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2457 Commits

Author SHA1 Message Date
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
Doug Kwan
5c9ee73eb9 Implement plog and pexp for AltiVec. 2015-07-30 11:12:42 -07:00
Gael Guennebaud
5a1cc5d24c bug #1053: fix SuplerLU::solve with EIGEN_DEFAULT_TO_ROW_MAJOR 2015-09-03 11:25:36 +02:00
Gael Guennebaud
2795ffd6a0 Fix Index vs StorageIndex naming convention 2015-09-03 11:18:27 +02:00
Gael Guennebaud
ef2b54f422 Fix AMD ordering when a column has only one off-diagonal non-zero (also fix bug #1045) 2015-09-03 11:04:06 +02:00
Christoph Hertzberg
5ad7981f73 Use full packet size for Dynamic-sized objects (otherwise, the unalignedcount unit test fails with AVX enabled) 2015-09-02 22:51:43 +02:00
Gael Guennebaud
aa768add0b Since there is no reason for evaluators to be nested by reference, let's remove the evaluator<>::nestedType indirection. 2015-09-02 22:10:39 +02:00
Gael Guennebaud
51455824ea Fix AlignedVector3 wrt previous change 2015-09-02 21:51:58 +02:00
Gael Guennebaud
f8976fdbe0 Make evaluators non-copyable. This guarantee that evaluators storing temporaries do not introduce unwanted copy overhead. 2015-09-02 21:39:49 +02:00
Gael Guennebaud
92b9f0e102 Cleaning pass on evaluators: remove the useless and error prone evaluator<>::type indirection. 2015-09-02 21:38:40 +02:00
Gael Guennebaud
cda55ab245 Fix compilation of cuda unit test 2015-09-02 16:59:07 +02:00
Gael Guennebaud
14458ec0a0 Fix packetmath unit test for exp and log 2015-09-02 15:47:58 +02:00
Gael Guennebaud
6b99afa5ae Fix LSCG::solve with a sparse destination. 2015-09-02 15:34:03 +02:00
Gael Guennebaud
b5ad3d2cf7 Remove deprecated Flagged expression. 2015-09-02 14:53:50 +02:00
Gael Guennebaud
6522c3a6f0 Add regression test for bug #817 2015-09-02 13:16:03 +02:00
Gael Guennebaud
be5e2ecc21 bug #505: add more examples of bad and correct usages of auto and eval(). 2015-09-02 13:04:30 +02:00
Gael Guennebaud
aba8c9ee17 Add a documentation page for common pitfalls 2015-09-02 11:23:55 +02:00
Gael Guennebaud
a75616887e bug #1057: fix a declaration missmatch with MSVC 2015-09-02 09:31:32 +02:00
Gael Guennebaud
280f93ff65 Fix FullPivLU::image documentation 2015-09-02 09:19:27 +02:00
Gael Guennebaud
6059188f9d Simplify implementation of the evaluation's iterator of Sparse*Diagonal products to help the compiler to generate better code. 2015-09-01 22:34:30 +02:00
Gael Guennebaud
0b2412df50 Remove duplicated temporary in Sparse to Sparse assignment 2015-09-01 22:31:30 +02:00
Gael Guennebaud
9001f4a46b Add missing specialization of evaluator of sub-sparse-matrices that can be seen as a SparseCompressedBase. This changeset enable faster iterator for such expressions. 2015-09-01 22:29:17 +02:00
Benoit Steiner
f41831e445 Added support for argmax/argmin 2015-08-31 08:18:53 -07:00
Benoit Steiner
2ab603316a Use numext::mini/numext::maxi instead of std::min/std::max in the tensor code 2015-08-28 08:14:15 -07:00
Benoit Steiner
2ed1495eec nvcc doesn't support std::min or std::max on GPU. Use our own custom implementation instead 2015-08-27 16:59:55 -07:00
Sergiu Dotenco
d4c24eb016 fixed Quaternion identity initialization for non-implicitly convertible types 2015-08-20 20:55:37 +02:00
Christoph Hertzberg
78358a7241 Fixed broken commit a09cfe650f
. Missing } and unprotected min/max calls and definitions.
2015-08-22 15:03:16 +02:00
Benoit Steiner
a09cfe650f std::numeric_limits doesn't work reliably on CUDA devices. Use our own definition of numeric_limit<T>::max() and numeric_limit<T>::min() instead of the stl ones. 2015-08-21 16:01:40 -07:00
Christoph Hertzberg
e5c78d85c8 bug #1043: Avoid integer conversion sign warning 2015-08-19 21:50:21 +02:00
Christoph Hertzberg
1bdd06a199 Fix some trivial warnings 2015-08-19 21:38:18 +02:00
Christoph Hertzberg
0721690dbb Use standard include syntax in Tensor module (<> for include-path and "" for relative path) 2015-08-18 14:34:00 +02:00
Christoph Hertzberg
8097d8d028 surpress some warnings 2015-08-17 21:50:52 +02:00
Christoph Hertzberg
d2e0927127 Define EIGEN_MAX_STATIC_ALIGN_BYTES to 0 for architectures that don't require stack alignment 2015-08-17 16:44:52 +02:00
Gael Guennebaud
dc2c103b3b merge 2015-08-16 14:22:02 +02:00
Christoph Hertzberg
d6a4805fdf Protect further isnan/isfinite/isinf calls 2015-08-16 14:00:02 +02:00
Christoph Hertzberg
a40f6ab276 Merged in ITimer/eigen (pull request PR-133)
[Doc] Fix a spelling error in TopicMultithreading.dox
2015-08-14 17:46:57 +02:00
Christoph Hertzberg
61e0977e10 Protect all calls to isnan, isinf and isfinite with parentheses. 2015-08-14 17:32:34 +02:00
Christoph Hertzberg
712e2fed17 bug #829: Introduce macro EIGEN_HAS_CXX11_CONTAINERS and do not specialize std-containers if it is enabled. 2015-08-14 16:09:48 +02:00
Christoph Hertzberg
a5d1bb2be8 bug #1054: Use set(EIGEN_CXX_FLAG_VERSION "/version") only for Intel compilers on Windows.
Also removed code calling `head -n1` and always use integrated REGEX functionality.
2015-08-14 15:30:59 +02:00
ITimer
93635cafee Fixed a spelling error 2015-08-10 15:11:10 +08:00
Gael Guennebaud
23aab82c0c merge 2015-08-09 21:24:20 +02:00
Gael Guennebaud
0d5e673baa Fix Tensor module wrt nullary functor recent change 2015-08-09 21:20:24 +02:00
Christoph Hertzberg
cac6b23033 bug #1053: SparseLU failed with EIGEN_DEFAULT_TO_ROW_MAJOR 2015-08-07 23:10:56 +02:00
Gael Guennebaud
febcce34f1 Enable vectorization with half-packets 2015-08-07 20:05:31 +02:00
Gael Guennebaud
6245591349 Fix prototype of plset and generalize linspace functor. 2015-08-07 19:27:59 +02:00
Gael Guennebaud
60e4260d0d Some functors were not generic wrt packet-type. 2015-08-07 17:41:39 +02:00
Gael Guennebaud
e68c7b8368 Include SSE packetmath when AVX is enabled, and enable AVX's sine function only in fast-math mode (as SSE) 2015-08-07 17:40:39 +02:00
Gael Guennebaud
65bfa5fce7 Allow to use arbitrary packet-types during evaluation.
This is implemented by adding a PacketType template parameter to packet and writePacket members of evaluator<>.
2015-08-07 12:01:39 +02:00
Gael Guennebaud
3602926ed5 Mark ALignedBit as deprecated. 2015-08-07 10:45:02 +02:00
Gael Guennebaud
ce57dbd937 Let unpacket_traits<> exposes the required alignment and make use of it everywhere 2015-08-07 10:44:01 +02:00
Gael Guennebaud
2afdef6a54 Generalize first_aligned to take the requested alignment as a template parameter, and add a first_default_aligned variante calling first_aligned with the requirement of the largest packet for the given scalar type. 2015-08-06 17:52:01 +02:00
Gael Guennebaud
1f5024332e First part of a big refactoring of alignment control to enable the handling of arbitrarily aligned buffers. It includes:
- AlignedBit flag is deprecated. Alignment is now specified by the evaluator through the 'Alignment' enum, e.g., evaluator<Xpr>::Alignment. Its value is in Bytes.
 - Add several enums to specify alignment: Aligned8, Aligned16, Aligned32, Aligned64, Aligned128. AlignedMax corresponds to EIGEN_MAX_ALIGN_BYTES. Such enums are used to define the above Alignment value, and as the 'Options' template parameter of Map<> and Ref<>.
 - The Aligned enum is now deprecated. It is now an alias for Aligned16.
 - Currently, traits<Matrix<>>, traits<Array<>>, traits<Ref<>>, traits<Map<>>, and traits<Block<>> also expose the Alignment enum.
2015-08-06 15:31:07 +02:00
Gael Guennebaud
65186ef18d Fix logic in compute_default_alignment, extend it to Dynamic size, and move it to XprHelper.h file. 2015-08-06 14:07:59 +02:00
Gael Guennebaud
becd89df29 Enable runtime stack alignment in gemm_blocking_space. 2015-08-06 14:00:26 +02:00
Gael Guennebaud
d4f5efc51a Add a EIGEN_DEFAULT_ALIGN_BYTES macro defining default alignment for alloca and aligned_malloc.
It is defined as the max of EIGEN_IDEAL_MAX_ALIGN_BYTES and EIGEN_MAX_ALIGN_BYTES
2015-08-06 13:56:53 +02:00
Gael Guennebaud
7e0d7a76b8 Remove dense nested loops in IncompleteCholesky 2015-08-04 18:01:38 +02:00
Gael Guennebaud
e31fc50280 Numerous fixes for IncompleteCholesky. Still have to make it fully exploit the sparse structure of the L factor, and improve robustness to illconditionned problems. 2015-08-04 16:16:02 +02:00
Gael Guennebaud
9a4713e505 Add a unit test for IncompleteCholesky 2015-08-04 16:14:06 +02:00
Gael Guennebaud
506964fc29 Propagate precondition info to the iterative solver. 2015-08-04 16:13:34 +02:00
Gael Guennebaud
db0f5c9d90 Fix conversion warning 2015-08-04 16:12:44 +02:00
Gael Guennebaud
b986c147cd Fix ForceNonZeroDiag for complexes 2015-08-04 16:12:16 +02:00
Benoit Steiner
cbce0e3b12 Fixed compilation warning 2015-08-03 21:52:29 -07:00
Benoit Steiner
a5dc49e7e8 Fixed 2 compilation warnings generated by llvm 2015-07-29 15:06:08 -07:00
Benoit Steiner
e1d28b7ea7 Added a test for shuffling 2015-07-29 15:01:21 -07:00
Benoit Steiner
0570594f2c Fixed a few compilation warnings triggered by clang 2015-07-29 11:48:38 -07:00
Benoit Steiner
099597406f Simplified and generalized the DividerTraits code 2015-07-29 10:02:42 -07:00
Gael Guennebaud
6db3a557f4 Add missing specialization of struct DividerTraits<long> 2015-07-29 11:38:53 +02:00
Gael Guennebaud
aec4814370 Many files were missing in previous changeset. 2015-07-29 11:11:23 +02:00
Gael Guennebaud
f7d5b9323d typo 2015-07-29 11:08:49 +02:00
Gael Guennebaud
175ed636ea bug #973: update macro-level control of alignement by introducing user-controllable EIGEN_MAX_ALIGN_BYTES and EIGEN_MAX_STATIC_ALIGN_BYTES macros. This changeset also removes EIGEN_ALIGN (replaced by EIGEN_MAX_ALIGN_BYTES>0), EIGEN_ALIGN_STATICALLY (replaced by EIGEN_MAX_STATIC_ALIGN_BYTES>0), EIGEN_USER_ALIGN*, EIGEN_ALIGN_DEFAULT (replaced by EIGEN_ALIGN_MAX). 2015-07-29 10:22:25 +02:00
Gael Guennebaud
76874b128e bug #1047: document the structure layout of class Matrix 2015-07-29 10:21:28 +02:00
Gael Guennebaud
41e1f3498c bug #1048: fix unused variable warning 2015-07-28 22:59:50 +02:00
Benoit Steiner
b9db19aec4 Pulled latest updates from trunk. 2015-07-27 09:39:57 -07:00
Benoit Steiner
f84417d97b Removed an incorrect assertion. 2015-07-27 09:25:22 -07:00
Benoit Steiner
1a30a8e7a2 Merged in godeffroy/eigen_tensor_generalized_contraction (pull request PR-130)
Allowed tensor contraction operation with an empty array of dimension pairs, which performs a tensor product.
2015-07-27 09:19:35 -07:00
Christoph Hertzberg
a44d022caf bug #792: SparseLU::factorize failed for structurally rank deficient matrices 2015-07-26 20:30:30 +02:00
Godeffroy Valet
2195822df6 Allowed tensor contraction operation with an empty array of dimension pairs, which performs a tensor product. 2015-07-25 11:58:36 +02:00
Benoit Steiner
f6282e451a Fixed a typo in an assertion. 2015-07-24 17:35:47 -07:00
Benoit Steiner
4b3052c54d Pulled latest update from trunk 2015-07-23 08:47:33 -07:00
Benoit Steiner
a446020b78 Reenable 2 tests previously disabled by mistake 2015-07-23 08:47:00 -07:00
Christoph Hertzberg
3d951df223 Re-enabled unit tests which were disabled in commit 4200bdec24
.
2015-07-23 10:55:03 +02:00
Benoit Steiner
6d6e6d0b88 Define EIGEN_VECTORIZE_AVX2 and EIGEN_VECTORIZE_FMA when the corresponding instructions can be used by the compiler 2015-07-22 18:22:16 -07:00
Benoit Steiner
ce65c2922a Pulled latest updates from trunk 2015-07-22 18:12:16 -07:00
Benoit Steiner
4200bdec24 Extended the range of value inputs for TensorIntDiv to support tensors with more than 4 billion elements. 2015-07-22 17:02:30 -07:00
Gael Guennebaud
3b0ad02c10 Remove wrongly pushed debugging statements 2015-07-22 14:33:57 +02:00
Jonas Adler
815fa0dbf6 Fixed some compiler bugs in NVCC, now compiles with CUDA.
(chtz: Manually joined sevaral commits to keep the history clean)
2015-07-22 12:29:18 +02:00
Benoit Steiner
d259b719d1 Made sure that the use const expressions are not enabled when compiling with nvcc even when gcc 4.9 is used as the host compiler. 2015-07-21 17:35:58 -07:00
Benoit Steiner
0dda72316f The eigen_check macro doesn't exist anymore: use assert instead 2015-07-21 17:34:15 -07:00
Gael Guennebaud
586d10f7e0 Fix compilation of tri(sparse) * dense with OpenMP 2015-07-21 22:52:21 +02:00
Gael Guennebaud
d3e5db9a80 add regression unit test for previous changeset 2015-07-21 22:23:17 +02:00
Valentin Roussellet
5e635f9ca1 AlignedVector3 accepts implicit conversions from more operators. 2015-07-21 16:42:52 +00:00
Gael Guennebaud
45ee14a13a Fix output of relative error, and add more support for long double 2015-07-21 22:22:12 +02:00
Gael Guennebaud
87f3e533f5 bug #1036: implement verify_is_approx_upto_permutation through a combinatorial search.
The previous implementation was subject to numerical cancellation issues.
2015-07-20 15:34:06 +02:00
Gael Guennebaud
ab8b497a7e Add pow(scalar,array) in quick ref 2015-07-20 13:59:21 +02:00
Gael Guennebaud
6544b49e59 Generalize pow(x,e) such that x and e can be a different expression type or a scalar for either x or e. Add x.pow(e) with e an array expression. 2015-07-20 13:57:55 +02:00
Gael Guennebaud
2d93060291 Fix trivial warnings. 2015-07-20 13:55:48 +02:00
Gael Guennebaud
c11971de37 Fix compilation of isnan(complex) 2015-07-20 12:56:01 +02:00
Gael Guennebaud
88e352adac Add support for replicate in CUDA 2015-07-20 10:53:03 +02:00
Benoit Steiner
6799c26cd6 Fixed a typo in a test and a compilation warning 2015-07-17 16:50:47 -07:00
Benoit Steiner
7a39439904 Rewrote Eigen::dimensions_match to prevent a static assertion when the rank of the tensors is different. 2015-07-17 16:46:30 -07:00
Benoit Steiner
e94f9eb637 Fixed a const correctness issue in TensorLayoutSwap 2015-07-17 15:44:26 -07:00
Benoit Steiner
513e357b48 Added support for prefetching on cuda devices 2015-07-17 15:35:16 -07:00
Benoit Steiner
943035e5bd Pulled latest updates from trunk 2015-07-17 09:42:45 -07:00
Benoit Steiner
06a22ca5bd Added support for sigmoid function to the tensor module 2015-07-17 09:29:00 -07:00
Nicolas Mellado
3275eddc24 Add const getters for LM parameters 2015-07-17 09:11:49 +02:00
Benoit Steiner
979b73cebf Fixed a typo in Macro.h 2015-07-16 14:17:50 -07:00
Benoit Steiner
a5ec25f11c Use the new EIGEN_HAS_INDEX_LIST define to enable the cxx11_tensor_index_list tests 2015-07-16 13:16:08 -07:00
Benoit Steiner
7a243959b4 Define EIGEN_HAS_INDEX_LIST whenever the class is defined. This makes it easier to support compilers that are cxx11 compliant and compilers that aren't. 2015-07-16 13:14:18 -07:00
Benoit Steiner
b756f6af5e Added missing APIs to the Eigen::Sizes class 2015-07-16 12:14:18 -07:00
Benoit Steiner
05787f8367 Added support for tensor inflation. 2015-07-16 09:04:05 -07:00
Benoit Steiner
b900fe47d5 Avoid relying on a default value for the Vectorizable template parameter of the EvalRange functor 2015-07-15 17:17:04 -07:00
Benoit Steiner
4b3d697e12 Fixed compilation error in a cuda test 2015-07-15 17:14:24 -07:00
Benoit Steiner
8315e025e1 Updated the cuda tests to use the new GpuDevice constructor 2015-07-15 12:39:26 -07:00
Benoit Steiner
e892524efe Added support for multi gpu configuration to the GpuDevice class 2015-07-15 12:38:34 -07:00
Gael Guennebaud
f5aa640862 Clean some previous changes and more cuda fixes 2015-07-15 10:57:55 +02:00
Nicolas Mellado
7cecd39a84 Merged eigen/eigen into default 2015-07-15 10:15:54 +02:00
Nicolas Mellado
592ee2a4b4 Add missing EIGEN_DEVICE_FUNC 2015-07-15 10:14:52 +02:00
Gael Guennebaud
6527dbb9f8 Merged in emartin/eigen (pull request PR-123)
Modify GEMM to handle m=0, n=0, and k=0 cases.
2015-07-13 23:58:30 +02:00
Benoit Steiner
b80036abec Enabled the construction of a fixed sized tensor directly from an expression. 2015-07-13 11:16:37 -07:00
Benoit Steiner
3912ca0d53 Fixed a bug in the integer division code that caused some large numerators to be incorrectly handled 2015-07-13 11:14:59 -07:00
Christoph Hertzberg
ea87561564 bug #1039: Redefining EIGEN_DEFAULT_DENSE_INDEX_TYPE may lead to errors 2015-07-13 16:08:25 +02:00
Gael Guennebaud
b8df8815f4 Fix operator<<(ostream,AlignedVector3) 2015-07-13 13:55:59 +02:00
Eric Martin
002c2923c2 Modify GEMM to handle m=0, n=0, and k=0 cases. 2015-07-11 21:46:13 -05:00
Nicolas Mellado
dbb3e2cf8a Cleaning 2015-07-11 18:15:31 +00:00
Nicolas Mellado
0d09845562 Revert files to remove EIGEN_USING_NUMEXT_MATH 2015-07-11 20:11:36 +02:00
Nicolas Mellado
20b96025fd Replace double constants by Scalar constants 2015-07-11 20:02:30 +02:00
Nicolas Mellado
1dd6a329e8 Cuda compatibility: remove explicit call to std math functions 2015-07-11 19:40:15 +02:00
Nicolas Mellado
bc40eb745d Merged eigen/eigen into default 2015-07-11 19:33:43 +02:00
Benoit Steiner
e6297741c9 Added support for generation of random complex numbers on CUDA devices 2015-07-07 17:40:49 -07:00
Benoit Steiner
6de6fa9483 Use NumTraits<T>::RequireInitialization instead of internal::is_arithmetic<T>::value to check whether it's possible to bypass the type constructor in the tensor code. 2015-07-07 15:23:56 -07:00
Benoit Steiner
7b7df7b6b8 Updated internal::is_arithmetic::value to be true for complex numbers 2015-07-07 12:57:35 -07:00
Benoit Steiner
6e55284e51 Pulled latest changes from trunk 2015-07-07 08:54:37 -07:00
Benoit Steiner
a93af65938 Improved and cleaned up the 2d patch extraction code 2015-07-07 08:52:14 -07:00
Gael Guennebaud
7fa6fe8d8c typo 2015-07-07 17:47:24 +02:00
Gael Guennebaud
fa17358c4b Rotation2D: fix slerp to take the shortest path, and add convenient method to get the angle in [-pi,pi] or [0,pi] 2015-07-07 17:27:12 +02:00
Benoit Steiner
3f2101b03b Use numext::swap instead of std::swap 2015-07-06 17:02:29 -07:00
Benoit Steiner
0485a2468d use Eigen smart_copy instead of std::copy 2015-07-06 17:01:51 -07:00
Benoit Steiner
ebdacfc5ea Fixed a compilation warning generated by clang 2015-07-06 15:03:11 -07:00
Benoit Steiner
81f9e968fd Only attempt to use the texture path on GPUs when it's supported by CUDA 2015-07-06 13:32:38 -07:00
Nicolas Mellado
66b30728f8 Merged eigen/eigen into default 2015-07-06 20:58:31 +02:00
Nicolas Mellado
5359e5cdb2 Protect against compilation errors with nvcc and numext/complex.
Disable functions explicitely involving std::complex when compiling with nvcc.
Improve code compatilibity using the new macro EIGEN_USING_NUMEXT_MATH (same spirit than EIGEN_USING_STD_MATH but for numext functions)
2015-07-06 20:55:01 +02:00
Benoit Steiner
864318e508 Misc small fixes to the tensor slicing code. 2015-07-06 11:45:56 -07:00
Gael Guennebaud
c2019dfeb3 Merged in Emie/eigen (pull request PR-121)
typo correction in mathFunction
2015-07-06 16:48:54 +02:00
Emilie Guy
ea7113dd0c typo correction in mathFunction 2015-07-06 14:31:08 +02:00
Nicolas Mellado
9115896590 Merged eigen/eigen into default 2015-07-03 00:41:11 +02:00
Benoit Steiner
95ef94f1ee Fixed a typo in the patch 2015-07-02 07:06:55 +00:00
Benoit Steiner
8f1d547c92 Added a default value for the cuda stream in the GpuDevice constructor 2015-07-01 18:32:18 -07:00
Benoit Steiner
1e911b276c Misc improvements and optimizations 2015-07-01 13:59:11 -07:00
Benoit Steiner
4ed213f97b Improved a previous fix 2015-07-01 13:06:30 -07:00
Benoit Steiner
56e155dd60 Fixed a couple of mistakes in the previous commit. 2015-07-01 12:40:27 -07:00
Benoit Steiner
925d0d375a Enabled the vectorized evaluation of several tensor expressions that was previously disabled by mistake 2015-07-01 11:32:04 -07:00
Benoit Steiner
44eedd8915 Marked the cast functions as EIGEN_DEVICE_FUNC to ensure that we can run casting on GPUs 2015-06-30 15:48:55 -07:00
Benoit Steiner
6021b68d8b Silenced a compilation warning 2015-06-30 15:42:25 -07:00
Benoit Steiner
f1f480b116 Added support for user defined custom tensor op. 2015-06-30 15:36:29 -07:00
Benoit Steiner
dc31fcb9ba Added support for 3D patch extraction 2015-06-30 14:48:26 -07:00
Benoit Steiner
f587075987 Made ThreadPoolDevice inherit from a new pure abstract ThreadPoolInterface class: this enables users to leverage their existing threadpool when using eigen tensors. 2015-06-30 14:21:24 -07:00
Benoit Steiner
28b36632ec Turned Eigen::array::size into a function to make the code compatible with std::array 2015-06-30 13:23:05 -07:00
Benoit Steiner
109005c6c9 Added a test for multithreaded full reductions 2015-06-30 13:08:12 -07:00
Benoit Steiner
a4aa7c6217 Fixed a few compilation warnings 2015-06-30 10:36:17 -07:00
Benoit Steiner
7d41e97fa9 Silenced a number of compilation warnings 2015-06-29 14:47:40 -07:00
Benoit Steiner
fffe63045c Added a test for full reductions on GPU 2015-06-29 14:10:32 -07:00
Benoit Steiner
db9dbbda32 Improved performance of full reduction by 2 order of magnitude on CPU and 3 orders of magnitude on GPU 2015-06-29 14:06:32 -07:00
Benoit Steiner
f0ce85b757 Improved support for fixed size tensors 2015-06-29 14:04:15 -07:00
Benoit Steiner
670c71d906 Express the full reduction operations (such as sum, max, min) using TensorDimensionList 2015-06-29 11:30:36 -07:00
Benoit Steiner
d8098ee7d5 Added support for tanh function to the tensor code 2015-06-29 11:14:42 -07:00
Benoit Steiner
3625734bc8 Moved some utilities to TensorMeta.h to make it easier to reuse them accross several tensor operations.
Created the TensorDimensionList class to encode the list of all the dimensions of a tensor of rank n. This could be done using TensorIndexList, however TensorIndexList require cxx11 which isn't yet supported as widely as we'd like.
2015-06-29 10:49:55 -07:00
Gael Guennebaud
392a30db82 Use VERIFY_IS_EQUAL instead of VERIFY(a==b) to get more feedback in case of failure 2015-06-26 16:22:49 +02:00
Gael Guennebaud
c911fc8dee split compiler intensive bdcsvd_1 unit test 2015-06-26 16:14:23 +02:00
Gael Guennebaud
98ff17eb9e Add special path for matrix<complex>/real.
This also fixes underflow issues when scaling complex matrices through complex/complex operator.
2015-06-26 16:08:15 +02:00
Gael Guennebaud
e102ddbf1f bug #1026: fix infinite loop for an empty input 2015-06-26 14:02:52 +02:00
Gael Guennebaud
555b9c6843 Doc: explain perf and multithreading issues in sparse iterative solvers 2015-06-26 10:49:40 +02:00
Gael Guennebaud
53b930887d Enable OpenMP parallelization of row-major-sparse * dense products.
I observed significant speed-up of the CG solver.
2015-06-26 10:32:34 +02:00
Gael Guennebaud
3f49cf4c90 More msvc 2013/2015 workarounds 2015-06-26 09:07:53 +02:00
Gael Guennebaud
7f824dd613 Optimize CG to enable faster spare row-major * dense vector products when the input matrix is complete (Upper|Lower) but column major. 2015-06-25 17:17:38 +02:00
Gael Guennebaud
c5f9eafcbc Fix assignement to selfadjoint-view when testing real-world problems 2015-06-25 17:08:58 +02:00
Gael Guennebaud
33e699c9fe Remove redundant accessors in Reverse 2015-06-25 14:14:59 +02:00
Gael Guennebaud
6b4d255cab Avoid division by a zero complex 2015-06-25 14:04:05 +02:00
Gael Guennebaud
973b0a90db Clarify documentation of the tolerance and error returned in iterative solvers 2015-06-25 13:51:13 +02:00
Gael Guennebaud
84264ceebc workaround msvc 2013/2015 wrong instanciation of isnan, isfinite, isinf 2015-06-25 10:00:26 +02:00
Gael Guennebaud
b4ab72678c bug #1000: MSVC 2013 does need the operator= workaround 2015-06-25 09:45:22 +02:00
Gael Guennebaud
788941d3b1 Workaround MSVC ambiguous instanciation 2015-06-24 23:35:17 +02:00
Gael Guennebaud
4c8cd13b35 Add explicit ctor for diagonal to sparse conversion 2015-06-24 18:11:06 +02:00
Gael Guennebaud
c38c195321 Document how cross behaves on complex numbers 2015-06-24 18:02:33 +02:00
Gael Guennebaud
23535ed31c Add unit test for dense = SparseQR::matrixQ 2015-06-24 17:55:41 +02:00
Gael Guennebaud
62f21e2d11 Add support for sparse = diagonal 2015-06-24 17:55:00 +02:00
Gael Guennebaud
763c833637 Make SparseSelfAdjointView, twists, and SparseQR more evaluator friendly 2015-06-24 17:54:09 +02:00
Gael Guennebaud
36643eec0c Add a call_assignment_no_alias_no_transpose shortcut 2015-06-24 17:50:43 +02:00
Gael Guennebaud
02db7c9bc6 Inherit operator+= and -= with 'using' kkeyword 2015-06-24 17:49:20 +02:00
Gael Guennebaud
53a61a067b Fallback to CMAKE_CXX_COMPILER_VERSION if VS version unknown 2015-06-24 15:17:37 +02:00
Gael Guennebaud
95e19be381 Fix compilation of MKL Pardiso support 2015-06-24 14:53:43 +02:00
Gael Guennebaud
2a33075aeb std::isnan is c++11 only 2015-06-24 10:29:17 +02:00
Gael Guennebaud
23da99492f Add unit-test for Visual2013 ambiguous call to operator= 2015-06-24 10:27:02 +02:00
Benoit Steiner
6441befbb3 Added more checks to test the correctness of the pexp implementation 2015-06-23 19:12:46 -07:00
Gael Guennebaud
c3e398d138 Fix overflow when checking SVD accuracy 2015-06-23 15:05:20 +02:00
Gael Guennebaud
b0d08869a9 Fix underflow in 3x3 tridiagonalization 2015-06-23 14:54:31 +02:00
Gael Guennebaud
18c9d155f3 Fix the fact that float(int) != float(int(float(int))) 2015-06-23 14:33:00 +02:00
Gael Guennebaud
71523a2e25 Fix a warning with icc 2015-06-23 14:20:20 +02:00
Gael Guennebaud
d9778f3391 Enable VML's pow wrapper on windows (the previous wrapper used the Fortran interface) 2015-06-23 14:04:50 +02:00
Gael Guennebaud
5f9630d7f9 bug #923: update support for Intel's VML wrt new evaluation mechanisms 2015-06-23 14:03:25 +02:00
Gael Guennebaud
793e4c6d77 bug #923: fix EIGEN_USE_BLAS mode 2015-06-23 11:13:24 +02:00
Gael Guennebaud
307c4fc292 Workaround missalignment produced by first_aligned for PacketSize==1 and size==1 2015-06-23 10:10:17 +02:00
Gael Guennebaud
bb3a9b4941 Use Ref<> to bypass forceAlignmentIf 2015-06-22 17:48:28 +02:00
Gael Guennebaud
476beed7f8 bug #1017: apply Christoph's patch preventing underflows in makeHouseholder 2015-06-22 16:51:45 +02:00
Gael Guennebaud
9fc1c92137 Fix isinf unit tests 2015-06-22 16:48:27 +02:00
Gael Guennebaud
9c7cfa7dab Update list of main modules 2015-06-22 14:17:24 +02:00
Gael Guennebaud
3ccd23efc0 Update coeff-wise quick-reference doc. 2015-06-22 14:08:54 +02:00
Gael Guennebaud
0848ba0a6e Fix return nullary return types: it must be based on the PlainObject type instead of the expression type. 2015-06-22 10:52:08 +02:00
Gael Guennebaud
b3b3dcad05 Reduce compiler memory consumption for SVD unit tests 2015-06-22 09:58:06 +02:00
Nicolas Mellado
ad5fdc7ddd Fix double to Scalar unwanted promotions 2015-06-21 20:21:23 +02:00
Gael Guennebaud
40821876ea Fix regression on CompressedStorage::operator= 2015-06-20 13:59:13 +02:00
Michael Abrahams
7043083be4 Use GCC flags in mingw 2015-06-20 18:54:41 +00:00
Gael Guennebaud
84aaef93ba Merged in vanhoucke/eigen_vanhoucke (pull request PR-118)
Fix two small undefined behaviors caught by static analysis.
2015-06-20 13:56:48 +02:00
Gael Guennebaud
6b33b29f00 Get rid of must_nest_by_value 2015-06-19 18:12:40 +02:00
Gael Guennebaud
846b227bb7 Get rid of class internal::nested<> (still have to updated Tensor module) 2015-06-19 17:56:39 +02:00
vanhoucke
368ea23406 Fix undefined behavior. When resizing a default-constructed SparseArray, we end up calling memcpy(ptr, 0, 0), which is technically UB and gets caught by static analysis. 2015-06-19 15:53:30 +00:00
vanhoucke
4cc0c961f3 Fix undefined behavior. 2015-06-19 15:46:46 +00:00
Gael Guennebaud
386d9e5ebd Fix usage of nested versus nested_eval 2015-06-19 17:42:27 +02:00
Gael Guennebaud
a5a7b68b76 Fix ambiguous instanciation using clean class-level SFINAE in product_evaluator 2015-06-19 17:25:13 +02:00
Gael Guennebaud
6fc5438205 Remove a few deprecated internal expressions 2015-06-19 17:06:12 +02:00
Gael Guennebaud
e9edb085c0 Check number of temporaries when applying permutations 2015-06-19 16:39:24 +02:00
Gael Guennebaud
6318d53b41 Factorize VERIFY_EVALUATION_COUNT in unit tests 2015-06-19 16:38:26 +02:00
Gael Guennebaud
5c84dd5665 Fix permutation/transposiitons products wrt nested_eval 2015-06-19 16:37:04 +02:00
Gael Guennebaud
0c8b0e007b Introduce a AliasFreeProduct option for Permutations and Transpositions 2015-06-19 15:38:19 +02:00
Gael Guennebaud
3f6aa4cd5d Remove useless specializations of evaluator_traits 2015-06-19 14:18:29 +02:00
Gael Guennebaud
4a8888dfbc Improbe compatibility of Transpositions and evaluators 2015-06-19 14:10:44 +02:00
Gael Guennebaud
3af4c6c1c9 Make Transpositions use evaluators 2015-06-19 11:50:24 +02:00
Gael Guennebaud
82b6ac0864 Enforce eigenvectors to be column-major (for performance reasons) 2015-06-19 11:25:46 +02:00
Gael Guennebaud
fad36cc814 Clean implementation of permutation * matrix products. 2015-06-19 10:51:57 +02:00
Gael Guennebaud
06036d8bb1 Fix compilation of BDCSVD with DEFAULT_TO_ROWMAJOR 2015-06-19 10:37:25 +02:00
Gael Guennebaud
d2db15016b Fix storage order computation in traits<Product> 2015-06-19 10:36:38 +02:00
Benoit Steiner
6a9a29e96f Fixed a compilation warning 2015-06-17 10:14:13 -07:00
Gael Guennebaud
bb6acc561e Workaround broken complex*real product on old clang versions 2015-06-17 16:11:58 +02:00
Gael Guennebaud
40f326ef2e workaround clang's broken complex division 2015-06-17 15:33:09 +02:00
Benoit Steiner
ab5db86fe9 Fixed merge conflict 2015-06-16 19:52:20 -07:00
Benoit Steiner
ea160a898c Pulled latest updates from trunk 2015-06-16 19:46:23 -07:00
Benoit Steiner
367794e668 Fixed compilation warnings triggered by clang 2015-06-16 19:43:49 -07:00
Gael Guennebaud
736a805883 Add unit test for bug #879 2015-06-16 22:11:41 +02:00
Gael Guennebaud
9ab8ac5c8b Fix compilation in TensorImagePatch 2015-06-16 14:50:08 +02:00
Gael Guennebaud
38874b1651 Fix shadow warnings in Tensor module 2015-06-16 14:43:46 +02:00
Gael Guennebaud
e2e66930c6 Fix compilation of alignedvector3 unit test 2015-06-16 14:40:55 +02:00
Gael Guennebaud
7baa1ba03e Remove the usage of result_of for DenseBase::redux as discussed in bug #1006 2015-06-15 22:40:18 +02:00
Gael Guennebaud
97cbe28829 Remove support of std::binder* in C++11 2015-06-15 15:34:05 +02:00
Gael Guennebaud
972a535288 Remove aligned-on-scalar assert and fallback to non vectorized path at runtime (first_aligned already had this runtime guard) 2015-06-14 15:04:07 +02:00
Gael Guennebaud
e5b490b654 Fix isfinite/isinf/isnan code snippets 2015-06-15 15:09:25 +02:00
Gael Guennebaud
a546be56e0 typo 2015-06-15 15:08:51 +02:00
Gael Guennebaud
3946c981b1 Relax tolerance when testing LDLT on singular problems 2015-06-15 15:08:16 +02:00
Gael Guennebaud
2212e40e95 Extend VERIFY_IS_APPROX to report the magnitude of the relative difference in case of failure. This will ease identifying strongest failing tests 2015-06-15 15:03:19 +02:00
Gael Guennebaud
321a2cbe3d Add missing forward declaration of AlignedBox 2015-06-15 15:01:20 +02:00
Gael Guennebaud
2f2a441a4d Fix use of unitialized buffers. 2015-06-13 22:19:40 +02:00
Gael Guennebaud
91b64a9c65 Relax aligned-on-scalar assert as in the 3.2 branch 2015-06-12 11:25:57 +02:00
Gael Guennebaud
84d103bee8 Enable C++11 math function in a more conservative manner. 2015-06-11 21:45:02 +02:00
Gael Guennebaud
916ef52fff merge 2015-06-11 09:35:49 +02:00
Gael Guennebaud
d93ba137f2 Introduce EIGEN_PI, get rid of M_PI and acos(-1.0) 2015-06-10 17:12:10 +02:00
Gael Guennebaud
9756c7fb4d Make more use of EIGEN_HAS_C99_MATH 2015-06-10 16:26:55 +02:00
Gael Guennebaud
93a62265dc fix isinf(complex(inf,NaN)) to return false. 2015-06-10 16:19:10 +02:00
Gael Guennebaud
b0d5aaafcc Rename free functions isFinite, isInf, isNaN to be compatible with c++11 2015-06-10 16:17:09 +02:00
Gael Guennebaud
25a98be948 bug #80: merge with d_hood branch on adding more coefficient-wise unary array functors 2015-06-10 15:52:05 +02:00
Gael Guennebaud
192bce2795 bug #890, add a more general routine to check that two dense object reference to the same data 2015-06-10 10:09:04 +02:00
Gael Guennebaud
e6832ce93d Add regression test for bug #890 2015-06-10 09:32:10 +02:00
Gael Guennebaud
0b2cbb2bdc bug #897: make umfpack support use Ref<> 2015-06-09 23:30:06 +02:00
Gael Guennebaud
feaf76c001 bug #910: add a StandardCompressedFormat option to Ref<SparseMatrix> to enforce standard compressed storage format.
If the input is not compressed, then this trigger a copy for a const Ref, and a runtime assert for non-const Ref.
2015-06-09 23:11:24 +02:00
Gael Guennebaud
f899aeb301 bug #650: fix sparse * dense wrt noalias and compound assignment 2015-06-09 18:33:24 +02:00
Gael Guennebaud
785b9c0127 bug #1003: assert in MapBase if the provided pointer is not aligned on scalar while it is expected to be. Also add a EIGEN_ALIGN8 macro. 2015-06-09 17:42:09 +02:00
Gael Guennebaud
0eb06f1391 Enable -Wshadow with clang 2015-06-09 17:44:18 +02:00
Gael Guennebaud
64753af3b7 code simplification 2015-06-09 15:35:34 +02:00
Gael Guennebaud
cacbc5679d formatting 2015-06-09 15:23:08 +02:00
Gael Guennebaud
04665ef9e1 remove redundant dynamic allocations in GMRES 2015-06-09 15:18:21 +02:00
Gael Guennebaud
d4c574707e fix some legitimate shadow warnings 2015-06-09 15:17:58 +02:00
Gael Guennebaud
f9350e70eb fix unused variable warning 2015-06-09 15:17:21 +02:00
Gael Guennebaud
4aba24a1b2 Clean argument names of some functions 2015-06-09 13:32:12 +02:00
Gael Guennebaud
302cf8ffe2 Add missing documentation for TriangularViewImpl<MatrixType,Mode,Sparse> 2015-06-09 11:40:07 +02:00
Gael Guennebaud
3a4299b245 bug #872: remove usage of deprecated bind1st. 2015-06-09 10:52:04 +02:00
Gael Guennebaud
9aef0db992 Skip too large real-world problems for solvers that do not scale (e.g., SimplicialLLT without reordering) 2015-06-09 09:29:53 +02:00
Gael Guennebaud
9a2447b0c9 Fix shadow warnings triggered by clang 2015-06-09 09:11:12 +02:00
Gael Guennebaud
cd8b996f99 Extend unit test and documentation of SelfAdjointEigenSolver::computeDirect 2015-06-08 16:16:42 +02:00
Gael Guennebaud
913a61870d Update utility for experimenting with 3x3 eigenvalues 2015-06-08 15:54:53 +02:00
Gael Guennebaud
8f031a3cee bug #997: add missing evaluators for m.lazyProduct(v.homogeneous()) 2015-06-08 15:43:41 +02:00
Gael Guennebaud
e6c5723dcd Add unit test for m.replicate(...)(index). 2015-06-08 15:42:15 +02:00
Gael Guennebaud
274b1f5d7e Fix homogeneous() for 1x1 matrix: in this case, homogeneous follows the storage order guaranteeing that v.transpose().homogeneous() == v.homogeneous().transpose() 2015-06-08 15:40:51 +02:00
Gael Guennebaud
cbe3a1a83e Add missing accessors for 1D index based access to Replicate<> expressions. 2015-06-08 15:39:09 +02:00
Gael Guennebaud
a7ae628c9f bug #1005: fix regression regarding sparse coeff-wise binary operator that did not trigger a static assertion for mismatched storage 2015-06-08 10:14:08 +02:00
Gael Guennebaud
0a9b5d1396 bug #705: fix handling of Lapack potrf return code 2015-06-05 15:59:13 +02:00
Gael Guennebaud
d0b7b5cb55 minor documentation fixes 2015-06-05 14:40:07 +02:00
Gael Guennebaud
56d4ef7ad6 BiCGSTAB: set default guess to 0, and improve restart mechanism by recomputing the accurate residual. 2015-06-05 14:37:57 +02:00
Gael Guennebaud
98a8d43457 Improve unit testing of real-word sparse problem (fix some shortcommings, use VERIFY, etc.) 2015-06-05 14:33:37 +02:00
Gael Guennebaud
b685660b22 Do go to full accuracy when testing BiCGSTAB. 2015-06-05 14:32:26 +02:00
Gael Guennebaud
8bc26562f4 Do not abort if the folder cannot be openned! 2015-06-05 14:31:29 +02:00
Gael Guennebaud
3e7bc8d686 Improve loading of symmetric sparse matrices in MatrixMarketIterator 2015-06-05 10:16:29 +02:00
Gael Guennebaud
acc761cf0c Merged in FlorianGeorge/eigen_blaze_fork_2 (pull request PR-60)
Use trans(X) instead of X.transpose() in Blaze Benchmark
2015-06-04 09:15:22 +02:00
Benoit Steiner
ea1190486f Fixed a compilation error triggered by nvcc 7 2015-05-28 11:57:51 -07:00
Benoit Steiner
0e5fed74e7 Worked around some constexpr related bugs in nvcc 7 2015-05-28 10:14:38 -07:00
Benoit Steiner
f13b3d4433 Added missing include files 2015-05-28 07:57:28 -07:00
Benoit Steiner
abec18bae0 Fixed potential compilation error 2015-05-26 10:11:15 -07:00
Benoit Steiner
9df186c140 Added a few more missing EIGEN_DEVICE_FUNC statements 2015-05-26 09:47:48 -07:00
Benoit Steiner
466bcc589e Added a few missing EIGEN_DEVICE_FUNC statements 2015-05-26 09:37:23 -07:00
Gael Guennebaud
d457734a19 Avoid calling smart_copy with null pointers. 2015-05-25 22:30:56 +02:00
Benoit Steiner
6b800744ce Moved away from std::async and std::future as the underlying mechnism for the thread pool device. On several platforms, the functions passed to std::async are not scheduled in the order in which they are given to std::async, which leads to massive performance issues in the contraction code.
Instead we now have a custom thread pool that ensures that the functions are picked up by the threads in the pool in the order in which they are enqueued in the pool.
2015-05-20 13:52:07 -07:00
Benoit Steiner
48f6b274e2 Fixed compilation error triggered by gcc 4.7 2015-05-20 08:54:44 -07:00
Benoit Steiner
2451679951 Avoid using the cuda memcpy for small tensor slices since the memcpy kernel is very expensive to launch 2015-05-19 15:19:01 -07:00
Benoit Steiner
a81d17b73a Added new version of the TensorIntDiv class optimized for 32 bit signed integers. It saves 1 register on CPU and 2 on GPU. 2015-05-19 13:59:52 -07:00
Benoit Jacob
051d5325cc Abandon blocking size lookup table approach. Not performing as well in real world as in microbenchmark. 2015-05-19 11:03:59 -04:00
Christoph Hertzberg
ebea530782 bug #1014: More stable direct computation of eigenvalues and -vectors for 3x3 matrices 2015-05-17 21:54:32 +02:00
Benoit Jacob
c88e1abaf3 also uninitialized here, see previous cset 2015-05-15 11:34:57 -04:00
Benoit Jacob
807793ec3b Fix uninitialized var warning. The compiler was clearing the register anyway, so this does not change resulting code 2015-05-15 11:15:53 -04:00
Pete Warden
140f85bb99 Check for the macro __ARM_NEON__ (with two underscores at the end) as well as __ARM_NEON. The second macro is correct according to the ARM language extensions specification, but historically the first one has been more common. Some older compilers (e.g. gcc v4.6 on a Beaglebone Black) only define the first, so without this patch NEON isn't enabled. 2015-05-12 16:03:43 -07:00
Gael Guennebaud
a852001196 Add regression test for bugs #854 and #1014, and check that the eigenvector matrix is unitary. 2015-05-12 18:45:39 +02:00
Gael Guennebaud
e66caf48e8 Make test matrices for eigensolver/selfadjoint even more tricky 2015-05-12 18:44:46 +02:00
Gael Guennebaud
ef81730625 Ignore denormal numbers in selfadjoint eigensolver. 2015-05-12 18:38:43 +02:00
Christoph Hertzberg
a605a1d7df Merged in MattPD/eigen/MattPD/doc-fix-wording-typos-in-templatekeywor-1431363009359 (pull request PR-116)
[Doc] Fix wording / typos in TemplateKeyword.dox
2015-05-11 23:37:52 +02:00
MattPD
447e060b81 [Doc] Fix wording / typos in TemplateKeyword.dox 2015-05-11 16:50:18 +00:00
Christoph Hertzberg
494fa991c3 bug #872: Avoid deprecated binder1st/binder2nd usage by providing custom functors for comparison operators 2015-05-07 17:28:40 +02:00
Gael Guennebaud
4a936974a5 bug #1013: fix 2x2 direct eigensolver for identical eiegnvalues 2015-05-07 15:55:12 +02:00
Gael Guennebaud
c2107d30ce Extend unit tests of sefladjoint-eigensolver 2015-05-07 15:54:07 +02:00
Gael Guennebaud
ebf8ca4fa8 Fix bug #1010: m_isInitialized was improperly updated 2015-05-07 14:20:42 +02:00
Konstantinos Margaritis
dd698e6680 Merged in doug_kwan/eigen (pull request PR-103)
Fix bug in pdiv<Packet1cd> which swaps 32-bit halves of a pair of
2015-05-05 20:50:14 +03:00
Benoit Steiner
1dded10cb7 Added a double-precision implementation of the exp() function for AVX. 2015-05-04 10:42:51 -07:00
Christoph Hertzberg
6273aca9b1 small typo 2015-05-04 15:26:31 +00:00
Christoph Hertzberg
4dd7d0b5dc Merged in mvdyck/eigen-3/mvdyck/doc-multithreading-fix-old-n-eigennbthr-1430750928880 (pull request PR-114)
[Doc] Multi-threading fix
2015-05-04 17:23:21 +02:00
michiel van dyck
4b9eddaef8 [Doc] Multi-threading fix
OLD: n = Eigen::nbThreads( n );
NEW: n = Eigen::nbThreads( );

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

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

I could not see any significant impact of this offset.

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

So let's just go with 0!

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

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

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

Example usage:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The documentation update script now wipes build/doc/html
before rebuilding stuff. Most of the time/cpu consuming is spent in
compiling snippets, so we don't loose that much.
2013-03-19 19:18:14 +01:00
Gael Guennebaud
f29b4c435b Make cpuid not use %%esi -> dangerous if someone is using it. 2013-03-19 14:11:59 +01:00
Michael Schmidt
0d5a418048 Fix bug #566: rbx register has to be saved when calling cpuid on x84_64 with -fPIC and medium or large code models. 2013-03-19 14:00:42 +01:00
Claas H. Köhler
d6d638c751 Forward compiler flags to Fortran workaround 2013-03-17 14:17:44 +01:00
Christoph Hertzberg
6357fd68da Patch by Kolja Brix <brix@igpm.rwth-aachen.de> that fixes bug #565 and adds a testcase to verify that. 2013-03-17 13:55:31 +01:00
Desire NUENTSA
f8addac4e1 Include SparseLU and SparseQR 2013-03-13 18:01:47 +01:00
Gael Guennebaud
5d1a74da0a Update matlab-eigen quick ascii reff 2013-03-11 21:20:12 +01:00
Desire NUENTSA
6c68f1d787 bug #563 : Sparse block assignments should be called on compressed matrices. Uncompressed matrices will be supported later 2013-03-11 19:21:18 +01:00
Jitse Niesen
79f93247c5 Relax tolerances in matrix_power tests to avoid intermittent failures. 2013-03-09 17:20:16 +00:00
Jitse Niesen
97c9e3c74f Handle special case in atanh2(x,y) when y = 0.
This fixes matrix_power unit test on clang.
2013-03-09 16:58:05 +00:00
Gael Guennebaud
03373f41cb Fix bug #561: remove useless sign macro 2013-03-07 23:35:26 +01:00
Gael Guennebaud
f82ee241ac Added tag 3.2-beta1 for changeset 2238592062 2013-03-07 08:51:23 +01:00
Gael Guennebaud
2238592062 bump to 3.2-beta1 (3.1.91) 2013-03-07 08:49:10 +01:00
Desire NUENTSA
4fdae4dda9 Fix bug in SparseLU kernel for 32bits indices 2013-03-06 16:35:12 +01:00
Gael Guennebaud
98ce4455dd fix sparse vector assignment from a sparse matrix 2013-03-06 11:58:22 +01:00
Desire NUENTSA
69bd334d2b Fix mismatched free/delete 2013-03-05 16:35:13 +01:00
Desire NUENTSA
a1ddf2e7a8 Update doc for the sparse module 2013-03-05 12:55:03 +01:00
Gael Guennebaud
24d81aeb20 Fix overlaping operands when calling memcpy 2013-03-04 17:47:45 +01:00
Gael Guennebaud
d2e5c9d892 Do not globally disable stupid warnings in our unit test since such warnings do affect user code. 2013-03-01 14:50:20 +01:00
Gael Guennebaud
b9fe79153b Fix a couple of remaining warnings (missing newlines, inline-noinline, meaningless type qualifiers) 2013-03-01 14:42:36 +01:00
Gael Guennebaud
87142237b5 Fix "missing return statement at end of non-void function" 2013-03-01 14:33:11 +01:00
Gael Guennebaud
210a56ff48 Update to latest mpreal. 2013-03-01 14:31:11 +01:00
Gael Guennebaud
d70366d011 Remove assumption on RowMajorBit==RowMajor and ColMajor==0 2013-03-01 14:23:31 +01:00
Gael Guennebaud
01c6308d6e Add missing template keyword in evaluators 2013-03-01 00:26:52 +01:00
Gael Guennebaud
858ac9ffe0 Add missing template keyword 2013-03-01 00:03:28 +01:00
Gael Guennebaud
1bb1945078 Fix "explicit instantiation of 'Eigen::Spline' must occur in namespace 'Eigen'" warnings 2013-02-28 20:22:26 +01:00
Gael Guennebaud
3930c9b086 Fix "routine is both "inline" and "noinline"" warnings 2013-02-28 19:31:03 +01:00
Gael Guennebaud
e5bf4440c0 Fix "type qualifiers are meaningless here" warnings 2013-02-28 19:29:32 +01:00
Gael Guennebaud
0fac91ac22 Fix "storage class is not first" warnings 2013-02-28 19:27:53 +01:00
Hauke Heibel
b5d8299ee7 Prevent calling .norm() on integer matrices in the unit tests. 2013-02-28 12:33:34 +01:00
Hauke Heibel
83aac6d54c MSVC fix; the compiler failed to detect the correct overload. 2013-02-28 11:38:34 +01:00
Hauke Heibel
5882f1631d Fixed compiler warning. 2013-02-28 10:15:19 +01:00
Hauke Heibel
5e8384df2e MSVC fix; the base class typedef shadowed the local template parameter. 2013-02-28 08:47:38 +01:00
Gael Guennebaud
6dd93fc76e The ref unit test cannot be easily written to work with EIGEN_DEFAULT_TO_ROW_MAJOR 2013-02-27 23:52:10 +01:00
Hauke Heibel
c754023e72 Fixed MSVC dashboard (Experimental/Continuous) build scripts. 2013-02-27 15:54:27 +01:00
Gael Guennebaud
455e6e38b6 Fix two numerical issues in unit tests. 2013-02-27 08:07:18 +01:00
Gael Guennebaud
61a2995d03 Remove ICC warning in nomalloc unit test. 2013-02-26 18:10:19 +01:00
Gael Guennebaud
fe2c8e1c36 Fix compilation with ICC that was unable to instanciate Scaling from Eigen's namespace. 2013-02-26 17:38:37 +01:00
Gael Guennebaud
fa17a6da75 Fix compilation with ICC that was unable to instanciate first_aligned 2013-02-26 17:32:42 +01:00
Gael Guennebaud
bb94f0ebc6 Add a unit test for Ref.h and fix an extra copy. 2013-02-26 15:10:00 +01:00
Gael Guennebaud
63135a7350 Fix computation of outer-stride when calling .real() or .imag() 2013-02-26 15:08:50 +01:00
Gael Guennebaud
e8ccd07671 Add the possibility to define a custom build-string suffix 2013-02-26 13:40:13 +01:00
Gael Guennebaud
0b187a40a1 workaround "may be used uninitialized in this function" warning 2013-02-26 12:09:08 +01:00
Gael Guennebaud
5dda7842ca Add assertion on the input matrix size in factorizations relying on permutations of 32bits int 2013-02-26 11:42:32 +01:00
Gael Guennebaud
b73baa1ea4 Workaround warning: assuming signed overflow does not occur when... 2013-02-26 10:29:24 +01:00
Gael Guennebaud
5108ef01fc Fix no newline warning. 2013-02-26 10:27:55 +01:00
Gael Guennebaud
b6dc2613ac Fix bug #552: disable EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED when compiling with -fsanitize=address, and allow users to manually tell whether EIGEN_MALLOC_ALREADY_ALIGNED. 2013-02-25 19:17:13 +01:00
Gael Guennebaud
12a1313b09 bug #482: pass scalar arguments by const references. Still remains a few cases that might affect the ABI (see the bug entry) 2013-02-25 18:05:57 +01:00
Desire NUENTSA
cc35c44256 Add reference for the default threshold in sparse QR 2013-02-25 14:26:55 +01:00
Desire NUENTSA
ced8dfc0d9 Fix the computation of the default pivot threshold for sparse QR 2013-02-25 13:41:59 +01:00
Gael Guennebaud
5a0c5c0393 Fix bug #483: optimize outer-products to skip setZero and a scalar multiple when not needed. 2013-02-25 13:31:42 +01:00
Gael Guennebaud
96ad13abba conservative_resize unit test was executed only once 2013-02-25 01:50:58 +01:00
Gael Guennebaud
41aa0fcc3b Output random generator seed in case of failure so that we have it in CDash. 2013-02-25 01:46:59 +01:00
Gael Guennebaud
698de91c8a Fix segfault in SparseBlock::InnerIterator 2013-02-25 01:30:18 +01:00
Gael Guennebaud
19bc418f5c Remove erroneously committed debugging stuff. 2013-02-25 01:17:44 +01:00
Gael Guennebaud
80d2a65465 Fix visitor unit test. 2013-02-25 01:12:07 +01:00
Gael Guennebaud
04367447ac Fix bug #496: generalize internal rank1_update implementation to accept uplo(A) += v * w and make A.triangularView() += v * w uses it.
Update unit tests and blas interface respectively.
2013-02-24 23:05:42 +01:00
Gael Guennebaud
08388cc712 Remove superfluous cast. 2013-02-24 20:37:52 +01:00
Gael Guennebaud
1c137496c6 Extend sparseqr unit test to check linearly dependent columns 2013-02-24 20:36:54 +01:00
Gael Guennebaud
4eeaff6d38 Cleaning pass on SparseQR 2013-02-24 20:36:28 +01:00
Gael Guennebaud
28e139ad60 Fix another issue related to summing up many signed values. 2013-02-23 23:06:45 +01:00
Gael Guennebaud
42af5870a4 Fix array unit test: isApprox(log(0),log(0)) is false, and summing up signed float value might result in very small values and thus large numerical errors 2013-02-23 22:58:14 +01:00
Gael Guennebaud
274c24c262 Avoid problematic ternary operator (http://forum.kde.org/viewtopic.php?f=74&t=109486) 2013-02-23 20:13:21 +01:00
Sebastien Barthelemy
74438f8aa9 Fix EIGEN_INITIALIZE_MATRICES_BY_NAN. 2013-02-22 15:09:03 +01:00
Gael Guennebaud
7fe6419171 remove double parenthesis 2013-02-22 14:50:47 +01:00
Gael Guennebaud
e71bc79f2a SparseLU does not accept row-major matrices for the destination. 2013-02-22 14:45:42 +01:00
Gael Guennebaud
bd8c9c69e4 Protect min with parenthesis in IncompleteLLT 2013-02-22 14:41:32 +01:00
Gael Guennebaud
d93c1c113b NVCC: EIGEN_NO_DEBUG must be defined before including Macro.h 2013-02-21 19:05:23 +01:00
Desire NUENTSA
59f9400420 Clarify the doc for column-pivoting QR 2013-02-21 13:33:31 +01:00
Gael Guennebaud
968f7591f8 Make it compile without nvcc 2013-02-21 12:51:58 +01:00
Jitse Niesen
986f60127d Guard against transposeInPlace on non-square non-resizable matrix.
Inspired by question by Martin Drozdik at stackoverflow.com/q/14954983
2013-02-20 14:03:14 +00:00
Jitse Niesen
a054b4ee27 Be more explicit about user-defined functions in Map tutorial.
See discussion on mailing list on 18 + 19 Feb 2013.
2013-02-20 13:44:40 +00:00
Desire NUENTSA
febf8e5d7b Set built-in sparse QR as the default sparse solver and add ComputationInfo for Levenberg Marquardt, 2013-02-20 14:10:14 +01:00
Desire NUENTSA
dca7190e15 Add setPivotThreshold to Sparse QR 2013-02-20 14:00:28 +01:00
Desire NUENTSA
19de016fef Correct the SPQR backend for rank-deficient matrices and delete some public functions 2013-02-20 13:59:56 +01:00
Desire NUENTSA
bc18e06fe3 Add matrixR() to get the triangular factor from the Householder QR 2013-02-20 13:58:26 +01:00
Desire NUENTSA
962c99d462 Metis ordering backend supports only squared matrices 2013-02-20 13:56:51 +01:00
Jitse Niesen
ba653105a2 Remove confusing transpose() in setLinSpaced() docs. 2013-02-18 17:27:41 +00:00
Jitse Niesen
b4f6aec195 Fix linear vectorized transversal in linspace (fixes bug #526). 2013-02-18 17:26:03 +00:00
Desire NUENTSA
1a056b408d Add a rank-revealing feature to sparse QR 2013-02-15 16:35:28 +01:00
Gael Guennebaud
9fd465ea2b Fix the following warning: "comparison between signed and unsigned integer expressions" 2013-02-15 14:31:38 +01:00
Gael Guennebaud
cf259ce590 Workaround the following warning: "assuming signed overflow does not occur when assuming that (X + c) < X is always false" 2013-02-15 14:28:20 +01:00
Gael Guennebaud
a1091caa43 Fix some unused or not initialized related warnings. 2013-02-15 14:05:37 +01:00
Gael Guennebaud
19f699ded0 "-Wno-psabi" option is not supported by all gcc version. 2013-02-15 14:01:30 +01:00
Gael Guennebaud
8745da14d8 Fix SSE plog<float> to return -INF on 0 2013-02-14 23:34:05 +01:00
Gael Guennebaud
912ba10efe Remove the following note made by gcc: "The ABI of passing structure with complex float member has changed in GCC 4.4" 2013-02-14 21:52:12 +01:00
Gael Guennebaud
24e4a3af2b Add missing using std::sqrt 2013-02-14 21:40:00 +01:00
Gael Guennebaud
a0fb885c82 Update adjoint unit test to avoid instantiating sqrt(int) 2013-02-14 21:33:42 +01:00
Gael Guennebaud
9cc016d3f9 Update basicstuff unit test to avoid instantiating the ambiguous sqrt(int) 2013-02-14 21:15:58 +01:00
Gael Guennebaud
f8407742c1 Fix some implicit int64 to int conversion warnings. However, the real issue
is that PermutationMatrix mixes the type of the stored indices and the "Index"
type used for the sizes, coeff indices, etc., which should be DenseIndex.
(transplanted from 66cbfd4d39
)
2013-02-14 18:16:51 +01:00
Gael Guennebaud
25bcbfb10c Fix bug in aligned_free with windows CE 2013-02-13 19:09:31 +01:00
Gael Guennebaud
a143c5b78c Fix bug #544: assertion in JacobiSVD when compiling with EIGEN_NO_AUTOMATIC_RESIZING 2013-02-12 19:56:48 +01:00
Gael Guennebaud
3cd32996f1 Fix bug #551: compilation issue when using EIGEN_DEFAULT_DENSE_INDEX_TYPE 2013-02-09 09:43:17 +01:00
Gael Guennebaud
5adcc6c7b4 Add support for NVCC5: most of the Core and part of LU are callable from CUDA code.
Still a lot to do.
2013-02-07 19:06:14 +01:00
Gael Guennebaud
5115f4c504 add EIGEN_INITIALIZE_MATRICES_BY_NAN 2013-02-07 18:07:07 +01:00
Gael Guennebaud
3c1ccca285 Add missing operator= in RefBase 2013-02-07 17:49:16 +01:00
Gael Guennebaud
e21dc15386 Add missing data member function in CwiseUnaryView 2013-02-07 17:44:24 +01:00
Gael Guennebaud
5154253933 Fix some MPL2/LGPL lisencing confusions 2013-02-06 11:30:33 +01:00
Jitse Niesen
14e2ab02b5 Replace assert() by eigen_assert() (fixes bug #548). 2013-02-02 22:04:42 +00:00
Desire NUENTSA
35647b0133 Correct bug in SPQR backend and replace matrixQR by matrixR 2013-01-29 17:48:30 +01:00
Desire NUENTSA
8bc00925e5 Change int to Index type for SparseLU 2013-01-29 16:21:24 +01:00
Hauke Heibel
57e50789f3 Added missing using std::sqrt. 2013-01-27 13:46:06 +01:00
Hauke Heibel
718535ac6c Added Visual Studio 2012 debug visualizers. 2013-01-26 17:32:14 +01:00
Desire NUENTSA
7f0f7ab5b4 Add additional methods in SparseLU and Improve the naming conventions 2013-01-25 20:38:26 +01:00
Desire NUENTSA
d58056bde4 Merged local branch with main trunk 2013-01-25 19:05:33 +01:00
Desire NUENTSA
81d4bfa8d9 add support for solving with sparse right hand side 2013-01-25 18:17:17 +01:00
Gael Guennebaud
e4ec63aee7 Suppress annoying "may be used uninitialized in this function" warning with gcc >= 4.6 2013-01-24 11:59:17 +01:00
Gael Guennebaud
b74c0a4413 Check that NeedsToAlign is properly sets before checking alignment 2013-01-24 11:42:04 +01:00
Gael Guennebaud
7282a45a0a Remove dummy code in MPRealSupport 2013-01-24 08:48:26 +01:00
Gael Guennebaud
29d395f769 Relax a bit the precision in mpreal unit test. 2013-01-23 23:57:28 +01:00
Gael Guennebaud
691e607d85 Specialize GEBP traits and kernel for mpreal to by-pass mpreal and remove the costly creation of many temporaries. 2013-01-23 23:56:57 +01:00
Gael Guennebaud
c22f7cef83 Workaround "error: floating-point literal cannot appear in a constant-expression" in mpreal.h when compiling with predantic.
(I really don't know how to properly fix this))
2013-01-23 20:51:38 +01:00
Gael Guennebaud
73026eab4d Fix SparseLU special gemm kernel on 32 bits system w/o SSE 2013-01-23 19:34:01 +01:00
Gael Guennebaud
ee36eaefc6 remove dummy code in ColPivHouseholderQR::solve 2013-01-23 18:34:29 +01:00
Gael Guennebaud
19c78cf510 Workaround gcc-4.7 bug #53900 (too aggressive optimization in our alignment check) 2013-01-22 22:59:09 +01:00
Gael Guennebaud
67b9f42528 Recent UMFPACK library requires to link to libSuiteSparse 2013-01-22 22:53:28 +01:00
Desire NUENTSA
ad798231ec Fix test for Metis 2013-01-21 15:43:15 +01:00
Desire NUENTSA
3d9150870d Fix documentation for SparseLU 2013-01-21 15:39:18 +01:00
Desire NUENTSA
d2dd5063b6 Documentation for the ordering methods 2013-01-21 15:37:47 +01:00
Desire NUENTSA
5b9bb00265 Test for the sparse Blue norm 2013-01-21 15:37:06 +01:00
Desire NUENTSA
5dcf6caa36 Unit test for the Metis Ordering package 2013-01-21 15:36:18 +01:00
Gael Guennebaud
392ffce3b9 Fix traits of Map<Quaternion>, and respectively extend the unit tests 2013-01-20 10:21:54 +01:00
Gael Guennebaud
fb89b66229 Some minor documentation fixes in Quaternion 2013-01-20 10:20:39 +01:00
Chen-Pang He
23c87fcde6 I think it's OK to let XprHelper.h determine the nested type. 2012-10-15 00:14:32 +08:00
Chen-Pang He
fe0ef8e609 Remove unused typedef (traits<MatrixPowerProduct>::PlainObject) for brevity. 2012-10-14 22:30:52 +08:00
Chen-Pang He
40fce01648 Simplify traits<MatrixPowerProduct>: StorageKind must be Dense because MatrixPowerProduct is derived from MatrixBase. 2012-10-14 18:36:17 +08:00
Chen-Pang He
c890cf5489 Use the nested type instead of const reference 2012-10-14 03:02:16 +08:00
Chen-Pang He
daa65c5bce Just tidy up: no need to specify template parameters inside class body. 2012-10-14 01:36:54 +08:00
Chen-Pang He
0017d8c58f Make MatrixPowerTriangularAtomic::computePade static because it should be. 2012-10-07 02:25:00 +08:00
Chen-Pang He
a5d348e30a Use simplified return type, trying to work around MSVC. 2012-10-03 19:42:02 +08:00
Chen-Pang He
4cfde4590f Make use of TRMM (speed up), and remove useless condition (the triangular don't need LU) 2012-10-02 23:04:23 +08:00
Chen-Pang He
21c2b4e327 Make better decision on PartialPivLU vs inverse(): We have specialized inverse() only for FIXED matrices. 2012-10-02 19:53:38 +08:00
Chen-Pang He
e92fe88159 Add test for real MatrixPowerTriangular. 2012-09-30 19:21:53 +08:00
Chen-Pang He
eb33d307af Avoid Schur decomposition on (quasi-)triangular matrices. (Huge speed up!) 2012-09-30 16:30:18 +08:00
Chen-Pang He
332eb36436 Implement complex MatrixPowerTriangular. There are still problems with real one. 2012-09-30 02:14:16 +08:00
Gael Guennebaud
209199a13e Move the definition of DenseBase::InnerIterator to Core module. (needed to make blueNorm generic) 2013-01-15 22:03:54 +01:00
Desire NUENTSA
f813e83bc3 Delete unused variable in SparseLU 2013-01-14 16:03:46 +01:00
Desire NUENTSA
c05848a330 Move SparseColEtree common to SparseLU and SparseQR to SparseCore and fix build issue of sparseqr 2013-01-14 15:59:46 +01:00
Desire NUENTSA
904c2f137b Fix the column permutation in SparseQR 2013-01-14 14:20:42 +01:00
Gael Guennebaud
a3b94d26c8 Remove TOC numbering, and minor improvement in overview. 2013-01-12 20:34:52 +01:00
Sergey Popov
761b3bbb69 Fix bug #540: SelfAdjointEigenSolver improperly used the upper triangular part to extract the scaling factor. 2013-01-12 12:07:49 +01:00
Gael Guennebaud
7262cf783c Cleaning documentation pass in ordering and ILUT 2013-01-12 11:56:56 +01:00
Gael Guennebaud
38fa432e07 Clean inclusion, namespace definition, and documentation of SparseLU 2013-01-12 11:55:16 +01:00
Gael Guennebaud
50625834e6 SparseQR: clean a bit the documentation, fix rows/cols methods, remove rowsQ methods and rename matrixQR to matrixR. 2013-01-12 09:40:31 +01:00
Gael Guennebaud
581e389c54 Fix installation path of SparseQR 2013-01-12 09:32:51 +01:00
Desire NUENTSA
121f3bdf04 Pass a const matrix to sparseQR 2013-01-11 17:47:32 +01:00
Desire NUENTSA
33febdb48b Add support for Schur decomposition of matrices in Hessenberg form 2013-01-11 17:36:45 +01:00
Desire NUENTSA
0f94e96342 Add support for sparse blueNorm 2013-01-11 17:27:12 +01:00
Desire NUENTSA
91b3b3aaab Add a sparse QR factorization and update the elimination tree in SparseLU 2013-01-11 17:16:14 +01:00
Gael Guennebaud
1ccd90a927 Make the MatrixFunctions documentation page looks a bit better 2013-01-11 10:48:43 +01:00
Gael Guennebaud
cc444bbbf9 update unsupported module documentation to be conformed with new documentation style 2013-01-11 10:41:26 +01:00
Gael Guennebaud
b0cb5e6d48 remove the 'Unsupported Modules' meta module 2013-01-11 10:40:35 +01:00
Gael Guennebaud
109cbb6ad3 typos 2013-01-09 17:44:25 +01:00
Gael Guennebaud
dcc1754f05 update javascript hacks for doxygen 1.8.3 2013-01-09 00:40:48 +01:00
Gael Guennebaud
2abe7d8c6e Rename the dox files: the number prefixes are not needed anymore 2013-01-06 23:57:54 +01:00
Gael Guennebaud
091a49cad5 Clean the manual page titles, links and intro. 2013-01-06 23:48:59 +01:00
Thomas Capricelli
c71c06b71f fix typo 2013-01-06 14:39:20 +01:00
Gael Guennebaud
8a50ed86f3 Check that minCoeff(int*)/maxCoeff(int*) always pick the first entry in case of multiple extrema. 2013-01-05 23:49:47 +01:00
Gael Guennebaud
f9927b4aca Fix _data() versus data() issue in SparseVector, and add a Storage typedef just like SparseMatrix. 2013-01-05 23:04:22 +01:00
Gael Guennebaud
86983fa1ff Update the overview page to reflect the new organisation 2013-01-05 21:25:41 +01:00
Gael Guennebaud
2de69c2f26 Doc presentation:
- remove the "modules|classes" link for module pages (they are already in the TOC)
 - fine tune the TOC css
2013-01-05 17:14:14 +01:00
Gael Guennebaud
93ee82b1fd Big changes in Eigen documentation:
- Organize the documentation into "chapters".
  - Each chapter include many documentation pages, reference pages organized as modules, and a quick reference page.
  - The "Chapters" tree is created using the defgroup/ingroup mechanism, even for the documentation pages (i.e., .dox files for which I added an \eigenManualPage macro that we can switch between \page or \defgroup ).
  - Add a "General topics" entry for all pages that do not fit well in the previous "chapters".
  - The highlevel struture is managed by a new eigendoxy_layout.xml file.
- remove the "index" and quite useless pages (namespace list, class hierarchy, member list, file list, etc.)
- add the javascript search-engine.
- add the "treeview" panel.
- remove \tableofcontents (replace them by a custom \eigenAutoToc macro to be able to easily re-enable if needed).
- add javascript to automatically generate a TOC from the h1/h2 tags of the current page, and put the TOC in the left side panel.
- overload various javascript function generated by doxygen to:
  - remove the root of the treeview
  - remove links to section/subsection from the treeview
  - automatically expand the "Chapters" section
  - automatically expand the current section
  - adjust the height of the treeview to take into account the TOC
- always use the default .css file, eigendoxy.css now only includes our modifications
- use Doxyfile to specify our logo
- remove cross references to unsupported modules (temporarily)
2013-01-05 16:37:11 +01:00
Jitse Niesen
eac676ff6c Set matrix to zero before inserting entries (partially fixes bug #539). 2013-01-03 18:00:45 +00:00
Chen-Pang He
8321b7ae74 Make KroneckerProductSparse inherit EigenBase instead of SparseMatrixBase, for it does not provide an InnerIterator. 2012-10-25 02:09:48 +08:00
Chen-Pang He
204a09cb82 Fix compile error caused by incomplete SparseMatrixBase. 2012-10-16 00:06:49 +08:00
Chen-Pang He
0508a0620b Let KroneckerProduct inherit ReturnByValue to eliminate temporary evaluation. It's uncommon to store the product back to one of the operands. 2012-10-15 19:45:50 +08:00
Chen-Pang He
8284e7134b Add doc for KroneckerProductSparse. 2012-10-15 00:31:09 +08:00
Chen-Pang He
c4b83461d9 Make kroneckerProduct take two arguments and return an expression, which is more straight-forward. 2012-10-15 00:21:12 +08:00
Chen-Pang He
f34db6578a KroneckerProduct: we have const_cast_derived so why not use it? 2012-10-14 01:38:38 +08:00
Jitse Niesen
20a984cd2e Remove #include of removed header file. 2013-01-03 16:44:15 +00:00
Gael Guennebaud
6fb3be9841 Remove useless empty file. 2013-01-03 17:05:20 +01:00
Gael Guennebaud
2ea1e49a08 Doc: replace manual TOC by doxygen's \tableofcontents command 2012-12-28 18:58:07 +01:00
Gael Guennebaud
ded70b8b58 Doc: remove page margins and limits to 60em paragraphes only instaead of the entire page (many declarations and tables are larger than 60em anyway) 2012-12-28 18:57:10 +01:00
Gael Guennebaud
3f82401890 Update doxygen files to doxygen version 1.8 2012-12-28 18:52:53 +01:00
Gael Guennebaud
f41d96deb9 Fix several documentation issues 2012-12-24 13:33:22 +01:00
Gael Guennebaud
f450303321 Fix MSVC compilation: std::log was not accessible. 2012-12-20 18:11:49 +01:00
Gael Guennebaud
85005ffbd1 Make sure sqrt and the likes are not compiled for integer type in cwiseop unit test. 2012-12-20 18:08:26 +01:00
Christoph Hertzberg
b7ea59556d Fix bug #507: Mark variable as unused in NDEBUG case 2012-12-20 11:21:47 +01:00
Christoph Hertzberg
0fe264869a Merge with 6300e8ca02 2012-12-17 17:01:24 +01:00
Christoph Hertzberg
6300e8ca02 replaced compiler specific __attribute__((noinline)) by EIGEN_DONT_INLINE 2012-12-17 16:55:14 +01:00
Christoph Hertzberg
c69577ea31 Fix bug #531: Empty line in <table> made doxygen render it as paragraphs 2012-12-17 16:13:42 +01:00
Jakob Schwendner
22e6741da9 updated geometry benchmark to handle additional cases 2012-12-17 09:33:22 +01:00
Jakob Schwendner
98798e904b added benchmark for test vectorization in geometry package 2012-12-16 23:30:56 +01:00
Gael Guennebaud
e90752d252 Fix bug #534: rm useless initialization of rowSpacer. 2012-12-16 20:32:48 +01:00
Gael Guennebaud
925a5b7d07 Fix bug #535: unused variable warnings 2012-12-16 20:21:28 +01:00
Gael Guennebaud
6c8cf15c06 Fix compilation of Block/SparseBlock with MSVC 2012-12-16 19:45:48 +01:00
David Harmon
23ce05971b Add arpack support module file 2012-12-16 19:11:24 +01:00
David Harmon
dbe1ab67ac Added ARPACK support for standard and generalized eigenvalue problems. Currently self-adjoint only. 2012-10-06 17:18:09 -06:00
Gael Guennebaud
8844f632fa Move work in progress Levenberg Marquardt module in unsupported 2012-12-08 18:22:23 +01:00
Gael Guennebaud
4cdcb6d793 Add missing minpack copyrights/license.
Fix LM header files and credits original MINPACK authors.
Move minimizeOneStep code into its own file to get it more properly credited.
2012-12-08 18:17:18 +01:00
Gael Guennebaud
d85253ccf4 Backed out changeset 363e506776 2012-12-07 20:53:19 +01:00
Desire NUENTSA
363e506776 Rename the old LevenbergMarquardt class to LevenbergMarquardtLegacy
Split the levenberg marquardt test and the hybrid nonlinear test
2012-12-07 15:51:25 +01:00
Desire NUENTSA
cc0fef9807 Add tests for dense and sparse levenberg-Marquardt 2012-12-07 15:48:21 +01:00
Desire NUENTSA
2aba6645f4 Move the Levenberg Marquardt to the supported branch
Add support for sparse computations... need SPQR module.
2012-12-07 15:47:13 +01:00
Desire NUENTSA
71cb78e1ba Fix Incomplete Cholesky factorization. Stable but need iterative robust shift 2012-12-07 15:33:26 +01:00
Desire NUENTSA
5afaacedc6 Update SPQR interface 2012-12-07 15:32:04 +01:00
Pavel Holoborodko
895d90d3e1 Fixed mpreal for IA64 architectures 2012-12-04 18:15:46 +09:00
Gael Guennebaud
8719b1bf16 fix geometry tutorial 2012-11-29 22:48:13 +08:00
Desire NUENTSA
36414d1f3e Update SPQR module for Sparse LM 2012-11-21 19:47:44 +01:00
Desire NUENTSA
9162a8492e ReverseInnerIterator for SparseBlock 2012-11-16 20:00:34 +01:00
Desire NUENTSA
4acc18f100 Move VectorBlock methods into plugin section 2012-11-16 19:59:11 +01:00
Gael Guennebaud
6a790058f5 remove deprecated InnerVectorSet for the deprecated DynamicSparseMatrix class 2012-11-16 09:03:42 +01:00
Gael Guennebaud
4e60283289 Remove Sparse/InnerVectorSet expression in favor of a more general Block<> specialization for Sparse expression.
The specializations for "InnerPanels" are still preserved for efficiency reasons and because they offer additional usefull features.
2012-11-16 09:02:50 +01:00
Gael Guennebaud
3dc8f8536a Generalize Block<> to support various implementation wrt StorageKind (just like other expression) 2012-11-16 09:00:27 +01:00
Gael Guennebaud
493319ae5f plugin header files can be included more than once 2012-11-15 14:33:30 +01:00
Desire NUENTSA
b40a5b8b48 Improve the IncompleteLLT ... not yet robust 2012-11-13 18:14:34 +01:00
Desire NUENTSA
0412dff97b Add more useful functions to SPQR interface 2012-11-13 18:13:13 +01:00
Desire NUENTSA
9cf77ce1d8 Add support for Sparse QR factorization 2012-11-12 15:20:37 +01:00
Desire NUENTSA
474716ec5b Add restarted GMRES with deflation 2012-11-12 10:47:55 +01:00
Gael Guennebaud
a76fbbf397 Fix bug #314:
- remove most of the metaprogramming kung fu in MathFunctions.h (only keep functions that differs from the std)
- remove the overloads for array expression that were in the std namespace
2012-11-06 15:25:50 +01:00
Gael Guennebaud
959ef37006 Fix FindUmfpack when specifying manually the related cmake variables. 2012-11-05 23:21:02 +01:00
Gael Guennebaud
691fb92690 Disable opengl demo if Qt4 or OpenGL cannot be found.
(transplanted from caf24f1c9e
)
2012-10-31 11:36:45 +01:00
Gael Guennebaud
aa858cb43a add first_multiple helper function 2012-10-30 16:27:52 +01:00
Gael Guennebaud
90fcaf11cf SparseLU: remove the "snode" path which appears to bring nearly zero speedup 2012-10-30 15:17:58 +01:00
Gael Guennebaud
ac8c694f3e add missing copyright 2012-10-30 15:16:47 +01:00
Gael Guennebaud
fea4220f37 SparseLU: add a specialized gemm kernel, and add padding to the supernodes such that supernodes columns are all properly aligned 2012-10-30 15:09:48 +01:00
Desire NUENTSA
f7e203fb0c Fix build error in matrixfunctions on MSVC 2012-10-30 11:30:37 +01:00
Gael Guennebaud
b3254c9af5 fix bug #524: Pardiso's parameter array does not have to be aligned! 2012-10-24 10:31:04 +02:00
Gael Guennebaud
138897cc06 fix bug #521: __cpuidex is not available on all architectures supported by MSVC 2012-10-24 10:21:41 +02:00
Gael Guennebaud
9b418afff6 Windows CE does not provide an aligned_malloc function. 2012-10-24 10:12:42 +02:00
Gael Guennebaud
0753463d70 Fix bug #519: AlignedBox::dim() was wrong for dynamic dimensions 2012-10-24 09:58:35 +02:00
Pavel Holoborodko
7857118f2e Fixed gcc warnings, John Westwood name and round_style function 2012-10-19 22:51:55 +09:00
Pavel Holoborodko
8b84e05739 Updated multiprecision module to support the most recent version of MPFR C++ 2012-10-19 18:12:31 +09:00
Desire NUENTSA
77f92bf0b1 the repeated solves are already present in check_sparse_solving() 2012-10-09 13:30:48 +02:00
dnuentsa
f757034001 MINRES solver 2012-10-09 13:07:09 +02:00
Desire NUENTSA
fe78c86b4a Discard failing tests in NonlinearOptimization 2012-10-09 12:20:21 +02:00
Desire NUENTSA
b722c405b7 Use Ref instead of VectorBlock 2012-10-09 12:18:47 +02:00
Desire NUENTSA
23e2de3cb6 RealShur for a already Hessenberg matrix 2012-10-09 12:16:54 +02:00
Gael Guennebaud
a67eea05c1 fix comma initializer when inserting empty matrices 2012-10-03 21:58:14 +02:00
Desire NUENTSA
cfa8032ffb bug #517 : Small typo in AsciiQuickReference 2012-10-03 09:48:33 +02:00
Gael Guennebaud
fec6df1f7d fix dense=sparse*diagonal (there was an issue in the values returned by the .outer() function of the related iterators) 2012-10-03 09:06:19 +02:00
Gael Guennebaud
f30ca7ed7e extend unit tests to check rectangular matrices for sparse*diagonal products 2012-10-02 23:03:06 +02:00
Gael Guennebaud
62b1f75a86 add an assertion when inserting an already existing element 2012-10-02 23:02:23 +02:00
giacomo po
bf81276dad spd test instead of square test. Still missing complex version of MINRES. 2012-10-01 12:23:03 -07:00
Jitse Niesen
2008f76120 Merge 2012-09-29 17:35:15 +01:00
Chen-Pang He
d7d96f6694 Make testExponentLaws in matrix_power quiet. It was too noisy. 2012-09-29 17:45:59 +08:00
Chen-Pang He
50c07e50e8 Avoid memory manipulation for simplicity, efficiency, and safety. 2012-09-29 17:41:51 +08:00
Chen-Pang He
5814a5f1a0 Abort the extension. MatrixSquareRootTriangular only takes upper triangular matrices. 2012-09-29 17:41:06 +08:00
Chen-Pang He
067a5a98c8 Extend MatrixPowerTriangularAtomic for future implementation for triangular matrix power. 2012-09-29 02:02:12 +08:00
Desire NUENTSA
b68102d9a2 MSVC needs parentheses around min and max 2012-09-28 10:44:25 +02:00
giacomo po
01cb88fff8 compiling (but failing) unit test 2012-09-27 17:44:54 -07:00
Gael Guennebaud
87074d97e5 old gcc versions do not have immintrin.h file... 2012-09-27 23:35:54 +02:00
Chen-Pang He
ed18d6f2ad Fix doc and tidy up 2012-09-28 02:08:14 +08:00
Desire NUENTSA
82c3ff3784 Fix Build error on MSVC 2012-09-27 12:04:59 +02:00
Desire NUENTSA
72bfed5e20 Add forgotten SparseLUBase 2012-09-27 11:34:56 +02:00
Chen-Pang He
3b88216d42 Move unshared items back to MatrixPower 2012-09-27 17:19:32 +08:00
Gael Guennebaud
8b83e66906 add scalar multiple to diagonal matrices
(transplanted from dc5b335f9f
)
2012-09-27 09:37:05 +02:00
Gael Guennebaud
1b004d5794 fix SparseMatrix option bit flag in eval<> helper 2012-09-27 09:22:10 +02:00
Gael Guennebaud
b648484dba fix bug #515: missing explicit scalar conversion
(transplanted from b0862dcb2f
)
2012-09-27 00:23:19 +02:00
Gael Guennebaud
44374788b5 fix bug #511: pretty printers on windows 2012-09-26 23:48:48 +02:00
Gael Guennebaud
7c4b55fda9 fix bug #509: warning with gcc 4.7 2012-09-26 23:32:22 +02:00
Chen-Pang He
73a0bfe261 Write doc on (matrix power) * (matrix expression) 2012-09-27 02:31:18 +08:00
Chen-Pang He
aa5acdb352 Create class MatrixPowerBase for further extension (like specialization for triangular or self-adjoint matrices) 2012-09-27 02:20:36 +08:00
Gael Guennebaud
7e97dd5bd8 we should not directly include the *mmintrin.h headers but include immintrin.h only 2012-09-26 19:28:57 +02:00
Gael Guennebaud
1edb396542 fix minor typo in doc 2012-09-26 19:24:41 +02:00
Desire NUENTSA
357fe3641d Correct reference to iterative scaling method 2012-09-25 11:55:33 +02:00
Desire NUENTSA
15a9f6b9c1 Doc for sparseLU 2012-09-25 11:48:18 +02:00
Hauke Heibel
5a3f49036b Removed scaling from the umeyama when it is not requested. 2012-09-25 11:39:40 +02:00
Desire NUENTSA
088379ac2f Fix MSVC compile error in SparseLU 2012-09-25 09:58:29 +02:00
Desire NUENTSA
a01371548d Define sparseLU functions as static 2012-09-25 09:53:40 +02:00
giacomo po
fd0441baee some clean-up and new comments. 2012-09-24 09:20:40 -07:00
Chen-Pang He
d387dfa9dc Remove unnecessary code. lazyAssign seems to fix all (noalias, initialization, etc.) 2012-09-24 23:36:19 +08:00
giacomo po
18c41aa04f Some minor optimization. 2012-09-24 08:33:11 -07:00
giacomo po
dd7ff3f493 moved MINRES to unsupported. Made unit test. 2012-09-24 07:47:38 -07:00
Chen-Pang He
334532b7f5 Remove class MatrixPowerEvaluator with enhanced existing MatrixPowerReturnValue to simplicity, but docs are not completed yet. 2012-09-23 23:49:50 +08:00
Chen-Pang He
1d402dac03 Fix bug in MatrixPower(expression) due to destruction of temporary objects. Sorry for ugly pointer manipulation but it prevents copying a PlainObject. 2012-09-23 18:49:44 +08:00
giacomo po
8c5e4fae61 working preconditioned MINRES solver 2012-09-22 15:29:00 -07:00
Chen-Pang He
963794b04a Eliminate unnecessary evaluations 2012-09-23 00:20:19 +08:00
Chen-Pang He
7e64f78f65 Avoid inefficient 2x2 LU 2012-09-22 22:06:22 +08:00
Chen-Pang He
d7b1049cab Fix my typo in MatrixPowerBase.h, no effect on the flow. 2012-09-22 19:13:02 +08:00
Chen-Pang He
dd8034bd1c Fix cost evaluation. (chain product for integral power) 2012-09-22 17:37:14 +08:00
Gael Guennebaud
7740127e3d Make Ref<> suitable for both Matrix and Array kinds. Note that Matrix kind objects can be implicitely converted to an Array kind Ref<> and vice versa 2012-09-22 11:11:26 +02:00
Chen-Pang He
446d14f6ad Implement matrix power-matrix product again 2012-09-22 03:26:00 +08:00
Chen-Pang He
87afd99433 Enable saving intermidiate (Schur decomposition) but disable unstable specialization for matrix power-matrix product. 2012-09-21 23:24:28 +08:00
Desire NUENTSA
7e0dd17312 Improve BiCGSTAB : With exact preconditioner, the solution should be found in one iteration 2012-09-19 18:32:02 +02:00
Chen-Pang He
d5d99dd1f0 Optimize matrix functions: m_fT is triangular and trmm is faster than gemm 2012-09-16 14:42:42 +08:00
Gael Guennebaud
48c4d48aec workaround weird compilation error with MSVC 2012-09-14 09:54:56 +02:00
Gael Guennebaud
0c584dcf4d fix compilation with m.array().min/max(scalar) 2012-09-12 17:50:07 +02:00
Gael Guennebaud
28528519e9 Merged in jdh8/eigen (pull request PR-17) 2012-09-11 21:36:05 +02:00
Gael Guennebaud
9e80822fc9 fix compilation on freebsd 2012-09-11 13:32:56 +02:00
Desire NUENTSA
45672e724e Incomplete Cholesky preconditioner... not yet stable 2012-09-11 12:12:19 +02:00
Benoit Jacob
504edbddb1 Replace COPYING.LGPL by a copy of the LGPL 2.1 (instead of LGPL 3).
Indeed, all the LGPL code we use, is licensed under LGPL 2.1 (with some files being "2.1 or later").
2012-09-10 13:27:44 -04:00
Desire NUENTSA
2d49d049d1 Clean the Colamd routine and add test for sparselu code 2012-09-10 14:41:17 +02:00
Desire NUENTSA
761fe49f37 Clean the Colamd routine 2012-09-10 14:28:28 +02:00
Desire NUENTSA
2c99d84133 add SparseLU in sparse bench 2012-09-10 12:41:26 +02:00
Chen-Pang He
04f315d692 Fix rank-1 update for self-adjoint packed matrices. 2012-09-10 18:25:30 +08:00
Chen-Pang He
65caa40a3d Implement packed triangular solver. 2012-09-10 06:29:02 +08:00
Chen-Pang He
3642ca4d46 Implement packed triangular matrix-vector product. 2012-09-09 23:34:45 +08:00
Chen-Pang He
2828c995c5 Use conj_expr_if to clarify what it's doing. 2012-09-09 21:35:28 +08:00
Chen-Pang He
669db3d776 Extend rank-1 updates for different storage orders. 2012-09-09 02:55:10 +08:00
Chen-Pang He
1b8f416408 Implement rank-1 update for self-adjoint packed matrices. 2012-09-08 22:51:40 +08:00
Chen-Pang He
dac5a8a37d Simplify Rank2Update.h 2012-09-08 22:20:05 +08:00
Chen-Pang He
17c746523e Comment FIXMEs on Rank2Update.h and remove unused files. 2012-09-08 21:25:09 +08:00
Gael Guennebaud
24f371bdb4 Merged in jdh8/eigen (pull request PR-16) 2012-09-08 12:16:49 +02:00
Gael Guennebaud
721671cc4e fix bug #501: remove aggressive mat/scalar optimization (was replaced by mat*(1/scalar) for non integer types) 2012-09-08 11:52:03 +02:00
Chen-Pang He
e4e7585a24 Implement rank-2 update for packed matrices. 2012-09-08 17:29:44 +08:00
Chen-Pang He
b5f9bec8ac Perform direct calls in xHEMV and xSYMV. 2012-09-08 15:47:33 +08:00
Gael Guennebaud
06d2fe453d remove stupid assert in blue norm. 2012-09-07 23:19:24 +02:00
Chen-Pang He
1b61aadcbe Implement SDSDOT with DSDOT and avoid allocating buffers in DSDOT. 2012-09-08 02:06:45 +08:00
Chen-Pang He
b0b9b4d6b2 Implement functors for rank-1 and rank-2 update. 2012-09-08 01:39:16 +08:00
Desire NUENTSA
5433986f5a multiple warnings for unused variable 2012-09-07 14:01:51 +02:00
Desire NUENTSA
fdd0f0c5fc merge Sparse LU branch 2012-09-07 13:18:16 +02:00
Desire NUENTSA
063705b5be Add tutorial for sparse solvers 2012-09-07 13:14:57 +02:00
Chen-Pang He
145f89cd5f Fix memory leak in DSDOT. 2012-09-07 15:21:57 +08:00
Chen-Pang He
c86d047c2f BLAS: implement DSDOT and SDSDOT; update test for them. 2012-09-05 18:59:32 +08:00
Desire NUENTSA
2280f2490e Init perf values 2012-09-04 12:21:07 +02:00
Desire NUENTSA
2e38666d01 correct bug in Blas 3 2D block update 2012-09-04 11:36:57 +02:00
Desire NUENTSA
3a22c47fb5 Bug in blas 3 2D block update 2012-09-03 14:49:03 +02:00
Desire NUENTSA
288e6aab14 Insert XSL styles into output XML files 2012-09-03 10:33:39 +02:00
Chen-Pang He
c4051d3d02 Fix a typo in blas/common.h 2012-09-03 15:31:19 +08:00
giacomo po
751501eade added preconditioner with preconditioned-Lanczos iteration 2012-09-01 21:59:06 +02:00
Chen-Pang He
d4144583bb Write dox for assertions 2012-08-31 21:53:02 +08:00
Chen-Pang He
d23134e4a7 Avoid inefficient 2x2 LU. Move atanh to internal for maintainability. 2012-08-30 23:40:30 +08:00
Gael Guennebaud
9da41cc527 forward resize() function from Array/Matrix-Wrapper to the nested expression such that mat.array().resize(a,b) is now allowed. 2012-08-30 16:28:53 +02:00
giacomo po
5f3880c5a8 some optimization in MINRES, not sure about convergence criterion 2012-08-30 13:10:08 +02:00
Gael Guennebaud
c5031edb92 Fix out-of-range memory access in GEMV (the memory was not used for the computation, only to assemble unaligned packets from aligned packet loads)
(transplanted from 221f54698c
)
2012-08-30 10:52:15 +02:00
giacomo po
064f3eff95 first working version. Still no preconditioning 2012-08-30 10:01:34 +02:00
Chen-Pang He
d0ee31aea6 Fix dox and tabbing 2012-08-29 01:56:23 +08:00
Chen-Pang He
ba4e886376 Tidy up and write dox. 2012-08-28 01:55:13 +08:00
Chen-Pang He
5252d823c9 Optimize matrix power 2012-08-26 02:15:41 +08:00
Chen-Pang He
1cd4279b03 Fix a lot in MatrixPower.h 2012-08-25 01:09:20 +08:00
Jitse Niesen
edc7a09ee7 merge 2012-08-27 22:57:39 +01:00
Chen-Pang He
bfaa7f4ffe Add test for matrix power.
Use Christoph Hertzberg's suggestion to use exponent laws.
2012-08-27 22:48:37 +01:00
Desire NUENTSA W.
fe9956defe Read real and complex bench matrices from a unique folder
Output and display bench results using XML and XSLT
2012-08-27 22:52:43 +02:00
Chen-Pang He
b55d260ada Replace atanh with atanh2 2012-08-27 21:43:41 +01:00
Gael Guennebaud
ebe511334f workaround clang bug (see http://forum.kde.org/viewtopic.php?f=74&t=102653) 2012-08-27 14:50:45 +02:00
Gael Guennebaud
576d62db64 fix a typo in commit 324ecf153b
(regarding MKL on windows)
2012-08-27 13:17:45 +02:00
Gael Guennebaud
75435079ca fix bug #499: the image was missing because of a dependency issue when building/executing the "special" examples 2012-08-27 11:11:25 +02:00
Gael Guennebaud
aa1aa36d6d simplify eigen-doc.tgz file generation, and make it more future proof 2012-08-27 10:56:44 +02:00
Gael Guennebaud
904c2e6cfb remove EXTRACT_ALL 2012-08-27 10:30:10 +02:00
Thomas Capricelli
edc496f087 add piwik code to documentation (web stats engine) 2012-08-21 22:36:29 +02:00
jdh8
1b4aed7255 Fix toc in dox and claim copyright 2012-08-20 03:04:28 +08:00
jdh8
573d88f81c Dox in MatrixFunctions 2012-08-19 18:12:04 +08:00
jdh8
15dabd4db7 Bugfix in MatrixLogarithm.h 2012-08-18 21:28:05 +08:00
Hauke Heibel
42c1b9a8dd Ensured that all branches of MatrixLogarithmAtomic::getPadeDegree return values. 2012-08-18 10:18:31 +02:00
jdh8
f047030104 Add specialization for float and long double 2012-08-18 02:27:47 +08:00
Jitse Niesen
dee866a99a Undo incorrect fix in previous commit, and fix real mistake instead. 2012-08-17 15:36:37 +01:00
Jitse Niesen
5eefca637e Documentation fixes. Thanks to Rodney Sparapani for reporting these. 2012-08-17 14:49:18 +01:00
jdh8
2337ea430b Remove useless code (abort specialization for complex exponent temporarily) 2012-08-15 20:56:03 +08:00
jdh8
4be172d84f matrix power: MatrixBase::pow(RealScalar) and MatrixBase::pow(T) where T is integral type 2012-08-15 00:34:20 +08:00
jdh8
c5800a2452 using std::frexp instead of frexp 2012-08-08 17:50:56 +08:00
jdh8
8cddcaf839 Optimize getting exponent from IEEE floating points. 2012-08-08 01:27:11 +08:00
Desire NUENTSA
63d2dcfb70 Clean the supernodal matrix class 2012-08-07 17:10:42 +02:00
Desire NUENTSA
43f74cb5b1 Bug in 2D block update, disable it for now 2012-08-07 13:55:50 +02:00
Desire NUENTSA
4d3b7e2a13 Add support for Metis fill-reducing ordering ; it is generally more efficient than COLAMD ordering 2012-08-06 14:55:02 +02:00
Gael Guennebaud
a1b405c92e Add missing const in some casts 2012-08-05 10:40:46 +02:00
Gael Guennebaud
af824091be Fix precision regression when attempting to fix underflow issues. 2012-08-05 09:57:31 +02:00
jdh8
93967b0dd6 Fix some typos in MatrixLogarithm to improve accuracy. 2012-08-03 23:54:42 +08:00
Desire NUENTSA
a51806993b Prefix with glu, the global structure 2012-08-03 16:43:12 +02:00
Desire NUENTSA
70db61c269 Prefix with glu, the global structure 2012-08-03 16:36:00 +02:00
Gael Guennebaud
03509d1387 SparseLU: add leverage level3 ops 2012-08-03 15:37:44 +02:00
Gael Guennebaud
48dc95f1da factorize column_dfs and panel_dfs 2012-08-02 18:28:16 +02:00
Desire NUENTSA
7dc39b7037 Add unit tests 2012-08-03 13:05:45 +02:00
Desire NUENTSA
6e8aa96e0f correct bug when solving with multiple Rhs 2012-08-03 13:05:27 +02:00
Gael Guennebaud
c73c3ec2f8 fix bug #495: remove too aggressive EIGEN_FLATTEN_ATTRIB attribute
(after some benchmarking, it was not useful anymore)
2012-08-02 12:22:22 +02:00
Desire NUENTSA
e3ac608e41 bug #493 : multiple calls to FindUmfPack
(transplanted from 1914024965
)
2012-08-02 10:00:23 +02:00
Desire NUENTSA
3a0f5a2a7f Update copyrights sections 2012-08-01 11:40:56 +02:00
Desire NUENTSA
02935b4249 switch to MPL license 2012-08-01 11:38:32 +02:00
Desire NUENTSA
390d6599ba Add missing .noalias() 2012-08-01 11:35:23 +02:00
Gael Guennebaud
7d98c864ff fix warning 2012-08-01 10:44:59 +02:00
Gael Guennebaud
22e0ebbc2c fix lower acceptable bound of SSE pexp for double 2012-07-31 23:11:04 +02:00
Gael Guennebaud
e88817cc51 add another missing .noalias() 2012-07-30 19:28:31 +02:00
Gael Guennebaud
8f6d5eacb4 optimize LU_kernel_bmod for small cases, and add an important .noalias() 2012-07-29 22:26:00 +02:00
Jitse Niesen
696b2f999f Eigenvalues module: Implement setMaxIterations() methods. 2012-07-28 21:30:09 +01:00
Gael Guennebaud
6f54269829 add an example for GeneralizedEigenSolver 2012-07-28 18:00:54 +02:00
Gael Guennebaud
8ab0e16e27 fix various regressions with MKL support 2012-07-28 16:32:43 +02:00
Alexey Korepanov
d937e67b48 RealQZ: added example and some code comments 2012-07-28 08:24:44 -05:00
Hauke Heibel
52a0e0d65e Added a default constructor for Splines which creates zero (constant) splines. 2012-07-28 13:37:29 +02:00
Gael Guennebaud
f23dd7c6f1 Fix typo in the doc: s/Succeeded/Success 2012-07-28 13:07:45 +02:00
Gael Guennebaud
e8aa1f00c5 add SSE pexp function for double, make use of _mm_floor_p* for pexp with SSE4.1 2012-07-27 23:40:04 +02:00
Desire NUENTSA W.
ce30d50e3e Improve the permutation 2012-07-27 16:38:20 +02:00
Gael Guennebaud
6eee2918d9 extend quotient functor to allow for mixed types (complex-real) 2012-07-27 11:56:20 +02:00
Desire NUENTSA W.
c0fa5811ec Refactoring codes for numeric updates 2012-07-27 11:36:58 +02:00
Gael Guennebaud
9e8d2dea80 Add a preliminary GeneralizedEigenSolver computing the eigenvalues of Av=lBv with A and B general real matrices.
Currently only the eigenvalues are reported.
2012-07-26 20:15:17 +02:00
Gael Guennebaud
cfb76b242f RealSchur: improve speed of computeNormOfT 2012-07-26 18:04:58 +02:00
Gael Guennebaud
4e60e2cdf6 RealQZ: improve computeNorms speed, improve shift accuracy (better to do a/b than a*(1/b)),
update API to set the maximum number of iterations
2012-07-26 18:03:10 +02:00
Gael Guennebaud
7518201de8 SparseMatrix: add missing ctor for ReturnByValue 2012-07-25 23:03:10 +02:00
Alexey Korepanov
ea310249f3 RealQZ: bug in pushDownZero fixed too 2012-07-25 12:49:18 -05:00
Alexey Korepanov
a3a9773ab6 RealQZ: bug in splitOffTwoRows fixed 2012-07-25 12:17:00 -05:00
Desire NUENTSA
925ace196c correct bug in the complex version 2012-07-19 18:15:23 +02:00
Desire NUENTSA
59642da88b Add exception handler to memory allocation 2012-07-19 18:03:44 +02:00
Desire NUENTSA
b0cba2d988 Add a draft (not clean ) version of the COLAMD ordering implementation 2012-07-18 16:59:00 +02:00
Jitse Niesen
bf7d986af6 Add static assert that objects on stacks are not too big (bug #491). 2012-07-17 22:15:42 +01:00
Gael Guennebaud
e75b1eb883 Fix aliasing issue in sparse matrix assignment.
(m=-m; or m=m.transpose(); with m sparse work again)
2012-07-25 09:33:50 +02:00
Gael Guennebaud
7b34b5f6f9 do not apply plane rotation when it is exactly the identity 2012-07-24 18:19:56 +02:00
Gael Guennebaud
e7c07de549 RealQZ: optimize general hessenberg to not apply rotations to zero entries. 2012-07-24 18:16:22 +02:00
Gael Guennebaud
c1cab7b8ed real QZ: update license 2012-07-24 18:11:41 +02:00
Desire NUENTSA
773804691a working version of sparse LU with unsymmetric supernodes and fill-reducing permutation 2012-07-13 17:32:25 +02:00
Alexey Korepanov
65db91ac2b Add a RealQZ class: a generalized Schur decomposition for real matrices 2012-07-11 16:38:03 -05:00
Jitse Niesen
ba5eecae53 Allow user to specify max number of iterations (bug #479). 2012-07-24 15:17:59 +01:00
Jitse Niesen
b7ac053b9c Use EISPACK's strategy re max number of iters in Schur decomposition (bug #479). 2012-07-22 22:02:50 +01:00
Jitse Niesen
fd5749f51c LDLT: Report sign consistent with D for indefinite matrices.
See http://forum.kde.org/viewtopic.php?f=74&t=106942
2012-07-22 21:39:38 +01:00
Jitse Niesen
907f4562ac Fix some illegal memory access in sparse conservativeResize() 2012-07-20 22:51:51 +01:00
Benjamin Piwowarski
6bf49ceac2 bug #449: add SparseMatrix::conservativeResize feature 2012-07-19 00:07:06 +02:00
Benoit Jacob
3f08a6a126 add COPYING.MINPACK 2012-07-15 11:46:22 -04:00
Benoit Jacob
df06e5662d MINPACK license is OK for MPL2 after all 2012-07-15 10:30:57 -04:00
Benoit Jacob
f28e95500b add COPYING.README 2012-07-15 10:29:09 -04:00
Benoit Jacob
9bf3ec134e add COPYING.MPL2 2012-07-15 10:20:59 -04:00
Benoit Jacob
b596f6c10c remove outdated "Eigen itself is part of the KDE project" outside of eigen2 files 2012-07-15 10:33:40 -04:00
Jitse Niesen
d3998de472 Silence clang warning about && inside || 2012-07-14 15:50:56 +01:00
Jitse Niesen
4ae3e0a9b8 Evaluators: Fixed bug caused by Diagonal dynamic index change.
This caused the evaluators unit test to fail.
2012-07-14 14:55:04 +01:00
Gael Guennebaud
79214745c7 clean Eigen2Support wrt KDE mentions 2012-07-14 10:15:45 +02:00
Gael Guennebaud
e59f95a9a0 clean old KDE mention and related 2012-07-14 10:04:26 +02:00
Gael Guennebaud
54559094ec document EIGEN_MPL2_ONLY 2012-07-14 09:56:03 +02:00
Gael Guennebaud
46b1c7a0ce fix bug #485: conflict between a typedef and template type parameter 2012-07-13 20:54:38 +02:00
Benoit Jacob
269be00925 Add a EIGEN_MPL2_ONLY build option to generate compiler errors when including non-MPL2 modules 2012-07-13 14:42:47 -04:00
Benoit Jacob
0733e622a3 Manual MPL2 relicensing fixes 2012-07-13 14:42:47 -04:00
Benoit Jacob
69124cfca2 Automatic relicensing to MPL2 using Keirs script. Manual fixup follows. 2012-07-13 14:42:47 -04:00
Keir Mierle
d4ca0963bc Add preliminary script to relicense Eigen to MPL2. 2012-07-11 11:29:52 -07:00
Gael Guennebaud
904ecdf9d8 Add a DynamicIndex constant for signed quantities and use it to fix the conflict
between Diagonal<S,-1> (the first sub diagonal) and a runtime super/sub diagonal which is now:
Diagonal<S,DynamicIndex>
2012-07-10 23:04:17 +02:00
Gael Guennebaud
3e6329a0d9 fix computation of fixed size sub/super diagonal size 2012-07-10 22:39:05 +02:00
Desire NUENTSA
e529bc9cc1 correct bug when applying column permutation 2012-07-10 19:18:50 +02:00
Desire NUENTSA
de2544cc9b working version of sparse LU without fill-reducing permutation 2012-07-10 19:16:57 +02:00
Gael Guennebaud
a2c3003be2 Fix possible underflow issues in SelfAdjointEigenSolver 2012-07-10 09:51:26 +02:00
Desire NUENTSA
3095e4a5f9 Correct bug for triangular solve within supernodes 2012-07-09 19:09:48 +02:00
Gael Guennebaud
ff12a6cd43 fix include path 2012-07-08 16:18:51 +02:00
Desire NUENTSA
b5a83867ca Update Ordering interface 2012-07-06 20:18:16 +02:00
Jitse Niesen
b1b6864c88 Evaluators: Remove member variables if known at compile-time.
Also, use composition instead of inheritance in EvalToTemp evaluator.
2012-07-06 14:50:03 +01:00
Desire NUENTSA
203a0343fd Update Ordering interface 2012-07-06 13:34:06 +02:00
Gael Guennebaud
7bfd8eabff fix compilation with MSVC 2012-07-05 21:58:01 +02:00
Gael Guennebaud
5dbdde0420 Fix bug #480: workaround the Android NDK defining isfinite as a macro 2012-07-05 17:22:25 +02:00
Gael Guennebaud
23df2eed46 bug #481 step 1: add a new Ref<> class for non templated function arguments 2012-07-05 17:00:28 +02:00
Jitse Niesen
60edf02f6f doc: Typo in CustomizingEigen, introduced in previous commit.
Thanks to Christoph Hertzberg for noting this.
2012-07-05 13:56:28 +01:00
Jitse Niesen
37d5825c5a merge 2012-07-05 13:39:06 +01:00
Jitse Niesen
b582b2ebdc doc: Add constructor to example for inheritance.
See "Error in Inheriting Eigen::Vector3d" on forum.
2012-07-05 13:36:02 +01:00
Gael Guennebaud
0a7ce6ad69 fix bug #486: template speacialization of member functions must be declared inline to avoid duplicate references 2012-07-05 13:32:23 +02:00
Jitse Niesen
cb9f3685d3 Move implementation of coeff() &c to Matrix/Array evaluator. 2012-07-05 11:09:42 +01:00
Christoph Hertzberg
03fe095622 bug #488: Add setShift method (and functionality) to Cholmod classes
Also check for Success of numerical factorization
2012-07-04 18:46:14 +02:00
Gael Guennebaud
54d55aeaf6 fix bug #487: isometry * scaling was not compiling 2012-07-04 18:25:07 +02:00
Konstantinos Margaritis
d878cf2227 fix typo 2012-07-04 11:28:59 +03:00
Konstantinos Margaritis
f737536744 fix NEON port, use vget_lane_*() instead of temporary variables (saves extra
load/store), following advice by  Josh Bleecher Snyder <josharian@gmail.com>.
Also implement pmadd() using vmla instead of nested padd/pmul.
2012-07-04 11:12:02 +03:00
Gael Guennebaud
9a97dac4d9 Doc: add an example for HouseholderQR::householderQ() 2012-07-02 16:33:32 +02:00
Gael Guennebaud
eee34f2da4 workaround compilation issue with MSVC 2005 2012-07-02 10:20:44 +02:00
Desire NUENTSA
15f1563533 Before moving to the new building 2012-06-29 17:45:10 +02:00
Jitse Niesen
746378868a Implement A.noalias() = B * C without temporaries
* Wrap expression inside EvalToTemp in copy_using_evaluators() if we
  assume aliasing for that expression (that is, for products)
* Remove temporary kludge of evaluating expression to temporary in
  AllAtOnce traversal
* Implement EvalToTemp expression object
2012-06-29 13:54:09 +01:00
Jitse Niesen
d0b873822f Make product eval-at-once.
* Make product EvalAtOnce in cases OuterProduct, GemmProduct and
  GemvProduct
* Ensure that product evaluators are nested inside EvalToTemp
  evaluator
* As temporary kludge, evaluate expression to temporary in AllAtOnce
  traversal and pass expression operator to evalTo()
2012-06-29 13:49:25 +01:00
Jitse Niesen
2393ceb380 Implement eval-at-once in evaluator.
- Add evaluator_traits with HasEvalTo flag, which is true if evaluator
  has evalTo() function.
- Add AllAtOnce traversal, which calls evalTo() in evaluator.
- If source evaluator in copy_using_evaluator has HasEvalTo set, then
  use AllAtOnce traversal.
2012-06-29 13:32:12 +01:00
Jitse Niesen
c1eb820e50 Implement interface for NoAlias assignments.
* Rename the old copy_using_evaluators to noalias_copy_using_evaluators.
* Write a new copy_using_evaluators which strips NoAlias expression, if present,
  and calls noalias_copy_using_evaluators; in future, it will also take care of
  aliasing in products.
* Add expression() getter to NoAlias.
2012-06-29 13:24:04 +01:00
Jitse Niesen
069fd0e4be Move (part of) evaluation of products to evaluator objects.
* Copy implementation from CoeffBasedProduct.
* Copy implementation from GeneralProduct in InnerProduct case.
* For GeneralProduct in other cases, call the evalTo() member function with
  expression objects in constructor of evaluator.
2012-06-29 13:07:21 +01:00
Gael Guennebaud
9629ba361a bug #482: pass scalar by const ref - pass on the sparse module
(also fix a compilation issue due to previous pass)
2012-06-28 21:01:02 +02:00
Jitse Niesen
23184527fa Resize lhs automatically in copy_using_evaluator(). 2012-06-28 15:25:25 +01:00
Gael Guennebaud
139c91bf30 fix implicit scalar conversion 2012-06-28 13:12:49 +02:00
Gael Guennebaud
a2ace4b79a bug #482: pass scalar arguments by const references. This changeset only concerns the Core and Geometry modules 2012-06-28 02:08:59 +02:00
Gael Guennebaud
cc964b6caf fix performance regression due to check_rows_cols_for_overflow and add appropriate assertions in the PlainObjectBase::resize() functions.
The fix consists in disabling this useless test for statically allocated objects.
2012-06-26 22:16:07 +02:00
Gael Guennebaud
57b5804974 remove dynamic allocation for fixed size object and triangular matrix-matrix products 2012-06-26 17:45:01 +02:00
Jitse Niesen
8994f9962a Fix bug in {Matrix,Array}Wrapper evaluator 2012-06-24 17:35:27 +01:00
Jitse Niesen
d0d077b212 Fix bug in evaluators with sliced vectorization. 2012-06-24 17:33:21 +01:00
Jitse Niesen
172af9facc Fix an evaluator test which was wrong and failed in debug builds. 2012-06-24 17:31:19 +01:00
Gael Guennebaud
7c32904766 typo 2012-06-24 10:13:28 +02:00
Gael Guennebaud
e46fc8c05c fix GMRES 2012-06-23 19:29:21 +02:00
Gael Guennebaud
cc6dd55028 put the resurected files into the Eigen namespace 2012-06-22 16:35:20 +02:00
Gael Guennebaud
62c504e7bf fix most of the shadow warnings in Core/*.h 2012-06-22 16:32:45 +02:00
Gael Guennebaud
5fae6c7848 resurrect expression evaluators 2012-06-22 09:39:35 +02:00
Gael Guennebaud
81e39e1bc6 bump default branch to 3.1.90 2012-06-22 09:27:24 +02:00
Desire NUENTSA
f0c34c6822 Build finished... start debugging 2012-06-15 17:23:54 +02:00
Desire NUENTSA
0c9b08e46e build complete... almost 2012-06-14 18:45:04 +02:00
Desire NUENTSA
f8a0745cb0 Build process... 2012-06-13 18:26:05 +02:00
Desire NUENTSA
c0ad109499 Checking Data structures and function prototypes 2012-06-12 18:19:59 +02:00
Desire NUENTSA
bccf64d342 Checking Syntax... 2012-06-11 18:52:26 +02:00
Desire NUENTSA W.
0591011d5c Sparse LU - End Triangular solve... start debugging 2012-06-10 23:36:38 +02:00
Desire NUENTSA
7bdaa60f6c triangular solve... almost finished 2012-06-08 17:23:38 +02:00
Desire NUENTSA
f091879d77 Memory management 2012-06-07 19:06:22 +02:00
Desire NUENTSA
268ba3b521 Memory expansion and few bugs 2012-06-06 18:23:39 +02:00
Desire NUENTSA
4e5655cc03 Supernodal Matrix 2012-06-01 18:44:51 +02:00
Desire NUENTSA
b26d6b02de Eliminate and prune columns in a panel 2012-05-31 17:10:29 +02:00
Desire NUENTSA
8608d08d65 Symbolic and numeric updates within the panel 2012-05-30 18:09:26 +02:00
Desire NUENTSA
8ab820b5b8 Symbolic and numeric update on a whole panel 2012-05-29 17:55:38 +02:00
Desire NUENTSA
b6267507ea Add preliminary files for SparseLU 2012-05-25 18:17:57 +02:00
1070 changed files with 117287 additions and 53748 deletions

3
.hgeol
View File

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

View File

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

3
.krazy
View File

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

View File

@@ -1,6 +1,6 @@
project(Eigen)
cmake_minimum_required(VERSION 2.6.2)
cmake_minimum_required(VERSION 2.8.4)
# guard against in-source builds
@@ -105,26 +105,82 @@ if(EIGEN_DEFAULT_TO_ROW_MAJOR)
add_definitions("-DEIGEN_DEFAULT_TO_ROW_MAJOR")
endif()
add_definitions("-DEIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS")
set(EIGEN_TEST_MAX_SIZE "320" CACHE STRING "Maximal matrix/vector size, default is 320")
if(CMAKE_COMPILER_IS_GNUCXX)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wnon-virtual-dtor -Wno-long-long -ansi -Wundef -Wcast-align -Wchar-subscripts -Wall -W -Wpointer-arith -Wwrite-strings -Wformat-security -fexceptions -fno-check-new -fno-common -fstrict-aliasing")
macro(ei_add_cxx_compiler_flag FLAG)
string(REGEX REPLACE "-" "" SFLAG1 ${FLAG})
string(REGEX REPLACE "\\+" "p" SFLAG ${SFLAG1})
check_cxx_compiler_flag(${FLAG} COMPILER_SUPPORT_${SFLAG})
if(COMPILER_SUPPORT_${SFLAG})
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${FLAG}")
endif()
endmacro(ei_add_cxx_compiler_flag)
if(NOT MSVC)
# We assume that other compilers are partly compatible with GNUCC
# set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fexceptions")
set(CMAKE_CXX_FLAGS_DEBUG "-g3")
set(CMAKE_CXX_FLAGS_RELEASE "-g0 -O2")
check_cxx_compiler_flag("-Wno-variadic-macros" COMPILER_SUPPORT_WNOVARIADICMACRO)
if(COMPILER_SUPPORT_WNOVARIADICMACRO)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-variadic-macros")
# clang outputs some warnings for unknwon flags that are not caught by check_cxx_compiler_flag
# adding -Werror turns such warnings into errors
check_cxx_compiler_flag("-Werror" COMPILER_SUPPORT_WERROR)
if(COMPILER_SUPPORT_WERROR)
set(CMAKE_REQUIRED_FLAGS "-Werror")
endif()
ei_add_cxx_compiler_flag("-pedantic")
ei_add_cxx_compiler_flag("-Wall")
ei_add_cxx_compiler_flag("-Wextra")
#ei_add_cxx_compiler_flag("-Weverything") # clang
ei_add_cxx_compiler_flag("-Wundef")
ei_add_cxx_compiler_flag("-Wcast-align")
ei_add_cxx_compiler_flag("-Wchar-subscripts")
ei_add_cxx_compiler_flag("-Wnon-virtual-dtor")
ei_add_cxx_compiler_flag("-Wunused-local-typedefs")
ei_add_cxx_compiler_flag("-Wpointer-arith")
ei_add_cxx_compiler_flag("-Wwrite-strings")
ei_add_cxx_compiler_flag("-Wformat-security")
ei_add_cxx_compiler_flag("-Wshorten-64-to-32")
ei_add_cxx_compiler_flag("-Wenum-conversion")
ei_add_cxx_compiler_flag("-Wc++11-extensions")
# -Wshadow is insanely too strict with gcc, hopefully it will become usable with gcc 6
# if(NOT CMAKE_COMPILER_IS_GNUCXX OR (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "5.0.0"))
if(NOT CMAKE_COMPILER_IS_GNUCXX)
ei_add_cxx_compiler_flag("-Wshadow")
endif()
ei_add_cxx_compiler_flag("-Wno-psabi")
ei_add_cxx_compiler_flag("-Wno-variadic-macros")
ei_add_cxx_compiler_flag("-Wno-long-long")
ei_add_cxx_compiler_flag("-fno-check-new")
ei_add_cxx_compiler_flag("-fno-common")
ei_add_cxx_compiler_flag("-fstrict-aliasing")
ei_add_cxx_compiler_flag("-wd981") # disable ICC's "operands are evaluated in unspecified order" remark
ei_add_cxx_compiler_flag("-wd2304") # disbale ICC's "warning #2304: non-explicit constructor with single argument may cause implicit type conversion" produced by -Wnon-virtual-dtor
# The -ansi flag must be added last, otherwise it is also used as a linker flag by check_cxx_compiler_flag making it fails
# Moreover we should not set both -strict-ansi and -ansi
check_cxx_compiler_flag("-strict-ansi" COMPILER_SUPPORT_STRICTANSI)
ei_add_cxx_compiler_flag("-Qunused-arguments") # disable clang warning: argument unused during compilation: '-ansi'
if(COMPILER_SUPPORT_STRICTANSI)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -strict-ansi")
else()
ei_add_cxx_compiler_flag("-ansi")
endif()
check_cxx_compiler_flag("-Wextra" COMPILER_SUPPORT_WEXTRA)
if(COMPILER_SUPPORT_WEXTRA)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wextra")
if(ANDROID_NDK)
ei_add_cxx_compiler_flag("-pie")
ei_add_cxx_compiler_flag("-fPIE")
endif()
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pedantic")
set(CMAKE_REQUIRED_FLAGS "")
option(EIGEN_TEST_SSE2 "Enable/Disable SSE2 in tests/examples" OFF)
if(EIGEN_TEST_SSE2)
@@ -156,18 +212,49 @@ if(CMAKE_COMPILER_IS_GNUCXX)
message(STATUS "Enabling SSE4.2 in tests/examples")
endif()
option(EIGEN_TEST_AVX "Enable/Disable AVX in tests/examples" OFF)
if(EIGEN_TEST_AVX)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mavx")
message(STATUS "Enabling AVX in tests/examples")
endif()
option(EIGEN_TEST_FMA "Enable/Disable FMA in tests/examples" OFF)
if(EIGEN_TEST_FMA AND NOT EIGEN_TEST_NEON)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mfma")
message(STATUS "Enabling FMA in tests/examples")
endif()
option(EIGEN_TEST_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=softfp")
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 "${CMAKE_CXX_FLAGS}")
message(STATUS "Enabling NEON in tests/examples")
endif()
check_cxx_compiler_flag("-fopenmp" COMPILER_SUPPORT_OPENMP)
if(COMPILER_SUPPORT_OPENMP)
option(EIGEN_TEST_OPENMP "Enable/Disable OpenMP in tests/examples" OFF)
@@ -177,9 +264,8 @@ if(CMAKE_COMPILER_IS_GNUCXX)
endif()
endif()
endif(CMAKE_COMPILER_IS_GNUCXX)
else(NOT MSVC)
if(MSVC)
# C4127 - conditional expression is constant
# C4714 - marked as __forceinline not inlined (I failed to deactivate it selectively)
# We can disable this warning in the unit tests since it is clear that it occurs
@@ -209,7 +295,7 @@ if(MSVC)
endif(NOT CMAKE_CL_64)
message(STATUS "Enabling SSE2 in tests/examples")
endif(EIGEN_TEST_SSE2)
endif(MSVC)
endif(NOT MSVC)
option(EIGEN_TEST_NO_EXPLICIT_VECTORIZATION "Disable explicit vectorization in tests/examples" OFF)
option(EIGEN_TEST_X87 "Force using X87 instructions. Implies no vectorization." OFF)
@@ -245,7 +331,13 @@ if(EIGEN_TEST_NO_EXPLICIT_ALIGNMENT)
message(STATUS "Disabling alignment in tests/examples")
endif()
option(EIGEN_TEST_C++0x "Enables all C++0x features." OFF)
option(EIGEN_TEST_NO_EXCEPTIONS "Disables C++ exceptions" OFF)
if(EIGEN_TEST_NO_EXCEPTIONS)
ei_add_cxx_compiler_flag("-fno-exceptions")
message(STATUS "Disabling exceptions in tests/examples")
endif()
option(EIGEN_TEST_CXX11 "Enable testing with C++11 and C++11 features (e.g. Tensor module)." OFF)
include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
@@ -308,6 +400,7 @@ add_subdirectory(Eigen)
add_subdirectory(doc EXCLUDE_FROM_ALL)
include(EigenConfigureTesting)
# fixme, not sure this line is still needed:
enable_testing() # must be called from the root CMakeLists, see man page
@@ -342,6 +435,8 @@ if(NOT WIN32)
add_subdirectory(bench/spbench EXCLUDE_FROM_ALL)
endif(NOT WIN32)
configure_file(scripts/cdashtesting.cmake.in cdashtesting.cmake @ONLY)
ei_testing_print_summary()
message(STATUS "")
@@ -369,6 +464,7 @@ if(cmake_generator_tolower MATCHES "makefile")
message(STATUS "make check | Build and run the unit-tests. Read this page:")
message(STATUS " | http://eigen.tuxfamily.org/index.php?title=Tests")
message(STATUS "make blas | Build BLAS library (not the same thing as Eigen)")
message(STATUS "make uninstall| Removes files installed by make install")
message(STATUS "--------------+--------------------------------------------------------------")
else()
message(STATUS "To build/run the unit tests, read this page:")
@@ -376,3 +472,35 @@ else()
endif()
message(STATUS "")
set ( EIGEN_CONFIG_CMAKE_PATH
lib${LIB_SUFFIX}/cmake/eigen3
CACHE PATH "The directory where the CMake files are installed"
)
if ( NOT IS_ABSOLUTE EIGEN_CONFIG_CMAKE_PATH )
set ( EIGEN_CONFIG_CMAKE_PATH ${CMAKE_INSTALL_PREFIX}/${EIGEN_CONFIG_CMAKE_PATH} )
endif ()
set ( EIGEN_USE_FILE ${EIGEN_CONFIG_CMAKE_PATH}/UseEigen3.cmake )
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 ${INCLUDE_INSTALL_DIR} )
set ( EIGEN_INCLUDE_DIRS ${EIGEN_INCLUDE_DIR} )
set ( EIGEN_ROOT_DIR ${CMAKE_INSTALL_PREFIX} )
configure_file ( ${CMAKE_CURRENT_SOURCE_DIR}/cmake/Eigen3Config.cmake.in
${CMAKE_CURRENT_BINARY_DIR}/Eigen3Config.cmake
@ONLY ESCAPE_QUOTES
)
install ( FILES ${CMAKE_CURRENT_SOURCE_DIR}/cmake/UseEigen3.cmake
${CMAKE_CURRENT_BINARY_DIR}/Eigen3Config.cmake
DESTINATION ${EIGEN_CONFIG_CMAKE_PATH}
)
# Add uninstall target
add_custom_target ( uninstall
COMMAND ${CMAKE_COMMAND} -P ${CMAKE_CURRENT_SOURCE_DIR}/cmake/EigenUninstall.cmake)

View File

@@ -1,165 +1,502 @@
GNU LESSER GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Version 2.1, February 1999
Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
Copyright (C) 1991, 1999 Free Software Foundation, Inc.
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
[This is the first released version of the Lesser GPL. It also counts
as the successor of the GNU Library Public License, version 2, hence
the version number 2.1.]
This version of the GNU Lesser General Public License incorporates
the terms and conditions of version 3 of the GNU General Public
License, supplemented by the additional permissions listed below.
Preamble
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General Public License, and the "GNU GPL" refers to version 3 of the GNU
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This license, the Lesser General Public License, applies to some
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This library is distributed in the hope that it will be useful,
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You should have received a copy of the GNU Lesser General Public
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Also add information on how to contact you by electronic and paper mail.
You should also get your employer (if you work as a programmer) or your
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necessary. Here is a sample; alter the names:
Yoyodyne, Inc., hereby disclaims all copyright interest in the
library `Frob' (a library for tweaking knobs) written by James Random Hacker.
<signature of Ty Coon>, 1 April 1990
Ty Coon, President of Vice
That's all there is to it!

View File

@@ -5,6 +5,9 @@ Eigen is primarily MPL2 licensed. See COPYING.MPL2 and these links:
Some files contain third-party code under BSD or LGPL licenses, whence the other
COPYING.* files here.
All the LGPL code is either LGPL 2.1-only, or LGPL 2.1-or-later.
For this reason, the COPYING.LGPL file contains the LGPL 2.1 text.
If you want to guarantee that the Eigen code that you are #including is licensed
under the MPL2 and possibly more permissive licenses (like BSD), #define this
preprocessor symbol:

View File

@@ -11,3 +11,7 @@ set(CTEST_DROP_METHOD "http")
set(CTEST_DROP_SITE "manao.inria.fr")
set(CTEST_DROP_LOCATION "/CDash/submit.php?project=Eigen")
set(CTEST_DROP_SITE_CDASH TRUE)
set(CTEST_PROJECT_SUBPROJECTS
Official
Unsupported
)

View File

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

View File

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

View File

@@ -10,16 +10,17 @@
*
*
* 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

View File

@@ -33,12 +33,8 @@ 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

@@ -14,37 +14,85 @@
// first thing Eigen does: stop the compiler from committing suicide
#include "src/Core/util/DisableStupidWarnings.h"
// Handle NVCC/CUDA
#ifdef __CUDACC__
// Do not try asserts on CUDA!
#ifndef EIGEN_NO_DEBUG
#define EIGEN_NO_DEBUG
#endif
#ifdef EIGEN_INTERNAL_DEBUGGING
#undef EIGEN_INTERNAL_DEBUGGING
#endif
// Do not try to vectorize on CUDA!
#ifndef EIGEN_DONT_VECTORIZE
#define EIGEN_DONT_VECTORIZE
#endif
#ifdef EIGEN_EXCEPTIONS
#undef EIGEN_EXCEPTIONS
#endif
// All functions callable from CUDA code must be qualified with __device__
#define EIGEN_DEVICE_FUNC __host__ __device__
#else
#define EIGEN_DEVICE_FUNC
#endif
#if defined(__CUDA_ARCH__)
#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)
#define EIGEN_EXCEPTIONS
#endif
#ifdef EIGEN_EXCEPTIONS
#include <new>
#endif
// then include this file where all our macros are defined. It's really important to do it first because
// it's where we do all the alignment settings (platform detection and honoring the user's will if he
// defined e.g. EIGEN_DONT_ALIGN) so it needs to be done before we do anything with vectorization.
#include "src/Core/util/Macros.h"
// Disable the ipa-cp-clone optimization flag with MinGW 6.x or newer (enabled by default with -O3)
// See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=556 for details.
#if EIGEN_COMP_MINGW && EIGEN_GNUC_AT_LEAST(4,6)
#pragma GCC optimize ("-fno-ipa-cp-clone")
#endif
#include <complex>
// this include file manages BLAS and MKL related macros
// and inclusion of their respective header files
#include "src/Core/util/MKL_support.h"
// if alignment is disabled, then disable vectorization. Note: EIGEN_ALIGN is the proper check, it takes into
// account both the user's will (EIGEN_DONT_ALIGN) and our own platform checks
#if !EIGEN_ALIGN
// if alignment is disabled, then disable vectorization. Note: EIGEN_MAX_ALIGN_BYTES is the proper check, it takes into
// account both the user's will (EIGEN_MAX_ALIGN_BYTES,EIGEN_DONT_ALIGN) and our own platform checks
#if EIGEN_MAX_ALIGN_BYTES==0
#ifndef EIGEN_DONT_VECTORIZE
#define EIGEN_DONT_VECTORIZE
#endif
#endif
#ifdef _MSC_VER
#if EIGEN_COMP_MSVC
#include <malloc.h> // for _aligned_malloc -- need it regardless of whether vectorization is enabled
#if (_MSC_VER >= 1500) // 2008 or later
#if (EIGEN_COMP_MSVC >= 1500) // 2008 or later
// Remember that usage of defined() in a #define is undefined by the standard.
// a user reported that in 64-bit mode, MSVC doesn't care to define _M_IX86_FP.
#if (defined(_M_IX86_FP) && (_M_IX86_FP >= 2)) || defined(_M_X64)
#if (defined(_M_IX86_FP) && (_M_IX86_FP >= 2)) || EIGEN_ARCH_x86_64
#define EIGEN_SSE2_ON_MSVC_2008_OR_LATER
#endif
#endif
#else
// Remember that usage of defined() in a #define is undefined by the standard
#if (defined __SSE2__) && ( (!defined __GNUC__) || EIGEN_GNUC_AT_LEAST(4,2) )
#if (defined __SSE2__) && ( (!EIGEN_COMP_GNUC) || EIGEN_COMP_ICC || EIGEN_GNUC_AT_LEAST(4,2) )
#define EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC
#endif
#endif
@@ -76,6 +124,19 @@
#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
// include files
@@ -87,21 +148,39 @@
// so, to avoid compile errors when windows.h is included after Eigen/Core, ensure intrinsics are extern "C" here too.
// notice that since these are C headers, the extern "C" is theoretically needed anyways.
extern "C" {
#include <emmintrin.h>
#include <xmmintrin.h>
#ifdef EIGEN_VECTORIZE_SSE3
#include <pmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSSE3
#include <tmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSE4_1
#include <smmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSE4_2
#include <nmmintrin.h>
// In theory we should only include immintrin.h and not the other *mmintrin.h header files directly.
// Doing so triggers some issues with ICC. However old gcc versions seems to not have this file, thus:
#if EIGEN_COMP_ICC >= 1110
#include <immintrin.h>
#else
#include <emmintrin.h>
#include <xmmintrin.h>
#ifdef EIGEN_VECTORIZE_SSE3
#include <pmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSSE3
#include <tmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSE4_1
#include <smmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSE4_2
#include <nmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_AVX
#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
@@ -111,13 +190,18 @@
#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>
#endif
#endif
#if defined __CUDACC__
#define EIGEN_VECTORIZE_CUDA
#include <vector_types.h>
#endif
#if (defined _OPENMP) && (!defined EIGEN_DONT_PARALLELIZE)
#define EIGEN_HAS_OPENMP
#endif
@@ -127,7 +211,7 @@
#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
@@ -153,23 +237,17 @@
#endif
// required for __cpuid, needs to be included after cmath
#if defined(_MSC_VER) && (defined(_M_IX86)||defined(_M_X64))
#if EIGEN_COMP_MSVC && EIGEN_ARCH_i386_OR_x86_64 && !EIGEN_OS_WINCE
#include <intrin.h>
#endif
#if defined(_CPPUNWIND) || defined(__EXCEPTIONS)
#define EIGEN_EXCEPTIONS
#endif
#ifdef EIGEN_EXCEPTIONS
#include <new>
#endif
/** \brief Namespace containing all symbols from the %Eigen library. */
namespace Eigen {
inline static const char *SimdInstructionSetsInUse(void) {
#if defined(EIGEN_VECTORIZE_SSE4_2)
#if defined(EIGEN_VECTORIZE_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";
@@ -181,6 +259,8 @@ 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";
#else
@@ -190,34 +270,9 @@ 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
@@ -236,50 +291,75 @@ using std::ptrdiff_t;
* \endcode
*/
/** \defgroup Support_modules Support modules [category]
* Category of modules which add support for external libraries.
*/
#include "src/Core/util/Constants.h"
#include "src/Core/util/ForwardDeclarations.h"
#include "src/Core/util/Meta.h"
#include "src/Core/util/XprHelper.h"
#include "src/Core/util/ForwardDeclarations.h"
#include "src/Core/util/StaticAssert.h"
#include "src/Core/util/XprHelper.h"
#include "src/Core/util/Memory.h"
#include "src/Core/NumTraits.h"
#include "src/Core/MathFunctions.h"
#include "src/Core/GenericPacketMath.h"
#if defined EIGEN_VECTORIZE_SSE
#if 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"
#endif
#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/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"
#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"
@@ -292,9 +372,10 @@ using std::ptrdiff_t;
#include "src/Core/SelfCwiseBinaryOp.h"
#include "src/Core/Dot.h"
#include "src/Core/StableNorm.h"
#include "src/Core/MapBase.h"
#include "src/Core/Stride.h"
#include "src/Core/MapBase.h"
#include "src/Core/Map.h"
#include "src/Core/Ref.h"
#include "src/Core/Block.h"
#include "src/Core/VectorBlock.h"
#include "src/Core/Transpose.h"
@@ -309,14 +390,14 @@ using std::ptrdiff_t;
#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/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"
@@ -330,6 +411,7 @@ using std::ptrdiff_t;
#include "src/Core/products/TriangularSolverMatrix.h"
#include "src/Core/products/TriangularSolverVector.h"
#include "src/Core/BandMatrix.h"
#include "src/Core/CoreIterators.h"
#include "src/Core/BooleanRedux.h"
#include "src/Core/Select.h"
@@ -337,7 +419,6 @@ 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
@@ -359,8 +440,4 @@ using std::ptrdiff_t;
#include "src/Core/util/ReenableStupidWarnings.h"
#ifdef EIGEN2_SUPPORT
#include "Eigen2Support"
#endif
#endif // EIGEN_CORE_H

View File

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

View File

@@ -1,82 +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
#include "src/Core/util/DisableStupidWarnings.h"
/** \ingroup Support_modules
* \defgroup Eigen2Support_Module Eigen2 support module
* This module provides a couple of deprecated functions improving the compatibility with Eigen2.
*
* To use it, define EIGEN2_SUPPORT before including any Eigen header
* \code
* #define EIGEN2_SUPPORT
* \endcode
*
*/
#include "src/Eigen2Support/Macros.h"
#include "src/Eigen2Support/Memory.h"
#include "src/Eigen2Support/Meta.h"
#include "src/Eigen2Support/Lazy.h"
#include "src/Eigen2Support/Cwise.h"
#include "src/Eigen2Support/CwiseOperators.h"
#include "src/Eigen2Support/TriangularSolver.h"
#include "src/Eigen2Support/Block.h"
#include "src/Eigen2Support/VectorBlock.h"
#include "src/Eigen2Support/Minor.h"
#include "src/Eigen2Support/MathFunctions.h"
#include "src/Core/util/ReenableStupidWarnings.h"
// Eigen2 used to include iostream
#include<iostream>
#define EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, SizeSuffix) \
using Eigen::Matrix##SizeSuffix##TypeSuffix; \
using Eigen::Vector##SizeSuffix##TypeSuffix; \
using Eigen::RowVector##SizeSuffix##TypeSuffix;
#define EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(TypeSuffix) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 2) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 3) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 4) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X) \
#define EIGEN_USING_MATRIX_TYPEDEFS \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(i) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(f) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(d) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(cf) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(cd)
#define USING_PART_OF_NAMESPACE_EIGEN \
EIGEN_USING_MATRIX_TYPEDEFS \
using Eigen::Matrix; \
using Eigen::MatrixBase; \
using Eigen::ei_random; \
using Eigen::ei_real; \
using Eigen::ei_imag; \
using Eigen::ei_conj; \
using Eigen::ei_abs; \
using Eigen::ei_abs2; \
using Eigen::ei_sqrt; \
using Eigen::ei_exp; \
using Eigen::ei_log; \
using Eigen::ei_sin; \
using Eigen::ei_cos;
#endif // EIGEN2SUPPORT_H

View File

@@ -33,6 +33,8 @@
#include "src/Eigenvalues/HessenbergDecomposition.h"
#include "src/Eigenvalues/ComplexSchur.h"
#include "src/Eigenvalues/ComplexEigenSolver.h"
#include "src/Eigenvalues/RealQZ.h"
#include "src/Eigenvalues/GeneralizedEigenSolver.h"
#include "src/Eigenvalues/MatrixBaseEigenvalues.h"
#ifdef EIGEN_USE_LAPACKE
#include "src/Eigenvalues/RealSchur_MKL.h"

View File

@@ -9,10 +9,6 @@
#include "LU"
#include <limits>
#ifndef M_PI
#define M_PI 3.14159265358979323846
#endif
/** \defgroup Geometry_Module Geometry module
*
*
@@ -33,27 +29,23 @@
#include "src/Geometry/OrthoMethods.h"
#include "src/Geometry/EulerAngles.h"
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
#include "src/Geometry/Homogeneous.h"
#include "src/Geometry/RotationBase.h"
#include "src/Geometry/Rotation2D.h"
#include "src/Geometry/Quaternion.h"
#include "src/Geometry/AngleAxis.h"
#include "src/Geometry/Transform.h"
#include "src/Geometry/Translation.h"
#include "src/Geometry/Scaling.h"
#include "src/Geometry/Hyperplane.h"
#include "src/Geometry/ParametrizedLine.h"
#include "src/Geometry/AlignedBox.h"
#include "src/Geometry/Umeyama.h"
#include "src/Geometry/Homogeneous.h"
#include "src/Geometry/RotationBase.h"
#include "src/Geometry/Rotation2D.h"
#include "src/Geometry/Quaternion.h"
#include "src/Geometry/AngleAxis.h"
#include "src/Geometry/Transform.h"
#include "src/Geometry/Translation.h"
#include "src/Geometry/Scaling.h"
#include "src/Geometry/Hyperplane.h"
#include "src/Geometry/ParametrizedLine.h"
#include "src/Geometry/AlignedBox.h"
#include "src/Geometry/Umeyama.h"
#if defined EIGEN_VECTORIZE_SSE
#include "src/Geometry/arch/Geometry_SSE.h"
#endif
#endif
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/Geometry/All.h"
// Use the SSE optimized version whenever possible. At the moment the
// SSE version doesn't compile when AVX is enabled
#if defined EIGEN_VECTORIZE_SSE && !defined EIGEN_VECTORIZE_AVX
#include "src/Geometry/arch/Geometry_SSE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"

View File

@@ -6,32 +6,32 @@
#include "src/Core/util/DisableStupidWarnings.h"
/** \ingroup Sparse_modules
/**
* \defgroup IterativeLinearSolvers_Module IterativeLinearSolvers module
*
* This module currently provides iterative methods to solve problems of the form \c A \c x = \c b, where \c A is a squared matrix, usually very large and sparse.
* Those solvers are accessible via the following classes:
* - ConjugateGradient for selfadjoint (hermitian) matrices,
* - LeastSquaresConjugateGradient for rectangular least-square problems,
* - BiCGSTAB for general square matrices.
*
* These iterative solvers are associated with some preconditioners:
* - IdentityPreconditioner - not really useful
* - DiagonalPreconditioner - also called JAcobi preconditioner, work very well on diagonal dominant matrices.
* - IncompleteILUT - incomplete LU factorization with dual thresholding
* - DiagonalPreconditioner - also called Jacobi preconditioner, work very well on diagonal dominant matrices.
* - IncompleteLUT - incomplete LU factorization with dual thresholding
*
* Such problems can also be solved using the direct sparse decomposition modules: SparseCholesky, CholmodSupport, UmfPackSupport, SuperLUSupport.
*
* \code
* #include <Eigen/IterativeLinearSolvers>
* \endcode
\code
#include <Eigen/IterativeLinearSolvers>
\endcode
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/IterativeLinearSolvers/SolveWithGuess.h"
#include "src/IterativeLinearSolvers/IterativeSolverBase.h"
#include "src/IterativeLinearSolvers/BasicPreconditioners.h"
#include "src/IterativeLinearSolvers/ConjugateGradient.h"
#include "src/IterativeLinearSolvers/LeastSquareConjugateGradient.h"
#include "src/IterativeLinearSolvers/BiCGSTAB.h"
#include "src/IterativeLinearSolvers/IncompleteLUT.h"

View File

@@ -16,7 +16,6 @@
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/misc/Kernel.h"
#include "src/misc/Image.h"
#include "src/LU/FullPivLU.h"
@@ -25,16 +24,14 @@
#include "src/LU/PartialPivLU_MKL.h"
#endif
#include "src/LU/Determinant.h"
#include "src/LU/Inverse.h"
#include "src/LU/InverseImpl.h"
#if defined EIGEN_VECTORIZE_SSE
// Use the SSE optimized version whenever possible. At the moment the
// SSE version doesn't compile when AVX is enabled
#if defined EIGEN_VECTORIZE_SSE && !defined EIGEN_VECTORIZE_AVX
#include "src/LU/arch/Inverse_SSE.h"
#endif
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/LU.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_LU_MODULE_H

View File

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

28
Eigen/MetisSupport Normal file
View File

@@ -0,0 +1,28 @@
#ifndef EIGEN_METISSUPPORT_MODULE_H
#define EIGEN_METISSUPPORT_MODULE_H
#include "SparseCore"
#include "src/Core/util/DisableStupidWarnings.h"
extern "C" {
#include <metis.h>
}
/** \ingroup Support_modules
* \defgroup MetisSupport_Module MetisSupport module
*
* \code
* #include <Eigen/MetisSupport>
* \endcode
* This module defines an interface to the METIS reordering package (http://glaros.dtc.umn.edu/gkhome/views/metis).
* It can be used just as any other built-in method as explained in \link OrderingMethods_Module here. \endlink
*/
#include "src/MetisSupport/MetisSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_METISSUPPORT_MODULE_H

View File

@@ -5,19 +5,62 @@
#include "src/Core/util/DisableStupidWarnings.h"
/** \ingroup Sparse_modules
/**
* \defgroup OrderingMethods_Module OrderingMethods module
*
* This module is currently for internal use only.
*
*
* This module is currently for internal use only
*
* It defines various built-in and external ordering methods for sparse matrices.
* They are typically used to reduce the number of elements during
* the sparse matrix decomposition (LLT, LU, QR).
* Precisely, in a preprocessing step, a permutation matrix P is computed using
* those ordering methods and applied to the columns of the matrix.
* Using for instance the sparse Cholesky decomposition, it is expected that
* the nonzeros elements in LLT(A*P) will be much smaller than that in LLT(A).
*
*
* Usage :
* \code
* #include <Eigen/OrderingMethods>
* \endcode
*
* A simple usage is as a template parameter in the sparse decomposition classes :
*
* \code
* SparseLU<MatrixType, COLAMDOrdering<int> > solver;
* \endcode
*
* \code
* SparseQR<MatrixType, COLAMDOrdering<int> > solver;
* \endcode
*
* It is possible as well to call directly a particular ordering method for your own purpose,
* \code
* AMDOrdering<int> ordering;
* PermutationMatrix<Dynamic, Dynamic, int> perm;
* SparseMatrix<double> A;
* //Fill the matrix ...
*
* ordering(A, perm); // Call AMD
* \endcode
*
* \note Some of these methods (like AMD or METIS), need the sparsity pattern
* of the input matrix to be symmetric. When the matrix is structurally unsymmetric,
* Eigen computes internally the pattern of \f$A^T*A\f$ before calling the method.
* If your matrix is already symmetric (at leat in structure), you can avoid that
* by calling the method with a SelfAdjointView type.
*
* \code
* // Call the ordering on the pattern of the lower triangular matrix A
* ordering(A.selfadjointView<Lower>(), perm);
* \endcode
*/
#ifndef EIGEN_MPL2_ONLY
#include "src/OrderingMethods/Amd.h"
#endif
#include "src/OrderingMethods/Ordering.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_ORDERINGMETHODS_MODULE_H

View File

@@ -35,12 +35,8 @@ 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

View File

@@ -15,14 +15,15 @@
*
* 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"
@@ -31,15 +32,7 @@
#include "src/QR/ColPivHouseholderQR_MKL.h"
#endif
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/QR.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: */

27
Eigen/SPQRSupport Normal file
View File

@@ -0,0 +1,27 @@
#ifndef EIGEN_SPQRSUPPORT_MODULE_H
#define EIGEN_SPQRSUPPORT_MODULE_H
#include "SparseCore"
#include "src/Core/util/DisableStupidWarnings.h"
#include "SuiteSparseQR.hpp"
/** \ingroup Support_modules
* \defgroup SPQRSupport_Module SuiteSparseQR module
*
* This module provides an interface to the SPQR library, which is part of the <a href="http://www.cise.ufl.edu/research/sparse/SuiteSparse/">suitesparse</a> package.
*
* \code
* #include <Eigen/SPQRSupport>
* \endcode
*
* In order to use this module, the SPQR headers must be accessible from the include paths, and your binary must be linked to the SPQR library and its dependencies (Cholmod, AMD, COLAMD,...).
* For a cmake based project, you can use our FindSPQR.cmake and FindCholmod.Cmake modules
*
*/
#include "src/CholmodSupport/CholmodSupport.h"
#include "src/SPQRSupport/SuiteSparseQRSupport.h"
#endif

View File

@@ -12,24 +12,25 @@
*
*
* 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/UpperBidiagonalization.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/SVD/JacobiSVD_MKL.h"
#endif
#include "src/SVD/UpperBidiagonalization.h"
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/SVD.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"

View File

@@ -1,22 +1,26 @@
#ifndef EIGEN_SPARSE_MODULE_H
#define EIGEN_SPARSE_MODULE_H
/** \defgroup Sparse_modules Sparse modules
/** \defgroup Sparse_Module Sparse meta-module
*
* Meta-module including all related modules:
* - SparseCore
* - OrderingMethods
* - SparseCholesky
* - IterativeLinearSolvers
* - \ref SparseCore_Module
* - \ref OrderingMethods_Module
* - \ref SparseCholesky_Module
* - \ref SparseLU_Module
* - \ref SparseQR_Module
* - \ref IterativeLinearSolvers_Module
*
* \code
* #include <Eigen/Sparse>
* \endcode
\code
#include <Eigen/Sparse>
\endcode
*/
#include "SparseCore"
#include "OrderingMethods"
#include "SparseCholesky"
#include "SparseLU"
#include "SparseQR"
#include "IterativeLinearSolvers"
#endif // EIGEN_SPARSE_MODULE_H

View File

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

View File

@@ -11,7 +11,7 @@
#include <cstring>
#include <algorithm>
/** \ingroup Sparse_modules
/**
* \defgroup SparseCore_Module SparseCore module
*
* This module provides a sparse matrix representation, and basic associatd matrix manipulations
@@ -26,39 +26,35 @@
* This module depends on: Core.
*/
namespace Eigen {
/** The type used to identify a general sparse storage. */
struct Sparse {};
}
#include "src/SparseCore/SparseUtil.h"
#include "src/SparseCore/SparseMatrixBase.h"
#include "src/SparseCore/SparseAssign.h"
#include "src/SparseCore/CompressedStorage.h"
#include "src/SparseCore/AmbiVector.h"
#include "src/SparseCore/SparseCompressedBase.h"
#include "src/SparseCore/SparseMatrix.h"
#include "src/SparseCore/SparseMap.h"
#include "src/SparseCore/MappedSparseMatrix.h"
#include "src/SparseCore/SparseVector.h"
#include "src/SparseCore/CoreIterators.h"
#include "src/SparseCore/SparseBlock.h"
#include "src/SparseCore/SparseTranspose.h"
#include "src/SparseCore/SparseRef.h"
#include "src/SparseCore/SparseCwiseUnaryOp.h"
#include "src/SparseCore/SparseCwiseBinaryOp.h"
#include "src/SparseCore/SparseTranspose.h"
#include "src/SparseCore/SparseBlock.h"
#include "src/SparseCore/SparseDot.h"
#include "src/SparseCore/SparsePermutation.h"
#include "src/SparseCore/SparseAssign.h"
#include "src/SparseCore/SparseRedux.h"
#include "src/SparseCore/SparseFuzzy.h"
#include "src/SparseCore/SparseView.h"
#include "src/SparseCore/SparseDiagonalProduct.h"
#include "src/SparseCore/ConservativeSparseSparseProduct.h"
#include "src/SparseCore/SparseSparseProductWithPruning.h"
#include "src/SparseCore/SparseProduct.h"
#include "src/SparseCore/SparseDenseProduct.h"
#include "src/SparseCore/SparseDiagonalProduct.h"
#include "src/SparseCore/SparseTriangularView.h"
#include "src/SparseCore/SparseSelfAdjointView.h"
#include "src/SparseCore/SparseTriangularView.h"
#include "src/SparseCore/TriangularSolver.h"
#include "src/SparseCore/SparseView.h"
#include "src/SparseCore/SparsePermutation.h"
#include "src/SparseCore/SparseFuzzy.h"
#include "src/SparseCore/SparseSolverBase.h"
#include "src/Core/util/ReenableStupidWarnings.h"

46
Eigen/SparseLU Normal file
View File

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

30
Eigen/SparseQR Normal file
View File

@@ -0,0 +1,30 @@
#ifndef EIGEN_SPARSEQR_MODULE_H
#define EIGEN_SPARSEQR_MODULE_H
#include "SparseCore"
#include "OrderingMethods"
#include "src/Core/util/DisableStupidWarnings.h"
/** \defgroup SparseQR_Module SparseQR module
* \brief Provides QR decomposition for sparse matrices
*
* This module provides a simplicial version of the left-looking Sparse QR decomposition.
* The columns of the input matrix should be reordered to limit the fill-in during the
* decomposition. Built-in methods (COLAMD, AMD) or external methods (METIS) can be used to this end.
* See the \link OrderingMethods_Module OrderingMethods\endlink module for the list
* of built-in and external ordering methods.
*
* \code
* #include <Eigen/SparseQR>
* \endcode
*
*
*/
#include "OrderingMethods"
#include "src/SparseCore/SparseColEtree.h"
#include "src/SparseQR/SparseQR.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif

View File

@@ -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(...)

View File

@@ -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

@@ -48,12 +48,8 @@ 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

@@ -26,9 +26,6 @@ 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

@@ -16,7 +16,10 @@
namespace Eigen {
namespace internal {
template<typename MatrixType, int UpLo> struct LDLT_Traits;
template<typename MatrixType, int UpLo> struct LDLT_Traits;
// PositiveSemiDef means positive semi-definite and non-zero; same for NegativeSemiDef
enum SignMatrix { PositiveSemiDef, NegativeSemiDef, ZeroSign, Indefinite };
}
/** \ingroup Cholesky_Module
@@ -40,7 +43,7 @@ template<typename MatrixType, int UpLo> struct LDLT_Traits;
* Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky
* decomposition to determine whether a system of equations has a solution.
*
* \sa MatrixBase::ldlt(), class LLT
* \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT
*/
template<typename _MatrixType, int _UpLo> class LDLT
{
@@ -56,7 +59,8 @@ template<typename _MatrixType, int _UpLo> class LDLT
};
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;
typedef Matrix<Scalar, RowsAtCompileTime, 1, Options, MaxRowsAtCompileTime, 1> TmpMatrixType;
typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
@@ -69,7 +73,12 @@ template<typename _MatrixType, int _UpLo> class LDLT
* The default constructor is useful in cases in which the user intends to
* perform decompositions via LDLT::compute(const MatrixType&).
*/
LDLT() : m_matrix(), m_transpositions(), m_isInitialized(false) {}
LDLT()
: m_matrix(),
m_transpositions(),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{}
/** \brief Default Constructor with memory preallocation
*
@@ -77,10 +86,11 @@ template<typename _MatrixType, int _UpLo> class LDLT
* according to the specified problem \a size.
* \sa LDLT()
*/
LDLT(Index size)
explicit LDLT(Index size)
: m_matrix(size, size),
m_transpositions(size),
m_temporary(size),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{}
@@ -89,10 +99,11 @@ template<typename _MatrixType, int _UpLo> class LDLT
* This calculates the decomposition for the input \a matrix.
* \sa LDLT(Index size)
*/
LDLT(const MatrixType& matrix)
explicit LDLT(const MatrixType& matrix)
: m_matrix(matrix.rows(), matrix.cols()),
m_transpositions(matrix.rows()),
m_temporary(matrix.rows()),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{
compute(matrix);
@@ -139,21 +150,14 @@ template<typename _MatrixType, int _UpLo> class LDLT
inline bool isPositive() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_sign == 1;
return m_sign == internal::PositiveSemiDef || m_sign == internal::ZeroSign;
}
#ifdef EIGEN2_SUPPORT
inline bool isPositiveDefinite() const
{
return isPositive();
}
#endif
/** \returns true if the matrix is negative (semidefinite) */
inline bool isNegative(void) const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_sign == -1;
return m_sign == internal::NegativeSemiDef || m_sign == internal::ZeroSign;
}
/** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
@@ -169,34 +173,25 @@ template<typename _MatrixType, int _UpLo> class LDLT
* 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 Derived>
LDLT& rankUpdate(const MatrixBase<Derived>& w,RealScalar alpha=1);
LDLT& rankUpdate(const MatrixBase<Derived>& w, const RealScalar& alpha=1);
/** \returns the internal LDLT decomposition matrix
*
@@ -223,8 +218,19 @@ template<typename _MatrixType, int _UpLo> class LDLT
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return Success;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename RhsType, typename DstType>
EIGEN_DEVICE_FUNC
void _solve_impl(const RhsType &rhs, DstType &dst) const;
#endif
protected:
static void check_template_parameters()
{
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
}
/** \internal
* Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U.
@@ -235,7 +241,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
MatrixType m_matrix;
TranspositionType m_transpositions;
TmpMatrixType m_temporary;
int m_sign;
internal::SignMatrix m_sign;
bool m_isInitialized;
};
@@ -246,50 +252,32 @@ template<int UpLo> struct ldlt_inplace;
template<> struct ldlt_inplace<Lower>
{
template<typename MatrixType, typename TranspositionType, typename Workspace>
static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, int* sign=0)
static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign)
{
using std::abs;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
typedef typename TranspositionType::StorageIndex IndexType;
eigen_assert(mat.rows()==mat.cols());
const Index size = mat.rows();
if (size <= 1)
{
transpositions.setIdentity();
if(sign)
*sign = real(mat.coeff(0,0))>0 ? 1:-1;
if (numext::real(mat.coeff(0,0)) > 0) sign = PositiveSemiDef;
else if (numext::real(mat.coeff(0,0)) < 0) sign = NegativeSemiDef;
else sign = ZeroSign;
return true;
}
RealScalar cutoff(0), biggest_in_corner;
for (Index k = 0; k < size; ++k)
{
// Find largest diagonal element
Index index_of_biggest_in_corner;
biggest_in_corner = mat.diagonal().tail(size-k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
mat.diagonal().tail(size-k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
index_of_biggest_in_corner += k;
if(k == 0)
{
// The biggest overall is the point of reference to which further diagonals
// are compared; if any diagonal is negligible compared
// to the largest overall, the algorithm bails.
cutoff = abs(NumTraits<Scalar>::epsilon() * biggest_in_corner);
if(sign)
*sign = real(mat.diagonal().coeff(index_of_biggest_in_corner)) > 0 ? 1 : -1;
}
// Finish early if the matrix is not full rank.
if(biggest_in_corner < cutoff)
{
for(Index i = k; i < size; i++) transpositions.coeffRef(i) = i;
break;
}
transpositions.coeffRef(k) = index_of_biggest_in_corner;
transpositions.coeffRef(k) = IndexType(index_of_biggest_in_corner);
if(k != index_of_biggest_in_corner)
{
// apply the transposition while taking care to consider only
@@ -298,14 +286,14 @@ template<> struct ldlt_inplace<Lower>
mat.row(k).head(k).swap(mat.row(index_of_biggest_in_corner).head(k));
mat.col(k).tail(s).swap(mat.col(index_of_biggest_in_corner).tail(s));
std::swap(mat.coeffRef(k,k),mat.coeffRef(index_of_biggest_in_corner,index_of_biggest_in_corner));
for(int i=k+1;i<index_of_biggest_in_corner;++i)
for(Index i=k+1;i<index_of_biggest_in_corner;++i)
{
Scalar tmp = mat.coeffRef(i,k);
mat.coeffRef(i,k) = conj(mat.coeffRef(index_of_biggest_in_corner,i));
mat.coeffRef(index_of_biggest_in_corner,i) = conj(tmp);
mat.coeffRef(i,k) = numext::conj(mat.coeffRef(index_of_biggest_in_corner,i));
mat.coeffRef(index_of_biggest_in_corner,i) = numext::conj(tmp);
}
if(NumTraits<Scalar>::IsComplex)
mat.coeffRef(index_of_biggest_in_corner,k) = conj(mat.coeff(index_of_biggest_in_corner,k));
mat.coeffRef(index_of_biggest_in_corner,k) = numext::conj(mat.coeff(index_of_biggest_in_corner,k));
}
// partition the matrix:
@@ -319,13 +307,28 @@ template<> struct ldlt_inplace<Lower>
if(k>0)
{
temp.head(k) = mat.diagonal().head(k).asDiagonal() * A10.adjoint();
temp.head(k) = mat.diagonal().real().head(k).asDiagonal() * A10.adjoint();
mat.coeffRef(k,k) -= (A10 * temp.head(k)).value();
if(rs>0)
A21.noalias() -= A20 * temp.head(k);
}
if((rs>0) && (abs(mat.coeffRef(k,k)) > cutoff))
A21 /= mat.coeffRef(k,k);
// In some previous versions of Eigen (e.g., 3.2.1), the scaling was omitted if the pivot
// was smaller than the cutoff value. However, since LDLT is not rank-revealing
// we should only make sure that we do not introduce INF or NaN values.
// Remark that LAPACK also uses 0 as the cutoff value.
RealScalar realAkk = numext::real(mat.coeffRef(k,k));
if((rs>0) && (abs(realAkk) > RealScalar(0)))
A21 /= realAkk;
if (sign == PositiveSemiDef) {
if (realAkk < 0) sign = Indefinite;
} else if (sign == NegativeSemiDef) {
if (realAkk > 0) sign = Indefinite;
} else if (sign == ZeroSign) {
if (realAkk > 0) sign = PositiveSemiDef;
else if (realAkk < 0) sign = NegativeSemiDef;
}
}
return true;
@@ -339,12 +342,11 @@ template<> struct ldlt_inplace<Lower>
// Here only rank-1 updates are implemented, to reduce the
// requirement for intermediate storage and improve accuracy
template<typename MatrixType, typename WDerived>
static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w, typename MatrixType::RealScalar sigma=1)
static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w, const typename MatrixType::RealScalar& sigma=1)
{
using internal::isfinite;
using numext::isfinite;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
const Index size = mat.rows();
eigen_assert(mat.cols() == size && w.size()==size);
@@ -359,9 +361,9 @@ template<> struct ldlt_inplace<Lower>
break;
// Update the diagonal terms
RealScalar dj = real(mat.coeff(j,j));
RealScalar dj = numext::real(mat.coeff(j,j));
Scalar wj = w.coeff(j);
RealScalar swj2 = sigma*abs2(wj);
RealScalar swj2 = sigma*numext::abs2(wj);
RealScalar gamma = dj*alpha + swj2;
mat.coeffRef(j,j) += swj2/alpha;
@@ -372,13 +374,13 @@ template<> struct ldlt_inplace<Lower>
Index rs = size-j-1;
w.tail(rs) -= wj * mat.col(j).tail(rs);
if(gamma != 0)
mat.col(j).tail(rs) += (sigma*conj(wj)/gamma)*w.tail(rs);
mat.col(j).tail(rs) += (sigma*numext::conj(wj)/gamma)*w.tail(rs);
}
return true;
}
template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w, typename MatrixType::RealScalar sigma=1)
static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w, const typename MatrixType::RealScalar& sigma=1)
{
// Apply the permutation to the input w
tmp = transpositions * w;
@@ -390,14 +392,14 @@ template<> struct ldlt_inplace<Lower>
template<> struct ldlt_inplace<Upper>
{
template<typename MatrixType, typename TranspositionType, typename Workspace>
static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, int* sign=0)
static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign)
{
Transpose<MatrixType> matt(mat);
return ldlt_inplace<Lower>::unblocked(matt, transpositions, temp, sign);
}
template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w, typename MatrixType::RealScalar sigma=1)
static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w, const typename MatrixType::RealScalar& sigma=1)
{
Transpose<MatrixType> matt(mat);
return ldlt_inplace<Lower>::update(matt, transpositions, tmp, w.conjugate(), sigma);
@@ -408,16 +410,16 @@ 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
@@ -427,6 +429,8 @@ template<typename MatrixType> struct LDLT_Traits<MatrixType,Upper>
template<typename MatrixType, int _UpLo>
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const MatrixType& a)
{
check_template_parameters();
eigen_assert(a.rows()==a.cols());
const Index size = a.rows();
@@ -435,8 +439,9 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const MatrixType& a)
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);
internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign);
m_isInitialized = true;
return *this;
@@ -449,8 +454,9 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const MatrixType& a)
*/
template<typename MatrixType, int _UpLo>
template<typename Derived>
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Derived>& w,typename NumTraits<typename MatrixType::Scalar>::Real sigma)
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Derived>& w, const typename LDLT<MatrixType,_UpLo>::RealScalar& sigma)
{
typedef typename TranspositionType::StorageIndex IndexType;
const Index size = w.rows();
if (m_isInitialized)
{
@@ -462,9 +468,9 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Deri
m_matrix.setZero();
m_transpositions.resize(size);
for (Index i = 0; i < size; i++)
m_transpositions.coeffRef(i) = i;
m_transpositions.coeffRef(i) = IndexType(i);
m_temporary.resize(size);
m_sign = sigma>=0 ? 1 : -1;
m_sign = sigma>=0 ? internal::PositiveSemiDef : internal::NegativeSemiDef;
m_isInitialized = true;
}
@@ -473,48 +479,45 @@ 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::Scalar Scalar;
typedef typename LDLTType::RealScalar RealScalar;
const Diagonal<const MatrixType> vectorD = dec().vectorD();
RealScalar tolerance = (max)(vectorD.array().abs().maxCoeff() * NumTraits<Scalar>::epsilon(),
RealScalar(1) / NumTraits<RealScalar>::highest()); // motivated by LAPACK's xGELSS
for (Index i = 0; i < vectorD.size(); ++i) {
if(abs(vectorD(i)) > tolerance)
dst.row(i) /= vectorD(i);
else
dst.row(i).setZero();
}
// dst = L^-T (D^-1 L^-1 P b)
dec().matrixU().solveInPlace(dst);
// dst = P^-1 (L^-T D^-1 L^-1 P b) = A^-1 b
dst = dec().transpositionsP().transpose() * dst;
if(abs(vecD(i)) > tolerance)
dst.row(i) /= vecD(i);
else
dst.row(i).setZero();
}
};
// dst = L^-T (D^-1 L^-1 P b)
matrixU().solveInPlace(dst);
// dst = P^-1 (L^-T D^-1 L^-1 P b) = A^-1 b
dst = m_transpositions.transpose() * dst;
}
#endif
/** \internal use x = ldlt_object.solve(x);
*
@@ -534,8 +537,7 @@ template<typename Derived>
bool LDLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
const Index size = m_matrix.rows();
eigen_assert(size == bAndX.rows());
eigen_assert(m_matrix.rows() == bAndX.rows());
bAndX = this->solve(bAndX);
@@ -558,7 +560,7 @@ MatrixType LDLT<MatrixType,_UpLo>::reconstructedMatrix() const
// L^* P
res = matrixU() * res;
// D(L^*P)
res = vectorD().asDiagonal() * res;
res = vectorD().real().asDiagonal() * res;
// L(DL^*P)
res = matrixL() * res;
// P^T (LDL^*P)
@@ -567,8 +569,10 @@ MatrixType LDLT<MatrixType,_UpLo>::reconstructedMatrix() const
return res;
}
#ifndef __CUDACC__
/** \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>
@@ -579,6 +583,7 @@ 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>
@@ -586,6 +591,7 @@ MatrixBase<Derived>::ldlt() const
{
return LDLT<PlainObject>(derived());
}
#endif // __CUDACC__
} // end namespace Eigen

View File

@@ -41,7 +41,7 @@ template<typename MatrixType, int UpLo> struct LLT_Traits;
* Example: \include LLT_example.cpp
* Output: \verbinclude LLT_example.out
*
* \sa MatrixBase::llt(), class LDLT
* \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,
@@ -59,7 +59,8 @@ template<typename _MatrixType, int _UpLo> class LLT
};
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,
@@ -83,10 +84,10 @@ 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)
explicit LLT(const MatrixType& matrix)
: m_matrix(matrix.rows(), matrix.cols()),
m_isInitialized(false)
{
@@ -115,29 +116,18 @@ 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;
@@ -172,8 +162,20 @@ template<typename _MatrixType, int _UpLo> class LLT
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.
@@ -188,18 +190,18 @@ namespace internal {
template<typename Scalar, int UpLo> struct llt_inplace;
template<typename MatrixType, typename VectorType>
static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma)
static Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma)
{
using std::sqrt;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
typedef typename MatrixType::ColXpr ColXpr;
typedef typename internal::remove_all<ColXpr>::type ColXprCleaned;
typedef typename ColXprCleaned::SegmentReturnType ColXprSegment;
typedef Matrix<Scalar,Dynamic,1> TempVectorType;
typedef typename TempVectorType::SegmentReturnType TempVecSegment;
int n = mat.cols();
Index n = mat.cols();
eigen_assert(mat.rows()==n && vec.size()==n);
TempVectorType temp;
@@ -211,12 +213,12 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
// i.e., for sigma > 0
temp = sqrt(sigma) * vec;
for(int i=0; i<n; ++i)
for(Index i=0; i<n; ++i)
{
JacobiRotation<Scalar> g;
g.makeGivens(mat(i,i), -temp(i), &mat(i,i));
int rs = n-i-1;
Index rs = n-i-1;
if(rs>0)
{
ColXprSegment x(mat.col(i).tail(rs));
@@ -229,12 +231,12 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
{
temp = vec;
RealScalar beta = 1;
for(int j=0; j<n; ++j)
for(Index j=0; j<n; ++j)
{
RealScalar Ljj = real(mat.coeff(j,j));
RealScalar dj = abs2(Ljj);
RealScalar Ljj = numext::real(mat.coeff(j,j));
RealScalar dj = numext::abs2(Ljj);
Scalar wj = temp.coeff(j);
RealScalar swj2 = sigma*abs2(wj);
RealScalar swj2 = sigma*numext::abs2(wj);
RealScalar gamma = dj*beta + swj2;
RealScalar x = dj + swj2/beta;
@@ -250,7 +252,7 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
{
temp.tail(rs) -= (wj/Ljj) * mat.col(j).tail(rs);
if(gamma != 0)
mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*conj(wj)/gamma)*temp.tail(rs);
mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*numext::conj(wj)/gamma)*temp.tail(rs);
}
}
}
@@ -261,9 +263,9 @@ template<typename Scalar> struct llt_inplace<Scalar, Lower>
{
typedef typename NumTraits<Scalar>::Real RealScalar;
template<typename MatrixType>
static typename MatrixType::Index unblocked(MatrixType& mat)
static Index unblocked(MatrixType& mat)
{
typedef typename MatrixType::Index Index;
using std::sqrt;
eigen_assert(mat.rows()==mat.cols());
const Index size = mat.rows();
@@ -275,7 +277,7 @@ template<typename Scalar> struct llt_inplace<Scalar, Lower>
Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
RealScalar x = real(mat.coeff(k,k));
RealScalar x = numext::real(mat.coeff(k,k));
if (k>0) x -= A10.squaredNorm();
if (x<=RealScalar(0))
return k;
@@ -287,9 +289,8 @@ 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)
@@ -320,7 +321,7 @@ template<typename Scalar> struct llt_inplace<Scalar, Lower>
}
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);
}
@@ -331,19 +332,19 @@ 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);
@@ -354,8 +355,8 @@ 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; }
};
@@ -364,8 +365,8 @@ 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; }
};
@@ -382,6 +383,8 @@ template<typename MatrixType> struct LLT_Traits<MatrixType,Upper>
template<typename MatrixType, int _UpLo>
LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const MatrixType& a)
{
check_template_parameters();
eigen_assert(a.rows()==a.cols());
const Index size = a.rows();
m_matrix.resize(size, size);
@@ -413,22 +416,16 @@ 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>
#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
{
typedef LLT<_MatrixType,UpLo> LLTType;
EIGEN_MAKE_SOLVE_HELPERS(LLTType,Rhs)
template<typename Dest> void evalTo(Dest& dst) const
{
dst = rhs();
dec().solveInPlace(dst);
}
};
dst = rhs;
solveInPlace(dst);
}
#endif
/** \internal use x = llt_object.solve(x);
*
@@ -463,8 +460,10 @@ MatrixType LLT<MatrixType,_UpLo>::reconstructedMatrix() const
return matrixL() * matrixL().adjoint().toDenseMatrix();
}
#ifndef __CUDACC__
/** \cholesky_module
* \returns the LLT decomposition of \c *this
* \sa SelfAdjointView::llt()
*/
template<typename Derived>
inline const LLT<typename MatrixBase<Derived>::PlainObject>
@@ -475,6 +474,7 @@ 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>
@@ -482,7 +482,8 @@ SelfAdjointView<MatrixType, UpLo>::llt() const
{
return LLT<PlainObject,UpLo>(m_matrix);
}
#endif // __CUDACC__
} // end namespace Eigen
#endif // EIGEN_LLT_H

View File

@@ -46,7 +46,7 @@ template<typename Scalar> struct mkl_llt;
template<> struct mkl_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; \
@@ -60,30 +60,30 @@ template<> struct mkl_llt<EIGTYPE> \
lda = m.outerStride(); \
\
info = LAPACKE_##MKLPREFIX##potrf( matrix_order, uplo, size, (MKLTYPE*)a, lda ); \
info = (info==0) ? Success : NumericalIssue; \
info = (info==0) ? -1 : info>0 ? info-1 : size; \
return info; \
} \
}; \
template<> struct llt_inplace<EIGTYPE, Lower> \
{ \
template<typename MatrixType> \
static typename MatrixType::Index blocked(MatrixType& m) \
static Index blocked(MatrixType& m) \
{ \
return mkl_llt<EIGTYPE>::potrf(m, 'L'); \
} \
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'); \
} \
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); \

View File

@@ -48,10 +48,9 @@ void cholmod_configure_matrix(CholmodType& mat)
/** 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(SparseMatrix<_Scalar,_Options,_StorageIndex>& mat)
{
typedef SparseMatrix<_Scalar,_Options,_Index> MatrixType;
cholmod_sparse res;
res.nzmax = mat.nonZeros();
res.nrow = mat.rows();;
@@ -59,10 +58,12 @@ cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
res.p = mat.outerIndexPtr();
res.i = mat.innerIndexPtr();
res.x = mat.valuePtr();
res.z = 0;
res.sorted = 1;
if(mat.isCompressed())
{
res.packed = 1;
res.nz = 0;
}
else
{
@@ -73,13 +74,17 @@ cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
res.dtype = 0;
res.stype = -1;
if (internal::is_same<_Index,int>::value)
if (internal::is_same<_StorageIndex,int>::value)
{
res.itype = CHOLMOD_INT;
}
else if (internal::is_same<_StorageIndex,UF_long>::value)
{
res.itype = CHOLMOD_LONG;
}
else
{
eigen_assert(false && "Index type different than int is not supported yet");
eigen_assert(false && "Index type not supported yet");
}
// setup res.xtype
@@ -100,7 +105,7 @@ const cholmod_sparse viewAsCholmod(const SparseMatrix<_Scalar,_Options,_Index>&
/** 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());
@@ -123,7 +128,7 @@ cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat)
res.ncol = mat.cols();
res.nzmax = res.nrow * res.ncol;
res.d = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride();
res.x = mat.derived().data();
res.x = (void*)(mat.derived().data());
res.z = 0;
internal::cholmod_configure_matrix<Scalar>(res);
@@ -133,12 +138,12 @@ 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, reinterpret_cast<Index*>(cm.p)[cm.ncol],
reinterpret_cast<Index*>(cm.p), reinterpret_cast<Index*>(cm.i),reinterpret_cast<Scalar*>(cm.x) );
return MappedSparseMatrix<Scalar,Flags,StorageIndex>
(cm.nrow, cm.ncol, static_cast<StorageIndex*>(cm.p)[cm.ncol],
static_cast<StorageIndex*>(cm.p), static_cast<StorageIndex*>(cm.i),static_cast<Scalar*>(cm.x) );
}
enum CholmodMode {
@@ -152,27 +157,33 @@ 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;
public:
CholmodBase()
: m_cholmodFactor(0), m_info(Success), m_isInitialized(false)
: m_cholmodFactor(0), m_info(Success)
{
m_shiftOffset[0] = m_shiftOffset[1] = RealScalar(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_shiftOffset[0] = m_shiftOffset[1] = RealScalar(0.0);
cholmod_start(&m_cholmod);
compute(matrix);
}
@@ -184,11 +195,8 @@ 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.
*
@@ -209,35 +217,7 @@ class CholmodBase : internal::noncopyable
return derived();
}
/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
*
* \sa compute()
*/
template<typename Rhs>
inline const internal::solve_retval<CholmodBase, Rhs>
solve(const MatrixBase<Rhs>& b) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(rows()==b.rows()
&& "CholmodDecomposition::solve(): invalid number of rows of the right hand side matrix b");
return internal::solve_retval<CholmodBase, Rhs>(*this, b.derived());
}
/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
*
* \sa compute()
*/
template<typename Rhs>
inline const internal::sparse_solve_retval<CholmodBase, Rhs>
solve(const SparseMatrixBase<Rhs>& b) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(rows()==b.rows()
&& "CholmodDecomposition::solve(): invalid number of rows of the right hand side matrix b");
return internal::sparse_solve_retval<CholmodBase, Rhs>(*this, b.derived());
}
/** Performs a symbolic decomposition on the sparcity of \a matrix.
/** Performs a symbolic decomposition on the sparsity pattern of \a matrix.
*
* This function is particularly useful when solving for several problems having the same structure.
*
@@ -261,7 +241,7 @@ class CholmodBase : internal::noncopyable
/** Performs a numeric decomposition of \a matrix
*
* The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
* The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been performed.
*
* \sa analyzePattern()
*/
@@ -269,9 +249,10 @@ class CholmodBase : internal::noncopyable
{
eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
cholmod_factorize(&A, m_cholmodFactor, &m_cholmod);
cholmod_factorize_p(&A, m_shiftOffset, 0, 0, m_cholmodFactor, &m_cholmod);
this->m_info = Success;
// If the factorization failed, minor is the column at which it did. On success minor == n.
this->m_info = (m_cholmodFactor->minor == m_cholmodFactor->n ? Success : NumericalIssue);
m_factorizationIsOk = true;
}
@@ -282,30 +263,34 @@ 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());
// note: cd stands for Cholmod Dense
cholmod_dense b_cd = viewAsCholmod(b.const_cast_derived());
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 carreful with alignment, etc.)
// TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
dest = Matrix<Scalar,Dest::RowsAtCompileTime,Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x),b.rows(),b.cols());
cholmod_free_dense(&x_cd, &m_cholmod);
}
/** \internal */
template<typename RhsScalar, int RhsOptions, typename RhsIndex, typename DestScalar, int DestOptions, typename DestIndex>
void _solve(const SparseMatrix<RhsScalar,RhsOptions,RhsIndex> &b, SparseMatrix<DestScalar,DestOptions,DestIndex> &dest) const
void _solve_impl(const SparseMatrix<RhsScalar,RhsOptions,RhsIndex> &b, SparseMatrix<DestScalar,DestOptions,DestIndex> &dest) const
{
eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
const Index size = m_cholmodFactor->n;
EIGEN_UNUSED_VARIABLE(size);
eigen_assert(size==b.rows());
// note: cs stands for Cholmod Sparse
@@ -314,22 +299,39 @@ class CholmodBase : internal::noncopyable
if(!x_cs)
{
this->m_info = NumericalIssue;
return;
}
// TODO optimize this copy by swapping when possible (be carreful with alignment, etc.)
// TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
dest = viewAsEigen<DestScalar,DestOptions,DestIndex>(*x_cs);
cholmod_free_sparse(&x_cs, &m_cholmod);
}
#endif // EIGEN_PARSED_BY_DOXYGEN
/** Sets the shift parameter that will be used to adjust the diagonal coefficients during the numerical factorization.
*
* During the numerical factorization, an offset term is added to the diagonal coefficients:\n
* \c d_ii = \a offset + \c d_ii
*
* The default is \a offset=0.
*
* \returns a reference to \c *this.
*/
Derived& setShift(const RealScalar& offset)
{
m_shiftOffset[0] = offset;
return derived();
}
template<typename Stream>
void dumpMemory(Stream& s)
void dumpMemory(Stream& /*s*/)
{}
protected:
mutable cholmod_common m_cholmod;
cholmod_factor* m_cholmodFactor;
RealScalar m_shiftOffset[2];
mutable ComputationInfo m_info;
bool m_isInitialized;
int m_factorizationIsOk;
int m_analysisIsOk;
};
@@ -340,8 +342,8 @@ class CholmodBase : internal::noncopyable
*
* This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization
* using the Cholmod library.
* This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Thefore, it has little practical interest.
* The sparse matrix A must be selfajoint and positive definite. The vectors or matrices
* This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Therefore, it has little practical interest.
* The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
@@ -367,7 +369,7 @@ class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimpl
CholmodSimplicialLLT(const MatrixType& matrix) : Base()
{
init();
compute(matrix);
this->compute(matrix);
}
~CholmodSimplicialLLT() {}
@@ -387,8 +389,8 @@ class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimpl
*
* This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization
* using the Cholmod library.
* This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Thefore, it has little practical interest.
* The sparse matrix A must be selfajoint and positive definite. The vectors or matrices
* This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Therefore, it has little practical interest.
* The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
@@ -414,7 +416,7 @@ class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimp
CholmodSimplicialLDLT(const MatrixType& matrix) : Base()
{
init();
compute(matrix);
this->compute(matrix);
}
~CholmodSimplicialLDLT() {}
@@ -433,7 +435,7 @@ class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimp
* This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization
* using the Cholmod library.
* This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM.
* The sparse matrix A must be selfajoint and positive definite. The vectors or matrices
* The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
@@ -459,7 +461,7 @@ class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSuper
CholmodSupernodalLLT(const MatrixType& matrix) : Base()
{
init();
compute(matrix);
this->compute(matrix);
}
~CholmodSupernodalLLT() {}
@@ -476,7 +478,7 @@ class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSuper
* \brief A general Cholesky factorization and solver based on Cholmod
*
* This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization
* using the Cholmod library. The sparse matrix A must be selfajoint and positive definite. The vectors or matrices
* using the Cholmod library. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* This variant permits to change the underlying Cholesky method at runtime.
@@ -506,7 +508,7 @@ class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecom
CholmodDecomposition(const MatrixType& matrix) : Base()
{
init();
compute(matrix);
this->compute(matrix);
}
~CholmodDecomposition() {}
@@ -544,36 +546,6 @@ 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

@@ -24,6 +24,9 @@ namespace Eigen {
* API for the %Matrix class provides easy access to linear-algebra
* operations.
*
* See documentation of class Matrix for detailed information on the template parameters
* storage layout.
*
* 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.
*
@@ -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.
@@ -107,122 +128,118 @@ class Array
*
* \sa resize(Index,Index)
*/
EIGEN_STRONG_INLINE explicit Array() : Base()
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array() : Base()
{
Base::_check_template_params();
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
// FIXME is it still needed ??
/** \internal */
EIGEN_DEVICE_FUNC
Array(internal::constructor_without_unaligned_array_assert)
: Base(internal::constructor_without_unaligned_array_assert())
{
Base::_check_template_params();
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#endif
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
Array(Array&& other)
: Base(std::move(other))
{
Base::_check_template_params();
if (RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic)
Base::_set_noalias(other);
}
EIGEN_DEVICE_FUNC
Array& operator=(Array&& other)
{
other.swap(*this);
return *this;
}
#endif
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit Array(const T& x)
{
Base::_check_template_params();
Base::template _init1<T>(x);
}
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const T0& val0, const T1& val1)
{
Base::_check_template_params();
this->template _init2<T0,T1>(val0, val1);
}
#else
/** \brief Constructs a fixed-sized array initialized with coefficients starting at \a data */
EIGEN_DEVICE_FUNC explicit Array(const Scalar *data);
/** Constructs a vector or row-vector with given dimension. \only_for_vectors
*
* Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
* it is redundant to pass the dimension here, so it makes more sense to use the default
* constructor Matrix() instead.
* constructor Array() instead.
*/
EIGEN_STRONG_INLINE explicit Array(Index dim)
: Base(dim, RowsAtCompileTime == 1 ? 1 : dim, ColsAtCompileTime == 1 ? 1 : dim)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Array)
eigen_assert(dim >= 0);
eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim);
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename T0, typename T1>
EIGEN_STRONG_INLINE Array(const T0& x, const T1& y)
{
Base::_check_template_params();
this->template _init2<T0,T1>(x, y);
}
#else
/** constructs an uninitialized matrix with \a rows rows and \a cols columns.
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit Array(Index dim);
/** constructs an initialized 1x1 Array with the given coefficient */
Array(const Scalar& value);
/** constructs an uninitialized array with \a rows rows and \a cols columns.
*
* This is useful for dynamic-size matrices. For fixed-size matrices,
* This is useful for dynamic-size arrays. For fixed-size arrays,
* it is redundant to pass these parameters, so one should use the default constructor
* Matrix() instead. */
* Array() instead. */
Array(Index rows, Index cols);
/** constructs an initialized 2D vector with given coefficients */
Array(const Scalar& x, const Scalar& y);
Array(const Scalar& val0, const Scalar& val1);
#endif
/** constructs an initialized 3D vector with given coefficients */
EIGEN_STRONG_INLINE Array(const Scalar& x, const Scalar& y, const Scalar& z)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
m_storage.data()[2] = z;
m_storage.data()[0] = val0;
m_storage.data()[1] = val1;
m_storage.data()[2] = val2;
}
/** constructs an initialized 4D vector with given coefficients */
EIGEN_STRONG_INLINE Array(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2, const Scalar& val3)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
m_storage.data()[2] = z;
m_storage.data()[3] = w;
m_storage.data()[0] = val0;
m_storage.data()[1] = val1;
m_storage.data()[2] = val2;
m_storage.data()[3] = val3;
}
explicit Array(const Scalar *data);
/** Constructor copying the value of the expression \a other */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Array(const ArrayBase<OtherDerived>& other)
: Base(other.rows() * other.cols(), other.rows(), other.cols())
{
Base::_check_template_params();
Base::_set_noalias(other);
}
/** Copy constructor */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const Array& other)
: Base(other.rows() * other.cols(), other.rows(), other.cols())
{
Base::_check_template_params();
Base::_set_noalias(other);
}
/** Copy constructor with in-place evaluation */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Array(const ReturnByValue<OtherDerived>& other)
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
other.evalTo(*this);
}
: Base(other)
{ }
/** \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(other.rows(), other.cols());
*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

@@ -50,7 +50,6 @@ template<typename Derived> class ArrayBase
typename NumTraits<typename internal::traits<Derived>::Scalar>::Real>::operator*;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
@@ -64,8 +63,7 @@ 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;
@@ -85,22 +83,10 @@ 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
@@ -118,40 +104,57 @@ template<typename Derived> class ArrayBase
/** 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
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
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
Derived& operator+=(const Scalar& scalar);
EIGEN_DEVICE_FUNC
Derived& operator-=(const Scalar& scalar);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator+=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator-=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator*=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator/=(const ArrayBase<OtherDerived>& other);
public:
EIGEN_DEVICE_FUNC
ArrayBase<Derived>& array() { return *this; }
EIGEN_DEVICE_FUNC
const ArrayBase<Derived>& array() const { return *this; }
/** \returns an \link MatrixBase Matrix \endlink expression of this array
/** \returns an \link Eigen::MatrixBase Matrix \endlink expression of this array
* \sa MatrixBase::array() */
MatrixWrapper<Derived> matrix() { return derived(); }
const MatrixWrapper<const Derived> matrix() const { return derived(); }
EIGEN_DEVICE_FUNC
MatrixWrapper<Derived> matrix() { return MatrixWrapper<Derived>(derived()); }
EIGEN_DEVICE_FUNC
const MatrixWrapper<const Derived> matrix() const { return MatrixWrapper<const Derived>(derived()); }
// template<typename Dest>
// inline void evalTo(Dest& dst) const { dst = matrix(); }
protected:
EIGEN_DEVICE_FUNC
ArrayBase() : Base() {}
private:
@@ -176,8 +179,7 @@ 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>());
return derived();
}
@@ -190,8 +192,7 @@ 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>());
return derived();
}
@@ -204,8 +205,7 @@ 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();
}
@@ -218,8 +218,7 @@ 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>());
return derived();
}

View File

@@ -29,6 +29,11 @@ struct traits<ArrayWrapper<ExpressionType> >
: public traits<typename remove_all<typename ExpressionType::Nested>::type >
{
typedef ArrayXpr XprKind;
// Let's remove NestByRefBit
enum {
Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
Flags = Flags0 & ~NestByRefBit
};
};
}
@@ -39,6 +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,
@@ -46,58 +52,71 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
const Scalar
>::type ScalarWithConstIfNotLvalue;
typedef typename internal::nested<ExpressionType>::type NestedExpressionType;
typedef typename internal::ref_selector<ExpressionType>::type NestedExpressionType;
inline ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
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.data(); }
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return m_expression.const_cast_derived().data(); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return m_expression.data(); }
inline CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index rowId, Index colId) const
{
return m_expression.coeff(row, col);
return m_expression.coeff(rowId, colId);
}
inline Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index rowId, Index colId)
{
return m_expression.const_cast_derived().coeffRef(row, col);
return m_expression.const_cast_derived().coeffRef(rowId, colId);
}
inline const Scalar& coeffRef(Index row, Index col) const
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.const_cast_derived().coeffRef(row, col);
return m_expression.const_cast_derived().coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index index) const
{
return m_expression.coeff(index);
}
EIGEN_DEVICE_FUNC
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 row, Index col) const
inline const PacketScalar packet(Index rowId, Index colId) const
{
return m_expression.template packet<LoadMode>(row, col);
return m_expression.template packet<LoadMode>(rowId, colId);
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(row, col, x);
m_expression.const_cast_derived().template writePacket<LoadMode>(rowId, colId, val);
}
template<int LoadMode>
@@ -107,20 +126,31 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
inline void writePacket(Index index, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(index, x);
m_expression.const_cast_derived().template writePacket<LoadMode>(index, val);
}
template<typename Dest>
EIGEN_DEVICE_FUNC
inline void evalTo(Dest& dst) const { dst = m_expression; }
const typename internal::remove_all<NestedExpressionType>::type&
EIGEN_DEVICE_FUNC
nestedExpression() const
{
return m_expression;
}
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index) */
EIGEN_DEVICE_FUNC
void resize(Index newSize) { m_expression.const_cast_derived().resize(newSize); }
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index,Index)*/
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols) { m_expression.const_cast_derived().resize(rows,cols); }
protected:
NestedExpressionType m_expression;
};
@@ -142,6 +172,11 @@ struct traits<MatrixWrapper<ExpressionType> >
: public traits<typename remove_all<typename ExpressionType::Nested>::type >
{
typedef MatrixXpr XprKind;
// Let's remove NestByRefBit
enum {
Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
Flags = Flags0 & ~NestByRefBit
};
};
}
@@ -152,6 +187,7 @@ 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,
@@ -159,58 +195,71 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
const Scalar
>::type ScalarWithConstIfNotLvalue;
typedef typename internal::nested<ExpressionType>::type NestedExpressionType;
typedef typename internal::ref_selector<ExpressionType>::type NestedExpressionType;
inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {}
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.data(); }
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return m_expression.const_cast_derived().data(); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return m_expression.data(); }
inline CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index rowId, Index colId) const
{
return m_expression.coeff(row, col);
return m_expression.coeff(rowId, colId);
}
inline Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index rowId, Index colId)
{
return m_expression.const_cast_derived().coeffRef(row, col);
return m_expression.const_cast_derived().coeffRef(rowId, colId);
}
inline const Scalar& coeffRef(Index row, Index col) const
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.derived().coeffRef(row, col);
return m_expression.derived().coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index index) const
{
return m_expression.coeff(index);
}
EIGEN_DEVICE_FUNC
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 row, Index col) const
inline const PacketScalar packet(Index rowId, Index colId) const
{
return m_expression.template packet<LoadMode>(row, col);
return m_expression.template packet<LoadMode>(rowId, colId);
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(row, col, x);
m_expression.const_cast_derived().template writePacket<LoadMode>(rowId, colId, val);
}
template<int LoadMode>
@@ -220,17 +269,27 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
inline void writePacket(Index index, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(index, x);
m_expression.const_cast_derived().template writePacket<LoadMode>(index, val);
}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<NestedExpressionType>::type&
nestedExpression() const
{
return m_expression;
}
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index) */
EIGEN_DEVICE_FUNC
void resize(Index newSize) { m_expression.const_cast_derived().resize(newSize); }
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index,Index)*/
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols) { m_expression.const_cast_derived().resize(rows,cols); }
protected:
NestedExpressionType m_expression;
};

View File

@@ -14,471 +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, int outer)
{
dst.copyCoeffByOuterInner(outer, Index, src);
assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src, outer);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &, int) {}
};
/***********************
*** Linear traversal ***
***********************/
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_LinearTraversal_CompleteUnrolling
{
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
dst.copyCoeff(Index, src);
assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {}
};
/**************************
*** Inner vectorization ***
**************************/
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_innervec_CompleteUnrolling
{
enum {
outer = Index / Derived1::InnerSizeAtCompileTime,
inner = Index % Derived1::InnerSizeAtCompileTime,
JointAlignment = assign_traits<Derived1,Derived2>::JointAlignment
};
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
dst.template copyPacketByOuterInner<Derived2, Aligned, JointAlignment>(outer, inner, src);
assign_innervec_CompleteUnrolling<Derived1, Derived2,
Index+packet_traits<typename Derived1::Scalar>::size, Stop>::run(dst, src);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_innervec_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {}
};
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_innervec_InnerUnrolling
{
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src, int outer)
{
dst.template copyPacketByOuterInner<Derived2, Aligned, Aligned>(outer, Index, src);
assign_innervec_InnerUnrolling<Derived1, Derived2,
Index+packet_traits<typename Derived1::Scalar>::size, Stop>::run(dst, src, outer);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_innervec_InnerUnrolling<Derived1, Derived2, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &, int) {}
};
/***************************************************************************
* Part 3 : implementation of all cases
***************************************************************************/
template<typename Derived1, typename Derived2,
int Traversal = assign_traits<Derived1, Derived2>::Traversal,
int Unrolling = assign_traits<Derived1, Derived2>::Unrolling,
int Version = Specialized>
struct assign_impl;
/************************
*** Default traversal ***
************************/
template<typename Derived1, typename Derived2, int Unrolling, int Version>
struct assign_impl<Derived1, Derived2, InvalidTraversal, Unrolling, Version>
{
static inline void run(Derived1 &, const Derived2 &) { }
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, DefaultTraversal, NoUnrolling, Version>
{
typedef typename Derived1::Index Index;
static inline void run(Derived1 &dst, const Derived2 &src)
{
const Index innerSize = dst.innerSize();
const Index outerSize = dst.outerSize();
for(Index outer = 0; outer < outerSize; ++outer)
for(Index inner = 0; inner < innerSize; ++inner)
dst.copyCoeffByOuterInner(outer, inner, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, DefaultTraversal, CompleteUnrolling, Version>
{
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
::run(dst, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, DefaultTraversal, InnerUnrolling, Version>
{
typedef typename Derived1::Index Index;
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
const Index outerSize = dst.outerSize();
for(Index outer = 0; outer < outerSize; ++outer)
assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, 0, Derived1::InnerSizeAtCompileTime>
::run(dst, src, outer);
}
};
/***********************
*** Linear traversal ***
***********************/
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, LinearTraversal, NoUnrolling, Version>
{
typedef typename Derived1::Index Index;
static inline void run(Derived1 &dst, const Derived2 &src)
{
const Index size = dst.size();
for(Index i = 0; i < size; ++i)
dst.copyCoeff(i, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, LinearTraversal, CompleteUnrolling, Version>
{
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
::run(dst, src);
}
};
/**************************
*** Inner vectorization ***
**************************/
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, NoUnrolling, Version>
{
typedef typename Derived1::Index Index;
static inline void run(Derived1 &dst, const Derived2 &src)
{
const Index innerSize = dst.innerSize();
const Index outerSize = dst.outerSize();
const Index packetSize = packet_traits<typename Derived1::Scalar>::size;
for(Index outer = 0; outer < outerSize; ++outer)
for(Index inner = 0; inner < innerSize; inner+=packetSize)
dst.template copyPacketByOuterInner<Derived2, Aligned, Aligned>(outer, inner, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, CompleteUnrolling, Version>
{
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
assign_innervec_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
::run(dst, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, InnerUnrolling, Version>
{
typedef typename Derived1::Index Index;
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
const Index outerSize = dst.outerSize();
for(Index outer = 0; outer < outerSize; ++outer)
assign_innervec_InnerUnrolling<Derived1, Derived2, 0, Derived1::InnerSizeAtCompileTime>
::run(dst, src, outer);
}
};
/***************************
*** Linear vectorization ***
***************************/
template <bool IsAligned = false>
struct unaligned_assign_impl
{
template <typename Derived, typename OtherDerived>
static EIGEN_STRONG_INLINE void run(const Derived&, OtherDerived&, typename Derived::Index, typename Derived::Index) {}
};
template <>
struct unaligned_assign_impl<false>
{
// MSVC must not inline this functions. If it does, it fails to optimize the
// packet access path.
#ifdef _MSC_VER
template <typename Derived, typename OtherDerived>
static EIGEN_DONT_INLINE void run(const Derived& src, OtherDerived& dst, typename Derived::Index start, typename Derived::Index end)
#else
template <typename Derived, typename OtherDerived>
static EIGEN_STRONG_INLINE void run(const Derived& src, OtherDerived& dst, typename Derived::Index start, typename Derived::Index end)
#endif
{
for (typename Derived::Index index = start; index < end; ++index)
dst.copyCoeff(index, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, LinearVectorizedTraversal, NoUnrolling, Version>
{
typedef typename Derived1::Index Index;
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
const Index size = dst.size();
typedef packet_traits<typename Derived1::Scalar> PacketTraits;
enum {
packetSize = PacketTraits::size,
dstAlignment = PacketTraits::AlignedOnScalar ? Aligned : int(assign_traits<Derived1,Derived2>::DstIsAligned) ,
srcAlignment = assign_traits<Derived1,Derived2>::JointAlignment
};
const Index alignedStart = assign_traits<Derived1,Derived2>::DstIsAligned ? 0
: internal::first_aligned(&dst.coeffRef(0), size);
const Index alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize;
unaligned_assign_impl<assign_traits<Derived1,Derived2>::DstIsAligned!=0>::run(src,dst,0,alignedStart);
for(Index index = alignedStart; index < alignedEnd; index += packetSize)
{
dst.template copyPacket<Derived2, dstAlignment, srcAlignment>(index, src);
}
unaligned_assign_impl<>::run(src,dst,alignedEnd,size);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, LinearVectorizedTraversal, CompleteUnrolling, Version>
{
typedef typename Derived1::Index Index;
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
enum { size = Derived1::SizeAtCompileTime,
packetSize = packet_traits<typename Derived1::Scalar>::size,
alignedSize = (size/packetSize)*packetSize };
assign_innervec_CompleteUnrolling<Derived1, Derived2, 0, alignedSize>::run(dst, src);
assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, alignedSize, size>::run(dst, src);
}
};
/**************************
*** Slice vectorization ***
***************************/
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, SliceVectorizedTraversal, NoUnrolling, Version>
{
typedef typename Derived1::Index Index;
static inline void run(Derived1 &dst, const Derived2 &src)
{
typedef packet_traits<typename Derived1::Scalar> PacketTraits;
enum {
packetSize = PacketTraits::size,
alignable = PacketTraits::AlignedOnScalar,
dstAlignment = alignable ? Aligned : int(assign_traits<Derived1,Derived2>::DstIsAligned) ,
srcAlignment = assign_traits<Derived1,Derived2>::JointAlignment
};
const Index packetAlignedMask = packetSize - 1;
const Index innerSize = dst.innerSize();
const Index outerSize = dst.outerSize();
const Index alignedStep = alignable ? (packetSize - dst.outerStride() % packetSize) & packetAlignedMask : 0;
Index alignedStart = ((!alignable) || assign_traits<Derived1,Derived2>::DstIsAligned) ? 0
: internal::first_aligned(&dst.coeffRef(0,0), innerSize);
for(Index outer = 0; outer < outerSize; ++outer)
{
const Index alignedEnd = alignedStart + ((innerSize-alignedStart) & ~packetAlignedMask);
// do the non-vectorizable part of the assignment
for(Index inner = 0; inner<alignedStart ; ++inner)
dst.copyCoeffByOuterInner(outer, inner, src);
// do the vectorizable part of the assignment
for(Index inner = alignedStart; inner<alignedEnd; inner+=packetSize)
dst.template copyPacketByOuterInner<Derived2, dstAlignment, Unaligned>(outer, inner, src);
// do the non-vectorizable part of the assignment
for(Index inner = alignedEnd; inner<innerSize ; ++inner)
dst.copyCoeffByOuterInner(outer, inner, src);
alignedStart = std::min<Index>((alignedStart+alignedStep)%packetSize, innerSize);
}
}
};
} // end namespace internal
/***************************************************************************
* Part 4 : implementation of DenseBase methods
***************************************************************************/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>
@@ -492,89 +27,61 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived)
EIGEN_STATIC_ASSERT(SameType,YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
#ifdef EIGEN_DEBUG_ASSIGN
internal::assign_traits<Derived, OtherDerived>::debug();
#endif
eigen_assert(rows() == other.rows() && cols() == other.cols());
internal::assign_impl<Derived, OtherDerived, int(SameType) ? int(internal::assign_traits<Derived, OtherDerived>::Traversal)
: int(InvalidTraversal)>::run(derived(),other.derived());
#ifndef EIGEN_NO_DEBUG
checkTransposeAliasing(other.derived());
#endif
internal::call_assignment_no_alias(derived(),other.derived());
return derived();
}
namespace internal {
template<typename Derived, typename OtherDerived,
bool EvalBeforeAssigning = (int(OtherDerived::Flags) & EvalBeforeAssigningBit) != 0,
bool NeedToTranspose = Derived::IsVectorAtCompileTime
&& OtherDerived::IsVectorAtCompileTime
&& ((int(Derived::RowsAtCompileTime) == 1 && int(OtherDerived::ColsAtCompileTime) == 1)
| // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
// revert to || as soon as not needed anymore.
(int(Derived::ColsAtCompileTime) == 1 && int(OtherDerived::RowsAtCompileTime) == 1))
&& int(Derived::SizeAtCompileTime) != 1>
struct assign_selector;
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,false,false> {
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.derived()); }
};
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,true,false> {
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.eval()); }
};
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,false,true> {
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose()); }
};
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,true,true> {
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose().eval()); }
};
} // end namespace internal
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
{
return internal::assign_selector<Derived,OtherDerived>::run(derived(), other.derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase& other)
{
return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const MatrixBase& other)
{
return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
template <typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
{
return internal::assign_selector<Derived,OtherDerived>::run(derived(), other.derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
template <typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const EigenBase<OtherDerived>& other)
{
other.derived().evalTo(derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
{
other.evalTo(derived());
other.derived().evalTo(derived());
return derived();
}

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@@ -0,0 +1,828 @@
// 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;
// TODO distinguish between linear traversal and inner-traversals
typedef typename find_best_packet<DstScalar,Dst::SizeAtCompileTime>::type PacketType;
enum {
DstFlags = DstEvaluator::Flags,
SrcFlags = SrcEvaluator::Flags,
RequiredAlignment = unpacket_traits<PacketType>::alignment
};
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),
MaxSizeAtCompileTime = Dst::SizeAtCompileTime,
PacketSize = unpacket_traits<PacketType>::size
};
enum {
DstIsRowMajor = DstFlags&RowMajorBit,
SrcIsRowMajor = SrcFlags&RowMajorBit,
StorageOrdersAgree = (int(DstIsRowMajor) == int(SrcIsRowMajor)),
MightVectorize = StorageOrdersAgree
&& (int(DstFlags) & int(SrcFlags) & ActualPacketAccessBit)
&& (functor_traits<AssignFunc>::PacketAccess),
MayInnerVectorize = MightVectorize && int(InnerSize)!=Dynamic && int(InnerSize)%int(PacketSize)==0
&& int(JointAlignment)>=int(RequiredAlignment),
MayLinearize = StorageOrdersAgree && (int(DstFlags) & int(SrcFlags) & LinearAccessBit),
MayLinearVectorize = MightVectorize && MayLinearize && DstHasDirectAccess
&& ((int(DstAlignment)>=int(RequiredAlignment)) || 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(Dst::SizeAtCompileTime) != Dynamic
&& int(SrcEvaluator::CoeffReadCost) != Dynamic
&& int(Dst::SizeAtCompileTime) * int(SrcEvaluator::CoeffReadCost) <= int(UnrollingLimit),
MayUnrollInner = int(InnerSize) != Dynamic
&& int(SrcEvaluator::CoeffReadCost) != Dynamic
&& int(InnerSize) * 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) && (int(DstAlignment)>=int(RequiredAlignment)) ? int(CompleteUnrolling)
: int(NoUnrolling) )
: int(Traversal) == int(LinearTraversal)
? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling)
: int(NoUnrolling) )
: 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);
EIGEN_DEBUG_VAR(DstFlags)
EIGEN_DEBUG_VAR(SrcFlags)
std::cerr.unsetf(std::ios::hex);
EIGEN_DEBUG_VAR(DstAlignment)
EIGEN_DEBUG_VAR(SrcAlignment)
EIGEN_DEBUG_VAR(RequiredAlignment)
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)
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,
JointAlignment = Kernel::AssignmentTraits::JointAlignment
};
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
kernel.template assignPacketByOuterInner<Aligned, JointAlignment, 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>
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<Aligned, Aligned, PacketType>(outer, Index_);
enum { NextIndex = Index_ + unpacket_traits<PacketType>::size };
copy_using_evaluator_innervec_InnerUnrolling<Kernel, NextIndex, Stop>::run(kernel, outer);
}
};
template<typename Kernel, int Stop>
struct copy_using_evaluator_innervec_InnerUnrolling<Kernel, Stop, Stop>
{
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 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>
{
typedef typename Kernel::StorageIndex StorageIndex;
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::RequiredAlignment,
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.dstEvaluator().coeffRef(0), 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>
{
typedef typename Kernel::StorageIndex StorageIndex;
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
enum { size = DstXprType::SizeAtCompileTime,
packetSize = packet_traits<typename Kernel::Scalar>::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;
EIGEN_DEVICE_FUNC static 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<Aligned, Aligned, 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>
{
typedef typename Kernel::StorageIndex StorageIndex;
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_innervec_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime>::run(kernel, outer);
}
};
/***********************
*** Linear traversal ***
***********************/
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearTraversal, NoUnrolling>
{
EIGEN_DEVICE_FUNC static 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::RequiredAlignment),
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.dstEvaluator().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 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 = std::min<Index>((alignedStart+alignedStep)%packetSize, innerSize);
}
}
};
/***************************************************************************
* 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 typename DstEvaluatorType::StorageIndex StorageIndex;
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(); }
// TODO get rid of this one:
EIGEN_DEVICE_FUNC DstXprType& dstExpression() const { return m_dstExpr; }
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 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 void assignCoeff(Index index)
{
m_functor.assignCoeff(m_dst.coeffRef(index), m_src.coeff(index));
}
/// \sa assignCoeff(Index,Index)
EIGEN_DEVICE_FUNC 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 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 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 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 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 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;
}
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 void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src, const Functor &func)
{
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
typedef evaluator<DstXprType> DstEvaluatorType;
typedef evaluator<SrcXprType> SrcEvaluatorType;
DstEvaluatorType dstEvaluator(dst);
SrcEvaluatorType srcEvaluator(src);
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 void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src)
{
call_dense_assignment_loop(dst, src, internal::assign_op<typename DstXprType::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 Scalar = typename DstXprType::Scalar>
struct Assignment;
// The only purpose of this call_assignment() function is to deal with noalias() / AssumeAliasing and automatic transposition.
// Indeed, I (Gael) think that this concept of AssumeAliasing was a mistake, and it makes thing quite complicated.
// So this intermediate function removes everything related to AssumeAliasing such that Assignment
// does not has to bother about these annoying details.
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC void call_assignment(Dst& dst, const Src& src)
{
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar>());
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC void call_assignment(const Dst& dst, const Src& src)
{
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar>());
}
// Deal with AssumeAliasing
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if<evaluator_traits<Src>::AssumeAliasing==1, 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 void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if<evaluator_traits<Src>::AssumeAliasing==0, void*>::type = 0)
{
call_assignment_no_alias(dst, src, func);
}
// by-pass AssumeAliasing
// FIXME the const version should probably not be needed
// 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 void call_assignment(const NoAlias<Dst,StorageBase>& dst, const Src& src, const Func& func)
{
call_assignment_no_alias(dst.expression(), src, func);
}
template<typename Dst, template <typename> class StorageBase, typename Src, typename Func>
EIGEN_DEVICE_FUNC 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 void call_assignment_no_alias(Dst& dst, const Src& src, const Func& func)
{
enum {
NeedToTranspose = ( (int(Dst::RowsAtCompileTime) == 1 && int(Src::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(Dst::ColsAtCompileTime) == 1 && int(Src::RowsAtCompileTime) == 1))
&& int(Dst::SizeAtCompileTime) != 1
};
Index dstRows = NeedToTranspose ? src.cols() : src.rows();
Index dstCols = NeedToTranspose ? src.rows() : src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
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)
// TODO this line is commented to allow matrix = permutation
// Actually, the "Scalar" type for a permutation matrix does not really make sense,
// perhaps it could be void, and EIGEN_CHECK_BINARY_COMPATIBILIY could allow micing void with anything...?
// 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 void call_assignment_no_alias(Dst& dst, const Src& src)
{
call_assignment_no_alias(dst, src, internal::assign_op<typename Dst::Scalar>());
}
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src, const Func& func)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
// 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)
Assignment<Dst,Src,Func>::run(dst, src, func);
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC 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>());
}
// forward declaration
template<typename Dst, typename Src> void check_for_aliasing(const Dst &dst, const Src &src);
// Generic Dense to Dense assignment
template< typename DstXprType, typename SrcXprType, typename Functor, typename Scalar>
struct Assignment<DstXprType, SrcXprType, Functor, Dense2Dense, Scalar>
{
EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
{
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
#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.
template< typename DstXprType, typename SrcXprType, typename Functor, typename Scalar>
struct Assignment<DstXprType, SrcXprType, Functor, EigenBase2EigenBase, Scalar>
{
EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar> &/*func*/)
{
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
src.evalTo(dst);
}
};
} // namespace internal
} // end namespace Eigen
#endif // EIGEN_ASSIGN_EVALUATOR_H

254
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 +54,118 @@ 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>, \
Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml,EIGENTYPE>::type> { \
typedef CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested> SrcXprType; \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<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> \
struct Assignment<DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested>, assign_op<EIGENTYPE>, \
Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml,EIGENTYPE>::type> { \
typedef CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested> SrcXprType; \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE> &/*func*/) { \
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); \
VMLTYPE exponent = reinterpret_cast<const VMLTYPE&>(src.functor().m_exponent); \
if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal) \
{ \
VMLOP( dst.size(), (const VMLTYPE*)src.nestedExpression().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.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, exponent, \
(VMLTYPE*)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_##VMLMODE)); \
} \
} \
} \
};
#define EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE) \
template<> struct vml_call< scalar_##EIGENOP##_op<EIGENTYPE> > { \
enum { IsSupported = 1 }; \
static inline void run( const scalar_##EIGENOP##_op<EIGENTYPE>& /*func*/, \
int size, const EIGENTYPE* src, EIGENTYPE* dst) { \
MKL_INT64 vmlMode = EIGEN_MKL_VML_MODE; \
VMLOP(size, (const VMLTYPE*)src, (VMLTYPE*)dst, vmlMode); \
} \
};
#define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE) \
template<> struct vml_call< scalar_##EIGENOP##_op<EIGENTYPE> > { \
enum { IsSupported = 1 }; \
static inline void run( const scalar_##EIGENOP##_op<EIGENTYPE>& func, \
int size, const EIGENTYPE* src, EIGENTYPE* dst) { \
EIGENTYPE exponent = func.m_exponent; \
MKL_INT64 vmlMode = EIGEN_MKL_VML_MODE; \
VMLOP(&size, (const VMLTYPE*)src, (const VMLTYPE*)&exponent, \
(VMLTYPE*)dst, &vmlMode); \
} \
};
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vs##VMLOP, float, float) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vd##VMLOP, double, double)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vc##VMLOP, scomplex, MKL_Complex8) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vz##VMLOP, dcomplex, MKL_Complex16)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX(EIGENOP, VMLOP)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL_LA(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vms##VMLOP, float, float) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmd##VMLOP, double, double)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX_LA(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmc##VMLOP, scomplex, MKL_Complex8) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmz##VMLOP, dcomplex, MKL_Complex16)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL_LA(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX_LA(EIGENOP, VMLOP)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(sin, Sin)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(asin, Asin)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(cos, Cos)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(acos, Acos)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(tan, Tan)
//EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs, Abs)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(exp, Exp)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(log, Ln)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(sqrt, Sqrt)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr)
// The vm*powx functions are not avaibale in the windows version of MKL.
#ifdef _WIN32
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmspowx_, float, float)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdpowx_, double, double)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcpowx_, scomplex, MKL_Complex8)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzpowx_, dcomplex, MKL_Complex16)
#endif
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmsPowx, float, float, LA)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdPowx, double, double, LA)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcPowx, scomplex, MKL_Complex8, LA)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzPowx, dcomplex, MKL_Complex16, LA)
} // end namespace internal

View File

@@ -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;
@@ -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)
{
@@ -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

@@ -21,7 +21,9 @@ namespace Eigen {
* \param XprType the type of the expression in which we are taking a block
* \param BlockRows the number of rows of the block we are taking at compile time (optional)
* \param BlockCols the number of columns of the block we are taking at compile time (optional)
* \param _DirectAccessStatus \internal used for partial specialization
* \param 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
@@ -47,13 +49,13 @@ namespace Eigen {
*/
namespace internal {
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess>
struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel, HasDirectAccess> > : traits<XprType>
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 ref_selector<XprType>::type XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
enum{
MatrixRows = traits<XprType>::RowsAtCompileTime,
@@ -66,6 +68,7 @@ struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel, HasDirectAccess>
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
@@ -78,35 +81,111 @@ struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel, HasDirectAccess>
OuterStrideAtCompileTime = HasSameStorageOrderAsXprType
? int(outer_stride_at_compile_time<XprType>::ret)
: int(inner_stride_at_compile_time<XprType>::ret),
MaskPacketAccessBit = (InnerSize == Dynamic || (InnerSize % packet_traits<Scalar>::size) == 0)
&& (InnerStrideAtCompileTime == 1)
? PacketAccessBit : 0,
MaskAlignedBit = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic) && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % 16) == 0)) ? AlignedBit : 0,
FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0,
// 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,
Flags0 = traits<XprType>::Flags & ( (HereditaryBits & ~RowMajorBit) |
DirectAccessBit |
MaskPacketAccessBit |
MaskAlignedBit),
Flags = Flags0 | FlagsLinearAccessBit | FlagsLvalueBit | FlagsRowMajorBit
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, int BlockCols, bool InnerPanel, bool HasDirectAccess> class Block
: public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel, HasDirectAccess> >::type
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>
{
typedef BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> Impl;
public:
//typedef typename Impl::Base Base;
typedef Impl Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(Block)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
typedef typename internal::remove_all<XprType>::type NestedExpression;
/** Column or Row constructor
*/
EIGEN_DEVICE_FUNC
inline Block(XprType& xpr, Index i) : Impl(xpr,i)
{
eigen_assert( (i>=0) && (
((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
}
/** Fixed-size constructor
*/
EIGEN_DEVICE_FUNC
inline Block(XprType& xpr, Index startRow, Index startCol)
: Impl(xpr, startRow, startCol)
{
EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
eigen_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows()
&& startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols());
}
/** Dynamic-size constructor
*/
EIGEN_DEVICE_FUNC
inline Block(XprType& xpr,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: Impl(xpr, startRow, startCol, blockRows, blockCols)
{
eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
&& (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
eigen_assert(startRow >= 0 && blockRows >= 0 && startRow <= xpr.rows() - blockRows
&& startCol >= 0 && blockCols >= 0 && startCol <= xpr.cols() - blockCols);
}
};
// The generic default implementation for dense block simplu forward to the internal::BlockImpl_dense
// that must be specialized for direct and non-direct access...
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
class BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, Dense>
: public internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel>
{
typedef internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> Impl;
typedef typename XprType::StorageIndex StorageIndex;
public:
typedef Impl Base;
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)
EIGEN_DEVICE_FUNC inline BlockImpl(XprType& xpr, Index i) : Impl(xpr,i) {}
EIGEN_DEVICE_FUNC inline BlockImpl(XprType& xpr, Index startRow, Index startCol) : Impl(xpr, startRow, startCol) {}
EIGEN_DEVICE_FUNC
inline BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
: Impl(xpr, startRow, startCol, blockRows, blockCols) {}
};
namespace internal {
/** \internal Internal implementation of dense Blocks in the general case. */
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess> class BlockImpl_dense
: public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel> >::type
{
typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
public:
typedef typename internal::dense_xpr_base<Block>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Block)
typedef typename internal::dense_xpr_base<BlockType>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
class InnerIterator;
// class InnerIterator; // FIXME apparently never used
/** Column or Row constructor
*/
inline Block(XprType& xpr, Index i)
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index i)
: m_xpr(xpr),
// It is a row if and only if BlockRows==1 and BlockCols==XprType::ColsAtCompileTime,
// and it is a column if and only if BlockRows==XprType::RowsAtCompileTime and BlockCols==1,
@@ -116,60 +195,51 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0),
m_blockRows(BlockRows==1 ? 1 : xpr.rows()),
m_blockCols(BlockCols==1 ? 1 : xpr.cols())
{
eigen_assert( (i>=0) && (
((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
}
{}
/** Fixed-size constructor
*/
inline Block(XprType& xpr, Index startRow, Index startCol)
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
: m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
m_blockRows(BlockRows), m_blockCols(BlockCols)
{
EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
eigen_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows()
&& startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols());
}
m_blockRows(BlockRows), m_blockCols(BlockCols)
{}
/** Dynamic-size constructor
*/
inline Block(XprType& xpr,
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
m_blockRows(blockRows), m_blockCols(blockCols)
{
eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
&& (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
eigen_assert(startRow >= 0 && blockRows >= 0 && startRow + blockRows <= xpr.rows()
&& startCol >= 0 && blockCols >= 0 && startCol + blockCols <= xpr.cols());
}
m_blockRows(blockRows), m_blockCols(blockCols)
{}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
EIGEN_DEVICE_FUNC inline Index rows() const { return m_blockRows.value(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_blockCols.value(); }
inline Index rows() const { return m_blockRows.value(); }
inline Index cols() const { return m_blockCols.value(); }
inline Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index rowId, Index colId)
{
EIGEN_STATIC_ASSERT_LVALUE(XprType)
return m_xpr.const_cast_derived()
.coeffRef(row + m_startRow.value(), col + m_startCol.value());
.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
}
inline const Scalar& coeffRef(Index row, Index col) const
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_xpr.derived()
.coeffRef(row + m_startRow.value(), col + m_startCol.value());
.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
}
EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const
{
return m_xpr.coeff(row + m_startRow.value(), col + m_startCol.value());
return m_xpr.coeff(rowId + m_startRow.value(), colId + m_startCol.value());
}
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_LVALUE(XprType)
@@ -178,6 +248,7 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
return m_xpr.const_cast_derived()
@@ -185,6 +256,7 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
EIGEN_DEVICE_FUNC
inline const CoeffReturnType coeff(Index index) const
{
return m_xpr
@@ -193,17 +265,17 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
}
template<int LoadMode>
inline PacketScalar packet(Index row, Index col) const
inline PacketScalar packet(Index rowId, Index colId) const
{
return m_xpr.template packet<Unaligned>
(row + m_startRow.value(), col + m_startCol.value());
(rowId + m_startRow.value(), colId + m_startCol.value());
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_xpr.const_cast_derived().template writePacket<Unaligned>
(row + m_startRow.value(), col + m_startCol.value(), x);
(rowId + m_startRow.value(), colId + m_startCol.value(), val);
}
template<int LoadMode>
@@ -215,31 +287,34 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
inline void writePacket(Index index, const PacketScalar& val)
{
m_xpr.const_cast_derived().template writePacket<Unaligned>
(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), x);
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), val);
}
#ifdef EIGEN_PARSED_BY_DOXYGEN
/** \sa MapBase::data() */
inline const Scalar* data() const;
inline Index innerStride() const;
inline Index outerStride() const;
EIGEN_DEVICE_FUNC inline const Scalar* data() const;
EIGEN_DEVICE_FUNC inline Index innerStride() const;
EIGEN_DEVICE_FUNC inline Index outerStride() const;
#endif
const typename internal::remove_all<typename XprType::Nested>::type& nestedExpression() const
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type& nestedExpression() const
{
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();
}
@@ -247,79 +322,79 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
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;
const internal::variable_if_dynamic<StorageIndex, XprType::RowsAtCompileTime == 1 ? 0 : Dynamic> m_startRow;
const internal::variable_if_dynamic<StorageIndex, XprType::ColsAtCompileTime == 1 ? 0 : Dynamic> m_startCol;
const internal::variable_if_dynamic<StorageIndex, RowsAtCompileTime> m_blockRows;
const internal::variable_if_dynamic<StorageIndex, ColsAtCompileTime> m_blockCols;
};
/** \internal */
/** \internal Internal implementation of dense Blocks in the direct access case.*/
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
class Block<XprType,BlockRows,BlockCols, InnerPanel,true>
: public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel, true> >
class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
: public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel> >
{
typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
enum {
XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0
};
public:
typedef MapBase<Block> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Block)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
typedef MapBase<BlockType> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
/** Column or Row constructor
*/
inline Block(XprType& xpr, Index i)
: Base(internal::const_cast_ptr(&xpr.coeffRef(
(BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0,
(BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0)),
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index i)
: Base(xpr.data() + i * ( ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && (!XprTypeIsRowMajor))
|| ((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && ( XprTypeIsRowMajor)) ? xpr.innerStride() : xpr.outerStride()),
BlockRows==1 ? 1 : xpr.rows(),
BlockCols==1 ? 1 : xpr.cols()),
m_xpr(xpr)
{
eigen_assert( (i>=0) && (
((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
init();
}
/** Fixed-size constructor
*/
inline Block(XprType& xpr, Index startRow, Index startCol)
: Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol))), m_xpr(xpr)
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
: Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol)),
m_xpr(xpr)
{
eigen_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows()
&& startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols());
init();
}
/** Dynamic-size constructor
*/
inline Block(XprType& xpr,
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol)), blockRows, blockCols),
: Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol), blockRows, blockCols),
m_xpr(xpr)
{
eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
&& (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
eigen_assert(startRow >= 0 && blockRows >= 0 && startRow + blockRows <= xpr.rows()
&& startCol >= 0 && blockCols >= 0 && startCol + blockCols <= xpr.cols());
init();
}
const typename internal::remove_all<typename XprType::Nested>::type& nestedExpression() const
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type& nestedExpression() const
{
return m_xpr;
}
/** \sa MapBase::innerStride() */
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return internal::traits<Block>::HasSameStorageOrderAsXprType
return internal::traits<BlockType>::HasSameStorageOrderAsXprType
? m_xpr.innerStride()
: m_xpr.outerStride();
}
/** \sa MapBase::outerStride() */
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return m_outerStride;
@@ -333,7 +408,8 @@ class Block<XprType,BlockRows,BlockCols, InnerPanel,true>
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal used by allowAligned() */
inline Block(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols)
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols)
: Base(data, blockRows, blockCols), m_xpr(xpr)
{
init();
@@ -341,9 +417,10 @@ class Block<XprType,BlockRows,BlockCols, InnerPanel,true>
#endif
protected:
EIGEN_DEVICE_FUNC
void init()
{
m_outerStride = internal::traits<Block>::HasSameStorageOrderAsXprType
m_outerStride = internal::traits<BlockType>::HasSameStorageOrderAsXprType
? m_xpr.outerStride()
: m_xpr.innerStride();
}
@@ -352,6 +429,8 @@ class Block<XprType,BlockRows,BlockCols, InnerPanel,true>
Index m_outerStride;
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_BLOCK_H

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)
@@ -29,9 +30,9 @@ struct all_unroller
};
template<typename Derived>
struct all_unroller<Derived, 1>
struct all_unroller<Derived, 0>
{
static inline bool run(const Derived &mat) { return mat.coeff(0, 0); }
static inline bool run(const Derived &/*mat*/) { return true; }
};
template<typename Derived>
@@ -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);
@@ -55,9 +57,9 @@ struct any_unroller
};
template<typename Derived>
struct any_unroller<Derived, 1>
struct any_unroller<Derived, 0>
{
static inline bool run(const Derived &mat) { return mat.coeff(0, 0); }
static inline bool run(const Derived & /*mat*/) { return false; }
};
template<typename Derived>
@@ -78,21 +80,21 @@ 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
&& Evaluator::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;
}
}
@@ -104,21 +106,21 @@ 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
&& Evaluator::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;
}
}
@@ -128,11 +130,31 @@ inline bool DenseBase<Derived>::any() const
* \sa all(), any()
*/
template<typename Derived>
inline typename DenseBase<Derived>::Index DenseBase<Derived>::count() const
inline Eigen::Index DenseBase<Derived>::count() const
{
return derived().template cast<bool>().template cast<Index>().sum();
}
/** \returns true is \c *this contains at least one Not A Number (NaN).
*
* \sa allFinite()
*/
template<typename Derived>
inline bool DenseBase<Derived>::hasNaN() const
{
return !((derived().array()==derived().array()).all());
}
/** \returns true if \c *this contains only finite numbers, i.e., no NaN and no +/-INF values.
*
* \sa hasNaN()
*/
template<typename Derived>
inline bool DenseBase<Derived>::allFinite() const
{
return !((derived()-derived()).hasNaN());
}
} // end namespace Eigen
#endif // EIGEN_ALLANDANY_H

View File

@@ -8,3 +8,4 @@ INSTALL(FILES
ADD_SUBDIRECTORY(products)
ADD_SUBDIRECTORY(util)
ADD_SUBDIRECTORY(arch)
ADD_SUBDIRECTORY(functors)

View File

@@ -28,8 +28,8 @@ 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)
{
@@ -37,13 +37,27 @@ struct CommaInitializer
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
inline CommaInitializer(XprType& xpr, const DenseBase<OtherDerived>& other)
: m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows())
{
m_xpr.block(0, 0, other.rows(), other.cols()) = other;
}
/* Copy/Move constructor which transfers ownership. This is crucial in
* absence of return value optimization to avoid assertions during destruction. */
// FIXME in C++11 mode this could be replaced by a proper RValue constructor
EIGEN_DEVICE_FUNC
inline CommaInitializer(const CommaInitializer& o)
: m_xpr(o.m_xpr), m_row(o.m_row), m_col(o.m_col), m_currentBlockRows(o.m_currentBlockRows) {
// Mark original object as finished. In absence of R-value references we need to const_cast:
const_cast<CommaInitializer&>(o).m_row = m_xpr.rows();
const_cast<CommaInitializer&>(o).m_col = m_xpr.cols();
const_cast<CommaInitializer&>(o).m_currentBlockRows = 0;
}
/* inserts a scalar value in the target matrix */
EIGEN_DEVICE_FUNC
CommaInitializer& operator,(const Scalar& s)
{
if (m_col==m_xpr.cols())
@@ -63,8 +77,11 @@ struct CommaInitializer
/* inserts a matrix expression in the target matrix */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
CommaInitializer& operator,(const DenseBase<OtherDerived>& other)
{
if(other.cols()==0 || other.rows()==0)
return *this;
if (m_col==m_xpr.cols())
{
m_row+=m_currentBlockRows;
@@ -86,7 +103,11 @@ struct CommaInitializer
return *this;
}
EIGEN_DEVICE_FUNC
inline ~CommaInitializer()
#if defined VERIFY_RAISES_ASSERT && (!defined EIGEN_NO_ASSERTION_CHECKING) && defined EIGEN_EXCEPTIONS
throw(Eigen::eigen_assert_exception)
#endif
{
eigen_assert((m_row+m_currentBlockRows) == m_xpr.rows()
&& m_col == m_xpr.cols()
@@ -100,9 +121,10 @@ struct CommaInitializer
* quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished());
* \endcode
*/
EIGEN_DEVICE_FUNC
inline XprType& finished() { 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
@@ -116,6 +138,8 @@ struct CommaInitializer
*
* Example: \include MatrixBase_set.cpp
* Output: \verbinclude MatrixBase_set.out
*
* \note According the c++ standard, the argument expressions of this comma initializer are evaluated in arbitrary order.
*
* \sa CommaInitializer::finished(), class CommaInitializer
*/

File diff suppressed because it is too large Load Diff

View File

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

View File

@@ -1,7 +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
@@ -56,81 +56,61 @@ struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
typename Rhs::Scalar
)
>::type Scalar;
typedef typename promote_storage_type<typename traits<Lhs>::StorageKind,
typename traits<Rhs>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<typename traits<Lhs>::Index,
typename traits<Rhs>::Index>::type Index;
typedef typename cwise_promote_storage_type<typename traits<Lhs>::StorageKind,
typename traits<Rhs>::StorageKind,
BinaryOp>::ret StorageKind;
typedef typename promote_index_type<typename traits<Lhs>::StorageIndex,
typename traits<Rhs>::StorageIndex>::type StorageIndex;
typedef typename Lhs::Nested LhsNested;
typedef typename Rhs::Nested RhsNested;
typedef typename remove_reference<LhsNested>::type _LhsNested;
typedef typename remove_reference<RhsNested>::type _RhsNested;
enum {
LhsCoeffReadCost = _LhsNested::CoeffReadCost,
RhsCoeffReadCost = _RhsNested::CoeffReadCost,
LhsFlags = _LhsNested::Flags,
RhsFlags = _RhsNested::Flags,
SameType = is_same<typename _LhsNested::Scalar,typename _RhsNested::Scalar>::value,
StorageOrdersAgree = (int(Lhs::Flags)&RowMajorBit)==(int(Rhs::Flags)&RowMajorBit),
Flags0 = (int(LhsFlags) | int(RhsFlags)) & (
HereditaryBits
| (int(LhsFlags) & int(RhsFlags) &
( AlignedBit
| (StorageOrdersAgree ? LinearAccessBit : 0)
| (functor_traits<BinaryOp>::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0)
)
)
),
Flags = (Flags0 & ~RowMajorBit) | (LhsFlags & RowMajorBit),
CoeffReadCost = LhsCoeffReadCost + RhsCoeffReadCost + functor_traits<BinaryOp>::Cost
Flags = _LhsNested::Flags & RowMajorBit
};
};
} // end namespace internal
// we require Lhs and Rhs to have the same scalar type. Currently there is no example of a binary functor
// that would take two operands of different types. If there were such an example, then this check should be
// moved to the BinaryOp functors, on a per-case basis. This would however require a change in the BinaryOp functors, as
// currently they take only one typename Scalar template parameter.
// It is tempting to always allow mixing different types but remember that this is often impossible in the vectorized paths.
// So allowing mixing different types gives very unexpected errors when enabling vectorization, when the user tries to
// add together a float matrix and a double matrix.
#define EIGEN_CHECK_BINARY_COMPATIBILIY(BINOP,LHS,RHS) \
EIGEN_STATIC_ASSERT((internal::functor_allows_mixing_real_and_complex<BINOP>::ret \
? int(internal::is_same<typename NumTraits<LHS>::Real, typename NumTraits<RHS>::Real>::value) \
: int(internal::is_same<LHS, RHS>::value)), \
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
class CwiseBinaryOpImpl;
template<typename BinaryOp, typename Lhs, typename Rhs>
class CwiseBinaryOp : internal::no_assignment_operator,
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<LhsType>::type Lhs;
typedef typename internal::remove_all<RhsType>::type Rhs;
typedef typename CwiseBinaryOpImpl<
BinaryOp, Lhs, Rhs,
typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind,
typename internal::traits<Rhs>::StorageKind>::ret>::Base Base;
BinaryOp, LhsType, RhsType,
typename internal::cwise_promote_storage_type<typename internal::traits<LhsType>::StorageKind,
typename internal::traits<Rhs>::StorageKind,
BinaryOp>::ret>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseBinaryOp)
typedef typename internal::nested<Lhs>::type LhsNested;
typedef typename internal::nested<Rhs>::type RhsNested;
typedef typename internal::ref_selector<LhsType>::type LhsNested;
typedef typename internal::ref_selector<RhsType>::type RhsNested;
typedef typename internal::remove_reference<LhsNested>::type _LhsNested;
typedef typename internal::remove_reference<RhsNested>::type _RhsNested;
EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& lhs, const Rhs& rhs, const BinaryOp& func = BinaryOp())
: m_lhs(lhs), m_rhs(rhs), m_functor(func)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& aLhs, const Rhs& aRhs, const BinaryOp& func = BinaryOp())
: m_lhs(aLhs), m_rhs(aRhs), m_functor(func)
{
EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename Rhs::Scalar);
// require the sizes to match
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs)
eigen_assert(lhs.rows() == rhs.rows() && lhs.cols() == rhs.cols());
eigen_assert(aLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rows() const {
// return the fixed size type if available to enable compile time optimizations
if (internal::traits<typename internal::remove_all<LhsNested>::type>::RowsAtCompileTime==Dynamic)
@@ -138,6 +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)
@@ -147,10 +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:
@@ -159,41 +143,13 @@ class CwiseBinaryOp : internal::no_assignment_operator,
const BinaryOp m_functor;
};
template<typename BinaryOp, typename Lhs, typename Rhs>
class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Dense>
: public internal::dense_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type
// Generic API dispatcher
template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
class CwiseBinaryOpImpl
: public internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type
{
typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> Derived;
public:
typedef typename internal::dense_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE( Derived )
EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
{
return derived().functor()(derived().lhs().coeff(row, col),
derived().rhs().coeff(row, col));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
{
return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(row, col),
derived().rhs().template packet<LoadMode>(row, col));
}
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
{
return derived().functor()(derived().lhs().coeff(index),
derived().rhs().coeff(index));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
{
return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(index),
derived().rhs().template packet<LoadMode>(index));
}
public:
typedef typename internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type Base;
};
/** replaces \c *this by \c *this - \a other.
@@ -205,8 +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>());
return derived();
}
@@ -219,11 +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>());
return derived();
}
} // end namespace Eigen
#endif // EIGEN_CWISE_BINARY_OP_H

View File

@@ -35,25 +35,20 @@ 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
Flags = traits<PlainObjectType>::Flags & RowMajorBit
};
};
}
template<typename NullaryOp, typename PlainObjectType>
class CwiseNullaryOp : internal::no_assignment_operator,
public internal::dense_xpr_base< CwiseNullaryOp<NullaryOp, PlainObjectType> >::type
class CwiseNullaryOp : public internal::dense_xpr_base< CwiseNullaryOp<NullaryOp, PlainObjectType> >::type, internal::no_assignment_operator
{
public:
typedef typename internal::dense_xpr_base<CwiseNullaryOp>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(CwiseNullaryOp)
EIGEN_DEVICE_FUNC
CwiseNullaryOp(Index rows, Index cols, const NullaryOp& func = NullaryOp())
: m_rows(rows), m_cols(cols), m_functor(func)
{
@@ -63,20 +58,24 @@ class CwiseNullaryOp : internal::no_assignment_operator,
&& (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rows() const { return m_rows.value(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const { return m_cols.value(); }
EIGEN_STRONG_INLINE const Scalar coeff(Index rows, Index cols) const
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar coeff(Index rowId, Index colId) const
{
return m_functor(rows, cols);
return m_functor(rowId, colId);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
{
return m_functor.packetOp(row, col);
return m_functor.packetOp(rowId, colId);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
{
return m_functor(index);
@@ -89,6 +88,7 @@ class CwiseNullaryOp : internal::no_assignment_operator,
}
/** \returns the functor representing the nullary operation */
EIGEN_DEVICE_FUNC
const NullaryOp& functor() const { return m_functor; }
protected:
@@ -113,10 +113,10 @@ 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 +132,19 @@ 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
@@ -155,10 +158,10 @@ 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
@@ -242,7 +245,7 @@ EIGEN_STRONG_INLINE const typename DenseBase<Derived>::SequentialLinSpacedReturn
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,false>(low,high,size));
}
/**
@@ -255,7 +258,7 @@ DenseBase<Derived>::LinSpaced(Sequential_t, const Scalar& low, const Scalar& hig
{
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,false>(low,high,Derived::SizeAtCompileTime));
}
/**
@@ -276,7 +279,7 @@ EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedRetu
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,true>(low,high,size));
}
/**
@@ -289,17 +292,18 @@ 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,true>(low,high,Derived::SizeAtCompileTime));
}
/** \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */
/** \returns true if all coefficients in this matrix are approximately equal to \a val, to within precision \a prec */
template<typename Derived>
bool DenseBase<Derived>::isApproxToConstant
(const Scalar& value, RealScalar prec) const
(const Scalar& val, const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if(!internal::isApprox(this->coeff(i, j), value, prec))
if(!internal::isApprox(self.coeff(i, j), val, prec))
return false;
return true;
}
@@ -309,19 +313,19 @@ bool DenseBase<Derived>::isApproxToConstant
* \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */
template<typename Derived>
bool DenseBase<Derived>::isConstant
(const Scalar& value, RealScalar prec) const
(const Scalar& val, const RealScalar& prec) const
{
return isApproxToConstant(value, prec);
return isApproxToConstant(val, prec);
}
/** Alias for setConstant(): sets all coefficients in this expression to \a value.
/** Alias for setConstant(): sets all coefficients in this expression to \a val.
*
* \sa setConstant(), Constant(), class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& value)
EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& val)
{
setConstant(value);
setConstant(val);
}
/** Sets all coefficients in this expression to \a value.
@@ -329,9 +333,9 @@ EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& value)
* \sa fill(), setConstant(Index,const Scalar&), setConstant(Index,Index,const Scalar&), setZero(), setOnes(), Constant(), class CwiseNullaryOp, setZero(), setOnes()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& value)
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& val)
{
return derived() = Constant(rows(), cols(), value);
return derived() = Constant(rows(), cols(), val);
}
/** Resizes to the given \a size, and sets all coefficients in this expression to the given \a value.
@@ -345,17 +349,17 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& value
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index size, const Scalar& value)
PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val)
{
resize(size);
return setConstant(value);
return setConstant(val);
}
/** Resizes to the given size, and sets all coefficients in this expression to the given \a value.
*
* \param rows the new number of rows
* \param cols the new number of columns
* \param value the value to which all coefficients are set
* \param val the value to which all coefficients are set
*
* Example: \include Matrix_setConstant_int_int.cpp
* Output: \verbinclude Matrix_setConstant_int_int.out
@@ -364,10 +368,10 @@ PlainObjectBase<Derived>::setConstant(Index size, const Scalar& value)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& value)
PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& val)
{
resize(rows, cols);
return setConstant(value);
return setConstant(val);
}
/**
@@ -384,10 +388,10 @@ PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& valu
* \sa CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index size, const Scalar& low, const Scalar& high)
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index newSize, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return derived() = Derived::NullaryExpr(size, internal::linspaced_op<Scalar,false>(low,high,size));
return derived() = Derived::NullaryExpr(newSize, internal::linspaced_op<Scalar,PacketScalar,false>(low,high,newSize));
}
/**
@@ -479,11 +483,12 @@ DenseBase<Derived>::Zero()
* \sa class CwiseNullaryOp, Zero()
*/
template<typename Derived>
bool DenseBase<Derived>::isZero(RealScalar prec) const
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;
}
@@ -512,9 +517,9 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index size)
PlainObjectBase<Derived>::setZero(Index newSize)
{
resize(size);
resize(newSize);
return setConstant(Scalar(0));
}
@@ -561,7 +566,7 @@ DenseBase<Derived>::Ones(Index rows, Index cols)
/** \returns an expression of a vector where all coefficients equal one.
*
* The parameter \a size is the size of the returned vector.
* The parameter \a newSize is the size of the returned vector.
* Must be compatible with this MatrixBase type.
*
* \only_for_vectors
@@ -577,9 +582,9 @@ DenseBase<Derived>::Ones(Index rows, Index cols)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index size)
DenseBase<Derived>::Ones(Index newSize)
{
return Constant(size, Scalar(1));
return Constant(newSize, Scalar(1));
}
/** \returns an expression of a fixed-size matrix or vector where all coefficients equal one.
@@ -609,7 +614,7 @@ DenseBase<Derived>::Ones()
*/
template<typename Derived>
bool DenseBase<Derived>::isOnes
(RealScalar prec) const
(const RealScalar& prec) const
{
return isApproxToConstant(Scalar(1), prec);
}
@@ -627,7 +632,7 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
return setConstant(Scalar(1));
}
/** Resizes to the given \a size, and sets all coefficients in this expression to one.
/** Resizes to the given \a newSize, and sets all coefficients in this expression to one.
*
* \only_for_vectors
*
@@ -638,9 +643,9 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index size)
PlainObjectBase<Derived>::setOnes(Index newSize)
{
resize(size);
resize(newSize);
return setConstant(Scalar(1));
}
@@ -714,20 +719,21 @@ MatrixBase<Derived>::Identity()
*/
template<typename Derived>
bool MatrixBase<Derived>::isIdentity
(RealScalar prec) const
(const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index j = 0; j < cols(); ++j)
{
for(Index i = 0; i < rows(); ++i)
{
if(i == j)
{
if(!internal::isApprox(this->coeff(i, j), static_cast<Scalar>(1), prec))
if(!internal::isApprox(self.coeff(i, j), static_cast<Scalar>(1), prec))
return false;
}
else
{
if(!internal::isMuchSmallerThan(this->coeff(i, j), static_cast<RealScalar>(1), prec))
if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<RealScalar>(1), prec))
return false;
}
}
@@ -740,6 +746,7 @@ 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());
@@ -749,7 +756,7 @@ 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();
@@ -798,10 +805,10 @@ EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index
* \sa MatrixBase::Unit(Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index size, Index i)
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index newSize, Index i)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return BasisReturnType(SquareMatrixType::Identity(size,size), i);
return BasisReturnType(SquareMatrixType::Identity(newSize,newSize), i);
}
/** \returns an expression of the i-th unit (basis) vector.

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
@@ -44,10 +44,7 @@ struct traits<CwiseUnaryOp<UnaryOp, XprType> >
typedef typename XprType::Nested XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
enum {
Flags = _XprTypeNested::Flags & (
HereditaryBits | LinearAccessBit | AlignedBit
| (functor_traits<UnaryOp>::PacketAccess ? PacketAccessBit : 0)),
CoeffReadCost = _XprTypeNested::CoeffReadCost + functor_traits<UnaryOp>::Cost
Flags = _XprTypeNested::Flags & RowMajorBit
};
};
}
@@ -56,28 +53,34 @@ 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::remove_all<XprType>::type NestedExpression;
inline CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
EIGEN_DEVICE_FUNC
explicit inline CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
: m_xpr(xpr), m_functor(func) {}
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
const UnaryOp& functor() const { return m_functor; }
/** \returns the nested expression */
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
nestedExpression() const { return m_xpr; }
/** \returns the nested expression */
EIGEN_DEVICE_FUNC
typename internal::remove_all<typename XprType::Nested>::type&
nestedExpression() { return m_xpr.const_cast_derived(); }
@@ -86,39 +89,13 @@ class CwiseUnaryOp : internal::no_assignment_operator,
const UnaryOp m_functor;
};
// This is the generic implementation for dense storage.
// It can be used for any expression types implementing the dense concept.
template<typename UnaryOp, typename XprType>
class CwiseUnaryOpImpl<UnaryOp,XprType,Dense>
: public internal::dense_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type
// Generic API dispatcher
template<typename UnaryOp, typename XprType, typename StorageKind>
class CwiseUnaryOpImpl
: public internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type
{
public:
typedef CwiseUnaryOp<UnaryOp, XprType> Derived;
typedef typename internal::dense_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
{
return derived().functor()(derived().nestedExpression().coeff(row, col));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
{
return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(row, col));
}
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
{
return derived().functor()(derived().nestedExpression().coeff(index));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
{
return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(index));
}
public:
typedef typename internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
};
} // end namespace Eigen

View File

@@ -37,16 +37,17 @@ struct traits<CwiseUnaryView<ViewOp, MatrixType> >
typedef typename MatrixType::Nested MatrixTypeNested;
typedef typename remove_all<MatrixTypeNested>::type _MatrixTypeNested;
enum {
Flags = (traits<_MatrixTypeNested>::Flags & (HereditaryBits | LvalueBit | LinearAccessBit | DirectAccessBit)),
CoeffReadCost = traits<_MatrixTypeNested>::CoeffReadCost + functor_traits<ViewOp>::Cost,
FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
Flags = traits<_MatrixTypeNested>::Flags & (RowMajorBit | FlagsLvalueBit | DirectAccessBit), // FIXME DirectAccessBit should not be handled by expressions
MatrixTypeInnerStride = inner_stride_at_compile_time<MatrixType>::ret,
// need to cast the sizeof's from size_t to int explicitly, otherwise:
// "error: no integral type can represent all of the enumerator values
InnerStrideAtCompileTime = MatrixTypeInnerStride == Dynamic
? int(Dynamic)
: int(MatrixTypeInnerStride)
* int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)),
OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret
: int(MatrixTypeInnerStride) * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)),
OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret == Dynamic
? int(Dynamic)
: outer_stride_at_compile_time<MatrixType>::ret * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar))
};
};
}
@@ -55,15 +56,15 @@ template<typename ViewOp, typename MatrixType, typename StorageKind>
class CwiseUnaryViewImpl;
template<typename ViewOp, typename MatrixType>
class CwiseUnaryView : internal::no_assignment_operator,
public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
{
public:
typedef typename CwiseUnaryViewImpl<ViewOp, MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView)
typedef typename internal::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)
@@ -83,11 +84,19 @@ class CwiseUnaryView : internal::no_assignment_operator,
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;
typename internal::ref_selector<MatrixType>::type 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
@@ -98,35 +107,19 @@ class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
typedef typename internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
EIGEN_DEVICE_FUNC inline Scalar* data() { return &(this->coeffRef(0)); }
EIGEN_DEVICE_FUNC inline const Scalar* data() const { return &(this->coeff(0)); }
inline Index innerStride() const
EIGEN_DEVICE_FUNC inline Index innerStride() const
{
return derived().nestedExpression().innerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
}
inline Index outerStride() const
EIGEN_DEVICE_FUNC inline Index outerStride() const
{
return derived().nestedExpression().outerStride();
}
EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const
{
return derived().functor()(derived().nestedExpression().coeff(row, col));
}
EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
return derived().functor()(derived().nestedExpression().coeff(index));
}
EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
{
return derived().functor()(const_cast_derived().nestedExpression().coeffRef(row, col));
}
EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
return derived().functor()(const_cast_derived().nestedExpression().coeffRef(index));
return derived().nestedExpression().outerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
}
};

View File

@@ -13,6 +13,16 @@
namespace Eigen {
namespace internal {
// The index type defined by EIGEN_DEFAULT_DENSE_INDEX_TYPE must be a signed type.
// This dummy function simply aims at checking that at compile time.
static inline void check_DenseIndex_is_signed() {
EIGEN_STATIC_ASSERT(NumTraits<DenseIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE);
}
} // end namespace internal
/** \class DenseBase
* \ingroup Core_Module
*
@@ -39,22 +49,37 @@ template<typename Derived> class DenseBase
public:
using internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar,
typename NumTraits<typename internal::traits<Derived>::Scalar>::Real>::operator*;
using internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar,
typename NumTraits<typename internal::traits<Derived>::Scalar>::Real>::operator/;
class InnerIterator;
/** Inner iterator type to iterate over the coefficients of a row or column.
* \sa class InnerIterator
*/
typedef Eigen::InnerIterator<Derived> InnerIterator;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
/** \brief The type of indices
* \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
* \sa \ref TopicPreprocessorDirectives.
*/
typedef typename internal::traits<Derived>::Index Index;
/**
* \brief The type used to store indices
* \details This typedef is relevant for types that store multiple indices such as
* PermutationMatrix or Transpositions, otherwise it defaults to Eigen::Index
* \sa \ref TopicPreprocessorDirectives, Eigen::Index, SparseMatrixBase.
*/
typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
/** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc. */
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
/** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc.
*
* It is an alias for the Scalar type */
typedef Scalar value_type;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef DenseCoeffsBase<Derived> Base;
typedef internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar,
typename NumTraits<typename internal::traits<Derived>::Scalar>::Real> Base;
using Base::derived;
using Base::const_cast_derived;
using Base::rows;
@@ -64,16 +89,6 @@ 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;
@@ -159,19 +174,46 @@ 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 true if either the number of rows or the number of columns is equal to 1.
* In other words, this function returns
@@ -183,6 +225,7 @@ 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
@@ -194,6 +237,7 @@ 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()
@@ -204,16 +248,18 @@ template<typename Derived> class DenseBase
* Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
* nothing else.
*/
void resize(Index size)
EIGEN_DEVICE_FUNC
void resize(Index newSize)
{
EIGEN_ONLY_USED_FOR_DEBUG(size);
eigen_assert(size == this->size()
EIGEN_ONLY_USED_FOR_DEBUG(newSize);
eigen_assert(newSize == this->size()
&& "DenseBase::resize() does not actually allow to resize.");
}
/** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
* Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
* nothing else.
*/
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols)
{
EIGEN_ONLY_USED_FOR_DEBUG(rows);
@@ -223,13 +269,12 @@ template<typename Derived> class DenseBase
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal Represents a matrix with all coefficients equal to one another*/
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType;
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> 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::linspaced_op<Scalar,PacketScalar,false>,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,true>,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;
@@ -237,130 +282,122 @@ template<typename Derived> class DenseBase
/** Copies \a other into *this. \returns a reference to *this. */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
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
Derived& operator=(const DenseBase& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator=(const EigenBase<OtherDerived> &other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator+=(const EigenBase<OtherDerived> &other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator-=(const EigenBase<OtherDerived> &other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator=(const ReturnByValue<OtherDerived>& func);
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** Copies \a other into *this without evaluating other. \returns a reference to *this. */
/** \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);
#endif // not EIGEN_PARSED_BY_DOXYGEN
EIGEN_DEVICE_FUNC
CommaInitializer<Derived> operator<< (const Scalar& s);
/** \deprecated it now returns \c *this */
template<unsigned int Added,unsigned int Removed>
const Flagged<Derived, Added, Removed> flagged() const;
EIGEN_DEPRECATED
const Derived& flagged() const
{ return derived(); }
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
CommaInitializer<Derived> operator<< (const DenseBase<OtherDerived>& other);
Eigen::Transpose<Derived> transpose();
typedef const Transpose<const Derived> ConstTransposeReturnType;
typedef Transpose<Derived> TransposeReturnType;
EIGEN_DEVICE_FUNC
TransposeReturnType transpose();
typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
EIGEN_DEVICE_FUNC
ConstTransposeReturnType transpose() const;
EIGEN_DEVICE_FUNC
void transposeInPlace();
#ifndef EIGEN_NO_DEBUG
protected:
template<typename OtherDerived>
void checkTransposeAliasing(const OtherDerived& other) const;
public:
#endif
typedef VectorBlock<Derived> SegmentReturnType;
typedef const VectorBlock<const Derived> ConstSegmentReturnType;
template<int Size> struct FixedSegmentReturnType { typedef VectorBlock<Derived, Size> Type; };
template<int Size> struct ConstFixedSegmentReturnType { typedef const VectorBlock<const Derived, Size> Type; };
// Note: The "DenseBase::" prefixes are added to help MSVC9 to match these declarations with the later implementations.
SegmentReturnType segment(Index start, Index size);
typename DenseBase::ConstSegmentReturnType segment(Index start, Index size) const;
SegmentReturnType head(Index size);
typename DenseBase::ConstSegmentReturnType head(Index size) const;
SegmentReturnType tail(Index size);
typename DenseBase::ConstSegmentReturnType tail(Index size) const;
template<int Size> typename FixedSegmentReturnType<Size>::Type head();
template<int Size> typename ConstFixedSegmentReturnType<Size>::Type head() const;
template<int Size> typename FixedSegmentReturnType<Size>::Type tail();
template<int Size> typename ConstFixedSegmentReturnType<Size>::Type tail() const;
template<int Size> typename FixedSegmentReturnType<Size>::Type segment(Index start);
template<int Size> typename ConstFixedSegmentReturnType<Size>::Type segment(Index start) const;
static const ConstantReturnType
EIGEN_DEVICE_FUNC static const ConstantReturnType
Constant(Index rows, Index cols, const Scalar& value);
static const ConstantReturnType
EIGEN_DEVICE_FUNC static const ConstantReturnType
Constant(Index size, const Scalar& value);
static const ConstantReturnType
EIGEN_DEVICE_FUNC static const ConstantReturnType
Constant(const Scalar& value);
static const SequentialLinSpacedReturnType
EIGEN_DEVICE_FUNC static const SequentialLinSpacedReturnType
LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high);
static const RandomAccessLinSpacedReturnType
EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
LinSpaced(Index size, const Scalar& low, const Scalar& high);
static const SequentialLinSpacedReturnType
EIGEN_DEVICE_FUNC static const SequentialLinSpacedReturnType
LinSpaced(Sequential_t, const Scalar& low, const Scalar& high);
static const RandomAccessLinSpacedReturnType
EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
LinSpaced(const Scalar& low, const Scalar& high);
template<typename CustomNullaryOp>
static const CwiseNullaryOp<CustomNullaryOp, Derived>
template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func);
template<typename CustomNullaryOp>
static const CwiseNullaryOp<CustomNullaryOp, Derived>
template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
NullaryExpr(Index size, const CustomNullaryOp& func);
template<typename CustomNullaryOp>
static const CwiseNullaryOp<CustomNullaryOp, Derived>
template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
NullaryExpr(const CustomNullaryOp& func);
static const ConstantReturnType Zero(Index rows, Index cols);
static const ConstantReturnType Zero(Index size);
static const ConstantReturnType Zero();
static const ConstantReturnType Ones(Index rows, Index cols);
static const ConstantReturnType Ones(Index size);
static const ConstantReturnType Ones();
EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index rows, Index cols);
EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index size);
EIGEN_DEVICE_FUNC static const ConstantReturnType Zero();
EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index rows, Index cols);
EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index size);
EIGEN_DEVICE_FUNC static const ConstantReturnType Ones();
void fill(const Scalar& value);
Derived& setConstant(const Scalar& value);
Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high);
Derived& setLinSpaced(const Scalar& low, const Scalar& high);
Derived& setZero();
Derived& setOnes();
Derived& setRandom();
EIGEN_DEVICE_FUNC void fill(const Scalar& value);
EIGEN_DEVICE_FUNC Derived& setConstant(const Scalar& value);
EIGEN_DEVICE_FUNC Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high);
EIGEN_DEVICE_FUNC Derived& setLinSpaced(const Scalar& low, const Scalar& high);
EIGEN_DEVICE_FUNC Derived& setZero();
EIGEN_DEVICE_FUNC Derived& setOnes();
EIGEN_DEVICE_FUNC Derived& setRandom();
template<typename OtherDerived>
template<typename OtherDerived> EIGEN_DEVICE_FUNC
bool isApprox(const DenseBase<OtherDerived>& other,
RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC
bool isMuchSmallerThan(const RealScalar& other,
RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
template<typename OtherDerived>
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
template<typename OtherDerived> EIGEN_DEVICE_FUNC
bool isMuchSmallerThan(const DenseBase<OtherDerived>& other,
RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isApproxToConstant(const Scalar& value, RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isConstant(const Scalar& value, RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isZero(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isOnes(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isApproxToConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isZero(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isOnes(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
inline bool hasNaN() const;
inline bool allFinite() const;
EIGEN_DEVICE_FUNC
inline Derived& operator*=(const Scalar& other);
EIGEN_DEVICE_FUNC
inline Derived& operator/=(const Scalar& other);
typedef typename internal::add_const_on_value_type<typename internal::eval<Derived>::type>::type EvalReturnType;
@@ -368,7 +405,10 @@ template<typename Derived> class DenseBase
*
* 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
@@ -376,61 +416,68 @@ 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)
@@ -438,10 +485,8 @@ template<typename Derived> class DenseBase
return derived().coeff(0,0);
}
/////////// Array module ///////////
bool all(void) const;
bool any(void) const;
bool all() const;
bool any() const;
Index count() const;
typedef VectorwiseOp<Derived, Horizontal> RowwiseReturnType;
@@ -449,14 +494,35 @@ 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>
@@ -465,23 +531,42 @@ template<typename Derived> class DenseBase
template<typename ThenDerived>
inline const Select<Derived,ThenDerived, typename ThenDerived::ConstantReturnType>
select(const DenseBase<ThenDerived>& thenMatrix, typename ThenDerived::Scalar elseScalar) const;
select(const DenseBase<ThenDerived>& thenMatrix, const typename ThenDerived::Scalar& elseScalar) const;
template<typename ElseDerived>
inline const Select<Derived, typename ElseDerived::ConstantReturnType, ElseDerived >
select(typename ElseDerived::Scalar thenScalar, const DenseBase<ElseDerived>& elseMatrix) const;
select(const typename ElseDerived::Scalar& thenScalar, const DenseBase<ElseDerived>& elseMatrix) const;
template<int p> RealScalar lpNorm() const;
template<int RowFactor, int ColFactor>
EIGEN_DEVICE_FUNC
const Replicate<Derived,RowFactor,ColFactor> replicate() const;
const Replicate<Derived,Dynamic,Dynamic> replicate(Index rowFacor,Index colFactor) const;
/**
* \return an expression of the replication of \c *this
*
* Example: \include MatrixBase_replicate_int_int.cpp
* Output: \verbinclude MatrixBase_replicate_int_int.out
*
* \sa VectorwiseOp::replicate(), DenseBase::replicate<int,int>(), class Replicate
*/
//Code moved here due to a CUDA compiler bug
EIGEN_DEVICE_FUNC
const Replicate<Derived, Dynamic, Dynamic> replicate(Index rowFactor, Index colFactor) const
{
return Replicate<Derived, Dynamic, Dynamic>(derived(), rowFactor, colFactor);
}
typedef Reverse<Derived, BothDirections> ReverseReturnType;
typedef const Reverse<const Derived, BothDirections> ConstReverseReturnType;
ReverseReturnType reverse();
ConstReverseReturnType reverse() const;
void reverseInPlace();
EIGEN_DEVICE_FUNC ReverseReturnType reverse();
/** This is the const version of reverse(). */
//Code moved here due to a CUDA compiler bug
EIGEN_DEVICE_FUNC ConstReverseReturnType reverse() const
{
return ConstReverseReturnType(derived());
}
EIGEN_DEVICE_FUNC void reverseInPlace();
#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::DenseBase
# include "../plugins/BlockMethods.h"
@@ -490,27 +575,18 @@ template<typename Derived> class DenseBase
# 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
// 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
@@ -523,9 +599,9 @@ 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 +134,12 @@ 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_internal_assert(index >= 0 && index < size());
return derived().coeff(index);
return internal::evaluator<Derived>(derived()).coeff(index);
}
@@ -146,15 +151,14 @@ 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.
@@ -167,30 +171,35 @@ 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]; }
/** equivalent to operator[](2). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
z() const { return (*this)[2]; }
/** equivalent to operator[](3). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
w() const { return (*this)[3]; }
@@ -207,9 +216,9 @@ 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 +243,9 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
template<int LoadMode>
EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
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 +288,6 @@ 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 +320,15 @@ 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 +341,13 @@ 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 +366,12 @@ 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_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 +381,14 @@ 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 +400,37 @@ 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]; }
/** equivalent to operator[](2). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
z() { return (*this)[2]; }
/** equivalent to operator[](3). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
w() { return (*this)[3]; }
/** \internal
* Stores the given packet of coefficients, at the given row and column of this expression. It is your responsibility
* to ensure that a packet really starts there. This method is only available on expressions having the
* PacketAccessBit.
*
* The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select
* the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
* starting at an address which is a multiple of the packet size.
*/
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket
(Index row, Index col, const typename internal::packet_traits<Scalar>::type& x)
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().template writePacket<StoreMode>(row,col,x);
}
/** \internal */
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacketByOuterInner
(Index outer, Index inner, const typename internal::packet_traits<Scalar>::type& x)
{
writePacket<StoreMode>(rowIndexByOuterInner(outer, inner),
colIndexByOuterInner(outer, inner),
x);
}
/** \internal
* Stores the given packet of coefficients, at the given index in this expression. It is your responsibility
* to ensure that a packet really starts there. This method is only available on expressions having the
* PacketAccessBit and the LinearAccessBit.
*
* The \a LoadMode parameter may have the value \a Aligned or \a Unaligned. Its effect is to select
* the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
* starting at an address which is a multiple of the packet size.
*/
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket
(Index index, const typename internal::packet_traits<Scalar>::type& x)
{
eigen_internal_assert(index >= 0 && index < size());
derived().template writePacket<StoreMode>(index,x);
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal Copies the coefficient at position (row,col) of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE void copyCoeff(Index row, Index col, const DenseBase<OtherDerived>& other)
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().coeffRef(row, col) = other.derived().coeff(row, col);
}
/** \internal Copies the coefficient at the given index of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE void copyCoeff(Index index, const DenseBase<OtherDerived>& other)
{
eigen_internal_assert(index >= 0 && index < size());
derived().coeffRef(index) = other.derived().coeff(index);
}
template<typename OtherDerived>
EIGEN_STRONG_INLINE void copyCoeffByOuterInner(Index outer, Index inner, const DenseBase<OtherDerived>& other)
{
const Index row = rowIndexByOuterInner(outer,inner);
const Index col = colIndexByOuterInner(outer,inner);
// derived() is important here: copyCoeff() may be reimplemented in Derived!
derived().copyCoeff(row, col, other);
}
/** \internal Copies the packet at position (row,col) of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived, int StoreMode, int LoadMode>
EIGEN_STRONG_INLINE void copyPacket(Index row, Index col, const DenseBase<OtherDerived>& other)
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().template writePacket<StoreMode>(row, col,
other.derived().template packet<LoadMode>(row, col));
}
/** \internal Copies the packet at the given index of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived, int StoreMode, int LoadMode>
EIGEN_STRONG_INLINE void copyPacket(Index index, const DenseBase<OtherDerived>& other)
{
eigen_internal_assert(index >= 0 && index < size());
derived().template writePacket<StoreMode>(index,
other.derived().template packet<LoadMode>(index));
}
/** \internal */
template<typename OtherDerived, int StoreMode, int LoadMode>
EIGEN_STRONG_INLINE void copyPacketByOuterInner(Index outer, Index inner, const DenseBase<OtherDerived>& other)
{
const Index row = rowIndexByOuterInner(outer,inner);
const Index col = colIndexByOuterInner(outer,inner);
// derived() is important here: copyCoeff() may be reimplemented in Derived!
derived().template copyPacket< OtherDerived, StoreMode, LoadMode>(row, col, other);
}
#endif
};
/** \brief Base class providing direct read-only coefficient access to matrices and arrays.
@@ -568,7 +450,6 @@ 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 +462,7 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
*
* \sa outerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return derived().innerStride();
@@ -591,6 +473,7 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
*
* \sa innerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return derived().outerStride();
@@ -606,6 +489,7 @@ 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 +499,7 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
*
* \sa innerStride(), outerStride(), rowStride()
*/
EIGEN_DEVICE_FUNC
inline Index colStride() const
{
return Derived::IsRowMajor ? innerStride() : outerStride();
@@ -639,7 +524,6 @@ 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 +536,7 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
*
* \sa outerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return derived().innerStride();
@@ -662,6 +547,7 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
*
* \sa innerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return derived().outerStride();
@@ -677,6 +563,7 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
*
* \sa innerStride(), outerStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index rowStride() const
{
return Derived::IsRowMajor ? outerStride() : innerStride();
@@ -686,6 +573,7 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
*
* \sa innerStride(), outerStride(), rowStride()
*/
EIGEN_DEVICE_FUNC
inline Index colStride() const
{
return Derived::IsRowMajor ? innerStride() : outerStride();
@@ -694,33 +582,42 @@ 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.const_cast_derived().coeffRef(0,0), 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 first_aligned<unpacket_traits<DefaultPacketType>::alignment>(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
@@ -24,45 +24,143 @@ namespace internal {
struct constructor_without_unaligned_array_assert {};
template<typename T, int Size>
EIGEN_DEVICE_FUNC
void check_static_allocation_size()
{
// if EIGEN_STACK_ALLOCATION_LIMIT is defined to 0, then no limit
#if EIGEN_STACK_ALLOCATION_LIMIT
EIGEN_STATIC_ASSERT(Size * sizeof(T) <= EIGEN_STACK_ALLOCATION_LIMIT, OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG);
#endif
}
/** \internal
* Static array. If the MatrixOrArrayOptions require auto-alignment, the array will be automatically aligned:
* to 16 bytes boundary if the total size is a multiple of 16 bytes.
*/
template <typename T, int Size, int MatrixOrArrayOptions,
int Alignment = (MatrixOrArrayOptions&DontAlign) ? 0
: (((Size*sizeof(T))%16)==0) ? 16
: 0 >
: compute_default_alignment<T,Size>::value >
struct plain_array
{
T array[Size];
plain_array() {}
plain_array(constructor_without_unaligned_array_assert) {}
EIGEN_DEVICE_FUNC
plain_array()
{
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
#ifdef EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT
#if defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask)
#elif EIGEN_GNUC_AT_LEAST(4,7)
// GCC 4.7 is too aggressive in its optimizations and remove the alignement test based on the fact the array is declared to be aligned.
// See this bug report: http://gcc.gnu.org/bugzilla/show_bug.cgi?id=53900
// Hiding the origin of the array pointer behind a function argument seems to do the trick even if the function is inlined:
template<typename PtrType>
EIGEN_ALWAYS_INLINE PtrType eigen_unaligned_array_assert_workaround_gcc47(PtrType array) { return array; }
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
eigen_assert((reinterpret_cast<size_t>(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((reinterpret_cast<size_t>(array) & (sizemask)) == 0 \
&& "this assertion is explained here: " \
"http://eigen.tuxfamily.org/dox-devel/TopicUnalignedArrayAssert.html" \
"http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
" **** READ THIS WEB PAGE !!! ****");
#endif
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 8>
{
EIGEN_ALIGN_TO_BOUNDARY(8) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(7);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 16>
{
EIGEN_USER_ALIGN16 T array[Size];
plain_array() { EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(0xf) }
plain_array(constructor_without_unaligned_array_assert) {}
EIGEN_ALIGN_TO_BOUNDARY(16) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(15);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 32>
{
EIGEN_ALIGN_TO_BOUNDARY(32) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(31);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 64>
{
EIGEN_ALIGN_TO_BOUNDARY(64) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(63);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
template <typename T, int MatrixOrArrayOptions, int Alignment>
struct plain_array<T, 0, MatrixOrArrayOptions, Alignment>
{
EIGEN_USER_ALIGN16 T array[1];
plain_array() {}
plain_array(constructor_without_unaligned_array_assert) {}
T array[1];
EIGEN_DEVICE_FUNC plain_array() {}
EIGEN_DEVICE_FUNC plain_array(constructor_without_unaligned_array_assert) {}
};
} // end namespace internal
@@ -86,33 +184,50 @@ template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseSt
{
internal::plain_array<T,Size,_Options> m_data;
public:
inline explicit DenseStorage() {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC DenseStorage() {}
EIGEN_DEVICE_FUNC
explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()) {}
inline DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); }
static inline DenseIndex rows(void) {return _Rows;}
static inline DenseIndex cols(void) {return _Cols;}
inline void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {}
inline void resize(DenseIndex,DenseIndex,DenseIndex) {}
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
EIGEN_DEVICE_FUNC
DenseStorage(const DenseStorage& other) : m_data(other.m_data) {}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other) m_data = other.m_data;
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) {
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
eigen_internal_assert(size==rows*cols && rows==_Rows && cols==_Cols);
EIGEN_UNUSED_VARIABLE(size);
EIGEN_UNUSED_VARIABLE(rows);
EIGEN_UNUSED_VARIABLE(cols);
}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); }
EIGEN_DEVICE_FUNC static Index rows(void) {return _Rows;}
EIGEN_DEVICE_FUNC static Index cols(void) {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC void resize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
};
// null matrix
template<typename T, int _Rows, int _Cols, int _Options> class DenseStorage<T, 0, _Rows, _Cols, _Options>
{
public:
inline explicit DenseStorage() {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert) {}
inline DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
inline void swap(DenseStorage& ) {}
static inline DenseIndex rows(void) {return _Rows;}
static inline DenseIndex cols(void) {return _Cols;}
inline void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {}
inline void resize(DenseIndex,DenseIndex,DenseIndex) {}
inline const T *data() const { return 0; }
inline T *data() { return 0; }
EIGEN_DEVICE_FUNC DenseStorage() {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) {}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage&) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage&) { return *this; }
EIGEN_DEVICE_FUNC DenseStorage(Index,Index,Index) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& ) {}
EIGEN_DEVICE_FUNC static Index rows(void) {return _Rows;}
EIGEN_DEVICE_FUNC static Index cols(void) {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC void resize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC const T *data() const { return 0; }
EIGEN_DEVICE_FUNC T *data() { return 0; }
};
// more specializations for null matrices; these are necessary to resolve ambiguities
@@ -129,86 +244,157 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, 0, Dynamic,
template<typename T, int Size, int _Options> class DenseStorage<T, Size, Dynamic, Dynamic, _Options>
{
internal::plain_array<T,Size,_Options> m_data;
DenseIndex m_rows;
DenseIndex m_cols;
Index m_rows;
Index m_cols;
public:
inline explicit DenseStorage() : m_rows(0), m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
inline DenseStorage(DenseIndex, DenseIndex rows, DenseIndex cols) : m_rows(rows), m_cols(cols) {}
inline void swap(DenseStorage& other)
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows), m_cols(other.m_cols) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
m_data = other.m_data;
m_rows = other.m_rows;
m_cols = other.m_cols;
}
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index cols) : m_rows(rows), m_cols(cols) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
{ std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
inline DenseIndex rows(void) const {return m_rows;}
inline DenseIndex cols(void) const {return m_cols;}
inline void conservativeResize(DenseIndex, DenseIndex rows, DenseIndex cols) { m_rows = rows; m_cols = cols; }
inline void resize(DenseIndex, DenseIndex rows, DenseIndex cols) { m_rows = rows; m_cols = cols; }
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
EIGEN_DEVICE_FUNC Index rows() const {return m_rows;}
EIGEN_DEVICE_FUNC Index cols() const {return m_cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols; }
EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
};
// dynamic-size matrix with fixed-size storage and fixed width
template<typename T, int Size, int _Cols, int _Options> class DenseStorage<T, Size, Dynamic, _Cols, _Options>
{
internal::plain_array<T,Size,_Options> m_data;
DenseIndex m_rows;
Index m_rows;
public:
inline explicit DenseStorage() : m_rows(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {}
inline DenseStorage(DenseIndex, DenseIndex rows, DenseIndex) : m_rows(rows) {}
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
inline DenseIndex rows(void) const {return m_rows;}
inline DenseIndex cols(void) const {return _Cols;}
inline void conservativeResize(DenseIndex, DenseIndex rows, DenseIndex) { m_rows = rows; }
inline void resize(DenseIndex, DenseIndex rows, DenseIndex) { m_rows = rows; }
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
m_data = other.m_data;
m_rows = other.m_rows;
}
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index) : m_rows(rows) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
EIGEN_DEVICE_FUNC Index rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index) { m_rows = rows; }
EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index) { m_rows = rows; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
};
// dynamic-size matrix with fixed-size storage and fixed height
template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Size, _Rows, Dynamic, _Options>
{
internal::plain_array<T,Size,_Options> m_data;
DenseIndex m_cols;
Index m_cols;
public:
inline explicit DenseStorage() : m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC DenseStorage() : m_cols(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {}
inline DenseStorage(DenseIndex, DenseIndex, DenseIndex cols) : m_cols(cols) {}
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
inline DenseIndex rows(void) const {return _Rows;}
inline DenseIndex cols(void) const {return m_cols;}
inline void conservativeResize(DenseIndex, DenseIndex, DenseIndex cols) { m_cols = cols; }
inline void resize(DenseIndex, DenseIndex, DenseIndex cols) { m_cols = cols; }
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_cols(other.m_cols) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
m_data = other.m_data;
m_cols = other.m_cols;
}
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index, Index, Index cols) : m_cols(cols) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
EIGEN_DEVICE_FUNC Index rows(void) const {return _Rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;}
void conservativeResize(Index, Index, Index cols) { m_cols = cols; }
void resize(Index, Index, Index cols) { m_cols = cols; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
};
// purely dynamic matrix.
template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynamic, _Options>
{
T *m_data;
DenseIndex m_rows;
DenseIndex m_cols;
Index m_rows;
Index m_cols;
public:
inline explicit DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(0), m_rows(0), m_cols(0) {}
inline DenseStorage(DenseIndex size, DenseIndex rows, DenseIndex cols)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows), m_cols(cols)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
inline void swap(DenseStorage& other)
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows), m_cols(cols)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
eigen_internal_assert(size==rows*cols && rows>=0 && cols >=0);
}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(other.m_rows*other.m_cols))
, m_rows(other.m_rows)
, m_cols(other.m_cols)
{
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;
}
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other)
: m_data(std::move(other.m_data))
, m_rows(std::move(other.m_rows))
, m_cols(std::move(other.m_cols))
{
other.m_data = nullptr;
other.m_rows = 0;
other.m_cols = 0;
}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other)
{
using std::swap;
swap(m_data, other.m_data);
swap(m_rows, other.m_rows);
swap(m_cols, other.m_cols);
return *this;
}
#endif
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
{ std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
inline DenseIndex rows(void) const {return m_rows;}
inline DenseIndex cols(void) const {return m_cols;}
inline void conservativeResize(DenseIndex size, DenseIndex rows, DenseIndex cols)
EIGEN_DEVICE_FUNC Index rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;}
void conservativeResize(Index size, Index rows, Index cols)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*m_cols);
m_rows = rows;
m_cols = cols;
}
void resize(DenseIndex size, DenseIndex rows, DenseIndex cols)
EIGEN_DEVICE_FUNC void resize(Index size, Index rows, Index cols)
{
if(size != m_rows*m_cols)
{
@@ -222,30 +408,67 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
m_rows = rows;
m_cols = cols;
}
inline const T *data() const { return m_data; }
inline T *data() { return m_data; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC T *data() { return m_data; }
};
// matrix with dynamic width and fixed height (so that matrix has dynamic size).
template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Rows, Dynamic, _Options>
{
T *m_data;
DenseIndex m_cols;
Index m_cols;
public:
inline explicit DenseStorage() : m_data(0), m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
inline DenseStorage(DenseIndex size, DenseIndex, DenseIndex cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(cols)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
static inline DenseIndex rows(void) {return _Rows;}
inline DenseIndex cols(void) const {return m_cols;}
inline void conservativeResize(DenseIndex size, DenseIndex, DenseIndex cols)
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_cols(0) {}
explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(cols)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
eigen_internal_assert(size==rows*cols && rows==_Rows && cols >=0);
EIGEN_UNUSED_VARIABLE(rows);
}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(_Rows*other.m_cols))
, m_cols(other.m_cols)
{
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;
}
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other)
: m_data(std::move(other.m_data))
, m_cols(std::move(other.m_cols))
{
other.m_data = nullptr;
other.m_cols = 0;
}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other)
{
using std::swap;
swap(m_data, other.m_data);
swap(m_cols, other.m_cols);
return *this;
}
#endif
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
EIGEN_DEVICE_FUNC static Index rows(void) {return _Rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index size, Index, Index cols)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, _Rows*m_cols);
m_cols = cols;
}
EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex, DenseIndex cols)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index, Index cols)
{
if(size != _Rows*m_cols)
{
@@ -258,30 +481,67 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
}
m_cols = cols;
}
inline const T *data() const { return m_data; }
inline T *data() { return m_data; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC T *data() { return m_data; }
};
// matrix with dynamic height and fixed width (so that matrix has dynamic size).
template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dynamic, _Cols, _Options>
{
T *m_data;
DenseIndex m_rows;
Index m_rows;
public:
inline explicit DenseStorage() : m_data(0), m_rows(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
inline DenseStorage(DenseIndex size, DenseIndex rows, DenseIndex) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
inline DenseIndex rows(void) const {return m_rows;}
static inline DenseIndex cols(void) {return _Cols;}
inline void conservativeResize(DenseIndex size, DenseIndex rows, DenseIndex)
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0) {}
explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
eigen_internal_assert(size==rows*cols && rows>=0 && cols == _Cols);
EIGEN_UNUSED_VARIABLE(cols);
}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(other.m_rows*_Cols))
, m_rows(other.m_rows)
{
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;
}
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other)
: m_data(std::move(other.m_data))
, m_rows(std::move(other.m_rows))
{
other.m_data = nullptr;
other.m_rows = 0;
}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other)
{
using std::swap;
swap(m_data, other.m_data);
swap(m_rows, other.m_rows);
return *this;
}
#endif
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
EIGEN_DEVICE_FUNC Index rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC static Index cols(void) {return _Cols;}
void conservativeResize(Index size, Index rows, Index)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*_Cols);
m_rows = rows;
}
EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex rows, DenseIndex)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index rows, Index)
{
if(size != m_rows*_Cols)
{
@@ -294,8 +554,8 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
}
m_rows = rows;
}
inline const T *data() const { return m_data; }
inline T *data() { return m_data; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC T *data() { return m_data; }
};
} // end namespace Eigen

View File

@@ -37,23 +37,22 @@ template<typename MatrixType, int DiagIndex>
struct traits<Diagonal<MatrixType,DiagIndex> >
: traits<MatrixType>
{
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
typedef typename MatrixType::StorageKind StorageKind;
enum {
RowsAtCompileTime = (int(DiagIndex) == Dynamic || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic
: (EIGEN_PLAIN_ENUM_MIN(MatrixType::RowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0),
MatrixType::ColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
RowsAtCompileTime = (int(DiagIndex) == DynamicIndex || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic
: (EIGEN_PLAIN_ENUM_MIN(MatrixType::RowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0),
MatrixType::ColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
ColsAtCompileTime = 1,
MaxRowsAtCompileTime = int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic
: DiagIndex == Dynamic ? EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime,
: DiagIndex == DynamicIndex ? EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime,
MatrixType::MaxColsAtCompileTime)
: (EIGEN_PLAIN_ENUM_MIN(MatrixType::MaxRowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0),
MatrixType::MaxColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
MaxColsAtCompileTime = 1,
MaskLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
Flags = (unsigned int)_MatrixTypeNested::Flags & (HereditaryBits | LinearAccessBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit,
CoeffReadCost = _MatrixTypeNested::CoeffReadCost,
Flags = (unsigned int)_MatrixTypeNested::Flags & (RowMajorBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit, // FIXME DirectAccessBit should not be handled by expressions
MatrixTypeOuterStride = outer_stride_at_compile_time<MatrixType>::ret,
InnerStrideAtCompileTime = MatrixTypeOuterStride == Dynamic ? Dynamic : MatrixTypeOuterStride+1,
OuterStrideAtCompileTime = 0
@@ -61,28 +60,37 @@ struct traits<Diagonal<MatrixType,DiagIndex> >
};
}
template<typename MatrixType, int DiagIndex> class Diagonal
: public internal::dense_xpr_base< Diagonal<MatrixType,DiagIndex> >::type
template<typename MatrixType, int _DiagIndex> class Diagonal
: public internal::dense_xpr_base< Diagonal<MatrixType,_DiagIndex> >::type
{
public:
enum { DiagIndex = _DiagIndex };
typedef typename internal::dense_xpr_base<Diagonal>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
inline Diagonal(MatrixType& matrix, Index index = DiagIndex) : m_matrix(matrix), m_index(index) {}
EIGEN_DEVICE_FUNC
explicit inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index) {}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
EIGEN_DEVICE_FUNC
inline Index rows() const
{ return m_index.value()<0 ? (std::min)(m_matrix.cols(),m_matrix.rows()+m_index.value()) : (std::min)(m_matrix.rows(),m_matrix.cols()-m_index.value()); }
{
return m_index.value()<0 ? numext::mini<Index>(m_matrix.cols(),m_matrix.rows()+m_index.value())
: numext::mini<Index>(m_matrix.rows(),m_matrix.cols()-m_index.value());
}
EIGEN_DEVICE_FUNC
inline Index cols() const { return 1; }
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return m_matrix.outerStride() + 1;
}
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return 0;
@@ -94,62 +102,75 @@ template<typename MatrixType, int DiagIndex> class Diagonal
const Scalar
>::type ScalarWithConstIfNotLvalue;
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return &(m_matrix.const_cast_derived().coeffRef(rowOffset(), colOffset())); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return &(m_matrix.const_cast_derived().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());
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index row, Index) const
{
return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset());
}
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index row, Index) const
{
return m_matrix.coeff(row+rowOffset(), row+colOffset());
}
inline Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index idx)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return m_matrix.const_cast_derived().coeffRef(index+rowOffset(), index+colOffset());
return m_matrix.const_cast_derived().coeffRef(idx+rowOffset(), idx+colOffset());
}
inline const Scalar& coeffRef(Index index) const
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index idx) const
{
return m_matrix.const_cast_derived().coeffRef(index+rowOffset(), index+colOffset());
return m_matrix.const_cast_derived().coeffRef(idx+rowOffset(), idx+colOffset());
}
inline CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index idx) const
{
return m_matrix.coeff(index+rowOffset(), index+colOffset());
return m_matrix.coeff(idx+rowOffset(), idx+colOffset());
}
const typename internal::remove_all<typename MatrixType::Nested>::type&
EIGEN_DEVICE_FUNC
inline const typename internal::remove_all<typename MatrixType::Nested>::type&
nestedExpression() const
{
return m_matrix;
}
int index() const
EIGEN_DEVICE_FUNC
inline Index index() const
{
return m_index.value();
}
protected:
typename MatrixType::Nested m_matrix;
const internal::variable_if_dynamic<Index, DiagIndex> m_index;
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;
};
@@ -166,12 +187,12 @@ template<typename Derived>
inline typename MatrixBase<Derived>::DiagonalReturnType
MatrixBase<Derived>::diagonal()
{
return derived();
return DiagonalReturnType(derived());
}
/** This is the const version of diagonal(). */
template<typename Derived>
inline const typename MatrixBase<Derived>::ConstDiagonalReturnType
inline typename MatrixBase<Derived>::ConstDiagonalReturnType
MatrixBase<Derived>::diagonal() const
{
return ConstDiagonalReturnType(derived());
@@ -189,18 +210,18 @@ MatrixBase<Derived>::diagonal() const
*
* \sa MatrixBase::diagonal(), class Diagonal */
template<typename Derived>
inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Dynamic>::Type
inline typename MatrixBase<Derived>::DiagonalDynamicIndexReturnType
MatrixBase<Derived>::diagonal(Index index)
{
return typename DiagonalIndexReturnType<Dynamic>::Type(derived(), index);
return DiagonalDynamicIndexReturnType(derived(), index);
}
/** This is the const version of diagonal(Index). */
template<typename Derived>
inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Dynamic>::Type
inline typename MatrixBase<Derived>::ConstDiagonalDynamicIndexReturnType
MatrixBase<Derived>::diagonal(Index index) const
{
return typename ConstDiagonalIndexReturnType<Dynamic>::Type(derived(), index);
return ConstDiagonalDynamicIndexReturnType(derived(), index);
}
/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
@@ -215,20 +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

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@@ -20,8 +20,9 @@ class DiagonalBase : public EigenBase<Derived>
public:
typedef typename internal::traits<Derived>::DiagonalVectorType DiagonalVectorType;
typedef typename DiagonalVectorType::Scalar Scalar;
typedef typename DiagonalVectorType::RealScalar RealScalar;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
enum {
RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
@@ -29,63 +30,62 @@ class DiagonalBase : public EigenBase<Derived>
MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
IsVectorAtCompileTime = 0,
Flags = 0
Flags = NoPreferredStorageOrderBit
};
typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime> DenseMatrixType;
typedef DenseMatrixType DenseType;
typedef DiagonalMatrix<Scalar,DiagonalVectorType::SizeAtCompileTime,DiagonalVectorType::MaxSizeAtCompileTime> PlainObject;
EIGEN_DEVICE_FUNC
inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
EIGEN_DEVICE_FUNC
inline Derived& derived() { return *static_cast<Derived*>(this); }
EIGEN_DEVICE_FUNC
DenseMatrixType toDenseMatrix() const { return derived(); }
template<typename DenseDerived>
void evalTo(MatrixBase<DenseDerived> &other) const;
template<typename DenseDerived>
void addTo(MatrixBase<DenseDerived> &other) const
{ other.diagonal() += diagonal(); }
template<typename DenseDerived>
void subTo(MatrixBase<DenseDerived> &other) const
{ other.diagonal() -= diagonal(); }
EIGEN_DEVICE_FUNC
inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); }
EIGEN_DEVICE_FUNC
inline DiagonalVectorType& diagonal() { return derived().diagonal(); }
EIGEN_DEVICE_FUNC
inline Index rows() const { return diagonal().size(); }
EIGEN_DEVICE_FUNC
inline Index cols() const { return diagonal().size(); }
template<typename MatrixDerived>
const DiagonalProduct<MatrixDerived, Derived, OnTheLeft>
operator*(const MatrixBase<MatrixDerived> &matrix) const;
EIGEN_DEVICE_FUNC
const Product<Derived,MatrixDerived,LazyProduct>
operator*(const MatrixBase<MatrixDerived> &matrix) const
{
return Product<Derived, MatrixDerived, LazyProduct>(derived(),matrix.derived());
}
inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> >
typedef DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> > InverseReturnType;
EIGEN_DEVICE_FUNC
inline const InverseReturnType
inverse() const
{
return diagonal().cwiseInverse();
return InverseReturnType(diagonal().cwiseInverse());
}
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
bool isApprox(const DiagonalBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
typedef DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> > ScalarMultipleReturnType;
EIGEN_DEVICE_FUNC
inline const ScalarMultipleReturnType
operator*(const Scalar& scalar) const
{
return diagonal().isApprox(other.diagonal(), precision);
return ScalarMultipleReturnType(diagonal() * scalar);
}
template<typename OtherDerived>
bool isApprox(const MatrixBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
EIGEN_DEVICE_FUNC
friend inline const ScalarMultipleReturnType
operator*(const Scalar& scalar, const DiagonalBase& other)
{
return toDenseMatrix().isApprox(other, precision);
return ScalarMultipleReturnType(other.diagonal() * scalar);
}
#endif
};
template<typename Derived>
template<typename DenseDerived>
void DiagonalBase<Derived>::evalTo(MatrixBase<DenseDerived> &other) const
{
other.setZero();
other.diagonal() = diagonal();
}
#endif
/** \class DiagonalMatrix
@@ -107,10 +107,9 @@ 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
};
};
}
@@ -124,7 +123,7 @@ 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:
@@ -134,24 +133,31 @@ 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
@@ -161,11 +167,13 @@ 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();
@@ -176,6 +184,7 @@ 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();
@@ -184,14 +193,19 @@ 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); }
};
@@ -215,14 +229,15 @@ 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
};
};
}
@@ -238,9 +253,11 @@ class DiagonalWrapper
#endif
/** Constructor from expression of diagonal coefficients to wrap. */
inline DiagonalWrapper(DiagonalVectorType& diagonal) : m_diagonal(diagonal) {}
EIGEN_DEVICE_FUNC
explicit inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_diagonal) {}
/** \returns a const reference to the wrapped expression of diagonal coefficients. */
EIGEN_DEVICE_FUNC
const DiagonalVectorType& diagonal() const { return m_diagonal; }
protected:
@@ -260,7 +277,7 @@ 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,
@@ -272,13 +289,14 @@ MatrixBase<Derived>::asDiagonal() const
* \sa asDiagonal()
*/
template<typename Derived>
bool MatrixBase<Derived>::isDiagonal(RealScalar prec) const
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 = internal::abs(coeff(j,j));
RealScalar absOnDiagonal = abs(coeff(j,j));
if(absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal;
}
for(Index j = 0; j < cols(); ++j)
@@ -290,6 +308,33 @@ bool MatrixBase<Derived>::isDiagonal(RealScalar prec) const
return true;
}
namespace internal {
template<> struct storage_kind_to_shape<DiagonalShape> { typedef DiagonalShape Shape; };
struct Diagonal2Dense {};
template<> struct AssignmentKind<DenseShape,DiagonalShape> { typedef Diagonal2Dense Kind; };
// Diagonal matrix to Dense assignment
template< typename DstXprType, typename SrcXprType, typename Functor, typename Scalar>
struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Dense, Scalar>
{
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar> &/*func*/)
{
dst.setZero();
dst.diagonal() = src.diagonal();
}
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar> &/*func*/)
{ dst.diagonal() += src.diagonal(); }
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar> &/*func*/)
{ dst.diagonal() -= src.diagonal(); }
};
} // namespace internal
} // end namespace Eigen
#endif // EIGEN_DIAGONALMATRIX_H

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@@ -13,109 +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,
_PacketOnDiag = !((int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft)
||(int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)),
_SameTypes = is_same<typename MatrixType::Scalar, typename DiagonalType::Scalar>::value,
// FIXME currently we need same types, but in the future the next rule should be the one
//_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagonalType::Flags)&PacketAccessBit))),
_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && _SameTypes && ((!_PacketOnDiag) || (bool(int(DiagonalType::Flags)&PacketAccessBit))),
Flags = (HereditaryBits & (unsigned int)(MatrixType::Flags)) | (_Vectorizable ? PacketAccessBit : 0),
CoeffReadCost = NumTraits<Scalar>::MulCost + MatrixType::CoeffReadCost + DiagonalType::DiagonalVectorType::CoeffReadCost
};
};
}
template<typename MatrixType, typename DiagonalType, int ProductOrder>
class DiagonalProduct : internal::no_assignment_operator,
public MatrixBase<DiagonalProduct<MatrixType, DiagonalType, ProductOrder> >
{
public:
typedef MatrixBase<DiagonalProduct> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(DiagonalProduct)
inline DiagonalProduct(const MatrixType& matrix, const DiagonalType& diagonal)
: m_matrix(matrix), m_diagonal(diagonal)
{
eigen_assert(diagonal.diagonal().size() == (ProductOrder == OnTheLeft ? matrix.rows() : matrix.cols()));
}
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
const Scalar coeff(Index row, Index col) const
{
return m_diagonal.diagonal().coeff(ProductOrder == OnTheLeft ? row : col) * m_matrix.coeff(row, col);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
{
enum {
StorageOrder = Flags & RowMajorBit ? RowMajor : ColMajor
};
const Index indexInDiagonalVector = ProductOrder == OnTheLeft ? row : col;
return packet_impl<LoadMode>(row,col,indexInDiagonalVector,typename internal::conditional<
((int(StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft)
||(int(StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)), internal::true_type, internal::false_type>::type());
}
protected:
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::true_type) const
{
return internal::pmul(m_matrix.template packet<LoadMode>(row, col),
internal::pset1<PacketScalar>(m_diagonal.diagonal().coeff(id)));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::false_type) const
{
enum {
InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
DiagonalVectorPacketLoadMode = (LoadMode == Aligned && ((InnerSize%16) == 0)) ? Aligned : Unaligned
};
return internal::pmul(m_matrix.template packet<LoadMode>(row, col),
m_diagonal.diagonal().template packet<DiagonalVectorPacketLoadMode>(id));
}
typename MatrixType::Nested m_matrix;
typename DiagonalType::Nested m_diagonal;
};
/** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal.
*/
template<typename Derived>
template<typename DiagonalDerived>
inline const DiagonalProduct<Derived, DiagonalDerived, OnTheRight>
MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &diagonal) const
inline const Product<Derived, DiagonalDerived, LazyProduct>
MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &a_diagonal) const
{
return DiagonalProduct<Derived, DiagonalDerived, OnTheRight>(derived(), diagonal.derived());
}
/** \returns the diagonal matrix product of \c *this by the matrix \a matrix.
*/
template<typename DiagonalDerived>
template<typename MatrixDerived>
inline const DiagonalProduct<MatrixDerived, DiagonalDerived, OnTheLeft>
DiagonalBase<DiagonalDerived>::operator*(const MatrixBase<MatrixDerived> &matrix) const
{
return DiagonalProduct<MatrixDerived, DiagonalDerived, OnTheLeft>(matrix.derived(), derived());
return Product<Derived, DiagonalDerived, LazyProduct>(derived(),a_diagonal.derived());
}
} // end namespace Eigen

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@@ -29,6 +29,7 @@ template<typename T, typename U,
struct dot_nocheck
{
typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType 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();
@@ -39,6 +40,7 @@ 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;
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();
@@ -59,6 +61,7 @@ struct dot_nocheck<T, U, true>
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
{
@@ -73,34 +76,6 @@ MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
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.
@@ -112,7 +87,7 @@ MatrixBase<Derived>::eigen2_dot(const MatrixBase<OtherDerived>& other) const
template<typename Derived>
EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const
{
return internal::real((*this).cwiseAbs2().sum());
return numext::real((*this).cwiseAbs2().sum());
}
/** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm.
@@ -124,7 +99,8 @@ EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scala
template<typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
{
return internal::sqrt(squaredNorm());
EIGEN_USING_STD_MATH(sqrt)
return sqrt(squaredNorm());
}
/** \returns an expression of the quotient of *this by its own norm.
@@ -137,8 +113,7 @@ 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();
}
@@ -163,8 +138,10 @@ template<typename Derived, int p>
struct lpNorm_selector
{
typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC
static inline RealScalar run(const MatrixBase<Derived>& m)
{
EIGEN_USING_STD_MATH(pow)
return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p);
}
};
@@ -172,6 +149,7 @@ 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();
@@ -181,6 +159,7 @@ 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();
@@ -190,6 +169,7 @@ struct lpNorm_selector<Derived, 2>
template<typename Derived>
struct lpNorm_selector<Derived, Infinity>
{
EIGEN_DEVICE_FUNC
static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
{
return m.cwiseAbs().maxCoeff();
@@ -223,11 +203,11 @@ MatrixBase<Derived>::lpNorm() const
template<typename Derived>
template<typename OtherDerived>
bool MatrixBase<Derived>::isOrthogonal
(const MatrixBase<OtherDerived>& other, RealScalar prec) const
(const MatrixBase<OtherDerived>& other, const RealScalar& prec) const
{
typename internal::nested<Derived,2>::type nested(derived());
typename internal::nested<OtherDerived,2>::type otherNested(other.derived());
return internal::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
typename internal::nested_eval<Derived,2>::type nested(derived());
typename internal::nested_eval<OtherDerived,2>::type otherNested(other.derived());
return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
}
/** \returns true if *this is approximately an unitary matrix,
@@ -242,15 +222,15 @@ bool MatrixBase<Derived>::isOrthogonal
* Output: \verbinclude MatrixBase_isUnitary.out
*/
template<typename Derived>
bool MatrixBase<Derived>::isUnitary(RealScalar prec) const
bool MatrixBase<Derived>::isUnitary(const RealScalar& prec) const
{
typename Derived::Nested nested(derived());
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index i = 0; i < cols(); ++i)
{
if(!internal::isApprox(nested.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
if(!internal::isApprox(self.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
return false;
for(Index j = 0; j < i; ++j)
if(!internal::isMuchSmallerThan(nested.col(i).dot(nested.col(j)), static_cast<Scalar>(1), prec))
if(!internal::isMuchSmallerThan(self.col(i).dot(self.col(j)), static_cast<Scalar>(1), prec))
return false;
}
return true;

View File

@@ -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.
*
@@ -26,34 +28,52 @@ namespace Eigen {
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>());
return derived();
}
@@ -122,39 +146,10 @@ 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>());
return derived();
}
/** replaces \c *this by \c *this * \a other.
*
* \returns a reference to \c *this
*/
template<typename Derived>
template<typename OtherDerived>
inline Derived&
MatrixBase<Derived>::operator*=(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheRight(derived());
return derived();
}
/** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=() */
template<typename Derived>
template<typename OtherDerived>
inline void MatrixBase<Derived>::applyOnTheRight(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheRight(derived());
}
/** replaces \c *this by \c *this * \a other. */
template<typename Derived>
template<typename OtherDerived>
inline void MatrixBase<Derived>::applyOnTheLeft(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheLeft(derived());
}
} // end namespace Eigen
#endif // EIGEN_EIGENBASE_H

View File

@@ -1,140 +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

View File

@@ -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

View File

@@ -1,989 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2010 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_FUNCTORS_H
#define EIGEN_FUNCTORS_H
namespace Eigen {
namespace internal {
// associative functors:
/** \internal
* \brief Template functor to compute the sum of two scalars
*
* \sa class CwiseBinaryOp, MatrixBase::operator+, class VectorwiseOp, MatrixBase::sum()
*/
template<typename Scalar> struct scalar_sum_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_sum_op)
EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a + b; }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::padd(a,b); }
template<typename Packet>
EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const
{ return internal::predux(a); }
};
template<typename Scalar>
struct functor_traits<scalar_sum_op<Scalar> > {
enum {
Cost = NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasAdd
};
};
/** \internal
* \brief Template functor to compute the product of two scalars
*
* \sa class CwiseBinaryOp, Cwise::operator*(), class VectorwiseOp, MatrixBase::redux()
*/
template<typename LhsScalar,typename RhsScalar> struct scalar_product_op {
enum {
// TODO vectorize mixed product
Vectorizable = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasMul && packet_traits<RhsScalar>::HasMul
};
typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_product_op)
EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a * b; }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pmul(a,b); }
template<typename Packet>
EIGEN_STRONG_INLINE const result_type predux(const Packet& a) const
{ return internal::predux_mul(a); }
};
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_product_op<LhsScalar,RhsScalar> > {
enum {
Cost = (NumTraits<LhsScalar>::MulCost + NumTraits<RhsScalar>::MulCost)/2, // rough estimate!
PacketAccess = scalar_product_op<LhsScalar,RhsScalar>::Vectorizable
};
};
/** \internal
* \brief Template functor to compute the conjugate product of two scalars
*
* This is a short cut for conj(x) * y which is needed for optimization purpose; in Eigen2 support mode, this becomes x * conj(y)
*/
template<typename LhsScalar,typename RhsScalar> struct scalar_conj_product_op {
enum {
Conj = NumTraits<LhsScalar>::IsComplex
};
typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_conj_product_op)
EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const
{ return conj_helper<LhsScalar,RhsScalar,Conj,false>().pmul(a,b); }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return conj_helper<Packet,Packet,Conj,false>().pmul(a,b); }
};
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_conj_product_op<LhsScalar,RhsScalar> > {
enum {
Cost = NumTraits<LhsScalar>::MulCost,
PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMul
};
};
/** \internal
* \brief Template functor to compute the min of two scalars
*
* \sa class CwiseBinaryOp, MatrixBase::cwiseMin, class VectorwiseOp, MatrixBase::minCoeff()
*/
template<typename Scalar> struct scalar_min_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_min_op)
EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { using std::min; return (min)(a, b); }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pmin(a,b); }
template<typename Packet>
EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const
{ return internal::predux_min(a); }
};
template<typename Scalar>
struct functor_traits<scalar_min_op<Scalar> > {
enum {
Cost = NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasMin
};
};
/** \internal
* \brief Template functor to compute the max of two scalars
*
* \sa class CwiseBinaryOp, MatrixBase::cwiseMax, class VectorwiseOp, MatrixBase::maxCoeff()
*/
template<typename Scalar> struct scalar_max_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_max_op)
EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { using std::max; return (max)(a, b); }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pmax(a,b); }
template<typename Packet>
EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const
{ return internal::predux_max(a); }
};
template<typename Scalar>
struct functor_traits<scalar_max_op<Scalar> > {
enum {
Cost = NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasMax
};
};
/** \internal
* \brief Template functor to compute the hypot of two scalars
*
* \sa MatrixBase::stableNorm(), class Redux
*/
template<typename Scalar> struct scalar_hypot_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_hypot_op)
// typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& _x, const Scalar& _y) const
{
using std::max;
using std::min;
Scalar p = (max)(_x, _y);
Scalar q = (min)(_x, _y);
Scalar qp = q/p;
return p * sqrt(Scalar(1) + qp*qp);
}
};
template<typename Scalar>
struct functor_traits<scalar_hypot_op<Scalar> > {
enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess=0 };
};
/** \internal
* \brief Template functor to compute the pow of two scalars
*/
template<typename Scalar, typename OtherScalar> struct scalar_binary_pow_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_binary_pow_op)
inline Scalar operator() (const Scalar& a, const OtherScalar& b) const { return internal::pow(a, b); }
};
template<typename Scalar, typename OtherScalar>
struct functor_traits<scalar_binary_pow_op<Scalar,OtherScalar> > {
enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false };
};
// other binary functors:
/** \internal
* \brief Template functor to compute the difference of two scalars
*
* \sa class CwiseBinaryOp, MatrixBase::operator-
*/
template<typename Scalar> struct scalar_difference_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_difference_op)
EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a - b; }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::psub(a,b); }
};
template<typename Scalar>
struct functor_traits<scalar_difference_op<Scalar> > {
enum {
Cost = NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasSub
};
};
/** \internal
* \brief Template functor to compute the quotient of two scalars
*
* \sa class CwiseBinaryOp, Cwise::operator/()
*/
template<typename Scalar> struct scalar_quotient_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_quotient_op)
EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a / b; }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pdiv(a,b); }
};
template<typename Scalar>
struct functor_traits<scalar_quotient_op<Scalar> > {
enum {
Cost = 2 * NumTraits<Scalar>::MulCost,
PacketAccess = packet_traits<Scalar>::HasDiv
};
};
/** \internal
* \brief Template functor to compute the and of two booleans
*
* \sa class CwiseBinaryOp, ArrayBase::operator&&
*/
struct scalar_boolean_and_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_and_op)
EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a && b; }
};
template<> struct functor_traits<scalar_boolean_and_op> {
enum {
Cost = NumTraits<bool>::AddCost,
PacketAccess = false
};
};
/** \internal
* \brief Template functor to compute the or of two booleans
*
* \sa class CwiseBinaryOp, ArrayBase::operator||
*/
struct scalar_boolean_or_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_or_op)
EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a || b; }
};
template<> struct functor_traits<scalar_boolean_or_op> {
enum {
Cost = NumTraits<bool>::AddCost,
PacketAccess = false
};
};
// unary functors:
/** \internal
* \brief Template functor to compute the opposite of a scalar
*
* \sa class CwiseUnaryOp, MatrixBase::operator-
*/
template<typename Scalar> struct scalar_opposite_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_opposite_op)
EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return -a; }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::pnegate(a); }
};
template<typename Scalar>
struct functor_traits<scalar_opposite_op<Scalar> >
{ enum {
Cost = NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasNegate };
};
/** \internal
* \brief Template functor to compute the absolute value of a scalar
*
* \sa class CwiseUnaryOp, Cwise::abs
*/
template<typename Scalar> struct scalar_abs_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_abs_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return internal::abs(a); }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::pabs(a); }
};
template<typename Scalar>
struct functor_traits<scalar_abs_op<Scalar> >
{
enum {
Cost = NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasAbs
};
};
/** \internal
* \brief Template functor to compute the squared absolute value of a scalar
*
* \sa class CwiseUnaryOp, Cwise::abs2
*/
template<typename Scalar> struct scalar_abs2_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_abs2_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return internal::abs2(a); }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::pmul(a,a); }
};
template<typename Scalar>
struct functor_traits<scalar_abs2_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasAbs2 }; };
/** \internal
* \brief Template functor to compute the conjugate of a complex value
*
* \sa class CwiseUnaryOp, MatrixBase::conjugate()
*/
template<typename Scalar> struct scalar_conjugate_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_conjugate_op)
EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return internal::conj(a); }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pconj(a); }
};
template<typename Scalar>
struct functor_traits<scalar_conjugate_op<Scalar> >
{
enum {
Cost = NumTraits<Scalar>::IsComplex ? NumTraits<Scalar>::AddCost : 0,
PacketAccess = packet_traits<Scalar>::HasConj
};
};
/** \internal
* \brief Template functor to cast a scalar to another type
*
* \sa class CwiseUnaryOp, MatrixBase::cast()
*/
template<typename Scalar, typename NewType>
struct scalar_cast_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
typedef NewType result_type;
EIGEN_STRONG_INLINE const NewType operator() (const Scalar& a) const { return cast<Scalar, NewType>(a); }
};
template<typename Scalar, typename NewType>
struct functor_traits<scalar_cast_op<Scalar,NewType> >
{ enum { Cost = is_same<Scalar, NewType>::value ? 0 : NumTraits<NewType>::AddCost, PacketAccess = false }; };
/** \internal
* \brief Template functor to extract the real part of a complex
*
* \sa class CwiseUnaryOp, MatrixBase::real()
*/
template<typename Scalar>
struct scalar_real_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_real_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return internal::real(a); }
};
template<typename Scalar>
struct functor_traits<scalar_real_op<Scalar> >
{ enum { Cost = 0, PacketAccess = false }; };
/** \internal
* \brief Template functor to extract the imaginary part of a complex
*
* \sa class CwiseUnaryOp, MatrixBase::imag()
*/
template<typename Scalar>
struct scalar_imag_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return internal::imag(a); }
};
template<typename Scalar>
struct functor_traits<scalar_imag_op<Scalar> >
{ enum { Cost = 0, PacketAccess = false }; };
/** \internal
* \brief Template functor to extract the real part of a complex as a reference
*
* \sa class CwiseUnaryOp, MatrixBase::real()
*/
template<typename Scalar>
struct scalar_real_ref_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_real_ref_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return internal::real_ref(*const_cast<Scalar*>(&a)); }
};
template<typename Scalar>
struct functor_traits<scalar_real_ref_op<Scalar> >
{ enum { Cost = 0, PacketAccess = false }; };
/** \internal
* \brief Template functor to extract the imaginary part of a complex as a reference
*
* \sa class CwiseUnaryOp, MatrixBase::imag()
*/
template<typename Scalar>
struct scalar_imag_ref_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_ref_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return internal::imag_ref(*const_cast<Scalar*>(&a)); }
};
template<typename Scalar>
struct functor_traits<scalar_imag_ref_op<Scalar> >
{ enum { Cost = 0, PacketAccess = false }; };
/** \internal
*
* \brief Template functor to compute the exponential of a scalar
*
* \sa class CwiseUnaryOp, Cwise::exp()
*/
template<typename Scalar> struct scalar_exp_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_exp_op)
inline const Scalar operator() (const Scalar& a) const { return internal::exp(a); }
typedef typename packet_traits<Scalar>::type Packet;
inline Packet packetOp(const Packet& a) const { return internal::pexp(a); }
};
template<typename Scalar>
struct functor_traits<scalar_exp_op<Scalar> >
{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasExp }; };
/** \internal
*
* \brief Template functor to compute the logarithm of a scalar
*
* \sa class CwiseUnaryOp, Cwise::log()
*/
template<typename Scalar> struct scalar_log_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_log_op)
inline const Scalar operator() (const Scalar& a) const { return internal::log(a); }
typedef typename packet_traits<Scalar>::type Packet;
inline Packet packetOp(const Packet& a) const { return internal::plog(a); }
};
template<typename Scalar>
struct functor_traits<scalar_log_op<Scalar> >
{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasLog }; };
/** \internal
* \brief Template functor to multiply a scalar by a fixed other one
*
* \sa class CwiseUnaryOp, MatrixBase::operator*, MatrixBase::operator/
*/
/* NOTE why doing the pset1() in packetOp *is* an optimization ?
* indeed it seems better to declare m_other as a Packet and do the pset1() once
* in the constructor. However, in practice:
* - GCC does not like m_other as a Packet and generate a load every time it needs it
* - on the other hand GCC is able to moves the pset1() away the loop :)
* - simpler code ;)
* (ICC and gcc 4.4 seems to perform well in both cases, the issue is visible with y = a*x + b*y)
*/
template<typename Scalar>
struct scalar_multiple_op {
typedef typename packet_traits<Scalar>::type Packet;
// FIXME default copy constructors seems bugged with std::complex<>
EIGEN_STRONG_INLINE scalar_multiple_op(const scalar_multiple_op& other) : m_other(other.m_other) { }
EIGEN_STRONG_INLINE scalar_multiple_op(const Scalar& other) : m_other(other) { }
EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a * m_other; }
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::pmul(a, pset1<Packet>(m_other)); }
typename add_const_on_value_type<typename NumTraits<Scalar>::Nested>::type m_other;
};
template<typename Scalar>
struct functor_traits<scalar_multiple_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
template<typename Scalar1, typename Scalar2>
struct scalar_multiple2_op {
typedef typename scalar_product_traits<Scalar1,Scalar2>::ReturnType result_type;
EIGEN_STRONG_INLINE scalar_multiple2_op(const scalar_multiple2_op& other) : m_other(other.m_other) { }
EIGEN_STRONG_INLINE scalar_multiple2_op(const Scalar2& other) : m_other(other) { }
EIGEN_STRONG_INLINE result_type operator() (const Scalar1& a) const { return a * m_other; }
typename add_const_on_value_type<typename NumTraits<Scalar2>::Nested>::type m_other;
};
template<typename Scalar1,typename Scalar2>
struct functor_traits<scalar_multiple2_op<Scalar1,Scalar2> >
{ enum { Cost = NumTraits<Scalar1>::MulCost, PacketAccess = false }; };
template<typename Scalar, bool IsInteger>
struct scalar_quotient1_impl {
typedef typename packet_traits<Scalar>::type Packet;
// FIXME default copy constructors seems bugged with std::complex<>
EIGEN_STRONG_INLINE scalar_quotient1_impl(const scalar_quotient1_impl& other) : m_other(other.m_other) { }
EIGEN_STRONG_INLINE scalar_quotient1_impl(const Scalar& other) : m_other(static_cast<Scalar>(1) / other) {}
EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a * m_other; }
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::pmul(a, pset1<Packet>(m_other)); }
const Scalar m_other;
};
template<typename Scalar>
struct functor_traits<scalar_quotient1_impl<Scalar,false> >
{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
template<typename Scalar>
struct scalar_quotient1_impl<Scalar,true> {
// FIXME default copy constructors seems bugged with std::complex<>
EIGEN_STRONG_INLINE scalar_quotient1_impl(const scalar_quotient1_impl& other) : m_other(other.m_other) { }
EIGEN_STRONG_INLINE scalar_quotient1_impl(const Scalar& other) : m_other(other) {}
EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a / m_other; }
typename add_const_on_value_type<typename NumTraits<Scalar>::Nested>::type m_other;
};
template<typename Scalar>
struct functor_traits<scalar_quotient1_impl<Scalar,true> >
{ enum { Cost = 2 * NumTraits<Scalar>::MulCost, PacketAccess = false }; };
/** \internal
* \brief Template functor to divide a scalar by a fixed other one
*
* This functor is used to implement the quotient of a matrix by
* a scalar where the scalar type is not necessarily a floating point type.
*
* \sa class CwiseUnaryOp, MatrixBase::operator/
*/
template<typename Scalar>
struct scalar_quotient1_op : scalar_quotient1_impl<Scalar, NumTraits<Scalar>::IsInteger > {
EIGEN_STRONG_INLINE scalar_quotient1_op(const Scalar& other)
: scalar_quotient1_impl<Scalar, NumTraits<Scalar>::IsInteger >(other) {}
};
template<typename Scalar>
struct functor_traits<scalar_quotient1_op<Scalar> >
: functor_traits<scalar_quotient1_impl<Scalar, NumTraits<Scalar>::IsInteger> >
{};
// nullary functors
template<typename Scalar>
struct scalar_constant_op {
typedef typename packet_traits<Scalar>::type Packet;
EIGEN_STRONG_INLINE scalar_constant_op(const scalar_constant_op& other) : m_other(other.m_other) { }
EIGEN_STRONG_INLINE scalar_constant_op(const Scalar& other) : m_other(other) { }
template<typename Index>
EIGEN_STRONG_INLINE const Scalar operator() (Index, Index = 0) const { return m_other; }
template<typename Index>
EIGEN_STRONG_INLINE const Packet packetOp(Index, Index = 0) const { return internal::pset1<Packet>(m_other); }
const Scalar m_other;
};
template<typename Scalar>
struct functor_traits<scalar_constant_op<Scalar> >
// FIXME replace this packet test by a safe one
{ enum { Cost = 1, PacketAccess = packet_traits<Scalar>::Vectorizable, IsRepeatable = true }; };
template<typename Scalar> struct scalar_identity_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_identity_op)
template<typename Index>
EIGEN_STRONG_INLINE const Scalar operator() (Index row, Index col) const { return row==col ? Scalar(1) : Scalar(0); }
};
template<typename Scalar>
struct functor_traits<scalar_identity_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = false, IsRepeatable = true }; };
template <typename Scalar, bool RandomAccess> struct linspaced_op_impl;
// linear access for packet ops:
// 1) initialization
// base = [low, ..., low] + ([step, ..., step] * [-size, ..., 0])
// 2) each step
// base += [size*step, ..., size*step]
template <typename Scalar>
struct linspaced_op_impl<Scalar,false>
{
typedef typename packet_traits<Scalar>::type Packet;
linspaced_op_impl(Scalar low, Scalar step) :
m_low(low), m_step(step),
m_packetStep(pset1<Packet>(packet_traits<Scalar>::size*step)),
m_base(padd(pset1<Packet>(low),pmul(pset1<Packet>(step),plset<Scalar>(-packet_traits<Scalar>::size)))) {}
template<typename Index>
EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return m_low+i*m_step; }
template<typename Index>
EIGEN_STRONG_INLINE const Packet packetOp(Index) const { return m_base = padd(m_base,m_packetStep); }
const Scalar m_low;
const Scalar m_step;
const Packet m_packetStep;
mutable Packet m_base;
};
// random access for packet ops:
// 1) each step
// [low, ..., low] + ( [step, ..., step] * ( [i, ..., i] + [0, ..., size] ) )
template <typename Scalar>
struct linspaced_op_impl<Scalar,true>
{
typedef typename packet_traits<Scalar>::type Packet;
linspaced_op_impl(Scalar low, Scalar step) :
m_low(low), m_step(step),
m_lowPacket(pset1<Packet>(m_low)), m_stepPacket(pset1<Packet>(m_step)), m_interPacket(plset<Scalar>(0)) {}
template<typename Index>
EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return m_low+i*m_step; }
template<typename Index>
EIGEN_STRONG_INLINE const Packet packetOp(Index i) const
{ return internal::padd(m_lowPacket, pmul(m_stepPacket, padd(pset1<Packet>(i),m_interPacket))); }
const Scalar m_low;
const Scalar m_step;
const Packet m_lowPacket;
const Packet m_stepPacket;
const Packet m_interPacket;
};
// ----- Linspace functor ----------------------------------------------------------------
// Forward declaration (we default to random access which does not really give
// us a speed gain when using packet access but it allows to use the functor in
// nested expressions).
template <typename Scalar, bool RandomAccess = true> struct linspaced_op;
template <typename Scalar, bool RandomAccess> struct functor_traits< linspaced_op<Scalar,RandomAccess> >
{ enum { Cost = 1, PacketAccess = packet_traits<Scalar>::HasSetLinear, IsRepeatable = true }; };
template <typename Scalar, bool RandomAccess> struct linspaced_op
{
typedef typename packet_traits<Scalar>::type Packet;
linspaced_op(Scalar low, Scalar high, int num_steps) : impl((num_steps==1 ? high : low), (num_steps==1 ? Scalar() : (high-low)/(num_steps-1))) {}
template<typename Index>
EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return impl(i); }
// We need this function when assigning e.g. a RowVectorXd to a MatrixXd since
// there row==0 and col is used for the actual iteration.
template<typename Index>
EIGEN_STRONG_INLINE const Scalar operator() (Index row, Index col) const
{
eigen_assert(col==0 || row==0);
return impl(col + row);
}
template<typename Index>
EIGEN_STRONG_INLINE const Packet packetOp(Index i) const { return impl.packetOp(i); }
// We need this function when assigning e.g. a RowVectorXd to a MatrixXd since
// there row==0 and col is used for the actual iteration.
template<typename Index>
EIGEN_STRONG_INLINE const Packet packetOp(Index row, Index col) const
{
eigen_assert(col==0 || row==0);
return impl.packetOp(col + row);
}
// This proxy object handles the actual required temporaries, the different
// implementations (random vs. sequential access) as well as the
// correct piping to size 2/4 packet operations.
const linspaced_op_impl<Scalar,RandomAccess> impl;
};
// all functors allow linear access, except scalar_identity_op. So we fix here a quick meta
// to indicate whether a functor allows linear access, just always answering 'yes' except for
// scalar_identity_op.
// FIXME move this to functor_traits adding a functor_default
template<typename Functor> struct functor_has_linear_access { enum { ret = 1 }; };
template<typename Scalar> struct functor_has_linear_access<scalar_identity_op<Scalar> > { enum { ret = 0 }; };
// in CwiseBinaryOp, we require the Lhs and Rhs to have the same scalar type, except for multiplication
// where we only require them to have the same _real_ scalar type so one may multiply, say, float by complex<float>.
// FIXME move this to functor_traits adding a functor_default
template<typename Functor> struct functor_allows_mixing_real_and_complex { enum { ret = 0 }; };
template<typename LhsScalar,typename RhsScalar> struct functor_allows_mixing_real_and_complex<scalar_product_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; };
template<typename LhsScalar,typename RhsScalar> struct functor_allows_mixing_real_and_complex<scalar_conj_product_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; };
/** \internal
* \brief Template functor to add a scalar to a fixed other one
* \sa class CwiseUnaryOp, Array::operator+
*/
/* If you wonder why doing the pset1() in packetOp() is an optimization check scalar_multiple_op */
template<typename Scalar>
struct scalar_add_op {
typedef typename packet_traits<Scalar>::type Packet;
// FIXME default copy constructors seems bugged with std::complex<>
inline scalar_add_op(const scalar_add_op& other) : m_other(other.m_other) { }
inline scalar_add_op(const Scalar& other) : m_other(other) { }
inline Scalar operator() (const Scalar& a) const { return a + m_other; }
inline const Packet packetOp(const Packet& a) const
{ return internal::padd(a, pset1<Packet>(m_other)); }
const Scalar m_other;
};
template<typename Scalar>
struct functor_traits<scalar_add_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasAdd }; };
/** \internal
* \brief Template functor to compute the square root of a scalar
* \sa class CwiseUnaryOp, Cwise::sqrt()
*/
template<typename Scalar> struct scalar_sqrt_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_sqrt_op)
inline const Scalar operator() (const Scalar& a) const { return internal::sqrt(a); }
typedef typename packet_traits<Scalar>::type Packet;
inline Packet packetOp(const Packet& a) const { return internal::psqrt(a); }
};
template<typename Scalar>
struct functor_traits<scalar_sqrt_op<Scalar> >
{ enum {
Cost = 5 * NumTraits<Scalar>::MulCost,
PacketAccess = packet_traits<Scalar>::HasSqrt
};
};
/** \internal
* \brief Template functor to compute the cosine of a scalar
* \sa class CwiseUnaryOp, ArrayBase::cos()
*/
template<typename Scalar> struct scalar_cos_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_cos_op)
inline Scalar operator() (const Scalar& a) const { return internal::cos(a); }
typedef typename packet_traits<Scalar>::type Packet;
inline Packet packetOp(const Packet& a) const { return internal::pcos(a); }
};
template<typename Scalar>
struct functor_traits<scalar_cos_op<Scalar> >
{
enum {
Cost = 5 * NumTraits<Scalar>::MulCost,
PacketAccess = packet_traits<Scalar>::HasCos
};
};
/** \internal
* \brief Template functor to compute the sine of a scalar
* \sa class CwiseUnaryOp, ArrayBase::sin()
*/
template<typename Scalar> struct scalar_sin_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_sin_op)
inline const Scalar operator() (const Scalar& a) const { return internal::sin(a); }
typedef typename packet_traits<Scalar>::type Packet;
inline Packet packetOp(const Packet& a) const { return internal::psin(a); }
};
template<typename Scalar>
struct functor_traits<scalar_sin_op<Scalar> >
{
enum {
Cost = 5 * NumTraits<Scalar>::MulCost,
PacketAccess = packet_traits<Scalar>::HasSin
};
};
/** \internal
* \brief Template functor to compute the tan of a scalar
* \sa class CwiseUnaryOp, ArrayBase::tan()
*/
template<typename Scalar> struct scalar_tan_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_tan_op)
inline const Scalar operator() (const Scalar& a) const { return internal::tan(a); }
typedef typename packet_traits<Scalar>::type Packet;
inline Packet packetOp(const Packet& a) const { return internal::ptan(a); }
};
template<typename Scalar>
struct functor_traits<scalar_tan_op<Scalar> >
{
enum {
Cost = 5 * NumTraits<Scalar>::MulCost,
PacketAccess = packet_traits<Scalar>::HasTan
};
};
/** \internal
* \brief Template functor to compute the arc cosine of a scalar
* \sa class CwiseUnaryOp, ArrayBase::acos()
*/
template<typename Scalar> struct scalar_acos_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_acos_op)
inline const Scalar operator() (const Scalar& a) const { return internal::acos(a); }
typedef typename packet_traits<Scalar>::type Packet;
inline Packet packetOp(const Packet& a) const { return internal::pacos(a); }
};
template<typename Scalar>
struct functor_traits<scalar_acos_op<Scalar> >
{
enum {
Cost = 5 * NumTraits<Scalar>::MulCost,
PacketAccess = packet_traits<Scalar>::HasACos
};
};
/** \internal
* \brief Template functor to compute the arc sine of a scalar
* \sa class CwiseUnaryOp, ArrayBase::asin()
*/
template<typename Scalar> struct scalar_asin_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_asin_op)
inline const Scalar operator() (const Scalar& a) const { return internal::asin(a); }
typedef typename packet_traits<Scalar>::type Packet;
inline Packet packetOp(const Packet& a) const { return internal::pasin(a); }
};
template<typename Scalar>
struct functor_traits<scalar_asin_op<Scalar> >
{
enum {
Cost = 5 * NumTraits<Scalar>::MulCost,
PacketAccess = packet_traits<Scalar>::HasASin
};
};
/** \internal
* \brief Template functor to raise a scalar to a power
* \sa class CwiseUnaryOp, Cwise::pow
*/
template<typename Scalar>
struct scalar_pow_op {
// FIXME default copy constructors seems bugged with std::complex<>
inline scalar_pow_op(const scalar_pow_op& other) : m_exponent(other.m_exponent) { }
inline scalar_pow_op(const Scalar& exponent) : m_exponent(exponent) {}
inline Scalar operator() (const Scalar& a) const { return internal::pow(a, m_exponent); }
const Scalar m_exponent;
};
template<typename Scalar>
struct functor_traits<scalar_pow_op<Scalar> >
{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; };
/** \internal
* \brief Template functor to compute the quotient between a scalar and array entries.
* \sa class CwiseUnaryOp, Cwise::inverse()
*/
template<typename Scalar>
struct scalar_inverse_mult_op {
scalar_inverse_mult_op(const Scalar& other) : m_other(other) {}
inline Scalar operator() (const Scalar& a) const { return m_other / a; }
template<typename Packet>
inline const Packet packetOp(const Packet& a) const
{ return internal::pdiv(pset1<Packet>(m_other),a); }
Scalar m_other;
};
/** \internal
* \brief Template functor to compute the inverse of a scalar
* \sa class CwiseUnaryOp, Cwise::inverse()
*/
template<typename Scalar>
struct scalar_inverse_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_inverse_op)
inline Scalar operator() (const Scalar& a) const { return Scalar(1)/a; }
template<typename Packet>
inline const Packet packetOp(const Packet& a) const
{ return internal::pdiv(pset1<Packet>(Scalar(1)),a); }
};
template<typename Scalar>
struct functor_traits<scalar_inverse_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasDiv }; };
/** \internal
* \brief Template functor to compute the square of a scalar
* \sa class CwiseUnaryOp, Cwise::square()
*/
template<typename Scalar>
struct scalar_square_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_square_op)
inline Scalar operator() (const Scalar& a) const { return a*a; }
template<typename Packet>
inline const Packet packetOp(const Packet& a) const
{ return internal::pmul(a,a); }
};
template<typename Scalar>
struct functor_traits<scalar_square_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
/** \internal
* \brief Template functor to compute the cube of a scalar
* \sa class CwiseUnaryOp, Cwise::cube()
*/
template<typename Scalar>
struct scalar_cube_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_cube_op)
inline Scalar operator() (const Scalar& a) const { return a*a*a; }
template<typename Packet>
inline const Packet packetOp(const Packet& a) const
{ return internal::pmul(a,pmul(a,a)); }
};
template<typename Scalar>
struct functor_traits<scalar_cube_op<Scalar> >
{ enum { Cost = 2*NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
// default functor traits for STL functors:
template<typename T>
struct functor_traits<std::multiplies<T> >
{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::divides<T> >
{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::plus<T> >
{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::minus<T> >
{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::negate<T> >
{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::logical_or<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::logical_and<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::logical_not<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::greater<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::less<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::greater_equal<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::less_equal<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::equal_to<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::not_equal_to<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::binder2nd<T> >
{ enum { Cost = functor_traits<T>::Cost, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::binder1st<T> >
{ enum { Cost = functor_traits<T>::Cost, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::unary_negate<T> >
{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; };
template<typename T>
struct functor_traits<std::binary_negate<T> >
{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; };
#ifdef EIGEN_STDEXT_SUPPORT
template<typename T0,typename T1>
struct functor_traits<std::project1st<T0,T1> >
{ enum { Cost = 0, PacketAccess = false }; };
template<typename T0,typename T1>
struct functor_traits<std::project2nd<T0,T1> >
{ enum { Cost = 0, PacketAccess = false }; };
template<typename T0,typename T1>
struct functor_traits<std::select2nd<std::pair<T0,T1> > >
{ enum { Cost = 0, PacketAccess = false }; };
template<typename T0,typename T1>
struct functor_traits<std::select1st<std::pair<T0,T1> > >
{ enum { Cost = 0, PacketAccess = false }; };
template<typename T0,typename T1>
struct functor_traits<std::unary_compose<T0,T1> >
{ enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost, PacketAccess = false }; };
template<typename T0,typename T1,typename T2>
struct functor_traits<std::binary_compose<T0,T1,T2> >
{ enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost + functor_traits<T2>::Cost, PacketAccess = false }; };
#endif // EIGEN_STDEXT_SUPPORT
// allow to add new functors and specializations of functor_traits from outside Eigen.
// this macro is really needed because functor_traits must be specialized after it is declared but before it is used...
#ifdef EIGEN_FUNCTORS_PLUGIN
#include EIGEN_FUNCTORS_PLUGIN
#endif
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_FUNCTORS_H

View File

@@ -19,19 +19,20 @@ namespace internal
template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isApprox_selector
{
static bool run(const Derived& x, const OtherDerived& y, typename Derived::RealScalar prec)
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
{
using std::min;
typename internal::nested<Derived,2>::type nested(x);
typename internal::nested<OtherDerived,2>::type otherNested(y);
return (nested - otherNested).cwiseAbs2().sum() <= prec * prec * (min)(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
typename internal::nested_eval<Derived,2>::type nested(x);
typename internal::nested_eval<OtherDerived,2>::type otherNested(y);
return (nested - otherNested).cwiseAbs2().sum() <= prec * prec * numext::mini(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
}
};
template<typename Derived, typename OtherDerived>
struct isApprox_selector<Derived, OtherDerived, true>
{
static bool run(const Derived& x, const OtherDerived& y, typename Derived::RealScalar)
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar&)
{
return x.matrix() == y.matrix();
}
@@ -40,16 +41,18 @@ struct isApprox_selector<Derived, OtherDerived, true>
template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isMuchSmallerThan_object_selector
{
static bool run(const Derived& x, const OtherDerived& y, typename Derived::RealScalar prec)
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
{
return x.cwiseAbs2().sum() <= abs2(prec) * y.cwiseAbs2().sum();
return x.cwiseAbs2().sum() <= numext::abs2(prec) * y.cwiseAbs2().sum();
}
};
template<typename Derived, typename OtherDerived>
struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true>
{
static bool run(const Derived& x, const OtherDerived&, typename Derived::RealScalar)
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived&, const typename Derived::RealScalar&)
{
return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
}
@@ -58,16 +61,18 @@ struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true>
template<typename Derived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isMuchSmallerThan_scalar_selector
{
static bool run(const Derived& x, const typename Derived::RealScalar& y, typename Derived::RealScalar prec)
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const typename Derived::RealScalar& y, const typename Derived::RealScalar& prec)
{
return x.cwiseAbs2().sum() <= abs2(prec * y);
return x.cwiseAbs2().sum() <= numext::abs2(prec * y);
}
};
template<typename Derived>
struct isMuchSmallerThan_scalar_selector<Derived, true>
{
static bool run(const Derived& x, const typename Derived::RealScalar&, typename Derived::RealScalar)
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const typename Derived::RealScalar&, const typename Derived::RealScalar&)
{
return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
}
@@ -97,7 +102,7 @@ template<typename Derived>
template<typename OtherDerived>
bool DenseBase<Derived>::isApprox(
const DenseBase<OtherDerived>& other,
RealScalar prec
const RealScalar& prec
) const
{
return internal::isApprox_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);
@@ -119,7 +124,7 @@ bool DenseBase<Derived>::isApprox(
template<typename Derived>
bool DenseBase<Derived>::isMuchSmallerThan(
const typename NumTraits<Scalar>::Real& other,
RealScalar prec
const RealScalar& prec
) const
{
return internal::isMuchSmallerThan_scalar_selector<Derived>::run(derived(), other, prec);
@@ -139,7 +144,7 @@ template<typename Derived>
template<typename OtherDerived>
bool DenseBase<Derived>::isMuchSmallerThan(
const DenseBase<OtherDerived>& other,
RealScalar prec
const RealScalar& prec
) const
{
return internal::isMuchSmallerThan_object_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);

View File

@@ -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,
@@ -59,15 +37,14 @@ 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 +59,8 @@ private:
public:
enum {
value = selector::ret
value = selector::ret,
ret = selector::ret
};
#ifdef EIGEN_DEBUG_PRODUCT
static void debug()
@@ -98,6 +76,31 @@ public:
#endif
};
// template<typename Lhs, typename Rhs> struct product_tag
// {
// private:
//
// typedef typename remove_all<Lhs>::type _Lhs;
// typedef typename remove_all<Rhs>::type _Rhs;
// enum {
// Rows = _Lhs::RowsAtCompileTime,
// Cols = _Rhs::ColsAtCompileTime,
// Depth = EIGEN_SIZE_MIN_PREFER_FIXED(_Lhs::ColsAtCompileTime, _Rhs::RowsAtCompileTime)
// };
//
// enum {
// rows_select = Rows==1 ? int(Rows) : int(Large),
// cols_select = Cols==1 ? int(Cols) : int(Large),
// depth_select = Depth==1 ? int(Depth) : int(Large)
// };
// typedef product_type_selector<rows_select, cols_select, depth_select> selector;
//
// public:
// enum {
// ret = selector::ret
// };
//
// };
/* The following allows to select the kind of product at compile time
* based on the three dimensions of the product.
@@ -128,54 +131,6 @@ template<> struct product_type_selector<Large,Large,Small> { enum
} // 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
***********************************************************************/
@@ -187,97 +142,10 @@ struct LazyProductReturnType : public ProductReturnType<Lhs,Rhs,LazyCoeffBasedPr
// product ends up to a row-vector times col-vector product... To tackle this use
// case, we could have a specialization for Block<MatrixType,1,1> with: operator=(Scalar x);
namespace internal {
template<typename Lhs, typename Rhs>
struct traits<GeneralProduct<Lhs,Rhs,InnerProduct> >
: traits<Matrix<typename scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> >
{};
}
template<typename Lhs, typename Rhs>
class GeneralProduct<Lhs, Rhs, InnerProduct>
: internal::no_assignment_operator,
public Matrix<typename internal::scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1>
{
typedef Matrix<typename internal::scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> Base;
public:
GeneralProduct(const Lhs& lhs, const Rhs& rhs)
{
EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::RealScalar, typename Rhs::RealScalar>::value),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
Base::coeffRef(0,0) = (lhs.transpose().cwiseProduct(rhs)).sum();
}
/** Convertion to scalar */
operator const typename Base::Scalar() const {
return Base::coeff(0,0);
}
};
/***********************************************************************
* Implementation of Outer Vector Vector Product
***********************************************************************/
namespace internal {
template<int StorageOrder> struct outer_product_selector;
template<typename Lhs, typename Rhs>
struct traits<GeneralProduct<Lhs,Rhs,OuterProduct> >
: traits<ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs> >
{};
}
template<typename Lhs, typename Rhs>
class GeneralProduct<Lhs, Rhs, OuterProduct>
: public ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs>
{
public:
EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct)
GeneralProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
{
EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::RealScalar, typename Rhs::RealScalar>::value),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
}
template<typename Dest> void scaleAndAddTo(Dest& dest, Scalar alpha) const
{
internal::outer_product_selector<(int(Dest::Flags)&RowMajorBit) ? RowMajor : ColMajor>::run(*this, dest, alpha);
}
};
namespace internal {
template<> struct outer_product_selector<ColMajor> {
template<typename ProductType, typename Dest>
static EIGEN_DONT_INLINE void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha) {
typedef typename Dest::Index Index;
// FIXME make sure lhs is sequentially stored
// FIXME not very good if rhs is real and lhs complex while alpha is real too
const Index cols = dest.cols();
for (Index j=0; j<cols; ++j)
dest.col(j) += (alpha * prod.rhs().coeff(j)) * prod.lhs();
}
};
template<> struct outer_product_selector<RowMajor> {
template<typename ProductType, typename Dest>
static EIGEN_DONT_INLINE void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha) {
typedef typename Dest::Index Index;
// FIXME make sure rhs is sequentially stored
// FIXME not very good if lhs is real and rhs complex while alpha is real too
const Index rows = dest.rows();
for (Index i=0; i<rows; ++i)
dest.row(i) += (alpha * prod.lhs().coeff(i)) * prod.rhs();
}
};
} // end namespace internal
/***********************************************************************
* Implementation of General Matrix Vector Product
***********************************************************************/
@@ -291,60 +159,13 @@ template<> struct outer_product_selector<RowMajor> {
*/
namespace internal {
template<typename Lhs, typename Rhs>
struct traits<GeneralProduct<Lhs,Rhs,GemvProduct> >
: traits<ProductBase<GeneralProduct<Lhs,Rhs,GemvProduct>, Lhs, Rhs> >
{};
template<int Side, int StorageOrder, bool BlasCompatible>
struct gemv_selector;
struct gemv_dense_sense_selector;
} // end namespace internal
template<typename Lhs, typename Rhs>
class GeneralProduct<Lhs, Rhs, GemvProduct>
: public ProductBase<GeneralProduct<Lhs,Rhs,GemvProduct>, Lhs, Rhs>
{
public:
EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct)
typedef typename Lhs::Scalar LhsScalar;
typedef typename Rhs::Scalar RhsScalar;
GeneralProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
{
// EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::Scalar, typename Rhs::Scalar>::value),
// YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
}
enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight };
typedef typename internal::conditional<int(Side)==OnTheRight,_LhsNested,_RhsNested>::type MatrixType;
template<typename Dest> void scaleAndAddTo(Dest& dst, Scalar alpha) const
{
eigen_assert(m_lhs.rows() == dst.rows() && m_rhs.cols() == dst.cols());
internal::gemv_selector<Side,(int(MatrixType::Flags)&RowMajorBit) ? RowMajor : ColMajor,
bool(internal::blas_traits<MatrixType>::HasUsableDirectAccess)>::run(*this, dst, alpha);
}
};
namespace internal {
// The vector is on the left => transposition
template<int StorageOrder, bool BlasCompatible>
struct gemv_selector<OnTheLeft,StorageOrder,BlasCompatible>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
{
Transpose<Dest> destT(dest);
enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor };
gemv_selector<OnTheRight,OtherStorageOrder,BlasCompatible>
::run(GeneralProduct<Transpose<const typename ProductType::_RhsNested>,Transpose<const typename ProductType::_LhsNested>, GemvProduct>
(prod.rhs().transpose(), prod.lhs().transpose()), destT, alpha);
}
};
template<typename Scalar,int Size,int MaxSize,bool Cond> struct gemv_static_vector_if;
template<typename Scalar,int Size,int MaxSize>
@@ -362,7 +183,7 @@ 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
#if EIGEN_MAX_STATIC_ALIGN_BYTES!=0
internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize),0> m_data;
EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; }
#else
@@ -375,33 +196,48 @@ struct gemv_static_vector_if<Scalar,Size,MaxSize,true>
internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize)+(ForceAlignment?PacketSize: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*>((reinterpret_cast<size_t>(m_data.array) & ~(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_sense_selector<OnTheLeft,StorageOrder,BlasCompatible>
{
template<typename ProductType, typename Dest>
static inline void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
template<typename Lhs, typename Rhs, typename Dest>
static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename ProductType::Index Index;
typedef typename ProductType::LhsScalar LhsScalar;
typedef typename ProductType::RhsScalar RhsScalar;
typedef typename ProductType::Scalar ResScalar;
typedef typename ProductType::RealScalar RealScalar;
typedef typename ProductType::ActualLhsType ActualLhsType;
typedef typename ProductType::ActualRhsType ActualRhsType;
typedef typename ProductType::LhsBlasTraits LhsBlasTraits;
typedef typename ProductType::RhsBlasTraits RhsBlasTraits;
Transpose<Dest> destT(dest);
enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor };
gemv_dense_sense_selector<OnTheRight,OtherStorageOrder,BlasCompatible>
::run(rhs.transpose(), lhs.transpose(), destT, alpha);
}
};
template<> struct gemv_dense_sense_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>, Aligned> MappedDest;
ActualLhsType actualLhs = LhsBlasTraits::extract(prod.lhs());
ActualRhsType actualRhs = RhsBlasTraits::extract(prod.rhs());
ActualLhsType actualLhs = LhsBlasTraits::extract(lhs);
ActualRhsType 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
@@ -413,18 +249,18 @@ template<> struct gemv_selector<OnTheRight,ColMajor,true>
gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
bool alphaIsCompatible = (!ComplexByReal) || (imag(actualAlpha)==RealScalar(0));
bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
const bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0));
const bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
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)
{
#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
int size = dest.size();
Index size = dest.size();
EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#endif
if(!alphaIsCompatible)
@@ -436,11 +272,13 @@ template<> struct gemv_selector<OnTheRight,ColMajor,true>
MappedDest(actualDestPtr, dest.size()) = dest;
}
typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper;
typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper;
general_matrix_vector_product
<Index,LhsScalar,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsBlasTraits::NeedToConjugate>::run(
<Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
actualLhs.rows(), actualLhs.cols(),
actualLhs.data(), actualLhs.outerStride(),
actualRhs.data(), actualRhs.innerStride(),
LhsMapper(actualLhs.data(), actualLhs.outerStride()),
RhsMapper(actualRhs.data(), actualRhs.innerStride()),
actualDestPtr, 1,
compatibleAlpha);
@@ -454,34 +292,34 @@ template<> struct gemv_selector<OnTheRight,ColMajor,true>
}
};
template<> struct gemv_selector<OnTheRight,RowMajor,true>
template<> struct gemv_dense_sense_selector<OnTheRight,RowMajor,true>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
template<typename Lhs, typename Rhs, typename Dest>
static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename ProductType::LhsScalar LhsScalar;
typedef typename ProductType::RhsScalar RhsScalar;
typedef typename ProductType::Scalar ResScalar;
typedef typename ProductType::Index Index;
typedef typename ProductType::ActualLhsType ActualLhsType;
typedef typename ProductType::ActualRhsType ActualRhsType;
typedef typename ProductType::_ActualRhsType _ActualRhsType;
typedef typename ProductType::LhsBlasTraits LhsBlasTraits;
typedef typename ProductType::RhsBlasTraits RhsBlasTraits;
typedef typename Lhs::Scalar LhsScalar;
typedef typename Rhs::Scalar RhsScalar;
typedef typename Dest::Scalar ResScalar;
typedef internal::blas_traits<Lhs> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(prod.lhs());
typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(prod.rhs());
typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(lhs);
typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(rhs);
ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs())
* RhsBlasTraits::extractScalarFactor(prod.rhs());
ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(lhs)
* RhsBlasTraits::extractScalarFactor(rhs);
enum {
// FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
// on, the other hand it is good for the cache to pack the vector anyways...
DirectlyUseRhs = _ActualRhsType::InnerStrideAtCompileTime==1
DirectlyUseRhs = ActualRhsTypeCleaned::InnerStrideAtCompileTime==1
};
gemv_static_vector_if<RhsScalar,_ActualRhsType::SizeAtCompileTime,_ActualRhsType::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs;
gemv_static_vector_if<RhsScalar,ActualRhsTypeCleaned::SizeAtCompileTime,ActualRhsTypeCleaned::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs;
ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(),
DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data());
@@ -489,45 +327,45 @@ 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.innerStride(),
actualAlpha);
}
};
template<> struct gemv_selector<OnTheRight,ColMajor,false>
template<> struct gemv_dense_sense_selector<OnTheRight,ColMajor,false>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
template<typename Lhs, typename Rhs, typename Dest>
static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename Dest::Index Index;
// TODO makes sure dest is sequentially stored in memory, otherwise use a temp
const Index size = prod.rhs().rows();
const Index size = rhs.rows();
for(Index k=0; k<size; ++k)
dest += (alpha*prod.rhs().coeff(k)) * prod.lhs().col(k);
dest += (alpha*rhs.coeff(k)) * lhs.col(k);
}
};
template<> struct gemv_selector<OnTheRight,RowMajor,false>
template<> struct gemv_dense_sense_selector<OnTheRight,RowMajor,false>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
template<typename Lhs, typename Rhs, typename Dest>
static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename Dest::Index Index;
// TODO makes sure rhs is sequentially stored in memory, otherwise use a temp
const Index rows = prod.rows();
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(rhs.transpose())).sum();
}
};
@@ -543,9 +381,11 @@ 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
@@ -570,9 +410,12 @@ 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
@@ -586,7 +429,7 @@ 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 {
@@ -605,7 +448,7 @@ 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 +42,27 @@ 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,
HasLog10 = 0,
HasPow = 0,
HasSin = 0,
@@ -64,17 +70,26 @@ struct default_packet_traits
HasTan = 0,
HasASin = 0,
HasACos = 0,
HasATan = 0
HasATan = 0,
HasSinh = 0,
HasCosh = 0,
HasTanh = 0,
HasRound = 0,
HasFloor = 0,
HasCeil = 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,
@@ -90,132 +105,256 @@ 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);
}
/** \internal \returns a + b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
padd(const Packet& a,
const Packet& b) { return a+b; }
/** \internal \returns a - b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
psub(const Packet& a,
const Packet& b) { return a-b; }
/** \internal \returns -a (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pnegate(const Packet& a) { return -a; }
/** \internal \returns conj(a) (coeff-wise) */
template<typename Packet> inline Packet
pconj(const Packet& a) { return conj(a); }
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pconj(const Packet& a) { return numext::conj(a); }
/** \internal \returns a * b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pmul(const Packet& a,
const Packet& b) { return a*b; }
/** \internal \returns a / b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pdiv(const Packet& a,
const Packet& b) { return a/b; }
/** \internal \returns the min of \a a and \a b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pmin(const Packet& a,
const Packet& b) { using std::min; return (min)(a, b); }
const Packet& b) { return numext::mini(a, b); }
/** \internal \returns the max of \a a and \a b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pmax(const Packet& a,
const Packet& b) { using std::max; return (max)(a, b); }
const Packet& b) { return numext::maxi(a, b); }
/** \internal \returns the absolute value of \a a */
template<typename Packet> inline Packet
pabs(const Packet& a) { return abs(a); }
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pabs(const Packet& a) { using std::abs; return abs(a); }
/** \internal \returns the phase angle of \a a */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
parg(const Packet& a) { using numext::arg; return arg(a); }
/** \internal \returns the bitwise and of \a a and \a b */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pand(const Packet& a, const Packet& b) { return a & b; }
/** \internal \returns the bitwise or of \a a and \a b */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
por(const Packet& a, const Packet& b) { return a | b; }
/** \internal \returns the bitwise xor of \a a and \a b */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pxor(const Packet& a, const Packet& b) { return a ^ b; }
/** \internal \returns the bitwise andnot of \a a and \a b */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pandnot(const Packet& a, const Packet& b) { return a & (!b); }
/** \internal \returns a packet version of \a *from, from must be 16 bytes aligned */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pload(const typename unpacket_traits<Packet>::type* from) { return *from; }
/** \internal \returns a packet version of \a *from, (un-aligned load) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
ploadu(const typename unpacket_traits<Packet>::type* from) { return *from; }
/** \internal \returns a packet with elements of \a *from duplicated, e.g.: (from[0],from[0],from[1],from[1]) */
template<typename Packet> inline Packet
ploaddup(const typename unpacket_traits<Packet>::type* from) { return *from; }
/** \internal \returns a packet with constant coefficients \a a, e.g.: (a,a,a,a) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pset1(const typename unpacket_traits<Packet>::type& a) { return a; }
/** \internal \returns a packet with constant coefficients \a a[0], e.g.: (a[0],a[0],a[0],a[0]) */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pload1(const typename unpacket_traits<Packet>::type *a) { return pset1<Packet>(*a); }
/** \internal \returns a packet with elements of \a *from duplicated.
* For instance, for a packet of 8 elements, 4 scalars will be read from \a *from and
* duplicated to form: {from[0],from[0],from[1],from[1],from[2],from[2],from[3],from[3]}
* Currently, this function is only used for scalar * complex products.
*/
template<typename Packet> EIGEN_DEVICE_FUNC 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)
{
#if !defined(_MSC_VER)
__builtin_prefetch(addr);
#ifdef __CUDA_ARCH__
#if defined(__LP64__)
// 64-bit pointer operand constraint for inlined asm
asm(" prefetch.L1 [ %1 ];" : "=l"(addr) : "l"(addr));
#else
// 32-bit pointer operand constraint for inlined asm
asm(" prefetch.L1 [ %1 ];" : "=r"(addr) : "r"(addr));
#endif
#elif !EIGEN_COMP_MSVC
__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
predux4(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; }
template<size_t offset, typename Packet>
struct protate_impl
{
// Empty so attempts to use this unimplemented path will fail to compile.
// Only specializations of this template should be used.
};
/** \internal \returns a packet with the coefficients rotated to the right in little-endian convention,
* by the given offset, e.g. for offset == 1:
* (packet[3], packet[2], packet[1], packet[0]) becomes (packet[0], packet[3], packet[2], packet[1])
*/
template<size_t offset, typename Packet> EIGEN_DEVICE_FUNC inline Packet protate(const Packet& a)
{
return offset ? protate_impl<offset, Packet>::run(a) : a;
}
/** \internal \returns \a a with real and imaginary part flipped (for complex type only) */
template<typename Packet> inline Packet pcplxflip(const Packet& a)
{ return Packet(imag(a),real(a)); }
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet pcplxflip(const Packet& a)
{
// FIXME: uncomment the following in case we drop the internal imag and real functions.
// using std::imag;
// using std::real;
return Packet(imag(a),real(a));
}
/**************************
* Special math functions
@@ -223,35 +362,73 @@ template<typename Packet> inline Packet pcplxflip(const Packet& a)
/** \internal \returns the sine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet psin(const Packet& a) { return sin(a); }
Packet psin(const Packet& a) { using std::sin; return sin(a); }
/** \internal \returns the cosine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pcos(const Packet& a) { return cos(a); }
Packet pcos(const Packet& a) { using std::cos; return cos(a); }
/** \internal \returns the tan of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet ptan(const Packet& a) { return tan(a); }
Packet ptan(const Packet& a) { using std::tan; return tan(a); }
/** \internal \returns the arc sine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pasin(const Packet& a) { return asin(a); }
Packet pasin(const Packet& a) { using std::asin; return asin(a); }
/** \internal \returns the arc cosine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pacos(const Packet& a) { return acos(a); }
Packet pacos(const Packet& a) { using std::acos; return acos(a); }
/** \internal \returns the arc tangent of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet patan(const Packet& a) { using std::atan; return atan(a); }
/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet psinh(const Packet& a) { using std::sinh; return sinh(a); }
/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pcosh(const Packet& a) { using std::cosh; return cosh(a); }
/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet ptanh(const Packet& a) { using std::tanh; return tanh(a); }
/** \internal \returns the exp of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pexp(const Packet& a) { return exp(a); }
Packet pexp(const Packet& a) { using std::exp; return exp(a); }
/** \internal \returns the log of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plog(const Packet& a) { return log(a); }
Packet plog(const Packet& a) { using std::log; return log(a); }
/** \internal \returns the log10 of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plog10(const Packet& a) { using std::log10; return log10(a); }
/** \internal \returns the square-root of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet psqrt(const Packet& a) { return sqrt(a); }
Packet psqrt(const Packet& a) { using std::sqrt; return sqrt(a); }
/** \internal \returns the reciprocal square-root of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet prsqrt(const Packet& a) {
return pdiv(pset1<Packet>(1), psqrt(a));
}
/** \internal \returns the rounded value of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pround(const Packet& a) { using numext::round; return round(a); }
/** \internal \returns the floor of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pfloor(const Packet& a) { using numext::floor; return floor(a); }
/** \internal \returns the ceil of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pceil(const Packet& a) { using numext::ceil; return ceil(a); }
/***************************************************************************
* The following functions might not have to be overwritten for vectorized types
@@ -266,34 +443,45 @@ 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
@@ -302,8 +490,21 @@ struct palign_impl
static inline void run(PacketType&, const PacketType&) {}
};
/** \internal update \a first using the concatenation of the \a Offset last elements
* of \a first and packet_size minus \a Offset first elements of \a second */
/** \internal update \a first using the concatenation of the packet_size minus \a Offset last elements
* of \a first and \a Offset first elements of \a second.
*
* This function is currently only used to optimize matrix-vector products on unligned matrices.
* It takes 2 packets that represent a contiguous memory array, and returns a packet starting
* at the position \a Offset. For instance, for packets of 4 elements, we have:
* Input:
* - first = {f0,f1,f2,f3}
* - second = {s0,s1,s2,s3}
* Output:
* - if Offset==0 then {f0,f1,f2,f3}
* - if Offset==1 then {f1,f2,f3,s0}
* - if Offset==2 then {f2,f3,s0,s1}
* - if Offset==3 then {f3,s0,s1,s3}
*/
template<int Offset,typename PacketType>
inline void palign(PacketType& first, const PacketType& second)
{
@@ -314,15 +515,46 @@ 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;
}
} // 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 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2010-2012 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
@@ -11,11 +11,11 @@
#ifndef EIGEN_GLOBAL_FUNCTIONS_H
#define EIGEN_GLOBAL_FUNCTIONS_H
#define EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(NAME,FUNCTOR) \
#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR) \
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) { \
return Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived>(x.derived()); \
}
#define EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(NAME,FUNCTOR) \
@@ -30,50 +30,93 @@
{ \
static inline typename NAME##_retval<ArrayBase<Derived> >::type run(const Eigen::ArrayBase<Derived>& x) \
{ \
return x.derived(); \
return typename NAME##_retval<ArrayBase<Derived> >::type(x.derived()); \
} \
};
namespace std
namespace Eigen
{
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(real,scalar_real_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(imag,scalar_imag_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(sin,scalar_sin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(cos,scalar_cos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(asin,scalar_asin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(acos,scalar_acos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(tan,scalar_tan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(exp,scalar_exp_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(log,scalar_log_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(abs,scalar_abs_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(sqrt,scalar_sqrt_op)
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(inverse,scalar_inverse_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sin,scalar_sin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cos,scalar_cos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tan,scalar_tan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atan,scalar_atan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asin,scalar_asin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acos,scalar_acos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sinh,scalar_sinh_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cosh,scalar_cosh_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tanh,scalar_tanh_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(exp,scalar_exp_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log,scalar_log_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log10,scalar_log10_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs,scalar_abs_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs2,scalar_abs2_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(arg,scalar_arg_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sqrt,scalar_sqrt_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(square,scalar_square_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cube,scalar_cube_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(round,scalar_round_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(floor,scalar_floor_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(ceil,scalar_ceil_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isnan,scalar_isnan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isinf,scalar_isinf_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isfinite,scalar_isfinite_op)
template<typename Derived>
inline const Eigen::CwiseUnaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar>, const Derived>
pow(const Eigen::ArrayBase<Derived>& x, const typename Derived::Scalar& exponent) {
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)
/** \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()
*/
template<typename Derived,typename ExponentDerived>
inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_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_binary_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>(
x.derived(),
exponents.derived()
);
}
}
namespace Eigen
{
/** \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.
* Beaware that the scalar type of the input scalar \a x and the exponents \a exponents must be the same.
*
* Example: \include Cwise_scalar_power_array.cpp
* Output: \verbinclude Cwise_scalar_power_array.out
*
* \sa ArrayBase::pow()
*/
template<typename Derived>
inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_pow_op<typename Derived::Scalar, typename Derived::Scalar>, const typename Derived::ConstantReturnType, const Derived>
pow(const typename Derived::Scalar& x, const Eigen::ArrayBase<Derived>& exponents)
{
typename Derived::ConstantReturnType constant_x(exponents.rows(), exponents.cols(), x);
return Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_pow_op<typename Derived::Scalar, typename Derived::Scalar>, const typename Derived::ConstantReturnType, const Derived>(
constant_x,
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/(typename Derived::Scalar s, const Eigen::ArrayBase<Derived>& a)
operator/(const typename Derived::Scalar& s, const Eigen::ArrayBase<Derived>& a)
{
return Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>, const Derived>(
a.derived(),
@@ -85,19 +128,10 @@ namespace Eigen
{
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(real,scalar_real_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(imag,scalar_imag_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(sin,scalar_sin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(cos,scalar_cos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(asin,scalar_asin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(acos,scalar_acos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(tan,scalar_tan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(exp,scalar_exp_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(log,scalar_log_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs,scalar_abs_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs2,scalar_abs2_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(sqrt,scalar_sqrt_op)
}
}
// TODO: cleanly disable those functions that are not supported on Array (internal::real_ref, internal::random, internal::isApprox...)
// TODO: cleanly disable those functions that are not supported on Array (numext::real_ref, internal::random, internal::isApprox...)
#endif // EIGEN_GLOBAL_FUNCTIONS_H

View File

@@ -49,15 +49,18 @@ std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat&
*/
struct IOFormat
{
/** Default contructor, see class IOFormat for the meaning of the parameters */
/** Default constructor, see class IOFormat for the meaning of the parameters */
IOFormat(int _precision = StreamPrecision, int _flags = 0,
const std::string& _coeffSeparator = " ",
const std::string& _rowSeparator = "\n", const std::string& _rowPrefix="", const std::string& _rowSuffix="",
const std::string& _matPrefix="", const std::string& _matSuffix="")
: matPrefix(_matPrefix), matSuffix(_matSuffix), rowPrefix(_rowPrefix), rowSuffix(_rowSuffix), rowSeparator(_rowSeparator),
coeffSeparator(_coeffSeparator), precision(_precision), flags(_flags)
rowSpacer(""), coeffSeparator(_coeffSeparator), precision(_precision), flags(_flags)
{
rowSpacer = "";
// TODO check if rowPrefix, rowSuffix or rowSeparator contains a newline
// don't add rowSpacer if columns are not to be aligned
if((flags & DontAlignCols))
return;
int i = int(matSuffix.length())-1;
while (i>=0 && matSuffix[i]!='\n')
{
@@ -129,6 +132,7 @@ struct significant_decimals_default_impl
static inline int run()
{
using std::ceil;
using std::log;
return cast<RealScalar,int>(ceil(-log(NumTraits<RealScalar>::epsilon())/log(RealScalar(10))));
}
};
@@ -160,7 +164,6 @@ 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;
@@ -185,21 +188,22 @@ std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat&
explicit_precision = fmt.precision;
}
std::streamsize old_precision = 0;
if(explicit_precision) old_precision = s.precision(explicit_precision);
bool align_cols = !(fmt.flags & DontAlignCols);
if(align_cols)
{
// compute the largest width
for(Index j = 1; j < m.cols(); ++j)
for(Index j = 0; j < m.cols(); ++j)
for(Index i = 0; i < m.rows(); ++i)
{
std::stringstream sstr;
if(explicit_precision) sstr.precision(explicit_precision);
sstr.copyfmt(s);
sstr << m.coeff(i,j);
width = std::max<Index>(width, Index(sstr.str().length()));
}
}
std::streamsize old_precision = 0;
if(explicit_precision) old_precision = s.precision(explicit_precision);
s << fmt.matPrefix;
for(Index i = 0; i < m.rows(); ++i)
{

126
Eigen/src/Core/Inverse.h Normal file
View File

@@ -0,0 +1,126 @@
// 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 {
// TODO move the general declaration in Core, and rename this file DenseInverseImpl.h, or something like this...
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 internal::ref_selector<XprType>::type XprTypeNested;
typedef typename internal::remove_all<XprTypeNested>::type XprTypeNestedCleaned;
explicit 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;
};
/** \internal
* Specialization of the Inverse expression for dense expressions.
* Direct access to the coefficients are discared.
* FIXME this intermediate class is probably not needed anymore.
*/
template<typename XprType>
class InverseImpl<XprType,Dense>
: public MatrixBase<Inverse<XprType> >
{
typedef Inverse<XprType> Derived;
public:
typedef MatrixBase<Derived> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
typedef typename internal::remove_all<XprType>::type NestedExpression;
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

@@ -19,7 +19,7 @@ namespace Eigen {
* \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.
@@ -70,8 +70,6 @@ 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)
@@ -79,22 +77,9 @@ struct traits<Map<PlainObjectType, MapOptions, StrideType> >
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 ) ),
Alignment = int(MapOptions)&int(AlignedMask),
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)
Flags = is_lvalue<PlainObjectType>::value ? int(Flags0) : (int(Flags0) & ~LvalueBit)
};
private:
enum { Options }; // Expressions don't have Options
@@ -110,19 +95,17 @@ 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()
@@ -133,36 +116,39 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
/** Constructor in the fixed-size case.
*
* \param data pointer to the array to map
* \param dataPtr pointer to the array to map
* \param stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType data, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(data)), m_stride(stride)
EIGEN_DEVICE_FUNC
explicit inline Map(PointerArgType dataPtr, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(dataPtr)), m_stride(stride)
{
PlainObjectType::Base::_check_template_params();
}
/** Constructor in the dynamic-size vector case.
*
* \param data pointer to the array to map
* \param dataPtr pointer to the array to map
* \param size the size of the vector expression
* \param stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType data, Index size, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(data), size), m_stride(stride)
EIGEN_DEVICE_FUNC
inline Map(PointerArgType dataPtr, Index size, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), size), m_stride(stride)
{
PlainObjectType::Base::_check_template_params();
}
/** Constructor in the dynamic-size matrix case.
*
* \param data pointer to the array to map
* \param dataPtr pointer to the array to map
* \param rows the number of rows of the matrix expression
* \param cols the number of columns of the matrix expression
* \param stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType data, Index rows, Index cols, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(data), rows, cols), m_stride(stride)
EIGEN_DEVICE_FUNC
inline Map(PointerArgType dataPtr, Index rows, Index cols, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), rows, cols), m_stride(stride)
{
PlainObjectType::Base::_check_template_params();
}
@@ -173,19 +159,6 @@ 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

@@ -12,7 +12,7 @@
#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 {
@@ -37,7 +37,6 @@ 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 +75,8 @@ 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(); }
EIGEN_DEVICE_FUNC inline Index rows() const { return m_rows.value(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_cols.value(); }
/** Returns a pointer to the first coefficient of the matrix or vector.
*
@@ -85,24 +84,28 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
*
* \sa innerStride(), outerStride()
*/
inline const Scalar* data() const { return m_data; }
EIGEN_DEVICE_FUNC inline const Scalar* data() const { return m_data; }
inline const Scalar& coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC
inline const Scalar& coeff(Index rowId, Index colId) const
{
return m_data[col * colStride() + row * rowStride()];
return m_data[colId * colStride() + rowId * rowStride()];
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeff(Index index) const
{
EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
return m_data[index * innerStride()];
}
inline const Scalar& coeffRef(Index row, Index col) const
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return this->m_data[col * colStride() + row * rowStride()];
return this->m_data[colId * colStride() + rowId * rowStride()];
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
@@ -110,10 +113,10 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
}
template<int LoadMode>
inline PacketScalar packet(Index row, Index col) const
inline PacketScalar packet(Index rowId, Index colId) const
{
return internal::ploadt<PacketScalar, LoadMode>
(m_data + (col * colStride() + row * rowStride()));
(m_data + (colId * colStride() + rowId * rowStride()));
}
template<int LoadMode>
@@ -123,27 +126,30 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
return internal::ploadt<PacketScalar, LoadMode>(m_data + index * innerStride());
}
inline MapBase(PointerType data) : m_data(data), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
EIGEN_DEVICE_FUNC
explicit inline MapBase(PointerType dataPtr) : m_data(dataPtr), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
checkSanity();
}
inline MapBase(PointerType data, Index size)
: m_data(data),
m_rows(RowsAtCompileTime == Dynamic ? size : Index(RowsAtCompileTime)),
m_cols(ColsAtCompileTime == Dynamic ? size : Index(ColsAtCompileTime))
EIGEN_DEVICE_FUNC
inline MapBase(PointerType dataPtr, Index vecSize)
: m_data(dataPtr),
m_rows(RowsAtCompileTime == Dynamic ? vecSize : Index(RowsAtCompileTime)),
m_cols(ColsAtCompileTime == Dynamic ? vecSize : Index(ColsAtCompileTime))
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
eigen_assert(size >= 0);
eigen_assert(data == 0 || SizeAtCompileTime == Dynamic || SizeAtCompileTime == size);
eigen_assert(vecSize >= 0);
eigen_assert(dataPtr == 0 || SizeAtCompileTime == Dynamic || SizeAtCompileTime == vecSize);
checkSanity();
}
inline MapBase(PointerType data, Index rows, Index cols)
: m_data(data), m_rows(rows), m_cols(cols)
EIGEN_DEVICE_FUNC
inline MapBase(PointerType dataPtr, Index rows, Index cols)
: m_data(dataPtr), m_rows(rows), m_cols(cols)
{
eigen_assert( (data == 0)
eigen_assert( (dataPtr == 0)
|| ( rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)));
checkSanity();
@@ -151,13 +157,12 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
protected:
EIGEN_DEVICE_FUNC
void checkSanity() const
{
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(internal::traits<Derived>::Flags&PacketAccessBit,
internal::inner_stride_at_compile_time<Derived>::ret==1),
PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1);
eigen_assert(EIGEN_IMPLIES(internal::traits<Derived>::Flags&AlignedBit, (size_t(m_data) % 16) == 0)
&& "data is not aligned");
#if EIGEN_MAX_ALIGN_BYTES>0
eigen_assert(((size_t(m_data) % EIGEN_PLAIN_ENUM_MAX(1,internal::traits<Derived>::Alignment)) == 0) && "data is not aligned");
#endif
}
PointerType m_data;
@@ -168,13 +173,14 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
template<typename Derived> class MapBase<Derived, WriteAccessors>
: public MapBase<Derived, ReadOnlyAccessors>
{
typedef MapBase<Derived, ReadOnlyAccessors> ReadOnlyMapBase;
public:
typedef MapBase<Derived, ReadOnlyAccessors> Base;
typedef typename Base::Scalar Scalar;
typedef typename Base::PacketScalar PacketScalar;
typedef typename Base::Index Index;
typedef typename Base::StorageIndex StorageIndex;
typedef typename Base::PointerType PointerType;
using Base::derived;
@@ -195,14 +201,18 @@ 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)
@@ -210,33 +220,38 @@ template<typename Derived> class MapBase<Derived, WriteAccessors>
}
template<int StoreMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
inline void writePacket(Index row, Index col, const PacketScalar& val)
{
internal::pstoret<Scalar, PacketScalar, StoreMode>
(this->m_data + (col * colStride() + row * rowStride()), x);
(this->m_data + (col * colStride() + row * rowStride()), val);
}
template<int StoreMode>
inline void writePacket(Index index, const PacketScalar& x)
inline void writePacket(Index index, const PacketScalar& val)
{
EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
internal::pstoret<Scalar, PacketScalar, StoreMode>
(this->m_data + index * innerStride(), x);
(this->m_data + index * innerStride(), val);
}
explicit inline MapBase(PointerType data) : Base(data) {}
inline MapBase(PointerType data, Index size) : Base(data, size) {}
inline MapBase(PointerType data, Index rows, Index cols) : Base(data, rows, cols) {}
EIGEN_DEVICE_FUNC explicit inline MapBase(PointerType dataPtr) : Base(dataPtr) {}
EIGEN_DEVICE_FUNC inline MapBase(PointerType dataPtr, Index vecSize) : Base(dataPtr, vecSize) {}
EIGEN_DEVICE_FUNC inline MapBase(PointerType dataPtr, Index rows, Index cols) : Base(dataPtr, rows, cols) {}
EIGEN_DEVICE_FUNC
Derived& operator=(const MapBase& other)
{
Base::Base::operator=(other);
ReadOnlyMapBase::Base::operator=(other);
return derived();
}
using Base::Base::operator=;
// In theory we could simply refer to Base:Base::operator=, but MSVC does not like Base::Base,
// see bugs 821 and 920.
using ReadOnlyMapBase::Base::operator=;
};
#undef EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS
} // end namespace Eigen
#endif // EIGEN_MAPBASE_H

File diff suppressed because it is too large Load Diff

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@@ -24,13 +24,13 @@ 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.
@@ -97,6 +97,40 @@ namespace Eigen {
* are the dimensions of the original matrix, while _Rows and _Cols are Dynamic.</dd>
* </dl>
*
* <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
*/
@@ -105,9 +139,23 @@ 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 && (actual_alignment>=required_alignment) ? PacketAccessBit : 0
};
public:
typedef _Scalar Scalar;
typedef Dense StorageKind;
typedef DenseIndex Index;
typedef Eigen::Index StorageIndex;
typedef MatrixXpr XprKind;
enum {
RowsAtCompileTime = _Rows,
@@ -115,10 +163,13 @@ struct traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
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
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
};
};
}
@@ -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);
@@ -200,52 +255,95 @@ class Matrix
*
* \sa resize(Index,Index)
*/
EIGEN_STRONG_INLINE explicit Matrix() : Base()
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix() : Base()
{
Base::_check_template_params();
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
// FIXME is it still needed
Matrix(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC
explicit Matrix(internal::constructor_without_unaligned_array_assert)
: Base(internal::constructor_without_unaligned_array_assert())
{ Base::_check_template_params(); EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED }
{ Base::_check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED }
/** \brief Constructs a vector or row-vector with given dimension. \only_for_vectors
*
* Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
* it is redundant to pass the dimension here, so it makes more sense to use the default
* constructor Matrix() instead.
*/
EIGEN_STRONG_INLINE explicit Matrix(Index dim)
: Base(dim, RowsAtCompileTime == 1 ? 1 : dim, ColsAtCompileTime == 1 ? 1 : dim)
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
Matrix(Matrix&& other)
: Base(std::move(other))
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Matrix)
eigen_assert(dim >= 0);
eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim);
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
if (RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic)
Base::_set_noalias(other);
}
EIGEN_DEVICE_FUNC
Matrix& operator=(Matrix&& other)
{
other.swap(*this);
return *this;
}
#endif
#ifndef EIGEN_PARSED_BY_DOXYGEN
// This constructor is for both 1x1 matrices and dynamic vectors
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit Matrix(const T& x)
{
Base::_check_template_params();
Base::template _init1<T>(x);
}
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const T0& x, const T1& y)
{
Base::_check_template_params();
Base::template _init2<T0,T1>(x, y);
}
#else
/** \brief Constructs a fixed-sized matrix initialized with coefficients starting at \a data */
EIGEN_DEVICE_FUNC
explicit Matrix(const Scalar *data);
/** \brief Constructs a vector or row-vector with given dimension. \only_for_vectors
*
* This is useful for dynamic-size vectors. For fixed-size vectors,
* it is redundant to pass these parameters, so one should use the default constructor
* Matrix() instead.
*
* \warning This constructor is disabled for fixed-size \c 1x1 matrices. For instance,
* calling Matrix<double,1,1>(1) will call the initialization constructor: Matrix(const Scalar&).
* For fixed-size \c 1x1 matrices it is therefore recommended to use the default
* constructor Matrix() instead, especially when using one of the non standard
* \c EIGEN_INITIALIZE_MATRICES_BY_{ZERO,\c NAN} macros (see \ref TopicPreprocessorDirectives).
*/
EIGEN_STRONG_INLINE explicit Matrix(Index dim);
/** \brief Constructs an initialized 1x1 matrix with the given coefficient */
Matrix(const Scalar& x);
/** \brief Constructs an uninitialized matrix with \a rows rows and \a cols columns.
*
* This is useful for dynamic-size matrices. For fixed-size matrices,
* it is redundant to pass these parameters, so one should use the default constructor
* Matrix() instead. */
* Matrix() instead.
*
* \warning This constructor is disabled for fixed-size \c 1x2 and \c 2x1 vectors. For instance,
* calling Matrix2f(2,1) will call the initialization constructor: Matrix(const Scalar& x, const Scalar& y).
* For fixed-size \c 1x2 or \c 2x1 vectors it is therefore recommended to use the default
* constructor Matrix() instead, especially when using one of the non standard
* \c EIGEN_INITIALIZE_MATRICES_BY_{ZERO,\c NAN} macros (see \ref TopicPreprocessorDirectives).
*/
EIGEN_DEVICE_FUNC
Matrix(Index rows, Index cols);
/** \brief Constructs an initialized 2D vector with given coefficients */
Matrix(const Scalar& x, const Scalar& y);
#endif
/** \brief Constructs an initialized 3D vector with given coefficients */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z)
{
Base::_check_template_params();
@@ -255,6 +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();
@@ -265,76 +364,33 @@ class Matrix
m_storage.data()[3] = w;
}
explicit Matrix(const Scalar *data);
/** \brief Constructor copying the value of the expression \a other */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Matrix(const MatrixBase<OtherDerived>& other)
: Base(other.rows() * other.cols(), other.rows(), other.cols())
{
// This test resides here, to bring the error messages closer to the user. Normally, these checks
// are performed deeply within the library, thus causing long and scary error traces.
EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
Base::_check_template_params();
Base::_set_noalias(other);
}
/** \brief Copy constructor */
EIGEN_STRONG_INLINE Matrix(const Matrix& other)
: Base(other.rows() * other.cols(), other.rows(), other.cols())
{
Base::_check_template_params();
Base::_set_noalias(other);
}
/** \brief Copy constructor with in-place evaluation */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Matrix(const ReturnByValue<OtherDerived>& other)
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
other.evalTo(*this);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const Matrix& other) : Base(other)
{ }
/** \brief Copy constructor for generic expressions.
* \sa MatrixBase::operator=(const EigenBase<OtherDerived>&)
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const EigenBase<OtherDerived> &other)
: Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
// FIXME/CHECK: isn't *this = other.derived() more efficient. it allows to
// go for pure _set() implementations, right?
*this = other;
}
: Base(other.derived())
{ }
/** \internal
* \brief Override MatrixBase::swap() since for dynamic-sized matrices
* of same type it is enough to swap the data pointers.
*/
template<typename OtherDerived>
void swap(MatrixBase<OtherDerived> const & other)
{ this->_swap(other.derived()); }
inline Index innerStride() const { return 1; }
inline Index outerStride() const { return this->innerSize(); }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return 1; }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return this->innerSize(); }
/////////// Geometry module ///////////
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
explicit Matrix(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Matrix& operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
explicit Matrix(const eigen2_RotationBase<OtherDerived,ColsAtCompileTime>& r);
template<typename OtherDerived>
Matrix& operator=(const eigen2_RotationBase<OtherDerived,ColsAtCompileTime>& r);
#endif
// allow to extend Matrix outside Eigen
#ifdef EIGEN_MATRIX_PLUGIN
#include EIGEN_MATRIX_PLUGIN

View File

@@ -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;
@@ -66,8 +66,7 @@ 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;
using Base::rows;
@@ -81,6 +80,8 @@ template<typename Derived> class MatrixBase
using Base::operator-=;
using Base::operator*=;
using Base::operator/=;
using Base::operator*;
using Base::operator/;
typedef typename Base::CoeffReturnType CoeffReturnType;
typedef typename Base::ConstTransposeReturnType ConstTransposeReturnType;
@@ -98,25 +99,14 @@ template<typename Derived> class MatrixBase
/** \returns the size of the main diagonal, which is min(rows(),cols()).
* \sa rows(), cols(), SizeAtCompileTime. */
EIGEN_DEVICE_FUNC
inline Index diagonalSize() const { return (std::min)(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 +115,7 @@ 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,
@@ -145,36 +135,48 @@ template<typename Derived> class MatrixBase
/** 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
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
Derived& operator=(const DenseBase<OtherDerived>& other);
template <typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator=(const EigenBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator=(const ReturnByValue<OtherDerived>& other);
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& lazyAssign(const ProductBase<ProductDerived, Lhs,Rhs>& other);
#endif // not EIGEN_PARSED_BY_DOXYGEN
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator+=(const MatrixBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
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>
@@ -187,100 +189,103 @@ 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>
EIGEN_DEVICE_FUNC
typename internal::scalar_product_traits<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 void normalize();
const AdjointReturnType adjoint() const;
void adjointInPlace();
EIGEN_DEVICE_FUNC const AdjointReturnType adjoint() const;
EIGEN_DEVICE_FUNC void adjointInPlace();
typedef Diagonal<Derived> DiagonalReturnType;
EIGEN_DEVICE_FUNC
DiagonalReturnType diagonal();
typedef const Diagonal<const Derived> ConstDiagonalReturnType;
const ConstDiagonalReturnType diagonal() const;
typedef typename internal::add_const<Diagonal<const Derived> >::type ConstDiagonalReturnType;
EIGEN_DEVICE_FUNC
ConstDiagonalReturnType diagonal() const;
template<int Index> struct DiagonalIndexReturnType { typedef Diagonal<Derived,Index> Type; };
template<int Index> struct ConstDiagonalIndexReturnType { typedef const Diagonal<const Derived,Index> Type; };
template<int Index> typename DiagonalIndexReturnType<Index>::Type diagonal();
template<int Index> typename ConstDiagonalIndexReturnType<Index>::Type diagonal() const;
template<int Index>
EIGEN_DEVICE_FUNC
typename DiagonalIndexReturnType<Index>::Type diagonal();
// Note: The "MatrixBase::" prefixes are added to help MSVC9 to match these declarations with the later implementations.
// On the other hand they confuse MSVC8...
#if (defined _MSC_VER) && (_MSC_VER >= 1500) // 2008 or later
typename MatrixBase::template DiagonalIndexReturnType<Dynamic>::Type diagonal(Index index);
typename MatrixBase::template ConstDiagonalIndexReturnType<Dynamic>::Type diagonal(Index index) const;
#else
typename DiagonalIndexReturnType<Dynamic>::Type diagonal(Index index);
typename ConstDiagonalIndexReturnType<Dynamic>::Type diagonal(Index index) const;
#endif
#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;
template<int Index>
EIGEN_DEVICE_FUNC
typename ConstDiagonalIndexReturnType<Index>::Type diagonal() const;
// huuuge hack. make Eigen2's matrix.part<Diagonal>() work in eigen3. Problem: Diagonal is now a class template instead
// of an integer constant. Solution: overload the part() method template wrt template parameters list.
template<template<typename T, int n> class U>
const DiagonalWrapper<ConstDiagonalReturnType> part() const
{ return diagonal().asDiagonal(); }
#endif // EIGEN2_SUPPORT
typedef Diagonal<Derived,DynamicIndex> DiagonalDynamicIndexReturnType;
typedef typename internal::add_const<Diagonal<const Derived,DynamicIndex> >::type ConstDiagonalDynamicIndexReturnType;
EIGEN_DEVICE_FUNC
DiagonalDynamicIndexReturnType diagonal(Index index);
EIGEN_DEVICE_FUNC
ConstDiagonalDynamicIndexReturnType diagonal(Index index) const;
template<unsigned int Mode> struct TriangularViewReturnType { typedef TriangularView<Derived, Mode> Type; };
template<unsigned int Mode> struct ConstTriangularViewReturnType { typedef const TriangularView<const Derived, Mode> Type; };
template<unsigned int Mode> typename TriangularViewReturnType<Mode>::Type triangularView();
template<unsigned int Mode> typename ConstTriangularViewReturnType<Mode>::Type triangularView() const;
template<unsigned int Mode>
EIGEN_DEVICE_FUNC
typename TriangularViewReturnType<Mode>::Type triangularView();
template<unsigned int Mode>
EIGEN_DEVICE_FUNC
typename ConstTriangularViewReturnType<Mode>::Type triangularView() const;
template<unsigned int UpLo> struct SelfAdjointViewReturnType { typedef SelfAdjointView<Derived, UpLo> Type; };
template<unsigned int UpLo> struct ConstSelfAdjointViewReturnType { typedef const SelfAdjointView<const Derived, UpLo> Type; };
template<unsigned int UpLo> typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView();
template<unsigned int UpLo> typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
template<unsigned int UpLo>
EIGEN_DEVICE_FUNC
typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView();
template<unsigned int UpLo>
EIGEN_DEVICE_FUNC
typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
const SparseView<Derived> sparseView(const Scalar& m_reference = Scalar(0),
typename NumTraits<Scalar>::Real m_epsilon = NumTraits<Scalar>::dummy_precision()) const;
static const IdentityReturnType Identity();
static const IdentityReturnType Identity(Index rows, Index cols);
static const BasisReturnType Unit(Index size, Index i);
static const BasisReturnType Unit(Index i);
static const BasisReturnType UnitX();
static const BasisReturnType UnitY();
static const BasisReturnType UnitZ();
static const BasisReturnType UnitW();
const typename NumTraits<Scalar>::Real& m_epsilon = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC static const IdentityReturnType Identity();
EIGEN_DEVICE_FUNC static const IdentityReturnType Identity(Index rows, Index cols);
EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index size, Index i);
EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index i);
EIGEN_DEVICE_FUNC static const BasisReturnType UnitX();
EIGEN_DEVICE_FUNC static const BasisReturnType UnitY();
EIGEN_DEVICE_FUNC static const BasisReturnType UnitZ();
EIGEN_DEVICE_FUNC static const BasisReturnType UnitW();
EIGEN_DEVICE_FUNC
const DiagonalWrapper<const Derived> asDiagonal() const;
const PermutationWrapper<const Derived> asPermutation() const;
EIGEN_DEVICE_FUNC
Derived& setIdentity();
EIGEN_DEVICE_FUNC
Derived& setIdentity(Index rows, Index cols);
bool isIdentity(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isDiagonal(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isIdentity(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isDiagonal(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isUpperTriangular(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isLowerTriangular(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isUpperTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isLowerTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
template<typename OtherDerived>
bool isOrthogonal(const MatrixBase<OtherDerived>& other,
RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isUnitary(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isUnitary(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
/** \returns true if each coefficients of \c *this and \a other are all exactly equal.
* \warning When using floating point scalar values you probably should rather use a
@@ -300,50 +305,37 @@ 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;
EIGEN_DEVICE_FUNC MatrixBase<Derived>& matrix() { return *this; }
EIGEN_DEVICE_FUNC const MatrixBase<Derived>& matrix() const { return *this; }
MatrixBase<Derived>& matrix() { return *this; }
const MatrixBase<Derived>& matrix() const { return *this; }
/** \returns an \link ArrayBase Array \endlink expression of this matrix
/** \returns an \link Eigen::ArrayBase Array \endlink expression of this matrix
* \sa ArrayBase::matrix() */
ArrayWrapper<Derived> array() { return derived(); }
const ArrayWrapper<const Derived> array() const { return derived(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ArrayWrapper<Derived> array() { return ArrayWrapper<Derived>(derived()); }
/** \returns a const \link Eigen::ArrayBase Array \endlink expression of this matrix
* \sa ArrayBase::matrix() */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const ArrayWrapper<const Derived> array() const { return ArrayWrapper<const Derived>(derived()); }
/////////// LU module ///////////
const FullPivLU<PlainObject> fullPivLu() const;
const PartialPivLU<PlainObject> partialPivLu() const;
EIGEN_DEVICE_FUNC const FullPivLU<PlainObject> fullPivLu() const;
EIGEN_DEVICE_FUNC const PartialPivLU<PlainObject> partialPivLu() const;
#if EIGEN2_SUPPORT_STAGE < STAGE20_RESOLVE_API_CONFLICTS
const LU<PlainObject> lu() const;
#endif
#ifdef EIGEN2_SUPPORT
const LU<PlainObject> eigen2_lu() const;
#endif
#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;
EIGEN_DEVICE_FUNC
const Inverse<Derived> inverse() const;
template<typename ResultType>
void computeInverseAndDetWithCheck(
ResultType& inverse,
@@ -369,10 +361,6 @@ template<typename Derived> class MatrixBase
const HouseholderQR<PlainObject> householderQr() const;
const ColPivHouseholderQR<PlainObject> colPivHouseholderQr() const;
const FullPivHouseholderQR<PlainObject> fullPivHouseholderQr() const;
#ifdef EIGEN2_SUPPORT
const QR<PlainObject> qr() const;
#endif
EigenvaluesReturnType eigenvalues() const;
RealScalar operatorNorm() const;
@@ -380,10 +368,7 @@ template<typename Derived> class MatrixBase
/////////// SVD module ///////////
JacobiSVD<PlainObject> jacobiSvd(unsigned int computationOptions = 0) const;
#ifdef EIGEN2_SUPPORT
SVD<PlainObject> svd() const;
#endif
BDCSVD<PlainObject> bdcSvd(unsigned int computationOptions = 0) const;
/////////// Geometry module ///////////
@@ -395,20 +380,25 @@ template<typename Derived> class MatrixBase
};
#endif // EIGEN_PARSED_BY_DOXYGEN
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
typename cross_product_return_type<OtherDerived>::type
cross(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
PlainObject cross3(const MatrixBase<OtherDerived>& other) const;
EIGEN_DEVICE_FUNC
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;
// 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
enum {
SizeMinusOne = SizeAtCompileTime==Dynamic ? Dynamic : SizeAtCompileTime-1
@@ -454,49 +444,16 @@ template<typename Derived> class MatrixBase
const MatrixFunctionReturnValue<Derived> sin() const;
const MatrixSquareRootReturnValue<Derived> sqrt() const;
const MatrixLogarithmReturnValue<Derived> log() const;
#ifdef EIGEN2_SUPPORT
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& operator+=(const Flagged<ProductBase<ProductDerived, Lhs,Rhs>, 0,
EvalBeforeAssigningBit>& other);
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& operator-=(const Flagged<ProductBase<ProductDerived, Lhs,Rhs>, 0,
EvalBeforeAssigningBit>& other);
/** \deprecated because .lazy() is deprecated
* Overloaded for cache friendly product evaluation */
template<typename OtherDerived>
Derived& lazyAssign(const Flagged<OtherDerived, 0, EvalBeforeAssigningBit>& other)
{ return lazyAssign(other._expression()); }
template<unsigned int Added>
const Flagged<Derived, Added, 0> marked() const;
const Flagged<Derived, 0, EvalBeforeAssigningBit> lazy() const;
inline const Cwise<Derived> cwise() const;
inline Cwise<Derived> cwise();
VectorBlock<Derived> start(Index size);
const VectorBlock<const Derived> start(Index size) const;
VectorBlock<Derived> end(Index size);
const VectorBlock<const Derived> end(Index size) const;
template<int Size> VectorBlock<Derived,Size> start();
template<int Size> const VectorBlock<const Derived,Size> start() const;
template<int Size> VectorBlock<Derived,Size> end();
template<int Size> const VectorBlock<const Derived,Size> end() const;
Minor<Derived> minor(Index row, Index col);
const Minor<Derived> minor(Index row, Index col) const;
#endif
const MatrixPowerReturnValue<Derived> pow(const RealScalar& p) const;
const MatrixComplexPowerReturnValue<Derived> pow(const std::complex<RealScalar>& p) const;
protected:
MatrixBase() : Base() {}
EIGEN_DEVICE_FUNC MatrixBase() : Base() {}
private:
explicit MatrixBase(int);
MatrixBase(int,int);
template<typename OtherDerived> explicit MatrixBase(const MatrixBase<OtherDerived>&);
EIGEN_DEVICE_FUNC explicit MatrixBase(int);
EIGEN_DEVICE_FUNC MatrixBase(int,int);
template<typename OtherDerived> EIGEN_DEVICE_FUNC explicit MatrixBase(const MatrixBase<OtherDerived>&);
protected:
// mixing arrays and matrices is not legal
template<typename OtherDerived> Derived& operator+=(const ArrayBase<OtherDerived>& )
@@ -506,6 +463,51 @@ template<typename Derived> class MatrixBase
{EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
};
/***************************************************************************
* Implementation of matrix base methods
***************************************************************************/
/** replaces \c *this by \c *this * \a other.
*
* \returns a reference to \c *this
*
* Example: \include MatrixBase_applyOnTheRight.cpp
* Output: \verbinclude MatrixBase_applyOnTheRight.out
*/
template<typename Derived>
template<typename OtherDerived>
inline Derived&
MatrixBase<Derived>::operator*=(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheRight(derived());
return derived();
}
/** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=().
*
* Example: \include MatrixBase_applyOnTheRight.cpp
* Output: \verbinclude MatrixBase_applyOnTheRight.out
*/
template<typename Derived>
template<typename OtherDerived>
inline void MatrixBase<Derived>::applyOnTheRight(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheRight(derived());
}
/** replaces \c *this by \a other * \c *this.
*
* Example: \include MatrixBase_applyOnTheLeft.cpp
* Output: \verbinclude MatrixBase_applyOnTheLeft.out
*/
template<typename Derived>
template<typename OtherDerived>
inline void MatrixBase<Derived>::applyOnTheLeft(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheLeft(derived());
}
} // end namespace Eigen
#endif // EIGEN_MATRIXBASE_H

View File

@@ -40,29 +40,29 @@ 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 +91,7 @@ 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

@@ -30,57 +30,40 @@ namespace Eigen {
template<typename ExpressionType, template <typename> class StorageBase>
class NoAlias
{
typedef typename ExpressionType::Scalar Scalar;
public:
NoAlias(ExpressionType& expression) : m_expression(expression) {}
/** Behaves like MatrixBase::lazyAssign(other)
* \sa MatrixBase::lazyAssign() */
typedef typename ExpressionType::Scalar Scalar;
explicit NoAlias(ExpressionType& expression) : m_expression(expression) {}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase<OtherDerived>& other)
{ return internal::assign_selector<ExpressionType,OtherDerived,false>::run(m_expression,other.derived()); }
/** \sa MatrixBase::operator+= */
{
call_assignment_no_alias(m_expression, other.derived(), internal::assign_op<Scalar>());
return m_expression;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator+=(const StorageBase<OtherDerived>& other)
{
typedef SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, ExpressionType, OtherDerived> SelfAdder;
SelfAdder tmp(m_expression);
typedef typename internal::nested<OtherDerived>::type OtherDerivedNested;
typedef typename internal::remove_all<OtherDerivedNested>::type _OtherDerivedNested;
internal::assign_selector<SelfAdder,_OtherDerivedNested,false>::run(tmp,OtherDerivedNested(other.derived()));
call_assignment_no_alias(m_expression, other.derived(), internal::add_assign_op<Scalar>());
return m_expression;
}
/** \sa MatrixBase::operator-= */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator-=(const StorageBase<OtherDerived>& other)
{
typedef SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, ExpressionType, OtherDerived> SelfAdder;
SelfAdder tmp(m_expression);
typedef typename internal::nested<OtherDerived>::type OtherDerivedNested;
typedef typename internal::remove_all<OtherDerivedNested>::type _OtherDerivedNested;
internal::assign_selector<SelfAdder,_OtherDerivedNested,false>::run(tmp,OtherDerivedNested(other.derived()));
call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op<Scalar>());
return m_expression;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename ProductDerived, typename Lhs, typename Rhs>
EIGEN_STRONG_INLINE ExpressionType& operator+=(const ProductBase<ProductDerived, Lhs,Rhs>& other)
{ other.derived().addTo(m_expression); return m_expression; }
template<typename ProductDerived, typename Lhs, typename Rhs>
EIGEN_STRONG_INLINE ExpressionType& operator-=(const ProductBase<ProductDerived, Lhs,Rhs>& other)
{ other.derived().subTo(m_expression); return m_expression; }
template<typename Lhs, typename Rhs, int NestingFlags>
EIGEN_STRONG_INLINE ExpressionType& operator+=(const CoeffBasedProduct<Lhs,Rhs,NestingFlags>& other)
{ return m_expression.derived() += CoeffBasedProduct<Lhs,Rhs,NestByRefBit>(other.lhs(), other.rhs()); }
template<typename Lhs, typename Rhs, int NestingFlags>
EIGEN_STRONG_INLINE ExpressionType& operator-=(const CoeffBasedProduct<Lhs,Rhs,NestingFlags>& other)
{ return m_expression.derived() -= CoeffBasedProduct<Lhs,Rhs,NestByRefBit>(other.lhs(), other.rhs()); }
#endif
EIGEN_DEVICE_FUNC
ExpressionType& expression() const
{
return m_expression;
}
protected:
ExpressionType& m_expression;
@@ -117,7 +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

@@ -68,21 +68,40 @@ template<typename T> struct GenericNumTraits
>::type NonInteger;
typedef T Nested;
static inline Real epsilon() { return std::numeric_limits<T>::epsilon(); }
EIGEN_DEVICE_FUNC
static inline Real epsilon()
{
#if defined(__CUDA_ARCH__)
return internal::device::numeric_limits<T>::epsilon();
#else
return std::numeric_limits<T>::epsilon();
#endif
}
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;
EIGEN_DEVICE_FUNC
static inline T highest() {
#if defined(__CUDA_ARCH__)
return (internal::device::numeric_limits<T>::max)();
#else
return (std::numeric_limits<T>::max)();
#endif
}
EIGEN_DEVICE_FUNC
static inline T lowest() {
#if defined(__CUDA_ARCH__)
return IsInteger ? (internal::device::numeric_limits<T>::min)() : (-(internal::device::numeric_limits<T>::max)());
#else
return IsInteger ? (std::numeric_limits<T>::min)() : (-(std::numeric_limits<T>::max)());
#endif
}
};
template<typename T> struct NumTraits : GenericNumTraits<T>
@@ -91,11 +110,13 @@ 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; }
};
@@ -140,6 +161,9 @@ struct NumTraits<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
AddCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::AddCost,
MulCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::MulCost
};
static inline RealScalar epsilon() { return NumTraits<RealScalar>::epsilon(); }
static inline RealScalar dummy_precision() { return NumTraits<RealScalar>::dummy_precision(); }
};
} // end namespace Eigen

View File

@@ -13,7 +13,8 @@
namespace Eigen {
template<int RowCol,typename IndicesType,typename MatrixType, typename StorageKind> class PermutedImpl;
// TODO: this does not seems to be needed at all:
// template<int RowCol,typename IndicesType,typename MatrixType, typename StorageKind> class PermutedImpl;
/** \class PermutationBase
* \ingroup Core_Module
@@ -41,10 +42,6 @@ template<int RowCol,typename IndicesType,typename MatrixType, typename StorageKi
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
@@ -60,19 +57,18 @@ 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;
using Base::derived;
typedef Transpose<PermutationBase> TransposeReturnType;
#endif
/** Copies the other permutation into *this */
@@ -105,20 +101,20 @@ class PermutationBase : public EigenBase<Derived>
#endif
/** \returns the number of rows */
inline Index rows() const { return indices().size(); }
inline Index rows() const { return Index(indices().size()); }
/** \returns the number of columns */
inline Index cols() const { return indices().size(); }
inline Index cols() const { return Index(indices().size()); }
/** \returns the size of a side of the respective square matrix, i.e., the number of indices */
inline Index size() const { return indices().size(); }
inline Index size() const { return Index(indices().size()); }
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename DenseDerived>
void evalTo(MatrixBase<DenseDerived>& other) const
{
other.setZero();
for (int i=0; i<rows();++i)
for (Index i=0; i<rows(); ++i)
other.coeffRef(indices().coeff(i),i) = typename DenseDerived::Scalar(1);
}
#endif
@@ -139,23 +135,24 @@ class PermutationBase : public EigenBase<Derived>
/** Resizes to given size.
*/
inline void resize(Index size)
inline void resize(Index newSize)
{
indices().resize(size);
indices().resize(newSize);
}
/** Sets *this to be the identity permutation matrix */
void setIdentity()
{
for(Index i = 0; i < size(); ++i)
StorageIndex n = StorageIndex(size());
for(StorageIndex i = 0; i < n; ++i)
indices().coeffRef(i) = i;
}
/** Sets *this to be the identity permutation matrix of given size.
*/
void setIdentity(Index size)
void setIdentity(Index newSize)
{
resize(size);
resize(newSize);
setIdentity();
}
@@ -163,18 +160,18 @@ 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 +182,7 @@ 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)
{
@@ -198,14 +195,14 @@ class PermutationBase : public EigenBase<Derived>
*
* \note \note_try_to_help_rvo
*/
inline Transpose<PermutationBase> inverse() const
{ return derived(); }
inline TransposeReturnType inverse() const
{ return TransposeReturnType(derived()); }
/** \returns the tranpose permutation matrix.
*
* \note \note_try_to_help_rvo
*/
inline Transpose<PermutationBase> transpose() const
{ return derived(); }
inline TransposeReturnType transpose() const
{ return TransposeReturnType(derived()); }
/**** multiplication helpers to hopefully get RVO ****/
@@ -215,13 +212,13 @@ 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
@@ -250,6 +247,35 @@ class PermutationBase : public EigenBase<Derived>
template<typename Other> friend
inline PlainPermutationType operator*(const Transpose<PermutationBase<Other> >& other, const PermutationBase& perm)
{ return PlainPermutationType(internal::PermPermProduct, other.eval(), perm); }
/** \returns the determinant of the permutation matrix, which is either 1 or -1 depending on the parity of the permutation.
*
* This function is O(\c n) procedure allocating a buffer of \c n booleans.
*/
Index determinant() const
{
Index res = 1;
Index n = size();
Matrix<bool,RowsAtCompileTime,1,0,MaxRowsAtCompileTime> mask(n);
mask.fill(false);
Index r = 0;
while(r < n)
{
// search for the next seed
while(r<n && mask[r]) r++;
if(r>=n)
break;
// we got one, let's follow it until we are back to the seed
Index k0 = r++;
mask.coeffRef(k0) = true;
for(Index k=indices().coeff(k0); k!=k0; k=indices().coeff(k))
{
mask.coeffRef(k) = true;
res = -res;
}
}
return res;
}
protected:
@@ -262,7 +288,7 @@ class PermutationBase : public EigenBase<Derived>
*
* \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
* \param StorageIndex the integer type of the indices
*
* This class represents a permutation matrix, internally stored as a vector of integers.
*
@@ -270,24 +296,28 @@ class PermutationBase : public EigenBase<Derived>
*/
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType>
struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType> >
: traits<Matrix<IndexType,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex> >
: traits<Matrix<_StorageIndex,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef IndexType Index;
typedef Matrix<IndexType, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
typedef PermutationStorage StorageKind;
typedef Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
typedef _StorageIndex StorageIndex;
};
}
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()
@@ -295,8 +325,10 @@ 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>
@@ -365,9 +397,12 @@ 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())
: m_indices(other.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.nestedExpression().indices().coeff(i)) = i;
}
template<typename Lhs,typename Rhs>
PermutationMatrix(internal::PermPermProduct_t, const Lhs& lhs, const Rhs& rhs)
@@ -384,18 +419,19 @@ 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;
};
}
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;
@@ -403,15 +439,15 @@ class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef typename Traits::IndicesType IndicesType;
typedef typename IndicesType::Scalar Index;
typedef typename IndicesType::Scalar StorageIndex;
#endif
inline Map(const Index* indices)
: m_indices(indices)
inline Map(const StorageIndex* indicesPtr)
: m_indices(indicesPtr)
{}
inline Map(const Index* indices, Index size)
: m_indices(indices,size)
inline Map(const StorageIndex* indicesPtr, Index size)
: m_indices(indicesPtr,size)
{}
/** Copies the other permutation into *this */
@@ -457,8 +493,6 @@ class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,
* \sa class PermutationBase, class PermutationMatrix
*/
struct PermutationStorage {};
template<typename _IndicesType> class TranspositionsWrapper;
namespace internal {
template<typename _IndicesType>
@@ -466,15 +500,14 @@ struct traits<PermutationWrapper<_IndicesType> >
{
typedef PermutationStorage StorageKind;
typedef typename _IndicesType::Scalar Scalar;
typedef typename _IndicesType::Scalar Index;
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
};
};
}
@@ -503,103 +536,33 @@ 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> >
{
typedef typename MatrixType::PlainObject ReturnType;
};
template<typename PermutationType, typename MatrixType, int Side, bool Transposed>
struct permut_matrix_product_retval
: public ReturnByValue<permut_matrix_product_retval<PermutationType, MatrixType, Side, Transposed> >
{
typedef typename remove_all<typename MatrixType::Nested>::type MatrixTypeNestedCleaned;
permut_matrix_product_retval(const PermutationType& perm, const MatrixType& matrix)
: m_permutation(perm), m_matrix(matrix)
{}
inline int rows() const { return m_matrix.rows(); }
inline int cols() const { return m_matrix.cols(); }
template<typename Dest> inline void evalTo(Dest& dst) const
{
const int n = Side==OnTheLeft ? rows() : cols();
if(is_same<MatrixTypeNestedCleaned,Dest>::value && extract_data(dst) == extract_data(m_matrix))
{
// apply the permutation inplace
Matrix<bool,PermutationType::RowsAtCompileTime,1,0,PermutationType::MaxRowsAtCompileTime> mask(m_permutation.size());
mask.fill(false);
int r = 0;
while(r < m_permutation.size())
{
// search for the next seed
while(r<m_permutation.size() && mask[r]) r++;
if(r>=m_permutation.size())
break;
// we got one, let's follow it until we are back to the seed
int k0 = r++;
int kPrev = k0;
mask.coeffRef(k0) = true;
for(int k=m_permutation.indices().coeff(k0); k!=k0; k=m_permutation.indices().coeff(k))
{
Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>(dst, k)
.swap(Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
(dst,((Side==OnTheLeft) ^ Transposed) ? k0 : kPrev));
mask.coeffRef(k) = true;
kPrev = k;
}
}
}
else
{
for(int i = 0; i < n; ++i)
{
Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
(dst, ((Side==OnTheLeft) ^ Transposed) ? m_permutation.indices().coeff(i) : i)
=
Block<const MatrixTypeNestedCleaned,Side==OnTheLeft ? 1 : MatrixType::RowsAtCompileTime,Side==OnTheRight ? 1 : MatrixType::ColsAtCompileTime>
(m_matrix, ((Side==OnTheRight) ^ Transposed) ? m_permutation.indices().coeff(i) : i);
}
}
}
protected:
const PermutationType& m_permutation;
typename MatrixType::Nested m_matrix;
};
/* Template partial specialization for transposed/inverse permutations */
template<typename Derived>
@@ -609,6 +572,8 @@ struct traits<Transpose<PermutationBase<Derived> > >
} // end namespace internal
// TODO: the specificties should be handled by the evaluator,
// at the very least we should only specialize TransposeImpl
template<typename Derived>
class Transpose<PermutationBase<Derived> >
: public EigenBase<Transpose<PermutationBase<Derived> > >
@@ -623,26 +588,26 @@ class Transpose<PermutationBase<Derived> >
typedef typename Derived::DenseMatrixType DenseMatrixType;
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::StorageIndex StorageIndex;
#endif
Transpose(const PermutationType& p) : m_permutation(p) {}
inline int rows() const { return m_permutation.rows(); }
inline int cols() const { return m_permutation.cols(); }
inline Index rows() const { return m_permutation.rows(); }
inline Index 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)
for (Index i=0; i<rows();++i)
other.coeffRef(i, m_permutation.indices().coeff(i)) = typename DenseDerived::Scalar(1);
}
#endif
@@ -655,22 +620,22 @@ class Transpose<PermutationBase<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>
const Product<OtherDerived, Transpose, AliasFreeProduct>
operator*(const MatrixBase<OtherDerived>& matrix, const Transpose& trPerm)
{
return internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheRight, true>(trPerm.m_permutation, matrix.derived());
return Product<OtherDerived, Transpose, 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<Transpose, OtherDerived, AliasFreeProduct>
operator*(const MatrixBase<OtherDerived>& matrix) const
{
return internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheLeft, true>(m_permutation, matrix.derived());
return Product<Transpose, OtherDerived, AliasFreeProduct>(*this, matrix.derived());
}
const PermutationType& nestedPermutation() const { return m_permutation; }
const PermutationType& nestedExpression() const { return m_permutation; }
protected:
const PermutationType& m_permutation;
@@ -682,6 +647,12 @@ 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

@@ -11,30 +11,48 @@
#ifndef EIGEN_DENSESTORAGEBASE_H
#define EIGEN_DENSESTORAGEBASE_H
#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
# define EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED for(int i=0;i<base().size();++i) coeffRef(i)=Scalar(0);
#if defined(EIGEN_INITIALIZE_MATRICES_BY_ZERO)
# define EIGEN_INITIALIZE_COEFFS
# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED for(int i=0;i<base().size();++i) coeffRef(i)=Scalar(0);
#elif defined(EIGEN_INITIALIZE_MATRICES_BY_NAN)
# define EIGEN_INITIALIZE_COEFFS
# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED for(int i=0;i<base().size();++i) coeffRef(i)=std::numeric_limits<Scalar>::quiet_NaN();
#else
# define EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
# undef EIGEN_INITIALIZE_COEFFS
# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
#endif
namespace Eigen {
namespace internal {
template<typename Index>
EIGEN_ALWAYS_INLINE void check_rows_cols_for_overflow(Index rows, Index cols)
{
// http://hg.mozilla.org/mozilla-central/file/6c8a909977d3/xpcom/ds/CheckedInt.h#l242
// we assume Index is signed
Index max_index = (size_t(1) << (8 * sizeof(Index) - 1)) - 1; // assume Index is signed
bool error = (rows < 0 || cols < 0) ? true
: (rows == 0 || cols == 0) ? false
: (rows > max_index / cols);
if (error)
throw_std_bad_alloc();
}
template<int MaxSizeAtCompileTime> struct check_rows_cols_for_overflow {
template<typename Index>
EIGEN_DEVICE_FUNC
static EIGEN_ALWAYS_INLINE void run(Index, Index)
{
}
};
template <typename Derived, typename OtherDerived = Derived, bool IsVector = bool(Derived::IsVectorAtCompileTime)> struct conservative_resize_like_impl;
template<> struct check_rows_cols_for_overflow<Dynamic> {
template<typename Index>
EIGEN_DEVICE_FUNC
static EIGEN_ALWAYS_INLINE void run(Index rows, Index cols)
{
// http://hg.mozilla.org/mozilla-central/file/6c8a909977d3/xpcom/ds/CheckedInt.h#l242
// we assume Index is signed
Index max_index = (size_t(1) << (8 * sizeof(Index) - 1)) - 1; // assume Index is signed
bool error = (rows == 0 || cols == 0) ? false
: (rows > max_index / cols);
if (error)
throw_std_bad_alloc();
}
};
template <typename Derived,
typename OtherDerived = Derived,
bool IsVector = bool(Derived::IsVectorAtCompileTime) && bool(OtherDerived::IsVectorAtCompileTime)>
struct conservative_resize_like_impl;
template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> struct matrix_swap_impl;
@@ -51,8 +69,9 @@ template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> struct m
#ifdef EIGEN_PARSED_BY_DOXYGEN
namespace internal {
// this is a warkaround to doxygen not being able to understand the inheritence logic
// 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.
/** This class is just a workaround for Doxygen and it does not not actually exist. */
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>
@@ -77,8 +96,8 @@ 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;
@@ -97,62 +116,75 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
typedef Eigen::Map<Derived, Unaligned> MapType;
friend class Eigen::Map<const Derived, Unaligned>;
typedef const Eigen::Map<const Derived, Unaligned> ConstMapType;
friend class Eigen::Map<Derived, Aligned>;
typedef Eigen::Map<Derived, Aligned> AlignedMapType;
friend class Eigen::Map<const Derived, Aligned>;
typedef const Eigen::Map<const Derived, Aligned> ConstAlignedMapType;
#if EIGEN_MAX_ALIGN_BYTES>0
// for EIGEN_MAX_ALIGN_BYTES==0, AlignedMax==Unaligned, and many compilers generate warnings for friend-ing a class twice.
friend class Eigen::Map<Derived, AlignedMax>;
friend class Eigen::Map<const Derived, AlignedMax>;
#endif
typedef Eigen::Map<Derived, AlignedMax> AlignedMapType;
typedef const Eigen::Map<const Derived, AlignedMax> ConstAlignedMapType;
template<typename StrideType> struct StridedMapType { typedef Eigen::Map<Derived, Unaligned, StrideType> type; };
template<typename StrideType> struct StridedConstMapType { typedef Eigen::Map<const Derived, Unaligned, StrideType> type; };
template<typename StrideType> struct StridedAlignedMapType { typedef Eigen::Map<Derived, Aligned, StrideType> type; };
template<typename StrideType> struct StridedConstAlignedMapType { typedef Eigen::Map<const Derived, Aligned, StrideType> type; };
template<typename StrideType> struct StridedAlignedMapType { typedef Eigen::Map<Derived, AlignedMax, StrideType> type; };
template<typename StrideType> struct StridedConstAlignedMapType { typedef Eigen::Map<const Derived, AlignedMax, StrideType> type; };
protected:
DenseStorage<Scalar, Base::MaxSizeAtCompileTime, Base::RowsAtCompileTime, Base::ColsAtCompileTime, Options> m_storage;
public:
enum { NeedsToAlign = SizeAtCompileTime != Dynamic && (internal::traits<Derived>::Flags & AlignedBit) != 0 };
enum { NeedsToAlign = (SizeAtCompileTime != Dynamic) && (internal::traits<Derived>::Alignment>0) };
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
EIGEN_DEVICE_FUNC
Base& base() { return *static_cast<Base*>(this); }
EIGEN_DEVICE_FUNC
const Base& base() const { return *static_cast<const Base*>(this); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rows() const { return m_storage.rows(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const { return m_storage.cols(); }
EIGEN_STRONG_INLINE const Scalar& coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeff(Index rowId, Index colId) const
{
if(Flags & RowMajorBit)
return m_storage.data()[col + row * m_storage.cols()];
return m_storage.data()[colId + rowId * m_storage.cols()];
else // column-major
return m_storage.data()[row + col * m_storage.rows()];
return m_storage.data()[rowId + colId * m_storage.rows()];
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
{
return m_storage.data()[index];
}
EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index rowId, Index colId)
{
if(Flags & RowMajorBit)
return m_storage.data()[col + row * m_storage.cols()];
return m_storage.data()[colId + rowId * m_storage.cols()];
else // column-major
return m_storage.data()[row + col * m_storage.rows()];
return m_storage.data()[rowId + colId * m_storage.rows()];
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
return m_storage.data()[index];
}
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index row, Index col) const
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index rowId, Index colId) const
{
if(Flags & RowMajorBit)
return m_storage.data()[col + row * m_storage.cols()];
return m_storage.data()[colId + rowId * m_storage.cols()];
else // column-major
return m_storage.data()[row + col * m_storage.rows()];
return m_storage.data()[rowId + colId * m_storage.rows()];
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index index) const
{
return m_storage.data()[index];
@@ -160,12 +192,12 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
/** \internal */
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
{
return internal::ploadt<PacketScalar, LoadMode>
(m_storage.data() + (Flags & RowMajorBit
? col + row * m_storage.cols()
: row + col * m_storage.rows()));
? colId + rowId * m_storage.cols()
: rowId + colId * m_storage.rows()));
}
/** \internal */
@@ -177,27 +209,27 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
/** \internal */
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket(Index row, Index col, const PacketScalar& x)
EIGEN_STRONG_INLINE void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
internal::pstoret<Scalar, PacketScalar, StoreMode>
(m_storage.data() + (Flags & RowMajorBit
? col + row * m_storage.cols()
: row + col * m_storage.rows()), x);
? colId + rowId * m_storage.cols()
: rowId + colId * m_storage.rows()), val);
}
/** \internal */
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket(Index index, const PacketScalar& x)
EIGEN_STRONG_INLINE void writePacket(Index index, const PacketScalar& val)
{
internal::pstoret<Scalar, PacketScalar, StoreMode>(m_storage.data() + index, x);
internal::pstoret<Scalar, PacketScalar, StoreMode>(m_storage.data() + index, val);
}
/** \returns a const pointer to the data array of this matrix */
EIGEN_STRONG_INLINE const Scalar *data() const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar *data() const
{ return m_storage.data(); }
/** \returns a pointer to the data array of this matrix */
EIGEN_STRONG_INLINE Scalar *data()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar *data()
{ return m_storage.data(); }
/** Resizes \c *this to a \a rows x \a cols matrix.
@@ -216,16 +248,22 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index) for vectors, resize(NoChange_t, Index), resize(Index, NoChange_t)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void resize(Index rows, Index cols)
{
#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
internal::check_rows_cols_for_overflow(rows, cols);
eigen_assert( EIGEN_IMPLIES(RowsAtCompileTime!=Dynamic,rows==RowsAtCompileTime)
&& EIGEN_IMPLIES(ColsAtCompileTime!=Dynamic,cols==ColsAtCompileTime)
&& EIGEN_IMPLIES(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic,rows<=MaxRowsAtCompileTime)
&& EIGEN_IMPLIES(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic,cols<=MaxColsAtCompileTime)
&& rows>=0 && cols>=0 && "Invalid sizes when resizing a matrix or array.");
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(rows, cols);
#ifdef EIGEN_INITIALIZE_COEFFS
Index size = rows*cols;
bool size_changed = size != this->size();
m_storage.resize(size, rows, cols);
if(size_changed) EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
#else
internal::check_rows_cols_for_overflow(rows, cols);
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(rows, cols);
m_storage.resize(rows*cols, rows, cols);
#endif
}
@@ -241,19 +279,20 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index,Index), resize(NoChange_t, Index), resize(Index, NoChange_t)
*/
EIGEN_DEVICE_FUNC
inline void resize(Index size)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(PlainObjectBase)
eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == size);
#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
eigen_assert(((SizeAtCompileTime == Dynamic && (MaxSizeAtCompileTime==Dynamic || size<=MaxSizeAtCompileTime)) || SizeAtCompileTime == size) && size>=0);
#ifdef EIGEN_INITIALIZE_COEFFS
bool size_changed = size != this->size();
#endif
if(RowsAtCompileTime == 1)
m_storage.resize(size, 1, size);
else
m_storage.resize(size, size, 1);
#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
if(size_changed) EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
#ifdef EIGEN_INITIALIZE_COEFFS
if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
#endif
}
@@ -265,6 +304,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index,Index)
*/
EIGEN_DEVICE_FUNC
inline void resize(NoChange_t, Index cols)
{
resize(rows(), cols);
@@ -278,6 +318,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index,Index)
*/
EIGEN_DEVICE_FUNC
inline void resize(Index rows, NoChange_t)
{
resize(rows, cols());
@@ -291,10 +332,11 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void resizeLike(const EigenBase<OtherDerived>& _other)
{
const OtherDerived& other = _other.derived();
internal::check_rows_cols_for_overflow(other.rows(), other.cols());
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(other.rows(), other.cols());
const Index othersize = other.rows()*other.cols();
if(RowsAtCompileTime == 1)
{
@@ -318,6 +360,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* Matrices are resized relative to the top-left element. In case values need to be
* appended to the matrix they will be uninitialized.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResize(Index rows, Index cols)
{
internal::conservative_resize_like_impl<Derived>::run(*this, rows, cols);
@@ -330,6 +373,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* In case the matrix is growing, new rows will be uninitialized.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResize(Index rows, NoChange_t)
{
// Note: see the comment in conservativeResize(Index,Index)
@@ -343,6 +387,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* In case the matrix is growing, new columns will be uninitialized.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResize(NoChange_t, Index cols)
{
// Note: see the comment in conservativeResize(Index,Index)
@@ -357,6 +402,7 @@ 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);
@@ -372,6 +418,7 @@ 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);
@@ -380,6 +427,7 @@ 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);
@@ -387,6 +435,7 @@ 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);
@@ -394,38 +443,63 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& operator=(const ReturnByValue<OtherDerived>& func)
{
resize(func.rows(), func.cols());
return Base::operator=(func);
}
EIGEN_STRONG_INLINE explicit PlainObjectBase() : m_storage()
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase() : m_storage()
{
// _check_template_params();
// EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
// EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
// FIXME is it still needed ?
/** \internal */
PlainObjectBase(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC
explicit PlainObjectBase(internal::constructor_without_unaligned_array_assert)
: m_storage(internal::constructor_without_unaligned_array_assert())
{
// _check_template_params(); EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
// _check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#endif
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
PlainObjectBase(PlainObjectBase&& other)
: m_storage( std::move(other.m_storage) )
{
}
EIGEN_DEVICE_FUNC
PlainObjectBase& operator=(PlainObjectBase&& other)
{
using std::swap;
swap(m_storage, other.m_storage);
return *this;
}
#endif
/** Copy constructor */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(const PlainObjectBase& other)
: Base(), m_storage(other.m_storage) { }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(Index size, Index rows, Index cols)
: m_storage(size, rows, cols)
{
// _check_template_params();
// EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
// EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
/** \copydoc MatrixBase::operator=(const EigenBase<OtherDerived>&)
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& operator=(const EigenBase<OtherDerived> &other)
{
_resize_to_match(other);
@@ -433,14 +507,36 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
return this->derived();
}
/** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
/** \sa PlainObjectBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
EIGEN_STRONG_INLINE PlainObjectBase(const EigenBase<OtherDerived> &other)
: m_storage(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(const DenseBase<OtherDerived> &other)
: m_storage()
{
_check_template_params();
internal::check_rows_cols_for_overflow(other.derived().rows(), other.derived().cols());
Base::operator=(other.derived());
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());
}
/** \name Map
@@ -517,16 +613,16 @@ 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& value);
EIGEN_DEVICE_FUNC Derived& setConstant(Index rows, Index cols, const Scalar& value);
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);
@@ -545,12 +641,14 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _resize_to_match(const EigenBase<OtherDerived>& other)
{
#ifdef EIGEN_NO_AUTOMATIC_RESIZING
eigen_assert((this->size()==0 || (IsVectorAtCompileTime ? (this->size() == other.size())
: (rows() == other.rows() && cols() == other.cols())))
&& "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined");
EIGEN_ONLY_USED_FOR_DEBUG(other);
#else
resizeLike(other);
#endif
@@ -570,25 +668,23 @@ 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
@@ -596,44 +692,166 @@ 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>());
return this->derived();
}
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init2(Index rows, Index cols, typename internal::enable_if<Base::SizeAtCompileTime!=2,T0>::type* = 0)
{
EIGEN_STATIC_ASSERT(bool(NumTraits<T0>::IsInteger) &&
bool(NumTraits<T1>::IsInteger),
FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED)
eigen_assert(rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
internal::check_rows_cols_for_overflow(rows, cols);
m_storage.resize(rows*cols,rows,cols);
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
resize(rows,cols);
}
template<typename T0, typename T1>
EIGEN_STRONG_INLINE void _init2(const Scalar& x, const Scalar& y, typename internal::enable_if<Base::SizeAtCompileTime==2,T0>::type* = 0)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init2(const Scalar& val0, const Scalar& val1, typename internal::enable_if<Base::SizeAtCompileTime==2,T0>::type* = 0)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
m_storage.data()[0] = val0;
m_storage.data()[1] = 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_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)
@@ -647,16 +865,16 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
&& (Options & (DontAlign|RowMajor)) == Options),
INVALID_MATRIX_TEMPLATE_PARAMETERS)
}
#endif
private:
enum { ThisConstantIsPrivateInPlainObjectBase };
enum { IsPlainObjectBase = 1 };
#endif
};
namespace internal {
template <typename Derived, typename OtherDerived, bool IsVector>
struct internal::conservative_resize_like_impl
struct conservative_resize_like_impl
{
typedef typename Derived::Index Index;
static void run(DenseBase<Derived>& _this, Index rows, Index cols)
{
if (_this.rows() == rows && _this.cols() == cols) return;
@@ -665,7 +883,7 @@ struct internal::conservative_resize_like_impl
if ( ( Derived::IsRowMajor && _this.cols() == cols) || // row-major and we change only the number of rows
(!Derived::IsRowMajor && _this.rows() == rows) ) // column-major and we change only the number of columns
{
internal::check_rows_cols_for_overflow(rows, cols);
internal::check_rows_cols_for_overflow<Derived::MaxSizeAtCompileTime>::run(rows, cols);
_this.derived().m_storage.conservativeResize(rows*cols,rows,cols);
}
else
@@ -714,12 +932,14 @@ struct internal::conservative_resize_like_impl
}
};
namespace internal {
// Here, the specialization for vectors inherits from the general matrix case
// to allow calling .conservativeResize(rows,cols) on vectors.
template <typename Derived, typename OtherDerived>
struct conservative_resize_like_impl<Derived,OtherDerived,true>
: conservative_resize_like_impl<Derived,OtherDerived,false>
{
typedef typename Derived::Index Index;
using conservative_resize_like_impl<Derived,OtherDerived,false>::run;
static void run(DenseBase<Derived>& _this, Index size)
{
const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : size;
@@ -745,6 +965,7 @@ 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);
@@ -754,6 +975,7 @@ struct matrix_swap_impl
template<typename MatrixTypeA, typename MatrixTypeB>
struct matrix_swap_impl<MatrixTypeA, MatrixTypeB, true>
{
EIGEN_DEVICE_FUNC
static inline void run(MatrixTypeA& a, MatrixTypeB& b)
{
static_cast<typename MatrixTypeA::Base&>(a).m_storage.swap(static_cast<typename MatrixTypeB::Base&>(b).m_storage);

View File

@@ -3,15 +3,16 @@
//
// 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/.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_PRODUCT_H
#define EIGEN_PRODUCT_H
template<typename Lhs, typename Rhs> class Product;
template<typename Lhs, typename Rhs, typename StorageKind> class ProductImpl;
namespace Eigen {
template<typename Lhs, typename Rhs, int Option, typename StorageKind> class ProductImpl;
/** \class Product
* \ingroup Core_Module
@@ -22,77 +23,223 @@ template<typename Lhs, typename Rhs, typename StorageKind> class ProductImpl;
* \param Rhs the type of the right-hand side expression
*
* This class represents an expression of the product of two arbitrary matrices.
*
* The other template parameters are:
* \tparam Option can be DefaultProduct, AliasFreeProduct, or LazyProduct
*
*/
namespace internal {
template<typename Lhs, typename Rhs>
struct traits<Product<Lhs, Rhs> >
// Determine the scalar of Product<Lhs, Rhs>. This is normally the same as Lhs::Scalar times
// Rhs::Scalar, but product with permutation matrices inherit the scalar of the other factor.
template<typename Lhs, typename Rhs, typename LhsShape = typename evaluator_traits<Lhs>::Shape,
typename RhsShape = typename evaluator_traits<Rhs>::Shape >
struct product_result_scalar
{
typedef typename scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType Scalar;
};
template<typename Lhs, typename Rhs, typename RhsShape>
struct product_result_scalar<Lhs, Rhs, PermutationShape, RhsShape>
{
typedef typename Rhs::Scalar Scalar;
};
template<typename Lhs, typename Rhs, typename LhsShape>
struct product_result_scalar<Lhs, Rhs, LhsShape, PermutationShape>
{
typedef typename Lhs::Scalar Scalar;
};
template<typename Lhs, typename Rhs, typename RhsShape>
struct product_result_scalar<Lhs, Rhs, TranspositionsShape, RhsShape>
{
typedef typename Rhs::Scalar Scalar;
};
template<typename Lhs, typename Rhs, typename LhsShape>
struct product_result_scalar<Lhs, Rhs, LhsShape, TranspositionsShape>
{
typedef typename Lhs::Scalar Scalar;
};
template<typename Lhs, typename Rhs, int Option>
struct traits<Product<Lhs, Rhs, Option> >
{
typedef MatrixXpr XprKind;
typedef typename remove_all<Lhs>::type LhsCleaned;
typedef typename remove_all<Rhs>::type RhsCleaned;
typedef typename scalar_product_traits<typename traits<LhsCleaned>::Scalar, typename traits<RhsCleaned>::Scalar>::ReturnType Scalar;
typedef typename promote_storage_type<typename traits<LhsCleaned>::StorageKind,
typename traits<RhsCleaned>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<typename traits<LhsCleaned>::Index,
typename traits<RhsCleaned>::Index>::type Index;
typedef traits<LhsCleaned> LhsTraits;
typedef traits<RhsCleaned> RhsTraits;
typedef MatrixXpr XprKind;
typedef typename product_result_scalar<LhsCleaned,RhsCleaned>::Scalar 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 = LhsCleaned::RowsAtCompileTime,
ColsAtCompileTime = RhsCleaned::ColsAtCompileTime,
MaxRowsAtCompileTime = LhsCleaned::MaxRowsAtCompileTime,
MaxColsAtCompileTime = RhsCleaned::MaxColsAtCompileTime,
Flags = (MaxRowsAtCompileTime==1 ? RowMajorBit : 0), // TODO should be no storage order
CoeffReadCost = 0 // TODO CoeffReadCost should not be part of the expression traits
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
template<typename Lhs, typename Rhs>
class Product : public ProductImpl<Lhs,Rhs,typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind,
typename internal::traits<Rhs>::StorageKind>::ret>
template<typename _Lhs, typename _Rhs, int Option>
class Product : public ProductImpl<_Lhs,_Rhs,Option,
typename internal::product_promote_storage_type<typename internal::traits<_Lhs>::StorageKind,
typename internal::traits<_Rhs>::StorageKind,
internal::product_type<_Lhs,_Rhs>::ret>::ret>
{
public:
typedef _Lhs Lhs;
typedef _Rhs Rhs;
typedef typename ProductImpl<
Lhs, Rhs,
typename internal::promote_storage_type<typename Lhs::StorageKind,
typename Rhs::StorageKind>::ret>::Base Base;
Lhs, Rhs, Option,
typename internal::product_promote_storage_type<typename internal::traits<Lhs>::StorageKind,
typename internal::traits<Rhs>::StorageKind,
internal::product_type<Lhs,Rhs>::ret>::ret>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(Product)
typedef typename Lhs::Nested LhsNested;
typedef typename Rhs::Nested RhsNested;
typedef typename internal::ref_selector<Lhs>::type LhsNested;
typedef typename internal::ref_selector<Rhs>::type RhsNested;
typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
Product(const Lhs& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs)
EIGEN_DEVICE_FUNC Product(const Lhs& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs)
{
eigen_assert(lhs.cols() == rhs.rows()
&& "invalid matrix product"
&& "if you wanted a coeff-wise or a dot product use the respective explicit functions");
}
inline Index rows() const { return m_lhs.rows(); }
inline Index cols() const { return m_rhs.cols(); }
EIGEN_DEVICE_FUNC inline Index rows() const { return m_lhs.rows(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_rhs.cols(); }
const LhsNestedCleaned& lhs() const { return m_lhs; }
const RhsNestedCleaned& rhs() const { return m_rhs; }
EIGEN_DEVICE_FUNC const LhsNestedCleaned& lhs() const { return m_lhs; }
EIGEN_DEVICE_FUNC const RhsNestedCleaned& rhs() const { return m_rhs; }
protected:
const LhsNested m_lhs;
const RhsNested m_rhs;
LhsNested m_lhs;
RhsNested m_rhs;
};
template<typename Lhs, typename Rhs>
class ProductImpl<Lhs,Rhs,Dense> : public internal::dense_xpr_base<Product<Lhs,Rhs> >::type
namespace internal {
template<typename Lhs, typename Rhs, int Option, int ProductTag = internal::product_type<Lhs,Rhs>::ret>
class dense_product_base
: public internal::dense_xpr_base<Product<Lhs,Rhs,Option> >::type
{};
/** Convertion to scalar for inner-products */
template<typename Lhs, typename Rhs, int Option>
class dense_product_base<Lhs, Rhs, Option, InnerProduct>
: public internal::dense_xpr_base<Product<Lhs,Rhs,Option> >::type
{
typedef Product<Lhs, Rhs> Derived;
public:
typedef typename internal::dense_xpr_base<Product<Lhs, Rhs> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
typedef Product<Lhs,Rhs,Option> ProductXpr;
typedef typename internal::dense_xpr_base<ProductXpr>::type Base;
public:
using Base::derived;
typedef typename Base::Scalar Scalar;
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);
}
};
/***************************************************************************
* Implementation of matrix base methods
***************************************************************************/
/** \internal used to test the evaluator only
*/
template<typename Lhs,typename Rhs>
const Product<Lhs,Rhs>
prod(const Lhs& lhs, const Rhs& rhs)
{
return Product<Lhs,Rhs>(lhs,rhs);
}
/** \internal used to test the evaluator only
*/
template<typename Lhs,typename Rhs>
const Product<Lhs,Rhs,LazyProduct>
lazyprod(const Lhs& lhs, const Rhs& rhs)
{
return Product<Lhs,Rhs,LazyProduct>(lhs,rhs);
}
} // end namespace Eigen
#endif // EIGEN_PRODUCT_H

View File

@@ -1,278 +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:
typedef typename Base::PlainObject PlainObject;
ProductBase(const Lhs& lhs, const Rhs& rhs)
: m_lhs(lhs), m_rhs(rhs)
{
eigen_assert(lhs.cols() == rhs.rows()
&& "invalid matrix product"
&& "if you wanted a coeff-wise or a dot product use the respective explicit functions");
}
inline Index rows() const { return m_lhs.rows(); }
inline Index cols() const { return m_rhs.cols(); }
template<typename Dest>
inline void evalTo(Dest& dst) const { dst.setZero(); scaleAndAddTo(dst,Scalar(1)); }
template<typename Dest>
inline void addTo(Dest& dst) const { scaleAndAddTo(dst,Scalar(1)); }
template<typename Dest>
inline void subTo(Dest& dst) const { scaleAndAddTo(dst,Scalar(-1)); }
template<typename Dest>
inline void scaleAndAddTo(Dest& dst,Scalar alpha) const { derived().scaleAndAddTo(dst,alpha); }
const _LhsNested& lhs() const { return m_lhs; }
const _RhsNested& rhs() const { return m_rhs; }
// Implicit conversion to the nested type (trigger the evaluation of the product)
operator const PlainObject& () const
{
m_result.resize(m_lhs.rows(), m_rhs.cols());
derived().evalTo(m_result);
return m_result;
}
const Diagonal<const FullyLazyCoeffBaseProductType,0> diagonal() const
{ return FullyLazyCoeffBaseProductType(m_lhs, m_rhs); }
template<int Index>
const Diagonal<FullyLazyCoeffBaseProductType,Index> diagonal() const
{ return FullyLazyCoeffBaseProductType(m_lhs, m_rhs); }
const Diagonal<FullyLazyCoeffBaseProductType,Dynamic> diagonal(Index index) const
{ return FullyLazyCoeffBaseProductType(m_lhs, m_rhs).diagonal(index); }
// restrict coeff accessors to 1x1 expressions. No need to care about mutators here since this isnt a Lvalue expression
typename Base::CoeffReturnType coeff(Index row, Index col) const
{
#ifdef EIGEN2_SUPPORT
return lhs().row(row).cwiseProduct(rhs().col(col).transpose()).sum();
#else
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
Matrix<Scalar,1,1> result = *this;
return result.coeff(row,col);
#endif
}
typename Base::CoeffReturnType coeff(Index i) const
{
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
Matrix<Scalar,1,1> result = *this;
return result.coeff(i);
}
const Scalar& coeffRef(Index row, Index col) const
{
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
return derived().coeffRef(row,col);
}
const Scalar& coeffRef(Index i) const
{
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
return derived().coeffRef(i);
}
protected:
LhsNested m_lhs;
RhsNested m_rhs;
mutable PlainObject m_result;
};
// here we need to overload the nested rule for products
// such that the nested type is a const reference to a plain matrix
namespace internal {
template<typename Lhs, typename Rhs, int Mode, int N, typename PlainObject>
struct nested<GeneralProduct<Lhs,Rhs,Mode>, N, PlainObject>
{
typedef PlainObject const& type;
};
}
template<typename NestedProduct>
class ScaledProduct;
// Note that these two operator* functions are not defined as member
// functions of ProductBase, because, otherwise we would have to
// define all overloads defined in MatrixBase. Furthermore, Using
// "using Base::operator*" would not work with MSVC.
//
// Also note that here we accept any compatible scalar types
template<typename Derived,typename Lhs,typename Rhs>
const ScaledProduct<Derived>
operator*(const ProductBase<Derived,Lhs,Rhs>& prod, typename Derived::Scalar x)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
typename internal::enable_if<!internal::is_same<typename Derived::Scalar,typename Derived::RealScalar>::value,
const ScaledProduct<Derived> >::type
operator*(const ProductBase<Derived,Lhs,Rhs>& prod, typename Derived::RealScalar x)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
const ScaledProduct<Derived>
operator*(typename Derived::Scalar x,const ProductBase<Derived,Lhs,Rhs>& prod)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
typename internal::enable_if<!internal::is_same<typename Derived::Scalar,typename Derived::RealScalar>::value,
const ScaledProduct<Derived> >::type
operator*(typename Derived::RealScalar x,const ProductBase<Derived,Lhs,Rhs>& prod)
{ return ScaledProduct<Derived>(prod.derived(), x); }
namespace internal {
template<typename NestedProduct>
struct traits<ScaledProduct<NestedProduct> >
: traits<ProductBase<ScaledProduct<NestedProduct>,
typename NestedProduct::_LhsNested,
typename NestedProduct::_RhsNested> >
{
typedef typename traits<NestedProduct>::StorageKind StorageKind;
};
}
template<typename NestedProduct>
class ScaledProduct
: public ProductBase<ScaledProduct<NestedProduct>,
typename NestedProduct::_LhsNested,
typename NestedProduct::_RhsNested>
{
public:
typedef ProductBase<ScaledProduct<NestedProduct>,
typename NestedProduct::_LhsNested,
typename NestedProduct::_RhsNested> Base;
typedef typename Base::Scalar Scalar;
typedef typename Base::PlainObject PlainObject;
// EIGEN_PRODUCT_PUBLIC_INTERFACE(ScaledProduct)
ScaledProduct(const NestedProduct& prod, Scalar x)
: Base(prod.lhs(),prod.rhs()), m_prod(prod), m_alpha(x) {}
template<typename Dest>
inline void evalTo(Dest& dst) const { dst.setZero(); scaleAndAddTo(dst, Scalar(1)); }
template<typename Dest>
inline void addTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(1)); }
template<typename Dest>
inline void subTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(-1)); }
template<typename Dest>
inline void scaleAndAddTo(Dest& dst,Scalar alpha) const { m_prod.derived().scaleAndAddTo(dst,alpha * m_alpha); }
const Scalar& alpha() const { return m_alpha; }
protected:
const NestedProduct& m_prod;
Scalar m_alpha;
};
/** \internal
* Overloaded to perform an efficient C = (A*B).lazy() */
template<typename Derived>
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& MatrixBase<Derived>::lazyAssign(const ProductBase<ProductDerived, Lhs,Rhs>& other)
{
other.derived().evalTo(derived());
return derived();
}
} // end namespace Eigen
#endif // EIGEN_PRODUCTBASE_H

1015
Eigen/src/Core/ProductEvaluators.h Executable file

File diff suppressed because it is too large Load Diff

View File

@@ -28,12 +28,18 @@ 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 +47,28 @@ 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 +81,10 @@ 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 +92,9 @@ 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 +104,13 @@ 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 +118,11 @@ 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
*
@@ -112,32 +134,41 @@ inline Derived& DenseBase<Derived>::setRandom()
return *this = Random(rows(), cols());
}
/** Resizes to the given \a size, and sets all coefficients in this expression to random values.
/** Resizes to the given \a newSize, and sets all coefficients in this expression to random values.
*
* Numbers are uniformly spread through their whole definition range for integer types,
* and in the [-1:1] range for floating point scalar types.
*
* \only_for_vectors
* \not_reentrant
*
* Example: \include Matrix_setRandom_int.cpp
* Output: \verbinclude Matrix_setRandom_int.out
*
* \sa MatrixBase::setRandom(), setRandom(Index,Index), class CwiseNullaryOp, MatrixBase::Random()
* \sa DenseBase::setRandom(), setRandom(Index,Index), class CwiseNullaryOp, DenseBase::Random()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setRandom(Index size)
PlainObjectBase<Derived>::setRandom(Index newSize)
{
resize(size);
resize(newSize);
return setRandom();
}
/** Resizes to the given size, and sets all coefficients in this expression to random values.
*
* Numbers are uniformly spread through their whole definition range for integer types,
* and in the [-1:1] range for floating point scalar types.
*
* \not_reentrant
*
* \param rows the new number of rows
* \param cols the new number of columns
*
* Example: \include Matrix_setRandom_int_int.cpp
* Output: \verbinclude Matrix_setRandom_int_int.out
*
* \sa MatrixBase::setRandom(), setRandom(Index), class CwiseNullaryOp, MatrixBase::Random()
* \sa DenseBase::setRandom(), setRandom(Index), class CwiseNullaryOp, DenseBase::Random()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&

View File

@@ -65,6 +65,25 @@ public:
? 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 +101,7 @@ 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 +119,7 @@ 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 +133,7 @@ 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(); }
};
@@ -143,7 +165,7 @@ struct redux_vec_unroller<Func, Derived, Start, 1>
index = Start * packet_traits<typename Derived::Scalar>::size,
outer = index / int(Derived::InnerSizeAtCompileTime),
inner = index % int(Derived::InnerSizeAtCompileTime),
alignment = (Derived::Flags & AlignedBit) ? Aligned : Unaligned
alignment = Derived::Alignment
};
typedef typename Derived::Scalar Scalar;
@@ -151,7 +173,7 @@ struct redux_vec_unroller<Func, Derived, Start, 1>
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 +191,8 @@ 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;
@@ -194,18 +216,18 @@ struct redux_impl<Func, Derived, LinearVectorizedTraversal, NoUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename Derived::Index Index;
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 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 +235,19 @@ 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);
@@ -251,10 +273,9 @@ template<typename Func, typename Derived>
struct redux_impl<Func, Derived, SliceVectorizedTraversal, NoUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename Derived::Index Index;
typedef typename packet_traits<Scalar>::type 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();
@@ -266,10 +287,10 @@ struct redux_impl<Func, Derived, SliceVectorizedTraversal, NoUnrolling>
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)
@@ -296,16 +317,83 @@ struct redux_impl<Func, Derived, LinearVectorizedTraversal, CompleteUnrolling>
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>
PacketReturnType packet(Index row, Index col) const
{ return m_evaluator.template packet<LoadMode,PacketType>(row, col); }
template<int LoadMode, typename PacketType>
PacketReturnType 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>
PacketReturnType 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
/***************************************************************************
@@ -316,36 +404,51 @@ 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");
// FIXME, eval_nest should be handled by redux_evaluator, however:
// - it is currently difficult to provide the right Flags since they are still handled by the expressions
// - handling it here might reduce the number of template instantiations
// typedef typename internal::nested_eval<Derived,1>::type ThisNested;
// typedef typename internal::remove_all<ThisNested>::type ThisNestedCleaned;
// typedef typename internal::redux_evaluator<ThisNestedCleaned> ThisEvaluator;
//
// ThisNested thisNested(derived());
// ThisEvaluator thisEval(thisNested);
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 *this
/** \returns the minimum of all coefficients of \c *this.
* \warning the result is undefined if \c *this contains NaN.
*/
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>());
}
/** \returns the maximum of all coefficients of *this
/** \returns the maximum of all coefficients of \c *this.
* \warning the result is undefined if \c *this contains NaN.
*/
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>());
}
/** \returns the sum of all coefficients of *this
@@ -358,7 +461,7 @@ 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>());
}
/** \returns the mean of all coefficients of *this
@@ -369,7 +472,7 @@ 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());
return Scalar(derived().redux(Eigen::internal::scalar_sum_op<Scalar>())) / Scalar(this->size());
}
/** \returns the product of all coefficients of *this
@@ -385,7 +488,7 @@ 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.

276
Eigen/src/Core/Ref.h Normal file
View File

@@ -0,0 +1,276 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_REF_H
#define EIGEN_REF_H
namespace Eigen {
/** \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 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, 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
*/
namespace internal {
template<typename _PlainObjectType, int _Options, typename _StrideType>
struct traits<Ref<_PlainObjectType, _Options, _StrideType> >
: public traits<Map<_PlainObjectType, _Options, _StrideType> >
{
typedef _PlainObjectType PlainObjectType;
typedef _StrideType StrideType;
enum {
Options = _Options,
Flags = traits<Map<_PlainObjectType, _Options, _StrideType> >::Flags | NestByRefBit,
Alignment = traits<Map<_PlainObjectType, _Options, _StrideType> >::Alignment
};
template<typename Derived> struct match {
enum {
HasDirectAccess = internal::has_direct_access<Derived>::ret,
StorageOrderMatch = PlainObjectType::IsVectorAtCompileTime || Derived::IsVectorAtCompileTime || ((PlainObjectType::Flags&RowMajorBit)==(Derived::Flags&RowMajorBit)),
InnerStrideMatch = int(StrideType::InnerStrideAtCompileTime)==int(Dynamic)
|| int(StrideType::InnerStrideAtCompileTime)==int(Derived::InnerStrideAtCompileTime)
|| (int(StrideType::InnerStrideAtCompileTime)==0 && int(Derived::InnerStrideAtCompileTime)==1),
OuterStrideMatch = Derived::IsVectorAtCompileTime
|| int(StrideType::OuterStrideAtCompileTime)==int(Dynamic) || int(StrideType::OuterStrideAtCompileTime)==int(Derived::OuterStrideAtCompileTime),
AlignmentMatch = (int(traits<PlainObjectType>::Alignment)==int(Unaligned)) || (int(evaluator<Derived>::Alignment) >= 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
};
typedef typename internal::conditional<MatchAtCompileTime,internal::true_type,internal::false_type>::type type;
};
};
template<typename Derived>
struct traits<RefBase<Derived> > : public traits<Derived> {};
}
template<typename Derived> class RefBase
: public MapBase<Derived>
{
typedef typename internal::traits<Derived>::PlainObjectType PlainObjectType;
typedef typename internal::traits<Derived>::StrideType StrideType;
public:
typedef MapBase<Derived> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(RefBase)
EIGEN_DEVICE_FUNC inline Index innerStride() const
{
return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1;
}
EIGEN_DEVICE_FUNC inline Index outerStride() const
{
return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
: IsVectorAtCompileTime ? this->size()
: int(Flags)&RowMajorBit ? this->cols()
: this->rows();
}
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,
StrideType::InnerStrideAtCompileTime==Dynamic?0:StrideType::InnerStrideAtCompileTime)
{}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(RefBase)
protected:
typedef Stride<StrideType::OuterStrideAtCompileTime,StrideType::InnerStrideAtCompileTime> StrideBase;
template<typename Expression>
EIGEN_DEVICE_FUNC void construct(Expression& expr)
{
if(PlainObjectType::RowsAtCompileTime==1)
{
eigen_assert(expr.rows()==1 || expr.cols()==1);
::new (static_cast<Base*>(this)) Base(expr.data(), 1, expr.size());
}
else if(PlainObjectType::ColsAtCompileTime==1)
{
eigen_assert(expr.rows()==1 || expr.cols()==1);
::new (static_cast<Base*>(this)) Base(expr.data(), expr.size(), 1);
}
else
::new (static_cast<Base*>(this)) Base(expr.data(), expr.rows(), expr.cols());
if(Expression::IsVectorAtCompileTime && (!PlainObjectType::IsVectorAtCompileTime) && ((Expression::Flags&RowMajorBit)!=(PlainObjectType::Flags&RowMajorBit)))
::new (&m_stride) StrideBase(expr.innerStride(), StrideType::InnerStrideAtCompileTime==0?0:1);
else
::new (&m_stride) StrideBase(StrideType::OuterStrideAtCompileTime==0?0:expr.outerStride(),
StrideType::InnerStrideAtCompileTime==0?0:expr.innerStride());
}
StrideBase m_stride;
};
template<typename PlainObjectType, int Options, typename StrideType> class Ref
: public RefBase<Ref<PlainObjectType, Options, StrideType> >
{
private:
typedef internal::traits<Ref> Traits;
template<typename Derived>
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;
EIGEN_DENSE_PUBLIC_INTERFACE(Ref)
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename Derived>
EIGEN_DEVICE_FUNC inline Ref(PlainObjectBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
{
EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
Base::construct(expr.derived());
}
template<typename Derived>
EIGEN_DEVICE_FUNC inline Ref(const DenseBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
#else
template<typename Derived>
inline Ref(DenseBase<Derived>& expr)
#endif
{
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());
}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Ref)
};
// this is the const ref version
template<typename TPlainObjectType, int Options, typename StrideType> class Ref<const TPlainObjectType, Options, StrideType>
: public RefBase<Ref<const TPlainObjectType, Options, StrideType> >
{
typedef internal::traits<Ref> Traits;
public:
typedef RefBase<Ref> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Ref)
template<typename Derived>
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());
}
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>
EIGEN_DEVICE_FUNC inline Ref(const RefBase<OtherRef>& other) {
construct(other.derived(), typename Traits::template match<OtherRef>::type());
}
protected:
template<typename Expression>
EIGEN_DEVICE_FUNC void construct(const Expression& expr,internal::true_type)
{
Base::construct(expr);
}
template<typename Expression>
EIGEN_DEVICE_FUNC void construct(const Expression& expr, internal::false_type)
{
internal::call_assignment_no_alias(m_object,expr,internal::assign_op<Scalar>());
Base::construct(m_object);
}
protected:
TPlainObjectType m_object;
};
} // end namespace Eigen
#endif // EIGEN_REF_H

View File

@@ -35,10 +35,7 @@ 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
@@ -53,8 +50,9 @@ 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
};
};
}
@@ -68,8 +66,10 @@ 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>
EIGEN_DEVICE_FUNC
inline explicit Replicate(const OriginalMatrixType& matrix)
: m_matrix(matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor)
{
@@ -79,6 +79,7 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
}
template<typename OriginalMatrixType>
EIGEN_DEVICE_FUNC
inline Replicate(const OriginalMatrixType& matrix, Index rowFactor, Index colFactor)
: m_matrix(matrix), m_rowFactor(rowFactor), m_colFactor(colFactor)
{
@@ -86,34 +87,12 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
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 row, Index col) const
{
// try to avoid using modulo; this is a pure optimization strategy
const Index actual_row = internal::traits<MatrixType>::RowsAtCompileTime==1 ? 0
: RowFactor==1 ? row
: row%m_matrix.rows();
const Index actual_col = internal::traits<MatrixType>::ColsAtCompileTime==1 ? 0
: ColFactor==1 ? col
: col%m_matrix.cols();
return m_matrix.coeff(actual_row, actual_col);
}
template<int LoadMode>
inline PacketScalar packet(Index row, Index col) const
{
const Index actual_row = internal::traits<MatrixType>::RowsAtCompileTime==1 ? 0
: RowFactor==1 ? row
: row%m_matrix.rows();
const Index actual_col = internal::traits<MatrixType>::ColsAtCompileTime==1 ? 0
: ColFactor==1 ? col
: col%m_matrix.cols();
return m_matrix.template packet<LoadMode>(actual_row, actual_col);
}
EIGEN_DEVICE_FUNC
const _MatrixTypeNested& nestedExpression() const
{
return m_matrix;
@@ -135,27 +114,12 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
*/
template<typename Derived>
template<int RowFactor, int ColFactor>
inline const Replicate<Derived,RowFactor,ColFactor>
const Replicate<Derived,RowFactor,ColFactor>
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>
inline const Replicate<Derived,Dynamic,Dynamic>
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

@@ -38,9 +38,10 @@ 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;
};
@@ -48,7 +49,7 @@ struct nested<ReturnByValue<Derived>, n, PlainObject>
} // end namespace internal
template<typename Derived> class ReturnByValue
: 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 +58,11 @@ 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
@@ -72,6 +74,7 @@ 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); }
#undef Unusable
#endif
};
@@ -83,6 +86,33 @@ Derived& DenseBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
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
#endif // EIGEN_RETURNBYVALUE_H

View File

@@ -37,32 +37,25 @@ struct traits<Reverse<MatrixType, Direction> >
typedef typename MatrixType::Scalar Scalar;
typedef typename traits<MatrixType>::StorageKind StorageKind;
typedef typename traits<MatrixType>::XprKind XprKind;
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
// let's enable LinearAccess only with vectorization because of the product overhead
LinearAccess = ( (Direction==BothDirections) && (int(_MatrixTypeNested::Flags)&PacketAccessBit) )
? LinearAccessBit : 0,
Flags = int(_MatrixTypeNested::Flags) & (HereditaryBits | LvalueBit | PacketAccessBit | LinearAccess),
CoeffReadCost = _MatrixTypeNested::CoeffReadCost
Flags = _MatrixTypeNested::Flags & (RowMajorBit | LvalueBit)
};
};
template<typename PacketScalar, bool ReversePacket> struct reverse_packet_cond
template<typename PacketType, bool ReversePacket> struct reverse_packet_cond
{
static inline PacketScalar run(const PacketScalar& x) { return preverse(x); }
static inline PacketType run(const PacketType& x) { return preverse(x); }
};
template<typename PacketScalar> struct reverse_packet_cond<PacketScalar,false>
template<typename PacketType> struct reverse_packet_cond<PacketType,false>
{
static inline PacketScalar run(const PacketScalar& x) { return x; }
static inline PacketType run(const PacketType& x) { return x; }
};
} // end namespace internal
@@ -74,12 +67,9 @@ template<typename MatrixType, int Direction> class Reverse
typedef typename internal::dense_xpr_base<Reverse>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Reverse)
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
using Base::IsRowMajor;
// next line is necessary because otherwise const version of operator()
// is hidden by non-const version defined in this file
using Base::operator();
protected:
enum {
PacketSize = internal::packet_traits<Scalar>::size,
@@ -95,82 +85,19 @@ template<typename MatrixType, int Direction> class Reverse
typedef internal::reverse_packet_cond<PacketScalar,ReversePacket> reverse_packet;
public:
inline Reverse(const MatrixType& matrix) : m_matrix(matrix) { }
EIGEN_DEVICE_FUNC explicit inline Reverse(const MatrixType& matrix) : m_matrix(matrix) { }
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Reverse)
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
EIGEN_DEVICE_FUNC inline Index rows() const { return m_matrix.rows(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_matrix.cols(); }
inline Index innerStride() const
EIGEN_DEVICE_FUNC inline Index innerStride() const
{
return -m_matrix.innerStride();
}
inline Scalar& operator()(Index row, Index col)
{
eigen_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
return coeffRef(row, col);
}
inline Scalar& coeffRef(Index row, Index col)
{
return m_matrix.const_cast_derived().coeffRef(ReverseRow ? m_matrix.rows() - row - 1 : row,
ReverseCol ? m_matrix.cols() - col - 1 : col);
}
inline CoeffReturnType coeff(Index row, Index col) const
{
return m_matrix.coeff(ReverseRow ? m_matrix.rows() - row - 1 : row,
ReverseCol ? m_matrix.cols() - col - 1 : col);
}
inline CoeffReturnType coeff(Index index) const
{
return m_matrix.coeff(m_matrix.size() - index - 1);
}
inline Scalar& coeffRef(Index index)
{
return m_matrix.const_cast_derived().coeffRef(m_matrix.size() - index - 1);
}
inline Scalar& operator()(Index index)
{
eigen_assert(index >= 0 && index < m_matrix.size());
return coeffRef(index);
}
template<int LoadMode>
inline const PacketScalar packet(Index row, Index col) const
{
return reverse_packet::run(m_matrix.template packet<LoadMode>(
ReverseRow ? m_matrix.rows() - row - OffsetRow : row,
ReverseCol ? m_matrix.cols() - col - OffsetCol : col));
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
{
m_matrix.const_cast_derived().template writePacket<LoadMode>(
ReverseRow ? m_matrix.rows() - row - OffsetRow : row,
ReverseCol ? m_matrix.cols() - col - OffsetCol : col,
reverse_packet::run(x));
}
template<int LoadMode>
inline const PacketScalar packet(Index index) const
{
return internal::preverse(m_matrix.template packet<LoadMode>( m_matrix.size() - index - PacketSize ));
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
{
m_matrix.const_cast_derived().template writePacket<LoadMode>(m_matrix.size() - index - PacketSize, internal::preverse(x));
}
const typename internal::remove_all<typename MatrixType::Nested>::type&
EIGEN_DEVICE_FUNC const typename internal::remove_all<typename MatrixType::Nested>::type&
nestedExpression() const
{
return m_matrix;
@@ -190,33 +117,93 @@ template<typename Derived>
inline typename DenseBase<Derived>::ReverseReturnType
DenseBase<Derived>::reverse()
{
return derived();
return ReverseReturnType(derived());
}
/** This is the const version of reverse(). */
template<typename Derived>
inline const typename DenseBase<Derived>::ConstReverseReturnType
DenseBase<Derived>::reverse() const
{
return derived();
}
//reverse const overload moved DenseBase.h due to a CUDA compiler bug
/** This is the "in place" version of reverse: it reverses \c *this.
*
* In most cases it is probably better to simply use the reversed expression
* of a matrix. However, when reversing the matrix data itself is really needed,
* then this "in-place" version is probably the right choice because it provides
* the following additional features:
* the following additional benefits:
* - less error prone: doing the same operation with .reverse() requires special care:
* \code m = m.reverse().eval(); \endcode
* - this API allows to avoid creating a temporary (the current implementation creates a temporary, but that could be avoided using swap)
* - this API enables reverse operations without the need for a temporary
* - it allows future optimizations (cache friendliness, etc.)
*
* \sa reverse() */
* \sa VectorwiseOp::reverseInPlace(), reverse() */
template<typename Derived>
inline void DenseBase<Derived>::reverseInPlace()
{
derived() = derived().reverse().eval();
if(cols()>rows())
{
Index half = cols()/2;
leftCols(half).swap(rightCols(half).reverse());
if((cols()%2)==1)
{
Index half2 = rows()/2;
col(half).head(half2).swap(col(half).tail(half2).reverse());
}
}
else
{
Index half = rows()/2;
topRows(half).swap(bottomRows(half).reverse());
if((rows()%2)==1)
{
Index half2 = cols()/2;
row(half).head(half2).swap(row(half).tail(half2).reverse());
}
}
}
namespace internal {
template<int Direction>
struct vectorwise_reverse_inplace_impl;
template<>
struct vectorwise_reverse_inplace_impl<Vertical>
{
template<typename ExpressionType>
static void run(ExpressionType &xpr)
{
Index half = xpr.rows()/2;
xpr.topRows(half).swap(xpr.bottomRows(half).colwise().reverse());
}
};
template<>
struct vectorwise_reverse_inplace_impl<Horizontal>
{
template<typename ExpressionType>
static void run(ExpressionType &xpr)
{
Index half = xpr.cols()/2;
xpr.leftCols(half).swap(xpr.rightCols(half).rowwise().reverse());
}
};
} // end namespace internal
/** This is the "in place" version of VectorwiseOp::reverse: it reverses each column or row of \c *this.
*
* In most cases it is probably better to simply use the reversed expression
* of a matrix. However, when reversing the matrix data itself is really needed,
* then this "in-place" version is probably the right choice because it provides
* the following additional benefits:
* - less error prone: doing the same operation with .reverse() requires special care:
* \code m = m.reverse().eval(); \endcode
* - this API enables reverse operations without the need for a temporary
*
* \sa DenseBase::reverseInPlace(), reverse() */
template<typename ExpressionType, int Direction>
void VectorwiseOp<ExpressionType,Direction>::reverseInPlace()
{
internal::vectorwise_reverse_inplace_impl<Direction>::run(_expression().const_cast_derived());
}
} // end namespace Eigen

View File

@@ -43,35 +43,34 @@ struct traits<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
ColsAtCompileTime = ConditionMatrixType::ColsAtCompileTime,
MaxRowsAtCompileTime = ConditionMatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = ConditionMatrixType::MaxColsAtCompileTime,
Flags = (unsigned int)ThenMatrixType::Flags & ElseMatrixType::Flags & HereditaryBits,
CoeffReadCost = traits<typename remove_all<ConditionMatrixNested>::type>::CoeffReadCost
+ EIGEN_SIZE_MAX(traits<typename remove_all<ThenMatrixNested>::type>::CoeffReadCost,
traits<typename remove_all<ElseMatrixNested>::type>::CoeffReadCost)
Flags = (unsigned int)ThenMatrixType::Flags & ElseMatrixType::Flags & RowMajorBit
};
};
}
template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType>
class Select : internal::no_assignment_operator,
public internal::dense_xpr_base< Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >::type
class Select : public internal::dense_xpr_base< Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >::type,
internal::no_assignment_operator
{
public:
typedef typename internal::dense_xpr_base<Select>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Select)
Select(const ConditionMatrixType& conditionMatrix,
const ThenMatrixType& thenMatrix,
const ElseMatrixType& elseMatrix)
: m_condition(conditionMatrix), m_then(thenMatrix), m_else(elseMatrix)
inline EIGEN_DEVICE_FUNC
Select(const ConditionMatrixType& a_conditionMatrix,
const ThenMatrixType& a_thenMatrix,
const ElseMatrixType& a_elseMatrix)
: m_condition(a_conditionMatrix), m_then(a_thenMatrix), m_else(a_elseMatrix)
{
eigen_assert(m_condition.rows() == m_then.rows() && m_condition.rows() == m_else.rows());
eigen_assert(m_condition.cols() == m_then.cols() && m_condition.cols() == m_else.cols());
}
Index rows() const { return m_condition.rows(); }
Index cols() const { return m_condition.cols(); }
inline EIGEN_DEVICE_FUNC Index rows() const { return m_condition.rows(); }
inline EIGEN_DEVICE_FUNC Index cols() const { return m_condition.cols(); }
inline EIGEN_DEVICE_FUNC
const Scalar coeff(Index i, Index j) const
{
if (m_condition.coeff(i,j))
@@ -80,6 +79,7 @@ class Select : internal::no_assignment_operator,
return m_else.coeff(i,j);
}
inline EIGEN_DEVICE_FUNC
const Scalar coeff(Index i) const
{
if (m_condition.coeff(i))
@@ -88,17 +88,17 @@ class Select : internal::no_assignment_operator,
return m_else.coeff(i);
}
const ConditionMatrixType& conditionMatrix() const
inline EIGEN_DEVICE_FUNC const ConditionMatrixType& conditionMatrix() const
{
return m_condition;
}
const ThenMatrixType& thenMatrix() const
inline EIGEN_DEVICE_FUNC const ThenMatrixType& thenMatrix() const
{
return m_then;
}
const ElseMatrixType& elseMatrix() const
inline EIGEN_DEVICE_FUNC const ElseMatrixType& elseMatrix() const
{
return m_else;
}
@@ -136,7 +136,7 @@ template<typename Derived>
template<typename ThenDerived>
inline const Select<Derived,ThenDerived, typename ThenDerived::ConstantReturnType>
DenseBase<Derived>::select(const DenseBase<ThenDerived>& thenMatrix,
typename ThenDerived::Scalar elseScalar) const
const typename ThenDerived::Scalar& elseScalar) const
{
return Select<Derived,ThenDerived,typename ThenDerived::ConstantReturnType>(
derived(), thenMatrix.derived(), ThenDerived::Constant(rows(),cols(),elseScalar));
@@ -150,8 +150,8 @@ DenseBase<Derived>::select(const DenseBase<ThenDerived>& thenMatrix,
template<typename Derived>
template<typename ElseDerived>
inline const Select<Derived, typename ElseDerived::ConstantReturnType, ElseDerived >
DenseBase<Derived>::select(typename ElseDerived::Scalar thenScalar,
const DenseBase<ElseDerived>& elseMatrix) const
DenseBase<Derived>::select(const typename ElseDerived::Scalar& thenScalar,
const DenseBase<ElseDerived>& elseMatrix) const
{
return Select<Derived,typename ElseDerived::ConstantReturnType,ElseDerived>(
derived(), ElseDerived::Constant(rows(),cols(),thenScalar), elseMatrix.derived());

View File

@@ -32,54 +32,57 @@ namespace internal {
template<typename MatrixType, unsigned int UpLo>
struct traits<SelfAdjointView<MatrixType, UpLo> > : traits<MatrixType>
{
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned;
typedef MatrixType ExpressionType;
typedef typename MatrixType::PlainObject DenseMatrixType;
typedef typename MatrixType::PlainObject FullMatrixType;
enum {
Mode = UpLo | SelfAdjoint,
Flags = MatrixTypeNestedCleaned::Flags & (HereditaryBits)
& (~(PacketAccessBit | DirectAccessBit | LinearAccessBit)), // FIXME these flags should be preserved
CoeffReadCost = MatrixTypeNestedCleaned::CoeffReadCost
FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
Flags = MatrixTypeNestedCleaned::Flags & (HereditaryBits|FlagsLvalueBit)
& (~(PacketAccessBit | DirectAccessBit | LinearAccessBit)) // FIXME these flags should be preserved
};
};
}
template <typename Lhs, int LhsMode, bool LhsIsVector,
typename Rhs, int RhsMode, bool RhsIsVector>
struct SelfadjointProductMatrix;
// FIXME could also be called SelfAdjointWrapper to be consistent with DiagonalWrapper ??
template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
: public TriangularBase<SelfAdjointView<MatrixType, UpLo> >
template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
: public TriangularBase<SelfAdjointView<_MatrixType, UpLo> >
{
public:
typedef _MatrixType MatrixType;
typedef TriangularBase<SelfAdjointView> Base;
typedef typename internal::traits<SelfAdjointView>::MatrixTypeNested MatrixTypeNested;
typedef typename internal::traits<SelfAdjointView>::MatrixTypeNestedCleaned MatrixTypeNestedCleaned;
/** \brief The type of coefficients in this matrix */
typedef typename internal::traits<SelfAdjointView>::Scalar Scalar;
typedef typename MatrixType::Index Index;
typedef typename MatrixType::StorageIndex StorageIndex;
enum {
Mode = internal::traits<SelfAdjointView>::Mode
Mode = internal::traits<SelfAdjointView>::Mode,
Flags = internal::traits<SelfAdjointView>::Flags
};
typedef typename MatrixType::PlainObject PlainObject;
inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix)
EIGEN_DEVICE_FUNC
explicit inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix)
{}
EIGEN_DEVICE_FUNC
inline Index rows() const { return m_matrix.rows(); }
EIGEN_DEVICE_FUNC
inline Index cols() const { return m_matrix.cols(); }
EIGEN_DEVICE_FUNC
inline Index outerStride() const { return m_matrix.outerStride(); }
EIGEN_DEVICE_FUNC
inline Index innerStride() const { return m_matrix.innerStride(); }
/** \sa MatrixBase::coeff()
* \warning the coordinates must fit into the referenced triangular part
*/
EIGEN_DEVICE_FUNC
inline Scalar coeff(Index row, Index col) const
{
Base::check_coordinates_internal(row, col);
@@ -89,36 +92,46 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
/** \sa MatrixBase::coeffRef()
* \warning the coordinates must fit into the referenced triangular part
*/
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index row, Index col)
{
EIGEN_STATIC_ASSERT_LVALUE(SelfAdjointView);
Base::check_coordinates_internal(row, col);
return m_matrix.const_cast_derived().coeffRef(row, col);
}
/** \internal */
EIGEN_DEVICE_FUNC
const MatrixTypeNestedCleaned& _expression() const { return m_matrix; }
EIGEN_DEVICE_FUNC
const MatrixTypeNestedCleaned& nestedExpression() const { return m_matrix; }
EIGEN_DEVICE_FUNC
MatrixTypeNestedCleaned& nestedExpression() { return *const_cast<MatrixTypeNestedCleaned*>(&m_matrix); }
/** Efficient self-adjoint matrix times vector/matrix product */
/** Efficient triangular matrix times vector/matrix product */
template<typename OtherDerived>
SelfadjointProductMatrix<MatrixType,Mode,false,OtherDerived,0,OtherDerived::IsVectorAtCompileTime>
EIGEN_DEVICE_FUNC
const Product<SelfAdjointView,OtherDerived>
operator*(const MatrixBase<OtherDerived>& rhs) const
{
return SelfadjointProductMatrix
<MatrixType,Mode,false,OtherDerived,0,OtherDerived::IsVectorAtCompileTime>
(m_matrix, rhs.derived());
return Product<SelfAdjointView,OtherDerived>(*this, rhs.derived());
}
/** Efficient vector/matrix times self-adjoint matrix product */
/** Efficient vector/matrix times triangular matrix product */
template<typename OtherDerived> friend
SelfadjointProductMatrix<OtherDerived,0,OtherDerived::IsVectorAtCompileTime,MatrixType,Mode,false>
EIGEN_DEVICE_FUNC
const Product<OtherDerived,SelfAdjointView>
operator*(const MatrixBase<OtherDerived>& lhs, const SelfAdjointView& rhs)
{
return SelfadjointProductMatrix
<OtherDerived,0,OtherDerived::IsVectorAtCompileTime,MatrixType,Mode,false>
(lhs.derived(),rhs.m_matrix);
return Product<OtherDerived,SelfAdjointView>(lhs.derived(),rhs);
}
friend EIGEN_DEVICE_FUNC
const SelfAdjointView<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>,MatrixType>,UpLo>
operator*(const Scalar& s, const SelfAdjointView& mat)
{
return (s*mat.nestedExpression()).template selfadjointView<UpLo>();
}
/** Perform a symmetric rank 2 update of the selfadjoint matrix \c *this:
@@ -132,7 +145,8 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
* \sa rankUpdate(const MatrixBase<DerivedU>&, Scalar)
*/
template<typename DerivedU, typename DerivedV>
SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, Scalar alpha = Scalar(1));
EIGEN_DEVICE_FUNC
SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, const Scalar& alpha = Scalar(1));
/** Perform a symmetric rank K update of the selfadjoint matrix \c *this:
* \f$ this = this + \alpha ( u u^* ) \f$ where \a u is a vector or matrix.
@@ -145,7 +159,8 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
* \sa rankUpdate(const MatrixBase<DerivedU>&, const MatrixBase<DerivedV>&, Scalar)
*/
template<typename DerivedU>
SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, Scalar alpha = Scalar(1));
EIGEN_DEVICE_FUNC
SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const Scalar& alpha = Scalar(1));
/////////// Cholesky module ///////////
@@ -159,31 +174,10 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
/** Return type of eigenvalues() */
typedef Matrix<RealScalar, internal::traits<MatrixType>::ColsAtCompileTime, 1> EigenvaluesReturnType;
EIGEN_DEVICE_FUNC
EigenvaluesReturnType eigenvalues() const;
EIGEN_DEVICE_FUNC
RealScalar operatorNorm() const;
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
SelfAdjointView& operator=(const MatrixBase<OtherDerived>& other)
{
enum {
OtherPart = UpLo == Upper ? StrictlyLower : StrictlyUpper
};
m_matrix.const_cast_derived().template triangularView<UpLo>() = other;
m_matrix.const_cast_derived().template triangularView<OtherPart>() = other.adjoint();
return *this;
}
template<typename OtherMatrixType, unsigned int OtherMode>
SelfAdjointView& operator=(const TriangularView<OtherMatrixType, OtherMode>& other)
{
enum {
OtherPart = UpLo == Upper ? StrictlyLower : StrictlyUpper
};
m_matrix.const_cast_derived().template triangularView<UpLo>() = other.toDenseMatrix();
m_matrix.const_cast_derived().template triangularView<OtherPart>() = other.toDenseMatrix().adjoint();
return *this;
}
#endif
protected:
MatrixTypeNested m_matrix;
@@ -201,90 +195,56 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
namespace internal {
template<typename Derived1, typename Derived2, int UnrollCount, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Upper), UnrollCount, ClearOpposite>
// TODO currently a selfadjoint expression has the form SelfAdjointView<.,.>
// in the future selfadjoint-ness should be defined by the expression traits
// such that Transpose<SelfAdjointView<.,.> > is valid. (currently TriangularBase::transpose() is overloaded to make it work)
template<typename MatrixType, unsigned int Mode>
struct evaluator_traits<SelfAdjointView<MatrixType,Mode> >
{
enum {
col = (UnrollCount-1) / Derived1::RowsAtCompileTime,
row = (UnrollCount-1) % Derived1::RowsAtCompileTime
};
typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
typedef SelfAdjointShape Shape;
static const int AssumeAliasing = 0;
};
static inline void run(Derived1 &dst, const Derived2 &src)
template<int UpLo, int SetOpposite, typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version>
class triangular_dense_assignment_kernel<UpLo,SelfAdjoint,SetOpposite,DstEvaluatorTypeT,SrcEvaluatorTypeT,Functor,Version>
: public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>
{
protected:
typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version> Base;
typedef typename Base::DstXprType DstXprType;
typedef typename Base::SrcXprType SrcXprType;
using Base::m_dst;
using Base::m_src;
using Base::m_functor;
public:
typedef typename Base::DstEvaluatorType DstEvaluatorType;
typedef typename Base::SrcEvaluatorType SrcEvaluatorType;
typedef typename Base::Scalar Scalar;
typedef typename Base::AssignmentTraits AssignmentTraits;
EIGEN_DEVICE_FUNC triangular_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr)
: Base(dst, src, func, dstExpr)
{}
EIGEN_DEVICE_FUNC void assignCoeff(Index row, Index col)
{
triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Upper), UnrollCount-1, ClearOpposite>::run(dst, src);
if(row == col)
dst.coeffRef(row, col) = real(src.coeff(row, col));
else if(row < col)
dst.coeffRef(col, row) = conj(dst.coeffRef(row, col) = src.coeff(row, col));
eigen_internal_assert(row!=col);
Scalar tmp = m_src.coeff(row,col);
m_functor.assignCoeff(m_dst.coeffRef(row,col), tmp);
m_functor.assignCoeff(m_dst.coeffRef(col,row), numext::conj(tmp));
}
};
template<typename Derived1, typename Derived2, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Upper, 0, ClearOpposite>
{
static inline void run(Derived1 &, const Derived2 &) {}
};
template<typename Derived1, typename Derived2, int UnrollCount, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Lower), UnrollCount, ClearOpposite>
{
enum {
col = (UnrollCount-1) / Derived1::RowsAtCompileTime,
row = (UnrollCount-1) % Derived1::RowsAtCompileTime
};
static inline void run(Derived1 &dst, const Derived2 &src)
EIGEN_DEVICE_FUNC void assignDiagonalCoeff(Index id)
{
triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Lower), UnrollCount-1, ClearOpposite>::run(dst, src);
if(row == col)
dst.coeffRef(row, col) = real(src.coeff(row, col));
else if(row > col)
dst.coeffRef(col, row) = conj(dst.coeffRef(row, col) = src.coeff(row, col));
}
};
template<typename Derived1, typename Derived2, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Lower, 0, ClearOpposite>
{
static inline void run(Derived1 &, const Derived2 &) {}
};
template<typename Derived1, typename Derived2, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Upper, Dynamic, ClearOpposite>
{
typedef typename Derived1::Index Index;
static inline void run(Derived1 &dst, const Derived2 &src)
{
for(Index j = 0; j < dst.cols(); ++j)
{
for(Index i = 0; i < j; ++i)
{
dst.copyCoeff(i, j, src);
dst.coeffRef(j,i) = conj(dst.coeff(i,j));
}
dst.copyCoeff(j, j, src);
}
}
};
template<typename Derived1, typename Derived2, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Lower, Dynamic, ClearOpposite>
{
static inline void run(Derived1 &dst, const Derived2 &src)
{
typedef typename Derived1::Index Index;
for(Index i = 0; i < dst.rows(); ++i)
{
for(Index j = 0; j < i; ++j)
{
dst.copyCoeff(i, j, src);
dst.coeffRef(j,i) = conj(dst.coeff(i,j));
}
dst.copyCoeff(i, i, src);
}
Base::assignCoeff(id,id);
}
EIGEN_DEVICE_FUNC void assignOppositeCoeff(Index, Index)
{ eigen_internal_assert(false && "should never be called"); }
};
} // end namespace internal
@@ -298,7 +258,7 @@ template<unsigned int UpLo>
typename MatrixBase<Derived>::template ConstSelfAdjointViewReturnType<UpLo>::Type
MatrixBase<Derived>::selfadjointView() const
{
return derived();
return typename ConstSelfAdjointViewReturnType<UpLo>::Type(derived());
}
template<typename Derived>
@@ -306,7 +266,7 @@ template<unsigned int UpLo>
typename MatrixBase<Derived>::template SelfAdjointViewReturnType<UpLo>::Type
MatrixBase<Derived>::selfadjointView()
{
return derived();
return typename SelfAdjointViewReturnType<UpLo>::Type(derived());
}
} // end namespace Eigen

View File

@@ -12,180 +12,35 @@
namespace Eigen {
/** \class SelfCwiseBinaryOp
* \ingroup Core_Module
*
* \internal
*
* \brief Internal helper class for optimizing operators like +=, -=
*
* This is a pseudo expression class re-implementing the copyCoeff/copyPacket
* method to directly performs a +=/-= operations in an optimal way. In particular,
* this allows to make sure that the input/output data are loaded only once using
* aligned packet loads.
*
* \sa class SwapWrapper for a similar trick.
*/
namespace internal {
template<typename BinaryOp, typename Lhs, typename Rhs>
struct traits<SelfCwiseBinaryOp<BinaryOp,Lhs,Rhs> >
: traits<CwiseBinaryOp<BinaryOp,Lhs,Rhs> >
{
enum {
// Note that it is still a good idea to preserve the DirectAccessBit
// so that assign can correctly align the data.
Flags = traits<CwiseBinaryOp<BinaryOp,Lhs,Rhs> >::Flags | (Lhs::Flags&DirectAccessBit) | (Lhs::Flags&LvalueBit),
OuterStrideAtCompileTime = Lhs::OuterStrideAtCompileTime,
InnerStrideAtCompileTime = Lhs::InnerStrideAtCompileTime
};
};
}
template<typename BinaryOp, typename Lhs, typename Rhs> class SelfCwiseBinaryOp
: public internal::dense_xpr_base< SelfCwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type
{
public:
typedef typename internal::dense_xpr_base<SelfCwiseBinaryOp>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(SelfCwiseBinaryOp)
typedef typename internal::packet_traits<Scalar>::type Packet;
inline SelfCwiseBinaryOp(Lhs& xpr, const BinaryOp& func = BinaryOp()) : m_matrix(xpr), m_functor(func) {}
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 const Scalar* data() const { return m_matrix.data(); }
// note that this function is needed by assign to correctly align loads/stores
// TODO make Assign use .data()
inline Scalar& coeffRef(Index row, Index col)
{
EIGEN_STATIC_ASSERT_LVALUE(Lhs)
return m_matrix.const_cast_derived().coeffRef(row, col);
}
inline const Scalar& coeffRef(Index row, Index col) const
{
return m_matrix.coeffRef(row, col);
}
// note that this function is needed by assign to correctly align loads/stores
// TODO make Assign use .data()
inline Scalar& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_LVALUE(Lhs)
return m_matrix.const_cast_derived().coeffRef(index);
}
inline const Scalar& coeffRef(Index index) const
{
return m_matrix.const_cast_derived().coeffRef(index);
}
template<typename OtherDerived>
void copyCoeff(Index row, Index col, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
Scalar& tmp = m_matrix.coeffRef(row,col);
tmp = m_functor(tmp, _other.coeff(row,col));
}
template<typename OtherDerived>
void copyCoeff(Index index, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(index >= 0 && index < m_matrix.size());
Scalar& tmp = m_matrix.coeffRef(index);
tmp = m_functor(tmp, _other.coeff(index));
}
template<typename OtherDerived, int StoreMode, int LoadMode>
void copyPacket(Index row, Index col, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
m_matrix.template writePacket<StoreMode>(row, col,
m_functor.packetOp(m_matrix.template packet<StoreMode>(row, col),_other.template packet<LoadMode>(row, col)) );
}
template<typename OtherDerived, int StoreMode, int LoadMode>
void copyPacket(Index index, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(index >= 0 && index < m_matrix.size());
m_matrix.template writePacket<StoreMode>(index,
m_functor.packetOp(m_matrix.template packet<StoreMode>(index),_other.template packet<LoadMode>(index)) );
}
// reimplement lazyAssign to handle complex *= real
// see CwiseBinaryOp ctor for details
template<typename RhsDerived>
EIGEN_STRONG_INLINE SelfCwiseBinaryOp& lazyAssign(const DenseBase<RhsDerived>& rhs)
{
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs,RhsDerived)
EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename RhsDerived::Scalar);
#ifdef EIGEN_DEBUG_ASSIGN
internal::assign_traits<SelfCwiseBinaryOp, RhsDerived>::debug();
#endif
eigen_assert(rows() == rhs.rows() && cols() == rhs.cols());
internal::assign_impl<SelfCwiseBinaryOp, RhsDerived>::run(*this,rhs.derived());
#ifndef EIGEN_NO_DEBUG
this->checkTransposeAliasing(rhs.derived());
#endif
return *this;
}
// overloaded to honor evaluation of special matrices
// maybe another solution would be to not use SelfCwiseBinaryOp
// at first...
SelfCwiseBinaryOp& operator=(const Rhs& _rhs)
{
typename internal::nested<Rhs>::type rhs(_rhs);
return Base::operator=(rhs);
}
Lhs& expression() const
{
return m_matrix;
}
const BinaryOp& functor() const
{
return m_functor;
}
protected:
Lhs& m_matrix;
const BinaryOp& m_functor;
private:
SelfCwiseBinaryOp& operator=(const SelfCwiseBinaryOp&);
};
template<typename Derived>
inline Derived& DenseBase<Derived>::operator*=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
SelfCwiseBinaryOp<internal::scalar_product_op<Scalar>, Derived, typename PlainObject::ConstantReturnType> tmp(derived());
tmp = PlainObject::Constant(rows(),cols(),other);
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar>());
return derived();
}
template<typename Derived>
inline Derived& ArrayBase<Derived>::operator+=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::add_assign_op<Scalar>());
return derived();
}
template<typename Derived>
inline Derived& ArrayBase<Derived>::operator-=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::sub_assign_op<Scalar>());
return derived();
}
template<typename Derived>
inline Derived& DenseBase<Derived>::operator/=(const Scalar& other)
{
typedef typename internal::conditional<NumTraits<Scalar>::IsInteger,
internal::scalar_quotient_op<Scalar>,
internal::scalar_product_op<Scalar> >::type BinOp;
typedef typename Derived::PlainObject PlainObject;
SelfCwiseBinaryOp<BinOp, Derived, typename PlainObject::ConstantReturnType> tmp(derived());
tmp = PlainObject::Constant(rows(),cols(), NumTraits<Scalar>::IsInteger ? other : Scalar(1)/other);
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar>());
return derived();
}

150
Eigen/src/Core/Solve.h Normal file
View File

@@ -0,0 +1,150 @@
// 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_SOLVE_H
#define EIGEN_SOLVE_H
namespace Eigen {
template<typename Decomposition, typename RhsType, typename StorageKind> class SolveImpl;
/** \class Solve
* \ingroup Core_Module
*
* \brief Pseudo expression representing a solving operation
*
* \tparam Decomposition the type of the matrix or decomposion object
* \tparam Rhstype the type of the right-hand side
*
* This class represents an expression of A.solve(B)
* and most of the time this is the only way it is used.
*
*/
namespace internal {
// this solve_traits class permits to determine the evaluation type with respect to storage kind (Dense vs Sparse)
template<typename Decomposition, typename RhsType,typename StorageKind> struct solve_traits;
template<typename Decomposition, typename RhsType>
struct solve_traits<Decomposition,RhsType,Dense>
{
typedef typename Decomposition::MatrixType MatrixType;
typedef Matrix<typename RhsType::Scalar,
MatrixType::ColsAtCompileTime,
RhsType::ColsAtCompileTime,
RhsType::PlainObject::Options,
MatrixType::MaxColsAtCompileTime,
RhsType::MaxColsAtCompileTime> PlainObject;
};
template<typename Decomposition, typename RhsType>
struct traits<Solve<Decomposition, RhsType> >
: traits<typename solve_traits<Decomposition,RhsType,typename internal::traits<RhsType>::StorageKind>::PlainObject>
{
typedef typename solve_traits<Decomposition,RhsType,typename internal::traits<RhsType>::StorageKind>::PlainObject PlainObject;
typedef typename promote_index_type<typename Decomposition::StorageIndex, typename RhsType::StorageIndex>::type StorageIndex;
typedef traits<PlainObject> BaseTraits;
enum {
Flags = BaseTraits::Flags & RowMajorBit,
CoeffReadCost = Dynamic
};
};
}
template<typename Decomposition, typename RhsType>
class Solve : public SolveImpl<Decomposition,RhsType,typename internal::traits<RhsType>::StorageKind>
{
public:
typedef typename internal::traits<Solve>::PlainObject PlainObject;
typedef typename internal::traits<Solve>::StorageIndex StorageIndex;
Solve(const Decomposition &dec, const RhsType &rhs)
: m_dec(dec), m_rhs(rhs)
{}
EIGEN_DEVICE_FUNC Index rows() const { return m_dec.cols(); }
EIGEN_DEVICE_FUNC Index cols() const { return m_rhs.cols(); }
EIGEN_DEVICE_FUNC const Decomposition& dec() const { return m_dec; }
EIGEN_DEVICE_FUNC const RhsType& rhs() const { return m_rhs; }
protected:
const Decomposition &m_dec;
const RhsType &m_rhs;
};
// Specialization of the Solve expression for dense results
template<typename Decomposition, typename RhsType>
class SolveImpl<Decomposition,RhsType,Dense>
: public MatrixBase<Solve<Decomposition,RhsType> >
{
typedef Solve<Decomposition,RhsType> Derived;
public:
typedef MatrixBase<Solve<Decomposition,RhsType> > Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
private:
Scalar coeff(Index row, Index col) const;
Scalar coeff(Index i) const;
};
// Generic API dispatcher
template<typename Decomposition, typename RhsType, typename StorageKind>
class SolveImpl : public internal::generic_xpr_base<Solve<Decomposition,RhsType>, MatrixXpr, StorageKind>::type
{
public:
typedef typename internal::generic_xpr_base<Solve<Decomposition,RhsType>, MatrixXpr, StorageKind>::type Base;
};
namespace internal {
// Evaluator of Solve -> eval into a temporary
template<typename Decomposition, typename RhsType>
struct evaluator<Solve<Decomposition,RhsType> >
: public evaluator<typename Solve<Decomposition,RhsType>::PlainObject>
{
typedef Solve<Decomposition,RhsType> SolveType;
typedef typename SolveType::PlainObject PlainObject;
typedef evaluator<PlainObject> Base;
EIGEN_DEVICE_FUNC explicit evaluator(const SolveType& solve)
: m_result(solve.rows(), solve.cols())
{
::new (static_cast<Base*>(this)) Base(m_result);
solve.dec()._solve_impl(solve.rhs(), m_result);
}
protected:
PlainObject m_result;
};
// Specialization for "dst = dec.solve(rhs)"
// NOTE we need to specialize it for Dense2Dense to avoid ambiguous specialization error and a Sparse2Sparse specialization must exist somewhere
template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
struct Assignment<DstXprType, Solve<DecType,RhsType>, internal::assign_op<Scalar>, Dense2Dense, Scalar>
{
typedef Solve<DecType,RhsType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
{
// FIXME shall we resize dst here?
src.dec()._solve_impl(src.rhs(), dst);
}
};
} // end namepsace internal
} // end namespace Eigen
#endif // EIGEN_SOLVE_H

View File

@@ -68,7 +68,7 @@ struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,1>
if(!useRhsDirectly)
MappedRhs(actualRhs,rhs.size()) = rhs;
triangular_solve_vector<LhsScalar, RhsScalar, typename Lhs::Index, Side, Mode, LhsProductTraits::NeedToConjugate,
triangular_solve_vector<LhsScalar, RhsScalar, Index, Side, Mode, LhsProductTraits::NeedToConjugate,
(int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor>
::run(actualLhs.cols(), actualLhs.data(), actualLhs.outerStride(), actualRhs);
@@ -82,7 +82,6 @@ template<typename Lhs, typename Rhs, int Side, int Mode>
struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,Dynamic>
{
typedef typename Rhs::Scalar Scalar;
typedef typename Rhs::Index Index;
typedef blas_traits<Lhs> LhsProductTraits;
typedef typename LhsProductTraits::DirectLinearAccessType ActualLhsType;
@@ -96,7 +95,7 @@ struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,Dynamic>
typedef internal::gemm_blocking_space<(Rhs::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar,
Rhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxRowsAtCompileTime,4> BlockingType;
BlockingType blocking(rhs.rows(), rhs.cols(), size);
BlockingType blocking(rhs.rows(), rhs.cols(), size, 1, false);
triangular_solve_matrix<Scalar,Index,Side,Mode,LhsProductTraits::NeedToConjugate,(int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor,
(Rhs::Flags&RowMajorBit) ? RowMajor : ColMajor>
@@ -171,10 +170,10 @@ struct triangular_solver_selector<Lhs,Rhs,OnTheRight,Mode,CompleteUnrolling,1> {
*/
template<typename MatrixType, unsigned int Mode>
template<int Side, typename OtherDerived>
void TriangularView<MatrixType,Mode>::solveInPlace(const MatrixBase<OtherDerived>& _other) const
void TriangularViewImpl<MatrixType,Mode,Dense>::solveInPlace(const MatrixBase<OtherDerived>& _other) const
{
OtherDerived& other = _other.const_cast_derived();
eigen_assert( cols() == rows() && ((Side==OnTheLeft && cols() == other.rows()) || (Side==OnTheRight && cols() == other.cols())) );
eigen_assert( derived().cols() == derived().rows() && ((Side==OnTheLeft && derived().cols() == other.rows()) || (Side==OnTheRight && derived().cols() == other.cols())) );
eigen_assert((!(Mode & ZeroDiag)) && bool(Mode & (Upper|Lower)));
enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit && OtherDerived::IsVectorAtCompileTime };
@@ -183,7 +182,7 @@ void TriangularView<MatrixType,Mode>::solveInPlace(const MatrixBase<OtherDerived
OtherCopy otherCopy(other);
internal::triangular_solver_selector<MatrixType, typename internal::remove_reference<OtherCopy>::type,
Side, Mode>::run(nestedExpression(), otherCopy);
Side, Mode>::run(derived().nestedExpression(), otherCopy);
if (copy)
other = otherCopy;
@@ -199,8 +198,8 @@ void TriangularView<MatrixType,Mode>::solveInPlace(const MatrixBase<OtherDerived
* diagonal must be non zero). It works as a forward (resp. backward) substitution if \c *this
* is an upper (resp. lower) triangular matrix.
*
* Example: \include MatrixBase_marked.cpp
* Output: \verbinclude MatrixBase_marked.out
* Example: \include Triangular_solve.cpp
* Output: \verbinclude Triangular_solve.out
*
* This function returns an expression of the inverse-multiply and can works in-place if it is assigned
* to the same matrix or vector \a other.
@@ -213,9 +212,9 @@ void TriangularView<MatrixType,Mode>::solveInPlace(const MatrixBase<OtherDerived
template<typename Derived, unsigned int Mode>
template<int Side, typename Other>
const internal::triangular_solve_retval<Side,TriangularView<Derived,Mode>,Other>
TriangularView<Derived,Mode>::solve(const MatrixBase<Other>& other) const
TriangularViewImpl<Derived,Mode,Dense>::solve(const MatrixBase<Other>& other) const
{
return internal::triangular_solve_retval<Side,TriangularView,Other>(*this, other.derived());
return internal::triangular_solve_retval<Side,TriangularViewType,Other>(derived(), other.derived());
}
namespace internal {
@@ -232,7 +231,6 @@ template<int Side, typename TriangularType, typename Rhs> struct triangular_solv
{
typedef typename remove_all<typename Rhs::Nested>::type RhsNestedCleaned;
typedef ReturnByValue<triangular_solve_retval> Base;
typedef typename Base::Index Index;
triangular_solve_retval(const TriangularType& tri, const Rhs& rhs)
: m_triangularMatrix(tri), m_rhs(rhs)

View File

@@ -13,22 +13,135 @@
namespace Eigen {
namespace internal {
template<typename ExpressionType, typename Scalar>
inline void stable_norm_kernel(const ExpressionType& bl, Scalar& ssq, Scalar& scale, Scalar& invScale)
{
Scalar max = bl.cwiseAbs().maxCoeff();
if (max>scale)
Scalar maxCoeff = bl.cwiseAbs().maxCoeff();
if(maxCoeff>scale)
{
ssq = ssq * abs2(scale/max);
scale = max;
invScale = Scalar(1)/scale;
ssq = ssq * numext::abs2(scale/maxCoeff);
Scalar tmp = Scalar(1)/maxCoeff;
if(tmp > NumTraits<Scalar>::highest())
{
invScale = NumTraits<Scalar>::highest();
scale = Scalar(1)/invScale;
}
else if(maxCoeff>NumTraits<Scalar>::highest()) // we got a INF
{
invScale = Scalar(1);
scale = maxCoeff;
}
else
{
scale = maxCoeff;
invScale = tmp;
}
}
// TODO if the max is much much smaller than the current scale,
else if(maxCoeff!=maxCoeff) // we got a NaN
{
scale = maxCoeff;
}
// TODO if the maxCoeff is much much smaller than the current scale,
// then we can neglect this sub vector
ssq += (bl*invScale).squaredNorm();
if(scale>Scalar(0)) // if scale==0, then bl is 0
ssq += (bl*invScale).squaredNorm();
}
template<typename Derived>
inline typename NumTraits<typename traits<Derived>::Scalar>::Real
blueNorm_impl(const EigenBase<Derived>& _vec)
{
typedef typename Derived::RealScalar RealScalar;
using std::pow;
using std::sqrt;
using std::abs;
const Derived& vec(_vec.derived());
static bool initialized = false;
static RealScalar b1, b2, s1m, s2m, rbig, relerr;
if(!initialized)
{
int ibeta, it, iemin, iemax, iexp;
RealScalar eps;
// This program calculates the machine-dependent constants
// bl, b2, slm, s2m, relerr overfl
// from the "basic" machine-dependent numbers
// nbig, ibeta, it, iemin, iemax, rbig.
// The following define the basic machine-dependent constants.
// For portability, the PORT subprograms "ilmaeh" and "rlmach"
// are used. For any specific computer, each of the assignment
// statements can be replaced
ibeta = std::numeric_limits<RealScalar>::radix; // base for floating-point numbers
it = std::numeric_limits<RealScalar>::digits; // number of base-beta digits in mantissa
iemin = std::numeric_limits<RealScalar>::min_exponent; // minimum exponent
iemax = std::numeric_limits<RealScalar>::max_exponent; // maximum exponent
rbig = (std::numeric_limits<RealScalar>::max)(); // largest floating-point number
iexp = -((1-iemin)/2);
b1 = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // lower boundary of midrange
iexp = (iemax + 1 - it)/2;
b2 = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // upper boundary of midrange
iexp = (2-iemin)/2;
s1m = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // scaling factor for lower range
iexp = - ((iemax+it)/2);
s2m = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // scaling factor for upper range
eps = RealScalar(pow(double(ibeta), 1-it));
relerr = sqrt(eps); // tolerance for neglecting asml
initialized = true;
}
Index n = vec.size();
RealScalar ab2 = b2 / RealScalar(n);
RealScalar asml = RealScalar(0);
RealScalar amed = RealScalar(0);
RealScalar abig = RealScalar(0);
for(typename Derived::InnerIterator it(vec, 0); it; ++it)
{
RealScalar ax = abs(it.value());
if(ax > ab2) abig += numext::abs2(ax*s2m);
else if(ax < b1) asml += numext::abs2(ax*s1m);
else amed += numext::abs2(ax);
}
if(amed!=amed)
return amed; // we got a NaN
if(abig > RealScalar(0))
{
abig = sqrt(abig);
if(abig > rbig) // overflow, or *this contains INF values
return abig; // return INF
if(amed > RealScalar(0))
{
abig = abig/s2m;
amed = sqrt(amed);
}
else
return abig/s2m;
}
else if(asml > RealScalar(0))
{
if (amed > RealScalar(0))
{
abig = sqrt(amed);
amed = sqrt(asml) / s1m;
}
else
return sqrt(asml)/s1m;
}
else
return sqrt(amed);
asml = numext::mini(abig, amed);
abig = numext::maxi(abig, amed);
if(asml <= abig*relerr)
return abig;
else
return abig * sqrt(RealScalar(1) + numext::abs2(asml/abig));
}
} // end namespace internal
/** \returns the \em l2 norm of \c *this avoiding underflow and overflow.
* This version use a blockwise two passes algorithm:
* 1 - find the absolute largest coefficient \c s
@@ -43,21 +156,34 @@ template<typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
MatrixBase<Derived>::stableNorm() const
{
using std::min;
using std::sqrt;
using std::abs;
const Index blockSize = 4096;
RealScalar scale(0);
RealScalar invScale(1);
RealScalar ssq(0); // sum of square
typedef typename internal::nested_eval<Derived,2>::type DerivedCopy;
typedef typename internal::remove_all<DerivedCopy>::type DerivedCopyClean;
DerivedCopy copy(derived());
enum {
Alignment = (int(Flags)&DirectAccessBit) || (int(Flags)&AlignedBit) ? 1 : 0
CanAlign = (int(Flags)&DirectAccessBit) || (int(internal::evaluator<DerivedCopyClean>::Alignment)>0) // FIXME
};
typedef typename internal::conditional<CanAlign, Ref<const Matrix<Scalar,Dynamic,1,0,blockSize,1>, internal::evaluator<DerivedCopyClean>::Alignment>,
typename DerivedCopyClean
::ConstSegmentReturnType>::type SegmentWrapper;
Index n = size();
Index bi = internal::first_aligned(derived());
if(n==1)
return abs(this->coeff(0));
Index bi = internal::first_default_aligned(copy);
if (bi>0)
internal::stable_norm_kernel(this->head(bi), ssq, scale, invScale);
internal::stable_norm_kernel(copy.head(bi), ssq, scale, invScale);
for (; bi<n; bi+=blockSize)
internal::stable_norm_kernel(this->segment(bi,(min)(blockSize, n - bi)).template forceAlignedAccessIf<Alignment>(), ssq, scale, invScale);
return scale * internal::sqrt(ssq);
internal::stable_norm_kernel(SegmentWrapper(copy.segment(bi,numext::mini(blockSize, n - bi))), ssq, scale, invScale);
return scale * sqrt(ssq);
}
/** \returns the \em l2 norm of \c *this using the Blue's algorithm.
@@ -73,93 +199,7 @@ template<typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
MatrixBase<Derived>::blueNorm() const
{
using std::pow;
using std::min;
using std::max;
static Index nmax = -1;
static RealScalar b1, b2, s1m, s2m, overfl, rbig, relerr;
if(nmax <= 0)
{
int nbig, ibeta, it, iemin, iemax, iexp;
RealScalar abig, eps;
// This program calculates the machine-dependent constants
// bl, b2, slm, s2m, relerr overfl, nmax
// from the "basic" machine-dependent numbers
// nbig, ibeta, it, iemin, iemax, rbig.
// The following define the basic machine-dependent constants.
// For portability, the PORT subprograms "ilmaeh" and "rlmach"
// are used. For any specific computer, each of the assignment
// statements can be replaced
nbig = (std::numeric_limits<Index>::max)(); // largest integer
ibeta = std::numeric_limits<RealScalar>::radix; // base for floating-point numbers
it = std::numeric_limits<RealScalar>::digits; // number of base-beta digits in mantissa
iemin = std::numeric_limits<RealScalar>::min_exponent; // minimum exponent
iemax = std::numeric_limits<RealScalar>::max_exponent; // maximum exponent
rbig = (std::numeric_limits<RealScalar>::max)(); // largest floating-point number
iexp = -((1-iemin)/2);
b1 = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // lower boundary of midrange
iexp = (iemax + 1 - it)/2;
b2 = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // upper boundary of midrange
iexp = (2-iemin)/2;
s1m = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // scaling factor for lower range
iexp = - ((iemax+it)/2);
s2m = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // scaling factor for upper range
overfl = rbig*s2m; // overflow boundary for abig
eps = RealScalar(pow(double(ibeta), 1-it));
relerr = internal::sqrt(eps); // tolerance for neglecting asml
abig = RealScalar(1.0/eps - 1.0);
if (RealScalar(nbig)>abig) nmax = int(abig); // largest safe n
else nmax = nbig;
}
Index n = size();
RealScalar ab2 = b2 / RealScalar(n);
RealScalar asml = RealScalar(0);
RealScalar amed = RealScalar(0);
RealScalar abig = RealScalar(0);
for(Index j=0; j<n; ++j)
{
RealScalar ax = internal::abs(coeff(j));
if(ax > ab2) abig += internal::abs2(ax*s2m);
else if(ax < b1) asml += internal::abs2(ax*s1m);
else amed += internal::abs2(ax);
}
if(abig > RealScalar(0))
{
abig = internal::sqrt(abig);
if(abig > overfl)
{
eigen_assert(false && "overflow");
return rbig;
}
if(amed > RealScalar(0))
{
abig = abig/s2m;
amed = internal::sqrt(amed);
}
else
return abig/s2m;
}
else if(asml > RealScalar(0))
{
if (amed > RealScalar(0))
{
abig = internal::sqrt(amed);
amed = internal::sqrt(asml) / s1m;
}
else
return internal::sqrt(asml)/s1m;
}
else
return internal::sqrt(amed);
asml = (min)(abig, amed);
abig = (max)(abig, amed);
if(asml <= abig*relerr)
return abig;
else
return abig * internal::sqrt(RealScalar(1) + internal::abs2(asml/abig));
return internal::blueNorm_impl(*this);
}
/** \returns the \em l2 norm of \c *this avoiding undeflow and overflow.

View File

@@ -44,13 +44,14 @@ template<int _OuterStrideAtCompileTime, int _InnerStrideAtCompileTime>
class Stride
{
public:
typedef DenseIndex Index;
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
enum {
InnerStrideAtCompileTime = _InnerStrideAtCompileTime,
OuterStrideAtCompileTime = _OuterStrideAtCompileTime
};
/** Default constructor, for use when strides are fixed at compile time */
EIGEN_DEVICE_FUNC
Stride()
: m_outer(OuterStrideAtCompileTime), m_inner(InnerStrideAtCompileTime)
{
@@ -58,6 +59,7 @@ class Stride
}
/** Constructor allowing to pass the strides at runtime */
EIGEN_DEVICE_FUNC
Stride(Index outerStride, Index innerStride)
: m_outer(outerStride), m_inner(innerStride)
{
@@ -65,13 +67,16 @@ class Stride
}
/** Copy constructor */
EIGEN_DEVICE_FUNC
Stride(const Stride& other)
: m_outer(other.outer()), m_inner(other.inner())
{}
/** \returns the outer stride */
EIGEN_DEVICE_FUNC
inline Index outer() const { return m_outer.value(); }
/** \returns the inner stride */
EIGEN_DEVICE_FUNC
inline Index inner() const { return m_inner.value(); }
protected:
@@ -81,26 +86,24 @@ class Stride
/** \brief Convenience specialization of Stride to specify only an inner stride
* See class Map for some examples */
template<int Value = Dynamic>
template<int Value>
class InnerStride : public Stride<0, Value>
{
typedef Stride<0, Value> Base;
public:
typedef DenseIndex Index;
InnerStride() : Base() {}
InnerStride(Index v) : Base(0, v) {}
EIGEN_DEVICE_FUNC InnerStride() : Base() {}
EIGEN_DEVICE_FUNC InnerStride(Index v) : Base(0, v) {} // FIXME making this explicit could break valid code
};
/** \brief Convenience specialization of Stride to specify only an outer stride
* See class Map for some examples */
template<int Value = Dynamic>
template<int Value>
class OuterStride : public Stride<Value, 0>
{
typedef Stride<Value, 0> Base;
public:
typedef DenseIndex Index;
OuterStride() : Base() {}
OuterStride(Index v) : Base(v,0) {}
EIGEN_DEVICE_FUNC OuterStride() : Base() {}
EIGEN_DEVICE_FUNC OuterStride(Index v) : Base(v,0) {} // FIXME making this explicit could break valid code
};
} // end namespace Eigen

View File

@@ -12,115 +12,56 @@
namespace Eigen {
/** \class SwapWrapper
* \ingroup Core_Module
*
* \internal
*
* \brief Internal helper class for swapping two expressions
*/
namespace internal {
template<typename ExpressionType>
struct traits<SwapWrapper<ExpressionType> > : traits<ExpressionType> {};
}
template<typename ExpressionType> class SwapWrapper
: public internal::dense_xpr_base<SwapWrapper<ExpressionType> >::type
// Overload default assignPacket behavior for swapping them
template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT>
class generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, Specialized>
: public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn>
{
public:
typedef typename internal::dense_xpr_base<SwapWrapper>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(SwapWrapper)
typedef typename internal::packet_traits<Scalar>::type Packet;
inline SwapWrapper(ExpressionType& xpr) : m_expression(xpr) {}
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(); }
typedef typename internal::conditional<
internal::is_lvalue<ExpressionType>::value,
Scalar,
const Scalar
>::type ScalarWithConstIfNotLvalue;
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
inline const Scalar* data() const { return m_expression.data(); }
inline Scalar& coeffRef(Index row, Index col)
{
return m_expression.const_cast_derived().coeffRef(row, col);
}
inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
}
inline Scalar& coeffRef(Index row, Index col) const
{
return m_expression.coeffRef(row, col);
}
inline Scalar& coeffRef(Index index) const
{
return m_expression.coeffRef(index);
}
template<typename OtherDerived>
void copyCoeff(Index row, Index col, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
Scalar tmp = m_expression.coeff(row, col);
m_expression.coeffRef(row, col) = _other.coeff(row, col);
_other.coeffRef(row, col) = tmp;
}
template<typename OtherDerived>
void copyCoeff(Index index, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(index >= 0 && index < m_expression.size());
Scalar tmp = m_expression.coeff(index);
m_expression.coeffRef(index) = _other.coeff(index);
_other.coeffRef(index) = tmp;
}
template<typename OtherDerived, int StoreMode, int LoadMode>
void copyPacket(Index row, Index col, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
Packet tmp = m_expression.template packet<StoreMode>(row, col);
m_expression.template writePacket<StoreMode>(row, col,
_other.template packet<LoadMode>(row, col)
);
_other.template writePacket<LoadMode>(row, col, tmp);
}
template<typename OtherDerived, int StoreMode, int LoadMode>
void copyPacket(Index index, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(index >= 0 && index < m_expression.size());
Packet tmp = m_expression.template packet<StoreMode>(index);
m_expression.template writePacket<StoreMode>(index,
_other.template packet<LoadMode>(index)
);
_other.template writePacket<LoadMode>(index, tmp);
}
ExpressionType& expression() const { return m_expression; }
protected:
ExpressionType& m_expression;
protected:
typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn> Base;
using Base::m_dst;
using Base::m_src;
using Base::m_functor;
public:
typedef typename Base::Scalar Scalar;
typedef typename Base::DstXprType DstXprType;
typedef swap_assign_op<Scalar> Functor;
EIGEN_DEVICE_FUNC generic_dense_assignment_kernel(DstEvaluatorTypeT &dst, const SrcEvaluatorTypeT &src, const Functor &func, DstXprType& dstExpr)
: Base(dst, src, func, dstExpr)
{}
template<int StoreMode, int LoadMode, typename PacketType>
void assignPacket(Index row, Index col)
{
PacketType tmp = m_src.template packet<LoadMode,PacketType>(row,col);
const_cast<SrcEvaluatorTypeT&>(m_src).template writePacket<LoadMode>(row,col, m_dst.template packet<StoreMode,PacketType>(row,col));
m_dst.template writePacket<StoreMode>(row,col,tmp);
}
template<int StoreMode, int LoadMode, typename PacketType>
void assignPacket(Index index)
{
PacketType tmp = m_src.template packet<LoadMode,PacketType>(index);
const_cast<SrcEvaluatorTypeT&>(m_src).template writePacket<LoadMode>(index, m_dst.template packet<StoreMode,PacketType>(index));
m_dst.template writePacket<StoreMode>(index,tmp);
}
// TODO find a simple way not to have to copy/paste this function from generic_dense_assignment_kernel, by simple I mean no CRTP (Gael)
template<int StoreMode, int LoadMode, typename PacketType>
void assignPacketByOuterInner(Index outer, Index inner)
{
Index row = Base::rowIndexByOuterInner(outer, inner);
Index col = Base::colIndexByOuterInner(outer, inner);
assignPacket<StoreMode,LoadMode,PacketType>(row, col);
}
};
} // namespace internal
} // end namespace Eigen
#endif // EIGEN_SWAP_H

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