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..

118 Commits
3.0.5 ... 2.0.7

Author SHA1 Message Date
Benoit Jacob
21d081c6da bump 2009-10-22 16:14:03 -04:00
Benoit Jacob
81fd1e9060 support gcc 3.3 2009-10-22 15:53:23 -04:00
Benoit Jacob
be8ae0d45f * CMakeLists: only pass -Wextra if it's supported (it's not on gcc 3.3)
* MapBase: put static first (fix gcc 3.3 warning)
* StdVector: add missing newline at end
2009-10-22 15:20:02 -04:00
Hauke Heibel
fc8b54c142 Code cleanup. 2009-10-22 20:06:05 +02:00
Hauke Heibel
76d578fb99 This does fix #61. See the comments in #61 for details. 2009-10-22 09:29:59 +02:00
Benoit Jacob
9af431e78e tentative fix for bug #61 2009-10-21 18:55:54 -04:00
Hauke Heibel
a4a3e511d0 Added missing resize case for m_outerSize==0. 2009-10-15 23:30:15 +02:00
Hauke Heibel
ffee27bf72 Fixed uninitialized variables in unit tests.
Fixed /W4 warning C4512 (LU was left out on purpose).
2009-10-14 08:33:36 +02:00
Benoit Jacob
5e24fbbead add assert for M>=N 2009-10-13 14:17:25 -04:00
Benoit Jacob
09364c8d05 fix compilation with gcc 3.3 2009-10-12 12:29:07 -04:00
Gael Guennebaud
3b8938ee1a backport d97d307fcf 2009-10-06 10:36:59 +02:00
Gael Guennebaud
e43d630d80 fix #10: the reallocateSparse function was half coded
(transplanted from 55de162cf6
)
2009-06-08 14:05:23 +02:00
Thomas Capricelli
eb1df142a3 backport changes in tip related to eigen_gen_docs 2009-10-04 03:38:13 +02:00
Thomas Capricelli
746d8b7ce9 update URL for adol-c (backport from tip) 2009-09-27 02:00:45 +02:00
Benoit Jacob
656c8faeb8 merge 2009-09-25 09:09:49 -04:00
Benoit Jacob
8084dbc86a copy the Memory.h file from the devel branch and remove some added trailing spaces.
This is now very harmless to do as the big change (EIGEN_ALIGN preprocessor stuff and the body of ei_aligned_malloc) was already introduced in 2.0.6.

Should address Björn's issue, and also improve FreeBSD platform detection.
2009-09-25 09:09:14 -04:00
Thomas Capricelli
79ebba4f52 clean tags... 2.0.6 was tagged three times, with two different values.
The best way to "push" a tag is to edit the .hgtags instead of using 'hg
tag xx' several times with the same value 'xx'.
2009-09-25 03:19:28 +02:00
Benoit Jacob
b362b45cff update .hgignore to ignore files created by eigen_gen_credits 2009-09-24 07:06:31 -04:00
Rhys Ulerich
c67b8b7ce0 Added pkgconfig support 2009-09-23 22:05:46 -04:00
Benoit Jacob
bcd621fcd5 Added tag 2.0.6 for changeset de88fb67d6 2009-09-23 12:11:26 -04:00
Benoit Jacob
de88fb67d6 bump that too 2009-09-23 12:11:19 -04:00
Benoit Jacob
4936720648 Added tag 2.0.6 for changeset 922e11e184 2009-09-23 12:08:22 -04:00
Benoit Jacob
922e11e184 bump 2009-09-23 12:08:16 -04:00
Benoit Jacob
8c2ace33c9 backport Rohit's vectorized quaternion product 2009-09-22 13:39:30 -04:00
Benoit Jacob
b66516e746 fix bug #42: add missing Transform::Identity() 2009-09-19 20:00:36 -04:00
Benoit Jacob
ecf64d2dc3 Allow to override EIGEN_RESTRICT, to satisfy a smart ass blogger who claims
that eigen2 owes all its performance to nonstandard restrict keyword.
well, this can also improve portability in case some compiler doesn't have __restrict.
2009-09-19 19:46:40 -04:00
Benoit Jacob
6af2c2c67a backported the following to 2.0:
* EIGEN_ALIGN and EIGEN_DONT_ALIGN and the corresponding logic in Macros.h
  (instead of using EIGEN_ARCH_WANTS_ALIGNMENT)
* The body of ei_aligned_malloc and ei_aligned_free

The reason for this backporting is that a user complained that with eigen 2.0 he got a warning at Memory.h:81 that the return value of posix_memalign was not used, and that function was declared with an attribute warn_unused_result.

Looking at this, it seemed that the body of this function was already overly complicated, and fixing this warning made it even worse, while the devel branch had a much simpler body and didn't suffer from that problem.

Then it was necessary to update ei_aligned_free too, and to backport EIGEN_ALIGN.

Inch' Allah....
2009-09-21 05:39:55 -04:00
Benoit Jacob
d0ac4fa479 explain how to get rid of it 2009-09-18 22:02:28 -04:00
Benoit Jacob
09f77b356d hg add the unit test 2009-09-16 14:29:44 -04:00
Benoit Jacob
8097487b9d backport bugfix in visitor (didn't work on rowvectors, fixed by splitting the vector case away from the matrix case) 2009-09-16 14:28:49 -04:00
Benoit Jacob
aaf1826384 backport: the first fix was the good one 2009-09-03 01:28:12 -04:00
Benoit Jacob
3590911de2 backport the fix to bug #50: compilation errors with swap 2009-09-02 17:04:34 -04:00
Benoit Jacob
21e97f07d8 update to reflect NewStdVector 2009-08-29 12:35:44 -04:00
Benoit Jacob
82df5b4a24 backport the new StdVector as NewStdVector
make StdVector be a wrapper around it if EIGEN_USE_NEW_STDVECTOR is defined
otherwise StdVector doesn't change ---> compatibility is preserved
backport unit-test
2009-08-29 12:13:52 -04:00
Benoit Jacob
35e88996c7 add missing parentheses after bug #42 2009-08-24 09:14:32 -04:00
Benoit Jacob
5dfb7204bd Added tag 2.0.5 for changeset e0cbf79e5a 2009-08-22 17:19:13 -04:00
Benoit Jacob
e0cbf79e5a bump to 2.0.5 2009-08-22 17:19:08 -04:00
Benoit Jacob
3af177058e fix nasty bug: when calling the cache friendly product, one used the product xpr flags instead of the destination flags, resulting in a transposed result when the storage orders didn't match. 2009-08-21 16:03:14 -04:00
Marcus D. Hanwell
258ea3ea02 Proper fix for linking to the Qt libraries (and others)
My initial fix was incorrect, the libraries must be quoted when being
passed to the add test macro, but must be unquoted when passed to the
target_link_libraries function.
2009-08-21 14:08:53 -04:00
Benoit Jacob
6580278e2c fix potential compilation issue 2009-08-21 12:08:59 -04:00
Benoit Jacob
dcefb66283 Fix bug #41, our resize() method didn't work with gcc 4.1 2009-08-21 11:53:04 -04:00
Benoit Jacob
9d64571963 disable fortran by default, because of bug http://public.kitware.com/Bug/view.php?id=9220 in cmake. 2009-08-21 11:48:59 -04:00
Benoit Jacob
65724def70 more useful error message about the including order 2009-08-20 12:27:01 -04:00
Benoit Jacob
7a44945a16 fix casting warning with MSVC 2009-08-18 07:41:17 -04:00
Gael Guennebaud
ed33d688e1 forgot to remove the link to the example list page 2009-08-17 18:23:21 +02:00
Gael Guennebaud
d7bf8b8581 remove the broken examplelist 2009-08-17 18:20:53 +02:00
Gael Guennebaud
a9c60954ed add EIGEN_TRANSFORM_PLUGIN 2009-08-17 09:16:04 +02:00
Gael Guennebaud
78ea8b2dbd fix #32 even though I agree this feature should be removed, or put somewhere else... 2009-08-15 22:35:33 +02:00
Benoit Jacob
d4e25e5acf in the 2.0 branch, that stuff isn't wanted anymore 2009-08-14 22:08:14 -04:00
Thomas Capricelli
36b324fe7b backport from main branch : basic .hgignore file 2009-08-15 03:43:40 +02:00
Thomas Capricelli
d37de5db30 todo list for the script eigen_gen_docs 2009-08-15 03:42:04 +02:00
Thomas Capricelli
456b6abed5 backport from the main branch : this script is used to create and upload
the documentation to the website
2009-08-15 03:39:08 +02:00
Gael Guennebaud
4a50ee8c21 fix issue #36 (missing return *this in Rotation2D
(transplanted from a4f6642518
)
2009-08-11 15:11:47 +02:00
Gael Guennebaud
c9f7a19053 make LU::solve() not to crash when rank=0
(transplanted from fe813911f2
)
2009-08-09 00:06:53 +02:00
Gael Guennebaud
47973c4963 set EIGEN_STACK_ALLOCATION_LIMIT as in the devel branch 2009-08-08 10:45:57 +02:00
Marcus D. Hanwell
65487176e3 Improved quoting of tests when added to the build.
This fixes an issue where multiple versions of the Qt libraries are
available, if the Qt library variable is not quoted an error was
generated as only the first part 'optimized' was used by the create test
macro.
2009-08-01 13:43:06 -04:00
Benoit Jacob
d28fae5bdf Added tag 2.0.4 for changeset d4f9515ca0 2009-08-01 00:58:16 +02:00
Benoit Jacob
d4f9515ca0 bump to 2.0.4 2009-08-01 00:58:09 +02:00
Gael Guennebaud
0361e8a7aa no more workaround, the -r option of clone works with branch name too 2009-07-31 17:24:57 +02:00
Gael Guennebaud
b7035b67b7 workaround to make the testsuite ctest script to work with the 2.0 branch, but that's only for unix systems 2009-07-31 17:07:43 +02:00
Gael Guennebaud
a1eae7ad00 update the ctest script for the 2.0 branch 2009-07-31 16:27:31 +02:00
Gael Guennebaud
30b605bef8 update the CTestConfig file to upload 2.0 reports to a different cdash project 2009-07-31 16:15:37 +02:00
Benoit Jacob
990615e884 backport 126284d08b
.
2009-07-31 13:30:12 +02:00
Gael Guennebaud
841ec959e5 s/std::atan2/ei_atan2 2009-07-31 10:08:23 +02:00
Manuel Yguel
2dce3311f7 add missing ei_atan2 without painfull warnings 2009-07-31 09:21:31 +02:00
Anthony Truchet
8eab0bccbf Bugfix in the Qt's QTransform and QMatrix support in Geometry/Transform.h
Function 'Transform<Scalar,Dim>::toQMatrix(void) const' :
  - 'other' was a hasty copy/paste to be replaced my m_matrix
	- 'coeffRef' was incorect for const Transform

Function 'Transform<Scalar,Dim>::toQTransform(void) const' :
	- return type was incorrect 'QMatrix' to be replaced by 'QTransform'
	- same bigfixes as in the previous point
2009-07-30 10:09:41 +02:00
Gael Guennebaud
f5a167b3e7 apply patch from Hauke Heibel cleaning overloaded operator new/detete 2009-05-07 20:33:48 +00:00
Gael Guennebaud
f845d15192 enable our own ctest dashboard 2009-07-20 23:55:43 +02:00
Gael Guennebaud
7ae2bc6109 compilation fix
(transplanted from c10b919edb
)
2009-07-20 10:56:03 +02:00
Gael Guennebaud
654fea39dc bugfix in operator*= (matrix product)
(transplanted from b3ad796d40
)
2009-07-20 10:44:07 +02:00
Gael Guennebaud
fa44566305 bugfix for a = a * b; when a has to be resized
(transplanted from a551107cce
)
2009-07-20 10:35:47 +02:00
Gael Guennebaud
8302ce6cdc remove the special version of ei_pow(int,int) for gcc >= 4.3 that was stupid
because gcc convert it to a pow(double,double)
2009-07-16 09:10:34 +02:00
Gael Guennebaud
c6eb9ef60e backporting bugfix in Quaternion::setFromTwoVectors() 2009-07-06 09:05:48 +02:00
Benoit Jacob
9bff5e4f67 some docs improvements 2009-07-05 01:52:42 +02:00
Gael Guennebaud
5f350c51b3 update the stack alignment doc 2009-06-22 10:46:03 +02:00
Benoit Jacob
df0b107243 Added tag 2.0.3 for changeset 55bf82c923 2009-06-21 17:46:35 +02:00
Benoit Jacob
55bf82c923 backport improvements to transpose documentation 2009-06-21 17:41:55 +02:00
Benoit Jacob
0b341486db document the "wrong stack alignment" issue 2009-06-21 17:34:17 +02:00
Benoit Jacob
9db0038c42 add Eigen/Eigen 2009-06-19 20:49:02 +02:00
Benoit Jacob
89d7ba0be0 add Dense header 2009-06-19 19:11:50 +02:00
Benoit Jacob
c3bab0edb7 fix #12, but the fix is not optimal, householder transformations need to be rethought in the complex case, see:
http://download.tuxfamily.org/eigen/complex-householder.pdf
2009-06-19 18:50:22 +02:00
Benoit Jacob
a1a26f45d3 fix #14: make llt::solve() and also ldlt::solve() work with uninitialized result 2009-06-19 17:01:32 +02:00
Benoit Jacob
f5ae3a4b5a result of our experiments with LU tuning: implement very simple formula, that
turns out to be similar to Higham's formula already in use in LDLt
2009-05-07 20:35:26 +00:00
Gael Guennebaud
8817798273 backporting accuracy fixes in QR module 2009-06-11 16:24:54 +02:00
Gael Guennebaud
287c7b8818 backporting LLT accuracy fixes 2009-06-11 16:18:37 +02:00
Benoit Jacob
5ec4922349 forgot to add the unsupported IterativeSolvers module needed by Step 2009-06-04 18:40:16 +02:00
Benoit Jacob
5a18f7545d this is essentially backporting all the changes made in the Sparse module up to KDE SVN revision r945600, aka changeset:
df9dfa1455


This is what is needed to make Step (in KDE/kdeedu) build.

The rest of Eigen (outside of Sparse) is unaffected except for a few trivial changes that were needed.

calling this 2.0.3, will tag if no problem.
2009-06-04 18:02:20 +02:00
Benoit Jacob
12570d97ce Added tag 2.0.2 for changeset 3fc53d2564 2009-05-22 15:41:27 +02:00
Benoit Jacob
3fc53d2564 bump version number 2009-05-22 15:41:22 +02:00
Benoit Jacob
9ff0baa680 the EIGEN_CAT macro is needed for the latest change in CacheFriendlyProduct 2009-05-22 15:03:40 +02:00
Gael Guennebaud
1c4b4e136b backporting warning fixes in cache friendly product 2009-05-19 02:20:04 +02:00
Thomas Capricelli
57934b9c30 backport ddb6e96d48
: fix warnings with recent gcc(4.3.3)
2009-05-19 00:05:33 +02:00
Thomas Capricelli
52aed8ac58 Remove this old file. It was stalling in history because of a bug in
svn, which did not prevent the commit (svn r722564) to 'svn copy' a
directory called 'Core/' on top of an existing file 'Core'

see http://websvn.kde.org/?view=rev&revision=722564
or
http://www.freehackers.org/thomas/2009/05/18/feedback-about-converting-eigen2-to-mercurial/
2009-05-18 15:20:56 +02:00
Benoit Jacob
1304e43f15 backport 964558: add missing setZero (etc) overloads that were mentioned in the tutorial
this should be safe as it's covered by the updated unit-test
2009-05-06 21:42:31 +00:00
Gael Guennebaud
e47593fb28 backporting 964177 (gcc 3.3 fix) 2009-05-06 09:41:36 +00:00
Gael Guennebaud
0104c34b7d backporting r964165 (gcc 3.3 fixes) 2009-05-06 09:40:41 +00:00
Benoit Jacob
f82d9bdf9a backport r963940, reimplement linearRegression on top of the better fitHyperplane 2009-05-05 17:16:45 +00:00
Benoit Jacob
c9edcc5acd backport 963931: fix linearRegression 2009-05-05 16:52:10 +00:00
Benoit Jacob
487edbf325 backport 963281, fix msvc detection on win64 2009-05-04 12:14:37 +00:00
Benoit Jacob
a29a390afa backport 958657: fix posix_memalign detection (Ross Smith) 2009-04-24 13:28:25 +00:00
Benoit Jacob
a16d18a632 update version number to 2.0.1 2009-04-14 14:32:00 +00:00
Benoit Jacob
3c3653b9de merge 953719: fix 4x4 inverse 2009-04-14 13:43:21 +00:00
Gael Guennebaud
c15842c374 backporting rev 951682 (compilation fix in aligned allocator) 2009-04-09 21:23:25 +00:00
Benoit Jacob
3c90fc2e64 patch by Hauke Heibel: compilation fix with VS 9 2009-04-09 12:05:36 +00:00
Benoit Jacob
d9c9508a12 backport 947492 -- fix static assertion / patch by Markus Moll 2009-03-31 16:08:06 +00:00
Gael Guennebaud
d6bb34fa5a backporting various bug fixes related to MapBase/Map/Block and new
StdVector workaround because the previous was really too limited. I hope
it is not a too big change for a "stable" branch.
2009-03-24 08:20:43 +00:00
Gael Guennebaud
e5b5ab53b8 backporting bugfix in SliceVectorization 2009-03-07 15:12:42 +00:00
Gael Guennebaud
f2829c1358 backporting rev 918446: fix MSVC internal compilation error 2009-03-06 22:18:26 +00:00
Benoit Jacob
d38504a4c8 backport 921254-921261 to the branch: disable alignment altogether on exotic platforms 2009-02-16 16:29:33 +00:00
Gael Guennebaud
95e4508b04 backporting rev925153 (bugfix in MapBase::coeffRef(int) ) 2009-02-12 15:32:32 +00:00
Benoit Jacob
b064b5e68e forgot to update version number 2009-02-02 16:18:42 +00:00
Benoit Jacob
f7df9f92ff backport 919961 and 920175 2009-02-02 14:26:40 +00:00
Benoit Jacob
d2dcca52a3 backport 920106: BSD's don't have aligned malloc 2009-02-02 13:24:17 +00:00
Gael Guennebaud
7408e923a7 backporting commit 918468 (fix MSVC internal error) 2009-01-29 23:14:51 +00:00
Gael Guennebaud
18ca438a62 backport r917694 (Patch from Frank fixing stupid MSVC internal crash) 2009-01-28 15:18:28 +00:00
Benoit Jacob
d286300362 backport unit-tests fixes 2009-01-27 20:56:47 +00:00
Benoit Jacob
02ba4e2f54 backport compilation fix 2009-01-27 17:46:02 +00:00
Benoit Jacob
2eef21a8d5 branch eigen 2.0 2009-01-27 17:26:44 +00:00
1109 changed files with 24068 additions and 154278 deletions

3
.hgeol
View File

@@ -1,3 +0,0 @@
[patterns]
**.* = native
eigen_autoexp_part.dat = CRLF

View File

@@ -5,7 +5,6 @@ qrc_*cxx
*.diff
diff
*.save
save
*.old
*.gmo
*.qm
@@ -13,7 +12,7 @@ core
core.*
*.bak
*~
build*
build
*.moc.*
*.moc
ui_*
@@ -23,10 +22,3 @@ tags
activity.png
*.out
*.php*
*.log
*.orig
*.rej
log
patch
a
a.*

View File

@@ -1,405 +1,105 @@
project(Eigen)
cmake_minimum_required(VERSION 2.6.2)
# guard against in-source builds
if(${CMAKE_SOURCE_DIR} STREQUAL ${CMAKE_BINARY_DIR})
message(FATAL_ERROR "In-source builds not allowed. Please make a new directory (called a build directory) and run CMake from there. You may need to remove CMakeCache.txt. ")
endif()
# guard against bad build-type strings
if (NOT CMAKE_BUILD_TYPE)
set(CMAKE_BUILD_TYPE "Release")
endif()
string(TOLOWER "${CMAKE_BUILD_TYPE}" cmake_build_type_tolower)
if( NOT cmake_build_type_tolower STREQUAL "debug"
AND NOT cmake_build_type_tolower STREQUAL "release"
AND NOT cmake_build_type_tolower STREQUAL "relwithdebinfo")
message(FATAL_ERROR "Unknown build type \"${CMAKE_BUILD_TYPE}\". Allowed values are Debug, Release, RelWithDebInfo (case-insensitive).")
endif()
#############################################################################
# retrieve version infomation #
#############################################################################
# automatically parse the version number
file(READ "${PROJECT_SOURCE_DIR}/Eigen/src/Core/util/Macros.h" _eigen_version_header)
string(REGEX MATCH "define[ \t]+EIGEN_WORLD_VERSION[ \t]+([0-9]+)" _eigen_world_version_match "${_eigen_version_header}")
set(EIGEN_WORLD_VERSION "${CMAKE_MATCH_1}")
string(REGEX MATCH "define[ \t]+EIGEN_MAJOR_VERSION[ \t]+([0-9]+)" _eigen_major_version_match "${_eigen_version_header}")
set(EIGEN_MAJOR_VERSION "${CMAKE_MATCH_1}")
string(REGEX MATCH "define[ \t]+EIGEN_MINOR_VERSION[ \t]+([0-9]+)" _eigen_minor_version_match "${_eigen_version_header}")
set(EIGEN_MINOR_VERSION "${CMAKE_MATCH_1}")
set(EIGEN_VERSION_NUMBER ${EIGEN_WORLD_VERSION}.${EIGEN_MAJOR_VERSION}.${EIGEN_MINOR_VERSION})
# if the mercurial program is absent, this will leave the EIGEN_HG_CHANGESET string empty,
# but won't stop CMake.
execute_process(COMMAND hg tip -R ${CMAKE_SOURCE_DIR} OUTPUT_VARIABLE EIGEN_HGTIP_OUTPUT)
execute_process(COMMAND hg branch -R ${CMAKE_SOURCE_DIR} OUTPUT_VARIABLE EIGEN_BRANCH_OUTPUT)
# if this is the default (aka development) branch, extract the mercurial changeset number from the hg tip output...
if(EIGEN_BRANCH_OUTPUT MATCHES "default")
string(REGEX MATCH "^changeset: *[0-9]*:([0-9;a-f]+).*" EIGEN_HG_CHANGESET_MATCH "${EIGEN_HGTIP_OUTPUT}")
set(EIGEN_HG_CHANGESET "${CMAKE_MATCH_1}")
endif(EIGEN_BRANCH_OUTPUT MATCHES "default")
#...and show it next to the version number
if(EIGEN_HG_CHANGESET)
set(EIGEN_VERSION "${EIGEN_VERSION_NUMBER} (mercurial changeset ${EIGEN_HG_CHANGESET})")
else(EIGEN_HG_CHANGESET)
set(EIGEN_VERSION "${EIGEN_VERSION_NUMBER}")
endif(EIGEN_HG_CHANGESET)
include(CheckCXXCompilerFlag)
set(EIGEN_VERSION_NUMBER "2.0.7")
set(EIGEN_VERSION "${EIGEN_VERSION_NUMBER}")
set(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake)
#############################################################################
# find how to link to the standard libraries #
#############################################################################
find_package(StandardMathLibrary)
set(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO "")
if(NOT STANDARD_MATH_LIBRARY_FOUND)
message(FATAL_ERROR
"Can't link to the standard math library. Please report to the Eigen developers, telling them about your platform.")
else()
if(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO)
set(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO "${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO} ${STANDARD_MATH_LIBRARY}")
else()
set(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO "${STANDARD_MATH_LIBRARY}")
endif()
endif()
if(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO)
message(STATUS "Standard libraries to link to explicitly: ${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO}")
else()
message(STATUS "Standard libraries to link to explicitly: none")
endif()
option(EIGEN_BUILD_TESTS "Build tests" OFF)
option(EIGEN_BUILD_DEMOS "Build demos" OFF)
if(NOT WIN32)
option(EIGEN_BUILD_LIB "Build the binary shared library" OFF)
endif(NOT WIN32)
option(EIGEN_BUILD_BTL "Build benchmark suite" OFF)
if(NOT WIN32)
option(EIGEN_BUILD_PKGCONFIG "Build pkg-config .pc file for Eigen" ON)
endif(NOT WIN32)
if(EIGEN_BUILD_LIB)
option(EIGEN_TEST_LIB "Build the unit tests using the library (disable -pedantic)" OFF)
endif(EIGEN_BUILD_LIB)
set(CMAKE_INCLUDE_CURRENT_DIR ON)
option(EIGEN_SPLIT_LARGE_TESTS "Split large tests into smaller executables" ON)
option(EIGEN_DEFAULT_TO_ROW_MAJOR "Use row-major as default matrix storage order" OFF)
if(EIGEN_DEFAULT_TO_ROW_MAJOR)
add_definitions("-DEIGEN_DEFAULT_TO_ROW_MAJOR")
endif()
add_definitions("-DEIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS")
if(CMAKE_COMPILER_IS_GNUCXX)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wnon-virtual-dtor -Wno-long-long -ansi -Wundef -Wcast-align -Wchar-subscripts -Wall -W -Wpointer-arith -Wwrite-strings -Wformat-security -fexceptions -fno-check-new -fno-common -fstrict-aliasing")
set(CMAKE_CXX_FLAGS_DEBUG "-g3")
set(CMAKE_CXX_FLAGS_RELEASE "-g0 -O2")
check_cxx_compiler_flag("-Wno-variadic-macros" COMPILER_SUPPORT_WNOVARIADICMACRO)
if(COMPILER_SUPPORT_WNOVARIADICMACRO)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-variadic-macros")
endif()
check_cxx_compiler_flag("-Wextra" COMPILER_SUPPORT_WEXTRA)
if(COMPILER_SUPPORT_WEXTRA)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wextra")
endif()
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pedantic")
option(EIGEN_TEST_SSE2 "Enable/Disable SSE2 in tests/examples" OFF)
if(EIGEN_TEST_SSE2)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -msse2")
message(STATUS "Enabling SSE2 in tests/examples")
endif()
option(EIGEN_TEST_SSE3 "Enable/Disable SSE3 in tests/examples" OFF)
if(EIGEN_TEST_SSE3)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -msse3")
message(STATUS "Enabling SSE3 in tests/examples")
endif()
option(EIGEN_TEST_SSSE3 "Enable/Disable SSSE3 in tests/examples" OFF)
if(EIGEN_TEST_SSSE3)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mssse3")
message(STATUS "Enabling SSSE3 in tests/examples")
endif()
option(EIGEN_TEST_SSE4_1 "Enable/Disable SSE4.1 in tests/examples" OFF)
if(EIGEN_TEST_SSE4_1)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -msse4.1")
message(STATUS "Enabling SSE4.1 in tests/examples")
endif()
option(EIGEN_TEST_SSE4_2 "Enable/Disable SSE4.2 in tests/examples" OFF)
if(EIGEN_TEST_SSE4_2)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -msse4.2")
message(STATUS "Enabling SSE4.2 in tests/examples")
endif()
option(EIGEN_TEST_ALTIVEC "Enable/Disable AltiVec in tests/examples" OFF)
if(EIGEN_TEST_ALTIVEC)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -maltivec -mabi=altivec")
message(STATUS "Enabling AltiVec in tests/examples")
endif()
option(EIGEN_TEST_NEON "Enable/Disable Neon in tests/examples" OFF)
if(EIGEN_TEST_NEON)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mfloat-abi=softfp -mfpu=neon -mcpu=cortex-a8")
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)
if(EIGEN_TEST_OPENMP)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fopenmp")
message(STATUS "Enabling OpenMP in tests/examples")
if(CMAKE_SYSTEM_NAME MATCHES Linux)
include(CheckCXXCompilerFlag)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wnon-virtual-dtor -Wno-long-long -ansi -Wundef -Wcast-align -Wchar-subscripts -Wall -W -Wpointer-arith -Wwrite-strings -Wformat-security -fno-exceptions -fno-check-new -fno-common -fstrict-aliasing")
check_cxx_compiler_flag("-Wextra" has_wextra)
if(has_wextra)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wextra")
endif()
endif()
if(NOT EIGEN_TEST_LIB)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pedantic")
endif(NOT EIGEN_TEST_LIB)
option(EIGEN_TEST_SSE2 "Enable/Disable SSE2 in tests/examples" OFF)
if(EIGEN_TEST_SSE2)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -msse2")
message("Enabling SSE2 in tests/examples")
endif(EIGEN_TEST_SSE2)
option(EIGEN_TEST_SSE3 "Enable/Disable SSE3 in tests/examples" OFF)
if(EIGEN_TEST_SSE3)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -msse3")
message("Enabling SSE3 in tests/examples")
endif(EIGEN_TEST_SSE3)
option(EIGEN_TEST_SSSE3 "Enable/Disable SSSE3 in tests/examples" OFF)
if(EIGEN_TEST_SSSE3)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mssse3")
message("Enabling SSSE3 in tests/examples")
endif(EIGEN_TEST_SSSE3)
option(EIGEN_TEST_ALTIVEC "Enable/Disable altivec in tests/examples" OFF)
if(EIGEN_TEST_ALTIVEC)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -maltivec -mabi=altivec")
message("Enabling AltiVec in tests/examples")
endif(EIGEN_TEST_ALTIVEC)
endif(CMAKE_SYSTEM_NAME MATCHES Linux)
endif(CMAKE_COMPILER_IS_GNUCXX)
if(MSVC)
# C4127 - conditional expression is constant
# C4714 - marked as __forceinline not inlined (I failed to deactivate it selectively)
# We can disable this warning in the unit tests since it is clear that it occurs
# because we are oftentimes returning objects that have a destructor or may
# throw exceptions - in particular in the unit tests we are throwing extra many
# exceptions to cover indexing errors.
# C4505 - unreferenced local function has been removed (impossible to deactive selectively)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /EHsc /wd4127 /wd4505 /wd4714")
# replace all /Wx by /W4
string(REGEX REPLACE "/W[0-9]" "/W4" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
check_cxx_compiler_flag("/openmp" COMPILER_SUPPORT_OPENMP)
if(COMPILER_SUPPORT_OPENMP)
option(EIGEN_TEST_OPENMP "Enable/Disable OpenMP in tests/examples" OFF)
if(EIGEN_TEST_OPENMP)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /openmp")
message(STATUS "Enabling OpenMP in tests/examples")
endif()
endif()
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ")
option(EIGEN_TEST_SSE2 "Enable/Disable SSE2 in tests/examples" OFF)
if(EIGEN_TEST_SSE2)
if(NOT CMAKE_CL_64)
# arch is not supported on 64 bit systems, SSE is enabled automatically.
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:SSE2")
endif(NOT CMAKE_CL_64)
message(STATUS "Enabling SSE2 in tests/examples")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:SSE2")
message("Enabling SSE2 in tests/examples")
endif(EIGEN_TEST_SSE2)
endif(MSVC)
option(EIGEN_TEST_NO_EXPLICIT_VECTORIZATION "Disable explicit vectorization in tests/examples" OFF)
option(EIGEN_TEST_X87 "Force using X87 instructions. Implies no vectorization." OFF)
option(EIGEN_TEST_32BIT "Force generating 32bit code." OFF)
if(EIGEN_TEST_X87)
set(EIGEN_TEST_NO_EXPLICIT_VECTORIZATION ON)
if(CMAKE_COMPILER_IS_GNUCXX)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mfpmath=387")
message(STATUS "Forcing use of x87 instructions in tests/examples")
else()
message(STATUS "EIGEN_TEST_X87 ignored on your compiler")
endif()
endif()
if(EIGEN_TEST_32BIT)
if(CMAKE_COMPILER_IS_GNUCXX)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -m32")
message(STATUS "Forcing generation of 32-bit code in tests/examples")
else()
message(STATUS "EIGEN_TEST_32BIT ignored on your compiler")
endif()
endif()
if(EIGEN_TEST_NO_EXPLICIT_VECTORIZATION)
add_definitions(-DEIGEN_DONT_VECTORIZE=1)
message(STATUS "Disabling vectorization in tests/examples")
endif()
option(EIGEN_TEST_NO_EXPLICIT_ALIGNMENT "Disable explicit alignment (hence vectorization) in tests/examples" OFF)
if(EIGEN_TEST_NO_EXPLICIT_ALIGNMENT)
add_definitions(-DEIGEN_DONT_ALIGN=1)
message(STATUS "Disabling alignment in tests/examples")
endif()
option(EIGEN_TEST_C++0x "Enables all C++0x features." OFF)
message("Disabling vectorization in tests/examples")
endif(EIGEN_TEST_NO_EXPLICIT_VECTORIZATION)
include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
# the user modifiable install path for header files
set(EIGEN_INCLUDE_INSTALL_DIR ${EIGEN_INCLUDE_INSTALL_DIR} CACHE PATH "The directory where we install the header files (optional)")
# set the internal install path for header files which depends on wether the user modifiable
# EIGEN_INCLUDE_INSTALL_DIR has been set by the user or not.
if(EIGEN_INCLUDE_INSTALL_DIR)
set(INCLUDE_INSTALL_DIR
${EIGEN_INCLUDE_INSTALL_DIR}
CACHE INTERNAL
"The directory where we install the header files (internal)"
)
else()
set(INCLUDE_INSTALL_DIR
"${CMAKE_INSTALL_PREFIX}/include/eigen3"
CACHE INTERNAL
"The directory where we install the header files (internal)"
)
endif()
# similar to set_target_properties but append the property instead of overwriting it
macro(ei_add_target_property target prop value)
get_target_property(previous ${target} ${prop})
# if the property wasn't previously set, ${previous} is now "previous-NOTFOUND" which cmake allows catching with plain if()
if(NOT previous)
set(previous "")
endif(NOT previous)
set_target_properties(${target} PROPERTIES ${prop} "${previous} ${value}")
endmacro(ei_add_target_property)
install(FILES
signature_of_eigen3_matrix_library
DESTINATION ${INCLUDE_INSTALL_DIR} COMPONENT Devel
)
if(EIGEN_BUILD_PKGCONFIG)
SET(path_separator ":")
STRING(REPLACE ${path_separator} ";" pkg_config_libdir_search "$ENV{PKG_CONFIG_LIBDIR}")
message(STATUS "searching for 'pkgconfig' directory in PKG_CONFIG_LIBDIR ( $ENV{PKG_CONFIG_LIBDIR} ), ${CMAKE_INSTALL_PREFIX}/share, and ${CMAKE_INSTALL_PREFIX}/lib")
FIND_PATH(pkg_config_libdir pkgconfig ${pkg_config_libdir_search} ${CMAKE_INSTALL_PREFIX}/share ${CMAKE_INSTALL_PREFIX}/lib ${pkg_config_libdir_search})
if(pkg_config_libdir)
SET(pkg_config_install_dir ${pkg_config_libdir})
message(STATUS "found ${pkg_config_libdir}/pkgconfig" )
else(pkg_config_libdir)
SET(pkg_config_install_dir ${CMAKE_INSTALL_PREFIX}/share)
message(STATUS "pkgconfig not found; installing in ${pkg_config_install_dir}" )
endif(pkg_config_libdir)
configure_file(eigen3.pc.in eigen3.pc)
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/eigen3.pc
DESTINATION ${pkg_config_install_dir}/pkgconfig
configure_file(eigen2.pc.in eigen2.pc)
install(FILES eigen2.pc
DESTINATION lib/pkgconfig
)
endif(EIGEN_BUILD_PKGCONFIG)
add_subdirectory(Eigen)
add_subdirectory(doc EXCLUDE_FROM_ALL)
add_custom_target(buildtests)
add_custom_target(check COMMAND "ctest")
add_dependencies(check buildtests)
# CMake/Ctest does not allow us to change the build command,
# so we have to workaround by directly editing the generated DartConfiguration.tcl file
# save CMAKE_MAKE_PROGRAM
set(CMAKE_MAKE_PROGRAM_SAVE ${CMAKE_MAKE_PROGRAM})
# and set a fake one
set(CMAKE_MAKE_PROGRAM "@EIGEN_MAKECOMMAND_PLACEHOLDER@")
include(CTest)
enable_testing() # must be called from the root CMakeLists, see man page
include(EigenTesting)
ei_init_testing()
# overwrite default DartConfiguration.tcl
# The worarounds are different for each version of the MSVC IDE
if(MSVC_IDE)
if(MSVC_VERSION EQUAL 1600) # MSVC 2010
set(EIGEN_MAKECOMMAND_PLACEHOLDER "${CMAKE_MAKE_PROGRAM_SAVE} buildtests.vcxproj /p:Configuration=\${CTEST_CONFIGURATION_TYPE} \n # ")
else() # MSVC 2008 (TODO check MSVC 2005)
set(EIGEN_MAKECOMMAND_PLACEHOLDER "${CMAKE_MAKE_PROGRAM_SAVE} /project buildtests")
endif()
else()
# for make and nmake
set(EIGEN_MAKECOMMAND_PLACEHOLDER "${CMAKE_MAKE_PROGRAM_SAVE} buildtests")
endif()
configure_file(${CMAKE_BINARY_DIR}/DartConfiguration.tcl ${CMAKE_BINARY_DIR}/DartConfiguration.tcl)
# restore default CMAKE_MAKE_PROGRAM
set(CMAKE_MAKE_PROGRAM ${CMAKE_MAKE_PROGRAM_SAVE})
# un-set temporary variables so that it is like they never existed.
# CMake 2.6.3 introduces the more logical unset() syntax for this.
set(CMAKE_MAKE_PROGRAM_SAVE)
set(EIGEN_MAKECOMMAND_PLACEHOLDER)
configure_file(${CMAKE_SOURCE_DIR}/CTestCustom.cmake.in ${CMAKE_BINARY_DIR}/CTestCustom.cmake)
if(EIGEN_LEAVE_TEST_IN_ALL_TARGET)
add_subdirectory(test) # can't do EXCLUDE_FROM_ALL here, breaks CTest
else()
add_subdirectory(test EXCLUDE_FROM_ALL)
endif()
if(NOT MSVC)
if(EIGEN_LEAVE_TEST_IN_ALL_TARGET)
add_subdirectory(blas)
add_subdirectory(lapack)
else()
add_subdirectory(blas EXCLUDE_FROM_ALL)
add_subdirectory(lapack EXCLUDE_FROM_ALL)
endif()
endif(NOT MSVC)
add_subdirectory(unsupported)
add_subdirectory(demos EXCLUDE_FROM_ALL)
if(EIGEN_BUILD_TESTS)
include(CTest)
add_subdirectory(test)
endif(EIGEN_BUILD_TESTS)
# must be after test and unsupported, for configuring buildtests.in
add_subdirectory(scripts EXCLUDE_FROM_ALL)
add_subdirectory(doc)
if(EIGEN_BUILD_DEMOS)
add_subdirectory(demos)
endif(EIGEN_BUILD_DEMOS)
# TODO: consider also replacing EIGEN_BUILD_BTL by a custom target "make btl"?
if(EIGEN_BUILD_BTL)
add_subdirectory(bench/btl EXCLUDE_FROM_ALL)
add_subdirectory(bench/btl)
endif(EIGEN_BUILD_BTL)
ei_testing_print_summary()
message(STATUS "")
message(STATUS "Configured Eigen ${EIGEN_VERSION_NUMBER}")
message(STATUS "")
option(EIGEN_FAILTEST "Enable failtests." OFF)
if(EIGEN_FAILTEST)
add_subdirectory(failtest)
endif()
string(TOLOWER "${CMAKE_GENERATOR}" cmake_generator_tolower)
if(cmake_generator_tolower MATCHES "makefile")
message(STATUS "Some things you can do now:")
message(STATUS "--------------+--------------------------------------------------------------")
message(STATUS "Command | Description")
message(STATUS "--------------+--------------------------------------------------------------")
message(STATUS "make install | Install to ${CMAKE_INSTALL_PREFIX}. To change that:")
message(STATUS " | cmake . -DCMAKE_INSTALL_PREFIX=yourpath")
message(STATUS " | Eigen headers will then be installed to:")
message(STATUS " | ${INCLUDE_INSTALL_DIR}")
message(STATUS " | To install Eigen headers to a separate location, do:")
message(STATUS " | cmake . -DEIGEN_INCLUDE_INSTALL_DIR=yourpath")
message(STATUS "make doc | Generate the API documentation, requires Doxygen & LaTeX")
message(STATUS "make check | Build and run the unit-tests. Read this page:")
message(STATUS " | http://eigen.tuxfamily.org/index.php?title=Tests")
message(STATUS "make blas | Build BLAS library (not the same thing as Eigen)")
message(STATUS "--------------+--------------------------------------------------------------")
else()
message(STATUS "To build/run the unit tests, read this page:")
message(STATUS " http://eigen.tuxfamily.org/index.php?title=Tests")
endif()
message(STATUS "")

View File

@@ -1,4 +1,4 @@
GNU LESSER GENERAL PUBLIC LICENSE
GNU LESSER GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>

View File

@@ -4,10 +4,10 @@
## # The following are required to uses Dart and the Cdash dashboard
## ENABLE_TESTING()
## INCLUDE(CTest)
set(CTEST_PROJECT_NAME "Eigen")
set(CTEST_NIGHTLY_START_TIME "00:00:00 UTC")
set(CTEST_PROJECT_NAME "Eigen 2.0")
set(CTEST_NIGHTLY_START_TIME "06:00:00 UTC")
set(CTEST_DROP_METHOD "http")
set(CTEST_DROP_SITE "eigen.tuxfamily.org")
set(CTEST_DROP_LOCATION "/CDash/submit.php?project=Eigen")
set(CTEST_DROP_LOCATION "/CDash/submit.php?project=Eigen+2.0")
set(CTEST_DROP_SITE_CDASH TRUE)

View File

@@ -1,4 +0,0 @@
## A tribute to Dynamic!
set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_WARNINGS "33331")
set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_ERRORS "33331")

281
Doxyfile Normal file
View File

@@ -0,0 +1,281 @@
# Doxyfile 1.5.3
#---------------------------------------------------------------------------
# Project related configuration options
#---------------------------------------------------------------------------
DOXYFILE_ENCODING = UTF-8
PROJECT_NAME = Eigen
PROJECT_NUMBER = 2.0
OUTPUT_DIRECTORY = ./
CREATE_SUBDIRS = NO
OUTPUT_LANGUAGE = English
BRIEF_MEMBER_DESC = YES
REPEAT_BRIEF = YES
ABBREVIATE_BRIEF = "The $name class" \
"The $name widget" \
"The $name file" \
is \
provides \
specifies \
contains \
represents \
a \
an \
the
ALWAYS_DETAILED_SEC = NO
INLINE_INHERITED_MEMB = NO
FULL_PATH_NAMES = NO
STRIP_FROM_PATH =
STRIP_FROM_INC_PATH =
SHORT_NAMES = NO
JAVADOC_AUTOBRIEF = NO
QT_AUTOBRIEF = NO
MULTILINE_CPP_IS_BRIEF = NO
DETAILS_AT_TOP = NO
INHERIT_DOCS = YES
SEPARATE_MEMBER_PAGES = NO
TAB_SIZE = 8
ALIASES =
OPTIMIZE_OUTPUT_FOR_C = NO
OPTIMIZE_OUTPUT_JAVA = NO
BUILTIN_STL_SUPPORT = NO
CPP_CLI_SUPPORT = NO
DISTRIBUTE_GROUP_DOC = NO
SUBGROUPING = YES
#---------------------------------------------------------------------------
# Build related configuration options
#---------------------------------------------------------------------------
EXTRACT_ALL = NO
EXTRACT_PRIVATE = NO
EXTRACT_STATIC = NO
EXTRACT_LOCAL_CLASSES = NO
EXTRACT_LOCAL_METHODS = NO
EXTRACT_ANON_NSPACES = NO
HIDE_UNDOC_MEMBERS = YES
HIDE_UNDOC_CLASSES = YES
HIDE_FRIEND_COMPOUNDS = YES
HIDE_IN_BODY_DOCS = NO
INTERNAL_DOCS = NO
CASE_SENSE_NAMES = YES
HIDE_SCOPE_NAMES = YES
SHOW_INCLUDE_FILES = YES
INLINE_INFO = YES
SORT_MEMBER_DOCS = YES
SORT_BRIEF_DOCS = NO
SORT_BY_SCOPE_NAME = NO
GENERATE_TODOLIST = YES
GENERATE_TESTLIST = YES
GENERATE_BUGLIST = YES
GENERATE_DEPRECATEDLIST= YES
ENABLED_SECTIONS =
MAX_INITIALIZER_LINES = 30
SHOW_USED_FILES = YES
SHOW_DIRECTORIES = NO
FILE_VERSION_FILTER =
#---------------------------------------------------------------------------
# configuration options related to warning and progress messages
#---------------------------------------------------------------------------
QUIET = NO
WARNINGS = YES
WARN_IF_UNDOCUMENTED = YES
WARN_IF_DOC_ERROR = YES
WARN_NO_PARAMDOC = NO
WARN_FORMAT = "$file:$line: $text"
WARN_LOGFILE =
#---------------------------------------------------------------------------
# configuration options related to the input files
#---------------------------------------------------------------------------
INPUT = ./
INPUT_ENCODING = UTF-8
FILE_PATTERNS = *.c \
*.cc \
*.cxx \
*.cpp \
*.c++ \
*.d \
*.java \
*.ii \
*.ixx \
*.ipp \
*.i++ \
*.inl \
*.h \
*.hh \
*.hxx \
*.hpp \
*.h++ \
*.idl \
*.odl \
*.cs \
*.php \
*.php3 \
*.inc \
*.m \
*.mm \
*.dox \
*.py \
*.C \
*.CC \
*.C++ \
*.II \
*.I++ \
*.H \
*.HH \
*.H++ \
*.CS \
*.PHP \
*.PHP3 \
*.M \
*.MM \
*.PY
RECURSIVE = NO
EXCLUDE =
EXCLUDE_SYMLINKS = NO
EXCLUDE_PATTERNS =
EXCLUDE_SYMBOLS =
EXAMPLE_PATH = doc/examples/
EXAMPLE_PATTERNS = *
EXAMPLE_RECURSIVE = NO
IMAGE_PATH =
INPUT_FILTER =
FILTER_PATTERNS =
FILTER_SOURCE_FILES = NO
#---------------------------------------------------------------------------
# configuration options related to source browsing
#---------------------------------------------------------------------------
SOURCE_BROWSER = NO
INLINE_SOURCES = NO
STRIP_CODE_COMMENTS = YES
REFERENCED_BY_RELATION = YES
REFERENCES_RELATION = YES
REFERENCES_LINK_SOURCE = YES
USE_HTAGS = NO
VERBATIM_HEADERS = YES
#---------------------------------------------------------------------------
# configuration options related to the alphabetical class index
#---------------------------------------------------------------------------
ALPHABETICAL_INDEX = NO
COLS_IN_ALPHA_INDEX = 5
IGNORE_PREFIX =
#---------------------------------------------------------------------------
# configuration options related to the HTML output
#---------------------------------------------------------------------------
GENERATE_HTML = YES
HTML_OUTPUT = html
HTML_FILE_EXTENSION = .html
HTML_HEADER =
HTML_FOOTER =
HTML_STYLESHEET =
HTML_ALIGN_MEMBERS = YES
GENERATE_HTMLHELP = NO
HTML_DYNAMIC_SECTIONS = NO
CHM_FILE =
HHC_LOCATION =
GENERATE_CHI = NO
BINARY_TOC = NO
TOC_EXPAND = NO
DISABLE_INDEX = NO
ENUM_VALUES_PER_LINE = 4
GENERATE_TREEVIEW = NO
TREEVIEW_WIDTH = 250
#---------------------------------------------------------------------------
# configuration options related to the LaTeX output
#---------------------------------------------------------------------------
GENERATE_LATEX = YES
LATEX_OUTPUT = latex
LATEX_CMD_NAME = latex
MAKEINDEX_CMD_NAME = makeindex
COMPACT_LATEX = NO
PAPER_TYPE = a4wide
EXTRA_PACKAGES =
LATEX_HEADER =
PDF_HYPERLINKS = NO
USE_PDFLATEX = NO
LATEX_BATCHMODE = NO
LATEX_HIDE_INDICES = NO
#---------------------------------------------------------------------------
# configuration options related to the RTF output
#---------------------------------------------------------------------------
GENERATE_RTF = NO
RTF_OUTPUT = rtf
COMPACT_RTF = NO
RTF_HYPERLINKS = NO
RTF_STYLESHEET_FILE =
RTF_EXTENSIONS_FILE =
#---------------------------------------------------------------------------
# configuration options related to the man page output
#---------------------------------------------------------------------------
GENERATE_MAN = NO
MAN_OUTPUT = man
MAN_EXTENSION = .3
MAN_LINKS = NO
#---------------------------------------------------------------------------
# configuration options related to the XML output
#---------------------------------------------------------------------------
GENERATE_XML = NO
XML_OUTPUT = xml
XML_SCHEMA =
XML_DTD =
XML_PROGRAMLISTING = YES
#---------------------------------------------------------------------------
# configuration options for the AutoGen Definitions output
#---------------------------------------------------------------------------
GENERATE_AUTOGEN_DEF = NO
#---------------------------------------------------------------------------
# configuration options related to the Perl module output
#---------------------------------------------------------------------------
GENERATE_PERLMOD = NO
PERLMOD_LATEX = NO
PERLMOD_PRETTY = YES
PERLMOD_MAKEVAR_PREFIX =
#---------------------------------------------------------------------------
# Configuration options related to the preprocessor
#---------------------------------------------------------------------------
ENABLE_PREPROCESSING = YES
MACRO_EXPANSION = NO
EXPAND_ONLY_PREDEF = NO
SEARCH_INCLUDES = YES
INCLUDE_PATH =
INCLUDE_FILE_PATTERNS =
PREDEFINED =
EXPAND_AS_DEFINED =
SKIP_FUNCTION_MACROS = YES
#---------------------------------------------------------------------------
# Configuration::additions related to external references
#---------------------------------------------------------------------------
TAGFILES =
GENERATE_TAGFILE =
ALLEXTERNALS = NO
EXTERNAL_GROUPS = YES
PERL_PATH = /usr/bin/perl
#---------------------------------------------------------------------------
# Configuration options related to the dot tool
#---------------------------------------------------------------------------
CLASS_DIAGRAMS = YES
MSCGEN_PATH =
HIDE_UNDOC_RELATIONS = YES
HAVE_DOT = YES
CLASS_GRAPH = YES
COLLABORATION_GRAPH = YES
GROUP_GRAPHS = YES
UML_LOOK = NO
TEMPLATE_RELATIONS = NO
INCLUDE_GRAPH = YES
INCLUDED_BY_GRAPH = YES
CALL_GRAPH = NO
CALLER_GRAPH = NO
GRAPHICAL_HIERARCHY = YES
DIRECTORY_GRAPH = YES
DOT_IMAGE_FORMAT = png
DOT_PATH =
DOTFILE_DIRS =
DOT_GRAPH_MAX_NODES = 50
MAX_DOT_GRAPH_DEPTH = 1000
DOT_TRANSPARENT = NO
DOT_MULTI_TARGETS = NO
GENERATE_LEGEND = YES
DOT_CLEANUP = YES
#---------------------------------------------------------------------------
# Configuration::additions related to the search engine
#---------------------------------------------------------------------------
SEARCHENGINE = NO

View File

@@ -1,11 +1,39 @@
#ifndef EIGEN_ARRAY_MODULE_H
#define EIGEN_ARRAY_MODULE_H
// include Core first to handle Eigen2 support macros
#include "Core"
#ifndef EIGEN2_SUPPORT
#error The Eigen/Array header does no longer exist in Eigen3. All that functionality has moved to Eigen/Core.
#endif
#include "src/Core/util/DisableMSVCWarnings.h"
namespace Eigen {
/** \defgroup Array_Module Array module
* This module provides several handy features to manipulate matrices as simple array of values.
* In addition to listed classes, it defines various methods of the Cwise interface
* (accessible from MatrixBase::cwise()), including:
* - matrix-scalar sum,
* - coeff-wise comparison operators,
* - sin, cos, sqrt, pow, exp, log, square, cube, inverse (reciprocal).
*
* This module also provides various MatrixBase methods, including:
* - \ref MatrixBase::all() "all", \ref MatrixBase::any() "any",
* - \ref MatrixBase::Random() "random matrix initialization"
*
* \code
* #include <Eigen/Array>
* \endcode
*/
#include "src/Array/CwiseOperators.h"
#include "src/Array/Functors.h"
#include "src/Array/BooleanRedux.h"
#include "src/Array/Select.h"
#include "src/Array/PartialRedux.h"
#include "src/Array/Random.h"
#include "src/Array/Norms.h"
} // namespace Eigen
#include "src/Core/util/EnableMSVCWarnings.h"
#endif // EIGEN_ARRAY_MODULE_H

View File

@@ -1,19 +1,34 @@
include(RegexUtils)
test_escape_string_as_regex()
set(Eigen_HEADERS Core LU Cholesky QR Geometry Sparse Array SVD LeastSquares QtAlignedMalloc StdVector)
file(GLOB Eigen_directory_files "*")
if(EIGEN_BUILD_LIB)
set(Eigen_SRCS
src/Core/CoreInstantiations.cpp
src/Cholesky/CholeskyInstantiations.cpp
src/QR/QrInstantiations.cpp
)
escape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}")
add_library(Eigen2 SHARED ${Eigen_SRCS})
foreach(f ${Eigen_directory_files})
if(NOT f MATCHES "\\.txt" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/src")
list(APPEND Eigen_directory_files_to_install ${f})
endif()
endforeach(f ${Eigen_directory_files})
install(TARGETS Eigen2
RUNTIME DESTINATION bin
LIBRARY DESTINATION lib
ARCHIVE DESTINATION lib)
endif(EIGEN_BUILD_LIB)
if(CMAKE_COMPILER_IS_GNUCXX)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g1 -O2")
set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} -g1 -O2")
endif(CMAKE_COMPILER_IS_GNUCXX)
set(INCLUDE_INSTALL_DIR
"${CMAKE_INSTALL_PREFIX}/include/eigen2"
CACHE PATH
"The directory where we install the header files"
FORCE)
install(FILES
${Eigen_directory_files_to_install}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen COMPONENT Devel
${Eigen_HEADERS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen
)
add_subdirectory(src)

View File

@@ -3,13 +3,22 @@
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
#include "src/Core/util/DisableMSVCWarnings.h"
// Note that EIGEN_HIDE_HEAVY_CODE has to be defined per module
#if (defined EIGEN_EXTERN_INSTANTIATIONS) && (EIGEN_EXTERN_INSTANTIATIONS>=2)
#ifndef EIGEN_HIDE_HEAVY_CODE
#define EIGEN_HIDE_HEAVY_CODE
#endif
#elif defined EIGEN_HIDE_HEAVY_CODE
#undef EIGEN_HIDE_HEAVY_CODE
#endif
namespace Eigen {
/** \defgroup Cholesky_Module Cholesky module
*
*
* \nonstableyet
*
* This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices.
* Those decompositions are accessible via the following MatrixBase methods:
@@ -21,13 +30,36 @@ namespace Eigen {
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/Array/CwiseOperators.h"
#include "src/Array/Functors.h"
#include "src/Cholesky/LLT.h"
#include "src/Cholesky/LDLT.h"
} // namespace Eigen
#include "src/Core/util/ReenableStupidWarnings.h"
#define EIGEN_CHOLESKY_MODULE_INSTANTIATE_TYPE(MATRIXTYPE,PREFIX) \
PREFIX template class LLT<MATRIXTYPE>; \
PREFIX template class LDLT<MATRIXTYPE>
#define EIGEN_CHOLESKY_MODULE_INSTANTIATE(PREFIX) \
EIGEN_CHOLESKY_MODULE_INSTANTIATE_TYPE(Matrix2f,PREFIX); \
EIGEN_CHOLESKY_MODULE_INSTANTIATE_TYPE(Matrix2d,PREFIX); \
EIGEN_CHOLESKY_MODULE_INSTANTIATE_TYPE(Matrix3f,PREFIX); \
EIGEN_CHOLESKY_MODULE_INSTANTIATE_TYPE(Matrix3d,PREFIX); \
EIGEN_CHOLESKY_MODULE_INSTANTIATE_TYPE(Matrix4f,PREFIX); \
EIGEN_CHOLESKY_MODULE_INSTANTIATE_TYPE(Matrix4d,PREFIX); \
EIGEN_CHOLESKY_MODULE_INSTANTIATE_TYPE(MatrixXf,PREFIX); \
EIGEN_CHOLESKY_MODULE_INSTANTIATE_TYPE(MatrixXd,PREFIX); \
EIGEN_CHOLESKY_MODULE_INSTANTIATE_TYPE(MatrixXcf,PREFIX); \
EIGEN_CHOLESKY_MODULE_INSTANTIATE_TYPE(MatrixXcd,PREFIX)
#ifdef EIGEN_EXTERN_INSTANTIATIONS
namespace Eigen {
EIGEN_CHOLESKY_MODULE_INSTANTIATE(extern);
} // namespace Eigen
#endif
#include "src/Core/util/EnableMSVCWarnings.h"
#endif // EIGEN_CHOLESKY_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -1,46 +1,8 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2007-2011 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_CORE_H
#define EIGEN_CORE_H
// first thing Eigen does: stop the compiler from committing suicide
#include "src/Core/util/DisableStupidWarnings.h"
// then include this file where all our macros are defined. It's really important to do it first because
// it's where we do all the alignment settings (platform detection and honoring the user's will if he
// defined e.g. EIGEN_DONT_ALIGN) so it needs to be done before we do anything with vectorization.
#include "src/Core/util/Macros.h"
// 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
#ifndef EIGEN_DONT_VECTORIZE
#define EIGEN_DONT_VECTORIZE
#endif
#endif
// first thing Eigen does: prevent MSVC from committing suicide
#include "src/Core/util/DisableMSVCWarnings.h"
#ifdef _MSC_VER
#include <malloc.h> // for _aligned_malloc -- need it regardless of whether vectorization is enabled
@@ -49,68 +11,33 @@
// 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)
#define EIGEN_SSE2_ON_MSVC_2008_OR_LATER
#endif
#endif
#else
// Remember that usage of defined() in a #define is undefined by the standard
#if (defined __SSE2__) && ( (!defined __GNUC__) || EIGEN_GNUC_AT_LEAST(4,2) )
#define EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC
#endif
#endif
#endif
#ifdef __GNUC__
#define EIGEN_GNUC_AT_LEAST(x,y) ((__GNUC__>=x && __GNUC_MINOR__>=y) || __GNUC__>x)
#else
#define EIGEN_GNUC_AT_LEAST(x,y) 0
#endif
// Remember that usage of defined() in a #define is undefined by the standard
#if (defined __SSE2__) && ( (!defined __GNUC__) || EIGEN_GNUC_AT_LEAST(4,2) )
#define EIGEN_SSE2_BUT_NOT_OLD_GCC
#endif
#ifndef EIGEN_DONT_VECTORIZE
#if defined (EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC) || defined(EIGEN_SSE2_ON_MSVC_2008_OR_LATER)
// Defines symbols for compile-time detection of which instructions are
// used.
// EIGEN_VECTORIZE_YY is defined if and only if the instruction set YY is used
#if defined (EIGEN_SSE2_BUT_NOT_OLD_GCC) || defined(EIGEN_SSE2_ON_MSVC_2008_OR_LATER)
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_SSE
#define EIGEN_VECTORIZE_SSE2
// Detect sse3/ssse3/sse4:
// gcc and icc defines __SSE3__, ...
// there is no way to know about this on msvc. You can define EIGEN_VECTORIZE_SSE* if you
// want to force the use of those instructions with msvc.
#include <emmintrin.h>
#include <xmmintrin.h>
#ifdef __SSE3__
#define EIGEN_VECTORIZE_SSE3
#include <pmmintrin.h>
#endif
#ifdef __SSSE3__
#define EIGEN_VECTORIZE_SSSE3
#endif
#ifdef __SSE4_1__
#define EIGEN_VECTORIZE_SSE4_1
#endif
#ifdef __SSE4_2__
#define EIGEN_VECTORIZE_SSE4_2
#endif
// include files
// This extern "C" works around a MINGW-w64 compilation issue
// https://sourceforge.net/tracker/index.php?func=detail&aid=3018394&group_id=202880&atid=983354
// In essence, intrin.h is included by windows.h and also declares intrinsics (just as emmintrin.h etc. below do).
// However, intrin.h uses an extern "C" declaration, and g++ thus complains of duplicate declarations
// with conflicting linkage. The linkage for intrinsics doesn't matter, but at that stage the compiler doesn't know;
// so, to avoid compile errors when windows.h is included after Eigen/Core, ensure intrinsics are extern "C" here too.
// notice that since these are C headers, the extern "C" is theoretically needed anyways.
extern "C" {
#include <emmintrin.h>
#include <xmmintrin.h>
#ifdef EIGEN_VECTORIZE_SSE3
#include <pmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSSE3
#include <tmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSE4_1
#include <smmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSE4_2
#include <nmmintrin.h>
#endif
} // end extern "C"
#endif
#elif defined __ALTIVEC__
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_ALTIVEC
@@ -120,54 +47,20 @@
#undef bool
#undef vector
#undef pixel
#elif defined __ARM_NEON__
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_NEON
#include <arm_neon.h>
#endif
#endif
#if (defined _OPENMP) && (!defined EIGEN_DONT_PARALLELIZE)
#define EIGEN_HAS_OPENMP
#endif
#ifdef EIGEN_HAS_OPENMP
#include <omp.h>
#endif
// MSVC for windows mobile does not have the errno.h file
#if !(defined(_MSC_VER) && defined(_WIN32_WCE))
#define EIGEN_HAS_ERRNO
#endif
#ifdef EIGEN_HAS_ERRNO
#include <cerrno>
#endif
#include <cstddef>
#include <cstdlib>
#include <cmath>
#include <complex>
#include <cassert>
#include <functional>
#include <iosfwd>
#include <iostream>
#include <cstring>
#include <string>
#include <limits>
#include <climits> // for CHAR_BIT
// for min/max:
#include <algorithm>
// for outputting debug info
#ifdef EIGEN_DEBUG_ASSIGN
#include <iostream>
#endif
// required for __cpuid, needs to be included after cmath
#if defined(_MSC_VER) && (defined(_M_IX86)||defined(_M_X64))
#include <intrin.h>
#endif
#if defined(_CPPUNWIND) || defined(__EXCEPTIONS)
#if (defined(_CPPUNWIND) || defined(__EXCEPTIONS)) && !defined(EIGEN_NO_EXCEPTIONS)
#define EIGEN_EXCEPTIONS
#endif
@@ -175,68 +68,15 @@
#include <new>
#endif
// defined in bits/termios.h
#undef B0
// this needs to be done after all possible windows C header includes and before any Eigen source includes
// (system C++ includes are supposed to be able to deal with this already):
// windows.h defines min and max macros which would make Eigen fail to compile.
#if defined(min) || defined(max)
#error The preprocessor symbols 'min' or 'max' are defined. If you are compiling on Windows, do #define NOMINMAX to prevent windows.h from defining these symbols.
#endif
/** \brief Namespace containing all symbols from the %Eigen library. */
namespace Eigen {
inline static const char *SimdInstructionSetsInUse(void) {
#if defined(EIGEN_VECTORIZE_SSE4_2)
return "SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
#elif defined(EIGEN_VECTORIZE_SSE4_1)
return "SSE, SSE2, SSE3, SSSE3, SSE4.1";
#elif defined(EIGEN_VECTORIZE_SSSE3)
return "SSE, SSE2, SSE3, SSSE3";
#elif defined(EIGEN_VECTORIZE_SSE3)
return "SSE, SSE2, SSE3";
#elif defined(EIGEN_VECTORIZE_SSE2)
return "SSE, SSE2";
#elif defined(EIGEN_VECTORIZE_ALTIVEC)
return "AltiVec";
#elif defined(EIGEN_VECTORIZE_NEON)
return "ARM NEON";
#else
return "None";
#endif
}
#define STAGE10_FULL_EIGEN2_API 10
#define STAGE20_RESOLVE_API_CONFLICTS 20
#define STAGE30_FULL_EIGEN3_API 30
#define STAGE40_FULL_EIGEN3_STRICTNESS 40
#define STAGE99_NO_EIGEN2_SUPPORT 99
#if defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS
#define EIGEN2_SUPPORT
#define EIGEN2_SUPPORT_STAGE STAGE40_FULL_EIGEN3_STRICTNESS
#elif defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API
#define EIGEN2_SUPPORT
#define EIGEN2_SUPPORT_STAGE STAGE30_FULL_EIGEN3_API
#elif defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS
#define EIGEN2_SUPPORT
#define EIGEN2_SUPPORT_STAGE STAGE20_RESOLVE_API_CONFLICTS
#elif defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API
#define EIGEN2_SUPPORT
#define EIGEN2_SUPPORT_STAGE STAGE10_FULL_EIGEN2_API
#elif defined EIGEN2_SUPPORT
// default to stage 3, that's what it's always meant
#define EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API
#define EIGEN2_SUPPORT_STAGE STAGE30_FULL_EIGEN3_API
#else
#define EIGEN2_SUPPORT_STAGE STAGE99_NO_EIGEN2_SUPPORT
#endif
#ifdef EIGEN2_SUPPORT
#undef minor
#endif
// we use size_t frequently and we'll never remember to prepend it with std:: everytime just to
// ensure QNX/QCC support
using std::size_t;
// gcc 4.6.0 wants std:: for ptrdiff_t
using std::ptrdiff_t;
/** \defgroup Core_Module Core module
* This is the main module of Eigen providing dense matrix and vector support
* (both fixed and dynamic size) with all the features corresponding to a BLAS library
@@ -247,6 +87,7 @@ using std::ptrdiff_t;
* \endcode
*/
#include "src/Core/util/Macros.h"
#include "src/Core/util/Constants.h"
#include "src/Core/util/ForwardDeclarations.h"
#include "src/Core/util/Meta.h"
@@ -260,101 +101,54 @@ using std::ptrdiff_t;
#if 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/AltiVec/PacketMath.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/Complex.h"
#endif
#include "src/Core/arch/Default/Settings.h"
#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 16
#endif
#include "src/Core/Functors.h"
#include "src/Core/DenseCoeffsBase.h"
#include "src/Core/DenseBase.h"
#include "src/Core/MatrixBase.h"
#include "src/Core/EigenBase.h"
#include "src/Core/Coeffs.h"
#ifndef EIGEN_PARSED_BY_DOXYGEN // work around Doxygen bug triggered by Assign.h r814874
// at least confirmed with Doxygen 1.5.5 and 1.5.6
#include "src/Core/Assign.h"
#endif
#include "src/Core/util/BlasUtil.h"
#include "src/Core/DenseStorage.h"
#include "src/Core/MatrixStorage.h"
#include "src/Core/NestByValue.h"
#include "src/Core/ForceAlignedAccess.h"
#include "src/Core/ReturnByValue.h"
#include "src/Core/NoAlias.h"
#include "src/Core/PlainObjectBase.h"
#include "src/Core/Flagged.h"
#include "src/Core/Matrix.h"
#include "src/Core/Array.h"
#include "src/Core/Cwise.h"
#include "src/Core/CwiseBinaryOp.h"
#include "src/Core/CwiseUnaryOp.h"
#include "src/Core/CwiseNullaryOp.h"
#include "src/Core/CwiseUnaryView.h"
#include "src/Core/SelfCwiseBinaryOp.h"
#include "src/Core/Dot.h"
#include "src/Core/StableNorm.h"
#include "src/Core/Product.h"
#include "src/Core/DiagonalProduct.h"
#include "src/Core/SolveTriangular.h"
#include "src/Core/MapBase.h"
#include "src/Core/Stride.h"
#include "src/Core/Map.h"
#include "src/Core/Block.h"
#include "src/Core/VectorBlock.h"
#include "src/Core/Minor.h"
#include "src/Core/Transpose.h"
#include "src/Core/DiagonalMatrix.h"
#include "src/Core/Diagonal.h"
#include "src/Core/DiagonalProduct.h"
#include "src/Core/PermutationMatrix.h"
#include "src/Core/Transpositions.h"
#include "src/Core/DiagonalCoeffs.h"
#include "src/Core/Sum.h"
#include "src/Core/Redux.h"
#include "src/Core/Visitor.h"
#include "src/Core/Fuzzy.h"
#include "src/Core/IO.h"
#include "src/Core/Swap.h"
#include "src/Core/CommaInitializer.h"
#include "src/Core/Flagged.h"
#include "src/Core/ProductBase.h"
#include "src/Core/Product.h"
#include "src/Core/TriangularMatrix.h"
#include "src/Core/SelfAdjointView.h"
#include "src/Core/SolveTriangular.h"
#include "src/Core/products/Parallelizer.h"
#include "src/Core/products/CoeffBasedProduct.h"
#include "src/Core/products/GeneralBlockPanelKernel.h"
#include "src/Core/products/GeneralMatrixVector.h"
#include "src/Core/products/GeneralMatrixMatrix.h"
#include "src/Core/products/GeneralMatrixMatrixTriangular.h"
#include "src/Core/products/SelfadjointMatrixVector.h"
#include "src/Core/products/SelfadjointMatrixMatrix.h"
#include "src/Core/products/SelfadjointProduct.h"
#include "src/Core/products/SelfadjointRank2Update.h"
#include "src/Core/products/TriangularMatrixVector.h"
#include "src/Core/products/TriangularMatrixMatrix.h"
#include "src/Core/products/TriangularSolverMatrix.h"
#include "src/Core/products/TriangularSolverVector.h"
#include "src/Core/BandMatrix.h"
#include "src/Core/BooleanRedux.h"
#include "src/Core/Select.h"
#include "src/Core/VectorwiseOp.h"
#include "src/Core/Random.h"
#include "src/Core/Replicate.h"
#include "src/Core/Reverse.h"
#include "src/Core/ArrayBase.h"
#include "src/Core/ArrayWrapper.h"
#include "src/Core/Part.h"
#include "src/Core/CacheFriendlyProduct.h"
} // namespace Eigen
#include "src/Core/GlobalFunctions.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#ifdef EIGEN2_SUPPORT
#include "Eigen2Support"
#endif
#include "src/Core/util/EnableMSVCWarnings.h"
#endif // EIGEN_CORE_H

View File

@@ -1,7 +1,8 @@
#include "Core"
#include "Array"
#include "LU"
#include "Cholesky"
#include "QR"
#include "SVD"
#include "Geometry"
#include "Eigenvalues"
#include "LeastSquares"

View File

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

View File

@@ -1,82 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN2SUPPORT_H
#define EIGEN2SUPPORT_H
#if (!defined(EIGEN2_SUPPORT)) || (!defined(EIGEN_CORE_H))
#error Eigen2 support must be enabled by defining EIGEN2_SUPPORT before including any Eigen header
#endif
#include "src/Core/util/DisableStupidWarnings.h"
namespace Eigen {
/** \defgroup Eigen2Support_Module Eigen2 support module
* This module provides a couple of deprecated functions improving the compatibility with Eigen2.
*
* To use it, define EIGEN2_SUPPORT before including any Eigen header
* \code
* #define EIGEN2_SUPPORT
* \endcode
*
*/
#include "src/Eigen2Support/Macros.h"
#include "src/Eigen2Support/Memory.h"
#include "src/Eigen2Support/Meta.h"
#include "src/Eigen2Support/Lazy.h"
#include "src/Eigen2Support/Cwise.h"
#include "src/Eigen2Support/CwiseOperators.h"
#include "src/Eigen2Support/TriangularSolver.h"
#include "src/Eigen2Support/Block.h"
#include "src/Eigen2Support/VectorBlock.h"
#include "src/Eigen2Support/Minor.h"
#include "src/Eigen2Support/MathFunctions.h"
} // namespace Eigen
#include "src/Core/util/ReenableStupidWarnings.h"
// Eigen2 used to include iostream
#include<iostream>
#define USING_PART_OF_NAMESPACE_EIGEN \
EIGEN_USING_MATRIX_TYPEDEFS \
using Eigen::Matrix; \
using Eigen::MatrixBase; \
using Eigen::ei_random; \
using Eigen::ei_real; \
using Eigen::ei_imag; \
using Eigen::ei_conj; \
using Eigen::ei_abs; \
using Eigen::ei_abs2; \
using Eigen::ei_sqrt; \
using Eigen::ei_exp; \
using Eigen::ei_log; \
using Eigen::ei_sin; \
using Eigen::ei_cos;
#endif // EIGEN2SUPPORT_H

View File

@@ -1,44 +0,0 @@
#ifndef EIGEN_EIGENVALUES_MODULE_H
#define EIGEN_EIGENVALUES_MODULE_H
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
#include "Cholesky"
#include "Jacobi"
#include "Householder"
#include "LU"
namespace Eigen {
/** \defgroup Eigenvalues_Module Eigenvalues module
*
*
*
* This module mainly provides various eigenvalue solvers.
* This module also provides some MatrixBase methods, including:
* - MatrixBase::eigenvalues(),
* - MatrixBase::operatorNorm()
*
* \code
* #include <Eigen/Eigenvalues>
* \endcode
*/
#include "src/Eigenvalues/Tridiagonalization.h"
#include "src/Eigenvalues/RealSchur.h"
#include "src/Eigenvalues/EigenSolver.h"
#include "src/Eigenvalues/SelfAdjointEigenSolver.h"
#include "src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h"
#include "src/Eigenvalues/HessenbergDecomposition.h"
#include "src/Eigenvalues/ComplexSchur.h"
#include "src/Eigenvalues/ComplexEigenSolver.h"
#include "src/Eigenvalues/MatrixBaseEigenvalues.h"
} // namespace Eigen
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_EIGENVALUES_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -3,10 +3,9 @@
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
#include "src/Core/util/DisableMSVCWarnings.h"
#include "SVD"
#include "LU"
#include "Array"
#include <limits>
#ifndef M_PI
@@ -17,7 +16,7 @@ namespace Eigen {
/** \defgroup Geometry_Module Geometry module
*
*
* \nonstableyet
*
* This module provides support for:
* - fixed-size homogeneous transformations
@@ -33,35 +32,20 @@ namespace Eigen {
*/
#include "src/Geometry/OrthoMethods.h"
#include "src/Geometry/RotationBase.h"
#include "src/Geometry/Rotation2D.h"
#include "src/Geometry/Quaternion.h"
#include "src/Geometry/AngleAxis.h"
#include "src/Geometry/EulerAngles.h"
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
#include "src/Geometry/Homogeneous.h"
#include "src/Geometry/RotationBase.h"
#include "src/Geometry/Rotation2D.h"
#include "src/Geometry/Quaternion.h"
#include "src/Geometry/AngleAxis.h"
#include "src/Geometry/Transform.h"
#include "src/Geometry/Translation.h"
#include "src/Geometry/Scaling.h"
#include "src/Geometry/Hyperplane.h"
#include "src/Geometry/ParametrizedLine.h"
#include "src/Geometry/AlignedBox.h"
#include "src/Geometry/Umeyama.h"
#if defined EIGEN_VECTORIZE_SSE
#include "src/Geometry/arch/Geometry_SSE.h"
#endif
#endif
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/Geometry/All.h"
#endif
#include "src/Geometry/Transform.h"
#include "src/Geometry/Translation.h"
#include "src/Geometry/Scaling.h"
#include "src/Geometry/Hyperplane.h"
#include "src/Geometry/ParametrizedLine.h"
#include "src/Geometry/AlignedBox.h"
} // namespace Eigen
#include "src/Core/util/ReenableStupidWarnings.h"
#include "src/Core/util/EnableMSVCWarnings.h"
#endif // EIGEN_GEOMETRY_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -1,27 +0,0 @@
#ifndef EIGEN_HOUSEHOLDER_MODULE_H
#define EIGEN_HOUSEHOLDER_MODULE_H
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
namespace Eigen {
/** \defgroup Householder_Module Householder module
* This module provides Householder transformations.
*
* \code
* #include <Eigen/Householder>
* \endcode
*/
#include "src/Householder/Householder.h"
#include "src/Householder/HouseholderSequence.h"
#include "src/Householder/BlockHouseholder.h"
} // namespace Eigen
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_HOUSEHOLDER_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -1,30 +0,0 @@
#ifndef EIGEN_JACOBI_MODULE_H
#define EIGEN_JACOBI_MODULE_H
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
namespace Eigen {
/** \defgroup Jacobi_Module Jacobi module
* This module provides Jacobi and Givens rotations.
*
* \code
* #include <Eigen/Jacobi>
* \endcode
*
* In addition to listed classes, it defines the two following MatrixBase methods to apply a Jacobi or Givens rotation:
* - MatrixBase::applyOnTheLeft()
* - MatrixBase::applyOnTheRight().
*/
#include "src/Jacobi/Jacobi.h"
} // namespace Eigen
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_JACOBI_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -3,7 +3,7 @@
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
#include "src/Core/util/DisableMSVCWarnings.h"
namespace Eigen {
@@ -18,25 +18,12 @@ namespace Eigen {
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/misc/Kernel.h"
#include "src/misc/Image.h"
#include "src/LU/FullPivLU.h"
#include "src/LU/PartialPivLU.h"
#include "src/LU/LU.h"
#include "src/LU/Determinant.h"
#include "src/LU/Inverse.h"
#if defined EIGEN_VECTORIZE_SSE
#include "src/LU/arch/Inverse_SSE.h"
#endif
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/LU.h"
#endif
} // namespace Eigen
#include "src/Core/util/ReenableStupidWarnings.h"
#include "src/Core/util/EnableMSVCWarnings.h"
#endif // EIGEN_LU_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -1,18 +1,11 @@
#ifndef EIGEN_REGRESSION_MODULE_H
#define EIGEN_REGRESSION_MODULE_H
#ifndef EIGEN2_SUPPORT
#error LeastSquares is only available in Eigen2 support mode (define EIGEN2_SUPPORT)
#endif
// exclude from normal eigen3-only documentation
#ifdef EIGEN2_SUPPORT
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
#include "src/Core/util/DisableMSVCWarnings.h"
#include "Eigenvalues"
#include "QR"
#include "Geometry"
namespace Eigen {
@@ -25,12 +18,10 @@ namespace Eigen {
* \endcode
*/
#include "src/Eigen2Support/LeastSquares.h"
#include "src/LeastSquares/LeastSquares.h"
} // namespace Eigen
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN2_SUPPORT
#include "src/Core/util/EnableMSVCWarnings.h"
#endif // EIGEN_REGRESSION_MODULE_H

View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
//
// Eigen is free software; you can redistribute it and/or
@@ -23,40 +23,68 @@
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_STDVECTOR_H
#define EIGEN_STDVECTOR_H
#ifndef EIGEN_STDVECTOR_MODULE_H
#define EIGEN_STDVECTOR_MODULE_H
#include "Eigen/src/StlSupport/details.h"
#include "Core"
#include <vector>
namespace Eigen {
// This one is needed to prevent reimplementing the whole std::vector.
template <class T>
class aligned_allocator_indirection : public aligned_allocator<T>
{
public:
typedef size_t size_type;
typedef ptrdiff_t difference_type;
typedef T* pointer;
typedef const T* const_pointer;
typedef T& reference;
typedef const T& const_reference;
typedef T value_type;
template<class U>
struct rebind
{
typedef aligned_allocator_indirection<U> other;
};
aligned_allocator_indirection() throw() {}
aligned_allocator_indirection(const aligned_allocator_indirection& ) throw() : aligned_allocator<T>() {}
aligned_allocator_indirection(const aligned_allocator<T>& ) throw() {}
template<class U>
aligned_allocator_indirection(const aligned_allocator_indirection<U>& ) throw() {}
template<class U>
aligned_allocator_indirection(const aligned_allocator<U>& ) throw() {}
~aligned_allocator_indirection() throw() {}
};
#ifdef _MSC_VER
// sometimes, MSVC detects, at compile time, that the argument x
// in std::vector::resize(size_t s,T x) won't be aligned and generate an error
// even if this function is never called. Whence this little wrapper.
#define EIGEN_WORKAROUND_MSVC_STD_VECTOR(T) Eigen::ei_workaround_msvc_std_vector<T>
template<typename T> struct ei_workaround_msvc_std_vector : public T
{
inline ei_workaround_msvc_std_vector() : T() {}
inline ei_workaround_msvc_std_vector(const T& other) : T(other) {}
inline operator T& () { return *static_cast<T*>(this); }
inline operator const T& () const { return *static_cast<const T*>(this); }
template<typename OtherT>
inline T& operator=(const OtherT& other)
{ T::operator=(other); return *this; }
inline ei_workaround_msvc_std_vector& operator=(const ei_workaround_msvc_std_vector& other)
{ T::operator=(other); return *this; }
};
#else
#define EIGEN_WORKAROUND_MSVC_STD_VECTOR(T) T
#endif
/**
* This section contains a convenience MACRO which allows an easy specialization of
* std::vector such that for data types with alignment issues the correct allocator
* is used automatically.
*/
#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...) \
namespace std \
{ \
template<> \
class vector<__VA_ARGS__, std::allocator<__VA_ARGS__> > \
: public vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \
{ \
typedef vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > vector_base; \
public: \
typedef __VA_ARGS__ value_type; \
typedef vector_base::allocator_type allocator_type; \
typedef vector_base::size_type size_type; \
typedef vector_base::iterator iterator; \
explicit vector(const allocator_type& a = allocator_type()) : vector_base(a) {} \
template<typename InputIterator> \
vector(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : vector_base(first, last, a) {} \
vector(const vector& c) : vector_base(c) {} \
explicit vector(size_type num, const value_type& val = value_type()) : vector_base(num, val) {} \
vector(iterator start, iterator end) : vector_base(start, end) {} \
vector& operator=(const vector& x) { \
vector_base::operator=(x); \
return *this; \
} \
}; \
}
namespace std {
@@ -80,13 +108,13 @@ namespace std {
return *this; \
}
template<typename T>
class vector<T,EIGEN_ALIGNED_ALLOCATOR<T> >
: public vector<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T),
Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> >
template<typename T>
class vector<T,Eigen::aligned_allocator<T> >
: public vector<EIGEN_WORKAROUND_MSVC_STD_VECTOR(T),
Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STD_VECTOR(T)> >
{
typedef vector<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T),
Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> > vector_base;
typedef vector<EIGEN_WORKAROUND_MSVC_STD_VECTOR(T),
Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STD_VECTOR(T)> > vector_base;
EIGEN_STD_VECTOR_SPECIALIZATION_BODY
void resize(size_type new_size)
@@ -108,13 +136,6 @@ namespace std {
{ return vector_base::insert(position,x); }
void insert(const_iterator position, size_type new_size, const value_type& x)
{ vector_base::insert(position, new_size, x); }
#elif defined(_GLIBCXX_VECTOR) && (!(EIGEN_GNUC_AT_LEAST(4,1)))
/* Note that before gcc-4.1 we already have: std::vector::resize(size_type,const T&).
* However, this specialization is still needed to make the above EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION trick to work. */
void resize(size_type new_size, const value_type& x)
{
vector_base::resize(new_size,x);
}
#elif defined(_GLIBCXX_VECTOR) && EIGEN_GNUC_AT_LEAST(4,2)
// workaround GCC std::vector implementation
void resize(size_type new_size, const value_type& x)
@@ -124,6 +145,10 @@ namespace std {
else
vector_base::insert(vector_base::end(), new_size - vector_base::size(), x);
}
#elif defined(_GLIBCXX_VECTOR) && (!EIGEN_GNUC_AT_LEAST(4,1))
// Note that before gcc-4.1 we already have: std::vector::resize(size_type,const T&),
// no no need to workaround !
using vector_base::resize;
#else
// either GCC 4.1 or non-GCC
// default implementation which should always work.
@@ -135,7 +160,9 @@ namespace std {
vector_base::insert(vector_base::end(), new_size - vector_base::size(), x);
}
#endif
};
};
}
#endif // EIGEN_STDVECTOR_H
#endif // EIGEN_STDVECTOR_MODULE_H

View File

@@ -3,43 +3,71 @@
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
#include "src/Core/util/DisableMSVCWarnings.h"
#include "Cholesky"
#include "Jacobi"
#include "Householder"
// Note that EIGEN_HIDE_HEAVY_CODE has to be defined per module
#if (defined EIGEN_EXTERN_INSTANTIATIONS) && (EIGEN_EXTERN_INSTANTIATIONS>=2)
#ifndef EIGEN_HIDE_HEAVY_CODE
#define EIGEN_HIDE_HEAVY_CODE
#endif
#elif defined EIGEN_HIDE_HEAVY_CODE
#undef EIGEN_HIDE_HEAVY_CODE
#endif
namespace Eigen {
/** \defgroup QR_Module QR module
*
* \nonstableyet
*
*
* This module provides various QR decompositions
* This module mainly provides QR decomposition and an eigen value solver.
* This module also provides some MatrixBase methods, including:
* - MatrixBase::qr(),
* - MatrixBase::eigenvalues(),
* - MatrixBase::operatorNorm()
*
* \code
* #include <Eigen/QR>
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/QR/HouseholderQR.h"
#include "src/QR/FullPivHouseholderQR.h"
#include "src/QR/ColPivHouseholderQR.h"
#include "src/QR/QR.h"
#include "src/QR/Tridiagonalization.h"
#include "src/QR/EigenSolver.h"
#include "src/QR/SelfAdjointEigenSolver.h"
#include "src/QR/HessenbergDecomposition.h"
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/QR.h"
#endif
// declare all classes for a given matrix type
#define EIGEN_QR_MODULE_INSTANTIATE_TYPE(MATRIXTYPE,PREFIX) \
PREFIX template class QR<MATRIXTYPE>; \
PREFIX template class Tridiagonalization<MATRIXTYPE>; \
PREFIX template class HessenbergDecomposition<MATRIXTYPE>; \
PREFIX template class SelfAdjointEigenSolver<MATRIXTYPE>
// removed because it does not support complex yet
// PREFIX template class EigenSolver<MATRIXTYPE>
// declare all class for all types
#define EIGEN_QR_MODULE_INSTANTIATE(PREFIX) \
EIGEN_QR_MODULE_INSTANTIATE_TYPE(Matrix2f,PREFIX); \
EIGEN_QR_MODULE_INSTANTIATE_TYPE(Matrix2d,PREFIX); \
EIGEN_QR_MODULE_INSTANTIATE_TYPE(Matrix3f,PREFIX); \
EIGEN_QR_MODULE_INSTANTIATE_TYPE(Matrix3d,PREFIX); \
EIGEN_QR_MODULE_INSTANTIATE_TYPE(Matrix4f,PREFIX); \
EIGEN_QR_MODULE_INSTANTIATE_TYPE(Matrix4d,PREFIX); \
EIGEN_QR_MODULE_INSTANTIATE_TYPE(MatrixXf,PREFIX); \
EIGEN_QR_MODULE_INSTANTIATE_TYPE(MatrixXd,PREFIX); \
EIGEN_QR_MODULE_INSTANTIATE_TYPE(MatrixXcf,PREFIX); \
EIGEN_QR_MODULE_INSTANTIATE_TYPE(MatrixXcd,PREFIX)
#ifdef EIGEN_EXTERN_INSTANTIATIONS
EIGEN_QR_MODULE_INSTANTIATE(extern);
#endif // EIGEN_EXTERN_INSTANTIATIONS
} // namespace Eigen
#include "src/Core/util/ReenableStupidWarnings.h"
#ifdef EIGEN2_SUPPORT
#include "Eigenvalues"
#endif
#include "src/Core/util/EnableMSVCWarnings.h"
#endif // EIGEN_QR_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -1,34 +1,49 @@
#ifndef EIGEN_QTMALLOC_MODULE_H
#define EIGEN_QTMALLOC_MODULE_H
#include "Core"
#if (!EIGEN_MALLOC_ALREADY_ALIGNED)
#include "src/Core/util/DisableStupidWarnings.h"
#ifdef QVECTOR_H
#error You must include <Eigen/QtAlignedMalloc> before <QtCore/QVector>.
#endif
void *qMalloc(size_t size)
#ifdef Q_DECL_IMPORT
#define Q_DECL_IMPORT_ORIG Q_DECL_IMPORT
#undef Q_DECL_IMPORT
#define Q_DECL_IMPORT
#else
#define Q_DECL_IMPORT
#endif
#include "Core"
#include <QtCore/QVector>
inline void *qMalloc(size_t size)
{
return Eigen::internal::aligned_malloc(size);
return Eigen::ei_aligned_malloc(size);
}
void qFree(void *ptr)
inline void qFree(void *ptr)
{
Eigen::internal::aligned_free(ptr);
Eigen::ei_aligned_free(ptr);
}
void *qRealloc(void *ptr, size_t size)
inline void *qRealloc(void *ptr, size_t size)
{
void* newPtr = Eigen::internal::aligned_malloc(size);
void* newPtr = Eigen::ei_aligned_malloc(size);
memcpy(newPtr, ptr, size);
Eigen::internal::aligned_free(ptr);
Eigen::ei_aligned_free(ptr);
return newPtr;
}
#include "src/Core/util/ReenableStupidWarnings.h"
#endif
#ifdef Q_DECL_IMPORT_ORIG
#define Q_DECL_IMPORT Q_DECL_IMPORT_ORIG
#undef Q_DECL_IMPORT_ORIG
#else
#undef Q_DECL_IMPORT
#endif
#endif // EIGEN_QTMALLOC_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -1,38 +1,29 @@
#ifndef EIGEN_SVD_MODULE_H
#define EIGEN_SVD_MODULE_H
#include "QR"
#include "Householder"
#include "Jacobi"
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
#include "src/Core/util/DisableMSVCWarnings.h"
namespace Eigen {
/** \defgroup SVD_Module SVD module
*
* \nonstableyet
*
*
* This module provides SVD decomposition for matrices (both real and complex).
* This module provides SVD decomposition for (currently) real matrices.
* This decomposition is accessible via the following MatrixBase method:
* - MatrixBase::jacobiSvd()
* - MatrixBase::svd()
*
* \code
* #include <Eigen/SVD>
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/SVD/JacobiSVD.h"
#include "src/SVD/UpperBidiagonalization.h"
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/SVD.h"
#endif
#include "src/SVD/SVD.h"
} // namespace Eigen
#include "src/Core/util/ReenableStupidWarnings.h"
#include "src/Core/util/EnableMSVCWarnings.h"
#endif // EIGEN_SVD_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -3,7 +3,7 @@
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
#include "src/Core/util/DisableMSVCWarnings.h"
#include <vector>
#include <map>
@@ -11,59 +11,122 @@
#include <cstring>
#include <algorithm>
#ifdef EIGEN2_SUPPORT
#define EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
#ifdef EIGEN_GOOGLEHASH_SUPPORT
#include <google/dense_hash_map>
#endif
#ifndef EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
#error The sparse module API is not stable yet. To use it anyway, please define the EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET preprocessor token.
#ifdef EIGEN_CHOLMOD_SUPPORT
extern "C" {
#include "cholmod.h"
}
#endif
#ifdef EIGEN_TAUCS_SUPPORT
// taucs.h declares a lot of mess
#define isnan
#define finite
#define isinf
extern "C" {
#include "taucs.h"
}
#undef isnan
#undef finite
#undef isinf
#ifdef min
#undef min
#endif
#ifdef max
#undef max
#endif
#ifdef complex
#undef complex
#endif
#endif
#ifdef EIGEN_SUPERLU_SUPPORT
typedef int int_t;
#include "superlu/slu_Cnames.h"
#include "superlu/supermatrix.h"
#include "superlu/slu_util.h"
namespace SuperLU_S {
#include "superlu/slu_sdefs.h"
}
namespace SuperLU_D {
#include "superlu/slu_ddefs.h"
}
namespace SuperLU_C {
#include "superlu/slu_cdefs.h"
}
namespace SuperLU_Z {
#include "superlu/slu_zdefs.h"
}
namespace Eigen { struct SluMatrix; }
#endif
#ifdef EIGEN_UMFPACK_SUPPORT
#include "umfpack.h"
#endif
namespace Eigen {
/** \defgroup Sparse_Module Sparse module
*
*
* \nonstableyet
*
* See the \ref TutorialSparse "Sparse tutorial"
*
* \code
* #include <Eigen/Sparse>
* #include <Eigen/QR>
* \endcode
*/
/** The type used to identify a general sparse storage. */
struct Sparse {};
#include "src/Sparse/SparseUtil.h"
#include "src/Sparse/SparseMatrixBase.h"
#include "src/Sparse/CompressedStorage.h"
#include "src/Sparse/AmbiVector.h"
#include "src/Sparse/RandomSetter.h"
#include "src/Sparse/SparseBlock.h"
#include "src/Sparse/SparseMatrix.h"
#include "src/Sparse/DynamicSparseMatrix.h"
#include "src/Sparse/MappedSparseMatrix.h"
#include "src/Sparse/SparseVector.h"
#include "src/Sparse/CoreIterators.h"
#include "src/Sparse/SparseBlock.h"
#include "src/Sparse/SparseTranspose.h"
#include "src/Sparse/SparseCwise.h"
#include "src/Sparse/SparseCwiseUnaryOp.h"
#include "src/Sparse/SparseCwiseBinaryOp.h"
#include "src/Sparse/SparseDot.h"
#include "src/Sparse/SparseAssign.h"
#include "src/Sparse/SparseRedux.h"
#include "src/Sparse/SparseFuzzy.h"
#include "src/Sparse/SparseFlagged.h"
#include "src/Sparse/SparseProduct.h"
#include "src/Sparse/SparseSparseProduct.h"
#include "src/Sparse/SparseDenseProduct.h"
#include "src/Sparse/SparseDiagonalProduct.h"
#include "src/Sparse/SparseTriangularView.h"
#include "src/Sparse/SparseSelfAdjointView.h"
#include "src/Sparse/TriangularSolver.h"
#include "src/Sparse/SparseView.h"
#include "src/Sparse/SparseLLT.h"
#include "src/Sparse/SparseLDLT.h"
#include "src/Sparse/SparseLU.h"
#ifdef EIGEN_CHOLMOD_SUPPORT
# include "src/Sparse/CholmodSupport.h"
#endif
#ifdef EIGEN_TAUCS_SUPPORT
# include "src/Sparse/TaucsSupport.h"
#endif
#ifdef EIGEN_SUPERLU_SUPPORT
# include "src/Sparse/SuperLUSupport.h"
#endif
#ifdef EIGEN_UMFPACK_SUPPORT
# include "src/Sparse/UmfPackSupport.h"
#endif
} // namespace Eigen
#include "src/Core/util/ReenableStupidWarnings.h"
#include "src/Core/util/EnableMSVCWarnings.h"
#endif // EIGEN_SPARSE_MODULE_H

View File

@@ -1,42 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_STDDEQUE_MODULE_H
#define EIGEN_STDDEQUE_MODULE_H
#include "Core"
#include <deque>
#if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */
#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...)
#else
#include "src/StlSupport/StdDeque.h"
#endif
#endif // EIGEN_STDDEQUE_MODULE_H

View File

@@ -1,41 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_STDLIST_MODULE_H
#define EIGEN_STDLIST_MODULE_H
#include "Core"
#include <list>
#if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */
#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...)
#else
#include "src/StlSupport/StdList.h"
#endif
#endif // EIGEN_STDLIST_MODULE_H

View File

@@ -1,42 +1,147 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifdef EIGEN_USE_NEW_STDVECTOR
#include "NewStdVector"
#else
#ifndef EIGEN_STDVECTOR_MODULE_H
#define EIGEN_STDVECTOR_MODULE_H
#include "Core"
#include <vector>
#if defined(_GLIBCXX_VECTOR) || defined(_VECTOR_)
#error you must include <Eigen/StdVector> before <vector>. Also note that <Eigen/Sparse> includes <vector>, so it must be included after <Eigen/StdVector> too.
#endif
#if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */
#ifndef EIGEN_GNUC_AT_LEAST
#ifdef __GNUC__
#define EIGEN_GNUC_AT_LEAST(x,y) ((__GNUC__>=x && __GNUC_MINOR__>=y) || __GNUC__>x)
#else
#define EIGEN_GNUC_AT_LEAST(x,y) 0
#endif
#endif
#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...)
#define vector std_vector
#include <vector>
#undef vector
namespace Eigen {
template<typename T> class aligned_allocator;
// meta programming to determine if a class has a given member
struct ei_does_not_have_aligned_operator_new_marker_sizeof {int a[1];};
struct ei_has_aligned_operator_new_marker_sizeof {int a[2];};
template<typename ClassType>
struct ei_has_aligned_operator_new {
template<typename T>
static ei_has_aligned_operator_new_marker_sizeof
test(T const *, typename T::ei_operator_new_marker_type const * = 0);
static ei_does_not_have_aligned_operator_new_marker_sizeof
test(...);
// note that the following indirection is needed for gcc-3.3
enum {ret = sizeof(test(static_cast<ClassType*>(0)))
== sizeof(ei_has_aligned_operator_new_marker_sizeof) };
};
#ifdef _MSC_VER
// sometimes, MSVC detects, at compile time, that the argument x
// in std::vector::resize(size_t s,T x) won't be aligned and generate an error
// even if this function is never called. Whence this little wrapper.
#define _EIGEN_WORKAROUND_MSVC_STD_VECTOR(T) Eigen::ei_workaround_msvc_std_vector<T>
template<typename T> struct ei_workaround_msvc_std_vector : public T
{
inline ei_workaround_msvc_std_vector() : T() {}
inline ei_workaround_msvc_std_vector(const T& other) : T(other) {}
inline operator T& () { return *static_cast<T*>(this); }
inline operator const T& () const { return *static_cast<const T*>(this); }
template<typename OtherT>
inline T& operator=(const OtherT& other)
{ T::operator=(other); return *this; }
inline ei_workaround_msvc_std_vector& operator=(const ei_workaround_msvc_std_vector& other)
{ T::operator=(other); return *this; }
};
#else
#include "src/StlSupport/StdVector.h"
#define _EIGEN_WORKAROUND_MSVC_STD_VECTOR(T) T
#endif
}
namespace std {
#define EIGEN_STD_VECTOR_SPECIALIZATION_BODY \
public: \
typedef T value_type; \
typedef typename vector_base::allocator_type allocator_type; \
typedef typename vector_base::size_type size_type; \
typedef typename vector_base::iterator iterator; \
explicit vector(const allocator_type& __a = allocator_type()) : vector_base(__a) {} \
vector(const vector& c) : vector_base(c) {} \
vector(size_type num, const value_type& val = value_type()) : vector_base(num, val) {} \
vector(iterator start, iterator end) : vector_base(start, end) {} \
vector& operator=(const vector& __x) { \
vector_base::operator=(__x); \
return *this; \
}
template<typename T,
typename AllocT = std::allocator<T>,
bool HasAlignedNew = Eigen::ei_has_aligned_operator_new<T>::ret>
class vector : public std::std_vector<T,AllocT>
{
typedef std_vector<T, AllocT> vector_base;
EIGEN_STD_VECTOR_SPECIALIZATION_BODY
};
template<typename T,typename DummyAlloc>
class vector<T,DummyAlloc,true>
: public std::std_vector<_EIGEN_WORKAROUND_MSVC_STD_VECTOR(T),
Eigen::aligned_allocator<_EIGEN_WORKAROUND_MSVC_STD_VECTOR(T)> >
{
typedef std_vector<_EIGEN_WORKAROUND_MSVC_STD_VECTOR(T),
Eigen::aligned_allocator<_EIGEN_WORKAROUND_MSVC_STD_VECTOR(T)> > vector_base;
EIGEN_STD_VECTOR_SPECIALIZATION_BODY
void resize(size_type __new_size)
{ resize(__new_size, T()); }
#if defined(_VECTOR_)
// workaround MSVC std::vector implementation
void resize(size_type __new_size, const value_type& __x)
{
if (vector_base::size() < __new_size)
vector_base::_Insert_n(vector_base::end(), __new_size - vector_base::size(), __x);
else if (__new_size < vector_base::size())
vector_base::erase(vector_base::begin() + __new_size, vector_base::end());
}
#elif defined(_GLIBCXX_VECTOR) && EIGEN_GNUC_AT_LEAST(4,2)
// workaround GCC std::vector implementation
void resize(size_type __new_size, const value_type& __x)
{
if (__new_size < vector_base::size())
vector_base::_M_erase_at_end(this->_M_impl._M_start + __new_size);
else
vector_base::insert(vector_base::end(), __new_size - vector_base::size(), __x);
}
#elif defined(_GLIBCXX_VECTOR) && EIGEN_GNUC_AT_LEAST(4,1)
void resize(size_type __new_size, const value_type& __x)
{
if (__new_size < vector_base::size())
vector_base::erase(vector_base::begin() + __new_size, vector_base::end());
else
vector_base::insert(vector_base::end(), __new_size - vector_base::size(), __x);
}
#else
// Before gcc-4.1 we already have: std::vector::resize(size_type,const T&),
// so no need for a workaround !
using vector_base::resize;
#endif
};
}
#endif // EIGEN_STDVECTOR_MODULE_H
#endif // EIGEN_USE_NEW_STDVECTOR

View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -25,10 +25,8 @@
#ifndef EIGEN_ALLANDANY_H
#define EIGEN_ALLANDANY_H
namespace internal {
template<typename Derived, int UnrollCount>
struct all_unroller
struct ei_all_unroller
{
enum {
col = (UnrollCount-1) / Derived::RowsAtCompileTime,
@@ -37,24 +35,24 @@ struct all_unroller
inline static bool run(const Derived &mat)
{
return all_unroller<Derived, UnrollCount-1>::run(mat) && mat.coeff(row, col);
return ei_all_unroller<Derived, UnrollCount-1>::run(mat) && mat.coeff(row, col);
}
};
template<typename Derived>
struct all_unroller<Derived, 1>
struct ei_all_unroller<Derived, 1>
{
inline static bool run(const Derived &mat) { return mat.coeff(0, 0); }
};
template<typename Derived>
struct all_unroller<Derived, Dynamic>
struct ei_all_unroller<Derived, Dynamic>
{
inline static bool run(const Derived &) { return false; }
};
template<typename Derived, int UnrollCount>
struct any_unroller
struct ei_any_unroller
{
enum {
col = (UnrollCount-1) / Derived::RowsAtCompileTime,
@@ -63,87 +61,85 @@ struct any_unroller
inline static bool run(const Derived &mat)
{
return any_unroller<Derived, UnrollCount-1>::run(mat) || mat.coeff(row, col);
return ei_any_unroller<Derived, UnrollCount-1>::run(mat) || mat.coeff(row, col);
}
};
template<typename Derived>
struct any_unroller<Derived, 1>
struct ei_any_unroller<Derived, 1>
{
inline static bool run(const Derived &mat) { return mat.coeff(0, 0); }
};
template<typename Derived>
struct any_unroller<Derived, Dynamic>
struct ei_any_unroller<Derived, Dynamic>
{
inline static bool run(const Derived &) { return false; }
};
} // end namespace internal
/** \returns true if all coefficients are true
/** \array_module
*
* \returns true if all coefficients are true
*
* \addexample CwiseAll \label How to check whether a point is inside a box (using operator< and all())
*
* Example: \include MatrixBase_all.cpp
* Output: \verbinclude MatrixBase_all.out
*
* \sa any(), Cwise::operator<()
* \sa MatrixBase::any(), Cwise::operator<()
*/
template<typename Derived>
inline bool DenseBase<Derived>::all() const
inline bool MatrixBase<Derived>::all() const
{
enum {
unroll = SizeAtCompileTime != Dynamic
&& CoeffReadCost != Dynamic
&& NumTraits<Scalar>::AddCost != Dynamic
&& SizeAtCompileTime * (CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
};
const bool unroll = SizeAtCompileTime * (CoeffReadCost + NumTraits<Scalar>::AddCost)
<= EIGEN_UNROLLING_LIMIT;
if(unroll)
return internal::all_unroller<Derived,
return ei_all_unroller<Derived,
unroll ? int(SizeAtCompileTime) : Dynamic
>::run(derived());
else
{
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
for(int j = 0; j < cols(); ++j)
for(int i = 0; i < rows(); ++i)
if (!coeff(i, j)) return false;
return true;
}
}
/** \returns true if at least one coefficient is true
/** \array_module
*
* \returns true if at least one coefficient is true
*
* \sa all()
* \sa MatrixBase::all()
*/
template<typename Derived>
inline bool DenseBase<Derived>::any() const
inline bool MatrixBase<Derived>::any() const
{
enum {
unroll = SizeAtCompileTime != Dynamic
&& CoeffReadCost != Dynamic
&& NumTraits<Scalar>::AddCost != Dynamic
&& SizeAtCompileTime * (CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
};
const bool unroll = SizeAtCompileTime * (CoeffReadCost + NumTraits<Scalar>::AddCost)
<= EIGEN_UNROLLING_LIMIT;
if(unroll)
return internal::any_unroller<Derived,
return ei_any_unroller<Derived,
unroll ? int(SizeAtCompileTime) : Dynamic
>::run(derived());
else
{
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
for(int j = 0; j < cols(); ++j)
for(int i = 0; i < rows(); ++i)
if (coeff(i, j)) return true;
return false;
}
}
/** \returns the number of coefficients which evaluate to true
/** \array_module
*
* \returns the number of coefficients which evaluate to true
*
* \sa all(), any()
* \sa MatrixBase::all(), MatrixBase::any()
*/
template<typename Derived>
inline typename DenseBase<Derived>::Index DenseBase<Derived>::count() const
inline int MatrixBase<Derived>::count() const
{
return derived().template cast<bool>().template cast<Index>().sum();
return this->cast<bool>().cast<int>().sum();
}
#endif // EIGEN_ALLANDANY_H

View File

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

View File

@@ -0,0 +1,453 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_ARRAY_CWISE_OPERATORS_H
#define EIGEN_ARRAY_CWISE_OPERATORS_H
// -- unary operators --
/** \array_module
*
* \returns an expression of the coefficient-wise square root of *this.
*
* Example: \include Cwise_sqrt.cpp
* Output: \verbinclude Cwise_sqrt.out
*
* \sa pow(), square()
*/
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_sqrt_op)
Cwise<ExpressionType>::sqrt() const
{
return _expression();
}
/** \array_module
*
* \returns an expression of the coefficient-wise exponential of *this.
*
* Example: \include Cwise_exp.cpp
* Output: \verbinclude Cwise_exp.out
*
* \sa pow(), log(), sin(), cos()
*/
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_exp_op)
Cwise<ExpressionType>::exp() const
{
return _expression();
}
/** \array_module
*
* \returns an expression of the coefficient-wise logarithm of *this.
*
* Example: \include Cwise_log.cpp
* Output: \verbinclude Cwise_log.out
*
* \sa exp()
*/
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_log_op)
Cwise<ExpressionType>::log() const
{
return _expression();
}
/** \array_module
*
* \returns an expression of the coefficient-wise cosine of *this.
*
* Example: \include Cwise_cos.cpp
* Output: \verbinclude Cwise_cos.out
*
* \sa sin(), exp()
*/
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_cos_op)
Cwise<ExpressionType>::cos() const
{
return _expression();
}
/** \array_module
*
* \returns an expression of the coefficient-wise sine of *this.
*
* Example: \include Cwise_sin.cpp
* Output: \verbinclude Cwise_sin.out
*
* \sa cos(), exp()
*/
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_sin_op)
Cwise<ExpressionType>::sin() const
{
return _expression();
}
/** \array_module
*
* \returns an expression of the coefficient-wise power of *this to the given exponent.
*
* Example: \include Cwise_pow.cpp
* Output: \verbinclude Cwise_pow.out
*
* \sa exp(), log()
*/
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_pow_op)
Cwise<ExpressionType>::pow(const Scalar& exponent) const
{
return EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_pow_op)(_expression(), ei_scalar_pow_op<Scalar>(exponent));
}
/** \array_module
*
* \returns an expression of the coefficient-wise inverse of *this.
*
* Example: \include Cwise_inverse.cpp
* Output: \verbinclude Cwise_inverse.out
*
* \sa operator/(), operator*()
*/
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_inverse_op)
Cwise<ExpressionType>::inverse() const
{
return _expression();
}
/** \array_module
*
* \returns an expression of the coefficient-wise square of *this.
*
* Example: \include Cwise_square.cpp
* Output: \verbinclude Cwise_square.out
*
* \sa operator/(), operator*(), abs2()
*/
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_square_op)
Cwise<ExpressionType>::square() const
{
return _expression();
}
/** \array_module
*
* \returns an expression of the coefficient-wise cube of *this.
*
* Example: \include Cwise_cube.cpp
* Output: \verbinclude Cwise_cube.out
*
* \sa square(), pow()
*/
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_cube_op)
Cwise<ExpressionType>::cube() const
{
return _expression();
}
// -- binary operators --
/** \array_module
*
* \returns an expression of the coefficient-wise \< operator of *this and \a other
*
* Example: \include Cwise_less.cpp
* Output: \verbinclude Cwise_less.out
*
* \sa MatrixBase::all(), MatrixBase::any(), operator>(), operator<=()
*/
template<typename ExpressionType>
template<typename OtherDerived>
inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::less)
Cwise<ExpressionType>::operator<(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(std::less)(_expression(), other.derived());
}
/** \array_module
*
* \returns an expression of the coefficient-wise \<= operator of *this and \a other
*
* Example: \include Cwise_less_equal.cpp
* Output: \verbinclude Cwise_less_equal.out
*
* \sa MatrixBase::all(), MatrixBase::any(), operator>=(), operator<()
*/
template<typename ExpressionType>
template<typename OtherDerived>
inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::less_equal)
Cwise<ExpressionType>::operator<=(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(std::less_equal)(_expression(), other.derived());
}
/** \array_module
*
* \returns an expression of the coefficient-wise \> operator of *this and \a other
*
* Example: \include Cwise_greater.cpp
* Output: \verbinclude Cwise_greater.out
*
* \sa MatrixBase::all(), MatrixBase::any(), operator>=(), operator<()
*/
template<typename ExpressionType>
template<typename OtherDerived>
inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::greater)
Cwise<ExpressionType>::operator>(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(std::greater)(_expression(), other.derived());
}
/** \array_module
*
* \returns an expression of the coefficient-wise \>= operator of *this and \a other
*
* Example: \include Cwise_greater_equal.cpp
* Output: \verbinclude Cwise_greater_equal.out
*
* \sa MatrixBase::all(), MatrixBase::any(), operator>(), operator<=()
*/
template<typename ExpressionType>
template<typename OtherDerived>
inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::greater_equal)
Cwise<ExpressionType>::operator>=(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(std::greater_equal)(_expression(), other.derived());
}
/** \array_module
*
* \returns an expression of the coefficient-wise == operator of *this and \a other
*
* \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
* In order to check for equality between two vectors or matrices with floating-point coefficients, it is
* generally a far better idea to use a fuzzy comparison as provided by MatrixBase::isApprox() and
* MatrixBase::isMuchSmallerThan().
*
* Example: \include Cwise_equal_equal.cpp
* Output: \verbinclude Cwise_equal_equal.out
*
* \sa MatrixBase::all(), MatrixBase::any(), MatrixBase::isApprox(), MatrixBase::isMuchSmallerThan()
*/
template<typename ExpressionType>
template<typename OtherDerived>
inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::equal_to)
Cwise<ExpressionType>::operator==(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(std::equal_to)(_expression(), other.derived());
}
/** \array_module
*
* \returns an expression of the coefficient-wise != operator of *this and \a other
*
* \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
* In order to check for equality between two vectors or matrices with floating-point coefficients, it is
* generally a far better idea to use a fuzzy comparison as provided by MatrixBase::isApprox() and
* MatrixBase::isMuchSmallerThan().
*
* Example: \include Cwise_not_equal.cpp
* Output: \verbinclude Cwise_not_equal.out
*
* \sa MatrixBase::all(), MatrixBase::any(), MatrixBase::isApprox(), MatrixBase::isMuchSmallerThan()
*/
template<typename ExpressionType>
template<typename OtherDerived>
inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::not_equal_to)
Cwise<ExpressionType>::operator!=(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(std::not_equal_to)(_expression(), other.derived());
}
// comparisons to scalar value
/** \array_module
*
* \returns an expression of the coefficient-wise \< operator of *this and a scalar \a s
*
* \sa operator<(const MatrixBase<OtherDerived> &) const
*/
template<typename ExpressionType>
inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::less)
Cwise<ExpressionType>::operator<(Scalar s) const
{
return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::less)(_expression(),
typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s));
}
/** \array_module
*
* \returns an expression of the coefficient-wise \<= operator of *this and a scalar \a s
*
* \sa operator<=(const MatrixBase<OtherDerived> &) const
*/
template<typename ExpressionType>
inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::less_equal)
Cwise<ExpressionType>::operator<=(Scalar s) const
{
return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::less_equal)(_expression(),
typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s));
}
/** \array_module
*
* \returns an expression of the coefficient-wise \> operator of *this and a scalar \a s
*
* \sa operator>(const MatrixBase<OtherDerived> &) const
*/
template<typename ExpressionType>
inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::greater)
Cwise<ExpressionType>::operator>(Scalar s) const
{
return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::greater)(_expression(),
typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s));
}
/** \array_module
*
* \returns an expression of the coefficient-wise \>= operator of *this and a scalar \a s
*
* \sa operator>=(const MatrixBase<OtherDerived> &) const
*/
template<typename ExpressionType>
inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::greater_equal)
Cwise<ExpressionType>::operator>=(Scalar s) const
{
return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::greater_equal)(_expression(),
typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s));
}
/** \array_module
*
* \returns an expression of the coefficient-wise == operator of *this and a scalar \a s
*
* \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
* In order to check for equality between two vectors or matrices with floating-point coefficients, it is
* generally a far better idea to use a fuzzy comparison as provided by MatrixBase::isApprox() and
* MatrixBase::isMuchSmallerThan().
*
* \sa operator==(const MatrixBase<OtherDerived> &) const
*/
template<typename ExpressionType>
inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::equal_to)
Cwise<ExpressionType>::operator==(Scalar s) const
{
return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::equal_to)(_expression(),
typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s));
}
/** \array_module
*
* \returns an expression of the coefficient-wise != operator of *this and a scalar \a s
*
* \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
* In order to check for equality between two vectors or matrices with floating-point coefficients, it is
* generally a far better idea to use a fuzzy comparison as provided by MatrixBase::isApprox() and
* MatrixBase::isMuchSmallerThan().
*
* \sa operator!=(const MatrixBase<OtherDerived> &) const
*/
template<typename ExpressionType>
inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::not_equal_to)
Cwise<ExpressionType>::operator!=(Scalar s) const
{
return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::not_equal_to)(_expression(),
typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s));
}
// scalar addition
/** \array_module
*
* \returns an expression of \c *this with each coeff incremented by the constant \a scalar
*
* Example: \include Cwise_plus.cpp
* Output: \verbinclude Cwise_plus.out
*
* \sa operator+=(), operator-()
*/
template<typename ExpressionType>
inline const typename Cwise<ExpressionType>::ScalarAddReturnType
Cwise<ExpressionType>::operator+(const Scalar& scalar) const
{
return typename Cwise<ExpressionType>::ScalarAddReturnType(m_matrix, ei_scalar_add_op<Scalar>(scalar));
}
/** \array_module
*
* Adds the given \a scalar to each coeff of this expression.
*
* Example: \include Cwise_plus_equal.cpp
* Output: \verbinclude Cwise_plus_equal.out
*
* \sa operator+(), operator-=()
*/
template<typename ExpressionType>
inline ExpressionType& Cwise<ExpressionType>::operator+=(const Scalar& scalar)
{
return m_matrix.const_cast_derived() = *this + scalar;
}
/** \array_module
*
* \returns an expression of \c *this with each coeff decremented by the constant \a scalar
*
* Example: \include Cwise_minus.cpp
* Output: \verbinclude Cwise_minus.out
*
* \sa operator+(), operator-=()
*/
template<typename ExpressionType>
inline const typename Cwise<ExpressionType>::ScalarAddReturnType
Cwise<ExpressionType>::operator-(const Scalar& scalar) const
{
return *this + (-scalar);
}
/** \array_module
*
* Substracts the given \a scalar from each coeff of this expression.
*
* Example: \include Cwise_minus_equal.cpp
* Output: \verbinclude Cwise_minus_equal.out
*
* \sa operator+=(), operator-()
*/
template<typename ExpressionType>
inline ExpressionType& Cwise<ExpressionType>::operator-=(const Scalar& scalar)
{
return m_matrix.const_cast_derived() = *this - scalar;
}
#endif // EIGEN_ARRAY_CWISE_OPERATORS_H

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_ARRAY_FUNCTORS_H
#define EIGEN_ARRAY_FUNCTORS_H
/** \internal
* \array_module
*
* \brief Template functor to add a scalar to a fixed other one
*
* \sa class CwiseUnaryOp, Array::operator+
*/
/* If you wonder why doing the ei_pset1() in packetOp() is an optimization check ei_scalar_multiple_op */
template<typename Scalar>
struct ei_scalar_add_op {
typedef typename ei_packet_traits<Scalar>::type PacketScalar;
// FIXME default copy constructors seems bugged with std::complex<>
inline ei_scalar_add_op(const ei_scalar_add_op& other) : m_other(other.m_other) { }
inline ei_scalar_add_op(const Scalar& other) : m_other(other) { }
inline Scalar operator() (const Scalar& a) const { return a + m_other; }
inline const PacketScalar packetOp(const PacketScalar& a) const
{ return ei_padd(a, ei_pset1(m_other)); }
const Scalar m_other;
private:
ei_scalar_add_op& operator=(const ei_scalar_add_op&);
};
template<typename Scalar>
struct ei_functor_traits<ei_scalar_add_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = ei_packet_traits<Scalar>::size>1 }; };
/** \internal
*
* \array_module
*
* \brief Template functor to compute the square root of a scalar
*
* \sa class CwiseUnaryOp, Cwise::sqrt()
*/
template<typename Scalar> struct ei_scalar_sqrt_op EIGEN_EMPTY_STRUCT {
inline const Scalar operator() (const Scalar& a) const { return ei_sqrt(a); }
};
template<typename Scalar>
struct ei_functor_traits<ei_scalar_sqrt_op<Scalar> >
{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; };
/** \internal
*
* \array_module
*
* \brief Template functor to compute the exponential of a scalar
*
* \sa class CwiseUnaryOp, Cwise::exp()
*/
template<typename Scalar> struct ei_scalar_exp_op EIGEN_EMPTY_STRUCT {
inline const Scalar operator() (const Scalar& a) const { return ei_exp(a); }
};
template<typename Scalar>
struct ei_functor_traits<ei_scalar_exp_op<Scalar> >
{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; };
/** \internal
*
* \array_module
*
* \brief Template functor to compute the logarithm of a scalar
*
* \sa class CwiseUnaryOp, Cwise::log()
*/
template<typename Scalar> struct ei_scalar_log_op EIGEN_EMPTY_STRUCT {
inline const Scalar operator() (const Scalar& a) const { return ei_log(a); }
};
template<typename Scalar>
struct ei_functor_traits<ei_scalar_log_op<Scalar> >
{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; };
/** \internal
*
* \array_module
*
* \brief Template functor to compute the cosine of a scalar
*
* \sa class CwiseUnaryOp, Cwise::cos()
*/
template<typename Scalar> struct ei_scalar_cos_op EIGEN_EMPTY_STRUCT {
inline const Scalar operator() (const Scalar& a) const { return ei_cos(a); }
};
template<typename Scalar>
struct ei_functor_traits<ei_scalar_cos_op<Scalar> >
{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; };
/** \internal
*
* \array_module
*
* \brief Template functor to compute the sine of a scalar
*
* \sa class CwiseUnaryOp, Cwise::sin()
*/
template<typename Scalar> struct ei_scalar_sin_op EIGEN_EMPTY_STRUCT {
inline const Scalar operator() (const Scalar& a) const { return ei_sin(a); }
};
template<typename Scalar>
struct ei_functor_traits<ei_scalar_sin_op<Scalar> >
{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; };
/** \internal
*
* \array_module
*
* \brief Template functor to raise a scalar to a power
*
* \sa class CwiseUnaryOp, Cwise::pow
*/
template<typename Scalar>
struct ei_scalar_pow_op {
// FIXME default copy constructors seems bugged with std::complex<>
inline ei_scalar_pow_op(const ei_scalar_pow_op& other) : m_exponent(other.m_exponent) { }
inline ei_scalar_pow_op(const Scalar& exponent) : m_exponent(exponent) {}
inline Scalar operator() (const Scalar& a) const { return ei_pow(a, m_exponent); }
const Scalar m_exponent;
private:
ei_scalar_pow_op& operator=(const ei_scalar_pow_op&);
};
template<typename Scalar>
struct ei_functor_traits<ei_scalar_pow_op<Scalar> >
{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; };
/** \internal
*
* \array_module
*
* \brief Template functor to compute the inverse of a scalar
*
* \sa class CwiseUnaryOp, Cwise::inverse()
*/
template<typename Scalar>
struct ei_scalar_inverse_op {
inline Scalar operator() (const Scalar& a) const { return Scalar(1)/a; }
template<typename PacketScalar>
inline const PacketScalar packetOp(const PacketScalar& a) const
{ return ei_pdiv(ei_pset1(Scalar(1)),a); }
};
template<typename Scalar>
struct ei_functor_traits<ei_scalar_inverse_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = int(ei_packet_traits<Scalar>::size)>1 }; };
/** \internal
*
* \array_module
*
* \brief Template functor to compute the square of a scalar
*
* \sa class CwiseUnaryOp, Cwise::square()
*/
template<typename Scalar>
struct ei_scalar_square_op {
inline Scalar operator() (const Scalar& a) const { return a*a; }
template<typename PacketScalar>
inline const PacketScalar packetOp(const PacketScalar& a) const
{ return ei_pmul(a,a); }
};
template<typename Scalar>
struct ei_functor_traits<ei_scalar_square_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = int(ei_packet_traits<Scalar>::size)>1 }; };
/** \internal
*
* \array_module
*
* \brief Template functor to compute the cube of a scalar
*
* \sa class CwiseUnaryOp, Cwise::cube()
*/
template<typename Scalar>
struct ei_scalar_cube_op {
inline Scalar operator() (const Scalar& a) const { return a*a*a; }
template<typename PacketScalar>
inline const PacketScalar packetOp(const PacketScalar& a) const
{ return ei_pmul(a,ei_pmul(a,a)); }
};
template<typename Scalar>
struct ei_functor_traits<ei_scalar_cube_op<Scalar> >
{ enum { Cost = 2*NumTraits<Scalar>::MulCost, PacketAccess = int(ei_packet_traits<Scalar>::size)>1 }; };
// default ei_functor_traits for STL functors:
template<typename T>
struct ei_functor_traits<std::multiplies<T> >
{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::divides<T> >
{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::plus<T> >
{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::minus<T> >
{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::negate<T> >
{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::logical_or<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::logical_and<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::logical_not<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::greater<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::less<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::greater_equal<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::less_equal<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::equal_to<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::not_equal_to<T> >
{ enum { Cost = 1, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::binder2nd<T> >
{ enum { Cost = ei_functor_traits<T>::Cost, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::binder1st<T> >
{ enum { Cost = ei_functor_traits<T>::Cost, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::unary_negate<T> >
{ enum { Cost = 1 + ei_functor_traits<T>::Cost, PacketAccess = false }; };
template<typename T>
struct ei_functor_traits<std::binary_negate<T> >
{ enum { Cost = 1 + ei_functor_traits<T>::Cost, PacketAccess = false }; };
#ifdef EIGEN_STDEXT_SUPPORT
template<typename T0,typename T1>
struct ei_functor_traits<std::project1st<T0,T1> >
{ enum { Cost = 0, PacketAccess = false }; };
template<typename T0,typename T1>
struct ei_functor_traits<std::project2nd<T0,T1> >
{ enum { Cost = 0, PacketAccess = false }; };
template<typename T0,typename T1>
struct ei_functor_traits<std::select2nd<std::pair<T0,T1> > >
{ enum { Cost = 0, PacketAccess = false }; };
template<typename T0,typename T1>
struct ei_functor_traits<std::select1st<std::pair<T0,T1> > >
{ enum { Cost = 0, PacketAccess = false }; };
template<typename T0,typename T1>
struct ei_functor_traits<std::unary_compose<T0,T1> >
{ enum { Cost = ei_functor_traits<T0>::Cost + ei_functor_traits<T1>::Cost, PacketAccess = false }; };
template<typename T0,typename T1,typename T2>
struct ei_functor_traits<std::binary_compose<T0,T1,T2> >
{ enum { Cost = ei_functor_traits<T0>::Cost + ei_functor_traits<T1>::Cost + ei_functor_traits<T2>::Cost, PacketAccess = false }; };
#endif // EIGEN_STDEXT_SUPPORT
#endif // EIGEN_ARRAY_FUNCTORS_H

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_ARRAY_NORMS_H
#define EIGEN_ARRAY_NORMS_H
template<typename Derived, int p>
struct ei_lpNorm_selector
{
typedef typename NumTraits<typename ei_traits<Derived>::Scalar>::Real RealScalar;
inline static RealScalar run(const MatrixBase<Derived>& m)
{
return ei_pow(m.cwise().abs().cwise().pow(p).sum(), RealScalar(1)/p);
}
};
template<typename Derived>
struct ei_lpNorm_selector<Derived, 1>
{
inline static typename NumTraits<typename ei_traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
{
return m.cwise().abs().sum();
}
};
template<typename Derived>
struct ei_lpNorm_selector<Derived, 2>
{
inline static typename NumTraits<typename ei_traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
{
return m.norm();
}
};
template<typename Derived>
struct ei_lpNorm_selector<Derived, Infinity>
{
inline static typename NumTraits<typename ei_traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
{
return m.cwise().abs().maxCoeff();
}
};
/** \array_module
*
* \returns the \f$ \ell^p \f$ norm of *this, that is, returns the p-th root of the sum of the p-th powers of the absolute values
* of the coefficients of *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^p\infty \f$
* norm, that is the maximum of the absolute values of the coefficients of *this.
*
* \sa norm()
*/
template<typename Derived>
template<int p>
inline typename NumTraits<typename ei_traits<Derived>::Scalar>::Real MatrixBase<Derived>::lpNorm() const
{
return ei_lpNorm_selector<Derived, p>::run(*this);
}
#endif // EIGEN_ARRAY_NORMS_H

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_PARTIAL_REDUX_H
#define EIGEN_PARTIAL_REDUX_H
/** \array_module \ingroup Array
*
* \class PartialReduxExpr
*
* \brief Generic expression of a partially reduxed matrix
*
* \param MatrixType the type of the matrix we are applying the redux operation
* \param MemberOp type of the member functor
* \param Direction indicates the direction of the redux (Vertical or Horizontal)
*
* This class represents an expression of a partial redux operator of a matrix.
* It is the return type of PartialRedux functions,
* and most of the time this is the only way it is used.
*
* \sa class PartialRedux
*/
template< typename MatrixType, typename MemberOp, int Direction>
class PartialReduxExpr;
template<typename MatrixType, typename MemberOp, int Direction>
struct ei_traits<PartialReduxExpr<MatrixType, MemberOp, Direction> >
{
typedef typename MemberOp::result_type Scalar;
typedef typename MatrixType::Scalar InputScalar;
typedef typename ei_nested<MatrixType>::type MatrixTypeNested;
typedef typename ei_cleantype<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,
Flags = (unsigned int)_MatrixTypeNested::Flags & HereditaryBits,
TraversalSize = Direction==Vertical ? RowsAtCompileTime : 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 * ei_traits<_MatrixTypeNested>::CoeffReadCost + int(CostOpType::value)
};
};
template< typename MatrixType, typename MemberOp, int Direction>
class PartialReduxExpr : ei_no_assignment_operator,
public MatrixBase<PartialReduxExpr<MatrixType, MemberOp, Direction> >
{
public:
EIGEN_GENERIC_PUBLIC_INTERFACE(PartialReduxExpr)
typedef typename ei_traits<PartialReduxExpr>::MatrixTypeNested MatrixTypeNested;
typedef typename ei_traits<PartialReduxExpr>::_MatrixTypeNested _MatrixTypeNested;
PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp())
: m_matrix(mat), m_functor(func) {}
int rows() const { return (Direction==Vertical ? 1 : m_matrix.rows()); }
int cols() const { return (Direction==Horizontal ? 1 : m_matrix.cols()); }
const Scalar coeff(int i, int j) const
{
if (Direction==Vertical)
return m_functor(m_matrix.col(j));
else
return m_functor(m_matrix.row(i));
}
protected:
const MatrixTypeNested m_matrix;
const MemberOp m_functor;
};
#define EIGEN_MEMBER_FUNCTOR(MEMBER,COST) \
template <typename ResultType> \
struct ei_member_##MEMBER EIGEN_EMPTY_STRUCT { \
typedef ResultType result_type; \
template<typename Scalar, int Size> struct Cost \
{ enum { value = COST }; }; \
template<typename Derived> \
inline ResultType operator()(const MatrixBase<Derived>& mat) const \
{ return mat.MEMBER(); } \
}
EIGEN_MEMBER_FUNCTOR(squaredNorm, Size * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MEMBER_FUNCTOR(norm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MEMBER_FUNCTOR(sum, (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MEMBER_FUNCTOR(minCoeff, (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MEMBER_FUNCTOR(maxCoeff, (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MEMBER_FUNCTOR(all, (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MEMBER_FUNCTOR(any, (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MEMBER_FUNCTOR(count, (Size-1)*NumTraits<Scalar>::AddCost);
/** \internal */
template <typename BinaryOp, typename Scalar>
struct ei_member_redux {
typedef typename ei_result_of<
BinaryOp(Scalar)
>::type result_type;
template<typename _Scalar, int Size> struct Cost
{ enum { value = (Size-1) * ei_functor_traits<BinaryOp>::Cost }; };
ei_member_redux(const BinaryOp func) : m_functor(func) {}
template<typename Derived>
inline result_type operator()(const MatrixBase<Derived>& mat) const
{ return mat.redux(m_functor); }
const BinaryOp m_functor;
private:
ei_member_redux& operator=(const ei_member_redux&);
};
/** \array_module \ingroup Array
*
* \class PartialRedux
*
* \brief Pseudo expression providing partial reduction operations
*
* \param ExpressionType the type of the object on which to do partial reductions
* \param Direction indicates the direction of the redux (Vertical or Horizontal)
*
* This class represents a pseudo expression with partial reduction features.
* It is the return type of MatrixBase::colwise() and MatrixBase::rowwise()
* and most of the time this is the only way it is used.
*
* Example: \include MatrixBase_colwise.cpp
* Output: \verbinclude MatrixBase_colwise.out
*
* \sa MatrixBase::colwise(), MatrixBase::rowwise(), class PartialReduxExpr
*/
template<typename ExpressionType, int Direction> class PartialRedux
{
public:
typedef typename ei_traits<ExpressionType>::Scalar Scalar;
typedef typename ei_meta_if<ei_must_nest_by_value<ExpressionType>::ret,
ExpressionType, const ExpressionType&>::ret ExpressionTypeNested;
template<template<typename _Scalar> class Functor> struct ReturnType
{
typedef PartialReduxExpr<ExpressionType,
Functor<typename ei_traits<ExpressionType>::Scalar>,
Direction
> Type;
};
template<typename BinaryOp> struct ReduxReturnType
{
typedef PartialReduxExpr<ExpressionType,
ei_member_redux<BinaryOp,typename ei_traits<ExpressionType>::Scalar>,
Direction
> Type;
};
typedef typename ExpressionType::PlainMatrixType CrossReturnType;
inline PartialRedux(const ExpressionType& matrix) : m_matrix(matrix) {}
/** \internal */
inline const ExpressionType& _expression() const { return m_matrix; }
template<typename BinaryOp>
const typename ReduxReturnType<BinaryOp>::Type
redux(const BinaryOp& func = BinaryOp()) const;
/** \returns a row (or column) vector expression of the smallest coefficient
* of each column (or row) of the referenced expression.
*
* Example: \include PartialRedux_minCoeff.cpp
* Output: \verbinclude PartialRedux_minCoeff.out
*
* \sa MatrixBase::minCoeff() */
const typename ReturnType<ei_member_minCoeff>::Type minCoeff() const
{ return _expression(); }
/** \returns a row (or column) vector expression of the largest coefficient
* of each column (or row) of the referenced expression.
*
* Example: \include PartialRedux_maxCoeff.cpp
* Output: \verbinclude PartialRedux_maxCoeff.out
*
* \sa MatrixBase::maxCoeff() */
const typename ReturnType<ei_member_maxCoeff>::Type maxCoeff() const
{ return _expression(); }
/** \returns a row (or column) vector expression of the squared norm
* of each column (or row) of the referenced expression.
*
* Example: \include PartialRedux_squaredNorm.cpp
* Output: \verbinclude PartialRedux_squaredNorm.out
*
* \sa MatrixBase::squaredNorm() */
const typename ReturnType<ei_member_squaredNorm>::Type squaredNorm() const
{ return _expression(); }
/** \returns a row (or column) vector expression of the norm
* of each column (or row) of the referenced expression.
*
* Example: \include PartialRedux_norm.cpp
* Output: \verbinclude PartialRedux_norm.out
*
* \sa MatrixBase::norm() */
const typename ReturnType<ei_member_norm>::Type norm() const
{ return _expression(); }
/** \returns a row (or column) vector expression of the sum
* of each column (or row) of the referenced expression.
*
* Example: \include PartialRedux_sum.cpp
* Output: \verbinclude PartialRedux_sum.out
*
* \sa MatrixBase::sum() */
const typename ReturnType<ei_member_sum>::Type sum() const
{ return _expression(); }
/** \returns a row (or column) vector expression representing
* whether \b all coefficients of each respective column (or row) are \c true.
*
* \sa MatrixBase::all() */
const typename ReturnType<ei_member_all>::Type all() const
{ return _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.
*
* \sa MatrixBase::any() */
const typename ReturnType<ei_member_any>::Type any() const
{ return _expression(); }
/** \returns a row (or column) vector expression representing
* the number of \c true coefficients of each respective column (or row).
*
* Example: \include PartialRedux_count.cpp
* Output: \verbinclude PartialRedux_count.out
*
* \sa MatrixBase::count() */
const PartialReduxExpr<ExpressionType, ei_member_count<int>, Direction> count() const
{ return _expression(); }
/** \returns a 3x3 matrix expression of the cross product
* of each column or row of the referenced expression with the \a other vector.
*
* \geometry_module
*
* \sa MatrixBase::cross() */
template<typename OtherDerived>
const CrossReturnType cross(const MatrixBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(CrossReturnType,3,3)
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,3)
EIGEN_STATIC_ASSERT((ei_is_same_type<Scalar, typename OtherDerived::Scalar>::ret),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
if(Direction==Vertical)
return (CrossReturnType()
<< _expression().col(0).cross(other),
_expression().col(1).cross(other),
_expression().col(2).cross(other)).finished();
else
return (CrossReturnType()
<< _expression().row(0).cross(other),
_expression().row(1).cross(other),
_expression().row(2).cross(other)).finished();
}
protected:
ExpressionTypeNested m_matrix;
private:
PartialRedux& operator=(const PartialRedux&);
};
/** \array_module
*
* \returns a PartialRedux wrapper of *this providing additional partial reduction operations
*
* Example: \include MatrixBase_colwise.cpp
* Output: \verbinclude MatrixBase_colwise.out
*
* \sa rowwise(), class PartialRedux
*/
template<typename Derived>
inline const PartialRedux<Derived,Vertical>
MatrixBase<Derived>::colwise() const
{
return derived();
}
/** \array_module
*
* \returns a PartialRedux wrapper of *this providing additional partial reduction operations
*
* Example: \include MatrixBase_rowwise.cpp
* Output: \verbinclude MatrixBase_rowwise.out
*
* \sa colwise(), class PartialRedux
*/
template<typename Derived>
inline const PartialRedux<Derived,Horizontal>
MatrixBase<Derived>::rowwise() const
{
return derived();
}
/** \returns a row or column vector expression of \c *this reduxed by \a func
*
* The template parameter \a BinaryOp is the type of the functor
* of the custom redux operator. Note that func must be an associative operator.
*
* \sa class PartialRedux, MatrixBase::colwise(), MatrixBase::rowwise()
*/
template<typename ExpressionType, int Direction>
template<typename BinaryOp>
const typename PartialRedux<ExpressionType,Direction>::template ReduxReturnType<BinaryOp>::Type
PartialRedux<ExpressionType,Direction>::redux(const BinaryOp& func) const
{
return typename ReduxReturnType<BinaryOp>::Type(_expression(), func);
}
#endif // EIGEN_PARTIAL_REDUX_H

View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -25,46 +25,42 @@
#ifndef EIGEN_RANDOM_H
#define EIGEN_RANDOM_H
namespace internal {
template<typename Scalar> struct scalar_random_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_random_op)
template<typename Index>
inline const Scalar operator() (Index, Index = 0) const { return random<Scalar>(); }
template<typename Scalar> struct ei_scalar_random_op EIGEN_EMPTY_STRUCT {
inline ei_scalar_random_op(void) {}
inline const Scalar operator() (int, int) const { return ei_random<Scalar>(); }
};
template<typename Scalar>
struct functor_traits<scalar_random_op<Scalar> >
struct ei_functor_traits<ei_scalar_random_op<Scalar> >
{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false, IsRepeatable = false }; };
} // end namespace internal
/** \returns a random matrix expression
/** \array_module
*
* \returns a random matrix (not an expression, the matrix is immediately evaluated).
*
* 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,
* it is redundant to pass \a rows and \a cols as arguments, so Random() should be used
* it is redundant to pass \a rows and \a cols as arguments, so ei_random() should be used
* instead.
*
* \addexample RandomExample \label How to create a matrix with random coefficients
*
* Example: \include MatrixBase_random_int_int.cpp
* Output: \verbinclude MatrixBase_random_int_int.out
*
* This expression has the "evaluate before nesting" flag so that it will be evaluated into
* a temporary matrix whenever it is nested in a larger expression. This prevents unexpected
* behavior with expressions involving random matrices.
*
* \sa MatrixBase::setRandom(), MatrixBase::Random(Index), MatrixBase::Random()
* \sa MatrixBase::setRandom(), MatrixBase::Random(int), MatrixBase::Random()
*/
template<typename Derived>
inline const CwiseNullaryOp<internal::scalar_random_op<typename internal::traits<Derived>::Scalar>, Derived>
DenseBase<Derived>::Random(Index rows, Index cols)
inline const CwiseNullaryOp<ei_scalar_random_op<typename ei_traits<Derived>::Scalar>, Derived>
MatrixBase<Derived>::Random(int rows, int cols)
{
return NullaryExpr(rows, cols, internal::scalar_random_op<Scalar>());
return NullaryExpr(rows, cols, ei_scalar_random_op<Scalar>());
}
/** \returns a random vector expression
/** \array_module
*
* \returns a random vector (not an expression, the vector is immediately evaluated).
*
* The parameter \a size is the size of the returned vector.
* Must be compatible with this MatrixBase type.
@@ -72,26 +68,25 @@ DenseBase<Derived>::Random(Index rows, Index cols)
* \only_for_vectors
*
* This variant is meant to be used for dynamic-size vector types. For fixed-size types,
* it is redundant to pass \a size as argument, so Random() should be used
* it is redundant to pass \a size as argument, so ei_random() should be used
* instead.
*
* Example: \include MatrixBase_random_int.cpp
* Output: \verbinclude MatrixBase_random_int.out
*
* This expression has the "evaluate before nesting" flag so that it will be evaluated into
* a temporary vector whenever it is nested in a larger expression. This prevents unexpected
* behavior with expressions involving random matrices.
*
* \sa MatrixBase::setRandom(), MatrixBase::Random(Index,Index), MatrixBase::Random()
* \sa MatrixBase::setRandom(), MatrixBase::Random(int,int), MatrixBase::Random()
*/
template<typename Derived>
inline const CwiseNullaryOp<internal::scalar_random_op<typename internal::traits<Derived>::Scalar>, Derived>
DenseBase<Derived>::Random(Index size)
inline const CwiseNullaryOp<ei_scalar_random_op<typename ei_traits<Derived>::Scalar>, Derived>
MatrixBase<Derived>::Random(int size)
{
return NullaryExpr(size, internal::scalar_random_op<Scalar>());
return NullaryExpr(size, ei_scalar_random_op<Scalar>());
}
/** \returns a fixed-size random matrix or vector expression
/** \array_module
*
* \returns a fixed-size random matrix or vector
* (not an expression, the matrix is immediately evaluated).
*
* This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
* need to use the variants taking size arguments.
@@ -99,28 +94,26 @@ DenseBase<Derived>::Random(Index size)
* Example: \include MatrixBase_random.cpp
* Output: \verbinclude MatrixBase_random.out
*
* This expression has the "evaluate before nesting" flag so that it will be evaluated into
* a temporary matrix whenever it is nested in a larger expression. This prevents unexpected
* behavior with expressions involving random matrices.
*
* \sa MatrixBase::setRandom(), MatrixBase::Random(Index,Index), MatrixBase::Random(Index)
* \sa MatrixBase::setRandom(), MatrixBase::Random(int,int), MatrixBase::Random(int)
*/
template<typename Derived>
inline const CwiseNullaryOp<internal::scalar_random_op<typename internal::traits<Derived>::Scalar>, Derived>
DenseBase<Derived>::Random()
inline const CwiseNullaryOp<ei_scalar_random_op<typename ei_traits<Derived>::Scalar>, Derived>
MatrixBase<Derived>::Random()
{
return NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_random_op<Scalar>());
return NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, ei_scalar_random_op<Scalar>());
}
/** Sets all coefficients in this expression to random values.
/** \array_module
*
* Sets all coefficients in this expression to random values.
*
* Example: \include MatrixBase_setRandom.cpp
* Output: \verbinclude MatrixBase_setRandom.out
*
* \sa class CwiseNullaryOp, setRandom(Index), setRandom(Index,Index)
* \sa class CwiseNullaryOp, setRandom(int), setRandom(int,int)
*/
template<typename Derived>
inline Derived& DenseBase<Derived>::setRandom()
inline Derived& MatrixBase<Derived>::setRandom()
{
return *this = Random(rows(), cols());
}
@@ -132,11 +125,11 @@ inline Derived& DenseBase<Derived>::setRandom()
* Example: \include Matrix_setRandom_int.cpp
* Output: \verbinclude Matrix_setRandom_int.out
*
* \sa MatrixBase::setRandom(), setRandom(Index,Index), class CwiseNullaryOp, MatrixBase::Random()
* \sa MatrixBase::setRandom(), setRandom(int,int), class CwiseNullaryOp, MatrixBase::Random()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setRandom(Index size)
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
EIGEN_STRONG_INLINE Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>&
Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::setRandom(int size)
{
resize(size);
return setRandom();
@@ -150,11 +143,11 @@ PlainObjectBase<Derived>::setRandom(Index size)
* Example: \include Matrix_setRandom_int_int.cpp
* Output: \verbinclude Matrix_setRandom_int_int.out
*
* \sa MatrixBase::setRandom(), setRandom(Index), class CwiseNullaryOp, MatrixBase::Random()
* \sa MatrixBase::setRandom(), setRandom(int), class CwiseNullaryOp, MatrixBase::Random()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setRandom(Index rows, Index cols)
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
EIGEN_STRONG_INLINE Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>&
Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::setRandom(int rows, int cols)
{
resize(rows, cols);
return setRandom();

View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -25,8 +25,9 @@
#ifndef EIGEN_SELECT_H
#define EIGEN_SELECT_H
/** \class Select
* \ingroup Core_Module
/** \array_module \ingroup Array
*
* \class Select
*
* \brief Expression of a coefficient wise version of the C++ ternary operator ?:
*
@@ -35,19 +36,15 @@
* \param ElseMatrixType the type of the \em else expression
*
* This class represents an expression of a coefficient wise version of the C++ ternary operator ?:.
* It is the return type of DenseBase::select() and most of the time this is the only way it is used.
* It is the return type of MatrixBase::select() and most of the time this is the only way it is used.
*
* \sa DenseBase::select(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&) const
* \sa MatrixBase::select(const MatrixBase<ThenDerived>&, const MatrixBase<ElseDerived>&) const
*/
namespace internal {
template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType>
struct traits<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
: traits<ThenMatrixType>
struct ei_traits<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
{
typedef typename traits<ThenMatrixType>::Scalar Scalar;
typedef Dense StorageKind;
typedef typename traits<ThenMatrixType>::XprKind XprKind;
typedef typename ei_traits<ThenMatrixType>::Scalar Scalar;
typedef typename ConditionMatrixType::Nested ConditionMatrixNested;
typedef typename ThenMatrixType::Nested ThenMatrixNested;
typedef typename ElseMatrixType::Nested ElseMatrixNested;
@@ -57,43 +54,41 @@ struct traits<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
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)
CoeffReadCost = ei_traits<typename ei_cleantype<ConditionMatrixNested>::type>::CoeffReadCost
+ EIGEN_ENUM_MAX(ei_traits<typename ei_cleantype<ThenMatrixNested>::type>::CoeffReadCost,
ei_traits<typename ei_cleantype<ElseMatrixNested>::type>::CoeffReadCost)
};
};
}
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 : ei_no_assignment_operator,
public MatrixBase<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
{
public:
typedef typename internal::dense_xpr_base<Select>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Select)
EIGEN_GENERIC_PUBLIC_INTERFACE(Select)
Select(const ConditionMatrixType& conditionMatrix,
const ThenMatrixType& thenMatrix,
const ElseMatrixType& elseMatrix)
: m_condition(conditionMatrix), m_then(thenMatrix), m_else(elseMatrix)
{
eigen_assert(m_condition.rows() == m_then.rows() && m_condition.rows() == m_else.rows());
eigen_assert(m_condition.cols() == m_then.cols() && m_condition.cols() == m_else.cols());
ei_assert(m_condition.rows() == m_then.rows() && m_condition.rows() == m_else.rows());
ei_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(); }
int rows() const { return m_condition.rows(); }
int cols() const { return m_condition.cols(); }
const Scalar coeff(Index i, Index j) const
const Scalar coeff(int i, int j) const
{
if (m_condition.coeff(i,j))
return m_then.coeff(i,j);
else
return m_else.coeff(i,j);
}
const Scalar coeff(Index i) const
const Scalar coeff(int i) const
{
if (m_condition.coeff(i))
return m_then.coeff(i);
@@ -108,7 +103,9 @@ class Select : internal::no_assignment_operator,
};
/** \returns a matrix where each coefficient (i,j) is equal to \a thenMatrix(i,j)
/** \array_module
*
* \returns a matrix where each coefficient (i,j) is equal to \a thenMatrix(i,j)
* if \c *this(i,j), and \a elseMatrix(i,j) otherwise.
*
* Example: \include MatrixBase_select.cpp
@@ -119,39 +116,43 @@ class Select : internal::no_assignment_operator,
template<typename Derived>
template<typename ThenDerived,typename ElseDerived>
inline const Select<Derived,ThenDerived,ElseDerived>
DenseBase<Derived>::select(const DenseBase<ThenDerived>& thenMatrix,
const DenseBase<ElseDerived>& elseMatrix) const
MatrixBase<Derived>::select(const MatrixBase<ThenDerived>& thenMatrix,
const MatrixBase<ElseDerived>& elseMatrix) const
{
return Select<Derived,ThenDerived,ElseDerived>(derived(), thenMatrix.derived(), elseMatrix.derived());
}
/** Version of DenseBase::select(const DenseBase&, const DenseBase&) with
/** \array_module
*
* Version of MatrixBase::select(const MatrixBase&, const MatrixBase&) with
* the \em else expression being a scalar value.
*
* \sa DenseBase::select(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&) const, class Select
* \sa MatrixBase::select(const MatrixBase<ThenDerived>&, const MatrixBase<ElseDerived>&) const, class Select
*/
template<typename Derived>
template<typename ThenDerived>
inline const Select<Derived,ThenDerived, typename ThenDerived::ConstantReturnType>
DenseBase<Derived>::select(const DenseBase<ThenDerived>& thenMatrix,
inline const Select<Derived,ThenDerived, NestByValue<typename ThenDerived::ConstantReturnType> >
MatrixBase<Derived>::select(const MatrixBase<ThenDerived>& thenMatrix,
typename ThenDerived::Scalar elseScalar) const
{
return Select<Derived,ThenDerived,typename ThenDerived::ConstantReturnType>(
return Select<Derived,ThenDerived,NestByValue<typename ThenDerived::ConstantReturnType> >(
derived(), thenMatrix.derived(), ThenDerived::Constant(rows(),cols(),elseScalar));
}
/** Version of DenseBase::select(const DenseBase&, const DenseBase&) with
/** \array_module
*
* Version of MatrixBase::select(const MatrixBase&, const MatrixBase&) with
* the \em then expression being a scalar value.
*
* \sa DenseBase::select(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&) const, class Select
* \sa MatrixBase::select(const MatrixBase<ThenDerived>&, const MatrixBase<ElseDerived>&) const, class Select
*/
template<typename Derived>
template<typename ElseDerived>
inline const Select<Derived, typename ElseDerived::ConstantReturnType, ElseDerived >
DenseBase<Derived>::select(typename ElseDerived::Scalar thenScalar,
const DenseBase<ElseDerived>& elseMatrix) const
inline const Select<Derived, NestByValue<typename ElseDerived::ConstantReturnType>, ElseDerived >
MatrixBase<Derived>::select(typename ElseDerived::Scalar thenScalar,
const MatrixBase<ElseDerived>& elseMatrix) const
{
return Select<Derived,typename ElseDerived::ConstantReturnType,ElseDerived>(
return Select<Derived,NestByValue<typename ElseDerived::ConstantReturnType>,ElseDerived>(
derived(), ElseDerived::Constant(rows(),cols(),thenScalar), elseMatrix.derived());
}

View File

@@ -1,7 +1,9 @@
file(GLOB Eigen_src_subdirectories "*")
escape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}")
foreach(f ${Eigen_src_subdirectories})
if(NOT f MATCHES "\\.txt" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+" )
add_subdirectory(${f})
endif()
endforeach()
ADD_SUBDIRECTORY(Core)
ADD_SUBDIRECTORY(LU)
ADD_SUBDIRECTORY(QR)
ADD_SUBDIRECTORY(SVD)
ADD_SUBDIRECTORY(Cholesky)
ADD_SUBDIRECTORY(Array)
ADD_SUBDIRECTORY(Geometry)
ADD_SUBDIRECTORY(LeastSquares)
ADD_SUBDIRECTORY(Sparse)

View File

@@ -2,5 +2,5 @@ FILE(GLOB Eigen_Cholesky_SRCS "*.h")
INSTALL(FILES
${Eigen_Cholesky_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Cholesky COMPONENT Devel
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Cholesky
)

View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -22,10 +22,14 @@
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_EIGENVALUES_COMMON_H
#define EIGEN_EIGENVALUES_COMMON_H
#ifndef EIGEN_EXTERN_INSTANTIATIONS
#define EIGEN_EXTERN_INSTANTIATIONS
#endif
#include "../../Core"
#undef EIGEN_EXTERN_INSTANTIATIONS
#include "../../Cholesky"
#endif // EIGEN_EIGENVALUES_COMMON_H
namespace Eigen {
EIGEN_CHOLESKY_MODULE_INSTANTIATE();
}

View File

@@ -1,9 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009 Keir Mierle <mierle@gmail.com>
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -27,458 +25,174 @@
#ifndef EIGEN_LDLT_H
#define EIGEN_LDLT_H
namespace internal {
template<typename MatrixType, int UpLo> struct LDLT_Traits;
}
/** \ingroup cholesky_Module
*
* \class LDLT
*
* \brief Robust Cholesky decomposition of a matrix with pivoting
* \brief Robust Cholesky decomposition of a matrix and associated features
*
* \param MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition
* \param MatrixType the type of the matrix of which we are computing the LDL^T Cholesky decomposition
*
* Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite
* matrix \f$ A \f$ such that \f$ A = P^TLDL^*P \f$, where P is a permutation matrix, L
* is lower triangular with a unit diagonal and D is a diagonal matrix.
* This class performs a Cholesky decomposition without square root of a symmetric, positive definite
* matrix A such that A = L D L^* = U^* D U, where L is lower triangular with a unit diagonal
* and D is a diagonal matrix.
*
* The decomposition uses pivoting to ensure stability, so that L will have
* zeros in the bottom right rank(A) - n submatrix. Avoiding the square root
* on D also stabilizes the computation.
* Compared to a standard Cholesky decomposition, avoiding the square roots allows for faster and more
* stable computation.
*
* Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky
* decomposition to determine whether a system of equations has a solution.
* Note that during the decomposition, only the upper triangular part of A is considered. Therefore,
* the strict lower part does not have to store correct values.
*
* \sa MatrixBase::ldlt(), class LLT
*/
/* THIS PART OF THE DOX IS CURRENTLY DISABLED BECAUSE INACCURATE BECAUSE OF BUG IN THE DECOMPOSITION CODE
* Note that during the decomposition, only the upper triangular part of A is considered. Therefore,
* the strict lower part does not have to store correct values.
*/
template<typename _MatrixType, int _UpLo> class LDLT
template<typename MatrixType> class LDLT
{
public:
typedef _MatrixType MatrixType;
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
Options = MatrixType::Options & ~RowMajorBit, // these are the options for the TmpMatrixType, we need a ColMajor matrix here!
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
UpLo = _UpLo
};
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef typename MatrixType::Index Index;
typedef Matrix<Scalar, RowsAtCompileTime, 1, Options, MaxRowsAtCompileTime, 1> TmpMatrixType;
typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType;
typedef internal::LDLT_Traits<MatrixType,UpLo> Traits;
/** \brief Default Constructor.
*
* The default constructor is useful in cases in which the user intends to
* perform decompositions via LDLT::compute(const MatrixType&).
*/
LDLT() : m_matrix(), m_transpositions(), m_isInitialized(false) {}
/** \brief Default Constructor with memory preallocation
*
* Like the default constructor but with preallocation of the internal data
* according to the specified problem \a size.
* \sa LDLT()
*/
LDLT(Index size)
: m_matrix(size, size),
m_transpositions(size),
m_temporary(size),
m_isInitialized(false)
{}
typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> VectorType;
LDLT(const MatrixType& matrix)
: m_matrix(matrix.rows(), matrix.cols()),
m_transpositions(matrix.rows()),
m_temporary(matrix.rows()),
m_isInitialized(false)
: m_matrix(matrix.rows(), matrix.cols())
{
compute(matrix);
}
/** \returns a view of the upper triangular matrix U */
inline typename Traits::MatrixU matrixU() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return Traits::getU(m_matrix);
}
/** \returns a view of the lower triangular matrix L */
inline typename Traits::MatrixL matrixL() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return Traits::getL(m_matrix);
}
/** \returns the permutation matrix P as a transposition sequence.
*/
inline const TranspositionType& transpositionsP() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_transpositions;
}
/** \returns the lower triangular matrix L */
inline Part<MatrixType, UnitLowerTriangular> matrixL(void) const { return m_matrix; }
/** \returns the coefficients of the diagonal matrix D */
inline Diagonal<const MatrixType> vectorD(void) const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_matrix.diagonal();
}
inline DiagonalCoeffs<MatrixType> vectorD(void) const { return m_matrix.diagonal(); }
/** \returns true if the matrix is positive (semidefinite) */
inline bool isPositive(void) const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_sign == 1;
}
#ifdef EIGEN2_SUPPORT
inline bool isPositiveDefinite() const
{
return isPositive();
}
#endif
/** \returns true if the matrix is positive definite */
inline bool isPositiveDefinite(void) const { return m_isPositiveDefinite; }
/** \returns true if the matrix is negative (semidefinite) */
inline bool isNegative(void) const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_sign == -1;
}
/** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
*
* This function also supports in-place solves using the syntax <tt>x = decompositionObject.solve(x)</tt> .
*
* \note_about_checking_solutions
*
* More precisely, this method solves \f$ A x = b \f$ using the decomposition \f$ A = P^T L D L^* P \f$
* by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$,
* \f$ L^* y_4 = y_3 \f$ and \f$ P x = y_4 \f$ in succession. If the matrix \f$ A \f$ is singular, then
* \f$ D \f$ will also be singular (all the other matrices are invertible). In that case, the
* least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function
* computes the least-square solution of \f$ A x = b \f$ is \f$ A \f$ is singular.
*
* \sa MatrixBase::ldlt()
*/
template<typename Rhs>
inline const internal::solve_retval<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());
}
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived, typename ResultType>
bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const
{
*result = this->solve(b);
return true;
}
#endif
template<typename RhsDerived, typename ResultType>
bool solve(const MatrixBase<RhsDerived> &b, ResultType *result) const;
template<typename Derived>
bool solveInPlace(MatrixBase<Derived> &bAndX) const;
LDLT& compute(const MatrixType& matrix);
/** \returns the internal LDLT decomposition matrix
*
* TODO: document the storage layout
*/
inline const MatrixType& matrixLDLT() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_matrix;
}
MatrixType reconstructedMatrix() const;
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
void compute(const MatrixType& matrix);
protected:
/** \internal
* Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U.
* Used to compute and store the cholesky decomposition A = L D L^* = U^* D U.
* The strict upper part is used during the decomposition, the strict lower
* part correspond to the coefficients of L (its diagonal is equal to 1 and
* is not stored), and the diagonal entries correspond to D.
*/
MatrixType m_matrix;
TranspositionType m_transpositions;
TmpMatrixType m_temporary;
int m_sign;
bool m_isInitialized;
bool m_isPositiveDefinite;
};
namespace internal {
template<int UpLo> struct ldlt_inplace;
template<> struct ldlt_inplace<Lower>
{
template<typename MatrixType, typename TranspositionType, typename Workspace>
static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, int* sign=0)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
eigen_assert(mat.rows()==mat.cols());
const Index size = mat.rows();
if (size <= 1)
{
transpositions.setIdentity();
if(sign)
*sign = real(mat.coeff(0,0))>0 ? 1:-1;
return true;
}
RealScalar cutoff = 0, biggest_in_corner;
for (Index k = 0; k < size; ++k)
{
// Find largest diagonal element
Index index_of_biggest_in_corner;
biggest_in_corner = mat.diagonal().tail(size-k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
index_of_biggest_in_corner += k;
if(k == 0)
{
// The biggest overall is the point of reference to which further diagonals
// are compared; if any diagonal is negligible compared
// to the largest overall, the algorithm bails.
cutoff = abs(NumTraits<Scalar>::epsilon() * biggest_in_corner);
if(sign)
*sign = real(mat.diagonal().coeff(index_of_biggest_in_corner)) > 0 ? 1 : -1;
}
// Finish early if the matrix is not full rank.
if(biggest_in_corner < cutoff)
{
for(Index i = k; i < size; i++) transpositions.coeffRef(i) = i;
break;
}
transpositions.coeffRef(k) = index_of_biggest_in_corner;
if(k != index_of_biggest_in_corner)
{
// apply the transposition while taking care to consider only
// the lower triangular part
Index s = size-index_of_biggest_in_corner-1; // trailing size after the biggest element
mat.row(k).head(k).swap(mat.row(index_of_biggest_in_corner).head(k));
mat.col(k).tail(s).swap(mat.col(index_of_biggest_in_corner).tail(s));
std::swap(mat.coeffRef(k,k),mat.coeffRef(index_of_biggest_in_corner,index_of_biggest_in_corner));
for(int i=k+1;i<index_of_biggest_in_corner;++i)
{
Scalar tmp = mat.coeffRef(i,k);
mat.coeffRef(i,k) = conj(mat.coeffRef(index_of_biggest_in_corner,i));
mat.coeffRef(index_of_biggest_in_corner,i) = conj(tmp);
}
if(NumTraits<Scalar>::IsComplex)
mat.coeffRef(index_of_biggest_in_corner,k) = conj(mat.coeff(index_of_biggest_in_corner,k));
}
// partition the matrix:
// A00 | - | -
// lu = A10 | A11 | -
// A20 | A21 | A22
Index rs = size - k - 1;
Block<MatrixType,Dynamic,1> A21(mat,k+1,k,rs,1);
Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
if(k>0)
{
temp.head(k) = mat.diagonal().head(k).asDiagonal() * A10.adjoint();
mat.coeffRef(k,k) -= (A10 * temp.head(k)).value();
if(rs>0)
A21.noalias() -= A20 * temp.head(k);
}
if((rs>0) && (abs(mat.coeffRef(k,k)) > cutoff))
A21 /= mat.coeffRef(k,k);
}
return true;
}
};
template<> struct ldlt_inplace<Upper>
{
template<typename MatrixType, typename TranspositionType, typename Workspace>
static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, int* sign=0)
{
Transpose<MatrixType> matt(mat);
return ldlt_inplace<Lower>::unblocked(matt, transpositions, temp, sign);
}
};
template<typename MatrixType> struct LDLT_Traits<MatrixType,Lower>
{
typedef TriangularView<MatrixType, UnitLower> MatrixL;
typedef TriangularView<typename MatrixType::AdjointReturnType, UnitUpper> MatrixU;
inline static MatrixL getL(const MatrixType& m) { return m; }
inline static MatrixU getU(const MatrixType& m) { return m.adjoint(); }
};
template<typename MatrixType> struct LDLT_Traits<MatrixType,Upper>
{
typedef TriangularView<typename MatrixType::AdjointReturnType, UnitLower> MatrixL;
typedef TriangularView<MatrixType, UnitUpper> MatrixU;
inline static MatrixL getL(const MatrixType& m) { return m.adjoint(); }
inline static MatrixU getU(const MatrixType& m) { return m; }
};
} // end namespace internal
/** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
/** Compute / recompute the LLT decomposition A = L D L^* = U^* D U of \a matrix
*/
template<typename MatrixType, int _UpLo>
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const MatrixType& a)
template<typename MatrixType>
void LDLT<MatrixType>::compute(const MatrixType& a)
{
eigen_assert(a.rows()==a.cols());
const Index size = a.rows();
assert(a.rows()==a.cols());
const int size = a.rows();
m_matrix.resize(size, size);
m_isPositiveDefinite = true;
const RealScalar eps = ei_sqrt(precision<Scalar>());
m_matrix = a;
m_transpositions.resize(size);
m_isInitialized = false;
m_temporary.resize(size);
internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, &m_sign);
m_isInitialized = true;
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>
{
typedef LDLT<_MatrixType,_UpLo> LDLTType;
EIGEN_MAKE_SOLVE_HELPERS(LDLTType,Rhs)
template<typename Dest> void evalTo(Dest& dst) const
if (size<=1)
{
eigen_assert(rhs().rows() == dec().matrixLDLT().rows());
// dst = P b
dst = dec().transpositionsP() * rhs();
m_matrix = a;
return;
}
// dst = L^-1 (P b)
dec().matrixL().solveInPlace(dst);
// Let's preallocate a temporay vector to evaluate the matrix-vector product into it.
// Unlike the standard LLT decomposition, here we cannot evaluate it to the destination
// matrix because it a sub-row which is not compatible suitable for efficient packet evaluation.
// (at least if we assume the matrix is col-major)
Matrix<Scalar,MatrixType::RowsAtCompileTime,1> _temporary(size);
// dst = D^-1 (L^-1 P b)
// more precisely, use pseudo-inverse of D (see bug 241)
using std::abs;
using std::max;
typedef typename LDLTType::MatrixType MatrixType;
typedef typename LDLTType::Scalar Scalar;
typedef typename LDLTType::RealScalar RealScalar;
const Diagonal<const MatrixType> vectorD = dec().vectorD();
RealScalar tolerance = (max)(vectorD.array().abs().maxCoeff() * NumTraits<Scalar>::epsilon(),
RealScalar(1) / NumTraits<RealScalar>::highest()); // motivated by LAPACK's xGELSS
for (Index i = 0; i < vectorD.size(); ++i) {
if(abs(vectorD(i)) > tolerance)
dst.row(i) /= vectorD(i);
else
dst.row(i).setZero();
// Note that, in this algorithm the rows of the strict upper part of m_matrix is used to store
// column vector, thus the strange .conjugate() and .transpose()...
m_matrix.row(0) = a.row(0).conjugate();
m_matrix.col(0).end(size-1) = m_matrix.row(0).end(size-1) / m_matrix.coeff(0,0);
for (int j = 1; j < size; ++j)
{
RealScalar tmp = ei_real(a.coeff(j,j) - (m_matrix.row(j).start(j) * m_matrix.col(j).start(j).conjugate()).coeff(0,0));
m_matrix.coeffRef(j,j) = tmp;
if (tmp < eps)
{
m_isPositiveDefinite = false;
return;
}
// dst = L^-T (D^-1 L^-1 P b)
dec().matrixU().solveInPlace(dst);
int endSize = size-j-1;
if (endSize>0)
{
_temporary.end(endSize) = ( m_matrix.block(j+1,0, endSize, j)
* m_matrix.col(j).start(j).conjugate() ).lazy();
// dst = P^-1 (L^-T D^-1 L^-1 P b) = A^-1 b
dst = dec().transpositionsP().transpose() * dst;
m_matrix.row(j).end(endSize) = a.row(j).end(endSize).conjugate()
- _temporary.end(endSize).transpose();
m_matrix.col(j).end(endSize) = m_matrix.row(j).end(endSize) / tmp;
}
}
};
}
/** \internal use x = ldlt_object.solve(x);
/** Computes the solution x of \f$ A x = b \f$ using the current decomposition of A.
* The result is stored in \a result
*
* This is the \em in-place version of solve().
* \returns true in case of success, false otherwise.
*
* In other words, it computes \f$ b = A^{-1} b \f$ with
* \f$ {L^{*}}^{-1} D^{-1} L^{-1} b \f$ from right to left.
*
* \sa LDLT::solveInPlace(), MatrixBase::ldlt()
*/
template<typename MatrixType>
template<typename RhsDerived, typename ResultType>
bool LDLT<MatrixType>
::solve(const MatrixBase<RhsDerived> &b, ResultType *result) const
{
const int size = m_matrix.rows();
ei_assert(size==b.rows() && "LLT::solve(): invalid number of rows of the right hand side matrix b");
*result = b;
return solveInPlace(*result);
}
/** This is the \em in-place version of solve().
*
* \param bAndX represents both the right-hand side matrix b and result x.
*
* \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD.
*
* This version avoids a copy when the right hand side matrix b is not
* needed anymore.
*
* \sa LDLT::solve(), MatrixBase::ldlt()
*/
template<typename MatrixType,int _UpLo>
template<typename MatrixType>
template<typename Derived>
bool LDLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
bool LDLT<MatrixType>::solveInPlace(MatrixBase<Derived> &bAndX) const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
const Index size = m_matrix.rows();
eigen_assert(size == bAndX.rows());
bAndX = this->solve(bAndX);
const int size = m_matrix.rows();
ei_assert(size==bAndX.rows());
if (!m_isPositiveDefinite)
return false;
matrixL().solveTriangularInPlace(bAndX);
bAndX = (m_matrix.cwise().inverse().template part<Diagonal>() * bAndX).lazy();
m_matrix.adjoint().template part<UnitUpperTriangular>().solveTriangularInPlace(bAndX);
return true;
}
/** \returns the matrix represented by the decomposition,
* i.e., it returns the product: P^T L D L^* P.
* This function is provided for debug purpose. */
template<typename MatrixType, int _UpLo>
MatrixType LDLT<MatrixType,_UpLo>::reconstructedMatrix() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
const Index size = m_matrix.rows();
MatrixType res(size,size);
// P
res.setIdentity();
res = transpositionsP() * res;
// L^* P
res = matrixU() * res;
// D(L^*P)
res = vectorD().asDiagonal() * res;
// L(DL^*P)
res = matrixL() * res;
// P^T (LDL^*P)
res = transpositionsP().transpose() * res;
return res;
}
/** \cholesky_module
* \returns the Cholesky decomposition with full pivoting without square root of \c *this
*/
template<typename MatrixType, unsigned int UpLo>
inline const LDLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
SelfAdjointView<MatrixType, UpLo>::ldlt() const
{
return LDLT<PlainObject,UpLo>(m_matrix);
}
/** \cholesky_module
* \returns the Cholesky decomposition with full pivoting without square root of \c *this
* \returns the Cholesky decomposition without square root of \c *this
*/
template<typename Derived>
inline const LDLT<typename MatrixBase<Derived>::PlainObject>
inline const LDLT<typename MatrixBase<Derived>::PlainMatrixType>
MatrixBase<Derived>::ldlt() const
{
return LDLT<PlainObject>(derived());
return derived();
}
#endif // EIGEN_LDLT_H

View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -25,10 +25,6 @@
#ifndef EIGEN_LLT_H
#define EIGEN_LLT_H
namespace internal{
template<typename MatrixType, int UpLo> struct LLT_Traits;
}
/** \ingroup cholesky_Module
*
* \class LLT
@@ -55,45 +51,28 @@ template<typename MatrixType, int UpLo> struct LLT_Traits;
* Note that during the decomposition, only the upper triangular part of A is considered. Therefore,
* the strict lower part does not have to store correct values.
*/
template<typename _MatrixType, int _UpLo> class LLT
template<typename MatrixType> class LLT
{
public:
typedef _MatrixType MatrixType;
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
Options = MatrixType::Options,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
};
private:
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef typename MatrixType::Index Index;
typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> VectorType;
enum {
PacketSize = internal::packet_traits<Scalar>::size,
AlignmentMask = int(PacketSize)-1,
UpLo = _UpLo
PacketSize = ei_packet_traits<Scalar>::size,
AlignmentMask = int(PacketSize)-1
};
typedef internal::LLT_Traits<MatrixType,UpLo> Traits;
public:
/**
* \brief Default Constructor.
*
* The default constructor is useful in cases in which the user intends to
* perform decompositions via LLT::compute(const MatrixType&).
*/
/**
* \brief Default Constructor.
*
* The default constructor is useful in cases in which the user intends to
* perform decompositions via LLT::compute(const MatrixType&).
*/
LLT() : m_matrix(), m_isInitialized(false) {}
/** \brief Default Constructor with memory preallocation
*
* Like the default constructor but with preallocation of the internal data
* according to the specified problem \a size.
* \sa LLT()
*/
LLT(Index size) : m_matrix(size, size),
m_isInitialized(false) {}
LLT(const MatrixType& matrix)
: m_matrix(matrix.rows(), matrix.cols()),
m_isInitialized(false)
@@ -101,82 +80,23 @@ template<typename _MatrixType, int _UpLo> class LLT
compute(matrix);
}
/** \returns a view of the upper triangular matrix U */
inline typename Traits::MatrixU matrixU() const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
return Traits::getU(m_matrix);
}
/** \returns a view of the lower triangular matrix L */
inline typename Traits::MatrixL matrixL() const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
return Traits::getL(m_matrix);
}
/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
*
* Since this LLT class assumes anyway that the matrix A is invertible, the solution
* theoretically exists and is unique regardless of b.
*
* Example: \include LLT_solve.cpp
* Output: \verbinclude LLT_solve.out
*
* \sa solveInPlace(), MatrixBase::llt()
*/
template<typename Rhs>
inline const internal::solve_retval<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());
}
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived, typename ResultType>
bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const
{
*result = this->solve(b);
return true;
/** \returns the lower triangular matrix L */
inline Part<MatrixType, LowerTriangular> matrixL(void) const
{
ei_assert(m_isInitialized && "LLT is not initialized.");
return m_matrix;
}
bool isPositiveDefinite() const { return true; }
#endif
/** \deprecated */
inline bool isPositiveDefinite(void) const { return m_isInitialized && m_isPositiveDefinite; }
template<typename RhsDerived, typename ResultType>
bool solve(const MatrixBase<RhsDerived> &b, ResultType *result) const;
template<typename Derived>
void solveInPlace(MatrixBase<Derived> &bAndX) const;
bool solveInPlace(MatrixBase<Derived> &bAndX) const;
LLT& compute(const MatrixType& matrix);
/** \returns the LLT decomposition matrix
*
* TODO: document the storage layout
*/
inline const MatrixType& matrixLLT() const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
return m_matrix;
}
MatrixType reconstructedMatrix() const;
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \c NumericalIssue if the matrix.appears to be negative.
*/
ComputationInfo info() const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
return m_info;
}
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
void compute(const MatrixType& matrix);
protected:
/** \internal
@@ -185,154 +105,85 @@ template<typename _MatrixType, int _UpLo> class LLT
*/
MatrixType m_matrix;
bool m_isInitialized;
ComputationInfo m_info;
bool m_isPositiveDefinite;
};
namespace internal {
template<int UpLo> struct llt_inplace;
template<> struct llt_inplace<Lower>
{
template<typename MatrixType>
static typename MatrixType::Index unblocked(MatrixType& mat)
{
typedef typename MatrixType::Index Index;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
eigen_assert(mat.rows()==mat.cols());
const Index size = mat.rows();
for(Index k = 0; k < size; ++k)
{
Index rs = size-k-1; // remaining size
Block<MatrixType,Dynamic,1> A21(mat,k+1,k,rs,1);
Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
RealScalar x = real(mat.coeff(k,k));
if (k>0) x -= A10.squaredNorm();
if (x<=RealScalar(0))
return k;
mat.coeffRef(k,k) = x = sqrt(x);
if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();
if (rs>0) A21 *= RealScalar(1)/x;
}
return -1;
}
template<typename MatrixType>
static typename MatrixType::Index blocked(MatrixType& m)
{
typedef typename MatrixType::Index Index;
eigen_assert(m.rows()==m.cols());
Index size = m.rows();
if(size<32)
return unblocked(m);
Index blockSize = size/8;
blockSize = (blockSize/16)*16;
blockSize = (std::min)((std::max)(blockSize,Index(8)), Index(128));
for (Index k=0; k<size; k+=blockSize)
{
// partition the matrix:
// A00 | - | -
// lu = A10 | A11 | -
// A20 | A21 | A22
Index bs = (std::min)(blockSize, size-k);
Index rs = size - k - bs;
Block<MatrixType,Dynamic,Dynamic> A11(m,k, k, bs,bs);
Block<MatrixType,Dynamic,Dynamic> A21(m,k+bs,k, rs,bs);
Block<MatrixType,Dynamic,Dynamic> A22(m,k+bs,k+bs,rs,rs);
Index ret;
if((ret=unblocked(A11))>=0) return k+ret;
if(rs>0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);
if(rs>0) A22.template selfadjointView<Lower>().rankUpdate(A21,-1); // bottleneck
}
return -1;
}
};
template<> struct llt_inplace<Upper>
{
template<typename MatrixType>
static EIGEN_STRONG_INLINE typename MatrixType::Index unblocked(MatrixType& mat)
{
Transpose<MatrixType> matt(mat);
return llt_inplace<Lower>::unblocked(matt);
}
template<typename MatrixType>
static EIGEN_STRONG_INLINE typename MatrixType::Index blocked(MatrixType& mat)
{
Transpose<MatrixType> matt(mat);
return llt_inplace<Lower>::blocked(matt);
}
};
template<typename MatrixType> struct LLT_Traits<MatrixType,Lower>
{
typedef TriangularView<MatrixType, Lower> MatrixL;
typedef TriangularView<typename MatrixType::AdjointReturnType, Upper> MatrixU;
inline static MatrixL getL(const MatrixType& m) { return m; }
inline static MatrixU getU(const MatrixType& m) { return m.adjoint(); }
static bool inplace_decomposition(MatrixType& m)
{ return llt_inplace<Lower>::blocked(m)==-1; }
};
template<typename MatrixType> struct LLT_Traits<MatrixType,Upper>
{
typedef TriangularView<typename MatrixType::AdjointReturnType, Lower> MatrixL;
typedef TriangularView<MatrixType, Upper> MatrixU;
inline static MatrixL getL(const MatrixType& m) { return m.adjoint(); }
inline static MatrixU getU(const MatrixType& m) { return m; }
static bool inplace_decomposition(MatrixType& m)
{ return llt_inplace<Upper>::blocked(m)==-1; }
};
} // end namespace internal
/** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix
*
*
* \returns a reference to *this
*/
template<typename MatrixType, int _UpLo>
LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const MatrixType& a)
template<typename MatrixType>
void LLT<MatrixType>::compute(const MatrixType& a)
{
assert(a.rows()==a.cols());
const Index size = a.rows();
m_isPositiveDefinite = true;
const int size = a.rows();
m_matrix.resize(size, size);
m_matrix = a;
// The biggest overall is the point of reference to which further diagonals
// are compared; if any diagonal is negligible compared
// to the largest overall, the algorithm bails. This cutoff is suggested
// in "Analysis of the Cholesky Decomposition of a Semi-definite Matrix" by
// Nicholas J. Higham. Also see "Accuracy and Stability of Numerical
// Algorithms" page 217, also by Higham.
const RealScalar cutoff = machine_epsilon<Scalar>() * size * a.diagonal().cwise().abs().maxCoeff();
RealScalar x;
x = ei_real(a.coeff(0,0));
m_matrix.coeffRef(0,0) = ei_sqrt(x);
if(size==1)
{
m_isInitialized = true;
return;
}
m_matrix.col(0).end(size-1) = a.row(0).end(size-1).adjoint() / ei_real(m_matrix.coeff(0,0));
for (int j = 1; j < size; ++j)
{
x = ei_real(a.coeff(j,j)) - m_matrix.row(j).start(j).squaredNorm();
if (x < cutoff)
{
m_isPositiveDefinite = false;
continue;
}
m_matrix.coeffRef(j,j) = x = ei_sqrt(x);
int endSize = size-j-1;
if (endSize>0) {
// Note that when all matrix columns have good alignment, then the following
// product is guaranteed to be optimal with respect to alignment.
m_matrix.col(j).end(endSize) =
(m_matrix.block(j+1, 0, endSize, j) * m_matrix.row(j).start(j).adjoint()).lazy();
// FIXME could use a.col instead of a.row
m_matrix.col(j).end(endSize) = (a.row(j).end(endSize).adjoint()
- m_matrix.col(j).end(endSize) ) / x;
}
}
m_isInitialized = true;
bool ok = Traits::inplace_decomposition(m_matrix);
m_info = ok ? Success : NumericalIssue;
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>
/** Computes the solution x of \f$ A x = b \f$ using the current decomposition of A.
* The result is stored in \a result
*
* \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD.
*
* In other words, it computes \f$ b = A^{-1} b \f$ with
* \f$ {L^{*}}^{-1} L^{-1} b \f$ from right to left.
*
* Example: \include LLT_solve.cpp
* Output: \verbinclude LLT_solve.out
*
* \sa LLT::solveInPlace(), MatrixBase::llt()
*/
template<typename MatrixType>
template<typename RhsDerived, typename ResultType>
bool LLT<MatrixType>::solve(const MatrixBase<RhsDerived> &b, ResultType *result) 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);
}
};
ei_assert(m_isInitialized && "LLT is not initialized.");
const int size = m_matrix.rows();
ei_assert(size==b.rows() && "LLT::solve(): invalid number of rows of the right hand side matrix b");
return solveInPlace((*result) = b);
}
/** \internal use x = llt_object.solve(x);
*
* This is the \em in-place version of solve().
/** This is the \em in-place version of solve().
*
* \param bAndX represents both the right-hand side matrix b and result x.
*
@@ -343,44 +194,26 @@ struct solve_retval<LLT<_MatrixType, UpLo>, Rhs>
*
* \sa LLT::solve(), MatrixBase::llt()
*/
template<typename MatrixType, int _UpLo>
template<typename MatrixType>
template<typename Derived>
void LLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
bool LLT<MatrixType>::solveInPlace(MatrixBase<Derived> &bAndX) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(m_matrix.rows()==bAndX.rows());
matrixL().solveInPlace(bAndX);
matrixU().solveInPlace(bAndX);
}
/** \returns the matrix represented by the decomposition,
* i.e., it returns the product: L L^*.
* This function is provided for debug purpose. */
template<typename MatrixType, int _UpLo>
MatrixType LLT<MatrixType,_UpLo>::reconstructedMatrix() const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
return matrixL() * matrixL().adjoint().toDenseMatrix();
ei_assert(m_isInitialized && "LLT is not initialized.");
const int size = m_matrix.rows();
ei_assert(size==bAndX.rows());
matrixL().solveTriangularInPlace(bAndX);
m_matrix.adjoint().template part<UpperTriangular>().solveTriangularInPlace(bAndX);
return true;
}
/** \cholesky_module
* \returns the LLT decomposition of \c *this
*/
template<typename Derived>
inline const LLT<typename MatrixBase<Derived>::PlainObject>
inline const LLT<typename MatrixBase<Derived>::PlainMatrixType>
MatrixBase<Derived>::llt() const
{
return LLT<PlainObject>(derived());
}
/** \cholesky_module
* \returns the LLT decomposition of \c *this
*/
template<typename MatrixType, unsigned int UpLo>
inline const LLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
SelfAdjointView<MatrixType, UpLo>::llt() const
{
return LLT<PlainObject,UpLo>(m_matrix);
return LLT<PlainMatrixType>(derived());
}
#endif // EIGEN_LLT_H

View File

@@ -1,320 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_ARRAY_H
#define EIGEN_ARRAY_H
/** \class Array
* \ingroup Core_Module
*
* \brief General-purpose arrays with easy API for coefficient-wise operations
*
* The %Array class is very similar to the Matrix class. It provides
* general-purpose one- and two-dimensional arrays. The difference between the
* %Array and the %Matrix class is primarily in the API: the API for the
* %Array class provides easy access to coefficient-wise operations, while the
* API for the %Matrix class provides easy access to linear-algebra
* operations.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
*
* \sa \ref TutorialArrayClass, \ref TopicClassHierarchy
*/
namespace internal {
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct traits<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > : traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
{
typedef ArrayXpr XprKind;
typedef ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > XprBase;
};
}
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
class Array
: public PlainObjectBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
{
public:
typedef PlainObjectBase<Array> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Array)
enum { Options = _Options };
typedef typename Base::PlainObject PlainObject;
protected:
template <typename Derived, typename OtherDerived, bool IsVector>
friend struct internal::conservative_resize_like_impl;
using Base::m_storage;
public:
using Base::base;
using Base::coeff;
using Base::coeffRef;
/**
* 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=.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE Array& operator=(const EigenBase<OtherDerived> &other)
{
return Base::operator=(other);
}
/** 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),
* it will be initialized.
*
* Note that copying a row-vector into a vector (and conversely) is allowed.
* The resizing, if any, is then done in the appropriate way so that row-vectors
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE Array& operator=(const ArrayBase<OtherDerived>& other)
{
return Base::_set(other);
}
/** This is a special case of the templated operator=. Its purpose is to
* prevent a default operator= from hiding the templated operator=.
*/
EIGEN_STRONG_INLINE Array& operator=(const Array& other)
{
return Base::_set(other);
}
/** Default constructor.
*
* For fixed-size matrices, does nothing.
*
* For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix
* is called a null matrix. This constructor is the unique way to create null matrices: resizing
* a matrix to 0 is not supported.
*
* \sa resize(Index,Index)
*/
EIGEN_STRONG_INLINE explicit Array() : Base()
{
Base::_check_template_params();
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
// FIXME is it still needed ??
/** \internal */
Array(internal::constructor_without_unaligned_array_assert)
: Base(internal::constructor_without_unaligned_array_assert())
{
Base::_check_template_params();
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
}
#endif
/** Constructs a vector or row-vector with given dimension. \only_for_vectors
*
* Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
* it is redundant to pass the dimension here, so it makes more sense to use the default
* constructor Matrix() instead.
*/
EIGEN_STRONG_INLINE explicit Array(Index dim)
: Base(dim, RowsAtCompileTime == 1 ? 1 : dim, ColsAtCompileTime == 1 ? 1 : dim)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Array)
eigen_assert(dim >= 0);
eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim);
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename T0, typename T1>
EIGEN_STRONG_INLINE Array(const T0& x, const T1& y)
{
Base::_check_template_params();
this->template _init2<T0,T1>(x, y);
}
#else
/** constructs an uninitialized matrix with \a rows rows and \a cols columns.
*
* This is useful for dynamic-size matrices. For fixed-size matrices,
* it is redundant to pass these parameters, so one should use the default constructor
* Matrix() instead. */
Array(Index rows, Index cols);
/** constructs an initialized 2D vector with given coefficients */
Array(const Scalar& x, const Scalar& y);
#endif
/** constructs an initialized 3D vector with given coefficients */
EIGEN_STRONG_INLINE Array(const Scalar& x, const Scalar& y, const Scalar& z)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
m_storage.data()[2] = z;
}
/** constructs an initialized 4D vector with given coefficients */
EIGEN_STRONG_INLINE Array(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
m_storage.data()[2] = z;
m_storage.data()[3] = w;
}
explicit Array(const Scalar *data);
/** Constructor copying the value of the expression \a other */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Array(const ArrayBase<OtherDerived>& other)
: Base(other.rows() * other.cols(), other.rows(), other.cols())
{
Base::_check_template_params();
Base::_set_noalias(other);
}
/** Copy constructor */
EIGEN_STRONG_INLINE Array(const Array& other)
: Base(other.rows() * other.cols(), other.rows(), other.cols())
{
Base::_check_template_params();
Base::_set_noalias(other);
}
/** Copy constructor with in-place evaluation */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Array(const ReturnByValue<OtherDerived>& other)
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
other.evalTo(*this);
}
/** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Array(const EigenBase<OtherDerived> &other)
: Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
*this = other;
}
/** Override MatrixBase::swap() since for dynamic-sized matrices of same type it is enough to swap the
* data pointers.
*/
template<typename OtherDerived>
void swap(ArrayBase<OtherDerived> const & other)
{ this->_swap(other.derived()); }
inline Index innerStride() const { return 1; }
inline Index outerStride() const { return this->innerSize(); }
#ifdef EIGEN_ARRAY_PLUGIN
#include EIGEN_ARRAY_PLUGIN
#endif
private:
template<typename MatrixType, typename OtherDerived, bool SwapPointers>
friend struct internal::matrix_swap_impl;
};
/** \defgroup arraytypedefs Global array typedefs
* \ingroup Core_Module
*
* Eigen defines several typedef shortcuts for most common 1D and 2D array types.
*
* The general patterns are the following:
*
* \c ArrayRowsColsType where \c Rows and \c Cols can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size,
* and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd
* for complex double.
*
* For example, \c Array33d is a fixed-size 3x3 array type of doubles, and \c ArrayXXf is a dynamic-size matrix of floats.
*
* There are also \c ArraySizeType which are self-explanatory. For example, \c Array4cf is
* a fixed-size 1D array of 4 complex floats.
*
* \sa class Array
*/
#define EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix) \
/** \ingroup arraytypedefs */ \
typedef Array<Type, Size, Size> Array##SizeSuffix##SizeSuffix##TypeSuffix; \
/** \ingroup arraytypedefs */ \
typedef Array<Type, Size, 1> Array##SizeSuffix##TypeSuffix;
#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, Size) \
/** \ingroup arraytypedefs */ \
typedef Array<Type, Size, Dynamic> Array##Size##X##TypeSuffix; \
/** \ingroup arraytypedefs */ \
typedef Array<Type, Dynamic, Size> Array##X##Size##TypeSuffix;
#define EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 2, 2) \
EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 3, 3) \
EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 4, 4) \
EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Dynamic, X) \
EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 2) \
EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 3) \
EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 4)
EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(int, i)
EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(float, f)
EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(double, d)
EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<float>, cf)
EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<double>, cd)
#undef EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES
#undef EIGEN_MAKE_ARRAY_TYPEDEFS
#undef EIGEN_MAKE_ARRAY_TYPEDEFS_LARGE
#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, SizeSuffix) \
using Eigen::Matrix##SizeSuffix##TypeSuffix; \
using Eigen::Vector##SizeSuffix##TypeSuffix; \
using Eigen::RowVector##SizeSuffix##TypeSuffix;
#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(TypeSuffix) \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 2) \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 3) \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 4) \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X) \
#define EIGEN_USING_ARRAY_TYPEDEFS \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(i) \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(f) \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(d) \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cf) \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cd)
#endif // EIGEN_ARRAY_H

View File

@@ -1,239 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_ARRAYBASE_H
#define EIGEN_ARRAYBASE_H
template<typename ExpressionType> class MatrixWrapper;
/** \class ArrayBase
* \ingroup Core_Module
*
* \brief Base class for all 1D and 2D array, and related expressions
*
* An array is similar to a dense vector or matrix. While matrices are mathematical
* objects with well defined linear algebra operators, an array is just a collection
* of scalar values arranged in a one or two dimensionnal fashion. As the main consequence,
* all operations applied to an array are performed coefficient wise. Furthermore,
* arrays support scalar math functions of the c++ standard library (e.g., std::sin(x)), and convenient
* constructors allowing to easily write generic code working for both scalar values
* and arrays.
*
* This class is the base that is inherited by all array expression types.
*
* \tparam Derived is the derived type, e.g., an array or an expression type.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
*
* \sa class MatrixBase, \ref TopicClassHierarchy
*/
template<typename Derived> class ArrayBase
: public DenseBase<Derived>
{
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** The base class for a given storage type. */
typedef ArrayBase StorageBaseType;
typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl;
using internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar,
typename NumTraits<typename internal::traits<Derived>::Scalar>::Real>::operator*;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef DenseBase<Derived> Base;
using Base::RowsAtCompileTime;
using Base::ColsAtCompileTime;
using Base::SizeAtCompileTime;
using Base::MaxRowsAtCompileTime;
using Base::MaxColsAtCompileTime;
using Base::MaxSizeAtCompileTime;
using Base::IsVectorAtCompileTime;
using Base::Flags;
using Base::CoeffReadCost;
using Base::derived;
using Base::const_cast_derived;
using Base::rows;
using Base::cols;
using Base::size;
using Base::coeff;
using Base::coeffRef;
using Base::lazyAssign;
using Base::operator=;
using Base::operator+=;
using Base::operator-=;
using Base::operator*=;
using Base::operator/=;
typedef typename Base::CoeffReturnType CoeffReturnType;
#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;
/** \internal Represents a matrix with all coefficients equal to one another*/
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType;
#endif // not EIGEN_PARSED_BY_DOXYGEN
#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::ArrayBase
# include "../plugins/CommonCwiseUnaryOps.h"
# include "../plugins/MatrixCwiseUnaryOps.h"
# include "../plugins/ArrayCwiseUnaryOps.h"
# include "../plugins/CommonCwiseBinaryOps.h"
# include "../plugins/MatrixCwiseBinaryOps.h"
# include "../plugins/ArrayCwiseBinaryOps.h"
# ifdef EIGEN_ARRAYBASE_PLUGIN
# include EIGEN_ARRAYBASE_PLUGIN
# endif
#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
/** Special case of the template operator=, in order to prevent the compiler
* from generating a default operator= (issue hit with g++ 4.1)
*/
Derived& operator=(const ArrayBase& other)
{
return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
}
Derived& operator+=(const Scalar& scalar)
{ return *this = derived() + scalar; }
Derived& operator-=(const Scalar& scalar)
{ return *this = derived() - scalar; }
template<typename OtherDerived>
Derived& operator+=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
Derived& operator-=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
Derived& operator*=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
Derived& operator/=(const ArrayBase<OtherDerived>& other);
public:
ArrayBase<Derived>& array() { return *this; }
const ArrayBase<Derived>& array() const { return *this; }
/** \returns an \link MatrixBase Matrix \endlink expression of this array
* \sa MatrixBase::array() */
MatrixWrapper<Derived> matrix() { return derived(); }
const MatrixWrapper<Derived> matrix() const { return derived(); }
// template<typename Dest>
// inline void evalTo(Dest& dst) const { dst = matrix(); }
protected:
ArrayBase() : Base() {}
private:
explicit ArrayBase(Index);
ArrayBase(Index,Index);
template<typename OtherDerived> explicit ArrayBase(const ArrayBase<OtherDerived>&);
protected:
// mixing arrays and matrices is not legal
template<typename OtherDerived> Derived& operator+=(const MatrixBase<OtherDerived>& )
{EIGEN_STATIC_ASSERT(sizeof(typename OtherDerived::Scalar)==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);}
// mixing arrays and matrices is not legal
template<typename OtherDerived> Derived& operator-=(const MatrixBase<OtherDerived>& )
{EIGEN_STATIC_ASSERT(sizeof(typename OtherDerived::Scalar)==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);}
};
/** replaces \c *this by \c *this - \a other.
*
* \returns a reference to \c *this
*/
template<typename Derived>
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();
return derived();
}
/** replaces \c *this by \c *this + \a other.
*
* \returns a reference to \c *this
*/
template<typename Derived>
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();
return derived();
}
/** replaces \c *this by \c *this * \a other coefficient wise.
*
* \returns a reference to \c *this
*/
template<typename Derived>
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();
return derived();
}
/** replaces \c *this by \c *this / \a other coefficient wise.
*
* \returns a reference to \c *this
*/
template<typename Derived>
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();
return derived();
}
#endif // EIGEN_ARRAYBASE_H

View File

@@ -1,239 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_ARRAYWRAPPER_H
#define EIGEN_ARRAYWRAPPER_H
/** \class ArrayWrapper
* \ingroup Core_Module
*
* \brief Expression of a mathematical vector or matrix as an array object
*
* This class is the return type of MatrixBase::array(), and most of the time
* this is the only way it is use.
*
* \sa MatrixBase::array(), class MatrixWrapper
*/
namespace internal {
template<typename ExpressionType>
struct traits<ArrayWrapper<ExpressionType> >
: public traits<typename remove_all<typename ExpressionType::Nested>::type >
{
typedef ArrayXpr XprKind;
};
}
template<typename ExpressionType>
class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
{
public:
typedef ArrayBase<ArrayWrapper> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(ArrayWrapper)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ArrayWrapper)
typedef typename internal::conditional<
internal::is_lvalue<ExpressionType>::value,
Scalar,
const Scalar
>::type ScalarWithConstIfNotLvalue;
typedef typename internal::nested<ExpressionType>::type NestedExpressionType;
inline ArrayWrapper(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(); }
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
inline const Scalar* data() const { return m_expression.data(); }
inline const CoeffReturnType coeff(Index row, Index col) const
{
return m_expression.coeff(row, col);
}
inline Scalar& coeffRef(Index row, Index col)
{
return m_expression.const_cast_derived().coeffRef(row, col);
}
inline const Scalar& coeffRef(Index row, Index col) const
{
return m_expression.const_cast_derived().coeffRef(row, col);
}
inline const CoeffReturnType coeff(Index index) const
{
return m_expression.coeff(index);
}
inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
}
inline const Scalar& coeffRef(Index index) const
{
return m_expression.const_cast_derived().coeffRef(index);
}
template<int LoadMode>
inline const PacketScalar packet(Index row, Index col) const
{
return m_expression.template packet<LoadMode>(row, col);
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(row, col, x);
}
template<int LoadMode>
inline const PacketScalar packet(Index index) const
{
return m_expression.template packet<LoadMode>(index);
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(index, x);
}
template<typename Dest>
inline void evalTo(Dest& dst) const { dst = m_expression; }
protected:
const NestedExpressionType m_expression;
};
/** \class MatrixWrapper
* \ingroup Core_Module
*
* \brief Expression of an array as a mathematical vector or matrix
*
* This class is the return type of ArrayBase::matrix(), and most of the time
* this is the only way it is use.
*
* \sa MatrixBase::matrix(), class ArrayWrapper
*/
namespace internal {
template<typename ExpressionType>
struct traits<MatrixWrapper<ExpressionType> >
: public traits<typename remove_all<typename ExpressionType::Nested>::type >
{
typedef MatrixXpr XprKind;
};
}
template<typename ExpressionType>
class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
{
public:
typedef MatrixBase<MatrixWrapper<ExpressionType> > Base;
EIGEN_DENSE_PUBLIC_INTERFACE(MatrixWrapper)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(MatrixWrapper)
typedef typename internal::conditional<
internal::is_lvalue<ExpressionType>::value,
Scalar,
const Scalar
>::type ScalarWithConstIfNotLvalue;
typedef typename internal::nested<ExpressionType>::type NestedExpressionType;
inline MatrixWrapper(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(); }
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
inline const Scalar* data() const { return m_expression.data(); }
inline const CoeffReturnType coeff(Index row, Index col) const
{
return m_expression.coeff(row, col);
}
inline Scalar& coeffRef(Index row, Index col)
{
return m_expression.const_cast_derived().coeffRef(row, col);
}
inline const Scalar& coeffRef(Index row, Index col) const
{
return m_expression.derived().coeffRef(row, col);
}
inline const CoeffReturnType coeff(Index index) const
{
return m_expression.coeff(index);
}
inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
}
inline const Scalar& coeffRef(Index index) const
{
return m_expression.const_cast_derived().coeffRef(index);
}
template<int LoadMode>
inline const PacketScalar packet(Index row, Index col) const
{
return m_expression.template packet<LoadMode>(row, col);
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(row, col, x);
}
template<int LoadMode>
inline const PacketScalar packet(Index index) const
{
return m_expression.template packet<LoadMode>(index);
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(index, x);
}
protected:
const NestedExpressionType m_expression;
};
#endif // EIGEN_ARRAYWRAPPER_H

View File

@@ -1,9 +1,9 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2007 Michael Olbrich <michael.olbrich@gmx.net>
// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -27,220 +27,169 @@
#ifndef EIGEN_ASSIGN_H
#define EIGEN_ASSIGN_H
namespace internal {
/***************************************************************************
* Part 1 : the logic deciding a strategy for traversal and unrolling *
* Part 1 : the logic deciding a strategy for vectorization and unrolling
***************************************************************************/
template <typename Derived, typename OtherDerived>
struct assign_traits
struct ei_assign_traits
{
public:
enum {
DstIsAligned = Derived::Flags & AlignedBit,
DstHasDirectAccess = Derived::Flags & DirectAccessBit,
SrcIsAligned = OtherDerived::Flags & AlignedBit,
JointAlignment = bool(DstIsAligned) && bool(SrcIsAligned) ? Aligned : Unaligned
SrcAlignment = DstIsAligned && 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
InnerSize = int(Derived::Flags)&RowMajorBit
? Derived::ColsAtCompileTime
: Derived::RowsAtCompileTime,
InnerMaxSize = int(Derived::Flags)&RowMajorBit
? Derived::MaxColsAtCompileTime
: Derived::MaxRowsAtCompileTime,
PacketSize = ei_packet_traits<typename Derived::Scalar>::size
};
enum {
StorageOrdersAgree = (int(Derived::IsRowMajor) == int(OtherDerived::IsRowMajor)),
MightVectorize = StorageOrdersAgree
&& (int(Derived::Flags) & int(OtherDerived::Flags) & ActualPacketAccessBit),
MightVectorize = (int(Derived::Flags) & int(OtherDerived::Flags) & ActualPacketAccessBit)
&& ((int(Derived::Flags)&RowMajorBit)==(int(OtherDerived::Flags)&RowMajorBit)),
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 */
MayLinearVectorize = MightVectorize && (int(Derived::Flags) & int(OtherDerived::Flags) & LinearAccessBit),
MaySliceVectorize = MightVectorize && 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
Vectorization = int(MayInnerVectorize) ? int(InnerVectorization)
: int(MayLinearVectorize) ? int(LinearVectorization)
: int(MaySliceVectorize) ? int(SliceVectorization)
: int(NoVectorization)
};
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)
UnrollingLimit = EIGEN_UNROLLING_LIMIT * (int(Vectorization) == int(NoVectorization) ? 1 : int(PacketSize)),
MayUnrollCompletely = int(Derived::SizeAtCompileTime) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit),
MayUnrollInner = int(InnerSize * 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) )
Unrolling = (int(Vectorization) == int(InnerVectorization) || int(Vectorization) == int(NoVectorization))
? (
int(MayUnrollCompletely) ? int(CompleteUnrolling)
: int(MayUnrollInner) ? int(InnerUnrolling)
: int(NoUnrolling)
)
: int(Vectorization) == int(LinearVectorization)
? ( int(MayUnrollCompletely) && int(DstIsAligned) ? int(CompleteUnrolling) : int(NoUnrolling) )
: int(NoUnrolling)
};
#ifdef EIGEN_DEBUG_ASSIGN
static void debug()
{
EIGEN_DEBUG_VAR(DstIsAligned)
EIGEN_DEBUG_VAR(SrcIsAligned)
EIGEN_DEBUG_VAR(JointAlignment)
EIGEN_DEBUG_VAR(InnerSize)
EIGEN_DEBUG_VAR(InnerMaxSize)
EIGEN_DEBUG_VAR(PacketSize)
EIGEN_DEBUG_VAR(StorageOrdersAgree)
EIGEN_DEBUG_VAR(MightVectorize)
EIGEN_DEBUG_VAR(MayLinearize)
EIGEN_DEBUG_VAR(MayInnerVectorize)
EIGEN_DEBUG_VAR(MayLinearVectorize)
EIGEN_DEBUG_VAR(MaySliceVectorize)
EIGEN_DEBUG_VAR(Traversal)
EIGEN_DEBUG_VAR(UnrollingLimit)
EIGEN_DEBUG_VAR(MayUnrollCompletely)
EIGEN_DEBUG_VAR(MayUnrollInner)
EIGEN_DEBUG_VAR(Unrolling)
}
#endif
};
/***************************************************************************
* Part 2 : meta-unrollers
***************************************************************************/
/************************
*** Default traversal ***
************************/
/***********************
*** No vectorization ***
***********************/
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_DefaultTraversal_CompleteUnrolling
struct ei_assign_novec_CompleteUnrolling
{
enum {
outer = Index / Derived1::InnerSizeAtCompileTime,
inner = Index % Derived1::InnerSizeAtCompileTime
row = int(Derived1::Flags)&RowMajorBit
? Index / int(Derived1::ColsAtCompileTime)
: Index % Derived1::RowsAtCompileTime,
col = int(Derived1::Flags)&RowMajorBit
? Index % int(Derived1::ColsAtCompileTime)
: Index / Derived1::RowsAtCompileTime
};
EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
{
dst.copyCoeffByOuterInner(outer, inner, src);
assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src);
dst.copyCoeff(row, col, src);
ei_assign_novec_CompleteUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
struct ei_assign_novec_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
{
EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &) {}
};
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_DefaultTraversal_InnerUnrolling
struct ei_assign_novec_InnerUnrolling
{
EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src, int outer)
EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src, int row_or_col)
{
dst.copyCoeffByOuterInner(outer, Index, src);
assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src, outer);
const bool rowMajor = int(Derived1::Flags)&RowMajorBit;
const int row = rowMajor ? row_or_col : Index;
const int col = rowMajor ? Index : row_or_col;
dst.copyCoeff(row, col, src);
ei_assign_novec_InnerUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src, row_or_col);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Stop, Stop>
struct ei_assign_novec_InnerUnrolling<Derived1, Derived2, Stop, Stop>
{
EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &, int) {}
};
/***********************
*** Linear traversal ***
***********************/
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_LinearTraversal_CompleteUnrolling
{
EIGEN_STRONG_INLINE static 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_STRONG_INLINE static void run(Derived1 &, const Derived2 &) {}
};
/**************************
*** Inner vectorization ***
**************************/
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_innervec_CompleteUnrolling
struct ei_assign_innervec_CompleteUnrolling
{
enum {
outer = Index / Derived1::InnerSizeAtCompileTime,
inner = Index % Derived1::InnerSizeAtCompileTime,
JointAlignment = assign_traits<Derived1,Derived2>::JointAlignment
row = int(Derived1::Flags)&RowMajorBit
? Index / int(Derived1::ColsAtCompileTime)
: Index % Derived1::RowsAtCompileTime,
col = int(Derived1::Flags)&RowMajorBit
? Index % int(Derived1::ColsAtCompileTime)
: Index / Derived1::RowsAtCompileTime,
SrcAlignment = ei_assign_traits<Derived1,Derived2>::SrcAlignment
};
EIGEN_STRONG_INLINE static 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);
dst.template copyPacket<Derived2, Aligned, SrcAlignment>(row, col, src);
ei_assign_innervec_CompleteUnrolling<Derived1, Derived2,
Index+ei_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>
struct ei_assign_innervec_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
{
EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &) {}
};
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_innervec_InnerUnrolling
struct ei_assign_innervec_InnerUnrolling
{
EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src, int outer)
EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src, int row_or_col)
{
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);
const int row = int(Derived1::Flags)&RowMajorBit ? row_or_col : Index;
const int col = int(Derived1::Flags)&RowMajorBit ? Index : row_or_col;
dst.template copyPacket<Derived2, Aligned, Aligned>(row, col, src);
ei_assign_innervec_InnerUnrolling<Derived1, Derived2,
Index+ei_packet_traits<typename Derived1::Scalar>::size, Stop>::run(dst, src, row_or_col);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_innervec_InnerUnrolling<Derived1, Derived2, Stop, Stop>
struct ei_assign_innervec_InnerUnrolling<Derived1, Derived2, Stop, Stop>
{
EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &, int) {}
};
@@ -250,122 +199,100 @@ struct assign_innervec_InnerUnrolling<Derived1, Derived2, Stop, Stop>
***************************************************************************/
template<typename Derived1, typename Derived2,
int Traversal = assign_traits<Derived1, Derived2>::Traversal,
int Unrolling = assign_traits<Derived1, Derived2>::Unrolling>
struct assign_impl;
/************************
*** Default traversal ***
************************/
template<typename Derived1, typename Derived2, int Unrolling>
struct assign_impl<Derived1, Derived2, InvalidTraversal, Unrolling>
{
inline static void run(Derived1 &, const Derived2 &) { }
};
template<typename Derived1, typename Derived2>
struct assign_impl<Derived1, Derived2, DefaultTraversal, NoUnrolling>
{
typedef typename Derived1::Index Index;
inline static 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>
struct assign_impl<Derived1, Derived2, DefaultTraversal, CompleteUnrolling>
{
EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
{
assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
::run(dst, src);
}
};
template<typename Derived1, typename Derived2>
struct assign_impl<Derived1, Derived2, DefaultTraversal, InnerUnrolling>
{
typedef typename Derived1::Index Index;
EIGEN_STRONG_INLINE static 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);
}
};
int Vectorization = ei_assign_traits<Derived1, Derived2>::Vectorization,
int Unrolling = ei_assign_traits<Derived1, Derived2>::Unrolling>
struct ei_assign_impl;
/***********************
*** Linear traversal ***
*** No vectorization ***
***********************/
template<typename Derived1, typename Derived2>
struct assign_impl<Derived1, Derived2, LinearTraversal, NoUnrolling>
struct ei_assign_impl<Derived1, Derived2, NoVectorization, NoUnrolling>
{
typedef typename Derived1::Index Index;
inline static void run(Derived1 &dst, const Derived2 &src)
{
const Index size = dst.size();
for(Index i = 0; i < size; ++i)
dst.copyCoeff(i, src);
const int innerSize = dst.innerSize();
const int outerSize = dst.outerSize();
for(int j = 0; j < outerSize; ++j)
for(int i = 0; i < innerSize; ++i)
{
if(int(Derived1::Flags)&RowMajorBit)
dst.copyCoeff(j, i, src);
else
dst.copyCoeff(i, j, src);
}
}
};
template<typename Derived1, typename Derived2>
struct assign_impl<Derived1, Derived2, LinearTraversal, CompleteUnrolling>
struct ei_assign_impl<Derived1, Derived2, NoVectorization, CompleteUnrolling>
{
EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
{
assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
ei_assign_novec_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
::run(dst, src);
}
};
template<typename Derived1, typename Derived2>
struct ei_assign_impl<Derived1, Derived2, NoVectorization, InnerUnrolling>
{
EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
{
const bool rowMajor = int(Derived1::Flags)&RowMajorBit;
const int innerSize = rowMajor ? Derived1::ColsAtCompileTime : Derived1::RowsAtCompileTime;
const int outerSize = dst.outerSize();
for(int j = 0; j < outerSize; ++j)
ei_assign_novec_InnerUnrolling<Derived1, Derived2, 0, innerSize>
::run(dst, src, j);
}
};
/**************************
*** Inner vectorization ***
**************************/
template<typename Derived1, typename Derived2>
struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, NoUnrolling>
struct ei_assign_impl<Derived1, Derived2, InnerVectorization, NoUnrolling>
{
typedef typename Derived1::Index Index;
inline static 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);
const int innerSize = dst.innerSize();
const int outerSize = dst.outerSize();
const int packetSize = ei_packet_traits<typename Derived1::Scalar>::size;
for(int j = 0; j < outerSize; ++j)
for(int i = 0; i < innerSize; i+=packetSize)
{
if(int(Derived1::Flags)&RowMajorBit)
dst.template copyPacket<Derived2, Aligned, Aligned>(j, i, src);
else
dst.template copyPacket<Derived2, Aligned, Aligned>(i, j, src);
}
}
};
template<typename Derived1, typename Derived2>
struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, CompleteUnrolling>
struct ei_assign_impl<Derived1, Derived2, InnerVectorization, CompleteUnrolling>
{
EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
{
assign_innervec_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
ei_assign_innervec_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
::run(dst, src);
}
};
template<typename Derived1, typename Derived2>
struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, InnerUnrolling>
struct ei_assign_impl<Derived1, Derived2, InnerVectorization, InnerUnrolling>
{
typedef typename Derived1::Index Index;
EIGEN_STRONG_INLINE static 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);
const bool rowMajor = int(Derived1::Flags)&RowMajorBit;
const int innerSize = rowMajor ? Derived1::ColsAtCompileTime : Derived1::RowsAtCompileTime;
const int outerSize = dst.outerSize();
for(int j = 0; j < outerSize; ++j)
ei_assign_innervec_InnerUnrolling<Derived1, Derived2, 0, innerSize>
::run(dst, src, j);
}
};
@@ -373,71 +300,41 @@ struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, InnerUnrolling>
*** Linear vectorization ***
***************************/
template <bool IsAligned = false>
struct unaligned_assign_impl
template<typename Derived1, typename Derived2>
struct ei_assign_impl<Derived1, Derived2, LinearVectorization, NoUnrolling>
{
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
inline static void run(Derived1 &dst, const Derived2 &src)
{
for (typename Derived::Index index = start; index < end; ++index)
const int size = dst.size();
const int packetSize = ei_packet_traits<typename Derived1::Scalar>::size;
const int alignedStart = ei_assign_traits<Derived1,Derived2>::DstIsAligned ? 0
: ei_alignmentOffset(&dst.coeffRef(0), size);
const int alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize;
for(int index = 0; index < alignedStart; ++index)
dst.copyCoeff(index, src);
for(int index = alignedStart; index < alignedEnd; index += packetSize)
{
dst.template copyPacket<Derived2, Aligned, ei_assign_traits<Derived1,Derived2>::SrcAlignment>(index, src);
}
for(int index = alignedEnd; index < size; ++index)
dst.copyCoeff(index, src);
}
};
template<typename Derived1, typename Derived2>
struct assign_impl<Derived1, Derived2, LinearVectorizedTraversal, NoUnrolling>
struct ei_assign_impl<Derived1, Derived2, LinearVectorization, CompleteUnrolling>
{
typedef typename Derived1::Index Index;
EIGEN_STRONG_INLINE static 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
: first_aligned(&dst.coeffRef(0), size);
const Index alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize;
const int size = Derived1::SizeAtCompileTime;
const int packetSize = ei_packet_traits<typename Derived1::Scalar>::size;
const int alignedSize = (size/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>
struct assign_impl<Derived1, Derived2, LinearVectorizedTraversal, CompleteUnrolling>
{
typedef typename Derived1::Index Index;
EIGEN_STRONG_INLINE static 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);
ei_assign_innervec_CompleteUnrolling<Derived1, Derived2, 0, alignedSize>::run(dst, src);
ei_assign_novec_CompleteUnrolling<Derived1, Derived2, alignedSize, size>::run(dst, src);
}
};
@@ -446,148 +343,103 @@ struct assign_impl<Derived1, Derived2, LinearVectorizedTraversal, CompleteUnroll
***************************/
template<typename Derived1, typename Derived2>
struct assign_impl<Derived1, Derived2, SliceVectorizedTraversal, NoUnrolling>
struct ei_assign_impl<Derived1, Derived2, SliceVectorization, NoUnrolling>
{
typedef typename Derived1::Index Index;
inline static 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
: first_aligned(&dst.coeffRef(0,0), innerSize);
const int packetSize = ei_packet_traits<typename Derived1::Scalar>::size;
const int packetAlignedMask = packetSize - 1;
const int innerSize = dst.innerSize();
const int outerSize = dst.outerSize();
const int alignedStep = (packetSize - dst.stride() % packetSize) & packetAlignedMask;
int alignedStart = ei_assign_traits<Derived1,Derived2>::DstIsAligned ? 0
: ei_alignmentOffset(&dst.coeffRef(0,0), innerSize);
for(Index outer = 0; outer < outerSize; ++outer)
for(int i = 0; i < outerSize; ++i)
{
const Index alignedEnd = alignedStart + ((innerSize-alignedStart) & ~packetAlignedMask);
const int 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);
for (int index = 0; index<alignedStart ; ++index)
{
if(Derived1::Flags&RowMajorBit)
dst.copyCoeff(i, index, src);
else
dst.copyCoeff(index, i, 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);
for (int index = alignedStart; index<alignedEnd; index+=packetSize)
{
if(Derived1::Flags&RowMajorBit)
dst.template copyPacket<Derived2, Aligned, Unaligned>(i, index, src);
else
dst.template copyPacket<Derived2, Aligned, Unaligned>(index, i, src);
}
// do the non-vectorizable part of the assignment
for(Index inner = alignedEnd; inner<innerSize ; ++inner)
dst.copyCoeffByOuterInner(outer, inner, src);
for (int index = alignedEnd; index<innerSize ; ++index)
{
if(Derived1::Flags&RowMajorBit)
dst.copyCoeff(i, index, src);
else
dst.copyCoeff(index, i, src);
}
alignedStart = std::min<Index>((alignedStart+alignedStep)%packetSize, innerSize);
alignedStart = std::min<int>((alignedStart+alignedStep)%packetSize, innerSize);
}
}
};
} // end namespace internal
/***************************************************************************
* Part 4 : implementation of DenseBase methods
* Part 4 : implementation of MatrixBase methods
***************************************************************************/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>
::lazyAssign(const DenseBase<OtherDerived>& other)
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>
::lazyAssign(const MatrixBase<OtherDerived>& other)
{
enum{
SameType = internal::is_same<typename Derived::Scalar,typename OtherDerived::Scalar>::value
};
EIGEN_STATIC_ASSERT_LVALUE(Derived)
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived)
EIGEN_STATIC_ASSERT(SameType,YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
#ifdef EIGEN_DEBUG_ASSIGN
internal::assign_traits<Derived, OtherDerived>::debug();
#endif
eigen_assert(rows() == other.rows() && cols() == other.cols());
internal::assign_impl<Derived, OtherDerived, int(SameType) ? int(internal::assign_traits<Derived, OtherDerived>::Traversal)
: int(InvalidTraversal)>::run(derived(),other.derived());
#ifndef EIGEN_NO_DEBUG
checkTransposeAliasing(other.derived());
#endif
EIGEN_STATIC_ASSERT((ei_is_same_type<typename Derived::Scalar, typename OtherDerived::Scalar>::ret),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
ei_assert(rows() == other.rows() && cols() == other.cols());
ei_assign_impl<Derived, OtherDerived>::run(derived(),other.derived());
return derived();
}
namespace internal {
template<typename Derived, typename OtherDerived,
bool EvalBeforeAssigning = (int(OtherDerived::Flags) & EvalBeforeAssigningBit) != 0,
bool NeedToTranspose = Derived::IsVectorAtCompileTime
&& OtherDerived::IsVectorAtCompileTime
&& ((int(Derived::RowsAtCompileTime) == 1 && int(OtherDerived::ColsAtCompileTime) == 1)
| // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
// revert to || as soon as not needed anymore.
(int(Derived::ColsAtCompileTime) == 1 && int(OtherDerived::RowsAtCompileTime) == 1))
&& int(Derived::RowsAtCompileTime) == int(OtherDerived::ColsAtCompileTime)
&& int(Derived::ColsAtCompileTime) == int(OtherDerived::RowsAtCompileTime)
&& int(Derived::SizeAtCompileTime) != 1>
struct assign_selector;
struct ei_assign_selector;
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,false,false> {
struct ei_assign_selector<Derived,OtherDerived,false,false> {
EIGEN_STRONG_INLINE static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.derived()); }
};
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,true,false> {
struct ei_assign_selector<Derived,OtherDerived,true,false> {
EIGEN_STRONG_INLINE static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.eval()); }
};
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,false,true> {
struct ei_assign_selector<Derived,OtherDerived,false,true> {
EIGEN_STRONG_INLINE static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose()); }
};
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,true,true> {
struct ei_assign_selector<Derived,OtherDerived,true,true> {
EIGEN_STRONG_INLINE static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose().eval()); }
};
} // end namespace internal
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>
::operator=(const MatrixBase<OtherDerived>& other)
{
return internal::assign_selector<Derived,OtherDerived>::run(derived(), other.derived());
}
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase& other)
{
return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
}
template<typename Derived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const MatrixBase& other)
{
return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
}
template<typename Derived>
template <typename OtherDerived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
{
return internal::assign_selector<Derived,OtherDerived>::run(derived(), other.derived());
}
template<typename Derived>
template <typename OtherDerived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const EigenBase<OtherDerived>& other)
{
other.derived().evalTo(derived());
return derived();
}
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
{
other.evalTo(derived());
return derived();
return ei_assign_selector<Derived,OtherDerived>::run(derived(), other.derived());
}
#endif // EIGEN_ASSIGN_H

View File

@@ -1,346 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_BANDMATRIX_H
#define EIGEN_BANDMATRIX_H
namespace internal {
template<typename Derived>
class BandMatrixBase : public EigenBase<Derived>
{
public:
enum {
Flags = internal::traits<Derived>::Flags,
CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
Supers = internal::traits<Derived>::Supers,
Subs = internal::traits<Derived>::Subs,
Options = internal::traits<Derived>::Options
};
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> DenseMatrixType;
typedef typename DenseMatrixType::Index Index;
typedef typename internal::traits<Derived>::CoefficientsType CoefficientsType;
typedef EigenBase<Derived> Base;
protected:
enum {
DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic))
? 1 + Supers + Subs
: Dynamic,
SizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime)
};
public:
using Base::derived;
using Base::rows;
using Base::cols;
/** \returns the number of super diagonals */
inline Index supers() const { return derived().supers(); }
/** \returns the number of sub diagonals */
inline Index subs() const { return derived().subs(); }
/** \returns an expression of the underlying coefficient matrix */
inline const CoefficientsType& coeffs() const { return derived().coeffs(); }
/** \returns an expression of the underlying coefficient matrix */
inline CoefficientsType& coeffs() { return derived().coeffs(); }
/** \returns a vector expression of the \a i -th column,
* only the meaningful part is returned.
* \warning the internal storage must be column major. */
inline Block<CoefficientsType,Dynamic,1> col(Index i)
{
EIGEN_STATIC_ASSERT((Options&RowMajor)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
Index start = 0;
Index len = coeffs().rows();
if (i<=supers())
{
start = supers()-i;
len = (std::min)(rows(),std::max<Index>(0,coeffs().rows() - (supers()-i)));
}
else if (i>=rows()-subs())
len = std::max<Index>(0,coeffs().rows() - (i + 1 - rows() + subs()));
return Block<CoefficientsType,Dynamic,1>(coeffs(), start, i, len, 1);
}
/** \returns a vector expression of the main diagonal */
inline Block<CoefficientsType,1,SizeAtCompileTime> diagonal()
{ return Block<CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
/** \returns a vector expression of the main diagonal (const version) */
inline const Block<const CoefficientsType,1,SizeAtCompileTime> diagonal() const
{ return Block<const CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
template<int Index> struct DiagonalIntReturnType {
enum {
ReturnOpposite = (Options&SelfAdjoint) && (((Index)>0 && Supers==0) || ((Index)<0 && Subs==0)),
Conjugate = ReturnOpposite && NumTraits<Scalar>::IsComplex,
ActualIndex = ReturnOpposite ? -Index : Index,
DiagonalSize = (RowsAtCompileTime==Dynamic || ColsAtCompileTime==Dynamic)
? Dynamic
: (ActualIndex<0
? EIGEN_SIZE_MIN_PREFER_DYNAMIC(ColsAtCompileTime, RowsAtCompileTime + ActualIndex)
: EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime, ColsAtCompileTime - ActualIndex))
};
typedef Block<CoefficientsType,1, DiagonalSize> BuildType;
typedef typename internal::conditional<Conjugate,
CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>,BuildType >,
BuildType>::type Type;
};
/** \returns a vector expression of the \a N -th sub or super diagonal */
template<int N> inline typename DiagonalIntReturnType<N>::Type diagonal()
{
return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
}
/** \returns a vector expression of the \a N -th sub or super diagonal */
template<int N> inline const typename DiagonalIntReturnType<N>::Type diagonal() const
{
return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
}
/** \returns a vector expression of the \a i -th sub or super diagonal */
inline Block<CoefficientsType,1,Dynamic> diagonal(Index i)
{
eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers()));
return Block<CoefficientsType,1,Dynamic>(coeffs(), supers()-i, std::max<Index>(0,i), 1, diagonalLength(i));
}
/** \returns a vector expression of the \a i -th sub or super diagonal */
inline const Block<const CoefficientsType,1,Dynamic> diagonal(Index i) const
{
eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers()));
return Block<const CoefficientsType,1,Dynamic>(coeffs(), supers()-i, std::max<Index>(0,i), 1, diagonalLength(i));
}
template<typename Dest> inline void evalTo(Dest& dst) const
{
dst.resize(rows(),cols());
dst.setZero();
dst.diagonal() = diagonal();
for (Index i=1; i<=supers();++i)
dst.diagonal(i) = diagonal(i);
for (Index i=1; i<=subs();++i)
dst.diagonal(-i) = diagonal(-i);
}
DenseMatrixType toDenseMatrix() const
{
DenseMatrixType res(rows(),cols());
evalTo(res);
return res;
}
protected:
inline Index diagonalLength(Index i) const
{ return i<0 ? (std::min)(cols(),rows()+i) : (std::min)(rows(),cols()-i); }
};
/**
* \class BandMatrix
* \ingroup Core_Module
*
* \brief Represents a rectangular matrix with a banded storage
*
* \param _Scalar Numeric type, i.e. float, double, int
* \param Rows Number of rows, or \b Dynamic
* \param Cols Number of columns, or \b Dynamic
* \param Supers Number of super diagonal
* \param Subs Number of sub diagonal
* \param _Options A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
* The former controls \ref TopicStorageOrders "storage order", and defaults to
* column-major. The latter controls whether the matrix represents a selfadjoint
* matrix in which case either Supers of Subs have to be null.
*
* \sa class TridiagonalMatrix
*/
template<typename _Scalar, int _Rows, int _Cols, int _Supers, int _Subs, int _Options>
struct traits<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
{
typedef _Scalar Scalar;
typedef Dense StorageKind;
typedef DenseIndex Index;
enum {
CoeffReadCost = NumTraits<Scalar>::ReadCost,
RowsAtCompileTime = _Rows,
ColsAtCompileTime = _Cols,
MaxRowsAtCompileTime = _Rows,
MaxColsAtCompileTime = _Cols,
Flags = LvalueBit,
Supers = _Supers,
Subs = _Subs,
Options = _Options,
DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic
};
typedef Matrix<Scalar,DataRowsAtCompileTime,ColsAtCompileTime,Options&RowMajor?RowMajor:ColMajor> CoefficientsType;
};
template<typename _Scalar, int Rows, int Cols, int Supers, int Subs, int Options>
class BandMatrix : public BandMatrixBase<BandMatrix<_Scalar,Rows,Cols,Supers,Subs,Options> >
{
public:
typedef typename internal::traits<BandMatrix>::Scalar Scalar;
typedef typename internal::traits<BandMatrix>::Index Index;
typedef typename internal::traits<BandMatrix>::CoefficientsType CoefficientsType;
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)
{
}
/** \returns the number of columns */
inline Index rows() const { return m_rows.value(); }
/** \returns the number of rows */
inline Index cols() const { return m_coeffs.cols(); }
/** \returns the number of super diagonals */
inline Index supers() const { return m_supers.value(); }
/** \returns the number of sub diagonals */
inline Index subs() const { return m_subs.value(); }
inline const CoefficientsType& coeffs() const { return m_coeffs; }
inline CoefficientsType& coeffs() { return m_coeffs; }
protected:
CoefficientsType m_coeffs;
internal::variable_if_dynamic<Index, Rows> m_rows;
internal::variable_if_dynamic<Index, Supers> m_supers;
internal::variable_if_dynamic<Index, Subs> m_subs;
};
template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
class BandMatrixWrapper;
template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
struct traits<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
{
typedef typename _CoefficientsType::Scalar Scalar;
typedef typename _CoefficientsType::StorageKind StorageKind;
typedef typename _CoefficientsType::Index Index;
enum {
CoeffReadCost = internal::traits<_CoefficientsType>::CoeffReadCost,
RowsAtCompileTime = _Rows,
ColsAtCompileTime = _Cols,
MaxRowsAtCompileTime = _Rows,
MaxColsAtCompileTime = _Cols,
Flags = LvalueBit,
Supers = _Supers,
Subs = _Subs,
Options = _Options,
DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic
};
typedef _CoefficientsType CoefficientsType;
};
template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
{
public:
typedef typename internal::traits<BandMatrixWrapper>::Scalar Scalar;
typedef typename internal::traits<BandMatrixWrapper>::CoefficientsType CoefficientsType;
typedef typename internal::traits<BandMatrixWrapper>::Index Index;
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)
{
EIGEN_UNUSED_VARIABLE(cols);
//internal::assert(coeffs.cols()==cols() && (supers()+subs()+1)==coeffs.rows());
}
/** \returns the number of columns */
inline Index rows() const { return m_rows.value(); }
/** \returns the number of rows */
inline Index cols() const { return m_coeffs.cols(); }
/** \returns the number of super diagonals */
inline Index supers() const { return m_supers.value(); }
/** \returns the number of sub diagonals */
inline Index subs() const { return m_subs.value(); }
inline const CoefficientsType& coeffs() const { return m_coeffs; }
protected:
const CoefficientsType& m_coeffs;
internal::variable_if_dynamic<Index, _Rows> m_rows;
internal::variable_if_dynamic<Index, _Supers> m_supers;
internal::variable_if_dynamic<Index, _Subs> m_subs;
};
/**
* \class TridiagonalMatrix
* \ingroup Core_Module
*
* \brief Represents a tridiagonal matrix with a compact banded storage
*
* \param _Scalar Numeric type, i.e. float, double, int
* \param Size Number of rows and cols, or \b Dynamic
* \param _Options Can be 0 or \b SelfAdjoint
*
* \sa class BandMatrix
*/
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;
public:
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>(); }
inline const typename Base::template DiagonalIntReturnType<1>::Type super() const
{ return Base::template diagonal<1>(); }
inline typename Base::template DiagonalIntReturnType<-1>::Type sub()
{ return Base::template diagonal<-1>(); }
inline const typename Base::template DiagonalIntReturnType<-1>::Type sub() const
{ return Base::template diagonal<-1>(); }
protected:
};
} // end namespace internal
#endif // EIGEN_BANDMATRIX_H

View File

@@ -1,8 +1,8 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -27,17 +27,20 @@
#define EIGEN_BLOCK_H
/** \class Block
* \ingroup Core_Module
*
* \brief Expression of a fixed-size or dynamic-size block
*
* \param XprType the type of the expression in which we are taking a block
* \param MatrixType the type of the object in which we are taking a block
* \param BlockRows the number of rows of the block we are taking at compile time (optional)
* \param BlockCols the number of columns of the block we are taking at compile time (optional)
* \param _PacketAccess allows to enforce aligned loads and stores if set to ForceAligned.
* The default is AsRequested. This parameter is internaly used by Eigen
* in expressions such as \code mat.block() += other; \endcode and most of
* the time this is the only way it is used.
* \param _DirectAccessStatus \internal used for partial specialization
*
* This class represents an expression of either a fixed-size or dynamic-size block. It is the return
* type of DenseBase::block(Index,Index,Index,Index) and DenseBase::block<int,int>(Index,Index) and
* type of MatrixBase::block(int,int,int,int) and MatrixBase::block<int,int>(int,int) and
* most of the time this is the only way it is used.
*
* However, if you want to directly maniputate block expressions,
@@ -48,7 +51,7 @@
* \include class_Block.cpp
* Output: \verbinclude class_Block.out
*
* \note Even though this expression has dynamic size, in the case where \a XprType
* \note Even though this expression has dynamic size, in the case where \a MatrixType
* has fixed size, this expression inherits a fixed maximal size which means that evaluating
* it does not cause a dynamic memory allocation.
*
@@ -56,294 +59,694 @@
* \include class_FixedBlock.cpp
* Output: \verbinclude class_FixedBlock.out
*
* \sa DenseBase::block(Index,Index,Index,Index), DenseBase::block(Index,Index), class VectorBlock
* \sa MatrixBase::block(int,int,int,int), MatrixBase::block(int,int), class VectorBlock
*/
namespace internal {
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess>
struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel, HasDirectAccess> > : traits<XprType>
template<typename MatrixType, int BlockRows, int BlockCols, int _PacketAccess, int _DirectAccessStatus>
struct ei_traits<Block<MatrixType, BlockRows, BlockCols, _PacketAccess, _DirectAccessStatus> >
{
typedef typename traits<XprType>::Scalar Scalar;
typedef typename traits<XprType>::StorageKind StorageKind;
typedef typename traits<XprType>::XprKind XprKind;
typedef typename nested<XprType>::type XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
typedef typename ei_traits<MatrixType>::Scalar Scalar;
typedef typename ei_nested<MatrixType>::type MatrixTypeNested;
typedef typename ei_unref<MatrixTypeNested>::type _MatrixTypeNested;
enum{
MatrixRows = traits<XprType>::RowsAtCompileTime,
MatrixCols = traits<XprType>::ColsAtCompileTime,
RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows,
ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols,
MaxRowsAtCompileTime = BlockRows==0 ? 0
: RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime)
: int(traits<XprType>::MaxRowsAtCompileTime),
MaxColsAtCompileTime = BlockCols==0 ? 0
: ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime)
: int(traits<XprType>::MaxColsAtCompileTime),
XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0,
IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
: (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
: XprTypeIsRowMajor,
HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
InnerStrideAtCompileTime = HasSameStorageOrderAsXprType
? int(inner_stride_at_compile_time<XprType>::ret)
: int(outer_stride_at_compile_time<XprType>::ret),
OuterStrideAtCompileTime = HasSameStorageOrderAsXprType
? int(outer_stride_at_compile_time<XprType>::ret)
: int(inner_stride_at_compile_time<XprType>::ret),
MaskPacketAccessBit = (InnerSize == Dynamic || (InnerSize % packet_traits<Scalar>::size) == 0)
&& (InnerStrideAtCompileTime == 1)
RowsAtCompileTime = ei_traits<MatrixType>::RowsAtCompileTime == 1 ? 1 : BlockRows,
ColsAtCompileTime = ei_traits<MatrixType>::ColsAtCompileTime == 1 ? 1 : BlockCols,
MaxRowsAtCompileTime = RowsAtCompileTime == 1 ? 1
: (BlockRows==Dynamic ? int(ei_traits<MatrixType>::MaxRowsAtCompileTime) : BlockRows),
MaxColsAtCompileTime = ColsAtCompileTime == 1 ? 1
: (BlockCols==Dynamic ? int(ei_traits<MatrixType>::MaxColsAtCompileTime) : BlockCols),
RowMajor = int(ei_traits<MatrixType>::Flags)&RowMajorBit,
InnerSize = RowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
InnerMaxSize = RowMajor ? int(MaxColsAtCompileTime) : int(MaxRowsAtCompileTime),
MaskPacketAccessBit = (InnerMaxSize == Dynamic || (InnerSize >= ei_packet_traits<Scalar>::size))
? PacketAccessBit : 0,
MaskAlignedBit = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic) && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % 16) == 0)) ? AlignedBit : 0,
FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0,
FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
Flags0 = traits<XprType>::Flags & ( (HereditaryBits & ~RowMajorBit) |
DirectAccessBit |
MaskPacketAccessBit |
MaskAlignedBit),
Flags = Flags0 | FlagsLinearAccessBit | FlagsLvalueBit | FlagsRowMajorBit
Flags = (ei_traits<MatrixType>::Flags & (HereditaryBits | MaskPacketAccessBit | DirectAccessBit)) | FlagsLinearAccessBit,
CoeffReadCost = ei_traits<MatrixType>::CoeffReadCost,
PacketAccess = _PacketAccess
};
typedef typename ei_meta_if<int(PacketAccess)==ForceAligned,
Block<MatrixType, BlockRows, BlockCols, _PacketAccess, _DirectAccessStatus>&,
Block<MatrixType, BlockRows, BlockCols, ForceAligned, _DirectAccessStatus> >::ret AlignedDerivedType;
};
}
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess> class Block
: public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel, HasDirectAccess> >::type
template<typename MatrixType, int BlockRows, int BlockCols, int PacketAccess, int _DirectAccessStatus> class Block
: public MatrixBase<Block<MatrixType, BlockRows, BlockCols, PacketAccess, _DirectAccessStatus> >
{
public:
typedef typename internal::dense_xpr_base<Block>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Block)
EIGEN_GENERIC_PUBLIC_INTERFACE(Block)
class InnerIterator;
/** Column or Row constructor
*/
inline Block(XprType& xpr, Index i)
: m_xpr(xpr),
// It is a row if and only if BlockRows==1 and BlockCols==XprType::ColsAtCompileTime,
// and it is a column if and only if BlockRows==XprType::RowsAtCompileTime and BlockCols==1,
inline Block(const MatrixType& matrix, int i)
: m_matrix(matrix),
// It is a row if and only if BlockRows==1 and BlockCols==MatrixType::ColsAtCompileTime,
// and it is a column if and only if BlockRows==MatrixType::RowsAtCompileTime and BlockCols==1,
// all other cases are invalid.
// The case a 1x1 matrix seems ambiguous, but the result is the same anyway.
m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0),
m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0),
m_blockRows(BlockRows==1 ? 1 : xpr.rows()),
m_blockCols(BlockCols==1 ? 1 : xpr.cols())
m_startRow( (BlockRows==1) && (BlockCols==MatrixType::ColsAtCompileTime) ? i : 0),
m_startCol( (BlockRows==MatrixType::RowsAtCompileTime) && (BlockCols==1) ? i : 0),
m_blockRows(matrix.rows()), // if it is a row, then m_blockRows has a fixed-size of 1, so no pb to try to overwrite it
m_blockCols(matrix.cols()) // same for m_blockCols
{
eigen_assert( (i>=0) && (
((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
ei_assert( (i>=0) && (
((BlockRows==1) && (BlockCols==MatrixType::ColsAtCompileTime) && i<matrix.rows())
||((BlockRows==MatrixType::RowsAtCompileTime) && (BlockCols==1) && i<matrix.cols())));
}
/** Fixed-size constructor
*/
inline Block(XprType& xpr, Index startRow, Index startCol)
: m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
m_blockRows(BlockRows), m_blockCols(BlockCols)
inline Block(const MatrixType& matrix, int startRow, int startCol)
: m_matrix(matrix), m_startRow(startRow), m_startCol(startCol),
m_blockRows(matrix.rows()), m_blockCols(matrix.cols())
{
EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
eigen_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows()
&& startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols());
ei_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= matrix.rows()
&& startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= matrix.cols());
}
/** Dynamic-size constructor
*/
inline Block(XprType& xpr,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
inline Block(const MatrixType& matrix,
int startRow, int startCol,
int blockRows, int blockCols)
: m_matrix(matrix), m_startRow(startRow), m_startCol(startCol),
m_blockRows(blockRows), m_blockCols(blockCols)
{
eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
ei_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
&& (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
eigen_assert(startRow >= 0 && blockRows >= 0 && startRow + blockRows <= xpr.rows()
&& startCol >= 0 && blockCols >= 0 && startCol + blockCols <= xpr.cols());
ei_assert(startRow >= 0 && blockRows >= 1 && startRow + blockRows <= matrix.rows()
&& startCol >= 0 && blockCols >= 1 && startCol + blockCols <= matrix.cols());
}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
inline Index rows() const { return m_blockRows.value(); }
inline Index cols() const { return m_blockCols.value(); }
inline int rows() const { return m_blockRows.value(); }
inline int cols() const { return m_blockCols.value(); }
inline Scalar& coeffRef(Index row, Index col)
inline Scalar& coeffRef(int row, int col)
{
EIGEN_STATIC_ASSERT_LVALUE(XprType)
return m_xpr.const_cast_derived()
return m_matrix.const_cast_derived()
.coeffRef(row + m_startRow.value(), col + m_startCol.value());
}
inline const Scalar& coeffRef(Index row, Index col) const
inline const Scalar coeff(int row, int col) const
{
return m_xpr.derived()
.coeffRef(row + m_startRow.value(), col + m_startCol.value());
return m_matrix.coeff(row + m_startRow.value(), col + m_startCol.value());
}
EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index row, Index col) const
inline Scalar& coeffRef(int index)
{
return m_xpr.coeff(row + m_startRow.value(), col + m_startCol.value());
}
inline Scalar& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_LVALUE(XprType)
return m_xpr.const_cast_derived()
return m_matrix.const_cast_derived()
.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
inline const Scalar& coeffRef(Index index) const
inline const Scalar coeff(int index) const
{
return m_xpr.const_cast_derived()
.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
inline const CoeffReturnType coeff(Index index) const
{
return m_xpr
return m_matrix
.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
template<int LoadMode>
inline PacketScalar packet(Index row, Index col) const
inline PacketScalar packet(int row, int col) const
{
return m_xpr.template packet<Unaligned>
return m_matrix.template packet<Unaligned>
(row + m_startRow.value(), col + m_startCol.value());
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
inline void writePacket(int row, int col, const PacketScalar& x)
{
m_xpr.const_cast_derived().template writePacket<Unaligned>
m_matrix.const_cast_derived().template writePacket<Unaligned>
(row + m_startRow.value(), col + m_startCol.value(), x);
}
template<int LoadMode>
inline PacketScalar packet(Index index) const
inline PacketScalar packet(int index) const
{
return m_xpr.template packet<Unaligned>
return m_matrix.template packet<Unaligned>
(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
inline void writePacket(int index, const PacketScalar& x)
{
m_xpr.const_cast_derived().template writePacket<Unaligned>
m_matrix.const_cast_derived().template writePacket<Unaligned>
(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), x);
}
#ifdef EIGEN_PARSED_BY_DOXYGEN
/** \sa MapBase::data() */
inline const Scalar* data() const;
inline Index innerStride() const;
inline Index outerStride() const;
#endif
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 typename MatrixType::Nested m_matrix;
const ei_int_if_dynamic<MatrixType::RowsAtCompileTime == 1 ? 0 : Dynamic> m_startRow;
const ei_int_if_dynamic<MatrixType::ColsAtCompileTime == 1 ? 0 : Dynamic> m_startCol;
const ei_int_if_dynamic<RowsAtCompileTime> m_blockRows;
const ei_int_if_dynamic<ColsAtCompileTime> m_blockCols;
};
/** \internal */
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
class Block<XprType,BlockRows,BlockCols, InnerPanel,true>
: public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel, true> >
template<typename MatrixType, int BlockRows, int BlockCols, int PacketAccess>
class Block<MatrixType,BlockRows,BlockCols,PacketAccess,HasDirectAccess>
: public MapBase<Block<MatrixType, BlockRows, BlockCols,PacketAccess,HasDirectAccess> >
{
public:
typedef MapBase<Block> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Block)
_EIGEN_GENERIC_PUBLIC_INTERFACE(Block, MapBase<Block>)
class InnerIterator;
typedef typename ei_traits<Block>::AlignedDerivedType AlignedDerivedType;
friend class Block<MatrixType,BlockRows,BlockCols,PacketAccess==AsRequested?ForceAligned:AsRequested,HasDirectAccess>;
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
AlignedDerivedType _convertToForceAligned()
{
return Block<MatrixType,BlockRows,BlockCols,ForceAligned,HasDirectAccess>
(m_matrix, Base::m_data, Base::m_rows.value(), Base::m_cols.value());
}
/** Column or Row constructor
*/
inline Block(XprType& xpr, Index i)
: Base(internal::const_cast_ptr(&xpr.coeffRef(
(BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0,
(BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0)),
BlockRows==1 ? 1 : xpr.rows(),
BlockCols==1 ? 1 : xpr.cols()),
m_xpr(xpr)
inline Block(const MatrixType& matrix, int i)
: Base(&matrix.const_cast_derived().coeffRef(
(BlockRows==1) && (BlockCols==MatrixType::ColsAtCompileTime) ? i : 0,
(BlockRows==MatrixType::RowsAtCompileTime) && (BlockCols==1) ? i : 0),
BlockRows==1 ? 1 : matrix.rows(),
BlockCols==1 ? 1 : matrix.cols()),
m_matrix(matrix)
{
eigen_assert( (i>=0) && (
((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
init();
ei_assert( (i>=0) && (
((BlockRows==1) && (BlockCols==MatrixType::ColsAtCompileTime) && i<matrix.rows())
||((BlockRows==MatrixType::RowsAtCompileTime) && (BlockCols==1) && i<matrix.cols())));
}
/** Fixed-size constructor
*/
inline Block(XprType& xpr, Index startRow, Index startCol)
: Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol))), m_xpr(xpr)
inline Block(const MatrixType& matrix, int startRow, int startCol)
: Base(&matrix.const_cast_derived().coeffRef(startRow,startCol)), m_matrix(matrix)
{
eigen_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows()
&& startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols());
init();
ei_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= matrix.rows()
&& startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= matrix.cols());
}
/** Dynamic-size constructor
*/
inline Block(XprType& xpr,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol)), blockRows, blockCols),
m_xpr(xpr)
inline Block(const MatrixType& matrix,
int startRow, int startCol,
int blockRows, int blockCols)
: Base(&matrix.const_cast_derived().coeffRef(startRow,startCol), blockRows, blockCols),
m_matrix(matrix)
{
eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
ei_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
&& (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
eigen_assert(startRow >= 0 && blockRows >= 0 && startRow + blockRows <= xpr.rows()
&& startCol >= 0 && blockCols >= 0 && startCol + blockCols <= xpr.cols());
init();
ei_assert(startRow >= 0 && blockRows >= 1 && startRow + blockRows <= matrix.rows()
&& startCol >= 0 && blockCols >= 1 && startCol + blockCols <= matrix.cols());
}
/** \sa MapBase::innerStride() */
inline Index innerStride() const
{
return internal::traits<Block>::HasSameStorageOrderAsXprType
? m_xpr.innerStride()
: m_xpr.outerStride();
}
inline int stride(void) const { return m_matrix.stride(); }
/** \sa MapBase::outerStride() */
inline Index outerStride() const
{
return m_outerStride;
}
#ifndef __SUNPRO_CC
// FIXME sunstudio is not friendly with the above friend...
// META-FIXME there is no 'friend' keyword around here. Is this obsolete?
protected:
#endif
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal used by allowAligned() */
inline Block(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols)
: Base(data, blockRows, blockCols), m_xpr(xpr)
{
init();
}
#endif
inline Block(const MatrixType& matrix, const Scalar* data, int blockRows, int blockCols)
: Base(data, blockRows, blockCols), m_matrix(matrix)
{}
protected:
void init()
{
m_outerStride = internal::traits<Block>::HasSameStorageOrderAsXprType
? m_xpr.outerStride()
: m_xpr.innerStride();
}
const typename XprType::Nested m_xpr;
int m_outerStride;
const typename MatrixType::Nested m_matrix;
};
/** \returns a dynamic-size expression of a block in *this.
*
* \param startRow the first row in the block
* \param startCol the first column in the block
* \param blockRows the number of rows in the block
* \param blockCols the number of columns in the block
*
* \addexample BlockIntIntIntInt \label How to reference a sub-matrix (dynamic-size)
*
* Example: \include MatrixBase_block_int_int_int_int.cpp
* Output: \verbinclude MatrixBase_block_int_int_int_int.out
*
* \note Even though the returned expression has dynamic size, in the case
* when it is applied to a fixed-size matrix, it inherits a fixed maximal size,
* which means that evaluating it does not cause a dynamic memory allocation.
*
* \sa class Block, block(int,int)
*/
template<typename Derived>
inline typename BlockReturnType<Derived>::Type MatrixBase<Derived>
::block(int startRow, int startCol, int blockRows, int blockCols)
{
return typename BlockReturnType<Derived>::Type(derived(), startRow, startCol, blockRows, blockCols);
}
/** This is the const version of block(int,int,int,int). */
template<typename Derived>
inline const typename BlockReturnType<Derived>::Type MatrixBase<Derived>
::block(int startRow, int startCol, int blockRows, int blockCols) const
{
return typename BlockReturnType<Derived>::Type(derived(), startRow, startCol, blockRows, blockCols);
}
/** \returns a dynamic-size expression of a segment (i.e. a vector block) in *this.
*
* \only_for_vectors
*
* \addexample SegmentIntInt \label How to reference a sub-vector (dynamic size)
*
* \param start the first coefficient in the segment
* \param size the number of coefficients in the segment
*
* Example: \include MatrixBase_segment_int_int.cpp
* Output: \verbinclude MatrixBase_segment_int_int.out
*
* \note Even though the returned expression has dynamic size, in the case
* when it is applied to a fixed-size vector, it inherits a fixed maximal size,
* which means that evaluating it does not cause a dynamic memory allocation.
*
* \sa class Block, segment(int)
*/
template<typename Derived>
inline typename BlockReturnType<Derived>::SubVectorType MatrixBase<Derived>
::segment(int start, int size)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return typename BlockReturnType<Derived>::SubVectorType(derived(), RowsAtCompileTime == 1 ? 0 : start,
ColsAtCompileTime == 1 ? 0 : start,
RowsAtCompileTime == 1 ? 1 : size,
ColsAtCompileTime == 1 ? 1 : size);
}
/** This is the const version of segment(int,int).*/
template<typename Derived>
inline const typename BlockReturnType<Derived>::SubVectorType
MatrixBase<Derived>::segment(int start, int size) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return typename BlockReturnType<Derived>::SubVectorType(derived(), RowsAtCompileTime == 1 ? 0 : start,
ColsAtCompileTime == 1 ? 0 : start,
RowsAtCompileTime == 1 ? 1 : size,
ColsAtCompileTime == 1 ? 1 : size);
}
/** \returns a dynamic-size expression of the first coefficients of *this.
*
* \only_for_vectors
*
* \param size the number of coefficients in the block
*
* \addexample BlockInt \label How to reference a sub-vector (fixed-size)
*
* Example: \include MatrixBase_start_int.cpp
* Output: \verbinclude MatrixBase_start_int.out
*
* \note Even though the returned expression has dynamic size, in the case
* when it is applied to a fixed-size vector, it inherits a fixed maximal size,
* which means that evaluating it does not cause a dynamic memory allocation.
*
* \sa class Block, block(int,int)
*/
template<typename Derived>
inline typename BlockReturnType<Derived,Dynamic>::SubVectorType
MatrixBase<Derived>::start(int size)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return Block<Derived,
RowsAtCompileTime == 1 ? 1 : Dynamic,
ColsAtCompileTime == 1 ? 1 : Dynamic>
(derived(), 0, 0,
RowsAtCompileTime == 1 ? 1 : size,
ColsAtCompileTime == 1 ? 1 : size);
}
/** This is the const version of start(int).*/
template<typename Derived>
inline const typename BlockReturnType<Derived,Dynamic>::SubVectorType
MatrixBase<Derived>::start(int size) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return Block<Derived,
RowsAtCompileTime == 1 ? 1 : Dynamic,
ColsAtCompileTime == 1 ? 1 : Dynamic>
(derived(), 0, 0,
RowsAtCompileTime == 1 ? 1 : size,
ColsAtCompileTime == 1 ? 1 : size);
}
/** \returns a dynamic-size expression of the last coefficients of *this.
*
* \only_for_vectors
*
* \param size the number of coefficients in the block
*
* \addexample BlockEnd \label How to reference the end of a vector (fixed-size)
*
* Example: \include MatrixBase_end_int.cpp
* Output: \verbinclude MatrixBase_end_int.out
*
* \note Even though the returned expression has dynamic size, in the case
* when it is applied to a fixed-size vector, it inherits a fixed maximal size,
* which means that evaluating it does not cause a dynamic memory allocation.
*
* \sa class Block, block(int,int)
*/
template<typename Derived>
inline typename BlockReturnType<Derived,Dynamic>::SubVectorType
MatrixBase<Derived>::end(int size)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return Block<Derived,
RowsAtCompileTime == 1 ? 1 : Dynamic,
ColsAtCompileTime == 1 ? 1 : Dynamic>
(derived(),
RowsAtCompileTime == 1 ? 0 : rows() - size,
ColsAtCompileTime == 1 ? 0 : cols() - size,
RowsAtCompileTime == 1 ? 1 : size,
ColsAtCompileTime == 1 ? 1 : size);
}
/** This is the const version of end(int).*/
template<typename Derived>
inline const typename BlockReturnType<Derived,Dynamic>::SubVectorType
MatrixBase<Derived>::end(int size) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return Block<Derived,
RowsAtCompileTime == 1 ? 1 : Dynamic,
ColsAtCompileTime == 1 ? 1 : Dynamic>
(derived(),
RowsAtCompileTime == 1 ? 0 : rows() - size,
ColsAtCompileTime == 1 ? 0 : cols() - size,
RowsAtCompileTime == 1 ? 1 : size,
ColsAtCompileTime == 1 ? 1 : size);
}
/** \returns a fixed-size expression of a segment (i.e. a vector block) in \c *this
*
* \only_for_vectors
*
* The template parameter \a Size is the number of coefficients in the block
*
* \param start the index of the first element of the sub-vector
*
* Example: \include MatrixBase_template_int_segment.cpp
* Output: \verbinclude MatrixBase_template_int_segment.out
*
* \sa class Block
*/
template<typename Derived>
template<int Size>
inline typename BlockReturnType<Derived,Size>::SubVectorType
MatrixBase<Derived>::segment(int start)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return Block<Derived, (RowsAtCompileTime == 1 ? 1 : Size),
(ColsAtCompileTime == 1 ? 1 : Size)>
(derived(), RowsAtCompileTime == 1 ? 0 : start,
ColsAtCompileTime == 1 ? 0 : start);
}
/** This is the const version of segment<int>(int).*/
template<typename Derived>
template<int Size>
inline const typename BlockReturnType<Derived,Size>::SubVectorType
MatrixBase<Derived>::segment(int start) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return Block<Derived, (RowsAtCompileTime == 1 ? 1 : Size),
(ColsAtCompileTime == 1 ? 1 : Size)>
(derived(), RowsAtCompileTime == 1 ? 0 : start,
ColsAtCompileTime == 1 ? 0 : start);
}
/** \returns a fixed-size expression of the first coefficients of *this.
*
* \only_for_vectors
*
* The template parameter \a Size is the number of coefficients in the block
*
* \addexample BlockStart \label How to reference the start of a vector (fixed-size)
*
* Example: \include MatrixBase_template_int_start.cpp
* Output: \verbinclude MatrixBase_template_int_start.out
*
* \sa class Block
*/
template<typename Derived>
template<int Size>
inline typename BlockReturnType<Derived,Size>::SubVectorType
MatrixBase<Derived>::start()
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return Block<Derived, (RowsAtCompileTime == 1 ? 1 : Size),
(ColsAtCompileTime == 1 ? 1 : Size)>(derived(), 0, 0);
}
/** This is the const version of start<int>().*/
template<typename Derived>
template<int Size>
inline const typename BlockReturnType<Derived,Size>::SubVectorType
MatrixBase<Derived>::start() const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return Block<Derived, (RowsAtCompileTime == 1 ? 1 : Size),
(ColsAtCompileTime == 1 ? 1 : Size)>(derived(), 0, 0);
}
/** \returns a fixed-size expression of the last coefficients of *this.
*
* \only_for_vectors
*
* The template parameter \a Size is the number of coefficients in the block
*
* Example: \include MatrixBase_template_int_end.cpp
* Output: \verbinclude MatrixBase_template_int_end.out
*
* \sa class Block
*/
template<typename Derived>
template<int Size>
inline typename BlockReturnType<Derived,Size>::SubVectorType
MatrixBase<Derived>::end()
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return Block<Derived, RowsAtCompileTime == 1 ? 1 : Size,
ColsAtCompileTime == 1 ? 1 : Size>
(derived(),
RowsAtCompileTime == 1 ? 0 : rows() - Size,
ColsAtCompileTime == 1 ? 0 : cols() - Size);
}
/** This is the const version of end<int>.*/
template<typename Derived>
template<int Size>
inline const typename BlockReturnType<Derived,Size>::SubVectorType
MatrixBase<Derived>::end() const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return Block<Derived, RowsAtCompileTime == 1 ? 1 : Size,
ColsAtCompileTime == 1 ? 1 : Size>
(derived(),
RowsAtCompileTime == 1 ? 0 : rows() - Size,
ColsAtCompileTime == 1 ? 0 : cols() - Size);
}
/** \returns a dynamic-size expression of a corner of *this.
*
* \param type the type of corner. Can be \a Eigen::TopLeft, \a Eigen::TopRight,
* \a Eigen::BottomLeft, \a Eigen::BottomRight.
* \param cRows the number of rows in the corner
* \param cCols the number of columns in the corner
*
* \addexample BlockCornerDynamicSize \label How to reference a sub-corner of a matrix
*
* Example: \include MatrixBase_corner_enum_int_int.cpp
* Output: \verbinclude MatrixBase_corner_enum_int_int.out
*
* \note Even though the returned expression has dynamic size, in the case
* when it is applied to a fixed-size matrix, it inherits a fixed maximal size,
* which means that evaluating it does not cause a dynamic memory allocation.
*
* \sa class Block, block(int,int,int,int)
*/
template<typename Derived>
inline typename BlockReturnType<Derived>::Type MatrixBase<Derived>
::corner(CornerType type, int cRows, int cCols)
{
switch(type)
{
default:
ei_assert(false && "Bad corner type.");
case TopLeft:
return typename BlockReturnType<Derived>::Type(derived(), 0, 0, cRows, cCols);
case TopRight:
return typename BlockReturnType<Derived>::Type(derived(), 0, cols() - cCols, cRows, cCols);
case BottomLeft:
return typename BlockReturnType<Derived>::Type(derived(), rows() - cRows, 0, cRows, cCols);
case BottomRight:
return typename BlockReturnType<Derived>::Type(derived(), rows() - cRows, cols() - cCols, cRows, cCols);
}
}
/** This is the const version of corner(CornerType, int, int).*/
template<typename Derived>
inline const typename BlockReturnType<Derived>::Type
MatrixBase<Derived>::corner(CornerType type, int cRows, int cCols) const
{
switch(type)
{
default:
ei_assert(false && "Bad corner type.");
case TopLeft:
return typename BlockReturnType<Derived>::Type(derived(), 0, 0, cRows, cCols);
case TopRight:
return typename BlockReturnType<Derived>::Type(derived(), 0, cols() - cCols, cRows, cCols);
case BottomLeft:
return typename BlockReturnType<Derived>::Type(derived(), rows() - cRows, 0, cRows, cCols);
case BottomRight:
return typename BlockReturnType<Derived>::Type(derived(), rows() - cRows, cols() - cCols, cRows, cCols);
}
}
/** \returns a fixed-size expression of a corner of *this.
*
* \param type the type of corner. Can be \a Eigen::TopLeft, \a Eigen::TopRight,
* \a Eigen::BottomLeft, \a Eigen::BottomRight.
*
* The template parameters CRows and CCols arethe number of rows and columns in the corner.
*
* Example: \include MatrixBase_template_int_int_corner_enum.cpp
* Output: \verbinclude MatrixBase_template_int_int_corner_enum.out
*
* \sa class Block, block(int,int,int,int)
*/
template<typename Derived>
template<int CRows, int CCols>
inline typename BlockReturnType<Derived, CRows, CCols>::Type
MatrixBase<Derived>::corner(CornerType type)
{
switch(type)
{
default:
ei_assert(false && "Bad corner type.");
case TopLeft:
return Block<Derived, CRows, CCols>(derived(), 0, 0);
case TopRight:
return Block<Derived, CRows, CCols>(derived(), 0, cols() - CCols);
case BottomLeft:
return Block<Derived, CRows, CCols>(derived(), rows() - CRows, 0);
case BottomRight:
return Block<Derived, CRows, CCols>(derived(), rows() - CRows, cols() - CCols);
}
}
/** This is the const version of corner<int, int>(CornerType).*/
template<typename Derived>
template<int CRows, int CCols>
inline const typename BlockReturnType<Derived, CRows, CCols>::Type
MatrixBase<Derived>::corner(CornerType type) const
{
switch(type)
{
default:
ei_assert(false && "Bad corner type.");
case TopLeft:
return Block<Derived, CRows, CCols>(derived(), 0, 0);
case TopRight:
return Block<Derived, CRows, CCols>(derived(), 0, cols() - CCols);
case BottomLeft:
return Block<Derived, CRows, CCols>(derived(), rows() - CRows, 0);
case BottomRight:
return Block<Derived, CRows, CCols>(derived(), rows() - CRows, cols() - CCols);
}
}
/** \returns a fixed-size expression of a block in *this.
*
* The template parameters \a BlockRows and \a BlockCols are the number of
* rows and columns in the block.
*
* \param startRow the first row in the block
* \param startCol the first column in the block
*
* \addexample BlockSubMatrixFixedSize \label How to reference a sub-matrix (fixed-size)
*
* Example: \include MatrixBase_block_int_int.cpp
* Output: \verbinclude MatrixBase_block_int_int.out
*
* \note since block is a templated member, the keyword template has to be used
* if the matrix type is also a template parameter: \code m.template block<3,3>(1,1); \endcode
*
* \sa class Block, block(int,int,int,int)
*/
template<typename Derived>
template<int BlockRows, int BlockCols>
inline typename BlockReturnType<Derived, BlockRows, BlockCols>::Type
MatrixBase<Derived>::block(int startRow, int startCol)
{
return Block<Derived, BlockRows, BlockCols>(derived(), startRow, startCol);
}
/** This is the const version of block<>(int, int). */
template<typename Derived>
template<int BlockRows, int BlockCols>
inline const typename BlockReturnType<Derived, BlockRows, BlockCols>::Type
MatrixBase<Derived>::block(int startRow, int startCol) const
{
return Block<Derived, BlockRows, BlockCols>(derived(), startRow, startCol);
}
/** \returns an expression of the \a i-th column of *this. Note that the numbering starts at 0.
*
* \addexample BlockColumn \label How to reference a single column of a matrix
*
* Example: \include MatrixBase_col.cpp
* Output: \verbinclude MatrixBase_col.out
*
* \sa row(), class Block */
template<typename Derived>
inline typename MatrixBase<Derived>::ColXpr
MatrixBase<Derived>::col(int i)
{
return ColXpr(derived(), i);
}
/** This is the const version of col(). */
template<typename Derived>
inline const typename MatrixBase<Derived>::ColXpr
MatrixBase<Derived>::col(int i) const
{
return ColXpr(derived(), i);
}
/** \returns an expression of the \a i-th row of *this. Note that the numbering starts at 0.
*
* \addexample BlockRow \label How to reference a single row of a matrix
*
* Example: \include MatrixBase_row.cpp
* Output: \verbinclude MatrixBase_row.out
*
* \sa col(), class Block */
template<typename Derived>
inline typename MatrixBase<Derived>::RowXpr
MatrixBase<Derived>::row(int i)
{
return RowXpr(derived(), i);
}
/** This is the const version of row(). */
template<typename Derived>
inline const typename MatrixBase<Derived>::RowXpr
MatrixBase<Derived>::row(int i) const
{
return RowXpr(derived(), i);
}
#endif // EIGEN_BLOCK_H

View File

@@ -2,9 +2,8 @@ FILE(GLOB Eigen_Core_SRCS "*.h")
INSTALL(FILES
${Eigen_Core_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core COMPONENT Devel
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core
)
ADD_SUBDIRECTORY(products)
ADD_SUBDIRECTORY(util)
ADD_SUBDIRECTORY(arch)

View File

@@ -0,0 +1,753 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_CACHE_FRIENDLY_PRODUCT_H
#define EIGEN_CACHE_FRIENDLY_PRODUCT_H
template <int L2MemorySize,typename Scalar>
struct ei_L2_block_traits {
enum {width = 8 * ei_meta_sqrt<L2MemorySize/(64*sizeof(Scalar))>::ret };
};
#ifndef EIGEN_EXTERN_INSTANTIATIONS
template<typename Scalar>
static void ei_cache_friendly_product(
int _rows, int _cols, int depth,
bool _lhsRowMajor, const Scalar* _lhs, int _lhsStride,
bool _rhsRowMajor, const Scalar* _rhs, int _rhsStride,
bool resRowMajor, Scalar* res, int resStride)
{
const Scalar* EIGEN_RESTRICT lhs;
const Scalar* EIGEN_RESTRICT rhs;
int lhsStride, rhsStride, rows, cols;
bool lhsRowMajor;
if (resRowMajor)
{
lhs = _rhs;
rhs = _lhs;
lhsStride = _rhsStride;
rhsStride = _lhsStride;
cols = _rows;
rows = _cols;
lhsRowMajor = !_rhsRowMajor;
ei_assert(_lhsRowMajor);
}
else
{
lhs = _lhs;
rhs = _rhs;
lhsStride = _lhsStride;
rhsStride = _rhsStride;
rows = _rows;
cols = _cols;
lhsRowMajor = _lhsRowMajor;
ei_assert(!_rhsRowMajor);
}
typedef typename ei_packet_traits<Scalar>::type PacketType;
enum {
PacketSize = sizeof(PacketType)/sizeof(Scalar),
#if (defined __i386__)
// i386 architecture provides only 8 xmm registers,
// so let's reduce the max number of rows processed at once.
MaxBlockRows = 4,
MaxBlockRows_ClampingMask = 0xFFFFFC,
#else
MaxBlockRows = 8,
MaxBlockRows_ClampingMask = 0xFFFFF8,
#endif
// maximal size of the blocks fitted in L2 cache
MaxL2BlockSize = ei_L2_block_traits<EIGEN_TUNE_FOR_CPU_CACHE_SIZE,Scalar>::width
};
const bool resIsAligned = (PacketSize==1) || (((resStride%PacketSize) == 0) && (size_t(res)%16==0));
const int remainingSize = depth % PacketSize;
const int size = depth - remainingSize; // third dimension of the product clamped to packet boundaries
const int l2BlockRows = MaxL2BlockSize > rows ? rows : MaxL2BlockSize;
const int l2BlockCols = MaxL2BlockSize > cols ? cols : MaxL2BlockSize;
const int l2BlockSize = MaxL2BlockSize > size ? size : MaxL2BlockSize;
const int l2BlockSizeAligned = (1 + std::max(l2BlockSize,l2BlockCols)/PacketSize)*PacketSize;
const bool needRhsCopy = (PacketSize>1) && ((rhsStride%PacketSize!=0) || (size_t(rhs)%16!=0));
Scalar* EIGEN_RESTRICT block = 0;
const int allocBlockSize = l2BlockRows*size;
block = ei_aligned_stack_new(Scalar, allocBlockSize);
Scalar* EIGEN_RESTRICT rhsCopy
= ei_aligned_stack_new(Scalar, l2BlockSizeAligned*l2BlockSizeAligned);
// loops on each L2 cache friendly blocks of the result
for(int l2i=0; l2i<rows; l2i+=l2BlockRows)
{
const int l2blockRowEnd = std::min(l2i+l2BlockRows, rows);
const int l2blockRowEndBW = l2blockRowEnd & MaxBlockRows_ClampingMask; // end of the rows aligned to bw
const int l2blockRemainingRows = l2blockRowEnd - l2blockRowEndBW; // number of remaining rows
//const int l2blockRowEndBWPlusOne = l2blockRowEndBW + (l2blockRemainingRows?0:MaxBlockRows);
// build a cache friendly blocky matrix
int count = 0;
// copy l2blocksize rows of m_lhs to blocks of ps x bw
for(int l2k=0; l2k<size; l2k+=l2BlockSize)
{
const int l2blockSizeEnd = std::min(l2k+l2BlockSize, size);
for (int i = l2i; i<l2blockRowEndBW/*PlusOne*/; i+=MaxBlockRows)
{
// TODO merge the "if l2blockRemainingRows" using something like:
// const int blockRows = std::min(i+MaxBlockRows, rows) - i;
for (int k=l2k; k<l2blockSizeEnd; k+=PacketSize)
{
// TODO write these loops using meta unrolling
// negligible for large matrices but useful for small ones
if (lhsRowMajor)
{
for (int w=0; w<MaxBlockRows; ++w)
for (int s=0; s<PacketSize; ++s)
block[count++] = lhs[(i+w)*lhsStride + (k+s)];
}
else
{
for (int w=0; w<MaxBlockRows; ++w)
for (int s=0; s<PacketSize; ++s)
block[count++] = lhs[(i+w) + (k+s)*lhsStride];
}
}
}
if (l2blockRemainingRows>0)
{
for (int k=l2k; k<l2blockSizeEnd; k+=PacketSize)
{
if (lhsRowMajor)
{
for (int w=0; w<l2blockRemainingRows; ++w)
for (int s=0; s<PacketSize; ++s)
block[count++] = lhs[(l2blockRowEndBW+w)*lhsStride + (k+s)];
}
else
{
for (int w=0; w<l2blockRemainingRows; ++w)
for (int s=0; s<PacketSize; ++s)
block[count++] = lhs[(l2blockRowEndBW+w) + (k+s)*lhsStride];
}
}
}
}
for(int l2j=0; l2j<cols; l2j+=l2BlockCols)
{
int l2blockColEnd = std::min(l2j+l2BlockCols, cols);
for(int l2k=0; l2k<size; l2k+=l2BlockSize)
{
// acumulate bw rows of lhs time a single column of rhs to a bw x 1 block of res
int l2blockSizeEnd = std::min(l2k+l2BlockSize, size);
// if not aligned, copy the rhs block
if (needRhsCopy)
for(int l1j=l2j; l1j<l2blockColEnd; l1j+=1)
{
ei_internal_assert(l2BlockSizeAligned*(l1j-l2j)+(l2blockSizeEnd-l2k) < l2BlockSizeAligned*l2BlockSizeAligned);
memcpy(rhsCopy+l2BlockSizeAligned*(l1j-l2j),&(rhs[l1j*rhsStride+l2k]),(l2blockSizeEnd-l2k)*sizeof(Scalar));
}
// for each bw x 1 result's block
for(int l1i=l2i; l1i<l2blockRowEndBW; l1i+=MaxBlockRows)
{
int offsetblock = l2k * (l2blockRowEnd-l2i) + (l1i-l2i)*(l2blockSizeEnd-l2k) - l2k*MaxBlockRows;
const Scalar* EIGEN_RESTRICT localB = &block[offsetblock];
for(int l1j=l2j; l1j<l2blockColEnd; l1j+=1)
{
const Scalar* EIGEN_RESTRICT rhsColumn;
if (needRhsCopy)
rhsColumn = &(rhsCopy[l2BlockSizeAligned*(l1j-l2j)-l2k]);
else
rhsColumn = &(rhs[l1j*rhsStride]);
PacketType dst[MaxBlockRows];
dst[3] = dst[2] = dst[1] = dst[0] = ei_pset1(Scalar(0.));
if (MaxBlockRows==8)
dst[7] = dst[6] = dst[5] = dst[4] = dst[0];
PacketType tmp;
for(int k=l2k; k<l2blockSizeEnd; k+=PacketSize)
{
tmp = ei_ploadu(&rhsColumn[k]);
PacketType A0, A1, A2, A3, A4, A5;
A0 = ei_pload(localB + k*MaxBlockRows);
A1 = ei_pload(localB + k*MaxBlockRows+1*PacketSize);
A2 = ei_pload(localB + k*MaxBlockRows+2*PacketSize);
A3 = ei_pload(localB + k*MaxBlockRows+3*PacketSize);
if (MaxBlockRows==8) A4 = ei_pload(localB + k*MaxBlockRows+4*PacketSize);
if (MaxBlockRows==8) A5 = ei_pload(localB + k*MaxBlockRows+5*PacketSize);
dst[0] = ei_pmadd(tmp, A0, dst[0]);
if (MaxBlockRows==8) A0 = ei_pload(localB + k*MaxBlockRows+6*PacketSize);
dst[1] = ei_pmadd(tmp, A1, dst[1]);
if (MaxBlockRows==8) A1 = ei_pload(localB + k*MaxBlockRows+7*PacketSize);
dst[2] = ei_pmadd(tmp, A2, dst[2]);
dst[3] = ei_pmadd(tmp, A3, dst[3]);
if (MaxBlockRows==8)
{
dst[4] = ei_pmadd(tmp, A4, dst[4]);
dst[5] = ei_pmadd(tmp, A5, dst[5]);
dst[6] = ei_pmadd(tmp, A0, dst[6]);
dst[7] = ei_pmadd(tmp, A1, dst[7]);
}
}
Scalar* EIGEN_RESTRICT localRes = &(res[l1i + l1j*resStride]);
if (PacketSize>1 && resIsAligned)
{
// the result is aligned: let's do packet reduction
ei_pstore(&(localRes[0]), ei_padd(ei_pload(&(localRes[0])), ei_preduxp(&dst[0])));
if (PacketSize==2)
ei_pstore(&(localRes[2]), ei_padd(ei_pload(&(localRes[2])), ei_preduxp(&(dst[2]))));
if (MaxBlockRows==8)
{
ei_pstore(&(localRes[4]), ei_padd(ei_pload(&(localRes[4])), ei_preduxp(&(dst[4]))));
if (PacketSize==2)
ei_pstore(&(localRes[6]), ei_padd(ei_pload(&(localRes[6])), ei_preduxp(&(dst[6]))));
}
}
else
{
// not aligned => per coeff packet reduction
localRes[0] += ei_predux(dst[0]);
localRes[1] += ei_predux(dst[1]);
localRes[2] += ei_predux(dst[2]);
localRes[3] += ei_predux(dst[3]);
if (MaxBlockRows==8)
{
localRes[4] += ei_predux(dst[4]);
localRes[5] += ei_predux(dst[5]);
localRes[6] += ei_predux(dst[6]);
localRes[7] += ei_predux(dst[7]);
}
}
}
}
if (l2blockRemainingRows>0)
{
int offsetblock = l2k * (l2blockRowEnd-l2i) + (l2blockRowEndBW-l2i)*(l2blockSizeEnd-l2k) - l2k*l2blockRemainingRows;
const Scalar* localB = &block[offsetblock];
for(int l1j=l2j; l1j<l2blockColEnd; l1j+=1)
{
const Scalar* EIGEN_RESTRICT rhsColumn;
if (needRhsCopy)
rhsColumn = &(rhsCopy[l2BlockSizeAligned*(l1j-l2j)-l2k]);
else
rhsColumn = &(rhs[l1j*rhsStride]);
PacketType dst[MaxBlockRows];
dst[3] = dst[2] = dst[1] = dst[0] = ei_pset1(Scalar(0.));
if (MaxBlockRows==8)
dst[7] = dst[6] = dst[5] = dst[4] = dst[0];
// let's declare a few other temporary registers
PacketType tmp;
for(int k=l2k; k<l2blockSizeEnd; k+=PacketSize)
{
tmp = ei_pload(&rhsColumn[k]);
dst[0] = ei_pmadd(tmp, ei_pload(&(localB[k*l2blockRemainingRows ])), dst[0]);
if (l2blockRemainingRows>=2) dst[1] = ei_pmadd(tmp, ei_pload(&(localB[k*l2blockRemainingRows+ PacketSize])), dst[1]);
if (l2blockRemainingRows>=3) dst[2] = ei_pmadd(tmp, ei_pload(&(localB[k*l2blockRemainingRows+2*PacketSize])), dst[2]);
if (l2blockRemainingRows>=4) dst[3] = ei_pmadd(tmp, ei_pload(&(localB[k*l2blockRemainingRows+3*PacketSize])), dst[3]);
if (MaxBlockRows==8)
{
if (l2blockRemainingRows>=5) dst[4] = ei_pmadd(tmp, ei_pload(&(localB[k*l2blockRemainingRows+4*PacketSize])), dst[4]);
if (l2blockRemainingRows>=6) dst[5] = ei_pmadd(tmp, ei_pload(&(localB[k*l2blockRemainingRows+5*PacketSize])), dst[5]);
if (l2blockRemainingRows>=7) dst[6] = ei_pmadd(tmp, ei_pload(&(localB[k*l2blockRemainingRows+6*PacketSize])), dst[6]);
if (l2blockRemainingRows>=8) dst[7] = ei_pmadd(tmp, ei_pload(&(localB[k*l2blockRemainingRows+7*PacketSize])), dst[7]);
}
}
Scalar* EIGEN_RESTRICT localRes = &(res[l2blockRowEndBW + l1j*resStride]);
// process the remaining rows once at a time
localRes[0] += ei_predux(dst[0]);
if (l2blockRemainingRows>=2) localRes[1] += ei_predux(dst[1]);
if (l2blockRemainingRows>=3) localRes[2] += ei_predux(dst[2]);
if (l2blockRemainingRows>=4) localRes[3] += ei_predux(dst[3]);
if (MaxBlockRows==8)
{
if (l2blockRemainingRows>=5) localRes[4] += ei_predux(dst[4]);
if (l2blockRemainingRows>=6) localRes[5] += ei_predux(dst[5]);
if (l2blockRemainingRows>=7) localRes[6] += ei_predux(dst[6]);
if (l2blockRemainingRows>=8) localRes[7] += ei_predux(dst[7]);
}
}
}
}
}
}
if (PacketSize>1 && remainingSize)
{
if (lhsRowMajor)
{
for (int j=0; j<cols; ++j)
for (int i=0; i<rows; ++i)
{
Scalar tmp = lhs[i*lhsStride+size] * rhs[j*rhsStride+size];
// FIXME this loop get vectorized by the compiler !
for (int k=1; k<remainingSize; ++k)
tmp += lhs[i*lhsStride+size+k] * rhs[j*rhsStride+size+k];
res[i+j*resStride] += tmp;
}
}
else
{
for (int j=0; j<cols; ++j)
for (int i=0; i<rows; ++i)
{
Scalar tmp = lhs[i+size*lhsStride] * rhs[j*rhsStride+size];
for (int k=1; k<remainingSize; ++k)
tmp += lhs[i+(size+k)*lhsStride] * rhs[j*rhsStride+size+k];
res[i+j*resStride] += tmp;
}
}
}
ei_aligned_stack_delete(Scalar, block, allocBlockSize);
ei_aligned_stack_delete(Scalar, rhsCopy, l2BlockSizeAligned*l2BlockSizeAligned);
}
#endif // EIGEN_EXTERN_INSTANTIATIONS
/* Optimized col-major matrix * vector product:
* This algorithm processes 4 columns at onces that allows to both reduce
* the number of load/stores of the result by a factor 4 and to reduce
* the instruction dependency. Moreover, we know that all bands have the
* same alignment pattern.
* TODO: since rhs gets evaluated only once, no need to evaluate it
*/
template<typename Scalar, typename RhsType>
static EIGEN_DONT_INLINE void ei_cache_friendly_product_colmajor_times_vector(
int size,
const Scalar* lhs, int lhsStride,
const RhsType& rhs,
Scalar* res)
{
#ifdef _EIGEN_ACCUMULATE_PACKETS
#error _EIGEN_ACCUMULATE_PACKETS has already been defined
#endif
#define _EIGEN_ACCUMULATE_PACKETS(A0,A13,A2) \
ei_pstore(&res[j], \
ei_padd(ei_pload(&res[j]), \
ei_padd( \
ei_padd(ei_pmul(ptmp0,EIGEN_CAT(ei_ploa , A0)(&lhs0[j])), \
ei_pmul(ptmp1,EIGEN_CAT(ei_ploa , A13)(&lhs1[j]))), \
ei_padd(ei_pmul(ptmp2,EIGEN_CAT(ei_ploa , A2)(&lhs2[j])), \
ei_pmul(ptmp3,EIGEN_CAT(ei_ploa , A13)(&lhs3[j]))) )))
typedef typename ei_packet_traits<Scalar>::type Packet;
const int PacketSize = sizeof(Packet)/sizeof(Scalar);
enum { AllAligned = 0, EvenAligned, FirstAligned, NoneAligned };
const int columnsAtOnce = 4;
const int peels = 2;
const int PacketAlignedMask = PacketSize-1;
const int PeelAlignedMask = PacketSize*peels-1;
// How many coeffs of the result do we have to skip to be aligned.
// Here we assume data are at least aligned on the base scalar type that is mandatory anyway.
const int alignedStart = ei_alignmentOffset(res,size);
const int alignedSize = PacketSize>1 ? alignedStart + ((size-alignedStart) & ~PacketAlignedMask) : 0;
const int peeledSize = peels>1 ? alignedStart + ((alignedSize-alignedStart) & ~PeelAlignedMask) : alignedStart;
const int alignmentStep = PacketSize>1 ? (PacketSize - lhsStride % PacketSize) & PacketAlignedMask : 0;
int alignmentPattern = alignmentStep==0 ? AllAligned
: alignmentStep==(PacketSize/2) ? EvenAligned
: FirstAligned;
// we cannot assume the first element is aligned because of sub-matrices
const int lhsAlignmentOffset = ei_alignmentOffset(lhs,size);
// find how many columns do we have to skip to be aligned with the result (if possible)
int skipColumns = 0;
if (PacketSize>1)
{
ei_internal_assert(size_t(lhs+lhsAlignmentOffset)%sizeof(Packet)==0 || size<PacketSize);
while (skipColumns<PacketSize &&
alignedStart != ((lhsAlignmentOffset + alignmentStep*skipColumns)%PacketSize))
++skipColumns;
if (skipColumns==PacketSize)
{
// nothing can be aligned, no need to skip any column
alignmentPattern = NoneAligned;
skipColumns = 0;
}
else
{
skipColumns = std::min(skipColumns,rhs.size());
// note that the skiped columns are processed later.
}
ei_internal_assert((alignmentPattern==NoneAligned) || (size_t(lhs+alignedStart+lhsStride*skipColumns)%sizeof(Packet))==0);
}
int offset1 = (FirstAligned && alignmentStep==1?3:1);
int offset3 = (FirstAligned && alignmentStep==1?1:3);
int columnBound = ((rhs.size()-skipColumns)/columnsAtOnce)*columnsAtOnce + skipColumns;
for (int i=skipColumns; i<columnBound; i+=columnsAtOnce)
{
Packet ptmp0 = ei_pset1(rhs[i]), ptmp1 = ei_pset1(rhs[i+offset1]),
ptmp2 = ei_pset1(rhs[i+2]), ptmp3 = ei_pset1(rhs[i+offset3]);
// this helps a lot generating better binary code
const Scalar *lhs0 = lhs + i*lhsStride, *lhs1 = lhs + (i+offset1)*lhsStride,
*lhs2 = lhs + (i+2)*lhsStride, *lhs3 = lhs + (i+offset3)*lhsStride;
if (PacketSize>1)
{
/* explicit vectorization */
// process initial unaligned coeffs
for (int j=0; j<alignedStart; ++j)
res[j] += ei_pfirst(ptmp0)*lhs0[j] + ei_pfirst(ptmp1)*lhs1[j] + ei_pfirst(ptmp2)*lhs2[j] + ei_pfirst(ptmp3)*lhs3[j];
if (alignedSize>alignedStart)
{
switch(alignmentPattern)
{
case AllAligned:
for (int j = alignedStart; j<alignedSize; j+=PacketSize)
_EIGEN_ACCUMULATE_PACKETS(d,d,d);
break;
case EvenAligned:
for (int j = alignedStart; j<alignedSize; j+=PacketSize)
_EIGEN_ACCUMULATE_PACKETS(d,du,d);
break;
case FirstAligned:
if(peels>1)
{
Packet A00, A01, A02, A03, A10, A11, A12, A13;
A01 = ei_pload(&lhs1[alignedStart-1]);
A02 = ei_pload(&lhs2[alignedStart-2]);
A03 = ei_pload(&lhs3[alignedStart-3]);
for (int j = alignedStart; j<peeledSize; j+=peels*PacketSize)
{
A11 = ei_pload(&lhs1[j-1+PacketSize]); ei_palign<1>(A01,A11);
A12 = ei_pload(&lhs2[j-2+PacketSize]); ei_palign<2>(A02,A12);
A13 = ei_pload(&lhs3[j-3+PacketSize]); ei_palign<3>(A03,A13);
A00 = ei_pload (&lhs0[j]);
A10 = ei_pload (&lhs0[j+PacketSize]);
A00 = ei_pmadd(ptmp0, A00, ei_pload(&res[j]));
A10 = ei_pmadd(ptmp0, A10, ei_pload(&res[j+PacketSize]));
A00 = ei_pmadd(ptmp1, A01, A00);
A01 = ei_pload(&lhs1[j-1+2*PacketSize]); ei_palign<1>(A11,A01);
A00 = ei_pmadd(ptmp2, A02, A00);
A02 = ei_pload(&lhs2[j-2+2*PacketSize]); ei_palign<2>(A12,A02);
A00 = ei_pmadd(ptmp3, A03, A00);
ei_pstore(&res[j],A00);
A03 = ei_pload(&lhs3[j-3+2*PacketSize]); ei_palign<3>(A13,A03);
A10 = ei_pmadd(ptmp1, A11, A10);
A10 = ei_pmadd(ptmp2, A12, A10);
A10 = ei_pmadd(ptmp3, A13, A10);
ei_pstore(&res[j+PacketSize],A10);
}
}
for (int j = peeledSize; j<alignedSize; j+=PacketSize)
_EIGEN_ACCUMULATE_PACKETS(d,du,du);
break;
default:
for (int j = alignedStart; j<alignedSize; j+=PacketSize)
_EIGEN_ACCUMULATE_PACKETS(du,du,du);
break;
}
}
} // end explicit vectorization
/* process remaining coeffs (or all if there is no explicit vectorization) */
for (int j=alignedSize; j<size; ++j)
res[j] += ei_pfirst(ptmp0)*lhs0[j] + ei_pfirst(ptmp1)*lhs1[j] + ei_pfirst(ptmp2)*lhs2[j] + ei_pfirst(ptmp3)*lhs3[j];
}
// process remaining first and last columns (at most columnsAtOnce-1)
int end = rhs.size();
int start = columnBound;
do
{
for (int i=start; i<end; ++i)
{
Packet ptmp0 = ei_pset1(rhs[i]);
const Scalar* lhs0 = lhs + i*lhsStride;
if (PacketSize>1)
{
/* explicit vectorization */
// process first unaligned result's coeffs
for (int j=0; j<alignedStart; ++j)
res[j] += ei_pfirst(ptmp0) * lhs0[j];
// process aligned result's coeffs
if ((size_t(lhs0+alignedStart)%sizeof(Packet))==0)
for (int j = alignedStart;j<alignedSize;j+=PacketSize)
ei_pstore(&res[j], ei_pmadd(ptmp0,ei_pload(&lhs0[j]),ei_pload(&res[j])));
else
for (int j = alignedStart;j<alignedSize;j+=PacketSize)
ei_pstore(&res[j], ei_pmadd(ptmp0,ei_ploadu(&lhs0[j]),ei_pload(&res[j])));
}
// process remaining scalars (or all if no explicit vectorization)
for (int j=alignedSize; j<size; ++j)
res[j] += ei_pfirst(ptmp0) * lhs0[j];
}
if (skipColumns)
{
start = 0;
end = skipColumns;
skipColumns = 0;
}
else
break;
} while(PacketSize>1);
#undef _EIGEN_ACCUMULATE_PACKETS
}
// TODO add peeling to mask unaligned load/stores
template<typename Scalar, typename ResType>
static EIGEN_DONT_INLINE void ei_cache_friendly_product_rowmajor_times_vector(
const Scalar* lhs, int lhsStride,
const Scalar* rhs, int rhsSize,
ResType& res)
{
#ifdef _EIGEN_ACCUMULATE_PACKETS
#error _EIGEN_ACCUMULATE_PACKETS has already been defined
#endif
#define _EIGEN_ACCUMULATE_PACKETS(A0,A13,A2) {\
Packet b = ei_pload(&rhs[j]); \
ptmp0 = ei_pmadd(b, EIGEN_CAT(ei_ploa,A0) (&lhs0[j]), ptmp0); \
ptmp1 = ei_pmadd(b, EIGEN_CAT(ei_ploa,A13)(&lhs1[j]), ptmp1); \
ptmp2 = ei_pmadd(b, EIGEN_CAT(ei_ploa,A2) (&lhs2[j]), ptmp2); \
ptmp3 = ei_pmadd(b, EIGEN_CAT(ei_ploa,A13)(&lhs3[j]), ptmp3); }
typedef typename ei_packet_traits<Scalar>::type Packet;
const int PacketSize = sizeof(Packet)/sizeof(Scalar);
enum { AllAligned=0, EvenAligned=1, FirstAligned=2, NoneAligned=3 };
const int rowsAtOnce = 4;
const int peels = 2;
const int PacketAlignedMask = PacketSize-1;
const int PeelAlignedMask = PacketSize*peels-1;
const int size = rhsSize;
// How many coeffs of the result do we have to skip to be aligned.
// Here we assume data are at least aligned on the base scalar type that is mandatory anyway.
const int alignedStart = ei_alignmentOffset(rhs, size);
const int alignedSize = PacketSize>1 ? alignedStart + ((size-alignedStart) & ~PacketAlignedMask) : 0;
const int peeledSize = peels>1 ? alignedStart + ((alignedSize-alignedStart) & ~PeelAlignedMask) : alignedStart;
const int alignmentStep = PacketSize>1 ? (PacketSize - lhsStride % PacketSize) & PacketAlignedMask : 0;
int alignmentPattern = alignmentStep==0 ? AllAligned
: alignmentStep==(PacketSize/2) ? EvenAligned
: FirstAligned;
// we cannot assume the first element is aligned because of sub-matrices
const int lhsAlignmentOffset = ei_alignmentOffset(lhs,size);
// find how many rows do we have to skip to be aligned with rhs (if possible)
int skipRows = 0;
if (PacketSize>1)
{
ei_internal_assert(size_t(lhs+lhsAlignmentOffset)%sizeof(Packet)==0 || size<PacketSize);
while (skipRows<PacketSize &&
alignedStart != ((lhsAlignmentOffset + alignmentStep*skipRows)%PacketSize))
++skipRows;
if (skipRows==PacketSize)
{
// nothing can be aligned, no need to skip any column
alignmentPattern = NoneAligned;
skipRows = 0;
}
else
{
skipRows = std::min(skipRows,res.size());
// note that the skiped columns are processed later.
}
ei_internal_assert((alignmentPattern==NoneAligned) || PacketSize==1
|| (size_t(lhs+alignedStart+lhsStride*skipRows)%sizeof(Packet))==0);
}
int offset1 = (FirstAligned && alignmentStep==1?3:1);
int offset3 = (FirstAligned && alignmentStep==1?1:3);
int rowBound = ((res.size()-skipRows)/rowsAtOnce)*rowsAtOnce + skipRows;
for (int i=skipRows; i<rowBound; i+=rowsAtOnce)
{
Scalar tmp0 = Scalar(0), tmp1 = Scalar(0), tmp2 = Scalar(0), tmp3 = Scalar(0);
// this helps the compiler generating good binary code
const Scalar *lhs0 = lhs + i*lhsStride, *lhs1 = lhs + (i+offset1)*lhsStride,
*lhs2 = lhs + (i+2)*lhsStride, *lhs3 = lhs + (i+offset3)*lhsStride;
if (PacketSize>1)
{
/* explicit vectorization */
Packet ptmp0 = ei_pset1(Scalar(0)), ptmp1 = ei_pset1(Scalar(0)), ptmp2 = ei_pset1(Scalar(0)), ptmp3 = ei_pset1(Scalar(0));
// process initial unaligned coeffs
// FIXME this loop get vectorized by the compiler !
for (int j=0; j<alignedStart; ++j)
{
Scalar b = rhs[j];
tmp0 += b*lhs0[j]; tmp1 += b*lhs1[j]; tmp2 += b*lhs2[j]; tmp3 += b*lhs3[j];
}
if (alignedSize>alignedStart)
{
switch(alignmentPattern)
{
case AllAligned:
for (int j = alignedStart; j<alignedSize; j+=PacketSize)
_EIGEN_ACCUMULATE_PACKETS(d,d,d);
break;
case EvenAligned:
for (int j = alignedStart; j<alignedSize; j+=PacketSize)
_EIGEN_ACCUMULATE_PACKETS(d,du,d);
break;
case FirstAligned:
if (peels>1)
{
/* Here we proccess 4 rows with with two peeled iterations to hide
* tghe overhead of unaligned loads. Moreover unaligned loads are handled
* using special shift/move operations between the two aligned packets
* overlaping the desired unaligned packet. This is *much* more efficient
* than basic unaligned loads.
*/
Packet A01, A02, A03, b, A11, A12, A13;
A01 = ei_pload(&lhs1[alignedStart-1]);
A02 = ei_pload(&lhs2[alignedStart-2]);
A03 = ei_pload(&lhs3[alignedStart-3]);
for (int j = alignedStart; j<peeledSize; j+=peels*PacketSize)
{
b = ei_pload(&rhs[j]);
A11 = ei_pload(&lhs1[j-1+PacketSize]); ei_palign<1>(A01,A11);
A12 = ei_pload(&lhs2[j-2+PacketSize]); ei_palign<2>(A02,A12);
A13 = ei_pload(&lhs3[j-3+PacketSize]); ei_palign<3>(A03,A13);
ptmp0 = ei_pmadd(b, ei_pload (&lhs0[j]), ptmp0);
ptmp1 = ei_pmadd(b, A01, ptmp1);
A01 = ei_pload(&lhs1[j-1+2*PacketSize]); ei_palign<1>(A11,A01);
ptmp2 = ei_pmadd(b, A02, ptmp2);
A02 = ei_pload(&lhs2[j-2+2*PacketSize]); ei_palign<2>(A12,A02);
ptmp3 = ei_pmadd(b, A03, ptmp3);
A03 = ei_pload(&lhs3[j-3+2*PacketSize]); ei_palign<3>(A13,A03);
b = ei_pload(&rhs[j+PacketSize]);
ptmp0 = ei_pmadd(b, ei_pload (&lhs0[j+PacketSize]), ptmp0);
ptmp1 = ei_pmadd(b, A11, ptmp1);
ptmp2 = ei_pmadd(b, A12, ptmp2);
ptmp3 = ei_pmadd(b, A13, ptmp3);
}
}
for (int j = peeledSize; j<alignedSize; j+=PacketSize)
_EIGEN_ACCUMULATE_PACKETS(d,du,du);
break;
default:
for (int j = alignedStart; j<alignedSize; j+=PacketSize)
_EIGEN_ACCUMULATE_PACKETS(du,du,du);
break;
}
tmp0 += ei_predux(ptmp0);
tmp1 += ei_predux(ptmp1);
tmp2 += ei_predux(ptmp2);
tmp3 += ei_predux(ptmp3);
}
} // end explicit vectorization
// process remaining coeffs (or all if no explicit vectorization)
// FIXME this loop get vectorized by the compiler !
for (int j=alignedSize; j<size; ++j)
{
Scalar b = rhs[j];
tmp0 += b*lhs0[j]; tmp1 += b*lhs1[j]; tmp2 += b*lhs2[j]; tmp3 += b*lhs3[j];
}
res[i] += tmp0; res[i+offset1] += tmp1; res[i+2] += tmp2; res[i+offset3] += tmp3;
}
// process remaining first and last rows (at most columnsAtOnce-1)
int end = res.size();
int start = rowBound;
do
{
for (int i=start; i<end; ++i)
{
Scalar tmp0 = Scalar(0);
Packet ptmp0 = ei_pset1(tmp0);
const Scalar* lhs0 = lhs + i*lhsStride;
// process first unaligned result's coeffs
// FIXME this loop get vectorized by the compiler !
for (int j=0; j<alignedStart; ++j)
tmp0 += rhs[j] * lhs0[j];
if (alignedSize>alignedStart)
{
// process aligned rhs coeffs
if ((size_t(lhs0+alignedStart)%sizeof(Packet))==0)
for (int j = alignedStart;j<alignedSize;j+=PacketSize)
ptmp0 = ei_pmadd(ei_pload(&rhs[j]), ei_pload(&lhs0[j]), ptmp0);
else
for (int j = alignedStart;j<alignedSize;j+=PacketSize)
ptmp0 = ei_pmadd(ei_pload(&rhs[j]), ei_ploadu(&lhs0[j]), ptmp0);
tmp0 += ei_predux(ptmp0);
}
// process remaining scalars
// FIXME this loop get vectorized by the compiler !
for (int j=alignedSize; j<size; ++j)
tmp0 += rhs[j] * lhs0[j];
res[i] += tmp0;
}
if (skipRows)
{
start = 0;
end = skipRows;
skipRows = 0;
}
else
break;
} while(PacketSize>1);
#undef _EIGEN_ACCUMULATE_PACKETS
}
#endif // EIGEN_CACHE_FRIENDLY_PRODUCT_H

384
Eigen/src/Core/Coeffs.h Normal file
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@@ -0,0 +1,384 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_COEFFS_H
#define EIGEN_COEFFS_H
/** Short version: don't use this function, use
* \link operator()(int,int) const \endlink instead.
*
* Long version: this function is similar to
* \link operator()(int,int) const \endlink, but without the assertion.
* Use this for limiting the performance cost of debugging code when doing
* repeated coefficient access. Only use this when it is guaranteed that the
* parameters \a row and \a col are in range.
*
* If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
* function equivalent to \link operator()(int,int) const \endlink.
*
* \sa operator()(int,int) const, coeffRef(int,int), coeff(int) const
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
::coeff(int row, int col) const
{
ei_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().coeff(row, col);
}
/** \returns the coefficient at given the given row and column.
*
* \sa operator()(int,int), operator[](int) const
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
::operator()(int row, int col) const
{
ei_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().coeff(row, col);
}
/** Short version: don't use this function, use
* \link operator()(int,int) \endlink instead.
*
* Long version: this function is similar to
* \link operator()(int,int) \endlink, but without the assertion.
* Use this for limiting the performance cost of debugging code when doing
* repeated coefficient access. Only use this when it is guaranteed that the
* parameters \a row and \a col are in range.
*
* If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
* function equivalent to \link operator()(int,int) \endlink.
*
* \sa operator()(int,int), coeff(int, int) const, coeffRef(int)
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
::coeffRef(int row, int col)
{
ei_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().coeffRef(row, col);
}
/** \returns a reference to the coefficient at given the given row and column.
*
* \sa operator()(int,int) const, operator[](int)
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
::operator()(int row, int col)
{
ei_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().coeffRef(row, col);
}
/** Short version: don't use this function, use
* \link operator[](int) const \endlink instead.
*
* Long version: this function is similar to
* \link operator[](int) const \endlink, but without the assertion.
* Use this for limiting the performance cost of debugging code when doing
* repeated coefficient access. Only use this when it is guaranteed that the
* parameter \a index is in range.
*
* If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
* function equivalent to \link operator[](int) const \endlink.
*
* \sa operator[](int) const, coeffRef(int), coeff(int,int) const
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
::coeff(int index) const
{
ei_internal_assert(index >= 0 && index < size());
return derived().coeff(index);
}
/** \returns the coefficient at given index.
*
* This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
*
* \sa operator[](int), operator()(int,int) const, x() const, y() const,
* z() const, w() const
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
::operator[](int index) const
{
ei_assert(index >= 0 && index < size());
return derived().coeff(index);
}
/** \returns the coefficient at given index.
*
* This is synonymous to operator[](int) const.
*
* This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
*
* \sa operator[](int), operator()(int,int) const, x() const, y() const,
* z() const, w() const
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
::operator()(int index) const
{
ei_assert(index >= 0 && index < size());
return derived().coeff(index);
}
/** Short version: don't use this function, use
* \link operator[](int) \endlink instead.
*
* Long version: this function is similar to
* \link operator[](int) \endlink, but without the assertion.
* Use this for limiting the performance cost of debugging code when doing
* repeated coefficient access. Only use this when it is guaranteed that the
* parameters \a row and \a col are in range.
*
* If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
* function equivalent to \link operator[](int) \endlink.
*
* \sa operator[](int), coeff(int) const, coeffRef(int,int)
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
::coeffRef(int index)
{
ei_internal_assert(index >= 0 && index < size());
return derived().coeffRef(index);
}
/** \returns a reference to the coefficient at given index.
*
* This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
*
* \sa operator[](int) const, operator()(int,int), x(), y(), z(), w()
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
::operator[](int index)
{
ei_assert(index >= 0 && index < size());
return derived().coeffRef(index);
}
/** \returns a reference to the coefficient at given index.
*
* This is synonymous to operator[](int).
*
* This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
*
* \sa operator[](int) const, operator()(int,int), x(), y(), z(), w()
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
::operator()(int index)
{
ei_assert(index >= 0 && index < size());
return derived().coeffRef(index);
}
/** equivalent to operator[](0). */
template<typename Derived>
EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
::x() const { return (*this)[0]; }
/** equivalent to operator[](1). */
template<typename Derived>
EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
::y() const { return (*this)[1]; }
/** equivalent to operator[](2). */
template<typename Derived>
EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
::z() const { return (*this)[2]; }
/** equivalent to operator[](3). */
template<typename Derived>
EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
::w() const { return (*this)[3]; }
/** equivalent to operator[](0). */
template<typename Derived>
EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
::x() { return (*this)[0]; }
/** equivalent to operator[](1). */
template<typename Derived>
EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
::y() { return (*this)[1]; }
/** equivalent to operator[](2). */
template<typename Derived>
EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
::z() { return (*this)[2]; }
/** equivalent to operator[](3). */
template<typename Derived>
EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
::w() { return (*this)[3]; }
/** \returns the packet of coefficients starting at the given row and column. 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<typename Derived>
template<int LoadMode>
EIGEN_STRONG_INLINE typename ei_packet_traits<typename ei_traits<Derived>::Scalar>::type
MatrixBase<Derived>::packet(int row, int col) const
{
ei_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().template packet<LoadMode>(row,col);
}
/** 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<typename Derived>
template<int StoreMode>
EIGEN_STRONG_INLINE void MatrixBase<Derived>::writePacket
(int row, int col, const typename ei_packet_traits<typename ei_traits<Derived>::Scalar>::type& x)
{
ei_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().template writePacket<StoreMode>(row,col,x);
}
/** \returns the packet of coefficients starting at the given index. 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<typename Derived>
template<int LoadMode>
EIGEN_STRONG_INLINE typename ei_packet_traits<typename ei_traits<Derived>::Scalar>::type
MatrixBase<Derived>::packet(int index) const
{
ei_internal_assert(index >= 0 && index < size());
return derived().template packet<LoadMode>(index);
}
/** 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<typename Derived>
template<int StoreMode>
EIGEN_STRONG_INLINE void MatrixBase<Derived>::writePacket
(int index, const typename ei_packet_traits<typename ei_traits<Derived>::Scalar>::type& x)
{
ei_internal_assert(index >= 0 && index < size());
derived().template writePacket<StoreMode>(index,x);
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal Copies the coefficient at position (row,col) of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE void MatrixBase<Derived>::copyCoeff(int row, int col, const MatrixBase<OtherDerived>& other)
{
ei_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 Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE void MatrixBase<Derived>::copyCoeff(int index, const MatrixBase<OtherDerived>& other)
{
ei_internal_assert(index >= 0 && index < size());
derived().coeffRef(index) = other.derived().coeff(index);
}
/** \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 Derived>
template<typename OtherDerived, int StoreMode, int LoadMode>
EIGEN_STRONG_INLINE void MatrixBase<Derived>::copyPacket(int row, int col, const MatrixBase<OtherDerived>& other)
{
ei_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 Derived>
template<typename OtherDerived, int StoreMode, int LoadMode>
EIGEN_STRONG_INLINE void MatrixBase<Derived>::copyPacket(int index, const MatrixBase<OtherDerived>& other)
{
ei_internal_assert(index >= 0 && index < size());
derived().template writePacket<StoreMode>(index,
other.derived().template packet<LoadMode>(index));
}
#endif
#endif // EIGEN_COEFFS_H

View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
@@ -27,7 +27,6 @@
#define EIGEN_COMMAINITIALIZER_H
/** \class CommaInitializer
* \ingroup Core_Module
*
* \brief Helper class used by the comma initializer operator
*
@@ -37,72 +36,70 @@
*
* \sa \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished()
*/
template<typename XprType>
template<typename MatrixType>
struct CommaInitializer
{
typedef typename XprType::Scalar Scalar;
typedef typename XprType::Index Index;
inline CommaInitializer(XprType& xpr, const Scalar& s)
: m_xpr(xpr), m_row(0), m_col(1), m_currentBlockRows(1)
typedef typename ei_traits<MatrixType>::Scalar Scalar;
inline CommaInitializer(MatrixType& mat, const Scalar& s)
: m_matrix(mat), m_row(0), m_col(1), m_currentBlockRows(1)
{
m_xpr.coeffRef(0,0) = s;
m_matrix.coeffRef(0,0) = s;
}
template<typename OtherDerived>
inline CommaInitializer(XprType& xpr, const DenseBase<OtherDerived>& other)
: m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows())
inline CommaInitializer(MatrixType& mat, const MatrixBase<OtherDerived>& other)
: m_matrix(mat), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows())
{
m_xpr.block(0, 0, other.rows(), other.cols()) = other;
m_matrix.block(0, 0, other.rows(), other.cols()) = other;
}
/* inserts a scalar value in the target matrix */
CommaInitializer& operator,(const Scalar& s)
{
if (m_col==m_xpr.cols())
if (m_col==m_matrix.cols())
{
m_row+=m_currentBlockRows;
m_col = 0;
m_currentBlockRows = 1;
eigen_assert(m_row<m_xpr.rows()
ei_assert(m_row<m_matrix.rows()
&& "Too many rows passed to comma initializer (operator<<)");
}
eigen_assert(m_col<m_xpr.cols()
ei_assert(m_col<m_matrix.cols()
&& "Too many coefficients passed to comma initializer (operator<<)");
eigen_assert(m_currentBlockRows==1);
m_xpr.coeffRef(m_row, m_col++) = s;
ei_assert(m_currentBlockRows==1);
m_matrix.coeffRef(m_row, m_col++) = s;
return *this;
}
/* inserts a matrix expression in the target matrix */
template<typename OtherDerived>
CommaInitializer& operator,(const DenseBase<OtherDerived>& other)
CommaInitializer& operator,(const MatrixBase<OtherDerived>& other)
{
if (m_col==m_xpr.cols())
if (m_col==m_matrix.cols())
{
m_row+=m_currentBlockRows;
m_col = 0;
m_currentBlockRows = other.rows();
eigen_assert(m_row+m_currentBlockRows<=m_xpr.rows()
ei_assert(m_row+m_currentBlockRows<=m_matrix.rows()
&& "Too many rows passed to comma initializer (operator<<)");
}
eigen_assert(m_col<m_xpr.cols()
ei_assert(m_col<m_matrix.cols()
&& "Too many coefficients passed to comma initializer (operator<<)");
eigen_assert(m_currentBlockRows==other.rows());
ei_assert(m_currentBlockRows==other.rows());
if (OtherDerived::SizeAtCompileTime != Dynamic)
m_xpr.template block<OtherDerived::RowsAtCompileTime != Dynamic ? OtherDerived::RowsAtCompileTime : 1,
m_matrix.template block<OtherDerived::RowsAtCompileTime != Dynamic ? OtherDerived::RowsAtCompileTime : 1,
OtherDerived::ColsAtCompileTime != Dynamic ? OtherDerived::ColsAtCompileTime : 1>
(m_row, m_col) = other;
else
m_xpr.block(m_row, m_col, other.rows(), other.cols()) = other;
m_matrix.block(m_row, m_col, other.rows(), other.cols()) = other;
m_col += other.cols();
return *this;
}
inline ~CommaInitializer()
{
eigen_assert((m_row+m_currentBlockRows) == m_xpr.rows()
&& m_col == m_xpr.cols()
ei_assert((m_row+m_currentBlockRows) == m_matrix.rows()
&& m_col == m_matrix.cols()
&& "Too few coefficients passed to comma initializer (operator<<)");
}
@@ -113,12 +110,15 @@ struct CommaInitializer
* quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished());
* \endcode
*/
inline XprType& finished() { return m_xpr; }
inline MatrixType& finished() { return m_matrix; }
XprType& m_xpr; // target expression
Index m_row; // current row id
Index m_col; // current col id
Index m_currentBlockRows; // current block height
MatrixType& m_matrix; // target matrix
int m_row; // current row id
int m_col; // current col id
int m_currentBlockRows; // current block height
private:
CommaInitializer& operator=(const CommaInitializer&);
};
/** \anchor MatrixBaseCommaInitRef
@@ -127,13 +127,15 @@ struct CommaInitializer
* The coefficients must be provided in a row major order and exactly match
* the size of the matrix. Otherwise an assertion is raised.
*
* \addexample CommaInit \label How to easily set all the coefficients of a matrix
*
* Example: \include MatrixBase_set.cpp
* Output: \verbinclude MatrixBase_set.out
*
* \sa CommaInitializer::finished(), class CommaInitializer
*/
template<typename Derived>
inline CommaInitializer<Derived> DenseBase<Derived>::operator<< (const Scalar& s)
inline CommaInitializer<Derived> MatrixBase<Derived>::operator<< (const Scalar& s)
{
return CommaInitializer<Derived>(*static_cast<Derived*>(this), s);
}
@@ -142,7 +144,7 @@ inline CommaInitializer<Derived> DenseBase<Derived>::operator<< (const Scalar& s
template<typename Derived>
template<typename OtherDerived>
inline CommaInitializer<Derived>
DenseBase<Derived>::operator<<(const DenseBase<OtherDerived>& other)
MatrixBase<Derived>::operator<<(const MatrixBase<OtherDerived>& other)
{
return CommaInitializer<Derived>(*static_cast<Derived *>(this), other);
}

View File

@@ -22,40 +22,26 @@
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#include "main.h"
#ifdef EIGEN_EXTERN_INSTANTIATIONS
#undef EIGEN_EXTERN_INSTANTIATIONS
#endif
void test_eigen2_commainitializer()
#include "../../Core"
namespace Eigen
{
Matrix3d m3;
Matrix4d m4;
VERIFY_RAISES_ASSERT( (m3 << 1, 2, 3, 4, 5, 6, 7, 8) );
#ifndef _MSC_VER
VERIFY_RAISES_ASSERT( (m3 << 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) );
#endif
#define EIGEN_INSTANTIATE_PRODUCT(TYPE) \
template static void ei_cache_friendly_product<TYPE>( \
int _rows, int _cols, int depth, \
bool _lhsRowMajor, const TYPE* _lhs, int _lhsStride, \
bool _rhsRowMajor, const TYPE* _rhs, int _rhsStride, \
bool resRowMajor, TYPE* res, int resStride)
double data[] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
Matrix3d ref = Map<Matrix<double,3,3,RowMajor> >(data);
EIGEN_INSTANTIATE_PRODUCT(float);
EIGEN_INSTANTIATE_PRODUCT(double);
EIGEN_INSTANTIATE_PRODUCT(int);
EIGEN_INSTANTIATE_PRODUCT(std::complex<float>);
EIGEN_INSTANTIATE_PRODUCT(std::complex<double>);
m3 = Matrix3d::Random();
m3 << 1, 2, 3, 4, 5, 6, 7, 8, 9;
VERIFY_IS_APPROX(m3, ref );
Vector3d vec[3];
vec[0] << 1, 4, 7;
vec[1] << 2, 5, 8;
vec[2] << 3, 6, 9;
m3 = Matrix3d::Random();
m3 << vec[0], vec[1], vec[2];
VERIFY_IS_APPROX(m3, ref);
vec[0] << 1, 2, 3;
vec[1] << 4, 5, 6;
vec[2] << 7, 8, 9;
m3 = Matrix3d::Random();
m3 << vec[0].transpose(),
4, 5, 6,
vec[2].transpose();
VERIFY_IS_APPROX(m3, ref);
}

View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
@@ -29,18 +29,30 @@
/** \internal
* convenient macro to defined the return type of a cwise binary operation */
#define EIGEN_CWISE_BINOP_RETURN_TYPE(OP) \
CwiseBinaryOp<OP<typename internal::traits<ExpressionType>::Scalar>, ExpressionType, OtherDerived>
CwiseBinaryOp<OP<typename ei_traits<ExpressionType>::Scalar>, ExpressionType, OtherDerived>
#define EIGEN_CWISE_PRODUCT_RETURN_TYPE \
CwiseBinaryOp< \
ei_scalar_product_op< \
typename ei_scalar_product_traits< \
typename ei_traits<ExpressionType>::Scalar, \
typename ei_traits<OtherDerived>::Scalar \
>::ReturnType \
>, \
ExpressionType, \
OtherDerived \
>
/** \internal
* convenient macro to defined the return type of a cwise unary operation */
#define EIGEN_CWISE_UNOP_RETURN_TYPE(OP) \
CwiseUnaryOp<OP<typename internal::traits<ExpressionType>::Scalar>, ExpressionType>
CwiseUnaryOp<OP<typename ei_traits<ExpressionType>::Scalar>, ExpressionType>
/** \internal
* convenient macro to defined the return type of a cwise comparison to a scalar */
#define EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(OP) \
CwiseBinaryOp<OP<typename internal::traits<ExpressionType>::Scalar>, ExpressionType, \
typename ExpressionType::ConstantReturnType >
CwiseBinaryOp<OP<typename ei_traits<ExpressionType>::Scalar>, ExpressionType, \
NestByValue<typename ExpressionType::ConstantReturnType> >
/** \class Cwise
*
@@ -52,22 +64,21 @@
* It is the return type of MatrixBase::cwise()
* and most of the time this is the only way it is used.
*
* Note that some methods are defined in the \ref Array module.
*
* Example: \include MatrixBase_cwise_const.cpp
* Output: \verbinclude MatrixBase_cwise_const.out
*
* 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_CWISE_PLUGIN.
*
* \sa MatrixBase::cwise() const, MatrixBase::cwise()
*/
template<typename ExpressionType> class Cwise
{
public:
typedef typename internal::traits<ExpressionType>::Scalar Scalar;
typedef typename internal::conditional<internal::must_nest_by_value<ExpressionType>::ret,
ExpressionType, const ExpressionType&>::type ExpressionTypeNested;
typedef CwiseUnaryOp<internal::scalar_add_op<Scalar>, ExpressionType> ScalarAddReturnType;
typedef typename ei_traits<ExpressionType>::Scalar Scalar;
typedef typename ei_meta_if<ei_must_nest_by_value<ExpressionType>::ret,
ExpressionType, const ExpressionType&>::ret ExpressionTypeNested;
typedef CwiseUnaryOp<ei_scalar_add_op<Scalar>, ExpressionType> ScalarAddReturnType;
inline Cwise(const ExpressionType& matrix) : m_matrix(matrix) {}
@@ -75,36 +86,32 @@ template<typename ExpressionType> class Cwise
inline const ExpressionType& _expression() const { return m_matrix; }
template<typename OtherDerived>
const EIGEN_CWISE_PRODUCT_RETURN_TYPE(ExpressionType,OtherDerived)
const EIGEN_CWISE_PRODUCT_RETURN_TYPE
operator*(const MatrixBase<OtherDerived> &other) const;
template<typename OtherDerived>
const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_quotient_op)
const EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_quotient_op)
operator/(const MatrixBase<OtherDerived> &other) const;
/** \deprecated ArrayBase::min() */
template<typename OtherDerived>
const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_min_op)
(min)(const MatrixBase<OtherDerived> &other) const
{ return EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_min_op)(_expression(), other.derived()); }
const EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_min_op)
min(const MatrixBase<OtherDerived> &other) const;
/** \deprecated ArrayBase::max() */
template<typename OtherDerived>
const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_max_op)
(max)(const MatrixBase<OtherDerived> &other) const
{ return EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_max_op)(_expression(), other.derived()); }
const EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_max_op)
max(const MatrixBase<OtherDerived> &other) const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_abs_op) abs() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_abs2_op) abs2() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_square_op) square() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_cube_op) cube() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_inverse_op) inverse() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_sqrt_op) sqrt() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_exp_op) exp() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_log_op) log() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_cos_op) cos() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_sin_op) sin() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_pow_op) pow(const Scalar& exponent) const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_abs_op) abs() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_abs2_op) abs2() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_square_op) square() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_cube_op) cube() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_inverse_op) inverse() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_sqrt_op) sqrt() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_exp_op) exp() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_log_op) log() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_cos_op) cos() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_sin_op) sin() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_pow_op) pow(const Scalar& exponent) const;
const ScalarAddReturnType
operator+(const Scalar& scalar) const;
@@ -171,8 +178,10 @@ template<typename ExpressionType> class Cwise
protected:
ExpressionTypeNested m_matrix;
};
private:
Cwise& operator=(const Cwise&);
};
/** \returns a Cwise wrapper of *this providing additional coefficient-wise operations
*
@@ -182,7 +191,8 @@ template<typename ExpressionType> class Cwise
* \sa class Cwise, cwise()
*/
template<typename Derived>
inline const Cwise<Derived> MatrixBase<Derived>::cwise() const
inline const Cwise<Derived>
MatrixBase<Derived>::cwise() const
{
return derived();
}
@@ -195,7 +205,8 @@ inline const Cwise<Derived> MatrixBase<Derived>::cwise() const
* \sa class Cwise, cwise() const
*/
template<typename Derived>
inline Cwise<Derived> MatrixBase<Derived>::cwise()
inline Cwise<Derived>
MatrixBase<Derived>::cwise()
{
return derived();
}

View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
@@ -27,144 +27,108 @@
#define EIGEN_CWISE_BINARY_OP_H
/** \class CwiseBinaryOp
* \ingroup Core_Module
*
* \brief Generic expression where a coefficient-wise binary operator is applied to two expressions
* \brief Generic expression of a coefficient-wise operator between two matrices or vectors
*
* \param BinaryOp template functor implementing the operator
* \param Lhs the type of the left-hand side
* \param Rhs the type of the right-hand side
*
* This class represents an expression where a coefficient-wise binary operator is applied to two expressions.
* It is the return type of binary operators, by which we mean only those binary operators where
* both the left-hand side and the right-hand side are Eigen expressions.
* For example, the return type of matrix1+matrix2 is a CwiseBinaryOp.
* This class represents an expression of a generic binary operator of two matrices or vectors.
* It is the return type of the operator+, operator-, and the Cwise methods, and most
* of the time this is the only way it is used.
*
* Most of the time, this is the only way that it is used, so you typically don't have to name
* CwiseBinaryOp types explicitly.
* However, if you want to write a function returning such an expression, you
* will need to use this class.
*
* \sa MatrixBase::binaryExpr(const MatrixBase<OtherDerived> &,const CustomBinaryOp &) const, class CwiseUnaryOp, class CwiseNullaryOp
*/
namespace internal {
template<typename BinaryOp, typename Lhs, typename Rhs>
struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
struct ei_traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
{
// we must not inherit from traits<Lhs> since it has
// the potential to cause problems with MSVC
typedef typename remove_all<Lhs>::type Ancestor;
typedef typename traits<Ancestor>::XprKind XprKind;
enum {
RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime,
MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime,
MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime
};
// even though we require Lhs and Rhs to have the same scalar type (see CwiseBinaryOp constructor),
// we still want to handle the case when the result type is different.
typedef typename result_of<
typedef typename ei_result_of<
BinaryOp(
typename Lhs::Scalar,
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 Lhs::Nested LhsNested;
typedef typename Rhs::Nested RhsNested;
typedef typename remove_reference<LhsNested>::type _LhsNested;
typedef typename remove_reference<RhsNested>::type _RhsNested;
typedef typename ei_unref<LhsNested>::type _LhsNested;
typedef typename ei_unref<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)) & (
RowsAtCompileTime = Lhs::RowsAtCompileTime,
ColsAtCompileTime = Lhs::ColsAtCompileTime,
MaxRowsAtCompileTime = Lhs::MaxRowsAtCompileTime,
MaxColsAtCompileTime = Lhs::MaxColsAtCompileTime,
Flags = (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
| (int(LhsFlags) & int(RhsFlags) & (LinearAccessBit | AlignedBit))
| (ei_functor_traits<BinaryOp>::PacketAccess && ((int(LhsFlags) & RowMajorBit)==(int(RhsFlags) & RowMajorBit))
? (int(LhsFlags) & int(RhsFlags) & PacketAccessBit) : 0)),
CoeffReadCost = LhsCoeffReadCost + RhsCoeffReadCost + ei_functor_traits<BinaryOp>::Cost
};
};
} // end namespace internal
// we require Lhs and Rhs to have the same scalar type. Currently there is no example of a binary functor
// that would take two operands of different types. If there were such an example, then this check should be
// moved to the BinaryOp functors, on a per-case basis. This would however require a change in the BinaryOp functors, as
// currently they take only one typename Scalar template parameter.
// It is tempting to always allow mixing different types but remember that this is often impossible in the vectorized paths.
// So allowing mixing different types gives very unexpected errors when enabling vectorization, when the user tries to
// add together a float matrix and a double matrix.
#define EIGEN_CHECK_BINARY_COMPATIBILIY(BINOP,LHS,RHS) \
EIGEN_STATIC_ASSERT((internal::functor_allows_mixing_real_and_complex<BINOP>::ret \
? int(internal::is_same<typename NumTraits<LHS>::Real, typename NumTraits<RHS>::Real>::value) \
: int(internal::is_same<LHS, RHS>::value)), \
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
class CwiseBinaryOpImpl;
template<typename BinaryOp, typename Lhs, typename Rhs>
class CwiseBinaryOp : internal::no_assignment_operator,
public CwiseBinaryOpImpl<
BinaryOp, Lhs, Rhs,
typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind,
typename internal::traits<Rhs>::StorageKind>::ret>
class CwiseBinaryOp : ei_no_assignment_operator,
public MatrixBase<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
{
public:
typedef typename CwiseBinaryOpImpl<
BinaryOp, Lhs, Rhs,
typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind,
typename internal::traits<Rhs>::StorageKind>::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::remove_reference<LhsNested>::type _LhsNested;
typedef typename internal::remove_reference<RhsNested>::type _RhsNested;
typedef typename ei_traits<CwiseBinaryOp>::LhsNested LhsNested;
typedef typename ei_traits<CwiseBinaryOp>::RhsNested RhsNested;
EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& lhs, const Rhs& rhs, const BinaryOp& func = BinaryOp())
: m_lhs(lhs), m_rhs(rhs), m_functor(func)
{
EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename Rhs::Scalar);
// 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.
EIGEN_STATIC_ASSERT((ei_functor_allows_mixing_real_and_complex<BinaryOp>::ret
? int(ei_is_same_type<typename Lhs::RealScalar, typename Rhs::RealScalar>::ret)
: int(ei_is_same_type<typename Lhs::Scalar, typename Rhs::Scalar>::ret)),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
// require the sizes to match
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs)
eigen_assert(lhs.rows() == rhs.rows() && lhs.cols() == rhs.cols());
ei_assert(lhs.rows() == rhs.rows() && lhs.cols() == rhs.cols());
}
EIGEN_STRONG_INLINE Index rows() const {
// return the fixed size type if available to enable compile time optimizations
if (internal::traits<typename internal::remove_all<LhsNested>::type>::RowsAtCompileTime==Dynamic)
return m_rhs.rows();
else
return m_lhs.rows();
}
EIGEN_STRONG_INLINE Index cols() const {
// return the fixed size type if available to enable compile time optimizations
if (internal::traits<typename internal::remove_all<LhsNested>::type>::ColsAtCompileTime==Dynamic)
return m_rhs.cols();
else
return m_lhs.cols();
EIGEN_STRONG_INLINE int rows() const { return m_lhs.rows(); }
EIGEN_STRONG_INLINE int cols() const { return m_lhs.cols(); }
EIGEN_STRONG_INLINE const Scalar coeff(int row, int col) const
{
return m_functor(m_lhs.coeff(row, col), m_rhs.coeff(row, col));
}
/** \returns the left hand side nested expression */
const _LhsNested& lhs() const { return m_lhs; }
/** \returns the right hand side nested expression */
const _RhsNested& rhs() const { return m_rhs; }
/** \returns the functor representing the binary operation */
const BinaryOp& functor() const { return m_functor; }
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(int row, int col) const
{
return m_functor.packetOp(m_lhs.template packet<LoadMode>(row, col), m_rhs.template packet<LoadMode>(row, col));
}
EIGEN_STRONG_INLINE const Scalar coeff(int index) const
{
return m_functor(m_lhs.coeff(index), m_rhs.coeff(index));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(int index) const
{
return m_functor.packetOp(m_lhs.template packet<LoadMode>(index), m_rhs.template packet<LoadMode>(index));
}
protected:
const LhsNested m_lhs;
@@ -172,42 +136,21 @@ 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
/**\returns an expression of the difference of \c *this and \a other
*
* \note If you want to substract a given scalar from all coefficients, see Cwise::operator-().
*
* \sa class CwiseBinaryOp, MatrixBase::operator-=(), Cwise::operator-()
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<ei_scalar_difference_op<typename ei_traits<Derived>::Scalar>,
Derived, OtherDerived>
MatrixBase<Derived>::operator-(const MatrixBase<OtherDerived> &other) const
{
typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> Derived;
public:
typedef typename internal::dense_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE( Derived )
EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
{
return derived().functor()(derived().lhs().coeff(row, col),
derived().rhs().coeff(row, col));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
{
return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(row, col),
derived().rhs().template packet<LoadMode>(row, col));
}
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
{
return derived().functor()(derived().lhs().coeff(index),
derived().rhs().coeff(index));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
{
return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(index),
derived().rhs().template packet<LoadMode>(index));
}
};
return CwiseBinaryOp<ei_scalar_difference_op<Scalar>,
Derived, OtherDerived>(derived(), other.derived());
}
/** replaces \c *this by \c *this - \a other.
*
@@ -218,9 +161,23 @@ 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();
return derived();
return *this = *this - other;
}
/** \relates MatrixBase
*
* \returns an expression of the sum of \c *this and \a other
*
* \note If you want to add a given scalar to all coefficients, see Cwise::operator+().
*
* \sa class CwiseBinaryOp, MatrixBase::operator+=(), Cwise::operator+()
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<ei_scalar_sum_op<typename ei_traits<Derived>::Scalar>, Derived, OtherDerived>
MatrixBase<Derived>::operator+(const MatrixBase<OtherDerived> &other) const
{
return CwiseBinaryOp<ei_scalar_sum_op<Scalar>, Derived, OtherDerived>(derived(), other.derived());
}
/** replaces \c *this by \c *this + \a other.
@@ -232,9 +189,116 @@ 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();
return derived();
return *this = *this + other;
}
/** \returns an expression of the Schur product (coefficient wise product) of *this and \a other
*
* Example: \include Cwise_product.cpp
* Output: \verbinclude Cwise_product.out
*
* \sa class CwiseBinaryOp, operator/(), square()
*/
template<typename ExpressionType>
template<typename OtherDerived>
EIGEN_STRONG_INLINE const EIGEN_CWISE_PRODUCT_RETURN_TYPE
Cwise<ExpressionType>::operator*(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_PRODUCT_RETURN_TYPE(_expression(), other.derived());
}
/** \returns an expression of the coefficient-wise quotient of *this and \a other
*
* Example: \include Cwise_quotient.cpp
* Output: \verbinclude Cwise_quotient.out
*
* \sa class CwiseBinaryOp, operator*(), inverse()
*/
template<typename ExpressionType>
template<typename OtherDerived>
EIGEN_STRONG_INLINE const EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_quotient_op)
Cwise<ExpressionType>::operator/(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_quotient_op)(_expression(), other.derived());
}
/** Replaces this expression by its coefficient-wise product with \a other.
*
* Example: \include Cwise_times_equal.cpp
* Output: \verbinclude Cwise_times_equal.out
*
* \sa operator*(), operator/=()
*/
template<typename ExpressionType>
template<typename OtherDerived>
inline ExpressionType& Cwise<ExpressionType>::operator*=(const MatrixBase<OtherDerived> &other)
{
return m_matrix.const_cast_derived() = *this * other;
}
/** Replaces this expression by its coefficient-wise quotient by \a other.
*
* Example: \include Cwise_slash_equal.cpp
* Output: \verbinclude Cwise_slash_equal.out
*
* \sa operator/(), operator*=()
*/
template<typename ExpressionType>
template<typename OtherDerived>
inline ExpressionType& Cwise<ExpressionType>::operator/=(const MatrixBase<OtherDerived> &other)
{
return m_matrix.const_cast_derived() = *this / other;
}
/** \returns an expression of the coefficient-wise min of *this and \a other
*
* Example: \include Cwise_min.cpp
* Output: \verbinclude Cwise_min.out
*
* \sa class CwiseBinaryOp
*/
template<typename ExpressionType>
template<typename OtherDerived>
EIGEN_STRONG_INLINE const EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_min_op)
Cwise<ExpressionType>::min(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_min_op)(_expression(), other.derived());
}
/** \returns an expression of the coefficient-wise max of *this and \a other
*
* Example: \include Cwise_max.cpp
* Output: \verbinclude Cwise_max.out
*
* \sa class CwiseBinaryOp
*/
template<typename ExpressionType>
template<typename OtherDerived>
EIGEN_STRONG_INLINE const EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_max_op)
Cwise<ExpressionType>::max(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_max_op)(_expression(), other.derived());
}
/** \returns an expression of a custom coefficient-wise operator \a func of *this and \a other
*
* The template parameter \a CustomBinaryOp is the type of the functor
* of the custom operator (see class CwiseBinaryOp for an example)
*
* \addexample CustomCwiseBinaryFunctors \label How to use custom coeff wise binary functors
*
* Here is an example illustrating the use of custom functors:
* \include class_CwiseBinaryOp.cpp
* Output: \verbinclude class_CwiseBinaryOp.out
*
* \sa class CwiseBinaryOp, MatrixBase::operator+, MatrixBase::operator-, Cwise::operator*, Cwise::operator/
*/
template<typename Derived>
template<typename CustomBinaryOp, typename OtherDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<CustomBinaryOp, Derived, OtherDerived>
MatrixBase<Derived>::binaryExpr(const MatrixBase<OtherDerived> &other, const CustomBinaryOp& func) const
{
return CwiseBinaryOp<CustomBinaryOp, Derived, OtherDerived>(derived(), other.derived(), func);
}
#endif // EIGEN_CWISE_BINARY_OP_H

View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -26,84 +26,81 @@
#define EIGEN_CWISE_NULLARY_OP_H
/** \class CwiseNullaryOp
* \ingroup Core_Module
*
* \brief Generic expression of a matrix where all coefficients are defined by a functor
*
* \param NullaryOp template functor implementing the operator
* \param PlainObjectType the underlying plain matrix/array type
*
* This class represents an expression of a generic nullary operator.
* It is the return type of the Ones(), Zero(), Constant(), Identity() and Random() methods,
* It is the return type of the Ones(), Zero(), Constant(), Identity() and Random() functions,
* and most of the time this is the only way it is used.
*
* However, if you want to write a function returning such an expression, you
* will need to use this class.
*
* \sa class CwiseUnaryOp, class CwiseBinaryOp, DenseBase::NullaryExpr()
* \sa class CwiseUnaryOp, class CwiseBinaryOp, MatrixBase::NullaryExpr()
*/
namespace internal {
template<typename NullaryOp, typename PlainObjectType>
struct traits<CwiseNullaryOp<NullaryOp, PlainObjectType> > : traits<PlainObjectType>
template<typename NullaryOp, typename MatrixType>
struct ei_traits<CwiseNullaryOp<NullaryOp, MatrixType> > : ei_traits<MatrixType>
{
enum {
Flags = (traits<PlainObjectType>::Flags
Flags = (ei_traits<MatrixType>::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
| (ei_functor_has_linear_access<NullaryOp>::ret ? LinearAccessBit : 0)
| (ei_functor_traits<NullaryOp>::PacketAccess ? PacketAccessBit : 0)))
| (ei_functor_traits<NullaryOp>::IsRepeatable ? 0 : EvalBeforeNestingBit),
CoeffReadCost = ei_functor_traits<NullaryOp>::Cost
};
};
}
template<typename NullaryOp, typename PlainObjectType>
class CwiseNullaryOp : internal::no_assignment_operator,
public internal::dense_xpr_base< CwiseNullaryOp<NullaryOp, PlainObjectType> >::type
template<typename NullaryOp, typename MatrixType>
class CwiseNullaryOp : ei_no_assignment_operator,
public MatrixBase<CwiseNullaryOp<NullaryOp, MatrixType> >
{
public:
typedef typename internal::dense_xpr_base<CwiseNullaryOp>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(CwiseNullaryOp)
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseNullaryOp)
CwiseNullaryOp(Index rows, Index cols, const NullaryOp& func = NullaryOp())
CwiseNullaryOp(int rows, int cols, const NullaryOp& func = NullaryOp())
: m_rows(rows), m_cols(cols), m_functor(func)
{
eigen_assert(rows >= 0
&& (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0
&& (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
ei_assert(rows > 0
&& (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols > 0
&& (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
}
EIGEN_STRONG_INLINE Index rows() const { return m_rows.value(); }
EIGEN_STRONG_INLINE Index cols() const { return m_cols.value(); }
EIGEN_STRONG_INLINE int rows() const { return m_rows.value(); }
EIGEN_STRONG_INLINE int cols() const { return m_cols.value(); }
EIGEN_STRONG_INLINE const Scalar coeff(Index rows, Index cols) const
EIGEN_STRONG_INLINE const Scalar coeff(int rows, int cols) const
{
return m_functor(rows, cols);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
EIGEN_STRONG_INLINE PacketScalar packet(int, int) const
{
return m_functor.packetOp(row, col);
return m_functor.packetOp();
}
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
EIGEN_STRONG_INLINE const Scalar coeff(int index) const
{
return m_functor(index);
if(RowsAtCompileTime == 1)
return m_functor(0, index);
else
return m_functor(index, 0);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
EIGEN_STRONG_INLINE PacketScalar packet(int) const
{
return m_functor.packetOp(index);
return m_functor.packetOp();
}
protected:
const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows;
const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols;
const ei_int_if_dynamic<RowsAtCompileTime> m_rows;
const ei_int_if_dynamic<ColsAtCompileTime> m_cols;
const NullaryOp m_functor;
};
@@ -124,7 +121,7 @@ class CwiseNullaryOp : internal::no_assignment_operator,
template<typename Derived>
template<typename CustomNullaryOp>
EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, Derived>
DenseBase<Derived>::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func)
MatrixBase<Derived>::NullaryExpr(int rows, int cols, const CustomNullaryOp& func)
{
return CwiseNullaryOp<CustomNullaryOp, Derived>(rows, cols, func);
}
@@ -147,16 +144,17 @@ DenseBase<Derived>::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& f
template<typename Derived>
template<typename CustomNullaryOp>
EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, Derived>
DenseBase<Derived>::NullaryExpr(Index size, const CustomNullaryOp& func)
MatrixBase<Derived>::NullaryExpr(int size, const CustomNullaryOp& func)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
ei_assert(IsVectorAtCompileTime);
if(RowsAtCompileTime == 1) return CwiseNullaryOp<CustomNullaryOp, Derived>(1, size, func);
else return CwiseNullaryOp<CustomNullaryOp, Derived>(size, 1, func);
}
/** \returns an expression of a matrix defined by a custom functor \a func
*
* This variant is only for fixed-size DenseBase types. For dynamic-size types, you
* This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
* need to use the variants taking size arguments.
*
* The template parameter \a CustomNullaryOp is the type of the functor.
@@ -166,7 +164,7 @@ DenseBase<Derived>::NullaryExpr(Index size, const CustomNullaryOp& func)
template<typename Derived>
template<typename CustomNullaryOp>
EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, Derived>
DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
MatrixBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
{
return CwiseNullaryOp<CustomNullaryOp, Derived>(RowsAtCompileTime, ColsAtCompileTime, func);
}
@@ -174,7 +172,7 @@ DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
/** \returns an expression of a constant matrix of value \a value
*
* 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.
* 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,
* it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
@@ -185,16 +183,16 @@ DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
* \sa class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(Index rows, Index cols, const Scalar& value)
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
MatrixBase<Derived>::Constant(int rows, int cols, const Scalar& value)
{
return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_constant_op<Scalar>(value));
return NullaryExpr(rows, cols, ei_scalar_constant_op<Scalar>(value));
}
/** \returns an expression of a constant matrix of value \a value
*
* The parameter \a size is the size of the returned vector.
* Must be compatible with this DenseBase type.
* Must be compatible with this MatrixBase type.
*
* \only_for_vectors
*
@@ -207,15 +205,15 @@ DenseBase<Derived>::Constant(Index rows, Index cols, const Scalar& value)
* \sa class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(Index size, const Scalar& value)
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
MatrixBase<Derived>::Constant(int size, const Scalar& value)
{
return DenseBase<Derived>::NullaryExpr(size, internal::scalar_constant_op<Scalar>(value));
return NullaryExpr(size, ei_scalar_constant_op<Scalar>(value));
}
/** \returns an expression of a constant matrix of value \a value
*
* This variant is only for fixed-size DenseBase types. For dynamic-size types, you
* This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
* need to use the variants taking size arguments.
*
* The template parameter \a CustomNullaryOp is the type of the functor.
@@ -223,90 +221,21 @@ DenseBase<Derived>::Constant(Index size, const Scalar& value)
* \sa class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(const Scalar& value)
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
MatrixBase<Derived>::Constant(const Scalar& value)
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
return DenseBase<Derived>::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_constant_op<Scalar>(value));
}
/**
* \brief Sets a linearly space vector.
*
* The function generates 'size' equally spaced values in the closed interval [low,high].
* This particular version of LinSpaced() uses sequential access, i.e. vector access is
* assumed to be a(0), a(1), ..., a(size). This assumption allows for better vectorization
* and yields faster code than the random access version.
*
* \only_for_vectors
*
* Example: \include DenseBase_LinSpaced_seq.cpp
* Output: \verbinclude DenseBase_LinSpaced_seq.out
*
* \sa setLinSpaced(Index,const Scalar&,const Scalar&), LinSpaced(Index,Scalar,Scalar), CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::SequentialLinSpacedReturnType
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));
}
/**
* \copydoc DenseBase::LinSpaced(Sequential_t, Index, const Scalar&, const Scalar&)
* Special version for fixed size types which does not require the size parameter.
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::SequentialLinSpacedReturnType
DenseBase<Derived>::LinSpaced(Sequential_t, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,false>(low,high,Derived::SizeAtCompileTime));
}
/**
* \brief Sets a linearly space vector.
*
* The function generates 'size' equally spaced values in the closed interval [low,high].
*
* \only_for_vectors
*
* Example: \include DenseBase_LinSpaced.cpp
* Output: \verbinclude DenseBase_LinSpaced.out
*
* \sa setLinSpaced(Index,const Scalar&,const Scalar&), LinSpaced(Sequential_t,Index,const Scalar&,const Scalar&,Index), CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(Index size, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar,true>(low,high,size));
}
/**
* \copydoc DenseBase::LinSpaced(Index, const Scalar&, const Scalar&)
* Special version for fixed size types which does not require the size parameter.
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,true>(low,high,Derived::SizeAtCompileTime));
return NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, ei_scalar_constant_op<Scalar>(value));
}
/** \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */
template<typename Derived>
bool DenseBase<Derived>::isApproxToConstant
bool MatrixBase<Derived>::isApproxToConstant
(const Scalar& value, RealScalar prec) const
{
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if(!internal::isApprox(this->coeff(i, j), value, prec))
for(int j = 0; j < cols(); ++j)
for(int i = 0; i < rows(); ++i)
if(!ei_isApprox(coeff(i, j), value, prec))
return false;
return true;
}
@@ -315,7 +244,7 @@ bool DenseBase<Derived>::isApproxToConstant
*
* \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */
template<typename Derived>
bool DenseBase<Derived>::isConstant
bool MatrixBase<Derived>::isConstant
(const Scalar& value, RealScalar prec) const
{
return isApproxToConstant(value, prec);
@@ -326,17 +255,17 @@ bool DenseBase<Derived>::isConstant
* \sa setConstant(), Constant(), class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& value)
EIGEN_STRONG_INLINE void MatrixBase<Derived>::fill(const Scalar& value)
{
setConstant(value);
}
/** Sets all coefficients in this expression to \a value.
*
* \sa fill(), setConstant(Index,const Scalar&), setConstant(Index,Index,const Scalar&), setZero(), setOnes(), Constant(), class CwiseNullaryOp, setZero(), setOnes()
* \sa fill(), setConstant(int,const Scalar&), setConstant(int,int,const Scalar&), setZero(), setOnes(), Constant(), class CwiseNullaryOp, setZero(), setOnes()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& value)
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setConstant(const Scalar& value)
{
return derived() = Constant(rows(), cols(), value);
}
@@ -345,14 +274,14 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& value
*
* \only_for_vectors
*
* Example: \include Matrix_setConstant_int.cpp
* Example: \include Matrix_set_int.cpp
* Output: \verbinclude Matrix_setConstant_int.out
*
* \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
* \sa MatrixBase::setConstant(const Scalar&), setConstant(int,int,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index size, const Scalar& value)
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
EIGEN_STRONG_INLINE Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>&
Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::setConstant(int size, const Scalar& value)
{
resize(size);
return setConstant(value);
@@ -362,39 +291,20 @@ PlainObjectBase<Derived>::setConstant(Index size, const Scalar& value)
*
* \param rows the new number of rows
* \param cols the new number of columns
* \param value the value to which all coefficients are set
*
* Example: \include Matrix_setConstant_int_int.cpp
* Output: \verbinclude Matrix_setConstant_int_int.out
*
* \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
* \sa MatrixBase::setConstant(const Scalar&), setConstant(int,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& value)
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
EIGEN_STRONG_INLINE Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>&
Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::setConstant(int rows, int cols, const Scalar& value)
{
resize(rows, cols);
return setConstant(value);
}
/**
* \brief Sets a linearly space vector.
*
* The function generates 'size' equally spaced values in the closed interval [low,high].
*
* \only_for_vectors
*
* Example: \include DenseBase_setLinSpaced.cpp
* Output: \verbinclude DenseBase_setLinSpaced.out
*
* \sa CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index size, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return derived() = Derived::NullaryExpr(size, internal::linspaced_op<Scalar,false>(low,high,size));
}
// zero:
@@ -407,14 +317,16 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index size, const
* it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
* instead.
*
* \addexample Zero \label How to take get a zero matrix
*
* Example: \include MatrixBase_zero_int_int.cpp
* Output: \verbinclude MatrixBase_zero_int_int.out
*
* \sa Zero(), Zero(Index)
* \sa Zero(), Zero(int)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero(Index rows, Index cols)
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
MatrixBase<Derived>::Zero(int rows, int cols)
{
return Constant(rows, cols, Scalar(0));
}
@@ -433,11 +345,11 @@ DenseBase<Derived>::Zero(Index rows, Index cols)
* Example: \include MatrixBase_zero_int.cpp
* Output: \verbinclude MatrixBase_zero_int.out
*
* \sa Zero(), Zero(Index,Index)
* \sa Zero(), Zero(int,int)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero(Index size)
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
MatrixBase<Derived>::Zero(int size)
{
return Constant(size, Scalar(0));
}
@@ -450,11 +362,11 @@ DenseBase<Derived>::Zero(Index size)
* Example: \include MatrixBase_zero.cpp
* Output: \verbinclude MatrixBase_zero.out
*
* \sa Zero(Index), Zero(Index,Index)
* \sa Zero(int), Zero(int,int)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero()
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
MatrixBase<Derived>::Zero()
{
return Constant(Scalar(0));
}
@@ -468,11 +380,11 @@ DenseBase<Derived>::Zero()
* \sa class CwiseNullaryOp, Zero()
*/
template<typename Derived>
bool DenseBase<Derived>::isZero(RealScalar prec) const
bool MatrixBase<Derived>::isZero(RealScalar prec) const
{
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))
for(int j = 0; j < cols(); ++j)
for(int i = 0; i < rows(); ++i)
if(!ei_isMuchSmallerThan(coeff(i, j), static_cast<Scalar>(1), prec))
return false;
return true;
}
@@ -485,7 +397,7 @@ bool DenseBase<Derived>::isZero(RealScalar prec) const
* \sa class CwiseNullaryOp, Zero()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setZero()
{
return setConstant(Scalar(0));
}
@@ -497,11 +409,11 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
* Example: \include Matrix_setZero_int.cpp
* Output: \verbinclude Matrix_setZero_int.out
*
* \sa DenseBase::setZero(), setZero(Index,Index), class CwiseNullaryOp, DenseBase::Zero()
* \sa MatrixBase::setZero(), setZero(int,int), class CwiseNullaryOp, MatrixBase::Zero()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index size)
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
EIGEN_STRONG_INLINE Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>&
Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::setZero(int size)
{
resize(size);
return setConstant(Scalar(0));
@@ -515,11 +427,11 @@ PlainObjectBase<Derived>::setZero(Index size)
* Example: \include Matrix_setZero_int_int.cpp
* Output: \verbinclude Matrix_setZero_int_int.out
*
* \sa DenseBase::setZero(), setZero(Index), class CwiseNullaryOp, DenseBase::Zero()
* \sa MatrixBase::setZero(), setZero(int), class CwiseNullaryOp, MatrixBase::Zero()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index rows, Index cols)
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
EIGEN_STRONG_INLINE Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>&
Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::setZero(int rows, int cols)
{
resize(rows, cols);
return setConstant(Scalar(0));
@@ -536,14 +448,16 @@ PlainObjectBase<Derived>::setZero(Index rows, Index cols)
* it is redundant to pass \a rows and \a cols as arguments, so Ones() should be used
* instead.
*
* \addexample One \label How to get a matrix with all coefficients equal one
*
* Example: \include MatrixBase_ones_int_int.cpp
* Output: \verbinclude MatrixBase_ones_int_int.out
*
* \sa Ones(), Ones(Index), isOnes(), class Ones
* \sa Ones(), Ones(int), isOnes(), class Ones
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index rows, Index cols)
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
MatrixBase<Derived>::Ones(int rows, int cols)
{
return Constant(rows, cols, Scalar(1));
}
@@ -562,11 +476,11 @@ DenseBase<Derived>::Ones(Index rows, Index cols)
* Example: \include MatrixBase_ones_int.cpp
* Output: \verbinclude MatrixBase_ones_int.out
*
* \sa Ones(), Ones(Index,Index), isOnes(), class Ones
* \sa Ones(), Ones(int,int), isOnes(), class Ones
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index size)
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
MatrixBase<Derived>::Ones(int size)
{
return Constant(size, Scalar(1));
}
@@ -579,11 +493,11 @@ DenseBase<Derived>::Ones(Index size)
* Example: \include MatrixBase_ones.cpp
* Output: \verbinclude MatrixBase_ones.out
*
* \sa Ones(Index), Ones(Index,Index), isOnes(), class Ones
* \sa Ones(int), Ones(int,int), isOnes(), class Ones
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones()
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
MatrixBase<Derived>::Ones()
{
return Constant(Scalar(1));
}
@@ -597,7 +511,7 @@ DenseBase<Derived>::Ones()
* \sa class CwiseNullaryOp, Ones()
*/
template<typename Derived>
bool DenseBase<Derived>::isOnes
bool MatrixBase<Derived>::isOnes
(RealScalar prec) const
{
return isApproxToConstant(Scalar(1), prec);
@@ -611,7 +525,7 @@ bool DenseBase<Derived>::isOnes
* \sa class CwiseNullaryOp, Ones()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setOnes()
{
return setConstant(Scalar(1));
}
@@ -623,11 +537,11 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
* Example: \include Matrix_setOnes_int.cpp
* Output: \verbinclude Matrix_setOnes_int.out
*
* \sa MatrixBase::setOnes(), setOnes(Index,Index), class CwiseNullaryOp, MatrixBase::Ones()
* \sa MatrixBase::setOnes(), setOnes(int,int), class CwiseNullaryOp, MatrixBase::Ones()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index size)
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
EIGEN_STRONG_INLINE Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>&
Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::setOnes(int size)
{
resize(size);
return setConstant(Scalar(1));
@@ -641,11 +555,11 @@ PlainObjectBase<Derived>::setOnes(Index size)
* Example: \include Matrix_setOnes_int_int.cpp
* Output: \verbinclude Matrix_setOnes_int_int.out
*
* \sa MatrixBase::setOnes(), setOnes(Index), class CwiseNullaryOp, MatrixBase::Ones()
* \sa MatrixBase::setOnes(), setOnes(int), class CwiseNullaryOp, MatrixBase::Ones()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
EIGEN_STRONG_INLINE Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>&
Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::setOnes(int rows, int cols)
{
resize(rows, cols);
return setConstant(Scalar(1));
@@ -662,6 +576,8 @@ PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
* it is redundant to pass \a rows and \a cols as arguments, so Identity() should be used
* instead.
*
* \addexample Identity \label How to get an identity matrix
*
* Example: \include MatrixBase_identity_int_int.cpp
* Output: \verbinclude MatrixBase_identity_int_int.out
*
@@ -669,9 +585,9 @@ PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
MatrixBase<Derived>::Identity(Index rows, Index cols)
MatrixBase<Derived>::Identity(int rows, int cols)
{
return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_identity_op<Scalar>());
return NullaryExpr(rows, cols, ei_scalar_identity_op<Scalar>());
}
/** \returns an expression of the identity matrix (not necessarily square).
@@ -682,14 +598,14 @@ MatrixBase<Derived>::Identity(Index rows, Index cols)
* Example: \include MatrixBase_identity.cpp
* Output: \verbinclude MatrixBase_identity.out
*
* \sa Identity(Index,Index), setIdentity(), isIdentity()
* \sa Identity(int,int), setIdentity(), isIdentity()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
MatrixBase<Derived>::Identity()
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
return MatrixBase<Derived>::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_identity_op<Scalar>());
return NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, ei_scalar_identity_op<Scalar>());
}
/** \returns true if *this is approximately equal to the identity matrix
@@ -699,24 +615,24 @@ MatrixBase<Derived>::Identity()
* Example: \include MatrixBase_isIdentity.cpp
* Output: \verbinclude MatrixBase_isIdentity.out
*
* \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), setIdentity()
* \sa class CwiseNullaryOp, Identity(), Identity(int,int), setIdentity()
*/
template<typename Derived>
bool MatrixBase<Derived>::isIdentity
(RealScalar prec) const
{
for(Index j = 0; j < cols(); ++j)
for(int j = 0; j < cols(); ++j)
{
for(Index i = 0; i < rows(); ++i)
for(int i = 0; i < rows(); ++i)
{
if(i == j)
{
if(!internal::isApprox(this->coeff(i, j), static_cast<Scalar>(1), prec))
if(!ei_isApprox(coeff(i, j), static_cast<Scalar>(1), prec))
return false;
}
else
{
if(!internal::isMuchSmallerThan(this->coeff(i, j), static_cast<RealScalar>(1), prec))
if(!ei_isMuchSmallerThan(coeff(i, j), static_cast<RealScalar>(1), prec))
return false;
}
}
@@ -724,10 +640,8 @@ bool MatrixBase<Derived>::isIdentity
return true;
}
namespace internal {
template<typename Derived, bool Big = (Derived::SizeAtCompileTime>=16)>
struct setIdentity_impl
struct ei_setIdentity_impl
{
static EIGEN_STRONG_INLINE Derived& run(Derived& m)
{
@@ -736,34 +650,31 @@ struct setIdentity_impl
};
template<typename Derived>
struct setIdentity_impl<Derived, true>
struct ei_setIdentity_impl<Derived, true>
{
typedef typename Derived::Index Index;
static EIGEN_STRONG_INLINE Derived& run(Derived& m)
{
m.setZero();
const Index size = (std::min)(m.rows(), m.cols());
for(Index i = 0; i < size; ++i) m.coeffRef(i,i) = typename Derived::Scalar(1);
const int size = std::min(m.rows(), m.cols());
for(int i = 0; i < size; ++i) m.coeffRef(i,i) = typename Derived::Scalar(1);
return m;
}
};
} // end namespace internal
/** Writes the identity expression (not necessarily square) into *this.
*
* Example: \include MatrixBase_setIdentity.cpp
* Output: \verbinclude MatrixBase_setIdentity.out
*
* \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), isIdentity()
* \sa class CwiseNullaryOp, Identity(), Identity(int,int), isIdentity()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
{
return internal::setIdentity_impl<Derived>::run(derived());
return ei_setIdentity_impl<Derived>::run(derived());
}
/** \brief Resizes to the given size, and writes the identity expression (not necessarily square) into *this.
/** Resizes to the given size, and writes the identity expression (not necessarily square) into *this.
*
* \param rows the new number of rows
* \param cols the new number of columns
@@ -773,10 +684,11 @@ EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
*
* \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Identity()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index cols)
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
EIGEN_STRONG_INLINE Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>&
Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::setIdentity(int rows, int cols)
{
derived().resize(rows, cols);
resize(rows, cols);
return setIdentity();
}
@@ -784,10 +696,10 @@ EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index
*
* \only_for_vectors
*
* \sa MatrixBase::Unit(Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
* \sa MatrixBase::Unit(int), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index size, Index i)
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(int size, int i)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return BasisReturnType(SquareMatrixType::Identity(size,size), i);
@@ -799,10 +711,10 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
*
* This variant is for fixed-size vector only.
*
* \sa MatrixBase::Unit(Index,Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
* \sa MatrixBase::Unit(int,int), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index i)
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(int i)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return BasisReturnType(SquareMatrixType::Identity(),i);
@@ -812,7 +724,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
*
* \only_for_vectors
*
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
* \sa MatrixBase::Unit(int,int), MatrixBase::Unit(int), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitX()
@@ -822,7 +734,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
*
* \only_for_vectors
*
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
* \sa MatrixBase::Unit(int,int), MatrixBase::Unit(int), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitY()
@@ -832,7 +744,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
*
* \only_for_vectors
*
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
* \sa MatrixBase::Unit(int,int), MatrixBase::Unit(int), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitZ()
@@ -842,7 +754,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
*
* \only_for_vectors
*
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
* \sa MatrixBase::Unit(int,int), MatrixBase::Unit(int), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitW()

View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
@@ -27,111 +27,203 @@
#define EIGEN_CWISE_UNARY_OP_H
/** \class CwiseUnaryOp
* \ingroup Core_Module
*
* \brief Generic expression where a coefficient-wise unary operator is applied to an expression
* \brief Generic expression of a coefficient-wise unary operator of a matrix or a vector
*
* \param UnaryOp template functor implementing the operator
* \param XprType the type of the expression to which we are applying the unary operator
* \param MatrixType the type of the matrix we are applying the unary operator
*
* This class represents an expression where a unary operator is applied to an expression.
* It is the return type of all operations taking exactly 1 input expression, regardless of the
* presence of other inputs such as scalars. For example, the operator* in the expression 3*matrix
* is considered unary, because only the right-hand side is an expression, and its
* return type is a specialization of CwiseUnaryOp.
*
* Most of the time, this is the only way that it is used, so you typically don't have to name
* CwiseUnaryOp types explicitly.
* This class represents an expression of a generic unary operator of a matrix or a vector.
* It is the return type of the unary operator-, of a matrix or a vector, and most
* of the time this is the only way it is used.
*
* \sa MatrixBase::unaryExpr(const CustomUnaryOp &) const, class CwiseBinaryOp, class CwiseNullaryOp
*/
namespace internal {
template<typename UnaryOp, typename XprType>
struct traits<CwiseUnaryOp<UnaryOp, XprType> >
: traits<XprType>
template<typename UnaryOp, typename MatrixType>
struct ei_traits<CwiseUnaryOp<UnaryOp, MatrixType> >
: ei_traits<MatrixType>
{
typedef typename result_of<
UnaryOp(typename XprType::Scalar)
typedef typename ei_result_of<
UnaryOp(typename MatrixType::Scalar)
>::type Scalar;
typedef typename XprType::Nested XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
typedef typename MatrixType::Nested MatrixTypeNested;
typedef typename ei_unref<MatrixTypeNested>::type _MatrixTypeNested;
enum {
Flags = _XprTypeNested::Flags & (
Flags = (_MatrixTypeNested::Flags & (
HereditaryBits | LinearAccessBit | AlignedBit
| (functor_traits<UnaryOp>::PacketAccess ? PacketAccessBit : 0)),
CoeffReadCost = _XprTypeNested::CoeffReadCost + functor_traits<UnaryOp>::Cost
| (ei_functor_traits<UnaryOp>::PacketAccess ? PacketAccessBit : 0))),
CoeffReadCost = _MatrixTypeNested::CoeffReadCost + ei_functor_traits<UnaryOp>::Cost
};
};
}
template<typename UnaryOp, typename XprType, typename StorageKind>
class CwiseUnaryOpImpl;
template<typename UnaryOp, typename XprType>
class CwiseUnaryOp : internal::no_assignment_operator,
public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind>
template<typename UnaryOp, typename MatrixType>
class CwiseUnaryOp : ei_no_assignment_operator,
public MatrixBase<CwiseUnaryOp<UnaryOp, MatrixType> >
{
public:
typedef typename CwiseUnaryOpImpl<UnaryOp, XprType,typename internal::traits<XprType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryOp)
inline CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
: m_xpr(xpr), m_functor(func) {}
inline CwiseUnaryOp(const MatrixType& mat, const UnaryOp& func = UnaryOp())
: m_matrix(mat), m_functor(func) {}
EIGEN_STRONG_INLINE Index rows() const { return m_xpr.rows(); }
EIGEN_STRONG_INLINE Index cols() const { return m_xpr.cols(); }
EIGEN_STRONG_INLINE int rows() const { return m_matrix.rows(); }
EIGEN_STRONG_INLINE int cols() const { return m_matrix.cols(); }
/** \returns the functor representing the unary operation */
const UnaryOp& functor() const { return m_functor; }
EIGEN_STRONG_INLINE const Scalar coeff(int row, int col) const
{
return m_functor(m_matrix.coeff(row, col));
}
/** \returns the nested expression */
const typename internal::remove_all<typename XprType::Nested>::type&
nestedExpression() const { return m_xpr; }
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(int row, int col) const
{
return m_functor.packetOp(m_matrix.template packet<LoadMode>(row, col));
}
/** \returns the nested expression */
typename internal::remove_all<typename XprType::Nested>::type&
nestedExpression() { return m_xpr.const_cast_derived(); }
EIGEN_STRONG_INLINE const Scalar coeff(int index) const
{
return m_functor(m_matrix.coeff(index));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(int index) const
{
return m_functor.packetOp(m_matrix.template packet<LoadMode>(index));
}
protected:
const typename XprType::Nested m_xpr;
const typename MatrixType::Nested m_matrix;
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
/** \returns an expression of a custom coefficient-wise unary operator \a func of *this
*
* The template parameter \a CustomUnaryOp is the type of the functor
* of the custom unary operator.
*
* \addexample CustomCwiseUnaryFunctors \label How to use custom coeff wise unary functors
*
* Example:
* \include class_CwiseUnaryOp.cpp
* Output: \verbinclude class_CwiseUnaryOp.out
*
* \sa class CwiseUnaryOp, class CwiseBinarOp, MatrixBase::operator-, Cwise::abs
*/
template<typename Derived>
template<typename CustomUnaryOp>
EIGEN_STRONG_INLINE const CwiseUnaryOp<CustomUnaryOp, Derived>
MatrixBase<Derived>::unaryExpr(const CustomUnaryOp& func) const
{
public:
return CwiseUnaryOp<CustomUnaryOp, Derived>(derived(), func);
}
typedef CwiseUnaryOp<UnaryOp, XprType> Derived;
typedef typename internal::dense_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
/** \returns an expression of the opposite of \c *this
*/
template<typename Derived>
EIGEN_STRONG_INLINE const CwiseUnaryOp<ei_scalar_opposite_op<typename ei_traits<Derived>::Scalar>,Derived>
MatrixBase<Derived>::operator-() const
{
return derived();
}
EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
{
return derived().functor()(derived().nestedExpression().coeff(row, col));
}
/** \returns an expression of the coefficient-wise absolute value of \c *this
*
* Example: \include Cwise_abs.cpp
* Output: \verbinclude Cwise_abs.out
*
* \sa abs2()
*/
template<typename ExpressionType>
EIGEN_STRONG_INLINE const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_abs_op)
Cwise<ExpressionType>::abs() const
{
return _expression();
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
{
return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(row, col));
}
/** \returns an expression of the coefficient-wise squared absolute value of \c *this
*
* Example: \include Cwise_abs2.cpp
* Output: \verbinclude Cwise_abs2.out
*
* \sa abs(), square()
*/
template<typename ExpressionType>
EIGEN_STRONG_INLINE const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_abs2_op)
Cwise<ExpressionType>::abs2() const
{
return _expression();
}
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
{
return derived().functor()(derived().nestedExpression().coeff(index));
}
/** \returns an expression of the complex conjugate of \c *this.
*
* \sa adjoint() */
template<typename Derived>
EIGEN_STRONG_INLINE typename MatrixBase<Derived>::ConjugateReturnType
MatrixBase<Derived>::conjugate() const
{
return ConjugateReturnType(derived());
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
{
return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(index));
}
};
/** \returns an expression of the real part of \c *this.
*
* \sa imag() */
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::RealReturnType
MatrixBase<Derived>::real() const { return derived(); }
/** \returns an expression of the imaginary part of \c *this.
*
* \sa real() */
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ImagReturnType
MatrixBase<Derived>::imag() const { return derived(); }
/** \returns an expression of *this with the \a Scalar type casted to
* \a NewScalar.
*
* The template parameter \a NewScalar is the type we are casting the scalars to.
*
* \sa class CwiseUnaryOp
*/
template<typename Derived>
template<typename NewType>
EIGEN_STRONG_INLINE const CwiseUnaryOp<ei_scalar_cast_op<typename ei_traits<Derived>::Scalar, NewType>, Derived>
MatrixBase<Derived>::cast() const
{
return derived();
}
/** \relates MatrixBase */
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ScalarMultipleReturnType
MatrixBase<Derived>::operator*(const Scalar& scalar) const
{
return CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, Derived>
(derived(), ei_scalar_multiple_op<Scalar>(scalar));
}
/** \relates MatrixBase */
template<typename Derived>
EIGEN_STRONG_INLINE const CwiseUnaryOp<ei_scalar_quotient1_op<typename ei_traits<Derived>::Scalar>, Derived>
MatrixBase<Derived>::operator/(const Scalar& scalar) const
{
return CwiseUnaryOp<ei_scalar_quotient1_op<Scalar>, Derived>
(derived(), ei_scalar_quotient1_op<Scalar>(scalar));
}
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
MatrixBase<Derived>::operator*=(const Scalar& other)
{
return *this = *this * other;
}
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
MatrixBase<Derived>::operator/=(const Scalar& other)
{
return *this = *this / other;
}
#endif // EIGEN_CWISE_UNARY_OP_H

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@@ -1,148 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_CWISE_UNARY_VIEW_H
#define EIGEN_CWISE_UNARY_VIEW_H
/** \class CwiseUnaryView
* \ingroup Core_Module
*
* \brief Generic lvalue expression of a coefficient-wise unary operator of a matrix or a vector
*
* \param ViewOp template functor implementing the view
* \param MatrixType the type of the matrix we are applying the unary operator
*
* This class represents a lvalue expression of a generic unary view operator of a matrix or a vector.
* It is the return type of real() and imag(), and most of the time this is the only way it is used.
*
* \sa MatrixBase::unaryViewExpr(const CustomUnaryOp &) const, class CwiseUnaryOp
*/
namespace internal {
template<typename ViewOp, typename MatrixType>
struct traits<CwiseUnaryView<ViewOp, MatrixType> >
: traits<MatrixType>
{
typedef typename result_of<
ViewOp(typename traits<MatrixType>::Scalar)
>::type Scalar;
typedef typename MatrixType::Nested MatrixTypeNested;
typedef typename remove_all<MatrixTypeNested>::type _MatrixTypeNested;
enum {
Flags = (traits<_MatrixTypeNested>::Flags & (HereditaryBits | LvalueBit | LinearAccessBit | DirectAccessBit)),
CoeffReadCost = traits<_MatrixTypeNested>::CoeffReadCost + functor_traits<ViewOp>::Cost,
MatrixTypeInnerStride = inner_stride_at_compile_time<MatrixType>::ret,
// need to cast the sizeof's from size_t to int explicitly, otherwise:
// "error: no integral type can represent all of the enumerator values
InnerStrideAtCompileTime = MatrixTypeInnerStride == Dynamic
? int(Dynamic)
: int(MatrixTypeInnerStride)
* int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)),
OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret
};
};
}
template<typename ViewOp, typename MatrixType, typename StorageKind>
class CwiseUnaryViewImpl;
template<typename ViewOp, typename MatrixType>
class CwiseUnaryView : internal::no_assignment_operator,
public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
{
public:
typedef typename CwiseUnaryViewImpl<ViewOp, MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView)
inline CwiseUnaryView(const MatrixType& mat, const ViewOp& func = ViewOp())
: m_matrix(mat), m_functor(func) {}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryView)
EIGEN_STRONG_INLINE Index rows() const { return m_matrix.rows(); }
EIGEN_STRONG_INLINE Index cols() const { return m_matrix.cols(); }
/** \returns the functor representing unary operation */
const ViewOp& functor() const { return m_functor; }
/** \returns the nested expression */
const typename internal::remove_all<typename MatrixType::Nested>::type&
nestedExpression() const { return m_matrix; }
/** \returns the nested expression */
typename internal::remove_all<typename MatrixType::Nested>::type&
nestedExpression() { return m_matrix.const_cast_derived(); }
protected:
// FIXME changed from MatrixType::Nested because of a weird compilation error with sun CC
const typename internal::nested<MatrixType>::type m_matrix;
ViewOp m_functor;
};
template<typename ViewOp, typename MatrixType>
class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
: public internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type
{
public:
typedef CwiseUnaryView<ViewOp, MatrixType> Derived;
typedef typename internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
inline Index innerStride() const
{
return derived().nestedExpression().innerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
}
inline Index outerStride() const
{
return derived().nestedExpression().outerStride();
}
EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const
{
return derived().functor()(derived().nestedExpression().coeff(row, col));
}
EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
return derived().functor()(derived().nestedExpression().coeff(index));
}
EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
{
return derived().functor()(const_cast_derived().nestedExpression().coeffRef(row, col));
}
EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
return derived().functor()(const_cast_derived().nestedExpression().coeffRef(index));
}
};
#endif // EIGEN_CWISE_UNARY_VIEW_H

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@@ -1,543 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_DENSEBASE_H
#define EIGEN_DENSEBASE_H
/** \class DenseBase
* \ingroup Core_Module
*
* \brief Base class for all dense matrices, vectors, and arrays
*
* This class is the base that is inherited by all dense objects (matrix, vector, arrays,
* and related expression types). The common Eigen API for dense objects is contained in this class.
*
* \tparam Derived is the derived type, e.g., a matrix type or an expression.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN.
*
* \sa \ref TopicClassHierarchy
*/
template<typename Derived> class DenseBase
#ifndef EIGEN_PARSED_BY_DOXYGEN
: public internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar,
typename NumTraits<typename internal::traits<Derived>::Scalar>::Real>
#else
: public DenseCoeffsBase<Derived>
#endif // not EIGEN_PARSED_BY_DOXYGEN
{
public:
using internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar,
typename NumTraits<typename internal::traits<Derived>::Scalar>::Real>::operator*;
class InnerIterator;
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;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef DenseCoeffsBase<Derived> Base;
using Base::derived;
using Base::const_cast_derived;
using Base::rows;
using Base::cols;
using Base::size;
using Base::rowIndexByOuterInner;
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;
using Base::y;
using Base::z;
using Base::w;
using Base::stride;
using Base::innerStride;
using Base::outerStride;
using Base::rowStride;
using Base::colStride;
typedef typename Base::CoeffReturnType CoeffReturnType;
enum {
RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
/**< The number of rows at compile-time. This is just a copy of the value provided
* by the \a Derived type. If a value is not known at compile-time,
* it is set to the \a Dynamic constant.
* \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
/**< The number of columns at compile-time. This is just a copy of the value provided
* by the \a Derived type. If a value is not known at compile-time,
* it is set to the \a Dynamic constant.
* \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime>::ret),
/**< This is equal to the number of coefficients, i.e. the number of
* rows times the number of columns, or to \a Dynamic if this is not
* known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
/**< This value is equal to the maximum possible number of rows that this expression
* might have. If this expression might have an arbitrarily high number of rows,
* this value is set to \a Dynamic.
*
* This value is useful to know when evaluating an expression, in order to determine
* whether it is possible to avoid doing a dynamic memory allocation.
*
* \sa RowsAtCompileTime, MaxColsAtCompileTime, MaxSizeAtCompileTime
*/
MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
/**< This value is equal to the maximum possible number of columns that this expression
* might have. If this expression might have an arbitrarily high number of columns,
* this value is set to \a Dynamic.
*
* This value is useful to know when evaluating an expression, in order to determine
* whether it is possible to avoid doing a dynamic memory allocation.
*
* \sa ColsAtCompileTime, MaxRowsAtCompileTime, MaxSizeAtCompileTime
*/
MaxSizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime>::ret),
/**< This value is equal to the maximum possible number of coefficients that this expression
* might have. If this expression might have an arbitrarily high number of coefficients,
* this value is set to \a Dynamic.
*
* This value is useful to know when evaluating an expression, in order to determine
* whether it is possible to avoid doing a dynamic memory allocation.
*
* \sa SizeAtCompileTime, MaxRowsAtCompileTime, MaxColsAtCompileTime
*/
IsVectorAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime == 1
|| internal::traits<Derived>::MaxColsAtCompileTime == 1,
/**< This is set to true if either the number of rows or the number of
* columns is known at compile-time to be equal to 1. Indeed, in that case,
* we are dealing with a column-vector (if there is only one column) or with
* a row-vector (if there is only one row). */
Flags = internal::traits<Derived>::Flags,
/**< This stores expression \ref flags flags which may or may not be inherited by new expressions
* constructed from this one. See the \ref flags "list of flags".
*/
IsRowMajor = int(Flags) & RowMajorBit, /**< True if this expression has row-major storage order. */
InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? SizeAtCompileTime
: int(IsRowMajor) ? ColsAtCompileTime : 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
};
enum { ThisConstantIsPrivateInPlainObjectBase };
/** \returns the number of nonzero coefficients which is in practice the number
* of stored coefficients. */
inline Index nonZeros() const { return size(); }
/** \returns true if either the number of rows or the number of columns is equal to 1.
* In other words, this function returns
* \code rows()==1 || cols()==1 \endcode
* \sa rows(), cols(), IsVectorAtCompileTime. */
/** \returns the outer size.
*
* \note For a vector, this returns just 1. For a matrix (non-vector), this is the major dimension
* with respect to the \ref TopicStorageOrders "storage order", i.e., the number of columns for a
* column-major matrix, and the number of rows for a row-major matrix. */
Index outerSize() const
{
return IsVectorAtCompileTime ? 1
: int(IsRowMajor) ? this->rows() : this->cols();
}
/** \returns the inner size.
*
* \note For a vector, this is just the size. For a matrix (non-vector), this is the minor dimension
* with respect to the \ref TopicStorageOrders "storage order", i.e., the number of rows for a
* column-major matrix, and the number of columns for a row-major matrix. */
Index innerSize() const
{
return IsVectorAtCompileTime ? this->size()
: int(IsRowMajor) ? this->cols() : this->rows();
}
/** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
* Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
* nothing else.
*/
void resize(Index size)
{
EIGEN_ONLY_USED_FOR_DEBUG(size);
eigen_assert(size == this->size()
&& "DenseBase::resize() does not actually allow to resize.");
}
/** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
* Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
* nothing else.
*/
void resize(Index rows, Index cols)
{
EIGEN_ONLY_USED_FOR_DEBUG(rows);
EIGEN_ONLY_USED_FOR_DEBUG(cols);
eigen_assert(rows == this->rows() && cols == this->cols()
&& "DenseBase::resize() does not actually allow to resize.");
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal Represents a matrix with all coefficients equal to one another*/
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType;
/** \internal Represents a vector with linearly spaced coefficients that allows sequential access only. */
typedef CwiseNullaryOp<internal::linspaced_op<Scalar,false>,Derived> SequentialLinSpacedReturnType;
/** \internal Represents a vector with linearly spaced coefficients that allows random access. */
typedef CwiseNullaryOp<internal::linspaced_op<Scalar,true>,Derived> RandomAccessLinSpacedReturnType;
/** \internal the return type of MatrixBase::eigenvalues() */
typedef Matrix<typename NumTraits<typename internal::traits<Derived>::Scalar>::Real, internal::traits<Derived>::ColsAtCompileTime, 1> EigenvaluesReturnType;
#endif // not EIGEN_PARSED_BY_DOXYGEN
/** Copies \a other into *this. \returns a reference to *this. */
template<typename OtherDerived>
Derived& operator=(const DenseBase<OtherDerived>& other);
/** Special case of the template operator=, in order to prevent the compiler
* from generating a default operator= (issue hit with g++ 4.1)
*/
Derived& operator=(const DenseBase& other);
template<typename OtherDerived>
Derived& operator=(const EigenBase<OtherDerived> &other);
template<typename OtherDerived>
Derived& operator+=(const EigenBase<OtherDerived> &other);
template<typename OtherDerived>
Derived& operator-=(const EigenBase<OtherDerived> &other);
template<typename OtherDerived>
Derived& operator=(const ReturnByValue<OtherDerived>& func);
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** Copies \a other into *this without evaluating other. \returns a reference to *this. */
template<typename OtherDerived>
Derived& lazyAssign(const DenseBase<OtherDerived>& other);
#endif // not EIGEN_PARSED_BY_DOXYGEN
CommaInitializer<Derived> operator<< (const Scalar& s);
template<unsigned int Added,unsigned int Removed>
const Flagged<Derived, Added, Removed> flagged() const;
template<typename OtherDerived>
CommaInitializer<Derived> operator<< (const DenseBase<OtherDerived>& other);
Eigen::Transpose<Derived> transpose();
typedef const Transpose<const Derived> ConstTransposeReturnType;
ConstTransposeReturnType transpose() const;
void transposeInPlace();
#ifndef EIGEN_NO_DEBUG
protected:
template<typename OtherDerived>
void checkTransposeAliasing(const OtherDerived& other) const;
public:
#endif
typedef VectorBlock<Derived> SegmentReturnType;
typedef const VectorBlock<const Derived> ConstSegmentReturnType;
template<int Size> struct FixedSegmentReturnType { typedef VectorBlock<Derived, Size> Type; };
template<int Size> struct ConstFixedSegmentReturnType { typedef const VectorBlock<const Derived, Size> Type; };
// Note: The "DenseBase::" prefixes are added to help MSVC9 to match these declarations with the later implementations.
SegmentReturnType segment(Index start, Index size);
typename DenseBase::ConstSegmentReturnType segment(Index start, Index size) const;
SegmentReturnType head(Index size);
typename DenseBase::ConstSegmentReturnType head(Index size) const;
SegmentReturnType tail(Index size);
typename DenseBase::ConstSegmentReturnType tail(Index size) const;
template<int Size> typename FixedSegmentReturnType<Size>::Type head();
template<int Size> typename ConstFixedSegmentReturnType<Size>::Type head() const;
template<int Size> typename FixedSegmentReturnType<Size>::Type tail();
template<int Size> typename ConstFixedSegmentReturnType<Size>::Type tail() const;
template<int Size> typename FixedSegmentReturnType<Size>::Type segment(Index start);
template<int Size> typename ConstFixedSegmentReturnType<Size>::Type segment(Index start) const;
static const ConstantReturnType
Constant(Index rows, Index cols, const Scalar& value);
static const ConstantReturnType
Constant(Index size, const Scalar& value);
static const ConstantReturnType
Constant(const Scalar& value);
static const SequentialLinSpacedReturnType
LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high);
static const RandomAccessLinSpacedReturnType
LinSpaced(Index size, const Scalar& low, const Scalar& high);
static const SequentialLinSpacedReturnType
LinSpaced(Sequential_t, const Scalar& low, const Scalar& high);
static const RandomAccessLinSpacedReturnType
LinSpaced(const Scalar& low, const Scalar& high);
template<typename CustomNullaryOp>
static const CwiseNullaryOp<CustomNullaryOp, Derived>
NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func);
template<typename CustomNullaryOp>
static const CwiseNullaryOp<CustomNullaryOp, Derived>
NullaryExpr(Index size, const CustomNullaryOp& func);
template<typename CustomNullaryOp>
static const CwiseNullaryOp<CustomNullaryOp, Derived>
NullaryExpr(const CustomNullaryOp& func);
static const ConstantReturnType Zero(Index rows, Index cols);
static const ConstantReturnType Zero(Index size);
static const ConstantReturnType Zero();
static const ConstantReturnType Ones(Index rows, Index cols);
static const ConstantReturnType Ones(Index size);
static const ConstantReturnType Ones();
void fill(const Scalar& value);
Derived& setConstant(const Scalar& value);
Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high);
Derived& setLinSpaced(const Scalar& low, const Scalar& high);
Derived& setZero();
Derived& setOnes();
Derived& setRandom();
template<typename OtherDerived>
bool isApprox(const DenseBase<OtherDerived>& other,
RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isMuchSmallerThan(const RealScalar& other,
RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
template<typename OtherDerived>
bool isMuchSmallerThan(const DenseBase<OtherDerived>& other,
RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isApproxToConstant(const Scalar& value, RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isConstant(const Scalar& value, RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isZero(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isOnes(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
inline Derived& operator*=(const Scalar& other);
inline Derived& operator/=(const Scalar& other);
/** \returns the matrix or vector obtained by evaluating this expression.
*
* Notice that in the case of a plain matrix or vector (not an expression) this function just returns
* a const reference, in order to avoid a useless copy.
*/
EIGEN_STRONG_INLINE const typename internal::eval<Derived>::type eval() const
{
// Even though MSVC does not honor strong inlining when the return type
// is a dynamic matrix, we desperately need strong inlining for fixed
// size types on MSVC.
return typename internal::eval<Derived>::type(derived());
}
/** swaps *this with the expression \a other.
*
*/
template<typename OtherDerived>
void swap(const DenseBase<OtherDerived>& other,
int = OtherDerived::ThisConstantIsPrivateInPlainObjectBase)
{
SwapWrapper<Derived>(derived()).lazyAssign(other.derived());
}
/** swaps *this with the matrix or array \a other.
*
*/
template<typename OtherDerived>
void swap(PlainObjectBase<OtherDerived>& other)
{
SwapWrapper<Derived>(derived()).lazyAssign(other.derived());
}
inline const NestByValue<Derived> nestByValue() const;
inline const ForceAlignedAccess<Derived> forceAlignedAccess() const;
inline ForceAlignedAccess<Derived> forceAlignedAccess();
template<bool Enable> inline const typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf() const;
template<bool Enable> inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf();
Scalar sum() const;
Scalar mean() const;
Scalar trace() const;
Scalar prod() const;
typename internal::traits<Derived>::Scalar minCoeff() const;
typename internal::traits<Derived>::Scalar maxCoeff() const;
template<typename IndexType>
typename internal::traits<Derived>::Scalar minCoeff(IndexType* row, IndexType* col) const;
template<typename IndexType>
typename internal::traits<Derived>::Scalar maxCoeff(IndexType* row, IndexType* col) const;
template<typename IndexType>
typename internal::traits<Derived>::Scalar minCoeff(IndexType* index) const;
template<typename IndexType>
typename internal::traits<Derived>::Scalar maxCoeff(IndexType* index) const;
template<typename BinaryOp>
typename internal::result_of<BinaryOp(typename internal::traits<Derived>::Scalar)>::type
redux(const BinaryOp& func) const;
template<typename Visitor>
void visit(Visitor& func) const;
inline const WithFormat<Derived> format(const IOFormat& fmt) const;
/** \returns the unique coefficient of a 1x1 expression */
CoeffReturnType value() const
{
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
return derived().coeff(0,0);
}
/////////// Array module ///////////
bool all(void) const;
bool any(void) const;
Index count() const;
typedef VectorwiseOp<Derived, Horizontal> RowwiseReturnType;
typedef const VectorwiseOp<const Derived, Horizontal> ConstRowwiseReturnType;
typedef VectorwiseOp<Derived, Vertical> ColwiseReturnType;
typedef const VectorwiseOp<const Derived, Vertical> ConstColwiseReturnType;
ConstRowwiseReturnType rowwise() const;
RowwiseReturnType rowwise();
ConstColwiseReturnType colwise() const;
ColwiseReturnType colwise();
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();
template<typename ThenDerived,typename ElseDerived>
const Select<Derived,ThenDerived,ElseDerived>
select(const DenseBase<ThenDerived>& thenMatrix,
const DenseBase<ElseDerived>& elseMatrix) const;
template<typename ThenDerived>
inline const Select<Derived,ThenDerived, typename ThenDerived::ConstantReturnType>
select(const DenseBase<ThenDerived>& thenMatrix, typename ThenDerived::Scalar elseScalar) const;
template<typename ElseDerived>
inline const Select<Derived, typename ElseDerived::ConstantReturnType, ElseDerived >
select(typename ElseDerived::Scalar thenScalar, const DenseBase<ElseDerived>& elseMatrix) const;
template<int p> RealScalar lpNorm() const;
template<int RowFactor, int ColFactor>
const Replicate<Derived,RowFactor,ColFactor> replicate() const;
const Replicate<Derived,Dynamic,Dynamic> replicate(Index rowFacor,Index colFactor) const;
typedef Reverse<Derived, BothDirections> ReverseReturnType;
typedef const Reverse<const Derived, BothDirections> ConstReverseReturnType;
ReverseReturnType reverse();
ConstReverseReturnType reverse() const;
void reverseInPlace();
#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::DenseBase
# include "../plugins/BlockMethods.h"
# ifdef EIGEN_DENSEBASE_PLUGIN
# include EIGEN_DENSEBASE_PLUGIN
# endif
#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
#ifdef EIGEN2_SUPPORT
Block<Derived> corner(CornerType type, Index cRows, Index cCols);
const Block<Derived> corner(CornerType type, Index cRows, Index cCols) const;
template<int CRows, int CCols>
Block<Derived, CRows, CCols> corner(CornerType type);
template<int CRows, int CCols>
const Block<Derived, CRows, CCols> corner(CornerType type) const;
#endif // EIGEN2_SUPPORT
// disable the use of evalTo for dense objects with a nice compilation error
template<typename Dest> inline void evalTo(Dest& ) const
{
EIGEN_STATIC_ASSERT((internal::is_same<Dest,void>::value),THE_EVAL_EVALTO_FUNCTION_SHOULD_NEVER_BE_CALLED_FOR_DENSE_OBJECTS);
}
protected:
/** Default constructor. Do nothing. */
DenseBase()
{
/* Just checks for self-consistency of the flags.
* Only do it when debugging Eigen, as this borders on paranoiac and could slow compilation down
*/
#ifdef EIGEN_INTERNAL_DEBUGGING
EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, int(IsRowMajor))
&& EIGEN_IMPLIES(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, int(!IsRowMajor))),
INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION)
#endif
}
private:
explicit DenseBase(int);
DenseBase(int,int);
template<typename OtherDerived> explicit DenseBase(const DenseBase<OtherDerived>&);
};
#endif // EIGEN_DENSEBASE_H

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@@ -1,765 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_DENSECOEFFSBASE_H
#define EIGEN_DENSECOEFFSBASE_H
namespace internal {
template<typename T> struct add_const_on_value_type_if_arithmetic
{
typedef typename conditional<is_arithmetic<T>::value, T, typename add_const_on_value_type<T>::type>::type type;
};
}
/** \brief Base class providing read-only coefficient access to matrices and arrays.
* \ingroup Core_Module
* \tparam Derived Type of the derived class
* \tparam #ReadOnlyAccessors Constant indicating read-only access
*
* This class defines the \c operator() \c const function and friends, which can be used to read specific
* entries of a matrix or array.
*
* \sa DenseCoeffsBase<Derived, WriteAccessors>, DenseCoeffsBase<Derived, DirectAccessors>,
* \ref TopicClassHierarchy
*/
template<typename Derived>
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;
// Explanation for this CoeffReturnType typedef.
// - This is the return type of the coeff() method.
// - The LvalueBit means exactly that we can offer a coeffRef() method, which means exactly that we can get references
// to coeffs, which means exactly that we can have coeff() return a const reference (as opposed to returning a value).
// - The is_artihmetic check is required since "const int", "const double", etc. will cause warnings on some systems
// while the declaration of "const T", where T is a non arithmetic type does not. Always returning "const Scalar&" is
// not possible, since the underlying expressions might not offer a valid address the reference could be referring to.
typedef typename internal::conditional<bool(internal::traits<Derived>::Flags&LvalueBit),
const Scalar&,
typename internal::conditional<internal::is_arithmetic<Scalar>::value, Scalar, const Scalar>::type
>::type CoeffReturnType;
typedef typename internal::add_const_on_value_type_if_arithmetic<
typename internal::packet_traits<Scalar>::type
>::type PacketReturnType;
typedef EigenBase<Derived> Base;
using Base::rows;
using Base::cols;
using Base::size;
using Base::derived;
EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner) const
{
return int(Derived::RowsAtCompileTime) == 1 ? 0
: int(Derived::ColsAtCompileTime) == 1 ? inner
: int(Derived::Flags)&RowMajorBit ? outer
: inner;
}
EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner) const
{
return int(Derived::ColsAtCompileTime) == 1 ? 0
: int(Derived::RowsAtCompileTime) == 1 ? inner
: int(Derived::Flags)&RowMajorBit ? inner
: outer;
}
/** Short version: don't use this function, use
* \link operator()(Index,Index) const \endlink instead.
*
* Long version: this function is similar to
* \link operator()(Index,Index) const \endlink, but without the assertion.
* Use this for limiting the performance cost of debugging code when doing
* repeated coefficient access. Only use this when it is guaranteed that the
* parameters \a row and \a col are in range.
*
* If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
* function equivalent to \link operator()(Index,Index) const \endlink.
*
* \sa operator()(Index,Index) const, coeffRef(Index,Index), coeff(Index) const
*/
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);
}
EIGEN_STRONG_INLINE CoeffReturnType coeffByOuterInner(Index outer, Index inner) const
{
return coeff(rowIndexByOuterInner(outer, inner),
colIndexByOuterInner(outer, inner));
}
/** \returns the coefficient at given the given row and column.
*
* \sa operator()(Index,Index), operator[](Index)
*/
EIGEN_STRONG_INLINE CoeffReturnType operator()(Index row, Index col) const
{
eigen_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().coeff(row, col);
}
/** Short version: don't use this function, use
* \link operator[](Index) const \endlink instead.
*
* Long version: this function is similar to
* \link operator[](Index) const \endlink, but without the assertion.
* Use this for limiting the performance cost of debugging code when doing
* repeated coefficient access. Only use this when it is guaranteed that the
* parameter \a index is in range.
*
* If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
* function equivalent to \link operator[](Index) const \endlink.
*
* \sa operator[](Index) const, coeffRef(Index), coeff(Index,Index) const
*/
EIGEN_STRONG_INLINE CoeffReturnType
coeff(Index index) const
{
eigen_internal_assert(index >= 0 && index < size());
return derived().coeff(index);
}
/** \returns the coefficient at given index.
*
* This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
*
* \sa operator[](Index), operator()(Index,Index) const, x() const, y() const,
* z() const, w() const
*/
EIGEN_STRONG_INLINE CoeffReturnType
operator[](Index index) const
{
#ifndef EIGEN2_SUPPORT
EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
#endif
eigen_assert(index >= 0 && index < size());
return derived().coeff(index);
}
/** \returns the coefficient at given index.
*
* This is synonymous to operator[](Index) const.
*
* This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
*
* \sa operator[](Index), operator()(Index,Index) const, x() const, y() const,
* z() const, w() const
*/
EIGEN_STRONG_INLINE CoeffReturnType
operator()(Index index) const
{
eigen_assert(index >= 0 && index < size());
return derived().coeff(index);
}
/** equivalent to operator[](0). */
EIGEN_STRONG_INLINE CoeffReturnType
x() const { return (*this)[0]; }
/** equivalent to operator[](1). */
EIGEN_STRONG_INLINE CoeffReturnType
y() const { return (*this)[1]; }
/** equivalent to operator[](2). */
EIGEN_STRONG_INLINE CoeffReturnType
z() const { return (*this)[2]; }
/** equivalent to operator[](3). */
EIGEN_STRONG_INLINE CoeffReturnType
w() const { return (*this)[3]; }
/** \internal
* \returns the packet of coefficients starting at the given row and column. 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 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);
}
/** \internal */
template<int LoadMode>
EIGEN_STRONG_INLINE PacketReturnType packetByOuterInner(Index outer, Index inner) const
{
return packet<LoadMode>(rowIndexByOuterInner(outer, inner),
colIndexByOuterInner(outer, inner));
}
/** \internal
* \returns the packet of coefficients starting at the given index. 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 LoadMode>
EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
eigen_internal_assert(index >= 0 && index < size());
return derived().template packet<LoadMode>(index);
}
protected:
// explanation: DenseBase is doing "using ..." on the methods from DenseCoeffsBase.
// But some methods are only available in the DirectAccess case.
// So we add dummy methods here with these names, so that "using... " doesn't fail.
// It's not private so that the child class DenseBase can access them, and it's not public
// either since it's an implementation detail, so has to be protected.
void coeffRef();
void coeffRefByOuterInner();
void writePacket();
void writePacketByOuterInner();
void copyCoeff();
void copyCoeffByOuterInner();
void copyPacket();
void copyPacketByOuterInner();
void stride();
void innerStride();
void outerStride();
void rowStride();
void colStride();
};
/** \brief Base class providing read/write coefficient access to matrices and arrays.
* \ingroup Core_Module
* \tparam Derived Type of the derived class
* \tparam #WriteAccessors Constant indicating read/write access
*
* This class defines the non-const \c operator() function and friends, which can be used to write specific
* entries of a matrix or array. This class inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which
* defines the const variant for reading specific entries.
*
* \sa DenseCoeffsBase<Derived, DirectAccessors>, \ref TopicClassHierarchy
*/
template<typename Derived>
class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors>
{
public:
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;
using Base::coeff;
using Base::rows;
using Base::cols;
using Base::size;
using Base::derived;
using Base::rowIndexByOuterInner;
using Base::colIndexByOuterInner;
using Base::operator[];
using Base::operator();
using Base::x;
using Base::y;
using Base::z;
using Base::w;
/** Short version: don't use this function, use
* \link operator()(Index,Index) \endlink instead.
*
* Long version: this function is similar to
* \link operator()(Index,Index) \endlink, but without the assertion.
* Use this for limiting the performance cost of debugging code when doing
* repeated coefficient access. Only use this when it is guaranteed that the
* parameters \a row and \a col are in range.
*
* If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
* function equivalent to \link operator()(Index,Index) \endlink.
*
* \sa operator()(Index,Index), coeff(Index, Index) const, coeffRef(Index)
*/
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);
}
EIGEN_STRONG_INLINE Scalar&
coeffRefByOuterInner(Index outer, Index inner)
{
return coeffRef(rowIndexByOuterInner(outer, inner),
colIndexByOuterInner(outer, inner));
}
/** \returns a reference to the coefficient at given the given row and column.
*
* \sa operator[](Index)
*/
EIGEN_STRONG_INLINE Scalar&
operator()(Index row, Index col)
{
eigen_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().coeffRef(row, col);
}
/** Short version: don't use this function, use
* \link operator[](Index) \endlink instead.
*
* Long version: this function is similar to
* \link operator[](Index) \endlink, but without the assertion.
* Use this for limiting the performance cost of debugging code when doing
* repeated coefficient access. Only use this when it is guaranteed that the
* parameters \a row and \a col are in range.
*
* If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
* function equivalent to \link operator[](Index) \endlink.
*
* \sa operator[](Index), coeff(Index) const, coeffRef(Index,Index)
*/
EIGEN_STRONG_INLINE Scalar&
coeffRef(Index index)
{
eigen_internal_assert(index >= 0 && index < size());
return derived().coeffRef(index);
}
/** \returns a reference to the coefficient at given index.
*
* This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
*
* \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
*/
EIGEN_STRONG_INLINE Scalar&
operator[](Index index)
{
#ifndef EIGEN2_SUPPORT
EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
#endif
eigen_assert(index >= 0 && index < size());
return derived().coeffRef(index);
}
/** \returns a reference to the coefficient at given index.
*
* This is synonymous to operator[](Index).
*
* This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
*
* \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
*/
EIGEN_STRONG_INLINE Scalar&
operator()(Index index)
{
eigen_assert(index >= 0 && index < size());
return derived().coeffRef(index);
}
/** equivalent to operator[](0). */
EIGEN_STRONG_INLINE Scalar&
x() { return (*this)[0]; }
/** equivalent to operator[](1). */
EIGEN_STRONG_INLINE Scalar&
y() { return (*this)[1]; }
/** equivalent to operator[](2). */
EIGEN_STRONG_INLINE Scalar&
z() { return (*this)[2]; }
/** equivalent to operator[](3). */
EIGEN_STRONG_INLINE Scalar&
w() { return (*this)[3]; }
/** \internal
* Stores the given packet of coefficients, at the given row and column of this expression. It is your responsibility
* to ensure that a packet really starts there. This method is only available on expressions having the
* PacketAccessBit.
*
* The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select
* the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
* starting at an address which is a multiple of the packet size.
*/
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket
(Index row, Index col, const typename internal::packet_traits<Scalar>::type& x)
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().template writePacket<StoreMode>(row,col,x);
}
/** \internal */
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacketByOuterInner
(Index outer, Index inner, const typename internal::packet_traits<Scalar>::type& x)
{
writePacket<StoreMode>(rowIndexByOuterInner(outer, inner),
colIndexByOuterInner(outer, inner),
x);
}
/** \internal
* Stores the given packet of coefficients, at the given index in this expression. It is your responsibility
* to ensure that a packet really starts there. This method is only available on expressions having the
* PacketAccessBit and the LinearAccessBit.
*
* The \a LoadMode parameter may have the value \a Aligned or \a Unaligned. Its effect is to select
* the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
* starting at an address which is a multiple of the packet size.
*/
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket
(Index index, const typename internal::packet_traits<Scalar>::type& x)
{
eigen_internal_assert(index >= 0 && index < size());
derived().template writePacket<StoreMode>(index,x);
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal Copies the coefficient at position (row,col) of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE void copyCoeff(Index row, Index col, const DenseBase<OtherDerived>& other)
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().coeffRef(row, col) = other.derived().coeff(row, col);
}
/** \internal Copies the coefficient at the given index of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE void copyCoeff(Index index, const DenseBase<OtherDerived>& other)
{
eigen_internal_assert(index >= 0 && index < size());
derived().coeffRef(index) = other.derived().coeff(index);
}
template<typename OtherDerived>
EIGEN_STRONG_INLINE void copyCoeffByOuterInner(Index outer, Index inner, const DenseBase<OtherDerived>& other)
{
const Index row = rowIndexByOuterInner(outer,inner);
const Index col = colIndexByOuterInner(outer,inner);
// derived() is important here: copyCoeff() may be reimplemented in Derived!
derived().copyCoeff(row, col, other);
}
/** \internal Copies the packet at position (row,col) of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived, int StoreMode, int LoadMode>
EIGEN_STRONG_INLINE void copyPacket(Index row, Index col, const DenseBase<OtherDerived>& other)
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().template writePacket<StoreMode>(row, col,
other.derived().template packet<LoadMode>(row, col));
}
/** \internal Copies the packet at the given index of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived, int StoreMode, int LoadMode>
EIGEN_STRONG_INLINE void copyPacket(Index index, const DenseBase<OtherDerived>& other)
{
eigen_internal_assert(index >= 0 && index < size());
derived().template writePacket<StoreMode>(index,
other.derived().template packet<LoadMode>(index));
}
/** \internal */
template<typename OtherDerived, int StoreMode, int LoadMode>
EIGEN_STRONG_INLINE void copyPacketByOuterInner(Index outer, Index inner, const DenseBase<OtherDerived>& other)
{
const Index row = rowIndexByOuterInner(outer,inner);
const Index col = colIndexByOuterInner(outer,inner);
// derived() is important here: copyCoeff() may be reimplemented in Derived!
derived().template copyPacket< OtherDerived, StoreMode, LoadMode>(row, col, other);
}
#endif
};
/** \brief Base class providing direct read-only coefficient access to matrices and arrays.
* \ingroup Core_Module
* \tparam Derived Type of the derived class
* \tparam #DirectAccessors Constant indicating direct access
*
* This class defines functions to work with strides which can be used to access entries directly. This class
* inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which defines functions to access entries read-only using
* \c operator() .
*
* \sa \ref TopicClassHierarchy
*/
template<typename Derived>
class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors>
{
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;
using Base::rows;
using Base::cols;
using Base::size;
using Base::derived;
/** \returns the pointer increment between two consecutive elements within a slice in the inner direction.
*
* \sa outerStride(), rowStride(), colStride()
*/
inline Index innerStride() const
{
return derived().innerStride();
}
/** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns
* in a column-major matrix).
*
* \sa innerStride(), rowStride(), colStride()
*/
inline Index outerStride() const
{
return derived().outerStride();
}
// FIXME shall we remove it ?
inline Index stride() const
{
return Derived::IsVectorAtCompileTime ? innerStride() : outerStride();
}
/** \returns the pointer increment between two consecutive rows.
*
* \sa innerStride(), outerStride(), colStride()
*/
inline Index rowStride() const
{
return Derived::IsRowMajor ? outerStride() : innerStride();
}
/** \returns the pointer increment between two consecutive columns.
*
* \sa innerStride(), outerStride(), rowStride()
*/
inline Index colStride() const
{
return Derived::IsRowMajor ? innerStride() : outerStride();
}
};
/** \brief Base class providing direct read/write coefficient access to matrices and arrays.
* \ingroup Core_Module
* \tparam Derived Type of the derived class
* \tparam #DirectWriteAccessors Constant indicating direct access
*
* This class defines functions to work with strides which can be used to access entries directly. This class
* inherits DenseCoeffsBase<Derived, WriteAccessors> which defines functions to access entries read/write using
* \c operator().
*
* \sa \ref TopicClassHierarchy
*/
template<typename Derived>
class DenseCoeffsBase<Derived, DirectWriteAccessors>
: public DenseCoeffsBase<Derived, WriteAccessors>
{
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;
using Base::rows;
using Base::cols;
using Base::size;
using Base::derived;
/** \returns the pointer increment between two consecutive elements within a slice in the inner direction.
*
* \sa outerStride(), rowStride(), colStride()
*/
inline Index innerStride() const
{
return derived().innerStride();
}
/** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns
* in a column-major matrix).
*
* \sa innerStride(), rowStride(), colStride()
*/
inline Index outerStride() const
{
return derived().outerStride();
}
// FIXME shall we remove it ?
inline Index stride() const
{
return Derived::IsVectorAtCompileTime ? innerStride() : outerStride();
}
/** \returns the pointer increment between two consecutive rows.
*
* \sa innerStride(), outerStride(), colStride()
*/
inline Index rowStride() const
{
return Derived::IsRowMajor ? outerStride() : innerStride();
}
/** \returns the pointer increment between two consecutive columns.
*
* \sa innerStride(), outerStride(), rowStride()
*/
inline Index colStride() const
{
return Derived::IsRowMajor ? innerStride() : outerStride();
}
};
namespace internal {
template<typename Derived, bool JustReturnZero>
struct first_aligned_impl
{
inline static typename Derived::Index run(const Derived&)
{ return 0; }
};
template<typename Derived>
struct first_aligned_impl<Derived, false>
{
inline static typename Derived::Index run(const Derived& m)
{
return first_aligned(&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.
*
* There is also the variant first_aligned(const Scalar*, Integer) defined in Memory.h. See it for more
* documentation.
*/
template<typename Derived>
inline static typename Derived::Index first_aligned(const Derived& m)
{
return first_aligned_impl
<Derived, (Derived::Flags & AlignedBit) || !(Derived::Flags & DirectAccessBit)>
::run(m);
}
template<typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret>
struct inner_stride_at_compile_time
{
enum { ret = traits<Derived>::InnerStrideAtCompileTime };
};
template<typename Derived>
struct inner_stride_at_compile_time<Derived, false>
{
enum { ret = 0 };
};
template<typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret>
struct outer_stride_at_compile_time
{
enum { ret = traits<Derived>::OuterStrideAtCompileTime };
};
template<typename Derived>
struct outer_stride_at_compile_time<Derived, false>
{
enum { ret = 0 };
};
} // end namespace internal
#endif // EIGEN_DENSECOEFFSBASE_H

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@@ -1,304 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_MATRIXSTORAGE_H
#define EIGEN_MATRIXSTORAGE_H
#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN EIGEN_DENSE_STORAGE_CTOR_PLUGIN;
#else
#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
#endif
namespace internal {
struct constructor_without_unaligned_array_assert {};
/** \internal
* Static array. If the MatrixOrArrayOptions require auto-alignment, the array will be automatically aligned:
* to 16 bytes boundary if the total size is a multiple of 16 bytes.
*/
template <typename T, int Size, int MatrixOrArrayOptions,
int Alignment = (MatrixOrArrayOptions&DontAlign) ? 0
: (((Size*sizeof(T))%16)==0) ? 16
: 0 >
struct plain_array
{
T array[Size];
plain_array() {}
plain_array(constructor_without_unaligned_array_assert) {}
};
#ifdef EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask)
#else
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
eigen_assert((reinterpret_cast<size_t>(array) & sizemask) == 0 \
&& "this assertion is explained here: " \
"http://eigen.tuxfamily.org/dox-devel/TopicUnalignedArrayAssert.html" \
" **** READ THIS WEB PAGE !!! ****");
#endif
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 16>
{
EIGEN_USER_ALIGN16 T array[Size];
plain_array() { EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(0xf) }
plain_array(constructor_without_unaligned_array_assert) {}
};
template <typename T, int MatrixOrArrayOptions, int Alignment>
struct plain_array<T, 0, MatrixOrArrayOptions, Alignment>
{
EIGEN_USER_ALIGN16 T array[1];
plain_array() {}
plain_array(constructor_without_unaligned_array_assert) {}
};
} // end namespace internal
/** \internal
*
* \class DenseStorage
* \ingroup Core_Module
*
* \brief Stores the data of a matrix
*
* This class stores the data of fixed-size, dynamic-size or mixed matrices
* in a way as compact as possible.
*
* \sa Matrix
*/
template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseStorage;
// purely fixed-size matrix
template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseStorage
{
internal::plain_array<T,Size,_Options> m_data;
public:
inline explicit DenseStorage() {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()) {}
inline DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); }
inline static DenseIndex rows(void) {return _Rows;}
inline static DenseIndex cols(void) {return _Cols;}
inline void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {}
inline void resize(DenseIndex,DenseIndex,DenseIndex) {}
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
};
// null matrix
template<typename T, int _Rows, int _Cols, int _Options> class DenseStorage<T, 0, _Rows, _Cols, _Options>
{
public:
inline explicit DenseStorage() {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert) {}
inline DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
inline void swap(DenseStorage& ) {}
inline static DenseIndex rows(void) {return _Rows;}
inline static DenseIndex cols(void) {return _Cols;}
inline void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {}
inline void resize(DenseIndex,DenseIndex,DenseIndex) {}
inline const T *data() const { return 0; }
inline T *data() { return 0; }
};
// dynamic-size matrix with fixed-size storage
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;
public:
inline explicit DenseStorage() : m_rows(0), m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
inline DenseStorage(DenseIndex, DenseIndex rows, DenseIndex cols) : m_rows(rows), m_cols(cols) {}
inline void swap(DenseStorage& other)
{ std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
inline DenseIndex rows(void) const {return m_rows;}
inline DenseIndex cols(void) const {return m_cols;}
inline void conservativeResize(DenseIndex, DenseIndex rows, DenseIndex cols) { m_rows = rows; m_cols = cols; }
inline void resize(DenseIndex, DenseIndex rows, DenseIndex cols) { m_rows = rows; m_cols = cols; }
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
};
// dynamic-size matrix with fixed-size storage and fixed width
template<typename T, int Size, int _Cols, int _Options> class DenseStorage<T, Size, Dynamic, _Cols, _Options>
{
internal::plain_array<T,Size,_Options> m_data;
DenseIndex m_rows;
public:
inline explicit DenseStorage() : m_rows(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {}
inline DenseStorage(DenseIndex, DenseIndex rows, DenseIndex) : m_rows(rows) {}
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
inline DenseIndex rows(void) const {return m_rows;}
inline DenseIndex cols(void) const {return _Cols;}
inline void conservativeResize(DenseIndex, DenseIndex rows, DenseIndex) { m_rows = rows; }
inline void resize(DenseIndex, DenseIndex rows, DenseIndex) { m_rows = rows; }
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
};
// dynamic-size matrix with fixed-size storage and fixed height
template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Size, _Rows, Dynamic, _Options>
{
internal::plain_array<T,Size,_Options> m_data;
DenseIndex m_cols;
public:
inline explicit DenseStorage() : m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {}
inline DenseStorage(DenseIndex, DenseIndex, DenseIndex cols) : m_cols(cols) {}
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
inline DenseIndex rows(void) const {return _Rows;}
inline DenseIndex cols(void) const {return m_cols;}
inline void conservativeResize(DenseIndex, DenseIndex, DenseIndex cols) { m_cols = cols; }
inline void resize(DenseIndex, DenseIndex, DenseIndex cols) { m_cols = cols; }
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
};
// purely dynamic matrix.
template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynamic, _Options>
{
T *m_data;
DenseIndex m_rows;
DenseIndex m_cols;
public:
inline explicit DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(0), m_rows(0), m_cols(0) {}
inline DenseStorage(DenseIndex size, DenseIndex rows, DenseIndex cols)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows), m_cols(cols)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
inline void swap(DenseStorage& other)
{ std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
inline DenseIndex rows(void) const {return m_rows;}
inline DenseIndex cols(void) const {return m_cols;}
inline void conservativeResize(DenseIndex size, DenseIndex rows, DenseIndex cols)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*m_cols);
m_rows = rows;
m_cols = cols;
}
void resize(DenseIndex size, DenseIndex rows, DenseIndex cols)
{
if(size != m_rows*m_cols)
{
internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols);
if (size)
m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
else
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
}
m_rows = rows;
m_cols = cols;
}
inline const T *data() const { return m_data; }
inline T *data() { return m_data; }
};
// matrix with dynamic width and fixed height (so that matrix has dynamic size).
template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Rows, Dynamic, _Options>
{
T *m_data;
DenseIndex m_cols;
public:
inline explicit DenseStorage() : m_data(0), m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
inline DenseStorage(DenseIndex size, DenseIndex, DenseIndex cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(cols)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
inline static DenseIndex rows(void) {return _Rows;}
inline DenseIndex cols(void) const {return m_cols;}
inline void conservativeResize(DenseIndex size, DenseIndex, DenseIndex cols)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, _Rows*m_cols);
m_cols = cols;
}
EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex, DenseIndex cols)
{
if(size != _Rows*m_cols)
{
internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols);
if (size)
m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
else
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
}
m_cols = cols;
}
inline const T *data() const { return m_data; }
inline T *data() { return m_data; }
};
// matrix with dynamic height and fixed width (so that matrix has dynamic size).
template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dynamic, _Cols, _Options>
{
T *m_data;
DenseIndex m_rows;
public:
inline explicit DenseStorage() : m_data(0), m_rows(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
inline DenseStorage(DenseIndex size, DenseIndex rows, DenseIndex) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
inline DenseIndex rows(void) const {return m_rows;}
inline static DenseIndex cols(void) {return _Cols;}
inline void conservativeResize(DenseIndex size, DenseIndex rows, DenseIndex)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*_Cols);
m_rows = rows;
}
EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex rows, DenseIndex)
{
if(size != m_rows*_Cols)
{
internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows);
if (size)
m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
else
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
}
m_rows = rows;
}
inline const T *data() const { return m_data; }
inline T *data() { return m_data; }
};
#endif // EIGEN_MATRIX_H

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@@ -1,227 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_DIAGONAL_H
#define EIGEN_DIAGONAL_H
/** \class Diagonal
* \ingroup Core_Module
*
* \brief Expression of a diagonal/subdiagonal/superdiagonal in a matrix
*
* \param MatrixType the type of the object in which we are taking a sub/main/super diagonal
* \param DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal.
* A positive value means a superdiagonal, a negative value means a subdiagonal.
* You can also use Dynamic so the index can be set at runtime.
*
* The matrix is not required to be square.
*
* This class represents an expression of the main diagonal, or any sub/super diagonal
* of a square matrix. It is the return type of MatrixBase::diagonal() and MatrixBase::diagonal(Index) and most of the
* time this is the only way it is used.
*
* \sa MatrixBase::diagonal(), MatrixBase::diagonal(Index)
*/
namespace internal {
template<typename MatrixType, int DiagIndex>
struct traits<Diagonal<MatrixType,DiagIndex> >
: traits<MatrixType>
{
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
typedef typename MatrixType::StorageKind StorageKind;
enum {
AbsDiagIndex = DiagIndex<0 ? -DiagIndex : DiagIndex, // only used if DiagIndex != Dynamic
// FIXME these computations are broken in the case where the matrix is rectangular and DiagIndex!=0
RowsAtCompileTime = (int(DiagIndex) == Dynamic || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic
: (EIGEN_SIZE_MIN_PREFER_DYNAMIC(MatrixType::RowsAtCompileTime,
MatrixType::ColsAtCompileTime) - AbsDiagIndex),
ColsAtCompileTime = 1,
MaxRowsAtCompileTime = int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic
: DiagIndex == Dynamic ? EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime,
MatrixType::MaxColsAtCompileTime)
: (EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime) - AbsDiagIndex),
MaxColsAtCompileTime = 1,
MaskLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
Flags = (unsigned int)_MatrixTypeNested::Flags & (HereditaryBits | LinearAccessBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit,
CoeffReadCost = _MatrixTypeNested::CoeffReadCost,
MatrixTypeOuterStride = outer_stride_at_compile_time<MatrixType>::ret,
InnerStrideAtCompileTime = MatrixTypeOuterStride == Dynamic ? Dynamic : MatrixTypeOuterStride+1,
OuterStrideAtCompileTime = 0
};
};
}
template<typename MatrixType, int DiagIndex> class Diagonal
: public internal::dense_xpr_base< Diagonal<MatrixType,DiagIndex> >::type
{
public:
typedef typename internal::dense_xpr_base<Diagonal>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
inline Diagonal(MatrixType& matrix, Index index = DiagIndex) : m_matrix(matrix), m_index(index) {}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
inline Index rows() const
{ return m_index.value()<0 ? (std::min)(m_matrix.cols(),m_matrix.rows()+m_index.value()) : (std::min)(m_matrix.rows(),m_matrix.cols()-m_index.value()); }
inline Index cols() const { return 1; }
inline Index innerStride() const
{
return m_matrix.outerStride() + 1;
}
inline Index outerStride() const
{
return 0;
}
inline Scalar& coeffRef(Index row, Index)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset());
}
inline const Scalar& coeffRef(Index row, Index) const
{
return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset());
}
inline CoeffReturnType coeff(Index row, Index) const
{
return m_matrix.coeff(row+rowOffset(), row+colOffset());
}
inline Scalar& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return m_matrix.const_cast_derived().coeffRef(index+rowOffset(), index+colOffset());
}
inline const Scalar& coeffRef(Index index) const
{
return m_matrix.const_cast_derived().coeffRef(index+rowOffset(), index+colOffset());
}
inline CoeffReturnType coeff(Index index) const
{
return m_matrix.coeff(index+rowOffset(), index+colOffset());
}
protected:
const typename MatrixType::Nested m_matrix;
const internal::variable_if_dynamic<Index, DiagIndex> m_index;
private:
// some compilers may fail to optimize std::max etc in case of compile-time constants...
EIGEN_STRONG_INLINE Index absDiagIndex() const { return m_index.value()>0 ? m_index.value() : -m_index.value(); }
EIGEN_STRONG_INLINE Index rowOffset() const { return m_index.value()>0 ? 0 : -m_index.value(); }
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
template<int LoadMode> typename MatrixType::PacketReturnType packet(Index) const;
template<int LoadMode> typename MatrixType::PacketReturnType packet(Index,Index) const;
};
/** \returns an expression of the main diagonal of the matrix \c *this
*
* \c *this is not required to be square.
*
* Example: \include MatrixBase_diagonal.cpp
* Output: \verbinclude MatrixBase_diagonal.out
*
* \sa class Diagonal */
template<typename Derived>
inline typename MatrixBase<Derived>::DiagonalReturnType
MatrixBase<Derived>::diagonal()
{
return derived();
}
/** This is the const version of diagonal(). */
template<typename Derived>
inline const typename MatrixBase<Derived>::ConstDiagonalReturnType
MatrixBase<Derived>::diagonal() const
{
return ConstDiagonalReturnType(derived());
}
/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
*
* \c *this is not required to be square.
*
* The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0
* and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal.
*
* Example: \include MatrixBase_diagonal_int.cpp
* Output: \verbinclude MatrixBase_diagonal_int.out
*
* \sa MatrixBase::diagonal(), class Diagonal */
template<typename Derived>
inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Dynamic>::Type
MatrixBase<Derived>::diagonal(Index index)
{
return typename DiagonalIndexReturnType<Dynamic>::Type(derived(), index);
}
/** This is the const version of diagonal(Index). */
template<typename Derived>
inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Dynamic>::Type
MatrixBase<Derived>::diagonal(Index index) const
{
return typename ConstDiagonalIndexReturnType<Dynamic>::Type(derived(), index);
}
/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
*
* \c *this is not required to be square.
*
* The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0
* and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal.
*
* Example: \include MatrixBase_diagonal_template_int.cpp
* Output: \verbinclude MatrixBase_diagonal_template_int.out
*
* \sa MatrixBase::diagonal(), class Diagonal */
template<typename Derived>
template<int Index>
inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Index>::Type
MatrixBase<Derived>::diagonal()
{
return derived();
}
/** This is the const version of diagonal<int>(). */
template<typename Derived>
template<int Index>
inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Index>::Type
MatrixBase<Derived>::diagonal() const
{
return derived();
}
#endif // EIGEN_DIAGONAL_H

View File

@@ -0,0 +1,124 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_DIAGONALCOEFFS_H
#define EIGEN_DIAGONALCOEFFS_H
/** \class DiagonalCoeffs
*
* \brief Expression of the main diagonal of a matrix
*
* \param MatrixType the type of the object in which we are taking the main diagonal
*
* The matrix is not required to be square.
*
* This class represents an expression of the main diagonal of a square matrix.
* It is the return type of MatrixBase::diagonal() and most of the time this is
* the only way it is used.
*
* \sa MatrixBase::diagonal()
*/
template<typename MatrixType>
struct ei_traits<DiagonalCoeffs<MatrixType> >
{
typedef typename MatrixType::Scalar Scalar;
typedef typename ei_nested<MatrixType>::type MatrixTypeNested;
typedef typename ei_unref<MatrixTypeNested>::type _MatrixTypeNested;
enum {
RowsAtCompileTime = int(MatrixType::SizeAtCompileTime) == Dynamic ? Dynamic
: EIGEN_ENUM_MIN(MatrixType::RowsAtCompileTime,
MatrixType::ColsAtCompileTime),
ColsAtCompileTime = 1,
MaxRowsAtCompileTime = int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic
: EIGEN_ENUM_MIN(MatrixType::MaxRowsAtCompileTime,
MatrixType::MaxColsAtCompileTime),
MaxColsAtCompileTime = 1,
Flags = (unsigned int)_MatrixTypeNested::Flags & (HereditaryBits | LinearAccessBit),
CoeffReadCost = _MatrixTypeNested::CoeffReadCost
};
};
template<typename MatrixType> class DiagonalCoeffs
: public MatrixBase<DiagonalCoeffs<MatrixType> >
{
public:
EIGEN_GENERIC_PUBLIC_INTERFACE(DiagonalCoeffs)
inline DiagonalCoeffs(const MatrixType& matrix) : m_matrix(matrix) {}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(DiagonalCoeffs)
inline int rows() const { return std::min(m_matrix.rows(), m_matrix.cols()); }
inline int cols() const { return 1; }
inline Scalar& coeffRef(int row, int)
{
return m_matrix.const_cast_derived().coeffRef(row, row);
}
inline const Scalar coeff(int row, int) const
{
return m_matrix.coeff(row, row);
}
inline Scalar& coeffRef(int index)
{
return m_matrix.const_cast_derived().coeffRef(index, index);
}
inline const Scalar coeff(int index) const
{
return m_matrix.coeff(index, index);
}
protected:
const typename MatrixType::Nested m_matrix;
};
/** \returns an expression of the main diagonal of the matrix \c *this
*
* \c *this is not required to be square.
*
* Example: \include MatrixBase_diagonal.cpp
* Output: \verbinclude MatrixBase_diagonal.out
*
* \sa class DiagonalCoeffs */
template<typename Derived>
inline DiagonalCoeffs<Derived>
MatrixBase<Derived>::diagonal()
{
return DiagonalCoeffs<Derived>(derived());
}
/** This is the const version of diagonal(). */
template<typename Derived>
inline const DiagonalCoeffs<Derived>
MatrixBase<Derived>::diagonal() const
{
return DiagonalCoeffs<Derived>(derived());
}
#endif // EIGEN_DIAGONALCOEFFS_H

View File

@@ -1,8 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -26,257 +25,95 @@
#ifndef EIGEN_DIAGONALMATRIX_H
#define EIGEN_DIAGONALMATRIX_H
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename Derived>
class DiagonalBase : public EigenBase<Derived>
{
public:
typedef typename internal::traits<Derived>::DiagonalVectorType DiagonalVectorType;
typedef typename DiagonalVectorType::Scalar Scalar;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
enum {
RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
IsVectorAtCompileTime = 0,
Flags = 0
};
typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime> DenseMatrixType;
typedef DenseMatrixType DenseType;
typedef DiagonalMatrix<Scalar,DiagonalVectorType::SizeAtCompileTime,DiagonalVectorType::MaxSizeAtCompileTime> PlainObject;
inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
inline Derived& derived() { return *static_cast<Derived*>(this); }
DenseMatrixType toDenseMatrix() const { return derived(); }
template<typename DenseDerived>
void evalTo(MatrixBase<DenseDerived> &other) const;
template<typename DenseDerived>
void addTo(MatrixBase<DenseDerived> &other) const
{ other.diagonal() += diagonal(); }
template<typename DenseDerived>
void subTo(MatrixBase<DenseDerived> &other) const
{ other.diagonal() -= diagonal(); }
inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); }
inline DiagonalVectorType& diagonal() { return derived().diagonal(); }
inline Index rows() const { return diagonal().size(); }
inline Index cols() const { return diagonal().size(); }
template<typename MatrixDerived>
const DiagonalProduct<MatrixDerived, Derived, OnTheLeft>
operator*(const MatrixBase<MatrixDerived> &matrix) const;
inline const DiagonalWrapper<CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> >
inverse() const
{
return diagonal().cwiseInverse();
}
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
bool isApprox(const DiagonalBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
{
return diagonal().isApprox(other.diagonal(), precision);
}
template<typename OtherDerived>
bool isApprox(const MatrixBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
{
return toDenseMatrix().isApprox(other, precision);
}
#endif
};
template<typename Derived>
template<typename DenseDerived>
void DiagonalBase<Derived>::evalTo(MatrixBase<DenseDerived> &other) const
{
other.setZero();
other.diagonal() = diagonal();
}
#endif
/** \class DiagonalMatrix
* \ingroup Core_Module
*
* \brief Represents a diagonal matrix with its storage
*
* \param _Scalar the type of coefficients
* \param SizeAtCompileTime the dimension of the matrix, or Dynamic
* \param MaxSizeAtCompileTime the dimension of the matrix, or Dynamic. This parameter is optional and defaults
* to SizeAtCompileTime. Most of the time, you do not need to specify it.
*
* \sa class DiagonalWrapper
*/
namespace internal {
template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime>
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;
enum {
Flags = LvalueBit
};
};
}
template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime>
class DiagonalMatrix
: public DiagonalBase<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> >
{
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef typename internal::traits<DiagonalMatrix>::DiagonalVectorType DiagonalVectorType;
typedef const DiagonalMatrix& Nested;
typedef _Scalar Scalar;
typedef typename internal::traits<DiagonalMatrix>::StorageKind StorageKind;
typedef typename internal::traits<DiagonalMatrix>::Index Index;
#endif
protected:
DiagonalVectorType m_diagonal;
public:
/** const version of diagonal(). */
inline const DiagonalVectorType& diagonal() const { return m_diagonal; }
/** \returns a reference to the stored vector of diagonal coefficients. */
inline DiagonalVectorType& diagonal() { return m_diagonal; }
/** Default constructor without initialization */
inline DiagonalMatrix() {}
/** Constructs a diagonal matrix with given dimension */
inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
/** 2D constructor. */
inline DiagonalMatrix(const Scalar& x, const Scalar& y) : m_diagonal(x,y) {}
/** 3D constructor. */
inline DiagonalMatrix(const Scalar& x, const Scalar& y, const Scalar& z) : m_diagonal(x,y,z) {}
/** Copy constructor. */
template<typename OtherDerived>
inline DiagonalMatrix(const DiagonalBase<OtherDerived>& other) : m_diagonal(other.diagonal()) {}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** copy constructor. prevent a default copy constructor from hiding the other templated constructor */
inline DiagonalMatrix(const DiagonalMatrix& other) : m_diagonal(other.diagonal()) {}
#endif
/** generic constructor from expression of the diagonal coefficients */
template<typename OtherDerived>
explicit inline DiagonalMatrix(const MatrixBase<OtherDerived>& other) : m_diagonal(other)
{}
/** Copy operator. */
template<typename OtherDerived>
DiagonalMatrix& operator=(const DiagonalBase<OtherDerived>& other)
{
m_diagonal = other.diagonal();
return *this;
}
#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=.
*/
DiagonalMatrix& operator=(const DiagonalMatrix& other)
{
m_diagonal = other.diagonal();
return *this;
}
#endif
/** Resizes to given size. */
inline void resize(Index size) { m_diagonal.resize(size); }
/** Sets all coefficients to zero. */
inline void setZero() { m_diagonal.setZero(); }
/** Resizes and sets all coefficients to zero. */
inline void setZero(Index size) { m_diagonal.setZero(size); }
/** Sets this matrix to be the identity matrix of the current size. */
inline void setIdentity() { m_diagonal.setOnes(); }
/** Sets this matrix to be the identity matrix of the given size. */
inline void setIdentity(Index size) { m_diagonal.setOnes(size); }
};
/** \class DiagonalWrapper
* \ingroup Core_Module
* \nonstableyet
*
* \brief Expression of a diagonal matrix
*
* \param _DiagonalVectorType the type of the vector of diagonal coefficients
* \param CoeffsVectorType the type of the vector of diagonal coefficients
*
* This class is an expression of a diagonal matrix, but not storing its own vector of diagonal coefficients,
* instead wrapping an existing vector expression. It is the return type of MatrixBase::asDiagonal()
* and most of the time this is the only way that it is used.
* This class is an expression of a diagonal matrix with given vector of diagonal
* coefficients. It is the return
* type of MatrixBase::diagonal(const OtherDerived&) and most of the time this is
* the only way it is used.
*
* \sa class DiagonalMatrix, class DiagonalBase, MatrixBase::asDiagonal()
* \sa MatrixBase::diagonal(const OtherDerived&)
*/
namespace internal {
template<typename _DiagonalVectorType>
struct traits<DiagonalWrapper<_DiagonalVectorType> >
template<typename CoeffsVectorType>
struct ei_traits<DiagonalMatrix<CoeffsVectorType> >
{
typedef _DiagonalVectorType DiagonalVectorType;
typedef typename DiagonalVectorType::Scalar Scalar;
typedef typename DiagonalVectorType::Index Index;
typedef typename DiagonalVectorType::StorageKind StorageKind;
typedef typename CoeffsVectorType::Scalar Scalar;
typedef typename ei_nested<CoeffsVectorType>::type CoeffsVectorTypeNested;
typedef typename ei_unref<CoeffsVectorTypeNested>::type _CoeffsVectorTypeNested;
enum {
RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
MaxRowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
MaxColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
Flags = traits<DiagonalVectorType>::Flags & LvalueBit
RowsAtCompileTime = CoeffsVectorType::SizeAtCompileTime,
ColsAtCompileTime = CoeffsVectorType::SizeAtCompileTime,
MaxRowsAtCompileTime = CoeffsVectorType::MaxSizeAtCompileTime,
MaxColsAtCompileTime = CoeffsVectorType::MaxSizeAtCompileTime,
Flags = (_CoeffsVectorTypeNested::Flags & HereditaryBits) | Diagonal,
CoeffReadCost = _CoeffsVectorTypeNested::CoeffReadCost
};
};
}
template<typename _DiagonalVectorType>
class DiagonalWrapper
: public DiagonalBase<DiagonalWrapper<_DiagonalVectorType> >, internal::no_assignment_operator
template<typename CoeffsVectorType>
class DiagonalMatrix : ei_no_assignment_operator,
public MatrixBase<DiagonalMatrix<CoeffsVectorType> >
{
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef _DiagonalVectorType DiagonalVectorType;
typedef DiagonalWrapper Nested;
#endif
/** Constructor from expression of diagonal coefficients to wrap. */
inline DiagonalWrapper(const DiagonalVectorType& diagonal) : m_diagonal(diagonal) {}
EIGEN_GENERIC_PUBLIC_INTERFACE(DiagonalMatrix)
typedef CoeffsVectorType _CoeffsVectorType;
/** \returns a const reference to the wrapped expression of diagonal coefficients. */
const DiagonalVectorType& diagonal() const { return m_diagonal; }
// needed to evaluate a DiagonalMatrix<Xpr> to a DiagonalMatrix<NestByValue<Vector> >
template<typename OtherCoeffsVectorType>
inline DiagonalMatrix(const DiagonalMatrix<OtherCoeffsVectorType>& other) : m_coeffs(other.diagonal())
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(CoeffsVectorType);
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherCoeffsVectorType);
ei_assert(m_coeffs.size() > 0);
}
inline DiagonalMatrix(const CoeffsVectorType& coeffs) : m_coeffs(coeffs)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(CoeffsVectorType);
ei_assert(coeffs.size() > 0);
}
inline int rows() const { return m_coeffs.size(); }
inline int cols() const { return m_coeffs.size(); }
inline const Scalar coeff(int row, int col) const
{
return row == col ? m_coeffs.coeff(row) : static_cast<Scalar>(0);
}
inline const CoeffsVectorType& diagonal() const { return m_coeffs; }
protected:
const typename DiagonalVectorType::Nested m_diagonal;
const typename CoeffsVectorType::Nested m_coeffs;
};
/** \returns a pseudo-expression of a diagonal matrix with *this as vector of diagonal coefficients
/** \nonstableyet
* \returns an expression of a diagonal matrix with *this as vector of diagonal coefficients
*
* \only_for_vectors
*
* \addexample AsDiagonalExample \label How to build a diagonal matrix from a vector
*
* Example: \include MatrixBase_asDiagonal.cpp
* Output: \verbinclude MatrixBase_asDiagonal.out
*
* \sa class DiagonalWrapper, class DiagonalMatrix, diagonal(), isDiagonal()
* \sa class DiagonalMatrix, isDiagonal()
**/
template<typename Derived>
inline const DiagonalWrapper<const Derived>
inline const DiagonalMatrix<Derived>
MatrixBase<Derived>::asDiagonal() const
{
return derived();
}
/** \returns true if *this is approximately equal to a diagonal matrix,
/** \nonstableyet
* \returns true if *this is approximately equal to a diagonal matrix,
* within the precision given by \a prec.
*
* Example: \include MatrixBase_isDiagonal.cpp
@@ -285,20 +122,21 @@ MatrixBase<Derived>::asDiagonal() const
* \sa asDiagonal()
*/
template<typename Derived>
bool MatrixBase<Derived>::isDiagonal(RealScalar prec) const
bool MatrixBase<Derived>::isDiagonal
(RealScalar prec) const
{
if(cols() != rows()) return false;
RealScalar maxAbsOnDiagonal = static_cast<RealScalar>(-1);
for(Index j = 0; j < cols(); ++j)
for(int j = 0; j < cols(); ++j)
{
RealScalar absOnDiagonal = internal::abs(coeff(j,j));
RealScalar absOnDiagonal = ei_abs(coeff(j,j));
if(absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal;
}
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < j; ++i)
for(int j = 0; j < cols(); ++j)
for(int i = 0; i < j; ++i)
{
if(!internal::isMuchSmallerThan(coeff(i, j), maxAbsOnDiagonal, prec)) return false;
if(!internal::isMuchSmallerThan(coeff(j, i), maxAbsOnDiagonal, prec)) return false;
if(!ei_isMuchSmallerThan(coeff(i, j), maxAbsOnDiagonal, prec)) return false;
if(!ei_isMuchSmallerThan(coeff(j, i), maxAbsOnDiagonal, prec)) return false;
}
return true;
}

View File

@@ -1,8 +1,8 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -26,110 +26,105 @@
#ifndef EIGEN_DIAGONALPRODUCT_H
#define EIGEN_DIAGONALPRODUCT_H
namespace internal {
template<typename MatrixType, typename DiagonalType, int ProductOrder>
struct traits<DiagonalProduct<MatrixType, DiagonalType, ProductOrder> >
: traits<MatrixType>
/** \internal Specialization of ei_nested for DiagonalMatrix.
* Unlike ei_nested, if the argument is a DiagonalMatrix and if it must be evaluated,
* then it evaluated to a DiagonalMatrix having its own argument evaluated.
*/
template<typename T, int N> struct ei_nested_diagonal : ei_nested<T,N> {};
template<typename T, int N> struct ei_nested_diagonal<DiagonalMatrix<T>,N >
: ei_nested<DiagonalMatrix<T>, N, DiagonalMatrix<NestByValue<typename ei_plain_matrix_type<T>::type> > >
{};
// specialization of ProductReturnType
template<typename Lhs, typename Rhs>
struct ProductReturnType<Lhs,Rhs,DiagonalProduct>
{
typedef typename scalar_product_traits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar;
typedef typename ei_nested_diagonal<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
typedef typename ei_nested_diagonal<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
typedef Product<LhsNested, RhsNested, DiagonalProduct> Type;
};
template<typename LhsNested, typename RhsNested>
struct ei_traits<Product<LhsNested, RhsNested, DiagonalProduct> >
{
// clean the nested types:
typedef typename ei_cleantype<LhsNested>::type _LhsNested;
typedef typename ei_cleantype<RhsNested>::type _RhsNested;
typedef typename _LhsNested::Scalar Scalar;
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
LhsFlags = _LhsNested::Flags,
RhsFlags = _RhsNested::Flags,
RowsAtCompileTime = _LhsNested::RowsAtCompileTime,
ColsAtCompileTime = _RhsNested::ColsAtCompileTime,
MaxRowsAtCompileTime = _LhsNested::MaxRowsAtCompileTime,
MaxColsAtCompileTime = _RhsNested::MaxColsAtCompileTime,
_StorageOrder = MatrixType::Flags & RowMajorBit ? RowMajor : ColMajor,
_PacketOnDiag = !((int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft)
||(int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)),
_SameTypes = is_same<typename MatrixType::Scalar, typename DiagonalType::Scalar>::value,
// FIXME currently we need same types, but in the future the next rule should be the one
//_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagonalType::Flags)&PacketAccessBit))),
_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && _SameTypes && ((!_PacketOnDiag) || (bool(int(DiagonalType::Flags)&PacketAccessBit))),
LhsIsDiagonal = (_LhsNested::Flags&Diagonal)==Diagonal,
RhsIsDiagonal = (_RhsNested::Flags&Diagonal)==Diagonal,
Flags = (HereditaryBits & (unsigned int)(MatrixType::Flags)) | (_Vectorizable ? PacketAccessBit : 0),
CoeffReadCost = NumTraits<Scalar>::MulCost + MatrixType::CoeffReadCost + DiagonalType::DiagonalVectorType::CoeffReadCost
CanVectorizeRhs = (!RhsIsDiagonal) && (RhsFlags & RowMajorBit) && (RhsFlags & PacketAccessBit)
&& (ColsAtCompileTime % ei_packet_traits<Scalar>::size == 0),
CanVectorizeLhs = (!LhsIsDiagonal) && (!(LhsFlags & RowMajorBit)) && (LhsFlags & PacketAccessBit)
&& (RowsAtCompileTime % ei_packet_traits<Scalar>::size == 0),
RemovedBits = ~((RhsFlags & RowMajorBit) && (!CanVectorizeLhs) ? 0 : RowMajorBit),
Flags = ((unsigned int)(LhsFlags | RhsFlags) & HereditaryBits & RemovedBits)
| (((CanVectorizeLhs&&RhsIsDiagonal) || (CanVectorizeRhs&&LhsIsDiagonal)) ? PacketAccessBit : 0),
CoeffReadCost = NumTraits<Scalar>::MulCost + _LhsNested::CoeffReadCost + _RhsNested::CoeffReadCost
};
};
}
template<typename MatrixType, typename DiagonalType, int ProductOrder>
class DiagonalProduct : internal::no_assignment_operator,
public MatrixBase<DiagonalProduct<MatrixType, DiagonalType, ProductOrder> >
template<typename LhsNested, typename RhsNested> class Product<LhsNested, RhsNested, DiagonalProduct> : ei_no_assignment_operator,
public MatrixBase<Product<LhsNested, RhsNested, DiagonalProduct> >
{
typedef typename ei_traits<Product>::_LhsNested _LhsNested;
typedef typename ei_traits<Product>::_RhsNested _RhsNested;
enum {
RhsIsDiagonal = (_RhsNested::Flags&Diagonal)==Diagonal
};
public:
typedef MatrixBase<DiagonalProduct> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(DiagonalProduct)
EIGEN_GENERIC_PUBLIC_INTERFACE(Product)
inline DiagonalProduct(const MatrixType& matrix, const DiagonalType& diagonal)
: m_matrix(matrix), m_diagonal(diagonal)
template<typename Lhs, typename Rhs>
inline Product(const Lhs& lhs, const Rhs& rhs)
: m_lhs(lhs), m_rhs(rhs)
{
eigen_assert(diagonal.diagonal().size() == (ProductOrder == OnTheLeft ? matrix.rows() : matrix.cols()));
ei_assert(lhs.cols() == rhs.rows());
}
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
inline int rows() const { return m_lhs.rows(); }
inline int cols() const { return m_rhs.cols(); }
const Scalar coeff(Index row, Index col) const
const Scalar coeff(int row, int col) const
{
return m_diagonal.diagonal().coeff(ProductOrder == OnTheLeft ? row : col) * m_matrix.coeff(row, col);
const int unique = RhsIsDiagonal ? col : row;
return m_lhs.coeff(row, unique) * m_rhs.coeff(unique, col);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
const PacketScalar packet(int row, int 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());
if (RhsIsDiagonal)
{
return ei_pmul(m_lhs.template packet<LoadMode>(row, col), ei_pset1(m_rhs.coeff(col, col)));
}
else
{
return ei_pmul(ei_pset1(m_lhs.coeff(row, row)), m_rhs.template packet<LoadMode>(row, col));
}
}
protected:
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::true_type) const
{
return internal::pmul(m_matrix.template packet<LoadMode>(row, col),
internal::pset1<PacketScalar>(m_diagonal.diagonal().coeff(id)));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::false_type) const
{
enum {
InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
DiagonalVectorPacketLoadMode = (LoadMode == Aligned && ((InnerSize%16) == 0)) ? Aligned : Unaligned
};
return internal::pmul(m_matrix.template packet<LoadMode>(row, col),
m_diagonal.diagonal().template packet<DiagonalVectorPacketLoadMode>(id));
}
const typename MatrixType::Nested m_matrix;
const typename DiagonalType::Nested m_diagonal;
const LhsNested m_lhs;
const RhsNested m_rhs;
};
/** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal.
*/
template<typename Derived>
template<typename DiagonalDerived>
inline const DiagonalProduct<Derived, DiagonalDerived, OnTheRight>
MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &diagonal) const
{
return DiagonalProduct<Derived, DiagonalDerived, OnTheRight>(derived(), diagonal.derived());
}
/** \returns the diagonal matrix product of \c *this by the matrix \a matrix.
*/
template<typename DiagonalDerived>
template<typename MatrixDerived>
inline const DiagonalProduct<MatrixDerived, DiagonalDerived, OnTheLeft>
DiagonalBase<DiagonalDerived>::operator*(const MatrixBase<MatrixDerived> &matrix) const
{
return DiagonalProduct<MatrixDerived, DiagonalDerived, OnTheLeft>(matrix.derived(), derived());
}
#endif // EIGEN_DIAGONALPRODUCT_H

View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2008, 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -25,119 +25,268 @@
#ifndef EIGEN_DOT_H
#define EIGEN_DOT_H
namespace internal {
/***************************************************************************
* Part 1 : the logic deciding a strategy for vectorization and unrolling
***************************************************************************/
// helper function for dot(). The problem is that if we put that in the body of dot(), then upon calling dot
// with mismatched types, the compiler emits errors about failing to instantiate cwiseProduct BEFORE
// looking at the static assertions. Thus this is a trick to get better compile errors.
template<typename T, typename U,
// the NeedToTranspose condition here is taken straight from Assign.h
bool NeedToTranspose = T::IsVectorAtCompileTime
&& U::IsVectorAtCompileTime
&& ((int(T::RowsAtCompileTime) == 1 && int(U::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(T::ColsAtCompileTime) == 1 && int(U::RowsAtCompileTime) == 1))
template<typename Derived1, typename Derived2>
struct ei_dot_traits
{
public:
enum {
Vectorization = (int(Derived1::Flags)&int(Derived2::Flags)&ActualPacketAccessBit)
&& (int(Derived1::Flags)&int(Derived2::Flags)&LinearAccessBit)
? LinearVectorization
: NoVectorization
};
private:
typedef typename Derived1::Scalar Scalar;
enum {
PacketSize = ei_packet_traits<Scalar>::size,
Cost = Derived1::SizeAtCompileTime * (Derived1::CoeffReadCost + Derived2::CoeffReadCost + NumTraits<Scalar>::MulCost)
+ (Derived1::SizeAtCompileTime-1) * NumTraits<Scalar>::AddCost,
UnrollingLimit = EIGEN_UNROLLING_LIMIT * (int(Vectorization) == int(NoVectorization) ? 1 : int(PacketSize))
};
public:
enum {
Unrolling = Cost <= UnrollingLimit
? CompleteUnrolling
: NoUnrolling
};
};
/***************************************************************************
* Part 2 : unrollers
***************************************************************************/
/*** no vectorization ***/
template<typename Derived1, typename Derived2, int Start, int Length>
struct ei_dot_novec_unroller
{
enum {
HalfLength = Length/2
};
typedef typename Derived1::Scalar Scalar;
inline static Scalar run(const Derived1& v1, const Derived2& v2)
{
return ei_dot_novec_unroller<Derived1, Derived2, Start, HalfLength>::run(v1, v2)
+ ei_dot_novec_unroller<Derived1, Derived2, Start+HalfLength, Length-HalfLength>::run(v1, v2);
}
};
template<typename Derived1, typename Derived2, int Start>
struct ei_dot_novec_unroller<Derived1, Derived2, Start, 1>
{
typedef typename Derived1::Scalar Scalar;
inline static Scalar run(const Derived1& v1, const Derived2& v2)
{
return v1.coeff(Start) * ei_conj(v2.coeff(Start));
}
};
/*** vectorization ***/
template<typename Derived1, typename Derived2, int Index, int Stop,
bool LastPacket = (Stop-Index == ei_packet_traits<typename Derived1::Scalar>::size)>
struct ei_dot_vec_unroller
{
typedef typename Derived1::Scalar Scalar;
typedef typename ei_packet_traits<Scalar>::type PacketScalar;
enum {
row1 = Derived1::RowsAtCompileTime == 1 ? 0 : Index,
col1 = Derived1::RowsAtCompileTime == 1 ? Index : 0,
row2 = Derived2::RowsAtCompileTime == 1 ? 0 : Index,
col2 = Derived2::RowsAtCompileTime == 1 ? Index : 0
};
inline static PacketScalar run(const Derived1& v1, const Derived2& v2)
{
return ei_pmadd(
v1.template packet<Aligned>(row1, col1),
v2.template packet<Aligned>(row2, col2),
ei_dot_vec_unroller<Derived1, Derived2, Index+ei_packet_traits<Scalar>::size, Stop>::run(v1, v2)
);
}
};
template<typename Derived1, typename Derived2, int Index, int Stop>
struct ei_dot_vec_unroller<Derived1, Derived2, Index, Stop, true>
{
enum {
row1 = Derived1::RowsAtCompileTime == 1 ? 0 : Index,
col1 = Derived1::RowsAtCompileTime == 1 ? Index : 0,
row2 = Derived2::RowsAtCompileTime == 1 ? 0 : Index,
col2 = Derived2::RowsAtCompileTime == 1 ? Index : 0,
alignment1 = (Derived1::Flags & AlignedBit) ? Aligned : Unaligned,
alignment2 = (Derived2::Flags & AlignedBit) ? Aligned : Unaligned
};
typedef typename Derived1::Scalar Scalar;
typedef typename ei_packet_traits<Scalar>::type PacketScalar;
inline static PacketScalar run(const Derived1& v1, const Derived2& v2)
{
return ei_pmul(v1.template packet<alignment1>(row1, col1), v2.template packet<alignment2>(row2, col2));
}
};
/***************************************************************************
* Part 3 : implementation of all cases
***************************************************************************/
template<typename Derived1, typename Derived2,
int Vectorization = ei_dot_traits<Derived1, Derived2>::Vectorization,
int Unrolling = ei_dot_traits<Derived1, Derived2>::Unrolling
>
struct dot_nocheck
struct ei_dot_impl;
template<typename Derived1, typename Derived2>
struct ei_dot_impl<Derived1, Derived2, NoVectorization, NoUnrolling>
{
typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar;
static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
typedef typename Derived1::Scalar Scalar;
static Scalar run(const Derived1& v1, const Derived2& v2)
{
return a.template binaryExpr<scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> >(b).sum();
ei_assert(v1.size()>0 && "you are using a non initialized vector");
Scalar res;
res = v1.coeff(0) * ei_conj(v2.coeff(0));
for(int i = 1; i < v1.size(); ++i)
res += v1.coeff(i) * ei_conj(v2.coeff(i));
return res;
}
};
template<typename T, typename U>
struct dot_nocheck<T, U, true>
template<typename Derived1, typename Derived2>
struct ei_dot_impl<Derived1, Derived2, NoVectorization, CompleteUnrolling>
: public ei_dot_novec_unroller<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
{};
template<typename Derived1, typename Derived2>
struct ei_dot_impl<Derived1, Derived2, LinearVectorization, NoUnrolling>
{
typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar;
static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
typedef typename Derived1::Scalar Scalar;
typedef typename ei_packet_traits<Scalar>::type PacketScalar;
static Scalar run(const Derived1& v1, const Derived2& v2)
{
return a.transpose().template binaryExpr<scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> >(b).sum();
const int size = v1.size();
const int packetSize = ei_packet_traits<Scalar>::size;
const int alignedSize = (size/packetSize)*packetSize;
enum {
alignment1 = (Derived1::Flags & AlignedBit) ? Aligned : Unaligned,
alignment2 = (Derived2::Flags & AlignedBit) ? Aligned : Unaligned
};
Scalar res;
// do the vectorizable part of the sum
if(size >= packetSize)
{
PacketScalar packet_res = ei_pmul(
v1.template packet<alignment1>(0),
v2.template packet<alignment2>(0)
);
for(int index = packetSize; index<alignedSize; index += packetSize)
{
packet_res = ei_pmadd(
v1.template packet<alignment1>(index),
v2.template packet<alignment2>(index),
packet_res
);
}
res = ei_predux(packet_res);
// now we must do the rest without vectorization.
if(alignedSize == size) return res;
}
else // too small to vectorize anything.
// since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize.
{
res = Scalar(0);
}
// do the remainder of the vector
for(int index = alignedSize; index < size; ++index)
{
res += v1.coeff(index) * v2.coeff(index);
}
return res;
}
};
} // end namespace internal
template<typename Derived1, typename Derived2>
struct ei_dot_impl<Derived1, Derived2, LinearVectorization, CompleteUnrolling>
{
typedef typename Derived1::Scalar Scalar;
typedef typename ei_packet_traits<Scalar>::type PacketScalar;
enum {
PacketSize = ei_packet_traits<Scalar>::size,
Size = Derived1::SizeAtCompileTime,
VectorizationSize = (Size / PacketSize) * PacketSize
};
static Scalar run(const Derived1& v1, const Derived2& v2)
{
Scalar res = ei_predux(ei_dot_vec_unroller<Derived1, Derived2, 0, VectorizationSize>::run(v1, v2));
if (VectorizationSize != Size)
res += ei_dot_novec_unroller<Derived1, Derived2, VectorizationSize, Size-VectorizationSize>::run(v1, v2);
return res;
}
};
/***************************************************************************
* Part 4 : implementation of MatrixBase methods
***************************************************************************/
/** \returns the dot product of *this with other.
*
* \only_for_vectors
*
* \note If the scalar type is complex numbers, then this function returns the hermitian
* (sesquilinear) dot product, conjugate-linear in the first variable and linear in the
* (sesquilinear) dot product, linear in the first variable and conjugate-linear in the
* second variable.
*
* \sa squaredNorm(), norm()
*/
template<typename Derived>
template<typename OtherDerived>
typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
typename ei_traits<Derived>::Scalar
MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
typedef internal::scalar_conj_product_op<Scalar,typename OtherDerived::Scalar> func;
EIGEN_CHECK_BINARY_COMPATIBILIY(func,Scalar,typename OtherDerived::Scalar);
eigen_assert(size() == other.size());
return internal::dot_nocheck<Derived,OtherDerived>::run(*this, other);
}
#ifdef EIGEN2_SUPPORT
/** \returns the dot product of *this with other, with the Eigen2 convention that the dot product is linear in the first variable
* (conjugating the second variable). Of course this only makes a difference in the complex case.
*
* This method is only available in EIGEN2_SUPPORT mode.
*
* \only_for_vectors
*
* \sa dot()
*/
template<typename Derived>
template<typename OtherDerived>
typename internal::traits<Derived>::Scalar
MatrixBase<Derived>::eigen2_dot(const MatrixBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
EIGEN_STATIC_ASSERT((ei_is_same_type<Scalar, typename OtherDerived::Scalar>::ret),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
eigen_assert(size() == other.size());
ei_assert(size() == other.size());
return internal::dot_nocheck<OtherDerived,Derived>::run(other,*this);
return ei_dot_impl<Derived, OtherDerived>::run(derived(), other.derived());
}
#endif
//---------- implementation of L2 norm and related functions ----------
/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the Frobenius norm.
* In both cases, it consists in the sum of the square of all the matrix entries.
* For vectors, this is also equals to the dot product of \c *this with itself.
/** \returns the squared \em l2 norm of *this, i.e., for vectors, the dot product of *this with itself.
*
* \sa dot(), norm()
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const
inline typename NumTraits<typename ei_traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const
{
return internal::real((*this).cwiseAbs2().sum());
return ei_real((*this).cwise().abs2().sum());
}
/** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm.
* In both cases, it consists in the square root of the sum of the square of all the matrix entries.
* For vectors, this is also equals to the square root of the dot product of \c *this with itself.
/** \returns the \em l2 norm of *this, i.e., for vectors, the square root of the dot product of *this with itself.
*
* \sa dot(), squaredNorm()
*/
template<typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
inline typename NumTraits<typename ei_traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
{
return internal::sqrt(squaredNorm());
return ei_sqrt(squaredNorm());
}
/** \returns an expression of the quotient of *this by its own norm.
@@ -147,11 +296,11 @@ inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real Matr
* \sa norm(), normalize()
*/
template<typename Derived>
inline const typename MatrixBase<Derived>::PlainObject
inline const typename MatrixBase<Derived>::PlainMatrixType
MatrixBase<Derived>::normalized() const
{
typedef typename internal::nested<Derived>::type Nested;
typedef typename internal::remove_reference<Nested>::type _Nested;
typedef typename ei_nested<Derived>::type Nested;
typedef typename ei_unref<Nested>::type _Nested;
_Nested n(derived());
return n / n.norm();
}
@@ -168,65 +317,6 @@ inline void MatrixBase<Derived>::normalize()
*this /= norm();
}
//---------- implementation of other norms ----------
namespace internal {
template<typename Derived, int p>
struct lpNorm_selector
{
typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
inline static RealScalar run(const MatrixBase<Derived>& m)
{
return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p);
}
};
template<typename Derived>
struct lpNorm_selector<Derived, 1>
{
inline static typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
{
return m.cwiseAbs().sum();
}
};
template<typename Derived>
struct lpNorm_selector<Derived, 2>
{
inline static typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
{
return m.norm();
}
};
template<typename Derived>
struct lpNorm_selector<Derived, Infinity>
{
inline static typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
{
return m.cwiseAbs().maxCoeff();
}
};
} // end namespace internal
/** \returns the \f$ \ell^p \f$ norm of *this, that is, returns the p-th root of the sum of the p-th powers of the absolute values
* of the coefficients of *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^\infty \f$
* norm, that is the maximum of the absolute values of the coefficients of *this.
*
* \sa norm()
*/
template<typename Derived>
template<int p>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
MatrixBase<Derived>::lpNorm() const
{
return internal::lpNorm_selector<Derived, p>::run(*this);
}
//---------- implementation of isOrthogonal / isUnitary ----------
/** \returns true if *this is approximately orthogonal to \a other,
* within the precision given by \a prec.
*
@@ -238,9 +328,9 @@ template<typename OtherDerived>
bool MatrixBase<Derived>::isOrthogonal
(const MatrixBase<OtherDerived>& other, RealScalar prec) const
{
typename internal::nested<Derived,2>::type nested(derived());
typename internal::nested<OtherDerived,2>::type otherNested(other.derived());
return internal::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
typename ei_nested<Derived,2>::type nested(derived());
typename ei_nested<OtherDerived,2>::type otherNested(other.derived());
return ei_abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
}
/** \returns true if *this is approximately an unitary matrix,
@@ -258,15 +348,14 @@ template<typename Derived>
bool MatrixBase<Derived>::isUnitary(RealScalar prec) const
{
typename Derived::Nested nested(derived());
for(Index i = 0; i < cols(); ++i)
for(int i = 0; i < cols(); ++i)
{
if(!internal::isApprox(nested.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
if(!ei_isApprox(nested.col(i).squaredNorm(), static_cast<Scalar>(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))
for(int j = 0; j < i; ++j)
if(!ei_isMuchSmallerThan(nested.col(i).dot(nested.col(j)), static_cast<Scalar>(1), prec))
return false;
}
return true;
}
#endif // EIGEN_DOT_H

View File

@@ -1,172 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_EIGENBASE_H
#define EIGEN_EIGENBASE_H
/** 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.
*
* Besides MatrixBase-derived classes, this also includes special matrix classes such as diagonal matrices, etc.
*
* Notice that this class is trivial, it is only used to disambiguate overloaded functions.
*
* \sa \ref TopicClassHierarchy
*/
template<typename Derived> struct EigenBase
{
// typedef typename internal::plain_matrix_type<Derived>::type PlainObject;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
/** \returns a reference to the derived object */
Derived& derived() { return *static_cast<Derived*>(this); }
/** \returns a const reference to the derived object */
const Derived& derived() const { return *static_cast<const Derived*>(this); }
inline Derived& const_cast_derived() const
{ return *static_cast<Derived*>(const_cast<EigenBase*>(this)); }
inline const Derived& const_derived() const
{ return *static_cast<const Derived*>(this); }
/** \returns the number of rows. \sa cols(), RowsAtCompileTime */
inline Index rows() const { return derived().rows(); }
/** \returns the number of columns. \sa rows(), ColsAtCompileTime*/
inline Index cols() const { return derived().cols(); }
/** \returns the number of coefficients, which is rows()*cols().
* \sa rows(), cols(), SizeAtCompileTime. */
inline Index size() const { return rows() * cols(); }
/** \internal Don't use it, but do the equivalent: \code dst = *this; \endcode */
template<typename Dest> inline void evalTo(Dest& dst) const
{ derived().evalTo(dst); }
/** \internal Don't use it, but do the equivalent: \code dst += *this; \endcode */
template<typename Dest> inline void addTo(Dest& dst) const
{
// This is the default implementation,
// derived class can reimplement it in a more optimized way.
typename Dest::PlainObject res(rows(),cols());
evalTo(res);
dst += res;
}
/** \internal Don't use it, but do the equivalent: \code dst -= *this; \endcode */
template<typename Dest> inline void subTo(Dest& dst) const
{
// This is the default implementation,
// derived class can reimplement it in a more optimized way.
typename Dest::PlainObject res(rows(),cols());
evalTo(res);
dst -= res;
}
/** \internal Don't use it, but do the equivalent: \code dst.applyOnTheRight(*this); \endcode */
template<typename Dest> inline void applyThisOnTheRight(Dest& dst) const
{
// This is the default implementation,
// derived class can reimplement it in a more optimized way.
dst = dst * this->derived();
}
/** \internal Don't use it, but do the equivalent: \code dst.applyOnTheLeft(*this); \endcode */
template<typename Dest> inline void applyThisOnTheLeft(Dest& dst) const
{
// This is the default implementation,
// derived class can reimplement it in a more optimized way.
dst = this->derived() * dst;
}
};
/***************************************************************************
* Implementation of matrix base methods
***************************************************************************/
/** \brief Copies the generic expression \a other into *this.
*
* \details The expression must provide a (templated) evalTo(Derived& dst) const
* function which does the actual job. In practice, this allows any user to write
* its own special matrix without having to modify MatrixBase
*
* \returns a reference to *this.
*/
template<typename Derived>
template<typename OtherDerived>
Derived& DenseBase<Derived>::operator=(const EigenBase<OtherDerived> &other)
{
other.derived().evalTo(derived());
return derived();
}
template<typename Derived>
template<typename OtherDerived>
Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
{
other.derived().addTo(derived());
return derived();
}
template<typename Derived>
template<typename OtherDerived>
Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
{
other.derived().subTo(derived());
return derived();
}
/** replaces \c *this by \c *this * \a other.
*
* \returns a reference to \c *this
*/
template<typename Derived>
template<typename OtherDerived>
inline Derived&
MatrixBase<Derived>::operator*=(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheRight(derived());
return derived();
}
/** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=() */
template<typename Derived>
template<typename OtherDerived>
inline void MatrixBase<Derived>::applyOnTheRight(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheRight(derived());
}
/** replaces \c *this by \c *this * \a other. */
template<typename Derived>
template<typename OtherDerived>
inline void MatrixBase<Derived>::applyOnTheLeft(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheLeft(derived());
}
#endif // EIGEN_EIGENBASE_H

View File

@@ -1,5 +1,5 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
@@ -26,7 +26,6 @@
#define EIGEN_FLAGGED_H
/** \class Flagged
* \ingroup Core_Module
*
* \brief Expression with modified flags
*
@@ -40,110 +39,109 @@
*
* \sa MatrixBase::flagged()
*/
namespace internal {
template<typename ExpressionType, unsigned int Added, unsigned int Removed>
struct traits<Flagged<ExpressionType, Added, Removed> > : traits<ExpressionType>
struct ei_traits<Flagged<ExpressionType, Added, Removed> > : ei_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;
EIGEN_GENERIC_PUBLIC_INTERFACE(Flagged)
typedef typename ei_meta_if<ei_must_nest_by_value<ExpressionType>::ret,
ExpressionType, const ExpressionType&>::ret 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 int rows() const { return m_matrix.rows(); }
inline int cols() const { return m_matrix.cols(); }
inline int stride() const { return m_matrix.stride(); }
inline CoeffReturnType coeff(Index row, Index col) const
inline const Scalar coeff(int row, int col) const
{
return m_matrix.coeff(row, col);
}
inline CoeffReturnType coeff(Index index) const
inline Scalar& coeffRef(int row, int col)
{
return m_matrix.const_cast_derived().coeffRef(row, col);
}
inline const Scalar coeff(int 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)
inline Scalar& coeffRef(int index)
{
return m_matrix.const_cast_derived().coeffRef(index);
}
template<int LoadMode>
inline const PacketScalar packet(Index row, Index col) const
inline const PacketScalar packet(int row, int col) const
{
return m_matrix.template packet<LoadMode>(row, col);
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
inline void writePacket(int row, int 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
inline const PacketScalar packet(int index) const
{
return m_matrix.template packet<LoadMode>(index);
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
inline void writePacket(int 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;
private:
Flagged& operator=(const Flagged&);
};
/** \returns an expression of *this with added and removed flags
/** \returns an expression of *this with added flags
*
* This is mostly for internal use.
* \addexample MarkExample \label How to mark a triangular matrix as triangular
*
* \sa class Flagged
* Example: \include MatrixBase_marked.cpp
* Output: \verbinclude MatrixBase_marked.out
*
* \sa class Flagged, extract(), part()
*/
template<typename Derived>
template<unsigned int Added,unsigned int Removed>
inline const Flagged<Derived, Added, Removed>
DenseBase<Derived>::flagged() const
template<unsigned int Added>
inline const Flagged<Derived, Added, 0>
MatrixBase<Derived>::marked() const
{
return derived();
}
/** \returns an expression of *this with the following flags removed:
* EvalBeforeNestingBit and EvalBeforeAssigningBit.
*
* Example: \include MatrixBase_lazy.cpp
* Output: \verbinclude MatrixBase_lazy.out
*
* \sa class Flagged, marked()
*/
template<typename Derived>
inline const Flagged<Derived, 0, EvalBeforeNestingBit | EvalBeforeAssigningBit>
MatrixBase<Derived>::lazy() const
{
return derived();
}

View File

@@ -1,157 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_FORCEALIGNEDACCESS_H
#define EIGEN_FORCEALIGNEDACCESS_H
/** \class ForceAlignedAccess
* \ingroup Core_Module
*
* \brief Enforce aligned packet loads and stores regardless of what is requested
*
* \param ExpressionType the type of the object of which we are forcing aligned packet access
*
* This class is the return type of MatrixBase::forceAlignedAccess()
* and most of the time this is the only way it is used.
*
* \sa MatrixBase::forceAlignedAccess()
*/
namespace internal {
template<typename ExpressionType>
struct traits<ForceAlignedAccess<ExpressionType> > : public traits<ExpressionType>
{};
}
template<typename ExpressionType> class ForceAlignedAccess
: public internal::dense_xpr_base< ForceAlignedAccess<ExpressionType> >::type
{
public:
typedef typename internal::dense_xpr_base<ForceAlignedAccess>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(ForceAlignedAccess)
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(); }
inline const CoeffReturnType coeff(Index row, Index col) const
{
return m_expression.coeff(row, col);
}
inline Scalar& coeffRef(Index row, Index col)
{
return m_expression.const_cast_derived().coeffRef(row, col);
}
inline const CoeffReturnType coeff(Index index) const
{
return m_expression.coeff(index);
}
inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
}
template<int LoadMode>
inline const PacketScalar packet(Index row, Index col) const
{
return m_expression.template packet<Aligned>(row, col);
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
{
m_expression.const_cast_derived().template writePacket<Aligned>(row, col, x);
}
template<int LoadMode>
inline const PacketScalar packet(Index index) const
{
return m_expression.template packet<Aligned>(index);
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
{
m_expression.const_cast_derived().template writePacket<Aligned>(index, x);
}
operator const ExpressionType&() const { return m_expression; }
protected:
const ExpressionType& m_expression;
private:
ForceAlignedAccess& operator=(const ForceAlignedAccess&);
};
/** \returns an expression of *this with forced aligned access
* \sa forceAlignedAccessIf(),class ForceAlignedAccess
*/
template<typename Derived>
inline const ForceAlignedAccess<Derived>
MatrixBase<Derived>::forceAlignedAccess() const
{
return ForceAlignedAccess<Derived>(derived());
}
/** \returns an expression of *this with forced aligned access
* \sa forceAlignedAccessIf(), class ForceAlignedAccess
*/
template<typename Derived>
inline ForceAlignedAccess<Derived>
MatrixBase<Derived>::forceAlignedAccess()
{
return ForceAlignedAccess<Derived>(derived());
}
/** \returns an expression of *this with forced aligned access if \a Enable is true.
* \sa forceAlignedAccess(), class ForceAlignedAccess
*/
template<typename Derived>
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();
}
/** \returns an expression of *this with forced aligned access if \a Enable is true.
* \sa forceAlignedAccess(), class ForceAlignedAccess
*/
template<typename Derived>
template<bool Enable>
inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type
MatrixBase<Derived>::forceAlignedAccessIf()
{
return derived();
}
#endif // EIGEN_FORCEALIGNEDACCESS_H

File diff suppressed because it is too large Load Diff

View File

@@ -1,8 +1,8 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -26,68 +26,7 @@
#ifndef EIGEN_FUZZY_H
#define EIGEN_FUZZY_H
namespace internal
{
template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isApprox_selector
{
static bool run(const Derived& x, const OtherDerived& y, typename Derived::RealScalar prec)
{
using std::min;
const typename internal::nested<Derived,2>::type nested(x);
const typename internal::nested<OtherDerived,2>::type otherNested(y);
return (nested - otherNested).cwiseAbs2().sum() <= prec * prec * (min)(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
}
};
template<typename Derived, typename OtherDerived>
struct isApprox_selector<Derived, OtherDerived, true>
{
static bool run(const Derived& x, const OtherDerived& y, typename Derived::RealScalar)
{
return x.matrix() == y.matrix();
}
};
template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isMuchSmallerThan_object_selector
{
static bool run(const Derived& x, const OtherDerived& y, typename Derived::RealScalar prec)
{
return x.cwiseAbs2().sum() <= abs2(prec) * y.cwiseAbs2().sum();
}
};
template<typename Derived, typename OtherDerived>
struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true>
{
static bool run(const Derived& x, const OtherDerived&, typename Derived::RealScalar)
{
return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
}
};
template<typename Derived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isMuchSmallerThan_scalar_selector
{
static bool run(const Derived& x, const typename Derived::RealScalar& y, typename Derived::RealScalar prec)
{
return x.cwiseAbs2().sum() <= abs2(prec * y);
}
};
template<typename Derived>
struct isMuchSmallerThan_scalar_selector<Derived, true>
{
static bool run(const Derived& x, const typename Derived::RealScalar&, typename Derived::RealScalar)
{
return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
}
};
} // end namespace internal
#ifndef EIGEN_LEGACY_COMPARES
/** \returns \c true if \c *this is approximately equal to \a other, within the precision
* determined by \a prec.
@@ -101,19 +40,21 @@ struct isMuchSmallerThan_scalar_selector<Derived, true>
* \note Because of the multiplicativeness of this comparison, one can't use this function
* to check whether \c *this is approximately equal to the zero matrix or vector.
* Indeed, \c isApprox(zero) returns false unless \c *this itself is exactly the zero matrix
* or vector. If you want to test whether \c *this is zero, use internal::isMuchSmallerThan(const
* or vector. If you want to test whether \c *this is zero, use ei_isMuchSmallerThan(const
* RealScalar&, RealScalar) instead.
*
* \sa internal::isMuchSmallerThan(const RealScalar&, RealScalar) const
* \sa ei_isMuchSmallerThan(const RealScalar&, RealScalar) const
*/
template<typename Derived>
template<typename OtherDerived>
bool DenseBase<Derived>::isApprox(
const DenseBase<OtherDerived>& other,
RealScalar prec
bool MatrixBase<Derived>::isApprox(
const MatrixBase<OtherDerived>& other,
typename NumTraits<Scalar>::Real prec
) const
{
return internal::isApprox_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);
const typename ei_nested<Derived,2>::type nested(derived());
const typename ei_nested<OtherDerived,2>::type otherNested(other.derived());
return (nested - otherNested).cwise().abs2().sum() <= prec * prec * std::min(nested.cwise().abs2().sum(), otherNested.cwise().abs2().sum());
}
/** \returns \c true if the norm of \c *this is much smaller than \a other,
@@ -127,15 +68,15 @@ bool DenseBase<Derived>::isApprox(
* the value of the reference scalar \a other should come from the Hilbert-Schmidt norm
* of a reference matrix of same dimensions.
*
* \sa isApprox(), isMuchSmallerThan(const DenseBase<OtherDerived>&, RealScalar) const
* \sa isApprox(), isMuchSmallerThan(const MatrixBase<OtherDerived>&, RealScalar) const
*/
template<typename Derived>
bool DenseBase<Derived>::isMuchSmallerThan(
bool MatrixBase<Derived>::isMuchSmallerThan(
const typename NumTraits<Scalar>::Real& other,
RealScalar prec
typename NumTraits<Scalar>::Real prec
) const
{
return internal::isMuchSmallerThan_scalar_selector<Derived>::run(derived(), other, prec);
return cwise().abs2().sum() <= prec * prec * other * other;
}
/** \returns \c true if the norm of \c *this is much smaller than the norm of \a other,
@@ -150,12 +91,144 @@ bool DenseBase<Derived>::isMuchSmallerThan(
*/
template<typename Derived>
template<typename OtherDerived>
bool DenseBase<Derived>::isMuchSmallerThan(
const DenseBase<OtherDerived>& other,
RealScalar prec
bool MatrixBase<Derived>::isMuchSmallerThan(
const MatrixBase<OtherDerived>& other,
typename NumTraits<Scalar>::Real prec
) const
{
return internal::isMuchSmallerThan_object_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);
return this->cwise().abs2().sum() <= prec * prec * other.cwise().abs2().sum();
}
#else
template<typename Derived, typename OtherDerived=Derived, bool IsVector=Derived::IsVectorAtCompileTime>
struct ei_fuzzy_selector;
/** \returns \c true if \c *this is approximately equal to \a other, within the precision
* determined by \a prec.
*
* \note The fuzzy compares are done multiplicatively. Two vectors \f$ v \f$ and \f$ w \f$
* are considered to be approximately equal within precision \f$ p \f$ if
* \f[ \Vert v - w \Vert \leqslant p\,\min(\Vert v\Vert, \Vert w\Vert). \f]
* For matrices, the comparison is done on all columns.
*
* \note Because of the multiplicativeness of this comparison, one can't use this function
* to check whether \c *this is approximately equal to the zero matrix or vector.
* Indeed, \c isApprox(zero) returns false unless \c *this itself is exactly the zero matrix
* or vector. If you want to test whether \c *this is zero, use ei_isMuchSmallerThan(const
* RealScalar&, RealScalar) instead.
*
* \sa ei_isMuchSmallerThan(const RealScalar&, RealScalar) const
*/
template<typename Derived>
template<typename OtherDerived>
bool MatrixBase<Derived>::isApprox(
const MatrixBase<OtherDerived>& other,
typename NumTraits<Scalar>::Real prec
) const
{
return ei_fuzzy_selector<Derived,OtherDerived>::isApprox(derived(), other.derived(), prec);
}
/** \returns \c true if the norm of \c *this is much smaller than \a other,
* within the precision determined by \a prec.
*
* \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is
* considered to be much smaller than \f$ x \f$ within precision \f$ p \f$ if
* \f[ \Vert v \Vert \leqslant p\,\vert x\vert. \f]
* For matrices, the comparison is done on all columns.
*
* \sa isApprox(), isMuchSmallerThan(const MatrixBase<OtherDerived>&, RealScalar) const
*/
template<typename Derived>
bool MatrixBase<Derived>::isMuchSmallerThan(
const typename NumTraits<Scalar>::Real& other,
typename NumTraits<Scalar>::Real prec
) const
{
return ei_fuzzy_selector<Derived>::isMuchSmallerThan(derived(), other, prec);
}
/** \returns \c true if the norm of \c *this is much smaller than the norm of \a other,
* within the precision determined by \a prec.
*
* \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is
* considered to be much smaller than a vector \f$ w \f$ within precision \f$ p \f$ if
* \f[ \Vert v \Vert \leqslant p\,\Vert w\Vert. \f]
* For matrices, the comparison is done on all columns.
*
* \sa isApprox(), isMuchSmallerThan(const RealScalar&, RealScalar) const
*/
template<typename Derived>
template<typename OtherDerived>
bool MatrixBase<Derived>::isMuchSmallerThan(
const MatrixBase<OtherDerived>& other,
typename NumTraits<Scalar>::Real prec
) const
{
return ei_fuzzy_selector<Derived,OtherDerived>::isMuchSmallerThan(derived(), other.derived(), prec);
}
template<typename Derived, typename OtherDerived>
struct ei_fuzzy_selector<Derived,OtherDerived,true>
{
typedef typename Derived::RealScalar RealScalar;
static bool isApprox(const Derived& self, const OtherDerived& other, RealScalar prec)
{
EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
ei_assert(self.size() == other.size());
return((self - other).squaredNorm() <= std::min(self.squaredNorm(), other.squaredNorm()) * prec * prec);
}
static bool isMuchSmallerThan(const Derived& self, const RealScalar& other, RealScalar prec)
{
return(self.squaredNorm() <= ei_abs2(other * prec));
}
static bool isMuchSmallerThan(const Derived& self, const OtherDerived& other, RealScalar prec)
{
EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
ei_assert(self.size() == other.size());
return(self.squaredNorm() <= other.squaredNorm() * prec * prec);
}
};
template<typename Derived, typename OtherDerived>
struct ei_fuzzy_selector<Derived,OtherDerived,false>
{
typedef typename Derived::RealScalar RealScalar;
static bool isApprox(const Derived& self, const OtherDerived& other, RealScalar prec)
{
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived)
ei_assert(self.rows() == other.rows() && self.cols() == other.cols());
typename Derived::Nested nested(self);
typename OtherDerived::Nested otherNested(other);
for(int i = 0; i < self.cols(); ++i)
if((nested.col(i) - otherNested.col(i)).squaredNorm()
> std::min(nested.col(i).squaredNorm(), otherNested.col(i).squaredNorm()) * prec * prec)
return false;
return true;
}
static bool isMuchSmallerThan(const Derived& self, const RealScalar& other, RealScalar prec)
{
typename Derived::Nested nested(self);
for(int i = 0; i < self.cols(); ++i)
if(nested.col(i).squaredNorm() > ei_abs2(other * prec))
return false;
return true;
}
static bool isMuchSmallerThan(const Derived& self, const OtherDerived& other, RealScalar prec)
{
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived)
ei_assert(self.rows() == other.rows() && self.cols() == other.cols());
typename Derived::Nested nested(self);
typename OtherDerived::Nested otherNested(other);
for(int i = 0; i < self.cols(); ++i)
if(nested.col(i).squaredNorm() > otherNested.col(i).squaredNorm() * prec * prec)
return false;
return true;
}
};
#endif
#endif // EIGEN_FUZZY_H

View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
@@ -26,8 +26,6 @@
#ifndef EIGEN_GENERIC_PACKET_MATH_H
#define EIGEN_GENERIC_PACKET_MATH_H
namespace internal {
/** \internal
* \file GenericPacketMath.h
*
@@ -36,280 +34,105 @@ namespace internal {
* of generic vectorized code.
*/
#ifndef EIGEN_DEBUG_ALIGNED_LOAD
#define EIGEN_DEBUG_ALIGNED_LOAD
#endif
#ifndef EIGEN_DEBUG_UNALIGNED_LOAD
#define EIGEN_DEBUG_UNALIGNED_LOAD
#endif
#ifndef EIGEN_DEBUG_ALIGNED_STORE
#define EIGEN_DEBUG_ALIGNED_STORE
#endif
#ifndef EIGEN_DEBUG_UNALIGNED_STORE
#define EIGEN_DEBUG_UNALIGNED_STORE
#endif
struct default_packet_traits
{
enum {
HasAdd = 1,
HasSub = 1,
HasMul = 1,
HasNegate = 1,
HasAbs = 1,
HasAbs2 = 1,
HasMin = 1,
HasMax = 1,
HasConj = 1,
HasSetLinear = 1,
HasDiv = 0,
HasSqrt = 0,
HasExp = 0,
HasLog = 0,
HasPow = 0,
HasSin = 0,
HasCos = 0,
HasTan = 0,
HasASin = 0,
HasACos = 0,
HasATan = 0
};
};
template<typename T> struct packet_traits : default_packet_traits
{
typedef T type;
enum {
Vectorizable = 0,
size = 1,
AlignedOnScalar = 0
};
enum {
HasAdd = 0,
HasSub = 0,
HasMul = 0,
HasNegate = 0,
HasAbs = 0,
HasAbs2 = 0,
HasMin = 0,
HasMax = 0,
HasConj = 0,
HasSetLinear = 0
};
};
/** \internal \returns a + b (coeff-wise) */
template<typename Packet> inline Packet
padd(const Packet& a,
ei_padd(const Packet& a,
const Packet& b) { return a+b; }
/** \internal \returns a - b (coeff-wise) */
template<typename Packet> inline Packet
psub(const Packet& a,
ei_psub(const Packet& a,
const Packet& b) { return a-b; }
/** \internal \returns -a (coeff-wise) */
template<typename Packet> inline Packet
pnegate(const Packet& a) { return -a; }
/** \internal \returns conj(a) (coeff-wise) */
template<typename Packet> inline Packet
pconj(const Packet& a) { return conj(a); }
/** \internal \returns a * b (coeff-wise) */
template<typename Packet> inline Packet
pmul(const Packet& a,
ei_pmul(const Packet& a,
const Packet& b) { return a*b; }
/** \internal \returns a / b (coeff-wise) */
template<typename Packet> inline Packet
pdiv(const Packet& a,
ei_pdiv(const Packet& a,
const Packet& b) { return a/b; }
/** \internal \returns the min of \a a and \a b (coeff-wise) */
template<typename Packet> inline Packet
pmin(const Packet& a,
const Packet& b) { using std::min; return (min)(a, b); }
ei_pmin(const Packet& a,
const Packet& b) { return std::min(a, b); }
/** \internal \returns the max of \a a and \a b (coeff-wise) */
template<typename Packet> inline Packet
pmax(const Packet& a,
const Packet& b) { using std::max; return (max)(a, b); }
/** \internal \returns the absolute value of \a a */
template<typename Packet> inline Packet
pabs(const Packet& a) { return abs(a); }
/** \internal \returns the bitwise and of \a a and \a b */
template<typename Packet> inline Packet
pand(const Packet& a, const Packet& b) { return a & b; }
/** \internal \returns the bitwise or of \a a and \a b */
template<typename Packet> inline Packet
por(const Packet& a, const Packet& b) { return a | b; }
/** \internal \returns the bitwise xor of \a a and \a b */
template<typename Packet> inline Packet
pxor(const Packet& a, const Packet& b) { return a ^ b; }
/** \internal \returns the bitwise andnot of \a a and \a b */
template<typename Packet> inline Packet
pandnot(const Packet& a, const Packet& b) { return a & (!b); }
ei_pmax(const Packet& a,
const Packet& b) { return std::max(a, b); }
/** \internal \returns a packet version of \a *from, from must be 16 bytes aligned */
template<typename Packet> inline Packet
pload(const typename unpacket_traits<Packet>::type* from) { return *from; }
template<typename Scalar> inline typename ei_packet_traits<Scalar>::type
ei_pload(const Scalar* from) { return *from; }
/** \internal \returns a packet version of \a *from, (un-aligned load) */
template<typename Packet> inline Packet
ploadu(const typename unpacket_traits<Packet>::type* from) { return *from; }
/** \internal \returns a packet with elements of \a *from duplicated, e.g.: (from[0],from[0],from[1],from[1]) */
template<typename Packet> inline Packet
ploaddup(const typename unpacket_traits<Packet>::type* from) { return *from; }
template<typename Scalar> inline typename ei_packet_traits<Scalar>::type
ei_ploadu(const Scalar* from) { return *from; }
/** \internal \returns a packet with constant coefficients \a a, e.g.: (a,a,a,a) */
template<typename Packet> inline Packet
pset1(const typename unpacket_traits<Packet>::type& a) { return 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 Scalar> inline typename ei_packet_traits<Scalar>::type
ei_pset1(const Scalar& a) { return a; }
/** \internal copy the packet \a from to \a *to, \a to must be 16 bytes aligned */
template<typename Scalar, typename Packet> inline void pstore(Scalar* to, const Packet& from)
template<typename Scalar, typename Packet> inline void ei_pstore(Scalar* to, const Packet& from)
{ (*to) = from; }
/** \internal copy the packet \a from to \a *to, (un-aligned store) */
template<typename Scalar, typename Packet> inline void pstoreu(Scalar* to, const Packet& from)
template<typename Scalar, typename Packet> inline void ei_pstoreu(Scalar* to, const Packet& from)
{ (*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);
#endif
}
/** \internal \returns the first element of a packet */
template<typename Packet> inline typename unpacket_traits<Packet>::type pfirst(const Packet& a)
template<typename Packet> inline typename ei_unpacket_traits<Packet>::type ei_pfirst(const Packet& a)
{ return a; }
/** \internal \returns a packet where the element i contains the sum of the packet of \a vec[i] */
template<typename Packet> inline Packet
preduxp(const Packet* vecs) { return vecs[0]; }
ei_preduxp(const Packet* vecs) { return vecs[0]; }
/** \internal \returns the sum of the elements of \a a*/
template<typename Packet> inline typename unpacket_traits<Packet>::type predux(const Packet& a)
template<typename Packet> inline typename ei_unpacket_traits<Packet>::type ei_predux(const Packet& a)
{ return a; }
/** \internal \returns the product of the elements of \a a*/
template<typename Packet> inline typename unpacket_traits<Packet>::type predux_mul(const Packet& a)
{ return a; }
/** \internal \returns the min of the elements of \a a*/
template<typename Packet> inline typename unpacket_traits<Packet>::type predux_min(const Packet& a)
{ return a; }
/** \internal \returns the max of the elements of \a a*/
template<typename Packet> inline typename unpacket_traits<Packet>::type predux_max(const Packet& a)
{ return a; }
/** \internal \returns the reversed elements of \a a*/
template<typename Packet> inline Packet preverse(const Packet& a)
{ return a; }
/** \internal \returns \a a with real and imaginary part flipped (for complex type only) */
template<typename Packet> inline Packet pcplxflip(const Packet& a)
{ return Packet(imag(a),real(a)); }
/**************************
* Special math functions
***************************/
/** \internal \returns the sine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet psin(const Packet& a) { return sin(a); }
/** \internal \returns the cosine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pcos(const Packet& a) { return cos(a); }
/** \internal \returns the tan of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet ptan(const Packet& a) { return tan(a); }
/** \internal \returns the arc sine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pasin(const Packet& a) { return asin(a); }
/** \internal \returns the arc cosine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pacos(const Packet& a) { return acos(a); }
/** \internal \returns the exp of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pexp(const Packet& a) { return exp(a); }
/** \internal \returns the log of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plog(const Packet& a) { return log(a); }
/** \internal \returns the square-root of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet psqrt(const Packet& a) { return sqrt(a); }
/***************************************************************************
* The following functions might not have to be overwritten for vectorized types
***************************************************************************/
/** \internal copy a packet with constant coeficient \a a (e.g., [a,a,a,a]) to \a *to. \a to must be 16 bytes aligned */
// NOTE: this function must really be templated on the packet type (think about different packet types for the same scalar type)
template<typename Packet>
inline void pstore1(typename unpacket_traits<Packet>::type* to, const typename unpacket_traits<Packet>::type& a)
{
pstore(to, pset1<Packet>(a));
}
/** \internal \returns a * b + c (coeff-wise) */
template<typename Packet> inline Packet
pmadd(const Packet& a,
ei_pmadd(const Packet& a,
const Packet& b,
const Packet& c)
{ return padd(pmul(a, b),c); }
{ return ei_padd(ei_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)
* \If LoadMode equals Aligned, \a from must be 16 bytes aligned */
template<typename Scalar, int LoadMode>
inline typename ei_packet_traits<Scalar>::type ei_ploadt(const Scalar* from)
{
if(LoadMode == Aligned)
return pload<Packet>(from);
return ei_pload(from);
else
return ploadu<Packet>(from);
return ei_ploadu(from);
}
/** \internal copy the packet \a from to \a *to.
* If StoreMode equals #Aligned, \a to must be 16 bytes aligned */
* 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)
inline void ei_pstoret(Scalar* to, const Packet& from)
{
if(LoadMode == Aligned)
pstore(to, from);
ei_pstore(to, from);
else
pstoreu(to, from);
ei_pstoreu(to, from);
}
/** \internal default implementation of palign() allowing partial specialization */
/** \internal default implementation of ei_palign() allowing partial specialization */
template<int Offset,typename PacketType>
struct palign_impl
struct ei_palign_impl
{
// by default data are aligned, so there is nothing to be done :)
inline static void run(PacketType&, const PacketType&) {}
@@ -318,22 +141,10 @@ struct palign_impl
/** \internal update \a first using the concatenation of the \a Offset last elements
* of \a first and packet_size minus \a Offset first elements of \a second */
template<int Offset,typename PacketType>
inline void palign(PacketType& first, const PacketType& second)
inline void ei_palign(PacketType& first, const PacketType& second)
{
palign_impl<Offset,PacketType>::run(first,second);
ei_palign_impl<Offset,PacketType>::run(first,second);
}
/***************************************************************************
* Fast complex products (GCC generates a function call which is very slow)
***************************************************************************/
template<> inline std::complex<float> pmul(const std::complex<float>& a, const std::complex<float>& b)
{ return std::complex<float>(real(a)*real(b) - imag(a)*imag(b), imag(a)*real(b) + real(a)*imag(b)); }
template<> inline std::complex<double> pmul(const std::complex<double>& a, const std::complex<double>& b)
{ return std::complex<double>(real(a)*real(b) - imag(a)*imag(b), imag(a)*real(b) + real(a)*imag(b)); }
} // end namespace internal
#endif // EIGEN_GENERIC_PACKET_MATH_H

View File

@@ -1,95 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_GLOBAL_FUNCTIONS_H
#define EIGEN_GLOBAL_FUNCTIONS_H
#define EIGEN_ARRAY_DECLARE_GLOBAL_STD_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(); \
}
#define EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(NAME,FUNCTOR) \
\
template<typename Derived> \
struct NAME##_retval<ArrayBase<Derived> > \
{ \
typedef const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> type; \
}; \
template<typename Derived> \
struct NAME##_impl<ArrayBase<Derived> > \
{ \
static inline typename NAME##_retval<ArrayBase<Derived> >::type run(const Eigen::ArrayBase<Derived>& x) \
{ \
return x.derived(); \
} \
};
namespace std
{
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(real,scalar_real_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(imag,scalar_imag_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(sin,scalar_sin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(cos,scalar_cos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(asin,scalar_asin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(acos,scalar_acos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(tan,scalar_tan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(exp,scalar_exp_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(log,scalar_log_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(abs,scalar_abs_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(sqrt,scalar_sqrt_op)
template<typename Derived>
inline const Eigen::CwiseUnaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar>, const Derived>
pow(const Eigen::ArrayBase<Derived>& x, const typename Derived::Scalar& exponent) { \
return x.derived().pow(exponent); \
}
}
namespace Eigen
{
namespace internal
{
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(real,scalar_real_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(imag,scalar_imag_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(sin,scalar_sin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(cos,scalar_cos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(asin,scalar_asin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(acos,scalar_acos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(tan,scalar_tan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(exp,scalar_exp_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(log,scalar_log_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs,scalar_abs_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs2,scalar_abs2_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(sqrt,scalar_sqrt_op)
}
}
// TODO: cleanly disable those functions that are not supported on Array (internal::real_ref, internal::random, internal::isApprox...)
#endif // EIGEN_GLOBAL_FUNCTIONS_H

View File

@@ -1,8 +1,8 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -26,28 +26,15 @@
#ifndef EIGEN_IO_H
#define EIGEN_IO_H
enum { DontAlignCols = 1 };
enum { StreamPrecision = -1,
FullPrecision = -2 };
namespace internal {
template<typename Derived>
std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt);
}
enum { Raw, AlignCols };
/** \class IOFormat
* \ingroup Core_Module
*
* \brief Stores a set of parameters controlling the way matrices are printed
*
* List of available parameters:
* - \b precision number of digits for floating point values, or one of the special constants \c StreamPrecision and \c FullPrecision.
* The default is the special value \c StreamPrecision which means to use the
* stream's own precision setting, as set for instance using \c cout.precision(3). The other special value
* \c FullPrecision means that the number of digits will be computed to match the full precision of each floating-point
* type.
* - \b flags an OR-ed combination of flags, the default value is 0, the only currently available flag is \c DontAlignCols which
* allows to disable the alignment of columns, resulting in faster code.
* - \b precision number of digits for floating point values
* - \b flags can be either Raw (default) or AlignCols which aligns all the columns
* - \b coeffSeparator string printed between two coefficients of the same row
* - \b rowSeparator string printed between two rows
* - \b rowPrefix string printed at the beginning of each row
@@ -58,12 +45,12 @@ std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat&
* Example: \include IOFormat.cpp
* Output: \verbinclude IOFormat.out
*
* \sa DenseBase::format(), class WithFormat
* \sa MatrixBase::format(), class WithFormat
*/
struct IOFormat
{
/** Default contructor, see class IOFormat for the meaning of the parameters */
IOFormat(int _precision = StreamPrecision, int _flags = 0,
IOFormat(int _precision=4, int _flags=Raw,
const std::string& _coeffSeparator = " ",
const std::string& _rowSeparator = "\n", const std::string& _rowPrefix="", const std::string& _rowSuffix="",
const std::string& _matPrefix="", const std::string& _matSuffix="")
@@ -86,19 +73,18 @@ struct IOFormat
};
/** \class WithFormat
* \ingroup Core_Module
*
* \brief Pseudo expression providing matrix output with given format
*
* \param ExpressionType the type of the object on which IO stream operations are performed
*
* This class represents an expression with stream operators controlled by a given IOFormat.
* It is the return type of DenseBase::format()
* It is the return type of MatrixBase::format()
* and most of the time this is the only way it is used.
*
* See class IOFormat for some examples.
*
* \sa DenseBase::format(), class IOFormat
* \sa MatrixBase::format(), class IOFormat
*/
template<typename ExpressionType>
class WithFormat
@@ -111,7 +97,7 @@ class WithFormat
friend std::ostream & operator << (std::ostream & s, const WithFormat& wf)
{
return internal::print_matrix(s, wf.m_matrix.eval(), wf.m_format);
return ei_print_matrix(s, wf.m_matrix.eval(), wf.m_format);
}
protected:
@@ -128,100 +114,41 @@ class WithFormat
*/
template<typename Derived>
inline const WithFormat<Derived>
DenseBase<Derived>::format(const IOFormat& fmt) const
MatrixBase<Derived>::format(const IOFormat& fmt) const
{
return WithFormat<Derived>(derived(), fmt);
}
namespace internal {
template<typename Scalar, bool IsInteger>
struct significant_decimals_default_impl
{
typedef typename NumTraits<Scalar>::Real RealScalar;
static inline int run()
{
using std::ceil;
return cast<RealScalar,int>(ceil(-log(NumTraits<RealScalar>::epsilon())/log(RealScalar(10))));
}
};
template<typename Scalar>
struct significant_decimals_default_impl<Scalar, true>
{
static inline int run()
{
return 0;
}
};
template<typename Scalar>
struct significant_decimals_impl
: significant_decimals_default_impl<Scalar, NumTraits<Scalar>::IsInteger>
{};
/** \internal
* print the matrix \a _m to the output stream \a s using the output format \a fmt */
template<typename Derived>
std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt)
std::ostream & ei_print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt)
{
if(_m.size() == 0)
{
s << fmt.matPrefix << fmt.matSuffix;
return s;
}
const typename Derived::Nested m = _m;
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Index Index;
Index width = 0;
std::streamsize explicit_precision;
if(fmt.precision == StreamPrecision)
{
explicit_precision = 0;
}
else if(fmt.precision == FullPrecision)
{
if (NumTraits<Scalar>::IsInteger)
{
explicit_precision = 0;
}
else
{
explicit_precision = significant_decimals_impl<Scalar>::run();
}
}
else
{
explicit_precision = fmt.precision;
}
bool align_cols = !(fmt.flags & DontAlignCols);
if(align_cols)
int width = 0;
if (fmt.flags & AlignCols)
{
// compute the largest width
for(Index j = 1; j < m.cols(); ++j)
for(Index i = 0; i < m.rows(); ++i)
for(int j = 1; j < m.cols(); ++j)
for(int i = 0; i < m.rows(); ++i)
{
std::stringstream sstr;
if(explicit_precision) sstr.precision(explicit_precision);
sstr.precision(fmt.precision);
sstr << m.coeff(i,j);
width = std::max<Index>(width, Index(sstr.str().length()));
width = std::max<int>(width, int(sstr.str().length()));
}
}
std::streamsize old_precision = 0;
if(explicit_precision) old_precision = s.precision(explicit_precision);
s.precision(fmt.precision);
s << fmt.matPrefix;
for(Index i = 0; i < m.rows(); ++i)
for(int i = 0; i < m.rows(); ++i)
{
if (i)
s << fmt.rowSpacer;
s << fmt.rowPrefix;
if(width) s.width(width);
s << m.coeff(i, 0);
for(Index j = 1; j < m.cols(); ++j)
for(int j = 1; j < m.cols(); ++j)
{
s << fmt.coeffSeparator;
if (width) s.width(width);
@@ -232,29 +159,26 @@ std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat&
s << fmt.rowSeparator;
}
s << fmt.matSuffix;
if(explicit_precision) s.precision(old_precision);
return s;
}
} // end namespace internal
/** \relates DenseBase
/** \relates MatrixBase
*
* Outputs the matrix, to the given stream.
*
* If you wish to print the matrix with a format different than the default, use DenseBase::format().
* If you wish to print the matrix with a format different than the default, use MatrixBase::format().
*
* It is also possible to change the default format by defining EIGEN_DEFAULT_IO_FORMAT before including Eigen headers.
* If not defined, this will automatically be defined to Eigen::IOFormat(), that is the Eigen::IOFormat with default parameters.
*
* \sa DenseBase::format()
* \sa MatrixBase::format()
*/
template<typename Derived>
std::ostream & operator <<
(std::ostream & s,
const DenseBase<Derived> & m)
const MatrixBase<Derived> & m)
{
return internal::print_matrix(s, m.eval(), EIGEN_DEFAULT_IO_FORMAT);
return ei_print_matrix(s, m.eval(), EIGEN_DEFAULT_IO_FORMAT);
}
#endif // EIGEN_IO_H

View File

@@ -1,8 +1,8 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -27,177 +27,85 @@
#define EIGEN_MAP_H
/** \class Map
* \ingroup Core_Module
*
* \brief A matrix or vector expression mapping an existing array of data.
*
* \tparam PlainObjectType the equivalent matrix type of the mapped data
* \tparam MapOptions specifies whether the pointer is \c #Aligned, or \c #Unaligned.
* 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.
* The type passed here must be a specialization of the Stride template, see examples below.
* \param MatrixType the equivalent matrix type of the mapped data
* \param _PacketAccess allows to enforce aligned loads and stores if set to ForceAligned.
* The default is AsRequested. This parameter is internaly used by Eigen
* in expressions such as \code Map<...>(...) += other; \endcode and most
* of the time this is the only way it is used.
*
* This class represents a matrix or vector expression mapping an existing array of data.
* It can be used to let Eigen interface without any overhead with non-Eigen data structures,
* such as plain C arrays or structures from other libraries. By default, it assumes that the
* data is laid out contiguously in memory. You can however override this by explicitly specifying
* inner and outer strides.
* such as plain C arrays or structures from other libraries.
*
* Here's an example of simply mapping a contiguous array as a \ref TopicStorageOrders "column-major" matrix:
* \include Map_simple.cpp
* Output: \verbinclude Map_simple.out
* This class is the return type of Matrix::Map() but can also be used directly.
*
* If you need to map non-contiguous arrays, you can do so by specifying strides:
*
* Here's an example of mapping an array as a vector, specifying an inner stride, that is, the pointer
* increment between two consecutive coefficients. Here, we're specifying the inner stride as a compile-time
* fixed value.
* \include Map_inner_stride.cpp
* Output: \verbinclude Map_inner_stride.out
*
* Here's an example of mapping an array while specifying an outer stride. Here, since we're mapping
* as a column-major matrix, 'outer stride' means the pointer increment between two consecutive columns.
* Here, we're specifying the outer stride as a runtime parameter. Note that here \c OuterStride<> is
* a short version of \c OuterStride<Dynamic> because the default template parameter of OuterStride
* is \c Dynamic
* \include Map_outer_stride.cpp
* Output: \verbinclude Map_outer_stride.out
*
* For more details and for an example of specifying both an inner and an outer stride, see class Stride.
*
* \b Tip: to change the array of data mapped by a Map object, you can use the C++
* placement new syntax:
*
* Example: \include Map_placement_new.cpp
* Output: \verbinclude Map_placement_new.out
*
* This class is the return type of PlainObjectBase::Map() but can also be used directly.
*
* \sa PlainObjectBase::Map(), \ref TopicStorageOrders
* \sa Matrix::Map()
*/
namespace internal {
template<typename PlainObjectType, int MapOptions, typename StrideType>
struct traits<Map<PlainObjectType, MapOptions, StrideType> >
: public traits<PlainObjectType>
template<typename MatrixType, int _PacketAccess>
struct ei_traits<Map<MatrixType, _PacketAccess> > : public ei_traits<MatrixType>
{
typedef traits<PlainObjectType> TraitsBase;
typedef typename PlainObjectType::Index Index;
typedef typename PlainObjectType::Scalar Scalar;
enum {
InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
? int(PlainObjectType::InnerStrideAtCompileTime)
: int(StrideType::InnerStrideAtCompileTime),
OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
? int(PlainObjectType::OuterStrideAtCompileTime)
: int(StrideType::OuterStrideAtCompileTime),
HasNoInnerStride = InnerStrideAtCompileTime == 1,
HasNoOuterStride = StrideType::OuterStrideAtCompileTime == 0,
HasNoStride = HasNoInnerStride && HasNoOuterStride,
IsAligned = bool(EIGEN_ALIGN) && ((int(MapOptions)&Aligned)==Aligned),
IsDynamicSize = PlainObjectType::SizeAtCompileTime==Dynamic,
KeepsPacketAccess = bool(HasNoInnerStride)
&& ( bool(IsDynamicSize)
|| HasNoOuterStride
|| ( OuterStrideAtCompileTime!=Dynamic
&& ((static_cast<int>(sizeof(Scalar))*OuterStrideAtCompileTime)%16)==0 ) ),
Flags0 = TraitsBase::Flags & (~NestByRefBit),
Flags1 = IsAligned ? (int(Flags0) | AlignedBit) : (int(Flags0) & ~AlignedBit),
Flags2 = (bool(HasNoStride) || bool(PlainObjectType::IsVectorAtCompileTime))
? int(Flags1) : int(Flags1 & ~LinearAccessBit),
Flags3 = is_lvalue<PlainObjectType>::value ? int(Flags2) : (int(Flags2) & ~LvalueBit),
Flags = KeepsPacketAccess ? int(Flags3) : (int(Flags3) & ~PacketAccessBit)
PacketAccess = _PacketAccess,
Flags = ei_traits<MatrixType>::Flags & ~AlignedBit
};
private:
enum { Options }; // Expressions don't have Options
typedef typename ei_meta_if<int(PacketAccess)==ForceAligned,
Map<MatrixType, _PacketAccess>&,
Map<MatrixType, ForceAligned> >::ret AlignedDerivedType;
};
}
template<typename PlainObjectType, int MapOptions, typename StrideType> class Map
: public MapBase<Map<PlainObjectType, MapOptions, StrideType> >
template<typename MatrixType, int PacketAccess> class Map
: public MapBase<Map<MatrixType, PacketAccess> >
{
public:
typedef MapBase<Map> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Map)
_EIGEN_GENERIC_PUBLIC_INTERFACE(Map, MapBase<Map>)
typedef typename ei_traits<Map>::AlignedDerivedType AlignedDerivedType;
typedef typename Base::PointerType PointerType;
#if EIGEN2_SUPPORT_STAGE <= STAGE30_FULL_EIGEN3_API
typedef const Scalar* PointerArgType;
inline PointerType cast_to_pointer_type(PointerArgType ptr) { return const_cast<PointerType>(ptr); }
#else
typedef PointerType PointerArgType;
inline PointerType cast_to_pointer_type(PointerArgType ptr) { return ptr; }
#endif
inline int stride() const { return this->innerSize(); }
inline Index innerStride() const
AlignedDerivedType _convertToForceAligned()
{
return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1;
return Map<MatrixType,ForceAligned>(Base::m_data, Base::m_rows.value(), Base::m_cols.value());
}
inline Index outerStride() const
inline Map(const Scalar* data) : Base(data) {}
inline Map(const Scalar* data, int size) : Base(data, size) {}
inline Map(const Scalar* data, int rows, int cols) : Base(data, rows, cols) {}
inline void resize(int rows, int cols)
{
return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
: IsVectorAtCompileTime ? this->size()
: int(Flags)&RowMajorBit ? this->cols()
: this->rows();
EIGEN_ONLY_USED_FOR_DEBUG(rows);
EIGEN_ONLY_USED_FOR_DEBUG(cols);
ei_assert(rows == this->rows());
ei_assert(cols == this->cols());
}
/** Constructor in the fixed-size case.
*
* \param data pointer to the array to map
* \param stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType data, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(data)), m_stride(stride)
inline void resize(int size)
{
PlainObjectType::Base::_check_template_params();
}
/** Constructor in the dynamic-size vector case.
*
* \param data pointer to the array to map
* \param size the size of the vector expression
* \param stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType data, Index size, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(data), size), m_stride(stride)
{
PlainObjectType::Base::_check_template_params();
}
/** Constructor in the dynamic-size matrix case.
*
* \param data pointer to the array to map
* \param rows the number of rows of the matrix expression
* \param cols the number of columns of the matrix expression
* \param stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType data, Index rows, Index cols, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(data), rows, cols), m_stride(stride)
{
PlainObjectType::Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_ONLY(MatrixType)
EIGEN_ONLY_USED_FOR_DEBUG(size);
ei_assert(size == this->size());
}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map)
protected:
StrideType m_stride;
};
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
inline Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>
::Array(const Scalar *data)
{
this->_set_noalias(Eigen::Map<const Array>(data));
}
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
inline Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>
/** Constructor copying an existing array of data.
* Only for fixed-size matrices and vectors.
* \param data The array of data to copy
*
* \sa Matrix::Map(const Scalar *)
*/
template<typename _Scalar, int _Rows, int _Cols, int _StorageOrder, int _MaxRows, int _MaxCols>
inline Matrix<_Scalar, _Rows, _Cols, _StorageOrder, _MaxRows, _MaxCols>
::Matrix(const Scalar *data)
{
this->_set_noalias(Eigen::Map<const Matrix>(data));
_set_noalias(Eigen::Map<Matrix>(data));
}
#endif // EIGEN_MAP_H

View File

@@ -1,8 +1,8 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -26,230 +26,177 @@
#ifndef EIGEN_MAPBASE_H
#define EIGEN_MAPBASE_H
#define EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) \
EIGEN_STATIC_ASSERT((int(internal::traits<Derived>::Flags) & LinearAccessBit) || Derived::IsVectorAtCompileTime, \
YOU_ARE_TRYING_TO_USE_AN_INDEX_BASED_ACCESSOR_ON_AN_EXPRESSION_THAT_DOES_NOT_SUPPORT_THAT)
/** \class MapBase
* \ingroup Core_Module
*
* \brief Base class for Map and Block expression with direct access
*
* Expression classes inheriting MapBase must define the constant \c PacketAccess,
* and type \c AlignedDerivedType in their respective ei_traits<> specialization structure.
* The value of \c PacketAccess can be either:
* - \b ForceAligned which enforces both aligned loads and stores
* - \b AsRequested which is the default behavior
* The type \c AlignedDerivedType should correspond to the equivalent expression type
* with \c PacketAccess being \c ForceAligned.
*
* \sa class Map, class Block
*/
template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
: public internal::dense_xpr_base<Derived>::type
template<typename Derived> class MapBase
: public MatrixBase<Derived>
{
public:
typedef typename internal::dense_xpr_base<Derived>::type Base;
typedef MatrixBase<Derived> Base;
enum {
RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
IsRowMajor = (int(ei_traits<Derived>::Flags) & RowMajorBit) ? 1 : 0,
PacketAccess = ei_traits<Derived>::PacketAccess,
RowsAtCompileTime = ei_traits<Derived>::RowsAtCompileTime,
ColsAtCompileTime = ei_traits<Derived>::ColsAtCompileTime,
SizeAtCompileTime = Base::SizeAtCompileTime
};
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 typename internal::conditional<
bool(internal::is_lvalue<Derived>::value),
Scalar *,
const Scalar *>::type
PointerType;
typedef typename ei_traits<Derived>::AlignedDerivedType AlignedDerivedType;
typedef typename ei_traits<Derived>::Scalar Scalar;
typedef typename Base::PacketScalar PacketScalar;
using Base::derived;
// using Base::RowsAtCompileTime;
// using Base::ColsAtCompileTime;
// using Base::SizeAtCompileTime;
using Base::MaxRowsAtCompileTime;
using Base::MaxColsAtCompileTime;
using Base::MaxSizeAtCompileTime;
using Base::IsVectorAtCompileTime;
using Base::Flags;
using Base::IsRowMajor;
using Base::rows;
using Base::cols;
using Base::size;
using Base::coeff;
using Base::coeffRef;
using Base::lazyAssign;
using Base::eval;
inline int rows() const { return m_rows.value(); }
inline int cols() const { return m_cols.value(); }
using Base::innerStride;
using Base::outerStride;
using Base::rowStride;
using Base::colStride;
// bug 217 - compile error on ICC 11.1
using Base::operator=;
typedef typename Base::CoeffReturnType CoeffReturnType;
inline Index rows() const { return m_rows.value(); }
inline Index cols() const { return m_cols.value(); }
/** Returns a pointer to the first coefficient of the matrix or vector.
*
* \note When addressing this data, make sure to honor the strides returned by innerStride() and outerStride().
*
* \sa innerStride(), outerStride()
*/
inline int stride() const { return derived().stride(); }
inline const Scalar* data() const { return m_data; }
inline const Scalar& coeff(Index row, Index col) const
template<bool IsForceAligned,typename Dummy> struct force_aligned_impl {
static AlignedDerivedType run(MapBase& a) { return a.derived(); }
};
template<typename Dummy> struct force_aligned_impl<false,Dummy> {
static AlignedDerivedType run(MapBase& a) { return a.derived()._convertToForceAligned(); }
};
/** \returns an expression equivalent to \c *this but having the \c PacketAccess constant
* set to \c ForceAligned. Must be reimplemented by the derived class. */
AlignedDerivedType forceAligned()
{
return m_data[col * colStride() + row * rowStride()];
return force_aligned_impl<int(PacketAccess)==int(ForceAligned),Derived>::run(*this);
}
inline const Scalar& coeff(Index index) const
inline const Scalar& coeff(int row, int col) const
{
EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
return m_data[index * innerStride()];
if(IsRowMajor)
return m_data[col + row * stride()];
else // column-major
return m_data[row + col * stride()];
}
inline const Scalar& coeffRef(Index row, Index col) const
inline Scalar& coeffRef(int row, int col)
{
return this->m_data[col * colStride() + row * rowStride()];
if(IsRowMajor)
return const_cast<Scalar*>(m_data)[col + row * stride()];
else // column-major
return const_cast<Scalar*>(m_data)[row + col * stride()];
}
inline const Scalar& coeffRef(Index index) const
inline const Scalar coeff(int index) const
{
EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
return this->m_data[index * innerStride()];
ei_assert(Derived::IsVectorAtCompileTime || (ei_traits<Derived>::Flags & LinearAccessBit));
if ( ((RowsAtCompileTime == 1) == IsRowMajor) )
return m_data[index];
else
return m_data[index*stride()];
}
inline Scalar& coeffRef(int index)
{
ei_assert(Derived::IsVectorAtCompileTime || (ei_traits<Derived>::Flags & LinearAccessBit));
if ( ((RowsAtCompileTime == 1) == IsRowMajor) )
return const_cast<Scalar*>(m_data)[index];
else
return const_cast<Scalar*>(m_data)[index*stride()];
}
template<int LoadMode>
inline PacketScalar packet(Index row, Index col) const
inline PacketScalar packet(int row, int col) const
{
return internal::ploadt<PacketScalar, LoadMode>
(m_data + (col * colStride() + row * rowStride()));
return ei_ploadt<Scalar, int(PacketAccess) == ForceAligned ? Aligned : LoadMode>
(m_data + (IsRowMajor ? col + row * stride()
: row + col * stride()));
}
template<int LoadMode>
inline PacketScalar packet(Index index) const
inline PacketScalar packet(int index) const
{
EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
return internal::ploadt<PacketScalar, LoadMode>(m_data + index * innerStride());
return ei_ploadt<Scalar, int(PacketAccess) == ForceAligned ? Aligned : LoadMode>(m_data + index);
}
inline MapBase(PointerType data) : m_data(data), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
template<int StoreMode>
inline void writePacket(int row, int col, const PacketScalar& x)
{
ei_pstoret<Scalar, PacketScalar, int(PacketAccess) == ForceAligned ? Aligned : StoreMode>
(const_cast<Scalar*>(m_data) + (IsRowMajor ? col + row * stride()
: row + col * stride()), x);
}
template<int StoreMode>
inline void writePacket(int index, const PacketScalar& x)
{
ei_pstoret<Scalar, PacketScalar, int(PacketAccess) == ForceAligned ? Aligned : StoreMode>
(const_cast<Scalar*>(m_data) + index, x);
}
inline MapBase(const Scalar* data) : m_data(data), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
checkSanity();
}
inline MapBase(PointerType data, Index size)
inline MapBase(const Scalar* data, int size)
: m_data(data),
m_rows(RowsAtCompileTime == Dynamic ? size : Index(RowsAtCompileTime)),
m_cols(ColsAtCompileTime == Dynamic ? size : Index(ColsAtCompileTime))
m_rows(RowsAtCompileTime == Dynamic ? size : RowsAtCompileTime),
m_cols(ColsAtCompileTime == Dynamic ? size : ColsAtCompileTime)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
eigen_assert(size >= 0);
eigen_assert(data == 0 || SizeAtCompileTime == Dynamic || SizeAtCompileTime == size);
checkSanity();
ei_assert(size > 0 || data == 0);
ei_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == size);
}
inline MapBase(PointerType data, Index rows, Index cols)
inline MapBase(const Scalar* data, int rows, int cols)
: m_data(data), m_rows(rows), m_cols(cols)
{
eigen_assert( (data == 0)
|| ( rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)));
checkSanity();
ei_assert( (data == 0)
|| ( rows > 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols > 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)));
}
protected:
void checkSanity() const
{
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(internal::traits<Derived>::Flags&PacketAccessBit,
internal::inner_stride_at_compile_time<Derived>::ret==1),
PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1);
eigen_assert(EIGEN_IMPLIES(internal::traits<Derived>::Flags&AlignedBit, (size_t(m_data) % 16) == 0)
&& "data is not aligned");
}
PointerType m_data;
const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows;
const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols;
};
template<typename Derived> class MapBase<Derived, WriteAccessors>
: public MapBase<Derived, ReadOnlyAccessors>
{
public:
typedef MapBase<Derived, ReadOnlyAccessors> Base;
typedef typename Base::Scalar Scalar;
typedef typename Base::PacketScalar PacketScalar;
typedef typename Base::Index Index;
typedef typename Base::PointerType PointerType;
using Base::derived;
using Base::rows;
using Base::cols;
using Base::size;
using Base::coeff;
using Base::coeffRef;
using Base::innerStride;
using Base::outerStride;
using Base::rowStride;
using Base::colStride;
typedef typename internal::conditional<
internal::is_lvalue<Derived>::value,
Scalar,
const Scalar
>::type ScalarWithConstIfNotLvalue;
inline const Scalar* data() const { return this->m_data; }
inline ScalarWithConstIfNotLvalue* data() { return this->m_data; } // no const-cast here so non-const-correct code will give a compile error
inline ScalarWithConstIfNotLvalue& coeffRef(Index row, Index col)
{
return this->m_data[col * colStride() + row * rowStride()];
}
inline ScalarWithConstIfNotLvalue& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
return this->m_data[index * innerStride()];
}
template<int StoreMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
{
internal::pstoret<Scalar, PacketScalar, StoreMode>
(this->m_data + (col * colStride() + row * rowStride()), x);
}
template<int StoreMode>
inline void writePacket(Index index, const PacketScalar& x)
{
EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
internal::pstoret<Scalar, PacketScalar, StoreMode>
(this->m_data + index * innerStride(), x);
}
explicit inline MapBase(PointerType data) : Base(data) {}
inline MapBase(PointerType data, Index size) : Base(data, size) {}
inline MapBase(PointerType data, Index rows, Index cols) : Base(data, rows, cols) {}
Derived& operator=(const MapBase& other)
{
Base::Base::operator=(other);
return derived();
return Base::operator=(other);
}
using Base::Base::operator=;
template<typename OtherDerived>
Derived& operator=(const MatrixBase<OtherDerived>& other)
{
return Base::operator=(other);
}
using Base::operator*=;
template<typename OtherDerived>
Derived& operator+=(const MatrixBase<OtherDerived>& other)
{ return derived() = forceAligned() + other; }
template<typename OtherDerived>
Derived& operator-=(const MatrixBase<OtherDerived>& other)
{ return derived() = forceAligned() - other; }
Derived& operator*=(const Scalar& other)
{ return derived() = forceAligned() * other; }
Derived& operator/=(const Scalar& other)
{ return derived() = forceAligned() / other; }
protected:
const Scalar* EIGEN_RESTRICT m_data;
const ei_int_if_dynamic<RowsAtCompileTime> m_rows;
const ei_int_if_dynamic<ColsAtCompileTime> m_cols;
};
#endif // EIGEN_MAPBASE_H

File diff suppressed because it is too large Load Diff

View File

@@ -1,8 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -26,8 +25,8 @@
#ifndef EIGEN_MATRIX_H
#define EIGEN_MATRIX_H
/** \class Matrix
* \ingroup Core_Module
*
* \brief The matrix class, also used for vectors and row-vectors
*
@@ -37,18 +36,17 @@
* 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 _Rows Number of rows, or \b Dynamic
* \tparam _Cols Number of columns, or \b Dynamic
* \param _Scalar Numeric type, i.e. float, double, int
* \param _Rows Number of rows, or \b Dynamic
* \param _Cols Number of columns, or \b Dynamic
*
* 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
* \b #AutoAlign or \b #DontAlign.
* The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required
* \param _Options A combination of either \b RowMajor or \b ColMajor, and of either
* \b AutoAlign or \b DontAlign.
* The former controls 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.
* \tparam _MaxRows Maximum number of rows. Defaults to \a _Rows (\ref maxrows "note").
* \tparam _MaxCols Maximum number of columns. Defaults to \a _Cols (\ref maxrows "note").
* \param _MaxRows Maximum number of rows. Defaults to \a _Rows (\ref maxrows "note").
* \param _MaxCols Maximum number of columns. Defaults to \a _Cols (\ref maxrows "note").
*
* Eigen provides a number of typedefs covering the usual cases. Here are some examples:
*
@@ -59,9 +57,6 @@
* \li \c MatrixXf is a dynamic-size matrix of floats (\c Matrix<float, Dynamic, Dynamic>)
* \li \c VectorXf is a dynamic-size vector of floats (\c Matrix<float, Dynamic, 1>)
*
* \li \c Matrix2Xf is a partially fixed-size (dynamic-size) matrix of floats (\c Matrix<float, 2, Dynamic>)
* \li \c MatrixX3d is a partially dynamic-size (fixed-size) matrix of double (\c Matrix<double, Dynamic, 3>)
*
* See \link matrixtypedefs this page \endlink for a complete list of predefined \em %Matrix and \em Vector typedefs.
*
* You can access elements of vectors and matrices using normal subscripting:
@@ -79,9 +74,6 @@
* m(0, 3) = 3;
* \endcode
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_MATRIX_PLUGIN.
*
* <i><b>Some notes:</b></i>
*
* <dl>
@@ -110,67 +102,154 @@
* are the dimensions of the original matrix, while _Rows and _Cols are Dynamic.</dd>
* </dl>
*
* \see MatrixBase for the majority of the API methods for matrices, \ref TopicClassHierarchy,
* \ref TopicStorageOrders
* \see MatrixBase for the majority of the API methods for matrices
*/
namespace internal {
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
struct ei_traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
{
typedef _Scalar Scalar;
typedef Dense StorageKind;
typedef DenseIndex Index;
typedef MatrixXpr XprKind;
enum {
RowsAtCompileTime = _Rows,
ColsAtCompileTime = _Cols,
MaxRowsAtCompileTime = _MaxRows,
MaxColsAtCompileTime = _MaxCols,
Flags = compute_matrix_flags<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::ret,
CoeffReadCost = NumTraits<Scalar>::ReadCost,
Options = _Options,
InnerStrideAtCompileTime = 1,
OuterStrideAtCompileTime = (Options&RowMajor) ? ColsAtCompileTime : RowsAtCompileTime
Flags = ei_compute_matrix_flags<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::ret,
CoeffReadCost = NumTraits<Scalar>::ReadCost
};
};
}
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
class Matrix
: public PlainObjectBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
: public MatrixBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
{
public:
/** \brief Base class typedef.
* \sa PlainObjectBase
*/
typedef PlainObjectBase<Matrix> Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(Matrix)
enum { Options = _Options };
friend class Eigen::Map<Matrix, Unaligned>;
typedef class Eigen::Map<Matrix, Unaligned> UnalignedMapType;
friend class Eigen::Map<Matrix, Aligned>;
typedef class Eigen::Map<Matrix, Aligned> AlignedMapType;
EIGEN_DENSE_PUBLIC_INTERFACE(Matrix)
protected:
ei_matrix_storage<Scalar, MaxSizeAtCompileTime, RowsAtCompileTime, ColsAtCompileTime, Options> m_storage;
typedef typename Base::PlainObject PlainObject;
public:
enum { NeedsToAlign = (Options&AutoAlign) == AutoAlign
&& SizeAtCompileTime!=Dynamic && ((sizeof(Scalar)*SizeAtCompileTime)%16)==0 };
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
Base& base() { return *static_cast<Base*>(this); }
const Base& base() const { return *static_cast<const Base*>(this); }
using Base::base;
using Base::coeffRef;
EIGEN_STRONG_INLINE int rows() const { return m_storage.rows(); }
EIGEN_STRONG_INLINE int cols() const { return m_storage.cols(); }
/**
* \brief Assigns matrices to each other.
*
* \note This is a special case of the templated operator=. Its purpose is
* to prevent a default operator= from hiding the templated operator=.
*
* \callgraph
*/
EIGEN_STRONG_INLINE Matrix& operator=(const Matrix& other)
EIGEN_STRONG_INLINE int stride(void) const
{
return Base::_set(other);
if(Flags & RowMajorBit)
return m_storage.cols();
else
return m_storage.rows();
}
/** \internal
* \brief Copies the value of the expression \a other into \c *this with automatic resizing.
EIGEN_STRONG_INLINE const Scalar& coeff(int row, int col) const
{
if(Flags & RowMajorBit)
return m_storage.data()[col + row * m_storage.cols()];
else // column-major
return m_storage.data()[row + col * m_storage.rows()];
}
EIGEN_STRONG_INLINE const Scalar& coeff(int index) const
{
return m_storage.data()[index];
}
EIGEN_STRONG_INLINE Scalar& coeffRef(int row, int col)
{
if(Flags & RowMajorBit)
return m_storage.data()[col + row * m_storage.cols()];
else // column-major
return m_storage.data()[row + col * m_storage.rows()];
}
EIGEN_STRONG_INLINE Scalar& coeffRef(int index)
{
return m_storage.data()[index];
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(int row, int col) const
{
return ei_ploadt<Scalar, LoadMode>
(m_storage.data() + (Flags & RowMajorBit
? col + row * m_storage.cols()
: row + col * m_storage.rows()));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(int index) const
{
return ei_ploadt<Scalar, LoadMode>(m_storage.data() + index);
}
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket(int row, int col, const PacketScalar& x)
{
ei_pstoret<Scalar, PacketScalar, StoreMode>
(m_storage.data() + (Flags & RowMajorBit
? col + row * m_storage.cols()
: row + col * m_storage.rows()), x);
}
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket(int index, const PacketScalar& x)
{
ei_pstoret<Scalar, PacketScalar, StoreMode>(m_storage.data() + index, x);
}
/** \returns a const pointer to the data array of this matrix */
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()
{ return m_storage.data(); }
/** Resizes \c *this to a \a rows x \a cols matrix.
*
* Makes sense for dynamic-size matrices only.
*
* If the current number of coefficients of \c *this exactly matches the
* product \a rows * \a cols, then no memory allocation is performed and
* the current values are left unchanged. In all other cases, including
* shrinking, the data is reallocated and all previous values are lost.
*
* \sa resize(int) for vectors.
*/
inline void resize(int rows, int cols)
{
ei_assert((MaxRowsAtCompileTime == Dynamic || MaxRowsAtCompileTime >= rows)
&& (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& (MaxColsAtCompileTime == Dynamic || MaxColsAtCompileTime >= cols)
&& (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
m_storage.resize(rows * cols, rows, cols);
}
/** Resizes \c *this to a vector of length \a size
*
* \sa resize(int,int) for the details.
*/
inline void resize(int size)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Matrix)
if(RowsAtCompileTime == 1)
m_storage.resize(size, 1, size);
else
m_storage.resize(size, size, 1);
}
/** 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),
* it will be initialized.
@@ -182,28 +261,23 @@ class Matrix
template<typename OtherDerived>
EIGEN_STRONG_INLINE Matrix& operator=(const MatrixBase<OtherDerived>& other)
{
return Base::_set(other);
return _set(other);
}
/* Here, doxygen failed to copy the brief information when using \copydoc */
/**
* \brief Copies the generic expression \a other into *this.
* \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other)
/** This is a special case of the templated operator=. Its purpose is to
* prevent a default operator= from hiding the templated operator=.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE Matrix& operator=(const EigenBase<OtherDerived> &other)
EIGEN_STRONG_INLINE Matrix& operator=(const Matrix& other)
{
return Base::operator=(other);
return _set(other);
}
template<typename OtherDerived>
EIGEN_STRONG_INLINE Matrix& operator=(const ReturnByValue<OtherDerived>& func)
{
return Base::operator=(func);
}
EIGEN_INHERIT_ASSIGNMENT_OPERATOR(Matrix, +=)
EIGEN_INHERIT_ASSIGNMENT_OPERATOR(Matrix, -=)
EIGEN_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Matrix, *=)
EIGEN_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Matrix, /=)
/** \brief Default constructor.
/** Default constructor.
*
* For fixed-size matrices, does nothing.
*
@@ -211,66 +285,89 @@ class Matrix
* is called a null matrix. This constructor is the unique way to create null matrices: resizing
* a matrix to 0 is not supported.
*
* \sa resize(Index,Index)
* \sa resize(int,int)
*/
EIGEN_STRONG_INLINE explicit Matrix() : Base()
EIGEN_STRONG_INLINE explicit Matrix() : m_storage()
{
Base::_check_template_params();
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
_check_template_params();
}
// FIXME is it still needed
Matrix(internal::constructor_without_unaligned_array_assert)
: Base(internal::constructor_without_unaligned_array_assert())
{ Base::_check_template_params(); EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED }
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal */
Matrix(ei_constructor_without_unaligned_array_assert)
: m_storage(ei_constructor_without_unaligned_array_assert())
{}
#endif
/** \brief Constructs a vector or row-vector with given dimension. \only_for_vectors
/** Constructs a vector or row-vector with given dimension. \only_for_vectors
*
* Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
* it is redundant to pass the dimension here, so it makes more sense to use the default
* constructor Matrix() instead.
*/
EIGEN_STRONG_INLINE explicit Matrix(Index dim)
: Base(dim, RowsAtCompileTime == 1 ? 1 : dim, ColsAtCompileTime == 1 ? 1 : dim)
EIGEN_STRONG_INLINE explicit Matrix(int dim)
: m_storage(dim, RowsAtCompileTime == 1 ? 1 : dim, ColsAtCompileTime == 1 ? 1 : dim)
{
Base::_check_template_params();
_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Matrix)
eigen_assert(dim >= 0);
eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim);
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
ei_assert(dim > 0);
ei_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim);
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename T0, typename T1>
EIGEN_STRONG_INLINE Matrix(const T0& x, const T1& y)
{
Base::_check_template_params();
Base::template _init2<T0,T1>(x, y);
}
#else
/** \brief Constructs an uninitialized matrix with \a rows rows and \a cols columns.
/** This constructor has two very different behaviors, depending on the type of *this.
*
* This is useful for dynamic-size matrices. For fixed-size matrices,
* it is redundant to pass these parameters, so one should use the default constructor
* Matrix() instead. */
Matrix(Index rows, Index cols);
/** \brief Constructs an initialized 2D vector with given coefficients */
Matrix(const Scalar& x, const Scalar& y);
#endif
/** \brief Constructs an initialized 3D vector with given coefficients */
* \li When Matrix is a fixed-size vector type of size 2, this constructor constructs
* an initialized vector. The parameters \a x, \a y are copied into the first and second
* coords of the vector respectively.
* \li Otherwise, this constructor constructs an uninitialized matrix with \a x rows and
* \a y columns. This is useful for dynamic-size matrices. For fixed-size matrices,
* it is redundant to pass these parameters, so one should use the default constructor
* Matrix() instead.
*/
EIGEN_STRONG_INLINE Matrix(int x, int y) : m_storage(x*y, x, y)
{
_check_template_params();
if((RowsAtCompileTime == 1 && ColsAtCompileTime == 2)
|| (RowsAtCompileTime == 2 && ColsAtCompileTime == 1))
{
m_storage.data()[0] = Scalar(x);
m_storage.data()[1] = Scalar(y);
}
else
{
ei_assert(x > 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == x)
&& y > 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == y));
}
}
/** constructs an initialized 2D vector with given coefficients */
EIGEN_STRONG_INLINE Matrix(const float& x, const float& y)
{
_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 2)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
}
/** constructs an initialized 2D vector with given coefficients */
EIGEN_STRONG_INLINE Matrix(const double& x, const double& y)
{
_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 2)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
}
/** constructs an initialized 3D vector with given coefficients */
EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z)
{
Base::_check_template_params();
_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 3)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
m_storage.data()[2] = z;
}
/** \brief Constructs an initialized 4D vector with given coefficients */
/** constructs an initialized 4D vector with given coefficients */
EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w)
{
Base::_check_template_params();
_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 4)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
@@ -280,86 +377,202 @@ class Matrix
explicit Matrix(const Scalar *data);
/** \brief Constructor copying the value of the expression \a other */
/** Constructor copying the value of the expression \a other */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Matrix(const MatrixBase<OtherDerived>& other)
: Base(other.rows() * other.cols(), other.rows(), other.cols())
: m_storage(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);
_check_template_params();
_set_noalias(other);
}
/** \brief Copy constructor */
/** Copy constructor */
EIGEN_STRONG_INLINE Matrix(const Matrix& other)
: Base(other.rows() * other.cols(), other.rows(), other.cols())
: Base(), m_storage(other.rows() * other.cols(), other.rows(), other.cols())
{
Base::_check_template_params();
Base::_set_noalias(other);
}
/** \brief Copy constructor with in-place evaluation */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Matrix(const ReturnByValue<OtherDerived>& other)
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
other.evalTo(*this);
_check_template_params();
_set_noalias(other);
}
/** Destructor */
inline ~Matrix() {}
/** \brief Copy constructor for generic expressions.
* \sa MatrixBase::operator=(const EigenBase<OtherDerived>&)
/** Override MatrixBase::swap() since for dynamic-sized matrices of same type it is enough to swap the
* data pointers.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE Matrix(const EigenBase<OtherDerived> &other)
: Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
// FIXME/CHECK: isn't *this = other.derived() more efficient. it allows to
// go for pure _set() implementations, right?
*this = other;
}
void swap(const MatrixBase<OtherDerived>& other);
/** \internal
* \brief Override MatrixBase::swap() since for dynamic-sized matrices
* of same type it is enough to swap the data pointers.
/** \name Map
* These are convenience functions returning Map objects. The Map() static functions return unaligned Map objects,
* while the AlignedMap() functions return aligned Map objects and thus should be called only with 16-byte-aligned
* \a data pointers.
*
* \see class Map
*/
template<typename OtherDerived>
void swap(MatrixBase<OtherDerived> const & other)
{ this->_swap(other.derived()); }
//@{
inline static const UnalignedMapType Map(const Scalar* data)
{ return UnalignedMapType(data); }
inline static UnalignedMapType Map(Scalar* data)
{ return UnalignedMapType(data); }
inline static const UnalignedMapType Map(const Scalar* data, int size)
{ return UnalignedMapType(data, size); }
inline static UnalignedMapType Map(Scalar* data, int size)
{ return UnalignedMapType(data, size); }
inline static const UnalignedMapType Map(const Scalar* data, int rows, int cols)
{ return UnalignedMapType(data, rows, cols); }
inline static UnalignedMapType Map(Scalar* data, int rows, int cols)
{ return UnalignedMapType(data, rows, cols); }
inline Index innerStride() const { return 1; }
inline Index outerStride() const { return this->innerSize(); }
inline static const AlignedMapType MapAligned(const Scalar* data)
{ return AlignedMapType(data); }
inline static AlignedMapType MapAligned(Scalar* data)
{ return AlignedMapType(data); }
inline static const AlignedMapType MapAligned(const Scalar* data, int size)
{ return AlignedMapType(data, size); }
inline static AlignedMapType MapAligned(Scalar* data, int size)
{ return AlignedMapType(data, size); }
inline static const AlignedMapType MapAligned(const Scalar* data, int rows, int cols)
{ return AlignedMapType(data, rows, cols); }
inline static AlignedMapType MapAligned(Scalar* data, int rows, int cols)
{ return AlignedMapType(data, rows, cols); }
//@}
/////////// Geometry module ///////////
using Base::setConstant;
Matrix& setConstant(int size, const Scalar& value);
Matrix& setConstant(int rows, int cols, const Scalar& value);
using Base::setZero;
Matrix& setZero(int size);
Matrix& setZero(int rows, int cols);
using Base::setOnes;
Matrix& setOnes(int size);
Matrix& setOnes(int rows, int cols);
using Base::setRandom;
Matrix& setRandom(int size);
Matrix& setRandom(int rows, int cols);
using Base::setIdentity;
Matrix& setIdentity(int rows, int cols);
/////////// Geometry module ///////////
template<typename OtherDerived>
explicit Matrix(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
template<typename OtherDerived>
Matrix& operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
explicit Matrix(const eigen2_RotationBase<OtherDerived,ColsAtCompileTime>& r);
template<typename OtherDerived>
Matrix& operator=(const eigen2_RotationBase<OtherDerived,ColsAtCompileTime>& r);
#endif
// allow to extend Matrix outside Eigen
#ifdef EIGEN_MATRIX_PLUGIN
#include EIGEN_MATRIX_PLUGIN
#endif
protected:
template <typename Derived, typename OtherDerived, bool IsVector>
friend struct internal::conservative_resize_like_impl;
private:
/** \internal Resizes *this in preparation for assigning \a other to it.
* Takes care of doing all the checking that's needed.
*
* Note that copying a row-vector into a vector (and conversely) is allowed.
* The resizing, if any, is then done in the appropriate way so that row-vectors
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE void _resize_to_match(const MatrixBase<OtherDerived>& other)
{
if(RowsAtCompileTime == 1)
{
ei_assert(other.isVector());
resize(1, other.size());
}
else if(ColsAtCompileTime == 1)
{
ei_assert(other.isVector());
resize(other.size(), 1);
}
else resize(other.rows(), other.cols());
}
using Base::m_storage;
/** \internal 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),
* it will be initialized.
*
* Note that copying a row-vector into a vector (and conversely) is allowed.
* The resizing, if any, is then done in the appropriate way so that row-vectors
* remain row-vectors and vectors remain vectors.
*
* \sa operator=(const MatrixBase<OtherDerived>&), _set_noalias()
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE Matrix& _set(const MatrixBase<OtherDerived>& other)
{
// this enum introduced to fix compilation with gcc 3.3
enum { cond = int(OtherDerived::Flags) & EvalBeforeAssigningBit };
_set_selector(other.derived(), typename ei_meta_if<bool(cond), ei_meta_true, ei_meta_false>::ret());
return *this;
}
template<typename OtherDerived>
EIGEN_STRONG_INLINE void _set_selector(const OtherDerived& other, const ei_meta_true&) { _set_noalias(other.eval()); }
template<typename OtherDerived>
EIGEN_STRONG_INLINE void _set_selector(const OtherDerived& other, const ei_meta_false&) { _set_noalias(other); }
/** \internal Like _set() but additionally makes the assumption that no aliasing effect can happen (which
* is the case when creating a new matrix) so one can enforce lazy evaluation.
*
* \sa operator=(const MatrixBase<OtherDerived>&), _set()
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE Matrix& _set_noalias(const MatrixBase<OtherDerived>& other)
{
_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 ei_assign_selector<Matrix,OtherDerived,false>::run(*this, other.derived());
}
static EIGEN_STRONG_INLINE void _check_template_params()
{
EIGEN_STATIC_ASSERT((_Rows > 0
&& _Cols > 0
&& _MaxRows <= _Rows
&& _MaxCols <= _Cols
&& (_Options & (AutoAlign|RowMajor)) == _Options),
INVALID_MATRIX_TEMPLATE_PARAMETERS)
}
template<typename MatrixType, typename OtherDerived, bool IsSameType, bool IsDynamicSize>
friend struct ei_matrix_swap_impl;
};
template<typename MatrixType, typename OtherDerived,
bool IsSameType = ei_is_same_type<MatrixType, OtherDerived>::ret,
bool IsDynamicSize = MatrixType::SizeAtCompileTime==Dynamic>
struct ei_matrix_swap_impl
{
static inline void run(MatrixType& matrix, MatrixBase<OtherDerived>& other)
{
matrix.base().swap(other);
}
};
template<typename MatrixType, typename OtherDerived>
struct ei_matrix_swap_impl<MatrixType, OtherDerived, true, true>
{
static inline void run(MatrixType& matrix, MatrixBase<OtherDerived>& other)
{
matrix.m_storage.swap(other.derived().m_storage);
}
};
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
template<typename OtherDerived>
inline void Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::swap(const MatrixBase<OtherDerived>& other)
{
ei_matrix_swap_impl<Matrix, OtherDerived>::run(*this, *const_cast<MatrixBase<OtherDerived>*>(&other));
}
/** \defgroup matrixtypedefs Global matrix typedefs
*
* \ingroup Core_Module
@@ -388,20 +601,11 @@ typedef Matrix<Type, Size, 1> Vector##SizeSuffix##TypeSuffix; \
/** \ingroup matrixtypedefs */ \
typedef Matrix<Type, 1, Size> RowVector##SizeSuffix##TypeSuffix;
#define EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, Size) \
/** \ingroup matrixtypedefs */ \
typedef Matrix<Type, Size, Dynamic> Matrix##Size##X##TypeSuffix; \
/** \ingroup matrixtypedefs */ \
typedef Matrix<Type, Dynamic, Size> Matrix##X##Size##TypeSuffix;
#define EIGEN_MAKE_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 2, 2) \
EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 3, 3) \
EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 4, 4) \
EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Dynamic, X) \
EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 2) \
EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 3) \
EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 4)
EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Dynamic, X)
EIGEN_MAKE_TYPEDEFS_ALL_SIZES(int, i)
EIGEN_MAKE_TYPEDEFS_ALL_SIZES(float, f)

View File

@@ -1,8 +1,8 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -27,18 +27,16 @@
#define EIGEN_MATRIXBASE_H
/** \class MatrixBase
* \ingroup Core_Module
*
* \brief Base class for all dense matrices, vectors, and expressions
* \brief Base class for all matrices, vectors, and expressions
*
* This class is the base that is inherited by all matrix, vector, and related expression
* types. Most of the Eigen API is contained in this class, and its base classes. Other important
* classes for the Eigen API are Matrix, and VectorwiseOp.
* This class is the base that is inherited by all matrix, vector, and expression
* types. Most of the Eigen API is contained in this class. Other important classes for
* the Eigen API are Matrix, Cwise, and PartialRedux.
*
* Note that some methods are defined in other modules such as the \ref LU_Module LU module
* for all functions related to matrix inversions.
* Note that some methods are defined in the \ref Array module.
*
* \tparam Derived is the derived type, e.g. a matrix type, or an expression, etc.
* \param Derived is the derived type, e.g. a matrix type, or an expression, etc.
*
* When writing a function taking Eigen objects as argument, if you want your function
* to take as argument any matrix, vector, or expression, just let it take a
@@ -53,469 +51,582 @@
}
* \endcode
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_MATRIXBASE_PLUGIN.
*
* \sa \ref TopicClassHierarchy
*/
template<typename Derived> class MatrixBase
: public DenseBase<Derived>
{
public:
#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>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
class InnerIterator;
typedef typename ei_traits<Derived>::Scalar Scalar;
typedef typename ei_packet_traits<Scalar>::type PacketScalar;
#endif // not EIGEN_PARSED_BY_DOXYGEN
enum {
RowsAtCompileTime = ei_traits<Derived>::RowsAtCompileTime,
/**< The number of rows at compile-time. This is just a copy of the value provided
* by the \a Derived type. If a value is not known at compile-time,
* it is set to the \a Dynamic constant.
* \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
ColsAtCompileTime = ei_traits<Derived>::ColsAtCompileTime,
/**< The number of columns at compile-time. This is just a copy of the value provided
* by the \a Derived type. If a value is not known at compile-time,
* it is set to the \a Dynamic constant.
* \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
SizeAtCompileTime = (ei_size_at_compile_time<ei_traits<Derived>::RowsAtCompileTime,
ei_traits<Derived>::ColsAtCompileTime>::ret),
/**< This is equal to the number of coefficients, i.e. the number of
* rows times the number of columns, or to \a Dynamic if this is not
* known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
MaxRowsAtCompileTime = ei_traits<Derived>::MaxRowsAtCompileTime,
/**< This value is equal to the maximum possible number of rows that this expression
* might have. If this expression might have an arbitrarily high number of rows,
* this value is set to \a Dynamic.
*
* This value is useful to know when evaluating an expression, in order to determine
* whether it is possible to avoid doing a dynamic memory allocation.
*
* \sa RowsAtCompileTime, MaxColsAtCompileTime, MaxSizeAtCompileTime
*/
MaxColsAtCompileTime = ei_traits<Derived>::MaxColsAtCompileTime,
/**< This value is equal to the maximum possible number of columns that this expression
* might have. If this expression might have an arbitrarily high number of columns,
* this value is set to \a Dynamic.
*
* This value is useful to know when evaluating an expression, in order to determine
* whether it is possible to avoid doing a dynamic memory allocation.
*
* \sa ColsAtCompileTime, MaxRowsAtCompileTime, MaxSizeAtCompileTime
*/
MaxSizeAtCompileTime = (ei_size_at_compile_time<ei_traits<Derived>::MaxRowsAtCompileTime,
ei_traits<Derived>::MaxColsAtCompileTime>::ret),
/**< This value is equal to the maximum possible number of coefficients that this expression
* might have. If this expression might have an arbitrarily high number of coefficients,
* this value is set to \a Dynamic.
*
* This value is useful to know when evaluating an expression, in order to determine
* whether it is possible to avoid doing a dynamic memory allocation.
*
* \sa SizeAtCompileTime, MaxRowsAtCompileTime, MaxColsAtCompileTime
*/
IsVectorAtCompileTime = ei_traits<Derived>::RowsAtCompileTime == 1
|| ei_traits<Derived>::ColsAtCompileTime == 1,
/**< This is set to true if either the number of rows or the number of
* columns is known at compile-time to be equal to 1. Indeed, in that case,
* we are dealing with a column-vector (if there is only one column) or with
* a row-vector (if there is only one row). */
Flags = ei_traits<Derived>::Flags,
/**< This stores expression \ref flags flags which may or may not be inherited by new expressions
* constructed from this one. See the \ref flags "list of flags".
*/
CoeffReadCost = ei_traits<Derived>::CoeffReadCost
/**< This is a rough measure of how expensive it is to read one coefficient from
* this expression.
*/
};
/** Default constructor. Just checks at compile-time for self-consistency of the flags. */
MatrixBase()
{
ei_assert(ei_are_flags_consistent<Flags>::ret);
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** This is the "real scalar" type; if the \a Scalar type is already real numbers
* (e.g. int, float or double) then \a RealScalar is just the same as \a Scalar. If
* \a Scalar is \a std::complex<T> then RealScalar is \a T.
*
* \sa class NumTraits
*/
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef DenseBase<Derived> Base;
using Base::RowsAtCompileTime;
using Base::ColsAtCompileTime;
using Base::SizeAtCompileTime;
using Base::MaxRowsAtCompileTime;
using Base::MaxColsAtCompileTime;
using Base::MaxSizeAtCompileTime;
using Base::IsVectorAtCompileTime;
using Base::Flags;
using Base::CoeffReadCost;
using Base::derived;
using Base::const_cast_derived;
using Base::rows;
using Base::cols;
using Base::size;
using Base::coeff;
using Base::coeffRef;
using Base::lazyAssign;
using Base::eval;
using Base::operator+=;
using Base::operator-=;
using Base::operator*=;
using Base::operator/=;
typedef typename Base::CoeffReturnType CoeffReturnType;
typedef typename Base::ConstTransposeReturnType ConstTransposeReturnType;
typedef typename Base::RowXpr RowXpr;
typedef typename Base::ColXpr ColXpr;
#endif // not EIGEN_PARSED_BY_DOXYGEN
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** type of the equivalent square matrix */
typedef Matrix<Scalar,EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime),
EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime)> SquareMatrixType;
typedef Matrix<Scalar,EIGEN_ENUM_MAX(RowsAtCompileTime,ColsAtCompileTime),
EIGEN_ENUM_MAX(RowsAtCompileTime,ColsAtCompileTime)> SquareMatrixType;
#endif // not EIGEN_PARSED_BY_DOXYGEN
/** \returns the size of the main diagonal, which is min(rows(),cols()).
/** \returns the number of rows. \sa cols(), RowsAtCompileTime */
inline int rows() const { return derived().rows(); }
/** \returns the number of columns. \sa rows(), ColsAtCompileTime*/
inline int cols() const { return derived().cols(); }
/** \returns the number of coefficients, which is \a rows()*cols().
* \sa rows(), cols(), SizeAtCompileTime. */
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;
inline int size() const { return rows() * cols(); }
/** \returns the number of nonzero coefficients which is in practice the number
* of stored coefficients. */
inline int nonZeros() const { return derived.nonZeros(); }
/** \returns true if either the number of rows or the number of columns is equal to 1.
* In other words, this function returns
* \code rows()==1 || cols()==1 \endcode
* \sa rows(), cols(), IsVectorAtCompileTime. */
inline bool isVector() const { return rows()==1 || cols()==1; }
/** \returns the size of the storage major dimension,
* i.e., the number of columns for a columns major matrix, and the number of rows otherwise */
int outerSize() const { return (int(Flags)&RowMajorBit) ? this->rows() : this->cols(); }
/** \returns the size of the inner dimension according to the storage order,
* i.e., the number of rows for a columns major matrix, and the number of cols otherwise */
int innerSize() const { return (int(Flags)&RowMajorBit) ? this->cols() : this->rows(); }
#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 guaranteed however, that the return type of eval() is either
* PlainMatrixType or const PlainMatrixType&.
*/
typedef typename ei_plain_matrix_type<Derived>::type PlainMatrixType;
/** \internal the column-major 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!
* The only difference from PlainMatrixType is that PlainMatrixType_ColMajor is guaranteed to be column-major.
*/
typedef typename ei_plain_matrix_type<Derived>::type PlainMatrixType_ColMajor;
/** \internal Represents a matrix with all coefficients equal to one another*/
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType;
typedef CwiseNullaryOp<ei_scalar_constant_op<Scalar>,Derived> ConstantReturnType;
/** \internal Represents a scalar multiple of a matrix */
typedef CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, Derived> ScalarMultipleReturnType;
/** \internal Represents a quotient of a matrix by a scalar*/
typedef CwiseUnaryOp<ei_scalar_quotient1_op<Scalar>, Derived> ScalarQuotient1ReturnType;
/** \internal the return type of MatrixBase::conjugate() */
typedef typename ei_meta_if<NumTraits<Scalar>::IsComplex,
const CwiseUnaryOp<ei_scalar_conjugate_op<Scalar>, Derived>,
const Derived&
>::ret ConjugateReturnType;
/** \internal the return type of MatrixBase::real() */
typedef CwiseUnaryOp<ei_scalar_real_op<Scalar>, Derived> RealReturnType;
/** \internal the return type of MatrixBase::imag() */
typedef CwiseUnaryOp<ei_scalar_imag_op<Scalar>, Derived> ImagReturnType;
/** \internal the return type of MatrixBase::adjoint() */
typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, ConstTransposeReturnType>,
ConstTransposeReturnType
>::type AdjointReturnType;
/** \internal Return type of eigenvalues() */
typedef Matrix<std::complex<RealScalar>, internal::traits<Derived>::ColsAtCompileTime, 1, ColMajor> EigenvaluesReturnType;
typedef Eigen::Transpose<NestByValue<typename ei_cleantype<ConjugateReturnType>::type> >
AdjointReturnType;
/** \internal the return type of MatrixBase::eigenvalues() */
typedef Matrix<typename NumTraits<typename ei_traits<Derived>::Scalar>::Real, ei_traits<Derived>::ColsAtCompileTime, 1> EigenvaluesReturnType;
/** \internal expression tyepe of a column */
typedef Block<Derived, ei_traits<Derived>::RowsAtCompileTime, 1> ColXpr;
/** \internal expression tyepe of a column */
typedef Block<Derived, 1, ei_traits<Derived>::ColsAtCompileTime> RowXpr;
/** \internal the return type of identity */
typedef CwiseNullaryOp<internal::scalar_identity_op<Scalar>,Derived> IdentityReturnType;
typedef CwiseNullaryOp<ei_scalar_identity_op<Scalar>,Derived> IdentityReturnType;
/** \internal the return type of unit vectors */
typedef Block<const CwiseNullaryOp<internal::scalar_identity_op<Scalar>, SquareMatrixType>,
internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime> BasisReturnType;
typedef Block<CwiseNullaryOp<ei_scalar_identity_op<Scalar>, SquareMatrixType>,
ei_traits<Derived>::RowsAtCompileTime,
ei_traits<Derived>::ColsAtCompileTime> BasisReturnType;
#endif // not EIGEN_PARSED_BY_DOXYGEN
#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::MatrixBase
# include "../plugins/CommonCwiseUnaryOps.h"
# include "../plugins/CommonCwiseBinaryOps.h"
# include "../plugins/MatrixCwiseUnaryOps.h"
# include "../plugins/MatrixCwiseBinaryOps.h"
# ifdef EIGEN_MATRIXBASE_PLUGIN
# include EIGEN_MATRIXBASE_PLUGIN
# endif
#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
/** Copies \a other into *this. \returns a reference to *this. */
template<typename OtherDerived>
Derived& operator=(const MatrixBase<OtherDerived>& other);
/** Special case of the template operator=, in order to prevent the compiler
* from generating a default operator= (issue hit with g++ 4.1)
*/
Derived& operator=(const MatrixBase& other);
// We cannot inherit here via Base::operator= since it is causing
// trouble with MSVC.
template <typename OtherDerived>
Derived& operator=(const DenseBase<OtherDerived>& other);
template <typename OtherDerived>
Derived& operator=(const EigenBase<OtherDerived>& other);
template<typename OtherDerived>
Derived& operator=(const ReturnByValue<OtherDerived>& other);
inline Derived& operator=(const MatrixBase& other)
{
return this->operator=<Derived>(other);
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& lazyAssign(const ProductBase<ProductDerived, Lhs,Rhs>& other);
/** Copies \a other into *this without evaluating other. \returns a reference to *this. */
template<typename OtherDerived>
Derived& lazyAssign(const MatrixBase<OtherDerived>& other);
/** Overloaded for cache friendly product evaluation */
template<typename Lhs, typename Rhs>
Derived& lazyAssign(const Product<Lhs,Rhs,CacheFriendlyProduct>& product);
/** Overloaded for cache friendly product evaluation */
template<typename OtherDerived>
Derived& lazyAssign(const Flagged<OtherDerived, 0, EvalBeforeNestingBit | EvalBeforeAssigningBit>& other)
{ return lazyAssign(other._expression()); }
#endif // not EIGEN_PARSED_BY_DOXYGEN
CommaInitializer<Derived> operator<< (const Scalar& s);
template<typename OtherDerived>
CommaInitializer<Derived> operator<< (const MatrixBase<OtherDerived>& other);
const Scalar coeff(int row, int col) const;
const Scalar operator()(int row, int col) const;
Scalar& coeffRef(int row, int col);
Scalar& operator()(int row, int col);
const Scalar coeff(int index) const;
const Scalar operator[](int index) const;
const Scalar operator()(int index) const;
Scalar& coeffRef(int index);
Scalar& operator[](int index);
Scalar& operator()(int index);
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename OtherDerived>
void copyCoeff(int row, int col, const MatrixBase<OtherDerived>& other);
template<typename OtherDerived>
void copyCoeff(int index, const MatrixBase<OtherDerived>& other);
template<typename OtherDerived, int StoreMode, int LoadMode>
void copyPacket(int row, int col, const MatrixBase<OtherDerived>& other);
template<typename OtherDerived, int StoreMode, int LoadMode>
void copyPacket(int index, const MatrixBase<OtherDerived>& other);
#endif // not EIGEN_PARSED_BY_DOXYGEN
template<int LoadMode>
PacketScalar packet(int row, int col) const;
template<int StoreMode>
void writePacket(int row, int col, const PacketScalar& x);
template<int LoadMode>
PacketScalar packet(int index) const;
template<int StoreMode>
void writePacket(int index, const PacketScalar& x);
const Scalar x() const;
const Scalar y() const;
const Scalar z() const;
const Scalar w() const;
Scalar& x();
Scalar& y();
Scalar& z();
Scalar& w();
const CwiseUnaryOp<ei_scalar_opposite_op<typename ei_traits<Derived>::Scalar>,Derived> operator-() const;
template<typename OtherDerived>
const CwiseBinaryOp<ei_scalar_sum_op<typename ei_traits<Derived>::Scalar>, Derived, OtherDerived>
operator+(const MatrixBase<OtherDerived> &other) const;
template<typename OtherDerived>
const CwiseBinaryOp<ei_scalar_difference_op<typename ei_traits<Derived>::Scalar>, Derived, OtherDerived>
operator-(const MatrixBase<OtherDerived> &other) const;
template<typename OtherDerived>
Derived& operator+=(const MatrixBase<OtherDerived>& other);
template<typename OtherDerived>
Derived& operator-=(const MatrixBase<OtherDerived>& other);
template<typename Lhs,typename Rhs>
Derived& operator+=(const Flagged<Product<Lhs,Rhs,CacheFriendlyProduct>, 0, EvalBeforeNestingBit | EvalBeforeAssigningBit>& other);
Derived& operator*=(const Scalar& other);
Derived& operator/=(const Scalar& other);
const ScalarMultipleReturnType operator*(const Scalar& scalar) const;
const CwiseUnaryOp<ei_scalar_quotient1_op<typename ei_traits<Derived>::Scalar>, Derived>
operator/(const Scalar& scalar) const;
inline friend const CwiseUnaryOp<ei_scalar_multiple_op<typename ei_traits<Derived>::Scalar>, Derived>
operator*(const Scalar& scalar, const MatrixBase& matrix)
{ return matrix*scalar; }
template<typename OtherDerived>
const typename ProductReturnType<Derived,OtherDerived>::Type
operator*(const MatrixBase<OtherDerived> &other) const;
template<typename OtherDerived>
const typename LazyProductReturnType<Derived,OtherDerived>::Type
lazyProduct(const MatrixBase<OtherDerived> &other) const;
Derived& operator*=(const MatrixBase<OtherDerived>& other);
template<typename OtherDerived>
Derived& operator*=(const EigenBase<OtherDerived>& other);
typename ei_plain_matrix_type_column_major<OtherDerived>::type
solveTriangular(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived>
void applyOnTheLeft(const EigenBase<OtherDerived>& other);
void solveTriangularInPlace(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived>
void applyOnTheRight(const EigenBase<OtherDerived>& other);
template<typename DiagonalDerived>
const DiagonalProduct<Derived, DiagonalDerived, OnTheRight>
operator*(const DiagonalBase<DiagonalDerived> &diagonal) const;
template<typename OtherDerived>
typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
dot(const MatrixBase<OtherDerived>& other) const;
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
Scalar eigen2_dot(const MatrixBase<OtherDerived>& other) const;
#endif
Scalar dot(const MatrixBase<OtherDerived>& other) const;
RealScalar squaredNorm() const;
RealScalar norm() const;
RealScalar stableNorm() const;
RealScalar blueNorm() const;
RealScalar hypotNorm() const;
const PlainObject normalized() const;
RealScalar norm() const;
const PlainMatrixType normalized() const;
void normalize();
Eigen::Transpose<Derived> transpose();
const Eigen::Transpose<Derived> transpose() const;
void transposeInPlace();
const AdjointReturnType adjoint() const;
void adjointInPlace();
typedef Diagonal<Derived> DiagonalReturnType;
DiagonalReturnType diagonal();
typedef const Diagonal<const Derived> ConstDiagonalReturnType;
const ConstDiagonalReturnType diagonal() const;
template<int Index> struct DiagonalIndexReturnType { typedef Diagonal<Derived,Index> Type; };
template<int Index> struct ConstDiagonalIndexReturnType { typedef const Diagonal<const Derived,Index> Type; };
RowXpr row(int i);
const RowXpr row(int i) const;
template<int Index> typename DiagonalIndexReturnType<Index>::Type diagonal();
template<int Index> typename ConstDiagonalIndexReturnType<Index>::Type diagonal() const;
ColXpr col(int i);
const ColXpr col(int i) 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<Dynamic>::Type diagonal(Index index);
typename MatrixBase::template ConstDiagonalIndexReturnType<Dynamic>::Type diagonal(Index index) const;
#else
typename DiagonalIndexReturnType<Dynamic>::Type diagonal(Index index);
typename ConstDiagonalIndexReturnType<Dynamic>::Type diagonal(Index index) const;
#endif
Minor<Derived> minor(int row, int col);
const Minor<Derived> minor(int row, int col) const;
#ifdef EIGEN2_SUPPORT
template<unsigned int Mode> typename internal::eigen2_part_return_type<Derived, Mode>::type part();
template<unsigned int Mode> const typename internal::eigen2_part_return_type<Derived, Mode>::type part() const;
// huuuge hack. make Eigen2's matrix.part<Diagonal>() work in eigen3. Problem: Diagonal is now a class template instead
// of an integer constant. Solution: overload the part() method template wrt template parameters list.
// Note: replacing next line by "template<template<typename T, int n> class U>" produces a mysterious error C2082 in MSVC.
template<template<typename, int> class U>
const DiagonalWrapper<ConstDiagonalReturnType> part() const
{ return diagonal().asDiagonal(); }
#endif // EIGEN2_SUPPORT
typename BlockReturnType<Derived>::Type block(int startRow, int startCol, int blockRows, int blockCols);
const typename BlockReturnType<Derived>::Type
block(int startRow, int startCol, int blockRows, int blockCols) 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; };
typename BlockReturnType<Derived>::SubVectorType segment(int start, int size);
const typename BlockReturnType<Derived>::SubVectorType segment(int start, int size) const;
template<unsigned int Mode> typename TriangularViewReturnType<Mode>::Type triangularView();
template<unsigned int Mode> typename ConstTriangularViewReturnType<Mode>::Type triangularView() const;
typename BlockReturnType<Derived,Dynamic>::SubVectorType start(int size);
const typename BlockReturnType<Derived,Dynamic>::SubVectorType start(int size) const;
template<unsigned int UpLo> struct SelfAdjointViewReturnType { typedef SelfAdjointView<Derived, UpLo> Type; };
template<unsigned int UpLo> struct ConstSelfAdjointViewReturnType { typedef const SelfAdjointView<const Derived, UpLo> Type; };
typename BlockReturnType<Derived,Dynamic>::SubVectorType end(int size);
const typename BlockReturnType<Derived,Dynamic>::SubVectorType end(int size) const;
template<unsigned int UpLo> typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView();
template<unsigned int UpLo> typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
typename BlockReturnType<Derived>::Type corner(CornerType type, int cRows, int cCols);
const typename BlockReturnType<Derived>::Type corner(CornerType type, int cRows, int cCols) const;
const SparseView<Derived> sparseView(const Scalar& m_reference = Scalar(0),
typename NumTraits<Scalar>::Real m_epsilon = NumTraits<Scalar>::dummy_precision()) const;
template<int BlockRows, int BlockCols>
typename BlockReturnType<Derived, BlockRows, BlockCols>::Type block(int startRow, int startCol);
template<int BlockRows, int BlockCols>
const typename BlockReturnType<Derived, BlockRows, BlockCols>::Type block(int startRow, int startCol) const;
template<int CRows, int CCols>
typename BlockReturnType<Derived, CRows, CCols>::Type corner(CornerType type);
template<int CRows, int CCols>
const typename BlockReturnType<Derived, CRows, CCols>::Type corner(CornerType type) const;
template<int Size> typename BlockReturnType<Derived,Size>::SubVectorType start(void);
template<int Size> const typename BlockReturnType<Derived,Size>::SubVectorType start() const;
template<int Size> typename BlockReturnType<Derived,Size>::SubVectorType end();
template<int Size> const typename BlockReturnType<Derived,Size>::SubVectorType end() const;
template<int Size> typename BlockReturnType<Derived,Size>::SubVectorType segment(int start);
template<int Size> const typename BlockReturnType<Derived,Size>::SubVectorType segment(int start) const;
DiagonalCoeffs<Derived> diagonal();
const DiagonalCoeffs<Derived> diagonal() const;
template<unsigned int Mode> Part<Derived, Mode> part();
template<unsigned int Mode> const Part<Derived, Mode> part() const;
static const ConstantReturnType
Constant(int rows, int cols, const Scalar& value);
static const ConstantReturnType
Constant(int size, const Scalar& value);
static const ConstantReturnType
Constant(const Scalar& value);
template<typename CustomNullaryOp>
static const CwiseNullaryOp<CustomNullaryOp, Derived>
NullaryExpr(int rows, int cols, const CustomNullaryOp& func);
template<typename CustomNullaryOp>
static const CwiseNullaryOp<CustomNullaryOp, Derived>
NullaryExpr(int size, const CustomNullaryOp& func);
template<typename CustomNullaryOp>
static const CwiseNullaryOp<CustomNullaryOp, Derived>
NullaryExpr(const CustomNullaryOp& func);
static const ConstantReturnType Zero(int rows, int cols);
static const ConstantReturnType Zero(int size);
static const ConstantReturnType Zero();
static const ConstantReturnType Ones(int rows, int cols);
static const ConstantReturnType Ones(int size);
static const ConstantReturnType Ones();
static const IdentityReturnType Identity();
static const IdentityReturnType Identity(Index rows, Index cols);
static const BasisReturnType Unit(Index size, Index i);
static const BasisReturnType Unit(Index i);
static const IdentityReturnType Identity(int rows, int cols);
static const BasisReturnType Unit(int size, int i);
static const BasisReturnType Unit(int i);
static const BasisReturnType UnitX();
static const BasisReturnType UnitY();
static const BasisReturnType UnitZ();
static const BasisReturnType UnitW();
const DiagonalWrapper<const Derived> asDiagonal() const;
const PermutationWrapper<const Derived> asPermutation() const;
const DiagonalMatrix<Derived> asDiagonal() const;
void fill(const Scalar& value);
Derived& setConstant(const Scalar& value);
Derived& setZero();
Derived& setOnes();
Derived& setRandom();
Derived& setIdentity();
Derived& setIdentity(Index rows, Index cols);
bool isIdentity(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isDiagonal(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isUpperTriangular(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isLowerTriangular(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
template<typename OtherDerived>
bool isApprox(const MatrixBase<OtherDerived>& other,
RealScalar prec = precision<Scalar>()) const;
bool isMuchSmallerThan(const RealScalar& other,
RealScalar prec = precision<Scalar>()) const;
template<typename OtherDerived>
bool isMuchSmallerThan(const MatrixBase<OtherDerived>& other,
RealScalar prec = precision<Scalar>()) const;
bool isApproxToConstant(const Scalar& value, RealScalar prec = precision<Scalar>()) const;
bool isConstant(const Scalar& value, RealScalar prec = precision<Scalar>()) const;
bool isZero(RealScalar prec = precision<Scalar>()) const;
bool isOnes(RealScalar prec = precision<Scalar>()) const;
bool isIdentity(RealScalar prec = precision<Scalar>()) const;
bool isDiagonal(RealScalar prec = precision<Scalar>()) const;
bool isUpperTriangular(RealScalar prec = precision<Scalar>()) const;
bool isLowerTriangular(RealScalar prec = precision<Scalar>()) const;
template<typename OtherDerived>
bool isOrthogonal(const MatrixBase<OtherDerived>& other,
RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isUnitary(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
RealScalar prec = precision<Scalar>()) const;
bool isUnitary(RealScalar prec = precision<Scalar>()) const;
/** \returns true if each coefficients of \c *this and \a other are all exactly equal.
* \warning When using floating point scalar values you probably should rather use a
* fuzzy comparison such as isApprox()
* \sa isApprox(), operator!= */
template<typename OtherDerived>
inline bool operator==(const MatrixBase<OtherDerived>& other) const
{ return cwiseEqual(other).all(); }
{ return (cwise() == other).all(); }
/** \returns true if at least one pair of coefficients of \c *this and \a other are not exactly equal to each other.
* \warning When using floating point scalar values you probably should rather use a
* fuzzy comparison such as isApprox()
* \sa isApprox(), operator== */
template<typename OtherDerived>
inline bool operator!=(const MatrixBase<OtherDerived>& other) const
{ return cwiseNotEqual(other).any(); }
{ return (cwise() != other).any(); }
NoAlias<Derived,Eigen::MatrixBase > noalias();
inline const ForceAlignedAccess<Derived> forceAlignedAccess() const;
inline ForceAlignedAccess<Derived> forceAlignedAccess();
template<bool Enable> inline typename internal::add_const_on_value_type<typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type>::type forceAlignedAccessIf() const;
template<bool Enable> inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf();
template<typename NewType>
const CwiseUnaryOp<ei_scalar_cast_op<typename ei_traits<Derived>::Scalar, NewType>, Derived> cast() const;
/** \returns the matrix or vector obtained by evaluating this expression.
*
* Notice that in the case of a plain matrix or vector (not an expression) this function just returns
* a const reference, in order to avoid a useless copy.
*/
EIGEN_STRONG_INLINE const typename ei_eval<Derived>::type eval() const
{ return typename ei_eval<Derived>::type(derived()); }
template<typename OtherDerived>
void swap(const MatrixBase<OtherDerived>& other);
template<unsigned int Added>
const Flagged<Derived, Added, 0> marked() const;
const Flagged<Derived, 0, EvalBeforeNestingBit | EvalBeforeAssigningBit> lazy() const;
/** \returns number of elements to skip to pass from one row (resp. column) to another
* for a row-major (resp. column-major) matrix.
* Combined with coeffRef() and the \ref flags flags, it allows a direct access to the data
* of the underlying matrix.
*/
inline int stride(void) const { return derived().stride(); }
inline const NestByValue<Derived> nestByValue() const;
ConjugateReturnType conjugate() const;
const RealReturnType real() const;
const ImagReturnType imag() const;
template<typename CustomUnaryOp>
const CwiseUnaryOp<CustomUnaryOp, Derived> unaryExpr(const CustomUnaryOp& func = CustomUnaryOp()) const;
template<typename CustomBinaryOp, typename OtherDerived>
const CwiseBinaryOp<CustomBinaryOp, Derived, OtherDerived>
binaryExpr(const MatrixBase<OtherDerived> &other, const CustomBinaryOp& func = CustomBinaryOp()) const;
Scalar sum() const;
Scalar trace() const;
typename ei_traits<Derived>::Scalar minCoeff() const;
typename ei_traits<Derived>::Scalar maxCoeff() const;
typename ei_traits<Derived>::Scalar minCoeff(int* row, int* col) const;
typename ei_traits<Derived>::Scalar maxCoeff(int* row, int* col) const;
typename ei_traits<Derived>::Scalar minCoeff(int* index) const;
typename ei_traits<Derived>::Scalar maxCoeff(int* index) const;
template<typename BinaryOp>
typename ei_result_of<BinaryOp(typename ei_traits<Derived>::Scalar)>::type
redux(const BinaryOp& func) const;
template<typename Visitor>
void visit(Visitor& func) const;
#ifndef EIGEN_PARSED_BY_DOXYGEN
inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
inline Derived& derived() { return *static_cast<Derived*>(this); }
inline Derived& const_cast_derived() const
{ return *static_cast<Derived*>(const_cast<MatrixBase*>(this)); }
#endif // not EIGEN_PARSED_BY_DOXYGEN
const Cwise<Derived> cwise() const;
Cwise<Derived> cwise();
inline const WithFormat<Derived> format(const IOFormat& fmt) const;
/////////// Array module ///////////
bool all(void) const;
bool any(void) const;
int count() const;
const PartialRedux<Derived,Horizontal> rowwise() const;
const PartialRedux<Derived,Vertical> colwise() const;
static const CwiseNullaryOp<ei_scalar_random_op<Scalar>,Derived> Random(int rows, int cols);
static const CwiseNullaryOp<ei_scalar_random_op<Scalar>,Derived> Random(int size);
static const CwiseNullaryOp<ei_scalar_random_op<Scalar>,Derived> Random();
template<typename ThenDerived,typename ElseDerived>
const Select<Derived,ThenDerived,ElseDerived>
select(const MatrixBase<ThenDerived>& thenMatrix,
const MatrixBase<ElseDerived>& elseMatrix) const;
template<typename ThenDerived>
inline const Select<Derived,ThenDerived, NestByValue<typename ThenDerived::ConstantReturnType> >
select(const MatrixBase<ThenDerived>& thenMatrix, typename ThenDerived::Scalar elseScalar) const;
template<typename ElseDerived>
inline const Select<Derived, NestByValue<typename ElseDerived::ConstantReturnType>, ElseDerived >
select(typename ElseDerived::Scalar thenScalar, const MatrixBase<ElseDerived>& elseMatrix) const;
template<int p> RealScalar lpNorm() const;
MatrixBase<Derived>& matrix() { return *this; }
const MatrixBase<Derived>& matrix() const { return *this; }
/** \returns an \link ArrayBase Array \endlink expression of this matrix
* \sa ArrayBase::matrix() */
ArrayWrapper<Derived> array() { return derived(); }
const ArrayWrapper<Derived> array() const { return derived(); }
/////////// LU module ///////////
const FullPivLU<PlainObject> fullPivLu() const;
const PartialPivLU<PlainObject> partialPivLu() const;
#if EIGEN2_SUPPORT_STAGE < STAGE20_RESOLVE_API_CONFLICTS
const LU<PlainObject> lu() const;
#endif
#ifdef EIGEN2_SUPPORT
const LU<PlainObject> eigen2_lu() const;
#endif
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
const PartialPivLU<PlainObject> lu() const;
#endif
#ifdef EIGEN2_SUPPORT
template<typename ResultType>
void computeInverse(MatrixBase<ResultType> *result) const {
*result = this->inverse();
}
#endif
const internal::inverse_impl<Derived> inverse() const;
template<typename ResultType>
void computeInverseAndDetWithCheck(
ResultType& inverse,
typename ResultType::Scalar& determinant,
bool& invertible,
const RealScalar& absDeterminantThreshold = NumTraits<Scalar>::dummy_precision()
) const;
template<typename ResultType>
void computeInverseWithCheck(
ResultType& inverse,
bool& invertible,
const RealScalar& absDeterminantThreshold = NumTraits<Scalar>::dummy_precision()
) const;
const LU<PlainMatrixType> lu() const;
const PlainMatrixType inverse() const;
void computeInverse(PlainMatrixType *result) const;
Scalar determinant() const;
/////////// Cholesky module ///////////
const LLT<PlainObject> llt() const;
const LDLT<PlainObject> ldlt() const;
const LLT<PlainMatrixType> llt() const;
const LDLT<PlainMatrixType> ldlt() const;
/////////// QR module ///////////
const HouseholderQR<PlainObject> householderQr() const;
const ColPivHouseholderQR<PlainObject> colPivHouseholderQr() const;
const FullPivHouseholderQR<PlainObject> fullPivHouseholderQr() const;
#ifdef EIGEN2_SUPPORT
const QR<PlainObject> qr() const;
#endif
const QR<PlainMatrixType> qr() const;
EigenvaluesReturnType eigenvalues() const;
RealScalar operatorNorm() const;
/////////// SVD module ///////////
JacobiSVD<PlainObject> jacobiSvd(unsigned int computationOptions = 0) const;
#ifdef EIGEN2_SUPPORT
SVD<PlainObject> svd() const;
#endif
SVD<PlainMatrixType> svd() const;
/////////// Geometry module ///////////
#ifndef EIGEN_PARSED_BY_DOXYGEN
/// \internal helper struct to form the return type of the cross product
template<typename OtherDerived> struct cross_product_return_type {
typedef typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType Scalar;
typedef Matrix<Scalar,MatrixBase::RowsAtCompileTime,MatrixBase::ColsAtCompileTime> type;
};
#endif // EIGEN_PARSED_BY_DOXYGEN
template<typename OtherDerived>
typename cross_product_return_type<OtherDerived>::type
cross(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived>
PlainObject cross3(const MatrixBase<OtherDerived>& other) const;
PlainObject unitOrthogonal(void) const;
Matrix<Scalar,3,1> eulerAngles(Index a0, Index a1, Index a2) const;
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
ScalarMultipleReturnType operator*(const UniformScaling<Scalar>& s) const;
// put this as separate enum value to work around possible GCC 4.3 bug (?)
enum { HomogeneousReturnTypeDirection = ColsAtCompileTime==1?Vertical:Horizontal };
typedef Homogeneous<Derived, HomogeneousReturnTypeDirection> HomogeneousReturnType;
HomogeneousReturnType homogeneous() const;
PlainMatrixType cross(const MatrixBase<OtherDerived>& other) const;
PlainMatrixType unitOrthogonal(void) const;
Matrix<Scalar,3,1> eulerAngles(int a0, int a1, int a2) const;
/////////// Sparse module ///////////
// dense = spasre * dense
template<typename Derived1, typename Derived2>
Derived& lazyAssign(const SparseProduct<Derived1,Derived2,SparseTimeDenseProduct>& product);
// dense = dense * spasre
template<typename Derived1, typename Derived2>
Derived& lazyAssign(const SparseProduct<Derived1,Derived2,DenseTimeSparseProduct>& product);
#ifdef EIGEN_MATRIXBASE_PLUGIN
#include EIGEN_MATRIXBASE_PLUGIN
#endif
enum {
SizeMinusOne = SizeAtCompileTime==Dynamic ? Dynamic : SizeAtCompileTime-1
};
typedef Block<const Derived,
internal::traits<Derived>::ColsAtCompileTime==1 ? SizeMinusOne : 1,
internal::traits<Derived>::ColsAtCompileTime==1 ? 1 : SizeMinusOne> ConstStartMinusOne;
typedef CwiseUnaryOp<internal::scalar_quotient1_op<typename internal::traits<Derived>::Scalar>,
const ConstStartMinusOne > HNormalizedReturnType;
const HNormalizedReturnType hnormalized() const;
////////// Householder module ///////////
void makeHouseholderInPlace(Scalar& tau, RealScalar& beta);
template<typename EssentialPart>
void makeHouseholder(EssentialPart& essential,
Scalar& tau, RealScalar& beta) const;
template<typename EssentialPart>
void applyHouseholderOnTheLeft(const EssentialPart& essential,
const Scalar& tau,
Scalar* workspace);
template<typename EssentialPart>
void applyHouseholderOnTheRight(const EssentialPart& essential,
const Scalar& tau,
Scalar* workspace);
///////// Jacobi module /////////
template<typename OtherScalar>
void applyOnTheLeft(Index p, Index q, const JacobiRotation<OtherScalar>& j);
template<typename OtherScalar>
void applyOnTheRight(Index p, Index q, const JacobiRotation<OtherScalar>& j);
///////// MatrixFunctions module /////////
typedef typename internal::stem_function<Scalar>::type StemFunction;
const MatrixExponentialReturnValue<Derived> exp() const;
const MatrixFunctionReturnValue<Derived> matrixFunction(StemFunction f) const;
const MatrixFunctionReturnValue<Derived> cosh() const;
const MatrixFunctionReturnValue<Derived> sinh() const;
const MatrixFunctionReturnValue<Derived> cos() const;
const MatrixFunctionReturnValue<Derived> sin() const;
#ifdef EIGEN2_SUPPORT
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& operator+=(const Flagged<ProductBase<ProductDerived, Lhs,Rhs>, 0,
EvalBeforeAssigningBit>& other);
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& operator-=(const Flagged<ProductBase<ProductDerived, Lhs,Rhs>, 0,
EvalBeforeAssigningBit>& other);
/** \deprecated because .lazy() is deprecated
* Overloaded for cache friendly product evaluation */
template<typename OtherDerived>
Derived& lazyAssign(const Flagged<OtherDerived, 0, EvalBeforeAssigningBit>& other)
{ return lazyAssign(other._expression()); }
template<unsigned int Added>
const Flagged<Derived, Added, 0> marked() const;
const Flagged<Derived, 0, EvalBeforeAssigningBit> lazy() const;
inline const Cwise<Derived> cwise() const;
inline Cwise<Derived> cwise();
VectorBlock<Derived> start(Index size);
const VectorBlock<const Derived> start(Index size) const;
VectorBlock<Derived> end(Index size);
const VectorBlock<const Derived> end(Index size) const;
template<int Size> VectorBlock<Derived,Size> start();
template<int Size> const VectorBlock<const Derived,Size> start() const;
template<int Size> VectorBlock<Derived,Size> end();
template<int Size> const VectorBlock<const Derived,Size> end() const;
Minor<Derived> minor(Index row, Index col);
const Minor<Derived> minor(Index row, Index col) const;
#endif
protected:
MatrixBase() : Base() {}
private:
explicit MatrixBase(int);
MatrixBase(int,int);
template<typename OtherDerived> explicit MatrixBase(const MatrixBase<OtherDerived>&);
protected:
// mixing arrays and matrices is not legal
template<typename OtherDerived> Derived& operator+=(const ArrayBase<OtherDerived>& )
{EIGEN_STATIC_ASSERT(sizeof(typename OtherDerived::Scalar)==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);}
// mixing arrays and matrices is not legal
template<typename OtherDerived> Derived& operator-=(const ArrayBase<OtherDerived>& )
{EIGEN_STATIC_ASSERT(sizeof(typename OtherDerived::Scalar)==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);}
};
#endif // EIGEN_MATRIXBASE_H

View File

@@ -0,0 +1,249 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_MATRIXSTORAGE_H
#define EIGEN_MATRIXSTORAGE_H
struct ei_constructor_without_unaligned_array_assert {};
/** \internal
* Static array automatically aligned if the total byte size is a multiple of 16 and the matrix options require auto alignment
*/
template <typename T, int Size, int MatrixOptions,
bool Align = (MatrixOptions&AutoAlign) && (((Size*sizeof(T))&0xf)==0)
> struct ei_matrix_array
{
EIGEN_ALIGN_128 T array[Size];
ei_matrix_array()
{
#ifndef EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT
ei_assert((reinterpret_cast<size_t>(array) & 0xf) == 0
&& "this assertion is explained here: http://eigen.tuxfamily.org/dox/UnalignedArrayAssert.html **** READ THIS WEB PAGE !!! ****");
#endif
}
ei_matrix_array(ei_constructor_without_unaligned_array_assert) {}
};
template <typename T, int Size, int MatrixOptions> struct ei_matrix_array<T,Size,MatrixOptions,false>
{
T array[Size];
ei_matrix_array() {}
ei_matrix_array(ei_constructor_without_unaligned_array_assert) {}
};
/** \internal
*
* \class ei_matrix_storage
*
* \brief Stores the data of a matrix
*
* This class stores the data of fixed-size, dynamic-size or mixed matrices
* in a way as compact as possible.
*
* \sa Matrix
*/
template<typename T, int Size, int _Rows, int _Cols, int _Options> class ei_matrix_storage;
// purely fixed-size matrix
template<typename T, int Size, int _Rows, int _Cols, int _Options> class ei_matrix_storage
{
ei_matrix_array<T,Size,_Options> m_data;
public:
inline explicit ei_matrix_storage() {}
inline ei_matrix_storage(ei_constructor_without_unaligned_array_assert)
: m_data(ei_constructor_without_unaligned_array_assert()) {}
inline ei_matrix_storage(int,int,int) {}
inline void swap(ei_matrix_storage& other) { std::swap(m_data,other.m_data); }
inline static int rows(void) {return _Rows;}
inline static int cols(void) {return _Cols;}
inline void resize(int,int,int) {}
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
};
// dynamic-size matrix with fixed-size storage
template<typename T, int Size, int _Options> class ei_matrix_storage<T, Size, Dynamic, Dynamic, _Options>
{
ei_matrix_array<T,Size,_Options> m_data;
int m_rows;
int m_cols;
public:
inline explicit ei_matrix_storage() : m_rows(0), m_cols(0) {}
inline ei_matrix_storage(ei_constructor_without_unaligned_array_assert)
: m_data(ei_constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
inline ei_matrix_storage(int, int rows, int cols) : m_rows(rows), m_cols(cols) {}
inline ~ei_matrix_storage() {}
inline void swap(ei_matrix_storage& other)
{ std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
inline int rows(void) const {return m_rows;}
inline int cols(void) const {return m_cols;}
inline void resize(int, int rows, int cols)
{
m_rows = rows;
m_cols = cols;
}
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
};
// dynamic-size matrix with fixed-size storage and fixed width
template<typename T, int Size, int _Cols, int _Options> class ei_matrix_storage<T, Size, Dynamic, _Cols, _Options>
{
ei_matrix_array<T,Size,_Options> m_data;
int m_rows;
public:
inline explicit ei_matrix_storage() : m_rows(0) {}
inline ei_matrix_storage(ei_constructor_without_unaligned_array_assert)
: m_data(ei_constructor_without_unaligned_array_assert()), m_rows(0) {}
inline ei_matrix_storage(int, int rows, int) : m_rows(rows) {}
inline ~ei_matrix_storage() {}
inline void swap(ei_matrix_storage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
inline int rows(void) const {return m_rows;}
inline int cols(void) const {return _Cols;}
inline void resize(int /*size*/, int rows, int)
{
m_rows = rows;
}
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
};
// dynamic-size matrix with fixed-size storage and fixed height
template<typename T, int Size, int _Rows, int _Options> class ei_matrix_storage<T, Size, _Rows, Dynamic, _Options>
{
ei_matrix_array<T,Size,_Options> m_data;
int m_cols;
public:
inline explicit ei_matrix_storage() : m_cols(0) {}
inline ei_matrix_storage(ei_constructor_without_unaligned_array_assert)
: m_data(ei_constructor_without_unaligned_array_assert()), m_cols(0) {}
inline ei_matrix_storage(int, int, int cols) : m_cols(cols) {}
inline ~ei_matrix_storage() {}
inline void swap(ei_matrix_storage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
inline int rows(void) const {return _Rows;}
inline int cols(void) const {return m_cols;}
inline void resize(int, int, int cols)
{
m_cols = cols;
}
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
};
// purely dynamic matrix.
template<typename T, int _Options> class ei_matrix_storage<T, Dynamic, Dynamic, Dynamic, _Options>
{
T *m_data;
int m_rows;
int m_cols;
public:
inline explicit ei_matrix_storage() : m_data(0), m_rows(0), m_cols(0) {}
inline ei_matrix_storage(ei_constructor_without_unaligned_array_assert)
: m_data(0), m_rows(0), m_cols(0) {}
inline ei_matrix_storage(int size, int rows, int cols)
: m_data(ei_aligned_new<T>(size)), m_rows(rows), m_cols(cols) {}
inline ~ei_matrix_storage() { ei_aligned_delete(m_data, m_rows*m_cols); }
inline void swap(ei_matrix_storage& other)
{ std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
inline int rows(void) const {return m_rows;}
inline int cols(void) const {return m_cols;}
void resize(int size, int rows, int cols)
{
if(size != m_rows*m_cols)
{
ei_aligned_delete(m_data, m_rows*m_cols);
if (size)
m_data = ei_aligned_new<T>(size);
else
m_data = 0;
}
m_rows = rows;
m_cols = cols;
}
inline const T *data() const { return m_data; }
inline T *data() { return m_data; }
};
// matrix with dynamic width and fixed height (so that matrix has dynamic size).
template<typename T, int _Rows, int _Options> class ei_matrix_storage<T, Dynamic, _Rows, Dynamic, _Options>
{
T *m_data;
int m_cols;
public:
inline explicit ei_matrix_storage() : m_data(0), m_cols(0) {}
inline ei_matrix_storage(ei_constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
inline ei_matrix_storage(int size, int, int cols) : m_data(ei_aligned_new<T>(size)), m_cols(cols) {}
inline ~ei_matrix_storage() { ei_aligned_delete(m_data, _Rows*m_cols); }
inline void swap(ei_matrix_storage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
inline static int rows(void) {return _Rows;}
inline int cols(void) const {return m_cols;}
void resize(int size, int, int cols)
{
if(size != _Rows*m_cols)
{
ei_aligned_delete(m_data, _Rows*m_cols);
if (size)
m_data = ei_aligned_new<T>(size);
else
m_data = 0;
}
m_cols = cols;
}
inline const T *data() const { return m_data; }
inline T *data() { return m_data; }
};
// matrix with dynamic height and fixed width (so that matrix has dynamic size).
template<typename T, int _Cols, int _Options> class ei_matrix_storage<T, Dynamic, Dynamic, _Cols, _Options>
{
T *m_data;
int m_rows;
public:
inline explicit ei_matrix_storage() : m_data(0), m_rows(0) {}
inline ei_matrix_storage(ei_constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
inline ei_matrix_storage(int size, int rows, int) : m_data(ei_aligned_new<T>(size)), m_rows(rows) {}
inline ~ei_matrix_storage() { ei_aligned_delete(m_data, _Cols*m_rows); }
inline void swap(ei_matrix_storage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
inline int rows(void) const {return m_rows;}
inline static int cols(void) {return _Cols;}
void resize(int size, int rows, int)
{
if(size != m_rows*_Cols)
{
ei_aligned_delete(m_data, _Cols*m_rows);
if (size)
m_data = ei_aligned_new<T>(size);
else
m_data = 0;
}
m_rows = rows;
}
inline const T *data() const { return m_data; }
inline T *data() { return m_data; }
};
#endif // EIGEN_MATRIX_H

View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -25,7 +25,7 @@
#ifndef EIGEN_MINOR_H
#define EIGEN_MINOR_H
/**
/** \nonstableyet
* \class Minor
*
* \brief Expression of a minor
@@ -38,15 +38,12 @@
*
* \sa MatrixBase::minor()
*/
namespace internal {
template<typename MatrixType>
struct traits<Minor<MatrixType> >
: traits<MatrixType>
struct ei_traits<Minor<MatrixType> >
{
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
typedef typename MatrixType::StorageKind StorageKind;
typedef typename MatrixType::Scalar Scalar;
typedef typename ei_nested<MatrixType>::type MatrixTypeNested;
typedef typename ei_unref<MatrixTypeNested>::type _MatrixTypeNested;
enum {
RowsAtCompileTime = (MatrixType::RowsAtCompileTime != Dynamic) ?
int(MatrixType::RowsAtCompileTime) - 1 : Dynamic,
@@ -56,50 +53,47 @@ struct traits<Minor<MatrixType> >
int(MatrixType::MaxRowsAtCompileTime) - 1 : Dynamic,
MaxColsAtCompileTime = (MatrixType::MaxColsAtCompileTime != Dynamic) ?
int(MatrixType::MaxColsAtCompileTime) - 1 : Dynamic,
Flags = _MatrixTypeNested::Flags & (HereditaryBits | LvalueBit),
CoeffReadCost = _MatrixTypeNested::CoeffReadCost // minor is used typically on tiny matrices,
// where loops are unrolled and the 'if' evaluates at compile time
Flags = _MatrixTypeNested::Flags & HereditaryBits,
CoeffReadCost = _MatrixTypeNested::CoeffReadCost
};
};
}
template<typename MatrixType> class Minor
: public MatrixBase<Minor<MatrixType> >
{
public:
typedef MatrixBase<Minor> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Minor)
EIGEN_GENERIC_PUBLIC_INTERFACE(Minor)
inline Minor(const MatrixType& matrix,
Index row, Index col)
int row, int col)
: m_matrix(matrix), m_row(row), m_col(col)
{
eigen_assert(row >= 0 && row < matrix.rows()
ei_assert(row >= 0 && row < matrix.rows()
&& col >= 0 && col < matrix.cols());
}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Minor)
inline Index rows() const { return m_matrix.rows() - 1; }
inline Index cols() const { return m_matrix.cols() - 1; }
inline int rows() const { return m_matrix.rows() - 1; }
inline int cols() const { return m_matrix.cols() - 1; }
inline Scalar& coeffRef(Index row, Index col)
inline Scalar& coeffRef(int row, int col)
{
return m_matrix.const_cast_derived().coeffRef(row + (row >= m_row), col + (col >= m_col));
}
inline const Scalar coeff(Index row, Index col) const
inline const Scalar coeff(int row, int col) const
{
return m_matrix.coeff(row + (row >= m_row), col + (col >= m_col));
}
protected:
const typename MatrixType::Nested m_matrix;
const Index m_row, m_col;
const int m_row, m_col;
};
/**
/** \nonstableyet
* \return an expression of the (\a row, \a col)-minor of *this,
* i.e. an expression constructed from *this by removing the specified
* row and column.
@@ -111,16 +105,16 @@ template<typename MatrixType> class Minor
*/
template<typename Derived>
inline Minor<Derived>
MatrixBase<Derived>::minor(Index row, Index col)
MatrixBase<Derived>::minor(int row, int col)
{
return Minor<Derived>(derived(), row, col);
}
/**
/** \nonstableyet
* This is the const version of minor(). */
template<typename Derived>
inline const Minor<Derived>
MatrixBase<Derived>::minor(Index row, Index col) const
MatrixBase<Derived>::minor(int row, int col) const
{
return Minor<Derived>(derived(), row, col);
}

View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
@@ -27,7 +27,6 @@
#define EIGEN_NESTBYVALUE_H
/** \class NestByValue
* \ingroup Core_Module
*
* \brief Expression which must be nested by value
*
@@ -38,83 +37,79 @@
*
* \sa MatrixBase::nestByValue()
*/
namespace internal {
template<typename ExpressionType>
struct traits<NestByValue<ExpressionType> > : public traits<ExpressionType>
struct ei_traits<NestByValue<ExpressionType> > : public ei_traits<ExpressionType>
{};
}
template<typename ExpressionType> class NestByValue
: public internal::dense_xpr_base< NestByValue<ExpressionType> >::type
: public MatrixBase<NestByValue<ExpressionType> >
{
public:
typedef typename internal::dense_xpr_base<NestByValue>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(NestByValue)
EIGEN_GENERIC_PUBLIC_INTERFACE(NestByValue)
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(); }
inline int rows() const { return m_expression.rows(); }
inline int cols() const { return m_expression.cols(); }
inline int stride() const { return m_expression.stride(); }
inline const CoeffReturnType coeff(Index row, Index col) const
inline const Scalar coeff(int row, int col) const
{
return m_expression.coeff(row, col);
}
inline Scalar& coeffRef(Index row, Index col)
inline Scalar& coeffRef(int row, int col)
{
return m_expression.const_cast_derived().coeffRef(row, col);
}
inline const CoeffReturnType coeff(Index index) const
inline const Scalar coeff(int index) const
{
return m_expression.coeff(index);
}
inline Scalar& coeffRef(Index index)
inline Scalar& coeffRef(int index)
{
return m_expression.const_cast_derived().coeffRef(index);
}
template<int LoadMode>
inline const PacketScalar packet(Index row, Index col) const
inline const PacketScalar packet(int row, int col) const
{
return m_expression.template packet<LoadMode>(row, col);
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
inline void writePacket(int row, int col, const PacketScalar& x)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(row, col, x);
}
template<int LoadMode>
inline const PacketScalar packet(Index index) const
inline const PacketScalar packet(int index) const
{
return m_expression.template packet<LoadMode>(index);
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
inline void writePacket(int index, const PacketScalar& x)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(index, x);
}
operator const ExpressionType&() const { return m_expression; }
protected:
const ExpressionType m_expression;
private:
NestByValue& operator=(const NestByValue&);
};
/** \returns an expression of the temporary version of *this.
*/
template<typename Derived>
inline const NestByValue<Derived>
DenseBase<Derived>::nestByValue() const
MatrixBase<Derived>::nestByValue() const
{
return NestByValue<Derived>(derived());
}

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@@ -1,136 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_NOALIAS_H
#define EIGEN_NOALIAS_H
/** \class NoAlias
* \ingroup Core_Module
*
* \brief Pseudo expression providing an operator = assuming no aliasing
*
* \param ExpressionType the type of the object on which to do the lazy assignment
*
* This class represents an expression with special assignment operators
* assuming no aliasing between the target expression and the source expression.
* More precisely it alloas to bypass the EvalBeforeAssignBit flag of the source expression.
* It is the return type of MatrixBase::noalias()
* and most of the time this is the only way it is used.
*
* \sa MatrixBase::noalias()
*/
template<typename ExpressionType, template <typename> class StorageBase>
class NoAlias
{
typedef typename ExpressionType::Scalar Scalar;
public:
NoAlias(ExpressionType& expression) : m_expression(expression) {}
/** Behaves like MatrixBase::lazyAssign(other)
* \sa MatrixBase::lazyAssign() */
template<typename OtherDerived>
EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase<OtherDerived>& other)
{ return internal::assign_selector<ExpressionType,OtherDerived,false>::run(m_expression,other.derived()); }
/** \sa MatrixBase::operator+= */
template<typename OtherDerived>
EIGEN_STRONG_INLINE ExpressionType& operator+=(const StorageBase<OtherDerived>& other)
{
typedef SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, ExpressionType, OtherDerived> SelfAdder;
SelfAdder tmp(m_expression);
typedef typename internal::nested<OtherDerived>::type OtherDerivedNested;
typedef typename internal::remove_all<OtherDerivedNested>::type _OtherDerivedNested;
internal::assign_selector<SelfAdder,_OtherDerivedNested,false>::run(tmp,OtherDerivedNested(other.derived()));
return m_expression;
}
/** \sa MatrixBase::operator-= */
template<typename OtherDerived>
EIGEN_STRONG_INLINE ExpressionType& operator-=(const StorageBase<OtherDerived>& other)
{
typedef SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, ExpressionType, OtherDerived> SelfAdder;
SelfAdder tmp(m_expression);
typedef typename internal::nested<OtherDerived>::type OtherDerivedNested;
typedef typename internal::remove_all<OtherDerivedNested>::type _OtherDerivedNested;
internal::assign_selector<SelfAdder,_OtherDerivedNested,false>::run(tmp,OtherDerivedNested(other.derived()));
return m_expression;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename ProductDerived, typename Lhs, typename Rhs>
EIGEN_STRONG_INLINE ExpressionType& operator+=(const ProductBase<ProductDerived, Lhs,Rhs>& other)
{ other.derived().addTo(m_expression); return m_expression; }
template<typename ProductDerived, typename Lhs, typename Rhs>
EIGEN_STRONG_INLINE ExpressionType& operator-=(const ProductBase<ProductDerived, Lhs,Rhs>& other)
{ other.derived().subTo(m_expression); return m_expression; }
template<typename Lhs, typename Rhs, int NestingFlags>
EIGEN_STRONG_INLINE ExpressionType& operator+=(const CoeffBasedProduct<Lhs,Rhs,NestingFlags>& other)
{ return m_expression.derived() += CoeffBasedProduct<Lhs,Rhs,NestByRefBit>(other.lhs(), other.rhs()); }
template<typename Lhs, typename Rhs, int NestingFlags>
EIGEN_STRONG_INLINE ExpressionType& operator-=(const CoeffBasedProduct<Lhs,Rhs,NestingFlags>& other)
{ return m_expression.derived() -= CoeffBasedProduct<Lhs,Rhs,NestByRefBit>(other.lhs(), other.rhs()); }
#endif
protected:
ExpressionType& m_expression;
};
/** \returns a pseudo expression of \c *this with an operator= assuming
* no aliasing between \c *this and the source expression.
*
* More precisely, noalias() allows to bypass the EvalBeforeAssignBit flag.
* Currently, even though several expressions may alias, only product
* expressions have this flag. Therefore, noalias() is only usefull when
* the source expression contains a matrix product.
*
* Here are some examples where noalias is usefull:
* \code
* D.noalias() = A * B;
* D.noalias() += A.transpose() * B;
* D.noalias() -= 2 * A * B.adjoint();
* \endcode
*
* On the other hand the following example will lead to a \b wrong result:
* \code
* A.noalias() = A * B;
* \endcode
* because the result matrix A is also an operand of the matrix product. Therefore,
* there is no alternative than evaluating A * B in a temporary, that is the default
* behavior when you write:
* \code
* A = A * B;
* \endcode
*
* \sa class NoAlias
*/
template<typename Derived>
NoAlias<Derived,MatrixBase> MatrixBase<Derived>::noalias()
{
return derived();
}
#endif // EIGEN_NOALIAS_H

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@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -26,135 +26,117 @@
#define EIGEN_NUMTRAITS_H
/** \class NumTraits
* \ingroup Core_Module
*
* \brief Holds information about the various numeric (i.e. scalar) types allowed by Eigen.
* \brief Holds some data about the various numeric (i.e. scalar) types allowed by Eigen.
*
* \param T the numeric type at hand
*
* This class stores enums, typedefs and static methods giving information about a numeric type.
* \param T the numeric type about which this class provides data. Recall that Eigen allows
* only the following types for \a T: \c int, \c float, \c double,
* \c std::complex<float>, \c std::complex<double>, and \c long \c double (especially
* useful to enforce x87 arithmetics when SSE is the default).
*
* The provided data consists of:
* \li A typedef \a Real, giving the "real part" type of \a T. If \a T is already real,
* then \a Real is just a typedef to \a T. If \a T is \c std::complex<U> then \a Real
* is a typedef to \a U.
* \li A typedef \a NonInteger, giving the type that should be used for operations producing non-integral values,
* such as quotients, square roots, etc. If \a T is a floating-point type, then this typedef just gives
* \a T again. Note however that many Eigen functions such as internal::sqrt simply refuse to
* take integers. Outside of a few cases, Eigen doesn't do automatic type promotion. Thus, this typedef is
* only intended as a helper for code that needs to explicitly promote types.
* \li A typedef \a Nested giving the type to use to nest a value inside of the expression tree. If you don't know what
* this means, just use \a T here.
* \li A typedef \a FloatingPoint, giving the "floating-point type" of \a T. If \a T is
* \c int, then \a FloatingPoint is a typedef to \c double. Otherwise, \a FloatingPoint
* is a typedef to \a T.
* \li An enum value \a IsComplex. It is equal to 1 if \a T is a \c std::complex
* type, and to 0 otherwise.
* \li An enum value \a IsInteger. It is equal to \c 1 if \a T is an integer type such as \c int,
* and to \c 0 otherwise.
* \li Enum values ReadCost, AddCost and MulCost representing a rough estimate of the number of CPU cycles needed
* to by move / add / mul instructions respectively, assuming the data is already stored in CPU registers.
* Stay vague here. No need to do architecture-specific stuff.
* \li An enum value \a IsSigned. It is equal to \c 1 if \a T is a signed type and to 0 if \a T is unsigned.
* \li An enum value \a RequireInitialization. It is equal to \c 1 if the constructor of the numeric type \a T must
* be called, and to 0 if it is safe not to call it. Default is 0 if \a T is an arithmetic type, and 1 otherwise.
* \li An epsilon() function which, unlike std::numeric_limits::epsilon(), returns a \a Real instead of a \a T.
* \li A dummy_precision() function returning a weak epsilon value. It is mainly used as a default
* value by the fuzzy comparison operators.
* \li highest() and lowest() functions returning the highest and lowest possible values respectively.
* \li An enum \a HasFloatingPoint. It is equal to \c 0 if \a T is \c int,
* and to \c 1 otherwise.
*/
template<typename T> struct NumTraits;
template<typename T> struct GenericNumTraits
template<> struct NumTraits<int>
{
typedef int Real;
typedef double FloatingPoint;
enum {
IsInteger = std::numeric_limits<T>::is_integer,
IsSigned = std::numeric_limits<T>::is_signed,
IsComplex = 0,
RequireInitialization = internal::is_arithmetic<T>::value ? 0 : 1,
HasFloatingPoint = 0,
ReadCost = 1,
AddCost = 1,
MulCost = 1
};
typedef T Real;
typedef typename internal::conditional<
IsInteger,
typename internal::conditional<sizeof(T)<=2, float, double>::type,
T
>::type NonInteger;
typedef T Nested;
inline static Real epsilon() { return std::numeric_limits<T>::epsilon(); }
inline static Real dummy_precision()
{
// make sure to override this for floating-point types
return Real(0);
}
inline static T highest() { return (std::numeric_limits<T>::max)(); }
inline static T lowest() { return IsInteger ? (std::numeric_limits<T>::min)() : (-(std::numeric_limits<T>::max)()); }
#ifdef EIGEN2_SUPPORT
enum {
HasFloatingPoint = !IsInteger
};
typedef NonInteger FloatingPoint;
#endif
};
template<typename T> struct NumTraits : GenericNumTraits<T>
{};
template<> struct NumTraits<float>
: GenericNumTraits<float>
{
inline static float dummy_precision() { return 1e-5f; }
typedef float Real;
typedef float FloatingPoint;
enum {
IsComplex = 0,
HasFloatingPoint = 1,
ReadCost = 1,
AddCost = 1,
MulCost = 1
};
};
template<> struct NumTraits<double> : GenericNumTraits<double>
template<> struct NumTraits<double>
{
inline static double dummy_precision() { return 1e-12; }
};
template<> struct NumTraits<long double>
: GenericNumTraits<long double>
{
static inline long double dummy_precision() { return 1e-15l; }
typedef double Real;
typedef double FloatingPoint;
enum {
IsComplex = 0,
HasFloatingPoint = 1,
ReadCost = 1,
AddCost = 1,
MulCost = 1
};
};
template<typename _Real> struct NumTraits<std::complex<_Real> >
: GenericNumTraits<std::complex<_Real> >
{
typedef _Real Real;
typedef std::complex<_Real> FloatingPoint;
enum {
IsComplex = 1,
RequireInitialization = NumTraits<_Real>::RequireInitialization,
ReadCost = 2 * NumTraits<_Real>::ReadCost,
HasFloatingPoint = NumTraits<Real>::HasFloatingPoint,
ReadCost = 2,
AddCost = 2 * NumTraits<Real>::AddCost,
MulCost = 4 * NumTraits<Real>::MulCost + 2 * NumTraits<Real>::AddCost
};
inline static Real epsilon() { return NumTraits<Real>::epsilon(); }
inline static Real dummy_precision() { return NumTraits<Real>::dummy_precision(); }
};
template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
struct NumTraits<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
template<> struct NumTraits<long long int>
{
typedef Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> ArrayType;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Array<RealScalar, Rows, Cols, Options, MaxRows, MaxCols> Real;
typedef typename NumTraits<Scalar>::NonInteger NonIntegerScalar;
typedef Array<NonIntegerScalar, Rows, Cols, Options, MaxRows, MaxCols> NonInteger;
typedef ArrayType & Nested;
typedef long long int Real;
typedef long double FloatingPoint;
enum {
IsComplex = NumTraits<Scalar>::IsComplex,
IsInteger = NumTraits<Scalar>::IsInteger,
IsSigned = NumTraits<Scalar>::IsSigned,
RequireInitialization = 1,
ReadCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::ReadCost,
AddCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::AddCost,
MulCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::MulCost
IsComplex = 0,
HasFloatingPoint = 0,
ReadCost = 1,
AddCost = 1,
MulCost = 1
};
};
template<> struct NumTraits<long double>
{
typedef long double Real;
typedef long double FloatingPoint;
enum {
IsComplex = 0,
HasFloatingPoint = 1,
ReadCost = 1,
AddCost = 1,
MulCost = 1
};
};
template<> struct NumTraits<bool>
{
typedef bool Real;
typedef float FloatingPoint;
enum {
IsComplex = 0,
HasFloatingPoint = 0,
ReadCost = 1,
AddCost = 1,
MulCost = 1
};
};
#endif // EIGEN_NUMTRAITS_H

377
Eigen/src/Core/Part.h Normal file
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@@ -0,0 +1,377 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_PART_H
#define EIGEN_PART_H
/** \nonstableyet
* \class Part
*
* \brief Expression of a triangular matrix extracted from a given matrix
*
* \param MatrixType the type of the object in which we are taking the triangular part
* \param Mode the kind of triangular matrix expression to construct. Can be UpperTriangular, StrictlyUpperTriangular,
* UnitUpperTriangular, LowerTriangular, StrictlyLowerTriangular, UnitLowerTriangular. This is in fact a bit field; it must have either
* UpperTriangularBit or LowerTriangularBit, and additionnaly it may have either ZeroDiagBit or
* UnitDiagBit.
*
* This class represents an expression of the upper or lower triangular part of
* a square matrix, possibly with a further assumption on the diagonal. It is the return type
* of MatrixBase::part() and most of the time this is the only way it is used.
*
* \sa MatrixBase::part()
*/
template<typename MatrixType, unsigned int Mode>
struct ei_traits<Part<MatrixType, Mode> > : ei_traits<MatrixType>
{
typedef typename ei_nested<MatrixType>::type MatrixTypeNested;
typedef typename ei_unref<MatrixTypeNested>::type _MatrixTypeNested;
enum {
Flags = (_MatrixTypeNested::Flags & (HereditaryBits) & (~(PacketAccessBit | DirectAccessBit | LinearAccessBit))) | Mode,
CoeffReadCost = _MatrixTypeNested::CoeffReadCost
};
};
template<typename MatrixType, unsigned int Mode> class Part
: public MatrixBase<Part<MatrixType, Mode> >
{
public:
EIGEN_GENERIC_PUBLIC_INTERFACE(Part)
inline Part(const MatrixType& matrix) : m_matrix(matrix)
{ ei_assert(ei_are_flags_consistent<Mode>::ret); }
/** \sa MatrixBase::operator+=() */
template<typename Other> Part& operator+=(const Other& other);
/** \sa MatrixBase::operator-=() */
template<typename Other> Part& operator-=(const Other& other);
/** \sa MatrixBase::operator*=() */
Part& operator*=(const typename ei_traits<MatrixType>::Scalar& other);
/** \sa MatrixBase::operator/=() */
Part& operator/=(const typename ei_traits<MatrixType>::Scalar& other);
/** \sa operator=(), MatrixBase::lazyAssign() */
template<typename Other> void lazyAssign(const Other& other);
/** \sa MatrixBase::operator=() */
template<typename Other> Part& operator=(const Other& other);
inline int rows() const { return m_matrix.rows(); }
inline int cols() const { return m_matrix.cols(); }
inline int stride() const { return m_matrix.stride(); }
inline Scalar coeff(int row, int col) const
{
// SelfAdjointBit doesn't play any role here: just because a matrix is selfadjoint doesn't say anything about
// each individual coefficient, except for the not-very-useful-here fact that diagonal coefficients are real.
if( ((Flags & LowerTriangularBit) && (col>row)) || ((Flags & UpperTriangularBit) && (row>col)) )
return (Scalar)0;
if(Flags & UnitDiagBit)
return col==row ? (Scalar)1 : m_matrix.coeff(row, col);
else if(Flags & ZeroDiagBit)
return col==row ? (Scalar)0 : m_matrix.coeff(row, col);
else
return m_matrix.coeff(row, col);
}
inline Scalar& coeffRef(int row, int col)
{
EIGEN_STATIC_ASSERT(!(Flags & UnitDiagBit), WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED)
EIGEN_STATIC_ASSERT(!(Flags & SelfAdjointBit), COEFFICIENT_WRITE_ACCESS_TO_SELFADJOINT_NOT_SUPPORTED)
ei_assert( (Mode==UpperTriangular && col>=row)
|| (Mode==LowerTriangular && col<=row)
|| (Mode==StrictlyUpperTriangular && col>row)
|| (Mode==StrictlyLowerTriangular && col<row));
return m_matrix.const_cast_derived().coeffRef(row, col);
}
/** \internal */
const MatrixType& _expression() const { return m_matrix; }
/** discard any writes to a row */
const Block<Part, 1, ColsAtCompileTime> row(int i) { return Base::row(i); }
const Block<Part, 1, ColsAtCompileTime> row(int i) const { return Base::row(i); }
/** discard any writes to a column */
const Block<Part, RowsAtCompileTime, 1> col(int i) { return Base::col(i); }
const Block<Part, RowsAtCompileTime, 1> col(int i) const { return Base::col(i); }
template<typename OtherDerived>
void swap(const MatrixBase<OtherDerived>& other)
{
Part<SwapWrapper<MatrixType>,Mode>(const_cast<MatrixType&>(m_matrix)).lazyAssign(other.derived());
}
protected:
const typename MatrixType::Nested m_matrix;
private:
Part& operator=(const Part&);
};
/** \nonstableyet
* \returns an expression of a triangular matrix extracted from the current matrix
*
* The parameter \a Mode can have the following values: \c UpperTriangular, \c StrictlyUpperTriangular, \c UnitUpperTriangular,
* \c LowerTriangular, \c StrictlyLowerTriangular, \c UnitLowerTriangular.
*
* \addexample PartExample \label How to extract a triangular part of an arbitrary matrix
*
* Example: \include MatrixBase_extract.cpp
* Output: \verbinclude MatrixBase_extract.out
*
* \sa class Part, part(), marked()
*/
template<typename Derived>
template<unsigned int Mode>
const Part<Derived, Mode> MatrixBase<Derived>::part() const
{
return derived();
}
template<typename MatrixType, unsigned int Mode>
template<typename Other>
inline Part<MatrixType, Mode>& Part<MatrixType, Mode>::operator=(const Other& other)
{
if(Other::Flags & EvalBeforeAssigningBit)
{
typename MatrixBase<Other>::PlainMatrixType other_evaluated(other.rows(), other.cols());
other_evaluated.template part<Mode>().lazyAssign(other);
lazyAssign(other_evaluated);
}
else
lazyAssign(other.derived());
return *this;
}
template<typename Derived1, typename Derived2, unsigned int Mode, int UnrollCount>
struct ei_part_assignment_impl
{
enum {
col = (UnrollCount-1) / Derived1::RowsAtCompileTime,
row = (UnrollCount-1) % Derived1::RowsAtCompileTime
};
inline static void run(Derived1 &dst, const Derived2 &src)
{
ei_part_assignment_impl<Derived1, Derived2, Mode, UnrollCount-1>::run(dst, src);
if(Mode == SelfAdjoint)
{
if(row == col)
dst.coeffRef(row, col) = ei_real(src.coeff(row, col));
else if(row < col)
dst.coeffRef(col, row) = ei_conj(dst.coeffRef(row, col) = src.coeff(row, col));
}
else
{
ei_assert(Mode == UpperTriangular || Mode == LowerTriangular || Mode == StrictlyUpperTriangular || Mode == StrictlyLowerTriangular);
if((Mode == UpperTriangular && row <= col)
|| (Mode == LowerTriangular && row >= col)
|| (Mode == StrictlyUpperTriangular && row < col)
|| (Mode == StrictlyLowerTriangular && row > col))
dst.copyCoeff(row, col, src);
}
}
};
template<typename Derived1, typename Derived2, unsigned int Mode>
struct ei_part_assignment_impl<Derived1, Derived2, Mode, 1>
{
inline static void run(Derived1 &dst, const Derived2 &src)
{
if(!(Mode & ZeroDiagBit))
dst.copyCoeff(0, 0, src);
}
};
// prevent buggy user code from causing an infinite recursion
template<typename Derived1, typename Derived2, unsigned int Mode>
struct ei_part_assignment_impl<Derived1, Derived2, Mode, 0>
{
inline static void run(Derived1 &, const Derived2 &) {}
};
template<typename Derived1, typename Derived2>
struct ei_part_assignment_impl<Derived1, Derived2, UpperTriangular, Dynamic>
{
inline static void run(Derived1 &dst, const Derived2 &src)
{
for(int j = 0; j < dst.cols(); ++j)
for(int i = 0; i <= j; ++i)
dst.copyCoeff(i, j, src);
}
};
template<typename Derived1, typename Derived2>
struct ei_part_assignment_impl<Derived1, Derived2, LowerTriangular, Dynamic>
{
inline static void run(Derived1 &dst, const Derived2 &src)
{
for(int j = 0; j < dst.cols(); ++j)
for(int i = j; i < dst.rows(); ++i)
dst.copyCoeff(i, j, src);
}
};
template<typename Derived1, typename Derived2>
struct ei_part_assignment_impl<Derived1, Derived2, StrictlyUpperTriangular, Dynamic>
{
inline static void run(Derived1 &dst, const Derived2 &src)
{
for(int j = 0; j < dst.cols(); ++j)
for(int i = 0; i < j; ++i)
dst.copyCoeff(i, j, src);
}
};
template<typename Derived1, typename Derived2>
struct ei_part_assignment_impl<Derived1, Derived2, StrictlyLowerTriangular, Dynamic>
{
inline static void run(Derived1 &dst, const Derived2 &src)
{
for(int j = 0; j < dst.cols(); ++j)
for(int i = j+1; i < dst.rows(); ++i)
dst.copyCoeff(i, j, src);
}
};
template<typename Derived1, typename Derived2>
struct ei_part_assignment_impl<Derived1, Derived2, SelfAdjoint, Dynamic>
{
inline static void run(Derived1 &dst, const Derived2 &src)
{
for(int j = 0; j < dst.cols(); ++j)
{
for(int i = 0; i < j; ++i)
dst.coeffRef(j, i) = ei_conj(dst.coeffRef(i, j) = src.coeff(i, j));
dst.coeffRef(j, j) = ei_real(src.coeff(j, j));
}
}
};
template<typename MatrixType, unsigned int Mode>
template<typename Other>
void Part<MatrixType, Mode>::lazyAssign(const Other& other)
{
const bool unroll = MatrixType::SizeAtCompileTime * Other::CoeffReadCost / 2 <= EIGEN_UNROLLING_LIMIT;
ei_assert(m_matrix.rows() == other.rows() && m_matrix.cols() == other.cols());
ei_part_assignment_impl
<MatrixType, Other, Mode,
unroll ? int(MatrixType::SizeAtCompileTime) : Dynamic
>::run(m_matrix.const_cast_derived(), other.derived());
}
/** \nonstableyet
* \returns a lvalue pseudo-expression allowing to perform special operations on \c *this.
*
* The \a Mode parameter can have the following values: \c UpperTriangular, \c StrictlyUpperTriangular, \c LowerTriangular,
* \c StrictlyLowerTriangular, \c SelfAdjoint.
*
* \addexample PartExample \label How to write to a triangular part of a matrix
*
* Example: \include MatrixBase_part.cpp
* Output: \verbinclude MatrixBase_part.out
*
* \sa class Part, MatrixBase::extract(), MatrixBase::marked()
*/
template<typename Derived>
template<unsigned int Mode>
inline Part<Derived, Mode> MatrixBase<Derived>::part()
{
return Part<Derived, Mode>(derived());
}
/** \returns true if *this is approximately equal to an upper triangular matrix,
* within the precision given by \a prec.
*
* \sa isLowerTriangular(), extract(), part(), marked()
*/
template<typename Derived>
bool MatrixBase<Derived>::isUpperTriangular(RealScalar prec) const
{
if(cols() != rows()) return false;
RealScalar maxAbsOnUpperTriangularPart = static_cast<RealScalar>(-1);
for(int j = 0; j < cols(); ++j)
for(int i = 0; i <= j; ++i)
{
RealScalar absValue = ei_abs(coeff(i,j));
if(absValue > maxAbsOnUpperTriangularPart) maxAbsOnUpperTriangularPart = absValue;
}
for(int j = 0; j < cols()-1; ++j)
for(int i = j+1; i < rows(); ++i)
if(!ei_isMuchSmallerThan(coeff(i, j), maxAbsOnUpperTriangularPart, prec)) return false;
return true;
}
/** \returns true if *this is approximately equal to a lower triangular matrix,
* within the precision given by \a prec.
*
* \sa isUpperTriangular(), extract(), part(), marked()
*/
template<typename Derived>
bool MatrixBase<Derived>::isLowerTriangular(RealScalar prec) const
{
if(cols() != rows()) return false;
RealScalar maxAbsOnLowerTriangularPart = static_cast<RealScalar>(-1);
for(int j = 0; j < cols(); ++j)
for(int i = j; i < rows(); ++i)
{
RealScalar absValue = ei_abs(coeff(i,j));
if(absValue > maxAbsOnLowerTriangularPart) maxAbsOnLowerTriangularPart = absValue;
}
for(int j = 1; j < cols(); ++j)
for(int i = 0; i < j; ++i)
if(!ei_isMuchSmallerThan(coeff(i, j), maxAbsOnLowerTriangularPart, prec)) return false;
return true;
}
template<typename MatrixType, unsigned int Mode>
template<typename Other>
inline Part<MatrixType, Mode>& Part<MatrixType, Mode>::operator+=(const Other& other)
{
return *this = m_matrix + other;
}
template<typename MatrixType, unsigned int Mode>
template<typename Other>
inline Part<MatrixType, Mode>& Part<MatrixType, Mode>::operator-=(const Other& other)
{
return *this = m_matrix - other;
}
template<typename MatrixType, unsigned int Mode>
inline Part<MatrixType, Mode>& Part<MatrixType, Mode>::operator*=
(const typename ei_traits<MatrixType>::Scalar& other)
{
return *this = m_matrix * other;
}
template<typename MatrixType, unsigned int Mode>
inline Part<MatrixType, Mode>& Part<MatrixType, Mode>::operator/=
(const typename ei_traits<MatrixType>::Scalar& other)
{
return *this = m_matrix / other;
}
#endif // EIGEN_PART_H

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@@ -1,696 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2009-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_PERMUTATIONMATRIX_H
#define EIGEN_PERMUTATIONMATRIX_H
template<int RowCol,typename IndicesType,typename MatrixType, typename StorageKind> class PermutedImpl;
/** \class PermutationBase
* \ingroup Core_Module
*
* \brief Base class for permutations
*
* \param Derived the derived class
*
* This class is the base class for all expressions representing a permutation matrix,
* internally stored as a vector of integers.
* The convention followed here is that if \f$ \sigma \f$ is a permutation, the corresponding permutation matrix
* \f$ P_\sigma \f$ is such that if \f$ (e_1,\ldots,e_p) \f$ is the canonical basis, we have:
* \f[ P_\sigma(e_i) = e_{\sigma(i)}. \f]
* This convention ensures that for any two permutations \f$ \sigma, \tau \f$, we have:
* \f[ P_{\sigma\circ\tau} = P_\sigma P_\tau. \f]
*
* Permutation matrices are square and invertible.
*
* Notice that in addition to the member functions and operators listed here, there also are non-member
* operator* to multiply any kind of permutation object with any kind of matrix expression (MatrixBase)
* on either side.
*
* \sa class PermutationMatrix, class PermutationWrapper
*/
namespace internal {
template<typename PermutationType, typename MatrixType, int Side, bool Transposed=false>
struct permut_matrix_product_retval;
enum PermPermProduct_t {PermPermProduct};
} // end namespace internal
template<typename Derived>
class PermutationBase : public EigenBase<Derived>
{
typedef internal::traits<Derived> Traits;
typedef EigenBase<Derived> Base;
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef typename Traits::IndicesType IndicesType;
enum {
Flags = Traits::Flags,
CoeffReadCost = Traits::CoeffReadCost,
RowsAtCompileTime = Traits::RowsAtCompileTime,
ColsAtCompileTime = Traits::ColsAtCompileTime,
MaxRowsAtCompileTime = Traits::MaxRowsAtCompileTime,
MaxColsAtCompileTime = Traits::MaxColsAtCompileTime
};
typedef typename Traits::Scalar Scalar;
typedef typename Traits::Index Index;
typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime,0,MaxRowsAtCompileTime,MaxColsAtCompileTime>
DenseMatrixType;
typedef PermutationMatrix<IndicesType::SizeAtCompileTime,IndicesType::MaxSizeAtCompileTime,Index>
PlainPermutationType;
using Base::derived;
#endif
/** Copies the other permutation into *this */
template<typename OtherDerived>
Derived& operator=(const PermutationBase<OtherDerived>& other)
{
indices() = other.indices();
return derived();
}
/** Assignment from the Transpositions \a tr */
template<typename OtherDerived>
Derived& operator=(const TranspositionsBase<OtherDerived>& tr)
{
setIdentity(tr.size());
for(Index k=size()-1; k>=0; --k)
applyTranspositionOnTheRight(k,tr.coeff(k));
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=.
*/
Derived& operator=(const PermutationBase& other)
{
indices() = other.indices();
return derived();
}
#endif
/** \returns the number of rows */
inline Index rows() const { return indices().size(); }
/** \returns the number of columns */
inline Index cols() const { return indices().size(); }
/** \returns the size of a side of the respective square matrix, i.e., the number of indices */
inline Index size() const { return indices().size(); }
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename DenseDerived>
void evalTo(MatrixBase<DenseDerived>& other) const
{
other.setZero();
for (int i=0; i<rows();++i)
other.coeffRef(indices().coeff(i),i) = typename DenseDerived::Scalar(1);
}
#endif
/** \returns a Matrix object initialized from this permutation matrix. Notice that it
* is inefficient to return this Matrix object by value. For efficiency, favor using
* the Matrix constructor taking EigenBase objects.
*/
DenseMatrixType toDenseMatrix() const
{
return derived();
}
/** const version of indices(). */
const IndicesType& indices() const { return derived().indices(); }
/** \returns a reference to the stored array representing the permutation. */
IndicesType& indices() { return derived().indices(); }
/** Resizes to given size.
*/
inline void resize(Index size)
{
indices().resize(size);
}
/** Sets *this to be the identity permutation matrix */
void setIdentity()
{
for(Index i = 0; i < size(); ++i)
indices().coeffRef(i) = i;
}
/** Sets *this to be the identity permutation matrix of given size.
*/
void setIdentity(Index size)
{
resize(size);
setIdentity();
}
/** Multiplies *this by the transposition \f$(ij)\f$ on the left.
*
* \returns a reference to *this.
*
* \warning This is much slower than applyTranspositionOnTheRight(int,int):
* this has linear complexity and requires a lot of branching.
*
* \sa applyTranspositionOnTheRight(int,int)
*/
Derived& applyTranspositionOnTheLeft(Index i, Index j)
{
eigen_assert(i>=0 && j>=0 && i<size() && j<size());
for(Index k = 0; k < size(); ++k)
{
if(indices().coeff(k) == i) indices().coeffRef(k) = j;
else if(indices().coeff(k) == j) indices().coeffRef(k) = i;
}
return derived();
}
/** Multiplies *this by the transposition \f$(ij)\f$ on the right.
*
* \returns a reference to *this.
*
* This is a fast operation, it only consists in swapping two indices.
*
* \sa applyTranspositionOnTheLeft(int,int)
*/
Derived& applyTranspositionOnTheRight(Index i, Index j)
{
eigen_assert(i>=0 && j>=0 && i<size() && j<size());
std::swap(indices().coeffRef(i), indices().coeffRef(j));
return derived();
}
/** \returns the inverse permutation matrix.
*
* \note \note_try_to_help_rvo
*/
inline Transpose<PermutationBase> inverse() const
{ return derived(); }
/** \returns the tranpose permutation matrix.
*
* \note \note_try_to_help_rvo
*/
inline Transpose<PermutationBase> transpose() const
{ return derived(); }
/**** multiplication helpers to hopefully get RVO ****/
#ifndef EIGEN_PARSED_BY_DOXYGEN
protected:
template<typename OtherDerived>
void assignTranspose(const PermutationBase<OtherDerived>& other)
{
for (int 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));
}
#endif
public:
/** \returns the product permutation matrix.
*
* \note \note_try_to_help_rvo
*/
template<typename Other>
inline PlainPermutationType operator*(const PermutationBase<Other>& other) const
{ return PlainPermutationType(internal::PermPermProduct, derived(), other.derived()); }
/** \returns the product of a permutation with another inverse permutation.
*
* \note \note_try_to_help_rvo
*/
template<typename Other>
inline PlainPermutationType operator*(const Transpose<PermutationBase<Other> >& other) const
{ return PlainPermutationType(internal::PermPermProduct, *this, other.eval()); }
/** \returns the product of an inverse permutation with another permutation.
*
* \note \note_try_to_help_rvo
*/
template<typename Other> friend
inline PlainPermutationType operator*(const Transpose<PermutationBase<Other> >& other, const PermutationBase& perm)
{ return PlainPermutationType(internal::PermPermProduct, other.eval(), perm); }
protected:
};
/** \class PermutationMatrix
* \ingroup Core_Module
*
* \brief Permutation matrix
*
* \param SizeAtCompileTime the number of rows/cols, or Dynamic
* \param MaxSizeAtCompileTime the maximum number of rows/cols, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it.
* \param IndexType the interger type of the indices
*
* This class represents a permutation matrix, internally stored as a vector of integers.
*
* \sa class PermutationBase, class PermutationWrapper, class DiagonalMatrix
*/
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType>
struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType> >
: traits<Matrix<IndexType,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef IndexType Index;
typedef Matrix<IndexType, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
};
}
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType>
class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType> >
{
typedef PermutationBase<PermutationMatrix> Base;
typedef internal::traits<PermutationMatrix> Traits;
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef typename Traits::IndicesType IndicesType;
#endif
inline PermutationMatrix()
{}
/** Constructs an uninitialized permutation matrix of given size.
*/
inline PermutationMatrix(int size) : m_indices(size)
{}
/** Copy constructor. */
template<typename OtherDerived>
inline PermutationMatrix(const PermutationBase<OtherDerived>& other)
: m_indices(other.indices()) {}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** Standard copy constructor. Defined only to prevent a default copy constructor
* from hiding the other templated constructor */
inline PermutationMatrix(const PermutationMatrix& other) : m_indices(other.indices()) {}
#endif
/** Generic constructor from expression of the indices. The indices
* array has the meaning that the permutations sends each integer i to indices[i].
*
* \warning It is your responsibility to check that the indices array that you passes actually
* describes a permutation, i.e., each value between 0 and n-1 occurs exactly once, where n is the
* array's size.
*/
template<typename Other>
explicit inline PermutationMatrix(const MatrixBase<Other>& indices) : m_indices(indices)
{}
/** Convert the Transpositions \a tr to a permutation matrix */
template<typename Other>
explicit PermutationMatrix(const TranspositionsBase<Other>& tr)
: m_indices(tr.size())
{
*this = tr;
}
/** Copies the other permutation into *this */
template<typename Other>
PermutationMatrix& operator=(const PermutationBase<Other>& other)
{
m_indices = other.indices();
return *this;
}
/** Assignment from the Transpositions \a tr */
template<typename Other>
PermutationMatrix& operator=(const TranspositionsBase<Other>& tr)
{
return Base::operator=(tr.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=.
*/
PermutationMatrix& operator=(const PermutationMatrix& other)
{
m_indices = other.m_indices;
return *this;
}
#endif
/** const version of indices(). */
const IndicesType& indices() const { return m_indices; }
/** \returns a reference to the stored array representing the permutation. */
IndicesType& indices() { return m_indices; }
/**** multiplication helpers to hopefully get RVO ****/
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename Other>
PermutationMatrix(const Transpose<PermutationBase<Other> >& other)
: m_indices(other.nestedPermutation().size())
{
for (int i=0; i<m_indices.size();++i) m_indices.coeffRef(other.nestedPermutation().indices().coeff(i)) = i;
}
template<typename Lhs,typename Rhs>
PermutationMatrix(internal::PermPermProduct_t, const Lhs& lhs, const Rhs& rhs)
: m_indices(lhs.indices().size())
{
Base::assignProduct(lhs,rhs);
}
#endif
protected:
IndicesType m_indices;
};
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType, int _PacketAccess>
struct traits<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,_PacketAccess> >
: traits<Matrix<IndexType,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef IndexType Index;
typedef Map<const Matrix<IndexType, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1>, _PacketAccess> IndicesType;
};
}
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType, int _PacketAccess>
class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,_PacketAccess>
: public PermutationBase<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,_PacketAccess> >
{
typedef PermutationBase<Map> Base;
typedef internal::traits<Map> Traits;
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef typename Traits::IndicesType IndicesType;
typedef typename IndicesType::Scalar Index;
#endif
inline Map(const Index* indices)
: m_indices(indices)
{}
inline Map(const Index* indices, Index size)
: m_indices(indices,size)
{}
/** Copies the other permutation into *this */
template<typename Other>
Map& operator=(const PermutationBase<Other>& other)
{ return Base::operator=(other.derived()); }
/** Assignment from the Transpositions \a tr */
template<typename Other>
Map& operator=(const TranspositionsBase<Other>& tr)
{ return Base::operator=(tr.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=.
*/
Map& operator=(const Map& other)
{
m_indices = other.m_indices;
return *this;
}
#endif
/** const version of indices(). */
const IndicesType& indices() const { return m_indices; }
/** \returns a reference to the stored array representing the permutation. */
IndicesType& indices() { return m_indices; }
protected:
IndicesType m_indices;
};
/** \class PermutationWrapper
* \ingroup Core_Module
*
* \brief Class to view a vector of integers as a permutation matrix
*
* \param _IndicesType the type of the vector of integer (can be any compatible expression)
*
* This class allows to view any vector expression of integers as a permutation matrix.
*
* \sa class PermutationBase, class PermutationMatrix
*/
struct PermutationStorage {};
template<typename _IndicesType> class TranspositionsWrapper;
namespace internal {
template<typename _IndicesType>
struct traits<PermutationWrapper<_IndicesType> >
{
typedef PermutationStorage StorageKind;
typedef typename _IndicesType::Scalar Scalar;
typedef typename _IndicesType::Scalar Index;
typedef _IndicesType IndicesType;
enum {
RowsAtCompileTime = _IndicesType::SizeAtCompileTime,
ColsAtCompileTime = _IndicesType::SizeAtCompileTime,
MaxRowsAtCompileTime = IndicesType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = IndicesType::MaxColsAtCompileTime,
Flags = 0,
CoeffReadCost = _IndicesType::CoeffReadCost
};
};
}
template<typename _IndicesType>
class PermutationWrapper : public PermutationBase<PermutationWrapper<_IndicesType> >
{
typedef PermutationBase<PermutationWrapper> Base;
typedef internal::traits<PermutationWrapper> Traits;
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef typename Traits::IndicesType IndicesType;
#endif
inline PermutationWrapper(const IndicesType& indices)
: m_indices(indices)
{}
/** const version of indices(). */
const typename internal::remove_all<typename IndicesType::Nested>::type&
indices() const { return m_indices; }
protected:
const 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)
{
return internal::permut_matrix_product_retval
<PermutationDerived, Derived, OnTheRight>
(permutation.derived(), matrix.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>
operator*(const PermutationBase<PermutationDerived> &permutation,
const MatrixBase<Derived>& matrix)
{
return internal::permut_matrix_product_retval
<PermutationDerived, Derived, OnTheLeft>
(permutation.derived(), matrix.derived());
}
namespace internal {
template<typename PermutationType, typename MatrixType, int Side, bool Transposed>
struct traits<permut_matrix_product_retval<PermutationType, MatrixType, Side, Transposed> >
{
typedef typename MatrixType::PlainObject ReturnType;
};
template<typename PermutationType, typename MatrixType, int Side, bool Transposed>
struct permut_matrix_product_retval
: public ReturnByValue<permut_matrix_product_retval<PermutationType, MatrixType, Side, Transposed> >
{
typedef typename remove_all<typename MatrixType::Nested>::type MatrixTypeNestedCleaned;
permut_matrix_product_retval(const PermutationType& perm, const MatrixType& matrix)
: m_permutation(perm), m_matrix(matrix)
{}
inline int rows() const { return m_matrix.rows(); }
inline int cols() const { return m_matrix.cols(); }
template<typename Dest> inline void evalTo(Dest& dst) const
{
const int n = Side==OnTheLeft ? rows() : cols();
if(is_same<MatrixTypeNestedCleaned,Dest>::value && extract_data(dst) == extract_data(m_matrix))
{
// apply the permutation inplace
Matrix<bool,PermutationType::RowsAtCompileTime,1,0,PermutationType::MaxRowsAtCompileTime> mask(m_permutation.size());
mask.fill(false);
int r = 0;
while(r < m_permutation.size())
{
// search for the next seed
while(r<m_permutation.size() && mask[r]) r++;
if(r>=m_permutation.size())
break;
// we got one, let's follow it until we are back to the seed
int k0 = r++;
int kPrev = k0;
mask.coeffRef(k0) = true;
for(int k=m_permutation.indices().coeff(k0); k!=k0; k=m_permutation.indices().coeff(k))
{
Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>(dst, k)
.swap(Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
(dst,((Side==OnTheLeft) ^ Transposed) ? k0 : kPrev));
mask.coeffRef(k) = true;
kPrev = k;
}
}
}
else
{
for(int i = 0; i < n; ++i)
{
Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
(dst, ((Side==OnTheLeft) ^ Transposed) ? m_permutation.indices().coeff(i) : i)
=
Block<const MatrixTypeNestedCleaned,Side==OnTheLeft ? 1 : MatrixType::RowsAtCompileTime,Side==OnTheRight ? 1 : MatrixType::ColsAtCompileTime>
(m_matrix, ((Side==OnTheRight) ^ Transposed) ? m_permutation.indices().coeff(i) : i);
}
}
}
protected:
const PermutationType& m_permutation;
const typename MatrixType::Nested m_matrix;
};
/* Template partial specialization for transposed/inverse permutations */
template<typename Derived>
struct traits<Transpose<PermutationBase<Derived> > >
: traits<Derived>
{};
} // end namespace internal
template<typename Derived>
class Transpose<PermutationBase<Derived> >
: public EigenBase<Transpose<PermutationBase<Derived> > >
{
typedef Derived PermutationType;
typedef typename PermutationType::IndicesType IndicesType;
typedef typename PermutationType::PlainPermutationType PlainPermutationType;
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef internal::traits<PermutationType> Traits;
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;
#endif
Transpose(const PermutationType& p) : m_permutation(p) {}
inline int rows() const { return m_permutation.rows(); }
inline int cols() const { return m_permutation.cols(); }
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename DenseDerived>
void evalTo(MatrixBase<DenseDerived>& other) const
{
other.setZero();
for (int i=0; i<rows();++i)
other.coeffRef(i, m_permutation.indices().coeff(i)) = typename DenseDerived::Scalar(1);
}
#endif
/** \return the equivalent permutation matrix */
PlainPermutationType eval() const { return *this; }
DenseMatrixType toDenseMatrix() const { return *this; }
/** \returns the matrix with the inverse permutation applied to the columns.
*/
template<typename OtherDerived> friend
inline const internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheRight, true>
operator*(const MatrixBase<OtherDerived>& matrix, const Transpose& trPerm)
{
return internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheRight, true>(trPerm.m_permutation, matrix.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>
operator*(const MatrixBase<OtherDerived>& matrix) const
{
return internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheLeft, true>(m_permutation, matrix.derived());
}
const PermutationType& nestedPermutation() const { return m_permutation; }
protected:
const PermutationType& m_permutation;
};
template<typename Derived>
const PermutationWrapper<const Derived> MatrixBase<Derived>::asPermutation() const
{
return derived();
}
#endif // EIGEN_PERMUTATIONMATRIX_H

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@@ -1,754 +0,0 @@
// 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) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_DENSESTORAGEBASE_H
#define EIGEN_DENSESTORAGEBASE_H
#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
# define EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED for(int i=0;i<base().size();++i) coeffRef(i)=Scalar(0);
#else
# define EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
#endif
namespace internal {
template<typename Index>
EIGEN_ALWAYS_INLINE void check_rows_cols_for_overflow(Index rows, Index cols)
{
// http://hg.mozilla.org/mozilla-central/file/6c8a909977d3/xpcom/ds/CheckedInt.h#l242
// we assume Index is signed
Index max_index = (size_t(1) << (8 * sizeof(Index) - 1)) - 1; // assume Index is signed
bool error = (rows < 0 || cols < 0) ? true
: (rows == 0 || cols == 0) ? false
: (rows > max_index / cols);
if (error)
throw_std_bad_alloc();
}
template <typename Derived, typename OtherDerived = Derived, bool IsVector = static_cast<bool>(Derived::IsVectorAtCompileTime)> struct conservative_resize_like_impl;
template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> struct matrix_swap_impl;
} // end namespace internal
/**
* \brief %Dense storage base class for matrices and arrays.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_PLAINOBJECTBASE_PLUGIN.
*
* \sa \ref TopicClassHierarchy
*/
template<typename Derived>
class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
{
public:
enum { Options = internal::traits<Derived>::Options };
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;
using Base::RowsAtCompileTime;
using Base::ColsAtCompileTime;
using Base::SizeAtCompileTime;
using Base::MaxRowsAtCompileTime;
using Base::MaxColsAtCompileTime;
using Base::MaxSizeAtCompileTime;
using Base::IsVectorAtCompileTime;
using Base::Flags;
template<typename PlainObjectType, int MapOptions, typename StrideType> friend class Eigen::Map;
friend class Eigen::Map<Derived, Unaligned>;
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;
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; };
protected:
DenseStorage<Scalar, Base::MaxSizeAtCompileTime, Base::RowsAtCompileTime, Base::ColsAtCompileTime, Options> m_storage;
public:
enum { NeedsToAlign = SizeAtCompileTime != Dynamic && (internal::traits<Derived>::Flags & AlignedBit) != 0 };
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
Base& base() { return *static_cast<Base*>(this); }
const Base& base() const { return *static_cast<const Base*>(this); }
EIGEN_STRONG_INLINE Index rows() const { return m_storage.rows(); }
EIGEN_STRONG_INLINE Index cols() const { return m_storage.cols(); }
EIGEN_STRONG_INLINE const Scalar& coeff(Index row, Index col) const
{
if(Flags & RowMajorBit)
return m_storage.data()[col + row * m_storage.cols()];
else // column-major
return m_storage.data()[row + col * m_storage.rows()];
}
EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
{
return m_storage.data()[index];
}
EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
{
if(Flags & RowMajorBit)
return m_storage.data()[col + row * m_storage.cols()];
else // column-major
return m_storage.data()[row + col * m_storage.rows()];
}
EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
return m_storage.data()[index];
}
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index row, Index col) const
{
if(Flags & RowMajorBit)
return m_storage.data()[col + row * m_storage.cols()];
else // column-major
return m_storage.data()[row + col * m_storage.rows()];
}
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index index) const
{
return m_storage.data()[index];
}
/** \internal */
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
{
return internal::ploadt<PacketScalar, LoadMode>
(m_storage.data() + (Flags & RowMajorBit
? col + row * m_storage.cols()
: row + col * m_storage.rows()));
}
/** \internal */
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
{
return internal::ploadt<PacketScalar, LoadMode>(m_storage.data() + index);
}
/** \internal */
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket(Index row, Index col, const PacketScalar& x)
{
internal::pstoret<Scalar, PacketScalar, StoreMode>
(m_storage.data() + (Flags & RowMajorBit
? col + row * m_storage.cols()
: row + col * m_storage.rows()), x);
}
/** \internal */
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket(Index index, const PacketScalar& x)
{
internal::pstoret<Scalar, PacketScalar, StoreMode>(m_storage.data() + index, x);
}
/** \returns a const pointer to the data array of this matrix */
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()
{ return m_storage.data(); }
/** Resizes \c *this to a \a rows x \a cols matrix.
*
* This method is intended for dynamic-size matrices, although it is legal to call it on any
* matrix as long as fixed dimensions are left unchanged. If you only want to change the number
* of rows and/or of columns, you can use resize(NoChange_t, Index), resize(Index, NoChange_t).
*
* If the current number of coefficients of \c *this exactly matches the
* product \a rows * \a cols, then no memory allocation is performed and
* the current values are left unchanged. In all other cases, including
* shrinking, the data is reallocated and all previous values are lost.
*
* Example: \include Matrix_resize_int_int.cpp
* Output: \verbinclude Matrix_resize_int_int.out
*
* \sa resize(Index) for vectors, resize(NoChange_t, Index), resize(Index, NoChange_t)
*/
EIGEN_STRONG_INLINE void resize(Index rows, Index cols)
{
#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
internal::check_rows_cols_for_overflow(rows, cols);
Index size = rows*cols;
bool size_changed = size != this->size();
m_storage.resize(size, rows, cols);
if(size_changed) EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
#else
internal::check_rows_cols_for_overflow(rows, cols);
m_storage.resize(rows*cols, rows, cols);
#endif
}
/** Resizes \c *this to a vector of length \a size
*
* \only_for_vectors. This method does not work for
* partially dynamic matrices when the static dimension is anything other
* than 1. For example it will not work with Matrix<double, 2, Dynamic>.
*
* Example: \include Matrix_resize_int.cpp
* Output: \verbinclude Matrix_resize_int.out
*
* \sa resize(Index,Index), resize(NoChange_t, Index), resize(Index, NoChange_t)
*/
inline void resize(Index size)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(PlainObjectBase)
eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == size);
#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
bool size_changed = size != this->size();
#endif
if(RowsAtCompileTime == 1)
m_storage.resize(size, 1, size);
else
m_storage.resize(size, size, 1);
#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
if(size_changed) EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
#endif
}
/** Resizes the matrix, changing only the number of columns. For the parameter of type NoChange_t, just pass the special value \c NoChange
* as in the example below.
*
* Example: \include Matrix_resize_NoChange_int.cpp
* Output: \verbinclude Matrix_resize_NoChange_int.out
*
* \sa resize(Index,Index)
*/
inline void resize(NoChange_t, Index cols)
{
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
* as in the example below.
*
* Example: \include Matrix_resize_int_NoChange.cpp
* Output: \verbinclude Matrix_resize_int_NoChange.out
*
* \sa resize(Index,Index)
*/
inline void resize(Index rows, NoChange_t)
{
resize(rows, cols());
}
/** Resizes \c *this to have the same dimensions as \a other.
* Takes care of doing all the checking that's needed.
*
* Note that copying a row-vector into a vector (and conversely) is allowed.
* The resizing, if any, is then done in the appropriate way so that row-vectors
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE void resizeLike(const EigenBase<OtherDerived>& _other)
{
const OtherDerived& other = _other.derived();
internal::check_rows_cols_for_overflow(other.rows(), other.cols());
const Index othersize = other.rows()*other.cols();
if(RowsAtCompileTime == 1)
{
eigen_assert(other.rows() == 1 || other.cols() == 1);
resize(1, othersize);
}
else if(ColsAtCompileTime == 1)
{
eigen_assert(other.rows() == 1 || other.cols() == 1);
resize(othersize, 1);
}
else resize(other.rows(), other.cols());
}
/** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
*
* The method is intended for matrices of dynamic size. If you only want to change the number
* of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or
* conservativeResize(Index, NoChange_t).
*
* Matrices are resized relative to the top-left element. In case values need to be
* appended to the matrix they will be uninitialized.
*/
EIGEN_STRONG_INLINE void conservativeResize(Index rows, Index cols)
{
internal::conservative_resize_like_impl<Derived>::run(*this, rows, cols);
}
/** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
*
* As opposed to conservativeResize(Index rows, Index cols), this version leaves
* the number of columns unchanged.
*
* In case the matrix is growing, new rows will be uninitialized.
*/
EIGEN_STRONG_INLINE void conservativeResize(Index rows, NoChange_t)
{
// Note: see the comment in conservativeResize(Index,Index)
conservativeResize(rows, cols());
}
/** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
*
* As opposed to conservativeResize(Index rows, Index cols), this version leaves
* the number of rows unchanged.
*
* In case the matrix is growing, new columns will be uninitialized.
*/
EIGEN_STRONG_INLINE void conservativeResize(NoChange_t, Index cols)
{
// Note: see the comment in conservativeResize(Index,Index)
conservativeResize(rows(), cols);
}
/** Resizes the vector to \a size while retaining old values.
*
* \only_for_vectors. This method does not work for
* partially dynamic matrices when the static dimension is anything other
* than 1. For example it will not work with Matrix<double, 2, Dynamic>.
*
* When values are appended, they will be uninitialized.
*/
EIGEN_STRONG_INLINE void conservativeResize(Index size)
{
internal::conservative_resize_like_impl<Derived>::run(*this, size);
}
/** Resizes the matrix to \a rows x \a cols of \c other, while leaving old values untouched.
*
* The method is intended for matrices of dynamic size. If you only want to change the number
* of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or
* conservativeResize(Index, NoChange_t).
*
* Matrices are resized relative to the top-left element. In case values need to be
* appended to the matrix they will copied from \c other.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE void conservativeResizeLike(const DenseBase<OtherDerived>& other)
{
internal::conservative_resize_like_impl<Derived,OtherDerived>::run(*this, other);
}
/** This is a special case of the templated operator=. Its purpose is to
* prevent a default operator= from hiding the templated operator=.
*/
EIGEN_STRONG_INLINE Derived& operator=(const PlainObjectBase& other)
{
return _set(other);
}
/** \sa MatrixBase::lazyAssign() */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived& lazyAssign(const DenseBase<OtherDerived>& other)
{
_resize_to_match(other);
return Base::lazyAssign(other.derived());
}
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived& operator=(const ReturnByValue<OtherDerived>& func)
{
resize(func.rows(), func.cols());
return Base::operator=(func);
}
EIGEN_STRONG_INLINE explicit PlainObjectBase() : m_storage()
{
// _check_template_params();
// EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
// FIXME is it still needed ?
/** \internal */
PlainObjectBase(internal::constructor_without_unaligned_array_assert)
: m_storage(internal::constructor_without_unaligned_array_assert())
{
// _check_template_params(); EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
}
#endif
EIGEN_STRONG_INLINE PlainObjectBase(Index size, Index rows, Index cols)
: m_storage(size, rows, cols)
{
// _check_template_params();
// EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
}
/** \copydoc MatrixBase::operator=(const EigenBase<OtherDerived>&)
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived& operator=(const EigenBase<OtherDerived> &other)
{
_resize_to_match(other);
Base::operator=(other.derived());
return this->derived();
}
/** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
EIGEN_STRONG_INLINE PlainObjectBase(const EigenBase<OtherDerived> &other)
: m_storage(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
{
_check_template_params();
internal::check_rows_cols_for_overflow(other.derived().rows(), other.derived().cols());
Base::operator=(other.derived());
}
/** \name Map
* These are convenience functions returning Map objects. The Map() static functions return unaligned Map objects,
* while the AlignedMap() functions return aligned Map objects and thus should be called only with 16-byte-aligned
* \a data pointers.
*
* \see class Map
*/
//@{
inline static ConstMapType Map(const Scalar* data)
{ return ConstMapType(data); }
inline static MapType Map(Scalar* data)
{ return MapType(data); }
inline static ConstMapType Map(const Scalar* data, Index size)
{ return ConstMapType(data, size); }
inline static MapType Map(Scalar* data, Index size)
{ return MapType(data, size); }
inline static ConstMapType Map(const Scalar* data, Index rows, Index cols)
{ return ConstMapType(data, rows, cols); }
inline static MapType Map(Scalar* data, Index rows, Index cols)
{ return MapType(data, rows, cols); }
inline static ConstAlignedMapType MapAligned(const Scalar* data)
{ return ConstAlignedMapType(data); }
inline static AlignedMapType MapAligned(Scalar* data)
{ return AlignedMapType(data); }
inline static ConstAlignedMapType MapAligned(const Scalar* data, Index size)
{ return ConstAlignedMapType(data, size); }
inline static AlignedMapType MapAligned(Scalar* data, Index size)
{ return AlignedMapType(data, size); }
inline static ConstAlignedMapType MapAligned(const Scalar* data, Index rows, Index cols)
{ return ConstAlignedMapType(data, rows, cols); }
inline static AlignedMapType MapAligned(Scalar* data, Index rows, Index cols)
{ return AlignedMapType(data, rows, cols); }
template<int Outer, int Inner>
inline static typename StridedConstMapType<Stride<Outer, Inner> >::type Map(const Scalar* data, const Stride<Outer, Inner>& stride)
{ return typename StridedConstMapType<Stride<Outer, Inner> >::type(data, stride); }
template<int Outer, int Inner>
inline static typename StridedMapType<Stride<Outer, Inner> >::type Map(Scalar* data, const Stride<Outer, Inner>& stride)
{ return typename StridedMapType<Stride<Outer, Inner> >::type(data, stride); }
template<int Outer, int Inner>
inline static typename StridedConstMapType<Stride<Outer, Inner> >::type Map(const Scalar* data, Index size, const Stride<Outer, Inner>& stride)
{ return typename StridedConstMapType<Stride<Outer, Inner> >::type(data, size, stride); }
template<int Outer, int Inner>
inline static typename StridedMapType<Stride<Outer, Inner> >::type Map(Scalar* data, Index size, const Stride<Outer, Inner>& stride)
{ return typename StridedMapType<Stride<Outer, Inner> >::type(data, size, stride); }
template<int Outer, int Inner>
inline static typename StridedConstMapType<Stride<Outer, Inner> >::type Map(const Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
{ return typename StridedConstMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
template<int Outer, int Inner>
inline static typename StridedMapType<Stride<Outer, Inner> >::type Map(Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
{ return typename StridedMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
template<int Outer, int Inner>
inline static typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type MapAligned(const Scalar* data, const Stride<Outer, Inner>& stride)
{ return typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type(data, stride); }
template<int Outer, int Inner>
inline static typename StridedAlignedMapType<Stride<Outer, Inner> >::type MapAligned(Scalar* data, const Stride<Outer, Inner>& stride)
{ return typename StridedAlignedMapType<Stride<Outer, Inner> >::type(data, stride); }
template<int Outer, int Inner>
inline static typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type MapAligned(const Scalar* data, Index size, const Stride<Outer, Inner>& stride)
{ return typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type(data, size, stride); }
template<int Outer, int Inner>
inline static typename StridedAlignedMapType<Stride<Outer, Inner> >::type MapAligned(Scalar* data, Index size, const Stride<Outer, Inner>& stride)
{ return typename StridedAlignedMapType<Stride<Outer, Inner> >::type(data, size, stride); }
template<int Outer, int Inner>
inline static typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type MapAligned(const Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
{ return typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
template<int Outer, int Inner>
inline static typename StridedAlignedMapType<Stride<Outer, Inner> >::type MapAligned(Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
{ return typename StridedAlignedMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
//@}
using Base::setConstant;
Derived& setConstant(Index size, const Scalar& value);
Derived& setConstant(Index rows, Index cols, const Scalar& value);
using Base::setZero;
Derived& setZero(Index size);
Derived& setZero(Index rows, Index cols);
using Base::setOnes;
Derived& setOnes(Index size);
Derived& setOnes(Index rows, Index cols);
using Base::setRandom;
Derived& setRandom(Index size);
Derived& setRandom(Index rows, Index cols);
#ifdef EIGEN_PLAINOBJECTBASE_PLUGIN
#include EIGEN_PLAINOBJECTBASE_PLUGIN
#endif
protected:
/** \internal Resizes *this in preparation for assigning \a other to it.
* Takes care of doing all the checking that's needed.
*
* Note that copying a row-vector into a vector (and conversely) is allowed.
* The resizing, if any, is then done in the appropriate way so that row-vectors
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE void _resize_to_match(const EigenBase<OtherDerived>& other)
{
#ifdef EIGEN_NO_AUTOMATIC_RESIZING
eigen_assert((this->size()==0 || (IsVectorAtCompileTime ? (this->size() == other.size())
: (rows() == other.rows() && cols() == other.cols())))
&& "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined");
#else
resizeLike(other);
#endif
}
/**
* \brief 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),
* it will be initialized.
*
* Note that copying a row-vector into a vector (and conversely) is allowed.
* The resizing, if any, is then done in the appropriate way so that row-vectors
* remain row-vectors and vectors remain vectors.
*
* \sa operator=(const MatrixBase<OtherDerived>&), _set_noalias()
*
* \internal
*/
template<typename OtherDerived>
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());
return this->derived();
}
template<typename OtherDerived>
EIGEN_STRONG_INLINE void _set_selector(const OtherDerived& other, const internal::true_type&) { _set_noalias(other.eval()); }
template<typename OtherDerived>
EIGEN_STRONG_INLINE void _set_selector(const OtherDerived& other, const internal::false_type&) { _set_noalias(other); }
/** \internal Like _set() but additionally makes the assumption that no aliasing effect can happen (which
* is the case when creating a new matrix) so one can enforce lazy evaluation.
*
* \sa operator=(const MatrixBase<OtherDerived>&), _set()
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived& _set_noalias(const DenseBase<OtherDerived>& other)
{
// I don't think we need this resize call since the lazyAssign will anyways resize
// and lazyAssign will be called by the assign selector.
//_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());
}
template<typename T0, typename T1>
EIGEN_STRONG_INLINE void _init2(Index rows, Index cols, typename internal::enable_if<Base::SizeAtCompileTime!=2,T0>::type* = 0)
{
eigen_assert(rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
internal::check_rows_cols_for_overflow(rows, cols);
m_storage.resize(rows*cols,rows,cols);
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
}
template<typename T0, typename T1>
EIGEN_STRONG_INLINE void _init2(const Scalar& x, const Scalar& y, typename internal::enable_if<Base::SizeAtCompileTime==2,T0>::type* = 0)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
}
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.
*/
template<typename OtherDerived>
void _swap(DenseBase<OtherDerived> const & 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());
}
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
EIGEN_STRONG_INLINE static void _check_template_params()
{
EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, (Options&RowMajor)==RowMajor)
&& EIGEN_IMPLIES(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, (Options&RowMajor)==0)
&& ((RowsAtCompileTime == Dynamic) || (RowsAtCompileTime >= 0))
&& ((ColsAtCompileTime == Dynamic) || (ColsAtCompileTime >= 0))
&& ((MaxRowsAtCompileTime == Dynamic) || (MaxRowsAtCompileTime >= 0))
&& ((MaxColsAtCompileTime == Dynamic) || (MaxColsAtCompileTime >= 0))
&& (MaxRowsAtCompileTime == RowsAtCompileTime || RowsAtCompileTime==Dynamic)
&& (MaxColsAtCompileTime == ColsAtCompileTime || ColsAtCompileTime==Dynamic)
&& (Options & (DontAlign|RowMajor)) == Options),
INVALID_MATRIX_TEMPLATE_PARAMETERS)
}
#endif
private:
enum { ThisConstantIsPrivateInPlainObjectBase };
};
template <typename Derived, typename OtherDerived, bool IsVector>
struct internal::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;
EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(Derived)
if ( ( Derived::IsRowMajor && _this.cols() == cols) || // row-major and we change only the number of rows
(!Derived::IsRowMajor && _this.rows() == rows) ) // column-major and we change only the number of columns
{
internal::check_rows_cols_for_overflow(rows, cols);
_this.derived().m_storage.conservativeResize(rows*cols,rows,cols);
}
else
{
// The storage order does not allow us to use reallocation.
typename Derived::PlainObject tmp(rows,cols);
const Index common_rows = (std::min)(rows, _this.rows());
const Index common_cols = (std::min)(cols, _this.cols());
tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols);
_this.derived().swap(tmp);
}
}
static void run(DenseBase<Derived>& _this, const DenseBase<OtherDerived>& other)
{
if (_this.rows() == other.rows() && _this.cols() == other.cols()) return;
// Note: Here is space for improvement. Basically, for conservativeResize(Index,Index),
// neither RowsAtCompileTime or ColsAtCompileTime must be Dynamic. If only one of the
// dimensions is dynamic, one could use either conservativeResize(Index rows, NoChange_t) or
// conservativeResize(NoChange_t, Index cols). For these methods new static asserts like
// EIGEN_STATIC_ASSERT_DYNAMIC_ROWS and EIGEN_STATIC_ASSERT_DYNAMIC_COLS would be good.
EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(Derived)
EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(OtherDerived)
if ( ( Derived::IsRowMajor && _this.cols() == other.cols()) || // row-major and we change only the number of rows
(!Derived::IsRowMajor && _this.rows() == other.rows()) ) // column-major and we change only the number of columns
{
const Index new_rows = other.rows() - _this.rows();
const Index new_cols = other.cols() - _this.cols();
_this.derived().m_storage.conservativeResize(other.size(),other.rows(),other.cols());
if (new_rows>0)
_this.bottomRightCorner(new_rows, other.cols()) = other.bottomRows(new_rows);
else if (new_cols>0)
_this.bottomRightCorner(other.rows(), new_cols) = other.rightCols(new_cols);
}
else
{
// The storage order does not allow us to use reallocation.
typename Derived::PlainObject tmp(other);
const Index common_rows = (std::min)(tmp.rows(), _this.rows());
const Index common_cols = (std::min)(tmp.cols(), _this.cols());
tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols);
_this.derived().swap(tmp);
}
}
};
namespace internal {
template <typename Derived, typename OtherDerived>
struct conservative_resize_like_impl<Derived,OtherDerived,true>
{
typedef typename Derived::Index Index;
static void run(DenseBase<Derived>& _this, Index size)
{
const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : size;
const Index new_cols = Derived::RowsAtCompileTime==1 ? size : 1;
_this.derived().m_storage.conservativeResize(size,new_rows,new_cols);
}
static void run(DenseBase<Derived>& _this, const DenseBase<OtherDerived>& other)
{
if (_this.rows() == other.rows() && _this.cols() == other.cols()) return;
const Index num_new_elements = other.size() - _this.size();
const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : other.rows();
const Index new_cols = Derived::RowsAtCompileTime==1 ? other.cols() : 1;
_this.derived().m_storage.conservativeResize(other.size(),new_rows,new_cols);
if (num_new_elements > 0)
_this.tail(num_new_elements) = other.tail(num_new_elements);
}
};
template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers>
struct matrix_swap_impl
{
static inline void run(MatrixTypeA& a, MatrixTypeB& b)
{
a.base().swap(b);
}
};
template<typename MatrixTypeA, typename MatrixTypeB>
struct matrix_swap_impl<MatrixTypeA, MatrixTypeB, true>
{
static inline void run(MatrixTypeA& a, MatrixTypeB& b)
{
static_cast<typename MatrixTypeA::Base&>(a).m_storage.swap(static_cast<typename MatrixTypeB::Base&>(b).m_storage);
}
};
} // end namespace internal
#endif // EIGEN_DENSESTORAGEBASE_H

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@@ -1,288 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_PRODUCTBASE_H
#define EIGEN_PRODUCTBASE_H
/** \class ProductBase
* \ingroup Core_Module
*
*/
namespace internal {
template<typename Derived, typename _Lhs, typename _Rhs>
struct traits<ProductBase<Derived,_Lhs,_Rhs> >
{
typedef MatrixXpr XprKind;
typedef typename remove_all<_Lhs>::type Lhs;
typedef typename remove_all<_Rhs>::type Rhs;
typedef typename scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType Scalar;
typedef typename promote_storage_type<typename traits<Lhs>::StorageKind,
typename traits<Rhs>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<typename traits<Lhs>::Index,
typename traits<Rhs>::Index>::type Index;
enum {
RowsAtCompileTime = traits<Lhs>::RowsAtCompileTime,
ColsAtCompileTime = traits<Rhs>::ColsAtCompileTime,
MaxRowsAtCompileTime = traits<Lhs>::MaxRowsAtCompileTime,
MaxColsAtCompileTime = traits<Rhs>::MaxColsAtCompileTime,
Flags = (MaxRowsAtCompileTime==1 ? RowMajorBit : 0)
| EvalBeforeNestingBit | EvalBeforeAssigningBit | NestByRefBit,
// Note that EvalBeforeNestingBit and NestByRefBit
// are not used in practice because nested is overloaded for products
CoeffReadCost = 0 // FIXME why is it needed ?
};
};
}
#define EIGEN_PRODUCT_PUBLIC_INTERFACE(Derived) \
typedef ProductBase<Derived, Lhs, Rhs > Base; \
EIGEN_DENSE_PUBLIC_INTERFACE(Derived) \
typedef typename Base::LhsNested LhsNested; \
typedef typename Base::_LhsNested _LhsNested; \
typedef typename Base::LhsBlasTraits LhsBlasTraits; \
typedef typename Base::ActualLhsType ActualLhsType; \
typedef typename Base::_ActualLhsType _ActualLhsType; \
typedef typename Base::RhsNested RhsNested; \
typedef typename Base::_RhsNested _RhsNested; \
typedef typename Base::RhsBlasTraits RhsBlasTraits; \
typedef typename Base::ActualRhsType ActualRhsType; \
typedef typename Base::_ActualRhsType _ActualRhsType; \
using Base::m_lhs; \
using Base::m_rhs;
template<typename Derived, typename Lhs, typename Rhs>
class ProductBase : public MatrixBase<Derived>
{
public:
typedef MatrixBase<Derived> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(ProductBase)
typedef typename Lhs::Nested LhsNested;
typedef typename internal::remove_all<LhsNested>::type _LhsNested;
typedef internal::blas_traits<_LhsNested> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
typedef typename internal::remove_all<ActualLhsType>::type _ActualLhsType;
typedef typename internal::traits<Lhs>::Scalar LhsScalar;
typedef typename Rhs::Nested RhsNested;
typedef typename internal::remove_all<RhsNested>::type _RhsNested;
typedef internal::blas_traits<_RhsNested> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
typedef typename internal::remove_all<ActualRhsType>::type _ActualRhsType;
typedef typename internal::traits<Rhs>::Scalar RhsScalar;
// Diagonal of a product: no need to evaluate the arguments because they are going to be evaluated only once
typedef CoeffBasedProduct<LhsNested, RhsNested, 0> FullyLazyCoeffBaseProductType;
public:
typedef typename Base::PlainObject PlainObject;
ProductBase(const Lhs& lhs, const Rhs& rhs)
: m_lhs(lhs), m_rhs(rhs)
{
eigen_assert(lhs.cols() == rhs.rows()
&& "invalid matrix product"
&& "if you wanted a coeff-wise or a dot product use the respective explicit functions");
}
inline Index rows() const { return m_lhs.rows(); }
inline Index cols() const { return m_rhs.cols(); }
template<typename Dest>
inline void evalTo(Dest& dst) const { dst.setZero(); scaleAndAddTo(dst,Scalar(1)); }
template<typename Dest>
inline void addTo(Dest& dst) const { scaleAndAddTo(dst,1); }
template<typename Dest>
inline void subTo(Dest& dst) const { scaleAndAddTo(dst,-1); }
template<typename Dest>
inline void scaleAndAddTo(Dest& dst,Scalar alpha) const { derived().scaleAndAddTo(dst,alpha); }
const _LhsNested& lhs() const { return m_lhs; }
const _RhsNested& rhs() const { return m_rhs; }
// Implicit conversion to the nested type (trigger the evaluation of the product)
operator const PlainObject& () const
{
m_result.resize(m_lhs.rows(), m_rhs.cols());
derived().evalTo(m_result);
return m_result;
}
const Diagonal<const FullyLazyCoeffBaseProductType,0> diagonal() const
{ return FullyLazyCoeffBaseProductType(m_lhs, m_rhs); }
template<int Index>
const Diagonal<FullyLazyCoeffBaseProductType,Index> diagonal() const
{ return FullyLazyCoeffBaseProductType(m_lhs, m_rhs); }
const Diagonal<FullyLazyCoeffBaseProductType,Dynamic> diagonal(Index index) const
{ return FullyLazyCoeffBaseProductType(m_lhs, m_rhs).diagonal(index); }
// restrict coeff accessors to 1x1 expressions. No need to care about mutators here since this isnt a Lvalue expression
typename Base::CoeffReturnType coeff(Index row, Index col) const
{
#ifdef EIGEN2_SUPPORT
return lhs().row(row).cwiseProduct(rhs().col(col).transpose()).sum();
#else
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
return derived().coeff(row,col);
#endif
}
typename Base::CoeffReturnType coeff(Index i) const
{
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
return derived().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:
const LhsNested m_lhs;
const RhsNested m_rhs;
mutable PlainObject m_result;
};
// here we need to overload the nested rule for products
// such that the nested type is a const reference to a plain matrix
namespace internal {
template<typename Lhs, typename Rhs, int Mode, int N, typename PlainObject>
struct nested<GeneralProduct<Lhs,Rhs,Mode>, N, PlainObject>
{
typedef PlainObject const& type;
};
}
template<typename NestedProduct>
class ScaledProduct;
// Note that these two operator* functions are not defined as member
// functions of ProductBase, because, otherwise we would have to
// define all overloads defined in MatrixBase. Furthermore, Using
// "using Base::operator*" would not work with MSVC.
//
// Also note that here we accept any compatible scalar types
template<typename Derived,typename Lhs,typename Rhs>
const ScaledProduct<Derived>
operator*(const ProductBase<Derived,Lhs,Rhs>& prod, typename Derived::Scalar x)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
typename internal::enable_if<!internal::is_same<typename Derived::Scalar,typename Derived::RealScalar>::value,
const ScaledProduct<Derived> >::type
operator*(const ProductBase<Derived,Lhs,Rhs>& prod, typename Derived::RealScalar x)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
const ScaledProduct<Derived>
operator*(typename Derived::Scalar x,const ProductBase<Derived,Lhs,Rhs>& prod)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
typename internal::enable_if<!internal::is_same<typename Derived::Scalar,typename Derived::RealScalar>::value,
const ScaledProduct<Derived> >::type
operator*(typename Derived::RealScalar x,const ProductBase<Derived,Lhs,Rhs>& prod)
{ return ScaledProduct<Derived>(prod.derived(), x); }
namespace internal {
template<typename NestedProduct>
struct traits<ScaledProduct<NestedProduct> >
: traits<ProductBase<ScaledProduct<NestedProduct>,
typename NestedProduct::_LhsNested,
typename NestedProduct::_RhsNested> >
{
typedef typename traits<NestedProduct>::StorageKind StorageKind;
};
}
template<typename NestedProduct>
class ScaledProduct
: public ProductBase<ScaledProduct<NestedProduct>,
typename NestedProduct::_LhsNested,
typename NestedProduct::_RhsNested>
{
public:
typedef ProductBase<ScaledProduct<NestedProduct>,
typename NestedProduct::_LhsNested,
typename NestedProduct::_RhsNested> Base;
typedef typename Base::Scalar Scalar;
typedef typename Base::PlainObject PlainObject;
// EIGEN_PRODUCT_PUBLIC_INTERFACE(ScaledProduct)
ScaledProduct(const NestedProduct& prod, Scalar x)
: Base(prod.lhs(),prod.rhs()), m_prod(prod), m_alpha(x) {}
template<typename Dest>
inline void evalTo(Dest& dst) const { dst.setZero(); scaleAndAddTo(dst, Scalar(1)); }
template<typename Dest>
inline void addTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(1)); }
template<typename Dest>
inline void subTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(-1)); }
template<typename Dest>
inline void scaleAndAddTo(Dest& dst,Scalar alpha) const { m_prod.derived().scaleAndAddTo(dst,alpha * m_alpha); }
const Scalar& alpha() const { return m_alpha; }
protected:
const NestedProduct& m_prod;
Scalar m_alpha;
};
/** \internal
* Overloaded to perform an efficient C = (A*B).lazy() */
template<typename Derived>
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& MatrixBase<Derived>::lazyAssign(const ProductBase<ProductDerived, Lhs,Rhs>& other)
{
other.derived().evalTo(derived());
return derived();
}
#endif // EIGEN_PRODUCTBASE_H

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@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
@@ -26,379 +26,92 @@
#ifndef EIGEN_REDUX_H
#define EIGEN_REDUX_H
namespace internal {
// TODO
// * implement other kind of vectorization
// * factorize code
/***************************************************************************
* Part 1 : the logic deciding a strategy for vectorization and unrolling
***************************************************************************/
template<typename Func, typename Derived>
struct redux_traits
{
public:
enum {
PacketSize = packet_traits<typename Derived::Scalar>::size,
InnerMaxSize = int(Derived::IsRowMajor)
? Derived::MaxColsAtCompileTime
: Derived::MaxRowsAtCompileTime
};
enum {
MightVectorize = (int(Derived::Flags)&ActualPacketAccessBit)
&& (functor_traits<Func>::PacketAccess),
MayLinearVectorize = MightVectorize && (int(Derived::Flags)&LinearAccessBit),
MaySliceVectorize = MightVectorize && int(InnerMaxSize)>=3*PacketSize
};
public:
enum {
Traversal = int(MayLinearVectorize) ? int(LinearVectorizedTraversal)
: int(MaySliceVectorize) ? int(SliceVectorizedTraversal)
: int(DefaultTraversal)
};
public:
enum {
Cost = ( Derived::SizeAtCompileTime == Dynamic
|| Derived::CoeffReadCost == Dynamic
|| (Derived::SizeAtCompileTime!=1 && functor_traits<Func>::Cost == Dynamic)
) ? Dynamic
: Derived::SizeAtCompileTime * Derived::CoeffReadCost
+ (Derived::SizeAtCompileTime-1) * functor_traits<Func>::Cost,
UnrollingLimit = EIGEN_UNROLLING_LIMIT * (int(Traversal) == int(DefaultTraversal) ? 1 : int(PacketSize))
};
public:
enum {
Unrolling = Cost != Dynamic && Cost <= UnrollingLimit
? CompleteUnrolling
: NoUnrolling
};
};
/***************************************************************************
* Part 2 : unrollers
***************************************************************************/
/*** no vectorization ***/
template<typename Func, typename Derived, int Start, int Length>
struct redux_novec_unroller
template<typename BinaryOp, typename Derived, int Start, int Length>
struct ei_redux_impl
{
enum {
HalfLength = Length/2
};
typedef typename Derived::Scalar Scalar;
typedef typename ei_result_of<BinaryOp(typename Derived::Scalar)>::type Scalar;
EIGEN_STRONG_INLINE static Scalar run(const Derived &mat, const Func& func)
static Scalar run(const Derived &mat, const BinaryOp& func)
{
return func(redux_novec_unroller<Func, Derived, Start, HalfLength>::run(mat,func),
redux_novec_unroller<Func, Derived, Start+HalfLength, Length-HalfLength>::run(mat,func));
return func(
ei_redux_impl<BinaryOp, Derived, Start, HalfLength>::run(mat, func),
ei_redux_impl<BinaryOp, Derived, Start+HalfLength, Length - HalfLength>::run(mat, func));
}
};
template<typename Func, typename Derived, int Start>
struct redux_novec_unroller<Func, Derived, Start, 1>
template<typename BinaryOp, typename Derived, int Start>
struct ei_redux_impl<BinaryOp, Derived, Start, 1>
{
enum {
outer = Start / Derived::InnerSizeAtCompileTime,
inner = Start % Derived::InnerSizeAtCompileTime
col = Start / Derived::RowsAtCompileTime,
row = Start % Derived::RowsAtCompileTime
};
typedef typename Derived::Scalar Scalar;
typedef typename ei_result_of<BinaryOp(typename Derived::Scalar)>::type Scalar;
EIGEN_STRONG_INLINE static Scalar run(const Derived &mat, const Func&)
static Scalar run(const Derived &mat, const BinaryOp &)
{
return mat.coeffByOuterInner(outer, inner);
return mat.coeff(row, col);
}
};
// This is actually dead code and will never be called. It is required
// to prevent false warnings regarding failed inlining though
// for 0 length run() will never be called at all.
template<typename Func, typename Derived, int Start>
struct redux_novec_unroller<Func, Derived, Start, 0>
template<typename BinaryOp, typename Derived, int Start>
struct ei_redux_impl<BinaryOp, Derived, Start, Dynamic>
{
typedef typename Derived::Scalar Scalar;
EIGEN_STRONG_INLINE static Scalar run(const Derived&, const Func&) { return Scalar(); }
};
/*** vectorization ***/
template<typename Func, typename Derived, int Start, int Length>
struct redux_vec_unroller
{
enum {
PacketSize = packet_traits<typename Derived::Scalar>::size,
HalfLength = Length/2
};
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
EIGEN_STRONG_INLINE static PacketScalar run(const Derived &mat, const Func& func)
typedef typename ei_result_of<BinaryOp(typename Derived::Scalar)>::type Scalar;
static Scalar run(const Derived& mat, const BinaryOp& func)
{
return func.packetOp(
redux_vec_unroller<Func, Derived, Start, HalfLength>::run(mat,func),
redux_vec_unroller<Func, Derived, Start+HalfLength, Length-HalfLength>::run(mat,func) );
}
};
template<typename Func, typename Derived, int Start>
struct redux_vec_unroller<Func, Derived, Start, 1>
{
enum {
index = Start * packet_traits<typename Derived::Scalar>::size,
outer = index / int(Derived::InnerSizeAtCompileTime),
inner = index % int(Derived::InnerSizeAtCompileTime),
alignment = (Derived::Flags & AlignedBit) ? Aligned : Unaligned
};
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
EIGEN_STRONG_INLINE static PacketScalar run(const Derived &mat, const Func&)
{
return mat.template packetByOuterInner<alignment>(outer, inner);
}
};
/***************************************************************************
* Part 3 : implementation of all cases
***************************************************************************/
template<typename Func, typename Derived,
int Traversal = redux_traits<Func, Derived>::Traversal,
int Unrolling = redux_traits<Func, Derived>::Unrolling
>
struct redux_impl;
template<typename Func, typename Derived>
struct redux_impl<Func, Derived, DefaultTraversal, NoUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Index Index;
static EIGEN_STRONG_INLINE Scalar run(const Derived& mat, const Func& func)
{
eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix");
ei_assert(mat.rows()>0 && mat.cols()>0 && "you are using a non initialized matrix");
Scalar res;
res = mat.coeffByOuterInner(0, 0);
for(Index i = 1; i < mat.innerSize(); ++i)
res = func(res, mat.coeffByOuterInner(0, i));
for(Index i = 1; i < mat.outerSize(); ++i)
for(Index j = 0; j < mat.innerSize(); ++j)
res = func(res, mat.coeffByOuterInner(i, j));
res = mat.coeff(0,0);
for(int i = 1; i < mat.rows(); ++i)
res = func(res, mat.coeff(i, 0));
for(int j = 1; j < mat.cols(); ++j)
for(int i = 0; i < mat.rows(); ++i)
res = func(res, mat.coeff(i, j));
return res;
}
};
template<typename Func, typename Derived>
struct redux_impl<Func,Derived, DefaultTraversal, CompleteUnrolling>
: public redux_novec_unroller<Func,Derived, 0, Derived::SizeAtCompileTime>
{};
template<typename Func, typename Derived>
struct redux_impl<Func, Derived, LinearVectorizedTraversal, NoUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename Derived::Index Index;
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 = first_aligned(mat);
enum {
alignment = bool(Derived::Flags & DirectAccessBit) || bool(Derived::Flags & AlignedBit)
? Aligned : Unaligned
};
const Index alignedSize = ((size-alignedStart)/packetSize)*packetSize;
const Index alignedEnd = alignedStart + alignedSize;
Scalar res;
if(alignedSize)
{
PacketScalar packet_res = mat.template packet<alignment>(alignedStart);
for(Index index = alignedStart + packetSize; index < alignedEnd; index += packetSize)
packet_res = func.packetOp(packet_res, mat.template packet<alignment>(index));
res = func.predux(packet_res);
for(Index index = 0; index < alignedStart; ++index)
res = func(res,mat.coeff(index));
for(Index index = alignedEnd; index < size; ++index)
res = func(res,mat.coeff(index));
}
else // too small to vectorize anything.
// since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize.
{
res = mat.coeff(0);
for(Index index = 1; index < size; ++index)
res = func(res,mat.coeff(index));
}
return res;
}
};
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;
static Scalar run(const Derived& mat, const Func& func)
{
eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix");
const Index innerSize = mat.innerSize();
const Index outerSize = mat.outerSize();
enum {
packetSize = packet_traits<Scalar>::size
};
const Index packetedInnerSize = ((innerSize)/packetSize)*packetSize;
Scalar res;
if(packetedInnerSize)
{
PacketScalar packet_res = mat.template packet<Unaligned>(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));
res = func.predux(packet_res);
for(Index j=0; j<outerSize; ++j)
for(Index i=packetedInnerSize; i<innerSize; ++i)
res = func(res, mat.coeffByOuterInner(j,i));
}
else // too small to vectorize anything.
// since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize.
{
res = redux_impl<Func, Derived, DefaultTraversal, NoUnrolling>::run(mat, func);
}
return res;
}
};
template<typename Func, typename Derived>
struct redux_impl<Func, Derived, LinearVectorizedTraversal, CompleteUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
enum {
PacketSize = packet_traits<Scalar>::size,
Size = Derived::SizeAtCompileTime,
VectorizedSize = (Size / PacketSize) * PacketSize
};
EIGEN_STRONG_INLINE static Scalar run(const Derived& mat, const Func& func)
{
eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix");
Scalar res = func.predux(redux_vec_unroller<Func, Derived, 0, Size / PacketSize>::run(mat,func));
if (VectorizedSize != Size)
res = func(res,redux_novec_unroller<Func, Derived, VectorizedSize, Size-VectorizedSize>::run(mat,func));
return res;
}
};
} // end namespace internal
/***************************************************************************
* Part 4 : public API
***************************************************************************/
/** \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 assiociative operator. Both current STL and TR1 functor styles are handled.
*
* \sa DenseBase::sum(), DenseBase::minCoeff(), DenseBase::maxCoeff(), MatrixBase::colwise(), MatrixBase::rowwise()
* \sa MatrixBase::sum(), MatrixBase::minCoeff(), MatrixBase::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
DenseBase<Derived>::redux(const Func& func) const
template<typename BinaryOp>
typename ei_result_of<BinaryOp(typename ei_traits<Derived>::Scalar)>::type
MatrixBase<Derived>::redux(const BinaryOp& func) const
{
typedef typename internal::remove_all<typename Derived::Nested>::type ThisNested;
return internal::redux_impl<Func, ThisNested>
const bool unroll = SizeAtCompileTime * CoeffReadCost
+ (SizeAtCompileTime-1) * ei_functor_traits<BinaryOp>::Cost
<= EIGEN_UNROLLING_LIMIT;
return ei_redux_impl<BinaryOp, Derived, 0, unroll ? int(SizeAtCompileTime) : Dynamic>
::run(derived(), func);
}
/** \returns the minimum of all coefficients of *this
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::minCoeff() const
inline typename ei_traits<Derived>::Scalar
MatrixBase<Derived>::minCoeff() const
{
return this->redux(Eigen::internal::scalar_min_op<Scalar>());
return this->redux(Eigen::ei_scalar_min_op<Scalar>());
}
/** \returns the maximum of all coefficients of *this
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::maxCoeff() const
inline typename ei_traits<Derived>::Scalar
MatrixBase<Derived>::maxCoeff() const
{
return this->redux(Eigen::internal::scalar_max_op<Scalar>());
}
/** \returns the sum of all coefficients of *this
*
* \sa trace(), prod(), mean()
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::sum() const
{
if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0))
return Scalar(0);
return this->redux(Eigen::internal::scalar_sum_op<Scalar>());
}
/** \returns the mean of all coefficients of *this
*
* \sa trace(), prod(), sum()
*/
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());
}
/** \returns the product of all coefficients of *this
*
* Example: \include MatrixBase_prod.cpp
* Output: \verbinclude MatrixBase_prod.out
*
* \sa sum(), mean(), trace()
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::prod() const
{
if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0))
return Scalar(1);
return this->redux(Eigen::internal::scalar_product_op<Scalar>());
}
/** \returns the trace of \c *this, i.e. the sum of the coefficients on the main diagonal.
*
* \c *this can be any matrix, not necessarily square.
*
* \sa diagonal(), sum()
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
MatrixBase<Derived>::trace() const
{
return derived().diagonal().sum();
return this->redux(Eigen::ei_scalar_max_op<Scalar>());
}
#endif // EIGEN_REDUX_H

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@@ -1,179 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_REPLICATE_H
#define EIGEN_REPLICATE_H
/**
* \class Replicate
* \ingroup Core_Module
*
* \brief Expression of the multiple replication of a matrix or vector
*
* \param MatrixType the type of the object we are replicating
*
* This class represents an expression of the multiple replication of a matrix or vector.
* It is the return type of DenseBase::replicate() and most of the time
* this is the only way it is used.
*
* \sa DenseBase::replicate()
*/
namespace internal {
template<typename MatrixType,int RowFactor,int ColFactor>
struct traits<Replicate<MatrixType,RowFactor,ColFactor> >
: traits<MatrixType>
{
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 remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
enum {
RowsAtCompileTime = RowFactor==Dynamic || int(MatrixType::RowsAtCompileTime)==Dynamic
? Dynamic
: RowFactor * MatrixType::RowsAtCompileTime,
ColsAtCompileTime = ColFactor==Dynamic || int(MatrixType::ColsAtCompileTime)==Dynamic
? Dynamic
: ColFactor * MatrixType::ColsAtCompileTime,
//FIXME we don't propagate the max sizes !!!
MaxRowsAtCompileTime = RowsAtCompileTime,
MaxColsAtCompileTime = ColsAtCompileTime,
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
};
};
}
template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
: public internal::dense_xpr_base< Replicate<MatrixType,RowFactor,ColFactor> >::type
{
public:
typedef typename internal::dense_xpr_base<Replicate>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Replicate)
template<typename OriginalMatrixType>
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)
eigen_assert(RowFactor!=Dynamic && ColFactor!=Dynamic);
}
template<typename OriginalMatrixType>
inline Replicate(const OriginalMatrixType& matrix, int rowFactor, int 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)
}
inline Index rows() const { return m_matrix.rows() * m_rowFactor.value(); }
inline Index cols() const { return m_matrix.cols() * m_colFactor.value(); }
inline Scalar coeff(Index row, Index col) const
{
// try to avoid using modulo; this is a pure optimization strategy
const Index actual_row = internal::traits<MatrixType>::RowsAtCompileTime==1 ? 0
: RowFactor==1 ? row
: row%m_matrix.rows();
const Index actual_col = internal::traits<MatrixType>::ColsAtCompileTime==1 ? 0
: ColFactor==1 ? col
: col%m_matrix.cols();
return m_matrix.coeff(actual_row, actual_col);
}
template<int LoadMode>
inline PacketScalar packet(Index row, Index col) const
{
const Index actual_row = internal::traits<MatrixType>::RowsAtCompileTime==1 ? 0
: RowFactor==1 ? row
: row%m_matrix.rows();
const Index actual_col = internal::traits<MatrixType>::ColsAtCompileTime==1 ? 0
: ColFactor==1 ? col
: col%m_matrix.cols();
return m_matrix.template packet<LoadMode>(actual_row, actual_col);
}
protected:
const typename MatrixType::Nested m_matrix;
const internal::variable_if_dynamic<Index, RowFactor> m_rowFactor;
const internal::variable_if_dynamic<Index, ColFactor> m_colFactor;
};
/**
* \return an expression of the replication of \c *this
*
* Example: \include MatrixBase_replicate.cpp
* Output: \verbinclude MatrixBase_replicate.out
*
* \sa VectorwiseOp::replicate(), DenseBase::replicate(Index,Index), class Replicate
*/
template<typename Derived>
template<int RowFactor, int ColFactor>
inline 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
*
* Example: \include DirectionWise_replicate_int.cpp
* Output: \verbinclude DirectionWise_replicate_int.out
*
* \sa VectorwiseOp::replicate(), DenseBase::replicate(), class Replicate
*/
template<typename ExpressionType, int Direction>
const typename VectorwiseOp<ExpressionType,Direction>::ReplicateReturnType
VectorwiseOp<ExpressionType,Direction>::replicate(Index factor) const
{
return typename VectorwiseOp<ExpressionType,Direction>::ReplicateReturnType
(_expression(),Direction==Vertical?factor:1,Direction==Horizontal?factor:1);
}
#endif // EIGEN_REPLICATE_H

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@@ -1,99 +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>
// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_RETURNBYVALUE_H
#define EIGEN_RETURNBYVALUE_H
/** \class ReturnByValue
* \ingroup Core_Module
*
*/
namespace internal {
template<typename Derived>
struct traits<ReturnByValue<Derived> >
: public traits<typename traits<Derived>::ReturnType>
{
enum {
// We're disabling the DirectAccess because e.g. the constructor of
// the Block-with-DirectAccess expression requires to have a coeffRef method.
// Also, we don't want to have to implement the stride stuff.
Flags = (traits<typename traits<Derived>::ReturnType>::Flags
| EvalBeforeNestingBit) & ~DirectAccessBit
};
};
/* The ReturnByValue object doesn't even have a coeff() method.
* So the only way that nesting it in an expression can work, is by evaluating it into a plain matrix.
* 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 ??
*/
template<typename Derived,int n,typename PlainObject>
struct nested<ReturnByValue<Derived>, n, PlainObject>
{
typedef typename traits<Derived>::ReturnType type;
};
} // end namespace internal
template<typename Derived> class ReturnByValue
: public internal::dense_xpr_base< ReturnByValue<Derived> >::type
{
public:
typedef typename internal::traits<Derived>::ReturnType ReturnType;
typedef typename internal::dense_xpr_base<ReturnByValue>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(ReturnByValue)
template<typename Dest>
inline void evalTo(Dest& dst) const
{ static_cast<const Derived*>(this)->evalTo(dst); }
inline Index rows() const { return static_cast<const Derived*>(this)->rows(); }
inline Index cols() const { return static_cast<const Derived*>(this)->cols(); }
#ifndef EIGEN_PARSED_BY_DOXYGEN
#define Unusable YOU_ARE_TRYING_TO_ACCESS_A_SINGLE_COEFFICIENT_IN_A_SPECIAL_EXPRESSION_WHERE_THAT_IS_NOT_ALLOWED_BECAUSE_THAT_WOULD_BE_INEFFICIENT
class Unusable{
Unusable(const Unusable&) {}
Unusable& operator=(const Unusable&) {return *this;}
};
const Unusable& coeff(Index) const { return *reinterpret_cast<const Unusable*>(this); }
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); }
#endif
};
template<typename Derived>
template<typename OtherDerived>
Derived& DenseBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
{
other.evalTo(derived());
return derived();
}
#endif // EIGEN_RETURNBYVALUE_H

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@@ -1,230 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2009 Ricard Marxer <email@ricardmarxer.com>
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_REVERSE_H
#define EIGEN_REVERSE_H
/** \class Reverse
* \ingroup Core_Module
*
* \brief Expression of the reverse of a vector or matrix
*
* \param MatrixType the type of the object of which we are taking the reverse
*
* This class represents an expression of the reverse of a vector.
* It is the return type of MatrixBase::reverse() and VectorwiseOp::reverse()
* and most of the time this is the only way it is used.
*
* \sa MatrixBase::reverse(), VectorwiseOp::reverse()
*/
namespace internal {
template<typename MatrixType, int Direction>
struct traits<Reverse<MatrixType, Direction> >
: traits<MatrixType>
{
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 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
};
};
template<typename PacketScalar, bool ReversePacket> struct reverse_packet_cond
{
static inline PacketScalar run(const PacketScalar& x) { return preverse(x); }
};
template<typename PacketScalar> struct reverse_packet_cond<PacketScalar,false>
{
static inline PacketScalar run(const PacketScalar& x) { return x; }
};
} // end namespace internal
template<typename MatrixType, int Direction> class Reverse
: public internal::dense_xpr_base< Reverse<MatrixType, Direction> >::type
{
public:
typedef typename internal::dense_xpr_base<Reverse>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Reverse)
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,
IsColMajor = !IsRowMajor,
ReverseRow = (Direction == Vertical) || (Direction == BothDirections),
ReverseCol = (Direction == Horizontal) || (Direction == BothDirections),
OffsetRow = ReverseRow && IsColMajor ? PacketSize : 1,
OffsetCol = ReverseCol && IsRowMajor ? PacketSize : 1,
ReversePacket = (Direction == BothDirections)
|| ((Direction == Vertical) && IsColMajor)
|| ((Direction == Horizontal) && IsRowMajor)
};
typedef internal::reverse_packet_cond<PacketScalar,ReversePacket> reverse_packet;
public:
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(); }
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));
}
protected:
const typename MatrixType::Nested m_matrix;
};
/** \returns an expression of the reverse of *this.
*
* Example: \include MatrixBase_reverse.cpp
* Output: \verbinclude MatrixBase_reverse.out
*
*/
template<typename Derived>
inline typename DenseBase<Derived>::ReverseReturnType
DenseBase<Derived>::reverse()
{
return derived();
}
/** This is the const version of reverse(). */
template<typename Derived>
inline const typename DenseBase<Derived>::ConstReverseReturnType
DenseBase<Derived>::reverse() const
{
return derived();
}
/** 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:
* - 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)
* - it allows future optimizations (cache friendliness, etc.)
*
* \sa reverse() */
template<typename Derived>
inline void DenseBase<Derived>::reverseInPlace()
{
derived() = derived().reverse().eval();
}
#endif // EIGEN_REVERSE_H

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@@ -1,325 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_SELFADJOINTMATRIX_H
#define EIGEN_SELFADJOINTMATRIX_H
/** \class SelfAdjointView
* \ingroup Core_Module
*
*
* \brief Expression of a selfadjoint matrix from a triangular part of a dense matrix
*
* \param MatrixType the type of the dense matrix storing the coefficients
* \param TriangularPart can be either \c #Lower or \c #Upper
*
* This class is an expression of a sefladjoint matrix from a triangular part of a matrix
* with given dense storage of the coefficients. It is the return type of MatrixBase::selfadjointView()
* and most of the time this is the only way that it is used.
*
* \sa class TriangularBase, MatrixBase::selfadjointView()
*/
namespace internal {
template<typename MatrixType, unsigned int UpLo>
struct traits<SelfAdjointView<MatrixType, UpLo> > : traits<MatrixType>
{
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned;
typedef MatrixType ExpressionType;
typedef typename MatrixType::PlainObject DenseMatrixType;
enum {
Mode = UpLo | SelfAdjoint,
Flags = MatrixTypeNestedCleaned::Flags & (HereditaryBits)
& (~(PacketAccessBit | DirectAccessBit | LinearAccessBit)), // FIXME these flags should be preserved
CoeffReadCost = MatrixTypeNestedCleaned::CoeffReadCost
};
};
}
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> >
{
public:
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;
enum {
Mode = internal::traits<SelfAdjointView>::Mode
};
typedef typename MatrixType::PlainObject PlainObject;
inline SelfAdjointView(const MatrixType& 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(); }
/** \sa MatrixBase::coeff()
* \warning the coordinates must fit into the referenced triangular part
*/
inline Scalar coeff(Index row, Index col) const
{
Base::check_coordinates_internal(row, col);
return m_matrix.coeff(row, col);
}
/** \sa MatrixBase::coeffRef()
* \warning the coordinates must fit into the referenced triangular part
*/
inline Scalar& coeffRef(Index row, Index col)
{
Base::check_coordinates_internal(row, col);
return m_matrix.const_cast_derived().coeffRef(row, col);
}
/** \internal */
const MatrixTypeNestedCleaned& _expression() const { return m_matrix; }
const MatrixTypeNestedCleaned& nestedExpression() const { return m_matrix; }
MatrixTypeNestedCleaned& nestedExpression() { return *const_cast<MatrixTypeNestedCleaned*>(&m_matrix); }
/** Efficient self-adjoint matrix times vector/matrix product */
template<typename OtherDerived>
SelfadjointProductMatrix<MatrixType,Mode,false,OtherDerived,0,OtherDerived::IsVectorAtCompileTime>
operator*(const MatrixBase<OtherDerived>& rhs) const
{
return SelfadjointProductMatrix
<MatrixType,Mode,false,OtherDerived,0,OtherDerived::IsVectorAtCompileTime>
(m_matrix, rhs.derived());
}
/** Efficient vector/matrix times self-adjoint matrix product */
template<typename OtherDerived> friend
SelfadjointProductMatrix<OtherDerived,0,OtherDerived::IsVectorAtCompileTime,MatrixType,Mode,false>
operator*(const MatrixBase<OtherDerived>& lhs, const SelfAdjointView& rhs)
{
return SelfadjointProductMatrix
<OtherDerived,0,OtherDerived::IsVectorAtCompileTime,MatrixType,Mode,false>
(lhs.derived(),rhs.m_matrix);
}
/** Perform a symmetric rank 2 update of the selfadjoint matrix \c *this:
* \f$ this = this + \alpha u v^* + conj(\alpha) v u^* \f$
* \returns a reference to \c *this
*
* The vectors \a u and \c v \b must be column vectors, however they can be
* a adjoint expression without any overhead. Only the meaningful triangular
* part of the matrix is updated, the rest is left unchanged.
*
* \sa rankUpdate(const MatrixBase<DerivedU>&, Scalar)
*/
template<typename DerivedU, typename DerivedV>
SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, Scalar alpha = Scalar(1));
/** Perform a symmetric rank K update of the selfadjoint matrix \c *this:
* \f$ this = this + \alpha ( u u^* ) \f$ where \a u is a vector or matrix.
*
* \returns a reference to \c *this
*
* Note that to perform \f$ this = this + \alpha ( u^* u ) \f$ you can simply
* call this function with u.adjoint().
*
* \sa rankUpdate(const MatrixBase<DerivedU>&, const MatrixBase<DerivedV>&, Scalar)
*/
template<typename DerivedU>
SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, Scalar alpha = Scalar(1));
/////////// Cholesky module ///////////
const LLT<PlainObject, UpLo> llt() const;
const LDLT<PlainObject, UpLo> ldlt() const;
/////////// Eigenvalue module ///////////
/** Real part of #Scalar */
typedef typename NumTraits<Scalar>::Real RealScalar;
/** Return type of eigenvalues() */
typedef Matrix<RealScalar, internal::traits<MatrixType>::ColsAtCompileTime, 1> EigenvaluesReturnType;
EigenvaluesReturnType eigenvalues() const;
RealScalar operatorNorm() const;
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
SelfAdjointView& operator=(const MatrixBase<OtherDerived>& other)
{
enum {
OtherPart = UpLo == Upper ? StrictlyLower : StrictlyUpper
};
m_matrix.const_cast_derived().template triangularView<UpLo>() = other;
m_matrix.const_cast_derived().template triangularView<OtherPart>() = other.adjoint();
return *this;
}
template<typename OtherMatrixType, unsigned int OtherMode>
SelfAdjointView& operator=(const TriangularView<OtherMatrixType, OtherMode>& other)
{
enum {
OtherPart = UpLo == Upper ? StrictlyLower : StrictlyUpper
};
m_matrix.const_cast_derived().template triangularView<UpLo>() = other.toDenseMatrix();
m_matrix.const_cast_derived().template triangularView<OtherPart>() = other.toDenseMatrix().adjoint();
return *this;
}
#endif
protected:
const MatrixTypeNested m_matrix;
};
// template<typename OtherDerived, typename MatrixType, unsigned int UpLo>
// internal::selfadjoint_matrix_product_returntype<OtherDerived,SelfAdjointView<MatrixType,UpLo> >
// operator*(const MatrixBase<OtherDerived>& lhs, const SelfAdjointView<MatrixType,UpLo>& rhs)
// {
// return internal::matrix_selfadjoint_product_returntype<OtherDerived,SelfAdjointView<MatrixType,UpLo> >(lhs.derived(),rhs);
// }
// selfadjoint to dense matrix
namespace internal {
template<typename Derived1, typename Derived2, int UnrollCount, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Upper), UnrollCount, ClearOpposite>
{
enum {
col = (UnrollCount-1) / Derived1::RowsAtCompileTime,
row = (UnrollCount-1) % Derived1::RowsAtCompileTime
};
inline static void run(Derived1 &dst, const Derived2 &src)
{
triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Upper), UnrollCount-1, ClearOpposite>::run(dst, src);
if(row == col)
dst.coeffRef(row, col) = real(src.coeff(row, col));
else if(row < col)
dst.coeffRef(col, row) = conj(dst.coeffRef(row, col) = src.coeff(row, col));
}
};
template<typename Derived1, typename Derived2, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Upper, 0, ClearOpposite>
{
inline static 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
};
inline static void run(Derived1 &dst, const Derived2 &src)
{
triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Lower), UnrollCount-1, ClearOpposite>::run(dst, src);
if(row == col)
dst.coeffRef(row, col) = real(src.coeff(row, col));
else if(row > col)
dst.coeffRef(col, row) = conj(dst.coeffRef(row, col) = src.coeff(row, col));
}
};
template<typename Derived1, typename Derived2, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Lower, 0, ClearOpposite>
{
inline static 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;
inline static void run(Derived1 &dst, const Derived2 &src)
{
for(Index j = 0; j < dst.cols(); ++j)
{
for(Index i = 0; i < j; ++i)
{
dst.copyCoeff(i, j, src);
dst.coeffRef(j,i) = conj(dst.coeff(i,j));
}
dst.copyCoeff(j, j, src);
}
}
};
template<typename Derived1, typename Derived2, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Lower, Dynamic, ClearOpposite>
{
inline static void run(Derived1 &dst, const Derived2 &src)
{
typedef typename Derived1::Index Index;
for(Index i = 0; i < dst.rows(); ++i)
{
for(Index j = 0; j < i; ++j)
{
dst.copyCoeff(i, j, src);
dst.coeffRef(j,i) = conj(dst.coeff(i,j));
}
dst.copyCoeff(i, i, src);
}
}
};
} // end namespace internal
/***************************************************************************
* Implementation of MatrixBase methods
***************************************************************************/
template<typename Derived>
template<unsigned int UpLo>
typename MatrixBase<Derived>::template ConstSelfAdjointViewReturnType<UpLo>::Type
MatrixBase<Derived>::selfadjointView() const
{
return derived();
}
template<typename Derived>
template<unsigned int UpLo>
typename MatrixBase<Derived>::template SelfAdjointViewReturnType<UpLo>::Type
MatrixBase<Derived>::selfadjointView()
{
return derived();
}
#endif // EIGEN_SELFADJOINTMATRIX_H

View File

@@ -1,195 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_SELFCWISEBINARYOP_H
#define EIGEN_SELFCWISEBINARYOP_H
/** \class SelfCwiseBinaryOp
* \ingroup Core_Module
*
* \internal
*
* \brief Internal helper class for optimizing operators like +=, -=
*
* This is a pseudo expression class re-implementing the copyCoeff/copyPacket
* method to directly performs a +=/-= operations in an optimal way. In particular,
* this allows to make sure that the input/output data are loaded only once using
* aligned packet loads.
*
* \sa class SwapWrapper for a similar trick.
*/
namespace internal {
template<typename BinaryOp, typename Lhs, typename Rhs>
struct traits<SelfCwiseBinaryOp<BinaryOp,Lhs,Rhs> >
: traits<CwiseBinaryOp<BinaryOp,Lhs,Rhs> >
{
enum {
// Note that it is still a good idea to preserve the DirectAccessBit
// so that assign can correctly align the data.
Flags = traits<CwiseBinaryOp<BinaryOp,Lhs,Rhs> >::Flags | (Lhs::Flags&DirectAccessBit) | (Lhs::Flags&LvalueBit),
OuterStrideAtCompileTime = Lhs::OuterStrideAtCompileTime,
InnerStrideAtCompileTime = Lhs::InnerStrideAtCompileTime
};
};
}
template<typename BinaryOp, typename Lhs, typename Rhs> class SelfCwiseBinaryOp
: public internal::dense_xpr_base< SelfCwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type
{
public:
typedef typename internal::dense_xpr_base<SelfCwiseBinaryOp>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(SelfCwiseBinaryOp)
typedef typename internal::packet_traits<Scalar>::type Packet;
inline SelfCwiseBinaryOp(Lhs& xpr, const BinaryOp& func = BinaryOp()) : m_matrix(xpr), m_functor(func) {}
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
inline Index outerStride() const { return m_matrix.outerStride(); }
inline Index innerStride() const { return m_matrix.innerStride(); }
inline const Scalar* data() const { return m_matrix.data(); }
// note that this function is needed by assign to correctly align loads/stores
// TODO make Assign use .data()
inline Scalar& coeffRef(Index row, Index col)
{
EIGEN_STATIC_ASSERT_LVALUE(Lhs)
return m_matrix.const_cast_derived().coeffRef(row, col);
}
inline const Scalar& coeffRef(Index row, Index col) const
{
return m_matrix.coeffRef(row, col);
}
// note that this function is needed by assign to correctly align loads/stores
// TODO make Assign use .data()
inline Scalar& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_LVALUE(Lhs)
return m_matrix.const_cast_derived().coeffRef(index);
}
inline const Scalar& coeffRef(Index index) const
{
return m_matrix.const_cast_derived().coeffRef(index);
}
template<typename OtherDerived>
void copyCoeff(Index row, Index col, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
Scalar& tmp = m_matrix.coeffRef(row,col);
tmp = m_functor(tmp, _other.coeff(row,col));
}
template<typename OtherDerived>
void copyCoeff(Index index, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(index >= 0 && index < m_matrix.size());
Scalar& tmp = m_matrix.coeffRef(index);
tmp = m_functor(tmp, _other.coeff(index));
}
template<typename OtherDerived, int StoreMode, int LoadMode>
void copyPacket(Index row, Index col, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
m_matrix.template writePacket<StoreMode>(row, col,
m_functor.packetOp(m_matrix.template packet<StoreMode>(row, col),_other.template packet<LoadMode>(row, col)) );
}
template<typename OtherDerived, int StoreMode, int LoadMode>
void copyPacket(Index index, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(index >= 0 && index < m_matrix.size());
m_matrix.template writePacket<StoreMode>(index,
m_functor.packetOp(m_matrix.template packet<StoreMode>(index),_other.template packet<LoadMode>(index)) );
}
// reimplement lazyAssign to handle complex *= real
// see CwiseBinaryOp ctor for details
template<typename RhsDerived>
EIGEN_STRONG_INLINE SelfCwiseBinaryOp& lazyAssign(const DenseBase<RhsDerived>& rhs)
{
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs,RhsDerived)
EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename RhsDerived::Scalar);
#ifdef EIGEN_DEBUG_ASSIGN
internal::assign_traits<SelfCwiseBinaryOp, RhsDerived>::debug();
#endif
eigen_assert(rows() == rhs.rows() && cols() == rhs.cols());
internal::assign_impl<SelfCwiseBinaryOp, RhsDerived>::run(*this,rhs.derived());
#ifndef EIGEN_NO_DEBUG
this->checkTransposeAliasing(rhs.derived());
#endif
return *this;
}
// overloaded to honor evaluation of special matrices
// maybe another solution would be to not use SelfCwiseBinaryOp
// at first...
SelfCwiseBinaryOp& operator=(const Rhs& _rhs)
{
typename internal::nested<Rhs>::type rhs(_rhs);
return Base::operator=(rhs);
}
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);
return derived();
}
template<typename Derived>
inline Derived& DenseBase<Derived>::operator/=(const Scalar& other)
{
typedef typename internal::conditional<NumTraits<Scalar>::IsInteger,
internal::scalar_quotient_op<Scalar>,
internal::scalar_product_op<Scalar> >::type BinOp;
typedef typename Derived::PlainObject PlainObject;
SelfCwiseBinaryOp<BinOp, Derived, typename PlainObject::ConstantReturnType> tmp(derived());
tmp = PlainObject::Constant(rows(),cols(), NumTraits<Scalar>::IsInteger ? other : Scalar(1)/other);
return derived();
}
#endif // EIGEN_SELFCWISEBINARYOP_H

View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -25,170 +25,227 @@
#ifndef EIGEN_SOLVETRIANGULAR_H
#define EIGEN_SOLVETRIANGULAR_H
namespace internal {
// Forward declarations:
// The following two routines are implemented in the products/TriangularSolver*.h files
template<typename LhsScalar, typename RhsScalar, typename Index, int Side, int Mode, bool Conjugate, int StorageOrder>
struct triangular_solve_vector;
template <typename Scalar, typename Index, int Side, int Mode, bool Conjugate, int TriStorageOrder, int OtherStorageOrder>
struct triangular_solve_matrix;
// small helper struct extracting some traits on the underlying solver operation
template<typename Lhs, typename Rhs, int Side>
class trsolve_traits
{
private:
enum {
RhsIsVectorAtCompileTime = (Side==OnTheLeft ? Rhs::ColsAtCompileTime : Rhs::RowsAtCompileTime)==1
};
public:
enum {
Unrolling = (RhsIsVectorAtCompileTime && Rhs::SizeAtCompileTime != Dynamic && Rhs::SizeAtCompileTime <= 8)
? CompleteUnrolling : NoUnrolling,
RhsVectors = RhsIsVectorAtCompileTime ? 1 : Dynamic
};
};
template<typename XprType> struct ei_is_part { enum {value=false}; };
template<typename XprType, unsigned int Mode> struct ei_is_part<Part<XprType,Mode> > { enum {value=true}; };
template<typename Lhs, typename Rhs,
int Side, // can be OnTheLeft/OnTheRight
int Mode, // can be Upper/Lower | UnitDiag
int Unrolling = trsolve_traits<Lhs,Rhs,Side>::Unrolling,
int RhsVectors = trsolve_traits<Lhs,Rhs,Side>::RhsVectors
int TriangularPart = (int(Lhs::Flags) & LowerTriangularBit)
? LowerTriangular
: (int(Lhs::Flags) & UpperTriangularBit)
? UpperTriangular
: -1,
int StorageOrder = ei_is_part<Lhs>::value ? -1 // this is to solve ambiguous specializations
: int(Lhs::Flags) & (RowMajorBit|SparseBit)
>
struct triangular_solver_selector;
struct ei_solve_triangular_selector;
template<typename Lhs, typename Rhs, int Side, int Mode>
struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,1>
// transform a Part xpr to a Flagged xpr
template<typename Lhs, unsigned int LhsMode, typename Rhs, int UpLo, int StorageOrder>
struct ei_solve_triangular_selector<Part<Lhs,LhsMode>,Rhs,UpLo,StorageOrder>
{
typedef typename Lhs::Scalar LhsScalar;
typedef typename Rhs::Scalar RhsScalar;
typedef blas_traits<Lhs> LhsProductTraits;
typedef typename LhsProductTraits::ExtractType ActualLhsType;
typedef Map<Matrix<RhsScalar,Dynamic,1>, Aligned> MappedRhs;
static void run(const Lhs& lhs, Rhs& rhs)
static void run(const Part<Lhs,LhsMode>& lhs, Rhs& other)
{
ActualLhsType actualLhs = LhsProductTraits::extract(lhs);
// FIXME find a way to allow an inner stride if packet_traits<Scalar>::size==1
bool useRhsDirectly = Rhs::InnerStrideAtCompileTime==1 || rhs.innerStride()==1;
ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhs,rhs.size(),
(useRhsDirectly ? rhs.data() : 0));
if(!useRhsDirectly)
MappedRhs(actualRhs,rhs.size()) = rhs;
triangular_solve_vector<LhsScalar, RhsScalar, typename Lhs::Index, Side, Mode, LhsProductTraits::NeedToConjugate,
(int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor>
::run(actualLhs.cols(), actualLhs.data(), actualLhs.outerStride(), actualRhs);
if(!useRhsDirectly)
rhs = MappedRhs(actualRhs, rhs.size());
ei_solve_triangular_selector<Flagged<Lhs,LhsMode,0>,Rhs>::run(lhs._expression(), other);
}
};
// the rhs is a matrix
template<typename Lhs, typename Rhs, int Side, int Mode>
struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,Dynamic>
// forward substitution, row-major
template<typename Lhs, typename Rhs, int UpLo>
struct ei_solve_triangular_selector<Lhs,Rhs,UpLo,RowMajor|IsDense>
{
typedef typename Rhs::Scalar Scalar;
typedef typename Rhs::Index Index;
typedef blas_traits<Lhs> LhsProductTraits;
typedef typename LhsProductTraits::DirectLinearAccessType ActualLhsType;
static void run(const Lhs& lhs, Rhs& rhs)
static void run(const Lhs& lhs, Rhs& other)
{
const ActualLhsType actualLhs = LhsProductTraits::extract(lhs);
triangular_solve_matrix<Scalar,Index,Side,Mode,LhsProductTraits::NeedToConjugate,(int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor,
(Rhs::Flags&RowMajorBit) ? RowMajor : ColMajor>
::run(lhs.rows(), Side==OnTheLeft? rhs.cols() : rhs.rows(), &actualLhs.coeffRef(0,0), actualLhs.outerStride(), &rhs.coeffRef(0,0), rhs.outerStride());
const bool IsLowerTriangular = (UpLo==LowerTriangular);
const int size = lhs.cols();
/* We perform the inverse product per block of 4 rows such that we perfectly match
* our optimized matrix * vector product. blockyStart represents the number of rows
* we have process first using the non-block version.
*/
int blockyStart = (std::max(size-5,0)/4)*4;
if (IsLowerTriangular)
blockyStart = size - blockyStart;
else
blockyStart -= 1;
for(int c=0 ; c<other.cols() ; ++c)
{
// process first rows using the non block version
if(!(Lhs::Flags & UnitDiagBit))
{
if (IsLowerTriangular)
other.coeffRef(0,c) = other.coeff(0,c)/lhs.coeff(0, 0);
else
other.coeffRef(size-1,c) = other.coeff(size-1, c)/lhs.coeff(size-1, size-1);
}
for(int i=(IsLowerTriangular ? 1 : size-2); IsLowerTriangular ? i<blockyStart : i>blockyStart; i += (IsLowerTriangular ? 1 : -1) )
{
Scalar tmp = other.coeff(i,c)
- (IsLowerTriangular ? ((lhs.row(i).start(i)) * other.col(c).start(i)).coeff(0,0)
: ((lhs.row(i).end(size-i-1)) * other.col(c).end(size-i-1)).coeff(0,0));
if (Lhs::Flags & UnitDiagBit)
other.coeffRef(i,c) = tmp;
else
other.coeffRef(i,c) = tmp/lhs.coeff(i,i);
}
// now let's process the remaining rows 4 at once
for(int i=blockyStart; IsLowerTriangular ? i<size : i>0; )
{
int startBlock = i;
int endBlock = startBlock + (IsLowerTriangular ? 4 : -4);
/* Process the i cols times 4 rows block, and keep the result in a temporary vector */
// FIXME use fixed size block but take care to small fixed size matrices...
Matrix<Scalar,Dynamic,1> btmp(4);
if (IsLowerTriangular)
btmp = lhs.block(startBlock,0,4,i) * other.col(c).start(i);
else
btmp = lhs.block(i-3,i+1,4,size-1-i) * other.col(c).end(size-1-i);
/* Let's process the 4x4 sub-matrix as usual.
* btmp stores the diagonal coefficients used to update the remaining part of the result.
*/
{
Scalar tmp = other.coeff(startBlock,c)-btmp.coeff(IsLowerTriangular?0:3);
if (Lhs::Flags & UnitDiagBit)
other.coeffRef(i,c) = tmp;
else
other.coeffRef(i,c) = tmp/lhs.coeff(i,i);
}
i += IsLowerTriangular ? 1 : -1;
for (;IsLowerTriangular ? i<endBlock : i>endBlock; i += IsLowerTriangular ? 1 : -1)
{
int remainingSize = IsLowerTriangular ? i-startBlock : startBlock-i;
Scalar tmp = other.coeff(i,c)
- btmp.coeff(IsLowerTriangular ? remainingSize : 3-remainingSize)
- ( lhs.row(i).segment(IsLowerTriangular ? startBlock : i+1, remainingSize)
* other.col(c).segment(IsLowerTriangular ? startBlock : i+1, remainingSize)).coeff(0,0);
if (Lhs::Flags & UnitDiagBit)
other.coeffRef(i,c) = tmp;
else
other.coeffRef(i,c) = tmp/lhs.coeff(i,i);
}
}
}
}
};
/***************************************************************************
* meta-unrolling implementation
***************************************************************************/
// Implements the following configurations:
// - inv(LowerTriangular, ColMajor) * Column vector
// - inv(LowerTriangular,UnitDiag,ColMajor) * Column vector
// - inv(UpperTriangular, ColMajor) * Column vector
// - inv(UpperTriangular,UnitDiag,ColMajor) * Column vector
template<typename Lhs, typename Rhs, int UpLo>
struct ei_solve_triangular_selector<Lhs,Rhs,UpLo,ColMajor|IsDense>
{
typedef typename Rhs::Scalar Scalar;
typedef typename ei_packet_traits<Scalar>::type Packet;
enum { PacketSize = ei_packet_traits<Scalar>::size };
template<typename Lhs, typename Rhs, int Mode, int Index, int Size,
bool Stop = Index==Size>
struct triangular_solver_unroller;
template<typename Lhs, typename Rhs, int Mode, int Index, int Size>
struct triangular_solver_unroller<Lhs,Rhs,Mode,Index,Size,false> {
enum {
IsLower = ((Mode&Lower)==Lower),
I = IsLower ? Index : Size - Index - 1,
S = IsLower ? 0 : I+1
};
static void run(const Lhs& lhs, Rhs& rhs)
static void run(const Lhs& lhs, Rhs& other)
{
if (Index>0)
rhs.coeffRef(I) -= lhs.row(I).template segment<Index>(S).transpose()
.cwiseProduct(rhs.template segment<Index>(S)).sum();
static const bool IsLowerTriangular = (UpLo==LowerTriangular);
const int size = lhs.cols();
for(int c=0 ; c<other.cols() ; ++c)
{
/* let's perform the inverse product per block of 4 columns such that we perfectly match
* our optimized matrix * vector product. blockyEnd represents the number of rows
* we can process using the block version.
*/
int blockyEnd = (std::max(size-5,0)/4)*4;
if (!IsLowerTriangular)
blockyEnd = size-1 - blockyEnd;
for(int i=IsLowerTriangular ? 0 : size-1; IsLowerTriangular ? i<blockyEnd : i>blockyEnd;)
{
/* Let's process the 4x4 sub-matrix as usual.
* btmp stores the diagonal coefficients used to update the remaining part of the result.
*/
int startBlock = i;
int endBlock = startBlock + (IsLowerTriangular ? 4 : -4);
Matrix<Scalar,4,1> btmp;
for (;IsLowerTriangular ? i<endBlock : i>endBlock;
i += IsLowerTriangular ? 1 : -1)
{
if(!(Lhs::Flags & UnitDiagBit))
other.coeffRef(i,c) /= lhs.coeff(i,i);
int remainingSize = IsLowerTriangular ? endBlock-i-1 : i-endBlock-1;
if (remainingSize>0)
other.col(c).segment((IsLowerTriangular ? i : endBlock) + 1, remainingSize) -=
other.coeffRef(i,c)
* Block<Lhs,Dynamic,1>(lhs, (IsLowerTriangular ? i : endBlock) + 1, i, remainingSize, 1);
btmp.coeffRef(IsLowerTriangular ? i-startBlock : remainingSize) = -other.coeffRef(i,c);
}
if(!(Mode & UnitDiag))
rhs.coeffRef(I) /= lhs.coeff(I,I);
/* Now we can efficiently update the remaining part of the result as a matrix * vector product.
* NOTE in order to reduce both compilation time and binary size, let's directly call
* the fast product implementation. It is equivalent to the following code:
* other.col(c).end(size-endBlock) += (lhs.block(endBlock, startBlock, size-endBlock, endBlock-startBlock)
* * other.col(c).block(startBlock,endBlock-startBlock)).lazy();
*/
// FIXME this is cool but what about conjugate/adjoint expressions ? do we want to evaluate them ?
// this is a more general problem though.
ei_cache_friendly_product_colmajor_times_vector(
IsLowerTriangular ? size-endBlock : endBlock+1,
&(lhs.const_cast_derived().coeffRef(IsLowerTriangular ? endBlock : 0, IsLowerTriangular ? startBlock : endBlock+1)),
lhs.stride(),
btmp, &(other.coeffRef(IsLowerTriangular ? endBlock : 0, c)));
// if (IsLowerTriangular)
// other.col(c).end(size-endBlock) += (lhs.block(endBlock, startBlock, size-endBlock, endBlock-startBlock)
// * other.col(c).block(startBlock,endBlock-startBlock)).lazy();
// else
// other.col(c).end(size-endBlock) += (lhs.block(endBlock, startBlock, size-endBlock, endBlock-startBlock)
// * other.col(c).block(startBlock,endBlock-startBlock)).lazy();
}
triangular_solver_unroller<Lhs,Rhs,Mode,Index+1,Size>::run(lhs,rhs);
/* Now we have to process the remaining part as usual */
int i;
for(i=blockyEnd; IsLowerTriangular ? i<size-1 : i>0; i += (IsLowerTriangular ? 1 : -1) )
{
if(!(Lhs::Flags & UnitDiagBit))
other.coeffRef(i,c) /= lhs.coeff(i,i);
/* NOTE we cannot use lhs.col(i).end(size-i-1) because Part::coeffRef gets called by .col() to
* get the address of the start of the row
*/
if(IsLowerTriangular)
other.col(c).end(size-i-1) -= other.coeffRef(i,c) * Block<Lhs,Dynamic,1>(lhs, i+1,i, size-i-1,1);
else
other.col(c).start(i) -= other.coeffRef(i,c) * Block<Lhs,Dynamic,1>(lhs, 0,i, i, 1);
}
if(!(Lhs::Flags & UnitDiagBit))
other.coeffRef(i,c) /= lhs.coeff(i,i);
}
}
};
template<typename Lhs, typename Rhs, int Mode, int Index, int Size>
struct triangular_solver_unroller<Lhs,Rhs,Mode,Index,Size,true> {
static void run(const Lhs&, Rhs&) {}
};
template<typename Lhs, typename Rhs, int Mode>
struct triangular_solver_selector<Lhs,Rhs,OnTheLeft,Mode,CompleteUnrolling,1> {
static void run(const Lhs& lhs, Rhs& rhs)
{ triangular_solver_unroller<Lhs,Rhs,Mode,0,Rhs::SizeAtCompileTime>::run(lhs,rhs); }
};
template<typename Lhs, typename Rhs, int Mode>
struct triangular_solver_selector<Lhs,Rhs,OnTheRight,Mode,CompleteUnrolling,1> {
static void run(const Lhs& lhs, Rhs& rhs)
{
Transpose<const Lhs> trLhs(lhs);
Transpose<Rhs> trRhs(rhs);
triangular_solver_unroller<Transpose<const Lhs>,Transpose<Rhs>,
((Mode&Upper)==Upper ? Lower : Upper) | (Mode&UnitDiag),
0,Rhs::SizeAtCompileTime>::run(trLhs,trRhs);
}
};
} // end namespace internal
/***************************************************************************
* TriangularView methods
***************************************************************************/
/** "in-place" version of TriangularView::solve() where the result is written in \a other
/** "in-place" version of MatrixBase::solveTriangular() where the result is written in \a other
*
* \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
* \nonstableyet
*
* The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
* This function will const_cast it, so constness isn't honored here.
*
* See TriangularView:solve() for the details.
* See MatrixBase:solveTriangular() for the details.
*/
template<typename MatrixType, unsigned int Mode>
template<int Side, typename OtherDerived>
void TriangularView<MatrixType,Mode>::solveInPlace(const MatrixBase<OtherDerived>& _other) const
template<typename Derived>
template<typename OtherDerived>
void MatrixBase<Derived>::solveTriangularInPlace(const MatrixBase<OtherDerived>& _other) const
{
OtherDerived& other = _other.const_cast_derived();
eigen_assert(cols() == rows());
eigen_assert( (Side==OnTheLeft && cols() == other.rows()) || (Side==OnTheRight && cols() == other.cols()) );
eigen_assert(!(Mode & ZeroDiag));
eigen_assert((Mode & (Upper|Lower)) != 0);
MatrixBase<OtherDerived>& other = _other.const_cast_derived();
ei_assert(derived().cols() == derived().rows());
ei_assert(derived().cols() == other.rows());
ei_assert(!(Flags & ZeroDiagBit));
ei_assert(Flags & (UpperTriangularBit|LowerTriangularBit));
enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit && OtherDerived::IsVectorAtCompileTime };
typedef typename internal::conditional<copy,
typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy;
OtherCopy otherCopy(other);
enum { copy = ei_traits<OtherDerived>::Flags & RowMajorBit };
internal::triangular_solver_selector<MatrixType, typename internal::remove_reference<OtherCopy>::type,
Side, Mode>::run(nestedExpression(), otherCopy);
typedef typename ei_meta_if<copy,
typename ei_plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::ret OtherCopy;
OtherCopy otherCopy(other.derived());
ei_solve_triangular_selector<Derived, typename ei_unref<OtherCopy>::type>::run(derived(), otherCopy);
if (copy)
other = otherCopy;
@@ -196,68 +253,45 @@ void TriangularView<MatrixType,Mode>::solveInPlace(const MatrixBase<OtherDerived
/** \returns the product of the inverse of \c *this with \a other, \a *this being triangular.
*
* This function computes the inverse-matrix matrix product inverse(\c *this) * \a other if
* \a Side==OnTheLeft (the default), or the right-inverse-multiply \a other * inverse(\c *this) if
* \a Side==OnTheRight.
* \nonstableyet
*
* This function computes the inverse-matrix matrix product inverse(\c *this) * \a other.
* The matrix \c *this must be triangular and invertible (i.e., all the coefficients of the
* diagonal must be non zero). It works as a forward (resp. backward) substitution if \c *this
* is an upper (resp. lower) triangular matrix.
*
* It is required that \c *this be marked as either an upper or a lower triangular matrix, which
* can be done by marked(), and that is automatically the case with expressions such as those returned
* by extract().
*
* \addexample SolveTriangular \label How to solve a triangular system (aka. how to multiply the inverse of a triangular matrix by another one)
*
* Example: \include MatrixBase_marked.cpp
* Output: \verbinclude MatrixBase_marked.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.
* This function is essentially a wrapper to the faster solveTriangularInPlace() function creating
* a temporary copy of \a other, calling solveTriangularInPlace() on the copy and returning it.
* Therefore, if \a other is not needed anymore, it is quite faster to call solveTriangularInPlace()
* instead of solveTriangular().
*
* For users coming from BLAS, this function (and more specifically solveInPlace()) offer
* For users coming from BLAS, this function (and more specifically solveTriangularInPlace()) offer
* all the operations supported by the \c *TRSV and \c *TRSM BLAS routines.
*
* \sa TriangularView::solveInPlace()
* \b Tips: to perform a \em "right-inverse-multiply" you can simply transpose the operation, e.g.:
* \code
* M * T^1 <=> T.transpose().solveTriangularInPlace(M.transpose());
* \endcode
*
* \sa solveTriangularInPlace(), marked(), extract()
*/
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
template<typename Derived>
template<typename OtherDerived>
typename ei_plain_matrix_type_column_major<OtherDerived>::type
MatrixBase<Derived>::solveTriangular(const MatrixBase<OtherDerived>& other) const
{
return internal::triangular_solve_retval<Side,TriangularView,Other>(*this, other.derived());
typename ei_plain_matrix_type_column_major<OtherDerived>::type res(other);
solveTriangularInPlace(res);
return res;
}
namespace internal {
template<int Side, typename TriangularType, typename Rhs>
struct traits<triangular_solve_retval<Side, TriangularType, Rhs> >
{
typedef typename internal::plain_matrix_type_column_major<Rhs>::type ReturnType;
};
template<int Side, typename TriangularType, typename Rhs> struct triangular_solve_retval
: public ReturnByValue<triangular_solve_retval<Side, TriangularType, Rhs> >
{
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)
{}
inline Index rows() const { return m_rhs.rows(); }
inline Index cols() const { return m_rhs.cols(); }
template<typename Dest> inline void evalTo(Dest& dst) const
{
if(!(is_same<RhsNestedCleaned,Dest>::value && extract_data(dst) == extract_data(m_rhs)))
dst = m_rhs;
m_triangularMatrix.template solveInPlace<Side>(dst);
}
protected:
const TriangularType& m_triangularMatrix;
const typename Rhs::Nested m_rhs;
};
} // namespace internal
#endif // EIGEN_SOLVETRIANGULAR_H

View File

@@ -1,190 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_STABLENORM_H
#define EIGEN_STABLENORM_H
namespace internal {
template<typename ExpressionType, typename Scalar>
inline void stable_norm_kernel(const ExpressionType& bl, Scalar& ssq, Scalar& scale, Scalar& invScale)
{
Scalar max = bl.cwiseAbs().maxCoeff();
if (max>scale)
{
ssq = ssq * abs2(scale/max);
scale = max;
invScale = Scalar(1)/scale;
}
// TODO if the max is much much smaller than the current scale,
// then we can neglect this sub vector
ssq += (bl*invScale).squaredNorm();
}
}
/** \returns the \em l2 norm of \c *this avoiding underflow and overflow.
* This version use a blockwise two passes algorithm:
* 1 - find the absolute largest coefficient \c s
* 2 - compute \f$ s \Vert \frac{*this}{s} \Vert \f$ in a standard way
*
* For architecture/scalar types supporting vectorization, this version
* is faster than blueNorm(). Otherwise the blueNorm() is much faster.
*
* \sa norm(), blueNorm(), hypotNorm()
*/
template<typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
MatrixBase<Derived>::stableNorm() const
{
using std::min;
const Index blockSize = 4096;
RealScalar scale = 0;
RealScalar invScale = 1;
RealScalar ssq = 0; // sum of square
enum {
Alignment = (int(Flags)&DirectAccessBit) || (int(Flags)&AlignedBit) ? 1 : 0
};
Index n = size();
Index bi = internal::first_aligned(derived());
if (bi>0)
internal::stable_norm_kernel(this->head(bi), ssq, scale, invScale);
for (; bi<n; bi+=blockSize)
internal::stable_norm_kernel(this->segment(bi,(min)(blockSize, n - bi)).template forceAlignedAccessIf<Alignment>(), ssq, scale, invScale);
return scale * internal::sqrt(ssq);
}
/** \returns the \em l2 norm of \c *this using the Blue's algorithm.
* A Portable Fortran Program to Find the Euclidean Norm of a Vector,
* ACM TOMS, Vol 4, Issue 1, 1978.
*
* For architecture/scalar types without vectorization, this version
* is much faster than stableNorm(). Otherwise the stableNorm() is faster.
*
* \sa norm(), stableNorm(), hypotNorm()
*/
template<typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
MatrixBase<Derived>::blueNorm() const
{
using std::pow;
using std::min;
using std::max;
static Index nmax = -1;
static RealScalar b1, b2, s1m, s2m, overfl, rbig, relerr;
if(nmax <= 0)
{
int nbig, ibeta, it, iemin, iemax, iexp;
RealScalar abig, eps;
// This program calculates the machine-dependent constants
// bl, b2, slm, s2m, relerr overfl, nmax
// from the "basic" machine-dependent numbers
// nbig, ibeta, it, iemin, iemax, rbig.
// The following define the basic machine-dependent constants.
// For portability, the PORT subprograms "ilmaeh" and "rlmach"
// are used. For any specific computer, each of the assignment
// statements can be replaced
nbig = (std::numeric_limits<Index>::max)(); // largest integer
ibeta = std::numeric_limits<RealScalar>::radix; // base for floating-point numbers
it = std::numeric_limits<RealScalar>::digits; // number of base-beta digits in mantissa
iemin = std::numeric_limits<RealScalar>::min_exponent; // minimum exponent
iemax = std::numeric_limits<RealScalar>::max_exponent; // maximum exponent
rbig = (std::numeric_limits<RealScalar>::max)(); // largest floating-point number
iexp = -((1-iemin)/2);
b1 = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // lower boundary of midrange
iexp = (iemax + 1 - it)/2;
b2 = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // upper boundary of midrange
iexp = (2-iemin)/2;
s1m = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // scaling factor for lower range
iexp = - ((iemax+it)/2);
s2m = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // scaling factor for upper range
overfl = rbig*s2m; // overflow boundary for abig
eps = RealScalar(pow(double(ibeta), 1-it));
relerr = internal::sqrt(eps); // tolerance for neglecting asml
abig = RealScalar(1.0/eps - 1.0);
if (RealScalar(nbig)>abig) nmax = int(abig); // largest safe n
else nmax = nbig;
}
Index n = size();
RealScalar ab2 = b2 / RealScalar(n);
RealScalar asml = RealScalar(0);
RealScalar amed = RealScalar(0);
RealScalar abig = RealScalar(0);
for(Index j=0; j<n; ++j)
{
RealScalar ax = internal::abs(coeff(j));
if(ax > ab2) abig += internal::abs2(ax*s2m);
else if(ax < b1) asml += internal::abs2(ax*s1m);
else amed += internal::abs2(ax);
}
if(abig > RealScalar(0))
{
abig = internal::sqrt(abig);
if(abig > overfl)
{
eigen_assert(false && "overflow");
return rbig;
}
if(amed > RealScalar(0))
{
abig = abig/s2m;
amed = internal::sqrt(amed);
}
else
return abig/s2m;
}
else if(asml > RealScalar(0))
{
if (amed > RealScalar(0))
{
abig = internal::sqrt(amed);
amed = internal::sqrt(asml) / s1m;
}
else
return internal::sqrt(asml)/s1m;
}
else
return internal::sqrt(amed);
asml = (min)(abig, amed);
abig = (max)(abig, amed);
if(asml <= abig*relerr)
return abig;
else
return abig * internal::sqrt(RealScalar(1) + internal::abs2(asml/abig));
}
/** \returns the \em l2 norm of \c *this avoiding undeflow and overflow.
* This version use a concatenation of hypot() calls, and it is very slow.
*
* \sa norm(), stableNorm()
*/
template<typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
MatrixBase<Derived>::hypotNorm() const
{
return this->cwiseAbs().redux(internal::scalar_hypot_op<RealScalar>());
}
#endif // EIGEN_STABLENORM_H

View File

@@ -1,119 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_STRIDE_H
#define EIGEN_STRIDE_H
/** \class Stride
* \ingroup Core_Module
*
* \brief Holds strides information for Map
*
* This class holds the strides information for mapping arrays with strides with class Map.
*
* It holds two values: the inner stride and the outer stride.
*
* The inner stride is the pointer increment between two consecutive entries within a given row of a
* row-major matrix or within a given column of a column-major matrix.
*
* The outer stride is the pointer increment between two consecutive rows of a row-major matrix or
* between two consecutive columns of a column-major matrix.
*
* These two values can be passed either at compile-time as template parameters, or at runtime as
* arguments to the constructor.
*
* Indeed, this class takes two template parameters:
* \param _OuterStrideAtCompileTime the outer stride, or Dynamic if you want to specify it at runtime.
* \param _InnerStrideAtCompileTime the inner stride, or Dynamic if you want to specify it at runtime.
*
* Here is an example:
* \include Map_general_stride.cpp
* Output: \verbinclude Map_general_stride.out
*
* \sa class InnerStride, class OuterStride, \ref TopicStorageOrders
*/
template<int _OuterStrideAtCompileTime, int _InnerStrideAtCompileTime>
class Stride
{
public:
typedef DenseIndex Index;
enum {
InnerStrideAtCompileTime = _InnerStrideAtCompileTime,
OuterStrideAtCompileTime = _OuterStrideAtCompileTime
};
/** Default constructor, for use when strides are fixed at compile time */
Stride()
: m_outer(OuterStrideAtCompileTime), m_inner(InnerStrideAtCompileTime)
{
eigen_assert(InnerStrideAtCompileTime != Dynamic && OuterStrideAtCompileTime != Dynamic);
}
/** Constructor allowing to pass the strides at runtime */
Stride(Index outerStride, Index innerStride)
: m_outer(outerStride), m_inner(innerStride)
{
eigen_assert(innerStride>=0 && outerStride>=0);
}
/** Copy constructor */
Stride(const Stride& other)
: m_outer(other.outer()), m_inner(other.inner())
{}
/** \returns the outer stride */
inline Index outer() const { return m_outer.value(); }
/** \returns the inner stride */
inline Index inner() const { return m_inner.value(); }
protected:
internal::variable_if_dynamic<Index, OuterStrideAtCompileTime> m_outer;
internal::variable_if_dynamic<Index, InnerStrideAtCompileTime> m_inner;
};
/** \brief Convenience specialization of Stride to specify only an inner stride
* See class Map for some examples */
template<int Value = Dynamic>
class InnerStride : public Stride<0, Value>
{
typedef Stride<0, Value> Base;
public:
typedef DenseIndex Index;
InnerStride() : Base() {}
InnerStride(Index v) : Base(0, v) {}
};
/** \brief Convenience specialization of Stride to specify only an outer stride
* See class Map for some examples */
template<int Value = Dynamic>
class OuterStride : public Stride<Value, 0>
{
typedef Stride<Value, 0> Base;
public:
typedef DenseIndex Index;
OuterStride() : Base() {}
OuterStride(Index v) : Base(v,0) {}
};
#endif // EIGEN_STRIDE_H

271
Eigen/src/Core/Sum.h Normal file
View File

@@ -0,0 +1,271 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_SUM_H
#define EIGEN_SUM_H
/***************************************************************************
* Part 1 : the logic deciding a strategy for vectorization and unrolling
***************************************************************************/
template<typename Derived>
struct ei_sum_traits
{
private:
enum {
PacketSize = ei_packet_traits<typename Derived::Scalar>::size
};
public:
enum {
Vectorization = (int(Derived::Flags)&ActualPacketAccessBit)
&& (int(Derived::Flags)&LinearAccessBit)
? LinearVectorization
: NoVectorization
};
private:
enum {
Cost = Derived::SizeAtCompileTime * Derived::CoeffReadCost
+ (Derived::SizeAtCompileTime-1) * NumTraits<typename Derived::Scalar>::AddCost,
UnrollingLimit = EIGEN_UNROLLING_LIMIT * (int(Vectorization) == int(NoVectorization) ? 1 : int(PacketSize))
};
public:
enum {
Unrolling = Cost <= UnrollingLimit
? CompleteUnrolling
: NoUnrolling
};
};
/***************************************************************************
* Part 2 : unrollers
***************************************************************************/
/*** no vectorization ***/
template<typename Derived, int Start, int Length>
struct ei_sum_novec_unroller
{
enum {
HalfLength = Length/2
};
typedef typename Derived::Scalar Scalar;
inline static Scalar run(const Derived &mat)
{
return ei_sum_novec_unroller<Derived, Start, HalfLength>::run(mat)
+ ei_sum_novec_unroller<Derived, Start+HalfLength, Length-HalfLength>::run(mat);
}
};
template<typename Derived, int Start>
struct ei_sum_novec_unroller<Derived, Start, 1>
{
enum {
col = Start / Derived::RowsAtCompileTime,
row = Start % Derived::RowsAtCompileTime
};
typedef typename Derived::Scalar Scalar;
inline static Scalar run(const Derived &mat)
{
return mat.coeff(row, col);
}
};
/*** vectorization ***/
template<typename Derived, int Start, int Length>
struct ei_sum_vec_unroller
{
enum {
PacketSize = ei_packet_traits<typename Derived::Scalar>::size,
HalfLength = Length/2
};
typedef typename Derived::Scalar Scalar;
typedef typename ei_packet_traits<Scalar>::type PacketScalar;
inline static PacketScalar run(const Derived &mat)
{
return ei_padd(
ei_sum_vec_unroller<Derived, Start, HalfLength>::run(mat),
ei_sum_vec_unroller<Derived, Start+HalfLength, Length-HalfLength>::run(mat) );
}
};
template<typename Derived, int Start>
struct ei_sum_vec_unroller<Derived, Start, 1>
{
enum {
index = Start * ei_packet_traits<typename Derived::Scalar>::size,
row = int(Derived::Flags)&RowMajorBit
? index / int(Derived::ColsAtCompileTime)
: index % Derived::RowsAtCompileTime,
col = int(Derived::Flags)&RowMajorBit
? index % int(Derived::ColsAtCompileTime)
: index / Derived::RowsAtCompileTime,
alignment = (Derived::Flags & AlignedBit) ? Aligned : Unaligned
};
typedef typename Derived::Scalar Scalar;
typedef typename ei_packet_traits<Scalar>::type PacketScalar;
inline static PacketScalar run(const Derived &mat)
{
return mat.template packet<alignment>(row, col);
}
};
/***************************************************************************
* Part 3 : implementation of all cases
***************************************************************************/
template<typename Derived,
int Vectorization = ei_sum_traits<Derived>::Vectorization,
int Unrolling = ei_sum_traits<Derived>::Unrolling
>
struct ei_sum_impl;
template<typename Derived>
struct ei_sum_impl<Derived, NoVectorization, NoUnrolling>
{
typedef typename Derived::Scalar Scalar;
static Scalar run(const Derived& mat)
{
ei_assert(mat.rows()>0 && mat.cols()>0 && "you are using a non initialized matrix");
Scalar res;
res = mat.coeff(0, 0);
for(int i = 1; i < mat.rows(); ++i)
res += mat.coeff(i, 0);
for(int j = 1; j < mat.cols(); ++j)
for(int i = 0; i < mat.rows(); ++i)
res += mat.coeff(i, j);
return res;
}
};
template<typename Derived>
struct ei_sum_impl<Derived, NoVectorization, CompleteUnrolling>
: public ei_sum_novec_unroller<Derived, 0, Derived::SizeAtCompileTime>
{};
template<typename Derived>
struct ei_sum_impl<Derived, LinearVectorization, NoUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename ei_packet_traits<Scalar>::type PacketScalar;
static Scalar run(const Derived& mat)
{
const int size = mat.size();
const int packetSize = ei_packet_traits<Scalar>::size;
const int alignedStart = (Derived::Flags & AlignedBit)
|| !(Derived::Flags & DirectAccessBit)
? 0
: ei_alignmentOffset(&mat.const_cast_derived().coeffRef(0), size);
enum {
alignment = (Derived::Flags & DirectAccessBit) || (Derived::Flags & AlignedBit)
? Aligned : Unaligned
};
const int alignedSize = ((size-alignedStart)/packetSize)*packetSize;
const int alignedEnd = alignedStart + alignedSize;
Scalar res;
if(alignedSize)
{
PacketScalar packet_res = mat.template packet<alignment>(alignedStart);
for(int index = alignedStart + packetSize; index < alignedEnd; index += packetSize)
packet_res = ei_padd(packet_res, mat.template packet<alignment>(index));
res = ei_predux(packet_res);
}
else // too small to vectorize anything.
// since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize.
{
res = Scalar(0);
}
for(int index = 0; index < alignedStart; ++index)
res += mat.coeff(index);
for(int index = alignedEnd; index < size; ++index)
res += mat.coeff(index);
return res;
}
};
template<typename Derived>
struct ei_sum_impl<Derived, LinearVectorization, CompleteUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename ei_packet_traits<Scalar>::type PacketScalar;
enum {
PacketSize = ei_packet_traits<Scalar>::size,
Size = Derived::SizeAtCompileTime,
VectorizationSize = (Size / PacketSize) * PacketSize
};
static Scalar run(const Derived& mat)
{
Scalar res = ei_predux(ei_sum_vec_unroller<Derived, 0, Size / PacketSize>::run(mat));
if (VectorizationSize != Size)
res += ei_sum_novec_unroller<Derived, VectorizationSize, Size-VectorizationSize>::run(mat);
return res;
}
};
/***************************************************************************
* Part 4 : implementation of MatrixBase methods
***************************************************************************/
/** \returns the sum of all coefficients of *this
*
* \sa trace()
*/
template<typename Derived>
inline typename ei_traits<Derived>::Scalar
MatrixBase<Derived>::sum() const
{
return ei_sum_impl<Derived>::run(derived());
}
/** \returns the trace of \c *this, i.e. the sum of the coefficients on the main diagonal.
*
* \c *this can be any matrix, not necessarily square.
*
* \sa diagonal(), sum()
*/
template<typename Derived>
inline typename ei_traits<Derived>::Scalar
MatrixBase<Derived>::trace() const
{
return diagonal().sum();
}
#endif // EIGEN_SUM_H

View File

@@ -1,5 +1,5 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
@@ -26,58 +26,54 @@
#define EIGEN_SWAP_H
/** \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> {};
}
struct ei_traits<SwapWrapper<ExpressionType> >
{
typedef typename ExpressionType::Scalar Scalar;
enum {
RowsAtCompileTime = ExpressionType::RowsAtCompileTime,
ColsAtCompileTime = ExpressionType::ColsAtCompileTime,
MaxRowsAtCompileTime = ExpressionType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = ExpressionType::MaxColsAtCompileTime,
Flags = ExpressionType::Flags,
CoeffReadCost = ExpressionType::CoeffReadCost
};
};
template<typename ExpressionType> class SwapWrapper
: public internal::dense_xpr_base<SwapWrapper<ExpressionType> >::type
: public MatrixBase<SwapWrapper<ExpressionType> >
{
public:
typedef typename internal::dense_xpr_base<SwapWrapper>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(SwapWrapper)
typedef typename internal::packet_traits<Scalar>::type Packet;
EIGEN_GENERIC_PUBLIC_INTERFACE(SwapWrapper)
typedef typename ei_packet_traits<Scalar>::type Packet;
inline SwapWrapper(ExpressionType& xpr) : m_expression(xpr) {}
inline Index rows() const { return m_expression.rows(); }
inline Index cols() const { return m_expression.cols(); }
inline Index outerStride() const { return m_expression.outerStride(); }
inline Index innerStride() const { return m_expression.innerStride(); }
inline int rows() const { return m_expression.rows(); }
inline int cols() const { return m_expression.cols(); }
inline int stride() const { return m_expression.stride(); }
inline Scalar& coeffRef(Index row, Index col)
inline Scalar& coeffRef(int row, int col)
{
return m_expression.const_cast_derived().coeffRef(row, col);
}
inline Scalar& coeffRef(Index index)
inline Scalar& coeffRef(int index)
{
return m_expression.const_cast_derived().coeffRef(index);
}
inline Scalar& coeffRef(Index row, Index col) const
{
return m_expression.coeffRef(row, col);
}
inline Scalar& coeffRef(Index index) const
{
return m_expression.coeffRef(index);
}
template<typename OtherDerived>
void copyCoeff(Index row, Index col, const DenseBase<OtherDerived>& other)
void copyCoeff(int row, int col, const MatrixBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(row >= 0 && row < rows()
ei_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
Scalar tmp = m_expression.coeff(row, col);
m_expression.coeffRef(row, col) = _other.coeff(row, col);
@@ -85,20 +81,20 @@ template<typename ExpressionType> class SwapWrapper
}
template<typename OtherDerived>
void copyCoeff(Index index, const DenseBase<OtherDerived>& other)
void copyCoeff(int index, const MatrixBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(index >= 0 && index < m_expression.size());
ei_internal_assert(index >= 0 && index < m_expression.size());
Scalar tmp = m_expression.coeff(index);
m_expression.coeffRef(index) = _other.coeff(index);
_other.coeffRef(index) = tmp;
}
template<typename OtherDerived, int StoreMode, int LoadMode>
void copyPacket(Index row, Index col, const DenseBase<OtherDerived>& other)
void copyPacket(int row, int col, const MatrixBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(row >= 0 && row < rows()
ei_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
Packet tmp = m_expression.template packet<StoreMode>(row, col);
m_expression.template writePacket<StoreMode>(row, col,
@@ -108,10 +104,10 @@ template<typename ExpressionType> class SwapWrapper
}
template<typename OtherDerived, int StoreMode, int LoadMode>
void copyPacket(Index index, const DenseBase<OtherDerived>& other)
void copyPacket(int index, const MatrixBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(index >= 0 && index < m_expression.size());
ei_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)
@@ -121,6 +117,29 @@ template<typename ExpressionType> class SwapWrapper
protected:
ExpressionType& m_expression;
private:
SwapWrapper& operator=(const SwapWrapper&);
};
/** swaps *this with the expression \a other.
*
* \note \a other is only marked for internal reasons, but of course
* it gets const-casted. One reason is that one will often call swap
* on temporary objects (hence non-const references are forbidden).
* Another reason is that lazyAssign takes a const argument anyway.
*/
template<typename Derived>
template<typename OtherDerived>
void MatrixBase<Derived>::swap(const MatrixBase<OtherDerived>& other)
{
(SwapWrapper<Derived>(derived())).lazyAssign(other);
}
#endif // EIGEN_SWAP_H

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