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4779 Commits
3.1.0 ... 3.3.5

Author SHA1 Message Date
Gael Guennebaud
81bdde705c Bump to 3.3.5 2018-07-23 11:33:42 +02:00
Gael Guennebaud
06fc5761fa Oopps, EIGEN_COMP_MSVC is not available before including Eigen.
(grafted from de70671937
)
2018-07-20 17:51:17 +02:00
Gael Guennebaud
a185bc485c Disable optimization for sparse_product unit test with MSVC 2013, otherwise it takes several hours to build.
(grafted from 56a750b6cc
)
2018-07-20 08:36:38 -07:00
Gael Guennebaud
96134409fc Fix weird issue with MSVC 2013
(grafted from 3a2dc3869e
)
2018-07-18 02:26:43 -07:00
Gael Guennebaud
ab3fa2e123 Fix GeneralizedEigenSolver when requesting for eigenvalues only.
(grafted from a87cff20df
)
2018-07-14 09:38:49 +02:00
Gael Guennebaud
ae6e5caa40 Fix unit test
(grafted from a7b313a16c
)
2018-07-01 22:45:47 +02:00
Gael Guennebaud
483beabab9 bug #1560 fix product with a 1x1 diagonal matrix
(grafted from ee5864f72e
)
2018-06-25 10:30:12 +02:00
Jayaram Bobba
5c59564bfb fix AVX512 plog
(grafted from b7b868d1c4
)
2018-04-20 13:39:18 -07:00
Gael Guennebaud
1939c971a3 AVX512: _mm512_rsqrt28_ps is available for AVX512ER only
(grafted from 40b4bf3d32
)
2018-04-03 14:36:27 +02:00
Gael Guennebaud
c2f9e6cb37 AVX512: fix psqrt and prsqrt
(grafted from 7b0630315f
)
2018-04-03 14:12:50 +02:00
Rasmus Munk Larsen
1641a6cdd5 Fix typo in pbend for AltiVec.
(grafted from bda71ad394
)
2018-06-22 15:04:35 -07:00
Rasmus Munk Larsen
fea50d40ea Fix oversharding bug in parallelFor.
(grafted from 5418154a45
)
2018-06-20 17:51:48 -07:00
Gael Guennebaud
c1128efb6c fix md5sum of lapack_addons
(grafted from b8271bb368
)
2018-06-15 14:21:29 +02:00
Gael Guennebaud
20ca86888e bug #1555: compilation fix with XLC 2018-06-21 10:28:58 +02:00
Gael Guennebaud
36a1cd87d9 Fiw some warnings in dox examples
(grafted from c25034710e
)
2018-06-07 16:09:22 +02:00
Gael Guennebaud
523e442a7b Fix warning
(grafted from c723ffd763
)
2018-06-07 15:56:20 +02:00
Gael Guennebaud
48048172e5 Fix int versus Index
(grafted from 37348d03ae
)
2018-06-07 15:56:43 +02:00
Gael Guennebaud
e9bd839b13 Fix warning
(grafted from af7c83b9a2
)
2018-06-07 15:45:24 +02:00
Gael Guennebaud
3df78d5afc Fix MSVC warning C4290: C++ exception specification ignored except to indicate a function is not __declspec(nothrow)
(grafted from 7fe29aceeb
)
2018-06-07 15:36:20 +02:00
Gael Guennebaud
352489edbe Fix short vs long 2018-06-07 15:26:04 +02:00
Gael Guennebaud
450c5e5d27 Fix compilation with MSVC by reverting to char* for _mm_prefetch except for PGI (the later being the one that has the wrong prototype).
(grafted from 7134fa7a2e
)
2018-06-07 09:33:10 +02:00
Gael Guennebaud
64cc5f8512 Don't run hg on non mercurial clone
(grafted from 84868da904
)
2018-05-31 21:21:57 +02:00
Gael Guennebaud
656712d48f Doc: add aliasing in common pitfaffs.
(grafted from 6af1433cb5
)
2018-05-29 22:37:47 +02:00
Gael Guennebaud
971b32440c Define pcast<> for SSE types even when AVX is enabled. (otherwise float are silently reinterpreted as int instead of being converted)
(grafted from 647b724a36
)
2018-05-29 20:46:46 +02:00
Gael Guennebaud
bb87f618bf Fix compilation and SSE support with PGI compiler
(grafted from 49262dfee6
)
2018-05-29 15:09:31 +02:00
Jeff Trull
2f9de52245 Add tests for sparseQR results (value and size) covering bugs #1522 and #1544 2018-04-21 10:26:30 -07:00
Jeff Trull
2136cfa17e Make sparse QR result sizes consistent with dense QR, with the following rules:
1) Q is always square
2) Q*R*P' is valid and recovers the original matrix

This implies that the size of Q is the number of rows in the original matrix, square,
and that the size of R is the size of the original matrix.
2018-02-15 15:00:31 -08:00
Christoph Hertzberg
39125654ce bug #1544: Generate correct Q matrix in complex case. Original patch was by Jeff Trull in PR-386. 2018-05-17 19:17:01 +02:00
Gael Guennebaud
927d023cea Fix compilation with NEON+MSVC
(grafted from 6e7118265d
)
2018-04-26 10:50:41 +02:00
Gael Guennebaud
1e2d2693b9 bug #1428: atempt to make NEON vectorization compilable by MSVC.
The workaround is to wrap NEON packet types to make them different c++ types.
(grafted from e8ca5166a9
)
2018-04-24 11:19:49 +02:00
Gael Guennebaud
7634a44bfe Fix "used uninitialized" warnings
(grafted from 2f3287da7d
)
2018-04-24 17:17:25 +02:00
Gael Guennebaud
2480d04ac7 Workaround warning
(grafted from 3ffd449ef5
)
2018-04-24 17:11:51 +02:00
Gael Guennebaud
c92536d926 workaround MSVC 2013 compilation issue (ambiguous call)
(grafted from a57e6e5f0f
)
2018-04-23 15:31:51 +02:00
Gael Guennebaud
80af7d6a47 bug #1543: fix linear indexing in generic block evaluation (this completes the fix in commit 12efc7d41b
)
(grafted from 5679e439e0
)
2018-04-23 14:40:16 +02:00
Gael Guennebaud
87f9e301f9 Fix unit test
(grafted from 35b31353ab
)
2018-04-22 22:49:08 +02:00
Christoph Hertzberg
542fb03968 Fix enum-compare warning 2018-04-20 23:11:37 +02:00
Christoph Hertzberg
f90d136c84 Add parenthesis to fix compiler warnings 2018-04-15 18:43:56 +02:00
Gael Guennebaud
877a2b64c9 fix const cast in NEON
(grafted from 686fb57233
)
2018-04-18 18:46:34 +02:00
Dmitriy Korchemkin
e6577f3c30 Cast zeros to Scalar in RealSchur 2018-04-18 13:52:46 +03:00
Gael Guennebaud
69e01a2999 update cdash 2018-04-17 17:22:56 +02:00
Christoph Hertzberg
5f71579a2d Another fix to make boost::multiprecision compile again 2018-04-13 20:22:57 +02:00
Christoph Hertzberg
686e0749a5 Recent Adolc versions require C++11 2018-04-13 19:10:23 +02:00
Christoph Hertzberg
385d8b5e42 Make hypot_impl compile again for types with expression-templates (e.g., boost::multiprecision) 2018-04-13 19:01:37 +02:00
Christoph Hertzberg
4662c610c1 SelfAdjointView<...,Mode> causes a static assert since commit d820ab9edc 2018-04-13 19:00:34 +02:00
Gael Guennebaud
906a98fe39 fix linking issue
(grafted from 7a9089c33c
)
2018-04-13 08:51:47 +02:00
Gael Guennebaud
1c4fdad7bd bug #1520: workaround some -Wfloat-equal warnings by calling std::equal_to 2018-04-11 15:24:13 +02:00
Gael Guennebaud
3f711f3356 extend doxygen splitter for huge screens
(grafted from 79266fec75
)
2018-04-11 11:31:17 +02:00
Gael Guennebaud
b02ab76847 Update header/footer for doxygen 1.8.13
(grafted from 426052ef6e
)
2018-04-11 11:30:34 +02:00
Gael Guennebaud
5fec52ced1 Fix javascript hacks for oxygen 1.8.13
(grafted from 9c8decffbf
)
2018-04-11 11:30:14 +02:00
Gael Guennebaud
bde2bfcee8 bug #1538: update manual pages regarding BDCSVD.
(grafted from e798466871
)
2018-04-11 10:46:11 +02:00
Gael Guennebaud
eab7afe252 Fix MKL backend for symmetric eigenvalues on row-major matrices.
(grafted from add15924ac
)
2018-04-09 13:29:26 +02:00
Gael Guennebaud
81e94eea02 Fix cmake scripts with no fortran compiler
(grafted from c2624c0318
)
2018-04-07 08:45:19 +02:00
Gael Guennebaud
a2a2c3c865 bug #1509: fix computeInverseWithCheck for complexes
(grafted from 2f833b1c64
)
2018-04-04 15:47:46 +02:00
Gael Guennebaud
90cd199d4b Factories code between numext::hypot and scalar_hyot_op functor.
(grafted from 4213b63f5c
)
2018-04-04 15:12:43 +02:00
Gael Guennebaud
b18e2d422b bug #1521: avoid signalling NaN in hypot and make it std::complex<> friendly.
(grafted from e116f6847e
)
2018-04-04 13:47:23 +02:00
Gael Guennebaud
892c0a79ce bug #1494: makes pmin/pmax behave on Altivec/VSX as on x86 regading NaNs
(grafted from e91e314347
)
2018-04-04 11:39:19 +02:00
Gael Guennebaud
59398aa2bb comment unreachable code
(grafted from 112c899304
)
2018-04-03 23:16:43 +02:00
Gael Guennebaud
170914dbbc Fix compilation of product with inverse transpositions (e.g., mat * Transpositions().inverse())
(grafted from a1292395d6
)
2018-04-03 23:06:44 +02:00
Gael Guennebaud
866d222d60 commit 45e9c9996da790b55ed9c4b0dfeae49492ac5c46 (HEAD -> memory_fix)
Author: George Burgess IV <gbiv@google.com>
Date:   Thu Mar 1 11:20:24 2018 -0800

    Prefer `::operator new` to `new`

    The C++ standard allows compilers much flexibility with `new`
    expressions, including eliding them entirely
    (https://godbolt.org/g/yS6i91). However, calls to `operator new` are
    required to be treated like opaque function calls.

    Since we're calling `new` for side-effects other than allocating heap
    memory, we should prefer the less flexible version.

    Signed-off-by: George Burgess IV <gbiv@google.com>
(grafted from 8c7b5158a1
)
2018-04-03 17:15:38 +02:00
Gael Guennebaud
86a939451c bug #1527: fix support for MKL's VML (destination was not properly resized)
(grafted from dd4cc6bd9e
)
2018-04-03 17:11:15 +02:00
Gael Guennebaud
9ff3150243 bug #1528: better use numeric_limits::min() instead of 1/highest() that with underflow.
(grafted from c5b56f1fb2
)
2018-04-03 16:49:35 +02:00
Benoit Steiner
a7144f8d6a Made the TensorStorage class compile with clang 3.9
(grafted from de7b0fdea9
)
2017-02-28 13:52:22 -08:00
Gael Guennebaud
273738ba6f bug #1516: add assertion for out-of-range diagonal index in MatrixBase::diagonal(i)
(grafted from 8d0ffe3655
)
2018-04-03 16:15:43 +02:00
Gael Guennebaud
3fb42ff7b2 bug #1532: disable stl::*_negate in C++17 (they are deprecated)
(grafted from 407e3e2621
)
2018-04-03 15:59:30 +02:00
Gael Guennebaud
e90a14609a Fix uninitialized output argument.
(grafted from 524119d32a
)
2018-04-03 10:56:10 +02:00
Gael Guennebaud
ece56baba0 Merged in bfierz/eigen/3.3 (pull request PR-345)
Adds missing EIGEN_STRONG_INLINE to support MSVC properly inlining small vector calculations
2018-03-27 07:40:13 +00:00
Gael Guennebaud
1724dae8b8 Add static assertion for fixed sizes Ref<>
(grafted from f7d17689a5
)
2018-03-09 10:11:13 +01:00
Gael Guennebaud
190b46dd1f Implement better static assertion checking to make sure that the first assertion is a static one and not a runtime one.
(grafted from f6be7289d7
)
2018-03-09 10:00:51 +01:00
Gael Guennebaud
74daf12e52 Add static assertion on selfadjoint-view's UpLo parameter.
(grafted from d820ab9edc
)
2018-03-09 09:33:43 +01:00
Gael Guennebaud
c24844195d bug #1517: fix triangular product with unit diagonal and nested scaling factor: (s*A).triangularView<UpperUnit>()*B
(grafted from 5deeb19e7b
)
2018-02-09 16:52:35 +01:00
Gael Guennebaud
15752027ec Fix linear indexing in generic block evaluation.
(grafted from 12efc7d41b
)
2018-02-09 16:45:49 +01:00
Eugene Chereshnev
bfc66e8b9a Fix incorrect ldvt in LAPACKE call from JacobiSVD
(grafted from f558ad2955
)
2018-01-03 12:55:52 -08:00
Gael Guennebaud
b60cbbef37 fix compilation with old compiler 2017-12-15 17:53:48 +01:00
Gael Guennebaud
33b972d8b3 Fix compilation of stableNorm with some expressions as input
(grafted from 06bf1047f9
)
2017-12-15 15:15:37 +01:00
Gael Guennebaud
bb28a2aada fix warning 2017-12-15 14:43:33 +01:00
Gael Guennebaud
acd0ce11aa Fix cmake warning
(grafted from 31e0bda2e3
)
2017-12-14 15:48:27 +01:00
Basil Fierz
01fb621733 Adds missing EIGEN_STRONG_INLINE to support MSVC properly inlining small vector calculations
When working with MSVC often small vector operations are not properly inlined. This behaviour is observed even on the most recent compiler versions.
2017-10-26 22:44:28 +02:00
Benoit Steiner
71d1198ccd Merged in henryiii/eigen/henryiii/device33 (pull request PR-344)
Branch 3.3: Fixing missing inlines on device functions for newer CUDA cards
2017-10-21 01:59:01 +00:00
Henry Schreiner
95ec3232c6 Restore __device__ 2017-10-21 00:48:05 +00:00
Henry Schreiner
243249718b Adding missing inlines for CUDA and ARCH 6 2017-10-20 13:00:23 +00:00
Gael Guennebaud
32a6db0f8c bug #1468 (1/2) : add missing std:: to memcpy
(grafted from 8579195169
)
2017-09-22 09:23:24 +02:00
Gael Guennebaud
6fc0f2be70 Update documentation for aligned_allocator
(grafted from 7ad07fc6f2
)
2017-09-20 10:22:00 +02:00
Gael Guennebaud
70ac6c9230 Add C++11 max_digits10 for half.
(grafted from 9c353dd145
)
2017-09-06 10:22:47 +02:00
Gael Guennebaud
609e425166 Implement true compile-time "if" for apply_rotation_in_the_plane. This fixes a compilation issue for vectorized real type with missing vectorization for complexes, e.g. AVX512.
(grafted from b35d1ce4a5
)
2017-09-06 10:02:49 +02:00
Gael Guennebaud
4ead16cdd6 Fix mixing types in sparse matrix products.
(grafted from 80142362ac
)
2017-09-02 22:50:20 +02:00
Gael Guennebaud
361102f88b Merged in dtrebbien/eigen/patch-1 (pull request PR-312)
Work around a compilation error seen with nvcc V8.0.61
(grafted from fc39d5954b
)
2017-08-22 12:17:37 +00:00
Gael Guennebaud
5d40715db6 Handle min/max/inf/etc issue in cuda_fp16.h directly in test/main.h
(grafted from 304ef29571
)
2017-08-24 11:26:41 +02:00
Gael Guennebaud
e7c065ec71 bug #1462: remove all occurences of the deprecated __CUDACC_VER__ macro by introducing EIGEN_CUDACC_VER 2017-08-24 11:06:47 +02:00
Gael Guennebaud
18868228ad bug #336: improve doc for PlainObjectBase::Map
(grafted from 39864ebe1e
)
2017-08-22 17:18:43 +02:00
Gael Guennebaud
fbb0c510c5 Add missing scalar conversion
(grafted from 600e52fc7f
)
2017-08-22 17:06:57 +02:00
Gael Guennebaud
a8d2459f8e bug #1449: fix redux_3 unit test
(grafted from bc4dae9aeb
)
2017-08-22 15:59:08 +02:00
Gael Guennebaud
9a266e5118 bug #1461: fix compilation of Map<const Quaternion>::x()
(grafted from bc91a2df8b
)
2017-08-22 15:10:42 +02:00
Gael Guennebaud
51e1aa1539 Doc: warn about constness in LLT::solveInPlace
(grafted from b223918ea9
)
2017-08-22 14:12:47 +02:00
Jim Radford
0137ed4f19 LLT: const the arg to solveInPlace() to allow passing .transpose(), .block(), etc.
(grafted from 0c226644d8
)
2017-01-04 14:42:57 -08:00
Jim Radford
9d03711df8 LLT: avoid making a copy when decomposing in place
(grafted from be281e5289
)
2017-01-04 14:43:56 -08:00
Gael Guennebaud
1ca9072b51 Gub 1453: fix Map with non-default inner-stride but no outer-stride.
(grafted from e27f17bf5c
)
2017-08-22 13:27:37 +02:00
Gael Guennebaud
9fd138e2b3 Re-enable hidden doc in LLT
(grafted from 2c3d70d915
)
2017-08-22 12:04:09 +02:00
Gael Guennebaud
55fbf4fedd bug #1456: add perf recommendation for LLT and storage format
(grafted from 21d0a0bcf5
)
2017-08-22 12:46:35 +02:00
Gael Guennebaud
b87875abf8 bug #1455: Cholesky module depends on Jacobi for rank-updates.
(grafted from a6e7a41a55
)
2017-08-22 11:37:32 +02:00
Gael Guennebaud
ac2c97edff bug #1458: fix documentation of LLT and LDLT info() method.
(grafted from e6021cc8cc
)
2017-08-22 11:32:55 +02:00
Gael Guennebaud
292dea7922 Clarify MKL_DIRECT_CALL doc.
(grafted from 2810ba194b
)
2017-08-17 22:12:26 +02:00
Gael Guennebaud
070b5958e0 use MKL's lapacke.h header when using MKL
(grafted from f727844658
)
2017-08-17 21:58:39 +02:00
Gael Guennebaud
3108fbf767 Clarify doc regarding the usage of MKL_DIRECT_CALL
(grafted from 8c858bd891
)
2017-08-17 12:17:45 +02:00
Gael Guennebaud
9df7f3d8e9 Fix support for MKL's BLAS when using MKL_DIRECT_CALL.
(grafted from b95f92843c
)
2017-08-17 12:07:10 +02:00
Gael Guennebaud
782fd81dee Disable BDCSVD preallocation check.
(grafted from d580a90c9a
)
2017-07-20 10:03:54 +02:00
Gael Guennebaud
fa77d71335 Fix lazyness of operator* with CUDA 2017-07-20 09:47:28 +02:00
Gael Guennebaud
3d1795da28 Fix gcc7 warning: Wint-in-bool-context 2017-06-27 14:32:36 +02:00
Gael Guennebaud
d1c2d6683c Fix a gcc7 warning: Wint-in-bool-context
(grafted from b651ce0ffa
)
2017-06-26 09:58:28 +02:00
Christoph Hertzberg
d8cf158e06 Make sure CMAKE_Fortran_COMPILER is set before checking for Fortran functions 2017-06-20 16:31:53 +02:00
Gael Guennebaud
bc837b7975 bug #1436: fix compilation of Jacobi rotations with ARM NEON, some specializations of internal::conj_helper were missing.
(grafted from b240080e64
)
2017-06-15 10:16:30 +02:00
Gael Guennebaud
68e8f2b833 Added tag 3.3.4 for changeset 3dc3a0ea2d 2017-06-15 09:10:26 +02:00
Gael Guennebaud
3dc3a0ea2d bump to 3.3.4 2017-06-15 09:10:20 +02:00
Gael Guennebaud
79120a4c63 Enable Array(EigenBase<>) ctor for compatible scalar types only. This prevents nested arrays to look as being convertible from/to simple arrays.
(grafted from 9fbdf02059
)
2017-06-12 22:30:32 +02:00
Gael Guennebaud
e0412f18fd Fix compilation of streaming nested Array, i.e., cout << Array<Array<>>
(grafted from e43d8fe9d7
)
2017-06-12 22:26:26 +02:00
Gael Guennebaud
40b0c43bda Fix 1x1 case in Solve expression with EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION==RowMajor
(grafted from d9d7bd6d62
)
2017-06-12 22:25:02 +02:00
Gael Guennebaud
72f3e20e74 Fix LeastSquareDiagonalPreconditioner for complexes (issue introduced in previous commit)
(grafted from a7be4cd1b1
)
2017-06-09 11:57:53 +02:00
Gael Guennebaud
676a7a3271 fix compilation in C++98
(grafted from 8640093af1
)
2017-06-09 12:45:01 +02:00
Gael Guennebaud
f843239452 bug #1414: doxygen, add EigenBase to CoreModule
(grafted from 90168c003d
)
2017-06-09 14:01:44 +02:00
Gael Guennebaud
a4ab0c6b6a Fix compilation with some compilers
(grafted from a4fd4233ad
)
2017-06-09 23:02:02 +02:00
Gael Guennebaud
ef955ea8e5 fix tipo
(grafted from 50e09cca0f
)
2017-06-11 15:30:36 +02:00
NeroBurner
8bd392ca0e add cmake-option to enable/disable creation of tests
* * *
disable unsupportet/test when test are disabled
* * *
rename EIGEN_ENABLE_TESTS to BUILD_TESTING
* * *
consider BUILD_TESTING in blas
(grafted from c4fc2611ba
)
2017-01-02 09:09:21 +01:00
Gael Guennebaud
8d2ac85797 fix unit test
(grafted from 26a2c6fc16
)
2017-12-14 15:11:04 +01:00
Gael Guennebaud
6d6e5fcd43 Add possibility to overwrite EIGEN_STRONG_INLINE. 2017-12-14 14:47:38 +01:00
Gael Guennebaud
9c9e90f6db Fix packet and alignment propagation logic of Block<Xpr> expressions. In particular, (A+B).col(j) lost vectorisation.
(grafted from 9c3aed9d48
)
2017-12-14 14:24:33 +01:00
Gael Guennebaud
7ffa27f347 ignore all *build* sub directories
(grafted from 76c7dae600
)
2017-12-14 14:22:14 +01:00
Gael Guennebaud
c20043c8fd bug #1479: fix failure detection in LDLT
(grafted from 672bdc126b
)
2017-11-16 17:55:24 +01:00
Gael Guennebaud
d18877f18d bug #1485: fix linking issue of non template functions
(grafted from 7cc503f9f5
)
2017-11-15 21:33:37 +01:00
Justin Carpentier
02c0cef97f Use col method for column-major matrix
(grafted from a020d9b134
)
2017-10-17 21:51:27 +02:00
Gael Guennebaud
c8e663fe87 bug #1484: restore deleted line for 128 bits long doubles, and improve dispatching logic.
(grafted from 0a1cc73942
)
2017-11-10 10:25:41 +01:00
Gael Guennebaud
7a875acfb0 Fix overflow issues in BDCSVD
(grafted from e8468ea91b
)
2017-11-08 10:24:28 +01:00
Gael Guennebaud
3ec11d8f17 Fix compilation
(grafted from 7713e20fd2
)
2016-12-27 22:04:58 +01:00
Gael Guennebaud
ec067ac5e3 bug #1403: more scalar conversions fixes in BDCSVD
(grafted from 731c8c704d
)
2017-06-09 15:45:49 +02:00
Gael Guennebaud
316969d839 bug #1403: fix implicit scalar type conversion.
(grafted from 1bbcf19029
)
2017-06-09 14:44:02 +02:00
Gael Guennebaud
7a0a9581b5 bug #1405: enable StrictlyLower/StrictlyUpper triangularView as the destination of matrix*matrix products.
(grafted from ba5cab576a
)
2017-06-09 14:38:04 +02:00
Gael Guennebaud
8880be60fa fix compilation of Half in C++98 (issue introduced in previous commit)
(grafted from 26f552c18d
)
2017-06-09 13:36:58 +02:00
Gael Guennebaud
e41713d52e Fix compilation with gcc 4.3 and ARM NEON
(grafted from 1d59ca2458
)
2017-06-09 13:20:52 +02:00
Gael Guennebaud
b69e465d7a bug #1410: fix lvalue propagation of Array/Matrix-Wrapper with a const nested expression.
(grafted from fb1ee04087
)
2017-06-09 13:13:03 +02:00
Joao Rui Leal
0db83fc571 it is now possible to change Umfpack control settings before factorizations; added access to the report functions of Umfpack
(grafted from 95b804c0fe
)
2016-12-19 10:45:59 +00:00
Gael Guennebaud
1ac703f641 bug #1424: add numext::abs specialization for unsigned integer types. 2017-06-09 11:53:49 +02:00
Gael Guennebaud
2c32368642 Add missing std::numeric_limits specialization for half, and complete NumTraits<half>
(grafted from d588822779
)
2017-06-09 11:51:53 +02:00
Gael Guennebaud
db40309e70 bug #1423: fix LSCG\'s Jacobi preconditioner for row-major matrices.
(grafted from 682b2ef17e
)
2017-06-08 15:06:27 +02:00
Gael Guennebaud
e36c1f7501 bug #1435: fix aliasing issue in exressions like: A = C - B*A;
(grafted from 4bbc320468
)
2017-06-08 12:55:25 +02:00
Mmanu Chaturvedi
3aef5c1a2f Specializing numeric_limits For AutoDiffScalar
(grafted from 2971503fed
)
2017-05-23 17:12:36 -04:00
Gael Guennebaud
ab6bb89980 Fix compilation of matrix log with Map as input
(grafted from 26e8f9171e
)
2017-06-07 10:51:23 +02:00
Gael Guennebaud
983ace99d4 bug #1411: fix usage of alignment information in vectorization of quaternion product and conjugate.
(grafted from f2a553fb7b
)
2017-06-07 10:10:30 +02:00
Gael Guennebaud
72fa6775e8 bug #1417: make LinSpace compatible with std::complex
(grafted from 8508db52ab
)
2017-06-06 17:25:56 +02:00
Gael Guennebaud
9f25cdf4f6 Fix dense * sparse-selfadjoint-view product.
(grafted from 891ac03483
)
2017-04-25 13:58:10 +02:00
Gael Guennebaud
6e5edd68d3 Improve mixing of complex and real in the vectorized path of apply_rotation_in_the_plane
(grafted from d9084ac8e1
)
2017-04-14 11:05:13 +02:00
Gael Guennebaud
e8978ffa99 Fix unwanted Real to Scalar to Real conversions in column-pivoting QR.
(grafted from f75dfdda7e
)
2017-04-14 10:34:30 +02:00
Gael Guennebaud
c753fe7cc3 Improve cmake scripts for Pastix and BLAS detection.
(grafted from 0f83aeb6b2
)
2017-04-14 10:22:12 +02:00
Gael Guennebaud
e59e345720 better check array index before using it
(grafted from 89fd0c3881
)
2017-03-15 15:18:03 +01:00
Benoit Jacob
07c2244440 ARM prefetch fixes: Implement prefetch on ARM64. Do not clobber cc on ARM32. 2017-03-15 06:53:35 -04:00
Gael Guennebaud
1865dccd58 bug #1401: fix compilation of "cond ? x : -x" with x an AutoDiffScalar
(grafted from 970ff78294
)
2017-03-08 16:16:53 +01:00
Gael Guennebaud
f2e6ee9687 remove UTF8 symbol
(grafted from 5694315fbb
)
2017-03-07 10:53:47 +01:00
Gael Guennebaud
9219307e13 remove UTF8 symbols
(grafted from e958c2baac
)
2017-03-07 10:47:40 +01:00
Gael Guennebaud
f2e8f96151 bug #1400: fix stableNorm with EIGEN_DONT_ALIGN_STATICALLY
(grafted from 659087b622
)
2017-03-07 10:02:34 +01:00
Gael Guennebaud
faf8af25ed bug #1396: add some missing EIGEN_DEVICE_FUNC
(grafted from 4e98a7b2f0
)
2017-02-28 09:47:38 +01:00
Gael Guennebaud
106ba41c2a Fix typo.
(grafted from 478a9f53be
)
2017-02-28 09:32:45 +01:00
Benoit Steiner
87939ea0dd Added missing EIGEN_DEVICE_FUNC to the SelfCwise binary ops
(grafted from 889c606f8f
)
2017-02-27 17:17:47 -08:00
Benoit Steiner
e813640aa1 Added missing EIGEN_DEVICE_FUNC qualifiers to several nullary op methods.
(grafted from 193939d6aa
)
2017-02-27 17:11:47 -08:00
Benoit Steiner
612b8f2749 Declared the plset, ploadt_ro, and ploaddup packet primitives as usable within a gpu kernel
(grafted from ed4dc9d01a
)
2017-02-27 16:57:01 -08:00
Benoit Steiner
ead8e1b796 Added missing EIGEN_DEVICE_FUNC qualifiers.
(grafted from b1fc7c9a09
)
2017-02-27 16:48:30 -08:00
Benoit Steiner
3d4265f2d5 Added EIGEN_DEVICE_FUNC to make the prototype of the EigenBase override match that of DenseBase
(grafted from 554116bec1
)
2017-02-27 16:45:31 -08:00
Benoit Steiner
d66586ac90 Avoid unecessary float to double conversions.
(grafted from 34d9fce93b
)
2017-02-27 16:33:33 -08:00
Gael Guennebaud
44920624fb Added tag 3.3.3 for changeset 208058b9ad 2017-02-21 14:36:39 +01:00
Gael Guennebaud
208058b9ad bump to 3.3.3 2017-02-21 14:36:34 +01:00
Gael Guennebaud
b4218b8473 Use int32_t instead of int in NEON code. Some platforms with 16 bytes int supports ARM NEON.
(grafted from cbbf88c4d7
)
2017-02-17 14:39:02 +01:00
Gael Guennebaud
3c2f0812f6 bug #1394: fix compilation of SelfAdjointEigenSolver<Matrix>(sparse*sparse);
(grafted from 76687f385c
)
2017-02-20 14:27:26 +01:00
Gael Guennebaud
17bbd82f7d bug #1380: for Map<> as input of matrix exponential
(grafted from d8b1f6cebd
)
2017-02-20 14:06:06 +01:00
Gael Guennebaud
e1385337ff bug #1395: fix the use of compile-time vectors as inputs of JacobiSVD.
(grafted from 6572825703
)
2017-02-20 13:44:37 +01:00
Gael Guennebaud
d367ecb475 Silent warning.
(grafted from a811a04696
)
2017-02-20 10:14:21 +01:00
Gael Guennebaud
c3b658b2c9 Fix tracking of temporaries in unit tests
(grafted from deefa54a54
)
2017-02-19 10:32:54 +01:00
Gael Guennebaud
f9d655a8c8 Fix compilation.
(grafted from f8a55cc062
)
2017-02-18 10:08:13 +01:00
Gael Guennebaud
ad3e4d1a49 bug #1393: enable Matrix/Array explicit ctor from types with conversion operators (was ok with 3.2)
(grafted from 582b5e39bf
)
2017-02-17 14:10:57 +01:00
Gael Guennebaud
222ed66f79 Fix usage of CUDACC_VER 2017-02-20 08:16:54 +01:00
Gael Guennebaud
6bceebfabf bug #1391: include IO.h before DenseBase to enable its usage in DenseBase plugins. 2017-02-13 09:46:20 +01:00
Gael Guennebaud
2ca3eb8407 bug #1392: fix #include <Eigen/Sparse> with mpl2-only
(grafted from c16ee72b20
)
2017-02-11 10:35:01 +01:00
Gael Guennebaud
698205cddf Suppress warning 2017-02-10 21:30:31 +01:00
Gael Guennebaud
2ecb33820f Fix prunning in (sparse*sparse).pruned() when the result is nearly dense.
(grafted from a1ff24f96a
)
2017-02-10 13:59:32 +01:00
Gael Guennebaud
a0de6eb4ce Include clang in the list of non strict MSVC (just to be sure) 2017-02-10 13:41:52 +01:00
Alexander Neumann
7962ac1a58 fixed inlining issue with clang-cl on visual studio 2017-02-08 23:50:38 +01:00
Alexander Neumann
9c97b053f3 fixed compiling issue using clang-cl with visual studio 2017-02-08 23:50:09 +01:00
Gael Guennebaud
f61b0d56f0 Improve multi-threading heuristic for matrix products with a small number of columns.
(grafted from fc8fd5fd24
)
2017-02-07 17:19:59 +01:00
Gael Guennebaud
5087e016eb bug #1389: MSVC's std containers do not properly align in 64 bits mode if the requested alignment is larger than 16 bytes (e.g., with AVX)
(grafted from 4254b3eda3
)
2017-02-03 15:22:35 +01:00
Gael Guennebaud
fa9f5d7170 Fix compilation of JacobiSVD for vectors type
(grafted from 645a8e32a5
)
2017-01-31 16:22:54 +01:00
Gael Guennebaud
6975534cb2 bug #478: fix regression in the eigen decomposition of zero matrices.
(grafted from 53026d29d4
)
2017-01-31 14:22:42 +01:00
Gael Guennebaud
95c6d8db75 bug #1380: fix matrix exponential with Map<>
(grafted from 63de19c000
)
2017-01-30 13:55:27 +01:00
Gael Guennebaud
e0548e9ff3 bug #1384: fix evaluation of "sparse/scalar" that used the wrong evaluation path.
(grafted from c86911ac73
)
2017-01-30 13:38:24 +01:00
Gael Guennebaud
c289ef20f3 bug #1383: fix regression in LinSpaced for integers and high<low
(grafted from 850ca961d2
)
2017-01-25 18:13:53 +01:00
Gael Guennebaud
b8cf157e8c bug #1381: fix sparse.diagonal() used as a rvalue.
The problem was that is "sparse" is not const, then sparse.diagonal() must have the
LValueBit flag meaning that sparse.diagonal().coeff(i) must returns a const reference,
const Scalar&. However, sparse::coeff() cannot returns a reference for a non-existing
zero coefficient. The trick is to return a reference to a local member of
evaluator<SparseMatrix>.
(grafted from 296d24be4d
)
2017-01-25 17:39:01 +01:00
Gael Guennebaud
b4d2b404b0 bug #1383: Fix regression from 3.2 with LinSpaced(n,0,n-1) with n==0.
(grafted from d06a48959a
)
2017-01-25 15:27:13 +01:00
Gael Guennebaud
70fcaf9bd8 bug #1365: fix another type mismatch warning
(sync is set from and compared to an Index)
2016-12-28 23:35:43 +01:00
Gael Guennebaud
2f31c6b1d8 bug #1369: fix type mismatch warning.
Returned values of omp thread id and numbers are int,
o let's use int instead of Index here.
(grafted from 97812ff0d3
)
2016-12-28 23:29:35 +01:00
Gael Guennebaud
9e55467b4c bug #1375: fix cmake installation with cmake 2.8
(grafted from 156e6234f1
)
2017-01-24 09:16:40 +01:00
Gael Guennebaud
35bf99c63e bug #1376: add missing assertion on size mismatch with compound assignment operators (e.g., mat += mat.col(j))
(grafted from ba3f977946
)
2017-01-23 22:06:08 +01:00
Gael Guennebaud
f9b8729597 bug #1382: move using std::size_t/ptrdiff_t to Eigen's namespace (still better than the global namespace!)
(grafted from b0db4eff36
)
2017-01-23 22:03:57 +01:00
Gael Guennebaud
4b2e7f26aa Add std:: namespace prefix to all (hopefully) instances if size_t/ptrdfiff_t 2017-01-23 22:02:53 +01:00
Gael Guennebaud
5202bc92e6 Use Index instead of size_t
(grafted from 4b607b5692
)
2017-01-23 22:00:33 +01:00
Gael Guennebaud
9d83411cc4 bug #1379: fix compilation in sparse*diagonal*dense with openmp
(grafted from 0fe278f7be
)
2017-01-21 23:27:01 +01:00
Gael Guennebaud
556c03a09d bug #1378: fix doc (DiagonalIndex vs Diagonal)
(grafted from 22a172751e
)
2017-01-21 22:09:59 +01:00
Gael Guennebaud
ce463b9fa4 Added tag 3.3.2 for changeset 477d1e8192 2017-01-18 15:06:46 +01:00
Gael Guennebaud
477d1e8192 Bump to 3.3.2 2017-01-18 15:06:40 +01:00
Gael Guennebaud
0eaff8fdf2 Defer set-to-zero in triangular = product so that no aliasing issue occur in the common:
A.triangularView() = B*A.sefladjointView()*B.adjoint()
case that used to work in 3.2.
(grafted from 655ba783f8
)
2017-01-17 18:03:35 +01:00
Gael Guennebaud
582c96691b Fix typo 2017-01-16 13:36:56 +01:00
Gael Guennebaud
0b22158d9f Add missing doc of SparseView
(grafted from 831fffe874
)
2017-01-06 18:01:29 +01:00
Gael Guennebaud
dafdb0d8a8 MSVC 2015 has all we want about c++11 and MSVC 2017 fails on binder1st/binder2nd
(grafted from e383d6159a
)
2017-01-06 15:44:13 +01:00
Gael Guennebaud
1d1686c62b Convert integers to real numbers when computing relative L2 error
(grafted from f3f026c9aa
)
2017-01-05 13:36:08 +01:00
Gael Guennebaud
ad95b924d0 Fix and workaround several doxygen issues/warnings
(grafted from 2299717fd5
)
2017-01-04 23:27:33 +01:00
Gael Guennebaud
9499684320 Add doc for sparse triangular solve functions
(grafted from ee6f7f6c0c
)
2017-01-04 23:10:36 +01:00
Gael Guennebaud
5b6a31626b Add missing snippet files.
(grafted from 5165de97a4
)
2017-01-04 23:08:27 +01:00
Gael Guennebaud
bc3fee2d8e bug #1336: workaround doxygen failing to include numerous members of MatriBase in Matrix
(grafted from a0a36ad0ef
)
2017-01-04 22:02:39 +01:00
Gael Guennebaud
eaa9223277 Document selfadjointView
(grafted from 29a1a58113
)
2017-01-04 22:01:50 +01:00
Gael Guennebaud
c9ba1165e7 bug #1336: fix doxygen issue regarding EIGEN_CWISE_BINARY_RETURN_TYPE
(grafted from a5ebc92f8d
)
2017-01-04 18:21:44 +01:00
Gael Guennebaud
dd2d5d67ff bug #1370: add doc for StorageIndex
(grafted from 8702562177
)
2017-01-03 11:25:41 +01:00
Gael Guennebaud
404322b64f bug #1370: rename _Index to _StorageIndex in SparseMatrix, and add a warning in the doc regarding the 3.2 to 3.3 change of SparseMatrix::Index
(grafted from 575c078759
)
2017-01-03 11:19:14 +01:00
Marco Falke
ce37bae2cd doc: Fix trivial typo in AsciiQuickReference.txt
* * *
fixup!
(grafted from 4ebf69394d
)
2017-01-01 13:25:48 +00:00
Gael Guennebaud
3900dbc341 Make sure that traits<CwiseBinaryOp>::Flags reports the correct storage order so that methods like .outerSize()/.innerSize() work properly.
(grafted from d32a43e33a
)
2016-12-27 16:35:45 +01:00
Gael Guennebaud
5f586c2bd0 Add missing .outer() member to iterators of evaluators of cwise sparse binary expression
(grafted from 7136267461
)
2016-12-27 16:34:30 +01:00
Gael Guennebaud
215f88a417 Fix check of storage order mismatch for "sparse cwiseop sparse".
(grafted from fe0ee72390
)
2016-12-27 16:33:19 +01:00
Gael Guennebaud
2257f40f4a Merged in angelos_m/eigen/3.3 (pull request PR-269)
Remove superfluous const's (can cause warnings on some Intel compilers)
2016-12-21 08:53:16 +01:00
Gael Guennebaud
9e0fa0ef6d Fix bug #1367: compilation fix for gcc 4.1!
(grafted from 94e8d8902f
)
2016-12-20 22:17:01 +01:00
Gael Guennebaud
0fddbf3dc7 Add transpose, adjoint, conjugate methods to SelfAdjointView (useful to write generic code)
(grafted from 684cfc762d
)
2016-12-20 16:33:53 +01:00
Gael Guennebaud
eda635bd58 Make sure that HyperPlane::transform manitains a unit normal vector in the Affine case.
(grafted from f5d644b415
)
2016-12-20 09:35:00 +01:00
Benoit Jacob
26197bb467 Use 32 registers on ARM64 2016-12-19 13:44:46 -05:00
Gael Guennebaud
772e59d475 bug #1360: fix sign issue with pmull on altivec
(grafted from 8c0e701504
)
2016-12-18 22:13:19 +00:00
Gael Guennebaud
e8f83cbb5d Fix unused warning
(grafted from fc94258e77
)
2016-12-18 22:11:48 +00:00
Gael Guennebaud
dce584d799 bug #1363: fix mingw's ABI issue
(grafted from 5d00fdf0e8
)
2016-12-15 11:58:31 +01:00
Gael Guennebaud
0bcef9557d bug #1358: fix compilation for sparse += sparse.selfadjointView();
(grafted from 11b492e993
)
2016-12-14 17:53:47 +01:00
Gael Guennebaud
2b3c876b2a bug #1359: fix compilation of col_major_sparse.row() *= scalar
(used to work in 3.2.9 though the expression is not really writable)
(grafted from e67397bfa7
)
2016-12-14 17:05:26 +01:00
Gael Guennebaud
a05f6aad0e bug #1359: fix sparse /=scalar and *=scalar implementation.
InnerIterators must be obtained from an evaluator.
(grafted from 98d7458275
)
2016-12-14 17:03:13 +01:00
Gael Guennebaud
59187285e1 bug #1361: fix compilation issue in mat=perm.inverse()
(grafted from c817ce3ba3
)
2016-12-13 23:10:27 +01:00
Angelos Mantzaflaris
1dd074ea7e Merged eigen/eigen/3.3 into 3.3 2016-12-07 01:01:50 +01:00
Angelos Mantzaflaris
24fa7a01bd merge 2016-12-07 00:43:55 +01:00
Angelos Mantzaflaris
e236d3443c Remove superfluous const's (can cause warnings on some Intel compilers) 2016-12-07 00:37:48 +01:00
Gael Guennebaud
4ec8833220 Added tag 3.3.1 for changeset dd3685cc6a 2016-12-06 11:44:02 +01:00
Gael Guennebaud
dd3685cc6a Bump to 3.3.1 2016-12-06 11:43:58 +01:00
Gael Guennebaud
487a6e6515 Explain how to choose your favorite Eigen version
(grafted from 0c4d05b009
)
2016-12-06 11:34:06 +01:00
Silvio Traversaro
75f0b8aae3 Added relocatable cmake support also for CMake before 3.0 and after 2.8.8
(grafted from e049a2a72a
)
2016-12-06 10:37:34 +01:00
Gael Guennebaud
23aca8a586 Optimize SparseLU::solve for rhs vectors
(grafted from 8640ffac65
)
2016-12-05 15:41:14 +01:00
Gael Guennebaud
28bf2bf070 remove temporary in SparseLU::solve
(grafted from 62acd67903
)
2016-12-05 15:11:57 +01:00
Silvio Traversaro
0164f4c682 Make CMake config file relocatable
(grafted from 18481b518f
)
2016-12-05 10:39:52 +01:00
Gael Guennebaud
bbff608a42 Merged in angelos_m/eigen/3.3 (pull request PR-264)
add explicit template to numext::abs2 and fix signed/unsigned warning
2016-12-05 21:56:01 +00:00
Gael Guennebaud
ea56d2ff2c Fix memory leak in Ref<Sparse>
(grafted from a6b971e291
)
2016-12-05 16:59:30 +01:00
Gael Guennebaud
a4c8701e9a bug #1356: fix calls to evaluator::coeffRef(0,0) to get the address of the destination
by adding a dstDataPtr() member to the kernel. This fixes undefined behavior if dst is empty (nullptr).
(grafted from 0db6d5b3f4
)
2016-12-05 15:08:09 +01:00
Gael Guennebaud
a9bb9796e0 Ease compiler job to generate clean and efficient code in mat*vec.
(grafted from 66f65ccc36
)
2016-12-02 22:41:26 +01:00
Gael Guennebaud
449883be74 Operators += and -= do not resize!
(grafted from fe696022ec
)
2016-12-02 22:40:25 +01:00
Angelos Mantzaflaris
0a08d4c60b use numext::abs 2016-12-02 11:48:06 +01:00
Angelos Mantzaflaris
4086187e49 1. Add explicit template to abs2 (resolves deduction for some arithmetic types)
2. Avoid signed-unsigned conversion in comparison (warning in case Scalar is unsigned)
2016-12-02 11:39:18 +01:00
Christoph Hertzberg
91864f85d3 bug #1355: Fixed wrong line-endings on two files
(grafted from 22f7d398e2
)
2016-12-02 11:22:05 +01:00
Gael Guennebaud
c3597106ab Merged in angelos_m/eigen/3.3 (pull request PR-263)
fix two warnings(unused typedef, unused variable) and a typo
2016-12-02 09:02:39 +00:00
Gael Guennebaud
aed1d6597f Clean up SparseCore module regarding ReverseInnerIterator
(grafted from 27873008d4
)
2016-12-01 21:55:10 +01:00
Angelos Mantzaflaris
b6f04a2dd4 typo UIntPtr 2016-12-01 21:25:58 +01:00
Angelos Mantzaflaris
a9aa3bcf50 fix two warnings(unused typedef, unused variable) and a typo 2016-12-01 21:23:43 +01:00
Gael Guennebaud
32b8da66e3 fix member order
(grafted from 181138a1cb
)
2016-12-01 17:06:20 +01:00
Gael Guennebaud
eb94179ea3 Merged in sergiu/eigen/cmake-imported-target (pull request PR-257)
CMake imported target (take #2)
2016-12-01 15:13:48 +00:00
Gael Guennebaud
52a7386aef Fix misleading-indentation warnings.
(grafted from 037b46762d
)
2016-12-01 16:05:42 +01:00
Gael Guennebaud
8cada1d894 Fix slection of product implementation for dynamic size matrices with fixed max size.
(grafted from 8df272af88
)
2016-11-30 22:21:33 +01:00
Gael Guennebaud
6e4a664c42 Fix a performance regression in (mat*mat)*vec for which mat*mat was evaluated multiple times.
(grafted from c927af60ed
)
2016-11-30 17:59:13 +01:00
Gael Guennebaud
1cd1a96d56 bug #1351: fix compilation of random with old compilers
(grafted from ab4ef5e66e
)
2016-11-30 17:37:53 +01:00
Sergiu Deitsch
86ab00cdcf cmake: remove architecture dependency from Eigen3ConfigVersion.cmake
Also, install Eigen3*.cmake under $prefix/share/eigen3/cmake by default.
2016-11-30 15:46:46 +01:00
Sergiu Deitsch
65f09be8d2 doc: mention the NO_MODULE option and target availability 2016-11-30 15:41:38 +01:00
Gael Guennebaud
400d756b82 bug #1348: Document EIGEN_MAX_ALIGN_BYTES and EIGEN_MAX_STATIC_ALIGN_BYTES,
and reflect in the doc that EIGEN_DONT_ALIGN* are deprecated.
(grafted from 21d0286d81
)
2016-11-23 22:15:03 +01:00
Gael Guennebaud
9d31798a84 update cdash project for 3.3 2016-11-23 14:13:08 +01:00
Gael Guennebaud
723ed92e0e Fix compilation with gcc and old ABI version
(grafted from e340866c81
)
2016-11-23 14:04:57 +01:00
Gael Guennebaud
0a7de0b273 Fix compilation issue with MSVC:
MSVC always messes up with shadowed template arguments, for instance in:
  struct B { typedef float T; }
  template<typename T> struct A : B {
    T g;
  };
The type of A<double>::g will be float and not double.
(grafted from a91de27e98
)
2016-11-23 12:24:48 +01:00
Gael Guennebaud
d6b9bc1ccd Optimize predux<Packet8f> (AVX)
(grafted from 74637fa4e3
)
2016-11-22 21:57:52 +01:00
Gael Guennebaud
0eff51e2ed Disable usage of SSE3 _mm_hadd_ps that is extremely slow.
(grafted from 178c084856
)
2016-11-22 21:53:14 +01:00
Gael Guennebaud
1b7dd46d94 Optimize predux<Packet4d> (AVX)
(grafted from 7dd894e40e
)
2016-11-22 21:41:30 +01:00
Gael Guennebaud
b2eb1bf3dc Disable usage of SSE3 haddpd that is extremely slow.
(grafted from f3fb0a1940
)
2016-11-22 16:58:31 +01:00
Gael Guennebaud
fe48c25682 Revert vec/y to vec*(1/y) in row-major TRSM:
- div is extremely costly
- this is consistent with the column-major case
- this is consistent with all other BLAS implementations
(grafted from eb621413c1
)
2016-12-06 15:04:50 +01:00
Gael Guennebaud
0ba6da3470 Fix BLAS backend for symmetric rank K updates.
(grafted from 8365c2c941
)
2016-12-06 14:47:09 +01:00
Sergiu Deitsch
a287140f72 cmake: added Eigen3::Eigen imported target 2016-11-22 12:25:06 +01:00
Gael Guennebaud
4d89ec8a00 Fix regression in assigment of sparse block to spasre block.
(grafted from 6a84246a6a
)
2016-11-21 21:46:42 +01:00
Chun Wang
441760f239 Workaround for error in VS2012 with /clr
(grafted from 0d0948c3b9
)
2016-11-17 17:54:27 -05:00
Gael Guennebaud
664162fb8a Fix compilation issue in mat = permutation (regression introduced in 8193ffb3d3
)
(grafted from 465ede0f20
)
2016-11-20 09:41:37 +01:00
Gael Guennebaud
aa3c761002 bug #1343: fix compilation regression in mat+=selfadjoint_view.
Generic EigenBase2EigenBase assignment was incomplete.
(grafted from 8193ffb3d3
)
2016-11-18 10:17:34 +01:00
Gael Guennebaud
94f2cfc9c7 bug #1343: fix compilation regression in array = matrix_product
(grafted from cebff7e3a2
)
2016-11-18 10:09:33 +01:00
Konstantinos Margaritis
4a13d79df6 replace sizeof(Packet) with PacketSize else it breaks for ZVector.Packet4f
(grafted from a1d5c503fa
)
2016-11-17 13:27:45 -05:00
Konstantinos Margaritis
463176cc44 implement float/std::complex<float> for ZVector as well, minor fixes to ZVector
(grafted from 672aa97d4d
)
2016-11-17 13:27:33 -05:00
Gael Guennebaud
5aab97fba6 Optimize sparse<bool> && sparse<bool> to use the same path as for coeff-wise products.
(grafted from 0ee92aa38e
)
2016-11-14 18:47:41 +01:00
Gael Guennebaud
89abc6806d bug #426: move operator && and || to MatrixBase and SparseMatrixBase.
(grafted from 2e334f5da0
)
2016-11-14 18:47:02 +01:00
Niels Ole Salscheider
baf793ebaa Make sure not to call numext::maxi on expression templates
(grafted from 51fef87408
)
2016-11-12 12:20:57 +01:00
Gael Guennebaud
b4ddafcfac Fix regression in SparseMatrix::ReverseInnerIterator
(grafted from eedb87f4ba
)
2016-11-14 14:05:53 +01:00
Gael Guennebaud
1079967710 Added tag 3.3.0 for changeset eeac81b8c0 2016-11-10 13:57:29 +01:00
Gael Guennebaud
eeac81b8c0 bump to 3.3.0 2016-11-10 13:55:14 +01:00
Gael Guennebaud
e80bc2ddb0 Fix printing of sparse expressions 2016-11-10 10:35:32 +01:00
Benoit Steiner
db3903498d Merged in benoitsteiner/opencl (pull request PR-246)
Improved support for OpenCL
2016-11-08 22:28:44 +00:00
Benoit Steiner
dcc14bee64 Fixed the formatting of the code 2016-11-08 14:24:46 -08:00
Benoit Steiner
b88c1117d4 Fixed the indentation of the cmake file 2016-11-08 14:22:36 -08:00
Luke Iwanski
912cb3d660 #if EIGEN_EXCEPTION -> #ifdef EIGEN_EXCEPTIONS. 2016-11-08 22:01:14 +00:00
Luke Iwanski
1b345b0895 Fix for SYCL queue initialisation. 2016-11-08 21:56:31 +00:00
Luke Iwanski
1b95717358 Use try/catch only when exceptions are enabled. 2016-11-08 21:08:53 +00:00
Mehdi Goli
d57430dd73 Converting all sycl buffers to uninitialised device only buffers; adding memcpyHostToDevice and memcpyDeviceToHost on syclDevice; modifying all examples to obey the new rules; moving sycl queue creating to the device based on Benoit suggestion; removing the sycl specefic condition for returning m_result in TensorReduction.h according to Benoit suggestion. 2016-11-08 17:08:02 +00:00
Gael Guennebaud
73985ead27 Extend unit test to check sparse solvers with a SparseVector as the rhs and result. 2016-11-06 20:29:57 +01:00
Gael Guennebaud
436a111792 Generalize Cholmod support to hanlde any sparse type as the rhs and result of the solve method 2016-11-06 20:29:23 +01:00
Gael Guennebaud
afc55b1885 Generalize IterativeSolverBase::solve to hanlde any sparse type as the results (instead of SparseMatrix only) 2016-11-06 20:28:18 +01:00
Gael Guennebaud
a5c2d8a3cc Generalize solve_sparse_through_dense_panels to handle SparseVector. 2016-11-06 15:20:58 +01:00
Gael Guennebaud
f8bfe10613 Add missing friend declaration 2016-11-06 15:20:30 +01:00
Gael Guennebaud
fc7180cda8 Add a default ctor to evaluator<SparseVector>.
Needed for evaluator<Solve>.
2016-11-06 15:20:00 +01:00
Gael Guennebaud
4d226ab5b5 Enable swapping between SparseMatrix and SparseVector 2016-11-06 15:15:03 +01:00
Benoit Steiner
ad086b03e4 Removed unnecessary statement 2016-11-05 12:43:27 -07:00
Benoit Steiner
dad177be01 Added missing includes 2016-11-05 10:04:42 -07:00
Gael Guennebaud
55b4fd1d40 Extend mpreal unit test to check LLT with complexes. 2016-11-05 11:28:53 +01:00
Gael Guennebaud
a354c3ca59 Fix compilation of LLT with complex<mpreal>. 2016-11-05 11:28:29 +01:00
Benoit Steiner
d46a36cc84 Merged eigen/eigen into default 2016-11-04 18:22:55 -07:00
Mehdi Goli
0ebe3808ca Removed the sycl include from Eigen/Core and moved it to Unsupported/Eigen/CXX11/Tensor; added TensorReduction for sycl (full reduction and partial reduction); added TensorReduction test case for sycl (full reduction and partial reduction); fixed the tile size on TensorSyclRun.h based on the device max work group size; 2016-11-04 18:18:19 +00:00
Gael Guennebaud
47d1b4a609 Added tag 3.3-rc2 for changeset ba05572dcb 2016-11-04 09:09:18 +01:00
Gael Guennebaud
ba05572dcb bump to 3.3-rc2 2016-11-04 09:09:06 +01:00
Benoit Steiner
5c3995769c Improved AVX512 configuration 2016-11-03 04:50:28 -07:00
Benoit Steiner
fbe672d599 Reenable the generation of dynamic blas libraries. 2016-11-03 04:08:43 -07:00
Benoit Steiner
ca0ba0d9a4 Improved AVX512 support 2016-11-03 04:00:49 -07:00
Benoit Steiner
c80587c92b Merged eigen/eigen into default 2016-11-03 03:55:11 -07:00
Gael Guennebaud
3f1d0cdc22 bug #1337: improve doc of homogeneous() and hnormalized() 2016-11-03 11:03:08 +01:00
Gael Guennebaud
78e93ac1ad bug #1330: Cholmod supports double precision only, so let's trigger a static assertion if the scalar type does not match this requirement. 2016-11-03 10:21:59 +01:00
Benoit Steiner
3e37166d0b Merged in benoitsteiner/opencl (pull request PR-244)
Disable vectorization on device only when compiling for sycl
2016-11-02 22:01:03 +00:00
Benoit Steiner
0585b2965d Disable vectorization on device only when compiling for sycl 2016-11-02 11:44:27 -07:00
Benoit Steiner
e6e77ed08b Don't call lgamma_r when compiling for an Apple device, since the function isn't available on MacOS 2016-11-02 09:55:39 -07:00
Benoit Steiner
b238f387b4 Pulled latest updates from trunk 2016-11-02 08:53:13 -07:00
Benoit Steiner
c8db17301e Special functions require math.h: make sure it is included. 2016-11-02 08:51:52 -07:00
Gael Guennebaud
a07bb428df bug #1004: improve accuracy of LinSpaced for abs(low) >> abs(high). 2016-11-02 11:34:38 +01:00
Gael Guennebaud
598de8b193 Add pinsertfirst function and implement pinsertlast for complex on SSE/AVX. 2016-11-02 10:38:13 +01:00
Benoit Steiner
e44519744e Merged in benoitsteiner/opencl (pull request PR-243)
Fixed the ambiguity in callig make_tuple for sycl backend.
2016-11-02 02:56:58 +00:00
Rasmus Munk Larsen
0a6ae41555 Merged eigen/eigen into default 2016-11-01 15:37:00 -07:00
Rasmus Munk Larsen
b730952414 Don't attempts to use lgamma_r for CUDA devices.
Fix type in lgamma_impl<double>.
2016-11-01 15:34:19 -07:00
Benoit Steiner
7a0e96b80d Gate the code that refers to cuda fp16 primitives more thoroughly 2016-11-01 12:08:09 -07:00
Mehdi Goli
51af6ae971 Fixed the ambiguity in callig make_tuple for sycl backend. 2016-10-31 16:35:51 +00:00
Benoit Steiner
0a9ad6fc72 Worked around Visual Studio compilation errors 2016-10-28 07:54:27 -07:00
Benoit Steiner
d5f88e2357 Sharded the tensor_image_patch test to help it run on low power devices 2016-10-27 21:48:21 -07:00
Benoit Steiner
0b4b0f11e8 Fixed a few more compilation warnings 2016-10-28 04:01:01 +00:00
Benoit Steiner
306daa24a3 Fixed a compilation warning 2016-10-28 03:50:31 +00:00
Benoit Steiner
8471cf1996 Fixed compilation warning 2016-10-28 03:46:08 +00:00
Benoit Steiner
b0c5bfdf78 Added missing template parameters 2016-10-28 03:43:41 +00:00
Rasmus Munk Larsen
2ebb314fa7 Use threadsafe versions of lgamma and lgammaf if possible. 2016-10-27 16:17:12 -07:00
Gael Guennebaud
530f20c21a Workaround MSVC issue. 2016-10-27 21:51:37 +02:00
Gael Guennebaud
c3ce4f9ac0 Merged in enricodetoma/eigen (pull request PR-241)
Always enable /bigobj for tests to avoid a compile error in MSVC 2015
2016-10-27 19:21:28 +00:00
Benoit Steiner
7d64e6752c Pulled latest updates from trunk 2016-10-26 18:48:06 -07:00
Benoit Steiner
0a4c4d40b4 Removed a template parameter for fixed sized tensors 2016-10-26 18:47:37 -07:00
Gael Guennebaud
3ecb343dc3 Fix regression in X = (X*X.transpose())/s with X rectangular by deferring resizing of the destination after the creation of the evaluator of the source expression. 2016-10-26 22:50:41 +02:00
enrico.detoma
6ed571744b Always enable /bigobj for tests to avoid a compile error in MSVC 2015 2016-10-26 22:48:46 +02:00
Gael Guennebaud
97feea9d39 add a generic EIGEN_HAS_CXX11 2016-10-26 15:53:13 +02:00
Gael Guennebaud
ca6a2a5248 Fix warning with ICC 2016-10-26 14:13:05 +02:00
Benoit Steiner
5f2dd503ff Replaced tabs with spaces 2016-10-25 20:40:58 -07:00
Benoit Steiner
1644bafe29 Code cleanup 2016-10-25 20:36:14 -07:00
Gael Guennebaud
b15a5dc3f4 Fix ICC warnings 2016-10-25 22:20:24 +02:00
Gael Guennebaud
aad72f3c6d Add missing inline keywords 2016-10-25 20:20:09 +02:00
Benoit Steiner
3e194a6a73 Fixed a typo 2016-10-25 08:42:15 -07:00
Gael Guennebaud
58146be99b bug #1004: one more rewrite of LinSpaced for floating point numbers to guarantee both interpolation and monotonicity.
This version simply does low+i*step plus a branch to return high if i==size-1.
Vectorization is accomplished with a branch and the help of pinsertlast.
Some quick benchmark revealed that the overhead is really marginal, even when filling small vectors.
2016-10-25 16:53:09 +02:00
Gael Guennebaud
13fc18d3a2 Add a pinsertlast function replacing the last entry of a packet by a scalar.
(useful to vectorize LinSpaced)
2016-10-25 16:48:49 +02:00
Gael Guennebaud
2634f9386c bug #1333: fix bad usage of const_cast_derived. Better use .data() for that purpose. 2016-10-24 22:22:35 +02:00
Gael Guennebaud
9e8f07d7b5 Cleanup ArrayWrapper and MatrixWrapper by removing redundant accessors. 2016-10-24 22:16:48 +02:00
Gael Guennebaud
b027d7a8cf bug #1004: remove the inaccurate "sequential" path for LinSpaced, mark respective function as deprecated, and enforce strict interpolation of the higher range using a correction term.
Now, even with floating point precision, both the 'low' and 'high' bounds are exactly reproduced at i=0 and i=size-1 respectively.
2016-10-24 20:27:21 +02:00
Benoit Steiner
b11aab5fcc Merged in benoitsteiner/opencl (pull request PR-238)
Added support for OpenCL to the Tensor Module
2016-10-24 15:30:45 +00:00
Gael Guennebaud
53c77061f0 bug #698: rewrite LinSpaced for integer scalar types to avoid overflow and guarantee an even spacing when possible.
Otherwise, the "high" bound is implicitly lowered to the largest value allowing for an even distribution.
This changeset also disable vectorization for this integer path.
2016-10-24 15:50:27 +02:00
Gael Guennebaud
e8e56c7642 Add unit test for overflow in LinSpaced 2016-10-24 15:43:51 +02:00
Gael Guennebaud
40f62974b7 bug #1328: workaround a compilation issue with gcc 4.2 2016-10-20 19:19:37 +02:00
Benoit Steiner
cf20b30d65 Merge latest updates from trunk 2016-10-20 09:42:05 -07:00
Luke Iwanski
03b63e182c Added SYCL include in Tensor. 2016-10-20 15:32:44 +01:00
Benoit Steiner
d3943cd50c Fixed a few typos in the ternary tensor expressions types 2016-10-19 12:56:12 -07:00
Mehdi Goli
8fb162fc85 Fixing the typo regarding missing #if needed for proper handling of exceptions in Eigen/Core. 2016-10-16 12:52:34 +01:00
Mehdi Goli
e36cb91c99 Fixing the code indentation in the TensorReduction.h file. 2016-10-14 18:03:00 +01:00
Luke Iwanski
2e188dd4d4 Merged ComputeCpp to default. 2016-10-14 16:47:40 +01:00
Mehdi Goli
15380f9a87 Applyiing Benoit's comment to return the missing line back in Eigen/Core 2016-10-14 16:39:41 +01:00
Gael Guennebaud
692b30ca95 Fix previous merge. 2016-10-14 17:16:28 +02:00
Gael Guennebaud
050c681bdd Merged in rmlarsen/eigen2 (pull request PR-232)
Improve performance of parallelized matrix multiply for rectangular matrices
2016-10-14 14:51:09 +00:00
Luke Iwanski
e742da8b28 Merged ComputeCpp into default. 2016-10-14 13:36:51 +01:00
Mehdi Goli
524fa4c46f Reducing the code by generalising sycl backend functions/structs. 2016-10-14 12:09:55 +01:00
Benoit Steiner
737e4152c3 Merged in lukier/eigen (pull request PR-234)
Enabling CUDA in Geometry
2016-10-13 18:09:28 +00:00
Benoit Steiner
d0ee2267d6 Relaxed the resizing checks so that they don't fail with gcc >= 5.3 2016-10-13 10:59:46 -07:00
Robert Lukierski
a94791b69a Fixes for min and abs after Benoit's comments, switched to numext. 2016-10-13 15:00:22 +01:00
Avi Ginsburg
ac63d6891c Patch to allow VS2015 & CUDA 8.0 to compile with Eigen included. I'm not sure
whether to limit the check to this compiler combination
(` || (EIGEN_COMP_MSVC == 1900 &&  __CUDACC_VER__) `)
or to leave it as it is. I also don't know if this will have any affect on
including Eigen in device code (I'm not in my current project).
2016-10-13 08:47:32 +00:00
Benoit Steiner
7e4a6754b2 Merged eigen/eigen into default 2016-10-12 22:42:33 -07:00
Benoit Steiner
38b6048e14 Deleted redundant implementation of predux 2016-10-12 14:37:56 -07:00
Gael Guennebaud
e74612b9a0 Remove double ;; 2016-10-12 22:49:47 +02:00
Benoit Steiner
78d2926508 Merged eigen/eigen into default 2016-10-12 13:46:29 -07:00
Benoit Steiner
2e2f48e30e Take advantage of AVX512 instructions whenever possible to speedup the processing of 16 bit floats. 2016-10-12 13:45:39 -07:00
Gael Guennebaud
f939c351cb Fix SPQR for rectangular matrices 2016-10-12 22:39:33 +02:00
Gael Guennebaud
091d373ee9 Fix outer-stride. 2016-10-12 21:47:52 +02:00
Robert Lukierski
471075f7ad Fixes min() warnings. 2016-10-12 18:59:05 +01:00
Gael Guennebaud
5c366fe1d7 Merged in rmlarsen/eigen (pull request PR-230)
Fix a bug in psqrt for SSE and AVX when EIGEN_FAST_MATH=1
2016-10-12 16:30:51 +00:00
Robert Lukierski
86711497c4 Adding EIGEN_DEVICE_FUNC in the Geometry module.
Additional CUDA necessary fixes in the Core (mostly usage of
EIGEN_USING_STD_MATH).
2016-10-12 16:35:17 +01:00
Rasmus Munk Larsen
47150af1c8 Fix copy-paste error: Must use _mm256_cmp_ps for AVX. 2016-10-12 08:34:39 -07:00
Gael Guennebaud
89e315152c bug #1325: fix compilation on NEON with clang 2016-10-12 16:55:47 +02:00
Benoit Steiner
7f0599b6eb Manually define int16_t and uint16_t when compiling with Visual Studio 2016-10-08 22:56:32 -07:00
Benoit Steiner
5727e4d89c Reenabled the use of variadic templates on tegra x1 provides that the latest version (i.e. JetPack 2.3) is used. 2016-10-08 22:19:03 +00:00
Benoit Steiner
5266ff8966 Cleaned up a regression test 2016-10-08 19:12:44 +00:00
Benoit Steiner
5c68051cd7 Merge the content of the ComputeCpp branch into the default branch 2016-10-07 11:04:16 -07:00
Gael Guennebaud
4860727ac2 Remove static qualifier of free-functions (inline is enough and this helps ICC to find the right overload) 2016-10-07 09:21:12 +02:00
Benoit Steiner
507b661106 Renamed predux_half into predux_downto4 2016-10-06 17:57:04 -07:00
Benoit Steiner
a498ff7df6 Fixed incorrect comment 2016-10-06 15:27:27 -07:00
Benoit Steiner
8ba3c41fcf Revergted unecessary change 2016-10-06 15:12:15 -07:00
Benoit Steiner
a7473d6d5a Fixed compilation error with gcc >= 5.3 2016-10-06 14:33:22 -07:00
Benoit Steiner
5e64cea896 Silenced a compilation warning 2016-10-06 14:24:17 -07:00
Benoit Steiner
33fba3f08d Merged in rryan/eigen/tensorfunctors (pull request PR-233)
Fully support complex types in SumReducer and MeanReducer when building for CUDA by using scalar_sum_op and scalar_product_op instead of operator+ and operator*.
2016-10-06 12:29:19 -07:00
RJ Ryan
bfc264abe8 Add a test that GPU complex product reductions match CPU reductions. 2016-10-06 11:10:14 -07:00
RJ Ryan
e2e9cdd169 Fully support complex types in SumReducer and MeanReducer when building for CUDA by using scalar_sum_op and scalar_product_op instead of operator+ and operator*. 2016-10-06 10:49:48 -07:00
Benoit Steiner
d485d12c51 Added missing AVX intrinsics for fp16: in particular, implemented predux which is required by the matrix-vector code. 2016-10-06 10:41:03 -07:00
Rasmus Munk Larsen
48c635e223 Add a simple cost model to prevent Eigen's parallel GEMM from using too many threads when the inner dimension is small.
Timing for square matrices is unchanged, but both CPU and Wall time are significantly improved for skinny matrices. The benchmarks below are for multiplying NxK * KxN matrices with test names of the form BM_OuterishProd/N/K.

Improvements in Wall time:

Run on [redacted] (12 X 3501 MHz CPUs); 2016-10-05T17:40:02.462497196-07:00
CPU: Intel Haswell with HyperThreading (6 cores) dL1:32KB dL2:256KB dL3:15MB
Benchmark                          Base (ns)  New (ns) Improvement
------------------------------------------------------------------
BM_OuterishProd/64/1                    3088      1610    +47.9%
BM_OuterishProd/64/4                    3562      2414    +32.2%
BM_OuterishProd/64/32                   8861      7815    +11.8%
BM_OuterishProd/128/1                  11363      6504    +42.8%
BM_OuterishProd/128/4                  11128      9794    +12.0%
BM_OuterishProd/128/64                 27691     27396     +1.1%
BM_OuterishProd/256/1                  33214     28123    +15.3%
BM_OuterishProd/256/4                  34312     36818     -7.3%
BM_OuterishProd/256/128               174866    176398     -0.9%
BM_OuterishProd/512/1                7963684    104224    +98.7%
BM_OuterishProd/512/4                7987913    112867    +98.6%
BM_OuterishProd/512/256              8198378   1306500    +84.1%
BM_OuterishProd/1k/1                 7356256    324432    +95.6%
BM_OuterishProd/1k/4                 8129616    331621    +95.9%
BM_OuterishProd/1k/512              27265418   7517538    +72.4%

Improvements in CPU time:

Run on [redacted] (12 X 3501 MHz CPUs); 2016-10-05T17:40:02.462497196-07:00
CPU: Intel Haswell with HyperThreading (6 cores) dL1:32KB dL2:256KB dL3:15MB
Benchmark                          Base (ns)  New (ns) Improvement
------------------------------------------------------------------
BM_OuterishProd/64/1                    6169      1608    +73.9%
BM_OuterishProd/64/4                    7117      2412    +66.1%
BM_OuterishProd/64/32                  17702     15616    +11.8%
BM_OuterishProd/128/1                  45415      6498    +85.7%
BM_OuterishProd/128/4                  44459      9786    +78.0%
BM_OuterishProd/128/64                110657    109489     +1.1%
BM_OuterishProd/256/1                 265158     28101    +89.4%
BM_OuterishProd/256/4                 274234    183885    +32.9%
BM_OuterishProd/256/128              1397160   1408776     -0.8%
BM_OuterishProd/512/1               78947048    520703    +99.3%
BM_OuterishProd/512/4               86955578   1349742    +98.4%
BM_OuterishProd/512/256             74701613  15584661    +79.1%
BM_OuterishProd/1k/1                78352601   3877911    +95.1%
BM_OuterishProd/1k/4                78521643   3966221    +94.9%
BM_OuterishProd/1k/512              258104736  89480530    +65.3%
2016-10-06 10:33:10 -07:00
Benoit Steiner
9f3276981c Enabling AVX512 should also enable AVX2. 2016-10-06 10:29:48 -07:00
Gael Guennebaud
80b5133789 Fix compilation of qr.inverse() for column and full pivoting variants. 2016-10-06 09:55:50 +02:00
Benoit Steiner
4131074818 Deleted unecessary CMakeLists.txt file 2016-10-05 18:54:35 -07:00
Benoit Steiner
cb5cd69872 Silenced a compilation warning. 2016-10-05 18:50:53 -07:00
Benoit Steiner
78b569f685 Merged latest updates from trunk 2016-10-05 18:48:55 -07:00
Benoit Steiner
9c2b6c049b Silenced a few compilation warnings 2016-10-05 18:37:31 -07:00
Benoit Steiner
6f3cd529af Pulled latest updates from trunk 2016-10-05 18:31:43 -07:00
Benoit Steiner
d7f9679a34 Fixed a couple of compilation warnings 2016-10-05 15:00:32 -07:00
Benoit Steiner
ae1385c7e4 Pull the latest updates from trunk 2016-10-05 14:54:36 -07:00
Benoit Steiner
73b0012945 Fixed compilation warnings 2016-10-05 14:24:24 -07:00
Benoit Steiner
c84084c0c0 Fixed compilation warning 2016-10-05 14:15:41 -07:00
Benoit Steiner
4387433acf Increased the robustness of the reduction tests on fp16 2016-10-05 10:42:41 -07:00
Benoit Steiner
aad20d700d Increase the tolerance to numerical noise. 2016-10-05 10:39:24 -07:00
Benoit Steiner
8b69d5d730 ::rand() returns a signed integer on win32 2016-10-05 08:55:02 -07:00
Benoit Steiner
ed7a220b04 Fixed a typo that impacts windows builds 2016-10-05 08:51:31 -07:00
Benoit Steiner
ceee1c008b Silenced compilation warning 2016-10-04 18:47:53 -07:00
Benoit Steiner
698ff69450 Properly characterize the CUDA packet primitives for fp16 as device only 2016-10-04 16:53:30 -07:00
Rasmus Munk Larsen
7f67e6dfdb Update comment for fast sqrt. 2016-10-04 15:09:11 -07:00
Rasmus Munk Larsen
765615609d Update comment for fast sqrt. 2016-10-04 15:08:41 -07:00
Rasmus Munk Larsen
3ed67cb0bb Fix a bug in the implementation of Carmack's fast sqrt algorithm in Eigen (enabled by EIGEN_FAST_MATH), which causes the vectorized parts of the computation to return -0.0 instead of NaN for negative arguments.
Benchmark speed in Giga-sqrts/s
Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
-----------------------------------------
                    SSE        AVX
Fast=1              2.529G     4.380G
Fast=0              1.944G     1.898G
Fast=1 fixed        2.214G     3.739G

This table illustrates the worst case in terms speed impact: It was measured by repeatedly computing the sqrt of an n=4096 float vector that fits in L1 cache. For large vectors the operation becomes memory bound and the differences between the different versions almost negligible.
2016-10-04 14:22:56 -07:00
Benoit Steiner
6af5ac7e27 Cleanup the cuda executor code. 2016-10-04 08:52:13 -07:00
Benoit Steiner
2f6d1607c8 Cleaned up the random number generation code. 2016-10-04 08:38:23 -07:00
Benoit Steiner
881b90e984 Use explicit type casting to generate packets of zeros. 2016-10-04 08:23:38 -07:00
Benoit Steiner
616a7a1912 Improved support for compiling CUDA code with clang as the host compiler 2016-10-03 17:09:33 -07:00
Benoit Steiner
409e887d78 Added support for constand std::complex numbers on GPU 2016-10-03 11:06:24 -07:00
Gael Guennebaud
9d6d0dff8f bug #1317: fix performance regression with some Block expressions and clang by helping it to remove dead code.
The trick is to get rid of the nested expression in the evaluator by copying only the required information (here, the strides).
2016-10-01 15:37:00 +02:00
Gael Guennebaud
8b84801f7f bug #1310: workaround a compilation regression from 3.2 regarding triangular * homogeneous 2016-09-30 22:49:59 +02:00
Benoit Steiner
422530946f Renamed the SYCL tests to follow the standard naming convention. 2016-09-30 08:22:10 -07:00
Gael Guennebaud
67b4f45836 Fix angle range 2016-09-30 12:46:33 +02:00
Gael Guennebaud
27f3970453 Remove std:: prefix 2016-09-30 12:40:41 +02:00
Gael Guennebaud
3860a0bc8f bug #1312: Quaternion to AxisAngle conversion now ensures the angle will be in the range [-pi,pi]. This also increases accuracy when q.w is negative. 2016-09-29 23:23:35 +02:00
Gael Guennebaud
33500050c3 bug #1308: fix compilation of some small products involving nullary-expressions. 2016-09-29 09:40:44 +02:00
Benoit Steiner
27d7628f16 Updated the list of warnings to reflect the new message ids introduced in cuda 8.0 2016-09-28 17:42:59 -07:00
Benoit Steiner
2bda1b0d93 Updated the tensor sum and mean reducer to enable them to process complex numbers on cuda gpus. 2016-09-28 17:08:41 -07:00
Mehdi Goli
dd602e62c8 Converting alias template to nested struct in order to be compatible with CXX-03 2016-09-27 16:21:19 +01:00
Gael Guennebaud
f3a00dd2b5 Merged in sergiu/eigen (pull request PR-229)
Disabled MSVC level 4 warning C4714
2016-09-27 09:28:08 +02:00
Gael Guennebaud
892afb9416 Add debug info. 2016-09-26 23:53:57 +02:00
Gael Guennebaud
779774f98c bug #1311: fix alignment logic in some cases of (scalar*small).lazyProduct(small) 2016-09-26 23:53:40 +02:00
Benoit Steiner
6565f8d60f Made the initialization of a CUDA device thread safe. 2016-09-26 11:00:32 -07:00
Gael Guennebaud
48dfe98abd bug #1308: fix compilation of vector * rowvector::nullary. 2016-09-25 14:54:35 +02:00
Sergiu Deitsch
fe29157d02 disabled MSVC level 4 warning C4714
The level 4 warning (/W4) warns about functions marked as __forceinline not
inlined, and generates a lot of noise.
2016-09-25 14:25:47 +02:00
Benoit Steiner
f6ac51a054 Made TensorEvalTo compatible with c++0x again. 2016-09-23 16:45:17 -07:00
Benoit Steiner
00d4e65f00 Deleted unused TensorMap data member 2016-09-23 16:44:45 -07:00
Gael Guennebaud
86caba838d bug #1304: fix Projective * scaling and Projective *= scaling 2016-09-23 13:41:21 +02:00
Gael Guennebaud
b9f7a17e47 Add missing file. 2016-09-23 10:26:08 +02:00
Benoit Steiner
1301d744f8 Made the gaussian generator usable on GPU 2016-09-22 19:04:44 -07:00
Benoit Steiner
2a69290ddb Added a specialization of Eigen::numext::real and Eigen::numext::imag for std::complex<T> to be used when compiling a cuda kernel. This is unfortunately necessary to be able to process complex numbers from a CUDA kernel on MacOS. 2016-09-22 15:52:23 -07:00
Gael Guennebaud
3946768916 Added tag 3.3-rc1 for changeset 77e27fbeee 2016-09-22 22:38:36 +02:00
Gael Guennebaud
77e27fbeee bump to 3.3-rc1 2016-09-22 22:37:39 +02:00
Gael Guennebaud
2ada122bc6 merge 2016-09-22 22:33:18 +02:00
Gael Guennebaud
8f2bdde373 merge 2016-09-22 22:32:55 +02:00
Gael Guennebaud
ba0f844d6b Backout changeset ce3557ca69 2016-09-22 22:28:51 +02:00
Gael Guennebaud
9bcdc8b756 Add a nullary-functor example performing index-based sub-matrices. 2016-09-22 22:27:54 +02:00
Benoit Steiner
50e3bbfc90 Calls x.imag() instead of imag(x) when x is a complex number since the former
is a constexpr while the later isn't. This fixes compilation errors triggered by nvcc on Mac.
2016-09-22 13:17:25 -07:00
Gael Guennebaud
ca3746c6f8 Bypass identity reflectors. 2016-09-22 22:07:13 +02:00
Felix Gruber
8bde7da086 fix documentation of LinSpaced
The index of the highest value in a LinSpace is size-1.
2016-09-22 14:50:07 +02:00
Gael Guennebaud
66cbabafed Add a note regarding gcc bug #72867 2016-09-22 11:18:52 +02:00
Christoph Hertzberg
4b377715d7 Do not manually add absolute path to boost-library.
Also set C++ standard for blaze to C++14
2016-09-22 00:10:47 +02:00
Gael Guennebaud
aecc51a3e8 fix typo 2016-09-21 21:53:00 +02:00
Gael Guennebaud
1fc3a21ed0 Disable a failure test if extended double precision is in use (x87) 2016-09-21 20:09:07 +02:00
Gael Guennebaud
9fa2c8650e Fix alignement of statically allocated temporaries in symv, and trmv. 2016-09-21 17:34:24 +02:00
Gael Guennebaud
ac5377e161 Improve cost estimation of complex division 2016-09-21 17:26:04 +02:00
Gael Guennebaud
5269d11935 Fix compilation if ICC. 2016-09-21 17:08:51 +02:00
Benoit Steiner
26f9907542 Added missing typedefs 2016-09-20 12:58:03 -07:00
RJ Ryan
608b1acd6d Don't use c++11 features and fix include. 2016-09-20 07:49:05 -07:00
RJ Ryan
b2c6dc48d9 Add CUDA-specific std::complex<T> specializations for scalar_sum_op, scalar_difference_op, scalar_product_op, and scalar_quotient_op. 2016-09-20 07:18:20 -07:00
Benoit Steiner
8a66ca4b10 Pulled latest updates from trunk 2016-09-19 14:13:55 -07:00
Benoit Steiner
59e9edfbf1 Removed EIGEN_DEVICE_FUNC qualifers for the lu(), fullPivLu(), partialPivLu(), and inverse() functions since they aren't ready to run on GPU 2016-09-19 14:13:20 -07:00
Gael Guennebaud
3ada6e4bed Merged hongkai-dai/eigen/tip into default (bug #1298) 2016-09-19 22:08:06 +02:00
Benoit Steiner
c3ca9b1e76 Deleted some unecessary and confusing EIGEN_DEVICE_FUNC 2016-09-19 11:33:39 -07:00
Hongkai Dai
5dcc6d301a remove ternary operator in euler angles 2016-09-19 10:30:30 -07:00
Luke Iwanski
c771df6bc3 Updated the owners of the file. 2016-09-19 14:09:25 +01:00
Luke Iwanski
b91e021172 Merged with default. 2016-09-19 14:03:54 +01:00
Luke Iwanski
cb81975714 Partial OpenCL support via SYCL compatible with ComputeCpp CE. 2016-09-19 12:44:13 +01:00
Gael Guennebaud
bf03820339 Silent warning. 2016-09-17 14:14:01 +02:00
Gael Guennebaud
de05a18fe0 fix compilation with boost::multiprec 2016-09-17 14:13:48 +02:00
Gael Guennebaud
4cc2c73e6a Fix alignement of statically allocated temporaries in gemv. 2016-09-17 12:52:27 +02:00
Christoph Hertzberg
ce3557ca69 Make makeHouseholder more stable for cases where real(c0) is not very small (but the rest is). 2016-09-16 14:24:47 +02:00
Emil Fresk
6edd2e2851 Made AutoDiffJacobian more intuitive to use and updated for C++11
Changes:
* Removed unnecessary types from the Functor by inferring from its types
* Removed inputs() function reference, replaced with .rows()
* Updated the forward constructor to use variadic templates
* Added optional parameters to the Fuctor for passing parameters,
  control signals, etc
* Has been tested with fixed size and dynamic matricies

Ammendment by chtz: overload operator() for compatibility with not fully conforming compilers
2016-09-16 14:03:55 +02:00
Gael Guennebaud
4adeababf9 Fix undeflow 2016-09-16 11:46:46 +02:00
Gael Guennebaud
18f6e47815 Fix order of "static inline". 2016-09-16 11:32:54 +02:00
Gael Guennebaud
ee62f168e6 Doc: add link from block methods to respective tutorial section. 2016-09-16 11:26:25 +02:00
Gael Guennebaud
ca7f061a5f bug #828: clarify documentation of SparseMatrixBase's methods returning a sub-matrix. 2016-09-16 11:23:19 +02:00
Gael Guennebaud
50e203c717 bug #828: clarify documentation of SparseMatrixBase's unary methods. 2016-09-16 10:40:50 +02:00
Gael Guennebaud
fa9049a544 Let be consistent and consider any denormal number as zero. 2016-09-15 11:24:03 +02:00
Gael Guennebaud
b33144e4df merge 2016-09-15 11:22:16 +02:00
Benoit Steiner
c0d56a543e Added several missing EIGEN_DEVICE_FUNC qualifiers 2016-09-14 14:06:21 -07:00
Benoit Steiner
488ad7dd1b Added missing EIGEN_DEVICE_FUNC qualifiers 2016-09-14 13:35:00 -07:00
Benoit Steiner
779faaaeba Fixed compilation warnings generated by nvcc 6.5 (and below) when compiling the EIGEN_THROW macro 2016-09-14 09:56:11 -07:00
Gael Guennebaud
1c8347e554 Fix product for custom complex type. (conjugation was ignored) 2016-09-14 18:28:49 +02:00
Benoit Steiner
ff47717f25 Suppress warning 2527 and 2529, which correspond to the "calling a __host__ function from a __host__ __device__ function is not allowed" message in nvcc 6.5. 2016-09-13 12:49:40 -07:00
Benoit Steiner
309190cf02 Suppress message 1222 when compiling with nvcc: this ensures that we don't warnings about unknown warning messages when compiling with older versions of nvcc 2016-09-13 12:42:13 -07:00
Gael Guennebaud
c10620b2b0 Fix typo in doc. 2016-09-13 09:25:07 +02:00
Gael Guennebaud
73c8f2f697 bug #1285: fix regression introduced in changeset 00c29c2cae 2016-09-13 07:58:39 +02:00
Benoit Steiner
e4d4d15588 Register the cxx11_tensor_device only for recent cuda architectures (i.e. >= 3.0) since the test instantiate contractions that require a modern gpu. 2016-09-12 19:01:52 -07:00
Benoit Steiner
4dfd888c92 CUDA contractions require arch >= 3.0: don't compile the cuda contraction tests on older architectures. 2016-09-12 18:49:01 -07:00
Benoit Steiner
028e299577 Fixed a bug impacting some outer reductions on GPU 2016-09-12 18:36:52 -07:00
Benoit Steiner
5f50f12d2c Added the ability to compute the absolute value of a complex number on GPU, as well as a test to catch the problem. 2016-09-12 13:46:13 -07:00
Benoit Steiner
8321dcce76 Merged latest updates from trunk 2016-09-12 10:33:05 -07:00
Benoit Steiner
eb6ba00cc8 Properly size the list of waiters 2016-09-12 10:31:55 -07:00
Benoit Steiner
a618094b62 Added a resize method to MaxSizeVector 2016-09-12 10:30:53 -07:00
Gael Guennebaud
228ae29591 Fix compilation on 32 bits systems. 2016-09-09 22:34:38 +02:00
Gael Guennebaud
471eac5399 bug #1195: move NumTraits::Div<>::Cost to internal::scalar_div_cost (with some specializations in arch/SSE and arch/AVX) 2016-09-08 08:36:27 +02:00
Gael Guennebaud
d780983f59 Doc: explain minimal requirements on nullary functors 2016-09-06 23:14:52 +02:00
Gael Guennebaud
85fb517eaf Generalize ScalarBinaryOpTraits to any complex-real combination as defined by NumTraits (instead of supporting std::complex only). 2016-09-06 17:23:15 +02:00
Gael Guennebaud
447f269561 Disable previous workaround. 2016-09-06 15:49:02 +02:00
Gael Guennebaud
b046a3f87d Workaround MSVC instantiation faillure of has_*ary_operator at the level of triats<Ref>::match so that the has_*ary_operator are really properly instantiated throughout the compilation unit. 2016-09-06 15:47:04 +02:00
Gael Guennebaud
3cb914f332 bug #1266: remove CUDA guards on MatrixBase::<decomposition> definitions. (those used to break old nvcc versions that we propably don't care anymore) 2016-09-06 09:55:50 +02:00
Gael Guennebaud
e1642f485c bug #1288: fix memory leak in arpack wrapper. 2016-09-05 18:01:30 +02:00
Gael Guennebaud
19a95b3309 Fix shadowing wrt Eigen::Index 2016-09-05 17:19:47 +02:00
Gael Guennebaud
dabc81751f Fix compilation when cuda_fp16.h does not exist. 2016-09-05 17:14:20 +02:00
Gael Guennebaud
e13071dd13 Workaround a weird msvc 2012 compilation error. 2016-09-05 15:50:41 +02:00
Gael Guennebaud
d123717e21 Fix for msvc 2012 and older 2016-09-05 15:26:56 +02:00
Benoit Steiner
87a8a1975e Fixed a regression test 2016-09-02 19:29:33 -07:00
Benoit Steiner
13df3441ae Use MaxSizeVector instead of std::vector: xcode sometimes assumes that std::vector allocates aligned memory and therefore issues aligned instruction to initialize it. This can result in random crashes when compiling with AVX instructions enabled. 2016-09-02 19:25:47 -07:00
Benoit Steiner
373c340b71 Fixed a typo 2016-09-02 15:41:17 -07:00
Benoit Steiner
cadd124d73 Pulled latest update from trunk 2016-09-02 15:30:02 -07:00
Benoit Steiner
05b0518077 Made the index type an explicit template parameter to help some compilers compile the code. 2016-09-02 15:29:34 -07:00
Benoit Steiner
adf864fec0 Merged in rmlarsen/eigen (pull request PR-222)
Fix CUDA build broken by changes to min and max reduction.
2016-09-02 14:11:20 -07:00
Benoit Steiner
5a6be66cef Turned the Index type used by the nullary wrapper into a template parameter. 2016-09-02 14:10:29 -07:00
Rasmus Munk Larsen
13e93ca8b7 Fix CUDA build broken by changes to min and max reduction. 2016-09-02 13:41:36 -07:00
Benoit Steiner
6c05c3dd49 Fix the cxx11_tensor_cuda.cu test on 32bit platforms. 2016-09-02 11:12:16 -07:00
Gael Guennebaud
49c0390ce0 merge 2016-09-02 15:24:14 +02:00
Gael Guennebaud
d6c8366d84 Fix compilation with MSVC 2012 2016-09-02 15:23:32 +02:00
Benoit Steiner
039e225f7f Added a test for nullary expressions on CUDA
Also check that we can mix 64 and 32 bit indices in the same compilation unit
2016-09-01 13:28:12 -07:00
Benoit Steiner
c53f783705 Updated the contraction code to support constant inputs. 2016-09-01 11:41:27 -07:00
Gael Guennebaud
ef54723dbe One more msvc fix iteration, the previous one was over-simplified for visual 2016-09-01 15:04:53 +02:00
Gael Guennebaud
46475eff9a Adjust Tensor module wrt recent change in nullary functor 2016-09-01 13:40:45 +02:00
Gael Guennebaud
72a4d49315 Fix compilation with CUDA 8 2016-09-01 13:39:33 +02:00
Gael Guennebaud
f9f32e9e2d Fix compilation with nvcc 2016-09-01 13:06:14 +02:00
Gael Guennebaud
3d946e42b3 Fix compilation with visual studio 2016-09-01 12:59:32 +02:00
Benoit Steiner
221f619bea Merged in rmlarsen/eigen (pull request PR-221)
Fix bugs to make min- and max reducers work with correctly with IEEE infinities.
2016-08-31 15:10:10 -07:00
Rasmus Munk Larsen
a1e092d1e8 Fix bugs to make min- and max reducers with correctly with IEEE infinities. 2016-08-31 15:04:16 -07:00
Gael Guennebaud
836fa25a82 Make sure sizeof is truelly needed, thus improving SFINAE portability. 2016-08-31 23:40:18 +02:00
Gael Guennebaud
84cf6e42ca minor tweaks in has_* helpers 2016-08-31 23:04:14 +02:00
Gael Guennebaud
7ae819123c Simplify CwiseNullaryOp example. 2016-08-31 15:46:04 +02:00
Gael Guennebaud
218c37beb4 bug #1286: automatically detect the available prototypes of functors passed to CwiseNullaryExpr such that functors have only to implement the operators that matters among:
operator()()
 operator()(i)
 operator()(i,j)
Linear access is also automatically detected based on the availability of operator()(i,j).
2016-08-31 15:45:25 +02:00
Gael Guennebaud
efe2c225c9 bug #1283: add regression unit test 2016-08-31 13:04:29 +02:00
Gael Guennebaud
3456247437 bug #1283: quick fix for products involving uncommon general block access to vectors. 2016-08-31 08:17:15 +02:00
Gael Guennebaud
8c48d42530 Fix 4x4 inverse with non-linear destination 2016-08-30 23:16:38 +02:00
Gael Guennebaud
e7fbbc2748 Doc: add links and discourage user to write their own expression (better use CwiseNullaryOp) 2016-08-30 15:57:46 +02:00
Gael Guennebaud
1e2ab8b0b3 Doc: add an exemple showing how custom expression can be advantageously implemented via CwiseNullaryOp. 2016-08-30 15:40:41 +02:00
Gael Guennebaud
9c9e23858e Doc: split customizing-eigen page into sub-pages and re-structure a bit the different topics 2016-08-30 11:10:08 +02:00
Gael Guennebaud
cffe8bbff7 Doc: add link to example 2016-08-30 10:45:27 +02:00
Gael Guennebaud
c57317035a Fix unit test for 1x1 matrices 2016-08-30 10:20:23 +02:00
Gael Guennebaud
1f84f0d33a merge EulerAngles module 2016-08-30 10:01:53 +02:00
Gael Guennebaud
68e803a26e Fix warning 2016-08-30 09:21:57 +02:00
Gael Guennebaud
e074f720c7 Include missing forward declaration of SparseMatrix 2016-08-29 18:56:46 +02:00
Gael Guennebaud
2915e1fc5d Revert part of changeset 5b3a6f51d3
to keep accuracy of smallest eigenvalues.
2016-08-29 14:14:18 +02:00
Gael Guennebaud
7e029d1d6e bug #1271: add SparseMatrix::coeffs() methods returning a 1D view of the non zero coefficients. 2016-08-29 12:06:37 +02:00
Gael Guennebaud
a93e354d92 Add some pre-allocation unit tests (not working yet) 2016-08-29 11:08:44 +02:00
Gael Guennebaud
6cd7b9ea6b Fix compilation with cuda 8 2016-08-29 11:06:08 +02:00
Gael Guennebaud
8f4b4ad5fb use ::hlog if available. 2016-08-29 11:05:32 +02:00
Gael Guennebaud
35a8e94577 bug #1167: simplify installation of header files using cmake's install(DIRECTORY ...) command. 2016-08-29 10:59:37 +02:00
Gael Guennebaud
0decc31aa8 Add generic implementation of conj_helper for custom complex types. 2016-08-29 09:42:29 +02:00
Gael Guennebaud
fd9caa1bc2 bug #1282: fix implicit double to float conversion warning 2016-08-28 22:45:56 +02:00
Gael Guennebaud
68d1897e8a Make sure that our log1p implementation is called as a last resort only. 2016-08-26 15:30:55 +02:00
Gael Guennebaud
fe60856fed Add overload of numext::log1p for float/double in CUDA 2016-08-26 15:28:59 +02:00
Gael Guennebaud
0f56b5a6de enable vectorization path when testing half on cuda, and add test for log1p 2016-08-26 14:55:51 +02:00
Gael Guennebaud
965e595f02 Add missing log1p method 2016-08-26 14:55:00 +02:00
Gael Guennebaud
1329c55875 Fix compilation with boost::multiprec. 2016-08-25 14:54:39 +02:00
Gael Guennebaud
441b7eaab2 Add support for non trivial scalar factor in sparse selfadjoint * dense products, and enable +=/-= assignement for such products.
This changeset also improves the performance by working on column of the result at once.
2016-08-24 13:06:34 +02:00
Gael Guennebaud
8132a12625 bug #1268: detect faillure in LDLT and report them through info() 2016-08-23 23:15:55 +02:00
Gael Guennebaud
bde9b456dc Typo 2016-08-23 21:36:36 +02:00
Gael Guennebaud
326320ec7b Fix compilation in non C++11 mode. 2016-08-23 19:28:57 +02:00
Gael Guennebaud
ea2e968257 Address several implicit scalar conversions. 2016-08-23 18:44:33 +02:00
Gael Guennebaud
0a6a50d1b0 Cleanup eiegnvector extraction: leverage matrix products and compile-time sizes, remove numerous useless temporaries. 2016-08-23 18:14:37 +02:00
Gael Guennebaud
00b2666853 bug #645: patch from Tobias Wood implementing the extraction of eigenvectors in GeneralizedEigenSolver 2016-08-23 17:37:38 +02:00
Gael Guennebaud
504a4404f1 Optimize expression matching "d?=a-b*c" as "d?=a; d?=b*c;" 2016-08-23 16:52:22 +02:00
Gael Guennebaud
e47a8928ec Fix compilation in check_for_aliasing due to ambiguous specializations 2016-08-23 16:19:10 +02:00
Gael Guennebaud
6739f6bb1b Merged in traversaro/eigen-1/traversaro/modify-findeigen3cmake-to-find-eigen3con-1469782761059 (pull request PR-213)
Modify FindEigen3.cmake to find Eigen3Config.cmake
2016-08-23 15:53:57 +02:00
Gael Guennebaud
ef3de20481 Cleanup cost of tanh 2016-08-23 14:39:55 +02:00
Gael Guennebaud
b3151bca40 Implement pmadd for float and double to make it consistent with the vectorized path when FMA is available. 2016-08-23 14:24:08 +02:00
Gael Guennebaud
a4c266f827 Factorize the 4 copies of tanh implementations, make numext::tanh consistent with array::tanh, enable fast tanh in fast-math mode only. 2016-08-23 14:23:08 +02:00
Gael Guennebaud
82147cefff Fix possible overflow and biais in integer random generator 2016-08-23 13:25:31 +02:00
Silvio Traversaro
068ccab9fe FindEigen3.cmake : search for package only if EIGEN3_INCLUDE_DIR is not already defined 2016-08-22 22:13:10 +00:00
Gael Guennebaud
581b6472d1 bug #1265: remove outdated notes 2016-08-22 23:25:39 +02:00
Igor Babuschkin
59bacfe520 Fix compilation on CUDA 8 by removing call to h2log1p 2016-08-15 23:38:05 +01:00
Benoit Steiner
34ae80179a Use array_prod instead of calling TotalSize since TotalSize is only available on DSize. 2016-08-15 10:29:14 -07:00
Benoit Steiner
2556565b4b Merged in ibab/eigen/extend-log1p (pull request PR-218)
Fix compilation on CUDA 8 due to missing h2log1p function
2016-08-15 08:31:03 -07:00
Benoit Steiner
30dd6f5e34 Close branch extend-log1p 2016-08-15 08:31:03 -07:00
Benoit Steiner
fe73648c98 Fixed a bug in the documentation. 2016-08-12 10:00:43 -07:00
Christoph Hertzberg
9636a8ed43 bug #1273: Add parentheses when redefining eigen_assert 2016-08-12 15:34:21 +02:00
Christoph Hertzberg
c83b754ee0 bug #1272: Disable assertion when total number of columns is zero.
Also moved assertion to finished() method and adapted unit-test
2016-08-12 15:15:34 +02:00
Benoit Steiner
e3a8dfb02f std::erfcf doesn't exist: use numext::erfc instead 2016-08-11 15:24:06 -07:00
Benoit Steiner
64e68cbe87 Don't attempt to optimize partial reductions when the optimized implementation doesn't buy anything. 2016-08-08 19:29:59 -07:00
Benoit Steiner
5157ce8cbf Merged in ibab/eigen/extend-log1p (pull request PR-217)
Add log1p support for CUDA and half floats
2016-08-08 14:50:00 -07:00
Igor Babuschkin
aee693ac52 Add log1p support for CUDA and half floats 2016-08-08 20:24:59 +01:00
Benoit Steiner
72096f3bd4 Merged in suiyuan2009/eigen/fix_tanh_inconsistent_for_tensorflow (pull request PR-215)
Fix_tanh_inconsistent_for_tensorflow
2016-08-08 09:06:45 -07:00
Christoph Hertzberg
3e4a33d4ba bug #1272: Let CommaInitializer work for more border cases (enhances fix of bug #1242).
The unit test tests all combinations of 2x2 block-sizes from 0 to 3.
2016-08-08 17:26:48 +02:00
Ziming Dong
1031223c09 fix tanh inconsistent 2016-08-06 19:48:50 +08:00
Ziming Dong
5cf1e4c79b create fix_tanh_inconsistent branch 2016-08-06 15:54:33 +08:00
Christoph Hertzberg
fe4b927e9c Add aliases Eigen_*_DIR to Eigen3_*_DIR
This is to make configuring work again after project was renamed from Eigen to Eigen3
2016-08-05 15:21:14 +02:00
Benoit Steiner
fe778427f2 Fixed the constructors of the new half_base class. 2016-08-04 18:32:26 -07:00
Benoit Steiner
5eea1c7f97 Fixed cut and paste bug in debud message 2016-08-04 17:34:13 -07:00
Benoit Steiner
9506343349 Fixed the isnan, isfinite and isinf operations on GPU 2016-08-04 17:25:53 -07:00
Benoit Steiner
b50d8f8c4a Extended a regression test to validate that we basic fp16 support works with cuda 7.0 2016-08-03 16:50:13 -07:00
Benoit Steiner
fad9828769 Deleted redundant regression test. 2016-08-03 16:08:37 -07:00
Benoit Steiner
373bb12dc6 Check that it's possible to forward declare the hlaf type. 2016-08-03 16:07:31 -07:00
Gael Guennebaud
17b9a55d98 Move Eigen::half_impl::half to Eigen::half while preserving the free functions to the Eigen::half_impl namespace together with ADL 2016-08-04 00:00:43 +02:00
Benoit Steiner
ca2cee2739 Merged in ibab/eigen (pull request PR-206)
Expose real and imag methods on Tensors
2016-08-03 11:53:04 -07:00
Benoit Steiner
d92df04ce8 Cleaned up the new float16 test a bit 2016-08-03 11:50:07 -07:00
Benoit Steiner
81099ef482 Added a test for fp16 2016-08-03 11:41:17 -07:00
Benoit Steiner
a20b58845f CUDA_ARCH isn't always defined, so avoid relying on it too much when figuring out which implementation to use for reductions. Instead rely on the device to tell us on which hardware version we're running. 2016-08-03 10:00:43 -07:00
Gael Guennebaud
819d0cea1b List PARDISO solver. 2016-08-02 23:32:41 +02:00
Christoph Hertzberg
f4404777ff Change project name to Eigen3, to be compatible with FindEigen3.cmake and Eigen3Config.cmake.
This is related to pull-requests 214.
2016-08-02 17:08:57 +00:00
Benoit Steiner
fd220dd8b0 Use numext::conj instead of std::conj 2016-08-01 18:16:16 -07:00
Benoit Steiner
e256acec7c Avoid unecessary object copies 2016-08-01 17:03:39 -07:00
Gael Guennebaud
7995cec90c Fix vectorization logic for coeff-based product for some corner cases. 2016-07-31 15:20:22 +02:00
Benoit Steiner
02fe89f5ef half implementation has been moved to half_impl namespace 2016-07-29 15:09:34 -07:00
Benoit Steiner
2693fd54bf bug #1266: half implementation has been moved to half_impl namespace 2016-07-29 13:45:56 -07:00
Christoph Hertzberg
c5b893f434 bug #1266: half implementation has been moved to half_impl namespace 2016-07-29 18:36:08 +02:00
Silvio Traversaro
5e51a361fe Modify FindEigen3.cmake to find Eigen3Config.cmake 2016-07-29 08:59:38 +00:00
klimpel
ca5effa16c MSVC-2010 is making problems with SFINAE again. But restricting to the variant for very old compilers (enum, template<typename C> for both function definitions) fixes the problem. 2016-07-28 15:58:17 +01:00
Gael Guennebaud
4057f9b1fc Enable slice-vectorization+inner-unrolling when unaligned vectorization is allowed. For instance, this permits to vectorize 5x5 matrices (including product) 2016-07-28 13:47:33 +02:00
Gael Guennebaud
5fbe7aa604 Update and fix Cholesky mini benchmark 2016-07-28 11:26:30 +02:00
Gael Guennebaud
a72752caac Vectorize more small product expressions by letting the general assignement logic decides on the sizes that are OK for vectorization. 2016-07-28 11:21:07 +02:00
Gael Guennebaud
cc2f6d68b1 bug #1264: fix compilation 2016-07-27 23:30:47 +02:00
Gael Guennebaud
188590db82 Add instructions for LAPACKE+Accelerate 2016-07-27 15:07:35 +02:00
Gael Guennebaud
8972323c08 Big 1261: add missing max(ADS,ADS) overload (same for min) 2016-07-27 14:52:48 +02:00
Gael Guennebaud
5d94dc85e5 bug #1260: add regression test 2016-07-27 14:38:30 +02:00
Gael Guennebaud
0d7039319c bug #1260: remove doubtful specializations of ScalarBinaryOpTraits 2016-07-27 14:35:52 +02:00
Christoph Hertzberg
d3d7c6245d Add brackets to block matrix and fixed some typos 2016-07-27 09:55:39 +02:00
Gael Guennebaud
0eece608b4 Added tag 3.3-beta2 for changeset f6b3cf8de9 2016-07-26 23:52:14 +02:00
Gael Guennebaud
f6b3cf8de9 Bump to 3.3-beta2 2016-07-26 23:51:59 +02:00
Gael Guennebaud
9d16b6e1cf Formatting 2016-07-26 23:51:43 +02:00
Gael Guennebaud
fd2f989b1d Fix testing of nearly zero input matrices. 2016-07-26 14:46:02 +02:00
Gael Guennebaud
c9e3e438eb Add more very small numbers in the list of nearly "zero" values when testing SVD and EVD algorithms 2016-07-26 14:45:44 +02:00
Gael Guennebaud
95113cb15c Improve robustness of 2x2 eigenvalue with shifting and scaling 2016-07-26 14:43:54 +02:00
Gael Guennebaud
7f7e84aa36 Fix compilation with MKL support 2016-07-26 13:31:29 +02:00
Gael Guennebaud
429028b652 Typo. 2016-07-26 12:12:53 +02:00
Gael Guennebaud
6b89fa802c Typos. 2016-07-26 12:08:04 +02:00
Gael Guennebaud
c581c8fa79 Fix with expession template scalar types. 2016-07-26 11:33:28 +02:00
Gael Guennebaud
8021aed89e Split BLAS/LAPACK versus MKL documentation 2016-07-26 11:11:59 +02:00
Gael Guennebaud
757971e7ea bug #1258: fix compilation of Map<SparseMatrix>::coeffRef 2016-07-26 09:40:19 +02:00
Gael Guennebaud
c9425492c8 Update doc. 2016-07-25 18:41:26 +02:00
Gael Guennebaud
0592b4cfbf merge 2016-07-25 18:20:22 +02:00
Gael Guennebaud
9c663e4ee8 Clean references to MKL in LAPACKe support. 2016-07-25 18:20:08 +02:00
Gael Guennebaud
0c06077efa Rename MKL files 2016-07-25 18:00:47 +02:00
Gael Guennebaud
4d54e3dd33 bug #173: remove dependency to MKL for LAPACKe backend. 2016-07-25 17:55:07 +02:00
Benoit Steiner
3d3d34e442 Deleted dead code. 2016-07-25 08:53:37 -07:00
Gael Guennebaud
34b483e25d bug #1249: enable use of __builtin_prefetch for GCC, clang, and ICC only. 2016-07-25 15:17:45 +02:00
Gael Guennebaud
6d5daf32f5 bug #1255: comment out broken and unsused line. 2016-07-25 14:48:30 +02:00
Gael Guennebaud
f9598d73b5 bug #1250: fix pow() for AutoDiffScalar with custom nested scalar type. 2016-07-25 14:42:19 +02:00
Gael Guennebaud
fd1117f2be Implement digits10 for mpreal 2016-07-25 14:38:55 +02:00
Gael Guennebaud
9908020d36 Add minimal support for Array<string>, and fix Tensor<string> 2016-07-25 14:25:56 +02:00
Gael Guennebaud
4184a3e544 Extend boost.multiprec unit test with ET on, complexes, and general/generalized eigenvalue solvers. 2016-07-25 12:36:22 +02:00
Gael Guennebaud
1b2049fbda Enforce scalar types in calls to max/min (helps with expression template scalar types) 2016-07-25 12:35:10 +02:00
Gael Guennebaud
b118bc76eb Add digits10 overload for complex. 2016-07-25 12:33:21 +02:00
Gael Guennebaud
c96af5381f Remove custom complex division function cdiv. 2016-07-25 12:31:58 +02:00
Gael Guennebaud
e1c7c5968a Update doc. 2016-07-25 11:18:04 +02:00
Gael Guennebaud
8fffc81606 Add NumTraits::digits10() function based on numeric_limits::digits10 and make use of it for printing matrices. 2016-07-25 11:13:01 +02:00
Gael Guennebaud
5f03584752 merge 2016-07-23 17:52:44 +02:00
Gael Guennebaud
1b0353c659 Fix misuse of dummy_precesion in eigenvalues solvers 2016-07-23 17:52:31 +02:00
Benoit Steiner
c6b0de2c21 Improved partial reductions in more cases 2016-07-22 17:18:20 -07:00
Gael Guennebaud
72744d93ef Allows the compiler to inline outer products (the change from default to dont-inline in changeset 737bed19c1
was not motivated)
2016-07-22 17:02:28 +02:00
Gael Guennebaud
32d95e86c9 merge 2016-07-22 16:43:12 +02:00
Gael Guennebaud
60d5980a41 add a note 2016-07-22 15:46:23 +02:00
Gael Guennebaud
d7a0e52478 Fix testing of log nearby 1 2016-07-22 15:44:26 +02:00
Gael Guennebaud
7acf23c14c Truely split unit test. 2016-07-22 15:41:23 +02:00
Gael Guennebaud
24af67a6cc Fix boostmultiprec for C++03 2016-07-22 15:30:54 +02:00
Gael Guennebaud
395c835f4b Fix CUDA compilation 2016-07-22 15:30:24 +02:00
Gael Guennebaud
d075d122ea Move half unit test from unsupported to main tests 2016-07-22 14:34:19 +02:00
Gael Guennebaud
47afc9a365 More cleaning in half:
- put its definition and functions in its own half_impl namespace such that the free function does not polute the Eigen namespace while still making them visible for half through ADL.
 - expose Eigen::half throguh a using statement
 - move operator<< from std to half_float namespace
2016-07-22 14:33:28 +02:00
Gael Guennebaud
0f350a8b7e Fix CUDA compilation 2016-07-21 18:47:07 +02:00
Gael Guennebaud
bf91a44f4a Use ADL and log10 for printing matrices. 2016-07-21 15:48:24 +02:00
Gael Guennebaud
82798162c0 Extend unit testing of half with ADL and arrays. 2016-07-21 15:47:21 +02:00
Gael Guennebaud
87fbda812f Add missing log10 and random generator for half. 2016-07-21 15:46:45 +02:00
Gael Guennebaud
01d12d3e82 Some cleanup in Halh: standard functions should be defined in the namespace of the class half to make ADL work, and thus the global is* functions can be removed. 2016-07-21 15:10:48 +02:00
Gael Guennebaud
007edee1ac Add a doc page summarizing the true speed of Eigen's decompositions. 2016-07-21 12:32:02 +02:00
Gael Guennebaud
9b76be9d21 Update benchmark for dense solver to stress least-squares pb, and to output a HTML table 2016-07-21 12:30:53 +02:00
Gael Guennebaud
72950effdf enable testing of Boost.Multiprecision with expression templates 2016-07-20 18:21:30 +02:00
Yi Lin
7b4abc2b1d Fixed a code comment error 2016-07-20 22:28:54 +08:00
Gael Guennebaud
b64b9d0172 Add a unit test to stress our solvers with Boost.Multiprecision 2016-07-20 15:20:14 +02:00
Gael Guennebaud
5e4dda8a12 Enable custom scalar types in some unit tests. 2016-07-20 15:19:17 +02:00
Gael Guennebaud
87d480d785 Make use of EIGEN_TEST_MAX_SIZE 2016-07-20 15:14:20 +02:00
Gael Guennebaud
7722913475 Fix ambiguous specialization with custom scalar type 2016-07-20 15:13:44 +02:00
Gael Guennebaud
fd057f86b3 Complete the coeff-wise math function table. 2016-07-20 12:14:10 +02:00
Gael Guennebaud
9e8476ef22 Add missing Eigen::rsqrt global function 2016-07-20 11:59:49 +02:00
Gael Guennebaud
4b4c296d6e Simplify ScalarBinaryOpTraits by removing the Defined enum, and extend its documentation. 2016-07-20 09:56:39 +02:00
Gael Guennebaud
e3bf874c83 Workaround MSVC 2010 compilation issue. 2016-07-18 15:17:25 +02:00
Gael Guennebaud
0f89c6d6b5 Add a summary of possible values for EIGEN_COMP_MSVC 2016-07-18 15:16:13 +02:00
Gael Guennebaud
18884f17d7 Remove static constant declaration: this enforces compiler to generate costly code for thread safety. 2016-07-18 15:05:17 +02:00
Gael Guennebaud
79574e384e Make scalar_product_op the default (instead of void) 2016-07-18 12:03:05 +02:00
Gael Guennebaud
6a3c451c1c Permits call to explicit ctor. 2016-07-18 12:02:20 +02:00
Gael Guennebaud
0c3fe4aca5 merge 2016-07-18 10:44:15 +02:00
Gael Guennebaud
db9b154193 Add missing non-const reverse method in VectorwiseOp. 2016-07-16 15:19:28 +02:00
Gael Guennebaud
461cd819c2 Workaround VS2015 bug 2016-07-13 18:46:01 +02:00
Gael Guennebaud
5ea0864c81 Fix regression in a previous commit: some diagonal entry might not be treated by the 2x2 real preconditioner. 2016-07-13 18:37:54 +02:00
Benoit Steiner
20f7ef2f89 An evalTo expression is only aligned iff both the lhs and the rhs are aligned. 2016-07-12 10:56:42 -07:00
Gael Guennebaud
b4343aa67e Avoid division by very small entries when extracting singularvalues, and explicitly handle the 1x1 complex case. 2016-07-12 17:22:03 +02:00
Gael Guennebaud
e2aa58b631 Consider denormals as zero in makeJacobi and 2x2 SVD.
This also fix serious issues with x387 for which values can be much smaller than the smallest denormal!
2016-07-12 17:21:03 +02:00
Gael Guennebaud
263993a7b6 Fix test for nearly null input 2016-07-12 17:19:26 +02:00
Gael Guennebaud
9ab35d8ba4 Fix compilation of doc 2016-07-12 16:47:39 +02:00
Gael Guennebaud
19614497ae Add some doxygen's images to support both old and recent doxygen versions
(with some vague definitions of old and recent ;) )
2016-07-12 16:45:43 +02:00
Gael Guennebaud
c98bac2966 Manually add -stdd=c++11 to nvcc for old cmake versions 2016-07-12 09:29:18 +02:00
Benoit Steiner
013a904237 Pulled latest updates from trunk 2016-07-11 14:29:05 -07:00
Benoit Steiner
40eb97516c reverted unintended change. 2016-07-11 14:28:03 -07:00
Benoit Steiner
03b71c273e Made the packetmath test compile again. A better fix would be to move the special function tests to the unsupported directory where the code now resides. 2016-07-11 13:50:24 -07:00
Benoit Steiner
3a2dd352ae Improved the contraction mapper to properly support tensor products 2016-07-11 13:43:41 -07:00
Benoit Steiner
0bc020be9d Improved the detection of packet size in the tensor scan evaluator. 2016-07-11 12:14:56 -07:00
Gael Guennebaud
a96a7ce3f7 Move CUDA's special functions to SpecialFunctions module. 2016-07-11 18:39:11 +02:00
Gael Guennebaud
bec35f4c55 Clarify that SpecialFunctions is unsupported 2016-07-11 18:38:40 +02:00
Gael Guennebaud
fd60966310 merge 2016-07-11 18:11:47 +02:00
Gael Guennebaud
7d636349dc Fix configuration of CUDA:
- preserve user defined CUDA_NVCC_FLAGS
 - remove the -ansi flag that conflicts with -std=c++11
 - do not add -std=c++11 if already there
2016-07-11 18:09:04 +02:00
klimpel
8b3fc31b55 compile fix (SFINAE variant apparently didn't work for all compilers) for the following compiler/platform:
gcc (GCC) 4.1.2 20080704 (Red Hat 4.1.2-46)
Copyright (C) 2006 Free Software Foundation, Inc.
2016-07-11 17:42:22 +02:00
Gael Guennebaud
3e348fdcf9 Workaround MSVC bug 2016-07-11 15:24:52 +02:00
Gael Guennebaud
131ee4bb8e Split test_slice_in_expr which seems to be huge for visual 2016-07-11 11:46:55 +02:00
Gael Guennebaud
194daa3048 Fix assertion (it did not make sense for static_val types) 2016-07-11 11:39:27 +02:00
Gael Guennebaud
18c35747ce Emulate _BitScanReverse64 for 32 bits builds 2016-07-11 11:38:04 +02:00
Konstantinos Margaritis
ef05463fcf Merged kmargar/eigen/tip into default, Altivec/VSX port should be working ok now. 2016-07-10 16:11:46 +03:00
Konstantinos Margaritis
9f7caa7e7d minor fixes for big endian altivec/vsx 2016-07-10 07:05:10 -03:00
Christoph Hertzberg
3c795c6923 bug #1119: Adjust call to ?gssvx for SuperLU 5
Also improved corresponding cmake module to detect versions 5.x

Based on patch by Christoph Grüninger.
2016-07-10 02:29:57 +02:00
Gael Guennebaud
57113e00f9 Relax strict equality 2016-07-09 23:37:11 +02:00
Gael Guennebaud
599f8ba617 Change runtime to compile-time conditional. 2016-07-08 11:39:43 +02:00
Gael Guennebaud
544935101a Fix warnings 2016-07-08 11:38:52 +02:00
Gael Guennebaud
59bf2774a3 Fix warnings 2016-07-08 11:38:11 +02:00
Gael Guennebaud
2f7e2614e7 bug #1232: refactor special functions as a new SpecialFunctions module, currently in unsupported/. 2016-07-08 11:13:55 +02:00
Gael Guennebaud
8b7431d8fd fix compilation with c++11 2016-07-07 15:18:23 +02:00
Gael Guennebaud
69378eed0b Split huge unit test 2016-07-07 15:18:04 +02:00
Gael Guennebaud
c684e37d32 Prevent division by zero. 2016-07-07 11:03:01 +02:00
Gael Guennebaud
179ebb88f9 Fix warning 2016-07-07 09:16:40 +02:00
Gael Guennebaud
5d2dada197 Fix warnings 2016-07-07 09:05:15 +02:00
Gael Guennebaud
f5e780fb05 split huge unit test 2016-07-07 08:59:59 +02:00
Gael Guennebaud
66917299a9 Add debug output 2016-07-06 22:27:15 +02:00
Gael Guennebaud
5ca2457fa5 Fix unit test. 2016-07-06 22:25:24 +02:00
Gael Guennebaud
9b68ed4537 Relax is_equal to is_approx because scaling might modify last bit. 2016-07-06 15:02:49 +02:00
Gael Guennebaud
c3b23d7dbf Fix support of Intel's VML 2016-07-06 14:07:32 +02:00
Gael Guennebaud
8ec4d6480d Fix compilation with recent updates of icc 2016 2016-07-06 14:07:14 +02:00
Gael Guennebaud
5b3a6f51d3 Improve numerical robustness of RealSchur: add scaling and compare sub-diag entries to largest diagonal entry instead of the 2 neighbors. 2016-07-06 13:45:30 +02:00
Gael Guennebaud
d2b5a19e0f Fix warning. 2016-07-06 11:05:30 +02:00
Gael Guennebaud
367ef66af3 Re-enable some specializations for Assignment<.,Product<>> 2016-07-05 22:58:14 +02:00
Gael Guennebaud
155d8d8603 Fix compilation with msvc 2016-07-05 14:43:42 +02:00
Gael Guennebaud
43696ede8f Revert unwanted changes. 2016-07-04 22:40:36 +02:00
Gael Guennebaud
b39fd8217f Fix nesting of SolveWithGuess, and add unit test. 2016-07-04 17:47:47 +02:00
Gael Guennebaud
ec02af1047 Fix template resolution. 2016-07-04 17:37:33 +02:00
Gael Guennebaud
fbcfc2f862 Add unit test for solveWithGuess, and fix template resolution. 2016-07-04 17:19:38 +02:00
Gael Guennebaud
7f7839c12f Add documentation and exemples for inplace decomposition. 2016-07-04 17:18:26 +02:00
Gael Guennebaud
32a41ee659 bug #707: add inplace decomposition through Ref<> for Cholesky, LU and QR decompositions. 2016-07-04 15:13:35 +02:00
Gael Guennebaud
75e80792cc Update relevent list of changesets. 2016-07-04 14:32:34 +02:00
Gael Guennebaud
dacc544b84 asm escape was not strong enough to prevent too aggressive compiler optimization let's fallback to no-inline. 2016-07-04 14:32:15 +02:00
Gael Guennebaud
b74e45906c Few fixes in perf-monitoring. 2016-07-04 14:30:50 +02:00
Gael Guennebaud
ce9fc0ce14 fix clang compilation 2016-07-04 12:59:02 +02:00
Gael Guennebaud
440020474c Workaround compilation issue with msvc 2016-07-04 12:49:19 +02:00
Gael Guennebaud
e61cee7a50 Fix compilation of some unit tests with msvc 2016-07-04 11:49:03 +02:00
Gael Guennebaud
91b3039013 Change the semantic of the last template parameter of Assignment from "Scalar" to "SFINAE" only.
The previous "Scalar" semantic was obsolete since we allow for different scalar types in the source and destination expressions.
On can still specialize on scalar types through SFINAE and/or assignment functor.
2016-07-04 11:02:00 +02:00
Gael Guennebaud
0fa9e4a15c Fix performance regression in dgemm introduced by changeset 5d51a7f12c 2016-07-02 17:35:08 +02:00
Gael Guennebaud
672076db5d Fix performance regression introduced in changeset e56aabf205
.
Register blocking sizes are better handled by the cache size heuristics.
The current code introduced very small blocks, for instance for 9x9 matrix,
thus killing performance.
2016-07-02 15:40:56 +02:00
Igor Babuschkin
78f37ca03c Expose real and imag methods on Tensors 2016-07-01 17:34:31 +01:00
Gael Guennebaud
d161b8f03a Merged in carpent/eigen (pull request PR-204)
Use complete nested namespace Eigen::internal, thus making the custom static assertion macros available outside the Eigen's namespace.
2016-07-01 09:56:44 +02:00
Benoit Steiner
cb2d8b8fa6 Made it possible to compile reductions for an old cuda architecture and run them on a recent gpu. 2016-06-29 15:42:01 -07:00
Benoit Steiner
b2a47641ce Made the code compile when using CUDA architecture < 300 2016-06-29 15:32:47 -07:00
Benoit Steiner
b047ca765f Merged in ibab/eigen/fix-tensor-scan-gpu (pull request PR-205)
Add missing CUDA kernel to tensor scan op
2016-06-29 14:52:19 -07:00
Igor Babuschkin
85699850d9 Add missing CUDA kernel to tensor scan op
The TensorScanOp implementation was missing a CUDA kernel launch.
This adds a simple placeholder implementation.
2016-06-29 11:54:35 +01:00
Justin Carpentier
6126886a67 Use complete nested namespace Eigen::internal 2016-06-28 20:09:25 +02:00
Benoit Jacob
328c5d876a Undo changes in AltiVec --- I don't have any way to test there. 2016-06-28 11:15:25 -04:00
Benoit Jacob
38fb606052 Avoid global variables with static constructors in NEON/Complex.h 2016-06-28 11:12:49 -04:00
Benoit Steiner
1a9f92e781 Added a test to validate the tensor scan evaluation on GPU. The test is currently disabled since the code segfaults. 2016-06-27 16:02:52 -07:00
Benoit Steiner
75c333f94c Don't store the scan axis in the evaluator of the tensor scan operation since it's only used in the constructor.
Also avoid taking references to values that may becomes stale after a copy construction.
2016-06-27 10:32:38 -07:00
xantares
c52c8d76da Disable pkgconfig only for native windows builds
ie enable it for MinGW
2016-06-27 16:43:08 +00:00
Gael Guennebaud
d937a420a2 Fix compilation with MSVC by using our portable numext::log1p implementation. 2016-08-22 15:44:21 +02:00
Gael Guennebaud
2d5731e40a bug #1270: bypass custom asm for pmadd and recent clang version 2016-08-22 15:38:03 +02:00
Gael Guennebaud
49b005181a Define EIGEN_COMP_CLANG to clang version as major*100+minor (e.g., 307 corresponds to clang 3.7) 2016-08-22 15:37:05 +02:00
Gael Guennebaud
130f891bb0 bug #1278: ease parsing 2016-08-22 15:00:29 +02:00
Benoit Steiner
7944d4431f Made the cost model cwiseMax and cwiseMin methods consts to help the PowerPC cuda compiler compile this code. 2016-08-18 13:46:36 -07:00
Benoit Steiner
647a51b426 Force the inlining of a simple accessor. 2016-08-18 12:31:02 -07:00
Benoit Steiner
a452dedb4f Merged in ibab/eigen/double-tensor-reduction (pull request PR-216)
Enable efficient Tensor reduction for doubles on the GPU (continued)
2016-08-18 12:29:54 -07:00
Igor Babuschkin
18c67df31c Fix remaining CUDA >= 300 checks 2016-08-18 17:18:30 +01:00
Igor Babuschkin
1569a7d7ab Add the necessary CUDA >= 300 checks back 2016-08-18 17:15:12 +01:00
Benoit Steiner
2b17f34574 Properly detect the type of the result of a contraction. 2016-08-16 16:00:30 -07:00
Igor Babuschkin
841e075154 Remove CUDA >= 300 checks and enable outer reductin for doubles 2016-08-06 18:07:50 +01:00
Igor Babuschkin
0425118e2a Merge upstream changes 2016-08-05 14:34:57 +01:00
Igor Babuschkin
9537e8b118 Make use of atomicExch for atomicExchCustom 2016-08-05 14:29:58 +01:00
Igor Babuschkin
eeb0d880ee Enable efficient Tensor reduction for doubles 2016-07-01 19:08:26 +01:00
Gael Guennebaud
d476cadbb8 bug #1247: fix regression in compilation of pow(integer,integer), and add respective unit tests. 2016-06-25 10:12:06 +02:00
Gael Guennebaud
cfff370549 Fix hyperbolic functions for autodiff. 2016-06-24 23:21:35 +02:00
Gael Guennebaud
c50c73cae2 Fix missing specialization. 2016-06-24 23:10:39 +02:00
Gael Guennebaud
3852351793 merge pull request 198 2016-06-24 11:48:17 +02:00
Gael Guennebaud
6dd9077070 Fix some unused typedef warnings. 2016-06-24 11:34:21 +02:00
Gael Guennebaud
ce90647fa5 Fix NumTraits<AutoDiff> 2016-06-24 11:34:02 +02:00
Gael Guennebaud
fa39f81b48 Fix instantiation of ScalarBinaryOpTraits for AutoDiff. 2016-06-24 11:33:30 +02:00
Gael Guennebaud
cd577a275c Relax promote_scalar_arg logic to enable promotion to Expr::Scalar if conversion to Expr::Literal fails.
This is useful to cancel expression template at the scalar level, e.g. with AutoDiff<AutoDiff<>>.
This patch also defers calls to NumTraits in cases for which types are not directly compatible.
2016-06-24 11:28:54 +02:00
Gael Guennebaud
deb45ad4bc bug #1245: fix compilation with msvc 2016-06-24 09:52:25 +02:00
Rasmus Munk Larsen
a9c1e4d7b7 Return -1 from CurrentThreadId when called by thread outside the pool. 2016-06-23 16:40:07 -07:00
Rasmus Munk Larsen
d39df320d2 Resolve merge. 2016-06-23 15:08:03 -07:00
Gael Guennebaud
361dbd246d Add unit test for printing empty tensors 2016-06-23 18:54:30 +02:00
Gael Guennebaud
360a743a10 bug #1241: does not emmit anything for empty tensors 2016-06-23 18:47:31 +02:00
Gael Guennebaud
55fc04e8b5 Fix operator priority 2016-06-23 15:36:42 +02:00
Gael Guennebaud
bf2d5edecc Fix warning. 2016-06-23 15:35:17 +02:00
Gael Guennebaud
7c6561485a merge PR 194 2016-06-23 15:29:57 +02:00
Konstantinos Margaritis
be107e387b fix compilation with clang 3.9, fix performance with pset1, use vector operators instead of intrinsics in some cases 2016-06-23 10:19:05 -03:00
Gael Guennebaud
76faf4a965 Introduce a NumTraits<T>::Literal type to be used for literals, and
improve mixing type support in operations between arrays and scalars:
 - 2 * ArrayXcf is now optimized in the sense that the integer 2 is properly promoted to a float instead of a complex<float> (fix a regression)
 - 2.1 * ArrayXi is now forbiden (previously, 2.1 was converted to 2)
 - This mechanism should be applicable to any custom scalar type, assuming NumTraits<T>::Literal is properly defined (it defaults to T)
2016-06-23 14:27:20 +02:00
Gael Guennebaud
a3f7edf7e7 Biug 1242: fix comma init with empty matrices. 2016-06-23 10:25:04 +02:00
Benoit Steiner
a29a2cb4ff Silenced a couple of compilation warnings generated by xcode 2016-06-22 16:43:02 -07:00
Benoit Steiner
f8fcd6b32d Turned the constructor of the PerThread struct into what is effectively a constant expression to make the code compatible with a wider range of compilers 2016-06-22 16:03:11 -07:00
Benoit Steiner
c58df31747 Handle empty tensors in the print functions 2016-06-21 09:22:43 -07:00
Benoit Steiner
de32f8d656 Fixed the printing of rank-0 tensors 2016-06-20 10:46:45 -07:00
Konstantinos Margaritis
8c34b5a0e3 mostly cleanups and modernizing code 2016-06-19 16:13:17 -03:00
Konstantinos Margaritis
b410d46482 mostly cleanups and modernizing code 2016-06-19 16:12:52 -03:00
Konstantinos Margaritis
b80379bda0 fixed pexp<Packet2d>, was failing tests 2016-06-19 16:11:58 -03:00
Tal Hadad
8e198d6835 Complete docs and add ostream operator for EulerAngles. 2016-06-19 20:42:45 +03:00
Benoit Steiner
b055590e91 Made log1p_impl usable inside a GPU kernel 2016-06-16 11:37:40 -07:00
Geoffrey Lalonde
72c95383e0 Add autodiff coverage for standard library hyperbolic functions, and tests.
* * *
Corrected tanh derivatived, moved test definitions.
* * *
Added more test cases, removed lingering lines
2016-06-15 23:33:19 -07:00
Gael Guennebaud
67c12531e5 Fix warnings with gcc 2016-06-15 18:11:33 +02:00
Gael Guennebaud
eb91345d64 Move scalar/expr to ArrayBase and fix documentation 2016-06-15 15:22:03 +02:00
Gael Guennebaud
4794834397 Propagate functor to ScalarBinaryOpTraits 2016-06-15 09:58:49 +02:00
Gael Guennebaud
c55035b9c0 Include the cost of stores in unrolling of triangular expressions. 2016-06-15 09:57:33 +02:00
Benoit Steiner
7d495d890a Merged in ibab/eigen (pull request PR-197)
Implement exclusive scan option for Tensor library
2016-06-14 17:54:59 -07:00
Benoit Steiner
aedc5be1d6 Avoid generating pseudo random numbers that are multiple of 5: this helps
spread the load over multiple cpus without havind to rely on work stealing.
2016-06-14 17:51:47 -07:00
Gael Guennebaud
4e7c3af874 Cleanup useless helper: internal::product_result_scalar 2016-06-15 00:04:10 +02:00
Gael Guennebaud
101ea26f5e Include the cost of stores in unrolling (also fix infinite unrolling with expression costing 0 like Constant) 2016-06-15 00:01:16 +02:00
Igor Babuschkin
c4d10e921f Implement exclusive scan option 2016-06-14 19:44:07 +01:00
Gael Guennebaud
76236cdea4 merge 2016-06-14 15:33:47 +02:00
Gael Guennebaud
1004c4df99 Cleanup unused functors. 2016-06-14 15:27:28 +02:00
Gael Guennebaud
70dad84b73 Generalize expr/expr and scalar/expr wrt scalar types. 2016-06-14 15:26:37 +02:00
Gael Guennebaud
62134082aa Update AutoDiffScalar wrt to scalar-multiple. 2016-06-14 15:06:35 +02:00
Gael Guennebaud
5d38203735 Update Tensor module to use bind1st_op and bind2nd_op 2016-06-14 15:06:03 +02:00
Gael Guennebaud
396d9cfb6e Generalize expr.pow(scalar), pow(expr,scalar) and pow(scalar,expr).
Internal: scalar_pow_op (unary) is removed, and scalar_binary_pow_op is renamed scalar_pow_op.
2016-06-14 14:10:07 +02:00
Gael Guennebaud
a9bb653a68 Update doc (scalar_add_op is now deprecated) 2016-06-14 12:07:00 +02:00
Gael Guennebaud
a8c08e8b8e Implement expr+scalar, scalar+expr, expr-scalar, and scalar-expr as binary expressions, and generalize supported scalar types.
The following functors are now deprecated: scalar_add_op, scalar_sub_op, and scalar_rsub_op.
2016-06-14 12:06:10 +02:00
Gael Guennebaud
756ac4a93d Fix doc. 2016-06-14 12:03:39 +02:00
Gael Guennebaud
f925dba3d9 Fix compilation of BVH example 2016-06-14 11:32:09 +02:00
Gael Guennebaud
12350d3ac7 Add unit test for AlignedBox::center 2016-06-14 11:31:52 +02:00
Gael Guennebaud
bcc0f38f98 Add unittesting plugins to scalar_product_op and scalar_quotient_op to help chaking that types are properly propagated. 2016-06-14 11:31:27 +02:00
Gael Guennebaud
f57fd78e30 Generalize coeff-wise sparse products to support different scalar types 2016-06-14 11:29:54 +02:00
Gael Guennebaud
f5b1c73945 Set cost of constant expression to 0 (the cost should be amortized through the expression) 2016-06-14 11:29:06 +02:00
Gael Guennebaud
deb8306e60 Move MatrixBase::operaotr*(UniformScaling) as a free function in Scaling.h, and fix return type. 2016-06-14 11:28:03 +02:00
Gael Guennebaud
64fcfd314f Implement scalar multiples and division by a scalar as a binary-expression with a constant expression.
This slightly complexifies the type of the expressions and implies that we now have to distinguish between scalar*expr and expr*scalar to catch scalar-multiple expression (e.g., see BlasUtil.h), but this brings several advantages:
- it makes it clear on each side the scalar is applied,
- it clearly reflects that we are dealing with a binary-expression,
- the complexity of the type is hidden through macros defined at the end of Macros.h,
- distinguishing between "scalar op expr" and "expr op scalar" is important to support non commutative fields (like quaternions)
- "scalar op expr" is now fully equivalent to "ConstantExpr(scalar) op expr"
- scalar_multiple_op, scalar_quotient1_op and scalar_quotient2_op are not used anymore in officially supported modules (still used in Tensor)
2016-06-14 11:26:57 +02:00
Gael Guennebaud
39781dc1e2 Fix compilation of evaluator unit test 2016-06-14 11:03:26 +02:00
Tal Hadad
6edfe8771b Little bit docs 2016-06-13 22:03:19 +03:00
Tal Hadad
6e1c086593 Add static assertion 2016-06-13 21:55:17 +03:00
Gael Guennebaud
3c12e24164 Add bind1st_op and bind2nd_op helpers to turn binary functors into unary ones, and implement scalar_multiple2 and scalar_quotient2 on top of them. 2016-06-13 16:18:59 +02:00
Gael Guennebaud
7a9ef7bbb4 Add default template parameters for the second scalar type of binary functors.
This enhences backward compatibility.
2016-06-13 16:17:23 +02:00
Gael Guennebaud
2ca2ffb65e check for mixing types in "array / scalar" expressions 2016-06-13 16:15:32 +02:00
Gael Guennebaud
4c61f00838 Add missing explicit scalar conversion 2016-06-12 22:42:13 +02:00
Tal Hadad
06206482d9 More docs, and minor code fixes 2016-06-12 23:40:17 +03:00
Gael Guennebaud
a3a4714aba Add debug output. 2016-06-11 14:41:53 +02:00
Gael Guennebaud
83904a21c1 Make sure T(i+1,i)==0 when diagonalizing T(i:i+1,i:i+1) 2016-06-11 14:41:36 +02:00
Benoit Steiner
65d33e5898 Merged in ibab/eigen (pull request PR-195)
Add small fixes to TensorScanOp
2016-06-10 19:31:17 -07:00
Benoit Steiner
a05607875a Don't refer to the half2 type unless it's been defined 2016-06-10 11:53:56 -07:00
Gael Guennebaud
fabae6c9a1 Cleanup 2016-06-10 15:58:33 +02:00
Gael Guennebaud
5de8d7036b Add real.pow(complex), complex.pow(real) unit tests. 2016-06-10 15:58:22 +02:00
Gael Guennebaud
5fdd703629 Enable mixing types in numext::pow 2016-06-10 15:58:04 +02:00
Gael Guennebaud
2e238bafb6 Big 279: enable mixing types for comparisons, min, and max. 2016-06-10 15:05:43 +02:00
Gael Guennebaud
0028049380 bug #1240: Remove any assumption on NEON vector types. 2016-06-09 23:08:11 +02:00
Igor Babuschkin
86aedc9282 Add small fixes to TensorScanOp 2016-06-07 20:06:38 +01:00
Christoph Hertzberg
db0118342c Fixed compilation of BVH_Example (required for make doc) 2016-06-07 19:17:18 +02:00
Benoit Steiner
84b2060a9e Fixed compilation error with gcc 4.4 2016-06-06 17:16:19 -07:00
Gael Guennebaud
2c462f4201 Clean handling for void type in EIGEN_CHECK_BINARY_COMPATIBILIY 2016-06-06 23:11:38 +02:00
Gael Guennebaud
3d71d3918e Disable shortcuts for res ?= prod when the scalar types do not match exactly. 2016-06-06 23:10:55 +02:00
Benoit Steiner
7ef9f47b58 Misc small improvements to the reduction code. 2016-06-06 14:09:46 -07:00
Benoit Steiner
ea75dba201 Added missing EIGEN_DEVICE_FUNC qualifiers to the unary array ops 2016-06-06 13:32:28 -07:00
Benoit Steiner
33f0340188 Implement result_of for the new ternary functors 2016-06-06 12:06:42 -07:00
Tal Hadad
e30133e439 Doc EulerAngles class, and minor fixes. 2016-06-06 22:01:40 +03:00
Gael Guennebaud
df24f4a01d bug #1201: improve code generation of affine*vec with MSVC 2016-06-06 16:46:46 +02:00
Benoit Steiner
9137f560f0 Moved assertions to the constructor to make the code more portable 2016-06-06 07:26:48 -07:00
Gael Guennebaud
66e99ab6a1 Relax mixing-type constraints for binary coefficient-wise operators:
- Replace internal::scalar_product_traits<A,B> by Eigen::ScalarBinaryOpTraits<A,B,OP>
- Remove the "functor_is_product_like" helper (was pretty ugly)
- Currently, OP is not used, but it is available to the user for fine grained tuning
- Currently, only the following operators have been generalized: *,/,+,-,=,*=,/=,+=,-=
- TODO: generalize all other binray operators (comparisons,pow,etc.)
- TODO: handle "scalar op array" operators (currently only * is handled)
- TODO: move the handling of the "void" scalar type to ScalarBinaryOpTraits
2016-06-06 15:11:41 +02:00
Benoit Steiner
1f1e0b9e30 Silenced compilation warning 2016-06-05 12:59:11 -07:00
Benoit Steiner
5b95b4daf9 Moved static assertions into the class constructor to make the code more portable 2016-06-05 12:57:48 -07:00
Christoph Hertzberg
d7e3e4bb04 Removed executable bits from header files. 2016-06-05 10:15:41 +02:00
Eugene Brevdo
c53687dd14 Add randomized properties tests for betainc special function. 2016-06-05 11:10:30 -07:00
Rasmus Munk Larsen
f1f2ff8208 size_t -> int 2016-06-03 18:06:37 -07:00
Rasmus Munk Larsen
76308e7fd2 Add CurrentThreadId and NumThreads methods to Eigen threadpools and TensorDeviceThreadPool. 2016-06-03 16:28:58 -07:00
Sean Templeton
bd21243821 Fix compile errors initializing packets on ARM DS-5 5.20
The ARM DS-5 5.20 compiler fails compiling with the following errors:

"src/Core/arch/NEON/PacketMath.h", line 113: Error:  #146: too many initializer values
    Packet4f countdown = EIGEN_INIT_NEON_PACKET4(0, 1, 2, 3);
                         ^
"src/Core/arch/NEON/PacketMath.h", line 118: Error:  #146: too many initializer values
    Packet4i countdown = EIGEN_INIT_NEON_PACKET4(0, 1, 2, 3);
                         ^
"src/Core/arch/NEON/Complex.h", line 30: Error:  #146: too many initializer values
  static uint32x4_t p4ui_CONJ_XOR = EIGEN_INIT_NEON_PACKET4(0x00000000, 0x80000000, 0x00000000, 0x80000000);
                                    ^
"src/Core/arch/NEON/Complex.h", line 31: Error:  #146: too many initializer values
  static uint32x2_t p2ui_CONJ_XOR = EIGEN_INIT_NEON_PACKET2(0x00000000, 0x80000000);
                                    ^

The vectors are implemented as two doubles, hence the too many initializer values error.
Changed the code to use intrinsic load functions which all compilers
implementing NEON should have.
2016-06-03 10:51:35 -05:00
Gael Guennebaud
1fc2746417 Make Arrays's ctor/assignment noexcept 2016-06-09 22:52:37 +02:00
Benoit Steiner
37638dafd7 Simplified the code that dispatches vectorized reductions on GPU 2016-06-09 10:29:52 -07:00
Benoit Steiner
66796e843d Fixed definition of some of the reducer_traits 2016-06-09 08:50:01 -07:00
Benoit Steiner
4434b16694 Pulled latest updates from trunk 2016-06-09 08:25:47 -07:00
Benoit Steiner
14a112ee15 Use signed integers more consistently to encode the number of threads to use to evaluate a tensor expression. 2016-06-09 08:25:22 -07:00
Benoit Steiner
8f92c26319 Improved code formatting 2016-06-09 08:23:42 -07:00
Benoit Steiner
aa33446dac Improved support for vectorization of 16-bit floats 2016-06-09 08:22:27 -07:00
Gael Guennebaud
e2b3836326 Include recent changesets that played with product's kernel 2016-06-09 17:13:33 +02:00
Gael Guennebaud
2bd59b0e0d Take advantage that T is already diagonal in the extraction of generalized complex eigenvalues. 2016-06-09 17:12:03 +02:00
Gael Guennebaud
c1f9ca9254 Update RealQZ to reduce 2x2 diagonal block of T corresponding to non reduced diagonal block of S to positive diagonal form.
This step involve a real 2x2 SVD problem. The respective routine is thus in src/misc/ to be shared by both EVD and AVD modules.
2016-06-09 17:11:03 +02:00
Gael Guennebaud
15890c304e Add unit test for non symmetric generalized eigenvalues 2016-06-09 16:17:27 +02:00
Gael Guennebaud
a20d2ec1c0 Fix shadow variable, and indexing. 2016-06-09 16:16:22 +02:00
Abhijit Kundu
0beabb4776 Fixed type conversion from int 2016-06-08 16:12:04 -04:00
Gael Guennebaud
df095cab10 Fixes for PARDISO: warnings, and defaults to metis+ in-core mode. 2016-06-08 18:31:19 +02:00
Gael Guennebaud
9fc8379328 Fix extraction of complex eigenvalue pairs in real generalized eigenvalue problems. 2016-06-08 16:39:11 +02:00
Christoph Hertzberg
9dd9d58273 Copied a regression test from 3.2 branch. 2016-06-08 15:36:42 +02:00
Benoit Steiner
8fd57a97f2 Enable the vectorization of adds and mults of fp16 2016-06-07 18:22:18 -07:00
Benoit Steiner
d6d39c7ddb Added missing EIGEN_DEVICE_FUNC 2016-06-07 14:35:08 -07:00
Gael Guennebaud
8d97ba6b22 bug #725: make move ctor/assignment noexcept. 2016-06-03 14:28:25 +02:00
Gael Guennebaud
e8b922ca63 Fix MatrixFunctions module. 2016-06-03 09:21:35 +02:00
Gael Guennebaud
82293f38d6 Fix unit test. 2016-06-03 08:12:14 +02:00
Gael Guennebaud
fe62c06d9b Fix compilation. 2016-06-03 07:47:38 +02:00
Gael Guennebaud
969b8959a0 Fix compilation: Matrix does not indirectly live in the internal namespace anymore! 2016-06-03 07:44:58 +02:00
Gael Guennebaud
f2c2465acc Fix function dependencies 2016-06-03 07:44:18 +02:00
Benoit Steiner
c3c8ad8046 Align the first element of the Waiter struct instead of padding it. This reduces its memory footprint a bit while achieving the goal of preventing false sharing 2016-06-02 21:17:41 -07:00
Eugene Brevdo
39baff850c Add TernaryFunctors and the betainc SpecialFunction.
TernaryFunctors and their executors allow operations on 3-tuples of inputs.
API fully implemented for Arrays and Tensors based on binary functors.

Ported the cephes betainc function (regularized incomplete beta
integral) to Eigen, with support for CPU and GPU, floats, doubles, and
half types.

Added unit tests in array.cpp and cxx11_tensor_cuda.cu


Collapsed revision
* Merged helper methods for betainc across floats and doubles.
* Added TensorGlobalFunctions with betainc().  Removed betainc() from TensorBase.
* Clean up CwiseTernaryOp checks, change igamma_helper to cephes_helper.
* betainc: merge incbcf and incbd into incbeta_cfe.  and more cleanup.
* Update TernaryOp and SpecialFunctions (betainc) based on review comments.
2016-06-02 17:04:19 -07:00
Benoit Steiner
02db4e1a82 Disable the tensor tests when using msvc since older versions of the compiler fail to handle this code 2016-06-04 08:21:17 -07:00
Benoit Steiner
c21eaedce6 Use array_prod to compute the number of elements contained in the input tensor expression 2016-06-04 07:47:04 -07:00
Benoit Steiner
36a4500822 Merged in ibab/eigen (pull request PR-192)
Add generic scan method
2016-06-03 17:28:33 -07:00
Benoit Steiner
c2a102345f Improved the performance of full reductions.
AFTER:
BM_fullReduction/10        4541       4543     154017  21.0M items/s
BM_fullReduction/64        5191       5193     100000  752.5M items/s
BM_fullReduction/512       9588       9588      71361  25.5G items/s
BM_fullReduction/4k      244314     244281       2863  64.0G items/s
BM_fullReduction/5k      359382     359363       1946  64.8G items/s

BEFORE:
BM_fullReduction/10        9085       9087      74395  10.5M items/s
BM_fullReduction/64        9478       9478      72014  412.1M items/s
BM_fullReduction/512      14643      14646      46902  16.7G items/s
BM_fullReduction/4k      260338     260384       2678  60.0G items/s
BM_fullReduction/5k      385076     385178       1818  60.5G items/s
2016-06-03 17:27:08 -07:00
Igor Babuschkin
dc03b8f3a1 Add generic scan method 2016-06-03 17:37:04 +01:00
Gael Guennebaud
5b77481d58 merge 2016-06-02 22:21:45 +02:00
Gael Guennebaud
53feb73b45 Remove dead code. 2016-06-02 22:19:55 +02:00
Gael Guennebaud
2c00ac0b53 Implement generic scalar*expr and expr*scalar operator based on scalar_product_traits.
This is especially useful for custom scalar types, e.g., to enable float*expr<multi_prec> without conversion.
2016-06-02 22:16:37 +02:00
Rasmus Munk Larsen
811aadbe00 Add syntactic sugar to Eigen tensors to allow more natural syntax.
Specifically, this enables expressions involving:

scalar + tensor
scalar * tensor
scalar / tensor
scalar - tensor
2016-06-02 12:41:28 -07:00
Tal Hadad
52e4cbf539 Merged eigen/eigen into default 2016-06-02 22:15:20 +03:00
Tal Hadad
2aaaf22623 Fix Gael reports (except documention)
- "Scalar angle(int) const"  should be  "const Vector& angles() const"
- then method "coeffs" could be removed.
- avoid one letter names like h, p, r -> use alpha(), beta(), gamma() ;)
- about the "fromRotation" methods:
 - replace the ones which are not static by operator= (as in Quaternion)
 - the others are actually static methods: use a capital F: FromRotation
- method "invert" should be removed.
- use a macro to define both float and double EulerAnglesXYZ* typedefs
- AddConstIf -> not used
- no needs for NegateIfXor, compilers are extremely good at optimizing away branches based on compile time constants:
  if(IsHeadingOpposite-=IsEven) res.alpha() = -res.alpha();
2016-06-02 22:12:57 +03:00
Benoit Steiner
6021c90fdf Merged in ibab/eigen (pull request PR-189)
Add scan op to Tensor module
2016-06-02 08:08:11 -07:00
Gael Guennebaud
8b6f53222b bug #1193: fix lpNorm<Infinity> for empty input. 2016-06-02 15:29:59 +02:00
Gael Guennebaud
d616a81294 Disable MSVC's "decorated name length exceeded, name was truncated" warning in unit tests. 2016-06-02 14:48:38 +02:00
Gael Guennebaud
61a32f2a4c Fix pointer to long conversion warning. 2016-06-02 14:45:45 +02:00
Igor Babuschkin
fbd7ed6ff7 Add tensor scan op
This is the initial implementation a generic scan operation.
Based on this, cumsum and cumprod method have been added to TensorBase.
2016-06-02 13:35:47 +01:00
Benoit Steiner
0ed08fd281 Use a single PacketSize variable 2016-06-01 21:19:05 -07:00
Benoit Steiner
8f6fedc55f Fixed compilation warning 2016-06-01 21:14:46 -07:00
Benoit Steiner
c3cada38e2 Speedup a test 2016-06-01 21:13:00 -07:00
Gael Guennebaud
360e311b66 Doc: add some cross references (also fix empty macro argument warning) 2016-06-01 23:34:09 +02:00
Benoit Steiner
873e6ac54b Silenced compilation warning generated by nvcc. 2016-06-01 14:20:50 -07:00
Benoit Steiner
d27b0ad4c8 Added support for mean reductions on fp16 2016-06-01 11:12:07 -07:00
Gael Guennebaud
cd221a62ee Doc: start of a table summarizing coefficient-wise math functions. 2016-06-01 17:09:48 +02:00
Gael Guennebaud
3c69afca4c Add missing ArrayBase::log1p 2016-06-01 17:08:47 +02:00
Gael Guennebaud
89099b0cf7 Expose log1p to Array. 2016-06-01 17:00:08 +02:00
Gael Guennebaud
afd33539dd Doc: makes the global unary math functions visible to doxygen (and docuement them) 2016-06-01 15:27:13 +02:00
Gael Guennebaud
77e652d8ad Doc: improve documentation of Map<SparseMatrix> 2016-06-01 10:03:32 +02:00
Gael Guennebaud
da4970ead2 Doc: disable inlining of inherited members, workaround Doxygen's limited C++ parsing abilities, and improve doc of MapBase. 2016-06-01 09:38:49 +02:00
Benoit Steiner
099b354ca7 Pulled latest updates from trunk 2016-05-31 10:34:16 -07:00
Benoit Steiner
5aeb3687c4 Only enable optimized reductions of fp16 if the reduction functor supports them 2016-05-31 10:33:40 -07:00
Benoit Steiner
b6e306f189 Improved support for CUDA 8.0 2016-05-31 09:47:59 -07:00
Gael Guennebaud
1d3b253329 bug #1181: help MSVC inlining. 2016-05-31 17:23:42 +02:00
Gael Guennebaud
d79eee05ef Fix compilation with old icc 2016-05-31 17:13:51 +02:00
Gael Guennebaud
2c1b56f4c1 bug #1238: fix SparseMatrix::sum() overload for un-compressed mode. 2016-05-31 10:56:53 +02:00
Benoit Steiner
c4bd3b1f21 Silenced some compilation warnings triggered by nvcc 8.0 2016-05-27 14:40:49 -07:00
Benoit Steiner
e2946d962d Reimplement clamp as a static function. 2016-05-27 12:58:43 -07:00
Benoit Steiner
e96d36d4cd Use NULL instead of nullptr to preserve the compatibility with cxx03 2016-05-27 12:54:06 -07:00
Benoit Steiner
abc815798b Added a new operation to enable more powerful tensorindexing. 2016-05-27 12:22:25 -07:00
Benoit Steiner
5707537592 Fixed option '--relaxed-constexpr' has been deprecated and replaced by option '--expt-relaxed-constexpr' warning generated by nvcc 7.5 2016-05-27 10:47:53 -07:00
Benoit Steiner
3a5d6a3c38 Disable the use of MMX instructions since the code is broken on many platforms 2016-05-27 09:13:26 -07:00
Christoph Hertzberg
f2c86384f4 Cleaner implementation of dont_over_optimize. 2016-05-27 11:13:38 +02:00
Gael Guennebaud
22a035db95 Fix compilation when defaulting to row-major 2016-05-27 10:31:11 +02:00
Gael Guennebaud
e0cb73b46b Fix compilation with old ICC version (use C99 types instead of C++11 ones) 2016-05-27 10:28:09 +02:00
Benoit Steiner
1ae2567861 Fixed some compilation warnings 2016-05-26 15:57:19 -07:00
Benoit Steiner
094f4a56c8 Deleted extra namespace 2016-05-26 14:49:51 -07:00
Benoit Steiner
1a47844529 Preserve the ability to vectorize the evaluation of an expression even when it involves a cast that isn't vectorized (e.g fp16 to float) 2016-05-26 14:37:09 -07:00
Benoit Steiner
36369ab63c Resolved merge conflicts 2016-05-26 13:39:39 -07:00
Benoit Steiner
28fcb5ca2a Merged latest reduction improvements 2016-05-26 12:19:33 -07:00
Benoit Steiner
b24cf21235 Merged latest code improvements 2016-05-26 11:57:50 -07:00
Benoit Steiner
c1c7f06c35 Improved the performance of inner reductions. 2016-05-26 11:53:59 -07:00
Benoit Steiner
22d02c9855 Improved the coverage of the fp16 reduction tests 2016-05-26 11:12:16 -07:00
Christoph Hertzberg
41dcd047d7 bug #1237: Redefine eigen_assert instead of disabling assertions for documentation snippets 2016-05-26 18:13:33 +02:00
Benoit Steiner
8288b0aec2 Code cleanup. 2016-05-26 09:00:04 -07:00
Gael Guennebaud
7ff5fadcc0 Disable usage of MMX with msvc. 2016-05-26 17:58:46 +02:00
Gael Guennebaud
e8cef383b7 bug #1236: fix possible integer overflow in density estimation. 2016-05-26 17:51:04 +02:00
Gael Guennebaud
35df3a32eb Disabled GCC6's ignored-attributes warning in packetmath unit test. 2016-05-26 17:42:58 +02:00
Gael Guennebaud
db62719eda Fix some conversion warnings in unit tests. 2016-05-26 17:42:12 +02:00
Gael Guennebaud
fdcad686ee Fix numerous pointer-to-integer conversion warnings in unit tests. 2016-05-26 17:41:28 +02:00
Gael Guennebaud
30d97c03ce Defer the allocation of the working space:
- it is not always needed,
- and this fixes a long-to-float conversion warning
2016-05-26 17:39:42 +02:00
Gael Guennebaud
e08f54e9eb Fix copy ctor prototype. 2016-05-26 17:37:25 +02:00
Gael Guennebaud
c7f54b11ec linspaced's divisor for integer is better stored as the underlying scalar type. 2016-05-26 17:36:54 +02:00
Gael Guennebaud
bebc5a2147 Fix/handle some int-to-long conversions. 2016-05-26 17:35:53 +02:00
Gael Guennebaud
00c29c2cae Store permutation's determinant as char.
This also fixes some long to float conversion warnings
2016-05-26 17:34:23 +02:00
Gael Guennebaud
2f56d91063 Fix a pointer to integer conversion warning 2016-05-26 17:31:45 +02:00
Gael Guennebaud
2a44a70142 Handle some Index to int conversions in BLAS/LAPACK support. 2016-05-26 17:29:04 +02:00
Gael Guennebaud
f253e19296 Disable some long to float conversion warnings 2016-05-26 17:27:14 +02:00
Christoph Hertzberg
2ee306e44a Temporary workaround for bug #1237. The snippet (expectedly) failed with enabled assertions. 2016-05-26 16:16:41 +02:00
Gael Guennebaud
37197b602b Remove debuging code. 2016-05-26 11:53:10 +02:00
Gael Guennebaud
27f0434233 Introduce internal's UIntPtr and IntPtr types for pointer to integer conversions.
This fixes "conversion from pointer to same-sized integral type" warnings by ICC.
Ideally, we would use the std::[u]intptr_t types all the time, but since they are C99/C++11 only,
let's be safe.
2016-05-26 10:52:12 +02:00
Gael Guennebaud
40e4637d79 Turn off ICC's conversion warning in is_convertible implementation 2016-05-26 10:48:43 +02:00
Gael Guennebaud
cc1ab64f29 Add missing inclusion of mmintrin.h 2016-05-26 09:51:50 +02:00
Benoit Steiner
2d7ed54ba2 Made the static storage class qualifier come first. 2016-05-25 22:16:15 -07:00
Benoit Steiner
e1fca8866e Deleted unnecessary explicit qualifiers. 2016-05-25 22:15:26 -07:00
Benoit Steiner
9b0aaf5113 Don't mark inline functions as static since it confuses the ICC compiler 2016-05-25 22:10:11 -07:00
Benoit Steiner
3585ff585e Silenced a compilation warning 2016-05-25 22:09:19 -07:00
Benoit Steiner
037a463fd5 Marked unused variables as such 2016-05-25 22:07:48 -07:00
Benoit Steiner
efeb89dcdb Specify the rounding mode in the correct location 2016-05-25 17:53:24 -07:00
Benoit Steiner
457204cb83 Updated the README file for the tensor benchmarks 2016-05-25 16:13:41 -07:00
Benoit Steiner
0322c66a3f Explicitly specify the rounding mode when converting floats to fp16 2016-05-25 15:56:15 -07:00
Benoit Steiner
3ac4045272 Made the IndexPair code compile in non cxx11 mode 2016-05-25 15:15:12 -07:00
Benoit Steiner
66556d0e05 Made the index pair list code more portable accross various compilers 2016-05-25 14:34:27 -07:00
Benoit Steiner
034aa3b2c0 Improved the performance of tensor padding 2016-05-25 11:43:08 -07:00
Benoit Steiner
58026905ae Added support for statically known lists of pairs of indices 2016-05-25 11:04:14 -07:00
Benoit Steiner
ed783872ab Disable the use of MMX instructions on x86_64 since too many compilers only support them in 32bit mode 2016-05-25 08:27:26 -07:00
Benoit Steiner
bcfff64f9e Use numext:: instead of std:: functions. 2016-05-25 08:08:21 -07:00
Gael Guennebaud
f57260a997 Fix typo in dont_over_optimize 2016-05-25 11:17:53 +02:00
Gael Guennebaud
2cd32be70b Fix warning. 2016-05-25 11:15:54 +02:00
Gael Guennebaud
bbf9109e25 Fix compilation with ICC. 2016-05-25 10:00:55 +02:00
Gael Guennebaud
2a1bff67fd Fix static/inline order. 2016-05-25 10:00:11 +02:00
Benoit Steiner
0835667329 There is no need to make the fp16 full reduction kernel a static function. 2016-05-24 23:11:56 -07:00
Benoit Steiner
b5d6b52a4d Fixed compilation warning 2016-05-24 23:10:57 -07:00
Benoit Steiner
d041a528da Cleaned up the fp16 code a little more 2016-05-24 22:43:26 -07:00
Benoit Steiner
cb26784d07 Pulled latest updates from trunk 2016-05-24 18:51:39 -07:00
Benoit Steiner
ff4a289572 Cleaned up the fp16 code 2016-05-24 18:50:09 -07:00
Gael Guennebaud
3f715e1701 update doc wrt to unaligned vectorization 2016-05-24 22:34:59 +02:00
Gael Guennebaud
9216abe28d Document EIGEN_UNALIGNED_VECTORIZE. 2016-05-24 22:14:34 +02:00
Gael Guennebaud
0fd953c217 Workaround clang/llvm bug in code generation. 2016-05-24 21:55:46 +02:00
Gael Guennebaud
e68e165a23 bug #256: enable vectorization with unaligned loads/stores.
This concerns all architectures and all sizes.
This new behavior can be disabled by defining EIGEN_UNALIGNED_VECTORIZE=0
2016-05-24 21:54:03 +02:00
Gael Guennebaud
78390e4189 Block<> should not disable vectorization based on inner-size, this is the responsibilty of the assignment logic. 2016-05-24 17:14:01 +02:00
Gael Guennebaud
64bb7576eb Clean propagation of Dest/Src alignments. 2016-05-24 17:12:12 +02:00
Benoit Jacob
40a16282c7 Remove now-unused protate PacketMath func 2016-05-24 11:01:18 -04:00
Benoit Jacob
6136f4fdd4 Remove the rotating kernel. It was only useful on some ARM CPUs (Qualcomm Krait) that are not as ubiquitous today as they were when I introduced it. 2016-05-24 10:00:32 -04:00
Benoit Steiner
e617711306 Don't attempt to use MMX instructions with visualstudio since they're only partially supported. 2016-05-24 06:43:58 -07:00
Benoit Steiner
334e76537f Worked around missing clang intrinsic 2016-05-24 00:29:28 -07:00
Benoit Steiner
b517ab349b Use the generic ploadquad intrinsics since it does the job 2016-05-24 00:11:17 -07:00
Benoit Steiner
646872cb3b Worked around missing clang intrinsics 2016-05-24 00:07:08 -07:00
Benoit Steiner
3dfc391a61 Added missing EIGEN_DEVICE_FUNC qualifier 2016-05-23 20:56:59 -07:00
Benoit Steiner
3d0741f027 Include mmintrin.h to make it possible to use mmx instructions when needed. For example, this will enable the definition of a half packet for the Packet4f type. 2016-05-23 20:43:48 -07:00
Benoit Steiner
33a94f5dc7 Use the Index type instead of integers to specify the strides in pgather/pscatter 2016-05-23 20:37:30 -07:00
Benoit Steiner
6bc684ab6a Added missing alignment in the fp16 packet traits 2016-05-23 20:32:30 -07:00
Benoit Steiner
283e33dea4 ptranspose is not a template. 2016-05-23 19:55:55 -07:00
Benoit Steiner
a5a3ba2b80 Avoid unnecessary float to double conversions 2016-05-23 17:16:09 -07:00
Benoit Steiner
5ba0ebe7c9 Avoid unnecessary float to double conversion. 2016-05-23 17:14:31 -07:00
Benoit Steiner
7d980d74e5 Started to vectorize the processing of 16bit floats on CPU. 2016-05-23 15:21:40 -07:00
Benoit Steiner
5d51a7f12c Don't optimize the processing of the last rows of a matrix matrix product in cases that violate the assumptions made by the optimized code path. 2016-05-23 15:13:16 -07:00
Benoit Steiner
7aa5bc9558 Fixed a typo in the array.cpp test 2016-05-23 14:39:51 -07:00
Benoit Steiner
a09cbf9905 Merged in rmlarsen/eigen (pull request PR-188)
Minor cleanups: 1. Get rid of a few unused variables. 2. Get rid of last uses of EIGEN_USE_COST_MODEL.
2016-05-23 12:55:12 -07:00
Christoph Hertzberg
88654762da Replace multiple constructors of half-type by a generic/templated constructor. This fixes an incompatibility with long double, exposed by the previous commit. 2016-05-23 10:03:03 +02:00
Christoph Hertzberg
718521d5cf Silenced several double-promotion warnings 2016-05-22 18:17:04 +02:00
Christoph Hertzberg
b5a7603822 fixed macro name 2016-05-22 16:49:29 +02:00
Christoph Hertzberg
25a03c02d6 Fix some sign-compare warnings 2016-05-22 16:42:27 +02:00
Christoph Hertzberg
0851d5d210 Identify clang++ even if it is not named llvm-clang++ 2016-05-22 15:21:14 +02:00
Gael Guennebaud
6a15e14cda Document EIGEN_MAX_CPP_VER and user controllable compiler features. 2016-05-20 15:26:09 +02:00
Gael Guennebaud
ccaace03c9 Make EIGEN_HAS_CONSTEXPR user configurable 2016-05-20 15:10:08 +02:00
Gael Guennebaud
c3410804cd Make EIGEN_HAS_VARIADIC_TEMPLATES user configurable 2016-05-20 15:05:38 +02:00
Gael Guennebaud
abd1c1af7a Make EIGEN_HAS_STD_RESULT_OF user configurable 2016-05-20 15:01:27 +02:00
Gael Guennebaud
1395056fc0 Make EIGEN_HAS_C99_MATH user configurable 2016-05-20 14:58:19 +02:00
Gael Guennebaud
48bf5ec216 Make EIGEN_HAS_RVALUE_REFERENCES user configurable 2016-05-20 14:54:20 +02:00
Gael Guennebaud
f43ae88892 Rename EIGEN_HAVE_RVALUE_REFERENCES to EIGEN_HAS_RVALUE_REFERENCES 2016-05-20 14:48:51 +02:00
Gael Guennebaud
8d6bd5691b polygamma is C99/C++11 only 2016-05-20 14:45:33 +02:00
Gael Guennebaud
998f2efc58 Add a EIGEN_MAX_CPP_VER option to limit the C++ version to be used. 2016-05-20 14:44:28 +02:00
Gael Guennebaud
c028d96089 Improve doc of special math functions 2016-05-20 14:18:48 +02:00
Gael Guennebaud
0ba32f99bd Rename UniformRandom to UnitRandom. 2016-05-20 13:21:34 +02:00
Gael Guennebaud
7a9d9cde94 Fix coding practice in Quaternion::UniformRandom 2016-05-20 13:19:52 +02:00
Joseph Mirabel
eb0cc2573a bug #823: add static method to Quaternion for uniform random rotations. 2016-05-20 13:15:40 +02:00
Gael Guennebaud
2f656ce447 Remove std:: to enable custom scalar types. 2016-05-19 23:13:47 +02:00
Rasmus Larsen
b1e080c752 Merged eigen/eigen into default 2016-05-18 15:21:50 -07:00
Rasmus Munk Larsen
5624219b6b Merge. 2016-05-18 15:16:06 -07:00
Rasmus Munk Larsen
7df811cfe5 Minor cleanups: 1. Get rid of unused variables. 2. Get rid of last uses of EIGEN_USE_COST_MODEL. 2016-05-18 15:09:48 -07:00
Benoit Steiner
bb3ff8e9d9 Advertize the packet api of the tensor reducers iff the corresponding packet primitives are available. 2016-05-18 14:52:49 -07:00
Gael Guennebaud
84df9142e7 bug #1231: fix compilation regression regarding complex_array/=real_array and add respective unit tests 2016-05-18 23:00:13 +02:00
Gael Guennebaud
21d692d054 Use coeff(i,j) instead of operator(). 2016-05-18 17:09:20 +02:00
Gael Guennebaud
8456bbbadb bug #1224: fix regression in (dense*dense).sparseView() by specializing evaluator<SparseView<Product>> for sparse products only. 2016-05-18 16:53:28 +02:00
Gael Guennebaud
b507b82326 Use default sorting strategy for square products. 2016-05-18 16:51:54 +02:00
Gael Guennebaud
1fa15ceee6 Extend sparse*sparse product unit test to check that the expected implementation is used (conservative vs auto pruning). 2016-05-18 16:50:54 +02:00
Gael Guennebaud
548a487800 bug #1229: bypass usage of Derived::Options which is available for plain matrix types only. Better use column-major storage anyway. 2016-05-18 16:44:05 +02:00
Gael Guennebaud
43790e009b Pass argument by const ref instead of by value in pow(AutoDiffScalar...) 2016-05-18 16:28:02 +02:00
Gael Guennebaud
1fbfab27a9 bug #1223: fix compilation of AutoDiffScalar's min/max operators, and add regression unit test. 2016-05-18 16:26:26 +02:00
Gael Guennebaud
448d9d943c bug #1222: fix compilation in AutoDiffScalar and add respective unit test 2016-05-18 16:00:11 +02:00
Gael Guennebaud
5a71eb5985 Big 1213: add regression unit test. 2016-05-18 14:03:03 +02:00
Gael Guennebaud
747e3290c0 bug #1213: rename some enums type for consistency. 2016-05-18 13:26:56 +02:00
Rasmus Munk Larsen
f519fca72b Reduce overhead for small tensors and cheap ops by short-circuiting the const computation and block size calculation in parallelFor. 2016-05-17 16:06:00 -07:00
Benoit Steiner
86ae94462e #if defined(EIGEN_USE_NONBLOCKING_THREAD_POOL) is now #if !defined(EIGEN_USE_SIMPLE_THREAD_POOL): the non blocking thread pool is the default since it's more scalable, and one needs to request the old thread pool explicitly. 2016-05-17 14:06:15 -07:00
Benoit Steiner
997c335970 Fixed compilation error 2016-05-17 12:54:18 -07:00
Benoit Steiner
ebf6ada5ee Fixed compilation error in the tensor thread pool 2016-05-17 12:33:46 -07:00
Rasmus Munk Larsen
0bb61b04ca Merge upstream. 2016-05-17 10:26:10 -07:00
Rasmus Munk Larsen
0dbd68145f Roll back changes to core. Move include of TensorFunctors.h up to satisfy dependence in TensorCostModel.h. 2016-05-17 10:25:19 -07:00
Rasmus Larsen
00228f2506 Merged eigen/eigen into default 2016-05-17 09:49:31 -07:00
Benoit Steiner
e7e64c3277 Enable the use of the packet api to evaluate tensor broadcasts. This speed things up quite a bit:
Before"
M_broadcasting/10        500000       3690    27.10 MFlops/s
BM_broadcasting/80        500000       4014  1594.24 MFlops/s
BM_broadcasting/640       100000      14770 27731.35 MFlops/s
BM_broadcasting/4K          5000     632711 39512.48 MFlops/s
After:
BM_broadcasting/10        500000       4287    23.33 MFlops/s
BM_broadcasting/80        500000       4455  1436.41 MFlops/s
BM_broadcasting/640       200000      10195 40173.01 MFlops/s
BM_broadcasting/4K          5000     423746 58997.57 MFlops/s
2016-05-17 09:24:35 -07:00
Benoit Steiner
5fa27574dd Allow vectorized padding on GPU. This helps speed things up a little
Before:
BM_padding/10            5000000        460   217.03 MFlops/s
BM_padding/80            5000000        460 13899.40 MFlops/s
BM_padding/640           5000000        461 888421.17 MFlops/s
BM_padding/4K            5000000        460 54316322.55 MFlops/s
After:
BM_padding/10            5000000        454   220.20 MFlops/s
BM_padding/80            5000000        455 14039.86 MFlops/s
BM_padding/640           5000000        452 904968.83 MFlops/s
BM_padding/4K            5000000        411 60750049.21 MFlops/s
2016-05-17 09:17:26 -07:00
Benoit Steiner
a910bcee43 Merged latest updates from trunk 2016-05-17 09:14:22 -07:00
Benoit Steiner
8d06c02ffd Allow vectorized padding on GPU. This helps speed things up a little.
Before:
BM_padding/10            5000000        460   217.03 MFlops/s
BM_padding/80            5000000        460 13899.40 MFlops/s
BM_padding/640           5000000        461 888421.17 MFlops/s
BM_padding/4K            5000000        460 54316322.55 MFlops/s
After:
BM_padding/10            5000000        454   220.20 MFlops/s
BM_padding/80            5000000        455 14039.86 MFlops/s
BM_padding/640           5000000        452 904968.83 MFlops/s
BM_padding/4K            5000000        411 60750049.21 MFlops/s
2016-05-17 09:13:27 -07:00
Benoit Steiner
86da77cb9b Pulled latest updates from trunk. 2016-05-17 07:21:48 -07:00
Benoit Steiner
92fc6add43 Don't rely on c++11 extension when we don't have to. 2016-05-17 07:21:22 -07:00
Benoit Steiner
2d74ef9682 Avoid float to double conversion 2016-05-17 07:20:11 -07:00
David Dement
ccc7563ac5 made a fix to the GMRES solver so that it now correctly reports the error achieved in the solution process 2016-05-16 14:26:41 -04:00
Gael Guennebaud
575bc44c3f Fix unit test. 2016-05-19 22:48:16 +02:00
Gael Guennebaud
ccb408ee6a Improve unit tests of zeta, polygamma, and digamma 2016-05-19 18:34:41 +02:00
Gael Guennebaud
6761c64d60 zeta and polygamma are not unary functions, but binary ones. 2016-05-19 18:34:16 +02:00
Gael Guennebaud
7a54032408 zeta and digamma do not require C++11/C99 2016-05-19 17:36:47 +02:00
Gael Guennebaud
ce12562710 Add some c++11 flags in documentation 2016-05-19 17:35:30 +02:00
Gael Guennebaud
b6ed8244b4 bug #1201: optimize affine*vector products 2016-05-19 16:09:15 +02:00
Gael Guennebaud
73693b5de6 bug #1221: disable gcc 6 warning: ignoring attributes on template argument 2016-05-19 15:21:53 +02:00
Gael Guennebaud
df9a5e13c6 Fix SelfAdjointEigenSolver for some input expression types, and add new regression unit tests for sparse and selfadjointview inputs. 2016-05-19 13:07:33 +02:00
Gael Guennebaud
6a2916df80 DiagonalWrapper is a vector, so it must expose the LinearAccessBit flag. 2016-05-19 13:06:21 +02:00
Gael Guennebaud
a226f6af6b Add support for SelfAdjointView::diagonal() 2016-05-19 13:05:33 +02:00
Gael Guennebaud
ee7da3c7c5 Fix SelfAdjointView::triangularView for complexes. 2016-05-19 13:01:51 +02:00
Gael Guennebaud
b6b8578a67 bug #1230: add support for SelfadjointView::triangularView. 2016-05-19 11:36:38 +02:00
Benoit Steiner
a80d875916 Added missing costPerCoeff method 2016-05-16 09:31:10 -07:00
Benoit Steiner
83ef39e055 Turn on the cost model by default. This results in some significant speedups for smaller tensors. For example, below are the results for the various tensor reductions.
Before:
BM_colReduction_12T/10       1000000       1949    51.29 MFlops/s
BM_colReduction_12T/80        100000      15636   409.29 MFlops/s
BM_colReduction_12T/640        20000      95100  4307.01 MFlops/s
BM_colReduction_12T/4K           500    4573423  5466.36 MFlops/s
BM_colReduction_4T/10        1000000       1867    53.56 MFlops/s
BM_colReduction_4T/80         500000       5288  1210.11 MFlops/s
BM_colReduction_4T/640         10000     106924  3830.75 MFlops/s
BM_colReduction_4T/4K            500    9946374  2513.48 MFlops/s
BM_colReduction_8T/10        1000000       1912    52.30 MFlops/s
BM_colReduction_8T/80         200000       8354   766.09 MFlops/s
BM_colReduction_8T/640         20000      85063  4815.22 MFlops/s
BM_colReduction_8T/4K            500    5445216  4591.19 MFlops/s
BM_rowReduction_12T/10       1000000       2041    48.99 MFlops/s
BM_rowReduction_12T/80        100000      15426   414.87 MFlops/s
BM_rowReduction_12T/640        50000      39117 10470.98 MFlops/s
BM_rowReduction_12T/4K           500    3034298  8239.14 MFlops/s
BM_rowReduction_4T/10        1000000       1834    54.51 MFlops/s
BM_rowReduction_4T/80         500000       5406  1183.81 MFlops/s
BM_rowReduction_4T/640         50000      35017 11697.16 MFlops/s
BM_rowReduction_4T/4K            500    3428527  7291.76 MFlops/s
BM_rowReduction_8T/10        1000000       1925    51.95 MFlops/s
BM_rowReduction_8T/80         200000       8519   751.23 MFlops/s
BM_rowReduction_8T/640         50000      33441 12248.42 MFlops/s
BM_rowReduction_8T/4K           1000    2852841  8763.19 MFlops/s


After:
BM_colReduction_12T/10      50000000         59  1678.30 MFlops/s
BM_colReduction_12T/80       5000000        725  8822.71 MFlops/s
BM_colReduction_12T/640        20000      90882  4506.93 MFlops/s
BM_colReduction_12T/4K           500    4668855  5354.63 MFlops/s
BM_colReduction_4T/10       50000000         59  1687.37 MFlops/s
BM_colReduction_4T/80        5000000        737  8681.24 MFlops/s
BM_colReduction_4T/640         50000     108637  3770.34 MFlops/s
BM_colReduction_4T/4K            500    7912954  3159.38 MFlops/s
BM_colReduction_8T/10       50000000         60  1657.21 MFlops/s
BM_colReduction_8T/80        5000000        726  8812.48 MFlops/s
BM_colReduction_8T/640         20000      91451  4478.90 MFlops/s
BM_colReduction_8T/4K            500    5441692  4594.16 MFlops/s
BM_rowReduction_12T/10      20000000         93  1065.28 MFlops/s
BM_rowReduction_12T/80       2000000        950  6730.96 MFlops/s
BM_rowReduction_12T/640        50000      38196 10723.48 MFlops/s
BM_rowReduction_12T/4K           500    3019217  8280.29 MFlops/s
BM_rowReduction_4T/10       20000000         93  1064.30 MFlops/s
BM_rowReduction_4T/80        2000000        959  6667.71 MFlops/s
BM_rowReduction_4T/640         50000      37433 10941.96 MFlops/s
BM_rowReduction_4T/4K            500    3036476  8233.23 MFlops/s
BM_rowReduction_8T/10       20000000         93  1072.47 MFlops/s
BM_rowReduction_8T/80        2000000        959  6670.04 MFlops/s
BM_rowReduction_8T/640         50000      38069 10759.37 MFlops/s
BM_rowReduction_8T/4K           1000    2758988  9061.29 MFlops/s
2016-05-16 08:55:21 -07:00
Benoit Steiner
b789a26804 Fixed syntax error 2016-05-16 08:51:08 -07:00
Benoit Steiner
83dfb40f66 Turnon the new thread pool by default since it scales much better over multiple cores. It is still possible to revert to the old thread pool by compiling with the EIGEN_USE_SIMPLE_THREAD_POOL define. 2016-05-13 17:23:15 -07:00
Benoit Steiner
97605c7b27 New multithreaded contraction that doesn't rely on the thread pool to run the closure in the order in which they are enqueued. This is needed in order to switch to the new non blocking thread pool since this new thread pool can execute the closure in any order. 2016-05-13 17:11:29 -07:00
Benoit Steiner
069a0b04d7 Added benchmarks for contraction on CPU. 2016-05-13 14:32:17 -07:00
Benoit Steiner
c4fc8b70ec Removed unnecessary thread synchronization 2016-05-13 10:49:38 -07:00
Benoit Steiner
7aa3557d31 Fixed compilation errors triggered by old versions of gcc 2016-05-12 18:59:04 -07:00
Rasmus Munk Larsen
5005b27fc8 Diasbled cost model by accident. Revert. 2016-05-12 16:55:21 -07:00
Rasmus Munk Larsen
989e419328 Address comments by bsteiner. 2016-05-12 16:54:19 -07:00
Rasmus Munk Larsen
e55deb21c5 Improvements to parallelFor.
Move some scalar functors from TensorFunctors. to Eigen core.
2016-05-12 14:07:22 -07:00
Benoit Steiner
ae9688f313 Worked around a compilation error triggered by nvcc when compiling a tensor concatenation kernel. 2016-05-12 12:06:51 -07:00
Benoit Steiner
2a54b70d45 Fixed potential race condition in the non blocking thread pool 2016-05-12 11:45:48 -07:00
Benoit Steiner
a071629fec Replace implicit cast with an explicit one 2016-05-12 10:40:07 -07:00
Benoit Steiner
2f9401b061 Worked around compilation errors with older versions of gcc 2016-05-11 23:39:20 -07:00
Benoit Steiner
09653e1f82 Improved the portability of the tensor code 2016-05-11 23:29:09 -07:00
Benoit Steiner
fae0493f98 Fixed a couple of bugs related to the Pascalfamily of GPUs
H: Enter commit message.  Lines beginning with 'HG:' are removed.
2016-05-11 23:02:26 -07:00
Benoit Steiner
886445ce4d Avoid unnecessary conversions between floats and doubles 2016-05-11 23:00:03 -07:00
Benoit Steiner
595e890391 Added more tests for half floats 2016-05-11 21:27:15 -07:00
Benoit Steiner
b6a517c47d Added the ability to load fp16 using the texture path.
Improved the performance of some reductions on fp16
2016-05-11 21:26:48 -07:00
Benoit Steiner
518149e868 Misc fixes for fp16 2016-05-11 20:11:14 -07:00
Benoit Steiner
56a1757d74 Made predux_min and predux_max on fp16 less noisy 2016-05-11 17:37:34 -07:00
Benoit Steiner
9091351dbe __ldg is only available with cuda architectures >= 3.5 2016-05-11 15:22:13 -07:00
Benoit Steiner
02f76dae2d Fixed a typo 2016-05-11 15:08:38 -07:00
Christoph Hertzberg
131e5a1a4a Do not copy for trivial 1x1 case. This also avoids a "maybe-uninitialized" warning in some situations. 2016-05-11 23:50:13 +02:00
Benoit Steiner
70195a5ff7 Added missing EIGEN_DEVICE_FUNC 2016-05-11 14:10:09 -07:00
Benoit Steiner
09a19c33a8 Added missing EIGEN_DEVICE_FUNC qualifiers 2016-05-11 14:07:43 -07:00
Christoph Hertzberg
1a1ce6ff61 Removed deprecated flag (which apparently was ignored anyway) 2016-05-11 23:05:37 +02:00
Christoph Hertzberg
2150f13d65 fixed some double-promotion and sign-compare warnings 2016-05-11 23:02:26 +02:00
Christoph Hertzberg
7268b10203 Split unit test 2016-05-11 19:41:53 +02:00
Christoph Hertzberg
8d4ef391b0 Don't flood test output with successful VERIFY_IS_NOT_EQUAL tests. 2016-05-11 19:40:45 +02:00
Christoph Hertzberg
bda21407dd Fix help output of buildtests and check scripts 2016-05-11 19:39:09 +02:00
Christoph Hertzberg
33ca7e3c8d bug #1207: Add and fix logical-op warnings 2016-05-11 19:36:34 +02:00
Benoit Steiner
217d984abc Fixed a typo in my previous commit 2016-05-11 10:22:15 -07:00
Benoit Steiner
08348b4e48 Fix potential race condition in the CUDA reduction code. 2016-05-11 10:08:51 -07:00
Benoit Steiner
cbb14ed47e Added a few tests to validate the generation of random tensors on GPU. 2016-05-11 10:05:56 -07:00
Benoit Steiner
6a5717dc74 Explicitely initialize all the atomic variables. 2016-05-11 10:04:41 -07:00
Christoph Hertzberg
0f61343893 Workaround maybe-uninitialized warning 2016-05-11 09:00:18 +02:00
Christoph Hertzberg
3bfc9b47ca Workaround "misleading-indentation" warnings 2016-05-11 08:41:36 +02:00
Benoit Steiner
4ede059de1 Properly gate the use of half2. 2016-05-10 17:04:01 -07:00
Benoit Steiner
bf185c3c28 Extended the tests for ptanh 2016-05-10 16:21:43 -07:00
Benoit Steiner
661e710092 Added support for fp16 to the sigmoid functor. 2016-05-10 12:25:27 -07:00
Benoit Steiner
0eb69b7552 Small improvement to the full reduction of fp16 2016-05-10 11:58:18 -07:00
Benoit Steiner
0b9e3dcd06 Added packet primitives to compute exp, log, sqrt and rsqrt on fp16. This improves the performance by 10 to 30%. 2016-05-10 11:05:33 -07:00
Benoit Steiner
6bf8273bc0 Added a test to validate the new non blocking thread pool 2016-05-10 10:49:34 -07:00
Benoit Steiner
4013b8feca Simplified the reduction code a little. 2016-05-10 09:40:42 -07:00
Benoit Steiner
75bd2bd32d Fixed compilation warning 2016-05-09 19:24:41 -07:00
Benoit Steiner
4670d7d5ce Improved the performance of full reductions on GPU:
Before:
BM_fullReduction/10       200000      11751     8.51 MFlops/s
BM_fullReduction/80         5000     523385    12.23 MFlops/s
BM_fullReduction/640          50   36179326    11.32 MFlops/s
BM_fullReduction/4K            1 2173517195    11.50 MFlops/s

After:
BM_fullReduction/10       500000       5987    16.70 MFlops/s
BM_fullReduction/80       200000      10636   601.73 MFlops/s
BM_fullReduction/640       50000      58428  7010.31 MFlops/s
BM_fullReduction/4K         1000    2006106 12461.95 MFlops/s
2016-05-09 17:09:54 -07:00
Benoit Steiner
c3859a2b58 Added the ability to use a scratch buffer in cuda kernels 2016-05-09 17:05:53 -07:00
Benoit Steiner
ba95e43ea2 Added a new parallelFor api to the thread pool device. 2016-05-09 10:45:12 -07:00
Benoit Steiner
dc7dbc2df7 Optimized the non blocking thread pool:
* Use a pseudo-random permutation of queue indices during random stealing. This ensures that all the queues are considered.
 * Directly pop from a non-empty queue when we are waiting for work,
instead of first noticing that there is a non-empty queue and
then doing another round of random stealing to re-discover the non-empty
queue.
 * Steal only 1 task from a remote queue instead of half of tasks.
2016-05-09 10:17:17 -07:00
Benoit Steiner
05c365fb16 Pulled latest updates from trunk 2016-05-07 13:39:04 -07:00
Benoit Steiner
691614bd2c Worked around a bug in nvcc on tegra x1 2016-05-07 13:28:53 -07:00
Benoit Steiner
a2d94fc216 Merged latest updates from trunk 2016-05-06 19:17:57 -07:00
Benoit Steiner
8adf5cc70f Added support for packet processing of fp16 on kepler and maxwell gpus 2016-05-06 19:16:43 -07:00
Benoit Steiner
1660e749b4 Avoid double promotion 2016-05-06 08:15:12 -07:00
Christoph Hertzberg
a11bd82dc3 bug #1213: Give names to anonymous enums 2016-05-06 11:31:56 +02:00
Benoit Steiner
c54ae65c83 Marked a few tensor operations as read only 2016-05-05 17:18:47 -07:00
Benoit Steiner
69a8a4e1f3 Added a test to validate full reduction on tensor of half floats 2016-05-05 16:52:50 -07:00
Benoit Steiner
678a17ba79 Made the testing of contractions on fp16 more robust 2016-05-05 16:36:39 -07:00
Benoit Steiner
e3d053e14e Refined the testing of log and exp on fp16 2016-05-05 16:24:15 -07:00
Benoit Steiner
9a48688d37 Further improved the testing of fp16 2016-05-05 15:58:05 -07:00
Benoit Steiner
0451940fa4 Relaxed the dummy precision for fp16 2016-05-05 15:40:01 -07:00
Benoit Steiner
910e013506 Relaxed an assertion that was tighter that necessary. 2016-05-05 15:38:16 -07:00
Benoit Steiner
f81e413180 Added a benchmark to measure the performance of full reductions of 16 bit floats 2016-05-05 14:15:11 -07:00
Benoit Steiner
28d5572658 Fixed some incorrect assertions 2016-05-05 10:02:26 -07:00
Benoit Steiner
2aba40d208 Avoid unecessary type promotion 2016-05-05 09:26:57 -07:00
Benoit Steiner
a4d6e8fef0 Strongly hint but don't force the compiler to unroll a some loops in the tensor executor. This results in up to 27% faster code. 2016-05-05 09:25:55 -07:00
Benoit Steiner
7875437ca0 Avoided unecessary type promotion 2016-05-05 09:08:42 -07:00
Benoit Steiner
f363e533aa Added tests for full contractions using thread pools and gpu devices.
Fixed a couple of issues in the corresponding code.
2016-05-05 09:05:45 -07:00
Benoit Steiner
06d774bf58 Updated the contraction code to ensure that full contraction return a tensor of rank 0 2016-05-05 08:37:47 -07:00
Christoph Hertzberg
b300a84989 Fixed some singed/unsigned comparison warnings 2016-05-05 13:36:28 +02:00
Christoph Hertzberg
dacb469bc9 Enable and fix -Wdouble-conversion warnings 2016-05-05 13:35:45 +02:00
Benoit Steiner
62b710072e Reduced the memory footprint of the cxx11_tensor_image_patch test 2016-05-04 21:08:22 -07:00
Benoit Steiner
dd2b45feed Removed extraneous 'explicit' keywords 2016-05-04 16:57:52 -07:00
Ola Røer Thorsen
be78aea6b3 fix double-promotion/float-conversion in Core/SpecialFunctions.h 2016-05-04 10:52:08 +02:00
Gael Guennebaud
75a94b9662 Improve documentation of BDCSVD 2016-05-04 12:53:14 +02:00
Benoit Steiner
968ec1c2ae Use numext::isfinite instead of std::isfinite 2016-05-03 19:56:40 -07:00
Gael Guennebaud
e2ca478485 bug #1214: consider denormals as zero in D&C SVD. This also workaround infinite binary search when compiling with ICC's unsafe optimizations. 2016-05-03 23:15:29 +02:00
Benoit Steiner
f899e08946 Enabled a number of tests previously disabled by mistake 2016-05-03 14:07:47 -07:00
Benoit Steiner
4c05fb03a3 Merged eigen/eigen into default 2016-05-03 13:15:00 -07:00
Benoit Steiner
577a07a86e Re-enabled the product_small test now that everything compiles correctly. 2016-05-03 13:11:38 -07:00
Benoit Steiner
2c5568a757 Added a test to validate the computation of exp and log on 16bit floats 2016-05-03 12:06:07 -07:00
Benoit Steiner
6c3e5b85bc Fixed compilation error with cuda >= 7.5 2016-05-03 09:38:42 -07:00
Benoit Steiner
aad9a04da4 Deleted superfluous explicit keyword. 2016-05-03 09:37:19 -07:00
Benoit Steiner
da50419df8 Made a cast explicit 2016-05-02 19:50:22 -07:00
Benoit Steiner
73ef5371e4 Pulled latest updates from trunk 2016-05-01 14:48:57 -07:00
Benoit Steiner
8a9228ed9b Fixed compilation error 2016-05-01 14:48:01 -07:00
Gael Guennebaud
b1bd53aa6b Fix performance regression: with AVX, unaligned stores were emitted instead of aligned ones for fixed size assignement. 2016-05-01 23:25:06 +02:00
Benoit Steiner
d6c9596fd8 Added missing accessors to fixed sized tensors 2016-04-29 18:51:33 -07:00
Benoit Steiner
17fe7f354e Deleted trailing commas 2016-04-29 18:39:01 -07:00
Benoit Steiner
e5f71aa6b2 Deleted useless trailing commas 2016-04-29 18:36:10 -07:00
Benoit Steiner
44f592dceb Deleted unnecessary trailing commas. 2016-04-29 18:33:46 -07:00
Benoit Steiner
2b890ae618 Fixed compilation errors generated by clang 2016-04-29 18:30:40 -07:00
Benoit Steiner
d217217842 Added a few tests to ensure that the dimensions of rank 0 tensors are correctly computed 2016-04-29 18:15:34 -07:00
Benoit Steiner
f100d1494c Return the proper size (ie 1) for tensors of rank 0 2016-04-29 18:14:33 -07:00
Benoit Steiner
d14105f158 Made several tensor tests compatible with cxx03 2016-04-29 17:22:37 -07:00
Benoit Steiner
c0882ef4d9 Moved a number of tensor tests that don't require cxx11 to work properly outside the EIGEN_TEST_CXX11 test section 2016-04-29 17:13:51 -07:00
Benoit Steiner
9d1dbd1ec0 Fixed teh cxx11_tensor_empty test to compile without requiring cxx11 support 2016-04-29 16:53:55 -07:00
Benoit Steiner
a8c0405cf5 Deleted unused default values for template parameters 2016-04-29 16:34:43 -07:00
Benoit Steiner
4f53178e62 Made a coupe of tensor tests compile without requiring c++11 support. 2016-04-29 16:09:54 -07:00
Benoit Steiner
1131a984a6 Made the cxx11_tensor_forced_eval compile without c++11. 2016-04-29 15:48:59 -07:00
Benoit Steiner
46bcb70969 Don't turn on const expressions when compiling with gcc >= 4.8 unless the -std=c++11 option has been used 2016-04-29 15:20:59 -07:00
Benoit Steiner
c07404f6a1 Restore Tensor support for non c++11 compilers 2016-04-29 15:19:19 -07:00
Benoit Steiner
ba32ded021 Fixed include path 2016-04-29 15:11:09 -07:00
Benoit Steiner
3b8da4be5a Extended the packetmath test to cover all the alignments made possible by avx512 instructions. 2016-04-29 14:13:43 -07:00
Benoit Steiner
2f28ccbea3 Update the makefile to make the tests compile with gcc 4.9 2016-04-29 14:11:09 -07:00
Benoit Steiner
7a4bd337d9 Resolved merge conflict 2016-04-29 13:42:22 -07:00
Benoit Steiner
07a247dcf4 Pulled latest updates from upstream 2016-04-29 13:41:26 -07:00
Benoit Steiner
fa5a8f055a Implemented palign_impl for AVX512 2016-04-29 13:30:13 -07:00
Benoit Steiner
ef3ac9d05a Fixed the AVX512 packet traits 2016-04-29 13:28:36 -07:00
Benoit Steiner
d7b75e8d86 Added pdiv packet primitives for avx512 2016-04-29 13:26:47 -07:00
Benoit Steiner
5e89ded685 Implemented preduxp for AVX512 2016-04-29 13:00:33 -07:00
Benoit Steiner
5f85662ad8 Implemented the pabs and preverse primitives for avx512. 2016-04-29 12:53:34 -07:00
Benoit Steiner
d37ee89ca8 Disabled some of the AVX512 primitives on compilers that don't support them 2016-04-29 12:50:29 -07:00
Gael Guennebaud
0f3c4c8ff4 Fix compilation of sparse.cast<>().transpose(). 2016-04-29 18:26:08 +02:00
Benoit Steiner
a524a26fdc Fixed a few memory leaks 2016-04-28 18:55:53 -07:00
Benoit Steiner
dacb23277e Fixed the igamma and igammac implementations to make them callable from a gpu kernel. 2016-04-28 18:54:54 -07:00
Benoit Steiner
a5d4545083 Deleted unused variable 2016-04-28 14:14:48 -07:00
Justin Lebar
40d1e2f8c7 Eliminate mutual recursion in igamma{,c}_impl::Run.
Presently, igammac_impl::Run calls igamma_impl::Run, which in turn calls
igammac_impl::Run.

This isn't actually mutual recursion; the calls are guarded such that we never
get into a loop.  Nonetheless, it's a stretch for clang to prove this.  As a
result, clang emits a recursive call in both igammac_impl::Run and
igamma_impl::Run.

That this is suboptimal code is bad enough, but it's particularly bad when
compiling for CUDA/nvptx.  nvptx allows recursion, but only begrudgingly: If
you have recursive calls in a kernel, it's on you to manually specify the
kernel's stack size.  Otherwise, ptxas will dump a warning, make a guess, and
who knows if it's right.

This change explicitly eliminates the mutual recursion in igammac_impl::Run and
igamma_impl::Run.
2016-04-28 13:57:08 -07:00
Konstantinos Margaritis
87294c84a6 define Packet2d constants with VSX only 2016-04-28 14:39:56 -03:00
Konstantinos Margaritis
6ed7a7281c remove accidentally pasted code 2016-04-28 14:35:55 -03:00
Konstantinos Margaritis
62f9093b31 improve state of MathFunctions as well 2016-04-28 14:33:09 -03:00
Konstantinos Margaritis
8ed26120c8 bring Altivec/VSX to a better state, implement some of the missing functions 2016-04-28 14:32:42 -03:00
Konstantinos Margaritis
950158f6d1 add name to copyrights 2016-04-28 14:32:11 -03:00
Konstantinos Margaritis
ee0459300b minor fix, add to copyright 2016-04-28 14:31:21 -03:00
Benoit Steiner
3ec81fc00f Fixed compilation error with clang. 2016-04-27 19:32:12 -07:00
Benoit Steiner
2b917291d9 Merged in rmlarsen/eigen2 (pull request PR-183)
Detect cxx_constexpr support when compiling with clang.
2016-04-27 15:19:54 -07:00
Rasmus Munk Larsen
09b9e951e3 Depend on the more extensive support for constexpr in clang:
http://clang.llvm.org/docs/LanguageExtensions.html#c-1y-relaxed-constexpr
2016-04-27 14:59:11 -07:00
Rasmus Munk Larsen
1a325ef71c Detect cxx_constexpr support when compiling with clang. 2016-04-27 14:33:51 -07:00
Benoit Steiner
1a97fd8b4e Merged latest update from trunk 2016-04-27 14:22:45 -07:00
Benoit Steiner
c61170e87d fpclassify isn't portable enough. In particular, the return values of the function are not available on all the platforms Eigen supportes: remove it from Eigen. 2016-04-27 14:22:20 -07:00
Gael Guennebaud
318e65e0ae Fix missing inclusion of Eigen/Core 2016-04-27 23:05:40 +02:00
Benoit Steiner
f629fe95c8 Made the index type a template parameter to evaluateProductBlockingSizes
Use numext::mini and numext::maxi instead of std::min/std::max to compute blocking sizes.
2016-04-27 13:11:19 -07:00
Benoit Steiner
66b215b742 Merged latest updates from trunk 2016-04-27 12:57:48 -07:00
Benoit Steiner
25141b69d4 Improved support for min and max on 16 bit floats when running on recent cuda gpus 2016-04-27 12:57:21 -07:00
Rasmus Larsen
ff33798acd Merged eigen/eigen into default 2016-04-27 12:27:00 -07:00
Rasmus Munk Larsen
463738ccbe Use computeProductBlockingSizes to compute blocking for both ShardByCol and ShardByRow cases. 2016-04-27 12:26:18 -07:00
Benoit Steiner
6744d776ba Added support for fpclassify in Eigen::Numext 2016-04-27 12:10:25 -07:00
Rasmus Munk Larsen
1f48f47ab7 Implement stricter argument checking for SYRK and SY2K and real matrices. To implement the BLAS API they should return info=2 if op='C' is passed for a complex matrix. Without this change, the Eigen BLAS fails the strict zblat3 and cblat3 tests in LAPACK 3.5. 2016-04-27 19:59:44 +02:00
Gael Guennebaud
3dddd34133 Refactor the unsupported CXX11/Core module to internal headers only. 2016-04-26 11:20:25 +02:00
Benoit Steiner
4a164d2c46 Fixed the partial evaluation of non vectorizable tensor subexpressions 2016-04-25 10:43:03 -07:00
Benoit Steiner
fd9401f260 Refined the cost of the striding operation. 2016-04-25 09:16:08 -07:00
Konstantinos Margaritis
3f80696ae1 Merged eigen/eigen into default 2016-04-22 15:05:21 +03:00
Benoit Steiner
5c372d19e3 Merged in rmlarsen/eigen (pull request PR-179)
Prevent crash in CompleteOrthogonalDecomposition if object was default constructed.
2016-04-21 18:06:36 -07:00
Benoit Steiner
4bbc97be5e Provide access to the base threadpool classes 2016-04-21 17:59:33 -07:00
Rasmus Munk Larsen
a3256d78d8 Prevent crash in CompleteOrthogonalDecomposition if object was default constructed. 2016-04-21 16:49:28 -07:00
Benoit Steiner
33adce5c3a Added the ability to switch to the new thread pool with a #define 2016-04-21 11:59:58 -07:00
Benoit Steiner
79b900375f Use index list for the striding benchmarks 2016-04-21 11:58:27 -07:00
Benoit Steiner
f670613e4b Fixed several compilation warnings 2016-04-21 11:03:02 -07:00
Benoit Steiner
6015422ee6 Added an option to enable the use of the F16C instruction set 2016-04-21 10:30:29 -07:00
Benoit Steiner
32ffce04fc Use EIGEN_THREAD_YIELD instead of std::this_thread::yield to make the code more portable. 2016-04-21 08:47:28 -07:00
Konstantinos Margaritis
e5b2ef47d5 Merged eigen/eigen into default 2016-04-21 18:03:08 +03:00
Benoit Steiner
2dde1b1028 Don't crash when attempting to reduce empty tensors. 2016-04-20 18:08:20 -07:00
Benoit Steiner
a792cd357d Added more tests 2016-04-20 17:33:58 -07:00
Benoit Steiner
80200a1828 Don't attempt to leverage the _cvtss_sh and _cvtsh_ss instructions when compiling with clang since it's unclear which versions of clang actually support these instruction. 2016-04-20 12:10:27 -07:00
Benoit Steiner
c7c2054bb5 Started to implement a portable way to yield. 2016-04-19 17:59:58 -07:00
Benoit Steiner
1d0238375d Made sure all the required header files are included when trying to use fp16 2016-04-19 17:44:12 -07:00
Benoit Steiner
2b72163028 Implemented a more portable version of thread local variables 2016-04-19 15:56:02 -07:00
Benoit Steiner
04f954956d Fixed a few typos 2016-04-19 15:27:09 -07:00
Benoit Steiner
5b1106c56b Fixed a compilation error with nvcc 7. 2016-04-19 14:57:57 -07:00
Benoit Steiner
7129d998db Simplified the code that launches cuda kernels. 2016-04-19 14:55:21 -07:00
Benoit Steiner
b9ea40c30d Don't take the address of a kernel on CUDA devices that don't support this feature. 2016-04-19 14:35:11 -07:00
Benoit Steiner
884c075058 Use numext::ceil instead of std::ceil 2016-04-19 14:33:30 -07:00
Benoit Steiner
a278414d1b Avoid an unnecessary copy of the evaluator. 2016-04-19 13:54:28 -07:00
Benoit Steiner
f953c60705 Fixed 2 recent regression tests 2016-04-19 12:57:39 -07:00
Benoit Steiner
50968a0a3e Use DenseIndex in the MeanReducer to avoid overflows when processing very large tensors. 2016-04-19 11:53:58 -07:00
Benoit Steiner
84543c8be2 Worked around the lack of a rand_r function on windows systems 2016-04-17 19:29:27 -07:00
Benoit Steiner
5fbcfe5eb4 Worked around the lack of a rand_r function on windows systems 2016-04-17 18:42:31 -07:00
Gael Guennebaud
e4fe611e2c Enable lazy-coeff-based-product for vector*(1x1) products 2016-04-16 15:17:39 +02:00
Benoit Steiner
c8e8f93d6c Move the evalGemm method into the TensorContractionEvaluatorBase class to make it accessible from both the single and multithreaded contraction evaluators. 2016-04-15 16:48:10 -07:00
Benoit Steiner
1a16fb1532 Deleted extraneous comma. 2016-04-15 15:50:13 -07:00
Benoit Steiner
7cff898e0a Deleted unnecessary variable 2016-04-15 15:46:14 -07:00
Benoit Steiner
6c43c49e4a Fixed a few compilation warnings 2016-04-15 15:34:34 -07:00
Benoit Steiner
eb669f989f Merged in rmlarsen/eigen (pull request PR-178)
Eigen Tensor cost model part 2: Thread scheduling for standard evaluators and reductions.
2016-04-15 14:53:15 -07:00
Gael Guennebaud
2a7115daca bug #1203: by-pass large stack-allocation in stableNorm if EIGEN_STACK_ALLOCATION_LIMIT is too small 2016-04-15 22:34:11 +02:00
Rasmus Munk Larsen
3718bf654b Get rid of void* casting when calling EvalRange::run. 2016-04-15 12:51:33 -07:00
Benoit Steiner
40c9923a8a Fixed compilation errors with msvc 2016-04-15 11:27:52 -07:00
Benoit Steiner
1d23430628 Improved the matrix multiplication blocking in the case where mr is not a power of 2 (e.g on Haswell CPUs). 2016-04-15 10:53:31 -07:00
Gael Guennebaud
1e80bddde3 Fix trmv for mixing types. 2016-04-15 17:58:36 +02:00
Konstantinos Margaritis
0e8fc31087 remove pgather/pscatter for std::complex<double> for s390x 2016-04-15 07:08:57 -04:00
Benoit Steiner
a62e924656 Added ability to access the cache sizes from the tensor devices 2016-04-14 21:25:06 -07:00
Benoit Steiner
18e6f67426 Added support for exclusive or 2016-04-14 20:37:46 -07:00
Rasmus Munk Larsen
07ac4f7e02 Eigen Tensor cost model part 2: Thread scheduling for standard evaluators and reductions. The cost model is turned off by default. 2016-04-14 18:28:23 -07:00
Benoit Steiner
9624a1ea3d Added missing definition of PacketSize in the gpu evaluator of convolution 2016-04-14 17:16:58 -07:00
Benoit Steiner
6fbedf5a4e Merged in rmlarsen/eigen (pull request PR-177)
Eigen Tensor cost model part 1.
2016-04-14 17:13:19 -07:00
Benoit Steiner
bebb89acfa Enabled the new threadpool tests 2016-04-14 16:44:10 -07:00
Benoit Steiner
9c064b5a97 Cleanup 2016-04-14 16:41:31 -07:00
Benoit Steiner
1372156c41 Prepared the migration to the new non blocking thread pool 2016-04-14 16:16:42 -07:00
Rasmus Munk Larsen
aeb5494a0b Improvements to cost model. 2016-04-14 15:52:58 -07:00
Benoit Steiner
00dfe18487 Merged latest updates from trunk 2016-04-14 15:25:20 -07:00
Benoit Steiner
a8e8837ba7 Added tests for the non blocking thread pool 2016-04-14 15:23:49 -07:00
Benoit Steiner
78a51abc12 Added a more scalable non blocking thread pool 2016-04-14 15:23:10 -07:00
Rasmus Munk Larsen
d2e95492e7 Merge upstream updates. 2016-04-14 13:59:50 -07:00
Rasmus Munk Larsen
235e83aba6 Eigen cost model part 1. This implements a basic recursive framework to estimate the cost of evaluating tensor expressions. 2016-04-14 13:57:35 -07:00
Gael Guennebaud
68897c52f3 Add extreme values to the imaginary part for SVD unit tests. 2016-04-14 22:47:30 +02:00
Gael Guennebaud
20f387fafa Improve numerical robustness of JacoviSVD:
- avoid noise amplification in complex to real conversion
 - compare off-diagonal entries to the current biggest diagonal entry: no need to bother about a 2x2 block containing ridiculously small entries compared to the rest of the matrix.
2016-04-14 22:46:55 +02:00
Benoit Steiner
7718749fee Force the inlining of the << operator on half floats 2016-04-14 11:51:54 -07:00
Benoit Steiner
5379d2b594 Inline the << operator on half floats 2016-04-14 11:40:48 -07:00
Benoit Steiner
5912ad877c Silenced a compilation warning 2016-04-14 11:40:14 -07:00
Benoit Steiner
2b6e3de02f Added tests to validate flooring and ceiling of fp16 2016-04-14 11:39:18 -07:00
Benoit Steiner
6f23e945f6 Added simple test for numext::sqrt and numext::pow on fp16 2016-04-14 10:32:52 -07:00
Benoit Steiner
72510c80e1 Added basic test for trigonometric functions on fp16 2016-04-14 10:27:24 -07:00
Benoit Steiner
7b3d7acebe Added support for fp16 to test_isApprox, test_isMuchSmallerThan, and test_isApproxOrLessThan 2016-04-14 10:25:50 -07:00
Benoit Steiner
5c13765ee3 Added ability to printf fp16 2016-04-14 10:24:52 -07:00
Benoit Steiner
c7167fee0e Added support for fp16 to the sigmoid function 2016-04-14 10:08:33 -07:00
Benoit Steiner
f6003f0873 Made the test msvc friendly 2016-04-14 09:47:26 -07:00
Gael Guennebaud
3551dea887 Cleaning pass on rcond estimator. 2016-04-14 16:45:41 +02:00
Gael Guennebaud
d8a3bdaa24 remove useless include 2016-04-14 15:18:56 +02:00
Gael Guennebaud
d402adc3d7 Better use .data() than &coeffRef(0) 2016-04-14 15:18:08 +02:00
Gael Guennebaud
ea7087ef31 Merged in rmlarsen/eigen (pull request PR-174)
Add matrix condition number estimation module.
2016-04-14 15:11:33 +02:00
Benoit Steiner
36f5a10198 Properly gate the definition of the error and gamma functions for fp16 2016-04-13 18:44:48 -07:00
Benoit Steiner
10b69810d1 Improved support for trigonometric functions on GPU 2016-04-13 16:00:51 -07:00
Benoit Steiner
d6105b53b8 Added basic implementation of the lgamma, digamma, igamma, igammac, polygamma, and zeta function for fp16 2016-04-13 15:26:02 -07:00
Gael Guennebaud
703251f10f merge 2016-04-13 23:45:10 +02:00
Gael Guennebaud
39211ba46b Fix JacobiSVD for complex when the complex-to-real update already gives a diagonal 2x2 block. 2016-04-13 23:43:26 +02:00
Benoit Steiner
2986253259 Cleaned up the implementation of digamma 2016-04-13 14:24:06 -07:00
Benoit Steiner
d5de1a8220 Pulled latest updates from trunk 2016-04-13 14:17:11 -07:00
Benoit Steiner
87ca15c4e8 Added support for sin, cos, tan, and tanh on fp16 2016-04-13 14:12:38 -07:00
Gael Guennebaud
2c9e4fa417 Add debug output for random unit test 2016-04-13 22:56:12 +02:00
Gael Guennebaud
7d1391d049 Turn a converge check to a warning 2016-04-13 22:50:54 +02:00
Gael Guennebaud
feef39e2d1 Fix underflow in JacoviSVD's complex to real preconditioner 2016-04-13 22:49:51 +02:00
Gael Guennebaud
f4e12272f1 Fix corner case in unit test. 2016-04-13 22:18:02 +02:00
Gael Guennebaud
a95e1a273e Fix warning in unit tests 2016-04-13 22:00:38 +02:00
Benoit Steiner
bf3f6688f0 Added support for computing cos, sin, tan, and tanh on GPU. 2016-04-13 11:55:08 -07:00
Benoit Steiner
473c8380ea Added constructors to convert unsigned integers into fp16 2016-04-13 11:03:37 -07:00
Gael Guennebaud
42a3352a3b Workaround a division by zero when outerstride==0 2016-04-13 19:02:02 +02:00
Gael Guennebaud
6f960b83ff Make use of is_same_dense helper instead of extract_data to detect input/outputs are the same. 2016-04-13 18:47:12 +02:00
Gael Guennebaud
b7716c0328 Fix incomplete previous patch on matrix comparision. 2016-04-13 18:32:56 +02:00
Gael Guennebaud
2630d97c62 Fix detection of same matrices when both matrices are not handled by extract_data. 2016-04-13 18:26:08 +02:00
Gael Guennebaud
512ba0ac76 Add regression unit tests for half-packet vectorization 2016-04-13 18:16:35 +02:00
Gael Guennebaud
06447e0a39 Improve half-packet vectorization logic to distinguish linear versus inner traversal modes. 2016-04-13 18:15:49 +02:00
Gael Guennebaud
bbb8854bf7 Enable half-packet in reduxions. 2016-04-13 13:02:34 +02:00
Benoit Steiner
e9b12cc1f7 Fixed compilation warnings generated by clang 2016-04-12 20:53:18 -07:00
Benoit Steiner
eaeb6ca93a Enable the benchmarks for algebraic and transcendental fnctions on fp16. 2016-04-12 16:29:00 -07:00
Benoit Steiner
aa1ba8bbd2 Don't put a command at the end of an enumerator list 2016-04-12 16:28:11 -07:00
Benoit Steiner
e49945ced4 Pulled latest update from trunk 2016-04-12 14:13:41 -07:00
Benoit Steiner
25d05c4b8f Fixed the vectorization logic test 2016-04-12 14:13:25 -07:00
Benoit Steiner
53121c0119 Turned on the contraction benchmarks for fp16 2016-04-12 14:11:52 -07:00
Gael Guennebaud
b67c983291 Enable the use of half-packet in coeff-based product.
For instance, Matrix4f*Vector4f is now vectorized again when using AVX.
2016-04-12 23:03:03 +02:00
Benoit Steiner
e3a184785c Fixed the zeta test 2016-04-12 11:12:36 -07:00
Benoit Steiner
3b76df64fc Defer the decision to vectorize tensor CUDA code to the meta kernel. This makes it possible to decide to vectorize or not depending on the capability of the target cuda architecture. In particular, this enables us to vectorize the processing of fp16 when running on device of capability >= 5.3 2016-04-12 10:58:51 -07:00
Benoit Steiner
8bfe739cd2 Updated the AVX512 PacketMath to properly leverage the AVX512DQ instructions 2016-04-11 18:40:16 -07:00
Rasmus Larsen
6498dadc2f Merged eigen/eigen into default 2016-04-11 17:42:05 -07:00
Benoit Steiner
d6e596174d Pull latest updates from upstream 2016-04-11 17:20:17 -07:00
Benoit Steiner
748c4c4599 More accurate cost estimates for exp, log, tanh, and sqrt. 2016-04-11 13:11:04 -07:00
Benoit Steiner
833efb39bf Added epsilon, dummy_precision, infinity and quiet_NaN NumTraits for fp16 2016-04-11 11:03:56 -07:00
Benoit Steiner
e939b087fe Pulled latest update from trunk 2016-04-11 11:03:02 -07:00
Gael Guennebaud
1744b5b5d2 Update doc regarding the genericity of EIGEN_USE_BLAS 2016-04-11 17:16:07 +02:00
Gael Guennebaud
91bf925fc1 Improve constness of level2 blas API. 2016-04-11 17:13:01 +02:00
Gael Guennebaud
0483430283 Move LAPACK declarations from blas.h to lapack.h and fix compatibility with EIGEN_USE_MKL 2016-04-11 17:12:31 +02:00
Gael Guennebaud
097d1e8823 Cleanup obsolete assign_scalar_eig2mkl helper. 2016-04-11 16:09:29 +02:00
Gael Guennebaud
fec4c334ba Remove all references to MKL in BLAS wrappers. 2016-04-11 16:04:09 +02:00
Gael Guennebaud
ddabc992fa Fix long to int conversion in BLAS API. 2016-04-11 15:52:01 +02:00
Gael Guennebaud
8191f373be Silent unused warning. 2016-04-11 15:37:16 +02:00
Gael Guennebaud
6a9ca88e7e Relax dependency on MKL for EIGEN_USE_BLAS 2016-04-11 15:17:14 +02:00
Gael Guennebaud
4e8e5888d7 Improve constness of blas level-3 interface. 2016-04-11 15:12:44 +02:00
Gael Guennebaud
675e0a2224 Fix static/inline keywords order. 2016-04-11 15:06:20 +02:00
Gael Guennebaud
fc6a0ebb1c Typos in doc. 2016-04-11 10:54:58 +02:00
Till Hoffmann
643b697649 Proper handling of domain errors. 2016-04-10 00:37:53 +01:00
Rasmus Munk Larsen
1f70bd4134 Merge. 2016-04-09 15:31:53 -07:00
Rasmus Munk Larsen
096e355f8e Add short-circuit to avoid calling matrix norm for empty matrix. 2016-04-09 15:29:56 -07:00
Rasmus Larsen
be80fb49fc Merged default (4a92b590a0
) into default
2016-04-09 13:13:01 -07:00
Rasmus Larsen
7a8176587b Merged eigen/eigen into default 2016-04-09 12:47:41 -07:00
Rasmus Munk Larsen
4a92b590a0 Merge. 2016-04-09 12:47:24 -07:00
Rasmus Munk Larsen
ee6c69733a A few tiny adjustments to short-circuit logic. 2016-04-09 12:45:49 -07:00
Till Hoffmann
7f4826890c Merge upstream 2016-04-09 20:08:07 +01:00
Till Hoffmann
de057ebe54 Added nans to zeta function. 2016-04-09 20:07:36 +01:00
Gael Guennebaud
af2161cdb4 bug #1197: fix/relax some LM unit tests 2016-04-09 11:14:02 +02:00
Gael Guennebaud
a05a683d83 bug #1160: fix and relax some lm unit tests by turning faillures to warnings 2016-04-09 10:49:19 +02:00
Benoit Steiner
5da90fc8dd Use numext::abs instead of std::abs in scalar_fuzzy_default_impl to make it usable inside GPU kernels. 2016-04-08 19:40:48 -07:00
Benoit Steiner
01bd577288 Fixed the implementation of Eigen::numext::isfinite, Eigen::numext::isnan, andEigen::numext::isinf on CUDA devices 2016-04-08 16:40:10 -07:00
Benoit Steiner
89a3dc35a3 Fixed isfinite_impl: NumTraits<T>::highest() and NumTraits<T>::lowest() are finite numbers. 2016-04-08 15:56:16 -07:00
Benoit Steiner
995f202cea Disabled the use of half2 on cuda devices of compute capability < 5.3 2016-04-08 14:43:36 -07:00
Benoit Steiner
8d22967bd9 Initial support for taking the power of fp16 2016-04-08 14:22:39 -07:00
Benoit Steiner
3394379319 Fixed the packet_traits for half floats. 2016-04-08 13:33:59 -07:00
Benoit Steiner
0d2a532fc3 Created the new EIGEN_TEST_CUDA_CLANG option to compile the CUDA tests using clang instead of nvcc 2016-04-08 13:16:08 -07:00
Rasmus Larsen
0b81a18d12 Merged eigen/eigen into default 2016-04-08 12:58:57 -07:00
Benoit Steiner
2d072b38c1 Don't test the division by 0 on float16 when compiling with msvc since msvc detects and errors out on divisions by 0. 2016-04-08 12:50:25 -07:00
Benoit Jacob
cd2b667ac8 Add references to filed LLVM bugs 2016-04-08 08:12:47 -04:00
Benoit Steiner
3bd16457e1 Properly handle complex numbers. 2016-04-07 23:28:04 -07:00
Benoit Steiner
63102ee43d Turn on the coeffWise benchmarks on fp16 2016-04-07 23:05:20 -07:00
Benoit Steiner
7c47d3e663 Fixed the type casting benchmarks for fp16 2016-04-07 22:50:25 -07:00
Benoit Steiner
166b56bc61 Fixed the type casting benchmark for float16 2016-04-07 22:45:54 -07:00
Benoit Steiner
2f2801f096 Merged in parthaEth/eigen (pull request PR-175)
Static casting scalar types so as to let chlesky module of eigen work with ceres
2016-04-07 22:10:14 -07:00
Benoit Steiner
d962fe6a99 Renamed float16 into cxx11_float16 since the test relies on c++11 features 2016-04-07 20:28:32 -07:00
Rasmus Larsen
c34e55c62b Merged eigen/eigen into default 2016-04-07 20:23:03 -07:00
Benoit Steiner
7d5b17087f Added missing EIGEN_DEVICE_FUNC to the tensor conversion code. 2016-04-07 20:01:19 -07:00
Benoit Steiner
a6d08be9b2 Fixed the benchmarking of fp16 coefficient wise operations 2016-04-07 17:13:44 -07:00
Rasmus Munk Larsen
283c51cd5e Widen short-circuiting ReciprocalConditionNumberEstimate so we don't call InverseMatrixL1NormEstimate for dec.rows() <= 1. 2016-04-07 16:45:40 -07:00
Rasmus Munk Larsen
d51803a728 Use Index instead of int for indexing and sizes. 2016-04-07 16:39:48 -07:00
Rasmus Munk Larsen
fd872aefb3 Remove transpose() method from LLT and LDLT classes as it would imply conjugation.
Explicitly cast constants to RealScalar in ConditionEstimator.h.
2016-04-07 16:28:44 -07:00
Rasmus Munk Larsen
0b5546d182 Use lpNorm<1>() to compute l1 norms in LLT and LDLT. 2016-04-07 15:49:30 -07:00
parthaEth
2d5bb375b7 Static casting scalar types so as to let chlesky module of eigen work with ceres 2016-04-08 00:14:44 +02:00
Benoit Steiner
a02ec09511 Worked around numerical noise in the test for the zeta function. 2016-04-07 12:11:02 -07:00
Benoit Steiner
c912b1d28c Fixed a typo in the polygamma test. 2016-04-07 11:51:07 -07:00
Benoit Steiner
74f64838c5 Updated the unary functors to use the numext implementation of typicall functions instead of the one provided in the standard library. The standard library functions aren't supported officially by cuda, so we're better off using the numext implementations. 2016-04-07 11:42:14 -07:00
Benoit Steiner
737644366f Move the functions operating on fp16 out of the std namespace and into the Eigen::numext namespace 2016-04-07 11:40:15 -07:00
Benoit Steiner
dc45aaeb93 Added tests for float16 2016-04-07 11:18:05 -07:00
Benoit Steiner
8db269e055 Fixed a typo in a test 2016-04-07 10:41:51 -07:00
Benoit Steiner
b89d3f78b2 Updated the isnan, isinf and isfinite functions to make compatible with cuda devices. 2016-04-07 10:08:49 -07:00
Benoit Steiner
48308ed801 Added support for isinf, isnan, and isfinite checks to the tensor api 2016-04-07 09:48:36 -07:00
Benoit Steiner
cfb34d808b Fixed a possible integer overflow. 2016-04-07 08:46:52 -07:00
Benoit Steiner
df838736e2 Fixed compilation warning triggered by msvc 2016-04-06 20:48:55 -07:00
Benoit Steiner
14ea7c7ec7 Fixed packet_traits<half> 2016-04-06 19:30:21 -07:00
Benoit Steiner
532fdf24cb Added support for hardware conversion between fp16 and full floats whenever
possible.
2016-04-06 17:11:31 -07:00
Benoit Steiner
165150e896 Fixed the tests for the zeta and polygamma functions 2016-04-06 14:31:01 -07:00
Benoit Steiner
7be1eaad1e Fixed typos in the implementation of the zeta and polygamma ops. 2016-04-06 14:15:37 -07:00
Benoit Steiner
58c1dbff19 Made the fp16 code more portable. 2016-04-06 13:44:08 -07:00
Benoit Steiner
cf7e73addd Added some missing conversions to the Half class, and fixed the implementation of the < operator on cuda devices. 2016-04-06 09:59:51 -07:00
Benoit Steiner
10bdd8e378 Merged in tillahoffmann/eigen (pull request PR-173)
Added zeta function of two arguments and polygamma function
2016-04-06 09:40:17 -07:00
Benoit Steiner
7781f865cb Renamed the EIGEN_TEST_NVCC cmake option into EIGEN_TEST_CUDA per the discussion in bug #1173. 2016-04-06 09:35:23 -07:00
Benoit Steiner
72abfa11dd Added support for isfinite on fp16 2016-04-06 09:07:30 -07:00
Rasmus Munk Larsen
4d07064a3d Fix bug in alternate lower bound calculation due to missing parentheses.
Make a few expressions more concise.
2016-04-05 16:40:48 -07:00
Konstantinos Margaritis
2bba4ee2cf Merged kmargar/eigen/tip into default 2016-04-05 22:22:08 +03:00
Konstantinos Margaritis
317384b397 complete the port, remove float support 2016-04-05 14:56:45 -04:00
tillahoffmann
726bd5f077 Merged eigen/eigen into default 2016-04-05 18:21:05 +01:00
Till Hoffmann
a350c25a39 Added accuracy comments. 2016-04-05 18:20:40 +01:00
Gael Guennebaud
4d7e230d2f bug #1189: fix pow/atan2 compilation for AutoDiffScalar 2016-04-05 14:49:41 +02:00
Konstantinos Margaritis
bc0ad363c6 add remaining includes 2016-04-05 06:01:17 -04:00
Konstantinos Margaritis
2d41dc9622 complete int/double specialized traits for ZVector 2016-04-05 06:00:51 -04:00
Konstantinos Margaritis
644d0f91d2 enable all tests again 2016-04-05 05:59:54 -04:00
Konstantinos Margaritis
988344daf1 enable the other includes as well 2016-04-05 05:59:30 -04:00
Rasmus Larsen
d7eeee0c1d Merged eigen/eigen into default 2016-04-04 15:58:27 -07:00
Rasmus Munk Larsen
513c372960 Fix docstrings to list all supported decompositions. 2016-04-04 14:34:59 -07:00
Rasmus Munk Larsen
86e0ed81f8 Addresses comments on Eigen pull request PR-174.
* Get rid of code-duplication for real vs. complex matrices.
* Fix flipped arguments to select.
* Make the condition estimation functions free functions.
* Use Vector::Unit() to generate canonical unit vectors.
* Misc. cleanup.
2016-04-04 14:20:01 -07:00
Benoit Jacob
158fea0f5e bug #1190 - Don't trust __ARM_FEATURE_FMA on Clang/ARM 2016-04-04 16:42:40 -04:00
Benoit Jacob
03f2997a11 bug #1191 - Prevent Clang/ARM from rewriting VMLA into VMUL+VADD 2016-04-04 16:41:47 -04:00
Till Hoffmann
b0143de177 Merge upstream. 2016-04-04 19:16:48 +01:00
Till Hoffmann
b97911dd18 Refactored code into type-specific helper functions. 2016-04-04 19:16:03 +01:00
Benoit Steiner
c4179dd470 Updated the scalar_abs_op struct to make it compatible with cuda devices. 2016-04-04 11:11:51 -07:00
Benoit Steiner
1108b4f218 Fixed the signature of numext::abs to make it compatible with complex numbers 2016-04-04 11:09:25 -07:00
tillahoffmann
b8245cc325 Merged eigen/eigen into default 2016-04-04 12:28:11 +01:00
Gael Guennebaud
2b457f8e5e Fix cross-compiling windows version detection 2016-04-04 11:47:46 +02:00
Rasmus Larsen
30242b7565 Merged eigen/eigen into default 2016-04-01 17:19:36 -07:00
Rasmus Munk Larsen
9d51f7c457 Add rcond method to LDLT. 2016-04-01 16:48:38 -07:00
Rasmus Munk Larsen
f54137606e Add condition estimation to Cholesky (LLT) factorization. 2016-04-01 16:19:45 -07:00
Rasmus Munk Larsen
fb8dccc23e Replace "inline static" with "static inline" for consistency. 2016-04-01 12:48:18 -07:00
Rasmus Munk Larsen
91414e0042 Fix comments in ConditionEstimator and minor cleanup. 2016-04-01 11:58:17 -07:00
Rasmus Munk Larsen
1aa89fb855 Add matrix condition estimator module that implements the Higham/Hager algorithm from http://www.maths.manchester.ac.uk/~higham/narep/narep135.pdf used in LPACK. Add rcond() methods to FullPivLU and PartialPivLU. 2016-04-01 10:27:59 -07:00
Till Hoffmann
80eba21ad0 Merge upstream. 2016-04-01 18:18:49 +01:00
Till Hoffmann
eb0ae602bd Added CUDA tests. 2016-04-01 18:17:45 +01:00
Till Hoffmann
ffd770ce94 Fixed CUDA signature. 2016-04-01 17:58:24 +01:00
Till Hoffmann
3cb0a237c1 Fixed suggestions by Eugene Brevdo. 2016-04-01 17:51:39 +01:00
tillahoffmann
49960adbdd Merged eigen/eigen into default 2016-04-01 14:36:15 +01:00
Till Hoffmann
57239f4a81 Added polygamma function. 2016-04-01 14:35:21 +01:00
Till Hoffmann
dd5d390daf Added zeta function. 2016-04-01 13:32:29 +01:00
Benoit Steiner
3da495e6b9 Relaxed the condition used to gate the fft code. 2016-03-31 18:11:51 -07:00
Benoit Steiner
0ea7ab4f62 Hashing was only officially introduced in c++11. Therefore only define an implementation of the hash function for float16 if c++11 is enabled. 2016-03-31 14:44:55 -07:00
Benoit Steiner
92b7f7b650 Improved code formating 2016-03-31 13:09:58 -07:00
Benoit Steiner
f197813f37 Added the ability to hash a fp16 2016-03-31 13:09:23 -07:00
Benoit Steiner
0f5cc504fe Properly gate the fft code 2016-03-31 12:59:39 -07:00
Benoit Steiner
4c859181da Made it possible to use the NumTraits for complex and Array in a cuda kernel. 2016-03-31 12:48:38 -07:00
Benoit Steiner
c36ab19902 Added __ldg primitive for fp16. 2016-03-31 10:55:03 -07:00
Benoit Steiner
b575fb1d02 Added NumTraits for half floats 2016-03-31 10:43:59 -07:00
Benoit Steiner
8c8a79cec1 Fixed a typo 2016-03-31 10:33:32 -07:00
Benoit Steiner
af4ef540bf Fixed a off-by-one bug in a debug assertion 2016-03-30 18:37:19 -07:00
Benoit Steiner
791e5cfb69 Added NumTraits for type2index. 2016-03-30 18:36:36 -07:00
Benoit Steiner
4f1a7e51c1 Pull math functions from the global namespace only when compiling cuda code with nvcc. When compiling with clang, we want to use the std namespace. 2016-03-30 17:59:49 -07:00
Benoit Steiner
bc68fc2fe7 Enable constant expressions when compiling cuda code with clang. 2016-03-30 17:58:32 -07:00
Benoit Steiner
483aaad10a Fixed compilation warning 2016-03-30 17:08:13 -07:00
Benoit Steiner
1b40abbf99 Added missing assignment operator to the TensorUInt128 class, and made misc small improvements 2016-03-30 13:17:03 -07:00
Benoit Jacob
01b5333e44 bug #1186 - vreinterpretq_u64_f64 fails to build on Android/Aarch64/Clang toolchain 2016-03-30 11:02:33 -04:00
Benoit Steiner
aa45ad2aac Fixed the formatting of the README. 2016-03-29 15:06:13 -07:00
Benoit Steiner
56df5ef1d7 Attempt to fix the formatting of the README 2016-03-29 15:03:38 -07:00
Benoit Steiner
1bcd82e31b Pulled latest updates from trunk 2016-03-29 13:36:18 -07:00
Gael Guennebaud
09ad31aa85 Add regression test for nesting type handling in blas_traits 2016-03-29 22:33:57 +02:00
Benoit Steiner
1841d6d4c3 Added missing cuda template specializations for numext::ceil 2016-03-29 13:29:34 -07:00
Benoit Steiner
7b7d2a9fa5 Use false instead of 0 as the expected value of a boolean 2016-03-29 11:50:17 -07:00
Benoit Steiner
e02b784ec3 Added support for standard mathematical functions and trancendentals(such as exp, log, abs, ...) on fp16 2016-03-29 09:20:36 -07:00
Benoit Steiner
c38295f0a0 Added support for fmod 2016-03-28 15:53:02 -07:00
Benoit Steiner
6772f653c3 Made it possible to customize the threadpool 2016-03-28 10:01:04 -07:00
Benoit Steiner
1bc81f7889 Fixed compilation warnings on arm 2016-03-28 09:21:04 -07:00
Benoit Steiner
78f83d6f6a Prevent potential overflow. 2016-03-28 09:18:04 -07:00
Konstantinos Margaritis
01e7298fe6 actually include ZVector files, passes most basic tests (float still fails) 2016-03-28 10:58:02 -04:00
Konstantinos Margaritis
f48011119e Merged eigen/eigen into default 2016-03-28 01:48:45 +03:00
Konstantinos Margaritis
ed6b9d08f1 some primitives ported, but missing intrinsics and crash with asm() are a problem 2016-03-27 18:47:49 -04:00
Benoit Steiner
74f91ed06c Improved support for integer modulo 2016-03-25 17:21:56 -07:00
Benoit Steiner
65716e99a5 Improved the cost estimate of the quotient op 2016-03-25 11:13:53 -07:00
Benoit Steiner
d94f6ba965 Started to model the cost of divisions more accurately. 2016-03-25 11:02:56 -07:00
Benoit Steiner
a86c9f037b Fixed compilation error on windows 2016-03-24 18:54:31 -07:00
Benoit Steiner
0968e925a0 Updated the benchmarking code to use Eigen::half instead of half 2016-03-24 18:00:33 -07:00
Benoit Steiner
044efea965 Made sure that the cxx11_tensor_cuda test can be compiled even without support for cxx11. 2016-03-23 20:02:11 -07:00
Benoit Steiner
2e4e4cb74d Use numext::abs instead of abs to avoid incorrect conversion to integer of the argument 2016-03-23 16:57:12 -07:00
Benoit Steiner
41434a8a85 Avoid unnecessary conversions 2016-03-23 16:52:38 -07:00
Benoit Steiner
92693b50eb Fixed compilation warning 2016-03-23 16:40:36 -07:00
Benoit Steiner
9bc9396e88 Use portable includes 2016-03-23 16:30:06 -07:00
Benoit Steiner
393bc3b16b Added comment 2016-03-23 16:22:15 -07:00
Benoit Steiner
81d340984a Removed executable bit from header files 2016-03-23 16:15:02 -07:00
Benoit Steiner
bff8cbad06 Removed executable bit from header files 2016-03-23 16:14:23 -07:00
Benoit Steiner
7a570e50ef Fixed contractions of fp16 2016-03-23 16:00:06 -07:00
Benoit Steiner
7168afde5e Made the tensor benchmarks compile on MacOS 2016-03-23 14:21:04 -07:00
Benoit Steiner
2062ee2d26 Added a test to verify that notifications are working properly 2016-03-23 13:39:00 -07:00
Benoit Steiner
fc3660285f Made type conversion explicit 2016-03-23 09:56:50 -07:00
Benoit Steiner
0e68882604 Added the ability to divide a half float by an index 2016-03-23 09:46:42 -07:00
Benoit Steiner
6971146ca9 Added more conversion operators for half floats 2016-03-23 09:44:52 -07:00
Christoph Hertzberg
9642fd7a93 Replace all M_PI by EIGEN_PI and add a check to the testsuite. 2016-03-23 15:37:45 +01:00
Benoit Steiner
28e02996df Merged patch 672 from Justin Lebar: Don't use long doubles with cuda 2016-03-22 16:53:57 -07:00
Benoit Steiner
3d1e857327 Fixed compilation error 2016-03-22 15:48:28 -07:00
Benoit Steiner
de7d92c259 Pulled latest updates from trunk 2016-03-22 15:24:49 -07:00
Benoit Steiner
002cf0d1c9 Use a single Barrier instead of a collection of Notifications to reduce the thread synchronization overhead 2016-03-22 15:24:23 -07:00
Benoit Steiner
bc2b802751 Fixed a couple of typos 2016-03-22 14:27:34 -07:00
Benoit Steiner
e7a468c5b7 Filter some compilation flags that nvcc warns about. 2016-03-22 14:26:50 -07:00
Benoit Steiner
6a31b7be3e Avoid using std::vector whenever possible 2016-03-22 14:02:50 -07:00
Benoit Steiner
65a7113a36 Use an enum instead of a static const int to prevent possible link error 2016-03-22 09:33:54 -07:00
Benoit Steiner
f9ad25e4d8 Fixed contractions of 16 bit floats 2016-03-22 09:30:23 -07:00
Benoit Steiner
8ef3181f15 Worked around a constness related issue 2016-03-21 11:24:05 -07:00
Benoit Steiner
7a07d6aa2b Small cleanup 2016-03-21 11:12:17 -07:00
Konstantinos Margaritis
a9a6710e15 add initial s390x(zEC13) ZVECTOR support 2016-03-21 13:46:47 -04:00
Benoit Steiner
e91f255301 Marked variables that's only used in debug mode as such 2016-03-21 10:02:00 -07:00
Benoit Steiner
db5c14de42 Explicitly cast the default value into the proper scalar type. 2016-03-21 09:52:58 -07:00
Christoph Hertzberg
b224771f40 bug #1178: Simplified modification of the SSE control register for better portability 2016-03-20 10:57:08 +01:00
Benoit Steiner
8e03333f06 Renamed some class members to make the code more readable. 2016-03-18 15:21:04 -07:00
Benoit Steiner
6c08943d9f Fixed a bug in the padding of extracted image patches. 2016-03-18 15:19:10 -07:00
Benoit Steiner
134d750eab Completed the implementation of vectorized type casting of half floats. 2016-03-18 13:36:28 -07:00
Benoit Steiner
7bd551b3a9 Make all the conversions explicit 2016-03-18 12:20:08 -07:00
Benoit Steiner
bb0e73c191 Gate all the CUDA tests under the EIGEN_TEST_NVCC option 2016-03-18 12:17:37 -07:00
Benoit Steiner
2db4a04827 Fixed a typo 2016-03-18 12:08:01 -07:00
Benoit Steiner
dd514de8a9 Added a test to validate the fallback path for half floats 2016-03-18 12:02:39 -07:00
Benoit Steiner
9a7ece9caf Worked around constness issue 2016-03-18 10:38:29 -07:00
Benoit Steiner
edc679f6c6 Fixed compilation warning 2016-03-18 07:12:34 -07:00
Benoit Steiner
53d498ef06 Fixed compilation warnings in the cuda tests 2016-03-18 07:04:54 -07:00
Benoit Steiner
e10e126cd0 pulled latest updates from trunk 2016-03-17 21:48:38 -07:00
Benoit Steiner
70eb70f5f8 Avoid mutable class members when possible 2016-03-17 21:47:18 -07:00
Benoit Steiner
7b98de1f15 Implemented some of the missing type casting for half floats 2016-03-17 21:45:45 -07:00
Benoit Steiner
afb81b7ded Made sure to use the hard abi when compiling with NEON instructions to avoid the "gnu/stubs-soft.h: No such file or directory" error 2016-03-17 21:24:24 -07:00
Benoit Steiner
95b8961a9b Allocate the mersenne twister used by the random number generators on the heap instead of on the stack since they tend to keep a lot of state (i.e. about 5k) around. 2016-03-17 15:23:51 -07:00
Benoit Steiner
f7329619da Fix bug in tensor contraction. The code assumes that contraction axis indices for the LHS (after possibly swapping to ColMajor!) is increasing. Explicitly sort the contraction axis pairs to make it so. 2016-03-17 15:08:02 -07:00
Christoph Hertzberg
46aa9772fc Merged in ebrevdo/eigen (pull request PR-169)
Bugfixes to cuda tests, igamma & igammac implemented, & tests for digamma, igamma, igammac on CPU & GPU.
2016-03-16 21:59:08 +01:00
Eugene Brevdo
f1f7181f53 Merge default branch. 2016-03-16 12:46:19 -07:00
Eugene Brevdo
1f69a1b65f Change the header guard around certain numext functions to be CUDA specific. 2016-03-16 12:44:35 -07:00
Benoit Steiner
ab9b749b45 Improved a test 2016-03-14 20:03:13 -07:00
Benoit Steiner
5a51366ea5 Fixed a typo. 2016-03-14 09:25:16 -07:00
Benoit Steiner
fcf59e1c37 Properly gate the use of cuda intrinsics in the code 2016-03-14 09:13:44 -07:00
Benoit Steiner
97a1f1c273 Make sure we only use the half float intrinsic when compiling with a version of CUDA that is recent enough to provide them 2016-03-14 08:37:58 -07:00
Eugene Brevdo
9550be925d Merge specfun branch. 2016-03-13 15:46:51 -07:00
Eugene Brevdo
b1a9afe9a9 Add tests in array.cpp that check igamma/igammac properties.
This adds to the set of existing tests, which compare a specific
set of values to third party calculated ground truth.
2016-03-13 15:45:34 -07:00
Benoit Steiner
e29c9676b1 Don't mark the cast operator as explicit, since this is a c++11 feature that's not supported by older compilers. 2016-03-12 00:15:58 -08:00
Benoit Steiner
eecd914864 Also replaced uint32_t with unsigned int to make the code more portable 2016-03-11 19:34:21 -08:00
Benoit Steiner
1ca8c1ec97 Replaced a couple more uint16_t with unsigned short 2016-03-11 19:28:28 -08:00
Benoit Steiner
0423b66187 Use unsigned short instead of uint16_t since they're more portable 2016-03-11 17:53:41 -08:00
Benoit Steiner
048c4d6efd Made half floats usable on hardware that doesn't support them natively. 2016-03-11 17:21:42 -08:00
Benoit Steiner
b72ffcb05e Made the comparison of Eigen::array GPU friendly 2016-03-11 16:37:59 -08:00
Benoit Steiner
25f69cb932 Added a comparison operator for Eigen::array
Alias Eigen::array to std::array when compiling with Visual Studio 2015
2016-03-11 15:20:37 -08:00
Benoit Steiner
c5b98a58b8 Updated the cxx11_meta test to work on the Eigen::array class when std::array isn't available. 2016-03-11 11:53:38 -08:00
Benoit Steiner
456e038a4e Fixed the +=, -=, *= and /= operators to return a reference 2016-03-10 15:17:44 -08:00
Benoit Steiner
86d45a3c83 Worked around visual studio compilation warnings. 2016-03-09 21:29:39 -08:00
Benoit Steiner
8fd4241377 Fixed a typo. 2016-03-10 02:28:46 +00:00
Benoit Steiner
a685a6beed Made the list reductions less ambiguous. 2016-03-09 17:41:52 -08:00
Benoit Steiner
3149b5b148 Avoid implicit cast 2016-03-09 17:35:17 -08:00
Benoit Steiner
b2100b83ad Made sure to include the <random> header file when compiling with visual studio 2016-03-09 16:03:16 -08:00
Benoit Steiner
f05fb449b8 Avoid unnecessary conversion from 32bit int to 64bit unsigned int 2016-03-09 15:27:45 -08:00
Benoit Steiner
1d566417d2 Enable the random number generators when compiling with visual studio 2016-03-09 10:55:11 -08:00
Eugene Brevdo
836e92a051 Update MathFunctions/SpecialFunctions with intelligent header guards. 2016-03-09 09:04:45 -08:00
Benoit Steiner
b084133dbf Fixed the integer division code on windows 2016-03-09 07:06:36 -08:00
Benoit Steiner
6d30683113 Fixed static assertion 2016-03-08 21:02:51 -08:00
Eugene Brevdo
5e7de771e3 Properly fix merge issues. 2016-03-08 17:35:05 -08:00
Eugene Brevdo
73220d2bb0 Resolve bad merge. 2016-03-08 17:28:21 -08:00
Eugene Brevdo
5f17de3393 Merge changes. 2016-03-08 17:22:26 -08:00
Eugene Brevdo
14f0fde51f Add certain functions to numext (log, exp, tan) because CUDA doesn't support std::
Use these in SpecialFunctions.
2016-03-08 17:17:44 -08:00
Benoit Steiner
46177c8d64 Replace std::vector with our own implementation, as using the stl when compiling with nvcc and avx enabled leads to many issues. 2016-03-08 16:37:27 -08:00
Benoit Steiner
6d6413f768 Simplified the full reduction code 2016-03-08 16:02:00 -08:00
Benoit Steiner
5a427a94a9 Fixed the tensor generator code 2016-03-08 13:28:06 -08:00
Benoit Steiner
a81b88bef7 Fixed the tensor concatenation code 2016-03-08 12:30:19 -08:00
Benoit Steiner
551ff11d0d Fixed the tensor layout swapping code 2016-03-08 12:28:10 -08:00
Benoit Steiner
8768c063f5 Fixed the tensor chipping code. 2016-03-08 12:26:49 -08:00
Benoit Steiner
e09eb835db Decoupled the packet type definition from the definition of the tensor ops. All the vectorization is now defined in the tensor evaluators. This will make it possible to relialably support devices with different packet types in the same compilation unit. 2016-03-08 12:07:33 -08:00
Benoit Steiner
3b614a2358 Use NumTraits::highest() and NumTraits::lowest() instead of the std::numeric_limits to make the tensor min and max functors more CUDA friendly. 2016-03-07 17:53:28 -08:00
Eugene Brevdo
dd6dcad6c2 Merge branch specfun. 2016-03-07 15:37:12 -08:00
Eugene Brevdo
0bb5de05a1 Finishing touches on igamma/igammac for GPU. Tests now pass. 2016-03-07 15:35:09 -08:00
Benoit Steiner
769685e74e Added the ability to pad a tensor using a non-zero value 2016-03-07 14:45:37 -08:00
Benoit Steiner
7f87cc3a3b Fix a couple of typos in the code. 2016-03-07 14:31:27 -08:00
Eugene Brevdo
5707004d6b Fix Eigen's building of sharded tests that use CUDA & more igamma/igammac bugfixes.
0. Prior to this PR, not a single sharded CUDA test was actually being *run*.
Fixed that.

GPU tests are still failing for igamma/igammac.

1. Add calls for igamma/igammac to TensorBase
2. Fix up CUDA-specific calls of igamma/igammac
3. Add unit tests for digamma, igamma, igammac in CUDA.
2016-03-07 14:08:56 -08:00
Benoit Steiner
e5f25622e2 Added a test to validate the behavior of some of the tensor syntactic sugar. 2016-03-07 09:04:27 -08:00
Benoit Steiner
9f5740cbc1 Added missing include 2016-03-06 22:03:18 -08:00
Benoit Steiner
5238e03fe1 Don't try to compile the uint128 test with compilers that don't support uint127 2016-03-06 21:59:40 -08:00
Benoit Steiner
9a54c3e32b Don't warn that msvc 2015 isn't c++11 compliant just because it doesn't claim to be. 2016-03-06 09:38:56 -08:00
Benoit Steiner
05bbca079a Turn on some of the cxx11 features when compiling with visual studio 2015 2016-03-05 10:52:08 -08:00
Benoit Steiner
6093eb9ff5 Don't test our 128bit emulation code when compiling with msvc 2016-03-05 10:37:11 -08:00
Benoit Steiner
57b263c5b9 Avoid using initializer lists in test since not all version of msvc support them 2016-03-05 08:35:26 -08:00
Benoit Steiner
23aed8f2e4 Use EIGEN_PI instead of redefining our own constant PI 2016-03-05 08:04:45 -08:00
Eugene Brevdo
0b9e0abc96 Make igamma and igammac work correctly.
This required replacing ::abs with std::abs.
Modified some unit tests.
2016-03-04 21:12:10 -08:00
Benoit Steiner
c23e0be18f Use the CMAKE_CXX_STANDARD variable to turn on cxx11 2016-03-04 20:18:01 -08:00
Benoit Steiner
ec35068edc Don't rely on the M_PI constant since not all compilers provide it. 2016-03-04 16:42:38 -08:00
Benoit Steiner
60d9df11c1 Fixed the computation of leading zeros when compiling with msvc. 2016-03-04 16:27:02 -08:00
Benoit Steiner
4e49fd5eb9 MSVC uses __uint128 while other compilers use __uint128_t to encode 128bit unsigned integers. Make the cxx11_tensor_uint128.cpp test work in both cases. 2016-03-04 14:49:18 -08:00
Benoit Steiner
667fcc2b53 Fixed syntax error 2016-03-04 14:37:51 -08:00
Benoit Steiner
4416a5dcff Added missing include 2016-03-04 14:35:43 -08:00
Benoit Steiner
c561eeb7bf Don't use implicit type conversions in initializer lists since not all compilers support them. 2016-03-04 14:12:45 -08:00
Benoit Steiner
174edf976b Made the contraction test more portable 2016-03-04 14:11:13 -08:00
Benoit Steiner
2c50fc878e Fixed a typo 2016-03-04 14:09:38 -08:00
Eugene Brevdo
7ea35bfa1c Initial implementation of igamma and igammac. 2016-03-03 19:39:41 -08:00
Benoit Steiner
deea866bbd Added tests to cover the new rounding, flooring and ceiling tensor operations. 2016-03-03 12:38:02 -08:00
Benoit Steiner
5cf4558c0a Added support for rounding, flooring, and ceiling to the tensor api 2016-03-03 12:36:55 -08:00
Benoit Steiner
dac58d7c35 Added a test to validate the conversion of half floats into floats on Kepler GPUs.
Restricted the testing of the random number generation code to GPU architecture greater than or equal to 3.5.
2016-03-03 10:37:25 -08:00
Benoit Steiner
1032441c6f Enable partial support for half floats on Kepler GPUs. 2016-03-03 10:34:20 -08:00
Benoit Steiner
1da10a7358 Enable the conversion between floats and half floats on older GPUs that support it. 2016-03-03 10:33:20 -08:00
Benoit Steiner
2de8cc9122 Merged in ebrevdo/eigen (pull request PR-167)
Add infinity() support to numext::numeric_limits, use it in lgamma.

I tested the code on my gtx-titan-black gpu, and it appears to work as expected.
2016-03-03 09:42:12 -08:00
Eugene Brevdo
ab3dc0b0fe Small bugfix to numeric_limits for CUDA. 2016-03-02 21:48:46 -08:00
Eugene Brevdo
6afea46838 Add infinity() support to numext::numeric_limits, use it in lgamma.
This makes the infinity access a __device__ function, removing
nvcc warnings.
2016-03-02 21:35:48 -08:00
Gael Guennebaud
3fccef6f50 bug #537: fix compilation with Apples's compiler 2016-03-02 13:22:46 +01:00
Benoit Steiner
fedaf19262 Pulled latest updates from trunk 2016-03-01 06:15:44 -08:00
Gael Guennebaud
dfa80b2060 Compilation fix 2016-03-01 12:48:56 +01:00
Gael Guennebaud
bee9efc203 Compilation fix 2016-03-01 12:47:27 +01:00
Benoit Steiner
68ac5c1738 Improved the performance of large outer reductions on cuda 2016-02-29 18:11:58 -08:00
Benoit Steiner
56a3ada670 Added benchmarks for full reduction 2016-02-29 14:57:52 -08:00
Benoit Steiner
b2075cb7a2 Made the signature of the inner and outer reducers consistent 2016-02-29 10:53:38 -08:00
Benoit Steiner
3284842045 Optimized the performance of narrow reductions on CUDA devices 2016-02-29 10:48:16 -08:00
Gael Guennebaud
e9bea614ec Fix shortcoming in fixed-value deduction of startRow/startCol 2016-02-29 10:31:27 +01:00
Benoit Steiner
609b3337a7 Print some information to stderr when a CUDA kernel fails 2016-02-27 20:42:57 +00:00
Benoit Steiner
1031b31571 Improved the README 2016-02-27 20:22:04 +00:00
Gael Guennebaud
8e6faab51e bug #1172: make valuePtr and innderIndexPtr properly return null for empty matrices. 2016-02-27 14:55:40 +01:00
Benoit Steiner
ac2e6e0d03 Properly vectorized the random number generators 2016-02-26 13:52:24 -08:00
Benoit Steiner
caa54d888f Made the TensorIndexList usable on GPU without having to use the -relaxed-constexpr compilation flag 2016-02-26 12:38:18 -08:00
Benoit Steiner
93485d86bc Added benchmarks for type casting of float16 2016-02-26 12:24:58 -08:00
Benoit Steiner
002824e32d Added benchmarks for fp16 2016-02-26 12:21:25 -08:00
Benoit Steiner
2cd32cad27 Reverted previous commit since it caused more problems than it solved 2016-02-26 13:21:44 +00:00
Benoit Steiner
d9d05dd96e Fixed handling of long doubles on aarch64 2016-02-26 04:13:58 -08:00
Benoit Steiner
af199b4658 Made the CUDA architecture level a build setting. 2016-02-25 09:06:18 -08:00
Benoit Steiner
c36c09169e Fixed a typo in the reduction code that could prevent large full reductionsx from running properly on old cuda devices. 2016-02-24 17:07:25 -08:00
Benoit Steiner
7a01cb8e4b Marked the And and Or reducers as stateless. 2016-02-24 16:43:01 -08:00
Gael Guennebaud
91e1375ba9 merge 2016-02-23 11:09:05 +01:00
Gael Guennebaud
055000a424 Fix startRow()/startCol() for dense Block with direct access:
the initial implementation failed for empty rows/columns for which are ambiguous.
2016-02-23 11:07:59 +01:00
Benoit Steiner
1d9256f7db Updated the padding code to work with half floats 2016-02-23 05:51:22 +00:00
Benoit Steiner
8cb9bfab87 Extended the tensor benchmark suite to support types other than floats 2016-02-23 05:28:02 +00:00
Benoit Steiner
f442a5a5b3 Updated the tensor benchmarking code to work with compilers that don't support cxx11. 2016-02-23 04:15:48 +00:00
Benoit Steiner
72d2cf642e Deleted the coordinate based evaluation of tensor expressions, since it's hardly ever used and started to cause some issues with some versions of xcode. 2016-02-22 15:29:41 -08:00
Benoit Steiner
6270d851e3 Declare the half float type as arithmetic. 2016-02-22 13:59:33 -08:00
Benoit Steiner
5cd00068c0 include <iostream> in the tensor header since we now use it to better report cuda initialization errors 2016-02-22 13:59:03 -08:00
Benoit Steiner
257b640463 Fixed compilation warning generated by clang 2016-02-21 22:43:37 -08:00
Benoit Steiner
584832cb3c Implemented the ptranspose function on half floats 2016-02-21 12:44:53 -08:00
Benoit Steiner
e644f60907 Pulled latest updates from trunk 2016-02-21 20:24:59 +00:00
Benoit Steiner
95fceb6452 Added the ability to compute the absolute value of a half float 2016-02-21 20:24:11 +00:00
Benoit Steiner
ed69cbeef0 Added some debugging information to the test to figure out why it fails sometimes 2016-02-21 11:20:20 -08:00
Benoit Steiner
96a24b05cc Optimized casting of tensors in the case where the casting happens to be a no-op 2016-02-21 11:16:15 -08:00
Benoit Steiner
203490017f Prevent unecessary Index to int conversions 2016-02-21 08:49:36 -08:00
Benoit Steiner
9ff269a1d3 Moved some of the fp16 operators outside the Eigen namespace to workaround some nvcc limitations. 2016-02-20 07:47:23 +00:00
Benoit Steiner
1e6fe6f046 Fixed the float16 tensor test. 2016-02-20 07:44:17 +00:00
Rasmus Munk Larsen
8eb127022b Get rid of duplicate code. 2016-02-19 16:33:30 -08:00
Rasmus Munk Larsen
d5e2ec7447 Speed up tensor FFT by up ~25-50%.
Benchmark                          Base (ns)  New (ns) Improvement
------------------------------------------------------------------
BM_tensor_fft_single_1D_cpu/8            132       134     -1.5%
BM_tensor_fft_single_1D_cpu/9           1162      1229     -5.8%
BM_tensor_fft_single_1D_cpu/16           199       195     +2.0%
BM_tensor_fft_single_1D_cpu/17          2587      2267    +12.4%
BM_tensor_fft_single_1D_cpu/32           373       341     +8.6%
BM_tensor_fft_single_1D_cpu/33          5922      4879    +17.6%
BM_tensor_fft_single_1D_cpu/64           797       675    +15.3%
BM_tensor_fft_single_1D_cpu/65         13580     10481    +22.8%
BM_tensor_fft_single_1D_cpu/128         1753      1375    +21.6%
BM_tensor_fft_single_1D_cpu/129        31426     22789    +27.5%
BM_tensor_fft_single_1D_cpu/256         4005      3008    +24.9%
BM_tensor_fft_single_1D_cpu/257        70910     49549    +30.1%
BM_tensor_fft_single_1D_cpu/512         8989      6524    +27.4%
BM_tensor_fft_single_1D_cpu/513       165402    107751    +34.9%
BM_tensor_fft_single_1D_cpu/999       198293    115909    +41.5%
BM_tensor_fft_single_1D_cpu/1ki        21289     14143    +33.6%
BM_tensor_fft_single_1D_cpu/1k        361980    233355    +35.5%
BM_tensor_fft_double_1D_cpu/8            138       131     +5.1%
BM_tensor_fft_double_1D_cpu/9           1253      1133     +9.6%
BM_tensor_fft_double_1D_cpu/16           218       200     +8.3%
BM_tensor_fft_double_1D_cpu/17          2770      2392    +13.6%
BM_tensor_fft_double_1D_cpu/32           406       368     +9.4%
BM_tensor_fft_double_1D_cpu/33          6418      5153    +19.7%
BM_tensor_fft_double_1D_cpu/64           856       728    +15.0%
BM_tensor_fft_double_1D_cpu/65         14666     11148    +24.0%
BM_tensor_fft_double_1D_cpu/128         1913      1502    +21.5%
BM_tensor_fft_double_1D_cpu/129        36414     24072    +33.9%
BM_tensor_fft_double_1D_cpu/256         4226      3216    +23.9%
BM_tensor_fft_double_1D_cpu/257        86638     52059    +39.9%
BM_tensor_fft_double_1D_cpu/512         9397      6939    +26.2%
BM_tensor_fft_double_1D_cpu/513       203208    114090    +43.9%
BM_tensor_fft_double_1D_cpu/999       237841    125583    +47.2%
BM_tensor_fft_double_1D_cpu/1ki        20921     15392    +26.4%
BM_tensor_fft_double_1D_cpu/1k        455183    250763    +44.9%
BM_tensor_fft_single_2D_cpu/8           1051      1005     +4.4%
BM_tensor_fft_single_2D_cpu/9          16784     14837    +11.6%
BM_tensor_fft_single_2D_cpu/16          4074      3772     +7.4%
BM_tensor_fft_single_2D_cpu/17         75802     63884    +15.7%
BM_tensor_fft_single_2D_cpu/32         20580     16931    +17.7%
BM_tensor_fft_single_2D_cpu/33        345798    278579    +19.4%
BM_tensor_fft_single_2D_cpu/64         97548     81237    +16.7%
BM_tensor_fft_single_2D_cpu/65       1592701   1227048    +23.0%
BM_tensor_fft_single_2D_cpu/128       472318    384303    +18.6%
BM_tensor_fft_single_2D_cpu/129      7038351   5445308    +22.6%
BM_tensor_fft_single_2D_cpu/256      2309474   1850969    +19.9%
BM_tensor_fft_single_2D_cpu/257     31849182  23797538    +25.3%
BM_tensor_fft_single_2D_cpu/512     10395194   8077499    +22.3%
BM_tensor_fft_single_2D_cpu/513     144053843  104242541    +27.6%
BM_tensor_fft_single_2D_cpu/999     279885833  208389718    +25.5%
BM_tensor_fft_single_2D_cpu/1ki     45967677  36070985    +21.5%
BM_tensor_fft_single_2D_cpu/1k      619727095  456489500    +26.3%
BM_tensor_fft_double_2D_cpu/8           1110      1016     +8.5%
BM_tensor_fft_double_2D_cpu/9          17957     15768    +12.2%
BM_tensor_fft_double_2D_cpu/16          4558      4000    +12.2%
BM_tensor_fft_double_2D_cpu/17         79237     66901    +15.6%
BM_tensor_fft_double_2D_cpu/32         21494     17699    +17.7%
BM_tensor_fft_double_2D_cpu/33        357962    290357    +18.9%
BM_tensor_fft_double_2D_cpu/64        105179     87435    +16.9%
BM_tensor_fft_double_2D_cpu/65       1617143   1288006    +20.4%
BM_tensor_fft_double_2D_cpu/128       512848    419397    +18.2%
BM_tensor_fft_double_2D_cpu/129      7271322   5636884    +22.5%
BM_tensor_fft_double_2D_cpu/256      2415529   1922032    +20.4%
BM_tensor_fft_double_2D_cpu/257     32517952  24462177    +24.8%
BM_tensor_fft_double_2D_cpu/512     10724898   8287617    +22.7%
BM_tensor_fft_double_2D_cpu/513     146007419  108603266    +25.6%
BM_tensor_fft_double_2D_cpu/999     296351330  221885776    +25.1%
BM_tensor_fft_double_2D_cpu/1ki     59334166  48357539    +18.5%
BM_tensor_fft_double_2D_cpu/1k      666660132  483840349    +27.4%
2016-02-19 16:29:23 -08:00
Gael Guennebaud
d90a2dac5e merge 2016-02-19 23:01:27 +01:00
Gael Guennebaud
485823b5f5 Add COD and BDCSVD in list of benched solvers. 2016-02-19 23:00:33 +01:00
Gael Guennebaud
2af04f1a57 Extend unit test to stress smart_copy with empty input/output. 2016-02-19 22:59:28 +01:00
Gael Guennebaud
6fa35bbd28 bug #1170: skip calls to memcpy/memmove for empty imput. 2016-02-19 22:58:52 +01:00
Benoit Steiner
46fc23f91c Print an error message to stderr when the initialization of the CUDA runtime fails. This helps debugging setup issues. 2016-02-19 13:44:22 -08:00
Gael Guennebaud
6f0992c05b Fix nesting type and complete reflection methods of Block expressions. 2016-02-19 22:21:02 +01:00
Gael Guennebaud
f3643eec57 Add typedefs for the return type of all block methods. 2016-02-19 22:15:01 +01:00
Benoit Steiner
670db7988d Updated the contraction code to make it compatible with half floats. 2016-02-19 13:03:26 -08:00
Benoit Steiner
180156ba1a Added support for tensor reductions on half floats 2016-02-19 10:05:59 -08:00
Benoit Steiner
5c4901b83a Implemented the scalar division of 2 half floats 2016-02-19 10:03:19 -08:00
Benoit Steiner
f268db1c4b Added the ability to query the minor version of a cuda device 2016-02-19 16:31:04 +00:00
Benoit Steiner
a08d2ff0c9 Started to work on contractions and reductions using half floats 2016-02-19 15:59:59 +00:00
Benoit Steiner
f3352e0fb0 Don't make the array constructors explicit 2016-02-19 15:58:57 +00:00
Benoit Steiner
f7cb755299 Added support for operators +=, -=, *= and /= on CUDA half floats 2016-02-19 15:57:26 +00:00
Benoit Steiner
dc26459b99 Implemented protate() for CUDA 2016-02-19 15:16:54 +00:00
Benoit Steiner
cd042dbbfd Fixed a bug in the tensor type converter 2016-02-19 15:03:26 +00:00
Benoit Steiner
ac5d706a94 Added support for simple coefficient wise tensor expression using half floats on CUDA devices 2016-02-19 08:19:12 +00:00
Benoit Steiner
0606a0a39b FP16 on CUDA are only available starting with cuda 7.5. Disable them when using an older version of CUDA 2016-02-18 23:15:23 -08:00
Benoit Steiner
f36c0c2c65 Added regression test for float16 2016-02-19 06:23:28 +00:00
Benoit Steiner
7151bd8768 Reverted unintended changes introduced by a bad merge 2016-02-19 06:20:50 +00:00
Benoit Steiner
1304e1fb5e Pulled latest updates from trunk 2016-02-19 06:17:02 +00:00
Benoit Steiner
17b9fbed34 Added preliminary support for half floats on CUDA GPU. For now we can simply convert floats into half floats and vice versa 2016-02-19 06:16:07 +00:00
Benoit Steiner
8ce46f9d89 Improved implementation of ptanh for SSE and AVX 2016-02-18 13:24:34 -08:00
Eugene Brevdo
832380c455 Merged eigen/eigen into default 2016-02-17 14:44:06 -08:00
Eugene Brevdo
06a2bc7c9c Tiny bugfix in SpecialFunctions: some compilers don't like doubles
implicitly downcast to floats in an array constructor.
2016-02-17 14:41:59 -08:00
Gael Guennebaud
f6f057bb7d bug #1166: fix shortcomming in gemv when the destination is not a vector at compile-time. 2016-02-15 21:43:07 +01:00
Gael Guennebaud
8e1f1ba6a6 Import wiki's paragraph: "I disabled vectorization, but I'm still getting annoyed about alignment issues" 2016-02-12 22:16:59 +01:00
Gael Guennebaud
c8b4c4b48a bug #795: mention allocate_shared as a condidate for aligned_allocator. 2016-02-12 22:09:16 +01:00
Gael Guennebaud
6eff3e5185 Fix triangularView versus triangularPart. 2016-02-12 17:09:28 +01:00
Gael Guennebaud
4252af6897 Remove dead code. 2016-02-12 16:13:35 +01:00
Gael Guennebaud
2f5f56a820 Fix usage of evaluator in sparse * permutation products. 2016-02-12 16:13:16 +01:00
Gael Guennebaud
0a537cb2d8 bug #901: fix triangular-view with unit diagonal of sparse rectangular matrices. 2016-02-12 15:58:31 +01:00
Gael Guennebaud
b35d1a122e Fix unit test: accessing elements in a deque by offsetting a pointer to another element causes undefined behavior. 2016-02-12 15:31:16 +01:00
Benoit Steiner
9e3f3a2d27 Deleted outdated comment 2016-02-11 17:27:35 -08:00
Benoit Steiner
de345eff2e Added a method to conjugate the content of a tensor or the result of a tensor expression. 2016-02-11 16:34:07 -08:00
Benoit Steiner
17e93ba148 Pulled latest updates from trunk 2016-02-11 15:05:38 -08:00
Benoit Steiner
3628f7655d Made it possible to run the scalar_binary_pow_op functor on GPU 2016-02-11 15:05:03 -08:00
Hauke Heibel
eeac46f980 bug #774: re-added comment referencing equations in the original paper 2016-02-11 19:38:37 +01:00
Benoit Steiner
c569cfe12a Inline the +=, -=, *= and /= operators consistently between DenseBase.h and SelfCwiseBinaryOp.h 2016-02-11 09:33:32 -08:00
Gael Guennebaud
8cc9232b9a bug #774: fix a numerical issue producing unwanted reflections. 2016-02-11 15:32:56 +01:00
Gael Guennebaud
2d35c0cb5f Merged in rmlarsen/eigen (pull request PR-163)
Implement complete orthogonal decomposition in Eigen.
2016-02-11 15:12:34 +01:00
Benoit Steiner
33e2373f01 Merged in nnyby/eigen/nnyby/doc-grammar-fix-linearly-space-linearly-1443742971203 (pull request PR-138)
[doc] grammar fix: "linearly space" -> "linearly spaced"
2016-02-10 23:29:59 -08:00
Benoit Steiner
6d8b1dce06 Avoid implicit cast from double to float. 2016-02-10 18:07:11 -08:00
Benoit Steiner
1dfaafe28a Added a regression test for tanh 2016-02-10 17:41:47 -08:00
Rasmus Munk Larsen
b6fdf7468c Rename inverse -> pseudoInverse. 2016-02-10 13:03:07 -08:00
Benoit Jacob
9d6f1ad398 I'm told to use __EMSCRIPTEN__ by an Emscripten dev. 2016-02-10 12:48:34 -05:00
Benoit Steiner
bfb3fcd94f Optimized implementation of the tanh function for SSE 2016-02-10 08:52:30 -08:00
Benoit Steiner
2d523332b3 Optimized implementation of the hyperbolic tangent function for AVX 2016-02-10 08:48:05 -08:00
Benoit Jacob
e6ee18d6b4 Make the GCC workaround for sqrt GCC-only; detect Emscripten as non-GCC 2016-02-10 11:11:49 -05:00
Benoit Steiner
2ac59e5d36 Pulled latest updates from trunk 2016-02-10 08:03:02 -08:00
Benoit Steiner
9a21b38ccc Worked around a few clang compilation warnings 2016-02-10 08:02:04 -08:00
Benoit Jacob
964a95bf5e Work around Emscripten bug - https://github.com/kripken/emscripten/issues/4088 2016-02-10 10:37:22 -05:00
Benoit Steiner
72ab7879f7 Fixed clang comilation warnings 2016-02-10 06:48:28 -08:00
Benoit Steiner
e88535634d Fixed some clang compilation warnings 2016-02-09 23:32:41 -08:00
Benoit Steiner
970751ece3 Disabling the nvcc warnings in addition to the clang warnings when clang is used as a frontend for nvcc 2016-02-09 20:55:50 -08:00
Benoit Steiner
6323851ea9 Fixed compilation warning 2016-02-09 20:43:41 -08:00
Rasmus Munk Larsen
bb8811c655 Enable inverse() method for computing pseudo-inverse. 2016-02-09 20:35:20 -08:00
Benoit Steiner
5cc0dd5f44 Fixed the code that disables the use of variadic templates when compiling with nvcc on ARM devices. 2016-02-09 10:32:01 -08:00
Benoit Steiner
a9cc6a06b9 Fixed compilation warning in the splines test 2016-02-09 05:10:06 +00:00
Benoit Steiner
d69946183d Updated the TensorIntDivisor code to work properly on LLP64 systems 2016-02-08 21:03:59 -08:00
Benoit Steiner
24d291cf16 Worked around nvcc crash when compiling Eigen on Tegra X1 2016-02-09 02:34:02 +00:00
Rasmus Munk Larsen
53f60e0afc Make applyZAdjointOnTheLeftInPlace protected. 2016-02-08 09:01:43 -08:00
Rasmus Munk Larsen
414efa47d3 Add missing calls to tests of COD.
Fix a few mistakes in 3.2 -> 3.3 port.
2016-02-08 08:50:34 -08:00
Gael Guennebaud
c2bf2f56ef Remove custom unaligned loads for SSE. They were only useful for core2 CPU. 2016-02-08 14:29:12 +01:00
Gael Guennebaud
a4c76f8d34 Improve inlining 2016-02-08 11:33:02 +01:00
Rasmus Munk Larsen
16ec450ca1 Nevermind. 2016-02-06 17:54:01 -08:00
Rasmus Munk Larsen
019fff9a00 Add my name to copyright notice in ColPivHouseholder.h, mostly for previous work on stable norm downdate formula. 2016-02-06 17:48:42 -08:00
Rasmus Munk Larsen
86d6201d7b Merge. 2016-02-06 16:36:56 -08:00
Rasmus Munk Larsen
d904c8ac8f Implement complete orthogonal decomposition in Eigen. 2016-02-06 16:32:00 -08:00
Gael Guennebaud
010afe1619 Add exemples for reshaping/slicing with Map. 2016-02-06 22:49:18 +01:00
Gael Guennebaud
8e599bc098 Fix warning in unit test 2016-02-06 20:26:59 +01:00
Gael Guennebaud
c6a12d1dc6 Fix warning with gcc < 4.8 2016-02-06 18:06:51 +01:00
Benoit Steiner
4d4211c04e Avoid unecessary type conversions 2016-02-05 18:19:41 -08:00
Benoit Steiner
d2cba52015 Only enable the cxx11_tensor_uint128 test on 64 bit machines since 32 bit systems don't support the __uin128_t type 2016-02-05 18:14:23 -08:00
Benoit Steiner
fb00a4af2b Made the tensor fft test compile on tegra x1 2016-02-06 01:42:14 +00:00
Gael Guennebaud
5b2d287878 bug #779: allow non aligned buffers for buffers smaller than the requested alignment. 2016-02-05 21:46:39 +01:00
Gael Guennebaud
e8e1d504d6 Add an explicit assersion on the alignment of the pointer returned by std::malloc 2016-02-05 21:38:16 +01:00
Gael Guennebaud
62a1c911cd Remove posix_memalign, _mm_malloc, and _aligned_malloc special paths. 2016-02-05 21:24:35 +01:00
Rasmus Munk Larsen
093f2b3c01 Merge. 2016-02-04 14:32:19 -08:00
Benoit Steiner
3ca1ae2bb7 Commented out the version of pexp<Packet8d> since it fails to compile with gcc 5.3 2016-02-04 13:49:06 -08:00
Rasmus Munk Larsen
2e39cc40a4 Fix condition that made the unit test spam stdout with bogus error messages. 2016-02-04 12:56:14 -08:00
Benoit Steiner
23f69ab936 Added implementations of pexp, plog, psqrt, and prsqrt optimized for AVX512 2016-02-04 10:36:36 -08:00
Benoit Steiner
6c9cf117c1 Fixed indentation 2016-02-04 10:34:10 -08:00
Benoit Steiner
bcdcdace48 Pulled latest updates from trunk 2016-02-04 08:56:49 -08:00
Gael Guennebaud
659fc9c159 Remove dead code 2016-02-04 09:55:09 +01:00
Gael Guennebaud
d5d7798b9d Improve heuritics for switching between coeff-based and general matrix product implementation. 2016-02-04 09:53:47 +01:00
Benoit Steiner
f535378995 Added support for vectorized type casting of int to char. 2016-02-03 18:58:29 -08:00
Benoit Steiner
4ab63a3f6f Fixed the initialization of the dummy member of the array class to make it compatible with pairs of element. 2016-02-03 17:23:07 -08:00
Benoit Steiner
727ff26960 Disable 2 more nvcc warning messages 2016-02-03 16:01:37 -08:00
Benoit Steiner
1cbb79cdfd Made sure the dummy element of size 0 array is always intialized to silence some compiler warnings 2016-02-03 15:58:26 -08:00
Benoit Steiner
bcbde37a11 Made sure the code compiles when EIGEN_HAS_C99_MATH isn't defined 2016-02-03 14:53:08 -08:00
Benoit Steiner
f933f69021 Added a few comments 2016-02-03 14:12:18 -08:00
Benoit Steiner
5d82e47ef6 Properly disable nvcc warning messages in user code. 2016-02-03 14:10:06 -08:00
Benoit Steiner
af8436b196 Silenced the "calling a __host__ function from a __host__ __device__ function is not allowed" messages 2016-02-03 13:48:36 -08:00
Benoit Steiner
d7742d22e4 Revert the nvcc messages to their default severity instead of the forcing them to be warnings 2016-02-03 13:47:28 -08:00
Benoit Steiner
ac26e1aaf3 Pulled latest updates from trunk 2016-02-03 12:52:20 -08:00
Benoit Steiner
492fe7ce02 Silenced some unhelpful warnings generated by nvcc. 2016-02-03 12:51:19 -08:00
Gael Guennebaud
b70db60e4d Merged in rmlarsen/eigen (pull request PR-161)
Change Eigen's ColPivHouseholderQR to use  numerically stable norm downdate formula
2016-02-03 21:37:06 +01:00
Rasmus Munk Larsen
5fb04ab2da Fix bad line break. Don't repeat Kahan matrix test since it is deterministic. 2016-02-03 10:12:10 -08:00
Rasmus Munk Larsen
d9a6f86cc0 Make the array of directly compute column norms a member to avoid allocation in computeInPlace. 2016-02-03 09:55:30 -08:00
Gael Guennebaud
70dc14e4e1 bug #1161: fix division by zero for huge scalar types 2016-02-03 18:25:41 +01:00
Damien R
c301f99208 bug #1164: fix list and deque specializations such that our aligned allocator is automatically activatived only when the user did not specified an allocator (or specified the default std::allocator). 2016-02-03 18:07:25 +01:00
Gael Guennebaud
eb6d9aea0e Clarify error message when writing to a read-only sparse-sub-matrix. 2016-02-03 16:58:23 +01:00
Gael Guennebaud
040cf33e8f merge 2016-02-03 16:09:51 +01:00
Gael Guennebaud
c85fbfd0b7 Clarify documentation on the restrictions of writable sparse block expressions. 2016-02-03 16:08:43 +01:00
Benoit Steiner
dc413dbe8a Merged in ville-k/eigen/explicit_long_constructors (pull request PR-158)
Add constructor for long types.
2016-02-02 20:58:06 -08:00
Ville Kallioniemi
783018d8f6 Use EIGEN_STATIC_ASSERT for backward compatibility. 2016-02-02 16:45:12 -07:00
Benoit Steiner
99cde88341 Don't try to use direct offsets when computing a tensor product, since the required stride isn't available. 2016-02-02 11:06:53 -08:00
Ville Kallioniemi
ff0a83aaf8 Use single template constructor to avoid overload resolution issues. 2016-02-02 00:33:25 -07:00
Ville Kallioniemi
aedea349aa Replace separate low word constructors with a single templated constructor. 2016-02-01 20:25:02 -07:00
Ville Kallioniemi
f0fdefa96f Rebase to latest. 2016-02-01 19:32:31 -07:00
Benoit Steiner
d93b71a301 Updated the packetmath test to call predux_half instead of predux4 2016-02-01 15:18:33 -08:00
Benoit Steiner
ef66f2887b Updated the matrix multiplication code to make it compile with AVX512 enabled. 2016-02-01 14:38:05 -08:00
Benoit Steiner
85b6d82b49 Generalized predux4 to support AVX512 packets, and renamed it predux_half.
Disabled the implementation of pabs for avx512 since the corresponding intrinsics are not shipped with gcc
2016-02-01 14:35:51 -08:00
Benoit Steiner
64ce78c2ec Cleaned up a tensor contraction test 2016-02-01 13:57:41 -08:00
Benoit Steiner
0ce5d32be5 Sharded the cxx11_tensor_contract_cuda test 2016-02-01 13:33:23 -08:00
Benoit Steiner
922b5f527b Silenced a few compilation warnings 2016-02-01 13:30:49 -08:00
Benoit Steiner
6b5dff875e Made it possible to limit the number of blocks that will be used to evaluate a tensor expression on a CUDA device. This makesit possible to set aside streaming multiprocessors for other computations. 2016-02-01 12:46:32 -08:00
Rasmus Munk Larsen
00f9ef6c76 merging. 2016-02-01 11:10:30 -08:00
Benoit Steiner
264f8141f8 Shared the tensor reduction test 2016-02-01 07:44:31 -08:00
Benoit Steiner
11bb71c8fc Sharded the tensor device test 2016-02-01 07:34:59 -08:00
Gael Guennebaud
ff1157bcbf bug #694: document that SparseQR::matrixR is not sorted. 2016-02-01 16:09:34 +01:00
Gael Guennebaud
ec469700dc bug #557: make InnerIterator of sparse storage types more versatile by adding default-ctor, copy-ctor/assignment 2016-02-01 15:04:33 +01:00
Gael Guennebaud
6e0a86194c Fix integer path for num_steps==1 2016-02-01 15:00:04 +01:00
Gael Guennebaud
e1d219e5c9 bug #698: fix linspaced for integer types. 2016-02-01 14:25:34 +01:00
Gael Guennebaud
2c3224924b Fix warning and replace min/max macros by calls to mini/maxi 2016-02-01 10:23:45 +01:00
Benoit Steiner
e80ed948e1 Fixed a number of compilation warnings generated by the cuda tests 2016-01-31 20:09:41 -08:00
Benoit Steiner
6720b38fbf Fixed a few compilation warnings 2016-01-31 16:48:50 -08:00
Benoit Steiner
3f1ee45833 Fixed compilation errors triggered by duplicate inline declaration 2016-01-31 10:48:49 -08:00
Benoit Steiner
70be6f6531 Pulled latest changes from trunk 2016-01-31 10:44:45 -08:00
Benoit Steiner
4a2ddfb81d Sharded the CUDA argmax tensor test 2016-01-31 10:44:15 -08:00
Gael Guennebaud
d142165942 bug #667: declare several critical functions as FORECE_INLINE to make ICC happier.
<g.gael@free.fr> HG: branch 'default' HG: changed Eigen/src/Core/ArrayBase.h HG: changed Eigen/src/Core/AssignEvaluator.h HG: changed
Eigen/src/Core/CoreEvaluators.h HG: changed Eigen/src/Core/CwiseUnaryOp.h HG: changed Eigen/src/Core/DenseBase.h HG: changed Eigen/src/Core/MatrixBase.h
2016-01-31 16:34:10 +01:00
Gael Guennebaud
a4e4542b89 Avoid overflow in unit test. 2016-01-30 22:26:17 +01:00
Gael Guennebaud
3ba8a3ab1a Disable underflow unit test on the i387 FPU. 2016-01-30 22:14:04 +01:00
Benoit Steiner
483082ef6e Fixed a few memory leaks in the cuda tests 2016-01-30 11:59:22 -08:00
Benoit Steiner
bd21aba181 Sharded the cxx11_tensor_cuda test and fixed a memory leak 2016-01-30 11:47:09 -08:00
Benoit Steiner
9de155d153 Added a test to cover threaded tensor shuffling 2016-01-30 10:56:47 -08:00
Benoit Steiner
32088c06a1 Made the comparison between single and multithreaded contraction results more resistant to numerical noise to prevent spurious test failures. 2016-01-30 10:51:14 -08:00
Benoit Steiner
2053478c56 Made sure to use a tensor of rank 0 to store the result of a full reduction in the tensor thread pool test 2016-01-30 10:46:36 -08:00
Benoit Steiner
d0db95f730 Sharded the tensor thread pool test 2016-01-30 10:43:57 -08:00
Benoit Steiner
ba27c8a7de Made the CUDA contract test more robust to numerical noise. 2016-01-30 10:28:43 -08:00
Benoit Steiner
4281eb1e2c Added 2 benchmarks to the suite of tensor benchmarks running on GPU 2016-01-30 10:20:43 -08:00
Gael Guennebaud
102fa96a96 Extend doc on dense+sparse 2016-01-30 14:58:21 +01:00
Gael Guennebaud
1bc207c528 backout changeset d4a9e61569
: the extended SparseView is not needed anymore
2016-01-30 14:43:21 +01:00
Gael Guennebaud
8ed1553d20 bug #632: implement general coefficient-wise "dense op sparse" operations through specialized evaluators instead of using SparseView.
This permits to deal with arbitrary storage order, and to by-pass the more complex iterator of the sparse-sparse case.
2016-01-30 14:39:50 +01:00
Gael Guennebaud
699634890a bug #946: generalize Cholmod::solve to handle any rhs expression 2016-01-29 23:02:22 +01:00
Gael Guennebaud
15084cf1ac bug #632: add support for "dense +/- sparse" operations. The current implementation is based on SparseView to make the dense subexpression compatible with the sparse one. 2016-01-29 22:09:45 +01:00
Gael Guennebaud
d4a9e61569 Extend SparseView to allow keeping explicit zeros. This is equivalent to sparseView(1,-1) but faster because the test is removed at compile-time. 2016-01-29 22:07:56 +01:00
Gael Guennebaud
d8d37349c3 bug #696: enable zero-sized block at compile-time by relaxing the respective assertion 2016-01-29 12:44:49 +01:00
Gael Guennebaud
e8ccc06fe5 merge 2016-01-29 09:40:38 +01:00
Benoit Steiner
963f2d2a8f Marked several methods EIGEN_DEVICE_FUNC 2016-01-28 23:37:48 -08:00
Benoit Steiner
c5d25bf1d0 Fixed a couple of compilation warnings. 2016-01-28 23:15:45 -08:00
Benoit Steiner
e4f83bae5d Fixed the tensor benchmarks on apple devices 2016-01-28 21:08:07 -08:00
Benoit Steiner
10bea90c4a Fixed clang related compilation error 2016-01-28 20:52:08 -08:00
Benoit Steiner
d3f533b395 Fixed compilation warning 2016-01-28 20:09:45 -08:00
Abhijit Kundu
3fde202215 Making ceil() functor generic w.r.t packet type 2016-01-28 21:27:00 -05:00
Benoit Steiner
211d350fc3 Fixed a typo 2016-01-28 17:13:04 -08:00
Benoit Steiner
bd2e5a788a Made sure the number of floating point operations done by a benchmark is computed using 64 bit integers to avoid overflows. 2016-01-28 17:10:40 -08:00
Benoit Steiner
120e13b1b6 Added a readme to explain how to compile the tensor benchmarks. 2016-01-28 17:06:00 -08:00
Benoit Steiner
a68864b6bc Updated the benchmarking code to print the number of flops processed instead of the number of bytes. 2016-01-28 16:51:40 -08:00
Benoit Steiner
8217281ae4 Merge latest updates from trunk 2016-01-28 16:20:53 -08:00
Benoit Steiner
c8d5f21941 Added extra tensor benchmarks 2016-01-28 16:20:36 -08:00
Benoit Steiner
7b3044d086 Made sure to call nvcc with the relaxed-constexpr flag. 2016-01-28 15:36:34 -08:00
Rasmus Munk Larsen
acce4dd050 Change Eigen's ColPivHouseholderQR to use the numerically stable norm downdate formula from http://www.netlib.org/lapack/lawnspdf/lawn176.pdf, which has been used in LAPACK's xGEQPF and xGEQP3 since 2006. With the old formula, the code chooses the wrong pivots and fails to correctly determine rank on graded matrices.
This change also adds additional checks for non-increasing diagonal in R11 to existing unit tests, and adds a new unit test with the Kahan matrix, which consistently fails for the original code.

Benchmark timings on Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz. Code compiled with AVX & FMA. I just ran on square matrices of 3 difference sizes.

Benchmark               Time(ns)     CPU(ns) Iterations
-------------------------------------------------------
Before:
BM_EigencolPivQR/64        53677       53627      12890
BM_EigencolPivQR/512    15265408    15250784         46
BM_EigencolPivQR/4k  15403556228 15388788368          2

After (non-vectorized version):
Benchmark               Time(ns)     CPU(ns) Iterations  Degradation
--------------------------------------------------------------------
BM_EigencolPivQR/64        63736       63669      10844         18.5%
BM_EigencolPivQR/512    16052546    16037381         43          5.1%
BM_EigencolPivQR/4k  15149263620 15132025316          2         -2.0%

Performance-wise there seems to be a ~18.5% degradation for small (64x64) matrices, probably due to the cost of more O(min(m,n)^2) sqrt operations that are not needed for the unstable formula.
2016-01-28 15:07:26 -08:00
Gael Guennebaud
b908e071a8 bug #178: get rid of some const_cast in SparseCore 2016-01-28 22:11:18 +01:00
Gael Guennebaud
c1d900af61 bug #178: remove additional const on nested expression, and remove several const_cast. 2016-01-28 21:43:20 +01:00
Benoit Steiner
12f8bd12a2 Merged in jiayq/eigen (pull request PR-159)
Modifications to the tensor benchmarks to allow compilation in a standalone fashion.
2016-01-28 11:28:55 -08:00
Yangqing Jia
270c4e1ecd bugfix 2016-01-28 11:11:45 -08:00
Yangqing Jia
c4e47630b1 benchmark modifications to make it compilable in a standalone fashion. 2016-01-28 10:35:14 -08:00
Gael Guennebaud
f50bb1e6f3 Fix compilation with gcc 2016-01-28 13:25:26 +01:00
Gael Guennebaud
ddf64babde merge 2016-01-28 13:21:48 +01:00
Gael Guennebaud
df15fbc452 bug #1158: PartialReduxExpr is a vector expression, and it thus must expose the LinearAccessBit flag 2016-01-28 13:16:30 +01:00
Gael Guennebaud
9bcadb7fd1 Disable stupid MSVC warning 2016-01-28 12:14:16 +01:00
Gael Guennebaud
b4d87fff4a Fix MSVC warning. 2016-01-28 12:12:30 +01:00
Gael Guennebaud
2bad3e78d9 bug #96, bug #1006: fix by value argument in result_of. 2016-01-28 12:12:06 +01:00
Gael Guennebaud
7802a6bb1c Fix unit test filename. 2016-01-28 09:35:37 +01:00
Benoit Steiner
4bf9eaf77a Deleted an invalid assertion that prevented the assignment of empty tensors. 2016-01-27 17:09:30 -08:00
Benoit Steiner
291069e885 Fixed some compilation problems with nvcc + clang 2016-01-27 15:37:03 -08:00
Benoit Steiner
47ca9dc809 Fixed the tensor_cuda test 2016-01-27 14:58:48 -08:00
Benoit Steiner
55a5204319 Fixed the flags passed to nvcc to compile the tensor code. 2016-01-27 14:46:34 -08:00
Gael Guennebaud
4865e1e732 Update link to suitesparse. 2016-01-27 22:48:40 +01:00
Benoit Steiner
9dfbd4fe8d Made the cuda tests compile using make check 2016-01-27 12:22:17 -08:00
Benoit Steiner
5973bcf939 Properly specify the namespace when calling cout/endl 2016-01-27 12:04:42 -08:00
Eugene Brevdo
c8d94ae944 digamma special function: merge shared code.
Moved type-specific code into a helper class digamma_impl_maybe_poly<Scalar>.
2016-01-27 09:52:29 -08:00
Gael Guennebaud
9c8f7dfe94 bug #1156: fix several function declarations whose arguments were passed by value instead of being passed by reference 2016-01-27 18:34:42 +01:00
Gael Guennebaud
9aa6fae123 bug #1154: move to dynamic scheduling for spmv products. 2016-01-27 18:03:51 +01:00
Gael Guennebaud
9ac8e8c6a1 Extend mixing type unit test with trmv, and the following not yet supported products: trmm, symv, symm 2016-01-27 17:29:53 +01:00
Gael Guennebaud
6da5d87f92 add nomalloc unit test for rank2 updates 2016-01-27 17:26:48 +01:00
Gael Guennebaud
9801c959e6 Fix tri = complex * real product, and add respective unit test. 2016-01-27 17:12:25 +01:00
Gael Guennebaud
21b5345782 Add meta_least_common_multiple helper. 2016-01-27 17:11:39 +01:00
Gael Guennebaud
fecea26d93 Extend doc on shifting strategy 2016-01-27 15:55:15 +01:00
Ville Kallioniemi
02db1228ed Add constructor for long types. 2016-01-26 23:41:01 -07:00
Gael Guennebaud
412bb5a631 Remove redundant test. 2016-01-26 23:35:30 +01:00
Gael Guennebaud
0f8d26c6a9 Doc: add flip* and arrayfun MatLab equivalent. 2016-01-26 23:34:48 +01:00
Gael Guennebaud
cfa21f8123 Remove dead code. 2016-01-26 23:33:15 +01:00
Gael Guennebaud
6850eab33b Re-enable blocking on rows in non-l3 blocking mode. 2016-01-26 23:32:48 +01:00
Gael Guennebaud
aa8c6a251e Make sure that micro-panel-size is smaller than blocking sizes (otherwise we might get a buffer overflow) 2016-01-26 23:31:48 +01:00
Gael Guennebaud
5b0a9ee003 Make sure that block sizes are smaller than input matrix sizes. 2016-01-26 23:30:24 +01:00
Benoit Jacob
639b1d864a bug #1152: Fix data race in static initialization of blas 2016-01-26 11:44:16 -05:00
Christoph Hertzberg
44d4674955 bug #1153: Don't rely on __GXX_EXPERIMENTAL_CXX0X__ to detect C++11 support 2016-01-26 16:45:33 +01:00
Hauke Heibel
5eb2790be0 Fixed minor typo in SplineFitting. 2016-01-25 22:17:52 +01:00
Gael Guennebaud
8328caa618 bug #51: add block preallocation mechanism to selfadjoit*matrix product. 2016-01-25 22:06:42 +01:00
Gael Guennebaud
2f9e6314b1 update BLAS interface to general_matrix_matrix_triangular_product 2016-01-25 21:56:05 +01:00
Gael Guennebaud
e58827d2ed bug #51: make general_matrix_matrix_triangular_product use L3-blocking helper so that general symmetric rank-updates and general-matrix-to-triangular products do not trigger dynamic memory allocation for fixed size matrices. 2016-01-25 17:16:33 +01:00
Gael Guennebaud
c10021c00a bug #1144: clarify the doc about aliasing in case of resizing and matrix product. 2016-01-25 15:50:55 +01:00
Gael Guennebaud
b114e6fd3b Improve documentation. 2016-01-25 11:56:25 +01:00
Gael Guennebaud
869b4443ac Add SparseVector::conservativeResize() method. 2016-01-25 11:55:39 +01:00
Benoit Steiner
e3a15a03a4 Don't explicitely evaluate the subexpression from TensorForcedEval::evalSubExprIfNeeded, as it will be done when executing the EvalTo subexpression 2016-01-24 23:04:50 -08:00
Benoit Steiner
bd207ce11e Added missing EIGEN_DEVICE_FUNC qualifier 2016-01-24 20:36:05 -08:00
Gael Guennebaud
acf6f7af6b Merged in larsmans/eigen (pull request PR-156)
Documentation fixes
2016-01-24 22:28:49 +01:00
Lars Buitinck
cc482e32f1 Method is called visit, not visitor 2016-01-24 15:50:59 +01:00
Lars Buitinck
19e437daf0 Copyedit documentation: typos, spelling 2016-01-24 15:50:36 +01:00
Gael Guennebaud
1cf85bd875 bug #977: add stableNormalize[d] methods: they are analogues to normalize[d] but with carefull handling of under/over-flow 2016-01-23 22:40:11 +01:00
Gael Guennebaud
369d6d1ae3 Add link to reference paper. 2016-01-23 22:16:03 +01:00
Gael Guennebaud
0caa4b1531 bug #1150: make IncompleteCholesky more robust by iteratively increase the shift until the factorization succeed (with at most 10 attempts). 2016-01-23 22:13:54 +01:00
Benoit Steiner
cb4e53ff7f Merged in ville-k/eigen/tensorflow_fix (pull request PR-153)
Add ctor for long
2016-01-22 19:11:31 -08:00
Ville Kallioniemi
9f94e030c1 Re-add executable flags to minimize changeset. 2016-01-22 20:08:45 -07:00
Benoit Steiner
3aeeca32af Leverage the new blocking code in the tensor contraction code. 2016-01-22 16:36:30 -08:00
Benoit Steiner
4beb447e27 Created a mechanism to enable contraction mappers to determine the best blocking strategy. 2016-01-22 14:37:26 -08:00
Gael Guennebaud
5358c38589 bug #1095: add Cholmod*::logDeterminant/determinant (from patch of Joshua Pritikin) 2016-01-22 16:05:29 +01:00
Gael Guennebaud
6a44ccb58b Backout changeset 690bc950f7 2016-01-22 15:03:53 +01:00
Gael Guennebaud
06971223ef Unify std::numeric_limits and device::numeric_limits within numext namespace 2016-01-22 15:02:21 +01:00
Ville Kallioniemi
9b6c72958a Update to latest default branch 2016-01-21 23:08:54 -07:00
Ville Kallioniemi
73aec9219b Make use of 32 bit ints explicit and remove executable bit from headers. 2016-01-21 23:00:32 -07:00
Benoit Steiner
7b68cf2e0f Pulled latest updates from trunk 2016-01-21 17:17:56 -08:00
Benoit Steiner
c33479324c Fixed a constness bug 2016-01-21 17:08:11 -08:00
Gael Guennebaud
ee37eb4eed bug #977: avoid division by 0 in normalize() and normalized(). 2016-01-21 20:43:42 +01:00
Gael Guennebaud
7cae8918c0 Fix compilation on old gcc+AVX 2016-01-21 20:30:32 +01:00
Gael Guennebaud
8dca9f97e3 Add numext::sqrt function to enable custom optimized implementation.
This changeset add two specializations for float/double on SSE. Those
are mostly usefull with GCC for which std::sqrt add an extra and costly
check on the result of _mm_sqrt_*. Clang does not add this burden.

In this changeset, only DenseBase::norm() makes use of it.
2016-01-21 20:18:51 +01:00
Gael Guennebaud
34340458cb bug #1151: remove useless critical section 2016-01-21 14:29:45 +01:00
Jan Prach
690bc950f7 fix clang warnings
"braces around scalar initializer"
2016-01-20 19:35:59 -08:00
Benoit Steiner
f2a842294f Pulled latest updates from the trunk 2016-01-20 18:12:53 -08:00
Benoit Steiner
7ce932edd3 Small cleanup and small fix to the contraction of row major tensors 2016-01-20 18:12:08 -08:00
Gael Guennebaud
62f7e77711 add upper|lower case in incomplete_cholesky unit test 2016-01-21 00:02:59 +01:00
Benoit Steiner
47076bf00e Reduce the register pressure exerted by the tensor mappers whenever possible. This improves the performance of the contraction of a matrix with a vector by about 35%. 2016-01-20 14:51:48 -08:00
Benoit Steiner
ebd3388ee6 Pulled latest updates from trunk 2016-01-20 13:56:43 -08:00
Gael Guennebaud
ed8ade9c65 bug #1149: fix Pastix*::*parm() 2016-01-20 19:01:24 +01:00
Gael Guennebaud
4c5e96aab6 bug #1148: silent Pastix by default 2016-01-20 18:56:17 +01:00
Gael Guennebaud
db237d0c75 bug #1145: fix PastixSupport LLT/LDLT wrappers (missing resize prior to calls to selfAdjointView) 2016-01-20 18:49:01 +01:00
Gael Guennebaud
0b7169d1f7 bug #1147: fix compilation of PastixSupport 2016-01-20 18:15:59 +01:00
Gael Guennebaud
234a1094b7 Add static assertion to y(), z(), w() accessors 2016-01-20 09:18:44 +01:00
Ville Kallioniemi
915e7667cd Remove executable bit from header files 2016-01-19 21:17:29 -07:00
Ville Kallioniemi
2832175a68 Use explicitly 32 bit integer types in constructors. 2016-01-19 20:12:17 -07:00
Benoit Steiner
df79c00901 Improved the formatting of the code 2016-01-19 17:24:08 -08:00
Benoit Steiner
6d472d8375 Moved the contraction mapping code to its own file to make the code more manageable. 2016-01-19 17:22:05 -08:00
Benoit Steiner
b3b722905f Improved code indentation 2016-01-19 17:09:47 -08:00
Benoit Steiner
5b7713dd33 Record whether the underlying tensor storage can be accessed directly during the evaluation of an expression. 2016-01-19 17:05:10 -08:00
Ville Kallioniemi
63fb66f53a Add ctor for long 2016-01-17 21:25:36 -07:00
Eugene Brevdo
6a75e7e0d5 Digamma cleanup
* Added permission from cephes author to use his code
* Cleanup in ArrayCwiseUnaryOps
2016-01-15 16:32:21 -08:00
Benoit Steiner
34057cff23 Fixed a race condition that could affect some reductions on CUDA devices. 2016-01-15 15:11:56 -08:00
Benoit Steiner
0461f0153e Made it possible to compare tensor dimensions inside a CUDA kernel. 2016-01-15 11:22:16 -08:00
Benoit Steiner
aed4cb1269 Use warp shuffles instead of shared memory access to speedup the inner reduction kernel. 2016-01-14 21:45:14 -08:00
Benoit Steiner
c1a42c2d0d Don't disable the AVX implementations of plset when compiling with AVX512 enabled 2016-01-14 17:21:39 -08:00
Benoit Steiner
0366478df8 Added alignment requirement to the AVX512 packet traits. 2016-01-14 17:02:39 -08:00
Benoit Steiner
3cfd16f3af Fixed the signature of the plset primitives for AVX512 2016-01-14 16:58:01 -08:00
Benoit Steiner
67f44365ea Fixed the AVX512 signature of the ptranspose primitives 2016-01-14 16:51:11 -08:00
Benoit Steiner
a282eb1363 pscatter/pgather use Index instead of int to specify the stride 2016-01-14 16:39:39 -08:00
Benoit Steiner
7832485575 Deleted unnecessary commas and semicolons 2016-01-14 16:36:29 -08:00
Benoit Steiner
8fe2532e70 Fixed a boundary condition bug in the outer reduction kernel 2016-01-14 09:29:48 -08:00
Benoit Steiner
9f013a9d86 Properly record the rank of reduced tensors in the tensor traits. 2016-01-13 14:24:37 -08:00
Benoit Steiner
79b69b7444 Trigger the optimized matrix vector path more conservatively. 2016-01-12 15:21:09 -08:00
Benoit Steiner
d920d57f38 Improved the performance of the contraction of a 2d tensor with a 1d tensor by a factor of 3 or more. This helps speedup LSTM neural networks. 2016-01-12 11:32:27 -08:00
Benoit Steiner
bd7d901da9 Reverted a previous change that tripped nvcc when compiling in debug mode. 2016-01-11 17:49:44 -08:00
Benoit Steiner
bbdabbb379 Made the blas utils usable from within a cuda kernel 2016-01-11 17:26:56 -08:00
Benoit Steiner
c5e6900400 Silenced a few compilation warnings. 2016-01-11 17:06:39 -08:00
Benoit Steiner
f894736d61 Updated the tensor traits: the alignment is not part of the Flags enum anymore 2016-01-11 16:42:18 -08:00
Benoit Steiner
4f7714d72c Enabled the use of fixed dimensions from within a cuda kernel. 2016-01-11 16:01:00 -08:00
Benoit Steiner
01c55d37e6 Deleted unused variable. 2016-01-11 15:53:19 -08:00
Benoit Steiner
0504c56ea7 Silenced a nvcc compilation warning 2016-01-11 15:49:21 -08:00
Benoit Steiner
b523771a24 Silenced several compilation warnings triggered by nvcc. 2016-01-11 14:25:43 -08:00
Benoit Steiner
2c3b13eded Merged in jeremy_barnes/eigen/shader-model-3.0 (pull request PR-152)
Alternative way of forcing instantiation of device kernels without causing warnings or requiring device to device kernel invocations.
2016-01-11 11:43:37 -08:00
Benoit Steiner
2ccb1c8634 Fixed a bug in the dispatch of optimized reduction kernels. 2016-01-11 10:36:37 -08:00
Benoit Steiner
780623261e Re-enabled the optimized reduction CUDA code. 2016-01-11 09:07:14 -08:00
Jeremy Barnes
91678f489a Cleaned up double-defined macro from last commit 2016-01-10 22:44:45 -05:00
Jeremy Barnes
403a7cb6c3 Alternative way of forcing instantiation of device kernels without
causing warnings or requiring device to device kernel invocations.

This allows Tensorflow to work on SM 3.0 (ie, Amazon EC2) machines.
2016-01-10 22:39:13 -05:00
Gael Guennebaud
b557662e58 merge 2016-01-09 08:37:01 +01:00
Gael Guennebaud
8b9dc9f0df bug #1144: fix regression in x=y+A*x (aliasing), and move evaluator_traits::AssumeAliasing to evaluator_assume_aliasing. 2016-01-09 08:30:38 +01:00
Benoit Steiner
e76904af1b Simplified the dispatch code. 2016-01-08 16:50:57 -08:00
Benoit Steiner
d726e864ac Made it possible to use array of size 0 on CUDA devices 2016-01-08 16:38:14 -08:00
Benoit Steiner
3358dfd5dd Reworked the dispatch of optimized cuda reduction kernels to workaround a nvcc bug that prevented the code from compiling in optimized mode in some cases 2016-01-08 16:28:53 -08:00
Benoit Steiner
53749ff415 Prevent nvcc from miscompiling the cuda metakernel. Unfortunately this reintroduces some compulation warnings but it's much better than having to deal with random assertion failures. 2016-01-08 13:53:40 -08:00
Gael Guennebaud
f9d71a1729 extend matlab conversion table 2016-01-08 22:24:45 +01:00
Benoit Steiner
6639b7d6e8 Removed a couple of partial specialization that confuse nvcc and result in errors such as this:
error: more than one partial specialization matches the template argument list of class "Eigen::internal::get<3, Eigen::internal::numeric_list<std::size_t, 1UL, 1UL, 1UL, 1UL>>"
            "Eigen::internal::get<n, Eigen::internal::numeric_list<T, a, as...>>"
            "Eigen::internal::get<n, Eigen::internal::numeric_list<T, as...>>"
2016-01-07 18:45:19 -08:00
Benoit Steiner
0cb2ca5de2 Fixed a typo. 2016-01-06 18:50:28 -08:00
Benoit Steiner
213459d818 Optimized the performance of broadcasting of scalars. 2016-01-06 18:47:45 -08:00
Gael Guennebaud
ee738321aa rm remaining debug code 2016-01-06 14:49:40 +01:00
Christoph Hertzberg
54bf582303 bug #1143: Work-around gcc bug 2016-01-06 11:59:24 +01:00
Benoit Steiner
99093c0fe0 Added support for AVX512 to the build files 2016-01-05 10:02:49 -08:00
Benoit Steiner
cfff40b1d4 Improved the performance of reductions on CUDA devices 2016-01-04 17:25:00 -08:00
Benoit Steiner
515dee0baf Added a 'divup' util to compute the floor of the quotient of two integers 2016-01-04 16:29:26 -08:00
Gael Guennebaud
715f6f049f Improve inline documentation of SparseCompressedBase and its derived classes 2016-01-03 21:56:30 +01:00
Gael Guennebaud
8b0d1eb0f7 Fix numerous doxygen shortcomings, and workaround some clang -Wdocumentation warnings 2016-01-01 21:45:06 +01:00
Gael Guennebaud
9900782e88 Mark AlignedBit and EvalBeforeNestingBit with deprecated attribute, and remove the remaining usages of EvalBeforeNestingBit. 2015-12-30 16:47:49 +01:00
Gael Guennebaud
70404e07c2 Workaround clang -Wdocumentation warning about "/*<" 2015-12-30 16:46:45 +01:00
Gael Guennebaud
addb7066e8 Workaround "empty paragraph" warning with clang -Wdocumentation 2015-12-30 16:45:44 +01:00
Gael Guennebaud
eadc377b3f Add missing doc of Derived template parameter 2015-12-30 16:43:19 +01:00
Gael Guennebaud
29bb599e03 Fix numerous doxygen issues in auto-link generation 2015-12-30 16:04:24 +01:00
Gael Guennebaud
162ccb2938 Fix links to Eigen2-to-Eigen3 porting helpers 2015-12-30 16:03:14 +01:00
Gael Guennebaud
5fae3750b5 Recent versions of doxygen miss-parsed Eigen/* headers 2015-12-30 16:02:05 +01:00
Gael Guennebaud
b84cefe61d Add missing snippets for erf/erfc/lgamma functions. 2015-12-30 15:12:15 +01:00
Gael Guennebaud
16dd82ed51 Add missing snippet for sign/cwiseSign functions. 2015-12-30 15:11:42 +01:00
Gael Guennebaud
978c379ed7 Add missing ctor from uint 2015-12-30 12:52:38 +01:00
Gael Guennebaud
25f2b8d824 bug #1141: add missing initialization of CholmodBase::m_*IsOk 2015-12-29 15:50:11 +01:00
Eugene Brevdo
f2471f31e0 Modify constants in SpecialFunctions to lowercase (avoid name conflicts). 2015-12-28 17:48:38 -08:00
Eugene Brevdo
afb35385bf Change PI* to M_PI* in SpecialFunctions to avoid possible breakage
with external DEFINEs.
2015-12-28 17:34:06 -08:00
Eugene Brevdo
14897600b7 Protect digamma tests behind a EIGEN_HAS_C99_MATH check. 2015-12-24 21:28:18 -08:00
Eugene Brevdo
cef81c9084 Merged eigen/eigen into default 2015-12-24 21:17:33 -08:00
Eugene Brevdo
f7362772e3 Add digamma for CPU + CUDA. Includes tests. 2015-12-24 21:15:38 -08:00
Gael Guennebaud
d2e288ae50 Workaround compilers that do not even define _mm256_set_m128. 2015-12-24 16:53:43 +01:00
Benoit Steiner
bdcbc66a5c Don't attempt to vectorize mean reductions of integers since we can't use
SSE or AVX instructions to divide 2 integers.
2015-12-22 17:51:55 -08:00
Benoit Steiner
a1e08fb2a5 Optimized the configuration of the outer reduction cuda kernel 2015-12-22 16:30:10 -08:00
Benoit Steiner
9c7d96697b Added missing define 2015-12-22 16:11:07 -08:00
Benoit Steiner
e7e6d01810 Made sure the optimized gpu reduction code is actually compiled. 2015-12-22 15:07:33 -08:00
Benoit Steiner
b5d2078c4a Optimized outer reduction on GPUs. 2015-12-22 15:06:17 -08:00
Benoit Steiner
3504ae47ca Made it possible to run the lgamma, erf, and erfc functors on a CUDA gpu. 2015-12-21 15:20:06 -08:00
Benoit Steiner
1c3e78319d Added missing const 2015-12-21 15:05:01 -08:00
Benoit Steiner
9f9d8d2f62 Disabled part of the matrix matrix peeling code that's incompatible with 512 bit registers 2015-12-21 13:04:52 -08:00
Benoit Steiner
b74887d5f2 Implemented most of the packet primitives for AVX512 2015-12-21 11:46:36 -08:00
Benoit Steiner
6ffb208c77 Make sure EIGEN_HAS_MM_MALLOC is set to 1 when using the avx512 instruction set. 2015-12-21 11:23:15 -08:00
Benoit Steiner
994d1c60b9 Free memory allocated using posix_memalign() with free() instead of std::free() 2015-12-21 11:21:39 -08:00
Benoit Steiner
b407948a77 Merged in connor-k/eigen (pull request PR-149)
[doc] Remove extra ';' in Advanced Initialization sample
2015-12-21 09:44:25 -08:00
Benoit Steiner
a6c243617b Fixed a typo in previous change. 2015-12-21 09:05:45 -08:00
Benoit Steiner
51be91f15e Added support for CUDA architectures that don's support for 3.5 capabilities 2015-12-21 08:42:58 -08:00
connor-k
95dd423cca [doc] Remove extra ';' in Tutorial_AdvancedInitialization_Join.cpp 2015-12-21 01:12:26 +00:00
Tal Hadad
c006ecace1 Fix comments 2015-12-20 20:07:06 +02:00
Tal Hadad
bfed274df3 Use RotationBase, test quaternions and support ranges. 2015-12-20 16:24:53 +02:00
Tal Hadad
b091b7e6ea Remove unneccesary comment. 2015-12-20 13:00:07 +02:00
Tal Hadad
fabd8474ff Merged eigen/eigen into default 2015-12-20 12:50:07 +02:00
Tal Hadad
6752a69aa5 Much better tests, and a little bit more functionality. 2015-12-20 12:49:12 +02:00
Benoit Steiner
6d777e1bc7 Fixed a typo. 2015-12-18 19:25:50 -08:00
Benoit Steiner
1b82969559 Add alignment requirement for local buffer used by the slicing op. 2015-12-18 14:36:35 -08:00
Benoit Steiner
75a7fa1919 Doubled the speed of full reductions on GPUs. 2015-12-18 14:07:31 -08:00
Gael Guennebaud
3abd8470ca bug #1140: remove custom definition and use of _mm256_setr_m128 2015-12-18 14:18:59 +01:00
Benoit Steiner
8dd17cbe80 Fixed a clang compilation warning triggered by the use of arrays of size 0. 2015-12-17 14:00:33 -08:00
Benoit Steiner
4aac55f684 Silenced some compilation warnings triggered by nvcc 2015-12-17 13:39:01 -08:00
Benoit Steiner
40e6250fc3 Made it possible to run tensor chipping operations on CUDA devices 2015-12-17 13:29:08 -08:00
Benoit Steiner
2ca55a3ae4 Fixed some compilation error triggered by the tensor code with msvc 2008 2015-12-16 20:45:58 -08:00
Gael Guennebaud
55aef139ff Added tag 3.3-beta1 for changeset 9f9de1aaa9 2015-12-16 21:49:02 +01:00
Gael Guennebaud
9f9de1aaa9 bump to 3.3-beta1 2015-12-16 21:48:48 +01:00
Christoph Hertzberg
49d96aee64 bug #1120: Make sure that SuperLU version is checked 2015-12-16 11:37:16 +01:00
Gael Guennebaud
ae8b217a01 Update doc to make it clear that only SuperLU 4.x is supported 2015-12-16 10:47:03 +01:00
Gael Guennebaud
35d8725c73 Disable AutoDiffScalar generic copy ctor for non compatible scalar types (fix ambiguous template instantiation) 2015-12-16 10:14:24 +01:00
Christoph Hertzberg
92655e7215 bug #1136: Protect isinf for Intel compilers. Also don't distinguish GCC from ICC and don't rely on EIGEN_NOT_A_MACRO, which might not be defined when including this. 2015-12-15 11:34:52 +01:00
Benoit Steiner
17352e2792 Made the entire TensorFixedSize api callable from a CUDA kernel. 2015-12-14 15:20:31 -08:00
Benoit Steiner
75e19fc7ca Marked the tensor constructors as EIGEN_DEVICE_FUNC: This makes it possible to call them from a CUDA kernel. 2015-12-14 15:12:55 -08:00
Gael Guennebaud
140f3a02a8 Fix MKL wrapper for ComplexSchur 2015-12-11 23:31:21 +01:00
Gael Guennebaud
4483c0fdf6 Fix unused variable warning. 2015-12-11 23:29:53 +01:00
Gael Guennebaud
774dba87c8 merge 2015-12-11 23:28:44 +01:00
Gael Guennebaud
c884a8e7f4 merge 2015-12-11 23:07:33 +01:00
Gael Guennebaud
4d708457d0 Increase axpy vector size 2015-12-11 23:07:22 +01:00
Benoit Steiner
b8861b0c25 Make sure the data is aligned on a 64 byte boundary when using avx512 instructions. 2015-12-11 09:19:57 -08:00
Gael Guennebaud
b60a8967f5 bug #1134: fix JacobiSVD pre-allocation
(grafted from f22036f5f8
)
2015-12-11 11:59:11 +01:00
Gael Guennebaud
ca39b1546e Merged in ebrevdo/eigen (pull request PR-148)
Add special functions to eigen: lgamma, erf, erfc.
2015-12-11 11:52:09 +01:00
Gael Guennebaud
82152f2ae6 bug #1132: add EIGEN_MAPBASE_PLUGIN 2015-12-11 11:43:49 +01:00
Gael Guennebaud
4519fd5d40 Fix MKL compilation issue 2015-12-11 11:11:38 +01:00
Gael Guennebaud
7385e6e2ef Remove useless explicit 2015-12-11 11:11:19 +01:00
Gael Guennebaud
bcb4f126a7 Fix compilation of PardisoSupport 2015-12-11 11:11:00 +01:00
Gael Guennebaud
30b5c4cd14 Remove useless "explicit", and fix inline/static order. 2015-12-11 10:59:39 +01:00
Gael Guennebaud
79c1e6d0a6 Fix compilation of MKL support. 2015-12-11 10:55:07 +01:00
Gael Guennebaud
c684a07eba merge 2015-12-11 10:06:38 +01:00
Gael Guennebaud
836da91b3f Fix unit tests wrt EIGEN_DEFAULT_TO_ROW_MAJOR 2015-12-11 10:06:28 +01:00
Benoit Steiner
6af52a1227 Fixed a typo in the constructor of tensors of rank 5. 2015-12-10 23:31:12 -08:00
Benoit Steiner
2d8f2e4042 Made 2 tests compile without cxx11.
HdG: --
2015-12-10 23:20:04 -08:00
Benoit Steiner
8d28a161b2 Use the proper accessor to refer to the value of a scalar tensor 2015-12-10 22:53:56 -08:00
Benoit Steiner
8e00ea9a92 Fixed the coefficient accessors use for the 2d and 3d case when compiling without cxx11 support. 2015-12-10 22:45:10 -08:00
Benoit Steiner
9db8316c93 Updated the cxx11_tensor_custom_op to not require cxx11. 2015-12-10 20:53:44 -08:00
Benoit Steiner
4e324ca6ae Updated the cxx11_tensor_assign test to make it compile without support for cxx11 2015-12-10 20:47:25 -08:00
Benoit Steiner
6acf2bd472 Fixed compilation error triggered by MSVC 2008 2015-12-10 17:17:42 -08:00
Benoit Steiner
9a415fb1e2 Preliminary support for AVX512 2015-12-10 15:34:57 -08:00
Benoit Steiner
b820b097b8 Created EIGEN_HAS_C99_MATH define as Gael suggested. 2015-12-10 13:52:05 -08:00
Gael Guennebaud
df6f54ff63 Fix storage order of PartialRedux 2015-12-10 22:24:58 +01:00
Gael Guennebaud
d1862967a8 Make sure ADOLC is recent enough by searching for adtl.h 2015-12-10 22:23:21 +01:00
Mark Borgerding
22dd368ea0 sign(complex) compiles for GPU 2015-12-10 16:14:29 -05:00
Benoit Steiner
8314962ce2 Only test the lgamma, erf and erfc function when using a C99 compliant compiler 2015-12-10 13:13:45 -08:00
Benoit Steiner
58e06447de Silence a compilation warning 2015-12-10 13:11:36 -08:00
Benoit Steiner
48877a6933 Only implement the lgamma, erf, and erfc functions when using a compiler compliant with the C99 specification. 2015-12-10 13:09:49 -08:00
Gael Guennebaud
46d2f6cd78 Workaround gcc issue with -O3 and the i387 FPU. 2015-12-10 21:33:43 +01:00
Gael Guennebaud
7ad1aaec1d bug #1103: fix neon vectorization of pmul(Packet1cd,Packet1cd) 2015-12-10 16:06:33 +01:00
Gael Guennebaud
b0a1d6f2e5 Improve handling of deprecated EIGEN_INCLUDE_INSTALL_DIR variable 2015-12-10 15:47:06 +01:00
Benoit Steiner
53b196aa5f Simplified the implementation of lgamma, erf, and erfc 2015-12-08 14:17:34 -08:00
Benoit Steiner
e535450573 Cleanup 2015-12-08 14:06:39 -08:00
Benoit Steiner
b630d10b62 Only disable the erf, erfc, and lgamma tests for older versions of c++. 2015-12-07 17:08:08 -08:00
Benoit Steiner
b1ae39794c Simplified the code a bit 2015-12-07 16:46:35 -08:00
Benoit Steiner
73b68d4370 Fixed a couple of typos
Cleaned up the code a bit.
2015-12-07 16:38:48 -08:00
Eugene Brevdo
fa4f933c0f Add special functions to Eigen: lgamma, erf, erfc.
Includes CUDA support and unit tests.
2015-12-07 15:24:49 -08:00
Benoit Steiner
7dfe75f445 Fixed compilation warnings 2015-12-07 08:12:30 -08:00
Gael Guennebaud
ad3d68400e Add matrix-free solver example 2015-12-07 12:33:38 +01:00
Gael Guennebaud
b37036afce Implement wrapper for matrix-free iterative solvers 2015-12-07 12:23:22 +01:00
Benoit Steiner
f4ca8ad917 Use signed integers instead of unsigned ones more consistently in the codebase. 2015-12-04 18:14:16 -08:00
Benoit Steiner
490d26e4c1 Use integers instead of std::size_t to encode the number of dimensions in the Tensor class since most of the code currently already use integers. 2015-12-04 10:15:11 -08:00
Benoit Steiner
d20efc974d Made it possible to use the sigmoid functor within a CUDA kernel. 2015-12-04 09:38:15 -08:00
Benoit Steiner
e25e3a041b Added rsqrt() method to the Array class: this method computes the coefficient-wise inverse square root much more efficiently than calling sqrt().inverse(). 2015-12-03 18:16:35 -08:00
Benoit Steiner
029052d276 Deleted redundant code 2015-12-03 17:08:47 -08:00
Benoit Steiner
c41e9e4bd0 Merged in Unril/eigen-1/Unril/fixes-internal-compiler-error-while-comp-1449156092576 (pull request PR-147)
Fixes internal compiler error while compiling with VC2015 Update1 x64.
2015-12-03 14:26:14 -08:00
Gael Guennebaud
1562e13aba Add missing Rotation2D::operator=(Matrix2x2) 2015-12-03 22:25:26 +01:00
Nikolay Fedorov
944647c0aa Fixes internal compiler error while compiling with VC2015 Update1 x64. 2015-12-03 15:21:43 +00:00
Benoit Steiner
d2d4c45d55 Made it possible to leverage several binary functor in a CUDA kernel
Explicitely specified the return type of the various scalar_cmp_op functors.
2015-12-02 17:21:33 -08:00
Gael Guennebaud
c5b86893e7 bug #1123: add missing documentation of angle() and axis() 2015-12-01 14:45:08 +01:00
Gael Guennebaud
0bb12fa614 Add LU::transpose().solve() and LU::adjoint().solve() API. 2015-12-01 14:38:47 +01:00
Rasmus Munk Larsen
1663d15da7 Add internal method _solve_impl_transposed() to LU decomposition classes that solves A^T x = b or A^* x = b. 2015-11-30 13:39:24 -08:00
Gael Guennebaud
274b2272b7 Make bench_gemm compatible with 3.2 2015-12-01 09:57:31 +01:00
Gael Guennebaud
6c02cbbb0f Fix matrix to quaternion (and angleaxis) conversion for matrix expression. 2015-12-01 09:45:56 +01:00
Gael Guennebaud
844561939f Do not check NeedsToAlign if no static alignment 2015-11-30 22:36:14 +01:00
Gael Guennebaud
1d906d883d Fix degenerate cases in syrk and trsm 2015-11-30 22:20:31 +01:00
Gael Guennebaud
e7a1c48185 Update BLAS API unit tests 2015-11-30 22:19:20 +01:00
Gael Guennebaud
034ca5a22d Clean hardcoded compilation options 2015-11-30 17:05:42 +01:00
Gael Guennebaud
fd727249ad Update ADOL-C support. 2015-11-30 16:00:22 +01:00
Gael Guennebaud
6fcd316f23 Extend superlu cmake script to check version 2015-11-30 14:48:11 +01:00
Gael Guennebaud
afa11d646d Fix UmfPackLU ctor for exppressions 2015-11-27 22:04:22 +01:00
Gael Guennebaud
6bdeb8cfbe bug #918, umfpack: add access to umfpack return code and parameters 2015-11-27 21:58:36 +01:00
Gael Guennebaud
3f32f5ec22 ArrayBase::sign: add unit test and fix doc 2015-11-27 16:27:53 +01:00
Gael Guennebaud
da46b1ed54 bug #1112: fix compilation on exotic architectures 2015-11-27 15:57:18 +01:00
Gael Guennebaud
1261d020c3 bug #1120, superlu: mem_usage_t is now uniquely defined, so let's use it. 2015-11-27 10:39:09 +01:00
Gael Guennebaud
0ff127e896 Preserve CMAKE_CXX_FLAGS in BTL 2015-11-27 10:18:39 +01:00
Gael Guennebaud
ca001d7c2a Big 1009, part 2/2: add static assertion on LinearAccessBit in coeff(index)-like methods. 2015-11-27 10:06:47 +01:00
Gael Guennebaud
91a7059459 bug #1009, part 1/2: make sure vector expressions expose LinearAccessBit flag. 2015-11-27 10:06:07 +01:00
Mark Borgerding
7ddcf97da7 added scalar_sign_op (both real,complex) 2015-11-24 17:15:07 -05:00
Benoit Steiner
44848ac39b Fixed a bug in TensorArgMax.h 2015-11-23 15:58:47 -08:00
Benoit Steiner
547a8608e5 Fixed the implementation of Eigen::internal::count_leading_zeros for MSVC.
Also updated the code to silence bogux warnings generated by nvcc when compilining this function.
2015-11-23 12:17:45 -08:00
Benoit Steiner
562078780a Don't create more cuda blocks than necessary 2015-11-23 11:00:10 -08:00
Benoit Steiner
df31ca3b9e Made it possible to refer t oa GPUDevice from code compile with a regular C++ compiler 2015-11-23 10:03:53 -08:00
Benoit Steiner
1e04059012 Deleted unused variable. 2015-11-23 08:36:54 -08:00
Benoit Steiner
4286b2d494 Pulled latest updates from trunk 2015-11-23 08:28:34 -08:00
Gael Guennebaud
f9fff67a56 Disable "decorated name length exceeded, name was truncated" MSVC warning. 2015-11-23 15:03:24 +01:00
Gael Guennebaud
f3dca16a1d bug #1117: workaround unused-local-typedefs warning when EIGEN_NO_STATIC_ASSERT and NDEBUG are both defined. 2015-11-23 14:07:52 +01:00
Gael Guennebaud
31b661e4ca Add a note on initParallel being optional in C++11. 2015-11-23 13:28:43 +01:00
Gael Guennebaud
8a2659f0cb Improve numerical robustness of some unit tests 2015-11-23 10:53:55 +01:00
Gael Guennebaud
82bd4e546a Merged in dr15jones/eigen (pull request PR-146)
Use a class constructor to initialize CPU cache sizes
2015-11-22 22:50:31 +01:00
Gael Guennebaud
35c17a3fc8 Use overload instead of template full specialization to please old MSVC 2015-11-22 22:09:57 +01:00
Gael Guennebaud
b265979a70 Make FullPivLU::solve use rank() instead of nonzeroPivots(). 2015-11-21 15:03:04 +01:00
Benoit Steiner
9fa65d3838 Split TensorDeviceType.h in 3 files to make it more manageable 2015-11-20 17:42:50 -08:00
Benoit Steiner
a367804856 Added option to force the usage of the Eigen array class instead of the std::array class. 2015-11-20 12:41:40 -08:00
Benoit Steiner
86486eee2d Pulled latest updates from trunk 2015-11-20 11:10:37 -08:00
Benoit Steiner
383d1cc2ed Added proper support for fast 64bit integer division on CUDA 2015-11-20 11:09:46 -08:00
Chris Jones
4946d758c9 Use a class constructor to initialize CPU cache sizes
Using a static instance of a class to initialize the values for
the CPU cache sizes guarantees thread-safe initialization of the
values when using C++11. Therefore under C++11 it is no longer
necessary to call Eigen::initParallel() before calling any eigen
functions on different threads.
2015-11-20 19:58:08 +01:00
Gael Guennebaud
027a846b34 Use .data() instead of &coeffRef(0). 2015-11-20 15:30:10 +01:00
Gael Guennebaud
4522ffd17c Add regression using test for array<complex>/real 2015-11-20 15:29:32 +01:00
Gael Guennebaud
4fc36079e7 Fix overload instantiation for clang 2015-11-20 15:29:03 +01:00
Gael Guennebaud
4a985e793c Workaround msvc broken complex/complex division in unit test 2015-11-20 14:52:08 +01:00
Gael Guennebaud
5c9c0dca4d Add missing using statement to enable fast Array<complex> / real operations. (was ok for Matrix only) 2015-11-20 14:51:36 +01:00
Gael Guennebaud
e1b27bcb0b Workaround MSVC missing overloads of std::fpclassify for integral types 2015-11-20 13:55:34 +01:00
Gael Guennebaud
e52d4f8d8d Add is_integral<> type traits 2015-11-20 13:54:28 +01:00
Benoit Steiner
0ad7c7b1ad Fixed another clang compilation warning 2015-11-19 15:52:51 -08:00
Benoit Steiner
66ff9b2c6c Fixed compilation warning generated by clang 2015-11-19 15:40:32 -08:00
Benoit Steiner
f37a5f1c53 Fixed compilation error triggered by nvcc 2015-11-19 14:34:26 -08:00
Benoit Steiner
04f1284f9a Shard the uint128 test 2015-11-19 14:08:08 -08:00
Benoit Steiner
e2859c6b71 Cleanup the integer division test 2015-11-19 14:07:50 -08:00
Benoit Steiner
f8df393165 Added support for 128bit integers on CUDA devices. 2015-11-19 13:57:27 -08:00
Benoit Steiner
7d1cedd0fe Added numeric limits for unsigned integers 2015-11-18 17:17:44 -08:00
Gael Guennebaud
1994999105 Add regression unit test for prod.maxCoeff(i) 2015-11-18 23:29:07 +01:00
Benoit Steiner
1dd444ea71 Avoid using the version of TensorIntDiv optimized for 32-bit integers when the divisor can be equal to one since it isn't supported. 2015-11-18 11:37:58 -08:00
Benoit Jacob
4926251f13 bug #1115: enable static alignment on ARM outside of old-GCC 2015-11-18 10:55:23 -05:00
Gael Guennebaud
a64156cae5 Workaround i387 issue in unit test 2015-11-16 13:33:54 +01:00
Benoit Steiner
bf792f59e3 Only enable the use of constexpr with nvcc if we're using version 7.5 or above 2015-11-13 12:24:22 -08:00
Benoit Steiner
f1fbd74db9 Added sanity check 2015-11-13 09:07:27 -08:00
Benoit Steiner
1e1755352d Made it possible to compute atan, tanh, sinh and cosh on GPU 2015-11-12 20:19:38 -08:00
Benoit Steiner
7815b84be4 Fixed a compilation warning 2015-11-12 20:16:59 -08:00
Benoit Steiner
10a91930cc Fixed a compilation warning triggered by nvcc 2015-11-12 20:10:52 -08:00
Benoit Steiner
ed4b37de02 Fixed a few compilation warnings 2015-11-12 20:08:01 -08:00
Benoit Steiner
b69248fa2a Added a couple of missing EIGEN_DEVICE_FUNC 2015-11-12 20:01:50 -08:00
Benoit Steiner
0aaa5941df Silenced some compilation warnings triggered by nvcc 2015-11-12 19:11:43 -08:00
Benoit Steiner
2c73633b28 Fixed a few more typos 2015-11-12 18:39:19 -08:00
Benoit Steiner
be08e82953 Fixed typos 2015-11-12 18:37:40 -08:00
Benoit Steiner
e4d45f3440 Only enable the use of const expression when nvcc is called with the -std=c++11 option 2015-11-12 18:18:35 -08:00
Benoit Steiner
150c12e138 Completed the IndexList rewrite 2015-11-12 18:11:56 -08:00
Benoit Steiner
8037826367 Simplified more of the IndexList code. 2015-11-12 17:19:45 -08:00
Benoit Steiner
e9ecfad796 Started to make the IndexList code compile by more compilers 2015-11-12 16:41:14 -08:00
Benoit Steiner
7a1316fcc5 Fixed compilation error with xcode. 2015-11-12 11:05:54 -08:00
Benoit Steiner
737d237722 Made it possible to run some of the CXXMeta functions on a CUDA device. 2015-11-12 09:02:59 -08:00
Benoit Steiner
1e072424e8 Moved the array code into it's own file. 2015-11-12 08:57:04 -08:00
Benoit Steiner
aa5f1ca714 gen_numeric_list takes a size_t, not a int 2015-11-12 08:30:10 -08:00
Gael Guennebaud
dfbb889fe9 Fix missing Dynamic versus HugeCost changes 2015-11-12 12:09:48 +01:00
Gael Guennebaud
e701cb2c7c Update EIGEN_FAST_MATH doc 2015-11-12 12:09:19 +01:00
Benoit Steiner
9fa10fe52d Don't use std::array when compiling with nvcc since nvidia doesn't support the use of STL containers on GPU. 2015-11-11 15:38:30 -08:00
Benoit Steiner
c587293e48 Fixed a compilation warning 2015-11-11 15:35:12 -08:00
Benoit Steiner
7f1c29fb0c Make it possible for a vectorized tensor expression to be executed in a CUDA kernel. 2015-11-11 15:22:50 -08:00
Benoit Steiner
4f471146fb Allow the vectorized version of the Binary and the Nullary functors to run on GPU 2015-11-11 15:19:00 -08:00
Benoit Steiner
99f4778506 Disable SFINAE when compiling with nvcc 2015-11-11 15:04:58 -08:00
Benoit Steiner
5cb18e5b5e Fixed CUDA compilation errors 2015-11-11 14:36:33 -08:00
Benoit Steiner
228edfe616 Use Eigen::NumTraits instead of std::numeric_limits 2015-11-11 09:26:23 -08:00
Taylor Braun-Jones
b836acb799 Further fixes for CMAKE_INSTALL_PREFIX correctness
And other related cmake cleanup, including:

- Use CMAKE_CURRENT_LIST_DIR to find UseEigen3.cmake
- Use INSTALL_DIR term consistently for variable names
- Drop unnecessary extra EIGEN_INCLUDE_INSTALL_DIR
- Fix some paths in generated eigen3.pc and Eigen3Config.cmake files
    missing CMAKE_INSTALL_PREFIX
- Fix pkgconfig directory choice ignored if it doesn't exist at configure
    time (bug #711)
2015-11-07 21:29:24 -05:00
Gael Guennebaud
e73ef4f25e bug #1109: use noexcept instead of throw for C++11 compilers 2015-12-10 14:21:23 +01:00
Gael Guennebaud
145ad5d800 Use more explicit names. 2015-12-10 12:03:38 +01:00
Gael Guennebaud
75f0fe3795 Fix usage of "Index" as a compile time integral. 2015-12-10 12:01:06 +01:00
Gael Guennebaud
f248249c1f bug #1113: fix name conflict with C99's "I". 2015-12-10 11:57:57 +01:00
Gael Guennebaud
21ed29e2c9 Disable complex scalar types because the compiler might aggressively vectorize
the initialization of complex coeffs to 0 before we can check for alignedness
2015-12-09 20:46:09 +01:00
Gael Guennebaud
fbe18d5507 Forbid the creation of SparseCompressedBase object 2015-12-09 15:47:32 +01:00
Gael Guennebaud
dc73430d4b bug #1074: forbid the creation of PlainObjectBase object by making its ctor protected 2015-12-09 15:47:08 +01:00
Gael Guennebaud
1257fbd2f9 Fix sign-unsigned issue in enum 2015-12-09 10:06:42 +01:00
Gael Guennebaud
4549549992 Fix and clarify documentation of Transform wrt operator*(MatrixBase) 2015-12-08 16:21:49 +01:00
Gael Guennebaud
543bd28a24 Fix Alignment in coeff-based product, and enable unaligned vectorization 2015-12-08 11:28:05 +01:00
Gael Guennebaud
03ad4fc504 Extend unit test of coeff-based product to check many more combinations 2015-12-08 11:27:43 +01:00
Benoit Steiner
20e2ab1121 Fixed another compilation warning 2015-12-07 16:17:57 -08:00
Benoit Steiner
d573efe303 Code cleanup 2015-11-06 14:54:28 -08:00
Benoit Steiner
9fa283339f Silenced a compilation warning 2015-11-06 11:44:22 -08:00
Benoit Steiner
53432a17b2 Added static assertions to avoid misuses of padding, broadcasting and concatenation ops. 2015-11-06 10:26:19 -08:00
Benoit Steiner
6857a35a11 Fixed typos 2015-11-06 09:42:05 -08:00
Benoit Steiner
33cbdc2d15 Added more missing EIGEN_DEVICE_FUNC 2015-11-06 09:29:59 -08:00
Benoit Steiner
d27e4f1cba Added missing EIGEN_DEVICE_FUNC statements 2015-11-06 09:23:58 -08:00
Benoit Steiner
ed1962b464 Reimplement the tensor comparison operators by using the scalar_cmp_op functors. This makes them more cuda friendly. 2015-11-06 09:18:43 -08:00
Gael Guennebaud
bfd6ee64f3 bug #1105: fix default preallocation when moving from compressed to uncompressed mode 2015-11-06 15:05:37 +01:00
Benoit Steiner
29038b982d Added support for modulo operation 2015-11-05 19:39:48 -08:00
Benoit Steiner
fbcf8cc8c1 Pulled latest updates from trunk 2015-11-05 14:30:02 -08:00
Benoit Steiner
0d15ad8019 Updated the regressions tests that cover full reductions 2015-11-05 14:22:30 -08:00
Benoit Steiner
c75a19f815 Misc fixes to full reductions 2015-11-05 14:21:20 -08:00
Benoit Steiner
ec5a81b45a Fixed a bug in the extraction of sizes of fixed sized tensors of rank 0 2015-11-05 13:39:48 -08:00
Gael Guennebaud
589b839ad0 Add unit test for Hessian via AutoDiffScalar 2015-11-05 14:54:05 +01:00
Gael Guennebaud
9ceaa8e445 bug #1063: nest AutoDiffScalar by value to avoid dead references 2015-11-05 13:54:26 +01:00
Gael Guennebaud
ae87f094eb Fix "," in non SSE4 mode 2015-11-05 12:08:36 +01:00
Gael Guennebaud
2844e7ae43 SPQR and UmfPack need to link to cholmod.
(grafted from 47592d31ea
)
2015-11-05 12:05:02 +01:00
Gael Guennebaud
780eeb3be7 prevent stack overflow in unit test 2015-11-05 00:32:48 -08:00
Benoit Steiner
beedd9630d Updated the reduction code so that full reductions now return a tensor of rank 0. 2015-11-04 13:57:36 -08:00
Gael Guennebaud
90323f1751 Fix AVX round/ceil/floor, and fix respective unit test 2015-11-04 22:15:57 +01:00
Gael Guennebaud
3dd24bdf99 Merged in aavenel/eigen (pull request PR-142)
Add round, ceil and floor for SSE4.1/AVX (Bug #70)
2015-11-04 18:26:38 +01:00
Gael Guennebaud
902750826b Add support for dense.cwiseProduct(sparse)
This also fixes a regression regarding (dense*sparse).diagonal()
2015-11-04 17:42:07 +01:00
Gael Guennebaud
f6b1deebab Fix compilation of sparse-triangular to dense assignment 2015-11-04 17:02:32 +01:00
Benoit Steiner
36cd6daaae Made the CUDA implementation of ploadt_ro compatible with cuda implementations older than 3.5 2015-11-03 16:36:30 -08:00
Gael Guennebaud
29a94c8055 compilation issue 2015-11-02 16:11:59 +01:00
Alexandre Avenel
38832e0791 Merge 2015-11-01 10:55:42 +01:00
Alexandre Avenel
d46e2c10a6 Add round, ceil and floor for SSE4.1/AVX (Bug #70) 2015-11-01 10:49:27 +01:00
Gael Guennebaud
c0352197a1 bug #1099: add missing incude for CUDA 2015-10-31 18:06:28 +01:00
Gael Guennebaud
b32948c642 bug #1102: fix multiple definition linking issue 2015-10-30 22:25:59 +01:00
Gael Guennebaud
5a2007f7e4 typo 2015-10-30 22:16:23 +01:00
Gael Guennebaud
8a3151de2e Limit matrix size for other eigen and schur decompositions 2015-10-30 18:06:03 +01:00
Gael Guennebaud
fdf3030ff8 Limit matrix sizes for trmm unit test and complexes. 2015-10-30 15:07:50 +01:00
Gael Guennebaud
9285647dfe Limit matrix size when testing for NaN: they can become prohibitively expensive when running on x87 fp unit 2015-10-30 14:44:22 +01:00
Gael Guennebaud
ddaaa2d381 bug #1101: typo 2015-10-30 12:02:52 +01:00
Gael Guennebaud
c8c8821038 Biug 1100: remove explicit CMAKE_INSTALL_PREFIX prefix to please cmake install's DESTINATION argument 2015-10-30 12:00:34 +01:00
Gael Guennebaud
0e6cb08f92 Fix shadow warning 2015-10-30 11:44:22 +01:00
Gael Guennebaud
27c56bf60f Workaround compilation issue with MSVC<=2013 2015-10-30 10:57:11 +01:00
Gael Guennebaud
213bd0253a Fix gcc 4.4 compilation issue 2015-10-30 08:44:37 +01:00
Benoit Steiner
6a02c2a85d Fixed a compilation warning 2015-10-29 20:21:29 -07:00
Benoit Steiner
ca12d4c3b3 Pulled latest updates from trunk 2015-10-29 17:57:48 -07:00
Benoit Steiner
31bdafac67 Added a few tests to cover rank-0 tensors 2015-10-29 17:56:48 -07:00
Benoit Steiner
ce19e38c1f Added support for tensor maps of rank 0. 2015-10-29 17:49:04 -07:00
Benoit Steiner
3785c69294 Added support for fixed sized tensors of rank 0 2015-10-29 17:31:03 -07:00
Benoit Steiner
0d7a23d34e Extended the reduction code so that reducing an empty set returns the neural element for the operation 2015-10-29 17:29:49 -07:00
Benoit Steiner
1b0685d09a Added support for rank-0 tensors 2015-10-29 17:27:38 -07:00
Benoit Steiner
c444a0a8c3 Consistently use the same index type in the fft codebase. 2015-10-29 16:39:47 -07:00
Benoit Steiner
09ea3a7acd Silenced a few more compilation warnings 2015-10-29 16:22:52 -07:00
Benoit Steiner
0974a57910 Silenced compiler warning 2015-10-29 15:00:06 -07:00
Benoit Steiner
ac142773a7 Don't call internal::check_rows_cols_for_overflow twice in PlainObjectBase::resize since this is extremely expensive for small arrays 2015-10-29 13:13:39 -07:00
Gael Guennebaud
05a0ee25df Fix warning. 2015-10-29 21:06:07 +01:00
Gael Guennebaud
7cfbe35e49 Fix duplicated declaration 2015-10-29 21:05:52 +01:00
Gael Guennebaud
568d488a27 Fusion the two similar specialization of Sparse2Dense Assignment.
This change also fixes a compilation issue with MSVC<=2013.
2015-10-29 13:16:15 +01:00
Gael Guennebaud
7a5f83ca60 Add overloads for real times sparse<complex> operations.
This avoids real to complex conversions, and also fixes a compilation issue with MSVC.
2015-10-29 03:55:39 -07:00
Gael Guennebaud
c688cc28d6 fix copy/paste typo 2015-10-28 20:20:05 +01:00
Gael Guennebaud
5b6cff5b0e fix typo 2015-10-28 20:18:00 +01:00
Gael Guennebaud
6759a21e49 CUDA support: define more accurate min/max values for device::numeric_limits of float and double using values from cfloat header 2015-10-28 16:49:15 +01:00
Gael Guennebaud
28ddb5158d Enable std::isfinite/nan/inf on MSVC 2013 and newer and clang. Fix isinf for gcc4.4 and older msvc with fast-math. 2015-10-28 16:27:20 +01:00
Ilya Popov
1a842c0dc4 Fix typo in TutorialSparse: laplace equation contains gradient symbol (\nabla) instead of laplacian (\Delta). 2015-10-28 09:52:55 +00:00
Gael Guennebaud
8531304858 Simplify cost computations based on HugeCost being smaller that unrolling limit 2015-10-28 13:39:02 +01:00
Gael Guennebaud
1f11dd6ced Add a unit test for large chains of products 2015-10-28 12:53:13 +01:00
Gael Guennebaud
902c2db5a5 Extend vectorwiseop unit test with column/row vectors as input. 2015-10-28 11:59:20 +01:00
Gael Guennebaud
77ff3386b7 Refactoring of the cost model:
- Dynamic is now an invalid value
 - introduce a HugeCost constant to be used for runtime-cost values or arbitrarily huge cost
 - add sanity checks for cost values: must be >=0 and not too large
This change provides several benefits:
 - it fixes shortcoming is some cost computation where the Dynamic case was not properly handled.
 - it simplifies cost computation logic, and should avoid future similar shortcomings.
 - it allows to distinguish between different level of dynamic/huge/infinite cost
 - it should enable further simplifications in the computation of costs (save compilation time)
2015-10-28 11:42:14 +01:00
Gael Guennebaud
827d8a9bad Fix false negative in redux test 2015-10-27 21:37:03 +01:00
Gael Guennebaud
d4cf436cb1 Enable mpreal unit test for C++11 compiler only 2015-10-27 17:35:54 +01:00
Gael Guennebaud
946f8850e8 bug #1008: add a unit test for fast-math mode and isinf/isnan/isfinite/etc. functions. 2015-10-27 16:44:45 +01:00
Gael Guennebaud
e3031d7bfa bug #1008: improve handling of fast-math mode for older gcc versions. 2015-10-27 16:43:23 +01:00
Gael Guennebaud
2475a1de48 bug #1008: stabilize isfinite/isinf/isnan/hasNaN/allFinite functions for fast-math mode. 2015-10-27 15:39:50 +01:00
Gael Guennebaud
699c33e76a merge 2015-10-27 11:10:11 +01:00
Gael Guennebaud
8c66b6bc61 Simplify evaluator::Flags for Map<> 2015-10-27 11:06:42 +01:00
Gael Guennebaud
12f50a4697 Fix assign vectorization logic with respect to fixed outer-stride 2015-10-27 11:04:19 +01:00
Gael Guennebaud
c1e0b6dde3 merge 2015-10-27 11:02:03 +01:00
Gael Guennebaud
73f692d16b Fix ambiguous instantiation 2015-10-27 11:01:37 +01:00
Gael Guennebaud
0fc8954282 Improve readibility of EIGEN_DEBUG_ASSIGN mode. 2015-10-27 10:38:49 +01:00
Benoit Steiner
1c8312c811 Started to add support for tensors of rank 0 2015-10-26 14:29:26 -07:00
Benoit Steiner
1f4c98abb1 Fixed compilation warning 2015-10-26 12:42:55 -07:00
Benoit Steiner
9dc236bc83 Fixed compilation warning 2015-10-26 12:41:48 -07:00
Benoit Steiner
9f721384e0 Added support for empty dimensions 2015-10-26 11:21:27 -07:00
Benoit Steiner
ded4336988 Pulled latest updates from trunk 2015-10-26 10:48:29 -07:00
Benoit Steiner
a3e144727c Fixed compilation warning 2015-10-26 10:48:11 -07:00
Benoit Steiner
f8e7b9590d Fixed compilation error triggered by gcc 4.7 2015-10-26 10:47:37 -07:00
Gael Guennebaud
e6f8c5c325 Add support to directly evaluate the product of two sparse matrices within a dense matrix. 2015-10-26 18:20:00 +01:00
Gael Guennebaud
a5324a131f bug #1092: fix iterative solver ctors for expressions as input 2015-10-26 16:16:24 +01:00
Gael Guennebaud
f93654ae16 bug #1098: fix regression introduced when generalizing some compute() methods in changeset 7031a851d4
.
2015-10-26 16:00:25 +01:00
Gael Guennebaud
af2e25d482 Merged in infinitei/eigen (pull request PR-140)
bug #1097 Added ArpackSupport to cmake install target
2015-10-26 15:31:39 +01:00
Gael Guennebaud
4704bdc9c0 Make the IterativeLinearSolvers module compatible with MPL2-only mode
by defaulting to COLAMDOrdering and NaturalOrdering for ILUT and ILLT respectively.
2015-10-26 15:17:52 +01:00
Gael Guennebaud
47d44c2f37 Add missing licence header to some top header files 2015-10-26 11:46:05 +01:00
Gael Guennebaud
8a211bb1a9 bug #1088: fix setIdenity for non-compressed sparse-matrix 2015-10-25 22:01:58 +01:00
Gael Guennebaud
ac6b2266b9 Fix SparseMatrix::insert/coeffRef for non-empty compressed matrix 2015-10-25 22:00:38 +01:00
Abhijit Kundu
0ed41bdefa ArpackSupport was missing here also. 2015-10-16 18:21:02 -07:00
Abhijit Kundu
1127ca8586 Added ArpackSupport to cmake install target 2015-10-16 16:41:33 -07:00
Gael Guennebaud
e99279f444 merge 2015-10-16 22:12:54 +02:00
Benoit Steiner
de1e9f29f4 Updated the custom indexing code: we can now use any container that provides the [] operator to index a tensor. Added unit tests to validate the use of std::map and a few more types as valid custom index containers 2015-10-15 14:58:49 -07:00
Benoit Steiner
6585efc553 Tightened the definition of isOfNormalIndex to take into account integer types in addition to arrays of indices
Only compile the custom index code  when EIGEN_HAS_SFINAE is defined. For the time beeing, EIGEN_HAS_SFINAE is a synonym for EIGEN_HAS_VARIADIC_TEMPLATES, but this might evolve in the future.
Moved some code around.
2015-10-14 09:31:37 -07:00
Gael Guennebaud
c0adf6e38d Fix perm*sparse return type and nesting, and add several sanity checks for perm*sparse 2015-10-14 10:16:48 +02:00
Gael Guennebaud
527fc4bc86 Fix ambiguous instantiation issues of product_evaluator. 2015-10-14 10:14:47 +02:00
Gael Guennebaud
2598f3987e Add a plain_object_eval<> helper returning a plain object type based on evaluator's Flags,
and base nested_eval on it.
2015-10-14 10:12:58 +02:00
Gael Guennebaud
b4c79ee1d3 Update custom setFromTripplets API to allow passing a functor object, and add a collapseDuplicates method to cleanup the API. Also add respective unit test 2015-10-13 11:30:41 +02:00
Gabriel Nützi
fc7478c04d name changes 2
user: Gabriel Nützi <gnuetzi@gmx.ch>
branch 'default'
changed unsupported/Eigen/CXX11/src/Tensor/Tensor.h
changed unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h
2015-10-09 19:10:08 +02:00
Gabriel Nützi
7b34834f64 name changes
user: Gabriel Nützi <gnuetzi@gmx.ch>
branch 'default'
changed unsupported/Eigen/CXX11/src/Tensor/Tensor.h
2015-10-09 19:08:14 +02:00
Gabriel Nützi
6edae2d30d added CustomIndex capability only to Tensor and not yet to TensorBase.
using Sfinae and is_base_of to select correct template which converts to array<Index,NumIndices>


 user: Gabriel Nützi <gnuetzi@gmx.ch>
 branch 'default'
 added unsupported/Eigen/CXX11/src/Tensor/TensorMetaMacros.h
 added unsupported/test/cxx11_tensor_customIndex.cpp
 changed unsupported/Eigen/CXX11/Tensor
 changed unsupported/Eigen/CXX11/src/Tensor/Tensor.h
 changed unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h
 changed unsupported/test/CMakeLists.txt
2015-10-09 18:52:48 +02:00
Calixte Denizet
b9d81c9150 Add a functor to setFromTriplets to handle duplicated entries 2015-10-06 13:29:41 +02:00
Gael Guennebaud
9acfc7c4f3 remove reference to internal method 2015-10-13 10:55:58 +02:00
Gael Guennebaud
a44d91a0b2 extend unit test for SparseMatrix::prune 2015-10-13 10:53:38 +02:00
Gael Guennebaud
ac22b66f1c Fix macro issues 2015-10-13 10:18:09 +02:00
Gael Guennebaud
3e32f6b554 update mpreal.h 2015-10-13 09:58:54 +02:00
Gael Guennebaud
ea9749fd6c Fix packetmath unit test for pdiv not being always defined 2015-10-13 09:53:46 +02:00
Gael Guennebaud
252e89b11b bug #1086: replace deprecated UF_long by SuiteSparse_long 2015-10-12 16:20:12 +02:00
Gael Guennebaud
6407e367ee Add missing epxlicit keyword, and fix regression in DynamicSparseMatrix 2015-10-12 09:49:05 +02:00
Gael Guennebaud
63e29e7765 Workaround ICC issue with first_aligned 2015-10-11 22:47:28 +02:00
Gael Guennebaud
6163db814c bug #1085: workaround gcc default ABI issue 2015-10-10 22:38:55 +02:00
Gael Guennebaud
6536b4bad7 Implement temporary-free path for "D.nolias() ?= C + A*B". (I thought it was already implemented) 2015-10-09 15:28:09 +02:00
Gael Guennebaud
a4cc4c1e5e Clarify note in nested_eval for evaluator creating temporaries. 2015-10-09 14:57:51 +02:00
Gael Guennebaud
ae38910693 The evalautor of Solve was missing the EvalBeforeNestingBit flag. 2015-10-09 14:57:19 +02:00
Gael Guennebaud
515ecddb97 Add unit test for nested_eval 2015-10-09 14:29:46 +02:00
Gael Guennebaud
78b8c344b5 Add unit test for CoeffReadCost 2015-10-09 14:28:48 +02:00
Gael Guennebaud
321cb56bf6 Add unit test to check nesting of complex expressions in redux() 2015-10-09 13:29:39 +02:00
Gael Guennebaud
2632b3446c Improve documentation of TriangularView. 2015-10-09 12:10:58 +02:00
Gael Guennebaud
1429daf850 Add lvalue check for TriangularView::swap, and fix deprecated TriangularView::lazyAssign 2015-10-09 12:10:48 +02:00
Gael Guennebaud
72bd05b6d8 Cleaning in Redux.h 2015-10-09 12:07:42 +02:00
Gael Guennebaud
2c516ba38f Remove auto references and referenced-by relation in doc. 2015-10-09 12:07:06 +02:00
Gael Guennebaud
041e038fef Remove dead code in selfadjoint_matrix_vector_product 2015-10-09 10:42:14 +02:00
Gael Guennebaud
c2d68b984f Optimize a bit complex selfadjoint * vector product. 2015-10-09 10:34:58 +02:00
Gael Guennebaud
1932a24760 Simplify EIGEN_DENSE_PUBLIC_INTERFACE 2015-10-09 10:21:54 +02:00
Gael Guennebaud
186ec1437c Cleanup EIGEN_SPARSE_PUBLIC_INTERFACE, it is now a simple alias to EIGEN_GENERIC_PUBLIC_INTERFACE 2015-10-08 22:06:49 +02:00
Gael Guennebaud
c9718514f5 Fix nesting sub-expression in outer-products 2015-10-08 21:41:53 +02:00
Gael Guennebaud
4140ee039d Fix propagation of AssumeAliasing for expression as: "scalar * (A*B)" 2015-10-08 21:41:27 +02:00
Gael Guennebaud
d866279364 Clean a bit the implementation of inverse permutations 2015-10-08 18:36:39 +02:00
Gael Guennebaud
8d00a953af Fix a nesting issue in some matrix-vector cases. 2015-10-08 17:36:57 +02:00
Gael Guennebaud
dd934ad057 Re-enable vectorization of LinSpaced, plus some cleaning 2015-10-08 17:27:01 +02:00
Gael Guennebaud
f6f6f50272 Clean evaluator<EvalToTemp> 2015-10-08 16:34:33 +02:00
Gael Guennebaud
67bfba07fd Fix some CUDA issues 2015-10-08 16:30:28 +02:00
Gael Guennebaud
412c049ba4 Fix a warning 2015-10-08 16:27:54 +02:00
Gael Guennebaud
aa6b1aebf3 Properly implement PartialReduxExpr on top of evaluators, and fix multiple evaluation of nested expression 2015-10-08 15:57:05 +02:00
Gael Guennebaud
5cc7251188 Some cleaning in evaluators 2015-10-08 15:22:04 +02:00
Gael Guennebaud
e30bc89190 Add missing include of std vector 2015-10-08 15:20:50 +02:00
Gael Guennebaud
5d7ebfb275 Update sparse solver list to make it more complete 2015-10-08 11:33:17 +02:00
Gael Guennebaud
1b148d9e2e Move IncompleteCholesky to official modules 2015-10-08 11:32:46 +02:00
Gael Guennebaud
632e7705b1 Improve doc of IncompleteCholesky 2015-10-08 10:54:36 +02:00
Gael Guennebaud
64242b8bf3 Doc: add link to doc of sparse solver concept 2015-10-08 10:50:39 +02:00
Gael Guennebaud
131db3c552 Fix return by value versus ref typo in IncompleteCholesky 2015-10-07 16:37:46 +02:00
Gael Guennebaud
13294b5152 Unify gemm and lazy_gemm benchmarks 2015-10-07 16:06:48 +02:00
Gael Guennebaud
247259f805 Add a perfromance regression benchmark for lazyProduct 2015-10-07 15:51:06 +02:00
Gael Guennebaud
c6eb17cbe9 Add helper routines to help bypassing some compiler otpimization when benchmarking 2015-10-07 15:50:42 +02:00
Gael Guennebaud
f047ecc36a _mm_hadd_epi32 is for SSSE3 only (and not SSE3) 2015-10-07 15:48:35 +02:00
Gael Guennebaud
aba1eda71e Help clang to inline some functions, thus fixing some regressions 2015-10-07 15:44:12 +02:00
Gael Guennebaud
41cc1f9033 Remove debuging prod() and lazyprod() function, plus some cleaning in noalias assignment 2015-10-07 15:41:22 +02:00
Gael Guennebaud
ca0dd7ae26 Fix implicit cast in unit test 2015-10-07 15:36:12 +02:00
Gael Guennebaud
8bb51a87f7 Re-enable some invalid scalar type conversion checks by disabling explicit vectorization 2015-10-06 17:24:01 +02:00
Gael Guennebaud
27a94299aa Add sparse vector to Ref<SparseMatrix> conversion unit tests, and improve output of sparse_ref unit test in case of failure. 2015-10-06 17:23:11 +02:00
Gael Guennebaud
2e0ece7b66 Fix wrong casting syntax 2015-10-06 17:22:12 +02:00
Gael Guennebaud
69a7897e72 Fix storage index type in empty permutations 2015-10-06 17:21:24 +02:00
Gael Guennebaud
26cde4db3c Define Permutation*<>::Scalar to 'void', re-enable scalar type compatibility check in assignment while relaxing this test for void types. 2015-10-06 17:18:06 +02:00
Gael Guennebaud
fb51bab272 Some cleaning 2015-10-06 17:14:56 +02:00
Gael Guennebaud
2c676ddb40 Handle various TODOs in SSE vectorization (remove splitted storeu, enable SSE3 integer vectorization, plus minor tweaks) 2015-10-06 15:43:27 +02:00
Gael Guennebaud
2d287a4898 Fix Ref<SparseMatrix> for Transpose<SparseVector> 2015-10-06 15:09:04 +02:00
Gael Guennebaud
752a0e5339 bug #1076: fix scaling in IncompleteCholesky, improve doc, add read-only access to the different factors, remove debugging code. 2015-10-06 13:25:45 +02:00
Gael Guennebaud
f25bdc707f Optimise assignment into a Block<SparseMatrix> by using Ref and avoiding useless updates in non-compressed mode. This make row-by-row filling of a row-major sparse matrix very efficient. 2015-10-06 11:59:08 +02:00
Gael Guennebaud
945b80c83e Optimize Ref<SparseMatrix> by removing useless default initialisation of SparseMapBase and SparseMatrix 2015-10-06 11:57:03 +02:00
Gael Guennebaud
9a070638de Enable to view a SparseVector as a Ref<SparseMatrix> 2015-10-06 11:53:19 +02:00
Gael Guennebaud
1b43860bc1 Make SparseVector derive from SparseCompressedBase, thus improving compatibility between sparse vectors and matrices 2015-10-06 11:41:03 +02:00
Gael Guennebaud
6100d1ae64 Improve counting of sparse temporaries 2015-10-06 11:32:02 +02:00
Gael Guennebaud
1879917d35 Propagate cmake generator 2015-10-05 16:18:22 +02:00
Gael Guennebaud
deb261f64b Make abs2 compatible with custom complex types 2015-10-02 10:33:25 +02:00
nnyby
ccc7b0ffea [doc] grammar fix: "linearly space" -> "linearly spaced" 2015-10-01 23:43:06 +00:00
Gael Guennebaud
75a60d3ac0 bug #1075: fix AlignedBox::sample for runtime dimension 2015-09-30 11:44:02 +02:00
Gael Guennebaud
9136b95219 Merged in doug_kwan/eigen (pull request PR-137)
Specified signedness of char type in test
2015-09-30 11:37:04 +02:00
Gael Guennebaud
781e8c38bd merge 2015-09-29 11:12:43 +02:00
Gael Guennebaud
b2b8c1d41e Fix performance regression in sparse * dense product where "sparse" is an expression 2015-09-29 11:11:40 +02:00
Doug Kwan
239c9946cd Specified signedness of char type in test so that test passes
consistently on different targets.
2015-09-28 14:26:10 -07:00
Benoit Steiner
d46bacb6bb Call numext::mini instead of std::min in several places. 2015-09-28 10:40:41 -07:00
Gael Guennebaud
ceafed519f Add support for permutation * homogenous 2015-09-28 16:56:11 +02:00
Gael Guennebaud
ddb5650530 bug #1070: propagate last three Matrix template arguments for NumTraits<AutoDiffScalar<>>::Real 2015-09-28 15:07:03 +02:00
Gael Guennebaud
02e940fc9f bug #1071: improve doc on lpNorm and add example for some operator norms 2015-09-28 11:55:36 +02:00
Gael Guennebaud
8c1ee3629f Add support for row/col-wise lpNorm() 2015-09-28 11:36:00 +02:00
Gael Guennebaud
75861f6650 bug #1069: fix AVX support on MSVC (use of non portable C-style cast) 2015-09-28 10:08:26 +02:00
Tal Hadad
5e0a178df2 Initial fork of unsupported module EulerAngles. 2015-09-27 16:51:24 +03:00
Gael Guennebaud
d16797cfc0 Fix bug #1067: naming conflict 2015-09-19 21:44:14 +02:00
Benoit Steiner
13aee4463e Cleaned up a test 2015-09-18 09:42:08 -07:00
Benoit Steiner
58a6453d48 Fixed compilation warning 2015-09-17 10:18:49 -07:00
Benoit Steiner
31afdcb4c2 Fix return type for TensorEvaluator<TensorSlicingOp>::data 2015-09-17 09:40:21 -07:00
Gael Guennebaud
9d993c709b Fix typo in Vectowise::any() 2015-09-16 22:31:19 +02:00
Christoph Hertzberg
43ba07d4d7 Merged in daalpa/eigen/daalpa/removed-documentation-that-did-not-match-1442148941751 (pull request PR-136)
Removed documentation that did not match the member function DenseBase::outerSize()
2015-09-13 16:35:32 +02:00
daalpa
fab96f2ff3 Removed documentation that did not match the member function DenseBase::outerSize() 2015-09-13 12:55:57 +00:00
Christoph Hertzberg
d6f762d955 Fixed cuda code: EIGEN_DEVICE_FUNC must come after template<...> 2015-09-10 11:46:27 +02:00
Gael Guennebaud
680d318352 Add unit tests for bug #981: valid and invalid usage of ternary operator 2015-09-09 11:38:25 +02:00
Benoit Steiner
84e0c27b61 Fixed a compilation warning 2015-09-08 17:05:35 -07:00
Benoit Steiner
05f2f94f2b Fixed a compilation warning 2015-09-08 17:04:03 -07:00
Benoit Steiner
98f8f0db9a Added support for predux_mul for CUDA devices 2015-09-08 15:37:25 -07:00
Christoph Hertzberg
e3f69eb60d Fixed minor regression caused by 7031a851d4 2015-09-08 10:53:10 +02:00
Gael Guennebaud
5bf971e5b8 MKL is now free of charge for opensource 2015-09-07 11:23:55 +02:00
Gael Guennebaud
73a86cfcd3 Add EIGEN_QUATERNION_PLUGIN 2015-09-07 11:12:30 +02:00
Gael Guennebaud
7fad309631 Fix link and code formating 2015-09-07 11:08:41 +02:00
Gael Guennebaud
7031a851d4 Generalize matrix ctor and compute() method of dense decomposition to 1) limit temporaries, 2) forward expressions to nested decompositions, 3) fix ambiguous ctor instanciation for square decomposition 2015-09-07 10:42:04 +02:00
Gael Guennebaud
1702fcb72e Added tag 3.3-alpha1 for changeset f9303cc7c5 2015-09-04 17:27:20 +02:00
Gael Guennebaud
f9303cc7c5 bump to 3.3-alpha1 2015-09-04 17:26:36 +02:00
Gael Guennebaud
b20a55a608 Workaround wrong instanciation made by VS2010 2015-09-04 15:25:58 +02:00
Gael Guennebaud
ed265258e4 Fix returned index type of inner iterators of sparse blocks. 2015-09-03 15:07:35 +02:00
Gael Guennebaud
a835dfca73 InnerIterator::index() should really return a StorageIndex 2015-09-03 14:53:51 +02:00
Gael Guennebaud
941a99ac1a Add a few missing EIGEN_DEVICE_FUNC declarations 2015-09-03 14:14:54 +02:00
Gael Guennebaud
d91db41a31 Fix documentation example 2015-09-03 14:14:14 +02:00
Gael Guennebaud
3942db9d7c Use inline versus static free functions. 2015-09-03 13:42:54 +02:00
Sergiu Dotenco
85afb61417 use explicit Scalar types for AngleAxis initialization
(grafted from 89a222ce50
)
2015-08-28 22:20:15 +02:00
Benoit Steiner
56983f6d43 Fixed compilation warning 2015-10-23 12:03:42 -07:00
Benoit Steiner
57857775b4 Added support for arrays of size 0 2015-10-23 10:20:51 -07:00
Benoit Steiner
c40c2ceb27 Reordered the code of fft constructor to prevent compilation warnings 2015-10-23 09:38:19 -07:00
Benoit Steiner
a586fdaa91 Reworked the tensor contraction mapper code to make it compile on Android 2015-10-23 09:33:41 -07:00
Benoit Steiner
29c3b7513e Pulled latest updates from trunk 2015-10-23 09:16:14 -07:00
Benoit Steiner
9ea39ce13c Refined the #ifdef __CUDACC__ guard to ensure that when trying to compile gpu code with a non cuda compiler results in a linking error instead of bogus code. 2015-10-23 09:15:34 -07:00
Gael Guennebaud
c244081490 disable usage of INTMAX_T 2015-10-23 14:48:54 +02:00
Gael Guennebaud
0905ed5390 remove useless cstdint header 2015-10-23 14:41:25 +02:00
Gael Guennebaud
54b23cce16 Switch to MPL2 2015-10-23 10:36:33 +02:00
Benoit Steiner
ac99b49249 Added missing glue logic 2015-10-22 16:54:21 -07:00
Benoit Steiner
2dd9446613 Added mapping between a specific device and the corresponding packet type 2015-10-22 16:53:36 -07:00
Benoit Steiner
2495e2479f Added tests for the fft code 2015-10-22 16:52:55 -07:00
Benoit Steiner
a147c62998 Added support for fourier transforms (code courtesy of thucjw@gmail.com) 2015-10-22 16:51:30 -07:00
Gael Guennebaud
71b473aab1 Remove invalid typename keyword 2015-10-22 21:58:18 +02:00
Gael Guennebaud
ebc1af1683 merge 2015-10-22 21:47:47 +02:00
Benoit Steiner
825146c8fd Fixed incorrect expected value 2015-10-22 11:56:00 -07:00
Benoit Steiner
4cf7da63de Added a constructor to simplify the construction of tensormap from tensor 2015-10-22 11:48:02 -07:00
Gael Guennebaud
0eb46508e2 Avoid any openmp calls if multi-threading is explicitely disabled at runtime. 2015-10-22 16:30:28 +02:00
Gael Guennebaud
6df8e99470 bug #1089: add a warning when using a MatrixBase method which is implemented within another module by declaring them inline. 2015-10-22 16:10:28 +02:00
Gael Guennebaud
e78bc111f1 bug #1090: fix a shortcoming in redux logic for which slice-vectorization plus unrolling might happen. 2015-10-21 20:58:33 +02:00
Benoit Steiner
b178cc3479 Added some syntactic sugar to make it simpler to compare a tensor to a scalar. 2015-10-21 11:28:28 -07:00
Gael Guennebaud
5ca2e25967 merge 2015-10-21 13:49:13 +02:00
Gael Guennebaud
8afd0ce955 add FIXME 2015-10-21 13:48:15 +02:00
Gael Guennebaud
8961265889 bug #1064: add support for Ref<SparseVector> 2015-10-21 09:47:43 +02:00
Benoit Steiner
0af63493fd Disable SFINAE for versions of gcc older than 4.8 2015-10-20 11:53:30 -07:00
Benoit Steiner
73b8e719ae Removed bogus assertion 2015-10-20 11:42:34 -07:00
Benoit Steiner
eaf4b98180 Added support for boolean reductions (ie 'and' & 'or' reductions) 2015-10-20 11:41:22 -07:00
Benoit Steiner
f5c1587e4e Fixed a bug in the tensor conversion op 2015-10-20 11:37:44 -07:00
Gael Guennebaud
fe630c9873 Improve numerical accuracy in LLT and triangular solve by using true scalar divisions (instead of x * (1/y)) 2015-10-18 22:15:01 +02:00
Doug Kwan
5c9ee73eb9 Implement plog and pexp for AltiVec. 2015-07-30 11:12:42 -07:00
Gael Guennebaud
5a1cc5d24c bug #1053: fix SuplerLU::solve with EIGEN_DEFAULT_TO_ROW_MAJOR 2015-09-03 11:25:36 +02:00
Gael Guennebaud
2795ffd6a0 Fix Index vs StorageIndex naming convention 2015-09-03 11:18:27 +02:00
Gael Guennebaud
ef2b54f422 Fix AMD ordering when a column has only one off-diagonal non-zero (also fix bug #1045) 2015-09-03 11:04:06 +02:00
Christoph Hertzberg
5ad7981f73 Use full packet size for Dynamic-sized objects (otherwise, the unalignedcount unit test fails with AVX enabled) 2015-09-02 22:51:43 +02:00
Gael Guennebaud
aa768add0b Since there is no reason for evaluators to be nested by reference, let's remove the evaluator<>::nestedType indirection. 2015-09-02 22:10:39 +02:00
Gael Guennebaud
51455824ea Fix AlignedVector3 wrt previous change 2015-09-02 21:51:58 +02:00
Gael Guennebaud
f8976fdbe0 Make evaluators non-copyable. This guarantee that evaluators storing temporaries do not introduce unwanted copy overhead. 2015-09-02 21:39:49 +02:00
Gael Guennebaud
92b9f0e102 Cleaning pass on evaluators: remove the useless and error prone evaluator<>::type indirection. 2015-09-02 21:38:40 +02:00
Gael Guennebaud
cda55ab245 Fix compilation of cuda unit test 2015-09-02 16:59:07 +02:00
Gael Guennebaud
14458ec0a0 Fix packetmath unit test for exp and log 2015-09-02 15:47:58 +02:00
Gael Guennebaud
6b99afa5ae Fix LSCG::solve with a sparse destination. 2015-09-02 15:34:03 +02:00
Gael Guennebaud
b5ad3d2cf7 Remove deprecated Flagged expression. 2015-09-02 14:53:50 +02:00
Gael Guennebaud
6522c3a6f0 Add regression test for bug #817 2015-09-02 13:16:03 +02:00
Gael Guennebaud
be5e2ecc21 bug #505: add more examples of bad and correct usages of auto and eval(). 2015-09-02 13:04:30 +02:00
Gael Guennebaud
aba8c9ee17 Add a documentation page for common pitfalls 2015-09-02 11:23:55 +02:00
Gael Guennebaud
a75616887e bug #1057: fix a declaration missmatch with MSVC 2015-09-02 09:31:32 +02:00
Gael Guennebaud
280f93ff65 Fix FullPivLU::image documentation 2015-09-02 09:19:27 +02:00
Gael Guennebaud
6059188f9d Simplify implementation of the evaluation's iterator of Sparse*Diagonal products to help the compiler to generate better code. 2015-09-01 22:34:30 +02:00
Gael Guennebaud
0b2412df50 Remove duplicated temporary in Sparse to Sparse assignment 2015-09-01 22:31:30 +02:00
Gael Guennebaud
9001f4a46b Add missing specialization of evaluator of sub-sparse-matrices that can be seen as a SparseCompressedBase. This changeset enable faster iterator for such expressions. 2015-09-01 22:29:17 +02:00
Benoit Steiner
f41831e445 Added support for argmax/argmin 2015-08-31 08:18:53 -07:00
Benoit Steiner
2ab603316a Use numext::mini/numext::maxi instead of std::min/std::max in the tensor code 2015-08-28 08:14:15 -07:00
Benoit Steiner
2ed1495eec nvcc doesn't support std::min or std::max on GPU. Use our own custom implementation instead 2015-08-27 16:59:55 -07:00
Sergiu Dotenco
d4c24eb016 fixed Quaternion identity initialization for non-implicitly convertible types 2015-08-20 20:55:37 +02:00
Christoph Hertzberg
78358a7241 Fixed broken commit a09cfe650f
. Missing } and unprotected min/max calls and definitions.
2015-08-22 15:03:16 +02:00
Benoit Steiner
a09cfe650f std::numeric_limits doesn't work reliably on CUDA devices. Use our own definition of numeric_limit<T>::max() and numeric_limit<T>::min() instead of the stl ones. 2015-08-21 16:01:40 -07:00
Christoph Hertzberg
e5c78d85c8 bug #1043: Avoid integer conversion sign warning 2015-08-19 21:50:21 +02:00
Christoph Hertzberg
1bdd06a199 Fix some trivial warnings 2015-08-19 21:38:18 +02:00
Christoph Hertzberg
0721690dbb Use standard include syntax in Tensor module (<> for include-path and "" for relative path) 2015-08-18 14:34:00 +02:00
Christoph Hertzberg
8097d8d028 surpress some warnings 2015-08-17 21:50:52 +02:00
Christoph Hertzberg
d2e0927127 Define EIGEN_MAX_STATIC_ALIGN_BYTES to 0 for architectures that don't require stack alignment 2015-08-17 16:44:52 +02:00
Gael Guennebaud
dc2c103b3b merge 2015-08-16 14:22:02 +02:00
Christoph Hertzberg
d6a4805fdf Protect further isnan/isfinite/isinf calls 2015-08-16 14:00:02 +02:00
Christoph Hertzberg
a40f6ab276 Merged in ITimer/eigen (pull request PR-133)
[Doc] Fix a spelling error in TopicMultithreading.dox
2015-08-14 17:46:57 +02:00
Christoph Hertzberg
61e0977e10 Protect all calls to isnan, isinf and isfinite with parentheses. 2015-08-14 17:32:34 +02:00
Christoph Hertzberg
712e2fed17 bug #829: Introduce macro EIGEN_HAS_CXX11_CONTAINERS and do not specialize std-containers if it is enabled. 2015-08-14 16:09:48 +02:00
Christoph Hertzberg
a5d1bb2be8 bug #1054: Use set(EIGEN_CXX_FLAG_VERSION "/version") only for Intel compilers on Windows.
Also removed code calling `head -n1` and always use integrated REGEX functionality.
2015-08-14 15:30:59 +02:00
ITimer
93635cafee Fixed a spelling error 2015-08-10 15:11:10 +08:00
Gael Guennebaud
23aab82c0c merge 2015-08-09 21:24:20 +02:00
Gael Guennebaud
0d5e673baa Fix Tensor module wrt nullary functor recent change 2015-08-09 21:20:24 +02:00
Christoph Hertzberg
cac6b23033 bug #1053: SparseLU failed with EIGEN_DEFAULT_TO_ROW_MAJOR 2015-08-07 23:10:56 +02:00
Gael Guennebaud
febcce34f1 Enable vectorization with half-packets 2015-08-07 20:05:31 +02:00
Gael Guennebaud
6245591349 Fix prototype of plset and generalize linspace functor. 2015-08-07 19:27:59 +02:00
Gael Guennebaud
60e4260d0d Some functors were not generic wrt packet-type. 2015-08-07 17:41:39 +02:00
Gael Guennebaud
e68c7b8368 Include SSE packetmath when AVX is enabled, and enable AVX's sine function only in fast-math mode (as SSE) 2015-08-07 17:40:39 +02:00
Gael Guennebaud
65bfa5fce7 Allow to use arbitrary packet-types during evaluation.
This is implemented by adding a PacketType template parameter to packet and writePacket members of evaluator<>.
2015-08-07 12:01:39 +02:00
Gael Guennebaud
3602926ed5 Mark ALignedBit as deprecated. 2015-08-07 10:45:02 +02:00
Gael Guennebaud
ce57dbd937 Let unpacket_traits<> exposes the required alignment and make use of it everywhere 2015-08-07 10:44:01 +02:00
Gael Guennebaud
2afdef6a54 Generalize first_aligned to take the requested alignment as a template parameter, and add a first_default_aligned variante calling first_aligned with the requirement of the largest packet for the given scalar type. 2015-08-06 17:52:01 +02:00
Gael Guennebaud
1f5024332e First part of a big refactoring of alignment control to enable the handling of arbitrarily aligned buffers. It includes:
- AlignedBit flag is deprecated. Alignment is now specified by the evaluator through the 'Alignment' enum, e.g., evaluator<Xpr>::Alignment. Its value is in Bytes.
 - Add several enums to specify alignment: Aligned8, Aligned16, Aligned32, Aligned64, Aligned128. AlignedMax corresponds to EIGEN_MAX_ALIGN_BYTES. Such enums are used to define the above Alignment value, and as the 'Options' template parameter of Map<> and Ref<>.
 - The Aligned enum is now deprecated. It is now an alias for Aligned16.
 - Currently, traits<Matrix<>>, traits<Array<>>, traits<Ref<>>, traits<Map<>>, and traits<Block<>> also expose the Alignment enum.
2015-08-06 15:31:07 +02:00
Gael Guennebaud
65186ef18d Fix logic in compute_default_alignment, extend it to Dynamic size, and move it to XprHelper.h file. 2015-08-06 14:07:59 +02:00
Gael Guennebaud
becd89df29 Enable runtime stack alignment in gemm_blocking_space. 2015-08-06 14:00:26 +02:00
Gael Guennebaud
d4f5efc51a Add a EIGEN_DEFAULT_ALIGN_BYTES macro defining default alignment for alloca and aligned_malloc.
It is defined as the max of EIGEN_IDEAL_MAX_ALIGN_BYTES and EIGEN_MAX_ALIGN_BYTES
2015-08-06 13:56:53 +02:00
Gael Guennebaud
7e0d7a76b8 Remove dense nested loops in IncompleteCholesky 2015-08-04 18:01:38 +02:00
Gael Guennebaud
e31fc50280 Numerous fixes for IncompleteCholesky. Still have to make it fully exploit the sparse structure of the L factor, and improve robustness to illconditionned problems. 2015-08-04 16:16:02 +02:00
Gael Guennebaud
9a4713e505 Add a unit test for IncompleteCholesky 2015-08-04 16:14:06 +02:00
Gael Guennebaud
506964fc29 Propagate precondition info to the iterative solver. 2015-08-04 16:13:34 +02:00
Gael Guennebaud
db0f5c9d90 Fix conversion warning 2015-08-04 16:12:44 +02:00
Gael Guennebaud
b986c147cd Fix ForceNonZeroDiag for complexes 2015-08-04 16:12:16 +02:00
Benoit Steiner
cbce0e3b12 Fixed compilation warning 2015-08-03 21:52:29 -07:00
Benoit Steiner
a5dc49e7e8 Fixed 2 compilation warnings generated by llvm 2015-07-29 15:06:08 -07:00
Benoit Steiner
e1d28b7ea7 Added a test for shuffling 2015-07-29 15:01:21 -07:00
Benoit Steiner
0570594f2c Fixed a few compilation warnings triggered by clang 2015-07-29 11:48:38 -07:00
Benoit Steiner
099597406f Simplified and generalized the DividerTraits code 2015-07-29 10:02:42 -07:00
Gael Guennebaud
6db3a557f4 Add missing specialization of struct DividerTraits<long> 2015-07-29 11:38:53 +02:00
Gael Guennebaud
aec4814370 Many files were missing in previous changeset. 2015-07-29 11:11:23 +02:00
Gael Guennebaud
f7d5b9323d typo 2015-07-29 11:08:49 +02:00
Gael Guennebaud
175ed636ea bug #973: update macro-level control of alignement by introducing user-controllable EIGEN_MAX_ALIGN_BYTES and EIGEN_MAX_STATIC_ALIGN_BYTES macros. This changeset also removes EIGEN_ALIGN (replaced by EIGEN_MAX_ALIGN_BYTES>0), EIGEN_ALIGN_STATICALLY (replaced by EIGEN_MAX_STATIC_ALIGN_BYTES>0), EIGEN_USER_ALIGN*, EIGEN_ALIGN_DEFAULT (replaced by EIGEN_ALIGN_MAX). 2015-07-29 10:22:25 +02:00
Gael Guennebaud
76874b128e bug #1047: document the structure layout of class Matrix 2015-07-29 10:21:28 +02:00
Gael Guennebaud
41e1f3498c bug #1048: fix unused variable warning 2015-07-28 22:59:50 +02:00
Benoit Steiner
b9db19aec4 Pulled latest updates from trunk. 2015-07-27 09:39:57 -07:00
Benoit Steiner
f84417d97b Removed an incorrect assertion. 2015-07-27 09:25:22 -07:00
Benoit Steiner
1a30a8e7a2 Merged in godeffroy/eigen_tensor_generalized_contraction (pull request PR-130)
Allowed tensor contraction operation with an empty array of dimension pairs, which performs a tensor product.
2015-07-27 09:19:35 -07:00
Christoph Hertzberg
a44d022caf bug #792: SparseLU::factorize failed for structurally rank deficient matrices 2015-07-26 20:30:30 +02:00
Godeffroy Valet
2195822df6 Allowed tensor contraction operation with an empty array of dimension pairs, which performs a tensor product. 2015-07-25 11:58:36 +02:00
Benoit Steiner
f6282e451a Fixed a typo in an assertion. 2015-07-24 17:35:47 -07:00
Benoit Steiner
4b3052c54d Pulled latest update from trunk 2015-07-23 08:47:33 -07:00
Benoit Steiner
a446020b78 Reenable 2 tests previously disabled by mistake 2015-07-23 08:47:00 -07:00
Christoph Hertzberg
3d951df223 Re-enabled unit tests which were disabled in commit 4200bdec24
.
2015-07-23 10:55:03 +02:00
Benoit Steiner
6d6e6d0b88 Define EIGEN_VECTORIZE_AVX2 and EIGEN_VECTORIZE_FMA when the corresponding instructions can be used by the compiler 2015-07-22 18:22:16 -07:00
Benoit Steiner
ce65c2922a Pulled latest updates from trunk 2015-07-22 18:12:16 -07:00
Benoit Steiner
4200bdec24 Extended the range of value inputs for TensorIntDiv to support tensors with more than 4 billion elements. 2015-07-22 17:02:30 -07:00
Gael Guennebaud
3b0ad02c10 Remove wrongly pushed debugging statements 2015-07-22 14:33:57 +02:00
Jonas Adler
815fa0dbf6 Fixed some compiler bugs in NVCC, now compiles with CUDA.
(chtz: Manually joined sevaral commits to keep the history clean)
2015-07-22 12:29:18 +02:00
Benoit Steiner
d259b719d1 Made sure that the use const expressions are not enabled when compiling with nvcc even when gcc 4.9 is used as the host compiler. 2015-07-21 17:35:58 -07:00
Benoit Steiner
0dda72316f The eigen_check macro doesn't exist anymore: use assert instead 2015-07-21 17:34:15 -07:00
Gael Guennebaud
586d10f7e0 Fix compilation of tri(sparse) * dense with OpenMP 2015-07-21 22:52:21 +02:00
Gael Guennebaud
d3e5db9a80 add regression unit test for previous changeset 2015-07-21 22:23:17 +02:00
Valentin Roussellet
5e635f9ca1 AlignedVector3 accepts implicit conversions from more operators. 2015-07-21 16:42:52 +00:00
Gael Guennebaud
45ee14a13a Fix output of relative error, and add more support for long double 2015-07-21 22:22:12 +02:00
Gael Guennebaud
87f3e533f5 bug #1036: implement verify_is_approx_upto_permutation through a combinatorial search.
The previous implementation was subject to numerical cancellation issues.
2015-07-20 15:34:06 +02:00
Gael Guennebaud
ab8b497a7e Add pow(scalar,array) in quick ref 2015-07-20 13:59:21 +02:00
Gael Guennebaud
6544b49e59 Generalize pow(x,e) such that x and e can be a different expression type or a scalar for either x or e. Add x.pow(e) with e an array expression. 2015-07-20 13:57:55 +02:00
Gael Guennebaud
2d93060291 Fix trivial warnings. 2015-07-20 13:55:48 +02:00
Gael Guennebaud
c11971de37 Fix compilation of isnan(complex) 2015-07-20 12:56:01 +02:00
Gael Guennebaud
88e352adac Add support for replicate in CUDA 2015-07-20 10:53:03 +02:00
Benoit Steiner
6799c26cd6 Fixed a typo in a test and a compilation warning 2015-07-17 16:50:47 -07:00
Benoit Steiner
7a39439904 Rewrote Eigen::dimensions_match to prevent a static assertion when the rank of the tensors is different. 2015-07-17 16:46:30 -07:00
Benoit Steiner
e94f9eb637 Fixed a const correctness issue in TensorLayoutSwap 2015-07-17 15:44:26 -07:00
Benoit Steiner
513e357b48 Added support for prefetching on cuda devices 2015-07-17 15:35:16 -07:00
Benoit Steiner
943035e5bd Pulled latest updates from trunk 2015-07-17 09:42:45 -07:00
Benoit Steiner
06a22ca5bd Added support for sigmoid function to the tensor module 2015-07-17 09:29:00 -07:00
Nicolas Mellado
3275eddc24 Add const getters for LM parameters 2015-07-17 09:11:49 +02:00
Benoit Steiner
979b73cebf Fixed a typo in Macro.h 2015-07-16 14:17:50 -07:00
Benoit Steiner
a5ec25f11c Use the new EIGEN_HAS_INDEX_LIST define to enable the cxx11_tensor_index_list tests 2015-07-16 13:16:08 -07:00
Benoit Steiner
7a243959b4 Define EIGEN_HAS_INDEX_LIST whenever the class is defined. This makes it easier to support compilers that are cxx11 compliant and compilers that aren't. 2015-07-16 13:14:18 -07:00
Benoit Steiner
b756f6af5e Added missing APIs to the Eigen::Sizes class 2015-07-16 12:14:18 -07:00
Benoit Steiner
05787f8367 Added support for tensor inflation. 2015-07-16 09:04:05 -07:00
Benoit Steiner
b900fe47d5 Avoid relying on a default value for the Vectorizable template parameter of the EvalRange functor 2015-07-15 17:17:04 -07:00
Benoit Steiner
4b3d697e12 Fixed compilation error in a cuda test 2015-07-15 17:14:24 -07:00
Benoit Steiner
8315e025e1 Updated the cuda tests to use the new GpuDevice constructor 2015-07-15 12:39:26 -07:00
Benoit Steiner
e892524efe Added support for multi gpu configuration to the GpuDevice class 2015-07-15 12:38:34 -07:00
Gael Guennebaud
f5aa640862 Clean some previous changes and more cuda fixes 2015-07-15 10:57:55 +02:00
Nicolas Mellado
7cecd39a84 Merged eigen/eigen into default 2015-07-15 10:15:54 +02:00
Nicolas Mellado
592ee2a4b4 Add missing EIGEN_DEVICE_FUNC 2015-07-15 10:14:52 +02:00
Gael Guennebaud
6527dbb9f8 Merged in emartin/eigen (pull request PR-123)
Modify GEMM to handle m=0, n=0, and k=0 cases.
2015-07-13 23:58:30 +02:00
Benoit Steiner
b80036abec Enabled the construction of a fixed sized tensor directly from an expression. 2015-07-13 11:16:37 -07:00
Benoit Steiner
3912ca0d53 Fixed a bug in the integer division code that caused some large numerators to be incorrectly handled 2015-07-13 11:14:59 -07:00
Christoph Hertzberg
ea87561564 bug #1039: Redefining EIGEN_DEFAULT_DENSE_INDEX_TYPE may lead to errors 2015-07-13 16:08:25 +02:00
Gael Guennebaud
b8df8815f4 Fix operator<<(ostream,AlignedVector3) 2015-07-13 13:55:59 +02:00
Eric Martin
002c2923c2 Modify GEMM to handle m=0, n=0, and k=0 cases. 2015-07-11 21:46:13 -05:00
Nicolas Mellado
dbb3e2cf8a Cleaning 2015-07-11 18:15:31 +00:00
Nicolas Mellado
0d09845562 Revert files to remove EIGEN_USING_NUMEXT_MATH 2015-07-11 20:11:36 +02:00
Nicolas Mellado
20b96025fd Replace double constants by Scalar constants 2015-07-11 20:02:30 +02:00
Nicolas Mellado
1dd6a329e8 Cuda compatibility: remove explicit call to std math functions 2015-07-11 19:40:15 +02:00
Nicolas Mellado
bc40eb745d Merged eigen/eigen into default 2015-07-11 19:33:43 +02:00
Benoit Steiner
e6297741c9 Added support for generation of random complex numbers on CUDA devices 2015-07-07 17:40:49 -07:00
Benoit Steiner
6de6fa9483 Use NumTraits<T>::RequireInitialization instead of internal::is_arithmetic<T>::value to check whether it's possible to bypass the type constructor in the tensor code. 2015-07-07 15:23:56 -07:00
Benoit Steiner
7b7df7b6b8 Updated internal::is_arithmetic::value to be true for complex numbers 2015-07-07 12:57:35 -07:00
Benoit Steiner
6e55284e51 Pulled latest changes from trunk 2015-07-07 08:54:37 -07:00
Benoit Steiner
a93af65938 Improved and cleaned up the 2d patch extraction code 2015-07-07 08:52:14 -07:00
Gael Guennebaud
7fa6fe8d8c typo 2015-07-07 17:47:24 +02:00
Gael Guennebaud
fa17358c4b Rotation2D: fix slerp to take the shortest path, and add convenient method to get the angle in [-pi,pi] or [0,pi] 2015-07-07 17:27:12 +02:00
Benoit Steiner
3f2101b03b Use numext::swap instead of std::swap 2015-07-06 17:02:29 -07:00
Benoit Steiner
0485a2468d use Eigen smart_copy instead of std::copy 2015-07-06 17:01:51 -07:00
Benoit Steiner
ebdacfc5ea Fixed a compilation warning generated by clang 2015-07-06 15:03:11 -07:00
Benoit Steiner
81f9e968fd Only attempt to use the texture path on GPUs when it's supported by CUDA 2015-07-06 13:32:38 -07:00
Nicolas Mellado
66b30728f8 Merged eigen/eigen into default 2015-07-06 20:58:31 +02:00
Nicolas Mellado
5359e5cdb2 Protect against compilation errors with nvcc and numext/complex.
Disable functions explicitely involving std::complex when compiling with nvcc.
Improve code compatilibity using the new macro EIGEN_USING_NUMEXT_MATH (same spirit than EIGEN_USING_STD_MATH but for numext functions)
2015-07-06 20:55:01 +02:00
Benoit Steiner
864318e508 Misc small fixes to the tensor slicing code. 2015-07-06 11:45:56 -07:00
Gael Guennebaud
c2019dfeb3 Merged in Emie/eigen (pull request PR-121)
typo correction in mathFunction
2015-07-06 16:48:54 +02:00
Emilie Guy
ea7113dd0c typo correction in mathFunction 2015-07-06 14:31:08 +02:00
Nicolas Mellado
9115896590 Merged eigen/eigen into default 2015-07-03 00:41:11 +02:00
Benoit Steiner
95ef94f1ee Fixed a typo in the patch 2015-07-02 07:06:55 +00:00
Benoit Steiner
8f1d547c92 Added a default value for the cuda stream in the GpuDevice constructor 2015-07-01 18:32:18 -07:00
Benoit Steiner
1e911b276c Misc improvements and optimizations 2015-07-01 13:59:11 -07:00
Benoit Steiner
4ed213f97b Improved a previous fix 2015-07-01 13:06:30 -07:00
Benoit Steiner
56e155dd60 Fixed a couple of mistakes in the previous commit. 2015-07-01 12:40:27 -07:00
Benoit Steiner
925d0d375a Enabled the vectorized evaluation of several tensor expressions that was previously disabled by mistake 2015-07-01 11:32:04 -07:00
Benoit Steiner
44eedd8915 Marked the cast functions as EIGEN_DEVICE_FUNC to ensure that we can run casting on GPUs 2015-06-30 15:48:55 -07:00
Benoit Steiner
6021b68d8b Silenced a compilation warning 2015-06-30 15:42:25 -07:00
Benoit Steiner
f1f480b116 Added support for user defined custom tensor op. 2015-06-30 15:36:29 -07:00
Benoit Steiner
dc31fcb9ba Added support for 3D patch extraction 2015-06-30 14:48:26 -07:00
Benoit Steiner
f587075987 Made ThreadPoolDevice inherit from a new pure abstract ThreadPoolInterface class: this enables users to leverage their existing threadpool when using eigen tensors. 2015-06-30 14:21:24 -07:00
Benoit Steiner
28b36632ec Turned Eigen::array::size into a function to make the code compatible with std::array 2015-06-30 13:23:05 -07:00
Benoit Steiner
109005c6c9 Added a test for multithreaded full reductions 2015-06-30 13:08:12 -07:00
Benoit Steiner
a4aa7c6217 Fixed a few compilation warnings 2015-06-30 10:36:17 -07:00
Benoit Steiner
7d41e97fa9 Silenced a number of compilation warnings 2015-06-29 14:47:40 -07:00
Benoit Steiner
fffe63045c Added a test for full reductions on GPU 2015-06-29 14:10:32 -07:00
Benoit Steiner
db9dbbda32 Improved performance of full reduction by 2 order of magnitude on CPU and 3 orders of magnitude on GPU 2015-06-29 14:06:32 -07:00
Benoit Steiner
f0ce85b757 Improved support for fixed size tensors 2015-06-29 14:04:15 -07:00
Benoit Steiner
670c71d906 Express the full reduction operations (such as sum, max, min) using TensorDimensionList 2015-06-29 11:30:36 -07:00
Benoit Steiner
d8098ee7d5 Added support for tanh function to the tensor code 2015-06-29 11:14:42 -07:00
Benoit Steiner
3625734bc8 Moved some utilities to TensorMeta.h to make it easier to reuse them accross several tensor operations.
Created the TensorDimensionList class to encode the list of all the dimensions of a tensor of rank n. This could be done using TensorIndexList, however TensorIndexList require cxx11 which isn't yet supported as widely as we'd like.
2015-06-29 10:49:55 -07:00
Gael Guennebaud
392a30db82 Use VERIFY_IS_EQUAL instead of VERIFY(a==b) to get more feedback in case of failure 2015-06-26 16:22:49 +02:00
Gael Guennebaud
c911fc8dee split compiler intensive bdcsvd_1 unit test 2015-06-26 16:14:23 +02:00
Gael Guennebaud
98ff17eb9e Add special path for matrix<complex>/real.
This also fixes underflow issues when scaling complex matrices through complex/complex operator.
2015-06-26 16:08:15 +02:00
Gael Guennebaud
e102ddbf1f bug #1026: fix infinite loop for an empty input 2015-06-26 14:02:52 +02:00
Gael Guennebaud
555b9c6843 Doc: explain perf and multithreading issues in sparse iterative solvers 2015-06-26 10:49:40 +02:00
Gael Guennebaud
53b930887d Enable OpenMP parallelization of row-major-sparse * dense products.
I observed significant speed-up of the CG solver.
2015-06-26 10:32:34 +02:00
Gael Guennebaud
3f49cf4c90 More msvc 2013/2015 workarounds 2015-06-26 09:07:53 +02:00
Gael Guennebaud
7f824dd613 Optimize CG to enable faster spare row-major * dense vector products when the input matrix is complete (Upper|Lower) but column major. 2015-06-25 17:17:38 +02:00
Gael Guennebaud
c5f9eafcbc Fix assignement to selfadjoint-view when testing real-world problems 2015-06-25 17:08:58 +02:00
Gael Guennebaud
33e699c9fe Remove redundant accessors in Reverse 2015-06-25 14:14:59 +02:00
Gael Guennebaud
6b4d255cab Avoid division by a zero complex 2015-06-25 14:04:05 +02:00
Gael Guennebaud
973b0a90db Clarify documentation of the tolerance and error returned in iterative solvers 2015-06-25 13:51:13 +02:00
Gael Guennebaud
84264ceebc workaround msvc 2013/2015 wrong instanciation of isnan, isfinite, isinf 2015-06-25 10:00:26 +02:00
Gael Guennebaud
b4ab72678c bug #1000: MSVC 2013 does need the operator= workaround 2015-06-25 09:45:22 +02:00
Gael Guennebaud
788941d3b1 Workaround MSVC ambiguous instanciation 2015-06-24 23:35:17 +02:00
Gael Guennebaud
4c8cd13b35 Add explicit ctor for diagonal to sparse conversion 2015-06-24 18:11:06 +02:00
Gael Guennebaud
c38c195321 Document how cross behaves on complex numbers 2015-06-24 18:02:33 +02:00
Gael Guennebaud
23535ed31c Add unit test for dense = SparseQR::matrixQ 2015-06-24 17:55:41 +02:00
Gael Guennebaud
62f21e2d11 Add support for sparse = diagonal 2015-06-24 17:55:00 +02:00
Gael Guennebaud
763c833637 Make SparseSelfAdjointView, twists, and SparseQR more evaluator friendly 2015-06-24 17:54:09 +02:00
Gael Guennebaud
36643eec0c Add a call_assignment_no_alias_no_transpose shortcut 2015-06-24 17:50:43 +02:00
Gael Guennebaud
02db7c9bc6 Inherit operator+= and -= with 'using' kkeyword 2015-06-24 17:49:20 +02:00
Gael Guennebaud
53a61a067b Fallback to CMAKE_CXX_COMPILER_VERSION if VS version unknown 2015-06-24 15:17:37 +02:00
Gael Guennebaud
95e19be381 Fix compilation of MKL Pardiso support 2015-06-24 14:53:43 +02:00
Gael Guennebaud
2a33075aeb std::isnan is c++11 only 2015-06-24 10:29:17 +02:00
Gael Guennebaud
23da99492f Add unit-test for Visual2013 ambiguous call to operator= 2015-06-24 10:27:02 +02:00
Benoit Steiner
6441befbb3 Added more checks to test the correctness of the pexp implementation 2015-06-23 19:12:46 -07:00
Gael Guennebaud
c3e398d138 Fix overflow when checking SVD accuracy 2015-06-23 15:05:20 +02:00
Gael Guennebaud
b0d08869a9 Fix underflow in 3x3 tridiagonalization 2015-06-23 14:54:31 +02:00
Gael Guennebaud
18c9d155f3 Fix the fact that float(int) != float(int(float(int))) 2015-06-23 14:33:00 +02:00
Gael Guennebaud
71523a2e25 Fix a warning with icc 2015-06-23 14:20:20 +02:00
Gael Guennebaud
d9778f3391 Enable VML's pow wrapper on windows (the previous wrapper used the Fortran interface) 2015-06-23 14:04:50 +02:00
Gael Guennebaud
5f9630d7f9 bug #923: update support for Intel's VML wrt new evaluation mechanisms 2015-06-23 14:03:25 +02:00
Gael Guennebaud
793e4c6d77 bug #923: fix EIGEN_USE_BLAS mode 2015-06-23 11:13:24 +02:00
Gael Guennebaud
307c4fc292 Workaround missalignment produced by first_aligned for PacketSize==1 and size==1 2015-06-23 10:10:17 +02:00
Gael Guennebaud
bb3a9b4941 Use Ref<> to bypass forceAlignmentIf 2015-06-22 17:48:28 +02:00
Gael Guennebaud
476beed7f8 bug #1017: apply Christoph's patch preventing underflows in makeHouseholder 2015-06-22 16:51:45 +02:00
Gael Guennebaud
9fc1c92137 Fix isinf unit tests 2015-06-22 16:48:27 +02:00
Gael Guennebaud
9c7cfa7dab Update list of main modules 2015-06-22 14:17:24 +02:00
Gael Guennebaud
3ccd23efc0 Update coeff-wise quick-reference doc. 2015-06-22 14:08:54 +02:00
Gael Guennebaud
0848ba0a6e Fix return nullary return types: it must be based on the PlainObject type instead of the expression type. 2015-06-22 10:52:08 +02:00
Gael Guennebaud
b3b3dcad05 Reduce compiler memory consumption for SVD unit tests 2015-06-22 09:58:06 +02:00
Nicolas Mellado
ad5fdc7ddd Fix double to Scalar unwanted promotions 2015-06-21 20:21:23 +02:00
Gael Guennebaud
40821876ea Fix regression on CompressedStorage::operator= 2015-06-20 13:59:13 +02:00
Michael Abrahams
7043083be4 Use GCC flags in mingw 2015-06-20 18:54:41 +00:00
Gael Guennebaud
84aaef93ba Merged in vanhoucke/eigen_vanhoucke (pull request PR-118)
Fix two small undefined behaviors caught by static analysis.
2015-06-20 13:56:48 +02:00
Gael Guennebaud
6b33b29f00 Get rid of must_nest_by_value 2015-06-19 18:12:40 +02:00
Gael Guennebaud
846b227bb7 Get rid of class internal::nested<> (still have to updated Tensor module) 2015-06-19 17:56:39 +02:00
vanhoucke
368ea23406 Fix undefined behavior. When resizing a default-constructed SparseArray, we end up calling memcpy(ptr, 0, 0), which is technically UB and gets caught by static analysis. 2015-06-19 15:53:30 +00:00
vanhoucke
4cc0c961f3 Fix undefined behavior. 2015-06-19 15:46:46 +00:00
Gael Guennebaud
386d9e5ebd Fix usage of nested versus nested_eval 2015-06-19 17:42:27 +02:00
Gael Guennebaud
a5a7b68b76 Fix ambiguous instanciation using clean class-level SFINAE in product_evaluator 2015-06-19 17:25:13 +02:00
Gael Guennebaud
6fc5438205 Remove a few deprecated internal expressions 2015-06-19 17:06:12 +02:00
Gael Guennebaud
e9edb085c0 Check number of temporaries when applying permutations 2015-06-19 16:39:24 +02:00
Gael Guennebaud
6318d53b41 Factorize VERIFY_EVALUATION_COUNT in unit tests 2015-06-19 16:38:26 +02:00
Gael Guennebaud
5c84dd5665 Fix permutation/transposiitons products wrt nested_eval 2015-06-19 16:37:04 +02:00
Gael Guennebaud
0c8b0e007b Introduce a AliasFreeProduct option for Permutations and Transpositions 2015-06-19 15:38:19 +02:00
Gael Guennebaud
3f6aa4cd5d Remove useless specializations of evaluator_traits 2015-06-19 14:18:29 +02:00
Gael Guennebaud
4a8888dfbc Improbe compatibility of Transpositions and evaluators 2015-06-19 14:10:44 +02:00
Gael Guennebaud
3af4c6c1c9 Make Transpositions use evaluators 2015-06-19 11:50:24 +02:00
Gael Guennebaud
82b6ac0864 Enforce eigenvectors to be column-major (for performance reasons) 2015-06-19 11:25:46 +02:00
Gael Guennebaud
fad36cc814 Clean implementation of permutation * matrix products. 2015-06-19 10:51:57 +02:00
Gael Guennebaud
06036d8bb1 Fix compilation of BDCSVD with DEFAULT_TO_ROWMAJOR 2015-06-19 10:37:25 +02:00
Gael Guennebaud
d2db15016b Fix storage order computation in traits<Product> 2015-06-19 10:36:38 +02:00
Benoit Steiner
6a9a29e96f Fixed a compilation warning 2015-06-17 10:14:13 -07:00
Gael Guennebaud
bb6acc561e Workaround broken complex*real product on old clang versions 2015-06-17 16:11:58 +02:00
Gael Guennebaud
40f326ef2e workaround clang's broken complex division 2015-06-17 15:33:09 +02:00
Benoit Steiner
ab5db86fe9 Fixed merge conflict 2015-06-16 19:52:20 -07:00
Benoit Steiner
ea160a898c Pulled latest updates from trunk 2015-06-16 19:46:23 -07:00
Benoit Steiner
367794e668 Fixed compilation warnings triggered by clang 2015-06-16 19:43:49 -07:00
Gael Guennebaud
736a805883 Add unit test for bug #879 2015-06-16 22:11:41 +02:00
Gael Guennebaud
9ab8ac5c8b Fix compilation in TensorImagePatch 2015-06-16 14:50:08 +02:00
Gael Guennebaud
38874b1651 Fix shadow warnings in Tensor module 2015-06-16 14:43:46 +02:00
Gael Guennebaud
e2e66930c6 Fix compilation of alignedvector3 unit test 2015-06-16 14:40:55 +02:00
Gael Guennebaud
7baa1ba03e Remove the usage of result_of for DenseBase::redux as discussed in bug #1006 2015-06-15 22:40:18 +02:00
Gael Guennebaud
97cbe28829 Remove support of std::binder* in C++11 2015-06-15 15:34:05 +02:00
Gael Guennebaud
972a535288 Remove aligned-on-scalar assert and fallback to non vectorized path at runtime (first_aligned already had this runtime guard) 2015-06-14 15:04:07 +02:00
Gael Guennebaud
e5b490b654 Fix isfinite/isinf/isnan code snippets 2015-06-15 15:09:25 +02:00
Gael Guennebaud
a546be56e0 typo 2015-06-15 15:08:51 +02:00
Gael Guennebaud
3946c981b1 Relax tolerance when testing LDLT on singular problems 2015-06-15 15:08:16 +02:00
Gael Guennebaud
2212e40e95 Extend VERIFY_IS_APPROX to report the magnitude of the relative difference in case of failure. This will ease identifying strongest failing tests 2015-06-15 15:03:19 +02:00
Gael Guennebaud
321a2cbe3d Add missing forward declaration of AlignedBox 2015-06-15 15:01:20 +02:00
Gael Guennebaud
2f2a441a4d Fix use of unitialized buffers. 2015-06-13 22:19:40 +02:00
Gael Guennebaud
91b64a9c65 Relax aligned-on-scalar assert as in the 3.2 branch 2015-06-12 11:25:57 +02:00
Gael Guennebaud
84d103bee8 Enable C++11 math function in a more conservative manner. 2015-06-11 21:45:02 +02:00
Gael Guennebaud
916ef52fff merge 2015-06-11 09:35:49 +02:00
Gael Guennebaud
d93ba137f2 Introduce EIGEN_PI, get rid of M_PI and acos(-1.0) 2015-06-10 17:12:10 +02:00
Gael Guennebaud
9756c7fb4d Make more use of EIGEN_HAS_C99_MATH 2015-06-10 16:26:55 +02:00
Gael Guennebaud
93a62265dc fix isinf(complex(inf,NaN)) to return false. 2015-06-10 16:19:10 +02:00
Gael Guennebaud
b0d5aaafcc Rename free functions isFinite, isInf, isNaN to be compatible with c++11 2015-06-10 16:17:09 +02:00
Gael Guennebaud
25a98be948 bug #80: merge with d_hood branch on adding more coefficient-wise unary array functors 2015-06-10 15:52:05 +02:00
Gael Guennebaud
192bce2795 bug #890, add a more general routine to check that two dense object reference to the same data 2015-06-10 10:09:04 +02:00
Gael Guennebaud
e6832ce93d Add regression test for bug #890 2015-06-10 09:32:10 +02:00
Gael Guennebaud
0b2cbb2bdc bug #897: make umfpack support use Ref<> 2015-06-09 23:30:06 +02:00
Gael Guennebaud
feaf76c001 bug #910: add a StandardCompressedFormat option to Ref<SparseMatrix> to enforce standard compressed storage format.
If the input is not compressed, then this trigger a copy for a const Ref, and a runtime assert for non-const Ref.
2015-06-09 23:11:24 +02:00
Gael Guennebaud
f899aeb301 bug #650: fix sparse * dense wrt noalias and compound assignment 2015-06-09 18:33:24 +02:00
Gael Guennebaud
785b9c0127 bug #1003: assert in MapBase if the provided pointer is not aligned on scalar while it is expected to be. Also add a EIGEN_ALIGN8 macro. 2015-06-09 17:42:09 +02:00
Gael Guennebaud
0eb06f1391 Enable -Wshadow with clang 2015-06-09 17:44:18 +02:00
Gael Guennebaud
64753af3b7 code simplification 2015-06-09 15:35:34 +02:00
Gael Guennebaud
cacbc5679d formatting 2015-06-09 15:23:08 +02:00
Gael Guennebaud
04665ef9e1 remove redundant dynamic allocations in GMRES 2015-06-09 15:18:21 +02:00
Gael Guennebaud
d4c574707e fix some legitimate shadow warnings 2015-06-09 15:17:58 +02:00
Gael Guennebaud
f9350e70eb fix unused variable warning 2015-06-09 15:17:21 +02:00
Gael Guennebaud
4aba24a1b2 Clean argument names of some functions 2015-06-09 13:32:12 +02:00
Gael Guennebaud
302cf8ffe2 Add missing documentation for TriangularViewImpl<MatrixType,Mode,Sparse> 2015-06-09 11:40:07 +02:00
Gael Guennebaud
3a4299b245 bug #872: remove usage of deprecated bind1st. 2015-06-09 10:52:04 +02:00
Gael Guennebaud
9aef0db992 Skip too large real-world problems for solvers that do not scale (e.g., SimplicialLLT without reordering) 2015-06-09 09:29:53 +02:00
Gael Guennebaud
9a2447b0c9 Fix shadow warnings triggered by clang 2015-06-09 09:11:12 +02:00
Gael Guennebaud
cd8b996f99 Extend unit test and documentation of SelfAdjointEigenSolver::computeDirect 2015-06-08 16:16:42 +02:00
Gael Guennebaud
913a61870d Update utility for experimenting with 3x3 eigenvalues 2015-06-08 15:54:53 +02:00
Gael Guennebaud
8f031a3cee bug #997: add missing evaluators for m.lazyProduct(v.homogeneous()) 2015-06-08 15:43:41 +02:00
Gael Guennebaud
e6c5723dcd Add unit test for m.replicate(...)(index). 2015-06-08 15:42:15 +02:00
Gael Guennebaud
274b1f5d7e Fix homogeneous() for 1x1 matrix: in this case, homogeneous follows the storage order guaranteeing that v.transpose().homogeneous() == v.homogeneous().transpose() 2015-06-08 15:40:51 +02:00
Gael Guennebaud
cbe3a1a83e Add missing accessors for 1D index based access to Replicate<> expressions. 2015-06-08 15:39:09 +02:00
Gael Guennebaud
a7ae628c9f bug #1005: fix regression regarding sparse coeff-wise binary operator that did not trigger a static assertion for mismatched storage 2015-06-08 10:14:08 +02:00
Gael Guennebaud
0a9b5d1396 bug #705: fix handling of Lapack potrf return code 2015-06-05 15:59:13 +02:00
Gael Guennebaud
d0b7b5cb55 minor documentation fixes 2015-06-05 14:40:07 +02:00
Gael Guennebaud
56d4ef7ad6 BiCGSTAB: set default guess to 0, and improve restart mechanism by recomputing the accurate residual. 2015-06-05 14:37:57 +02:00
Gael Guennebaud
98a8d43457 Improve unit testing of real-word sparse problem (fix some shortcommings, use VERIFY, etc.) 2015-06-05 14:33:37 +02:00
Gael Guennebaud
b685660b22 Do go to full accuracy when testing BiCGSTAB. 2015-06-05 14:32:26 +02:00
Gael Guennebaud
8bc26562f4 Do not abort if the folder cannot be openned! 2015-06-05 14:31:29 +02:00
Gael Guennebaud
3e7bc8d686 Improve loading of symmetric sparse matrices in MatrixMarketIterator 2015-06-05 10:16:29 +02:00
Gael Guennebaud
acc761cf0c Merged in FlorianGeorge/eigen_blaze_fork_2 (pull request PR-60)
Use trans(X) instead of X.transpose() in Blaze Benchmark
2015-06-04 09:15:22 +02:00
Benoit Steiner
ea1190486f Fixed a compilation error triggered by nvcc 7 2015-05-28 11:57:51 -07:00
Benoit Steiner
0e5fed74e7 Worked around some constexpr related bugs in nvcc 7 2015-05-28 10:14:38 -07:00
Benoit Steiner
f13b3d4433 Added missing include files 2015-05-28 07:57:28 -07:00
Benoit Steiner
abec18bae0 Fixed potential compilation error 2015-05-26 10:11:15 -07:00
Benoit Steiner
9df186c140 Added a few more missing EIGEN_DEVICE_FUNC statements 2015-05-26 09:47:48 -07:00
Benoit Steiner
466bcc589e Added a few missing EIGEN_DEVICE_FUNC statements 2015-05-26 09:37:23 -07:00
Gael Guennebaud
d457734a19 Avoid calling smart_copy with null pointers. 2015-05-25 22:30:56 +02:00
Benoit Steiner
6b800744ce Moved away from std::async and std::future as the underlying mechnism for the thread pool device. On several platforms, the functions passed to std::async are not scheduled in the order in which they are given to std::async, which leads to massive performance issues in the contraction code.
Instead we now have a custom thread pool that ensures that the functions are picked up by the threads in the pool in the order in which they are enqueued in the pool.
2015-05-20 13:52:07 -07:00
Benoit Steiner
48f6b274e2 Fixed compilation error triggered by gcc 4.7 2015-05-20 08:54:44 -07:00
Benoit Steiner
2451679951 Avoid using the cuda memcpy for small tensor slices since the memcpy kernel is very expensive to launch 2015-05-19 15:19:01 -07:00
Benoit Steiner
a81d17b73a Added new version of the TensorIntDiv class optimized for 32 bit signed integers. It saves 1 register on CPU and 2 on GPU. 2015-05-19 13:59:52 -07:00
Benoit Jacob
051d5325cc Abandon blocking size lookup table approach. Not performing as well in real world as in microbenchmark. 2015-05-19 11:03:59 -04:00
Christoph Hertzberg
ebea530782 bug #1014: More stable direct computation of eigenvalues and -vectors for 3x3 matrices 2015-05-17 21:54:32 +02:00
Benoit Jacob
c88e1abaf3 also uninitialized here, see previous cset 2015-05-15 11:34:57 -04:00
Benoit Jacob
807793ec3b Fix uninitialized var warning. The compiler was clearing the register anyway, so this does not change resulting code 2015-05-15 11:15:53 -04:00
Pete Warden
140f85bb99 Check for the macro __ARM_NEON__ (with two underscores at the end) as well as __ARM_NEON. The second macro is correct according to the ARM language extensions specification, but historically the first one has been more common. Some older compilers (e.g. gcc v4.6 on a Beaglebone Black) only define the first, so without this patch NEON isn't enabled. 2015-05-12 16:03:43 -07:00
Gael Guennebaud
a852001196 Add regression test for bugs #854 and #1014, and check that the eigenvector matrix is unitary. 2015-05-12 18:45:39 +02:00
Gael Guennebaud
e66caf48e8 Make test matrices for eigensolver/selfadjoint even more tricky 2015-05-12 18:44:46 +02:00
Gael Guennebaud
ef81730625 Ignore denormal numbers in selfadjoint eigensolver. 2015-05-12 18:38:43 +02:00
Christoph Hertzberg
a605a1d7df Merged in MattPD/eigen/MattPD/doc-fix-wording-typos-in-templatekeywor-1431363009359 (pull request PR-116)
[Doc] Fix wording / typos in TemplateKeyword.dox
2015-05-11 23:37:52 +02:00
MattPD
447e060b81 [Doc] Fix wording / typos in TemplateKeyword.dox 2015-05-11 16:50:18 +00:00
Christoph Hertzberg
494fa991c3 bug #872: Avoid deprecated binder1st/binder2nd usage by providing custom functors for comparison operators 2015-05-07 17:28:40 +02:00
Gael Guennebaud
4a936974a5 bug #1013: fix 2x2 direct eigensolver for identical eiegnvalues 2015-05-07 15:55:12 +02:00
Gael Guennebaud
c2107d30ce Extend unit tests of sefladjoint-eigensolver 2015-05-07 15:54:07 +02:00
Gael Guennebaud
ebf8ca4fa8 Fix bug #1010: m_isInitialized was improperly updated 2015-05-07 14:20:42 +02:00
Konstantinos Margaritis
dd698e6680 Merged in doug_kwan/eigen (pull request PR-103)
Fix bug in pdiv<Packet1cd> which swaps 32-bit halves of a pair of
2015-05-05 20:50:14 +03:00
Benoit Steiner
1dded10cb7 Added a double-precision implementation of the exp() function for AVX. 2015-05-04 10:42:51 -07:00
Christoph Hertzberg
6273aca9b1 small typo 2015-05-04 15:26:31 +00:00
Christoph Hertzberg
4dd7d0b5dc Merged in mvdyck/eigen-3/mvdyck/doc-multithreading-fix-old-n-eigennbthr-1430750928880 (pull request PR-114)
[Doc] Multi-threading fix
2015-05-04 17:23:21 +02:00
michiel van dyck
4b9eddaef8 [Doc] Multi-threading fix
OLD: n = Eigen::nbThreads( n );
NEW: n = Eigen::nbThreads( );

from:
You can query the number of threads that will be used with:
\code
n = Eigen::nbThreads( );
\endcode

Kr Michiel
2015-05-04 14:48:52 +00:00
Christoph Hertzberg
28a4c92cbf bug #998: Started fixing doxygen warnings 2015-05-01 22:10:41 +02:00
Christoph Hertzberg
173b34e9ab bug #999: clarify that behavior of empty AlignedBoxes is undefined, and further improvements in documentation 2015-04-30 19:30:36 +02:00
Christoph Hertzberg
da2baf685d Regression test for bug #302
(transplanted from 80fd8fab87
)
Changed DenseIndex to Index
2015-04-26 21:05:33 +02:00
Christoph Hertzberg
8c6a3b5ace Fix trivial warnings in LevenbergMarquardt module and test 2015-04-24 21:35:30 +02:00
Gael Guennebaud
de18cd413d Disable posix_memalign on Solaris and SunOS, and allows to by-pass built-in posix_memalign detection rules. 2015-04-24 11:26:51 +02:00
Gael Guennebaud
1681a665d9 Extend unit test of Map<,,Stride<>> with stack allocated buffers and less trivial operations. 2015-04-24 10:38:28 +02:00
Gael Guennebaud
834f66e9fc Extend unit test of Map<> with stack allocated buffers and less trivial operations. 2015-04-24 10:10:19 +02:00
Gael Guennebaud
40258078c6 bug #360: add value_type typedef to DenseBase/SparseMatrixBase 2015-04-24 09:44:24 +02:00
Christoph Hertzberg
c460af414e Fix bug #1000: Manually inherit assignment operators for MSVC 2013 and later (as required by the standard). 2015-04-23 13:39:03 +02:00
Benoit Steiner
fd1d4bd86c Silenced a few compilation warnings 2015-04-22 16:16:15 -07:00
Benoit Steiner
91359e1d0a Added the ability to generate a tensor from a custom user defined 'generator'. This simplifies the creation of constant tensors initialized using specific regular patterns.
Created a gaussian window generator as a first use case.
2015-04-22 11:14:58 -07:00
Benoit Steiner
8838ed39f4 Added support for non-deterministic random number generation on GPU 2015-04-22 09:14:38 -07:00
Christoph Hertzberg
e7457e419d Merge with dfa991cbae 2015-04-22 03:39:32 +02:00
Benoit Steiner
dfa991cbae Make sure that the copy constructor of the evaluator is always called before launching the evaluation of a tensor expression on a cuda device. 2015-04-21 16:15:45 -07:00
Gael Guennebaud
dbd12b4cda Make sure that BlockImpl<const SparseMatrix> ctor is called with the right type 2015-04-21 10:15:36 +02:00
Gael Guennebaud
d6a8b43b39 Fix typo in the definition of EIGEN_COMP_GNUC_STRICT 2015-04-21 10:12:38 +02:00
Benoit Steiner
e709488361 Silenced a few compilation warnings 2015-04-20 17:39:45 -07:00
Benoit Steiner
10a1f81822 Sped up the assignment of a tensor to a tensor slice, as well as the assigment of a constant slice to a tensor 2015-04-20 17:34:11 -07:00
Deanna Hood
e5048b5501 Use std::isfinite when available 2015-04-20 14:59:57 -04:00
Deanna Hood
249c48ba00 Incorporate C++11 check into EIGEN_HAS_C99_MATH macro 2015-04-20 14:57:04 -04:00
Deanna Hood
0250f4a9f2 Merged default into unary-array-cwise-functors 2015-04-20 14:01:35 -04:00
Deanna Hood
0339502a4f Only use std::isnan and std::isinf if they are available 2015-04-20 13:14:06 -04:00
Benoit Steiner
43eb2ca6e1 Improved the tensor random number generators:
* Use a mersenne twister whenebver possible instead of the default entropy source since the default one isn't very good at all.
 * Added the ability to seed the generators with a time based seed to make them non-deterministic.
2015-04-20 09:24:09 -07:00
Christoph Hertzberg
016c29f207 Merge with 70bc3b0668 2015-04-20 08:33:39 +02:00
Benoit Steiner
70bc3b0668 Silenced a warning in the tensor code 2015-04-19 12:38:00 -07:00
Benoit Steiner
3220eb2b93 Fixed some compilation warnings 2015-04-19 12:36:35 -07:00
Gael Guennebaud
fc2d5b86ce simplify previous changeset: sub-expressions are nested by value 2015-04-18 22:50:16 +02:00
Gael Guennebaud
5a3c48e3c6 bug #942: fix dangling references in evaluator of diagonal * sparse products. 2015-04-18 22:43:27 +02:00
Benoit Steiner
3b429b71e6 Fixed compilation warning triggered by gcc 4.7 2015-04-18 13:41:06 -07:00
Benoit Steiner
9c6b82bcd5 Use ptrdiff_t instead of size_t to encode fixed sizes. This silences several clang compilation warnings
(transplanted from 4400e4436ac7c5bbd305a03c21aa4bce24ae199b)
2015-04-17 09:12:18 -07:00
Christoph Hertzberg
4f126b862d Add internal assertions to purely fixed-size DenseStorage, mark optional variables always as unused 2015-04-17 11:36:21 +02:00
Benoit Steiner
da5b98a94d Updated the cxx11_tensor_convolution test to avoid using cxx11 features. This should enable the test to compile with gcc 4.7 and older 2015-04-16 12:29:16 -07:00
Benoit Steiner
d19d09ae6a Updated a regression test to avoid compilation errors when compiling with gcc 4.7 2015-04-16 12:15:27 -07:00
Christoph Hertzberg
9d7843d0d0 Add internal assertions to DenseStorage constructor 2015-04-16 15:47:06 +02:00
Christoph Hertzberg
3be9f5c4d7 Constructing a Matrix/Array with implicit transpose could lead to memory leaks.
Also reduced code duplication for Matrix/Array constructors
2015-04-16 13:25:20 +02:00
Gael Guennebaud
e0cff9ae0d Fix bug #996: fix comparisons to 0 instead of Scalar(0) 2015-04-15 14:48:53 +02:00
Gael Guennebaud
5dbe758dc3 Backed out changeset 04c8c5d9ef 2015-04-15 14:47:08 +02:00
Gael Guennebaud
04c8c5d9ef Fix bug #996: fix comparisons to 0 instead of Scalar(0) 2015-04-15 14:44:57 +02:00
Benoit Steiner
0f82399fe9 Pulled latest changes from trunk 2015-04-14 19:13:34 -07:00
Christoph Hertzberg
761691f18d Make conversion from 0 to Scalar explicit (issue reported by Brad Bell) 2015-04-13 17:15:00 +02:00
Benoit Steiner
5401fbcc50 Improved the blocking strategy to speedup multithreaded tensor contractions. 2015-04-09 16:44:10 -07:00
Deanna Hood
085aa8e601 Don't use M_PI since it's only guaranteed to be defined in Eigen/Geometry 2015-04-08 13:59:18 -05:00
Gael Guennebaud
0eb220c00d add a note on bug #992 2015-04-08 09:25:34 +02:00
Benoit Jacob
d7f51feb07 bug #992: don't select a 3p GEMM path with non-vectorizable scalar types, this hits unsupported paths in symm/triangular products code 2015-04-07 15:13:55 -04:00
Benoit Jacob
0e9753c8df Fix compiler flags on Android/ARM:
- generate position-independent code (PIE), a requirement to run binaries on Android 5.0+ devices;
 - correctly handle EIGEN_TEST_FMA + EIGEN_TEST_NEON to pass -mfpu=neon-vfpv4.
2015-04-07 14:03:21 -04:00
Benoit Steiner
1de49ef4c2 Fixed a bug when chipping tensors laid out in row major order. 2015-04-07 10:44:13 -07:00
Benoit Steiner
a1f1e1e51d Fixed the order of 2 #includes 2015-04-06 10:41:39 -07:00
Benoit Steiner
7c18ab921c Pulled latest updates from trunk 2015-04-04 20:07:04 -07:00
Gael Guennebaud
15b5adb327 Fix regression in DynamicSparseMatrix and SuperLUSupport wrt recent change on nonZeros/nonZerosEstimate 2015-04-02 22:21:41 +02:00
Benoit Steiner
74e558cfa8 Pulled latest updates from trunk 2015-04-01 23:24:11 -07:00
Benoit Steiner
03a0df2010 Fixed some compilation warnings triggered by pre-cxx11 comoilers 2015-04-01 22:51:33 -07:00
Benoit Steiner
b8b7807269 Fixed some compilation warning triggered by the cxx11 emulation code 2015-04-01 21:48:18 -07:00
Benoit Steiner
383b6dfafe Fixed 2 typos 2015-04-01 16:44:36 -07:00
Gael Guennebaud
5861cfb55e Remove unused GenericSparseBlockInnerIteratorImpl code. 2015-04-01 22:29:29 +02:00
Gael Guennebaud
3105986e71 bug #875: remove broken SparseMatrixBase::nonZeros and introduce a nonZerosEstimate() method to sparse evaluators for internal uses.
Factorize some code in SparseCompressedBase.
2015-04-01 22:27:34 +02:00
Gael Guennebaud
39dcd01b0a bug #973: enable alignment of multiples of half-packet size (e.g., Vector6d with AVX) 2015-04-01 13:55:09 +02:00
Gael Guennebaud
8481dc21ea bug #986: add support for coefficient-based product with 0 depth. 2015-04-01 13:15:23 +02:00
Gael Guennebaud
79b4e6acaf Fix bug #987: wrong alignement guess in diagonal product. 2015-03-31 23:35:12 +02:00
Gael Guennebaud
3c38589984 Remove most of the dynamic memory allocations that occured in D&C SVD. Still remains the calls to JacobiSVD and UpperBidiagonalization. 2015-03-31 22:54:47 +02:00
Gael Guennebaud
8313fb7df7 Add row/column-wise reverseInPlace feature. 2015-03-31 21:35:53 +02:00
Gael Guennebaud
dfb674a25e Make reverseInPlace really work in-place. 2015-03-31 20:17:10 +02:00
Gael Guennebaud
20d030f207 Fix vectorization of swap for non trivial expressions 2015-03-31 20:16:02 +02:00
Benoit Steiner
678207e02a Added regression tests for tensor convolutions 2015-03-31 09:08:08 -07:00
Benoit Steiner
68d4afe985 Added support for convolution of tensors laid out in RowMajor mode 2015-03-31 09:07:09 -07:00
Benoit Steiner
f873686602 Added documentation for the convolution operation 2015-03-31 08:27:23 -07:00
Benoit Jacob
73cdeae1d3 Only use blocking sizes LUTs for single-thread products for now 2015-03-31 11:17:23 -04:00
Benoit Jacob
0cbd5ae3cb Correctly detect Android with ndk_build 2015-03-31 11:17:21 -04:00
Gael Guennebaud
ae01c05e18 Fix computeProductBlockingSizes with m==0, and add respective unit test. 2015-03-31 15:19:57 +02:00
Gael Guennebaud
bd76d837e6 Fix sign of SuperLU::determinant 2015-03-31 14:57:32 +02:00
Gael Guennebaud
35d3053d55 Fix regression introduced in 3b169d792d 2015-03-31 09:23:53 +02:00
Benoit Steiner
731d7b84b4 Sharded a large test 2015-03-30 23:26:45 -07:00
Christoph Hertzberg
7bd578d11d Change CMake warning to simple message for old Metis versions 2015-03-31 00:50:04 +02:00
Christoph Hertzberg
3b169d792d Suppress unused variable warning 2015-03-31 00:49:08 +02:00
Christoph Hertzberg
3238ca6abc Addendum to last patch: k is Index and not int 2015-03-31 00:42:14 +02:00
Christoph Hertzberg
1efae98fee bug #985: RealQZ failed when either matrix had zero rows or columns (report and patch by Ben Goodrich)
Also added a regression test
2015-03-30 23:56:20 +02:00
Benoit Steiner
35722fa022 Made the index type a template parameter of the tensor class instead of encoding it in the options. 2015-03-30 14:55:54 -07:00
Benoit Steiner
71950f02e5 Deleted unnecessary semicolons 2015-03-30 14:49:10 -07:00
Christoph Hertzberg
58af8bf90c bug #982: Make sure numext::maxi and numext::mini are called correctly, in case Scalar expressions return expression templates. 2015-03-30 16:47:22 +02:00
Gael Guennebaud
2adbf6b8ca fix stupid warning with old GCC 2015-03-28 22:34:54 +01:00
Gael Guennebaud
41e20248f8 merge 2015-03-28 14:43:35 +01:00
Christoph Hertzberg
09a5361d1b bug #983: Pass Vector3 by const reference and not by value 2015-03-28 12:36:24 +01:00
Christoph Hertzberg
266a84558f Optionally build the documentation when building unit tests. 2015-03-27 16:36:59 +01:00
Christoph Hertzberg
1b4bb20cf1 Merged in d_hood/eigen/sparse-tutorial-doc-fix (pull request PR-107)
[Doc] Fix missing image in sparse tutorial
2015-03-27 16:22:16 +01:00
Gael Guennebaud
eb7e4c2b9c Pass Vector3 type by reference 2015-03-27 12:11:24 +01:00
Gael Guennebaud
ad044008da Fix transpose versus adjoint. 2015-03-27 12:07:14 +01:00
Gael Guennebaud
79cb875249 merge 2015-03-27 10:56:04 +01:00
Gael Guennebaud
7e225b6fa4 Suppress some false negatives in SVD unit test 2015-03-27 10:55:53 +01:00
Gael Guennebaud
1b8cc9af43 Slight numerical stability improvement in 2x2 svd 2015-03-27 10:55:00 +01:00
Gael Guennebaud
3d59ae0203 Fix hypot(0,0). 2015-03-27 09:59:24 +01:00
Benoit Steiner
4df8b5a75e Avoid making an unecessary copy of the tensor expression when evaluating it on a GPU device 2015-03-25 14:36:07 -07:00
Benoit Steiner
b3343bfdae Fixed the vectorized implementation of the Tensor select() method 2015-03-25 13:25:53 -07:00
Benoit Steiner
ccf290a65c Cleaned up the TensorDevice code a little bit. 2015-03-25 12:37:38 -07:00
Benoit Steiner
d3f7915aeb Pulled latest update from the eigen main codebase 2015-03-24 13:12:14 -07:00
Benoit Steiner
abdbe8562e Fixed the CUDA packet primitives 2015-03-24 10:45:46 -07:00
Gael Guennebaud
29eaa2b0f1 Make MatrixBase::is* methods aware of nested_eval. 2015-03-24 13:42:42 +01:00
Gael Guennebaud
f42b105f73 Add the possibility to make VERIFY* checks to output a warning instead of abording. 2015-03-24 13:39:14 +01:00
Gael Guennebaud
d27968eb7e D&C SVD: directly falls back to JacobiSVD for very small problems (by-pass upper-bidiagonalization) 2015-03-24 13:38:07 +01:00
Gael Guennebaud
4472f3e578 Avoid SVD: consider denormalized small numbers as zero when computing the rank of the matrix 2015-03-23 09:40:21 +01:00
Deanna Hood
83e5b7656b Use M_PI instead of acos(-1) for pi 2015-03-22 06:04:31 +10:00
Deanna Hood
4bab4790c0 Add \sa tags of isFinite/isInf for each other 2015-03-22 05:39:08 +10:00
Gael Guennebaud
4e2b18d909 Update approx. minimum ordering method to push and keep structural empty diagonal elements to the bottom-right part of the matrix 2015-03-20 16:33:48 +01:00
Gael Guennebaud
8d9bfb3a7b fix loadMarket wrt Index versus int 2015-03-20 16:00:10 +01:00
Benoit Steiner
a6a628ca6b Added the -= operator to the device classes 2015-03-19 23:22:19 -07:00
Benoit Steiner
e134226a03 Fixed a bug in the handling of packets by the MeanReducer 2015-03-19 23:11:42 -07:00
Gael Guennebaud
9ee62fdcd5 Fix random unit test for 32bits systems. 2015-03-19 21:39:37 +01:00
Gael Guennebaud
d6b2f300db Fix MSVC compilation: aligned type must be passed by reference 2015-03-19 17:28:32 +01:00
Gael Guennebaud
61c45d7cfd Fix comparison warning 2015-03-19 17:13:22 +01:00
Gael Guennebaud
d7698c18b7 Split sparse_basic unit test 2015-03-19 15:11:05 +01:00
Gael Guennebaud
f329d0908a Improve random number generation for integer and add unit test 2015-03-19 15:10:36 +01:00
Deanna Hood
2ab4922431 Make html directory before generating output image there 2015-03-18 07:24:13 +10:00
Deanna Hood
41b717de25 More extensive unit tests for recent array-wise functors 2015-03-18 03:11:03 +10:00
Benoit Steiner
cc0f89eb3b Changed the way lvalue operations are declared in TensorBase: this fixes constness isses that prevented some expressions mixing lvalues and rvalues from compiling. 2015-03-17 09:57:20 -07:00
Benoit Jacob
dc04f12967 use unsigned short instead of uint16_t which doesn't exist in c++98 2015-03-17 10:31:45 -04:00
Deanna Hood
8878e1c1de Remove ambiguity with recent numext methods isNaN and isInf 2015-03-17 22:39:51 +10:00
Deanna Hood
596be3cd86 Use std::arg for real numbers when c++11 is used, custom implementation otherwise 2015-03-17 15:28:12 +10:00
Deanna Hood
e26134ed62 Use std::round when c++11 is used, custom implementation otherwise 2015-03-17 14:55:14 +10:00
Deanna Hood
e21e29a088 Update cost of arg call to depend on if the scalar is complex or not 2015-03-17 14:04:33 +10:00
Deanna Hood
447a5a6b01 Fix VML declarations to only be for real/complex as appropriate 2015-03-17 13:33:31 +10:00
Deanna Hood
f52b78491c Remove packet isNaN, isInf, isFinite 2015-03-17 09:26:24 +10:00
Deanna Hood
1c78d6f2a6 Add boolean not operator (!) array support 2015-03-17 08:29:57 +10:00
Deanna Hood
85da0c2281 Remove test of now-missing floor, ceil, round complex implementations 2015-03-17 06:56:47 +10:00
Benoit Jacob
364cfd529d Similar to cset 3589a9c115
, also in 2px4 kernel: actual_panel_rows computation should always be resilient to parameters not consistent with the known L1 cache size, see comment
2015-03-16 16:28:44 -04:00
Benoit Steiner
25664afacd Pulled latest updates from trunk 2015-03-16 13:25:45 -07:00
Deanna Hood
e1d6e6c972 Make cube, inverse and abs2 free-functions 2015-03-17 06:25:24 +10:00
Benoit Jacob
577056aa94 Include stdint.h. Not going for cstdint because it is a C++11 addition. Needed for uint16_t at least, in lookup-table code. 2015-03-16 16:21:50 -04:00
Benoit Steiner
5144f66728 Fixed compilation warning 2015-03-16 13:17:52 -07:00
Benoit Steiner
0fd6d52724 Fixed compilation error with clang 2015-03-16 13:16:12 -07:00
Benoit Jacob
eb6929cb19 fix bug in maxsize calculation, which would cause products of size > 2048 to address the lookup table out of bounds 2015-03-16 16:15:47 -04:00
Benoit Steiner
f218c0181d Fixes the Lvalue computation by actually setting the LvalueBit properly when instantiating tensors of const T. Added a test to check the fix. 2015-03-16 13:05:00 -07:00
Deanna Hood
fef4e071d7 Rename isinf to isInf 2015-03-17 05:58:47 +10:00
Deanna Hood
46cf9cda32 Add isfinite array support as isFinite 2015-03-17 04:33:12 +10:00
Deanna Hood
7b829940d1 Add code snippets for new methods 2015-03-17 03:40:28 +10:00
Deanna Hood
1d76ceab55 Remove floor, ceil, round for complex numbers 2015-03-17 02:36:07 +10:00
Deanna Hood
717b7954ce Update cost of coeff-wise arg call 2015-03-17 02:11:57 +10:00
Deanna Hood
fb68b149cb Rename isnan to isNaN 2015-03-17 02:04:42 +10:00
Benoit Jacob
35c3a8bb84 Update Nexus 5 lookup table from combining now 2 runs of the benchmark, using the analyze-blocking-sizes partition tool. Gives better worst-case performance. 2015-03-16 11:05:51 -04:00
Benoit Jacob
e274607d7f fix compilation with GCC 4.8 2015-03-16 10:48:27 -04:00
Benoit Jacob
151b8b95c6 Fix bug in case where EIGEN_TEST_SPECIFIC_BLOCKING_SIZE is defined but false 2015-03-15 19:10:51 -04:00
Benoit Jacob
02babb9c0f Provide a empirical lookup table for blocking sizes measured on a Nexus 5. Only for float, only for Android on ARM 32bit for now. 2015-03-15 18:13:12 -04:00
Benoit Jacob
3589a9c115 actual_panel_rows computation should always be resilient to parameters not consistent with the known L1 cache size, see comment 2015-03-15 18:12:18 -04:00
Benoit Jacob
1dd3d89818 Fix a unused-var warning 2015-03-15 18:07:19 -04:00
Benoit Jacob
ca5c12587b Polish lookup tables generation 2015-03-15 18:05:53 -04:00
Benoit Jacob
e56aabf205 Refactor computeProductBlockingSizes to make room for the possibility of using lookup tables 2015-03-15 18:05:12 -04:00
Benoit Jacob
b6b88c0808 update perf_monitoring/gemm/changesets.txt 2015-03-13 14:57:05 -07:00
Benoit Jacob
488c15615a organize a little our default cache sizes, and use a saner default L1 outside of x86 (10% faster on Nexus 5) 2015-03-13 14:51:26 -07:00
Gael Guennebaud
9f58524cbd merge 2015-03-13 21:16:39 +01:00
Gael Guennebaud
1330f8bbd1 bug #973, improve AVX support by enabling vectorization of Vector4i-like types, and enforcing alignement of Vector4f/Vector2d-like types to preserve compatibility with SSE and future Eigen versions that will vectorize them with AVX enabled. 2015-03-13 21:15:50 +01:00
Gael Guennebaud
d99ab35f9e Fix internal::random(x,y) for integer types. The previous implementation could return y+1. The new implementation uses rejection sampling to get an unbiased behabior. 2015-03-13 21:12:46 +01:00
Gael Guennebaud
8580eb6808 bug #949: add static assertion for incompatible scalar types in dense end-user decompositions. 2015-03-13 21:06:20 +01:00
Gael Guennebaud
a9df28c95b SparseMatrix::insert: switch to a fully uncompressed mode if sequential insertion is not possible (otherwise an arbitrary large amount of memory was preallocated in some cases) 2015-03-13 21:00:21 +01:00
Gael Guennebaud
5ffe29cb9f Bound pre-allocation to the maximal size representable by StorageIndex and throw bad_alloc if that's not possible. 2015-03-13 20:57:33 +01:00
Benoit Jacob
d73ccd717e Add support for dumping blocking sizes tables 2015-03-13 10:36:01 -07:00
Gael Guennebaud
2f6f8bf31c Add missing coeff/coeffRef members to Block<sparse>, and extend unit tests. 2015-03-13 16:24:40 +01:00
Benoit Jacob
f2c3e2b10f Add --only-cubic-sizes option to analyze-blocking-sizes tool 2015-03-12 13:16:33 -07:00
Doug Kwan
657407227e Fix bug in pdiv<Packet1cd> which swaps 32-bit halves of a pair of
doubles instead of swapping the doubles.
2015-03-11 15:13:37 -07:00
Deanna Hood
f89fcefa79 Add hyperbolic trigonometric functions from std array support 2015-03-11 13:13:30 +10:00
Deanna Hood
a5e49976f5 Add log10 array support 2015-03-11 08:56:42 +10:00
Deanna Hood
19a71056ae Allow calling of square(array) in addition to array.square() 2015-03-11 06:59:28 +10:00
Deanna Hood
31fdd67756 Additional unary coeff-wise functors (isnan, round, arg, e.g.) 2015-03-11 06:39:23 +10:00
Gael Guennebaud
fd78874888 Fix compilation of iterative solvers with dense matrices 2015-03-09 21:31:03 +01:00
Gael Guennebaud
d4317a85e8 Add typedefs for return types of SparseMatrixBase::selfadjointView 2015-03-09 21:29:46 +01:00
Gael Guennebaud
9e885fb766 Add unit tests for CG and sparse-LLT for long int as storage-index 2015-03-09 14:33:15 +01:00
Gael Guennebaud
224a1fe4c6 bug #963: make IncompleteLUT compatible with non-default storage index types. 2015-03-09 13:55:20 +01:00
Gael Guennebaud
cf9940e17b Make sparse unit-test helpers aware of StorageIndex 2015-03-09 13:54:05 +01:00
Benoit Jacob
39228cb224 deserialization assumed benchmarks in same order, but we shuffle them. 2015-03-06 19:29:01 -05:00
Benoit Jacob
a4f956b1da merge 2015-03-06 19:13:36 -05:00
Benoit Jacob
19bf13aa62 Automatically serialize partial results to disk, reboot, and resume, when timings are getting bad 2015-03-06 19:11:50 -05:00
Gael Guennebaud
0ee391863e Avoid undeflow when blocking size are tuned manually. 2015-03-06 21:51:09 +01:00
Gael Guennebaud
14a5f135a3 bug #969: workaround abiguous calls to Ref using enable_if. 2015-03-06 17:51:31 +01:00
Gael Guennebaud
d23fcc0672 bug #978: add unit test for zero-sized products 2015-03-06 16:12:08 +01:00
Gael Guennebaud
87681e508f bug #978: early return for vanishing products 2015-03-06 16:11:22 +01:00
Gael Guennebaud
4c8eeeaed6 update gemm changeset list 2015-03-06 15:08:20 +01:00
Gael Guennebaud
cd3bbffa73 Improve blocking heuristic: if the lhs fit within L1, then block on the rhs in L1 (allows to keep packed rhs in L1) 2015-03-06 14:31:39 +01:00
Gael Guennebaud
eedd5063fd Update gemm performance monitoring tool:
- permit to recompute a subset of changesets
 - update changeset list
 - add a few more cases
2015-03-06 11:47:13 +01:00
Gael Guennebaud
58740ce4c6 Improve product kernel: replace the previous dynamic loop swaping strategy by a more general one:
It consists in increasing the actual number of rows of lhs's micro horizontal panel for small depth such that L1 cache is fully exploited.
2015-03-06 10:30:35 +01:00
Benoit Jacob
4ab01f7c21 slightly increase tolerance to clock speed variation 2015-03-05 14:41:16 -05:00
Benoit Jacob
5db2baa573 Make benchmark-blocking-sizes detect changes to clock speed and be resilient to that. 2015-03-05 13:44:20 -05:00
Gael Guennebaud
4c8b95d5c5 Rename LSCG to LeastSquaresConjugateGradient 2015-03-05 10:16:32 +01:00
Gael Guennebaud
7550107028 Product optimization: implement a dynamic loop-swapping startegy to improve memory accesses to the destination matrix in the case of K-rank-update like products, i.e., for products of the kind: "large x small" * "small x large" 2015-03-05 10:03:46 +01:00
Gael Guennebaud
2dc968e453 bug #824: improve accuracy of Quaternion::angularDistance using atan2 instead of acos. 2015-03-04 17:03:13 +01:00
Benoit Jacob
2231b3dece output to cout, not cerr, the actual results 2015-03-04 09:45:12 -05:00
Benoit Jacob
00ea121881 Complete the tool to analyze the efficiency of default sizes. 2015-03-04 09:30:56 -05:00
Benoit Steiner
0196141938 Fixed the optimized AVX implementation of the fast rsqrt function 2015-03-02 13:49:39 -08:00
Benoit Steiner
b0f2b6f297 Updated the tensor type casting code as follow: in the case where TgtRatio < SrcRatio, disable the vectorization of the source expression unless is has direct-access. 2015-03-02 10:11:40 -08:00
Benoit Steiner
d9cb604a5d Disabled the use of aligned memory loads when converting a tensor from float to doubles since alignment can't always be guaranteed. 2015-03-02 09:41:36 -08:00
Benoit Steiner
4fd7f47692 Added an optimized version of rsqrt for SSE and AVX that is used when EIGEN_FAST_MATH is defined. 2015-03-02 09:38:47 -08:00
Benoit Steiner
ae73859a0a Fixed incorrect assertion 2015-02-28 08:02:02 -08:00
Benoit Steiner
131449298f Fixed clang compilation warning 2015-02-28 03:01:19 -08:00
Benoit Steiner
56ea45ff0f Silenced some compilation warnings 2015-02-28 02:37:41 -08:00
Benoit Steiner
bb483313f6 Fixed another batch of compilation warnings 2015-02-28 02:32:46 -08:00
Benoit Steiner
fb53384b0f Improved the default implementation of prsqrt 2015-02-28 01:51:26 -08:00
Benoit Steiner
61409d9449 Silenced one more comilation warning 2015-02-28 01:49:09 -08:00
Benoit Steiner
1a7b84dc75 Silenced a few compilation warnings 2015-02-28 01:45:15 -08:00
Benoit Steiner
37357a310f Fixed compilation warnings 2015-02-27 23:54:24 -08:00
Benoit Steiner
cf1eea11de Fixed compilation warnings 2015-02-27 23:52:02 -08:00
Benoit Steiner
78732186ee Fixed compilation warnings 2015-02-27 23:51:16 -08:00
Benoit Steiner
4250a0cab0 Fixed compilation warnings 2015-02-27 21:59:10 -08:00
Benoit Steiner
a4e37b0617 Reverted the README 2015-02-27 13:09:49 -08:00
Benoit Steiner
306fceccbe Pulled latest updates from trunk 2015-02-27 13:05:26 -08:00
Benoit Steiner
75e7f381c8 Pulled latest updates from trunk 2015-02-27 12:57:55 -08:00
Benoit Steiner
2386fc8528 Added support for 32bit index on a per tensor/tensor expression. This enables us to use 32bit indices to evaluate expressions on GPU faster while keeping the ability to use 64 bit indices to manipulate large tensors on CPU in the same binary. 2015-02-27 12:57:13 -08:00
Benoit Steiner
e1f6a45b14 README.md edited online with Bitbucket 2015-02-27 20:44:24 +00:00
Benoit Steiner
90893bbe18 README.md edited online with Bitbucket 2015-02-27 20:44:10 +00:00
Benoit Steiner
473e6d4c3d README.md edited online with Bitbucket 2015-02-27 20:41:45 +00:00
Benoit Steiner
4369538227 README.md edited online with Bitbucket 2015-02-27 20:41:33 +00:00
Benoit Steiner
99cfbd6e84 README.md edited online with Bitbucket 2015-02-27 20:41:14 +00:00
Benoit Jacob
6466fa63be Reimplement the selection between rotating and non-rotating kernels
using templates instead of macros and if()'s.
That was needed to fix the build of unit tests on ARM, which I had
broken. My bad for not testing earlier.
2015-02-27 15:30:10 -05:00
Benoit Steiner
05089aba75 Switch to truncated casting when converting floating point types to integer. This ensures that vectorized casts are consistent with scalar casts 2015-02-27 09:27:30 -08:00
Benoit Steiner
bf9877a92a Pulled latest updates from trunk 2015-02-27 09:23:22 -08:00
Benoit Steiner
90f4e90f1d Fixed off-by-one error that prevented the evaluation of small tensor expressions from being vectorized 2015-02-27 09:22:37 -08:00
Benoit Steiner
573b377110 Added support for vectorized type casting of tensors 2015-02-27 08:46:04 -08:00
Benoit Jacob
2fc3b484d7 remove trailing comma 2015-02-27 11:37:45 -05:00
Benoit Jacob
33669348c4 Disable Packet2f/2i halfpacket support in NEON.
I believe that it was erroneously turned on, since Packet2f/2i intrinsics are unimplemented,
and code trying to use halfpackets just fails to compile on NEON, as it tries to use the
default implementation of pload/pstore and the types don't match.
2015-02-27 11:35:37 -05:00
Benoit Jacob
f5ff4d826f Fix NEON build flags: in the current NDK, at least with the clang-3.5 toolchain,
-mfpu=neon is not enough to activate NEON, since it's incompatible with the default float ABI,
and I have to pass -mfloat-abi=softfp (which is what everyone does in practice).
In fact, it would be a good idea to pass -mfloat-abi=softfp all the time, regardless of NEON.
Also removing the -mcpu=cortex-a8, as 1) it's not needed and 2) if we really wanted to pass
a specific -mcpu flag, that would presumably to tune performance for benchmarks, and it would
then not really make sense to tune for the very old cortex-a8 (it reflects ARM CPUs from 5 years ago).
2015-02-27 10:56:50 -05:00
Benoit Jacob
b7fc8746e0 Replace a static assert by a runtime one, fixes the build of unit tests on ARM
Also safely assert in the non-implemented path that should never be taken in practice,
and would return wrong results.
2015-02-27 10:01:59 -05:00
Benoit Steiner
f074bb4b5f Fixed another compilation problem with TensorIntDiv.h 2015-02-26 11:14:23 -08:00
Benoit Steiner
57154fdb32 Can now use the tensor 'reverse' operation as a lvalue 2015-02-26 11:13:42 -08:00
Benoit Steiner
f41b1f1666 Added support for fast reciprocal square root computation. 2015-02-26 09:42:41 -08:00
Benoit Steiner
2fffe69b1b Added missing copy constructor 2015-02-26 09:27:53 -08:00
Gael Guennebaud
bcf9bb5c1f Avoid packing rhs multiple-times when blocking on the lhs only. 2015-02-26 17:01:33 +01:00
Gael Guennebaud
4ec3f04b3a Make sure that the block size computation is tested by our unit test. 2015-02-26 17:00:36 +01:00
Gael Guennebaud
2e9cb06a87 Update changeset list to be checked by perf_monitoring/gemm. 2015-02-26 16:13:33 +01:00
Gael Guennebaud
a46061ab7b Make perf_monitoring/gemm script more flexible:
- skip existing dataset
  - add a "-up" option to recompute the dataset (see script header)
  - allow to specify a filename prefix
2015-02-26 16:12:58 +01:00
Gael Guennebaud
a8ad8887bf Implement a more generic blocking-size selection algorithm. See explanations inlines.
It performs extremely well on Haswell. The main issue is to reliably and quickly find the
actual cache size to be used for our 2nd level of blocking, that is: max(l2,l3/nb_core_sharing_l3)
2015-02-26 16:04:35 +01:00
Gael Guennebaud
400becc591 Fix typos in block-size testing code, and set peeling on k to 8. 2015-02-26 15:57:06 +01:00
Benoit Steiner
bffb6bdf45 Made TensorIntDiv.h compile with MSVC 2015-02-25 23:54:43 -08:00
Benoit Steiner
27f3fb2bcc Fixed another clang warning 2015-02-25 22:54:20 -08:00
Benoit Steiner
f8fbb3f9a6 Fixed several compilation warnings reported by clang 2015-02-25 22:22:37 -08:00
Benoit Steiner
8e817b65d0 Silenced a few more compilation warnings generated by nvcc 2015-02-25 17:46:20 -08:00
Benoit Steiner
410070e5ab Added more tests to validate support for tensors laid out in RowMajor order. 2015-02-25 16:14:59 -08:00
Benoit Steiner
1cfd51908c Added support for RowMajor layout to the tensor patch extraction cofde. 2015-02-25 13:29:12 -08:00
Benoit Steiner
eb21a8173e Pulled latest changes from trunk 2015-02-25 09:49:44 -08:00
Benoit Steiner
8afce86e64 Added support for RowMajor layout to the image patch extraction code
Speeded up the unsupported_cxx11_tensor_image_patch test and reduced its memory footprint
2015-02-25 09:48:54 -08:00
Benoit Jacob
692136350b So I extensively measured the impact of the offset in this prefetch. I tried offset values from 0 to 128 (on this float* pointer, so implicitly times 4 bytes).
On x86, I tested a Sandy Bridge with AVX with 12M cache and a Haswell with AVX+FMA with 6M cache on MatrixXf sizes up to 2400.

I could not see any significant impact of this offset.

On Nexus 5, the offset has a slight effect: values around 32 (times sizeof float) are worst. Anything else is the same: the current 64 (8*pk), or... 0.

So let's just go with 0!

Note that we needed a fix anyway for not accounting for the value of RhsProgress. 0 nicely avoids the issue altogether!
2015-02-25 12:37:14 -05:00
Christoph Hertzberg
531fa9de77 bug #970: Add EIGEN_DEVICE_FUNC to RValue functions, in case Cuda supports RValue-references. 2015-02-24 21:03:28 +01:00
Benoit Jacob
26275b250a Fix my recent prefetch changes:
- the first prefetch is actually harmful on Haswell with FMA,
   but it is the most beneficial on ARM.
 - the second prefetch... I was very stupid and multiplied by sizeof(scalar)
   and offset of a scalar* pointer. The old offset was 64; pk = 8, so 64=pk*8.
   So this effectively restores the older offset. Actually, there were
   two prefetches here, one with offset 48 and one with offset 64. I could not
   confirm any benefit from this strange 48 offset on either the haswell or
   my ARM device.
2015-02-23 16:55:17 -05:00
Benoit Jacob
488874781b Add analyze-blocking-sizes program under bench/ to analyze multiple logs
generated by benchmark-blocking-sizes.
2015-02-23 14:02:29 -05:00
Christoph Hertzberg
052b6b40f1 Fix two trivial warnings 2015-02-22 12:40:51 +01:00
Christoph Hertzberg
ecbf2a6656 log1p is defined only for real Scalars in C++11 2015-02-21 19:58:24 +01:00
Christoph Hertzberg
6af6cf0c2e I can reproduce any problems that justified this hack. However it makes builds fail in C++11 mode. 2015-02-21 19:43:56 +01:00
Gael Guennebaud
3cf642baa3 Fix compilation of unit tests disabling assertion cheking 2015-02-21 14:13:48 +01:00
Benoit Jacob
458cf91cd9 Add benchmark-blocking-sizes.cpp to bench/ per mailing list discussion. 2015-02-20 17:08:04 -05:00
Gael Guennebaud
03ec601ff7 Initial version of a small script to help tracking performance regressions 2015-02-20 19:20:34 +01:00
Gael Guennebaud
333b497383 update bench_gemm 2015-02-20 11:59:49 +01:00
Gael Guennebaud
2da1594750 Fix doc of Ref<> 2015-02-20 11:52:22 +01:00
Gael Guennebaud
01b8440579 With C++11 Matrix<float> + Matrix<complex<float>> does not even compile 2015-02-20 09:32:49 +01:00
Gael Guennebaud
3594451ee0 Remove EIGEN_TEST_C++0x option and let EIGEN_TEST_CXX11 adds the -std=c++11 flag 2015-02-20 09:31:27 +01:00
Gael Guennebaud
b192e29eae In C++11 destructors do not throw by default (fix CommaInitializer unit test) 2015-02-20 09:28:34 +01:00
Benoit Steiner
ab41652d81 Pulled latest changes from trunk 2015-02-19 21:23:37 -08:00
Benoit Steiner
7765039f1c Marked the CUDA packet primitives as EIGEN_DEVICE_FUNC since they'll end up being executed on the GPU device. 2015-02-19 21:22:51 -08:00
Gael Guennebaud
a66f5fc2fd Fix regression with C++11 support of lambda: now internal::result_of falls back to std::result_of in C++11. 2015-02-19 23:32:12 +01:00
Gael Guennebaud
ece6b440f9 Fix a C++11 compilation issue in unit test 2015-02-19 23:31:08 +01:00
Gael Guennebaud
1b7e12847d Fix some calls to result_of on binary functors as unary ones. 2015-02-19 23:30:41 +01:00
Gael Guennebaud
0f4dd15dfc Declare const some const variables 2015-02-19 23:28:57 +01:00
Benoit Steiner
92ceb02c6d Pulle latest updates from trunk 2015-02-19 11:59:52 -08:00
Benoit Steiner
110fb90250 Improved the documentations 2015-02-19 11:59:04 -08:00
Gael Guennebaud
829dddd0fd Add support for C++11 result_of/lambdas 2015-02-19 15:18:37 +01:00
Benoit Jacob
db05f2d01e rotating kernel: avoid compiling anything outside of ARM 2015-02-18 15:43:52 -05:00
Benoit Jacob
0ed00d5438 remove a newly introduced redundant typedef - sorry. 2015-02-18 15:05:01 -05:00
Benoit Jacob
9bd8a4bab5 bug #955 - Implement a rotating kernel alternative in the 3px4 gebp path
This is substantially faster on ARM, where it's important to minimize the number of loads.

This is specific to the case where all packet types are of size 4. I made my best attempt to minimize how dirty this is... opinions welcome.

Eventually one could have a generic rotated kernel, but it would take some work to get there. Also, on sandy bridge, in my experience, it's not beneficial (even about 1% slower).
2015-02-18 15:03:35 -05:00
Hauke Heibel
ee27d50633 Fixed template parameter. 2015-02-18 18:51:08 +01:00
Gael Guennebaud
73a24de424 merge 2015-02-18 15:51:00 +01:00
Gael Guennebaud
63eb0f6fe6 Clean a bit computeProductBlockingSizes (use Index type, remove CEIL macro) 2015-02-18 15:49:05 +01:00
Gael Guennebaud
fc5c3e85e2 Fix bug #961: eigen-doc.tgz included part of itself. 2015-02-18 15:47:01 +01:00
Benoit Jacob
4a3e6c8be1 bug #958 - Allow testing specific blocking sizes
This is only a debugging/testing patch. It allows testing specific
product blocking sizes, typically to study the impact on performance.

Example usage:

int testk, testm, testn;
#define EIGEN_TEST_SPECIFIC_BLOCKING_SIZES
#define EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_K testk
#define EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_M testm
#define EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_N testn
#include <Eigen/Core>
2015-02-18 09:43:55 -05:00
Gael Guennebaud
c7bb1e8ea8 Fix a regression when using OpenMP, and fix bug #714: the number of threads might be lower than the number of requested ones 2015-02-18 15:19:23 +01:00
Jan Blechta
168ceb271e Really use zero guess in ConjugateGradients::solve as documented
and expected for consistency with other methods.
2015-02-18 14:26:10 +01:00
Gael Guennebaud
8fdcaded5e merge 2015-03-04 10:18:08 +01:00
Gael Guennebaud
c43154bbc5 Check for no-reallocation in SparseMatrix::insert (bug #974) 2015-03-04 10:16:46 +01:00
Gael Guennebaud
1ce0178363 Improve efficiency of SparseMatrix::insert/coeffRef for sequential outer-index insertion strategies (bug #974) 2015-03-04 09:39:26 +01:00
Gael Guennebaud
3dca4a1efc Update manual wrt new LSCG solver. 2015-03-04 09:35:30 +01:00
Gael Guennebaud
05274219a7 Add a CG-based solver for rectangular least-square problems (bug #975). 2015-03-04 09:34:27 +01:00
Benoit Jacob
2aa09e6b4e Fix asm comments in 1px1 kernel 2015-03-03 13:44:00 -05:00
Benoit Steiner
5d2fd64a1a Fixed compilation error when compiling with gcc4.7 2015-03-03 08:56:49 -08:00
Benoit Jacob
f64b4480af Add missing copyright notices 2015-03-03 11:43:56 -05:00
Benoit Jacob
eae8e27b7d Add a benchmark-default-sizes action to benchmark-blocking-sizes.cpp 2015-03-03 11:41:21 -05:00
Marc Glisse
37a93c4263 New scoring functor to select the pivot.
This is can be useful for non-floating point scalars, where choosing the biggest element is generally not the best choice.
2015-03-03 17:08:28 +01:00
Benoit Jacob
ccc1277a42 must also disable complex<double> when disabling double vectorization 2015-03-03 10:17:05 -05:00
Benoit Jacob
f839099512 Work around an ICE in Clang 3.5 in the iOS toolchain with double NEON intrinsics. 2015-03-03 09:35:22 -05:00
Benoit Jacob
9930e9583b Improve analyze-blocking-sizes, and in particular give it a evaluate-defaults tool
that shows the efficiency of Eigen's default blocking sizes choices, using a
previously computed table from benchmark-blocking-sizes.
2015-03-02 18:08:38 -05:00
Benoit Jacob
1ec0f4fadf HalfPacket also needed to be disabled for double, on ARMv8. 2015-03-02 16:08:54 -05:00
Gael Guennebaud
3109f0e74e Add SSE vectorization of Quaternion::conjugate. Significant speed-up when combined with products like q1*q2.conjugate() 2015-03-02 20:09:33 +01:00
Abhijit Kundu
ef09ce4552 Fix for TensorIO for Fixed sized Tensors.
The following code snippet was failing to compile:

TensorFixedSize<double, Sizes<4, 3> > t_4x3;
cout << 4x3;
2015-02-28 21:30:31 -05:00
Abhijit Kundu
3a4b6827b4 Merged eigen/eigen into default 2015-02-28 20:15:28 -05:00
Christoph Hertzberg
31e2ffe82c Replaced POSIX random() by internal::random 2015-02-28 18:39:37 +01:00
Christoph Hertzberg
73dd95e7b0 Use @CMAKE_MAKE_PROGRAM@ instead of make in buildtests.sh 2015-02-28 16:51:53 +01:00
Christoph Hertzberg
682196e9fc Fixed MPRealSupport 2015-02-28 16:41:00 +01:00
Christoph Hertzberg
33f40b2883 Cygwin does not like weak linking either. 2015-02-28 14:53:11 +01:00
Christoph Hertzberg
0f82a1d7b7 bug #967: Automatically add cxx11 suffix when building in C++11 mode 2015-02-28 14:52:26 +01:00
Gael Guennebaud
9aee1e300a Increase unit-test L1 cache size to ensure we are doing at least 2 peeled loop within product kernel. 2015-02-27 22:55:12 +01:00
Gael Guennebaud
b10cd3afd2 Re-enbale detection of min/max parentheses protection, and re-enable mpreal_support unit test. 2015-02-27 22:38:00 +01:00
Abhijit Kundu
4084dce038 Added CMake support for Tensor module. CMake now installs CXX11 Tensor module like the rest of the unsupported modules 2015-02-26 16:50:09 -05:00
Gael Guennebaud
548b781380 Fix bug #945: workaround MSVC warning 2015-02-18 12:53:49 +01:00
Gael Guennebaud
6f4adc9e94 Add missing install directives for arch/CUDA 2015-02-18 11:40:06 +01:00
Gael Guennebaud
371d3bef36 Workaround dead store warnings in unit tests. 2015-02-18 11:30:44 +01:00
Gael Guennebaud
63464754ef Add an internal assertion in makeCompressed to catch a possible risk of null-pointer access. 2015-02-18 11:29:54 +01:00
Gael Guennebaud
eb563049f7 Remove some dead stores. 2015-02-18 11:26:48 +01:00
Gael Guennebaud
dc7e6acc05 Fix possible usage of a null pointer in CholmodSupport 2015-02-18 11:26:25 +01:00
Gael Guennebaud
d4eda01488 Big 957, workaround MSVC/ICC compilation issue 2015-02-18 11:24:32 +01:00
Christoph Hertzberg
24d65ac0b0 Removed redundant typedef which confused old gcc versions. 2015-02-18 01:03:32 +01:00
Gael Guennebaud
20cac72b82 Packet must be passed by const reference and not by value to avoid alignment issue. 2015-02-17 22:58:32 +01:00
Benoit Steiner
36c9d08274 Pulled latest updates from trunk 2015-02-17 10:04:25 -08:00
Benoit Steiner
f77054f43c Silenced compilation warning 2015-02-17 10:02:04 -08:00
Benoit Steiner
1d3b64d32b Added support for tensor concatenation as lvalue 2015-02-17 09:57:41 -08:00
Benoit Steiner
00f048d44f Added support for tensor concatenation as lvalue 2015-02-17 09:54:40 -08:00
Christoph Hertzberg
97a36ecba4 Suppress some remaining Index conversion warnings 2015-02-17 18:52:39 +01:00
Gael Guennebaud
159fb181c2 Disable __m128* wrappers when compiling with AVX and -fabi-version=4 2015-02-17 16:27:20 +01:00
Gael Guennebaud
91ab2489dd Fix compilation with GCC/AVX (workaround __m128 and __m256 being the same type with default ABI) 2015-02-17 16:08:07 +01:00
Gael Guennebaud
9daf8eba6f Fix compilation of Cholmod*(matrix) ctor 2015-02-17 15:24:52 +01:00
Gael Guennebaud
3373c903b3 Fix compilation of int*complex with gcc 2015-02-16 19:18:12 +01:00
Gael Guennebaud
9f49f00feb Extend sparse-determinant unitests 2015-02-16 19:09:48 +01:00
Gael Guennebaud
f0b1b1df9b Fix SparseLU::signDeterminant() method, and add a SparseLU::determinant() method. 2015-02-16 19:09:22 +01:00
Gael Guennebaud
8768ff3c31 Add PermutationMatrix::determinant method. 2015-02-16 19:08:25 +01:00
Martin Drozdik
64b29e06b9 bug #956: Fixed bug in move constructors of DenseStorage which caused "moved-from" objects to be in an invalid state. 2015-02-16 18:18:46 +09:00
Gael Guennebaud
1c0e8bcf09 Fix unused variable warning. 2015-02-16 17:21:30 +01:00
Gael Guennebaud
69fa405096 Update circulant custom expression example 2015-02-16 17:21:16 +01:00
Gael Guennebaud
0f464d9d87 bug #897: fix regression in BiCGSTAB(mat) ctor (an all other iterative solvers).
Add respective regression unit test.
2015-02-16 17:05:10 +01:00
Gael Guennebaud
470d26d580 Remove some useless typedefs 2015-02-16 16:48:21 +01:00
Gael Guennebaud
4dded73227 bug #914: fix compiler detection on windows
(grafted from 77af14fb62
)
2015-02-16 16:26:47 +01:00
Gael Guennebaud
953d5ccfd5 Doc: explain how to free allocated memory in SparseMAtrix 2015-02-16 15:56:11 +01:00
Gael Guennebaud
98604576d1 Merged in chtz/eigen-indexconversion (pull request PR-92)
bug #877, bug #572: Get rid of Index conversion warnings, summary of changes:

- Introduce a global typedef Eigen::Index making Eigen::DenseIndex and AnyExpr<>::Index deprecated (default is std::ptrdiff_t).

 - Eigen::Index is used throughout the API to represent indices, offsets, and sizes.

 - Classes storing an array of indices uses the type StorageIndex to store them. This is a template parameter of the class. Default is int.

 - Methods that *explicitly* set or return an element of such an array take or return a StorageIndex type. In all other cases, the Index type is used.
2015-02-16 15:29:00 +01:00
Gael Guennebaud
45cbb0bbb1 The usage of DenseIndex is deprecated, so let's replace DenseIndex by Index 2015-02-16 15:05:41 +01:00
Gael Guennebaud
cc641aabb7 Remove deprecated usage of expr::Index. 2015-02-16 14:46:51 +01:00
Gael Guennebaud
aa6c516ec1 Fix many long to int conversion warnings:
- fix usage of Index (API) versus StorageIndex (when multiple indexes are stored)
 - use StorageIndex(val) when the input has already been check
 - use internal::convert_index<StorageIndex>(val) when val is potentially unsafe (directly comes from user input)
2015-02-16 13:19:05 +01:00
Christoph Hertzberg
bd511dde9d bug #952: Missing \endcode made doxygen fail to build ColPivHouseholderQR 2015-02-15 06:08:25 +01:00
Benoit Steiner
e2cfddf75f Pulled latest updates from trunk 2015-02-13 16:21:59 -08:00
Benoit Steiner
0927801a84 Optimized version of the sin(), exp(), log() and sqrt() function for AVX 2015-02-13 16:07:08 -08:00
Benoit Jacob
e972b55ec4 bug #953 - Fix prefetches in 3px4 product kernel
This gives a 10% speedup on nexus 4 and on nexus 5.
2015-02-13 14:52:36 -05:00
Gael Guennebaud
fc202bab39 Index refactoring: StorageIndex must be used for storage only (and locally when it make sense). In all other cases use the global Index type. 2015-02-13 18:57:41 +01:00
Gael Guennebaud
fe51319980 Merge Index-refactoring branch with default, fix PastixSupport, remove some useless typedefs 2015-02-13 10:03:53 +01:00
Gael Guennebaud
0918c51e60 merge Tensor module within Eigen/unsupported and update gemv BLAS wrapper 2015-02-12 21:48:41 +01:00
Gael Guennebaud
409547a0c8 update EIGEN_FAST_MATH documentation 2015-02-12 21:04:31 +01:00
Benoit Steiner
4470c99975 Added a test to validate tensor casting on cuda devices 2015-02-10 14:40:18 -08:00
Benoit Steiner
6620aaa4b3 Silenced a few compilation warnings generated by nvcc 2015-02-10 14:34:42 -08:00
Benoit Steiner
f669f5656a Marked a few functions as EIGEN_DEVICE_FUNC to enable the use of tensors in cuda kernels. 2015-02-10 14:29:47 -08:00
Gael Guennebaud
029d236ceb merge 2015-02-10 23:12:47 +01:00
Gael Guennebaud
fe25f3b8e3 FMA has been wrongly disabled 2015-02-10 23:11:35 +01:00
Benoit Steiner
ceb4c9c10b Pulled latest changes from trunk 2015-02-10 14:03:17 -08:00
Benoit Steiner
cc5d7ff523 Added vectorized implementation of the exponential function for ARM/NEON 2015-02-10 14:02:38 -08:00
Gael Guennebaud
d771295554 remove useless include 2015-02-10 22:59:27 +01:00
Benoit Steiner
fefec723aa Fixed compilation error triggered when trying to vectorize a non vectorizable cuda kernel. 2015-02-10 13:16:22 -08:00
Benoit Steiner
780b2422e2 Silenced the last batch of compilation warnings triggered by gcc 4.8 2015-02-10 12:43:55 -08:00
Benoit Steiner
c21e45fbc5 Fixed a few more compilation warnings 2015-02-10 12:36:26 -08:00
Benoit Steiner
057cfd2f02 Silenced more compilation warnings 2015-02-10 12:25:02 -08:00
Benoit Steiner
114e863f08 Silcenced a few compilation warnings 2015-02-10 12:20:24 -08:00
Benoit Steiner
410895a7e4 Silenced several compilation warnings 2015-02-10 12:13:19 -08:00
Benoit Steiner
4716c2c666 Fixed compilation error 2015-02-10 12:06:19 -08:00
Benoit Steiner
91fe3a3004 Removed a debug printf statement. 2015-02-10 10:29:28 -08:00
Jan Blechta
c3f3580b8f Fix bug #733: step by step solving is not a good example for solveWithGuess 2015-02-10 14:24:39 +01:00
Gael Guennebaud
deecff97ed typo 2015-02-10 19:22:05 +01:00
Gael Guennebaud
c6e8caf090 Allows Lower|Upper as a template argument of CG and MINRES: in this case the full matrix will be considered. 2015-02-10 18:57:41 +01:00
Gael Guennebaud
d10d6a40dd bug #897: Update unsupported iterative solvers based on IterativeSolverBased. 2015-02-10 13:02:59 +01:00
Gael Guennebaud
87629cd639 bug #897: makes iterative sparse solvers use a Ref<SparseMatrix> instead of a SparseMatrix pointer. This fixes usage of iterative solvers with a Map<SparseMatrix>. 2015-02-09 11:41:25 +01:00
Gael Guennebaud
bde98df03f merge 2015-02-09 11:15:37 +01:00
Gael Guennebaud
d4ec48575e Make Block<SparseMatrix> inherit SparseCompressedBase in the case of an inner-panels and fix valuePtr() innerIndexPtr() 2015-02-09 11:14:36 +01:00
Gael Guennebaud
554aa9b31d Add failtests for Ref<SparseMatrix> 2015-02-09 10:24:07 +01:00
Gael Guennebaud
3af29caae8 Cleaning and add more unit tests for Ref<SparseMatrix> and Map<SparseMatrix> 2015-02-09 10:23:45 +01:00
Gael Guennebaud
f2ff8c091e Add a Ref<SparseMatrix> specialization. 2015-02-07 22:04:18 +01:00
Gael Guennebaud
f3be317614 Add a Map<SparseMatrix> specialization. 2015-02-07 22:03:25 +01:00
Gael Guennebaud
08081f8293 Make SparseTranspose inherit SparseCompressBase when possible 2015-02-07 22:02:14 +01:00
Gael Guennebaud
7838fda82c Add a SparseCompressedBase class providing (un)compressed accessors (like data()/*Stride() for dense matrices),
and a CompressedAccessBit flag (similar to DirectAccessBit for dense matrices).
2015-02-07 22:00:46 +01:00
Benoit Steiner
3ba6647398 Fixed the cxx11_meta test 2015-02-06 06:00:59 -08:00
Benoit Steiner
01f7918788 Pulled latest fixes 2015-02-06 05:30:20 -08:00
Gael Guennebaud
b50ffaddf2 merge 2015-02-06 14:27:12 +01:00
Gael Guennebaud
74e460b995 Fix symmetric product 2015-02-06 14:26:24 +01:00
Gael Guennebaud
c03c73c9b7 Fix clang compilation 2015-02-06 14:26:12 +01:00
Gael Guennebaud
668518aed6 Fix non initialized entries and comparison of very small numbers 2015-02-06 14:25:41 +01:00
Benoit Steiner
c739102ef9 Pulled the latest changes from the trunk 2015-02-06 05:25:03 -08:00
Benoit Steiner
2559fa9b0f Fixed compilation error in the tensor broadcasting test 2015-02-06 02:55:18 -08:00
Benoit Steiner
dcb2a8b184 Added the EIGEN_HAS_CONSTEXPR define
Gate the tensor index list code based on the value of EIGEN_HAS_CONSTEXPR
2015-02-06 02:51:59 -08:00
Filippo Basso
a8f2c6eec7 Using numext::pow instead of std::pow in poly_eval function. 2015-02-04 18:37:51 +00:00
Gael Guennebaud
b1eca55328 Use Ref<> to ensure that both x and b in Ax=b are compatible with Umfpack/SuperLU expectations 2015-02-03 23:46:05 +01:00
Gael Guennebaud
ebdf6a2dbb SPQR: fix default threshold value 2015-02-03 22:32:34 +01:00
Benoit Steiner
f64045a060 Silenced a few more compilation warnings 2015-01-30 19:52:01 -08:00
Benoit Steiner
590f4b0aa3 Silenced some compilation warnings 2015-01-30 19:46:30 -08:00
Benoit Jacob
5ef95fabee bug #936, patch 3/3: Properly detect FMA support on ARM (requires VFPv4)
and use it instead of MLA when available, because it's both more accurate,
and faster.
2015-01-30 17:45:03 -05:00
Benoit Jacob
0f21613698 bug #936, patch 2/3: Remove EIGEN_VECTORIZE_FMA, was redundant with EIGEN_HAS_SINGLE_INSTRUCTION_MADD 2015-01-30 17:44:26 -05:00
Benoit Jacob
340b8afb14 bug #936, patch 1.5/3: rename _FUSED_ macros to _SINGLE_INSTRUCTION_,
because this is what they are about. "Fused" means "no intermediate rounding
between the mul and the add, only one rounding at the end". Instead,
what we are concerned about here is whether a temporary register is needed,
i.e. whether the MUL and ADD are separate instructions.
Concretely, on ARM NEON, a single-instruction mul-add is always available: VMLA.
But a true fused mul-add is only available on VFPv4: VFMA.
2015-01-31 14:15:57 -05:00
Benoit Jacob
9f99f61e69 bug #936, patch 1/3: some cleanup and renaming for consistency. 2015-01-30 17:43:56 -05:00
Benoit Jacob
759bd92a85 bug #935: Add asm comments in GEBP kernels to work around a bug
in both GCC and Clang on ARM/NEON, whereby they spill registers,
severely harming performance. The reason why the asm comments
make a difference is that they prevent the compiler from
reordering code across these boundaries, which has the effect
of extending the lifetime of local variables and increasing
register pressure on this register-tight code.
2015-01-30 17:27:56 -05:00
Gael Guennebaud
f1092d2f73 bug #941: fix accuracy issue in ColPivHouseholderQR, do not stop decomposition on a small pivot 2015-01-30 19:04:04 +01:00
Gael Guennebaud
9d82f7e30d Supernodes was disabled. 2015-01-30 17:24:40 +01:00
Benoit Steiner
e896c0ade7 Marked the contraction operation as read only, since its result can't be assigned. 2015-01-29 10:29:47 -08:00
Benoit Steiner
5a6ea4edf6 Added more tests to cover tensor reductions 2015-01-28 10:02:47 -08:00
Gael Guennebaud
a727a2c4ed bug #933: RealSchur, do not consider the input matrix norm to check negligible sub-diag entries. This also makes this test consistent with the complex and self-adjoint cases. 2015-01-28 16:07:51 +01:00
Benoit Steiner
9dfdbd7e56 mproved the performance of tensor reductions that preserve the inner most dimension(s). 2015-01-27 14:15:31 -08:00
Benoit Steiner
46fc881e4a Added a few benchmarks for the tensor code 2015-01-26 17:46:40 -08:00
Gael Guennebaud
c6eb84aabc Enable vectorization of transposeInPlace for PacketSize x PacketSize matrices 2015-01-26 17:09:01 +01:00
Gael Guennebaud
e1f1091fde Add support for dense ?= diagonal 2015-01-24 10:32:49 +01:00
Gael Guennebaud
b9d314ae19 bug #329: fix typo 2015-01-17 21:55:33 +01:00
Benoit Steiner
14f537c296 gcc doesn't consider that
template<typename OtherDerived> TensorStridingOp& operator = (const OtherDerived& other)
provides a valid assignment operator for the striding operation, and therefore refuses to compile code like:
result.stride(foo) = source.stride(bar);

Added the explicit
   TensorStridingOp& operator = (const TensorStridingOp& other)

as a workaround to get the code to compile, and did the same in all the operations that can be used as lvalues.
2015-01-16 09:09:23 -08:00
Benoit Steiner
641e824c56 Added cube() operation 2015-01-15 11:11:48 -08:00
Benoit Steiner
b5124e7cfd Created many additional tests 2015-01-14 15:46:04 -08:00
Benoit Steiner
54e3633b43 Updated the list of include files 2015-01-14 15:43:38 -08:00
Benoit Steiner
f697df7237 Improved support for RowMajor tensors
Misc fixes and API cleanups.
2015-01-14 15:38:48 -08:00
Benoit Steiner
6559d09c60 Ensured that each thread has it's own copy of the TensorEvaluator: this avoid race conditions when the evaluator calls a non thread safe functor, eg when generating random numbers. 2015-01-14 15:34:50 -08:00
Benoit Steiner
8a382aa119 Improved the resizing of tensors 2015-01-14 15:33:11 -08:00
Benoit Steiner
703c526355 Misc improvements 2015-01-14 15:31:52 -08:00
Benoit Steiner
4cdf3fe427 Misc fixes 2015-01-14 15:30:47 -08:00
Benoit Steiner
0feff6e987 Expanded the functionality of index lists 2015-01-14 15:29:48 -08:00
Gael Guennebaud
cd679f2c47 Fix doc: setConstant does not exist for SparseMatrix. 2015-01-14 22:06:09 +01:00
Benoit Steiner
1ac8600126 Fixed the return type of coefficient wise operations. For example, the abs function returns a floating point value when called on a complex input. 2015-01-14 12:47:46 -08:00
Benoit Steiner
378bdfb7f0 Added missing apis to the TensorMap class 2015-01-14 12:45:20 -08:00
Benoit Steiner
0526dc1bb4 Added missing apis to the tensor class 2015-01-14 12:44:08 -08:00
Benoit Steiner
1a36590e84 Fixed the printing of RowMajor tensors 2015-01-14 12:43:20 -08:00
Benoit Steiner
7e0b6c56b4 Added ability to initialize a tensor using an initializer list 2015-01-14 12:41:30 -08:00
Benoit Steiner
b12dd1ae3c Misc improvements for fixed size tensors 2015-01-14 12:39:34 -08:00
Benoit Steiner
71676eaddd Added support for RowMajor inputs to the contraction code. 2015-01-14 12:36:57 -08:00
Benoit Steiner
0a0ab6dd15 Increased the functionality of the tensor devices 2015-01-14 11:45:17 -08:00
Benoit Steiner
5692723c58 Improved the performance of the contraction code on CUDA 2015-01-14 11:42:52 -08:00
Benoit Steiner
8f4b8d204b Improved the performance of tensor reductions
Added the ability to generate random numbers following a normal distribution
Created a test to validate the ability to generate random numbers.
2015-01-14 10:19:33 -08:00
Benoit Steiner
3bd2b41b2e Created a test for tensor type casting 2015-01-14 10:17:02 -08:00
Benoit Steiner
4928ea1212 Added ability to reverse the order of the coefficients in a tensor 2015-01-14 10:15:58 -08:00
Benoit Steiner
b00fe1590d Added ability to swap the layout of a tensor 2015-01-14 10:14:46 -08:00
Benoit Steiner
c94174b4fe Improved tensor references 2015-01-14 10:13:08 -08:00
Benoit Steiner
91dd53e54d Created some documentation 2015-01-13 16:07:51 -08:00
Gael Guennebaud
279786e987 Fix missing evaluator in outer-product 2015-01-13 10:25:50 +01:00
Gael Guennebaud
ae4644cc68 bug #907, ARM64: workaround ICE in xcode/clang 2015-01-13 10:03:00 +01:00
Gael Guennebaud
36f7c1337f bug #907, ARM64: workaround vreinterpretq_u64_* not defined in xcode/clang 2015-01-13 09:57:37 +01:00
Gael Guennebaud
63974bcb88 Big 907: workaround some missing intrinsics in current NDK's gcc version (ARM64) 2015-01-07 09:44:25 +01:00
Gael Guennebaud
79f4a59ed9 bug #907: fix compilation with ARM64 2015-01-07 09:41:56 +01:00
Benoit Steiner
9f98650d0a Ensured that contractions that can be reduced to a matrix vector product work correctly even when the input coefficients aren't aligned. 2015-01-06 09:29:13 -08:00
Gael Guennebaud
db5b0741b5 Fix bug #925: typo in MatLab versions of middleRows 2015-01-04 21:39:50 +01:00
Gael Guennebaud
f5f6e2c6f4 bug #921: fix utilization of bitwise operation on enums in first_aligned 2014-12-19 14:41:59 +01:00
Gael Guennebaud
25c7d9164f bug #920: fix MSVC 2015 compilation issues 2014-12-18 22:58:15 +01:00
Gael Guennebaud
b8d9eaa19b Use true compile time "if" for Transform::makeAffine 2014-12-13 22:16:39 +01:00
Gael Guennebaud
f806c23012 Fix false negatives in geo_transformations unit tests 2014-12-16 16:50:30 +01:00
Gael Guennebaud
99501a2c4c Fix wrong negative in nullary unit test when extended precision is used (FPU). 2014-12-16 16:23:47 +01:00
Gael Guennebaud
7dad5f797e bug #821: workaround MSVC 2013 issue with using Base::Base::operator= 2014-12-16 13:33:43 +01:00
Christoph Hertzberg
dcad508986 At least CMAKE 2.8.4 is required for WORKING_DIRECTORY option in add_test 2014-12-15 12:45:29 +01:00
Christoph Hertzberg
608733415a Free functions should only be declared as static in separate compilation units
(grafted from d85abc89c5
)
2014-12-12 12:01:03 +01:00
Gael Guennebaud
57ec399ec9 Remove unused fortran files 2014-12-13 21:41:25 +01:00
Gael Guennebaud
56ca44ad1a Use f2c generated code instead of the original fortran code, except for dotc/dotu. 2014-12-11 17:03:41 +01:00
Christoph Hertzberg
e8cdbedefb bug #877, bug #572: Introduce a global Index typedef. Rename Sparse*::Index to StorageIndex, make Dense*::StorageIndex an alias to DenseIndex. Overall this commit gets rid of all Index conversion warnings. 2014-12-04 22:48:53 +01:00
Gael Guennebaud
6ccf97f3e6 Fix GL support wrt evaluators 2014-12-04 22:05:28 +01:00
Gael Guennebaud
433bce5c3a UmfPack support: fix redundant evaluation/copies when calling compute() and support generic expressions as input 2014-12-02 17:30:57 +01:00
Gael Guennebaud
775f7e5fbb bug #697: make sure empty classes are at the end in case of multiple inheritence 2014-12-02 14:40:19 +01:00
Gael Guennebaud
a819fa148d Fix MSVC compilation issue 2014-12-02 14:35:31 +01:00
Gael Guennebaud
1a8dc85142 bug #897: fix UmfPack usage with mapped sparse matrices 2014-12-02 13:57:13 +01:00
Gael Guennebaud
4974d1d2b4 Fix bug #911: m_extractedDataAreDirty was not initialized in UmfPackLU 2014-12-02 13:54:06 +01:00
Gael Guennebaud
e2f3e4e4aa Document non-const SparseMatrix::diagonal() method. 2014-12-01 14:45:15 +01:00
Gael Guennebaud
b26e697182 Make SparseMatrix::coeff() returns a const reference and add a non const version of SparseMatrix::diagonal() 2014-12-01 14:41:39 +01:00
Gael Guennebaud
b1f9f603a0 Simplify return type of diagonal(Index) (and ease compiler job) 2014-11-28 14:39:47 +01:00
Gael Guennebaud
5384e89147 Disable MatrixBase::bdcSvd with CUDA (just like MatrixBase::jacobiSvd 2014-11-26 22:29:29 +01:00
Gael Guennebaud
8518ba0bbc Fix Hyperplane::Through(a,b,c) when points are aligned or identical. We use the stratgey as in Quaternion::setFromTwoVectors. 2014-11-26 15:01:53 +01:00
Tim Murray
80cae358b0 Adds a modified f2c-generated C implmentation for BLAS.
This adds an optional implementation for the BLAS library that does
not require the use of a FORTRAN compiler. It can be enabled with
EIGEN_USE_F2C_BLAS.

The C implementation uses the standard gfortran calling convention
and does not require the use of -ff2c when compiled with gfortran.
2014-11-24 10:56:30 -08:00
Gael Guennebaud
0efaff9b3b Fix out-of-bounds write 2014-12-11 16:15:20 +01:00
Gael Guennebaud
41a20994cc In simplicial cholesky: avoid deep copy of the input matrix is this later can be used readily 2014-12-08 17:56:33 +01:00
Gael Guennebaud
a910a7466e Fix inner iterator type 2014-12-08 17:55:31 +01:00
Gael Guennebaud
4371911861 Remove useless and non standard numext::atanh2 function. 2014-12-08 16:44:34 +01:00
Gael Guennebaud
5fc4ce6449 bug #876: remove usage of atanh2 in matrix power 2014-12-08 16:44:05 +01:00
Gael Guennebaud
77294047d6 bug #876, matrix_log_compute_2x2: directly use logp1 instead of atanh2 2014-12-08 16:28:06 +01:00
Gael Guennebaud
bea36925db bug #876: implement a portable log1p function 2014-12-08 16:26:53 +01:00
Gael Guennebaud
7f7a712062 Optimize Simplicial Cholesky when NaturalOrdering is used. 2014-12-08 15:02:25 +01:00
Gael Guennebaud
30c849669d Fix dynamic allocation in JacobiSVD (regression) 2014-12-08 14:45:04 +01:00
Gael Guennebaud
e0a8615b94 Merged in infinitei/eigen (pull request PR-91)
Added cmake uninstall target
2014-12-05 15:04:19 +01:00
Gael Guennebaud
8efd9142b3 Merged in infinitei/eigen-opengl-fixes (pull request PR-90)
Adding missing OPENGL_LIBRARIES for openglsupport test.
2014-12-05 12:54:57 +01:00
Gael Guennebaud
80ed5bd90c Workaround various "returning reference to temporary" warnings. 2014-12-05 12:49:30 +01:00
Abhijit Kundu
eb3695d2fc Added cmake uninstall target.
This adds a cmake command make uninstall
Running make uninstall removes the files installed by running make install
2014-12-04 02:57:03 -05:00
Abhijit Kundu
48db34a7b9 Adding missing OPENGL_LIBRARIES for openglsupport test. Also adding OpenGL include directories as a better pratice even though these are system include directories in most systems. 2014-12-04 01:18:47 -05:00
Gael Guennebaud
da584912b6 Fix memory pre-allocation when permuting inner vectors of a sparse matrix. 2014-11-24 17:31:59 +01:00
Benoit Steiner
509e4ddc02 Added reduction packet primitives for CUDA 2014-11-19 10:34:11 -08:00
Benoit Steiner
b33cf92878 Fixed the evaluation of expressions involving tensors of 2 or 3 elements on CUDA devices. 2014-11-18 14:32:41 -08:00
Benoit Steiner
1d3c8306f8 Fixed compilation errors with clang.
H: Enter commit message.  Lines beginning with 'HG:' are removed.
2014-11-13 19:13:17 -08:00
Benoit Steiner
ec785b0180 Added support for extraction of patches from images 2014-11-13 09:28:54 -08:00
Benoit Steiner
eeabf7975e Optimized broadcasting 2014-11-12 22:35:44 -08:00
Benoit Steiner
c2d1074932 Added support for static list of indices 2014-11-12 22:25:38 -08:00
Gael Guennebaud
722916e19d bug #903: clean swap API regarding extra enable_if parameters, and add failtests for swap 2014-11-06 09:25:26 +01:00
Benoit Steiner
cb37f818ca Fixed a compilation error triggered by some operations on fixed sized tensors 2014-11-05 23:25:11 -08:00
Benoit Steiner
9a06a71627 Fixed a test 2014-11-05 07:49:51 -08:00
Gael Guennebaud
c6fefe5d8e Big 853: replace enable_if in Ref<> ctor by static assertions and add failtests for Ref<> 2014-11-05 16:15:17 +01:00
Gael Guennebaud
ee06f78679 Introduce unified macros to identify compiler, OS, and architecture. They are all defined in util/Macros.h and prefixed with EIGEN_COMP_, EIGEN_OS_, and EIGEN_ARCH_ respectively. 2014-11-04 21:58:52 +01:00
Benoit Steiner
9ea09179b5 Fixed the return type of the coefficient-wise tensor operations. 2014-11-04 10:24:42 -08:00
Benoit Steiner
b1789c112b Improved handling of 1d tensors 2014-11-03 08:51:33 -08:00
Benoit Steiner
2dde63499c Generalized the matrix vector product code. 2014-10-31 16:33:51 -07:00
Benoit Steiner
7f2c6ed2fa Fixed a compilation warning 2014-10-31 11:45:21 -07:00
Christoph Hertzberg
c5a3777666 Regression test for (invalid) bug #900. We should make it possible somehow to increase the problem size depending on the available RAM. 2014-10-31 17:19:05 +01:00
Christoph Hertzberg
0833b82efd Run sparse_basic unit tests also for rectangular matrices.
TriangularView with UnitDiag does not work properly yet (bug #901)
2014-10-31 17:12:13 +01:00
Benoit Steiner
85c3389b28 Fixed a test 2014-10-31 00:04:13 -07:00
Benoit Steiner
67fcf47ecb Merged from trunk 2014-10-30 21:59:22 -07:00
Benoit Steiner
fcecafde3a Fixed a compilation error with clang 2014-10-30 21:58:14 -07:00
Benoit Steiner
d62bfe73a9 Use the proper index type in the padding code 2014-10-30 18:15:05 -07:00
Benoit Steiner
bc99c5f7db fixed some potential alignment issues. 2014-10-30 18:09:53 -07:00
Benoit Steiner
1946cc4478 Added missing packet primitives for CUDA. 2014-10-30 17:52:32 -07:00
Benoit Steiner
5e62427e22 Use the proper index type 2014-10-30 17:49:39 -07:00
Christoph Hertzberg
4ec2f07a5b Fixed bug in SparseBlock which caused a segfault in sparse_extra_3 test 2014-10-30 21:34:10 +01:00
Christoph Hertzberg
883168ed94 Make select CUDA compatible (comparison operators aren't yet, so no test case yet) 2014-10-30 20:16:16 +01:00
Christoph Hertzberg
e5f134006b EIGEN_UNUSED_VARIABLE works better than casting to void. Make this also usable from CUDA code 2014-10-30 19:59:09 +01:00
Christoph Hertzberg
d2fc597d5b Removed deprecated header (unsupported/Eigen/BDCSVD is included in Eigen/SVD now) 2014-10-29 17:51:14 +01:00
Christoph Hertzberg
3d25b1f5b8 Split up some test cases 2014-10-29 17:46:54 +01:00
Christoph Hertzberg
acecb7b09f Fixed include in bdcsvd.cpp 2014-10-29 17:46:33 +01:00
Gael Guennebaud
21c0a2ce0c Move D&C SVD to official SVD module. 2014-10-29 11:29:33 +01:00
Benoit Steiner
debc97821c Added support for tensor references 2014-10-28 23:10:13 -07:00
Christoph Hertzberg
e2e7ba9f85 bug #898: add inline hint to const_cast_ptr 2014-10-28 14:49:44 +01:00
Christoph Hertzberg
bd2d330b25 Temporary workaround for bug #875:
Let TriangularView<Sparse>::nonZeros() return nonZeros() of the nested expression
2014-10-28 13:31:00 +01:00
Konstantinos Margaritis
79225db0b6 Merged in kmargar/eigen (pull request PR-87)
Extend NEON to add ARMv8 64-bit double support
2014-10-28 13:08:53 +02:00
Benjamin Chrétien
c426054767 BDCSVD: fix CMake install (missing separator). 2014-10-24 15:10:56 +02:00
Christoph Hertzberg
1fa793cb97 Removed weird self assignment. 2014-10-24 13:19:19 +02:00
Christoph Hertzberg
04ffb9956e Replace TEST_SET_BUT_UNUSED_VARIABLE by already defined EIGEN_UNUSED_VARIABLE 2014-10-24 13:18:23 +02:00
Konstantinos Margaritis
94ed7c81e6 Bug #896: Swap order of checking __VSX__/__ALTIVEC__ 2014-10-22 06:15:18 -04:00
Konstantinos Margaritis
fcb3573d17 Merged eigen/eigen into default 2014-10-22 10:42:18 +03:00
Konstantinos Margaritis
fae4fd7a26 Added ARMv8 support 2014-10-22 07:39:49 +00:00
Christoph Hertzberg
cf09c5f687 Prevent CUDA calling a __host__ function from a __host__ __device__ function is not allowed error. 2014-10-21 20:40:09 +02:00
Konstantinos Margaritis
b508619392 working 64-bit support in PacketMath.h, Complex.h needed 2014-10-21 18:10:33 +00:00
Konstantinos Margaritis
0f65f2762d add EIGEN_TEST_NEON64, but it's a dummy, AArch64 implies NEON support so extra CXXFLAGS are needed 2014-10-21 18:10:01 +00:00
Konstantinos Margaritis
87524922dc check for __ARM_NEON instead as it's defined in arm64 as well 2014-10-21 18:08:50 +00:00
Gael Guennebaud
a303b6a733 bug #670: add unit test for mapped input in sparse solver. 2014-10-20 16:46:47 +02:00
Gael Guennebaud
fe57b2f963 bug #701: workaround (min) and (max) blocking ADL by introducing numext::mini and numext::maxi internal functions and a EIGEN_NOT_A_MACRO macro. 2014-10-20 15:55:32 +02:00
Christoph Hertzberg
c12b7896d0 bug #766: Check minimum CUDA version 2014-10-20 14:23:11 +02:00
Gael Guennebaud
973e6a035f bug #718: Introduce a compilation error when using the wrong InnerIterator type with a SparseVector 2014-10-20 14:07:08 +02:00
Christoph Hertzberg
84aaa03182 Addendum to bug #859: pexp(NaN) for double did not return NaN, also, plog(NaN) did not return NaN.
psqrt(NaN) and psqrt(-1) shall return NaN if EIGEN_FAST_MATH==0
2014-10-20 13:13:43 +02:00
Gael Guennebaud
aa5f79206f Fix bug #859: pexp(NaN) returned Inf instead of NaN 2014-10-20 11:38:51 +02:00
Gael Guennebaud
b4a9b3f496 Add unit tests for Rotation2D's inverse(), operator*, slerp, and fix regression wrt explicit ctor change 2014-10-20 11:04:32 +02:00
Gael Guennebaud
d04f23260d Fix bug #894: the sign of LDLT was not re-initialized at each call of compute() 2014-10-20 10:48:40 +02:00
Gael Guennebaud
8838b0a1ff Fix SparseQR::rank for a completely empty matrix. 2014-10-19 22:42:20 +02:00
Benoit Steiner
f786897e4b Added access to the unerlying raw data of a tnsor slice/chip whenever possible 2014-10-17 15:33:27 -07:00
Benoit Steiner
7acd38d19e Created some benchmarks for the tensor code 2014-10-17 09:49:03 -07:00
Gael Guennebaud
b50e5bc816 merge 2014-10-17 16:53:18 +02:00
Gael Guennebaud
a370b1f2e2 Fix SparseLU::absDeterminant and add respective unit test 2014-10-17 16:52:56 +02:00
Gael Guennebaud
a13bc22204 Ignore automalically imported lapack source files 2014-10-17 15:34:39 +02:00
Gael Guennebaud
4b7c3abbea Fix D&C SVD wrt zero matrices 2014-10-17 15:32:55 +02:00
Gael Guennebaud
feacfa5f83 Fix JacobiSVD wrt undeR/overflow by doing scaling prior to QR preconditioning 2014-10-17 15:32:06 +02:00
Gael Guennebaud
8472e697ca Add lapack interface to JacobiSVD and BDCSVD 2014-10-17 15:31:11 +02:00
Benoit Steiner
65af852b54 Silenced one last warning 2014-10-16 15:02:30 -07:00
Benoit Steiner
ae697b471c Silenced a few compilation warnings
Generalized a TensorMap constructor
2014-10-16 14:52:50 -07:00
Benoit Steiner
94e47798f4 Fixed the return types of unary and binary expressions to properly handle the case where it is different from the input type (e.g. abs(complex<float>)) 2014-10-16 10:41:07 -07:00
Benoit Steiner
d853adffdb Avoid calling get_future() more than once on a given promise. 2014-10-16 10:10:04 -07:00
Mark Borgerding
880e72c130 quieted more g++ warnings of the form: warning: typedef XXX locally defined but not used [-Wunused-local-typedefs] 2014-10-16 09:19:32 -04:00
Benoit Steiner
bfdd9f3ac9 Made the blocking computation aware of the l3 cache
Also optimized the blocking parameters to take into account the number of threads used for a computation
2014-10-15 15:32:59 -07:00
Gael Guennebaud
c566cfe2ba Make SVD unit test even more tough 2014-10-15 23:37:47 +02:00
Benoit Steiner
dba55041ab Added support for promises
Started to improve multithreaded contractions
2014-10-15 11:20:36 -07:00
Gael Guennebaud
fd1aaf4772 merge 2014-10-15 16:33:14 +02:00
Gael Guennebaud
c806009453 Extend svd unit tests to stress problems with duplicated singular values. 2014-10-15 16:32:16 +02:00
Gael Guennebaud
2cc41dbe83 D&C SVD: fix some numerical issues by truly skipping deflated singular values when computing them 2014-10-15 15:21:12 +02:00
Gael Guennebaud
c26e8a1af3 D&C SVD: fix deflation of repeated singular values, fix sorting of singular values, fix case of complete deflation 2014-10-15 11:59:21 +02:00
Christoph Hertzberg
0ec1fc9e11 bug #891: Determine sizeof(void*) via CMAKE variable instead of test program 2014-10-14 14:14:25 +02:00
Benoit Steiner
99d75235a9 Misc improvements and cleanups 2014-10-13 17:02:09 -07:00
Benoit Steiner
4c70b0a762 Added support for patch extraction 2014-10-13 10:04:04 -07:00
Christoph Hertzberg
d3f52debc6 Make cuda_basic test compile again by adding lots of EIGEN_DEVICE_FUNC.
Although the test passes now, there might still be some missing.
2014-10-13 17:18:26 +02:00
Benoit Steiner
0219f8aed4 Added ability to print a tensor using an iostream. 2014-10-10 16:17:26 -07:00
Benoit Steiner
2ed1838aeb Added support for tensor chips 2014-10-10 16:11:27 -07:00
Benoit Steiner
4b36c3591f Fixed the tensor shuffling test 2014-10-10 15:43:21 -07:00
Benoit Steiner
a991f94c0e Fixed the thread pool test 2014-10-10 15:20:37 -07:00
Benoit Steiner
498b7eed25 Rewrote the TensorBase::random method to support the generation of random number on gpu. 2014-10-09 15:39:13 -07:00
Benoit Steiner
767424af18 Improved the functors defined for standard reductions
Added a functor to encapsulate the generation of random numbers on cpu and gpu.
2014-10-09 15:36:23 -07:00
Gael Guennebaud
a80e17cfe8 Remove unused and dangerous CompressedStorage::Map function 2014-10-09 23:42:33 +02:00
Gael Guennebaud
349c2c9235 bug #367: fix double copies in atWithInsertion, and add respective unit-test 2014-10-09 23:35:49 +02:00
Gael Guennebaud
48d537f59f Fix indentation 2014-10-09 23:35:26 +02:00
Gael Guennebaud
538c059aa4 bug #887: fix CompressedStorage::reallocate wrt memory leaks 2014-10-09 23:35:05 +02:00
Gael Guennebaud
a48b82eece Add a scoped_array helper class to handle locally allocated/used arrays 2014-10-09 23:34:05 +02:00
Gael Guennebaud
ccd70ba123 Various numerical fixes in D&C SVD: I cannot make it fail with double, but still need to tune for single precision, and carefully test with duplicated singular values 2014-10-09 23:29:01 +02:00
Benoit Steiner
44beee9d68 Removed dead code 2014-10-08 14:14:20 -07:00
Benoit Steiner
0a07ac574e Added support for the *= and /* operators to TensorBase 2014-10-08 13:32:41 -07:00
Benoit Steiner
6c047d398d Fixed a comment 2014-10-08 13:29:36 -07:00
Gael Guennebaud
4b886e6b39 bug #889: fix protected typedef 2014-10-08 07:48:30 +02:00
Gael Guennebaud
5741349294 bug #882: fix various const-correctness issues with *View classes. 2014-10-07 18:29:28 +02:00
Gael Guennebaud
118b1113d9 Workaround MSVC issue. 2014-10-07 09:53:39 +02:00
Gael Guennebaud
503c176d8e Fix missing outer() member in DynamicSparseMatrix 2014-10-07 09:53:27 +02:00
Gael Guennebaud
dbdd8b0883 D&C SVD: add scaling to avoid overflow, fix handling of fixed size matrices 2014-10-06 19:35:57 +02:00
Gael Guennebaud
d44d432baa Re-enable products with triangular views of sparse matrices: we simply have to treat them as a sparse matrix. 2014-10-06 16:11:26 +02:00
Gael Guennebaud
893bfcf95f bug #887: use ei_declare_aligned_stack_constructed_variable instead of manual new[]/delete[] pairs in AMD and Paralellizer 2014-10-06 11:54:30 +02:00
Gael Guennebaud
fb53ff1eda Fix SparseLU regarding uncompressed inputs and avoid manual new/delete calls. 2014-10-06 11:42:31 +02:00
Gael Guennebaud
7a17639953 Extend unit tests to check uncompressed sparse inputs in sparse solvers 2014-10-06 11:41:50 +02:00
Benoit Steiner
bbce6fa65d define EIGEN_VECTORIZE_CUDA when compiling with nvcc 2014-10-03 19:55:35 -07:00
Benoit Steiner
95a430a2ca Vector primitives for CUDA 2014-10-03 19:45:19 -07:00
Benoit Steiner
152f3218ac Improved contraction test 2014-10-03 19:33:44 -07:00
Benoit Steiner
af2e5995e2 Improved support for CUDA devices.
Improved contractions on GPU
2014-10-03 19:18:07 -07:00
Benoit Steiner
1269392822 Created the IndexPair type to store pair of tensor indices. CUDA doesn't support std::pair so we can't use them when targeting GPUs.
Improved the performance on tensor contractions
2014-10-03 10:16:59 -07:00
Benoit Steiner
b7271dffb5 Generalized the gebp apis 2014-10-02 16:51:57 -07:00
Benoit Steiner
8b2afe33a1 Fixes for the forced evaluation of tensor expressions
More tests
2014-10-02 10:39:36 -07:00
Benoit Steiner
5cc23199be More tests to validate the const-correctness of the tensor code. 2014-10-02 10:30:44 -07:00
Benoit Steiner
7caaf6453b Added support for tensor reductions and concatenations 2014-10-01 20:38:22 -07:00
Benoit Steiner
1c236f4c9a Added tests for tensors of const values and tensors of stringswwq:: 2014-10-01 20:21:42 -07:00
Christoph Hertzberg
1fa6fe2abd template keyword not allowed before non-template function call 2014-10-01 14:33:55 +02:00
Konstantinos Margaritis
9d3c69952b fixed to make big-endian VSX work as well 2014-10-01 09:43:56 +00:00
Gael Guennebaud
5180bb5e47 Add missing default ctor in Rotation2D 2014-09-30 16:59:28 +02:00
Christoph Hertzberg
0187504912 Avoid `unneeded-internal-declaration' warning 2014-09-30 16:43:52 +02:00
Christoph Hertzberg
6d26deb894 Missing outerStride in AlignedVector3 resulted in infinite recursion 2014-09-30 16:43:19 +02:00
Christoph Hertzberg
81517eebc1 Missing explicit 2014-09-30 16:42:04 +02:00
Christoph Hertzberg
12d59465cb bug #884: Copy constructor of Ref shall never malloc, constructing from other RefBase shall only malloc if the memory layout is incompatible. 2014-09-30 14:57:54 +02:00
Christoph Hertzberg
e404841235 make sure that regex does not match cmake 2014-09-29 19:28:10 +00:00
Christoph Hertzberg
15c946338f Related to bug #880: Accept make as well a gmake when searching the MakeCommand. And don't include \n in match expression 2014-09-29 19:20:01 +02:00
Gael Guennebaud
56a0bbbbee Fix compilation with GCC 2014-09-29 18:28:18 +02:00
Gael Guennebaud
842e31cf5c Let KroneckerProduct exploits the recently introduced generic InnerIterator class. 2014-09-29 13:37:49 +02:00
Gael Guennebaud
abd3502e9e Introduce a generic InnerIterator classes compatible with evaluators. 2014-09-29 13:36:57 +02:00
Gael Guennebaud
76c3cf6949 Re-enable -Wshorten-64-to-32 compilation flag. 2014-09-29 10:33:16 +02:00
Georg Drenkhahn
bc34ee3365 Using Index type instead of hard coded int type to prevent potential implicit integer conversion. 2014-09-22 18:56:36 +02:00
Georg Drenkhahn
9a04cd307c Added implicit integer conversion by using explicit integer type conversion. Adding assert to catch overflow. 2014-09-22 18:47:33 +02:00
Gael Guennebaud
f0a62c90bc Avoid comparisons between different index types. 2014-09-29 10:27:51 +02:00
Georg Drenkhahn
2946992ad4 Using StorageIndexType for loop assigning initial permutation. Adding assert for index overflow. 2014-09-22 17:59:02 +02:00
Georg Drenkhahn
821ff0ecfb Using Index instead of hard coded int type to prevent potential implicit integer conversion 2014-09-22 16:12:35 +02:00
Georg Drenkhahn
2c4cace56c Using Index instead of hard coded int type to prevent potential implicit integer conversion 2014-09-22 15:54:34 +02:00
Georg Drenkhahn
8a502233d8 Correcting the ReturnType in traits<KroneckerProduct<>> to include the correct Index type.
Fixed mixup of types Rhs::Index and Lhs:Index in various loop variables.
Added explicit type conversion for arithmetic expressions which may return a wider type.
2014-09-21 23:19:29 +02:00
Georg Drenkhahn
b2755edcdd Replaced hard coded int types with Index types preventing implicit integer conversions. 2014-09-21 23:15:35 +02:00
Georg Drenkhahn
d1ef3c3546 Changed Diagonal::index() to return an Index type instead of int to prevent possible implicit conversion from long to int.
Added inline keyword to member methods.
2014-09-21 10:21:20 +02:00
Georg Drenkhahn
edaefeb978 Using Kernel::Index type instead of int to prevent possible implicit conversion from long to int. 2014-09-21 10:01:12 +02:00
Georg Drenkhahn
3bd31e21b5 Fixed compiler warning on implicit integer conversion by separating index type for matrix and permutation matrix which may not be equal. 2014-09-20 15:00:36 +02:00
Georg Drenkhahn
75e269c77b Fixed warning on implicit integer conversion in test case code by using type VectorXd::Index instead of int. 2014-09-20 14:57:42 +02:00
Gael Guennebaud
74cde0c925 Add missing return derived() in ArrayBase::operator= 2014-09-28 09:16:13 +02:00
Jitse Niesen
ce2035af86 New doc page on implementing a new expression class. 2014-09-27 23:25:58 +01:00
Konstantinos Margaritis
6d0f0b8cec add VSX identifier 2014-09-25 16:06:16 +00:00
Christoph Hertzberg
4ba8aa1482 Fix bug #884: No malloc for zero-sized matrices or for Ref without temporaries 2014-09-25 16:05:17 +02:00
Christoph Hertzberg
27d6b4daf9 Tridiagonalization::diagonal() and ::subDiagonal() did not work. Added unit-test 2014-09-24 14:37:13 +02:00
Gael Guennebaud
446001ef51 Fix nested_eval<Product<> > which wrongly returned a Product<> expression 2014-09-24 09:39:09 +02:00
Gael Guennebaud
13cbc751c9 bug #880: automatically preserves buildtool flags when modifying DartConfiguration.tcl file. 2014-09-23 22:10:32 +02:00
Christoph Hertzberg
421feea3b2 member_redux constructor is explicit too. Renamed some typedefs for more consistency. 2014-09-23 18:55:42 +02:00
Christoph Hertzberg
7817bc19a4 Removed FIXME, as it is actually necessary. 2014-09-23 17:23:34 +02:00
Christoph Hertzberg
eb13ada3aa Renamed CwiseInverseReturnType to InverseReturnType for ArrayBase::inverse() 2014-09-23 17:21:27 +02:00
Christoph Hertzberg
36448c9e28 Make constructors explicit if they could lead to unintended implicit conversion 2014-09-23 14:28:23 +02:00
Christoph Hertzberg
de0d8a010e Suppress stupid gcc-4.4 warning 2014-09-23 12:58:14 +02:00
Gael Guennebaud
72569f17ec bug #882: add const-correctness failtests for CwiseUnaryView, TriangularView, and SelfAdjointView. 2014-09-23 10:26:02 +02:00
Gael Guennebaud
3878e6f170 Add a true ctest unit test for failtests 2014-09-23 10:25:12 +02:00
Gael Guennebaud
ff46ec0f24 bug #881: make SparseMatrixBase::isApprox(SparseMatrixBase) exploits sparse computations instead of converting the operands to dense matrices. 2014-09-22 23:33:28 +02:00
Gael Guennebaud
ae514ddfe5 bug #880: manually edit the DartConfiguration.tcl file to get it working with cmake 3.0.x 2014-09-22 22:49:20 +02:00
Gael Guennebaud
f9d6d3780f bug #879: fix compilation of tri1=mat*tri2 by copying tri2 into a full temporary. 2014-09-22 17:34:17 +02:00
Gael Guennebaud
abba11bdcf Many improvements in Divide&Conquer SVD:
- Fix many numerical issues, in particular regarding deflation.
- Add heavy debugging output to help track numerical issues (there are still fews)
- Make use of Eiegn's apply-inplane-rotation feature.
2014-09-22 15:22:52 +02:00
Christoph Hertzberg
d9e0336a78 Merged in kmargar/eigen (pull request PR-84)
Add VSX support
2014-09-22 12:57:06 +02:00
Jitse Niesen
333905b0c2 Fix typos in docs for IterativeLinearSolvers module 2014-09-21 14:20:08 +01:00
Jitse Niesen
5fa69422a2 Fix copy-and-paste typo in SolveWithGuess assignment
This fixes compilation of code snippets in BiCGSTAB docs.
2014-09-21 14:19:23 +01:00
Konstantinos Margaritis
de38ff2499 prefetch are noops on VSX, actually disable the prefetch trait 2014-09-21 11:56:07 +00:00
Konstantinos Margaritis
60e093a9dc Merged eigen/eigen into default 2014-09-21 14:02:51 +03:00
Konstantinos Margaritis
56408504e4 fix compile error on big endian altivec 2014-09-21 13:59:30 +03:00
Konstantinos Margaritis
974fe38ca3 prefetch are noops on VSX 2014-09-21 11:24:30 +00:00
Konstantinos Margaritis
c0205ca4af VSX supports vec_div, implement where appropriate (float/doubles) 2014-09-21 08:12:22 +00:00
Konstantinos Margaritis
10f8aabb61 VSX port passes packetmath_[1-5] tests! 2014-09-20 22:31:31 +00:00
Jitse Niesen
80de35b6c5 Remove double return statement in PlainObjectBase::_set() 2014-09-19 22:05:18 +01:00
Konstantinos Margaritis
60663a510a 32-bit floats/ints, 64-bit doubles pass packetmath tests, complex 32/64-bit remaining 2014-09-19 21:05:01 +00:00
Gael Guennebaud
03dd4dd91a Unify unit test for BDC and Jacobi SVD. This reveals some numerical issues in BDCSVD. 2014-09-19 15:25:48 +02:00
Gael Guennebaud
0a18eecab3 bug #100: add support for explicit scalar to Array conversion (as enable implicit conversion is much more tricky) 2014-09-19 13:25:28 +02:00
Gael Guennebaud
7b044c0ead Added tag before-evaluators for changeset 9452eb38f8 2014-09-19 10:10:29 +02:00
Gael Guennebaud
755e77266f Fix SparseQR for row-major inputs. 2014-09-19 09:58:56 +02:00
Gael Guennebaud
07c5500d70 Introduce a compilation error when using the wrong InnerIterator type. 2014-09-19 09:58:20 +02:00
Gael Guennebaud
e70506dd8f Fix inner-stride of AlignedVector3 2014-09-18 22:46:46 +02:00
Gael Guennebaud
2ae20d558b Update KroneckerProduct wrt evaluator changes 2014-09-18 22:08:49 +02:00
Gael Guennebaud
62bce6e5e6 Make MatrixFunction use nested_eval instead of nested 2014-09-18 17:31:17 +02:00
Gael Guennebaud
060e835ee9 Add evaluator for the experimental AlignedVector3 2014-09-18 17:30:21 +02:00
Gael Guennebaud
0ca43f7e9a Remove deprecated code not used by evaluators 2014-09-18 15:15:27 +02:00
Gael Guennebaud
8b3be4907d log2(int) must be inlined. 2014-09-18 10:53:53 +02:00
Gael Guennebaud
0bf5894861 workaround one more shadowing issue with MSVC 2014-09-16 18:21:39 -07:00
Gael Guennebaud
e44d78dab3 workaround ambiguous call 2014-09-16 17:10:25 -07:00
Gael Guennebaud
c2f66c65aa workaround MSVC compilation issue (shadow issue) 2014-09-16 16:23:45 -07:00
Gael Guennebaud
125619146b workaround weird MSVC compilation issue: a typdedef in a base class shadows a template parameter of a derived class 2014-09-16 16:06:32 -07:00
Gael Guennebaud
341ae8665d avoid division by 0 2014-09-16 16:05:06 -07:00
Gael Guennebaud
fc23e93707 Add a portable log2 function for integers 2014-09-17 09:56:07 +02:00
Gael Guennebaud
0f0580b97c Remove not needed template keyword. 2014-09-17 09:55:44 +02:00
Gael Guennebaud
486ca277a0 Workaround MSVC ICE 2014-09-16 10:29:29 -07:00
Benoit Steiner
10a79ca3a3 Merged latest updates from the Eigen trunk. 2014-09-15 09:18:16 -07:00
Gael Guennebaud
466d6d41c6 Avoid a potential risk of recursive definition using traits to get he scalar type 2014-09-15 17:40:17 +02:00
Gael Guennebaud
8514179aa3 Fix traits<Quaternion>::IsAligned when using evaluators 2014-09-15 13:53:52 +02:00
Gael Guennebaud
0403d49006 Fix inverse unit test making sure we try to invert an invertible matrix 2014-09-14 20:12:07 +02:00
Gael Guennebaud
c83e01f2d6 Favor column major storage for inner products 2014-09-14 19:38:49 +02:00
Gael Guennebaud
26db954776 Re-enable aliasing checks when using evaluators 2014-09-14 19:06:08 +02:00
Gael Guennebaud
fda680f9cf Adapt changeset 51b3f558bb
to evaluators:
(Fix bug #822: outer products needed linear access, and add respective unit tests)
2014-09-14 18:31:29 +02:00
Gael Guennebaud
dfc54e1bbf Fix /= when using evaluator as in changeset 2d90484450 2014-09-14 18:27:48 +02:00
Gael Guennebaud
749b56f6af merge with default branch 2014-09-14 17:34:54 +02:00
Gael Guennebaud
af9c9f7706 Fix comparison to block size 2014-09-14 17:33:39 +02:00
Jitse Niesen
9452eb38f8 Make UpperBidiagonalization accept row-major matrices (bug #769)
* Give temporary workspace the same storage order as original matrix
* Take storage order into account when determining inner stride
  of rows and columns
* Change one test to use a row-major matrix.
2014-09-12 14:52:35 +01:00
Konstantinos Margaritis
470aa15c35 First time it compiles, but fails to pass the tests. 2014-09-09 16:58:48 +00:00
Gael Guennebaud
188a13f9fe Fix compilation of coeff(Index) on sub-inner-panels 2014-09-08 09:50:03 +02:00
Benoit Steiner
efdff15749 Fixed a typo in the contraction code 2014-09-06 13:28:24 -07:00
Gael Guennebaud
dacd39ea76 Exploit sparse structure in naiveU and naiveV when updating them. 2014-09-05 17:51:46 +02:00
Benoit Steiner
74db22455a Misc fixes. 2014-09-05 07:47:43 -07:00
Gael Guennebaud
b23556bbbd Oops, a block size of 1 is not very useful, set it to 48 as in HouseholderQR 2014-09-05 08:50:50 +02:00
Benoit Steiner
1abe4ed14c Created more regression tests 2014-09-04 20:27:28 -07:00
Benoit Steiner
d43f737b4a Added support for evaluation of tensor shuffling operations as lvalues 2014-09-04 20:02:28 -07:00
Benoit Steiner
f50548e86a Added missing tensor copy constructors. As a result it is now possible to declare and initialize a tensor on the same line, as in:
Tensor<bla> T = A + B;  or
  Tensor<bla> T(A.reshape(new_shape));
2014-09-04 19:50:27 -07:00
Gael Guennebaud
15bad3670b Apply Householder U and V in-place. 2014-09-04 09:17:01 +02:00
Gael Guennebaud
8846aa6d1b Optimization: enable cache-efficient application of HouseholderSequence. 2014-09-04 09:15:59 +02:00
Gael Guennebaud
80993b95d3 Disable a test which had never worked without evalautors 2014-09-03 22:56:39 +02:00
Benoit Steiner
b24fe22b1a Improved the performance of the tensor convolution code by a factor of about 4. 2014-09-03 11:38:13 -07:00
Gael Guennebaud
c82dc227f1 Cleaning in BDCSVD (formating, handling of transpose case, remove some for loops) 2014-09-03 10:15:24 +02:00
Gael Guennebaud
a96f3d629c Clean bdcsvd 2014-09-02 22:30:23 +02:00
Gael Guennebaud
47829e2d16 Disable solve_ret_val like mechanism with evaluator enabled 2014-09-01 18:32:59 +02:00
Gael Guennebaud
1f398dfc82 Factorize *SVD::solve to SVDBase 2014-09-01 18:31:54 +02:00
Gael Guennebaud
b3a0365429 merge with default branch 2014-09-01 18:21:01 +02:00
Gael Guennebaud
eb39296028 Reafctoring in D&C SVD unsupported module: clean and merge the SVDBase class to Eigen/SVD, rm copy/pasted JacobiSVD.h file 2014-09-01 18:16:20 +02:00
Gael Guennebaud
72c4f8ca8f Disable a few unit tests in unsupported 2014-09-01 17:35:58 +02:00
Gael Guennebaud
b121eecf60 Fix regression is sparse-sparse product 2014-09-01 17:34:55 +02:00
Gael Guennebaud
8754341848 Fix remaining garbage during a merge. 2014-09-01 17:25:13 +02:00
Gael Guennebaud
daad9585a3 Fix Kronecker product in legacy mode. 2014-09-01 17:24:07 +02:00
Gael Guennebaud
b051bbd64f Make unsupport sparse solvers use SparseSolverBase 2014-09-01 17:21:47 +02:00
Gael Guennebaud
b3d63b4db2 Add evaluator for DynamicSparseMatrix 2014-09-01 17:21:05 +02:00
Gael Guennebaud
1c4b69c5fb Factorize solveWithGuess in IterativeSolverBase 2014-09-01 17:19:51 +02:00
Gael Guennebaud
8a74ce922c Make IncompleteLUT use SparseSolverBase. 2014-09-01 17:19:16 +02:00
Gael Guennebaud
863b7362bc Fix usage of m_isInitialized in SparseLU and Pastix support. 2014-09-01 17:16:32 +02:00
Gael Guennebaud
1bf3b34849 Fix regression in sparse-sparse product 2014-09-01 17:15:08 +02:00
Gael Guennebaud
f9580a3473 Fix Cholmod support without evaluators 2014-09-01 17:14:30 +02:00
Gael Guennebaud
fbb53b6cbb Fix sparse matrix times sparse vector. 2014-09-01 16:53:52 +02:00
Gael Guennebaud
85c7659574 Refactoring of sparse solvers through a SparseSolverBase class and usage of the Solve<> expression. Introduce a SolveWithGuess expression on top of Solve. 2014-09-01 15:00:19 +02:00
Gael Guennebaud
bc065c75d2 Implement the missing bits to make Solve compatible with sparse rhs 2014-09-01 14:50:59 +02:00
Gael Guennebaud
e6cc24cbd6 Fix compilation in legacy mode 2014-09-01 14:20:11 +02:00
Gael Guennebaud
0369db12af bug #871: fix compilation on ARM/Neon regarding __has_builtin usage 2014-09-01 10:52:58 +02:00
Konstantinos Margaritis
7ff266e3ce Initial VSX commit 2014-08-29 20:03:49 +00:00
Gael Guennebaud
b4a709520d merge 2014-08-29 15:31:54 +02:00
Gael Guennebaud
c1d0f15bde Enable evaluators by default 2014-08-29 15:31:32 +02:00
Gael Guennebaud
124d12a915 merge default branch 2014-08-29 15:20:31 +02:00
Gael Guennebaud
f29dbec321 undef Unsable macro 2014-08-29 15:12:03 +02:00
Gael Guennebaud
01f3ca3e8d Merged in georg_drenkhahn/eigen/georg_d/fix_warn_minmax (pull request PR-81)
Fix for warning on macro definitions of max() and min() in test.h
2014-08-29 14:34:00 +02:00
Gael Guennebaud
aec3d90ca6 Optimization in sparse-sparse matrix products for small ones 2014-08-29 14:19:03 +02:00
Gael Guennebaud
460662cbcc Fix SparseVector::coeffRef(i,j) and add missing SparseVector::insert*Unordered 2014-08-29 14:18:23 +02:00
Gael Guennebaud
1ed9e2d004 In sparse matrix product, enable sorted insertion when doing two transposition is defenitely not optimal. 2014-08-29 11:55:03 +02:00
Georg Drenkhahn
e49e84d979 Added missing STL include of <list> in main.h
Removed duplicated include of <sstream>
Added comments on the background of min/max macro definitions and STL header includes
2014-08-29 10:41:05 +02:00
Freddie Witherden
c3e4080474 Allow LevenbergMarquardt to work with non-standard types. 2014-08-27 15:24:51 +01:00
Benoit Steiner
2959045f2f Optimized the tensor padding code. 2014-08-26 09:47:18 -07:00
Benoit Steiner
36fffe48f7 Misc api improvements and cleanups 2014-08-23 14:35:41 -07:00
Benoit Steiner
fb5c1e9097 Optimized and cleaned up the tensor morphing code 2014-08-23 13:18:30 -07:00
Georg Drenkhahn
0ba490cf80 Fixed CMakeLists.txt files to prevent CMake 3.0.0 warnings about deprecated LOCATION target property.
Small whitespace cleanup in CMakelLists.txt.
2014-08-22 12:13:07 +02:00
Gael Guennebaud
25a3e65a68 In SparseQR, calling factorize() without analyzePattern() was broken. 2014-08-26 23:32:32 +02:00
Gael Guennebaud
be3477e206 bug #857: workaround MSVC compilation issue. 2014-08-26 12:52:29 +02:00
Gael Guennebaud
2e50289ba3 bug #861: enable posix_memalign with PGI 2014-08-26 12:54:19 +02:00
Gael Guennebaud
b49ef99617 Do not apply the preconditioner before starting the iterations as this might destroy a very good initial guess. 2014-08-21 22:14:25 +02:00
Gael Guennebaud
9c0aa81fbf bug #854: fix numerical issue in SelfAdjointEigenSolver::computeDirect for 3x3 matrices. The tolerance to detect stable cross products was too optimistic.
Add respective unit tests.
2014-08-21 10:49:09 +02:00
Benoit Steiner
3d298da269 Added support for broadcasting 2014-08-20 17:00:50 -07:00
Christoph Hertzberg
eeadc06e83 EIGEN_EXCEPTIONS was not defined in test/main.h, therefore all VERIFY_RAISES_ASSERT tests were not enabled 2014-08-20 16:39:25 +02:00
Christoph Hertzberg
4403800e11 Merged in vladimir_ch/eigen-1/vladimir_ch/fix-uninitialized-variable-warning-in-sp-1408513228472 (pull request PR-77)
Fix uninitialized variable warning in SparseQR
2014-08-20 11:12:18 +02:00
Christoph Hertzberg
9062f74d13 Merged in traversaro/eigen/traversaro/findeigen3cmake-add-reading-hints-of-eig-1407426517521 (pull request PR-76)
FindEigen3.cmake: Add reading hints of Eigen directory location from environment variables EIGEN3_ROOT and EIGEN3_ROOT_DIR .
2014-08-20 11:11:52 +02:00
Vladimir Chalupecky
6a3423da80 Fix uninitialized variable warning in SparseQR 2014-08-20 05:42:22 +00:00
Benoit Steiner
9ac3c821ea Improved the speed of convolutions when running on cuda devices 2014-08-19 16:57:10 -07:00
Benoit Steiner
33c702c79f Added support for fast integer divisions by a constant
Sped up tensor slicing by a factor of 3 by using these fast integer divisions.
2014-08-14 22:13:21 -07:00
Benoit Steiner
756292f8aa Fixed compilation errors 2014-08-14 00:32:59 -07:00
Benoit Steiner
8c8db49331 Added a few regression tests 2014-08-14 00:25:22 -07:00
Benoit Steiner
eeb43f9e2b Added support for padding, stridding, and shuffling 2014-08-14 00:22:47 -07:00
Benoit Steiner
16047c8d4a Pulled in the latest changes from the Eigen trunk 2014-08-13 22:25:29 -07:00
Benoit Steiner
916ef48846 Added ability to get the nth element from an abstract array type. 2014-08-13 08:44:47 -07:00
Benoit Steiner
f1d8c13dbc Fixed misc typos. 2014-08-13 08:40:26 -07:00
Benoit Steiner
9faad2932f Added missing apis. 2014-08-13 08:36:33 -07:00
Benoit Steiner
f8fad09301 Updated the convolution and contraction evaluators to follow the new EvalSubExprsIfNeeded apu. 2014-08-13 08:33:18 -07:00
Benoit Steiner
72e7529708 Fixed a typo. 2014-08-13 08:29:40 -07:00
Benoit Steiner
1aa2bf8274 Support for in place evaluation of expressions containing slicing and reshaping operations 2014-08-13 08:27:58 -07:00
Benoit Steiner
b1892ab14d Added suppor for in place evaluation to simple tensor expressions.
Use mempy to speedup tensor copies whenever possible.
2014-08-13 08:26:44 -07:00
Benoit Steiner
439feca139 Reworked the TensorExecutor code to support in place evaluation. 2014-08-13 08:22:05 -07:00
Kevin Locke
e6d55c081b Fix bug #852: define Traits type in general_matrix_matrix_product when EIGEN_USE_BLAS is defined 2014-08-08 04:05:28 -04:00
Gael Guennebaud
57f71a5552 Update bench_norm utility 2014-09-11 10:27:46 +02:00
Gael Guennebaud
5e890d3ad7 Improve further the accuracy of JacobiSVD wrt under/overflow while improving speed for small matrices (hypot is very slow). 2014-09-10 23:11:58 +02:00
Gael Guennebaud
2d90484450 mat/=scalar was transformed into mat*=(1/scalar) thus laking accuracy. This was also inconsistent with mat = mat/scalar. 2014-09-10 23:10:01 +02:00
Gael Guennebaud
84a7ead059 Add one more regression test for bug #791. 2014-09-10 11:59:45 +02:00
Gael Guennebaud
d6236d3b26 Fix bug #791: infinite loop in JacobiSVD in the presence of NaN. 2014-09-10 11:54:20 +02:00
Gael Guennebaud
921a645481 ArrayWrapper and MatrixWrapper classes should not be nested by reference. 2014-09-10 10:33:19 +02:00
Yan Zhou
4b678b96eb fix for MKL_BLAS not defined in MKL 11.2 2014-09-08 17:37:58 +08:00
Gael Guennebaud
51b3f558bb Fix bug #822: outer products needed linear access, and add respective unit tests 2014-09-08 10:21:22 +02:00
Gael Guennebaud
6162672dc5 Runtime alignement is not possible if AlignedOnScalar is not true (e.g., for complex<double>) 2014-09-08 10:04:26 +02:00
Gael Guennebaud
e54898f53e bug #619: workaround MSVC 2008 implementing std::abs for int only on WINCE 2014-09-07 23:02:30 +02:00
Gael Guennebaud
fafc829424 bug #804: copy group__TopicUnalignedArrayAssert.html to TopicUnalignedArrayAssert.html as the second is linked to by old Eigen versions. 2014-09-07 22:38:09 +02:00
Jitse Niesen
abb33258ce Doc: difference between array and matrix cosine etc (bug #830) 2014-09-06 14:59:44 +01:00
Jitse Niesen
25bceefb4e Replace asm by __asm__ (bug #873) 2014-09-06 11:47:24 +01:00
Gael Guennebaud
60314beb38 Update reference value for testNistLanczos1 test 2014-09-02 17:35:11 +02:00
Gael Guennebaud
280661e67d Remove LM::sqrt_() member function in favor of a shortcut for sqrt(epsilon()) 2014-09-02 17:29:06 +02:00
Gael Guennebaud
ff9bfc45f7 relax some LM unit tests 2014-09-02 17:10:17 +02:00
Gael Guennebaud
42e27d41a2 Fix hypot() and hypotNorm() wrt NaN and INF values. 2014-09-02 16:09:39 +02:00
Gael Guennebaud
a44a343f03 Fix blueNorm wrt NaN/INF. 2014-09-02 15:06:24 +02:00
Gael Guennebaud
18fbe7e7d4 Fix stableNorm() with respect to NaN and inf, and add respective unit tests. blueNorm() and hypotNorm() are broken wrt to NaN/inf 2014-09-02 14:49:23 +02:00
Gael Guennebaud
3eb5253ca1 Optimization: "matrix<complex> * real" did not call the special path and the real was converted to a complex. Add respective unit test to avoid future regression. 2014-09-02 14:41:14 +02:00
Gael Guennebaud
305aa1f9c5 Add examples for hnormalized and homogenous (fix bug #846) 2014-09-02 10:47:40 +02:00
Silvio Traversaro
50085d2c28 FindEigen3.cmake: Add reading hints of Eigen directory location from environment variables EIGEN3_ROOT and EIGEN3_ROOT_DIR . 2014-08-07 15:48:53 +00:00
Gael Guennebaud
e51da9c3a8 Memory allocated on the stack is freed at the function exit, so reduce iteration count to avoid stack overflow 2014-08-04 12:46:00 +02:00
Kolja Brix
953ec08089 Correct GMRES:
* Fix error in calculation of residual at restart.
* Use relative residual as stopping criterion.
* Improve documentation.
2014-08-02 18:39:15 +02:00
Gael Guennebaud
3e59163a24 Fix bug #850: workaround MSVC 2008 weird compilation bug 2014-08-02 02:47:30 +02:00
Gael Guennebaud
4dd55a2958 Optimize reduxions for Homogeneous 2014-08-01 17:00:20 +02:00
Gael Guennebaud
f25338f4d7 Fix nesting of Homogenous evaluator 2014-08-01 16:49:44 +02:00
Gael Guennebaud
51357a6622 Fix geo_orthomethods unit test for complexes 2014-08-01 16:26:23 +02:00
Gael Guennebaud
107bb308c3 Fix various small issues detected by gcc 2014-08-01 16:24:23 +02:00
Gael Guennebaud
c2ff44cbf3 Make assignment from general EigenBase object call evaluator, and support dense X= sparse 2014-08-01 16:23:30 +02:00
Benjamin Chrétien
c53f88297c Fix more typos in Ref.h (doc). 2014-08-01 15:43:47 +02:00
Benjamin Chrétien
6f58a41097 Fix typos in Ref.h (doc). 2014-08-01 15:35:45 +02:00
Gael Guennebaud
2a3c3c49a1 Fix numerous nested versus nested_eval shortcomings 2014-08-01 14:48:22 +02:00
Gael Guennebaud
fc13b37c55 Make cross product uses nested/nested_eval 2014-08-01 14:47:33 +02:00
Benjamin Chrétien
db76193bc7 Fix typo in PermutationMatrix (doc). 2014-08-01 14:41:49 +02:00
Benoit Steiner
647622281e The tensor assignment code now resizes the destination tensor as needed. 2014-07-31 17:39:04 -07:00
Gael Guennebaud
d79516660c Make loadMarket use the sparse-matrix index type, thus enabling loading huge matrices. 2014-07-31 16:43:19 +02:00
Gael Guennebaud
26d2cdefd4 Fix 4x4 inverse via SSE for submatrices 2014-07-31 16:24:29 +02:00
Gael Guennebaud
db183ca7b3 Make minimal changes to make homogenous compatible with evaluators 2014-07-31 14:54:54 +02:00
Gael Guennebaud
702a3c17db Make Transform exposes sizes: Dim+1 x Dim+1 for projective transform, and Dim x Dim+1 for all others 2014-07-31 14:54:00 +02:00
Gael Guennebaud
5f5a8d97c0 Re-enable main unit tests which are now compiling and running fine with evaluators 2014-07-31 13:43:19 +02:00
Gael Guennebaud
bae2e3327b Call product_generic_impl by default, and remove lot of boilerplate code 2014-07-31 13:35:49 +02:00
Gael Guennebaud
cd0ff253ec Make permutation compatible with sparse matrices 2014-07-30 15:22:50 +02:00
Gael Guennebaud
929e77192c Various minor fixes 2014-07-30 11:39:52 +02:00
Gael Guennebaud
ba694ce8cf add missing delete operator overloads 2014-07-30 09:32:35 +02:00
Benoit Steiner
2116e261fb Made sure that the data stored in fixed sized tensor is aligned. 2014-07-25 09:47:59 -07:00
Jitse Niesen
5f3d542b8a Fix typo in MatrixExponential noticed by Markos. 2014-07-25 13:34:03 +01:00
Gael Guennebaud
a0a87410d0 Fix bug #61: gemm was broken since we changed the blocking order 2014-07-24 22:08:10 +02:00
Konstantinos Margaritis
2c625ec9ba Simplification of some Altivec constants, reuse existing constants and avoid loading from RAM esp in the case of p16uc_COMPLEX_TRANSPOSE* 2014-07-22 20:46:03 +00:00
Benoit Steiner
1f371e78e6 Added a few tests to validate the behavior of the assignment operator. 2014-07-22 10:32:40 -07:00
Benoit Steiner
f7bb7ee3f3 Fixed the assignment operator of the Tensor and TensorMap classes. 2014-07-22 10:31:21 -07:00
Gael Guennebaud
d1e9f39a9a Ambiguous call fixes for clang. 2014-07-22 18:28:19 +02:00
Gael Guennebaud
7f15f27a9e Workaround ambiguous call of init1 with MSVC. 2014-07-22 17:01:34 +02:00
Gael Guennebaud
922694a2d1 Extend fixed-size ctor unit test and fix conversion warning. 2014-07-22 16:57:14 +02:00
Gael Guennebaud
baa77ffe38 Fix max sizes at compile time of DiagonalWrapper 2014-07-22 16:13:56 +02:00
Christoph Hertzberg
a8283e0ed2 Define EIGEN_TRY, EIGEN_CATCH, EIGEN_THROW as suggested by Moritz Klammer.
Make it possible to run unit-tests with exceptions disabled via EIGEN_TEST_NO_EXCEPTIONS flag.
Enhanced ctorleak unit-test
2014-07-22 13:16:44 +02:00
Gael Guennebaud
4aac87251f Re-enable a couple of unit tests with evaluators. 2014-07-22 12:54:03 +02:00
Gael Guennebaud
6daa6a0d16 Refactor TriangularView to handle both dense and sparse objects. Introduce a glu_shape<S1,S2> helper to assemble sparse/dense shapes with triagular/seladjoint views. 2014-07-22 11:35:56 +02:00
Gael Guennebaud
2a251ffab0 Implement evaluator for sparse-selfadjoint products 2014-07-22 09:32:40 +02:00
Gael Guennebaud
9b729f93a1 Resizing is done by call_assignment_noalias, so no need to perform it when dealing with aliasing. 2014-07-21 11:46:47 +02:00
Gael Guennebaud
946b99dd5c Extend qr unit test 2014-07-21 11:45:54 +02:00
Gael Guennebaud
50eef6dfc3 Compilation fixes 2014-07-20 15:16:34 +02:00
Gael Guennebaud
62f332fc04 Make sure we evaluate into temporaries matching evaluator storage order requirements 2014-07-19 15:19:10 +02:00
Gael Guennebaud
3eba5e1101 Implement evaluator for sparse outer products 2014-07-19 14:55:56 +02:00
Moritz Klammler
529e6cb552 Applied changes suggested by Christoph Hertzberg to c'tor leak fix.
- Enclose exception handling in '#ifdef EIGEN_EXCEPTIONS'.
- Use an object counter to demonstrate the bug more readily.
2014-07-18 23:19:56 +02:00
Gael Guennebaud
36e6c9064f bug #770: fix out of bounds access 2014-07-18 14:19:18 +02:00
Gael Guennebaud
a325d1cb1e merge with default branch 2014-07-18 11:02:22 +02:00
Gael Guennebaud
da62eb22e4 bug #843: fix jacobisvd for complexes and extend respective unit test to chack with random tricky matrices 2014-07-17 17:09:15 +02:00
Gael Guennebaud
77af4cc3c9 bug #397: add a warning for 64 to 32 bit integer conversion and fix many of these warning by splitting the index type used for storage and as size/coefficient indexes in PermutationMatrix and Transpositions. 2014-07-17 13:34:26 +02:00
Gael Guennebaud
5e72151ca5 bug #842: warn user about MPFR++ being under the GPL 2014-07-17 12:06:20 +02:00
Gael Guennebaud
2cd38a6634 merge 2014-07-17 12:01:55 +02:00
Gael Guennebaud
84ad8ce7e3 Fix bug #770: workaround thread safety in mpreal 2014-07-17 12:00:56 +02:00
Gael Guennebaud
40b74411e4 bug #842: update mpreal copy (fix compilation with clang) 2014-07-17 11:59:51 +02:00
Christoph Hertzberg
14c8793a70 Remove unnecessary <bench/BenchTimer.h>include 2014-07-17 11:14:14 +02:00
Gael Guennebaud
424c3ad266 bug #842: fix specialized product for mpreal 2014-07-17 09:41:33 +02:00
Gael Guennebaud
a53f2b0e43 bug #838: add unit test for fill-in in sparse outer product and fix abusive fill-in. 2014-07-16 17:00:54 +02:00
Christoph Hertzberg
cd0b433540 Regression test for bug #714.
Note that the bug only occurs on some compilers and is not fixed yet
2014-07-16 15:41:10 +02:00
Gael Guennebaud
338d2ec42b bug #826: fix is_convertible for MSVC and add minimalistic unit test for is_convertible 2014-07-16 13:17:06 +02:00
Konstantinos Margaritis
0a945687b7 Added HasDiv=1 to Altivec PacketMath.h, now vectorization_logic test passes.
Added comments to the constants, indicative of the actual values
2014-07-15 11:02:51 +00:00
Gael Guennebaud
a0d1aac6c5 Extend unit test of dense triangular solvers 2014-07-15 11:15:36 +02:00
Gael Guennebaud
2bdb3b1afd Extend dense*sparse product unit tests 2014-07-15 11:00:16 +02:00
Gael Guennebaud
3c7686630d merge with default branch 2014-07-15 10:55:03 +02:00
Christoph Hertzberg
4f440b8123 Test vectorization logic for int 2014-07-14 14:36:20 +02:00
Gael Guennebaud
a20e2462bf Fix bug #838: detect outer products from either the lhs or rhs 2014-07-11 17:15:26 +02:00
Gael Guennebaud
c0f76ce2cf Fix bug #838: fix dense * sparse and sparse * dense outer products 2014-07-11 16:25:36 +02:00
Gael Guennebaud
df604e4f49 Fix inner iterator on an outer-vector 2014-07-11 16:24:49 +02:00
Hauke Heibel
5f1eedd655 Merged in complexzeros/eigen (pull request PR-69)
Added Spline interpolation with derivatives.
2014-07-11 12:03:10 +02:00
Gael Guennebaud
296cb40161 merge with default branch 2014-07-10 22:04:45 +02:00
Benoit Steiner
40bb98e76a Added primitives to compare tensor dimensions 2014-07-10 11:29:51 -07:00
Benoit Steiner
9b7a6f0122 Added tests for tensor slicing 2014-07-10 11:27:27 -07:00
Benoit Steiner
ffd3654f67 Vectorized the evaluation of expressions involving tensor slices. 2014-07-10 11:09:46 -07:00
Jeff
b1169ce40c Fixed index that would cause crash with two point, two derivative interpolation. Added static_cast. 2014-07-10 12:03:42 -06:00
Christoph Hertzberg
d1460d9278 stride must be DenseIndex not int 2014-07-10 16:23:20 +02:00
Christoph Hertzberg
cf7cf7b490 Backed out of changeset 6089:f27f55bee3efc2cafd01cb07d3faadf7eb490f66
Unfortunately this breaks things at other places
2014-07-10 16:12:13 +02:00
Christoph Hertzberg
f27f55bee3 Make MatrixBase::makeHouseholder resize its output vector if it is zero 2014-07-10 14:59:18 +02:00
Kolja Brix
e955725ff1 Fix GMRES: Initialize essential Householder vector with correct dimension. Add check if initial guess is already a sufficient approximation. 2014-07-10 08:20:55 +02:00
Benoit Steiner
25b2f6624d Improved the speed of slicing operations. 2014-07-09 12:48:34 -07:00
Gael Guennebaud
23bb592a2d Fix unit test when using 80bits FPU 2014-07-09 17:21:16 +02:00
Christoph Hertzberg
75d19bb087 Determine version of Metis library. Apparently, at least version 5.x is needed for Eigen/MetisSupport.
Marked some internal variables as advanced
2014-07-09 16:54:15 +02:00
Gael Guennebaud
62f948c56a Generalize unit testing of pscatter 2014-07-09 16:01:24 +02:00
Gael Guennebaud
da1e356306 Merged in jdh8/eigen (pull request PR-72)
Fix bug #839
2014-07-09 13:07:39 +02:00
Gael Guennebaud
54fbbe7b4e Add unit test for bug #839. 2014-07-09 13:06:06 +02:00
Benoit Steiner
ea0906dfd8 Improved evaluation of tensor expressions when used as rvalues 2014-07-08 16:43:28 -07:00
Benoit Steiner
cc1bacea5b Improved the efficiency of the tensor evaluation code on thread pools and gpus. 2014-07-08 16:39:28 -07:00
Benoit Steiner
c285fda7f4 Extended the functionality of the TensorDeviceType classes 2014-07-08 16:30:48 -07:00
Chen-Pang He
1967e7f2f3 Fix bug #839 2014-07-09 03:32:32 +08:00
Gael Guennebaud
77d57cd681 bug #808: fix implicit conversions from int/longint to float/double 2014-07-08 19:07:58 +02:00
Gael Guennebaud
e3557e8dd2 bug #808: use double instead of float for the increasing size ratio in CompressedStorage::resize
(grafted from 0e0ae40084
)
2014-07-08 18:58:41 +02:00
Gael Guennebaud
5214466b7a Fix implicit long to int conversions in blas interface 2014-07-08 19:01:49 +02:00
Gael Guennebaud
5c4733f6e4 Fix bug #809: unused variable warning 2014-07-08 18:38:34 +02:00
Gael Guennebaud
b47ef1431f Fix many long to int implicit conversions 2014-07-08 16:47:11 +02:00
Christoph Hertzberg
e25e674852 bug #837: Always re-align the result of EIGEN_ALLOCA. 2014-07-08 13:57:26 +02:00
Gael Guennebaud
4b6b76463a Merged in jdh8/eigen (pull request PR-71)
Find benchmark opponents more aggressively
2014-07-08 13:13:16 +02:00
Gael Guennebaud
904509fbb6 Move using std::abs from Eigen's namespace to function scope. 2014-07-08 10:28:09 +02:00
Gael Guennebaud
0dfb73d46a Fix LDLT with semi-definite complex matrices: owing to round-off errors, the diagonal was not real. Also exploit the fact that the diagonal is real in the rest of LDLT 2014-07-08 10:04:27 +02:00
Gael Guennebaud
7fa83e7374 Fix LDLT with semi-definite complex matrices: owing to round-off errors, the diagonal was not real. Also exploit the fact that the diagonal is real in the rest of LDLT 2014-07-08 09:56:09 +02:00
Benoit Steiner
7d53633e05 Added support for tensor slicing 2014-07-07 14:10:36 -07:00
Benoit Steiner
bc072c5cba Added support for tensor slicing 2014-07-07 14:08:45 -07:00
Benoit Steiner
47981c5925 Added support for tensor slicing 2014-07-07 14:07:57 -07:00
Chen-Pang He
1eefa5a841 Find benchmark opponents also in /usr/lib64 2014-07-07 22:55:28 +08:00
Chen-Pang He
e4b6979334 Find OpenBLAS more aggressively. This made a difference on Fedora 20 2014-07-07 21:32:33 +08:00
Chen-Pang He
b9ee880f07 chmod -x Eigen/src/Core/GenericPacketMath.h 2014-07-07 21:28:00 +08:00
Chen-Pang He
2bf58316ee Fix dox at internal::tridiagonal_qr_step 2014-07-06 13:49:43 +08:00
Chen-Pang He
85777fc131 Mark internal namespace as \internal 2014-07-06 13:45:54 +08:00
Moritz Klammler
58687aa5e6 Avoid memory leak when constructor of user-defined type throws exception.
The added check `ctorleak.cpp` demonstrates how the leak can be reproduced.
The test appears to pass but it is leaking the storage of the (not created)
matrix.  I don't know how to make this test fail in the existing test suite but
you can run it through Valgrind (or another debugger) to verify the leak.

    $ ./check.sh ctorleak && valgrind --leak-check=full ./test/ctorleak

This patch fixes this leak by adding some try-catch-delete-rethrow blocks to
`Eigen/src/Core/util/Memory.h`.
2014-07-06 06:58:13 +02:00
Gael Guennebaud
339f14b8d1 bug #826: document caveats in 1x1 and 2x1 constructors. 2014-07-21 13:43:48 +02:00
Gael Guennebaud
d4cc1bdc7f Make the ordering method of SimplicialL[D]LT user configurable. 2014-07-20 14:22:58 +02:00
Gael Guennebaud
8e19027130 bug #826: fix 64 to 32 bits conversion warning when calling Matrix<int,1,1>(long) 2014-07-20 14:03:22 +02:00
Christoph Hertzberg
ef4a86d6b8 Fix trivial warnings in MPRealSupport 2014-07-18 16:39:58 +02:00
Christoph Hertzberg
68eafc10b1 Add note to EIGEN_DONT_PARALLELIZE into preprocessor documentation page (requested in IRC) 2014-07-18 15:42:12 +02:00
Christoph Hertzberg
1cb71a8782 bug #138: Make building of internal documentation configurable via cmake flag 2014-07-18 14:34:58 +02:00
Gael Guennebaud
ac1bb3e5b3 bug #770: fix out of bounds access 2014-07-18 14:22:33 +02:00
Chen-Pang He
4860da2de1 Percent "Eigen" in dox to prevent linking if not referring to the Eigen namespace 2014-07-05 23:01:27 +08:00
Chen-Pang He
7a915f6846 Move Doxygen-only stuff to *.dox 2014-07-05 22:41:58 +08:00
Chen-Pang He
1a817d3b70 Document internal namespace 2014-07-05 21:50:05 +08:00
Chen-Pang He
8ee38d2db6 Fix dox for namespaces 2014-07-05 21:48:48 +08:00
Christoph Hertzberg
f365380496 Fix regression introduced by 3117036b80
:
Matrix<Scalar,1,1>(int) did not compile if Scalar is not constructible from int. Now this falls back to the (Index size) constructor.
2014-07-04 12:52:55 +02:00
Christoph Hertzberg
3a9f9faada Fix unused typedef warning 2014-07-04 12:48:24 +02:00
Gael Guennebaud
998455a570 LDLT is not rank-revealing, so we should not attempt to use the biggest diagonal elements as thresholds. 2014-07-02 23:04:46 +02:00
Gael Guennebaud
0a8e4712d1 Do not attempt to include <intrin.h> on Windows CE 2014-07-02 16:13:05 +02:00
Gael Guennebaud
61b88d2feb merge with default branch 2014-07-02 09:35:37 +02:00
Gael Guennebaud
bf334b8ae5 Fix regeression in bicgstab: the threshold used to detect the need for a restart was much too large. 2014-07-01 22:29:04 +02:00
Gael Guennebaud
8f4cdbbc8f Fix typo in dense * diagonal evaluator. 2014-07-01 18:04:30 +02:00
Gael Guennebaud
7390af91b6 Implement evaluators for sparse*dense products 2014-07-01 17:53:18 +02:00
Gael Guennebaud
1e6f53e070 Use DiagonalShape as the storage kind of DiagonalBase<>. 2014-07-01 17:52:58 +02:00
Gael Guennebaud
6f846ef9c6 Split StorageKind promotion into two helpers: one for products, and one for coefficient-wise operations. 2014-07-01 17:51:53 +02:00
Christoph Hertzberg
324e7e8fc9 Removed the deprecated EIGEN2_SUPPORT, as previously announced. A compilation error is raised, if this compile-switch is defined. The documentation references to the corresponding pages from Eigen3.2 now. Also, the Eigen2 testsuite has been removed. 2014-07-01 16:58:11 +02:00
Gael Guennebaud
3c63446507 Update copyright dates 2014-07-01 13:27:35 +02:00
Gael Guennebaud
746d2db6ed Implement evaluators for sparse * sparse with auto pruning. 2014-07-01 13:18:56 +02:00
Gael Guennebaud
441f97b2df Implement evaluators for sparse * sparse products 2014-07-01 11:50:20 +02:00
Gael Guennebaud
0ad7a644df Implement nonZeros() for Transpose<sparse> 2014-07-01 11:49:46 +02:00
Gael Guennebaud
7ffd55c980 Do not bypass aliasing in sparse e assignments 2014-07-01 11:48:49 +02:00
Gael Guennebaud
75e574275c Fix bug #836: extend SparseQR to support more columns than rows. 2014-07-01 10:24:46 +02:00
Gael Guennebaud
c401167712 Fix double constructions of the nested CwiseBinaryOp evaluator in sparse*diagonal product iterator. 2014-06-27 16:41:45 +02:00
Gael Guennebaud
73e686c6a4 Implement evaluators for sparse times diagonal products. 2014-06-27 15:54:44 +02:00
Gael Guennebaud
ae039dde13 Add a NoPreferredStorageOrderBit flag for expression having no preferred storage order.
It is currently only used in Product.
2014-06-27 15:53:51 +02:00
Gael Guennebaud
f0648f8860 Implement evaluator for sparse views. 2014-06-26 13:52:19 +02:00
Jeff
08c615f1e4 IndexArray is now a typename.
Changed interpolate with derivatives test to use VERIFY_IS_APPROX.
2014-06-25 19:02:57 -06:00
Gael Guennebaud
54607665ab Fix inverse evaluator 2014-06-25 23:44:59 +02:00
Christoph Hertzberg
d73ee84d37 Disabled HIDE_SCOPE_NAMES (default doxygen setting). This might help to avoid API confusions as in bug #830. 2014-06-25 22:44:43 +02:00
Gael Guennebaud
a7bd4c455a Update sparse reduxions and sparse-vectors to evaluators. 2014-06-25 17:24:43 +02:00
Gael Guennebaud
b868bfb84a Make operator=(EigenBase<>) uses the new assignment mechanism and introduce a generic EigenBase to EigenBase assignment kind based on the previous evalTo mechanism. 2014-06-25 17:23:52 +02:00
Gael Guennebaud
3b19b466a7 Generalize static assertions on matching sizes to avoid the need for SizeAtCompileTime 2014-06-25 17:22:12 +02:00
Gael Guennebaud
199ac3f2e7 Implement evaluators for sparse coeff-wise views 2014-06-25 17:21:04 +02:00
Gael Guennebaud
e3ba5329ff Implement evaluators for sparse Block. 2014-06-25 09:58:26 +02:00
Gael Guennebaud
17f119689e implement evaluator for SparseVector 2014-06-25 09:58:03 +02:00
Jeff
f9496d341f Merged. 2014-06-23 20:24:31 -06:00
Jeff
e745a450de Removed tabs and fixed indentation. 2014-06-23 20:18:16 -06:00
Jeff
e86adc87e9 Fixed type mixing issues. 2014-06-23 19:52:42 -06:00
Jeff
b59f045c07 Using LU decomposition with complete pivoting for better accuracy. 2014-06-23 19:04:52 -06:00
Christoph Hertzberg
755be9016a Workaround clang error introduced by 3117036b80
:
"template argument for non-type template parameter is treated as function type 'bool (bool)'"
2014-06-23 22:33:36 +02:00
Jeff
957c2c291b Changed uint to unsigned int. 2014-06-23 08:34:11 -06:00
Christoph Hertzberg
15c2c083e8 Additional unit tests for bug #826 by Gael 2014-06-23 11:21:40 +02:00
Christoph Hertzberg
3117036b80 Fix bug #826: Allow initialization of 1x1 Arrays/Matrices by passing a value. 2014-06-23 11:15:42 +02:00
Christoph Hertzberg
1c3843bf86 Fix bug #729: Use alloca if it is defined 2014-06-23 11:04:12 +02:00
Christoph Hertzberg
0ddde223e9 Fixed typos 2014-06-23 11:00:52 +02:00
Gael Guennebaud
3849cc65ee Implement binaryop and transpose evaluators for sparse matrices 2014-06-23 10:40:03 +02:00
Jeff
5dbbe6b400 Added Spline interpolation with derivatives. 2014-06-20 22:12:45 -06:00
Gael Guennebaud
ec0a8b2e6d rm conflict 2014-06-20 16:30:34 +02:00
Gael Guennebaud
7fa87a8b12 Backport changes from old to new expression engines 2014-06-20 16:17:57 +02:00
Gael Guennebaud
b29b81a1f4 merge with default branch 2014-06-20 15:55:44 +02:00
Gael Guennebaud
47585c8ab2 merge 2014-06-20 15:49:07 +02:00
Gael Guennebaud
c415b627a7 Started to move the SparseCore module to evaluators: implemented assignment and cwise-unary evaluator 2014-06-20 15:42:13 +02:00
Gael Guennebaud
78bb808337 1- Introduce sub-evaluator types for unary, binary, product, and map expressions to ease specializing them.
2- Remove a lot of code which should not be there with evaluators, in particular coeff/packet methods implemented in the expressions.
2014-06-20 15:39:38 +02:00
Gael Guennebaud
963d338922 Fix bug #827: improve accuracy of quaternion to angle-axis conversion 2014-06-20 15:09:42 +02:00
Gael Guennebaud
98ef44fe55 Add assertion and warning on the requirements of SparseQR and COLAMDOrdering 2014-06-20 14:43:47 +02:00
Gael Guennebaud
1fdef63d1f Explain how to export sparse linear problems in matrix-market format. 2014-06-20 13:23:33 +02:00
Jitse Niesen
de150b1e14 Add documentation and very simple test for array atan(), part 2
(files I forget in the previous commit).
2014-06-19 15:12:33 +01:00
Jitse Niesen
55453c51e8 Add documentation and very simple test for array atan(). 2014-06-19 15:07:42 +01:00
Roger Martin
eb49100de9 Add component-wise atan() function (see bug #80). 2014-06-19 14:55:14 +01:00
Mark Borgerding
afb1a8c124 fixed warning: -Wunused-local-typedefs 2014-06-17 18:25:56 -04:00
Gael Guennebaud
c06ec0f464 Fix Jacobi preconditioner with zero diagonal entries 2014-06-17 23:47:30 +02:00
Gael Guennebaud
95ecd582a3 Update decompositions tables 2014-06-17 09:37:07 +02:00
Gael Guennebaud
b0979b8598 Merged in vladimir_ch/eigen/vladimir_ch/bug-796-fix-eigen3config.cmake (pull request PR-67)
Change variable names in Eigen3Config.cmake to EIGEN3_*
2014-06-17 09:20:37 +02:00
Benoit Steiner
774c3c1e0a Created additional unit tests for the tensor code and improved existing ones. 2014-06-13 10:20:28 -07:00
Benoit Steiner
f80c8e17eb Silenced a compilation warning 2014-06-13 10:12:12 -07:00
Benoit Steiner
38ab7e6ed0 Reworked the expression evaluation mechanism in order to make it possible to efficiently compute convolutions and contractions in the future:
* The scheduling of computation is moved out the the assignment code and into a new TensorExecutor class
 * The assignment itself is now a regular node on the expression tree
 * The expression evaluators start by recursively evaluating all their subexpressions if needed
2014-06-13 09:56:51 -07:00
Vladimir Chalupecky
1ee4e2db15 Change variable names in Eigen3Config.cmake to EIGEN3_* 2014-06-12 10:51:02 +09:00
Benoit Steiner
aa664eabb9 Fixed a few compilation errors. 2014-06-10 10:31:29 -07:00
Benoit Steiner
4304c73542 Pulled latest updates from the Eigen main trunk. 2014-06-10 10:23:32 -07:00
Benoit Steiner
925fb6b937 TensorEval are now typed on the device: this will make it possible to use partial template specialization to optimize the strategy of each evaluator for each device type.
Started work on partial evaluations.
2014-06-10 09:14:44 -07:00
Benoit Steiner
a77458a8ff Fixes compilation errors triggered when compiling the tensor contraction code with cxx11 enabled. 2014-06-09 10:06:57 -07:00
Benoit Steiner
a669052f12 Improved support for rvalues in tensor expressions. 2014-06-09 09:45:30 -07:00
Benoit Steiner
36a2b2e9dc Prevent the generation of unlaunchable cuda kernels when compiling in debug mode. 2014-06-09 09:43:51 -07:00
Benoit Steiner
2859a31ac8 Fixed compilation error 2014-06-09 09:42:34 -07:00
Benoit Steiner
d13711a363 Pulled latest changes from the main branch 2014-06-09 09:35:04 -07:00
Benoit Steiner
fe102248ac Fixed the threadpool test 2014-06-09 09:19:21 -07:00
Benoit Steiner
8c8ae2d819 Fixed a typo 2014-06-07 11:24:38 -07:00
Benoit Steiner
29aebf96e6 Created the pblend packet primitive and implemented it using SSE and AVX instructions. 2014-06-06 20:18:44 -07:00
Benoit Steiner
79085e08e9 Fixed a typo 2014-06-06 20:16:13 -07:00
Benoit Steiner
a961d72e65 Added support for convolution and reshaping of tensors. 2014-06-06 16:25:16 -07:00
Gael Guennebaud
abc1ca0af1 The BLAS interface is complete. 2014-06-06 11:21:19 +02:00
Gael Guennebaud
c331ce6b8e Fix bug #738: use the "current" version of cmake project directories to ease the inclusion of Eigen within other projects. 2014-06-06 11:06:44 +02:00
Gael Guennebaud
ed37c44765 Enable LinearAccessBit in Block expression for inner-panels 2014-06-06 11:02:20 +02:00
Benoit Steiner
8998f4099e Created additional tests for the tensor code. 2014-06-05 10:49:34 -07:00
Christian Seiler
96cb58fa3b unsupported/TensorSymmetry: factor out completely from Tensor module
Remove the symCoeff() method of the the Tensor module and move the
functionality into a new operator() of the symmetry classes. This makes
the Tensor module now completely self-contained without symmetry
support (even though previously it was only a forward declaration and a
otherwise harmless trivial templated method) and also removes the
inconsistency with the rest of eigen w.r.t. the method's naming scheme.
2014-06-04 20:44:22 +02:00
Christian Seiler
ea99433523 unsupported/TensorSymmetry: make symgroup construction autodetect number of indices
When constructing a symmetry group, make the code automatically detect
the number of indices required from the indices of the group's
generators. Also, allow the symmetry group to be applied to lists of
indices that are larger than the number of indices of the symmetry
group.

Before:
SGroup<4, Symmetry<0, 1>, Symmetry<2,3>> group;
group.apply<SomeOp, int>(std::array<int,4>{{0, 1, 2, 3}}, 0);

After:
SGroup<Symmetry<0, 1>, Symmetry<2,3>> group;
group.apply<SomeOp, int>(std::array<int,4>{{0, 1, 2, 3}}, 0);
group.apply<SomeOp, int>(std::array<int,5>{{0, 1, 2, 3, 4}}, 0);

This should make the symmetry group easier to use - especially if one
wants to reuse the same symmetry group for different tensors of maybe
different rank.

static/runtime asserts remain for the case where the length of the
index list to which a symmetry group is to be applied is too small.
2014-06-04 20:27:42 +02:00
Christian Seiler
cee62018fc unsupported/CXX11/Core: allow gen_numeric_list to have a starting point
Add a template parameter to gen_numeric_list that acts as a starting
point for the list, i.e. gen_numeric_list<int, 5, 4> will generate a
numeric_list<int, 4, 5, 6, 7, 8>.
2014-06-04 19:54:22 +02:00
Christian Seiler
58cfac9a12 unsupported/ C++11 workarounds: don't use hack for libc++ if not required
libc++ from 3.4 onwards supports constexpr std::get, but only if
compiled with -std=c++1y. Change the detection so that libc++'s
internals are only used if either -std=c++1y is not specified or the
library is too old, making the whole hack a bit more future-proof.
2014-06-04 18:47:42 +02:00
Christian Seiler
45515779d3 Fix compilation for CXX11/Tensor module if unsupported is not in include path 2014-06-04 18:31:02 +02:00
Benoit Steiner
6fa6cdd2b9 Added support for tensor contractions
Updated expression evaluation mechanism to also compute the size of the tensor result
Misc fixes and improvements.
2014-06-04 09:21:48 -07:00
Gael Guennebaud
0f1e321dd4 Fic bug #819: include path of details.h 2014-06-04 11:58:01 +02:00
Jitse Niesen
789674809f Fix test: EigenSolver on 1x1 matrix with NaN sets info to NumericalIssue.
This was changed in 3c66bb136b
.
2014-06-02 11:42:42 +01:00
Jitse Niesen
eb56461ac2 Fix doc'n of FullPivLU re permutation matrices (bug #815). 2014-05-31 23:05:18 +01:00
Benoit Steiner
736267cf6b Added support for additional tensor operations:
* comparison (<, <=, ==, !=, ...)
  * selection
  * nullary ops such as random or constant generation
  * misc unary ops such as log(), exp(), or a user defined unaryExpr()
Cleaned up the code a little.
2014-05-22 16:22:35 -07:00
Benoit Steiner
7402fea0a8 Vectorized the evaluation of tensor expression (using SSE, AVX, NEON, ...)
Added the ability to parallelize the evaluation of a tensor expression over multiple cpu cores.
Added the ability to offload the evaluation of a tensor expression to a GPU.
2014-05-16 15:08:05 -07:00
Mark Borgerding
e3ab46b8c9 AsciiQuickReference: added .real(), .imag()
(transplanted from 11462c1a29
)
2014-05-16 13:45:35 -04:00
Mark Borgerding
c794099e69 fixed AsciiQuickReference typo: LinSpace -> LinSpaced
(transplanted from e667819055
)
2014-05-08 15:14:12 -04:00
Christoph Hertzberg
aa524604b7 README.md edited online with Bitbucket 2014-05-21 14:08:04 +00:00
Benjamin Chrétien
c55c5763fe PolynomialSolver: fix typo. 2014-05-19 19:24:02 +02:00
Benjamin Chrétien
eda79321be PolynomialSolver: fix bugs related to linear polynomials. 2014-05-19 19:08:51 +02:00
Benjamin Chrétien
df92649379 PolynomialSolver: add missing constructors. 2014-05-19 18:40:29 +02:00
Benjamin Chrétien
0f94607947 PolynomialSolver: test template constructor in test suite. 2014-05-19 18:34:10 +02:00
Benjamin Chrétien
edebb15275 PolynomialSolver: add a test to reveal a bug. 2014-05-19 18:21:29 +02:00
Christoph Hertzberg
9aa3dc4e21 Merged in benoitsteiner/eigen-fixes (pull request PR-62)
Made it possible to call the assignment operator on an Eigen::Block from a CUDA kernel.
2014-05-08 17:06:28 +02:00
Benoit Steiner
881aab14b4 Made it possible to call the assignment operator on an Eigen::Block from a CUDA kernel. 2014-05-07 13:34:46 -07:00
Benoit Steiner
0320f7e3a7 Added support for fixed sized tensors.
Improved support for tensor expressions.
2014-05-06 11:18:37 -07:00
Christoph Hertzberg
5811e68dd1 Disabled unused warnings in Eigen2-tests 2014-05-06 12:53:18 +02:00
Christoph Hertzberg
9217de8bf2 Missed to remove IACA_END in previous commit 2014-05-05 15:10:18 +02:00
Christoph Hertzberg
84cb1d72b8 Removed IACA-defines
This caused redefinition warnings if IACA headers were included from elsewhere. For a clean solution we should define our own EIGEN_IACA_* macros
2014-05-05 15:06:37 +02:00
Christoph Hertzberg
56de8d3816 Fixed unused variable warnings 2014-05-05 15:03:29 +02:00
Christoph Hertzberg
b4beba72a2 Fix bug #807: Missing scalar type cast in umeyama() 2014-05-05 14:23:52 +02:00
Christoph Hertzberg
b5e3d76aa5 Fixed bug #806: Missing scalar type cast in Quaternion::setFromTwoVectors() 2014-05-05 14:22:27 +02:00
Florian George
f56d452c7e Enable atv in Blaze Benchmark 2014-05-04 17:07:17 +02:00
Florian George
af79b158a1 Use trans(X) instead of X.transpose() in Blaze Benchmark 2014-05-04 17:06:34 +02:00
Benjamin Chretien
0b7f95a03f Fix typo in SparseMatrix assert. 2014-05-03 12:41:37 +02:00
Gael Guennebaud
d67aa1549b Add missing add_subdirectory directive 2014-05-03 10:46:11 +02:00
Gael Guennebaud
07986189b7 Fix bug #803: avoid char* to int* conversion 2014-05-01 23:03:54 +02:00
Benoit Steiner
c0f2cb016e Extended support for Tensors:
* Added ability to map a region of the memory to a tensor
  * Added basic support for unary and binary coefficient wise expressions, such as addition or square root
  * Provided an emulation layer to make it possible to compile the code with compilers (such as nvcc) that don't support cxx11.
2014-04-28 10:32:27 -07:00
Gael Guennebaud
2fb64578aa Add a small benchmark to compare dense solvers for small to large problems. 2014-04-28 16:16:29 +02:00
Kolja Brix
ecf1f1d589 Make gdb pretty printer Python3-compatible (bug #800). 2014-04-28 14:10:22 +01:00
Gael Guennebaud
e7ef26fa44 TRMM: Make sure we have enough memory in rhs block to enforce alignment. 2014-04-25 23:36:22 +02:00
Gael Guennebaud
450d0c3de0 Make sure that calls to broadcast4 are 16 bytes aligned 2014-04-25 22:25:48 +02:00
Gael Guennebaud
f9d2f3903e Product kernel: skip loop on columns if there is no remaining rows 2014-04-25 16:54:30 +02:00
Gael Guennebaud
6f64b0b487 Fix sizeof unit test 2014-04-25 14:05:54 +02:00
Gael Guennebaud
c20e3641de Fix for mixed products 2014-04-25 13:22:34 +02:00
Gael Guennebaud
2dbfd83424 Implement pbroadcast4 on altivec 2014-04-25 02:46:57 -07:00
Gael Guennebaud
7388fdf560 pbroadcast4/2 assume aligned memory 2014-04-25 02:46:22 -07:00
Gael Guennebaud
c9788d55b9 Disable 3pX4 kernel on Altivec: despite this platform has 32 registers, this version seems significantly slower. 2014-04-25 11:48:22 +02:00
Gael Guennebaud
ae4d9434e2 Add unit test for pbroadcast4/2 2014-04-25 11:21:18 +02:00
Gael Guennebaud
4def7b1fa5 Fix ptranspose overload prototypes for NEON 2014-04-25 11:15:13 +02:00
Gael Guennebaud
c79bd4b64b Minor optimizations in product kernel:
- use pbroadcast4 (helpful when AVX is not available)
- process all remaining rows at once (significant speedup for small matrices)
2014-04-25 11:06:03 +02:00
Gael Guennebaud
cf7eaed38d Avoid blocking-size mismatch in unit tests calling Eigen's blas interface. 2014-04-25 11:04:02 +02:00
Gael Guennebaud
3d8d0f6269 Enable vectorization of pack_rhs with a column-major RHS.
Rename and generalize Kernel<*> to PacketBlock<*,N>.
2014-04-25 10:56:18 +02:00
Gael Guennebaud
b0e19db1cf Enable fused madd for Altivec 2014-04-24 23:17:18 +02:00
Gael Guennebaud
8d85ce88e1 Implement ptranspose on altivec and fix pgather/pscatter 2014-04-24 05:47:53 -07:00
Benoit Steiner
4eb92e5647 Fixed the NEON implementation of predux_max<Packet4i>. 2014-04-23 18:23:07 -07:00
Benoit Steiner
ccb4dec719 Created a NEON version of the ptranspose packet primitives 2014-04-23 18:22:10 -07:00
Gael Guennebaud
82b09fcb91 Add Altivec implementation of pgather/pscatter (not tested) 2014-04-23 13:09:26 +02:00
Gael Guennebaud
ecbd67a15a Fix EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT macro 2014-04-22 17:03:57 +02:00
Gael Guennebaud
934ce93886 merge with default branch 2014-04-22 17:00:38 +02:00
Gael Guennebaud
5c5231ab71 Workaround gcc's default ABI not being able to distinghish between vector types of different sizes. 2014-04-22 16:03:19 +02:00
Gael Guennebaud
2606abed53 Fix 128bit packet size assumptions in unit tests. 2014-04-18 21:14:40 +02:00
Gael Guennebaud
a7d20038df Fix alignment assertion. 2014-04-18 17:06:31 +02:00
Gael Guennebaud
3454b4e5f1 Fix calls to lazy products (lazy product does not like matrices with 0 length) 2014-04-18 17:06:03 +02:00
Gael Guennebaud
94684721bd Smarter block size computation 2014-04-18 15:35:34 +02:00
Gael Guennebaud
1388f4f9fd Fix typo (was working with clang\!) 2014-04-18 11:43:13 +02:00
Gael Guennebaud
6d665d446b Fixes for fixed sizes and non vectorizable types 2014-04-17 23:26:34 +02:00
Gael Guennebaud
2c3c95990d merge 2014-04-17 22:50:49 +02:00
Benoit Steiner
6d6df90c9a Implemented the pgather/pscatter packet primitives for the arm/NEON architecture 2014-04-17 12:28:01 -07:00
Gael Guennebaud
c354bd47f7 Make our gemm bench a little more powerful. 2014-04-17 21:03:26 +02:00
Gael Guennebaud
9777a5ca60 Various minor fixes in BTL 2014-04-17 21:01:45 +02:00
Gael Guennebaud
9746396d1b Optimize AVX pset1 for complexes and ploaddup 2014-04-17 20:51:04 +02:00
Benjamin Chretien
e5d0cb54a5 Fix typo in Reductions tutorial. 2014-04-17 18:49:23 +02:00
Gael Guennebaud
1dd015fea6 Reduce block sizes in unit tests. 2014-04-17 16:27:58 +02:00
Gael Guennebaud
45a4aad572 add unit tests for ploadquad and predux4, and split packetmath unit test wrt real/complex 2014-04-17 16:27:22 +02:00
Gael Guennebaud
e1d461352e Extend mixingtype unit test to check transposed cases. 2014-04-17 16:26:35 +02:00
Gael Guennebaud
11fbdcbc38 Fix and optimize mixed products 2014-04-17 16:04:30 +02:00
Gael Guennebaud
0fa8290366 Optimize ploaddup for AVX 2014-04-17 16:02:27 +02:00
Gael Guennebaud
d936ddc3d1 Fallback to lazy products for very small ones. 2014-04-16 23:15:42 +02:00
Gael Guennebaud
de8336a9bc Enable alloca on MAC OSX 2014-04-16 23:14:58 +02:00
Jitse Niesen
ffc995c9e4 Implement evaluator<ReturnByValue>.
All supported tests pass apart from Sparse and Geometry,
except test in adjoint_4 that a = a.transpose() raises an assert.
2014-04-16 18:16:36 +01:00
Gael Guennebaud
d5a795f673 New gebp kernel handling up to 3 packets x 4 register-level blocks. Huge speeup on Haswell.
This changeset also introduce new vector functions: ploadquad and predux4.
2014-04-16 17:05:11 +02:00
Jitse Niesen
b30706bd5c Fix typo in Inverse.h 2014-04-15 22:51:46 +01:00
Mark Borgerding
e0dbb68c2f Check IMKL version for compatibility with Eigen 2014-04-15 13:57:03 -04:00
Jitse Niesen
59f5f155c2 Port products with permutation matrices to evaluators. 2014-04-15 15:21:38 +01:00
Gael Guennebaud
20c840be15 Merged in benoitsteiner/eigen-fixes/nvcc_fixes (pull request PR-56)
Fixed a typo in CXX11Meta.h
2014-04-15 10:38:25 +02:00
Benoit Steiner
1afd50e0f3 Fixed a typo in CXX11Meta.h 2014-04-14 14:26:30 -07:00
Gael Guennebaud
3c66bb136b bug #793: detect NaN and INF in EigenSolver instead of aborting with an assert. 2014-04-14 22:00:27 +02:00
Gael Guennebaud
7098e6d976 Add isfinite overload for complexes. 2014-04-14 21:57:49 +02:00
Benoit Steiner
feaf7c7e6d Optimized SSE unaligned loads and stores when compiling a 64bit target with a recent version of gcc (ie gcc 4.8). 2014-04-14 10:44:17 -07:00
Gael Guennebaud
d567e3b893 Merged in benoitsteiner/eigen-fixes (pull request PR-55)
CUDA fixes
2014-04-14 14:33:50 +02:00
Gael Guennebaud
148acf8e4f bug #790: fix overflow in real_2x2_jacobi_svd 2014-04-14 13:52:16 +02:00
Gael Guennebaud
0587db8bf5 bug #793: fix overflow in EigenSolver and add respective regression unit test 2014-04-14 11:43:08 +02:00
Benoit Steiner
7903d3f27b Updated the compiler flags to enable nvcc to work with clang. 2014-04-12 23:39:37 -07:00
Benoit Steiner
a803ff18a9 Fixed a typo in cuda_basic.cu 2014-04-12 20:24:05 -07:00
Freddie Witherden
91288e9bf9 Add include LevenbergMarquardt in CMakeLists.txt.
This fixes bug #768.
2014-04-12 12:53:09 +01:00
Jitse Niesen
fbd5eac7cf Merged in benoitsteiner/eigen-fixes/nvcc_fixes (pull request PR-53)
Silenced a compilation warning produced by nvcc.
2014-04-11 14:16:08 +01:00
Benoit Steiner
1b333c89c9 Updated my previous fix to avoid introducing a compilation warning on ARM platforms. 2014-04-10 17:43:13 -07:00
Benoit Steiner
a1fcf599fa Silenced a compilation warning produced by nvcc. 2014-04-10 11:19:37 -07:00
Jitse Niesen
a91a7a1964 doc: Add references to Cholesky methods in SelfAdjointView. 2014-04-07 14:14:48 +01:00
Benoit Steiner
3b2321e3ab Updated the geo_parametrizedline_2 test for AVX. 2014-04-04 17:08:47 -07:00
Benoit Steiner
b446ff037e Deleted some dead code. 2014-04-04 14:12:24 -07:00
Jitse Niesen
5afcb4965c Remove out-dated comment in cholesky test. 2014-04-04 16:48:13 +01:00
Christoph Hertzberg
096af59799 Fix bug #784: Assert if assigning a product to a triangularView does not match the size. 2014-04-04 17:48:37 +02:00
Benoit Steiner
8044b00a7f bug #782: Workaround for gcc <= 4.4 compilation error on the NEON PacketMath code. 2014-04-03 23:41:47 +02:00
Benoit Steiner
aecc78325a Pulled the latest updates from the eigen trunk. 2014-04-01 22:07:05 -07:00
Christoph Hertzberg
1cb8de1250 Make some actual verifications inside the autodiff unit test 2014-04-01 17:44:48 +02:00
Florian George
56c4851323 Fixed typo: symmretric -> symmetric 2014-04-01 15:52:25 +02:00
Gael Guennebaud
ceae5b4145 Fix lapack build 2014-04-01 11:52:23 +02:00
Gael Guennebaud
ec65e6648c bug #775: propagate generator when workingaround cmake bug #9220 2014-04-01 11:45:43 +02:00
Gael Guennebaud
d992634fbc Fix bug #776: it seems that mingw does not support weak linking 2014-04-01 11:31:21 +02:00
Benoit Steiner
5e8622477b Rename the vector() factories defined in blas/common.h into make_vector() to prevent a possible name conflict with std::vector. 2014-04-01 11:23:28 +02:00
Gael Guennebaud
1221dd90aa Fix no newline at end of file warning 2014-04-01 11:21:14 +02:00
Gael Guennebaud
93870d95b7 BTL: add blaze 2014-03-31 10:59:55 +02:00
Gael Guennebaud
f603823ef3 BTL: fix warnings and extend to 5k matrices, update GotoBlas to OpenBlas, etc. 2014-03-31 10:58:30 +02:00
Gael Guennebaud
8d0441052e Finally, prefetching seems to help getting more stable performance 2014-03-31 10:42:19 +02:00
Gael Guennebaud
82c8163067 Enable repetition in mixing type unit test 2014-03-31 10:41:40 +02:00
Gael Guennebaud
1c0728043a Workaround alignment warnings 2014-03-30 22:43:47 +02:00
Gael Guennebaud
e497a27ddc Optimize gebp kernel:
1 - increase peeling level along the depth dimention (+5% for large matrices, i.e., >1000)
2 - improve pipelining when dealing with latest rows of the lhs
2014-03-30 21:57:05 +02:00
Benoit Steiner
ad59ade116 Vectorized the loop peeling of the inner loop of the block-panel matrix multiplication code. This speeds up the multiplication of matrices which size is not a multiple of the packet size. 2014-03-28 12:11:23 -07:00
Benoit Steiner
39bfbd43f0 Properly align the input data to prevent false failures of the packetmath.cpp test. 2014-03-28 12:00:08 -07:00
Gael Guennebaud
10aa14592a Add a mechanism to recursively access to half-size packet types 2014-03-28 10:18:04 +01:00
Gael Guennebaud
8d2bb2c20d merge with default branch 2014-03-28 09:24:18 +01:00
Gael Guennebaud
c94fde118a Enable vectorization of gemv for PacketSize>4 through unaligned loads (still better than no vectorization) 2014-03-28 09:11:06 +01:00
Benoit Steiner
51e85c936d Merged latest changes from parent. 2014-03-27 18:32:15 -07:00
Benoit Steiner
8a94cb3edd Implemented the SSE version of the gather and scatter packet primitives. 2014-03-27 18:29:01 -07:00
Benoit Steiner
7f3162f707 Implemented the AVX version of the gather and scatter packet primitives. 2014-03-27 17:42:25 -07:00
Benoit Steiner
ee86679096 Introduced pscatter/pgather packet primitives. They will be used to optimize the loop peeling code of the block-panel matrix multiplication kernel. 2014-03-27 16:03:03 -07:00
Gael Guennebaud
58fe2fc2b2 enforce the use of vfmadd231ps for pmadd (gcc and clang stupidely generates the other fmadd variants plus some register moves...) 2014-03-27 23:38:50 +01:00
Benoit Steiner
729363114f Fixed compilation error when FMA instructions are enabled. 2014-03-27 11:20:41 -07:00
Benoit Steiner
1697d7a179 Silenced "unused variable" warnings when compiling with FMA. 2014-03-27 11:00:47 -07:00
Benoit Steiner
3e1fe8e416 Vectorized the packing of a col-major matrix used as the right hand side argument in a matrix-matrix product when AVX instructions are used. No vectorization takes place when SSE instructions are used, however this doesn't seem to impact performance. 2014-03-27 10:38:41 -07:00
Benoit Steiner
b776458ccb Vectorized the packing of a row-major matrix used as the left hand side argument in a matrix-matrix product. 2014-03-27 10:02:24 -07:00
Benoit Steiner
c4902a3d01 Implemented the AVX version of the ptranspose packet primitive. 2014-03-27 09:34:51 -07:00
Gael Guennebaud
7d73c7f18b Change abi version when enabling AVX with GCC 2014-03-27 15:38:40 +01:00
Gael Guennebaud
6f123d209e Fix geo_* unit tests with respect to AVX 2014-03-27 15:29:56 +01:00
Gael Guennebaud
052aedd394 Implement pcplflip, palign, predux and the likes from AVC/complexes 2014-03-27 14:47:00 +01:00
Gael Guennebaud
fb03b56647 Fix warning 2014-03-27 11:38:35 +01:00
Jitse Niesen
6a81594771 Merged in infinitei/eigen (pull request PR-50)
Fixed compilation error due to obsolete internal::abs and internal::sqrt function calls
2014-03-27 10:12:25 +00:00
Mark Borgerding
9ce0d78513 immintrin.h did not come until intel version 11 2014-03-26 22:26:07 -04:00
Benoit Steiner
a419cea4a0 Created the ptranspose packet primitive that can transpose an array of N packets, where N is the number of words in each packet. This primitive will be used to complete the vectorization of the gemm_pack_lhs and gemm_pack_rhs functions.
Implemented the primitive using SSE instructions.
2014-03-26 19:03:07 -07:00
Abhijit Kundu
ba3457cab2 Fixed compilation error due to obsolete internal::abs and internal::sqrt function calls 2014-03-26 22:02:48 -04:00
Benoit Steiner
14bc4b9704 Made sure that the version of gemm_pack_rhs specialized for row major matrices is vectorized when nr == 2*PacketSize (which is the case for SSE when compiling in 64bit mode). 2014-03-26 17:35:18 -07:00
Benoit Steiner
e45a6bed45 Specialized the pload1 packet primitive for Packet8f and Packet4d in order to take advantage of the vbroadcastss and vbroadcastsd instructions whenever possible. 2014-03-26 15:58:13 -07:00
Benoit Steiner
cc73164aa8 Merged latest updates from the parent branch 2014-03-26 15:23:59 -07:00
Gael Guennebaud
f0a4c9d5ab Update gebp kernel to process a panle of 4 columns at once for the remaining ones. 2014-03-26 23:22:36 +01:00
Gael Guennebaud
8be011e776 Remove remaining bits of the dead working buffer 2014-03-26 23:14:44 +01:00
Benoit Steiner
a078f442a3 Vectorized the multiplication and division of complex numbers using AVX instructions. 2014-03-26 15:11:18 -07:00
Benoit Steiner
cf1a7bfbe1 Used AVX instructions to vectorize the complex version of the pfirst and ploaddup packet primitives.
Silenced a few compilation warnings.
2014-03-26 12:03:31 -07:00
Gael Guennebaud
bc401eb6fa Implement new 1 packet x 8 gebp kernel 2014-03-26 18:53:00 +01:00
Gael Guennebaud
b286a1e75c add pbroadcast2/4 generic intrinsics 2014-03-26 16:46:36 +01:00
Benoit Steiner
6bf3cc2732 Use AVX instructions to vectorize pset1<Packet2cd>, pset1<Packet4cf>, preverse<Packet2cd>, and preverse<Packet4cf> 2014-03-25 09:00:43 -07:00
Benoit Steiner
7ae9b0805d Used AVX instructions to vectorize the predux_min<Packet8f>, predux_min<Packet4d>, predux_max<Packet8f>, and predux_max<Packet4d> packet primitives. 2014-03-24 13:33:40 -07:00
Benoit Steiner
08f7b3221d Added proper support for AVX and FMA in the makefiles. 2014-03-24 09:52:45 -07:00
Benoit Steiner
72707a8664 Made sure that EIGEN_ALIGN is defined when EIGEN_DONT_VECTORIZE is set to true to prevent build failures when vectorization is disabled. 2014-03-21 11:40:29 -07:00
Benoit Steiner
8a0845ebd7 Merged latest changes from the parent 2014-03-18 12:58:08 -07:00
giacomo po
3e42b775ea MINRES, bug #715: add support for zero rhs, and remove square test. 2014-03-17 16:33:52 -07:00
Bo Li
dead9085c0 fixed Spline constructor dimension bug 2014-03-16 22:26:57 +08:00
Bo Li
4fe56a0e02 fix Spline constructor 2014-03-15 08:42:20 +08:00
Christoph Hertzberg
35a2c9cde7 clang does not accept this without template keyword 2014-03-14 16:48:29 +01:00
Gael Guennebaud
bb4b67cf39 Relax Ref such that Ref<MatrixXf> accepts a RowVectorXf which can be seen as a degenerate MatrixXf(1,N) 2014-03-13 18:04:19 +01:00
Gael Guennebaud
0a6c472335 A bit of cleaning 2014-03-13 15:44:20 +01:00
Christoph Hertzberg
2db792852f Silence stupid parenthesis warnings for old GCC versions (<= 4.6.x) 2014-03-13 12:58:57 +01:00
Gael Guennebaud
847d801a4c Fix bug #760: complete Eigen's lapack interface with default Lapack for SPQR if there is no fortran compiler. 2014-03-12 21:33:45 +01:00
Gael Guennebaud
aceae8314b Resurect EvalBeforeNestingBit to control nested_eval 2014-03-12 20:25:36 +01:00
Gael Guennebaud
16d4c7a5e8 Conditionally disable unit tests that are not supported by evaluators yet 2014-03-12 20:23:44 +01:00
Gael Guennebaud
a395024d44 More debug info and use lazyProd instead of operator* to query the right flags 2014-03-12 18:14:58 +01:00
Gael Guennebaud
f74ed34539 Fix regressions in redux_evaluator flags and evaluator<Block> flags 2014-03-12 18:14:08 +01:00
Gael Guennebaud
5e26b7cf9d Extend evaluation traits debuging info 2014-03-12 18:13:18 +01:00
Gael Guennebaud
74b1d79d77 merge default and evaluator branches 2014-03-12 16:24:25 +01:00
Gael Guennebaud
0b362e0c9a This file is not needed anymore 2014-03-12 16:18:54 +01:00
Gael Guennebaud
a6be1952f4 Fix a few regression when moving the flags 2014-03-12 16:18:34 +01:00
Christoph Hertzberg
2379ccffcb bug #755: CommaInitializer produced wrong assertions in absence of ReturnValueOptimization. 2014-03-12 13:48:09 +01:00
Christoph Hertzberg
88aa18df64 bug #759: Removed hard-coded double-math from Quaternion::angularDistance.
Some documentation improvements
2014-03-12 13:43:19 +01:00
Gael Guennebaud
0bd5671b9e Fix Eigenvalues module 2014-03-12 13:35:44 +01:00
Gael Guennebaud
8dd3b716e3 Move evaluation related flags from traits to evaluator and fix evaluators of MapBase and Replicate 2014-03-12 13:34:11 +01:00
Gael Guennebaud
7eefdb948c Migrate JacobiSVD to Solver 2014-03-11 13:43:46 +01:00
Gael Guennebaud
082f7ddc37 Port Cholesky module to evaluators 2014-03-11 13:33:44 +01:00
Christoph Hertzberg
bbc0ada12a Avoid stupid "enumeral mismatch in conditional expression" warnings in GCC 2014-03-11 12:18:32 +01:00
Gael Guennebaud
9be72cda2a Port QR module to Solve/Inverse 2014-03-11 11:47:32 +01:00
Gael Guennebaud
ae40583965 Fix CoeffReadCost issues 2014-03-11 11:47:14 +01:00
Gael Guennebaud
5806e73800 It is not clear what XprType::Nested should be, so let's use nested<Xpr>::type as much as possible 2014-03-11 11:44:11 +01:00
Gael Guennebaud
2bf63c6b4a Even ReturnByValue should not evaluate when assembling the expression 2014-03-11 11:42:07 +01:00
Christoph Hertzberg
1b3d7fc04c Merged in abachrac/eigen (pull request PR-47)
Move the Base typedef's from private to public scope
2014-03-11 11:01:36 +01:00
Gael Guennebaud
da6ec81282 Move CoeffReadCost mechanism to evaluators 2014-03-10 23:24:40 +01:00
Gael Guennebaud
354bd8a428 Hide legacy dense assignment routines with EIGEN_TEST_EVALUATORS 2014-03-10 09:30:58 +01:00
Gael Guennebaud
5c0f294098 Fix evaluators unit test (i.e., when only EIGEN_ENABLE_EVALUATORS is defined 2014-03-10 09:28:00 +01:00
Abraham Bachrach
804ef2350d Move the Base typedef's from private to public scope
Move the Quaternion::Base typedef from private to public scope so that one may
create child classes of Quaternion.

NOTE: This matches the semantics of MatrixBase.
2014-03-09 16:56:44 -07:00
Gael Guennebaud
a6b130c63c swap 3.2 <-> default CTestConfig.cmake file 2014-03-05 10:07:44 +01:00
Benoit Steiner
8eac97138a Merged latest changes from the main trunk 2014-02-24 13:59:43 -08:00
Benoit Steiner
1dd176b0b0 Pulled latest changes from the Eigen main trunk 2014-02-24 13:56:01 -08:00
Benoit Steiner
131027ee0a Merged eigen/eigen into default 2014-02-24 13:54:07 -08:00
Benoit Steiner
db7d49efbb Added support for FMA instructions 2014-02-24 13:45:32 -08:00
Gael Guennebaud
9fdc6258cf Implement bug #317: use a template function call to suppress unused variable warnings. This also fix the issue of the previous changeset in a much nicer way. 2014-02-24 18:13:49 +01:00
Gael Guennebaud
21fecd5252 Workaround clang ABI change with unsed arguments (ugly fix) 2014-02-24 17:12:17 +01:00
Jitse Niesen
6fecb6f1b6 Fix bug #748 - array_5 test fails for seed 1392781168. 2014-02-24 14:10:17 +00:00
Christoph Hertzberg
3e439889e0 Specify what non-resizeable objects are in transposeInPlace and adjointInPlace (cf bug #749) 2014-02-24 13:12:10 +01:00
Gael Guennebaud
cbc572caf7 Split LU/Inverse.h to Core/Inverse.h for the generic Inverse expression, and LU/InverseImpl.h for the dense implementation of dense.inverse() 2014-02-24 11:49:30 +01:00
Gael Guennebaud
1e0c2f6ddb Hide some deprecated classes. 2014-02-24 11:41:19 +01:00
Gael Guennebaud
c98881e130 By-pass ProductBase for triangular and selfadjoint products and get rid of ProductBase 2014-02-23 22:51:13 +01:00
Gael Guennebaud
d67548f345 Get rid of GeneralProduct<> for GemvProduct 2014-02-21 17:13:28 +01:00
Gael Guennebaud
6c7ab50811 Get rid of GeneralProduct<> for GemmProduct 2014-02-21 16:43:03 +01:00
Gael Guennebaud
728c3d2cb9 Get rid of GeneralProduct for outer-products, and get rid of ScaledProduct 2014-02-21 16:27:24 +01:00
Gael Guennebaud
af31b6c37a Generalize evaluator<Inverse<>> such that there is no need to specialize it 2014-02-21 15:22:08 +01:00
Gael Guennebaud
93125e372d Port LU module to evaluators (except image() and kernel()) 2014-02-20 15:26:15 +01:00
Gael Guennebaud
b2e1453e1e Some bit flags and internal structures are deprecated 2014-02-20 15:25:06 +01:00
Gael Guennebaud
9621333545 Fix dimension of Solve expression 2014-02-20 15:24:21 +01:00
Gael Guennebaud
5f6ec95291 Propagate LvalueBit flag to TriangularView 2014-02-20 15:24:00 +01:00
Gael Guennebaud
ecd2c8f37b Add general Inverse<> expression with evaluator 2014-02-20 14:18:24 +01:00
Gael Guennebaud
5960befc20 More int versus Index fixes 2014-02-19 21:42:29 +01:00
Gael Guennebaud
2eee6eaf3c Fix mixing scalar types with evaluators 2014-02-19 16:30:17 +01:00
Gael Guennebaud
8af02d19b2 ExprType::Nested has a new meaning now... 2014-02-19 15:16:11 +01:00
Gael Guennebaud
95b0a6707b evaluator<Replicate> must evaluate its argument to avoid redundant evaluations 2014-02-19 14:51:46 +01:00
Gael Guennebaud
b1ab6a8e0b Add missing assertion in swap() 2014-02-19 14:06:35 +01:00
Gael Guennebaud
61cff28618 Disable Flagged and ForceAlignedAccess 2014-02-19 14:05:56 +01:00
Gael Guennebaud
68e8ddaf94 Fix vectorization logic wrt assignment functors 2014-02-19 13:26:07 +01:00
Gael Guennebaud
3a735a6cf1 Fix lazy evaluation in Ref 2014-02-19 13:17:41 +01:00
Gael Guennebaud
ccc41128fb Add a Solve expression for uniform treatment of solve() methods. 2014-02-19 11:33:29 +01:00
Gael Guennebaud
b3a07eecc5 Fix CoeffReadCost of products to handle Dynamic costs 2014-02-19 11:32:04 +01:00
Gael Guennebaud
c16b80746a isApprox must honors nested_eval 2014-02-19 11:30:58 +01:00
Benoit Steiner
cbd7e98174 Merged the latest version of the code from eigen/eigen 2014-02-18 18:51:24 -08:00
Benoit Steiner
7ed9441ea4 Reverted the definition of the EIGEN_ALIGN to its former meaning (i.e. a boolean)
Created a new EIGEN_ALIGN_BYTES define to encode how the data should be aligned
Fixed a few remaining alignment issues exposed when the Eigen code is compiled with avx enabled.
Created a new EIGEN_ALIGN_DEFAULT define, which is set to the minimum alignment value required for the chosen instruction set. Use this value instead of EIGEN_ALIGN32 to preserve the existing alignment on SSE/Altivec/Neon.
2014-02-18 18:06:44 -08:00
Gael Guennebaud
5b78780def Add evaluator shortcut for triangular ?= product 2014-02-18 17:43:16 +01:00
Gael Guennebaud
8169c6ac59 Simplify implementation of coeff-based products to fully exploit our reduxion mechanisms.
If this results in performance regressions, then we should optimize reduxion rather than
somehow duplicate the code.
2014-02-18 16:57:25 +01:00
Gael Guennebaud
463554c254 Merge with default branch 2014-02-18 15:45:39 +01:00
Gael Guennebaud
82c066b3c4 Cleaning 2014-02-18 15:44:32 +01:00
Gael Guennebaud
0543cb51b5 Product::coeff method are also OK for lazy products (including diagonal products) 2014-02-18 14:51:41 +01:00
Gael Guennebaud
99e27916cf Fix all()/any() for evaluators 2014-02-18 14:26:25 +01:00
Gael Guennebaud
37a1d736bf _MatrixTypeNested must be public in sparse Block 2014-02-18 13:35:24 +01:00
Gael Guennebaud
06545058bb Temporary workaround for permutations 2014-02-18 13:33:04 +01:00
Gael Guennebaud
7002aa858f Support Product::coeff(0,0) even for dynamic matrices 2014-02-18 13:32:30 +01:00
Gael Guennebaud
8cfb138e73 Finally, the simplest remains to deffer resizing at the latest 2014-02-18 13:31:44 +01:00
Gael Guennebaud
1b5de5a37b Add evaluator for Ref 2014-02-18 13:30:16 +01:00
Gael Guennebaud
a08cba6b5f Move is_diagonal to XprHelper, forward declare Ref 2014-02-18 11:03:59 +01:00
Gael Guennebaud
573c587e3d New design for handling automatic transposition 2014-02-18 10:53:14 +01:00
Gael Guennebaud
551bf5c66a Get rid of DiagonalProduct 2014-02-18 10:52:26 +01:00
Gael Guennebaud
2d136d3d7f Get rid of SeflCwiseBinaryOp 2014-02-18 10:52:00 +01:00
Gael Guennebaud
873401032b Fix scalar * product optimization when 'product' includes a selfadjoint matrix 2014-02-17 19:00:45 +01:00
Gael Guennebaud
d595fd31f5 Deal with automatic transposition in call_assignment, fix a few shortcomings 2014-02-17 16:11:55 +01:00
Gael Guennebaud
bffa15142c Add evaluator support for diagonal products 2014-02-17 16:10:55 +01:00
Christoph Hertzberg
b14a4628af Relaxed umeyama test. Problem was ill-posed if linear part was scaled with very small number. This should fix bug #744. 2014-02-17 13:48:00 +01:00
Gael Guennebaud
3573a10712 Fix support for row (resp. column) of a column-major (resp. row-major) sparse matrix 2014-02-17 13:46:17 +01:00
Gael Guennebaud
bd6eca059d Fix compilation of SPlines module 2014-02-17 10:00:38 +01:00
Gael Guennebaud
ed461ba9bc Fix sparse_product/sparse_extra unit tests 2014-02-17 09:57:47 +01:00
Gael Guennebaud
3bb57e21a8 Fix FFTW unit test with clang 2014-02-17 09:56:46 +01:00
Gael Guennebaud
4b6b3f310f Fix a few Index to int buggy conversions 2014-02-15 09:35:23 +01:00
Gael Guennebaud
cd606bbc94 Fix infinite loop in sparselu 2014-02-14 23:10:16 +01:00
Gael Guennebaud
0508af4287 Merged in martinhofernandes/eigen (pull request PR-40)
Better fix for bug #503
2014-02-14 15:31:39 +01:00
Gael Guennebaud
3283d98d13 optimize sparse-sparse Kronecker product 2014-02-14 14:46:01 +01:00
Gael Guennebaud
0d3f496233 Upload the 3.2 testing result to its own CDash project 2014-02-14 10:18:14 +01:00
Gael Guennebaud
6df3bee687 reduce false negative in the qr unit test 2014-02-14 09:58:30 +01:00
Gael Guennebaud
97965dde9b alloca is not necessarily alligned on windows 2014-02-14 00:04:38 +01:00
Gael Guennebaud
0b1430ae10 Fix propagation of index type 2014-02-13 23:58:28 +01:00
Gael Guennebaud
c0e08e9e4b fix stable norm benchmark 2014-02-13 15:53:51 +01:00
Gael Guennebaud
0715d49908 Fix stable_norm unit test for complexes 2014-02-13 15:49:54 +01:00
Gael Guennebaud
3291580630 Fix bug #740: overflow issue in stableNorm 2014-02-13 15:44:01 +01:00
Gael Guennebaud
14422decc2 Fix Fortran compiler detection 2014-02-13 09:21:13 +01:00
Jitse Niesen
7ea6ef8969 Fix documentation of MatrixBase::applyOnTheLeft (bug #739)
Add examples; move methods from EigenBase.h to MatrixBase.h
2014-02-12 14:03:39 +00:00
Gael Guennebaud
31c63ef0b4 fix compilation of Transform * UniformScaling 2014-02-12 13:37:23 +01:00
Christoph Hertzberg
e170e7070b Added examples for casting, made better examples for Maps 2014-02-11 17:27:14 +01:00
Jitse Niesen
c1921ad3e2 Remove unused typedef in polynomialsolver test. 2014-02-08 20:31:35 +00:00
Jitse Niesen
c4f08cfc05 Merged in maksqwe/eigen/maksqwe/fix-typo-in-evalSolverSugarFunction (pull request PR-44)
fix typo in evalSolverSugarFunction()
2014-02-08 20:27:13 +00:00
Naumov Maks
9e71ecbeec fix typo in evalSolverSugarFunction() 2014-02-08 10:40:51 +00:00
Jitse Niesen
ff8d81762d Fix bug #736: LDLT isPositive returns false for a positive semidefinite matrix
Add unit test covering this case.
2014-02-06 11:06:06 +00:00
Hauke Heibel
6c527bd811 Fixed assignment from QMatrix to Transform for compact storage. 2014-02-04 07:02:34 +01:00
Hauke Heibel
e722f36ffa Fixed issue #734 (thanks to Philipp Büttgenbach for reporting the issue and proposing a fix).
Kept ColMajor layout if possible in order to keep derivatives of the same order adjacent in memory.
2014-02-01 20:49:48 +01:00
Christoph Hertzberg
febfc7b9b4 Fix bug #730: Path of OpenGL headers is different on MacOS 2014-01-29 22:05:39 +01:00
Benoit Steiner
64a85800bd Added support for AVX to Eigen. 2014-01-29 11:43:05 -08:00
Gael Guennebaud
94acccc126 Fix Random().normalized() by introducing a nested_eval helper (recall that the old nested<> class is deprecated) 2014-01-26 15:35:44 +01:00
Gael Guennebaud
34694d8828 Fix evaluator<Replicate> for fixed size objects 2014-01-26 15:34:26 +01:00
Gael Guennebaud
ee1c55f923 Add missing template keyword 2014-01-26 14:55:25 +01:00
Gael Guennebaud
f54e62e4a9 Port evaluation from selfadjoint to full to evaluators 2014-01-26 12:18:36 +01:00
Gael Guennebaud
5fa7262e4c Refactor triangular assignment 2014-01-25 23:02:14 +01:00
Gael Guennebaud
fef534f52e fix scalar * prod in evaluators unit test 2014-01-25 19:06:07 +01:00
Gael Guennebaud
a7621809fe Remove useless register keyword, and optimize predux_min/max for SSE4 2014-01-25 16:54:13 +01:00
Gael Guennebaud
6cf938df53 Add a minimalistic page on CUDA with Eigen. 2014-01-24 13:24:30 +01:00
Gael Guennebaud
afcfb560a2 NVCC: add more debug info 2014-01-24 12:51:33 +01:00
Gael Guennebaud
40c42d9788 NVCC: no need to enforce host compiler 2014-01-24 12:51:05 +01:00
Gael Guennebaud
deab937d45 NVCC: fix closed-form eigenvalue decomposition, workaround gcc4.7/nvcc5.5 issue 2014-01-24 12:50:29 +01:00
Christoph Hertzberg
66f1c56aab sparse_solve_retval_base::defaultEvalTo created extremely oversized temporary matrices in some cases 2014-01-19 03:04:51 +01:00
Jitse Niesen
aa0db35185 Add doc page on computing Least Squares. 2014-01-18 01:16:17 +00:00
Martinho Fernandes
4c08385b74 Merged eigen/eigen into default 2014-01-10 11:22:24 +01:00
Martinho Fernandes
4ccff2d028 Placement new must use void* to avoid user-specific overloads. 2014-01-10 11:20:40 +01:00
Martinho Fernandes
3a4616d6e3 Add C++11 allocator overloads to avoid implicit conversions. 2014-01-10 11:02:11 +01:00
Gael Guennebaud
92190a1caf Add an example showing how to use C++11 random distributions 2014-01-07 20:23:35 +01:00
Gael Guennebaud
ac409f51f1 Document the fact that Random and setRandom are not reentrant (so not thread-safe) 2014-01-07 20:17:59 +01:00
Gael Guennebaud
a6a57748dd Fix typo 2014-01-05 14:24:41 +01:00
Benoit Steiner
c8c81c1e74 Improved the efficiency if the block-panel matrix multiplication code: the change reduces the pressure on the L1 cache by removing the calls to gebp_traits::unpackRhs(). Instead the packetization of the rhs blocks is done on the fly in gebp_traits::loadRhs(). This adds numerous calls to pset1<ResPacket> (since we're packetizing on the fly in the inner loop) but this is more than compensated by the fact that we're decreasing the memory transfers by a factor RhsPacketSize. 2014-01-02 16:18:32 -08:00
Christoph Hertzberg
60cd361ebe Fix bug #222. Make temporary matrix column-major independently of EIGEN_DEFAULT_TO_ROW_MAJOR 2014-03-26 17:48:30 +01:00
Gael Guennebaud
c8bfbf4a7e Merged in prclibo/eigen (pull request PR-49)
fixed a template type conversion bug in AngleAxis found by Pei Luo
2014-03-25 10:54:40 +01:00
Gael Guennebaud
01fd880424 Revert previous change and introduce a new workaround regarding gcc generating a shufps instruction instead of the more efficient pshufd instruction.
The trick consists in introducing a new pload1 function to be used in low level product kernels for which bug #203 does not apply.
Indeed, it turned out that using inline assembly prevents gcc of doing a good job at instructtion reordering.
2014-03-20 16:03:46 +01:00
Bo Li
e3fb190edf merged incoming udpates 2014-03-20 22:11:13 +08:00
Bo Li
cfd3d6ce9c fixed a template type conversion bug in AngleAxis found by Pei Luo 2014-03-20 22:05:40 +08:00
Gael Guennebaud
c39a3fa7a1 Makes gcc to generate a pshufd instruction for pset1 2014-03-20 10:14:26 +01:00
Gael Guennebaud
2a564695f0 Simpler and hopefully more future-proof fix for bug #503 (aligned_allocator with c++11) 2014-03-19 13:28:50 +01:00
Jitse Niesen
a58325ac2f Minor corrections in QR docs. 2013-12-31 18:06:28 +00:00
Anton Gladky
4cd4be97a7 Port unsupported constrained CG to Eigen3 2014-01-15 17:49:52 +01:00
Gael Guennebaud
548216b7ca QuaternionBase::slerp was documented twice and one explanation was ambiguous. 2014-01-12 11:09:06 +01:00
Gael Guennebaud
e15cb9f4f8 Make geo_hyperplane unit test more stable (bug #539) 2014-01-11 20:04:36 +01:00
Christoph Hertzberg
bbf373bbe9 Applied patch from Richard JW Roberts, resolving bug #704 2013-12-21 22:14:03 +01:00
Christoph Hertzberg
1200bd2ef0 Grafted from 5725:cdedc9e90d21099e8b3191f95425680ebe710d6f
and resolved conflicts
2013-12-21 21:46:27 +01:00
Christoph Hertzberg
8a49dd5626 Fixed typos in comments 2013-12-19 11:55:17 +01:00
Benoit Steiner
ce99b502ce Use vectorization when packing row-major rhs matrices. (bug #717) 2013-12-17 10:49:43 -08:00
Henry de Valence
033ee7f6d9 Fix typo: 'explicitely' -> 'explicitly' 2014-03-08 00:44:56 -05:00
Gael Guennebaud
ba2f79e680 Fix selfadjoint_matrix_vector_product for complex with packet size > 2 (e.g., AVX) 2014-03-07 23:18:20 +01:00
Gael Guennebaud
72461be962 Fix typo and formating 2014-03-07 23:13:14 +01:00
Gael Guennebaud
33ca9b4ee6 Add support for OSX in BTL and fix a few warnings 2014-03-07 23:11:38 +01:00
Gael Guennebaud
ce41b72eb8 Extend sizeof unit test 2014-03-07 23:09:39 +01:00
Christoph Hertzberg
d5cc083782 Fixed bug #754. Only inserted (!defined(_WIN32_WCE)) analog to alloc and free implementation (not tested, but should be correct). 2014-03-05 14:50:00 +01:00
Gael Guennebaud
7313f32efa Help MSVC to inline some trivial functions 2014-03-04 17:24:00 +01:00
Christoph Hertzberg
04e1e38eed bug #289: Removed useless static keywords 2014-03-04 15:10:29 +01:00
Olivier Saut
47679c50ae Typo in the example for Eigen::SelfAdjointEigenSolver::eigenvectors, the first eigenvector should be col(0) not col(1) 2014-03-03 14:44:39 +01:00
Gael Guennebaud
76d2ca27e5 Fix PaStiX support for Pastix 5.2 2014-02-28 13:11:39 +01:00
Christoph Hertzberg
41e89c73c7 Regression test for bug #752 2014-02-27 12:57:24 +01:00
Gael Guennebaud
ac69d8769f Remove early termination in LDLT: the zero on the diagonal of the input matrix does not mean the matrix is not full rank. Typical examples are matrices coming from LS with linear equality constraints. 2014-02-26 10:12:27 +01:00
Christoph Hertzberg
6b6071866b Make pivoting HouseholderQR compatible with custom scalar types 2014-02-25 18:55:16 +01:00
Gael Guennebaud
d357bbd9c0 Fix a few regression regarding temporaries and products 2013-12-14 22:53:47 +01:00
Gael Guennebaud
27c068e9d6 Make selfqdjoint products use evaluators 2013-12-13 18:09:07 +01:00
Gael Guennebaud
e94fe4cc3e fix resizing in noalias for blocks, and make -=/+= use evaluators 2013-12-13 18:06:58 +01:00
Gael Guennebaud
2ca0ccd2f2 Add support for triangular products with evaluators 2013-12-07 17:17:47 +01:00
Gael Guennebaud
8d8acc3ab4 Move inner product special functions to a base class to avoid ambiguous calls 2013-12-04 22:58:19 +01:00
Gael Guennebaud
6c5e915e9a Enable use of evaluators for noalias and lazyProduct, add conversion to scalar for inner products 2013-12-03 17:17:53 +01:00
Gael Guennebaud
f0b82c3ab9 Make reductions compatible with evaluators 2013-12-02 17:54:38 +01:00
Gael Guennebaud
6f1a0479b3 fix a typo triangular assignment 2013-12-02 17:54:15 +01:00
Gael Guennebaud
b5fd774775 Fix flags of Product<> 2013-12-02 17:53:26 +01:00
Gael Guennebaud
34ca81b1bf Add direct assignment of products 2013-12-02 16:37:58 +01:00
Gael Guennebaud
7f917807c6 Fix product evaluator when TEST_EVALUATOR in not ON 2013-12-02 16:19:14 +01:00
Gael Guennebaud
8af1ba5346 Make swap unit test work with evaluators 2013-12-02 15:07:45 +01:00
Gael Guennebaud
c6f7337032 Get rid of call_dense_swap_loop 2013-12-02 14:44:13 +01:00
Gael Guennebaud
626821b0e3 Add evaluator/assignment to TriangularView expressions 2013-12-02 14:06:17 +01:00
Gael Guennebaud
27ca9437a1 Fix usage of Dense versus DenseShape 2013-12-02 14:05:34 +01:00
Gael Guennebaud
d0261bd26c Fix swap in DenseBase 2013-11-30 10:42:23 +01:00
Christoph Hertzberg
276801b25a Fixed and simplified Matlab code and added further block-related examples 2013-11-29 19:54:01 +01:00
Christoph Hertzberg
d61345f366 Fix bug #609: Euler angles are in Range [0:pi]x[-pi:pi]x[-pi:pi].
Now the unit test verifies this (also that it is bijective in this range).
2013-11-29 19:42:11 +01:00
Gael Guennebaud
c15c65990f First step toward the generalization of evaluators to triangular, sparse and other fancyness.
Remove product_tag template parameter to Product.
2013-11-29 17:50:59 +01:00
Gael Guennebaud
fb6e32a62f Get rid of evalautor_impl 2013-11-29 16:45:47 +01:00
Gael Guennebaud
d331def6cc add definition of product_tag 2013-11-29 16:18:22 +01:00
Gael Guennebaud
5584275325 Remove HasEvalTo and all at once eval mode 2013-11-29 13:38:59 +01:00
Gael Guennebaud
cc6dd878ee Refactor dense product evaluators 2013-11-27 17:32:57 +01:00
Gael Guennebaud
fc6ecebc69 Simplify evaluator of EvalToTemp 2013-11-27 11:32:07 +01:00
Gael Guennebaud
49034d1570 Fix bug #708: add placement new/delete for array 2013-11-27 09:46:59 +01:00
Gael Guennebaud
230f5c3aa9 Evaluator: introduce the main Assignment class, add call_assignment to bypass NoAlias and AssumeAliasing, and some bits of cleaning 2013-11-25 15:20:31 +01:00
Gael Guennebaud
c550a0e634 extend Map unit test to check buffers allocated on the stack 2013-11-21 10:39:47 +01:00
Gael Guennebaud
28b2abdbea Fix FullPivHouseholderQR ctors for non squared fixed size matrix types 2013-11-19 12:53:46 +01:00
Gael Guennebaud
654eab3bd6 Add scaling in JacobiSVD to avoid overflows 2013-11-19 11:53:48 +01:00
Gael Guennebaud
5d1291a4de Document how to reproduce matlab's rot90 2013-11-19 11:51:16 +01:00
Gael Guennebaud
8b4dd78d57 Merged in chris-se/eigen/tensor-for-merge (pull request PR-39)
Tensor support for Eigen
2013-11-16 11:12:05 +01:00
Christian Seiler
f6bac196d5 C++11/Tensor: Fix copyright headers 2013-11-16 00:03:23 +01:00
Gael Guennebaud
46dd1bb1be Workaround fixing aliasing issue in x = SparseLU::solve(x) 2013-11-15 11:19:19 +01:00
Gael Guennebaud
6b471f205e fix overflow and ambiguity in SparseLU memory allocation 2013-11-15 10:59:19 +01:00
Christian Seiler
03a956925a CXX11/TensorSymmetry: add symmetry support for Tensor class
Add a symCoeff() method to the Tensor class template that allows the
user of the class to set multiple elements of a tensor at once if they
are connected by a symmetry operation with respect to the tensor's
indices (symmetry/antisymmetry/hermiticity/antihermiticity under
echange of two indices and combination thereof for different pairs of
indices).

A compile-time resolution of the required symmetry groups via meta
templates is also implemented. For small enough groups this is used to
unroll the loop that goes through all the elements of the Tensor that
are connected by this group. For larger groups or groups where the
symmetries are defined at run time, a standard run-time implementation
of the same algorithm is provided.

For example, the following code completely initializes all elements of
the totally antisymmetric tensor in three dimensions ('epsilon
tensor'):

SGroup<3, AntiSymmetry<0,1>, AntiSymmetry<1,2>> sym;
Eigen::Tensor<double, 3> epsilon(3,3,3);
epsilon.setZero();
epsilon.symCoeff(sym, 0, 1, 2) =  1;
2013-11-14 23:35:11 +01:00
Christian Seiler
f97b3cd024 CXX11/Tensor: add simple initial tensor implementation
This commit adds an initial implementation of a class template Tensor
that allows for the storage of objects with more than two indices.
Currently, only storing data and setting the object to zero for POD
data types are implemented.
2013-11-14 22:52:37 +01:00
Christian Seiler
5e28c41549 C++11: add template metaprogramming helpers
Create a new directory CXX11 under unsupported/Eigen that contains code
that requires C++11. In that directory, add a few generic templates
useful for any module relying on C++11. These templates may be included
with #include <[unsupported/]Eigen/CXX11/Core>. At the moment, this
will only provide templates in the Eigen::internal namespace.
2013-11-14 22:27:06 +01:00
Christoph Hertzberg
e59b38abef Implement boolean reductions for zero-sized objects 2013-11-13 16:47:02 +01:00
Gael Guennebaud
8f2d068e84 Use the specialization of Block<SparseMatrix> for const matrices too 2013-11-10 16:16:50 +01:00
Gael Guennebaud
5c2d1b4710 Add missing nonZeros() overload in Block<SparseMatrixBase<>> 2013-11-10 15:26:07 +01:00
Leszek Swirski
b93520b1a5 Install functor folder with cmake 2013-11-08 14:07:11 +00:00
Gael Guennebaud
cb8da751a0 fix broken commit 2013-11-07 22:44:37 +01:00
Gael Guennebaud
fe0b44e876 Fix stupid mistake in CMakeLists.txt 2013-11-07 18:48:17 +01:00
Christoph Hertzberg
ae83f5ede9 Fixed bug #702 and added unit test.
Thanks to Alexander Werner for the report.
2013-11-07 18:32:24 +01:00
Gael Guennebaud
76c230a84d Add an option to test evaluators globally 2013-11-07 16:38:14 +01:00
Gael Guennebaud
57327cc2d5 Drop evaluators for SwapWrapper and SelfCwiseBinaryOp 2013-11-07 14:07:27 +01:00
Gael Guennebaud
5887e82729 Clean evaluator_impl_base. It will probably be removed in the future 2013-11-07 14:02:47 +01:00
Gael Guennebaud
af9851d1d7 bug #99: move the creation of the evaluator to a central place, and make generic_dense_assignment_kernel hold the destination and source evaluators 2013-11-07 12:03:12 +01:00
Gael Guennebaud
8fe609311d Move internal::swap to numext to fix ambiguous call with std::swap 2013-11-07 09:01:26 +01:00
Gael Guennebaud
8edc964734 bug #99: refactor assignment and compound assignment mechanism through "assignment functors" and "assignement kernels".
The former is very low level and generic. The later abstarct the former for dense expressions. This refactoring permits
to get rid of the very ugly SwapWrapper and SelfCwiseBinaryOp classes.
In the future, this will also permit to simplify all these evaluation loops and perhaps to reuse them for reduxions.
That will also permit to specialize for operations like expr1 += expr2 outside Eigen, and so for any kind
of expressions (dense, sparse, tensor, etc.)
2013-11-06 18:17:59 +01:00
Gael Guennebaud
a37bdfc955 Fix static/inline order 2013-11-06 11:13:31 +01:00
Gael Guennebaud
03de5c2410 Split the huge Functors.h file 2013-11-06 10:36:10 +01:00
Gael Guennebaud
4f572e4c14 Add minimalistic unit tests for NVCC support 2013-11-05 15:41:45 +01:00
Gael Guennebaud
87aee5fda1 Allow calling attributes of dynamic size objects from device 2013-11-05 15:40:58 +01:00
Gael Guennebaud
1bb1a57ef7 merge with default branch 2013-11-05 10:31:59 +01:00
Gael Guennebaud
7c9cdd6030 SparseLU: fix estimated non-zeros in U 2013-11-05 00:12:14 +01:00
Gael Guennebaud
a236e15048 JacobiSVD: fix a 0/0 issue for complexes 2013-11-04 23:58:18 +01:00
Gael Guennebaud
ad1dc50b57 Check for minimal norm solutions 2013-11-03 13:19:55 +01:00
Gael Guennebaud
019dcfc21d JacobiSVD: move from Lapack to Matlab strategy for the default threshold 2013-11-03 13:18:56 +01:00
Gael Guennebaud
19521c83b8 bug #677: fix usage of pld instrinsics for ccomplexes 2013-11-02 12:10:48 +01:00
Gael Guennebaud
bbd49d194a Add a rank method with threshold control to JacobiSVD, and make solve uses it to return the minimal norm solution for rank-deficient problems 2013-11-01 18:21:46 +01:00
Gael Guennebaud
8f496cd3a3 Fix changeset 2702788da7
for fixed size matrices
2013-11-01 18:17:55 +01:00
Gael Guennebaud
6dc0e59b1e Fix bug #677: compilation issue on arm64 which does not have the PLD instruction 2013-10-31 13:52:43 +01:00
Gael Guennebaud
2702788da7 Fix bug #678: vectors of row and columns transpositions were not properly resized in FullPivQR 2013-10-29 18:02:18 +01:00
Gael Guennebaud
58c0a6f0fd Fix unused variable warnings 2013-10-29 17:51:19 +01:00
Gael Guennebaud
5974685866 Fix parenthesis min/max issue in mpreal 2013-10-29 17:43:21 +01:00
Christoph Hertzberg
7fae9b358d Use aligned loads in Matrix-Vector product where possible. Fixes bug #689 2013-10-29 12:42:46 +01:00
Gael Guennebaud
e14f529dac Merged in martinhofernandes/eigen (pull request PR-33)
Fix for bug #503
2013-10-29 11:39:20 +01:00
Gael Guennebaud
fe2f437642 Merged in xantares/eigen (pull request PR-36)
Add cmake config files
2013-10-29 11:31:28 +01:00
Gael Guennebaud
90b5d303db Fix bug #672: use exceptions in SuperLU if they are enabled only 2013-10-29 11:26:52 +01:00
Gael Guennebaud
9b863c1830 Merged in vanhoucke/eigen_vanhoucke_unused_variable (pull request PR-34)
Silence unused variable warning.
2013-10-29 11:04:47 +01:00
Gael Guennebaud
11fbbc51fa Fix bug #359: fix AlignedBit flag of CoeffBasedProduct thus enabling the vectorization of more matrix products 2013-10-28 17:48:32 +01:00
Gael Guennebaud
d3e84b747a Clarify the meaning of AlignedBit (bug #359) 2013-10-28 17:44:07 +01:00
Gael Guennebaud
2e606394b1 Fix bug #685: document the range of Random and setRandom 2013-10-28 17:16:03 +01:00
Gael Guennebaud
285112fc55 Fix bug #688: make it clearer that CG is for both dense and sparse matrices. 2013-10-28 15:56:30 +01:00
Gael Guennebaud
9f3f42d66a fix a few "dead stores" warnings 2013-10-26 13:59:02 +02:00
Gael Guennebaud
a0e8577b49 Fix bug #684: optimize vectorization of array-scalar and scalar-array 2013-10-18 14:56:36 +02:00
Thomas Capricelli
a6bff116f9 simplify/uniformize eigen_gen_docs 2013-10-18 12:56:15 +02:00
Christoph Hertzberg
36052c4911 Added comparisons scalar to array (previously only the array to scalar was possible) (Fixes bug #147)
Extended the unit test for that
2013-10-17 15:37:29 +02:00
Christoph Hertzberg
3d2a3bc755 Copy all format flags (not only precision) from actual output stream when calculating the maximal width 2013-10-17 14:30:09 +02:00
Christoph Hertzberg
ad9dc05663 consider all columns for aligned output (fixes bug #616) 2013-10-17 14:14:06 +02:00
Christoph Hertzberg
ff075def5c Copy and paste mistake in last commit 2013-10-17 14:02:00 +02:00
Christoph Hertzberg
4d7dfafbe7 Don't add rowSpacer if columns are not to be aligned 2013-10-17 13:49:56 +02:00
Christoph Hertzberg
3390db099a Fixes bug #681
Also fixed some spelling issues in the documentation
2013-10-17 00:03:00 +02:00
Gael Guennebaud
c6da881849 Fix bug #674: typo in documentation example for BiCGSTAB. They are now proper snippet files. 2013-10-16 15:25:39 +02:00
Christoph Hertzberg
b61facb08b Use != instead of < to check for emptiness of iterator range (fixes bug #664) 2013-10-16 13:10:15 +02:00
Christoph Hertzberg
4a42843513 Make index type of Triplet default to SparseMatrix::Index as suggested by Kolja Brix. Fixes bug #665. 2013-10-16 13:08:09 +02:00
Gael Guennebaud
b433fb2857 Allow .conservativeResize(rows,cols) on vectors 2013-10-16 12:07:33 +02:00
Gael Guennebaud
2c0303c89e bug #679: add respective unit test 2013-10-15 23:51:01 +02:00
Christoph Hertzberg
0bce534c8f Fix bug #679 2013-10-15 19:09:09 +02:00
Thomas Capricelli
6bef527f9d uniformize piwik code among branches 2013-10-11 20:46:18 +02:00
xantares
2d186da58a Add cmake config files 2013-10-09 10:25:50 +02:00
vanhoucke
3736e00ae7 Silence unused variable warning. 2013-10-04 00:21:03 +00:00
Gael Guennebaud
40f1548b32 Sparse is stable now, so Eigen/Eigen should include Sparse 2013-10-02 23:31:59 +02:00
Gael Guennebaud
446320b226 Fix dot*w to return 0 for empty vectors (BLAS interface) 2013-10-01 22:37:10 +02:00
Desire NUENTSA
54e576c88a Fix SPQR Solve() when assigning to a Map object 2013-09-26 15:00:22 +02:00
Desire NUENTSA
fe19f972e1 Fix leaked memory for successive calls to SPQR 2013-09-24 15:56:56 +02:00
Gael Guennebaud
00dc45d0f9 Reduce explicit zeros when applying SparseQR's matrix Q 2013-09-20 23:28:10 +02:00
Desire NUENTSA
4bb1c48f25 Add a block sparse matrix class. tests to be added 2013-09-20 18:54:17 +02:00
Desire NUENTSA
bd21c82a94 Fix assert bug in sparseQR 2013-09-20 18:49:32 +02:00
Gael Guennebaud
1b4623e713 Fix elimination tree and SparseQR with rows<cols 2013-09-12 22:16:35 +02:00
Martinho Fernandes
a1f056cf2a Fix bug #503
C++11 support on simple allocators comes for free. `aligned_allocator` does not
need to add any `construct` overloads to work with C++11 compilers.
2013-09-10 17:08:04 +02:00
Gael Guennebaud
4612a1cd87 Fix ploaddup and lin-spaced with AltiVec. 2013-09-10 16:13:59 +02:00
Gael Guennebaud
07417bd03f Fix bug #654: allow implicit transposition in Array to Matrix and Matrix to Array constructors 2013-09-07 00:01:04 +02:00
Gael Guennebaud
7fa007e8bf Fix sparse block 2013-09-07 00:00:13 +02:00
Gael Guennebaud
ed78a76161 Merged in advanpix/eigen-mp-devs (pull request PR-32)
Fixes for SparseMatrix to support non-POD scalar types
2013-09-03 22:05:14 +02:00
Gael Guennebaud
eda2f8948a Another compilation fix with ICC/MSVC combo 2013-09-03 21:42:59 +02:00
Jitse Niesen
16cbd3d72d BDCSVD: Use rational interpolation to solve secular equation.
Algorithm is rather ad-hoc and falls back on bisection if required.
2013-08-27 15:30:11 +01:00
Hauke Heibel
86daf2f75c Added missing inline statements in order to prevent linker errors. 2013-08-27 15:41:18 +02:00
Hauke Heibel
69c057ccb1 Fixed InnerPanel definition in the Transformation class.
Added some inital documentation on InnerPanel.
2013-08-27 14:54:57 +02:00
Gael Guennebaud
94a7a1ec00 Use unblocked version if the matrix is too small, plus some cleaning. 2013-08-27 13:47:15 +02:00
Gael Guennebaud
5864e3fbd5 Implement a blocked upper-bidiagonalization algorithm. The computeUnblocked function is currently for benchmarking purpose. 2013-08-27 07:23:31 +02:00
Pavel Holoborodko
d2c4f4ab21 Updated mpfr::mpreal. Move semantic support, RVO, other new features 2013-08-26 00:22:18 +09:00
Pavel Holoborodko
41321e4366 Replaced memcpy & memmove to smart_* alternatives for non-POD scalar types 2013-08-25 18:12:15 +09:00
Pavel Holoborodko
e6462c2ce3 Switched to smart_copy to support non-trivial scalar types 2013-08-25 18:03:49 +09:00
Pavel Holoborodko
1472f4bc61 Fixed bug #647 by using smart_copy instead of bitwise memcpy. 2013-08-25 18:02:07 +09:00
Pavel Holoborodko
a147500dee Added smart_memmove with support of non-POD scalars (e.g. needed in SparseBlock.h). 2013-08-25 18:00:28 +09:00
Jitse Niesen
d1c48f1606 BDCSVD: Use HouseholderSeq directly. 2013-08-21 14:34:48 +01:00
Gael Guennebaud
1b8394f71f Fix compilation with ICC/MSVC combo 2013-08-21 15:28:53 +02:00
Gael Guennebaud
4ecfdc4716 Add explanations of the logic behind the matrix-vector products 2013-08-21 14:29:53 +02:00
Gael Guennebaud
d9381598bc Allows EIGEN_STACK_ALLOCATION_LIMIT to be 0 for no limit 2013-08-21 14:29:00 +02:00
Jitse Niesen
403be74861 BDCSVD: Compute SVD of combined problem directly.
First step at implementing final stage in BDCSVD algorithm.
Uses bisection method to solve nonlinear equation.
Still lots of room for optimization.
2013-08-20 14:10:55 +01:00
Gael Guennebaud
1c61e28b32 Fix indentation 2013-08-20 14:13:41 +02:00
Gael Guennebaud
c06e373beb Fix compilation with non-msvc compilers. 2013-08-20 14:12:42 +02:00
Gael Guennebaud
7bca2910c7 Make the static assertions on maximal fixed size object use EIGEN_STACK_ALLOCATION_LIMIT, and raise its default value to 128KB 2013-08-20 13:59:33 +02:00
Gael Guennebaud
2cf513e973 Merged in advanpix/eigen-mp-devs (pull request PR-31)
Added support for custom scalars in SparseLU
2013-08-20 12:10:38 +02:00
Gael Guennebaud
150c9fe536 Make FullPivHouseholderQR::solve returns the least-square solution instead of aborting if no exact solution exist 2013-08-20 11:52:48 +02:00
Pavel Holoborodko
e4ffb7729a Removed unnecessary parentheses 2013-08-20 16:06:13 +09:00
Pavel Holoborodko
d908ccc01c Added support for custom scalars 2013-08-20 15:00:28 +09:00
Gael Guennebaud
2b15e00106 Make ArrayBase operator+=(scalar) and -=(scalar) use SelfCwiseBinaryOp optimization 2013-08-19 16:40:50 +02:00
Gael Guennebaud
127d7f2071 Fix bug #643: enable vectorization of compound assignement for fixed size objects 2013-08-19 16:34:09 +02:00
Gael Guennebaud
c47010e3d2 typo 2013-08-19 16:10:00 +02:00
Gael Guennebaud
d4dd6aaed2 Fix bug #642: add vectorization of sqrt for doubles, and make sqrt really safe if EIGEN_FAST_MATH is disabled 2013-08-19 16:02:27 +02:00
Jitse Niesen
d3635b08da Merged in advanpix/eigen-mp-devs (pull request PR-30)
Added support for custom-scalars
2013-08-19 11:41:22 +01:00
Pavel Holoborodko
ebd6a7a46c Added support for custom-scalars 2013-09-02 19:09:39 +09:00
Christoph Hertzberg
e0dbc2913a Documentation of deprecated struct. Closing bug #426. 2013-08-16 16:43:02 +02:00
Christoph Hertzberg
1d89554f1b Deprecate boolean sum operator (bug #426) 2013-08-13 14:54:09 +02:00
Gael Guennebaud
ace2ed7b87 Fix broken link on transforming normals 2013-08-12 13:38:25 +02:00
Gael Guennebaud
956251b738 bug #638: fix typos in sparse tutorial 2013-08-12 13:37:47 +02:00
Hauke Heibel
6f5f488a80 Switched to MPL2 license. 2013-08-12 07:39:24 +02:00
Gael Guennebaud
916d29e58f Backout parts of changeset 6719e56b5b
(these changes were not intended to be commited)
2013-08-11 19:26:41 +02:00
Gael Guennebaud
bffdc491b3 Fix cost evaluation of partial reduxions -> improve performance of vectorwise/replicate expressions involving partial reduxions 2013-08-11 19:21:43 +02:00
Gael Guennebaud
6719e56b5b Ref<> objects must be nested by reference because they potentially store a temporary object 2013-08-11 17:52:43 +02:00
Jitse Niesen
c13e9bbabf QuickReference.dox: std::tan(array) --> tan(array), same for other functions. 2013-08-11 10:17:23 +01:00
Hauke Heibel
e4acd6e2fd Added copy constructor and assignment to DenseStorage.
Required by the standard even when its not used but elided.
Added a test for DenseStorage copying and assignment.
2013-08-10 19:13:46 +02:00
Hauke Heibel
8a89ba9275 Added alternative C++11 detection. 2013-08-10 19:11:03 +02:00
Hauke Heibel
097a105603 Disabled std::log1p on Cygwin. 2013-08-10 19:10:23 +02:00
Jitse Niesen
306ce33e1c BDCSVD: Streamline compute() and copyUV() 2013-08-07 16:34:34 +01:00
Jitse Niesen
616f9cc593 doc: Explain type of result for VectorwiseOp member functions.
Prompted by a question on the forum.
2013-08-06 09:49:44 +01:00
Jitse Niesen
2f0faf117e Remove LinearLeastSquares.dox , which should not have been added.
Accidentally included in changeset e37ff98bbb
 .
2013-08-06 08:03:39 +01:00
Hauke Heibel
8710440951 Removed errornous swap for stack storage. 2013-08-03 10:09:31 +02:00
Jitse Niesen
8fdffdd573 Move inheritance from Eigen example in stand-alone file.
Also fix a small mistake (Vector3d instead of VectorXd).
2013-08-02 22:33:12 +01:00
Hauke Heibel
3444f06f68 Removed a warning when rvalue references are not unsed. 2013-08-02 22:54:01 +02:00
Hauke Heibel
8f4d93a4b7 Fix compilation.
The Matrix is required to be mutable but it also needs to be a reference and
temporaries do not bind to non-const references - thus we need a hack and
cast away the constness.
2013-08-02 22:40:36 +02:00
Hauke Heibel
51b361b3bb Ensure that (potentially aligned) stack objects are passed by reference. 2013-08-02 21:07:39 +02:00
Hauke Heibel
7c99b38b7c Added move support for Matrix and Array.
Added EIGEN_HAVE_RVALUE_REFERENCES define.
Added move unit tests.
Removed superfluous 'inline' declarations in DenseStorage.
2013-08-02 19:59:43 +02:00
Gael Guennebaud
b72a686830 Fix bug #635: add isCompressed to MappedSparseMatrix for compatibility 2013-08-02 11:11:21 +02:00
Gael Guennebaud
e3058dd88b Make Pardiso solvers non copyabe 2013-08-02 11:09:02 +02:00
Gael Guennebaud
8ea7413a64 Fix compilation and warning of PARDISO 2013-08-02 11:05:00 +02:00
Gael Guennebaud
e90229a429 reduce cancellation probablity 2013-08-02 00:36:06 +02:00
Hauke Heibel
cf884a9815 Added build name support for VC11 and its service packs. 2013-08-01 16:38:05 +02:00
Gael Guennebaud
ddf7753631 Add nvcc support for small eigenvalues decompositions and workaround lack of support for std::swap and std::numeric_limits 2013-08-01 16:26:57 +02:00
Hauke Heibel
222eedf5f3 Removed unused testing files. 2013-08-01 12:14:03 +02:00
Gael Guennebaud
d0e543be26 Remove superfluous testing files (as changeset e41bc6cbbf
but more complete)
2013-07-31 23:11:43 +02:00
Hauke Heibel
8e6d0cba5f Added a pattern which forces LF line endings for *.sh files. 2013-07-31 18:20:58 +02:00
Hauke Heibel
32d46dd9b8 Backed out changeset: e41bc6cbbf 2013-07-31 18:03:34 +02:00
Hauke Heibel
e41bc6cbbf Removed unused test files. 2013-07-31 17:21:32 +02:00
Gael Guennebaud
55b57fcba6 Disable some shortcuts with nvcc 2013-07-31 16:56:31 +02:00
Hauke Heibel
39491e3b75 Enable support for minimal rebuilds. 2013-07-31 16:16:08 +02:00
Jitse Niesen
68168e9eae MatrixFunctions: replace eval() by nested.
This eliminates an unnecessary copy in some situations, e.g. Map.
2013-07-31 14:57:20 +01:00
Gael Guennebaud
6126ad801f Extend support for nvcc to Array objects and wrappers 2013-07-31 15:30:50 +02:00
Hauke Heibel
43df1e707c Merged in advanpix/eigen-mp-3.2 (pull request PR-29)
Quick fix in order to be custom-scalar friendly.
2013-07-30 08:11:39 +02:00
Hauke Heibel
b1f4601bf9 Removed non-standard conforming (17.4.3.1.2/1) leading underscore. 2013-07-30 08:05:10 +02:00
Pavel Holoborodko
acb82c7f16 Quick fix in order to be custom-scalar friendly. 2013-07-29 20:13:52 +09:00
Hauke Heibel
9ef3645cc7 Removed 'T' prefix from types and thus fixed compilation for GCC. 2013-07-29 12:08:50 +02:00
Sven Strothoff
5f11db695b bug #502: add bool intersects() methods to AlignedBox 2013-07-28 23:59:37 +02:00
Hauke Heibel
2437215221 Fixed constness in Array- and MatrixWrapper.
This also fixes the compilation on VC11.
2013-07-28 22:46:38 +02:00
Hauke Heibel
dd27b5c4a8 Fixed dummy_precision evaluation. 2013-07-28 19:31:33 +02:00
Jitse Niesen
70131120ab Fix bug in MatrixFunctions for matrices with multiple eigenvalues.
Store indices, not eigenvalues, in clusters.
Bug was introduced in changeset a3a55357db
.
2013-07-26 15:39:18 +01:00
Jitse Niesen
6d86cd7224 merge 2013-07-26 14:30:28 +01:00
Hauke Heibel
75dab1ce5e Fixed floating point warning.
Fixed evaluation of matrix_exp_computeUV.
2013-07-26 15:13:54 +02:00
Jitse Niesen
e43934d60f MatrixFunctions: Clean up StemFunction.h 2013-07-26 13:51:10 +01:00
Hauke Heibel
75edc7cc8b Fixed VC11 compilation.
The typedefs Lhs/Rhs in the base class are now accessible by derived classes.
2013-07-26 11:05:21 +02:00
Hauke Heibel
5897695e8a Merged simple geometry asserts. 2013-07-25 21:21:21 +02:00
Jitse Niesen
a3a55357db Clean up MatrixFunction and MatrixLogarithm. 2013-07-25 15:08:53 +01:00
Jitse Niesen
084dc63b4c Clean-up of MatrixSquareRoot. 2013-07-22 13:56:15 +01:00
Jitse Niesen
463343fb37 Clean-up of MatrixExponential:
* put internal stuff in the internal namespace
* replace member functions by free functions
2013-07-21 21:31:15 +01:00
Jitse Niesen
5879937f58 Merge in jdh8's branch.
* Enable singular matrix power and complex exponents.
* Eliminate unnecessary copying for sparse Kronecker product.
2013-07-21 20:50:15 +01:00
Chen-Pang He
01190b3544 Directly code failing example, or it breaks make doc. 2013-07-21 18:09:11 +08:00
Chen-Pang He
c00f688c64 Fix doc. (It is also used by computeFracPower) 2013-07-21 05:40:56 +08:00
Chen-Pang He
51573da3a4 Warn about power of a matrix with non-semisimple 0 eigenvalue. 2013-07-21 01:00:36 +08:00
Chen-Pang He
1191949e87 Improve documentation on Kronecker product module. 2013-07-21 00:19:46 +08:00
Chen-Pang He
3d94ed9fa0 Document on MatrixExponential::ScalingOp 2013-07-21 00:18:19 +08:00
Chen-Pang He
ede27f5780 Apply argument-dependent lookup on user-defined types. (using std::) 2013-07-20 23:30:37 +08:00
Chen-Pang He
dda869051d Optimize MatrixPower::computeIntPower 2013-07-20 18:47:54 +08:00
Chen-Pang He
2320073e41 Comment on private members of MatrixPower. 2013-07-20 17:58:12 +08:00
Chen-Pang He
c587e63631 Simplify MatrixPower::split 2013-07-20 17:49:38 +08:00
Gael Guennebaud
660b905e12 Fix ICE with ICC 11 2013-07-19 11:46:54 +02:00
Gael Guennebaud
4f0bd557a4 Previous isFinite->hasNonFinite change was broken. After discussion let's rename it to allFinite 2013-07-18 11:27:04 +02:00
Desire NUENTSA
736fe99fbf Fix bug #326 : expose tridiagonal eigensolver to end-users through ComputeFromTridiagonal() 2013-07-18 10:32:31 +02:00
Gael Guennebaud
6fab4012a3 Rename isFinite to hasNonFinite to avoid future naming collisions. 2013-07-17 21:13:45 +02:00
Gael Guennebaud
2f593ee67c merge with main branch 2013-07-17 13:21:35 +02:00
Gael Guennebaud
20e535e142 Bump default branch to 3.2.90 2013-07-17 10:04:20 +02:00
Gael Guennebaud
bbaef8ebba SparseLU: make COLAMDOrdering the default ordering method. 2013-07-17 09:30:25 +02:00
Gael Guennebaud
bd689ccc28 IncompleteLUT should not raise an assert in compute if factorize failed. 2013-07-17 09:21:07 +02:00
Gael Guennebaud
e3774e93b7 Fix vompilation of bdcsvd with ICC. 2013-07-17 09:20:30 +02:00
Gael Guennebaud
db8e88c936 Fix testing issues with x87 extra precision. 2013-07-16 17:35:08 +02:00
Desire NUENTSA
cfd7f9b84a avoid unneeded const_cast 2013-07-16 15:56:05 +02:00
Desire NUENTSA
3e094af410 Fix Sparse LU for matrices in non compressed mode 2013-07-16 15:15:53 +02:00
Gael Guennebaud
adeaa657eb Expose InnerSizeAtCompileTime in SparseMatrixBase (it was already present in DenseBase) and simplify sparse_vector_assign_selector (this also fix a stupid warning in old gcc versions) 2013-07-16 09:49:01 +02:00
Gael Guennebaud
f2aba7a768 Remove obsolete sentence on LPGL in MKL doc. 2013-07-15 23:25:01 +02:00
Gael Guennebaud
d02e329218 Fix adjoint unit test: test_isApproxWithRef works for positive quantities only. 2013-07-15 21:21:14 +02:00
Gael Guennebaud
c76990664b Add bdcsvd unit test in CMakeLists 2013-07-15 21:16:57 +02:00
Chen-Pang He
4b780553e0 Eliminate unnecessary copying for sparse Kronecker product. 2013-07-15 09:10:17 +08:00
Chen-Pang He
9be658f701 generateTestMatrix can use processTriangularMatrix 2013-07-15 00:43:14 +08:00
Chen-Pang He
b8f0364a1c Test singular matrix power with square roots. Exponent laws are too unstable. 2013-07-15 00:10:17 +08:00
Gael Guennebaud
ee244d54f4 SparseVector::assign: it is not always possible to reserve according to given non-zeros. 2013-07-14 11:56:08 +02:00
Chen-Pang He
cbe92de2b5 Fix typo in testSingular. 2013-07-14 17:27:44 +08:00
Chen-Pang He
eeb744dc8d Add test3dRotation. 2013-07-14 02:00:50 +08:00
Gael Guennebaud
4bb0fff151 Rationalize assignment to sparse vectors 2013-07-13 19:45:05 +02:00
Chen-Pang He
d5501d3a90 Document on MatrixPowerAtomic. 2013-07-13 23:13:07 +08:00
Chen-Pang He
3c423ccfe2 Document on complex matrix power. 2013-07-13 22:12:09 +08:00
Chen-Pang He
738d75d3eb Document on the return type of MatrixPower::operator() 2013-07-13 22:11:36 +08:00
Gael Guennebaud
9a16519d62 Extend the "functions taking Eigen type" doc page to present the Ref<> option. 2013-07-13 12:36:55 +02:00
Gael Guennebaud
06a5bcecf6 Stabilize eulerangle unit test. 2013-07-13 10:55:04 +02:00
Gael Guennebaud
7ee378d89d Fix various scalar type conversion warnings. 2013-07-12 16:40:02 +02:00
Gael Guennebaud
61c3f55362 Relax slerp unit test 2013-07-12 14:30:28 +02:00
Gael Guennebaud
5431473d67 Fix SparseMatrix::conservativeResize() when one dimension is null 2013-07-12 14:10:02 +02:00
Desire Nuentsa
444c09e313 Fix constness of diagonal() and transpose() for MSVC. 2013-07-11 12:36:57 +02:00
Gael Guennebaud
84f52ad317 Remove double const qualifier 2013-07-10 23:54:53 +02:00
Gael Guennebaud
6d1f5dbaae Add no_assignment_operator to a few classes that must not be assigned, and fix a couple of warnings. 2013-07-10 23:48:26 +02:00
Gael Guennebaud
71cccf0ed8 Rename map unit test to mapped_matrix: without splitting unit tests, this created a "map" binary file in the include path, not a good idea! 2013-07-10 23:26:35 +02:00
Gael Guennebaud
5a4519d2b4 Revisit the implementation of random_default_impl for integer to make sure avoid overflows and compiler warnings. 2013-07-10 21:11:41 +02:00
Chen-Pang He
a992fa74eb Make non-conversion unary constructors explicit. 2013-07-11 02:31:13 +08:00
Chen-Pang He
4466875d54 The only(?) way to test complex matrix power. 2013-07-10 02:59:16 +08:00
Chen-Pang He
5c95892b83 Test power of singular matrices. 2013-07-10 02:57:54 +08:00
Chen-Pang He
639d03d900 These casts are unnecessary because isApprox already casts them. 2013-07-10 02:53:15 +08:00
Chen-Pang He
d204bb57d0 Remove unused struct definition in test. 2013-07-10 02:48:17 +08:00
Chen-Pang He
c52cbd9de9 Write doc for positive power of a matrix with a semisimple zero eigenvalue. 2013-07-10 02:44:38 +08:00
Chen-Pang He
159a3bed9e Write doc for complex power of a matrix. 2013-07-10 02:43:10 +08:00
Chen-Pang He
25544dbec3 Add assertion against undefined matrix power. 2013-07-10 02:36:34 +08:00
Jitse Niesen
f850550e3e merge 2013-07-08 14:11:25 +01:00
Chen-Pang He
04bd1e3fc0 Slightly optimize atanh2. 2013-07-08 16:49:27 +08:00
Gael Guennebaud
0567cf96cc Ease setting build options when running ctest -D 2013-07-07 17:25:58 +02:00
Chen-Pang He
00e30a5fc4 We need not prohibit assignment here. Thanks to changeset 3edd4681f2
.
2013-07-07 19:57:23 +08:00
Chen-Pang He
55ec3cc6d5 Prevent copying with internal::noncopyable. 2013-07-07 19:34:13 +08:00
Gael Guennebaud
4f28ccdd0e Rationalize the use of Index type in iterators 2013-07-06 22:05:49 +02:00
Gael Guennebaud
9b833aff42 Use numeric_limits to get NaN and inf 2013-07-06 22:01:14 +02:00
Gael Guennebaud
3edd4681f2 ReturnByValue should not be assignable! 2013-07-06 20:26:02 +02:00
Gael Guennebaud
d0142e963b Fix ambiguity from the origin of Index type in BlockImpl<Sparse>::InnerIterator 2013-07-06 17:33:49 +02:00
Gael Guennebaud
8ba7ccf16a bug #63: add lapack unit tests. They are automatically downloaded and configured if EIGEN_ENABLE_LAPACK_TESTS is ON. 2013-07-06 15:08:42 +02:00
Gael Guennebaud
cc03c9d683 bug #556: workaround mingw bug with -O3 or -fipa-cp-clone 2013-07-05 23:47:40 +02:00
Gael Guennebaud
4f14b3fa72 Fix bug #611: diag * sparse * diag 2013-07-05 22:42:46 +02:00
Chen-Pang He
9e2b4eeac0 Const-correct the scaling functor. 2013-07-05 23:28:57 +08:00
Gael Guennebaud
9b9177f1ce Fix a couple of warnings in unit tests. 2013-07-05 13:35:34 +02:00
Gael Guennebaud
7d8823c8b7 Use true compile-time branching in SparseVector::assign to handle automatic transposition. 2013-07-05 09:14:32 +02:00
Chen-Pang He
c273a6c37c Avoid pow(Scalar, int) for C++11 conformance. 2013-07-05 03:33:56 +08:00
Chen-Pang He
04a9ad6e10 Let complex power fall back to "log, scale, exp". 2013-07-05 00:28:28 +08:00
Chen-Pang He
4e26057f66 Remove unused declarations for MatrixPowerProduct. 2013-07-05 00:08:11 +08:00
Desire NUENTSA
edba612f68 Fix unresolved typename bug for MSVC 2013-07-04 16:56:01 +02:00
Chen-Pang He
cce68d4e91 Remove unused inclusions. 2013-07-04 18:39:33 +08:00
Chen-Pang He
75b3391e3f Enable singular matrix power using unitary similarities. 2013-07-04 18:37:46 +08:00
Gael Guennebaud
4020d4286f Fix bug in sparse documentation. 2013-07-04 06:49:24 +02:00
Chen-Pang He
3cda1deb52 Simplify class hierarchy. 2013-07-04 05:10:43 +08:00
Chen-Pang He
eaf92ef48c Remove unreachable MatrixPowerTriangular, paving the way to future cleanups. 2013-07-04 04:42:02 +08:00
Gael Guennebaud
155fa0ca83 Add missing namespace prefix in pconj 2013-07-03 11:36:12 +02:00
Jitse Niesen
4e458d309c Fix some doxygen errors and warnings. 2013-07-02 14:08:12 +01:00
Jitse Niesen
419b5cff44 doc: Mention vec=vec.head(n) in aliasing page. 2013-07-02 13:35:36 +01:00
Gael Guennebaud
1caeb814f0 Fix bicgstab for complexes, and avoid a duplicate computation 2013-07-02 08:14:10 +02:00
Gael Guennebaud
f8e325356a It's better to check that eigen_assert does raise an assert rather than testing the definition of NDEBUG 2013-07-01 13:48:21 +02:00
Gael Guennebaud
65cc51288a On windows CE, assert.h defines NDEBUG if DEBUG is not defined 2013-07-01 13:47:25 +02:00
Gael Guennebaud
22820e950e Improve BiCGSTAB robustness: fix a divide by zero and allow to restart with a new initial residual reference. 2013-07-01 11:49:23 +02:00
Gael Guennebaud
99bef0957b Add missing sparse matrix constructor from sparse self-adjoint views, and add documentation for sparse time selfadjoint matrix 2013-06-28 22:56:26 +02:00
Desire NUENTSA
9f035c876a Fiw bug #553: add support for sparse matrix time sparse self-adjoint view products 2013-06-28 22:27:45 +02:00
Gael Guennebaud
fc27cbd914 Fix bug #611: fix const qualifier in cwiseProduct(sparse,dense) and SparseDiagonalProduct::InnerIterator 2013-06-28 17:10:53 +02:00
Gael Guennebaud
a915f0292e Fix bug #626: add assertion on input ranges for coeff* and insert members for sparse objects 2013-06-28 16:16:02 +02:00
Gael Guennebaud
4cf742525f bug #626: add compiletime check of the Options template parameter of SparseMatrix and SparseVector. Fix eval and plain_object for sparse objects. 2013-06-28 15:56:43 +02:00
Gael Guennebaud
487d94f495 Fix bug #623: inlining test_is_equal leads to failures with x87 2013-06-27 22:30:46 +02:00
Gael Guennebaud
74beb218d2 Fix bug #554: include unistd.h before checking the presence of posix_memalign. 2013-06-26 22:49:14 +02:00
Jitse Niesen
ffbe04ae78 Merged in jdh8/eigen (pull request PR-27): Matrix power cleanup 2013-06-25 13:05:37 +01:00
Gael Guennebaud
95f8a738ea Introduce a TEST_SET_BUT_UNUSED_VARIABLE macro for initialized but unused variables in the unit tests and also fix a few other warnings. 2013-06-25 11:42:04 +02:00
Gael Guennebaud
231d4a6fda Workarounf nvcc not being able to find RowMajor when declaring a Matrix<...> inside another namespace. 2013-06-25 10:08:50 +02:00
Chen-Pang He
7b6e94fb58 Clean namespace pollution. 2013-06-25 02:56:30 +08:00
Chen-Pang He
b9543ce237 Matrix square root can process 0 eigenvalue. 2013-06-24 23:57:57 +08:00
Chen-Pang He
b9fc9d8f32 Remove mat.pow * vec specialization, which causes segfault for mat.pow * mat.pow 2013-06-24 23:56:17 +08:00
Gael Guennebaud
4cc9377941 fix casting from double* to void* in SuperLU and Cholmod support 2013-06-24 17:24:32 +02:00
Chen-Pang He
ee8a28fb85 Fix segfault and bug with equal eivals in matrix power (bug #614). 2013-06-24 13:58:51 +01:00
Gael Guennebaud
1330ca611b CwiseUnaryView should not inherit no_assignment_operator! 2013-06-24 13:45:33 +02:00
Gael Guennebaud
c21a04bcf9 fix compilation of ArrayBase::transposeInPlace 2013-06-24 13:35:13 +02:00
Gael Guennebaud
c695cbf0fa fix compilation of ArrayBase::transposeInPlace 2013-06-24 13:33:44 +02:00
Gael Guennebaud
8bbde351e7 bug #620: fix robustness issue in JacobiSVD::solve (also fix a perf. issue) 2013-06-24 13:08:09 +02:00
Gael Guennebaud
d1d7a1ade9 Workaround a bunch of stupid warnings in unit tests 2013-06-23 19:11:32 +02:00
Simon Pilgrim
fab0235369 Fix bug #590: NEON Duplicate lane load 2013-06-23 14:13:21 +02:00
Gael Guennebaud
bea4a67c92 that's getting harder and harder to make ICC, GCC and clang all happy: one wants type_name to be static and if it is so then the other one triggers 'unused function' warnings -> a forward declaration seems to do the trick 2013-06-22 10:51:45 +02:00
Gael Guennebaud
260a923334 explicit template specialization cannot have a storage class 2013-06-22 10:30:26 +02:00
Gael Guennebaud
3ed919e0ed Fix an shut down a few ICC's remarks 2013-06-22 10:19:50 +02:00
Gael Guennebaud
dd964ec08c Fix a couple of warnings 2013-06-21 19:06:45 +02:00
Gael Guennebaud
620e4277bc Disable ASM comments on non x86 architecture and do not redfine if EIGEN_ASM_COMMENT is already defined 2013-06-21 17:49:36 +02:00
Gael Guennebaud
8cc9b12589 Add missing using std::pow in lpNorm. 2013-06-21 11:37:33 +02:00
Gael Guennebaud
cf5c5ed725 Fix warning typedef XXX locally defined but not used 2013-06-21 09:27:38 +02:00
Gael Guennebaud
7adfca5af2 Shutdown clang warning: argument unused during compilation: '-ansi' at linking time 2013-06-21 09:24:57 +02:00
Gael Guennebaud
c0cad44da6 Reduce maximum number of warnings/errors. (they took GBs even for limited period of time) 2013-06-20 17:39:15 +02:00
Gauthier Brun
8105b5ed3f new unsupported and not finished SVD, using a divide and conquert algorithm, with tests and benchmark 2013-06-19 00:03:27 +02:00
Gael Guennebaud
ba79e39c5c bug #71: enable vectorization of diagonal products in more cases. 2013-06-18 17:44:25 +02:00
Gael Guennebaud
eef8d98139 Fix bug #542: fix detection of compiler version on systems without the head command. 2013-06-18 17:25:37 +02:00
Jitse Niesen
4e6d746514 Avoid phrase "static allocation" for local storage on stack (bug #615). 2013-06-18 14:35:12 +01:00
Jitse Niesen
e37ff98bbb Implement mixed static/dynamic-size .block() (bug #579) 2013-06-18 14:29:15 +01:00
Kolja Brix
05da15bf40 bug #230, fix compilation issues and wrong static assertions 2013-06-18 09:44:40 +02:00
Gael Guennebaud
33788b97dd Fix compilation issue with some compilers (when doing using Base::foo;, foo must be visible in the direct base class) 2013-06-18 00:48:47 +02:00
Jitse Niesen
79bd6fa5ee Require at least cmake version 2.8.2 (bug #606). 2013-06-17 22:12:01 +01:00
Jitse Niesen
a8494787f4 Merged in RhysU/eigen//fix-documentation-typo-1371479301909 (pull request PR-25)
Fix documentation typo
2013-06-17 15:35:44 +01:00
Rhys Ulerich
437e26d000 Fix documentation typo 2013-06-17 14:28:42 +00:00
Gael Guennebaud
55365566b2 Fix HouseholderSequence::conjugate() and ::adjoint() and add respective unit tests. 2013-06-17 00:14:42 +02:00
Gael Guennebaud
9f11f80db1 Make psqrt works with numeric_limits<float>::min 2013-06-14 10:55:05 +02:00
Gael Guennebaud
5f178e54e9 Extend sparse-block unit test to explicitly cover bug #584 2013-06-14 10:52:19 +02:00
Jeff Dean
d5fa5001a7 Fix bug #613: psqrt was incorrect for small numbers 2013-06-13 18:17:27 +02:00
Gael Guennebaud
3352b8d873 Extend the magnitude range of tested numbers in packet math unit tests 2013-06-13 18:12:58 +02:00
Gael Guennebaud
d541765e85 Fix copy constructor signature 2013-06-12 18:02:13 +02:00
Gael Guennebaud
f75419c711 Add missing changes. 2013-06-12 17:56:15 +02:00
Gael Guennebaud
f3a029e957 Remove meaningless explicit qualifier 2013-06-12 13:05:23 +02:00
Gael Guennebaud
1b92d2ca33 Suppress warning #2304: non-explicit constructor with single argument may cause implicit type conversion 2013-06-12 13:02:30 +02:00
Gael Guennebaud
f6c1841316 compilation fixes in unsupported 2013-06-12 12:52:41 +02:00
Gael Guennebaud
65c59307e2 Fix sparse_basic unit test conflict 2013-06-12 10:37:15 +02:00
Gael Guennebaud
62670c83a0 Fix bug #314: move remaining math functions from internal to numext namespace 2013-06-10 23:40:56 +02:00
Gael Guennebaud
827843bbbd Complete the lapack interface to make it complete enough for suitesparse QR. 2013-06-12 10:12:50 +02:00
Gael Guennebaud
76f4820560 Improve SuiteSparse cmake scripts 2013-06-12 10:12:05 +02:00
Gael Guennebaud
f0efe60924 Fix implicit conversion warnings 2013-06-12 09:25:58 +02:00
Gael Guennebaud
92eb807c30 Fix warning: explicitely initialize all member of IOFormat 2013-06-12 09:24:07 +02:00
Gael Guennebaud
7742eacfeb Add default value for IsRepeatable in functor_traits 2013-06-12 09:22:59 +02:00
Gael Guennebaud
f3af423c70 Add missing dependency in SparseSholesky header 2013-06-11 21:13:30 +02:00
Desire NUENTSA
1bf18bd57f Fix bug in SparseLU dfs for dense matrices 2013-06-11 14:48:04 +02:00
Desire NUENTSA
9266f65318 Fix bug #588 : Compute a determinant using SparseLU 2013-06-11 14:46:13 +02:00
Desire NUENTSA
4cd8245c96 Add support with unit test for off-diagonal sparse matrix views 2013-06-11 14:42:29 +02:00
Desire NUENTSA
b3fff170a0 Restore internal math functions for unit tests 2013-06-11 14:31:31 +02:00
Gael Guennebaud
18e476107e Fix bug #583: add compile-time check that DenseIndex is signed 2013-06-10 17:16:16 +02:00
Simon Pilgrim
ca67c60150 Fix bug #591: minor optimization in NEON vectorization support 2013-06-10 15:59:03 +02:00
Gael Guennebaud
05c9be65ce Fix bug #595: typo 2013-06-10 13:10:36 +02:00
Gael Guennebaud
a4a575e2a3 fix bug #597: typo in sparse documentation 2013-06-10 12:13:31 +02:00
Gael Guennebaud
26c35b95c7 Fix bug #598: add explicit cast to Scalar type 2013-06-10 12:03:55 +02:00
Gael Guennebaud
0525874a03 Fix bug #599: add missing documentation of some members in QR module. 2013-06-10 11:58:28 +02:00
Gael Guennebaud
2b6528effc HouseholderSequence should expose standard enums (Rows/Cols, etc.)) 2013-06-10 11:42:14 +02:00
Gael Guennebaud
47e89026d0 Check sparse matrices with short indices 2013-06-10 10:34:03 +02:00
Gael Guennebaud
e8c963568c Simplify and generalize assign_selector logic 2013-06-10 10:32:29 +02:00
Gael Guennebaud
b6d3fcf6f2 Fix bug #605: ambiguous call to std::min when calling .diagonal() on a sparse matrix with non default index type 2013-06-10 10:11:29 +02:00
Gael Guennebaud
e392948548 Fix bug #607: handle implicit transposition from sparse vector to dense vector 2013-06-10 00:06:40 +02:00
Gael Guennebaud
4811b4526c Add regression test for bug #608 2013-06-09 23:30:04 +02:00
Gael Guennebaud
a69b4b092b Fix bug #608: the sign computation in LDLT was broken 2013-06-09 23:19:32 +02:00
Gael Guennebaud
c98fd7a6ca Fix bug #609: avoid if statement and improve consistency of eulerAngles method 2013-06-09 23:14:45 +02:00
Gael Guennebaud
e04b59929e fix unused variable warning 2013-06-09 21:03:32 +02:00
Gael Guennebaud
64054ee396 Add nvcc support for normalize, initializers, and fuzzy comparisons 2013-06-05 15:38:33 +02:00
Gael Guennebaud
b3adc4face Add missing pconj specializations 2013-05-17 17:25:29 +02:00
Thomas Capricelli
62e337eb01 fix a weird typo I commited in ae76c97704
(Nov 10th, 2009)
2013-06-03 23:09:33 +02:00
Desire NUENTSA
d7cd957f10 Include misc struct declarations 2013-05-29 10:15:40 +02:00
Desire NUENTSA
e0566a817f Delete unneeded resize in SparseQR 2013-05-22 10:44:12 +02:00
Desire NUENTSA
8e050bd681 Optimize Sparse setIdentity and add a unit test 2013-05-22 10:43:12 +02:00
Desire NUENTSA
cf939f154f Fix bug #596 : Recover plain SparseMatrix from SparseQR matrixQ() 2013-05-21 17:35:10 +02:00
Gael Guennebaud
bd7511fc36 Fix return type of TriangularView::ReverseInnerIterator::operator++ 2013-05-17 14:40:32 +02:00
Gael Guennebaud
bd0474adbb Fix A=A with A a SparseMatrix 2013-05-17 14:39:31 +02:00
Gael Guennebaud
9ab3811cc5 Disallow implicit scalar conversion of SparseMatrix 2013-05-17 14:02:20 +02:00
Gael Guennebaud
b5e5b6aa57 Fix non const data() member in Array and Matrix wrappers. 2013-05-16 10:18:19 +02:00
Hauke Heibel
12e69ec896 Added asserts to AngleAxis class which verify that the initial axis is
normalized.
2013-05-15 12:05:01 +02:00
Hauke Heibel
8556ca3de5 Adapted settings for the eol extension. 2013-05-15 13:45:24 +02:00
Desire NUENTSA
f7bdbf69e1 Add support in SparseLU to solve with L and U factors independently 2013-05-14 17:15:23 +02:00
Desire NUENTSA
83736e9c61 Set back the default ordering method in SPQR support 2013-05-13 13:08:13 +02:00
Desire NUENTSA
122b16d841 fix memory leak from Cholmod data in SPQR support 2013-05-13 13:04:12 +02:00
Gael Guennebaud
43bb942365 Add missing support for x.noalias() = ReturnByValue<...> 2013-05-13 10:39:50 +02:00
Gael Guennebaud
fcdbfabf7a Fix setFromTripplet with empty inputs 2013-05-03 14:28:37 +02:00
Gael Guennebaud
aa8b897607 document the evaluation order of the comma initializer 2013-04-19 14:03:16 +02:00
Gael Guennebaud
9cd2d14005 merge with default branch 2013-04-19 11:21:39 +02:00
Gael Guennebaud
4e2e615a7c actually assertion are incompatible with nvcc even on host code 2013-04-19 11:14:17 +02:00
Gael Guennebaud
46755648ec Add a few missing standard functions for ScalarWithExceptions type. 2013-04-17 10:24:31 +02:00
Gael Guennebaud
41b3c56e61 Disable "operands are evaluated in unspecified order" ICC's remark 2013-04-17 10:23:08 +02:00
Gael Guennebaud
9a4caf2b0f Extend internal doc of ploaddup and palign 2013-04-17 09:17:34 +02:00
Gael Guennebaud
94e20f485c Big 564: add hasNaN and isFinite members 2013-04-16 15:10:40 +02:00
Desire NUENTSA
d4b0c19a46 Fix a bug in Supernodal Matrix Iterator 2013-04-15 17:24:49 +02:00
Gael Guennebaud
db43205dc6 Fix ICC warning when defining both -ansi and -strict-ansi 2013-04-12 15:51:40 +02:00
Gael Guennebaud
9816e8532e Fix bug #482: pass scalar value by const reference (it remained a few cases) 2013-04-12 15:26:55 +02:00
Gael Guennebaud
43f4fd4d71 generalize testing flags to clang and ICC 2013-04-12 15:24:41 +02:00
Gael Guennebaud
7450b23fbb Fix bug #563: assignement to Block<SparseMatrix> is now allowed on non-compressed matrices 2013-04-12 13:20:13 +02:00
Gael Guennebaud
6eaff5a098 Enable SSE with ICC even when it mimics a gcc version lower than 4.2 2013-04-11 19:48:34 +02:00
Gael Guennebaud
1e38928c64 workaround strange compilation issue with ICC and -strict-ansi 2013-04-10 17:30:25 +02:00
Gael Guennebaud
ff661a7b6f Add temporary check for triangularView += product 2013-04-10 23:13:04 +02:00
Gael Guennebaud
899c0c2b6c Clean source code and unit tests with respect to -Wunused-local-typedefs 2013-04-10 22:27:35 +02:00
Gael Guennebaud
7e04d7db02 Fix a serious bug in handmade_aligned_realloc: original data have to be moved if the alignment offset differs. 2013-04-10 13:58:20 +02:00
Gael Guennebaud
f7e52d22d4 Fix missuse of unitialized values in unit tests 2013-04-10 09:46:16 +02:00
Gael Guennebaud
84637ca58c Remove a useless variable in blueNorm 2013-04-10 09:41:42 +02:00
Gael Guennebaud
d7f3cfb56e bug #564: document the fact that minCoeff/maxCoeff members have undefined behavior if the matrix contains NaN. 2013-04-09 11:27:54 +02:00
Gael Guennebaud
3cb6e21f80 Fix bug #562: add vector-wise normalized and normalize functions 2013-04-09 11:12:35 +02:00
Gael Guennebaud
d8f1035355 Fix a couple of int versus Index issues. 2013-04-09 09:43:00 +02:00
Gael Guennebaud
bff264283d Add missing epsilon/dummy_precision function in NumTraits<Array> 2013-04-09 09:31:26 +02:00
Gael Guennebaud
8f44205671 Fix bug #581: remove useless piece of code is blueNorm 2013-04-09 09:23:40 +02:00
Desire NUENTSA
d97cd746ae Replace int by Index 2013-04-08 08:51:58 +02:00
Gael Guennebaud
12439e1249 Port SelfCwiseBinaryOp and Dot.h to nvcc, fix portability issue with std::min/max 2013-04-05 16:35:49 +02:00
Christoph Hertzberg
9b33ab62da Fixing bug #578. Thanks to Angelos <filiatra@gmail.com> 2013-04-03 16:29:16 +02:00
Gael Guennebaud
c3a6fa03a2 elif/elseif typo 2013-03-26 11:52:43 +01:00
Gael Guennebaud
0a1d9fb9ae Fix warning: implicit conversion loses integer precision in SparseMatrix. No need to use std::ptrdiff_t instead of Index since this later is requested to be signed. 2013-03-20 21:58:24 +01:00
Gael Guennebaud
225fd0f579 adapt AutoDiff to scalar_product_traits 2013-03-20 21:20:13 +01:00
Gael Guennebaud
c519be2bac Allow multiplication like binary operators to be applied on type couples supported by scalar_product_traits 2013-03-20 21:19:16 +01:00
Desire NUENTSA
f350f34560 Add complex support to dgmres and the unit test 2013-03-20 18:38:22 +01:00
Gael Guennebaud
d63712163c Add SSE4 min/max for integers 2013-03-20 18:28:40 +01:00
Desire NUENTSA
da6219b19d Bug567 : Fix iterative solvers to immediately return when the initial guess is the true solution and for trivial solution 2013-03-20 16:15:18 +01:00
Desire NUENTSA
22460edb49 Use a template Index for COLAMD ordering 2013-03-20 16:02:03 +01:00
Desire NUENTSA
4107b371e3 Handle zero right hand side in CG and GMRES 2013-03-20 11:22:45 +01:00
Gael Guennebaud
9bfeeba1c5 Add Official/Unsupported labels to unit tests and add a ctest driver to submit subprojects to cdash 2013-03-20 08:40:13 +01:00
Thomas Capricelli
11a9091084 fix a weird bug where a space was missing before a link 2013-03-19 20:09:13 +01:00
Thomas Capricelli
aba50d842e fixes #568
(files from previous build were kept on the server, with outdated/garbled
information)

The documentation update script now wipes build/doc/html
before rebuilding stuff. Most of the time/cpu consuming is spent in
compiling snippets, so we don't loose that much.
2013-03-19 19:18:14 +01:00
Gael Guennebaud
f29b4c435b Make cpuid not use %%esi -> dangerous if someone is using it. 2013-03-19 14:11:59 +01:00
Michael Schmidt
0d5a418048 Fix bug #566: rbx register has to be saved when calling cpuid on x84_64 with -fPIC and medium or large code models. 2013-03-19 14:00:42 +01:00
Claas H. Köhler
d6d638c751 Forward compiler flags to Fortran workaround 2013-03-17 14:17:44 +01:00
Christoph Hertzberg
6357fd68da Patch by Kolja Brix <brix@igpm.rwth-aachen.de> that fixes bug #565 and adds a testcase to verify that. 2013-03-17 13:55:31 +01:00
Desire NUENTSA
f8addac4e1 Include SparseLU and SparseQR 2013-03-13 18:01:47 +01:00
Gael Guennebaud
5d1a74da0a Update matlab-eigen quick ascii reff 2013-03-11 21:20:12 +01:00
Desire NUENTSA
6c68f1d787 bug #563 : Sparse block assignments should be called on compressed matrices. Uncompressed matrices will be supported later 2013-03-11 19:21:18 +01:00
Jitse Niesen
79f93247c5 Relax tolerances in matrix_power tests to avoid intermittent failures. 2013-03-09 17:20:16 +00:00
Jitse Niesen
97c9e3c74f Handle special case in atanh2(x,y) when y = 0.
This fixes matrix_power unit test on clang.
2013-03-09 16:58:05 +00:00
Gael Guennebaud
03373f41cb Fix bug #561: remove useless sign macro 2013-03-07 23:35:26 +01:00
Gael Guennebaud
f82ee241ac Added tag 3.2-beta1 for changeset 2238592062 2013-03-07 08:51:23 +01:00
Gael Guennebaud
2238592062 bump to 3.2-beta1 (3.1.91) 2013-03-07 08:49:10 +01:00
Desire NUENTSA
4fdae4dda9 Fix bug in SparseLU kernel for 32bits indices 2013-03-06 16:35:12 +01:00
Gael Guennebaud
98ce4455dd fix sparse vector assignment from a sparse matrix 2013-03-06 11:58:22 +01:00
Desire NUENTSA
69bd334d2b Fix mismatched free/delete 2013-03-05 16:35:13 +01:00
Desire NUENTSA
a1ddf2e7a8 Update doc for the sparse module 2013-03-05 12:55:03 +01:00
Gael Guennebaud
24d81aeb20 Fix overlaping operands when calling memcpy 2013-03-04 17:47:45 +01:00
Gael Guennebaud
d2e5c9d892 Do not globally disable stupid warnings in our unit test since such warnings do affect user code. 2013-03-01 14:50:20 +01:00
Gael Guennebaud
b9fe79153b Fix a couple of remaining warnings (missing newlines, inline-noinline, meaningless type qualifiers) 2013-03-01 14:42:36 +01:00
Gael Guennebaud
87142237b5 Fix "missing return statement at end of non-void function" 2013-03-01 14:33:11 +01:00
Gael Guennebaud
210a56ff48 Update to latest mpreal. 2013-03-01 14:31:11 +01:00
Gael Guennebaud
d70366d011 Remove assumption on RowMajorBit==RowMajor and ColMajor==0 2013-03-01 14:23:31 +01:00
Gael Guennebaud
01c6308d6e Add missing template keyword in evaluators 2013-03-01 00:26:52 +01:00
Gael Guennebaud
858ac9ffe0 Add missing template keyword 2013-03-01 00:03:28 +01:00
Gael Guennebaud
1bb1945078 Fix "explicit instantiation of 'Eigen::Spline' must occur in namespace 'Eigen'" warnings 2013-02-28 20:22:26 +01:00
Gael Guennebaud
3930c9b086 Fix "routine is both "inline" and "noinline"" warnings 2013-02-28 19:31:03 +01:00
Gael Guennebaud
e5bf4440c0 Fix "type qualifiers are meaningless here" warnings 2013-02-28 19:29:32 +01:00
Gael Guennebaud
0fac91ac22 Fix "storage class is not first" warnings 2013-02-28 19:27:53 +01:00
Hauke Heibel
b5d8299ee7 Prevent calling .norm() on integer matrices in the unit tests. 2013-02-28 12:33:34 +01:00
Hauke Heibel
83aac6d54c MSVC fix; the compiler failed to detect the correct overload. 2013-02-28 11:38:34 +01:00
Hauke Heibel
5882f1631d Fixed compiler warning. 2013-02-28 10:15:19 +01:00
Hauke Heibel
5e8384df2e MSVC fix; the base class typedef shadowed the local template parameter. 2013-02-28 08:47:38 +01:00
Gael Guennebaud
6dd93fc76e The ref unit test cannot be easily written to work with EIGEN_DEFAULT_TO_ROW_MAJOR 2013-02-27 23:52:10 +01:00
Hauke Heibel
c754023e72 Fixed MSVC dashboard (Experimental/Continuous) build scripts. 2013-02-27 15:54:27 +01:00
Gael Guennebaud
455e6e38b6 Fix two numerical issues in unit tests. 2013-02-27 08:07:18 +01:00
Gael Guennebaud
61a2995d03 Remove ICC warning in nomalloc unit test. 2013-02-26 18:10:19 +01:00
Gael Guennebaud
fe2c8e1c36 Fix compilation with ICC that was unable to instanciate Scaling from Eigen's namespace. 2013-02-26 17:38:37 +01:00
Gael Guennebaud
fa17a6da75 Fix compilation with ICC that was unable to instanciate first_aligned 2013-02-26 17:32:42 +01:00
Gael Guennebaud
bb94f0ebc6 Add a unit test for Ref.h and fix an extra copy. 2013-02-26 15:10:00 +01:00
Gael Guennebaud
63135a7350 Fix computation of outer-stride when calling .real() or .imag() 2013-02-26 15:08:50 +01:00
Gael Guennebaud
e8ccd07671 Add the possibility to define a custom build-string suffix 2013-02-26 13:40:13 +01:00
Gael Guennebaud
0b187a40a1 workaround "may be used uninitialized in this function" warning 2013-02-26 12:09:08 +01:00
Gael Guennebaud
5dda7842ca Add assertion on the input matrix size in factorizations relying on permutations of 32bits int 2013-02-26 11:42:32 +01:00
Gael Guennebaud
b73baa1ea4 Workaround warning: assuming signed overflow does not occur when... 2013-02-26 10:29:24 +01:00
Gael Guennebaud
5108ef01fc Fix no newline warning. 2013-02-26 10:27:55 +01:00
Gael Guennebaud
b6dc2613ac Fix bug #552: disable EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED when compiling with -fsanitize=address, and allow users to manually tell whether EIGEN_MALLOC_ALREADY_ALIGNED. 2013-02-25 19:17:13 +01:00
Gael Guennebaud
12a1313b09 bug #482: pass scalar arguments by const references. Still remains a few cases that might affect the ABI (see the bug entry) 2013-02-25 18:05:57 +01:00
Desire NUENTSA
cc35c44256 Add reference for the default threshold in sparse QR 2013-02-25 14:26:55 +01:00
Desire NUENTSA
ced8dfc0d9 Fix the computation of the default pivot threshold for sparse QR 2013-02-25 13:41:59 +01:00
Gael Guennebaud
5a0c5c0393 Fix bug #483: optimize outer-products to skip setZero and a scalar multiple when not needed. 2013-02-25 13:31:42 +01:00
Gael Guennebaud
96ad13abba conservative_resize unit test was executed only once 2013-02-25 01:50:58 +01:00
Gael Guennebaud
41aa0fcc3b Output random generator seed in case of failure so that we have it in CDash. 2013-02-25 01:46:59 +01:00
Gael Guennebaud
698de91c8a Fix segfault in SparseBlock::InnerIterator 2013-02-25 01:30:18 +01:00
Gael Guennebaud
19bc418f5c Remove erroneously committed debugging stuff. 2013-02-25 01:17:44 +01:00
Gael Guennebaud
80d2a65465 Fix visitor unit test. 2013-02-25 01:12:07 +01:00
Gael Guennebaud
04367447ac Fix bug #496: generalize internal rank1_update implementation to accept uplo(A) += v * w and make A.triangularView() += v * w uses it.
Update unit tests and blas interface respectively.
2013-02-24 23:05:42 +01:00
Gael Guennebaud
08388cc712 Remove superfluous cast. 2013-02-24 20:37:52 +01:00
Gael Guennebaud
1c137496c6 Extend sparseqr unit test to check linearly dependent columns 2013-02-24 20:36:54 +01:00
Gael Guennebaud
4eeaff6d38 Cleaning pass on SparseQR 2013-02-24 20:36:28 +01:00
Gael Guennebaud
28e139ad60 Fix another issue related to summing up many signed values. 2013-02-23 23:06:45 +01:00
Gael Guennebaud
42af5870a4 Fix array unit test: isApprox(log(0),log(0)) is false, and summing up signed float value might result in very small values and thus large numerical errors 2013-02-23 22:58:14 +01:00
Gael Guennebaud
274c24c262 Avoid problematic ternary operator (http://forum.kde.org/viewtopic.php?f=74&t=109486) 2013-02-23 20:13:21 +01:00
Sebastien Barthelemy
74438f8aa9 Fix EIGEN_INITIALIZE_MATRICES_BY_NAN. 2013-02-22 15:09:03 +01:00
Gael Guennebaud
7fe6419171 remove double parenthesis 2013-02-22 14:50:47 +01:00
Gael Guennebaud
e71bc79f2a SparseLU does not accept row-major matrices for the destination. 2013-02-22 14:45:42 +01:00
Gael Guennebaud
bd8c9c69e4 Protect min with parenthesis in IncompleteLLT 2013-02-22 14:41:32 +01:00
Gael Guennebaud
d93c1c113b NVCC: EIGEN_NO_DEBUG must be defined before including Macro.h 2013-02-21 19:05:23 +01:00
Desire NUENTSA
59f9400420 Clarify the doc for column-pivoting QR 2013-02-21 13:33:31 +01:00
Gael Guennebaud
968f7591f8 Make it compile without nvcc 2013-02-21 12:51:58 +01:00
Jitse Niesen
986f60127d Guard against transposeInPlace on non-square non-resizable matrix.
Inspired by question by Martin Drozdik at stackoverflow.com/q/14954983
2013-02-20 14:03:14 +00:00
Jitse Niesen
a054b4ee27 Be more explicit about user-defined functions in Map tutorial.
See discussion on mailing list on 18 + 19 Feb 2013.
2013-02-20 13:44:40 +00:00
Desire NUENTSA
febf8e5d7b Set built-in sparse QR as the default sparse solver and add ComputationInfo for Levenberg Marquardt, 2013-02-20 14:10:14 +01:00
Desire NUENTSA
dca7190e15 Add setPivotThreshold to Sparse QR 2013-02-20 14:00:28 +01:00
Desire NUENTSA
19de016fef Correct the SPQR backend for rank-deficient matrices and delete some public functions 2013-02-20 13:59:56 +01:00
Desire NUENTSA
bc18e06fe3 Add matrixR() to get the triangular factor from the Householder QR 2013-02-20 13:58:26 +01:00
Desire NUENTSA
962c99d462 Metis ordering backend supports only squared matrices 2013-02-20 13:56:51 +01:00
Jitse Niesen
ba653105a2 Remove confusing transpose() in setLinSpaced() docs. 2013-02-18 17:27:41 +00:00
Jitse Niesen
b4f6aec195 Fix linear vectorized transversal in linspace (fixes bug #526). 2013-02-18 17:26:03 +00:00
Desire NUENTSA
1a056b408d Add a rank-revealing feature to sparse QR 2013-02-15 16:35:28 +01:00
Gael Guennebaud
9fd465ea2b Fix the following warning: "comparison between signed and unsigned integer expressions" 2013-02-15 14:31:38 +01:00
Gael Guennebaud
cf259ce590 Workaround the following warning: "assuming signed overflow does not occur when assuming that (X + c) < X is always false" 2013-02-15 14:28:20 +01:00
Gael Guennebaud
a1091caa43 Fix some unused or not initialized related warnings. 2013-02-15 14:05:37 +01:00
Gael Guennebaud
19f699ded0 "-Wno-psabi" option is not supported by all gcc version. 2013-02-15 14:01:30 +01:00
Gael Guennebaud
8745da14d8 Fix SSE plog<float> to return -INF on 0 2013-02-14 23:34:05 +01:00
Gael Guennebaud
912ba10efe Remove the following note made by gcc: "The ABI of passing structure with complex float member has changed in GCC 4.4" 2013-02-14 21:52:12 +01:00
Gael Guennebaud
24e4a3af2b Add missing using std::sqrt 2013-02-14 21:40:00 +01:00
Gael Guennebaud
a0fb885c82 Update adjoint unit test to avoid instantiating sqrt(int) 2013-02-14 21:33:42 +01:00
Gael Guennebaud
9cc016d3f9 Update basicstuff unit test to avoid instantiating the ambiguous sqrt(int) 2013-02-14 21:15:58 +01:00
Gael Guennebaud
f8407742c1 Fix some implicit int64 to int conversion warnings. However, the real issue
is that PermutationMatrix mixes the type of the stored indices and the "Index"
type used for the sizes, coeff indices, etc., which should be DenseIndex.
(transplanted from 66cbfd4d39
)
2013-02-14 18:16:51 +01:00
Gael Guennebaud
25bcbfb10c Fix bug in aligned_free with windows CE 2013-02-13 19:09:31 +01:00
Gael Guennebaud
a143c5b78c Fix bug #544: assertion in JacobiSVD when compiling with EIGEN_NO_AUTOMATIC_RESIZING 2013-02-12 19:56:48 +01:00
Gael Guennebaud
3cd32996f1 Fix bug #551: compilation issue when using EIGEN_DEFAULT_DENSE_INDEX_TYPE 2013-02-09 09:43:17 +01:00
Gael Guennebaud
5adcc6c7b4 Add support for NVCC5: most of the Core and part of LU are callable from CUDA code.
Still a lot to do.
2013-02-07 19:06:14 +01:00
Gael Guennebaud
5115f4c504 add EIGEN_INITIALIZE_MATRICES_BY_NAN 2013-02-07 18:07:07 +01:00
Gael Guennebaud
3c1ccca285 Add missing operator= in RefBase 2013-02-07 17:49:16 +01:00
Gael Guennebaud
e21dc15386 Add missing data member function in CwiseUnaryView 2013-02-07 17:44:24 +01:00
Gael Guennebaud
5154253933 Fix some MPL2/LGPL lisencing confusions 2013-02-06 11:30:33 +01:00
Jitse Niesen
14e2ab02b5 Replace assert() by eigen_assert() (fixes bug #548). 2013-02-02 22:04:42 +00:00
Desire NUENTSA
35647b0133 Correct bug in SPQR backend and replace matrixQR by matrixR 2013-01-29 17:48:30 +01:00
Desire NUENTSA
8bc00925e5 Change int to Index type for SparseLU 2013-01-29 16:21:24 +01:00
Hauke Heibel
57e50789f3 Added missing using std::sqrt. 2013-01-27 13:46:06 +01:00
Hauke Heibel
718535ac6c Added Visual Studio 2012 debug visualizers. 2013-01-26 17:32:14 +01:00
Desire NUENTSA
7f0f7ab5b4 Add additional methods in SparseLU and Improve the naming conventions 2013-01-25 20:38:26 +01:00
Desire NUENTSA
d58056bde4 Merged local branch with main trunk 2013-01-25 19:05:33 +01:00
Desire NUENTSA
81d4bfa8d9 add support for solving with sparse right hand side 2013-01-25 18:17:17 +01:00
Gael Guennebaud
e4ec63aee7 Suppress annoying "may be used uninitialized in this function" warning with gcc >= 4.6 2013-01-24 11:59:17 +01:00
Gael Guennebaud
b74c0a4413 Check that NeedsToAlign is properly sets before checking alignment 2013-01-24 11:42:04 +01:00
Gael Guennebaud
7282a45a0a Remove dummy code in MPRealSupport 2013-01-24 08:48:26 +01:00
Gael Guennebaud
29d395f769 Relax a bit the precision in mpreal unit test. 2013-01-23 23:57:28 +01:00
Gael Guennebaud
691e607d85 Specialize GEBP traits and kernel for mpreal to by-pass mpreal and remove the costly creation of many temporaries. 2013-01-23 23:56:57 +01:00
Gael Guennebaud
c22f7cef83 Workaround "error: floating-point literal cannot appear in a constant-expression" in mpreal.h when compiling with predantic.
(I really don't know how to properly fix this))
2013-01-23 20:51:38 +01:00
Gael Guennebaud
73026eab4d Fix SparseLU special gemm kernel on 32 bits system w/o SSE 2013-01-23 19:34:01 +01:00
Gael Guennebaud
ee36eaefc6 remove dummy code in ColPivHouseholderQR::solve 2013-01-23 18:34:29 +01:00
Gael Guennebaud
19c78cf510 Workaround gcc-4.7 bug #53900 (too aggressive optimization in our alignment check) 2013-01-22 22:59:09 +01:00
Gael Guennebaud
67b9f42528 Recent UMFPACK library requires to link to libSuiteSparse 2013-01-22 22:53:28 +01:00
Desire NUENTSA
ad798231ec Fix test for Metis 2013-01-21 15:43:15 +01:00
Desire NUENTSA
3d9150870d Fix documentation for SparseLU 2013-01-21 15:39:18 +01:00
Desire NUENTSA
d2dd5063b6 Documentation for the ordering methods 2013-01-21 15:37:47 +01:00
Desire NUENTSA
5b9bb00265 Test for the sparse Blue norm 2013-01-21 15:37:06 +01:00
Desire NUENTSA
5dcf6caa36 Unit test for the Metis Ordering package 2013-01-21 15:36:18 +01:00
Gael Guennebaud
392ffce3b9 Fix traits of Map<Quaternion>, and respectively extend the unit tests 2013-01-20 10:21:54 +01:00
Gael Guennebaud
fb89b66229 Some minor documentation fixes in Quaternion 2013-01-20 10:20:39 +01:00
Chen-Pang He
23c87fcde6 I think it's OK to let XprHelper.h determine the nested type. 2012-10-15 00:14:32 +08:00
Chen-Pang He
fe0ef8e609 Remove unused typedef (traits<MatrixPowerProduct>::PlainObject) for brevity. 2012-10-14 22:30:52 +08:00
Chen-Pang He
40fce01648 Simplify traits<MatrixPowerProduct>: StorageKind must be Dense because MatrixPowerProduct is derived from MatrixBase. 2012-10-14 18:36:17 +08:00
Chen-Pang He
c890cf5489 Use the nested type instead of const reference 2012-10-14 03:02:16 +08:00
Chen-Pang He
daa65c5bce Just tidy up: no need to specify template parameters inside class body. 2012-10-14 01:36:54 +08:00
Chen-Pang He
0017d8c58f Make MatrixPowerTriangularAtomic::computePade static because it should be. 2012-10-07 02:25:00 +08:00
Chen-Pang He
a5d348e30a Use simplified return type, trying to work around MSVC. 2012-10-03 19:42:02 +08:00
Chen-Pang He
4cfde4590f Make use of TRMM (speed up), and remove useless condition (the triangular don't need LU) 2012-10-02 23:04:23 +08:00
Chen-Pang He
21c2b4e327 Make better decision on PartialPivLU vs inverse(): We have specialized inverse() only for FIXED matrices. 2012-10-02 19:53:38 +08:00
Chen-Pang He
e92fe88159 Add test for real MatrixPowerTriangular. 2012-09-30 19:21:53 +08:00
Chen-Pang He
eb33d307af Avoid Schur decomposition on (quasi-)triangular matrices. (Huge speed up!) 2012-09-30 16:30:18 +08:00
Chen-Pang He
332eb36436 Implement complex MatrixPowerTriangular. There are still problems with real one. 2012-09-30 02:14:16 +08:00
Gael Guennebaud
209199a13e Move the definition of DenseBase::InnerIterator to Core module. (needed to make blueNorm generic) 2013-01-15 22:03:54 +01:00
Desire NUENTSA
f813e83bc3 Delete unused variable in SparseLU 2013-01-14 16:03:46 +01:00
Desire NUENTSA
c05848a330 Move SparseColEtree common to SparseLU and SparseQR to SparseCore and fix build issue of sparseqr 2013-01-14 15:59:46 +01:00
Desire NUENTSA
904c2f137b Fix the column permutation in SparseQR 2013-01-14 14:20:42 +01:00
Gael Guennebaud
a3b94d26c8 Remove TOC numbering, and minor improvement in overview. 2013-01-12 20:34:52 +01:00
Sergey Popov
761b3bbb69 Fix bug #540: SelfAdjointEigenSolver improperly used the upper triangular part to extract the scaling factor. 2013-01-12 12:07:49 +01:00
Gael Guennebaud
7262cf783c Cleaning documentation pass in ordering and ILUT 2013-01-12 11:56:56 +01:00
Gael Guennebaud
38fa432e07 Clean inclusion, namespace definition, and documentation of SparseLU 2013-01-12 11:55:16 +01:00
Gael Guennebaud
50625834e6 SparseQR: clean a bit the documentation, fix rows/cols methods, remove rowsQ methods and rename matrixQR to matrixR. 2013-01-12 09:40:31 +01:00
Gael Guennebaud
581e389c54 Fix installation path of SparseQR 2013-01-12 09:32:51 +01:00
Desire NUENTSA
121f3bdf04 Pass a const matrix to sparseQR 2013-01-11 17:47:32 +01:00
Desire NUENTSA
33febdb48b Add support for Schur decomposition of matrices in Hessenberg form 2013-01-11 17:36:45 +01:00
Desire NUENTSA
0f94e96342 Add support for sparse blueNorm 2013-01-11 17:27:12 +01:00
Desire NUENTSA
91b3b3aaab Add a sparse QR factorization and update the elimination tree in SparseLU 2013-01-11 17:16:14 +01:00
Gael Guennebaud
1ccd90a927 Make the MatrixFunctions documentation page looks a bit better 2013-01-11 10:48:43 +01:00
Gael Guennebaud
cc444bbbf9 update unsupported module documentation to be conformed with new documentation style 2013-01-11 10:41:26 +01:00
Gael Guennebaud
b0cb5e6d48 remove the 'Unsupported Modules' meta module 2013-01-11 10:40:35 +01:00
Gael Guennebaud
109cbb6ad3 typos 2013-01-09 17:44:25 +01:00
Gael Guennebaud
dcc1754f05 update javascript hacks for doxygen 1.8.3 2013-01-09 00:40:48 +01:00
Gael Guennebaud
2abe7d8c6e Rename the dox files: the number prefixes are not needed anymore 2013-01-06 23:57:54 +01:00
Gael Guennebaud
091a49cad5 Clean the manual page titles, links and intro. 2013-01-06 23:48:59 +01:00
Thomas Capricelli
c71c06b71f fix typo 2013-01-06 14:39:20 +01:00
Gael Guennebaud
8a50ed86f3 Check that minCoeff(int*)/maxCoeff(int*) always pick the first entry in case of multiple extrema. 2013-01-05 23:49:47 +01:00
Gael Guennebaud
f9927b4aca Fix _data() versus data() issue in SparseVector, and add a Storage typedef just like SparseMatrix. 2013-01-05 23:04:22 +01:00
Gael Guennebaud
86983fa1ff Update the overview page to reflect the new organisation 2013-01-05 21:25:41 +01:00
Gael Guennebaud
2de69c2f26 Doc presentation:
- remove the "modules|classes" link for module pages (they are already in the TOC)
 - fine tune the TOC css
2013-01-05 17:14:14 +01:00
Gael Guennebaud
93ee82b1fd Big changes in Eigen documentation:
- Organize the documentation into "chapters".
  - Each chapter include many documentation pages, reference pages organized as modules, and a quick reference page.
  - The "Chapters" tree is created using the defgroup/ingroup mechanism, even for the documentation pages (i.e., .dox files for which I added an \eigenManualPage macro that we can switch between \page or \defgroup ).
  - Add a "General topics" entry for all pages that do not fit well in the previous "chapters".
  - The highlevel struture is managed by a new eigendoxy_layout.xml file.
- remove the "index" and quite useless pages (namespace list, class hierarchy, member list, file list, etc.)
- add the javascript search-engine.
- add the "treeview" panel.
- remove \tableofcontents (replace them by a custom \eigenAutoToc macro to be able to easily re-enable if needed).
- add javascript to automatically generate a TOC from the h1/h2 tags of the current page, and put the TOC in the left side panel.
- overload various javascript function generated by doxygen to:
  - remove the root of the treeview
  - remove links to section/subsection from the treeview
  - automatically expand the "Chapters" section
  - automatically expand the current section
  - adjust the height of the treeview to take into account the TOC
- always use the default .css file, eigendoxy.css now only includes our modifications
- use Doxyfile to specify our logo
- remove cross references to unsupported modules (temporarily)
2013-01-05 16:37:11 +01:00
Jitse Niesen
eac676ff6c Set matrix to zero before inserting entries (partially fixes bug #539). 2013-01-03 18:00:45 +00:00
Chen-Pang He
8321b7ae74 Make KroneckerProductSparse inherit EigenBase instead of SparseMatrixBase, for it does not provide an InnerIterator. 2012-10-25 02:09:48 +08:00
Chen-Pang He
204a09cb82 Fix compile error caused by incomplete SparseMatrixBase. 2012-10-16 00:06:49 +08:00
Chen-Pang He
0508a0620b Let KroneckerProduct inherit ReturnByValue to eliminate temporary evaluation. It's uncommon to store the product back to one of the operands. 2012-10-15 19:45:50 +08:00
Chen-Pang He
8284e7134b Add doc for KroneckerProductSparse. 2012-10-15 00:31:09 +08:00
Chen-Pang He
c4b83461d9 Make kroneckerProduct take two arguments and return an expression, which is more straight-forward. 2012-10-15 00:21:12 +08:00
Chen-Pang He
f34db6578a KroneckerProduct: we have const_cast_derived so why not use it? 2012-10-14 01:38:38 +08:00
Jitse Niesen
20a984cd2e Remove #include of removed header file. 2013-01-03 16:44:15 +00:00
Gael Guennebaud
6fb3be9841 Remove useless empty file. 2013-01-03 17:05:20 +01:00
Gael Guennebaud
2ea1e49a08 Doc: replace manual TOC by doxygen's \tableofcontents command 2012-12-28 18:58:07 +01:00
Gael Guennebaud
ded70b8b58 Doc: remove page margins and limits to 60em paragraphes only instaead of the entire page (many declarations and tables are larger than 60em anyway) 2012-12-28 18:57:10 +01:00
Gael Guennebaud
3f82401890 Update doxygen files to doxygen version 1.8 2012-12-28 18:52:53 +01:00
Gael Guennebaud
f41d96deb9 Fix several documentation issues 2012-12-24 13:33:22 +01:00
Gael Guennebaud
f450303321 Fix MSVC compilation: std::log was not accessible. 2012-12-20 18:11:49 +01:00
Gael Guennebaud
85005ffbd1 Make sure sqrt and the likes are not compiled for integer type in cwiseop unit test. 2012-12-20 18:08:26 +01:00
Christoph Hertzberg
b7ea59556d Fix bug #507: Mark variable as unused in NDEBUG case 2012-12-20 11:21:47 +01:00
Christoph Hertzberg
0fe264869a Merge with 6300e8ca02 2012-12-17 17:01:24 +01:00
Christoph Hertzberg
6300e8ca02 replaced compiler specific __attribute__((noinline)) by EIGEN_DONT_INLINE 2012-12-17 16:55:14 +01:00
Christoph Hertzberg
c69577ea31 Fix bug #531: Empty line in <table> made doxygen render it as paragraphs 2012-12-17 16:13:42 +01:00
Jakob Schwendner
22e6741da9 updated geometry benchmark to handle additional cases 2012-12-17 09:33:22 +01:00
Jakob Schwendner
98798e904b added benchmark for test vectorization in geometry package 2012-12-16 23:30:56 +01:00
Gael Guennebaud
e90752d252 Fix bug #534: rm useless initialization of rowSpacer. 2012-12-16 20:32:48 +01:00
Gael Guennebaud
925a5b7d07 Fix bug #535: unused variable warnings 2012-12-16 20:21:28 +01:00
Gael Guennebaud
6c8cf15c06 Fix compilation of Block/SparseBlock with MSVC 2012-12-16 19:45:48 +01:00
David Harmon
23ce05971b Add arpack support module file 2012-12-16 19:11:24 +01:00
David Harmon
dbe1ab67ac Added ARPACK support for standard and generalized eigenvalue problems. Currently self-adjoint only. 2012-10-06 17:18:09 -06:00
Gael Guennebaud
8844f632fa Move work in progress Levenberg Marquardt module in unsupported 2012-12-08 18:22:23 +01:00
Gael Guennebaud
4cdcb6d793 Add missing minpack copyrights/license.
Fix LM header files and credits original MINPACK authors.
Move minimizeOneStep code into its own file to get it more properly credited.
2012-12-08 18:17:18 +01:00
Gael Guennebaud
d85253ccf4 Backed out changeset 363e506776 2012-12-07 20:53:19 +01:00
Desire NUENTSA
363e506776 Rename the old LevenbergMarquardt class to LevenbergMarquardtLegacy
Split the levenberg marquardt test and the hybrid nonlinear test
2012-12-07 15:51:25 +01:00
Desire NUENTSA
cc0fef9807 Add tests for dense and sparse levenberg-Marquardt 2012-12-07 15:48:21 +01:00
Desire NUENTSA
2aba6645f4 Move the Levenberg Marquardt to the supported branch
Add support for sparse computations... need SPQR module.
2012-12-07 15:47:13 +01:00
Desire NUENTSA
71cb78e1ba Fix Incomplete Cholesky factorization. Stable but need iterative robust shift 2012-12-07 15:33:26 +01:00
Desire NUENTSA
5afaacedc6 Update SPQR interface 2012-12-07 15:32:04 +01:00
Pavel Holoborodko
895d90d3e1 Fixed mpreal for IA64 architectures 2012-12-04 18:15:46 +09:00
Gael Guennebaud
8719b1bf16 fix geometry tutorial 2012-11-29 22:48:13 +08:00
Desire NUENTSA
36414d1f3e Update SPQR module for Sparse LM 2012-11-21 19:47:44 +01:00
Desire NUENTSA
9162a8492e ReverseInnerIterator for SparseBlock 2012-11-16 20:00:34 +01:00
Desire NUENTSA
4acc18f100 Move VectorBlock methods into plugin section 2012-11-16 19:59:11 +01:00
Gael Guennebaud
6a790058f5 remove deprecated InnerVectorSet for the deprecated DynamicSparseMatrix class 2012-11-16 09:03:42 +01:00
Gael Guennebaud
4e60283289 Remove Sparse/InnerVectorSet expression in favor of a more general Block<> specialization for Sparse expression.
The specializations for "InnerPanels" are still preserved for efficiency reasons and because they offer additional usefull features.
2012-11-16 09:02:50 +01:00
Gael Guennebaud
3dc8f8536a Generalize Block<> to support various implementation wrt StorageKind (just like other expression) 2012-11-16 09:00:27 +01:00
Gael Guennebaud
493319ae5f plugin header files can be included more than once 2012-11-15 14:33:30 +01:00
Desire NUENTSA
b40a5b8b48 Improve the IncompleteLLT ... not yet robust 2012-11-13 18:14:34 +01:00
Desire NUENTSA
0412dff97b Add more useful functions to SPQR interface 2012-11-13 18:13:13 +01:00
Desire NUENTSA
9cf77ce1d8 Add support for Sparse QR factorization 2012-11-12 15:20:37 +01:00
Desire NUENTSA
474716ec5b Add restarted GMRES with deflation 2012-11-12 10:47:55 +01:00
Gael Guennebaud
a76fbbf397 Fix bug #314:
- remove most of the metaprogramming kung fu in MathFunctions.h (only keep functions that differs from the std)
- remove the overloads for array expression that were in the std namespace
2012-11-06 15:25:50 +01:00
Gael Guennebaud
959ef37006 Fix FindUmfpack when specifying manually the related cmake variables. 2012-11-05 23:21:02 +01:00
Gael Guennebaud
691fb92690 Disable opengl demo if Qt4 or OpenGL cannot be found.
(transplanted from caf24f1c9e
)
2012-10-31 11:36:45 +01:00
Gael Guennebaud
aa858cb43a add first_multiple helper function 2012-10-30 16:27:52 +01:00
Gael Guennebaud
90fcaf11cf SparseLU: remove the "snode" path which appears to bring nearly zero speedup 2012-10-30 15:17:58 +01:00
Gael Guennebaud
ac8c694f3e add missing copyright 2012-10-30 15:16:47 +01:00
Gael Guennebaud
fea4220f37 SparseLU: add a specialized gemm kernel, and add padding to the supernodes such that supernodes columns are all properly aligned 2012-10-30 15:09:48 +01:00
Desire NUENTSA
f7e203fb0c Fix build error in matrixfunctions on MSVC 2012-10-30 11:30:37 +01:00
Gael Guennebaud
b3254c9af5 fix bug #524: Pardiso's parameter array does not have to be aligned! 2012-10-24 10:31:04 +02:00
Gael Guennebaud
138897cc06 fix bug #521: __cpuidex is not available on all architectures supported by MSVC 2012-10-24 10:21:41 +02:00
Gael Guennebaud
9b418afff6 Windows CE does not provide an aligned_malloc function. 2012-10-24 10:12:42 +02:00
Gael Guennebaud
0753463d70 Fix bug #519: AlignedBox::dim() was wrong for dynamic dimensions 2012-10-24 09:58:35 +02:00
Pavel Holoborodko
7857118f2e Fixed gcc warnings, John Westwood name and round_style function 2012-10-19 22:51:55 +09:00
Pavel Holoborodko
8b84e05739 Updated multiprecision module to support the most recent version of MPFR C++ 2012-10-19 18:12:31 +09:00
Desire NUENTSA
77f92bf0b1 the repeated solves are already present in check_sparse_solving() 2012-10-09 13:30:48 +02:00
dnuentsa
f757034001 MINRES solver 2012-10-09 13:07:09 +02:00
Desire NUENTSA
fe78c86b4a Discard failing tests in NonlinearOptimization 2012-10-09 12:20:21 +02:00
Desire NUENTSA
b722c405b7 Use Ref instead of VectorBlock 2012-10-09 12:18:47 +02:00
Desire NUENTSA
23e2de3cb6 RealShur for a already Hessenberg matrix 2012-10-09 12:16:54 +02:00
Gael Guennebaud
a67eea05c1 fix comma initializer when inserting empty matrices 2012-10-03 21:58:14 +02:00
Desire NUENTSA
cfa8032ffb bug #517 : Small typo in AsciiQuickReference 2012-10-03 09:48:33 +02:00
Gael Guennebaud
fec6df1f7d fix dense=sparse*diagonal (there was an issue in the values returned by the .outer() function of the related iterators) 2012-10-03 09:06:19 +02:00
Gael Guennebaud
f30ca7ed7e extend unit tests to check rectangular matrices for sparse*diagonal products 2012-10-02 23:03:06 +02:00
Gael Guennebaud
62b1f75a86 add an assertion when inserting an already existing element 2012-10-02 23:02:23 +02:00
giacomo po
bf81276dad spd test instead of square test. Still missing complex version of MINRES. 2012-10-01 12:23:03 -07:00
Jitse Niesen
2008f76120 Merge 2012-09-29 17:35:15 +01:00
Chen-Pang He
d7d96f6694 Make testExponentLaws in matrix_power quiet. It was too noisy. 2012-09-29 17:45:59 +08:00
Chen-Pang He
50c07e50e8 Avoid memory manipulation for simplicity, efficiency, and safety. 2012-09-29 17:41:51 +08:00
Chen-Pang He
5814a5f1a0 Abort the extension. MatrixSquareRootTriangular only takes upper triangular matrices. 2012-09-29 17:41:06 +08:00
Chen-Pang He
067a5a98c8 Extend MatrixPowerTriangularAtomic for future implementation for triangular matrix power. 2012-09-29 02:02:12 +08:00
Desire NUENTSA
b68102d9a2 MSVC needs parentheses around min and max 2012-09-28 10:44:25 +02:00
giacomo po
01cb88fff8 compiling (but failing) unit test 2012-09-27 17:44:54 -07:00
Gael Guennebaud
87074d97e5 old gcc versions do not have immintrin.h file... 2012-09-27 23:35:54 +02:00
Chen-Pang He
ed18d6f2ad Fix doc and tidy up 2012-09-28 02:08:14 +08:00
Desire NUENTSA
82c3ff3784 Fix Build error on MSVC 2012-09-27 12:04:59 +02:00
Desire NUENTSA
72bfed5e20 Add forgotten SparseLUBase 2012-09-27 11:34:56 +02:00
Chen-Pang He
3b88216d42 Move unshared items back to MatrixPower 2012-09-27 17:19:32 +08:00
Gael Guennebaud
8b83e66906 add scalar multiple to diagonal matrices
(transplanted from dc5b335f9f
)
2012-09-27 09:37:05 +02:00
Gael Guennebaud
1b004d5794 fix SparseMatrix option bit flag in eval<> helper 2012-09-27 09:22:10 +02:00
Gael Guennebaud
b648484dba fix bug #515: missing explicit scalar conversion
(transplanted from b0862dcb2f
)
2012-09-27 00:23:19 +02:00
Gael Guennebaud
44374788b5 fix bug #511: pretty printers on windows 2012-09-26 23:48:48 +02:00
Gael Guennebaud
7c4b55fda9 fix bug #509: warning with gcc 4.7 2012-09-26 23:32:22 +02:00
Chen-Pang He
73a0bfe261 Write doc on (matrix power) * (matrix expression) 2012-09-27 02:31:18 +08:00
Chen-Pang He
aa5acdb352 Create class MatrixPowerBase for further extension (like specialization for triangular or self-adjoint matrices) 2012-09-27 02:20:36 +08:00
Gael Guennebaud
7e97dd5bd8 we should not directly include the *mmintrin.h headers but include immintrin.h only 2012-09-26 19:28:57 +02:00
Gael Guennebaud
1edb396542 fix minor typo in doc 2012-09-26 19:24:41 +02:00
Desire NUENTSA
357fe3641d Correct reference to iterative scaling method 2012-09-25 11:55:33 +02:00
Desire NUENTSA
15a9f6b9c1 Doc for sparseLU 2012-09-25 11:48:18 +02:00
Hauke Heibel
5a3f49036b Removed scaling from the umeyama when it is not requested. 2012-09-25 11:39:40 +02:00
Desire NUENTSA
088379ac2f Fix MSVC compile error in SparseLU 2012-09-25 09:58:29 +02:00
Desire NUENTSA
a01371548d Define sparseLU functions as static 2012-09-25 09:53:40 +02:00
giacomo po
fd0441baee some clean-up and new comments. 2012-09-24 09:20:40 -07:00
Chen-Pang He
d387dfa9dc Remove unnecessary code. lazyAssign seems to fix all (noalias, initialization, etc.) 2012-09-24 23:36:19 +08:00
giacomo po
18c41aa04f Some minor optimization. 2012-09-24 08:33:11 -07:00
giacomo po
dd7ff3f493 moved MINRES to unsupported. Made unit test. 2012-09-24 07:47:38 -07:00
Chen-Pang He
334532b7f5 Remove class MatrixPowerEvaluator with enhanced existing MatrixPowerReturnValue to simplicity, but docs are not completed yet. 2012-09-23 23:49:50 +08:00
Chen-Pang He
1d402dac03 Fix bug in MatrixPower(expression) due to destruction of temporary objects. Sorry for ugly pointer manipulation but it prevents copying a PlainObject. 2012-09-23 18:49:44 +08:00
giacomo po
8c5e4fae61 working preconditioned MINRES solver 2012-09-22 15:29:00 -07:00
Chen-Pang He
963794b04a Eliminate unnecessary evaluations 2012-09-23 00:20:19 +08:00
Chen-Pang He
7e64f78f65 Avoid inefficient 2x2 LU 2012-09-22 22:06:22 +08:00
Chen-Pang He
d7b1049cab Fix my typo in MatrixPowerBase.h, no effect on the flow. 2012-09-22 19:13:02 +08:00
Chen-Pang He
dd8034bd1c Fix cost evaluation. (chain product for integral power) 2012-09-22 17:37:14 +08:00
Gael Guennebaud
7740127e3d Make Ref<> suitable for both Matrix and Array kinds. Note that Matrix kind objects can be implicitely converted to an Array kind Ref<> and vice versa 2012-09-22 11:11:26 +02:00
Chen-Pang He
446d14f6ad Implement matrix power-matrix product again 2012-09-22 03:26:00 +08:00
Chen-Pang He
87afd99433 Enable saving intermidiate (Schur decomposition) but disable unstable specialization for matrix power-matrix product. 2012-09-21 23:24:28 +08:00
Desire NUENTSA
7e0dd17312 Improve BiCGSTAB : With exact preconditioner, the solution should be found in one iteration 2012-09-19 18:32:02 +02:00
Chen-Pang He
d5d99dd1f0 Optimize matrix functions: m_fT is triangular and trmm is faster than gemm 2012-09-16 14:42:42 +08:00
Gael Guennebaud
48c4d48aec workaround weird compilation error with MSVC 2012-09-14 09:54:56 +02:00
Gael Guennebaud
0c584dcf4d fix compilation with m.array().min/max(scalar) 2012-09-12 17:50:07 +02:00
Gael Guennebaud
28528519e9 Merged in jdh8/eigen (pull request PR-17) 2012-09-11 21:36:05 +02:00
Gael Guennebaud
9e80822fc9 fix compilation on freebsd 2012-09-11 13:32:56 +02:00
Desire NUENTSA
45672e724e Incomplete Cholesky preconditioner... not yet stable 2012-09-11 12:12:19 +02:00
Benoit Jacob
504edbddb1 Replace COPYING.LGPL by a copy of the LGPL 2.1 (instead of LGPL 3).
Indeed, all the LGPL code we use, is licensed under LGPL 2.1 (with some files being "2.1 or later").
2012-09-10 13:27:44 -04:00
Desire NUENTSA
2d49d049d1 Clean the Colamd routine and add test for sparselu code 2012-09-10 14:41:17 +02:00
Desire NUENTSA
761fe49f37 Clean the Colamd routine 2012-09-10 14:28:28 +02:00
Desire NUENTSA
2c99d84133 add SparseLU in sparse bench 2012-09-10 12:41:26 +02:00
Chen-Pang He
04f315d692 Fix rank-1 update for self-adjoint packed matrices. 2012-09-10 18:25:30 +08:00
Chen-Pang He
65caa40a3d Implement packed triangular solver. 2012-09-10 06:29:02 +08:00
Chen-Pang He
3642ca4d46 Implement packed triangular matrix-vector product. 2012-09-09 23:34:45 +08:00
Chen-Pang He
2828c995c5 Use conj_expr_if to clarify what it's doing. 2012-09-09 21:35:28 +08:00
Chen-Pang He
669db3d776 Extend rank-1 updates for different storage orders. 2012-09-09 02:55:10 +08:00
Chen-Pang He
1b8f416408 Implement rank-1 update for self-adjoint packed matrices. 2012-09-08 22:51:40 +08:00
Chen-Pang He
dac5a8a37d Simplify Rank2Update.h 2012-09-08 22:20:05 +08:00
Chen-Pang He
17c746523e Comment FIXMEs on Rank2Update.h and remove unused files. 2012-09-08 21:25:09 +08:00
Gael Guennebaud
24f371bdb4 Merged in jdh8/eigen (pull request PR-16) 2012-09-08 12:16:49 +02:00
Gael Guennebaud
721671cc4e fix bug #501: remove aggressive mat/scalar optimization (was replaced by mat*(1/scalar) for non integer types) 2012-09-08 11:52:03 +02:00
Chen-Pang He
e4e7585a24 Implement rank-2 update for packed matrices. 2012-09-08 17:29:44 +08:00
Chen-Pang He
b5f9bec8ac Perform direct calls in xHEMV and xSYMV. 2012-09-08 15:47:33 +08:00
Gael Guennebaud
06d2fe453d remove stupid assert in blue norm. 2012-09-07 23:19:24 +02:00
Chen-Pang He
1b61aadcbe Implement SDSDOT with DSDOT and avoid allocating buffers in DSDOT. 2012-09-08 02:06:45 +08:00
Chen-Pang He
b0b9b4d6b2 Implement functors for rank-1 and rank-2 update. 2012-09-08 01:39:16 +08:00
Desire NUENTSA
5433986f5a multiple warnings for unused variable 2012-09-07 14:01:51 +02:00
Desire NUENTSA
fdd0f0c5fc merge Sparse LU branch 2012-09-07 13:18:16 +02:00
Desire NUENTSA
063705b5be Add tutorial for sparse solvers 2012-09-07 13:14:57 +02:00
Chen-Pang He
145f89cd5f Fix memory leak in DSDOT. 2012-09-07 15:21:57 +08:00
Chen-Pang He
c86d047c2f BLAS: implement DSDOT and SDSDOT; update test for them. 2012-09-05 18:59:32 +08:00
Desire NUENTSA
2280f2490e Init perf values 2012-09-04 12:21:07 +02:00
Desire NUENTSA
2e38666d01 correct bug in Blas 3 2D block update 2012-09-04 11:36:57 +02:00
Desire NUENTSA
3a22c47fb5 Bug in blas 3 2D block update 2012-09-03 14:49:03 +02:00
Desire NUENTSA
288e6aab14 Insert XSL styles into output XML files 2012-09-03 10:33:39 +02:00
Chen-Pang He
c4051d3d02 Fix a typo in blas/common.h 2012-09-03 15:31:19 +08:00
giacomo po
751501eade added preconditioner with preconditioned-Lanczos iteration 2012-09-01 21:59:06 +02:00
Chen-Pang He
d4144583bb Write dox for assertions 2012-08-31 21:53:02 +08:00
Chen-Pang He
d23134e4a7 Avoid inefficient 2x2 LU. Move atanh to internal for maintainability. 2012-08-30 23:40:30 +08:00
Gael Guennebaud
9da41cc527 forward resize() function from Array/Matrix-Wrapper to the nested expression such that mat.array().resize(a,b) is now allowed. 2012-08-30 16:28:53 +02:00
giacomo po
5f3880c5a8 some optimization in MINRES, not sure about convergence criterion 2012-08-30 13:10:08 +02:00
Gael Guennebaud
c5031edb92 Fix out-of-range memory access in GEMV (the memory was not used for the computation, only to assemble unaligned packets from aligned packet loads)
(transplanted from 221f54698c
)
2012-08-30 10:52:15 +02:00
giacomo po
064f3eff95 first working version. Still no preconditioning 2012-08-30 10:01:34 +02:00
Chen-Pang He
d0ee31aea6 Fix dox and tabbing 2012-08-29 01:56:23 +08:00
Chen-Pang He
ba4e886376 Tidy up and write dox. 2012-08-28 01:55:13 +08:00
Chen-Pang He
5252d823c9 Optimize matrix power 2012-08-26 02:15:41 +08:00
Chen-Pang He
1cd4279b03 Fix a lot in MatrixPower.h 2012-08-25 01:09:20 +08:00
Jitse Niesen
edc7a09ee7 merge 2012-08-27 22:57:39 +01:00
Chen-Pang He
bfaa7f4ffe Add test for matrix power.
Use Christoph Hertzberg's suggestion to use exponent laws.
2012-08-27 22:48:37 +01:00
Desire NUENTSA W.
fe9956defe Read real and complex bench matrices from a unique folder
Output and display bench results using XML and XSLT
2012-08-27 22:52:43 +02:00
Chen-Pang He
b55d260ada Replace atanh with atanh2 2012-08-27 21:43:41 +01:00
Gael Guennebaud
ebe511334f workaround clang bug (see http://forum.kde.org/viewtopic.php?f=74&t=102653) 2012-08-27 14:50:45 +02:00
Gael Guennebaud
576d62db64 fix a typo in commit 324ecf153b
(regarding MKL on windows)
2012-08-27 13:17:45 +02:00
Gael Guennebaud
75435079ca fix bug #499: the image was missing because of a dependency issue when building/executing the "special" examples 2012-08-27 11:11:25 +02:00
Gael Guennebaud
aa1aa36d6d simplify eigen-doc.tgz file generation, and make it more future proof 2012-08-27 10:56:44 +02:00
Gael Guennebaud
904c2e6cfb remove EXTRACT_ALL 2012-08-27 10:30:10 +02:00
Thomas Capricelli
edc496f087 add piwik code to documentation (web stats engine) 2012-08-21 22:36:29 +02:00
jdh8
1b4aed7255 Fix toc in dox and claim copyright 2012-08-20 03:04:28 +08:00
jdh8
573d88f81c Dox in MatrixFunctions 2012-08-19 18:12:04 +08:00
jdh8
15dabd4db7 Bugfix in MatrixLogarithm.h 2012-08-18 21:28:05 +08:00
Hauke Heibel
42c1b9a8dd Ensured that all branches of MatrixLogarithmAtomic::getPadeDegree return values. 2012-08-18 10:18:31 +02:00
jdh8
f047030104 Add specialization for float and long double 2012-08-18 02:27:47 +08:00
Jitse Niesen
dee866a99a Undo incorrect fix in previous commit, and fix real mistake instead. 2012-08-17 15:36:37 +01:00
Jitse Niesen
5eefca637e Documentation fixes. Thanks to Rodney Sparapani for reporting these. 2012-08-17 14:49:18 +01:00
jdh8
2337ea430b Remove useless code (abort specialization for complex exponent temporarily) 2012-08-15 20:56:03 +08:00
jdh8
4be172d84f matrix power: MatrixBase::pow(RealScalar) and MatrixBase::pow(T) where T is integral type 2012-08-15 00:34:20 +08:00
jdh8
c5800a2452 using std::frexp instead of frexp 2012-08-08 17:50:56 +08:00
jdh8
8cddcaf839 Optimize getting exponent from IEEE floating points. 2012-08-08 01:27:11 +08:00
Desire NUENTSA
63d2dcfb70 Clean the supernodal matrix class 2012-08-07 17:10:42 +02:00
Desire NUENTSA
43f74cb5b1 Bug in 2D block update, disable it for now 2012-08-07 13:55:50 +02:00
Desire NUENTSA
4d3b7e2a13 Add support for Metis fill-reducing ordering ; it is generally more efficient than COLAMD ordering 2012-08-06 14:55:02 +02:00
Gael Guennebaud
a1b405c92e Add missing const in some casts 2012-08-05 10:40:46 +02:00
Gael Guennebaud
af824091be Fix precision regression when attempting to fix underflow issues. 2012-08-05 09:57:31 +02:00
jdh8
93967b0dd6 Fix some typos in MatrixLogarithm to improve accuracy. 2012-08-03 23:54:42 +08:00
Desire NUENTSA
a51806993b Prefix with glu, the global structure 2012-08-03 16:43:12 +02:00
Desire NUENTSA
70db61c269 Prefix with glu, the global structure 2012-08-03 16:36:00 +02:00
Gael Guennebaud
03509d1387 SparseLU: add leverage level3 ops 2012-08-03 15:37:44 +02:00
Gael Guennebaud
48dc95f1da factorize column_dfs and panel_dfs 2012-08-02 18:28:16 +02:00
Desire NUENTSA
7dc39b7037 Add unit tests 2012-08-03 13:05:45 +02:00
Desire NUENTSA
6e8aa96e0f correct bug when solving with multiple Rhs 2012-08-03 13:05:27 +02:00
Gael Guennebaud
c73c3ec2f8 fix bug #495: remove too aggressive EIGEN_FLATTEN_ATTRIB attribute
(after some benchmarking, it was not useful anymore)
2012-08-02 12:22:22 +02:00
Desire NUENTSA
e3ac608e41 bug #493 : multiple calls to FindUmfPack
(transplanted from 1914024965
)
2012-08-02 10:00:23 +02:00
Desire NUENTSA
3a0f5a2a7f Update copyrights sections 2012-08-01 11:40:56 +02:00
Desire NUENTSA
02935b4249 switch to MPL license 2012-08-01 11:38:32 +02:00
Desire NUENTSA
390d6599ba Add missing .noalias() 2012-08-01 11:35:23 +02:00
Gael Guennebaud
7d98c864ff fix warning 2012-08-01 10:44:59 +02:00
Gael Guennebaud
22e0ebbc2c fix lower acceptable bound of SSE pexp for double 2012-07-31 23:11:04 +02:00
Gael Guennebaud
e88817cc51 add another missing .noalias() 2012-07-30 19:28:31 +02:00
Gael Guennebaud
8f6d5eacb4 optimize LU_kernel_bmod for small cases, and add an important .noalias() 2012-07-29 22:26:00 +02:00
Jitse Niesen
696b2f999f Eigenvalues module: Implement setMaxIterations() methods. 2012-07-28 21:30:09 +01:00
Gael Guennebaud
6f54269829 add an example for GeneralizedEigenSolver 2012-07-28 18:00:54 +02:00
Gael Guennebaud
8ab0e16e27 fix various regressions with MKL support 2012-07-28 16:32:43 +02:00
Alexey Korepanov
d937e67b48 RealQZ: added example and some code comments 2012-07-28 08:24:44 -05:00
Hauke Heibel
52a0e0d65e Added a default constructor for Splines which creates zero (constant) splines. 2012-07-28 13:37:29 +02:00
Gael Guennebaud
f23dd7c6f1 Fix typo in the doc: s/Succeeded/Success 2012-07-28 13:07:45 +02:00
Gael Guennebaud
e8aa1f00c5 add SSE pexp function for double, make use of _mm_floor_p* for pexp with SSE4.1 2012-07-27 23:40:04 +02:00
Desire NUENTSA W.
ce30d50e3e Improve the permutation 2012-07-27 16:38:20 +02:00
Gael Guennebaud
6eee2918d9 extend quotient functor to allow for mixed types (complex-real) 2012-07-27 11:56:20 +02:00
Desire NUENTSA W.
c0fa5811ec Refactoring codes for numeric updates 2012-07-27 11:36:58 +02:00
Gael Guennebaud
9e8d2dea80 Add a preliminary GeneralizedEigenSolver computing the eigenvalues of Av=lBv with A and B general real matrices.
Currently only the eigenvalues are reported.
2012-07-26 20:15:17 +02:00
Gael Guennebaud
cfb76b242f RealSchur: improve speed of computeNormOfT 2012-07-26 18:04:58 +02:00
Gael Guennebaud
4e60e2cdf6 RealQZ: improve computeNorms speed, improve shift accuracy (better to do a/b than a*(1/b)),
update API to set the maximum number of iterations
2012-07-26 18:03:10 +02:00
Gael Guennebaud
7518201de8 SparseMatrix: add missing ctor for ReturnByValue 2012-07-25 23:03:10 +02:00
Alexey Korepanov
ea310249f3 RealQZ: bug in pushDownZero fixed too 2012-07-25 12:49:18 -05:00
Alexey Korepanov
a3a9773ab6 RealQZ: bug in splitOffTwoRows fixed 2012-07-25 12:17:00 -05:00
Desire NUENTSA
925ace196c correct bug in the complex version 2012-07-19 18:15:23 +02:00
Desire NUENTSA
59642da88b Add exception handler to memory allocation 2012-07-19 18:03:44 +02:00
Desire NUENTSA
b0cba2d988 Add a draft (not clean ) version of the COLAMD ordering implementation 2012-07-18 16:59:00 +02:00
Jitse Niesen
bf7d986af6 Add static assert that objects on stacks are not too big (bug #491). 2012-07-17 22:15:42 +01:00
Gael Guennebaud
e75b1eb883 Fix aliasing issue in sparse matrix assignment.
(m=-m; or m=m.transpose(); with m sparse work again)
2012-07-25 09:33:50 +02:00
Gael Guennebaud
7b34b5f6f9 do not apply plane rotation when it is exactly the identity 2012-07-24 18:19:56 +02:00
Gael Guennebaud
e7c07de549 RealQZ: optimize general hessenberg to not apply rotations to zero entries. 2012-07-24 18:16:22 +02:00
Gael Guennebaud
c1cab7b8ed real QZ: update license 2012-07-24 18:11:41 +02:00
Desire NUENTSA
773804691a working version of sparse LU with unsymmetric supernodes and fill-reducing permutation 2012-07-13 17:32:25 +02:00
Alexey Korepanov
65db91ac2b Add a RealQZ class: a generalized Schur decomposition for real matrices 2012-07-11 16:38:03 -05:00
Jitse Niesen
ba5eecae53 Allow user to specify max number of iterations (bug #479). 2012-07-24 15:17:59 +01:00
Jitse Niesen
b7ac053b9c Use EISPACK's strategy re max number of iters in Schur decomposition (bug #479). 2012-07-22 22:02:50 +01:00
Jitse Niesen
fd5749f51c LDLT: Report sign consistent with D for indefinite matrices.
See http://forum.kde.org/viewtopic.php?f=74&t=106942
2012-07-22 21:39:38 +01:00
Jitse Niesen
907f4562ac Fix some illegal memory access in sparse conservativeResize() 2012-07-20 22:51:51 +01:00
Benjamin Piwowarski
6bf49ceac2 bug #449: add SparseMatrix::conservativeResize feature 2012-07-19 00:07:06 +02:00
Benoit Jacob
3f08a6a126 add COPYING.MINPACK 2012-07-15 11:46:22 -04:00
Benoit Jacob
df06e5662d MINPACK license is OK for MPL2 after all 2012-07-15 10:30:57 -04:00
Benoit Jacob
f28e95500b add COPYING.README 2012-07-15 10:29:09 -04:00
Benoit Jacob
9bf3ec134e add COPYING.MPL2 2012-07-15 10:20:59 -04:00
Benoit Jacob
b596f6c10c remove outdated "Eigen itself is part of the KDE project" outside of eigen2 files 2012-07-15 10:33:40 -04:00
Jitse Niesen
d3998de472 Silence clang warning about && inside || 2012-07-14 15:50:56 +01:00
Jitse Niesen
4ae3e0a9b8 Evaluators: Fixed bug caused by Diagonal dynamic index change.
This caused the evaluators unit test to fail.
2012-07-14 14:55:04 +01:00
Gael Guennebaud
79214745c7 clean Eigen2Support wrt KDE mentions 2012-07-14 10:15:45 +02:00
Gael Guennebaud
e59f95a9a0 clean old KDE mention and related 2012-07-14 10:04:26 +02:00
Gael Guennebaud
54559094ec document EIGEN_MPL2_ONLY 2012-07-14 09:56:03 +02:00
Gael Guennebaud
46b1c7a0ce fix bug #485: conflict between a typedef and template type parameter 2012-07-13 20:54:38 +02:00
Benoit Jacob
269be00925 Add a EIGEN_MPL2_ONLY build option to generate compiler errors when including non-MPL2 modules 2012-07-13 14:42:47 -04:00
Benoit Jacob
0733e622a3 Manual MPL2 relicensing fixes 2012-07-13 14:42:47 -04:00
Benoit Jacob
69124cfca2 Automatic relicensing to MPL2 using Keirs script. Manual fixup follows. 2012-07-13 14:42:47 -04:00
Keir Mierle
d4ca0963bc Add preliminary script to relicense Eigen to MPL2. 2012-07-11 11:29:52 -07:00
Gael Guennebaud
904ecdf9d8 Add a DynamicIndex constant for signed quantities and use it to fix the conflict
between Diagonal<S,-1> (the first sub diagonal) and a runtime super/sub diagonal which is now:
Diagonal<S,DynamicIndex>
2012-07-10 23:04:17 +02:00
Gael Guennebaud
3e6329a0d9 fix computation of fixed size sub/super diagonal size 2012-07-10 22:39:05 +02:00
Desire NUENTSA
e529bc9cc1 correct bug when applying column permutation 2012-07-10 19:18:50 +02:00
Desire NUENTSA
de2544cc9b working version of sparse LU without fill-reducing permutation 2012-07-10 19:16:57 +02:00
Gael Guennebaud
a2c3003be2 Fix possible underflow issues in SelfAdjointEigenSolver 2012-07-10 09:51:26 +02:00
Desire NUENTSA
3095e4a5f9 Correct bug for triangular solve within supernodes 2012-07-09 19:09:48 +02:00
Gael Guennebaud
ff12a6cd43 fix include path 2012-07-08 16:18:51 +02:00
Desire NUENTSA
b5a83867ca Update Ordering interface 2012-07-06 20:18:16 +02:00
Jitse Niesen
b1b6864c88 Evaluators: Remove member variables if known at compile-time.
Also, use composition instead of inheritance in EvalToTemp evaluator.
2012-07-06 14:50:03 +01:00
Desire NUENTSA
203a0343fd Update Ordering interface 2012-07-06 13:34:06 +02:00
Gael Guennebaud
7bfd8eabff fix compilation with MSVC 2012-07-05 21:58:01 +02:00
Gael Guennebaud
5dbdde0420 Fix bug #480: workaround the Android NDK defining isfinite as a macro 2012-07-05 17:22:25 +02:00
Gael Guennebaud
23df2eed46 bug #481 step 1: add a new Ref<> class for non templated function arguments 2012-07-05 17:00:28 +02:00
Jitse Niesen
60edf02f6f doc: Typo in CustomizingEigen, introduced in previous commit.
Thanks to Christoph Hertzberg for noting this.
2012-07-05 13:56:28 +01:00
Jitse Niesen
37d5825c5a merge 2012-07-05 13:39:06 +01:00
Jitse Niesen
b582b2ebdc doc: Add constructor to example for inheritance.
See "Error in Inheriting Eigen::Vector3d" on forum.
2012-07-05 13:36:02 +01:00
Gael Guennebaud
0a7ce6ad69 fix bug #486: template speacialization of member functions must be declared inline to avoid duplicate references 2012-07-05 13:32:23 +02:00
Jitse Niesen
cb9f3685d3 Move implementation of coeff() &c to Matrix/Array evaluator. 2012-07-05 11:09:42 +01:00
Christoph Hertzberg
03fe095622 bug #488: Add setShift method (and functionality) to Cholmod classes
Also check for Success of numerical factorization
2012-07-04 18:46:14 +02:00
Gael Guennebaud
54d55aeaf6 fix bug #487: isometry * scaling was not compiling 2012-07-04 18:25:07 +02:00
Konstantinos Margaritis
d878cf2227 fix typo 2012-07-04 11:28:59 +03:00
Konstantinos Margaritis
f737536744 fix NEON port, use vget_lane_*() instead of temporary variables (saves extra
load/store), following advice by  Josh Bleecher Snyder <josharian@gmail.com>.
Also implement pmadd() using vmla instead of nested padd/pmul.
2012-07-04 11:12:02 +03:00
Gael Guennebaud
9a97dac4d9 Doc: add an example for HouseholderQR::householderQ() 2012-07-02 16:33:32 +02:00
Gael Guennebaud
eee34f2da4 workaround compilation issue with MSVC 2005 2012-07-02 10:20:44 +02:00
Desire NUENTSA
15f1563533 Before moving to the new building 2012-06-29 17:45:10 +02:00
Jitse Niesen
746378868a Implement A.noalias() = B * C without temporaries
* Wrap expression inside EvalToTemp in copy_using_evaluators() if we
  assume aliasing for that expression (that is, for products)
* Remove temporary kludge of evaluating expression to temporary in
  AllAtOnce traversal
* Implement EvalToTemp expression object
2012-06-29 13:54:09 +01:00
Jitse Niesen
d0b873822f Make product eval-at-once.
* Make product EvalAtOnce in cases OuterProduct, GemmProduct and
  GemvProduct
* Ensure that product evaluators are nested inside EvalToTemp
  evaluator
* As temporary kludge, evaluate expression to temporary in AllAtOnce
  traversal and pass expression operator to evalTo()
2012-06-29 13:49:25 +01:00
Jitse Niesen
2393ceb380 Implement eval-at-once in evaluator.
- Add evaluator_traits with HasEvalTo flag, which is true if evaluator
  has evalTo() function.
- Add AllAtOnce traversal, which calls evalTo() in evaluator.
- If source evaluator in copy_using_evaluator has HasEvalTo set, then
  use AllAtOnce traversal.
2012-06-29 13:32:12 +01:00
Jitse Niesen
c1eb820e50 Implement interface for NoAlias assignments.
* Rename the old copy_using_evaluators to noalias_copy_using_evaluators.
* Write a new copy_using_evaluators which strips NoAlias expression, if present,
  and calls noalias_copy_using_evaluators; in future, it will also take care of
  aliasing in products.
* Add expression() getter to NoAlias.
2012-06-29 13:24:04 +01:00
Jitse Niesen
069fd0e4be Move (part of) evaluation of products to evaluator objects.
* Copy implementation from CoeffBasedProduct.
* Copy implementation from GeneralProduct in InnerProduct case.
* For GeneralProduct in other cases, call the evalTo() member function with
  expression objects in constructor of evaluator.
2012-06-29 13:07:21 +01:00
Gael Guennebaud
9629ba361a bug #482: pass scalar by const ref - pass on the sparse module
(also fix a compilation issue due to previous pass)
2012-06-28 21:01:02 +02:00
Jitse Niesen
23184527fa Resize lhs automatically in copy_using_evaluator(). 2012-06-28 15:25:25 +01:00
Gael Guennebaud
139c91bf30 fix implicit scalar conversion 2012-06-28 13:12:49 +02:00
Gael Guennebaud
a2ace4b79a bug #482: pass scalar arguments by const references. This changeset only concerns the Core and Geometry modules 2012-06-28 02:08:59 +02:00
Gael Guennebaud
cc964b6caf fix performance regression due to check_rows_cols_for_overflow and add appropriate assertions in the PlainObjectBase::resize() functions.
The fix consists in disabling this useless test for statically allocated objects.
2012-06-26 22:16:07 +02:00
Gael Guennebaud
57b5804974 remove dynamic allocation for fixed size object and triangular matrix-matrix products 2012-06-26 17:45:01 +02:00
Jitse Niesen
8994f9962a Fix bug in {Matrix,Array}Wrapper evaluator 2012-06-24 17:35:27 +01:00
Jitse Niesen
d0d077b212 Fix bug in evaluators with sliced vectorization. 2012-06-24 17:33:21 +01:00
Jitse Niesen
172af9facc Fix an evaluator test which was wrong and failed in debug builds. 2012-06-24 17:31:19 +01:00
Gael Guennebaud
7c32904766 typo 2012-06-24 10:13:28 +02:00
Gael Guennebaud
e46fc8c05c fix GMRES 2012-06-23 19:29:21 +02:00
Gael Guennebaud
cc6dd55028 put the resurected files into the Eigen namespace 2012-06-22 16:35:20 +02:00
Gael Guennebaud
62c504e7bf fix most of the shadow warnings in Core/*.h 2012-06-22 16:32:45 +02:00
Gael Guennebaud
5fae6c7848 resurrect expression evaluators 2012-06-22 09:39:35 +02:00
Gael Guennebaud
81e39e1bc6 bump default branch to 3.1.90 2012-06-22 09:27:24 +02:00
Desire NUENTSA
f0c34c6822 Build finished... start debugging 2012-06-15 17:23:54 +02:00
Desire NUENTSA
0c9b08e46e build complete... almost 2012-06-14 18:45:04 +02:00
Desire NUENTSA
f8a0745cb0 Build process... 2012-06-13 18:26:05 +02:00
Desire NUENTSA
c0ad109499 Checking Data structures and function prototypes 2012-06-12 18:19:59 +02:00
Desire NUENTSA
bccf64d342 Checking Syntax... 2012-06-11 18:52:26 +02:00
Desire NUENTSA W.
0591011d5c Sparse LU - End Triangular solve... start debugging 2012-06-10 23:36:38 +02:00
Desire NUENTSA
7bdaa60f6c triangular solve... almost finished 2012-06-08 17:23:38 +02:00
Desire NUENTSA
f091879d77 Memory management 2012-06-07 19:06:22 +02:00
Desire NUENTSA
268ba3b521 Memory expansion and few bugs 2012-06-06 18:23:39 +02:00
Desire NUENTSA
4e5655cc03 Supernodal Matrix 2012-06-01 18:44:51 +02:00
Desire NUENTSA
b26d6b02de Eliminate and prune columns in a panel 2012-05-31 17:10:29 +02:00
Desire NUENTSA
8608d08d65 Symbolic and numeric updates within the panel 2012-05-30 18:09:26 +02:00
Desire NUENTSA
8ab820b5b8 Symbolic and numeric update on a whole panel 2012-05-29 17:55:38 +02:00
Desire NUENTSA
b6267507ea Add preliminary files for SparseLU 2012-05-25 18:17:57 +02:00
1333 changed files with 181537 additions and 68485 deletions

3
.hgeol
View File

@@ -1,6 +1,9 @@
[patterns]
*.sh = LF
*.MINPACK = CRLF
scripts/*.in = LF
debug/msvc/*.dat = CRLF
debug/msvc/*.natvis = CRLF
unsupported/test/mpreal/*.* = CRLF
** = native

View File

@@ -13,7 +13,7 @@ core
core.*
*.bak
*~
build*
*build*
*.moc.*
*.moc
ui_*
@@ -30,3 +30,5 @@ log
patch
a
a.*
lapack/testing
lapack/reference

3
.krazy
View File

@@ -1,3 +0,0 @@
SKIP /disabled/
SKIP /bench/
SKIP /build/

View File

@@ -1,6 +1,6 @@
project(Eigen)
project(Eigen3)
cmake_minimum_required(VERSION 2.6.2)
cmake_minimum_required(VERSION 2.8.5)
# guard against in-source builds
@@ -8,6 +8,11 @@ if(${CMAKE_SOURCE_DIR} STREQUAL ${CMAKE_BINARY_DIR})
message(FATAL_ERROR "In-source builds not allowed. Please make a new directory (called a build directory) and run CMake from there. You may need to remove CMakeCache.txt. ")
endif()
# Alias Eigen_*_DIR to Eigen3_*_DIR:
set(Eigen_SOURCE_DIR ${Eigen3_SOURCE_DIR})
set(Eigen_BINARY_DIR ${Eigen3_BINARY_DIR})
# guard against bad build-type strings
if (NOT CMAKE_BUILD_TYPE)
@@ -36,10 +41,13 @@ string(REGEX MATCH "define[ \t]+EIGEN_MINOR_VERSION[ \t]+([0-9]+)" _eigen_minor_
set(EIGEN_MINOR_VERSION "${CMAKE_MATCH_1}")
set(EIGEN_VERSION_NUMBER ${EIGEN_WORLD_VERSION}.${EIGEN_MAJOR_VERSION}.${EIGEN_MINOR_VERSION})
# if the mercurial program is absent, this will leave the EIGEN_HG_CHANGESET string empty,
# but won't stop CMake.
execute_process(COMMAND hg tip -R ${CMAKE_SOURCE_DIR} OUTPUT_VARIABLE EIGEN_HGTIP_OUTPUT)
execute_process(COMMAND hg branch -R ${CMAKE_SOURCE_DIR} OUTPUT_VARIABLE EIGEN_BRANCH_OUTPUT)
# if we are not in a mercurial clone
if(IS_DIRECTORY ${CMAKE_SOURCE_DIR}/.hg)
# if the mercurial program is absent or this will leave the EIGEN_HG_CHANGESET string empty,
# but won't stop CMake.
execute_process(COMMAND hg tip -R ${CMAKE_SOURCE_DIR} OUTPUT_VARIABLE EIGEN_HGTIP_OUTPUT)
execute_process(COMMAND hg branch -R ${CMAKE_SOURCE_DIR} OUTPUT_VARIABLE EIGEN_BRANCH_OUTPUT)
endif()
# if this is the default (aka development) branch, extract the mercurial changeset number from the hg tip output...
if(EIGEN_BRANCH_OUTPUT MATCHES "default")
@@ -55,6 +63,7 @@ endif(EIGEN_HG_CHANGESET)
include(CheckCXXCompilerFlag)
include(GNUInstallDirs)
set(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake)
@@ -92,9 +101,11 @@ else()
endif()
option(EIGEN_BUILD_BTL "Build benchmark suite" OFF)
if(NOT WIN32)
# Disable pkgconfig only for native Windows builds
if(NOT WIN32 OR NOT CMAKE_HOST_SYSTEM_NAME MATCHES Windows)
option(EIGEN_BUILD_PKGCONFIG "Build pkg-config .pc file for Eigen" ON)
endif(NOT WIN32)
endif()
set(CMAKE_INCLUDE_CURRENT_DIR ON)
@@ -105,26 +116,80 @@ if(EIGEN_DEFAULT_TO_ROW_MAJOR)
add_definitions("-DEIGEN_DEFAULT_TO_ROW_MAJOR")
endif()
add_definitions("-DEIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS")
set(EIGEN_TEST_MAX_SIZE "320" CACHE STRING "Maximal matrix/vector size, default is 320")
if(CMAKE_COMPILER_IS_GNUCXX)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wnon-virtual-dtor -Wno-long-long -ansi -Wundef -Wcast-align -Wchar-subscripts -Wall -W -Wpointer-arith -Wwrite-strings -Wformat-security -fexceptions -fno-check-new -fno-common -fstrict-aliasing")
set(CMAKE_CXX_FLAGS_DEBUG "-g3")
set(CMAKE_CXX_FLAGS_RELEASE "-g0 -O2")
macro(ei_add_cxx_compiler_flag FLAG)
string(REGEX REPLACE "-" "" SFLAG1 ${FLAG})
string(REGEX REPLACE "\\+" "p" SFLAG ${SFLAG1})
check_cxx_compiler_flag(${FLAG} COMPILER_SUPPORT_${SFLAG})
if(COMPILER_SUPPORT_${SFLAG})
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${FLAG}")
endif()
endmacro(ei_add_cxx_compiler_flag)
check_cxx_compiler_flag("-Wno-variadic-macros" COMPILER_SUPPORT_WNOVARIADICMACRO)
if(COMPILER_SUPPORT_WNOVARIADICMACRO)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-variadic-macros")
if(NOT MSVC)
# We assume that other compilers are partly compatible with GNUCC
# clang outputs some warnings for unknown flags that are not caught by check_cxx_compiler_flag
# adding -Werror turns such warnings into errors
check_cxx_compiler_flag("-Werror" COMPILER_SUPPORT_WERROR)
if(COMPILER_SUPPORT_WERROR)
set(CMAKE_REQUIRED_FLAGS "-Werror")
endif()
ei_add_cxx_compiler_flag("-pedantic")
ei_add_cxx_compiler_flag("-Wall")
ei_add_cxx_compiler_flag("-Wextra")
#ei_add_cxx_compiler_flag("-Weverything") # clang
ei_add_cxx_compiler_flag("-Wundef")
ei_add_cxx_compiler_flag("-Wcast-align")
ei_add_cxx_compiler_flag("-Wchar-subscripts")
ei_add_cxx_compiler_flag("-Wnon-virtual-dtor")
ei_add_cxx_compiler_flag("-Wunused-local-typedefs")
ei_add_cxx_compiler_flag("-Wpointer-arith")
ei_add_cxx_compiler_flag("-Wwrite-strings")
ei_add_cxx_compiler_flag("-Wformat-security")
ei_add_cxx_compiler_flag("-Wshorten-64-to-32")
ei_add_cxx_compiler_flag("-Wlogical-op")
ei_add_cxx_compiler_flag("-Wenum-conversion")
ei_add_cxx_compiler_flag("-Wc++11-extensions")
ei_add_cxx_compiler_flag("-Wdouble-promotion")
# ei_add_cxx_compiler_flag("-Wconversion")
# -Wshadow is insanely too strict with gcc, hopefully it will become usable with gcc 6
# if(NOT CMAKE_COMPILER_IS_GNUCXX OR (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "5.0.0"))
if(NOT CMAKE_COMPILER_IS_GNUCXX)
ei_add_cxx_compiler_flag("-Wshadow")
endif()
ei_add_cxx_compiler_flag("-Wno-psabi")
ei_add_cxx_compiler_flag("-Wno-variadic-macros")
ei_add_cxx_compiler_flag("-Wno-long-long")
ei_add_cxx_compiler_flag("-fno-check-new")
ei_add_cxx_compiler_flag("-fno-common")
ei_add_cxx_compiler_flag("-fstrict-aliasing")
ei_add_cxx_compiler_flag("-wd981") # disable ICC's "operands are evaluated in unspecified order" remark
ei_add_cxx_compiler_flag("-wd2304") # disable ICC's "warning #2304: non-explicit constructor with single argument may cause implicit type conversion" produced by -Wnon-virtual-dtor
# The -ansi flag must be added last, otherwise it is also used as a linker flag by check_cxx_compiler_flag making it fails
# Moreover we should not set both -strict-ansi and -ansi
check_cxx_compiler_flag("-strict-ansi" COMPILER_SUPPORT_STRICTANSI)
ei_add_cxx_compiler_flag("-Qunused-arguments") # disable clang warning: argument unused during compilation: '-ansi'
if(COMPILER_SUPPORT_STRICTANSI)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -strict-ansi")
else()
ei_add_cxx_compiler_flag("-ansi")
endif()
check_cxx_compiler_flag("-Wextra" COMPILER_SUPPORT_WEXTRA)
if(COMPILER_SUPPORT_WEXTRA)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wextra")
if(ANDROID_NDK)
ei_add_cxx_compiler_flag("-pie")
ei_add_cxx_compiler_flag("-fPIE")
endif()
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pedantic")
set(CMAKE_REQUIRED_FLAGS "")
option(EIGEN_TEST_SSE2 "Enable/Disable SSE2 in tests/examples" OFF)
if(EIGEN_TEST_SSE2)
@@ -156,18 +221,65 @@ if(CMAKE_COMPILER_IS_GNUCXX)
message(STATUS "Enabling SSE4.2 in tests/examples")
endif()
option(EIGEN_TEST_AVX "Enable/Disable AVX in tests/examples" OFF)
if(EIGEN_TEST_AVX)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mavx")
message(STATUS "Enabling AVX in tests/examples")
endif()
option(EIGEN_TEST_FMA "Enable/Disable FMA in tests/examples" OFF)
if(EIGEN_TEST_FMA AND NOT EIGEN_TEST_NEON)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mfma")
message(STATUS "Enabling FMA in tests/examples")
endif()
option(EIGEN_TEST_AVX512 "Enable/Disable AVX512 in tests/examples" OFF)
if(EIGEN_TEST_AVX512)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mavx512f -fabi-version=6 -DEIGEN_ENABLE_AVX512")
message(STATUS "Enabling AVX512 in tests/examples")
endif()
option(EIGEN_TEST_F16C "Enable/Disable F16C in tests/examples" OFF)
if(EIGEN_TEST_F16C)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mf16c")
message(STATUS "Enabling F16C in tests/examples")
endif()
option(EIGEN_TEST_ALTIVEC "Enable/Disable AltiVec in tests/examples" OFF)
if(EIGEN_TEST_ALTIVEC)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -maltivec -mabi=altivec")
message(STATUS "Enabling AltiVec in tests/examples")
endif()
option(EIGEN_TEST_VSX "Enable/Disable VSX in tests/examples" OFF)
if(EIGEN_TEST_VSX)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -m64 -mvsx")
message(STATUS "Enabling VSX in tests/examples")
endif()
option(EIGEN_TEST_NEON "Enable/Disable Neon in tests/examples" OFF)
if(EIGEN_TEST_NEON)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mfpu=neon -mcpu=cortex-a8")
if(EIGEN_TEST_FMA)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mfpu=neon-vfpv4")
else()
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mfpu=neon")
endif()
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mfloat-abi=hard")
message(STATUS "Enabling NEON in tests/examples")
endif()
option(EIGEN_TEST_NEON64 "Enable/Disable Neon in tests/examples" OFF)
if(EIGEN_TEST_NEON64)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
message(STATUS "Enabling NEON in tests/examples")
endif()
option(EIGEN_TEST_ZVECTOR "Enable/Disable S390X(zEC13) ZVECTOR in tests/examples" OFF)
if(EIGEN_TEST_ZVECTOR)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -march=z13 -mzvector")
message(STATUS "Enabling S390X(zEC13) ZVECTOR in tests/examples")
endif()
check_cxx_compiler_flag("-fopenmp" COMPILER_SUPPORT_OPENMP)
if(COMPILER_SUPPORT_OPENMP)
option(EIGEN_TEST_OPENMP "Enable/Disable OpenMP in tests/examples" OFF)
@@ -177,9 +289,8 @@ if(CMAKE_COMPILER_IS_GNUCXX)
endif()
endif()
endif(CMAKE_COMPILER_IS_GNUCXX)
else(NOT MSVC)
if(MSVC)
# C4127 - conditional expression is constant
# C4714 - marked as __forceinline not inlined (I failed to deactivate it selectively)
# We can disable this warning in the unit tests since it is clear that it occurs
@@ -209,7 +320,7 @@ if(MSVC)
endif(NOT CMAKE_CL_64)
message(STATUS "Enabling SSE2 in tests/examples")
endif(EIGEN_TEST_SSE2)
endif(MSVC)
endif(NOT MSVC)
option(EIGEN_TEST_NO_EXPLICIT_VECTORIZATION "Disable explicit vectorization in tests/examples" OFF)
option(EIGEN_TEST_X87 "Force using X87 instructions. Implies no vectorization." OFF)
@@ -245,28 +356,41 @@ if(EIGEN_TEST_NO_EXPLICIT_ALIGNMENT)
message(STATUS "Disabling alignment in tests/examples")
endif()
option(EIGEN_TEST_C++0x "Enables all C++0x features." OFF)
option(EIGEN_TEST_NO_EXCEPTIONS "Disables C++ exceptions" OFF)
if(EIGEN_TEST_NO_EXCEPTIONS)
ei_add_cxx_compiler_flag("-fno-exceptions")
message(STATUS "Disabling exceptions in tests/examples")
endif()
option(EIGEN_TEST_CXX11 "Enable testing with C++11 and C++11 features (e.g. Tensor module)." OFF)
set(EIGEN_CUDA_COMPUTE_ARCH 30 CACHE STRING "The CUDA compute architecture level to target when compiling CUDA code")
include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
# the user modifiable install path for header files
set(EIGEN_INCLUDE_INSTALL_DIR ${EIGEN_INCLUDE_INSTALL_DIR} CACHE PATH "The directory where we install the header files (optional)")
# set the internal install path for header files which depends on wether the user modifiable
# EIGEN_INCLUDE_INSTALL_DIR has been set by the user or not.
# Backward compatibility support for EIGEN_INCLUDE_INSTALL_DIR
if(EIGEN_INCLUDE_INSTALL_DIR)
set(INCLUDE_INSTALL_DIR
${EIGEN_INCLUDE_INSTALL_DIR}
CACHE INTERNAL
"The directory where we install the header files (internal)"
)
message(WARNING "EIGEN_INCLUDE_INSTALL_DIR is deprecated. Use INCLUDE_INSTALL_DIR instead.")
endif()
if(EIGEN_INCLUDE_INSTALL_DIR AND NOT INCLUDE_INSTALL_DIR)
set(INCLUDE_INSTALL_DIR ${EIGEN_INCLUDE_INSTALL_DIR}
CACHE PATH "The directory relative to CMAKE_PREFIX_PATH where Eigen header files are installed")
else()
set(INCLUDE_INSTALL_DIR
"${CMAKE_INSTALL_PREFIX}/include/eigen3"
CACHE INTERNAL
"The directory where we install the header files (internal)"
)
"${CMAKE_INSTALL_INCLUDEDIR}/eigen3"
CACHE PATH "The directory relative to CMAKE_PREFIX_PATH where Eigen header files are installed"
)
endif()
set(CMAKEPACKAGE_INSTALL_DIR
"${CMAKE_INSTALL_DATADIR}/eigen3/cmake"
CACHE PATH "The directory relative to CMAKE_PREFIX_PATH where Eigen3Config.cmake is installed"
)
set(PKGCONFIG_INSTALL_DIR
"${CMAKE_INSTALL_DATADIR}/pkgconfig"
CACHE PATH "The directory relative to CMAKE_PREFIX_PATH where eigen3.pc is installed"
)
# similar to set_target_properties but append the property instead of overwriting it
macro(ei_add_target_property target prop value)
@@ -285,37 +409,25 @@ install(FILES
)
if(EIGEN_BUILD_PKGCONFIG)
SET(path_separator ":")
STRING(REPLACE ${path_separator} ";" pkg_config_libdir_search "$ENV{PKG_CONFIG_LIBDIR}")
message(STATUS "searching for 'pkgconfig' directory in PKG_CONFIG_LIBDIR ( $ENV{PKG_CONFIG_LIBDIR} ), ${CMAKE_INSTALL_PREFIX}/share, and ${CMAKE_INSTALL_PREFIX}/lib")
FIND_PATH(pkg_config_libdir pkgconfig ${pkg_config_libdir_search} ${CMAKE_INSTALL_PREFIX}/share ${CMAKE_INSTALL_PREFIX}/lib ${pkg_config_libdir_search})
if(pkg_config_libdir)
SET(pkg_config_install_dir ${pkg_config_libdir})
message(STATUS "found ${pkg_config_libdir}/pkgconfig" )
else(pkg_config_libdir)
SET(pkg_config_install_dir ${CMAKE_INSTALL_PREFIX}/share)
message(STATUS "pkgconfig not found; installing in ${pkg_config_install_dir}" )
endif(pkg_config_libdir)
configure_file(eigen3.pc.in eigen3.pc)
configure_file(eigen3.pc.in eigen3.pc @ONLY)
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/eigen3.pc
DESTINATION ${pkg_config_install_dir}/pkgconfig
DESTINATION ${PKGCONFIG_INSTALL_DIR}
)
endif(EIGEN_BUILD_PKGCONFIG)
endif()
add_subdirectory(Eigen)
add_subdirectory(doc EXCLUDE_FROM_ALL)
include(EigenConfigureTesting)
# fixme, not sure this line is still needed:
enable_testing() # must be called from the root CMakeLists, see man page
option(BUILD_TESTING "Enable creation of Eigen tests." ON)
if(BUILD_TESTING)
include(EigenConfigureTesting)
if(EIGEN_LEAVE_TEST_IN_ALL_TARGET)
add_subdirectory(test) # can't do EXCLUDE_FROM_ALL here, breaks CTest
else()
add_subdirectory(test EXCLUDE_FROM_ALL)
if(EIGEN_LEAVE_TEST_IN_ALL_TARGET)
add_subdirectory(test) # can't do EXCLUDE_FROM_ALL here, breaks CTest
else()
add_subdirectory(test EXCLUDE_FROM_ALL)
endif()
endif()
if(EIGEN_LEAVE_TEST_IN_ALL_TARGET)
@@ -326,6 +438,13 @@ else()
add_subdirectory(lapack EXCLUDE_FROM_ALL)
endif()
# add SYCL
option(EIGEN_TEST_SYCL "Add Sycl support." OFF)
if(EIGEN_TEST_SYCL)
set (CMAKE_MODULE_PATH "${CMAKE_ROOT}/Modules" "cmake/Modules/" "${CMAKE_MODULE_PATH}")
include(FindComputeCpp)
endif()
add_subdirectory(unsupported)
add_subdirectory(demos EXCLUDE_FROM_ALL)
@@ -342,7 +461,11 @@ if(NOT WIN32)
add_subdirectory(bench/spbench EXCLUDE_FROM_ALL)
endif(NOT WIN32)
ei_testing_print_summary()
configure_file(scripts/cdashtesting.cmake.in cdashtesting.cmake @ONLY)
if(BUILD_TESTING)
ei_testing_print_summary()
endif()
message(STATUS "")
message(STATUS "Configured Eigen ${EIGEN_VERSION_NUMBER}")
@@ -359,16 +482,20 @@ if(cmake_generator_tolower MATCHES "makefile")
message(STATUS "--------------+--------------------------------------------------------------")
message(STATUS "Command | Description")
message(STATUS "--------------+--------------------------------------------------------------")
message(STATUS "make install | Install to ${CMAKE_INSTALL_PREFIX}. To change that:")
message(STATUS " | cmake . -DCMAKE_INSTALL_PREFIX=yourpath")
message(STATUS " | Eigen headers will then be installed to:")
message(STATUS " | ${INCLUDE_INSTALL_DIR}")
message(STATUS " | To install Eigen headers to a separate location, do:")
message(STATUS " | cmake . -DEIGEN_INCLUDE_INSTALL_DIR=yourpath")
message(STATUS "make install | Install Eigen. Headers will be installed to:")
message(STATUS " | <CMAKE_INSTALL_PREFIX>/<INCLUDE_INSTALL_DIR>")
message(STATUS " | Using the following values:")
message(STATUS " | CMAKE_INSTALL_PREFIX: ${CMAKE_INSTALL_PREFIX}")
message(STATUS " | INCLUDE_INSTALL_DIR: ${INCLUDE_INSTALL_DIR}")
message(STATUS " | Change the install location of Eigen headers using:")
message(STATUS " | cmake . -DCMAKE_INSTALL_PREFIX=yourprefix")
message(STATUS " | Or:")
message(STATUS " | cmake . -DINCLUDE_INSTALL_DIR=yourdir")
message(STATUS "make doc | Generate the API documentation, requires Doxygen & LaTeX")
message(STATUS "make check | Build and run the unit-tests. Read this page:")
message(STATUS " | http://eigen.tuxfamily.org/index.php?title=Tests")
message(STATUS "make blas | Build BLAS library (not the same thing as Eigen)")
message(STATUS "make uninstall| Removes files installed by make install")
message(STATUS "--------------+--------------------------------------------------------------")
else()
message(STATUS "To build/run the unit tests, read this page:")
@@ -376,3 +503,98 @@ else()
endif()
message(STATUS "")
set ( EIGEN_VERSION_STRING ${EIGEN_VERSION_NUMBER} )
set ( EIGEN_VERSION_MAJOR ${EIGEN_WORLD_VERSION} )
set ( EIGEN_VERSION_MINOR ${EIGEN_MAJOR_VERSION} )
set ( EIGEN_VERSION_PATCH ${EIGEN_MINOR_VERSION} )
set ( EIGEN_DEFINITIONS "")
set ( EIGEN_INCLUDE_DIR "${CMAKE_INSTALL_PREFIX}/${INCLUDE_INSTALL_DIR}" )
set ( EIGEN_ROOT_DIR ${CMAKE_INSTALL_PREFIX} )
# Interface libraries require at least CMake 3.0
if (NOT CMAKE_VERSION VERSION_LESS 3.0)
include (CMakePackageConfigHelpers)
# Imported target support
add_library (eigen INTERFACE)
target_compile_definitions (eigen INTERFACE ${EIGEN_DEFINITIONS})
target_include_directories (eigen INTERFACE
$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}>
$<INSTALL_INTERFACE:${INCLUDE_INSTALL_DIR}>
)
# Export as title case Eigen
set_target_properties (eigen PROPERTIES EXPORT_NAME Eigen)
install (TARGETS eigen EXPORT Eigen3Targets)
configure_package_config_file (
${CMAKE_CURRENT_SOURCE_DIR}/cmake/Eigen3Config.cmake.in
${CMAKE_CURRENT_BINARY_DIR}/Eigen3Config.cmake
PATH_VARS EIGEN_INCLUDE_DIR EIGEN_ROOT_DIR
INSTALL_DESTINATION ${CMAKEPACKAGE_INSTALL_DIR}
NO_CHECK_REQUIRED_COMPONENTS_MACRO # Eigen does not provide components
)
# Remove CMAKE_SIZEOF_VOID_P from Eigen3ConfigVersion.cmake since Eigen does
# not depend on architecture specific settings or libraries. More
# specifically, an Eigen3Config.cmake generated from a 64 bit target can be
# used for 32 bit targets as well (and vice versa).
set (_Eigen3_CMAKE_SIZEOF_VOID_P ${CMAKE_SIZEOF_VOID_P})
unset (CMAKE_SIZEOF_VOID_P)
write_basic_package_version_file (Eigen3ConfigVersion.cmake
VERSION ${EIGEN_VERSION_NUMBER}
COMPATIBILITY SameMajorVersion)
set (CMAKE_SIZEOF_VOID_P ${_Eigen3_CMAKE_SIZEOF_VOID_P})
# The Eigen target will be located in the Eigen3 namespace. Other CMake
# targets can refer to it using Eigen3::Eigen.
export (TARGETS eigen NAMESPACE Eigen3:: FILE Eigen3Targets.cmake)
# Export Eigen3 package to CMake registry such that it can be easily found by
# CMake even if it has not been installed to a standard directory.
export (PACKAGE Eigen3)
install (EXPORT Eigen3Targets NAMESPACE Eigen3:: DESTINATION ${CMAKEPACKAGE_INSTALL_DIR})
else (NOT CMAKE_VERSION VERSION_LESS 3.0)
# Fallback to legacy Eigen3Config.cmake without the imported target
# If CMakePackageConfigHelpers module is available (CMake >= 2.8.8)
# create a relocatable Config file, otherwise leave the hardcoded paths
include(CMakePackageConfigHelpers OPTIONAL RESULT_VARIABLE CPCH_PATH)
if(CPCH_PATH)
configure_package_config_file (
${CMAKE_CURRENT_SOURCE_DIR}/cmake/Eigen3ConfigLegacy.cmake.in
${CMAKE_CURRENT_BINARY_DIR}/Eigen3Config.cmake
PATH_VARS EIGEN_INCLUDE_DIR EIGEN_ROOT_DIR
INSTALL_DESTINATION ${CMAKEPACKAGE_INSTALL_DIR}
NO_CHECK_REQUIRED_COMPONENTS_MACRO # Eigen does not provide components
)
else()
# The PACKAGE_* variables are defined by the configure_package_config_file
# but without it we define them manually to the hardcoded paths
set(PACKAGE_INIT "")
set(PACKAGE_EIGEN_INCLUDE_DIR ${EIGEN_INCLUDE_DIR})
set(PACKAGE_EIGEN_ROOT_DIR ${EIGEN_ROOT_DIR})
configure_file ( ${CMAKE_CURRENT_SOURCE_DIR}/cmake/Eigen3ConfigLegacy.cmake.in
${CMAKE_CURRENT_BINARY_DIR}/Eigen3Config.cmake
@ONLY ESCAPE_QUOTES )
endif()
write_basic_package_version_file( Eigen3ConfigVersion.cmake
VERSION ${EIGEN_VERSION_NUMBER}
COMPATIBILITY SameMajorVersion )
endif (NOT CMAKE_VERSION VERSION_LESS 3.0)
install ( FILES ${CMAKE_CURRENT_SOURCE_DIR}/cmake/UseEigen3.cmake
${CMAKE_CURRENT_BINARY_DIR}/Eigen3Config.cmake
${CMAKE_CURRENT_BINARY_DIR}/Eigen3ConfigVersion.cmake
DESTINATION ${CMAKEPACKAGE_INSTALL_DIR} )
# Add uninstall target
add_custom_target ( uninstall
COMMAND ${CMAKE_COMMAND} -P ${CMAKE_CURRENT_SOURCE_DIR}/cmake/EigenUninstall.cmake)

View File

@@ -1,165 +1,502 @@
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Version 3, 29 June 2007
Version 2.1, February 1999
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Copyright (C) 1991, 1999 Free Software Foundation, Inc.
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Everyone is permitted to copy and distribute verbatim copies
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based on the Library, uncombined with any other library
facilities. This must be distributed under the terms of the
Sections above.
b) Give prominent notice with the combined library of the fact
that part of it is a work based on the Library, and explaining
where to find the accompanying uncombined form of the same work.
8. You may not copy, modify, sublicense, link with, or distribute
the Library except as expressly provided under this License. Any
attempt otherwise to copy, modify, sublicense, link with, or
distribute the Library is void, and will automatically terminate your
rights under this License. However, parties who have received copies,
or rights, from you under this License will not have their licenses
terminated so long as such parties remain in full compliance.
9. You are not required to accept this License, since you have not
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distribute the Library or its derivative works. These actions are
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modifying or distributing the Library (or any work based on the
Library), you indicate your acceptance of this License to do so, and
all its terms and conditions for copying, distributing or modifying
the Library or works based on it.
10. Each time you redistribute the Library (or any work based on the
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If any portion of this section is held invalid or unenforceable under any
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This section is intended to make thoroughly clear what is believed to
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12. If the distribution and/or use of the Library is restricted in
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an explicit geographical distribution limitation excluding those countries,
so that distribution is permitted only in or among countries not thus
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13. The Free Software Foundation may publish revised and/or new
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Such new versions will be similar in spirit to the present version,
but may differ in detail to address new problems or concerns.
Each version is given a distinguishing version number. If the Library
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14. If you wish to incorporate parts of the Library into other free
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decision will be guided by the two goals of preserving the free status
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NO WARRANTY
15. BECAUSE THE LIBRARY IS LICENSED FREE OF CHARGE, THERE IS NO
WARRANTY FOR THE LIBRARY, TO THE EXTENT PERMITTED BY APPLICABLE LAW.
EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR
OTHER PARTIES PROVIDE THE LIBRARY "AS IS" WITHOUT WARRANTY OF ANY
KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE
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PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE
LIBRARY IS WITH YOU. SHOULD THE LIBRARY PROVE DEFECTIVE, YOU ASSUME
THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN
WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY
AND/OR REDISTRIBUTE THE LIBRARY AS PERMITTED ABOVE, BE LIABLE TO YOU
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CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE
LIBRARY (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING
RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A
FAILURE OF THE LIBRARY TO OPERATE WITH ANY OTHER SOFTWARE), EVEN IF
SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH
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END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Libraries
If you develop a new library, and you want it to be of the greatest
possible use to the public, we recommend making it free software that
everyone can redistribute and change. You can do so by permitting
redistribution under these terms (or, alternatively, under the terms of the
ordinary General Public License).
To apply these terms, attach the following notices to the library. It is
safest to attach them to the start of each source file to most effectively
convey the exclusion of warranty; and each file should have at least the
"copyright" line and a pointer to where the full notice is found.
<one line to give the library's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Also add information on how to contact you by electronic and paper mail.
You should also get your employer (if you work as a programmer) or your
school, if any, to sign a "copyright disclaimer" for the library, if
necessary. Here is a sample; alter the names:
Yoyodyne, Inc., hereby disclaims all copyright interest in the
library `Frob' (a library for tweaking knobs) written by James Random Hacker.
<signature of Ty Coon>, 1 April 1990
Ty Coon, President of Vice
That's all there is to it!

52
COPYING.MINPACK Normal file
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@@ -0,0 +1,52 @@
Minpack Copyright Notice (1999) University of Chicago. All rights reserved
Redistribution and use in source and binary forms, with or
without modification, are permitted provided that the
following conditions are met:
1. Redistributions of source code must retain the above
copyright notice, this list of conditions and the following
disclaimer.
2. Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following
disclaimer in the documentation and/or other materials
provided with the distribution.
3. The end-user documentation included with the
redistribution, if any, must include the following
acknowledgment:
"This product includes software developed by the
University of Chicago, as Operator of Argonne National
Laboratory.
Alternately, this acknowledgment may appear in the software
itself, if and wherever such third-party acknowledgments
normally appear.
4. WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
BE CORRECTED.
5. LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
(INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
POSSIBILITY OF SUCH LOSS OR DAMAGES.

373
COPYING.MPL2 Normal file
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@@ -0,0 +1,373 @@
Mozilla Public License Version 2.0
==================================
1. Definitions
--------------
1.1. "Contributor"
means each individual or legal entity that creates, contributes to
the creation of, or owns Covered Software.
1.2. "Contributor Version"
means the combination of the Contributions of others (if any) used
by a Contributor and that particular Contributor's Contribution.
1.3. "Contribution"
means Covered Software of a particular Contributor.
1.4. "Covered Software"
means Source Code Form to which the initial Contributor has attached
the notice in Exhibit A, the Executable Form of such Source Code
Form, and Modifications of such Source Code Form, in each case
including portions thereof.
1.5. "Incompatible With Secondary Licenses"
means
(a) that the initial Contributor has attached the notice described
in Exhibit B to the Covered Software; or
(b) that the Covered Software was made available under the terms of
version 1.1 or earlier of the License, but not also under the
terms of a Secondary License.
1.6. "Executable Form"
means any form of the work other than Source Code Form.
1.7. "Larger Work"
means a work that combines Covered Software with other material, in
a separate file or files, that is not Covered Software.
1.8. "License"
means this document.
1.9. "Licensable"
means having the right to grant, to the maximum extent possible,
whether at the time of the initial grant or subsequently, any and
all of the rights conveyed by this License.
1.10. "Modifications"
means any of the following:
(a) any file in Source Code Form that results from an addition to,
deletion from, or modification of the contents of Covered
Software; or
(b) any new file in Source Code Form that contains any Covered
Software.
1.11. "Patent Claims" of a Contributor
means any patent claim(s), including without limitation, method,
process, and apparatus claims, in any patent Licensable by such
Contributor that would be infringed, but for the grant of the
License, by the making, using, selling, offering for sale, having
made, import, or transfer of either its Contributions or its
Contributor Version.
1.12. "Secondary License"
means either the GNU General Public License, Version 2.0, the GNU
Lesser General Public License, Version 2.1, the GNU Affero General
Public License, Version 3.0, or any later versions of those
licenses.
1.13. "Source Code Form"
means the form of the work preferred for making modifications.
1.14. "You" (or "Your")
means an individual or a legal entity exercising rights under this
License. For legal entities, "You" includes any entity that
controls, is controlled by, or is under common control with You. For
purposes of this definition, "control" means (a) the power, direct
or indirect, to cause the direction or management of such entity,
whether by contract or otherwise, or (b) ownership of more than
fifty percent (50%) of the outstanding shares or beneficial
ownership of such entity.
2. License Grants and Conditions
--------------------------------
2.1. Grants
Each Contributor hereby grants You a world-wide, royalty-free,
non-exclusive license:
(a) under intellectual property rights (other than patent or trademark)
Licensable by such Contributor to use, reproduce, make available,
modify, display, perform, distribute, and otherwise exploit its
Contributions, either on an unmodified basis, with Modifications, or
as part of a Larger Work; and
(b) under Patent Claims of such Contributor to make, use, sell, offer
for sale, have made, import, and otherwise transfer either its
Contributions or its Contributor Version.
2.2. Effective Date
The licenses granted in Section 2.1 with respect to any Contribution
become effective for each Contribution on the date the Contributor first
distributes such Contribution.
2.3. Limitations on Grant Scope
The licenses granted in this Section 2 are the only rights granted under
this License. No additional rights or licenses will be implied from the
distribution or licensing of Covered Software under this License.
Notwithstanding Section 2.1(b) above, no patent license is granted by a
Contributor:
(a) for any code that a Contributor has removed from Covered Software;
or
(b) for infringements caused by: (i) Your and any other third party's
modifications of Covered Software, or (ii) the combination of its
Contributions with other software (except as part of its Contributor
Version); or
(c) under Patent Claims infringed by Covered Software in the absence of
its Contributions.
This License does not grant any rights in the trademarks, service marks,
or logos of any Contributor (except as may be necessary to comply with
the notice requirements in Section 3.4).
2.4. Subsequent Licenses
No Contributor makes additional grants as a result of Your choice to
distribute the Covered Software under a subsequent version of this
License (see Section 10.2) or under the terms of a Secondary License (if
permitted under the terms of Section 3.3).
2.5. Representation
Each Contributor represents that the Contributor believes its
Contributions are its original creation(s) or it has sufficient rights
to grant the rights to its Contributions conveyed by this License.
2.6. Fair Use
This License is not intended to limit any rights You have under
applicable copyright doctrines of fair use, fair dealing, or other
equivalents.
2.7. Conditions
Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted
in Section 2.1.
3. Responsibilities
-------------------
3.1. Distribution of Source Form
All distribution of Covered Software in Source Code Form, including any
Modifications that You create or to which You contribute, must be under
the terms of this License. You must inform recipients that the Source
Code Form of the Covered Software is governed by the terms of this
License, and how they can obtain a copy of this License. You may not
attempt to alter or restrict the recipients' rights in the Source Code
Form.
3.2. Distribution of Executable Form
If You distribute Covered Software in Executable Form then:
(a) such Covered Software must also be made available in Source Code
Form, as described in Section 3.1, and You must inform recipients of
the Executable Form how they can obtain a copy of such Source Code
Form by reasonable means in a timely manner, at a charge no more
than the cost of distribution to the recipient; and
(b) You may distribute such Executable Form under the terms of this
License, or sublicense it under different terms, provided that the
license for the Executable Form does not attempt to limit or alter
the recipients' rights in the Source Code Form under this License.
3.3. Distribution of a Larger Work
You may create and distribute a Larger Work under terms of Your choice,
provided that You also comply with the requirements of this License for
the Covered Software. If the Larger Work is a combination of Covered
Software with a work governed by one or more Secondary Licenses, and the
Covered Software is not Incompatible With Secondary Licenses, this
License permits You to additionally distribute such Covered Software
under the terms of such Secondary License(s), so that the recipient of
the Larger Work may, at their option, further distribute the Covered
Software under the terms of either this License or such Secondary
License(s).
3.4. Notices
You may not remove or alter the substance of any license notices
(including copyright notices, patent notices, disclaimers of warranty,
or limitations of liability) contained within the Source Code Form of
the Covered Software, except that You may alter any license notices to
the extent required to remedy known factual inaccuracies.
3.5. Application of Additional Terms
You may choose to offer, and to charge a fee for, warranty, support,
indemnity or liability obligations to one or more recipients of Covered
Software. However, You may do so only on Your own behalf, and not on
behalf of any Contributor. You must make it absolutely clear that any
such warranty, support, indemnity, or liability obligation is offered by
You alone, and You hereby agree to indemnify every Contributor for any
liability incurred by such Contributor as a result of warranty, support,
indemnity or liability terms You offer. You may include additional
disclaimers of warranty and limitations of liability specific to any
jurisdiction.
4. Inability to Comply Due to Statute or Regulation
---------------------------------------------------
If it is impossible for You to comply with any of the terms of this
License with respect to some or all of the Covered Software due to
statute, judicial order, or regulation then You must: (a) comply with
the terms of this License to the maximum extent possible; and (b)
describe the limitations and the code they affect. Such description must
be placed in a text file included with all distributions of the Covered
Software under this License. Except to the extent prohibited by statute
or regulation, such description must be sufficiently detailed for a
recipient of ordinary skill to be able to understand it.
5. Termination
--------------
5.1. The rights granted under this License will terminate automatically
if You fail to comply with any of its terms. However, if You become
compliant, then the rights granted under this License from a particular
Contributor are reinstated (a) provisionally, unless and until such
Contributor explicitly and finally terminates Your grants, and (b) on an
ongoing basis, if such Contributor fails to notify You of the
non-compliance by some reasonable means prior to 60 days after You have
come back into compliance. Moreover, Your grants from a particular
Contributor are reinstated on an ongoing basis if such Contributor
notifies You of the non-compliance by some reasonable means, this is the
first time You have received notice of non-compliance with this License
from such Contributor, and You become compliant prior to 30 days after
Your receipt of the notice.
5.2. If You initiate litigation against any entity by asserting a patent
infringement claim (excluding declaratory judgment actions,
counter-claims, and cross-claims) alleging that a Contributor Version
directly or indirectly infringes any patent, then the rights granted to
You by any and all Contributors for the Covered Software under Section
2.1 of this License shall terminate.
5.3. In the event of termination under Sections 5.1 or 5.2 above, all
end user license agreements (excluding distributors and resellers) which
have been validly granted by You or Your distributors under this License
prior to termination shall survive termination.
************************************************************************
* *
* 6. Disclaimer of Warranty *
* ------------------------- *
* *
* Covered Software is provided under this License on an "as is" *
* basis, without warranty of any kind, either expressed, implied, or *
* statutory, including, without limitation, warranties that the *
* Covered Software is free of defects, merchantable, fit for a *
* particular purpose or non-infringing. The entire risk as to the *
* quality and performance of the Covered Software is with You. *
* Should any Covered Software prove defective in any respect, You *
* (not any Contributor) assume the cost of any necessary servicing, *
* repair, or correction. This disclaimer of warranty constitutes an *
* essential part of this License. No use of any Covered Software is *
* authorized under this License except under this disclaimer. *
* *
************************************************************************
************************************************************************
* *
* 7. Limitation of Liability *
* -------------------------- *
* *
* Under no circumstances and under no legal theory, whether tort *
* (including negligence), contract, or otherwise, shall any *
* Contributor, or anyone who distributes Covered Software as *
* permitted above, be liable to You for any direct, indirect, *
* special, incidental, or consequential damages of any character *
* including, without limitation, damages for lost profits, loss of *
* goodwill, work stoppage, computer failure or malfunction, or any *
* and all other commercial damages or losses, even if such party *
* shall have been informed of the possibility of such damages. This *
* limitation of liability shall not apply to liability for death or *
* personal injury resulting from such party's negligence to the *
* extent applicable law prohibits such limitation. Some *
* jurisdictions do not allow the exclusion or limitation of *
* incidental or consequential damages, so this exclusion and *
* limitation may not apply to You. *
* *
************************************************************************
8. Litigation
-------------
Any litigation relating to this License may be brought only in the
courts of a jurisdiction where the defendant maintains its principal
place of business and such litigation shall be governed by laws of that
jurisdiction, without reference to its conflict-of-law provisions.
Nothing in this Section shall prevent a party's ability to bring
cross-claims or counter-claims.
9. Miscellaneous
----------------
This License represents the complete agreement concerning the subject
matter hereof. If any provision of this License is held to be
unenforceable, such provision shall be reformed only to the extent
necessary to make it enforceable. Any law or regulation which provides
that the language of a contract shall be construed against the drafter
shall not be used to construe this License against a Contributor.
10. Versions of the License
---------------------------
10.1. New Versions
Mozilla Foundation is the license steward. Except as provided in Section
10.3, no one other than the license steward has the right to modify or
publish new versions of this License. Each version will be given a
distinguishing version number.
10.2. Effect of New Versions
You may distribute the Covered Software under the terms of the version
of the License under which You originally received the Covered Software,
or under the terms of any subsequent version published by the license
steward.
10.3. Modified Versions
If you create software not governed by this License, and you want to
create a new license for such software, you may create and use a
modified version of this License if you rename the license and remove
any references to the name of the license steward (except to note that
such modified license differs from this License).
10.4. Distributing Source Code Form that is Incompatible With Secondary
Licenses
If You choose to distribute Source Code Form that is Incompatible With
Secondary Licenses under the terms of this version of the License, the
notice described in Exhibit B of this License must be attached.
Exhibit A - Source Code Form License Notice
-------------------------------------------
This Source Code Form is subject to the terms of the Mozilla Public
License, v. 2.0. If a copy of the MPL was not distributed with this
file, You can obtain one at http://mozilla.org/MPL/2.0/.
If it is not possible or desirable to put the notice in a particular
file, then You may include the notice in a location (such as a LICENSE
file in a relevant directory) where a recipient would be likely to look
for such a notice.
You may add additional accurate notices of copyright ownership.
Exhibit B - "Incompatible With Secondary Licenses" Notice
---------------------------------------------------------
This Source Code Form is "Incompatible With Secondary Licenses", as
defined by the Mozilla Public License, v. 2.0.

18
COPYING.README Normal file
View File

@@ -0,0 +1,18 @@
Eigen is primarily MPL2 licensed. See COPYING.MPL2 and these links:
http://www.mozilla.org/MPL/2.0/
http://www.mozilla.org/MPL/2.0/FAQ.html
Some files contain third-party code under BSD or LGPL licenses, whence the other
COPYING.* files here.
All the LGPL code is either LGPL 2.1-only, or LGPL 2.1-or-later.
For this reason, the COPYING.LGPL file contains the LGPL 2.1 text.
If you want to guarantee that the Eigen code that you are #including is licensed
under the MPL2 and possibly more permissive licenses (like BSD), #define this
preprocessor symbol:
EIGEN_MPL2_ONLY
For example, with most compilers, you could add this to your project CXXFLAGS:
-DEIGEN_MPL2_ONLY
This will cause a compilation error to be generated if you #include any code that is
LGPL licensed.

View File

@@ -4,10 +4,10 @@
## # The following are required to uses Dart and the Cdash dashboard
## ENABLE_TESTING()
## INCLUDE(CTest)
set(CTEST_PROJECT_NAME "Eigen")
set(CTEST_PROJECT_NAME "Eigen 3.3")
set(CTEST_NIGHTLY_START_TIME "00:00:00 UTC")
set(CTEST_DROP_METHOD "http")
set(CTEST_DROP_SITE "manao.inria.fr")
set(CTEST_DROP_LOCATION "/CDash/submit.php?project=Eigen")
set(CTEST_DROP_LOCATION "/CDash/submit.php?project=Eigen+3.3")
set(CTEST_DROP_SITE_CDASH TRUE)

View File

@@ -1,4 +1,3 @@
## A tribute to Dynamic!
set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_WARNINGS "33331")
set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_ERRORS "33331")
set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_WARNINGS "2000")
set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_ERRORS "2000")

View File

@@ -1,11 +0,0 @@
#ifndef EIGEN_ARRAY_MODULE_H
#define EIGEN_ARRAY_MODULE_H
// include Core first to handle Eigen2 support macros
#include "Core"
#ifndef EIGEN2_SUPPORT
#error The Eigen/Array header does no longer exist in Eigen3. All that functionality has moved to Eigen/Core.
#endif
#endif // EIGEN_ARRAY_MODULE_H

View File

@@ -16,4 +16,4 @@ install(FILES
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen COMPONENT Devel
)
add_subdirectory(src)
install(DIRECTORY src DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen COMPONENT Devel FILES_MATCHING PATTERN "*.h")

View File

@@ -1,7 +1,15 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CHOLESKY_MODULE_H
#define EIGEN_CHOLESKY_MODULE_H
#include "Core"
#include "Jacobi"
#include "src/Core/util/DisableStupidWarnings.h"
@@ -10,20 +18,26 @@
*
*
* This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices.
* Those decompositions are accessible via the following MatrixBase methods:
* - MatrixBase::llt(),
* Those decompositions are also accessible via the following methods:
* - MatrixBase::llt()
* - MatrixBase::ldlt()
* - SelfAdjointView::llt()
* - SelfAdjointView::ldlt()
*
* \code
* #include <Eigen/Cholesky>
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/Cholesky/LLT.h"
#include "src/Cholesky/LDLT.h"
#ifdef EIGEN_USE_LAPACKE
#include "src/Cholesky/LLT_MKL.h"
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/Cholesky/LLT_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"

View File

@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
#define EIGEN_CHOLMODSUPPORT_MODULE_H
@@ -12,7 +19,7 @@ extern "C" {
/** \ingroup Support_modules
* \defgroup CholmodSupport_Module CholmodSupport module
*
* This module provides an interface to the Cholmod library which is part of the <a href="http://www.cise.ufl.edu/research/sparse/SuiteSparse/">suitesparse</a> package.
* This module provides an interface to the Cholmod library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
* It provides the two following main factorization classes:
* - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization.
* - class CholmodDecomposiiton: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of the underlying factorization method (supernodal or simplicial).
@@ -33,12 +40,8 @@ extern "C" {
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/CholmodSupport/CholmodSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_CHOLMODSUPPORT_MODULE_H

View File

@@ -4,24 +4,9 @@
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2007-2011 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CORE_H
#define EIGEN_CORE_H
@@ -29,37 +14,111 @@
// first thing Eigen does: stop the compiler from committing suicide
#include "src/Core/util/DisableStupidWarnings.h"
#if defined(__CUDACC__) && !defined(EIGEN_NO_CUDA)
#define EIGEN_CUDACC __CUDACC__
#endif
#if defined(__CUDA_ARCH__) && !defined(EIGEN_NO_CUDA)
#define EIGEN_CUDA_ARCH __CUDA_ARCH__
#endif
#if defined(__CUDACC_VER_MAJOR__) && (__CUDACC_VER_MAJOR__ >= 9)
#define EIGEN_CUDACC_VER ((__CUDACC_VER_MAJOR__ * 10000) + (__CUDACC_VER_MINOR__ * 100))
#elif defined(__CUDACC_VER__)
#define EIGEN_CUDACC_VER __CUDACC_VER__
#else
#define EIGEN_CUDACC_VER 0
#endif
// Handle NVCC/CUDA/SYCL
#if defined(__CUDACC__) || defined(__SYCL_DEVICE_ONLY__)
// Do not try asserts on CUDA and SYCL!
#ifndef EIGEN_NO_DEBUG
#define EIGEN_NO_DEBUG
#endif
#ifdef EIGEN_INTERNAL_DEBUGGING
#undef EIGEN_INTERNAL_DEBUGGING
#endif
#ifdef EIGEN_EXCEPTIONS
#undef EIGEN_EXCEPTIONS
#endif
// All functions callable from CUDA code must be qualified with __device__
#ifdef __CUDACC__
// Do not try to vectorize on CUDA and SYCL!
#ifndef EIGEN_DONT_VECTORIZE
#define EIGEN_DONT_VECTORIZE
#endif
#define EIGEN_DEVICE_FUNC __host__ __device__
// We need math_functions.hpp to ensure that that EIGEN_USING_STD_MATH macro
// works properly on the device side
#include <math_functions.hpp>
#else
#define EIGEN_DEVICE_FUNC
#endif
#else
#define EIGEN_DEVICE_FUNC
#endif
// When compiling CUDA device code with NVCC, pull in math functions from the
// global namespace. In host mode, and when device doee with clang, use the
// std versions.
#if defined(__CUDA_ARCH__) && defined(__NVCC__)
#define EIGEN_USING_STD_MATH(FUNC) using ::FUNC;
#else
#define EIGEN_USING_STD_MATH(FUNC) using std::FUNC;
#endif
#if (defined(_CPPUNWIND) || defined(__EXCEPTIONS)) && !defined(__CUDA_ARCH__) && !defined(EIGEN_EXCEPTIONS) && !defined(EIGEN_USE_SYCL)
#define EIGEN_EXCEPTIONS
#endif
#ifdef EIGEN_EXCEPTIONS
#include <new>
#endif
// then include this file where all our macros are defined. It's really important to do it first because
// it's where we do all the alignment settings (platform detection and honoring the user's will if he
// defined e.g. EIGEN_DONT_ALIGN) so it needs to be done before we do anything with vectorization.
#include "src/Core/util/Macros.h"
// Disable the ipa-cp-clone optimization flag with MinGW 6.x or newer (enabled by default with -O3)
// See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=556 for details.
#if EIGEN_COMP_MINGW && EIGEN_GNUC_AT_LEAST(4,6)
#pragma GCC optimize ("-fno-ipa-cp-clone")
#endif
#include <complex>
// this include file manages BLAS and MKL related macros
// and inclusion of their respective header files
#include "src/Core/util/MKL_support.h"
// if alignment is disabled, then disable vectorization. Note: EIGEN_ALIGN is the proper check, it takes into
// account both the user's will (EIGEN_DONT_ALIGN) and our own platform checks
#if !EIGEN_ALIGN
// if alignment is disabled, then disable vectorization. Note: EIGEN_MAX_ALIGN_BYTES is the proper check, it takes into
// account both the user's will (EIGEN_MAX_ALIGN_BYTES,EIGEN_DONT_ALIGN) and our own platform checks
#if EIGEN_MAX_ALIGN_BYTES==0
#ifndef EIGEN_DONT_VECTORIZE
#define EIGEN_DONT_VECTORIZE
#endif
#endif
#ifdef _MSC_VER
#if EIGEN_COMP_MSVC
#include <malloc.h> // for _aligned_malloc -- need it regardless of whether vectorization is enabled
#if (_MSC_VER >= 1500) // 2008 or later
#if (EIGEN_COMP_MSVC >= 1500) // 2008 or later
// Remember that usage of defined() in a #define is undefined by the standard.
// a user reported that in 64-bit mode, MSVC doesn't care to define _M_IX86_FP.
#if (defined(_M_IX86_FP) && (_M_IX86_FP >= 2)) || defined(_M_X64)
#if (defined(_M_IX86_FP) && (_M_IX86_FP >= 2)) || EIGEN_ARCH_x86_64
#define EIGEN_SSE2_ON_MSVC_2008_OR_LATER
#endif
#endif
#else
// Remember that usage of defined() in a #define is undefined by the standard
#if (defined __SSE2__) && ( (!defined __GNUC__) || EIGEN_GNUC_AT_LEAST(4,2) )
#if (defined __SSE2__) && ( (!EIGEN_COMP_GNUC) || EIGEN_COMP_ICC || EIGEN_GNUC_AT_LEAST(4,2) )
#define EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC
#endif
#endif
@@ -91,6 +150,31 @@
#ifdef __SSE4_2__
#define EIGEN_VECTORIZE_SSE4_2
#endif
#ifdef __AVX__
#define EIGEN_VECTORIZE_AVX
#define EIGEN_VECTORIZE_SSE3
#define EIGEN_VECTORIZE_SSSE3
#define EIGEN_VECTORIZE_SSE4_1
#define EIGEN_VECTORIZE_SSE4_2
#endif
#ifdef __AVX2__
#define EIGEN_VECTORIZE_AVX2
#endif
#ifdef __FMA__
#define EIGEN_VECTORIZE_FMA
#endif
#if defined(__AVX512F__) && defined(EIGEN_ENABLE_AVX512)
#define EIGEN_VECTORIZE_AVX512
#define EIGEN_VECTORIZE_AVX2
#define EIGEN_VECTORIZE_AVX
#define EIGEN_VECTORIZE_FMA
#ifdef __AVX512DQ__
#define EIGEN_VECTORIZE_AVX512DQ
#endif
#ifdef __AVX512ER__
#define EIGEN_VECTORIZE_AVX512ER
#endif
#endif
// include files
@@ -102,21 +186,40 @@
// so, to avoid compile errors when windows.h is included after Eigen/Core, ensure intrinsics are extern "C" here too.
// notice that since these are C headers, the extern "C" is theoretically needed anyways.
extern "C" {
#include <emmintrin.h>
#include <xmmintrin.h>
#ifdef EIGEN_VECTORIZE_SSE3
#include <pmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSSE3
#include <tmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSE4_1
#include <smmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSE4_2
#include <nmmintrin.h>
// In theory we should only include immintrin.h and not the other *mmintrin.h header files directly.
// Doing so triggers some issues with ICC. However old gcc versions seems to not have this file, thus:
#if EIGEN_COMP_ICC >= 1110
#include <immintrin.h>
#else
#include <mmintrin.h>
#include <emmintrin.h>
#include <xmmintrin.h>
#ifdef EIGEN_VECTORIZE_SSE3
#include <pmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSSE3
#include <tmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSE4_1
#include <smmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSE4_2
#include <nmmintrin.h>
#endif
#if defined(EIGEN_VECTORIZE_AVX) || defined(EIGEN_VECTORIZE_AVX512)
#include <immintrin.h>
#endif
#endif
} // end extern "C"
#elif defined __VSX__
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_VSX
#include <altivec.h>
// We need to #undef all these ugly tokens defined in <altivec.h>
// => use __vector instead of vector
#undef bool
#undef vector
#undef pixel
#elif defined __ALTIVEC__
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_ALTIVEC
@@ -126,13 +229,35 @@
#undef bool
#undef vector
#undef pixel
#elif defined __ARM_NEON__
#elif (defined __ARM_NEON) || (defined __ARM_NEON__)
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_NEON
#include <arm_neon.h>
#elif (defined __s390x__ && defined __VEC__)
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_ZVECTOR
#include <vecintrin.h>
#endif
#endif
#if defined(__F16C__) && !defined(EIGEN_COMP_CLANG)
// We can use the optimized fp16 to float and float to fp16 conversion routines
#define EIGEN_HAS_FP16_C
#endif
#if defined __CUDACC__
#define EIGEN_VECTORIZE_CUDA
#include <vector_types.h>
#if EIGEN_CUDACC_VER >= 70500
#define EIGEN_HAS_CUDA_FP16
#endif
#endif
#if defined EIGEN_HAS_CUDA_FP16
#include <host_defines.h>
#include <cuda_fp16.h>
#endif
#if (defined _OPENMP) && (!defined EIGEN_DONT_PARALLELIZE)
#define EIGEN_HAS_OPENMP
#endif
@@ -142,7 +267,7 @@
#endif
// MSVC for windows mobile does not have the errno.h file
#if !(defined(_MSC_VER) && defined(_WIN32_WCE)) && !defined(__ARMCC_VERSION)
#if !(EIGEN_COMP_MSVC && EIGEN_OS_WINCE) && !EIGEN_COMP_ARM
#define EIGEN_HAS_ERRNO
#endif
@@ -162,29 +287,30 @@
// for min/max:
#include <algorithm>
// for std::is_nothrow_move_assignable
#ifdef EIGEN_INCLUDE_TYPE_TRAITS
#include <type_traits>
#endif
// for outputting debug info
#ifdef EIGEN_DEBUG_ASSIGN
#include <iostream>
#endif
// required for __cpuid, needs to be included after cmath
#if defined(_MSC_VER) && (defined(_M_IX86)||defined(_M_X64))
#if EIGEN_COMP_MSVC && EIGEN_ARCH_i386_OR_x86_64 && !EIGEN_OS_WINCE
#include <intrin.h>
#endif
#if defined(_CPPUNWIND) || defined(__EXCEPTIONS)
#define EIGEN_EXCEPTIONS
#endif
#ifdef EIGEN_EXCEPTIONS
#include <new>
#endif
/** \brief Namespace containing all symbols from the %Eigen library. */
namespace Eigen {
inline static const char *SimdInstructionSetsInUse(void) {
#if defined(EIGEN_VECTORIZE_SSE4_2)
#if defined(EIGEN_VECTORIZE_AVX512)
return "AVX512, FMA, AVX2, AVX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
#elif defined(EIGEN_VECTORIZE_AVX)
return "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
#elif defined(EIGEN_VECTORIZE_SSE4_2)
return "SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
#elif defined(EIGEN_VECTORIZE_SSE4_1)
return "SSE, SSE2, SSE3, SSSE3, SSE4.1";
@@ -196,8 +322,12 @@ inline static const char *SimdInstructionSetsInUse(void) {
return "SSE, SSE2";
#elif defined(EIGEN_VECTORIZE_ALTIVEC)
return "AltiVec";
#elif defined(EIGEN_VECTORIZE_VSX)
return "VSX";
#elif defined(EIGEN_VECTORIZE_NEON)
return "ARM NEON";
#elif defined(EIGEN_VECTORIZE_ZVECTOR)
return "S390X ZVECTOR";
#else
return "None";
#endif
@@ -205,42 +335,21 @@ inline static const char *SimdInstructionSetsInUse(void) {
} // end namespace Eigen
#define STAGE10_FULL_EIGEN2_API 10
#define STAGE20_RESOLVE_API_CONFLICTS 20
#define STAGE30_FULL_EIGEN3_API 30
#define STAGE40_FULL_EIGEN3_STRICTNESS 40
#define STAGE99_NO_EIGEN2_SUPPORT 99
#if defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS
#define EIGEN2_SUPPORT
#define EIGEN2_SUPPORT_STAGE STAGE40_FULL_EIGEN3_STRICTNESS
#elif defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API
#define EIGEN2_SUPPORT
#define EIGEN2_SUPPORT_STAGE STAGE30_FULL_EIGEN3_API
#elif defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS
#define EIGEN2_SUPPORT
#define EIGEN2_SUPPORT_STAGE STAGE20_RESOLVE_API_CONFLICTS
#elif defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API
#define EIGEN2_SUPPORT
#define EIGEN2_SUPPORT_STAGE STAGE10_FULL_EIGEN2_API
#elif defined EIGEN2_SUPPORT
// default to stage 3, that's what it's always meant
#define EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API
#define EIGEN2_SUPPORT_STAGE STAGE30_FULL_EIGEN3_API
#else
#define EIGEN2_SUPPORT_STAGE STAGE99_NO_EIGEN2_SUPPORT
#if defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS || defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API || defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS || defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API || defined EIGEN2_SUPPORT
// This will generate an error message:
#error Eigen2-support is only available up to version 3.2. Please go to "http://eigen.tuxfamily.org/index.php?title=Eigen2" for further information
#endif
#ifdef EIGEN2_SUPPORT
#undef minor
#endif
namespace Eigen {
// we use size_t frequently and we'll never remember to prepend it with std:: everytime just to
// ensure QNX/QCC support
using std::size_t;
// gcc 4.6.0 wants std:: for ptrdiff_t
// gcc 4.6.0 wants std:: for ptrdiff_t
using std::ptrdiff_t;
}
/** \defgroup Core_Module Core module
* This is the main module of Eigen providing dense matrix and vector support
* (both fixed and dynamic size) with all the features corresponding to a BLAS library
@@ -251,55 +360,104 @@ using std::ptrdiff_t;
* \endcode
*/
/** \defgroup Support_modules Support modules [category]
* Category of modules which add support for external libraries.
*/
#include "src/Core/util/Constants.h"
#include "src/Core/util/ForwardDeclarations.h"
#include "src/Core/util/Meta.h"
#include "src/Core/util/XprHelper.h"
#include "src/Core/util/ForwardDeclarations.h"
#include "src/Core/util/StaticAssert.h"
#include "src/Core/util/XprHelper.h"
#include "src/Core/util/Memory.h"
#include "src/Core/NumTraits.h"
#include "src/Core/MathFunctions.h"
#include "src/Core/GenericPacketMath.h"
#include "src/Core/MathFunctionsImpl.h"
#include "src/Core/arch/Default/ConjHelper.h"
#if defined EIGEN_VECTORIZE_SSE
#if defined EIGEN_VECTORIZE_AVX512
#include "src/Core/arch/SSE/PacketMath.h"
#include "src/Core/arch/AVX/PacketMath.h"
#include "src/Core/arch/AVX512/PacketMath.h"
#include "src/Core/arch/AVX512/MathFunctions.h"
#elif defined EIGEN_VECTORIZE_AVX
// Use AVX for floats and doubles, SSE for integers
#include "src/Core/arch/SSE/PacketMath.h"
#include "src/Core/arch/SSE/Complex.h"
#include "src/Core/arch/SSE/MathFunctions.h"
#include "src/Core/arch/AVX/PacketMath.h"
#include "src/Core/arch/AVX/MathFunctions.h"
#include "src/Core/arch/AVX/Complex.h"
#include "src/Core/arch/AVX/TypeCasting.h"
#include "src/Core/arch/SSE/TypeCasting.h"
#elif defined EIGEN_VECTORIZE_SSE
#include "src/Core/arch/SSE/PacketMath.h"
#include "src/Core/arch/SSE/MathFunctions.h"
#include "src/Core/arch/SSE/Complex.h"
#elif defined EIGEN_VECTORIZE_ALTIVEC
#include "src/Core/arch/SSE/TypeCasting.h"
#elif defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX)
#include "src/Core/arch/AltiVec/PacketMath.h"
#include "src/Core/arch/AltiVec/MathFunctions.h"
#include "src/Core/arch/AltiVec/Complex.h"
#elif defined EIGEN_VECTORIZE_NEON
#include "src/Core/arch/NEON/PacketMath.h"
#include "src/Core/arch/NEON/MathFunctions.h"
#include "src/Core/arch/NEON/Complex.h"
#elif defined EIGEN_VECTORIZE_ZVECTOR
#include "src/Core/arch/ZVector/PacketMath.h"
#include "src/Core/arch/ZVector/MathFunctions.h"
#include "src/Core/arch/ZVector/Complex.h"
#endif
// Half float support
#include "src/Core/arch/CUDA/Half.h"
#include "src/Core/arch/CUDA/PacketMathHalf.h"
#include "src/Core/arch/CUDA/TypeCasting.h"
#if defined EIGEN_VECTORIZE_CUDA
#include "src/Core/arch/CUDA/PacketMath.h"
#include "src/Core/arch/CUDA/MathFunctions.h"
#endif
#include "src/Core/arch/Default/Settings.h"
#include "src/Core/Functors.h"
#include "src/Core/functors/TernaryFunctors.h"
#include "src/Core/functors/BinaryFunctors.h"
#include "src/Core/functors/UnaryFunctors.h"
#include "src/Core/functors/NullaryFunctors.h"
#include "src/Core/functors/StlFunctors.h"
#include "src/Core/functors/AssignmentFunctors.h"
// Specialized functors to enable the processing of complex numbers
// on CUDA devices
#include "src/Core/arch/CUDA/Complex.h"
#include "src/Core/IO.h"
#include "src/Core/DenseCoeffsBase.h"
#include "src/Core/DenseBase.h"
#include "src/Core/MatrixBase.h"
#include "src/Core/EigenBase.h"
#include "src/Core/Product.h"
#include "src/Core/CoreEvaluators.h"
#include "src/Core/AssignEvaluator.h"
#ifndef EIGEN_PARSED_BY_DOXYGEN // work around Doxygen bug triggered by Assign.h r814874
// at least confirmed with Doxygen 1.5.5 and 1.5.6
#include "src/Core/Assign.h"
#endif
#include "src/Core/ArrayBase.h"
#include "src/Core/util/BlasUtil.h"
#include "src/Core/DenseStorage.h"
#include "src/Core/NestByValue.h"
#include "src/Core/ForceAlignedAccess.h"
// #include "src/Core/ForceAlignedAccess.h"
#include "src/Core/ReturnByValue.h"
#include "src/Core/NoAlias.h"
#include "src/Core/PlainObjectBase.h"
#include "src/Core/Matrix.h"
#include "src/Core/Array.h"
#include "src/Core/CwiseTernaryOp.h"
#include "src/Core/CwiseBinaryOp.h"
#include "src/Core/CwiseUnaryOp.h"
#include "src/Core/CwiseNullaryOp.h"
@@ -307,31 +465,32 @@ using std::ptrdiff_t;
#include "src/Core/SelfCwiseBinaryOp.h"
#include "src/Core/Dot.h"
#include "src/Core/StableNorm.h"
#include "src/Core/MapBase.h"
#include "src/Core/Stride.h"
#include "src/Core/MapBase.h"
#include "src/Core/Map.h"
#include "src/Core/Ref.h"
#include "src/Core/Block.h"
#include "src/Core/VectorBlock.h"
#include "src/Core/Transpose.h"
#include "src/Core/DiagonalMatrix.h"
#include "src/Core/Diagonal.h"
#include "src/Core/DiagonalProduct.h"
#include "src/Core/PermutationMatrix.h"
#include "src/Core/Transpositions.h"
#include "src/Core/Redux.h"
#include "src/Core/Visitor.h"
#include "src/Core/Fuzzy.h"
#include "src/Core/IO.h"
#include "src/Core/Swap.h"
#include "src/Core/CommaInitializer.h"
#include "src/Core/Flagged.h"
#include "src/Core/ProductBase.h"
#include "src/Core/GeneralProduct.h"
#include "src/Core/Solve.h"
#include "src/Core/Inverse.h"
#include "src/Core/SolverBase.h"
#include "src/Core/PermutationMatrix.h"
#include "src/Core/Transpositions.h"
#include "src/Core/TriangularMatrix.h"
#include "src/Core/SelfAdjointView.h"
#include "src/Core/products/GeneralBlockPanelKernel.h"
#include "src/Core/products/Parallelizer.h"
#include "src/Core/products/CoeffBasedProduct.h"
#include "src/Core/ProductEvaluators.h"
#include "src/Core/products/GeneralMatrixVector.h"
#include "src/Core/products/GeneralMatrixMatrix.h"
#include "src/Core/SolveTriangular.h"
@@ -345,6 +504,8 @@ using std::ptrdiff_t;
#include "src/Core/products/TriangularSolverMatrix.h"
#include "src/Core/products/TriangularSolverVector.h"
#include "src/Core/BandMatrix.h"
#include "src/Core/CoreIterators.h"
#include "src/Core/ConditionEstimator.h"
#include "src/Core/BooleanRedux.h"
#include "src/Core/Select.h"
@@ -352,18 +513,17 @@ using std::ptrdiff_t;
#include "src/Core/Random.h"
#include "src/Core/Replicate.h"
#include "src/Core/Reverse.h"
#include "src/Core/ArrayBase.h"
#include "src/Core/ArrayWrapper.h"
#ifdef EIGEN_USE_BLAS
#include "src/Core/products/GeneralMatrixMatrix_MKL.h"
#include "src/Core/products/GeneralMatrixVector_MKL.h"
#include "src/Core/products/GeneralMatrixMatrixTriangular_MKL.h"
#include "src/Core/products/SelfadjointMatrixMatrix_MKL.h"
#include "src/Core/products/SelfadjointMatrixVector_MKL.h"
#include "src/Core/products/TriangularMatrixMatrix_MKL.h"
#include "src/Core/products/TriangularMatrixVector_MKL.h"
#include "src/Core/products/TriangularSolverMatrix_MKL.h"
#include "src/Core/products/GeneralMatrixMatrix_BLAS.h"
#include "src/Core/products/GeneralMatrixVector_BLAS.h"
#include "src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h"
#include "src/Core/products/SelfadjointMatrixMatrix_BLAS.h"
#include "src/Core/products/SelfadjointMatrixVector_BLAS.h"
#include "src/Core/products/TriangularMatrixMatrix_BLAS.h"
#include "src/Core/products/TriangularMatrixVector_BLAS.h"
#include "src/Core/products/TriangularSolverMatrix_BLAS.h"
#endif // EIGEN_USE_BLAS
#ifdef EIGEN_USE_MKL_VML
@@ -374,8 +534,4 @@ using std::ptrdiff_t;
#include "src/Core/util/ReenableStupidWarnings.h"
#ifdef EIGEN2_SUPPORT
#include "Eigen2Support"
#endif
#endif // EIGEN_CORE_H

View File

@@ -1,2 +1,2 @@
#include "Dense"
//#include "Sparse"
#include "Sparse"

View File

@@ -1,97 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN2SUPPORT_H
#define EIGEN2SUPPORT_H
#if (!defined(EIGEN2_SUPPORT)) || (!defined(EIGEN_CORE_H))
#error Eigen2 support must be enabled by defining EIGEN2_SUPPORT before including any Eigen header
#endif
#include "src/Core/util/DisableStupidWarnings.h"
/** \ingroup Support_modules
* \defgroup Eigen2Support_Module Eigen2 support module
* This module provides a couple of deprecated functions improving the compatibility with Eigen2.
*
* To use it, define EIGEN2_SUPPORT before including any Eigen header
* \code
* #define EIGEN2_SUPPORT
* \endcode
*
*/
#include "src/Eigen2Support/Macros.h"
#include "src/Eigen2Support/Memory.h"
#include "src/Eigen2Support/Meta.h"
#include "src/Eigen2Support/Lazy.h"
#include "src/Eigen2Support/Cwise.h"
#include "src/Eigen2Support/CwiseOperators.h"
#include "src/Eigen2Support/TriangularSolver.h"
#include "src/Eigen2Support/Block.h"
#include "src/Eigen2Support/VectorBlock.h"
#include "src/Eigen2Support/Minor.h"
#include "src/Eigen2Support/MathFunctions.h"
#include "src/Core/util/ReenableStupidWarnings.h"
// Eigen2 used to include iostream
#include<iostream>
#define EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, SizeSuffix) \
using Eigen::Matrix##SizeSuffix##TypeSuffix; \
using Eigen::Vector##SizeSuffix##TypeSuffix; \
using Eigen::RowVector##SizeSuffix##TypeSuffix;
#define EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(TypeSuffix) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 2) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 3) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 4) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X) \
#define EIGEN_USING_MATRIX_TYPEDEFS \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(i) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(f) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(d) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(cf) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(cd)
#define USING_PART_OF_NAMESPACE_EIGEN \
EIGEN_USING_MATRIX_TYPEDEFS \
using Eigen::Matrix; \
using Eigen::MatrixBase; \
using Eigen::ei_random; \
using Eigen::ei_real; \
using Eigen::ei_imag; \
using Eigen::ei_conj; \
using Eigen::ei_abs; \
using Eigen::ei_abs2; \
using Eigen::ei_sqrt; \
using Eigen::ei_exp; \
using Eigen::ei_log; \
using Eigen::ei_sin; \
using Eigen::ei_cos;
#endif // EIGEN2SUPPORT_H

View File

@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_EIGENVALUES_MODULE_H
#define EIGEN_EIGENVALUES_MODULE_H
@@ -25,6 +32,7 @@
* \endcode
*/
#include "src/misc/RealSvd2x2.h"
#include "src/Eigenvalues/Tridiagonalization.h"
#include "src/Eigenvalues/RealSchur.h"
#include "src/Eigenvalues/EigenSolver.h"
@@ -33,11 +41,18 @@
#include "src/Eigenvalues/HessenbergDecomposition.h"
#include "src/Eigenvalues/ComplexSchur.h"
#include "src/Eigenvalues/ComplexEigenSolver.h"
#include "src/Eigenvalues/RealQZ.h"
#include "src/Eigenvalues/GeneralizedEigenSolver.h"
#include "src/Eigenvalues/MatrixBaseEigenvalues.h"
#ifdef EIGEN_USE_LAPACKE
#include "src/Eigenvalues/RealSchur_MKL.h"
#include "src/Eigenvalues/ComplexSchur_MKL.h"
#include "src/Eigenvalues/SelfAdjointEigenSolver_MKL.h"
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/Eigenvalues/RealSchur_LAPACKE.h"
#include "src/Eigenvalues/ComplexSchur_LAPACKE.h"
#include "src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"

View File

@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_GEOMETRY_MODULE_H
#define EIGEN_GEOMETRY_MODULE_H
@@ -9,21 +16,17 @@
#include "LU"
#include <limits>
#ifndef M_PI
#define M_PI 3.14159265358979323846
#endif
/** \defgroup Geometry_Module Geometry module
*
*
*
* This module provides support for:
* - fixed-size homogeneous transformations
* - translation, scaling, 2D and 3D rotations
* - quaternions
* - \ref MatrixBase::cross() "cross product"
* - \ref MatrixBase::unitOrthogonal() "orthognal vector generation"
* - some linear components: parametrized-lines and hyperplanes
* - \link Quaternion quaternions \endlink
* - cross products (\ref MatrixBase::cross, \ref MatrixBase::cross3)
* - orthognal vector generation (\ref MatrixBase::unitOrthogonal)
* - some linear components: \link ParametrizedLine parametrized-lines \endlink and \link Hyperplane hyperplanes \endlink
* - \link AlignedBox axis aligned bounding boxes \endlink
* - \link umeyama least-square transformation fitting \endlink
*
* \code
* #include <Eigen/Geometry>
@@ -33,27 +36,23 @@
#include "src/Geometry/OrthoMethods.h"
#include "src/Geometry/EulerAngles.h"
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
#include "src/Geometry/Homogeneous.h"
#include "src/Geometry/RotationBase.h"
#include "src/Geometry/Rotation2D.h"
#include "src/Geometry/Quaternion.h"
#include "src/Geometry/AngleAxis.h"
#include "src/Geometry/Transform.h"
#include "src/Geometry/Translation.h"
#include "src/Geometry/Scaling.h"
#include "src/Geometry/Hyperplane.h"
#include "src/Geometry/ParametrizedLine.h"
#include "src/Geometry/AlignedBox.h"
#include "src/Geometry/Umeyama.h"
#include "src/Geometry/Homogeneous.h"
#include "src/Geometry/RotationBase.h"
#include "src/Geometry/Rotation2D.h"
#include "src/Geometry/Quaternion.h"
#include "src/Geometry/AngleAxis.h"
#include "src/Geometry/Transform.h"
#include "src/Geometry/Translation.h"
#include "src/Geometry/Scaling.h"
#include "src/Geometry/Hyperplane.h"
#include "src/Geometry/ParametrizedLine.h"
#include "src/Geometry/AlignedBox.h"
#include "src/Geometry/Umeyama.h"
#if defined EIGEN_VECTORIZE_SSE
#include "src/Geometry/arch/Geometry_SSE.h"
#endif
#endif
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/Geometry/All.h"
// Use the SSE optimized version whenever possible. At the moment the
// SSE version doesn't compile when AVX is enabled
#if defined EIGEN_VECTORIZE_SSE && !defined EIGEN_VECTORIZE_AVX
#include "src/Geometry/arch/Geometry_SSE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"

View File

@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_HOUSEHOLDER_MODULE_H
#define EIGEN_HOUSEHOLDER_MODULE_H

View File

@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
#define EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
@@ -6,34 +13,35 @@
#include "src/Core/util/DisableStupidWarnings.h"
/** \ingroup Sparse_modules
/**
* \defgroup IterativeLinearSolvers_Module IterativeLinearSolvers module
*
* This module currently provides iterative methods to solve problems of the form \c A \c x = \c b, where \c A is a squared matrix, usually very large and sparse.
* Those solvers are accessible via the following classes:
* - ConjugateGradient for selfadjoint (hermitian) matrices,
* - LeastSquaresConjugateGradient for rectangular least-square problems,
* - BiCGSTAB for general square matrices.
*
* These iterative solvers are associated with some preconditioners:
* - IdentityPreconditioner - not really useful
* - DiagonalPreconditioner - also called JAcobi preconditioner, work very well on diagonal dominant matrices.
* - IncompleteILUT - incomplete LU factorization with dual thresholding
* - DiagonalPreconditioner - also called Jacobi preconditioner, work very well on diagonal dominant matrices.
* - IncompleteLUT - incomplete LU factorization with dual thresholding
*
* Such problems can also be solved using the direct sparse decomposition modules: SparseCholesky, CholmodSupport, UmfPackSupport, SuperLUSupport.
*
* \code
* #include <Eigen/IterativeLinearSolvers>
* \endcode
\code
#include <Eigen/IterativeLinearSolvers>
\endcode
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/IterativeLinearSolvers/SolveWithGuess.h"
#include "src/IterativeLinearSolvers/IterativeSolverBase.h"
#include "src/IterativeLinearSolvers/BasicPreconditioners.h"
#include "src/IterativeLinearSolvers/ConjugateGradient.h"
#include "src/IterativeLinearSolvers/LeastSquareConjugateGradient.h"
#include "src/IterativeLinearSolvers/BiCGSTAB.h"
#include "src/IterativeLinearSolvers/IncompleteLUT.h"
#include "src/IterativeLinearSolvers/IncompleteCholesky.h"
#include "src/Core/util/ReenableStupidWarnings.h"

View File

@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_JACOBI_MODULE_H
#define EIGEN_JACOBI_MODULE_H

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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_LU_MODULE_H
#define EIGEN_LU_MODULE_H
@@ -16,25 +23,27 @@
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/misc/Kernel.h"
#include "src/misc/Image.h"
#include "src/LU/FullPivLU.h"
#include "src/LU/PartialPivLU.h"
#ifdef EIGEN_USE_LAPACKE
#include "src/LU/PartialPivLU_MKL.h"
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/LU/PartialPivLU_LAPACKE.h"
#endif
#include "src/LU/Determinant.h"
#include "src/LU/Inverse.h"
#include "src/LU/InverseImpl.h"
#if defined EIGEN_VECTORIZE_SSE
// Use the SSE optimized version whenever possible. At the moment the
// SSE version doesn't compile when AVX is enabled
#if defined EIGEN_VECTORIZE_SSE && !defined EIGEN_VECTORIZE_AVX
#include "src/LU/arch/Inverse_SSE.h"
#endif
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/LU.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_LU_MODULE_H

View File

@@ -1,32 +0,0 @@
#ifndef EIGEN_REGRESSION_MODULE_H
#define EIGEN_REGRESSION_MODULE_H
#ifndef EIGEN2_SUPPORT
#error LeastSquares is only available in Eigen2 support mode (define EIGEN2_SUPPORT)
#endif
// exclude from normal eigen3-only documentation
#ifdef EIGEN2_SUPPORT
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
#include "Eigenvalues"
#include "Geometry"
/** \defgroup LeastSquares_Module LeastSquares module
* This module provides linear regression and related features.
*
* \code
* #include <Eigen/LeastSquares>
* \endcode
*/
#include "src/Eigen2Support/LeastSquares.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN2_SUPPORT
#endif // EIGEN_REGRESSION_MODULE_H

35
Eigen/MetisSupport Normal file
View File

@@ -0,0 +1,35 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_METISSUPPORT_MODULE_H
#define EIGEN_METISSUPPORT_MODULE_H
#include "SparseCore"
#include "src/Core/util/DisableStupidWarnings.h"
extern "C" {
#include <metis.h>
}
/** \ingroup Support_modules
* \defgroup MetisSupport_Module MetisSupport module
*
* \code
* #include <Eigen/MetisSupport>
* \endcode
* This module defines an interface to the METIS reordering package (http://glaros.dtc.umn.edu/gkhome/views/metis).
* It can be used just as any other built-in method as explained in \link OrderingMethods_Module here. \endlink
*/
#include "src/MetisSupport/MetisSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_METISSUPPORT_MODULE_H

View File

@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ORDERINGMETHODS_MODULE_H
#define EIGEN_ORDERINGMETHODS_MODULE_H
@@ -5,19 +12,62 @@
#include "src/Core/util/DisableStupidWarnings.h"
/** \ingroup Sparse_modules
/**
* \defgroup OrderingMethods_Module OrderingMethods module
*
* This module is currently for internal use only.
*
*
* This module is currently for internal use only
*
* It defines various built-in and external ordering methods for sparse matrices.
* They are typically used to reduce the number of elements during
* the sparse matrix decomposition (LLT, LU, QR).
* Precisely, in a preprocessing step, a permutation matrix P is computed using
* those ordering methods and applied to the columns of the matrix.
* Using for instance the sparse Cholesky decomposition, it is expected that
* the nonzeros elements in LLT(A*P) will be much smaller than that in LLT(A).
*
*
* Usage :
* \code
* #include <Eigen/OrderingMethods>
* \endcode
*
* A simple usage is as a template parameter in the sparse decomposition classes :
*
* \code
* SparseLU<MatrixType, COLAMDOrdering<int> > solver;
* \endcode
*
* \code
* SparseQR<MatrixType, COLAMDOrdering<int> > solver;
* \endcode
*
* It is possible as well to call directly a particular ordering method for your own purpose,
* \code
* AMDOrdering<int> ordering;
* PermutationMatrix<Dynamic, Dynamic, int> perm;
* SparseMatrix<double> A;
* //Fill the matrix ...
*
* ordering(A, perm); // Call AMD
* \endcode
*
* \note Some of these methods (like AMD or METIS), need the sparsity pattern
* of the input matrix to be symmetric. When the matrix is structurally unsymmetric,
* Eigen computes internally the pattern of \f$A^T*A\f$ before calling the method.
* If your matrix is already symmetric (at leat in structure), you can avoid that
* by calling the method with a SelfAdjointView type.
*
* \code
* // Call the ordering on the pattern of the lower triangular matrix A
* ordering(A.selfadjointView<Lower>(), perm);
* \endcode
*/
#ifndef EIGEN_MPL2_ONLY
#include "src/OrderingMethods/Amd.h"
#endif
#include "src/OrderingMethods/Ordering.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_ORDERINGMETHODS_MODULE_H

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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_PASTIXSUPPORT_MODULE_H
#define EIGEN_PASTIXSUPPORT_MODULE_H
@@ -5,7 +12,6 @@
#include "src/Core/util/DisableStupidWarnings.h"
#include <complex.h>
extern "C" {
#include <pastix_nompi.h>
#include <pastix.h>
@@ -35,12 +41,8 @@ extern "C" {
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/PaStiXSupport/PaStiXSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_PASTIXSUPPORT_MODULE_H

9
Eigen/PardisoSupport Normal file → Executable file
View File

@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_PARDISOSUPPORT_MODULE_H
#define EIGEN_PARDISOSUPPORT_MODULE_H
@@ -7,8 +14,6 @@
#include <mkl_pardiso.h>
#include <unsupported/Eigen/SparseExtra>
/** \ingroup Support_modules
* \defgroup PardisoSupport_Module PardisoSupport module
*

View File

@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_QR_MODULE_H
#define EIGEN_QR_MODULE_H
@@ -15,31 +22,30 @@
*
* This module provides various QR decompositions
* This module also provides some MatrixBase methods, including:
* - MatrixBase::qr(),
* - MatrixBase::householderQr()
* - MatrixBase::colPivHouseholderQr()
* - MatrixBase::fullPivHouseholderQr()
*
* \code
* #include <Eigen/QR>
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/QR/HouseholderQR.h"
#include "src/QR/FullPivHouseholderQR.h"
#include "src/QR/ColPivHouseholderQR.h"
#include "src/QR/CompleteOrthogonalDecomposition.h"
#ifdef EIGEN_USE_LAPACKE
#include "src/QR/HouseholderQR_MKL.h"
#include "src/QR/ColPivHouseholderQR_MKL.h"
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/QR.h"
#include "src/QR/HouseholderQR_LAPACKE.h"
#include "src/QR/ColPivHouseholderQR_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#ifdef EIGEN2_SUPPORT
#include "Eigenvalues"
#endif
#endif // EIGEN_QR_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -1,3 +1,9 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_QTMALLOC_MODULE_H
#define EIGEN_QTMALLOC_MODULE_H
@@ -8,7 +14,7 @@
#include "src/Core/util/DisableStupidWarnings.h"
void *qMalloc(size_t size)
void *qMalloc(std::size_t size)
{
return Eigen::internal::aligned_malloc(size);
}
@@ -18,10 +24,10 @@ void qFree(void *ptr)
Eigen::internal::aligned_free(ptr);
}
void *qRealloc(void *ptr, size_t size)
void *qRealloc(void *ptr, std::size_t size)
{
void* newPtr = Eigen::internal::aligned_malloc(size);
memcpy(newPtr, ptr, size);
std::memcpy(newPtr, ptr, size);
Eigen::internal::aligned_free(ptr);
return newPtr;
}

34
Eigen/SPQRSupport Normal file
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@@ -0,0 +1,34 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPQRSUPPORT_MODULE_H
#define EIGEN_SPQRSUPPORT_MODULE_H
#include "SparseCore"
#include "src/Core/util/DisableStupidWarnings.h"
#include "SuiteSparseQR.hpp"
/** \ingroup Support_modules
* \defgroup SPQRSupport_Module SuiteSparseQR module
*
* This module provides an interface to the SPQR library, which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
*
* \code
* #include <Eigen/SPQRSupport>
* \endcode
*
* In order to use this module, the SPQR headers must be accessible from the include paths, and your binary must be linked to the SPQR library and its dependencies (Cholmod, AMD, COLAMD,...).
* For a cmake based project, you can use our FindSPQR.cmake and FindCholmod.Cmake modules
*
*/
#include "src/CholmodSupport/CholmodSupport.h"
#include "src/SPQRSupport/SuiteSparseQRSupport.h"
#endif

View File

@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SVD_MODULE_H
#define EIGEN_SVD_MODULE_H
@@ -12,23 +19,30 @@
*
*
* This module provides SVD decomposition for matrices (both real and complex).
* This decomposition is accessible via the following MatrixBase method:
* Two decomposition algorithms are provided:
* - JacobiSVD implementing two-sided Jacobi iterations is numerically very accurate, fast for small matrices, but very slow for larger ones.
* - BDCSVD implementing a recursive divide & conquer strategy on top of an upper-bidiagonalization which remains fast for large problems.
* These decompositions are accessible via the respective classes and following MatrixBase methods:
* - MatrixBase::jacobiSvd()
* - MatrixBase::bdcSvd()
*
* \code
* #include <Eigen/SVD>
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/SVD/JacobiSVD.h"
#if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT)
#include "src/SVD/JacobiSVD_MKL.h"
#endif
#include "src/misc/RealSvd2x2.h"
#include "src/SVD/UpperBidiagonalization.h"
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/SVD.h"
#include "src/SVD/SVDBase.h"
#include "src/SVD/JacobiSVD.h"
#include "src/SVD/BDCSVD.h"
#if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT)
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/SVD/JacobiSVD_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"

View File

@@ -1,22 +1,35 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPARSE_MODULE_H
#define EIGEN_SPARSE_MODULE_H
/** \defgroup Sparse_modules Sparse modules
/** \defgroup Sparse_Module Sparse meta-module
*
* Meta-module including all related modules:
* - SparseCore
* - OrderingMethods
* - SparseCholesky
* - IterativeLinearSolvers
* - \ref SparseCore_Module
* - \ref OrderingMethods_Module
* - \ref SparseCholesky_Module
* - \ref SparseLU_Module
* - \ref SparseQR_Module
* - \ref IterativeLinearSolvers_Module
*
* \code
* #include <Eigen/Sparse>
* \endcode
\code
#include <Eigen/Sparse>
\endcode
*/
#include "SparseCore"
#include "OrderingMethods"
#ifndef EIGEN_MPL2_ONLY
#include "SparseCholesky"
#endif
#include "SparseLU"
#include "SparseQR"
#include "IterativeLinearSolvers"
#endif // EIGEN_SPARSE_MODULE_H

View File

@@ -1,11 +1,21 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2013 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPARSECHOLESKY_MODULE_H
#define EIGEN_SPARSECHOLESKY_MODULE_H
#include "SparseCore"
#include "OrderingMethods"
#include "src/Core/util/DisableStupidWarnings.h"
/** \ingroup Sparse_modules
/**
* \defgroup SparseCholesky_Module SparseCholesky module
*
* This module currently provides two variants of the direct sparse Cholesky decomposition for selfadjoint (hermitian) matrices.
@@ -20,11 +30,16 @@
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#ifdef EIGEN_MPL2_ONLY
#error The SparseCholesky module has nothing to offer in MPL2 only mode
#endif
#include "src/SparseCholesky/SimplicialCholesky.h"
#ifndef EIGEN_MPL2_ONLY
#include "src/SparseCholesky/SimplicialCholesky_impl.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_SPARSECHOLESKY_MODULE_H

View File

@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPARSECORE_MODULE_H
#define EIGEN_SPARSECORE_MODULE_H
@@ -11,10 +18,10 @@
#include <cstring>
#include <algorithm>
/** \ingroup Sparse_modules
/**
* \defgroup SparseCore_Module SparseCore module
*
* This module provides a sparse matrix representation, and basic associatd matrix manipulations
* This module provides a sparse matrix representation, and basic associated matrix manipulations
* and operations.
*
* See the \ref TutorialSparse "Sparse tutorial"
@@ -26,39 +33,35 @@
* This module depends on: Core.
*/
namespace Eigen {
/** The type used to identify a general sparse storage. */
struct Sparse {};
}
#include "src/SparseCore/SparseUtil.h"
#include "src/SparseCore/SparseMatrixBase.h"
#include "src/SparseCore/SparseAssign.h"
#include "src/SparseCore/CompressedStorage.h"
#include "src/SparseCore/AmbiVector.h"
#include "src/SparseCore/SparseCompressedBase.h"
#include "src/SparseCore/SparseMatrix.h"
#include "src/SparseCore/SparseMap.h"
#include "src/SparseCore/MappedSparseMatrix.h"
#include "src/SparseCore/SparseVector.h"
#include "src/SparseCore/CoreIterators.h"
#include "src/SparseCore/SparseBlock.h"
#include "src/SparseCore/SparseTranspose.h"
#include "src/SparseCore/SparseRef.h"
#include "src/SparseCore/SparseCwiseUnaryOp.h"
#include "src/SparseCore/SparseCwiseBinaryOp.h"
#include "src/SparseCore/SparseTranspose.h"
#include "src/SparseCore/SparseBlock.h"
#include "src/SparseCore/SparseDot.h"
#include "src/SparseCore/SparsePermutation.h"
#include "src/SparseCore/SparseAssign.h"
#include "src/SparseCore/SparseRedux.h"
#include "src/SparseCore/SparseFuzzy.h"
#include "src/SparseCore/SparseView.h"
#include "src/SparseCore/SparseDiagonalProduct.h"
#include "src/SparseCore/ConservativeSparseSparseProduct.h"
#include "src/SparseCore/SparseSparseProductWithPruning.h"
#include "src/SparseCore/SparseProduct.h"
#include "src/SparseCore/SparseDenseProduct.h"
#include "src/SparseCore/SparseDiagonalProduct.h"
#include "src/SparseCore/SparseTriangularView.h"
#include "src/SparseCore/SparseSelfAdjointView.h"
#include "src/SparseCore/SparseTriangularView.h"
#include "src/SparseCore/TriangularSolver.h"
#include "src/SparseCore/SparseView.h"
#include "src/SparseCore/SparsePermutation.h"
#include "src/SparseCore/SparseFuzzy.h"
#include "src/SparseCore/SparseSolverBase.h"
#include "src/Core/util/ReenableStupidWarnings.h"

46
Eigen/SparseLU Normal file
View File

@@ -0,0 +1,46 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPARSELU_MODULE_H
#define EIGEN_SPARSELU_MODULE_H
#include "SparseCore"
/**
* \defgroup SparseLU_Module SparseLU module
* This module defines a supernodal factorization of general sparse matrices.
* The code is fully optimized for supernode-panel updates with specialized kernels.
* Please, see the documentation of the SparseLU class for more details.
*/
// Ordering interface
#include "OrderingMethods"
#include "src/SparseLU/SparseLU_gemm_kernel.h"
#include "src/SparseLU/SparseLU_Structs.h"
#include "src/SparseLU/SparseLU_SupernodalMatrix.h"
#include "src/SparseLU/SparseLUImpl.h"
#include "src/SparseCore/SparseColEtree.h"
#include "src/SparseLU/SparseLU_Memory.h"
#include "src/SparseLU/SparseLU_heap_relax_snode.h"
#include "src/SparseLU/SparseLU_relax_snode.h"
#include "src/SparseLU/SparseLU_pivotL.h"
#include "src/SparseLU/SparseLU_panel_dfs.h"
#include "src/SparseLU/SparseLU_kernel_bmod.h"
#include "src/SparseLU/SparseLU_panel_bmod.h"
#include "src/SparseLU/SparseLU_column_dfs.h"
#include "src/SparseLU/SparseLU_column_bmod.h"
#include "src/SparseLU/SparseLU_copy_to_ucol.h"
#include "src/SparseLU/SparseLU_pruneL.h"
#include "src/SparseLU/SparseLU_Utils.h"
#include "src/SparseLU/SparseLU.h"
#endif // EIGEN_SPARSELU_MODULE_H

37
Eigen/SparseQR Normal file
View File

@@ -0,0 +1,37 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPARSEQR_MODULE_H
#define EIGEN_SPARSEQR_MODULE_H
#include "SparseCore"
#include "OrderingMethods"
#include "src/Core/util/DisableStupidWarnings.h"
/** \defgroup SparseQR_Module SparseQR module
* \brief Provides QR decomposition for sparse matrices
*
* This module provides a simplicial version of the left-looking Sparse QR decomposition.
* The columns of the input matrix should be reordered to limit the fill-in during the
* decomposition. Built-in methods (COLAMD, AMD) or external methods (METIS) can be used to this end.
* See the \link OrderingMethods_Module OrderingMethods\endlink module for the list
* of built-in and external ordering methods.
*
* \code
* #include <Eigen/SparseQR>
* \endcode
*
*
*/
#include "OrderingMethods"
#include "src/SparseCore/SparseColEtree.h"
#include "src/SparseQR/SparseQR.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif

View File

@@ -4,24 +4,9 @@
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_STDDEQUE_MODULE_H
#define EIGEN_STDDEQUE_MODULE_H
@@ -29,7 +14,7 @@
#include "Core"
#include <deque>
#if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */
#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...)

View File

@@ -3,24 +3,9 @@
//
// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_STDLIST_MODULE_H
#define EIGEN_STDLIST_MODULE_H
@@ -28,7 +13,7 @@
#include "Core"
#include <list>
#if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */
#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...)

View File

@@ -4,24 +4,9 @@
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_STDVECTOR_MODULE_H
#define EIGEN_STDVECTOR_MODULE_H
@@ -29,7 +14,7 @@
#include "Core"
#include <vector>
#if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */
#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...)

View File

@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SUPERLUSUPPORT_MODULE_H
#define EIGEN_SUPERLUSUPPORT_MODULE_H
@@ -36,6 +43,8 @@ namespace Eigen { struct SluMatrix; }
* - class SuperLU: a supernodal sequential LU factorization.
* - class SuperILU: a supernodal sequential incomplete LU factorization (to be used as a preconditioner for iterative methods).
*
* \warning This wrapper requires at least versions 4.0 of SuperLU. The 3.x versions are not supported.
*
* \warning When including this module, you have to use SUPERLU_EMPTY instead of EMPTY which is no longer defined because it is too polluting.
*
* \code
@@ -48,12 +57,8 @@ namespace Eigen { struct SluMatrix; }
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/SuperLUSupport/SuperLUSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_SUPERLUSUPPORT_MODULE_H

View File

@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_UMFPACKSUPPORT_MODULE_H
#define EIGEN_UMFPACKSUPPORT_MODULE_H
@@ -12,7 +19,7 @@ extern "C" {
/** \ingroup Support_modules
* \defgroup UmfPackSupport_Module UmfPackSupport module
*
* This module provides an interface to the UmfPack library which is part of the <a href="http://www.cise.ufl.edu/research/sparse/SuiteSparse/">suitesparse</a> package.
* This module provides an interface to the UmfPack library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
* It provides the following factorization class:
* - class UmfPackLU: a multifrontal sequential LU factorization.
*
@@ -26,9 +33,6 @@ extern "C" {
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/UmfPackSupport/UmfPackSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"

View File

@@ -1,7 +0,0 @@
file(GLOB Eigen_src_subdirectories "*")
escape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}")
foreach(f ${Eigen_src_subdirectories})
if(NOT f MATCHES "\\.txt" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+" )
add_subdirectory(${f})
endif()
endforeach()

View File

@@ -1,6 +0,0 @@
FILE(GLOB Eigen_Cholesky_SRCS "*.h")
INSTALL(FILES
${Eigen_Cholesky_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Cholesky COMPONENT Devel
)

View File

@@ -6,32 +6,20 @@
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2011 Timothy E. Holy <tim.holy@gmail.com >
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_LDLT_H
#define EIGEN_LDLT_H
namespace Eigen {
namespace Eigen {
namespace internal {
template<typename MatrixType, int UpLo> struct LDLT_Traits;
template<typename MatrixType, int UpLo> struct LDLT_Traits;
// PositiveSemiDef means positive semi-definite and non-zero; same for NegativeSemiDef
enum SignMatrix { PositiveSemiDef, NegativeSemiDef, ZeroSign, Indefinite };
}
/** \ingroup Cholesky_Module
@@ -40,8 +28,8 @@ template<typename MatrixType, int UpLo> struct LDLT_Traits;
*
* \brief Robust Cholesky decomposition of a matrix with pivoting
*
* \param MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition
* \param UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
* \tparam _MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition
* \tparam _UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
* The other triangular part won't be read.
*
* Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite
@@ -55,7 +43,9 @@ template<typename MatrixType, int UpLo> struct LDLT_Traits;
* Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky
* decomposition to determine whether a system of equations has a solution.
*
* \sa MatrixBase::ldlt(), class LLT
* This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
*
* \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT
*/
template<typename _MatrixType, int _UpLo> class LDLT
{
@@ -64,15 +54,15 @@ template<typename _MatrixType, int _UpLo> class LDLT
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
Options = MatrixType::Options & ~RowMajorBit, // these are the options for the TmpMatrixType, we need a ColMajor matrix here!
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
UpLo = _UpLo
};
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef typename MatrixType::Index Index;
typedef Matrix<Scalar, RowsAtCompileTime, 1, Options, MaxRowsAtCompileTime, 1> TmpMatrixType;
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
typedef typename MatrixType::StorageIndex StorageIndex;
typedef Matrix<Scalar, RowsAtCompileTime, 1, 0, MaxRowsAtCompileTime, 1> TmpMatrixType;
typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType;
@@ -84,7 +74,12 @@ template<typename _MatrixType, int _UpLo> class LDLT
* The default constructor is useful in cases in which the user intends to
* perform decompositions via LDLT::compute(const MatrixType&).
*/
LDLT() : m_matrix(), m_transpositions(), m_isInitialized(false) {}
LDLT()
: m_matrix(),
m_transpositions(),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{}
/** \brief Default Constructor with memory preallocation
*
@@ -92,25 +87,46 @@ template<typename _MatrixType, int _UpLo> class LDLT
* according to the specified problem \a size.
* \sa LDLT()
*/
LDLT(Index size)
explicit LDLT(Index size)
: m_matrix(size, size),
m_transpositions(size),
m_temporary(size),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{}
/** \brief Constructor with decomposition
*
* This calculates the decomposition for the input \a matrix.
*
* \sa LDLT(Index size)
*/
LDLT(const MatrixType& matrix)
template<typename InputType>
explicit LDLT(const EigenBase<InputType>& matrix)
: m_matrix(matrix.rows(), matrix.cols()),
m_transpositions(matrix.rows()),
m_temporary(matrix.rows()),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{
compute(matrix);
compute(matrix.derived());
}
/** \brief Constructs a LDLT factorization from a given matrix
*
* This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
*
* \sa LDLT(const EigenBase&)
*/
template<typename InputType>
explicit LDLT(EigenBase<InputType>& matrix)
: m_matrix(matrix.derived()),
m_transpositions(matrix.rows()),
m_temporary(matrix.rows()),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{
compute(matrix.derived());
}
/** Clear any existing decomposition
@@ -154,21 +170,14 @@ template<typename _MatrixType, int _UpLo> class LDLT
inline bool isPositive() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_sign == 1;
return m_sign == internal::PositiveSemiDef || m_sign == internal::ZeroSign;
}
#ifdef EIGEN2_SUPPORT
inline bool isPositiveDefinite() const
{
return isPositive();
}
#endif
/** \returns true if the matrix is negative (semidefinite) */
inline bool isNegative(void) const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_sign == -1;
return m_sign == internal::NegativeSemiDef || m_sign == internal::ZeroSign;
}
/** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
@@ -178,40 +187,41 @@ template<typename _MatrixType, int _UpLo> class LDLT
* \note_about_checking_solutions
*
* More precisely, this method solves \f$ A x = b \f$ using the decomposition \f$ A = P^T L D L^* P \f$
* by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$,
* by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$,
* \f$ L^* y_4 = y_3 \f$ and \f$ P x = y_4 \f$ in succession. If the matrix \f$ A \f$ is singular, then
* \f$ D \f$ will also be singular (all the other matrices are invertible). In that case, the
* least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function
* computes the least-square solution of \f$ A x = b \f$ is \f$ A \f$ is singular.
*
* \sa MatrixBase::ldlt()
* \sa MatrixBase::ldlt(), SelfAdjointView::ldlt()
*/
template<typename Rhs>
inline const internal::solve_retval<LDLT, Rhs>
inline const Solve<LDLT, Rhs>
solve(const MatrixBase<Rhs>& b) const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
eigen_assert(m_matrix.rows()==b.rows()
&& "LDLT::solve(): invalid number of rows of the right hand side matrix b");
return internal::solve_retval<LDLT, Rhs>(*this, b.derived());
return Solve<LDLT, Rhs>(*this, b.derived());
}
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived, typename ResultType>
bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const
{
*result = this->solve(b);
return true;
}
#endif
template<typename Derived>
bool solveInPlace(MatrixBase<Derived> &bAndX) const;
LDLT& compute(const MatrixType& matrix);
template<typename InputType>
LDLT& compute(const EigenBase<InputType>& matrix);
/** \returns an estimate of the reciprocal condition number of the matrix of
* which \c *this is the LDLT decomposition.
*/
RealScalar rcond() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return internal::rcond_estimate_helper(m_l1_norm, *this);
}
template <typename Derived>
LDLT& rankUpdate(const MatrixBase<Derived>& w,RealScalar alpha=1);
LDLT& rankUpdate(const MatrixBase<Derived>& w, const RealScalar& alpha=1);
/** \returns the internal LDLT decomposition matrix
*
@@ -225,22 +235,40 @@ template<typename _MatrixType, int _UpLo> class LDLT
MatrixType reconstructedMatrix() const;
/** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint.
*
* This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
* \code x = decomposition.adjoint().solve(b) \endcode
*/
const LDLT& adjoint() const { return *this; };
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \c NumericalIssue if the matrix.appears to be negative.
* \c NumericalIssue if the factorization failed because of a zero pivot.
*/
ComputationInfo info() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return Success;
return m_info;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename RhsType, typename DstType>
EIGEN_DEVICE_FUNC
void _solve_impl(const RhsType &rhs, DstType &dst) const;
#endif
protected:
static void check_template_parameters()
{
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
}
/** \internal
* Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U.
* The strict upper part is used during the decomposition, the strict lower
@@ -248,10 +276,12 @@ template<typename _MatrixType, int _UpLo> class LDLT
* is not stored), and the diagonal entries correspond to D.
*/
MatrixType m_matrix;
RealScalar m_l1_norm;
TranspositionType m_transpositions;
TmpMatrixType m_temporary;
int m_sign;
internal::SignMatrix m_sign;
bool m_isInitialized;
ComputationInfo m_info;
};
namespace internal {
@@ -261,50 +291,34 @@ template<int UpLo> struct ldlt_inplace;
template<> struct ldlt_inplace<Lower>
{
template<typename MatrixType, typename TranspositionType, typename Workspace>
static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, int* sign=0)
static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign)
{
using std::abs;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
typedef typename TranspositionType::StorageIndex IndexType;
eigen_assert(mat.rows()==mat.cols());
const Index size = mat.rows();
bool found_zero_pivot = false;
bool ret = true;
if (size <= 1)
{
transpositions.setIdentity();
if(sign)
*sign = real(mat.coeff(0,0))>0 ? 1:-1;
if (numext::real(mat.coeff(0,0)) > static_cast<RealScalar>(0) ) sign = PositiveSemiDef;
else if (numext::real(mat.coeff(0,0)) < static_cast<RealScalar>(0)) sign = NegativeSemiDef;
else sign = ZeroSign;
return true;
}
RealScalar cutoff(0), biggest_in_corner;
for (Index k = 0; k < size; ++k)
{
// Find largest diagonal element
Index index_of_biggest_in_corner;
biggest_in_corner = mat.diagonal().tail(size-k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
mat.diagonal().tail(size-k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
index_of_biggest_in_corner += k;
if(k == 0)
{
// The biggest overall is the point of reference to which further diagonals
// are compared; if any diagonal is negligible compared
// to the largest overall, the algorithm bails.
cutoff = abs(NumTraits<Scalar>::epsilon() * biggest_in_corner);
if(sign)
*sign = real(mat.diagonal().coeff(index_of_biggest_in_corner)) > 0 ? 1 : -1;
}
// Finish early if the matrix is not full rank.
if(biggest_in_corner < cutoff)
{
for(Index i = k; i < size; i++) transpositions.coeffRef(i) = i;
break;
}
transpositions.coeffRef(k) = index_of_biggest_in_corner;
transpositions.coeffRef(k) = IndexType(index_of_biggest_in_corner);
if(k != index_of_biggest_in_corner)
{
// apply the transposition while taking care to consider only
@@ -313,14 +327,14 @@ template<> struct ldlt_inplace<Lower>
mat.row(k).head(k).swap(mat.row(index_of_biggest_in_corner).head(k));
mat.col(k).tail(s).swap(mat.col(index_of_biggest_in_corner).tail(s));
std::swap(mat.coeffRef(k,k),mat.coeffRef(index_of_biggest_in_corner,index_of_biggest_in_corner));
for(int i=k+1;i<index_of_biggest_in_corner;++i)
for(Index i=k+1;i<index_of_biggest_in_corner;++i)
{
Scalar tmp = mat.coeffRef(i,k);
mat.coeffRef(i,k) = conj(mat.coeffRef(index_of_biggest_in_corner,i));
mat.coeffRef(index_of_biggest_in_corner,i) = conj(tmp);
mat.coeffRef(i,k) = numext::conj(mat.coeffRef(index_of_biggest_in_corner,i));
mat.coeffRef(index_of_biggest_in_corner,i) = numext::conj(tmp);
}
if(NumTraits<Scalar>::IsComplex)
mat.coeffRef(index_of_biggest_in_corner,k) = conj(mat.coeff(index_of_biggest_in_corner,k));
mat.coeffRef(index_of_biggest_in_corner,k) = numext::conj(mat.coeff(index_of_biggest_in_corner,k));
}
// partition the matrix:
@@ -334,16 +348,51 @@ template<> struct ldlt_inplace<Lower>
if(k>0)
{
temp.head(k) = mat.diagonal().head(k).asDiagonal() * A10.adjoint();
temp.head(k) = mat.diagonal().real().head(k).asDiagonal() * A10.adjoint();
mat.coeffRef(k,k) -= (A10 * temp.head(k)).value();
if(rs>0)
A21.noalias() -= A20 * temp.head(k);
}
if((rs>0) && (abs(mat.coeffRef(k,k)) > cutoff))
A21 /= mat.coeffRef(k,k);
// In some previous versions of Eigen (e.g., 3.2.1), the scaling was omitted if the pivot
// was smaller than the cutoff value. However, since LDLT is not rank-revealing
// we should only make sure that we do not introduce INF or NaN values.
// Remark that LAPACK also uses 0 as the cutoff value.
RealScalar realAkk = numext::real(mat.coeffRef(k,k));
bool pivot_is_valid = (abs(realAkk) > RealScalar(0));
if(k==0 && !pivot_is_valid)
{
// The entire diagonal is zero, there is nothing more to do
// except filling the transpositions, and checking whether the matrix is zero.
sign = ZeroSign;
for(Index j = 0; j<size; ++j)
{
transpositions.coeffRef(j) = IndexType(j);
ret = ret && (mat.col(j).tail(size-j-1).array()==Scalar(0)).all();
}
return ret;
}
if((rs>0) && pivot_is_valid)
A21 /= realAkk;
else if(rs>0)
ret = ret && (A21.array()==Scalar(0)).all();
if(found_zero_pivot && pivot_is_valid) ret = false; // factorization failed
else if(!pivot_is_valid) found_zero_pivot = true;
if (sign == PositiveSemiDef) {
if (realAkk < static_cast<RealScalar>(0)) sign = Indefinite;
} else if (sign == NegativeSemiDef) {
if (realAkk > static_cast<RealScalar>(0)) sign = Indefinite;
} else if (sign == ZeroSign) {
if (realAkk > static_cast<RealScalar>(0)) sign = PositiveSemiDef;
else if (realAkk < static_cast<RealScalar>(0)) sign = NegativeSemiDef;
}
}
return true;
return ret;
}
// Reference for the algorithm: Davis and Hager, "Multiple Rank
@@ -354,12 +403,11 @@ template<> struct ldlt_inplace<Lower>
// Here only rank-1 updates are implemented, to reduce the
// requirement for intermediate storage and improve accuracy
template<typename MatrixType, typename WDerived>
static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w, typename MatrixType::RealScalar sigma=1)
static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w, const typename MatrixType::RealScalar& sigma=1)
{
using internal::isfinite;
using numext::isfinite;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
const Index size = mat.rows();
eigen_assert(mat.cols() == size && w.size()==size);
@@ -370,13 +418,13 @@ template<> struct ldlt_inplace<Lower>
for (Index j = 0; j < size; j++)
{
// Check for termination due to an original decomposition of low-rank
if (!isfinite(alpha))
if (!(isfinite)(alpha))
break;
// Update the diagonal terms
RealScalar dj = real(mat.coeff(j,j));
RealScalar dj = numext::real(mat.coeff(j,j));
Scalar wj = w.coeff(j);
RealScalar swj2 = sigma*abs2(wj);
RealScalar swj2 = sigma*numext::abs2(wj);
RealScalar gamma = dj*alpha + swj2;
mat.coeffRef(j,j) += swj2/alpha;
@@ -387,13 +435,13 @@ template<> struct ldlt_inplace<Lower>
Index rs = size-j-1;
w.tail(rs) -= wj * mat.col(j).tail(rs);
if(gamma != 0)
mat.col(j).tail(rs) += (sigma*conj(wj)/gamma)*w.tail(rs);
mat.col(j).tail(rs) += (sigma*numext::conj(wj)/gamma)*w.tail(rs);
}
return true;
}
template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w, typename MatrixType::RealScalar sigma=1)
static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w, const typename MatrixType::RealScalar& sigma=1)
{
// Apply the permutation to the input w
tmp = transpositions * w;
@@ -405,14 +453,14 @@ template<> struct ldlt_inplace<Lower>
template<> struct ldlt_inplace<Upper>
{
template<typename MatrixType, typename TranspositionType, typename Workspace>
static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, int* sign=0)
static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign)
{
Transpose<MatrixType> matt(mat);
return ldlt_inplace<Lower>::unblocked(matt, transpositions, temp, sign);
}
template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w, typename MatrixType::RealScalar sigma=1)
static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w, const typename MatrixType::RealScalar& sigma=1)
{
Transpose<MatrixType> matt(mat);
return ldlt_inplace<Lower>::update(matt, transpositions, tmp, w.conjugate(), sigma);
@@ -423,16 +471,16 @@ template<typename MatrixType> struct LDLT_Traits<MatrixType,Lower>
{
typedef const TriangularView<const MatrixType, UnitLower> MatrixL;
typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitUpper> MatrixU;
static inline MatrixL getL(const MatrixType& m) { return m; }
static inline MatrixU getU(const MatrixType& m) { return m.adjoint(); }
static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
};
template<typename MatrixType> struct LDLT_Traits<MatrixType,Upper>
{
typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitLower> MatrixL;
typedef const TriangularView<const MatrixType, UnitUpper> MatrixU;
static inline MatrixL getL(const MatrixType& m) { return m.adjoint(); }
static inline MatrixU getU(const MatrixType& m) { return m; }
static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
};
} // end namespace internal
@@ -440,18 +488,35 @@ template<typename MatrixType> struct LDLT_Traits<MatrixType,Upper>
/** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
*/
template<typename MatrixType, int _UpLo>
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const MatrixType& a)
template<typename InputType>
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a)
{
check_template_parameters();
eigen_assert(a.rows()==a.cols());
const Index size = a.rows();
m_matrix = a;
m_matrix = a.derived();
// Compute matrix L1 norm = max abs column sum.
m_l1_norm = RealScalar(0);
// TODO move this code to SelfAdjointView
for (Index col = 0; col < size; ++col) {
RealScalar abs_col_sum;
if (_UpLo == Lower)
abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
else
abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
if (abs_col_sum > m_l1_norm)
m_l1_norm = abs_col_sum;
}
m_transpositions.resize(size);
m_isInitialized = false;
m_temporary.resize(size);
m_sign = internal::ZeroSign;
internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, &m_sign);
m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success : NumericalIssue;
m_isInitialized = true;
return *this;
@@ -464,22 +529,23 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const MatrixType& a)
*/
template<typename MatrixType, int _UpLo>
template<typename Derived>
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Derived>& w,typename NumTraits<typename MatrixType::Scalar>::Real sigma)
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Derived>& w, const typename LDLT<MatrixType,_UpLo>::RealScalar& sigma)
{
typedef typename TranspositionType::StorageIndex IndexType;
const Index size = w.rows();
if (m_isInitialized)
{
eigen_assert(m_matrix.rows()==size);
}
else
{
{
m_matrix.resize(size,size);
m_matrix.setZero();
m_transpositions.resize(size);
for (Index i = 0; i < size; i++)
m_transpositions.coeffRef(i) = i;
m_transpositions.coeffRef(i) = IndexType(i);
m_temporary.resize(size);
m_sign = sigma>=0 ? 1 : -1;
m_sign = sigma>=0 ? internal::PositiveSemiDef : internal::NegativeSemiDef;
m_isInitialized = true;
}
@@ -488,48 +554,46 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Deri
return *this;
}
namespace internal {
template<typename _MatrixType, int _UpLo, typename Rhs>
struct solve_retval<LDLT<_MatrixType,_UpLo>, Rhs>
: solve_retval_base<LDLT<_MatrixType,_UpLo>, Rhs>
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename _MatrixType, int _UpLo>
template<typename RhsType, typename DstType>
void LDLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
{
typedef LDLT<_MatrixType,_UpLo> LDLTType;
EIGEN_MAKE_SOLVE_HELPERS(LDLTType,Rhs)
eigen_assert(rhs.rows() == rows());
// dst = P b
dst = m_transpositions * rhs;
template<typename Dest> void evalTo(Dest& dst) const
// dst = L^-1 (P b)
matrixL().solveInPlace(dst);
// dst = D^-1 (L^-1 P b)
// more precisely, use pseudo-inverse of D (see bug 241)
using std::abs;
const typename Diagonal<const MatrixType>::RealReturnType vecD(vectorD());
// In some previous versions, tolerance was set to the max of 1/highest (or rather numeric_limits::min())
// and the maximal diagonal entry * epsilon as motivated by LAPACK's xGELSS:
// RealScalar tolerance = numext::maxi(vecD.array().abs().maxCoeff() * NumTraits<RealScalar>::epsilon(),RealScalar(1) / NumTraits<RealScalar>::highest());
// However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the highest
// diagonal element is not well justified and leads to numerical issues in some cases.
// Moreover, Lapack's xSYTRS routines use 0 for the tolerance.
// Using numeric_limits::min() gives us more robustness to denormals.
RealScalar tolerance = (std::numeric_limits<RealScalar>::min)();
for (Index i = 0; i < vecD.size(); ++i)
{
eigen_assert(rhs().rows() == dec().matrixLDLT().rows());
// dst = P b
dst = dec().transpositionsP() * rhs();
// dst = L^-1 (P b)
dec().matrixL().solveInPlace(dst);
// dst = D^-1 (L^-1 P b)
// more precisely, use pseudo-inverse of D (see bug 241)
using std::abs;
using std::max;
typedef typename LDLTType::MatrixType MatrixType;
typedef typename LDLTType::Scalar Scalar;
typedef typename LDLTType::RealScalar RealScalar;
const Diagonal<const MatrixType> vectorD = dec().vectorD();
RealScalar tolerance = (max)(vectorD.array().abs().maxCoeff() * NumTraits<Scalar>::epsilon(),
RealScalar(1) / NumTraits<RealScalar>::highest()); // motivated by LAPACK's xGELSS
for (Index i = 0; i < vectorD.size(); ++i) {
if(abs(vectorD(i)) > tolerance)
dst.row(i) /= vectorD(i);
else
dst.row(i).setZero();
}
// dst = L^-T (D^-1 L^-1 P b)
dec().matrixU().solveInPlace(dst);
// dst = P^-1 (L^-T D^-1 L^-1 P b) = A^-1 b
dst = dec().transpositionsP().transpose() * dst;
if(abs(vecD(i)) > tolerance)
dst.row(i) /= vecD(i);
else
dst.row(i).setZero();
}
};
// dst = L^-T (D^-1 L^-1 P b)
matrixU().solveInPlace(dst);
// dst = P^-1 (L^-T D^-1 L^-1 P b) = A^-1 b
dst = m_transpositions.transpose() * dst;
}
#endif
/** \internal use x = ldlt_object.solve(x);
*
@@ -549,8 +613,7 @@ template<typename Derived>
bool LDLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
const Index size = m_matrix.rows();
eigen_assert(size == bAndX.rows());
eigen_assert(m_matrix.rows() == bAndX.rows());
bAndX = this->solve(bAndX);
@@ -573,7 +636,7 @@ MatrixType LDLT<MatrixType,_UpLo>::reconstructedMatrix() const
// L^* P
res = matrixU() * res;
// D(L^*P)
res = vectorD().asDiagonal() * res;
res = vectorD().real().asDiagonal() * res;
// L(DL^*P)
res = matrixL() * res;
// P^T (LDL^*P)
@@ -584,6 +647,7 @@ MatrixType LDLT<MatrixType,_UpLo>::reconstructedMatrix() const
/** \cholesky_module
* \returns the Cholesky decomposition with full pivoting without square root of \c *this
* \sa MatrixBase::ldlt()
*/
template<typename MatrixType, unsigned int UpLo>
inline const LDLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
@@ -594,6 +658,7 @@ SelfAdjointView<MatrixType, UpLo>::ldlt() const
/** \cholesky_module
* \returns the Cholesky decomposition with full pivoting without square root of \c *this
* \sa SelfAdjointView::ldlt()
*/
template<typename Derived>
inline const LDLT<typename MatrixBase<Derived>::PlainObject>

View File

@@ -3,29 +3,14 @@
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_LLT_H
#define EIGEN_LLT_H
namespace Eigen {
namespace Eigen {
namespace internal{
template<typename MatrixType, int UpLo> struct LLT_Traits;
@@ -37,9 +22,9 @@ template<typename MatrixType, int UpLo> struct LLT_Traits;
*
* \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features
*
* \param MatrixType the type of the matrix of which we are computing the LL^T Cholesky decomposition
* \param UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
* The other triangular part won't be read.
* \tparam _MatrixType the type of the matrix of which we are computing the LL^T Cholesky decomposition
* \tparam _UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
* The other triangular part won't be read.
*
* This class performs a LL^T Cholesky decomposition of a symmetric, positive definite
* matrix A such that A = LL^* = U^*U, where L is lower triangular.
@@ -55,12 +40,18 @@ template<typename MatrixType, int UpLo> struct LLT_Traits;
*
* Example: \include LLT_example.cpp
* Output: \verbinclude LLT_example.out
*
* \sa MatrixBase::llt(), class LDLT
*/
/* HEY THIS DOX IS DISABLED BECAUSE THERE's A BUG EITHER HERE OR IN LDLT ABOUT THAT (OR BOTH)
* Note that during the decomposition, only the upper triangular part of A is considered. Therefore,
* the strict lower part does not have to store correct values.
*
* \b Performance: for best performance, it is recommended to use a column-major storage format
* with the Lower triangular part (the default), or, equivalently, a row-major storage format
* with the Upper triangular part. Otherwise, you might get a 20% slowdown for the full factorization
* step, and rank-updates can be up to 3 times slower.
*
* This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
*
* Note that during the decomposition, only the lower (or upper, as defined by _UpLo) triangular part of A is considered.
* Therefore, the strict lower part does not have to store correct values.
*
* \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
*/
template<typename _MatrixType, int _UpLo> class LLT
{
@@ -69,12 +60,12 @@ template<typename _MatrixType, int _UpLo> class LLT
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
Options = MatrixType::Options,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
};
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef typename MatrixType::Index Index;
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
typedef typename MatrixType::StorageIndex StorageIndex;
enum {
PacketSize = internal::packet_traits<Scalar>::size,
@@ -98,14 +89,30 @@ template<typename _MatrixType, int _UpLo> class LLT
* according to the specified problem \a size.
* \sa LLT()
*/
LLT(Index size) : m_matrix(size, size),
explicit LLT(Index size) : m_matrix(size, size),
m_isInitialized(false) {}
LLT(const MatrixType& matrix)
template<typename InputType>
explicit LLT(const EigenBase<InputType>& matrix)
: m_matrix(matrix.rows(), matrix.cols()),
m_isInitialized(false)
{
compute(matrix);
compute(matrix.derived());
}
/** \brief Constructs a LDLT factorization from a given matrix
*
* This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when
* \c MatrixType is a Eigen::Ref.
*
* \sa LLT(const EigenBase&)
*/
template<typename InputType>
explicit LLT(EigenBase<InputType>& matrix)
: m_matrix(matrix.derived()),
m_isInitialized(false)
{
compute(matrix.derived());
}
/** \returns a view of the upper triangular matrix U */
@@ -130,33 +137,33 @@ template<typename _MatrixType, int _UpLo> class LLT
* Example: \include LLT_solve.cpp
* Output: \verbinclude LLT_solve.out
*
* \sa solveInPlace(), MatrixBase::llt()
* \sa solveInPlace(), MatrixBase::llt(), SelfAdjointView::llt()
*/
template<typename Rhs>
inline const internal::solve_retval<LLT, Rhs>
inline const Solve<LLT, Rhs>
solve(const MatrixBase<Rhs>& b) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(m_matrix.rows()==b.rows()
&& "LLT::solve(): invalid number of rows of the right hand side matrix b");
return internal::solve_retval<LLT, Rhs>(*this, b.derived());
return Solve<LLT, Rhs>(*this, b.derived());
}
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived, typename ResultType>
bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const
{
*result = this->solve(b);
return true;
}
bool isPositiveDefinite() const { return true; }
#endif
template<typename Derived>
void solveInPlace(MatrixBase<Derived> &bAndX) const;
void solveInPlace(const MatrixBase<Derived> &bAndX) const;
LLT& compute(const MatrixType& matrix);
template<typename InputType>
LLT& compute(const EigenBase<InputType>& matrix);
/** \returns an estimate of the reciprocal condition number of the matrix of
* which \c *this is the Cholesky decomposition.
*/
RealScalar rcond() const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(m_info == Success && "LLT failed because matrix appears to be negative");
return internal::rcond_estimate_helper(m_l1_norm, *this);
}
/** \returns the LLT decomposition matrix
*
@@ -174,7 +181,7 @@ template<typename _MatrixType, int _UpLo> class LLT
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \c NumericalIssue if the matrix.appears to be negative.
* \c NumericalIssue if the matrix.appears not to be positive definite.
*/
ComputationInfo info() const
{
@@ -182,18 +189,38 @@ template<typename _MatrixType, int _UpLo> class LLT
return m_info;
}
/** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint.
*
* This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
* \code x = decomposition.adjoint().solve(b) \endcode
*/
const LLT& adjoint() const { return *this; };
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
template<typename VectorType>
LLT rankUpdate(const VectorType& vec, const RealScalar& sigma = 1);
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename RhsType, typename DstType>
EIGEN_DEVICE_FUNC
void _solve_impl(const RhsType &rhs, DstType &dst) const;
#endif
protected:
static void check_template_parameters()
{
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
}
/** \internal
* Used to compute and store L
* The strict upper part is not used and even not initialized.
*/
MatrixType m_matrix;
RealScalar m_l1_norm;
bool m_isInitialized;
ComputationInfo m_info;
};
@@ -203,18 +230,18 @@ namespace internal {
template<typename Scalar, int UpLo> struct llt_inplace;
template<typename MatrixType, typename VectorType>
static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma)
static Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma)
{
using std::sqrt;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
typedef typename MatrixType::ColXpr ColXpr;
typedef typename internal::remove_all<ColXpr>::type ColXprCleaned;
typedef typename ColXprCleaned::SegmentReturnType ColXprSegment;
typedef Matrix<Scalar,Dynamic,1> TempVectorType;
typedef typename TempVectorType::SegmentReturnType TempVecSegment;
int n = mat.cols();
Index n = mat.cols();
eigen_assert(mat.rows()==n && vec.size()==n);
TempVectorType temp;
@@ -226,12 +253,12 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
// i.e., for sigma > 0
temp = sqrt(sigma) * vec;
for(int i=0; i<n; ++i)
for(Index i=0; i<n; ++i)
{
JacobiRotation<Scalar> g;
g.makeGivens(mat(i,i), -temp(i), &mat(i,i));
int rs = n-i-1;
Index rs = n-i-1;
if(rs>0)
{
ColXprSegment x(mat.col(i).tail(rs));
@@ -244,12 +271,12 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
{
temp = vec;
RealScalar beta = 1;
for(int j=0; j<n; ++j)
for(Index j=0; j<n; ++j)
{
RealScalar Ljj = real(mat.coeff(j,j));
RealScalar dj = abs2(Ljj);
RealScalar Ljj = numext::real(mat.coeff(j,j));
RealScalar dj = numext::abs2(Ljj);
Scalar wj = temp.coeff(j);
RealScalar swj2 = sigma*abs2(wj);
RealScalar swj2 = sigma*numext::abs2(wj);
RealScalar gamma = dj*beta + swj2;
RealScalar x = dj + swj2/beta;
@@ -265,7 +292,7 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
{
temp.tail(rs) -= (wj/Ljj) * mat.col(j).tail(rs);
if(gamma != 0)
mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*conj(wj)/gamma)*temp.tail(rs);
mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*numext::conj(wj)/gamma)*temp.tail(rs);
}
}
}
@@ -276,10 +303,10 @@ template<typename Scalar> struct llt_inplace<Scalar, Lower>
{
typedef typename NumTraits<Scalar>::Real RealScalar;
template<typename MatrixType>
static typename MatrixType::Index unblocked(MatrixType& mat)
static Index unblocked(MatrixType& mat)
{
typedef typename MatrixType::Index Index;
using std::sqrt;
eigen_assert(mat.rows()==mat.cols());
const Index size = mat.rows();
for(Index k = 0; k < size; ++k)
@@ -290,21 +317,20 @@ template<typename Scalar> struct llt_inplace<Scalar, Lower>
Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
RealScalar x = real(mat.coeff(k,k));
RealScalar x = numext::real(mat.coeff(k,k));
if (k>0) x -= A10.squaredNorm();
if (x<=RealScalar(0))
return k;
mat.coeffRef(k,k) = x = sqrt(x);
if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();
if (rs>0) A21 *= RealScalar(1)/x;
if (rs>0) A21 /= x;
}
return -1;
}
template<typename MatrixType>
static typename MatrixType::Index blocked(MatrixType& m)
static Index blocked(MatrixType& m)
{
typedef typename MatrixType::Index Index;
eigen_assert(m.rows()==m.cols());
Index size = m.rows();
if(size<32)
@@ -329,36 +355,36 @@ template<typename Scalar> struct llt_inplace<Scalar, Lower>
Index ret;
if((ret=unblocked(A11))>=0) return k+ret;
if(rs>0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);
if(rs>0) A22.template selfadjointView<Lower>().rankUpdate(A21,-1); // bottleneck
if(rs>0) A22.template selfadjointView<Lower>().rankUpdate(A21,typename NumTraits<RealScalar>::Literal(-1)); // bottleneck
}
return -1;
}
template<typename MatrixType, typename VectorType>
static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
{
return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
}
};
template<typename Scalar> struct llt_inplace<Scalar, Upper>
{
typedef typename NumTraits<Scalar>::Real RealScalar;
template<typename MatrixType>
static EIGEN_STRONG_INLINE typename MatrixType::Index unblocked(MatrixType& mat)
static EIGEN_STRONG_INLINE Index unblocked(MatrixType& mat)
{
Transpose<MatrixType> matt(mat);
return llt_inplace<Scalar, Lower>::unblocked(matt);
}
template<typename MatrixType>
static EIGEN_STRONG_INLINE typename MatrixType::Index blocked(MatrixType& mat)
static EIGEN_STRONG_INLINE Index blocked(MatrixType& mat)
{
Transpose<MatrixType> matt(mat);
return llt_inplace<Scalar, Lower>::blocked(matt);
}
template<typename MatrixType, typename VectorType>
static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
{
Transpose<MatrixType> matt(mat);
return llt_inplace<Scalar, Lower>::rankUpdate(matt, vec.conjugate(), sigma);
@@ -369,8 +395,8 @@ template<typename MatrixType> struct LLT_Traits<MatrixType,Lower>
{
typedef const TriangularView<const MatrixType, Lower> MatrixL;
typedef const TriangularView<const typename MatrixType::AdjointReturnType, Upper> MatrixU;
static inline MatrixL getL(const MatrixType& m) { return m; }
static inline MatrixU getU(const MatrixType& m) { return m.adjoint(); }
static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
static bool inplace_decomposition(MatrixType& m)
{ return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }
};
@@ -379,8 +405,8 @@ template<typename MatrixType> struct LLT_Traits<MatrixType,Upper>
{
typedef const TriangularView<const typename MatrixType::AdjointReturnType, Lower> MatrixL;
typedef const TriangularView<const MatrixType, Upper> MatrixU;
static inline MatrixL getL(const MatrixType& m) { return m.adjoint(); }
static inline MatrixU getU(const MatrixType& m) { return m; }
static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
static bool inplace_decomposition(MatrixType& m)
{ return llt_inplace<typename MatrixType::Scalar, Upper>::blocked(m)==-1; }
};
@@ -395,12 +421,29 @@ template<typename MatrixType> struct LLT_Traits<MatrixType,Upper>
* Output: \verbinclude TutorialLinAlgComputeTwice.out
*/
template<typename MatrixType, int _UpLo>
LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const MatrixType& a)
template<typename InputType>
LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a)
{
check_template_parameters();
eigen_assert(a.rows()==a.cols());
const Index size = a.rows();
m_matrix.resize(size, size);
m_matrix = a;
if (!internal::is_same_dense(m_matrix, a.derived()))
m_matrix = a.derived();
// Compute matrix L1 norm = max abs column sum.
m_l1_norm = RealScalar(0);
// TODO move this code to SelfAdjointView
for (Index col = 0; col < size; ++col) {
RealScalar abs_col_sum;
if (_UpLo == Lower)
abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
else
abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
if (abs_col_sum > m_l1_norm)
m_l1_norm = abs_col_sum;
}
m_isInitialized = true;
bool ok = Traits::inplace_decomposition(m_matrix);
@@ -428,39 +471,33 @@ LLT<_MatrixType,_UpLo> LLT<_MatrixType,_UpLo>::rankUpdate(const VectorType& v, c
return *this;
}
namespace internal {
template<typename _MatrixType, int UpLo, typename Rhs>
struct solve_retval<LLT<_MatrixType, UpLo>, Rhs>
: solve_retval_base<LLT<_MatrixType, UpLo>, Rhs>
{
typedef LLT<_MatrixType,UpLo> LLTType;
EIGEN_MAKE_SOLVE_HELPERS(LLTType,Rhs)
template<typename Dest> void evalTo(Dest& dst) const
{
dst = rhs();
dec().solveInPlace(dst);
}
};
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename _MatrixType,int _UpLo>
template<typename RhsType, typename DstType>
void LLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
{
dst = rhs;
solveInPlace(dst);
}
#endif
/** \internal use x = llt_object.solve(x);
*
*
* This is the \em in-place version of solve().
*
* \param bAndX represents both the right-hand side matrix b and result x.
*
* \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD.
* This version avoids a copy when the right hand side matrix b is not needed anymore.
*
* This version avoids a copy when the right hand side matrix b is not
* needed anymore.
* \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
* This function will const_cast it, so constness isn't honored here.
*
* \sa LLT::solve(), MatrixBase::llt()
*/
template<typename MatrixType, int _UpLo>
template<typename Derived>
void LLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
void LLT<MatrixType,_UpLo>::solveInPlace(const MatrixBase<Derived> &bAndX) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(m_matrix.rows()==bAndX.rows());
@@ -480,6 +517,7 @@ MatrixType LLT<MatrixType,_UpLo>::reconstructedMatrix() const
/** \cholesky_module
* \returns the LLT decomposition of \c *this
* \sa SelfAdjointView::llt()
*/
template<typename Derived>
inline const LLT<typename MatrixBase<Derived>::PlainObject>
@@ -490,6 +528,7 @@ MatrixBase<Derived>::llt() const
/** \cholesky_module
* \returns the LLT decomposition of \c *this
* \sa SelfAdjointView::llt()
*/
template<typename MatrixType, unsigned int UpLo>
inline const LLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>

View File

@@ -25,78 +25,75 @@
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
********************************************************************************
* Content : Eigen bindings to Intel(R) MKL
* Content : Eigen bindings to LAPACKe
* LLt decomposition based on LAPACKE_?potrf function.
********************************************************************************
*/
#ifndef EIGEN_LLT_MKL_H
#define EIGEN_LLT_MKL_H
#include "Eigen/src/Core/util/MKL_support.h"
#include <iostream>
#ifndef EIGEN_LLT_LAPACKE_H
#define EIGEN_LLT_LAPACKE_H
namespace Eigen {
namespace internal {
template<typename Scalar> struct mkl_llt;
template<typename Scalar> struct lapacke_llt;
#define EIGEN_MKL_LLT(EIGTYPE, MKLTYPE, MKLPREFIX) \
template<> struct mkl_llt<EIGTYPE> \
#define EIGEN_LAPACKE_LLT(EIGTYPE, BLASTYPE, LAPACKE_PREFIX) \
template<> struct lapacke_llt<EIGTYPE> \
{ \
template<typename MatrixType> \
static inline typename MatrixType::Index potrf(MatrixType& m, char uplo) \
static inline Index potrf(MatrixType& m, char uplo) \
{ \
lapack_int matrix_order; \
lapack_int size, lda, info, StorageOrder; \
EIGTYPE* a; \
eigen_assert(m.rows()==m.cols()); \
/* Set up parameters for ?potrf */ \
size = m.rows(); \
size = convert_index<lapack_int>(m.rows()); \
StorageOrder = MatrixType::Flags&RowMajorBit?RowMajor:ColMajor; \
matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \
a = &(m.coeffRef(0,0)); \
lda = m.outerStride(); \
lda = convert_index<lapack_int>(m.outerStride()); \
\
info = LAPACKE_##MKLPREFIX##potrf( matrix_order, uplo, size, (MKLTYPE*)a, lda ); \
info = (info==0) ? Success : NumericalIssue; \
info = LAPACKE_##LAPACKE_PREFIX##potrf( matrix_order, uplo, size, (BLASTYPE*)a, lda ); \
info = (info==0) ? -1 : info>0 ? info-1 : size; \
return info; \
} \
}; \
template<> struct llt_inplace<EIGTYPE, Lower> \
{ \
template<typename MatrixType> \
static typename MatrixType::Index blocked(MatrixType& m) \
static Index blocked(MatrixType& m) \
{ \
return mkl_llt<EIGTYPE>::potrf(m, 'L'); \
return lapacke_llt<EIGTYPE>::potrf(m, 'L'); \
} \
template<typename MatrixType, typename VectorType> \
static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
{ return Eigen::internal::llt_rank_update_lower(mat, vec, sigma); } \
}; \
template<> struct llt_inplace<EIGTYPE, Upper> \
{ \
template<typename MatrixType> \
static typename MatrixType::Index blocked(MatrixType& m) \
static Index blocked(MatrixType& m) \
{ \
return mkl_llt<EIGTYPE>::potrf(m, 'U'); \
return lapacke_llt<EIGTYPE>::potrf(m, 'U'); \
} \
template<typename MatrixType, typename VectorType> \
static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
{ \
Transpose<MatrixType> matt(mat); \
return llt_inplace<EIGTYPE, Lower>::rankUpdate(matt, vec.conjugate(), sigma); \
} \
};
EIGEN_MKL_LLT(double, double, d)
EIGEN_MKL_LLT(float, float, s)
EIGEN_MKL_LLT(dcomplex, MKL_Complex16, z)
EIGEN_MKL_LLT(scomplex, MKL_Complex8, c)
EIGEN_LAPACKE_LLT(double, double, d)
EIGEN_LAPACKE_LLT(float, float, s)
EIGEN_LAPACKE_LLT(dcomplex, lapack_complex_double, z)
EIGEN_LAPACKE_LLT(scomplex, lapack_complex_float, c)
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_LLT_MKL_H
#endif // EIGEN_LLT_LAPACKE_H

View File

@@ -1,6 +0,0 @@
FILE(GLOB Eigen_CholmodSupport_SRCS "*.h")
INSTALL(FILES
${Eigen_CholmodSupport_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/CholmodSupport COMPONENT Devel
)

View File

@@ -3,24 +3,9 @@
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CHOLMODSUPPORT_H
#define EIGEN_CHOLMODSUPPORT_H
@@ -29,55 +14,62 @@ namespace Eigen {
namespace internal {
template<typename Scalar, typename CholmodType>
void cholmod_configure_matrix(CholmodType& mat)
{
if (internal::is_same<Scalar,float>::value)
{
mat.xtype = CHOLMOD_REAL;
mat.dtype = CHOLMOD_SINGLE;
}
else if (internal::is_same<Scalar,double>::value)
{
template<typename Scalar> struct cholmod_configure_matrix;
template<> struct cholmod_configure_matrix<double> {
template<typename CholmodType>
static void run(CholmodType& mat) {
mat.xtype = CHOLMOD_REAL;
mat.dtype = CHOLMOD_DOUBLE;
}
else if (internal::is_same<Scalar,std::complex<float> >::value)
{
mat.xtype = CHOLMOD_COMPLEX;
mat.dtype = CHOLMOD_SINGLE;
}
else if (internal::is_same<Scalar,std::complex<double> >::value)
{
};
template<> struct cholmod_configure_matrix<std::complex<double> > {
template<typename CholmodType>
static void run(CholmodType& mat) {
mat.xtype = CHOLMOD_COMPLEX;
mat.dtype = CHOLMOD_DOUBLE;
}
else
{
eigen_assert(false && "Scalar type not supported by CHOLMOD");
}
}
};
// Other scalar types are not yet suppotred by Cholmod
// template<> struct cholmod_configure_matrix<float> {
// template<typename CholmodType>
// static void run(CholmodType& mat) {
// mat.xtype = CHOLMOD_REAL;
// mat.dtype = CHOLMOD_SINGLE;
// }
// };
//
// template<> struct cholmod_configure_matrix<std::complex<float> > {
// template<typename CholmodType>
// static void run(CholmodType& mat) {
// mat.xtype = CHOLMOD_COMPLEX;
// mat.dtype = CHOLMOD_SINGLE;
// }
// };
} // namespace internal
/** Wraps the Eigen sparse matrix \a mat into a Cholmod sparse matrix object.
* Note that the data are shared.
*/
template<typename _Scalar, int _Options, typename _Index>
cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
template<typename _Scalar, int _Options, typename _StorageIndex>
cholmod_sparse viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_StorageIndex> > mat)
{
typedef SparseMatrix<_Scalar,_Options,_Index> MatrixType;
cholmod_sparse res;
res.nzmax = mat.nonZeros();
res.nrow = mat.rows();;
res.nrow = mat.rows();
res.ncol = mat.cols();
res.p = mat.outerIndexPtr();
res.i = mat.innerIndexPtr();
res.x = mat.valuePtr();
res.z = 0;
res.sorted = 1;
if(mat.isCompressed())
{
res.packed = 1;
res.nz = 0;
}
else
{
@@ -88,17 +80,21 @@ cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
res.dtype = 0;
res.stype = -1;
if (internal::is_same<_Index,int>::value)
if (internal::is_same<_StorageIndex,int>::value)
{
res.itype = CHOLMOD_INT;
}
else if (internal::is_same<_StorageIndex,long>::value)
{
res.itype = CHOLMOD_LONG;
}
else
{
eigen_assert(false && "Index type different than int is not supported yet");
eigen_assert(false && "Index type not supported yet");
}
// setup res.xtype
internal::cholmod_configure_matrix<_Scalar>(res);
internal::cholmod_configure_matrix<_Scalar>::run(res);
res.stype = 0;
@@ -108,16 +104,23 @@ cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
template<typename _Scalar, int _Options, typename _Index>
const cholmod_sparse viewAsCholmod(const SparseMatrix<_Scalar,_Options,_Index>& mat)
{
cholmod_sparse res = viewAsCholmod(mat.const_cast_derived());
cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.const_cast_derived()));
return res;
}
template<typename _Scalar, int _Options, typename _Index>
const cholmod_sparse viewAsCholmod(const SparseVector<_Scalar,_Options,_Index>& mat)
{
cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.const_cast_derived()));
return res;
}
/** Returns a view of the Eigen sparse matrix \a mat as Cholmod sparse matrix.
* The data are not copied but shared. */
template<typename _Scalar, int _Options, typename _Index, unsigned int UpLo>
cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<SparseMatrix<_Scalar,_Options,_Index>, UpLo>& mat)
cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<const SparseMatrix<_Scalar,_Options,_Index>, UpLo>& mat)
{
cholmod_sparse res = viewAsCholmod(mat.matrix().const_cast_derived());
cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.matrix().const_cast_derived()));
if(UpLo==Upper) res.stype = 1;
if(UpLo==Lower) res.stype = -1;
@@ -138,22 +141,22 @@ cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat)
res.ncol = mat.cols();
res.nzmax = res.nrow * res.ncol;
res.d = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride();
res.x = mat.derived().data();
res.x = (void*)(mat.derived().data());
res.z = 0;
internal::cholmod_configure_matrix<Scalar>(res);
internal::cholmod_configure_matrix<Scalar>::run(res);
return res;
}
/** Returns a view of the Cholmod sparse matrix \a cm as an Eigen sparse matrix.
* The data are not copied but shared. */
template<typename Scalar, int Flags, typename Index>
MappedSparseMatrix<Scalar,Flags,Index> viewAsEigen(cholmod_sparse& cm)
template<typename Scalar, int Flags, typename StorageIndex>
MappedSparseMatrix<Scalar,Flags,StorageIndex> viewAsEigen(cholmod_sparse& cm)
{
return MappedSparseMatrix<Scalar,Flags,Index>
(cm.nrow, cm.ncol, reinterpret_cast<Index*>(cm.p)[cm.ncol],
reinterpret_cast<Index*>(cm.p), reinterpret_cast<Index*>(cm.i),reinterpret_cast<Scalar*>(cm.x) );
return MappedSparseMatrix<Scalar,Flags,StorageIndex>
(cm.nrow, cm.ncol, static_cast<StorageIndex*>(cm.p)[cm.ncol],
static_cast<StorageIndex*>(cm.p), static_cast<StorageIndex*>(cm.i),static_cast<Scalar*>(cm.x) );
}
enum CholmodMode {
@@ -167,27 +170,39 @@ enum CholmodMode {
* \sa class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT
*/
template<typename _MatrixType, int _UpLo, typename Derived>
class CholmodBase : internal::noncopyable
class CholmodBase : public SparseSolverBase<Derived>
{
protected:
typedef SparseSolverBase<Derived> Base;
using Base::derived;
using Base::m_isInitialized;
public:
typedef _MatrixType MatrixType;
enum { UpLo = _UpLo };
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef MatrixType CholMatrixType;
typedef typename MatrixType::Index Index;
typedef typename MatrixType::StorageIndex StorageIndex;
enum {
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
};
public:
CholmodBase()
: m_cholmodFactor(0), m_info(Success), m_isInitialized(false)
: m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false)
{
EIGEN_STATIC_ASSERT((internal::is_same<double,RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
cholmod_start(&m_cholmod);
}
CholmodBase(const MatrixType& matrix)
: m_cholmodFactor(0), m_info(Success), m_isInitialized(false)
explicit CholmodBase(const MatrixType& matrix)
: m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false)
{
EIGEN_STATIC_ASSERT((internal::is_same<double,RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
cholmod_start(&m_cholmod);
compute(matrix);
}
@@ -199,11 +214,8 @@ class CholmodBase : internal::noncopyable
cholmod_finish(&m_cholmod);
}
inline Index cols() const { return m_cholmodFactor->n; }
inline Index rows() const { return m_cholmodFactor->n; }
Derived& derived() { return *static_cast<Derived*>(this); }
const Derived& derived() const { return *static_cast<const Derived*>(this); }
inline StorageIndex cols() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
inline StorageIndex rows() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
/** \brief Reports whether previous computation was successful.
*
@@ -224,35 +236,7 @@ class CholmodBase : internal::noncopyable
return derived();
}
/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
*
* \sa compute()
*/
template<typename Rhs>
inline const internal::solve_retval<CholmodBase, Rhs>
solve(const MatrixBase<Rhs>& b) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(rows()==b.rows()
&& "CholmodDecomposition::solve(): invalid number of rows of the right hand side matrix b");
return internal::solve_retval<CholmodBase, Rhs>(*this, b.derived());
}
/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
*
* \sa compute()
*/
template<typename Rhs>
inline const internal::sparse_solve_retval<CholmodBase, Rhs>
solve(const SparseMatrixBase<Rhs>& b) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(rows()==b.rows()
&& "CholmodDecomposition::solve(): invalid number of rows of the right hand side matrix b");
return internal::sparse_solve_retval<CholmodBase, Rhs>(*this, b.derived());
}
/** Performs a symbolic decomposition on the sparcity of \a matrix.
/** Performs a symbolic decomposition on the sparsity pattern of \a matrix.
*
* This function is particularly useful when solving for several problems having the same structure.
*
@@ -276,7 +260,7 @@ class CholmodBase : internal::noncopyable
/** Performs a numeric decomposition of \a matrix
*
* The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
* The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been performed.
*
* \sa analyzePattern()
*/
@@ -284,9 +268,10 @@ class CholmodBase : internal::noncopyable
{
eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
cholmod_factorize(&A, m_cholmodFactor, &m_cholmod);
this->m_info = Success;
cholmod_factorize_p(&A, m_shiftOffset, 0, 0, m_cholmodFactor, &m_cholmod);
// If the factorization failed, minor is the column at which it did. On success minor == n.
this->m_info = (m_cholmodFactor->minor == m_cholmodFactor->n ? Success : NumericalIssue);
m_factorizationIsOk = true;
}
@@ -297,54 +282,128 @@ class CholmodBase : internal::noncopyable
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal */
template<typename Rhs,typename Dest>
void _solve(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
{
eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
const Index size = m_cholmodFactor->n;
EIGEN_UNUSED_VARIABLE(size);
eigen_assert(size==b.rows());
// Cholmod needs column-major stoarge without inner-stride, which corresponds to the default behavior of Ref.
Ref<const Matrix<typename Rhs::Scalar,Dynamic,Dynamic,ColMajor> > b_ref(b.derived());
// note: cd stands for Cholmod Dense
cholmod_dense b_cd = viewAsCholmod(b.const_cast_derived());
cholmod_dense b_cd = viewAsCholmod(b_ref);
cholmod_dense* x_cd = cholmod_solve(CHOLMOD_A, m_cholmodFactor, &b_cd, &m_cholmod);
if(!x_cd)
{
this->m_info = NumericalIssue;
return;
}
// TODO optimize this copy by swapping when possible (be carreful with alignment, etc.)
// TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
dest = Matrix<Scalar,Dest::RowsAtCompileTime,Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x),b.rows(),b.cols());
cholmod_free_dense(&x_cd, &m_cholmod);
}
/** \internal */
template<typename RhsScalar, int RhsOptions, typename RhsIndex, typename DestScalar, int DestOptions, typename DestIndex>
void _solve(const SparseMatrix<RhsScalar,RhsOptions,RhsIndex> &b, SparseMatrix<DestScalar,DestOptions,DestIndex> &dest) const
template<typename RhsDerived, typename DestDerived>
void _solve_impl(const SparseMatrixBase<RhsDerived> &b, SparseMatrixBase<DestDerived> &dest) const
{
eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
const Index size = m_cholmodFactor->n;
EIGEN_UNUSED_VARIABLE(size);
eigen_assert(size==b.rows());
// note: cs stands for Cholmod Sparse
cholmod_sparse b_cs = viewAsCholmod(b);
Ref<SparseMatrix<typename RhsDerived::Scalar,ColMajor,typename RhsDerived::StorageIndex> > b_ref(b.const_cast_derived());
cholmod_sparse b_cs = viewAsCholmod(b_ref);
cholmod_sparse* x_cs = cholmod_spsolve(CHOLMOD_A, m_cholmodFactor, &b_cs, &m_cholmod);
if(!x_cs)
{
this->m_info = NumericalIssue;
return;
}
// TODO optimize this copy by swapping when possible (be carreful with alignment, etc.)
dest = viewAsEigen<DestScalar,DestOptions,DestIndex>(*x_cs);
// TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
dest.derived() = viewAsEigen<typename DestDerived::Scalar,ColMajor,typename DestDerived::StorageIndex>(*x_cs);
cholmod_free_sparse(&x_cs, &m_cholmod);
}
#endif // EIGEN_PARSED_BY_DOXYGEN
/** Sets the shift parameter that will be used to adjust the diagonal coefficients during the numerical factorization.
*
* During the numerical factorization, an offset term is added to the diagonal coefficients:\n
* \c d_ii = \a offset + \c d_ii
*
* The default is \a offset=0.
*
* \returns a reference to \c *this.
*/
Derived& setShift(const RealScalar& offset)
{
m_shiftOffset[0] = double(offset);
return derived();
}
/** \returns the determinant of the underlying matrix from the current factorization */
Scalar determinant() const
{
using std::exp;
return exp(logDeterminant());
}
/** \returns the log determinant of the underlying matrix from the current factorization */
Scalar logDeterminant() const
{
using std::log;
using numext::real;
eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
RealScalar logDet = 0;
Scalar *x = static_cast<Scalar*>(m_cholmodFactor->x);
if (m_cholmodFactor->is_super)
{
// Supernodal factorization stored as a packed list of dense column-major blocs,
// as described by the following structure:
// super[k] == index of the first column of the j-th super node
StorageIndex *super = static_cast<StorageIndex*>(m_cholmodFactor->super);
// pi[k] == offset to the description of row indices
StorageIndex *pi = static_cast<StorageIndex*>(m_cholmodFactor->pi);
// px[k] == offset to the respective dense block
StorageIndex *px = static_cast<StorageIndex*>(m_cholmodFactor->px);
Index nb_super_nodes = m_cholmodFactor->nsuper;
for (Index k=0; k < nb_super_nodes; ++k)
{
StorageIndex ncols = super[k + 1] - super[k];
StorageIndex nrows = pi[k + 1] - pi[k];
Map<const Array<Scalar,1,Dynamic>, 0, InnerStride<> > sk(x + px[k], ncols, InnerStride<>(nrows+1));
logDet += sk.real().log().sum();
}
}
else
{
// Simplicial factorization stored as standard CSC matrix.
StorageIndex *p = static_cast<StorageIndex*>(m_cholmodFactor->p);
Index size = m_cholmodFactor->n;
for (Index k=0; k<size; ++k)
logDet += log(real( x[p[k]] ));
}
if (m_cholmodFactor->is_ll)
logDet *= 2.0;
return logDet;
};
template<typename Stream>
void dumpMemory(Stream& s)
void dumpMemory(Stream& /*s*/)
{}
protected:
mutable cholmod_common m_cholmod;
cholmod_factor* m_cholmodFactor;
double m_shiftOffset[2];
mutable ComputationInfo m_info;
bool m_isInitialized;
int m_factorizationIsOk;
int m_analysisIsOk;
};
@@ -355,17 +414,21 @@ class CholmodBase : internal::noncopyable
*
* This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization
* using the Cholmod library.
* This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Thefore, it has little practical interest.
* The sparse matrix A must be selfajoint and positive definite. The vectors or matrices
* This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Therefore, it has little practical interest.
* The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
* or Upper. Default is Lower.
*
* \implsparsesolverconcept
*
* This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
*
* \sa \ref TutorialSparseDirectSolvers, class CholmodSupernodalLLT, class SimplicialLLT
* \warning Only double precision real and complex scalar types are supported by Cholmod.
*
* \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLLT
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT<_MatrixType, _UpLo> >
@@ -382,7 +445,7 @@ class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimpl
CholmodSimplicialLLT(const MatrixType& matrix) : Base()
{
init();
compute(matrix);
this->compute(matrix);
}
~CholmodSimplicialLLT() {}
@@ -402,17 +465,21 @@ class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimpl
*
* This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization
* using the Cholmod library.
* This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Thefore, it has little practical interest.
* The sparse matrix A must be selfajoint and positive definite. The vectors or matrices
* This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Therefore, it has little practical interest.
* The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
* or Upper. Default is Lower.
*
* \implsparsesolverconcept
*
* This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
*
* \sa \ref TutorialSparseDirectSolvers, class CholmodSupernodalLLT, class SimplicialLDLT
* \warning Only double precision real and complex scalar types are supported by Cholmod.
*
* \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLDLT
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT<_MatrixType, _UpLo> >
@@ -429,7 +496,7 @@ class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimp
CholmodSimplicialLDLT(const MatrixType& matrix) : Base()
{
init();
compute(matrix);
this->compute(matrix);
}
~CholmodSimplicialLDLT() {}
@@ -448,16 +515,20 @@ class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimp
* This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization
* using the Cholmod library.
* This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM.
* The sparse matrix A must be selfajoint and positive definite. The vectors or matrices
* The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
* or Upper. Default is Lower.
*
* \implsparsesolverconcept
*
* This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
*
* \sa \ref TutorialSparseDirectSolvers
* \warning Only double precision real and complex scalar types are supported by Cholmod.
*
* \sa \ref TutorialSparseSolverConcept
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT<_MatrixType, _UpLo> >
@@ -474,7 +545,7 @@ class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSuper
CholmodSupernodalLLT(const MatrixType& matrix) : Base()
{
init();
compute(matrix);
this->compute(matrix);
}
~CholmodSupernodalLLT() {}
@@ -491,7 +562,7 @@ class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSuper
* \brief A general Cholesky factorization and solver based on Cholmod
*
* This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization
* using the Cholmod library. The sparse matrix A must be selfajoint and positive definite. The vectors or matrices
* using the Cholmod library. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* This variant permits to change the underlying Cholesky method at runtime.
@@ -502,9 +573,13 @@ class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSuper
* \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
* or Upper. Default is Lower.
*
* \implsparsesolverconcept
*
* This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
*
* \sa \ref TutorialSparseDirectSolvers
* \warning Only double precision real and complex scalar types are supported by Cholmod.
*
* \sa \ref TutorialSparseSolverConcept
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecomposition<_MatrixType, _UpLo> >
@@ -521,7 +596,7 @@ class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecom
CholmodDecomposition(const MatrixType& matrix) : Base()
{
init();
compute(matrix);
this->compute(matrix);
}
~CholmodDecomposition() {}
@@ -559,36 +634,6 @@ class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecom
}
};
namespace internal {
template<typename _MatrixType, int _UpLo, typename Derived, typename Rhs>
struct solve_retval<CholmodBase<_MatrixType,_UpLo,Derived>, Rhs>
: solve_retval_base<CholmodBase<_MatrixType,_UpLo,Derived>, Rhs>
{
typedef CholmodBase<_MatrixType,_UpLo,Derived> Dec;
EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
template<typename Dest> void evalTo(Dest& dst) const
{
dec()._solve(rhs(),dst);
}
};
template<typename _MatrixType, int _UpLo, typename Derived, typename Rhs>
struct sparse_solve_retval<CholmodBase<_MatrixType,_UpLo,Derived>, Rhs>
: sparse_solve_retval_base<CholmodBase<_MatrixType,_UpLo,Derived>, Rhs>
{
typedef CholmodBase<_MatrixType,_UpLo,Derived> Dec;
EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs)
template<typename Dest> void evalTo(Dest& dst) const
{
dec()._solve(rhs(),dst);
}
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_CHOLMODSUPPORT_H

View File

@@ -3,31 +3,25 @@
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ARRAY_H
#define EIGEN_ARRAY_H
namespace Eigen {
/** \class Array
namespace internal {
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct traits<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > : traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
{
typedef ArrayXpr XprKind;
typedef ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > XprBase;
};
}
/** \class Array
* \ingroup Core_Module
*
* \brief General-purpose arrays with easy API for coefficient-wise operations
@@ -39,20 +33,14 @@ namespace Eigen {
* API for the %Matrix class provides easy access to linear-algebra
* operations.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
* See documentation of class Matrix for detailed information on the template parameters
* storage layout.
*
* \sa \ref TutorialArrayClass, \ref TopicClassHierarchy
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
*
* \sa \blank \ref TutorialArrayClass, \ref TopicClassHierarchy
*/
namespace internal {
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct traits<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > : traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
{
typedef ArrayXpr XprKind;
typedef ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > XprBase;
};
}
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
class Array
: public PlainObjectBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
@@ -84,11 +72,27 @@ class Array
* the usage of 'using'. This should be done only for operator=.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array& operator=(const EigenBase<OtherDerived> &other)
{
return Base::operator=(other);
}
/** Set all the entries to \a value.
* \sa DenseBase::setConstant(), DenseBase::fill()
*/
/* This overload is needed because the usage of
* using Base::operator=;
* fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
* the usage of 'using'. This should be done only for operator=.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array& operator=(const Scalar &value)
{
Base::setConstant(value);
return *this;
}
/** Copies the value of the expression \a other into \c *this with automatic resizing.
*
* *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
@@ -99,7 +103,8 @@ class Array
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE Array& operator=(const ArrayBase<OtherDerived>& other)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array& operator=(const DenseBase<OtherDerived>& other)
{
return Base::_set(other);
}
@@ -107,11 +112,12 @@ class Array
/** This is a special case of the templated operator=. Its purpose is to
* prevent a default operator= from hiding the templated operator=.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array& operator=(const Array& other)
{
return Base::_set(other);
}
/** Default constructor.
*
* For fixed-size matrices, does nothing.
@@ -122,122 +128,124 @@ class Array
*
* \sa resize(Index,Index)
*/
EIGEN_STRONG_INLINE explicit Array() : Base()
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array() : Base()
{
Base::_check_template_params();
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
// FIXME is it still needed ??
/** \internal */
EIGEN_DEVICE_FUNC
Array(internal::constructor_without_unaligned_array_assert)
: Base(internal::constructor_without_unaligned_array_assert())
{
Base::_check_template_params();
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#endif
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
Array(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
: Base(std::move(other))
{
Base::_check_template_params();
if (RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic)
Base::_set_noalias(other);
}
EIGEN_DEVICE_FUNC
Array& operator=(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
{
other.swap(*this);
return *this;
}
#endif
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit Array(const T& x)
{
Base::_check_template_params();
Base::template _init1<T>(x);
}
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const T0& val0, const T1& val1)
{
Base::_check_template_params();
this->template _init2<T0,T1>(val0, val1);
}
#else
/** \brief Constructs a fixed-sized array initialized with coefficients starting at \a data */
EIGEN_DEVICE_FUNC explicit Array(const Scalar *data);
/** Constructs a vector or row-vector with given dimension. \only_for_vectors
*
* Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
* it is redundant to pass the dimension here, so it makes more sense to use the default
* constructor Matrix() instead.
* constructor Array() instead.
*/
EIGEN_STRONG_INLINE explicit Array(Index dim)
: Base(dim, RowsAtCompileTime == 1 ? 1 : dim, ColsAtCompileTime == 1 ? 1 : dim)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Array)
eigen_assert(dim >= 0);
eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim);
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename T0, typename T1>
EIGEN_STRONG_INLINE Array(const T0& x, const T1& y)
{
Base::_check_template_params();
this->template _init2<T0,T1>(x, y);
}
#else
/** constructs an uninitialized matrix with \a rows rows and \a cols columns.
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit Array(Index dim);
/** constructs an initialized 1x1 Array with the given coefficient */
Array(const Scalar& value);
/** constructs an uninitialized array with \a rows rows and \a cols columns.
*
* This is useful for dynamic-size matrices. For fixed-size matrices,
* This is useful for dynamic-size arrays. For fixed-size arrays,
* it is redundant to pass these parameters, so one should use the default constructor
* Matrix() instead. */
* Array() instead. */
Array(Index rows, Index cols);
/** constructs an initialized 2D vector with given coefficients */
Array(const Scalar& x, const Scalar& y);
Array(const Scalar& val0, const Scalar& val1);
#endif
/** constructs an initialized 3D vector with given coefficients */
EIGEN_STRONG_INLINE Array(const Scalar& x, const Scalar& y, const Scalar& z)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
m_storage.data()[2] = z;
m_storage.data()[0] = val0;
m_storage.data()[1] = val1;
m_storage.data()[2] = val2;
}
/** constructs an initialized 4D vector with given coefficients */
EIGEN_STRONG_INLINE Array(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2, const Scalar& val3)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
m_storage.data()[2] = z;
m_storage.data()[3] = w;
m_storage.data()[0] = val0;
m_storage.data()[1] = val1;
m_storage.data()[2] = val2;
m_storage.data()[3] = val3;
}
explicit Array(const Scalar *data);
/** Constructor copying the value of the expression \a other */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Array(const ArrayBase<OtherDerived>& other)
: Base(other.rows() * other.cols(), other.rows(), other.cols())
{
Base::_check_template_params();
Base::_set_noalias(other);
}
/** Copy constructor */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const Array& other)
: Base(other.rows() * other.cols(), other.rows(), other.cols())
{
Base::_check_template_params();
Base::_set_noalias(other);
}
/** Copy constructor with in-place evaluation */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Array(const ReturnByValue<OtherDerived>& other)
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
other.evalTo(*this);
}
: Base(other)
{ }
private:
struct PrivateType {};
public:
/** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Array(const EigenBase<OtherDerived> &other)
: Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
*this = other;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const EigenBase<OtherDerived> &other,
typename internal::enable_if<internal::is_convertible<typename OtherDerived::Scalar,Scalar>::value,
PrivateType>::type = PrivateType())
: Base(other.derived())
{ }
/** Override MatrixBase::swap() since for dynamic-sized matrices of same type it is enough to swap the
* data pointers.
*/
template<typename OtherDerived>
void swap(ArrayBase<OtherDerived> const & other)
{ this->_swap(other.derived()); }
inline Index innerStride() const { return 1; }
inline Index outerStride() const { return this->innerSize(); }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return 1; }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return this->innerSize(); }
#ifdef EIGEN_ARRAY_PLUGIN
#include EIGEN_ARRAY_PLUGIN

View File

@@ -3,24 +3,9 @@
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ARRAYBASE_H
#define EIGEN_ARRAYBASE_H
@@ -47,7 +32,7 @@ template<typename ExpressionType> class MatrixWrapper;
* \tparam Derived is the derived type, e.g., an array or an expression type.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
*
* \sa class MatrixBase, \ref TopicClassHierarchy
*/
@@ -61,11 +46,7 @@ template<typename Derived> class ArrayBase
typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl;
using internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar,
typename NumTraits<typename internal::traits<Derived>::Scalar>::Real>::operator*;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
@@ -79,8 +60,7 @@ template<typename Derived> class ArrayBase
using Base::MaxSizeAtCompileTime;
using Base::IsVectorAtCompileTime;
using Base::Flags;
using Base::CoeffReadCost;
using Base::derived;
using Base::const_cast_derived;
using Base::rows;
@@ -100,25 +80,14 @@ template<typename Derived> class ArrayBase
#endif // not EIGEN_PARSED_BY_DOXYGEN
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal the plain matrix type corresponding to this expression. Note that is not necessarily
* exactly the return type of eval(): in the case of plain matrices, the return type of eval() is a const
* reference to a matrix, not a matrix! It is however guaranteed that the return type of eval() is either
* PlainObject or const PlainObject&.
*/
typedef Array<typename internal::traits<Derived>::Scalar,
internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime,
AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime
> PlainObject;
typedef typename Base::PlainObject PlainObject;
/** \internal Represents a matrix with all coefficients equal to one another*/
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType;
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
#endif // not EIGEN_PARSED_BY_DOXYGEN
#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::ArrayBase
#define EIGEN_DOC_UNARY_ADDONS(X,Y)
# include "../plugins/CommonCwiseUnaryOps.h"
# include "../plugins/MatrixCwiseUnaryOps.h"
# include "../plugins/ArrayCwiseUnaryOps.h"
@@ -129,44 +98,62 @@ template<typename Derived> class ArrayBase
# include EIGEN_ARRAYBASE_PLUGIN
# endif
#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
#undef EIGEN_DOC_UNARY_ADDONS
/** Special case of the template operator=, in order to prevent the compiler
* from generating a default operator= (issue hit with g++ 4.1)
*/
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const ArrayBase& other)
{
return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
/** Set all the entries to \a value.
* \sa DenseBase::setConstant(), DenseBase::fill() */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const Scalar &value)
{ Base::setConstant(value); return derived(); }
Derived& operator+=(const Scalar& scalar)
{ return *this = derived() + scalar; }
Derived& operator-=(const Scalar& scalar)
{ return *this = derived() - scalar; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator+=(const Scalar& scalar);
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator-=(const Scalar& scalar);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator+=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator-=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator*=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator/=(const ArrayBase<OtherDerived>& other);
public:
EIGEN_DEVICE_FUNC
ArrayBase<Derived>& array() { return *this; }
EIGEN_DEVICE_FUNC
const ArrayBase<Derived>& array() const { return *this; }
/** \returns an \link MatrixBase Matrix \endlink expression of this array
/** \returns an \link Eigen::MatrixBase Matrix \endlink expression of this array
* \sa MatrixBase::array() */
MatrixWrapper<Derived> matrix() { return derived(); }
const MatrixWrapper<const Derived> matrix() const { return derived(); }
EIGEN_DEVICE_FUNC
MatrixWrapper<Derived> matrix() { return MatrixWrapper<Derived>(derived()); }
EIGEN_DEVICE_FUNC
const MatrixWrapper<const Derived> matrix() const { return MatrixWrapper<const Derived>(derived()); }
// template<typename Dest>
// inline void evalTo(Dest& dst) const { dst = matrix(); }
protected:
EIGEN_DEVICE_FUNC
ArrayBase() : Base() {}
private:
@@ -188,11 +175,10 @@ template<typename Derived> class ArrayBase
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator-=(const ArrayBase<OtherDerived> &other)
{
SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, Derived, OtherDerived> tmp(derived());
tmp = other.derived();
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@@ -202,11 +188,10 @@ ArrayBase<Derived>::operator-=(const ArrayBase<OtherDerived> &other)
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator+=(const ArrayBase<OtherDerived>& other)
{
SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, Derived, OtherDerived> tmp(derived());
tmp = other.derived();
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@@ -216,11 +201,10 @@ ArrayBase<Derived>::operator+=(const ArrayBase<OtherDerived>& other)
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator*=(const ArrayBase<OtherDerived>& other)
{
SelfCwiseBinaryOp<internal::scalar_product_op<Scalar>, Derived, OtherDerived> tmp(derived());
tmp = other.derived();
call_assignment(derived(), other.derived(), internal::mul_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@@ -230,11 +214,10 @@ ArrayBase<Derived>::operator*=(const ArrayBase<OtherDerived>& other)
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator/=(const ArrayBase<OtherDerived>& other)
{
SelfCwiseBinaryOp<internal::scalar_quotient_op<Scalar>, Derived, OtherDerived> tmp(derived());
tmp = other.derived();
call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}

View File

@@ -3,24 +3,9 @@
//
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ARRAYWRAPPER_H
#define EIGEN_ARRAYWRAPPER_H
@@ -44,6 +29,12 @@ struct traits<ArrayWrapper<ExpressionType> >
: public traits<typename remove_all<typename ExpressionType::Nested>::type >
{
typedef ArrayXpr XprKind;
// Let's remove NestByRefBit
enum {
Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
};
};
}
@@ -54,6 +45,7 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
typedef ArrayBase<ArrayWrapper> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(ArrayWrapper)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ArrayWrapper)
typedef typename internal::remove_all<ExpressionType>::type NestedExpression;
typedef typename internal::conditional<
internal::is_lvalue<ExpressionType>::value,
@@ -61,81 +53,59 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
const Scalar
>::type ScalarWithConstIfNotLvalue;
typedef typename internal::nested<ExpressionType>::type NestedExpressionType;
typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
inline ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
using Base::coeffRef;
EIGEN_DEVICE_FUNC
explicit EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
EIGEN_DEVICE_FUNC
inline Index rows() const { return m_expression.rows(); }
EIGEN_DEVICE_FUNC
inline Index cols() const { return m_expression.cols(); }
EIGEN_DEVICE_FUNC
inline Index outerStride() const { return m_expression.outerStride(); }
EIGEN_DEVICE_FUNC
inline Index innerStride() const { return m_expression.innerStride(); }
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return m_expression.data(); }
inline CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.coeff(row, col);
}
inline Scalar& coeffRef(Index row, Index col)
{
return m_expression.const_cast_derived().coeffRef(row, col);
}
inline const Scalar& coeffRef(Index row, Index col) const
{
return m_expression.const_cast_derived().coeffRef(row, col);
}
inline CoeffReturnType coeff(Index index) const
{
return m_expression.coeff(index);
}
inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
return m_expression.coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
return m_expression.const_cast_derived().coeffRef(index);
}
template<int LoadMode>
inline const PacketScalar packet(Index row, Index col) const
{
return m_expression.template packet<LoadMode>(row, col);
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(row, col, x);
}
template<int LoadMode>
inline const PacketScalar packet(Index index) const
{
return m_expression.template packet<LoadMode>(index);
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(index, x);
return m_expression.coeffRef(index);
}
template<typename Dest>
EIGEN_DEVICE_FUNC
inline void evalTo(Dest& dst) const { dst = m_expression; }
const typename internal::remove_all<NestedExpressionType>::type&
EIGEN_DEVICE_FUNC
nestedExpression() const
{
return m_expression;
}
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index) */
EIGEN_DEVICE_FUNC
void resize(Index newSize) { m_expression.resize(newSize); }
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index,Index)*/
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols) { m_expression.resize(rows,cols); }
protected:
NestedExpressionType m_expression;
};
@@ -157,6 +127,12 @@ struct traits<MatrixWrapper<ExpressionType> >
: public traits<typename remove_all<typename ExpressionType::Nested>::type >
{
typedef MatrixXpr XprKind;
// Let's remove NestByRefBit
enum {
Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
};
};
}
@@ -167,6 +143,7 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
typedef MatrixBase<MatrixWrapper<ExpressionType> > Base;
EIGEN_DENSE_PUBLIC_INTERFACE(MatrixWrapper)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(MatrixWrapper)
typedef typename internal::remove_all<ExpressionType>::type NestedExpression;
typedef typename internal::conditional<
internal::is_lvalue<ExpressionType>::value,
@@ -174,78 +151,55 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
const Scalar
>::type ScalarWithConstIfNotLvalue;
typedef typename internal::nested<ExpressionType>::type NestedExpressionType;
typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {}
using Base::coeffRef;
EIGEN_DEVICE_FUNC
explicit inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {}
EIGEN_DEVICE_FUNC
inline Index rows() const { return m_expression.rows(); }
EIGEN_DEVICE_FUNC
inline Index cols() const { return m_expression.cols(); }
EIGEN_DEVICE_FUNC
inline Index outerStride() const { return m_expression.outerStride(); }
EIGEN_DEVICE_FUNC
inline Index innerStride() const { return m_expression.innerStride(); }
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return m_expression.data(); }
inline CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.coeff(row, col);
}
inline Scalar& coeffRef(Index row, Index col)
{
return m_expression.const_cast_derived().coeffRef(row, col);
}
inline const Scalar& coeffRef(Index row, Index col) const
{
return m_expression.derived().coeffRef(row, col);
}
inline CoeffReturnType coeff(Index index) const
{
return m_expression.coeff(index);
}
inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
return m_expression.derived().coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
return m_expression.const_cast_derived().coeffRef(index);
}
template<int LoadMode>
inline const PacketScalar packet(Index row, Index col) const
{
return m_expression.template packet<LoadMode>(row, col);
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(row, col, x);
}
template<int LoadMode>
inline const PacketScalar packet(Index index) const
{
return m_expression.template packet<LoadMode>(index);
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(index, x);
return m_expression.coeffRef(index);
}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<NestedExpressionType>::type&
nestedExpression() const
{
return m_expression;
}
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index) */
EIGEN_DEVICE_FUNC
void resize(Index newSize) { m_expression.resize(newSize); }
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index,Index)*/
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols) { m_expression.resize(rows,cols); }
protected:
NestedExpressionType m_expression;
};

View File

@@ -5,495 +5,15 @@
// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ASSIGN_H
#define EIGEN_ASSIGN_H
namespace Eigen {
namespace internal {
/***************************************************************************
* Part 1 : the logic deciding a strategy for traversal and unrolling *
***************************************************************************/
template <typename Derived, typename OtherDerived>
struct assign_traits
{
public:
enum {
DstIsAligned = Derived::Flags & AlignedBit,
DstHasDirectAccess = Derived::Flags & DirectAccessBit,
SrcIsAligned = OtherDerived::Flags & AlignedBit,
JointAlignment = bool(DstIsAligned) && bool(SrcIsAligned) ? Aligned : Unaligned
};
private:
enum {
InnerSize = int(Derived::IsVectorAtCompileTime) ? int(Derived::SizeAtCompileTime)
: int(Derived::Flags)&RowMajorBit ? int(Derived::ColsAtCompileTime)
: int(Derived::RowsAtCompileTime),
InnerMaxSize = int(Derived::IsVectorAtCompileTime) ? int(Derived::MaxSizeAtCompileTime)
: int(Derived::Flags)&RowMajorBit ? int(Derived::MaxColsAtCompileTime)
: int(Derived::MaxRowsAtCompileTime),
MaxSizeAtCompileTime = Derived::SizeAtCompileTime,
PacketSize = packet_traits<typename Derived::Scalar>::size
};
enum {
StorageOrdersAgree = (int(Derived::IsRowMajor) == int(OtherDerived::IsRowMajor)),
MightVectorize = StorageOrdersAgree
&& (int(Derived::Flags) & int(OtherDerived::Flags) & ActualPacketAccessBit),
MayInnerVectorize = MightVectorize && int(InnerSize)!=Dynamic && int(InnerSize)%int(PacketSize)==0
&& int(DstIsAligned) && int(SrcIsAligned),
MayLinearize = StorageOrdersAgree && (int(Derived::Flags) & int(OtherDerived::Flags) & LinearAccessBit),
MayLinearVectorize = MightVectorize && MayLinearize && DstHasDirectAccess
&& (DstIsAligned || MaxSizeAtCompileTime == Dynamic),
/* If the destination isn't aligned, we have to do runtime checks and we don't unroll,
so it's only good for large enough sizes. */
MaySliceVectorize = MightVectorize && DstHasDirectAccess
&& (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=3*PacketSize)
/* slice vectorization can be slow, so we only want it if the slices are big, which is
indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block
in a fixed-size matrix */
};
public:
enum {
Traversal = int(MayInnerVectorize) ? int(InnerVectorizedTraversal)
: int(MayLinearVectorize) ? int(LinearVectorizedTraversal)
: int(MaySliceVectorize) ? int(SliceVectorizedTraversal)
: int(MayLinearize) ? int(LinearTraversal)
: int(DefaultTraversal),
Vectorized = int(Traversal) == InnerVectorizedTraversal
|| int(Traversal) == LinearVectorizedTraversal
|| int(Traversal) == SliceVectorizedTraversal
};
private:
enum {
UnrollingLimit = EIGEN_UNROLLING_LIMIT * (Vectorized ? int(PacketSize) : 1),
MayUnrollCompletely = int(Derived::SizeAtCompileTime) != Dynamic
&& int(OtherDerived::CoeffReadCost) != Dynamic
&& int(Derived::SizeAtCompileTime) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit),
MayUnrollInner = int(InnerSize) != Dynamic
&& int(OtherDerived::CoeffReadCost) != Dynamic
&& int(InnerSize) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit)
};
public:
enum {
Unrolling = (int(Traversal) == int(InnerVectorizedTraversal) || int(Traversal) == int(DefaultTraversal))
? (
int(MayUnrollCompletely) ? int(CompleteUnrolling)
: int(MayUnrollInner) ? int(InnerUnrolling)
: int(NoUnrolling)
)
: int(Traversal) == int(LinearVectorizedTraversal)
? ( bool(MayUnrollCompletely) && bool(DstIsAligned) ? int(CompleteUnrolling) : int(NoUnrolling) )
: int(Traversal) == int(LinearTraversal)
? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling) : int(NoUnrolling) )
: int(NoUnrolling)
};
#ifdef EIGEN_DEBUG_ASSIGN
static void debug()
{
EIGEN_DEBUG_VAR(DstIsAligned)
EIGEN_DEBUG_VAR(SrcIsAligned)
EIGEN_DEBUG_VAR(JointAlignment)
EIGEN_DEBUG_VAR(InnerSize)
EIGEN_DEBUG_VAR(InnerMaxSize)
EIGEN_DEBUG_VAR(PacketSize)
EIGEN_DEBUG_VAR(StorageOrdersAgree)
EIGEN_DEBUG_VAR(MightVectorize)
EIGEN_DEBUG_VAR(MayLinearize)
EIGEN_DEBUG_VAR(MayInnerVectorize)
EIGEN_DEBUG_VAR(MayLinearVectorize)
EIGEN_DEBUG_VAR(MaySliceVectorize)
EIGEN_DEBUG_VAR(Traversal)
EIGEN_DEBUG_VAR(UnrollingLimit)
EIGEN_DEBUG_VAR(MayUnrollCompletely)
EIGEN_DEBUG_VAR(MayUnrollInner)
EIGEN_DEBUG_VAR(Unrolling)
}
#endif
};
/***************************************************************************
* Part 2 : meta-unrollers
***************************************************************************/
/************************
*** Default traversal ***
************************/
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_DefaultTraversal_CompleteUnrolling
{
enum {
outer = Index / Derived1::InnerSizeAtCompileTime,
inner = Index % Derived1::InnerSizeAtCompileTime
};
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
dst.copyCoeffByOuterInner(outer, inner, src);
assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {}
};
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_DefaultTraversal_InnerUnrolling
{
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src, int outer)
{
dst.copyCoeffByOuterInner(outer, Index, src);
assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src, outer);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &, int) {}
};
/***********************
*** Linear traversal ***
***********************/
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_LinearTraversal_CompleteUnrolling
{
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
dst.copyCoeff(Index, src);
assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {}
};
/**************************
*** Inner vectorization ***
**************************/
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_innervec_CompleteUnrolling
{
enum {
outer = Index / Derived1::InnerSizeAtCompileTime,
inner = Index % Derived1::InnerSizeAtCompileTime,
JointAlignment = assign_traits<Derived1,Derived2>::JointAlignment
};
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
dst.template copyPacketByOuterInner<Derived2, Aligned, JointAlignment>(outer, inner, src);
assign_innervec_CompleteUnrolling<Derived1, Derived2,
Index+packet_traits<typename Derived1::Scalar>::size, Stop>::run(dst, src);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_innervec_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {}
};
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_innervec_InnerUnrolling
{
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src, int outer)
{
dst.template copyPacketByOuterInner<Derived2, Aligned, Aligned>(outer, Index, src);
assign_innervec_InnerUnrolling<Derived1, Derived2,
Index+packet_traits<typename Derived1::Scalar>::size, Stop>::run(dst, src, outer);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_innervec_InnerUnrolling<Derived1, Derived2, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &, int) {}
};
/***************************************************************************
* Part 3 : implementation of all cases
***************************************************************************/
template<typename Derived1, typename Derived2,
int Traversal = assign_traits<Derived1, Derived2>::Traversal,
int Unrolling = assign_traits<Derived1, Derived2>::Unrolling,
int Version = Specialized>
struct assign_impl;
/************************
*** Default traversal ***
************************/
template<typename Derived1, typename Derived2, int Unrolling, int Version>
struct assign_impl<Derived1, Derived2, InvalidTraversal, Unrolling, Version>
{
static inline void run(Derived1 &, const Derived2 &) { }
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, DefaultTraversal, NoUnrolling, Version>
{
typedef typename Derived1::Index Index;
static inline void run(Derived1 &dst, const Derived2 &src)
{
const Index innerSize = dst.innerSize();
const Index outerSize = dst.outerSize();
for(Index outer = 0; outer < outerSize; ++outer)
for(Index inner = 0; inner < innerSize; ++inner)
dst.copyCoeffByOuterInner(outer, inner, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, DefaultTraversal, CompleteUnrolling, Version>
{
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
::run(dst, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, DefaultTraversal, InnerUnrolling, Version>
{
typedef typename Derived1::Index Index;
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
const Index outerSize = dst.outerSize();
for(Index outer = 0; outer < outerSize; ++outer)
assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, 0, Derived1::InnerSizeAtCompileTime>
::run(dst, src, outer);
}
};
/***********************
*** Linear traversal ***
***********************/
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, LinearTraversal, NoUnrolling, Version>
{
typedef typename Derived1::Index Index;
static inline void run(Derived1 &dst, const Derived2 &src)
{
const Index size = dst.size();
for(Index i = 0; i < size; ++i)
dst.copyCoeff(i, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, LinearTraversal, CompleteUnrolling, Version>
{
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
::run(dst, src);
}
};
/**************************
*** Inner vectorization ***
**************************/
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, NoUnrolling, Version>
{
typedef typename Derived1::Index Index;
static inline void run(Derived1 &dst, const Derived2 &src)
{
const Index innerSize = dst.innerSize();
const Index outerSize = dst.outerSize();
const Index packetSize = packet_traits<typename Derived1::Scalar>::size;
for(Index outer = 0; outer < outerSize; ++outer)
for(Index inner = 0; inner < innerSize; inner+=packetSize)
dst.template copyPacketByOuterInner<Derived2, Aligned, Aligned>(outer, inner, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, CompleteUnrolling, Version>
{
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
assign_innervec_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
::run(dst, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, InnerUnrolling, Version>
{
typedef typename Derived1::Index Index;
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
const Index outerSize = dst.outerSize();
for(Index outer = 0; outer < outerSize; ++outer)
assign_innervec_InnerUnrolling<Derived1, Derived2, 0, Derived1::InnerSizeAtCompileTime>
::run(dst, src, outer);
}
};
/***************************
*** Linear vectorization ***
***************************/
template <bool IsAligned = false>
struct unaligned_assign_impl
{
template <typename Derived, typename OtherDerived>
static EIGEN_STRONG_INLINE void run(const Derived&, OtherDerived&, typename Derived::Index, typename Derived::Index) {}
};
template <>
struct unaligned_assign_impl<false>
{
// MSVC must not inline this functions. If it does, it fails to optimize the
// packet access path.
#ifdef _MSC_VER
template <typename Derived, typename OtherDerived>
static EIGEN_DONT_INLINE void run(const Derived& src, OtherDerived& dst, typename Derived::Index start, typename Derived::Index end)
#else
template <typename Derived, typename OtherDerived>
static EIGEN_STRONG_INLINE void run(const Derived& src, OtherDerived& dst, typename Derived::Index start, typename Derived::Index end)
#endif
{
for (typename Derived::Index index = start; index < end; ++index)
dst.copyCoeff(index, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, LinearVectorizedTraversal, NoUnrolling, Version>
{
typedef typename Derived1::Index Index;
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
const Index size = dst.size();
typedef packet_traits<typename Derived1::Scalar> PacketTraits;
enum {
packetSize = PacketTraits::size,
dstAlignment = PacketTraits::AlignedOnScalar ? Aligned : int(assign_traits<Derived1,Derived2>::DstIsAligned) ,
srcAlignment = assign_traits<Derived1,Derived2>::JointAlignment
};
const Index alignedStart = assign_traits<Derived1,Derived2>::DstIsAligned ? 0
: internal::first_aligned(&dst.coeffRef(0), size);
const Index alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize;
unaligned_assign_impl<assign_traits<Derived1,Derived2>::DstIsAligned!=0>::run(src,dst,0,alignedStart);
for(Index index = alignedStart; index < alignedEnd; index += packetSize)
{
dst.template copyPacket<Derived2, dstAlignment, srcAlignment>(index, src);
}
unaligned_assign_impl<>::run(src,dst,alignedEnd,size);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, LinearVectorizedTraversal, CompleteUnrolling, Version>
{
typedef typename Derived1::Index Index;
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
enum { size = Derived1::SizeAtCompileTime,
packetSize = packet_traits<typename Derived1::Scalar>::size,
alignedSize = (size/packetSize)*packetSize };
assign_innervec_CompleteUnrolling<Derived1, Derived2, 0, alignedSize>::run(dst, src);
assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, alignedSize, size>::run(dst, src);
}
};
/**************************
*** Slice vectorization ***
***************************/
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, SliceVectorizedTraversal, NoUnrolling, Version>
{
typedef typename Derived1::Index Index;
static inline void run(Derived1 &dst, const Derived2 &src)
{
typedef packet_traits<typename Derived1::Scalar> PacketTraits;
enum {
packetSize = PacketTraits::size,
alignable = PacketTraits::AlignedOnScalar,
dstAlignment = alignable ? Aligned : int(assign_traits<Derived1,Derived2>::DstIsAligned) ,
srcAlignment = assign_traits<Derived1,Derived2>::JointAlignment
};
const Index packetAlignedMask = packetSize - 1;
const Index innerSize = dst.innerSize();
const Index outerSize = dst.outerSize();
const Index alignedStep = alignable ? (packetSize - dst.outerStride() % packetSize) & packetAlignedMask : 0;
Index alignedStart = ((!alignable) || assign_traits<Derived1,Derived2>::DstIsAligned) ? 0
: internal::first_aligned(&dst.coeffRef(0,0), innerSize);
for(Index outer = 0; outer < outerSize; ++outer)
{
const Index alignedEnd = alignedStart + ((innerSize-alignedStart) & ~packetAlignedMask);
// do the non-vectorizable part of the assignment
for(Index inner = 0; inner<alignedStart ; ++inner)
dst.copyCoeffByOuterInner(outer, inner, src);
// do the vectorizable part of the assignment
for(Index inner = alignedStart; inner<alignedEnd; inner+=packetSize)
dst.template copyPacketByOuterInner<Derived2, dstAlignment, Unaligned>(outer, inner, src);
// do the non-vectorizable part of the assignment
for(Index inner = alignedEnd; inner<innerSize ; ++inner)
dst.copyCoeffByOuterInner(outer, inner, src);
alignedStart = std::min<Index>((alignedStart+alignedStep)%packetSize, innerSize);
}
}
};
} // end namespace internal
/***************************************************************************
* Part 4 : implementation of DenseBase methods
***************************************************************************/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>
@@ -507,89 +27,61 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived)
EIGEN_STATIC_ASSERT(SameType,YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
#ifdef EIGEN_DEBUG_ASSIGN
internal::assign_traits<Derived, OtherDerived>::debug();
#endif
eigen_assert(rows() == other.rows() && cols() == other.cols());
internal::assign_impl<Derived, OtherDerived, int(SameType) ? int(internal::assign_traits<Derived, OtherDerived>::Traversal)
: int(InvalidTraversal)>::run(derived(),other.derived());
#ifndef EIGEN_NO_DEBUG
checkTransposeAliasing(other.derived());
#endif
internal::call_assignment_no_alias(derived(),other.derived());
return derived();
}
namespace internal {
template<typename Derived, typename OtherDerived,
bool EvalBeforeAssigning = (int(OtherDerived::Flags) & EvalBeforeAssigningBit) != 0,
bool NeedToTranspose = Derived::IsVectorAtCompileTime
&& OtherDerived::IsVectorAtCompileTime
&& ((int(Derived::RowsAtCompileTime) == 1 && int(OtherDerived::ColsAtCompileTime) == 1)
| // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
// revert to || as soon as not needed anymore.
(int(Derived::ColsAtCompileTime) == 1 && int(OtherDerived::RowsAtCompileTime) == 1))
&& int(Derived::SizeAtCompileTime) != 1>
struct assign_selector;
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,false,false> {
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.derived()); }
};
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,true,false> {
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.eval()); }
};
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,false,true> {
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose()); }
};
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,true,true> {
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose().eval()); }
};
} // end namespace internal
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
{
return internal::assign_selector<Derived,OtherDerived>::run(derived(), other.derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase& other)
{
return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const MatrixBase& other)
{
return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
template <typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
{
return internal::assign_selector<Derived,OtherDerived>::run(derived(), other.derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
template <typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const EigenBase<OtherDerived>& other)
{
other.derived().evalTo(derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
{
other.evalTo(derived());
other.derived().evalTo(derived());
return derived();
}

View File

@@ -0,0 +1,935 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2011-2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ASSIGN_EVALUATOR_H
#define EIGEN_ASSIGN_EVALUATOR_H
namespace Eigen {
// This implementation is based on Assign.h
namespace internal {
/***************************************************************************
* Part 1 : the logic deciding a strategy for traversal and unrolling *
***************************************************************************/
// copy_using_evaluator_traits is based on assign_traits
template <typename DstEvaluator, typename SrcEvaluator, typename AssignFunc>
struct copy_using_evaluator_traits
{
typedef typename DstEvaluator::XprType Dst;
typedef typename Dst::Scalar DstScalar;
enum {
DstFlags = DstEvaluator::Flags,
SrcFlags = SrcEvaluator::Flags
};
public:
enum {
DstAlignment = DstEvaluator::Alignment,
SrcAlignment = SrcEvaluator::Alignment,
DstHasDirectAccess = (DstFlags & DirectAccessBit) == DirectAccessBit,
JointAlignment = EIGEN_PLAIN_ENUM_MIN(DstAlignment,SrcAlignment)
};
private:
enum {
InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
: int(DstFlags)&RowMajorBit ? int(Dst::ColsAtCompileTime)
: int(Dst::RowsAtCompileTime),
InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
: int(DstFlags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime)
: int(Dst::MaxRowsAtCompileTime),
OuterStride = int(outer_stride_at_compile_time<Dst>::ret),
MaxSizeAtCompileTime = Dst::SizeAtCompileTime
};
// TODO distinguish between linear traversal and inner-traversals
typedef typename find_best_packet<DstScalar,Dst::SizeAtCompileTime>::type LinearPacketType;
typedef typename find_best_packet<DstScalar,InnerSize>::type InnerPacketType;
enum {
LinearPacketSize = unpacket_traits<LinearPacketType>::size,
InnerPacketSize = unpacket_traits<InnerPacketType>::size
};
public:
enum {
LinearRequiredAlignment = unpacket_traits<LinearPacketType>::alignment,
InnerRequiredAlignment = unpacket_traits<InnerPacketType>::alignment
};
private:
enum {
DstIsRowMajor = DstFlags&RowMajorBit,
SrcIsRowMajor = SrcFlags&RowMajorBit,
StorageOrdersAgree = (int(DstIsRowMajor) == int(SrcIsRowMajor)),
MightVectorize = bool(StorageOrdersAgree)
&& (int(DstFlags) & int(SrcFlags) & ActualPacketAccessBit)
&& bool(functor_traits<AssignFunc>::PacketAccess),
MayInnerVectorize = MightVectorize
&& int(InnerSize)!=Dynamic && int(InnerSize)%int(InnerPacketSize)==0
&& int(OuterStride)!=Dynamic && int(OuterStride)%int(InnerPacketSize)==0
&& (EIGEN_UNALIGNED_VECTORIZE || int(JointAlignment)>=int(InnerRequiredAlignment)),
MayLinearize = bool(StorageOrdersAgree) && (int(DstFlags) & int(SrcFlags) & LinearAccessBit),
MayLinearVectorize = bool(MightVectorize) && bool(MayLinearize) && bool(DstHasDirectAccess)
&& (EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment)) || MaxSizeAtCompileTime == Dynamic),
/* If the destination isn't aligned, we have to do runtime checks and we don't unroll,
so it's only good for large enough sizes. */
MaySliceVectorize = bool(MightVectorize) && bool(DstHasDirectAccess)
&& (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=(EIGEN_UNALIGNED_VECTORIZE?InnerPacketSize:(3*InnerPacketSize)))
/* slice vectorization can be slow, so we only want it if the slices are big, which is
indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block
in a fixed-size matrix
However, with EIGEN_UNALIGNED_VECTORIZE and unrolling, slice vectorization is still worth it */
};
public:
enum {
Traversal = int(MayLinearVectorize) && (LinearPacketSize>InnerPacketSize) ? int(LinearVectorizedTraversal)
: int(MayInnerVectorize) ? int(InnerVectorizedTraversal)
: int(MayLinearVectorize) ? int(LinearVectorizedTraversal)
: int(MaySliceVectorize) ? int(SliceVectorizedTraversal)
: int(MayLinearize) ? int(LinearTraversal)
: int(DefaultTraversal),
Vectorized = int(Traversal) == InnerVectorizedTraversal
|| int(Traversal) == LinearVectorizedTraversal
|| int(Traversal) == SliceVectorizedTraversal
};
typedef typename conditional<int(Traversal)==LinearVectorizedTraversal, LinearPacketType, InnerPacketType>::type PacketType;
private:
enum {
ActualPacketSize = int(Traversal)==LinearVectorizedTraversal ? LinearPacketSize
: Vectorized ? InnerPacketSize
: 1,
UnrollingLimit = EIGEN_UNROLLING_LIMIT * ActualPacketSize,
MayUnrollCompletely = int(Dst::SizeAtCompileTime) != Dynamic
&& int(Dst::SizeAtCompileTime) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit),
MayUnrollInner = int(InnerSize) != Dynamic
&& int(InnerSize) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit)
};
public:
enum {
Unrolling = (int(Traversal) == int(InnerVectorizedTraversal) || int(Traversal) == int(DefaultTraversal))
? (
int(MayUnrollCompletely) ? int(CompleteUnrolling)
: int(MayUnrollInner) ? int(InnerUnrolling)
: int(NoUnrolling)
)
: int(Traversal) == int(LinearVectorizedTraversal)
? ( bool(MayUnrollCompletely) && ( EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment)))
? int(CompleteUnrolling)
: int(NoUnrolling) )
: int(Traversal) == int(LinearTraversal)
? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling)
: int(NoUnrolling) )
#if EIGEN_UNALIGNED_VECTORIZE
: int(Traversal) == int(SliceVectorizedTraversal)
? ( bool(MayUnrollInner) ? int(InnerUnrolling)
: int(NoUnrolling) )
#endif
: int(NoUnrolling)
};
#ifdef EIGEN_DEBUG_ASSIGN
static void debug()
{
std::cerr << "DstXpr: " << typeid(typename DstEvaluator::XprType).name() << std::endl;
std::cerr << "SrcXpr: " << typeid(typename SrcEvaluator::XprType).name() << std::endl;
std::cerr.setf(std::ios::hex, std::ios::basefield);
std::cerr << "DstFlags" << " = " << DstFlags << " (" << demangle_flags(DstFlags) << " )" << std::endl;
std::cerr << "SrcFlags" << " = " << SrcFlags << " (" << demangle_flags(SrcFlags) << " )" << std::endl;
std::cerr.unsetf(std::ios::hex);
EIGEN_DEBUG_VAR(DstAlignment)
EIGEN_DEBUG_VAR(SrcAlignment)
EIGEN_DEBUG_VAR(LinearRequiredAlignment)
EIGEN_DEBUG_VAR(InnerRequiredAlignment)
EIGEN_DEBUG_VAR(JointAlignment)
EIGEN_DEBUG_VAR(InnerSize)
EIGEN_DEBUG_VAR(InnerMaxSize)
EIGEN_DEBUG_VAR(LinearPacketSize)
EIGEN_DEBUG_VAR(InnerPacketSize)
EIGEN_DEBUG_VAR(ActualPacketSize)
EIGEN_DEBUG_VAR(StorageOrdersAgree)
EIGEN_DEBUG_VAR(MightVectorize)
EIGEN_DEBUG_VAR(MayLinearize)
EIGEN_DEBUG_VAR(MayInnerVectorize)
EIGEN_DEBUG_VAR(MayLinearVectorize)
EIGEN_DEBUG_VAR(MaySliceVectorize)
std::cerr << "Traversal" << " = " << Traversal << " (" << demangle_traversal(Traversal) << ")" << std::endl;
EIGEN_DEBUG_VAR(SrcEvaluator::CoeffReadCost)
EIGEN_DEBUG_VAR(UnrollingLimit)
EIGEN_DEBUG_VAR(MayUnrollCompletely)
EIGEN_DEBUG_VAR(MayUnrollInner)
std::cerr << "Unrolling" << " = " << Unrolling << " (" << demangle_unrolling(Unrolling) << ")" << std::endl;
std::cerr << std::endl;
}
#endif
};
/***************************************************************************
* Part 2 : meta-unrollers
***************************************************************************/
/************************
*** Default traversal ***
************************/
template<typename Kernel, int Index, int Stop>
struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling
{
// FIXME: this is not very clean, perhaps this information should be provided by the kernel?
typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
typedef typename DstEvaluatorType::XprType DstXprType;
enum {
outer = Index / DstXprType::InnerSizeAtCompileTime,
inner = Index % DstXprType::InnerSizeAtCompileTime
};
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
kernel.assignCoeffByOuterInner(outer, inner);
copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Index+1, Stop>::run(kernel);
}
};
template<typename Kernel, int Stop>
struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Stop, Stop>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
};
template<typename Kernel, int Index_, int Stop>
struct copy_using_evaluator_DefaultTraversal_InnerUnrolling
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, Index outer)
{
kernel.assignCoeffByOuterInner(outer, Index_);
copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Index_+1, Stop>::run(kernel, outer);
}
};
template<typename Kernel, int Stop>
struct copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Stop, Stop>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index) { }
};
/***********************
*** Linear traversal ***
***********************/
template<typename Kernel, int Index, int Stop>
struct copy_using_evaluator_LinearTraversal_CompleteUnrolling
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel)
{
kernel.assignCoeff(Index);
copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Index+1, Stop>::run(kernel);
}
};
template<typename Kernel, int Stop>
struct copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Stop, Stop>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
};
/**************************
*** Inner vectorization ***
**************************/
template<typename Kernel, int Index, int Stop>
struct copy_using_evaluator_innervec_CompleteUnrolling
{
// FIXME: this is not very clean, perhaps this information should be provided by the kernel?
typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
typedef typename DstEvaluatorType::XprType DstXprType;
typedef typename Kernel::PacketType PacketType;
enum {
outer = Index / DstXprType::InnerSizeAtCompileTime,
inner = Index % DstXprType::InnerSizeAtCompileTime,
SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
DstAlignment = Kernel::AssignmentTraits::DstAlignment
};
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
enum { NextIndex = Index + unpacket_traits<PacketType>::size };
copy_using_evaluator_innervec_CompleteUnrolling<Kernel, NextIndex, Stop>::run(kernel);
}
};
template<typename Kernel, int Stop>
struct copy_using_evaluator_innervec_CompleteUnrolling<Kernel, Stop, Stop>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
};
template<typename Kernel, int Index_, int Stop, int SrcAlignment, int DstAlignment>
struct copy_using_evaluator_innervec_InnerUnrolling
{
typedef typename Kernel::PacketType PacketType;
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, Index outer)
{
kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, Index_);
enum { NextIndex = Index_ + unpacket_traits<PacketType>::size };
copy_using_evaluator_innervec_InnerUnrolling<Kernel, NextIndex, Stop, SrcAlignment, DstAlignment>::run(kernel, outer);
}
};
template<typename Kernel, int Stop, int SrcAlignment, int DstAlignment>
struct copy_using_evaluator_innervec_InnerUnrolling<Kernel, Stop, Stop, SrcAlignment, DstAlignment>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &, Index) { }
};
/***************************************************************************
* Part 3 : implementation of all cases
***************************************************************************/
// dense_assignment_loop is based on assign_impl
template<typename Kernel,
int Traversal = Kernel::AssignmentTraits::Traversal,
int Unrolling = Kernel::AssignmentTraits::Unrolling>
struct dense_assignment_loop;
/************************
*** Default traversal ***
************************/
template<typename Kernel>
struct dense_assignment_loop<Kernel, DefaultTraversal, NoUnrolling>
{
EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE run(Kernel &kernel)
{
for(Index outer = 0; outer < kernel.outerSize(); ++outer) {
for(Index inner = 0; inner < kernel.innerSize(); ++inner) {
kernel.assignCoeffByOuterInner(outer, inner);
}
}
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, DefaultTraversal, CompleteUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, DefaultTraversal, InnerUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
const Index outerSize = kernel.outerSize();
for(Index outer = 0; outer < outerSize; ++outer)
copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime>::run(kernel, outer);
}
};
/***************************
*** Linear vectorization ***
***************************/
// The goal of unaligned_dense_assignment_loop is simply to factorize the handling
// of the non vectorizable beginning and ending parts
template <bool IsAligned = false>
struct unaligned_dense_assignment_loop
{
// if IsAligned = true, then do nothing
template <typename Kernel>
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index, Index) {}
};
template <>
struct unaligned_dense_assignment_loop<false>
{
// MSVC must not inline this functions. If it does, it fails to optimize the
// packet access path.
// FIXME check which version exhibits this issue
#if EIGEN_COMP_MSVC
template <typename Kernel>
static EIGEN_DONT_INLINE void run(Kernel &kernel,
Index start,
Index end)
#else
template <typename Kernel>
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel,
Index start,
Index end)
#endif
{
for (Index index = start; index < end; ++index)
kernel.assignCoeff(index);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, NoUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
const Index size = kernel.size();
typedef typename Kernel::Scalar Scalar;
typedef typename Kernel::PacketType PacketType;
enum {
requestedAlignment = Kernel::AssignmentTraits::LinearRequiredAlignment,
packetSize = unpacket_traits<PacketType>::size,
dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment)>=int(requestedAlignment),
dstAlignment = packet_traits<Scalar>::AlignedOnScalar ? int(requestedAlignment)
: int(Kernel::AssignmentTraits::DstAlignment),
srcAlignment = Kernel::AssignmentTraits::JointAlignment
};
const Index alignedStart = dstIsAligned ? 0 : internal::first_aligned<requestedAlignment>(kernel.dstDataPtr(), size);
const Index alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize;
unaligned_dense_assignment_loop<dstIsAligned!=0>::run(kernel, 0, alignedStart);
for(Index index = alignedStart; index < alignedEnd; index += packetSize)
kernel.template assignPacket<dstAlignment, srcAlignment, PacketType>(index);
unaligned_dense_assignment_loop<>::run(kernel, alignedEnd, size);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, CompleteUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
typedef typename Kernel::PacketType PacketType;
enum { size = DstXprType::SizeAtCompileTime,
packetSize =unpacket_traits<PacketType>::size,
alignedSize = (size/packetSize)*packetSize };
copy_using_evaluator_innervec_CompleteUnrolling<Kernel, 0, alignedSize>::run(kernel);
copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, alignedSize, size>::run(kernel);
}
};
/**************************
*** Inner vectorization ***
**************************/
template<typename Kernel>
struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, NoUnrolling>
{
typedef typename Kernel::PacketType PacketType;
enum {
SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
DstAlignment = Kernel::AssignmentTraits::DstAlignment
};
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
const Index innerSize = kernel.innerSize();
const Index outerSize = kernel.outerSize();
const Index packetSize = unpacket_traits<PacketType>::size;
for(Index outer = 0; outer < outerSize; ++outer)
for(Index inner = 0; inner < innerSize; inner+=packetSize)
kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, CompleteUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
copy_using_evaluator_innervec_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, InnerUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
typedef typename Kernel::AssignmentTraits Traits;
const Index outerSize = kernel.outerSize();
for(Index outer = 0; outer < outerSize; ++outer)
copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime,
Traits::SrcAlignment, Traits::DstAlignment>::run(kernel, outer);
}
};
/***********************
*** Linear traversal ***
***********************/
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearTraversal, NoUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
const Index size = kernel.size();
for(Index i = 0; i < size; ++i)
kernel.assignCoeff(i);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearTraversal, CompleteUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
}
};
/**************************
*** Slice vectorization ***
***************************/
template<typename Kernel>
struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::Scalar Scalar;
typedef typename Kernel::PacketType PacketType;
enum {
packetSize = unpacket_traits<PacketType>::size,
requestedAlignment = int(Kernel::AssignmentTraits::InnerRequiredAlignment),
alignable = packet_traits<Scalar>::AlignedOnScalar || int(Kernel::AssignmentTraits::DstAlignment)>=sizeof(Scalar),
dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment)>=int(requestedAlignment),
dstAlignment = alignable ? int(requestedAlignment)
: int(Kernel::AssignmentTraits::DstAlignment)
};
const Scalar *dst_ptr = kernel.dstDataPtr();
if((!bool(dstIsAligned)) && (UIntPtr(dst_ptr) % sizeof(Scalar))>0)
{
// the pointer is not aligend-on scalar, so alignment is not possible
return dense_assignment_loop<Kernel,DefaultTraversal,NoUnrolling>::run(kernel);
}
const Index packetAlignedMask = packetSize - 1;
const Index innerSize = kernel.innerSize();
const Index outerSize = kernel.outerSize();
const Index alignedStep = alignable ? (packetSize - kernel.outerStride() % packetSize) & packetAlignedMask : 0;
Index alignedStart = ((!alignable) || bool(dstIsAligned)) ? 0 : internal::first_aligned<requestedAlignment>(dst_ptr, innerSize);
for(Index outer = 0; outer < outerSize; ++outer)
{
const Index alignedEnd = alignedStart + ((innerSize-alignedStart) & ~packetAlignedMask);
// do the non-vectorizable part of the assignment
for(Index inner = 0; inner<alignedStart ; ++inner)
kernel.assignCoeffByOuterInner(outer, inner);
// do the vectorizable part of the assignment
for(Index inner = alignedStart; inner<alignedEnd; inner+=packetSize)
kernel.template assignPacketByOuterInner<dstAlignment, Unaligned, PacketType>(outer, inner);
// do the non-vectorizable part of the assignment
for(Index inner = alignedEnd; inner<innerSize ; ++inner)
kernel.assignCoeffByOuterInner(outer, inner);
alignedStart = numext::mini((alignedStart+alignedStep)%packetSize, innerSize);
}
}
};
#if EIGEN_UNALIGNED_VECTORIZE
template<typename Kernel>
struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, InnerUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
typedef typename Kernel::PacketType PacketType;
enum { size = DstXprType::InnerSizeAtCompileTime,
packetSize =unpacket_traits<PacketType>::size,
vectorizableSize = (size/packetSize)*packetSize };
for(Index outer = 0; outer < kernel.outerSize(); ++outer)
{
copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, vectorizableSize, 0, 0>::run(kernel, outer);
copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, vectorizableSize, size>::run(kernel, outer);
}
}
};
#endif
/***************************************************************************
* Part 4 : Generic dense assignment kernel
***************************************************************************/
// This class generalize the assignment of a coefficient (or packet) from one dense evaluator
// to another dense writable evaluator.
// It is parametrized by the two evaluators, and the actual assignment functor.
// This abstraction level permits to keep the evaluation loops as simple and as generic as possible.
// One can customize the assignment using this generic dense_assignment_kernel with different
// functors, or by completely overloading it, by-passing a functor.
template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version = Specialized>
class generic_dense_assignment_kernel
{
protected:
typedef typename DstEvaluatorTypeT::XprType DstXprType;
typedef typename SrcEvaluatorTypeT::XprType SrcXprType;
public:
typedef DstEvaluatorTypeT DstEvaluatorType;
typedef SrcEvaluatorTypeT SrcEvaluatorType;
typedef typename DstEvaluatorType::Scalar Scalar;
typedef copy_using_evaluator_traits<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor> AssignmentTraits;
typedef typename AssignmentTraits::PacketType PacketType;
EIGEN_DEVICE_FUNC generic_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr)
: m_dst(dst), m_src(src), m_functor(func), m_dstExpr(dstExpr)
{
#ifdef EIGEN_DEBUG_ASSIGN
AssignmentTraits::debug();
#endif
}
EIGEN_DEVICE_FUNC Index size() const { return m_dstExpr.size(); }
EIGEN_DEVICE_FUNC Index innerSize() const { return m_dstExpr.innerSize(); }
EIGEN_DEVICE_FUNC Index outerSize() const { return m_dstExpr.outerSize(); }
EIGEN_DEVICE_FUNC Index rows() const { return m_dstExpr.rows(); }
EIGEN_DEVICE_FUNC Index cols() const { return m_dstExpr.cols(); }
EIGEN_DEVICE_FUNC Index outerStride() const { return m_dstExpr.outerStride(); }
EIGEN_DEVICE_FUNC DstEvaluatorType& dstEvaluator() { return m_dst; }
EIGEN_DEVICE_FUNC const SrcEvaluatorType& srcEvaluator() const { return m_src; }
/// Assign src(row,col) to dst(row,col) through the assignment functor.
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Index row, Index col)
{
m_functor.assignCoeff(m_dst.coeffRef(row,col), m_src.coeff(row,col));
}
/// \sa assignCoeff(Index,Index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Index index)
{
m_functor.assignCoeff(m_dst.coeffRef(index), m_src.coeff(index));
}
/// \sa assignCoeff(Index,Index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeffByOuterInner(Index outer, Index inner)
{
Index row = rowIndexByOuterInner(outer, inner);
Index col = colIndexByOuterInner(outer, inner);
assignCoeff(row, col);
}
template<int StoreMode, int LoadMode, typename PacketType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index row, Index col)
{
m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(row,col), m_src.template packet<LoadMode,PacketType>(row,col));
}
template<int StoreMode, int LoadMode, typename PacketType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index index)
{
m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(index), m_src.template packet<LoadMode,PacketType>(index));
}
template<int StoreMode, int LoadMode, typename PacketType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacketByOuterInner(Index outer, Index inner)
{
Index row = rowIndexByOuterInner(outer, inner);
Index col = colIndexByOuterInner(outer, inner);
assignPacket<StoreMode,LoadMode,PacketType>(row, col);
}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner)
{
typedef typename DstEvaluatorType::ExpressionTraits Traits;
return int(Traits::RowsAtCompileTime) == 1 ? 0
: int(Traits::ColsAtCompileTime) == 1 ? inner
: int(DstEvaluatorType::Flags)&RowMajorBit ? outer
: inner;
}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner)
{
typedef typename DstEvaluatorType::ExpressionTraits Traits;
return int(Traits::ColsAtCompileTime) == 1 ? 0
: int(Traits::RowsAtCompileTime) == 1 ? inner
: int(DstEvaluatorType::Flags)&RowMajorBit ? inner
: outer;
}
EIGEN_DEVICE_FUNC const Scalar* dstDataPtr() const
{
return m_dstExpr.data();
}
protected:
DstEvaluatorType& m_dst;
const SrcEvaluatorType& m_src;
const Functor &m_functor;
// TODO find a way to avoid the needs of the original expression
DstXprType& m_dstExpr;
};
/***************************************************************************
* Part 5 : Entry point for dense rectangular assignment
***************************************************************************/
template<typename DstXprType,typename SrcXprType, typename Functor>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void resize_if_allowed(DstXprType &dst, const SrcXprType& src, const Functor &/*func*/)
{
EIGEN_ONLY_USED_FOR_DEBUG(dst);
EIGEN_ONLY_USED_FOR_DEBUG(src);
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
}
template<typename DstXprType,typename SrcXprType, typename T1, typename T2>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void resize_if_allowed(DstXprType &dst, const SrcXprType& src, const internal::assign_op<T1,T2> &/*func*/)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if(((dst.rows()!=dstRows) || (dst.cols()!=dstCols)))
dst.resize(dstRows, dstCols);
eigen_assert(dst.rows() == dstRows && dst.cols() == dstCols);
}
template<typename DstXprType, typename SrcXprType, typename Functor>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src, const Functor &func)
{
typedef evaluator<DstXprType> DstEvaluatorType;
typedef evaluator<SrcXprType> SrcEvaluatorType;
SrcEvaluatorType srcEvaluator(src);
// NOTE To properly handle A = (A*A.transpose())/s with A rectangular,
// we need to resize the destination after the source evaluator has been created.
resize_if_allowed(dst, src, func);
DstEvaluatorType dstEvaluator(dst);
typedef generic_dense_assignment_kernel<DstEvaluatorType,SrcEvaluatorType,Functor> Kernel;
Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
dense_assignment_loop<Kernel>::run(kernel);
}
template<typename DstXprType, typename SrcXprType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src)
{
call_dense_assignment_loop(dst, src, internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
}
/***************************************************************************
* Part 6 : Generic assignment
***************************************************************************/
// Based on the respective shapes of the destination and source,
// the class AssignmentKind determine the kind of assignment mechanism.
// AssignmentKind must define a Kind typedef.
template<typename DstShape, typename SrcShape> struct AssignmentKind;
// Assignement kind defined in this file:
struct Dense2Dense {};
struct EigenBase2EigenBase {};
template<typename,typename> struct AssignmentKind { typedef EigenBase2EigenBase Kind; };
template<> struct AssignmentKind<DenseShape,DenseShape> { typedef Dense2Dense Kind; };
// This is the main assignment class
template< typename DstXprType, typename SrcXprType, typename Functor,
typename Kind = typename AssignmentKind< typename evaluator_traits<DstXprType>::Shape , typename evaluator_traits<SrcXprType>::Shape >::Kind,
typename EnableIf = void>
struct Assignment;
// The only purpose of this call_assignment() function is to deal with noalias() / "assume-aliasing" and automatic transposition.
// Indeed, I (Gael) think that this concept of "assume-aliasing" was a mistake, and it makes thing quite complicated.
// So this intermediate function removes everything related to "assume-aliasing" such that Assignment
// does not has to bother about these annoying details.
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(Dst& dst, const Src& src)
{
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(const Dst& dst, const Src& src)
{
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
// Deal with "assume-aliasing"
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if< evaluator_assume_aliasing<Src>::value, void*>::type = 0)
{
typename plain_matrix_type<Src>::type tmp(src);
call_assignment_no_alias(dst, tmp, func);
}
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if<!evaluator_assume_aliasing<Src>::value, void*>::type = 0)
{
call_assignment_no_alias(dst, src, func);
}
// by-pass "assume-aliasing"
// When there is no aliasing, we require that 'dst' has been properly resized
template<typename Dst, template <typename> class StorageBase, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(NoAlias<Dst,StorageBase>& dst, const Src& src, const Func& func)
{
call_assignment_no_alias(dst.expression(), src, func);
}
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment_no_alias(Dst& dst, const Src& src, const Func& func)
{
enum {
NeedToTranspose = ( (int(Dst::RowsAtCompileTime) == 1 && int(Src::ColsAtCompileTime) == 1)
|| (int(Dst::ColsAtCompileTime) == 1 && int(Src::RowsAtCompileTime) == 1)
) && int(Dst::SizeAtCompileTime) != 1
};
typedef typename internal::conditional<NeedToTranspose, Transpose<Dst>, Dst>::type ActualDstTypeCleaned;
typedef typename internal::conditional<NeedToTranspose, Transpose<Dst>, Dst&>::type ActualDstType;
ActualDstType actualDst(dst);
// TODO check whether this is the right place to perform these checks:
EIGEN_STATIC_ASSERT_LVALUE(Dst)
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(ActualDstTypeCleaned,Src)
EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename ActualDstTypeCleaned::Scalar,typename Src::Scalar);
Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment_no_alias(Dst& dst, const Src& src)
{
call_assignment_no_alias(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src, const Func& func)
{
// TODO check whether this is the right place to perform these checks:
EIGEN_STATIC_ASSERT_LVALUE(Dst)
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Dst,Src)
EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename Dst::Scalar,typename Src::Scalar);
Assignment<Dst,Src,Func>::run(dst, src, func);
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src)
{
call_assignment_no_alias_no_transpose(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
// forward declaration
template<typename Dst, typename Src> void check_for_aliasing(const Dst &dst, const Src &src);
// Generic Dense to Dense assignment
// Note that the last template argument "Weak" is needed to make it possible to perform
// both partial specialization+SFINAE without ambiguous specialization
template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
struct Assignment<DstXprType, SrcXprType, Functor, Dense2Dense, Weak>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
{
#ifndef EIGEN_NO_DEBUG
internal::check_for_aliasing(dst, src);
#endif
call_dense_assignment_loop(dst, src, func);
}
};
// Generic assignment through evalTo.
// TODO: not sure we have to keep that one, but it helps porting current code to new evaluator mechanism.
// Note that the last template argument "Weak" is needed to make it possible to perform
// both partial specialization+SFINAE without ambiguous specialization
template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
struct Assignment<DstXprType, SrcXprType, Functor, EigenBase2EigenBase, Weak>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
src.evalTo(dst);
}
// NOTE The following two functions are templated to avoid their instanciation if not needed
// This is needed because some expressions supports evalTo only and/or have 'void' as scalar type.
template<typename SrcScalarType>
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,SrcScalarType> &/*func*/)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
src.addTo(dst);
}
template<typename SrcScalarType>
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,SrcScalarType> &/*func*/)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
src.subTo(dst);
}
};
} // namespace internal
} // end namespace Eigen
#endif // EIGEN_ASSIGN_EVALUATOR_H

258
Eigen/src/Core/Assign_MKL.h Normal file → Executable file
View File

@@ -1,6 +1,7 @@
/*
Copyright (c) 2011, Intel Corporation. All rights reserved.
Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
@@ -37,17 +38,13 @@ namespace Eigen {
namespace internal {
template<typename Op> struct vml_call
{ enum { IsSupported = 0 }; };
template<typename Dst, typename Src, typename UnaryOp>
template<typename Dst, typename Src>
class vml_assign_traits
{
private:
enum {
DstHasDirectAccess = Dst::Flags & DirectAccessBit,
SrcHasDirectAccess = Src::Flags & DirectAccessBit,
StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)),
InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
: int(Dst::Flags)&RowMajorBit ? int(Dst::ColsAtCompileTime)
@@ -57,165 +54,122 @@ class vml_assign_traits
: int(Dst::MaxRowsAtCompileTime),
MaxSizeAtCompileTime = Dst::SizeAtCompileTime,
MightEnableVml = vml_call<UnaryOp>::IsSupported && StorageOrdersAgree && DstHasDirectAccess && SrcHasDirectAccess
&& Src::InnerStrideAtCompileTime==1 && Dst::InnerStrideAtCompileTime==1,
MightEnableVml = StorageOrdersAgree && DstHasDirectAccess && SrcHasDirectAccess && Src::InnerStrideAtCompileTime==1 && Dst::InnerStrideAtCompileTime==1,
MightLinearize = MightEnableVml && (int(Dst::Flags) & int(Src::Flags) & LinearAccessBit),
VmlSize = MightLinearize ? MaxSizeAtCompileTime : InnerMaxSize,
LargeEnough = VmlSize==Dynamic || VmlSize>=EIGEN_MKL_VML_THRESHOLD,
MayEnableVml = MightEnableVml && LargeEnough,
MayLinearize = MayEnableVml && MightLinearize
LargeEnough = VmlSize==Dynamic || VmlSize>=EIGEN_MKL_VML_THRESHOLD
};
public:
enum {
Traversal = MayLinearize ? LinearVectorizedTraversal
: MayEnableVml ? InnerVectorizedTraversal
: DefaultTraversal
EnableVml = MightEnableVml && LargeEnough,
Traversal = MightLinearize ? LinearTraversal : DefaultTraversal
};
};
template<typename Derived1, typename Derived2, typename UnaryOp, int Traversal, int Unrolling,
int VmlTraversal = vml_assign_traits<Derived1, Derived2, UnaryOp>::Traversal >
struct vml_assign_impl
: assign_impl<Derived1, Eigen::CwiseUnaryOp<UnaryOp, Derived2>,Traversal,Unrolling,BuiltIn>
{
};
template<typename Derived1, typename Derived2, typename UnaryOp, int Traversal, int Unrolling>
struct vml_assign_impl<Derived1, Derived2, UnaryOp, Traversal, Unrolling, InnerVectorizedTraversal>
{
typedef typename Derived1::Scalar Scalar;
typedef typename Derived1::Index Index;
static inline void run(Derived1& dst, const CwiseUnaryOp<UnaryOp, Derived2>& src)
{
// in case we want to (or have to) skip VML at runtime we can call:
// assign_impl<Derived1,Eigen::CwiseUnaryOp<UnaryOp, Derived2>,Traversal,Unrolling,BuiltIn>::run(dst,src);
const Index innerSize = dst.innerSize();
const Index outerSize = dst.outerSize();
for(Index outer = 0; outer < outerSize; ++outer) {
const Scalar *src_ptr = src.IsRowMajor ? &(src.nestedExpression().coeffRef(outer,0)) :
&(src.nestedExpression().coeffRef(0, outer));
Scalar *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer));
vml_call<UnaryOp>::run(src.functor(), innerSize, src_ptr, dst_ptr );
}
}
};
template<typename Derived1, typename Derived2, typename UnaryOp, int Traversal, int Unrolling>
struct vml_assign_impl<Derived1, Derived2, UnaryOp, Traversal, Unrolling, LinearVectorizedTraversal>
{
static inline void run(Derived1& dst, const CwiseUnaryOp<UnaryOp, Derived2>& src)
{
// in case we want to (or have to) skip VML at runtime we can call:
// assign_impl<Derived1,Eigen::CwiseUnaryOp<UnaryOp, Derived2>,Traversal,Unrolling,BuiltIn>::run(dst,src);
vml_call<UnaryOp>::run(src.functor(), dst.size(), src.nestedExpression().data(), dst.data() );
}
};
// Macroses
#define EIGEN_MKL_VML_SPECIALIZE_ASSIGN(TRAVERSAL,UNROLLING) \
template<typename Derived1, typename Derived2, typename UnaryOp> \
struct assign_impl<Derived1, Eigen::CwiseUnaryOp<UnaryOp, Derived2>, TRAVERSAL, UNROLLING, Specialized> { \
static inline void run(Derived1 &dst, const Eigen::CwiseUnaryOp<UnaryOp, Derived2> &src) { \
vml_assign_impl<Derived1,Derived2,UnaryOp,TRAVERSAL,UNROLLING>::run(dst, src); \
} \
};
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(DefaultTraversal,NoUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(DefaultTraversal,CompleteUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(DefaultTraversal,InnerUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearTraversal,NoUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearTraversal,CompleteUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(InnerVectorizedTraversal,NoUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(InnerVectorizedTraversal,CompleteUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(InnerVectorizedTraversal,InnerUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearVectorizedTraversal,CompleteUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearVectorizedTraversal,NoUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(SliceVectorizedTraversal,NoUnrolling)
#define EIGEN_PP_EXPAND(ARG) ARG
#if !defined (EIGEN_FAST_MATH) || (EIGEN_FAST_MATH != 1)
#define EIGEN_MKL_VML_MODE VML_HA
#define EIGEN_VMLMODE_EXPAND_LA , VML_HA
#else
#define EIGEN_MKL_VML_MODE VML_LA
#define EIGEN_VMLMODE_EXPAND_LA , VML_LA
#endif
#define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE) \
template<> struct vml_call< scalar_##EIGENOP##_op<EIGENTYPE> > { \
enum { IsSupported = 1 }; \
static inline void run( const scalar_##EIGENOP##_op<EIGENTYPE>& /*func*/, \
int size, const EIGENTYPE* src, EIGENTYPE* dst) { \
VMLOP(size, (const VMLTYPE*)src, (VMLTYPE*)dst); \
} \
#define EIGEN_VMLMODE_EXPAND__
#define EIGEN_VMLMODE_PREFIX_LA vm
#define EIGEN_VMLMODE_PREFIX__ v
#define EIGEN_VMLMODE_PREFIX(VMLMODE) EIGEN_CAT(EIGEN_VMLMODE_PREFIX_,VMLMODE)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE) \
template< typename DstXprType, typename SrcXprNested> \
struct Assignment<DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested>, assign_op<EIGENTYPE,EIGENTYPE>, \
Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>::type> { \
typedef CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested> SrcXprType; \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &func) { \
resize_if_allowed(dst, src, func); \
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); \
if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal) { \
VMLOP(dst.size(), (const VMLTYPE*)src.nestedExpression().data(), \
(VMLTYPE*)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_##VMLMODE) ); \
} else { \
const Index outerSize = dst.outerSize(); \
for(Index outer = 0; outer < outerSize; ++outer) { \
const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.nestedExpression().coeffRef(outer,0)) : \
&(src.nestedExpression().coeffRef(0, outer)); \
EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer)); \
VMLOP( dst.innerSize(), (const VMLTYPE*)src_ptr, \
(VMLTYPE*)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_##VMLMODE)); \
} \
} \
} \
}; \
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),s##VMLOP), float, float, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),d##VMLOP), double, double, VMLMODE)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),c##VMLOP), scomplex, MKL_Complex8, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),z##VMLOP), dcomplex, MKL_Complex16, VMLMODE)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sin, Sin, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(asin, Asin, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sinh, Sinh, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cos, Cos, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(acos, Acos, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cosh, Cosh, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tan, Tan, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(atan, Atan, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tanh, Tanh, LA)
// EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs, Abs, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(exp, Exp, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log, Ln, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log10, Log10, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sqrt, Sqrt, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(arg, Arg, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(round, Round, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(floor, Floor, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(ceil, Ceil, _)
#define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE) \
template< typename DstXprType, typename SrcXprNested, typename Plain> \
struct Assignment<DstXprType, CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested, \
const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> >, assign_op<EIGENTYPE,EIGENTYPE>, \
Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>::type> { \
typedef CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested, \
const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> > SrcXprType; \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &func) { \
resize_if_allowed(dst, src, func); \
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); \
VMLTYPE exponent = reinterpret_cast<const VMLTYPE&>(src.rhs().functor().m_other); \
if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal) \
{ \
VMLOP( dst.size(), (const VMLTYPE*)src.lhs().data(), exponent, \
(VMLTYPE*)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_##VMLMODE) ); \
} else { \
const Index outerSize = dst.outerSize(); \
for(Index outer = 0; outer < outerSize; ++outer) { \
const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.lhs().coeffRef(outer,0)) : \
&(src.lhs().coeffRef(0, outer)); \
EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer)); \
VMLOP( dst.innerSize(), (const VMLTYPE*)src_ptr, exponent, \
(VMLTYPE*)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_##VMLMODE)); \
} \
} \
} \
};
#define EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE) \
template<> struct vml_call< scalar_##EIGENOP##_op<EIGENTYPE> > { \
enum { IsSupported = 1 }; \
static inline void run( const scalar_##EIGENOP##_op<EIGENTYPE>& /*func*/, \
int size, const EIGENTYPE* src, EIGENTYPE* dst) { \
MKL_INT64 vmlMode = EIGEN_MKL_VML_MODE; \
VMLOP(size, (const VMLTYPE*)src, (VMLTYPE*)dst, vmlMode); \
} \
};
#define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE) \
template<> struct vml_call< scalar_##EIGENOP##_op<EIGENTYPE> > { \
enum { IsSupported = 1 }; \
static inline void run( const scalar_##EIGENOP##_op<EIGENTYPE>& func, \
int size, const EIGENTYPE* src, EIGENTYPE* dst) { \
EIGENTYPE exponent = func.m_exponent; \
MKL_INT64 vmlMode = EIGEN_MKL_VML_MODE; \
VMLOP(&size, (const VMLTYPE*)src, (const VMLTYPE*)&exponent, \
(VMLTYPE*)dst, &vmlMode); \
} \
};
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vs##VMLOP, float, float) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vd##VMLOP, double, double)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vc##VMLOP, scomplex, MKL_Complex8) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vz##VMLOP, dcomplex, MKL_Complex16)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX(EIGENOP, VMLOP)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL_LA(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vms##VMLOP, float, float) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmd##VMLOP, double, double)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX_LA(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmc##VMLOP, scomplex, MKL_Complex8) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmz##VMLOP, dcomplex, MKL_Complex16)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL_LA(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX_LA(EIGENOP, VMLOP)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(sin, Sin)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(asin, Asin)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(cos, Cos)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(acos, Acos)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(tan, Tan)
//EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs, Abs)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(exp, Exp)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(log, Ln)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(sqrt, Sqrt)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr)
// The vm*powx functions are not avaibale in the windows version of MKL.
#ifdef _WIN32
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmspowx_, float, float)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdpowx_, double, double)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcpowx_, scomplex, MKL_Complex8)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzpowx_, dcomplex, MKL_Complex16)
#endif
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmsPowx, float, float, LA)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdPowx, double, double, LA)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcPowx, scomplex, MKL_Complex8, LA)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzPowx, dcomplex, MKL_Complex16, LA)
} // end namespace internal

View File

@@ -3,24 +3,9 @@
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_BANDMATRIX_H
#define EIGEN_BANDMATRIX_H
@@ -47,7 +32,7 @@ class BandMatrixBase : public EigenBase<Derived>
};
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> DenseMatrixType;
typedef typename DenseMatrixType::Index Index;
typedef typename DenseMatrixType::StorageIndex StorageIndex;
typedef typename internal::traits<Derived>::CoefficientsType CoefficientsType;
typedef EigenBase<Derived> Base;
@@ -176,15 +161,15 @@ class BandMatrixBase : public EigenBase<Derived>
*
* \brief Represents a rectangular matrix with a banded storage
*
* \param _Scalar Numeric type, i.e. float, double, int
* \param Rows Number of rows, or \b Dynamic
* \param Cols Number of columns, or \b Dynamic
* \param Supers Number of super diagonal
* \param Subs Number of sub diagonal
* \param _Options A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
* The former controls \ref TopicStorageOrders "storage order", and defaults to
* column-major. The latter controls whether the matrix represents a selfadjoint
* matrix in which case either Supers of Subs have to be null.
* \tparam _Scalar Numeric type, i.e. float, double, int
* \tparam _Rows Number of rows, or \b Dynamic
* \tparam _Cols Number of columns, or \b Dynamic
* \tparam _Supers Number of super diagonal
* \tparam _Subs Number of sub diagonal
* \tparam _Options A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
* The former controls \ref TopicStorageOrders "storage order", and defaults to
* column-major. The latter controls whether the matrix represents a selfadjoint
* matrix in which case either Supers of Subs have to be null.
*
* \sa class TridiagonalMatrix
*/
@@ -194,7 +179,7 @@ struct traits<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
{
typedef _Scalar Scalar;
typedef Dense StorageKind;
typedef DenseIndex Index;
typedef Eigen::Index StorageIndex;
enum {
CoeffReadCost = NumTraits<Scalar>::ReadCost,
RowsAtCompileTime = _Rows,
@@ -216,10 +201,10 @@ class BandMatrix : public BandMatrixBase<BandMatrix<_Scalar,Rows,Cols,Supers,Sub
public:
typedef typename internal::traits<BandMatrix>::Scalar Scalar;
typedef typename internal::traits<BandMatrix>::Index Index;
typedef typename internal::traits<BandMatrix>::StorageIndex StorageIndex;
typedef typename internal::traits<BandMatrix>::CoefficientsType CoefficientsType;
inline BandMatrix(Index rows=Rows, Index cols=Cols, Index supers=Supers, Index subs=Subs)
explicit inline BandMatrix(Index rows=Rows, Index cols=Cols, Index supers=Supers, Index subs=Subs)
: m_coeffs(1+supers+subs,cols),
m_rows(rows), m_supers(supers), m_subs(subs)
{
@@ -256,7 +241,7 @@ struct traits<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Opt
{
typedef typename _CoefficientsType::Scalar Scalar;
typedef typename _CoefficientsType::StorageKind StorageKind;
typedef typename _CoefficientsType::Index Index;
typedef typename _CoefficientsType::StorageIndex StorageIndex;
enum {
CoeffReadCost = internal::traits<_CoefficientsType>::CoeffReadCost,
RowsAtCompileTime = _Rows,
@@ -279,9 +264,9 @@ class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<_CoefficientsT
typedef typename internal::traits<BandMatrixWrapper>::Scalar Scalar;
typedef typename internal::traits<BandMatrixWrapper>::CoefficientsType CoefficientsType;
typedef typename internal::traits<BandMatrixWrapper>::Index Index;
typedef typename internal::traits<BandMatrixWrapper>::StorageIndex StorageIndex;
inline BandMatrixWrapper(const CoefficientsType& coeffs, Index rows=_Rows, Index cols=_Cols, Index supers=_Supers, Index subs=_Subs)
explicit inline BandMatrixWrapper(const CoefficientsType& coeffs, Index rows=_Rows, Index cols=_Cols, Index supers=_Supers, Index subs=_Subs)
: m_coeffs(coeffs),
m_rows(rows), m_supers(supers), m_subs(subs)
{
@@ -317,9 +302,9 @@ class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<_CoefficientsT
*
* \brief Represents a tridiagonal matrix with a compact banded storage
*
* \param _Scalar Numeric type, i.e. float, double, int
* \param Size Number of rows and cols, or \b Dynamic
* \param _Options Can be 0 or \b SelfAdjoint
* \tparam Scalar Numeric type, i.e. float, double, int
* \tparam Size Number of rows and cols, or \b Dynamic
* \tparam Options Can be 0 or \b SelfAdjoint
*
* \sa class BandMatrix
*/
@@ -327,9 +312,9 @@ template<typename Scalar, int Size, int Options>
class TridiagonalMatrix : public BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor>
{
typedef BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor> Base;
typedef typename Base::Index Index;
typedef typename Base::StorageIndex StorageIndex;
public:
TridiagonalMatrix(Index size = Size) : Base(size,size,Options&SelfAdjoint?0:1,1) {}
explicit TridiagonalMatrix(Index size = Size) : Base(size,size,Options&SelfAdjoint?0:1,1) {}
inline typename Base::template DiagonalIntReturnType<1>::Type super()
{ return Base::template diagonal<1>(); }
@@ -342,6 +327,25 @@ class TridiagonalMatrix : public BandMatrix<Scalar,Size,Size,Options&SelfAdjoint
protected:
};
struct BandShape {};
template<typename _Scalar, int _Rows, int _Cols, int _Supers, int _Subs, int _Options>
struct evaluator_traits<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
: public evaluator_traits_base<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
{
typedef BandShape Shape;
};
template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
struct evaluator_traits<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
: public evaluator_traits_base<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
{
typedef BandShape Shape;
};
template<> struct AssignmentKind<DenseShape,BandShape> { typedef EigenBase2EigenBase Kind; };
} // end namespace internal
} // end namespace Eigen

View File

@@ -4,39 +4,79 @@
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_BLOCK_H
#define EIGEN_BLOCK_H
namespace Eigen {
namespace internal {
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel> > : traits<XprType>
{
typedef typename traits<XprType>::Scalar Scalar;
typedef typename traits<XprType>::StorageKind StorageKind;
typedef typename traits<XprType>::XprKind XprKind;
typedef typename ref_selector<XprType>::type XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
enum{
MatrixRows = traits<XprType>::RowsAtCompileTime,
MatrixCols = traits<XprType>::ColsAtCompileTime,
RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows,
ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols,
MaxRowsAtCompileTime = BlockRows==0 ? 0
: RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime)
: int(traits<XprType>::MaxRowsAtCompileTime),
MaxColsAtCompileTime = BlockCols==0 ? 0
: ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime)
: int(traits<XprType>::MaxColsAtCompileTime),
XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0,
IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
: (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
: XprTypeIsRowMajor,
HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
InnerStrideAtCompileTime = HasSameStorageOrderAsXprType
? int(inner_stride_at_compile_time<XprType>::ret)
: int(outer_stride_at_compile_time<XprType>::ret),
OuterStrideAtCompileTime = HasSameStorageOrderAsXprType
? int(outer_stride_at_compile_time<XprType>::ret)
: int(inner_stride_at_compile_time<XprType>::ret),
// FIXME, this traits is rather specialized for dense object and it needs to be cleaned further
FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
Flags = (traits<XprType>::Flags & (DirectAccessBit | (InnerPanel?CompressedAccessBit:0))) | FlagsLvalueBit | FlagsRowMajorBit,
// FIXME DirectAccessBit should not be handled by expressions
//
// Alignment is needed by MapBase's assertions
// We can sefely set it to false here. Internal alignment errors will be detected by an eigen_internal_assert in the respective evaluator
Alignment = 0
};
};
template<typename XprType, int BlockRows=Dynamic, int BlockCols=Dynamic, bool InnerPanel = false,
bool HasDirectAccess = internal::has_direct_access<XprType>::ret> class BlockImpl_dense;
} // end namespace internal
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, typename StorageKind> class BlockImpl;
/** \class Block
* \ingroup Core_Module
*
* \brief Expression of a fixed-size or dynamic-size block
*
* \param XprType the type of the expression in which we are taking a block
* \param BlockRows the number of rows of the block we are taking at compile time (optional)
* \param BlockCols the number of columns of the block we are taking at compile time (optional)
* \param _DirectAccessStatus \internal used for partial specialization
* \tparam XprType the type of the expression in which we are taking a block
* \tparam BlockRows the number of rows of the block we are taking at compile time (optional)
* \tparam BlockCols the number of columns of the block we are taking at compile time (optional)
* \tparam InnerPanel is true, if the block maps to a set of rows of a row major matrix or
* to set of columns of a column major matrix (optional). The parameter allows to determine
* at compile time whether aligned access is possible on the block expression.
*
* This class represents an expression of either a fixed-size or dynamic-size block. It is the return
* type of DenseBase::block(Index,Index,Index,Index) and DenseBase::block<int,int>(Index,Index) and
@@ -60,68 +100,92 @@ namespace Eigen {
*
* \sa DenseBase::block(Index,Index,Index,Index), DenseBase::block(Index,Index), class VectorBlock
*/
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> class Block
: public BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind>
{
typedef BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> Impl;
public:
//typedef typename Impl::Base Base;
typedef Impl Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(Block)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
typedef typename internal::remove_all<XprType>::type NestedExpression;
/** Column or Row constructor
*/
EIGEN_DEVICE_FUNC
inline Block(XprType& xpr, Index i) : Impl(xpr,i)
{
eigen_assert( (i>=0) && (
((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
}
/** Fixed-size constructor
*/
EIGEN_DEVICE_FUNC
inline Block(XprType& xpr, Index startRow, Index startCol)
: Impl(xpr, startRow, startCol)
{
EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
eigen_assert(startRow >= 0 && BlockRows >= 0 && startRow + BlockRows <= xpr.rows()
&& startCol >= 0 && BlockCols >= 0 && startCol + BlockCols <= xpr.cols());
}
/** Dynamic-size constructor
*/
EIGEN_DEVICE_FUNC
inline Block(XprType& xpr,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: Impl(xpr, startRow, startCol, blockRows, blockCols)
{
eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
&& (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
eigen_assert(startRow >= 0 && blockRows >= 0 && startRow <= xpr.rows() - blockRows
&& startCol >= 0 && blockCols >= 0 && startCol <= xpr.cols() - blockCols);
}
};
// The generic default implementation for dense block simplu forward to the internal::BlockImpl_dense
// that must be specialized for direct and non-direct access...
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
class BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, Dense>
: public internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel>
{
typedef internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> Impl;
typedef typename XprType::StorageIndex StorageIndex;
public:
typedef Impl Base;
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)
EIGEN_DEVICE_FUNC inline BlockImpl(XprType& xpr, Index i) : Impl(xpr,i) {}
EIGEN_DEVICE_FUNC inline BlockImpl(XprType& xpr, Index startRow, Index startCol) : Impl(xpr, startRow, startCol) {}
EIGEN_DEVICE_FUNC
inline BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
: Impl(xpr, startRow, startCol, blockRows, blockCols) {}
};
namespace internal {
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess>
struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel, HasDirectAccess> > : traits<XprType>
{
typedef typename traits<XprType>::Scalar Scalar;
typedef typename traits<XprType>::StorageKind StorageKind;
typedef typename traits<XprType>::XprKind XprKind;
typedef typename nested<XprType>::type XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
enum{
MatrixRows = traits<XprType>::RowsAtCompileTime,
MatrixCols = traits<XprType>::ColsAtCompileTime,
RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows,
ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols,
MaxRowsAtCompileTime = BlockRows==0 ? 0
: RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime)
: int(traits<XprType>::MaxRowsAtCompileTime),
MaxColsAtCompileTime = BlockCols==0 ? 0
: ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime)
: int(traits<XprType>::MaxColsAtCompileTime),
XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0,
IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
: (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
: XprTypeIsRowMajor,
HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
InnerStrideAtCompileTime = HasSameStorageOrderAsXprType
? int(inner_stride_at_compile_time<XprType>::ret)
: int(outer_stride_at_compile_time<XprType>::ret),
OuterStrideAtCompileTime = HasSameStorageOrderAsXprType
? int(outer_stride_at_compile_time<XprType>::ret)
: int(inner_stride_at_compile_time<XprType>::ret),
MaskPacketAccessBit = (InnerSize == Dynamic || (InnerSize % packet_traits<Scalar>::size) == 0)
&& (InnerStrideAtCompileTime == 1)
? PacketAccessBit : 0,
MaskAlignedBit = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic) && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % 16) == 0)) ? AlignedBit : 0,
FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0,
FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
Flags0 = traits<XprType>::Flags & ( (HereditaryBits & ~RowMajorBit) |
DirectAccessBit |
MaskPacketAccessBit |
MaskAlignedBit),
Flags = Flags0 | FlagsLinearAccessBit | FlagsLvalueBit | FlagsRowMajorBit
};
};
}
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess> class Block
: public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel, HasDirectAccess> >::type
/** \internal Internal implementation of dense Blocks in the general case. */
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess> class BlockImpl_dense
: public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel> >::type
{
typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
public:
typedef typename internal::dense_xpr_base<Block>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Block)
typedef typename internal::dense_xpr_base<BlockType>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
class InnerIterator;
// class InnerIterator; // FIXME apparently never used
/** Column or Row constructor
*/
inline Block(XprType& xpr, Index i)
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index i)
: m_xpr(xpr),
// It is a row if and only if BlockRows==1 and BlockCols==XprType::ColsAtCompileTime,
// and it is a column if and only if BlockRows==XprType::RowsAtCompileTime and BlockCols==1,
@@ -131,94 +195,80 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0),
m_blockRows(BlockRows==1 ? 1 : xpr.rows()),
m_blockCols(BlockCols==1 ? 1 : xpr.cols())
{
eigen_assert( (i>=0) && (
((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
}
{}
/** Fixed-size constructor
*/
inline Block(XprType& xpr, Index startRow, Index startCol)
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
: m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
m_blockRows(BlockRows), m_blockCols(BlockCols)
{
EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
eigen_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows()
&& startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols());
}
m_blockRows(BlockRows), m_blockCols(BlockCols)
{}
/** Dynamic-size constructor
*/
inline Block(XprType& xpr,
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
m_blockRows(blockRows), m_blockCols(blockCols)
{
eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
&& (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
eigen_assert(startRow >= 0 && blockRows >= 0 && startRow + blockRows <= xpr.rows()
&& startCol >= 0 && blockCols >= 0 && startCol + blockCols <= xpr.cols());
}
m_blockRows(blockRows), m_blockCols(blockCols)
{}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
EIGEN_DEVICE_FUNC inline Index rows() const { return m_blockRows.value(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_blockCols.value(); }
inline Index rows() const { return m_blockRows.value(); }
inline Index cols() const { return m_blockCols.value(); }
inline Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index rowId, Index colId)
{
EIGEN_STATIC_ASSERT_LVALUE(XprType)
return m_xpr.const_cast_derived()
.coeffRef(row + m_startRow.value(), col + m_startCol.value());
return m_xpr.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
}
inline const Scalar& coeffRef(Index row, Index col) const
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_xpr.derived()
.coeffRef(row + m_startRow.value(), col + m_startCol.value());
return m_xpr.derived().coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
}
EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const
{
return m_xpr.coeff(row + m_startRow.value(), col + m_startCol.value());
return m_xpr.coeff(rowId + m_startRow.value(), colId + m_startCol.value());
}
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_LVALUE(XprType)
return m_xpr.const_cast_derived()
.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
return m_xpr.const_cast_derived()
.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
EIGEN_DEVICE_FUNC
inline const CoeffReturnType coeff(Index index) const
{
return m_xpr
.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
return m_xpr.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
template<int LoadMode>
inline PacketScalar packet(Index row, Index col) const
inline PacketScalar packet(Index rowId, Index colId) const
{
return m_xpr.template packet<Unaligned>
(row + m_startRow.value(), col + m_startCol.value());
return m_xpr.template packet<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value());
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_xpr.const_cast_derived().template writePacket<Unaligned>
(row + m_startRow.value(), col + m_startCol.value(), x);
m_xpr.template writePacket<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value(), val);
}
template<int LoadMode>
@@ -230,116 +280,140 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
inline void writePacket(Index index, const PacketScalar& val)
{
m_xpr.const_cast_derived().template writePacket<Unaligned>
m_xpr.template writePacket<Unaligned>
(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), x);
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), val);
}
#ifdef EIGEN_PARSED_BY_DOXYGEN
/** \sa MapBase::data() */
inline const Scalar* data() const;
inline Index innerStride() const;
inline Index outerStride() const;
EIGEN_DEVICE_FUNC inline const Scalar* data() const;
EIGEN_DEVICE_FUNC inline Index innerStride() const;
EIGEN_DEVICE_FUNC inline Index outerStride() const;
#endif
const typename internal::remove_all<typename XprType::Nested>::type& nestedExpression() const
EIGEN_DEVICE_FUNC
const typename internal::remove_all<XprTypeNested>::type& nestedExpression() const
{
return m_xpr;
}
EIGEN_DEVICE_FUNC
XprType& nestedExpression() { return m_xpr; }
Index startRow() const
EIGEN_DEVICE_FUNC
StorageIndex startRow() const
{
return m_startRow.value();
}
Index startCol() const
EIGEN_DEVICE_FUNC
StorageIndex startCol() const
{
return m_startCol.value();
}
protected:
const typename XprType::Nested m_xpr;
const internal::variable_if_dynamic<Index, XprType::RowsAtCompileTime == 1 ? 0 : Dynamic> m_startRow;
const internal::variable_if_dynamic<Index, XprType::ColsAtCompileTime == 1 ? 0 : Dynamic> m_startCol;
const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_blockRows;
const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_blockCols;
XprTypeNested m_xpr;
const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows==1) ? 0 : Dynamic> m_startRow;
const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols==1) ? 0 : Dynamic> m_startCol;
const internal::variable_if_dynamic<StorageIndex, RowsAtCompileTime> m_blockRows;
const internal::variable_if_dynamic<StorageIndex, ColsAtCompileTime> m_blockCols;
};
/** \internal */
/** \internal Internal implementation of dense Blocks in the direct access case.*/
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
class Block<XprType,BlockRows,BlockCols, InnerPanel,true>
: public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel, true> >
class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
: public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel> >
{
typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
enum {
XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0
};
public:
typedef MapBase<Block> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Block)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
typedef MapBase<BlockType> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
/** Column or Row constructor
*/
inline Block(XprType& xpr, Index i)
: Base(internal::const_cast_ptr(&xpr.coeffRef(
(BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0,
(BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0)),
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index i)
: Base(xpr.data() + i * ( ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && (!XprTypeIsRowMajor))
|| ((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && ( XprTypeIsRowMajor)) ? xpr.innerStride() : xpr.outerStride()),
BlockRows==1 ? 1 : xpr.rows(),
BlockCols==1 ? 1 : xpr.cols()),
m_xpr(xpr)
m_xpr(xpr),
m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0),
m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0)
{
eigen_assert( (i>=0) && (
((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
init();
}
/** Fixed-size constructor
*/
inline Block(XprType& xpr, Index startRow, Index startCol)
: Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol))), m_xpr(xpr)
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
: Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol)),
m_xpr(xpr), m_startRow(startRow), m_startCol(startCol)
{
eigen_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows()
&& startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols());
init();
}
/** Dynamic-size constructor
*/
inline Block(XprType& xpr,
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol)), blockRows, blockCols),
m_xpr(xpr)
: Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol), blockRows, blockCols),
m_xpr(xpr), m_startRow(startRow), m_startCol(startCol)
{
eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
&& (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
eigen_assert(startRow >= 0 && blockRows >= 0 && startRow + blockRows <= xpr.rows()
&& startCol >= 0 && blockCols >= 0 && startCol + blockCols <= xpr.cols());
init();
}
const typename internal::remove_all<typename XprType::Nested>::type& nestedExpression() const
EIGEN_DEVICE_FUNC
const typename internal::remove_all<XprTypeNested>::type& nestedExpression() const
{
return m_xpr;
}
EIGEN_DEVICE_FUNC
XprType& nestedExpression() { return m_xpr; }
/** \sa MapBase::innerStride() */
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return internal::traits<Block>::HasSameStorageOrderAsXprType
return internal::traits<BlockType>::HasSameStorageOrderAsXprType
? m_xpr.innerStride()
: m_xpr.outerStride();
}
/** \sa MapBase::outerStride() */
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return m_outerStride;
}
EIGEN_DEVICE_FUNC
StorageIndex startRow() const
{
return m_startRow.value();
}
EIGEN_DEVICE_FUNC
StorageIndex startCol() const
{
return m_startCol.value();
}
#ifndef __SUNPRO_CC
// FIXME sunstudio is not friendly with the above friend...
// META-FIXME there is no 'friend' keyword around here. Is this obsolete?
@@ -348,7 +422,8 @@ class Block<XprType,BlockRows,BlockCols, InnerPanel,true>
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal used by allowAligned() */
inline Block(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols)
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols)
: Base(data, blockRows, blockCols), m_xpr(xpr)
{
init();
@@ -356,17 +431,22 @@ class Block<XprType,BlockRows,BlockCols, InnerPanel,true>
#endif
protected:
EIGEN_DEVICE_FUNC
void init()
{
m_outerStride = internal::traits<Block>::HasSameStorageOrderAsXprType
m_outerStride = internal::traits<BlockType>::HasSameStorageOrderAsXprType
? m_xpr.outerStride()
: m_xpr.innerStride();
}
typename XprType::Nested m_xpr;
XprTypeNested m_xpr;
const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows==1) ? 0 : Dynamic> m_startRow;
const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols==1) ? 0 : Dynamic> m_startCol;
Index m_outerStride;
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_BLOCK_H

View File

@@ -3,24 +3,9 @@
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ALLANDANY_H
#define EIGEN_ALLANDANY_H
@@ -32,9 +17,10 @@ namespace internal {
template<typename Derived, int UnrollCount>
struct all_unroller
{
typedef typename Derived::ExpressionTraits Traits;
enum {
col = (UnrollCount-1) / Derived::RowsAtCompileTime,
row = (UnrollCount-1) % Derived::RowsAtCompileTime
col = (UnrollCount-1) / Traits::RowsAtCompileTime,
row = (UnrollCount-1) % Traits::RowsAtCompileTime
};
static inline bool run(const Derived &mat)
@@ -44,9 +30,9 @@ struct all_unroller
};
template<typename Derived>
struct all_unroller<Derived, 1>
struct all_unroller<Derived, 0>
{
static inline bool run(const Derived &mat) { return mat.coeff(0, 0); }
static inline bool run(const Derived &/*mat*/) { return true; }
};
template<typename Derived>
@@ -58,11 +44,12 @@ struct all_unroller<Derived, Dynamic>
template<typename Derived, int UnrollCount>
struct any_unroller
{
typedef typename Derived::ExpressionTraits Traits;
enum {
col = (UnrollCount-1) / Derived::RowsAtCompileTime,
row = (UnrollCount-1) % Derived::RowsAtCompileTime
col = (UnrollCount-1) / Traits::RowsAtCompileTime,
row = (UnrollCount-1) % Traits::RowsAtCompileTime
};
static inline bool run(const Derived &mat)
{
return any_unroller<Derived, UnrollCount-1>::run(mat) || mat.coeff(row, col);
@@ -70,9 +57,9 @@ struct any_unroller
};
template<typename Derived>
struct any_unroller<Derived, 1>
struct any_unroller<Derived, 0>
{
static inline bool run(const Derived &mat) { return mat.coeff(0, 0); }
static inline bool run(const Derived & /*mat*/) { return false; }
};
template<typename Derived>
@@ -93,21 +80,19 @@ struct any_unroller<Derived, Dynamic>
template<typename Derived>
inline bool DenseBase<Derived>::all() const
{
typedef internal::evaluator<Derived> Evaluator;
enum {
unroll = SizeAtCompileTime != Dynamic
&& CoeffReadCost != Dynamic
&& NumTraits<Scalar>::AddCost != Dynamic
&& SizeAtCompileTime * (CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
&& SizeAtCompileTime * (Evaluator::CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
};
Evaluator evaluator(derived());
if(unroll)
return internal::all_unroller<Derived,
unroll ? int(SizeAtCompileTime) : Dynamic
>::run(derived());
return internal::all_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic>::run(evaluator);
else
{
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if (!coeff(i, j)) return false;
if (!evaluator.coeff(i, j)) return false;
return true;
}
}
@@ -119,21 +104,19 @@ inline bool DenseBase<Derived>::all() const
template<typename Derived>
inline bool DenseBase<Derived>::any() const
{
typedef internal::evaluator<Derived> Evaluator;
enum {
unroll = SizeAtCompileTime != Dynamic
&& CoeffReadCost != Dynamic
&& NumTraits<Scalar>::AddCost != Dynamic
&& SizeAtCompileTime * (CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
&& SizeAtCompileTime * (Evaluator::CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
};
Evaluator evaluator(derived());
if(unroll)
return internal::any_unroller<Derived,
unroll ? int(SizeAtCompileTime) : Dynamic
>::run(derived());
return internal::any_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic>::run(evaluator);
else
{
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if (coeff(i, j)) return true;
if (evaluator.coeff(i, j)) return true;
return false;
}
}
@@ -143,11 +126,39 @@ inline bool DenseBase<Derived>::any() const
* \sa all(), any()
*/
template<typename Derived>
inline typename DenseBase<Derived>::Index DenseBase<Derived>::count() const
inline Eigen::Index DenseBase<Derived>::count() const
{
return derived().template cast<bool>().template cast<Index>().sum();
}
/** \returns true is \c *this contains at least one Not A Number (NaN).
*
* \sa allFinite()
*/
template<typename Derived>
inline bool DenseBase<Derived>::hasNaN() const
{
#if EIGEN_COMP_MSVC || (defined __FAST_MATH__)
return derived().array().isNaN().any();
#else
return !((derived().array()==derived().array()).all());
#endif
}
/** \returns true if \c *this contains only finite numbers, i.e., no NaN and no +/-INF values.
*
* \sa hasNaN()
*/
template<typename Derived>
inline bool DenseBase<Derived>::allFinite() const
{
#if EIGEN_COMP_MSVC || (defined __FAST_MATH__)
return derived().array().isFinite().all();
#else
return !((derived()-derived()).hasNaN());
#endif
}
} // end namespace Eigen
#endif // EIGEN_ALLANDANY_H

View File

@@ -1,10 +0,0 @@
FILE(GLOB Eigen_Core_SRCS "*.h")
INSTALL(FILES
${Eigen_Core_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core COMPONENT Devel
)
ADD_SUBDIRECTORY(products)
ADD_SUBDIRECTORY(util)
ADD_SUBDIRECTORY(arch)

View File

@@ -4,24 +4,9 @@
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_COMMAINITIALIZER_H
#define EIGEN_COMMAINITIALIZER_H
@@ -37,14 +22,14 @@ namespace Eigen {
* the return type of MatrixBase::operator<<, and most of the time this is the only
* way it is used.
*
* \sa \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished()
* \sa \blank \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished()
*/
template<typename XprType>
struct CommaInitializer
{
typedef typename XprType::Scalar Scalar;
typedef typename XprType::Index Index;
EIGEN_DEVICE_FUNC
inline CommaInitializer(XprType& xpr, const Scalar& s)
: m_xpr(xpr), m_row(0), m_col(1), m_currentBlockRows(1)
{
@@ -52,13 +37,27 @@ struct CommaInitializer
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
inline CommaInitializer(XprType& xpr, const DenseBase<OtherDerived>& other)
: m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows())
{
m_xpr.block(0, 0, other.rows(), other.cols()) = other;
}
/* Copy/Move constructor which transfers ownership. This is crucial in
* absence of return value optimization to avoid assertions during destruction. */
// FIXME in C++11 mode this could be replaced by a proper RValue constructor
EIGEN_DEVICE_FUNC
inline CommaInitializer(const CommaInitializer& o)
: m_xpr(o.m_xpr), m_row(o.m_row), m_col(o.m_col), m_currentBlockRows(o.m_currentBlockRows) {
// Mark original object as finished. In absence of R-value references we need to const_cast:
const_cast<CommaInitializer&>(o).m_row = m_xpr.rows();
const_cast<CommaInitializer&>(o).m_col = m_xpr.cols();
const_cast<CommaInitializer&>(o).m_currentBlockRows = 0;
}
/* inserts a scalar value in the target matrix */
EIGEN_DEVICE_FUNC
CommaInitializer& operator,(const Scalar& s)
{
if (m_col==m_xpr.cols())
@@ -78,9 +77,10 @@ struct CommaInitializer
/* inserts a matrix expression in the target matrix */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
CommaInitializer& operator,(const DenseBase<OtherDerived>& other)
{
if (m_col==m_xpr.cols())
if (m_col==m_xpr.cols() && (other.cols()!=0 || other.rows()!=m_currentBlockRows))
{
m_row+=m_currentBlockRows;
m_col = 0;
@@ -88,24 +88,22 @@ struct CommaInitializer
eigen_assert(m_row+m_currentBlockRows<=m_xpr.rows()
&& "Too many rows passed to comma initializer (operator<<)");
}
eigen_assert(m_col<m_xpr.cols()
eigen_assert((m_col + other.cols() <= m_xpr.cols())
&& "Too many coefficients passed to comma initializer (operator<<)");
eigen_assert(m_currentBlockRows==other.rows());
if (OtherDerived::SizeAtCompileTime != Dynamic)
m_xpr.template block<OtherDerived::RowsAtCompileTime != Dynamic ? OtherDerived::RowsAtCompileTime : 1,
OtherDerived::ColsAtCompileTime != Dynamic ? OtherDerived::ColsAtCompileTime : 1>
(m_row, m_col) = other;
else
m_xpr.block(m_row, m_col, other.rows(), other.cols()) = other;
m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>
(m_row, m_col, other.rows(), other.cols()) = other;
m_col += other.cols();
return *this;
}
EIGEN_DEVICE_FUNC
inline ~CommaInitializer()
#if defined VERIFY_RAISES_ASSERT && (!defined EIGEN_NO_ASSERTION_CHECKING) && defined EIGEN_EXCEPTIONS
EIGEN_EXCEPTION_SPEC(Eigen::eigen_assert_exception)
#endif
{
eigen_assert((m_row+m_currentBlockRows) == m_xpr.rows()
&& m_col == m_xpr.cols()
&& "Too few coefficients passed to comma initializer (operator<<)");
finished();
}
/** \returns the built matrix once all its coefficients have been set.
@@ -115,9 +113,15 @@ struct CommaInitializer
* quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished());
* \endcode
*/
inline XprType& finished() { return m_xpr; }
EIGEN_DEVICE_FUNC
inline XprType& finished() {
eigen_assert(((m_row+m_currentBlockRows) == m_xpr.rows() || m_xpr.cols() == 0)
&& m_col == m_xpr.cols()
&& "Too few coefficients passed to comma initializer (operator<<)");
return m_xpr;
}
XprType& m_xpr; // target expression
XprType& m_xpr; // target expression
Index m_row; // current row id
Index m_col; // current col id
Index m_currentBlockRows; // current block height
@@ -131,6 +135,8 @@ struct CommaInitializer
*
* Example: \include MatrixBase_set.cpp
* Output: \verbinclude MatrixBase_set.out
*
* \note According the c++ standard, the argument expressions of this comma initializer are evaluated in arbitrary order.
*
* \sa CommaInitializer::finished(), class CommaInitializer
*/

View File

@@ -0,0 +1,175 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016 Rasmus Munk Larsen (rmlarsen@google.com)
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CONDITIONESTIMATOR_H
#define EIGEN_CONDITIONESTIMATOR_H
namespace Eigen {
namespace internal {
template <typename Vector, typename RealVector, bool IsComplex>
struct rcond_compute_sign {
static inline Vector run(const Vector& v) {
const RealVector v_abs = v.cwiseAbs();
return (v_abs.array() == static_cast<typename Vector::RealScalar>(0))
.select(Vector::Ones(v.size()), v.cwiseQuotient(v_abs));
}
};
// Partial specialization to avoid elementwise division for real vectors.
template <typename Vector>
struct rcond_compute_sign<Vector, Vector, false> {
static inline Vector run(const Vector& v) {
return (v.array() < static_cast<typename Vector::RealScalar>(0))
.select(-Vector::Ones(v.size()), Vector::Ones(v.size()));
}
};
/**
* \returns an estimate of ||inv(matrix)||_1 given a decomposition of
* \a matrix that implements .solve() and .adjoint().solve() methods.
*
* This function implements Algorithms 4.1 and 5.1 from
* http://www.maths.manchester.ac.uk/~higham/narep/narep135.pdf
* which also forms the basis for the condition number estimators in
* LAPACK. Since at most 10 calls to the solve method of dec are
* performed, the total cost is O(dims^2), as opposed to O(dims^3)
* needed to compute the inverse matrix explicitly.
*
* The most common usage is in estimating the condition number
* ||matrix||_1 * ||inv(matrix)||_1. The first term ||matrix||_1 can be
* computed directly in O(n^2) operations.
*
* Supports the following decompositions: FullPivLU, PartialPivLU, LDLT, and
* LLT.
*
* \sa FullPivLU, PartialPivLU, LDLT, LLT.
*/
template <typename Decomposition>
typename Decomposition::RealScalar rcond_invmatrix_L1_norm_estimate(const Decomposition& dec)
{
typedef typename Decomposition::MatrixType MatrixType;
typedef typename Decomposition::Scalar Scalar;
typedef typename Decomposition::RealScalar RealScalar;
typedef typename internal::plain_col_type<MatrixType>::type Vector;
typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVector;
const bool is_complex = (NumTraits<Scalar>::IsComplex != 0);
eigen_assert(dec.rows() == dec.cols());
const Index n = dec.rows();
if (n == 0)
return 0;
// Disable Index to float conversion warning
#ifdef __INTEL_COMPILER
#pragma warning push
#pragma warning ( disable : 2259 )
#endif
Vector v = dec.solve(Vector::Ones(n) / Scalar(n));
#ifdef __INTEL_COMPILER
#pragma warning pop
#endif
// lower_bound is a lower bound on
// ||inv(matrix)||_1 = sup_v ||inv(matrix) v||_1 / ||v||_1
// and is the objective maximized by the ("super-") gradient ascent
// algorithm below.
RealScalar lower_bound = v.template lpNorm<1>();
if (n == 1)
return lower_bound;
// Gradient ascent algorithm follows: We know that the optimum is achieved at
// one of the simplices v = e_i, so in each iteration we follow a
// super-gradient to move towards the optimal one.
RealScalar old_lower_bound = lower_bound;
Vector sign_vector(n);
Vector old_sign_vector;
Index v_max_abs_index = -1;
Index old_v_max_abs_index = v_max_abs_index;
for (int k = 0; k < 4; ++k)
{
sign_vector = internal::rcond_compute_sign<Vector, RealVector, is_complex>::run(v);
if (k > 0 && !is_complex && sign_vector == old_sign_vector) {
// Break if the solution stagnated.
break;
}
// v_max_abs_index = argmax |real( inv(matrix)^T * sign_vector )|
v = dec.adjoint().solve(sign_vector);
v.real().cwiseAbs().maxCoeff(&v_max_abs_index);
if (v_max_abs_index == old_v_max_abs_index) {
// Break if the solution stagnated.
break;
}
// Move to the new simplex e_j, where j = v_max_abs_index.
v = dec.solve(Vector::Unit(n, v_max_abs_index)); // v = inv(matrix) * e_j.
lower_bound = v.template lpNorm<1>();
if (lower_bound <= old_lower_bound) {
// Break if the gradient step did not increase the lower_bound.
break;
}
if (!is_complex) {
old_sign_vector = sign_vector;
}
old_v_max_abs_index = v_max_abs_index;
old_lower_bound = lower_bound;
}
// The following calculates an independent estimate of ||matrix||_1 by
// multiplying matrix by a vector with entries of slowly increasing
// magnitude and alternating sign:
// v_i = (-1)^{i} (1 + (i / (dim-1))), i = 0,...,dim-1.
// This improvement to Hager's algorithm above is due to Higham. It was
// added to make the algorithm more robust in certain corner cases where
// large elements in the matrix might otherwise escape detection due to
// exact cancellation (especially when op and op_adjoint correspond to a
// sequence of backsubstitutions and permutations), which could cause
// Hager's algorithm to vastly underestimate ||matrix||_1.
Scalar alternating_sign(RealScalar(1));
for (Index i = 0; i < n; ++i) {
// The static_cast is needed when Scalar is a complex and RealScalar implements expression templates
v[i] = alternating_sign * static_cast<RealScalar>(RealScalar(1) + (RealScalar(i) / (RealScalar(n - 1))));
alternating_sign = -alternating_sign;
}
v = dec.solve(v);
const RealScalar alternate_lower_bound = (2 * v.template lpNorm<1>()) / (3 * RealScalar(n));
return numext::maxi(lower_bound, alternate_lower_bound);
}
/** \brief Reciprocal condition number estimator.
*
* Computing a decomposition of a dense matrix takes O(n^3) operations, while
* this method estimates the condition number quickly and reliably in O(n^2)
* operations.
*
* \returns an estimate of the reciprocal condition number
* (1 / (||matrix||_1 * ||inv(matrix)||_1)) of matrix, given ||matrix||_1 and
* its decomposition. Supports the following decompositions: FullPivLU,
* PartialPivLU, LDLT, and LLT.
*
* \sa FullPivLU, PartialPivLU, LDLT, LLT.
*/
template <typename Decomposition>
typename Decomposition::RealScalar
rcond_estimate_helper(typename Decomposition::RealScalar matrix_norm, const Decomposition& dec)
{
typedef typename Decomposition::RealScalar RealScalar;
eigen_assert(dec.rows() == dec.cols());
if (dec.rows() == 0) return RealScalar(1);
if (matrix_norm == RealScalar(0)) return RealScalar(0);
if (dec.rows() == 1) return RealScalar(1);
const RealScalar inverse_matrix_norm = rcond_invmatrix_L1_norm_estimate(dec);
return (inverse_matrix_norm == RealScalar(0) ? RealScalar(0)
: (RealScalar(1) / inverse_matrix_norm) / matrix_norm);
}
} // namespace internal
} // namespace Eigen
#endif

File diff suppressed because it is too large Load Diff

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@@ -0,0 +1,127 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_COREITERATORS_H
#define EIGEN_COREITERATORS_H
namespace Eigen {
/* This file contains the respective InnerIterator definition of the expressions defined in Eigen/Core
*/
namespace internal {
template<typename XprType, typename EvaluatorKind>
class inner_iterator_selector;
}
/** \class InnerIterator
* \brief An InnerIterator allows to loop over the element of any matrix expression.
*
* \warning To be used with care because an evaluator is constructed every time an InnerIterator iterator is constructed.
*
* TODO: add a usage example
*/
template<typename XprType>
class InnerIterator
{
protected:
typedef internal::inner_iterator_selector<XprType, typename internal::evaluator_traits<XprType>::Kind> IteratorType;
typedef internal::evaluator<XprType> EvaluatorType;
typedef typename internal::traits<XprType>::Scalar Scalar;
public:
/** Construct an iterator over the \a outerId -th row or column of \a xpr */
InnerIterator(const XprType &xpr, const Index &outerId)
: m_eval(xpr), m_iter(m_eval, outerId, xpr.innerSize())
{}
/// \returns the value of the current coefficient.
EIGEN_STRONG_INLINE Scalar value() const { return m_iter.value(); }
/** Increment the iterator \c *this to the next non-zero coefficient.
* Explicit zeros are not skipped over. To skip explicit zeros, see class SparseView
*/
EIGEN_STRONG_INLINE InnerIterator& operator++() { m_iter.operator++(); return *this; }
/// \returns the column or row index of the current coefficient.
EIGEN_STRONG_INLINE Index index() const { return m_iter.index(); }
/// \returns the row index of the current coefficient.
EIGEN_STRONG_INLINE Index row() const { return m_iter.row(); }
/// \returns the column index of the current coefficient.
EIGEN_STRONG_INLINE Index col() const { return m_iter.col(); }
/// \returns \c true if the iterator \c *this still references a valid coefficient.
EIGEN_STRONG_INLINE operator bool() const { return m_iter; }
protected:
EvaluatorType m_eval;
IteratorType m_iter;
private:
// If you get here, then you're not using the right InnerIterator type, e.g.:
// SparseMatrix<double,RowMajor> A;
// SparseMatrix<double>::InnerIterator it(A,0);
template<typename T> InnerIterator(const EigenBase<T>&,Index outer);
};
namespace internal {
// Generic inner iterator implementation for dense objects
template<typename XprType>
class inner_iterator_selector<XprType, IndexBased>
{
protected:
typedef evaluator<XprType> EvaluatorType;
typedef typename traits<XprType>::Scalar Scalar;
enum { IsRowMajor = (XprType::Flags&RowMajorBit)==RowMajorBit };
public:
EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &innerSize)
: m_eval(eval), m_inner(0), m_outer(outerId), m_end(innerSize)
{}
EIGEN_STRONG_INLINE Scalar value() const
{
return (IsRowMajor) ? m_eval.coeff(m_outer, m_inner)
: m_eval.coeff(m_inner, m_outer);
}
EIGEN_STRONG_INLINE inner_iterator_selector& operator++() { m_inner++; return *this; }
EIGEN_STRONG_INLINE Index index() const { return m_inner; }
inline Index row() const { return IsRowMajor ? m_outer : index(); }
inline Index col() const { return IsRowMajor ? index() : m_outer; }
EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; }
protected:
const EvaluatorType& m_eval;
Index m_inner;
const Index m_outer;
const Index m_end;
};
// For iterator-based evaluator, inner-iterator is already implemented as
// evaluator<>::InnerIterator
template<typename XprType>
class inner_iterator_selector<XprType, IteratorBased>
: public evaluator<XprType>::InnerIterator
{
protected:
typedef typename evaluator<XprType>::InnerIterator Base;
typedef evaluator<XprType> EvaluatorType;
public:
EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &/*innerSize*/)
: Base(eval, outerId)
{}
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_COREITERATORS_H

View File

@@ -1,53 +1,18 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CWISE_BINARY_OP_H
#define EIGEN_CWISE_BINARY_OP_H
namespace Eigen {
/** \class CwiseBinaryOp
* \ingroup Core_Module
*
* \brief Generic expression where a coefficient-wise binary operator is applied to two expressions
*
* \param BinaryOp template functor implementing the operator
* \param Lhs the type of the left-hand side
* \param Rhs the type of the right-hand side
*
* This class represents an expression where a coefficient-wise binary operator is applied to two expressions.
* It is the return type of binary operators, by which we mean only those binary operators where
* both the left-hand side and the right-hand side are Eigen expressions.
* For example, the return type of matrix1+matrix2 is a CwiseBinaryOp.
*
* Most of the time, this is the only way that it is used, so you typically don't have to name
* CwiseBinaryOp types explicitly.
*
* \sa MatrixBase::binaryExpr(const MatrixBase<OtherDerived> &,const CustomBinaryOp &) const, class CwiseUnaryOp, class CwiseNullaryOp
*/
namespace internal {
template<typename BinaryOp, typename Lhs, typename Rhs>
struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
@@ -67,85 +32,85 @@ struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
// we still want to handle the case when the result type is different.
typedef typename result_of<
BinaryOp(
typename Lhs::Scalar,
typename Rhs::Scalar
const typename Lhs::Scalar&,
const typename Rhs::Scalar&
)
>::type Scalar;
typedef typename promote_storage_type<typename traits<Lhs>::StorageKind,
typename traits<Rhs>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<typename traits<Lhs>::Index,
typename traits<Rhs>::Index>::type Index;
typedef typename cwise_promote_storage_type<typename traits<Lhs>::StorageKind,
typename traits<Rhs>::StorageKind,
BinaryOp>::ret StorageKind;
typedef typename promote_index_type<typename traits<Lhs>::StorageIndex,
typename traits<Rhs>::StorageIndex>::type StorageIndex;
typedef typename Lhs::Nested LhsNested;
typedef typename Rhs::Nested RhsNested;
typedef typename remove_reference<LhsNested>::type _LhsNested;
typedef typename remove_reference<RhsNested>::type _RhsNested;
enum {
LhsCoeffReadCost = _LhsNested::CoeffReadCost,
RhsCoeffReadCost = _RhsNested::CoeffReadCost,
LhsFlags = _LhsNested::Flags,
RhsFlags = _RhsNested::Flags,
SameType = is_same<typename _LhsNested::Scalar,typename _RhsNested::Scalar>::value,
StorageOrdersAgree = (int(Lhs::Flags)&RowMajorBit)==(int(Rhs::Flags)&RowMajorBit),
Flags0 = (int(LhsFlags) | int(RhsFlags)) & (
HereditaryBits
| (int(LhsFlags) & int(RhsFlags) &
( AlignedBit
| (StorageOrdersAgree ? LinearAccessBit : 0)
| (functor_traits<BinaryOp>::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0)
)
)
),
Flags = (Flags0 & ~RowMajorBit) | (LhsFlags & RowMajorBit),
CoeffReadCost = LhsCoeffReadCost + RhsCoeffReadCost + functor_traits<BinaryOp>::Cost
Flags = cwise_promote_storage_order<typename traits<Lhs>::StorageKind,typename traits<Rhs>::StorageKind,_LhsNested::Flags & RowMajorBit,_RhsNested::Flags & RowMajorBit>::value
};
};
} // end namespace internal
// we require Lhs and Rhs to have the same scalar type. Currently there is no example of a binary functor
// that would take two operands of different types. If there were such an example, then this check should be
// moved to the BinaryOp functors, on a per-case basis. This would however require a change in the BinaryOp functors, as
// currently they take only one typename Scalar template parameter.
// It is tempting to always allow mixing different types but remember that this is often impossible in the vectorized paths.
// So allowing mixing different types gives very unexpected errors when enabling vectorization, when the user tries to
// add together a float matrix and a double matrix.
#define EIGEN_CHECK_BINARY_COMPATIBILIY(BINOP,LHS,RHS) \
EIGEN_STATIC_ASSERT((internal::functor_allows_mixing_real_and_complex<BINOP>::ret \
? int(internal::is_same<typename NumTraits<LHS>::Real, typename NumTraits<RHS>::Real>::value) \
: int(internal::is_same<LHS, RHS>::value)), \
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
class CwiseBinaryOpImpl;
template<typename BinaryOp, typename Lhs, typename Rhs>
class CwiseBinaryOp : internal::no_assignment_operator,
/** \class CwiseBinaryOp
* \ingroup Core_Module
*
* \brief Generic expression where a coefficient-wise binary operator is applied to two expressions
*
* \tparam BinaryOp template functor implementing the operator
* \tparam LhsType the type of the left-hand side
* \tparam RhsType the type of the right-hand side
*
* This class represents an expression where a coefficient-wise binary operator is applied to two expressions.
* It is the return type of binary operators, by which we mean only those binary operators where
* both the left-hand side and the right-hand side are Eigen expressions.
* For example, the return type of matrix1+matrix2 is a CwiseBinaryOp.
*
* Most of the time, this is the only way that it is used, so you typically don't have to name
* CwiseBinaryOp types explicitly.
*
* \sa MatrixBase::binaryExpr(const MatrixBase<OtherDerived> &,const CustomBinaryOp &) const, class CwiseUnaryOp, class CwiseNullaryOp
*/
template<typename BinaryOp, typename LhsType, typename RhsType>
class CwiseBinaryOp :
public CwiseBinaryOpImpl<
BinaryOp, Lhs, Rhs,
typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind,
typename internal::traits<Rhs>::StorageKind>::ret>
BinaryOp, LhsType, RhsType,
typename internal::cwise_promote_storage_type<typename internal::traits<LhsType>::StorageKind,
typename internal::traits<RhsType>::StorageKind,
BinaryOp>::ret>,
internal::no_assignment_operator
{
public:
typedef typename internal::remove_all<BinaryOp>::type Functor;
typedef typename internal::remove_all<LhsType>::type Lhs;
typedef typename internal::remove_all<RhsType>::type Rhs;
typedef typename CwiseBinaryOpImpl<
BinaryOp, Lhs, Rhs,
typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind,
typename internal::traits<Rhs>::StorageKind>::ret>::Base Base;
BinaryOp, LhsType, RhsType,
typename internal::cwise_promote_storage_type<typename internal::traits<LhsType>::StorageKind,
typename internal::traits<Rhs>::StorageKind,
BinaryOp>::ret>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseBinaryOp)
typedef typename internal::nested<Lhs>::type LhsNested;
typedef typename internal::nested<Rhs>::type RhsNested;
typedef typename internal::ref_selector<LhsType>::type LhsNested;
typedef typename internal::ref_selector<RhsType>::type RhsNested;
typedef typename internal::remove_reference<LhsNested>::type _LhsNested;
typedef typename internal::remove_reference<RhsNested>::type _RhsNested;
EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& lhs, const Rhs& rhs, const BinaryOp& func = BinaryOp())
: m_lhs(lhs), m_rhs(rhs), m_functor(func)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& aLhs, const Rhs& aRhs, const BinaryOp& func = BinaryOp())
: m_lhs(aLhs), m_rhs(aRhs), m_functor(func)
{
EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename Rhs::Scalar);
// require the sizes to match
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs)
eigen_assert(lhs.rows() == rhs.rows() && lhs.cols() == rhs.cols());
eigen_assert(aLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rows() const {
// return the fixed size type if available to enable compile time optimizations
if (internal::traits<typename internal::remove_all<LhsNested>::type>::RowsAtCompileTime==Dynamic)
@@ -153,6 +118,7 @@ class CwiseBinaryOp : internal::no_assignment_operator,
else
return m_lhs.rows();
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const {
// return the fixed size type if available to enable compile time optimizations
if (internal::traits<typename internal::remove_all<LhsNested>::type>::ColsAtCompileTime==Dynamic)
@@ -162,10 +128,13 @@ class CwiseBinaryOp : internal::no_assignment_operator,
}
/** \returns the left hand side nested expression */
EIGEN_DEVICE_FUNC
const _LhsNested& lhs() const { return m_lhs; }
/** \returns the right hand side nested expression */
EIGEN_DEVICE_FUNC
const _RhsNested& rhs() const { return m_rhs; }
/** \returns the functor representing the binary operation */
EIGEN_DEVICE_FUNC
const BinaryOp& functor() const { return m_functor; }
protected:
@@ -174,41 +143,13 @@ class CwiseBinaryOp : internal::no_assignment_operator,
const BinaryOp m_functor;
};
template<typename BinaryOp, typename Lhs, typename Rhs>
class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Dense>
: public internal::dense_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type
// Generic API dispatcher
template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
class CwiseBinaryOpImpl
: public internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type
{
typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> Derived;
public:
typedef typename internal::dense_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE( Derived )
EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
{
return derived().functor()(derived().lhs().coeff(row, col),
derived().rhs().coeff(row, col));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
{
return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(row, col),
derived().rhs().template packet<LoadMode>(row, col));
}
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
{
return derived().functor()(derived().lhs().coeff(index),
derived().rhs().coeff(index));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
{
return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(index),
derived().rhs().template packet<LoadMode>(index));
}
public:
typedef typename internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type Base;
};
/** replaces \c *this by \c *this - \a other.
@@ -220,8 +161,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
MatrixBase<Derived>::operator-=(const MatrixBase<OtherDerived> &other)
{
SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, Derived, OtherDerived> tmp(derived());
tmp = other.derived();
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@@ -234,11 +174,11 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
MatrixBase<Derived>::operator+=(const MatrixBase<OtherDerived>& other)
{
SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, Derived, OtherDerived> tmp(derived());
tmp = other.derived();
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
} // end namespace Eigen
#endif // EIGEN_CWISE_BINARY_OP_H

View File

@@ -3,37 +3,33 @@
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CWISE_NULLARY_OP_H
#define EIGEN_CWISE_NULLARY_OP_H
namespace Eigen {
namespace internal {
template<typename NullaryOp, typename PlainObjectType>
struct traits<CwiseNullaryOp<NullaryOp, PlainObjectType> > : traits<PlainObjectType>
{
enum {
Flags = traits<PlainObjectType>::Flags & RowMajorBit
};
};
} // namespace internal
/** \class CwiseNullaryOp
* \ingroup Core_Module
*
* \brief Generic expression of a matrix where all coefficients are defined by a functor
*
* \param NullaryOp template functor implementing the operator
* \param PlainObjectType the underlying plain matrix/array type
* \tparam NullaryOp template functor implementing the operator
* \tparam PlainObjectType the underlying plain matrix/array type
*
* This class represents an expression of a generic nullary operator.
* It is the return type of the Ones(), Zero(), Constant(), Identity() and Random() methods,
@@ -42,33 +38,33 @@ namespace Eigen {
* However, if you want to write a function returning such an expression, you
* will need to use this class.
*
* \sa class CwiseUnaryOp, class CwiseBinaryOp, DenseBase::NullaryExpr()
* The functor NullaryOp must expose one of the following method:
<table class="manual">
<tr ><td>\c operator()() </td><td>if the procedural generation does not depend on the coefficient entries (e.g., random numbers)</td></tr>
<tr class="alt"><td>\c operator()(Index i)</td><td>if the procedural generation makes sense for vectors only and that it depends on the coefficient index \c i (e.g., linspace) </td></tr>
<tr ><td>\c operator()(Index i,Index j)</td><td>if the procedural generation depends on the matrix coordinates \c i, \c j (e.g., to generate a checkerboard with 0 and 1)</td></tr>
</table>
* It is also possible to expose the last two operators if the generation makes sense for matrices but can be optimized for vectors.
*
* See DenseBase::NullaryExpr(Index,const CustomNullaryOp&) for an example binding
* C++11 random number generators.
*
* A nullary expression can also be used to implement custom sophisticated matrix manipulations
* that cannot be covered by the existing set of natively supported matrix manipulations.
* See this \ref TopicCustomizing_NullaryExpr "page" for some examples and additional explanations
* on the behavior of CwiseNullaryOp.
*
* \sa class CwiseUnaryOp, class CwiseBinaryOp, DenseBase::NullaryExpr
*/
namespace internal {
template<typename NullaryOp, typename PlainObjectType>
struct traits<CwiseNullaryOp<NullaryOp, PlainObjectType> > : traits<PlainObjectType>
{
enum {
Flags = (traits<PlainObjectType>::Flags
& ( HereditaryBits
| (functor_has_linear_access<NullaryOp>::ret ? LinearAccessBit : 0)
| (functor_traits<NullaryOp>::PacketAccess ? PacketAccessBit : 0)))
| (functor_traits<NullaryOp>::IsRepeatable ? 0 : EvalBeforeNestingBit),
CoeffReadCost = functor_traits<NullaryOp>::Cost
};
};
}
template<typename NullaryOp, typename PlainObjectType>
class CwiseNullaryOp : internal::no_assignment_operator,
public internal::dense_xpr_base< CwiseNullaryOp<NullaryOp, PlainObjectType> >::type
class CwiseNullaryOp : public internal::dense_xpr_base< CwiseNullaryOp<NullaryOp, PlainObjectType> >::type, internal::no_assignment_operator
{
public:
typedef typename internal::dense_xpr_base<CwiseNullaryOp>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(CwiseNullaryOp)
EIGEN_DEVICE_FUNC
CwiseNullaryOp(Index rows, Index cols, const NullaryOp& func = NullaryOp())
: m_rows(rows), m_cols(cols), m_functor(func)
{
@@ -78,32 +74,13 @@ class CwiseNullaryOp : internal::no_assignment_operator,
&& (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rows() const { return m_rows.value(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const { return m_cols.value(); }
EIGEN_STRONG_INLINE const Scalar coeff(Index rows, Index cols) const
{
return m_functor(rows, cols);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
{
return m_functor.packetOp(row, col);
}
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
{
return m_functor(index);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
{
return m_functor.packetOp(index);
}
/** \returns the functor representing the nullary operation */
EIGEN_DEVICE_FUNC
const NullaryOp& functor() const { return m_functor; }
protected:
@@ -128,10 +105,10 @@ class CwiseNullaryOp : internal::no_assignment_operator,
*/
template<typename Derived>
template<typename CustomNullaryOp>
EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
DenseBase<Derived>::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func)
{
return CwiseNullaryOp<CustomNullaryOp, Derived>(rows, cols, func);
return CwiseNullaryOp<CustomNullaryOp, PlainObject>(rows, cols, func);
}
/** \returns an expression of a matrix defined by a custom functor \a func
@@ -147,16 +124,19 @@ DenseBase<Derived>::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& f
*
* The template parameter \a CustomNullaryOp is the type of the functor.
*
* Here is an example with C++11 random generators: \include random_cpp11.cpp
* Output: \verbinclude random_cpp11.out
*
* \sa class CwiseNullaryOp
*/
template<typename Derived>
template<typename CustomNullaryOp>
EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, Derived>
EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
DenseBase<Derived>::NullaryExpr(Index size, const CustomNullaryOp& func)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
if(RowsAtCompileTime == 1) return CwiseNullaryOp<CustomNullaryOp, Derived>(1, size, func);
else return CwiseNullaryOp<CustomNullaryOp, Derived>(size, 1, func);
if(RowsAtCompileTime == 1) return CwiseNullaryOp<CustomNullaryOp, PlainObject>(1, size, func);
else return CwiseNullaryOp<CustomNullaryOp, PlainObject>(size, 1, func);
}
/** \returns an expression of a matrix defined by a custom functor \a func
@@ -170,10 +150,10 @@ DenseBase<Derived>::NullaryExpr(Index size, const CustomNullaryOp& func)
*/
template<typename Derived>
template<typename CustomNullaryOp>
EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
{
return CwiseNullaryOp<CustomNullaryOp, Derived>(RowsAtCompileTime, ColsAtCompileTime, func);
return CwiseNullaryOp<CustomNullaryOp, PlainObject>(RowsAtCompileTime, ColsAtCompileTime, func);
}
/** \returns an expression of a constant matrix of value \a value
@@ -212,7 +192,7 @@ DenseBase<Derived>::Constant(Index rows, Index cols, const Scalar& value)
* \sa class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(Index size, const Scalar& value)
{
return DenseBase<Derived>::NullaryExpr(size, internal::scalar_constant_op<Scalar>(value));
@@ -228,53 +208,40 @@ DenseBase<Derived>::Constant(Index size, const Scalar& value)
* \sa class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(const Scalar& value)
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
return DenseBase<Derived>::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_constant_op<Scalar>(value));
}
/**
* \brief Sets a linearly space vector.
/** \deprecated because of accuracy loss. In Eigen 3.3, it is an alias for LinSpaced(Index,const Scalar&,const Scalar&)
*
* The function generates 'size' equally spaced values in the closed interval [low,high].
* This particular version of LinSpaced() uses sequential access, i.e. vector access is
* assumed to be a(0), a(1), ..., a(size). This assumption allows for better vectorization
* and yields faster code than the random access version.
*
* When size is set to 1, a vector of length 1 containing 'high' is returned.
*
* \only_for_vectors
*
* Example: \include DenseBase_LinSpaced_seq.cpp
* Output: \verbinclude DenseBase_LinSpaced_seq.out
*
* \sa setLinSpaced(Index,const Scalar&,const Scalar&), LinSpaced(Index,Scalar,Scalar), CwiseNullaryOp
* \sa LinSpaced(Index,Scalar,Scalar), setLinSpaced(Index,const Scalar&,const Scalar&)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::SequentialLinSpacedReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar,false>(low,high,size));
return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar,PacketScalar>(low,high,size));
}
/**
* \copydoc DenseBase::LinSpaced(Sequential_t, Index, const Scalar&, const Scalar&)
* Special version for fixed size types which does not require the size parameter.
/** \deprecated because of accuracy loss. In Eigen 3.3, it is an alias for LinSpaced(const Scalar&,const Scalar&)
*
* \sa LinSpaced(Scalar,Scalar)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::SequentialLinSpacedReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(Sequential_t, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,false>(low,high,Derived::SizeAtCompileTime));
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,PacketScalar>(low,high,Derived::SizeAtCompileTime));
}
/**
* \brief Sets a linearly space vector.
* \brief Sets a linearly spaced vector.
*
* The function generates 'size' equally spaced values in the closed interval [low,high].
* When size is set to 1, a vector of length 1 containing 'high' is returned.
@@ -284,14 +251,24 @@ DenseBase<Derived>::LinSpaced(Sequential_t, const Scalar& low, const Scalar& hig
* Example: \include DenseBase_LinSpaced.cpp
* Output: \verbinclude DenseBase_LinSpaced.out
*
* \sa setLinSpaced(Index,const Scalar&,const Scalar&), LinSpaced(Sequential_t,Index,const Scalar&,const Scalar&,Index), CwiseNullaryOp
* For integer scalar types, an even spacing is possible if and only if the length of the range,
* i.e., \c high-low is a scalar multiple of \c size-1, or if \c size is a scalar multiple of the
* number of values \c high-low+1 (meaning each value can be repeated the same number of time).
* If one of these two considions is not satisfied, then \c high is lowered to the largest value
* satisfying one of this constraint.
* Here are some examples:
*
* Example: \include DenseBase_LinSpacedInt.cpp
* Output: \verbinclude DenseBase_LinSpacedInt.out
*
* \sa setLinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(Index size, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar,true>(low,high,size));
return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar,PacketScalar>(low,high,size));
}
/**
@@ -299,22 +276,23 @@ DenseBase<Derived>::LinSpaced(Index size, const Scalar& low, const Scalar& high)
* Special version for fixed size types which does not require the size parameter.
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,true>(low,high,Derived::SizeAtCompileTime));
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,PacketScalar>(low,high,Derived::SizeAtCompileTime));
}
/** \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */
/** \returns true if all coefficients in this matrix are approximately equal to \a val, to within precision \a prec */
template<typename Derived>
bool DenseBase<Derived>::isApproxToConstant
(const Scalar& value, RealScalar prec) const
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isApproxToConstant
(const Scalar& val, const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if(!internal::isApprox(this->coeff(i, j), value, prec))
if(!internal::isApprox(self.coeff(i, j), val, prec))
return false;
return true;
}
@@ -323,33 +301,33 @@ bool DenseBase<Derived>::isApproxToConstant
*
* \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */
template<typename Derived>
bool DenseBase<Derived>::isConstant
(const Scalar& value, RealScalar prec) const
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isConstant
(const Scalar& val, const RealScalar& prec) const
{
return isApproxToConstant(value, prec);
return isApproxToConstant(val, prec);
}
/** Alias for setConstant(): sets all coefficients in this expression to \a value.
/** Alias for setConstant(): sets all coefficients in this expression to \a val.
*
* \sa setConstant(), Constant(), class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& value)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& val)
{
setConstant(value);
setConstant(val);
}
/** Sets all coefficients in this expression to \a value.
/** Sets all coefficients in this expression to value \a val.
*
* \sa fill(), setConstant(Index,const Scalar&), setConstant(Index,Index,const Scalar&), setZero(), setOnes(), Constant(), class CwiseNullaryOp, setZero(), setOnes()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& value)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& val)
{
return derived() = Constant(rows(), cols(), value);
return derived() = Constant(rows(), cols(), val);
}
/** Resizes to the given \a size, and sets all coefficients in this expression to the given \a value.
/** Resizes to the given \a size, and sets all coefficients in this expression to the given value \a val.
*
* \only_for_vectors
*
@@ -359,18 +337,18 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& value
* \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index size, const Scalar& value)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val)
{
resize(size);
return setConstant(value);
return setConstant(val);
}
/** Resizes to the given size, and sets all coefficients in this expression to the given \a value.
/** Resizes to the given size, and sets all coefficients in this expression to the given value \a val.
*
* \param rows the new number of rows
* \param cols the new number of columns
* \param value the value to which all coefficients are set
* \param val the value to which all coefficients are set
*
* Example: \include Matrix_setConstant_int_int.cpp
* Output: \verbinclude Matrix_setConstant_int_int.out
@@ -378,15 +356,15 @@ PlainObjectBase<Derived>::setConstant(Index size, const Scalar& value)
* \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& value)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& val)
{
resize(rows, cols);
return setConstant(value);
return setConstant(val);
}
/**
* \brief Sets a linearly space vector.
* \brief Sets a linearly spaced vector.
*
* The function generates 'size' equally spaced values in the closed interval [low,high].
* When size is set to 1, a vector of length 1 containing 'high' is returned.
@@ -396,27 +374,33 @@ PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& valu
* Example: \include DenseBase_setLinSpaced.cpp
* Output: \verbinclude DenseBase_setLinSpaced.out
*
* \sa CwiseNullaryOp
* For integer scalar types, do not miss the explanations on the definition
* of \link LinSpaced(Index,const Scalar&,const Scalar&) even spacing \endlink.
*
* \sa LinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index size, const Scalar& low, const Scalar& high)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index newSize, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return derived() = Derived::NullaryExpr(size, internal::linspaced_op<Scalar,false>(low,high,size));
return derived() = Derived::NullaryExpr(newSize, internal::linspaced_op<Scalar,PacketScalar>(low,high,newSize));
}
/**
* \brief Sets a linearly space vector.
* \brief Sets a linearly spaced vector.
*
* The function fill *this with equally spaced values in the closed interval [low,high].
* The function fills \c *this with equally spaced values in the closed interval [low,high].
* When size is set to 1, a vector of length 1 containing 'high' is returned.
*
* \only_for_vectors
*
* \sa setLinSpaced(Index, const Scalar&, const Scalar&), CwiseNullaryOp
* For integer scalar types, do not miss the explanations on the definition
* of \link LinSpaced(Index,const Scalar&,const Scalar&) even spacing \endlink.
*
* \sa LinSpaced(Index,const Scalar&,const Scalar&), setLinSpaced(Index, const Scalar&, const Scalar&), CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low, const Scalar& high)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return setLinSpaced(size(), low, high);
@@ -439,7 +423,7 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low,
* \sa Zero(), Zero(Index)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero(Index rows, Index cols)
{
return Constant(rows, cols, Scalar(0));
@@ -462,7 +446,7 @@ DenseBase<Derived>::Zero(Index rows, Index cols)
* \sa Zero(), Zero(Index,Index)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero(Index size)
{
return Constant(size, Scalar(0));
@@ -479,7 +463,7 @@ DenseBase<Derived>::Zero(Index size)
* \sa Zero(Index), Zero(Index,Index)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero()
{
return Constant(Scalar(0));
@@ -494,11 +478,12 @@ DenseBase<Derived>::Zero()
* \sa class CwiseNullaryOp, Zero()
*/
template<typename Derived>
bool DenseBase<Derived>::isZero(RealScalar prec) const
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isZero(const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if(!internal::isMuchSmallerThan(this->coeff(i, j), static_cast<Scalar>(1), prec))
if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<Scalar>(1), prec))
return false;
return true;
}
@@ -511,7 +496,7 @@ bool DenseBase<Derived>::isZero(RealScalar prec) const
* \sa class CwiseNullaryOp, Zero()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
{
return setConstant(Scalar(0));
}
@@ -526,10 +511,10 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
* \sa DenseBase::setZero(), setZero(Index,Index), class CwiseNullaryOp, DenseBase::Zero()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index size)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index newSize)
{
resize(size);
resize(newSize);
return setConstant(Scalar(0));
}
@@ -544,7 +529,7 @@ PlainObjectBase<Derived>::setZero(Index size)
* \sa DenseBase::setZero(), setZero(Index), class CwiseNullaryOp, DenseBase::Zero()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index rows, Index cols)
{
resize(rows, cols);
@@ -568,7 +553,7 @@ PlainObjectBase<Derived>::setZero(Index rows, Index cols)
* \sa Ones(), Ones(Index), isOnes(), class Ones
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index rows, Index cols)
{
return Constant(rows, cols, Scalar(1));
@@ -576,7 +561,7 @@ DenseBase<Derived>::Ones(Index rows, Index cols)
/** \returns an expression of a vector where all coefficients equal one.
*
* The parameter \a size is the size of the returned vector.
* The parameter \a newSize is the size of the returned vector.
* Must be compatible with this MatrixBase type.
*
* \only_for_vectors
@@ -591,10 +576,10 @@ DenseBase<Derived>::Ones(Index rows, Index cols)
* \sa Ones(), Ones(Index,Index), isOnes(), class Ones
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index size)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index newSize)
{
return Constant(size, Scalar(1));
return Constant(newSize, Scalar(1));
}
/** \returns an expression of a fixed-size matrix or vector where all coefficients equal one.
@@ -608,7 +593,7 @@ DenseBase<Derived>::Ones(Index size)
* \sa Ones(Index), Ones(Index,Index), isOnes(), class Ones
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones()
{
return Constant(Scalar(1));
@@ -623,8 +608,8 @@ DenseBase<Derived>::Ones()
* \sa class CwiseNullaryOp, Ones()
*/
template<typename Derived>
bool DenseBase<Derived>::isOnes
(RealScalar prec) const
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isOnes
(const RealScalar& prec) const
{
return isApproxToConstant(Scalar(1), prec);
}
@@ -637,12 +622,12 @@ bool DenseBase<Derived>::isOnes
* \sa class CwiseNullaryOp, Ones()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
{
return setConstant(Scalar(1));
}
/** Resizes to the given \a size, and sets all coefficients in this expression to one.
/** Resizes to the given \a newSize, and sets all coefficients in this expression to one.
*
* \only_for_vectors
*
@@ -652,10 +637,10 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
* \sa MatrixBase::setOnes(), setOnes(Index,Index), class CwiseNullaryOp, MatrixBase::Ones()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index size)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index newSize)
{
resize(size);
resize(newSize);
return setConstant(Scalar(1));
}
@@ -670,7 +655,7 @@ PlainObjectBase<Derived>::setOnes(Index size)
* \sa MatrixBase::setOnes(), setOnes(Index), class CwiseNullaryOp, MatrixBase::Ones()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
{
resize(rows, cols);
@@ -694,7 +679,7 @@ PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
* \sa Identity(), setIdentity(), isIdentity()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
MatrixBase<Derived>::Identity(Index rows, Index cols)
{
return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_identity_op<Scalar>());
@@ -711,7 +696,7 @@ MatrixBase<Derived>::Identity(Index rows, Index cols)
* \sa Identity(Index,Index), setIdentity(), isIdentity()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
MatrixBase<Derived>::Identity()
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
@@ -729,20 +714,21 @@ MatrixBase<Derived>::Identity()
*/
template<typename Derived>
bool MatrixBase<Derived>::isIdentity
(RealScalar prec) const
(const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index j = 0; j < cols(); ++j)
{
for(Index i = 0; i < rows(); ++i)
{
if(i == j)
{
if(!internal::isApprox(this->coeff(i, j), static_cast<Scalar>(1), prec))
if(!internal::isApprox(self.coeff(i, j), static_cast<Scalar>(1), prec))
return false;
}
else
{
if(!internal::isMuchSmallerThan(this->coeff(i, j), static_cast<RealScalar>(1), prec))
if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<RealScalar>(1), prec))
return false;
}
}
@@ -755,6 +741,7 @@ namespace internal {
template<typename Derived, bool Big = (Derived::SizeAtCompileTime>=16)>
struct setIdentity_impl
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& run(Derived& m)
{
return m = Derived::Identity(m.rows(), m.cols());
@@ -764,11 +751,11 @@ struct setIdentity_impl
template<typename Derived>
struct setIdentity_impl<Derived, true>
{
typedef typename Derived::Index Index;
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& run(Derived& m)
{
m.setZero();
const Index size = (std::min)(m.rows(), m.cols());
const Index size = numext::mini(m.rows(), m.cols());
for(Index i = 0; i < size; ++i) m.coeffRef(i,i) = typename Derived::Scalar(1);
return m;
}
@@ -784,7 +771,7 @@ struct setIdentity_impl<Derived, true>
* \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), isIdentity()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
{
return internal::setIdentity_impl<Derived>::run(derived());
}
@@ -800,7 +787,7 @@ EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
* \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Identity()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index cols)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index cols)
{
derived().resize(rows, cols);
return setIdentity();
@@ -813,10 +800,10 @@ EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index
* \sa MatrixBase::Unit(Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index size, Index i)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index newSize, Index i)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return BasisReturnType(SquareMatrixType::Identity(size,size), i);
return BasisReturnType(SquareMatrixType::Identity(newSize,newSize), i);
}
/** \returns an expression of the i-th unit (basis) vector.
@@ -828,7 +815,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
* \sa MatrixBase::Unit(Index,Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index i)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index i)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return BasisReturnType(SquareMatrixType::Identity(),i);
@@ -841,7 +828,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitX()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitX()
{ return Derived::Unit(0); }
/** \returns an expression of the Y axis unit vector (0,1{,0}^*)
@@ -851,7 +838,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitY()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitY()
{ return Derived::Unit(1); }
/** \returns an expression of the Z axis unit vector (0,0,1{,0}^*)
@@ -861,7 +848,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitZ()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitZ()
{ return Derived::Unit(2); }
/** \returns an expression of the W axis unit vector (0,0,0,1)
@@ -871,7 +858,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitW()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitW()
{ return Derived::Unit(3); }
} // end namespace Eigen

View File

@@ -0,0 +1,197 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CWISE_TERNARY_OP_H
#define EIGEN_CWISE_TERNARY_OP_H
namespace Eigen {
namespace internal {
template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
struct traits<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> > {
// we must not inherit from traits<Arg1> since it has
// the potential to cause problems with MSVC
typedef typename remove_all<Arg1>::type Ancestor;
typedef typename traits<Ancestor>::XprKind XprKind;
enum {
RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime,
MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime,
MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime
};
// even though we require Arg1, Arg2, and Arg3 to have the same scalar type
// (see CwiseTernaryOp constructor),
// we still want to handle the case when the result type is different.
typedef typename result_of<TernaryOp(
const typename Arg1::Scalar&, const typename Arg2::Scalar&,
const typename Arg3::Scalar&)>::type Scalar;
typedef typename internal::traits<Arg1>::StorageKind StorageKind;
typedef typename internal::traits<Arg1>::StorageIndex StorageIndex;
typedef typename Arg1::Nested Arg1Nested;
typedef typename Arg2::Nested Arg2Nested;
typedef typename Arg3::Nested Arg3Nested;
typedef typename remove_reference<Arg1Nested>::type _Arg1Nested;
typedef typename remove_reference<Arg2Nested>::type _Arg2Nested;
typedef typename remove_reference<Arg3Nested>::type _Arg3Nested;
enum { Flags = _Arg1Nested::Flags & RowMajorBit };
};
} // end namespace internal
template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3,
typename StorageKind>
class CwiseTernaryOpImpl;
/** \class CwiseTernaryOp
* \ingroup Core_Module
*
* \brief Generic expression where a coefficient-wise ternary operator is
* applied to two expressions
*
* \tparam TernaryOp template functor implementing the operator
* \tparam Arg1Type the type of the first argument
* \tparam Arg2Type the type of the second argument
* \tparam Arg3Type the type of the third argument
*
* This class represents an expression where a coefficient-wise ternary
* operator is applied to three expressions.
* It is the return type of ternary operators, by which we mean only those
* ternary operators where
* all three arguments are Eigen expressions.
* For example, the return type of betainc(matrix1, matrix2, matrix3) is a
* CwiseTernaryOp.
*
* Most of the time, this is the only way that it is used, so you typically
* don't have to name
* CwiseTernaryOp types explicitly.
*
* \sa MatrixBase::ternaryExpr(const MatrixBase<Argument2> &, const
* MatrixBase<Argument3> &, const CustomTernaryOp &) const, class CwiseBinaryOp,
* class CwiseUnaryOp, class CwiseNullaryOp
*/
template <typename TernaryOp, typename Arg1Type, typename Arg2Type,
typename Arg3Type>
class CwiseTernaryOp : public CwiseTernaryOpImpl<
TernaryOp, Arg1Type, Arg2Type, Arg3Type,
typename internal::traits<Arg1Type>::StorageKind>,
internal::no_assignment_operator
{
public:
typedef typename internal::remove_all<Arg1Type>::type Arg1;
typedef typename internal::remove_all<Arg2Type>::type Arg2;
typedef typename internal::remove_all<Arg3Type>::type Arg3;
typedef typename CwiseTernaryOpImpl<
TernaryOp, Arg1Type, Arg2Type, Arg3Type,
typename internal::traits<Arg1Type>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseTernaryOp)
typedef typename internal::ref_selector<Arg1Type>::type Arg1Nested;
typedef typename internal::ref_selector<Arg2Type>::type Arg2Nested;
typedef typename internal::ref_selector<Arg3Type>::type Arg3Nested;
typedef typename internal::remove_reference<Arg1Nested>::type _Arg1Nested;
typedef typename internal::remove_reference<Arg2Nested>::type _Arg2Nested;
typedef typename internal::remove_reference<Arg3Nested>::type _Arg3Nested;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CwiseTernaryOp(const Arg1& a1, const Arg2& a2,
const Arg3& a3,
const TernaryOp& func = TernaryOp())
: m_arg1(a1), m_arg2(a2), m_arg3(a3), m_functor(func) {
// require the sizes to match
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg2)
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg3)
// The index types should match
EIGEN_STATIC_ASSERT((internal::is_same<
typename internal::traits<Arg1Type>::StorageKind,
typename internal::traits<Arg2Type>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
EIGEN_STATIC_ASSERT((internal::is_same<
typename internal::traits<Arg1Type>::StorageKind,
typename internal::traits<Arg3Type>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
eigen_assert(a1.rows() == a2.rows() && a1.cols() == a2.cols() &&
a1.rows() == a3.rows() && a1.cols() == a3.cols());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rows() const {
// return the fixed size type if available to enable compile time
// optimizations
if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
RowsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg2Nested>::type>::
RowsAtCompileTime == Dynamic)
return m_arg3.rows();
else if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
RowsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg3Nested>::type>::
RowsAtCompileTime == Dynamic)
return m_arg2.rows();
else
return m_arg1.rows();
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const {
// return the fixed size type if available to enable compile time
// optimizations
if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
ColsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg2Nested>::type>::
ColsAtCompileTime == Dynamic)
return m_arg3.cols();
else if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
ColsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg3Nested>::type>::
ColsAtCompileTime == Dynamic)
return m_arg2.cols();
else
return m_arg1.cols();
}
/** \returns the first argument nested expression */
EIGEN_DEVICE_FUNC
const _Arg1Nested& arg1() const { return m_arg1; }
/** \returns the first argument nested expression */
EIGEN_DEVICE_FUNC
const _Arg2Nested& arg2() const { return m_arg2; }
/** \returns the third argument nested expression */
EIGEN_DEVICE_FUNC
const _Arg3Nested& arg3() const { return m_arg3; }
/** \returns the functor representing the ternary operation */
EIGEN_DEVICE_FUNC
const TernaryOp& functor() const { return m_functor; }
protected:
Arg1Nested m_arg1;
Arg2Nested m_arg2;
Arg3Nested m_arg3;
const TernaryOp m_functor;
};
// Generic API dispatcher
template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3,
typename StorageKind>
class CwiseTernaryOpImpl
: public internal::generic_xpr_base<
CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >::type {
public:
typedef typename internal::generic_xpr_base<
CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >::type Base;
};
} // end namespace Eigen
#endif // EIGEN_CWISE_TERNARY_OP_H

View File

@@ -1,40 +1,44 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CWISE_UNARY_OP_H
#define EIGEN_CWISE_UNARY_OP_H
namespace Eigen {
namespace internal {
template<typename UnaryOp, typename XprType>
struct traits<CwiseUnaryOp<UnaryOp, XprType> >
: traits<XprType>
{
typedef typename result_of<
UnaryOp(const typename XprType::Scalar&)
>::type Scalar;
typedef typename XprType::Nested XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
enum {
Flags = _XprTypeNested::Flags & RowMajorBit
};
};
}
template<typename UnaryOp, typename XprType, typename StorageKind>
class CwiseUnaryOpImpl;
/** \class CwiseUnaryOp
* \ingroup Core_Module
*
* \brief Generic expression where a coefficient-wise unary operator is applied to an expression
*
* \param UnaryOp template functor implementing the operator
* \param XprType the type of the expression to which we are applying the unary operator
* \tparam UnaryOp template functor implementing the operator
* \tparam XprType the type of the expression to which we are applying the unary operator
*
* This class represents an expression where a unary operator is applied to an expression.
* It is the return type of all operations taking exactly 1 input expression, regardless of the
@@ -47,93 +51,51 @@ namespace Eigen {
*
* \sa MatrixBase::unaryExpr(const CustomUnaryOp &) const, class CwiseBinaryOp, class CwiseNullaryOp
*/
namespace internal {
template<typename UnaryOp, typename XprType>
struct traits<CwiseUnaryOp<UnaryOp, XprType> >
: traits<XprType>
{
typedef typename result_of<
UnaryOp(typename XprType::Scalar)
>::type Scalar;
typedef typename XprType::Nested XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
enum {
Flags = _XprTypeNested::Flags & (
HereditaryBits | LinearAccessBit | AlignedBit
| (functor_traits<UnaryOp>::PacketAccess ? PacketAccessBit : 0)),
CoeffReadCost = _XprTypeNested::CoeffReadCost + functor_traits<UnaryOp>::Cost
};
};
}
template<typename UnaryOp, typename XprType, typename StorageKind>
class CwiseUnaryOpImpl;
template<typename UnaryOp, typename XprType>
class CwiseUnaryOp : internal::no_assignment_operator,
public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind>
class CwiseUnaryOp : public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind>, internal::no_assignment_operator
{
public:
typedef typename CwiseUnaryOpImpl<UnaryOp, XprType,typename internal::traits<XprType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryOp)
typedef typename internal::ref_selector<XprType>::type XprTypeNested;
typedef typename internal::remove_all<XprType>::type NestedExpression;
inline CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
explicit CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
: m_xpr(xpr), m_functor(func) {}
EIGEN_STRONG_INLINE Index rows() const { return m_xpr.rows(); }
EIGEN_STRONG_INLINE Index cols() const { return m_xpr.cols(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Index rows() const { return m_xpr.rows(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Index cols() const { return m_xpr.cols(); }
/** \returns the functor representing the unary operation */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const UnaryOp& functor() const { return m_functor; }
/** \returns the nested expression */
const typename internal::remove_all<typename XprType::Nested>::type&
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const typename internal::remove_all<XprTypeNested>::type&
nestedExpression() const { return m_xpr; }
/** \returns the nested expression */
typename internal::remove_all<typename XprType::Nested>::type&
nestedExpression() { return m_xpr.const_cast_derived(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
typename internal::remove_all<XprTypeNested>::type&
nestedExpression() { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
XprTypeNested m_xpr;
const UnaryOp m_functor;
};
// This is the generic implementation for dense storage.
// It can be used for any expression types implementing the dense concept.
template<typename UnaryOp, typename XprType>
class CwiseUnaryOpImpl<UnaryOp,XprType,Dense>
: public internal::dense_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type
// Generic API dispatcher
template<typename UnaryOp, typename XprType, typename StorageKind>
class CwiseUnaryOpImpl
: public internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type
{
public:
typedef CwiseUnaryOp<UnaryOp, XprType> Derived;
typedef typename internal::dense_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
{
return derived().functor()(derived().nestedExpression().coeff(row, col));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
{
return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(row, col));
}
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
{
return derived().functor()(derived().nestedExpression().coeff(index));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
{
return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(index));
}
public:
typedef typename internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
};
} // end namespace Eigen

View File

@@ -3,65 +3,37 @@
//
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CWISE_UNARY_VIEW_H
#define EIGEN_CWISE_UNARY_VIEW_H
namespace Eigen {
/** \class CwiseUnaryView
* \ingroup Core_Module
*
* \brief Generic lvalue expression of a coefficient-wise unary operator of a matrix or a vector
*
* \param ViewOp template functor implementing the view
* \param MatrixType the type of the matrix we are applying the unary operator
*
* This class represents a lvalue expression of a generic unary view operator of a matrix or a vector.
* It is the return type of real() and imag(), and most of the time this is the only way it is used.
*
* \sa MatrixBase::unaryViewExpr(const CustomUnaryOp &) const, class CwiseUnaryOp
*/
namespace internal {
template<typename ViewOp, typename MatrixType>
struct traits<CwiseUnaryView<ViewOp, MatrixType> >
: traits<MatrixType>
{
typedef typename result_of<
ViewOp(typename traits<MatrixType>::Scalar)
ViewOp(const typename traits<MatrixType>::Scalar&)
>::type Scalar;
typedef typename MatrixType::Nested MatrixTypeNested;
typedef typename remove_all<MatrixTypeNested>::type _MatrixTypeNested;
enum {
Flags = (traits<_MatrixTypeNested>::Flags & (HereditaryBits | LvalueBit | LinearAccessBit | DirectAccessBit)),
CoeffReadCost = traits<_MatrixTypeNested>::CoeffReadCost + functor_traits<ViewOp>::Cost,
FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
Flags = traits<_MatrixTypeNested>::Flags & (RowMajorBit | FlagsLvalueBit | DirectAccessBit), // FIXME DirectAccessBit should not be handled by expressions
MatrixTypeInnerStride = inner_stride_at_compile_time<MatrixType>::ret,
// need to cast the sizeof's from size_t to int explicitly, otherwise:
// "error: no integral type can represent all of the enumerator values
InnerStrideAtCompileTime = MatrixTypeInnerStride == Dynamic
? int(Dynamic)
: int(MatrixTypeInnerStride)
* int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)),
OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret
: int(MatrixTypeInnerStride) * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)),
OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret == Dynamic
? int(Dynamic)
: outer_stride_at_compile_time<MatrixType>::ret * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar))
};
};
}
@@ -69,16 +41,30 @@ struct traits<CwiseUnaryView<ViewOp, MatrixType> >
template<typename ViewOp, typename MatrixType, typename StorageKind>
class CwiseUnaryViewImpl;
/** \class CwiseUnaryView
* \ingroup Core_Module
*
* \brief Generic lvalue expression of a coefficient-wise unary operator of a matrix or a vector
*
* \tparam ViewOp template functor implementing the view
* \tparam MatrixType the type of the matrix we are applying the unary operator
*
* This class represents a lvalue expression of a generic unary view operator of a matrix or a vector.
* It is the return type of real() and imag(), and most of the time this is the only way it is used.
*
* \sa MatrixBase::unaryViewExpr(const CustomUnaryOp &) const, class CwiseUnaryOp
*/
template<typename ViewOp, typename MatrixType>
class CwiseUnaryView : internal::no_assignment_operator,
public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
{
public:
typedef typename CwiseUnaryViewImpl<ViewOp, MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView)
typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
inline CwiseUnaryView(const MatrixType& mat, const ViewOp& func = ViewOp())
explicit inline CwiseUnaryView(MatrixType& mat, const ViewOp& func = ViewOp())
: m_matrix(mat), m_functor(func) {}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryView)
@@ -90,19 +76,27 @@ class CwiseUnaryView : internal::no_assignment_operator,
const ViewOp& functor() const { return m_functor; }
/** \returns the nested expression */
const typename internal::remove_all<typename MatrixType::Nested>::type&
const typename internal::remove_all<MatrixTypeNested>::type&
nestedExpression() const { return m_matrix; }
/** \returns the nested expression */
typename internal::remove_all<typename MatrixType::Nested>::type&
typename internal::remove_reference<MatrixTypeNested>::type&
nestedExpression() { return m_matrix.const_cast_derived(); }
protected:
// FIXME changed from MatrixType::Nested because of a weird compilation error with sun CC
typename internal::nested<MatrixType>::type m_matrix;
MatrixTypeNested m_matrix;
ViewOp m_functor;
};
// Generic API dispatcher
template<typename ViewOp, typename XprType, typename StorageKind>
class CwiseUnaryViewImpl
: public internal::generic_xpr_base<CwiseUnaryView<ViewOp, XprType> >::type
{
public:
typedef typename internal::generic_xpr_base<CwiseUnaryView<ViewOp, XprType> >::type Base;
};
template<typename ViewOp, typename MatrixType>
class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
: public internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type
@@ -113,35 +107,19 @@ class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
typedef typename internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
EIGEN_DEVICE_FUNC inline Scalar* data() { return &(this->coeffRef(0)); }
EIGEN_DEVICE_FUNC inline const Scalar* data() const { return &(this->coeff(0)); }
inline Index innerStride() const
EIGEN_DEVICE_FUNC inline Index innerStride() const
{
return derived().nestedExpression().innerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
}
inline Index outerStride() const
EIGEN_DEVICE_FUNC inline Index outerStride() const
{
return derived().nestedExpression().outerStride();
}
EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const
{
return derived().functor()(derived().nestedExpression().coeff(row, col));
}
EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
return derived().functor()(derived().nestedExpression().coeff(index));
}
EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
{
return derived().functor()(const_cast_derived().nestedExpression().coeffRef(row, col));
}
EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
return derived().functor()(const_cast_derived().nestedExpression().coeffRef(index));
return derived().nestedExpression().outerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
}
};

View File

@@ -4,30 +4,25 @@
// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_DENSEBASE_H
#define EIGEN_DENSEBASE_H
namespace Eigen {
namespace internal {
// The index type defined by EIGEN_DEFAULT_DENSE_INDEX_TYPE must be a signed type.
// This dummy function simply aims at checking that at compile time.
static inline void check_DenseIndex_is_signed() {
EIGEN_STATIC_ASSERT(NumTraits<DenseIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE);
}
} // end namespace internal
/** \class DenseBase
* \ingroup Core_Module
*
@@ -39,37 +34,45 @@ namespace Eigen {
* \tparam Derived is the derived type, e.g., a matrix type or an expression.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN.
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN.
*
* \sa \ref TopicClassHierarchy
* \sa \blank \ref TopicClassHierarchy
*/
template<typename Derived> class DenseBase
#ifndef EIGEN_PARSED_BY_DOXYGEN
: public internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar,
typename NumTraits<typename internal::traits<Derived>::Scalar>::Real>
#else
: public DenseCoeffsBase<Derived>
#else
: public DenseCoeffsBase<Derived,DirectWriteAccessors>
#endif // not EIGEN_PARSED_BY_DOXYGEN
{
public:
using internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar,
typename NumTraits<typename internal::traits<Derived>::Scalar>::Real>::operator*;
class InnerIterator;
/** Inner iterator type to iterate over the coefficients of a row or column.
* \sa class InnerIterator
*/
typedef Eigen::InnerIterator<Derived> InnerIterator;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
/** \brief The type of indices
* \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
* \sa \ref TopicPreprocessorDirectives.
*/
typedef typename internal::traits<Derived>::Index Index;
/**
* \brief The type used to store indices
* \details This typedef is relevant for types that store multiple indices such as
* PermutationMatrix or Transpositions, otherwise it defaults to Eigen::Index
* \sa \blank \ref TopicPreprocessorDirectives, Eigen::Index, SparseMatrixBase.
*/
typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
/** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc. */
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
/** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc.
*
* It is an alias for the Scalar type */
typedef Scalar value_type;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef DenseCoeffsBase<Derived> Base;
using Base::derived;
using Base::const_cast_derived;
using Base::rows;
@@ -79,16 +82,6 @@ template<typename Derived> class DenseBase
using Base::colIndexByOuterInner;
using Base::coeff;
using Base::coeffByOuterInner;
using Base::packet;
using Base::packetByOuterInner;
using Base::writePacket;
using Base::writePacketByOuterInner;
using Base::coeffRef;
using Base::coeffRefByOuterInner;
using Base::copyCoeff;
using Base::copyCoeffByOuterInner;
using Base::copyPacket;
using Base::copyPacketByOuterInner;
using Base::operator();
using Base::operator[];
using Base::x;
@@ -174,30 +167,54 @@ template<typename Derived> class DenseBase
InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime)
: int(IsRowMajor) ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
/**< This is a rough measure of how expensive it is to read one coefficient from
* this expression.
*/
InnerStrideAtCompileTime = internal::inner_stride_at_compile_time<Derived>::ret,
OuterStrideAtCompileTime = internal::outer_stride_at_compile_time<Derived>::ret
};
typedef typename internal::find_best_packet<Scalar,SizeAtCompileTime>::type PacketScalar;
enum { ThisConstantIsPrivateInPlainObjectBase };
enum { IsPlainObjectBase = 0 };
/** The plain matrix type corresponding to this expression.
* \sa PlainObject */
typedef Matrix<typename internal::traits<Derived>::Scalar,
internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime,
AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime
> PlainMatrix;
/** The plain array type corresponding to this expression.
* \sa PlainObject */
typedef Array<typename internal::traits<Derived>::Scalar,
internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime,
AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime
> PlainArray;
/** \brief The plain matrix or array type corresponding to this expression.
*
* This is not necessarily exactly the return type of eval(). In the case of plain matrices,
* the return type of eval() is a const reference to a matrix, not a matrix! It is however guaranteed
* that the return type of eval() is either PlainObject or const PlainObject&.
*/
typedef typename internal::conditional<internal::is_same<typename internal::traits<Derived>::XprKind,MatrixXpr >::value,
PlainMatrix, PlainArray>::type PlainObject;
/** \returns the number of nonzero coefficients which is in practice the number
* of stored coefficients. */
EIGEN_DEVICE_FUNC
inline Index nonZeros() const { return size(); }
/** \returns true if either the number of rows or the number of columns is equal to 1.
* In other words, this function returns
* \code rows()==1 || cols()==1 \endcode
* \sa rows(), cols(), IsVectorAtCompileTime. */
/** \returns the outer size.
*
* \note For a vector, this returns just 1. For a matrix (non-vector), this is the major dimension
* with respect to the \ref TopicStorageOrders "storage order", i.e., the number of columns for a
* column-major matrix, and the number of rows for a row-major matrix. */
EIGEN_DEVICE_FUNC
Index outerSize() const
{
return IsVectorAtCompileTime ? 1
@@ -209,6 +226,7 @@ template<typename Derived> class DenseBase
* \note For a vector, this is just the size. For a matrix (non-vector), this is the minor dimension
* with respect to the \ref TopicStorageOrders "storage order", i.e., the number of rows for a
* column-major matrix, and the number of columns for a row-major matrix. */
EIGEN_DEVICE_FUNC
Index innerSize() const
{
return IsVectorAtCompileTime ? this->size()
@@ -219,16 +237,18 @@ template<typename Derived> class DenseBase
* Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
* nothing else.
*/
void resize(Index size)
EIGEN_DEVICE_FUNC
void resize(Index newSize)
{
EIGEN_ONLY_USED_FOR_DEBUG(size);
eigen_assert(size == this->size()
EIGEN_ONLY_USED_FOR_DEBUG(newSize);
eigen_assert(newSize == this->size()
&& "DenseBase::resize() does not actually allow to resize.");
}
/** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
* Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
* nothing else.
*/
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols)
{
EIGEN_ONLY_USED_FOR_DEBUG(rows);
@@ -238,13 +258,12 @@ template<typename Derived> class DenseBase
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal Represents a matrix with all coefficients equal to one another*/
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType;
/** \internal Represents a vector with linearly spaced coefficients that allows sequential access only. */
typedef CwiseNullaryOp<internal::linspaced_op<Scalar,false>,Derived> SequentialLinSpacedReturnType;
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
/** \internal \deprecated Represents a vector with linearly spaced coefficients that allows sequential access only. */
typedef CwiseNullaryOp<internal::linspaced_op<Scalar,PacketScalar>,PlainObject> SequentialLinSpacedReturnType;
/** \internal Represents a vector with linearly spaced coefficients that allows random access. */
typedef CwiseNullaryOp<internal::linspaced_op<Scalar,true>,Derived> RandomAccessLinSpacedReturnType;
typedef CwiseNullaryOp<internal::linspaced_op<Scalar,PacketScalar>,PlainObject> RandomAccessLinSpacedReturnType;
/** \internal the return type of MatrixBase::eigenvalues() */
typedef Matrix<typename NumTraits<typename internal::traits<Derived>::Scalar>::Real, internal::traits<Derived>::ColsAtCompileTime, 1> EigenvaluesReturnType;
@@ -252,138 +271,133 @@ template<typename Derived> class DenseBase
/** Copies \a other into *this. \returns a reference to *this. */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const DenseBase<OtherDerived>& other);
/** Special case of the template operator=, in order to prevent the compiler
* from generating a default operator= (issue hit with g++ 4.1)
*/
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const DenseBase& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator=(const EigenBase<OtherDerived> &other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator+=(const EigenBase<OtherDerived> &other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator-=(const EigenBase<OtherDerived> &other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator=(const ReturnByValue<OtherDerived>& func);
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** Copies \a other into *this without evaluating other. \returns a reference to *this. */
/** \internal
* Copies \a other into *this without evaluating other. \returns a reference to *this.
* \deprecated */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& lazyAssign(const DenseBase<OtherDerived>& other);
#endif // not EIGEN_PARSED_BY_DOXYGEN
EIGEN_DEVICE_FUNC
CommaInitializer<Derived> operator<< (const Scalar& s);
/** \deprecated it now returns \c *this */
template<unsigned int Added,unsigned int Removed>
const Flagged<Derived, Added, Removed> flagged() const;
EIGEN_DEPRECATED
const Derived& flagged() const
{ return derived(); }
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
CommaInitializer<Derived> operator<< (const DenseBase<OtherDerived>& other);
Eigen::Transpose<Derived> transpose();
typedef const Transpose<const Derived> ConstTransposeReturnType;
typedef Transpose<Derived> TransposeReturnType;
EIGEN_DEVICE_FUNC
TransposeReturnType transpose();
typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
EIGEN_DEVICE_FUNC
ConstTransposeReturnType transpose() const;
EIGEN_DEVICE_FUNC
void transposeInPlace();
#ifndef EIGEN_NO_DEBUG
protected:
template<typename OtherDerived>
void checkTransposeAliasing(const OtherDerived& other) const;
public:
#endif
typedef VectorBlock<Derived> SegmentReturnType;
typedef const VectorBlock<const Derived> ConstSegmentReturnType;
template<int Size> struct FixedSegmentReturnType { typedef VectorBlock<Derived, Size> Type; };
template<int Size> struct ConstFixedSegmentReturnType { typedef const VectorBlock<const Derived, Size> Type; };
// Note: The "DenseBase::" prefixes are added to help MSVC9 to match these declarations with the later implementations.
SegmentReturnType segment(Index start, Index size);
typename DenseBase::ConstSegmentReturnType segment(Index start, Index size) const;
SegmentReturnType head(Index size);
typename DenseBase::ConstSegmentReturnType head(Index size) const;
SegmentReturnType tail(Index size);
typename DenseBase::ConstSegmentReturnType tail(Index size) const;
template<int Size> typename FixedSegmentReturnType<Size>::Type head();
template<int Size> typename ConstFixedSegmentReturnType<Size>::Type head() const;
template<int Size> typename FixedSegmentReturnType<Size>::Type tail();
template<int Size> typename ConstFixedSegmentReturnType<Size>::Type tail() const;
template<int Size> typename FixedSegmentReturnType<Size>::Type segment(Index start);
template<int Size> typename ConstFixedSegmentReturnType<Size>::Type segment(Index start) const;
static const ConstantReturnType
EIGEN_DEVICE_FUNC static const ConstantReturnType
Constant(Index rows, Index cols, const Scalar& value);
static const ConstantReturnType
EIGEN_DEVICE_FUNC static const ConstantReturnType
Constant(Index size, const Scalar& value);
static const ConstantReturnType
EIGEN_DEVICE_FUNC static const ConstantReturnType
Constant(const Scalar& value);
static const SequentialLinSpacedReturnType
EIGEN_DEVICE_FUNC static const SequentialLinSpacedReturnType
LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high);
static const RandomAccessLinSpacedReturnType
EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
LinSpaced(Index size, const Scalar& low, const Scalar& high);
static const SequentialLinSpacedReturnType
EIGEN_DEVICE_FUNC static const SequentialLinSpacedReturnType
LinSpaced(Sequential_t, const Scalar& low, const Scalar& high);
static const RandomAccessLinSpacedReturnType
EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
LinSpaced(const Scalar& low, const Scalar& high);
template<typename CustomNullaryOp>
static const CwiseNullaryOp<CustomNullaryOp, Derived>
template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func);
template<typename CustomNullaryOp>
static const CwiseNullaryOp<CustomNullaryOp, Derived>
template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
NullaryExpr(Index size, const CustomNullaryOp& func);
template<typename CustomNullaryOp>
static const CwiseNullaryOp<CustomNullaryOp, Derived>
template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
NullaryExpr(const CustomNullaryOp& func);
static const ConstantReturnType Zero(Index rows, Index cols);
static const ConstantReturnType Zero(Index size);
static const ConstantReturnType Zero();
static const ConstantReturnType Ones(Index rows, Index cols);
static const ConstantReturnType Ones(Index size);
static const ConstantReturnType Ones();
EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index rows, Index cols);
EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index size);
EIGEN_DEVICE_FUNC static const ConstantReturnType Zero();
EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index rows, Index cols);
EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index size);
EIGEN_DEVICE_FUNC static const ConstantReturnType Ones();
void fill(const Scalar& value);
Derived& setConstant(const Scalar& value);
Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high);
Derived& setLinSpaced(const Scalar& low, const Scalar& high);
Derived& setZero();
Derived& setOnes();
Derived& setRandom();
EIGEN_DEVICE_FUNC void fill(const Scalar& value);
EIGEN_DEVICE_FUNC Derived& setConstant(const Scalar& value);
EIGEN_DEVICE_FUNC Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high);
EIGEN_DEVICE_FUNC Derived& setLinSpaced(const Scalar& low, const Scalar& high);
EIGEN_DEVICE_FUNC Derived& setZero();
EIGEN_DEVICE_FUNC Derived& setOnes();
EIGEN_DEVICE_FUNC Derived& setRandom();
template<typename OtherDerived>
template<typename OtherDerived> EIGEN_DEVICE_FUNC
bool isApprox(const DenseBase<OtherDerived>& other,
RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC
bool isMuchSmallerThan(const RealScalar& other,
RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
template<typename OtherDerived>
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
template<typename OtherDerived> EIGEN_DEVICE_FUNC
bool isMuchSmallerThan(const DenseBase<OtherDerived>& other,
RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isApproxToConstant(const Scalar& value, RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isConstant(const Scalar& value, RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isZero(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isOnes(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isApproxToConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isZero(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isOnes(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
inline bool hasNaN() const;
inline bool allFinite() const;
inline Derived& operator*=(const Scalar& other);
inline Derived& operator/=(const Scalar& other);
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator*=(const Scalar& other);
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator/=(const Scalar& other);
typedef typename internal::add_const_on_value_type<typename internal::eval<Derived>::type>::type EvalReturnType;
/** \returns the matrix or vector obtained by evaluating this expression.
*
* Notice that in the case of a plain matrix or vector (not an expression) this function just returns
* a const reference, in order to avoid a useless copy.
*
* \warning Be carefull with eval() and the auto C++ keyword, as detailed in this \link TopicPitfalls_auto_keyword page \endlink.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE EvalReturnType eval() const
{
// Even though MSVC does not honor strong inlining when the return type
@@ -391,61 +405,78 @@ template<typename Derived> class DenseBase
// size types on MSVC.
return typename internal::eval<Derived>::type(derived());
}
/** swaps *this with the expression \a other.
*
*/
template<typename OtherDerived>
void swap(const DenseBase<OtherDerived>& other,
int = OtherDerived::ThisConstantIsPrivateInPlainObjectBase)
EIGEN_DEVICE_FUNC
void swap(const DenseBase<OtherDerived>& other)
{
SwapWrapper<Derived>(derived()).lazyAssign(other.derived());
EIGEN_STATIC_ASSERT(!OtherDerived::IsPlainObjectBase,THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
eigen_assert(rows()==other.rows() && cols()==other.cols());
call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
}
/** swaps *this with the matrix or array \a other.
*
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
void swap(PlainObjectBase<OtherDerived>& other)
{
SwapWrapper<Derived>(derived()).lazyAssign(other.derived());
eigen_assert(rows()==other.rows() && cols()==other.cols());
call_assignment(derived(), other.derived(), internal::swap_assign_op<Scalar>());
}
EIGEN_DEVICE_FUNC inline const NestByValue<Derived> nestByValue() const;
EIGEN_DEVICE_FUNC inline const ForceAlignedAccess<Derived> forceAlignedAccess() const;
EIGEN_DEVICE_FUNC inline ForceAlignedAccess<Derived> forceAlignedAccess();
template<bool Enable> EIGEN_DEVICE_FUNC
inline const typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf() const;
template<bool Enable> EIGEN_DEVICE_FUNC
inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf();
inline const NestByValue<Derived> nestByValue() const;
inline const ForceAlignedAccess<Derived> forceAlignedAccess() const;
inline ForceAlignedAccess<Derived> forceAlignedAccess();
template<bool Enable> inline const typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf() const;
template<bool Enable> inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf();
EIGEN_DEVICE_FUNC Scalar sum() const;
EIGEN_DEVICE_FUNC Scalar mean() const;
EIGEN_DEVICE_FUNC Scalar trace() const;
Scalar sum() const;
Scalar mean() const;
Scalar trace() const;
EIGEN_DEVICE_FUNC Scalar prod() const;
Scalar prod() const;
EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar minCoeff() const;
EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar maxCoeff() const;
typename internal::traits<Derived>::Scalar minCoeff() const;
typename internal::traits<Derived>::Scalar maxCoeff() const;
template<typename IndexType>
template<typename IndexType> EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar minCoeff(IndexType* row, IndexType* col) const;
template<typename IndexType>
template<typename IndexType> EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar maxCoeff(IndexType* row, IndexType* col) const;
template<typename IndexType>
template<typename IndexType> EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar minCoeff(IndexType* index) const;
template<typename IndexType>
template<typename IndexType> EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar maxCoeff(IndexType* index) const;
template<typename BinaryOp>
typename internal::result_of<BinaryOp(typename internal::traits<Derived>::Scalar)>::type
redux(const BinaryOp& func) const;
EIGEN_DEVICE_FUNC
Scalar redux(const BinaryOp& func) const;
template<typename Visitor>
EIGEN_DEVICE_FUNC
void visit(Visitor& func) const;
inline const WithFormat<Derived> format(const IOFormat& fmt) const;
/** \returns a WithFormat proxy object allowing to print a matrix the with given
* format \a fmt.
*
* See class IOFormat for some examples.
*
* \sa class IOFormat, class WithFormat
*/
inline const WithFormat<Derived> format(const IOFormat& fmt) const
{
return WithFormat<Derived>(derived(), fmt);
}
/** \returns the unique coefficient of a 1x1 expression */
EIGEN_DEVICE_FUNC
CoeffReturnType value() const
{
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
@@ -453,25 +484,44 @@ template<typename Derived> class DenseBase
return derived().coeff(0,0);
}
/////////// Array module ///////////
bool all(void) const;
bool any(void) const;
Index count() const;
EIGEN_DEVICE_FUNC bool all() const;
EIGEN_DEVICE_FUNC bool any() const;
EIGEN_DEVICE_FUNC Index count() const;
typedef VectorwiseOp<Derived, Horizontal> RowwiseReturnType;
typedef const VectorwiseOp<const Derived, Horizontal> ConstRowwiseReturnType;
typedef VectorwiseOp<Derived, Vertical> ColwiseReturnType;
typedef const VectorwiseOp<const Derived, Vertical> ConstColwiseReturnType;
ConstRowwiseReturnType rowwise() const;
RowwiseReturnType rowwise();
ConstColwiseReturnType colwise() const;
ColwiseReturnType colwise();
/** \returns a VectorwiseOp wrapper of *this providing additional partial reduction operations
*
* Example: \include MatrixBase_rowwise.cpp
* Output: \verbinclude MatrixBase_rowwise.out
*
* \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
*/
//Code moved here due to a CUDA compiler bug
EIGEN_DEVICE_FUNC inline ConstRowwiseReturnType rowwise() const {
return ConstRowwiseReturnType(derived());
}
EIGEN_DEVICE_FUNC RowwiseReturnType rowwise();
static const CwiseNullaryOp<internal::scalar_random_op<Scalar>,Derived> Random(Index rows, Index cols);
static const CwiseNullaryOp<internal::scalar_random_op<Scalar>,Derived> Random(Index size);
static const CwiseNullaryOp<internal::scalar_random_op<Scalar>,Derived> Random();
/** \returns a VectorwiseOp wrapper of *this providing additional partial reduction operations
*
* Example: \include MatrixBase_colwise.cpp
* Output: \verbinclude MatrixBase_colwise.out
*
* \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
*/
EIGEN_DEVICE_FUNC inline ConstColwiseReturnType colwise() const {
return ConstColwiseReturnType(derived());
}
EIGEN_DEVICE_FUNC ColwiseReturnType colwise();
typedef CwiseNullaryOp<internal::scalar_random_op<Scalar>,PlainObject> RandomReturnType;
static const RandomReturnType Random(Index rows, Index cols);
static const RandomReturnType Random(Index size);
static const RandomReturnType Random();
template<typename ThenDerived,typename ElseDerived>
const Select<Derived,ThenDerived,ElseDerived>
@@ -480,52 +530,65 @@ template<typename Derived> class DenseBase
template<typename ThenDerived>
inline const Select<Derived,ThenDerived, typename ThenDerived::ConstantReturnType>
select(const DenseBase<ThenDerived>& thenMatrix, typename ThenDerived::Scalar elseScalar) const;
select(const DenseBase<ThenDerived>& thenMatrix, const typename ThenDerived::Scalar& elseScalar) const;
template<typename ElseDerived>
inline const Select<Derived, typename ElseDerived::ConstantReturnType, ElseDerived >
select(typename ElseDerived::Scalar thenScalar, const DenseBase<ElseDerived>& elseMatrix) const;
select(const typename ElseDerived::Scalar& thenScalar, const DenseBase<ElseDerived>& elseMatrix) const;
template<int p> RealScalar lpNorm() const;
template<int RowFactor, int ColFactor>
EIGEN_DEVICE_FUNC
const Replicate<Derived,RowFactor,ColFactor> replicate() const;
const Replicate<Derived,Dynamic,Dynamic> replicate(Index rowFacor,Index colFactor) const;
/**
* \return an expression of the replication of \c *this
*
* Example: \include MatrixBase_replicate_int_int.cpp
* Output: \verbinclude MatrixBase_replicate_int_int.out
*
* \sa VectorwiseOp::replicate(), DenseBase::replicate<int,int>(), class Replicate
*/
//Code moved here due to a CUDA compiler bug
EIGEN_DEVICE_FUNC
const Replicate<Derived, Dynamic, Dynamic> replicate(Index rowFactor, Index colFactor) const
{
return Replicate<Derived, Dynamic, Dynamic>(derived(), rowFactor, colFactor);
}
typedef Reverse<Derived, BothDirections> ReverseReturnType;
typedef const Reverse<const Derived, BothDirections> ConstReverseReturnType;
ReverseReturnType reverse();
ConstReverseReturnType reverse() const;
void reverseInPlace();
EIGEN_DEVICE_FUNC ReverseReturnType reverse();
/** This is the const version of reverse(). */
//Code moved here due to a CUDA compiler bug
EIGEN_DEVICE_FUNC ConstReverseReturnType reverse() const
{
return ConstReverseReturnType(derived());
}
EIGEN_DEVICE_FUNC void reverseInPlace();
#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::DenseBase
#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND)
# include "../plugins/BlockMethods.h"
# ifdef EIGEN_DENSEBASE_PLUGIN
# include EIGEN_DENSEBASE_PLUGIN
# endif
#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
#ifdef EIGEN2_SUPPORT
Block<Derived> corner(CornerType type, Index cRows, Index cCols);
const Block<Derived> corner(CornerType type, Index cRows, Index cCols) const;
template<int CRows, int CCols>
Block<Derived, CRows, CCols> corner(CornerType type);
template<int CRows, int CCols>
const Block<Derived, CRows, CCols> corner(CornerType type) const;
#endif // EIGEN2_SUPPORT
#undef EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
#undef EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF
// disable the use of evalTo for dense objects with a nice compilation error
template<typename Dest> inline void evalTo(Dest& ) const
template<typename Dest>
EIGEN_DEVICE_FUNC
inline void evalTo(Dest& ) const
{
EIGEN_STATIC_ASSERT((internal::is_same<Dest,void>::value),THE_EVAL_EVALTO_FUNCTION_SHOULD_NEVER_BE_CALLED_FOR_DENSE_OBJECTS);
}
protected:
/** Default constructor. Do nothing. */
DenseBase()
EIGEN_DEVICE_FUNC DenseBase()
{
/* Just checks for self-consistency of the flags.
* Only do it when debugging Eigen, as this borders on paranoiac and could slow compilation down
@@ -538,9 +601,9 @@ template<typename Derived> class DenseBase
}
private:
explicit DenseBase(int);
DenseBase(int,int);
template<typename OtherDerived> explicit DenseBase(const DenseBase<OtherDerived>&);
EIGEN_DEVICE_FUNC explicit DenseBase(int);
EIGEN_DEVICE_FUNC DenseBase(int,int);
template<typename OtherDerived> EIGEN_DEVICE_FUNC explicit DenseBase(const DenseBase<OtherDerived>&);
};
} // end namespace Eigen

View File

@@ -3,24 +3,9 @@
//
// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_DENSECOEFFSBASE_H
#define EIGEN_DENSECOEFFSBASE_H
@@ -50,7 +35,6 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
{
public:
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
@@ -76,6 +60,7 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
using Base::size;
using Base::derived;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner) const
{
return int(Derived::RowsAtCompileTime) == 1 ? 0
@@ -84,6 +69,7 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
: inner;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner) const
{
return int(Derived::ColsAtCompileTime) == 1 ? 0
@@ -106,13 +92,15 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
*
* \sa operator()(Index,Index) const, coeffRef(Index,Index), coeff(Index) const
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().coeff(row, col);
&& col >= 0 && col < cols());
return internal::evaluator<Derived>(derived()).coeff(row,col);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType coeffByOuterInner(Index outer, Index inner) const
{
return coeff(rowIndexByOuterInner(outer, inner),
@@ -123,11 +111,12 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
*
* \sa operator()(Index,Index), operator[](Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType operator()(Index row, Index col) const
{
eigen_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().coeff(row, col);
return coeff(row, col);
}
/** Short version: don't use this function, use
@@ -145,11 +134,14 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
* \sa operator[](Index) const, coeffRef(Index), coeff(Index,Index) const
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
coeff(Index index) const
{
EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
eigen_internal_assert(index >= 0 && index < size());
return derived().coeff(index);
return internal::evaluator<Derived>(derived()).coeff(index);
}
@@ -161,15 +153,14 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
* z() const, w() const
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
operator[](Index index) const
{
#ifndef EIGEN2_SUPPORT
EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
#endif
eigen_assert(index >= 0 && index < size());
return derived().coeff(index);
return coeff(index);
}
/** \returns the coefficient at given index.
@@ -182,32 +173,49 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
* z() const, w() const
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
operator()(Index index) const
{
eigen_assert(index >= 0 && index < size());
return derived().coeff(index);
return coeff(index);
}
/** equivalent to operator[](0). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
x() const { return (*this)[0]; }
/** equivalent to operator[](1). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
y() const { return (*this)[1]; }
y() const
{
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=2, OUT_OF_RANGE_ACCESS);
return (*this)[1];
}
/** equivalent to operator[](2). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
z() const { return (*this)[2]; }
z() const
{
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=3, OUT_OF_RANGE_ACCESS);
return (*this)[2];
}
/** equivalent to operator[](3). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
w() const { return (*this)[3]; }
w() const
{
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=4, OUT_OF_RANGE_ACCESS);
return (*this)[3];
}
/** \internal
* \returns the packet of coefficients starting at the given row and column. It is your responsibility
@@ -222,9 +230,9 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
template<int LoadMode>
EIGEN_STRONG_INLINE PacketReturnType packet(Index row, Index col) const
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().template packet<LoadMode>(row,col);
typedef typename internal::packet_traits<Scalar>::type DefaultPacketType;
eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
return internal::evaluator<Derived>(derived()).template packet<LoadMode,DefaultPacketType>(row,col);
}
@@ -249,8 +257,11 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
template<int LoadMode>
EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
typedef typename internal::packet_traits<Scalar>::type DefaultPacketType;
eigen_internal_assert(index >= 0 && index < size());
return derived().template packet<LoadMode>(index);
return internal::evaluator<Derived>(derived()).template packet<LoadMode,DefaultPacketType>(index);
}
protected:
@@ -293,7 +304,6 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
@@ -326,13 +336,15 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
*
* \sa operator()(Index,Index), coeff(Index, Index) const, coeffRef(Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().coeffRef(row, col);
&& col >= 0 && col < cols());
return internal::evaluator<Derived>(derived()).coeffRef(row,col);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
coeffRefByOuterInner(Index outer, Index inner)
{
@@ -345,12 +357,13 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
* \sa operator[](Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
operator()(Index row, Index col)
{
eigen_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().coeffRef(row, col);
return coeffRef(row, col);
}
@@ -369,11 +382,14 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
* \sa operator[](Index), coeff(Index) const, coeffRef(Index,Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
coeffRef(Index index)
{
EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
eigen_internal_assert(index >= 0 && index < size());
return derived().coeffRef(index);
return internal::evaluator<Derived>(derived()).coeffRef(index);
}
/** \returns a reference to the coefficient at given index.
@@ -383,15 +399,14 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
* \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
operator[](Index index)
{
#ifndef EIGEN2_SUPPORT
EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
#endif
eigen_assert(index >= 0 && index < size());
return derived().coeffRef(index);
return coeffRef(index);
}
/** \returns a reference to the coefficient at given index.
@@ -403,167 +418,49 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
* \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
operator()(Index index)
{
eigen_assert(index >= 0 && index < size());
return derived().coeffRef(index);
return coeffRef(index);
}
/** equivalent to operator[](0). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
x() { return (*this)[0]; }
/** equivalent to operator[](1). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
y() { return (*this)[1]; }
y()
{
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=2, OUT_OF_RANGE_ACCESS);
return (*this)[1];
}
/** equivalent to operator[](2). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
z() { return (*this)[2]; }
z()
{
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=3, OUT_OF_RANGE_ACCESS);
return (*this)[2];
}
/** equivalent to operator[](3). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
w() { return (*this)[3]; }
/** \internal
* Stores the given packet of coefficients, at the given row and column of this expression. It is your responsibility
* to ensure that a packet really starts there. This method is only available on expressions having the
* PacketAccessBit.
*
* The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select
* the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
* starting at an address which is a multiple of the packet size.
*/
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket
(Index row, Index col, const typename internal::packet_traits<Scalar>::type& x)
w()
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().template writePacket<StoreMode>(row,col,x);
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=4, OUT_OF_RANGE_ACCESS);
return (*this)[3];
}
/** \internal */
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacketByOuterInner
(Index outer, Index inner, const typename internal::packet_traits<Scalar>::type& x)
{
writePacket<StoreMode>(rowIndexByOuterInner(outer, inner),
colIndexByOuterInner(outer, inner),
x);
}
/** \internal
* Stores the given packet of coefficients, at the given index in this expression. It is your responsibility
* to ensure that a packet really starts there. This method is only available on expressions having the
* PacketAccessBit and the LinearAccessBit.
*
* The \a LoadMode parameter may have the value \a Aligned or \a Unaligned. Its effect is to select
* the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
* starting at an address which is a multiple of the packet size.
*/
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket
(Index index, const typename internal::packet_traits<Scalar>::type& x)
{
eigen_internal_assert(index >= 0 && index < size());
derived().template writePacket<StoreMode>(index,x);
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal Copies the coefficient at position (row,col) of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE void copyCoeff(Index row, Index col, const DenseBase<OtherDerived>& other)
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().coeffRef(row, col) = other.derived().coeff(row, col);
}
/** \internal Copies the coefficient at the given index of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE void copyCoeff(Index index, const DenseBase<OtherDerived>& other)
{
eigen_internal_assert(index >= 0 && index < size());
derived().coeffRef(index) = other.derived().coeff(index);
}
template<typename OtherDerived>
EIGEN_STRONG_INLINE void copyCoeffByOuterInner(Index outer, Index inner, const DenseBase<OtherDerived>& other)
{
const Index row = rowIndexByOuterInner(outer,inner);
const Index col = colIndexByOuterInner(outer,inner);
// derived() is important here: copyCoeff() may be reimplemented in Derived!
derived().copyCoeff(row, col, other);
}
/** \internal Copies the packet at position (row,col) of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived, int StoreMode, int LoadMode>
EIGEN_STRONG_INLINE void copyPacket(Index row, Index col, const DenseBase<OtherDerived>& other)
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().template writePacket<StoreMode>(row, col,
other.derived().template packet<LoadMode>(row, col));
}
/** \internal Copies the packet at the given index of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived, int StoreMode, int LoadMode>
EIGEN_STRONG_INLINE void copyPacket(Index index, const DenseBase<OtherDerived>& other)
{
eigen_internal_assert(index >= 0 && index < size());
derived().template writePacket<StoreMode>(index,
other.derived().template packet<LoadMode>(index));
}
/** \internal */
template<typename OtherDerived, int StoreMode, int LoadMode>
EIGEN_STRONG_INLINE void copyPacketByOuterInner(Index outer, Index inner, const DenseBase<OtherDerived>& other)
{
const Index row = rowIndexByOuterInner(outer,inner);
const Index col = colIndexByOuterInner(outer,inner);
// derived() is important here: copyCoeff() may be reimplemented in Derived!
derived().template copyPacket< OtherDerived, StoreMode, LoadMode>(row, col, other);
}
#endif
};
/** \brief Base class providing direct read-only coefficient access to matrices and arrays.
@@ -575,7 +472,7 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
* inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which defines functions to access entries read-only using
* \c operator() .
*
* \sa \ref TopicClassHierarchy
* \sa \blank \ref TopicClassHierarchy
*/
template<typename Derived>
class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors>
@@ -583,7 +480,6 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
public:
typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
@@ -596,6 +492,7 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
*
* \sa outerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return derived().innerStride();
@@ -606,6 +503,7 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
*
* \sa innerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return derived().outerStride();
@@ -621,6 +519,7 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
*
* \sa innerStride(), outerStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index rowStride() const
{
return Derived::IsRowMajor ? outerStride() : innerStride();
@@ -630,6 +529,7 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
*
* \sa innerStride(), outerStride(), rowStride()
*/
EIGEN_DEVICE_FUNC
inline Index colStride() const
{
return Derived::IsRowMajor ? innerStride() : outerStride();
@@ -645,7 +545,7 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
* inherits DenseCoeffsBase<Derived, WriteAccessors> which defines functions to access entries read/write using
* \c operator().
*
* \sa \ref TopicClassHierarchy
* \sa \blank \ref TopicClassHierarchy
*/
template<typename Derived>
class DenseCoeffsBase<Derived, DirectWriteAccessors>
@@ -654,7 +554,6 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
public:
typedef DenseCoeffsBase<Derived, WriteAccessors> Base;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
@@ -667,6 +566,7 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
*
* \sa outerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return derived().innerStride();
@@ -677,6 +577,7 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
*
* \sa innerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return derived().outerStride();
@@ -692,6 +593,7 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
*
* \sa innerStride(), outerStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index rowStride() const
{
return Derived::IsRowMajor ? outerStride() : innerStride();
@@ -701,6 +603,7 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
*
* \sa innerStride(), outerStride(), rowStride()
*/
EIGEN_DEVICE_FUNC
inline Index colStride() const
{
return Derived::IsRowMajor ? innerStride() : outerStride();
@@ -709,33 +612,42 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
namespace internal {
template<typename Derived, bool JustReturnZero>
template<int Alignment, typename Derived, bool JustReturnZero>
struct first_aligned_impl
{
static inline typename Derived::Index run(const Derived&)
static inline Index run(const Derived&)
{ return 0; }
};
template<typename Derived>
struct first_aligned_impl<Derived, false>
template<int Alignment, typename Derived>
struct first_aligned_impl<Alignment, Derived, false>
{
static inline typename Derived::Index run(const Derived& m)
static inline Index run(const Derived& m)
{
return internal::first_aligned(&m.const_cast_derived().coeffRef(0,0), m.size());
return internal::first_aligned<Alignment>(m.data(), m.size());
}
};
/** \internal \returns the index of the first element of the array that is well aligned for vectorization.
/** \internal \returns the index of the first element of the array stored by \a m that is properly aligned with respect to \a Alignment for vectorization.
*
* \tparam Alignment requested alignment in Bytes.
*
* There is also the variant first_aligned(const Scalar*, Integer) defined in Memory.h. See it for more
* documentation.
*/
template<typename Derived>
static inline typename Derived::Index first_aligned(const Derived& m)
template<int Alignment, typename Derived>
static inline Index first_aligned(const DenseBase<Derived>& m)
{
return first_aligned_impl
<Derived, (Derived::Flags & AlignedBit) || !(Derived::Flags & DirectAccessBit)>
::run(m);
enum { ReturnZero = (int(evaluator<Derived>::Alignment) >= Alignment) || !(Derived::Flags & DirectAccessBit) };
return first_aligned_impl<Alignment, Derived, ReturnZero>::run(m.derived());
}
template<typename Derived>
static inline Index first_default_aligned(const DenseBase<Derived>& m)
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type DefaultPacketType;
return internal::first_aligned<int(unpacket_traits<DefaultPacketType>::alignment),Derived>(m);
}
template<typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret>

View File

@@ -3,34 +3,19 @@
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com>
// Copyright (C) 2010-2013 Hauke Heibel <hauke.heibel@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_MATRIXSTORAGE_H
#define EIGEN_MATRIXSTORAGE_H
#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN EIGEN_DENSE_STORAGE_CTOR_PLUGIN;
#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X) X; EIGEN_DENSE_STORAGE_CTOR_PLUGIN;
#else
#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X)
#endif
namespace Eigen {
@@ -39,45 +24,143 @@ namespace internal {
struct constructor_without_unaligned_array_assert {};
template<typename T, int Size>
EIGEN_DEVICE_FUNC
void check_static_allocation_size()
{
// if EIGEN_STACK_ALLOCATION_LIMIT is defined to 0, then no limit
#if EIGEN_STACK_ALLOCATION_LIMIT
EIGEN_STATIC_ASSERT(Size * sizeof(T) <= EIGEN_STACK_ALLOCATION_LIMIT, OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG);
#endif
}
/** \internal
* Static array. If the MatrixOrArrayOptions require auto-alignment, the array will be automatically aligned:
* to 16 bytes boundary if the total size is a multiple of 16 bytes.
*/
template <typename T, int Size, int MatrixOrArrayOptions,
int Alignment = (MatrixOrArrayOptions&DontAlign) ? 0
: (((Size*sizeof(T))%16)==0) ? 16
: 0 >
: compute_default_alignment<T,Size>::value >
struct plain_array
{
T array[Size];
plain_array() {}
plain_array(constructor_without_unaligned_array_assert) {}
EIGEN_DEVICE_FUNC
plain_array()
{
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
#ifdef EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT
#if defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask)
#elif EIGEN_GNUC_AT_LEAST(4,7)
// GCC 4.7 is too aggressive in its optimizations and remove the alignement test based on the fact the array is declared to be aligned.
// See this bug report: http://gcc.gnu.org/bugzilla/show_bug.cgi?id=53900
// Hiding the origin of the array pointer behind a function argument seems to do the trick even if the function is inlined:
template<typename PtrType>
EIGEN_ALWAYS_INLINE PtrType eigen_unaligned_array_assert_workaround_gcc47(PtrType array) { return array; }
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
eigen_assert((internal::UIntPtr(eigen_unaligned_array_assert_workaround_gcc47(array)) & (sizemask)) == 0 \
&& "this assertion is explained here: " \
"http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
" **** READ THIS WEB PAGE !!! ****");
#else
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
eigen_assert((reinterpret_cast<size_t>(array) & sizemask) == 0 \
eigen_assert((internal::UIntPtr(array) & (sizemask)) == 0 \
&& "this assertion is explained here: " \
"http://eigen.tuxfamily.org/dox-devel/TopicUnalignedArrayAssert.html" \
"http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
" **** READ THIS WEB PAGE !!! ****");
#endif
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 8>
{
EIGEN_ALIGN_TO_BOUNDARY(8) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(7);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 16>
{
EIGEN_USER_ALIGN16 T array[Size];
plain_array() { EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(0xf) }
plain_array(constructor_without_unaligned_array_assert) {}
EIGEN_ALIGN_TO_BOUNDARY(16) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(15);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 32>
{
EIGEN_ALIGN_TO_BOUNDARY(32) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(31);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 64>
{
EIGEN_ALIGN_TO_BOUNDARY(64) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(63);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
template <typename T, int MatrixOrArrayOptions, int Alignment>
struct plain_array<T, 0, MatrixOrArrayOptions, Alignment>
{
EIGEN_USER_ALIGN16 T array[1];
plain_array() {}
plain_array(constructor_without_unaligned_array_assert) {}
T array[1];
EIGEN_DEVICE_FUNC plain_array() {}
EIGEN_DEVICE_FUNC plain_array(constructor_without_unaligned_array_assert) {}
};
} // end namespace internal
@@ -101,33 +184,54 @@ template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseSt
{
internal::plain_array<T,Size,_Options> m_data;
public:
inline explicit DenseStorage() {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC DenseStorage() {
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
}
EIGEN_DEVICE_FUNC
explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()) {}
inline DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); }
static inline DenseIndex rows(void) {return _Rows;}
static inline DenseIndex cols(void) {return _Cols;}
inline void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {}
inline void resize(DenseIndex,DenseIndex,DenseIndex) {}
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
EIGEN_DEVICE_FUNC
DenseStorage(const DenseStorage& other) : m_data(other.m_data) {
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other) m_data = other.m_data;
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) {
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
eigen_internal_assert(size==rows*cols && rows==_Rows && cols==_Cols);
EIGEN_UNUSED_VARIABLE(size);
EIGEN_UNUSED_VARIABLE(rows);
EIGEN_UNUSED_VARIABLE(cols);
}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); }
EIGEN_DEVICE_FUNC static Index rows(void) {return _Rows;}
EIGEN_DEVICE_FUNC static Index cols(void) {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC void resize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
};
// null matrix
template<typename T, int _Rows, int _Cols, int _Options> class DenseStorage<T, 0, _Rows, _Cols, _Options>
{
public:
inline explicit DenseStorage() {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert) {}
inline DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
inline void swap(DenseStorage& ) {}
static inline DenseIndex rows(void) {return _Rows;}
static inline DenseIndex cols(void) {return _Cols;}
inline void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {}
inline void resize(DenseIndex,DenseIndex,DenseIndex) {}
inline const T *data() const { return 0; }
inline T *data() { return 0; }
EIGEN_DEVICE_FUNC DenseStorage() {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) {}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage&) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage&) { return *this; }
EIGEN_DEVICE_FUNC DenseStorage(Index,Index,Index) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& ) {}
EIGEN_DEVICE_FUNC static Index rows(void) {return _Rows;}
EIGEN_DEVICE_FUNC static Index cols(void) {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC void resize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC const T *data() const { return 0; }
EIGEN_DEVICE_FUNC T *data() { return 0; }
};
// more specializations for null matrices; these are necessary to resolve ambiguities
@@ -144,86 +248,158 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, 0, Dynamic,
template<typename T, int Size, int _Options> class DenseStorage<T, Size, Dynamic, Dynamic, _Options>
{
internal::plain_array<T,Size,_Options> m_data;
DenseIndex m_rows;
DenseIndex m_cols;
Index m_rows;
Index m_cols;
public:
inline explicit DenseStorage() : m_rows(0), m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
inline DenseStorage(DenseIndex, DenseIndex rows, DenseIndex cols) : m_rows(rows), m_cols(cols) {}
inline void swap(DenseStorage& other)
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows), m_cols(other.m_cols) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
m_data = other.m_data;
m_rows = other.m_rows;
m_cols = other.m_cols;
}
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index cols) : m_rows(rows), m_cols(cols) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
{ std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
inline DenseIndex rows(void) const {return m_rows;}
inline DenseIndex cols(void) const {return m_cols;}
inline void conservativeResize(DenseIndex, DenseIndex rows, DenseIndex cols) { m_rows = rows; m_cols = cols; }
inline void resize(DenseIndex, DenseIndex rows, DenseIndex cols) { m_rows = rows; m_cols = cols; }
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
EIGEN_DEVICE_FUNC Index rows() const {return m_rows;}
EIGEN_DEVICE_FUNC Index cols() const {return m_cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols; }
EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
};
// dynamic-size matrix with fixed-size storage and fixed width
template<typename T, int Size, int _Cols, int _Options> class DenseStorage<T, Size, Dynamic, _Cols, _Options>
{
internal::plain_array<T,Size,_Options> m_data;
DenseIndex m_rows;
Index m_rows;
public:
inline explicit DenseStorage() : m_rows(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {}
inline DenseStorage(DenseIndex, DenseIndex rows, DenseIndex) : m_rows(rows) {}
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
inline DenseIndex rows(void) const {return m_rows;}
inline DenseIndex cols(void) const {return _Cols;}
inline void conservativeResize(DenseIndex, DenseIndex rows, DenseIndex) { m_rows = rows; }
inline void resize(DenseIndex, DenseIndex rows, DenseIndex) { m_rows = rows; }
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
m_data = other.m_data;
m_rows = other.m_rows;
}
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index) : m_rows(rows) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
EIGEN_DEVICE_FUNC Index rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index) { m_rows = rows; }
EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index) { m_rows = rows; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
};
// dynamic-size matrix with fixed-size storage and fixed height
template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Size, _Rows, Dynamic, _Options>
{
internal::plain_array<T,Size,_Options> m_data;
DenseIndex m_cols;
Index m_cols;
public:
inline explicit DenseStorage() : m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC DenseStorage() : m_cols(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {}
inline DenseStorage(DenseIndex, DenseIndex, DenseIndex cols) : m_cols(cols) {}
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
inline DenseIndex rows(void) const {return _Rows;}
inline DenseIndex cols(void) const {return m_cols;}
inline void conservativeResize(DenseIndex, DenseIndex, DenseIndex cols) { m_cols = cols; }
inline void resize(DenseIndex, DenseIndex, DenseIndex cols) { m_cols = cols; }
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_cols(other.m_cols) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
m_data = other.m_data;
m_cols = other.m_cols;
}
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index, Index, Index cols) : m_cols(cols) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
EIGEN_DEVICE_FUNC Index rows(void) const {return _Rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;}
void conservativeResize(Index, Index, Index cols) { m_cols = cols; }
void resize(Index, Index, Index cols) { m_cols = cols; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
};
// purely dynamic matrix.
template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynamic, _Options>
{
T *m_data;
DenseIndex m_rows;
DenseIndex m_cols;
Index m_rows;
Index m_cols;
public:
inline explicit DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(0), m_rows(0), m_cols(0) {}
inline DenseStorage(DenseIndex size, DenseIndex rows, DenseIndex cols)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows), m_cols(cols)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
inline void swap(DenseStorage& other)
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows), m_cols(cols)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
eigen_internal_assert(size==rows*cols && rows>=0 && cols >=0);
}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(other.m_rows*other.m_cols))
, m_rows(other.m_rows)
, m_cols(other.m_cols)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows*m_cols)
internal::smart_copy(other.m_data, other.m_data+other.m_rows*other.m_cols, m_data);
}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
DenseStorage tmp(other);
this->swap(tmp);
}
return *this;
}
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
: m_data(std::move(other.m_data))
, m_rows(std::move(other.m_rows))
, m_cols(std::move(other.m_cols))
{
other.m_data = nullptr;
other.m_rows = 0;
other.m_cols = 0;
}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_data, other.m_data);
swap(m_rows, other.m_rows);
swap(m_cols, other.m_cols);
return *this;
}
#endif
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
{ std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
inline DenseIndex rows(void) const {return m_rows;}
inline DenseIndex cols(void) const {return m_cols;}
inline void conservativeResize(DenseIndex size, DenseIndex rows, DenseIndex cols)
EIGEN_DEVICE_FUNC Index rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;}
void conservativeResize(Index size, Index rows, Index cols)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*m_cols);
m_rows = rows;
m_cols = cols;
}
void resize(DenseIndex size, DenseIndex rows, DenseIndex cols)
EIGEN_DEVICE_FUNC void resize(Index size, Index rows, Index cols)
{
if(size != m_rows*m_cols)
{
@@ -232,35 +408,73 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
else
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
}
m_rows = rows;
m_cols = cols;
}
inline const T *data() const { return m_data; }
inline T *data() { return m_data; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC T *data() { return m_data; }
};
// matrix with dynamic width and fixed height (so that matrix has dynamic size).
template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Rows, Dynamic, _Options>
{
T *m_data;
DenseIndex m_cols;
Index m_cols;
public:
inline explicit DenseStorage() : m_data(0), m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
inline DenseStorage(DenseIndex size, DenseIndex, DenseIndex cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(cols)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
static inline DenseIndex rows(void) {return _Rows;}
inline DenseIndex cols(void) const {return m_cols;}
inline void conservativeResize(DenseIndex size, DenseIndex, DenseIndex cols)
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_cols(0) {}
explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(cols)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
eigen_internal_assert(size==rows*cols && rows==_Rows && cols >=0);
EIGEN_UNUSED_VARIABLE(rows);
}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(_Rows*other.m_cols))
, m_cols(other.m_cols)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_cols*_Rows)
internal::smart_copy(other.m_data, other.m_data+_Rows*m_cols, m_data);
}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
DenseStorage tmp(other);
this->swap(tmp);
}
return *this;
}
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
: m_data(std::move(other.m_data))
, m_cols(std::move(other.m_cols))
{
other.m_data = nullptr;
other.m_cols = 0;
}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_data, other.m_data);
swap(m_cols, other.m_cols);
return *this;
}
#endif
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
EIGEN_DEVICE_FUNC static Index rows(void) {return _Rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index size, Index, Index cols)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, _Rows*m_cols);
m_cols = cols;
}
EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex, DenseIndex cols)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index, Index cols)
{
if(size != _Rows*m_cols)
{
@@ -269,34 +483,72 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
else
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
}
m_cols = cols;
}
inline const T *data() const { return m_data; }
inline T *data() { return m_data; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC T *data() { return m_data; }
};
// matrix with dynamic height and fixed width (so that matrix has dynamic size).
template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dynamic, _Cols, _Options>
{
T *m_data;
DenseIndex m_rows;
Index m_rows;
public:
inline explicit DenseStorage() : m_data(0), m_rows(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
inline DenseStorage(DenseIndex size, DenseIndex rows, DenseIndex) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
inline DenseIndex rows(void) const {return m_rows;}
static inline DenseIndex cols(void) {return _Cols;}
inline void conservativeResize(DenseIndex size, DenseIndex rows, DenseIndex)
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0) {}
explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
eigen_internal_assert(size==rows*cols && rows>=0 && cols == _Cols);
EIGEN_UNUSED_VARIABLE(cols);
}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(other.m_rows*_Cols))
, m_rows(other.m_rows)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows*_Cols)
internal::smart_copy(other.m_data, other.m_data+other.m_rows*_Cols, m_data);
}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
DenseStorage tmp(other);
this->swap(tmp);
}
return *this;
}
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
: m_data(std::move(other.m_data))
, m_rows(std::move(other.m_rows))
{
other.m_data = nullptr;
other.m_rows = 0;
}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_data, other.m_data);
swap(m_rows, other.m_rows);
return *this;
}
#endif
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
EIGEN_DEVICE_FUNC Index rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC static Index cols(void) {return _Cols;}
void conservativeResize(Index size, Index rows, Index)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*_Cols);
m_rows = rows;
}
EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex rows, DenseIndex)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index rows, Index)
{
if(size != m_rows*_Cols)
{
@@ -305,12 +557,12 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
else
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
}
m_rows = rows;
}
inline const T *data() const { return m_data; }
inline T *data() { return m_data; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC T *data() { return m_data; }
};
} // end namespace Eigen

View File

@@ -4,24 +4,9 @@
// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_DIAGONAL_H
#define EIGEN_DIAGONAL_H
@@ -36,7 +21,7 @@ namespace Eigen {
* \param MatrixType the type of the object in which we are taking a sub/main/super diagonal
* \param DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal.
* A positive value means a superdiagonal, a negative value means a subdiagonal.
* You can also use Dynamic so the index can be set at runtime.
* You can also use DynamicIndex so the index can be set at runtime.
*
* The matrix is not required to be square.
*
@@ -52,24 +37,22 @@ template<typename MatrixType, int DiagIndex>
struct traits<Diagonal<MatrixType,DiagIndex> >
: traits<MatrixType>
{
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
typedef typename MatrixType::StorageKind StorageKind;
enum {
AbsDiagIndex = DiagIndex<0 ? -DiagIndex : DiagIndex, // only used if DiagIndex != Dynamic
// FIXME these computations are broken in the case where the matrix is rectangular and DiagIndex!=0
RowsAtCompileTime = (int(DiagIndex) == Dynamic || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic
: (EIGEN_SIZE_MIN_PREFER_DYNAMIC(MatrixType::RowsAtCompileTime,
MatrixType::ColsAtCompileTime) - AbsDiagIndex),
RowsAtCompileTime = (int(DiagIndex) == DynamicIndex || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic
: (EIGEN_PLAIN_ENUM_MIN(MatrixType::RowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0),
MatrixType::ColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
ColsAtCompileTime = 1,
MaxRowsAtCompileTime = int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic
: DiagIndex == Dynamic ? EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime,
MatrixType::MaxColsAtCompileTime)
: (EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime) - AbsDiagIndex),
: DiagIndex == DynamicIndex ? EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime,
MatrixType::MaxColsAtCompileTime)
: (EIGEN_PLAIN_ENUM_MIN(MatrixType::MaxRowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0),
MatrixType::MaxColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
MaxColsAtCompileTime = 1,
MaskLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
Flags = (unsigned int)_MatrixTypeNested::Flags & (HereditaryBits | LinearAccessBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit,
CoeffReadCost = _MatrixTypeNested::CoeffReadCost,
Flags = (unsigned int)_MatrixTypeNested::Flags & (RowMajorBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit, // FIXME DirectAccessBit should not be handled by expressions
MatrixTypeOuterStride = outer_stride_at_compile_time<MatrixType>::ret,
InnerStrideAtCompileTime = MatrixTypeOuterStride == Dynamic ? Dynamic : MatrixTypeOuterStride+1,
OuterStrideAtCompileTime = 0
@@ -77,28 +60,40 @@ struct traits<Diagonal<MatrixType,DiagIndex> >
};
}
template<typename MatrixType, int DiagIndex> class Diagonal
: public internal::dense_xpr_base< Diagonal<MatrixType,DiagIndex> >::type
template<typename MatrixType, int _DiagIndex> class Diagonal
: public internal::dense_xpr_base< Diagonal<MatrixType,_DiagIndex> >::type
{
public:
enum { DiagIndex = _DiagIndex };
typedef typename internal::dense_xpr_base<Diagonal>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
inline Diagonal(MatrixType& matrix, Index index = DiagIndex) : m_matrix(matrix), m_index(index) {}
EIGEN_DEVICE_FUNC
explicit inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index)
{
eigen_assert( a_index <= m_matrix.cols() && -a_index <= m_matrix.rows() );
}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
EIGEN_DEVICE_FUNC
inline Index rows() const
{ return m_index.value()<0 ? (std::min)(m_matrix.cols(),m_matrix.rows()+m_index.value()) : (std::min)(m_matrix.rows(),m_matrix.cols()-m_index.value()); }
{
return m_index.value()<0 ? numext::mini<Index>(m_matrix.cols(),m_matrix.rows()+m_index.value())
: numext::mini<Index>(m_matrix.rows(),m_matrix.cols()-m_index.value());
}
EIGEN_DEVICE_FUNC
inline Index cols() const { return 1; }
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return m_matrix.outerStride() + 1;
}
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return 0;
@@ -110,62 +105,75 @@ template<typename MatrixType, int DiagIndex> class Diagonal
const Scalar
>::type ScalarWithConstIfNotLvalue;
inline ScalarWithConstIfNotLvalue* data() { return &(m_matrix.const_cast_derived().coeffRef(rowOffset(), colOffset())); }
inline const Scalar* data() const { return &(m_matrix.const_cast_derived().coeffRef(rowOffset(), colOffset())); }
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index row, Index)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset());
return m_matrix.coeffRef(row+rowOffset(), row+colOffset());
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index row, Index) const
{
return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset());
return m_matrix.coeffRef(row+rowOffset(), row+colOffset());
}
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index row, Index) const
{
return m_matrix.coeff(row+rowOffset(), row+colOffset());
}
inline Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index idx)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return m_matrix.const_cast_derived().coeffRef(index+rowOffset(), index+colOffset());
return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset());
}
inline const Scalar& coeffRef(Index index) const
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index idx) const
{
return m_matrix.const_cast_derived().coeffRef(index+rowOffset(), index+colOffset());
return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset());
}
inline CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index idx) const
{
return m_matrix.coeff(index+rowOffset(), index+colOffset());
return m_matrix.coeff(idx+rowOffset(), idx+colOffset());
}
const typename internal::remove_all<typename MatrixType::Nested>::type&
EIGEN_DEVICE_FUNC
inline const typename internal::remove_all<typename MatrixType::Nested>::type&
nestedExpression() const
{
return m_matrix;
}
int index() const
EIGEN_DEVICE_FUNC
inline Index index() const
{
return m_index.value();
}
protected:
typename MatrixType::Nested m_matrix;
const internal::variable_if_dynamic<Index, DiagIndex> m_index;
typename internal::ref_selector<MatrixType>::non_const_type m_matrix;
const internal::variable_if_dynamicindex<Index, DiagIndex> m_index;
private:
// some compilers may fail to optimize std::max etc in case of compile-time constants...
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index absDiagIndex() const { return m_index.value()>0 ? m_index.value() : -m_index.value(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rowOffset() const { return m_index.value()>0 ? 0 : -m_index.value(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index colOffset() const { return m_index.value()>0 ? m_index.value() : 0; }
// triger a compile time error is someone try to call packet
// trigger a compile-time error if someone try to call packet
template<int LoadMode> typename MatrixType::PacketReturnType packet(Index) const;
template<int LoadMode> typename MatrixType::PacketReturnType packet(Index,Index) const;
};
@@ -182,12 +190,12 @@ template<typename Derived>
inline typename MatrixBase<Derived>::DiagonalReturnType
MatrixBase<Derived>::diagonal()
{
return derived();
return DiagonalReturnType(derived());
}
/** This is the const version of diagonal(). */
template<typename Derived>
inline const typename MatrixBase<Derived>::ConstDiagonalReturnType
inline typename MatrixBase<Derived>::ConstDiagonalReturnType
MatrixBase<Derived>::diagonal() const
{
return ConstDiagonalReturnType(derived());
@@ -205,18 +213,18 @@ MatrixBase<Derived>::diagonal() const
*
* \sa MatrixBase::diagonal(), class Diagonal */
template<typename Derived>
inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Dynamic>::Type
inline typename MatrixBase<Derived>::DiagonalDynamicIndexReturnType
MatrixBase<Derived>::diagonal(Index index)
{
return typename DiagonalIndexReturnType<Dynamic>::Type(derived(), index);
return DiagonalDynamicIndexReturnType(derived(), index);
}
/** This is the const version of diagonal(Index). */
template<typename Derived>
inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Dynamic>::Type
inline typename MatrixBase<Derived>::ConstDiagonalDynamicIndexReturnType
MatrixBase<Derived>::diagonal(Index index) const
{
return typename ConstDiagonalIndexReturnType<Dynamic>::Type(derived(), index);
return ConstDiagonalDynamicIndexReturnType(derived(), index);
}
/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
@@ -231,20 +239,20 @@ MatrixBase<Derived>::diagonal(Index index) const
*
* \sa MatrixBase::diagonal(), class Diagonal */
template<typename Derived>
template<int Index>
inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Index>::Type
template<int Index_>
inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Index_>::Type
MatrixBase<Derived>::diagonal()
{
return derived();
return typename DiagonalIndexReturnType<Index_>::Type(derived());
}
/** This is the const version of diagonal<int>(). */
template<typename Derived>
template<int Index>
inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Index>::Type
template<int Index_>
inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Index_>::Type
MatrixBase<Derived>::diagonal() const
{
return derived();
return typename ConstDiagonalIndexReturnType<Index_>::Type(derived());
}
} // end namespace Eigen

View File

@@ -4,24 +4,9 @@
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_DIAGONALMATRIX_H
#define EIGEN_DIAGONALMATRIX_H
@@ -35,8 +20,9 @@ class DiagonalBase : public EigenBase<Derived>
public:
typedef typename internal::traits<Derived>::DiagonalVectorType DiagonalVectorType;
typedef typename DiagonalVectorType::Scalar Scalar;
typedef typename DiagonalVectorType::RealScalar RealScalar;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
enum {
RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
@@ -44,63 +30,61 @@ class DiagonalBase : public EigenBase<Derived>
MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
IsVectorAtCompileTime = 0,
Flags = 0
Flags = NoPreferredStorageOrderBit
};
typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime> DenseMatrixType;
typedef DenseMatrixType DenseType;
typedef DiagonalMatrix<Scalar,DiagonalVectorType::SizeAtCompileTime,DiagonalVectorType::MaxSizeAtCompileTime> PlainObject;
EIGEN_DEVICE_FUNC
inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
EIGEN_DEVICE_FUNC
inline Derived& derived() { return *static_cast<Derived*>(this); }
EIGEN_DEVICE_FUNC
DenseMatrixType toDenseMatrix() const { return derived(); }
template<typename DenseDerived>
void evalTo(MatrixBase<DenseDerived> &other) const;
template<typename DenseDerived>
void addTo(MatrixBase<DenseDerived> &other) const
{ other.diagonal() += diagonal(); }
template<typename DenseDerived>
void subTo(MatrixBase<DenseDerived> &other) const
{ other.diagonal() -= diagonal(); }
EIGEN_DEVICE_FUNC
inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); }
EIGEN_DEVICE_FUNC
inline DiagonalVectorType& diagonal() { return derived().diagonal(); }
EIGEN_DEVICE_FUNC
inline Index rows() const { return diagonal().size(); }
EIGEN_DEVICE_FUNC
inline Index cols() const { return diagonal().size(); }
template<typename MatrixDerived>
const DiagonalProduct<MatrixDerived, Derived, OnTheLeft>
operator*(const MatrixBase<MatrixDerived> &matrix) const;
EIGEN_DEVICE_FUNC
const Product<Derived,MatrixDerived,LazyProduct>
operator*(const MatrixBase<MatrixDerived> &matrix) const
{
return Product<Derived, MatrixDerived, LazyProduct>(derived(),matrix.derived());
}
inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> >
typedef DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> > InverseReturnType;
EIGEN_DEVICE_FUNC
inline const InverseReturnType
inverse() const
{
return diagonal().cwiseInverse();
return InverseReturnType(diagonal().cwiseInverse());
}
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
bool isApprox(const DiagonalBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
EIGEN_DEVICE_FUNC
inline const DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >
operator*(const Scalar& scalar) const
{
return diagonal().isApprox(other.diagonal(), precision);
return DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >(diagonal() * scalar);
}
template<typename OtherDerived>
bool isApprox(const MatrixBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
EIGEN_DEVICE_FUNC
friend inline const DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >
operator*(const Scalar& scalar, const DiagonalBase& other)
{
return toDenseMatrix().isApprox(other, precision);
return DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >(scalar * other.diagonal());
}
#endif
};
template<typename Derived>
template<typename DenseDerived>
void DiagonalBase<Derived>::evalTo(MatrixBase<DenseDerived> &other) const
{
other.setZero();
other.diagonal() = diagonal();
}
#endif
/** \class DiagonalMatrix
@@ -122,10 +106,9 @@ struct traits<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> >
: traits<Matrix<_Scalar,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef Matrix<_Scalar,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1> DiagonalVectorType;
typedef Dense StorageKind;
typedef DenseIndex Index;
typedef DiagonalShape StorageKind;
enum {
Flags = LvalueBit
Flags = LvalueBit | NoPreferredStorageOrderBit
};
};
}
@@ -139,7 +122,7 @@ class DiagonalMatrix
typedef const DiagonalMatrix& Nested;
typedef _Scalar Scalar;
typedef typename internal::traits<DiagonalMatrix>::StorageKind StorageKind;
typedef typename internal::traits<DiagonalMatrix>::Index Index;
typedef typename internal::traits<DiagonalMatrix>::StorageIndex StorageIndex;
#endif
protected:
@@ -149,24 +132,31 @@ class DiagonalMatrix
public:
/** const version of diagonal(). */
EIGEN_DEVICE_FUNC
inline const DiagonalVectorType& diagonal() const { return m_diagonal; }
/** \returns a reference to the stored vector of diagonal coefficients. */
EIGEN_DEVICE_FUNC
inline DiagonalVectorType& diagonal() { return m_diagonal; }
/** Default constructor without initialization */
EIGEN_DEVICE_FUNC
inline DiagonalMatrix() {}
/** Constructs a diagonal matrix with given dimension */
inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
EIGEN_DEVICE_FUNC
explicit inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
/** 2D constructor. */
EIGEN_DEVICE_FUNC
inline DiagonalMatrix(const Scalar& x, const Scalar& y) : m_diagonal(x,y) {}
/** 3D constructor. */
EIGEN_DEVICE_FUNC
inline DiagonalMatrix(const Scalar& x, const Scalar& y, const Scalar& z) : m_diagonal(x,y,z) {}
/** Copy constructor. */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
inline DiagonalMatrix(const DiagonalBase<OtherDerived>& other) : m_diagonal(other.diagonal()) {}
#ifndef EIGEN_PARSED_BY_DOXYGEN
@@ -176,11 +166,13 @@ class DiagonalMatrix
/** generic constructor from expression of the diagonal coefficients */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
explicit inline DiagonalMatrix(const MatrixBase<OtherDerived>& other) : m_diagonal(other)
{}
/** Copy operator. */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
DiagonalMatrix& operator=(const DiagonalBase<OtherDerived>& other)
{
m_diagonal = other.diagonal();
@@ -191,6 +183,7 @@ class DiagonalMatrix
/** This is a special case of the templated operator=. Its purpose is to
* prevent a default operator= from hiding the templated operator=.
*/
EIGEN_DEVICE_FUNC
DiagonalMatrix& operator=(const DiagonalMatrix& other)
{
m_diagonal = other.diagonal();
@@ -199,14 +192,19 @@ class DiagonalMatrix
#endif
/** Resizes to given size. */
EIGEN_DEVICE_FUNC
inline void resize(Index size) { m_diagonal.resize(size); }
/** Sets all coefficients to zero. */
EIGEN_DEVICE_FUNC
inline void setZero() { m_diagonal.setZero(); }
/** Resizes and sets all coefficients to zero. */
EIGEN_DEVICE_FUNC
inline void setZero(Index size) { m_diagonal.setZero(size); }
/** Sets this matrix to be the identity matrix of the current size. */
EIGEN_DEVICE_FUNC
inline void setIdentity() { m_diagonal.setOnes(); }
/** Sets this matrix to be the identity matrix of the given size. */
EIGEN_DEVICE_FUNC
inline void setIdentity(Index size) { m_diagonal.setOnes(size); }
};
@@ -230,14 +228,15 @@ struct traits<DiagonalWrapper<_DiagonalVectorType> >
{
typedef _DiagonalVectorType DiagonalVectorType;
typedef typename DiagonalVectorType::Scalar Scalar;
typedef typename DiagonalVectorType::Index Index;
typedef typename DiagonalVectorType::StorageKind StorageKind;
typedef typename DiagonalVectorType::StorageIndex StorageIndex;
typedef DiagonalShape StorageKind;
typedef typename traits<DiagonalVectorType>::XprKind XprKind;
enum {
RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
MaxRowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
MaxColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
Flags = traits<DiagonalVectorType>::Flags & LvalueBit
MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
Flags = (traits<DiagonalVectorType>::Flags & LvalueBit) | NoPreferredStorageOrderBit
};
};
}
@@ -253,9 +252,11 @@ class DiagonalWrapper
#endif
/** Constructor from expression of diagonal coefficients to wrap. */
inline DiagonalWrapper(DiagonalVectorType& diagonal) : m_diagonal(diagonal) {}
EIGEN_DEVICE_FUNC
explicit inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_diagonal) {}
/** \returns a const reference to the wrapped expression of diagonal coefficients. */
EIGEN_DEVICE_FUNC
const DiagonalVectorType& diagonal() const { return m_diagonal; }
protected:
@@ -275,7 +276,7 @@ template<typename Derived>
inline const DiagonalWrapper<const Derived>
MatrixBase<Derived>::asDiagonal() const
{
return derived();
return DiagonalWrapper<const Derived>(derived());
}
/** \returns true if *this is approximately equal to a diagonal matrix,
@@ -287,13 +288,13 @@ MatrixBase<Derived>::asDiagonal() const
* \sa asDiagonal()
*/
template<typename Derived>
bool MatrixBase<Derived>::isDiagonal(RealScalar prec) const
bool MatrixBase<Derived>::isDiagonal(const RealScalar& prec) const
{
if(cols() != rows()) return false;
RealScalar maxAbsOnDiagonal = static_cast<RealScalar>(-1);
for(Index j = 0; j < cols(); ++j)
{
RealScalar absOnDiagonal = internal::abs(coeff(j,j));
RealScalar absOnDiagonal = numext::abs(coeff(j,j));
if(absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal;
}
for(Index j = 0; j < cols(); ++j)
@@ -305,6 +306,38 @@ bool MatrixBase<Derived>::isDiagonal(RealScalar prec) const
return true;
}
namespace internal {
template<> struct storage_kind_to_shape<DiagonalShape> { typedef DiagonalShape Shape; };
struct Diagonal2Dense {};
template<> struct AssignmentKind<DenseShape,DiagonalShape> { typedef Diagonal2Dense Kind; };
// Diagonal matrix to Dense assignment
template< typename DstXprType, typename SrcXprType, typename Functor>
struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Dense>
{
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
dst.setZero();
dst.diagonal() = src.diagonal();
}
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{ dst.diagonal() += src.diagonal(); }
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{ dst.diagonal() -= src.diagonal(); }
};
} // namespace internal
} // end namespace Eigen
#endif // EIGEN_DIAGONALMATRIX_H

View File

@@ -4,133 +4,23 @@
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_DIAGONALPRODUCT_H
#define EIGEN_DIAGONALPRODUCT_H
namespace Eigen {
namespace internal {
template<typename MatrixType, typename DiagonalType, int ProductOrder>
struct traits<DiagonalProduct<MatrixType, DiagonalType, ProductOrder> >
: traits<MatrixType>
{
typedef typename scalar_product_traits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar;
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
_StorageOrder = MatrixType::Flags & RowMajorBit ? RowMajor : ColMajor,
_PacketOnDiag = !((int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft)
||(int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)),
_SameTypes = is_same<typename MatrixType::Scalar, typename DiagonalType::Scalar>::value,
// FIXME currently we need same types, but in the future the next rule should be the one
//_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagonalType::Flags)&PacketAccessBit))),
_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && _SameTypes && ((!_PacketOnDiag) || (bool(int(DiagonalType::Flags)&PacketAccessBit))),
Flags = (HereditaryBits & (unsigned int)(MatrixType::Flags)) | (_Vectorizable ? PacketAccessBit : 0),
CoeffReadCost = NumTraits<Scalar>::MulCost + MatrixType::CoeffReadCost + DiagonalType::DiagonalVectorType::CoeffReadCost
};
};
}
template<typename MatrixType, typename DiagonalType, int ProductOrder>
class DiagonalProduct : internal::no_assignment_operator,
public MatrixBase<DiagonalProduct<MatrixType, DiagonalType, ProductOrder> >
{
public:
typedef MatrixBase<DiagonalProduct> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(DiagonalProduct)
inline DiagonalProduct(const MatrixType& matrix, const DiagonalType& diagonal)
: m_matrix(matrix), m_diagonal(diagonal)
{
eigen_assert(diagonal.diagonal().size() == (ProductOrder == OnTheLeft ? matrix.rows() : matrix.cols()));
}
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
const Scalar coeff(Index row, Index col) const
{
return m_diagonal.diagonal().coeff(ProductOrder == OnTheLeft ? row : col) * m_matrix.coeff(row, col);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
{
enum {
StorageOrder = Flags & RowMajorBit ? RowMajor : ColMajor
};
const Index indexInDiagonalVector = ProductOrder == OnTheLeft ? row : col;
return packet_impl<LoadMode>(row,col,indexInDiagonalVector,typename internal::conditional<
((int(StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft)
||(int(StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)), internal::true_type, internal::false_type>::type());
}
protected:
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::true_type) const
{
return internal::pmul(m_matrix.template packet<LoadMode>(row, col),
internal::pset1<PacketScalar>(m_diagonal.diagonal().coeff(id)));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::false_type) const
{
enum {
InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
DiagonalVectorPacketLoadMode = (LoadMode == Aligned && ((InnerSize%16) == 0)) ? Aligned : Unaligned
};
return internal::pmul(m_matrix.template packet<LoadMode>(row, col),
m_diagonal.diagonal().template packet<DiagonalVectorPacketLoadMode>(id));
}
typename MatrixType::Nested m_matrix;
typename DiagonalType::Nested m_diagonal;
};
/** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal.
*/
template<typename Derived>
template<typename DiagonalDerived>
inline const DiagonalProduct<Derived, DiagonalDerived, OnTheRight>
MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &diagonal) const
inline const Product<Derived, DiagonalDerived, LazyProduct>
MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &a_diagonal) const
{
return DiagonalProduct<Derived, DiagonalDerived, OnTheRight>(derived(), diagonal.derived());
}
/** \returns the diagonal matrix product of \c *this by the matrix \a matrix.
*/
template<typename DiagonalDerived>
template<typename MatrixDerived>
inline const DiagonalProduct<MatrixDerived, DiagonalDerived, OnTheLeft>
DiagonalBase<DiagonalDerived>::operator*(const MatrixBase<MatrixDerived> &matrix) const
{
return DiagonalProduct<MatrixDerived, DiagonalDerived, OnTheLeft>(matrix.derived(), derived());
return Product<Derived, DiagonalDerived, LazyProduct>(derived(),a_diagonal.derived());
}
} // end namespace Eigen

View File

@@ -3,24 +3,9 @@
//
// Copyright (C) 2006-2008, 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_DOT_H
#define EIGEN_DOT_H
@@ -43,26 +28,33 @@ template<typename T, typename U,
>
struct dot_nocheck
{
typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar;
static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
typedef typename conj_prod::result_type ResScalar;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE
static ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
{
return a.template binaryExpr<scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> >(b).sum();
return a.template binaryExpr<conj_prod>(b).sum();
}
};
template<typename T, typename U>
struct dot_nocheck<T, U, true>
{
typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar;
static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
typedef typename conj_prod::result_type ResScalar;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE
static ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
{
return a.transpose().template binaryExpr<scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> >(b).sum();
return a.transpose().template binaryExpr<conj_prod>(b).sum();
}
};
} // end namespace internal
/** \returns the dot product of *this with other.
/** \fn MatrixBase::dot
* \returns the dot product of *this with other.
*
* \only_for_vectors
*
@@ -74,100 +66,134 @@ struct dot_nocheck<T, U, true>
*/
template<typename Derived>
template<typename OtherDerived>
typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE
typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
#if !(defined(EIGEN_NO_STATIC_ASSERT) && defined(EIGEN_NO_DEBUG))
typedef internal::scalar_conj_product_op<Scalar,typename OtherDerived::Scalar> func;
EIGEN_CHECK_BINARY_COMPATIBILIY(func,Scalar,typename OtherDerived::Scalar);
#endif
eigen_assert(size() == other.size());
return internal::dot_nocheck<Derived,OtherDerived>::run(*this, other);
}
#ifdef EIGEN2_SUPPORT
/** \returns the dot product of *this with other, with the Eigen2 convention that the dot product is linear in the first variable
* (conjugating the second variable). Of course this only makes a difference in the complex case.
*
* This method is only available in EIGEN2_SUPPORT mode.
*
* \only_for_vectors
*
* \sa dot()
*/
template<typename Derived>
template<typename OtherDerived>
typename internal::traits<Derived>::Scalar
MatrixBase<Derived>::eigen2_dot(const MatrixBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
eigen_assert(size() == other.size());
return internal::dot_nocheck<OtherDerived,Derived>::run(other,*this);
}
#endif
//---------- implementation of L2 norm and related functions ----------
/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the Frobenius norm.
* In both cases, it consists in the sum of the square of all the matrix entries.
* For vectors, this is also equals to the dot product of \c *this with itself.
*
* \sa dot(), norm()
* \sa dot(), norm(), lpNorm()
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const
{
return internal::real((*this).cwiseAbs2().sum());
return numext::real((*this).cwiseAbs2().sum());
}
/** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm.
* In both cases, it consists in the square root of the sum of the square of all the matrix entries.
* For vectors, this is also equals to the square root of the dot product of \c *this with itself.
*
* \sa dot(), squaredNorm()
* \sa lpNorm(), dot(), squaredNorm()
*/
template<typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
{
return internal::sqrt(squaredNorm());
return numext::sqrt(squaredNorm());
}
/** \returns an expression of the quotient of *this by its own norm.
/** \returns an expression of the quotient of \c *this by its own norm.
*
* \warning If the input vector is too small (i.e., this->norm()==0),
* then this function returns a copy of the input.
*
* \only_for_vectors
*
* \sa norm(), normalize()
*/
template<typename Derived>
inline const typename MatrixBase<Derived>::PlainObject
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
MatrixBase<Derived>::normalized() const
{
typedef typename internal::nested<Derived>::type Nested;
typedef typename internal::remove_reference<Nested>::type _Nested;
typedef typename internal::nested_eval<Derived,2>::type _Nested;
_Nested n(derived());
return n / n.norm();
RealScalar z = n.squaredNorm();
// NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
if(z>RealScalar(0))
return n / numext::sqrt(z);
else
return n;
}
/** Normalizes the vector, i.e. divides it by its own norm.
*
* \only_for_vectors
*
* \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
*
* \sa norm(), normalized()
*/
template<typename Derived>
inline void MatrixBase<Derived>::normalize()
EIGEN_STRONG_INLINE void MatrixBase<Derived>::normalize()
{
*this /= norm();
RealScalar z = squaredNorm();
// NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
if(z>RealScalar(0))
derived() /= numext::sqrt(z);
}
/** \returns an expression of the quotient of \c *this by its own norm while avoiding underflow and overflow.
*
* \only_for_vectors
*
* This method is analogue to the normalized() method, but it reduces the risk of
* underflow and overflow when computing the norm.
*
* \warning If the input vector is too small (i.e., this->norm()==0),
* then this function returns a copy of the input.
*
* \sa stableNorm(), stableNormalize(), normalized()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
MatrixBase<Derived>::stableNormalized() const
{
typedef typename internal::nested_eval<Derived,3>::type _Nested;
_Nested n(derived());
RealScalar w = n.cwiseAbs().maxCoeff();
RealScalar z = (n/w).squaredNorm();
if(z>RealScalar(0))
return n / (numext::sqrt(z)*w);
else
return n;
}
/** Normalizes the vector while avoid underflow and overflow
*
* \only_for_vectors
*
* This method is analogue to the normalize() method, but it reduces the risk of
* underflow and overflow when computing the norm.
*
* \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
*
* \sa stableNorm(), stableNormalized(), normalize()
*/
template<typename Derived>
EIGEN_STRONG_INLINE void MatrixBase<Derived>::stableNormalize()
{
RealScalar w = cwiseAbs().maxCoeff();
RealScalar z = (derived()/w).squaredNorm();
if(z>RealScalar(0))
derived() /= numext::sqrt(z)*w;
}
//---------- implementation of other norms ----------
@@ -178,8 +204,10 @@ template<typename Derived, int p>
struct lpNorm_selector
{
typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC
static inline RealScalar run(const MatrixBase<Derived>& m)
{
EIGEN_USING_STD_MATH(pow)
return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p);
}
};
@@ -187,6 +215,7 @@ struct lpNorm_selector
template<typename Derived>
struct lpNorm_selector<Derived, 1>
{
EIGEN_DEVICE_FUNC
static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
{
return m.cwiseAbs().sum();
@@ -196,6 +225,7 @@ struct lpNorm_selector<Derived, 1>
template<typename Derived>
struct lpNorm_selector<Derived, 2>
{
EIGEN_DEVICE_FUNC
static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
{
return m.norm();
@@ -205,23 +235,35 @@ struct lpNorm_selector<Derived, 2>
template<typename Derived>
struct lpNorm_selector<Derived, Infinity>
{
static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC
static inline RealScalar run(const MatrixBase<Derived>& m)
{
if(Derived::SizeAtCompileTime==0 || (Derived::SizeAtCompileTime==Dynamic && m.size()==0))
return RealScalar(0);
return m.cwiseAbs().maxCoeff();
}
};
} // end namespace internal
/** \returns the \f$ \ell^p \f$ norm of *this, that is, returns the p-th root of the sum of the p-th powers of the absolute values
* of the coefficients of *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^\infty \f$
* norm, that is the maximum of the absolute values of the coefficients of *this.
/** \returns the \b coefficient-wise \f$ \ell^p \f$ norm of \c *this, that is, returns the p-th root of the sum of the p-th powers of the absolute values
* of the coefficients of \c *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^\infty \f$
* norm, that is the maximum of the absolute values of the coefficients of \c *this.
*
* In all cases, if \c *this is empty, then the value 0 is returned.
*
* \note For matrices, this function does not compute the <a href="https://en.wikipedia.org/wiki/Operator_norm">operator-norm</a>. That is, if \c *this is a matrix, then its coefficients are interpreted as a 1D vector. Nonetheless, you can easily compute the 1-norm and \f$\infty\f$-norm matrix operator norms using \link TutorialReductionsVisitorsBroadcastingReductionsNorm partial reductions \endlink.
*
* \sa norm()
*/
template<typename Derived>
template<int p>
#ifndef EIGEN_PARSED_BY_DOXYGEN
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
#else
MatrixBase<Derived>::RealScalar
#endif
MatrixBase<Derived>::lpNorm() const
{
return internal::lpNorm_selector<Derived, p>::run(*this);
@@ -238,11 +280,11 @@ MatrixBase<Derived>::lpNorm() const
template<typename Derived>
template<typename OtherDerived>
bool MatrixBase<Derived>::isOrthogonal
(const MatrixBase<OtherDerived>& other, RealScalar prec) const
(const MatrixBase<OtherDerived>& other, const RealScalar& prec) const
{
typename internal::nested<Derived,2>::type nested(derived());
typename internal::nested<OtherDerived,2>::type otherNested(other.derived());
return internal::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
typename internal::nested_eval<Derived,2>::type nested(derived());
typename internal::nested_eval<OtherDerived,2>::type otherNested(other.derived());
return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
}
/** \returns true if *this is approximately an unitary matrix,
@@ -257,15 +299,15 @@ bool MatrixBase<Derived>::isOrthogonal
* Output: \verbinclude MatrixBase_isUnitary.out
*/
template<typename Derived>
bool MatrixBase<Derived>::isUnitary(RealScalar prec) const
bool MatrixBase<Derived>::isUnitary(const RealScalar& prec) const
{
typename Derived::Nested nested(derived());
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index i = 0; i < cols(); ++i)
{
if(!internal::isApprox(nested.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
if(!internal::isApprox(self.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
return false;
for(Index j = 0; j < i; ++j)
if(!internal::isMuchSmallerThan(nested.col(i).dot(nested.col(j)), static_cast<Scalar>(1), prec))
if(!internal::isMuchSmallerThan(self.col(i).dot(self.col(j)), static_cast<Scalar>(1), prec))
return false;
}
return true;

View File

@@ -4,31 +4,19 @@
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_EIGENBASE_H
#define EIGEN_EIGENBASE_H
namespace Eigen {
/** Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T).
/** \class EigenBase
* \ingroup Core_Module
*
* Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T).
*
* In other words, an EigenBase object is an object that can be copied into a MatrixBase.
*
@@ -36,39 +24,57 @@ namespace Eigen {
*
* Notice that this class is trivial, it is only used to disambiguate overloaded functions.
*
* \sa \ref TopicClassHierarchy
* \sa \blank \ref TopicClassHierarchy
*/
template<typename Derived> struct EigenBase
{
// typedef typename internal::plain_matrix_type<Derived>::type PlainObject;
/** \brief The interface type of indices
* \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
* \deprecated Since Eigen 3.3, its usage is deprecated. Use Eigen::Index instead.
* \sa StorageIndex, \ref TopicPreprocessorDirectives.
*/
typedef Eigen::Index Index;
// FIXME is it needed?
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
/** \returns a reference to the derived object */
EIGEN_DEVICE_FUNC
Derived& derived() { return *static_cast<Derived*>(this); }
/** \returns a const reference to the derived object */
EIGEN_DEVICE_FUNC
const Derived& derived() const { return *static_cast<const Derived*>(this); }
EIGEN_DEVICE_FUNC
inline Derived& const_cast_derived() const
{ return *static_cast<Derived*>(const_cast<EigenBase*>(this)); }
EIGEN_DEVICE_FUNC
inline const Derived& const_derived() const
{ return *static_cast<const Derived*>(this); }
/** \returns the number of rows. \sa cols(), RowsAtCompileTime */
EIGEN_DEVICE_FUNC
inline Index rows() const { return derived().rows(); }
/** \returns the number of columns. \sa rows(), ColsAtCompileTime*/
EIGEN_DEVICE_FUNC
inline Index cols() const { return derived().cols(); }
/** \returns the number of coefficients, which is rows()*cols().
* \sa rows(), cols(), SizeAtCompileTime. */
EIGEN_DEVICE_FUNC
inline Index size() const { return rows() * cols(); }
/** \internal Don't use it, but do the equivalent: \code dst = *this; \endcode */
template<typename Dest> inline void evalTo(Dest& dst) const
template<typename Dest>
EIGEN_DEVICE_FUNC
inline void evalTo(Dest& dst) const
{ derived().evalTo(dst); }
/** \internal Don't use it, but do the equivalent: \code dst += *this; \endcode */
template<typename Dest> inline void addTo(Dest& dst) const
template<typename Dest>
EIGEN_DEVICE_FUNC
inline void addTo(Dest& dst) const
{
// This is the default implementation,
// derived class can reimplement it in a more optimized way.
@@ -78,7 +84,9 @@ template<typename Derived> struct EigenBase
}
/** \internal Don't use it, but do the equivalent: \code dst -= *this; \endcode */
template<typename Dest> inline void subTo(Dest& dst) const
template<typename Dest>
EIGEN_DEVICE_FUNC
inline void subTo(Dest& dst) const
{
// This is the default implementation,
// derived class can reimplement it in a more optimized way.
@@ -88,7 +96,8 @@ template<typename Derived> struct EigenBase
}
/** \internal Don't use it, but do the equivalent: \code dst.applyOnTheRight(*this); \endcode */
template<typename Dest> inline void applyThisOnTheRight(Dest& dst) const
template<typename Dest>
EIGEN_DEVICE_FUNC inline void applyThisOnTheRight(Dest& dst) const
{
// This is the default implementation,
// derived class can reimplement it in a more optimized way.
@@ -96,7 +105,8 @@ template<typename Derived> struct EigenBase
}
/** \internal Don't use it, but do the equivalent: \code dst.applyOnTheLeft(*this); \endcode */
template<typename Dest> inline void applyThisOnTheLeft(Dest& dst) const
template<typename Dest>
EIGEN_DEVICE_FUNC inline void applyThisOnTheLeft(Dest& dst) const
{
// This is the default implementation,
// derived class can reimplement it in a more optimized way.
@@ -119,57 +129,31 @@ template<typename Derived> struct EigenBase
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& DenseBase<Derived>::operator=(const EigenBase<OtherDerived> &other)
{
other.derived().evalTo(derived());
call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
{
other.derived().addTo(derived());
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
{
other.derived().subTo(derived());
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
/** replaces \c *this by \c *this * \a other.
*
* \returns a reference to \c *this
*/
template<typename Derived>
template<typename OtherDerived>
inline Derived&
MatrixBase<Derived>::operator*=(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheRight(derived());
return derived();
}
/** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=() */
template<typename Derived>
template<typename OtherDerived>
inline void MatrixBase<Derived>::applyOnTheRight(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheRight(derived());
}
/** replaces \c *this by \c *this * \a other. */
template<typename Derived>
template<typename OtherDerived>
inline void MatrixBase<Derived>::applyOnTheLeft(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheLeft(derived());
}
} // end namespace Eigen
#endif // EIGEN_EIGENBASE_H

View File

@@ -1,155 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_FLAGGED_H
#define EIGEN_FLAGGED_H
namespace Eigen {
/** \class Flagged
* \ingroup Core_Module
*
* \brief Expression with modified flags
*
* \param ExpressionType the type of the object of which we are modifying the flags
* \param Added the flags added to the expression
* \param Removed the flags removed from the expression (has priority over Added).
*
* This class represents an expression whose flags have been modified.
* It is the return type of MatrixBase::flagged()
* and most of the time this is the only way it is used.
*
* \sa MatrixBase::flagged()
*/
namespace internal {
template<typename ExpressionType, unsigned int Added, unsigned int Removed>
struct traits<Flagged<ExpressionType, Added, Removed> > : traits<ExpressionType>
{
enum { Flags = (ExpressionType::Flags | Added) & ~Removed };
};
}
template<typename ExpressionType, unsigned int Added, unsigned int Removed> class Flagged
: public MatrixBase<Flagged<ExpressionType, Added, Removed> >
{
public:
typedef MatrixBase<Flagged> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Flagged)
typedef typename internal::conditional<internal::must_nest_by_value<ExpressionType>::ret,
ExpressionType, const ExpressionType&>::type ExpressionTypeNested;
typedef typename ExpressionType::InnerIterator InnerIterator;
inline Flagged(const ExpressionType& matrix) : m_matrix(matrix) {}
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
inline Index outerStride() const { return m_matrix.outerStride(); }
inline Index innerStride() const { return m_matrix.innerStride(); }
inline CoeffReturnType coeff(Index row, Index col) const
{
return m_matrix.coeff(row, col);
}
inline CoeffReturnType coeff(Index index) const
{
return m_matrix.coeff(index);
}
inline const Scalar& coeffRef(Index row, Index col) const
{
return m_matrix.const_cast_derived().coeffRef(row, col);
}
inline const Scalar& coeffRef(Index index) const
{
return m_matrix.const_cast_derived().coeffRef(index);
}
inline Scalar& coeffRef(Index row, Index col)
{
return m_matrix.const_cast_derived().coeffRef(row, col);
}
inline Scalar& coeffRef(Index index)
{
return m_matrix.const_cast_derived().coeffRef(index);
}
template<int LoadMode>
inline const PacketScalar packet(Index row, Index col) const
{
return m_matrix.template packet<LoadMode>(row, col);
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
{
m_matrix.const_cast_derived().template writePacket<LoadMode>(row, col, x);
}
template<int LoadMode>
inline const PacketScalar packet(Index index) const
{
return m_matrix.template packet<LoadMode>(index);
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
{
m_matrix.const_cast_derived().template writePacket<LoadMode>(index, x);
}
const ExpressionType& _expression() const { return m_matrix; }
template<typename OtherDerived>
typename ExpressionType::PlainObject solveTriangular(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived>
void solveTriangularInPlace(const MatrixBase<OtherDerived>& other) const;
protected:
ExpressionTypeNested m_matrix;
};
/** \returns an expression of *this with added and removed flags
*
* This is mostly for internal use.
*
* \sa class Flagged
*/
template<typename Derived>
template<unsigned int Added,unsigned int Removed>
inline const Flagged<Derived, Added, Removed>
DenseBase<Derived>::flagged() const
{
return derived();
}
} // end namespace Eigen
#endif // EIGEN_FLAGGED_H

View File

@@ -3,24 +3,9 @@
//
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_FORCEALIGNEDACCESS_H
#define EIGEN_FORCEALIGNEDACCESS_H
@@ -54,29 +39,29 @@ template<typename ExpressionType> class ForceAlignedAccess
typedef typename internal::dense_xpr_base<ForceAlignedAccess>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(ForceAlignedAccess)
inline ForceAlignedAccess(const ExpressionType& matrix) : m_expression(matrix) {}
EIGEN_DEVICE_FUNC explicit inline ForceAlignedAccess(const ExpressionType& matrix) : m_expression(matrix) {}
inline Index rows() const { return m_expression.rows(); }
inline Index cols() const { return m_expression.cols(); }
inline Index outerStride() const { return m_expression.outerStride(); }
inline Index innerStride() const { return m_expression.innerStride(); }
EIGEN_DEVICE_FUNC inline Index rows() const { return m_expression.rows(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_expression.cols(); }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return m_expression.outerStride(); }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return m_expression.innerStride(); }
inline const CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index row, Index col) const
{
return m_expression.coeff(row, col);
}
inline Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index col)
{
return m_expression.const_cast_derived().coeffRef(row, col);
}
inline const CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index index) const
{
return m_expression.coeff(index);
}
inline Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
}
@@ -105,7 +90,7 @@ template<typename ExpressionType> class ForceAlignedAccess
m_expression.const_cast_derived().template writePacket<Aligned>(index, x);
}
operator const ExpressionType&() const { return m_expression; }
EIGEN_DEVICE_FUNC operator const ExpressionType&() const { return m_expression; }
protected:
const ExpressionType& m_expression;
@@ -142,7 +127,7 @@ template<bool Enable>
inline typename internal::add_const_on_value_type<typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type>::type
MatrixBase<Derived>::forceAlignedAccessIf() const
{
return derived();
return derived(); // FIXME This should not work but apparently is never used
}
/** \returns an expression of *this with forced aligned access if \a Enable is true.
@@ -153,7 +138,7 @@ template<bool Enable>
inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type
MatrixBase<Derived>::forceAlignedAccessIf()
{
return derived();
return derived(); // FIXME This should not work but apparently is never used
}
} // end namespace Eigen

File diff suppressed because it is too large Load Diff

View File

@@ -4,24 +4,9 @@
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_FUZZY_H
#define EIGEN_FUZZY_H
@@ -34,19 +19,20 @@ namespace internal
template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isApprox_selector
{
static bool run(const Derived& x, const OtherDerived& y, typename Derived::RealScalar prec)
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
{
using std::min;
typename internal::nested<Derived,2>::type nested(x);
typename internal::nested<OtherDerived,2>::type otherNested(y);
return (nested - otherNested).cwiseAbs2().sum() <= prec * prec * (min)(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
typename internal::nested_eval<Derived,2>::type nested(x);
typename internal::nested_eval<OtherDerived,2>::type otherNested(y);
return (nested - otherNested).cwiseAbs2().sum() <= prec * prec * numext::mini(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
}
};
template<typename Derived, typename OtherDerived>
struct isApprox_selector<Derived, OtherDerived, true>
{
static bool run(const Derived& x, const OtherDerived& y, typename Derived::RealScalar)
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar&)
{
return x.matrix() == y.matrix();
}
@@ -55,16 +41,18 @@ struct isApprox_selector<Derived, OtherDerived, true>
template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isMuchSmallerThan_object_selector
{
static bool run(const Derived& x, const OtherDerived& y, typename Derived::RealScalar prec)
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
{
return x.cwiseAbs2().sum() <= abs2(prec) * y.cwiseAbs2().sum();
return x.cwiseAbs2().sum() <= numext::abs2(prec) * y.cwiseAbs2().sum();
}
};
template<typename Derived, typename OtherDerived>
struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true>
{
static bool run(const Derived& x, const OtherDerived&, typename Derived::RealScalar)
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived&, const typename Derived::RealScalar&)
{
return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
}
@@ -73,16 +61,18 @@ struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true>
template<typename Derived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isMuchSmallerThan_scalar_selector
{
static bool run(const Derived& x, const typename Derived::RealScalar& y, typename Derived::RealScalar prec)
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const typename Derived::RealScalar& y, const typename Derived::RealScalar& prec)
{
return x.cwiseAbs2().sum() <= abs2(prec * y);
return x.cwiseAbs2().sum() <= numext::abs2(prec * y);
}
};
template<typename Derived>
struct isMuchSmallerThan_scalar_selector<Derived, true>
{
static bool run(const Derived& x, const typename Derived::RealScalar&, typename Derived::RealScalar)
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const typename Derived::RealScalar&, const typename Derived::RealScalar&)
{
return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
}
@@ -112,7 +102,7 @@ template<typename Derived>
template<typename OtherDerived>
bool DenseBase<Derived>::isApprox(
const DenseBase<OtherDerived>& other,
RealScalar prec
const RealScalar& prec
) const
{
return internal::isApprox_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);
@@ -134,7 +124,7 @@ bool DenseBase<Derived>::isApprox(
template<typename Derived>
bool DenseBase<Derived>::isMuchSmallerThan(
const typename NumTraits<Scalar>::Real& other,
RealScalar prec
const RealScalar& prec
) const
{
return internal::isMuchSmallerThan_scalar_selector<Derived>::run(derived(), other, prec);
@@ -154,7 +144,7 @@ template<typename Derived>
template<typename OtherDerived>
bool DenseBase<Derived>::isMuchSmallerThan(
const DenseBase<OtherDerived>& other,
RealScalar prec
const RealScalar& prec
) const
{
return internal::isMuchSmallerThan_object_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);

View File

@@ -4,51 +4,14 @@
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_GENERAL_PRODUCT_H
#define EIGEN_GENERAL_PRODUCT_H
namespace Eigen {
/** \class GeneralProduct
* \ingroup Core_Module
*
* \brief Expression of the product of two general matrices or vectors
*
* \param LhsNested the type used to store the left-hand side
* \param RhsNested the type used to store the right-hand side
* \param ProductMode the type of the product
*
* This class represents an expression of the product of two general matrices.
* We call a general matrix, a dense matrix with full storage. For instance,
* This excludes triangular, selfadjoint, and sparse matrices.
* It is the return type of the operator* between general matrices. Its template
* arguments are determined automatically by ProductReturnType. Therefore,
* GeneralProduct should never be used direclty. To determine the result type of a
* function which involves a matrix product, use ProductReturnType::Type.
*
* \sa ProductReturnType, MatrixBase::operator*(const MatrixBase<OtherDerived>&)
*/
template<typename Lhs, typename Rhs, int ProductType = internal::product_type<Lhs,Rhs>::value>
class GeneralProduct;
namespace Eigen {
enum {
Large = 2,
@@ -61,11 +24,17 @@ template<int Rows, int Cols, int Depth> struct product_type_selector;
template<int Size, int MaxSize> struct product_size_category
{
enum { is_large = MaxSize == Dynamic ||
Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD,
value = is_large ? Large
: Size == 1 ? 1
: Small
enum {
#ifndef EIGEN_CUDA_ARCH
is_large = MaxSize == Dynamic ||
Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD ||
(Size==Dynamic && MaxSize>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD),
#else
is_large = 0,
#endif
value = is_large ? Large
: Size == 1 ? 1
: Small
};
};
@@ -74,15 +43,14 @@ template<typename Lhs, typename Rhs> struct product_type
typedef typename remove_all<Lhs>::type _Lhs;
typedef typename remove_all<Rhs>::type _Rhs;
enum {
MaxRows = _Lhs::MaxRowsAtCompileTime,
Rows = _Lhs::RowsAtCompileTime,
MaxCols = _Rhs::MaxColsAtCompileTime,
Cols = _Rhs::ColsAtCompileTime,
MaxDepth = EIGEN_SIZE_MIN_PREFER_FIXED(_Lhs::MaxColsAtCompileTime,
_Rhs::MaxRowsAtCompileTime),
Depth = EIGEN_SIZE_MIN_PREFER_FIXED(_Lhs::ColsAtCompileTime,
_Rhs::RowsAtCompileTime),
LargeThreshold = EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
MaxRows = traits<_Lhs>::MaxRowsAtCompileTime,
Rows = traits<_Lhs>::RowsAtCompileTime,
MaxCols = traits<_Rhs>::MaxColsAtCompileTime,
Cols = traits<_Rhs>::ColsAtCompileTime,
MaxDepth = EIGEN_SIZE_MIN_PREFER_FIXED(traits<_Lhs>::MaxColsAtCompileTime,
traits<_Rhs>::MaxRowsAtCompileTime),
Depth = EIGEN_SIZE_MIN_PREFER_FIXED(traits<_Lhs>::ColsAtCompileTime,
traits<_Rhs>::RowsAtCompileTime)
};
// the splitting into different lines of code here, introducing the _select enums and the typedef below,
@@ -97,7 +65,8 @@ private:
public:
enum {
value = selector::ret
value = selector::ret,
ret = selector::ret
};
#ifdef EIGEN_DEBUG_PRODUCT
static void debug()
@@ -113,12 +82,13 @@ public:
#endif
};
/* The following allows to select the kind of product at compile time
* based on the three dimensions of the product.
* This is a compile time mapping from {1,Small,Large}^3 -> {product types} */
// FIXME I'm not sure the current mapping is the ideal one.
template<int M, int N> struct product_type_selector<M,N,1> { enum { ret = OuterProduct }; };
template<int M> struct product_type_selector<M, 1, 1> { enum { ret = LazyCoeffBasedProductMode }; };
template<int N> struct product_type_selector<1, N, 1> { enum { ret = LazyCoeffBasedProductMode }; };
template<int Depth> struct product_type_selector<1, 1, Depth> { enum { ret = InnerProduct }; };
template<> struct product_type_selector<1, 1, 1> { enum { ret = InnerProduct }; };
template<> struct product_type_selector<Small,1, Small> { enum { ret = CoeffBasedProductMode }; };
@@ -137,60 +107,12 @@ template<> struct product_type_selector<Small,Small,Large> { enum
template<> struct product_type_selector<Large,Small,Large> { enum { ret = GemmProduct }; };
template<> struct product_type_selector<Small,Large,Large> { enum { ret = GemmProduct }; };
template<> struct product_type_selector<Large,Large,Large> { enum { ret = GemmProduct }; };
template<> struct product_type_selector<Large,Small,Small> { enum { ret = GemmProduct }; };
template<> struct product_type_selector<Small,Large,Small> { enum { ret = GemmProduct }; };
template<> struct product_type_selector<Large,Small,Small> { enum { ret = CoeffBasedProductMode }; };
template<> struct product_type_selector<Small,Large,Small> { enum { ret = CoeffBasedProductMode }; };
template<> struct product_type_selector<Large,Large,Small> { enum { ret = GemmProduct }; };
} // end namespace internal
/** \class ProductReturnType
* \ingroup Core_Module
*
* \brief Helper class to get the correct and optimized returned type of operator*
*
* \param Lhs the type of the left-hand side
* \param Rhs the type of the right-hand side
* \param ProductMode the type of the product (determined automatically by internal::product_mode)
*
* This class defines the typename Type representing the optimized product expression
* between two matrix expressions. In practice, using ProductReturnType<Lhs,Rhs>::Type
* is the recommended way to define the result type of a function returning an expression
* which involve a matrix product. The class Product should never be
* used directly.
*
* \sa class Product, MatrixBase::operator*(const MatrixBase<OtherDerived>&)
*/
template<typename Lhs, typename Rhs, int ProductType>
struct ProductReturnType
{
// TODO use the nested type to reduce instanciations ????
// typedef typename internal::nested<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
// typedef typename internal::nested<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
typedef GeneralProduct<Lhs/*Nested*/, Rhs/*Nested*/, ProductType> Type;
};
template<typename Lhs, typename Rhs>
struct ProductReturnType<Lhs,Rhs,CoeffBasedProductMode>
{
typedef typename internal::nested<Lhs, Rhs::ColsAtCompileTime, typename internal::plain_matrix_type<Lhs>::type >::type LhsNested;
typedef typename internal::nested<Rhs, Lhs::RowsAtCompileTime, typename internal::plain_matrix_type<Rhs>::type >::type RhsNested;
typedef CoeffBasedProduct<LhsNested, RhsNested, EvalBeforeAssigningBit | EvalBeforeNestingBit> Type;
};
template<typename Lhs, typename Rhs>
struct ProductReturnType<Lhs,Rhs,LazyCoeffBasedProductMode>
{
typedef typename internal::nested<Lhs, Rhs::ColsAtCompileTime, typename internal::plain_matrix_type<Lhs>::type >::type LhsNested;
typedef typename internal::nested<Rhs, Lhs::RowsAtCompileTime, typename internal::plain_matrix_type<Rhs>::type >::type RhsNested;
typedef CoeffBasedProduct<LhsNested, RhsNested, NestByRefBit> Type;
};
// this is a workaround for sun CC
template<typename Lhs, typename Rhs>
struct LazyProductReturnType : public ProductReturnType<Lhs,Rhs,LazyCoeffBasedProductMode>
{};
/***********************************************************************
* Implementation of Inner Vector Vector Product
***********************************************************************/
@@ -202,97 +124,10 @@ struct LazyProductReturnType : public ProductReturnType<Lhs,Rhs,LazyCoeffBasedPr
// product ends up to a row-vector times col-vector product... To tackle this use
// case, we could have a specialization for Block<MatrixType,1,1> with: operator=(Scalar x);
namespace internal {
template<typename Lhs, typename Rhs>
struct traits<GeneralProduct<Lhs,Rhs,InnerProduct> >
: traits<Matrix<typename scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> >
{};
}
template<typename Lhs, typename Rhs>
class GeneralProduct<Lhs, Rhs, InnerProduct>
: internal::no_assignment_operator,
public Matrix<typename internal::scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1>
{
typedef Matrix<typename internal::scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> Base;
public:
GeneralProduct(const Lhs& lhs, const Rhs& rhs)
{
EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::RealScalar, typename Rhs::RealScalar>::value),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
Base::coeffRef(0,0) = (lhs.transpose().cwiseProduct(rhs)).sum();
}
/** Convertion to scalar */
operator const typename Base::Scalar() const {
return Base::coeff(0,0);
}
};
/***********************************************************************
* Implementation of Outer Vector Vector Product
***********************************************************************/
namespace internal {
template<int StorageOrder> struct outer_product_selector;
template<typename Lhs, typename Rhs>
struct traits<GeneralProduct<Lhs,Rhs,OuterProduct> >
: traits<ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs> >
{};
}
template<typename Lhs, typename Rhs>
class GeneralProduct<Lhs, Rhs, OuterProduct>
: public ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs>
{
public:
EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct)
GeneralProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
{
EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::RealScalar, typename Rhs::RealScalar>::value),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
}
template<typename Dest> void scaleAndAddTo(Dest& dest, Scalar alpha) const
{
internal::outer_product_selector<(int(Dest::Flags)&RowMajorBit) ? RowMajor : ColMajor>::run(*this, dest, alpha);
}
};
namespace internal {
template<> struct outer_product_selector<ColMajor> {
template<typename ProductType, typename Dest>
static EIGEN_DONT_INLINE void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha) {
typedef typename Dest::Index Index;
// FIXME make sure lhs is sequentially stored
// FIXME not very good if rhs is real and lhs complex while alpha is real too
const Index cols = dest.cols();
for (Index j=0; j<cols; ++j)
dest.col(j) += (alpha * prod.rhs().coeff(j)) * prod.lhs();
}
};
template<> struct outer_product_selector<RowMajor> {
template<typename ProductType, typename Dest>
static EIGEN_DONT_INLINE void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha) {
typedef typename Dest::Index Index;
// FIXME make sure rhs is sequentially stored
// FIXME not very good if lhs is real and rhs complex while alpha is real too
const Index rows = dest.rows();
for (Index i=0; i<rows; ++i)
dest.row(i) += (alpha * prod.lhs().coeff(i)) * prod.rhs();
}
};
} // end namespace internal
/***********************************************************************
* Implementation of General Matrix Vector Product
***********************************************************************/
@@ -306,60 +141,13 @@ template<> struct outer_product_selector<RowMajor> {
*/
namespace internal {
template<typename Lhs, typename Rhs>
struct traits<GeneralProduct<Lhs,Rhs,GemvProduct> >
: traits<ProductBase<GeneralProduct<Lhs,Rhs,GemvProduct>, Lhs, Rhs> >
{};
template<int Side, int StorageOrder, bool BlasCompatible>
struct gemv_selector;
struct gemv_dense_selector;
} // end namespace internal
template<typename Lhs, typename Rhs>
class GeneralProduct<Lhs, Rhs, GemvProduct>
: public ProductBase<GeneralProduct<Lhs,Rhs,GemvProduct>, Lhs, Rhs>
{
public:
EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct)
typedef typename Lhs::Scalar LhsScalar;
typedef typename Rhs::Scalar RhsScalar;
GeneralProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
{
// EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::Scalar, typename Rhs::Scalar>::value),
// YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
}
enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight };
typedef typename internal::conditional<int(Side)==OnTheRight,_LhsNested,_RhsNested>::type MatrixType;
template<typename Dest> void scaleAndAddTo(Dest& dst, Scalar alpha) const
{
eigen_assert(m_lhs.rows() == dst.rows() && m_rhs.cols() == dst.cols());
internal::gemv_selector<Side,(int(MatrixType::Flags)&RowMajorBit) ? RowMajor : ColMajor,
bool(internal::blas_traits<MatrixType>::HasUsableDirectAccess)>::run(*this, dst, alpha);
}
};
namespace internal {
// The vector is on the left => transposition
template<int StorageOrder, bool BlasCompatible>
struct gemv_selector<OnTheLeft,StorageOrder,BlasCompatible>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
{
Transpose<Dest> destT(dest);
enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor };
gemv_selector<OnTheRight,OtherStorageOrder,BlasCompatible>
::run(GeneralProduct<Transpose<const typename ProductType::_RhsNested>,Transpose<const typename ProductType::_LhsNested>, GemvProduct>
(prod.rhs().transpose(), prod.lhs().transpose()), destT, alpha);
}
};
template<typename Scalar,int Size,int MaxSize,bool Cond> struct gemv_static_vector_if;
template<typename Scalar,int Size,int MaxSize>
@@ -377,126 +165,161 @@ struct gemv_static_vector_if<Scalar,Size,Dynamic,true>
template<typename Scalar,int Size,int MaxSize>
struct gemv_static_vector_if<Scalar,Size,MaxSize,true>
{
#if EIGEN_ALIGN_STATICALLY
internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize),0> m_data;
EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; }
#else
// Some architectures cannot align on the stack,
// => let's manually enforce alignment by allocating more data and return the address of the first aligned element.
enum {
ForceAlignment = internal::packet_traits<Scalar>::Vectorizable,
PacketSize = internal::packet_traits<Scalar>::size
};
internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize)+(ForceAlignment?PacketSize:0),0> m_data;
#if EIGEN_MAX_STATIC_ALIGN_BYTES!=0
internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize),0,EIGEN_PLAIN_ENUM_MIN(AlignedMax,PacketSize)> m_data;
EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; }
#else
// Some architectures cannot align on the stack,
// => let's manually enforce alignment by allocating more data and return the address of the first aligned element.
internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize)+(ForceAlignment?EIGEN_MAX_ALIGN_BYTES:0),0> m_data;
EIGEN_STRONG_INLINE Scalar* data() {
return ForceAlignment
? reinterpret_cast<Scalar*>((reinterpret_cast<size_t>(m_data.array) & ~(size_t(15))) + 16)
? reinterpret_cast<Scalar*>((internal::UIntPtr(m_data.array) & ~(std::size_t(EIGEN_MAX_ALIGN_BYTES-1))) + EIGEN_MAX_ALIGN_BYTES)
: m_data.array;
}
#endif
};
template<> struct gemv_selector<OnTheRight,ColMajor,true>
// The vector is on the left => transposition
template<int StorageOrder, bool BlasCompatible>
struct gemv_dense_selector<OnTheLeft,StorageOrder,BlasCompatible>
{
template<typename ProductType, typename Dest>
static inline void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
template<typename Lhs, typename Rhs, typename Dest>
static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename ProductType::Index Index;
typedef typename ProductType::LhsScalar LhsScalar;
typedef typename ProductType::RhsScalar RhsScalar;
typedef typename ProductType::Scalar ResScalar;
typedef typename ProductType::RealScalar RealScalar;
typedef typename ProductType::ActualLhsType ActualLhsType;
typedef typename ProductType::ActualRhsType ActualRhsType;
typedef typename ProductType::LhsBlasTraits LhsBlasTraits;
typedef typename ProductType::RhsBlasTraits RhsBlasTraits;
typedef Map<Matrix<ResScalar,Dynamic,1>, Aligned> MappedDest;
Transpose<Dest> destT(dest);
enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor };
gemv_dense_selector<OnTheRight,OtherStorageOrder,BlasCompatible>
::run(rhs.transpose(), lhs.transpose(), destT, alpha);
}
};
ActualLhsType actualLhs = LhsBlasTraits::extract(prod.lhs());
ActualRhsType actualRhs = RhsBlasTraits::extract(prod.rhs());
template<> struct gemv_dense_selector<OnTheRight,ColMajor,true>
{
template<typename Lhs, typename Rhs, typename Dest>
static inline void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename Lhs::Scalar LhsScalar;
typedef typename Rhs::Scalar RhsScalar;
typedef typename Dest::Scalar ResScalar;
typedef typename Dest::RealScalar RealScalar;
typedef internal::blas_traits<Lhs> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
typedef Map<Matrix<ResScalar,Dynamic,1>, EIGEN_PLAIN_ENUM_MIN(AlignedMax,internal::packet_traits<ResScalar>::size)> MappedDest;
ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs())
* RhsBlasTraits::extractScalarFactor(prod.rhs());
ActualLhsType actualLhs = LhsBlasTraits::extract(lhs);
ActualRhsType actualRhs = RhsBlasTraits::extract(rhs);
ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(lhs)
* RhsBlasTraits::extractScalarFactor(rhs);
// make sure Dest is a compile-time vector type (bug 1166)
typedef typename conditional<Dest::IsVectorAtCompileTime, Dest, typename Dest::ColXpr>::type ActualDest;
enum {
// FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
// on, the other hand it is good for the cache to pack the vector anyways...
EvalToDestAtCompileTime = Dest::InnerStrideAtCompileTime==1,
EvalToDestAtCompileTime = (ActualDest::InnerStrideAtCompileTime==1),
ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex),
MightCannotUseDest = (Dest::InnerStrideAtCompileTime!=1) || ComplexByReal
MightCannotUseDest = (!EvalToDestAtCompileTime) || ComplexByReal
};
gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
bool alphaIsCompatible = (!ComplexByReal) || (imag(actualAlpha)==RealScalar(0));
bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper;
typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper;
RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha);
ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
evalToDest ? dest.data() : static_dest.data());
if(!evalToDest)
if(!MightCannotUseDest)
{
#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
int size = dest.size();
EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#endif
if(!alphaIsCompatible)
{
MappedDest(actualDestPtr, dest.size()).setZero();
compatibleAlpha = RhsScalar(1);
}
else
MappedDest(actualDestPtr, dest.size()) = dest;
// shortcut if we are sure to be able to use dest directly,
// this ease the compiler to generate cleaner and more optimzized code for most common cases
general_matrix_vector_product
<Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
actualLhs.rows(), actualLhs.cols(),
LhsMapper(actualLhs.data(), actualLhs.outerStride()),
RhsMapper(actualRhs.data(), actualRhs.innerStride()),
dest.data(), 1,
compatibleAlpha);
}
general_matrix_vector_product
<Index,LhsScalar,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsBlasTraits::NeedToConjugate>::run(
actualLhs.rows(), actualLhs.cols(),
actualLhs.data(), actualLhs.outerStride(),
actualRhs.data(), actualRhs.innerStride(),
actualDestPtr, 1,
compatibleAlpha);
if (!evalToDest)
else
{
if(!alphaIsCompatible)
dest += actualAlpha * MappedDest(actualDestPtr, dest.size());
else
dest = MappedDest(actualDestPtr, dest.size());
gemv_static_vector_if<ResScalar,ActualDest::SizeAtCompileTime,ActualDest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
const bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0));
const bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
evalToDest ? dest.data() : static_dest.data());
if(!evalToDest)
{
#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
Index size = dest.size();
EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#endif
if(!alphaIsCompatible)
{
MappedDest(actualDestPtr, dest.size()).setZero();
compatibleAlpha = RhsScalar(1);
}
else
MappedDest(actualDestPtr, dest.size()) = dest;
}
general_matrix_vector_product
<Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
actualLhs.rows(), actualLhs.cols(),
LhsMapper(actualLhs.data(), actualLhs.outerStride()),
RhsMapper(actualRhs.data(), actualRhs.innerStride()),
actualDestPtr, 1,
compatibleAlpha);
if (!evalToDest)
{
if(!alphaIsCompatible)
dest.matrix() += actualAlpha * MappedDest(actualDestPtr, dest.size());
else
dest = MappedDest(actualDestPtr, dest.size());
}
}
}
};
template<> struct gemv_selector<OnTheRight,RowMajor,true>
template<> struct gemv_dense_selector<OnTheRight,RowMajor,true>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
template<typename Lhs, typename Rhs, typename Dest>
static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename ProductType::LhsScalar LhsScalar;
typedef typename ProductType::RhsScalar RhsScalar;
typedef typename ProductType::Scalar ResScalar;
typedef typename ProductType::Index Index;
typedef typename ProductType::ActualLhsType ActualLhsType;
typedef typename ProductType::ActualRhsType ActualRhsType;
typedef typename ProductType::_ActualRhsType _ActualRhsType;
typedef typename ProductType::LhsBlasTraits LhsBlasTraits;
typedef typename ProductType::RhsBlasTraits RhsBlasTraits;
typedef typename Lhs::Scalar LhsScalar;
typedef typename Rhs::Scalar RhsScalar;
typedef typename Dest::Scalar ResScalar;
typedef internal::blas_traits<Lhs> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(prod.lhs());
typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(prod.rhs());
typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(lhs);
typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(rhs);
ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs())
* RhsBlasTraits::extractScalarFactor(prod.rhs());
ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(lhs)
* RhsBlasTraits::extractScalarFactor(rhs);
enum {
// FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
// on, the other hand it is good for the cache to pack the vector anyways...
DirectlyUseRhs = _ActualRhsType::InnerStrideAtCompileTime==1
DirectlyUseRhs = ActualRhsTypeCleaned::InnerStrideAtCompileTime==1
};
gemv_static_vector_if<RhsScalar,_ActualRhsType::SizeAtCompileTime,_ActualRhsType::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs;
gemv_static_vector_if<RhsScalar,ActualRhsTypeCleaned::SizeAtCompileTime,ActualRhsTypeCleaned::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs;
ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(),
DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data());
@@ -504,45 +327,48 @@ template<> struct gemv_selector<OnTheRight,RowMajor,true>
if(!DirectlyUseRhs)
{
#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
int size = actualRhs.size();
Index size = actualRhs.size();
EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#endif
Map<typename _ActualRhsType::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
}
typedef const_blas_data_mapper<LhsScalar,Index,RowMajor> LhsMapper;
typedef const_blas_data_mapper<RhsScalar,Index,ColMajor> RhsMapper;
general_matrix_vector_product
<Index,LhsScalar,RowMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsBlasTraits::NeedToConjugate>::run(
<Index,LhsScalar,LhsMapper,RowMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
actualLhs.rows(), actualLhs.cols(),
actualLhs.data(), actualLhs.outerStride(),
actualRhsPtr, 1,
dest.data(), dest.innerStride(),
LhsMapper(actualLhs.data(), actualLhs.outerStride()),
RhsMapper(actualRhsPtr, 1),
dest.data(), dest.col(0).innerStride(), //NOTE if dest is not a vector at compile-time, then dest.innerStride() might be wrong. (bug 1166)
actualAlpha);
}
};
template<> struct gemv_selector<OnTheRight,ColMajor,false>
template<> struct gemv_dense_selector<OnTheRight,ColMajor,false>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
template<typename Lhs, typename Rhs, typename Dest>
static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename Dest::Index Index;
// TODO makes sure dest is sequentially stored in memory, otherwise use a temp
const Index size = prod.rhs().rows();
EIGEN_STATIC_ASSERT((!nested_eval<Lhs,1>::Evaluate),EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE);
// TODO if rhs is large enough it might be beneficial to make sure that dest is sequentially stored in memory, otherwise use a temp
typename nested_eval<Rhs,1>::type actual_rhs(rhs);
const Index size = rhs.rows();
for(Index k=0; k<size; ++k)
dest += (alpha*prod.rhs().coeff(k)) * prod.lhs().col(k);
dest += (alpha*actual_rhs.coeff(k)) * lhs.col(k);
}
};
template<> struct gemv_selector<OnTheRight,RowMajor,false>
template<> struct gemv_dense_selector<OnTheRight,RowMajor,false>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
template<typename Lhs, typename Rhs, typename Dest>
static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename Dest::Index Index;
// TODO makes sure rhs is sequentially stored in memory, otherwise use a temp
const Index rows = prod.rows();
EIGEN_STATIC_ASSERT((!nested_eval<Lhs,1>::Evaluate),EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE);
typename nested_eval<Rhs,Lhs::RowsAtCompileTime>::type actual_rhs(rhs);
const Index rows = dest.rows();
for(Index i=0; i<rows; ++i)
dest.coeffRef(i) += alpha * (prod.lhs().row(i).cwiseProduct(prod.rhs().transpose())).sum();
dest.coeffRef(i) += alpha * (lhs.row(i).cwiseProduct(actual_rhs.transpose())).sum();
}
};
@@ -560,7 +386,7 @@ template<> struct gemv_selector<OnTheRight,RowMajor,false>
*/
template<typename Derived>
template<typename OtherDerived>
inline const typename ProductReturnType<Derived, OtherDerived>::Type
inline const Product<Derived, OtherDerived>
MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
{
// A note regarding the function declaration: In MSVC, this function will sometimes
@@ -585,7 +411,8 @@ MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
#ifdef EIGEN_DEBUG_PRODUCT
internal::product_type<Derived,OtherDerived>::debug();
#endif
return typename ProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
return Product<Derived, OtherDerived>(derived(), other.derived());
}
/** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation.
@@ -601,7 +428,7 @@ MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
*/
template<typename Derived>
template<typename OtherDerived>
const typename LazyProductReturnType<Derived,OtherDerived>::Type
const Product<Derived,OtherDerived,LazyProduct>
MatrixBase<Derived>::lazyProduct(const MatrixBase<OtherDerived> &other) const
{
enum {
@@ -620,7 +447,7 @@ MatrixBase<Derived>::lazyProduct(const MatrixBase<OtherDerived> &other) const
INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
return typename LazyProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
return Product<Derived,OtherDerived,LazyProduct>(derived(), other.derived());
}
} // end namespace Eigen

View File

@@ -4,24 +4,9 @@
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_GENERIC_PACKET_MATH_H
#define EIGEN_GENERIC_PACKET_MATH_H
@@ -57,21 +42,28 @@ namespace internal {
struct default_packet_traits
{
enum {
HasHalfPacket = 0,
HasAdd = 1,
HasSub = 1,
HasMul = 1,
HasNegate = 1,
HasAbs = 1,
HasArg = 0,
HasAbs2 = 1,
HasMin = 1,
HasMax = 1,
HasConj = 1,
HasSetLinear = 1,
HasBlend = 0,
HasDiv = 0,
HasSqrt = 0,
HasRsqrt = 0,
HasExp = 0,
HasLog = 0,
HasLog1p = 0,
HasLog10 = 0,
HasPow = 0,
HasSin = 0,
@@ -79,17 +71,37 @@ struct default_packet_traits
HasTan = 0,
HasASin = 0,
HasACos = 0,
HasATan = 0
HasATan = 0,
HasSinh = 0,
HasCosh = 0,
HasTanh = 0,
HasLGamma = 0,
HasDiGamma = 0,
HasZeta = 0,
HasPolygamma = 0,
HasErf = 0,
HasErfc = 0,
HasIGamma = 0,
HasIGammac = 0,
HasBetaInc = 0,
HasRound = 0,
HasFloor = 0,
HasCeil = 0,
HasSign = 0
};
};
template<typename T> struct packet_traits : default_packet_traits
{
typedef T type;
typedef T half;
enum {
Vectorizable = 0,
size = 1,
AlignedOnScalar = 0
AlignedOnScalar = 0,
HasHalfPacket = 0
};
enum {
HasAdd = 0,
@@ -105,132 +117,245 @@ template<typename T> struct packet_traits : default_packet_traits
};
};
template<typename T> struct packet_traits<const T> : packet_traits<T> { };
template <typename Src, typename Tgt> struct type_casting_traits {
enum {
VectorizedCast = 0,
SrcCoeffRatio = 1,
TgtCoeffRatio = 1
};
};
/** \internal \returns static_cast<TgtType>(a) (coeff-wise) */
template <typename SrcPacket, typename TgtPacket>
EIGEN_DEVICE_FUNC inline TgtPacket
pcast(const SrcPacket& a) {
return static_cast<TgtPacket>(a);
}
template <typename SrcPacket, typename TgtPacket>
EIGEN_DEVICE_FUNC inline TgtPacket
pcast(const SrcPacket& a, const SrcPacket& /*b*/) {
return static_cast<TgtPacket>(a);
}
template <typename SrcPacket, typename TgtPacket>
EIGEN_DEVICE_FUNC inline TgtPacket
pcast(const SrcPacket& a, const SrcPacket& /*b*/, const SrcPacket& /*c*/, const SrcPacket& /*d*/) {
return static_cast<TgtPacket>(a);
}
/** \internal \returns a + b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
padd(const Packet& a,
const Packet& b) { return a+b; }
/** \internal \returns a - b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
psub(const Packet& a,
const Packet& b) { return a-b; }
/** \internal \returns -a (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pnegate(const Packet& a) { return -a; }
/** \internal \returns conj(a) (coeff-wise) */
template<typename Packet> inline Packet
pconj(const Packet& a) { return conj(a); }
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pconj(const Packet& a) { return numext::conj(a); }
/** \internal \returns a * b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pmul(const Packet& a,
const Packet& b) { return a*b; }
/** \internal \returns a / b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pdiv(const Packet& a,
const Packet& b) { return a/b; }
/** \internal \returns the min of \a a and \a b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pmin(const Packet& a,
const Packet& b) { using std::min; return (min)(a, b); }
const Packet& b) { return numext::mini(a, b); }
/** \internal \returns the max of \a a and \a b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pmax(const Packet& a,
const Packet& b) { using std::max; return (max)(a, b); }
const Packet& b) { return numext::maxi(a, b); }
/** \internal \returns the absolute value of \a a */
template<typename Packet> inline Packet
pabs(const Packet& a) { return abs(a); }
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pabs(const Packet& a) { using std::abs; return abs(a); }
/** \internal \returns the phase angle of \a a */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
parg(const Packet& a) { using numext::arg; return arg(a); }
/** \internal \returns the bitwise and of \a a and \a b */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pand(const Packet& a, const Packet& b) { return a & b; }
/** \internal \returns the bitwise or of \a a and \a b */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
por(const Packet& a, const Packet& b) { return a | b; }
/** \internal \returns the bitwise xor of \a a and \a b */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pxor(const Packet& a, const Packet& b) { return a ^ b; }
/** \internal \returns the bitwise andnot of \a a and \a b */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pandnot(const Packet& a, const Packet& b) { return a & (!b); }
/** \internal \returns a packet version of \a *from, from must be 16 bytes aligned */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pload(const typename unpacket_traits<Packet>::type* from) { return *from; }
/** \internal \returns a packet version of \a *from, (un-aligned load) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
ploadu(const typename unpacket_traits<Packet>::type* from) { return *from; }
/** \internal \returns a packet with elements of \a *from duplicated, e.g.: (from[0],from[0],from[1],from[1]) */
template<typename Packet> inline Packet
ploaddup(const typename unpacket_traits<Packet>::type* from) { return *from; }
/** \internal \returns a packet with constant coefficients \a a, e.g.: (a,a,a,a) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pset1(const typename unpacket_traits<Packet>::type& a) { return a; }
/** \internal \returns a packet with constant coefficients \a a[0], e.g.: (a[0],a[0],a[0],a[0]) */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pload1(const typename unpacket_traits<Packet>::type *a) { return pset1<Packet>(*a); }
/** \internal \returns a packet with elements of \a *from duplicated.
* For instance, for a packet of 8 elements, 4 scalars will be read from \a *from and
* duplicated to form: {from[0],from[0],from[1],from[1],from[2],from[2],from[3],from[3]}
* Currently, this function is only used for scalar * complex products.
*/
template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet
ploaddup(const typename unpacket_traits<Packet>::type* from) { return *from; }
/** \internal \returns a packet with elements of \a *from quadrupled.
* For instance, for a packet of 8 elements, 2 scalars will be read from \a *from and
* replicated to form: {from[0],from[0],from[0],from[0],from[1],from[1],from[1],from[1]}
* Currently, this function is only used in matrix products.
* For packet-size smaller or equal to 4, this function is equivalent to pload1
*/
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
ploadquad(const typename unpacket_traits<Packet>::type* from)
{ return pload1<Packet>(from); }
/** \internal equivalent to
* \code
* a0 = pload1(a+0);
* a1 = pload1(a+1);
* a2 = pload1(a+2);
* a3 = pload1(a+3);
* \endcode
* \sa pset1, pload1, ploaddup, pbroadcast2
*/
template<typename Packet> EIGEN_DEVICE_FUNC
inline void pbroadcast4(const typename unpacket_traits<Packet>::type *a,
Packet& a0, Packet& a1, Packet& a2, Packet& a3)
{
a0 = pload1<Packet>(a+0);
a1 = pload1<Packet>(a+1);
a2 = pload1<Packet>(a+2);
a3 = pload1<Packet>(a+3);
}
/** \internal equivalent to
* \code
* a0 = pload1(a+0);
* a1 = pload1(a+1);
* \endcode
* \sa pset1, pload1, ploaddup, pbroadcast4
*/
template<typename Packet> EIGEN_DEVICE_FUNC
inline void pbroadcast2(const typename unpacket_traits<Packet>::type *a,
Packet& a0, Packet& a1)
{
a0 = pload1<Packet>(a+0);
a1 = pload1<Packet>(a+1);
}
/** \internal \brief Returns a packet with coefficients (a,a+1,...,a+packet_size-1). */
template<typename Scalar> inline typename packet_traits<Scalar>::type
plset(const Scalar& a) { return a; }
template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet
plset(const typename unpacket_traits<Packet>::type& a) { return a; }
/** \internal copy the packet \a from to \a *to, \a to must be 16 bytes aligned */
template<typename Scalar, typename Packet> inline void pstore(Scalar* to, const Packet& from)
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstore(Scalar* to, const Packet& from)
{ (*to) = from; }
/** \internal copy the packet \a from to \a *to, (un-aligned store) */
template<typename Scalar, typename Packet> inline void pstoreu(Scalar* to, const Packet& from)
{ (*to) = from; }
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstoreu(Scalar* to, const Packet& from)
{ (*to) = from; }
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline Packet pgather(const Scalar* from, Index /*stride*/)
{ return ploadu<Packet>(from); }
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pscatter(Scalar* to, const Packet& from, Index /*stride*/)
{ pstore(to, from); }
/** \internal tries to do cache prefetching of \a addr */
template<typename Scalar> inline void prefetch(const Scalar* addr)
template<typename Scalar> EIGEN_DEVICE_FUNC inline void prefetch(const Scalar* addr)
{
#if !defined(_MSC_VER)
__builtin_prefetch(addr);
#ifdef __CUDA_ARCH__
#if defined(__LP64__)
// 64-bit pointer operand constraint for inlined asm
asm(" prefetch.L1 [ %1 ];" : "=l"(addr) : "l"(addr));
#else
// 32-bit pointer operand constraint for inlined asm
asm(" prefetch.L1 [ %1 ];" : "=r"(addr) : "r"(addr));
#endif
#elif (!EIGEN_COMP_MSVC) && (EIGEN_COMP_GNUC || EIGEN_COMP_CLANG || EIGEN_COMP_ICC)
__builtin_prefetch(addr);
#endif
}
/** \internal \returns the first element of a packet */
template<typename Packet> inline typename unpacket_traits<Packet>::type pfirst(const Packet& a)
template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type pfirst(const Packet& a)
{ return a; }
/** \internal \returns a packet where the element i contains the sum of the packet of \a vec[i] */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
preduxp(const Packet* vecs) { return vecs[0]; }
/** \internal \returns the sum of the elements of \a a*/
template<typename Packet> inline typename unpacket_traits<Packet>::type predux(const Packet& a)
template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux(const Packet& a)
{ return a; }
/** \internal \returns the sum of the elements of \a a by block of 4 elements.
* For a packet {a0, a1, a2, a3, a4, a5, a6, a7}, it returns a half packet {a0+a4, a1+a5, a2+a6, a3+a7}
* For packet-size smaller or equal to 4, this boils down to a noop.
*/
template<typename Packet> EIGEN_DEVICE_FUNC inline
typename conditional<(unpacket_traits<Packet>::size%8)==0,typename unpacket_traits<Packet>::half,Packet>::type
predux_downto4(const Packet& a)
{ return a; }
/** \internal \returns the product of the elements of \a a*/
template<typename Packet> inline typename unpacket_traits<Packet>::type predux_mul(const Packet& a)
template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_mul(const Packet& a)
{ return a; }
/** \internal \returns the min of the elements of \a a*/
template<typename Packet> inline typename unpacket_traits<Packet>::type predux_min(const Packet& a)
template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(const Packet& a)
{ return a; }
/** \internal \returns the max of the elements of \a a*/
template<typename Packet> inline typename unpacket_traits<Packet>::type predux_max(const Packet& a)
template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_max(const Packet& a)
{ return a; }
/** \internal \returns the reversed elements of \a a*/
template<typename Packet> inline Packet preverse(const Packet& a)
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet preverse(const Packet& a)
{ return a; }
/** \internal \returns \a a with real and imaginary part flipped (for complex type only) */
template<typename Packet> inline Packet pcplxflip(const Packet& a)
{ return Packet(imag(a),real(a)); }
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet pcplxflip(const Packet& a)
{
// FIXME: uncomment the following in case we drop the internal imag and real functions.
// using std::imag;
// using std::real;
return Packet(imag(a),real(a));
}
/**************************
* Special math functions
@@ -238,35 +363,77 @@ template<typename Packet> inline Packet pcplxflip(const Packet& a)
/** \internal \returns the sine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet psin(const Packet& a) { return sin(a); }
Packet psin(const Packet& a) { using std::sin; return sin(a); }
/** \internal \returns the cosine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pcos(const Packet& a) { return cos(a); }
Packet pcos(const Packet& a) { using std::cos; return cos(a); }
/** \internal \returns the tan of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet ptan(const Packet& a) { return tan(a); }
Packet ptan(const Packet& a) { using std::tan; return tan(a); }
/** \internal \returns the arc sine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pasin(const Packet& a) { return asin(a); }
Packet pasin(const Packet& a) { using std::asin; return asin(a); }
/** \internal \returns the arc cosine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pacos(const Packet& a) { return acos(a); }
Packet pacos(const Packet& a) { using std::acos; return acos(a); }
/** \internal \returns the arc tangent of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet patan(const Packet& a) { using std::atan; return atan(a); }
/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet psinh(const Packet& a) { using std::sinh; return sinh(a); }
/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pcosh(const Packet& a) { using std::cosh; return cosh(a); }
/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet ptanh(const Packet& a) { using std::tanh; return tanh(a); }
/** \internal \returns the exp of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pexp(const Packet& a) { return exp(a); }
Packet pexp(const Packet& a) { using std::exp; return exp(a); }
/** \internal \returns the log of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plog(const Packet& a) { return log(a); }
Packet plog(const Packet& a) { using std::log; return log(a); }
/** \internal \returns the log1p of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plog1p(const Packet& a) { return numext::log1p(a); }
/** \internal \returns the log10 of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plog10(const Packet& a) { using std::log10; return log10(a); }
/** \internal \returns the square-root of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet psqrt(const Packet& a) { return sqrt(a); }
Packet psqrt(const Packet& a) { using std::sqrt; return sqrt(a); }
/** \internal \returns the reciprocal square-root of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet prsqrt(const Packet& a) {
return pdiv(pset1<Packet>(1), psqrt(a));
}
/** \internal \returns the rounded value of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pround(const Packet& a) { using numext::round; return round(a); }
/** \internal \returns the floor of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pfloor(const Packet& a) { using numext::floor; return floor(a); }
/** \internal \returns the ceil of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pceil(const Packet& a) { using numext::ceil; return ceil(a); }
/***************************************************************************
* The following functions might not have to be overwritten for vectorized types
@@ -281,34 +448,45 @@ inline void pstore1(typename unpacket_traits<Packet>::type* to, const typename u
}
/** \internal \returns a * b + c (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pmadd(const Packet& a,
const Packet& b,
const Packet& c)
{ return padd(pmul(a, b),c); }
/** \internal \returns a packet version of \a *from.
* If LoadMode equals #Aligned, \a from must be 16 bytes aligned */
template<typename Packet, int LoadMode>
inline Packet ploadt(const typename unpacket_traits<Packet>::type* from)
* The pointer \a from must be aligned on a \a Alignment bytes boundary. */
template<typename Packet, int Alignment>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt(const typename unpacket_traits<Packet>::type* from)
{
if(LoadMode == Aligned)
if(Alignment >= unpacket_traits<Packet>::alignment)
return pload<Packet>(from);
else
return ploadu<Packet>(from);
}
/** \internal copy the packet \a from to \a *to.
* If StoreMode equals #Aligned, \a to must be 16 bytes aligned */
template<typename Scalar, typename Packet, int LoadMode>
inline void pstoret(Scalar* to, const Packet& from)
* The pointer \a from must be aligned on a \a Alignment bytes boundary. */
template<typename Scalar, typename Packet, int Alignment>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret(Scalar* to, const Packet& from)
{
if(LoadMode == Aligned)
if(Alignment >= unpacket_traits<Packet>::alignment)
pstore(to, from);
else
pstoreu(to, from);
}
/** \internal \returns a packet version of \a *from.
* Unlike ploadt, ploadt_ro takes advantage of the read-only memory path on the
* hardware if available to speedup the loading of data that won't be modified
* by the current computation.
*/
template<typename Packet, int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt_ro(const typename unpacket_traits<Packet>::type* from)
{
return ploadt<Packet, LoadMode>(from);
}
/** \internal default implementation of palign() allowing partial specialization */
template<int Offset,typename PacketType>
struct palign_impl
@@ -317,8 +495,21 @@ struct palign_impl
static inline void run(PacketType&, const PacketType&) {}
};
/** \internal update \a first using the concatenation of the \a Offset last elements
* of \a first and packet_size minus \a Offset first elements of \a second */
/** \internal update \a first using the concatenation of the packet_size minus \a Offset last elements
* of \a first and \a Offset first elements of \a second.
*
* This function is currently only used to optimize matrix-vector products on unligned matrices.
* It takes 2 packets that represent a contiguous memory array, and returns a packet starting
* at the position \a Offset. For instance, for packets of 4 elements, we have:
* Input:
* - first = {f0,f1,f2,f3}
* - second = {s0,s1,s2,s3}
* Output:
* - if Offset==0 then {f0,f1,f2,f3}
* - if Offset==1 then {f1,f2,f3,s0}
* - if Offset==2 then {f2,f3,s0,s1}
* - if Offset==3 then {f3,s0,s1,s3}
*/
template<int Offset,typename PacketType>
inline void palign(PacketType& first, const PacketType& second)
{
@@ -329,15 +520,74 @@ inline void palign(PacketType& first, const PacketType& second)
* Fast complex products (GCC generates a function call which is very slow)
***************************************************************************/
// Eigen+CUDA does not support complexes.
#ifndef __CUDACC__
template<> inline std::complex<float> pmul(const std::complex<float>& a, const std::complex<float>& b)
{ return std::complex<float>(real(a)*real(b) - imag(a)*imag(b), imag(a)*real(b) + real(a)*imag(b)); }
template<> inline std::complex<double> pmul(const std::complex<double>& a, const std::complex<double>& b)
{ return std::complex<double>(real(a)*real(b) - imag(a)*imag(b), imag(a)*real(b) + real(a)*imag(b)); }
#endif
/***************************************************************************
* PacketBlock, that is a collection of N packets where the number of words
* in the packet is a multiple of N.
***************************************************************************/
template <typename Packet,int N=unpacket_traits<Packet>::size> struct PacketBlock {
Packet packet[N];
};
template<typename Packet> EIGEN_DEVICE_FUNC inline void
ptranspose(PacketBlock<Packet,1>& /*kernel*/) {
// Nothing to do in the scalar case, i.e. a 1x1 matrix.
}
/***************************************************************************
* Selector, i.e. vector of N boolean values used to select (i.e. blend)
* words from 2 packets.
***************************************************************************/
template <size_t N> struct Selector {
bool select[N];
};
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pblend(const Selector<unpacket_traits<Packet>::size>& ifPacket, const Packet& thenPacket, const Packet& elsePacket) {
return ifPacket.select[0] ? thenPacket : elsePacket;
}
/** \internal \returns \a a with the first coefficient replaced by the scalar b */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pinsertfirst(const Packet& a, typename unpacket_traits<Packet>::type b)
{
// Default implementation based on pblend.
// It must be specialized for higher performance.
Selector<unpacket_traits<Packet>::size> mask;
mask.select[0] = true;
// This for loop should be optimized away by the compiler.
for(Index i=1; i<unpacket_traits<Packet>::size; ++i)
mask.select[i] = false;
return pblend(mask, pset1<Packet>(b), a);
}
/** \internal \returns \a a with the last coefficient replaced by the scalar b */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pinsertlast(const Packet& a, typename unpacket_traits<Packet>::type b)
{
// Default implementation based on pblend.
// It must be specialized for higher performance.
Selector<unpacket_traits<Packet>::size> mask;
// This for loop should be optimized away by the compiler.
for(Index i=0; i<unpacket_traits<Packet>::size-1; ++i)
mask.select[i] = false;
mask.select[unpacket_traits<Packet>::size-1] = true;
return pblend(mask, pset1<Packet>(b), a);
}
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_GENERIC_PACKET_MATH_H

View File

@@ -1,38 +1,40 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2010-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_GLOBAL_FUNCTIONS_H
#define EIGEN_GLOBAL_FUNCTIONS_H
#define EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(NAME,FUNCTOR) \
#ifdef EIGEN_PARSED_BY_DOXYGEN
#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR,DOC_OP,DOC_DETAILS) \
/** \returns an expression of the coefficient-wise DOC_OP of \a x
DOC_DETAILS
\sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_##NAME">Math functions</a>, class CwiseUnaryOp
*/ \
template<typename Derived> \
inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> \
NAME(const Eigen::ArrayBase<Derived>& x) { \
return x.derived(); \
NAME(const Eigen::ArrayBase<Derived>& x);
#else
#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR,DOC_OP,DOC_DETAILS) \
template<typename Derived> \
inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> \
(NAME)(const Eigen::ArrayBase<Derived>& x) { \
return Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived>(x.derived()); \
}
#endif // EIGEN_PARSED_BY_DOXYGEN
#define EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(NAME,FUNCTOR) \
\
template<typename Derived> \
@@ -45,74 +47,141 @@
{ \
static inline typename NAME##_retval<ArrayBase<Derived> >::type run(const Eigen::ArrayBase<Derived>& x) \
{ \
return x.derived(); \
return typename NAME##_retval<ArrayBase<Derived> >::type(x.derived()); \
} \
};
namespace std
namespace Eigen
{
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(real,scalar_real_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(imag,scalar_imag_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(sin,scalar_sin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(cos,scalar_cos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(asin,scalar_asin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(acos,scalar_acos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(tan,scalar_tan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(exp,scalar_exp_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(log,scalar_log_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(abs,scalar_abs_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(sqrt,scalar_sqrt_op)
template<typename Derived>
inline const Eigen::CwiseUnaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar>, const Derived>
pow(const Eigen::ArrayBase<Derived>& x, const typename Derived::Scalar& exponent) {
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real,scalar_real_op,real part,\sa ArrayBase::real)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(imag,scalar_imag_op,imaginary part,\sa ArrayBase::imag)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(conj,scalar_conjugate_op,complex conjugate,\sa ArrayBase::conjugate)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(inverse,scalar_inverse_op,inverse,\sa ArrayBase::inverse)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sin,scalar_sin_op,sine,\sa ArrayBase::sin)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cos,scalar_cos_op,cosine,\sa ArrayBase::cos)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tan,scalar_tan_op,tangent,\sa ArrayBase::tan)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atan,scalar_atan_op,arc-tangent,\sa ArrayBase::atan)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asin,scalar_asin_op,arc-sine,\sa ArrayBase::asin)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acos,scalar_acos_op,arc-consine,\sa ArrayBase::acos)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sinh,scalar_sinh_op,hyperbolic sine,\sa ArrayBase::sinh)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cosh,scalar_cosh_op,hyperbolic cosine,\sa ArrayBase::cosh)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tanh,scalar_tanh_op,hyperbolic tangent,\sa ArrayBase::tanh)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(lgamma,scalar_lgamma_op,natural logarithm of the gamma function,\sa ArrayBase::lgamma)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(digamma,scalar_digamma_op,derivative of lgamma,\sa ArrayBase::digamma)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erf,scalar_erf_op,error function,\sa ArrayBase::erf)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erfc,scalar_erfc_op,complement error function,\sa ArrayBase::erfc)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(exp,scalar_exp_op,exponential,\sa ArrayBase::exp)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log,scalar_log_op,natural logarithm,\sa Eigen::log10 DOXCOMMA ArrayBase::log)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log1p,scalar_log1p_op,natural logarithm of 1 plus the value,\sa ArrayBase::log1p)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log10,scalar_log10_op,base 10 logarithm,\sa Eigen::log DOXCOMMA ArrayBase::log)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs,scalar_abs_op,absolute value,\sa ArrayBase::abs DOXCOMMA MatrixBase::cwiseAbs)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs2,scalar_abs2_op,squared absolute value,\sa ArrayBase::abs2 DOXCOMMA MatrixBase::cwiseAbs2)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(arg,scalar_arg_op,complex argument,\sa ArrayBase::arg)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sqrt,scalar_sqrt_op,square root,\sa ArrayBase::sqrt DOXCOMMA MatrixBase::cwiseSqrt)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(rsqrt,scalar_rsqrt_op,reciprocal square root,\sa ArrayBase::rsqrt)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(square,scalar_square_op,square (power 2),\sa Eigen::abs2 DOXCOMMA Eigen::pow DOXCOMMA ArrayBase::square)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cube,scalar_cube_op,cube (power 3),\sa Eigen::pow DOXCOMMA ArrayBase::cube)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(round,scalar_round_op,nearest integer,\sa Eigen::floor DOXCOMMA Eigen::ceil DOXCOMMA ArrayBase::round)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(floor,scalar_floor_op,nearest integer not greater than the giben value,\sa Eigen::ceil DOXCOMMA ArrayBase::floor)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(ceil,scalar_ceil_op,nearest integer not less than the giben value,\sa Eigen::floor DOXCOMMA ArrayBase::ceil)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isnan,scalar_isnan_op,not-a-number test,\sa Eigen::isinf DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isnan)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isinf,scalar_isinf_op,infinite value test,\sa Eigen::isnan DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isinf)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isfinite,scalar_isfinite_op,finite value test,\sa Eigen::isinf DOXCOMMA Eigen::isnan DOXCOMMA ArrayBase::isfinite)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sign,scalar_sign_op,sign (or 0),\sa ArrayBase::sign)
/** \returns an expression of the coefficient-wise power of \a x to the given constant \a exponent.
*
* \tparam ScalarExponent is the scalar type of \a exponent. It must be compatible with the scalar type of the given expression (\c Derived::Scalar).
*
* \sa ArrayBase::pow()
*
* \relates ArrayBase
*/
#ifdef EIGEN_PARSED_BY_DOXYGEN
template<typename Derived,typename ScalarExponent>
inline const CwiseBinaryOp<internal::scalar_pow_op<Derived::Scalar,ScalarExponent>,Derived,Constant<ScalarExponent> >
pow(const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent);
#else
template<typename Derived,typename ScalarExponent>
inline typename internal::enable_if< !(internal::is_same<typename Derived::Scalar,ScalarExponent>::value) && EIGEN_SCALAR_BINARY_SUPPORTED(pow,typename Derived::Scalar,ScalarExponent),
const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,ScalarExponent,pow) >::type
pow(const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent) {
return x.derived().pow(exponent);
}
template<typename Derived>
inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_pow_op<typename Derived::Scalar, typename Derived::Scalar>, const Derived, const Derived>
pow(const Eigen::ArrayBase<Derived>& x, const Eigen::ArrayBase<Derived>& exponents)
inline const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,typename Derived::Scalar,pow)
pow(const Eigen::ArrayBase<Derived>& x, const typename Derived::Scalar& exponent) {
return x.derived().pow(exponent);
}
#endif
/** \returns an expression of the coefficient-wise power of \a x to the given array of \a exponents.
*
* This function computes the coefficient-wise power.
*
* Example: \include Cwise_array_power_array.cpp
* Output: \verbinclude Cwise_array_power_array.out
*
* \sa ArrayBase::pow()
*
* \relates ArrayBase
*/
template<typename Derived,typename ExponentDerived>
inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>
pow(const Eigen::ArrayBase<Derived>& x, const Eigen::ArrayBase<ExponentDerived>& exponents)
{
return Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_pow_op<typename Derived::Scalar, typename Derived::Scalar>, const Derived, const Derived>(
return Eigen::CwiseBinaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>(
x.derived(),
exponents.derived()
);
}
}
namespace Eigen
{
/**
* \brief Component-wise division of a scalar by array elements.
**/
template <typename Derived>
inline const Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>, const Derived>
operator/(typename Derived::Scalar s, const Eigen::ArrayBase<Derived>& a)
/** \returns an expression of the coefficient-wise power of the scalar \a x to the given array of \a exponents.
*
* This function computes the coefficient-wise power between a scalar and an array of exponents.
*
* \tparam Scalar is the scalar type of \a x. It must be compatible with the scalar type of the given array expression (\c Derived::Scalar).
*
* Example: \include Cwise_scalar_power_array.cpp
* Output: \verbinclude Cwise_scalar_power_array.out
*
* \sa ArrayBase::pow()
*
* \relates ArrayBase
*/
#ifdef EIGEN_PARSED_BY_DOXYGEN
template<typename Scalar,typename Derived>
inline const CwiseBinaryOp<internal::scalar_pow_op<Scalar,Derived::Scalar>,Constant<Scalar>,Derived>
pow(const Scalar& x,const Eigen::ArrayBase<Derived>& x);
#else
template<typename Scalar, typename Derived>
inline typename internal::enable_if< !(internal::is_same<typename Derived::Scalar,Scalar>::value) && EIGEN_SCALAR_BINARY_SUPPORTED(pow,Scalar,typename Derived::Scalar),
const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,Derived,pow) >::type
pow(const Scalar& x, const Eigen::ArrayBase<Derived>& exponents)
{
return Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>, const Derived>(
a.derived(),
Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>(s)
);
return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,Derived,pow)(
typename internal::plain_constant_type<Derived,Scalar>::type(exponents.rows(), exponents.cols(), x), exponents.derived() );
}
template<typename Derived>
inline const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename Derived::Scalar,Derived,pow)
pow(const typename Derived::Scalar& x, const Eigen::ArrayBase<Derived>& exponents)
{
return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename Derived::Scalar,Derived,pow)(
typename internal::plain_constant_type<Derived,typename Derived::Scalar>::type(exponents.rows(), exponents.cols(), x), exponents.derived() );
}
#endif
namespace internal
{
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(real,scalar_real_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(imag,scalar_imag_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(sin,scalar_sin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(cos,scalar_cos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(asin,scalar_asin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(acos,scalar_acos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(tan,scalar_tan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(exp,scalar_exp_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(log,scalar_log_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs,scalar_abs_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs2,scalar_abs2_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(sqrt,scalar_sqrt_op)
}
}
// TODO: cleanly disable those functions that are not supported on Array (internal::real_ref, internal::random, internal::isApprox...)
// TODO: cleanly disable those functions that are not supported on Array (numext::real_ref, internal::random, internal::isApprox...)
#endif // EIGEN_GLOBAL_FUNCTIONS_H

View File

@@ -4,24 +4,9 @@
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_IO_H
#define EIGEN_IO_H
@@ -64,15 +49,18 @@ std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat&
*/
struct IOFormat
{
/** Default contructor, see class IOFormat for the meaning of the parameters */
/** Default constructor, see class IOFormat for the meaning of the parameters */
IOFormat(int _precision = StreamPrecision, int _flags = 0,
const std::string& _coeffSeparator = " ",
const std::string& _rowSeparator = "\n", const std::string& _rowPrefix="", const std::string& _rowSuffix="",
const std::string& _matPrefix="", const std::string& _matSuffix="")
: matPrefix(_matPrefix), matSuffix(_matSuffix), rowPrefix(_rowPrefix), rowSuffix(_rowSuffix), rowSeparator(_rowSeparator),
coeffSeparator(_coeffSeparator), precision(_precision), flags(_flags)
rowSpacer(""), coeffSeparator(_coeffSeparator), precision(_precision), flags(_flags)
{
rowSpacer = "";
// TODO check if rowPrefix, rowSuffix or rowSeparator contains a newline
// don't add rowSpacer if columns are not to be aligned
if((flags & DontAlignCols))
return;
int i = int(matSuffix.length())-1;
while (i>=0 && matSuffix[i]!='\n')
{
@@ -92,7 +80,7 @@ struct IOFormat
*
* \brief Pseudo expression providing matrix output with given format
*
* \param ExpressionType the type of the object on which IO stream operations are performed
* \tparam ExpressionType the type of the object on which IO stream operations are performed
*
* This class represents an expression with stream operators controlled by a given IOFormat.
* It is the return type of DenseBase::format()
@@ -117,50 +105,23 @@ class WithFormat
}
protected:
const typename ExpressionType::Nested m_matrix;
typename ExpressionType::Nested m_matrix;
IOFormat m_format;
};
/** \returns a WithFormat proxy object allowing to print a matrix the with given
* format \a fmt.
*
* See class IOFormat for some examples.
*
* \sa class IOFormat, class WithFormat
*/
template<typename Derived>
inline const WithFormat<Derived>
DenseBase<Derived>::format(const IOFormat& fmt) const
{
return WithFormat<Derived>(derived(), fmt);
}
namespace internal {
template<typename Scalar, bool IsInteger>
struct significant_decimals_default_impl
{
typedef typename NumTraits<Scalar>::Real RealScalar;
static inline int run()
{
using std::ceil;
return cast<RealScalar,int>(ceil(-log(NumTraits<RealScalar>::epsilon())/log(RealScalar(10))));
}
};
template<typename Scalar>
struct significant_decimals_default_impl<Scalar, true>
{
static inline int run()
{
return 0;
}
};
// NOTE: This helper is kept for backward compatibility with previous code specializing
// this internal::significant_decimals_impl structure. In the future we should directly
// call digits10() which has been introduced in July 2016 in 3.3.
template<typename Scalar>
struct significant_decimals_impl
: significant_decimals_default_impl<Scalar, NumTraits<Scalar>::IsInteger>
{};
{
static inline int run()
{
return NumTraits<Scalar>::digits10();
}
};
/** \internal
* print the matrix \a _m to the output stream \a s using the output format \a fmt */
@@ -175,7 +136,6 @@ std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat&
typename Derived::Nested m = _m;
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Index Index;
Index width = 0;
@@ -200,21 +160,22 @@ std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat&
explicit_precision = fmt.precision;
}
std::streamsize old_precision = 0;
if(explicit_precision) old_precision = s.precision(explicit_precision);
bool align_cols = !(fmt.flags & DontAlignCols);
if(align_cols)
{
// compute the largest width
for(Index j = 1; j < m.cols(); ++j)
for(Index j = 0; j < m.cols(); ++j)
for(Index i = 0; i < m.rows(); ++i)
{
std::stringstream sstr;
if(explicit_precision) sstr.precision(explicit_precision);
sstr.copyfmt(s);
sstr << m.coeff(i,j);
width = std::max<Index>(width, Index(sstr.str().length()));
}
}
std::streamsize old_precision = 0;
if(explicit_precision) old_precision = s.precision(explicit_precision);
s << fmt.matPrefix;
for(Index i = 0; i < m.rows(); ++i)
{

118
Eigen/src/Core/Inverse.h Normal file
View File

@@ -0,0 +1,118 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_INVERSE_H
#define EIGEN_INVERSE_H
namespace Eigen {
template<typename XprType,typename StorageKind> class InverseImpl;
namespace internal {
template<typename XprType>
struct traits<Inverse<XprType> >
: traits<typename XprType::PlainObject>
{
typedef typename XprType::PlainObject PlainObject;
typedef traits<PlainObject> BaseTraits;
enum {
Flags = BaseTraits::Flags & RowMajorBit
};
};
} // end namespace internal
/** \class Inverse
*
* \brief Expression of the inverse of another expression
*
* \tparam XprType the type of the expression we are taking the inverse
*
* This class represents an abstract expression of A.inverse()
* and most of the time this is the only way it is used.
*
*/
template<typename XprType>
class Inverse : public InverseImpl<XprType,typename internal::traits<XprType>::StorageKind>
{
public:
typedef typename XprType::StorageIndex StorageIndex;
typedef typename XprType::PlainObject PlainObject;
typedef typename XprType::Scalar Scalar;
typedef typename internal::ref_selector<XprType>::type XprTypeNested;
typedef typename internal::remove_all<XprTypeNested>::type XprTypeNestedCleaned;
typedef typename internal::ref_selector<Inverse>::type Nested;
typedef typename internal::remove_all<XprType>::type NestedExpression;
explicit EIGEN_DEVICE_FUNC Inverse(const XprType &xpr)
: m_xpr(xpr)
{}
EIGEN_DEVICE_FUNC Index rows() const { return m_xpr.rows(); }
EIGEN_DEVICE_FUNC Index cols() const { return m_xpr.cols(); }
EIGEN_DEVICE_FUNC const XprTypeNestedCleaned& nestedExpression() const { return m_xpr; }
protected:
XprTypeNested m_xpr;
};
// Generic API dispatcher
template<typename XprType, typename StorageKind>
class InverseImpl
: public internal::generic_xpr_base<Inverse<XprType> >::type
{
public:
typedef typename internal::generic_xpr_base<Inverse<XprType> >::type Base;
typedef typename XprType::Scalar Scalar;
private:
Scalar coeff(Index row, Index col) const;
Scalar coeff(Index i) const;
};
namespace internal {
/** \internal
* \brief Default evaluator for Inverse expression.
*
* This default evaluator for Inverse expression simply evaluate the inverse into a temporary
* by a call to internal::call_assignment_no_alias.
* Therefore, inverse implementers only have to specialize Assignment<Dst,Inverse<...>, ...> for
* there own nested expression.
*
* \sa class Inverse
*/
template<typename ArgType>
struct unary_evaluator<Inverse<ArgType> >
: public evaluator<typename Inverse<ArgType>::PlainObject>
{
typedef Inverse<ArgType> InverseType;
typedef typename InverseType::PlainObject PlainObject;
typedef evaluator<PlainObject> Base;
enum { Flags = Base::Flags | EvalBeforeNestingBit };
unary_evaluator(const InverseType& inv_xpr)
: m_result(inv_xpr.rows(), inv_xpr.cols())
{
::new (static_cast<Base*>(this)) Base(m_result);
internal::call_assignment_no_alias(m_result, inv_xpr);
}
protected:
PlainObject m_result;
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_INVERSE_H

View File

@@ -4,37 +4,50 @@
// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_MAP_H
#define EIGEN_MAP_H
namespace Eigen {
namespace internal {
template<typename PlainObjectType, int MapOptions, typename StrideType>
struct traits<Map<PlainObjectType, MapOptions, StrideType> >
: public traits<PlainObjectType>
{
typedef traits<PlainObjectType> TraitsBase;
enum {
PlainObjectTypeInnerSize = ((traits<PlainObjectType>::Flags&RowMajorBit)==RowMajorBit)
? PlainObjectType::ColsAtCompileTime
: PlainObjectType::RowsAtCompileTime,
InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
? int(PlainObjectType::InnerStrideAtCompileTime)
: int(StrideType::InnerStrideAtCompileTime),
OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
? (InnerStrideAtCompileTime==Dynamic || PlainObjectTypeInnerSize==Dynamic
? Dynamic
: int(InnerStrideAtCompileTime) * int(PlainObjectTypeInnerSize))
: int(StrideType::OuterStrideAtCompileTime),
Alignment = int(MapOptions)&int(AlignedMask),
Flags0 = TraitsBase::Flags & (~NestByRefBit),
Flags = is_lvalue<PlainObjectType>::value ? int(Flags0) : (int(Flags0) & ~LvalueBit)
};
private:
enum { Options }; // Expressions don't have Options
};
}
/** \class Map
* \ingroup Core_Module
*
* \brief A matrix or vector expression mapping an existing array of data.
*
* \tparam PlainObjectType the equivalent matrix type of the mapped data
* \tparam MapOptions specifies whether the pointer is \c #Aligned, or \c #Unaligned.
* \tparam MapOptions specifies the pointer alignment in bytes. It can be: \c #Aligned128, , \c #Aligned64, \c #Aligned32, \c #Aligned16, \c #Aligned8 or \c #Unaligned.
* The default is \c #Unaligned.
* \tparam StrideType optionally specifies strides. By default, Map assumes the memory layout
* of an ordinary, contiguous array. This can be overridden by specifying strides.
@@ -78,44 +91,6 @@ namespace Eigen {
*
* \sa PlainObjectBase::Map(), \ref TopicStorageOrders
*/
namespace internal {
template<typename PlainObjectType, int MapOptions, typename StrideType>
struct traits<Map<PlainObjectType, MapOptions, StrideType> >
: public traits<PlainObjectType>
{
typedef traits<PlainObjectType> TraitsBase;
typedef typename PlainObjectType::Index Index;
typedef typename PlainObjectType::Scalar Scalar;
enum {
InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
? int(PlainObjectType::InnerStrideAtCompileTime)
: int(StrideType::InnerStrideAtCompileTime),
OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
? int(PlainObjectType::OuterStrideAtCompileTime)
: int(StrideType::OuterStrideAtCompileTime),
HasNoInnerStride = InnerStrideAtCompileTime == 1,
HasNoOuterStride = StrideType::OuterStrideAtCompileTime == 0,
HasNoStride = HasNoInnerStride && HasNoOuterStride,
IsAligned = bool(EIGEN_ALIGN) && ((int(MapOptions)&Aligned)==Aligned),
IsDynamicSize = PlainObjectType::SizeAtCompileTime==Dynamic,
KeepsPacketAccess = bool(HasNoInnerStride)
&& ( bool(IsDynamicSize)
|| HasNoOuterStride
|| ( OuterStrideAtCompileTime!=Dynamic
&& ((static_cast<int>(sizeof(Scalar))*OuterStrideAtCompileTime)%16)==0 ) ),
Flags0 = TraitsBase::Flags & (~NestByRefBit),
Flags1 = IsAligned ? (int(Flags0) | AlignedBit) : (int(Flags0) & ~AlignedBit),
Flags2 = (bool(HasNoStride) || bool(PlainObjectType::IsVectorAtCompileTime))
? int(Flags1) : int(Flags1 & ~LinearAccessBit),
Flags3 = is_lvalue<PlainObjectType>::value ? int(Flags2) : (int(Flags2) & ~LvalueBit),
Flags = KeepsPacketAccess ? int(Flags3) : (int(Flags3) & ~PacketAccessBit)
};
private:
enum { Options }; // Expressions don't have Options
};
}
template<typename PlainObjectType, int MapOptions, typename StrideType> class Map
: public MapBase<Map<PlainObjectType, MapOptions, StrideType> >
{
@@ -125,59 +100,61 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
EIGEN_DENSE_PUBLIC_INTERFACE(Map)
typedef typename Base::PointerType PointerType;
#if EIGEN2_SUPPORT_STAGE <= STAGE30_FULL_EIGEN3_API
typedef const Scalar* PointerArgType;
inline PointerType cast_to_pointer_type(PointerArgType ptr) { return const_cast<PointerType>(ptr); }
#else
typedef PointerType PointerArgType;
EIGEN_DEVICE_FUNC
inline PointerType cast_to_pointer_type(PointerArgType ptr) { return ptr; }
#endif
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1;
}
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
: IsVectorAtCompileTime ? this->size()
: int(Flags)&RowMajorBit ? this->cols()
: this->rows();
return int(StrideType::OuterStrideAtCompileTime) != 0 ? m_stride.outer()
: int(internal::traits<Map>::OuterStrideAtCompileTime) != Dynamic ? Index(internal::traits<Map>::OuterStrideAtCompileTime)
: IsVectorAtCompileTime ? (this->size() * innerStride())
: (int(Flags)&RowMajorBit) ? (this->cols() * innerStride())
: (this->rows() * innerStride());
}
/** Constructor in the fixed-size case.
*
* \param data pointer to the array to map
* \param dataPtr pointer to the array to map
* \param stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType data, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(data)), m_stride(stride)
EIGEN_DEVICE_FUNC
explicit inline Map(PointerArgType dataPtr, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(dataPtr)), m_stride(stride)
{
PlainObjectType::Base::_check_template_params();
}
/** Constructor in the dynamic-size vector case.
*
* \param data pointer to the array to map
* \param dataPtr pointer to the array to map
* \param size the size of the vector expression
* \param stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType data, Index size, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(data), size), m_stride(stride)
EIGEN_DEVICE_FUNC
inline Map(PointerArgType dataPtr, Index size, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), size), m_stride(stride)
{
PlainObjectType::Base::_check_template_params();
}
/** Constructor in the dynamic-size matrix case.
*
* \param data pointer to the array to map
* \param dataPtr pointer to the array to map
* \param rows the number of rows of the matrix expression
* \param cols the number of columns of the matrix expression
* \param stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType data, Index rows, Index cols, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(data), rows, cols), m_stride(stride)
EIGEN_DEVICE_FUNC
inline Map(PointerArgType dataPtr, Index rows, Index cols, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), rows, cols), m_stride(stride)
{
PlainObjectType::Base::_check_template_params();
}
@@ -188,19 +165,6 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
StrideType m_stride;
};
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
inline Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>
::Array(const Scalar *data)
{
this->_set_noalias(Eigen::Map<const Array>(data));
}
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
inline Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>
::Matrix(const Scalar *data)
{
this->_set_noalias(Eigen::Map<const Matrix>(data));
}
} // end namespace Eigen

View File

@@ -4,38 +4,33 @@
// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_MAPBASE_H
#define EIGEN_MAPBASE_H
#define EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) \
EIGEN_STATIC_ASSERT((int(internal::traits<Derived>::Flags) & LinearAccessBit) || Derived::IsVectorAtCompileTime, \
EIGEN_STATIC_ASSERT((int(internal::evaluator<Derived>::Flags) & LinearAccessBit) || Derived::IsVectorAtCompileTime, \
YOU_ARE_TRYING_TO_USE_AN_INDEX_BASED_ACCESSOR_ON_AN_EXPRESSION_THAT_DOES_NOT_SUPPORT_THAT)
namespace Eigen {
/** \class MapBase
* \ingroup Core_Module
/** \ingroup Core_Module
*
* \brief Base class for Map and Block expression with direct access
* \brief Base class for dense Map and Block expression with direct access
*
* This base class provides the const low-level accessors (e.g. coeff, coeffRef) of dense
* Map and Block objects with direct access.
* Typical users do not have to directly deal with this class.
*
* This class can be extended by through the macro plugin \c EIGEN_MAPBASE_PLUGIN.
* See \link TopicCustomizing_Plugins customizing Eigen \endlink for details.
*
* The \c Derived class has to provide the following two methods describing the memory layout:
* \code Index innerStride() const; \endcode
* \code Index outerStride() const; \endcode
*
* \sa class Map, class Block
*/
@@ -52,7 +47,6 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
};
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
@@ -91,8 +85,10 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
typedef typename Base::CoeffReturnType CoeffReturnType;
inline Index rows() const { return m_rows.value(); }
inline Index cols() const { return m_cols.value(); }
/** \copydoc DenseBase::rows() */
EIGEN_DEVICE_FUNC inline Index rows() const { return m_rows.value(); }
/** \copydoc DenseBase::cols() */
EIGEN_DEVICE_FUNC inline Index cols() const { return m_cols.value(); }
/** Returns a pointer to the first coefficient of the matrix or vector.
*
@@ -100,37 +96,47 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
*
* \sa innerStride(), outerStride()
*/
inline const Scalar* data() const { return m_data; }
EIGEN_DEVICE_FUNC inline const Scalar* data() const { return m_data; }
inline const Scalar& coeff(Index row, Index col) const
/** \copydoc PlainObjectBase::coeff(Index,Index) const */
EIGEN_DEVICE_FUNC
inline const Scalar& coeff(Index rowId, Index colId) const
{
return m_data[col * colStride() + row * rowStride()];
return m_data[colId * colStride() + rowId * rowStride()];
}
/** \copydoc PlainObjectBase::coeff(Index) const */
EIGEN_DEVICE_FUNC
inline const Scalar& coeff(Index index) const
{
EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
return m_data[index * innerStride()];
}
inline const Scalar& coeffRef(Index row, Index col) const
/** \copydoc PlainObjectBase::coeffRef(Index,Index) const */
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return this->m_data[col * colStride() + row * rowStride()];
return this->m_data[colId * colStride() + rowId * rowStride()];
}
/** \copydoc PlainObjectBase::coeffRef(Index) const */
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
return this->m_data[index * innerStride()];
}
/** \internal */
template<int LoadMode>
inline PacketScalar packet(Index row, Index col) const
inline PacketScalar packet(Index rowId, Index colId) const
{
return internal::ploadt<PacketScalar, LoadMode>
(m_data + (col * colStride() + row * rowStride()));
(m_data + (colId * colStride() + rowId * rowStride()));
}
/** \internal */
template<int LoadMode>
inline PacketScalar packet(Index index) const
{
@@ -138,58 +144,85 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
return internal::ploadt<PacketScalar, LoadMode>(m_data + index * innerStride());
}
inline MapBase(PointerType data) : m_data(data), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
/** \internal Constructor for fixed size matrices or vectors */
EIGEN_DEVICE_FUNC
explicit inline MapBase(PointerType dataPtr) : m_data(dataPtr), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
checkSanity();
checkSanity<Derived>();
}
inline MapBase(PointerType data, Index size)
: m_data(data),
m_rows(RowsAtCompileTime == Dynamic ? size : Index(RowsAtCompileTime)),
m_cols(ColsAtCompileTime == Dynamic ? size : Index(ColsAtCompileTime))
/** \internal Constructor for dynamically sized vectors */
EIGEN_DEVICE_FUNC
inline MapBase(PointerType dataPtr, Index vecSize)
: m_data(dataPtr),
m_rows(RowsAtCompileTime == Dynamic ? vecSize : Index(RowsAtCompileTime)),
m_cols(ColsAtCompileTime == Dynamic ? vecSize : Index(ColsAtCompileTime))
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
eigen_assert(size >= 0);
eigen_assert(data == 0 || SizeAtCompileTime == Dynamic || SizeAtCompileTime == size);
checkSanity();
eigen_assert(vecSize >= 0);
eigen_assert(dataPtr == 0 || SizeAtCompileTime == Dynamic || SizeAtCompileTime == vecSize);
checkSanity<Derived>();
}
inline MapBase(PointerType data, Index rows, Index cols)
: m_data(data), m_rows(rows), m_cols(cols)
/** \internal Constructor for dynamically sized matrices */
EIGEN_DEVICE_FUNC
inline MapBase(PointerType dataPtr, Index rows, Index cols)
: m_data(dataPtr), m_rows(rows), m_cols(cols)
{
eigen_assert( (data == 0)
eigen_assert( (dataPtr == 0)
|| ( rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)));
checkSanity();
checkSanity<Derived>();
}
#ifdef EIGEN_MAPBASE_PLUGIN
#include EIGEN_MAPBASE_PLUGIN
#endif
protected:
void checkSanity() const
template<typename T>
EIGEN_DEVICE_FUNC
void checkSanity(typename internal::enable_if<(internal::traits<T>::Alignment>0),void*>::type = 0) const
{
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(internal::traits<Derived>::Flags&PacketAccessBit,
internal::inner_stride_at_compile_time<Derived>::ret==1),
PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1);
eigen_assert(EIGEN_IMPLIES(internal::traits<Derived>::Flags&AlignedBit, (size_t(m_data) % 16) == 0)
&& "data is not aligned");
#if EIGEN_MAX_ALIGN_BYTES>0
eigen_assert(( ((internal::UIntPtr(m_data) % internal::traits<Derived>::Alignment) == 0)
|| (cols() * rows() * innerStride() * sizeof(Scalar)) < internal::traits<Derived>::Alignment ) && "data is not aligned");
#endif
}
template<typename T>
EIGEN_DEVICE_FUNC
void checkSanity(typename internal::enable_if<internal::traits<T>::Alignment==0,void*>::type = 0) const
{}
PointerType m_data;
const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows;
const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols;
};
/** \ingroup Core_Module
*
* \brief Base class for non-const dense Map and Block expression with direct access
*
* This base class provides the non-const low-level accessors (e.g. coeff and coeffRef) of
* dense Map and Block objects with direct access.
* It inherits MapBase<Derived, ReadOnlyAccessors> which defines the const variant for reading specific entries.
*
* \sa class Map, class Block
*/
template<typename Derived> class MapBase<Derived, WriteAccessors>
: public MapBase<Derived, ReadOnlyAccessors>
{
typedef MapBase<Derived, ReadOnlyAccessors> ReadOnlyMapBase;
public:
typedef MapBase<Derived, ReadOnlyAccessors> Base;
typedef typename Base::Scalar Scalar;
typedef typename Base::PacketScalar PacketScalar;
typedef typename Base::Index Index;
typedef typename Base::StorageIndex StorageIndex;
typedef typename Base::PointerType PointerType;
using Base::derived;
@@ -210,14 +243,18 @@ template<typename Derived> class MapBase<Derived, WriteAccessors>
const Scalar
>::type ScalarWithConstIfNotLvalue;
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return this->m_data; }
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return this->m_data; } // no const-cast here so non-const-correct code will give a compile error
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue& coeffRef(Index row, Index col)
{
return this->m_data[col * colStride() + row * rowStride()];
}
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
@@ -225,33 +262,38 @@ template<typename Derived> class MapBase<Derived, WriteAccessors>
}
template<int StoreMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
inline void writePacket(Index row, Index col, const PacketScalar& val)
{
internal::pstoret<Scalar, PacketScalar, StoreMode>
(this->m_data + (col * colStride() + row * rowStride()), x);
(this->m_data + (col * colStride() + row * rowStride()), val);
}
template<int StoreMode>
inline void writePacket(Index index, const PacketScalar& x)
inline void writePacket(Index index, const PacketScalar& val)
{
EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
internal::pstoret<Scalar, PacketScalar, StoreMode>
(this->m_data + index * innerStride(), x);
(this->m_data + index * innerStride(), val);
}
explicit inline MapBase(PointerType data) : Base(data) {}
inline MapBase(PointerType data, Index size) : Base(data, size) {}
inline MapBase(PointerType data, Index rows, Index cols) : Base(data, rows, cols) {}
EIGEN_DEVICE_FUNC explicit inline MapBase(PointerType dataPtr) : Base(dataPtr) {}
EIGEN_DEVICE_FUNC inline MapBase(PointerType dataPtr, Index vecSize) : Base(dataPtr, vecSize) {}
EIGEN_DEVICE_FUNC inline MapBase(PointerType dataPtr, Index rows, Index cols) : Base(dataPtr, rows, cols) {}
EIGEN_DEVICE_FUNC
Derived& operator=(const MapBase& other)
{
Base::Base::operator=(other);
ReadOnlyMapBase::Base::operator=(other);
return derived();
}
using Base::Base::operator=;
// In theory we could simply refer to Base:Base::operator=, but MSVC does not like Base::Base,
// see bugs 821 and 920.
using ReadOnlyMapBase::Base::operator=;
};
#undef EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS
} // end namespace Eigen
#endif // EIGEN_MAPBASE_H

File diff suppressed because it is too large Load Diff

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@@ -0,0 +1,101 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_MATHFUNCTIONSIMPL_H
#define EIGEN_MATHFUNCTIONSIMPL_H
namespace Eigen {
namespace internal {
/** \internal \returns the hyperbolic tan of \a a (coeff-wise)
Doesn't do anything fancy, just a 13/6-degree rational interpolant which
is accurate up to a couple of ulp in the range [-9, 9], outside of which
the tanh(x) = +/-1.
This implementation works on both scalars and packets.
*/
template<typename T>
T generic_fast_tanh_float(const T& a_x)
{
// Clamp the inputs to the range [-9, 9] since anything outside
// this range is +/-1.0f in single-precision.
const T plus_9 = pset1<T>(9.f);
const T minus_9 = pset1<T>(-9.f);
// NOTE GCC prior to 6.3 might improperly optimize this max/min
// step such that if a_x is nan, x will be either 9 or -9,
// and tanh will return 1 or -1 instead of nan.
// This is supposed to be fixed in gcc6.3,
// see: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
const T x = pmax(minus_9,pmin(plus_9,a_x));
// The monomial coefficients of the numerator polynomial (odd).
const T alpha_1 = pset1<T>(4.89352455891786e-03f);
const T alpha_3 = pset1<T>(6.37261928875436e-04f);
const T alpha_5 = pset1<T>(1.48572235717979e-05f);
const T alpha_7 = pset1<T>(5.12229709037114e-08f);
const T alpha_9 = pset1<T>(-8.60467152213735e-11f);
const T alpha_11 = pset1<T>(2.00018790482477e-13f);
const T alpha_13 = pset1<T>(-2.76076847742355e-16f);
// The monomial coefficients of the denominator polynomial (even).
const T beta_0 = pset1<T>(4.89352518554385e-03f);
const T beta_2 = pset1<T>(2.26843463243900e-03f);
const T beta_4 = pset1<T>(1.18534705686654e-04f);
const T beta_6 = pset1<T>(1.19825839466702e-06f);
// Since the polynomials are odd/even, we need x^2.
const T x2 = pmul(x, x);
// Evaluate the numerator polynomial p.
T p = pmadd(x2, alpha_13, alpha_11);
p = pmadd(x2, p, alpha_9);
p = pmadd(x2, p, alpha_7);
p = pmadd(x2, p, alpha_5);
p = pmadd(x2, p, alpha_3);
p = pmadd(x2, p, alpha_1);
p = pmul(x, p);
// Evaluate the denominator polynomial p.
T q = pmadd(x2, beta_6, beta_4);
q = pmadd(x2, q, beta_2);
q = pmadd(x2, q, beta_0);
// Divide the numerator by the denominator.
return pdiv(p, q);
}
template<typename RealScalar>
EIGEN_STRONG_INLINE
RealScalar positive_real_hypot(const RealScalar& x, const RealScalar& y)
{
EIGEN_USING_STD_MATH(sqrt);
RealScalar p, qp;
p = numext::maxi(x,y);
if(p==RealScalar(0)) return RealScalar(0);
qp = numext::mini(y,x) / p;
return p * sqrt(RealScalar(1) + qp*qp);
}
template<typename Scalar>
struct hypot_impl
{
typedef typename NumTraits<Scalar>::Real RealScalar;
static inline RealScalar run(const Scalar& x, const Scalar& y)
{
EIGEN_USING_STD_MATH(abs);
return positive_real_hypot<RealScalar>(abs(x), abs(y));
}
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_MATHFUNCTIONSIMPL_H

View File

@@ -4,30 +4,54 @@
// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_MATRIX_H
#define EIGEN_MATRIX_H
namespace Eigen {
namespace internal {
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
{
private:
enum { size = internal::size_at_compile_time<_Rows,_Cols>::ret };
typedef typename find_best_packet<_Scalar,size>::type PacketScalar;
enum {
row_major_bit = _Options&RowMajor ? RowMajorBit : 0,
is_dynamic_size_storage = _MaxRows==Dynamic || _MaxCols==Dynamic,
max_size = is_dynamic_size_storage ? Dynamic : _MaxRows*_MaxCols,
default_alignment = compute_default_alignment<_Scalar,max_size>::value,
actual_alignment = ((_Options&DontAlign)==0) ? default_alignment : 0,
required_alignment = unpacket_traits<PacketScalar>::alignment,
packet_access_bit = (packet_traits<_Scalar>::Vectorizable && (EIGEN_UNALIGNED_VECTORIZE || (actual_alignment>=required_alignment))) ? PacketAccessBit : 0
};
public:
typedef _Scalar Scalar;
typedef Dense StorageKind;
typedef Eigen::Index StorageIndex;
typedef MatrixXpr XprKind;
enum {
RowsAtCompileTime = _Rows,
ColsAtCompileTime = _Cols,
MaxRowsAtCompileTime = _MaxRows,
MaxColsAtCompileTime = _MaxCols,
Flags = compute_matrix_flags<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::ret,
Options = _Options,
InnerStrideAtCompileTime = 1,
OuterStrideAtCompileTime = (Options&RowMajor) ? ColsAtCompileTime : RowsAtCompileTime,
// FIXME, the following flag in only used to define NeedsToAlign in PlainObjectBase
EvaluatorFlags = LinearAccessBit | DirectAccessBit | packet_access_bit | row_major_bit,
Alignment = actual_alignment
};
};
}
/** \class Matrix
* \ingroup Core_Module
*
@@ -39,13 +63,13 @@ namespace Eigen {
* The %Matrix class encompasses \em both fixed-size and dynamic-size objects (\ref fixedsize "note").
*
* The first three template parameters are required:
* \tparam _Scalar \anchor matrix_tparam_scalar Numeric type, e.g. float, double, int or std::complex<float>.
* User defined sclar types are supported as well (see \ref user_defined_scalars "here").
* \tparam _Scalar Numeric type, e.g. float, double, int or std::complex<float>.
* User defined scalar types are supported as well (see \ref user_defined_scalars "here").
* \tparam _Rows Number of rows, or \b Dynamic
* \tparam _Cols Number of columns, or \b Dynamic
*
* The remaining template parameters are optional -- in most cases you don't have to worry about them.
* \tparam _Options \anchor matrix_tparam_options A combination of either \b #RowMajor or \b #ColMajor, and of either
* \tparam _Options A combination of either \b #RowMajor or \b #ColMajor, and of either
* \b #AutoAlign or \b #DontAlign.
* The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required
* for vectorization. It defaults to aligning matrices except for fixed sizes that aren't a multiple of the packet size.
@@ -82,7 +106,7 @@ namespace Eigen {
* \endcode
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_MATRIX_PLUGIN.
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_MATRIX_PLUGIN.
*
* <i><b>Some notes:</b></i>
*
@@ -112,32 +136,44 @@ namespace Eigen {
* are the dimensions of the original matrix, while _Rows and _Cols are Dynamic.</dd>
* </dl>
*
* \see MatrixBase for the majority of the API methods for matrices, \ref TopicClassHierarchy,
* \ref TopicStorageOrders
* <i><b>ABI and storage layout</b></i>
*
* The table below summarizes the ABI of some possible Matrix instances which is fixed thorough the lifetime of Eigen 3.
* <table class="manual">
* <tr><th>Matrix type</th><th>Equivalent C structure</th></tr>
* <tr><td>\code Matrix<T,Dynamic,Dynamic> \endcode</td><td>\code
* struct {
* T *data; // with (size_t(data)%EIGEN_MAX_ALIGN_BYTES)==0
* Eigen::Index rows, cols;
* };
* \endcode</td></tr>
* <tr class="alt"><td>\code
* Matrix<T,Dynamic,1>
* Matrix<T,1,Dynamic> \endcode</td><td>\code
* struct {
* T *data; // with (size_t(data)%EIGEN_MAX_ALIGN_BYTES)==0
* Eigen::Index size;
* };
* \endcode</td></tr>
* <tr><td>\code Matrix<T,Rows,Cols> \endcode</td><td>\code
* struct {
* T data[Rows*Cols]; // with (size_t(data)%A(Rows*Cols*sizeof(T)))==0
* };
* \endcode</td></tr>
* <tr class="alt"><td>\code Matrix<T,Dynamic,Dynamic,0,MaxRows,MaxCols> \endcode</td><td>\code
* struct {
* T data[MaxRows*MaxCols]; // with (size_t(data)%A(MaxRows*MaxCols*sizeof(T)))==0
* Eigen::Index rows, cols;
* };
* \endcode</td></tr>
* </table>
* Note that in this table Rows, Cols, MaxRows and MaxCols are all positive integers. A(S) is defined to the largest possible power-of-two
* smaller to EIGEN_MAX_STATIC_ALIGN_BYTES.
*
* \see MatrixBase for the majority of the API methods for matrices, \ref TopicClassHierarchy,
* \ref TopicStorageOrders
*/
namespace internal {
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
{
typedef _Scalar Scalar;
typedef Dense StorageKind;
typedef DenseIndex Index;
typedef MatrixXpr XprKind;
enum {
RowsAtCompileTime = _Rows,
ColsAtCompileTime = _Cols,
MaxRowsAtCompileTime = _MaxRows,
MaxColsAtCompileTime = _MaxCols,
Flags = compute_matrix_flags<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::ret,
CoeffReadCost = NumTraits<Scalar>::ReadCost,
Options = _Options,
InnerStrideAtCompileTime = 1,
OuterStrideAtCompileTime = (Options&RowMajor) ? ColsAtCompileTime : RowsAtCompileTime
};
};
}
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
class Matrix
: public PlainObjectBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
@@ -166,6 +202,7 @@ class Matrix
*
* \callgraph
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix& operator=(const Matrix& other)
{
return Base::_set(other);
@@ -182,7 +219,8 @@ class Matrix
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE Matrix& operator=(const MatrixBase<OtherDerived>& other)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix& operator=(const DenseBase<OtherDerived>& other)
{
return Base::_set(other);
}
@@ -194,12 +232,14 @@ class Matrix
* \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other)
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix& operator=(const EigenBase<OtherDerived> &other)
{
return Base::operator=(other);
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix& operator=(const ReturnByValue<OtherDerived>& func)
{
return Base::operator=(func);
@@ -215,52 +255,95 @@ class Matrix
*
* \sa resize(Index,Index)
*/
EIGEN_STRONG_INLINE explicit Matrix() : Base()
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix() : Base()
{
Base::_check_template_params();
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
// FIXME is it still needed
Matrix(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC
explicit Matrix(internal::constructor_without_unaligned_array_assert)
: Base(internal::constructor_without_unaligned_array_assert())
{ Base::_check_template_params(); EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED }
{ Base::_check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED }
/** \brief Constructs a vector or row-vector with given dimension. \only_for_vectors
*
* Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
* it is redundant to pass the dimension here, so it makes more sense to use the default
* constructor Matrix() instead.
*/
EIGEN_STRONG_INLINE explicit Matrix(Index dim)
: Base(dim, RowsAtCompileTime == 1 ? 1 : dim, ColsAtCompileTime == 1 ? 1 : dim)
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
Matrix(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
: Base(std::move(other))
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Matrix)
eigen_assert(dim >= 0);
eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim);
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
if (RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic)
Base::_set_noalias(other);
}
EIGEN_DEVICE_FUNC
Matrix& operator=(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
{
other.swap(*this);
return *this;
}
#endif
#ifndef EIGEN_PARSED_BY_DOXYGEN
// This constructor is for both 1x1 matrices and dynamic vectors
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit Matrix(const T& x)
{
Base::_check_template_params();
Base::template _init1<T>(x);
}
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const T0& x, const T1& y)
{
Base::_check_template_params();
Base::template _init2<T0,T1>(x, y);
}
#else
/** \brief Constructs a fixed-sized matrix initialized with coefficients starting at \a data */
EIGEN_DEVICE_FUNC
explicit Matrix(const Scalar *data);
/** \brief Constructs a vector or row-vector with given dimension. \only_for_vectors
*
* This is useful for dynamic-size vectors. For fixed-size vectors,
* it is redundant to pass these parameters, so one should use the default constructor
* Matrix() instead.
*
* \warning This constructor is disabled for fixed-size \c 1x1 matrices. For instance,
* calling Matrix<double,1,1>(1) will call the initialization constructor: Matrix(const Scalar&).
* For fixed-size \c 1x1 matrices it is therefore recommended to use the default
* constructor Matrix() instead, especially when using one of the non standard
* \c EIGEN_INITIALIZE_MATRICES_BY_{ZERO,\c NAN} macros (see \ref TopicPreprocessorDirectives).
*/
EIGEN_STRONG_INLINE explicit Matrix(Index dim);
/** \brief Constructs an initialized 1x1 matrix with the given coefficient */
Matrix(const Scalar& x);
/** \brief Constructs an uninitialized matrix with \a rows rows and \a cols columns.
*
* This is useful for dynamic-size matrices. For fixed-size matrices,
* it is redundant to pass these parameters, so one should use the default constructor
* Matrix() instead. */
* Matrix() instead.
*
* \warning This constructor is disabled for fixed-size \c 1x2 and \c 2x1 vectors. For instance,
* calling Matrix2f(2,1) will call the initialization constructor: Matrix(const Scalar& x, const Scalar& y).
* For fixed-size \c 1x2 or \c 2x1 vectors it is therefore recommended to use the default
* constructor Matrix() instead, especially when using one of the non standard
* \c EIGEN_INITIALIZE_MATRICES_BY_{ZERO,\c NAN} macros (see \ref TopicPreprocessorDirectives).
*/
EIGEN_DEVICE_FUNC
Matrix(Index rows, Index cols);
/** \brief Constructs an initialized 2D vector with given coefficients */
Matrix(const Scalar& x, const Scalar& y);
#endif
/** \brief Constructs an initialized 3D vector with given coefficients */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z)
{
Base::_check_template_params();
@@ -270,6 +353,7 @@ class Matrix
m_storage.data()[2] = z;
}
/** \brief Constructs an initialized 4D vector with given coefficients */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w)
{
Base::_check_template_params();
@@ -280,76 +364,33 @@ class Matrix
m_storage.data()[3] = w;
}
explicit Matrix(const Scalar *data);
/** \brief Constructor copying the value of the expression \a other */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Matrix(const MatrixBase<OtherDerived>& other)
: Base(other.rows() * other.cols(), other.rows(), other.cols())
{
// This test resides here, to bring the error messages closer to the user. Normally, these checks
// are performed deeply within the library, thus causing long and scary error traces.
EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
Base::_check_template_params();
Base::_set_noalias(other);
}
/** \brief Copy constructor */
EIGEN_STRONG_INLINE Matrix(const Matrix& other)
: Base(other.rows() * other.cols(), other.rows(), other.cols())
{
Base::_check_template_params();
Base::_set_noalias(other);
}
/** \brief Copy constructor with in-place evaluation */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Matrix(const ReturnByValue<OtherDerived>& other)
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
other.evalTo(*this);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const Matrix& other) : Base(other)
{ }
/** \brief Copy constructor for generic expressions.
* \sa MatrixBase::operator=(const EigenBase<OtherDerived>&)
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const EigenBase<OtherDerived> &other)
: Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
// FIXME/CHECK: isn't *this = other.derived() more efficient. it allows to
// go for pure _set() implementations, right?
*this = other;
}
: Base(other.derived())
{ }
/** \internal
* \brief Override MatrixBase::swap() since for dynamic-sized matrices
* of same type it is enough to swap the data pointers.
*/
template<typename OtherDerived>
void swap(MatrixBase<OtherDerived> const & other)
{ this->_swap(other.derived()); }
inline Index innerStride() const { return 1; }
inline Index outerStride() const { return this->innerSize(); }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return 1; }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return this->innerSize(); }
/////////// Geometry module ///////////
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
explicit Matrix(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Matrix& operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
explicit Matrix(const eigen2_RotationBase<OtherDerived,ColsAtCompileTime>& r);
template<typename OtherDerived>
Matrix& operator=(const eigen2_RotationBase<OtherDerived,ColsAtCompileTime>& r);
#endif
// allow to extend Matrix outside Eigen
#ifdef EIGEN_MATRIX_PLUGIN
#include EIGEN_MATRIX_PLUGIN

View File

@@ -4,24 +4,9 @@
// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_MATRIXBASE_H
#define EIGEN_MATRIXBASE_H
@@ -56,9 +41,9 @@ namespace Eigen {
* \endcode
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_MATRIXBASE_PLUGIN.
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_MATRIXBASE_PLUGIN.
*
* \sa \ref TopicClassHierarchy
* \sa \blank \ref TopicClassHierarchy
*/
template<typename Derived> class MatrixBase
: public DenseBase<Derived>
@@ -67,7 +52,7 @@ template<typename Derived> class MatrixBase
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef MatrixBase StorageBaseType;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
@@ -81,7 +66,6 @@ template<typename Derived> class MatrixBase
using Base::MaxSizeAtCompileTime;
using Base::IsVectorAtCompileTime;
using Base::Flags;
using Base::CoeffReadCost;
using Base::derived;
using Base::const_cast_derived;
@@ -113,25 +97,14 @@ template<typename Derived> class MatrixBase
/** \returns the size of the main diagonal, which is min(rows(),cols()).
* \sa rows(), cols(), SizeAtCompileTime. */
inline Index diagonalSize() const { return (std::min)(rows(),cols()); }
EIGEN_DEVICE_FUNC
inline Index diagonalSize() const { return (numext::mini)(rows(),cols()); }
/** \brief The plain matrix type corresponding to this expression.
*
* This is not necessarily exactly the return type of eval(). In the case of plain matrices,
* the return type of eval() is a const reference to a matrix, not a matrix! It is however guaranteed
* that the return type of eval() is either PlainObject or const PlainObject&.
*/
typedef Matrix<typename internal::traits<Derived>::Scalar,
internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime,
AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime
> PlainObject;
typedef typename Base::PlainObject PlainObject;
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal Represents a matrix with all coefficients equal to one another*/
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType;
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
/** \internal the return type of MatrixBase::adjoint() */
typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, ConstTransposeReturnType>,
@@ -140,7 +113,7 @@ template<typename Derived> class MatrixBase
/** \internal Return type of eigenvalues() */
typedef Matrix<std::complex<RealScalar>, internal::traits<Derived>::ColsAtCompileTime, 1, ColMajor> EigenvaluesReturnType;
/** \internal the return type of identity */
typedef CwiseNullaryOp<internal::scalar_identity_op<Scalar>,Derived> IdentityReturnType;
typedef CwiseNullaryOp<internal::scalar_identity_op<Scalar>,PlainObject> IdentityReturnType;
/** \internal the return type of unit vectors */
typedef Block<const CwiseNullaryOp<internal::scalar_identity_op<Scalar>, SquareMatrixType>,
internal::traits<Derived>::RowsAtCompileTime,
@@ -148,6 +121,7 @@ template<typename Derived> class MatrixBase
#endif // not EIGEN_PARSED_BY_DOXYGEN
#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::MatrixBase
#define EIGEN_DOC_UNARY_ADDONS(X,Y)
# include "../plugins/CommonCwiseUnaryOps.h"
# include "../plugins/CommonCwiseBinaryOps.h"
# include "../plugins/MatrixCwiseUnaryOps.h"
@@ -156,40 +130,44 @@ template<typename Derived> class MatrixBase
# include EIGEN_MATRIXBASE_PLUGIN
# endif
#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
#undef EIGEN_DOC_UNARY_ADDONS
/** Special case of the template operator=, in order to prevent the compiler
* from generating a default operator= (issue hit with g++ 4.1)
*/
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const MatrixBase& other);
// We cannot inherit here via Base::operator= since it is causing
// trouble with MSVC.
template <typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const DenseBase<OtherDerived>& other);
template <typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator=(const EigenBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator=(const ReturnByValue<OtherDerived>& other);
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& lazyAssign(const ProductBase<ProductDerived, Lhs,Rhs>& other);
#endif // not EIGEN_PARSED_BY_DOXYGEN
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator+=(const MatrixBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator-=(const MatrixBase<OtherDerived>& other);
template<typename OtherDerived>
const typename ProductReturnType<Derived,OtherDerived>::Type
EIGEN_DEVICE_FUNC
const Product<Derived,OtherDerived>
operator*(const MatrixBase<OtherDerived> &other) const;
template<typename OtherDerived>
const typename LazyProductReturnType<Derived,OtherDerived>::Type
EIGEN_DEVICE_FUNC
const Product<Derived,OtherDerived,LazyProduct>
lazyProduct(const MatrixBase<OtherDerived> &other) const;
template<typename OtherDerived>
@@ -202,107 +180,112 @@ template<typename Derived> class MatrixBase
void applyOnTheRight(const EigenBase<OtherDerived>& other);
template<typename DiagonalDerived>
const DiagonalProduct<Derived, DiagonalDerived, OnTheRight>
EIGEN_DEVICE_FUNC
const Product<Derived, DiagonalDerived, LazyProduct>
operator*(const DiagonalBase<DiagonalDerived> &diagonal) const;
template<typename OtherDerived>
typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
EIGEN_DEVICE_FUNC
typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
dot(const MatrixBase<OtherDerived>& other) const;
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
Scalar eigen2_dot(const MatrixBase<OtherDerived>& other) const;
#endif
RealScalar squaredNorm() const;
RealScalar norm() const;
EIGEN_DEVICE_FUNC RealScalar squaredNorm() const;
EIGEN_DEVICE_FUNC RealScalar norm() const;
RealScalar stableNorm() const;
RealScalar blueNorm() const;
RealScalar hypotNorm() const;
const PlainObject normalized() const;
void normalize();
EIGEN_DEVICE_FUNC const PlainObject normalized() const;
EIGEN_DEVICE_FUNC const PlainObject stableNormalized() const;
EIGEN_DEVICE_FUNC void normalize();
EIGEN_DEVICE_FUNC void stableNormalize();
const AdjointReturnType adjoint() const;
void adjointInPlace();
EIGEN_DEVICE_FUNC const AdjointReturnType adjoint() const;
EIGEN_DEVICE_FUNC void adjointInPlace();
typedef Diagonal<Derived> DiagonalReturnType;
EIGEN_DEVICE_FUNC
DiagonalReturnType diagonal();
typedef const Diagonal<const Derived> ConstDiagonalReturnType;
const ConstDiagonalReturnType diagonal() const;
typedef typename internal::add_const<Diagonal<const Derived> >::type ConstDiagonalReturnType;
EIGEN_DEVICE_FUNC
ConstDiagonalReturnType diagonal() const;
template<int Index> struct DiagonalIndexReturnType { typedef Diagonal<Derived,Index> Type; };
template<int Index> struct ConstDiagonalIndexReturnType { typedef const Diagonal<const Derived,Index> Type; };
template<int Index> typename DiagonalIndexReturnType<Index>::Type diagonal();
template<int Index> typename ConstDiagonalIndexReturnType<Index>::Type diagonal() const;
template<int Index>
EIGEN_DEVICE_FUNC
typename DiagonalIndexReturnType<Index>::Type diagonal();
// Note: The "MatrixBase::" prefixes are added to help MSVC9 to match these declarations with the later implementations.
// On the other hand they confuse MSVC8...
#if (defined _MSC_VER) && (_MSC_VER >= 1500) // 2008 or later
typename MatrixBase::template DiagonalIndexReturnType<Dynamic>::Type diagonal(Index index);
typename MatrixBase::template ConstDiagonalIndexReturnType<Dynamic>::Type diagonal(Index index) const;
#else
typename DiagonalIndexReturnType<Dynamic>::Type diagonal(Index index);
typename ConstDiagonalIndexReturnType<Dynamic>::Type diagonal(Index index) const;
#endif
template<int Index>
EIGEN_DEVICE_FUNC
typename ConstDiagonalIndexReturnType<Index>::Type diagonal() const;
#ifdef EIGEN2_SUPPORT
template<unsigned int Mode> typename internal::eigen2_part_return_type<Derived, Mode>::type part();
template<unsigned int Mode> const typename internal::eigen2_part_return_type<Derived, Mode>::type part() const;
// huuuge hack. make Eigen2's matrix.part<Diagonal>() work in eigen3. Problem: Diagonal is now a class template instead
// of an integer constant. Solution: overload the part() method template wrt template parameters list.
template<template<typename T, int n> class U>
const DiagonalWrapper<ConstDiagonalReturnType> part() const
{ return diagonal().asDiagonal(); }
#endif // EIGEN2_SUPPORT
typedef Diagonal<Derived,DynamicIndex> DiagonalDynamicIndexReturnType;
typedef typename internal::add_const<Diagonal<const Derived,DynamicIndex> >::type ConstDiagonalDynamicIndexReturnType;
EIGEN_DEVICE_FUNC
DiagonalDynamicIndexReturnType diagonal(Index index);
EIGEN_DEVICE_FUNC
ConstDiagonalDynamicIndexReturnType diagonal(Index index) const;
template<unsigned int Mode> struct TriangularViewReturnType { typedef TriangularView<Derived, Mode> Type; };
template<unsigned int Mode> struct ConstTriangularViewReturnType { typedef const TriangularView<const Derived, Mode> Type; };
template<unsigned int Mode> typename TriangularViewReturnType<Mode>::Type triangularView();
template<unsigned int Mode> typename ConstTriangularViewReturnType<Mode>::Type triangularView() const;
template<unsigned int Mode>
EIGEN_DEVICE_FUNC
typename TriangularViewReturnType<Mode>::Type triangularView();
template<unsigned int Mode>
EIGEN_DEVICE_FUNC
typename ConstTriangularViewReturnType<Mode>::Type triangularView() const;
template<unsigned int UpLo> struct SelfAdjointViewReturnType { typedef SelfAdjointView<Derived, UpLo> Type; };
template<unsigned int UpLo> struct ConstSelfAdjointViewReturnType { typedef const SelfAdjointView<const Derived, UpLo> Type; };
template<unsigned int UpLo> typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView();
template<unsigned int UpLo> typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
template<unsigned int UpLo>
EIGEN_DEVICE_FUNC
typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView();
template<unsigned int UpLo>
EIGEN_DEVICE_FUNC
typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
const SparseView<Derived> sparseView(const Scalar& m_reference = Scalar(0),
typename NumTraits<Scalar>::Real m_epsilon = NumTraits<Scalar>::dummy_precision()) const;
static const IdentityReturnType Identity();
static const IdentityReturnType Identity(Index rows, Index cols);
static const BasisReturnType Unit(Index size, Index i);
static const BasisReturnType Unit(Index i);
static const BasisReturnType UnitX();
static const BasisReturnType UnitY();
static const BasisReturnType UnitZ();
static const BasisReturnType UnitW();
const typename NumTraits<Scalar>::Real& m_epsilon = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC static const IdentityReturnType Identity();
EIGEN_DEVICE_FUNC static const IdentityReturnType Identity(Index rows, Index cols);
EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index size, Index i);
EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index i);
EIGEN_DEVICE_FUNC static const BasisReturnType UnitX();
EIGEN_DEVICE_FUNC static const BasisReturnType UnitY();
EIGEN_DEVICE_FUNC static const BasisReturnType UnitZ();
EIGEN_DEVICE_FUNC static const BasisReturnType UnitW();
EIGEN_DEVICE_FUNC
const DiagonalWrapper<const Derived> asDiagonal() const;
const PermutationWrapper<const Derived> asPermutation() const;
EIGEN_DEVICE_FUNC
Derived& setIdentity();
EIGEN_DEVICE_FUNC
Derived& setIdentity(Index rows, Index cols);
bool isIdentity(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isDiagonal(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isIdentity(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isDiagonal(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isUpperTriangular(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isLowerTriangular(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isUpperTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isLowerTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
template<typename OtherDerived>
bool isOrthogonal(const MatrixBase<OtherDerived>& other,
RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isUnitary(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isUnitary(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
/** \returns true if each coefficients of \c *this and \a other are all exactly equal.
* \warning When using floating point scalar values you probably should rather use a
* fuzzy comparison such as isApprox()
* \sa isApprox(), operator!= */
template<typename OtherDerived>
inline bool operator==(const MatrixBase<OtherDerived>& other) const
EIGEN_DEVICE_FUNC inline bool operator==(const MatrixBase<OtherDerived>& other) const
{ return cwiseEqual(other).all(); }
/** \returns true if at least one pair of coefficients of \c *this and \a other are not exactly equal to each other.
@@ -310,64 +293,50 @@ template<typename Derived> class MatrixBase
* fuzzy comparison such as isApprox()
* \sa isApprox(), operator== */
template<typename OtherDerived>
inline bool operator!=(const MatrixBase<OtherDerived>& other) const
EIGEN_DEVICE_FUNC inline bool operator!=(const MatrixBase<OtherDerived>& other) const
{ return cwiseNotEqual(other).any(); }
NoAlias<Derived,Eigen::MatrixBase > noalias();
inline const ForceAlignedAccess<Derived> forceAlignedAccess() const;
inline ForceAlignedAccess<Derived> forceAlignedAccess();
template<bool Enable> inline typename internal::add_const_on_value_type<typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type>::type forceAlignedAccessIf() const;
template<bool Enable> inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf();
// TODO forceAlignedAccess is temporarily disabled
// Need to find a nicer workaround.
inline const Derived& forceAlignedAccess() const { return derived(); }
inline Derived& forceAlignedAccess() { return derived(); }
template<bool Enable> inline const Derived& forceAlignedAccessIf() const { return derived(); }
template<bool Enable> inline Derived& forceAlignedAccessIf() { return derived(); }
Scalar trace() const;
EIGEN_DEVICE_FUNC Scalar trace() const;
/////////// Array module ///////////
template<int p> EIGEN_DEVICE_FUNC RealScalar lpNorm() const;
template<int p> RealScalar lpNorm() const;
EIGEN_DEVICE_FUNC MatrixBase<Derived>& matrix() { return *this; }
EIGEN_DEVICE_FUNC const MatrixBase<Derived>& matrix() const { return *this; }
MatrixBase<Derived>& matrix() { return *this; }
const MatrixBase<Derived>& matrix() const { return *this; }
/** \returns an \link ArrayBase Array \endlink expression of this matrix
/** \returns an \link Eigen::ArrayBase Array \endlink expression of this matrix
* \sa ArrayBase::matrix() */
ArrayWrapper<Derived> array() { return derived(); }
const ArrayWrapper<const Derived> array() const { return derived(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ArrayWrapper<Derived> array() { return ArrayWrapper<Derived>(derived()); }
/** \returns a const \link Eigen::ArrayBase Array \endlink expression of this matrix
* \sa ArrayBase::matrix() */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const ArrayWrapper<const Derived> array() const { return ArrayWrapper<const Derived>(derived()); }
/////////// LU module ///////////
const FullPivLU<PlainObject> fullPivLu() const;
const PartialPivLU<PlainObject> partialPivLu() const;
inline const FullPivLU<PlainObject> fullPivLu() const;
inline const PartialPivLU<PlainObject> partialPivLu() const;
#if EIGEN2_SUPPORT_STAGE < STAGE20_RESOLVE_API_CONFLICTS
const LU<PlainObject> lu() const;
#endif
inline const PartialPivLU<PlainObject> lu() const;
#ifdef EIGEN2_SUPPORT
const LU<PlainObject> eigen2_lu() const;
#endif
inline const Inverse<Derived> inverse() const;
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
const PartialPivLU<PlainObject> lu() const;
#endif
#ifdef EIGEN2_SUPPORT
template<typename ResultType>
void computeInverse(MatrixBase<ResultType> *result) const {
*result = this->inverse();
}
#endif
const internal::inverse_impl<Derived> inverse() const;
template<typename ResultType>
void computeInverseAndDetWithCheck(
inline void computeInverseAndDetWithCheck(
ResultType& inverse,
typename ResultType::Scalar& determinant,
bool& invertible,
const RealScalar& absDeterminantThreshold = NumTraits<Scalar>::dummy_precision()
) const;
template<typename ResultType>
void computeInverseWithCheck(
inline void computeInverseWithCheck(
ResultType& inverse,
bool& invertible,
const RealScalar& absDeterminantThreshold = NumTraits<Scalar>::dummy_precision()
@@ -376,65 +345,70 @@ template<typename Derived> class MatrixBase
/////////// Cholesky module ///////////
const LLT<PlainObject> llt() const;
const LDLT<PlainObject> ldlt() const;
inline const LLT<PlainObject> llt() const;
inline const LDLT<PlainObject> ldlt() const;
/////////// QR module ///////////
const HouseholderQR<PlainObject> householderQr() const;
const ColPivHouseholderQR<PlainObject> colPivHouseholderQr() const;
const FullPivHouseholderQR<PlainObject> fullPivHouseholderQr() const;
#ifdef EIGEN2_SUPPORT
const QR<PlainObject> qr() const;
#endif
inline const HouseholderQR<PlainObject> householderQr() const;
inline const ColPivHouseholderQR<PlainObject> colPivHouseholderQr() const;
inline const FullPivHouseholderQR<PlainObject> fullPivHouseholderQr() const;
inline const CompleteOrthogonalDecomposition<PlainObject> completeOrthogonalDecomposition() const;
EigenvaluesReturnType eigenvalues() const;
RealScalar operatorNorm() const;
/////////// Eigenvalues module ///////////
inline EigenvaluesReturnType eigenvalues() const;
inline RealScalar operatorNorm() const;
/////////// SVD module ///////////
JacobiSVD<PlainObject> jacobiSvd(unsigned int computationOptions = 0) const;
#ifdef EIGEN2_SUPPORT
SVD<PlainObject> svd() const;
#endif
inline JacobiSVD<PlainObject> jacobiSvd(unsigned int computationOptions = 0) const;
inline BDCSVD<PlainObject> bdcSvd(unsigned int computationOptions = 0) const;
/////////// Geometry module ///////////
#ifndef EIGEN_PARSED_BY_DOXYGEN
/// \internal helper struct to form the return type of the cross product
template<typename OtherDerived> struct cross_product_return_type {
typedef typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType Scalar;
typedef typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType Scalar;
typedef Matrix<Scalar,MatrixBase::RowsAtCompileTime,MatrixBase::ColsAtCompileTime> type;
};
#endif // EIGEN_PARSED_BY_DOXYGEN
template<typename OtherDerived>
typename cross_product_return_type<OtherDerived>::type
EIGEN_DEVICE_FUNC
#ifndef EIGEN_PARSED_BY_DOXYGEN
inline typename cross_product_return_type<OtherDerived>::type
#else
inline PlainObject
#endif
cross(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived>
PlainObject cross3(const MatrixBase<OtherDerived>& other) const;
PlainObject unitOrthogonal(void) const;
Matrix<Scalar,3,1> eulerAngles(Index a0, Index a1, Index a2) const;
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
ScalarMultipleReturnType operator*(const UniformScaling<Scalar>& s) const;
EIGEN_DEVICE_FUNC
inline PlainObject cross3(const MatrixBase<OtherDerived>& other) const;
EIGEN_DEVICE_FUNC
inline PlainObject unitOrthogonal(void) const;
EIGEN_DEVICE_FUNC
inline Matrix<Scalar,3,1> eulerAngles(Index a0, Index a1, Index a2) const;
// put this as separate enum value to work around possible GCC 4.3 bug (?)
enum { HomogeneousReturnTypeDirection = ColsAtCompileTime==1?Vertical:Horizontal };
enum { HomogeneousReturnTypeDirection = ColsAtCompileTime==1&&RowsAtCompileTime==1 ? ((internal::traits<Derived>::Flags&RowMajorBit)==RowMajorBit ? Horizontal : Vertical)
: ColsAtCompileTime==1 ? Vertical : Horizontal };
typedef Homogeneous<Derived, HomogeneousReturnTypeDirection> HomogeneousReturnType;
HomogeneousReturnType homogeneous() const;
#endif
EIGEN_DEVICE_FUNC
inline HomogeneousReturnType homogeneous() const;
enum {
SizeMinusOne = SizeAtCompileTime==Dynamic ? Dynamic : SizeAtCompileTime-1
};
typedef Block<const Derived,
internal::traits<Derived>::ColsAtCompileTime==1 ? SizeMinusOne : 1,
internal::traits<Derived>::ColsAtCompileTime==1 ? 1 : SizeMinusOne> ConstStartMinusOne;
typedef CwiseUnaryOp<internal::scalar_quotient1_op<typename internal::traits<Derived>::Scalar>,
const ConstStartMinusOne > HNormalizedReturnType;
const HNormalizedReturnType hnormalized() const;
typedef EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(ConstStartMinusOne,Scalar,quotient) HNormalizedReturnType;
EIGEN_DEVICE_FUNC
inline const HNormalizedReturnType hnormalized() const;
////////// Householder module ///////////
@@ -458,6 +432,15 @@ template<typename Derived> class MatrixBase
template<typename OtherScalar>
void applyOnTheRight(Index p, Index q, const JacobiRotation<OtherScalar>& j);
///////// SparseCore module /////////
template<typename OtherDerived>
EIGEN_STRONG_INLINE const typename SparseMatrixBase<OtherDerived>::template CwiseProductDenseReturnType<Derived>::Type
cwiseProduct(const SparseMatrixBase<OtherDerived> &other) const
{
return other.cwiseProduct(derived());
}
///////// MatrixFunctions module /////////
typedef typename internal::stem_function<Scalar>::type StemFunction;
@@ -469,49 +452,16 @@ template<typename Derived> class MatrixBase
const MatrixFunctionReturnValue<Derived> sin() const;
const MatrixSquareRootReturnValue<Derived> sqrt() const;
const MatrixLogarithmReturnValue<Derived> log() const;
#ifdef EIGEN2_SUPPORT
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& operator+=(const Flagged<ProductBase<ProductDerived, Lhs,Rhs>, 0,
EvalBeforeAssigningBit>& other);
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& operator-=(const Flagged<ProductBase<ProductDerived, Lhs,Rhs>, 0,
EvalBeforeAssigningBit>& other);
/** \deprecated because .lazy() is deprecated
* Overloaded for cache friendly product evaluation */
template<typename OtherDerived>
Derived& lazyAssign(const Flagged<OtherDerived, 0, EvalBeforeAssigningBit>& other)
{ return lazyAssign(other._expression()); }
template<unsigned int Added>
const Flagged<Derived, Added, 0> marked() const;
const Flagged<Derived, 0, EvalBeforeAssigningBit> lazy() const;
inline const Cwise<Derived> cwise() const;
inline Cwise<Derived> cwise();
VectorBlock<Derived> start(Index size);
const VectorBlock<const Derived> start(Index size) const;
VectorBlock<Derived> end(Index size);
const VectorBlock<const Derived> end(Index size) const;
template<int Size> VectorBlock<Derived,Size> start();
template<int Size> const VectorBlock<const Derived,Size> start() const;
template<int Size> VectorBlock<Derived,Size> end();
template<int Size> const VectorBlock<const Derived,Size> end() const;
Minor<Derived> minor(Index row, Index col);
const Minor<Derived> minor(Index row, Index col) const;
#endif
const MatrixPowerReturnValue<Derived> pow(const RealScalar& p) const;
const MatrixComplexPowerReturnValue<Derived> pow(const std::complex<RealScalar>& p) const;
protected:
MatrixBase() : Base() {}
EIGEN_DEVICE_FUNC MatrixBase() : Base() {}
private:
explicit MatrixBase(int);
MatrixBase(int,int);
template<typename OtherDerived> explicit MatrixBase(const MatrixBase<OtherDerived>&);
EIGEN_DEVICE_FUNC explicit MatrixBase(int);
EIGEN_DEVICE_FUNC MatrixBase(int,int);
template<typename OtherDerived> EIGEN_DEVICE_FUNC explicit MatrixBase(const MatrixBase<OtherDerived>&);
protected:
// mixing arrays and matrices is not legal
template<typename OtherDerived> Derived& operator+=(const ArrayBase<OtherDerived>& )
@@ -521,6 +471,51 @@ template<typename Derived> class MatrixBase
{EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
};
/***************************************************************************
* Implementation of matrix base methods
***************************************************************************/
/** replaces \c *this by \c *this * \a other.
*
* \returns a reference to \c *this
*
* Example: \include MatrixBase_applyOnTheRight.cpp
* Output: \verbinclude MatrixBase_applyOnTheRight.out
*/
template<typename Derived>
template<typename OtherDerived>
inline Derived&
MatrixBase<Derived>::operator*=(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheRight(derived());
return derived();
}
/** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=().
*
* Example: \include MatrixBase_applyOnTheRight.cpp
* Output: \verbinclude MatrixBase_applyOnTheRight.out
*/
template<typename Derived>
template<typename OtherDerived>
inline void MatrixBase<Derived>::applyOnTheRight(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheRight(derived());
}
/** replaces \c *this by \a other * \c *this.
*
* Example: \include MatrixBase_applyOnTheLeft.cpp
* Output: \verbinclude MatrixBase_applyOnTheLeft.out
*/
template<typename Derived>
template<typename OtherDerived>
inline void MatrixBase<Derived>::applyOnTheLeft(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheLeft(derived());
}
} // end namespace Eigen
#endif // EIGEN_MATRIXBASE_H

View File

@@ -4,49 +4,33 @@
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_NESTBYVALUE_H
#define EIGEN_NESTBYVALUE_H
namespace Eigen {
/** \class NestByValue
* \ingroup Core_Module
*
* \brief Expression which must be nested by value
*
* \param ExpressionType the type of the object of which we are requiring nesting-by-value
*
* This class is the return type of MatrixBase::nestByValue()
* and most of the time this is the only way it is used.
*
* \sa MatrixBase::nestByValue()
*/
namespace internal {
template<typename ExpressionType>
struct traits<NestByValue<ExpressionType> > : public traits<ExpressionType>
{};
}
/** \class NestByValue
* \ingroup Core_Module
*
* \brief Expression which must be nested by value
*
* \tparam ExpressionType the type of the object of which we are requiring nesting-by-value
*
* This class is the return type of MatrixBase::nestByValue()
* and most of the time this is the only way it is used.
*
* \sa MatrixBase::nestByValue()
*/
template<typename ExpressionType> class NestByValue
: public internal::dense_xpr_base< NestByValue<ExpressionType> >::type
{
@@ -55,29 +39,29 @@ template<typename ExpressionType> class NestByValue
typedef typename internal::dense_xpr_base<NestByValue>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(NestByValue)
inline NestByValue(const ExpressionType& matrix) : m_expression(matrix) {}
EIGEN_DEVICE_FUNC explicit inline NestByValue(const ExpressionType& matrix) : m_expression(matrix) {}
inline Index rows() const { return m_expression.rows(); }
inline Index cols() const { return m_expression.cols(); }
inline Index outerStride() const { return m_expression.outerStride(); }
inline Index innerStride() const { return m_expression.innerStride(); }
EIGEN_DEVICE_FUNC inline Index rows() const { return m_expression.rows(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_expression.cols(); }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return m_expression.outerStride(); }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return m_expression.innerStride(); }
inline const CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index row, Index col) const
{
return m_expression.coeff(row, col);
}
inline Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index col)
{
return m_expression.const_cast_derived().coeffRef(row, col);
}
inline const CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index index) const
{
return m_expression.coeff(index);
}
inline Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
}
@@ -106,7 +90,7 @@ template<typename ExpressionType> class NestByValue
m_expression.const_cast_derived().template writePacket<LoadMode>(index, x);
}
operator const ExpressionType&() const { return m_expression; }
EIGEN_DEVICE_FUNC operator const ExpressionType&() const { return m_expression; }
protected:
const ExpressionType m_expression;

View File

@@ -3,24 +3,9 @@
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_NOALIAS_H
#define EIGEN_NOALIAS_H
@@ -32,7 +17,7 @@ namespace Eigen {
*
* \brief Pseudo expression providing an operator = assuming no aliasing
*
* \param ExpressionType the type of the object on which to do the lazy assignment
* \tparam ExpressionType the type of the object on which to do the lazy assignment
*
* This class represents an expression with special assignment operators
* assuming no aliasing between the target expression and the source expression.
@@ -45,57 +30,40 @@ namespace Eigen {
template<typename ExpressionType, template <typename> class StorageBase>
class NoAlias
{
typedef typename ExpressionType::Scalar Scalar;
public:
NoAlias(ExpressionType& expression) : m_expression(expression) {}
/** Behaves like MatrixBase::lazyAssign(other)
* \sa MatrixBase::lazyAssign() */
typedef typename ExpressionType::Scalar Scalar;
explicit NoAlias(ExpressionType& expression) : m_expression(expression) {}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase<OtherDerived>& other)
{ return internal::assign_selector<ExpressionType,OtherDerived,false>::run(m_expression,other.derived()); }
/** \sa MatrixBase::operator+= */
{
call_assignment_no_alias(m_expression, other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
return m_expression;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator+=(const StorageBase<OtherDerived>& other)
{
typedef SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, ExpressionType, OtherDerived> SelfAdder;
SelfAdder tmp(m_expression);
typedef typename internal::nested<OtherDerived>::type OtherDerivedNested;
typedef typename internal::remove_all<OtherDerivedNested>::type _OtherDerivedNested;
internal::assign_selector<SelfAdder,_OtherDerivedNested,false>::run(tmp,OtherDerivedNested(other.derived()));
call_assignment_no_alias(m_expression, other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return m_expression;
}
/** \sa MatrixBase::operator-= */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator-=(const StorageBase<OtherDerived>& other)
{
typedef SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, ExpressionType, OtherDerived> SelfAdder;
SelfAdder tmp(m_expression);
typedef typename internal::nested<OtherDerived>::type OtherDerivedNested;
typedef typename internal::remove_all<OtherDerivedNested>::type _OtherDerivedNested;
internal::assign_selector<SelfAdder,_OtherDerivedNested,false>::run(tmp,OtherDerivedNested(other.derived()));
call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return m_expression;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename ProductDerived, typename Lhs, typename Rhs>
EIGEN_STRONG_INLINE ExpressionType& operator+=(const ProductBase<ProductDerived, Lhs,Rhs>& other)
{ other.derived().addTo(m_expression); return m_expression; }
template<typename ProductDerived, typename Lhs, typename Rhs>
EIGEN_STRONG_INLINE ExpressionType& operator-=(const ProductBase<ProductDerived, Lhs,Rhs>& other)
{ other.derived().subTo(m_expression); return m_expression; }
template<typename Lhs, typename Rhs, int NestingFlags>
EIGEN_STRONG_INLINE ExpressionType& operator+=(const CoeffBasedProduct<Lhs,Rhs,NestingFlags>& other)
{ return m_expression.derived() += CoeffBasedProduct<Lhs,Rhs,NestByRefBit>(other.lhs(), other.rhs()); }
template<typename Lhs, typename Rhs, int NestingFlags>
EIGEN_STRONG_INLINE ExpressionType& operator-=(const CoeffBasedProduct<Lhs,Rhs,NestingFlags>& other)
{ return m_expression.derived() -= CoeffBasedProduct<Lhs,Rhs,NestByRefBit>(other.lhs(), other.rhs()); }
#endif
EIGEN_DEVICE_FUNC
ExpressionType& expression() const
{
return m_expression;
}
protected:
ExpressionType& m_expression;
@@ -132,7 +100,7 @@ class NoAlias
template<typename Derived>
NoAlias<Derived,MatrixBase> MatrixBase<Derived>::noalias()
{
return derived();
return NoAlias<Derived, Eigen::MatrixBase >(derived());
}
} // end namespace Eigen

View File

@@ -3,48 +3,66 @@
//
// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_NUMTRAITS_H
#define EIGEN_NUMTRAITS_H
namespace Eigen {
namespace internal {
// default implementation of digits10(), based on numeric_limits if specialized,
// 0 for integer types, and log10(epsilon()) otherwise.
template< typename T,
bool use_numeric_limits = std::numeric_limits<T>::is_specialized,
bool is_integer = NumTraits<T>::IsInteger>
struct default_digits10_impl
{
static int run() { return std::numeric_limits<T>::digits10; }
};
template<typename T>
struct default_digits10_impl<T,false,false> // Floating point
{
static int run() {
using std::log10;
using std::ceil;
typedef typename NumTraits<T>::Real Real;
return int(ceil(-log10(NumTraits<Real>::epsilon())));
}
};
template<typename T>
struct default_digits10_impl<T,false,true> // Integer
{
static int run() { return 0; }
};
} // end namespace internal
/** \class NumTraits
* \ingroup Core_Module
*
* \brief Holds information about the various numeric (i.e. scalar) types allowed by Eigen.
*
* \param T the numeric type at hand
* \tparam T the numeric type at hand
*
* This class stores enums, typedefs and static methods giving information about a numeric type.
*
* The provided data consists of:
* \li A typedef \a Real, giving the "real part" type of \a T. If \a T is already real,
* then \a Real is just a typedef to \a T. If \a T is \c std::complex<U> then \a Real
* \li A typedef \c Real, giving the "real part" type of \a T. If \a T is already real,
* then \c Real is just a typedef to \a T. If \a T is \c std::complex<U> then \c Real
* is a typedef to \a U.
* \li A typedef \a NonInteger, giving the type that should be used for operations producing non-integral values,
* \li A typedef \c NonInteger, giving the type that should be used for operations producing non-integral values,
* such as quotients, square roots, etc. If \a T is a floating-point type, then this typedef just gives
* \a T again. Note however that many Eigen functions such as internal::sqrt simply refuse to
* take integers. Outside of a few cases, Eigen doesn't do automatic type promotion. Thus, this typedef is
* only intended as a helper for code that needs to explicitly promote types.
* \li A typedef \c Literal giving the type to use for numeric literals such as "2" or "0.5". For instance, for \c std::complex<U>, Literal is defined as \c U.
* Of course, this type must be fully compatible with \a T. In doubt, just use \a T here.
* \li A typedef \a Nested giving the type to use to nest a value inside of the expression tree. If you don't know what
* this means, just use \a T here.
* \li An enum value \a IsComplex. It is equal to 1 if \a T is a \c std::complex
@@ -57,10 +75,14 @@ namespace Eigen {
* \li An enum value \a IsSigned. It is equal to \c 1 if \a T is a signed type and to 0 if \a T is unsigned.
* \li An enum value \a RequireInitialization. It is equal to \c 1 if the constructor of the numeric type \a T must
* be called, and to 0 if it is safe not to call it. Default is 0 if \a T is an arithmetic type, and 1 otherwise.
* \li An epsilon() function which, unlike std::numeric_limits::epsilon(), returns a \a Real instead of a \a T.
* \li An epsilon() function which, unlike <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/epsilon">std::numeric_limits::epsilon()</a>,
* it returns a \a Real instead of a \a T.
* \li A dummy_precision() function returning a weak epsilon value. It is mainly used as a default
* value by the fuzzy comparison operators.
* \li highest() and lowest() functions returning the highest and lowest possible values respectively.
* \li digits10() function returning the number of decimal digits that can be represented without change. This is
* the analogue of <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/digits10">std::numeric_limits<T>::digits10</a>
* which is used as the default implementation if specialized.
*/
template<typename T> struct GenericNumTraits
@@ -82,22 +104,47 @@ template<typename T> struct GenericNumTraits
T
>::type NonInteger;
typedef T Nested;
typedef T Literal;
static inline Real epsilon() { return std::numeric_limits<T>::epsilon(); }
EIGEN_DEVICE_FUNC
static inline Real epsilon()
{
return numext::numeric_limits<T>::epsilon();
}
EIGEN_DEVICE_FUNC
static inline int digits10()
{
return internal::default_digits10_impl<T>::run();
}
EIGEN_DEVICE_FUNC
static inline Real dummy_precision()
{
// make sure to override this for floating-point types
return Real(0);
}
static inline T highest() { return (std::numeric_limits<T>::max)(); }
static inline T lowest() { return IsInteger ? (std::numeric_limits<T>::min)() : (-(std::numeric_limits<T>::max)()); }
#ifdef EIGEN2_SUPPORT
enum {
HasFloatingPoint = !IsInteger
};
typedef NonInteger FloatingPoint;
#endif
EIGEN_DEVICE_FUNC
static inline T highest() {
return (numext::numeric_limits<T>::max)();
}
EIGEN_DEVICE_FUNC
static inline T lowest() {
return IsInteger ? (numext::numeric_limits<T>::min)() : (-(numext::numeric_limits<T>::max)());
}
EIGEN_DEVICE_FUNC
static inline T infinity() {
return numext::numeric_limits<T>::infinity();
}
EIGEN_DEVICE_FUNC
static inline T quiet_NaN() {
return numext::numeric_limits<T>::quiet_NaN();
}
};
template<typename T> struct NumTraits : GenericNumTraits<T>
@@ -106,11 +153,13 @@ template<typename T> struct NumTraits : GenericNumTraits<T>
template<> struct NumTraits<float>
: GenericNumTraits<float>
{
EIGEN_DEVICE_FUNC
static inline float dummy_precision() { return 1e-5f; }
};
template<> struct NumTraits<double> : GenericNumTraits<double>
{
EIGEN_DEVICE_FUNC
static inline double dummy_precision() { return 1e-12; }
};
@@ -124,6 +173,7 @@ template<typename _Real> struct NumTraits<std::complex<_Real> >
: GenericNumTraits<std::complex<_Real> >
{
typedef _Real Real;
typedef typename NumTraits<_Real>::Literal Literal;
enum {
IsComplex = 1,
RequireInitialization = NumTraits<_Real>::RequireInitialization,
@@ -132,8 +182,12 @@ template<typename _Real> struct NumTraits<std::complex<_Real> >
MulCost = 4 * NumTraits<Real>::MulCost + 2 * NumTraits<Real>::AddCost
};
EIGEN_DEVICE_FUNC
static inline Real epsilon() { return NumTraits<Real>::epsilon(); }
EIGEN_DEVICE_FUNC
static inline Real dummy_precision() { return NumTraits<Real>::dummy_precision(); }
EIGEN_DEVICE_FUNC
static inline int digits10() { return NumTraits<Real>::digits10(); }
};
template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
@@ -145,18 +199,50 @@ struct NumTraits<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
typedef typename NumTraits<Scalar>::NonInteger NonIntegerScalar;
typedef Array<NonIntegerScalar, Rows, Cols, Options, MaxRows, MaxCols> NonInteger;
typedef ArrayType & Nested;
typedef typename NumTraits<Scalar>::Literal Literal;
enum {
IsComplex = NumTraits<Scalar>::IsComplex,
IsInteger = NumTraits<Scalar>::IsInteger,
IsSigned = NumTraits<Scalar>::IsSigned,
RequireInitialization = 1,
ReadCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::ReadCost,
AddCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::AddCost,
MulCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::MulCost
ReadCost = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::ReadCost,
AddCost = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::AddCost,
MulCost = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::MulCost
};
EIGEN_DEVICE_FUNC
static inline RealScalar epsilon() { return NumTraits<RealScalar>::epsilon(); }
EIGEN_DEVICE_FUNC
static inline RealScalar dummy_precision() { return NumTraits<RealScalar>::dummy_precision(); }
static inline int digits10() { return NumTraits<Scalar>::digits10(); }
};
template<> struct NumTraits<std::string>
: GenericNumTraits<std::string>
{
enum {
RequireInitialization = 1,
ReadCost = HugeCost,
AddCost = HugeCost,
MulCost = HugeCost
};
static inline int digits10() { return 0; }
private:
static inline std::string epsilon();
static inline std::string dummy_precision();
static inline std::string lowest();
static inline std::string highest();
static inline std::string infinity();
static inline std::string quiet_NaN();
};
// Empty specialization for void to allow template specialization based on NumTraits<T>::Real with T==void and SFINAE.
template<> struct NumTraits<void> {};
} // end namespace Eigen
#endif // EIGEN_NUMTRAITS_H

View File

@@ -2,40 +2,29 @@
// for linear algebra.
//
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2009-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_PERMUTATIONMATRIX_H
#define EIGEN_PERMUTATIONMATRIX_H
namespace Eigen {
template<int RowCol,typename IndicesType,typename MatrixType, typename StorageKind> class PermutedImpl;
namespace internal {
enum PermPermProduct_t {PermPermProduct};
} // end namespace internal
/** \class PermutationBase
* \ingroup Core_Module
*
* \brief Base class for permutations
*
* \param Derived the derived class
* \tparam Derived the derived class
*
* This class is the base class for all expressions representing a permutation matrix,
* internally stored as a vector of integers.
@@ -53,17 +42,6 @@ template<int RowCol,typename IndicesType,typename MatrixType, typename StorageKi
*
* \sa class PermutationMatrix, class PermutationWrapper
*/
namespace internal {
template<typename PermutationType, typename MatrixType, int Side, bool Transposed=false>
struct permut_matrix_product_retval;
template<typename PermutationType, typename MatrixType, int Side, bool Transposed=false>
struct permut_sparsematrix_product_retval;
enum PermPermProduct_t {PermPermProduct};
} // end namespace internal
template<typename Derived>
class PermutationBase : public EigenBase<Derived>
{
@@ -75,19 +53,20 @@ class PermutationBase : public EigenBase<Derived>
typedef typename Traits::IndicesType IndicesType;
enum {
Flags = Traits::Flags,
CoeffReadCost = Traits::CoeffReadCost,
RowsAtCompileTime = Traits::RowsAtCompileTime,
ColsAtCompileTime = Traits::ColsAtCompileTime,
MaxRowsAtCompileTime = Traits::MaxRowsAtCompileTime,
MaxColsAtCompileTime = Traits::MaxColsAtCompileTime
};
typedef typename Traits::Scalar Scalar;
typedef typename Traits::Index Index;
typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime,0,MaxRowsAtCompileTime,MaxColsAtCompileTime>
typedef typename Traits::StorageIndex StorageIndex;
typedef Matrix<StorageIndex,RowsAtCompileTime,ColsAtCompileTime,0,MaxRowsAtCompileTime,MaxColsAtCompileTime>
DenseMatrixType;
typedef PermutationMatrix<IndicesType::SizeAtCompileTime,IndicesType::MaxSizeAtCompileTime,Index>
typedef PermutationMatrix<IndicesType::SizeAtCompileTime,IndicesType::MaxSizeAtCompileTime,StorageIndex>
PlainPermutationType;
typedef PlainPermutationType PlainObject;
using Base::derived;
typedef Inverse<Derived> InverseReturnType;
typedef void Scalar;
#endif
/** Copies the other permutation into *this */
@@ -120,20 +99,20 @@ class PermutationBase : public EigenBase<Derived>
#endif
/** \returns the number of rows */
inline Index rows() const { return indices().size(); }
inline Index rows() const { return Index(indices().size()); }
/** \returns the number of columns */
inline Index cols() const { return indices().size(); }
inline Index cols() const { return Index(indices().size()); }
/** \returns the size of a side of the respective square matrix, i.e., the number of indices */
inline Index size() const { return indices().size(); }
inline Index size() const { return Index(indices().size()); }
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename DenseDerived>
void evalTo(MatrixBase<DenseDerived>& other) const
{
other.setZero();
for (int i=0; i<rows();++i)
for (Index i=0; i<rows(); ++i)
other.coeffRef(indices().coeff(i),i) = typename DenseDerived::Scalar(1);
}
#endif
@@ -154,23 +133,24 @@ class PermutationBase : public EigenBase<Derived>
/** Resizes to given size.
*/
inline void resize(Index size)
inline void resize(Index newSize)
{
indices().resize(size);
indices().resize(newSize);
}
/** Sets *this to be the identity permutation matrix */
void setIdentity()
{
for(Index i = 0; i < size(); ++i)
StorageIndex n = StorageIndex(size());
for(StorageIndex i = 0; i < n; ++i)
indices().coeffRef(i) = i;
}
/** Sets *this to be the identity permutation matrix of given size.
*/
void setIdentity(Index size)
void setIdentity(Index newSize)
{
resize(size);
resize(newSize);
setIdentity();
}
@@ -178,18 +158,18 @@ class PermutationBase : public EigenBase<Derived>
*
* \returns a reference to *this.
*
* \warning This is much slower than applyTranspositionOnTheRight(int,int):
* \warning This is much slower than applyTranspositionOnTheRight(Index,Index):
* this has linear complexity and requires a lot of branching.
*
* \sa applyTranspositionOnTheRight(int,int)
* \sa applyTranspositionOnTheRight(Index,Index)
*/
Derived& applyTranspositionOnTheLeft(Index i, Index j)
{
eigen_assert(i>=0 && j>=0 && i<size() && j<size());
for(Index k = 0; k < size(); ++k)
{
if(indices().coeff(k) == i) indices().coeffRef(k) = j;
else if(indices().coeff(k) == j) indices().coeffRef(k) = i;
if(indices().coeff(k) == i) indices().coeffRef(k) = StorageIndex(j);
else if(indices().coeff(k) == j) indices().coeffRef(k) = StorageIndex(i);
}
return derived();
}
@@ -200,7 +180,7 @@ class PermutationBase : public EigenBase<Derived>
*
* This is a fast operation, it only consists in swapping two indices.
*
* \sa applyTranspositionOnTheLeft(int,int)
* \sa applyTranspositionOnTheLeft(Index,Index)
*/
Derived& applyTranspositionOnTheRight(Index i, Index j)
{
@@ -211,16 +191,16 @@ class PermutationBase : public EigenBase<Derived>
/** \returns the inverse permutation matrix.
*
* \note \note_try_to_help_rvo
* \note \blank \note_try_to_help_rvo
*/
inline Transpose<PermutationBase> inverse() const
{ return derived(); }
inline InverseReturnType inverse() const
{ return InverseReturnType(derived()); }
/** \returns the tranpose permutation matrix.
*
* \note \note_try_to_help_rvo
* \note \blank \note_try_to_help_rvo
*/
inline Transpose<PermutationBase> transpose() const
{ return derived(); }
inline InverseReturnType transpose() const
{ return InverseReturnType(derived()); }
/**** multiplication helpers to hopefully get RVO ****/
@@ -230,13 +210,13 @@ class PermutationBase : public EigenBase<Derived>
template<typename OtherDerived>
void assignTranspose(const PermutationBase<OtherDerived>& other)
{
for (int i=0; i<rows();++i) indices().coeffRef(other.indices().coeff(i)) = i;
for (Index i=0; i<rows();++i) indices().coeffRef(other.indices().coeff(i)) = i;
}
template<typename Lhs,typename Rhs>
void assignProduct(const Lhs& lhs, const Rhs& rhs)
{
eigen_assert(lhs.cols() == rhs.rows());
for (int i=0; i<rows();++i) indices().coeffRef(i) = lhs.indices().coeff(rhs.indices().coeff(i));
for (Index i=0; i<rows();++i) indices().coeffRef(i) = lhs.indices().coeff(rhs.indices().coeff(i));
}
#endif
@@ -244,7 +224,7 @@ class PermutationBase : public EigenBase<Derived>
/** \returns the product permutation matrix.
*
* \note \note_try_to_help_rvo
* \note \blank \note_try_to_help_rvo
*/
template<typename Other>
inline PlainPermutationType operator*(const PermutationBase<Other>& other) const
@@ -252,57 +232,90 @@ class PermutationBase : public EigenBase<Derived>
/** \returns the product of a permutation with another inverse permutation.
*
* \note \note_try_to_help_rvo
* \note \blank \note_try_to_help_rvo
*/
template<typename Other>
inline PlainPermutationType operator*(const Transpose<PermutationBase<Other> >& other) const
inline PlainPermutationType operator*(const InverseImpl<Other,PermutationStorage>& other) const
{ return PlainPermutationType(internal::PermPermProduct, *this, other.eval()); }
/** \returns the product of an inverse permutation with another permutation.
*
* \note \note_try_to_help_rvo
* \note \blank \note_try_to_help_rvo
*/
template<typename Other> friend
inline PlainPermutationType operator*(const Transpose<PermutationBase<Other> >& other, const PermutationBase& perm)
inline PlainPermutationType operator*(const InverseImpl<Other, PermutationStorage>& other, const PermutationBase& perm)
{ return PlainPermutationType(internal::PermPermProduct, other.eval(), perm); }
/** \returns the determinant of the permutation matrix, which is either 1 or -1 depending on the parity of the permutation.
*
* This function is O(\c n) procedure allocating a buffer of \c n booleans.
*/
Index determinant() const
{
Index res = 1;
Index n = size();
Matrix<bool,RowsAtCompileTime,1,0,MaxRowsAtCompileTime> mask(n);
mask.fill(false);
Index r = 0;
while(r < n)
{
// search for the next seed
while(r<n && mask[r]) r++;
if(r>=n)
break;
// we got one, let's follow it until we are back to the seed
Index k0 = r++;
mask.coeffRef(k0) = true;
for(Index k=indices().coeff(k0); k!=k0; k=indices().coeff(k))
{
mask.coeffRef(k) = true;
res = -res;
}
}
return res;
}
protected:
};
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex> >
: traits<Matrix<_StorageIndex,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef PermutationStorage StorageKind;
typedef Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
typedef _StorageIndex StorageIndex;
typedef void Scalar;
};
}
/** \class PermutationMatrix
* \ingroup Core_Module
*
* \brief Permutation matrix
*
* \param SizeAtCompileTime the number of rows/cols, or Dynamic
* \param MaxSizeAtCompileTime the maximum number of rows/cols, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it.
* \param IndexType the interger type of the indices
* \tparam SizeAtCompileTime the number of rows/cols, or Dynamic
* \tparam MaxSizeAtCompileTime the maximum number of rows/cols, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it.
* \tparam _StorageIndex the integer type of the indices
*
* This class represents a permutation matrix, internally stored as a vector of integers.
*
* \sa class PermutationBase, class PermutationWrapper, class DiagonalMatrix
*/
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType>
struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType> >
: traits<Matrix<IndexType,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef IndexType Index;
typedef Matrix<IndexType, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
};
}
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType>
class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex> >
{
typedef PermutationBase<PermutationMatrix> Base;
typedef internal::traits<PermutationMatrix> Traits;
public:
typedef const PermutationMatrix& Nested;
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef typename Traits::IndicesType IndicesType;
typedef typename Traits::StorageIndex StorageIndex;
#endif
inline PermutationMatrix()
@@ -310,8 +323,10 @@ class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompile
/** Constructs an uninitialized permutation matrix of given size.
*/
inline PermutationMatrix(int size) : m_indices(size)
{}
explicit inline PermutationMatrix(Index size) : m_indices(size)
{
eigen_internal_assert(size <= NumTraits<StorageIndex>::highest());
}
/** Copy constructor. */
template<typename OtherDerived>
@@ -379,10 +394,13 @@ class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompile
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename Other>
PermutationMatrix(const Transpose<PermutationBase<Other> >& other)
: m_indices(other.nestedPermutation().size())
PermutationMatrix(const InverseImpl<Other,PermutationStorage>& other)
: m_indices(other.derived().nestedExpression().size())
{
for (int i=0; i<m_indices.size();++i) m_indices.coeffRef(other.nestedPermutation().indices().coeff(i)) = i;
eigen_internal_assert(m_indices.size() <= NumTraits<StorageIndex>::highest());
StorageIndex end = StorageIndex(m_indices.size());
for (StorageIndex i=0; i<end;++i)
m_indices.coeffRef(other.derived().nestedExpression().indices().coeff(i)) = i;
}
template<typename Lhs,typename Rhs>
PermutationMatrix(internal::PermPermProduct_t, const Lhs& lhs, const Rhs& rhs)
@@ -399,18 +417,20 @@ class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompile
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType, int _PacketAccess>
struct traits<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,_PacketAccess> >
: traits<Matrix<IndexType,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int _PacketAccess>
struct traits<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex>,_PacketAccess> >
: traits<Matrix<_StorageIndex,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef IndexType Index;
typedef Map<const Matrix<IndexType, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1>, _PacketAccess> IndicesType;
typedef PermutationStorage StorageKind;
typedef Map<const Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1>, _PacketAccess> IndicesType;
typedef _StorageIndex StorageIndex;
typedef void Scalar;
};
}
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType, int _PacketAccess>
class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,_PacketAccess>
: public PermutationBase<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,_PacketAccess> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int _PacketAccess>
class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex>,_PacketAccess>
: public PermutationBase<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex>,_PacketAccess> >
{
typedef PermutationBase<Map> Base;
typedef internal::traits<Map> Traits;
@@ -418,15 +438,15 @@ class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef typename Traits::IndicesType IndicesType;
typedef typename IndicesType::Scalar Index;
typedef typename IndicesType::Scalar StorageIndex;
#endif
inline Map(const Index* indices)
: m_indices(indices)
inline Map(const StorageIndex* indicesPtr)
: m_indices(indicesPtr)
{}
inline Map(const Index* indices, Index size)
: m_indices(indices,size)
inline Map(const StorageIndex* indicesPtr, Index size)
: m_indices(indicesPtr,size)
{}
/** Copies the other permutation into *this */
@@ -460,40 +480,36 @@ class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,
IndicesType m_indices;
};
/** \class PermutationWrapper
* \ingroup Core_Module
*
* \brief Class to view a vector of integers as a permutation matrix
*
* \param _IndicesType the type of the vector of integer (can be any compatible expression)
*
* This class allows to view any vector expression of integers as a permutation matrix.
*
* \sa class PermutationBase, class PermutationMatrix
*/
struct PermutationStorage {};
template<typename _IndicesType> class TranspositionsWrapper;
namespace internal {
template<typename _IndicesType>
struct traits<PermutationWrapper<_IndicesType> >
{
typedef PermutationStorage StorageKind;
typedef typename _IndicesType::Scalar Scalar;
typedef typename _IndicesType::Scalar Index;
typedef void Scalar;
typedef typename _IndicesType::Scalar StorageIndex;
typedef _IndicesType IndicesType;
enum {
RowsAtCompileTime = _IndicesType::SizeAtCompileTime,
ColsAtCompileTime = _IndicesType::SizeAtCompileTime,
MaxRowsAtCompileTime = IndicesType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = IndicesType::MaxColsAtCompileTime,
Flags = 0,
CoeffReadCost = _IndicesType::CoeffReadCost
MaxRowsAtCompileTime = IndicesType::MaxSizeAtCompileTime,
MaxColsAtCompileTime = IndicesType::MaxSizeAtCompileTime,
Flags = 0
};
};
}
/** \class PermutationWrapper
* \ingroup Core_Module
*
* \brief Class to view a vector of integers as a permutation matrix
*
* \tparam _IndicesType the type of the vector of integer (can be any compatible expression)
*
* This class allows to view any vector expression of integers as a permutation matrix.
*
* \sa class PermutationBase, class PermutationMatrix
*/
template<typename _IndicesType>
class PermutationWrapper : public PermutationBase<PermutationWrapper<_IndicesType> >
{
@@ -518,177 +534,86 @@ class PermutationWrapper : public PermutationBase<PermutationWrapper<_IndicesTyp
typename IndicesType::Nested m_indices;
};
/** \returns the matrix with the permutation applied to the columns.
*/
template<typename Derived, typename PermutationDerived>
inline const internal::permut_matrix_product_retval<PermutationDerived, Derived, OnTheRight>
operator*(const MatrixBase<Derived>& matrix,
const PermutationBase<PermutationDerived> &permutation)
template<typename MatrixDerived, typename PermutationDerived>
EIGEN_DEVICE_FUNC
const Product<MatrixDerived, PermutationDerived, AliasFreeProduct>
operator*(const MatrixBase<MatrixDerived> &matrix,
const PermutationBase<PermutationDerived>& permutation)
{
return internal::permut_matrix_product_retval
<PermutationDerived, Derived, OnTheRight>
(permutation.derived(), matrix.derived());
return Product<MatrixDerived, PermutationDerived, AliasFreeProduct>
(matrix.derived(), permutation.derived());
}
/** \returns the matrix with the permutation applied to the rows.
*/
template<typename Derived, typename PermutationDerived>
inline const internal::permut_matrix_product_retval
<PermutationDerived, Derived, OnTheLeft>
template<typename PermutationDerived, typename MatrixDerived>
EIGEN_DEVICE_FUNC
const Product<PermutationDerived, MatrixDerived, AliasFreeProduct>
operator*(const PermutationBase<PermutationDerived> &permutation,
const MatrixBase<Derived>& matrix)
const MatrixBase<MatrixDerived>& matrix)
{
return internal::permut_matrix_product_retval
<PermutationDerived, Derived, OnTheLeft>
(permutation.derived(), matrix.derived());
return Product<PermutationDerived, MatrixDerived, AliasFreeProduct>
(permutation.derived(), matrix.derived());
}
namespace internal {
template<typename PermutationType, typename MatrixType, int Side, bool Transposed>
struct traits<permut_matrix_product_retval<PermutationType, MatrixType, Side, Transposed> >
template<typename PermutationType>
class InverseImpl<PermutationType, PermutationStorage>
: public EigenBase<Inverse<PermutationType> >
{
typedef typename MatrixType::PlainObject ReturnType;
};
template<typename PermutationType, typename MatrixType, int Side, bool Transposed>
struct permut_matrix_product_retval
: public ReturnByValue<permut_matrix_product_retval<PermutationType, MatrixType, Side, Transposed> >
{
typedef typename remove_all<typename MatrixType::Nested>::type MatrixTypeNestedCleaned;
permut_matrix_product_retval(const PermutationType& perm, const MatrixType& matrix)
: m_permutation(perm), m_matrix(matrix)
{}
inline int rows() const { return m_matrix.rows(); }
inline int cols() const { return m_matrix.cols(); }
template<typename Dest> inline void evalTo(Dest& dst) const
{
const int n = Side==OnTheLeft ? rows() : cols();
if(is_same<MatrixTypeNestedCleaned,Dest>::value && extract_data(dst) == extract_data(m_matrix))
{
// apply the permutation inplace
Matrix<bool,PermutationType::RowsAtCompileTime,1,0,PermutationType::MaxRowsAtCompileTime> mask(m_permutation.size());
mask.fill(false);
int r = 0;
while(r < m_permutation.size())
{
// search for the next seed
while(r<m_permutation.size() && mask[r]) r++;
if(r>=m_permutation.size())
break;
// we got one, let's follow it until we are back to the seed
int k0 = r++;
int kPrev = k0;
mask.coeffRef(k0) = true;
for(int k=m_permutation.indices().coeff(k0); k!=k0; k=m_permutation.indices().coeff(k))
{
Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>(dst, k)
.swap(Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
(dst,((Side==OnTheLeft) ^ Transposed) ? k0 : kPrev));
mask.coeffRef(k) = true;
kPrev = k;
}
}
}
else
{
for(int i = 0; i < n; ++i)
{
Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
(dst, ((Side==OnTheLeft) ^ Transposed) ? m_permutation.indices().coeff(i) : i)
=
Block<const MatrixTypeNestedCleaned,Side==OnTheLeft ? 1 : MatrixType::RowsAtCompileTime,Side==OnTheRight ? 1 : MatrixType::ColsAtCompileTime>
(m_matrix, ((Side==OnTheRight) ^ Transposed) ? m_permutation.indices().coeff(i) : i);
}
}
}
protected:
const PermutationType& m_permutation;
typename MatrixType::Nested m_matrix;
};
/* Template partial specialization for transposed/inverse permutations */
template<typename Derived>
struct traits<Transpose<PermutationBase<Derived> > >
: traits<Derived>
{};
} // end namespace internal
template<typename Derived>
class Transpose<PermutationBase<Derived> >
: public EigenBase<Transpose<PermutationBase<Derived> > >
{
typedef Derived PermutationType;
typedef typename PermutationType::IndicesType IndicesType;
typedef typename PermutationType::PlainPermutationType PlainPermutationType;
typedef internal::traits<PermutationType> PermTraits;
protected:
InverseImpl() {}
public:
typedef Inverse<PermutationType> InverseType;
using EigenBase<Inverse<PermutationType> >::derived;
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef internal::traits<PermutationType> Traits;
typedef typename Derived::DenseMatrixType DenseMatrixType;
typedef typename PermutationType::DenseMatrixType DenseMatrixType;
enum {
Flags = Traits::Flags,
CoeffReadCost = Traits::CoeffReadCost,
RowsAtCompileTime = Traits::RowsAtCompileTime,
ColsAtCompileTime = Traits::ColsAtCompileTime,
MaxRowsAtCompileTime = Traits::MaxRowsAtCompileTime,
MaxColsAtCompileTime = Traits::MaxColsAtCompileTime
RowsAtCompileTime = PermTraits::RowsAtCompileTime,
ColsAtCompileTime = PermTraits::ColsAtCompileTime,
MaxRowsAtCompileTime = PermTraits::MaxRowsAtCompileTime,
MaxColsAtCompileTime = PermTraits::MaxColsAtCompileTime
};
typedef typename Traits::Scalar Scalar;
#endif
Transpose(const PermutationType& p) : m_permutation(p) {}
inline int rows() const { return m_permutation.rows(); }
inline int cols() const { return m_permutation.cols(); }
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename DenseDerived>
void evalTo(MatrixBase<DenseDerived>& other) const
{
other.setZero();
for (int i=0; i<rows();++i)
other.coeffRef(i, m_permutation.indices().coeff(i)) = typename DenseDerived::Scalar(1);
for (Index i=0; i<derived().rows();++i)
other.coeffRef(i, derived().nestedExpression().indices().coeff(i)) = typename DenseDerived::Scalar(1);
}
#endif
/** \return the equivalent permutation matrix */
PlainPermutationType eval() const { return *this; }
PlainPermutationType eval() const { return derived(); }
DenseMatrixType toDenseMatrix() const { return *this; }
DenseMatrixType toDenseMatrix() const { return derived(); }
/** \returns the matrix with the inverse permutation applied to the columns.
*/
template<typename OtherDerived> friend
inline const internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheRight, true>
operator*(const MatrixBase<OtherDerived>& matrix, const Transpose& trPerm)
const Product<OtherDerived, InverseType, AliasFreeProduct>
operator*(const MatrixBase<OtherDerived>& matrix, const InverseType& trPerm)
{
return internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheRight, true>(trPerm.m_permutation, matrix.derived());
return Product<OtherDerived, InverseType, AliasFreeProduct>(matrix.derived(), trPerm.derived());
}
/** \returns the matrix with the inverse permutation applied to the rows.
*/
template<typename OtherDerived>
inline const internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheLeft, true>
const Product<InverseType, OtherDerived, AliasFreeProduct>
operator*(const MatrixBase<OtherDerived>& matrix) const
{
return internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheLeft, true>(m_permutation, matrix.derived());
return Product<InverseType, OtherDerived, AliasFreeProduct>(derived(), matrix.derived());
}
const PermutationType& nestedPermutation() const { return m_permutation; }
protected:
const PermutationType& m_permutation;
};
template<typename Derived>
@@ -697,6 +622,12 @@ const PermutationWrapper<const Derived> MatrixBase<Derived>::asPermutation() con
return derived();
}
namespace internal {
template<> struct AssignmentKind<DenseShape,PermutationShape> { typedef EigenBase2EigenBase Kind; };
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_PERMUTATIONMATRIX_H

View File

@@ -4,84 +4,95 @@
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_DENSESTORAGEBASE_H
#define EIGEN_DENSESTORAGEBASE_H
#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
# define EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED for(int i=0;i<base().size();++i) coeffRef(i)=Scalar(0);
#if defined(EIGEN_INITIALIZE_MATRICES_BY_ZERO)
# define EIGEN_INITIALIZE_COEFFS
# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED for(int i=0;i<base().size();++i) coeffRef(i)=Scalar(0);
#elif defined(EIGEN_INITIALIZE_MATRICES_BY_NAN)
# define EIGEN_INITIALIZE_COEFFS
# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED for(int i=0;i<base().size();++i) coeffRef(i)=std::numeric_limits<Scalar>::quiet_NaN();
#else
# define EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
# undef EIGEN_INITIALIZE_COEFFS
# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
#endif
namespace Eigen {
namespace internal {
template<typename Index>
EIGEN_ALWAYS_INLINE void check_rows_cols_for_overflow(Index rows, Index cols)
{
// http://hg.mozilla.org/mozilla-central/file/6c8a909977d3/xpcom/ds/CheckedInt.h#l242
// we assume Index is signed
Index max_index = (size_t(1) << (8 * sizeof(Index) - 1)) - 1; // assume Index is signed
bool error = (rows < 0 || cols < 0) ? true
: (rows == 0 || cols == 0) ? false
: (rows > max_index / cols);
if (error)
throw_std_bad_alloc();
}
template<int MaxSizeAtCompileTime> struct check_rows_cols_for_overflow {
template<typename Index>
EIGEN_DEVICE_FUNC
static EIGEN_ALWAYS_INLINE void run(Index, Index)
{
}
};
template <typename Derived, typename OtherDerived = Derived, bool IsVector = bool(Derived::IsVectorAtCompileTime)> struct conservative_resize_like_impl;
template<> struct check_rows_cols_for_overflow<Dynamic> {
template<typename Index>
EIGEN_DEVICE_FUNC
static EIGEN_ALWAYS_INLINE void run(Index rows, Index cols)
{
// http://hg.mozilla.org/mozilla-central/file/6c8a909977d3/xpcom/ds/CheckedInt.h#l242
// we assume Index is signed
Index max_index = (std::size_t(1) << (8 * sizeof(Index) - 1)) - 1; // assume Index is signed
bool error = (rows == 0 || cols == 0) ? false
: (rows > max_index / cols);
if (error)
throw_std_bad_alloc();
}
};
template <typename Derived,
typename OtherDerived = Derived,
bool IsVector = bool(Derived::IsVectorAtCompileTime) && bool(OtherDerived::IsVectorAtCompileTime)>
struct conservative_resize_like_impl;
template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> struct matrix_swap_impl;
} // end namespace internal
#ifdef EIGEN_PARSED_BY_DOXYGEN
namespace doxygen {
// This is a workaround to doxygen not being able to understand the inheritance logic
// when it is hidden by the dense_xpr_base helper struct.
// Moreover, doxygen fails to include members that are not documented in the declaration body of
// MatrixBase if we inherits MatrixBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >,
// this is why we simply inherits MatrixBase, though this does not make sense.
/** This class is just a workaround for Doxygen and it does not not actually exist. */
template<typename Derived> struct dense_xpr_base_dispatcher;
/** This class is just a workaround for Doxygen and it does not not actually exist. */
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct dense_xpr_base_dispatcher<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
: public MatrixBase {};
/** This class is just a workaround for Doxygen and it does not not actually exist. */
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct dense_xpr_base_dispatcher<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
: public ArrayBase {};
} // namespace doxygen
/** \class PlainObjectBase
* \ingroup Core_Module
* \brief %Dense storage base class for matrices and arrays.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_PLAINOBJECTBASE_PLUGIN.
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_PLAINOBJECTBASE_PLUGIN.
*
* \tparam Derived is the derived type, e.g., a Matrix or Array
*
* \sa \ref TopicClassHierarchy
*/
#ifdef EIGEN_PARSED_BY_DOXYGEN
namespace internal {
// this is a warkaround to doxygen not being able to understand the inheritence logic
// when it is hidden by the dense_xpr_base helper struct.
template<typename Derived> struct dense_xpr_base_dispatcher_for_doxygen;// : public MatrixBase<Derived> {};
/** This class is just a workaround for Doxygen and it does not not actually exist. */
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct dense_xpr_base_dispatcher_for_doxygen<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
: public MatrixBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > {};
/** This class is just a workaround for Doxygen and it does not not actually exist. */
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct dense_xpr_base_dispatcher_for_doxygen<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
: public ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > {};
} // namespace internal
template<typename Derived>
class PlainObjectBase : public internal::dense_xpr_base_dispatcher_for_doxygen<Derived>
class PlainObjectBase : public doxygen::dense_xpr_base_dispatcher<Derived>
#else
template<typename Derived>
class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
@@ -92,8 +103,8 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
typedef typename internal::dense_xpr_base<Derived>::type Base;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Derived DenseType;
@@ -112,62 +123,95 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
typedef Eigen::Map<Derived, Unaligned> MapType;
friend class Eigen::Map<const Derived, Unaligned>;
typedef const Eigen::Map<const Derived, Unaligned> ConstMapType;
friend class Eigen::Map<Derived, Aligned>;
typedef Eigen::Map<Derived, Aligned> AlignedMapType;
friend class Eigen::Map<const Derived, Aligned>;
typedef const Eigen::Map<const Derived, Aligned> ConstAlignedMapType;
#if EIGEN_MAX_ALIGN_BYTES>0
// for EIGEN_MAX_ALIGN_BYTES==0, AlignedMax==Unaligned, and many compilers generate warnings for friend-ing a class twice.
friend class Eigen::Map<Derived, AlignedMax>;
friend class Eigen::Map<const Derived, AlignedMax>;
#endif
typedef Eigen::Map<Derived, AlignedMax> AlignedMapType;
typedef const Eigen::Map<const Derived, AlignedMax> ConstAlignedMapType;
template<typename StrideType> struct StridedMapType { typedef Eigen::Map<Derived, Unaligned, StrideType> type; };
template<typename StrideType> struct StridedConstMapType { typedef Eigen::Map<const Derived, Unaligned, StrideType> type; };
template<typename StrideType> struct StridedAlignedMapType { typedef Eigen::Map<Derived, Aligned, StrideType> type; };
template<typename StrideType> struct StridedConstAlignedMapType { typedef Eigen::Map<const Derived, Aligned, StrideType> type; };
template<typename StrideType> struct StridedAlignedMapType { typedef Eigen::Map<Derived, AlignedMax, StrideType> type; };
template<typename StrideType> struct StridedConstAlignedMapType { typedef Eigen::Map<const Derived, AlignedMax, StrideType> type; };
protected:
DenseStorage<Scalar, Base::MaxSizeAtCompileTime, Base::RowsAtCompileTime, Base::ColsAtCompileTime, Options> m_storage;
public:
enum { NeedsToAlign = SizeAtCompileTime != Dynamic && (internal::traits<Derived>::Flags & AlignedBit) != 0 };
enum { NeedsToAlign = (SizeAtCompileTime != Dynamic) && (internal::traits<Derived>::Alignment>0) };
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
EIGEN_DEVICE_FUNC
Base& base() { return *static_cast<Base*>(this); }
EIGEN_DEVICE_FUNC
const Base& base() const { return *static_cast<const Base*>(this); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rows() const { return m_storage.rows(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const { return m_storage.cols(); }
EIGEN_STRONG_INLINE const Scalar& coeff(Index row, Index col) const
/** This is an overloaded version of DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index,Index) const
* provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
*
* See DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const for details. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeff(Index rowId, Index colId) const
{
if(Flags & RowMajorBit)
return m_storage.data()[col + row * m_storage.cols()];
return m_storage.data()[colId + rowId * m_storage.cols()];
else // column-major
return m_storage.data()[row + col * m_storage.rows()];
return m_storage.data()[rowId + colId * m_storage.rows()];
}
/** This is an overloaded version of DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const
* provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
*
* See DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const for details. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
{
return m_storage.data()[index];
}
EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
/** This is an overloaded version of DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index,Index) const
* provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
*
* See DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index,Index) const for details. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index rowId, Index colId)
{
if(Flags & RowMajorBit)
return m_storage.data()[col + row * m_storage.cols()];
return m_storage.data()[colId + rowId * m_storage.cols()];
else // column-major
return m_storage.data()[row + col * m_storage.rows()];
return m_storage.data()[rowId + colId * m_storage.rows()];
}
/** This is an overloaded version of DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index) const
* provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
*
* See DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index) const for details. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
return m_storage.data()[index];
}
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index row, Index col) const
/** This is the const version of coeffRef(Index,Index) which is thus synonym of coeff(Index,Index).
* It is provided for convenience. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index rowId, Index colId) const
{
if(Flags & RowMajorBit)
return m_storage.data()[col + row * m_storage.cols()];
return m_storage.data()[colId + rowId * m_storage.cols()];
else // column-major
return m_storage.data()[row + col * m_storage.rows()];
return m_storage.data()[rowId + colId * m_storage.rows()];
}
/** This is the const version of coeffRef(Index) which is thus synonym of coeff(Index).
* It is provided for convenience. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index index) const
{
return m_storage.data()[index];
@@ -175,12 +219,12 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
/** \internal */
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
{
return internal::ploadt<PacketScalar, LoadMode>
(m_storage.data() + (Flags & RowMajorBit
? col + row * m_storage.cols()
: row + col * m_storage.rows()));
? colId + rowId * m_storage.cols()
: rowId + colId * m_storage.rows()));
}
/** \internal */
@@ -192,27 +236,27 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
/** \internal */
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket(Index row, Index col, const PacketScalar& x)
EIGEN_STRONG_INLINE void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
internal::pstoret<Scalar, PacketScalar, StoreMode>
(m_storage.data() + (Flags & RowMajorBit
? col + row * m_storage.cols()
: row + col * m_storage.rows()), x);
? colId + rowId * m_storage.cols()
: rowId + colId * m_storage.rows()), val);
}
/** \internal */
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket(Index index, const PacketScalar& x)
EIGEN_STRONG_INLINE void writePacket(Index index, const PacketScalar& val)
{
internal::pstoret<Scalar, PacketScalar, StoreMode>(m_storage.data() + index, x);
internal::pstoret<Scalar, PacketScalar, StoreMode>(m_storage.data() + index, val);
}
/** \returns a const pointer to the data array of this matrix */
EIGEN_STRONG_INLINE const Scalar *data() const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar *data() const
{ return m_storage.data(); }
/** \returns a pointer to the data array of this matrix */
EIGEN_STRONG_INLINE Scalar *data()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar *data()
{ return m_storage.data(); }
/** Resizes \c *this to a \a rows x \a cols matrix.
@@ -231,16 +275,21 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index) for vectors, resize(NoChange_t, Index), resize(Index, NoChange_t)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void resize(Index rows, Index cols)
{
#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
internal::check_rows_cols_for_overflow(rows, cols);
eigen_assert( EIGEN_IMPLIES(RowsAtCompileTime!=Dynamic,rows==RowsAtCompileTime)
&& EIGEN_IMPLIES(ColsAtCompileTime!=Dynamic,cols==ColsAtCompileTime)
&& EIGEN_IMPLIES(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic,rows<=MaxRowsAtCompileTime)
&& EIGEN_IMPLIES(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic,cols<=MaxColsAtCompileTime)
&& rows>=0 && cols>=0 && "Invalid sizes when resizing a matrix or array.");
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(rows, cols);
#ifdef EIGEN_INITIALIZE_COEFFS
Index size = rows*cols;
bool size_changed = size != this->size();
m_storage.resize(size, rows, cols);
if(size_changed) EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
#else
internal::check_rows_cols_for_overflow(rows, cols);
m_storage.resize(rows*cols, rows, cols);
#endif
}
@@ -256,19 +305,20 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index,Index), resize(NoChange_t, Index), resize(Index, NoChange_t)
*/
EIGEN_DEVICE_FUNC
inline void resize(Index size)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(PlainObjectBase)
eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == size);
#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
eigen_assert(((SizeAtCompileTime == Dynamic && (MaxSizeAtCompileTime==Dynamic || size<=MaxSizeAtCompileTime)) || SizeAtCompileTime == size) && size>=0);
#ifdef EIGEN_INITIALIZE_COEFFS
bool size_changed = size != this->size();
#endif
if(RowsAtCompileTime == 1)
m_storage.resize(size, 1, size);
else
m_storage.resize(size, size, 1);
#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
if(size_changed) EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
#ifdef EIGEN_INITIALIZE_COEFFS
if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
#endif
}
@@ -280,6 +330,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index,Index)
*/
EIGEN_DEVICE_FUNC
inline void resize(NoChange_t, Index cols)
{
resize(rows(), cols);
@@ -293,6 +344,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index,Index)
*/
EIGEN_DEVICE_FUNC
inline void resize(Index rows, NoChange_t)
{
resize(rows, cols());
@@ -306,10 +358,11 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void resizeLike(const EigenBase<OtherDerived>& _other)
{
const OtherDerived& other = _other.derived();
internal::check_rows_cols_for_overflow(other.rows(), other.cols());
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(other.rows(), other.cols());
const Index othersize = other.rows()*other.cols();
if(RowsAtCompileTime == 1)
{
@@ -333,6 +386,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* Matrices are resized relative to the top-left element. In case values need to be
* appended to the matrix they will be uninitialized.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResize(Index rows, Index cols)
{
internal::conservative_resize_like_impl<Derived>::run(*this, rows, cols);
@@ -345,6 +399,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* In case the matrix is growing, new rows will be uninitialized.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResize(Index rows, NoChange_t)
{
// Note: see the comment in conservativeResize(Index,Index)
@@ -358,6 +413,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* In case the matrix is growing, new columns will be uninitialized.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResize(NoChange_t, Index cols)
{
// Note: see the comment in conservativeResize(Index,Index)
@@ -372,6 +428,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* When values are appended, they will be uninitialized.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResize(Index size)
{
internal::conservative_resize_like_impl<Derived>::run(*this, size);
@@ -387,6 +444,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* appended to the matrix they will copied from \c other.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResizeLike(const DenseBase<OtherDerived>& other)
{
internal::conservative_resize_like_impl<Derived,OtherDerived>::run(*this, other);
@@ -395,6 +453,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
/** This is a special case of the templated operator=. Its purpose is to
* prevent a default operator= from hiding the templated operator=.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& operator=(const PlainObjectBase& other)
{
return _set(other);
@@ -402,6 +461,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
/** \sa MatrixBase::lazyAssign() */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& lazyAssign(const DenseBase<OtherDerived>& other)
{
_resize_to_match(other);
@@ -409,38 +469,102 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& operator=(const ReturnByValue<OtherDerived>& func)
{
resize(func.rows(), func.cols());
return Base::operator=(func);
}
EIGEN_STRONG_INLINE explicit PlainObjectBase() : m_storage()
// Prevent user from trying to instantiate PlainObjectBase objects
// by making all its constructor protected. See bug 1074.
protected:
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase() : m_storage()
{
// _check_template_params();
// EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
// EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
// FIXME is it still needed ?
/** \internal */
PlainObjectBase(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC
explicit PlainObjectBase(internal::constructor_without_unaligned_array_assert)
: m_storage(internal::constructor_without_unaligned_array_assert())
{
// _check_template_params(); EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
// _check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#endif
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
PlainObjectBase(PlainObjectBase&& other) EIGEN_NOEXCEPT
: m_storage( std::move(other.m_storage) )
{
}
EIGEN_DEVICE_FUNC
PlainObjectBase& operator=(PlainObjectBase&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_storage, other.m_storage);
return *this;
}
#endif
/** Copy constructor */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(const PlainObjectBase& other)
: Base(), m_storage(other.m_storage) { }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(Index size, Index rows, Index cols)
: m_storage(size, rows, cols)
{
// _check_template_params();
// EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
// EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
/** \copydoc MatrixBase::operator=(const EigenBase<OtherDerived>&)
/** \sa PlainObjectBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(const DenseBase<OtherDerived> &other)
: m_storage()
{
_check_template_params();
resizeLike(other);
_set_noalias(other);
}
/** \sa PlainObjectBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(const EigenBase<OtherDerived> &other)
: m_storage()
{
_check_template_params();
resizeLike(other);
*this = other.derived();
}
/** \brief Copy constructor with in-place evaluation */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(const ReturnByValue<OtherDerived>& other)
{
_check_template_params();
// FIXME this does not automatically transpose vectors if necessary
resize(other.rows(), other.cols());
other.evalTo(this->derived());
}
public:
/** \brief Copies the generic expression \a other into *this.
* \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other)
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& operator=(const EigenBase<OtherDerived> &other)
{
_resize_to_match(other);
@@ -448,21 +572,15 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
return this->derived();
}
/** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
EIGEN_STRONG_INLINE PlainObjectBase(const EigenBase<OtherDerived> &other)
: m_storage(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
{
_check_template_params();
internal::check_rows_cols_for_overflow(other.derived().rows(), other.derived().cols());
Base::operator=(other.derived());
}
/** \name Map
* These are convenience functions returning Map objects. The Map() static functions return unaligned Map objects,
* while the AlignedMap() functions return aligned Map objects and thus should be called only with 16-byte-aligned
* \a data pointers.
*
* Here is an example using strides:
* \include Matrix_Map_stride.cpp
* Output: \verbinclude Matrix_Map_stride.out
*
* \see class Map
*/
//@{
@@ -532,16 +650,16 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
//@}
using Base::setConstant;
Derived& setConstant(Index size, const Scalar& value);
Derived& setConstant(Index rows, Index cols, const Scalar& value);
EIGEN_DEVICE_FUNC Derived& setConstant(Index size, const Scalar& val);
EIGEN_DEVICE_FUNC Derived& setConstant(Index rows, Index cols, const Scalar& val);
using Base::setZero;
Derived& setZero(Index size);
Derived& setZero(Index rows, Index cols);
EIGEN_DEVICE_FUNC Derived& setZero(Index size);
EIGEN_DEVICE_FUNC Derived& setZero(Index rows, Index cols);
using Base::setOnes;
Derived& setOnes(Index size);
Derived& setOnes(Index rows, Index cols);
EIGEN_DEVICE_FUNC Derived& setOnes(Index size);
EIGEN_DEVICE_FUNC Derived& setOnes(Index rows, Index cols);
using Base::setRandom;
Derived& setRandom(Index size);
@@ -560,12 +678,14 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _resize_to_match(const EigenBase<OtherDerived>& other)
{
#ifdef EIGEN_NO_AUTOMATIC_RESIZING
eigen_assert((this->size()==0 || (IsVectorAtCompileTime ? (this->size() == other.size())
: (rows() == other.rows() && cols() == other.cols())))
&& "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined");
EIGEN_ONLY_USED_FOR_DEBUG(other);
#else
resizeLike(other);
#endif
@@ -585,25 +705,23 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \internal
*/
// aliasing is dealt once in internall::call_assignment
// so at this stage we have to assume aliasing... and resising has to be done later.
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& _set(const DenseBase<OtherDerived>& other)
{
_set_selector(other.derived(), typename internal::conditional<static_cast<bool>(int(OtherDerived::Flags) & EvalBeforeAssigningBit), internal::true_type, internal::false_type>::type());
internal::call_assignment(this->derived(), other.derived());
return this->derived();
}
template<typename OtherDerived>
EIGEN_STRONG_INLINE void _set_selector(const OtherDerived& other, const internal::true_type&) { _set_noalias(other.eval()); }
template<typename OtherDerived>
EIGEN_STRONG_INLINE void _set_selector(const OtherDerived& other, const internal::false_type&) { _set_noalias(other); }
/** \internal Like _set() but additionally makes the assumption that no aliasing effect can happen (which
* is the case when creating a new matrix) so one can enforce lazy evaluation.
*
* \sa operator=(const MatrixBase<OtherDerived>&), _set()
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& _set_noalias(const DenseBase<OtherDerived>& other)
{
// I don't think we need this resize call since the lazyAssign will anyways resize
@@ -611,44 +729,175 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
//_resize_to_match(other);
// the 'false' below means to enforce lazy evaluation. We don't use lazyAssign() because
// it wouldn't allow to copy a row-vector into a column-vector.
return internal::assign_selector<Derived,OtherDerived,false>::run(this->derived(), other.derived());
internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
return this->derived();
}
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init2(Index rows, Index cols, typename internal::enable_if<Base::SizeAtCompileTime!=2,T0>::type* = 0)
{
EIGEN_STATIC_ASSERT(bool(NumTraits<T0>::IsInteger) &&
bool(NumTraits<T1>::IsInteger),
FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED)
eigen_assert(rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
internal::check_rows_cols_for_overflow(rows, cols);
m_storage.resize(rows*cols,rows,cols);
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
resize(rows,cols);
}
template<typename T0, typename T1>
EIGEN_STRONG_INLINE void _init2(const Scalar& x, const Scalar& y, typename internal::enable_if<Base::SizeAtCompileTime==2,T0>::type* = 0)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init2(const T0& val0, const T1& val1, typename internal::enable_if<Base::SizeAtCompileTime==2,T0>::type* = 0)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
m_storage.data()[0] = Scalar(val0);
m_storage.data()[1] = Scalar(val1);
}
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init2(const Index& val0, const Index& val1,
typename internal::enable_if< (!internal::is_same<Index,Scalar>::value)
&& (internal::is_same<T0,Index>::value)
&& (internal::is_same<T1,Index>::value)
&& Base::SizeAtCompileTime==2,T1>::type* = 0)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
m_storage.data()[0] = Scalar(val0);
m_storage.data()[1] = Scalar(val1);
}
// The argument is convertible to the Index type and we either have a non 1x1 Matrix, or a dynamic-sized Array,
// then the argument is meant to be the size of the object.
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(Index size, typename internal::enable_if< (Base::SizeAtCompileTime!=1 || !internal::is_convertible<T, Scalar>::value)
&& ((!internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value || Base::SizeAtCompileTime==Dynamic)),T>::type* = 0)
{
// NOTE MSVC 2008 complains if we directly put bool(NumTraits<T>::IsInteger) as the EIGEN_STATIC_ASSERT argument.
const bool is_integer = NumTraits<T>::IsInteger;
EIGEN_UNUSED_VARIABLE(is_integer);
EIGEN_STATIC_ASSERT(is_integer,
FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED)
resize(size);
}
// We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type can be implicitely converted)
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const Scalar& val0, typename internal::enable_if<Base::SizeAtCompileTime==1 && internal::is_convertible<T, Scalar>::value,T>::type* = 0)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1)
m_storage.data()[0] = val0;
}
// We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type match the index type)
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const Index& val0,
typename internal::enable_if< (!internal::is_same<Index,Scalar>::value)
&& (internal::is_same<Index,T>::value)
&& Base::SizeAtCompileTime==1
&& internal::is_convertible<T, Scalar>::value,T*>::type* = 0)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1)
m_storage.data()[0] = Scalar(val0);
}
// Initialize a fixed size matrix from a pointer to raw data
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const Scalar* data){
this->_set_noalias(ConstMapType(data));
}
// Initialize an arbitrary matrix from a dense expression
template<typename T, typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const DenseBase<OtherDerived>& other){
this->_set_noalias(other);
}
// Initialize an arbitrary matrix from an object convertible to the Derived type.
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const Derived& other){
this->_set_noalias(other);
}
// Initialize an arbitrary matrix from a generic Eigen expression
template<typename T, typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const EigenBase<OtherDerived>& other){
this->derived() = other;
}
template<typename T, typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const ReturnByValue<OtherDerived>& other)
{
resize(other.rows(), other.cols());
other.evalTo(this->derived());
}
template<typename T, typename OtherDerived, int ColsAtCompileTime>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const RotationBase<OtherDerived,ColsAtCompileTime>& r)
{
this->derived() = r;
}
// For fixed-size Array<Scalar,...>
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const Scalar& val0,
typename internal::enable_if< Base::SizeAtCompileTime!=Dynamic
&& Base::SizeAtCompileTime!=1
&& internal::is_convertible<T, Scalar>::value
&& internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value,T>::type* = 0)
{
Base::setConstant(val0);
}
// For fixed-size Array<Index,...>
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const Index& val0,
typename internal::enable_if< (!internal::is_same<Index,Scalar>::value)
&& (internal::is_same<Index,T>::value)
&& Base::SizeAtCompileTime!=Dynamic
&& Base::SizeAtCompileTime!=1
&& internal::is_convertible<T, Scalar>::value
&& internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value,T*>::type* = 0)
{
Base::setConstant(val0);
}
template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers>
friend struct internal::matrix_swap_impl;
/** \internal generic implementation of swap for dense storage since for dynamic-sized matrices of same type it is enough to swap the
* data pointers.
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal
* \brief Override DenseBase::swap() since for dynamic-sized matrices
* of same type it is enough to swap the data pointers.
*/
template<typename OtherDerived>
void _swap(DenseBase<OtherDerived> const & other)
EIGEN_DEVICE_FUNC
void swap(DenseBase<OtherDerived> & other)
{
enum { SwapPointers = internal::is_same<Derived, OtherDerived>::value && Base::SizeAtCompileTime==Dynamic };
internal::matrix_swap_impl<Derived, OtherDerived, bool(SwapPointers)>::run(this->derived(), other.const_cast_derived());
internal::matrix_swap_impl<Derived, OtherDerived, bool(SwapPointers)>::run(this->derived(), other.derived());
}
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal
* \brief const version forwarded to DenseBase::swap
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
void swap(DenseBase<OtherDerived> const & other)
{ Base::swap(other.derived()); }
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void _check_template_params()
{
EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, (Options&RowMajor)==RowMajor)
@@ -662,16 +911,16 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
&& (Options & (DontAlign|RowMajor)) == Options),
INVALID_MATRIX_TEMPLATE_PARAMETERS)
}
#endif
private:
enum { ThisConstantIsPrivateInPlainObjectBase };
enum { IsPlainObjectBase = 1 };
#endif
};
namespace internal {
template <typename Derived, typename OtherDerived, bool IsVector>
struct internal::conservative_resize_like_impl
struct conservative_resize_like_impl
{
typedef typename Derived::Index Index;
static void run(DenseBase<Derived>& _this, Index rows, Index cols)
{
if (_this.rows() == rows && _this.cols() == cols) return;
@@ -680,15 +929,15 @@ struct internal::conservative_resize_like_impl
if ( ( Derived::IsRowMajor && _this.cols() == cols) || // row-major and we change only the number of rows
(!Derived::IsRowMajor && _this.rows() == rows) ) // column-major and we change only the number of columns
{
internal::check_rows_cols_for_overflow(rows, cols);
internal::check_rows_cols_for_overflow<Derived::MaxSizeAtCompileTime>::run(rows, cols);
_this.derived().m_storage.conservativeResize(rows*cols,rows,cols);
}
else
{
// The storage order does not allow us to use reallocation.
typename Derived::PlainObject tmp(rows,cols);
const Index common_rows = (std::min)(rows, _this.rows());
const Index common_cols = (std::min)(cols, _this.cols());
const Index common_rows = numext::mini(rows, _this.rows());
const Index common_cols = numext::mini(cols, _this.cols());
tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols);
_this.derived().swap(tmp);
}
@@ -721,20 +970,22 @@ struct internal::conservative_resize_like_impl
{
// The storage order does not allow us to use reallocation.
typename Derived::PlainObject tmp(other);
const Index common_rows = (std::min)(tmp.rows(), _this.rows());
const Index common_cols = (std::min)(tmp.cols(), _this.cols());
const Index common_rows = numext::mini(tmp.rows(), _this.rows());
const Index common_cols = numext::mini(tmp.cols(), _this.cols());
tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols);
_this.derived().swap(tmp);
}
}
};
namespace internal {
// Here, the specialization for vectors inherits from the general matrix case
// to allow calling .conservativeResize(rows,cols) on vectors.
template <typename Derived, typename OtherDerived>
struct conservative_resize_like_impl<Derived,OtherDerived,true>
: conservative_resize_like_impl<Derived,OtherDerived,false>
{
typedef typename Derived::Index Index;
using conservative_resize_like_impl<Derived,OtherDerived,false>::run;
static void run(DenseBase<Derived>& _this, Index size)
{
const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : size;
@@ -760,6 +1011,7 @@ struct conservative_resize_like_impl<Derived,OtherDerived,true>
template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers>
struct matrix_swap_impl
{
EIGEN_DEVICE_FUNC
static inline void run(MatrixTypeA& a, MatrixTypeB& b)
{
a.base().swap(b);
@@ -769,6 +1021,7 @@ struct matrix_swap_impl
template<typename MatrixTypeA, typename MatrixTypeB>
struct matrix_swap_impl<MatrixTypeA, MatrixTypeB, true>
{
EIGEN_DEVICE_FUNC
static inline void run(MatrixTypeA& a, MatrixTypeB& b)
{
static_cast<typename MatrixTypeA::Base&>(a).m_storage.swap(static_cast<typename MatrixTypeB::Base&>(b).m_storage);

View File

@@ -3,111 +3,184 @@
//
// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_PRODUCT_H
#define EIGEN_PRODUCT_H
template<typename Lhs, typename Rhs> class Product;
template<typename Lhs, typename Rhs, typename StorageKind> class ProductImpl;
namespace Eigen {
template<typename Lhs, typename Rhs, int Option, typename StorageKind> class ProductImpl;
namespace internal {
template<typename Lhs, typename Rhs, int Option>
struct traits<Product<Lhs, Rhs, Option> >
{
typedef typename remove_all<Lhs>::type LhsCleaned;
typedef typename remove_all<Rhs>::type RhsCleaned;
typedef traits<LhsCleaned> LhsTraits;
typedef traits<RhsCleaned> RhsTraits;
typedef MatrixXpr XprKind;
typedef typename ScalarBinaryOpTraits<typename traits<LhsCleaned>::Scalar, typename traits<RhsCleaned>::Scalar>::ReturnType Scalar;
typedef typename product_promote_storage_type<typename LhsTraits::StorageKind,
typename RhsTraits::StorageKind,
internal::product_type<Lhs,Rhs>::ret>::ret StorageKind;
typedef typename promote_index_type<typename LhsTraits::StorageIndex,
typename RhsTraits::StorageIndex>::type StorageIndex;
enum {
RowsAtCompileTime = LhsTraits::RowsAtCompileTime,
ColsAtCompileTime = RhsTraits::ColsAtCompileTime,
MaxRowsAtCompileTime = LhsTraits::MaxRowsAtCompileTime,
MaxColsAtCompileTime = RhsTraits::MaxColsAtCompileTime,
// FIXME: only needed by GeneralMatrixMatrixTriangular
InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(LhsTraits::ColsAtCompileTime, RhsTraits::RowsAtCompileTime),
// The storage order is somewhat arbitrary here. The correct one will be determined through the evaluator.
Flags = (MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1) ? RowMajorBit
: (MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1) ? 0
: ( ((LhsTraits::Flags&NoPreferredStorageOrderBit) && (RhsTraits::Flags&RowMajorBit))
|| ((RhsTraits::Flags&NoPreferredStorageOrderBit) && (LhsTraits::Flags&RowMajorBit)) ) ? RowMajorBit
: NoPreferredStorageOrderBit
};
};
} // end namespace internal
/** \class Product
* \ingroup Core_Module
*
* \brief Expression of the product of two arbitrary matrices or vectors
*
* \param Lhs the type of the left-hand side expression
* \param Rhs the type of the right-hand side expression
* \tparam _Lhs the type of the left-hand side expression
* \tparam _Rhs the type of the right-hand side expression
*
* This class represents an expression of the product of two arbitrary matrices.
*
* The other template parameters are:
* \tparam Option can be DefaultProduct, AliasFreeProduct, or LazyProduct
*
*/
namespace internal {
template<typename Lhs, typename Rhs>
struct traits<Product<Lhs, Rhs> >
{
typedef MatrixXpr XprKind;
typedef typename remove_all<Lhs>::type LhsCleaned;
typedef typename remove_all<Rhs>::type RhsCleaned;
typedef typename scalar_product_traits<typename traits<LhsCleaned>::Scalar, typename traits<RhsCleaned>::Scalar>::ReturnType Scalar;
typedef typename promote_storage_type<typename traits<LhsCleaned>::StorageKind,
typename traits<RhsCleaned>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<typename traits<LhsCleaned>::Index,
typename traits<RhsCleaned>::Index>::type Index;
enum {
RowsAtCompileTime = LhsCleaned::RowsAtCompileTime,
ColsAtCompileTime = RhsCleaned::ColsAtCompileTime,
MaxRowsAtCompileTime = LhsCleaned::MaxRowsAtCompileTime,
MaxColsAtCompileTime = RhsCleaned::MaxColsAtCompileTime,
Flags = (MaxRowsAtCompileTime==1 ? RowMajorBit : 0), // TODO should be no storage order
CoeffReadCost = 0 // TODO CoeffReadCost should not be part of the expression traits
};
};
} // end namespace internal
template<typename Lhs, typename Rhs>
class Product : public ProductImpl<Lhs,Rhs,typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind,
typename internal::traits<Rhs>::StorageKind>::ret>
template<typename _Lhs, typename _Rhs, int Option>
class Product : public ProductImpl<_Lhs,_Rhs,Option,
typename internal::product_promote_storage_type<typename internal::traits<_Lhs>::StorageKind,
typename internal::traits<_Rhs>::StorageKind,
internal::product_type<_Lhs,_Rhs>::ret>::ret>
{
public:
typedef _Lhs Lhs;
typedef _Rhs Rhs;
typedef typename ProductImpl<
Lhs, Rhs,
typename internal::promote_storage_type<typename Lhs::StorageKind,
typename Rhs::StorageKind>::ret>::Base Base;
Lhs, Rhs, Option,
typename internal::product_promote_storage_type<typename internal::traits<Lhs>::StorageKind,
typename internal::traits<Rhs>::StorageKind,
internal::product_type<Lhs,Rhs>::ret>::ret>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(Product)
typedef typename Lhs::Nested LhsNested;
typedef typename Rhs::Nested RhsNested;
typedef typename internal::ref_selector<Lhs>::type LhsNested;
typedef typename internal::ref_selector<Rhs>::type RhsNested;
typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
Product(const Lhs& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs)
EIGEN_DEVICE_FUNC Product(const Lhs& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs)
{
eigen_assert(lhs.cols() == rhs.rows()
&& "invalid matrix product"
&& "if you wanted a coeff-wise or a dot product use the respective explicit functions");
}
inline Index rows() const { return m_lhs.rows(); }
inline Index cols() const { return m_rhs.cols(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rows() const { return m_lhs.rows(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index cols() const { return m_rhs.cols(); }
const LhsNestedCleaned& lhs() const { return m_lhs; }
const RhsNestedCleaned& rhs() const { return m_rhs; }
EIGEN_DEVICE_FUNC const LhsNestedCleaned& lhs() const { return m_lhs; }
EIGEN_DEVICE_FUNC const RhsNestedCleaned& rhs() const { return m_rhs; }
protected:
const LhsNested m_lhs;
const RhsNested m_rhs;
LhsNested m_lhs;
RhsNested m_rhs;
};
template<typename Lhs, typename Rhs>
class ProductImpl<Lhs,Rhs,Dense> : public internal::dense_xpr_base<Product<Lhs,Rhs> >::type
namespace internal {
template<typename Lhs, typename Rhs, int Option, int ProductTag = internal::product_type<Lhs,Rhs>::ret>
class dense_product_base
: public internal::dense_xpr_base<Product<Lhs,Rhs,Option> >::type
{};
/** Convertion to scalar for inner-products */
template<typename Lhs, typename Rhs, int Option>
class dense_product_base<Lhs, Rhs, Option, InnerProduct>
: public internal::dense_xpr_base<Product<Lhs,Rhs,Option> >::type
{
typedef Product<Lhs, Rhs> Derived;
public:
typedef typename internal::dense_xpr_base<Product<Lhs, Rhs> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
typedef Product<Lhs,Rhs,Option> ProductXpr;
typedef typename internal::dense_xpr_base<ProductXpr>::type Base;
public:
using Base::derived;
typedef typename Base::Scalar Scalar;
EIGEN_STRONG_INLINE operator const Scalar() const
{
return internal::evaluator<ProductXpr>(derived()).coeff(0,0);
}
};
} // namespace internal
// Generic API dispatcher
template<typename Lhs, typename Rhs, int Option, typename StorageKind>
class ProductImpl : public internal::generic_xpr_base<Product<Lhs,Rhs,Option>, MatrixXpr, StorageKind>::type
{
public:
typedef typename internal::generic_xpr_base<Product<Lhs,Rhs,Option>, MatrixXpr, StorageKind>::type Base;
};
template<typename Lhs, typename Rhs, int Option>
class ProductImpl<Lhs,Rhs,Option,Dense>
: public internal::dense_product_base<Lhs,Rhs,Option>
{
typedef Product<Lhs, Rhs, Option> Derived;
public:
typedef typename internal::dense_product_base<Lhs, Rhs, Option> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
protected:
enum {
IsOneByOne = (RowsAtCompileTime == 1 || RowsAtCompileTime == Dynamic) &&
(ColsAtCompileTime == 1 || ColsAtCompileTime == Dynamic),
EnableCoeff = IsOneByOne || Option==LazyProduct
};
public:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index row, Index col) const
{
EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS);
eigen_assert( (Option==LazyProduct) || (this->rows() == 1 && this->cols() == 1) );
return internal::evaluator<Derived>(derived()).coeff(row,col);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index i) const
{
EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS);
eigen_assert( (Option==LazyProduct) || (this->rows() == 1 && this->cols() == 1) );
return internal::evaluator<Derived>(derived()).coeff(i);
}
};
} // end namespace Eigen
#endif // EIGEN_PRODUCT_H

View File

@@ -1,293 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_PRODUCTBASE_H
#define EIGEN_PRODUCTBASE_H
namespace Eigen {
/** \class ProductBase
* \ingroup Core_Module
*
*/
namespace internal {
template<typename Derived, typename _Lhs, typename _Rhs>
struct traits<ProductBase<Derived,_Lhs,_Rhs> >
{
typedef MatrixXpr XprKind;
typedef typename remove_all<_Lhs>::type Lhs;
typedef typename remove_all<_Rhs>::type Rhs;
typedef typename scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType Scalar;
typedef typename promote_storage_type<typename traits<Lhs>::StorageKind,
typename traits<Rhs>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<typename traits<Lhs>::Index,
typename traits<Rhs>::Index>::type Index;
enum {
RowsAtCompileTime = traits<Lhs>::RowsAtCompileTime,
ColsAtCompileTime = traits<Rhs>::ColsAtCompileTime,
MaxRowsAtCompileTime = traits<Lhs>::MaxRowsAtCompileTime,
MaxColsAtCompileTime = traits<Rhs>::MaxColsAtCompileTime,
Flags = (MaxRowsAtCompileTime==1 ? RowMajorBit : 0)
| EvalBeforeNestingBit | EvalBeforeAssigningBit | NestByRefBit,
// Note that EvalBeforeNestingBit and NestByRefBit
// are not used in practice because nested is overloaded for products
CoeffReadCost = 0 // FIXME why is it needed ?
};
};
}
#define EIGEN_PRODUCT_PUBLIC_INTERFACE(Derived) \
typedef ProductBase<Derived, Lhs, Rhs > Base; \
EIGEN_DENSE_PUBLIC_INTERFACE(Derived) \
typedef typename Base::LhsNested LhsNested; \
typedef typename Base::_LhsNested _LhsNested; \
typedef typename Base::LhsBlasTraits LhsBlasTraits; \
typedef typename Base::ActualLhsType ActualLhsType; \
typedef typename Base::_ActualLhsType _ActualLhsType; \
typedef typename Base::RhsNested RhsNested; \
typedef typename Base::_RhsNested _RhsNested; \
typedef typename Base::RhsBlasTraits RhsBlasTraits; \
typedef typename Base::ActualRhsType ActualRhsType; \
typedef typename Base::_ActualRhsType _ActualRhsType; \
using Base::m_lhs; \
using Base::m_rhs;
template<typename Derived, typename Lhs, typename Rhs>
class ProductBase : public MatrixBase<Derived>
{
public:
typedef MatrixBase<Derived> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(ProductBase)
typedef typename Lhs::Nested LhsNested;
typedef typename internal::remove_all<LhsNested>::type _LhsNested;
typedef internal::blas_traits<_LhsNested> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
typedef typename internal::remove_all<ActualLhsType>::type _ActualLhsType;
typedef typename internal::traits<Lhs>::Scalar LhsScalar;
typedef typename Rhs::Nested RhsNested;
typedef typename internal::remove_all<RhsNested>::type _RhsNested;
typedef internal::blas_traits<_RhsNested> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
typedef typename internal::remove_all<ActualRhsType>::type _ActualRhsType;
typedef typename internal::traits<Rhs>::Scalar RhsScalar;
// Diagonal of a product: no need to evaluate the arguments because they are going to be evaluated only once
typedef CoeffBasedProduct<LhsNested, RhsNested, 0> FullyLazyCoeffBaseProductType;
public:
typedef typename Base::PlainObject PlainObject;
ProductBase(const Lhs& lhs, const Rhs& rhs)
: m_lhs(lhs), m_rhs(rhs)
{
eigen_assert(lhs.cols() == rhs.rows()
&& "invalid matrix product"
&& "if you wanted a coeff-wise or a dot product use the respective explicit functions");
}
inline Index rows() const { return m_lhs.rows(); }
inline Index cols() const { return m_rhs.cols(); }
template<typename Dest>
inline void evalTo(Dest& dst) const { dst.setZero(); scaleAndAddTo(dst,Scalar(1)); }
template<typename Dest>
inline void addTo(Dest& dst) const { scaleAndAddTo(dst,Scalar(1)); }
template<typename Dest>
inline void subTo(Dest& dst) const { scaleAndAddTo(dst,Scalar(-1)); }
template<typename Dest>
inline void scaleAndAddTo(Dest& dst,Scalar alpha) const { derived().scaleAndAddTo(dst,alpha); }
const _LhsNested& lhs() const { return m_lhs; }
const _RhsNested& rhs() const { return m_rhs; }
// Implicit conversion to the nested type (trigger the evaluation of the product)
operator const PlainObject& () const
{
m_result.resize(m_lhs.rows(), m_rhs.cols());
derived().evalTo(m_result);
return m_result;
}
const Diagonal<const FullyLazyCoeffBaseProductType,0> diagonal() const
{ return FullyLazyCoeffBaseProductType(m_lhs, m_rhs); }
template<int Index>
const Diagonal<FullyLazyCoeffBaseProductType,Index> diagonal() const
{ return FullyLazyCoeffBaseProductType(m_lhs, m_rhs); }
const Diagonal<FullyLazyCoeffBaseProductType,Dynamic> diagonal(Index index) const
{ return FullyLazyCoeffBaseProductType(m_lhs, m_rhs).diagonal(index); }
// restrict coeff accessors to 1x1 expressions. No need to care about mutators here since this isnt a Lvalue expression
typename Base::CoeffReturnType coeff(Index row, Index col) const
{
#ifdef EIGEN2_SUPPORT
return lhs().row(row).cwiseProduct(rhs().col(col).transpose()).sum();
#else
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
Matrix<Scalar,1,1> result = *this;
return result.coeff(row,col);
#endif
}
typename Base::CoeffReturnType coeff(Index i) const
{
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
Matrix<Scalar,1,1> result = *this;
return result.coeff(i);
}
const Scalar& coeffRef(Index row, Index col) const
{
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
return derived().coeffRef(row,col);
}
const Scalar& coeffRef(Index i) const
{
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
return derived().coeffRef(i);
}
protected:
LhsNested m_lhs;
RhsNested m_rhs;
mutable PlainObject m_result;
};
// here we need to overload the nested rule for products
// such that the nested type is a const reference to a plain matrix
namespace internal {
template<typename Lhs, typename Rhs, int Mode, int N, typename PlainObject>
struct nested<GeneralProduct<Lhs,Rhs,Mode>, N, PlainObject>
{
typedef PlainObject const& type;
};
}
template<typename NestedProduct>
class ScaledProduct;
// Note that these two operator* functions are not defined as member
// functions of ProductBase, because, otherwise we would have to
// define all overloads defined in MatrixBase. Furthermore, Using
// "using Base::operator*" would not work with MSVC.
//
// Also note that here we accept any compatible scalar types
template<typename Derived,typename Lhs,typename Rhs>
const ScaledProduct<Derived>
operator*(const ProductBase<Derived,Lhs,Rhs>& prod, typename Derived::Scalar x)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
typename internal::enable_if<!internal::is_same<typename Derived::Scalar,typename Derived::RealScalar>::value,
const ScaledProduct<Derived> >::type
operator*(const ProductBase<Derived,Lhs,Rhs>& prod, typename Derived::RealScalar x)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
const ScaledProduct<Derived>
operator*(typename Derived::Scalar x,const ProductBase<Derived,Lhs,Rhs>& prod)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
typename internal::enable_if<!internal::is_same<typename Derived::Scalar,typename Derived::RealScalar>::value,
const ScaledProduct<Derived> >::type
operator*(typename Derived::RealScalar x,const ProductBase<Derived,Lhs,Rhs>& prod)
{ return ScaledProduct<Derived>(prod.derived(), x); }
namespace internal {
template<typename NestedProduct>
struct traits<ScaledProduct<NestedProduct> >
: traits<ProductBase<ScaledProduct<NestedProduct>,
typename NestedProduct::_LhsNested,
typename NestedProduct::_RhsNested> >
{
typedef typename traits<NestedProduct>::StorageKind StorageKind;
};
}
template<typename NestedProduct>
class ScaledProduct
: public ProductBase<ScaledProduct<NestedProduct>,
typename NestedProduct::_LhsNested,
typename NestedProduct::_RhsNested>
{
public:
typedef ProductBase<ScaledProduct<NestedProduct>,
typename NestedProduct::_LhsNested,
typename NestedProduct::_RhsNested> Base;
typedef typename Base::Scalar Scalar;
typedef typename Base::PlainObject PlainObject;
// EIGEN_PRODUCT_PUBLIC_INTERFACE(ScaledProduct)
ScaledProduct(const NestedProduct& prod, Scalar x)
: Base(prod.lhs(),prod.rhs()), m_prod(prod), m_alpha(x) {}
template<typename Dest>
inline void evalTo(Dest& dst) const { dst.setZero(); scaleAndAddTo(dst, Scalar(1)); }
template<typename Dest>
inline void addTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(1)); }
template<typename Dest>
inline void subTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(-1)); }
template<typename Dest>
inline void scaleAndAddTo(Dest& dst,Scalar alpha) const { m_prod.derived().scaleAndAddTo(dst,alpha * m_alpha); }
const Scalar& alpha() const { return m_alpha; }
protected:
const NestedProduct& m_prod;
Scalar m_alpha;
};
/** \internal
* Overloaded to perform an efficient C = (A*B).lazy() */
template<typename Derived>
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& MatrixBase<Derived>::lazyAssign(const ProductBase<ProductDerived, Lhs,Rhs>& other)
{
other.derived().evalTo(derived());
return derived();
}
} // end namespace Eigen
#endif // EIGEN_PRODUCTBASE_H

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@@ -3,24 +3,9 @@
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_RANDOM_H
#define EIGEN_RANDOM_H
@@ -31,8 +16,7 @@ namespace internal {
template<typename Scalar> struct scalar_random_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_random_op)
template<typename Index>
inline const Scalar operator() (Index, Index = 0) const { return random<Scalar>(); }
inline const Scalar operator() () const { return random<Scalar>(); }
};
template<typename Scalar>
@@ -43,12 +27,18 @@ struct functor_traits<scalar_random_op<Scalar> >
/** \returns a random matrix expression
*
* Numbers are uniformly spread through their whole definition range for integer types,
* and in the [-1:1] range for floating point scalar types.
*
* The parameters \a rows and \a cols are the number of rows and of columns of
* the returned matrix. Must be compatible with this MatrixBase type.
*
* \not_reentrant
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
* it is redundant to pass \a rows and \a cols as arguments, so Random() should be used
* instead.
*
*
* Example: \include MatrixBase_random_int_int.cpp
* Output: \verbinclude MatrixBase_random_int_int.out
@@ -56,22 +46,28 @@ struct functor_traits<scalar_random_op<Scalar> >
* This expression has the "evaluate before nesting" flag so that it will be evaluated into
* a temporary matrix whenever it is nested in a larger expression. This prevents unexpected
* behavior with expressions involving random matrices.
*
* See DenseBase::NullaryExpr(Index, const CustomNullaryOp&) for an example using C++11 random generators.
*
* \sa MatrixBase::setRandom(), MatrixBase::Random(Index), MatrixBase::Random()
* \sa DenseBase::setRandom(), DenseBase::Random(Index), DenseBase::Random()
*/
template<typename Derived>
inline const CwiseNullaryOp<internal::scalar_random_op<typename internal::traits<Derived>::Scalar>, Derived>
inline const typename DenseBase<Derived>::RandomReturnType
DenseBase<Derived>::Random(Index rows, Index cols)
{
return NullaryExpr(rows, cols, internal::scalar_random_op<Scalar>());
}
/** \returns a random vector expression
*
* Numbers are uniformly spread through their whole definition range for integer types,
* and in the [-1:1] range for floating point scalar types.
*
* The parameter \a size is the size of the returned vector.
* Must be compatible with this MatrixBase type.
*
* \only_for_vectors
* \not_reentrant
*
* This variant is meant to be used for dynamic-size vector types. For fixed-size types,
* it is redundant to pass \a size as argument, so Random() should be used
@@ -84,10 +80,10 @@ DenseBase<Derived>::Random(Index rows, Index cols)
* a temporary vector whenever it is nested in a larger expression. This prevents unexpected
* behavior with expressions involving random matrices.
*
* \sa MatrixBase::setRandom(), MatrixBase::Random(Index,Index), MatrixBase::Random()
* \sa DenseBase::setRandom(), DenseBase::Random(Index,Index), DenseBase::Random()
*/
template<typename Derived>
inline const CwiseNullaryOp<internal::scalar_random_op<typename internal::traits<Derived>::Scalar>, Derived>
inline const typename DenseBase<Derived>::RandomReturnType
DenseBase<Derived>::Random(Index size)
{
return NullaryExpr(size, internal::scalar_random_op<Scalar>());
@@ -95,6 +91,9 @@ DenseBase<Derived>::Random(Index size)
/** \returns a fixed-size random matrix or vector expression
*
* Numbers are uniformly spread through their whole definition range for integer types,
* and in the [-1:1] range for floating point scalar types.
*
* This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
* need to use the variants taking size arguments.
*
@@ -104,11 +103,13 @@ DenseBase<Derived>::Random(Index size)
* This expression has the "evaluate before nesting" flag so that it will be evaluated into
* a temporary matrix whenever it is nested in a larger expression. This prevents unexpected
* behavior with expressions involving random matrices.
*
* \not_reentrant
*
* \sa MatrixBase::setRandom(), MatrixBase::Random(Index,Index), MatrixBase::Random(Index)
* \sa DenseBase::setRandom(), DenseBase::Random(Index,Index), DenseBase::Random(Index)
*/
template<typename Derived>
inline const CwiseNullaryOp<internal::scalar_random_op<typename internal::traits<Derived>::Scalar>, Derived>
inline const typename DenseBase<Derived>::RandomReturnType
DenseBase<Derived>::Random()
{
return NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_random_op<Scalar>());
@@ -116,6 +117,11 @@ DenseBase<Derived>::Random()
/** Sets all coefficients in this expression to random values.
*
* Numbers are uniformly spread through their whole definition range for integer types,
* and in the [-1:1] range for floating point scalar types.
*
* \not_reentrant
*
* Example: \include MatrixBase_setRandom.cpp
* Output: \verbinclude MatrixBase_setRandom.out
*
@@ -127,32 +133,41 @@ inline Derived& DenseBase<Derived>::setRandom()
return *this = Random(rows(), cols());
}
/** Resizes to the given \a size, and sets all coefficients in this expression to random values.
/** Resizes to the given \a newSize, and sets all coefficients in this expression to random values.
*
* Numbers are uniformly spread through their whole definition range for integer types,
* and in the [-1:1] range for floating point scalar types.
*
* \only_for_vectors
* \not_reentrant
*
* Example: \include Matrix_setRandom_int.cpp
* Output: \verbinclude Matrix_setRandom_int.out
*
* \sa MatrixBase::setRandom(), setRandom(Index,Index), class CwiseNullaryOp, MatrixBase::Random()
* \sa DenseBase::setRandom(), setRandom(Index,Index), class CwiseNullaryOp, DenseBase::Random()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setRandom(Index size)
PlainObjectBase<Derived>::setRandom(Index newSize)
{
resize(size);
resize(newSize);
return setRandom();
}
/** Resizes to the given size, and sets all coefficients in this expression to random values.
*
* Numbers are uniformly spread through their whole definition range for integer types,
* and in the [-1:1] range for floating point scalar types.
*
* \not_reentrant
*
* \param rows the new number of rows
* \param cols the new number of columns
*
* Example: \include Matrix_setRandom_int_int.cpp
* Output: \verbinclude Matrix_setRandom_int_int.out
*
* \sa MatrixBase::setRandom(), setRandom(Index), class CwiseNullaryOp, MatrixBase::Random()
* \sa DenseBase::setRandom(), setRandom(Index), class CwiseNullaryOp, DenseBase::Random()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&

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