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2759 Commits
3.2 ... 3.3.2

Author SHA1 Message Date
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
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
1102 changed files with 137021 additions and 45174 deletions

41
.gitignore vendored
View File

@@ -1,41 +0,0 @@
qrc_*cxx
*.orig
*.pyc
*.diff
diff
*.save
save
*.old
*.gmo
*.qm
core
core.*
*.bak
*~
*.build*
*.moc.*
*.moc
ui_*
CMakeCache.txt
tags
.*.swp
activity.png
*.out
*.php*
*.log
*.orig
*.rej
log
patch
*.patch
a
a.*
lapack/testing
lapack/reference
.*project
.settings
Makefile
!ci/build.gitlab-ci.yml
!scripts/buildtests.in
!Eigen/Core
!Eigen/src/Core

View File

@@ -1,28 +0,0 @@
# This file is part of Eigen, a lightweight C++ template library
# for linear algebra.
#
# Copyright (C) 2023, The Eigen Authors
#
# 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/.
stages:
- build
- deploy
variables:
# CMake build directory.
EIGEN_CI_BUILDDIR: .build
# Specify the CMake build target.
EIGEN_CI_BUILD_TARGET: ""
# If a test regex is specified, that will be selected.
# Otherwise, we will try a label if specified.
EIGEN_CI_CTEST_REGEX: ""
EIGEN_CI_CTEST_LABEL: ""
EIGEN_CI_CTEST_ARGS: ""
include:
- "/ci/common.gitlab-ci.yml"
- "/ci/build.linux.gitlab-ci.yml"
- "/ci/deploy.gitlab-ci.yml"

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

@@ -30,3 +30,5 @@ log
patch
a
a.*
lapack/testing
lapack/reference

View File

@@ -1,4 +1,5 @@
project(Eigen)
project(Eigen3)
cmake_minimum_required(VERSION 2.8.5)
# guard against in-source builds
@@ -7,6 +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)
@@ -92,9 +98,11 @@ 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)
@@ -108,7 +116,8 @@ endif()
set(EIGEN_TEST_MAX_SIZE "320" CACHE STRING "Maximal matrix/vector size, default is 320")
macro(ei_add_cxx_compiler_flag FLAG)
string(REGEX REPLACE "-" "" SFLAG ${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} ${FLAG}")
@@ -117,18 +126,13 @@ endmacro(ei_add_cxx_compiler_flag)
if(NOT MSVC)
# We assume that other compilers are partly compatible with GNUCC
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fexceptions")
set(CMAKE_CXX_FLAGS_DEBUG "-g3")
set(CMAKE_CXX_FLAGS_RELEASE "-g0 -O2")
# clang outputs some warnings for unknwon flags that are not caught by check_cxx_compiler_flag
# 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")
@@ -142,6 +146,18 @@ if(NOT MSVC)
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")
@@ -151,7 +167,8 @@ if(NOT MSVC)
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") # disbale ICC's "warning #2304: non-explicit constructor with single argument may cause implicit type conversion" produced by -Wnon-virtual-dtor
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
@@ -163,6 +180,11 @@ if(NOT MSVC)
else()
ei_add_cxx_compiler_flag("-ansi")
endif()
if(ANDROID_NDK)
ei_add_cxx_compiler_flag("-pie")
ei_add_cxx_compiler_flag("-fPIE")
endif()
set(CMAKE_REQUIRED_FLAGS "")
@@ -196,18 +218,65 @@ if(NOT MSVC)
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)
@@ -284,11 +353,23 @@ 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})
# Backward compatibility support for EIGEN_INCLUDE_INSTALL_DIR
if(EIGEN_INCLUDE_INSTALL_DIR)
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 ${EIGEN_INCLUDE_INSTALL_DIR}
CACHE PATH "The directory relative to CMAKE_PREFIX_PATH where Eigen header files are installed")
@@ -298,9 +379,8 @@ else()
CACHE PATH "The directory relative to CMAKE_PREFIX_PATH where Eigen header files are installed"
)
endif()
set(CMAKEPACKAGE_INSTALL_DIR
"${CMAKE_INSTALL_LIBDIR}/cmake/eigen3"
"${CMAKE_INSTALL_DATADIR}/eigen3/cmake"
CACHE PATH "The directory relative to CMAKE_PREFIX_PATH where Eigen3Config.cmake is installed"
)
set(PKGCONFIG_INSTALL_DIR
@@ -308,6 +388,7 @@ set(PKGCONFIG_INSTALL_DIR
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)
@@ -329,7 +410,7 @@ if(EIGEN_BUILD_PKGCONFIG)
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/eigen3.pc
DESTINATION ${PKGCONFIG_INSTALL_DIR}
)
endif(EIGEN_BUILD_PKGCONFIG)
endif()
add_subdirectory(Eigen)
@@ -355,6 +436,13 @@ else()
add_subdirectory(lapack EXCLUDE_FROM_ALL)
endif()
# add SYCL
option(EIGEN_TEST_SYCL "Add Sycl support." OFF)
if(EIGEN_TEST_SYCL)
set (CMAKE_MODULE_PATH "${CMAKE_ROOT}/Modules" "cmake/Modules/" "${CMAKE_MODULE_PATH}")
include(FindComputeCpp)
endif()
add_subdirectory(unsupported)
add_subdirectory(demos EXCLUDE_FROM_ALL)
@@ -403,6 +491,7 @@ if(cmake_generator_tolower MATCHES "makefile")
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:")
@@ -410,3 +499,98 @@ 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})
install (FILES
${CMAKE_CURRENT_BINARY_DIR}/Eigen3Config.cmake
${CMAKE_CURRENT_BINARY_DIR}/Eigen3ConfigVersion.cmake
${CMAKE_CURRENT_SOURCE_DIR}/cmake/UseEigen3.cmake
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()
install ( FILES ${CMAKE_CURRENT_SOURCE_DIR}/cmake/UseEigen3.cmake
${CMAKE_CURRENT_BINARY_DIR}/Eigen3Config.cmake
DESTINATION ${CMAKEPACKAGE_INSTALL_DIR}
)
endif (NOT CMAKE_VERSION VERSION_LESS 3.0)
# Add uninstall target
add_custom_target ( uninstall
COMMAND ${CMAKE_COMMAND} -P ${CMAKE_CURRENT_SOURCE_DIR}/cmake/EigenUninstall.cmake)

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 "Eigen3.2")
set(CTEST_PROJECT_NAME "Eigen3.3")
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=Eigen3.2")
set(CTEST_DROP_LOCATION "/CDash/submit.php?project=Eigen3.3")
set(CTEST_DROP_SITE_CDASH TRUE)

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

@@ -1,3 +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_CHOLESKY_MODULE_H
#define EIGEN_CHOLESKY_MODULE_H
@@ -10,20 +17,22 @@
*
*
* 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"
#include "src/misc/lapacke.h"
#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
@@ -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

@@ -14,6 +14,58 @@
// first thing Eigen does: stop the compiler from committing suicide
#include "src/Core/util/DisableStupidWarnings.h"
// 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.
@@ -21,7 +73,7 @@
// 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 defined(__MINGW32__) && EIGEN_GNUC_AT_LEAST(4,6)
#if EIGEN_COMP_MINGW && EIGEN_GNUC_AT_LEAST(4,6)
#pragma GCC optimize ("-fno-ipa-cp-clone")
#endif
@@ -31,26 +83,26 @@
// 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__) || (defined __INTEL_COMPILER) || 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
@@ -82,6 +134,28 @@
#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
#endif
// include files
@@ -95,9 +169,10 @@
extern "C" {
// 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 defined(__INTEL_COMPILER) && __INTEL_COMPILER >= 1110
#if EIGEN_COMP_ICC >= 1110
#include <immintrin.h>
#else
#include <mmintrin.h>
#include <emmintrin.h>
#include <xmmintrin.h>
#ifdef EIGEN_VECTORIZE_SSE3
@@ -112,8 +187,20 @@
#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
@@ -123,13 +210,35 @@
#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 defined __CUDACC_VER__ && __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
@@ -139,7 +248,7 @@
#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
@@ -159,29 +268,30 @@
// 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)) && (!defined(_WIN32_WCE))
#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";
@@ -193,8 +303,12 @@ 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
@@ -202,40 +316,15 @@ 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
#endif
#ifdef EIGEN2_SUPPORT
#undef minor
#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
// 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
@@ -249,8 +338,8 @@ using std::ptrdiff_t;
*/
#include "src/Core/util/Constants.h"
#include "src/Core/util/ForwardDeclarations.h"
#include "src/Core/util/Meta.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"
@@ -258,41 +347,91 @@ using std::ptrdiff_t;
#include "src/Core/NumTraits.h"
#include "src/Core/MathFunctions.h"
#include "src/Core/GenericPacketMath.h"
#include "src/Core/MathFunctionsImpl.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"
#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/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"
@@ -300,32 +439,33 @@ 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/Ref.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"
@@ -340,6 +480,7 @@ using std::ptrdiff_t;
#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"
@@ -347,18 +488,17 @@ 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
@@ -369,8 +509,4 @@ using std::ptrdiff_t;
#include "src/Core/util/ReenableStupidWarnings.h"
#ifdef EIGEN2_SUPPORT
#include "Eigen2Support"
#endif
#endif // EIGEN_CORE_H

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

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@@ -1,95 +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>
//
// 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 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
#ifndef EIGEN_NO_EIGEN2_DEPRECATED_WARNING
#if defined(__GNUC__) || defined(__INTEL_COMPILER) || defined(__clang__)
#warning "Eigen2 support is deprecated in Eigen 3.2.x and it will be removed in Eigen 3.3. (Define EIGEN_NO_EIGEN2_DEPRECATED_WARNING to disable this warning)"
#else
#pragma message ("Eigen2 support is deprecated in Eigen 3.2.x and it will be removed in Eigen 3.3. (Define EIGEN_NO_EIGEN2_DEPRECATED_WARNING to disable this warning)")
#endif
#endif // EIGEN_NO_EIGEN2_DEPRECATED_WARNING
#include "src/Core/util/DisableStupidWarnings.h"
/** \ingroup Support_modules
* \defgroup Eigen2Support_Module Eigen2 support module
*
* \warning Eigen2 support is deprecated in Eigen 3.2.x and it will be removed in Eigen 3.3.
*
* 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

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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_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"
@@ -37,9 +45,10 @@
#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"
#include "src/misc/lapacke.h"
#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"

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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_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"

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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_HOUSEHOLDER_MODULE_H
#define EIGEN_HOUSEHOLDER_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_ITERATIVELINEARSOLVERS_MODULE_H
#define EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
@@ -12,28 +19,29 @@
* 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"

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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_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 +23,23 @@
* \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"
#include "src/misc/lapacke.h"
#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

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@@ -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

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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_METISSUPPORT_MODULE_H
#define EIGEN_METISSUPPORT_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_ORDERINGMETHODS_MODULE_H
#define 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
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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_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
*

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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_QR_MODULE_H
#define EIGEN_QR_MODULE_H
@@ -15,31 +22,26 @@
*
* 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"
#endif
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/QR.h"
#include "src/misc/lapacke.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: */

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@@ -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

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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_SPQRSUPPORT_MODULE_H
#define EIGEN_SPQRSUPPORT_MODULE_H
@@ -21,8 +28,6 @@
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/CholmodSupport/CholmodSupport.h"
#include "src/SPQRSupport/SuiteSparseQRSupport.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_SVD_MODULE_H
#define EIGEN_SVD_MODULE_H
@@ -12,23 +19,26 @@
*
*
* 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)
#include "src/misc/lapacke.h"
#include "src/SVD/JacobiSVD_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.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_SPARSE_MODULE_H
#define EIGEN_SPARSE_MODULE_H
@@ -11,9 +18,9 @@
* - \ref SparseQR_Module
* - \ref IterativeLinearSolvers_Module
*
* \code
* #include <Eigen/Sparse>
* \endcode
\code
#include <Eigen/Sparse>
\endcode
*/
#include "SparseCore"

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@@ -34,8 +34,6 @@
#error The SparseCholesky module has nothing to offer in MPL2 only mode
#endif
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/SparseCholesky/SimplicialCholesky.h"
#ifndef EIGEN_MPL2_ONLY

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
@@ -26,37 +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/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/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"

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@@ -20,9 +20,6 @@
* Please, see the documentation of the SparseLU class for more details.
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
// Ordering interface
#include "OrderingMethods"

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_SPARSEQR_MODULE_H
#define EIGEN_SPARSEQR_MODULE_H
@@ -21,9 +28,6 @@
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "OrderingMethods"
#include "src/SparseCore/SparseColEtree.h"
#include "src/SparseQR/SparseQR.h"

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@@ -14,7 +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 /* MSVC auto aligns in 64 bit builds */
#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...)

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@@ -13,7 +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 /* MSVC auto aligns in 64 bit builds */
#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...)

View File

@@ -14,7 +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 /* MSVC auto aligns 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
@@ -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

@@ -13,7 +13,7 @@
#ifndef EIGEN_LDLT_H
#define EIGEN_LDLT_H
namespace Eigen {
namespace Eigen {
namespace internal {
template<typename MatrixType, int UpLo> struct LDLT_Traits;
@@ -28,8 +28,8 @@ namespace internal {
*
* \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
@@ -43,7 +43,9 @@ namespace internal {
* 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
{
@@ -52,15 +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;
@@ -72,11 +74,11 @@ 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(),
LDLT()
: m_matrix(),
m_transpositions(),
m_sign(internal::ZeroSign),
m_isInitialized(false)
m_isInitialized(false)
{}
/** \brief Default Constructor with memory preallocation
@@ -85,7 +87,7 @@ 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),
@@ -96,16 +98,35 @@ template<typename _MatrixType, int _UpLo> class LDLT
/** \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
@@ -151,13 +172,6 @@ template<typename _MatrixType, int _UpLo> class LDLT
eigen_assert(m_isInitialized && "LDLT is not initialized.");
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
@@ -173,37 +187,38 @@ 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, const RealScalar& alpha=1);
@@ -220,6 +235,13 @@ 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(); }
@@ -231,11 +253,17 @@ template<typename _MatrixType, int _UpLo> class LDLT
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);
@@ -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;
internal::SignMatrix m_sign;
bool m_isInitialized;
ComputationInfo m_info;
};
namespace internal {
@@ -266,15 +296,17 @@ template<> struct ldlt_inplace<Lower>
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 (numext::real(mat.coeff(0,0)) > 0) sign = PositiveSemiDef;
else if (numext::real(mat.coeff(0,0)) < 0) sign = NegativeSemiDef;
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;
}
@@ -286,7 +318,7 @@ template<> struct ldlt_inplace<Lower>
mat.diagonal().tail(size-k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
index_of_biggest_in_corner += k;
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
@@ -295,7 +327,7 @@ 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) = numext::conj(mat.coeffRef(index_of_biggest_in_corner,i));
@@ -321,26 +353,44 @@ template<> struct ldlt_inplace<Lower>
if(rs>0)
A21.noalias() -= A20 * temp.head(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, soince LDLT is not rank-revealing
// we should only make sure we do not introduce INF or NaN values.
// LAPACK also uses 0 as the cutoff value.
// 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));
if((rs>0) && (abs(realAkk) > RealScalar(0)))
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;
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 < 0) sign = Indefinite;
if (realAkk < static_cast<RealScalar>(0)) sign = Indefinite;
} else if (sign == NegativeSemiDef) {
if (realAkk > 0) sign = Indefinite;
if (realAkk > static_cast<RealScalar>(0)) sign = Indefinite;
} else if (sign == ZeroSign) {
if (realAkk > 0) sign = PositiveSemiDef;
else if (realAkk < 0) sign = NegativeSemiDef;
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
@@ -356,7 +406,6 @@ template<> struct ldlt_inplace<Lower>
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);
@@ -420,16 +469,16 @@ 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
@@ -437,21 +486,35 @@ 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;
@@ -466,18 +529,19 @@ template<typename MatrixType, int _UpLo>
template<typename Derived>
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 ? internal::PositiveSemiDef : internal::NegativeSemiDef;
m_isInitialized = true;
@@ -488,53 +552,45 @@ 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 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.
RealScalar tolerance = RealScalar(1) / NumTraits<RealScalar>::highest();
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::RealScalar RealScalar;
const typename Diagonal<const MatrixType>::RealReturnType vectorD(dec().vectorD());
// In some previous versions, tolerance was set to the max of 1/highest and the maximal diagonal entry * epsilon
// as motivated by LAPACK's xGELSS:
// RealScalar tolerance = (max)(vectorD.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 to numerical issues in some cases.
// Moreover, Lapack's xSYTRS routines use 0 for the tolerance.
RealScalar tolerance = RealScalar(1) / NumTraits<RealScalar>::highest();
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);
*
@@ -588,6 +644,7 @@ 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>
@@ -598,6 +655,7 @@ 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

@@ -10,7 +10,7 @@
#ifndef EIGEN_LLT_H
#define EIGEN_LLT_H
namespace Eigen {
namespace Eigen {
namespace internal{
template<typename MatrixType, int UpLo> struct LLT_Traits;
@@ -22,8 +22,8 @@ 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.
* \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
@@ -40,8 +40,10 @@ template<typename MatrixType, int UpLo> struct LLT_Traits;
*
* Example: \include LLT_example.cpp
* Output: \verbinclude LLT_example.out
*
* \sa MatrixBase::llt(), class LDLT
*
* This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
*
* \sa MatrixBase::llt(), SelfAdjointView::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,
@@ -54,12 +56,12 @@ 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,
@@ -83,14 +85,30 @@ 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 */
@@ -115,33 +133,33 @@ 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;
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
*
@@ -167,24 +185,38 @@ 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;
};
@@ -194,12 +226,11 @@ 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;
@@ -268,11 +299,10 @@ 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)
{
using std::sqrt;
typedef typename MatrixType::Index Index;
eigen_assert(mat.rows()==mat.cols());
const Index size = mat.rows();
for(Index k = 0; k < size; ++k)
@@ -295,9 +325,8 @@ template<typename Scalar> struct llt_inplace<Scalar, Lower>
}
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)
@@ -322,36 +351,36 @@ 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);
@@ -362,8 +391,8 @@ 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; }
};
@@ -372,8 +401,8 @@ 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; }
};
@@ -388,14 +417,28 @@ 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;
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);
@@ -423,33 +466,24 @@ 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.
*
* \sa LLT::solve(), MatrixBase::llt()
*/
@@ -475,6 +509,7 @@ 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>
@@ -485,6 +520,7 @@ 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

@@ -25,41 +25,38 @@
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 = LAPACKE_##LAPACKE_PREFIX##potrf( matrix_order, uplo, size, (BLASTYPE*)a, lda ); \
info = (info==0) ? -1 : info>0 ? info-1 : size; \
return info; \
} \
@@ -67,36 +64,36 @@ template<> struct mkl_llt<EIGTYPE> \
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

@@ -54,12 +54,12 @@ template<> struct cholmod_configure_matrix<std::complex<double> > {
/** 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)
{
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();
@@ -80,11 +80,11 @@ 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<_Index,SuiteSparse_long>::value)
else if (internal::is_same<_StorageIndex,long>::value)
{
res.itype = CHOLMOD_LONG;
}
@@ -104,16 +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;
@@ -144,12 +151,12 @@ cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat)
/** 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, static_cast<Index*>(cm.p)[cm.ncol],
static_cast<Index*>(cm.p), static_cast<Index*>(cm.i),static_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 {
@@ -163,28 +170,36 @@ 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;
@@ -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.
*
@@ -224,34 +236,6 @@ 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 sparsity pattern of \a matrix.
*
* This function is particularly useful when solving for several problems having the same structure.
@@ -298,20 +282,22 @@ 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
Rhs& b_ref(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 careful with alignment, etc.)
dest = Matrix<Scalar,Dest::RowsAtCompileTime,Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x),b.rows(),b.cols());
@@ -319,8 +305,8 @@ class CholmodBase : internal::noncopyable
}
/** \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;
@@ -328,14 +314,16 @@ class CholmodBase : internal::noncopyable
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 careful with alignment, etc.)
dest = viewAsEigen<DestScalar,DestOptions,DestIndex>(*x_cs);
dest.derived() = viewAsEigen<typename DestDerived::Scalar,ColMajor,typename DestDerived::StorageIndex>(*x_cs);
cholmod_free_sparse(&x_cs, &m_cholmod);
}
#endif // EIGEN_PARSED_BY_DOXYGEN
@@ -356,6 +344,57 @@ class CholmodBase : internal::noncopyable
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*/)
{}
@@ -365,7 +404,6 @@ class CholmodBase : internal::noncopyable
cholmod_factor* m_cholmodFactor;
double m_shiftOffset[2];
mutable ComputationInfo m_info;
bool m_isInitialized;
int m_factorizationIsOk;
int m_analysisIsOk;
};
@@ -384,11 +422,13 @@ class CholmodBase : internal::noncopyable
* \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.
*
* \warning Only double precision real and complex scalar types are supported by Cholmod.
*
* \sa \ref TutorialSparseDirectSolvers, class CholmodSupernodalLLT, class SimplicialLLT
* \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLLT
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT<_MatrixType, _UpLo> >
@@ -405,7 +445,7 @@ class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimpl
CholmodSimplicialLLT(const MatrixType& matrix) : Base()
{
init();
Base::compute(matrix);
this->compute(matrix);
}
~CholmodSimplicialLLT() {}
@@ -433,11 +473,13 @@ class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimpl
* \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.
*
* \warning Only double precision real and complex scalar types are supported by Cholmod.
*
* \sa \ref TutorialSparseDirectSolvers, class CholmodSupernodalLLT, class SimplicialLDLT
* \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLDLT
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT<_MatrixType, _UpLo> >
@@ -454,7 +496,7 @@ class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimp
CholmodSimplicialLDLT(const MatrixType& matrix) : Base()
{
init();
Base::compute(matrix);
this->compute(matrix);
}
~CholmodSimplicialLDLT() {}
@@ -480,11 +522,13 @@ class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimp
* \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.
*
* \warning Only double precision real and complex scalar types are supported by Cholmod.
*
* \sa \ref TutorialSparseDirectSolvers
* \sa \ref TutorialSparseSolverConcept
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT<_MatrixType, _UpLo> >
@@ -501,7 +545,7 @@ class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSuper
CholmodSupernodalLLT(const MatrixType& matrix) : Base()
{
init();
Base::compute(matrix);
this->compute(matrix);
}
~CholmodSupernodalLLT() {}
@@ -529,11 +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.
*
* \warning Only double precision real and complex scalar types are supported by Cholmod.
*
* \sa \ref TutorialSparseDirectSolvers
* \sa \ref TutorialSparseSolverConcept
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecomposition<_MatrixType, _UpLo> >
@@ -550,7 +596,7 @@ class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecom
CholmodDecomposition(const MatrixType& matrix) : Base()
{
init();
Base::compute(matrix);
this->compute(matrix);
}
~CholmodDecomposition() {}
@@ -588,36 +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

@@ -12,7 +12,16 @@
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
@@ -24,20 +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> >
@@ -69,11 +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),
@@ -84,7 +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);
}
@@ -92,11 +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.
@@ -107,6 +128,7 @@ class Array
*
* \sa resize(Index,Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array() : Base()
{
Base::_check_template_params();
@@ -116,6 +138,7 @@ class Array
#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())
{
@@ -124,56 +147,64 @@ class Array
}
#endif
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
Array(Array&& other)
#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);
}
Array& operator=(Array&& other)
EIGEN_DEVICE_FUNC
Array& operator=(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
{
other.swap(*this);
return *this;
}
#endif
/** 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 Array(Index dim)
: Base(dim, RowsAtCompileTime == 1 ? 1 : dim, ColsAtCompileTime == 1 ? 1 : dim)
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit Array(const T& x)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Array)
eigen_assert(dim >= 0);
eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim);
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
Base::template _init1<T>(x);
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
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
/** constructs an uninitialized matrix with \a rows rows and \a cols columns.
/** \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
*
* This is useful for dynamic-size matrices. For fixed-size matrices,
* 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 Array() instead.
*/
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 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& val0, const Scalar& val1);
#endif
/** constructs an initialized 3D vector with given coefficients */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2)
{
Base::_check_template_params();
@@ -183,6 +214,7 @@ class Array
m_storage.data()[2] = val2;
}
/** constructs an initialized 4D vector with given coefficients */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2, const Scalar& val3)
{
Base::_check_template_params();
@@ -193,51 +225,21 @@ class Array
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)
{ }
/** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
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_to_match(other);
*this = other;
}
: 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

@@ -32,7 +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
*/
@@ -47,13 +47,11 @@ template<typename Derived> class ArrayBase
typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl;
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 DenseBase<Derived> Base;
using Base::operator*;
using Base::RowsAtCompileTime;
using Base::ColsAtCompileTime;
using Base::SizeAtCompileTime;
@@ -62,8 +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;
@@ -83,25 +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"
@@ -112,44 +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 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:
@@ -174,8 +178,7 @@ template<typename OtherDerived>
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();
}
@@ -188,8 +191,7 @@ template<typename OtherDerived>
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();
}
@@ -202,8 +204,7 @@ template<typename OtherDerived>
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();
}
@@ -216,8 +217,7 @@ template<typename OtherDerived>
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

@@ -44,6 +44,7 @@ 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,
@@ -51,76 +52,45 @@ 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(); }
inline ScalarWithConstIfNotLvalue* data() { return m_expression.const_cast_derived().data(); }
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 rowId, Index colId) const
{
return m_expression.coeff(rowId, colId);
}
inline Scalar& coeffRef(Index rowId, Index colId)
{
return m_expression.const_cast_derived().coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.const_cast_derived().coeffRef(rowId, colId);
}
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 rowId, Index colId) const
{
return m_expression.template packet<LoadMode>(rowId, colId);
}
template<int LoadMode>
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(rowId, colId, val);
}
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& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(index, val);
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;
@@ -128,10 +98,12 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index) */
void resize(Index newSize) { m_expression.const_cast_derived().resize(newSize); }
EIGEN_DEVICE_FUNC
void resize(Index newSize) { m_expression.resize(newSize); }
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index,Index)*/
void resize(Index nbRows, Index nbCols) { m_expression.const_cast_derived().resize(nbRows,nbCols); }
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols) { m_expression.resize(rows,cols); }
protected:
NestedExpressionType m_expression;
@@ -169,6 +141,7 @@ 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,
@@ -176,72 +149,40 @@ 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& a_matrix) : m_expression(a_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(); }
inline ScalarWithConstIfNotLvalue* data() { return m_expression.const_cast_derived().data(); }
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 rowId, Index colId) const
{
return m_expression.coeff(rowId, colId);
}
inline Scalar& coeffRef(Index rowId, Index colId)
{
return m_expression.const_cast_derived().coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.derived().coeffRef(rowId, colId);
}
inline CoeffReturnType coeff(Index index) const
{
return m_expression.coeff(index);
}
inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
}
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 rowId, Index colId) const
{
return m_expression.template packet<LoadMode>(rowId, colId);
}
template<int LoadMode>
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(rowId, colId, val);
}
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& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(index, val);
return m_expression.coeffRef(index);
}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<NestedExpressionType>::type&
nestedExpression() const
{
@@ -250,10 +191,12 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index) */
void resize(Index newSize) { m_expression.const_cast_derived().resize(newSize); }
EIGEN_DEVICE_FUNC
void resize(Index newSize) { m_expression.resize(newSize); }
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index,Index)*/
void resize(Index nbRows, Index nbCols) { m_expression.const_cast_derived().resize(nbRows,nbCols); }
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols) { m_expression.resize(rows,cols); }
protected:
NestedExpressionType m_expression;

View File

@@ -14,478 +14,6 @@
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, typename Derived1::Index 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 &, typename Derived1::Index) {}
};
/***********************
*** 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, typename Derived1::Index 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 &, typename Derived1::Index) {}
};
/***************************************************************************
* 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 typename Derived1::Scalar Scalar;
typedef packet_traits<Scalar> PacketTraits;
enum {
packetSize = PacketTraits::size,
alignable = PacketTraits::AlignedOnScalar,
dstIsAligned = assign_traits<Derived1,Derived2>::DstIsAligned,
dstAlignment = alignable ? Aligned : int(dstIsAligned),
srcAlignment = assign_traits<Derived1,Derived2>::JointAlignment
};
const Scalar *dst_ptr = &dst.coeffRef(0,0);
if((!bool(dstIsAligned)) && (size_t(dst_ptr) % sizeof(Scalar))>0)
{
// the pointer is not aligend-on scalar, so alignment is not possible
return assign_impl<Derived1,Derived2,DefaultTraversal,NoUnrolling>::run(dst, src);
}
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) || bool(dstIsAligned)) ? 0 : internal::first_aligned(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)
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>
@@ -499,90 +27,62 @@ 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(internal::traits<OtherDerived>::Flags) & EvalBeforeAssigningBit) != 0,
bool NeedToTranspose = ((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 ActualDerived, typename ActualOtherDerived>
static EIGEN_STRONG_INLINE Derived& evalTo(ActualDerived& dst, const ActualOtherDerived& other) { other.evalTo(dst); return dst; }
};
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 ActualDerived, typename ActualOtherDerived>
static EIGEN_STRONG_INLINE Derived& evalTo(ActualDerived& dst, const ActualOtherDerived& other) { Transpose<ActualDerived> dstTrans(dst); other.evalTo(dstTrans); return dst; }
};
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)
{
return internal::assign_selector<Derived,OtherDerived,false>::evalTo(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 ReturnByValue<OtherDerived>& other)
{
return internal::assign_selector<Derived,OtherDerived,false>::evalTo(derived(), other.derived());
other.derived().evalTo(derived());
return derived();
}
} // end namespace Eigen

View File

@@ -0,0 +1,918 @@
// 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,
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) && MayLinearize && 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 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 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 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.
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
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

256
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 +54,120 @@ 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*/) { \
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*/) { \
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.
#ifndef _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

@@ -32,7 +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;
@@ -161,15 +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
*/
@@ -179,7 +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,
@@ -201,10 +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)
{
@@ -241,7 +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,
@@ -264,9 +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)
{
@@ -302,9 +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
*/
@@ -312,9 +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>(); }
@@ -327,6 +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

@@ -13,14 +13,70 @@
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)
* \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
@@ -44,61 +100,6 @@ namespace Eigen {
*
* \sa DenseBase::block(Index,Index,Index,Index), DenseBase::block(Index,Index), class VectorBlock
*/
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 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 || (InnerPanel && (traits<XprType>::Flags&LinearAccessBit))) ? 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=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;
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> class Block
: public BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind>
{
@@ -108,9 +109,12 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> class
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) && (
@@ -120,25 +124,27 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> class
/** Fixed-size constructor
*/
inline Block(XprType& xpr, Index a_startRow, Index a_startCol)
: Impl(xpr, a_startRow, a_startCol)
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(a_startRow >= 0 && BlockRows >= 1 && a_startRow + BlockRows <= xpr.rows()
&& a_startCol >= 0 && BlockCols >= 1 && a_startCol + BlockCols <= xpr.cols());
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 a_startRow, Index a_startCol,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: Impl(xpr, a_startRow, a_startCol, blockRows, blockCols)
: Impl(xpr, startRow, startCol, blockRows, blockCols)
{
eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
&& (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
eigen_assert(a_startRow >= 0 && blockRows >= 0 && a_startRow <= xpr.rows() - blockRows
&& a_startCol >= 0 && blockCols >= 0 && a_startCol <= xpr.cols() - blockCols);
eigen_assert(startRow >= 0 && blockRows >= 0 && startRow <= xpr.rows() - blockRows
&& startCol >= 0 && blockCols >= 0 && startCol <= xpr.cols() - blockCols);
}
};
@@ -149,14 +155,15 @@ 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::Index Index;
typedef typename XprType::StorageIndex StorageIndex;
public:
typedef Impl Base;
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)
inline BlockImpl(XprType& xpr, Index i) : Impl(xpr,i) {}
inline BlockImpl(XprType& xpr, Index a_startRow, Index a_startCol) : Impl(xpr, a_startRow, a_startCol) {}
inline BlockImpl(XprType& xpr, Index a_startRow, Index a_startCol, Index blockRows, Index blockCols)
: Impl(xpr, a_startRow, a_startCol, blockRows, blockCols) {}
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 {
@@ -166,16 +173,18 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
: 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<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
*/
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,
@@ -190,75 +199,76 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
/** Fixed-size constructor
*/
inline BlockImpl_dense(XprType& xpr, Index a_startRow, Index a_startCol)
: m_xpr(xpr), m_startRow(a_startRow), m_startCol(a_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)
{}
/** Dynamic-size constructor
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr,
Index a_startRow, Index a_startCol,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: m_xpr(xpr), m_startRow(a_startRow), m_startCol(a_startCol),
: m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
m_blockRows(blockRows), m_blockCols(blockCols)
{}
inline Index rows() const { return m_blockRows.value(); }
inline Index cols() const { return m_blockCols.value(); }
EIGEN_DEVICE_FUNC inline Index rows() const { return m_blockRows.value(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_blockCols.value(); }
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index rowId, Index colId)
{
EIGEN_STATIC_ASSERT_LVALUE(XprType)
return m_xpr.const_cast_derived()
.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
return m_xpr.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_xpr.derived()
.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
return m_xpr.derived().coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const
{
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 rowId, Index colId) const
{
return m_xpr.template packet<Unaligned>
(rowId + m_startRow.value(), colId + m_startCol.value());
return m_xpr.template packet<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value());
}
template<int LoadMode>
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_xpr.const_cast_derived().template writePacket<Unaligned>
(rowId + m_startRow.value(), colId + m_startCol.value(), val);
m_xpr.template writePacket<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value(), val);
}
template<int LoadMode>
@@ -272,40 +282,46 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
template<int LoadMode>
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), 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 implementation of dense Blocks in the direct access case.*/
@@ -314,6 +330,10 @@ 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<BlockType> Base;
@@ -322,42 +342,52 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
/** Column or Row constructor
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(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)),
: 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)
{
init();
}
/** Fixed-size constructor
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
: Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol))), m_xpr(xpr)
: Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol)),
m_xpr(xpr), m_startRow(startRow), m_startCol(startCol)
{
init();
}
/** Dynamic-size constructor
*/
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)
{
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<BlockType>::HasSameStorageOrderAsXprType
@@ -366,11 +396,24 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
}
/** \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?
@@ -379,6 +422,7 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal used by allowAligned() */
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols)
: Base(data, blockRows, blockCols), m_xpr(xpr)
{
@@ -387,6 +431,7 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
#endif
protected:
EIGEN_DEVICE_FUNC
void init()
{
m_outerStride = internal::traits<BlockType>::HasSameStorageOrderAsXprType
@@ -394,7 +439,9 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
: 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;
};

View File

@@ -17,9 +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)
@@ -43,11 +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);
@@ -78,19 +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;
}
}
@@ -102,19 +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;
}
}
@@ -124,7 +126,7 @@ 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();
}
@@ -136,7 +138,11 @@ inline typename DenseBase<Derived>::Index DenseBase<Derived>::count() const
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.
@@ -146,7 +152,11 @@ inline bool DenseBase<Derived>::hasNaN() const
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

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

@@ -22,14 +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)
{
@@ -37,6 +37,7 @@ 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())
{
@@ -46,6 +47,7 @@ struct CommaInitializer
/* 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:
@@ -55,6 +57,7 @@ struct CommaInitializer
}
/* inserts a scalar value in the target matrix */
EIGEN_DEVICE_FUNC
CommaInitializer& operator,(const Scalar& s)
{
if (m_col==m_xpr.cols())
@@ -74,6 +77,7 @@ 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() && (other.cols()!=0 || other.rows()!=m_currentBlockRows))
@@ -93,7 +97,11 @@ struct CommaInitializer
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
{
finished();
}
@@ -105,6 +113,7 @@ struct CommaInitializer
* quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished());
* \endcode
*/
EIGEN_DEVICE_FUNC
inline XprType& finished() {
eigen_assert(((m_row+m_currentBlockRows) == m_xpr.rows() || m_xpr.cols() == 0)
&& m_col == m_xpr.cols()
@@ -112,7 +121,7 @@ struct CommaInitializer
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

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

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@@ -1,7 +1,7 @@
// 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>
//
// 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
@@ -15,47 +15,113 @@ namespace Eigen {
/* This file contains the respective InnerIterator definition of the expressions defined in Eigen/Core
*/
/** \ingroup SparseCore_Module
* \class InnerIterator
* \brief An InnerIterator allows to loop over the element of a sparse (or dense) matrix or expression
*
* todo
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
*/
// generic version for dense matrix and expressions
template<typename Derived> class DenseBase<Derived>::InnerIterator
template<typename XprType>
class InnerIterator
{
protected:
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Index Index;
enum { IsRowMajor = (Derived::Flags&RowMajorBit)==RowMajorBit };
public:
EIGEN_STRONG_INLINE InnerIterator(const Derived& expr, Index outer)
: m_expression(expr), m_inner(0), m_outer(outer), m_end(expr.innerSize())
{}
EIGEN_STRONG_INLINE Scalar value() const
{
return (IsRowMajor) ? m_expression.coeff(m_outer, m_inner)
: m_expression.coeff(m_inner, m_outer);
}
EIGEN_STRONG_INLINE InnerIterator& 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 Derived& m_expression;
Index m_inner;
const Index m_outer;
const Index m_end;
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

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@@ -1,7 +1,7 @@
// 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>
//
// This Source Code Form is subject to the terms of the Mozilla
@@ -13,26 +13,6 @@
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> >
@@ -52,77 +32,75 @@ 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),
Cost0 = EIGEN_ADD_COST(LhsCoeffReadCost,RhsCoeffReadCost),
CoeffReadCost = EIGEN_ADD_COST(Cost0,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_is_product_like<BINOP>::ret \
? int(internal::scalar_product_traits<LHS, RHS>::Defined) \
: 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_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)
{
@@ -132,6 +110,7 @@ class CwiseBinaryOp : internal::no_assignment_operator,
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)
@@ -139,6 +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)
@@ -148,10 +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:
@@ -160,41 +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 rowId, Index colId) const
{
return derived().functor()(derived().lhs().coeff(rowId, colId),
derived().rhs().coeff(rowId, colId));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
{
return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(rowId, colId),
derived().rhs().template packet<LoadMode>(rowId, colId));
}
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.
@@ -206,8 +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();
}
@@ -220,11 +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

@@ -12,13 +12,24 @@
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,
@@ -27,68 +38,49 @@ 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)
CwiseNullaryOp(Index nbRows, Index nbCols, const NullaryOp& func = NullaryOp())
: m_rows(nbRows), m_cols(nbCols), m_functor(func)
EIGEN_DEVICE_FUNC
CwiseNullaryOp(Index rows, Index cols, const NullaryOp& func = NullaryOp())
: m_rows(rows), m_cols(cols), m_functor(func)
{
eigen_assert(nbRows >= 0
&& (RowsAtCompileTime == Dynamic || RowsAtCompileTime == nbRows)
&& nbCols >= 0
&& (ColsAtCompileTime == Dynamic || ColsAtCompileTime == nbCols));
eigen_assert(rows >= 0
&& (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0
&& (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 rowId, Index colId) const
{
return m_functor(rowId, colId);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
{
return m_functor.packetOp(rowId, colId);
}
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:
@@ -113,10 +105,10 @@ class CwiseNullaryOp : internal::no_assignment_operator,
*/
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 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
@@ -132,16 +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
@@ -155,19 +150,19 @@ DenseBase<Derived>::NullaryExpr(Index size, const CustomNullaryOp& func)
*/
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(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
*
* The parameters \a nbRows and \a nbCols are the number of rows and of columns of
* The parameters \a rows and \a cols are the number of rows and of columns of
* the returned matrix. Must be compatible with this DenseBase type.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
* it is redundant to pass \a nbRows and \a nbCols as arguments, so Zero() should be used
* it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
* instead.
*
* The template parameter \a CustomNullaryOp is the type of the functor.
@@ -176,9 +171,9 @@ DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(Index nbRows, Index nbCols, const Scalar& value)
DenseBase<Derived>::Constant(Index rows, Index cols, const Scalar& value)
{
return DenseBase<Derived>::NullaryExpr(nbRows, nbCols, internal::scalar_constant_op<Scalar>(value));
return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_constant_op<Scalar>(value));
}
/** \returns an expression of a constant matrix of value \a value
@@ -220,46 +215,33 @@ DenseBase<Derived>::Constant(const Scalar& value)
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_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_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.
@@ -269,14 +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
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));
}
/**
@@ -289,7 +281,7 @@ 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 val, to within precision \a prec */
@@ -297,9 +289,10 @@ template<typename Derived>
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), val, prec))
if(!internal::isApprox(self.coeff(i, j), val, prec))
return false;
return true;
}
@@ -324,7 +317,7 @@ EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& val)
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()
*/
@@ -334,7 +327,7 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& val)
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
*
@@ -351,10 +344,10 @@ PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val)
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 nbRows the new number of rows
* \param nbCols the new number of columns
* \param rows the new number of rows
* \param cols the new number of columns
* \param val the value to which all coefficients are set
*
* Example: \include Matrix_setConstant_int_int.cpp
@@ -364,14 +357,14 @@ PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index nbRows, Index nbCols, const Scalar& val)
PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& val)
{
resize(nbRows, nbCols);
resize(rows, cols);
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.
@@ -381,24 +374,30 @@ PlainObjectBase<Derived>::setConstant(Index nbRows, Index nbCols, const Scalar&
* 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 newSize, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return derived() = Derived::NullaryExpr(newSize, internal::linspaced_op<Scalar,false>(low,high,newSize));
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)
@@ -425,9 +424,9 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low,
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero(Index nbRows, Index nbCols)
DenseBase<Derived>::Zero(Index rows, Index cols)
{
return Constant(nbRows, nbCols, Scalar(0));
return Constant(rows, cols, Scalar(0));
}
/** \returns an expression of a zero vector.
@@ -481,9 +480,10 @@ DenseBase<Derived>::Zero()
template<typename Derived>
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;
}
@@ -520,8 +520,8 @@ PlainObjectBase<Derived>::setZero(Index newSize)
/** Resizes to the given size, and sets all coefficients in this expression to zero.
*
* \param nbRows the new number of rows
* \param nbCols the new number of columns
* \param rows the new number of rows
* \param cols the new number of columns
*
* Example: \include Matrix_setZero_int_int.cpp
* Output: \verbinclude Matrix_setZero_int_int.out
@@ -530,9 +530,9 @@ PlainObjectBase<Derived>::setZero(Index newSize)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index nbRows, Index nbCols)
PlainObjectBase<Derived>::setZero(Index rows, Index cols)
{
resize(nbRows, nbCols);
resize(rows, cols);
return setConstant(Scalar(0));
}
@@ -540,7 +540,7 @@ PlainObjectBase<Derived>::setZero(Index nbRows, Index nbCols)
/** \returns an expression of a matrix where all coefficients equal one.
*
* The parameters \a nbRows and \a nbCols are the number of rows and of columns of
* 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.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
@@ -554,9 +554,9 @@ PlainObjectBase<Derived>::setZero(Index nbRows, Index nbCols)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index nbRows, Index nbCols)
DenseBase<Derived>::Ones(Index rows, Index cols)
{
return Constant(nbRows, nbCols, Scalar(1));
return Constant(rows, cols, Scalar(1));
}
/** \returns an expression of a vector where all coefficients equal one.
@@ -646,8 +646,8 @@ PlainObjectBase<Derived>::setOnes(Index newSize)
/** Resizes to the given size, and sets all coefficients in this expression to one.
*
* \param nbRows the new number of rows
* \param nbCols the new number of columns
* \param rows the new number of rows
* \param cols the new number of columns
*
* Example: \include Matrix_setOnes_int_int.cpp
* Output: \verbinclude Matrix_setOnes_int_int.out
@@ -656,9 +656,9 @@ PlainObjectBase<Derived>::setOnes(Index newSize)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index nbRows, Index nbCols)
PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
{
resize(nbRows, nbCols);
resize(rows, cols);
return setConstant(Scalar(1));
}
@@ -666,7 +666,7 @@ PlainObjectBase<Derived>::setOnes(Index nbRows, Index nbCols)
/** \returns an expression of the identity matrix (not necessarily square).
*
* The parameters \a nbRows and \a nbCols are the number of rows and of columns of
* 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.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
@@ -680,9 +680,9 @@ PlainObjectBase<Derived>::setOnes(Index nbRows, Index nbCols)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
MatrixBase<Derived>::Identity(Index nbRows, Index nbCols)
MatrixBase<Derived>::Identity(Index rows, Index cols)
{
return DenseBase<Derived>::NullaryExpr(nbRows, nbCols, internal::scalar_identity_op<Scalar>());
return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_identity_op<Scalar>());
}
/** \returns an expression of the identity matrix (not necessarily square).
@@ -716,18 +716,19 @@ template<typename Derived>
bool MatrixBase<Derived>::isIdentity
(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;
}
}
@@ -740,6 +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());
@@ -749,11 +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;
}
@@ -776,8 +778,8 @@ EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
/** \brief Resizes to the given size, and writes the identity expression (not necessarily square) into *this.
*
* \param nbRows the new number of rows
* \param nbCols the new number of columns
* \param rows the new number of rows
* \param cols the new number of columns
*
* Example: \include Matrix_setIdentity_int_int.cpp
* Output: \verbinclude Matrix_setIdentity_int_int.out
@@ -785,9 +787,9 @@ 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 nbRows, Index nbCols)
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index cols)
{
derived().resize(nbRows, nbCols);
derived().resize(rows, cols);
return setIdentity();
}

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

@@ -1,7 +1,7 @@
// 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>
//
// This Source Code Form is subject to the terms of the Mozilla
@@ -13,13 +13,32 @@
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
@@ -32,93 +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 = EIGEN_ADD_COST(_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 rowId, Index colId) const
{
return derived().functor()(derived().nestedExpression().coeff(rowId, colId));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
{
return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(rowId, colId));
}
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

@@ -12,33 +12,19 @@
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 = EIGEN_ADD_COST(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
@@ -55,6 +41,19 @@ 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 : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
{
@@ -62,8 +61,10 @@ class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename in
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)
@@ -75,19 +76,27 @@ class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename in
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
@@ -100,38 +109,18 @@ class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
inline Scalar* data() { return &coeffRef(0); }
inline const Scalar* data() const { return &coeff(0); }
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() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
}
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));
}
};
} // end namespace Eigen

View File

@@ -34,37 +34,45 @@ static inline void check_DenseIndex_is_signed() {
* \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,
DenseCoeffsBase<Derived> >
#else
: public DenseCoeffsBase<Derived>
#else
: public DenseCoeffsBase<Derived,DirectWriteAccessors>
#endif // not EIGEN_PARSED_BY_DOXYGEN
{
public:
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 internal::special_scalar_op_base<Derived,Scalar,RealScalar, DenseCoeffsBase<Derived> > Base;
typedef DenseCoeffsBase<Derived> Base;
using Base::operator*;
using Base::derived;
using Base::const_cast_derived;
using Base::rows;
@@ -74,16 +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;
@@ -169,19 +167,46 @@ 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 the outer size.
@@ -189,6 +214,7 @@ template<typename Derived> class DenseBase
* \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
@@ -200,6 +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()
@@ -210,6 +237,7 @@ 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.
*/
EIGEN_DEVICE_FUNC
void resize(Index newSize)
{
EIGEN_ONLY_USED_FOR_DEBUG(newSize);
@@ -220,22 +248,22 @@ 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 nbRows, Index nbCols)
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols)
{
EIGEN_ONLY_USED_FOR_DEBUG(nbRows);
EIGEN_ONLY_USED_FOR_DEBUG(nbCols);
eigen_assert(nbRows == this->rows() && nbCols == this->cols()
EIGEN_ONLY_USED_FOR_DEBUG(rows);
EIGEN_ONLY_USED_FOR_DEBUG(cols);
eigen_assert(rows == this->rows() && cols == this->cols()
&& "DenseBase::resize() does not actually allow to resize.");
}
#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;
@@ -243,120 +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);
/** \internal Copies \a other into *this without evaluating other. \returns a reference to *this. */
/** \ínternal
* 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);
/** \internal Evaluates \a other into *this. \returns a reference to *this. */
template<typename OtherDerived>
Derived& lazyAssign(const ReturnByValue<OtherDerived>& other);
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 typename internal::add_const<Transpose<const Derived> >::type 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
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,
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC
bool isMuchSmallerThan(const RealScalar& other,
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
template<typename OtherDerived>
template<typename OtherDerived> EIGEN_DEVICE_FUNC
bool isMuchSmallerThan(const DenseBase<OtherDerived>& other,
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isApproxToConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isZero(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isOnes(const 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
@@ -364,61 +405,68 @@ 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 the unique coefficient of a 1x1 expression */
EIGEN_DEVICE_FUNC
CoeffReturnType value() const
{
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
@@ -426,8 +474,8 @@ template<typename Derived> class DenseBase
return derived().coeff(0,0);
}
bool all(void) const;
bool any(void) const;
bool all() const;
bool any() const;
Index count() const;
typedef VectorwiseOp<Derived, Horizontal> RowwiseReturnType;
@@ -435,14 +483,35 @@ template<typename Derived> class DenseBase
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>
@@ -460,45 +529,56 @@ template<typename Derived> class DenseBase
template<int p> RealScalar lpNorm() const;
template<int RowFactor, int ColFactor>
inline const Replicate<Derived,RowFactor,ColFactor> replicate() const;
typedef Replicate<Derived,Dynamic,Dynamic> ReplicateReturnType;
inline const ReplicateReturnType replicate(Index rowFacor,Index colFactor) const;
EIGEN_DEVICE_FUNC
const Replicate<Derived,RowFactor,ColFactor> replicate() 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
@@ -511,9 +591,9 @@ 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

@@ -35,7 +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;
@@ -61,6 +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
@@ -69,6 +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
@@ -91,13 +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),
@@ -108,11 +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
@@ -130,11 +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);
}
@@ -146,15 +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.
@@ -167,32 +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
@@ -207,9 +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);
}
@@ -234,8 +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:
@@ -278,7 +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;
@@ -311,13 +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)
{
@@ -330,12 +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);
}
@@ -354,11 +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.
@@ -368,15 +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.
@@ -388,167 +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& val)
w()
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().template writePacket<StoreMode>(row,col,val);
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& val)
{
writePacket<StoreMode>(rowIndexByOuterInner(outer, inner),
colIndexByOuterInner(outer, inner),
val);
}
/** \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& val)
{
eigen_internal_assert(index >= 0 && index < size());
derived().template writePacket<StoreMode>(index,val);
}
#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.
@@ -560,7 +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>
@@ -568,7 +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;
@@ -581,6 +492,7 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
*
* \sa outerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return derived().innerStride();
@@ -591,6 +503,7 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
*
* \sa innerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return derived().outerStride();
@@ -606,6 +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();
@@ -615,6 +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();
@@ -630,7 +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>
@@ -639,7 +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;
@@ -652,6 +566,7 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
*
* \sa outerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return derived().innerStride();
@@ -662,6 +577,7 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
*
* \sa innerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return derived().outerStride();
@@ -677,6 +593,7 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
*
* \sa innerStride(), outerStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index rowStride() const
{
return Derived::IsRowMajor ? outerStride() : innerStride();
@@ -686,6 +603,7 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
*
* \sa innerStride(), outerStride(), rowStride()
*/
EIGEN_DEVICE_FUNC
inline Index colStride() const
{
return Derived::IsRowMajor ? innerStride() : outerStride();
@@ -694,33 +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

@@ -3,7 +3,7 @@
//
// 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>
//
// 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
@@ -24,7 +24,9 @@ namespace internal {
struct constructor_without_unaligned_array_assert {};
template<typename T, int Size> void check_static_allocation_size()
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
@@ -38,18 +40,19 @@ template<typename T, int Size> void check_static_allocation_size()
*/
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()
EIGEN_DEVICE_FUNC
plain_array()
{
check_static_allocation_size<T,Size>();
}
plain_array(constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
@@ -64,29 +67,88 @@ struct plain_array
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((reinterpret_cast<size_t>(eigen_unaligned_array_assert_workaround_gcc47(array)) & sizemask) == 0 \
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/group__TopicUnalignedArrayAssert.html" \
" **** READ THIS WEB PAGE !!! ****");
#endif
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 16>
struct plain_array<T, Size, MatrixOrArrayOptions, 8>
{
EIGEN_USER_ALIGN16 T array[Size];
EIGEN_ALIGN_TO_BOUNDARY(8) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(0xf);
{
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_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>();
@@ -96,9 +158,9 @@ struct plain_array<T, Size, MatrixOrArrayOptions, 16>
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
@@ -122,41 +184,50 @@ template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseSt
{
internal::plain_array<T,Size,_Options> m_data;
public:
DenseStorage() {}
DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC DenseStorage() {}
EIGEN_DEVICE_FUNC
explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()) {}
EIGEN_DEVICE_FUNC
DenseStorage(const DenseStorage& other) : m_data(other.m_data) {}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(const DenseStorage& other)
{
{
if (this != &other) m_data = other.m_data;
return *this;
return *this;
}
DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
void swap(DenseStorage& other) { std::swap(m_data,other.m_data); }
static DenseIndex rows(void) {return _Rows;}
static DenseIndex cols(void) {return _Cols;}
void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {}
void resize(DenseIndex,DenseIndex,DenseIndex) {}
const T *data() const { return m_data.array; }
T *data() { return m_data.array; }
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:
DenseStorage() {}
DenseStorage(internal::constructor_without_unaligned_array_assert) {}
DenseStorage(const DenseStorage&) {}
DenseStorage& operator=(const DenseStorage&) { return *this; }
DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
void swap(DenseStorage& ) {}
static DenseIndex rows(void) {return _Rows;}
static DenseIndex cols(void) {return _Cols;}
void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {}
void resize(DenseIndex,DenseIndex,DenseIndex) {}
const T *data() const { return 0; }
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
@@ -173,74 +244,74 @@ 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:
DenseStorage() : m_rows(0), m_cols(0) {}
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) {}
DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows), m_cols(other.m_cols) {}
DenseStorage& operator=(const 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;
return *this;
}
DenseStorage(DenseIndex, DenseIndex nbRows, DenseIndex nbCols) : m_rows(nbRows), m_cols(nbCols) {}
void swap(DenseStorage& other)
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); }
DenseIndex rows() const {return m_rows;}
DenseIndex cols() const {return m_cols;}
void conservativeResize(DenseIndex, DenseIndex nbRows, DenseIndex nbCols) { m_rows = nbRows; m_cols = nbCols; }
void resize(DenseIndex, DenseIndex nbRows, DenseIndex nbCols) { m_rows = nbRows; m_cols = nbCols; }
const T *data() const { return m_data.array; }
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:
DenseStorage() : m_rows(0) {}
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) {}
DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows) {}
DenseStorage& operator=(const DenseStorage& other)
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;
return *this;
}
DenseStorage(DenseIndex, DenseIndex nbRows, DenseIndex) : m_rows(nbRows) {}
void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
DenseIndex rows(void) const {return m_rows;}
DenseIndex cols(void) const {return _Cols;}
void conservativeResize(DenseIndex, DenseIndex nbRows, DenseIndex) { m_rows = nbRows; }
void resize(DenseIndex, DenseIndex nbRows, DenseIndex) { m_rows = nbRows; }
const T *data() const { return m_data.array; }
T *data() { return m_data.array; }
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:
DenseStorage() : m_cols(0) {}
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) {}
DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_cols(other.m_cols) {}
DenseStorage& operator=(const DenseStorage& other)
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)
{
@@ -249,38 +320,61 @@ template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Si
}
return *this;
}
DenseStorage(DenseIndex, DenseIndex, DenseIndex nbCols) : m_cols(nbCols) {}
void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
DenseIndex rows(void) const {return _Rows;}
DenseIndex cols(void) const {return m_cols;}
void conservativeResize(DenseIndex, DenseIndex, DenseIndex nbCols) { m_cols = nbCols; }
void resize(DenseIndex, DenseIndex, DenseIndex nbCols) { m_cols = nbCols; }
const T *data() const { return m_data.array; }
T *data() { return m_data.array; }
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:
DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
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) {}
DenseStorage(DenseIndex size, DenseIndex nbRows, DenseIndex nbCols)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(nbRows), m_cols(nbCols)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
DenseStorage(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)
{
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;
}
DenseStorage& operator=(DenseStorage&& other)
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_data, other.m_data);
@@ -289,18 +383,18 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
return *this;
}
#endif
~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
void swap(DenseStorage& other)
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); }
DenseIndex rows(void) const {return m_rows;}
DenseIndex cols(void) const {return m_cols;}
void conservativeResize(DenseIndex size, DenseIndex nbRows, DenseIndex nbCols)
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 = nbRows;
m_cols = nbCols;
m_rows = rows;
m_cols = cols;
}
void resize(DenseIndex size, DenseIndex nbRows, DenseIndex nbCols)
EIGEN_DEVICE_FUNC void resize(Index size, Index rows, Index cols)
{
if(size != m_rows*m_cols)
{
@@ -311,34 +405,53 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
}
m_rows = nbRows;
m_cols = nbCols;
m_rows = rows;
m_cols = cols;
}
const T *data() const { return m_data; }
T *data() { return m_data; }
private:
DenseStorage(const DenseStorage&);
DenseStorage& operator=(const DenseStorage&);
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:
DenseStorage() : m_data(0), m_cols(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
DenseStorage(DenseIndex size, DenseIndex, DenseIndex nbCols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(nbCols)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
DenseStorage(DenseStorage&& other)
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)
{
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;
}
DenseStorage& operator=(DenseStorage&& other)
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_data, other.m_data);
@@ -346,16 +459,16 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
return *this;
}
#endif
~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
static DenseIndex rows(void) {return _Rows;}
DenseIndex cols(void) const {return m_cols;}
void conservativeResize(DenseIndex size, DenseIndex, DenseIndex nbCols)
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 = nbCols;
m_cols = cols;
}
EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex, DenseIndex nbCols)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index, Index cols)
{
if(size != _Rows*m_cols)
{
@@ -366,33 +479,52 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
}
m_cols = nbCols;
m_cols = cols;
}
const T *data() const { return m_data; }
T *data() { return m_data; }
private:
DenseStorage(const DenseStorage&);
DenseStorage& operator=(const DenseStorage&);
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:
DenseStorage() : m_data(0), m_rows(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
DenseStorage(DenseIndex size, DenseIndex nbRows, DenseIndex) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(nbRows)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
DenseStorage(DenseStorage&& other)
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)
{
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;
}
DenseStorage& operator=(DenseStorage&& other)
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_data, other.m_data);
@@ -400,16 +532,16 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
return *this;
}
#endif
~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
DenseIndex rows(void) const {return m_rows;}
static DenseIndex cols(void) {return _Cols;}
void conservativeResize(DenseIndex size, DenseIndex nbRows, DenseIndex)
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 = nbRows;
m_rows = rows;
}
EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex nbRows, DenseIndex)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index rows, Index)
{
if(size != m_rows*_Cols)
{
@@ -420,13 +552,10 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
}
m_rows = nbRows;
m_rows = rows;
}
const T *data() const { return m_data; }
T *data() { return m_data; }
private:
DenseStorage(const DenseStorage&);
DenseStorage& operator=(const DenseStorage&);
EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC T *data() { return m_data; }
};
} // end namespace Eigen

View File

@@ -37,7 +37,7 @@ 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 {
@@ -52,8 +52,7 @@ struct traits<Diagonal<MatrixType,DiagIndex> >
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
@@ -70,20 +69,28 @@ template<typename MatrixType, int _DiagIndex> class Diagonal
typedef typename internal::dense_xpr_base<Diagonal>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index) {}
EIGEN_DEVICE_FUNC
explicit inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index) {}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
EIGEN_DEVICE_FUNC
inline Index rows() const
{ return m_index.value()<0 ? (std::min<Index>)(m_matrix.cols(),m_matrix.rows()+m_index.value()) : (std::min<Index>)(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;
@@ -95,62 +102,75 @@ 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());
}
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index idx)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return m_matrix.const_cast_derived().coeffRef(idx+rowOffset(), idx+colOffset());
return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset());
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index idx) const
{
return m_matrix.const_cast_derived().coeffRef(idx+rowOffset(), idx+colOffset());
return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset());
}
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index idx) const
{
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;
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;
};
@@ -167,7 +187,7 @@ template<typename Derived>
inline typename MatrixBase<Derived>::DiagonalReturnType
MatrixBase<Derived>::diagonal()
{
return derived();
return DiagonalReturnType(derived());
}
/** This is the const version of diagonal(). */
@@ -216,20 +236,20 @@ 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

@@ -22,7 +22,7 @@ class DiagonalBase : public EigenBase<Derived>
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,
@@ -30,79 +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>
inline void addTo(MatrixBase<DenseDerived> &other) const
{ other.diagonal() += diagonal(); }
template<typename DenseDerived>
inline 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(); }
/** \returns the diagonal matrix product of \c *this by the matrix \a matrix.
*/
template<typename MatrixDerived>
const DiagonalProduct<MatrixDerived, Derived, OnTheLeft>
EIGEN_DEVICE_FUNC
const Product<Derived,MatrixDerived,LazyProduct>
operator*(const MatrixBase<MatrixDerived> &matrix) const
{
return DiagonalProduct<MatrixDerived, Derived, OnTheLeft>(matrix.derived(), derived());
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());
}
inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> >
EIGEN_DEVICE_FUNC
inline const DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >
operator*(const Scalar& scalar) const
{
return diagonal() * scalar;
return DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >(diagonal() * scalar);
}
friend inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> >
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 other.diagonal() * scalar;
return DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >(scalar * other.diagonal());
}
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
bool isApprox(const DiagonalBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
{
return diagonal().isApprox(other.diagonal(), precision);
}
template<typename OtherDerived>
bool isApprox(const MatrixBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
{
return toDenseMatrix().isApprox(other, precision);
}
#endif
};
template<typename Derived>
template<typename DenseDerived>
inline void DiagonalBase<Derived>::evalTo(MatrixBase<DenseDerived> &other) const
{
other.setZero();
other.diagonal() = diagonal();
}
#endif
/** \class DiagonalMatrix
@@ -124,10 +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
};
};
}
@@ -141,7 +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:
@@ -151,24 +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
@@ -178,11 +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();
@@ -193,6 +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();
@@ -201,14 +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); }
};
@@ -232,14 +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
};
};
}
@@ -255,9 +252,11 @@ class DiagonalWrapper
#endif
/** Constructor from expression of diagonal coefficients to wrap. */
inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_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:
@@ -277,7 +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,
@@ -291,12 +290,11 @@ MatrixBase<Derived>::asDiagonal() const
template<typename Derived>
bool MatrixBase<Derived>::isDiagonal(const RealScalar& prec) const
{
using std::abs;
if(cols() != rows()) return false;
RealScalar maxAbsOnDiagonal = static_cast<RealScalar>(-1);
for(Index j = 0; j < cols(); ++j)
{
RealScalar absOnDiagonal = abs(coeff(j,j));
RealScalar absOnDiagonal = numext::abs(coeff(j,j));
if(absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal;
}
for(Index j = 0; j < cols(); ++j)
@@ -308,6 +306,38 @@ bool MatrixBase<Derived>::isDiagonal(const 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

@@ -13,117 +13,14 @@
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,
_ScalarAccessOnDiag = !((int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheLeft)
||(int(_StorageOrder) == RowMajor && 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::DiagonalVectorType::Flags)&PacketAccessBit))),
_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && _SameTypes && (_ScalarAccessOnDiag || (bool(int(DiagonalType::DiagonalVectorType::Flags)&PacketAccessBit))),
_LinearAccessMask = (RowsAtCompileTime==1 || ColsAtCompileTime==1) ? LinearAccessBit : 0,
Flags = ((HereditaryBits|_LinearAccessMask|AlignedBit) & (unsigned int)(MatrixType::Flags)) | (_Vectorizable ? PacketAccessBit : 0),//(int(MatrixType::Flags)&int(DiagonalType::DiagonalVectorType::Flags)&AlignedBit),
Cost0 = EIGEN_ADD_COST(NumTraits<Scalar>::MulCost, MatrixType::CoeffReadCost),
CoeffReadCost = EIGEN_ADD_COST(Cost0,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()));
}
EIGEN_STRONG_INLINE Index rows() const { return m_matrix.rows(); }
EIGEN_STRONG_INLINE Index cols() const { return m_matrix.cols(); }
EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
{
return m_diagonal.diagonal().coeff(ProductOrder == OnTheLeft ? row : col) * m_matrix.coeff(row, col);
}
EIGEN_STRONG_INLINE const Scalar coeff(Index idx) const
{
enum {
StorageOrder = int(MatrixType::Flags) & RowMajorBit ? RowMajor : ColMajor
};
return coeff(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
}
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());
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index idx) const
{
enum {
StorageOrder = int(MatrixType::Flags) & RowMajorBit ? RowMajor : ColMajor
};
return packet<LoadMode>(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
}
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) || (int(DiagonalType::DiagonalVectorType::Flags)&AlignedBit)==AlignedBit) ? 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>
inline const Product<Derived, DiagonalDerived, LazyProduct>
MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &a_diagonal) const
{
return DiagonalProduct<Derived, DiagonalDerived, OnTheRight>(derived(), a_diagonal.derived());
return Product<Derived, DiagonalDerived, LazyProduct>(derived(),a_diagonal.derived());
}
} // end namespace Eigen

View File

@@ -28,26 +28,31 @@ template<typename T, typename U,
>
struct dot_nocheck
{
typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar;
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
static inline 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;
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
static inline 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
*
@@ -59,55 +64,30 @@ struct dot_nocheck<T, U, true>
*/
template<typename Derived>
template<typename OtherDerived>
inline 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
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
@@ -119,16 +99,18 @@ EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scala
* 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
{
using std::sqrt;
return 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
*
@@ -138,22 +120,77 @@ template<typename Derived>
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()
{
*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>
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>
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 ----------
@@ -164,9 +201,10 @@ 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)
{
using std::pow;
EIGEN_USING_STD_MATH(pow)
return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p);
}
};
@@ -174,6 +212,7 @@ 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();
@@ -183,6 +222,7 @@ 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();
@@ -192,23 +232,35 @@ 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);
@@ -227,8 +279,8 @@ template<typename OtherDerived>
bool MatrixBase<Derived>::isOrthogonal
(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());
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();
}
@@ -246,13 +298,13 @@ bool MatrixBase<Derived>::isOrthogonal
template<typename Derived>
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

@@ -13,7 +13,9 @@
namespace Eigen {
/** Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T).
/** \class EigenBase
*
* 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.
*
@@ -21,39 +23,57 @@ 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.
@@ -63,7 +83,9 @@ 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.
@@ -73,7 +95,8 @@ 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.
@@ -81,7 +104,8 @@ 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.
@@ -106,7 +130,7 @@ template<typename Derived>
template<typename OtherDerived>
Derived& DenseBase<Derived>::operator=(const EigenBase<OtherDerived> &other)
{
other.derived().evalTo(derived());
call_assignment(derived(), other.derived());
return derived();
}
@@ -114,7 +138,7 @@ template<typename Derived>
template<typename OtherDerived>
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();
}
@@ -122,7 +146,7 @@ template<typename Derived>
template<typename OtherDerived>
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();
}

View File

@@ -1,140 +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>
//
// 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_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

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@@ -39,29 +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);
}
@@ -90,7 +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;
@@ -127,7 +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.
@@ -138,7 +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

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@@ -19,18 +19,19 @@ namespace internal
template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isApprox_selector
{
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>
{
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar&)
{
return x.matrix() == y.matrix();
@@ -40,6 +41,7 @@ struct isApprox_selector<Derived, OtherDerived, true>
template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isMuchSmallerThan_object_selector
{
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
{
return x.cwiseAbs2().sum() <= numext::abs2(prec) * y.cwiseAbs2().sum();
@@ -49,6 +51,7 @@ struct isMuchSmallerThan_object_selector
template<typename Derived, typename OtherDerived>
struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true>
{
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();
@@ -58,6 +61,7 @@ struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true>
template<typename Derived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isMuchSmallerThan_scalar_selector
{
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const typename Derived::RealScalar& y, const typename Derived::RealScalar& prec)
{
return x.cwiseAbs2().sum() <= numext::abs2(prec * y);
@@ -67,6 +71,7 @@ struct isMuchSmallerThan_scalar_selector
template<typename Derived>
struct isMuchSmallerThan_scalar_selector<Derived, true>
{
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();

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@@ -11,29 +11,7 @@
#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,
@@ -47,7 +25,8 @@ 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,
Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD ||
(Size==Dynamic && MaxSize>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD),
value = is_large ? Large
: Size == 1 ? 1
: Small
@@ -59,15 +38,14 @@ 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,
@@ -82,7 +60,8 @@ private:
public:
enum {
value = selector::ret
value = selector::ret,
ret = selector::ret
};
#ifdef EIGEN_DEBUG_PRODUCT
static void debug()
@@ -98,12 +77,13 @@ 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 }; };
@@ -122,60 +102,12 @@ 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
***********************************************************************/
@@ -187,114 +119,10 @@ 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)
{
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 {
// Column major
template<typename ProductType, typename Dest, typename Func>
EIGEN_DONT_INLINE void outer_product_selector_run(const ProductType& prod, Dest& dest, const Func& func, const false_type&)
{
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)
func(dest.col(j), prod.rhs().coeff(0,j) * prod.lhs());
}
// Row major
template<typename ProductType, typename Dest, typename Func>
EIGEN_DONT_INLINE void outer_product_selector_run(const ProductType& prod, Dest& dest, const Func& func, const true_type&) {
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)
func(dest.row(i), prod.lhs().coeff(i,0) * prod.rhs());
}
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>
{
template<typename T> struct is_row_major : internal::conditional<(int(T::Flags)&RowMajorBit), internal::true_type, internal::false_type>::type {};
public:
EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct)
GeneralProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
{
}
struct set { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() = src; } };
struct add { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() += src; } };
struct sub { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() -= src; } };
struct adds {
Scalar m_scale;
adds(const Scalar& s) : m_scale(s) {}
template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const {
dst.const_cast_derived() += m_scale * src;
}
};
template<typename Dest>
inline void evalTo(Dest& dest) const {
internal::outer_product_selector_run(*this, dest, set(), is_row_major<Dest>());
}
template<typename Dest>
inline void addTo(Dest& dest) const {
internal::outer_product_selector_run(*this, dest, add(), is_row_major<Dest>());
}
template<typename Dest>
inline void subTo(Dest& dest) const {
internal::outer_product_selector_run(*this, dest, sub(), is_row_major<Dest>());
}
template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const
{
internal::outer_product_selector_run(*this, dest, adds(alpha), is_row_major<Dest>());
}
};
/***********************************************************************
* Implementation of General Matrix Vector Product
***********************************************************************/
@@ -308,60 +136,13 @@ class GeneralProduct<Lhs, Rhs, OuterProduct>
*/
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& a_lhs, const Rhs& a_rhs) : Base(a_lhs,a_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, const 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, const 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>
@@ -379,46 +160,61 @@ 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, const 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;
@@ -428,80 +224,97 @@ template<> struct gemv_selector<OnTheRight,ColMajor,true>
// on, the other hand it is good for the cache to pack the vector anyways...
EvalToDestAtCompileTime = (ActualDest::InnerStrideAtCompileTime==1),
ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex),
MightCannotUseDest = (ActualDest::InnerStrideAtCompileTime!=1) || ComplexByReal
MightCannotUseDest = (!EvalToDestAtCompileTime) || ComplexByReal
};
gemv_static_vector_if<ResScalar,ActualDest::SizeAtCompileTime,ActualDest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
bool alphaIsCompatible = (!ComplexByReal) || (numext::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, const 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());
@@ -509,45 +322,48 @@ 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,
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, const 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, const 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();
}
};
@@ -563,9 +379,11 @@ template<> struct gemv_selector<OnTheRight,RowMajor,false>
*
* \sa lazyProduct(), operator*=(const MatrixBase&), Cwise::operator*()
*/
#ifndef __CUDACC__
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
@@ -590,9 +408,12 @@ 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());
}
#endif // __CUDACC__
/** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation.
*
* The returned product will behave like any other expressions: the coefficients of the product will be
@@ -606,7 +427,7 @@ 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 {
@@ -625,7 +446,7 @@ 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

@@ -42,21 +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,
@@ -64,17 +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,
@@ -90,135 +117,239 @@ 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
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
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.
* For instance, for a packet of 8 elements, 4 scalar 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> 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 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> 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 (!EIGEN_COMP_MSVC) && (EIGEN_COMP_GNUC || EIGEN_COMP_CLANG || EIGEN_COMP_ICC)
#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)
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;
@@ -250,6 +381,22 @@ Packet pasin(const Packet& a) { using std::asin; return asin(a); }
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
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) { using std::exp; return exp(a); }
@@ -258,10 +405,36 @@ Packet pexp(const Packet& a) { using std::exp; return exp(a); }
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
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) { 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
***************************************************************************/
@@ -275,34 +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>
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
@@ -336,15 +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

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010-2012 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>
//
// This Source Code Form is subject to the terms of the Mozilla
@@ -11,13 +11,30 @@
#ifndef EIGEN_GLOBAL_FUNCTIONS_H
#define EIGEN_GLOBAL_FUNCTIONS_H
#define EIGEN_ARRAY_DECLARE_GLOBAL_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> \
@@ -30,55 +47,133 @@
{ \
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 Eigen
{
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real,scalar_real_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(imag,scalar_imag_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(conj,scalar_conjugate_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sin,scalar_sin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cos,scalar_cos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asin,scalar_asin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acos,scalar_acos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tan,scalar_tan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(exp,scalar_exp_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log,scalar_log_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs,scalar_abs_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sqrt,scalar_sqrt_op)
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)
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) {
/** \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()
);
}
/**
* \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/(const 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)

View File

@@ -49,7 +49,7 @@ 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="",
@@ -57,6 +57,10 @@ struct IOFormat
: matPrefix(_matPrefix), matSuffix(_matSuffix), rowPrefix(_rowPrefix), rowSuffix(_rowSuffix), rowSeparator(_rowSeparator),
rowSpacer(""), coeffSeparator(_coeffSeparator), precision(_precision), flags(_flags)
{
// 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')
{
@@ -76,7 +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()
@@ -101,7 +105,7 @@ class WithFormat
}
protected:
const typename ExpressionType::Nested m_matrix;
typename ExpressionType::Nested m_matrix;
IOFormat m_format;
};
@@ -121,31 +125,17 @@ DenseBase<Derived>::format(const IOFormat& fmt) const
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;
using std::log;
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 */
@@ -160,7 +150,6 @@ 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;

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

@@ -13,13 +13,35 @@
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 {
InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
? int(PlainObjectType::InnerStrideAtCompileTime)
: int(StrideType::InnerStrideAtCompileTime),
OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
? int(PlainObjectType::OuterStrideAtCompileTime)
: 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.
@@ -63,44 +85,6 @@ 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> >
{
@@ -110,19 +94,17 @@ 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()
@@ -134,10 +116,11 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
/** Constructor in the fixed-size case.
*
* \param dataPtr pointer to the array to map
* \param a_stride optional Stride object, passing the strides.
* \param stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType dataPtr, const StrideType& a_stride = StrideType())
: Base(cast_to_pointer_type(dataPtr)), m_stride(a_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();
}
@@ -145,11 +128,12 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
/** Constructor in the dynamic-size vector case.
*
* \param dataPtr pointer to the array to map
* \param a_size the size of the vector expression
* \param a_stride optional Stride object, passing the strides.
* \param size the size of the vector expression
* \param stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType dataPtr, Index a_size, const StrideType& a_stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), a_size), m_stride(a_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();
}
@@ -157,12 +141,13 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
/** Constructor in the dynamic-size matrix case.
*
* \param dataPtr pointer to the array to map
* \param nbRows the number of rows of the matrix expression
* \param nbCols the number of columns of the matrix expression
* \param a_stride optional Stride object, passing the strides.
* \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 dataPtr, Index nbRows, Index nbCols, const StrideType& a_stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), nbRows, nbCols), m_stride(a_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();
}
@@ -173,19 +158,6 @@ 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

@@ -12,15 +12,25 @@
#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
*/
@@ -37,7 +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;
@@ -76,8 +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.
*
@@ -85,30 +96,39 @@ 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; }
/** \copydoc PlainObjectBase::coeff(Index,Index) const */
EIGEN_DEVICE_FUNC
inline const Scalar& coeff(Index rowId, Index colId) const
{
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()];
}
/** \copydoc PlainObjectBase::coeffRef(Index,Index) const */
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
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 rowId, Index colId) const
{
@@ -116,6 +136,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
(m_data + (colId * colStride() + rowId * rowStride()));
}
/** \internal */
template<int LoadMode>
inline PacketScalar packet(Index index) const
{
@@ -123,12 +144,16 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
return internal::ploadt<PacketScalar, LoadMode>(m_data + index * innerStride());
}
/** \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>();
}
/** \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)),
@@ -137,16 +162,18 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
eigen_assert(vecSize >= 0);
eigen_assert(dataPtr == 0 || SizeAtCompileTime == Dynamic || SizeAtCompileTime == vecSize);
checkSanity();
checkSanity<Derived>();
}
inline MapBase(PointerType dataPtr, Index nbRows, Index nbCols)
: m_data(dataPtr), m_rows(nbRows), m_cols(nbCols)
/** \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( (dataPtr == 0)
|| ( nbRows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == nbRows)
&& nbCols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == nbCols)));
checkSanity();
|| ( rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)));
checkSanity<Derived>();
}
#ifdef EIGEN_MAPBASE_PLUGIN
@@ -155,20 +182,36 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
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)
&& "input pointer is not aligned on a 16 byte boundary");
#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>
{
@@ -179,7 +222,7 @@ template<typename Derived> class MapBase<Derived, WriteAccessors>
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;
@@ -200,14 +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)
@@ -229,10 +276,11 @@ template<typename Derived> class MapBase<Derived, WriteAccessors>
(this->m_data + index * innerStride(), val);
}
explicit inline MapBase(PointerType dataPtr) : Base(dataPtr) {}
inline MapBase(PointerType dataPtr, Index vecSize) : Base(dataPtr, vecSize) {}
inline MapBase(PointerType dataPtr, Index nbRows, Index nbCols) : Base(dataPtr, nbRows, nbCols) {}
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)
{
ReadOnlyMapBase::Base::operator=(other);

File diff suppressed because it is too large Load Diff

View File

@@ -0,0 +1,78 @@
// 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);
}
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_MATHFUNCTIONSIMPL_H

View File

@@ -13,6 +13,45 @@
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
*
@@ -24,13 +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.
@@ -67,7 +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>
*
@@ -97,32 +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> >
@@ -151,6 +202,7 @@ class Matrix
*
* \callgraph
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix& operator=(const Matrix& other)
{
return Base::_set(other);
@@ -167,7 +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);
}
@@ -179,12 +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);
@@ -200,6 +255,7 @@ class Matrix
*
* \sa resize(Index,Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix() : Base()
{
Base::_check_template_params();
@@ -207,60 +263,87 @@ class Matrix
}
// 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_COEFFS_IF_THAT_OPTION_IS_ENABLED }
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
Matrix(Matrix&& other)
#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();
if (RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic)
Base::_set_noalias(other);
}
Matrix& operator=(Matrix&& other)
EIGEN_DEVICE_FUNC
Matrix& operator=(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
{
other.swap(*this);
return *this;
}
#endif
/** \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)
#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();
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Matrix)
eigen_assert(dim >= 0);
eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim);
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
Base::template _init1<T>(x);
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
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_to_match(other);
// 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

@@ -41,9 +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>
@@ -52,7 +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;
@@ -66,7 +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;
@@ -98,25 +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>,
@@ -125,7 +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,
@@ -133,6 +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"
@@ -141,41 +130,53 @@ 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);
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& lazyAssign(const ProductBase<ProductDerived, Lhs,Rhs>& other);
template<typename MatrixPower, typename Lhs, typename Rhs>
Derived& lazyAssign(const MatrixPowerProduct<MatrixPower, Lhs,Rhs>& other);
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);
#ifdef __CUDACC__
template<typename OtherDerived>
const typename ProductReturnType<Derived,OtherDerived>::Type
operator*(const MatrixBase<OtherDerived> &other) const;
EIGEN_DEVICE_FUNC
const Product<Derived,OtherDerived,LazyProduct>
operator*(const MatrixBase<OtherDerived> &other) const
{ return this->lazyProduct(other); }
#else
template<typename OtherDerived>
const typename LazyProductReturnType<Derived,OtherDerived>::Type
const Product<Derived,OtherDerived>
operator*(const MatrixBase<OtherDerived> &other) const;
#endif
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
const Product<Derived,OtherDerived,LazyProduct>
lazyProduct(const MatrixBase<OtherDerived> &other) const;
template<typename OtherDerived>
@@ -188,84 +189,93 @@ 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 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();
template<int Index>
EIGEN_DEVICE_FUNC
typename ConstDiagonalIndexReturnType<Index>::Type diagonal() const;
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;
#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
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),
const 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();
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(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
@@ -297,59 +307,45 @@ template<typename Derived> class MatrixBase
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;
MatrixBase<Derived>& matrix() { return *this; }
const MatrixBase<Derived>& matrix() const { return *this; }
EIGEN_DEVICE_FUNC MatrixBase<Derived>& matrix() { return *this; }
EIGEN_DEVICE_FUNC const MatrixBase<Derived>& matrix() const { return *this; }
/** \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()
@@ -358,65 +354,70 @@ 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 ///////////
@@ -461,49 +462,15 @@ template<typename Derived> class MatrixBase
const MatrixSquareRootReturnValue<Derived> sqrt() const;
const MatrixLogarithmReturnValue<Derived> log() const;
const MatrixPowerReturnValue<Derived> pow(const RealScalar& p) 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 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>& )

View File

@@ -13,25 +13,24 @@
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
{
@@ -40,29 +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);
}
@@ -91,7 +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

@@ -17,7 +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.
@@ -30,62 +30,36 @@ 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() */
template<typename OtherDerived>
EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase<OtherDerived>& other)
{ return internal::assign_selector<ExpressionType,OtherDerived,false>::run(m_expression,other.derived()); }
/** \sa MatrixBase::operator+= */
template<typename OtherDerived>
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()));
return m_expression;
}
/** \sa MatrixBase::operator-= */
template<typename OtherDerived>
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()));
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()); }
typedef typename ExpressionType::Scalar Scalar;
explicit NoAlias(ExpressionType& expression) : m_expression(expression) {}
template<typename OtherDerived>
ExpressionType& operator=(const ReturnByValue<OtherDerived>& func)
{ return m_expression = func; }
#endif
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase<OtherDerived>& other)
{
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)
{
call_assignment_no_alias(m_expression, other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return m_expression;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator-=(const StorageBase<OtherDerived>& other)
{
call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return m_expression;
}
EIGEN_DEVICE_FUNC
ExpressionType& expression() const
{
return m_expression;
@@ -126,7 +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

@@ -12,24 +12,57 @@
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
@@ -42,10 +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
@@ -67,22 +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>
@@ -91,11 +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; }
};
@@ -109,6 +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,
@@ -117,8 +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>
@@ -130,21 +199,48 @@ 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(); }
};
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

@@ -2,7 +2,7 @@
// 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>
//
// 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
@@ -13,14 +13,18 @@
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.
@@ -38,17 +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>
{
@@ -60,19 +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 */
@@ -118,7 +112,7 @@ class PermutationBase : public EigenBase<Derived>
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
@@ -147,7 +141,8 @@ class PermutationBase : public EigenBase<Derived>
/** 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;
}
@@ -163,18 +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();
}
@@ -185,7 +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)
{
@@ -196,16 +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 ****/
@@ -215,13 +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
@@ -229,7 +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
@@ -237,18 +232,18 @@ 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.
@@ -284,39 +279,43 @@ class PermutationBase : public EigenBase<Derived>
};
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()
@@ -324,8 +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>
@@ -346,7 +347,7 @@ class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompile
* array's size.
*/
template<typename Other>
explicit inline PermutationMatrix(const MatrixBase<Other>& a_indices) : m_indices(a_indices)
explicit inline PermutationMatrix(const MatrixBase<Other>& indices) : m_indices(indices)
{}
/** Convert the Transpositions \a tr to a permutation matrix */
@@ -393,10 +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)
@@ -413,18 +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;
@@ -432,14 +438,14 @@ 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* indicesPtr)
inline Map(const StorageIndex* indicesPtr)
: m_indices(indicesPtr)
{}
inline Map(const Index* indicesPtr, Index size)
inline Map(const StorageIndex* indicesPtr, Index size)
: m_indices(indicesPtr,size)
{}
@@ -474,40 +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> >
{
@@ -519,8 +521,8 @@ class PermutationWrapper : public PermutationBase<PermutationWrapper<_IndicesTyp
typedef typename Traits::IndicesType IndicesType;
#endif
inline PermutationWrapper(const IndicesType& a_indices)
: m_indices(a_indices)
inline PermutationWrapper(const IndicesType& indices)
: m_indices(indices)
{}
/** const version of indices(). */
@@ -532,183 +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;
typedef typename MatrixType::Index Index;
permut_matrix_product_retval(const PermutationType& perm, const MatrixType& matrix)
: m_permutation(perm), m_matrix(matrix)
{}
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
template<typename Dest> inline void evalTo(Dest& dst) const
{
const Index n = Side==OnTheLeft ? rows() : cols();
// FIXME we need an is_same for expression that is not sensitive to constness. For instance
// is_same_xpr<Block<const Matrix>, Block<Matrix> >::value should be true.
const typename Dest::Scalar *dst_data = internal::extract_data(dst);
if( is_same<MatrixTypeNestedCleaned,Dest>::value
&& blas_traits<MatrixTypeNestedCleaned>::HasUsableDirectAccess
&& blas_traits<Dest>::HasUsableDirectAccess
&& dst_data!=0 && dst_data == extract_data(m_matrix))
{
// apply the permutation inplace
Matrix<bool,PermutationType::RowsAtCompileTime,1,0,PermutationType::MaxRowsAtCompileTime> mask(m_permutation.size());
mask.fill(false);
Index 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
Index k0 = r++;
Index kPrev = k0;
mask.coeffRef(k0) = true;
for(Index 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>
@@ -717,6 +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

@@ -28,6 +28,7 @@ namespace internal {
template<int MaxSizeAtCompileTime> struct check_rows_cols_for_overflow {
template<typename Index>
EIGEN_DEVICE_FUNC
static EIGEN_ALWAYS_INLINE void run(Index, Index)
{
}
@@ -35,6 +36,7 @@ template<int MaxSizeAtCompileTime> struct check_rows_cols_for_overflow {
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
@@ -56,33 +58,41 @@ template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> struct m
} // 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
@@ -93,8 +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;
@@ -113,28 +123,40 @@ 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(); }
/** 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)
@@ -143,11 +165,21 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
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];
}
/** 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)
@@ -156,11 +188,19 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
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];
}
/** 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)
@@ -169,6 +209,9 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
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];
@@ -209,11 +252,11 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
}
/** \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.
@@ -232,22 +275,22 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index) for vectors, resize(NoChange_t, Index), resize(Index, NoChange_t)
*/
EIGEN_STRONG_INLINE void resize(Index nbRows, Index nbCols)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void resize(Index rows, Index cols)
{
eigen_assert( EIGEN_IMPLIES(RowsAtCompileTime!=Dynamic,nbRows==RowsAtCompileTime)
&& EIGEN_IMPLIES(ColsAtCompileTime!=Dynamic,nbCols==ColsAtCompileTime)
&& EIGEN_IMPLIES(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic,nbRows<=MaxRowsAtCompileTime)
&& EIGEN_IMPLIES(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic,nbCols<=MaxColsAtCompileTime)
&& nbRows>=0 && nbCols>=0 && "Invalid sizes when resizing a matrix or array.");
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(nbRows, nbCols);
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 = nbRows*nbCols;
Index size = rows*cols;
bool size_changed = size != this->size();
m_storage.resize(size, nbRows, nbCols);
m_storage.resize(size, rows, cols);
if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
#else
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(nbRows, nbCols);
m_storage.resize(nbRows*nbCols, nbRows, nbCols);
m_storage.resize(rows*cols, rows, cols);
#endif
}
@@ -262,6 +305,7 @@ 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)
@@ -286,9 +330,10 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index,Index)
*/
inline void resize(NoChange_t, Index nbCols)
EIGEN_DEVICE_FUNC
inline void resize(NoChange_t, Index cols)
{
resize(rows(), nbCols);
resize(rows(), cols);
}
/** Resizes the matrix, changing only the number of rows. For the parameter of type NoChange_t, just pass the special value \c NoChange
@@ -299,9 +344,10 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index,Index)
*/
inline void resize(Index nbRows, NoChange_t)
EIGEN_DEVICE_FUNC
inline void resize(Index rows, NoChange_t)
{
resize(nbRows, cols());
resize(rows, cols());
}
/** Resizes \c *this to have the same dimensions as \a other.
@@ -312,11 +358,12 @@ 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<MaxSizeAtCompileTime>::run(Index(other.rows()), Index(other.cols()));
const Index othersize = Index(other.rows())*Index(other.cols());
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(other.rows(), other.cols());
const Index othersize = other.rows()*other.cols();
if(RowsAtCompileTime == 1)
{
eigen_assert(other.rows() == 1 || other.cols() == 1);
@@ -339,9 +386,10 @@ 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_STRONG_INLINE void conservativeResize(Index nbRows, Index nbCols)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResize(Index rows, Index cols)
{
internal::conservative_resize_like_impl<Derived>::run(*this, nbRows, nbCols);
internal::conservative_resize_like_impl<Derived>::run(*this, rows, cols);
}
/** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
@@ -351,10 +399,11 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* In case the matrix is growing, new rows will be uninitialized.
*/
EIGEN_STRONG_INLINE void conservativeResize(Index nbRows, NoChange_t)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResize(Index rows, NoChange_t)
{
// Note: see the comment in conservativeResize(Index,Index)
conservativeResize(nbRows, cols());
conservativeResize(rows, cols());
}
/** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
@@ -364,10 +413,11 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* In case the matrix is growing, new columns will be uninitialized.
*/
EIGEN_STRONG_INLINE void conservativeResize(NoChange_t, Index nbCols)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResize(NoChange_t, Index cols)
{
// Note: see the comment in conservativeResize(Index,Index)
conservativeResize(rows(), nbCols);
conservativeResize(rows(), cols);
}
/** Resizes the vector to \a size while retaining old values.
@@ -378,6 +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);
@@ -393,6 +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);
@@ -401,6 +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);
@@ -408,6 +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);
@@ -415,12 +469,18 @@ 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);
}
// 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();
@@ -430,20 +490,23 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
#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_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#endif
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
PlainObjectBase(PlainObjectBase&& other)
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
PlainObjectBase(PlainObjectBase&& other) EIGEN_NOEXCEPT
: m_storage( std::move(other.m_storage) )
{
}
PlainObjectBase& operator=(PlainObjectBase&& other)
EIGEN_DEVICE_FUNC
PlainObjectBase& operator=(PlainObjectBase&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_storage, other.m_storage);
@@ -452,31 +515,56 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
#endif
/** Copy constructor */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(const PlainObjectBase& other)
: m_storage()
{
_check_template_params();
lazyAssign(other);
}
template<typename OtherDerived>
EIGEN_STRONG_INLINE PlainObjectBase(const DenseBase<OtherDerived> &other)
: m_storage()
{
_check_template_params();
lazyAssign(other);
}
EIGEN_STRONG_INLINE PlainObjectBase(Index a_size, Index nbRows, Index nbCols)
: m_storage(a_size, nbRows, nbCols)
: 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_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);
@@ -484,16 +572,6 @@ 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(Index(other.derived().rows()) * Index(other.derived().cols()), other.derived().rows(), other.derived().cols())
{
_check_template_params();
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(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
@@ -568,16 +646,16 @@ 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);
@@ -596,6 +674,7 @@ 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
@@ -603,8 +682,6 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
: (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);
if(this->size()==0)
resizeLike(other);
#else
resizeLike(other);
#endif
@@ -624,25 +701,23 @@ 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
@@ -650,40 +725,167 @@ 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_STRONG_INLINE void _init2(Index nbRows, Index nbCols, typename internal::enable_if<Base::SizeAtCompileTime!=2,T0>::type* = 0)
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)
resize(nbRows,nbCols);
resize(rows,cols);
}
template<typename T0, typename T1>
EIGEN_STRONG_INLINE void _init2(const Scalar& val0, const Scalar& val1, 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] = val0;
m_storage.data()[1] = val1;
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 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 arrays:
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);
}
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)
@@ -697,10 +899,9 @@ 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 {
@@ -708,7 +909,6 @@ namespace internal {
template <typename Derived, typename OtherDerived, bool IsVector>
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;
@@ -724,8 +924,8 @@ struct conservative_resize_like_impl
{
// 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);
}
@@ -758,8 +958,8 @@ struct 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);
}
@@ -774,7 +974,6 @@ struct conservative_resize_like_impl<Derived,OtherDerived,true>
{
using conservative_resize_like_impl<Derived,OtherDerived,false>::run;
typedef typename Derived::Index Index;
static void run(DenseBase<Derived>& _this, Index size)
{
const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : size;
@@ -800,6 +999,7 @@ 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);
@@ -809,6 +1009,7 @@ 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);

186
Eigen/src/Core/Product.h Normal file
View File

@@ -0,0 +1,186 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2011 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_PRODUCT_H
#define EIGEN_PRODUCT_H
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
*
* \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
*
*/
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, 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 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;
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");
}
EIGEN_DEVICE_FUNC inline Index rows() const { return m_lhs.rows(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_rhs.cols(); }
EIGEN_DEVICE_FUNC const LhsNestedCleaned& lhs() const { return m_lhs; }
EIGEN_DEVICE_FUNC const RhsNestedCleaned& rhs() const { return m_rhs; }
protected:
LhsNested m_lhs;
RhsNested m_rhs;
};
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,Option> ProductXpr;
typedef typename internal::dense_xpr_base<ProductXpr>::type Base;
public:
using Base::derived;
typedef typename Base::Scalar Scalar;
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 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 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

@@ -1,290 +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>
//
// 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_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:
#ifndef EIGEN_NO_MALLOC
typedef typename Base::PlainObject BasePlainObject;
typedef Matrix<Scalar,RowsAtCompileTime==1?1:Dynamic,ColsAtCompileTime==1?1:Dynamic,BasePlainObject::Options> DynPlainObject;
typedef typename internal::conditional<(BasePlainObject::SizeAtCompileTime==Dynamic) || (BasePlainObject::SizeAtCompileTime*int(sizeof(Scalar)) < int(EIGEN_STACK_ALLOCATION_LIMIT)),
BasePlainObject, DynPlainObject>::type PlainObject;
#else
typedef typename Base::PlainObject PlainObject;
#endif
ProductBase(const Lhs& a_lhs, const Rhs& a_rhs)
: m_lhs(a_lhs), m_rhs(a_rhs)
{
eigen_assert(a_lhs.cols() == a_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, const 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 typename GeneralProduct<Lhs,Rhs,Mode>::PlainObject const& type;
};
template<typename Lhs, typename Rhs, int Mode, int N, typename PlainObject>
struct nested<const GeneralProduct<Lhs,Rhs,Mode>, N, PlainObject>
{
typedef typename GeneralProduct<Lhs,Rhs,Mode>::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, const 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, const typename Derived::RealScalar& x)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
const ScaledProduct<Derived>
operator*(const 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*(const 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, const 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, const Scalar& a_alpha) const { m_prod.derived().scaleAndAddTo(dst,a_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

File diff suppressed because it is too large Load Diff

View File

@@ -16,8 +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>
@@ -28,12 +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
@@ -41,22 +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
@@ -69,10 +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>());
@@ -80,6 +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.
*
@@ -89,11 +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>());
@@ -101,6 +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
*
@@ -114,12 +135,16 @@ inline Derived& DenseBase<Derived>::setRandom()
/** 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&
@@ -131,19 +156,24 @@ PlainObjectBase<Derived>::setRandom(Index newSize)
/** Resizes to the given size, and sets all coefficients in this expression to random values.
*
* \param nbRows the new number of rows
* \param nbCols the new number of columns
* 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&
PlainObjectBase<Derived>::setRandom(Index nbRows, Index nbCols)
PlainObjectBase<Derived>::setRandom(Index rows, Index cols)
{
resize(nbRows, nbCols);
resize(rows, cols);
return setRandom();
}

View File

@@ -27,8 +27,9 @@ template<typename Func, typename Derived>
struct redux_traits
{
public:
typedef typename find_best_packet<typename Derived::Scalar,Derived::SizeAtCompileTime>::type PacketType;
enum {
PacketSize = packet_traits<typename Derived::Scalar>::size,
PacketSize = unpacket_traits<PacketType>::size,
InnerMaxSize = int(Derived::IsRowMajor)
? Derived::MaxColsAtCompileTime
: Derived::MaxRowsAtCompileTime
@@ -37,8 +38,8 @@ public:
enum {
MightVectorize = (int(Derived::Flags)&ActualPacketAccessBit)
&& (functor_traits<Func>::PacketAccess),
MayLinearVectorize = MightVectorize && (int(Derived::Flags)&LinearAccessBit),
MaySliceVectorize = MightVectorize && int(InnerMaxSize)>=3*PacketSize
MayLinearVectorize = bool(MightVectorize) && (int(Derived::Flags)&LinearAccessBit),
MaySliceVectorize = bool(MightVectorize) && int(InnerMaxSize)>=3*PacketSize
};
public:
@@ -50,21 +51,34 @@ public:
public:
enum {
Cost = ( Derived::SizeAtCompileTime == Dynamic
|| Derived::CoeffReadCost == Dynamic
|| (Derived::SizeAtCompileTime!=1 && functor_traits<Func>::Cost == Dynamic)
) ? Dynamic
: Derived::SizeAtCompileTime * Derived::CoeffReadCost
+ (Derived::SizeAtCompileTime-1) * functor_traits<Func>::Cost,
Cost = Derived::SizeAtCompileTime == Dynamic ? HugeCost
: Derived::SizeAtCompileTime * Derived::CoeffReadCost + (Derived::SizeAtCompileTime-1) * functor_traits<Func>::Cost,
UnrollingLimit = EIGEN_UNROLLING_LIMIT * (int(Traversal) == int(DefaultTraversal) ? 1 : int(PacketSize))
};
public:
enum {
Unrolling = Cost != Dynamic && Cost <= UnrollingLimit
? CompleteUnrolling
: NoUnrolling
Unrolling = Cost <= UnrollingLimit ? CompleteUnrolling : NoUnrolling
};
#ifdef EIGEN_DEBUG_ASSIGN
static void debug()
{
std::cerr << "Xpr: " << typeid(typename Derived::XprType).name() << std::endl;
std::cerr.setf(std::ios::hex, std::ios::basefield);
EIGEN_DEBUG_VAR(Derived::Flags)
std::cerr.unsetf(std::ios::hex);
EIGEN_DEBUG_VAR(InnerMaxSize)
EIGEN_DEBUG_VAR(PacketSize)
EIGEN_DEBUG_VAR(MightVectorize)
EIGEN_DEBUG_VAR(MayLinearVectorize)
EIGEN_DEBUG_VAR(MaySliceVectorize)
EIGEN_DEBUG_VAR(Traversal)
EIGEN_DEBUG_VAR(UnrollingLimit)
EIGEN_DEBUG_VAR(Unrolling)
std::cerr << std::endl;
}
#endif
};
/***************************************************************************
@@ -82,6 +96,7 @@ struct redux_novec_unroller
typedef typename Derived::Scalar Scalar;
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func& func)
{
return func(redux_novec_unroller<Func, Derived, Start, HalfLength>::run(mat,func),
@@ -99,6 +114,7 @@ struct redux_novec_unroller<Func, Derived, Start, 1>
typedef typename Derived::Scalar Scalar;
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func&)
{
return mat.coeffByOuterInner(outer, inner);
@@ -112,6 +128,7 @@ template<typename Func, typename Derived, int Start>
struct redux_novec_unroller<Func, Derived, Start, 0>
{
typedef typename Derived::Scalar Scalar;
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Scalar run(const Derived&, const Func&) { return Scalar(); }
};
@@ -121,12 +138,12 @@ template<typename Func, typename Derived, int Start, int Length>
struct redux_vec_unroller
{
enum {
PacketSize = packet_traits<typename Derived::Scalar>::size,
PacketSize = redux_traits<Func, Derived>::PacketSize,
HalfLength = Length/2
};
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename redux_traits<Func, Derived>::PacketType PacketScalar;
static EIGEN_STRONG_INLINE PacketScalar run(const Derived &mat, const Func& func)
{
@@ -140,18 +157,18 @@ template<typename Func, typename Derived, int Start>
struct redux_vec_unroller<Func, Derived, Start, 1>
{
enum {
index = Start * packet_traits<typename Derived::Scalar>::size,
index = Start * redux_traits<Func, Derived>::PacketSize,
outer = index / int(Derived::InnerSizeAtCompileTime),
inner = index % int(Derived::InnerSizeAtCompileTime),
alignment = (Derived::Flags & AlignedBit) ? Aligned : Unaligned
alignment = Derived::Alignment
};
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename redux_traits<Func, Derived>::PacketType PacketScalar;
static EIGEN_STRONG_INLINE PacketScalar run(const Derived &mat, const Func&)
{
return mat.template packetByOuterInner<alignment>(outer, inner);
return mat.template packetByOuterInner<alignment,PacketScalar>(outer, inner);
}
};
@@ -169,8 +186,8 @@ template<typename Func, typename Derived>
struct redux_impl<Func, Derived, DefaultTraversal, NoUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Index Index;
static EIGEN_STRONG_INLINE Scalar run(const Derived& mat, const Func& func)
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func& func)
{
eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix");
Scalar res;
@@ -193,19 +210,19 @@ template<typename Func, typename Derived>
struct redux_impl<Func, Derived, LinearVectorizedTraversal, NoUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename Derived::Index Index;
typedef typename redux_traits<Func, Derived>::PacketType PacketScalar;
static Scalar run(const Derived& mat, const Func& func)
static Scalar run(const Derived &mat, const Func& func)
{
const Index size = mat.size();
eigen_assert(size && "you are using an empty matrix");
const Index packetSize = packet_traits<Scalar>::size;
const Index alignedStart = internal::first_aligned(mat);
const Index packetSize = redux_traits<Func, Derived>::PacketSize;
const int packetAlignment = unpacket_traits<PacketScalar>::alignment;
enum {
alignment = bool(Derived::Flags & DirectAccessBit) || bool(Derived::Flags & AlignedBit)
? Aligned : Unaligned
alignment0 = (bool(Derived::Flags & DirectAccessBit) && bool(packet_traits<Scalar>::AlignedOnScalar)) ? int(packetAlignment) : int(Unaligned),
alignment = EIGEN_PLAIN_ENUM_MAX(alignment0, Derived::Alignment)
};
const Index alignedStart = internal::first_default_aligned(mat.nestedExpression());
const Index alignedSize2 = ((size-alignedStart)/(2*packetSize))*(2*packetSize);
const Index alignedSize = ((size-alignedStart)/(packetSize))*(packetSize);
const Index alignedEnd2 = alignedStart + alignedSize2;
@@ -213,19 +230,19 @@ struct redux_impl<Func, Derived, LinearVectorizedTraversal, NoUnrolling>
Scalar res;
if(alignedSize)
{
PacketScalar packet_res0 = mat.template packet<alignment>(alignedStart);
PacketScalar packet_res0 = mat.template packet<alignment,PacketScalar>(alignedStart);
if(alignedSize>packetSize) // we have at least two packets to partly unroll the loop
{
PacketScalar packet_res1 = mat.template packet<alignment>(alignedStart+packetSize);
PacketScalar packet_res1 = mat.template packet<alignment,PacketScalar>(alignedStart+packetSize);
for(Index index = alignedStart + 2*packetSize; index < alignedEnd2; index += 2*packetSize)
{
packet_res0 = func.packetOp(packet_res0, mat.template packet<alignment>(index));
packet_res1 = func.packetOp(packet_res1, mat.template packet<alignment>(index+packetSize));
packet_res0 = func.packetOp(packet_res0, mat.template packet<alignment,PacketScalar>(index));
packet_res1 = func.packetOp(packet_res1, mat.template packet<alignment,PacketScalar>(index+packetSize));
}
packet_res0 = func.packetOp(packet_res0,packet_res1);
if(alignedEnd>alignedEnd2)
packet_res0 = func.packetOp(packet_res0, mat.template packet<alignment>(alignedEnd2));
packet_res0 = func.packetOp(packet_res0, mat.template packet<alignment,PacketScalar>(alignedEnd2));
}
res = func.predux(packet_res0);
@@ -252,25 +269,24 @@ template<typename Func, typename Derived, int Unrolling>
struct redux_impl<Func, Derived, SliceVectorizedTraversal, Unrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename Derived::Index Index;
typedef typename redux_traits<Func, Derived>::PacketType PacketType;
static Scalar run(const Derived& mat, const Func& func)
EIGEN_DEVICE_FUNC static Scalar run(const Derived &mat, const Func& func)
{
eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix");
const Index innerSize = mat.innerSize();
const Index outerSize = mat.outerSize();
enum {
packetSize = packet_traits<Scalar>::size
packetSize = redux_traits<Func, Derived>::PacketSize
};
const Index packetedInnerSize = ((innerSize)/packetSize)*packetSize;
Scalar res;
if(packetedInnerSize)
{
PacketScalar packet_res = mat.template packet<Unaligned>(0,0);
PacketType packet_res = mat.template packet<Unaligned,PacketType>(0,0);
for(Index j=0; j<outerSize; ++j)
for(Index i=(j==0?packetSize:0); i<packetedInnerSize; i+=Index(packetSize))
packet_res = func.packetOp(packet_res, mat.template packetByOuterInner<Unaligned>(j,i));
packet_res = func.packetOp(packet_res, mat.template packetByOuterInner<Unaligned,PacketType>(j,i));
res = func.predux(packet_res);
for(Index j=0; j<outerSize; ++j)
@@ -291,22 +307,90 @@ template<typename Func, typename Derived>
struct redux_impl<Func, Derived, LinearVectorizedTraversal, CompleteUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename redux_traits<Func, Derived>::PacketType PacketScalar;
enum {
PacketSize = packet_traits<Scalar>::size,
PacketSize = redux_traits<Func, Derived>::PacketSize,
Size = Derived::SizeAtCompileTime,
VectorizedSize = (Size / PacketSize) * PacketSize
};
static EIGEN_STRONG_INLINE Scalar run(const Derived& mat, const Func& func)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func& func)
{
eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix");
Scalar res = func.predux(redux_vec_unroller<Func, Derived, 0, Size / PacketSize>::run(mat,func));
if (VectorizedSize != Size)
res = func(res,redux_novec_unroller<Func, Derived, VectorizedSize, Size-VectorizedSize>::run(mat,func));
return res;
if (VectorizedSize > 0) {
Scalar res = func.predux(redux_vec_unroller<Func, Derived, 0, Size / PacketSize>::run(mat,func));
if (VectorizedSize != Size)
res = func(res,redux_novec_unroller<Func, Derived, VectorizedSize, Size-VectorizedSize>::run(mat,func));
return res;
}
else {
return redux_novec_unroller<Func, Derived, 0, Size>::run(mat,func);
}
}
};
// evaluator adaptor
template<typename _XprType>
class redux_evaluator
{
public:
typedef _XprType XprType;
EIGEN_DEVICE_FUNC explicit redux_evaluator(const XprType &xpr) : m_evaluator(xpr), m_xpr(xpr) {}
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketScalar PacketScalar;
typedef typename XprType::PacketReturnType PacketReturnType;
enum {
MaxRowsAtCompileTime = XprType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = XprType::MaxColsAtCompileTime,
// TODO we should not remove DirectAccessBit and rather find an elegant way to query the alignment offset at runtime from the evaluator
Flags = evaluator<XprType>::Flags & ~DirectAccessBit,
IsRowMajor = XprType::IsRowMajor,
SizeAtCompileTime = XprType::SizeAtCompileTime,
InnerSizeAtCompileTime = XprType::InnerSizeAtCompileTime,
CoeffReadCost = evaluator<XprType>::CoeffReadCost,
Alignment = evaluator<XprType>::Alignment
};
EIGEN_DEVICE_FUNC Index rows() const { return m_xpr.rows(); }
EIGEN_DEVICE_FUNC Index cols() const { return m_xpr.cols(); }
EIGEN_DEVICE_FUNC Index size() const { return m_xpr.size(); }
EIGEN_DEVICE_FUNC Index innerSize() const { return m_xpr.innerSize(); }
EIGEN_DEVICE_FUNC Index outerSize() const { return m_xpr.outerSize(); }
EIGEN_DEVICE_FUNC
CoeffReturnType coeff(Index row, Index col) const
{ return m_evaluator.coeff(row, col); }
EIGEN_DEVICE_FUNC
CoeffReturnType coeff(Index index) const
{ return m_evaluator.coeff(index); }
template<int LoadMode, typename PacketType>
PacketType packet(Index row, Index col) const
{ return m_evaluator.template packet<LoadMode,PacketType>(row, col); }
template<int LoadMode, typename PacketType>
PacketType packet(Index index) const
{ return m_evaluator.template packet<LoadMode,PacketType>(index); }
EIGEN_DEVICE_FUNC
CoeffReturnType coeffByOuterInner(Index outer, Index inner) const
{ return m_evaluator.coeff(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
template<int LoadMode, typename PacketType>
PacketType packetByOuterInner(Index outer, Index inner) const
{ return m_evaluator.template packet<LoadMode,PacketType>(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
const XprType & nestedExpression() const { return m_xpr; }
protected:
internal::evaluator<XprType> m_evaluator;
const XprType &m_xpr;
};
} // end namespace internal
/***************************************************************************
@@ -317,18 +401,21 @@ struct redux_impl<Func, Derived, LinearVectorizedTraversal, CompleteUnrolling>
/** \returns the result of a full redux operation on the whole matrix or vector using \a func
*
* The template parameter \a BinaryOp is the type of the functor \a func which must be
* an associative operator. Both current STL and TR1 functor styles are handled.
* an associative operator. Both current C++98 and C++11 functor styles are handled.
*
* \sa DenseBase::sum(), DenseBase::minCoeff(), DenseBase::maxCoeff(), MatrixBase::colwise(), MatrixBase::rowwise()
*/
template<typename Derived>
template<typename Func>
EIGEN_STRONG_INLINE typename internal::result_of<Func(typename internal::traits<Derived>::Scalar)>::type
typename internal::traits<Derived>::Scalar
DenseBase<Derived>::redux(const Func& func) const
{
typedef typename internal::remove_all<typename Derived::Nested>::type ThisNested;
return internal::redux_impl<Func, ThisNested>
::run(derived(), func);
eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
typedef typename internal::redux_evaluator<Derived> ThisEvaluator;
ThisEvaluator thisEval(derived());
return internal::redux_impl<Func, ThisEvaluator>::run(thisEval, func);
}
/** \returns the minimum of all coefficients of \c *this.
@@ -338,7 +425,7 @@ template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::minCoeff() const
{
return this->redux(Eigen::internal::scalar_min_op<Scalar>());
return derived().redux(Eigen::internal::scalar_min_op<Scalar,Scalar>());
}
/** \returns the maximum of all coefficients of \c *this.
@@ -348,10 +435,12 @@ template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::maxCoeff() const
{
return this->redux(Eigen::internal::scalar_max_op<Scalar>());
return derived().redux(Eigen::internal::scalar_max_op<Scalar,Scalar>());
}
/** \returns the sum of all coefficients of *this
/** \returns the sum of all coefficients of \c *this
*
* If \c *this is empty, then the value 0 is returned.
*
* \sa trace(), prod(), mean()
*/
@@ -361,7 +450,7 @@ DenseBase<Derived>::sum() const
{
if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0))
return Scalar(0);
return this->redux(Eigen::internal::scalar_sum_op<Scalar>());
return derived().redux(Eigen::internal::scalar_sum_op<Scalar,Scalar>());
}
/** \returns the mean of all coefficients of *this
@@ -372,7 +461,14 @@ template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::mean() const
{
return Scalar(this->redux(Eigen::internal::scalar_sum_op<Scalar>())) / Scalar(this->size());
#ifdef __INTEL_COMPILER
#pragma warning push
#pragma warning ( disable : 2259 )
#endif
return Scalar(derived().redux(Eigen::internal::scalar_sum_op<Scalar,Scalar>())) / Scalar(this->size());
#ifdef __INTEL_COMPILER
#pragma warning pop
#endif
}
/** \returns the product of all coefficients of *this
@@ -388,7 +484,7 @@ DenseBase<Derived>::prod() const
{
if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0))
return Scalar(1);
return this->redux(Eigen::internal::scalar_product_op<Scalar>());
return derived().redux(Eigen::internal::scalar_product_op<Scalar>());
}
/** \returns the trace of \c *this, i.e. the sum of the coefficients on the main diagonal.

View File

@@ -12,79 +12,6 @@
namespace Eigen {
template<typename Derived> class RefBase;
template<typename PlainObjectType, int Options = 0,
typename StrideType = typename internal::conditional<PlainObjectType::IsVectorAtCompileTime,InnerStride<1>,OuterStride<> >::type > class Ref;
/** \class Ref
* \ingroup Core_Module
*
* \brief A matrix or vector expression mapping an existing expressions
*
* \tparam PlainObjectType the equivalent matrix type of the mapped data
* \tparam Options specifies whether the pointer is \c #Aligned, or \c #Unaligned.
* The default is \c #Unaligned.
* \tparam StrideType optionally specifies strides. By default, Ref implies a contiguous storage along the inner dimension (inner stride==1),
* but accept a variable outer stride (leading dimension).
* This can be overridden by specifying strides.
* The type passed here must be a specialization of the Stride template, see examples below.
*
* This class permits to write non template functions taking Eigen's object as parameters while limiting the number of copies.
* A Ref<> object can represent either a const expression or a l-value:
* \code
* // in-out argument:
* void foo1(Ref<VectorXf> x);
*
* // read-only const argument:
* void foo2(const Ref<const VectorXf>& x);
* \endcode
*
* In the in-out case, the input argument must satisfies the constraints of the actual Ref<> type, otherwise a compilation issue will be triggered.
* By default, a Ref<VectorXf> can reference any dense vector expression of float having a contiguous memory layout.
* Likewise, a Ref<MatrixXf> can reference any column major dense matrix expression of float whose column's elements are contiguously stored with
* the possibility to have a constant space inbetween each column, i.e.: the inner stride mmust be equal to 1, but the outer-stride (or leading dimension),
* can be greater than the number of rows.
*
* In the const case, if the input expression does not match the above requirement, then it is evaluated into a temporary before being passed to the function.
* Here are some examples:
* \code
* MatrixXf A;
* VectorXf a;
* foo1(a.head()); // OK
* foo1(A.col()); // OK
* foo1(A.row()); // compilation error because here innerstride!=1
* foo2(A.row()); // The row is copied into a contiguous temporary
* foo2(2*a); // The expression is evaluated into a temporary
* foo2(A.col().segment(2,4)); // No temporary
* \endcode
*
* The range of inputs that can be referenced without temporary can be enlarged using the last two template parameter.
* Here is an example accepting an innerstride!=1:
* \code
* // in-out argument:
* void foo3(Ref<VectorXf,0,InnerStride<> > x);
* foo3(A.row()); // OK
* \endcode
* The downside here is that the function foo3 might be significantly slower than foo1 because it won't be able to exploit vectorization, and will involved more
* expensive address computations even if the input is contiguously stored in memory. To overcome this issue, one might propose to overloads internally calling a
* template function, e.g.:
* \code
* // in the .h:
* void foo(const Ref<MatrixXf>& A);
* void foo(const Ref<MatrixXf,0,Stride<> >& A);
*
* // in the .cpp:
* template<typename TypeOfA> void foo_impl(const TypeOfA& A) {
* ... // crazy code goes here
* }
* void foo(const Ref<MatrixXf>& A) { foo_impl(A); }
* void foo(const Ref<MatrixXf,0,Stride<> >& A) { foo_impl(A); }
* \endcode
*
*
* \sa PlainObjectBase::Map(), \ref TopicStorageOrders
*/
namespace internal {
template<typename _PlainObjectType, int _Options, typename _StrideType>
@@ -95,7 +22,8 @@ struct traits<Ref<_PlainObjectType, _Options, _StrideType> >
typedef _StrideType StrideType;
enum {
Options = _Options,
Flags = traits<Map<_PlainObjectType, _Options, _StrideType> >::Flags | NestByRefBit
Flags = traits<Map<_PlainObjectType, _Options, _StrideType> >::Flags | NestByRefBit,
Alignment = traits<Map<_PlainObjectType, _Options, _StrideType> >::Alignment
};
template<typename Derived> struct match {
@@ -107,7 +35,13 @@ struct traits<Ref<_PlainObjectType, _Options, _StrideType> >
|| (int(StrideType::InnerStrideAtCompileTime)==0 && int(Derived::InnerStrideAtCompileTime)==1),
OuterStrideMatch = Derived::IsVectorAtCompileTime
|| int(StrideType::OuterStrideAtCompileTime)==int(Dynamic) || int(StrideType::OuterStrideAtCompileTime)==int(Derived::OuterStrideAtCompileTime),
AlignmentMatch = (_Options!=Aligned) || ((PlainObjectType::Flags&AlignedBit)==0) || ((traits<Derived>::Flags&AlignedBit)==AlignedBit),
// NOTE, this indirection of evaluator<Derived>::Alignment is needed
// to workaround a very strange bug in MSVC related to the instantiation
// of has_*ary_operator in evaluator<CwiseNullaryOp>.
// This line is surprisingly very sensitive. For instance, simply adding parenthesis
// as "DerivedAlignment = (int(evaluator<Derived>::Alignment))," will make MSVC fail...
DerivedAlignment = int(evaluator<Derived>::Alignment),
AlignmentMatch = (int(traits<PlainObjectType>::Alignment)==int(Unaligned)) || (DerivedAlignment >= int(Alignment)), // FIXME the first condition is not very clear, it should be replaced by the required alignment
ScalarTypeMatch = internal::is_same<typename PlainObjectType::Scalar, typename Derived::Scalar>::value,
MatchAtCompileTime = HasDirectAccess && StorageOrderMatch && InnerStrideMatch && OuterStrideMatch && AlignmentMatch && ScalarTypeMatch
};
@@ -132,12 +66,12 @@ public:
typedef MapBase<Derived> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(RefBase)
inline Index innerStride() const
EIGEN_DEVICE_FUNC inline Index innerStride() const
{
return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1;
}
inline Index outerStride() const
EIGEN_DEVICE_FUNC inline Index outerStride() const
{
return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
: IsVectorAtCompileTime ? this->size()
@@ -145,7 +79,7 @@ public:
: this->rows();
}
RefBase()
EIGEN_DEVICE_FUNC RefBase()
: Base(0,RowsAtCompileTime==Dynamic?0:RowsAtCompileTime,ColsAtCompileTime==Dynamic?0:ColsAtCompileTime),
// Stride<> does not allow default ctor for Dynamic strides, so let' initialize it with dummy values:
m_stride(StrideType::OuterStrideAtCompileTime==Dynamic?0:StrideType::OuterStrideAtCompileTime,
@@ -159,7 +93,7 @@ protected:
typedef Stride<StrideType::OuterStrideAtCompileTime,StrideType::InnerStrideAtCompileTime> StrideBase;
template<typename Expression>
void construct(Expression& expr)
EIGEN_DEVICE_FUNC void construct(Expression& expr)
{
if(PlainObjectType::RowsAtCompileTime==1)
{
@@ -184,15 +118,83 @@ protected:
StrideBase m_stride;
};
/** \class Ref
* \ingroup Core_Module
*
* \brief A matrix or vector expression mapping an existing expression
*
* \tparam PlainObjectType the equivalent matrix type of the mapped data
* \tparam Options 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, Ref implies a contiguous storage along the inner dimension (inner stride==1),
* but accepts a variable outer stride (leading dimension).
* This can be overridden by specifying strides.
* The type passed here must be a specialization of the Stride template, see examples below.
*
* This class provides a way to write non-template functions taking Eigen objects as parameters while limiting the number of copies.
* A Ref<> object can represent either a const expression or a l-value:
* \code
* // in-out argument:
* void foo1(Ref<VectorXf> x);
*
* // read-only const argument:
* void foo2(const Ref<const VectorXf>& x);
* \endcode
*
* In the in-out case, the input argument must satisfy the constraints of the actual Ref<> type, otherwise a compilation issue will be triggered.
* By default, a Ref<VectorXf> can reference any dense vector expression of float having a contiguous memory layout.
* Likewise, a Ref<MatrixXf> can reference any column-major dense matrix expression of float whose column's elements are contiguously stored with
* the possibility to have a constant space in-between each column, i.e. the inner stride must be equal to 1, but the outer stride (or leading dimension)
* can be greater than the number of rows.
*
* In the const case, if the input expression does not match the above requirement, then it is evaluated into a temporary before being passed to the function.
* Here are some examples:
* \code
* MatrixXf A;
* VectorXf a;
* foo1(a.head()); // OK
* foo1(A.col()); // OK
* foo1(A.row()); // Compilation error because here innerstride!=1
* foo2(A.row()); // Compilation error because A.row() is a 1xN object while foo2 is expecting a Nx1 object
* foo2(A.row().transpose()); // The row is copied into a contiguous temporary
* foo2(2*a); // The expression is evaluated into a temporary
* foo2(A.col().segment(2,4)); // No temporary
* \endcode
*
* The range of inputs that can be referenced without temporary can be enlarged using the last two template parameters.
* Here is an example accepting an innerstride!=1:
* \code
* // in-out argument:
* void foo3(Ref<VectorXf,0,InnerStride<> > x);
* foo3(A.row()); // OK
* \endcode
* The downside here is that the function foo3 might be significantly slower than foo1 because it won't be able to exploit vectorization, and will involve more
* expensive address computations even if the input is contiguously stored in memory. To overcome this issue, one might propose to overload internally calling a
* template function, e.g.:
* \code
* // in the .h:
* void foo(const Ref<MatrixXf>& A);
* void foo(const Ref<MatrixXf,0,Stride<> >& A);
*
* // in the .cpp:
* template<typename TypeOfA> void foo_impl(const TypeOfA& A) {
* ... // crazy code goes here
* }
* void foo(const Ref<MatrixXf>& A) { foo_impl(A); }
* void foo(const Ref<MatrixXf,0,Stride<> >& A) { foo_impl(A); }
* \endcode
*
*
* \sa PlainObjectBase::Map(), \ref TopicStorageOrders
*/
template<typename PlainObjectType, int Options, typename StrideType> class Ref
: public RefBase<Ref<PlainObjectType, Options, StrideType> >
{
private:
typedef internal::traits<Ref> Traits;
template<typename Derived>
inline Ref(const PlainObjectBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0);
EIGEN_DEVICE_FUNC inline Ref(const PlainObjectBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0);
public:
typedef RefBase<Ref> Base;
@@ -201,23 +203,24 @@ template<typename PlainObjectType, int Options, typename StrideType> class Ref
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename Derived>
inline Ref(PlainObjectBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
EIGEN_DEVICE_FUNC inline Ref(PlainObjectBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
{
EIGEN_STATIC_ASSERT(static_cast<bool>(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
Base::construct(expr.derived());
}
template<typename Derived>
inline Ref(const DenseBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
EIGEN_DEVICE_FUNC inline Ref(const DenseBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
#else
/** Implicit constructor from any dense expression */
template<typename Derived>
inline Ref(DenseBase<Derived>& expr)
#endif
{
EIGEN_STATIC_ASSERT(static_cast<bool>(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
EIGEN_STATIC_ASSERT(static_cast<bool>(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
enum { THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY = Derived::ThisConstantIsPrivateInPlainObjectBase};
EIGEN_STATIC_ASSERT(bool(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
EIGEN_STATIC_ASSERT(!Derived::IsPlainObjectBase,THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
Base::construct(expr.const_cast_derived());
}
@@ -236,36 +239,36 @@ template<typename TPlainObjectType, int Options, typename StrideType> class Ref<
EIGEN_DENSE_PUBLIC_INTERFACE(Ref)
template<typename Derived>
inline Ref(const DenseBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::ScalarTypeMatch),Derived>::type* = 0)
EIGEN_DEVICE_FUNC inline Ref(const DenseBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::ScalarTypeMatch),Derived>::type* = 0)
{
// std::cout << match_helper<Derived>::HasDirectAccess << "," << match_helper<Derived>::OuterStrideMatch << "," << match_helper<Derived>::InnerStrideMatch << "\n";
// std::cout << int(StrideType::OuterStrideAtCompileTime) << " - " << int(Derived::OuterStrideAtCompileTime) << "\n";
// std::cout << int(StrideType::InnerStrideAtCompileTime) << " - " << int(Derived::InnerStrideAtCompileTime) << "\n";
construct(expr.derived(), typename Traits::template match<Derived>::type());
}
inline Ref(const Ref& other) : Base(other) {
EIGEN_DEVICE_FUNC inline Ref(const Ref& other) : Base(other) {
// copy constructor shall not copy the m_object, to avoid unnecessary malloc and copy
}
template<typename OtherRef>
inline Ref(const RefBase<OtherRef>& other) {
EIGEN_DEVICE_FUNC inline Ref(const RefBase<OtherRef>& other) {
construct(other.derived(), typename Traits::template match<OtherRef>::type());
}
protected:
template<typename Expression>
void construct(const Expression& expr,internal::true_type)
EIGEN_DEVICE_FUNC void construct(const Expression& expr,internal::true_type)
{
Base::construct(expr);
}
template<typename Expression>
void construct(const Expression& expr, internal::false_type)
EIGEN_DEVICE_FUNC void construct(const Expression& expr, internal::false_type)
{
m_object.lazyAssign(expr);
internal::call_assignment_no_alias(m_object,expr,internal::assign_op<Scalar,Scalar>());
Base::construct(m_object);
}

View File

@@ -12,21 +12,6 @@
namespace Eigen {
/**
* \class Replicate
* \ingroup Core_Module
*
* \brief Expression of the multiple replication of a matrix or vector
*
* \param MatrixType the type of the object we are replicating
*
* This class represents an expression of the multiple replication of a matrix or vector.
* It is the return type of DenseBase::replicate() and most of the time
* this is the only way it is used.
*
* \sa DenseBase::replicate()
*/
namespace internal {
template<typename MatrixType,int RowFactor,int ColFactor>
struct traits<Replicate<MatrixType,RowFactor,ColFactor> >
@@ -35,10 +20,7 @@ struct traits<Replicate<MatrixType,RowFactor,ColFactor> >
typedef typename MatrixType::Scalar Scalar;
typedef typename traits<MatrixType>::StorageKind StorageKind;
typedef typename traits<MatrixType>::XprKind XprKind;
enum {
Factor = (RowFactor==Dynamic || ColFactor==Dynamic) ? Dynamic : RowFactor*ColFactor
};
typedef typename nested<MatrixType,Factor>::type MatrixTypeNested;
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
enum {
RowsAtCompileTime = RowFactor==Dynamic || int(MatrixType::RowsAtCompileTime)==Dynamic
@@ -53,12 +35,29 @@ struct traits<Replicate<MatrixType,RowFactor,ColFactor> >
IsRowMajor = MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1 ? 1
: MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1 ? 0
: (MatrixType::Flags & RowMajorBit) ? 1 : 0,
Flags = (_MatrixTypeNested::Flags & HereditaryBits & ~RowMajorBit) | (IsRowMajor ? RowMajorBit : 0),
CoeffReadCost = _MatrixTypeNested::CoeffReadCost
// FIXME enable DirectAccess with negative strides?
Flags = IsRowMajor ? RowMajorBit : 0
};
};
}
/**
* \class Replicate
* \ingroup Core_Module
*
* \brief Expression of the multiple replication of a matrix or vector
*
* \tparam MatrixType the type of the object we are replicating
* \tparam RowFactor number of repetitions at compile time along the vertical direction, can be Dynamic.
* \tparam ColFactor number of repetitions at compile time along the horizontal direction, can be Dynamic.
*
* This class represents an expression of the multiple replication of a matrix or vector.
* It is the return type of DenseBase::replicate() and most of the time
* this is the only way it is used.
*
* \sa DenseBase::replicate()
*/
template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
: public internal::dense_xpr_base< Replicate<MatrixType,RowFactor,ColFactor> >::type
{
@@ -68,10 +67,12 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
typedef typename internal::dense_xpr_base<Replicate>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Replicate)
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
template<typename OriginalMatrixType>
inline explicit Replicate(const OriginalMatrixType& a_matrix)
: m_matrix(a_matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor)
EIGEN_DEVICE_FUNC
inline explicit Replicate(const OriginalMatrixType& matrix)
: m_matrix(matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor)
{
EIGEN_STATIC_ASSERT((internal::is_same<typename internal::remove_const<MatrixType>::type,OriginalMatrixType>::value),
THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
@@ -79,41 +80,20 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
}
template<typename OriginalMatrixType>
inline Replicate(const OriginalMatrixType& a_matrix, Index rowFactor, Index colFactor)
: m_matrix(a_matrix), m_rowFactor(rowFactor), m_colFactor(colFactor)
EIGEN_DEVICE_FUNC
inline Replicate(const OriginalMatrixType& matrix, Index rowFactor, Index colFactor)
: m_matrix(matrix), m_rowFactor(rowFactor), m_colFactor(colFactor)
{
EIGEN_STATIC_ASSERT((internal::is_same<typename internal::remove_const<MatrixType>::type,OriginalMatrixType>::value),
THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
}
EIGEN_DEVICE_FUNC
inline Index rows() const { return m_matrix.rows() * m_rowFactor.value(); }
EIGEN_DEVICE_FUNC
inline Index cols() const { return m_matrix.cols() * m_colFactor.value(); }
inline Scalar coeff(Index rowId, Index colId) const
{
// try to avoid using modulo; this is a pure optimization strategy
const Index actual_row = internal::traits<MatrixType>::RowsAtCompileTime==1 ? 0
: RowFactor==1 ? rowId
: rowId%m_matrix.rows();
const Index actual_col = internal::traits<MatrixType>::ColsAtCompileTime==1 ? 0
: ColFactor==1 ? colId
: colId%m_matrix.cols();
return m_matrix.coeff(actual_row, actual_col);
}
template<int LoadMode>
inline PacketScalar packet(Index rowId, Index colId) const
{
const Index actual_row = internal::traits<MatrixType>::RowsAtCompileTime==1 ? 0
: RowFactor==1 ? rowId
: rowId%m_matrix.rows();
const Index actual_col = internal::traits<MatrixType>::ColsAtCompileTime==1 ? 0
: ColFactor==1 ? colId
: colId%m_matrix.cols();
return m_matrix.template packet<LoadMode>(actual_row, actual_col);
}
EIGEN_DEVICE_FUNC
const _MatrixTypeNested& nestedExpression() const
{
return m_matrix;
@@ -141,21 +121,6 @@ DenseBase<Derived>::replicate() const
return Replicate<Derived,RowFactor,ColFactor>(derived());
}
/**
* \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
*/
template<typename Derived>
const typename DenseBase<Derived>::ReplicateReturnType
DenseBase<Derived>::replicate(Index rowFactor,Index colFactor) const
{
return Replicate<Derived,Dynamic,Dynamic>(derived(),rowFactor,colFactor);
}
/**
* \return an expression of the replication of each column (or row) of \c *this
*

View File

@@ -13,11 +13,6 @@
namespace Eigen {
/** \class ReturnByValue
* \ingroup Core_Module
*
*/
namespace internal {
template<typename Derived>
@@ -38,17 +33,22 @@ struct traits<ReturnByValue<Derived> >
* So internal::nested always gives the plain return matrix type.
*
* FIXME: I don't understand why we need this specialization: isn't this taken care of by the EvalBeforeNestingBit ??
* Answer: EvalBeforeNestingBit should be deprecated since we have the evaluators
*/
template<typename Derived,int n,typename PlainObject>
struct nested<ReturnByValue<Derived>, n, PlainObject>
struct nested_eval<ReturnByValue<Derived>, n, PlainObject>
{
typedef typename traits<Derived>::ReturnType type;
};
} // end namespace internal
/** \class ReturnByValue
* \ingroup Core_Module
*
*/
template<typename Derived> class ReturnByValue
: internal::no_assignment_operator, public internal::dense_xpr_base< ReturnByValue<Derived> >::type
: public internal::dense_xpr_base< ReturnByValue<Derived> >::type, internal::no_assignment_operator
{
public:
typedef typename internal::traits<Derived>::ReturnType ReturnType;
@@ -57,10 +57,11 @@ template<typename Derived> class ReturnByValue
EIGEN_DENSE_PUBLIC_INTERFACE(ReturnByValue)
template<typename Dest>
EIGEN_DEVICE_FUNC
inline void evalTo(Dest& dst) const
{ static_cast<const Derived*>(this)->evalTo(dst); }
inline Index rows() const { return static_cast<const Derived*>(this)->rows(); }
inline Index cols() const { return static_cast<const Derived*>(this)->cols(); }
EIGEN_DEVICE_FUNC inline Index rows() const { return static_cast<const Derived*>(this)->rows(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return static_cast<const Derived*>(this)->cols(); }
#ifndef EIGEN_PARSED_BY_DOXYGEN
#define Unusable YOU_ARE_TRYING_TO_ACCESS_A_SINGLE_COEFFICIENT_IN_A_SPECIAL_EXPRESSION_WHERE_THAT_IS_NOT_ALLOWED_BECAUSE_THAT_WOULD_BE_INEFFICIENT
@@ -72,8 +73,7 @@ template<typename Derived> class ReturnByValue
const Unusable& coeff(Index,Index) const { return *reinterpret_cast<const Unusable*>(this); }
Unusable& coeffRef(Index) { return *reinterpret_cast<Unusable*>(this); }
Unusable& coeffRef(Index,Index) { return *reinterpret_cast<Unusable*>(this); }
template<int LoadMode> Unusable& packet(Index) const;
template<int LoadMode> Unusable& packet(Index, Index) const;
#undef Unusable
#endif
};
@@ -85,14 +85,32 @@ Derived& DenseBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
return derived();
}
template<typename Derived>
template<typename OtherDerived>
Derived& DenseBase<Derived>::lazyAssign(const ReturnByValue<OtherDerived>& other)
{
other.evalTo(derived());
return derived();
}
namespace internal {
// Expression is evaluated in a temporary; default implementation of Assignment is bypassed so that
// when a ReturnByValue expression is assigned, the evaluator is not constructed.
// TODO: Finalize port to new regime; ReturnByValue should not exist in the expression world
template<typename Derived>
struct evaluator<ReturnByValue<Derived> >
: public evaluator<typename internal::traits<Derived>::ReturnType>
{
typedef ReturnByValue<Derived> XprType;
typedef typename internal::traits<Derived>::ReturnType PlainObject;
typedef evaluator<PlainObject> Base;
EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr)
: m_result(xpr.rows(), xpr.cols())
{
::new (static_cast<Base*>(this)) Base(m_result);
xpr.evalTo(m_result);
}
protected:
PlainObject m_result;
};
} // end namespace internal
} // end namespace Eigen

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