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1221 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
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
72c4f8ca8f Disable a few unit tests in unsupported 2014-09-01 17:35:58 +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
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
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
Benoit Steiner
3d298da269 Added support for broadcasting 2014-08-20 17:00:50 -07:00
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
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
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
Benoit Steiner
647622281e The tensor assignment code now resizes the destination tensor as needed. 2014-07-31 17:39:04 -07:00
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
Benoit Steiner
2116e261fb Made sure that the data stored in fixed sized tensor is aligned. 2014-07-25 09:47:59 -07:00
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
baa77ffe38 Fix max sizes at compile time of DiagonalWrapper 2014-07-22 16:13:56 +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
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
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
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
Benoit Steiner
25b2f6624d Improved the speed of slicing operations. 2014-07-09 12:48:34 -07:00
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
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
Gael Guennebaud
61b88d2feb merge with default branch 2014-07-02 09:35:37 +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
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
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
Gael Guennebaud
54607665ab Fix inverse evaluator 2014-06-25 23:44:59 +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
Gael Guennebaud
3849cc65ee Implement binaryop and transpose evaluators for sparse matrices 2014-06-23 10:40:03 +02:00
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
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
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
Benoit Steiner
8998f4099e Created additional tests for the tensor code. 2014-06-05 10:49:34 -07:00
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
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
Benoit Steiner
0320f7e3a7 Added support for fixed sized tensors.
Improved support for tensor expressions.
2014-05-06 11:18:37 -07:00
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
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
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
Jitse Niesen
b30706bd5c Fix typo in Inverse.h 2014-04-15 22:51:46 +01:00
Jitse Niesen
59f5f155c2 Port products with permutation matrices to evaluators. 2014-04-15 15:21:38 +01:00
Gael Guennebaud
0a6c472335 A bit of cleaning 2014-03-13 15:44:20 +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
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
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
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
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
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
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
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
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
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
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
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
651 changed files with 63824 additions and 21626 deletions

View File

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

View File

@@ -1,6 +1,6 @@
project(Eigen)
cmake_minimum_required(VERSION 2.8.2)
cmake_minimum_required(VERSION 2.8.4)
# guard against in-source builds
@@ -147,6 +147,12 @@ if(NOT MSVC)
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")
@@ -168,6 +174,11 @@ if(NOT MSVC)
else()
ei_add_cxx_compiler_flag("-ansi")
endif()
if(ANDROID_NDK)
ei_add_cxx_compiler_flag("-pie")
ei_add_cxx_compiler_flag("-fPIE")
endif()
set(CMAKE_REQUIRED_FLAGS "")
@@ -208,7 +219,7 @@ if(NOT MSVC)
endif()
option(EIGEN_TEST_FMA "Enable/Disable FMA in tests/examples" OFF)
if(EIGEN_TEST_FMA)
if(EIGEN_TEST_FMA AND NOT EIGEN_TEST_NEON)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mfma")
message(STATUS "Enabling FMA in tests/examples")
endif()
@@ -219,12 +230,31 @@ if(NOT MSVC)
message(STATUS "Enabling AltiVec in tests/examples")
endif()
option(EIGEN_TEST_VSX "Enable/Disable VSX in tests/examples" OFF)
if(EIGEN_TEST_VSX)
set(CMAKE_CXX_FLAGS "${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 "${CMAKE_CXX_FLAGS}")
message(STATUS "Enabling NEON in tests/examples")
endif()
check_cxx_compiler_flag("-fopenmp" COMPILER_SUPPORT_OPENMP)
if(COMPILER_SUPPORT_OPENMP)
option(EIGEN_TEST_OPENMP "Enable/Disable OpenMP in tests/examples" OFF)
@@ -307,7 +337,7 @@ if(EIGEN_TEST_NO_EXCEPTIONS)
message(STATUS "Disabling exceptions in tests/examples")
endif()
option(EIGEN_TEST_C++0x "Enables all C++0x features." OFF)
option(EIGEN_TEST_CXX11 "Enable testing with C++11 and C++11 features (e.g. Tensor module)." OFF)
include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
@@ -434,6 +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:")
@@ -469,3 +500,7 @@ 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

@@ -21,7 +21,6 @@
* \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

@@ -24,9 +24,15 @@
#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__
@@ -57,7 +63,7 @@
// Disable the ipa-cp-clone optimization flag with MinGW 6.x or newer (enabled by default with -O3)
// See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=556 for details.
#if defined(__MINGW32__) && EIGEN_GNUC_AT_LEAST(4,6)
#if EIGEN_COMP_MINGW && EIGEN_GNUC_AT_LEAST(4,6)
#pragma GCC optimize ("-fno-ipa-cp-clone")
#endif
@@ -67,26 +73,26 @@
// and inclusion of their respective header files
#include "src/Core/util/MKL_support.h"
// if alignment is disabled, then disable vectorization. Note: EIGEN_ALIGN is the proper check, it takes into
// account both the user's will (EIGEN_DONT_ALIGN) and our own platform checks
#if !EIGEN_ALIGN
// if alignment is disabled, then disable vectorization. Note: EIGEN_MAX_ALIGN_BYTES is the proper check, it takes into
// account both the user's will (EIGEN_MAX_ALIGN_BYTES,EIGEN_DONT_ALIGN) and our own platform checks
#if EIGEN_MAX_ALIGN_BYTES==0
#ifndef EIGEN_DONT_VECTORIZE
#define EIGEN_DONT_VECTORIZE
#endif
#endif
#ifdef _MSC_VER
#if EIGEN_COMP_MSVC
#include <malloc.h> // for _aligned_malloc -- need it regardless of whether vectorization is enabled
#if (_MSC_VER >= 1500) // 2008 or later
#if (EIGEN_COMP_MSVC >= 1500) // 2008 or later
// Remember that usage of defined() in a #define is undefined by the standard.
// a user reported that in 64-bit mode, MSVC doesn't care to define _M_IX86_FP.
#if (defined(_M_IX86_FP) && (_M_IX86_FP >= 2)) || defined(_M_X64)
#if (defined(_M_IX86_FP) && (_M_IX86_FP >= 2)) || EIGEN_ARCH_x86_64
#define EIGEN_SSE2_ON_MSVC_2008_OR_LATER
#endif
#endif
#else
// Remember that usage of defined() in a #define is undefined by the standard
#if (defined __SSE2__) && ( (!defined __GNUC__) || (defined __INTEL_COMPILER) || EIGEN_GNUC_AT_LEAST(4,2) )
#if (defined __SSE2__) && ( (!EIGEN_COMP_GNUC) || EIGEN_COMP_ICC || EIGEN_GNUC_AT_LEAST(4,2) )
#define EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC
#endif
#endif
@@ -125,9 +131,13 @@
#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
// This extern "C" works around a MINGW-w64 compilation issue
@@ -140,7 +150,7 @@
extern "C" {
// In theory we should only include immintrin.h and not the other *mmintrin.h header files directly.
// Doing so triggers some issues with ICC. However old gcc versions seems to not have this file, thus:
#if defined(__INTEL_COMPILER) && __INTEL_COMPILER >= 1110
#if EIGEN_COMP_ICC >= 1110
#include <immintrin.h>
#else
#include <emmintrin.h>
@@ -162,6 +172,15 @@
#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
@@ -171,13 +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
@@ -187,7 +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
@@ -213,7 +237,7 @@
#endif
// required for __cpuid, needs to be included after cmath
#if defined(_MSC_VER) && (defined(_M_IX86)||defined(_M_X64)) && (!defined(_WIN32_WCE))
#if EIGEN_COMP_MSVC && EIGEN_ARCH_i386_OR_x86_64 && !EIGEN_OS_WINCE
#include <intrin.h>
#endif
@@ -235,6 +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
@@ -266,8 +292,8 @@ using std::ptrdiff_t;
*/
#include "src/Core/util/Constants.h"
#include "src/Core/util/ForwardDeclarations.h"
#include "src/Core/util/Meta.h"
#include "src/Core/util/ForwardDeclarations.h"
#include "src/Core/util/StaticAssert.h"
#include "src/Core/util/XprHelper.h"
#include "src/Core/util/Memory.h"
@@ -280,39 +306,47 @@ using std::ptrdiff_t;
// 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/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"
#ifdef EIGEN_ENABLE_EVALUATORS
#include "src/Core/functors/AssignmentFunctors.h"
#include "src/Core/Product.h"
#include "src/Core/CoreEvaluators.h"
#include "src/Core/AssignEvaluator.h"
#include "src/Core/ProductEvaluators.h"
#endif
#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
@@ -323,7 +357,9 @@ using std::ptrdiff_t;
#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"
@@ -336,12 +372,12 @@ using std::ptrdiff_t;
#include "src/Core/SelfCwiseBinaryOp.h"
#include "src/Core/Dot.h"
#include "src/Core/StableNorm.h"
#include "src/Core/MapBase.h"
#include "src/Core/Stride.h"
#include "src/Core/MapBase.h"
#include "src/Core/Map.h"
#include "src/Core/Ref.h"
#include "src/Core/Block.h"
#include "src/Core/VectorBlock.h"
#include "src/Core/Ref.h"
#include "src/Core/Transpose.h"
#include "src/Core/DiagonalMatrix.h"
#include "src/Core/Diagonal.h"
@@ -354,14 +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"

View File

@@ -9,10 +9,6 @@
#include "LU"
#include <limits>
#ifndef M_PI
#define M_PI 3.14159265358979323846
#endif
/** \defgroup Geometry_Module Geometry module
*
*

View File

@@ -12,26 +12,26 @@
* 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"
@@ -25,7 +24,7 @@
#include "src/LU/PartialPivLU_MKL.h"
#endif
#include "src/LU/Determinant.h"
#include "src/LU/Inverse.h"
#include "src/LU/InverseImpl.h"
// Use the SSE optimized version whenever possible. At the moment the
// SSE version doesn't compile when AVX is enabled

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

@@ -24,7 +24,6 @@
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/QR/HouseholderQR.h"
#include "src/QR/FullPivHouseholderQR.h"
#include "src/QR/ColPivHouseholderQR.h"

View File

@@ -21,8 +21,6 @@
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/CholmodSupport/CholmodSupport.h"
#include "src/SPQRSupport/SuiteSparseQRSupport.h"

View File

@@ -12,21 +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"
#include "src/Core/util/ReenableStupidWarnings.h"

View File

@@ -11,9 +11,9 @@
* - \ref SparseQR_Module
* - \ref IterativeLinearSolvers_Module
*
* \code
* #include <Eigen/Sparse>
* \endcode
\code
#include <Eigen/Sparse>
\endcode
*/
#include "SparseCore"

View File

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

View File

@@ -26,37 +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/SparseBlock.h"
#include "src/SparseCore/SparseTranspose.h"
#include "src/SparseCore/SparseRef.h"
#include "src/SparseCore/SparseCwiseUnaryOp.h"
#include "src/SparseCore/SparseCwiseBinaryOp.h"
#include "src/SparseCore/SparseTranspose.h"
#include "src/SparseCore/SparseBlock.h"
#include "src/SparseCore/SparseDot.h"
#include "src/SparseCore/SparsePermutation.h"
#include "src/SparseCore/SparseRedux.h"
#include "src/SparseCore/SparseFuzzy.h"
#include "src/SparseCore/SparseView.h"
#include "src/SparseCore/SparseDiagonalProduct.h"
#include "src/SparseCore/ConservativeSparseSparseProduct.h"
#include "src/SparseCore/SparseSparseProductWithPruning.h"
#include "src/SparseCore/SparseProduct.h"
#include "src/SparseCore/SparseDenseProduct.h"
#include "src/SparseCore/SparseDiagonalProduct.h"
#include "src/SparseCore/SparseTriangularView.h"
#include "src/SparseCore/SparseSelfAdjointView.h"
#include "src/SparseCore/SparseTriangularView.h"
#include "src/SparseCore/TriangularSolver.h"
#include "src/SparseCore/SparseView.h"
#include "src/SparseCore/SparsePermutation.h"
#include "src/SparseCore/SparseFuzzy.h"
#include "src/SparseCore/SparseSolverBase.h"
#include "src/Core/util/ReenableStupidWarnings.h"

View File

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

View File

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

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

@@ -59,7 +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;
@@ -85,7 +86,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
* according to the specified problem \a size.
* \sa LDLT()
*/
LDLT(Index size)
explicit LDLT(Index size)
: m_matrix(size, size),
m_transpositions(size),
m_temporary(size),
@@ -98,7 +99,7 @@ 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()),
@@ -175,13 +176,13 @@ template<typename _MatrixType, int _UpLo> class 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());
}
template<typename Derived>
@@ -217,8 +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.
@@ -245,8 +257,7 @@ template<> struct ldlt_inplace<Lower>
using std::abs;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
typedef typename TranspositionType::StorageIndexType IndexType;
typedef typename TranspositionType::StorageIndex IndexType;
eigen_assert(mat.rows()==mat.cols());
const Index size = mat.rows();
@@ -303,9 +314,9 @@ template<> struct ldlt_inplace<Lower>
}
// In some previous versions of Eigen (e.g., 3.2.1), the scaling was omitted if the pivot
// was smaller than the cutoff value. However, soince LDLT is not rank-revealing
// we should only make sure we do not introduce INF or NaN values.
// LAPACK also uses 0 as the cutoff value.
// was smaller than the cutoff value. However, since LDLT is not rank-revealing
// we should only make sure that we do not introduce INF or NaN values.
// Remark that LAPACK also uses 0 as the cutoff value.
RealScalar realAkk = numext::real(mat.coeffRef(k,k));
if((rs>0) && (abs(realAkk) > RealScalar(0)))
A21 /= realAkk;
@@ -336,7 +347,6 @@ template<> struct ldlt_inplace<Lower>
using numext::isfinite;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
const Index size = mat.rows();
eigen_assert(mat.cols() == size && w.size()==size);
@@ -400,16 +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
@@ -419,6 +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();
@@ -427,6 +439,7 @@ 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);
@@ -441,9 +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, const 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::StorageIndexType IndexType;
typedef typename TranspositionType::StorageIndex IndexType;
const Index size = w.rows();
if (m_isInitialized)
{
@@ -466,52 +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;
EIGEN_USING_STD_MATH(max);
typedef typename LDLTType::MatrixType MatrixType;
typedef typename LDLTType::RealScalar RealScalar;
const typename Diagonal<const MatrixType>::RealReturnType vectorD(dec().vectorD());
// In some previous versions, tolerance was set to the max of 1/highest and the maximal diagonal entry * epsilon
// as motivated by LAPACK's xGELSS:
// RealScalar tolerance = (max)(vectorD.array().abs().maxCoeff() *NumTraits<RealScalar>::epsilon(),RealScalar(1) / NumTraits<RealScalar>::highest());
// However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the highest
// diagonal element is not well justified and to numerical issues in some cases.
// Moreover, Lapack's xSYTRS routines use 0 for the tolerance.
RealScalar tolerance = RealScalar(1) / NumTraits<RealScalar>::highest();
for (Index i = 0; i < vectorD.size(); ++i) {
if(abs(vectorD(i)) > tolerance)
dst.row(i) /= vectorD(i);
else
dst.row(i).setZero();
}
// dst = L^-T (D^-1 L^-1 P b)
dec().matrixU().solveInPlace(dst);
// dst = P^-1 (L^-T D^-1 L^-1 P b) = A^-1 b
dst = dec().transpositionsP().transpose() * dst;
if(abs(vecD(i)) > tolerance)
dst.row(i) /= vecD(i);
else
dst.row(i).setZero();
}
};
// dst = L^-T (D^-1 L^-1 P b)
matrixU().solveInPlace(dst);
// dst = P^-1 (L^-T D^-1 L^-1 P b) = A^-1 b
dst = m_transpositions.transpose() * dst;
}
#endif
/** \internal use x = ldlt_object.solve(x);
*

View File

@@ -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)
{
@@ -118,13 +119,13 @@ template<typename _MatrixType, int _UpLo> class 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());
}
template<typename Derived>
@@ -161,8 +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.
@@ -177,12 +190,11 @@ namespace internal {
template<typename Scalar, int UpLo> struct llt_inplace;
template<typename MatrixType, typename VectorType>
static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma)
static Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma)
{
using std::sqrt;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
typedef typename MatrixType::ColXpr ColXpr;
typedef typename internal::remove_all<ColXpr>::type ColXprCleaned;
typedef typename ColXprCleaned::SegmentReturnType ColXprSegment;
@@ -251,10 +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)
{
using std::sqrt;
typedef typename MatrixType::Index Index;
eigen_assert(mat.rows()==mat.cols());
const Index size = mat.rows();
@@ -278,9 +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)
@@ -311,7 +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);
}
@@ -322,19 +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);
@@ -345,8 +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; }
};
@@ -355,8 +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; }
};
@@ -373,6 +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);
@@ -404,22 +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);
*

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

@@ -48,8 +48,8 @@ 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)
{
cholmod_sparse res;
res.nzmax = mat.nonZeros();
@@ -74,11 +74,11 @@ cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
res.dtype = 0;
res.stype = -1;
if (internal::is_same<_Index,int>::value)
if (internal::is_same<_StorageIndex,int>::value)
{
res.itype = CHOLMOD_INT;
}
else if (internal::is_same<_Index,UF_long>::value)
else if (internal::is_same<_StorageIndex,UF_long>::value)
{
res.itype = CHOLMOD_LONG;
}
@@ -105,7 +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());
@@ -138,12 +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, static_cast<Index*>(cm.p)[cm.ncol],
static_cast<Index*>(cm.p), static_cast<Index*>(cm.i),static_cast<Scalar*>(cm.x) );
return MappedSparseMatrix<Scalar,Flags,StorageIndex>
(cm.nrow, cm.ncol, static_cast<StorageIndex*>(cm.p)[cm.ncol],
static_cast<StorageIndex*>(cm.p), static_cast<StorageIndex*>(cm.i),static_cast<Scalar*>(cm.x) );
}
enum CholmodMode {
@@ -157,27 +157,31 @@ 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);
@@ -191,11 +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.
*
@@ -216,34 +217,6 @@ class CholmodBase : internal::noncopyable
return derived();
}
/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
*
* \sa compute()
*/
template<typename Rhs>
inline const internal::solve_retval<CholmodBase, Rhs>
solve(const MatrixBase<Rhs>& b) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(rows()==b.rows()
&& "CholmodDecomposition::solve(): invalid number of rows of the right hand side matrix b");
return internal::solve_retval<CholmodBase, Rhs>(*this, b.derived());
}
/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
*
* \sa compute()
*/
template<typename Rhs>
inline const internal::sparse_solve_retval<CholmodBase, Rhs>
solve(const SparseMatrixBase<Rhs>& b) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(rows()==b.rows()
&& "CholmodDecomposition::solve(): invalid number of rows of the right hand side matrix b");
return internal::sparse_solve_retval<CholmodBase, Rhs>(*this, b.derived());
}
/** Performs a symbolic decomposition on the sparsity pattern of \a matrix.
*
* This function is particularly useful when solving for several problems having the same structure.
@@ -290,7 +263,7 @@ 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;
@@ -304,6 +277,7 @@ class CholmodBase : internal::noncopyable
if(!x_cd)
{
this->m_info = NumericalIssue;
return;
}
// TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
dest = Matrix<Scalar,Dest::RowsAtCompileTime,Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x),b.rows(),b.cols());
@@ -312,7 +286,7 @@ class CholmodBase : internal::noncopyable
/** \internal */
template<typename RhsScalar, int RhsOptions, typename RhsIndex, typename DestScalar, int DestOptions, typename DestIndex>
void _solve(const SparseMatrix<RhsScalar,RhsOptions,RhsIndex> &b, SparseMatrix<DestScalar,DestOptions,DestIndex> &dest) const
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;
@@ -325,6 +299,7 @@ class CholmodBase : internal::noncopyable
if(!x_cs)
{
this->m_info = NumericalIssue;
return;
}
// TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
dest = viewAsEigen<DestScalar,DestOptions,DestIndex>(*x_cs);
@@ -357,7 +332,6 @@ class CholmodBase : internal::noncopyable
cholmod_factor* m_cholmodFactor;
RealScalar m_shiftOffset[2];
mutable ComputationInfo m_info;
bool m_isInitialized;
int m_factorizationIsOk;
int m_analysisIsOk;
};
@@ -395,7 +369,7 @@ class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimpl
CholmodSimplicialLLT(const MatrixType& matrix) : Base()
{
init();
compute(matrix);
this->compute(matrix);
}
~CholmodSimplicialLLT() {}
@@ -442,7 +416,7 @@ class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimp
CholmodSimplicialLDLT(const MatrixType& matrix) : Base()
{
init();
compute(matrix);
this->compute(matrix);
}
~CholmodSimplicialLDLT() {}
@@ -487,7 +461,7 @@ class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSuper
CholmodSupernodalLLT(const MatrixType& matrix) : Base()
{
init();
compute(matrix);
this->compute(matrix);
}
~CholmodSupernodalLLT() {}
@@ -534,7 +508,7 @@ class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecom
CholmodDecomposition(const MatrixType& matrix) : Base()
{
init();
compute(matrix);
this->compute(matrix);
}
~CholmodDecomposition() {}
@@ -572,36 +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.
*
@@ -75,6 +78,21 @@ class Array
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),
@@ -86,7 +104,7 @@ class Array
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array& operator=(const ArrayBase<OtherDerived>& other)
EIGEN_STRONG_INLINE Array& operator=(const DenseBase<OtherDerived>& other)
{
return Base::_set(other);
}
@@ -99,7 +117,7 @@ class Array
{
return Base::_set(other);
}
/** Default constructor.
*
* For fixed-size matrices, does nothing.
@@ -130,6 +148,7 @@ class Array
#endif
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
Array(Array&& other)
: Base(std::move(other))
{
@@ -137,6 +156,7 @@ class Array
if (RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic)
Base::_set_noalias(other);
}
EIGEN_DEVICE_FUNC
Array& operator=(Array&& other)
{
other.swap(*this);
@@ -144,7 +164,6 @@ class Array
}
#endif
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename T>
EIGEN_DEVICE_FUNC
@@ -206,50 +225,18 @@ class Array
m_storage.data()[3] = val3;
}
/** Constructor copying the value of the expression \a other */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
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_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const ReturnByValue<OtherDerived>& other)
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
other.evalTo(*this);
}
: Base(other)
{ }
/** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const EigenBase<OtherDerived> &other)
: Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
{
Base::_check_template_params();
Base::_resize_to_match(other);
*this = other;
}
/** 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()); }
: Base(other.derived())
{ }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return 1; }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return this->innerSize(); }

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
@@ -121,8 +107,15 @@ template<typename Derived> class ArrayBase
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(); }
EIGEN_DEVICE_FUNC
Derived& operator+=(const Scalar& scalar);
@@ -153,9 +146,9 @@ template<typename Derived> class ArrayBase
/** \returns an \link Eigen::MatrixBase Matrix \endlink expression of this array
* \sa MatrixBase::array() */
EIGEN_DEVICE_FUNC
MatrixWrapper<Derived> matrix() { return derived(); }
MatrixWrapper<Derived> matrix() { return MatrixWrapper<Derived>(derived()); }
EIGEN_DEVICE_FUNC
const MatrixWrapper<const Derived> matrix() const { return derived(); }
const MatrixWrapper<const Derived> matrix() const { return MatrixWrapper<const Derived>(derived()); }
// template<typename Dest>
// inline void evalTo(Dest& dst) const { dst = matrix(); }
@@ -186,8 +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();
}
@@ -200,8 +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();
}
@@ -214,8 +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();
}
@@ -228,8 +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

@@ -44,6 +44,7 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
typedef ArrayBase<ArrayWrapper> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(ArrayWrapper)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ArrayWrapper)
typedef typename internal::remove_all<ExpressionType>::type NestedExpression;
typedef typename internal::conditional<
internal::is_lvalue<ExpressionType>::value,
@@ -51,10 +52,10 @@ 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;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
explicit EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
EIGEN_DEVICE_FUNC
inline Index rows() const { return m_expression.rows(); }
@@ -148,7 +149,7 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index,Index)*/
EIGEN_DEVICE_FUNC
void resize(Index nbRows, Index nbCols) { m_expression.const_cast_derived().resize(nbRows,nbCols); }
void resize(Index rows, Index cols) { m_expression.const_cast_derived().resize(rows,cols); }
protected:
NestedExpressionType m_expression;
@@ -186,6 +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,
@@ -193,10 +195,10 @@ 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;
EIGEN_DEVICE_FUNC
inline MatrixWrapper(ExpressionType& a_matrix) : m_expression(a_matrix) {}
explicit inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {}
EIGEN_DEVICE_FUNC
inline Index rows() const { return m_expression.rows(); }
@@ -286,7 +288,7 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index,Index)*/
EIGEN_DEVICE_FUNC
void resize(Index nbRows, Index nbCols) { m_expression.const_cast_derived().resize(nbRows,nbCols); }
void resize(Index rows, Index cols) { m_expression.const_cast_derived().resize(rows,cols); }
protected:
NestedExpressionType m_expression;

View File

@@ -14,485 +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(Derived::SizeAtCompileTime)
EIGEN_DEBUG_VAR(OtherDerived::CoeffReadCost)
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
};
EIGEN_DEVICE_FUNC
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>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {}
};
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_DefaultTraversal_InnerUnrolling
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src, typename Derived1::Index outer)
{
dst.copyCoeffByOuterInner(outer, Index, src);
assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src, outer);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Stop, Stop>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &, typename Derived1::Index) {}
};
/***********************
*** Linear traversal ***
***********************/
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_LinearTraversal_CompleteUnrolling
{
EIGEN_DEVICE_FUNC
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>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {}
};
/**************************
*** Inner vectorization ***
**************************/
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_innervec_CompleteUnrolling
{
enum {
outer = Index / Derived1::InnerSizeAtCompileTime,
inner = Index % Derived1::InnerSizeAtCompileTime,
JointAlignment = assign_traits<Derived1,Derived2>::JointAlignment
};
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
dst.template copyPacketByOuterInner<Derived2, Aligned, JointAlignment>(outer, inner, src);
assign_innervec_CompleteUnrolling<Derived1, Derived2,
Index+packet_traits<typename Derived1::Scalar>::size, Stop>::run(dst, src);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_innervec_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {}
};
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_innervec_InnerUnrolling
{
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src, typename Derived1::Index outer)
{
dst.template copyPacketByOuterInner<Derived2, Aligned, Aligned>(outer, Index, src);
assign_innervec_InnerUnrolling<Derived1, Derived2,
Index+packet_traits<typename Derived1::Scalar>::size, Stop>::run(dst, src, outer);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_innervec_InnerUnrolling<Derived1, Derived2, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &, typename Derived1::Index) {}
};
/***************************************************************************
* Part 3 : implementation of all cases
***************************************************************************/
template<typename Derived1, typename Derived2,
int Traversal = assign_traits<Derived1, Derived2>::Traversal,
int Unrolling = assign_traits<Derived1, Derived2>::Unrolling,
int Version = Specialized>
struct assign_impl;
/************************
*** Default traversal ***
************************/
template<typename Derived1, typename Derived2, int Unrolling, int Version>
struct assign_impl<Derived1, Derived2, InvalidTraversal, Unrolling, Version>
{
EIGEN_DEVICE_FUNC
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;
EIGEN_DEVICE_FUNC
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>
{
EIGEN_DEVICE_FUNC
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;
EIGEN_DEVICE_FUNC
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;
EIGEN_DEVICE_FUNC
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>
{
EIGEN_DEVICE_FUNC
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>
@@ -506,91 +27,35 @@ 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_TEST_EVALUATORS
#ifdef EIGEN_DEBUG_ASSIGN
internal::copy_using_evaluator_traits<Derived, OtherDerived>::debug();
#endif
eigen_assert(rows() == other.rows() && cols() == other.cols());
internal::call_dense_assignment_loop(derived(),other.derived());
internal::call_assignment_no_alias(derived(),other.derived());
#else // EIGEN_TEST_EVALUATORS
#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());
#endif // EIGEN_TEST_EVALUATORS
#ifndef EIGEN_NO_DEBUG
checkTransposeAliasing(other.derived());
#endif
return derived();
}
namespace internal {
template<typename Derived, typename OtherDerived,
bool EvalBeforeAssigning = (int(internal::traits<OtherDerived>::Flags) & EvalBeforeAssigningBit) != 0,
bool NeedToTranspose = ((int(Derived::RowsAtCompileTime) == 1 && int(OtherDerived::ColsAtCompileTime) == 1)
| // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
// revert to || as soon as not needed anymore.
(int(Derived::ColsAtCompileTime) == 1 && int(OtherDerived::RowsAtCompileTime) == 1))
&& int(Derived::SizeAtCompileTime) != 1>
struct assign_selector;
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,false,false> {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.derived()); }
template<typename ActualDerived, typename ActualOtherDerived>
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& evalTo(ActualDerived& dst, const ActualOtherDerived& other) { other.evalTo(dst); return dst; }
};
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,true,false> {
EIGEN_DEVICE_FUNC
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> {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose()); }
template<typename ActualDerived, typename ActualOtherDerived>
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& evalTo(ActualDerived& dst, const ActualOtherDerived& other) { Transpose<ActualDerived> dstTrans(dst); other.evalTo(dstTrans); return dst; }
};
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,true,true> {
EIGEN_DEVICE_FUNC
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>
@@ -598,7 +63,8 @@ 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>
@@ -606,7 +72,8 @@ template <typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const EigenBase<OtherDerived>& other)
{
return internal::assign_selector<Derived,OtherDerived,false>::evalTo(derived(), other.derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
@@ -614,7 +81,8 @@ template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
{
return internal::assign_selector<Derived,OtherDerived,false>::evalTo(derived(), other.derived());
other.derived().evalTo(derived());
return derived();
}
} // end namespace Eigen

View File

@@ -2,7 +2,7 @@
// for linear algebra.
//
// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2011-2013 Gael Guennebaud <gael.guennebaud@inria.fr>
// 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
@@ -24,39 +24,52 @@ namespace internal {
// copy_using_evaluator_traits is based on assign_traits
template <typename Derived, typename OtherDerived>
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 {
DstIsAligned = Derived::Flags & AlignedBit,
DstHasDirectAccess = Derived::Flags & DirectAccessBit,
SrcIsAligned = OtherDerived::Flags & AlignedBit,
JointAlignment = bool(DstIsAligned) && bool(SrcIsAligned) ? Aligned : Unaligned,
SrcEvalBeforeAssign = (evaluator_traits<OtherDerived>::HasEvalTo == 1)
DstAlignment = DstEvaluator::Alignment,
SrcAlignment = SrcEvaluator::Alignment,
DstHasDirectAccess = DstFlags & DirectAccessBit,
JointAlignment = EIGEN_PLAIN_ENUM_MIN(DstAlignment,SrcAlignment)
};
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
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 {
StorageOrdersAgree = (int(Derived::IsRowMajor) == int(OtherDerived::IsRowMajor)),
DstIsRowMajor = DstFlags&RowMajorBit,
SrcIsRowMajor = SrcFlags&RowMajorBit,
StorageOrdersAgree = (int(DstIsRowMajor) == int(SrcIsRowMajor)),
MightVectorize = StorageOrdersAgree
&& (int(Derived::Flags) & int(OtherDerived::Flags) & ActualPacketAccessBit),
&& (int(DstFlags) & int(SrcFlags) & ActualPacketAccessBit)
&& (functor_traits<AssignFunc>::PacketAccess),
MayInnerVectorize = MightVectorize && int(InnerSize)!=Dynamic && int(InnerSize)%int(PacketSize)==0
&& int(DstIsAligned) && int(SrcIsAligned),
MayLinearize = StorageOrdersAgree && (int(Derived::Flags) & int(OtherDerived::Flags) & LinearAccessBit),
&& int(JointAlignment)>=int(RequiredAlignment),
MayLinearize = StorageOrdersAgree && (int(DstFlags) & int(SrcFlags) & LinearAccessBit),
MayLinearVectorize = MightVectorize && MayLinearize && DstHasDirectAccess
&& (DstIsAligned || MaxSizeAtCompileTime == Dynamic),
&& ((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
@@ -68,8 +81,7 @@ private:
public:
enum {
Traversal = int(SrcEvalBeforeAssign) ? int(AllAtOnceTraversal)
: int(MayInnerVectorize) ? int(InnerVectorizedTraversal)
Traversal = int(MayInnerVectorize) ? int(InnerVectorizedTraversal)
: int(MayLinearVectorize) ? int(LinearVectorizedTraversal)
: int(MaySliceVectorize) ? int(SliceVectorizedTraversal)
: int(MayLinearize) ? int(LinearTraversal)
@@ -82,12 +94,12 @@ public:
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),
MayUnrollCompletely = int(Dst::SizeAtCompileTime) != Dynamic
&& int(SrcEvaluator::CoeffReadCost) != Dynamic
&& int(Dst::SizeAtCompileTime) * int(SrcEvaluator::CoeffReadCost) <= int(UnrollingLimit),
MayUnrollInner = int(InnerSize) != Dynamic
&& int(OtherDerived::CoeffReadCost) != Dynamic
&& int(InnerSize) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit)
&& int(SrcEvaluator::CoeffReadCost) != Dynamic
&& int(InnerSize) * int(SrcEvaluator::CoeffReadCost) <= int(UnrollingLimit)
};
public:
@@ -99,8 +111,8 @@ public:
: int(NoUnrolling)
)
: int(Traversal) == int(LinearVectorizedTraversal)
? ( bool(MayUnrollCompletely) && bool(DstIsAligned) ? int(CompleteUnrolling)
: int(NoUnrolling) )
? ( bool(MayUnrollCompletely) && (int(DstAlignment)>=int(RequiredAlignment)) ? int(CompleteUnrolling)
: int(NoUnrolling) )
: int(Traversal) == int(LinearTraversal)
? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling)
: int(NoUnrolling) )
@@ -110,8 +122,15 @@ public:
#ifdef EIGEN_DEBUG_ASSIGN
static void debug()
{
EIGEN_DEBUG_VAR(DstIsAligned)
EIGEN_DEBUG_VAR(SrcIsAligned)
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)
@@ -127,6 +146,7 @@ public:
EIGEN_DEBUG_VAR(MayUnrollCompletely)
EIGEN_DEBUG_VAR(MayUnrollInner)
EIGEN_DEBUG_VAR(Unrolling)
std::cerr << std::endl;
}
#endif
};
@@ -142,6 +162,7 @@ public:
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;
@@ -150,7 +171,7 @@ struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling
inner = Index % DstXprType::InnerSizeAtCompileTime
};
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
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);
@@ -160,23 +181,23 @@ struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling
template<typename Kernel, int Stop>
struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Kernel&) { }
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
};
template<typename Kernel, int Index, int Stop>
template<typename Kernel, int Index_, int Stop>
struct copy_using_evaluator_DefaultTraversal_InnerUnrolling
{
static EIGEN_STRONG_INLINE void run(Kernel &kernel, int outer)
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);
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>
{
static EIGEN_STRONG_INLINE void run(Kernel&, int) { }
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index) { }
};
/***********************
@@ -186,7 +207,7 @@ struct copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Stop, Stop>
template<typename Kernel, int Index, int Stop>
struct copy_using_evaluator_LinearTraversal_CompleteUnrolling
{
static EIGEN_STRONG_INLINE void run(Kernel& kernel)
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);
@@ -196,7 +217,7 @@ struct copy_using_evaluator_LinearTraversal_CompleteUnrolling
template<typename Kernel, int Stop>
struct copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Kernel&) { }
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
};
/**************************
@@ -206,19 +227,21 @@ struct copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Stop, Stop
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
};
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
kernel.template assignPacketByOuterInner<Aligned, JointAlignment>(outer, inner);
enum { NextIndex = Index + packet_traits<typename DstXprType::Scalar>::size };
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);
}
};
@@ -226,17 +249,17 @@ struct copy_using_evaluator_innervec_CompleteUnrolling
template<typename Kernel, int Stop>
struct copy_using_evaluator_innervec_CompleteUnrolling<Kernel, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Kernel&) { }
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
};
template<typename Kernel, int Index, int Stop>
template<typename Kernel, int Index_, int Stop>
struct copy_using_evaluator_innervec_InnerUnrolling
{
static EIGEN_STRONG_INLINE void run(Kernel &kernel, int outer)
typedef typename Kernel::PacketType PacketType;
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, Index outer)
{
kernel.template assignPacketByOuterInner<Aligned, Aligned>(outer, Index);
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
enum { NextIndex = Index + packet_traits<typename DstXprType::Scalar>::size };
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);
}
};
@@ -244,7 +267,7 @@ struct copy_using_evaluator_innervec_InnerUnrolling
template<typename Kernel, int Stop>
struct copy_using_evaluator_innervec_InnerUnrolling<Kernel, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Kernel &, int) { }
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &, Index) { }
};
/***************************************************************************
@@ -265,10 +288,8 @@ struct dense_assignment_loop;
template<typename Kernel>
struct dense_assignment_loop<Kernel, DefaultTraversal, NoUnrolling>
{
static void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static void run(Kernel &kernel)
{
typedef typename Kernel::Index Index;
for(Index outer = 0; outer < kernel.outerSize(); ++outer) {
for(Index inner = 0; inner < kernel.innerSize(); ++inner) {
kernel.assignCoeffByOuterInner(outer, inner);
@@ -280,7 +301,7 @@ struct dense_assignment_loop<Kernel, DefaultTraversal, NoUnrolling>
template<typename Kernel>
struct dense_assignment_loop<Kernel, DefaultTraversal, CompleteUnrolling>
{
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
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);
@@ -290,8 +311,8 @@ struct dense_assignment_loop<Kernel, DefaultTraversal, CompleteUnrolling>
template<typename Kernel>
struct dense_assignment_loop<Kernel, DefaultTraversal, InnerUnrolling>
{
typedef typename Kernel::Index Index;
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
typedef typename Kernel::StorageIndex StorageIndex;
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
@@ -314,7 +335,7 @@ struct unaligned_dense_assignment_loop
{
// if IsAligned = true, then do nothing
template <typename Kernel>
static EIGEN_STRONG_INLINE void run(Kernel&, typename Kernel::Index, typename Kernel::Index) {}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index, Index) {}
};
template <>
@@ -323,19 +344,19 @@ 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
#ifdef _MSC_VER
#if EIGEN_COMP_MSVC
template <typename Kernel>
static EIGEN_DONT_INLINE void run(Kernel &kernel,
typename Kernel::Index start,
typename Kernel::Index end)
Index start,
Index end)
#else
template <typename Kernel>
static EIGEN_STRONG_INLINE void run(Kernel &kernel,
typename Kernel::Index start,
typename Kernel::Index end)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel,
Index start,
Index end)
#endif
{
for (typename Kernel::Index index = start; index < end; ++index)
for (Index index = start; index < end; ++index)
kernel.assignCoeff(index);
}
};
@@ -343,25 +364,26 @@ struct unaligned_dense_assignment_loop<false>
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, NoUnrolling>
{
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::Index Index;
const Index size = kernel.size();
typedef packet_traits<typename Kernel::Scalar> PacketTraits;
typedef typename Kernel::Scalar Scalar;
typedef typename Kernel::PacketType PacketType;
enum {
packetSize = PacketTraits::size,
dstIsAligned = int(Kernel::AssignmentTraits::DstIsAligned),
dstAlignment = PacketTraits::AlignedOnScalar ? Aligned : dstIsAligned,
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(&kernel.dstEvaluator().coeffRef(0), size);
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>(index);
kernel.template assignPacket<dstAlignment, srcAlignment, PacketType>(index);
unaligned_dense_assignment_loop<>::run(kernel, alignedEnd, size);
}
@@ -370,8 +392,8 @@ struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, NoUnrolling>
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, CompleteUnrolling>
{
typedef typename Kernel::Index Index;
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
typedef typename Kernel::StorageIndex StorageIndex;
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
@@ -391,23 +413,22 @@ struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, CompleteUnrollin
template<typename Kernel>
struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, NoUnrolling>
{
static inline void run(Kernel &kernel)
typedef typename Kernel::PacketType PacketType;
EIGEN_DEVICE_FUNC static inline void run(Kernel &kernel)
{
typedef typename Kernel::Index Index;
const Index innerSize = kernel.innerSize();
const Index outerSize = kernel.outerSize();
const Index packetSize = packet_traits<typename Kernel::Scalar>::size;
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>(outer, inner);
kernel.template assignPacketByOuterInner<Aligned, Aligned, PacketType>(outer, inner);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, CompleteUnrolling>
{
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
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);
@@ -417,8 +438,8 @@ struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, CompleteUnrolling
template<typename Kernel>
struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, InnerUnrolling>
{
typedef typename Kernel::Index Index;
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
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();
@@ -434,9 +455,8 @@ struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, InnerUnrolling>
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearTraversal, NoUnrolling>
{
static inline void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static inline void run(Kernel &kernel)
{
typedef typename Kernel::Index Index;
const Index size = kernel.size();
for(Index i = 0; i < size; ++i)
kernel.assignCoeff(i);
@@ -446,7 +466,7 @@ struct dense_assignment_loop<Kernel, LinearTraversal, NoUnrolling>
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearTraversal, CompleteUnrolling>
{
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
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);
@@ -460,21 +480,29 @@ struct dense_assignment_loop<Kernel, LinearTraversal, CompleteUnrolling>
template<typename Kernel>
struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
{
static inline void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static inline void run(Kernel &kernel)
{
typedef typename Kernel::Index Index;
typedef packet_traits<typename Kernel::Scalar> PacketTraits;
typedef typename Kernel::Scalar Scalar;
typedef typename Kernel::PacketType PacketType;
enum {
packetSize = PacketTraits::size,
alignable = PacketTraits::AlignedOnScalar,
dstAlignment = alignable ? Aligned : int(Kernel::AssignmentTraits::DstIsAligned)
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) || Kernel::AssignmentTraits::DstIsAligned) ? 0
: internal::first_aligned(&kernel.dstEvaluator().coeffRef(0,0), innerSize);
Index alignedStart = ((!alignable) || bool(dstIsAligned)) ? 0 : internal::first_aligned<requestedAlignment>(dst_ptr, innerSize);
for(Index outer = 0; outer < outerSize; ++outer)
{
@@ -485,7 +513,7 @@ struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
// do the vectorizable part of the assignment
for(Index inner = alignedStart; inner<alignedEnd; inner+=packetSize)
kernel.template assignPacketByOuterInner<dstAlignment, Unaligned>(outer, inner);
kernel.template assignPacketByOuterInner<dstAlignment, Unaligned, PacketType>(outer, inner);
// do the non-vectorizable part of the assignment
for(Index inner = alignedEnd; inner<innerSize ; ++inner)
@@ -496,25 +524,8 @@ struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
}
};
/****************************
*** All-at-once traversal ***
****************************/
// TODO: this 'AllAtOnceTraversal' should be dropped or caught earlier (Gael)
// Indeed, what to do with the kernel's functor??
template<typename Kernel>
struct dense_assignment_loop<Kernel, AllAtOnceTraversal, NoUnrolling>
{
static inline void run(Kernel & kernel)
{
// Evaluate rhs in temporary to prevent aliasing problems in a = a * a;
// TODO: Do not pass the xpr object to evalTo() (Jitse)
kernel.srcEvaluator().evalTo(kernel.dstEvaluator(), kernel.dstExpression());
}
};
/***************************************************************************
* Part 4 : Generic Assignment routine
* Part 4 : Generic dense assignment kernel
***************************************************************************/
// This class generalize the assignment of a coefficient (or packet) from one dense evaluator
@@ -523,7 +534,7 @@ struct dense_assignment_loop<Kernel, AllAtOnceTraversal, NoUnrolling>
// 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>
template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version = Specialized>
class generic_dense_assignment_kernel
{
protected:
@@ -534,36 +545,46 @@ public:
typedef DstEvaluatorTypeT DstEvaluatorType;
typedef SrcEvaluatorTypeT SrcEvaluatorType;
typedef typename DstEvaluatorType::Scalar Scalar;
typedef typename DstEvaluatorType::Index Index;
typedef copy_using_evaluator_traits<DstXprType, SrcXprType> AssignmentTraits;
typedef typename DstEvaluatorType::StorageIndex StorageIndex;
typedef copy_using_evaluator_traits<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor> AssignmentTraits;
typedef typename AssignmentTraits::PacketType PacketType;
generic_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr)
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
}
Index size() const { return m_dstExpr.size(); }
Index innerSize() const { return m_dstExpr.innerSize(); }
Index outerSize() const { return m_dstExpr.outerSize(); }
Index outerStride() const { return m_dstExpr.outerStride(); }
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:
DstXprType& dstExpression() const { return m_dstExpr; }
EIGEN_DEVICE_FUNC DstXprType& dstExpression() const { return m_dstExpr; }
DstEvaluatorType& dstEvaluator() { return m_dst; }
const SrcEvaluatorType& srcEvaluator() const { return m_src; }
EIGEN_DEVICE_FUNC DstEvaluatorType& dstEvaluator() { return m_dst; }
EIGEN_DEVICE_FUNC const SrcEvaluatorType& srcEvaluator() const { return m_src; }
void assignCoeff(Index row, Index col)
/// 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));
}
void assignCoeff(Index index)
/// \sa assignCoeff(Index,Index)
EIGEN_DEVICE_FUNC void assignCoeff(Index index)
{
m_functor.assignCoeff(m_dst.coeffRef(index), m_src.coeff(index));
}
void assignCoeffByOuterInner(Index outer, Index inner)
/// \sa assignCoeff(Index,Index)
EIGEN_DEVICE_FUNC void assignCoeffByOuterInner(Index outer, Index inner)
{
Index row = rowIndexByOuterInner(outer, inner);
Index col = colIndexByOuterInner(outer, inner);
@@ -571,41 +592,41 @@ public:
}
template<int StoreMode, int LoadMode>
void assignPacket(Index row, Index 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>(row,col));
m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(row,col), m_src.template packet<LoadMode,PacketType>(row,col));
}
template<int StoreMode, int LoadMode>
void assignPacket(Index index)
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>(index));
m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(index), m_src.template packet<LoadMode,PacketType>(index));
}
template<int StoreMode, int LoadMode>
void assignPacketByOuterInner(Index outer, Index inner)
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>(row, col);
assignPacket<StoreMode,LoadMode,PacketType>(row, col);
}
static Index rowIndexByOuterInner(Index outer, Index inner)
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(Traits::Flags)&RowMajorBit ? outer
: int(DstEvaluatorType::Flags)&RowMajorBit ? outer
: inner;
}
static Index colIndexByOuterInner(Index outer, Index 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(Traits::Flags)&RowMajorBit ? inner
: int(DstEvaluatorType::Flags)&RowMajorBit ? inner
: outer;
}
@@ -617,17 +638,17 @@ protected:
DstXprType& m_dstExpr;
};
/***************************************************************************
* Part 5 : Entry point for dense rectangular assignment
***************************************************************************/
template<typename DstXprType, typename SrcXprType, typename Functor>
void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src, const Functor &func)
EIGEN_DEVICE_FUNC void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src, const Functor &func)
{
#ifdef EIGEN_DEBUG_ASSIGN
// TODO these traits should be computed from information provided by the evaluators
internal::copy_using_evaluator_traits<DstXprType, SrcXprType>::debug();
#endif
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
typedef typename evaluator<DstXprType>::type DstEvaluatorType;
typedef typename evaluator<SrcXprType>::type SrcEvaluatorType;
typedef evaluator<DstXprType> DstEvaluatorType;
typedef evaluator<SrcXprType> SrcEvaluatorType;
DstEvaluatorType dstEvaluator(dst);
SrcEvaluatorType srcEvaluator(src);
@@ -639,201 +660,166 @@ void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src, co
}
template<typename DstXprType, typename SrcXprType>
void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src)
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 5 : Entry points
* Part 6 : Generic assignment
***************************************************************************/
// Based on DenseBase::LazyAssign()
// The following functions are just for testing and they are meant to be moved to operator= and the likes.
// 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;
template<typename DstXprType, template <typename> class StorageBase, typename SrcXprType>
EIGEN_STRONG_INLINE
const DstXprType& copy_using_evaluator(const NoAlias<DstXprType, StorageBase>& dst,
const EigenBase<SrcXprType>& src)
{
return noalias_copy_using_evaluator(dst.expression(), src.derived(), internal::assign_op<typename DstXprType::Scalar>());
}
// Assignement kind defined in this file:
struct Dense2Dense {};
struct EigenBase2EigenBase {};
template<typename XprType, int AssumeAliasing = evaluator_traits<XprType>::AssumeAliasing>
struct AddEvalIfAssumingAliasing;
template<typename XprType>
struct AddEvalIfAssumingAliasing<XprType, 0>
{
static const XprType& run(const XprType& xpr)
{
return xpr;
}
};
template<typename XprType>
struct AddEvalIfAssumingAliasing<XprType, 1>
{
static const EvalToTemp<XprType> run(const XprType& xpr)
{
return EvalToTemp<XprType>(xpr);
}
};
template<typename DstXprType, typename SrcXprType, typename Functor>
EIGEN_STRONG_INLINE
const DstXprType& copy_using_evaluator(const EigenBase<DstXprType>& dst, const EigenBase<SrcXprType>& src, const Functor &func)
{
return noalias_copy_using_evaluator(dst.const_cast_derived(),
AddEvalIfAssumingAliasing<SrcXprType>::run(src.derived()),
func
);
}
// this mimics operator=
template<typename DstXprType, typename SrcXprType>
EIGEN_STRONG_INLINE
const DstXprType& copy_using_evaluator(const EigenBase<DstXprType>& dst, const EigenBase<SrcXprType>& src)
{
return copy_using_evaluator(dst.const_cast_derived(), src.derived(), internal::assign_op<typename DstXprType::Scalar>());
}
template<typename DstXprType, typename SrcXprType, typename Functor>
EIGEN_STRONG_INLINE
const DstXprType& noalias_copy_using_evaluator(const PlainObjectBase<DstXprType>& dst, const EigenBase<SrcXprType>& src, const Functor &func)
{
#ifdef EIGEN_DEBUG_ASSIGN
internal::copy_using_evaluator_traits<DstXprType, SrcXprType>::debug();
#endif
#ifdef EIGEN_NO_AUTOMATIC_RESIZING
eigen_assert((dst.size()==0 || (IsVectorAtCompileTime ? (dst.size() == src.size())
: (dst.rows() == src.rows() && dst.cols() == src.cols())))
&& "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined");
#else
dst.const_cast_derived().resizeLike(src.derived());
#endif
call_dense_assignment_loop(dst.const_cast_derived(), src.derived(), func);
return dst.derived();
}
template<typename DstXprType, typename SrcXprType, typename Functor>
EIGEN_STRONG_INLINE
const DstXprType& noalias_copy_using_evaluator(const EigenBase<DstXprType>& dst, const EigenBase<SrcXprType>& src, const Functor &func)
{
call_dense_assignment_loop(dst.const_cast_derived(), src.derived(), func);
return dst.derived();
}
// Based on DenseBase::swap()
// TODO: Check whether we need to do something special for swapping two
// Arrays or Matrices. (Jitse)
// Overload default assignPacket behavior for swapping them
template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT>
class swap_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar> >
{
typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar> > Base;
typedef typename DstEvaluatorTypeT::PacketScalar PacketScalar;
using Base::m_dst;
using Base::m_src;
using Base::m_functor;
public:
typedef typename Base::Scalar Scalar;
typedef typename Base::Index Index;
typedef typename Base::DstXprType DstXprType;
swap_kernel(DstEvaluatorTypeT &dst, const SrcEvaluatorTypeT &src, DstXprType& dstExpr)
: Base(dst, src, swap_assign_op<Scalar>(), dstExpr)
{}
template<int StoreMode, int LoadMode>
void assignPacket(Index row, Index col)
{
m_functor.template swapPacket<StoreMode,LoadMode,PacketScalar>(&m_dst.coeffRef(row,col), &const_cast<SrcEvaluatorTypeT&>(m_src).coeffRef(row,col));
}
template<int StoreMode, int LoadMode>
void assignPacket(Index index)
{
m_functor.template swapPacket<StoreMode,LoadMode,PacketScalar>(&m_dst.coeffRef(index), &const_cast<SrcEvaluatorTypeT&>(m_src).coeffRef(index));
}
// 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>
void assignPacketByOuterInner(Index outer, Index inner)
{
Index row = Base::rowIndexByOuterInner(outer, inner);
Index col = Base::colIndexByOuterInner(outer, inner);
assignPacket<StoreMode,LoadMode>(row, col);
}
};
template<typename DstXprType, typename SrcXprType>
void swap_using_evaluator(const DstXprType& dst, const SrcXprType& src)
{
// TODO there is too much redundancy with call_dense_assignment_loop
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
typedef typename evaluator<DstXprType>::type DstEvaluatorType;
typedef typename evaluator<SrcXprType>::type SrcEvaluatorType;
DstEvaluatorType dstEvaluator(dst);
SrcEvaluatorType srcEvaluator(src);
template<typename,typename> struct AssignmentKind { typedef EigenBase2EigenBase Kind; };
template<> struct AssignmentKind<DenseShape,DenseShape> { typedef Dense2Dense Kind; };
typedef swap_kernel<DstEvaluatorType,SrcEvaluatorType> Kernel;
Kernel kernel(dstEvaluator, srcEvaluator, dst.const_cast_derived());
// 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);
dense_assignment_loop<Kernel>::run(kernel);
}
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)
// Based on MatrixBase::operator+= (in CwiseBinaryOp.h)
template<typename DstXprType, typename SrcXprType>
void add_assign_using_evaluator(const MatrixBase<DstXprType>& dst, const MatrixBase<SrcXprType>& 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)
{
typedef typename DstXprType::Scalar Scalar;
copy_using_evaluator(dst.derived(), src.derived(), add_assign_op<Scalar>());
call_assignment_no_alias(dst, src, internal::assign_op<typename Dst::Scalar>());
}
// Based on ArrayBase::operator+=
template<typename DstXprType, typename SrcXprType>
void add_assign_using_evaluator(const ArrayBase<DstXprType>& dst, const ArrayBase<SrcXprType>& src)
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)
{
typedef typename DstXprType::Scalar Scalar;
copy_using_evaluator(dst.derived(), src.derived(), add_assign_op<Scalar>());
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);
}
// TODO: Add add_assign_using_evaluator for EigenBase ? (Jitse)
template<typename DstXprType, typename SrcXprType>
void subtract_assign_using_evaluator(const MatrixBase<DstXprType>& dst, const MatrixBase<SrcXprType>& src)
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src)
{
typedef typename DstXprType::Scalar Scalar;
copy_using_evaluator(dst.derived(), src.derived(), sub_assign_op<Scalar>());
call_assignment_no_alias_no_transpose(dst, src, internal::assign_op<typename Dst::Scalar>());
}
template<typename DstXprType, typename SrcXprType>
void subtract_assign_using_evaluator(const ArrayBase<DstXprType>& dst, const ArrayBase<SrcXprType>& src)
// 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>
{
typedef typename DstXprType::Scalar Scalar;
copy_using_evaluator(dst.derived(), src.derived(), sub_assign_op<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);
}
};
template<typename DstXprType, typename SrcXprType>
void multiply_assign_using_evaluator(const ArrayBase<DstXprType>& dst, const ArrayBase<SrcXprType>& src)
// 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>
{
typedef typename DstXprType::Scalar Scalar;
copy_using_evaluator(dst.derived(), src.derived(), mul_assign_op<Scalar>());
}
template<typename DstXprType, typename SrcXprType>
void divide_assign_using_evaluator(const ArrayBase<DstXprType>& dst, const ArrayBase<SrcXprType>& src)
{
typedef typename DstXprType::Scalar Scalar;
copy_using_evaluator(dst.derived(), src.derived(), div_assign_op<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

255
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)
@@ -57,166 +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_LA(atan, Atan)
//EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs, Abs)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(exp, Exp)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(log, Ln)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(sqrt, Sqrt)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr)
// The vm*powx functions are not avaibale in the windows version of MKL.
#ifndef _WIN32
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmspowx_, float, float)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdpowx_, double, double)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcpowx_, scomplex, MKL_Complex8)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzpowx_, dcomplex, MKL_Complex16)
#endif
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmsPowx, float, float, LA)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdPowx, double, double, LA)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcPowx, scomplex, MKL_Complex8, LA)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzPowx, dcomplex, MKL_Complex16, LA)
} // end namespace internal

View File

@@ -32,7 +32,7 @@ class BandMatrixBase : public EigenBase<Derived>
};
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> DenseMatrixType;
typedef typename DenseMatrixType::Index Index;
typedef typename DenseMatrixType::StorageIndex StorageIndex;
typedef typename internal::traits<Derived>::CoefficientsType CoefficientsType;
typedef EigenBase<Derived> Base;
@@ -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

@@ -55,7 +55,7 @@ struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel> > : traits<XprTyp
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,
@@ -68,6 +68,7 @@ struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel> > : traits<XprTyp
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
@@ -80,18 +81,16 @@ struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel> > : traits<XprTyp
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))) % EIGEN_ALIGN_BYTES) == 0)) ? AlignedBit : 0,
FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1 || (InnerPanel && (traits<XprType>::Flags&LinearAccessBit))) ? 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
};
};
@@ -111,6 +110,8 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> class
typedef Impl Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(Block)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
typedef typename internal::remove_all<XprType>::type NestedExpression;
/** Column or Row constructor
*/
@@ -125,26 +126,26 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> class
/** Fixed-size constructor
*/
EIGEN_DEVICE_FUNC
inline Block(XprType& xpr, Index a_startRow, Index a_startCol)
: Impl(xpr, a_startRow, a_startCol)
inline Block(XprType& xpr, Index startRow, Index startCol)
: Impl(xpr, startRow, startCol)
{
EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
eigen_assert(a_startRow >= 0 && BlockRows >= 1 && a_startRow + BlockRows <= xpr.rows()
&& a_startCol >= 0 && BlockCols >= 1 && a_startCol + BlockCols <= xpr.cols());
eigen_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows()
&& startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols());
}
/** Dynamic-size constructor
*/
EIGEN_DEVICE_FUNC
inline Block(XprType& xpr,
Index a_startRow, Index a_startCol,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: Impl(xpr, a_startRow, a_startCol, blockRows, blockCols)
: Impl(xpr, startRow, startCol, blockRows, blockCols)
{
eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
&& (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
eigen_assert(a_startRow >= 0 && blockRows >= 0 && a_startRow <= xpr.rows() - blockRows
&& a_startCol >= 0 && blockCols >= 0 && a_startCol <= xpr.cols() - blockCols);
eigen_assert(startRow >= 0 && blockRows >= 0 && startRow <= xpr.rows() - blockRows
&& startCol >= 0 && blockCols >= 0 && startCol <= xpr.cols() - blockCols);
}
};
@@ -155,15 +156,15 @@ class BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, Dense>
: public internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel>
{
typedef internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> Impl;
typedef typename XprType::Index Index;
typedef typename XprType::StorageIndex StorageIndex;
public:
typedef Impl Base;
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)
EIGEN_DEVICE_FUNC inline BlockImpl(XprType& xpr, Index i) : Impl(xpr,i) {}
EIGEN_DEVICE_FUNC inline BlockImpl(XprType& xpr, Index a_startRow, Index a_startCol) : Impl(xpr, a_startRow, a_startCol) {}
EIGEN_DEVICE_FUNC inline BlockImpl(XprType& xpr, Index startRow, Index startCol) : Impl(xpr, startRow, startCol) {}
EIGEN_DEVICE_FUNC
inline BlockImpl(XprType& xpr, Index a_startRow, Index a_startCol, Index blockRows, Index blockCols)
: Impl(xpr, a_startRow, a_startCol, blockRows, blockCols) {}
inline BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
: Impl(xpr, startRow, startCol, blockRows, blockCols) {}
};
namespace internal {
@@ -179,7 +180,7 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
class InnerIterator;
// class InnerIterator; // FIXME apparently never used
/** Column or Row constructor
*/
@@ -199,8 +200,8 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
/** Fixed-size constructor
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index a_startRow, Index a_startCol)
: m_xpr(xpr), m_startRow(a_startRow), m_startCol(a_startCol),
inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
: m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
m_blockRows(BlockRows), m_blockCols(BlockCols)
{}
@@ -208,9 +209,9 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr,
Index a_startRow, Index a_startCol,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: m_xpr(xpr), m_startRow(a_startRow), m_startCol(a_startCol),
: m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
m_blockRows(blockRows), m_blockCols(blockCols)
{}
@@ -307,13 +308,13 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
}
EIGEN_DEVICE_FUNC
Index startRow() const
StorageIndex startRow() const
{
return m_startRow.value();
}
EIGEN_DEVICE_FUNC
Index startCol() const
StorageIndex startCol() const
{
return m_startCol.value();
}
@@ -321,10 +322,10 @@ 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 implementation of dense Blocks in the direct access case.*/
@@ -333,6 +334,9 @@ 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<BlockType> Base;
@@ -343,9 +347,8 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index i)
: Base(internal::const_cast_ptr(&xpr.coeffRef(
(BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0,
(BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0)),
: Base(xpr.data() + i * ( ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && (!XprTypeIsRowMajor))
|| ((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && ( XprTypeIsRowMajor)) ? xpr.innerStride() : xpr.outerStride()),
BlockRows==1 ? 1 : xpr.rows(),
BlockCols==1 ? 1 : xpr.cols()),
m_xpr(xpr)
@@ -357,7 +360,8 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
: Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol))), m_xpr(xpr)
: Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol)),
m_xpr(xpr)
{
init();
}
@@ -368,7 +372,7 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
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)
{
init();

View File

@@ -17,9 +17,10 @@ namespace internal {
template<typename Derived, int UnrollCount>
struct all_unroller
{
typedef typename Derived::ExpressionTraits Traits;
enum {
col = (UnrollCount-1) / Derived::RowsAtCompileTime,
row = (UnrollCount-1) % Derived::RowsAtCompileTime
col = (UnrollCount-1) / Traits::RowsAtCompileTime,
row = (UnrollCount-1) % Traits::RowsAtCompileTime
};
static inline bool run(const Derived &mat)
@@ -43,11 +44,12 @@ struct all_unroller<Derived, Dynamic>
template<typename Derived, int UnrollCount>
struct any_unroller
{
typedef typename Derived::ExpressionTraits Traits;
enum {
col = (UnrollCount-1) / Derived::RowsAtCompileTime,
row = (UnrollCount-1) % Derived::RowsAtCompileTime
col = (UnrollCount-1) / Traits::RowsAtCompileTime,
row = (UnrollCount-1) % Traits::RowsAtCompileTime
};
static inline bool run(const Derived &mat)
{
return any_unroller<Derived, UnrollCount-1>::run(mat) || mat.coeff(row, col);
@@ -78,19 +80,21 @@ 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;
}
}
@@ -102,19 +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;
}
}
@@ -124,7 +130,7 @@ inline bool DenseBase<Derived>::any() const
* \sa all(), any()
*/
template<typename Derived>
inline typename DenseBase<Derived>::Index DenseBase<Derived>::count() const
inline Eigen::Index DenseBase<Derived>::count() const
{
return derived().template cast<bool>().template cast<Index>().sum();
}

View File

@@ -28,7 +28,6 @@ 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)
@@ -106,6 +105,9 @@ struct CommaInitializer
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()

File diff suppressed because it is too large Load Diff

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>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
@@ -15,47 +15,113 @@ namespace Eigen {
/* This file contains the respective InnerIterator definition of the expressions defined in Eigen/Core
*/
/** \ingroup SparseCore_Module
* \class InnerIterator
* \brief An InnerIterator allows to loop over the element of a sparse (or dense) matrix or expression
*
* todo
namespace internal {
template<typename XprType, typename EvaluatorKind>
class inner_iterator_selector;
}
/** \class InnerIterator
* \brief An InnerIterator allows to loop over the element of any matrix expression.
*
* \warning To be used with care because an evaluator is constructed every time an InnerIterator iterator is constructed.
*
* TODO: add a usage example
*/
// generic version for dense matrix and expressions
template<typename Derived> class DenseBase<Derived>::InnerIterator
template<typename XprType>
class InnerIterator
{
protected:
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Index Index;
enum { IsRowMajor = (Derived::Flags&RowMajorBit)==RowMajorBit };
public:
EIGEN_STRONG_INLINE InnerIterator(const Derived& expr, Index outer)
: m_expression(expr), m_inner(0), m_outer(outer), m_end(expr.innerSize())
{}
EIGEN_STRONG_INLINE Scalar value() const
{
return (IsRowMajor) ? m_expression.coeff(m_outer, m_inner)
: m_expression.coeff(m_inner, m_outer);
}
EIGEN_STRONG_INLINE InnerIterator& operator++() { m_inner++; return *this; }
EIGEN_STRONG_INLINE Index index() const { return m_inner; }
inline Index row() const { return IsRowMajor ? m_outer : index(); }
inline Index col() const { return IsRowMajor ? index() : m_outer; }
EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; }
protected:
const Derived& m_expression;
Index m_inner;
const Index m_outer;
const Index m_end;
protected:
typedef internal::inner_iterator_selector<XprType, typename internal::evaluator_traits<XprType>::Kind> IteratorType;
typedef internal::evaluator<XprType> EvaluatorType;
typedef typename internal::traits<XprType>::Scalar Scalar;
public:
/** Construct an iterator over the \a outerId -th row or column of \a xpr */
InnerIterator(const XprType &xpr, const Index &outerId)
: m_eval(xpr), m_iter(m_eval, outerId, xpr.innerSize())
{}
/// \returns the value of the current coefficient.
EIGEN_STRONG_INLINE Scalar value() const { return m_iter.value(); }
/** Increment the iterator \c *this to the next non-zero coefficient.
* Explicit zeros are not skipped over. To skip explicit zeros, see class SparseView
*/
EIGEN_STRONG_INLINE InnerIterator& operator++() { m_iter.operator++(); return *this; }
/// \returns the column or row index of the current coefficient.
EIGEN_STRONG_INLINE Index index() const { return m_iter.index(); }
/// \returns the row index of the current coefficient.
EIGEN_STRONG_INLINE Index row() const { return m_iter.row(); }
/// \returns the column index of the current coefficient.
EIGEN_STRONG_INLINE Index col() const { return m_iter.col(); }
/// \returns \c true if the iterator \c *this still references a valid coefficient.
EIGEN_STRONG_INLINE operator bool() const { return m_iter; }
protected:
EvaluatorType m_eval;
IteratorType m_iter;
private:
// If you get here, then you're not using the right InnerIterator type, e.g.:
// SparseMatrix<double,RowMajor> A;
// SparseMatrix<double>::InnerIterator it(A,0);
template<typename T> InnerIterator(const EigenBase<T>&,Index outer);
};
namespace internal {
// Generic inner iterator implementation for dense objects
template<typename XprType>
class inner_iterator_selector<XprType, IndexBased>
{
protected:
typedef evaluator<XprType> EvaluatorType;
typedef typename traits<XprType>::Scalar Scalar;
enum { IsRowMajor = (XprType::Flags&RowMajorBit)==RowMajorBit };
public:
EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &innerSize)
: m_eval(eval), m_inner(0), m_outer(outerId), m_end(innerSize)
{}
EIGEN_STRONG_INLINE Scalar value() const
{
return (IsRowMajor) ? m_eval.coeff(m_outer, m_inner)
: m_eval.coeff(m_inner, m_outer);
}
EIGEN_STRONG_INLINE inner_iterator_selector& operator++() { m_inner++; return *this; }
EIGEN_STRONG_INLINE Index index() const { return m_inner; }
inline Index row() const { return IsRowMajor ? m_outer : index(); }
inline Index col() const { return IsRowMajor ? index() : m_outer; }
EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; }
protected:
const EvaluatorType& m_eval;
Index m_inner;
const Index m_outer;
const Index m_end;
};
// For iterator-based evaluator, inner-iterator is already implemented as
// evaluator<>::InnerIterator
template<typename XprType>
class inner_iterator_selector<XprType, IteratorBased>
: public evaluator<XprType>::InnerIterator
{
protected:
typedef typename evaluator<XprType>::InnerIterator Base;
typedef evaluator<XprType> EvaluatorType;
public:
EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &/*innerSize*/)
: Base(eval, outerId)
{}
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_COREITERATORS_H

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
@@ -56,69 +56,47 @@ 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_is_product_like<BINOP>::ret \
? int(internal::scalar_product_traits<LHS, RHS>::Defined) \
: int(internal::is_same<LHS, RHS>::value)), \
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
class CwiseBinaryOpImpl;
template<typename BinaryOp, typename Lhs, typename Rhs>
class CwiseBinaryOp : internal::no_assignment_operator,
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;
@@ -165,43 +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_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar coeff(Index rowId, Index colId) const
{
return derived().functor()(derived().lhs().coeff(rowId, colId),
derived().rhs().coeff(rowId, colId));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
{
return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(rowId, colId),
derived().rhs().template packet<LoadMode>(rowId, colId));
}
EIGEN_DEVICE_FUNC
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.
@@ -213,8 +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();
}
@@ -227,8 +174,7 @@ 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();
}

View File

@@ -35,19 +35,13 @@ 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:
@@ -55,13 +49,13 @@ class CwiseNullaryOp : internal::no_assignment_operator,
EIGEN_DENSE_PUBLIC_INTERFACE(CwiseNullaryOp)
EIGEN_DEVICE_FUNC
CwiseNullaryOp(Index nbRows, Index nbCols, const NullaryOp& func = NullaryOp())
: m_rows(nbRows), m_cols(nbCols), m_functor(func)
CwiseNullaryOp(Index rows, Index cols, const NullaryOp& func = NullaryOp())
: m_rows(rows), m_cols(cols), m_functor(func)
{
eigen_assert(nbRows >= 0
&& (RowsAtCompileTime == Dynamic || RowsAtCompileTime == nbRows)
&& nbCols >= 0
&& (ColsAtCompileTime == Dynamic || ColsAtCompileTime == nbCols));
eigen_assert(rows >= 0
&& (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0
&& (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
}
EIGEN_DEVICE_FUNC
@@ -119,10 +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
@@ -145,12 +139,12 @@ DenseBase<Derived>::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& f
*/
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
@@ -164,19 +158,19 @@ DenseBase<Derived>::NullaryExpr(Index size, const CustomNullaryOp& func)
*/
template<typename Derived>
template<typename CustomNullaryOp>
EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, Derived>
EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
{
return CwiseNullaryOp<CustomNullaryOp, Derived>(RowsAtCompileTime, ColsAtCompileTime, func);
return CwiseNullaryOp<CustomNullaryOp, PlainObject>(RowsAtCompileTime, ColsAtCompileTime, func);
}
/** \returns an expression of a constant matrix of value \a value
*
* The parameters \a nbRows and \a nbCols are the number of rows and of columns of
* The parameters \a rows and \a cols are the number of rows and of columns of
* the returned matrix. Must be compatible with this DenseBase type.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
* it is redundant to pass \a nbRows and \a nbCols as arguments, so Zero() should be used
* it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
* instead.
*
* The template parameter \a CustomNullaryOp is the type of the functor.
@@ -185,9 +179,9 @@ DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(Index nbRows, Index nbCols, const Scalar& value)
DenseBase<Derived>::Constant(Index rows, Index cols, const Scalar& value)
{
return DenseBase<Derived>::NullaryExpr(nbRows, nbCols, internal::scalar_constant_op<Scalar>(value));
return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_constant_op<Scalar>(value));
}
/** \returns an expression of a constant matrix of value \a value
@@ -251,7 +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));
}
/**
@@ -264,7 +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));
}
/**
@@ -285,7 +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));
}
/**
@@ -298,7 +292,7 @@ DenseBase<Derived>::LinSpaced(const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,true>(low,high,Derived::SizeAtCompileTime));
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,PacketScalar,true>(low,high,Derived::SizeAtCompileTime));
}
/** \returns true if all coefficients in this matrix are approximately equal to \a val, to within precision \a prec */
@@ -306,9 +300,10 @@ template<typename Derived>
bool DenseBase<Derived>::isApproxToConstant
(const Scalar& val, const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if(!internal::isApprox(this->coeff(i, j), val, prec))
if(!internal::isApprox(self.coeff(i, j), val, prec))
return false;
return true;
}
@@ -362,8 +357,8 @@ PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val)
/** Resizes to the given size, and sets all coefficients in this expression to the given \a value.
*
* \param nbRows the new number of rows
* \param nbCols the new number of columns
* \param rows the new number of rows
* \param cols the new number of columns
* \param val the value to which all coefficients are set
*
* Example: \include Matrix_setConstant_int_int.cpp
@@ -373,9 +368,9 @@ PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index nbRows, Index nbCols, const Scalar& val)
PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& val)
{
resize(nbRows, nbCols);
resize(rows, cols);
return setConstant(val);
}
@@ -396,7 +391,7 @@ template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index newSize, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return derived() = Derived::NullaryExpr(newSize, internal::linspaced_op<Scalar,false>(low,high,newSize));
return derived() = Derived::NullaryExpr(newSize, internal::linspaced_op<Scalar,PacketScalar,false>(low,high,newSize));
}
/**
@@ -434,9 +429,9 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low,
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero(Index nbRows, Index nbCols)
DenseBase<Derived>::Zero(Index rows, Index cols)
{
return Constant(nbRows, nbCols, Scalar(0));
return Constant(rows, cols, Scalar(0));
}
/** \returns an expression of a zero vector.
@@ -490,9 +485,10 @@ DenseBase<Derived>::Zero()
template<typename Derived>
bool DenseBase<Derived>::isZero(const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if(!internal::isMuchSmallerThan(this->coeff(i, j), static_cast<Scalar>(1), prec))
if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<Scalar>(1), prec))
return false;
return true;
}
@@ -529,8 +525,8 @@ PlainObjectBase<Derived>::setZero(Index newSize)
/** Resizes to the given size, and sets all coefficients in this expression to zero.
*
* \param nbRows the new number of rows
* \param nbCols the new number of columns
* \param rows the new number of rows
* \param cols the new number of columns
*
* Example: \include Matrix_setZero_int_int.cpp
* Output: \verbinclude Matrix_setZero_int_int.out
@@ -539,9 +535,9 @@ PlainObjectBase<Derived>::setZero(Index newSize)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index nbRows, Index nbCols)
PlainObjectBase<Derived>::setZero(Index rows, Index cols)
{
resize(nbRows, nbCols);
resize(rows, cols);
return setConstant(Scalar(0));
}
@@ -549,7 +545,7 @@ PlainObjectBase<Derived>::setZero(Index nbRows, Index nbCols)
/** \returns an expression of a matrix where all coefficients equal one.
*
* The parameters \a nbRows and \a nbCols are the number of rows and of columns of
* The parameters \a rows and \a cols are the number of rows and of columns of
* the returned matrix. Must be compatible with this MatrixBase type.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
@@ -563,9 +559,9 @@ PlainObjectBase<Derived>::setZero(Index nbRows, Index nbCols)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index nbRows, Index nbCols)
DenseBase<Derived>::Ones(Index rows, Index cols)
{
return Constant(nbRows, nbCols, Scalar(1));
return Constant(rows, cols, Scalar(1));
}
/** \returns an expression of a vector where all coefficients equal one.
@@ -655,8 +651,8 @@ PlainObjectBase<Derived>::setOnes(Index newSize)
/** Resizes to the given size, and sets all coefficients in this expression to one.
*
* \param nbRows the new number of rows
* \param nbCols the new number of columns
* \param rows the new number of rows
* \param cols the new number of columns
*
* Example: \include Matrix_setOnes_int_int.cpp
* Output: \verbinclude Matrix_setOnes_int_int.out
@@ -665,9 +661,9 @@ PlainObjectBase<Derived>::setOnes(Index newSize)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index nbRows, Index nbCols)
PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
{
resize(nbRows, nbCols);
resize(rows, cols);
return setConstant(Scalar(1));
}
@@ -675,7 +671,7 @@ PlainObjectBase<Derived>::setOnes(Index nbRows, Index nbCols)
/** \returns an expression of the identity matrix (not necessarily square).
*
* The parameters \a nbRows and \a nbCols are the number of rows and of columns of
* The parameters \a rows and \a cols are the number of rows and of columns of
* the returned matrix. Must be compatible with this MatrixBase type.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
@@ -689,9 +685,9 @@ PlainObjectBase<Derived>::setOnes(Index nbRows, Index nbCols)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
MatrixBase<Derived>::Identity(Index nbRows, Index nbCols)
MatrixBase<Derived>::Identity(Index rows, Index cols)
{
return DenseBase<Derived>::NullaryExpr(nbRows, nbCols, internal::scalar_identity_op<Scalar>());
return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_identity_op<Scalar>());
}
/** \returns an expression of the identity matrix (not necessarily square).
@@ -725,18 +721,19 @@ template<typename Derived>
bool MatrixBase<Derived>::isIdentity
(const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index j = 0; j < cols(); ++j)
{
for(Index i = 0; i < rows(); ++i)
{
if(i == j)
{
if(!internal::isApprox(this->coeff(i, j), static_cast<Scalar>(1), prec))
if(!internal::isApprox(self.coeff(i, j), static_cast<Scalar>(1), prec))
return false;
}
else
{
if(!internal::isMuchSmallerThan(this->coeff(i, j), static_cast<RealScalar>(1), prec))
if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<RealScalar>(1), prec))
return false;
}
}
@@ -759,7 +756,6 @@ 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)
{
@@ -787,8 +783,8 @@ EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
/** \brief Resizes to the given size, and writes the identity expression (not necessarily square) into *this.
*
* \param nbRows the new number of rows
* \param nbCols the new number of columns
* \param rows the new number of rows
* \param cols the new number of columns
*
* Example: \include Matrix_setIdentity_int_int.cpp
* Output: \verbinclude Matrix_setIdentity_int_int.out
@@ -796,9 +792,9 @@ EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
* \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Identity()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index nbRows, Index nbCols)
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index cols)
{
derived().resize(nbRows, nbCols);
derived().resize(rows, cols);
return setIdentity();
}

View File

@@ -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
};
};
}
@@ -56,16 +53,16 @@ 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;
EIGEN_DEVICE_FUNC
inline CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
explicit inline CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
: m_xpr(xpr), m_functor(func) {}
EIGEN_DEVICE_FUNC
@@ -92,42 +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_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar coeff(Index rowId, Index colId) const
{
return derived().functor()(derived().nestedExpression().coeff(rowId, colId));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
{
return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(rowId, colId));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
{
return derived().functor()(derived().nestedExpression().coeff(index));
}
template<int LoadMode>
EIGEN_DEVICE_FUNC
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

@@ -37,8 +37,8 @@ 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
@@ -62,8 +62,9 @@ class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename in
typedef typename CwiseUnaryViewImpl<ViewOp, MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView)
typedef typename internal::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 : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename in
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
@@ -100,38 +109,18 @@ class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
inline Scalar* data() { return &coeffRef(0); }
inline const Scalar* data() const { return &coeff(0); }
EIGEN_DEVICE_FUNC inline Scalar* data() { return &(this->coeffRef(0)); }
EIGEN_DEVICE_FUNC inline const Scalar* data() const { return &(this->coeff(0)); }
inline Index innerStride() const
EIGEN_DEVICE_FUNC inline Index innerStride() const
{
return derived().nestedExpression().innerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
}
inline Index outerStride() const
EIGEN_DEVICE_FUNC inline Index outerStride() const
{
return derived().nestedExpression().outerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
}
EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const
{
return derived().functor()(derived().nestedExpression().coeff(row, col));
}
EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
return derived().functor()(derived().nestedExpression().coeff(index));
}
EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
{
return derived().functor()(const_cast_derived().nestedExpression().coeffRef(row, col));
}
EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
return derived().functor()(const_cast_derived().nestedExpression().coeffRef(index));
}
};
} // end namespace Eigen

View File

@@ -49,22 +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;
@@ -74,16 +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;
@@ -169,16 +174,42 @@ 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. */
@@ -229,22 +260,21 @@ template<typename Derived> class DenseBase
* nothing else.
*/
EIGEN_DEVICE_FUNC
void resize(Index nbRows, Index nbCols)
void resize(Index rows, Index cols)
{
EIGEN_ONLY_USED_FOR_DEBUG(nbRows);
EIGEN_ONLY_USED_FOR_DEBUG(nbCols);
eigen_assert(nbRows == this->rows() && nbCols == this->cols()
EIGEN_ONLY_USED_FOR_DEBUG(rows);
EIGEN_ONLY_USED_FOR_DEBUG(cols);
eigen_assert(rows == this->rows() && cols == this->cols()
&& "DenseBase::resize() does not actually allow to resize.");
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal Represents a matrix with all coefficients equal to one another*/
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType;
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;
@@ -277,37 +307,34 @@ template<typename Derived> class DenseBase
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);
typedef Transpose<Derived> TransposeReturnType;
EIGEN_DEVICE_FUNC
Eigen::Transpose<Derived> transpose();
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
EIGEN_DEVICE_FUNC static const ConstantReturnType
Constant(Index rows, Index cols, const Scalar& value);
@@ -326,13 +353,13 @@ template<typename Derived> class DenseBase
LinSpaced(const Scalar& low, const Scalar& high);
template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
static const CwiseNullaryOp<CustomNullaryOp, Derived>
static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func);
template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
static const CwiseNullaryOp<CustomNullaryOp, Derived>
static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
NullaryExpr(Index size, const CustomNullaryOp& func);
template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
static const CwiseNullaryOp<CustomNullaryOp, Derived>
static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
NullaryExpr(const CustomNullaryOp& func);
EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index rows, Index cols);
@@ -378,6 +405,8 @@ 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
@@ -387,16 +416,17 @@ 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>
EIGEN_DEVICE_FUNC
void swap(const DenseBase<OtherDerived>& other,
int = OtherDerived::ThisConstantIsPrivateInPlainObjectBase)
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.
@@ -406,10 +436,10 @@ template<typename Derived> class DenseBase
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();
@@ -438,8 +468,7 @@ template<typename Derived> class DenseBase
template<typename BinaryOp>
EIGEN_DEVICE_FUNC
typename internal::result_of<BinaryOp(typename internal::traits<Derived>::Scalar)>::type
redux(const BinaryOp& func) const;
Scalar redux(const BinaryOp& func) const;
template<typename Visitor>
EIGEN_DEVICE_FUNC
@@ -465,14 +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>
@@ -490,14 +540,33 @@ template<typename Derived> class DenseBase
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"

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;
@@ -97,8 +96,8 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
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
@@ -117,7 +116,7 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
{
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
@@ -140,7 +139,7 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
coeff(Index index) const
{
eigen_internal_assert(index >= 0 && index < size());
return derived().coeff(index);
return internal::evaluator<Derived>(derived()).coeff(index);
}
@@ -159,7 +158,7 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
eigen_assert(index >= 0 && index < size());
return derived().coeff(index);
return coeff(index);
}
/** \returns the coefficient at given index.
@@ -177,7 +176,7 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
operator()(Index index) const
{
eigen_assert(index >= 0 && index < size());
return derived().coeff(index);
return coeff(index);
}
/** equivalent to operator[](0). */
@@ -217,9 +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);
}
@@ -244,8 +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:
@@ -288,7 +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;
@@ -325,8 +324,8 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
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
@@ -348,7 +347,7 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
{
eigen_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().coeffRef(row, col);
return coeffRef(row, col);
}
@@ -372,7 +371,7 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
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.
@@ -389,7 +388,7 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
eigen_assert(index >= 0 && index < size());
return derived().coeffRef(index);
return coeffRef(index);
}
/** \returns a reference to the coefficient at given index.
@@ -406,7 +405,7 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
operator()(Index index)
{
eigen_assert(index >= 0 && index < size());
return derived().coeffRef(index);
return coeffRef(index);
}
/** equivalent to operator[](0). */
@@ -432,144 +431,6 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
w() { return (*this)[3]; }
/** \internal
* Stores the given packet of coefficients, at the given row and column of this expression. It is your responsibility
* to ensure that a packet really starts there. This method is only available on expressions having the
* PacketAccessBit.
*
* The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select
* the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
* starting at an address which is a multiple of the packet size.
*/
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket
(Index row, Index col, const typename internal::packet_traits<Scalar>::type& val)
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().template writePacket<StoreMode>(row,col,val);
}
/** \internal */
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacketByOuterInner
(Index outer, Index inner, const typename internal::packet_traits<Scalar>::type& val)
{
writePacket<StoreMode>(rowIndexByOuterInner(outer, inner),
colIndexByOuterInner(outer, inner),
val);
}
/** \internal
* Stores the given packet of coefficients, at the given index in this expression. It is your responsibility
* to ensure that a packet really starts there. This method is only available on expressions having the
* PacketAccessBit and the LinearAccessBit.
*
* The \a LoadMode parameter may have the value \a Aligned or \a Unaligned. Its effect is to select
* the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
* starting at an address which is a multiple of the packet size.
*/
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket
(Index index, const typename internal::packet_traits<Scalar>::type& val)
{
eigen_internal_assert(index >= 0 && index < size());
derived().template writePacket<StoreMode>(index,val);
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal Copies the coefficient at position (row,col) of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
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_DEVICE_FUNC
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_DEVICE_FUNC
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.
@@ -589,7 +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;
@@ -664,7 +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;
@@ -723,33 +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

@@ -40,8 +40,7 @@ void check_static_allocation_size()
*/
template <typename T, int Size, int MatrixOrArrayOptions,
int Alignment = (MatrixOrArrayOptions&DontAlign) ? 0
: (((Size*sizeof(T))%EIGEN_ALIGN_BYTES)==0) ? EIGEN_ALIGN_BYTES
: 0 >
: compute_default_alignment<T,Size>::value >
struct plain_array
{
T array[Size];
@@ -81,14 +80,71 @@ struct plain_array
#endif
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, EIGEN_ALIGN_BYTES>
struct plain_array<T, Size, MatrixOrArrayOptions, 8>
{
EIGEN_USER_ALIGN_DEFAULT T array[Size];
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_ALIGN_TO_BOUNDARY(16) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(EIGEN_ALIGN_BYTES-1);
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>();
}
@@ -102,7 +158,7 @@ struct plain_array<T, Size, MatrixOrArrayOptions, EIGEN_ALIGN_BYTES>
template <typename T, int MatrixOrArrayOptions, int Alignment>
struct plain_array<T, 0, MatrixOrArrayOptions, Alignment>
{
EIGEN_USER_ALIGN_DEFAULT T array[1];
T array[1];
EIGEN_DEVICE_FUNC plain_array() {}
EIGEN_DEVICE_FUNC plain_array(constructor_without_unaligned_array_assert) {}
};
@@ -130,7 +186,7 @@ template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseSt
public:
EIGEN_DEVICE_FUNC DenseStorage() {}
EIGEN_DEVICE_FUNC
DenseStorage(internal::constructor_without_unaligned_array_assert)
explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()) {}
EIGEN_DEVICE_FUNC
DenseStorage(const DenseStorage& other) : m_data(other.m_data) {}
@@ -140,12 +196,18 @@ template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseSt
if (this != &other) m_data = other.m_data;
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
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 DenseIndex rows(void) {return _Rows;}
EIGEN_DEVICE_FUNC static DenseIndex cols(void) {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {}
EIGEN_DEVICE_FUNC void resize(DenseIndex,DenseIndex,DenseIndex) {}
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; }
};
@@ -155,15 +217,15 @@ template<typename T, int _Rows, int _Cols, int _Options> class DenseStorage<T, 0
{
public:
EIGEN_DEVICE_FUNC DenseStorage() {}
EIGEN_DEVICE_FUNC DenseStorage(internal::constructor_without_unaligned_array_assert) {}
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(DenseIndex,DenseIndex,DenseIndex) {}
EIGEN_DEVICE_FUNC DenseStorage(Index,Index,Index) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& ) {}
EIGEN_DEVICE_FUNC static DenseIndex rows(void) {return _Rows;}
EIGEN_DEVICE_FUNC static DenseIndex cols(void) {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {}
EIGEN_DEVICE_FUNC void resize(DenseIndex,DenseIndex,DenseIndex) {}
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; }
};
@@ -182,14 +244,14 @@ 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:
EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0), m_cols(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows), m_cols(other.m_cols) {}
DenseStorage& operator=(const DenseStorage& other)
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows), m_cols(other.m_cols) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
@@ -199,13 +261,13 @@ template<typename T, int Size, int _Options> class DenseStorage<T, Size, Dynamic
}
return *this;
}
DenseStorage(DenseIndex, DenseIndex nbRows, DenseIndex nbCols) : m_rows(nbRows), m_cols(nbCols) {}
void swap(DenseStorage& other)
EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index cols) : m_rows(rows), m_cols(cols) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
{ std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
EIGEN_DEVICE_FUNC DenseIndex rows() const {return m_rows;}
EIGEN_DEVICE_FUNC DenseIndex cols() const {return m_cols;}
void conservativeResize(DenseIndex, DenseIndex nbRows, DenseIndex nbCols) { m_rows = nbRows; m_cols = nbCols; }
void resize(DenseIndex, DenseIndex nbRows, DenseIndex nbCols) { m_rows = nbRows; m_cols = nbCols; }
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; }
};
@@ -214,13 +276,13 @@ template<typename T, int Size, int _Options> class DenseStorage<T, Size, Dynamic
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:
EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {}
DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows) {}
DenseStorage& operator=(const DenseStorage& other)
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
@@ -229,12 +291,12 @@ template<typename T, int Size, int _Cols, int _Options> class DenseStorage<T, Si
}
return *this;
}
DenseStorage(DenseIndex, DenseIndex nbRows, DenseIndex) : m_rows(nbRows) {}
void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
EIGEN_DEVICE_FUNC DenseIndex rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC DenseIndex cols(void) const {return _Cols;}
void conservativeResize(DenseIndex, DenseIndex nbRows, DenseIndex) { m_rows = nbRows; }
void resize(DenseIndex, DenseIndex nbRows, DenseIndex) { m_rows = nbRows; }
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; }
};
@@ -243,13 +305,13 @@ template<typename T, int Size, int _Cols, int _Options> class DenseStorage<T, Si
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:
EIGEN_DEVICE_FUNC DenseStorage() : m_cols(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {}
DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_cols(other.m_cols) {}
DenseStorage& operator=(const DenseStorage& other)
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_cols(other.m_cols) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
@@ -258,12 +320,12 @@ template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Si
}
return *this;
}
DenseStorage(DenseIndex, DenseIndex, DenseIndex nbCols) : m_cols(nbCols) {}
void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
EIGEN_DEVICE_FUNC DenseIndex rows(void) const {return _Rows;}
EIGEN_DEVICE_FUNC DenseIndex cols(void) const {return m_cols;}
void conservativeResize(DenseIndex, DenseIndex, DenseIndex nbCols) { m_cols = nbCols; }
void resize(DenseIndex, DenseIndex, DenseIndex nbCols) { m_cols = nbCols; }
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; }
};
@@ -272,23 +334,26 @@ template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Si
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:
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(0), m_rows(0), m_cols(0) {}
DenseStorage(DenseIndex size, DenseIndex nbRows, DenseIndex nbCols)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(nbRows), m_cols(nbCols)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
DenseStorage(const 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);
}
DenseStorage& operator=(const DenseStorage& other)
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
@@ -298,13 +363,17 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
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;
@@ -314,18 +383,18 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
return *this;
}
#endif
~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
void swap(DenseStorage& other)
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
{ std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
EIGEN_DEVICE_FUNC DenseIndex rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC DenseIndex cols(void) const {return m_cols;}
void conservativeResize(DenseIndex size, DenseIndex nbRows, DenseIndex nbCols)
EIGEN_DEVICE_FUNC Index rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;}
void conservativeResize(Index size, Index rows, Index cols)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*m_cols);
m_rows = nbRows;
m_cols = nbCols;
m_rows = rows;
m_cols = cols;
}
void resize(DenseIndex size, DenseIndex nbRows, DenseIndex nbCols)
EIGEN_DEVICE_FUNC void resize(Index size, Index rows, Index cols)
{
if(size != m_rows*m_cols)
{
@@ -336,8 +405,8 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
}
m_rows = nbRows;
m_cols = nbCols;
m_rows = rows;
m_cols = cols;
}
EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC T *data() { return m_data; }
@@ -347,19 +416,23 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Rows, Dynamic, _Options>
{
T *m_data;
DenseIndex m_cols;
Index m_cols;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_cols(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
DenseStorage(DenseIndex size, DenseIndex, DenseIndex nbCols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(nbCols)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
DenseStorage(const DenseStorage& other)
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);
}
DenseStorage& operator=(const DenseStorage& other)
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
@@ -369,12 +442,15 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
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;
@@ -383,16 +459,16 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
return *this;
}
#endif
~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
EIGEN_DEVICE_FUNC static DenseIndex rows(void) {return _Rows;}
EIGEN_DEVICE_FUNC DenseIndex cols(void) const {return m_cols;}
void conservativeResize(DenseIndex size, DenseIndex, DenseIndex nbCols)
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
EIGEN_DEVICE_FUNC static Index rows(void) {return _Rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index size, Index, Index cols)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, _Rows*m_cols);
m_cols = nbCols;
m_cols = cols;
}
EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex, DenseIndex nbCols)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index, Index cols)
{
if(size != _Rows*m_cols)
{
@@ -403,7 +479,7 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
}
m_cols = nbCols;
m_cols = cols;
}
EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC T *data() { return m_data; }
@@ -413,19 +489,23 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dynamic, _Cols, _Options>
{
T *m_data;
DenseIndex m_rows;
Index m_rows;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
DenseStorage(DenseIndex size, DenseIndex nbRows, DenseIndex) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(nbRows)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
DenseStorage(const DenseStorage& other)
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);
}
DenseStorage& operator=(const DenseStorage& other)
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
@@ -435,12 +515,15 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
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;
@@ -449,16 +532,16 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
return *this;
}
#endif
~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
EIGEN_DEVICE_FUNC DenseIndex rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC static DenseIndex cols(void) {return _Cols;}
void conservativeResize(DenseIndex size, DenseIndex nbRows, DenseIndex)
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
EIGEN_DEVICE_FUNC Index rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC static Index cols(void) {return _Cols;}
void conservativeResize(Index size, Index rows, Index)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*_Cols);
m_rows = nbRows;
m_rows = rows;
}
EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex nbRows, DenseIndex)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index rows, Index)
{
if(size != m_rows*_Cols)
{
@@ -469,7 +552,7 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
}
m_rows = nbRows;
m_rows = rows;
}
EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC T *data() { return m_data; }

View File

@@ -37,7 +37,7 @@ template<typename MatrixType, int DiagIndex>
struct traits<Diagonal<MatrixType,DiagIndex> >
: traits<MatrixType>
{
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
typedef typename MatrixType::StorageKind StorageKind;
enum {
@@ -52,8 +52,7 @@ struct traits<Diagonal<MatrixType,DiagIndex> >
MatrixType::MaxColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
MaxColsAtCompileTime = 1,
MaskLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
Flags = (unsigned int)_MatrixTypeNested::Flags & (HereditaryBits | LinearAccessBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit,
CoeffReadCost = _MatrixTypeNested::CoeffReadCost,
Flags = (unsigned int)_MatrixTypeNested::Flags & (RowMajorBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit, // FIXME DirectAccessBit should not be handled by expressions
MatrixTypeOuterStride = outer_stride_at_compile_time<MatrixType>::ret,
InnerStrideAtCompileTime = MatrixTypeOuterStride == Dynamic ? Dynamic : MatrixTypeOuterStride+1,
OuterStrideAtCompileTime = 0
@@ -71,17 +70,15 @@ template<typename MatrixType, int _DiagIndex> class Diagonal
EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
EIGEN_DEVICE_FUNC
inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index) {}
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
{
EIGEN_USING_STD_MATH(min);
return m_index.value()<0 ? (min)(Index(m_matrix.cols()),Index(m_matrix.rows()+m_index.value()))
: (min)(Index(m_matrix.rows()),Index(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
@@ -149,14 +146,14 @@ template<typename MatrixType, int _DiagIndex> class Diagonal
}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename MatrixType::Nested>::type&
inline const typename internal::remove_all<typename MatrixType::Nested>::type&
nestedExpression() const
{
return m_matrix;
}
EIGEN_DEVICE_FUNC
int index() const
inline Index index() const
{
return m_index.value();
}
@@ -173,7 +170,7 @@ template<typename MatrixType, int _DiagIndex> class Diagonal
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;
};
@@ -190,7 +187,7 @@ template<typename Derived>
inline typename MatrixBase<Derived>::DiagonalReturnType
MatrixBase<Derived>::diagonal()
{
return derived();
return DiagonalReturnType(derived());
}
/** This is the const version of diagonal(). */
@@ -213,18 +210,18 @@ MatrixBase<Derived>::diagonal() const
*
* \sa MatrixBase::diagonal(), class Diagonal */
template<typename Derived>
inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<DynamicIndex>::Type
inline typename MatrixBase<Derived>::DiagonalDynamicIndexReturnType
MatrixBase<Derived>::diagonal(Index index)
{
return typename DiagonalIndexReturnType<DynamicIndex>::Type(derived(), index);
return DiagonalDynamicIndexReturnType(derived(), index);
}
/** This is the const version of diagonal(Index). */
template<typename Derived>
inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<DynamicIndex>::Type
inline typename MatrixBase<Derived>::ConstDiagonalDynamicIndexReturnType
MatrixBase<Derived>::diagonal(Index index) const
{
return typename ConstDiagonalIndexReturnType<DynamicIndex>::Type(derived(), index);
return ConstDiagonalDynamicIndexReturnType(derived(), index);
}
/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
@@ -239,20 +236,20 @@ MatrixBase<Derived>::diagonal(Index index) const
*
* \sa MatrixBase::diagonal(), class Diagonal */
template<typename Derived>
template<int Index>
inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Index>::Type
template<int Index_>
inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Index_>::Type
MatrixBase<Derived>::diagonal()
{
return derived();
return typename DiagonalIndexReturnType<Index_>::Type(derived());
}
/** This is the const version of diagonal<int>(). */
template<typename Derived>
template<int Index>
inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Index>::Type
template<int Index_>
inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Index_>::Type
MatrixBase<Derived>::diagonal() const
{
return derived();
return typename ConstDiagonalIndexReturnType<Index_>::Type(derived());
}
} // end namespace Eigen

View File

@@ -22,7 +22,7 @@ class DiagonalBase : public EigenBase<Derived>
typedef typename DiagonalVectorType::Scalar Scalar;
typedef typename DiagonalVectorType::RealScalar RealScalar;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
enum {
RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
@@ -30,7 +30,7 @@ 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;
@@ -44,18 +44,7 @@ class DiagonalBase : public EigenBase<Derived>
EIGEN_DEVICE_FUNC
DenseMatrixType toDenseMatrix() const { return derived(); }
template<typename DenseDerived>
EIGEN_DEVICE_FUNC
void evalTo(MatrixBase<DenseDerived> &other) const;
template<typename DenseDerived>
EIGEN_DEVICE_FUNC
void addTo(MatrixBase<DenseDerived> &other) const
{ other.diagonal() += diagonal(); }
template<typename DenseDerived>
EIGEN_DEVICE_FUNC
void subTo(MatrixBase<DenseDerived> &other) const
{ other.diagonal() -= diagonal(); }
EIGEN_DEVICE_FUNC
inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); }
EIGEN_DEVICE_FUNC
@@ -66,44 +55,37 @@ class DiagonalBase : public EigenBase<Derived>
EIGEN_DEVICE_FUNC
inline Index cols() const { return diagonal().size(); }
/** \returns the diagonal matrix product of \c *this by the matrix \a matrix.
*/
template<typename MatrixDerived>
EIGEN_DEVICE_FUNC
const DiagonalProduct<MatrixDerived, Derived, OnTheLeft>
const Product<Derived,MatrixDerived,LazyProduct>
operator*(const MatrixBase<MatrixDerived> &matrix) const
{
return DiagonalProduct<MatrixDerived, Derived, OnTheLeft>(matrix.derived(), derived());
return Product<Derived, MatrixDerived, LazyProduct>(derived(),matrix.derived());
}
typedef DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> > InverseReturnType;
EIGEN_DEVICE_FUNC
inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> >
inline const InverseReturnType
inverse() const
{
return diagonal().cwiseInverse();
return InverseReturnType(diagonal().cwiseInverse());
}
typedef DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> > ScalarMultipleReturnType;
EIGEN_DEVICE_FUNC
inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> >
inline const ScalarMultipleReturnType
operator*(const Scalar& scalar) const
{
return diagonal() * scalar;
return ScalarMultipleReturnType(diagonal() * scalar);
}
EIGEN_DEVICE_FUNC
friend inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> >
friend inline const ScalarMultipleReturnType
operator*(const Scalar& scalar, const DiagonalBase& other)
{
return other.diagonal() * scalar;
return ScalarMultipleReturnType(other.diagonal() * scalar);
}
};
template<typename Derived>
template<typename DenseDerived>
void DiagonalBase<Derived>::evalTo(MatrixBase<DenseDerived> &other) const
{
other.setZero();
other.diagonal() = diagonal();
}
#endif
/** \class DiagonalMatrix
@@ -125,10 +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
};
};
}
@@ -142,7 +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:
@@ -164,7 +145,7 @@ class DiagonalMatrix
/** Constructs a diagonal matrix with given dimension */
EIGEN_DEVICE_FUNC
inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
explicit inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
/** 2D constructor. */
EIGEN_DEVICE_FUNC
@@ -248,14 +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
};
};
}
@@ -272,7 +254,7 @@ class DiagonalWrapper
/** Constructor from expression of diagonal coefficients to wrap. */
EIGEN_DEVICE_FUNC
inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_diagonal) {}
explicit inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_diagonal) {}
/** \returns a const reference to the wrapped expression of diagonal coefficients. */
EIGEN_DEVICE_FUNC
@@ -295,7 +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,
@@ -326,6 +308,33 @@ bool MatrixBase<Derived>::isDiagonal(const RealScalar& prec) const
return true;
}
namespace internal {
template<> struct storage_kind_to_shape<DiagonalShape> { typedef DiagonalShape Shape; };
struct Diagonal2Dense {};
template<> struct AssignmentKind<DenseShape,DiagonalShape> { typedef Diagonal2Dense Kind; };
// Diagonal matrix to Dense assignment
template< typename DstXprType, typename SrcXprType, typename Functor, 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

View File

@@ -13,116 +13,14 @@
namespace Eigen {
namespace internal {
template<typename MatrixType, typename DiagonalType, int ProductOrder>
struct traits<DiagonalProduct<MatrixType, DiagonalType, ProductOrder> >
: traits<MatrixType>
{
typedef typename scalar_product_traits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar;
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
_StorageOrder = MatrixType::Flags & RowMajorBit ? RowMajor : ColMajor,
_ScalarAccessOnDiag = !((int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheLeft)
||(int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheRight)),
_SameTypes = is_same<typename MatrixType::Scalar, typename DiagonalType::Scalar>::value,
// FIXME currently we need same types, but in the future the next rule should be the one
//_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagonalType::DiagonalVectorType::Flags)&PacketAccessBit))),
_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && _SameTypes && (_ScalarAccessOnDiag || (bool(int(DiagonalType::DiagonalVectorType::Flags)&PacketAccessBit))),
_LinearAccessMask = (RowsAtCompileTime==1 || ColsAtCompileTime==1) ? LinearAccessBit : 0,
Flags = ((HereditaryBits|_LinearAccessMask) & (unsigned int)(MatrixType::Flags)) | (_Vectorizable ? PacketAccessBit : 0) | AlignedBit,//(int(MatrixType::Flags)&int(DiagonalType::DiagonalVectorType::Flags)&AlignedBit),
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()));
}
EIGEN_STRONG_INLINE Index rows() const { return m_matrix.rows(); }
EIGEN_STRONG_INLINE Index cols() const { return m_matrix.cols(); }
EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
{
return m_diagonal.diagonal().coeff(ProductOrder == OnTheLeft ? row : col) * m_matrix.coeff(row, col);
}
EIGEN_STRONG_INLINE const Scalar coeff(Index idx) const
{
enum {
StorageOrder = int(MatrixType::Flags) & RowMajorBit ? RowMajor : ColMajor
};
return coeff(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
{
enum {
StorageOrder = Flags & RowMajorBit ? RowMajor : ColMajor
};
const Index indexInDiagonalVector = ProductOrder == OnTheLeft ? row : col;
return packet_impl<LoadMode>(row,col,indexInDiagonalVector,typename internal::conditional<
((int(StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft)
||(int(StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)), internal::true_type, internal::false_type>::type());
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index idx) const
{
enum {
StorageOrder = int(MatrixType::Flags) & RowMajorBit ? RowMajor : ColMajor
};
return packet<LoadMode>(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
}
protected:
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::true_type) const
{
return internal::pmul(m_matrix.template packet<LoadMode>(row, col),
internal::pset1<PacketScalar>(m_diagonal.diagonal().coeff(id)));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::false_type) const
{
enum {
InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
DiagonalVectorPacketLoadMode = (LoadMode == Aligned && (((InnerSize%16) == 0) || (int(DiagonalType::DiagonalVectorType::Flags)&AlignedBit)==AlignedBit) ? Aligned : Unaligned)
};
return internal::pmul(m_matrix.template packet<LoadMode>(row, col),
m_diagonal.diagonal().template packet<DiagonalVectorPacketLoadMode>(id));
}
typename MatrixType::Nested m_matrix;
typename DiagonalType::Nested m_diagonal;
};
/** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal.
*/
template<typename Derived>
template<typename DiagonalDerived>
inline const DiagonalProduct<Derived, DiagonalDerived, OnTheRight>
inline const Product<Derived, DiagonalDerived, LazyProduct>
MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &a_diagonal) const
{
return DiagonalProduct<Derived, DiagonalDerived, OnTheRight>(derived(), a_diagonal.derived());
return Product<Derived, DiagonalDerived, LazyProduct>(derived(),a_diagonal.derived());
}
} // end namespace Eigen

View File

@@ -99,7 +99,7 @@ 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
{
using std::sqrt;
EIGEN_USING_STD_MATH(sqrt)
return sqrt(squaredNorm());
}
@@ -113,8 +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();
}
@@ -142,7 +141,7 @@ struct lpNorm_selector
EIGEN_DEVICE_FUNC
static inline RealScalar run(const MatrixBase<Derived>& m)
{
using std::pow;
EIGEN_USING_STD_MATH(pow)
return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p);
}
};
@@ -206,8 +205,8 @@ template<typename OtherDerived>
bool MatrixBase<Derived>::isOrthogonal
(const MatrixBase<OtherDerived>& other, const RealScalar& prec) const
{
typename internal::nested<Derived,2>::type nested(derived());
typename internal::nested<OtherDerived,2>::type otherNested(other.derived());
typename internal::nested_eval<Derived,2>::type nested(derived());
typename internal::nested_eval<OtherDerived,2>::type otherNested(other.derived());
return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
}
@@ -225,13 +224,13 @@ bool MatrixBase<Derived>::isOrthogonal
template<typename Derived>
bool MatrixBase<Derived>::isUnitary(const RealScalar& prec) const
{
typename Derived::Nested nested(derived());
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index i = 0; i < cols(); ++i)
{
if(!internal::isApprox(nested.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
if(!internal::isApprox(self.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
return false;
for(Index j = 0; j < i; ++j)
if(!internal::isMuchSmallerThan(nested.col(i).dot(nested.col(j)), static_cast<Scalar>(1), prec))
if(!internal::isMuchSmallerThan(self.col(i).dot(self.col(j)), static_cast<Scalar>(1), prec))
return false;
}
return true;

View File

@@ -13,7 +13,9 @@
namespace Eigen {
/** Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T).
/** \class EigenBase
*
* Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T).
*
* In other words, an EigenBase object is an object that can be copied into a MatrixBase.
*
@@ -26,9 +28,16 @@ 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
@@ -121,7 +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();
}
@@ -129,7 +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();
}
@@ -137,7 +146,7 @@ template<typename Derived>
template<typename OtherDerived>
Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
{
other.derived().subTo(derived());
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar>());
return derived();
}

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@@ -1,140 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_FLAGGED_H
#define EIGEN_FLAGGED_H
namespace Eigen {
/** \class Flagged
* \ingroup Core_Module
*
* \brief Expression with modified flags
*
* \param ExpressionType the type of the object of which we are modifying the flags
* \param Added the flags added to the expression
* \param Removed the flags removed from the expression (has priority over Added).
*
* This class represents an expression whose flags have been modified.
* It is the return type of MatrixBase::flagged()
* and most of the time this is the only way it is used.
*
* \sa MatrixBase::flagged()
*/
namespace internal {
template<typename ExpressionType, unsigned int Added, unsigned int Removed>
struct traits<Flagged<ExpressionType, Added, Removed> > : traits<ExpressionType>
{
enum { Flags = (ExpressionType::Flags | Added) & ~Removed };
};
}
template<typename ExpressionType, unsigned int Added, unsigned int Removed> class Flagged
: public MatrixBase<Flagged<ExpressionType, Added, Removed> >
{
public:
typedef MatrixBase<Flagged> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Flagged)
typedef typename internal::conditional<internal::must_nest_by_value<ExpressionType>::ret,
ExpressionType, const ExpressionType&>::type ExpressionTypeNested;
typedef typename ExpressionType::InnerIterator InnerIterator;
inline Flagged(const ExpressionType& matrix) : m_matrix(matrix) {}
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
inline Index outerStride() const { return m_matrix.outerStride(); }
inline Index innerStride() const { return m_matrix.innerStride(); }
inline CoeffReturnType coeff(Index row, Index col) const
{
return m_matrix.coeff(row, col);
}
inline CoeffReturnType coeff(Index index) const
{
return m_matrix.coeff(index);
}
inline const Scalar& coeffRef(Index row, Index col) const
{
return m_matrix.const_cast_derived().coeffRef(row, col);
}
inline const Scalar& coeffRef(Index index) const
{
return m_matrix.const_cast_derived().coeffRef(index);
}
inline Scalar& coeffRef(Index row, Index col)
{
return m_matrix.const_cast_derived().coeffRef(row, col);
}
inline Scalar& coeffRef(Index index)
{
return m_matrix.const_cast_derived().coeffRef(index);
}
template<int LoadMode>
inline const PacketScalar packet(Index row, Index col) const
{
return m_matrix.template packet<LoadMode>(row, col);
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
{
m_matrix.const_cast_derived().template writePacket<LoadMode>(row, col, x);
}
template<int LoadMode>
inline const PacketScalar packet(Index index) const
{
return m_matrix.template packet<LoadMode>(index);
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
{
m_matrix.const_cast_derived().template writePacket<LoadMode>(index, x);
}
const ExpressionType& _expression() const { return m_matrix; }
template<typename OtherDerived>
typename ExpressionType::PlainObject solveTriangular(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived>
void solveTriangularInPlace(const MatrixBase<OtherDerived>& other) const;
protected:
ExpressionTypeNested m_matrix;
};
/** \returns an expression of *this with added and removed flags
*
* This is mostly for internal use.
*
* \sa class Flagged
*/
template<typename Derived>
template<unsigned int Added,unsigned int Removed>
inline const Flagged<Derived, Added, Removed>
DenseBase<Derived>::flagged() const
{
return derived();
}
} // end namespace Eigen
#endif // EIGEN_FLAGGED_H

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@@ -39,29 +39,29 @@ template<typename ExpressionType> class ForceAlignedAccess
typedef typename internal::dense_xpr_base<ForceAlignedAccess>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(ForceAlignedAccess)
inline ForceAlignedAccess(const ExpressionType& matrix) : m_expression(matrix) {}
EIGEN_DEVICE_FUNC explicit inline ForceAlignedAccess(const ExpressionType& matrix) : m_expression(matrix) {}
inline Index rows() const { return m_expression.rows(); }
inline Index cols() const { return m_expression.cols(); }
inline Index outerStride() const { return m_expression.outerStride(); }
inline Index innerStride() const { return m_expression.innerStride(); }
EIGEN_DEVICE_FUNC inline Index rows() const { return m_expression.rows(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_expression.cols(); }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return m_expression.outerStride(); }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return m_expression.innerStride(); }
inline const CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index row, Index col) const
{
return m_expression.coeff(row, col);
}
inline Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index col)
{
return m_expression.const_cast_derived().coeffRef(row, col);
}
inline const CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index index) const
{
return m_expression.coeff(index);
}
inline Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
}
@@ -90,7 +90,7 @@ template<typename ExpressionType> class ForceAlignedAccess
m_expression.const_cast_derived().template writePacket<Aligned>(index, x);
}
operator const ExpressionType&() const { return m_expression; }
EIGEN_DEVICE_FUNC operator const ExpressionType&() const { return m_expression; }
protected:
const ExpressionType& m_expression;
@@ -127,7 +127,7 @@ template<bool Enable>
inline typename internal::add_const_on_value_type<typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type>::type
MatrixBase<Derived>::forceAlignedAccessIf() const
{
return derived();
return derived(); // FIXME This should not work but apparently is never used
}
/** \returns an expression of *this with forced aligned access if \a Enable is true.
@@ -138,7 +138,7 @@ template<bool Enable>
inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type
MatrixBase<Derived>::forceAlignedAccessIf()
{
return derived();
return derived(); // FIXME This should not work but apparently is never used
}
} // end namespace Eigen

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@@ -22,10 +22,9 @@ struct isApprox_selector
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
{
EIGEN_USING_STD_MATH(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());
}
};

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@@ -11,29 +11,7 @@
#ifndef EIGEN_GENERAL_PRODUCT_H
#define EIGEN_GENERAL_PRODUCT_H
namespace Eigen {
/** \class GeneralProduct
* \ingroup Core_Module
*
* \brief Expression of the product of two general matrices or vectors
*
* \param LhsNested the type used to store the left-hand side
* \param RhsNested the type used to store the right-hand side
* \param ProductMode the type of the product
*
* This class represents an expression of the product of two general matrices.
* We call a general matrix, a dense matrix with full storage. For instance,
* This excludes triangular, selfadjoint, and sparse matrices.
* It is the return type of the operator* between general matrices. Its template
* arguments are determined automatically by ProductReturnType. Therefore,
* GeneralProduct should never be used direclty. To determine the result type of a
* function which involves a matrix product, use ProductReturnType::Type.
*
* \sa ProductReturnType, MatrixBase::operator*(const MatrixBase<OtherDerived>&)
*/
template<typename Lhs, typename Rhs, int ProductType = internal::product_type<Lhs,Rhs>::value>
class GeneralProduct;
namespace Eigen {
enum {
Large = 2,
@@ -59,14 +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)
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,
@@ -81,7 +59,8 @@ private:
public:
enum {
value = selector::ret
value = selector::ret,
ret = selector::ret
};
#ifdef EIGEN_DEBUG_PRODUCT
static void debug()
@@ -97,6 +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.
@@ -127,54 +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
***********************************************************************/
@@ -186,119 +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 {
// Column major
template<typename ProductType, typename Dest, typename Func>
EIGEN_DONT_INLINE void outer_product_selector_run(const ProductType& prod, Dest& dest, const Func& func, const false_type&)
{
typedef typename Dest::Index Index;
// FIXME make sure lhs is sequentially stored
// FIXME not very good if rhs is real and lhs complex while alpha is real too
const Index cols = dest.cols();
for (Index j=0; j<cols; ++j)
func(dest.col(j), prod.rhs().coeff(0,j) * prod.lhs());
}
// Row major
template<typename ProductType, typename Dest, typename Func>
EIGEN_DONT_INLINE void outer_product_selector_run(const ProductType& prod, Dest& dest, const Func& func, const true_type&) {
typedef typename Dest::Index Index;
// FIXME make sure rhs is sequentially stored
// FIXME not very good if lhs is real and rhs complex while alpha is real too
const Index rows = dest.rows();
for (Index i=0; i<rows; ++i)
func(dest.row(i), prod.lhs().coeff(i,0) * prod.rhs());
}
template<typename Lhs, typename Rhs>
struct traits<GeneralProduct<Lhs,Rhs,OuterProduct> >
: traits<ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs> >
{};
}
template<typename Lhs, typename Rhs>
class GeneralProduct<Lhs, Rhs, OuterProduct>
: public ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs>
{
template<typename T> struct IsRowMajor : internal::conditional<(int(T::Flags)&RowMajorBit), internal::true_type, internal::false_type>::type {};
public:
EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct)
GeneralProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
{
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)
}
struct set { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() = src; } };
struct add { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() += src; } };
struct sub { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() -= src; } };
struct adds {
Scalar m_scale;
adds(const Scalar& s) : m_scale(s) {}
template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const {
dst.const_cast_derived() += m_scale * src;
}
};
template<typename Dest>
inline void evalTo(Dest& dest) const {
internal::outer_product_selector_run(*this, dest, set(), IsRowMajor<Dest>());
}
template<typename Dest>
inline void addTo(Dest& dest) const {
internal::outer_product_selector_run(*this, dest, add(), IsRowMajor<Dest>());
}
template<typename Dest>
inline void subTo(Dest& dest) const {
internal::outer_product_selector_run(*this, dest, sub(), IsRowMajor<Dest>());
}
template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const
{
internal::outer_product_selector_run(*this, dest, adds(alpha), IsRowMajor<Dest>());
}
};
/***********************************************************************
* Implementation of General Matrix Vector Product
***********************************************************************/
@@ -312,60 +159,13 @@ class GeneralProduct<Lhs, Rhs, OuterProduct>
*/
namespace internal {
template<typename Lhs, typename Rhs>
struct traits<GeneralProduct<Lhs,Rhs,GemvProduct> >
: traits<ProductBase<GeneralProduct<Lhs,Rhs,GemvProduct>, Lhs, Rhs> >
{};
template<int Side, int StorageOrder, bool BlasCompatible>
struct gemv_selector;
struct gemv_dense_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& a_lhs, const Rhs& a_rhs) : Base(a_lhs,a_rhs)
{
// EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::Scalar, typename Rhs::Scalar>::value),
// YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
}
enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight };
typedef typename internal::conditional<int(Side)==OnTheRight,_LhsNested,_RhsNested>::type MatrixType;
template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const
{
eigen_assert(m_lhs.rows() == dst.rows() && m_rhs.cols() == dst.cols());
internal::gemv_selector<Side,(int(MatrixType::Flags)&RowMajorBit) ? RowMajor : ColMajor,
bool(internal::blas_traits<MatrixType>::HasUsableDirectAccess)>::run(*this, dst, alpha);
}
};
namespace internal {
// The vector is on the left => transposition
template<int StorageOrder, bool BlasCompatible>
struct gemv_selector<OnTheLeft,StorageOrder,BlasCompatible>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha)
{
Transpose<Dest> destT(dest);
enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor };
gemv_selector<OnTheRight,OtherStorageOrder,BlasCompatible>
::run(GeneralProduct<Transpose<const typename ProductType::_RhsNested>,Transpose<const typename ProductType::_LhsNested>, GemvProduct>
(prod.rhs().transpose(), prod.lhs().transpose()), destT, alpha);
}
};
template<typename Scalar,int Size,int MaxSize,bool Cond> struct gemv_static_vector_if;
template<typename Scalar,int Size,int MaxSize>
@@ -383,7 +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
@@ -396,33 +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(EIGEN_ALIGN_BYTES-1))) + EIGEN_ALIGN_BYTES)
? 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, const typename ProductType::Scalar& alpha)
template<typename Lhs, typename Rhs, typename Dest>
static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename ProductType::Index Index;
typedef typename ProductType::LhsScalar LhsScalar;
typedef typename ProductType::RhsScalar RhsScalar;
typedef typename ProductType::Scalar ResScalar;
typedef typename ProductType::RealScalar RealScalar;
typedef typename ProductType::ActualLhsType ActualLhsType;
typedef typename ProductType::ActualRhsType ActualRhsType;
typedef typename ProductType::LhsBlasTraits LhsBlasTraits;
typedef typename ProductType::RhsBlasTraits RhsBlasTraits;
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
@@ -434,14 +249,14 @@ template<> struct gemv_selector<OnTheRight,ColMajor,true>
gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
bool alphaIsCompatible = (!ComplexByReal) || (numext::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
@@ -457,11 +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);
@@ -475,34 +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, const typename ProductType::Scalar& alpha)
template<typename Lhs, typename Rhs, typename Dest>
static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename ProductType::LhsScalar LhsScalar;
typedef typename ProductType::RhsScalar RhsScalar;
typedef typename ProductType::Scalar ResScalar;
typedef typename ProductType::Index Index;
typedef typename ProductType::ActualLhsType ActualLhsType;
typedef typename ProductType::ActualRhsType ActualRhsType;
typedef typename ProductType::_ActualRhsType _ActualRhsType;
typedef typename ProductType::LhsBlasTraits LhsBlasTraits;
typedef typename ProductType::RhsBlasTraits RhsBlasTraits;
typedef typename Lhs::Scalar LhsScalar;
typedef typename Rhs::Scalar RhsScalar;
typedef typename Dest::Scalar ResScalar;
typedef internal::blas_traits<Lhs> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(prod.lhs());
typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(prod.rhs());
typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(lhs);
typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(rhs);
ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs())
* RhsBlasTraits::extractScalarFactor(prod.rhs());
ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(lhs)
* RhsBlasTraits::extractScalarFactor(rhs);
enum {
// FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
// on, the other hand it is good for the cache to pack the vector anyways...
DirectlyUseRhs = _ActualRhsType::InnerStrideAtCompileTime==1
DirectlyUseRhs = ActualRhsTypeCleaned::InnerStrideAtCompileTime==1
};
gemv_static_vector_if<RhsScalar,_ActualRhsType::SizeAtCompileTime,_ActualRhsType::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs;
gemv_static_vector_if<RhsScalar,ActualRhsTypeCleaned::SizeAtCompileTime,ActualRhsTypeCleaned::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs;
ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(),
DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data());
@@ -513,42 +330,42 @@ template<> struct gemv_selector<OnTheRight,RowMajor,true>
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, const typename ProductType::Scalar& alpha)
template<typename Lhs, typename Rhs, typename Dest>
static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename Dest::Index Index;
// TODO makes sure dest is sequentially stored in memory, otherwise use a temp
const Index size = prod.rhs().rows();
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, const typename ProductType::Scalar& alpha)
template<typename Lhs, typename Rhs, typename Dest>
static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename Dest::Index Index;
// TODO makes sure rhs is sequentially stored in memory, otherwise use a temp
const Index rows = prod.rows();
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();
}
};
@@ -566,7 +383,6 @@ template<> struct gemv_selector<OnTheRight,RowMajor,false>
*/
#ifndef __CUDACC__
#ifdef EIGEN_TEST_EVALUATORS
template<typename Derived>
template<typename OtherDerived>
inline const Product<Derived, OtherDerived>
@@ -594,42 +410,12 @@ MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
#ifdef EIGEN_DEBUG_PRODUCT
internal::product_type<Derived,OtherDerived>::debug();
#endif
return Product<Derived, OtherDerived>(derived(), other.derived());
}
#else
template<typename Derived>
template<typename OtherDerived>
inline const typename ProductReturnType<Derived, OtherDerived>::Type
MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
{
// A note regarding the function declaration: In MSVC, this function will sometimes
// not be inlined since DenseStorage is an unwindable object for dynamic
// matrices and product types are holding a member to store the result.
// Thus it does not help tagging this function with EIGEN_STRONG_INLINE.
enum {
ProductIsValid = Derived::ColsAtCompileTime==Dynamic
|| OtherDerived::RowsAtCompileTime==Dynamic
|| int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime),
AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived)
};
// note to the lost user:
// * for a dot product use: v1.dot(v2)
// * for a coeff-wise product use: v1.cwiseProduct(v2)
EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
#ifdef EIGEN_DEBUG_PRODUCT
internal::product_type<Derived,OtherDerived>::debug();
#endif
return typename ProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
}
#endif
#endif
#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
@@ -643,7 +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 {
@@ -662,7 +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

@@ -49,16 +49,20 @@ struct default_packet_traits
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,
@@ -66,7 +70,14 @@ 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
};
};
@@ -94,6 +105,30 @@ 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> EIGEN_DEVICE_FUNC inline Packet
padd(const Packet& a,
@@ -126,17 +161,21 @@ pdiv(const Packet& a,
/** \internal \returns the min of \a a and \a b (coeff-wise) */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pmin(const Packet& a,
const Packet& b) { EIGEN_USING_STD_MATH(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> EIGEN_DEVICE_FUNC inline Packet
pmax(const Packet& a,
const Packet& b) { EIGEN_USING_STD_MATH(max); return (max)(a, b); }
const Packet& b) { return numext::maxi(a, b); }
/** \internal \returns the absolute value of \a 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> EIGEN_DEVICE_FUNC inline Packet
pand(const Packet& a, const Packet& b) { return a & b; }
@@ -222,8 +261,8 @@ inline void pbroadcast2(const typename unpacket_traits<Packet>::type *a,
}
/** \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> EIGEN_DEVICE_FUNC inline void pstore(Scalar* to, const Packet& from)
@@ -233,17 +272,25 @@ template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstore(
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, DenseIndex /*stride*/)
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, DenseIndex /*stride*/)
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
}
@@ -284,6 +331,21 @@ template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Pack
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> EIGEN_DEVICE_FUNC inline Packet pcplxflip(const Packet& a)
@@ -318,10 +380,22 @@ Packet pasin(const Packet& a) { using std::asin; return asin(a); }
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pacos(const Packet& a) { using std::acos; return acos(a); }
/** \internal \returns the atan of \a a (coeff-wise) */
/** \internal \returns the arc tangent of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet patan(const Packet& a) { using std::atan; return atan(a); }
/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet psinh(const Packet& a) { using std::sinh; return sinh(a); }
/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pcosh(const Packet& a) { using std::cosh; return cosh(a); }
/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet ptanh(const Packet& a) { using std::tanh; return tanh(a); }
/** \internal \returns the exp of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pexp(const Packet& a) { using std::exp; return exp(a); }
@@ -330,10 +404,32 @@ Packet pexp(const Packet& a) { using std::exp; return exp(a); }
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plog(const Packet& a) { using std::log; return log(a); }
/** \internal \returns the log10 of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plog10(const Packet& a) { using std::log10; return log10(a); }
/** \internal \returns the square-root of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet psqrt(const Packet& a) { using std::sqrt; return sqrt(a); }
/** \internal \returns the reciprocal square-root of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet prsqrt(const Packet& a) {
return pdiv(pset1<Packet>(1), psqrt(a));
}
/** \internal \returns the rounded value of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pround(const Packet& a) { using numext::round; return round(a); }
/** \internal \returns the floor of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pfloor(const Packet& a) { using numext::floor; return floor(a); }
/** \internal \returns the ceil of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pceil(const Packet& a) { using numext::ceil; return ceil(a); }
/***************************************************************************
* The following functions might not have to be overwritten for vectorized types
***************************************************************************/
@@ -354,27 +450,38 @@ pmadd(const Packet& a,
{ 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
@@ -433,6 +540,19 @@ 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

View File

@@ -14,8 +14,8 @@
#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) \
@@ -30,26 +30,40 @@
{ \
static inline typename NAME##_retval<ArrayBase<Derived> >::type run(const Eigen::ArrayBase<Derived>& x) \
{ \
return x.derived(); \
return typename NAME##_retval<ArrayBase<Derived> >::type(x.derived()); \
} \
};
namespace Eigen
{
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real,scalar_real_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(imag,scalar_imag_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(conj,scalar_conjugate_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(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(asin,scalar_asin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acos,scalar_acos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tan,scalar_tan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(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>
@@ -57,16 +71,46 @@ namespace Eigen
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()
);
}
/** \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.
**/

View File

@@ -164,7 +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;

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)%EIGEN_ALIGN_BYTES)==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
@@ -132,11 +117,11 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
/** Constructor in the fixed-size case.
*
* \param dataPtr pointer to the array to map
* \param a_stride optional Stride object, passing the strides.
* \param stride optional Stride object, passing the strides.
*/
EIGEN_DEVICE_FUNC
inline Map(PointerArgType dataPtr, const StrideType& a_stride = StrideType())
: Base(cast_to_pointer_type(dataPtr)), m_stride(a_stride)
explicit inline Map(PointerArgType dataPtr, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(dataPtr)), m_stride(stride)
{
PlainObjectType::Base::_check_template_params();
}
@@ -144,12 +129,12 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
/** Constructor in the dynamic-size vector case.
*
* \param dataPtr pointer to the array to map
* \param a_size the size of the vector expression
* \param a_stride optional Stride object, passing the strides.
* \param size the size of the vector expression
* \param stride optional Stride object, passing the strides.
*/
EIGEN_DEVICE_FUNC
inline Map(PointerArgType dataPtr, Index a_size, const StrideType& a_stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), a_size), m_stride(a_stride)
inline Map(PointerArgType dataPtr, Index size, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), size), m_stride(stride)
{
PlainObjectType::Base::_check_template_params();
}
@@ -157,13 +142,13 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
/** Constructor in the dynamic-size matrix case.
*
* \param dataPtr pointer to the array to map
* \param nbRows the number of rows of the matrix expression
* \param nbCols the number of columns of the matrix expression
* \param a_stride optional Stride object, passing the strides.
* \param rows the number of rows of the matrix expression
* \param cols the number of columns of the matrix expression
* \param stride optional Stride object, passing the strides.
*/
EIGEN_DEVICE_FUNC
inline Map(PointerArgType dataPtr, Index nbRows, Index nbCols, const StrideType& a_stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), nbRows, nbCols), m_stride(a_stride)
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();
}

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;
@@ -85,7 +84,7 @@ 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; }
EIGEN_DEVICE_FUNC
inline const Scalar& coeff(Index rowId, Index colId) const
@@ -128,7 +127,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
}
EIGEN_DEVICE_FUNC
inline MapBase(PointerType dataPtr) : m_data(dataPtr), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
explicit inline MapBase(PointerType dataPtr) : m_data(dataPtr), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
checkSanity();
@@ -147,12 +146,12 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
}
EIGEN_DEVICE_FUNC
inline MapBase(PointerType dataPtr, Index nbRows, Index nbCols)
: m_data(dataPtr), m_rows(nbRows), m_cols(nbCols)
inline MapBase(PointerType dataPtr, Index rows, Index cols)
: m_data(dataPtr), m_rows(rows), m_cols(cols)
{
eigen_assert( (dataPtr == 0)
|| ( nbRows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == nbRows)
&& nbCols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == nbCols)));
|| ( rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)));
checkSanity();
}
@@ -161,11 +160,9 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
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) % EIGEN_ALIGN_BYTES) == 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;
@@ -176,13 +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;
@@ -238,16 +236,18 @@ template<typename Derived> class MapBase<Derived, WriteAccessors>
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 nbRows, Index nbCols) : Base(dataPtr, nbRows, nbCols) {}
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

View File

@@ -10,11 +10,14 @@
#ifndef EIGEN_MATHFUNCTIONS_H
#define EIGEN_MATHFUNCTIONS_H
// source: http://www.geom.uiuc.edu/~huberty/math5337/groupe/digits.html
#define EIGEN_PI 3.141592653589793238462643383279502884197169399375105820974944592307816406
namespace Eigen {
// On WINCE, std::abs is defined for int only, so let's defined our own overloads:
// This issue has been confirmed with MSVC 2008 only, but the issue might exist for more recent versions too.
#if defined(_WIN32_WCE) && defined(_MSC_VER) && _MSC_VER<=1500
#if EIGEN_OS_WINCE && EIGEN_COMP_MSVC && EIGEN_COMP_MSVC<=1500
long abs(long x) { return (labs(x)); }
double abs(double x) { return (fabs(x)); }
float abs(float x) { return (fabsf(x)); }
@@ -276,7 +279,7 @@ struct norm1_default_impl
EIGEN_DEVICE_FUNC
static inline RealScalar run(const Scalar& x)
{
using std::abs;
EIGEN_USING_STD_MATH(abs);
return abs(real(x)) + abs(imag(x));
}
};
@@ -287,7 +290,7 @@ struct norm1_default_impl<Scalar, false>
EIGEN_DEVICE_FUNC
static inline Scalar run(const Scalar& x)
{
using std::abs;
EIGEN_USING_STD_MATH(abs);
return abs(x);
}
};
@@ -313,8 +316,8 @@ struct hypot_impl
{
EIGEN_USING_STD_MATH(max);
EIGEN_USING_STD_MATH(min);
using std::abs;
using std::sqrt;
EIGEN_USING_STD_MATH(abs);
EIGEN_USING_STD_MATH(sqrt);
RealScalar _x = abs(x);
RealScalar _y = abs(y);
Scalar p, qp;
@@ -328,6 +331,7 @@ struct hypot_impl
p = _y;
qp = _x / p;
}
if(p==RealScalar(0)) return RealScalar(0);
return p * sqrt(RealScalar(1) + qp*qp);
}
};
@@ -345,6 +349,7 @@ struct hypot_retval
template<typename OldType, typename NewType>
struct cast_impl
{
EIGEN_DEVICE_FUNC
static inline NewType run(const OldType& x)
{
return static_cast<NewType>(x);
@@ -354,56 +359,121 @@ struct cast_impl
// here, for once, we're plainly returning NewType: we don't want cast to do weird things.
template<typename OldType, typename NewType>
EIGEN_DEVICE_FUNC
inline NewType cast(const OldType& x)
{
return cast_impl<OldType, NewType>::run(x);
}
/****************************************************************************
* Implementation of atanh2 *
* Implementation of round *
****************************************************************************/
#if EIGEN_HAS_CXX11_MATH
template<typename Scalar>
struct round_impl {
static inline Scalar run(const Scalar& x)
{
EIGEN_STATIC_ASSERT((!NumTraits<Scalar>::IsComplex), NUMERIC_TYPE_MUST_BE_REAL)
using std::round;
return round(x);
}
};
#else
template<typename Scalar>
struct round_impl
{
static inline Scalar run(const Scalar& x)
{
EIGEN_STATIC_ASSERT((!NumTraits<Scalar>::IsComplex), NUMERIC_TYPE_MUST_BE_REAL)
EIGEN_USING_STD_MATH(floor);
EIGEN_USING_STD_MATH(ceil);
return (x > Scalar(0)) ? floor(x + Scalar(0.5)) : ceil(x - Scalar(0.5));
}
};
#endif
template<typename Scalar>
struct atanh2_impl
struct round_retval
{
static inline Scalar run(const Scalar& x, const Scalar& r)
typedef Scalar type;
};
/****************************************************************************
* Implementation of arg *
****************************************************************************/
#if EIGEN_HAS_CXX11_MATH
template<typename Scalar>
struct arg_impl {
static inline Scalar run(const Scalar& x)
{
EIGEN_USING_STD_MATH(arg);
return arg(x);
}
};
#else
template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
struct arg_default_impl
{
typedef typename NumTraits<Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC
static inline RealScalar run(const Scalar& x)
{
return (x < Scalar(0)) ? Scalar(EIGEN_PI) : Scalar(0); }
};
template<typename Scalar>
struct arg_default_impl<Scalar,true>
{
typedef typename NumTraits<Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC
static inline RealScalar run(const Scalar& x)
{
EIGEN_USING_STD_MATH(arg);
return arg(x);
}
};
template<typename Scalar> struct arg_impl : arg_default_impl<Scalar> {};
#endif
template<typename Scalar>
struct arg_retval
{
typedef typename NumTraits<Scalar>::Real type;
};
/****************************************************************************
* Implementation of log1p *
****************************************************************************/
template<typename Scalar, bool isComplex = NumTraits<Scalar>::IsComplex >
struct log1p_impl
{
static inline Scalar run(const Scalar& x)
{
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
#if (__cplusplus >= 201103L) && !defined(__CYGWIN__)
using std::log1p;
return log1p(2 * x / (r - x)) / 2;
#else
using std::abs;
using std::log;
using std::sqrt;
Scalar z = x / r;
if (r == 0 || abs(z) > sqrt(NumTraits<Scalar>::epsilon()))
return log((r + x) / (r - x)) / 2;
else
return z + z*z*z / 3;
#endif
typedef typename NumTraits<Scalar>::Real RealScalar;
EIGEN_USING_STD_MATH(log);
Scalar x1p = RealScalar(1) + x;
return ( x1p == Scalar(1) ) ? x : x * ( log(x1p) / (x1p - RealScalar(1)) );
}
};
template<typename RealScalar>
struct atanh2_impl<std::complex<RealScalar> >
{
typedef std::complex<RealScalar> Scalar;
static inline Scalar run(const Scalar& x, const Scalar& r)
#if EIGEN_HAS_CXX11_MATH
template<typename Scalar>
struct log1p_impl<Scalar, false> {
static inline Scalar run(const Scalar& x)
{
using std::log;
using std::norm;
using std::sqrt;
Scalar z = x / r;
if (r == Scalar(0) || norm(z) > NumTraits<RealScalar>::epsilon())
return RealScalar(0.5) * log((r + x) / (r - x));
else
return z + z*z*z / RealScalar(3);
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
using std::log1p;
return log1p(x);
}
};
#endif
template<typename Scalar>
struct atanh2_retval
struct log1p_retval
{
typedef Scalar type;
};
@@ -418,7 +488,7 @@ struct pow_default_impl
typedef Scalar retval;
static inline Scalar run(const Scalar& x, const Scalar& y)
{
using std::pow;
EIGEN_USING_STD_MATH(pow);
return pow(x, y);
}
};
@@ -486,48 +556,48 @@ struct random_default_impl<Scalar, false, false>
};
enum {
floor_log2_terminate,
floor_log2_move_up,
floor_log2_move_down,
floor_log2_bogus
meta_floor_log2_terminate,
meta_floor_log2_move_up,
meta_floor_log2_move_down,
meta_floor_log2_bogus
};
template<unsigned int n, int lower, int upper> struct floor_log2_selector
template<unsigned int n, int lower, int upper> struct meta_floor_log2_selector
{
enum { middle = (lower + upper) / 2,
value = (upper <= lower + 1) ? int(floor_log2_terminate)
: (n < (1 << middle)) ? int(floor_log2_move_down)
: (n==0) ? int(floor_log2_bogus)
: int(floor_log2_move_up)
value = (upper <= lower + 1) ? int(meta_floor_log2_terminate)
: (n < (1 << middle)) ? int(meta_floor_log2_move_down)
: (n==0) ? int(meta_floor_log2_bogus)
: int(meta_floor_log2_move_up)
};
};
template<unsigned int n,
int lower = 0,
int upper = sizeof(unsigned int) * CHAR_BIT - 1,
int selector = floor_log2_selector<n, lower, upper>::value>
struct floor_log2 {};
int selector = meta_floor_log2_selector<n, lower, upper>::value>
struct meta_floor_log2 {};
template<unsigned int n, int lower, int upper>
struct floor_log2<n, lower, upper, floor_log2_move_down>
struct meta_floor_log2<n, lower, upper, meta_floor_log2_move_down>
{
enum { value = floor_log2<n, lower, floor_log2_selector<n, lower, upper>::middle>::value };
enum { value = meta_floor_log2<n, lower, meta_floor_log2_selector<n, lower, upper>::middle>::value };
};
template<unsigned int n, int lower, int upper>
struct floor_log2<n, lower, upper, floor_log2_move_up>
struct meta_floor_log2<n, lower, upper, meta_floor_log2_move_up>
{
enum { value = floor_log2<n, floor_log2_selector<n, lower, upper>::middle, upper>::value };
enum { value = meta_floor_log2<n, meta_floor_log2_selector<n, lower, upper>::middle, upper>::value };
};
template<unsigned int n, int lower, int upper>
struct floor_log2<n, lower, upper, floor_log2_terminate>
struct meta_floor_log2<n, lower, upper, meta_floor_log2_terminate>
{
enum { value = (n >= ((unsigned int)(1) << (lower+1))) ? lower+1 : lower };
};
template<unsigned int n, int lower, int upper>
struct floor_log2<n, lower, upper, floor_log2_bogus>
struct meta_floor_log2<n, lower, upper, meta_floor_log2_bogus>
{
// no value, error at compile time
};
@@ -535,11 +605,24 @@ struct floor_log2<n, lower, upper, floor_log2_bogus>
template<typename Scalar>
struct random_default_impl<Scalar, false, true>
{
typedef typename NumTraits<Scalar>::NonInteger NonInteger;
static inline Scalar run(const Scalar& x, const Scalar& y)
{
return x + Scalar((NonInteger(y)-x+1) * std::rand() / (RAND_MAX + NonInteger(1)));
{
using std::max;
using std::min;
typedef typename conditional<NumTraits<Scalar>::IsSigned,std::ptrdiff_t,std::size_t>::type ScalarX;
if(y<x)
return x;
std::size_t range = ScalarX(y)-ScalarX(x);
std::size_t offset = 0;
// rejection sampling
std::size_t divisor = (range+RAND_MAX-1)/(range+1);
std::size_t multiplier = (range+RAND_MAX-1)/std::size_t(RAND_MAX);
do {
offset = ( (std::size_t(std::rand()) * multiplier) / divisor );
} while (offset > range);
return Scalar(ScalarX(x) + offset);
}
static inline Scalar run()
@@ -547,7 +630,7 @@ struct random_default_impl<Scalar, false, true>
#ifdef EIGEN_MAKING_DOCS
return run(Scalar(NumTraits<Scalar>::IsSigned ? -10 : 0), Scalar(10));
#else
enum { rand_bits = floor_log2<(unsigned int)(RAND_MAX)+1>::value,
enum { rand_bits = meta_floor_log2<(unsigned int)(RAND_MAX)+1>::value,
scalar_bits = sizeof(Scalar) * CHAR_BIT,
shift = EIGEN_PLAIN_ENUM_MAX(0, int(rand_bits) - int(scalar_bits)),
offset = NumTraits<Scalar>::IsSigned ? (1 << (EIGEN_PLAIN_ENUM_MIN(rand_bits,scalar_bits)-1)) : 0
@@ -587,11 +670,55 @@ inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random()
} // end namespace internal
/****************************************************************************
* Generic math function *
* Generic math functions *
****************************************************************************/
namespace numext {
#ifndef __CUDA_ARCH__
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_ALWAYS_INLINE T mini(const T& x, const T& y)
{
EIGEN_USING_STD_MATH(min);
return min EIGEN_NOT_A_MACRO (x,y);
}
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_ALWAYS_INLINE T maxi(const T& x, const T& y)
{
EIGEN_USING_STD_MATH(max);
return max EIGEN_NOT_A_MACRO (x,y);
}
#else
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_ALWAYS_INLINE T mini(const T& x, const T& y)
{
return y < x ? y : x;
}
template<>
EIGEN_DEVICE_FUNC
EIGEN_ALWAYS_INLINE float mini(const float& x, const float& y)
{
return fmin(x, y);
}
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_ALWAYS_INLINE T maxi(const T& x, const T& y)
{
return x < y ? y : x;
}
template<>
EIGEN_DEVICE_FUNC
EIGEN_ALWAYS_INLINE float maxi(const float& x, const float& y)
{
return fmax(x, y);
}
#endif
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(real, Scalar) real(const Scalar& x)
@@ -620,6 +747,13 @@ inline EIGEN_MATHFUNC_RETVAL(imag, Scalar) imag(const Scalar& x)
return EIGEN_MATHFUNC_IMPL(imag, Scalar)::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(arg, Scalar) arg(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(arg, Scalar)::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline typename internal::add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) >::type imag_ref(const Scalar& x)
@@ -664,9 +798,9 @@ inline EIGEN_MATHFUNC_RETVAL(hypot, Scalar) hypot(const Scalar& x, const Scalar&
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(atanh2, Scalar) atanh2(const Scalar& x, const Scalar& y)
inline EIGEN_MATHFUNC_RETVAL(log1p, Scalar) log1p(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(atanh2, Scalar)::run(x, y);
return EIGEN_MATHFUNC_IMPL(log1p, Scalar)::run(x);
}
template<typename Scalar>
@@ -676,22 +810,96 @@ inline EIGEN_MATHFUNC_RETVAL(pow, Scalar) pow(const Scalar& x, const Scalar& y)
return EIGEN_MATHFUNC_IMPL(pow, Scalar)::run(x, y);
}
// std::isfinite is non standard, so let's define our own version,
// even though it is not very efficient.
template<typename T>
EIGEN_DEVICE_FUNC
bool (isfinite)(const T& x)
{
return x<NumTraits<T>::highest() && x>NumTraits<T>::lowest();
#if EIGEN_HAS_CXX11_MATH
using std::isfinite;
return isfinite EIGEN_NOT_A_MACRO (x);
#else
return x<NumTraits<T>::highest() && x>NumTraits<T>::lowest();
#endif
}
template<typename T>
EIGEN_DEVICE_FUNC
bool (isnan)(const T& x)
{
#if EIGEN_HAS_CXX11_MATH
using std::isnan;
return isnan EIGEN_NOT_A_MACRO (x);
#else
return x != x;
#endif
}
template<typename T>
EIGEN_DEVICE_FUNC
bool (isinf)(const T& x)
{
#if EIGEN_HAS_CXX11_MATH
using std::isinf;
return isinf EIGEN_NOT_A_MACRO (x);
#else
return x>NumTraits<T>::highest() || x<NumTraits<T>::lowest();
#endif
}
template<typename T>
bool (isfinite)(const std::complex<T>& x)
{
using std::real;
using std::imag;
return isfinite(real(x)) && isfinite(imag(x));
return (numext::isfinite)(numext::real(x)) && (numext::isfinite)(numext::imag(x));
}
template<typename T>
bool (isnan)(const std::complex<T>& x)
{
return (numext::isnan)(numext::real(x)) || (numext::isnan)(numext::imag(x));
}
template<typename T>
bool (isinf)(const std::complex<T>& x)
{
return ((numext::isinf)(numext::real(x)) || (numext::isinf)(numext::imag(x))) && (!(numext::isnan)(x));
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(round, Scalar) round(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(round, Scalar)::run(x);
}
template<typename T>
EIGEN_DEVICE_FUNC
T (floor)(const T& x)
{
EIGEN_USING_STD_MATH(floor);
return floor(x);
}
template<typename T>
EIGEN_DEVICE_FUNC
T (ceil)(const T& x)
{
EIGEN_USING_STD_MATH(ceil);
return ceil(x);
}
// Log base 2 for 32 bits positive integers.
// Conveniently returns 0 for x==0.
inline int log2(int x)
{
eigen_assert(x>=0);
unsigned int v(x);
static const int table[32] = { 0, 9, 1, 10, 13, 21, 2, 29, 11, 14, 16, 18, 22, 25, 3, 30, 8, 12, 20, 28, 15, 17, 24, 7, 19, 27, 23, 6, 26, 5, 4, 31 };
v |= v >> 1;
v |= v >> 2;
v |= v >> 4;
v |= v >> 8;
v |= v >> 16;
return table[(v * 0x07C4ACDDU) >> 27];
}
} // end namespace numext
@@ -714,14 +922,14 @@ struct scalar_fuzzy_default_impl<Scalar, false, false>
template<typename OtherScalar> EIGEN_DEVICE_FUNC
static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, const RealScalar& prec)
{
using std::abs;
EIGEN_USING_STD_MATH(abs);
return abs(x) <= abs(y) * prec;
}
EIGEN_DEVICE_FUNC
static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec)
{
EIGEN_USING_STD_MATH(min);
using std::abs;
EIGEN_USING_STD_MATH(abs);
return abs(x - y) <= (min)(abs(x), abs(y)) * prec;
}
EIGEN_DEVICE_FUNC

View File

@@ -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
};
};
}
@@ -169,7 +220,7 @@ class Matrix
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix& operator=(const MatrixBase<OtherDerived>& other)
EIGEN_STRONG_INLINE Matrix& operator=(const DenseBase<OtherDerived>& other)
{
return Base::_set(other);
}
@@ -213,11 +264,12 @@ class Matrix
// FIXME is it still needed
EIGEN_DEVICE_FUNC
Matrix(internal::constructor_without_unaligned_array_assert)
explicit Matrix(internal::constructor_without_unaligned_array_assert)
: Base(internal::constructor_without_unaligned_array_assert())
{ Base::_check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED }
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
Matrix(Matrix&& other)
: Base(std::move(other))
{
@@ -225,6 +277,7 @@ class Matrix
if (RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic)
Base::_set_noalias(other);
}
EIGEN_DEVICE_FUNC
Matrix& operator=(Matrix&& other)
{
other.swap(*this);
@@ -263,8 +316,8 @@ class Matrix
*
* \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 thefore recommended to use the default
* constructor Matrix() instead, especilly when using one of the non standard
* 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);
@@ -278,8 +331,8 @@ class Matrix
*
* \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 thefore recommended to use the default
* constructor Matrix() instead, especilly when using one of the non standard
* 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
@@ -312,37 +365,10 @@ class Matrix
}
/** \brief Constructor copying the value of the expression \a other */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
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_DEVICE_FUNC
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_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const ReturnByValue<OtherDerived>& other)
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
other.evalTo(*this);
}
EIGEN_STRONG_INLINE Matrix(const Matrix& other) : Base(other)
{ }
/** \brief Copy constructor for generic expressions.
* \sa MatrixBase::operator=(const EigenBase<OtherDerived>&)
@@ -350,23 +376,8 @@ class Matrix
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const EigenBase<OtherDerived> &other)
: Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
{
Base::_check_template_params();
Base::_resize_to_match(other);
// FIXME/CHECK: isn't *this = other.derived() more efficient. it allows to
// go for pure _set() implementations, right?
*this = other;
}
/** \internal
* \brief Override MatrixBase::swap() since for dynamic-sized matrices
* of same type it is enough to swap the data pointers.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
void swap(MatrixBase<OtherDerived> const & other)
{ this->_swap(other.derived()); }
: Base(other.derived())
{ }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return 1; }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return this->innerSize(); }

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;
@@ -82,6 +81,7 @@ template<typename Derived> class MatrixBase
using Base::operator*=;
using Base::operator/=;
using Base::operator*;
using Base::operator/;
typedef typename Base::CoeffReturnType CoeffReturnType;
typedef typename Base::ConstTransposeReturnType ConstTransposeReturnType;
@@ -102,23 +102,11 @@ template<typename Derived> class MatrixBase
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>,
@@ -127,7 +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,
@@ -165,12 +153,6 @@ template<typename Derived> class MatrixBase
EIGEN_DEVICE_FUNC
Derived& operator=(const ReturnByValue<OtherDerived>& other);
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename ProductDerived, typename Lhs, typename Rhs>
EIGEN_DEVICE_FUNC
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);
@@ -181,26 +163,20 @@ template<typename Derived> class MatrixBase
#ifdef __CUDACC__
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
const typename LazyProductReturnType<Derived,OtherDerived>::Type
const Product<Derived,OtherDerived,LazyProduct>
operator*(const MatrixBase<OtherDerived> &other) const
{ return this->lazyProduct(other); }
#else
#ifdef EIGEN_TEST_EVALUATORS
template<typename OtherDerived>
const Product<Derived,OtherDerived>
operator*(const MatrixBase<OtherDerived> &other) const;
#else
template<typename OtherDerived>
const typename ProductReturnType<Derived,OtherDerived>::Type
operator*(const MatrixBase<OtherDerived> &other) const;
#endif
#endif
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
const typename LazyProductReturnType<Derived,OtherDerived>::Type
const Product<Derived,OtherDerived,LazyProduct>
lazyProduct(const MatrixBase<OtherDerived> &other) const;
template<typename OtherDerived>
@@ -214,7 +190,7 @@ template<typename Derived> class MatrixBase
template<typename DiagonalDerived>
EIGEN_DEVICE_FUNC
const DiagonalProduct<Derived, DiagonalDerived, OnTheRight>
const Product<Derived, DiagonalDerived, LazyProduct>
operator*(const DiagonalBase<DiagonalDerived> &diagonal) const;
template<typename OtherDerived>
@@ -251,19 +227,14 @@ template<typename Derived> class MatrixBase
template<int Index>
EIGEN_DEVICE_FUNC
typename ConstDiagonalIndexReturnType<Index>::Type diagonal() const;
// 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<DynamicIndex>::Type diagonal(Index index);
typename MatrixBase::template ConstDiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index) const;
#else
EIGEN_DEVICE_FUNC
typename DiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index);
typedef Diagonal<Derived,DynamicIndex> DiagonalDynamicIndexReturnType;
typedef typename internal::add_const<Diagonal<const Derived,DynamicIndex> >::type ConstDiagonalDynamicIndexReturnType;
EIGEN_DEVICE_FUNC
typename ConstDiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index) const;
#endif
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; };
@@ -334,12 +305,14 @@ 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;
template<int p> EIGEN_DEVICE_FUNC RealScalar lpNorm() const;
@@ -348,10 +321,10 @@ template<typename Derived> class MatrixBase
/** \returns an \link Eigen::ArrayBase Array \endlink expression of this matrix
* \sa ArrayBase::matrix() */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ArrayWrapper<Derived> array() { 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 derived(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const ArrayWrapper<const Derived> array() const { return ArrayWrapper<const Derived>(derived()); }
/////////// LU module ///////////
@@ -361,7 +334,8 @@ template<typename Derived> class MatrixBase
const PartialPivLU<PlainObject> lu() const;
EIGEN_DEVICE_FUNC
const internal::inverse_impl<Derived> inverse() const;
const Inverse<Derived> inverse() const;
template<typename ResultType>
void computeInverseAndDetWithCheck(
ResultType& inverse,
@@ -394,6 +368,7 @@ template<typename Derived> class MatrixBase
/////////// SVD module ///////////
JacobiSVD<PlainObject> jacobiSvd(unsigned int computationOptions = 0) const;
BDCSVD<PlainObject> bdcSvd(unsigned int computationOptions = 0) const;
/////////// Geometry module ///////////
@@ -420,7 +395,8 @@ template<typename Derived> class MatrixBase
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;

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

@@ -30,68 +30,35 @@ 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_DEVICE_FUNC
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_DEVICE_FUNC
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_DEVICE_FUNC
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 OtherDerived>
ExpressionType& operator=(const ReturnByValue<OtherDerived>& func)
{ return m_expression = func; }
#endif
EIGEN_DEVICE_FUNC
ExpressionType& expression() const
{
@@ -133,7 +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

@@ -83,8 +83,25 @@ template<typename T> struct GenericNumTraits
// 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)()); }
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>

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::StorageIndexType StorageIndexType;
typedef typename Traits::Index Index;
typedef Matrix<StorageIndexType,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,StorageIndexType>
typedef PermutationMatrix<IndicesType::SizeAtCompileTime,IndicesType::MaxSizeAtCompileTime,StorageIndex>
PlainPermutationType;
using Base::derived;
typedef Transpose<PermutationBase> TransposeReturnType;
#endif
/** Copies the other permutation into *this */
@@ -118,7 +114,7 @@ class PermutationBase : public EigenBase<Derived>
void evalTo(MatrixBase<DenseDerived>& other) const
{
other.setZero();
for (int i=0; i<rows();++i)
for (Index i=0; i<rows(); ++i)
other.coeffRef(indices().coeff(i),i) = typename DenseDerived::Scalar(1);
}
#endif
@@ -147,7 +143,8 @@ class PermutationBase : public EigenBase<Derived>
/** Sets *this to be the identity permutation matrix */
void setIdentity()
{
for(StorageIndexType i = 0; i < size(); ++i)
StorageIndex n = StorageIndex(size());
for(StorageIndex i = 0; i < n; ++i)
indices().coeffRef(i) = i;
}
@@ -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) = StorageIndexType(j);
else if(indices().coeff(k) == j) indices().coeffRef(k) = StorageIndexType(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 StorageIndexType the integer 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.
*
@@ -270,27 +296,28 @@ class PermutationBase : public EigenBase<Derived>
*/
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndexType>
struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndexType> >
: traits<Matrix<_StorageIndexType,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 Matrix<_StorageIndexType, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
typedef typename IndicesType::Index Index;
typedef _StorageIndexType StorageIndexType;
typedef PermutationStorage StorageKind;
typedef Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
typedef _StorageIndex StorageIndex;
};
}
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndexType>
class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndexType> >
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::StorageIndexType StorageIndexType;
typedef typename Traits::Index Index;
typedef typename Traits::StorageIndex StorageIndex;
#endif
inline PermutationMatrix()
@@ -298,8 +325,10 @@ class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompile
/** Constructs an uninitialized permutation matrix of given size.
*/
inline PermutationMatrix(Index size) : m_indices(size)
{}
explicit inline PermutationMatrix(Index size) : m_indices(size)
{
eigen_internal_assert(size <= NumTraits<StorageIndex>::highest());
}
/** Copy constructor. */
template<typename OtherDerived>
@@ -320,7 +349,7 @@ class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompile
* array's size.
*/
template<typename Other>
explicit inline PermutationMatrix(const MatrixBase<Other>& a_indices) : m_indices(a_indices)
explicit inline PermutationMatrix(const MatrixBase<Other>& indices) : m_indices(indices)
{}
/** Convert the Transpositions \a tr to a permutation matrix */
@@ -368,9 +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)
@@ -387,19 +419,19 @@ class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompile
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndexType, int _PacketAccess>
struct traits<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndexType>,_PacketAccess> >
: traits<Matrix<_StorageIndexType,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 Map<const Matrix<_StorageIndexType, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1>, _PacketAccess> IndicesType;
typedef typename IndicesType::Index Index;
typedef _StorageIndexType StorageIndexType;
typedef PermutationStorage StorageKind;
typedef Map<const Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1>, _PacketAccess> IndicesType;
typedef _StorageIndex StorageIndex;
};
}
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndexType, int _PacketAccess>
class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndexType>,_PacketAccess>
: public PermutationBase<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndexType>,_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;
@@ -407,15 +439,14 @@ class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageInd
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef typename Traits::IndicesType IndicesType;
typedef typename IndicesType::Scalar StorageIndexType;
typedef typename IndicesType::Index Index;
typedef typename IndicesType::Scalar StorageIndex;
#endif
inline Map(const StorageIndexType* indicesPtr)
inline Map(const StorageIndex* indicesPtr)
: m_indices(indicesPtr)
{}
inline Map(const StorageIndexType* indicesPtr, Index size)
inline Map(const StorageIndex* indicesPtr, Index size)
: m_indices(indicesPtr,size)
{}
@@ -462,8 +493,6 @@ class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageInd
* \sa class PermutationBase, class PermutationMatrix
*/
struct PermutationStorage {};
template<typename _IndicesType> class TranspositionsWrapper;
namespace internal {
template<typename _IndicesType>
@@ -471,16 +500,14 @@ struct traits<PermutationWrapper<_IndicesType> >
{
typedef PermutationStorage StorageKind;
typedef typename _IndicesType::Scalar Scalar;
typedef typename _IndicesType::Scalar StorageIndexType;
typedef typename _IndicesType::Index 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
};
};
}
@@ -496,8 +523,8 @@ class PermutationWrapper : public PermutationBase<PermutationWrapper<_IndicesTyp
typedef typename Traits::IndicesType IndicesType;
#endif
inline PermutationWrapper(const IndicesType& a_indices)
: m_indices(a_indices)
inline PermutationWrapper(const IndicesType& indices)
: m_indices(indices)
{}
/** const version of indices(). */
@@ -509,105 +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;
typedef typename MatrixType::Index Index;
permut_matrix_product_retval(const PermutationType& perm, const MatrixType& matrix)
: m_permutation(perm), m_matrix(matrix)
{}
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
template<typename Dest> inline void evalTo(Dest& dst) const
{
const Index n = Side==OnTheLeft ? rows() : cols();
// FIXME we need an is_same for expression that is not sensitive to constness. For instance
// is_same_xpr<Block<const Matrix>, Block<Matrix> >::value should be true.
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);
Index r = 0;
while(r < m_permutation.size())
{
// search for the next seed
while(r<m_permutation.size() && mask[r]) r++;
if(r>=m_permutation.size())
break;
// we got one, let's follow it until we are back to the seed
Index k0 = r++;
Index kPrev = k0;
mask.coeffRef(k0) = true;
for(Index k=m_permutation.indices().coeff(k0); k!=k0; k=m_permutation.indices().coeff(k))
{
Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>(dst, k)
.swap(Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
(dst,((Side==OnTheLeft) ^ Transposed) ? k0 : kPrev));
mask.coeffRef(k) = true;
kPrev = k;
}
}
}
else
{
for(int i = 0; i < n; ++i)
{
Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
(dst, ((Side==OnTheLeft) ^ Transposed) ? m_permutation.indices().coeff(i) : i)
=
Block<const MatrixTypeNestedCleaned,Side==OnTheLeft ? 1 : MatrixType::RowsAtCompileTime,Side==OnTheRight ? 1 : MatrixType::ColsAtCompileTime>
(m_matrix, ((Side==OnTheRight) ^ Transposed) ? m_permutation.indices().coeff(i) : i);
}
}
}
protected:
const PermutationType& m_permutation;
typename MatrixType::Nested m_matrix;
};
/* Template partial specialization for transposed/inverse permutations */
template<typename Derived>
@@ -617,6 +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> > >
@@ -631,26 +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
@@ -663,22 +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;
@@ -690,6 +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

@@ -69,8 +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>
@@ -95,8 +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;
@@ -115,20 +116,23 @@ 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
@@ -221,11 +225,11 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
}
/** \returns a const pointer to the data array of this matrix */
EIGEN_STRONG_INLINE const Scalar *data() const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar *data() const
{ return m_storage.data(); }
/** \returns a pointer to the data array of this matrix */
EIGEN_STRONG_INLINE Scalar *data()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar *data()
{ return m_storage.data(); }
/** Resizes \c *this to a \a rows x \a cols matrix.
@@ -245,22 +249,22 @@ 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 nbRows, Index nbCols)
EIGEN_STRONG_INLINE void resize(Index rows, Index cols)
{
eigen_assert( EIGEN_IMPLIES(RowsAtCompileTime!=Dynamic,nbRows==RowsAtCompileTime)
&& EIGEN_IMPLIES(ColsAtCompileTime!=Dynamic,nbCols==ColsAtCompileTime)
&& EIGEN_IMPLIES(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic,nbRows<=MaxRowsAtCompileTime)
&& EIGEN_IMPLIES(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic,nbCols<=MaxColsAtCompileTime)
&& nbRows>=0 && nbCols>=0 && "Invalid sizes when resizing a matrix or array.");
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(nbRows, nbCols);
eigen_assert( EIGEN_IMPLIES(RowsAtCompileTime!=Dynamic,rows==RowsAtCompileTime)
&& EIGEN_IMPLIES(ColsAtCompileTime!=Dynamic,cols==ColsAtCompileTime)
&& EIGEN_IMPLIES(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic,rows<=MaxRowsAtCompileTime)
&& EIGEN_IMPLIES(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic,cols<=MaxColsAtCompileTime)
&& rows>=0 && cols>=0 && "Invalid sizes when resizing a matrix or array.");
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(rows, cols);
#ifdef EIGEN_INITIALIZE_COEFFS
Index size = nbRows*nbCols;
Index size = rows*cols;
bool size_changed = size != this->size();
m_storage.resize(size, nbRows, nbCols);
m_storage.resize(size, rows, cols);
if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
#else
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(nbRows, nbCols);
m_storage.resize(nbRows*nbCols, nbRows, nbCols);
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(rows, cols);
m_storage.resize(rows*cols, rows, cols);
#endif
}
@@ -301,9 +305,9 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* \sa resize(Index,Index)
*/
EIGEN_DEVICE_FUNC
inline void resize(NoChange_t, Index nbCols)
inline void resize(NoChange_t, Index cols)
{
resize(rows(), nbCols);
resize(rows(), cols);
}
/** Resizes the matrix, changing only the number of rows. For the parameter of type NoChange_t, just pass the special value \c NoChange
@@ -315,9 +319,9 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* \sa resize(Index,Index)
*/
EIGEN_DEVICE_FUNC
inline void resize(Index nbRows, NoChange_t)
inline void resize(Index rows, NoChange_t)
{
resize(nbRows, cols());
resize(rows, cols());
}
/** Resizes \c *this to have the same dimensions as \a other.
@@ -357,9 +361,9 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* appended to the matrix they will be uninitialized.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResize(Index nbRows, Index nbCols)
EIGEN_STRONG_INLINE void conservativeResize(Index rows, Index cols)
{
internal::conservative_resize_like_impl<Derived>::run(*this, nbRows, nbCols);
internal::conservative_resize_like_impl<Derived>::run(*this, rows, cols);
}
/** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
@@ -370,10 +374,10 @@ 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 nbRows, NoChange_t)
EIGEN_STRONG_INLINE void conservativeResize(Index rows, NoChange_t)
{
// Note: see the comment in conservativeResize(Index,Index)
conservativeResize(nbRows, cols());
conservativeResize(rows, cols());
}
/** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
@@ -384,10 +388,10 @@ 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 nbCols)
EIGEN_STRONG_INLINE void conservativeResize(NoChange_t, Index cols)
{
// Note: see the comment in conservativeResize(Index,Index)
conservativeResize(rows(), nbCols);
conservativeResize(rows(), cols);
}
/** Resizes the vector to \a size while retaining old values.
@@ -457,7 +461,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
// FIXME is it still needed ?
/** \internal */
EIGEN_DEVICE_FUNC
PlainObjectBase(internal::constructor_without_unaligned_array_assert)
explicit PlainObjectBase(internal::constructor_without_unaligned_array_assert)
: m_storage(internal::constructor_without_unaligned_array_assert())
{
// _check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
@@ -480,9 +484,13 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
}
#endif
/** Copy constructor */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(Index a_size, Index nbRows, Index nbCols)
: m_storage(a_size, nbRows, nbCols)
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_COEFFS_IF_THAT_OPTION_IS_ENABLED
@@ -499,15 +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_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(const DenseBase<OtherDerived> &other)
: m_storage()
{
_check_template_params();
resizeLike(other);
_set_noalias(other);
}
/** \sa PlainObjectBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(const EigenBase<OtherDerived> &other)
: m_storage(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
: m_storage()
{
_check_template_params();
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(other.derived().rows(), other.derived().cols());
Base::operator=(other.derived());
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
@@ -639,22 +668,16 @@ 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_DEVICE_FUNC
EIGEN_STRONG_INLINE void _set_selector(const OtherDerived& other, const internal::true_type&) { _set_noalias(other.eval()); }
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
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.
*
@@ -669,17 +692,18 @@ 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 nbRows, Index nbCols, typename internal::enable_if<Base::SizeAtCompileTime!=2,T0>::type* = 0)
EIGEN_STRONG_INLINE void _init2(Index rows, Index cols, typename internal::enable_if<Base::SizeAtCompileTime!=2,T0>::type* = 0)
{
EIGEN_STATIC_ASSERT(bool(NumTraits<T0>::IsInteger) &&
bool(NumTraits<T1>::IsInteger),
FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED)
resize(nbRows,nbCols);
resize(rows,cols);
}
template<typename T0, typename T1>
@@ -704,9 +728,12 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
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,T>::type* = 0)
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;
@@ -714,6 +741,8 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
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)
@@ -722,6 +751,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
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,
@@ -734,18 +764,21 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
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){
@@ -766,23 +799,58 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
{
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>
EIGEN_DEVICE_FUNC
void _swap(DenseBase<OtherDerived> const & other)
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()
{
@@ -797,10 +865,9 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
&& (Options & (DontAlign|RowMajor)) == Options),
INVALID_MATRIX_TEMPLATE_PARAMETERS)
}
#endif
private:
enum { ThisConstantIsPrivateInPlainObjectBase };
enum { IsPlainObjectBase = 1 };
#endif
};
namespace internal {
@@ -808,7 +875,6 @@ namespace internal {
template <typename Derived, typename OtherDerived, bool IsVector>
struct conservative_resize_like_impl
{
typedef typename Derived::Index Index;
static void run(DenseBase<Derived>& _this, Index rows, Index cols)
{
if (_this.rows() == rows && _this.cols() == cols) return;
@@ -874,7 +940,6 @@ struct conservative_resize_like_impl<Derived,OtherDerived,true>
{
using conservative_resize_like_impl<Derived,OtherDerived,false>::run;
typedef typename Derived::Index Index;
static void run(DenseBase<Derived>& _this, Index size)
{
const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : size;

View File

@@ -12,8 +12,7 @@
namespace Eigen {
template<typename Lhs, typename Rhs> class Product;
template<typename Lhs, typename Rhs, typename StorageKind> class ProductImpl;
template<typename Lhs, typename Rhs, int Option, typename StorageKind> class ProductImpl;
/** \class Product
* \ingroup Core_Module
@@ -24,53 +23,121 @@ 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
*
*/
// Use ProductReturnType to get correct traits, in particular vectorization flags
namespace internal {
template<typename Lhs, typename Rhs>
struct traits<Product<Lhs, Rhs> >
: traits<typename ProductReturnType<Lhs, Rhs>::Type>
{
// We want A+B*C to be of type Product<Matrix, Sum> and not Product<Matrix, Matrix>
// TODO: This flag should eventually go in a separate evaluator traits class
// 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 typename remove_all<Lhs>::type LhsCleaned;
typedef typename remove_all<Rhs>::type RhsCleaned;
typedef traits<LhsCleaned> LhsTraits;
typedef traits<RhsCleaned> RhsTraits;
typedef MatrixXpr XprKind;
typedef typename 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 {
Flags = traits<typename ProductReturnType<Lhs, Rhs>::Type>::Flags & ~(EvalBeforeNestingBit | DirectAccessBit)
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:
@@ -78,14 +145,76 @@ class Product : public ProductImpl<Lhs,Rhs,typename internal::promote_storage_ty
RhsNested m_rhs;
};
template<typename Lhs, typename Rhs>
class ProductImpl<Lhs,Rhs,Dense> : public internal::dense_xpr_base<Product<Lhs,Rhs> >::type
{
typedef Product<Lhs, Rhs> Derived;
public:
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
{};
typedef typename internal::dense_xpr_base<Product<Lhs, Rhs> >::type Base;
/** Convertion to scalar for inner-products */
template<typename Lhs, typename Rhs, int Option>
class dense_product_base<Lhs, Rhs, Option, InnerProduct>
: public internal::dense_xpr_base<Product<Lhs,Rhs,Option> >::type
{
typedef Product<Lhs,Rhs,Option> ProductXpr;
typedef typename internal::dense_xpr_base<ProductXpr>::type Base;
public:
using Base::derived;
typedef typename Base::Scalar Scalar;
operator const Scalar() const
{
return internal::evaluator<ProductXpr>(derived()).coeff(0,0);
}
};
} // namespace internal
// Generic API dispatcher
template<typename Lhs, typename Rhs, int Option, typename StorageKind>
class ProductImpl : public internal::generic_xpr_base<Product<Lhs,Rhs,Option>, MatrixXpr, StorageKind>::type
{
public:
typedef typename internal::generic_xpr_base<Product<Lhs,Rhs,Option>, MatrixXpr, StorageKind>::type Base;
};
template<typename Lhs, typename Rhs, int Option>
class ProductImpl<Lhs,Rhs,Option,Dense>
: public internal::dense_product_base<Lhs,Rhs,Option>
{
typedef Product<Lhs, Rhs, Option> Derived;
public:
typedef typename internal::dense_product_base<Lhs, Rhs, Option> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
protected:
enum {
IsOneByOne = (RowsAtCompileTime == 1 || RowsAtCompileTime == Dynamic) &&
(ColsAtCompileTime == 1 || ColsAtCompileTime == Dynamic),
EnableCoeff = IsOneByOne || Option==LazyProduct
};
public:
EIGEN_DEVICE_FUNC Scalar coeff(Index row, Index col) const
{
EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS);
eigen_assert( (Option==LazyProduct) || (this->rows() == 1 && this->cols() == 1) );
return internal::evaluator<Derived>(derived()).coeff(row,col);
}
EIGEN_DEVICE_FUNC Scalar coeff(Index i) const
{
EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS);
eigen_assert( (Option==LazyProduct) || (this->rows() == 1 && this->cols() == 1) );
return internal::evaluator<Derived>(derived()).coeff(i);
}
};
/***************************************************************************
@@ -102,6 +231,15 @@ 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

@@ -1,274 +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& a_lhs, const Rhs& a_rhs)
: m_lhs(a_lhs), m_rhs(a_rhs)
{
eigen_assert(a_lhs.cols() == a_rhs.rows()
&& "invalid matrix product"
&& "if you wanted a coeff-wise or a dot product use the respective explicit functions");
}
inline Index rows() const { return m_lhs.rows(); }
inline Index cols() const { return m_rhs.cols(); }
template<typename Dest>
inline void evalTo(Dest& dst) const { dst.setZero(); scaleAndAddTo(dst,Scalar(1)); }
template<typename Dest>
inline void addTo(Dest& dst) const { scaleAndAddTo(dst,Scalar(1)); }
template<typename Dest>
inline void subTo(Dest& dst) const { scaleAndAddTo(dst,Scalar(-1)); }
template<typename Dest>
inline void scaleAndAddTo(Dest& dst, const Scalar& alpha) const { derived().scaleAndAddTo(dst,alpha); }
const _LhsNested& lhs() const { return m_lhs; }
const _RhsNested& rhs() const { return m_rhs; }
// Implicit conversion to the nested type (trigger the evaluation of the product)
operator const PlainObject& () const
{
m_result.resize(m_lhs.rows(), m_rhs.cols());
derived().evalTo(m_result);
return m_result;
}
const Diagonal<const FullyLazyCoeffBaseProductType,0> diagonal() const
{ return FullyLazyCoeffBaseProductType(m_lhs, m_rhs); }
template<int Index>
const Diagonal<FullyLazyCoeffBaseProductType,Index> diagonal() const
{ return FullyLazyCoeffBaseProductType(m_lhs, m_rhs); }
const Diagonal<FullyLazyCoeffBaseProductType,Dynamic> diagonal(Index index) const
{ return FullyLazyCoeffBaseProductType(m_lhs, m_rhs).diagonal(index); }
// restrict coeff accessors to 1x1 expressions. No need to care about mutators here since this isn't an Lvalue expression
typename Base::CoeffReturnType coeff(Index row, Index col) const
{
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);
}
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, const typename Derived::Scalar& x)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
typename internal::enable_if<!internal::is_same<typename Derived::Scalar,typename Derived::RealScalar>::value,
const ScaledProduct<Derived> >::type
operator*(const ProductBase<Derived,Lhs,Rhs>& prod, const typename Derived::RealScalar& x)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
const ScaledProduct<Derived>
operator*(const typename Derived::Scalar& x,const ProductBase<Derived,Lhs,Rhs>& prod)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
typename internal::enable_if<!internal::is_same<typename Derived::Scalar,typename Derived::RealScalar>::value,
const ScaledProduct<Derived> >::type
operator*(const typename Derived::RealScalar& x,const ProductBase<Derived,Lhs,Rhs>& prod)
{ return ScaledProduct<Derived>(prod.derived(), x); }
namespace internal {
template<typename NestedProduct>
struct traits<ScaledProduct<NestedProduct> >
: traits<ProductBase<ScaledProduct<NestedProduct>,
typename NestedProduct::_LhsNested,
typename NestedProduct::_RhsNested> >
{
typedef typename traits<NestedProduct>::StorageKind StorageKind;
};
}
template<typename NestedProduct>
class ScaledProduct
: public ProductBase<ScaledProduct<NestedProduct>,
typename NestedProduct::_LhsNested,
typename NestedProduct::_RhsNested>
{
public:
typedef ProductBase<ScaledProduct<NestedProduct>,
typename NestedProduct::_LhsNested,
typename NestedProduct::_RhsNested> Base;
typedef typename Base::Scalar Scalar;
typedef typename Base::PlainObject PlainObject;
// EIGEN_PRODUCT_PUBLIC_INTERFACE(ScaledProduct)
ScaledProduct(const NestedProduct& prod, const Scalar& x)
: Base(prod.lhs(),prod.rhs()), m_prod(prod), m_alpha(x) {}
template<typename Dest>
inline void evalTo(Dest& dst) const { dst.setZero(); scaleAndAddTo(dst, Scalar(1)); }
template<typename Dest>
inline void addTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(1)); }
template<typename Dest>
inline void subTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(-1)); }
template<typename Dest>
inline void scaleAndAddTo(Dest& dst, const Scalar& a_alpha) const { m_prod.derived().scaleAndAddTo(dst,a_alpha * m_alpha); }
const Scalar& alpha() const { return m_alpha; }
protected:
const NestedProduct& m_prod;
Scalar m_alpha;
};
/** \internal
* Overloaded to perform an efficient C = (A*B).lazy() */
template<typename Derived>
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& MatrixBase<Derived>::lazyAssign(const ProductBase<ProductDerived, Lhs,Rhs>& other)
{
other.derived().evalTo(derived());
return derived();
}
} // end namespace Eigen
#endif // EIGEN_PRODUCTBASE_H

1116
Eigen/src/Core/ProductEvaluators.h Normal file → Executable file

File diff suppressed because it is too large Load Diff

View File

@@ -53,7 +53,7 @@ struct functor_traits<scalar_random_op<Scalar> >
* \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>());
@@ -84,7 +84,7 @@ DenseBase<Derived>::Random(Index rows, Index cols)
* \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>());
@@ -110,7 +110,7 @@ DenseBase<Derived>::Random(Index size)
* \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>());
@@ -162,8 +162,8 @@ PlainObjectBase<Derived>::setRandom(Index newSize)
*
* \not_reentrant
*
* \param nbRows the new number of rows
* \param nbCols the new number of columns
* \param rows the new number of rows
* \param cols the new number of columns
*
* Example: \include Matrix_setRandom_int_int.cpp
* Output: \verbinclude Matrix_setRandom_int_int.out
@@ -172,9 +172,9 @@ PlainObjectBase<Derived>::setRandom(Index newSize)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setRandom(Index nbRows, Index nbCols)
PlainObjectBase<Derived>::setRandom(Index rows, Index cols)
{
resize(nbRows, nbCols);
resize(rows, cols);
return setRandom();
}

View File

@@ -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
};
/***************************************************************************
@@ -146,7 +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;
@@ -154,7 +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);
}
};
@@ -172,9 +191,8 @@ template<typename Func, typename Derived>
struct redux_impl<Func, Derived, DefaultTraversal, NoUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Index Index;
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Scalar run(const Derived& mat, const Func& 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;
@@ -198,18 +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(packet_traits<Scalar>::AlignedOnScalar)) || 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;
@@ -217,19 +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);
@@ -255,10 +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();
@@ -270,10 +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)
@@ -300,7 +317,7 @@ 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");
if (VectorizedSize > 0) {
@@ -315,6 +332,68 @@ struct redux_impl<Func, Derived, LinearVectorizedTraversal, CompleteUnrolling>
}
};
// 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
/***************************************************************************
@@ -325,18 +404,31 @@ 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 \c *this.
@@ -346,7 +438,7 @@ template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::minCoeff() const
{
return this->redux(Eigen::internal::scalar_min_op<Scalar>());
return derived().redux(Eigen::internal::scalar_min_op<Scalar>());
}
/** \returns the maximum of all coefficients of \c *this.
@@ -356,7 +448,7 @@ 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
@@ -369,7 +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
@@ -380,7 +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
@@ -396,7 +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.

View File

@@ -12,17 +12,13 @@
namespace Eigen {
template<typename Derived> class RefBase;
template<typename PlainObjectType, int Options = 0,
typename StrideType = typename internal::conditional<PlainObjectType::IsVectorAtCompileTime,InnerStride<1>,OuterStride<> >::type > class Ref;
/** \class Ref
* \ingroup Core_Module
*
* \brief A matrix or vector expression mapping an existing expression
*
* \tparam PlainObjectType the equivalent matrix type of the mapped data
* \tparam Options 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, Ref implies a contiguous storage along the inner dimension (inner stride==1),
* but accepts a variable outer stride (leading dimension).
@@ -52,8 +48,9 @@ template<typename PlainObjectType, int Options = 0,
* VectorXf a;
* foo1(a.head()); // OK
* foo1(A.col()); // OK
* foo1(A.row()); // compilation error because here innerstride!=1
* foo2(A.row()); // The row is copied into a contiguous temporary
* 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
@@ -95,7 +92,8 @@ struct traits<Ref<_PlainObjectType, _Options, _StrideType> >
typedef _StrideType StrideType;
enum {
Options = _Options,
Flags = traits<Map<_PlainObjectType, _Options, _StrideType> >::Flags | NestByRefBit
Flags = traits<Map<_PlainObjectType, _Options, _StrideType> >::Flags | NestByRefBit,
Alignment = traits<Map<_PlainObjectType, _Options, _StrideType> >::Alignment
};
template<typename Derived> struct match {
@@ -107,8 +105,9 @@ struct traits<Ref<_PlainObjectType, _Options, _StrideType> >
|| (int(StrideType::InnerStrideAtCompileTime)==0 && int(Derived::InnerStrideAtCompileTime)==1),
OuterStrideMatch = Derived::IsVectorAtCompileTime
|| int(StrideType::OuterStrideAtCompileTime)==int(Dynamic) || int(StrideType::OuterStrideAtCompileTime)==int(Derived::OuterStrideAtCompileTime),
AlignmentMatch = (_Options!=Aligned) || ((PlainObjectType::Flags&AlignedBit)==0) || ((traits<Derived>::Flags&AlignedBit)==AlignedBit),
MatchAtCompileTime = HasDirectAccess && StorageOrderMatch && InnerStrideMatch && OuterStrideMatch && AlignmentMatch
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;
};
@@ -131,12 +130,12 @@ public:
typedef MapBase<Derived> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(RefBase)
inline Index innerStride() const
EIGEN_DEVICE_FUNC inline Index innerStride() const
{
return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1;
}
inline Index outerStride() const
EIGEN_DEVICE_FUNC inline Index outerStride() const
{
return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
: IsVectorAtCompileTime ? this->size()
@@ -144,7 +143,7 @@ public:
: this->rows();
}
RefBase()
EIGEN_DEVICE_FUNC RefBase()
: Base(0,RowsAtCompileTime==Dynamic?0:RowsAtCompileTime,ColsAtCompileTime==Dynamic?0:ColsAtCompileTime),
// Stride<> does not allow default ctor for Dynamic strides, so let' initialize it with dummy values:
m_stride(StrideType::OuterStrideAtCompileTime==Dynamic?0:StrideType::OuterStrideAtCompileTime,
@@ -158,7 +157,7 @@ protected:
typedef Stride<StrideType::OuterStrideAtCompileTime,StrideType::InnerStrideAtCompileTime> StrideBase;
template<typename Expression>
void construct(Expression& expr)
EIGEN_DEVICE_FUNC void construct(Expression& expr)
{
if(PlainObjectType::RowsAtCompileTime==1)
{
@@ -187,7 +186,11 @@ protected:
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;
@@ -196,20 +199,23 @@ template<typename PlainObjectType, int Options, typename StrideType> class Ref
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename Derived>
inline Ref(PlainObjectBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
EIGEN_DEVICE_FUNC inline Ref(PlainObjectBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
{
Base::construct(expr);
EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
Base::construct(expr.derived());
}
template<typename Derived>
inline Ref(const DenseBase<Derived>& expr,
typename internal::enable_if<bool(internal::is_lvalue<Derived>::value&&bool(Traits::template match<Derived>::MatchAtCompileTime)),Derived>::type* = 0,
int = Derived::ThisConstantIsPrivateInPlainObjectBase)
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());
}
@@ -228,7 +234,8 @@ template<typename TPlainObjectType, int Options, typename StrideType> class Ref<
EIGEN_DENSE_PUBLIC_INTERFACE(Ref)
template<typename Derived>
inline Ref(const DenseBase<Derived>& expr)
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";
@@ -236,18 +243,27 @@ template<typename TPlainObjectType, int Options, typename StrideType> class Ref<
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>
void construct(const Expression& expr,internal::true_type)
EIGEN_DEVICE_FUNC void construct(const Expression& expr,internal::true_type)
{
Base::construct(expr);
}
template<typename Expression>
void construct(const Expression& expr, internal::false_type)
EIGEN_DEVICE_FUNC void construct(const Expression& expr, internal::false_type)
{
m_object.lazyAssign(expr);
internal::call_assignment_no_alias(m_object,expr,internal::assign_op<Scalar>());
Base::construct(m_object);
}

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
};
};
}
@@ -68,10 +66,12 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
typedef typename internal::dense_xpr_base<Replicate>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Replicate)
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
template<typename OriginalMatrixType>
inline explicit Replicate(const OriginalMatrixType& a_matrix)
: m_matrix(a_matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor)
EIGEN_DEVICE_FUNC
inline explicit Replicate(const OriginalMatrixType& matrix)
: m_matrix(matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor)
{
EIGEN_STATIC_ASSERT((internal::is_same<typename internal::remove_const<MatrixType>::type,OriginalMatrixType>::value),
THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
@@ -79,41 +79,20 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
}
template<typename OriginalMatrixType>
inline Replicate(const OriginalMatrixType& a_matrix, Index rowFactor, Index colFactor)
: m_matrix(a_matrix), m_rowFactor(rowFactor), m_colFactor(colFactor)
EIGEN_DEVICE_FUNC
inline Replicate(const OriginalMatrixType& matrix, Index rowFactor, Index colFactor)
: m_matrix(matrix), m_rowFactor(rowFactor), m_colFactor(colFactor)
{
EIGEN_STATIC_ASSERT((internal::is_same<typename internal::remove_const<MatrixType>::type,OriginalMatrixType>::value),
THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
}
EIGEN_DEVICE_FUNC
inline Index rows() const { return m_matrix.rows() * m_rowFactor.value(); }
EIGEN_DEVICE_FUNC
inline Index cols() const { return m_matrix.cols() * m_colFactor.value(); }
inline Scalar coeff(Index rowId, Index colId) const
{
// try to avoid using modulo; this is a pure optimization strategy
const Index actual_row = internal::traits<MatrixType>::RowsAtCompileTime==1 ? 0
: RowFactor==1 ? rowId
: rowId%m_matrix.rows();
const Index actual_col = internal::traits<MatrixType>::ColsAtCompileTime==1 ? 0
: ColFactor==1 ? colId
: colId%m_matrix.cols();
return m_matrix.coeff(actual_row, actual_col);
}
template<int LoadMode>
inline PacketScalar packet(Index rowId, Index colId) const
{
const Index actual_row = internal::traits<MatrixType>::RowsAtCompileTime==1 ? 0
: RowFactor==1 ? rowId
: rowId%m_matrix.rows();
const Index actual_col = internal::traits<MatrixType>::ColsAtCompileTime==1 ? 0
: ColFactor==1 ? colId
: colId%m_matrix.cols();
return m_matrix.template packet<LoadMode>(actual_row, actual_col);
}
EIGEN_DEVICE_FUNC
const _MatrixTypeNested& nestedExpression() const
{
return m_matrix;
@@ -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
: internal::no_assignment_operator, public internal::dense_xpr_base< ReturnByValue<Derived> >::type
: public internal::dense_xpr_base< ReturnByValue<Derived> >::type, internal::no_assignment_operator
{
public:
typedef typename internal::traits<Derived>::ReturnType ReturnType;
@@ -73,6 +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
};
@@ -84,6 +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

@@ -43,23 +43,21 @@ 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)
inline EIGEN_DEVICE_FUNC
Select(const ConditionMatrixType& a_conditionMatrix,
const ThenMatrixType& a_thenMatrix,
const ElseMatrixType& a_elseMatrix)
@@ -69,9 +67,10 @@ class Select : internal::no_assignment_operator,
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;
}

View File

@@ -32,45 +32,42 @@ 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;
EIGEN_DEVICE_FUNC
inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix)
explicit inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix)
{}
EIGEN_DEVICE_FUNC
@@ -98,6 +95,7 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
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);
}
@@ -111,26 +109,29 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
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>
EIGEN_DEVICE_FUNC
SelfadjointProductMatrix<MatrixType,Mode,false,OtherDerived,0,OtherDerived::IsVectorAtCompileTime>
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
EIGEN_DEVICE_FUNC
SelfadjointProductMatrix<OtherDerived,0,OtherDerived::IsVectorAtCompileTime,MatrixType,Mode,false>
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:
@@ -194,96 +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;
};
EIGEN_DEVICE_FUNC
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) = numext::real(src.coeff(row, col));
else if(row < col)
dst.coeffRef(col, row) = numext::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>
{
EIGEN_DEVICE_FUNC
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
};
EIGEN_DEVICE_FUNC
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) = numext::real(src.coeff(row, col));
else if(row > col)
dst.coeffRef(col, row) = numext::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>
{
EIGEN_DEVICE_FUNC
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;
EIGEN_DEVICE_FUNC
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) = numext::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>
{
EIGEN_DEVICE_FUNC
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) = numext::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
@@ -297,7 +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>
@@ -305,7 +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

@@ -12,179 +12,11 @@
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&AlignedBit) | (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;
EIGEN_DEVICE_FUNC
inline SelfCwiseBinaryOp(Lhs& xpr, const BinaryOp& func = BinaryOp()) : m_matrix(xpr), m_functor(func) {}
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(); }
EIGEN_DEVICE_FUNC 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()
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index row, Index col)
{
EIGEN_STATIC_ASSERT_LVALUE(Lhs)
return m_matrix.const_cast_derived().coeffRef(row, col);
}
EIGEN_DEVICE_FUNC
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()
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_LVALUE(Lhs)
return m_matrix.const_cast_derived().coeffRef(index);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
return m_matrix.const_cast_derived().coeffRef(index);
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
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>
EIGEN_DEVICE_FUNC
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_DEVICE_FUNC
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...
EIGEN_DEVICE_FUNC
SelfCwiseBinaryOp& operator=(const Rhs& _rhs)
{
typename internal::nested<Rhs>::type rhs(_rhs);
return Base::operator=(rhs);
}
EIGEN_DEVICE_FUNC
Lhs& expression() const
{
return m_matrix;
}
EIGEN_DEVICE_FUNC
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();
}
@@ -192,8 +24,7 @@ template<typename Derived>
inline Derived& ArrayBase<Derived>::operator+=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
SelfCwiseBinaryOp<internal::scalar_sum_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::add_assign_op<Scalar>());
return derived();
}
@@ -201,8 +32,7 @@ template<typename Derived>
inline Derived& ArrayBase<Derived>::operator-=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
SelfCwiseBinaryOp<internal::scalar_difference_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::sub_assign_op<Scalar>());
return derived();
}
@@ -210,8 +40,7 @@ template<typename Derived>
inline Derived& DenseBase<Derived>::operator/=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
SelfCwiseBinaryOp<internal::scalar_quotient_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::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

@@ -17,7 +17,6 @@ namespace internal {
template<typename ExpressionType, typename Scalar>
inline void stable_norm_kernel(const ExpressionType& bl, Scalar& ssq, Scalar& scale, Scalar& invScale)
{
using std::max;
Scalar maxCoeff = bl.cwiseAbs().maxCoeff();
if(maxCoeff>scale)
@@ -56,10 +55,7 @@ inline typename NumTraits<typename traits<Derived>::Scalar>::Real
blueNorm_impl(const EigenBase<Derived>& _vec)
{
typedef typename Derived::RealScalar RealScalar;
typedef typename Derived::Index Index;
using std::pow;
EIGEN_USING_STD_MATH(min);
EIGEN_USING_STD_MATH(max);
using std::sqrt;
using std::abs;
const Derived& vec(_vec.derived());
@@ -136,8 +132,8 @@ blueNorm_impl(const EigenBase<Derived>& _vec)
}
else
return sqrt(amed);
asml = (min)(abig, amed);
abig = (max)(abig, amed);
asml = numext::mini(abig, amed);
abig = numext::maxi(abig, amed);
if(asml <= abig*relerr)
return abig;
else
@@ -160,21 +156,33 @@ template<typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
MatrixBase<Derived>::stableNorm() const
{
EIGEN_USING_STD_MATH(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);
internal::stable_norm_kernel(SegmentWrapper(copy.segment(bi,numext::mini(blockSize, n - bi))), ssq, scale, invScale);
return scale * sqrt(ssq);
}

View File

@@ -44,7 +44,7 @@ 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
@@ -86,26 +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;
EIGEN_DEVICE_FUNC InnerStride() : Base() {}
EIGEN_DEVICE_FUNC InnerStride(Index v) : Base(0, v) {}
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;
EIGEN_DEVICE_FUNC OuterStride() : Base() {}
EIGEN_DEVICE_FUNC OuterStride(Index v) : Base(v,0) {}
EIGEN_DEVICE_FUNC OuterStride(Index v) : Base(v,0) {} // FIXME making this explicit could break valid code
};
} // end namespace Eigen

View File

@@ -12,129 +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;
EIGEN_DEVICE_FUNC
inline SwapWrapper(ExpressionType& xpr) : m_expression(xpr) {}
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(); }
typedef typename internal::conditional<
internal::is_lvalue<ExpressionType>::value,
Scalar,
const Scalar
>::type ScalarWithConstIfNotLvalue;
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return m_expression.data(); }
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index rowId, Index colId)
{
return m_expression.const_cast_derived().coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
}
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index index) const
{
return m_expression.coeffRef(index);
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
void copyCoeff(Index rowId, Index colId, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(rowId >= 0 && rowId < rows()
&& colId >= 0 && colId < cols());
Scalar tmp = m_expression.coeff(rowId, colId);
m_expression.coeffRef(rowId, colId) = _other.coeff(rowId, colId);
_other.coeffRef(rowId, colId) = tmp;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
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 rowId, Index colId, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(rowId >= 0 && rowId < rows()
&& colId >= 0 && colId < cols());
Packet tmp = m_expression.template packet<StoreMode>(rowId, colId);
m_expression.template writePacket<StoreMode>(rowId, colId,
_other.template packet<LoadMode>(rowId, colId)
);
_other.template writePacket<LoadMode>(rowId, colId, 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);
}
EIGEN_DEVICE_FUNC
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

View File

@@ -2,7 +2,7 @@
// for linear algebra.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009-2014 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
@@ -29,13 +29,10 @@ namespace Eigen {
namespace internal {
template<typename MatrixType>
struct traits<Transpose<MatrixType> > : traits<MatrixType>
struct traits<Transpose<MatrixType> > : public traits<MatrixType>
{
typedef typename MatrixType::Scalar Scalar;
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type MatrixTypeNestedPlain;
typedef typename traits<MatrixType>::StorageKind StorageKind;
typedef typename traits<MatrixType>::XprKind XprKind;
enum {
RowsAtCompileTime = MatrixType::ColsAtCompileTime,
ColsAtCompileTime = MatrixType::RowsAtCompileTime,
@@ -45,7 +42,6 @@ struct traits<Transpose<MatrixType> > : traits<MatrixType>
Flags0 = MatrixTypeNestedPlain::Flags & ~(LvalueBit | NestByRefBit),
Flags1 = Flags0 | FlagsLvalueBit,
Flags = Flags1 ^ RowMajorBit,
CoeffReadCost = MatrixTypeNestedPlain::CoeffReadCost,
InnerStrideAtCompileTime = inner_stride_at_compile_time<MatrixType>::ret,
OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret
};
@@ -61,9 +57,10 @@ template<typename MatrixType> class Transpose
typedef typename TransposeImpl<MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(Transpose)
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
EIGEN_DEVICE_FUNC
inline Transpose(MatrixType& a_matrix) : m_matrix(a_matrix) {}
explicit inline Transpose(MatrixType& matrix) : m_matrix(matrix) {}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Transpose)
@@ -100,12 +97,22 @@ struct TransposeImpl_base<MatrixType, false>
} // end namespace internal
// Generic API dispatcher
template<typename XprType, typename StorageKind>
class TransposeImpl
: public internal::generic_xpr_base<Transpose<XprType> >::type
{
public:
typedef typename internal::generic_xpr_base<Transpose<XprType> >::type Base;
};
template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
: public internal::TransposeImpl_base<MatrixType>::type
{
public:
typedef typename internal::TransposeImpl_base<MatrixType>::type Base;
using Base::coeffRef;
EIGEN_DENSE_PUBLIC_INTERFACE(Transpose<MatrixType>)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(TransposeImpl)
@@ -118,23 +125,10 @@ template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
const Scalar
>::type ScalarWithConstIfNotLvalue;
inline ScalarWithConstIfNotLvalue* data() { return derived().nestedExpression().data(); }
inline const Scalar* data() const { return derived().nestedExpression().data(); }
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue& coeffRef(Index rowId, Index colId)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return derived().nestedExpression().const_cast_derived().coeffRef(colId, rowId);
}
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return derived().nestedExpression().const_cast_derived().coeffRef(index);
}
EIGEN_DEVICE_FUNC inline ScalarWithConstIfNotLvalue* data() { return derived().nestedExpression().data(); }
EIGEN_DEVICE_FUNC inline const Scalar* data() const { return derived().nestedExpression().data(); }
// FIXME: shall we keep the const version of coeffRef?
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
@@ -146,42 +140,6 @@ template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
{
return derived().nestedExpression().coeffRef(index);
}
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index rowId, Index colId) const
{
return derived().nestedExpression().coeff(colId, rowId);
}
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index index) const
{
return derived().nestedExpression().coeff(index);
}
template<int LoadMode>
inline const PacketScalar packet(Index rowId, Index colId) const
{
return derived().nestedExpression().template packet<LoadMode>(colId, rowId);
}
template<int LoadMode>
inline void writePacket(Index rowId, Index colId, const PacketScalar& x)
{
derived().nestedExpression().const_cast_derived().template writePacket<LoadMode>(colId, rowId, x);
}
template<int LoadMode>
inline const PacketScalar packet(Index index) const
{
return derived().nestedExpression().template packet<LoadMode>(index);
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
{
derived().nestedExpression().const_cast_derived().template writePacket<LoadMode>(index, x);
}
};
/** \returns an expression of the transpose of *this.
@@ -207,7 +165,7 @@ template<typename Derived>
inline Transpose<Derived>
DenseBase<Derived>::transpose()
{
return derived();
return TransposeReturnType(derived());
}
/** This is the const version of transpose().
@@ -245,8 +203,7 @@ template<typename Derived>
inline const typename MatrixBase<Derived>::AdjointReturnType
MatrixBase<Derived>::adjoint() const
{
return this->transpose(); // in the complex case, the .conjugate() is be implicit here
// due to implicit conversion to return type
return AdjointReturnType(this->transpose());
}
/***************************************************************************
@@ -256,18 +213,38 @@ MatrixBase<Derived>::adjoint() const
namespace internal {
template<typename MatrixType,
bool IsSquare = (MatrixType::RowsAtCompileTime == MatrixType::ColsAtCompileTime) && MatrixType::RowsAtCompileTime!=Dynamic>
bool IsSquare = (MatrixType::RowsAtCompileTime == MatrixType::ColsAtCompileTime) && MatrixType::RowsAtCompileTime!=Dynamic,
bool MatchPacketSize =
(int(MatrixType::RowsAtCompileTime) == int(internal::packet_traits<typename MatrixType::Scalar>::size))
&& (internal::evaluator<MatrixType>::Flags&PacketAccessBit) >
struct inplace_transpose_selector;
template<typename MatrixType>
struct inplace_transpose_selector<MatrixType,true> { // square matrix
struct inplace_transpose_selector<MatrixType,true,false> { // square matrix
static void run(MatrixType& m) {
m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose());
}
};
// TODO: vectorized path is currently limited to LargestPacketSize x LargestPacketSize cases only.
template<typename MatrixType>
struct inplace_transpose_selector<MatrixType,false> { // non square matrix
struct inplace_transpose_selector<MatrixType,true,true> { // PacketSize x PacketSize
static void run(MatrixType& m) {
typedef typename MatrixType::Scalar Scalar;
typedef typename internal::packet_traits<typename MatrixType::Scalar>::type Packet;
const Index PacketSize = internal::packet_traits<Scalar>::size;
const Index Alignment = internal::evaluator<MatrixType>::Alignment;
PacketBlock<Packet> A;
for (Index i=0; i<PacketSize; ++i)
A.packet[i] = m.template packetByOuterInner<Alignment>(i,0);
internal::ptranspose(A);
for (Index i=0; i<PacketSize; ++i)
m.template writePacket<Alignment>(m.rowIndexByOuterInner(i,0), m.colIndexByOuterInner(i,0), A.packet[i]);
}
};
template<typename MatrixType,bool MatchPacketSize>
struct inplace_transpose_selector<MatrixType,false,MatchPacketSize> { // non square matrix
static void run(MatrixType& m) {
if (m.rows()==m.cols())
m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose());
@@ -340,14 +317,6 @@ inline void MatrixBase<Derived>::adjointInPlace()
namespace internal {
template<typename BinOp,typename NestedXpr,typename Rhs>
struct blas_traits<SelfCwiseBinaryOp<BinOp,NestedXpr,Rhs> >
: blas_traits<NestedXpr>
{
typedef SelfCwiseBinaryOp<BinOp,NestedXpr,Rhs> XprType;
static inline const XprType extract(const XprType& x) { return x; }
};
template<bool DestIsTransposed, typename OtherDerived>
struct check_transpose_aliasing_compile_time_selector
{
@@ -413,15 +382,15 @@ struct checkTransposeAliasing_impl<Derived, OtherDerived, false>
}
};
template<typename Dst, typename Src>
void check_for_aliasing(const Dst &dst, const Src &src)
{
internal::checkTransposeAliasing_impl<Dst, Src>::run(dst, src);
}
} // end namespace internal
template<typename Derived>
template<typename OtherDerived>
void DenseBase<Derived>::checkTransposeAliasing(const OtherDerived& other) const
{
internal::checkTransposeAliasing_impl<Derived, OtherDerived>::run(derived(), other);
}
#endif
#endif // EIGEN_NO_DEBUG
} // end namespace Eigen

View File

@@ -41,10 +41,6 @@ namespace Eigen {
* \sa class PermutationMatrix
*/
namespace internal {
template<typename TranspositionType, typename MatrixType, int Side, bool Transposed=false> struct transposition_matrix_product_retval;
}
template<typename Derived>
class TranspositionsBase
{
@@ -53,8 +49,8 @@ class TranspositionsBase
public:
typedef typename Traits::IndicesType IndicesType;
typedef typename IndicesType::Scalar StorageIndexType;
typedef typename IndicesType::Index Index;
typedef typename IndicesType::Scalar StorageIndex;
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
Derived& derived() { return *static_cast<Derived*>(this); }
const Derived& derived() const { return *static_cast<const Derived*>(this); }
@@ -66,7 +62,7 @@ class TranspositionsBase
indices() = other.indices();
return derived();
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** This is a special case of the templated operator=. Its purpose is to
* prevent a default operator= from hiding the templated operator=.
@@ -79,20 +75,24 @@ class TranspositionsBase
#endif
/** \returns the number of transpositions */
inline Index size() const { return indices().size(); }
Index size() const { return indices().size(); }
/** \returns the number of rows of the equivalent permutation matrix */
Index rows() const { return indices().size(); }
/** \returns the number of columns of the equivalent permutation matrix */
Index cols() const { return indices().size(); }
/** Direct access to the underlying index vector */
inline const StorageIndexType& coeff(Index i) const { return indices().coeff(i); }
inline const StorageIndex& coeff(Index i) const { return indices().coeff(i); }
/** Direct access to the underlying index vector */
inline StorageIndexType& coeffRef(Index i) { return indices().coeffRef(i); }
inline StorageIndex& coeffRef(Index i) { return indices().coeffRef(i); }
/** Direct access to the underlying index vector */
inline const StorageIndexType& operator()(Index i) const { return indices()(i); }
inline const StorageIndex& operator()(Index i) const { return indices()(i); }
/** Direct access to the underlying index vector */
inline StorageIndexType& operator()(Index i) { return indices()(i); }
inline StorageIndex& operator()(Index i) { return indices()(i); }
/** Direct access to the underlying index vector */
inline const StorageIndexType& operator[](Index i) const { return indices()(i); }
inline const StorageIndex& operator[](Index i) const { return indices()(i); }
/** Direct access to the underlying index vector */
inline StorageIndexType& operator[](Index i) { return indices()(i); }
inline StorageIndex& operator[](Index i) { return indices()(i); }
/** const version of indices(). */
const IndicesType& indices() const { return derived().indices(); }
@@ -108,7 +108,7 @@ class TranspositionsBase
/** Sets \c *this to represents an identity transformation */
void setIdentity()
{
for(StorageIndexType i = 0; i < indices().size(); ++i)
for(StorageIndex i = 0; i < indices().size(); ++i)
coeffRef(i) = i;
}
@@ -145,26 +145,24 @@ class TranspositionsBase
};
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndexType>
struct traits<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndexType> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
struct traits<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
: traits<PermutationMatrix<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
{
typedef Matrix<_StorageIndexType, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
typedef typename IndicesType::Index Index;
typedef _StorageIndexType StorageIndexType;
typedef Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
typedef TranspositionsStorage StorageKind;
};
}
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndexType>
class Transpositions : public TranspositionsBase<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndexType> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
class Transpositions : public TranspositionsBase<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
{
typedef internal::traits<Transpositions> Traits;
public:
typedef TranspositionsBase<Transpositions> Base;
typedef typename Traits::IndicesType IndicesType;
typedef typename IndicesType::Scalar StorageIndexType;
typedef typename IndicesType::Index Index;
typedef typename IndicesType::Scalar StorageIndex;
inline Transpositions() {}
@@ -181,7 +179,7 @@ class Transpositions : public TranspositionsBase<Transpositions<SizeAtCompileTim
/** Generic constructor from expression of the transposition indices. */
template<typename Other>
explicit inline Transpositions(const MatrixBase<Other>& a_indices) : m_indices(a_indices)
explicit inline Transpositions(const MatrixBase<Other>& indices) : m_indices(indices)
{}
/** Copies the \a other transpositions into \c *this */
@@ -219,32 +217,32 @@ class Transpositions : public TranspositionsBase<Transpositions<SizeAtCompileTim
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndexType, int _PacketAccess>
struct traits<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndexType>,_PacketAccess> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int _PacketAccess>
struct traits<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex>,_PacketAccess> >
: traits<PermutationMatrix<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
{
typedef Map<const Matrix<_StorageIndexType,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1>, _PacketAccess> IndicesType;
typedef typename IndicesType::Index Index;
typedef _StorageIndexType StorageIndexType;
typedef Map<const Matrix<_StorageIndex,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1>, _PacketAccess> IndicesType;
typedef _StorageIndex StorageIndex;
typedef TranspositionsStorage StorageKind;
};
}
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndexType, int PacketAccess>
class Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndexType>,PacketAccess>
: public TranspositionsBase<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndexType>,PacketAccess> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int PacketAccess>
class Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex>,PacketAccess>
: public TranspositionsBase<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex>,PacketAccess> >
{
typedef internal::traits<Map> Traits;
public:
typedef TranspositionsBase<Map> Base;
typedef typename Traits::IndicesType IndicesType;
typedef typename IndicesType::Scalar StorageIndexType;
typedef typename IndicesType::Index Index;
typedef typename IndicesType::Scalar StorageIndex;
inline Map(const StorageIndexType* indicesPtr)
explicit inline Map(const StorageIndex* indicesPtr)
: m_indices(indicesPtr)
{}
inline Map(const StorageIndexType* indicesPtr, Index size)
inline Map(const StorageIndex* indicesPtr, Index size)
: m_indices(indicesPtr,size)
{}
@@ -280,10 +278,9 @@ class Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndexTyp
namespace internal {
template<typename _IndicesType>
struct traits<TranspositionsWrapper<_IndicesType> >
: traits<PermutationWrapper<_IndicesType> >
{
typedef typename _IndicesType::Scalar StorageIndexType;
typedef typename _IndicesType::Index Index;
typedef _IndicesType IndicesType;
typedef TranspositionsStorage StorageKind;
};
}
@@ -296,11 +293,10 @@ class TranspositionsWrapper
typedef TranspositionsBase<TranspositionsWrapper> Base;
typedef typename Traits::IndicesType IndicesType;
typedef typename IndicesType::Scalar StorageIndexType;
typedef typename IndicesType::Index Index;
typedef typename IndicesType::Scalar StorageIndex;
inline TranspositionsWrapper(IndicesType& a_indices)
: m_indices(a_indices)
explicit inline TranspositionsWrapper(IndicesType& indices)
: m_indices(indices)
{}
/** Copies the \a other transpositions into \c *this */
@@ -332,81 +328,43 @@ class TranspositionsWrapper
const typename IndicesType::Nested m_indices;
};
/** \returns the \a matrix with the \a transpositions applied to the columns.
*/
template<typename Derived, typename TranspositionsDerived>
inline const internal::transposition_matrix_product_retval<TranspositionsDerived, Derived, OnTheRight>
operator*(const MatrixBase<Derived>& matrix,
const TranspositionsBase<TranspositionsDerived> &transpositions)
template<typename MatrixDerived, typename TranspositionsDerived>
EIGEN_DEVICE_FUNC
const Product<MatrixDerived, TranspositionsDerived, AliasFreeProduct>
operator*(const MatrixBase<MatrixDerived> &matrix,
const TranspositionsBase<TranspositionsDerived>& transpositions)
{
return internal::transposition_matrix_product_retval
<TranspositionsDerived, Derived, OnTheRight>
(transpositions.derived(), matrix.derived());
return Product<MatrixDerived, TranspositionsDerived, AliasFreeProduct>
(matrix.derived(), transpositions.derived());
}
/** \returns the \a matrix with the \a transpositions applied to the rows.
*/
template<typename Derived, typename TranspositionDerived>
inline const internal::transposition_matrix_product_retval
<TranspositionDerived, Derived, OnTheLeft>
operator*(const TranspositionsBase<TranspositionDerived> &transpositions,
const MatrixBase<Derived>& matrix)
template<typename TranspositionsDerived, typename MatrixDerived>
EIGEN_DEVICE_FUNC
const Product<TranspositionsDerived, MatrixDerived, AliasFreeProduct>
operator*(const TranspositionsBase<TranspositionsDerived> &transpositions,
const MatrixBase<MatrixDerived>& matrix)
{
return internal::transposition_matrix_product_retval
<TranspositionDerived, Derived, OnTheLeft>
(transpositions.derived(), matrix.derived());
return Product<TranspositionsDerived, MatrixDerived, AliasFreeProduct>
(transpositions.derived(), matrix.derived());
}
// Template partial specialization for transposed/inverse transpositions
namespace internal {
template<typename TranspositionType, typename MatrixType, int Side, bool Transposed>
struct traits<transposition_matrix_product_retval<TranspositionType, MatrixType, Side, Transposed> >
{
typedef typename MatrixType::PlainObject ReturnType;
};
template<typename TranspositionType, typename MatrixType, int Side, bool Transposed>
struct transposition_matrix_product_retval
: public ReturnByValue<transposition_matrix_product_retval<TranspositionType, MatrixType, Side, Transposed> >
{
typedef typename remove_all<typename MatrixType::Nested>::type MatrixTypeNestedCleaned;
typedef typename TranspositionType::Index Index;
typedef typename TranspositionType::StorageIndexType StorageIndexType;
transposition_matrix_product_retval(const TranspositionType& tr, const MatrixType& matrix)
: m_transpositions(tr), m_matrix(matrix)
{}
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
template<typename Dest> inline void evalTo(Dest& dst) const
{
const Index size = m_transpositions.size();
StorageIndexType j = 0;
if(!(is_same<MatrixTypeNestedCleaned,Dest>::value && extract_data(dst) == extract_data(m_matrix)))
dst = m_matrix;
for(Index k=(Transposed?size-1:0) ; Transposed?k>=0:k<size ; Transposed?--k:++k)
if(Index(j=m_transpositions.coeff(k))!=k)
{
if(Side==OnTheLeft)
dst.row(k).swap(dst.row(j));
else if(Side==OnTheRight)
dst.col(k).swap(dst.col(j));
}
}
protected:
const TranspositionType& m_transpositions;
typename MatrixType::Nested m_matrix;
};
template<typename Derived>
struct traits<Transpose<TranspositionsBase<Derived> > >
: traits<Derived>
{};
} // end namespace internal
/* Template partial specialization for transposed/inverse transpositions */
template<typename TranspositionsDerived>
class Transpose<TranspositionsBase<TranspositionsDerived> >
{
@@ -414,27 +372,31 @@ class Transpose<TranspositionsBase<TranspositionsDerived> >
typedef typename TranspositionType::IndicesType IndicesType;
public:
Transpose(const TranspositionType& t) : m_transpositions(t) {}
explicit Transpose(const TranspositionType& t) : m_transpositions(t) {}
inline int size() const { return m_transpositions.size(); }
Index size() const { return m_transpositions.size(); }
Index rows() const { return m_transpositions.size(); }
Index cols() const { return m_transpositions.size(); }
/** \returns the \a matrix with the inverse transpositions applied to the columns.
*/
template<typename Derived> friend
inline const internal::transposition_matrix_product_retval<TranspositionType, Derived, OnTheRight, true>
operator*(const MatrixBase<Derived>& matrix, const Transpose& trt)
template<typename OtherDerived> friend
const Product<OtherDerived, Transpose, AliasFreeProduct>
operator*(const MatrixBase<OtherDerived>& matrix, const Transpose& trt)
{
return internal::transposition_matrix_product_retval<TranspositionType, Derived, OnTheRight, true>(trt.m_transpositions, matrix.derived());
return Product<OtherDerived, Transpose, AliasFreeProduct>(matrix.derived(), trt.derived());
}
/** \returns the \a matrix with the inverse transpositions applied to the rows.
*/
template<typename Derived>
inline const internal::transposition_matrix_product_retval<TranspositionType, Derived, OnTheLeft, true>
operator*(const MatrixBase<Derived>& matrix) const
template<typename OtherDerived>
const Product<Transpose, OtherDerived, AliasFreeProduct>
operator*(const MatrixBase<OtherDerived>& matrix) const
{
return internal::transposition_matrix_product_retval<TranspositionType, Derived, OnTheLeft, true>(m_transpositions, matrix.derived());
return Product<Transpose, OtherDerived, AliasFreeProduct>(*this, matrix.derived());
}
const TranspositionType& nestedExpression() const { return m_transpositions; }
protected:
const TranspositionType& m_transpositions;

File diff suppressed because it is too large Load Diff

View File

@@ -41,32 +41,22 @@ struct traits<PartialReduxExpr<MatrixType, MemberOp, Direction> >
typedef typename traits<MatrixType>::StorageKind StorageKind;
typedef typename traits<MatrixType>::XprKind XprKind;
typedef typename MatrixType::Scalar InputScalar;
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_all<MatrixTypeNested>::type _MatrixTypeNested;
enum {
RowsAtCompileTime = Direction==Vertical ? 1 : MatrixType::RowsAtCompileTime,
ColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::ColsAtCompileTime,
MaxRowsAtCompileTime = Direction==Vertical ? 1 : MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::MaxColsAtCompileTime,
Flags0 = (unsigned int)_MatrixTypeNested::Flags & HereditaryBits,
Flags = (Flags0 & ~RowMajorBit) | (RowsAtCompileTime == 1 ? RowMajorBit : 0),
Flags = RowsAtCompileTime == 1 ? RowMajorBit : 0,
TraversalSize = Direction==Vertical ? MatrixType::RowsAtCompileTime : MatrixType::ColsAtCompileTime
};
#if EIGEN_GNUC_AT_LEAST(3,4)
typedef typename MemberOp::template Cost<InputScalar,int(TraversalSize)> CostOpType;
#else
typedef typename MemberOp::template Cost<InputScalar,TraversalSize> CostOpType;
#endif
enum {
CoeffReadCost = TraversalSize==Dynamic ? Dynamic
: TraversalSize * traits<_MatrixTypeNested>::CoeffReadCost + int(CostOpType::value)
};
};
}
template< typename MatrixType, typename MemberOp, int Direction>
class PartialReduxExpr : internal::no_assignment_operator,
public internal::dense_xpr_base< PartialReduxExpr<MatrixType, MemberOp, Direction> >::type
class PartialReduxExpr : public internal::dense_xpr_base< PartialReduxExpr<MatrixType, MemberOp, Direction> >::type,
internal::no_assignment_operator
{
public:
@@ -75,13 +65,16 @@ class PartialReduxExpr : internal::no_assignment_operator,
typedef typename internal::traits<PartialReduxExpr>::MatrixTypeNested MatrixTypeNested;
typedef typename internal::traits<PartialReduxExpr>::_MatrixTypeNested _MatrixTypeNested;
PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp())
EIGEN_DEVICE_FUNC
explicit PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp())
: m_matrix(mat), m_functor(func) {}
EIGEN_DEVICE_FUNC
Index rows() const { return (Direction==Vertical ? 1 : m_matrix.rows()); }
EIGEN_DEVICE_FUNC
Index cols() const { return (Direction==Horizontal ? 1 : m_matrix.cols()); }
EIGEN_STRONG_INLINE const Scalar coeff(Index i, Index j) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index i, Index j) const
{
if (Direction==Vertical)
return m_functor(m_matrix.col(j));
@@ -89,7 +82,7 @@ class PartialReduxExpr : internal::no_assignment_operator,
return m_functor(m_matrix.row(i));
}
const Scalar coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
{
if (Direction==Vertical)
return m_functor(m_matrix.col(index));
@@ -110,7 +103,8 @@ class PartialReduxExpr : internal::no_assignment_operator,
template<typename Scalar, int Size> struct Cost \
{ enum { value = COST }; }; \
template<typename XprType> \
EIGEN_STRONG_INLINE ResultType operator()(const XprType& mat) const \
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE \
ResultType operator()(const XprType& mat) const \
{ return mat.MEMBER(); } \
}
@@ -134,13 +128,13 @@ EIGEN_MEMBER_FUNCTOR(prod, (Size-1)*NumTraits<Scalar>::MulCost);
template <typename BinaryOp, typename Scalar>
struct member_redux {
typedef typename result_of<
BinaryOp(Scalar)
BinaryOp(Scalar,Scalar)
>::type result_type;
template<typename _Scalar, int Size> struct Cost
{ enum { value = (Size-1) * functor_traits<BinaryOp>::Cost }; };
member_redux(const BinaryOp func) : m_functor(func) {}
EIGEN_DEVICE_FUNC explicit member_redux(const BinaryOp func) : m_functor(func) {}
template<typename Derived>
inline result_type operator()(const DenseBase<Derived>& mat) const
EIGEN_DEVICE_FUNC inline result_type operator()(const DenseBase<Derived>& mat) const
{ return mat.redux(m_functor); }
const BinaryOp m_functor;
};
@@ -169,16 +163,15 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
typedef typename ExpressionType::Scalar Scalar;
typedef typename ExpressionType::RealScalar RealScalar;
typedef typename ExpressionType::Index Index;
typedef typename internal::conditional<internal::must_nest_by_value<ExpressionType>::ret,
ExpressionType, ExpressionType&>::type ExpressionTypeNested;
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
typedef typename internal::ref_selector<ExpressionType>::non_const_type ExpressionTypeNested;
typedef typename internal::remove_all<ExpressionTypeNested>::type ExpressionTypeNestedCleaned;
template<template<typename _Scalar> class Functor,
typename Scalar=typename internal::traits<ExpressionType>::Scalar> struct ReturnType
typename Scalar_=Scalar> struct ReturnType
{
typedef PartialReduxExpr<ExpressionType,
Functor<Scalar>,
Functor<Scalar_>,
Direction
> Type;
};
@@ -186,23 +179,24 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
template<typename BinaryOp> struct ReduxReturnType
{
typedef PartialReduxExpr<ExpressionType,
internal::member_redux<BinaryOp,typename internal::traits<ExpressionType>::Scalar>,
internal::member_redux<BinaryOp,Scalar>,
Direction
> Type;
};
enum {
IsVertical = (Direction==Vertical) ? 1 : 0,
IsHorizontal = (Direction==Horizontal) ? 1 : 0
isVertical = (Direction==Vertical) ? 1 : 0,
isHorizontal = (Direction==Horizontal) ? 1 : 0
};
protected:
/** \internal
* \returns the i-th subvector according to the \c Direction */
typedef typename internal::conditional<Direction==Vertical,
typedef typename internal::conditional<isVertical,
typename ExpressionType::ColXpr,
typename ExpressionType::RowXpr>::type SubVector;
EIGEN_DEVICE_FUNC
SubVector subVector(Index i)
{
return SubVector(m_matrix.derived(),i);
@@ -210,58 +204,62 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** \internal
* \returns the number of subvectors in the direction \c Direction */
EIGEN_DEVICE_FUNC
Index subVectors() const
{ return Direction==Vertical?m_matrix.cols():m_matrix.rows(); }
{ return isVertical?m_matrix.cols():m_matrix.rows(); }
template<typename OtherDerived> struct ExtendedType {
typedef Replicate<OtherDerived,
Direction==Vertical ? 1 : ExpressionType::RowsAtCompileTime,
Direction==Horizontal ? 1 : ExpressionType::ColsAtCompileTime> Type;
isVertical ? 1 : ExpressionType::RowsAtCompileTime,
isHorizontal ? 1 : ExpressionType::ColsAtCompileTime> Type;
};
/** \internal
* Replicates a vector to match the size of \c *this */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
typename ExtendedType<OtherDerived>::Type
extendedTo(const DenseBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Vertical, OtherDerived::MaxColsAtCompileTime==1),
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isVertical, OtherDerived::MaxColsAtCompileTime==1),
YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Horizontal, OtherDerived::MaxRowsAtCompileTime==1),
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isHorizontal, OtherDerived::MaxRowsAtCompileTime==1),
YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED)
return typename ExtendedType<OtherDerived>::Type
(other.derived(),
Direction==Vertical ? 1 : m_matrix.rows(),
Direction==Horizontal ? 1 : m_matrix.cols());
isVertical ? 1 : m_matrix.rows(),
isHorizontal ? 1 : m_matrix.cols());
}
template<typename OtherDerived> struct OppositeExtendedType {
typedef Replicate<OtherDerived,
Direction==Horizontal ? 1 : ExpressionType::RowsAtCompileTime,
Direction==Vertical ? 1 : ExpressionType::ColsAtCompileTime> Type;
isHorizontal ? 1 : ExpressionType::RowsAtCompileTime,
isVertical ? 1 : ExpressionType::ColsAtCompileTime> Type;
};
/** \internal
* Replicates a vector in the opposite direction to match the size of \c *this */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
typename OppositeExtendedType<OtherDerived>::Type
extendedToOpposite(const DenseBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Horizontal, OtherDerived::MaxColsAtCompileTime==1),
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isHorizontal, OtherDerived::MaxColsAtCompileTime==1),
YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Vertical, OtherDerived::MaxRowsAtCompileTime==1),
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isVertical, OtherDerived::MaxRowsAtCompileTime==1),
YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED)
return typename OppositeExtendedType<OtherDerived>::Type
(other.derived(),
Direction==Horizontal ? 1 : m_matrix.rows(),
Direction==Vertical ? 1 : m_matrix.cols());
isHorizontal ? 1 : m_matrix.rows(),
isVertical ? 1 : m_matrix.cols());
}
public:
inline VectorwiseOp(ExpressionType& matrix) : m_matrix(matrix) {}
EIGEN_DEVICE_FUNC
explicit inline VectorwiseOp(ExpressionType& matrix) : m_matrix(matrix) {}
/** \internal */
EIGEN_DEVICE_FUNC
inline const ExpressionType& _expression() const { return m_matrix; }
/** \returns a row or column vector expression of \c *this reduxed by \a func
@@ -272,9 +270,25 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* \sa class VectorwiseOp, DenseBase::colwise(), DenseBase::rowwise()
*/
template<typename BinaryOp>
EIGEN_DEVICE_FUNC
const typename ReduxReturnType<BinaryOp>::Type
redux(const BinaryOp& func = BinaryOp()) const
{ return typename ReduxReturnType<BinaryOp>::Type(_expression(), func); }
{ return typename ReduxReturnType<BinaryOp>::Type(_expression(), internal::member_redux<BinaryOp,Scalar>(func)); }
typedef typename ReturnType<internal::member_minCoeff>::Type MinCoeffReturnType;
typedef typename ReturnType<internal::member_maxCoeff>::Type MaxCoeffReturnType;
typedef typename ReturnType<internal::member_squaredNorm,RealScalar>::Type SquaredNormReturnType;
typedef typename ReturnType<internal::member_norm,RealScalar>::Type NormReturnType;
typedef typename ReturnType<internal::member_blueNorm,RealScalar>::Type BlueNormReturnType;
typedef typename ReturnType<internal::member_stableNorm,RealScalar>::Type StableNormReturnType;
typedef typename ReturnType<internal::member_hypotNorm,RealScalar>::Type HypotNormReturnType;
typedef typename ReturnType<internal::member_sum>::Type SumReturnType;
typedef typename ReturnType<internal::member_mean>::Type MeanReturnType;
typedef typename ReturnType<internal::member_all>::Type AllReturnType;
typedef typename ReturnType<internal::member_any>::Type AnyReturnType;
typedef PartialReduxExpr<ExpressionType, internal::member_count<Index>, Direction> CountReturnType;
typedef typename ReturnType<internal::member_prod>::Type ProdReturnType;
typedef Reverse<ExpressionType, Direction> ReverseReturnType;
/** \returns a row (or column) vector expression of the smallest coefficient
* of each column (or row) of the referenced expression.
@@ -285,8 +299,9 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* Output: \verbinclude PartialRedux_minCoeff.out
*
* \sa DenseBase::minCoeff() */
const typename ReturnType<internal::member_minCoeff>::Type minCoeff() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
const MinCoeffReturnType minCoeff() const
{ return MinCoeffReturnType(_expression()); }
/** \returns a row (or column) vector expression of the largest coefficient
* of each column (or row) of the referenced expression.
@@ -297,8 +312,9 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* Output: \verbinclude PartialRedux_maxCoeff.out
*
* \sa DenseBase::maxCoeff() */
const typename ReturnType<internal::member_maxCoeff>::Type maxCoeff() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
const MaxCoeffReturnType maxCoeff() const
{ return MaxCoeffReturnType(_expression()); }
/** \returns a row (or column) vector expression of the squared norm
* of each column (or row) of the referenced expression.
@@ -308,8 +324,9 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* Output: \verbinclude PartialRedux_squaredNorm.out
*
* \sa DenseBase::squaredNorm() */
const typename ReturnType<internal::member_squaredNorm,RealScalar>::Type squaredNorm() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
const SquaredNormReturnType squaredNorm() const
{ return SquaredNormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the norm
* of each column (or row) of the referenced expression.
@@ -319,8 +336,9 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* Output: \verbinclude PartialRedux_norm.out
*
* \sa DenseBase::norm() */
const typename ReturnType<internal::member_norm,RealScalar>::Type norm() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
const NormReturnType norm() const
{ return NormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the norm
@@ -329,8 +347,9 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* This is a vector with real entries, even if the original matrix has complex entries.
*
* \sa DenseBase::blueNorm() */
const typename ReturnType<internal::member_blueNorm,RealScalar>::Type blueNorm() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
const BlueNormReturnType blueNorm() const
{ return BlueNormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the norm
@@ -339,8 +358,9 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* This is a vector with real entries, even if the original matrix has complex entries.
*
* \sa DenseBase::stableNorm() */
const typename ReturnType<internal::member_stableNorm,RealScalar>::Type stableNorm() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
const StableNormReturnType stableNorm() const
{ return StableNormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the norm
@@ -349,8 +369,9 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* This is a vector with real entries, even if the original matrix has complex entries.
*
* \sa DenseBase::hypotNorm() */
const typename ReturnType<internal::member_hypotNorm,RealScalar>::Type hypotNorm() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
const HypotNormReturnType hypotNorm() const
{ return HypotNormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the sum
* of each column (or row) of the referenced expression.
@@ -359,31 +380,35 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* Output: \verbinclude PartialRedux_sum.out
*
* \sa DenseBase::sum() */
const typename ReturnType<internal::member_sum>::Type sum() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
const SumReturnType sum() const
{ return SumReturnType(_expression()); }
/** \returns a row (or column) vector expression of the mean
* of each column (or row) of the referenced expression.
*
* \sa DenseBase::mean() */
const typename ReturnType<internal::member_mean>::Type mean() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
const MeanReturnType mean() const
{ return MeanReturnType(_expression()); }
/** \returns a row (or column) vector expression representing
* whether \b all coefficients of each respective column (or row) are \c true.
* This expression can be assigned to a vector with entries of type \c bool.
*
* \sa DenseBase::all() */
const typename ReturnType<internal::member_all>::Type all() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
const AllReturnType all() const
{ return AllReturnType(_expression()); }
/** \returns a row (or column) vector expression representing
* whether \b at \b least one coefficient of each respective column (or row) is \c true.
* This expression can be assigned to a vector with entries of type \c bool.
*
* \sa DenseBase::any() */
const typename ReturnType<internal::member_any>::Type any() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
const AnyReturnType any() const
{ return Any(_expression()); }
/** \returns a row (or column) vector expression representing
* the number of \c true coefficients of each respective column (or row).
@@ -394,8 +419,9 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* Output: \verbinclude PartialRedux_count.out
*
* \sa DenseBase::count() */
const PartialReduxExpr<ExpressionType, internal::member_count<Index>, Direction> count() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
const CountReturnType count() const
{ return CountReturnType(_expression()); }
/** \returns a row (or column) vector expression of the product
* of each column (or row) of the referenced expression.
@@ -404,8 +430,9 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* Output: \verbinclude PartialRedux_prod.out
*
* \sa DenseBase::prod() */
const typename ReturnType<internal::member_prod>::Type prod() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
const ProdReturnType prod() const
{ return ProdReturnType(_expression()); }
/** \returns a matrix expression
@@ -415,10 +442,12 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* Output: \verbinclude Vectorwise_reverse.out
*
* \sa DenseBase::reverse() */
const Reverse<ExpressionType, Direction> reverse() const
{ return Reverse<ExpressionType, Direction>( _expression() ); }
EIGEN_DEVICE_FUNC
const ReverseReturnType reverse() const
{ return ReverseReturnType( _expression() ); }
typedef Replicate<ExpressionType,Direction==Vertical?Dynamic:1,Direction==Horizontal?Dynamic:1> ReplicateReturnType;
typedef Replicate<ExpressionType,(isVertical?Dynamic:1),(isHorizontal?Dynamic:1)> ReplicateReturnType;
EIGEN_DEVICE_FUNC
const ReplicateReturnType replicate(Index factor) const;
/**
@@ -430,17 +459,20 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* \sa VectorwiseOp::replicate(Index), DenseBase::replicate(), class Replicate
*/
// NOTE implemented here because of sunstudio's compilation errors
template<int Factor> const Replicate<ExpressionType,(IsVertical?Factor:1),(IsHorizontal?Factor:1)>
// isVertical*Factor+isHorizontal instead of (isVertical?Factor:1) to handle CUDA bug with ternary operator
template<int Factor> const Replicate<ExpressionType,isVertical*Factor+isHorizontal,isHorizontal*Factor+isVertical>
EIGEN_DEVICE_FUNC
replicate(Index factor = Factor) const
{
return Replicate<ExpressionType,Direction==Vertical?Factor:1,Direction==Horizontal?Factor:1>
(_expression(),Direction==Vertical?factor:1,Direction==Horizontal?factor:1);
return Replicate<ExpressionType,(isVertical?Factor:1),(isHorizontal?Factor:1)>
(_expression(),isVertical?factor:1,isHorizontal?factor:1);
}
/////////// Artithmetic operators ///////////
/** Copies the vector \a other to each subvector of \c *this */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator=(const DenseBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
@@ -451,6 +483,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Adds the vector \a other to each subvector of \c *this */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator+=(const DenseBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
@@ -460,6 +493,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Substracts the vector \a other to each subvector of \c *this */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator-=(const DenseBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
@@ -469,6 +503,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Multiples each subvector of \c *this by the vector \a other */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator*=(const DenseBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
@@ -480,6 +515,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Divides each subvector of \c *this by the vector \a other */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator/=(const DenseBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
@@ -490,7 +526,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
}
/** Returns the expression of the sum of the vector \a other to each subvector of \c *this */
template<typename OtherDerived> EIGEN_STRONG_INLINE
template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_sum_op<Scalar>,
const ExpressionTypeNestedCleaned,
const typename ExtendedType<OtherDerived>::Type>
@@ -503,6 +539,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Returns the expression of the difference between each subvector of \c *this and the vector \a other */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_difference_op<Scalar>,
const ExpressionTypeNestedCleaned,
const typename ExtendedType<OtherDerived>::Type>
@@ -515,10 +552,11 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Returns the expression where each subvector is the product of the vector \a other
* by the corresponding subvector of \c *this */
template<typename OtherDerived> EIGEN_STRONG_INLINE
template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_product_op<Scalar>,
const ExpressionTypeNestedCleaned,
const typename ExtendedType<OtherDerived>::Type>
EIGEN_DEVICE_FUNC
operator*(const DenseBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
@@ -530,6 +568,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Returns the expression where each subvector is the quotient of the corresponding
* subvector of \c *this by the vector \a other */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_quotient_op<Scalar>,
const ExpressionTypeNestedCleaned,
const typename ExtendedType<OtherDerived>::Type>
@@ -545,6 +584,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* The referenced matrix is \b not modified.
* \sa MatrixBase::normalized(), normalize()
*/
EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_quotient_op<Scalar>,
const ExpressionTypeNestedCleaned,
const typename OppositeExtendedType<typename ReturnType<internal::member_norm,RealScalar>::Type>::Type>
@@ -554,16 +594,20 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Normalize in-place each row or columns of the referenced matrix.
* \sa MatrixBase::normalize(), normalized()
*/
void normalize() {
EIGEN_DEVICE_FUNC void normalize() {
m_matrix = this->normalized();
}
EIGEN_DEVICE_FUNC inline void reverseInPlace();
/////////// Geometry module ///////////
Homogeneous<ExpressionType,Direction> homogeneous() const;
typedef Homogeneous<ExpressionType,Direction> HomogeneousReturnType;
HomogeneousReturnType homogeneous() const;
typedef typename ExpressionType::PlainObject CrossReturnType;
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
const CrossReturnType cross(const MatrixBase<OtherDerived>& other) const;
enum {
@@ -594,19 +638,8 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
ExpressionTypeNested m_matrix;
};
/** \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
*/
template<typename Derived>
inline const typename DenseBase<Derived>::ConstColwiseReturnType
DenseBase<Derived>::colwise() const
{
return derived();
}
//const colwise moved to DenseBase.h due to CUDA compiler bug
/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations
*
@@ -616,22 +649,11 @@ template<typename Derived>
inline typename DenseBase<Derived>::ColwiseReturnType
DenseBase<Derived>::colwise()
{
return derived();
return ColwiseReturnType(derived());
}
/** \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
*/
template<typename Derived>
inline const typename DenseBase<Derived>::ConstRowwiseReturnType
DenseBase<Derived>::rowwise() const
{
return derived();
}
//const rowwise moved to DenseBase.h due to CUDA compiler bug
/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations
*
@@ -641,7 +663,7 @@ template<typename Derived>
inline typename DenseBase<Derived>::RowwiseReturnType
DenseBase<Derived>::rowwise()
{
return derived();
return RowwiseReturnType(derived());
}
} // end namespace Eigen

View File

@@ -22,6 +22,7 @@ struct visitor_impl
row = (UnrollCount-1) % Derived::RowsAtCompileTime
};
EIGEN_DEVICE_FUNC
static inline void run(const Derived &mat, Visitor& visitor)
{
visitor_impl<Visitor, Derived, UnrollCount-1>::run(mat, visitor);
@@ -32,6 +33,7 @@ struct visitor_impl
template<typename Visitor, typename Derived>
struct visitor_impl<Visitor, Derived, 1>
{
EIGEN_DEVICE_FUNC
static inline void run(const Derived &mat, Visitor& visitor)
{
return visitor.init(mat.coeff(0, 0), 0, 0);
@@ -41,7 +43,7 @@ struct visitor_impl<Visitor, Derived, 1>
template<typename Visitor, typename Derived>
struct visitor_impl<Visitor, Derived, Dynamic>
{
typedef typename Derived::Index Index;
EIGEN_DEVICE_FUNC
static inline void run(const Derived& mat, Visitor& visitor)
{
visitor.init(mat.coeff(0,0), 0, 0);
@@ -53,6 +55,33 @@ struct visitor_impl<Visitor, Derived, Dynamic>
}
};
// evaluator adaptor
template<typename XprType>
class visitor_evaluator
{
public:
EIGEN_DEVICE_FUNC
explicit visitor_evaluator(const XprType &xpr) : m_evaluator(xpr), m_xpr(xpr) {}
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
enum {
RowsAtCompileTime = XprType::RowsAtCompileTime,
CoeffReadCost = internal::evaluator<XprType>::CoeffReadCost
};
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 CoeffReturnType coeff(Index row, Index col) const
{ return m_evaluator.coeff(row, col); }
protected:
internal::evaluator<XprType> m_evaluator;
const XprType &m_xpr;
};
} // end namespace internal
/** Applies the visitor \a visitor to the whole coefficients of the matrix or vector.
@@ -74,16 +103,20 @@ struct visitor_impl<Visitor, Derived, Dynamic>
*/
template<typename Derived>
template<typename Visitor>
EIGEN_DEVICE_FUNC
void DenseBase<Derived>::visit(Visitor& visitor) const
{
enum { unroll = SizeAtCompileTime != Dynamic
&& CoeffReadCost != Dynamic
&& (SizeAtCompileTime == 1 || internal::functor_traits<Visitor>::Cost != Dynamic)
&& SizeAtCompileTime * CoeffReadCost + (SizeAtCompileTime-1) * internal::functor_traits<Visitor>::Cost
<= EIGEN_UNROLLING_LIMIT };
return internal::visitor_impl<Visitor, Derived,
typedef typename internal::visitor_evaluator<Derived> ThisEvaluator;
ThisEvaluator thisEval(derived());
enum { unroll = SizeAtCompileTime != Dynamic
&& ThisEvaluator::CoeffReadCost != Dynamic
&& (SizeAtCompileTime == 1 || internal::functor_traits<Visitor>::Cost != Dynamic)
&& SizeAtCompileTime * ThisEvaluator::CoeffReadCost + (SizeAtCompileTime-1) * internal::functor_traits<Visitor>::Cost
<= EIGEN_UNROLLING_LIMIT };
return internal::visitor_impl<Visitor, ThisEvaluator,
unroll ? int(SizeAtCompileTime) : Dynamic
>::run(derived(), visitor);
>::run(thisEval, visitor);
}
namespace internal {
@@ -94,10 +127,10 @@ namespace internal {
template <typename Derived>
struct coeff_visitor
{
typedef typename Derived::Index Index;
typedef typename Derived::Scalar Scalar;
Index row, col;
Scalar res;
EIGEN_DEVICE_FUNC
inline void init(const Scalar& value, Index i, Index j)
{
res = value;
@@ -114,8 +147,8 @@ struct coeff_visitor
template <typename Derived>
struct min_coeff_visitor : coeff_visitor<Derived>
{
typedef typename Derived::Index Index;
typedef typename Derived::Scalar Scalar;
EIGEN_DEVICE_FUNC
void operator() (const Scalar& value, Index i, Index j)
{
if(value < this->res)
@@ -142,8 +175,8 @@ struct functor_traits<min_coeff_visitor<Scalar> > {
template <typename Derived>
struct max_coeff_visitor : coeff_visitor<Derived>
{
typedef typename Derived::Index Index;
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Scalar Scalar;
EIGEN_DEVICE_FUNC
void operator() (const Scalar& value, Index i, Index j)
{
if(value > this->res)
@@ -171,6 +204,7 @@ struct functor_traits<max_coeff_visitor<Scalar> > {
*/
template<typename Derived>
template<typename IndexType>
EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar
DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const
{
@@ -188,6 +222,7 @@ DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const
*/
template<typename Derived>
template<typename IndexType>
EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar
DenseBase<Derived>::minCoeff(IndexType* index) const
{
@@ -205,6 +240,7 @@ DenseBase<Derived>::minCoeff(IndexType* index) const
*/
template<typename Derived>
template<typename IndexType>
EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar
DenseBase<Derived>::maxCoeff(IndexType* rowPtr, IndexType* colPtr) const
{
@@ -222,6 +258,7 @@ DenseBase<Derived>::maxCoeff(IndexType* rowPtr, IndexType* colPtr) const
*/
template<typename Derived>
template<typename IndexType>
EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar
DenseBase<Derived>::maxCoeff(IndexType* index) const
{

View File

@@ -45,7 +45,7 @@ template<> struct packet_traits<std::complex<float> > : default_packet_traits
};
};
template<> struct unpacket_traits<Packet4cf> { typedef std::complex<float> type; enum {size=4}; typedef Packet2cf half; };
template<> struct unpacket_traits<Packet4cf> { typedef std::complex<float> type; enum {size=4, alignment=Aligned32}; typedef Packet2cf half; };
template<> EIGEN_STRONG_INLINE Packet4cf padd<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_add_ps(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet4cf psub<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_sub_ps(a.v,b.v)); }
@@ -92,7 +92,7 @@ template<> EIGEN_STRONG_INLINE Packet4cf ploaddup<Packet4cf>(const std::complex<
template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float>* to, const Packet4cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore(&numext::real_ref(*to), from.v); }
template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to, const Packet4cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), from.v); }
template<> EIGEN_DEVICE_FUNC inline Packet4cf pgather<std::complex<float>, Packet4cf>(const std::complex<float>* from, DenseIndex stride)
template<> EIGEN_DEVICE_FUNC inline Packet4cf pgather<std::complex<float>, Packet4cf>(const std::complex<float>* from, Index stride)
{
return Packet4cf(_mm256_set_ps(std::imag(from[3*stride]), std::real(from[3*stride]),
std::imag(from[2*stride]), std::real(from[2*stride]),
@@ -100,7 +100,7 @@ template<> EIGEN_DEVICE_FUNC inline Packet4cf pgather<std::complex<float>, Packe
std::imag(from[0*stride]), std::real(from[0*stride])));
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet4cf>(std::complex<float>* to, const Packet4cf& from, DenseIndex stride)
template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet4cf>(std::complex<float>* to, const Packet4cf& from, Index stride)
{
__m128 low = _mm256_extractf128_ps(from.v, 0);
to[stride*0] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(low, low, 0)),
@@ -267,7 +267,7 @@ template<> struct packet_traits<std::complex<double> > : default_packet_traits
};
};
template<> struct unpacket_traits<Packet2cd> { typedef std::complex<double> type; enum {size=2}; typedef Packet1cd half; };
template<> struct unpacket_traits<Packet2cd> { typedef std::complex<double> type; enum {size=2, alignment=Aligned32}; typedef Packet1cd half; };
template<> EIGEN_STRONG_INLINE Packet2cd padd<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_add_pd(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cd psub<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_sub_pd(a.v,b.v)); }
@@ -310,13 +310,13 @@ template<> EIGEN_STRONG_INLINE Packet2cd ploaddup<Packet2cd>(const std::complex<
template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> * to, const Packet2cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); }
template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> * to, const Packet2cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); }
template<> EIGEN_DEVICE_FUNC inline Packet2cd pgather<std::complex<double>, Packet2cd>(const std::complex<double>* from, DenseIndex stride)
template<> EIGEN_DEVICE_FUNC inline Packet2cd pgather<std::complex<double>, Packet2cd>(const std::complex<double>* from, Index stride)
{
return Packet2cd(_mm256_set_pd(std::imag(from[1*stride]), std::real(from[1*stride]),
std::imag(from[0*stride]), std::real(from[0*stride])));
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet2cd>(std::complex<double>* to, const Packet2cd& from, DenseIndex stride)
template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet2cd>(std::complex<double>* to, const Packet2cd& from, Index stride)
{
__m128d low = _mm256_extractf128_pd(from.v, 0);
to[stride*0] = std::complex<double>(_mm_cvtsd_f64(low), _mm_cvtsd_f64(_mm_shuffle_pd(low, low, 1)));

View File

@@ -0,0 +1,441 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@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_MATH_FUNCTIONS_AVX_H
#define EIGEN_MATH_FUNCTIONS_AVX_H
// For some reason, this function didn't make it into the avxintirn.h
// used by the compiler, so we'll just wrap it.
#define _mm256_setr_m128(lo, hi) \
_mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1)
/* The sin, cos, exp, and log functions of this file are loosely derived from
* Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
*/
namespace Eigen {
namespace internal {
// Sine function
// Computes sin(x) by wrapping x to the interval [-Pi/4,3*Pi/4] and
// evaluating interpolants in [-Pi/4,Pi/4] or [Pi/4,3*Pi/4]. The interpolants
// are (anti-)symmetric and thus have only odd/even coefficients
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
psin<Packet8f>(const Packet8f& _x) {
Packet8f x = _x;
// Some useful values.
_EIGEN_DECLARE_CONST_Packet8i(one, 1);
_EIGEN_DECLARE_CONST_Packet8f(one, 1.0f);
_EIGEN_DECLARE_CONST_Packet8f(two, 2.0f);
_EIGEN_DECLARE_CONST_Packet8f(one_over_four, 0.25f);
_EIGEN_DECLARE_CONST_Packet8f(one_over_pi, 3.183098861837907e-01f);
_EIGEN_DECLARE_CONST_Packet8f(neg_pi_first, -3.140625000000000e+00);
_EIGEN_DECLARE_CONST_Packet8f(neg_pi_second, -9.670257568359375e-04);
_EIGEN_DECLARE_CONST_Packet8f(neg_pi_third, -6.278329571784980e-07);
_EIGEN_DECLARE_CONST_Packet8f(four_over_pi, 1.273239544735163e+00);
// Map x from [-Pi/4,3*Pi/4] to z in [-1,3] and subtract the shifted period.
Packet8f z = pmul(x, p8f_one_over_pi);
Packet8f shift = _mm256_floor_ps(padd(z, p8f_one_over_four));
x = pmadd(shift, p8f_neg_pi_first, x);
x = pmadd(shift, p8f_neg_pi_second, x);
x = pmadd(shift, p8f_neg_pi_third, x);
z = pmul(x, p8f_four_over_pi);
// Make a mask for the entries that need flipping, i.e. wherever the shift
// is odd.
Packet8i shift_ints = _mm256_cvtps_epi32(shift);
Packet8i shift_isodd =
(__m256i)_mm256_and_ps((__m256)shift_ints, (__m256)p8i_one);
#ifdef EIGEN_VECTORIZE_AVX2
Packet8i sign_flip_mask = _mm256_slli_epi32(shift_isodd, 31);
#else
__m128i lo =
_mm_slli_epi32(_mm256_extractf128_si256((__m256i)shift_isodd, 0), 31);
__m128i hi =
_mm_slli_epi32(_mm256_extractf128_si256((__m256i)shift_isodd, 1), 31);
Packet8i sign_flip_mask = _mm256_setr_m128(lo, hi);
#endif
// Create a mask for which interpolant to use, i.e. if z > 1, then the mask
// is set to ones for that entry.
Packet8f ival_mask = _mm256_cmp_ps(z, p8f_one, _CMP_GT_OQ);
// Evaluate the polynomial for the interval [1,3] in z.
_EIGEN_DECLARE_CONST_Packet8f(coeff_right_0, 9.999999724233232e-01f);
_EIGEN_DECLARE_CONST_Packet8f(coeff_right_2, -3.084242535619928e-01);
_EIGEN_DECLARE_CONST_Packet8f(coeff_right_4, 1.584991525700324e-02);
_EIGEN_DECLARE_CONST_Packet8f(coeff_right_6, -3.188805084631342e-04);
Packet8f z_minus_two = psub(z, p8f_two);
Packet8f z_minus_two2 = pmul(z_minus_two, z_minus_two);
Packet8f right = pmadd(p8f_coeff_right_6, z_minus_two2, p8f_coeff_right_4);
right = pmadd(right, z_minus_two2, p8f_coeff_right_2);
right = pmadd(right, z_minus_two2, p8f_coeff_right_0);
// Evaluate the polynomial for the interval [-1,1] in z.
_EIGEN_DECLARE_CONST_Packet8f(coeff_left_1, 7.853981525427295e-01);
_EIGEN_DECLARE_CONST_Packet8f(coeff_left_3, -8.074536727092352e-02);
_EIGEN_DECLARE_CONST_Packet8f(coeff_left_5, 2.489871967827018e-03);
_EIGEN_DECLARE_CONST_Packet8f(coeff_left_7, -3.587725841214251e-05);
Packet8f z2 = pmul(z, z);
Packet8f left = pmadd(p8f_coeff_left_7, z2, p8f_coeff_left_5);
left = pmadd(left, z2, p8f_coeff_left_3);
left = pmadd(left, z2, p8f_coeff_left_1);
left = pmul(left, z);
// Assemble the results, i.e. select the left and right polynomials.
left = _mm256_andnot_ps(ival_mask, left);
right = _mm256_and_ps(ival_mask, right);
Packet8f res = _mm256_or_ps(left, right);
// Flip the sign on the odd intervals and return the result.
res = _mm256_xor_ps(res, (__m256)sign_flip_mask);
return res;
}
// Natural logarithm
// Computes log(x) as log(2^e * m) = C*e + log(m), where the constant C =log(2)
// and m is in the range [sqrt(1/2),sqrt(2)). In this range, the logarithm can
// be easily approximated by a polynomial centered on m=1 for stability.
// TODO(gonnet): Further reduce the interval allowing for lower-degree
// polynomial interpolants -> ... -> profit!
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
plog<Packet8f>(const Packet8f& _x) {
Packet8f x = _x;
_EIGEN_DECLARE_CONST_Packet8f(1, 1.0f);
_EIGEN_DECLARE_CONST_Packet8f(half, 0.5f);
_EIGEN_DECLARE_CONST_Packet8f(126f, 126.0f);
_EIGEN_DECLARE_CONST_Packet8f_FROM_INT(inv_mant_mask, ~0x7f800000);
// The smallest non denormalized float number.
_EIGEN_DECLARE_CONST_Packet8f_FROM_INT(min_norm_pos, 0x00800000);
_EIGEN_DECLARE_CONST_Packet8f_FROM_INT(minus_inf, 0xff800000);
// Polynomial coefficients.
_EIGEN_DECLARE_CONST_Packet8f(cephes_SQRTHF, 0.707106781186547524f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_log_p0, 7.0376836292E-2f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_log_p1, -1.1514610310E-1f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_log_p2, 1.1676998740E-1f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_log_p3, -1.2420140846E-1f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_log_p4, +1.4249322787E-1f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_log_p5, -1.6668057665E-1f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_log_p6, +2.0000714765E-1f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_log_p7, -2.4999993993E-1f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_log_p8, +3.3333331174E-1f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_log_q1, -2.12194440e-4f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_log_q2, 0.693359375f);
Packet8f invalid_mask = _mm256_cmp_ps(x, _mm256_setzero_ps(), _CMP_NGE_UQ); // not greater equal is true if x is NaN
Packet8f iszero_mask = _mm256_cmp_ps(x, _mm256_setzero_ps(), _CMP_EQ_OQ);
// Truncate input values to the minimum positive normal.
x = pmax(x, p8f_min_norm_pos);
// Extract the shifted exponents (No bitwise shifting in regular AVX, so
// convert to SSE and do it there).
#ifdef EIGEN_VECTORIZE_AVX2
Packet8f emm0 = _mm256_cvtepi32_ps(_mm256_srli_epi32((__m256i)x, 23));
#else
__m128i lo = _mm_srli_epi32(_mm256_extractf128_si256((__m256i)x, 0), 23);
__m128i hi = _mm_srli_epi32(_mm256_extractf128_si256((__m256i)x, 1), 23);
Packet8f emm0 = _mm256_cvtepi32_ps(_mm256_setr_m128(lo, hi));
#endif
Packet8f e = _mm256_sub_ps(emm0, p8f_126f);
// Set the exponents to -1, i.e. x are in the range [0.5,1).
x = _mm256_and_ps(x, p8f_inv_mant_mask);
x = _mm256_or_ps(x, p8f_half);
// part2: Shift the inputs from the range [0.5,1) to [sqrt(1/2),sqrt(2))
// and shift by -1. The values are then centered around 0, which improves
// the stability of the polynomial evaluation.
// if( x < SQRTHF ) {
// e -= 1;
// x = x + x - 1.0;
// } else { x = x - 1.0; }
Packet8f mask = _mm256_cmp_ps(x, p8f_cephes_SQRTHF, _CMP_LT_OQ);
Packet8f tmp = _mm256_and_ps(x, mask);
x = psub(x, p8f_1);
e = psub(e, _mm256_and_ps(p8f_1, mask));
x = padd(x, tmp);
Packet8f x2 = pmul(x, x);
Packet8f x3 = pmul(x2, x);
// Evaluate the polynomial approximant of degree 8 in three parts, probably
// to improve instruction-level parallelism.
Packet8f y, y1, y2;
y = pmadd(p8f_cephes_log_p0, x, p8f_cephes_log_p1);
y1 = pmadd(p8f_cephes_log_p3, x, p8f_cephes_log_p4);
y2 = pmadd(p8f_cephes_log_p6, x, p8f_cephes_log_p7);
y = pmadd(y, x, p8f_cephes_log_p2);
y1 = pmadd(y1, x, p8f_cephes_log_p5);
y2 = pmadd(y2, x, p8f_cephes_log_p8);
y = pmadd(y, x3, y1);
y = pmadd(y, x3, y2);
y = pmul(y, x3);
// Add the logarithm of the exponent back to the result of the interpolation.
y1 = pmul(e, p8f_cephes_log_q1);
tmp = pmul(x2, p8f_half);
y = padd(y, y1);
x = psub(x, tmp);
y2 = pmul(e, p8f_cephes_log_q2);
x = padd(x, y);
x = padd(x, y2);
// Filter out invalid inputs, i.e. negative arg will be NAN, 0 will be -INF.
return _mm256_or_ps(
_mm256_andnot_ps(iszero_mask, _mm256_or_ps(x, invalid_mask)),
_mm256_and_ps(iszero_mask, p8f_minus_inf));
}
// Exponential function. Works by writing "x = m*log(2) + r" where
// "m = floor(x/log(2)+1/2)" and "r" is the remainder. The result is then
// "exp(x) = 2^m*exp(r)" where exp(r) is in the range [-1,1).
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
pexp<Packet8f>(const Packet8f& _x) {
_EIGEN_DECLARE_CONST_Packet8f(1, 1.0f);
_EIGEN_DECLARE_CONST_Packet8f(half, 0.5f);
_EIGEN_DECLARE_CONST_Packet8f(127, 127.0f);
_EIGEN_DECLARE_CONST_Packet8f(exp_hi, 88.3762626647950f);
_EIGEN_DECLARE_CONST_Packet8f(exp_lo, -88.3762626647949f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_LOG2EF, 1.44269504088896341f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_exp_p0, 1.9875691500E-4f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_exp_p1, 1.3981999507E-3f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_exp_p2, 8.3334519073E-3f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_exp_p3, 4.1665795894E-2f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_exp_p4, 1.6666665459E-1f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_exp_p5, 5.0000001201E-1f);
// Clamp x.
Packet8f x = pmax(pmin(_x, p8f_exp_hi), p8f_exp_lo);
// Express exp(x) as exp(m*ln(2) + r), start by extracting
// m = floor(x/ln(2) + 0.5).
Packet8f m = _mm256_floor_ps(pmadd(x, p8f_cephes_LOG2EF, p8f_half));
// Get r = x - m*ln(2). If no FMA instructions are available, m*ln(2) is
// subtracted out in two parts, m*C1+m*C2 = m*ln(2), to avoid accumulating
// truncation errors. Note that we don't use the "pmadd" function here to
// ensure that a precision-preserving FMA instruction is used.
#ifdef EIGEN_VECTORIZE_FMA
_EIGEN_DECLARE_CONST_Packet8f(nln2, -0.6931471805599453f);
Packet8f r = _mm256_fmadd_ps(m, p8f_nln2, x);
#else
_EIGEN_DECLARE_CONST_Packet8f(cephes_exp_C1, 0.693359375f);
_EIGEN_DECLARE_CONST_Packet8f(cephes_exp_C2, -2.12194440e-4f);
Packet8f r = psub(x, pmul(m, p8f_cephes_exp_C1));
r = psub(r, pmul(m, p8f_cephes_exp_C2));
#endif
Packet8f r2 = pmul(r, r);
// TODO(gonnet): Split into odd/even polynomials and try to exploit
// instruction-level parallelism.
Packet8f y = p8f_cephes_exp_p0;
y = pmadd(y, r, p8f_cephes_exp_p1);
y = pmadd(y, r, p8f_cephes_exp_p2);
y = pmadd(y, r, p8f_cephes_exp_p3);
y = pmadd(y, r, p8f_cephes_exp_p4);
y = pmadd(y, r, p8f_cephes_exp_p5);
y = pmadd(y, r2, r);
y = padd(y, p8f_1);
// Build emm0 = 2^m.
Packet8i emm0 = _mm256_cvttps_epi32(padd(m, p8f_127));
#ifdef EIGEN_VECTORIZE_AVX2
emm0 = _mm256_slli_epi32(emm0, 23);
#else
__m128i lo = _mm_slli_epi32(_mm256_extractf128_si256(emm0, 0), 23);
__m128i hi = _mm_slli_epi32(_mm256_extractf128_si256(emm0, 1), 23);
emm0 = _mm256_setr_m128(lo, hi);
#endif
// Return 2^m * exp(r).
return pmax(pmul(y, _mm256_castsi256_ps(emm0)), _x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4d
pexp<Packet4d>(const Packet4d& _x) {
Packet4d x = _x;
_EIGEN_DECLARE_CONST_Packet4d(1, 1.0);
_EIGEN_DECLARE_CONST_Packet4d(2, 2.0);
_EIGEN_DECLARE_CONST_Packet4d(half, 0.5);
_EIGEN_DECLARE_CONST_Packet4d(exp_hi, 709.437);
_EIGEN_DECLARE_CONST_Packet4d(exp_lo, -709.436139303);
_EIGEN_DECLARE_CONST_Packet4d(cephes_LOG2EF, 1.4426950408889634073599);
_EIGEN_DECLARE_CONST_Packet4d(cephes_exp_p0, 1.26177193074810590878e-4);
_EIGEN_DECLARE_CONST_Packet4d(cephes_exp_p1, 3.02994407707441961300e-2);
_EIGEN_DECLARE_CONST_Packet4d(cephes_exp_p2, 9.99999999999999999910e-1);
_EIGEN_DECLARE_CONST_Packet4d(cephes_exp_q0, 3.00198505138664455042e-6);
_EIGEN_DECLARE_CONST_Packet4d(cephes_exp_q1, 2.52448340349684104192e-3);
_EIGEN_DECLARE_CONST_Packet4d(cephes_exp_q2, 2.27265548208155028766e-1);
_EIGEN_DECLARE_CONST_Packet4d(cephes_exp_q3, 2.00000000000000000009e0);
_EIGEN_DECLARE_CONST_Packet4d(cephes_exp_C1, 0.693145751953125);
_EIGEN_DECLARE_CONST_Packet4d(cephes_exp_C2, 1.42860682030941723212e-6);
_EIGEN_DECLARE_CONST_Packet4i(1023, 1023);
Packet4d tmp, fx;
// clamp x
x = pmax(pmin(x, p4d_exp_hi), p4d_exp_lo);
// Express exp(x) as exp(g + n*log(2)).
fx = pmadd(p4d_cephes_LOG2EF, x, p4d_half);
// Get the integer modulus of log(2), i.e. the "n" described above.
fx = _mm256_floor_pd(fx);
// Get the remainder modulo log(2), i.e. the "g" described above. Subtract
// n*log(2) out in two steps, i.e. n*C1 + n*C2, C1+C2=log2 to get the last
// digits right.
tmp = pmul(fx, p4d_cephes_exp_C1);
Packet4d z = pmul(fx, p4d_cephes_exp_C2);
x = psub(x, tmp);
x = psub(x, z);
Packet4d x2 = pmul(x, x);
// Evaluate the numerator polynomial of the rational interpolant.
Packet4d px = p4d_cephes_exp_p0;
px = pmadd(px, x2, p4d_cephes_exp_p1);
px = pmadd(px, x2, p4d_cephes_exp_p2);
px = pmul(px, x);
// Evaluate the denominator polynomial of the rational interpolant.
Packet4d qx = p4d_cephes_exp_q0;
qx = pmadd(qx, x2, p4d_cephes_exp_q1);
qx = pmadd(qx, x2, p4d_cephes_exp_q2);
qx = pmadd(qx, x2, p4d_cephes_exp_q3);
// I don't really get this bit, copied from the SSE2 routines, so...
// TODO(gonnet): Figure out what is going on here, perhaps find a better
// rational interpolant?
x = _mm256_div_pd(px, psub(qx, px));
x = pmadd(p4d_2, x, p4d_1);
// Build e=2^n by constructing the exponents in a 128-bit vector and
// shifting them to where they belong in double-precision values.
__m128i emm0 = _mm256_cvtpd_epi32(fx);
emm0 = _mm_add_epi32(emm0, p4i_1023);
emm0 = _mm_shuffle_epi32(emm0, _MM_SHUFFLE(3, 1, 2, 0));
__m128i lo = _mm_slli_epi64(emm0, 52);
__m128i hi = _mm_slli_epi64(_mm_srli_epi64(emm0, 32), 52);
__m256i e = _mm256_insertf128_si256(_mm256_setzero_si256(), lo, 0);
e = _mm256_insertf128_si256(e, hi, 1);
// Construct the result 2^n * exp(g) = e * x. The max is used to catch
// non-finite values in the input.
return pmax(pmul(x, Packet4d(e)), _x);
}
// Functions for sqrt.
// The EIGEN_FAST_MATH version uses the _mm_rsqrt_ps approximation and one step
// of Newton's method, at a cost of 1-2 bits of precision as opposed to the
// exact solution. The main advantage of this approach is not just speed, but
// also the fact that it can be inlined and pipelined with other computations,
// further reducing its effective latency.
#if EIGEN_FAST_MATH
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
psqrt<Packet8f>(const Packet8f& _x) {
_EIGEN_DECLARE_CONST_Packet8f(one_point_five, 1.5f);
_EIGEN_DECLARE_CONST_Packet8f(minus_half, -0.5f);
_EIGEN_DECLARE_CONST_Packet8f_FROM_INT(flt_min, 0x00800000);
Packet8f neg_half = pmul(_x, p8f_minus_half);
// select only the inverse sqrt of positive normal inputs (denormals are
// flushed to zero and cause infs as well).
Packet8f non_zero_mask = _mm256_cmp_ps(_x, p8f_flt_min, _CMP_GE_OQ);
Packet8f x = _mm256_and_ps(non_zero_mask, _mm256_rsqrt_ps(_x));
// Do a single step of Newton's iteration.
x = pmul(x, pmadd(neg_half, pmul(x, x), p8f_one_point_five));
// Multiply the original _x by it's reciprocal square root to extract the
// square root.
return pmul(_x, x);
}
#else
template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet8f psqrt<Packet8f>(const Packet8f& x) {
return _mm256_sqrt_ps(x);
}
#endif
template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet4d psqrt<Packet4d>(const Packet4d& x) {
return _mm256_sqrt_pd(x);
}
#if EIGEN_FAST_MATH
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet8f prsqrt<Packet8f>(const Packet8f& _x) {
_EIGEN_DECLARE_CONST_Packet8f_FROM_INT(inf, 0x7f800000);
_EIGEN_DECLARE_CONST_Packet8f_FROM_INT(nan, 0x7fc00000);
_EIGEN_DECLARE_CONST_Packet8f(one_point_five, 1.5f);
_EIGEN_DECLARE_CONST_Packet8f(minus_half, -0.5f);
_EIGEN_DECLARE_CONST_Packet8f_FROM_INT(flt_min, 0x00800000);
Packet8f neg_half = pmul(_x, p8f_minus_half);
// select only the inverse sqrt of positive normal inputs (denormals are
// flushed to zero and cause infs as well).
Packet8f le_zero_mask = _mm256_cmp_ps(_x, p8f_flt_min, _CMP_LT_OQ);
Packet8f x = _mm256_andnot_ps(le_zero_mask, _mm256_rsqrt_ps(_x));
// Fill in NaNs and Infs for the negative/zero entries.
Packet8f neg_mask = _mm256_cmp_ps(_x, _mm256_setzero_ps(), _CMP_LT_OQ);
Packet8f zero_mask = _mm256_andnot_ps(neg_mask, le_zero_mask);
Packet8f infs_and_nans = _mm256_or_ps(_mm256_and_ps(neg_mask, p8f_nan),
_mm256_and_ps(zero_mask, p8f_inf));
// Do a single step of Newton's iteration.
x = pmul(x, pmadd(neg_half, pmul(x, x), p8f_one_point_five));
// Insert NaNs and Infs in all the right places.
return _mm256_or_ps(x, infs_and_nans);
}
#else
template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet8f prsqrt<Packet8f>(const Packet8f& x) {
_EIGEN_DECLARE_CONST_Packet8f(one, 1.0f);
return _mm256_div_ps(p8f_one, _mm256_sqrt_ps(x));
}
#endif
template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet4d prsqrt<Packet4d>(const Packet4d& x) {
_EIGEN_DECLARE_CONST_Packet4d(one, 1.0);
return _mm256_div_pd(p4d_one, _mm256_sqrt_pd(x));
}
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_MATH_FUNCTIONS_AVX_H

View File

@@ -22,9 +22,9 @@ namespace internal {
#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS (2*sizeof(void*))
#endif
#ifdef EIGEN_VECTORIZE_FMA
#ifndef EIGEN_HAS_FUSED_MADD
#define EIGEN_HAS_FUSED_MADD 1
#ifdef __FMA__
#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
#endif
#endif
@@ -42,6 +42,12 @@ template<> struct is_arithmetic<__m256d> { enum { value = true }; };
#define _EIGEN_DECLARE_CONST_Packet4d(NAME,X) \
const Packet4d p4d_##NAME = pset1<Packet4d>(X)
#define _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(NAME,X) \
const Packet8f p8f_##NAME = (__m256)pset1<Packet8i>(X)
#define _EIGEN_DECLARE_CONST_Packet8i(NAME,X) \
const Packet8i p8i_##NAME = pset1<Packet8i>(X)
template<> struct packet_traits<float> : default_packet_traits
{
@@ -54,13 +60,15 @@ template<> struct packet_traits<float> : default_packet_traits
HasHalfPacket = 1,
HasDiv = 1,
HasSin = 0,
HasSin = EIGEN_FAST_MATH,
HasCos = 0,
HasLog = 0,
HasExp = 0,
HasSqrt = 0
HasLog = 1,
HasExp = 1,
HasSqrt = 1,
HasRsqrt = 1,
HasBlend = 1
};
};
};
template<> struct packet_traits<double> : default_packet_traits
{
typedef Packet4d type;
@@ -72,7 +80,10 @@ template<> struct packet_traits<double> : default_packet_traits
HasHalfPacket = 1,
HasDiv = 1,
HasExp = 0
HasExp = 1,
HasSqrt = 1,
HasRsqrt = 1,
HasBlend = 1
};
};
@@ -89,9 +100,9 @@ template<> struct packet_traits<int> : default_packet_traits
};
*/
template<> struct unpacket_traits<Packet8f> { typedef float type; typedef Packet4f half; enum {size=8}; };
template<> struct unpacket_traits<Packet4d> { typedef double type; typedef Packet2d half; enum {size=4}; };
template<> struct unpacket_traits<Packet8i> { typedef int type; typedef Packet4i half; enum {size=8}; };
template<> struct unpacket_traits<Packet8f> { typedef float type; typedef Packet4f half; enum {size=8, alignment=Aligned32}; };
template<> struct unpacket_traits<Packet4d> { typedef double type; typedef Packet2d half; enum {size=4, alignment=Aligned32}; };
template<> struct unpacket_traits<Packet8i> { typedef int type; typedef Packet4i half; enum {size=8, alignment=Aligned32}; };
template<> EIGEN_STRONG_INLINE Packet8f pset1<Packet8f>(const float& from) { return _mm256_set1_ps(from); }
template<> EIGEN_STRONG_INLINE Packet4d pset1<Packet4d>(const double& from) { return _mm256_set1_pd(from); }
@@ -100,8 +111,8 @@ template<> EIGEN_STRONG_INLINE Packet8i pset1<Packet8i>(const int& from) { re
template<> EIGEN_STRONG_INLINE Packet8f pload1<Packet8f>(const float* from) { return _mm256_broadcast_ss(from); }
template<> EIGEN_STRONG_INLINE Packet4d pload1<Packet4d>(const double* from) { return _mm256_broadcast_sd(from); }
template<> EIGEN_STRONG_INLINE Packet8f plset<float>(const float& a) { return _mm256_add_ps(_mm256_set1_ps(a), _mm256_set_ps(7.0,6.0,5.0,4.0,3.0,2.0,1.0,0.0)); }
template<> EIGEN_STRONG_INLINE Packet4d plset<double>(const double& a) { return _mm256_add_pd(_mm256_set1_pd(a), _mm256_set_pd(3.0,2.0,1.0,0.0)); }
template<> EIGEN_STRONG_INLINE Packet8f plset<Packet8f>(const float& a) { return _mm256_add_ps(_mm256_set1_ps(a), _mm256_set_ps(7.0,6.0,5.0,4.0,3.0,2.0,1.0,0.0)); }
template<> EIGEN_STRONG_INLINE Packet4d plset<Packet4d>(const double& a) { return _mm256_add_pd(_mm256_set1_pd(a), _mm256_set_pd(3.0,2.0,1.0,0.0)); }
template<> EIGEN_STRONG_INLINE Packet8f padd<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_add_ps(a,b); }
template<> EIGEN_STRONG_INLINE Packet4d padd<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_add_pd(a,b); }
@@ -133,9 +144,9 @@ template<> EIGEN_STRONG_INLINE Packet8i pdiv<Packet8i>(const Packet8i& /*a*/, co
return pset1<Packet8i>(0);
}
#ifdef EIGEN_VECTORIZE_FMA
#ifdef __FMA__
template<> EIGEN_STRONG_INLINE Packet8f pmadd(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
#if defined(__clang__) || defined(__GNUC__)
#if EIGEN_COMP_GNUC || EIGEN_COMP_CLANG
// clang stupidly generates a vfmadd213ps instruction plus some vmovaps on registers,
// and gcc stupidly generates a vfmadd132ps instruction,
// so let's enforce it to generate a vfmadd231ps instruction since the most common use case is to accumulate
@@ -148,7 +159,7 @@ template<> EIGEN_STRONG_INLINE Packet8f pmadd(const Packet8f& a, const Packet8f&
#endif
}
template<> EIGEN_STRONG_INLINE Packet4d pmadd(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
#if defined(__clang__) || defined(__GNUC__)
#if EIGEN_COMP_GNUC || EIGEN_COMP_CLANG
// see above
Packet4d res = c;
__asm__("vfmadd231pd %[a], %[b], %[c]" : [c] "+x" (res) : [a] "x" (a), [b] "x" (b));
@@ -224,17 +235,17 @@ template<> EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet8i&
// NOTE: leverage _mm256_i32gather_ps and _mm256_i32gather_pd if AVX2 instructions are available
// NOTE: for the record the following seems to be slower: return _mm256_i32gather_ps(from, _mm256_set1_epi32(stride), 4);
template<> EIGEN_DEVICE_FUNC inline Packet8f pgather<float, Packet8f>(const float* from, DenseIndex stride)
template<> EIGEN_DEVICE_FUNC inline Packet8f pgather<float, Packet8f>(const float* from, Index stride)
{
return _mm256_set_ps(from[7*stride], from[6*stride], from[5*stride], from[4*stride],
from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
}
template<> EIGEN_DEVICE_FUNC inline Packet4d pgather<double, Packet4d>(const double* from, DenseIndex stride)
template<> EIGEN_DEVICE_FUNC inline Packet4d pgather<double, Packet4d>(const double* from, Index stride)
{
return _mm256_set_pd(from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet8f>(float* to, const Packet8f& from, DenseIndex stride)
template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet8f>(float* to, const Packet8f& from, Index stride)
{
__m128 low = _mm256_extractf128_ps(from, 0);
to[stride*0] = _mm_cvtss_f32(low);
@@ -248,7 +259,7 @@ template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet8f>(float* to, co
to[stride*6] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 2));
to[stride*7] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 3));
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet4d>(double* to, const Packet4d& from, DenseIndex stride)
template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet4d>(double* to, const Packet4d& from, Index stride)
{
__m128d low = _mm256_extractf128_pd(from, 0);
to[stride*0] = _mm_cvtsd_f64(low);
@@ -423,26 +434,30 @@ struct palign_impl<Offset,Packet8f>
if (Offset==1)
{
first = _mm256_blend_ps(first, second, 1);
Packet8f tmp = _mm256_permute_ps (first, _MM_SHUFFLE(0,3,2,1));
first = _mm256_blend_ps(tmp, _mm256_permute2f128_ps (tmp, tmp, 1), 0x88);
Packet8f tmp1 = _mm256_permute_ps (first, _MM_SHUFFLE(0,3,2,1));
Packet8f tmp2 = _mm256_permute2f128_ps (tmp1, tmp1, 1);
first = _mm256_blend_ps(tmp1, tmp2, 0x88);
}
else if (Offset==2)
{
first = _mm256_blend_ps(first, second, 3);
Packet8f tmp = _mm256_permute_ps (first, _MM_SHUFFLE(1,0,3,2));
first = _mm256_blend_ps(tmp, _mm256_permute2f128_ps (tmp, tmp, 1), 0xcc);
Packet8f tmp1 = _mm256_permute_ps (first, _MM_SHUFFLE(1,0,3,2));
Packet8f tmp2 = _mm256_permute2f128_ps (tmp1, tmp1, 1);
first = _mm256_blend_ps(tmp1, tmp2, 0xcc);
}
else if (Offset==3)
{
first = _mm256_blend_ps(first, second, 7);
Packet8f tmp = _mm256_permute_ps (first, _MM_SHUFFLE(2,1,0,3));
first = _mm256_blend_ps(tmp, _mm256_permute2f128_ps (tmp, tmp, 1), 0xee);
Packet8f tmp1 = _mm256_permute_ps (first, _MM_SHUFFLE(2,1,0,3));
Packet8f tmp2 = _mm256_permute2f128_ps (tmp1, tmp1, 1);
first = _mm256_blend_ps(tmp1, tmp2, 0xee);
}
else if (Offset==4)
{
first = _mm256_blend_ps(first, second, 15);
Packet8f tmp = _mm256_permute_ps (first, _MM_SHUFFLE(3,2,1,0));
first = _mm256_permute_ps(_mm256_permute2f128_ps (tmp, tmp, 1), _MM_SHUFFLE(3,2,1,0));
Packet8f tmp1 = _mm256_permute_ps (first, _MM_SHUFFLE(3,2,1,0));
Packet8f tmp2 = _mm256_permute2f128_ps (tmp1, tmp1, 1);
first = _mm256_permute_ps(tmp2, _MM_SHUFFLE(3,2,1,0));
}
else if (Offset==5)
{
@@ -557,6 +572,19 @@ ptranspose(PacketBlock<Packet4d,4>& kernel) {
kernel.packet[2] = _mm256_permute2f128_pd(T1, T3, 49);
}
template<> EIGEN_STRONG_INLINE Packet8f pblend(const Selector<8>& ifPacket, const Packet8f& thenPacket, const Packet8f& elsePacket) {
const __m256 zero = _mm256_setzero_ps();
const __m256 select = _mm256_set_ps(ifPacket.select[7], ifPacket.select[6], ifPacket.select[5], ifPacket.select[4], ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
__m256 false_mask = _mm256_cmp_ps(select, zero, _CMP_EQ_UQ);
return _mm256_blendv_ps(thenPacket, elsePacket, false_mask);
}
template<> EIGEN_STRONG_INLINE Packet4d pblend(const Selector<4>& ifPacket, const Packet4d& thenPacket, const Packet4d& elsePacket) {
const __m256d zero = _mm256_setzero_pd();
const __m256d select = _mm256_set_pd(ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
__m256d false_mask = _mm256_cmp_pd(select, zero, _CMP_EQ_UQ);
return _mm256_blendv_pd(thenPacket, elsePacket, false_mask);
}
} // end namespace internal
} // end namespace Eigen

View File

@@ -0,0 +1,51 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@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_TYPE_CASTING_AVX_H
#define EIGEN_TYPE_CASTING_AVX_H
namespace Eigen {
namespace internal {
// For now we use SSE to handle integers, so we can't use AVX instructions to cast
// from int to float
template <>
struct type_casting_traits<float, int> {
enum {
VectorizedCast = 0,
SrcCoeffRatio = 1,
TgtCoeffRatio = 1
};
};
template <>
struct type_casting_traits<int, float> {
enum {
VectorizedCast = 0,
SrcCoeffRatio = 1,
TgtCoeffRatio = 1
};
};
template<> EIGEN_STRONG_INLINE Packet8i pcast<Packet8f, Packet8i>(const Packet8f& a) {
return _mm256_cvtps_epi32(a);
}
template<> EIGEN_STRONG_INLINE Packet8f pcast<Packet8i, Packet8f>(const Packet8i& a) {
return _mm256_cvtepi32_ps(a);
}
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_TYPE_CASTING_AVX_H

View File

@@ -7,23 +7,21 @@
// 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_COMPLEX_ALTIVEC_H
#define EIGEN_COMPLEX_ALTIVEC_H
#ifndef EIGEN_COMPLEX32_ALTIVEC_H
#define EIGEN_COMPLEX32_ALTIVEC_H
namespace Eigen {
namespace internal {
static Packet4ui p4ui_CONJ_XOR = vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_ZERO_);//{ 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
static Packet16uc p16uc_COMPLEX_RE = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
static Packet16uc p16uc_COMPLEX_IM = vec_sld(p16uc_DUPLICATE, (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
static Packet16uc p16uc_COMPLEX_REV = vec_sld(p16uc_REVERSE, p16uc_REVERSE, 8);//{ 4,5,6,7, 0,1,2,3, 12,13,14,15, 8,9,10,11 };
static Packet16uc p16uc_COMPLEX_REV2 = vec_sld(p16uc_FORWARD, p16uc_FORWARD, 8);//{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
static Packet16uc p16uc_PSET_HI = (Packet16uc) vec_mergeh((Packet4ui)p16uc_COMPLEX_RE, (Packet4ui)p16uc_COMPLEX_IM);//{ 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 };
static Packet16uc p16uc_PSET_LO = (Packet16uc) vec_mergel((Packet4ui)p16uc_COMPLEX_RE, (Packet4ui)p16uc_COMPLEX_IM);//{ 8,9,10,11, 12,13,14,15, 8,9,10,11, 12,13,14,15 };
static Packet16uc p16uc_COMPLEX_MASK16 = vec_sld((Packet16uc)p4i_ZERO, vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 3), 8);//{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
static Packet16uc p16uc_COMPLEX_TRANSPOSE_0 = vec_add(p16uc_PSET_HI, p16uc_COMPLEX_MASK16);//{ 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23};
static Packet16uc p16uc_COMPLEX_TRANSPOSE_1 = vec_add(p16uc_PSET_LO, p16uc_COMPLEX_MASK16);//{ 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};
#ifdef _BIG_ENDIAN
static Packet2ul p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2d_ZERO_, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
static Packet2ul p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO, (Packet4ui) p2d_ZERO_, 8);//{ 0x8000000000000000, 0x0000000000000000 };
#else
static Packet2ul p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO, (Packet4ui) p2d_ZERO_, 8);//{ 0x8000000000000000, 0x0000000000000000 };
static Packet2ul p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2d_ZERO_, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
#endif
//---------- float ----------
struct Packet2cf
@@ -55,7 +53,7 @@ template<> struct packet_traits<std::complex<float> > : default_packet_traits
};
};
template<> struct unpacket_traits<Packet2cf> { typedef std::complex<float> type; enum {size=2}; typedef Packet2cf half; };
template<> struct unpacket_traits<Packet2cf> { typedef std::complex<float> type; enum {size=2, alignment=Aligned16}; typedef Packet2cf half; };
template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from)
{
@@ -65,18 +63,18 @@ template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<flo
res.v = pload<Packet4f>((const float *)&from);
else
res.v = ploadu<Packet4f>((const float *)&from);
res.v = vec_perm(res.v, res.v, p16uc_PSET_HI);
res.v = vec_perm(res.v, res.v, p16uc_PSET64_HI);
return res;
}
template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from, DenseIndex stride)
template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from, Index stride)
{
std::complex<float> EIGEN_ALIGN16 af[2];
af[0] = from[0*stride];
af[1] = from[1*stride];
return Packet2cf(vec_ld(0, (const float*)af));
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, DenseIndex stride)
template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, Index stride)
{
std::complex<float> EIGEN_ALIGN16 af[2];
vec_st(from.v, 0, (float*)af);
@@ -95,16 +93,16 @@ template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, con
Packet4f v1, v2;
// Permute and multiply the real parts of a and b
v1 = vec_perm(a.v, a.v, p16uc_COMPLEX_RE);
v1 = vec_perm(a.v, a.v, p16uc_PSET32_WODD);
// Get the imaginary parts of a
v2 = vec_perm(a.v, a.v, p16uc_COMPLEX_IM);
v2 = vec_perm(a.v, a.v, p16uc_PSET32_WEVEN);
// multiply a_re * b
v1 = vec_madd(v1, b.v, p4f_ZERO);
// multiply a_im * b and get the conjugate result
v2 = vec_madd(v2, b.v, p4f_ZERO);
v2 = (Packet4f) vec_xor((Packet4ui)v2, p4ui_CONJ_XOR);
// permute back to a proper order
v2 = vec_perm(v2, v2, p16uc_COMPLEX_REV);
v2 = vec_perm(v2, v2, p16uc_COMPLEX32_REV);
return Packet2cf(vec_add(v1, v2));
}
@@ -138,7 +136,7 @@ template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Pack
template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a)
{
Packet4f rev_a;
rev_a = vec_perm(a.v, a.v, p16uc_COMPLEX_REV2);
rev_a = vec_perm(a.v, a.v, p16uc_COMPLEX32_REV2);
return Packet2cf(rev_a);
}
@@ -153,9 +151,13 @@ template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packe
template<> EIGEN_STRONG_INLINE Packet2cf preduxp<Packet2cf>(const Packet2cf* vecs)
{
Packet4f b1, b2;
#ifdef _BIG_ENDIAN
b1 = (Packet4f) vec_sld(vecs[0].v, vecs[1].v, 8);
b2 = (Packet4f) vec_sld(vecs[1].v, vecs[0].v, 8);
#else
b1 = (Packet4f) vec_sld(vecs[1].v, vecs[0].v, 8);
b2 = (Packet4f) vec_sld(vecs[0].v, vecs[1].v, 8);
#endif
b2 = (Packet4f) vec_sld(b2, b2, 8);
b2 = padd(b1, b2);
@@ -179,7 +181,11 @@ struct palign_impl<Offset,Packet2cf>
{
if (Offset==1)
{
#ifdef _BIG_ENDIAN
first.v = vec_sld(first.v, second.v, 8);
#else
first.v = vec_sld(second.v, first.v, 8);
#endif
}
}
};
@@ -222,23 +228,203 @@ template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, con
// TODO optimize it for AltiVec
Packet2cf res = conj_helper<Packet2cf,Packet2cf,false,true>().pmul(a,b);
Packet4f s = vec_madd(b.v, b.v, p4f_ZERO);
return Packet2cf(pdiv(res.v, vec_add(s,vec_perm(s, s, p16uc_COMPLEX_REV))));
return Packet2cf(pdiv(res.v, vec_add(s,vec_perm(s, s, p16uc_COMPLEX32_REV))));
}
template<> EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& x)
{
return Packet2cf(vec_perm(x.v, x.v, p16uc_COMPLEX_REV));
return Packet2cf(vec_perm(x.v, x.v, p16uc_COMPLEX32_REV));
}
EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel)
{
Packet4f tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_COMPLEX_TRANSPOSE_0);
kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_COMPLEX_TRANSPOSE_1);
Packet4f tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
kernel.packet[0].v = tmp;
}
//---------- double ----------
#ifdef __VSX__
struct Packet1cd
{
EIGEN_STRONG_INLINE Packet1cd() {}
EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {}
Packet2d v;
};
template<> struct packet_traits<std::complex<double> > : default_packet_traits
{
typedef Packet1cd type;
typedef Packet1cd half;
enum {
Vectorizable = 1,
AlignedOnScalar = 0,
size = 1,
HasHalfPacket = 0,
HasAdd = 1,
HasSub = 1,
HasMul = 1,
HasDiv = 1,
HasNegate = 1,
HasAbs = 0,
HasAbs2 = 0,
HasMin = 0,
HasMax = 0,
HasSetLinear = 0
};
};
template<> struct unpacket_traits<Packet1cd> { typedef std::complex<double> type; enum {size=1, alignment=Aligned16}; typedef Packet1cd half; };
template<> EIGEN_STRONG_INLINE Packet1cd pload <Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from)); }
template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from)); }
template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); }
template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); }
template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>& from)
{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); }
template<> EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from, Index stride)
{
std::complex<double> EIGEN_ALIGN16 af[2];
af[0] = from[0*stride];
af[1] = from[1*stride];
return pload<Packet1cd>(af);
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from, Index stride)
{
std::complex<double> EIGEN_ALIGN16 af[2];
pstore<std::complex<double> >(af, from);
to[0*stride] = af[0];
to[1*stride] = af[1];
}
template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_add(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_sub(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate(Packet2d(a.v))); }
template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) { return Packet1cd((Packet2d)vec_xor((Packet2d)a.v, (Packet2d)p2ul_CONJ_XOR2)); }
template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
{
Packet2d a_re, a_im, v1, v2;
// Permute and multiply the real parts of a and b
a_re = vec_perm(a.v, a.v, p16uc_PSET64_HI);
// Get the imaginary parts of a
a_im = vec_perm(a.v, a.v, p16uc_PSET64_LO);
// multiply a_re * b
v1 = vec_madd(a_re, b.v, p2d_ZERO);
// multiply a_im * b and get the conjugate result
v2 = vec_madd(a_im, b.v, p2d_ZERO);
v2 = (Packet2d) vec_sld((Packet4ui)v2, (Packet4ui)v2, 8);
v2 = (Packet2d) vec_xor((Packet2d)v2, (Packet2d) p2ul_CONJ_XOR1);
return Packet1cd(vec_add(v1, v2));
}
template<> EIGEN_STRONG_INLINE Packet1cd pand <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_and(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd por <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_or(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd pxor <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_xor(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_and(a.v, vec_nor(b.v,b.v))); }
template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from)
{
return pset1<Packet1cd>(*from);
}
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> * addr) { vec_dstt((long *)addr, DST_CTRL(2,2,32), DST_CHAN); }
template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a)
{
std::complex<double> EIGEN_ALIGN16 res[2];
pstore<std::complex<double> >(res, a);
return res[0];
}
template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; }
template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a)
{
return pfirst(a);
}
template<> EIGEN_STRONG_INLINE Packet1cd preduxp<Packet1cd>(const Packet1cd* vecs)
{
return vecs[0];
}
template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a)
{
return pfirst(a);
}
template<int Offset>
struct palign_impl<Offset,Packet1cd>
{
static EIGEN_STRONG_INLINE void run(Packet1cd& /*first*/, const Packet1cd& /*second*/)
{
// FIXME is it sure we never have to align a Packet1cd?
// Even though a std::complex<double> has 16 bytes, it is not necessarily aligned on a 16 bytes boundary...
}
};
template<> struct conj_helper<Packet1cd, Packet1cd, false,true>
{
EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const
{
return internal::pmul(a, pconj(b));
}
};
template<> struct conj_helper<Packet1cd, Packet1cd, true,false>
{
EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const
{
return internal::pmul(pconj(a), b);
}
};
template<> struct conj_helper<Packet1cd, Packet1cd, true,true>
{
EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const
{
return pconj(internal::pmul(a, b));
}
};
template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
{
// TODO optimize it for AltiVec
Packet1cd res = conj_helper<Packet1cd,Packet1cd,false,true>().pmul(a,b);
Packet2d s = vec_madd(b.v, b.v, p2d_ZERO_);
return Packet1cd(pdiv(res.v, vec_add(s,vec_perm(s, s, p16uc_REVERSE64))));
}
EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x)
{
return Packet1cd(preverse(Packet2d(x.v)));
}
EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd,2>& kernel)
{
Packet2d tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
kernel.packet[0].v = tmp;
}
#endif // __VSX__
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_COMPLEX_ALTIVEC_H
#endif // EIGEN_COMPLEX32_ALTIVEC_H

View File

@@ -0,0 +1,290 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2007 Julien Pommier
// 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/.
/* The sin, cos, exp, and log functions of this file come from
* Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
*/
#ifndef EIGEN_MATH_FUNCTIONS_ALTIVEC_H
#define EIGEN_MATH_FUNCTIONS_ALTIVEC_H
namespace Eigen {
namespace internal {
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet4f plog<Packet4f>(const Packet4f& _x)
{
Packet4f x = _x;
_EIGEN_DECLARE_CONST_Packet4f(1 , 1.0f);
_EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
_EIGEN_DECLARE_CONST_Packet4i(0x7f, 0x7f);
_EIGEN_DECLARE_CONST_Packet4i(23, 23);
_EIGEN_DECLARE_CONST_Packet4f_FROM_INT(inv_mant_mask, ~0x7f800000);
/* the smallest non denormalized float number */
_EIGEN_DECLARE_CONST_Packet4f_FROM_INT(min_norm_pos, 0x00800000);
_EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_inf, 0xff800000); // -1.f/0.f
_EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_nan, 0xffffffff);
/* natural logarithm computed for 4 simultaneous float
return NaN for x <= 0
*/
_EIGEN_DECLARE_CONST_Packet4f(cephes_SQRTHF, 0.707106781186547524f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p0, 7.0376836292E-2f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p1, - 1.1514610310E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p2, 1.1676998740E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p3, - 1.2420140846E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p4, + 1.4249322787E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p5, - 1.6668057665E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p6, + 2.0000714765E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p7, - 2.4999993993E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p8, + 3.3333331174E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_q1, -2.12194440e-4f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_q2, 0.693359375f);
Packet4i emm0;
/* isvalid_mask is 0 if x < 0 or x is NaN. */
Packet4ui isvalid_mask = reinterpret_cast<Packet4ui>(vec_cmpge(x, p4f_ZERO));
Packet4ui iszero_mask = reinterpret_cast<Packet4ui>(vec_cmpeq(x, p4f_ZERO));
x = pmax(x, p4f_min_norm_pos); /* cut off denormalized stuff */
emm0 = vec_sr(reinterpret_cast<Packet4i>(x),
reinterpret_cast<Packet4ui>(p4i_23));
/* keep only the fractional part */
x = pand(x, p4f_inv_mant_mask);
x = por(x, p4f_half);
emm0 = psub(emm0, p4i_0x7f);
Packet4f e = padd(vec_ctf(emm0, 0), p4f_1);
/* part2:
if( x < SQRTHF ) {
e -= 1;
x = x + x - 1.0;
} else { x = x - 1.0; }
*/
Packet4f mask = reinterpret_cast<Packet4f>(vec_cmplt(x, p4f_cephes_SQRTHF));
Packet4f tmp = pand(x, mask);
x = psub(x, p4f_1);
e = psub(e, pand(p4f_1, mask));
x = padd(x, tmp);
Packet4f x2 = pmul(x,x);
Packet4f x3 = pmul(x2,x);
Packet4f y, y1, y2;
y = pmadd(p4f_cephes_log_p0, x, p4f_cephes_log_p1);
y1 = pmadd(p4f_cephes_log_p3, x, p4f_cephes_log_p4);
y2 = pmadd(p4f_cephes_log_p6, x, p4f_cephes_log_p7);
y = pmadd(y , x, p4f_cephes_log_p2);
y1 = pmadd(y1, x, p4f_cephes_log_p5);
y2 = pmadd(y2, x, p4f_cephes_log_p8);
y = pmadd(y, x3, y1);
y = pmadd(y, x3, y2);
y = pmul(y, x3);
y1 = pmul(e, p4f_cephes_log_q1);
tmp = pmul(x2, p4f_half);
y = padd(y, y1);
x = psub(x, tmp);
y2 = pmul(e, p4f_cephes_log_q2);
x = padd(x, y);
x = padd(x, y2);
// negative arg will be NAN, 0 will be -INF
x = vec_sel(x, p4f_minus_inf, iszero_mask);
x = vec_sel(p4f_minus_nan, x, isvalid_mask);
return x;
}
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet4f pexp<Packet4f>(const Packet4f& _x)
{
Packet4f x = _x;
_EIGEN_DECLARE_CONST_Packet4f(1 , 1.0f);
_EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
_EIGEN_DECLARE_CONST_Packet4i(0x7f, 0x7f);
_EIGEN_DECLARE_CONST_Packet4i(23, 23);
_EIGEN_DECLARE_CONST_Packet4f(exp_hi, 88.3762626647950f);
_EIGEN_DECLARE_CONST_Packet4f(exp_lo, -88.3762626647949f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_LOG2EF, 1.44269504088896341f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C1, 0.693359375f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C2, -2.12194440e-4f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p0, 1.9875691500E-4f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p1, 1.3981999507E-3f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p2, 8.3334519073E-3f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p3, 4.1665795894E-2f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p4, 1.6666665459E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p5, 5.0000001201E-1f);
Packet4f tmp, fx;
Packet4i emm0;
// clamp x
x = vec_max(vec_min(x, p4f_exp_hi), p4f_exp_lo);
/* express exp(x) as exp(g + n*log(2)) */
fx = pmadd(x, p4f_cephes_LOG2EF, p4f_half);
fx = vec_floor(fx);
tmp = pmul(fx, p4f_cephes_exp_C1);
Packet4f z = pmul(fx, p4f_cephes_exp_C2);
x = psub(x, tmp);
x = psub(x, z);
z = pmul(x,x);
Packet4f y = p4f_cephes_exp_p0;
y = pmadd(y, x, p4f_cephes_exp_p1);
y = pmadd(y, x, p4f_cephes_exp_p2);
y = pmadd(y, x, p4f_cephes_exp_p3);
y = pmadd(y, x, p4f_cephes_exp_p4);
y = pmadd(y, x, p4f_cephes_exp_p5);
y = pmadd(y, z, x);
y = padd(y, p4f_1);
// build 2^n
emm0 = vec_cts(fx, 0);
emm0 = vec_add(emm0, p4i_0x7f);
emm0 = vec_sl(emm0, reinterpret_cast<Packet4ui>(p4i_23));
// Altivec's max & min operators just drop silent NaNs. Check NaNs in
// inputs and return them unmodified.
Packet4ui isnumber_mask = reinterpret_cast<Packet4ui>(vec_cmpeq(_x, _x));
return vec_sel(_x, pmax(pmul(y, reinterpret_cast<Packet4f>(emm0)), _x),
isnumber_mask);
}
#ifdef __VSX__
// VSX support varies between different compilers and even different
// versions of the same compiler. For gcc version >= 4.9.3, we can use
// vec_cts to efficiently convert Packet2d to Packet2l. Otherwise, use
// a slow version that works with older compilers.
static inline Packet2l ConvertToPacket2l(const Packet2d& x) {
#if EIGEN_GNUC_AT_LEAST(5, 0) || \
(EIGEN_GNUC_AT(4, 9) && __GNUC_PATCHLEVEL__ >= 3)
return vec_cts(x, 0); // TODO: check clang version.
#else
double tmp[2];
memcpy(tmp, &x, sizeof(tmp));
Packet2l l = { static_cast<long long>(tmp[0]),
static_cast<long long>(tmp[1]) };
return l;
#endif
}
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet2d pexp<Packet2d>(const Packet2d& _x)
{
Packet2d x = _x;
_EIGEN_DECLARE_CONST_Packet2d(1 , 1.0);
_EIGEN_DECLARE_CONST_Packet2d(2 , 2.0);
_EIGEN_DECLARE_CONST_Packet2d(half, 0.5);
_EIGEN_DECLARE_CONST_Packet2d(exp_hi, 709.437);
_EIGEN_DECLARE_CONST_Packet2d(exp_lo, -709.436139303);
_EIGEN_DECLARE_CONST_Packet2d(cephes_LOG2EF, 1.4426950408889634073599);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p0, 1.26177193074810590878e-4);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p1, 3.02994407707441961300e-2);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p2, 9.99999999999999999910e-1);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q0, 3.00198505138664455042e-6);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q1, 2.52448340349684104192e-3);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q2, 2.27265548208155028766e-1);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q3, 2.00000000000000000009e0);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C1, 0.693145751953125);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C2, 1.42860682030941723212e-6);
Packet2d tmp, fx;
Packet2l emm0;
// clamp x
x = pmax(pmin(x, p2d_exp_hi), p2d_exp_lo);
/* express exp(x) as exp(g + n*log(2)) */
fx = pmadd(p2d_cephes_LOG2EF, x, p2d_half);
fx = vec_floor(fx);
tmp = pmul(fx, p2d_cephes_exp_C1);
Packet2d z = pmul(fx, p2d_cephes_exp_C2);
x = psub(x, tmp);
x = psub(x, z);
Packet2d x2 = pmul(x,x);
Packet2d px = p2d_cephes_exp_p0;
px = pmadd(px, x2, p2d_cephes_exp_p1);
px = pmadd(px, x2, p2d_cephes_exp_p2);
px = pmul (px, x);
Packet2d qx = p2d_cephes_exp_q0;
qx = pmadd(qx, x2, p2d_cephes_exp_q1);
qx = pmadd(qx, x2, p2d_cephes_exp_q2);
qx = pmadd(qx, x2, p2d_cephes_exp_q3);
x = pdiv(px,psub(qx,px));
x = pmadd(p2d_2,x,p2d_1);
// build 2^n
emm0 = ConvertToPacket2l(fx);
#ifdef __POWER8_VECTOR__
static const Packet2l p2l_1023 = { 1023, 1023 };
static const Packet2ul p2ul_52 = { 52, 52 };
emm0 = vec_add(emm0, p2l_1023);
emm0 = vec_sl(emm0, p2ul_52);
#else
// Code is a bit complex for POWER7. There is actually a
// vec_xxsldi intrinsic but it is not supported by some gcc versions.
// So we shift (52-32) bits and do a word swap with zeros.
_EIGEN_DECLARE_CONST_Packet4i(1023, 1023);
_EIGEN_DECLARE_CONST_Packet4i(20, 20); // 52 - 32
Packet4i emm04i = reinterpret_cast<Packet4i>(emm0);
emm04i = vec_add(emm04i, p4i_1023);
emm04i = vec_sl(emm04i, reinterpret_cast<Packet4ui>(p4i_20));
static const Packet16uc perm = {
0x14, 0x15, 0x16, 0x17, 0x00, 0x01, 0x02, 0x03,
0x1c, 0x1d, 0x1e, 0x1f, 0x08, 0x09, 0x0a, 0x0b };
#ifdef _BIG_ENDIAN
emm0 = reinterpret_cast<Packet2l>(vec_perm(p4i_ZERO, emm04i, perm));
#else
emm0 = reinterpret_cast<Packet2l>(vec_perm(emm04i, p4i_ZERO, perm));
#endif
#endif
// Altivec's max & min operators just drop silent NaNs. Check NaNs in
// inputs and return them unmodified.
Packet2ul isnumber_mask = reinterpret_cast<Packet2ul>(vec_cmpeq(_x, _x));
return vec_sel(_x, pmax(pmul(x, reinterpret_cast<Packet2d>(emm0)), _x),
isnumber_mask);
}
#endif
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_MATH_FUNCTIONS_ALTIVEC_H

View File

@@ -18,17 +18,17 @@ namespace internal {
#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 4
#endif
#ifndef EIGEN_HAS_FUSED_MADD
#define EIGEN_HAS_FUSED_MADD 1
#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
#endif
#ifndef EIGEN_HAS_FUSE_CJMADD
#define EIGEN_HAS_FUSE_CJMADD 1
#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_CJMADD
#define EIGEN_HAS_SINGLE_INSTRUCTION_CJMADD
#endif
// NOTE Altivec has 32 registers, but Eigen only accepts a value of 8 or 16
#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 16
#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
#endif
typedef __vector float Packet4f;
@@ -50,30 +50,77 @@ typedef __vector unsigned char Packet16uc;
#define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
Packet4f p4f_##NAME = pset1<Packet4f>(X)
#define _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \
Packet4f p4f_##NAME = vreinterpretq_f32_u32(pset1<int>(X))
#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
Packet4i p4i_##NAME = pset1<Packet4i>(X)
#define _EIGEN_DECLARE_CONST_Packet2d(NAME,X) \
Packet2d p2d_##NAME = pset1<Packet2d>(X)
#define _EIGEN_DECLARE_CONST_Packet2l(NAME,X) \
Packet2l p2l_##NAME = pset1<Packet2l>(X)
#define _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \
const Packet4f p4f_##NAME = reinterpret_cast<Packet4f>(pset1<Packet4i>(X))
#define DST_CHAN 1
#define DST_CTRL(size, count, stride) (((size) << 24) | ((count) << 16) | (stride))
// Define global static constants:
static Packet4f p4f_COUNTDOWN = { 0.0, 1.0, 2.0, 3.0 };
static Packet4i p4i_COUNTDOWN = { 0, 1, 2, 3 };
static Packet16uc p16uc_REVERSE = { 12,13,14,15, 8,9,10,11, 4,5,6,7, 0,1,2,3};
static Packet16uc p16uc_FORWARD = vec_lvsl(0, (float*)0); //{ 0,1,2,3, 4,5,6,7, 8,9,10,11, 12,13,14,15}
static Packet16uc p16uc_DUPLICATE = { 0,1,2,3, 0,1,2,3, 4,5,6,7, 4,5,6,7};
// These constants are endian-agnostic
static _EIGEN_DECLARE_CONST_FAST_Packet4f(ZERO, 0); //{ 0.0, 0.0, 0.0, 0.0}
static _EIGEN_DECLARE_CONST_FAST_Packet4i(ZERO, 0); //{ 0, 0, 0, 0,}
#ifndef __VSX__
static _EIGEN_DECLARE_CONST_FAST_Packet4i(ONE,1); //{ 1, 1, 1, 1}
static Packet4f p4f_ONE = vec_ctf(p4i_ONE, 0); //{ 1.0, 1.0, 1.0, 1.0}
#endif
static _EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS16,-16); //{ -16, -16, -16, -16}
static _EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS1,-1); //{ -1, -1, -1, -1}
static Packet4f p4f_ONE = vec_ctf(p4i_ONE, 0); //{ 1.0, 1.0, 1.0, 1.0}
static Packet4f p4f_ZERO_ = (Packet4f) vec_sl((Packet4ui)p4i_MINUS1, (Packet4ui)p4i_MINUS1); //{ 0x80000000, 0x80000000, 0x80000000, 0x80000000}
static Packet4f p4f_COUNTDOWN = { 0.0, 1.0, 2.0, 3.0 };
static Packet4i p4i_COUNTDOWN = { 0, 1, 2, 3 };
static Packet16uc p16uc_REVERSE32 = { 12,13,14,15, 8,9,10,11, 4,5,6,7, 0,1,2,3 };
static Packet16uc p16uc_DUPLICATE32_HI = { 0,1,2,3, 0,1,2,3, 4,5,6,7, 4,5,6,7 };
// Mask alignment
#ifdef __PPC64__
#define _EIGEN_MASK_ALIGNMENT 0xfffffffffffffff0
#else
#define _EIGEN_MASK_ALIGNMENT 0xfffffff0
#endif
#define _EIGEN_ALIGNED_PTR(x) ((ptrdiff_t)(x) & _EIGEN_MASK_ALIGNMENT)
// Handle endianness properly while loading constants
// Define global static constants:
#ifdef _BIG_ENDIAN
static Packet16uc p16uc_FORWARD = vec_lvsl(0, (float*)0);
static Packet16uc p16uc_REVERSE64 = { 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
static Packet16uc p16uc_PSET32_WODD = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
static Packet16uc p16uc_PSET32_WEVEN = vec_sld(p16uc_DUPLICATE32_HI, (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
static Packet16uc p16uc_HALF64_0_16 = vec_sld((Packet16uc)p4i_ZERO, vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 3), 8); //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
#else
static Packet16uc p16uc_FORWARD = p16uc_REVERSE32;
static Packet16uc p16uc_REVERSE64 = { 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
static Packet16uc p16uc_PSET32_WODD = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 1), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
static Packet16uc p16uc_PSET32_WEVEN = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
static Packet16uc p16uc_HALF64_0_16 = vec_sld(vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 0), (Packet16uc)p4i_ZERO, 8); //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
#endif // _BIG_ENDIAN
static Packet16uc p16uc_PSET64_HI = (Packet16uc) vec_mergeh((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN); //{ 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 };
static Packet16uc p16uc_PSET64_LO = (Packet16uc) vec_mergel((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN); //{ 8,9,10,11, 12,13,14,15, 8,9,10,11, 12,13,14,15 };
static Packet16uc p16uc_TRANSPOSE64_HI = vec_add(p16uc_PSET64_HI, p16uc_HALF64_0_16); //{ 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23};
static Packet16uc p16uc_TRANSPOSE64_LO = vec_add(p16uc_PSET64_LO, p16uc_HALF64_0_16); //{ 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};
static Packet16uc p16uc_COMPLEX32_REV = vec_sld(p16uc_REVERSE32, p16uc_REVERSE32, 8); //{ 4,5,6,7, 0,1,2,3, 12,13,14,15, 8,9,10,11 };
#ifdef _BIG_ENDIAN
static Packet16uc p16uc_COMPLEX32_REV2 = vec_sld(p16uc_FORWARD, p16uc_FORWARD, 8); //{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
#else
static Packet16uc p16uc_COMPLEX32_REV2 = vec_sld(p16uc_PSET64_HI, p16uc_PSET64_LO, 8); //{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
#endif // _BIG_ENDIAN
template<> struct packet_traits<float> : default_packet_traits
{
typedef Packet4f type;
@@ -88,8 +135,8 @@ template<> struct packet_traits<float> : default_packet_traits
HasDiv = 1,
HasSin = 0,
HasCos = 0,
HasLog = 0,
HasExp = 0,
HasLog = 1,
HasExp = 1,
HasSqrt = 0
};
};
@@ -105,9 +152,22 @@ template<> struct packet_traits<int> : default_packet_traits
};
};
template<> struct unpacket_traits<Packet4f> { typedef float type; enum {size=4}; typedef Packet4f half; };
template<> struct unpacket_traits<Packet4i> { typedef int type; enum {size=4}; typedef Packet4i half; };
/*
template<> struct unpacket_traits<Packet4f> { typedef float type; enum {size=4, alignment=Aligned16}; typedef Packet4f half; };
template<> struct unpacket_traits<Packet4i> { typedef int type; enum {size=4, alignment=Aligned16}; typedef Packet4i half; };
inline std::ostream & operator <<(std::ostream & s, const Packet16uc & v)
{
union {
Packet16uc v;
unsigned char n[16];
} vt;
vt.v = v;
for (int i=0; i< 16; i++)
s << (int)vt.n[i] << ", ";
return s;
}
inline std::ostream & operator <<(std::ostream & s, const Packet4f & v)
{
union {
@@ -140,7 +200,7 @@ inline std::ostream & operator <<(std::ostream & s, const Packet4ui & v)
s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3];
return s;
}
/*
inline std::ostream & operator <<(std::ostream & s, const Packetbi & v)
{
union {
@@ -150,14 +210,21 @@ inline std::ostream & operator <<(std::ostream & s, const Packetbi & v)
vt.v = v;
s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3];
return s;
}
*/
}*/
// Need to define them first or we get specialization after instantiation errors
template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) { EIGEN_DEBUG_ALIGNED_LOAD return vec_ld(0, from); }
template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int* from) { EIGEN_DEBUG_ALIGNED_LOAD return vec_ld(0, from); }
template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) { EIGEN_DEBUG_ALIGNED_STORE vec_st(from, 0, to); }
template<> EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet4i& from) { EIGEN_DEBUG_ALIGNED_STORE vec_st(from, 0, to); }
template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) {
// Taken from http://developer.apple.com/hardwaredrivers/ve/alignment.html
float EIGEN_ALIGN16 af[4];
af[0] = from;
Packet4f vc = vec_ld(0, af);
Packet4f vc = pload<Packet4f>(af);
vc = vec_splat(vc, 0);
return vc;
}
@@ -165,17 +232,15 @@ template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) {
template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int& from) {
int EIGEN_ALIGN16 ai[4];
ai[0] = from;
Packet4i vc = vec_ld(0, ai);
Packet4i vc = pload<Packet4i>(ai);
vc = vec_splat(vc, 0);
return vc;
}
template<> EIGEN_STRONG_INLINE void
pbroadcast4<Packet4f>(const float *a,
Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
{
a3 = vec_ld(0,a);
a3 = pload<Packet4f>(a);
a0 = vec_splat(a3, 0);
a1 = vec_splat(a3, 1);
a2 = vec_splat(a3, 2);
@@ -185,52 +250,52 @@ template<> EIGEN_STRONG_INLINE void
pbroadcast4<Packet4i>(const int *a,
Packet4i& a0, Packet4i& a1, Packet4i& a2, Packet4i& a3)
{
a3 = vec_ld(0,a);
a3 = pload<Packet4i>(a);
a0 = vec_splat(a3, 0);
a1 = vec_splat(a3, 1);
a2 = vec_splat(a3, 2);
a3 = vec_splat(a3, 3);
}
template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, DenseIndex stride)
template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride)
{
float EIGEN_ALIGN16 af[4];
af[0] = from[0*stride];
af[1] = from[1*stride];
af[2] = from[2*stride];
af[3] = from[3*stride];
return vec_ld(0, af);
return pload<Packet4f>(af);
}
template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* from, DenseIndex stride)
template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* from, Index stride)
{
int EIGEN_ALIGN16 ai[4];
ai[0] = from[0*stride];
ai[1] = from[1*stride];
ai[2] = from[2*stride];
ai[3] = from[3*stride];
return vec_ld(0, ai);
return pload<Packet4i>(ai);
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, DenseIndex stride)
template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
{
float EIGEN_ALIGN16 af[4];
vec_st(from, 0, af);
pstore<float>(af, from);
to[0*stride] = af[0];
to[1*stride] = af[1];
to[2*stride] = af[2];
to[3*stride] = af[3];
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const Packet4i& from, DenseIndex stride)
template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const Packet4i& from, Index stride)
{
int EIGEN_ALIGN16 ai[4];
vec_st(from, 0, ai);
pstore<int>((int *)ai, from);
to[0*stride] = ai[0];
to[1*stride] = ai[1];
to[2*stride] = ai[2];
to[3*stride] = ai[3];
}
template<> EIGEN_STRONG_INLINE Packet4f plset<float>(const float& a) { return vec_add(pset1<Packet4f>(a), p4f_COUNTDOWN); }
template<> EIGEN_STRONG_INLINE Packet4i plset<int>(const int& a) { return vec_add(pset1<Packet4i>(a), p4i_COUNTDOWN); }
template<> EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a) { return vec_add(pset1<Packet4f>(a), p4f_COUNTDOWN); }
template<> EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int& a) { return vec_add(pset1<Packet4i>(a), p4i_COUNTDOWN); }
template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_add(a,b); }
template<> EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_add(a,b); }
@@ -283,7 +348,8 @@ template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const
*/
template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b)
{
Packet4f t, y_0, y_1, res;
#ifndef __VSX__ // VSX actually provides a div instruction
Packet4f t, y_0, y_1;
// Altivec does not offer a divide instruction, we have to do a reciprocal approximation
y_0 = vec_re(b);
@@ -292,8 +358,10 @@ template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const
t = vec_nmsub(y_0, b, p4f_ONE);
y_1 = vec_madd(y_0, t, y_0);
res = vec_madd(a, y_1, p4f_ZERO);
return res;
return vec_madd(a, y_1, p4f_ZERO);
#else
return vec_div(a, b);
#endif
}
template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, const Packet4i& /*b*/)
@@ -311,7 +379,6 @@ template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const
template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_max(a, b); }
template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_max(a, b); }
// Logical Operations are not supported for float, so we have to reinterpret casts using NEON intrinsics
template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_and(a, b); }
template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_and(a, b); }
@@ -324,13 +391,10 @@ template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const
template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_and(a, vec_nor(b, b)); }
template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_and(a, vec_nor(b, b)); }
template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) { EIGEN_DEBUG_ALIGNED_LOAD return vec_ld(0, from); }
template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int* from) { EIGEN_DEBUG_ALIGNED_LOAD return vec_ld(0, from); }
#ifdef _BIG_ENDIAN
template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from)
{
EIGEN_DEBUG_ALIGNED_LOAD
// Taken from http://developer.apple.com/hardwaredrivers/ve/alignment.html
Packet16uc MSQ, LSQ;
Packet16uc mask;
MSQ = vec_ld(0, (unsigned char *)from); // most significant quadword
@@ -350,25 +414,36 @@ template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from)
mask = vec_lvsl(0, from); // create the permute mask
return (Packet4i) vec_perm(MSQ, LSQ, mask); // align the data
}
#else
// We also need ot redefine little endian loading of Packet4i/Packet4f using VSX
template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from)
{
EIGEN_DEBUG_ALIGNED_LOAD
return (Packet4i) vec_vsx_ld((long)from & 15, (const int*) _EIGEN_ALIGNED_PTR(from));
}
template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from)
{
EIGEN_DEBUG_ALIGNED_LOAD
return (Packet4f) vec_vsx_ld((long)from & 15, (const float*) _EIGEN_ALIGNED_PTR(from));
}
#endif
template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from)
{
Packet4f p;
if((ptrdiff_t(from) % 16) == 0) p = pload<Packet4f>(from);
else p = ploadu<Packet4f>(from);
return vec_perm(p, p, p16uc_DUPLICATE);
return vec_perm(p, p, p16uc_DUPLICATE32_HI);
}
template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int* from)
{
Packet4i p;
if((ptrdiff_t(from) % 16) == 0) p = pload<Packet4i>(from);
else p = ploadu<Packet4i>(from);
return vec_perm(p, p, p16uc_DUPLICATE);
return vec_perm(p, p, p16uc_DUPLICATE32_HI);
}
template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) { EIGEN_DEBUG_ALIGNED_STORE vec_st(from, 0, to); }
template<> EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet4i& from) { EIGEN_DEBUG_ALIGNED_STORE vec_st(from, 0, to); }
#ifdef _BIG_ENDIAN
template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from)
{
EIGEN_DEBUG_UNALIGNED_STORE
@@ -405,15 +480,30 @@ template<> EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet4i& f
vec_st( LSQ, 15, (unsigned char *)to ); // Store the LSQ part first
vec_st( MSQ, 0, (unsigned char *)to ); // Store the MSQ part
}
#else
// We also need ot redefine little endian loading of Packet4i/Packet4f using VSX
template<> EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet4i& from)
{
EIGEN_DEBUG_ALIGNED_STORE
vec_vsx_st(from, (long)to & 15, (int*) _EIGEN_ALIGNED_PTR(to));
}
template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from)
{
EIGEN_DEBUG_ALIGNED_STORE
vec_vsx_st(from, (long)to & 15, (float*) _EIGEN_ALIGNED_PTR(to));
}
#endif
#ifndef __VSX__
template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { vec_dstt(addr, DST_CTRL(2,2,32), DST_CHAN); }
template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { vec_dstt(addr, DST_CTRL(2,2,32), DST_CHAN); }
#endif
template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { float EIGEN_ALIGN16 x[4]; vec_st(a, 0, x); return x[0]; }
template<> EIGEN_STRONG_INLINE int pfirst<Packet4i>(const Packet4i& a) { int EIGEN_ALIGN16 x[4]; vec_st(a, 0, x); return x[0]; }
template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) { return (Packet4f)vec_perm((Packet16uc)a,(Packet16uc)a, p16uc_REVERSE); }
template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) { return (Packet4i)vec_perm((Packet16uc)a,(Packet16uc)a, p16uc_REVERSE); }
template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) { return (Packet4f)vec_perm((Packet16uc)a,(Packet16uc)a, p16uc_REVERSE32); }
template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) { return (Packet4i)vec_perm((Packet16uc)a,(Packet16uc)a, p16uc_REVERSE32); }
template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) { return vec_abs(a); }
template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) { return vec_abs(a); }
@@ -460,7 +550,11 @@ template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a)
{
Packet4i sum;
sum = vec_sums(a, p4i_ZERO);
#ifdef _BIG_ENDIAN
sum = vec_sld(sum, p4i_ZERO, 12);
#else
sum = vec_sld(p4i_ZERO, sum, 4);
#endif
return pfirst(sum);
}
@@ -547,8 +641,25 @@ struct palign_impl<Offset,Packet4f>
{
static EIGEN_STRONG_INLINE void run(Packet4f& first, const Packet4f& second)
{
if (Offset!=0)
first = vec_sld(first, second, Offset*4);
#ifdef _BIG_ENDIAN
switch (Offset % 4) {
case 1:
first = vec_sld(first, second, 4); break;
case 2:
first = vec_sld(first, second, 8); break;
case 3:
first = vec_sld(first, second, 12); break;
}
#else
switch (Offset % 4) {
case 1:
first = vec_sld(second, first, 12); break;
case 2:
first = vec_sld(second, first, 8); break;
case 3:
first = vec_sld(second, first, 4); break;
}
#endif
}
};
@@ -557,8 +668,25 @@ struct palign_impl<Offset,Packet4i>
{
static EIGEN_STRONG_INLINE void run(Packet4i& first, const Packet4i& second)
{
if (Offset!=0)
first = vec_sld(first, second, Offset*4);
#ifdef _BIG_ENDIAN
switch (Offset % 4) {
case 1:
first = vec_sld(first, second, 4); break;
case 2:
first = vec_sld(first, second, 8); break;
case 3:
first = vec_sld(first, second, 12); break;
}
#else
switch (Offset % 4) {
case 1:
first = vec_sld(second, first, 12); break;
case 2:
first = vec_sld(second, first, 8); break;
case 3:
first = vec_sld(second, first, 4); break;
}
#endif
}
};
@@ -588,6 +716,222 @@ ptranspose(PacketBlock<Packet4i,4>& kernel) {
kernel.packet[3] = vec_mergel(t1, t3);
}
//---------- double ----------
#ifdef __VSX__
typedef __vector double Packet2d;
typedef __vector unsigned long long Packet2ul;
typedef __vector long long Packet2l;
static Packet2l p2l_ZERO = (Packet2l) p4i_ZERO;
static Packet2d p2d_ONE = { 1.0, 1.0 };
static Packet2d p2d_ZERO = (Packet2d) p4f_ZERO;
static Packet2d p2d_ZERO_ = { -0.0, -0.0 };
#ifdef _BIG_ENDIAN
static Packet2d p2d_COUNTDOWN = (Packet2d) vec_sld((Packet16uc) p2d_ZERO, (Packet16uc) p2d_ONE, 8);
#else
static Packet2d p2d_COUNTDOWN = (Packet2d) vec_sld((Packet16uc) p2d_ONE, (Packet16uc) p2d_ZERO, 8);
#endif
static EIGEN_STRONG_INLINE Packet2d vec_splat_dbl(Packet2d& a, int index)
{
switch (index) {
case 0:
return (Packet2d) vec_perm(a, a, p16uc_PSET64_HI);
case 1:
return (Packet2d) vec_perm(a, a, p16uc_PSET64_LO);
}
return a;
}
template<> struct packet_traits<double> : default_packet_traits
{
typedef Packet2d type;
typedef Packet2d half;
enum {
Vectorizable = 1,
AlignedOnScalar = 1,
size=2,
HasHalfPacket = 0,
HasDiv = 1,
HasExp = 1,
HasSqrt = 0
};
};
template<> struct unpacket_traits<Packet2d> { typedef double type; enum {size=2, alignment=Aligned16}; typedef Packet2d half; };
inline std::ostream & operator <<(std::ostream & s, const Packet2d & v)
{
union {
Packet2d v;
double n[2];
} vt;
vt.v = v;
s << vt.n[0] << ", " << vt.n[1];
return s;
}
// Need to define them first or we get specialization after instantiation errors
template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) { EIGEN_DEBUG_ALIGNED_LOAD return (Packet2d) vec_ld(0, (const float *) from); } //FIXME
template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) { EIGEN_DEBUG_ALIGNED_STORE vec_st((Packet4f)from, 0, (float *)to); }
template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) {
double EIGEN_ALIGN16 af[2];
af[0] = from;
Packet2d vc = pload<Packet2d>(af);
vc = vec_splat_dbl(vc, 0);
return vc;
}
template<> EIGEN_STRONG_INLINE void
pbroadcast4<Packet2d>(const double *a,
Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
{
a1 = pload<Packet2d>(a);
a0 = vec_splat_dbl(a1, 0);
a1 = vec_splat_dbl(a1, 1);
a3 = pload<Packet2d>(a+2);
a2 = vec_splat_dbl(a3, 0);
a3 = vec_splat_dbl(a3, 1);
}
template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, Index stride)
{
double EIGEN_ALIGN16 af[2];
af[0] = from[0*stride];
af[1] = from[1*stride];
return pload<Packet2d>(af);
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride)
{
double EIGEN_ALIGN16 af[2];
pstore<double>(af, from);
to[0*stride] = af[0];
to[1*stride] = af[1];
}
template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) { return vec_add(pset1<Packet2d>(a), p2d_COUNTDOWN); }
template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_add(a,b); }
template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_sub(a,b); }
template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return psub<Packet2d>(p2d_ZERO, a); }
template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_madd(a,b,p2d_ZERO); }
template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_div(a,b); }
// for some weird raisons, it has to be overloaded for packet of integers
template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vec_madd(a, b, c); }
template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_min(a, b); }
template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_max(a, b); }
template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, b); }
template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_or(a, b); }
template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_xor(a, b); }
template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, vec_nor(b, b)); }
template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from)
{
EIGEN_DEBUG_ALIGNED_LOAD
return (Packet2d) vec_vsx_ld((long)from & 15, (const float*) _EIGEN_ALIGNED_PTR(from));
}
template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from)
{
Packet2d p;
if((ptrdiff_t(from) % 16) == 0) p = pload<Packet2d>(from);
else p = ploadu<Packet2d>(from);
return vec_perm(p, p, p16uc_PSET64_HI);
}
template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from)
{
EIGEN_DEBUG_ALIGNED_STORE
vec_vsx_st((Packet4f)from, (long)to & 15, (float*) _EIGEN_ALIGNED_PTR(to));
}
template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { vec_dstt((const float *) addr, DST_CTRL(2,2,32), DST_CHAN); }
template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { double EIGEN_ALIGN16 x[2]; pstore(x, a); return x[0]; }
template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) { return (Packet2d)vec_perm((Packet16uc)a,(Packet16uc)a, p16uc_REVERSE64); }
template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) { return vec_abs(a); }
template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
{
Packet2d b, sum;
b = (Packet2d) vec_sld((Packet4ui) a, (Packet4ui)a, 8);
sum = vec_add(a, b);
return pfirst(sum);
}
template<> EIGEN_STRONG_INLINE Packet2d preduxp<Packet2d>(const Packet2d* vecs)
{
Packet2d v[2], sum;
v[0] = vec_add(vecs[0], (Packet2d) vec_sld((Packet4ui) vecs[0], (Packet4ui) vecs[0], 8));
v[1] = vec_add(vecs[1], (Packet2d) vec_sld((Packet4ui) vecs[1], (Packet4ui) vecs[1], 8));
#ifdef _BIG_ENDIAN
sum = (Packet2d) vec_sld((Packet4ui) v[0], (Packet4ui) v[1], 8);
#else
sum = (Packet2d) vec_sld((Packet4ui) v[1], (Packet4ui) v[0], 8);
#endif
return sum;
}
// Other reduction functions:
// mul
template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
{
return pfirst(pmul(a, (Packet2d)vec_sld((Packet4ui) a, (Packet4ui) a, 8)));
}
// min
template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a)
{
return pfirst(vec_min(a, (Packet2d) vec_sld((Packet4ui) a, (Packet4ui) a, 8)));
}
// max
template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a)
{
return pfirst(vec_max(a, (Packet2d) vec_sld((Packet4ui) a, (Packet4ui) a, 8)));
}
template<int Offset>
struct palign_impl<Offset,Packet2d>
{
static EIGEN_STRONG_INLINE void run(Packet2d& first, const Packet2d& second)
{
if (Offset == 1)
#ifdef _BIG_ENDIAN
first = (Packet2d) vec_sld((Packet4ui) first, (Packet4ui) second, 8);
#else
first = (Packet2d) vec_sld((Packet4ui) second, (Packet4ui) first, 8);
#endif
}
};
EIGEN_DEVICE_FUNC inline void
ptranspose(PacketBlock<Packet2d,2>& kernel) {
Packet2d t0, t1;
t0 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_HI);
t1 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_LO);
kernel.packet[0] = t0;
kernel.packet[1] = t1;
}
#endif // __VSX__
} // end namespace internal
} // end namespace Eigen

View File

@@ -1,5 +1,9 @@
ADD_SUBDIRECTORY(SSE)
ADD_SUBDIRECTORY(AltiVec)
ADD_SUBDIRECTORY(NEON)
ADD_SUBDIRECTORY(AVX)
ADD_SUBDIRECTORY(CUDA)
ADD_SUBDIRECTORY(Default)
ADD_SUBDIRECTORY(NEON)
ADD_SUBDIRECTORY(SSE)

View File

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

View File

@@ -0,0 +1,75 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@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_MATH_FUNCTIONS_CUDA_H
#define EIGEN_MATH_FUNCTIONS_CUDA_H
namespace Eigen {
namespace internal {
// Make sure this is only available when targeting a GPU: we don't want to
// introduce conflicts between these packet_traits definitions and the ones
// we'll use on the host side (SSE, AVX, ...)
#if defined(__CUDACC__) && defined(EIGEN_USE_GPU)
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
float4 plog<float4>(const float4& a)
{
return make_float4(logf(a.x), logf(a.y), logf(a.z), logf(a.w));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
double2 plog<double2>(const double2& a)
{
return make_double2(log(a.x), log(a.y));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
float4 pexp<float4>(const float4& a)
{
return make_float4(expf(a.x), expf(a.y), expf(a.z), expf(a.w));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
double2 pexp<double2>(const double2& a)
{
return make_double2(exp(a.x), exp(a.y));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
float4 psqrt<float4>(const float4& a)
{
return make_float4(sqrtf(a.x), sqrtf(a.y), sqrtf(a.z), sqrtf(a.w));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
double2 psqrt<double2>(const double2& a)
{
return make_double2(sqrt(a.x), sqrt(a.y));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
float4 prsqrt<float4>(const float4& a)
{
return make_float4(rsqrtf(a.x), rsqrtf(a.y), rsqrtf(a.z), rsqrtf(a.w));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
double2 prsqrt<double2>(const double2& a)
{
return make_double2(rsqrt(a.x), rsqrt(a.y));
}
#endif
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_MATH_FUNCTIONS_CUDA_H

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