diff options
| author | Stanislaw Halik <sthalik@misaki.pl> | 2019-03-03 21:09:10 +0100 |
|---|---|---|
| committer | Stanislaw Halik <sthalik@misaki.pl> | 2019-03-03 21:10:13 +0100 |
| commit | f0238cfb6997c4acfc2bd200de7295f3fa36968f (patch) | |
| tree | b215183760e4f615b9c1dabc1f116383b72a1b55 /eigen/test | |
| parent | 543edd372a5193d04b3de9f23c176ab439e51b31 (diff) | |
don't index Eigen
Diffstat (limited to 'eigen/test')
146 files changed, 0 insertions, 24332 deletions
diff --git a/eigen/test/CMakeLists.txt b/eigen/test/CMakeLists.txt deleted file mode 100644 index 0747aa6..0000000 --- a/eigen/test/CMakeLists.txt +++ /dev/null @@ -1,390 +0,0 @@ -# generate split test header file only if it does not yet exist -# in order to prevent a rebuild everytime cmake is configured -if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/split_test_helper.h) - file(WRITE ${CMAKE_CURRENT_BINARY_DIR}/split_test_helper.h "") - foreach(i RANGE 1 999) - file(APPEND ${CMAKE_CURRENT_BINARY_DIR}/split_test_helper.h - "#ifdef EIGEN_TEST_PART_${i}\n" - "#define CALL_SUBTEST_${i}(FUNC) CALL_SUBTEST(FUNC)\n" - "#else\n" - "#define CALL_SUBTEST_${i}(FUNC)\n" - "#endif\n\n" - ) - endforeach() -endif() - -# check if we have a Fortran compiler -include("../cmake/language_support.cmake") - -workaround_9220(Fortran EIGEN_Fortran_COMPILER_WORKS) - -if(EIGEN_Fortran_COMPILER_WORKS) - enable_language(Fortran OPTIONAL) - if(NOT CMAKE_Fortran_COMPILER) - set(EIGEN_Fortran_COMPILER_WORKS OFF) - endif() -endif() - -if(NOT EIGEN_Fortran_COMPILER_WORKS) - # search for a default Lapack library to complete Eigen's one - find_package(LAPACK QUIET) -endif() - -# configure blas/lapack (use Eigen's ones) -set(EIGEN_BLAS_LIBRARIES eigen_blas) -set(EIGEN_LAPACK_LIBRARIES eigen_lapack) - -set(EIGEN_TEST_MATRIX_DIR "" CACHE STRING "Enable testing of realword sparse matrices contained in the specified path") -if(EIGEN_TEST_MATRIX_DIR) - if(NOT WIN32) - message(STATUS "Test realworld sparse matrices: ${EIGEN_TEST_MATRIX_DIR}") - add_definitions( -DTEST_REAL_CASES="${EIGEN_TEST_MATRIX_DIR}" ) - else(NOT WIN32) - message(STATUS "REAL CASES CAN NOT BE CURRENTLY TESTED ON WIN32") - endif(NOT WIN32) -endif(EIGEN_TEST_MATRIX_DIR) - -set(SPARSE_LIBS " ") - -find_package(Cholmod) -if(CHOLMOD_FOUND) - add_definitions("-DEIGEN_CHOLMOD_SUPPORT") - include_directories(${CHOLMOD_INCLUDES}) - set(SPARSE_LIBS ${SPARSE_LIBS} ${CHOLMOD_LIBRARIES} ${EIGEN_BLAS_LIBRARIES} ${EIGEN_LAPACK_LIBRARIES}) - set(CHOLMOD_ALL_LIBS ${CHOLMOD_LIBRARIES} ${EIGEN_BLAS_LIBRARIES} ${EIGEN_LAPACK_LIBRARIES}) - ei_add_property(EIGEN_TESTED_BACKENDS "Cholmod, ") -else() - ei_add_property(EIGEN_MISSING_BACKENDS "Cholmod, ") -endif() - -find_package(Umfpack) -if(UMFPACK_FOUND) - add_definitions("-DEIGEN_UMFPACK_SUPPORT") - include_directories(${UMFPACK_INCLUDES}) - set(SPARSE_LIBS ${SPARSE_LIBS} ${UMFPACK_LIBRARIES} ${EIGEN_BLAS_LIBRARIES}) - set(UMFPACK_ALL_LIBS ${UMFPACK_LIBRARIES} ${EIGEN_BLAS_LIBRARIES}) - ei_add_property(EIGEN_TESTED_BACKENDS "UmfPack, ") -else() - ei_add_property(EIGEN_MISSING_BACKENDS "UmfPack, ") -endif() - -find_package(SuperLU 4.0) -if(SUPERLU_FOUND) - add_definitions("-DEIGEN_SUPERLU_SUPPORT") - include_directories(${SUPERLU_INCLUDES}) - set(SPARSE_LIBS ${SPARSE_LIBS} ${SUPERLU_LIBRARIES} ${EIGEN_BLAS_LIBRARIES}) - set(SUPERLU_ALL_LIBS ${SUPERLU_LIBRARIES} ${EIGEN_BLAS_LIBRARIES}) - ei_add_property(EIGEN_TESTED_BACKENDS "SuperLU, ") -else() - ei_add_property(EIGEN_MISSING_BACKENDS "SuperLU, ") -endif() - - -find_package(PASTIX QUIET COMPONENTS METIS SCOTCH) -# check that the PASTIX found is a version without MPI -find_path(PASTIX_pastix_nompi.h_INCLUDE_DIRS - NAMES pastix_nompi.h - HINTS ${PASTIX_INCLUDE_DIRS} -) -if (NOT PASTIX_pastix_nompi.h_INCLUDE_DIRS) - message(STATUS "A version of Pastix has been found but pastix_nompi.h does not exist in the include directory." - " Because Eigen tests require a version without MPI, we disable the Pastix backend.") -endif() -if(PASTIX_FOUND AND PASTIX_pastix_nompi.h_INCLUDE_DIRS) - add_definitions("-DEIGEN_PASTIX_SUPPORT") - include_directories(${PASTIX_INCLUDE_DIRS_DEP}) - if(SCOTCH_FOUND) - include_directories(${SCOTCH_INCLUDE_DIRS}) - set(PASTIX_LIBRARIES ${PASTIX_LIBRARIES} ${SCOTCH_LIBRARIES}) - elseif(METIS_FOUND) - include_directories(${METIS_INCLUDE_DIRS}) - set(PASTIX_LIBRARIES ${PASTIX_LIBRARIES} ${METIS_LIBRARIES}) - else(SCOTCH_FOUND) - ei_add_property(EIGEN_MISSING_BACKENDS "PaStiX, ") - endif(SCOTCH_FOUND) - set(SPARSE_LIBS ${SPARSE_LIBS} ${PASTIX_LIBRARIES_DEP} ${ORDERING_LIBRARIES}) - set(PASTIX_ALL_LIBS ${PASTIX_LIBRARIES_DEP}) - ei_add_property(EIGEN_TESTED_BACKENDS "PaStiX, ") -else() - ei_add_property(EIGEN_MISSING_BACKENDS "PaStiX, ") -endif() - -if(METIS_FOUND) - add_definitions("-DEIGEN_METIS_SUPPORT") - include_directories(${METIS_INCLUDE_DIRS}) - ei_add_property(EIGEN_TESTED_BACKENDS "METIS, ") -else() - ei_add_property(EIGEN_MISSING_BACKENDS "METIS, ") -endif() - -find_package(SPQR) -if(SPQR_FOUND AND CHOLMOD_FOUND AND (EIGEN_Fortran_COMPILER_WORKS OR LAPACK_FOUND) ) - add_definitions("-DEIGEN_SPQR_SUPPORT") - include_directories(${SPQR_INCLUDES}) - set(SPQR_ALL_LIBS ${SPQR_LIBRARIES} ${CHOLMOD_LIBRARIES} ${EIGEN_LAPACK_LIBRARIES} ${EIGEN_BLAS_LIBRARIES} ${LAPACK_LIBRARIES}) - set(SPARSE_LIBS ${SPARSE_LIBS} ${SPQR_ALL_LIBS}) - ei_add_property(EIGEN_TESTED_BACKENDS "SPQR, ") -else() - ei_add_property(EIGEN_MISSING_BACKENDS "SPQR, ") -endif() - -option(EIGEN_TEST_NOQT "Disable Qt support in unit tests" OFF) -if(NOT EIGEN_TEST_NOQT) - find_package(Qt4) - if(QT4_FOUND) - include(${QT_USE_FILE}) - ei_add_property(EIGEN_TESTED_BACKENDS "Qt4 support, ") - else() - ei_add_property(EIGEN_MISSING_BACKENDS "Qt4 support, ") - endif() -endif(NOT EIGEN_TEST_NOQT) - -if(TEST_LIB) - add_definitions("-DEIGEN_EXTERN_INSTANTIATIONS=1") -endif(TEST_LIB) - -set_property(GLOBAL PROPERTY EIGEN_CURRENT_SUBPROJECT "Official") -add_custom_target(BuildOfficial) - -ei_add_test(rand) -ei_add_test(meta) -ei_add_test(numext) -ei_add_test(sizeof) -ei_add_test(dynalloc) -ei_add_test(nomalloc) -ei_add_test(first_aligned) -ei_add_test(nullary) -ei_add_test(mixingtypes) -ei_add_test(packetmath "-DEIGEN_FAST_MATH=1") -ei_add_test(unalignedassert) -ei_add_test(vectorization_logic) -ei_add_test(basicstuff) -ei_add_test(constructor) -ei_add_test(linearstructure) -ei_add_test(integer_types) -ei_add_test(unalignedcount) -if(NOT EIGEN_TEST_NO_EXCEPTIONS) - ei_add_test(exceptions) -endif() -ei_add_test(redux) -ei_add_test(visitor) -ei_add_test(block) -ei_add_test(corners) -ei_add_test(swap) -ei_add_test(resize) -ei_add_test(conservative_resize) -ei_add_test(product_small) -ei_add_test(product_large) -ei_add_test(product_extra) -ei_add_test(diagonalmatrices) -ei_add_test(adjoint) -ei_add_test(diagonal) -ei_add_test(miscmatrices) -ei_add_test(commainitializer) -ei_add_test(smallvectors) -ei_add_test(mapped_matrix) -ei_add_test(mapstride) -ei_add_test(mapstaticmethods) -ei_add_test(array) -ei_add_test(array_for_matrix) -ei_add_test(array_replicate) -ei_add_test(array_reverse) -ei_add_test(ref) -ei_add_test(is_same_dense) -ei_add_test(triangular) -ei_add_test(selfadjoint) -ei_add_test(product_selfadjoint) -ei_add_test(product_symm) -ei_add_test(product_syrk) -ei_add_test(product_trmv) -ei_add_test(product_trmm) -ei_add_test(product_trsolve) -ei_add_test(product_mmtr) -ei_add_test(product_notemporary) -ei_add_test(stable_norm) -ei_add_test(permutationmatrices) -ei_add_test(bandmatrix) -ei_add_test(cholesky) -ei_add_test(lu) -ei_add_test(determinant) -ei_add_test(inverse) -ei_add_test(qr) -ei_add_test(qr_colpivoting) -ei_add_test(qr_fullpivoting) -ei_add_test(upperbidiagonalization) -ei_add_test(hessenberg) -ei_add_test(schur_real) -ei_add_test(schur_complex) -ei_add_test(eigensolver_selfadjoint) -ei_add_test(eigensolver_generic) -ei_add_test(eigensolver_complex) -ei_add_test(real_qz) -ei_add_test(eigensolver_generalized_real) -ei_add_test(jacobi) -ei_add_test(jacobisvd) -ei_add_test(bdcsvd) -ei_add_test(householder) -ei_add_test(geo_orthomethods) -ei_add_test(geo_quaternion) -ei_add_test(geo_eulerangles) -ei_add_test(geo_parametrizedline) -ei_add_test(geo_alignedbox) -ei_add_test(geo_hyperplane) -ei_add_test(geo_transformations) -ei_add_test(geo_homogeneous) -ei_add_test(stdvector) -ei_add_test(stdvector_overload) -ei_add_test(stdlist) -ei_add_test(stdlist_overload) -ei_add_test(stddeque) -ei_add_test(stddeque_overload) -ei_add_test(sparse_basic) -ei_add_test(sparse_block) -ei_add_test(sparse_vector) -ei_add_test(sparse_product) -ei_add_test(sparse_ref) -ei_add_test(sparse_solvers) -ei_add_test(sparse_permutations) -ei_add_test(simplicial_cholesky) -ei_add_test(conjugate_gradient) -ei_add_test(incomplete_cholesky) -ei_add_test(bicgstab) -ei_add_test(lscg) -ei_add_test(sparselu) -ei_add_test(sparseqr) -ei_add_test(umeyama) -ei_add_test(nesting_ops "${CMAKE_CXX_FLAGS_DEBUG}") -ei_add_test(zerosized) -ei_add_test(dontalign) -ei_add_test(evaluators) -if(NOT EIGEN_TEST_NO_EXCEPTIONS) - ei_add_test(sizeoverflow) -endif() -ei_add_test(prec_inverse_4x4) -ei_add_test(vectorwiseop) -ei_add_test(special_numbers) -ei_add_test(rvalue_types) -ei_add_test(dense_storage) -ei_add_test(ctorleak) -ei_add_test(mpl2only) -ei_add_test(inplace_decomposition) -ei_add_test(half_float) -ei_add_test(array_of_string) - -add_executable(bug1213 bug1213.cpp bug1213_main.cpp) - -check_cxx_compiler_flag("-ffast-math" COMPILER_SUPPORT_FASTMATH) -if(COMPILER_SUPPORT_FASTMATH) - set(EIGEN_FASTMATH_FLAGS "-ffast-math") -else() - check_cxx_compiler_flag("/fp:fast" COMPILER_SUPPORT_FPFAST) - if(COMPILER_SUPPORT_FPFAST) - set(EIGEN_FASTMATH_FLAGS "/fp:fast") - endif() -endif() - -ei_add_test(fastmath " ${EIGEN_FASTMATH_FLAGS} ") - -# # ei_add_test(denseLM) - -if(QT4_FOUND) - ei_add_test(qtvector "" "${QT_QTCORE_LIBRARY}") -endif(QT4_FOUND) - -if(UMFPACK_FOUND) - ei_add_test(umfpack_support "" "${UMFPACK_ALL_LIBS}") -endif() - -if(SUPERLU_FOUND) - ei_add_test(superlu_support "" "${SUPERLU_ALL_LIBS}") -endif() - -if(CHOLMOD_FOUND) - ei_add_test(cholmod_support "" "${CHOLMOD_ALL_LIBS}") -endif() - -if(PARDISO_FOUND) - ei_add_test(pardiso_support "" "${PARDISO_ALL_LIBS}") -endif() - -if(PASTIX_FOUND AND (SCOTCH_FOUND OR METIS_FOUND)) - ei_add_test(pastix_support "" "${PASTIX_ALL_LIBS}") -endif() - -if(SPQR_FOUND AND CHOLMOD_FOUND) - ei_add_test(spqr_support "" "${SPQR_ALL_LIBS}") -endif() - -if(METIS_FOUND) -ei_add_test(metis_support "" "${METIS_LIBRARIES}") -endif() - -string(TOLOWER "${CMAKE_CXX_COMPILER}" cmake_cxx_compiler_tolower) -if(cmake_cxx_compiler_tolower MATCHES "qcc") - set(CXX_IS_QCC "ON") -endif() - -ei_add_property(EIGEN_TESTING_SUMMARY "CXX: ${CMAKE_CXX_COMPILER}\n") -if(CMAKE_COMPILER_IS_GNUCXX AND NOT CXX_IS_QCC) - execute_process(COMMAND ${CMAKE_CXX_COMPILER} --version COMMAND head -n 1 OUTPUT_VARIABLE EIGEN_CXX_VERSION_STRING OUTPUT_STRIP_TRAILING_WHITESPACE) - ei_add_property(EIGEN_TESTING_SUMMARY "CXX_VERSION: ${EIGEN_CXX_VERSION_STRING}\n") -endif() -ei_add_property(EIGEN_TESTING_SUMMARY "CXX_FLAGS: ${CMAKE_CXX_FLAGS}\n") -ei_add_property(EIGEN_TESTING_SUMMARY "Sparse lib flags: ${SPARSE_LIBS}\n") - -option(EIGEN_TEST_EIGEN2 "Run whole Eigen2 test suite against EIGEN2_SUPPORT" OFF) -mark_as_advanced(EIGEN_TEST_EIGEN2) -if(EIGEN_TEST_EIGEN2) - message(WARNING "The Eigen2 test suite has been removed") -endif() - -# boost MP unit test -find_package(Boost) -if(Boost_FOUND) - include_directories(${Boost_INCLUDE_DIRS}) - ei_add_test(boostmultiprec "" "${Boost_LIBRARIES}") - ei_add_property(EIGEN_TESTED_BACKENDS "Boost.Multiprecision, ") -else() - ei_add_property(EIGEN_MISSING_BACKENDS "Boost.Multiprecision, ") -endif() - - -# CUDA unit tests -option(EIGEN_TEST_CUDA "Enable CUDA support in unit tests" OFF) -option(EIGEN_TEST_CUDA_CLANG "Use clang instead of nvcc to compile the CUDA tests" OFF) - -if(EIGEN_TEST_CUDA_CLANG AND NOT CMAKE_CXX_COMPILER MATCHES "clang") - message(WARNING "EIGEN_TEST_CUDA_CLANG is set, but CMAKE_CXX_COMPILER does not appear to be clang.") -endif() - -if(EIGEN_TEST_CUDA) - -find_package(CUDA 5.0) -if(CUDA_FOUND) - - set(CUDA_PROPAGATE_HOST_FLAGS OFF) - if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") - set(CUDA_NVCC_FLAGS "-ccbin ${CMAKE_C_COMPILER}" CACHE STRING "nvcc flags" FORCE) - endif() - if(EIGEN_TEST_CUDA_CLANG) - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 --cuda-gpu-arch=sm_30") - endif() - cuda_include_directories(${CMAKE_CURRENT_BINARY_DIR}) - set(EIGEN_ADD_TEST_FILENAME_EXTENSION "cu") - - ei_add_test(cuda_basic) - - unset(EIGEN_ADD_TEST_FILENAME_EXTENSION) - -endif(CUDA_FOUND) - -endif(EIGEN_TEST_CUDA) - - -file(MAKE_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/failtests) -add_test(NAME failtests WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/failtests COMMAND ${CMAKE_COMMAND} ${Eigen_SOURCE_DIR} -G "${CMAKE_GENERATOR}" -DEIGEN_FAILTEST=ON) - -option(EIGEN_TEST_BUILD_DOCUMENTATION "Test building the doxygen documentation" OFF) -IF(EIGEN_TEST_BUILD_DOCUMENTATION) - add_dependencies(buildtests doc) -ENDIF() diff --git a/eigen/test/adjoint.cpp b/eigen/test/adjoint.cpp deleted file mode 100644 index 37032d2..0000000 --- a/eigen/test/adjoint.cpp +++ /dev/null @@ -1,199 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_NO_STATIC_ASSERT - -#include "main.h" - -template<bool IsInteger> struct adjoint_specific; - -template<> struct adjoint_specific<true> { - template<typename Vec, typename Mat, typename Scalar> - static void run(const Vec& v1, const Vec& v2, Vec& v3, const Mat& square, Scalar s1, Scalar s2) { - VERIFY(test_isApproxWithRef((s1 * v1 + s2 * v2).dot(v3), numext::conj(s1) * v1.dot(v3) + numext::conj(s2) * v2.dot(v3), 0)); - VERIFY(test_isApproxWithRef(v3.dot(s1 * v1 + s2 * v2), s1*v3.dot(v1)+s2*v3.dot(v2), 0)); - - // check compatibility of dot and adjoint - VERIFY(test_isApproxWithRef(v1.dot(square * v2), (square.adjoint() * v1).dot(v2), 0)); - } -}; - -template<> struct adjoint_specific<false> { - template<typename Vec, typename Mat, typename Scalar> - static void run(const Vec& v1, const Vec& v2, Vec& v3, const Mat& square, Scalar s1, Scalar s2) { - typedef typename NumTraits<Scalar>::Real RealScalar; - using std::abs; - - RealScalar ref = NumTraits<Scalar>::IsInteger ? RealScalar(0) : (std::max)((s1 * v1 + s2 * v2).norm(),v3.norm()); - VERIFY(test_isApproxWithRef((s1 * v1 + s2 * v2).dot(v3), numext::conj(s1) * v1.dot(v3) + numext::conj(s2) * v2.dot(v3), ref)); - VERIFY(test_isApproxWithRef(v3.dot(s1 * v1 + s2 * v2), s1*v3.dot(v1)+s2*v3.dot(v2), ref)); - - VERIFY_IS_APPROX(v1.squaredNorm(), v1.norm() * v1.norm()); - // check normalized() and normalize() - VERIFY_IS_APPROX(v1, v1.norm() * v1.normalized()); - v3 = v1; - v3.normalize(); - VERIFY_IS_APPROX(v1, v1.norm() * v3); - VERIFY_IS_APPROX(v3, v1.normalized()); - VERIFY_IS_APPROX(v3.norm(), RealScalar(1)); - - // check null inputs - VERIFY_IS_APPROX((v1*0).normalized(), (v1*0)); -#if (!EIGEN_ARCH_i386) || defined(EIGEN_VECTORIZE) - RealScalar very_small = (std::numeric_limits<RealScalar>::min)(); - VERIFY( (v1*very_small).norm() == 0 ); - VERIFY_IS_APPROX((v1*very_small).normalized(), (v1*very_small)); - v3 = v1*very_small; - v3.normalize(); - VERIFY_IS_APPROX(v3, (v1*very_small)); -#endif - - // check compatibility of dot and adjoint - ref = NumTraits<Scalar>::IsInteger ? 0 : (std::max)((std::max)(v1.norm(),v2.norm()),(std::max)((square * v2).norm(),(square.adjoint() * v1).norm())); - VERIFY(internal::isMuchSmallerThan(abs(v1.dot(square * v2) - (square.adjoint() * v1).dot(v2)), ref, test_precision<Scalar>())); - - // check that Random().normalized() works: tricky as the random xpr must be evaluated by - // normalized() in order to produce a consistent result. - VERIFY_IS_APPROX(Vec::Random(v1.size()).normalized().norm(), RealScalar(1)); - } -}; - -template<typename MatrixType> void adjoint(const MatrixType& m) -{ - /* this test covers the following files: - Transpose.h Conjugate.h Dot.h - */ - using std::abs; - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType; - const Index PacketSize = internal::packet_traits<Scalar>::size; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols), - square = SquareMatrixType::Random(rows, rows); - VectorType v1 = VectorType::Random(rows), - v2 = VectorType::Random(rows), - v3 = VectorType::Random(rows), - vzero = VectorType::Zero(rows); - - Scalar s1 = internal::random<Scalar>(), - s2 = internal::random<Scalar>(); - - // check basic compatibility of adjoint, transpose, conjugate - VERIFY_IS_APPROX(m1.transpose().conjugate().adjoint(), m1); - VERIFY_IS_APPROX(m1.adjoint().conjugate().transpose(), m1); - - // check multiplicative behavior - VERIFY_IS_APPROX((m1.adjoint() * m2).adjoint(), m2.adjoint() * m1); - VERIFY_IS_APPROX((s1 * m1).adjoint(), numext::conj(s1) * m1.adjoint()); - - // check basic properties of dot, squaredNorm - VERIFY_IS_APPROX(numext::conj(v1.dot(v2)), v2.dot(v1)); - VERIFY_IS_APPROX(numext::real(v1.dot(v1)), v1.squaredNorm()); - - adjoint_specific<NumTraits<Scalar>::IsInteger>::run(v1, v2, v3, square, s1, s2); - - VERIFY_IS_MUCH_SMALLER_THAN(abs(vzero.dot(v1)), static_cast<RealScalar>(1)); - - // like in testBasicStuff, test operator() to check const-qualification - Index r = internal::random<Index>(0, rows-1), - c = internal::random<Index>(0, cols-1); - VERIFY_IS_APPROX(m1.conjugate()(r,c), numext::conj(m1(r,c))); - VERIFY_IS_APPROX(m1.adjoint()(c,r), numext::conj(m1(r,c))); - - // check inplace transpose - m3 = m1; - m3.transposeInPlace(); - VERIFY_IS_APPROX(m3,m1.transpose()); - m3.transposeInPlace(); - VERIFY_IS_APPROX(m3,m1); - - if(PacketSize<m3.rows() && PacketSize<m3.cols()) - { - m3 = m1; - Index i = internal::random<Index>(0,m3.rows()-PacketSize); - Index j = internal::random<Index>(0,m3.cols()-PacketSize); - m3.template block<PacketSize,PacketSize>(i,j).transposeInPlace(); - VERIFY_IS_APPROX( (m3.template block<PacketSize,PacketSize>(i,j)), (m1.template block<PacketSize,PacketSize>(i,j).transpose()) ); - m3.template block<PacketSize,PacketSize>(i,j).transposeInPlace(); - VERIFY_IS_APPROX(m3,m1); - } - - // check inplace adjoint - m3 = m1; - m3.adjointInPlace(); - VERIFY_IS_APPROX(m3,m1.adjoint()); - m3.transposeInPlace(); - VERIFY_IS_APPROX(m3,m1.conjugate()); - - // check mixed dot product - typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealVectorType; - RealVectorType rv1 = RealVectorType::Random(rows); - VERIFY_IS_APPROX(v1.dot(rv1.template cast<Scalar>()), v1.dot(rv1)); - VERIFY_IS_APPROX(rv1.template cast<Scalar>().dot(v1), rv1.dot(v1)); -} - -void test_adjoint() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( adjoint(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( adjoint(Matrix3d()) ); - CALL_SUBTEST_3( adjoint(Matrix4f()) ); - - CALL_SUBTEST_4( adjoint(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) ); - CALL_SUBTEST_5( adjoint(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_6( adjoint(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - - // Complement for 128 bits vectorization: - CALL_SUBTEST_8( adjoint(Matrix2d()) ); - CALL_SUBTEST_9( adjoint(Matrix<int,4,4>()) ); - - // 256 bits vectorization: - CALL_SUBTEST_10( adjoint(Matrix<float,8,8>()) ); - CALL_SUBTEST_11( adjoint(Matrix<double,4,4>()) ); - CALL_SUBTEST_12( adjoint(Matrix<int,8,8>()) ); - } - // test a large static matrix only once - CALL_SUBTEST_7( adjoint(Matrix<float, 100, 100>()) ); - -#ifdef EIGEN_TEST_PART_13 - { - MatrixXcf a(10,10), b(10,10); - VERIFY_RAISES_ASSERT(a = a.transpose()); - VERIFY_RAISES_ASSERT(a = a.transpose() + b); - VERIFY_RAISES_ASSERT(a = b + a.transpose()); - VERIFY_RAISES_ASSERT(a = a.conjugate().transpose()); - VERIFY_RAISES_ASSERT(a = a.adjoint()); - VERIFY_RAISES_ASSERT(a = a.adjoint() + b); - VERIFY_RAISES_ASSERT(a = b + a.adjoint()); - - // no assertion should be triggered for these cases: - a.transpose() = a.transpose(); - a.transpose() += a.transpose(); - a.transpose() += a.transpose() + b; - a.transpose() = a.adjoint(); - a.transpose() += a.adjoint(); - a.transpose() += a.adjoint() + b; - - // regression tests for check_for_aliasing - MatrixXd c(10,10); - c = 1.0 * MatrixXd::Ones(10,10) + c; - c = MatrixXd::Ones(10,10) * 1.0 + c; - c = c + MatrixXd::Ones(10,10) .cwiseProduct( MatrixXd::Zero(10,10) ); - c = MatrixXd::Ones(10,10) * MatrixXd::Zero(10,10); - } -#endif -} - diff --git a/eigen/test/array.cpp b/eigen/test/array.cpp deleted file mode 100644 index 7afd3ed..0000000 --- a/eigen/test/array.cpp +++ /dev/null @@ -1,490 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename ArrayType> void array(const ArrayType& m) -{ - typedef typename ArrayType::Scalar Scalar; - typedef typename ArrayType::RealScalar RealScalar; - typedef Array<Scalar, ArrayType::RowsAtCompileTime, 1> ColVectorType; - typedef Array<Scalar, 1, ArrayType::ColsAtCompileTime> RowVectorType; - - Index rows = m.rows(); - Index cols = m.cols(); - - ArrayType m1 = ArrayType::Random(rows, cols), - m2 = ArrayType::Random(rows, cols), - m3(rows, cols); - ArrayType m4 = m1; // copy constructor - VERIFY_IS_APPROX(m1, m4); - - ColVectorType cv1 = ColVectorType::Random(rows); - RowVectorType rv1 = RowVectorType::Random(cols); - - Scalar s1 = internal::random<Scalar>(), - s2 = internal::random<Scalar>(); - - // scalar addition - VERIFY_IS_APPROX(m1 + s1, s1 + m1); - VERIFY_IS_APPROX(m1 + s1, ArrayType::Constant(rows,cols,s1) + m1); - VERIFY_IS_APPROX(s1 - m1, (-m1)+s1 ); - VERIFY_IS_APPROX(m1 - s1, m1 - ArrayType::Constant(rows,cols,s1)); - VERIFY_IS_APPROX(s1 - m1, ArrayType::Constant(rows,cols,s1) - m1); - VERIFY_IS_APPROX((m1*Scalar(2)) - s2, (m1+m1) - ArrayType::Constant(rows,cols,s2) ); - m3 = m1; - m3 += s2; - VERIFY_IS_APPROX(m3, m1 + s2); - m3 = m1; - m3 -= s1; - VERIFY_IS_APPROX(m3, m1 - s1); - - // scalar operators via Maps - m3 = m1; - ArrayType::Map(m1.data(), m1.rows(), m1.cols()) -= ArrayType::Map(m2.data(), m2.rows(), m2.cols()); - VERIFY_IS_APPROX(m1, m3 - m2); - - m3 = m1; - ArrayType::Map(m1.data(), m1.rows(), m1.cols()) += ArrayType::Map(m2.data(), m2.rows(), m2.cols()); - VERIFY_IS_APPROX(m1, m3 + m2); - - m3 = m1; - ArrayType::Map(m1.data(), m1.rows(), m1.cols()) *= ArrayType::Map(m2.data(), m2.rows(), m2.cols()); - VERIFY_IS_APPROX(m1, m3 * m2); - - m3 = m1; - m2 = ArrayType::Random(rows,cols); - m2 = (m2==0).select(1,m2); - ArrayType::Map(m1.data(), m1.rows(), m1.cols()) /= ArrayType::Map(m2.data(), m2.rows(), m2.cols()); - VERIFY_IS_APPROX(m1, m3 / m2); - - // reductions - VERIFY_IS_APPROX(m1.abs().colwise().sum().sum(), m1.abs().sum()); - VERIFY_IS_APPROX(m1.abs().rowwise().sum().sum(), m1.abs().sum()); - using std::abs; - VERIFY_IS_MUCH_SMALLER_THAN(abs(m1.colwise().sum().sum() - m1.sum()), m1.abs().sum()); - VERIFY_IS_MUCH_SMALLER_THAN(abs(m1.rowwise().sum().sum() - m1.sum()), m1.abs().sum()); - if (!internal::isMuchSmallerThan(abs(m1.sum() - (m1+m2).sum()), m1.abs().sum(), test_precision<Scalar>())) - VERIFY_IS_NOT_APPROX(((m1+m2).rowwise().sum()).sum(), m1.sum()); - VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar,Scalar>())); - - // vector-wise ops - m3 = m1; - VERIFY_IS_APPROX(m3.colwise() += cv1, m1.colwise() + cv1); - m3 = m1; - VERIFY_IS_APPROX(m3.colwise() -= cv1, m1.colwise() - cv1); - m3 = m1; - VERIFY_IS_APPROX(m3.rowwise() += rv1, m1.rowwise() + rv1); - m3 = m1; - VERIFY_IS_APPROX(m3.rowwise() -= rv1, m1.rowwise() - rv1); - - // Conversion from scalar - VERIFY_IS_APPROX((m3 = s1), ArrayType::Constant(rows,cols,s1)); - VERIFY_IS_APPROX((m3 = 1), ArrayType::Constant(rows,cols,1)); - VERIFY_IS_APPROX((m3.topLeftCorner(rows,cols) = 1), ArrayType::Constant(rows,cols,1)); - typedef Array<Scalar, - ArrayType::RowsAtCompileTime==Dynamic?2:ArrayType::RowsAtCompileTime, - ArrayType::ColsAtCompileTime==Dynamic?2:ArrayType::ColsAtCompileTime, - ArrayType::Options> FixedArrayType; - FixedArrayType f1(s1); - VERIFY_IS_APPROX(f1, FixedArrayType::Constant(s1)); - FixedArrayType f2(numext::real(s1)); - VERIFY_IS_APPROX(f2, FixedArrayType::Constant(numext::real(s1))); - FixedArrayType f3((int)100*numext::real(s1)); - VERIFY_IS_APPROX(f3, FixedArrayType::Constant((int)100*numext::real(s1))); - f1.setRandom(); - FixedArrayType f4(f1.data()); - VERIFY_IS_APPROX(f4, f1); - - // pow - VERIFY_IS_APPROX(m1.pow(2), m1.square()); - VERIFY_IS_APPROX(pow(m1,2), m1.square()); - VERIFY_IS_APPROX(m1.pow(3), m1.cube()); - VERIFY_IS_APPROX(pow(m1,3), m1.cube()); - VERIFY_IS_APPROX((-m1).pow(3), -m1.cube()); - VERIFY_IS_APPROX(pow(2*m1,3), 8*m1.cube()); - ArrayType exponents = ArrayType::Constant(rows, cols, RealScalar(2)); - VERIFY_IS_APPROX(Eigen::pow(m1,exponents), m1.square()); - VERIFY_IS_APPROX(m1.pow(exponents), m1.square()); - VERIFY_IS_APPROX(Eigen::pow(2*m1,exponents), 4*m1.square()); - VERIFY_IS_APPROX((2*m1).pow(exponents), 4*m1.square()); - VERIFY_IS_APPROX(Eigen::pow(m1,2*exponents), m1.square().square()); - VERIFY_IS_APPROX(m1.pow(2*exponents), m1.square().square()); - VERIFY_IS_APPROX(Eigen::pow(m1(0,0), exponents), ArrayType::Constant(rows,cols,m1(0,0)*m1(0,0))); - - // Check possible conflicts with 1D ctor - typedef Array<Scalar, Dynamic, 1> OneDArrayType; - OneDArrayType o1(rows); - VERIFY(o1.size()==rows); - OneDArrayType o4((int)rows); - VERIFY(o4.size()==rows); -} - -template<typename ArrayType> void comparisons(const ArrayType& m) -{ - using std::abs; - typedef typename ArrayType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - Index rows = m.rows(); - Index cols = m.cols(); - - Index r = internal::random<Index>(0, rows-1), - c = internal::random<Index>(0, cols-1); - - ArrayType m1 = ArrayType::Random(rows, cols), - m2 = ArrayType::Random(rows, cols), - m3(rows, cols), - m4 = m1; - - m4 = (m4.abs()==Scalar(0)).select(1,m4); - - VERIFY(((m1 + Scalar(1)) > m1).all()); - VERIFY(((m1 - Scalar(1)) < m1).all()); - if (rows*cols>1) - { - m3 = m1; - m3(r,c) += 1; - VERIFY(! (m1 < m3).all() ); - VERIFY(! (m1 > m3).all() ); - } - VERIFY(!(m1 > m2 && m1 < m2).any()); - VERIFY((m1 <= m2 || m1 >= m2).all()); - - // comparisons array to scalar - VERIFY( (m1 != (m1(r,c)+1) ).any() ); - VERIFY( (m1 > (m1(r,c)-1) ).any() ); - VERIFY( (m1 < (m1(r,c)+1) ).any() ); - VERIFY( (m1 == m1(r,c) ).any() ); - - // comparisons scalar to array - VERIFY( ( (m1(r,c)+1) != m1).any() ); - VERIFY( ( (m1(r,c)-1) < m1).any() ); - VERIFY( ( (m1(r,c)+1) > m1).any() ); - VERIFY( ( m1(r,c) == m1).any() ); - - // test Select - VERIFY_IS_APPROX( (m1<m2).select(m1,m2), m1.cwiseMin(m2) ); - VERIFY_IS_APPROX( (m1>m2).select(m1,m2), m1.cwiseMax(m2) ); - Scalar mid = (m1.cwiseAbs().minCoeff() + m1.cwiseAbs().maxCoeff())/Scalar(2); - for (int j=0; j<cols; ++j) - for (int i=0; i<rows; ++i) - m3(i,j) = abs(m1(i,j))<mid ? 0 : m1(i,j); - VERIFY_IS_APPROX( (m1.abs()<ArrayType::Constant(rows,cols,mid)) - .select(ArrayType::Zero(rows,cols),m1), m3); - // shorter versions: - VERIFY_IS_APPROX( (m1.abs()<ArrayType::Constant(rows,cols,mid)) - .select(0,m1), m3); - VERIFY_IS_APPROX( (m1.abs()>=ArrayType::Constant(rows,cols,mid)) - .select(m1,0), m3); - // even shorter version: - VERIFY_IS_APPROX( (m1.abs()<mid).select(0,m1), m3); - - // count - VERIFY(((m1.abs()+1)>RealScalar(0.1)).count() == rows*cols); - - // and/or - VERIFY( (m1<RealScalar(0) && m1>RealScalar(0)).count() == 0); - VERIFY( (m1<RealScalar(0) || m1>=RealScalar(0)).count() == rows*cols); - RealScalar a = m1.abs().mean(); - VERIFY( (m1<-a || m1>a).count() == (m1.abs()>a).count()); - - typedef Array<typename ArrayType::Index, Dynamic, 1> ArrayOfIndices; - - // TODO allows colwise/rowwise for array - VERIFY_IS_APPROX(((m1.abs()+1)>RealScalar(0.1)).colwise().count(), ArrayOfIndices::Constant(cols,rows).transpose()); - VERIFY_IS_APPROX(((m1.abs()+1)>RealScalar(0.1)).rowwise().count(), ArrayOfIndices::Constant(rows, cols)); -} - -template<typename ArrayType> void array_real(const ArrayType& m) -{ - using std::abs; - using std::sqrt; - typedef typename ArrayType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - Index rows = m.rows(); - Index cols = m.cols(); - - ArrayType m1 = ArrayType::Random(rows, cols), - m2 = ArrayType::Random(rows, cols), - m3(rows, cols), - m4 = m1; - - m4 = (m4.abs()==Scalar(0)).select(1,m4); - - Scalar s1 = internal::random<Scalar>(); - - // these tests are mostly to check possible compilation issues with free-functions. - VERIFY_IS_APPROX(m1.sin(), sin(m1)); - VERIFY_IS_APPROX(m1.cos(), cos(m1)); - VERIFY_IS_APPROX(m1.tan(), tan(m1)); - VERIFY_IS_APPROX(m1.asin(), asin(m1)); - VERIFY_IS_APPROX(m1.acos(), acos(m1)); - VERIFY_IS_APPROX(m1.atan(), atan(m1)); - VERIFY_IS_APPROX(m1.sinh(), sinh(m1)); - VERIFY_IS_APPROX(m1.cosh(), cosh(m1)); - VERIFY_IS_APPROX(m1.tanh(), tanh(m1)); - - VERIFY_IS_APPROX(m1.arg(), arg(m1)); - VERIFY_IS_APPROX(m1.round(), round(m1)); - VERIFY_IS_APPROX(m1.floor(), floor(m1)); - VERIFY_IS_APPROX(m1.ceil(), ceil(m1)); - VERIFY((m1.isNaN() == (Eigen::isnan)(m1)).all()); - VERIFY((m1.isInf() == (Eigen::isinf)(m1)).all()); - VERIFY((m1.isFinite() == (Eigen::isfinite)(m1)).all()); - VERIFY_IS_APPROX(m1.inverse(), inverse(m1)); - VERIFY_IS_APPROX(m1.abs(), abs(m1)); - VERIFY_IS_APPROX(m1.abs2(), abs2(m1)); - VERIFY_IS_APPROX(m1.square(), square(m1)); - VERIFY_IS_APPROX(m1.cube(), cube(m1)); - VERIFY_IS_APPROX(cos(m1+RealScalar(3)*m2), cos((m1+RealScalar(3)*m2).eval())); - VERIFY_IS_APPROX(m1.sign(), sign(m1)); - - - // avoid NaNs with abs() so verification doesn't fail - m3 = m1.abs(); - VERIFY_IS_APPROX(m3.sqrt(), sqrt(abs(m1))); - VERIFY_IS_APPROX(m3.rsqrt(), Scalar(1)/sqrt(abs(m1))); - VERIFY_IS_APPROX(rsqrt(m3), Scalar(1)/sqrt(abs(m1))); - VERIFY_IS_APPROX(m3.log(), log(m3)); - VERIFY_IS_APPROX(m3.log1p(), log1p(m3)); - VERIFY_IS_APPROX(m3.log10(), log10(m3)); - - - VERIFY((!(m1>m2) == (m1<=m2)).all()); - - VERIFY_IS_APPROX(sin(m1.asin()), m1); - VERIFY_IS_APPROX(cos(m1.acos()), m1); - VERIFY_IS_APPROX(tan(m1.atan()), m1); - VERIFY_IS_APPROX(sinh(m1), 0.5*(exp(m1)-exp(-m1))); - VERIFY_IS_APPROX(cosh(m1), 0.5*(exp(m1)+exp(-m1))); - VERIFY_IS_APPROX(tanh(m1), (0.5*(exp(m1)-exp(-m1)))/(0.5*(exp(m1)+exp(-m1)))); - VERIFY_IS_APPROX(arg(m1), ((m1<0).template cast<Scalar>())*std::acos(-1.0)); - VERIFY((round(m1) <= ceil(m1) && round(m1) >= floor(m1)).all()); - VERIFY((Eigen::isnan)((m1*0.0)/0.0).all()); - VERIFY((Eigen::isinf)(m4/0.0).all()); - VERIFY(((Eigen::isfinite)(m1) && (!(Eigen::isfinite)(m1*0.0/0.0)) && (!(Eigen::isfinite)(m4/0.0))).all()); - VERIFY_IS_APPROX(inverse(inverse(m1)),m1); - VERIFY((abs(m1) == m1 || abs(m1) == -m1).all()); - VERIFY_IS_APPROX(m3, sqrt(abs2(m1))); - VERIFY_IS_APPROX( m1.sign(), -(-m1).sign() ); - VERIFY_IS_APPROX( m1*m1.sign(),m1.abs()); - VERIFY_IS_APPROX(m1.sign() * m1.abs(), m1); - - VERIFY_IS_APPROX(numext::abs2(numext::real(m1)) + numext::abs2(numext::imag(m1)), numext::abs2(m1)); - VERIFY_IS_APPROX(numext::abs2(real(m1)) + numext::abs2(imag(m1)), numext::abs2(m1)); - if(!NumTraits<Scalar>::IsComplex) - VERIFY_IS_APPROX(numext::real(m1), m1); - - // shift argument of logarithm so that it is not zero - Scalar smallNumber = NumTraits<Scalar>::dummy_precision(); - VERIFY_IS_APPROX((m3 + smallNumber).log() , log(abs(m1) + smallNumber)); - VERIFY_IS_APPROX((m3 + smallNumber + 1).log() , log1p(abs(m1) + smallNumber)); - - VERIFY_IS_APPROX(m1.exp() * m2.exp(), exp(m1+m2)); - VERIFY_IS_APPROX(m1.exp(), exp(m1)); - VERIFY_IS_APPROX(m1.exp() / m2.exp(),(m1-m2).exp()); - - VERIFY_IS_APPROX(m3.pow(RealScalar(0.5)), m3.sqrt()); - VERIFY_IS_APPROX(pow(m3,RealScalar(0.5)), m3.sqrt()); - - VERIFY_IS_APPROX(m3.pow(RealScalar(-0.5)), m3.rsqrt()); - VERIFY_IS_APPROX(pow(m3,RealScalar(-0.5)), m3.rsqrt()); - - VERIFY_IS_APPROX(log10(m3), log(m3)/log(10)); - - // scalar by array division - const RealScalar tiny = sqrt(std::numeric_limits<RealScalar>::epsilon()); - s1 += Scalar(tiny); - m1 += ArrayType::Constant(rows,cols,Scalar(tiny)); - VERIFY_IS_APPROX(s1/m1, s1 * m1.inverse()); - - // check inplace transpose - m3 = m1; - m3.transposeInPlace(); - VERIFY_IS_APPROX(m3, m1.transpose()); - m3.transposeInPlace(); - VERIFY_IS_APPROX(m3, m1); -} - -template<typename ArrayType> void array_complex(const ArrayType& m) -{ - typedef typename ArrayType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - Index rows = m.rows(); - Index cols = m.cols(); - - ArrayType m1 = ArrayType::Random(rows, cols), - m2(rows, cols), - m4 = m1; - - m4.real() = (m4.real().abs()==RealScalar(0)).select(RealScalar(1),m4.real()); - m4.imag() = (m4.imag().abs()==RealScalar(0)).select(RealScalar(1),m4.imag()); - - Array<RealScalar, -1, -1> m3(rows, cols); - - for (Index i = 0; i < m.rows(); ++i) - for (Index j = 0; j < m.cols(); ++j) - m2(i,j) = sqrt(m1(i,j)); - - // these tests are mostly to check possible compilation issues with free-functions. - VERIFY_IS_APPROX(m1.sin(), sin(m1)); - VERIFY_IS_APPROX(m1.cos(), cos(m1)); - VERIFY_IS_APPROX(m1.tan(), tan(m1)); - VERIFY_IS_APPROX(m1.sinh(), sinh(m1)); - VERIFY_IS_APPROX(m1.cosh(), cosh(m1)); - VERIFY_IS_APPROX(m1.tanh(), tanh(m1)); - VERIFY_IS_APPROX(m1.arg(), arg(m1)); - VERIFY((m1.isNaN() == (Eigen::isnan)(m1)).all()); - VERIFY((m1.isInf() == (Eigen::isinf)(m1)).all()); - VERIFY((m1.isFinite() == (Eigen::isfinite)(m1)).all()); - VERIFY_IS_APPROX(m1.inverse(), inverse(m1)); - VERIFY_IS_APPROX(m1.log(), log(m1)); - VERIFY_IS_APPROX(m1.log10(), log10(m1)); - VERIFY_IS_APPROX(m1.abs(), abs(m1)); - VERIFY_IS_APPROX(m1.abs2(), abs2(m1)); - VERIFY_IS_APPROX(m1.sqrt(), sqrt(m1)); - VERIFY_IS_APPROX(m1.square(), square(m1)); - VERIFY_IS_APPROX(m1.cube(), cube(m1)); - VERIFY_IS_APPROX(cos(m1+RealScalar(3)*m2), cos((m1+RealScalar(3)*m2).eval())); - VERIFY_IS_APPROX(m1.sign(), sign(m1)); - - - VERIFY_IS_APPROX(m1.exp() * m2.exp(), exp(m1+m2)); - VERIFY_IS_APPROX(m1.exp(), exp(m1)); - VERIFY_IS_APPROX(m1.exp() / m2.exp(),(m1-m2).exp()); - - VERIFY_IS_APPROX(sinh(m1), 0.5*(exp(m1)-exp(-m1))); - VERIFY_IS_APPROX(cosh(m1), 0.5*(exp(m1)+exp(-m1))); - VERIFY_IS_APPROX(tanh(m1), (0.5*(exp(m1)-exp(-m1)))/(0.5*(exp(m1)+exp(-m1)))); - - for (Index i = 0; i < m.rows(); ++i) - for (Index j = 0; j < m.cols(); ++j) - m3(i,j) = std::atan2(imag(m1(i,j)), real(m1(i,j))); - VERIFY_IS_APPROX(arg(m1), m3); - - std::complex<RealScalar> zero(0.0,0.0); - VERIFY((Eigen::isnan)(m1*zero/zero).all()); -#if EIGEN_COMP_MSVC - // msvc complex division is not robust - VERIFY((Eigen::isinf)(m4/RealScalar(0)).all()); -#else -#if EIGEN_COMP_CLANG - // clang's complex division is notoriously broken too - if((numext::isinf)(m4(0,0)/RealScalar(0))) { -#endif - VERIFY((Eigen::isinf)(m4/zero).all()); -#if EIGEN_COMP_CLANG - } - else - { - VERIFY((Eigen::isinf)(m4.real()/zero.real()).all()); - } -#endif -#endif // MSVC - - VERIFY(((Eigen::isfinite)(m1) && (!(Eigen::isfinite)(m1*zero/zero)) && (!(Eigen::isfinite)(m1/zero))).all()); - - VERIFY_IS_APPROX(inverse(inverse(m1)),m1); - VERIFY_IS_APPROX(conj(m1.conjugate()), m1); - VERIFY_IS_APPROX(abs(m1), sqrt(square(real(m1))+square(imag(m1)))); - VERIFY_IS_APPROX(abs(m1), sqrt(abs2(m1))); - VERIFY_IS_APPROX(log10(m1), log(m1)/log(10)); - - VERIFY_IS_APPROX( m1.sign(), -(-m1).sign() ); - VERIFY_IS_APPROX( m1.sign() * m1.abs(), m1); - - // scalar by array division - Scalar s1 = internal::random<Scalar>(); - const RealScalar tiny = std::sqrt(std::numeric_limits<RealScalar>::epsilon()); - s1 += Scalar(tiny); - m1 += ArrayType::Constant(rows,cols,Scalar(tiny)); - VERIFY_IS_APPROX(s1/m1, s1 * m1.inverse()); - - // check inplace transpose - m2 = m1; - m2.transposeInPlace(); - VERIFY_IS_APPROX(m2, m1.transpose()); - m2.transposeInPlace(); - VERIFY_IS_APPROX(m2, m1); - -} - -template<typename ArrayType> void min_max(const ArrayType& m) -{ - typedef typename ArrayType::Scalar Scalar; - - Index rows = m.rows(); - Index cols = m.cols(); - - ArrayType m1 = ArrayType::Random(rows, cols); - - // min/max with array - Scalar maxM1 = m1.maxCoeff(); - Scalar minM1 = m1.minCoeff(); - - VERIFY_IS_APPROX(ArrayType::Constant(rows,cols, minM1), (m1.min)(ArrayType::Constant(rows,cols, minM1))); - VERIFY_IS_APPROX(m1, (m1.min)(ArrayType::Constant(rows,cols, maxM1))); - - VERIFY_IS_APPROX(ArrayType::Constant(rows,cols, maxM1), (m1.max)(ArrayType::Constant(rows,cols, maxM1))); - VERIFY_IS_APPROX(m1, (m1.max)(ArrayType::Constant(rows,cols, minM1))); - - // min/max with scalar input - VERIFY_IS_APPROX(ArrayType::Constant(rows,cols, minM1), (m1.min)( minM1)); - VERIFY_IS_APPROX(m1, (m1.min)( maxM1)); - - VERIFY_IS_APPROX(ArrayType::Constant(rows,cols, maxM1), (m1.max)( maxM1)); - VERIFY_IS_APPROX(m1, (m1.max)( minM1)); - -} - -void test_array() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( array(Array<float, 1, 1>()) ); - CALL_SUBTEST_2( array(Array22f()) ); - CALL_SUBTEST_3( array(Array44d()) ); - CALL_SUBTEST_4( array(ArrayXXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_5( array(ArrayXXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_6( array(ArrayXXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( comparisons(Array<float, 1, 1>()) ); - CALL_SUBTEST_2( comparisons(Array22f()) ); - CALL_SUBTEST_3( comparisons(Array44d()) ); - CALL_SUBTEST_5( comparisons(ArrayXXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_6( comparisons(ArrayXXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( min_max(Array<float, 1, 1>()) ); - CALL_SUBTEST_2( min_max(Array22f()) ); - CALL_SUBTEST_3( min_max(Array44d()) ); - CALL_SUBTEST_5( min_max(ArrayXXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_6( min_max(ArrayXXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( array_real(Array<float, 1, 1>()) ); - CALL_SUBTEST_2( array_real(Array22f()) ); - CALL_SUBTEST_3( array_real(Array44d()) ); - CALL_SUBTEST_5( array_real(ArrayXXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_4( array_complex(ArrayXXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } - - VERIFY((internal::is_same< internal::global_math_functions_filtering_base<int>::type, int >::value)); - VERIFY((internal::is_same< internal::global_math_functions_filtering_base<float>::type, float >::value)); - VERIFY((internal::is_same< internal::global_math_functions_filtering_base<Array2i>::type, ArrayBase<Array2i> >::value)); - typedef CwiseUnaryOp<internal::scalar_abs_op<double>, ArrayXd > Xpr; - VERIFY((internal::is_same< internal::global_math_functions_filtering_base<Xpr>::type, - ArrayBase<Xpr> - >::value)); -} diff --git a/eigen/test/array_for_matrix.cpp b/eigen/test/array_for_matrix.cpp deleted file mode 100644 index a05bba1..0000000 --- a/eigen/test/array_for_matrix.cpp +++ /dev/null @@ -1,300 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename MatrixType> void array_for_matrix(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVectorType; - typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols); - - ColVectorType cv1 = ColVectorType::Random(rows); - RowVectorType rv1 = RowVectorType::Random(cols); - - Scalar s1 = internal::random<Scalar>(), - s2 = internal::random<Scalar>(); - - // scalar addition - VERIFY_IS_APPROX(m1.array() + s1, s1 + m1.array()); - VERIFY_IS_APPROX((m1.array() + s1).matrix(), MatrixType::Constant(rows,cols,s1) + m1); - VERIFY_IS_APPROX(((m1*Scalar(2)).array() - s2).matrix(), (m1+m1) - MatrixType::Constant(rows,cols,s2) ); - m3 = m1; - m3.array() += s2; - VERIFY_IS_APPROX(m3, (m1.array() + s2).matrix()); - m3 = m1; - m3.array() -= s1; - VERIFY_IS_APPROX(m3, (m1.array() - s1).matrix()); - - // reductions - VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum().sum() - m1.sum(), m1.squaredNorm()); - VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum().sum() - m1.sum(), m1.squaredNorm()); - VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum() + m2.colwise().sum() - (m1+m2).colwise().sum(), (m1+m2).squaredNorm()); - VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum() - m2.rowwise().sum() - (m1-m2).rowwise().sum(), (m1-m2).squaredNorm()); - VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar,Scalar>())); - - // vector-wise ops - m3 = m1; - VERIFY_IS_APPROX(m3.colwise() += cv1, m1.colwise() + cv1); - m3 = m1; - VERIFY_IS_APPROX(m3.colwise() -= cv1, m1.colwise() - cv1); - m3 = m1; - VERIFY_IS_APPROX(m3.rowwise() += rv1, m1.rowwise() + rv1); - m3 = m1; - VERIFY_IS_APPROX(m3.rowwise() -= rv1, m1.rowwise() - rv1); - - // empty objects - VERIFY_IS_APPROX(m1.block(0,0,0,cols).colwise().sum(), RowVectorType::Zero(cols)); - VERIFY_IS_APPROX(m1.block(0,0,rows,0).rowwise().prod(), ColVectorType::Ones(rows)); - - // verify the const accessors exist - const Scalar& ref_m1 = m.matrix().array().coeffRef(0); - const Scalar& ref_m2 = m.matrix().array().coeffRef(0,0); - const Scalar& ref_a1 = m.array().matrix().coeffRef(0); - const Scalar& ref_a2 = m.array().matrix().coeffRef(0,0); - VERIFY(&ref_a1 == &ref_m1); - VERIFY(&ref_a2 == &ref_m2); - - // Check write accessors: - m1.array().coeffRef(0,0) = 1; - VERIFY_IS_APPROX(m1(0,0),Scalar(1)); - m1.array()(0,0) = 2; - VERIFY_IS_APPROX(m1(0,0),Scalar(2)); - m1.array().matrix().coeffRef(0,0) = 3; - VERIFY_IS_APPROX(m1(0,0),Scalar(3)); - m1.array().matrix()(0,0) = 4; - VERIFY_IS_APPROX(m1(0,0),Scalar(4)); -} - -template<typename MatrixType> void comparisons(const MatrixType& m) -{ - using std::abs; - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - Index rows = m.rows(); - Index cols = m.cols(); - - Index r = internal::random<Index>(0, rows-1), - c = internal::random<Index>(0, cols-1); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols); - - VERIFY(((m1.array() + Scalar(1)) > m1.array()).all()); - VERIFY(((m1.array() - Scalar(1)) < m1.array()).all()); - if (rows*cols>1) - { - m3 = m1; - m3(r,c) += 1; - VERIFY(! (m1.array() < m3.array()).all() ); - VERIFY(! (m1.array() > m3.array()).all() ); - } - - // comparisons to scalar - VERIFY( (m1.array() != (m1(r,c)+1) ).any() ); - VERIFY( (m1.array() > (m1(r,c)-1) ).any() ); - VERIFY( (m1.array() < (m1(r,c)+1) ).any() ); - VERIFY( (m1.array() == m1(r,c) ).any() ); - VERIFY( m1.cwiseEqual(m1(r,c)).any() ); - - // test Select - VERIFY_IS_APPROX( (m1.array()<m2.array()).select(m1,m2), m1.cwiseMin(m2) ); - VERIFY_IS_APPROX( (m1.array()>m2.array()).select(m1,m2), m1.cwiseMax(m2) ); - Scalar mid = (m1.cwiseAbs().minCoeff() + m1.cwiseAbs().maxCoeff())/Scalar(2); - for (int j=0; j<cols; ++j) - for (int i=0; i<rows; ++i) - m3(i,j) = abs(m1(i,j))<mid ? 0 : m1(i,j); - VERIFY_IS_APPROX( (m1.array().abs()<MatrixType::Constant(rows,cols,mid).array()) - .select(MatrixType::Zero(rows,cols),m1), m3); - // shorter versions: - VERIFY_IS_APPROX( (m1.array().abs()<MatrixType::Constant(rows,cols,mid).array()) - .select(0,m1), m3); - VERIFY_IS_APPROX( (m1.array().abs()>=MatrixType::Constant(rows,cols,mid).array()) - .select(m1,0), m3); - // even shorter version: - VERIFY_IS_APPROX( (m1.array().abs()<mid).select(0,m1), m3); - - // count - VERIFY(((m1.array().abs()+1)>RealScalar(0.1)).count() == rows*cols); - - // and/or - VERIFY( ((m1.array()<RealScalar(0)).matrix() && (m1.array()>RealScalar(0)).matrix()).count() == 0); - VERIFY( ((m1.array()<RealScalar(0)).matrix() || (m1.array()>=RealScalar(0)).matrix()).count() == rows*cols); - RealScalar a = m1.cwiseAbs().mean(); - VERIFY( ((m1.array()<-a).matrix() || (m1.array()>a).matrix()).count() == (m1.cwiseAbs().array()>a).count()); - - typedef Matrix<typename MatrixType::Index, Dynamic, 1> VectorOfIndices; - - // TODO allows colwise/rowwise for array - VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().colwise().count(), VectorOfIndices::Constant(cols,rows).transpose()); - VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().rowwise().count(), VectorOfIndices::Constant(rows, cols)); -} - -template<typename VectorType> void lpNorm(const VectorType& v) -{ - using std::sqrt; - typedef typename VectorType::RealScalar RealScalar; - VectorType u = VectorType::Random(v.size()); - - if(v.size()==0) - { - VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), RealScalar(0)); - VERIFY_IS_APPROX(u.template lpNorm<1>(), RealScalar(0)); - VERIFY_IS_APPROX(u.template lpNorm<2>(), RealScalar(0)); - VERIFY_IS_APPROX(u.template lpNorm<5>(), RealScalar(0)); - } - else - { - VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), u.cwiseAbs().maxCoeff()); - } - - VERIFY_IS_APPROX(u.template lpNorm<1>(), u.cwiseAbs().sum()); - VERIFY_IS_APPROX(u.template lpNorm<2>(), sqrt(u.array().abs().square().sum())); - VERIFY_IS_APPROX(numext::pow(u.template lpNorm<5>(), typename VectorType::RealScalar(5)), u.array().abs().pow(5).sum()); -} - -template<typename MatrixType> void cwise_min_max(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols); - - // min/max with array - Scalar maxM1 = m1.maxCoeff(); - Scalar minM1 = m1.minCoeff(); - - VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1), m1.cwiseMin(MatrixType::Constant(rows,cols, minM1))); - VERIFY_IS_APPROX(m1, m1.cwiseMin(MatrixType::Constant(rows,cols, maxM1))); - - VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1), m1.cwiseMax(MatrixType::Constant(rows,cols, maxM1))); - VERIFY_IS_APPROX(m1, m1.cwiseMax(MatrixType::Constant(rows,cols, minM1))); - - // min/max with scalar input - VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1), m1.cwiseMin( minM1)); - VERIFY_IS_APPROX(m1, m1.cwiseMin(maxM1)); - VERIFY_IS_APPROX(-m1, (-m1).cwiseMin(-minM1)); - VERIFY_IS_APPROX(-m1.array(), ((-m1).array().min)( -minM1)); - - VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1), m1.cwiseMax( maxM1)); - VERIFY_IS_APPROX(m1, m1.cwiseMax(minM1)); - VERIFY_IS_APPROX(-m1, (-m1).cwiseMax(-maxM1)); - VERIFY_IS_APPROX(-m1.array(), ((-m1).array().max)(-maxM1)); - - VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1).array(), (m1.array().min)( minM1)); - VERIFY_IS_APPROX(m1.array(), (m1.array().min)( maxM1)); - - VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1).array(), (m1.array().max)( maxM1)); - VERIFY_IS_APPROX(m1.array(), (m1.array().max)( minM1)); - -} - -template<typename MatrixTraits> void resize(const MatrixTraits& t) -{ - typedef typename MatrixTraits::Scalar Scalar; - typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType; - typedef Array<Scalar,Dynamic,Dynamic> Array2DType; - typedef Matrix<Scalar,Dynamic,1> VectorType; - typedef Array<Scalar,Dynamic,1> Array1DType; - - Index rows = t.rows(), cols = t.cols(); - - MatrixType m(rows,cols); - VectorType v(rows); - Array2DType a2(rows,cols); - Array1DType a1(rows); - - m.array().resize(rows+1,cols+1); - VERIFY(m.rows()==rows+1 && m.cols()==cols+1); - a2.matrix().resize(rows+1,cols+1); - VERIFY(a2.rows()==rows+1 && a2.cols()==cols+1); - v.array().resize(cols); - VERIFY(v.size()==cols); - a1.matrix().resize(cols); - VERIFY(a1.size()==cols); -} - -template<int> -void regression_bug_654() -{ - ArrayXf a = RowVectorXf(3); - VectorXf v = Array<float,1,Dynamic>(3); -} - -// Check propagation of LvalueBit through Array/Matrix-Wrapper -template<int> -void regrrssion_bug_1410() -{ - const Matrix4i M; - const Array4i A; - ArrayWrapper<const Matrix4i> MA = M.array(); - MA.row(0); - MatrixWrapper<const Array4i> AM = A.matrix(); - AM.row(0); - - VERIFY((internal::traits<ArrayWrapper<const Matrix4i> >::Flags&LvalueBit)==0); - VERIFY((internal::traits<MatrixWrapper<const Array4i> >::Flags&LvalueBit)==0); - - VERIFY((internal::traits<ArrayWrapper<Matrix4i> >::Flags&LvalueBit)==LvalueBit); - VERIFY((internal::traits<MatrixWrapper<Array4i> >::Flags&LvalueBit)==LvalueBit); -} - -void test_array_for_matrix() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( array_for_matrix(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( array_for_matrix(Matrix2f()) ); - CALL_SUBTEST_3( array_for_matrix(Matrix4d()) ); - CALL_SUBTEST_4( array_for_matrix(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_5( array_for_matrix(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_6( array_for_matrix(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( comparisons(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( comparisons(Matrix2f()) ); - CALL_SUBTEST_3( comparisons(Matrix4d()) ); - CALL_SUBTEST_5( comparisons(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_6( comparisons(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( cwise_min_max(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( cwise_min_max(Matrix2f()) ); - CALL_SUBTEST_3( cwise_min_max(Matrix4d()) ); - CALL_SUBTEST_5( cwise_min_max(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_6( cwise_min_max(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( lpNorm(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( lpNorm(Vector2f()) ); - CALL_SUBTEST_7( lpNorm(Vector3d()) ); - CALL_SUBTEST_8( lpNorm(Vector4f()) ); - CALL_SUBTEST_5( lpNorm(VectorXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_4( lpNorm(VectorXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } - CALL_SUBTEST_5( lpNorm(VectorXf(0)) ); - CALL_SUBTEST_4( lpNorm(VectorXcf(0)) ); - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_4( resize(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_5( resize(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_6( resize(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } - CALL_SUBTEST_6( regression_bug_654<0>() ); - CALL_SUBTEST_6( regrrssion_bug_1410<0>() ); -} diff --git a/eigen/test/array_of_string.cpp b/eigen/test/array_of_string.cpp deleted file mode 100644 index e23b7c5..0000000 --- a/eigen/test/array_of_string.cpp +++ /dev/null @@ -1,32 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -void test_array_of_string() -{ - typedef Array<std::string,1,Dynamic> ArrayXs; - ArrayXs a1(3), a2(3), a3(3), a3ref(3); - a1 << "one", "two", "three"; - a2 << "1", "2", "3"; - a3ref << "one (1)", "two (2)", "three (3)"; - std::stringstream s1; - s1 << a1; - VERIFY_IS_EQUAL(s1.str(), std::string(" one two three")); - a3 = a1 + std::string(" (") + a2 + std::string(")"); - VERIFY((a3==a3ref).all()); - - a3 = a1; - a3 += std::string(" (") + a2 + std::string(")"); - VERIFY((a3==a3ref).all()); - - a1.swap(a3); - VERIFY((a1==a3ref).all()); - VERIFY((a3!=a3ref).all()); -} diff --git a/eigen/test/array_replicate.cpp b/eigen/test/array_replicate.cpp deleted file mode 100644 index 0dad5ba..0000000 --- a/eigen/test/array_replicate.cpp +++ /dev/null @@ -1,81 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename MatrixType> void replicate(const MatrixType& m) -{ - /* this test covers the following files: - Replicate.cpp - */ - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - typedef Matrix<Scalar, Dynamic, Dynamic> MatrixX; - typedef Matrix<Scalar, Dynamic, 1> VectorX; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols); - - VectorType v1 = VectorType::Random(rows); - - MatrixX x1, x2; - VectorX vx1; - - int f1 = internal::random<int>(1,10), - f2 = internal::random<int>(1,10); - - x1.resize(rows*f1,cols*f2); - for(int j=0; j<f2; j++) - for(int i=0; i<f1; i++) - x1.block(i*rows,j*cols,rows,cols) = m1; - VERIFY_IS_APPROX(x1, m1.replicate(f1,f2)); - - x2.resize(2*rows,3*cols); - x2 << m2, m2, m2, - m2, m2, m2; - VERIFY_IS_APPROX(x2, (m2.template replicate<2,3>())); - - x2.resize(rows,3*cols); - x2 << m2, m2, m2; - VERIFY_IS_APPROX(x2, (m2.template replicate<1,3>())); - - vx1.resize(3*rows,cols); - vx1 << m2, m2, m2; - VERIFY_IS_APPROX(vx1+vx1, vx1+(m2.template replicate<3,1>())); - - vx1=m2+(m2.colwise().replicate(1)); - - if(m2.cols()==1) - VERIFY_IS_APPROX(m2.coeff(0), (m2.template replicate<3,1>().coeff(m2.rows()))); - - x2.resize(rows,f1); - for (int j=0; j<f1; ++j) - x2.col(j) = v1; - VERIFY_IS_APPROX(x2, v1.rowwise().replicate(f1)); - - vx1.resize(rows*f2); - for (int j=0; j<f2; ++j) - vx1.segment(j*rows,rows) = v1; - VERIFY_IS_APPROX(vx1, v1.colwise().replicate(f2)); -} - -void test_array_replicate() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( replicate(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( replicate(Vector2f()) ); - CALL_SUBTEST_3( replicate(Vector3d()) ); - CALL_SUBTEST_4( replicate(Vector4f()) ); - CALL_SUBTEST_5( replicate(VectorXf(16)) ); - CALL_SUBTEST_6( replicate(VectorXcd(10)) ); - } -} diff --git a/eigen/test/array_reverse.cpp b/eigen/test/array_reverse.cpp deleted file mode 100644 index 9d5b9a6..0000000 --- a/eigen/test/array_reverse.cpp +++ /dev/null @@ -1,145 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <iostream> - -using namespace std; - -template<typename MatrixType> void reverse(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - - Index rows = m.rows(); - Index cols = m.cols(); - - // this test relies a lot on Random.h, and there's not much more that we can do - // to test it, hence I consider that we will have tested Random.h - MatrixType m1 = MatrixType::Random(rows, cols), m2; - VectorType v1 = VectorType::Random(rows); - - MatrixType m1_r = m1.reverse(); - // Verify that MatrixBase::reverse() works - for ( int i = 0; i < rows; i++ ) { - for ( int j = 0; j < cols; j++ ) { - VERIFY_IS_APPROX(m1_r(i, j), m1(rows - 1 - i, cols - 1 - j)); - } - } - - Reverse<MatrixType> m1_rd(m1); - // Verify that a Reverse default (in both directions) of an expression works - for ( int i = 0; i < rows; i++ ) { - for ( int j = 0; j < cols; j++ ) { - VERIFY_IS_APPROX(m1_rd(i, j), m1(rows - 1 - i, cols - 1 - j)); - } - } - - Reverse<MatrixType, BothDirections> m1_rb(m1); - // Verify that a Reverse in both directions of an expression works - for ( int i = 0; i < rows; i++ ) { - for ( int j = 0; j < cols; j++ ) { - VERIFY_IS_APPROX(m1_rb(i, j), m1(rows - 1 - i, cols - 1 - j)); - } - } - - Reverse<MatrixType, Vertical> m1_rv(m1); - // Verify that a Reverse in the vertical directions of an expression works - for ( int i = 0; i < rows; i++ ) { - for ( int j = 0; j < cols; j++ ) { - VERIFY_IS_APPROX(m1_rv(i, j), m1(rows - 1 - i, j)); - } - } - - Reverse<MatrixType, Horizontal> m1_rh(m1); - // Verify that a Reverse in the horizontal directions of an expression works - for ( int i = 0; i < rows; i++ ) { - for ( int j = 0; j < cols; j++ ) { - VERIFY_IS_APPROX(m1_rh(i, j), m1(i, cols - 1 - j)); - } - } - - VectorType v1_r = v1.reverse(); - // Verify that a VectorType::reverse() of an expression works - for ( int i = 0; i < rows; i++ ) { - VERIFY_IS_APPROX(v1_r(i), v1(rows - 1 - i)); - } - - MatrixType m1_cr = m1.colwise().reverse(); - // Verify that PartialRedux::reverse() works (for colwise()) - for ( int i = 0; i < rows; i++ ) { - for ( int j = 0; j < cols; j++ ) { - VERIFY_IS_APPROX(m1_cr(i, j), m1(rows - 1 - i, j)); - } - } - - MatrixType m1_rr = m1.rowwise().reverse(); - // Verify that PartialRedux::reverse() works (for rowwise()) - for ( int i = 0; i < rows; i++ ) { - for ( int j = 0; j < cols; j++ ) { - VERIFY_IS_APPROX(m1_rr(i, j), m1(i, cols - 1 - j)); - } - } - - Scalar x = internal::random<Scalar>(); - - Index r = internal::random<Index>(0, rows-1), - c = internal::random<Index>(0, cols-1); - - m1.reverse()(r, c) = x; - VERIFY_IS_APPROX(x, m1(rows - 1 - r, cols - 1 - c)); - - m2 = m1; - m2.reverseInPlace(); - VERIFY_IS_APPROX(m2,m1.reverse().eval()); - - m2 = m1; - m2.col(0).reverseInPlace(); - VERIFY_IS_APPROX(m2.col(0),m1.col(0).reverse().eval()); - - m2 = m1; - m2.row(0).reverseInPlace(); - VERIFY_IS_APPROX(m2.row(0),m1.row(0).reverse().eval()); - - m2 = m1; - m2.rowwise().reverseInPlace(); - VERIFY_IS_APPROX(m2,m1.rowwise().reverse().eval()); - - m2 = m1; - m2.colwise().reverseInPlace(); - VERIFY_IS_APPROX(m2,m1.colwise().reverse().eval()); - - m1.colwise().reverse()(r, c) = x; - VERIFY_IS_APPROX(x, m1(rows - 1 - r, c)); - - m1.rowwise().reverse()(r, c) = x; - VERIFY_IS_APPROX(x, m1(r, cols - 1 - c)); -} - -void test_array_reverse() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( reverse(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( reverse(Matrix2f()) ); - CALL_SUBTEST_3( reverse(Matrix4f()) ); - CALL_SUBTEST_4( reverse(Matrix4d()) ); - CALL_SUBTEST_5( reverse(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_6( reverse(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_7( reverse(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_8( reverse(Matrix<float, 100, 100>()) ); - CALL_SUBTEST_9( reverse(Matrix<float,Dynamic,Dynamic,RowMajor>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } -#ifdef EIGEN_TEST_PART_3 - Vector4f x; x << 1, 2, 3, 4; - Vector4f y; y << 4, 3, 2, 1; - VERIFY(x.reverse()[1] == 3); - VERIFY(x.reverse() == y); -#endif -} diff --git a/eigen/test/bandmatrix.cpp b/eigen/test/bandmatrix.cpp deleted file mode 100644 index f8c38f7..0000000 --- a/eigen/test/bandmatrix.cpp +++ /dev/null @@ -1,71 +0,0 @@ -// This file is triangularView of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename MatrixType> void bandmatrix(const MatrixType& _m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrixType; - - Index rows = _m.rows(); - Index cols = _m.cols(); - Index supers = _m.supers(); - Index subs = _m.subs(); - - MatrixType m(rows,cols,supers,subs); - - DenseMatrixType dm1(rows,cols); - dm1.setZero(); - - m.diagonal().setConstant(123); - dm1.diagonal().setConstant(123); - for (int i=1; i<=m.supers();++i) - { - m.diagonal(i).setConstant(static_cast<RealScalar>(i)); - dm1.diagonal(i).setConstant(static_cast<RealScalar>(i)); - } - for (int i=1; i<=m.subs();++i) - { - m.diagonal(-i).setConstant(-static_cast<RealScalar>(i)); - dm1.diagonal(-i).setConstant(-static_cast<RealScalar>(i)); - } - //std::cerr << m.m_data << "\n\n" << m.toDense() << "\n\n" << dm1 << "\n\n\n\n"; - VERIFY_IS_APPROX(dm1,m.toDenseMatrix()); - - for (int i=0; i<cols; ++i) - { - m.col(i).setConstant(static_cast<RealScalar>(i+1)); - dm1.col(i).setConstant(static_cast<RealScalar>(i+1)); - } - Index d = (std::min)(rows,cols); - Index a = std::max<Index>(0,cols-d-supers); - Index b = std::max<Index>(0,rows-d-subs); - if(a>0) dm1.block(0,d+supers,rows,a).setZero(); - dm1.block(0,supers+1,cols-supers-1-a,cols-supers-1-a).template triangularView<Upper>().setZero(); - dm1.block(subs+1,0,rows-subs-1-b,rows-subs-1-b).template triangularView<Lower>().setZero(); - if(b>0) dm1.block(d+subs,0,b,cols).setZero(); - //std::cerr << m.m_data << "\n\n" << m.toDense() << "\n\n" << dm1 << "\n\n"; - VERIFY_IS_APPROX(dm1,m.toDenseMatrix()); - -} - -using Eigen::internal::BandMatrix; - -void test_bandmatrix() -{ - for(int i = 0; i < 10*g_repeat ; i++) { - Index rows = internal::random<Index>(1,10); - Index cols = internal::random<Index>(1,10); - Index sups = internal::random<Index>(0,cols-1); - Index subs = internal::random<Index>(0,rows-1); - CALL_SUBTEST(bandmatrix(BandMatrix<float>(rows,cols,sups,subs)) ); - } -} diff --git a/eigen/test/basicstuff.cpp b/eigen/test/basicstuff.cpp deleted file mode 100644 index 2e532f7..0000000 --- a/eigen/test/basicstuff.cpp +++ /dev/null @@ -1,278 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_NO_STATIC_ASSERT - -#include "main.h" - -template<typename MatrixType> void basicStuff(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType; - - Index rows = m.rows(); - Index cols = m.cols(); - - // this test relies a lot on Random.h, and there's not much more that we can do - // to test it, hence I consider that we will have tested Random.h - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols), - mzero = MatrixType::Zero(rows, cols), - square = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>::Random(rows, rows); - VectorType v1 = VectorType::Random(rows), - vzero = VectorType::Zero(rows); - SquareMatrixType sm1 = SquareMatrixType::Random(rows,rows), sm2(rows,rows); - - Scalar x = 0; - while(x == Scalar(0)) x = internal::random<Scalar>(); - - Index r = internal::random<Index>(0, rows-1), - c = internal::random<Index>(0, cols-1); - - m1.coeffRef(r,c) = x; - VERIFY_IS_APPROX(x, m1.coeff(r,c)); - m1(r,c) = x; - VERIFY_IS_APPROX(x, m1(r,c)); - v1.coeffRef(r) = x; - VERIFY_IS_APPROX(x, v1.coeff(r)); - v1(r) = x; - VERIFY_IS_APPROX(x, v1(r)); - v1[r] = x; - VERIFY_IS_APPROX(x, v1[r]); - - VERIFY_IS_APPROX( v1, v1); - VERIFY_IS_NOT_APPROX( v1, 2*v1); - VERIFY_IS_MUCH_SMALLER_THAN( vzero, v1); - VERIFY_IS_MUCH_SMALLER_THAN( vzero, v1.squaredNorm()); - VERIFY_IS_NOT_MUCH_SMALLER_THAN(v1, v1); - VERIFY_IS_APPROX( vzero, v1-v1); - VERIFY_IS_APPROX( m1, m1); - VERIFY_IS_NOT_APPROX( m1, 2*m1); - VERIFY_IS_MUCH_SMALLER_THAN( mzero, m1); - VERIFY_IS_NOT_MUCH_SMALLER_THAN(m1, m1); - VERIFY_IS_APPROX( mzero, m1-m1); - - // always test operator() on each read-only expression class, - // in order to check const-qualifiers. - // indeed, if an expression class (here Zero) is meant to be read-only, - // hence has no _write() method, the corresponding MatrixBase method (here zero()) - // should return a const-qualified object so that it is the const-qualified - // operator() that gets called, which in turn calls _read(). - VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows,cols)(r,c), static_cast<Scalar>(1)); - - // now test copying a row-vector into a (column-)vector and conversely. - square.col(r) = square.row(r).eval(); - Matrix<Scalar, 1, MatrixType::RowsAtCompileTime> rv(rows); - Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> cv(rows); - rv = square.row(r); - cv = square.col(r); - - VERIFY_IS_APPROX(rv, cv.transpose()); - - if(cols!=1 && rows!=1 && MatrixType::SizeAtCompileTime!=Dynamic) - { - VERIFY_RAISES_ASSERT(m1 = (m2.block(0,0, rows-1, cols-1))); - } - - if(cols!=1 && rows!=1) - { - VERIFY_RAISES_ASSERT(m1[0]); - VERIFY_RAISES_ASSERT((m1+m1)[0]); - } - - VERIFY_IS_APPROX(m3 = m1,m1); - MatrixType m4; - VERIFY_IS_APPROX(m4 = m1,m1); - - m3.real() = m1.real(); - VERIFY_IS_APPROX(static_cast<const MatrixType&>(m3).real(), static_cast<const MatrixType&>(m1).real()); - VERIFY_IS_APPROX(static_cast<const MatrixType&>(m3).real(), m1.real()); - - // check == / != operators - VERIFY(m1==m1); - VERIFY(m1!=m2); - VERIFY(!(m1==m2)); - VERIFY(!(m1!=m1)); - m1 = m2; - VERIFY(m1==m2); - VERIFY(!(m1!=m2)); - - // check automatic transposition - sm2.setZero(); - for(typename MatrixType::Index i=0;i<rows;++i) - sm2.col(i) = sm1.row(i); - VERIFY_IS_APPROX(sm2,sm1.transpose()); - - sm2.setZero(); - for(typename MatrixType::Index i=0;i<rows;++i) - sm2.col(i).noalias() = sm1.row(i); - VERIFY_IS_APPROX(sm2,sm1.transpose()); - - sm2.setZero(); - for(typename MatrixType::Index i=0;i<rows;++i) - sm2.col(i).noalias() += sm1.row(i); - VERIFY_IS_APPROX(sm2,sm1.transpose()); - - sm2.setZero(); - for(typename MatrixType::Index i=0;i<rows;++i) - sm2.col(i).noalias() -= sm1.row(i); - VERIFY_IS_APPROX(sm2,-sm1.transpose()); - - // check ternary usage - { - bool b = internal::random<int>(0,10)>5; - m3 = b ? m1 : m2; - if(b) VERIFY_IS_APPROX(m3,m1); - else VERIFY_IS_APPROX(m3,m2); - m3 = b ? -m1 : m2; - if(b) VERIFY_IS_APPROX(m3,-m1); - else VERIFY_IS_APPROX(m3,m2); - m3 = b ? m1 : -m2; - if(b) VERIFY_IS_APPROX(m3,m1); - else VERIFY_IS_APPROX(m3,-m2); - } -} - -template<typename MatrixType> void basicStuffComplex(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime> RealMatrixType; - - Index rows = m.rows(); - Index cols = m.cols(); - - Scalar s1 = internal::random<Scalar>(), - s2 = internal::random<Scalar>(); - - VERIFY(numext::real(s1)==numext::real_ref(s1)); - VERIFY(numext::imag(s1)==numext::imag_ref(s1)); - numext::real_ref(s1) = numext::real(s2); - numext::imag_ref(s1) = numext::imag(s2); - VERIFY(internal::isApprox(s1, s2, NumTraits<RealScalar>::epsilon())); - // extended precision in Intel FPUs means that s1 == s2 in the line above is not guaranteed. - - RealMatrixType rm1 = RealMatrixType::Random(rows,cols), - rm2 = RealMatrixType::Random(rows,cols); - MatrixType cm(rows,cols); - cm.real() = rm1; - cm.imag() = rm2; - VERIFY_IS_APPROX(static_cast<const MatrixType&>(cm).real(), rm1); - VERIFY_IS_APPROX(static_cast<const MatrixType&>(cm).imag(), rm2); - rm1.setZero(); - rm2.setZero(); - rm1 = cm.real(); - rm2 = cm.imag(); - VERIFY_IS_APPROX(static_cast<const MatrixType&>(cm).real(), rm1); - VERIFY_IS_APPROX(static_cast<const MatrixType&>(cm).imag(), rm2); - cm.real().setZero(); - VERIFY(static_cast<const MatrixType&>(cm).real().isZero()); - VERIFY(!static_cast<const MatrixType&>(cm).imag().isZero()); -} - -#ifdef EIGEN_TEST_PART_2 -void casting() -{ - Matrix4f m = Matrix4f::Random(), m2; - Matrix4d n = m.cast<double>(); - VERIFY(m.isApprox(n.cast<float>())); - m2 = m.cast<float>(); // check the specialization when NewType == Type - VERIFY(m.isApprox(m2)); -} -#endif - -template <typename Scalar> -void fixedSizeMatrixConstruction() -{ - Scalar raw[4]; - for(int k=0; k<4; ++k) - raw[k] = internal::random<Scalar>(); - - { - Matrix<Scalar,4,1> m(raw); - Array<Scalar,4,1> a(raw); - for(int k=0; k<4; ++k) VERIFY(m(k) == raw[k]); - for(int k=0; k<4; ++k) VERIFY(a(k) == raw[k]); - VERIFY_IS_EQUAL(m,(Matrix<Scalar,4,1>(raw[0],raw[1],raw[2],raw[3]))); - VERIFY((a==(Array<Scalar,4,1>(raw[0],raw[1],raw[2],raw[3]))).all()); - } - { - Matrix<Scalar,3,1> m(raw); - Array<Scalar,3,1> a(raw); - for(int k=0; k<3; ++k) VERIFY(m(k) == raw[k]); - for(int k=0; k<3; ++k) VERIFY(a(k) == raw[k]); - VERIFY_IS_EQUAL(m,(Matrix<Scalar,3,1>(raw[0],raw[1],raw[2]))); - VERIFY((a==Array<Scalar,3,1>(raw[0],raw[1],raw[2])).all()); - } - { - Matrix<Scalar,2,1> m(raw), m2( (DenseIndex(raw[0])), (DenseIndex(raw[1])) ); - Array<Scalar,2,1> a(raw), a2( (DenseIndex(raw[0])), (DenseIndex(raw[1])) ); - for(int k=0; k<2; ++k) VERIFY(m(k) == raw[k]); - for(int k=0; k<2; ++k) VERIFY(a(k) == raw[k]); - VERIFY_IS_EQUAL(m,(Matrix<Scalar,2,1>(raw[0],raw[1]))); - VERIFY((a==Array<Scalar,2,1>(raw[0],raw[1])).all()); - for(int k=0; k<2; ++k) VERIFY(m2(k) == DenseIndex(raw[k])); - for(int k=0; k<2; ++k) VERIFY(a2(k) == DenseIndex(raw[k])); - } - { - Matrix<Scalar,1,2> m(raw), - m2( (DenseIndex(raw[0])), (DenseIndex(raw[1])) ), - m3( (int(raw[0])), (int(raw[1])) ), - m4( (float(raw[0])), (float(raw[1])) ); - Array<Scalar,1,2> a(raw), a2( (DenseIndex(raw[0])), (DenseIndex(raw[1])) ); - for(int k=0; k<2; ++k) VERIFY(m(k) == raw[k]); - for(int k=0; k<2; ++k) VERIFY(a(k) == raw[k]); - VERIFY_IS_EQUAL(m,(Matrix<Scalar,1,2>(raw[0],raw[1]))); - VERIFY((a==Array<Scalar,1,2>(raw[0],raw[1])).all()); - for(int k=0; k<2; ++k) VERIFY(m2(k) == DenseIndex(raw[k])); - for(int k=0; k<2; ++k) VERIFY(a2(k) == DenseIndex(raw[k])); - for(int k=0; k<2; ++k) VERIFY(m3(k) == int(raw[k])); - for(int k=0; k<2; ++k) VERIFY((m4(k)) == Scalar(float(raw[k]))); - } - { - Matrix<Scalar,1,1> m(raw), m1(raw[0]), m2( (DenseIndex(raw[0])) ), m3( (int(raw[0])) ); - Array<Scalar,1,1> a(raw), a1(raw[0]), a2( (DenseIndex(raw[0])) ); - VERIFY(m(0) == raw[0]); - VERIFY(a(0) == raw[0]); - VERIFY(m1(0) == raw[0]); - VERIFY(a1(0) == raw[0]); - VERIFY(m2(0) == DenseIndex(raw[0])); - VERIFY(a2(0) == DenseIndex(raw[0])); - VERIFY(m3(0) == int(raw[0])); - VERIFY_IS_EQUAL(m,(Matrix<Scalar,1,1>(raw[0]))); - VERIFY((a==Array<Scalar,1,1>(raw[0])).all()); - } -} - -void test_basicstuff() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( basicStuff(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( basicStuff(Matrix4d()) ); - CALL_SUBTEST_3( basicStuff(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_4( basicStuff(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_5( basicStuff(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_6( basicStuff(Matrix<float, 100, 100>()) ); - CALL_SUBTEST_7( basicStuff(Matrix<long double,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - - CALL_SUBTEST_3( basicStuffComplex(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_5( basicStuffComplex(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } - - CALL_SUBTEST_1(fixedSizeMatrixConstruction<unsigned char>()); - CALL_SUBTEST_1(fixedSizeMatrixConstruction<float>()); - CALL_SUBTEST_1(fixedSizeMatrixConstruction<double>()); - CALL_SUBTEST_1(fixedSizeMatrixConstruction<int>()); - CALL_SUBTEST_1(fixedSizeMatrixConstruction<long int>()); - CALL_SUBTEST_1(fixedSizeMatrixConstruction<std::ptrdiff_t>()); - - CALL_SUBTEST_2(casting()); -} diff --git a/eigen/test/bdcsvd.cpp b/eigen/test/bdcsvd.cpp deleted file mode 100644 index 6c7b096..0000000 --- a/eigen/test/bdcsvd.cpp +++ /dev/null @@ -1,112 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com> -// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr> -// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr> -// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/ - -// discard stack allocation as that too bypasses malloc -#define EIGEN_STACK_ALLOCATION_LIMIT 0 -#define EIGEN_RUNTIME_NO_MALLOC - -#include "main.h" -#include <Eigen/SVD> -#include <iostream> -#include <Eigen/LU> - - -#define SVD_DEFAULT(M) BDCSVD<M> -#define SVD_FOR_MIN_NORM(M) BDCSVD<M> -#include "svd_common.h" - -// Check all variants of JacobiSVD -template<typename MatrixType> -void bdcsvd(const MatrixType& a = MatrixType(), bool pickrandom = true) -{ - MatrixType m = a; - if(pickrandom) - svd_fill_random(m); - - CALL_SUBTEST(( svd_test_all_computation_options<BDCSVD<MatrixType> >(m, false) )); -} - -template<typename MatrixType> -void bdcsvd_method() -{ - enum { Size = MatrixType::RowsAtCompileTime }; - typedef typename MatrixType::RealScalar RealScalar; - typedef Matrix<RealScalar, Size, 1> RealVecType; - MatrixType m = MatrixType::Identity(); - VERIFY_IS_APPROX(m.bdcSvd().singularValues(), RealVecType::Ones()); - VERIFY_RAISES_ASSERT(m.bdcSvd().matrixU()); - VERIFY_RAISES_ASSERT(m.bdcSvd().matrixV()); - VERIFY_IS_APPROX(m.bdcSvd(ComputeFullU|ComputeFullV).solve(m), m); -} - -// compare the Singular values returned with Jacobi and Bdc -template<typename MatrixType> -void compare_bdc_jacobi(const MatrixType& a = MatrixType(), unsigned int computationOptions = 0) -{ - MatrixType m = MatrixType::Random(a.rows(), a.cols()); - BDCSVD<MatrixType> bdc_svd(m); - JacobiSVD<MatrixType> jacobi_svd(m); - VERIFY_IS_APPROX(bdc_svd.singularValues(), jacobi_svd.singularValues()); - if(computationOptions & ComputeFullU) VERIFY_IS_APPROX(bdc_svd.matrixU(), jacobi_svd.matrixU()); - if(computationOptions & ComputeThinU) VERIFY_IS_APPROX(bdc_svd.matrixU(), jacobi_svd.matrixU()); - if(computationOptions & ComputeFullV) VERIFY_IS_APPROX(bdc_svd.matrixV(), jacobi_svd.matrixV()); - if(computationOptions & ComputeThinV) VERIFY_IS_APPROX(bdc_svd.matrixV(), jacobi_svd.matrixV()); -} - -void test_bdcsvd() -{ - CALL_SUBTEST_3(( svd_verify_assert<BDCSVD<Matrix3f> >(Matrix3f()) )); - CALL_SUBTEST_4(( svd_verify_assert<BDCSVD<Matrix4d> >(Matrix4d()) )); - CALL_SUBTEST_7(( svd_verify_assert<BDCSVD<MatrixXf> >(MatrixXf(10,12)) )); - CALL_SUBTEST_8(( svd_verify_assert<BDCSVD<MatrixXcd> >(MatrixXcd(7,5)) )); - - CALL_SUBTEST_101(( svd_all_trivial_2x2(bdcsvd<Matrix2cd>) )); - CALL_SUBTEST_102(( svd_all_trivial_2x2(bdcsvd<Matrix2d>) )); - - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_3(( bdcsvd<Matrix3f>() )); - CALL_SUBTEST_4(( bdcsvd<Matrix4d>() )); - CALL_SUBTEST_5(( bdcsvd<Matrix<float,3,5> >() )); - - int r = internal::random<int>(1, EIGEN_TEST_MAX_SIZE/2), - c = internal::random<int>(1, EIGEN_TEST_MAX_SIZE/2); - - TEST_SET_BUT_UNUSED_VARIABLE(r) - TEST_SET_BUT_UNUSED_VARIABLE(c) - - CALL_SUBTEST_6(( bdcsvd(Matrix<double,Dynamic,2>(r,2)) )); - CALL_SUBTEST_7(( bdcsvd(MatrixXf(r,c)) )); - CALL_SUBTEST_7(( compare_bdc_jacobi(MatrixXf(r,c)) )); - CALL_SUBTEST_10(( bdcsvd(MatrixXd(r,c)) )); - CALL_SUBTEST_10(( compare_bdc_jacobi(MatrixXd(r,c)) )); - CALL_SUBTEST_8(( bdcsvd(MatrixXcd(r,c)) )); - CALL_SUBTEST_8(( compare_bdc_jacobi(MatrixXcd(r,c)) )); - - // Test on inf/nan matrix - CALL_SUBTEST_7( (svd_inf_nan<BDCSVD<MatrixXf>, MatrixXf>()) ); - CALL_SUBTEST_10( (svd_inf_nan<BDCSVD<MatrixXd>, MatrixXd>()) ); - } - - // test matrixbase method - CALL_SUBTEST_1(( bdcsvd_method<Matrix2cd>() )); - CALL_SUBTEST_3(( bdcsvd_method<Matrix3f>() )); - - // Test problem size constructors - CALL_SUBTEST_7( BDCSVD<MatrixXf>(10,10) ); - - // Check that preallocation avoids subsequent mallocs - // Disbaled because not supported by BDCSVD - // CALL_SUBTEST_9( svd_preallocate<void>() ); - - CALL_SUBTEST_2( svd_underoverflow<void>() ); -} - diff --git a/eigen/test/bicgstab.cpp b/eigen/test/bicgstab.cpp deleted file mode 100644 index 4cc0dd3..0000000 --- a/eigen/test/bicgstab.cpp +++ /dev/null @@ -1,34 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "sparse_solver.h" -#include <Eigen/IterativeLinearSolvers> - -template<typename T, typename I> void test_bicgstab_T() -{ - BiCGSTAB<SparseMatrix<T,0,I>, DiagonalPreconditioner<T> > bicgstab_colmajor_diag; - BiCGSTAB<SparseMatrix<T,0,I>, IdentityPreconditioner > bicgstab_colmajor_I; - BiCGSTAB<SparseMatrix<T,0,I>, IncompleteLUT<T,I> > bicgstab_colmajor_ilut; - //BiCGSTAB<SparseMatrix<T>, SSORPreconditioner<T> > bicgstab_colmajor_ssor; - - bicgstab_colmajor_diag.setTolerance(NumTraits<T>::epsilon()*4); - bicgstab_colmajor_ilut.setTolerance(NumTraits<T>::epsilon()*4); - - CALL_SUBTEST( check_sparse_square_solving(bicgstab_colmajor_diag) ); -// CALL_SUBTEST( check_sparse_square_solving(bicgstab_colmajor_I) ); - CALL_SUBTEST( check_sparse_square_solving(bicgstab_colmajor_ilut) ); - //CALL_SUBTEST( check_sparse_square_solving(bicgstab_colmajor_ssor) ); -} - -void test_bicgstab() -{ - CALL_SUBTEST_1((test_bicgstab_T<double,int>()) ); - CALL_SUBTEST_2((test_bicgstab_T<std::complex<double>, int>())); - CALL_SUBTEST_3((test_bicgstab_T<double,long int>())); -} diff --git a/eigen/test/block.cpp b/eigen/test/block.cpp deleted file mode 100644 index ca9c21f..0000000 --- a/eigen/test/block.cpp +++ /dev/null @@ -1,276 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_NO_STATIC_ASSERT // otherwise we fail at compile time on unused paths -#include "main.h" - -template<typename MatrixType, typename Index, typename Scalar> -typename Eigen::internal::enable_if<!NumTraits<typename MatrixType::Scalar>::IsComplex,typename MatrixType::Scalar>::type -block_real_only(const MatrixType &m1, Index r1, Index r2, Index c1, Index c2, const Scalar& s1) { - // check cwise-Functions: - VERIFY_IS_APPROX(m1.row(r1).cwiseMax(s1), m1.cwiseMax(s1).row(r1)); - VERIFY_IS_APPROX(m1.col(c1).cwiseMin(s1), m1.cwiseMin(s1).col(c1)); - - VERIFY_IS_APPROX(m1.block(r1,c1,r2-r1+1,c2-c1+1).cwiseMin(s1), m1.cwiseMin(s1).block(r1,c1,r2-r1+1,c2-c1+1)); - VERIFY_IS_APPROX(m1.block(r1,c1,r2-r1+1,c2-c1+1).cwiseMax(s1), m1.cwiseMax(s1).block(r1,c1,r2-r1+1,c2-c1+1)); - - return Scalar(0); -} - -template<typename MatrixType, typename Index, typename Scalar> -typename Eigen::internal::enable_if<NumTraits<typename MatrixType::Scalar>::IsComplex,typename MatrixType::Scalar>::type -block_real_only(const MatrixType &, Index, Index, Index, Index, const Scalar&) { - return Scalar(0); -} - - -template<typename MatrixType> void block(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType; - typedef Matrix<Scalar, Dynamic, Dynamic, MatrixType::IsRowMajor?RowMajor:ColMajor> DynamicMatrixType; - typedef Matrix<Scalar, Dynamic, 1> DynamicVectorType; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m1_copy = m1, - m2 = MatrixType::Random(rows, cols), - m3(rows, cols), - ones = MatrixType::Ones(rows, cols); - VectorType v1 = VectorType::Random(rows); - - Scalar s1 = internal::random<Scalar>(); - - Index r1 = internal::random<Index>(0,rows-1); - Index r2 = internal::random<Index>(r1,rows-1); - Index c1 = internal::random<Index>(0,cols-1); - Index c2 = internal::random<Index>(c1,cols-1); - - block_real_only(m1, r1, r2, c1, c1, s1); - - //check row() and col() - VERIFY_IS_EQUAL(m1.col(c1).transpose(), m1.transpose().row(c1)); - //check operator(), both constant and non-constant, on row() and col() - m1 = m1_copy; - m1.row(r1) += s1 * m1_copy.row(r2); - VERIFY_IS_APPROX(m1.row(r1), m1_copy.row(r1) + s1 * m1_copy.row(r2)); - // check nested block xpr on lhs - m1.row(r1).row(0) += s1 * m1_copy.row(r2); - VERIFY_IS_APPROX(m1.row(r1), m1_copy.row(r1) + Scalar(2) * s1 * m1_copy.row(r2)); - m1 = m1_copy; - m1.col(c1) += s1 * m1_copy.col(c2); - VERIFY_IS_APPROX(m1.col(c1), m1_copy.col(c1) + s1 * m1_copy.col(c2)); - m1.col(c1).col(0) += s1 * m1_copy.col(c2); - VERIFY_IS_APPROX(m1.col(c1), m1_copy.col(c1) + Scalar(2) * s1 * m1_copy.col(c2)); - - - //check block() - Matrix<Scalar,Dynamic,Dynamic> b1(1,1); b1(0,0) = m1(r1,c1); - - RowVectorType br1(m1.block(r1,0,1,cols)); - VectorType bc1(m1.block(0,c1,rows,1)); - VERIFY_IS_EQUAL(b1, m1.block(r1,c1,1,1)); - VERIFY_IS_EQUAL(m1.row(r1), br1); - VERIFY_IS_EQUAL(m1.col(c1), bc1); - //check operator(), both constant and non-constant, on block() - m1.block(r1,c1,r2-r1+1,c2-c1+1) = s1 * m2.block(0, 0, r2-r1+1,c2-c1+1); - m1.block(r1,c1,r2-r1+1,c2-c1+1)(r2-r1,c2-c1) = m2.block(0, 0, r2-r1+1,c2-c1+1)(0,0); - - enum { - BlockRows = 2, - BlockCols = 5 - }; - if (rows>=5 && cols>=8) - { - // test fixed block() as lvalue - m1.template block<BlockRows,BlockCols>(1,1) *= s1; - // test operator() on fixed block() both as constant and non-constant - m1.template block<BlockRows,BlockCols>(1,1)(0, 3) = m1.template block<2,5>(1,1)(1,2); - // check that fixed block() and block() agree - Matrix<Scalar,Dynamic,Dynamic> b = m1.template block<BlockRows,BlockCols>(3,3); - VERIFY_IS_EQUAL(b, m1.block(3,3,BlockRows,BlockCols)); - - // same tests with mixed fixed/dynamic size - m1.template block<BlockRows,Dynamic>(1,1,BlockRows,BlockCols) *= s1; - m1.template block<BlockRows,Dynamic>(1,1,BlockRows,BlockCols)(0,3) = m1.template block<2,5>(1,1)(1,2); - Matrix<Scalar,Dynamic,Dynamic> b2 = m1.template block<Dynamic,BlockCols>(3,3,2,5); - VERIFY_IS_EQUAL(b2, m1.block(3,3,BlockRows,BlockCols)); - } - - if (rows>2) - { - // test sub vectors - VERIFY_IS_EQUAL(v1.template head<2>(), v1.block(0,0,2,1)); - VERIFY_IS_EQUAL(v1.template head<2>(), v1.head(2)); - VERIFY_IS_EQUAL(v1.template head<2>(), v1.segment(0,2)); - VERIFY_IS_EQUAL(v1.template head<2>(), v1.template segment<2>(0)); - Index i = rows-2; - VERIFY_IS_EQUAL(v1.template tail<2>(), v1.block(i,0,2,1)); - VERIFY_IS_EQUAL(v1.template tail<2>(), v1.tail(2)); - VERIFY_IS_EQUAL(v1.template tail<2>(), v1.segment(i,2)); - VERIFY_IS_EQUAL(v1.template tail<2>(), v1.template segment<2>(i)); - i = internal::random<Index>(0,rows-2); - VERIFY_IS_EQUAL(v1.segment(i,2), v1.template segment<2>(i)); - } - - // stress some basic stuffs with block matrices - VERIFY(numext::real(ones.col(c1).sum()) == RealScalar(rows)); - VERIFY(numext::real(ones.row(r1).sum()) == RealScalar(cols)); - - VERIFY(numext::real(ones.col(c1).dot(ones.col(c2))) == RealScalar(rows)); - VERIFY(numext::real(ones.row(r1).dot(ones.row(r2))) == RealScalar(cols)); - - // check that linear acccessors works on blocks - m1 = m1_copy; - if((MatrixType::Flags&RowMajorBit)==0) - VERIFY_IS_EQUAL(m1.leftCols(c1).coeff(r1+c1*rows), m1(r1,c1)); - else - VERIFY_IS_EQUAL(m1.topRows(r1).coeff(c1+r1*cols), m1(r1,c1)); - - - // now test some block-inside-of-block. - - // expressions with direct access - VERIFY_IS_EQUAL( (m1.block(r1,c1,rows-r1,cols-c1).block(r2-r1,c2-c1,rows-r2,cols-c2)) , (m1.block(r2,c2,rows-r2,cols-c2)) ); - VERIFY_IS_EQUAL( (m1.block(r1,c1,r2-r1+1,c2-c1+1).row(0)) , (m1.row(r1).segment(c1,c2-c1+1)) ); - VERIFY_IS_EQUAL( (m1.block(r1,c1,r2-r1+1,c2-c1+1).col(0)) , (m1.col(c1).segment(r1,r2-r1+1)) ); - VERIFY_IS_EQUAL( (m1.block(r1,c1,r2-r1+1,c2-c1+1).transpose().col(0)) , (m1.row(r1).segment(c1,c2-c1+1)).transpose() ); - VERIFY_IS_EQUAL( (m1.transpose().block(c1,r1,c2-c1+1,r2-r1+1).col(0)) , (m1.row(r1).segment(c1,c2-c1+1)).transpose() ); - - // expressions without direct access - VERIFY_IS_APPROX( ((m1+m2).block(r1,c1,rows-r1,cols-c1).block(r2-r1,c2-c1,rows-r2,cols-c2)) , ((m1+m2).block(r2,c2,rows-r2,cols-c2)) ); - VERIFY_IS_APPROX( ((m1+m2).block(r1,c1,r2-r1+1,c2-c1+1).row(0)) , ((m1+m2).row(r1).segment(c1,c2-c1+1)) ); - VERIFY_IS_APPROX( ((m1+m2).block(r1,c1,r2-r1+1,c2-c1+1).col(0)) , ((m1+m2).col(c1).segment(r1,r2-r1+1)) ); - VERIFY_IS_APPROX( ((m1+m2).block(r1,c1,r2-r1+1,c2-c1+1).transpose().col(0)) , ((m1+m2).row(r1).segment(c1,c2-c1+1)).transpose() ); - VERIFY_IS_APPROX( ((m1+m2).transpose().block(c1,r1,c2-c1+1,r2-r1+1).col(0)) , ((m1+m2).row(r1).segment(c1,c2-c1+1)).transpose() ); - - VERIFY_IS_APPROX( (m1*1).topRows(r1), m1.topRows(r1) ); - VERIFY_IS_APPROX( (m1*1).leftCols(c1), m1.leftCols(c1) ); - VERIFY_IS_APPROX( (m1*1).transpose().topRows(c1), m1.transpose().topRows(c1) ); - VERIFY_IS_APPROX( (m1*1).transpose().leftCols(r1), m1.transpose().leftCols(r1) ); - VERIFY_IS_APPROX( (m1*1).transpose().middleRows(c1,c2-c1+1), m1.transpose().middleRows(c1,c2-c1+1) ); - VERIFY_IS_APPROX( (m1*1).transpose().middleCols(r1,r2-r1+1), m1.transpose().middleCols(r1,r2-r1+1) ); - - // evaluation into plain matrices from expressions with direct access (stress MapBase) - DynamicMatrixType dm; - DynamicVectorType dv; - dm.setZero(); - dm = m1.block(r1,c1,rows-r1,cols-c1).block(r2-r1,c2-c1,rows-r2,cols-c2); - VERIFY_IS_EQUAL(dm, (m1.block(r2,c2,rows-r2,cols-c2))); - dm.setZero(); - dv.setZero(); - dm = m1.block(r1,c1,r2-r1+1,c2-c1+1).row(0).transpose(); - dv = m1.row(r1).segment(c1,c2-c1+1); - VERIFY_IS_EQUAL(dv, dm); - dm.setZero(); - dv.setZero(); - dm = m1.col(c1).segment(r1,r2-r1+1); - dv = m1.block(r1,c1,r2-r1+1,c2-c1+1).col(0); - VERIFY_IS_EQUAL(dv, dm); - dm.setZero(); - dv.setZero(); - dm = m1.block(r1,c1,r2-r1+1,c2-c1+1).transpose().col(0); - dv = m1.row(r1).segment(c1,c2-c1+1); - VERIFY_IS_EQUAL(dv, dm); - dm.setZero(); - dv.setZero(); - dm = m1.row(r1).segment(c1,c2-c1+1).transpose(); - dv = m1.transpose().block(c1,r1,c2-c1+1,r2-r1+1).col(0); - VERIFY_IS_EQUAL(dv, dm); - - VERIFY_IS_EQUAL( (m1.template block<Dynamic,1>(1,0,0,1)), m1.block(1,0,0,1)); - VERIFY_IS_EQUAL( (m1.template block<1,Dynamic>(0,1,1,0)), m1.block(0,1,1,0)); - VERIFY_IS_EQUAL( ((m1*1).template block<Dynamic,1>(1,0,0,1)), m1.block(1,0,0,1)); - VERIFY_IS_EQUAL( ((m1*1).template block<1,Dynamic>(0,1,1,0)), m1.block(0,1,1,0)); - - if (rows>=2 && cols>=2) - { - VERIFY_RAISES_ASSERT( m1 += m1.col(0) ); - VERIFY_RAISES_ASSERT( m1 -= m1.col(0) ); - VERIFY_RAISES_ASSERT( m1.array() *= m1.col(0).array() ); - VERIFY_RAISES_ASSERT( m1.array() /= m1.col(0).array() ); - } -} - - -template<typename MatrixType> -void compare_using_data_and_stride(const MatrixType& m) -{ - Index rows = m.rows(); - Index cols = m.cols(); - Index size = m.size(); - Index innerStride = m.innerStride(); - Index outerStride = m.outerStride(); - Index rowStride = m.rowStride(); - Index colStride = m.colStride(); - const typename MatrixType::Scalar* data = m.data(); - - for(int j=0;j<cols;++j) - for(int i=0;i<rows;++i) - VERIFY(m.coeff(i,j) == data[i*rowStride + j*colStride]); - - if(!MatrixType::IsVectorAtCompileTime) - { - for(int j=0;j<cols;++j) - for(int i=0;i<rows;++i) - VERIFY(m.coeff(i,j) == data[(MatrixType::Flags&RowMajorBit) - ? i*outerStride + j*innerStride - : j*outerStride + i*innerStride]); - } - - if(MatrixType::IsVectorAtCompileTime) - { - VERIFY(innerStride == int((&m.coeff(1))-(&m.coeff(0)))); - for (int i=0;i<size;++i) - VERIFY(m.coeff(i) == data[i*innerStride]); - } -} - -template<typename MatrixType> -void data_and_stride(const MatrixType& m) -{ - Index rows = m.rows(); - Index cols = m.cols(); - - Index r1 = internal::random<Index>(0,rows-1); - Index r2 = internal::random<Index>(r1,rows-1); - Index c1 = internal::random<Index>(0,cols-1); - Index c2 = internal::random<Index>(c1,cols-1); - - MatrixType m1 = MatrixType::Random(rows, cols); - compare_using_data_and_stride(m1.block(r1, c1, r2-r1+1, c2-c1+1)); - compare_using_data_and_stride(m1.transpose().block(c1, r1, c2-c1+1, r2-r1+1)); - compare_using_data_and_stride(m1.row(r1)); - compare_using_data_and_stride(m1.col(c1)); - compare_using_data_and_stride(m1.row(r1).transpose()); - compare_using_data_and_stride(m1.col(c1).transpose()); -} - -void test_block() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( block(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( block(Matrix4d()) ); - CALL_SUBTEST_3( block(MatrixXcf(3, 3)) ); - CALL_SUBTEST_4( block(MatrixXi(8, 12)) ); - CALL_SUBTEST_5( block(MatrixXcd(20, 20)) ); - CALL_SUBTEST_6( block(MatrixXf(20, 20)) ); - - CALL_SUBTEST_8( block(Matrix<float,Dynamic,4>(3, 4)) ); - -#ifndef EIGEN_DEFAULT_TO_ROW_MAJOR - CALL_SUBTEST_6( data_and_stride(MatrixXf(internal::random(5,50), internal::random(5,50))) ); - CALL_SUBTEST_7( data_and_stride(Matrix<int,Dynamic,Dynamic,RowMajor>(internal::random(5,50), internal::random(5,50))) ); -#endif - } -} diff --git a/eigen/test/boostmultiprec.cpp b/eigen/test/boostmultiprec.cpp deleted file mode 100644 index e06e9bd..0000000 --- a/eigen/test/boostmultiprec.cpp +++ /dev/null @@ -1,201 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include <sstream> - -#ifdef EIGEN_TEST_MAX_SIZE -#undef EIGEN_TEST_MAX_SIZE -#endif - -#define EIGEN_TEST_MAX_SIZE 50 - -#ifdef EIGEN_TEST_PART_1 -#include "cholesky.cpp" -#endif - -#ifdef EIGEN_TEST_PART_2 -#include "lu.cpp" -#endif - -#ifdef EIGEN_TEST_PART_3 -#include "qr.cpp" -#endif - -#ifdef EIGEN_TEST_PART_4 -#include "qr_colpivoting.cpp" -#endif - -#ifdef EIGEN_TEST_PART_5 -#include "qr_fullpivoting.cpp" -#endif - -#ifdef EIGEN_TEST_PART_6 -#include "eigensolver_selfadjoint.cpp" -#endif - -#ifdef EIGEN_TEST_PART_7 -#include "eigensolver_generic.cpp" -#endif - -#ifdef EIGEN_TEST_PART_8 -#include "eigensolver_generalized_real.cpp" -#endif - -#ifdef EIGEN_TEST_PART_9 -#include "jacobisvd.cpp" -#endif - -#ifdef EIGEN_TEST_PART_10 -#include "bdcsvd.cpp" -#endif - -#include <Eigen/Dense> - -#undef min -#undef max -#undef isnan -#undef isinf -#undef isfinite - -#include <boost/multiprecision/cpp_dec_float.hpp> -#include <boost/multiprecision/number.hpp> -#include <boost/math/special_functions.hpp> -#include <boost/math/complex.hpp> - -namespace mp = boost::multiprecision; -typedef mp::number<mp::cpp_dec_float<100>, mp::et_on> Real; - -namespace Eigen { - template<> struct NumTraits<Real> : GenericNumTraits<Real> { - static inline Real dummy_precision() { return 1e-50; } - }; - - template<typename T1,typename T2,typename T3,typename T4,typename T5> - struct NumTraits<boost::multiprecision::detail::expression<T1,T2,T3,T4,T5> > : NumTraits<Real> {}; - - template<> - Real test_precision<Real>() { return 1e-50; } - - // needed in C++93 mode where number does not support explicit cast. - namespace internal { - template<typename NewType> - struct cast_impl<Real,NewType> { - static inline NewType run(const Real& x) { - return x.template convert_to<NewType>(); - } - }; - - template<> - struct cast_impl<Real,std::complex<Real> > { - static inline std::complex<Real> run(const Real& x) { - return std::complex<Real>(x); - } - }; - } -} - -namespace boost { -namespace multiprecision { - // to make ADL works as expected: - using boost::math::isfinite; - using boost::math::isnan; - using boost::math::isinf; - using boost::math::copysign; - using boost::math::hypot; - - // The following is needed for std::complex<Real>: - Real fabs(const Real& a) { return abs EIGEN_NOT_A_MACRO (a); } - Real fmax(const Real& a, const Real& b) { using std::max; return max(a,b); } - - // some specialization for the unit tests: - inline bool test_isMuchSmallerThan(const Real& a, const Real& b) { - return internal::isMuchSmallerThan(a, b, test_precision<Real>()); - } - - inline bool test_isApprox(const Real& a, const Real& b) { - return internal::isApprox(a, b, test_precision<Real>()); - } - - inline bool test_isApproxOrLessThan(const Real& a, const Real& b) { - return internal::isApproxOrLessThan(a, b, test_precision<Real>()); - } - - Real get_test_precision(const Real&) { - return test_precision<Real>(); - } - - Real test_relative_error(const Real &a, const Real &b) { - using Eigen::numext::abs2; - return sqrt(abs2<Real>(a-b)/Eigen::numext::mini<Real>(abs2(a),abs2(b))); - } -} -} - -namespace Eigen { - -} - -void test_boostmultiprec() -{ - typedef Matrix<Real,Dynamic,Dynamic> Mat; - typedef Matrix<std::complex<Real>,Dynamic,Dynamic> MatC; - - std::cout << "NumTraits<Real>::epsilon() = " << NumTraits<Real>::epsilon() << std::endl; - std::cout << "NumTraits<Real>::dummy_precision() = " << NumTraits<Real>::dummy_precision() << std::endl; - std::cout << "NumTraits<Real>::lowest() = " << NumTraits<Real>::lowest() << std::endl; - std::cout << "NumTraits<Real>::highest() = " << NumTraits<Real>::highest() << std::endl; - std::cout << "NumTraits<Real>::digits10() = " << NumTraits<Real>::digits10() << std::endl; - - // chekc stream output - { - Mat A(10,10); - A.setRandom(); - std::stringstream ss; - ss << A; - } - { - MatC A(10,10); - A.setRandom(); - std::stringstream ss; - ss << A; - } - - for(int i = 0; i < g_repeat; i++) { - int s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE); - - CALL_SUBTEST_1( cholesky(Mat(s,s)) ); - - CALL_SUBTEST_2( lu_non_invertible<Mat>() ); - CALL_SUBTEST_2( lu_invertible<Mat>() ); - CALL_SUBTEST_2( lu_non_invertible<MatC>() ); - CALL_SUBTEST_2( lu_invertible<MatC>() ); - - CALL_SUBTEST_3( qr(Mat(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_3( qr_invertible<Mat>() ); - - CALL_SUBTEST_4( qr<Mat>() ); - CALL_SUBTEST_4( cod<Mat>() ); - CALL_SUBTEST_4( qr_invertible<Mat>() ); - - CALL_SUBTEST_5( qr<Mat>() ); - CALL_SUBTEST_5( qr_invertible<Mat>() ); - - CALL_SUBTEST_6( selfadjointeigensolver(Mat(s,s)) ); - - CALL_SUBTEST_7( eigensolver(Mat(s,s)) ); - - CALL_SUBTEST_8( generalized_eigensolver_real(Mat(s,s)) ); - - TEST_SET_BUT_UNUSED_VARIABLE(s) - } - - CALL_SUBTEST_9(( jacobisvd(Mat(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2))) )); - CALL_SUBTEST_10(( bdcsvd(Mat(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2))) )); -} - diff --git a/eigen/test/bug1213.cpp b/eigen/test/bug1213.cpp deleted file mode 100644 index 581760c..0000000 --- a/eigen/test/bug1213.cpp +++ /dev/null @@ -1,13 +0,0 @@ - -// This anonymous enum is essential to trigger the linking issue -enum { - Foo -}; - -#include "bug1213.h" - -bool bug1213_1(const Eigen::Vector3f& x) -{ - return bug1213_2(x); -} - diff --git a/eigen/test/bug1213.h b/eigen/test/bug1213.h deleted file mode 100644 index 040e5a4..0000000 --- a/eigen/test/bug1213.h +++ /dev/null @@ -1,8 +0,0 @@ - -#include <Eigen/Core> - -template<typename T, int dim> -bool bug1213_2(const Eigen::Matrix<T,dim,1>& x); - -bool bug1213_1(const Eigen::Vector3f& x); - diff --git a/eigen/test/bug1213_main.cpp b/eigen/test/bug1213_main.cpp deleted file mode 100644 index 4802c00..0000000 --- a/eigen/test/bug1213_main.cpp +++ /dev/null @@ -1,18 +0,0 @@ - -// This is a regression unit regarding a weird linking issue with gcc. - -#include "bug1213.h" - -int main() -{ - return 0; -} - - -template<typename T, int dim> -bool bug1213_2(const Eigen::Matrix<T,dim,1>& ) -{ - return true; -} - -template bool bug1213_2<float,3>(const Eigen::Vector3f&); diff --git a/eigen/test/cholesky.cpp b/eigen/test/cholesky.cpp deleted file mode 100644 index 5cf842d..0000000 --- a/eigen/test/cholesky.cpp +++ /dev/null @@ -1,529 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_NO_ASSERTION_CHECKING -#define EIGEN_NO_ASSERTION_CHECKING -#endif - -#define TEST_ENABLE_TEMPORARY_TRACKING - -#include "main.h" -#include <Eigen/Cholesky> -#include <Eigen/QR> - -template<typename MatrixType, int UpLo> -typename MatrixType::RealScalar matrix_l1_norm(const MatrixType& m) { - if(m.cols()==0) return typename MatrixType::RealScalar(0); - MatrixType symm = m.template selfadjointView<UpLo>(); - return symm.cwiseAbs().colwise().sum().maxCoeff(); -} - -template<typename MatrixType,template <typename,int> class CholType> void test_chol_update(const MatrixType& symm) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - - MatrixType symmLo = symm.template triangularView<Lower>(); - MatrixType symmUp = symm.template triangularView<Upper>(); - MatrixType symmCpy = symm; - - CholType<MatrixType,Lower> chollo(symmLo); - CholType<MatrixType,Upper> cholup(symmUp); - - for (int k=0; k<10; ++k) - { - VectorType vec = VectorType::Random(symm.rows()); - RealScalar sigma = internal::random<RealScalar>(); - symmCpy += sigma * vec * vec.adjoint(); - - // we are doing some downdates, so it might be the case that the matrix is not SPD anymore - CholType<MatrixType,Lower> chol(symmCpy); - if(chol.info()!=Success) - break; - - chollo.rankUpdate(vec, sigma); - VERIFY_IS_APPROX(symmCpy, chollo.reconstructedMatrix()); - - cholup.rankUpdate(vec, sigma); - VERIFY_IS_APPROX(symmCpy, cholup.reconstructedMatrix()); - } -} - -template<typename MatrixType> void cholesky(const MatrixType& m) -{ - /* this test covers the following files: - LLT.h LDLT.h - */ - Index rows = m.rows(); - Index cols = m.cols(); - - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - - MatrixType a0 = MatrixType::Random(rows,cols); - VectorType vecB = VectorType::Random(rows), vecX(rows); - MatrixType matB = MatrixType::Random(rows,cols), matX(rows,cols); - SquareMatrixType symm = a0 * a0.adjoint(); - // let's make sure the matrix is not singular or near singular - for (int k=0; k<3; ++k) - { - MatrixType a1 = MatrixType::Random(rows,cols); - symm += a1 * a1.adjoint(); - } - - { - SquareMatrixType symmUp = symm.template triangularView<Upper>(); - SquareMatrixType symmLo = symm.template triangularView<Lower>(); - - LLT<SquareMatrixType,Lower> chollo(symmLo); - VERIFY_IS_APPROX(symm, chollo.reconstructedMatrix()); - vecX = chollo.solve(vecB); - VERIFY_IS_APPROX(symm * vecX, vecB); - matX = chollo.solve(matB); - VERIFY_IS_APPROX(symm * matX, matB); - - const MatrixType symmLo_inverse = chollo.solve(MatrixType::Identity(rows,cols)); - RealScalar rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Lower>(symmLo)) / - matrix_l1_norm<MatrixType, Lower>(symmLo_inverse); - RealScalar rcond_est = chollo.rcond(); - // Verify that the estimated condition number is within a factor of 10 of the - // truth. - VERIFY(rcond_est >= rcond / 10 && rcond_est <= rcond * 10); - - // test the upper mode - LLT<SquareMatrixType,Upper> cholup(symmUp); - VERIFY_IS_APPROX(symm, cholup.reconstructedMatrix()); - vecX = cholup.solve(vecB); - VERIFY_IS_APPROX(symm * vecX, vecB); - matX = cholup.solve(matB); - VERIFY_IS_APPROX(symm * matX, matB); - - // Verify that the estimated condition number is within a factor of 10 of the - // truth. - const MatrixType symmUp_inverse = cholup.solve(MatrixType::Identity(rows,cols)); - rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Upper>(symmUp)) / - matrix_l1_norm<MatrixType, Upper>(symmUp_inverse); - rcond_est = cholup.rcond(); - VERIFY(rcond_est >= rcond / 10 && rcond_est <= rcond * 10); - - - MatrixType neg = -symmLo; - chollo.compute(neg); - VERIFY(neg.size()==0 || chollo.info()==NumericalIssue); - - VERIFY_IS_APPROX(MatrixType(chollo.matrixL().transpose().conjugate()), MatrixType(chollo.matrixU())); - VERIFY_IS_APPROX(MatrixType(chollo.matrixU().transpose().conjugate()), MatrixType(chollo.matrixL())); - VERIFY_IS_APPROX(MatrixType(cholup.matrixL().transpose().conjugate()), MatrixType(cholup.matrixU())); - VERIFY_IS_APPROX(MatrixType(cholup.matrixU().transpose().conjugate()), MatrixType(cholup.matrixL())); - - // test some special use cases of SelfCwiseBinaryOp: - MatrixType m1 = MatrixType::Random(rows,cols), m2(rows,cols); - m2 = m1; - m2 += symmLo.template selfadjointView<Lower>().llt().solve(matB); - VERIFY_IS_APPROX(m2, m1 + symmLo.template selfadjointView<Lower>().llt().solve(matB)); - m2 = m1; - m2 -= symmLo.template selfadjointView<Lower>().llt().solve(matB); - VERIFY_IS_APPROX(m2, m1 - symmLo.template selfadjointView<Lower>().llt().solve(matB)); - m2 = m1; - m2.noalias() += symmLo.template selfadjointView<Lower>().llt().solve(matB); - VERIFY_IS_APPROX(m2, m1 + symmLo.template selfadjointView<Lower>().llt().solve(matB)); - m2 = m1; - m2.noalias() -= symmLo.template selfadjointView<Lower>().llt().solve(matB); - VERIFY_IS_APPROX(m2, m1 - symmLo.template selfadjointView<Lower>().llt().solve(matB)); - } - - // LDLT - { - int sign = internal::random<int>()%2 ? 1 : -1; - - if(sign == -1) - { - symm = -symm; // test a negative matrix - } - - SquareMatrixType symmUp = symm.template triangularView<Upper>(); - SquareMatrixType symmLo = symm.template triangularView<Lower>(); - - LDLT<SquareMatrixType,Lower> ldltlo(symmLo); - VERIFY(ldltlo.info()==Success); - VERIFY_IS_APPROX(symm, ldltlo.reconstructedMatrix()); - vecX = ldltlo.solve(vecB); - VERIFY_IS_APPROX(symm * vecX, vecB); - matX = ldltlo.solve(matB); - VERIFY_IS_APPROX(symm * matX, matB); - - const MatrixType symmLo_inverse = ldltlo.solve(MatrixType::Identity(rows,cols)); - RealScalar rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Lower>(symmLo)) / - matrix_l1_norm<MatrixType, Lower>(symmLo_inverse); - RealScalar rcond_est = ldltlo.rcond(); - // Verify that the estimated condition number is within a factor of 10 of the - // truth. - VERIFY(rcond_est >= rcond / 10 && rcond_est <= rcond * 10); - - - LDLT<SquareMatrixType,Upper> ldltup(symmUp); - VERIFY(ldltup.info()==Success); - VERIFY_IS_APPROX(symm, ldltup.reconstructedMatrix()); - vecX = ldltup.solve(vecB); - VERIFY_IS_APPROX(symm * vecX, vecB); - matX = ldltup.solve(matB); - VERIFY_IS_APPROX(symm * matX, matB); - - // Verify that the estimated condition number is within a factor of 10 of the - // truth. - const MatrixType symmUp_inverse = ldltup.solve(MatrixType::Identity(rows,cols)); - rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Upper>(symmUp)) / - matrix_l1_norm<MatrixType, Upper>(symmUp_inverse); - rcond_est = ldltup.rcond(); - VERIFY(rcond_est >= rcond / 10 && rcond_est <= rcond * 10); - - VERIFY_IS_APPROX(MatrixType(ldltlo.matrixL().transpose().conjugate()), MatrixType(ldltlo.matrixU())); - VERIFY_IS_APPROX(MatrixType(ldltlo.matrixU().transpose().conjugate()), MatrixType(ldltlo.matrixL())); - VERIFY_IS_APPROX(MatrixType(ldltup.matrixL().transpose().conjugate()), MatrixType(ldltup.matrixU())); - VERIFY_IS_APPROX(MatrixType(ldltup.matrixU().transpose().conjugate()), MatrixType(ldltup.matrixL())); - - if(MatrixType::RowsAtCompileTime==Dynamic) - { - // note : each inplace permutation requires a small temporary vector (mask) - - // check inplace solve - matX = matB; - VERIFY_EVALUATION_COUNT(matX = ldltlo.solve(matX), 0); - VERIFY_IS_APPROX(matX, ldltlo.solve(matB).eval()); - - - matX = matB; - VERIFY_EVALUATION_COUNT(matX = ldltup.solve(matX), 0); - VERIFY_IS_APPROX(matX, ldltup.solve(matB).eval()); - } - - // restore - if(sign == -1) - symm = -symm; - - // check matrices coming from linear constraints with Lagrange multipliers - if(rows>=3) - { - SquareMatrixType A = symm; - Index c = internal::random<Index>(0,rows-2); - A.bottomRightCorner(c,c).setZero(); - // Make sure a solution exists: - vecX.setRandom(); - vecB = A * vecX; - vecX.setZero(); - ldltlo.compute(A); - VERIFY_IS_APPROX(A, ldltlo.reconstructedMatrix()); - vecX = ldltlo.solve(vecB); - VERIFY_IS_APPROX(A * vecX, vecB); - } - - // check non-full rank matrices - if(rows>=3) - { - Index r = internal::random<Index>(1,rows-1); - Matrix<Scalar,Dynamic,Dynamic> a = Matrix<Scalar,Dynamic,Dynamic>::Random(rows,r); - SquareMatrixType A = a * a.adjoint(); - // Make sure a solution exists: - vecX.setRandom(); - vecB = A * vecX; - vecX.setZero(); - ldltlo.compute(A); - VERIFY_IS_APPROX(A, ldltlo.reconstructedMatrix()); - vecX = ldltlo.solve(vecB); - VERIFY_IS_APPROX(A * vecX, vecB); - } - - // check matrices with a wide spectrum - if(rows>=3) - { - using std::pow; - using std::sqrt; - RealScalar s = (std::min)(16,std::numeric_limits<RealScalar>::max_exponent10/8); - Matrix<Scalar,Dynamic,Dynamic> a = Matrix<Scalar,Dynamic,Dynamic>::Random(rows,rows); - Matrix<RealScalar,Dynamic,1> d = Matrix<RealScalar,Dynamic,1>::Random(rows); - for(Index k=0; k<rows; ++k) - d(k) = d(k)*pow(RealScalar(10),internal::random<RealScalar>(-s,s)); - SquareMatrixType A = a * d.asDiagonal() * a.adjoint(); - // Make sure a solution exists: - vecX.setRandom(); - vecB = A * vecX; - vecX.setZero(); - ldltlo.compute(A); - VERIFY_IS_APPROX(A, ldltlo.reconstructedMatrix()); - vecX = ldltlo.solve(vecB); - - if(ldltlo.vectorD().real().cwiseAbs().minCoeff()>RealScalar(0)) - { - VERIFY_IS_APPROX(A * vecX,vecB); - } - else - { - RealScalar large_tol = sqrt(test_precision<RealScalar>()); - VERIFY((A * vecX).isApprox(vecB, large_tol)); - - ++g_test_level; - VERIFY_IS_APPROX(A * vecX,vecB); - --g_test_level; - } - } - } - - // update/downdate - CALL_SUBTEST(( test_chol_update<SquareMatrixType,LLT>(symm) )); - CALL_SUBTEST(( test_chol_update<SquareMatrixType,LDLT>(symm) )); -} - -template<typename MatrixType> void cholesky_cplx(const MatrixType& m) -{ - // classic test - cholesky(m); - - // test mixing real/scalar types - - Index rows = m.rows(); - Index cols = m.cols(); - - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> RealMatrixType; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - - RealMatrixType a0 = RealMatrixType::Random(rows,cols); - VectorType vecB = VectorType::Random(rows), vecX(rows); - MatrixType matB = MatrixType::Random(rows,cols), matX(rows,cols); - RealMatrixType symm = a0 * a0.adjoint(); - // let's make sure the matrix is not singular or near singular - for (int k=0; k<3; ++k) - { - RealMatrixType a1 = RealMatrixType::Random(rows,cols); - symm += a1 * a1.adjoint(); - } - - { - RealMatrixType symmLo = symm.template triangularView<Lower>(); - - LLT<RealMatrixType,Lower> chollo(symmLo); - VERIFY_IS_APPROX(symm, chollo.reconstructedMatrix()); - vecX = chollo.solve(vecB); - VERIFY_IS_APPROX(symm * vecX, vecB); -// matX = chollo.solve(matB); -// VERIFY_IS_APPROX(symm * matX, matB); - } - - // LDLT - { - int sign = internal::random<int>()%2 ? 1 : -1; - - if(sign == -1) - { - symm = -symm; // test a negative matrix - } - - RealMatrixType symmLo = symm.template triangularView<Lower>(); - - LDLT<RealMatrixType,Lower> ldltlo(symmLo); - VERIFY(ldltlo.info()==Success); - VERIFY_IS_APPROX(symm, ldltlo.reconstructedMatrix()); - vecX = ldltlo.solve(vecB); - VERIFY_IS_APPROX(symm * vecX, vecB); -// matX = ldltlo.solve(matB); -// VERIFY_IS_APPROX(symm * matX, matB); - } -} - -// regression test for bug 241 -template<typename MatrixType> void cholesky_bug241(const MatrixType& m) -{ - eigen_assert(m.rows() == 2 && m.cols() == 2); - - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - - MatrixType matA; - matA << 1, 1, 1, 1; - VectorType vecB; - vecB << 1, 1; - VectorType vecX = matA.ldlt().solve(vecB); - VERIFY_IS_APPROX(matA * vecX, vecB); -} - -// LDLT is not guaranteed to work for indefinite matrices, but happens to work fine if matrix is diagonal. -// This test checks that LDLT reports correctly that matrix is indefinite. -// See http://forum.kde.org/viewtopic.php?f=74&t=106942 and bug 736 -template<typename MatrixType> void cholesky_definiteness(const MatrixType& m) -{ - eigen_assert(m.rows() == 2 && m.cols() == 2); - MatrixType mat; - LDLT<MatrixType> ldlt(2); - - { - mat << 1, 0, 0, -1; - ldlt.compute(mat); - VERIFY(ldlt.info()==Success); - VERIFY(!ldlt.isNegative()); - VERIFY(!ldlt.isPositive()); - VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix()); - } - { - mat << 1, 2, 2, 1; - ldlt.compute(mat); - VERIFY(ldlt.info()==Success); - VERIFY(!ldlt.isNegative()); - VERIFY(!ldlt.isPositive()); - VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix()); - } - { - mat << 0, 0, 0, 0; - ldlt.compute(mat); - VERIFY(ldlt.info()==Success); - VERIFY(ldlt.isNegative()); - VERIFY(ldlt.isPositive()); - VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix()); - } - { - mat << 0, 0, 0, 1; - ldlt.compute(mat); - VERIFY(ldlt.info()==Success); - VERIFY(!ldlt.isNegative()); - VERIFY(ldlt.isPositive()); - VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix()); - } - { - mat << -1, 0, 0, 0; - ldlt.compute(mat); - VERIFY(ldlt.info()==Success); - VERIFY(ldlt.isNegative()); - VERIFY(!ldlt.isPositive()); - VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix()); - } -} - -template<typename> -void cholesky_faillure_cases() -{ - MatrixXd mat; - LDLT<MatrixXd> ldlt; - - { - mat.resize(2,2); - mat << 0, 1, 1, 0; - ldlt.compute(mat); - VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix()); - VERIFY(ldlt.info()==NumericalIssue); - } -#if (!EIGEN_ARCH_i386) || defined(EIGEN_VECTORIZE_SSE2) - { - mat.resize(3,3); - mat << -1, -3, 3, - -3, -8.9999999999999999999, 1, - 3, 1, 0; - ldlt.compute(mat); - VERIFY(ldlt.info()==NumericalIssue); - VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix()); - } -#endif - { - mat.resize(3,3); - mat << 1, 2, 3, - 2, 4, 1, - 3, 1, 0; - ldlt.compute(mat); - VERIFY(ldlt.info()==NumericalIssue); - VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix()); - } - - { - mat.resize(8,8); - mat << 0.1, 0, -0.1, 0, 0, 0, 1, 0, - 0, 4.24667, 0, 2.00333, 0, 0, 0, 0, - -0.1, 0, 0.2, 0, -0.1, 0, 0, 0, - 0, 2.00333, 0, 8.49333, 0, 2.00333, 0, 0, - 0, 0, -0.1, 0, 0.1, 0, 0, 1, - 0, 0, 0, 2.00333, 0, 4.24667, 0, 0, - 1, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 1, 0, 0, 0; - ldlt.compute(mat); - VERIFY(ldlt.info()==NumericalIssue); - VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix()); - } - - // bug 1479 - { - mat.resize(4,4); - mat << 1, 2, 0, 1, - 2, 4, 0, 2, - 0, 0, 0, 1, - 1, 2, 1, 1; - ldlt.compute(mat); - VERIFY(ldlt.info()==NumericalIssue); - VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix()); - } -} - -template<typename MatrixType> void cholesky_verify_assert() -{ - MatrixType tmp; - - LLT<MatrixType> llt; - VERIFY_RAISES_ASSERT(llt.matrixL()) - VERIFY_RAISES_ASSERT(llt.matrixU()) - VERIFY_RAISES_ASSERT(llt.solve(tmp)) - VERIFY_RAISES_ASSERT(llt.solveInPlace(&tmp)) - - LDLT<MatrixType> ldlt; - VERIFY_RAISES_ASSERT(ldlt.matrixL()) - VERIFY_RAISES_ASSERT(ldlt.permutationP()) - VERIFY_RAISES_ASSERT(ldlt.vectorD()) - VERIFY_RAISES_ASSERT(ldlt.isPositive()) - VERIFY_RAISES_ASSERT(ldlt.isNegative()) - VERIFY_RAISES_ASSERT(ldlt.solve(tmp)) - VERIFY_RAISES_ASSERT(ldlt.solveInPlace(&tmp)) -} - -void test_cholesky() -{ - int s = 0; - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( cholesky(Matrix<double,1,1>()) ); - CALL_SUBTEST_3( cholesky(Matrix2d()) ); - CALL_SUBTEST_3( cholesky_bug241(Matrix2d()) ); - CALL_SUBTEST_3( cholesky_definiteness(Matrix2d()) ); - CALL_SUBTEST_4( cholesky(Matrix3f()) ); - CALL_SUBTEST_5( cholesky(Matrix4d()) ); - - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE); - CALL_SUBTEST_2( cholesky(MatrixXd(s,s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2); - CALL_SUBTEST_6( cholesky_cplx(MatrixXcd(s,s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - } - // empty matrix, regression test for Bug 785: - CALL_SUBTEST_2( cholesky(MatrixXd(0,0)) ); - - // This does not work yet: - // CALL_SUBTEST_2( cholesky(Matrix<double,0,0>()) ); - - CALL_SUBTEST_4( cholesky_verify_assert<Matrix3f>() ); - CALL_SUBTEST_7( cholesky_verify_assert<Matrix3d>() ); - CALL_SUBTEST_8( cholesky_verify_assert<MatrixXf>() ); - CALL_SUBTEST_2( cholesky_verify_assert<MatrixXd>() ); - - // Test problem size constructors - CALL_SUBTEST_9( LLT<MatrixXf>(10) ); - CALL_SUBTEST_9( LDLT<MatrixXf>(10) ); - - CALL_SUBTEST_2( cholesky_faillure_cases<void>() ); - - TEST_SET_BUT_UNUSED_VARIABLE(nb_temporaries) -} diff --git a/eigen/test/cholmod_support.cpp b/eigen/test/cholmod_support.cpp deleted file mode 100644 index a7eda28..0000000 --- a/eigen/test/cholmod_support.cpp +++ /dev/null @@ -1,57 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_NO_DEBUG_SMALL_PRODUCT_BLOCKS -#include "sparse_solver.h" - -#include <Eigen/CholmodSupport> - -template<typename T> void test_cholmod_T() -{ - CholmodDecomposition<SparseMatrix<T>, Lower> g_chol_colmajor_lower; g_chol_colmajor_lower.setMode(CholmodSupernodalLLt); - CholmodDecomposition<SparseMatrix<T>, Upper> g_chol_colmajor_upper; g_chol_colmajor_upper.setMode(CholmodSupernodalLLt); - CholmodDecomposition<SparseMatrix<T>, Lower> g_llt_colmajor_lower; g_llt_colmajor_lower.setMode(CholmodSimplicialLLt); - CholmodDecomposition<SparseMatrix<T>, Upper> g_llt_colmajor_upper; g_llt_colmajor_upper.setMode(CholmodSimplicialLLt); - CholmodDecomposition<SparseMatrix<T>, Lower> g_ldlt_colmajor_lower; g_ldlt_colmajor_lower.setMode(CholmodLDLt); - CholmodDecomposition<SparseMatrix<T>, Upper> g_ldlt_colmajor_upper; g_ldlt_colmajor_upper.setMode(CholmodLDLt); - - CholmodSupernodalLLT<SparseMatrix<T>, Lower> chol_colmajor_lower; - CholmodSupernodalLLT<SparseMatrix<T>, Upper> chol_colmajor_upper; - CholmodSimplicialLLT<SparseMatrix<T>, Lower> llt_colmajor_lower; - CholmodSimplicialLLT<SparseMatrix<T>, Upper> llt_colmajor_upper; - CholmodSimplicialLDLT<SparseMatrix<T>, Lower> ldlt_colmajor_lower; - CholmodSimplicialLDLT<SparseMatrix<T>, Upper> ldlt_colmajor_upper; - - check_sparse_spd_solving(g_chol_colmajor_lower); - check_sparse_spd_solving(g_chol_colmajor_upper); - check_sparse_spd_solving(g_llt_colmajor_lower); - check_sparse_spd_solving(g_llt_colmajor_upper); - check_sparse_spd_solving(g_ldlt_colmajor_lower); - check_sparse_spd_solving(g_ldlt_colmajor_upper); - - check_sparse_spd_solving(chol_colmajor_lower); - check_sparse_spd_solving(chol_colmajor_upper); - check_sparse_spd_solving(llt_colmajor_lower); - check_sparse_spd_solving(llt_colmajor_upper); - check_sparse_spd_solving(ldlt_colmajor_lower); - check_sparse_spd_solving(ldlt_colmajor_upper); - - check_sparse_spd_determinant(chol_colmajor_lower); - check_sparse_spd_determinant(chol_colmajor_upper); - check_sparse_spd_determinant(llt_colmajor_lower); - check_sparse_spd_determinant(llt_colmajor_upper); - check_sparse_spd_determinant(ldlt_colmajor_lower); - check_sparse_spd_determinant(ldlt_colmajor_upper); -} - -void test_cholmod_support() -{ - CALL_SUBTEST_1(test_cholmod_T<double>()); - CALL_SUBTEST_2(test_cholmod_T<std::complex<double> >()); -} diff --git a/eigen/test/commainitializer.cpp b/eigen/test/commainitializer.cpp deleted file mode 100644 index 9844adb..0000000 --- a/eigen/test/commainitializer.cpp +++ /dev/null @@ -1,106 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - - -template<int M1, int M2, int N1, int N2> -void test_blocks() -{ - Matrix<int, M1+M2, N1+N2> m_fixed; - MatrixXi m_dynamic(M1+M2, N1+N2); - - Matrix<int, M1, N1> mat11; mat11.setRandom(); - Matrix<int, M1, N2> mat12; mat12.setRandom(); - Matrix<int, M2, N1> mat21; mat21.setRandom(); - Matrix<int, M2, N2> mat22; mat22.setRandom(); - - MatrixXi matx11 = mat11, matx12 = mat12, matx21 = mat21, matx22 = mat22; - - { - VERIFY_IS_EQUAL((m_fixed << mat11, mat12, mat21, matx22).finished(), (m_dynamic << mat11, matx12, mat21, matx22).finished()); - VERIFY_IS_EQUAL((m_fixed.template topLeftCorner<M1,N1>()), mat11); - VERIFY_IS_EQUAL((m_fixed.template topRightCorner<M1,N2>()), mat12); - VERIFY_IS_EQUAL((m_fixed.template bottomLeftCorner<M2,N1>()), mat21); - VERIFY_IS_EQUAL((m_fixed.template bottomRightCorner<M2,N2>()), mat22); - VERIFY_IS_EQUAL((m_fixed << mat12, mat11, matx21, mat22).finished(), (m_dynamic << mat12, matx11, matx21, mat22).finished()); - } - - if(N1 > 0) - { - VERIFY_RAISES_ASSERT((m_fixed << mat11, mat12, mat11, mat21, mat22)); - VERIFY_RAISES_ASSERT((m_fixed << mat11, mat12, mat21, mat21, mat22)); - } - else - { - // allow insertion of zero-column blocks: - VERIFY_IS_EQUAL((m_fixed << mat11, mat12, mat11, mat11, mat21, mat21, mat22).finished(), (m_dynamic << mat12, mat22).finished()); - } - if(M1 != M2) - { - VERIFY_RAISES_ASSERT((m_fixed << mat11, mat21, mat12, mat22)); - } -} - - -template<int N> -struct test_block_recursion -{ - static void run() - { - test_blocks<(N>>6)&3, (N>>4)&3, (N>>2)&3, N & 3>(); - test_block_recursion<N-1>::run(); - } -}; - -template<> -struct test_block_recursion<-1> -{ - static void run() { } -}; - -void test_commainitializer() -{ - 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 - - double data[] = {1, 2, 3, 4, 5, 6, 7, 8, 9}; - Matrix3d ref = Map<Matrix<double,3,3,RowMajor> >(data); - - 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); - - - // recursively test all block-sizes from 0 to 3: - test_block_recursion<(1<<8) - 1>(); -} diff --git a/eigen/test/conjugate_gradient.cpp b/eigen/test/conjugate_gradient.cpp deleted file mode 100644 index 9622fd8..0000000 --- a/eigen/test/conjugate_gradient.cpp +++ /dev/null @@ -1,34 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "sparse_solver.h" -#include <Eigen/IterativeLinearSolvers> - -template<typename T, typename I> void test_conjugate_gradient_T() -{ - typedef SparseMatrix<T,0,I> SparseMatrixType; - ConjugateGradient<SparseMatrixType, Lower > cg_colmajor_lower_diag; - ConjugateGradient<SparseMatrixType, Upper > cg_colmajor_upper_diag; - ConjugateGradient<SparseMatrixType, Lower|Upper> cg_colmajor_loup_diag; - ConjugateGradient<SparseMatrixType, Lower, IdentityPreconditioner> cg_colmajor_lower_I; - ConjugateGradient<SparseMatrixType, Upper, IdentityPreconditioner> cg_colmajor_upper_I; - - CALL_SUBTEST( check_sparse_spd_solving(cg_colmajor_lower_diag) ); - CALL_SUBTEST( check_sparse_spd_solving(cg_colmajor_upper_diag) ); - CALL_SUBTEST( check_sparse_spd_solving(cg_colmajor_loup_diag) ); - CALL_SUBTEST( check_sparse_spd_solving(cg_colmajor_lower_I) ); - CALL_SUBTEST( check_sparse_spd_solving(cg_colmajor_upper_I) ); -} - -void test_conjugate_gradient() -{ - CALL_SUBTEST_1(( test_conjugate_gradient_T<double,int>() )); - CALL_SUBTEST_2(( test_conjugate_gradient_T<std::complex<double>, int>() )); - CALL_SUBTEST_3(( test_conjugate_gradient_T<double,long int>() )); -} diff --git a/eigen/test/conservative_resize.cpp b/eigen/test/conservative_resize.cpp deleted file mode 100644 index 21a1db4..0000000 --- a/eigen/test/conservative_resize.cpp +++ /dev/null @@ -1,133 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Hauke Heibel <hauke.heibel@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/Core> - -using namespace Eigen; - -template <typename Scalar, int Storage> -void run_matrix_tests() -{ - typedef Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Storage> MatrixType; - - MatrixType m, n; - - // boundary cases ... - m = n = MatrixType::Random(50,50); - m.conservativeResize(1,50); - VERIFY_IS_APPROX(m, n.block(0,0,1,50)); - - m = n = MatrixType::Random(50,50); - m.conservativeResize(50,1); - VERIFY_IS_APPROX(m, n.block(0,0,50,1)); - - m = n = MatrixType::Random(50,50); - m.conservativeResize(50,50); - VERIFY_IS_APPROX(m, n.block(0,0,50,50)); - - // random shrinking ... - for (int i=0; i<25; ++i) - { - const Index rows = internal::random<Index>(1,50); - const Index cols = internal::random<Index>(1,50); - m = n = MatrixType::Random(50,50); - m.conservativeResize(rows,cols); - VERIFY_IS_APPROX(m, n.block(0,0,rows,cols)); - } - - // random growing with zeroing ... - for (int i=0; i<25; ++i) - { - const Index rows = internal::random<Index>(50,75); - const Index cols = internal::random<Index>(50,75); - m = n = MatrixType::Random(50,50); - m.conservativeResizeLike(MatrixType::Zero(rows,cols)); - VERIFY_IS_APPROX(m.block(0,0,n.rows(),n.cols()), n); - VERIFY( rows<=50 || m.block(50,0,rows-50,cols).sum() == Scalar(0) ); - VERIFY( cols<=50 || m.block(0,50,rows,cols-50).sum() == Scalar(0) ); - } -} - -template <typename Scalar> -void run_vector_tests() -{ - typedef Matrix<Scalar, 1, Eigen::Dynamic> VectorType; - - VectorType m, n; - - // boundary cases ... - m = n = VectorType::Random(50); - m.conservativeResize(1); - VERIFY_IS_APPROX(m, n.segment(0,1)); - - m = n = VectorType::Random(50); - m.conservativeResize(50); - VERIFY_IS_APPROX(m, n.segment(0,50)); - - m = n = VectorType::Random(50); - m.conservativeResize(m.rows(),1); - VERIFY_IS_APPROX(m, n.segment(0,1)); - - m = n = VectorType::Random(50); - m.conservativeResize(m.rows(),50); - VERIFY_IS_APPROX(m, n.segment(0,50)); - - // random shrinking ... - for (int i=0; i<50; ++i) - { - const int size = internal::random<int>(1,50); - m = n = VectorType::Random(50); - m.conservativeResize(size); - VERIFY_IS_APPROX(m, n.segment(0,size)); - - m = n = VectorType::Random(50); - m.conservativeResize(m.rows(), size); - VERIFY_IS_APPROX(m, n.segment(0,size)); - } - - // random growing with zeroing ... - for (int i=0; i<50; ++i) - { - const int size = internal::random<int>(50,100); - m = n = VectorType::Random(50); - m.conservativeResizeLike(VectorType::Zero(size)); - VERIFY_IS_APPROX(m.segment(0,50), n); - VERIFY( size<=50 || m.segment(50,size-50).sum() == Scalar(0) ); - - m = n = VectorType::Random(50); - m.conservativeResizeLike(Matrix<Scalar,Dynamic,Dynamic>::Zero(1,size)); - VERIFY_IS_APPROX(m.segment(0,50), n); - VERIFY( size<=50 || m.segment(50,size-50).sum() == Scalar(0) ); - } -} - -void test_conservative_resize() -{ - for(int i=0; i<g_repeat; ++i) - { - CALL_SUBTEST_1((run_matrix_tests<int, Eigen::RowMajor>())); - CALL_SUBTEST_1((run_matrix_tests<int, Eigen::ColMajor>())); - CALL_SUBTEST_2((run_matrix_tests<float, Eigen::RowMajor>())); - CALL_SUBTEST_2((run_matrix_tests<float, Eigen::ColMajor>())); - CALL_SUBTEST_3((run_matrix_tests<double, Eigen::RowMajor>())); - CALL_SUBTEST_3((run_matrix_tests<double, Eigen::ColMajor>())); - CALL_SUBTEST_4((run_matrix_tests<std::complex<float>, Eigen::RowMajor>())); - CALL_SUBTEST_4((run_matrix_tests<std::complex<float>, Eigen::ColMajor>())); - CALL_SUBTEST_5((run_matrix_tests<std::complex<double>, Eigen::RowMajor>())); - CALL_SUBTEST_6((run_matrix_tests<std::complex<double>, Eigen::ColMajor>())); - - CALL_SUBTEST_1((run_vector_tests<int>())); - CALL_SUBTEST_2((run_vector_tests<float>())); - CALL_SUBTEST_3((run_vector_tests<double>())); - CALL_SUBTEST_4((run_vector_tests<std::complex<float> >())); - CALL_SUBTEST_5((run_vector_tests<std::complex<double> >())); - } -} diff --git a/eigen/test/constructor.cpp b/eigen/test/constructor.cpp deleted file mode 100644 index eec9e21..0000000 --- a/eigen/test/constructor.cpp +++ /dev/null @@ -1,84 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - - -#define TEST_ENABLE_TEMPORARY_TRACKING - -#include "main.h" - -template<typename MatrixType> struct Wrapper -{ - MatrixType m_mat; - inline Wrapper(const MatrixType &x) : m_mat(x) {} - inline operator const MatrixType& () const { return m_mat; } - inline operator MatrixType& () { return m_mat; } -}; - -template<typename MatrixType> void ctor_init1(const MatrixType& m) -{ - // Check logic in PlainObjectBase::_init1 - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m0 = MatrixType::Random(rows,cols); - - VERIFY_EVALUATION_COUNT( MatrixType m1(m0), 1); - VERIFY_EVALUATION_COUNT( MatrixType m2(m0+m0), 1); - VERIFY_EVALUATION_COUNT( MatrixType m2(m0.block(0,0,rows,cols)) , 1); - - Wrapper<MatrixType> wrapper(m0); - VERIFY_EVALUATION_COUNT( MatrixType m3(wrapper) , 1); -} - - -void test_constructor() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( ctor_init1(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_1( ctor_init1(Matrix4d()) ); - CALL_SUBTEST_1( ctor_init1(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_1( ctor_init1(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } - { - Matrix<Index,1,1> a(123); - VERIFY_IS_EQUAL(a[0], 123); - } - { - Matrix<Index,1,1> a(123.0); - VERIFY_IS_EQUAL(a[0], 123); - } - { - Matrix<float,1,1> a(123); - VERIFY_IS_EQUAL(a[0], 123.f); - } - { - Array<Index,1,1> a(123); - VERIFY_IS_EQUAL(a[0], 123); - } - { - Array<Index,1,1> a(123.0); - VERIFY_IS_EQUAL(a[0], 123); - } - { - Array<float,1,1> a(123); - VERIFY_IS_EQUAL(a[0], 123.f); - } - { - Array<Index,3,3> a(123); - VERIFY_IS_EQUAL(a(4), 123); - } - { - Array<Index,3,3> a(123.0); - VERIFY_IS_EQUAL(a(4), 123); - } - { - Array<float,3,3> a(123); - VERIFY_IS_EQUAL(a(4), 123.f); - } -} diff --git a/eigen/test/corners.cpp b/eigen/test/corners.cpp deleted file mode 100644 index 32edadb..0000000 --- a/eigen/test/corners.cpp +++ /dev/null @@ -1,117 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#define COMPARE_CORNER(A,B) \ - VERIFY_IS_EQUAL(matrix.A, matrix.B); \ - VERIFY_IS_EQUAL(const_matrix.A, const_matrix.B); - -template<typename MatrixType> void corners(const MatrixType& m) -{ - Index rows = m.rows(); - Index cols = m.cols(); - - Index r = internal::random<Index>(1,rows); - Index c = internal::random<Index>(1,cols); - - MatrixType matrix = MatrixType::Random(rows,cols); - const MatrixType const_matrix = MatrixType::Random(rows,cols); - - COMPARE_CORNER(topLeftCorner(r,c), block(0,0,r,c)); - COMPARE_CORNER(topRightCorner(r,c), block(0,cols-c,r,c)); - COMPARE_CORNER(bottomLeftCorner(r,c), block(rows-r,0,r,c)); - COMPARE_CORNER(bottomRightCorner(r,c), block(rows-r,cols-c,r,c)); - - Index sr = internal::random<Index>(1,rows) - 1; - Index nr = internal::random<Index>(1,rows-sr); - Index sc = internal::random<Index>(1,cols) - 1; - Index nc = internal::random<Index>(1,cols-sc); - - COMPARE_CORNER(topRows(r), block(0,0,r,cols)); - COMPARE_CORNER(middleRows(sr,nr), block(sr,0,nr,cols)); - COMPARE_CORNER(bottomRows(r), block(rows-r,0,r,cols)); - COMPARE_CORNER(leftCols(c), block(0,0,rows,c)); - COMPARE_CORNER(middleCols(sc,nc), block(0,sc,rows,nc)); - COMPARE_CORNER(rightCols(c), block(0,cols-c,rows,c)); -} - -template<typename MatrixType, int CRows, int CCols, int SRows, int SCols> void corners_fixedsize() -{ - MatrixType matrix = MatrixType::Random(); - const MatrixType const_matrix = MatrixType::Random(); - - enum { - rows = MatrixType::RowsAtCompileTime, - cols = MatrixType::ColsAtCompileTime, - r = CRows, - c = CCols, - sr = SRows, - sc = SCols - }; - - VERIFY_IS_EQUAL((matrix.template topLeftCorner<r,c>()), (matrix.template block<r,c>(0,0))); - VERIFY_IS_EQUAL((matrix.template topRightCorner<r,c>()), (matrix.template block<r,c>(0,cols-c))); - VERIFY_IS_EQUAL((matrix.template bottomLeftCorner<r,c>()), (matrix.template block<r,c>(rows-r,0))); - VERIFY_IS_EQUAL((matrix.template bottomRightCorner<r,c>()), (matrix.template block<r,c>(rows-r,cols-c))); - - VERIFY_IS_EQUAL((matrix.template topLeftCorner<r,c>()), (matrix.template topLeftCorner<r,Dynamic>(r,c))); - VERIFY_IS_EQUAL((matrix.template topRightCorner<r,c>()), (matrix.template topRightCorner<r,Dynamic>(r,c))); - VERIFY_IS_EQUAL((matrix.template bottomLeftCorner<r,c>()), (matrix.template bottomLeftCorner<r,Dynamic>(r,c))); - VERIFY_IS_EQUAL((matrix.template bottomRightCorner<r,c>()), (matrix.template bottomRightCorner<r,Dynamic>(r,c))); - - VERIFY_IS_EQUAL((matrix.template topLeftCorner<r,c>()), (matrix.template topLeftCorner<Dynamic,c>(r,c))); - VERIFY_IS_EQUAL((matrix.template topRightCorner<r,c>()), (matrix.template topRightCorner<Dynamic,c>(r,c))); - VERIFY_IS_EQUAL((matrix.template bottomLeftCorner<r,c>()), (matrix.template bottomLeftCorner<Dynamic,c>(r,c))); - VERIFY_IS_EQUAL((matrix.template bottomRightCorner<r,c>()), (matrix.template bottomRightCorner<Dynamic,c>(r,c))); - - VERIFY_IS_EQUAL((matrix.template topRows<r>()), (matrix.template block<r,cols>(0,0))); - VERIFY_IS_EQUAL((matrix.template middleRows<r>(sr)), (matrix.template block<r,cols>(sr,0))); - VERIFY_IS_EQUAL((matrix.template bottomRows<r>()), (matrix.template block<r,cols>(rows-r,0))); - VERIFY_IS_EQUAL((matrix.template leftCols<c>()), (matrix.template block<rows,c>(0,0))); - VERIFY_IS_EQUAL((matrix.template middleCols<c>(sc)), (matrix.template block<rows,c>(0,sc))); - VERIFY_IS_EQUAL((matrix.template rightCols<c>()), (matrix.template block<rows,c>(0,cols-c))); - - VERIFY_IS_EQUAL((const_matrix.template topLeftCorner<r,c>()), (const_matrix.template block<r,c>(0,0))); - VERIFY_IS_EQUAL((const_matrix.template topRightCorner<r,c>()), (const_matrix.template block<r,c>(0,cols-c))); - VERIFY_IS_EQUAL((const_matrix.template bottomLeftCorner<r,c>()), (const_matrix.template block<r,c>(rows-r,0))); - VERIFY_IS_EQUAL((const_matrix.template bottomRightCorner<r,c>()), (const_matrix.template block<r,c>(rows-r,cols-c))); - - VERIFY_IS_EQUAL((const_matrix.template topLeftCorner<r,c>()), (const_matrix.template topLeftCorner<r,Dynamic>(r,c))); - VERIFY_IS_EQUAL((const_matrix.template topRightCorner<r,c>()), (const_matrix.template topRightCorner<r,Dynamic>(r,c))); - VERIFY_IS_EQUAL((const_matrix.template bottomLeftCorner<r,c>()), (const_matrix.template bottomLeftCorner<r,Dynamic>(r,c))); - VERIFY_IS_EQUAL((const_matrix.template bottomRightCorner<r,c>()), (const_matrix.template bottomRightCorner<r,Dynamic>(r,c))); - - VERIFY_IS_EQUAL((const_matrix.template topLeftCorner<r,c>()), (const_matrix.template topLeftCorner<Dynamic,c>(r,c))); - VERIFY_IS_EQUAL((const_matrix.template topRightCorner<r,c>()), (const_matrix.template topRightCorner<Dynamic,c>(r,c))); - VERIFY_IS_EQUAL((const_matrix.template bottomLeftCorner<r,c>()), (const_matrix.template bottomLeftCorner<Dynamic,c>(r,c))); - VERIFY_IS_EQUAL((const_matrix.template bottomRightCorner<r,c>()), (const_matrix.template bottomRightCorner<Dynamic,c>(r,c))); - - VERIFY_IS_EQUAL((const_matrix.template topRows<r>()), (const_matrix.template block<r,cols>(0,0))); - VERIFY_IS_EQUAL((const_matrix.template middleRows<r>(sr)), (const_matrix.template block<r,cols>(sr,0))); - VERIFY_IS_EQUAL((const_matrix.template bottomRows<r>()), (const_matrix.template block<r,cols>(rows-r,0))); - VERIFY_IS_EQUAL((const_matrix.template leftCols<c>()), (const_matrix.template block<rows,c>(0,0))); - VERIFY_IS_EQUAL((const_matrix.template middleCols<c>(sc)), (const_matrix.template block<rows,c>(0,sc))); - VERIFY_IS_EQUAL((const_matrix.template rightCols<c>()), (const_matrix.template block<rows,c>(0,cols-c))); -} - -void test_corners() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( corners(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( corners(Matrix4d()) ); - CALL_SUBTEST_3( corners(Matrix<int,10,12>()) ); - CALL_SUBTEST_4( corners(MatrixXcf(5, 7)) ); - CALL_SUBTEST_5( corners(MatrixXf(21, 20)) ); - - CALL_SUBTEST_1(( corners_fixedsize<Matrix<float, 1, 1>, 1, 1, 0, 0>() )); - CALL_SUBTEST_2(( corners_fixedsize<Matrix4d,2,2,1,1>() )); - CALL_SUBTEST_3(( corners_fixedsize<Matrix<int,10,12>,4,7,5,2>() )); - } -} diff --git a/eigen/test/ctorleak.cpp b/eigen/test/ctorleak.cpp deleted file mode 100644 index c158f5e..0000000 --- a/eigen/test/ctorleak.cpp +++ /dev/null @@ -1,69 +0,0 @@ -#include "main.h" - -#include <exception> // std::exception - -struct Foo -{ - static Index object_count; - static Index object_limit; - int dummy; - - Foo() - { -#ifdef EIGEN_EXCEPTIONS - // TODO: Is this the correct way to handle this? - if (Foo::object_count > Foo::object_limit) { std::cout << "\nThrow!\n"; throw Foo::Fail(); } -#endif - std::cout << '+'; - ++Foo::object_count; - } - - ~Foo() - { - std::cout << '-'; - --Foo::object_count; - } - - class Fail : public std::exception {}; -}; - -Index Foo::object_count = 0; -Index Foo::object_limit = 0; - -#undef EIGEN_TEST_MAX_SIZE -#define EIGEN_TEST_MAX_SIZE 3 - -void test_ctorleak() -{ - typedef Matrix<Foo, Dynamic, Dynamic> MatrixX; - typedef Matrix<Foo, Dynamic, 1> VectorX; - Foo::object_count = 0; - for(int i = 0; i < g_repeat; i++) { - Index rows = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE), cols = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE); - Foo::object_limit = internal::random<Index>(0, rows*cols - 2); - std::cout << "object_limit =" << Foo::object_limit << std::endl; -#ifdef EIGEN_EXCEPTIONS - try - { -#endif - std::cout << "\nMatrixX m(" << rows << ", " << cols << ");\n"; - MatrixX m(rows, cols); -#ifdef EIGEN_EXCEPTIONS - VERIFY(false); // not reached if exceptions are enabled - } - catch (const Foo::Fail&) { /* ignore */ } -#endif - VERIFY_IS_EQUAL(Index(0), Foo::object_count); - - { - Foo::object_limit = (rows+1)*(cols+1); - MatrixX A(rows, cols); - VERIFY_IS_EQUAL(Foo::object_count, rows*cols); - VectorX v=A.row(0); - VERIFY_IS_EQUAL(Foo::object_count, (rows+1)*cols); - v = A.col(0); - VERIFY_IS_EQUAL(Foo::object_count, rows*(cols+1)); - } - VERIFY_IS_EQUAL(Index(0), Foo::object_count); - } -} diff --git a/eigen/test/cuda_basic.cu b/eigen/test/cuda_basic.cu deleted file mode 100644 index ce66c2c..0000000 --- a/eigen/test/cuda_basic.cu +++ /dev/null @@ -1,170 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015-2016 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// workaround issue between gcc >= 4.7 and cuda 5.5 -#if (defined __GNUC__) && (__GNUC__>4 || __GNUC_MINOR__>=7) - #undef _GLIBCXX_ATOMIC_BUILTINS - #undef _GLIBCXX_USE_INT128 -#endif - -#define EIGEN_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_NO_COMPLEX -#define EIGEN_TEST_FUNC cuda_basic -#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int - -#include <math_constants.h> -#include <cuda.h> -#include "main.h" -#include "cuda_common.h" - -// Check that dense modules can be properly parsed by nvcc -#include <Eigen/Dense> - -// struct Foo{ -// EIGEN_DEVICE_FUNC -// void operator()(int i, const float* mats, float* vecs) const { -// using namespace Eigen; -// // Matrix3f M(data); -// // Vector3f x(data+9); -// // Map<Vector3f>(data+9) = M.inverse() * x; -// Matrix3f M(mats+i/16); -// Vector3f x(vecs+i*3); -// // using std::min; -// // using std::sqrt; -// Map<Vector3f>(vecs+i*3) << x.minCoeff(), 1, 2;// / x.dot(x);//(M.inverse() * x) / x.x(); -// //x = x*2 + x.y() * x + x * x.maxCoeff() - x / x.sum(); -// } -// }; - -template<typename T> -struct coeff_wise { - EIGEN_DEVICE_FUNC - void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const - { - using namespace Eigen; - T x1(in+i); - T x2(in+i+1); - T x3(in+i+2); - Map<T> res(out+i*T::MaxSizeAtCompileTime); - - res.array() += (in[0] * x1 + x2).array() * x3.array(); - } -}; - -template<typename T> -struct replicate { - EIGEN_DEVICE_FUNC - void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const - { - using namespace Eigen; - T x1(in+i); - int step = x1.size() * 4; - int stride = 3 * step; - - typedef Map<Array<typename T::Scalar,Dynamic,Dynamic> > MapType; - MapType(out+i*stride+0*step, x1.rows()*2, x1.cols()*2) = x1.replicate(2,2); - MapType(out+i*stride+1*step, x1.rows()*3, x1.cols()) = in[i] * x1.colwise().replicate(3); - MapType(out+i*stride+2*step, x1.rows(), x1.cols()*3) = in[i] * x1.rowwise().replicate(3); - } -}; - -template<typename T> -struct redux { - EIGEN_DEVICE_FUNC - void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const - { - using namespace Eigen; - int N = 10; - T x1(in+i); - out[i*N+0] = x1.minCoeff(); - out[i*N+1] = x1.maxCoeff(); - out[i*N+2] = x1.sum(); - out[i*N+3] = x1.prod(); - out[i*N+4] = x1.matrix().squaredNorm(); - out[i*N+5] = x1.matrix().norm(); - out[i*N+6] = x1.colwise().sum().maxCoeff(); - out[i*N+7] = x1.rowwise().maxCoeff().sum(); - out[i*N+8] = x1.matrix().colwise().squaredNorm().sum(); - } -}; - -template<typename T1, typename T2> -struct prod_test { - EIGEN_DEVICE_FUNC - void operator()(int i, const typename T1::Scalar* in, typename T1::Scalar* out) const - { - using namespace Eigen; - typedef Matrix<typename T1::Scalar, T1::RowsAtCompileTime, T2::ColsAtCompileTime> T3; - T1 x1(in+i); - T2 x2(in+i+1); - Map<T3> res(out+i*T3::MaxSizeAtCompileTime); - res += in[i] * x1 * x2; - } -}; - -template<typename T1, typename T2> -struct diagonal { - EIGEN_DEVICE_FUNC - void operator()(int i, const typename T1::Scalar* in, typename T1::Scalar* out) const - { - using namespace Eigen; - T1 x1(in+i); - Map<T2> res(out+i*T2::MaxSizeAtCompileTime); - res += x1.diagonal(); - } -}; - -template<typename T> -struct eigenvalues { - EIGEN_DEVICE_FUNC - void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const - { - using namespace Eigen; - typedef Matrix<typename T::Scalar, T::RowsAtCompileTime, 1> Vec; - T M(in+i); - Map<Vec> res(out+i*Vec::MaxSizeAtCompileTime); - T A = M*M.adjoint(); - SelfAdjointEigenSolver<T> eig; - eig.computeDirect(M); - res = eig.eigenvalues(); - } -}; - -void test_cuda_basic() -{ - ei_test_init_cuda(); - - int nthreads = 100; - Eigen::VectorXf in, out; - - #ifndef __CUDA_ARCH__ - int data_size = nthreads * 512; - in.setRandom(data_size); - out.setRandom(data_size); - #endif - - CALL_SUBTEST( run_and_compare_to_cuda(coeff_wise<Vector3f>(), nthreads, in, out) ); - CALL_SUBTEST( run_and_compare_to_cuda(coeff_wise<Array44f>(), nthreads, in, out) ); - - CALL_SUBTEST( run_and_compare_to_cuda(replicate<Array4f>(), nthreads, in, out) ); - CALL_SUBTEST( run_and_compare_to_cuda(replicate<Array33f>(), nthreads, in, out) ); - - CALL_SUBTEST( run_and_compare_to_cuda(redux<Array4f>(), nthreads, in, out) ); - CALL_SUBTEST( run_and_compare_to_cuda(redux<Matrix3f>(), nthreads, in, out) ); - - CALL_SUBTEST( run_and_compare_to_cuda(prod_test<Matrix3f,Matrix3f>(), nthreads, in, out) ); - CALL_SUBTEST( run_and_compare_to_cuda(prod_test<Matrix4f,Vector4f>(), nthreads, in, out) ); - - CALL_SUBTEST( run_and_compare_to_cuda(diagonal<Matrix3f,Vector3f>(), nthreads, in, out) ); - CALL_SUBTEST( run_and_compare_to_cuda(diagonal<Matrix4f,Vector4f>(), nthreads, in, out) ); - - CALL_SUBTEST( run_and_compare_to_cuda(eigenvalues<Matrix3f>(), nthreads, in, out) ); - CALL_SUBTEST( run_and_compare_to_cuda(eigenvalues<Matrix2f>(), nthreads, in, out) ); - -} diff --git a/eigen/test/cuda_common.h b/eigen/test/cuda_common.h deleted file mode 100644 index 9737693..0000000 --- a/eigen/test/cuda_common.h +++ /dev/null @@ -1,101 +0,0 @@ - -#ifndef EIGEN_TEST_CUDA_COMMON_H -#define EIGEN_TEST_CUDA_COMMON_H - -#include <cuda.h> -#include <cuda_runtime.h> -#include <cuda_runtime_api.h> -#include <iostream> - -#ifndef __CUDACC__ -dim3 threadIdx, blockDim, blockIdx; -#endif - -template<typename Kernel, typename Input, typename Output> -void run_on_cpu(const Kernel& ker, int n, const Input& in, Output& out) -{ - for(int i=0; i<n; i++) - ker(i, in.data(), out.data()); -} - - -template<typename Kernel, typename Input, typename Output> -__global__ -void run_on_cuda_meta_kernel(const Kernel ker, int n, const Input* in, Output* out) -{ - int i = threadIdx.x + blockIdx.x*blockDim.x; - if(i<n) { - ker(i, in, out); - } -} - - -template<typename Kernel, typename Input, typename Output> -void run_on_cuda(const Kernel& ker, int n, const Input& in, Output& out) -{ - typename Input::Scalar* d_in; - typename Output::Scalar* d_out; - std::ptrdiff_t in_bytes = in.size() * sizeof(typename Input::Scalar); - std::ptrdiff_t out_bytes = out.size() * sizeof(typename Output::Scalar); - - cudaMalloc((void**)(&d_in), in_bytes); - cudaMalloc((void**)(&d_out), out_bytes); - - cudaMemcpy(d_in, in.data(), in_bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_out, out.data(), out_bytes, cudaMemcpyHostToDevice); - - // Simple and non-optimal 1D mapping assuming n is not too large - // That's only for unit testing! - dim3 Blocks(128); - dim3 Grids( (n+int(Blocks.x)-1)/int(Blocks.x) ); - - cudaThreadSynchronize(); - run_on_cuda_meta_kernel<<<Grids,Blocks>>>(ker, n, d_in, d_out); - cudaThreadSynchronize(); - - // check inputs have not been modified - cudaMemcpy(const_cast<typename Input::Scalar*>(in.data()), d_in, in_bytes, cudaMemcpyDeviceToHost); - cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost); - - cudaFree(d_in); - cudaFree(d_out); -} - - -template<typename Kernel, typename Input, typename Output> -void run_and_compare_to_cuda(const Kernel& ker, int n, const Input& in, Output& out) -{ - Input in_ref, in_cuda; - Output out_ref, out_cuda; - #ifndef __CUDA_ARCH__ - in_ref = in_cuda = in; - out_ref = out_cuda = out; - #endif - run_on_cpu (ker, n, in_ref, out_ref); - run_on_cuda(ker, n, in_cuda, out_cuda); - #ifndef __CUDA_ARCH__ - VERIFY_IS_APPROX(in_ref, in_cuda); - VERIFY_IS_APPROX(out_ref, out_cuda); - #endif -} - - -void ei_test_init_cuda() -{ - int device = 0; - cudaDeviceProp deviceProp; - cudaGetDeviceProperties(&deviceProp, device); - std::cout << "CUDA device info:\n"; - std::cout << " name: " << deviceProp.name << "\n"; - std::cout << " capability: " << deviceProp.major << "." << deviceProp.minor << "\n"; - std::cout << " multiProcessorCount: " << deviceProp.multiProcessorCount << "\n"; - std::cout << " maxThreadsPerMultiProcessor: " << deviceProp.maxThreadsPerMultiProcessor << "\n"; - std::cout << " warpSize: " << deviceProp.warpSize << "\n"; - std::cout << " regsPerBlock: " << deviceProp.regsPerBlock << "\n"; - std::cout << " concurrentKernels: " << deviceProp.concurrentKernels << "\n"; - std::cout << " clockRate: " << deviceProp.clockRate << "\n"; - std::cout << " canMapHostMemory: " << deviceProp.canMapHostMemory << "\n"; - std::cout << " computeMode: " << deviceProp.computeMode << "\n"; -} - -#endif // EIGEN_TEST_CUDA_COMMON_H diff --git a/eigen/test/denseLM.cpp b/eigen/test/denseLM.cpp deleted file mode 100644 index 0aa736e..0000000 --- a/eigen/test/denseLM.cpp +++ /dev/null @@ -1,190 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr> -// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include <iostream> -#include <fstream> -#include <iomanip> - -#include "main.h" -#include <Eigen/LevenbergMarquardt> -using namespace std; -using namespace Eigen; - -template<typename Scalar> -struct DenseLM : DenseFunctor<Scalar> -{ - typedef DenseFunctor<Scalar> Base; - typedef typename Base::JacobianType JacobianType; - typedef Matrix<Scalar,Dynamic,1> VectorType; - - DenseLM(int n, int m) : DenseFunctor<Scalar>(n,m) - { } - - VectorType model(const VectorType& uv, VectorType& x) - { - VectorType y; // Should change to use expression template - int m = Base::values(); - int n = Base::inputs(); - eigen_assert(uv.size()%2 == 0); - eigen_assert(uv.size() == n); - eigen_assert(x.size() == m); - y.setZero(m); - int half = n/2; - VectorBlock<const VectorType> u(uv, 0, half); - VectorBlock<const VectorType> v(uv, half, half); - for (int j = 0; j < m; j++) - { - for (int i = 0; i < half; i++) - y(j) += u(i)*std::exp(-(x(j)-i)*(x(j)-i)/(v(i)*v(i))); - } - return y; - - } - void initPoints(VectorType& uv_ref, VectorType& x) - { - m_x = x; - m_y = this->model(uv_ref, x); - } - - int operator()(const VectorType& uv, VectorType& fvec) - { - - int m = Base::values(); - int n = Base::inputs(); - eigen_assert(uv.size()%2 == 0); - eigen_assert(uv.size() == n); - eigen_assert(fvec.size() == m); - int half = n/2; - VectorBlock<const VectorType> u(uv, 0, half); - VectorBlock<const VectorType> v(uv, half, half); - for (int j = 0; j < m; j++) - { - fvec(j) = m_y(j); - for (int i = 0; i < half; i++) - { - fvec(j) -= u(i) *std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i))); - } - } - - return 0; - } - int df(const VectorType& uv, JacobianType& fjac) - { - int m = Base::values(); - int n = Base::inputs(); - eigen_assert(n == uv.size()); - eigen_assert(fjac.rows() == m); - eigen_assert(fjac.cols() == n); - int half = n/2; - VectorBlock<const VectorType> u(uv, 0, half); - VectorBlock<const VectorType> v(uv, half, half); - for (int j = 0; j < m; j++) - { - for (int i = 0; i < half; i++) - { - fjac.coeffRef(j,i) = -std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i))); - fjac.coeffRef(j,i+half) = -2.*u(i)*(m_x(j)-i)*(m_x(j)-i)/(std::pow(v(i),3)) * std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i))); - } - } - return 0; - } - VectorType m_x, m_y; //Data Points -}; - -template<typename FunctorType, typename VectorType> -int test_minimizeLM(FunctorType& functor, VectorType& uv) -{ - LevenbergMarquardt<FunctorType> lm(functor); - LevenbergMarquardtSpace::Status info; - - info = lm.minimize(uv); - - VERIFY_IS_EQUAL(info, 1); - //FIXME Check other parameters - return info; -} - -template<typename FunctorType, typename VectorType> -int test_lmder(FunctorType& functor, VectorType& uv) -{ - typedef typename VectorType::Scalar Scalar; - LevenbergMarquardtSpace::Status info; - LevenbergMarquardt<FunctorType> lm(functor); - info = lm.lmder1(uv); - - VERIFY_IS_EQUAL(info, 1); - //FIXME Check other parameters - return info; -} - -template<typename FunctorType, typename VectorType> -int test_minimizeSteps(FunctorType& functor, VectorType& uv) -{ - LevenbergMarquardtSpace::Status info; - LevenbergMarquardt<FunctorType> lm(functor); - info = lm.minimizeInit(uv); - if (info==LevenbergMarquardtSpace::ImproperInputParameters) - return info; - do - { - info = lm.minimizeOneStep(uv); - } while (info==LevenbergMarquardtSpace::Running); - - VERIFY_IS_EQUAL(info, 1); - //FIXME Check other parameters - return info; -} - -template<typename T> -void test_denseLM_T() -{ - typedef Matrix<T,Dynamic,1> VectorType; - - int inputs = 10; - int values = 1000; - DenseLM<T> dense_gaussian(inputs, values); - VectorType uv(inputs),uv_ref(inputs); - VectorType x(values); - - // Generate the reference solution - uv_ref << -2, 1, 4 ,8, 6, 1.8, 1.2, 1.1, 1.9 , 3; - - //Generate the reference data points - x.setRandom(); - x = 10*x; - x.array() += 10; - dense_gaussian.initPoints(uv_ref, x); - - // Generate the initial parameters - VectorBlock<VectorType> u(uv, 0, inputs/2); - VectorBlock<VectorType> v(uv, inputs/2, inputs/2); - - // Solve the optimization problem - - //Solve in one go - u.setOnes(); v.setOnes(); - test_minimizeLM(dense_gaussian, uv); - - //Solve until the machine precision - u.setOnes(); v.setOnes(); - test_lmder(dense_gaussian, uv); - - // Solve step by step - v.setOnes(); u.setOnes(); - test_minimizeSteps(dense_gaussian, uv); - -} - -void test_denseLM() -{ - CALL_SUBTEST_2(test_denseLM_T<double>()); - - // CALL_SUBTEST_2(test_sparseLM_T<std::complex<double>()); -} diff --git a/eigen/test/dense_storage.cpp b/eigen/test/dense_storage.cpp deleted file mode 100644 index e63712b..0000000 --- a/eigen/test/dense_storage.cpp +++ /dev/null @@ -1,76 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2013 Hauke Heibel <hauke.heibel@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/Core> - -template <typename T, int Rows, int Cols> -void dense_storage_copy() -{ - static const int Size = ((Rows==Dynamic || Cols==Dynamic) ? Dynamic : Rows*Cols); - typedef DenseStorage<T,Size, Rows,Cols, 0> DenseStorageType; - - const int rows = (Rows==Dynamic) ? 4 : Rows; - const int cols = (Cols==Dynamic) ? 3 : Cols; - const int size = rows*cols; - DenseStorageType reference(size, rows, cols); - T* raw_reference = reference.data(); - for (int i=0; i<size; ++i) - raw_reference[i] = static_cast<T>(i); - - DenseStorageType copied_reference(reference); - const T* raw_copied_reference = copied_reference.data(); - for (int i=0; i<size; ++i) - VERIFY_IS_EQUAL(raw_reference[i], raw_copied_reference[i]); -} - -template <typename T, int Rows, int Cols> -void dense_storage_assignment() -{ - static const int Size = ((Rows==Dynamic || Cols==Dynamic) ? Dynamic : Rows*Cols); - typedef DenseStorage<T,Size, Rows,Cols, 0> DenseStorageType; - - const int rows = (Rows==Dynamic) ? 4 : Rows; - const int cols = (Cols==Dynamic) ? 3 : Cols; - const int size = rows*cols; - DenseStorageType reference(size, rows, cols); - T* raw_reference = reference.data(); - for (int i=0; i<size; ++i) - raw_reference[i] = static_cast<T>(i); - - DenseStorageType copied_reference; - copied_reference = reference; - const T* raw_copied_reference = copied_reference.data(); - for (int i=0; i<size; ++i) - VERIFY_IS_EQUAL(raw_reference[i], raw_copied_reference[i]); -} - -void test_dense_storage() -{ - dense_storage_copy<int,Dynamic,Dynamic>(); - dense_storage_copy<int,Dynamic,3>(); - dense_storage_copy<int,4,Dynamic>(); - dense_storage_copy<int,4,3>(); - - dense_storage_copy<float,Dynamic,Dynamic>(); - dense_storage_copy<float,Dynamic,3>(); - dense_storage_copy<float,4,Dynamic>(); - dense_storage_copy<float,4,3>(); - - dense_storage_assignment<int,Dynamic,Dynamic>(); - dense_storage_assignment<int,Dynamic,3>(); - dense_storage_assignment<int,4,Dynamic>(); - dense_storage_assignment<int,4,3>(); - - dense_storage_assignment<float,Dynamic,Dynamic>(); - dense_storage_assignment<float,Dynamic,3>(); - dense_storage_assignment<float,4,Dynamic>(); - dense_storage_assignment<float,4,3>(); -} diff --git a/eigen/test/determinant.cpp b/eigen/test/determinant.cpp deleted file mode 100644 index b8c9bab..0000000 --- a/eigen/test/determinant.cpp +++ /dev/null @@ -1,66 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/LU> - -template<typename MatrixType> void determinant(const MatrixType& m) -{ - /* this test covers the following files: - Determinant.h - */ - Index size = m.rows(); - - MatrixType m1(size, size), m2(size, size); - m1.setRandom(); - m2.setRandom(); - typedef typename MatrixType::Scalar Scalar; - Scalar x = internal::random<Scalar>(); - VERIFY_IS_APPROX(MatrixType::Identity(size, size).determinant(), Scalar(1)); - VERIFY_IS_APPROX((m1*m2).eval().determinant(), m1.determinant() * m2.determinant()); - if(size==1) return; - Index i = internal::random<Index>(0, size-1); - Index j; - do { - j = internal::random<Index>(0, size-1); - } while(j==i); - m2 = m1; - m2.row(i).swap(m2.row(j)); - VERIFY_IS_APPROX(m2.determinant(), -m1.determinant()); - m2 = m1; - m2.col(i).swap(m2.col(j)); - VERIFY_IS_APPROX(m2.determinant(), -m1.determinant()); - VERIFY_IS_APPROX(m2.determinant(), m2.transpose().determinant()); - VERIFY_IS_APPROX(numext::conj(m2.determinant()), m2.adjoint().determinant()); - m2 = m1; - m2.row(i) += x*m2.row(j); - VERIFY_IS_APPROX(m2.determinant(), m1.determinant()); - m2 = m1; - m2.row(i) *= x; - VERIFY_IS_APPROX(m2.determinant(), m1.determinant() * x); - - // check empty matrix - VERIFY_IS_APPROX(m2.block(0,0,0,0).determinant(), Scalar(1)); -} - -void test_determinant() -{ - for(int i = 0; i < g_repeat; i++) { - int s = 0; - CALL_SUBTEST_1( determinant(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( determinant(Matrix<double, 2, 2>()) ); - CALL_SUBTEST_3( determinant(Matrix<double, 3, 3>()) ); - CALL_SUBTEST_4( determinant(Matrix<double, 4, 4>()) ); - CALL_SUBTEST_5( determinant(Matrix<std::complex<double>, 10, 10>()) ); - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4); - CALL_SUBTEST_6( determinant(MatrixXd(s, s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - } -} diff --git a/eigen/test/diagonal.cpp b/eigen/test/diagonal.cpp deleted file mode 100644 index 8ed9b46..0000000 --- a/eigen/test/diagonal.cpp +++ /dev/null @@ -1,105 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename MatrixType> void diagonal(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols); - - Scalar s1 = internal::random<Scalar>(); - - //check diagonal() - VERIFY_IS_APPROX(m1.diagonal(), m1.transpose().diagonal()); - m2.diagonal() = 2 * m1.diagonal(); - m2.diagonal()[0] *= 3; - - if (rows>2) - { - enum { - N1 = MatrixType::RowsAtCompileTime>2 ? 2 : 0, - N2 = MatrixType::RowsAtCompileTime>1 ? -1 : 0 - }; - - // check sub/super diagonal - if(MatrixType::SizeAtCompileTime!=Dynamic) - { - VERIFY(m1.template diagonal<N1>().RowsAtCompileTime == m1.diagonal(N1).size()); - VERIFY(m1.template diagonal<N2>().RowsAtCompileTime == m1.diagonal(N2).size()); - } - - m2.template diagonal<N1>() = 2 * m1.template diagonal<N1>(); - VERIFY_IS_APPROX(m2.template diagonal<N1>(), static_cast<Scalar>(2) * m1.diagonal(N1)); - m2.template diagonal<N1>()[0] *= 3; - VERIFY_IS_APPROX(m2.template diagonal<N1>()[0], static_cast<Scalar>(6) * m1.template diagonal<N1>()[0]); - - - m2.template diagonal<N2>() = 2 * m1.template diagonal<N2>(); - m2.template diagonal<N2>()[0] *= 3; - VERIFY_IS_APPROX(m2.template diagonal<N2>()[0], static_cast<Scalar>(6) * m1.template diagonal<N2>()[0]); - - m2.diagonal(N1) = 2 * m1.diagonal(N1); - VERIFY_IS_APPROX(m2.template diagonal<N1>(), static_cast<Scalar>(2) * m1.diagonal(N1)); - m2.diagonal(N1)[0] *= 3; - VERIFY_IS_APPROX(m2.diagonal(N1)[0], static_cast<Scalar>(6) * m1.diagonal(N1)[0]); - - m2.diagonal(N2) = 2 * m1.diagonal(N2); - VERIFY_IS_APPROX(m2.template diagonal<N2>(), static_cast<Scalar>(2) * m1.diagonal(N2)); - m2.diagonal(N2)[0] *= 3; - VERIFY_IS_APPROX(m2.diagonal(N2)[0], static_cast<Scalar>(6) * m1.diagonal(N2)[0]); - - m2.diagonal(N2).x() = s1; - VERIFY_IS_APPROX(m2.diagonal(N2).x(), s1); - m2.diagonal(N2).coeffRef(0) = Scalar(2)*s1; - VERIFY_IS_APPROX(m2.diagonal(N2).coeff(0), Scalar(2)*s1); - } - - VERIFY( m1.diagonal( cols).size()==0 ); - VERIFY( m1.diagonal(-rows).size()==0 ); -} - -template<typename MatrixType> void diagonal_assert(const MatrixType& m) { - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols); - - if (rows>=2 && cols>=2) - { - VERIFY_RAISES_ASSERT( m1 += m1.diagonal() ); - VERIFY_RAISES_ASSERT( m1 -= m1.diagonal() ); - VERIFY_RAISES_ASSERT( m1.array() *= m1.diagonal().array() ); - VERIFY_RAISES_ASSERT( m1.array() /= m1.diagonal().array() ); - } - - VERIFY_RAISES_ASSERT( m1.diagonal(cols+1) ); - VERIFY_RAISES_ASSERT( m1.diagonal(-(rows+1)) ); -} - -void test_diagonal() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( diagonal(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_1( diagonal(Matrix<float, 4, 9>()) ); - CALL_SUBTEST_1( diagonal(Matrix<float, 7, 3>()) ); - CALL_SUBTEST_2( diagonal(Matrix4d()) ); - CALL_SUBTEST_2( diagonal(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_2( diagonal(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_2( diagonal(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_1( diagonal(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_1( diagonal(Matrix<float,Dynamic,4>(3, 4)) ); - CALL_SUBTEST_1( diagonal_assert(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } -} diff --git a/eigen/test/diagonalmatrices.cpp b/eigen/test/diagonalmatrices.cpp deleted file mode 100644 index c55733d..0000000 --- a/eigen/test/diagonalmatrices.cpp +++ /dev/null @@ -1,166 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -using namespace std; -template<typename MatrixType> void diagonalmatrices(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime }; - typedef Matrix<Scalar, Rows, 1> VectorType; - typedef Matrix<Scalar, 1, Cols> RowVectorType; - typedef Matrix<Scalar, Rows, Rows> SquareMatrixType; - typedef Matrix<Scalar, Dynamic, Dynamic> DynMatrixType; - typedef DiagonalMatrix<Scalar, Rows> LeftDiagonalMatrix; - typedef DiagonalMatrix<Scalar, Cols> RightDiagonalMatrix; - typedef Matrix<Scalar, Rows==Dynamic?Dynamic:2*Rows, Cols==Dynamic?Dynamic:2*Cols> BigMatrix; - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols); - VectorType v1 = VectorType::Random(rows), - v2 = VectorType::Random(rows); - RowVectorType rv1 = RowVectorType::Random(cols), - rv2 = RowVectorType::Random(cols); - - LeftDiagonalMatrix ldm1(v1), ldm2(v2); - RightDiagonalMatrix rdm1(rv1), rdm2(rv2); - - Scalar s1 = internal::random<Scalar>(); - - SquareMatrixType sq_m1 (v1.asDiagonal()); - VERIFY_IS_APPROX(sq_m1, v1.asDiagonal().toDenseMatrix()); - sq_m1 = v1.asDiagonal(); - VERIFY_IS_APPROX(sq_m1, v1.asDiagonal().toDenseMatrix()); - SquareMatrixType sq_m2 = v1.asDiagonal(); - VERIFY_IS_APPROX(sq_m1, sq_m2); - - ldm1 = v1.asDiagonal(); - LeftDiagonalMatrix ldm3(v1); - VERIFY_IS_APPROX(ldm1.diagonal(), ldm3.diagonal()); - LeftDiagonalMatrix ldm4 = v1.asDiagonal(); - VERIFY_IS_APPROX(ldm1.diagonal(), ldm4.diagonal()); - - sq_m1.block(0,0,rows,rows) = ldm1; - VERIFY_IS_APPROX(sq_m1, ldm1.toDenseMatrix()); - sq_m1.transpose() = ldm1; - VERIFY_IS_APPROX(sq_m1, ldm1.toDenseMatrix()); - - Index i = internal::random<Index>(0, rows-1); - Index j = internal::random<Index>(0, cols-1); - - VERIFY_IS_APPROX( ((ldm1 * m1)(i,j)) , ldm1.diagonal()(i) * m1(i,j) ); - VERIFY_IS_APPROX( ((ldm1 * (m1+m2))(i,j)) , ldm1.diagonal()(i) * (m1+m2)(i,j) ); - VERIFY_IS_APPROX( ((m1 * rdm1)(i,j)) , rdm1.diagonal()(j) * m1(i,j) ); - VERIFY_IS_APPROX( ((v1.asDiagonal() * m1)(i,j)) , v1(i) * m1(i,j) ); - VERIFY_IS_APPROX( ((m1 * rv1.asDiagonal())(i,j)) , rv1(j) * m1(i,j) ); - VERIFY_IS_APPROX( (((v1+v2).asDiagonal() * m1)(i,j)) , (v1+v2)(i) * m1(i,j) ); - VERIFY_IS_APPROX( (((v1+v2).asDiagonal() * (m1+m2))(i,j)) , (v1+v2)(i) * (m1+m2)(i,j) ); - VERIFY_IS_APPROX( ((m1 * (rv1+rv2).asDiagonal())(i,j)) , (rv1+rv2)(j) * m1(i,j) ); - VERIFY_IS_APPROX( (((m1+m2) * (rv1+rv2).asDiagonal())(i,j)) , (rv1+rv2)(j) * (m1+m2)(i,j) ); - - if(rows>1) - { - DynMatrixType tmp = m1.topRows(rows/2), res; - VERIFY_IS_APPROX( (res = m1.topRows(rows/2) * rv1.asDiagonal()), tmp * rv1.asDiagonal() ); - VERIFY_IS_APPROX( (res = v1.head(rows/2).asDiagonal()*m1.topRows(rows/2)), v1.head(rows/2).asDiagonal()*tmp ); - } - - BigMatrix big; - big.setZero(2*rows, 2*cols); - - big.block(i,j,rows,cols) = m1; - big.block(i,j,rows,cols) = v1.asDiagonal() * big.block(i,j,rows,cols); - - VERIFY_IS_APPROX((big.block(i,j,rows,cols)) , v1.asDiagonal() * m1 ); - - big.block(i,j,rows,cols) = m1; - big.block(i,j,rows,cols) = big.block(i,j,rows,cols) * rv1.asDiagonal(); - VERIFY_IS_APPROX((big.block(i,j,rows,cols)) , m1 * rv1.asDiagonal() ); - - - // scalar multiple - VERIFY_IS_APPROX(LeftDiagonalMatrix(ldm1*s1).diagonal(), ldm1.diagonal() * s1); - VERIFY_IS_APPROX(LeftDiagonalMatrix(s1*ldm1).diagonal(), s1 * ldm1.diagonal()); - - VERIFY_IS_APPROX(m1 * (rdm1 * s1), (m1 * rdm1) * s1); - VERIFY_IS_APPROX(m1 * (s1 * rdm1), (m1 * rdm1) * s1); - - // Diagonal to dense - sq_m1.setRandom(); - sq_m2 = sq_m1; - VERIFY_IS_APPROX( (sq_m1 += (s1*v1).asDiagonal()), sq_m2 += (s1*v1).asDiagonal().toDenseMatrix() ); - VERIFY_IS_APPROX( (sq_m1 -= (s1*v1).asDiagonal()), sq_m2 -= (s1*v1).asDiagonal().toDenseMatrix() ); - VERIFY_IS_APPROX( (sq_m1 = (s1*v1).asDiagonal()), (s1*v1).asDiagonal().toDenseMatrix() ); - - sq_m1.setRandom(); - sq_m2 = v1.asDiagonal(); - sq_m2 = sq_m1 * sq_m2; - VERIFY_IS_APPROX( (sq_m1*v1.asDiagonal()).col(i), sq_m2.col(i) ); - VERIFY_IS_APPROX( (sq_m1*v1.asDiagonal()).row(i), sq_m2.row(i) ); -} - -template<typename MatrixType> void as_scalar_product(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - typedef Matrix<Scalar, Dynamic, Dynamic> DynMatrixType; - typedef Matrix<Scalar, Dynamic, 1> DynVectorType; - typedef Matrix<Scalar, 1, Dynamic> DynRowVectorType; - - Index rows = m.rows(); - Index depth = internal::random<Index>(1,EIGEN_TEST_MAX_SIZE); - - VectorType v1 = VectorType::Random(rows); - DynVectorType dv1 = DynVectorType::Random(depth); - DynRowVectorType drv1 = DynRowVectorType::Random(depth); - DynMatrixType dm1 = dv1; - DynMatrixType drm1 = drv1; - - Scalar s = v1(0); - - VERIFY_IS_APPROX( v1.asDiagonal() * drv1, s*drv1 ); - VERIFY_IS_APPROX( dv1 * v1.asDiagonal(), dv1*s ); - - VERIFY_IS_APPROX( v1.asDiagonal() * drm1, s*drm1 ); - VERIFY_IS_APPROX( dm1 * v1.asDiagonal(), dm1*s ); -} - -template<int> -void bug987() -{ - Matrix3Xd points = Matrix3Xd::Random(3, 3); - Vector2d diag = Vector2d::Random(); - Matrix2Xd tmp1 = points.topRows<2>(), res1, res2; - VERIFY_IS_APPROX( res1 = diag.asDiagonal() * points.topRows<2>(), res2 = diag.asDiagonal() * tmp1 ); - Matrix2d tmp2 = points.topLeftCorner<2,2>(); - VERIFY_IS_APPROX(( res1 = points.topLeftCorner<2,2>()*diag.asDiagonal()) , res2 = tmp2*diag.asDiagonal() ); -} - -void test_diagonalmatrices() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( diagonalmatrices(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_1( as_scalar_product(Matrix<float, 1, 1>()) ); - - CALL_SUBTEST_2( diagonalmatrices(Matrix3f()) ); - CALL_SUBTEST_3( diagonalmatrices(Matrix<double,3,3,RowMajor>()) ); - CALL_SUBTEST_4( diagonalmatrices(Matrix4d()) ); - CALL_SUBTEST_5( diagonalmatrices(Matrix<float,4,4,RowMajor>()) ); - CALL_SUBTEST_6( diagonalmatrices(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_6( as_scalar_product(MatrixXcf(1,1)) ); - CALL_SUBTEST_7( diagonalmatrices(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_8( diagonalmatrices(Matrix<double,Dynamic,Dynamic,RowMajor>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_9( diagonalmatrices(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_9( diagonalmatrices(MatrixXf(1,1)) ); - CALL_SUBTEST_9( as_scalar_product(MatrixXf(1,1)) ); - } - CALL_SUBTEST_10( bug987<0>() ); -} diff --git a/eigen/test/dontalign.cpp b/eigen/test/dontalign.cpp deleted file mode 100644 index ac00112..0000000 --- a/eigen/test/dontalign.cpp +++ /dev/null @@ -1,62 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#if defined EIGEN_TEST_PART_1 || defined EIGEN_TEST_PART_2 || defined EIGEN_TEST_PART_3 || defined EIGEN_TEST_PART_4 -#define EIGEN_DONT_ALIGN -#elif defined EIGEN_TEST_PART_5 || defined EIGEN_TEST_PART_6 || defined EIGEN_TEST_PART_7 || defined EIGEN_TEST_PART_8 -#define EIGEN_DONT_ALIGN_STATICALLY -#endif - -#include "main.h" -#include <Eigen/Dense> - -template<typename MatrixType> -void dontalign(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType a = MatrixType::Random(rows,cols); - SquareMatrixType square = SquareMatrixType::Random(rows,rows); - VectorType v = VectorType::Random(rows); - - VERIFY_IS_APPROX(v, square * square.colPivHouseholderQr().solve(v)); - square = square.inverse().eval(); - a = square * a; - square = square*square; - v = square * v; - v = a.adjoint() * v; - VERIFY(square.determinant() != Scalar(0)); - - // bug 219: MapAligned() was giving an assert with EIGEN_DONT_ALIGN, because Map Flags were miscomputed - Scalar* array = internal::aligned_new<Scalar>(rows); - v = VectorType::MapAligned(array, rows); - internal::aligned_delete(array, rows); -} - -void test_dontalign() -{ -#if defined EIGEN_TEST_PART_1 || defined EIGEN_TEST_PART_5 - dontalign(Matrix3d()); - dontalign(Matrix4f()); -#elif defined EIGEN_TEST_PART_2 || defined EIGEN_TEST_PART_6 - dontalign(Matrix3cd()); - dontalign(Matrix4cf()); -#elif defined EIGEN_TEST_PART_3 || defined EIGEN_TEST_PART_7 - dontalign(Matrix<float, 32, 32>()); - dontalign(Matrix<std::complex<float>, 32, 32>()); -#elif defined EIGEN_TEST_PART_4 || defined EIGEN_TEST_PART_8 - dontalign(MatrixXd(32, 32)); - dontalign(MatrixXcf(32, 32)); -#endif -} diff --git a/eigen/test/dynalloc.cpp b/eigen/test/dynalloc.cpp deleted file mode 100644 index f1cc70b..0000000 --- a/eigen/test/dynalloc.cpp +++ /dev/null @@ -1,175 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#if EIGEN_MAX_ALIGN_BYTES>0 -#define ALIGNMENT EIGEN_MAX_ALIGN_BYTES -#else -#define ALIGNMENT 1 -#endif - -typedef Matrix<float,8,1> Vector8f; - -void check_handmade_aligned_malloc() -{ - for(int i = 1; i < 1000; i++) - { - char *p = (char*)internal::handmade_aligned_malloc(i); - VERIFY(internal::UIntPtr(p)%ALIGNMENT==0); - // if the buffer is wrongly allocated this will give a bad write --> check with valgrind - for(int j = 0; j < i; j++) p[j]=0; - internal::handmade_aligned_free(p); - } -} - -void check_aligned_malloc() -{ - for(int i = ALIGNMENT; i < 1000; i++) - { - char *p = (char*)internal::aligned_malloc(i); - VERIFY(internal::UIntPtr(p)%ALIGNMENT==0); - // if the buffer is wrongly allocated this will give a bad write --> check with valgrind - for(int j = 0; j < i; j++) p[j]=0; - internal::aligned_free(p); - } -} - -void check_aligned_new() -{ - for(int i = ALIGNMENT; i < 1000; i++) - { - float *p = internal::aligned_new<float>(i); - VERIFY(internal::UIntPtr(p)%ALIGNMENT==0); - // if the buffer is wrongly allocated this will give a bad write --> check with valgrind - for(int j = 0; j < i; j++) p[j]=0; - internal::aligned_delete(p,i); - } -} - -void check_aligned_stack_alloc() -{ - for(int i = ALIGNMENT; i < 400; i++) - { - ei_declare_aligned_stack_constructed_variable(float,p,i,0); - VERIFY(internal::UIntPtr(p)%ALIGNMENT==0); - // if the buffer is wrongly allocated this will give a bad write --> check with valgrind - for(int j = 0; j < i; j++) p[j]=0; - } -} - - -// test compilation with both a struct and a class... -struct MyStruct -{ - EIGEN_MAKE_ALIGNED_OPERATOR_NEW - char dummychar; - Vector8f avec; -}; - -class MyClassA -{ - public: - EIGEN_MAKE_ALIGNED_OPERATOR_NEW - char dummychar; - Vector8f avec; -}; - -template<typename T> void check_dynaligned() -{ - // TODO have to be updated once we support multiple alignment values - if(T::SizeAtCompileTime % ALIGNMENT == 0) - { - T* obj = new T; - VERIFY(T::NeedsToAlign==1); - VERIFY(internal::UIntPtr(obj)%ALIGNMENT==0); - delete obj; - } -} - -template<typename T> void check_custom_new_delete() -{ - { - T* t = new T; - delete t; - } - - { - std::size_t N = internal::random<std::size_t>(1,10); - T* t = new T[N]; - delete[] t; - } - -#if EIGEN_MAX_ALIGN_BYTES>0 - { - T* t = static_cast<T *>((T::operator new)(sizeof(T))); - (T::operator delete)(t, sizeof(T)); - } - - { - T* t = static_cast<T *>((T::operator new)(sizeof(T))); - (T::operator delete)(t); - } -#endif -} - -void test_dynalloc() -{ - // low level dynamic memory allocation - CALL_SUBTEST(check_handmade_aligned_malloc()); - CALL_SUBTEST(check_aligned_malloc()); - CALL_SUBTEST(check_aligned_new()); - CALL_SUBTEST(check_aligned_stack_alloc()); - - for (int i=0; i<g_repeat*100; ++i) - { - CALL_SUBTEST( check_custom_new_delete<Vector4f>() ); - CALL_SUBTEST( check_custom_new_delete<Vector2f>() ); - CALL_SUBTEST( check_custom_new_delete<Matrix4f>() ); - CALL_SUBTEST( check_custom_new_delete<MatrixXi>() ); - } - - // check static allocation, who knows ? - #if EIGEN_MAX_STATIC_ALIGN_BYTES - for (int i=0; i<g_repeat*100; ++i) - { - CALL_SUBTEST(check_dynaligned<Vector4f>() ); - CALL_SUBTEST(check_dynaligned<Vector2d>() ); - CALL_SUBTEST(check_dynaligned<Matrix4f>() ); - CALL_SUBTEST(check_dynaligned<Vector4d>() ); - CALL_SUBTEST(check_dynaligned<Vector4i>() ); - CALL_SUBTEST(check_dynaligned<Vector8f>() ); - } - - { - MyStruct foo0; VERIFY(internal::UIntPtr(foo0.avec.data())%ALIGNMENT==0); - MyClassA fooA; VERIFY(internal::UIntPtr(fooA.avec.data())%ALIGNMENT==0); - } - - // dynamic allocation, single object - for (int i=0; i<g_repeat*100; ++i) - { - MyStruct *foo0 = new MyStruct(); VERIFY(internal::UIntPtr(foo0->avec.data())%ALIGNMENT==0); - MyClassA *fooA = new MyClassA(); VERIFY(internal::UIntPtr(fooA->avec.data())%ALIGNMENT==0); - delete foo0; - delete fooA; - } - - // dynamic allocation, array - const int N = 10; - for (int i=0; i<g_repeat*100; ++i) - { - MyStruct *foo0 = new MyStruct[N]; VERIFY(internal::UIntPtr(foo0->avec.data())%ALIGNMENT==0); - MyClassA *fooA = new MyClassA[N]; VERIFY(internal::UIntPtr(fooA->avec.data())%ALIGNMENT==0); - delete[] foo0; - delete[] fooA; - } - #endif - -} diff --git a/eigen/test/eigen2support.cpp b/eigen/test/eigen2support.cpp deleted file mode 100644 index ac6931a..0000000 --- a/eigen/test/eigen2support.cpp +++ /dev/null @@ -1,65 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN2_SUPPORT - -#include "main.h" - -template<typename MatrixType> void eigen2support(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m3(rows, cols); - - Scalar s1 = internal::random<Scalar>(), - s2 = internal::random<Scalar>(); - - // scalar addition - VERIFY_IS_APPROX(m1.cwise() + s1, s1 + m1.cwise()); - VERIFY_IS_APPROX(m1.cwise() + s1, MatrixType::Constant(rows,cols,s1) + m1); - VERIFY_IS_APPROX((m1*Scalar(2)).cwise() - s2, (m1+m1) - MatrixType::Constant(rows,cols,s2) ); - m3 = m1; - m3.cwise() += s2; - VERIFY_IS_APPROX(m3, m1.cwise() + s2); - m3 = m1; - m3.cwise() -= s1; - VERIFY_IS_APPROX(m3, m1.cwise() - s1); - - VERIFY_IS_EQUAL((m1.corner(TopLeft,1,1)), (m1.block(0,0,1,1))); - VERIFY_IS_EQUAL((m1.template corner<1,1>(TopLeft)), (m1.template block<1,1>(0,0))); - VERIFY_IS_EQUAL((m1.col(0).start(1)), (m1.col(0).segment(0,1))); - VERIFY_IS_EQUAL((m1.col(0).template start<1>()), (m1.col(0).segment(0,1))); - VERIFY_IS_EQUAL((m1.col(0).end(1)), (m1.col(0).segment(rows-1,1))); - VERIFY_IS_EQUAL((m1.col(0).template end<1>()), (m1.col(0).segment(rows-1,1))); - - using std::cos; - using numext::real; - using numext::abs2; - VERIFY_IS_EQUAL(ei_cos(s1), cos(s1)); - VERIFY_IS_EQUAL(ei_real(s1), real(s1)); - VERIFY_IS_EQUAL(ei_abs2(s1), abs2(s1)); - - m1.minor(0,0); -} - -void test_eigen2support() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( eigen2support(Matrix<double,1,1>()) ); - CALL_SUBTEST_2( eigen2support(MatrixXd(1,1)) ); - CALL_SUBTEST_4( eigen2support(Matrix3f()) ); - CALL_SUBTEST_5( eigen2support(Matrix4d()) ); - CALL_SUBTEST_2( eigen2support(MatrixXf(200,200)) ); - CALL_SUBTEST_6( eigen2support(MatrixXcd(100,100)) ); - } -} diff --git a/eigen/test/eigensolver_complex.cpp b/eigen/test/eigensolver_complex.cpp deleted file mode 100644 index 7269452..0000000 --- a/eigen/test/eigensolver_complex.cpp +++ /dev/null @@ -1,176 +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) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <limits> -#include <Eigen/Eigenvalues> -#include <Eigen/LU> - -template<typename MatrixType> bool find_pivot(typename MatrixType::Scalar tol, MatrixType &diffs, Index col=0) -{ - bool match = diffs.diagonal().sum() <= tol; - if(match || col==diffs.cols()) - { - return match; - } - else - { - Index n = diffs.cols(); - std::vector<std::pair<Index,Index> > transpositions; - for(Index i=col; i<n; ++i) - { - Index best_index(0); - if(diffs.col(col).segment(col,n-i).minCoeff(&best_index) > tol) - break; - - best_index += col; - - diffs.row(col).swap(diffs.row(best_index)); - if(find_pivot(tol,diffs,col+1)) return true; - diffs.row(col).swap(diffs.row(best_index)); - - // move current pivot to the end - diffs.row(n-(i-col)-1).swap(diffs.row(best_index)); - transpositions.push_back(std::pair<Index,Index>(n-(i-col)-1,best_index)); - } - // restore - for(Index k=transpositions.size()-1; k>=0; --k) - diffs.row(transpositions[k].first).swap(diffs.row(transpositions[k].second)); - } - return false; -} - -/* Check that two column vectors are approximately equal upto permutations. - * Initially, this method checked that the k-th power sums are equal for all k = 1, ..., vec1.rows(), - * however this strategy is numerically inacurate because of numerical cancellation issues. - */ -template<typename VectorType> -void verify_is_approx_upto_permutation(const VectorType& vec1, const VectorType& vec2) -{ - typedef typename VectorType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - VERIFY(vec1.cols() == 1); - VERIFY(vec2.cols() == 1); - VERIFY(vec1.rows() == vec2.rows()); - - Index n = vec1.rows(); - RealScalar tol = test_precision<RealScalar>()*test_precision<RealScalar>()*numext::maxi(vec1.squaredNorm(),vec2.squaredNorm()); - Matrix<RealScalar,Dynamic,Dynamic> diffs = (vec1.rowwise().replicate(n) - vec2.rowwise().replicate(n).transpose()).cwiseAbs2(); - - VERIFY( find_pivot(tol, diffs) ); -} - - -template<typename MatrixType> void eigensolver(const MatrixType& m) -{ - /* this test covers the following files: - ComplexEigenSolver.h, and indirectly ComplexSchur.h - */ - Index rows = m.rows(); - Index cols = m.cols(); - - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - MatrixType a = MatrixType::Random(rows,cols); - MatrixType symmA = a.adjoint() * a; - - ComplexEigenSolver<MatrixType> ei0(symmA); - VERIFY_IS_EQUAL(ei0.info(), Success); - VERIFY_IS_APPROX(symmA * ei0.eigenvectors(), ei0.eigenvectors() * ei0.eigenvalues().asDiagonal()); - - ComplexEigenSolver<MatrixType> ei1(a); - VERIFY_IS_EQUAL(ei1.info(), Success); - VERIFY_IS_APPROX(a * ei1.eigenvectors(), ei1.eigenvectors() * ei1.eigenvalues().asDiagonal()); - // Note: If MatrixType is real then a.eigenvalues() uses EigenSolver and thus - // another algorithm so results may differ slightly - verify_is_approx_upto_permutation(a.eigenvalues(), ei1.eigenvalues()); - - ComplexEigenSolver<MatrixType> ei2; - ei2.setMaxIterations(ComplexSchur<MatrixType>::m_maxIterationsPerRow * rows).compute(a); - VERIFY_IS_EQUAL(ei2.info(), Success); - VERIFY_IS_EQUAL(ei2.eigenvectors(), ei1.eigenvectors()); - VERIFY_IS_EQUAL(ei2.eigenvalues(), ei1.eigenvalues()); - if (rows > 2) { - ei2.setMaxIterations(1).compute(a); - VERIFY_IS_EQUAL(ei2.info(), NoConvergence); - VERIFY_IS_EQUAL(ei2.getMaxIterations(), 1); - } - - ComplexEigenSolver<MatrixType> eiNoEivecs(a, false); - VERIFY_IS_EQUAL(eiNoEivecs.info(), Success); - VERIFY_IS_APPROX(ei1.eigenvalues(), eiNoEivecs.eigenvalues()); - - // Regression test for issue #66 - MatrixType z = MatrixType::Zero(rows,cols); - ComplexEigenSolver<MatrixType> eiz(z); - VERIFY((eiz.eigenvalues().cwiseEqual(0)).all()); - - MatrixType id = MatrixType::Identity(rows, cols); - VERIFY_IS_APPROX(id.operatorNorm(), RealScalar(1)); - - if (rows > 1 && rows < 20) - { - // Test matrix with NaN - a(0,0) = std::numeric_limits<typename MatrixType::RealScalar>::quiet_NaN(); - ComplexEigenSolver<MatrixType> eiNaN(a); - VERIFY_IS_EQUAL(eiNaN.info(), NoConvergence); - } - - // regression test for bug 1098 - { - ComplexEigenSolver<MatrixType> eig(a.adjoint() * a); - eig.compute(a.adjoint() * a); - } - - // regression test for bug 478 - { - a.setZero(); - ComplexEigenSolver<MatrixType> ei3(a); - VERIFY_IS_EQUAL(ei3.info(), Success); - VERIFY_IS_MUCH_SMALLER_THAN(ei3.eigenvalues().norm(),RealScalar(1)); - VERIFY((ei3.eigenvectors().transpose()*ei3.eigenvectors().transpose()).eval().isIdentity()); - } -} - -template<typename MatrixType> void eigensolver_verify_assert(const MatrixType& m) -{ - ComplexEigenSolver<MatrixType> eig; - VERIFY_RAISES_ASSERT(eig.eigenvectors()); - VERIFY_RAISES_ASSERT(eig.eigenvalues()); - - MatrixType a = MatrixType::Random(m.rows(),m.cols()); - eig.compute(a, false); - VERIFY_RAISES_ASSERT(eig.eigenvectors()); -} - -void test_eigensolver_complex() -{ - int s = 0; - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( eigensolver(Matrix4cf()) ); - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4); - CALL_SUBTEST_2( eigensolver(MatrixXcd(s,s)) ); - CALL_SUBTEST_3( eigensolver(Matrix<std::complex<float>, 1, 1>()) ); - CALL_SUBTEST_4( eigensolver(Matrix3f()) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - } - CALL_SUBTEST_1( eigensolver_verify_assert(Matrix4cf()) ); - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4); - CALL_SUBTEST_2( eigensolver_verify_assert(MatrixXcd(s,s)) ); - CALL_SUBTEST_3( eigensolver_verify_assert(Matrix<std::complex<float>, 1, 1>()) ); - CALL_SUBTEST_4( eigensolver_verify_assert(Matrix3f()) ); - - // Test problem size constructors - CALL_SUBTEST_5(ComplexEigenSolver<MatrixXf> tmp(s)); - - TEST_SET_BUT_UNUSED_VARIABLE(s) -} diff --git a/eigen/test/eigensolver_generalized_real.cpp b/eigen/test/eigensolver_generalized_real.cpp deleted file mode 100644 index 9dd44c8..0000000 --- a/eigen/test/eigensolver_generalized_real.cpp +++ /dev/null @@ -1,103 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012-2016 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_RUNTIME_NO_MALLOC -#include "main.h" -#include <limits> -#include <Eigen/Eigenvalues> -#include <Eigen/LU> - -template<typename MatrixType> void generalized_eigensolver_real(const MatrixType& m) -{ - /* this test covers the following files: - GeneralizedEigenSolver.h - */ - Index rows = m.rows(); - Index cols = m.cols(); - - typedef typename MatrixType::Scalar Scalar; - typedef std::complex<Scalar> ComplexScalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - - MatrixType a = MatrixType::Random(rows,cols); - MatrixType b = MatrixType::Random(rows,cols); - MatrixType a1 = MatrixType::Random(rows,cols); - MatrixType b1 = MatrixType::Random(rows,cols); - MatrixType spdA = a.adjoint() * a + a1.adjoint() * a1; - MatrixType spdB = b.adjoint() * b + b1.adjoint() * b1; - - // lets compare to GeneralizedSelfAdjointEigenSolver - { - GeneralizedSelfAdjointEigenSolver<MatrixType> symmEig(spdA, spdB); - GeneralizedEigenSolver<MatrixType> eig(spdA, spdB); - - VERIFY_IS_EQUAL(eig.eigenvalues().imag().cwiseAbs().maxCoeff(), 0); - - VectorType realEigenvalues = eig.eigenvalues().real(); - std::sort(realEigenvalues.data(), realEigenvalues.data()+realEigenvalues.size()); - VERIFY_IS_APPROX(realEigenvalues, symmEig.eigenvalues()); - - // check eigenvectors - typename GeneralizedEigenSolver<MatrixType>::EigenvectorsType D = eig.eigenvalues().asDiagonal(); - typename GeneralizedEigenSolver<MatrixType>::EigenvectorsType V = eig.eigenvectors(); - VERIFY_IS_APPROX(spdA*V, spdB*V*D); - } - - // non symmetric case: - { - GeneralizedEigenSolver<MatrixType> eig(rows); - // TODO enable full-prealocation of required memory, this probably requires an in-place mode for HessenbergDecomposition - //Eigen::internal::set_is_malloc_allowed(false); - eig.compute(a,b); - //Eigen::internal::set_is_malloc_allowed(true); - for(Index k=0; k<cols; ++k) - { - Matrix<ComplexScalar,Dynamic,Dynamic> tmp = (eig.betas()(k)*a).template cast<ComplexScalar>() - eig.alphas()(k)*b; - if(tmp.size()>1 && tmp.norm()>(std::numeric_limits<Scalar>::min)()) - tmp /= tmp.norm(); - VERIFY_IS_MUCH_SMALLER_THAN( std::abs(tmp.determinant()), Scalar(1) ); - } - // check eigenvectors - typename GeneralizedEigenSolver<MatrixType>::EigenvectorsType D = eig.eigenvalues().asDiagonal(); - typename GeneralizedEigenSolver<MatrixType>::EigenvectorsType V = eig.eigenvectors(); - VERIFY_IS_APPROX(a*V, b*V*D); - } - - // regression test for bug 1098 - { - GeneralizedSelfAdjointEigenSolver<MatrixType> eig1(a.adjoint() * a,b.adjoint() * b); - eig1.compute(a.adjoint() * a,b.adjoint() * b); - GeneralizedEigenSolver<MatrixType> eig2(a.adjoint() * a,b.adjoint() * b); - eig2.compute(a.adjoint() * a,b.adjoint() * b); - } - - // check without eigenvectors - { - GeneralizedEigenSolver<MatrixType> eig1(spdA, spdB, true); - GeneralizedEigenSolver<MatrixType> eig2(spdA, spdB, false); - VERIFY_IS_APPROX(eig1.eigenvalues(), eig2.eigenvalues()); - } -} - -void test_eigensolver_generalized_real() -{ - for(int i = 0; i < g_repeat; i++) { - int s = 0; - CALL_SUBTEST_1( generalized_eigensolver_real(Matrix4f()) ); - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4); - CALL_SUBTEST_2( generalized_eigensolver_real(MatrixXd(s,s)) ); - - // some trivial but implementation-wise special cases - CALL_SUBTEST_2( generalized_eigensolver_real(MatrixXd(1,1)) ); - CALL_SUBTEST_2( generalized_eigensolver_real(MatrixXd(2,2)) ); - CALL_SUBTEST_3( generalized_eigensolver_real(Matrix<double,1,1>()) ); - CALL_SUBTEST_4( generalized_eigensolver_real(Matrix2d()) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - } -} diff --git a/eigen/test/eigensolver_generic.cpp b/eigen/test/eigensolver_generic.cpp deleted file mode 100644 index 07bf65e..0000000 --- a/eigen/test/eigensolver_generic.cpp +++ /dev/null @@ -1,165 +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) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <limits> -#include <Eigen/Eigenvalues> - -template<typename MatrixType> void eigensolver(const MatrixType& m) -{ - /* this test covers the following files: - EigenSolver.h - */ - Index rows = m.rows(); - Index cols = m.cols(); - - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealVectorType; - typedef typename std::complex<typename NumTraits<typename MatrixType::Scalar>::Real> Complex; - - MatrixType a = MatrixType::Random(rows,cols); - MatrixType a1 = MatrixType::Random(rows,cols); - MatrixType symmA = a.adjoint() * a + a1.adjoint() * a1; - - EigenSolver<MatrixType> ei0(symmA); - VERIFY_IS_EQUAL(ei0.info(), Success); - VERIFY_IS_APPROX(symmA * ei0.pseudoEigenvectors(), ei0.pseudoEigenvectors() * ei0.pseudoEigenvalueMatrix()); - VERIFY_IS_APPROX((symmA.template cast<Complex>()) * (ei0.pseudoEigenvectors().template cast<Complex>()), - (ei0.pseudoEigenvectors().template cast<Complex>()) * (ei0.eigenvalues().asDiagonal())); - - EigenSolver<MatrixType> ei1(a); - VERIFY_IS_EQUAL(ei1.info(), Success); - VERIFY_IS_APPROX(a * ei1.pseudoEigenvectors(), ei1.pseudoEigenvectors() * ei1.pseudoEigenvalueMatrix()); - VERIFY_IS_APPROX(a.template cast<Complex>() * ei1.eigenvectors(), - ei1.eigenvectors() * ei1.eigenvalues().asDiagonal()); - VERIFY_IS_APPROX(ei1.eigenvectors().colwise().norm(), RealVectorType::Ones(rows).transpose()); - VERIFY_IS_APPROX(a.eigenvalues(), ei1.eigenvalues()); - - EigenSolver<MatrixType> ei2; - ei2.setMaxIterations(RealSchur<MatrixType>::m_maxIterationsPerRow * rows).compute(a); - VERIFY_IS_EQUAL(ei2.info(), Success); - VERIFY_IS_EQUAL(ei2.eigenvectors(), ei1.eigenvectors()); - VERIFY_IS_EQUAL(ei2.eigenvalues(), ei1.eigenvalues()); - if (rows > 2) { - ei2.setMaxIterations(1).compute(a); - VERIFY_IS_EQUAL(ei2.info(), NoConvergence); - VERIFY_IS_EQUAL(ei2.getMaxIterations(), 1); - } - - EigenSolver<MatrixType> eiNoEivecs(a, false); - VERIFY_IS_EQUAL(eiNoEivecs.info(), Success); - VERIFY_IS_APPROX(ei1.eigenvalues(), eiNoEivecs.eigenvalues()); - VERIFY_IS_APPROX(ei1.pseudoEigenvalueMatrix(), eiNoEivecs.pseudoEigenvalueMatrix()); - - MatrixType id = MatrixType::Identity(rows, cols); - VERIFY_IS_APPROX(id.operatorNorm(), RealScalar(1)); - - if (rows > 2 && rows < 20) - { - // Test matrix with NaN - a(0,0) = std::numeric_limits<typename MatrixType::RealScalar>::quiet_NaN(); - EigenSolver<MatrixType> eiNaN(a); - VERIFY_IS_EQUAL(eiNaN.info(), NoConvergence); - } - - // regression test for bug 1098 - { - EigenSolver<MatrixType> eig(a.adjoint() * a); - eig.compute(a.adjoint() * a); - } - - // regression test for bug 478 - { - a.setZero(); - EigenSolver<MatrixType> ei3(a); - VERIFY_IS_EQUAL(ei3.info(), Success); - VERIFY_IS_MUCH_SMALLER_THAN(ei3.eigenvalues().norm(),RealScalar(1)); - VERIFY((ei3.eigenvectors().transpose()*ei3.eigenvectors().transpose()).eval().isIdentity()); - } -} - -template<typename MatrixType> void eigensolver_verify_assert(const MatrixType& m) -{ - EigenSolver<MatrixType> eig; - VERIFY_RAISES_ASSERT(eig.eigenvectors()); - VERIFY_RAISES_ASSERT(eig.pseudoEigenvectors()); - VERIFY_RAISES_ASSERT(eig.pseudoEigenvalueMatrix()); - VERIFY_RAISES_ASSERT(eig.eigenvalues()); - - MatrixType a = MatrixType::Random(m.rows(),m.cols()); - eig.compute(a, false); - VERIFY_RAISES_ASSERT(eig.eigenvectors()); - VERIFY_RAISES_ASSERT(eig.pseudoEigenvectors()); -} - -void test_eigensolver_generic() -{ - int s = 0; - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( eigensolver(Matrix4f()) ); - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4); - CALL_SUBTEST_2( eigensolver(MatrixXd(s,s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - - // some trivial but implementation-wise tricky cases - CALL_SUBTEST_2( eigensolver(MatrixXd(1,1)) ); - CALL_SUBTEST_2( eigensolver(MatrixXd(2,2)) ); - CALL_SUBTEST_3( eigensolver(Matrix<double,1,1>()) ); - CALL_SUBTEST_4( eigensolver(Matrix2d()) ); - } - - CALL_SUBTEST_1( eigensolver_verify_assert(Matrix4f()) ); - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4); - CALL_SUBTEST_2( eigensolver_verify_assert(MatrixXd(s,s)) ); - CALL_SUBTEST_3( eigensolver_verify_assert(Matrix<double,1,1>()) ); - CALL_SUBTEST_4( eigensolver_verify_assert(Matrix2d()) ); - - // Test problem size constructors - CALL_SUBTEST_5(EigenSolver<MatrixXf> tmp(s)); - - // regression test for bug 410 - CALL_SUBTEST_2( - { - MatrixXd A(1,1); - A(0,0) = std::sqrt(-1.); // is Not-a-Number - Eigen::EigenSolver<MatrixXd> solver(A); - VERIFY_IS_EQUAL(solver.info(), NumericalIssue); - } - ); - -#ifdef EIGEN_TEST_PART_2 - { - // regression test for bug 793 - MatrixXd a(3,3); - a << 0, 0, 1, - 1, 1, 1, - 1, 1e+200, 1; - Eigen::EigenSolver<MatrixXd> eig(a); - double scale = 1e-200; // scale to avoid overflow during the comparisons - VERIFY_IS_APPROX(a * eig.pseudoEigenvectors()*scale, eig.pseudoEigenvectors() * eig.pseudoEigenvalueMatrix()*scale); - VERIFY_IS_APPROX(a * eig.eigenvectors()*scale, eig.eigenvectors() * eig.eigenvalues().asDiagonal()*scale); - } - { - // check a case where all eigenvalues are null. - MatrixXd a(2,2); - a << 1, 1, - -1, -1; - Eigen::EigenSolver<MatrixXd> eig(a); - VERIFY_IS_APPROX(eig.pseudoEigenvectors().squaredNorm(), 2.); - VERIFY_IS_APPROX((a * eig.pseudoEigenvectors()).norm()+1., 1.); - VERIFY_IS_APPROX((eig.pseudoEigenvectors() * eig.pseudoEigenvalueMatrix()).norm()+1., 1.); - VERIFY_IS_APPROX((a * eig.eigenvectors()).norm()+1., 1.); - VERIFY_IS_APPROX((eig.eigenvectors() * eig.eigenvalues().asDiagonal()).norm()+1., 1.); - } -#endif - - TEST_SET_BUT_UNUSED_VARIABLE(s) -} diff --git a/eigen/test/eigensolver_selfadjoint.cpp b/eigen/test/eigensolver_selfadjoint.cpp deleted file mode 100644 index 0e39b53..0000000 --- a/eigen/test/eigensolver_selfadjoint.cpp +++ /dev/null @@ -1,273 +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) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include "svd_fill.h" -#include <limits> -#include <Eigen/Eigenvalues> -#include <Eigen/SparseCore> - - -template<typename MatrixType> void selfadjointeigensolver_essential_check(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - RealScalar eival_eps = numext::mini<RealScalar>(test_precision<RealScalar>(), NumTraits<Scalar>::dummy_precision()*20000); - - SelfAdjointEigenSolver<MatrixType> eiSymm(m); - VERIFY_IS_EQUAL(eiSymm.info(), Success); - - RealScalar scaling = m.cwiseAbs().maxCoeff(); - - if(scaling<(std::numeric_limits<RealScalar>::min)()) - { - VERIFY(eiSymm.eigenvalues().cwiseAbs().maxCoeff() <= (std::numeric_limits<RealScalar>::min)()); - } - else - { - VERIFY_IS_APPROX((m.template selfadjointView<Lower>() * eiSymm.eigenvectors())/scaling, - (eiSymm.eigenvectors() * eiSymm.eigenvalues().asDiagonal())/scaling); - } - VERIFY_IS_APPROX(m.template selfadjointView<Lower>().eigenvalues(), eiSymm.eigenvalues()); - VERIFY_IS_UNITARY(eiSymm.eigenvectors()); - - if(m.cols()<=4) - { - SelfAdjointEigenSolver<MatrixType> eiDirect; - eiDirect.computeDirect(m); - VERIFY_IS_EQUAL(eiDirect.info(), Success); - if(! eiSymm.eigenvalues().isApprox(eiDirect.eigenvalues(), eival_eps) ) - { - std::cerr << "reference eigenvalues: " << eiSymm.eigenvalues().transpose() << "\n" - << "obtained eigenvalues: " << eiDirect.eigenvalues().transpose() << "\n" - << "diff: " << (eiSymm.eigenvalues()-eiDirect.eigenvalues()).transpose() << "\n" - << "error (eps): " << (eiSymm.eigenvalues()-eiDirect.eigenvalues()).norm() / eiSymm.eigenvalues().norm() << " (" << eival_eps << ")\n"; - } - if(scaling<(std::numeric_limits<RealScalar>::min)()) - { - VERIFY(eiDirect.eigenvalues().cwiseAbs().maxCoeff() <= (std::numeric_limits<RealScalar>::min)()); - } - else - { - VERIFY_IS_APPROX(eiSymm.eigenvalues()/scaling, eiDirect.eigenvalues()/scaling); - VERIFY_IS_APPROX((m.template selfadjointView<Lower>() * eiDirect.eigenvectors())/scaling, - (eiDirect.eigenvectors() * eiDirect.eigenvalues().asDiagonal())/scaling); - VERIFY_IS_APPROX(m.template selfadjointView<Lower>().eigenvalues()/scaling, eiDirect.eigenvalues()/scaling); - } - - VERIFY_IS_UNITARY(eiDirect.eigenvectors()); - } -} - -template<typename MatrixType> void selfadjointeigensolver(const MatrixType& m) -{ - /* this test covers the following files: - EigenSolver.h, SelfAdjointEigenSolver.h (and indirectly: Tridiagonalization.h) - */ - Index rows = m.rows(); - Index cols = m.cols(); - - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - RealScalar largerEps = 10*test_precision<RealScalar>(); - - MatrixType a = MatrixType::Random(rows,cols); - MatrixType a1 = MatrixType::Random(rows,cols); - MatrixType symmA = a.adjoint() * a + a1.adjoint() * a1; - MatrixType symmC = symmA; - - svd_fill_random(symmA,Symmetric); - - symmA.template triangularView<StrictlyUpper>().setZero(); - symmC.template triangularView<StrictlyUpper>().setZero(); - - MatrixType b = MatrixType::Random(rows,cols); - MatrixType b1 = MatrixType::Random(rows,cols); - MatrixType symmB = b.adjoint() * b + b1.adjoint() * b1; - symmB.template triangularView<StrictlyUpper>().setZero(); - - CALL_SUBTEST( selfadjointeigensolver_essential_check(symmA) ); - - SelfAdjointEigenSolver<MatrixType> eiSymm(symmA); - // generalized eigen pb - GeneralizedSelfAdjointEigenSolver<MatrixType> eiSymmGen(symmC, symmB); - - SelfAdjointEigenSolver<MatrixType> eiSymmNoEivecs(symmA, false); - VERIFY_IS_EQUAL(eiSymmNoEivecs.info(), Success); - VERIFY_IS_APPROX(eiSymm.eigenvalues(), eiSymmNoEivecs.eigenvalues()); - - // generalized eigen problem Ax = lBx - eiSymmGen.compute(symmC, symmB,Ax_lBx); - VERIFY_IS_EQUAL(eiSymmGen.info(), Success); - VERIFY((symmC.template selfadjointView<Lower>() * eiSymmGen.eigenvectors()).isApprox( - symmB.template selfadjointView<Lower>() * (eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps)); - - // generalized eigen problem BAx = lx - eiSymmGen.compute(symmC, symmB,BAx_lx); - VERIFY_IS_EQUAL(eiSymmGen.info(), Success); - VERIFY((symmB.template selfadjointView<Lower>() * (symmC.template selfadjointView<Lower>() * eiSymmGen.eigenvectors())).isApprox( - (eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps)); - - // generalized eigen problem ABx = lx - eiSymmGen.compute(symmC, symmB,ABx_lx); - VERIFY_IS_EQUAL(eiSymmGen.info(), Success); - VERIFY((symmC.template selfadjointView<Lower>() * (symmB.template selfadjointView<Lower>() * eiSymmGen.eigenvectors())).isApprox( - (eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps)); - - - eiSymm.compute(symmC); - MatrixType sqrtSymmA = eiSymm.operatorSqrt(); - VERIFY_IS_APPROX(MatrixType(symmC.template selfadjointView<Lower>()), sqrtSymmA*sqrtSymmA); - VERIFY_IS_APPROX(sqrtSymmA, symmC.template selfadjointView<Lower>()*eiSymm.operatorInverseSqrt()); - - MatrixType id = MatrixType::Identity(rows, cols); - VERIFY_IS_APPROX(id.template selfadjointView<Lower>().operatorNorm(), RealScalar(1)); - - SelfAdjointEigenSolver<MatrixType> eiSymmUninitialized; - VERIFY_RAISES_ASSERT(eiSymmUninitialized.info()); - VERIFY_RAISES_ASSERT(eiSymmUninitialized.eigenvalues()); - VERIFY_RAISES_ASSERT(eiSymmUninitialized.eigenvectors()); - VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorSqrt()); - VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorInverseSqrt()); - - eiSymmUninitialized.compute(symmA, false); - VERIFY_RAISES_ASSERT(eiSymmUninitialized.eigenvectors()); - VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorSqrt()); - VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorInverseSqrt()); - - // test Tridiagonalization's methods - Tridiagonalization<MatrixType> tridiag(symmC); - VERIFY_IS_APPROX(tridiag.diagonal(), tridiag.matrixT().diagonal()); - VERIFY_IS_APPROX(tridiag.subDiagonal(), tridiag.matrixT().template diagonal<-1>()); - Matrix<RealScalar,Dynamic,Dynamic> T = tridiag.matrixT(); - if(rows>1 && cols>1) { - // FIXME check that upper and lower part are 0: - //VERIFY(T.topRightCorner(rows-2, cols-2).template triangularView<Upper>().isZero()); - } - VERIFY_IS_APPROX(tridiag.diagonal(), T.diagonal()); - VERIFY_IS_APPROX(tridiag.subDiagonal(), T.template diagonal<1>()); - VERIFY_IS_APPROX(MatrixType(symmC.template selfadjointView<Lower>()), tridiag.matrixQ() * tridiag.matrixT().eval() * MatrixType(tridiag.matrixQ()).adjoint()); - VERIFY_IS_APPROX(MatrixType(symmC.template selfadjointView<Lower>()), tridiag.matrixQ() * tridiag.matrixT() * tridiag.matrixQ().adjoint()); - - // Test computation of eigenvalues from tridiagonal matrix - if(rows > 1) - { - SelfAdjointEigenSolver<MatrixType> eiSymmTridiag; - eiSymmTridiag.computeFromTridiagonal(tridiag.matrixT().diagonal(), tridiag.matrixT().diagonal(-1), ComputeEigenvectors); - VERIFY_IS_APPROX(eiSymm.eigenvalues(), eiSymmTridiag.eigenvalues()); - VERIFY_IS_APPROX(tridiag.matrixT(), eiSymmTridiag.eigenvectors().real() * eiSymmTridiag.eigenvalues().asDiagonal() * eiSymmTridiag.eigenvectors().real().transpose()); - } - - if (rows > 1 && rows < 20) - { - // Test matrix with NaN - symmC(0,0) = std::numeric_limits<typename MatrixType::RealScalar>::quiet_NaN(); - SelfAdjointEigenSolver<MatrixType> eiSymmNaN(symmC); - VERIFY_IS_EQUAL(eiSymmNaN.info(), NoConvergence); - } - - // regression test for bug 1098 - { - SelfAdjointEigenSolver<MatrixType> eig(a.adjoint() * a); - eig.compute(a.adjoint() * a); - } - - // regression test for bug 478 - { - a.setZero(); - SelfAdjointEigenSolver<MatrixType> ei3(a); - VERIFY_IS_EQUAL(ei3.info(), Success); - VERIFY_IS_MUCH_SMALLER_THAN(ei3.eigenvalues().norm(),RealScalar(1)); - VERIFY((ei3.eigenvectors().transpose()*ei3.eigenvectors().transpose()).eval().isIdentity()); - } -} - -template<int> -void bug_854() -{ - Matrix3d m; - m << 850.961, 51.966, 0, - 51.966, 254.841, 0, - 0, 0, 0; - selfadjointeigensolver_essential_check(m); -} - -template<int> -void bug_1014() -{ - Matrix3d m; - m << 0.11111111111111114658, 0, 0, - 0, 0.11111111111111109107, 0, - 0, 0, 0.11111111111111107719; - selfadjointeigensolver_essential_check(m); -} - -template<int> -void bug_1225() -{ - Matrix3d m1, m2; - m1.setRandom(); - m1 = m1*m1.transpose(); - m2 = m1.triangularView<Upper>(); - SelfAdjointEigenSolver<Matrix3d> eig1(m1); - SelfAdjointEigenSolver<Matrix3d> eig2(m2.selfadjointView<Upper>()); - VERIFY_IS_APPROX(eig1.eigenvalues(), eig2.eigenvalues()); -} - -template<int> -void bug_1204() -{ - SparseMatrix<double> A(2,2); - A.setIdentity(); - SelfAdjointEigenSolver<Eigen::SparseMatrix<double> > eig(A); -} - -void test_eigensolver_selfadjoint() -{ - int s = 0; - for(int i = 0; i < g_repeat; i++) { - // trivial test for 1x1 matrices: - CALL_SUBTEST_1( selfadjointeigensolver(Matrix<float, 1, 1>())); - CALL_SUBTEST_1( selfadjointeigensolver(Matrix<double, 1, 1>())); - // very important to test 3x3 and 2x2 matrices since we provide special paths for them - CALL_SUBTEST_12( selfadjointeigensolver(Matrix2f()) ); - CALL_SUBTEST_12( selfadjointeigensolver(Matrix2d()) ); - CALL_SUBTEST_13( selfadjointeigensolver(Matrix3f()) ); - CALL_SUBTEST_13( selfadjointeigensolver(Matrix3d()) ); - CALL_SUBTEST_2( selfadjointeigensolver(Matrix4d()) ); - - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4); - CALL_SUBTEST_3( selfadjointeigensolver(MatrixXf(s,s)) ); - CALL_SUBTEST_4( selfadjointeigensolver(MatrixXd(s,s)) ); - CALL_SUBTEST_5( selfadjointeigensolver(MatrixXcd(s,s)) ); - CALL_SUBTEST_9( selfadjointeigensolver(Matrix<std::complex<double>,Dynamic,Dynamic,RowMajor>(s,s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - - // some trivial but implementation-wise tricky cases - CALL_SUBTEST_4( selfadjointeigensolver(MatrixXd(1,1)) ); - CALL_SUBTEST_4( selfadjointeigensolver(MatrixXd(2,2)) ); - CALL_SUBTEST_6( selfadjointeigensolver(Matrix<double,1,1>()) ); - CALL_SUBTEST_7( selfadjointeigensolver(Matrix<double,2,2>()) ); - } - - CALL_SUBTEST_13( bug_854<0>() ); - CALL_SUBTEST_13( bug_1014<0>() ); - CALL_SUBTEST_13( bug_1204<0>() ); - CALL_SUBTEST_13( bug_1225<0>() ); - - // Test problem size constructors - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4); - CALL_SUBTEST_8(SelfAdjointEigenSolver<MatrixXf> tmp1(s)); - CALL_SUBTEST_8(Tridiagonalization<MatrixXf> tmp2(s)); - - TEST_SET_BUT_UNUSED_VARIABLE(s) -} - diff --git a/eigen/test/evaluator_common.h b/eigen/test/evaluator_common.h deleted file mode 100644 index e69de29..0000000 --- a/eigen/test/evaluator_common.h +++ /dev/null diff --git a/eigen/test/evaluators.cpp b/eigen/test/evaluators.cpp deleted file mode 100644 index aed5a05..0000000 --- a/eigen/test/evaluators.cpp +++ /dev/null @@ -1,499 +0,0 @@ - -#include "main.h" - -namespace Eigen { - - template<typename Lhs,typename Rhs> - const Product<Lhs,Rhs> - prod(const Lhs& lhs, const Rhs& rhs) - { - return Product<Lhs,Rhs>(lhs,rhs); - } - - template<typename Lhs,typename Rhs> - const Product<Lhs,Rhs,LazyProduct> - lazyprod(const Lhs& lhs, const Rhs& rhs) - { - return Product<Lhs,Rhs,LazyProduct>(lhs,rhs); - } - - template<typename DstXprType, typename SrcXprType> - EIGEN_STRONG_INLINE - DstXprType& copy_using_evaluator(const EigenBase<DstXprType> &dst, const SrcXprType &src) - { - call_assignment(dst.const_cast_derived(), src.derived(), internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>()); - return dst.const_cast_derived(); - } - - template<typename DstXprType, template <typename> class StorageBase, typename SrcXprType> - EIGEN_STRONG_INLINE - const DstXprType& copy_using_evaluator(const NoAlias<DstXprType, StorageBase>& dst, const SrcXprType &src) - { - call_assignment(dst, src.derived(), internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>()); - return dst.expression(); - } - - template<typename DstXprType, typename SrcXprType> - EIGEN_STRONG_INLINE - DstXprType& copy_using_evaluator(const PlainObjectBase<DstXprType> &dst, const SrcXprType &src) - { - #ifdef EIGEN_NO_AUTOMATIC_RESIZING - eigen_assert((dst.size()==0 || (IsVectorAtCompileTime ? (dst.size() == src.size()) - : (dst.rows() == src.rows() && dst.cols() == src.cols()))) - && "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined"); - #else - dst.const_cast_derived().resizeLike(src.derived()); - #endif - - call_assignment(dst.const_cast_derived(), src.derived(), internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>()); - return dst.const_cast_derived(); - } - - template<typename DstXprType, typename SrcXprType> - void add_assign_using_evaluator(const DstXprType& dst, const SrcXprType& src) - { - typedef typename DstXprType::Scalar Scalar; - call_assignment(const_cast<DstXprType&>(dst), src.derived(), internal::add_assign_op<Scalar,typename SrcXprType::Scalar>()); - } - - template<typename DstXprType, typename SrcXprType> - void subtract_assign_using_evaluator(const DstXprType& dst, const SrcXprType& src) - { - typedef typename DstXprType::Scalar Scalar; - call_assignment(const_cast<DstXprType&>(dst), src.derived(), internal::sub_assign_op<Scalar,typename SrcXprType::Scalar>()); - } - - template<typename DstXprType, typename SrcXprType> - void multiply_assign_using_evaluator(const DstXprType& dst, const SrcXprType& src) - { - typedef typename DstXprType::Scalar Scalar; - call_assignment(dst.const_cast_derived(), src.derived(), internal::mul_assign_op<Scalar,typename SrcXprType::Scalar>()); - } - - template<typename DstXprType, typename SrcXprType> - void divide_assign_using_evaluator(const DstXprType& dst, const SrcXprType& src) - { - typedef typename DstXprType::Scalar Scalar; - call_assignment(dst.const_cast_derived(), src.derived(), internal::div_assign_op<Scalar,typename SrcXprType::Scalar>()); - } - - template<typename DstXprType, typename SrcXprType> - void swap_using_evaluator(const DstXprType& dst, const SrcXprType& src) - { - typedef typename DstXprType::Scalar Scalar; - call_assignment(dst.const_cast_derived(), src.const_cast_derived(), internal::swap_assign_op<Scalar>()); - } - - namespace internal { - template<typename Dst, template <typename> class StorageBase, typename Src, typename Func> - EIGEN_DEVICE_FUNC void call_assignment(const NoAlias<Dst,StorageBase>& dst, const Src& src, const Func& func) - { - call_assignment_no_alias(dst.expression(), src, func); - } - } - -} - -template<typename XprType> long get_cost(const XprType& ) { return Eigen::internal::evaluator<XprType>::CoeffReadCost; } - -using namespace std; - -#define VERIFY_IS_APPROX_EVALUATOR(DEST,EXPR) VERIFY_IS_APPROX(copy_using_evaluator(DEST,(EXPR)), (EXPR).eval()); -#define VERIFY_IS_APPROX_EVALUATOR2(DEST,EXPR,REF) VERIFY_IS_APPROX(copy_using_evaluator(DEST,(EXPR)), (REF).eval()); - -void test_evaluators() -{ - // Testing Matrix evaluator and Transpose - Vector2d v = Vector2d::Random(); - const Vector2d v_const(v); - Vector2d v2; - RowVector2d w; - - VERIFY_IS_APPROX_EVALUATOR(v2, v); - VERIFY_IS_APPROX_EVALUATOR(v2, v_const); - - // Testing Transpose - VERIFY_IS_APPROX_EVALUATOR(w, v.transpose()); // Transpose as rvalue - VERIFY_IS_APPROX_EVALUATOR(w, v_const.transpose()); - - copy_using_evaluator(w.transpose(), v); // Transpose as lvalue - VERIFY_IS_APPROX(w,v.transpose().eval()); - - copy_using_evaluator(w.transpose(), v_const); - VERIFY_IS_APPROX(w,v_const.transpose().eval()); - - // Testing Array evaluator - { - ArrayXXf a(2,3); - ArrayXXf b(3,2); - a << 1,2,3, 4,5,6; - const ArrayXXf a_const(a); - - VERIFY_IS_APPROX_EVALUATOR(b, a.transpose()); - - VERIFY_IS_APPROX_EVALUATOR(b, a_const.transpose()); - - // Testing CwiseNullaryOp evaluator - copy_using_evaluator(w, RowVector2d::Random()); - VERIFY((w.array() >= -1).all() && (w.array() <= 1).all()); // not easy to test ... - - VERIFY_IS_APPROX_EVALUATOR(w, RowVector2d::Zero()); - - VERIFY_IS_APPROX_EVALUATOR(w, RowVector2d::Constant(3)); - - // mix CwiseNullaryOp and transpose - VERIFY_IS_APPROX_EVALUATOR(w, Vector2d::Zero().transpose()); - } - - { - // test product expressions - int s = internal::random<int>(1,100); - MatrixXf a(s,s), b(s,s), c(s,s), d(s,s); - a.setRandom(); - b.setRandom(); - c.setRandom(); - d.setRandom(); - VERIFY_IS_APPROX_EVALUATOR(d, (a + b)); - VERIFY_IS_APPROX_EVALUATOR(d, (a + b).transpose()); - VERIFY_IS_APPROX_EVALUATOR2(d, prod(a,b), a*b); - VERIFY_IS_APPROX_EVALUATOR2(d.noalias(), prod(a,b), a*b); - VERIFY_IS_APPROX_EVALUATOR2(d, prod(a,b) + c, a*b + c); - VERIFY_IS_APPROX_EVALUATOR2(d, s * prod(a,b), s * a*b); - VERIFY_IS_APPROX_EVALUATOR2(d, prod(a,b).transpose(), (a*b).transpose()); - VERIFY_IS_APPROX_EVALUATOR2(d, prod(a,b) + prod(b,c), a*b + b*c); - - // check that prod works even with aliasing present - c = a*a; - copy_using_evaluator(a, prod(a,a)); - VERIFY_IS_APPROX(a,c); - - // check compound assignment of products - d = c; - add_assign_using_evaluator(c.noalias(), prod(a,b)); - d.noalias() += a*b; - VERIFY_IS_APPROX(c, d); - - d = c; - subtract_assign_using_evaluator(c.noalias(), prod(a,b)); - d.noalias() -= a*b; - VERIFY_IS_APPROX(c, d); - } - - { - // test product with all possible sizes - int s = internal::random<int>(1,100); - Matrix<float, 1, 1> m11, res11; m11.setRandom(1,1); - Matrix<float, 1, 4> m14, res14; m14.setRandom(1,4); - Matrix<float, 1,Dynamic> m1X, res1X; m1X.setRandom(1,s); - Matrix<float, 4, 1> m41, res41; m41.setRandom(4,1); - Matrix<float, 4, 4> m44, res44; m44.setRandom(4,4); - Matrix<float, 4,Dynamic> m4X, res4X; m4X.setRandom(4,s); - Matrix<float,Dynamic, 1> mX1, resX1; mX1.setRandom(s,1); - Matrix<float,Dynamic, 4> mX4, resX4; mX4.setRandom(s,4); - Matrix<float,Dynamic,Dynamic> mXX, resXX; mXX.setRandom(s,s); - - VERIFY_IS_APPROX_EVALUATOR2(res11, prod(m11,m11), m11*m11); - VERIFY_IS_APPROX_EVALUATOR2(res11, prod(m14,m41), m14*m41); - VERIFY_IS_APPROX_EVALUATOR2(res11, prod(m1X,mX1), m1X*mX1); - VERIFY_IS_APPROX_EVALUATOR2(res14, prod(m11,m14), m11*m14); - VERIFY_IS_APPROX_EVALUATOR2(res14, prod(m14,m44), m14*m44); - VERIFY_IS_APPROX_EVALUATOR2(res14, prod(m1X,mX4), m1X*mX4); - VERIFY_IS_APPROX_EVALUATOR2(res1X, prod(m11,m1X), m11*m1X); - VERIFY_IS_APPROX_EVALUATOR2(res1X, prod(m14,m4X), m14*m4X); - VERIFY_IS_APPROX_EVALUATOR2(res1X, prod(m1X,mXX), m1X*mXX); - VERIFY_IS_APPROX_EVALUATOR2(res41, prod(m41,m11), m41*m11); - VERIFY_IS_APPROX_EVALUATOR2(res41, prod(m44,m41), m44*m41); - VERIFY_IS_APPROX_EVALUATOR2(res41, prod(m4X,mX1), m4X*mX1); - VERIFY_IS_APPROX_EVALUATOR2(res44, prod(m41,m14), m41*m14); - VERIFY_IS_APPROX_EVALUATOR2(res44, prod(m44,m44), m44*m44); - VERIFY_IS_APPROX_EVALUATOR2(res44, prod(m4X,mX4), m4X*mX4); - VERIFY_IS_APPROX_EVALUATOR2(res4X, prod(m41,m1X), m41*m1X); - VERIFY_IS_APPROX_EVALUATOR2(res4X, prod(m44,m4X), m44*m4X); - VERIFY_IS_APPROX_EVALUATOR2(res4X, prod(m4X,mXX), m4X*mXX); - VERIFY_IS_APPROX_EVALUATOR2(resX1, prod(mX1,m11), mX1*m11); - VERIFY_IS_APPROX_EVALUATOR2(resX1, prod(mX4,m41), mX4*m41); - VERIFY_IS_APPROX_EVALUATOR2(resX1, prod(mXX,mX1), mXX*mX1); - VERIFY_IS_APPROX_EVALUATOR2(resX4, prod(mX1,m14), mX1*m14); - VERIFY_IS_APPROX_EVALUATOR2(resX4, prod(mX4,m44), mX4*m44); - VERIFY_IS_APPROX_EVALUATOR2(resX4, prod(mXX,mX4), mXX*mX4); - VERIFY_IS_APPROX_EVALUATOR2(resXX, prod(mX1,m1X), mX1*m1X); - VERIFY_IS_APPROX_EVALUATOR2(resXX, prod(mX4,m4X), mX4*m4X); - VERIFY_IS_APPROX_EVALUATOR2(resXX, prod(mXX,mXX), mXX*mXX); - } - - { - ArrayXXf a(2,3); - ArrayXXf b(3,2); - a << 1,2,3, 4,5,6; - const ArrayXXf a_const(a); - - // this does not work because Random is eval-before-nested: - // copy_using_evaluator(w, Vector2d::Random().transpose()); - - // test CwiseUnaryOp - VERIFY_IS_APPROX_EVALUATOR(v2, 3 * v); - VERIFY_IS_APPROX_EVALUATOR(w, (3 * v).transpose()); - VERIFY_IS_APPROX_EVALUATOR(b, (a + 3).transpose()); - VERIFY_IS_APPROX_EVALUATOR(b, (2 * a_const + 3).transpose()); - - // test CwiseBinaryOp - VERIFY_IS_APPROX_EVALUATOR(v2, v + Vector2d::Ones()); - VERIFY_IS_APPROX_EVALUATOR(w, (v + Vector2d::Ones()).transpose().cwiseProduct(RowVector2d::Constant(3))); - - // dynamic matrices and arrays - MatrixXd mat1(6,6), mat2(6,6); - VERIFY_IS_APPROX_EVALUATOR(mat1, MatrixXd::Identity(6,6)); - VERIFY_IS_APPROX_EVALUATOR(mat2, mat1); - copy_using_evaluator(mat2.transpose(), mat1); - VERIFY_IS_APPROX(mat2.transpose(), mat1); - - ArrayXXd arr1(6,6), arr2(6,6); - VERIFY_IS_APPROX_EVALUATOR(arr1, ArrayXXd::Constant(6,6, 3.0)); - VERIFY_IS_APPROX_EVALUATOR(arr2, arr1); - - // test automatic resizing - mat2.resize(3,3); - VERIFY_IS_APPROX_EVALUATOR(mat2, mat1); - arr2.resize(9,9); - VERIFY_IS_APPROX_EVALUATOR(arr2, arr1); - - // test direct traversal - Matrix3f m3; - Array33f a3; - VERIFY_IS_APPROX_EVALUATOR(m3, Matrix3f::Identity()); // matrix, nullary - // TODO: find a way to test direct traversal with array - VERIFY_IS_APPROX_EVALUATOR(m3.transpose(), Matrix3f::Identity().transpose()); // transpose - VERIFY_IS_APPROX_EVALUATOR(m3, 2 * Matrix3f::Identity()); // unary - VERIFY_IS_APPROX_EVALUATOR(m3, Matrix3f::Identity() + Matrix3f::Zero()); // binary - VERIFY_IS_APPROX_EVALUATOR(m3.block(0,0,2,2), Matrix3f::Identity().block(1,1,2,2)); // block - - // test linear traversal - VERIFY_IS_APPROX_EVALUATOR(m3, Matrix3f::Zero()); // matrix, nullary - VERIFY_IS_APPROX_EVALUATOR(a3, Array33f::Zero()); // array - VERIFY_IS_APPROX_EVALUATOR(m3.transpose(), Matrix3f::Zero().transpose()); // transpose - VERIFY_IS_APPROX_EVALUATOR(m3, 2 * Matrix3f::Zero()); // unary - VERIFY_IS_APPROX_EVALUATOR(m3, Matrix3f::Zero() + m3); // binary - - // test inner vectorization - Matrix4f m4, m4src = Matrix4f::Random(); - Array44f a4, a4src = Matrix4f::Random(); - VERIFY_IS_APPROX_EVALUATOR(m4, m4src); // matrix - VERIFY_IS_APPROX_EVALUATOR(a4, a4src); // array - VERIFY_IS_APPROX_EVALUATOR(m4.transpose(), m4src.transpose()); // transpose - // TODO: find out why Matrix4f::Zero() does not allow inner vectorization - VERIFY_IS_APPROX_EVALUATOR(m4, 2 * m4src); // unary - VERIFY_IS_APPROX_EVALUATOR(m4, m4src + m4src); // binary - - // test linear vectorization - MatrixXf mX(6,6), mXsrc = MatrixXf::Random(6,6); - ArrayXXf aX(6,6), aXsrc = ArrayXXf::Random(6,6); - VERIFY_IS_APPROX_EVALUATOR(mX, mXsrc); // matrix - VERIFY_IS_APPROX_EVALUATOR(aX, aXsrc); // array - VERIFY_IS_APPROX_EVALUATOR(mX.transpose(), mXsrc.transpose()); // transpose - VERIFY_IS_APPROX_EVALUATOR(mX, MatrixXf::Zero(6,6)); // nullary - VERIFY_IS_APPROX_EVALUATOR(mX, 2 * mXsrc); // unary - VERIFY_IS_APPROX_EVALUATOR(mX, mXsrc + mXsrc); // binary - - // test blocks and slice vectorization - VERIFY_IS_APPROX_EVALUATOR(m4, (mXsrc.block<4,4>(1,0))); - VERIFY_IS_APPROX_EVALUATOR(aX, ArrayXXf::Constant(10, 10, 3.0).block(2, 3, 6, 6)); - - Matrix4f m4ref = m4; - copy_using_evaluator(m4.block(1, 1, 2, 3), m3.bottomRows(2)); - m4ref.block(1, 1, 2, 3) = m3.bottomRows(2); - VERIFY_IS_APPROX(m4, m4ref); - - mX.setIdentity(20,20); - MatrixXf mXref = MatrixXf::Identity(20,20); - mXsrc = MatrixXf::Random(9,12); - copy_using_evaluator(mX.block(4, 4, 9, 12), mXsrc); - mXref.block(4, 4, 9, 12) = mXsrc; - VERIFY_IS_APPROX(mX, mXref); - - // test Map - const float raw[3] = {1,2,3}; - float buffer[3] = {0,0,0}; - Vector3f v3; - Array3f a3f; - VERIFY_IS_APPROX_EVALUATOR(v3, Map<const Vector3f>(raw)); - VERIFY_IS_APPROX_EVALUATOR(a3f, Map<const Array3f>(raw)); - Vector3f::Map(buffer) = 2*v3; - VERIFY(buffer[0] == 2); - VERIFY(buffer[1] == 4); - VERIFY(buffer[2] == 6); - - // test CwiseUnaryView - mat1.setRandom(); - mat2.setIdentity(); - MatrixXcd matXcd(6,6), matXcd_ref(6,6); - copy_using_evaluator(matXcd.real(), mat1); - copy_using_evaluator(matXcd.imag(), mat2); - matXcd_ref.real() = mat1; - matXcd_ref.imag() = mat2; - VERIFY_IS_APPROX(matXcd, matXcd_ref); - - // test Select - VERIFY_IS_APPROX_EVALUATOR(aX, (aXsrc > 0).select(aXsrc, -aXsrc)); - - // test Replicate - mXsrc = MatrixXf::Random(6, 6); - VectorXf vX = VectorXf::Random(6); - mX.resize(6, 6); - VERIFY_IS_APPROX_EVALUATOR(mX, mXsrc.colwise() + vX); - matXcd.resize(12, 12); - VERIFY_IS_APPROX_EVALUATOR(matXcd, matXcd_ref.replicate(2,2)); - VERIFY_IS_APPROX_EVALUATOR(matXcd, (matXcd_ref.replicate<2,2>())); - - // test partial reductions - VectorXd vec1(6); - VERIFY_IS_APPROX_EVALUATOR(vec1, mat1.rowwise().sum()); - VERIFY_IS_APPROX_EVALUATOR(vec1, mat1.colwise().sum().transpose()); - - // test MatrixWrapper and ArrayWrapper - mat1.setRandom(6,6); - arr1.setRandom(6,6); - VERIFY_IS_APPROX_EVALUATOR(mat2, arr1.matrix()); - VERIFY_IS_APPROX_EVALUATOR(arr2, mat1.array()); - VERIFY_IS_APPROX_EVALUATOR(mat2, (arr1 + 2).matrix()); - VERIFY_IS_APPROX_EVALUATOR(arr2, mat1.array() + 2); - mat2.array() = arr1 * arr1; - VERIFY_IS_APPROX(mat2, (arr1 * arr1).matrix()); - arr2.matrix() = MatrixXd::Identity(6,6); - VERIFY_IS_APPROX(arr2, MatrixXd::Identity(6,6).array()); - - // test Reverse - VERIFY_IS_APPROX_EVALUATOR(arr2, arr1.reverse()); - VERIFY_IS_APPROX_EVALUATOR(arr2, arr1.colwise().reverse()); - VERIFY_IS_APPROX_EVALUATOR(arr2, arr1.rowwise().reverse()); - arr2.reverse() = arr1; - VERIFY_IS_APPROX(arr2, arr1.reverse()); - mat2.array() = mat1.array().reverse(); - VERIFY_IS_APPROX(mat2.array(), mat1.array().reverse()); - - // test Diagonal - VERIFY_IS_APPROX_EVALUATOR(vec1, mat1.diagonal()); - vec1.resize(5); - VERIFY_IS_APPROX_EVALUATOR(vec1, mat1.diagonal(1)); - VERIFY_IS_APPROX_EVALUATOR(vec1, mat1.diagonal<-1>()); - vec1.setRandom(); - - mat2 = mat1; - copy_using_evaluator(mat1.diagonal(1), vec1); - mat2.diagonal(1) = vec1; - VERIFY_IS_APPROX(mat1, mat2); - - copy_using_evaluator(mat1.diagonal<-1>(), mat1.diagonal(1)); - mat2.diagonal<-1>() = mat2.diagonal(1); - VERIFY_IS_APPROX(mat1, mat2); - } - - { - // test swapping - MatrixXd mat1, mat2, mat1ref, mat2ref; - mat1ref = mat1 = MatrixXd::Random(6, 6); - mat2ref = mat2 = 2 * mat1 + MatrixXd::Identity(6, 6); - swap_using_evaluator(mat1, mat2); - mat1ref.swap(mat2ref); - VERIFY_IS_APPROX(mat1, mat1ref); - VERIFY_IS_APPROX(mat2, mat2ref); - - swap_using_evaluator(mat1.block(0, 0, 3, 3), mat2.block(3, 3, 3, 3)); - mat1ref.block(0, 0, 3, 3).swap(mat2ref.block(3, 3, 3, 3)); - VERIFY_IS_APPROX(mat1, mat1ref); - VERIFY_IS_APPROX(mat2, mat2ref); - - swap_using_evaluator(mat1.row(2), mat2.col(3).transpose()); - mat1.row(2).swap(mat2.col(3).transpose()); - VERIFY_IS_APPROX(mat1, mat1ref); - VERIFY_IS_APPROX(mat2, mat2ref); - } - - { - // test compound assignment - const Matrix4d mat_const = Matrix4d::Random(); - Matrix4d mat, mat_ref; - mat = mat_ref = Matrix4d::Identity(); - add_assign_using_evaluator(mat, mat_const); - mat_ref += mat_const; - VERIFY_IS_APPROX(mat, mat_ref); - - subtract_assign_using_evaluator(mat.row(1), 2*mat.row(2)); - mat_ref.row(1) -= 2*mat_ref.row(2); - VERIFY_IS_APPROX(mat, mat_ref); - - const ArrayXXf arr_const = ArrayXXf::Random(5,3); - ArrayXXf arr, arr_ref; - arr = arr_ref = ArrayXXf::Constant(5, 3, 0.5); - multiply_assign_using_evaluator(arr, arr_const); - arr_ref *= arr_const; - VERIFY_IS_APPROX(arr, arr_ref); - - divide_assign_using_evaluator(arr.row(1), arr.row(2) + 1); - arr_ref.row(1) /= (arr_ref.row(2) + 1); - VERIFY_IS_APPROX(arr, arr_ref); - } - - { - // test triangular shapes - MatrixXd A = MatrixXd::Random(6,6), B(6,6), C(6,6), D(6,6); - A.setRandom();B.setRandom(); - VERIFY_IS_APPROX_EVALUATOR2(B, A.triangularView<Upper>(), MatrixXd(A.triangularView<Upper>())); - - A.setRandom();B.setRandom(); - VERIFY_IS_APPROX_EVALUATOR2(B, A.triangularView<UnitLower>(), MatrixXd(A.triangularView<UnitLower>())); - - A.setRandom();B.setRandom(); - VERIFY_IS_APPROX_EVALUATOR2(B, A.triangularView<UnitUpper>(), MatrixXd(A.triangularView<UnitUpper>())); - - A.setRandom();B.setRandom(); - C = B; C.triangularView<Upper>() = A; - copy_using_evaluator(B.triangularView<Upper>(), A); - VERIFY(B.isApprox(C) && "copy_using_evaluator(B.triangularView<Upper>(), A)"); - - A.setRandom();B.setRandom(); - C = B; C.triangularView<Lower>() = A.triangularView<Lower>(); - copy_using_evaluator(B.triangularView<Lower>(), A.triangularView<Lower>()); - VERIFY(B.isApprox(C) && "copy_using_evaluator(B.triangularView<Lower>(), A.triangularView<Lower>())"); - - - A.setRandom();B.setRandom(); - C = B; C.triangularView<Lower>() = A.triangularView<Upper>().transpose(); - copy_using_evaluator(B.triangularView<Lower>(), A.triangularView<Upper>().transpose()); - VERIFY(B.isApprox(C) && "copy_using_evaluator(B.triangularView<Lower>(), A.triangularView<Lower>().transpose())"); - - - A.setRandom();B.setRandom(); C = B; D = A; - C.triangularView<Upper>().swap(D.triangularView<Upper>()); - swap_using_evaluator(B.triangularView<Upper>(), A.triangularView<Upper>()); - VERIFY(B.isApprox(C) && "swap_using_evaluator(B.triangularView<Upper>(), A.triangularView<Upper>())"); - - - VERIFY_IS_APPROX_EVALUATOR2(B, prod(A.triangularView<Upper>(),A), MatrixXd(A.triangularView<Upper>()*A)); - - VERIFY_IS_APPROX_EVALUATOR2(B, prod(A.selfadjointView<Upper>(),A), MatrixXd(A.selfadjointView<Upper>()*A)); - } - - { - // test diagonal shapes - VectorXd d = VectorXd::Random(6); - MatrixXd A = MatrixXd::Random(6,6), B(6,6); - A.setRandom();B.setRandom(); - - VERIFY_IS_APPROX_EVALUATOR2(B, lazyprod(d.asDiagonal(),A), MatrixXd(d.asDiagonal()*A)); - VERIFY_IS_APPROX_EVALUATOR2(B, lazyprod(A,d.asDiagonal()), MatrixXd(A*d.asDiagonal())); - } - - { - // test CoeffReadCost - Matrix4d a, b; - VERIFY_IS_EQUAL( get_cost(a), 1 ); - VERIFY_IS_EQUAL( get_cost(a+b), 3); - VERIFY_IS_EQUAL( get_cost(2*a+b), 4); - VERIFY_IS_EQUAL( get_cost(a*b), 1); - VERIFY_IS_EQUAL( get_cost(a.lazyProduct(b)), 15); - VERIFY_IS_EQUAL( get_cost(a*(a*b)), 1); - VERIFY_IS_EQUAL( get_cost(a.lazyProduct(a*b)), 15); - VERIFY_IS_EQUAL( get_cost(a*(a+b)), 1); - VERIFY_IS_EQUAL( get_cost(a.lazyProduct(a+b)), 15); - } -} diff --git a/eigen/test/exceptions.cpp b/eigen/test/exceptions.cpp deleted file mode 100644 index b83fb82..0000000 --- a/eigen/test/exceptions.cpp +++ /dev/null @@ -1,113 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - - -// Various sanity tests with exceptions: -// - no memory leak when a custom scalar type trow an exceptions -// - todo: complete the list of tests! - -#define EIGEN_STACK_ALLOCATION_LIMIT 100000000 - -#include "main.h" - -struct my_exception -{ - my_exception() {} - ~my_exception() {} -}; - -class ScalarWithExceptions -{ - public: - ScalarWithExceptions() { init(); } - ScalarWithExceptions(const float& _v) { init(); *v = _v; } - ScalarWithExceptions(const ScalarWithExceptions& other) { init(); *v = *(other.v); } - ~ScalarWithExceptions() { - delete v; - instances--; - } - - void init() { - v = new float; - instances++; - } - - ScalarWithExceptions operator+(const ScalarWithExceptions& other) const - { - countdown--; - if(countdown<=0) - throw my_exception(); - return ScalarWithExceptions(*v+*other.v); - } - - ScalarWithExceptions operator-(const ScalarWithExceptions& other) const - { return ScalarWithExceptions(*v-*other.v); } - - ScalarWithExceptions operator*(const ScalarWithExceptions& other) const - { return ScalarWithExceptions((*v)*(*other.v)); } - - ScalarWithExceptions& operator+=(const ScalarWithExceptions& other) - { *v+=*other.v; return *this; } - ScalarWithExceptions& operator-=(const ScalarWithExceptions& other) - { *v-=*other.v; return *this; } - ScalarWithExceptions& operator=(const ScalarWithExceptions& other) - { *v = *(other.v); return *this; } - - bool operator==(const ScalarWithExceptions& other) const - { return *v==*other.v; } - bool operator!=(const ScalarWithExceptions& other) const - { return *v!=*other.v; } - - float* v; - static int instances; - static int countdown; -}; - -ScalarWithExceptions real(const ScalarWithExceptions &x) { return x; } -ScalarWithExceptions imag(const ScalarWithExceptions & ) { return 0; } -ScalarWithExceptions conj(const ScalarWithExceptions &x) { return x; } - -int ScalarWithExceptions::instances = 0; -int ScalarWithExceptions::countdown = 0; - - -#define CHECK_MEMLEAK(OP) { \ - ScalarWithExceptions::countdown = 100; \ - int before = ScalarWithExceptions::instances; \ - bool exception_thrown = false; \ - try { OP; } \ - catch (my_exception) { \ - exception_thrown = true; \ - VERIFY(ScalarWithExceptions::instances==before && "memory leak detected in " && EIGEN_MAKESTRING(OP)); \ - } \ - VERIFY(exception_thrown && " no exception thrown in " && EIGEN_MAKESTRING(OP)); \ - } - -void memoryleak() -{ - typedef Eigen::Matrix<ScalarWithExceptions,Dynamic,1> VectorType; - typedef Eigen::Matrix<ScalarWithExceptions,Dynamic,Dynamic> MatrixType; - - { - int n = 50; - VectorType v0(n), v1(n); - MatrixType m0(n,n), m1(n,n), m2(n,n); - v0.setOnes(); v1.setOnes(); - m0.setOnes(); m1.setOnes(); m2.setOnes(); - CHECK_MEMLEAK(v0 = m0 * m1 * v1); - CHECK_MEMLEAK(m2 = m0 * m1 * m2); - CHECK_MEMLEAK((v0+v1).dot(v0+v1)); - } - VERIFY(ScalarWithExceptions::instances==0 && "global memory leak detected in " && EIGEN_MAKESTRING(OP)); \ -} - -void test_exceptions() -{ - CALL_SUBTEST( memoryleak() ); -} diff --git a/eigen/test/fastmath.cpp b/eigen/test/fastmath.cpp deleted file mode 100644 index cc5db07..0000000 --- a/eigen/test/fastmath.cpp +++ /dev/null @@ -1,99 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -void check(bool b, bool ref) -{ - std::cout << b; - if(b==ref) - std::cout << " OK "; - else - std::cout << " BAD "; -} - -#if EIGEN_COMP_MSVC && EIGEN_COMP_MSVC < 1800 -namespace std { - template<typename T> bool (isfinite)(T x) { return _finite(x); } - template<typename T> bool (isnan)(T x) { return _isnan(x); } - template<typename T> bool (isinf)(T x) { return _fpclass(x)==_FPCLASS_NINF || _fpclass(x)==_FPCLASS_PINF; } -} -#endif - -template<typename T> -void check_inf_nan(bool dryrun) { - Matrix<T,Dynamic,1> m(10); - m.setRandom(); - m(3) = std::numeric_limits<T>::quiet_NaN(); - - if(dryrun) - { - std::cout << "std::isfinite(" << m(3) << ") = "; check((std::isfinite)(m(3)),false); std::cout << " ; numext::isfinite = "; check((numext::isfinite)(m(3)), false); std::cout << "\n"; - std::cout << "std::isinf(" << m(3) << ") = "; check((std::isinf)(m(3)),false); std::cout << " ; numext::isinf = "; check((numext::isinf)(m(3)), false); std::cout << "\n"; - std::cout << "std::isnan(" << m(3) << ") = "; check((std::isnan)(m(3)),true); std::cout << " ; numext::isnan = "; check((numext::isnan)(m(3)), true); std::cout << "\n"; - std::cout << "allFinite: "; check(m.allFinite(), 0); std::cout << "\n"; - std::cout << "hasNaN: "; check(m.hasNaN(), 1); std::cout << "\n"; - std::cout << "\n"; - } - else - { - VERIFY( !(numext::isfinite)(m(3)) ); - VERIFY( !(numext::isinf)(m(3)) ); - VERIFY( (numext::isnan)(m(3)) ); - VERIFY( !m.allFinite() ); - VERIFY( m.hasNaN() ); - } - T hidden_zero = (std::numeric_limits<T>::min)()*(std::numeric_limits<T>::min)(); - m(4) /= hidden_zero; - if(dryrun) - { - std::cout << "std::isfinite(" << m(4) << ") = "; check((std::isfinite)(m(4)),false); std::cout << " ; numext::isfinite = "; check((numext::isfinite)(m(4)), false); std::cout << "\n"; - std::cout << "std::isinf(" << m(4) << ") = "; check((std::isinf)(m(4)),true); std::cout << " ; numext::isinf = "; check((numext::isinf)(m(4)), true); std::cout << "\n"; - std::cout << "std::isnan(" << m(4) << ") = "; check((std::isnan)(m(4)),false); std::cout << " ; numext::isnan = "; check((numext::isnan)(m(4)), false); std::cout << "\n"; - std::cout << "allFinite: "; check(m.allFinite(), 0); std::cout << "\n"; - std::cout << "hasNaN: "; check(m.hasNaN(), 1); std::cout << "\n"; - std::cout << "\n"; - } - else - { - VERIFY( !(numext::isfinite)(m(4)) ); - VERIFY( (numext::isinf)(m(4)) ); - VERIFY( !(numext::isnan)(m(4)) ); - VERIFY( !m.allFinite() ); - VERIFY( m.hasNaN() ); - } - m(3) = 0; - if(dryrun) - { - std::cout << "std::isfinite(" << m(3) << ") = "; check((std::isfinite)(m(3)),true); std::cout << " ; numext::isfinite = "; check((numext::isfinite)(m(3)), true); std::cout << "\n"; - std::cout << "std::isinf(" << m(3) << ") = "; check((std::isinf)(m(3)),false); std::cout << " ; numext::isinf = "; check((numext::isinf)(m(3)), false); std::cout << "\n"; - std::cout << "std::isnan(" << m(3) << ") = "; check((std::isnan)(m(3)),false); std::cout << " ; numext::isnan = "; check((numext::isnan)(m(3)), false); std::cout << "\n"; - std::cout << "allFinite: "; check(m.allFinite(), 0); std::cout << "\n"; - std::cout << "hasNaN: "; check(m.hasNaN(), 0); std::cout << "\n"; - std::cout << "\n\n"; - } - else - { - VERIFY( (numext::isfinite)(m(3)) ); - VERIFY( !(numext::isinf)(m(3)) ); - VERIFY( !(numext::isnan)(m(3)) ); - VERIFY( !m.allFinite() ); - VERIFY( !m.hasNaN() ); - } -} - -void test_fastmath() { - std::cout << "*** float *** \n\n"; check_inf_nan<float>(true); - std::cout << "*** double ***\n\n"; check_inf_nan<double>(true); - std::cout << "*** long double *** \n\n"; check_inf_nan<long double>(true); - - check_inf_nan<float>(false); - check_inf_nan<double>(false); - check_inf_nan<long double>(false); -} diff --git a/eigen/test/first_aligned.cpp b/eigen/test/first_aligned.cpp deleted file mode 100644 index ae2d4bc..0000000 --- a/eigen/test/first_aligned.cpp +++ /dev/null @@ -1,51 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename Scalar> -void test_first_aligned_helper(Scalar *array, int size) -{ - const int packet_size = sizeof(Scalar) * internal::packet_traits<Scalar>::size; - VERIFY(((size_t(array) + sizeof(Scalar) * internal::first_default_aligned(array, size)) % packet_size) == 0); -} - -template<typename Scalar> -void test_none_aligned_helper(Scalar *array, int size) -{ - EIGEN_UNUSED_VARIABLE(array); - EIGEN_UNUSED_VARIABLE(size); - VERIFY(internal::packet_traits<Scalar>::size == 1 || internal::first_default_aligned(array, size) == size); -} - -struct some_non_vectorizable_type { float x; }; - -void test_first_aligned() -{ - EIGEN_ALIGN16 float array_float[100]; - test_first_aligned_helper(array_float, 50); - test_first_aligned_helper(array_float+1, 50); - test_first_aligned_helper(array_float+2, 50); - test_first_aligned_helper(array_float+3, 50); - test_first_aligned_helper(array_float+4, 50); - test_first_aligned_helper(array_float+5, 50); - - EIGEN_ALIGN16 double array_double[100]; - test_first_aligned_helper(array_double, 50); - test_first_aligned_helper(array_double+1, 50); - test_first_aligned_helper(array_double+2, 50); - - double *array_double_plus_4_bytes = (double*)(internal::UIntPtr(array_double)+4); - test_none_aligned_helper(array_double_plus_4_bytes, 50); - test_none_aligned_helper(array_double_plus_4_bytes+1, 50); - - some_non_vectorizable_type array_nonvec[100]; - test_first_aligned_helper(array_nonvec, 100); - test_none_aligned_helper(array_nonvec, 100); -} diff --git a/eigen/test/geo_alignedbox.cpp b/eigen/test/geo_alignedbox.cpp deleted file mode 100644 index b64ea3b..0000000 --- a/eigen/test/geo_alignedbox.cpp +++ /dev/null @@ -1,187 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/Geometry> -#include <Eigen/LU> -#include <Eigen/QR> - -#include<iostream> -using namespace std; - -template<typename T> EIGEN_DONT_INLINE -void kill_extra_precision(T& x) { eigen_assert((void*)(&x) != (void*)0); } - - -template<typename BoxType> void alignedbox(const BoxType& _box) -{ - /* this test covers the following files: - AlignedBox.h - */ - typedef typename BoxType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, BoxType::AmbientDimAtCompileTime, 1> VectorType; - - const Index dim = _box.dim(); - - VectorType p0 = VectorType::Random(dim); - VectorType p1 = VectorType::Random(dim); - while( p1 == p0 ){ - p1 = VectorType::Random(dim); } - RealScalar s1 = internal::random<RealScalar>(0,1); - - BoxType b0(dim); - BoxType b1(VectorType::Random(dim),VectorType::Random(dim)); - BoxType b2; - - kill_extra_precision(b1); - kill_extra_precision(p0); - kill_extra_precision(p1); - - b0.extend(p0); - b0.extend(p1); - VERIFY(b0.contains(p0*s1+(Scalar(1)-s1)*p1)); - VERIFY(b0.contains(b0.center())); - VERIFY_IS_APPROX(b0.center(),(p0+p1)/Scalar(2)); - - (b2 = b0).extend(b1); - VERIFY(b2.contains(b0)); - VERIFY(b2.contains(b1)); - VERIFY_IS_APPROX(b2.clamp(b0), b0); - - // intersection - BoxType box1(VectorType::Random(dim)); - box1.extend(VectorType::Random(dim)); - BoxType box2(VectorType::Random(dim)); - box2.extend(VectorType::Random(dim)); - - VERIFY(box1.intersects(box2) == !box1.intersection(box2).isEmpty()); - - // alignment -- make sure there is no memory alignment assertion - BoxType *bp0 = new BoxType(dim); - BoxType *bp1 = new BoxType(dim); - bp0->extend(*bp1); - delete bp0; - delete bp1; - - // sampling - for( int i=0; i<10; ++i ) - { - VectorType r = b0.sample(); - VERIFY(b0.contains(r)); - } - -} - - - -template<typename BoxType> -void alignedboxCastTests(const BoxType& _box) -{ - // casting - typedef typename BoxType::Scalar Scalar; - typedef Matrix<Scalar, BoxType::AmbientDimAtCompileTime, 1> VectorType; - - const Index dim = _box.dim(); - - VectorType p0 = VectorType::Random(dim); - VectorType p1 = VectorType::Random(dim); - - BoxType b0(dim); - - b0.extend(p0); - b0.extend(p1); - - const int Dim = BoxType::AmbientDimAtCompileTime; - typedef typename GetDifferentType<Scalar>::type OtherScalar; - AlignedBox<OtherScalar,Dim> hp1f = b0.template cast<OtherScalar>(); - VERIFY_IS_APPROX(hp1f.template cast<Scalar>(),b0); - AlignedBox<Scalar,Dim> hp1d = b0.template cast<Scalar>(); - VERIFY_IS_APPROX(hp1d.template cast<Scalar>(),b0); -} - - -void specificTest1() -{ - Vector2f m; m << -1.0f, -2.0f; - Vector2f M; M << 1.0f, 5.0f; - - typedef AlignedBox2f BoxType; - BoxType box( m, M ); - - Vector2f sides = M-m; - VERIFY_IS_APPROX(sides, box.sizes() ); - VERIFY_IS_APPROX(sides[1], box.sizes()[1] ); - VERIFY_IS_APPROX(sides[1], box.sizes().maxCoeff() ); - VERIFY_IS_APPROX(sides[0], box.sizes().minCoeff() ); - - VERIFY_IS_APPROX( 14.0f, box.volume() ); - VERIFY_IS_APPROX( 53.0f, box.diagonal().squaredNorm() ); - VERIFY_IS_APPROX( std::sqrt( 53.0f ), box.diagonal().norm() ); - - VERIFY_IS_APPROX( m, box.corner( BoxType::BottomLeft ) ); - VERIFY_IS_APPROX( M, box.corner( BoxType::TopRight ) ); - Vector2f bottomRight; bottomRight << M[0], m[1]; - Vector2f topLeft; topLeft << m[0], M[1]; - VERIFY_IS_APPROX( bottomRight, box.corner( BoxType::BottomRight ) ); - VERIFY_IS_APPROX( topLeft, box.corner( BoxType::TopLeft ) ); -} - - -void specificTest2() -{ - Vector3i m; m << -1, -2, 0; - Vector3i M; M << 1, 5, 3; - - typedef AlignedBox3i BoxType; - BoxType box( m, M ); - - Vector3i sides = M-m; - VERIFY_IS_APPROX(sides, box.sizes() ); - VERIFY_IS_APPROX(sides[1], box.sizes()[1] ); - VERIFY_IS_APPROX(sides[1], box.sizes().maxCoeff() ); - VERIFY_IS_APPROX(sides[0], box.sizes().minCoeff() ); - - VERIFY_IS_APPROX( 42, box.volume() ); - VERIFY_IS_APPROX( 62, box.diagonal().squaredNorm() ); - - VERIFY_IS_APPROX( m, box.corner( BoxType::BottomLeftFloor ) ); - VERIFY_IS_APPROX( M, box.corner( BoxType::TopRightCeil ) ); - Vector3i bottomRightFloor; bottomRightFloor << M[0], m[1], m[2]; - Vector3i topLeftFloor; topLeftFloor << m[0], M[1], m[2]; - VERIFY_IS_APPROX( bottomRightFloor, box.corner( BoxType::BottomRightFloor ) ); - VERIFY_IS_APPROX( topLeftFloor, box.corner( BoxType::TopLeftFloor ) ); -} - - -void test_geo_alignedbox() -{ - for(int i = 0; i < g_repeat; i++) - { - CALL_SUBTEST_1( alignedbox(AlignedBox2f()) ); - CALL_SUBTEST_2( alignedboxCastTests(AlignedBox2f()) ); - - CALL_SUBTEST_3( alignedbox(AlignedBox3f()) ); - CALL_SUBTEST_4( alignedboxCastTests(AlignedBox3f()) ); - - CALL_SUBTEST_5( alignedbox(AlignedBox4d()) ); - CALL_SUBTEST_6( alignedboxCastTests(AlignedBox4d()) ); - - CALL_SUBTEST_7( alignedbox(AlignedBox1d()) ); - CALL_SUBTEST_8( alignedboxCastTests(AlignedBox1d()) ); - - CALL_SUBTEST_9( alignedbox(AlignedBox1i()) ); - CALL_SUBTEST_10( alignedbox(AlignedBox2i()) ); - CALL_SUBTEST_11( alignedbox(AlignedBox3i()) ); - - CALL_SUBTEST_14( alignedbox(AlignedBox<double,Dynamic>(4)) ); - } - CALL_SUBTEST_12( specificTest1() ); - CALL_SUBTEST_13( specificTest2() ); -} diff --git a/eigen/test/geo_eulerangles.cpp b/eigen/test/geo_eulerangles.cpp deleted file mode 100644 index 932ebe7..0000000 --- a/eigen/test/geo_eulerangles.cpp +++ /dev/null @@ -1,112 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2012 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/Geometry> -#include <Eigen/LU> -#include <Eigen/SVD> - - -template<typename Scalar> -void verify_euler(const Matrix<Scalar,3,1>& ea, int i, int j, int k) -{ - typedef Matrix<Scalar,3,3> Matrix3; - typedef Matrix<Scalar,3,1> Vector3; - typedef AngleAxis<Scalar> AngleAxisx; - using std::abs; - Matrix3 m(AngleAxisx(ea[0], Vector3::Unit(i)) * AngleAxisx(ea[1], Vector3::Unit(j)) * AngleAxisx(ea[2], Vector3::Unit(k))); - Vector3 eabis = m.eulerAngles(i, j, k); - Matrix3 mbis(AngleAxisx(eabis[0], Vector3::Unit(i)) * AngleAxisx(eabis[1], Vector3::Unit(j)) * AngleAxisx(eabis[2], Vector3::Unit(k))); - VERIFY_IS_APPROX(m, mbis); - /* If I==K, and ea[1]==0, then there no unique solution. */ - /* The remark apply in the case where I!=K, and |ea[1]| is close to pi/2. */ - if( (i!=k || ea[1]!=0) && (i==k || !internal::isApprox(abs(ea[1]),Scalar(EIGEN_PI/2),test_precision<Scalar>())) ) - VERIFY((ea-eabis).norm() <= test_precision<Scalar>()); - - // approx_or_less_than does not work for 0 - VERIFY(0 < eabis[0] || test_isMuchSmallerThan(eabis[0], Scalar(1))); - VERIFY_IS_APPROX_OR_LESS_THAN(eabis[0], Scalar(EIGEN_PI)); - VERIFY_IS_APPROX_OR_LESS_THAN(-Scalar(EIGEN_PI), eabis[1]); - VERIFY_IS_APPROX_OR_LESS_THAN(eabis[1], Scalar(EIGEN_PI)); - VERIFY_IS_APPROX_OR_LESS_THAN(-Scalar(EIGEN_PI), eabis[2]); - VERIFY_IS_APPROX_OR_LESS_THAN(eabis[2], Scalar(EIGEN_PI)); -} - -template<typename Scalar> void check_all_var(const Matrix<Scalar,3,1>& ea) -{ - verify_euler(ea, 0,1,2); - verify_euler(ea, 0,1,0); - verify_euler(ea, 0,2,1); - verify_euler(ea, 0,2,0); - - verify_euler(ea, 1,2,0); - verify_euler(ea, 1,2,1); - verify_euler(ea, 1,0,2); - verify_euler(ea, 1,0,1); - - verify_euler(ea, 2,0,1); - verify_euler(ea, 2,0,2); - verify_euler(ea, 2,1,0); - verify_euler(ea, 2,1,2); -} - -template<typename Scalar> void eulerangles() -{ - typedef Matrix<Scalar,3,3> Matrix3; - typedef Matrix<Scalar,3,1> Vector3; - typedef Array<Scalar,3,1> Array3; - typedef Quaternion<Scalar> Quaternionx; - typedef AngleAxis<Scalar> AngleAxisx; - - Scalar a = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI)); - Quaternionx q1; - q1 = AngleAxisx(a, Vector3::Random().normalized()); - Matrix3 m; - m = q1; - - Vector3 ea = m.eulerAngles(0,1,2); - check_all_var(ea); - ea = m.eulerAngles(0,1,0); - check_all_var(ea); - - // Check with purely random Quaternion: - q1.coeffs() = Quaternionx::Coefficients::Random().normalized(); - m = q1; - ea = m.eulerAngles(0,1,2); - check_all_var(ea); - ea = m.eulerAngles(0,1,0); - check_all_var(ea); - - // Check with random angles in range [0:pi]x[-pi:pi]x[-pi:pi]. - ea = (Array3::Random() + Array3(1,0,0))*Scalar(EIGEN_PI)*Array3(0.5,1,1); - check_all_var(ea); - - ea[2] = ea[0] = internal::random<Scalar>(0,Scalar(EIGEN_PI)); - check_all_var(ea); - - ea[0] = ea[1] = internal::random<Scalar>(0,Scalar(EIGEN_PI)); - check_all_var(ea); - - ea[1] = 0; - check_all_var(ea); - - ea.head(2).setZero(); - check_all_var(ea); - - ea.setZero(); - check_all_var(ea); -} - -void test_geo_eulerangles() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( eulerangles<float>() ); - CALL_SUBTEST_2( eulerangles<double>() ); - } -} diff --git a/eigen/test/geo_homogeneous.cpp b/eigen/test/geo_homogeneous.cpp deleted file mode 100644 index 2187c7b..0000000 --- a/eigen/test/geo_homogeneous.cpp +++ /dev/null @@ -1,125 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/Geometry> - -template<typename Scalar,int Size> void homogeneous(void) -{ - /* this test covers the following files: - Homogeneous.h - */ - - typedef Matrix<Scalar,Size,Size> MatrixType; - typedef Matrix<Scalar,Size,1, ColMajor> VectorType; - - typedef Matrix<Scalar,Size+1,Size> HMatrixType; - typedef Matrix<Scalar,Size+1,1> HVectorType; - - typedef Matrix<Scalar,Size,Size+1> T1MatrixType; - typedef Matrix<Scalar,Size+1,Size+1> T2MatrixType; - typedef Matrix<Scalar,Size+1,Size> T3MatrixType; - - VectorType v0 = VectorType::Random(), - ones = VectorType::Ones(); - - HVectorType hv0 = HVectorType::Random(); - - MatrixType m0 = MatrixType::Random(); - - HMatrixType hm0 = HMatrixType::Random(); - - hv0 << v0, 1; - VERIFY_IS_APPROX(v0.homogeneous(), hv0); - VERIFY_IS_APPROX(v0, hv0.hnormalized()); - - VERIFY_IS_APPROX(v0.homogeneous().sum(), hv0.sum()); - VERIFY_IS_APPROX(v0.homogeneous().minCoeff(), hv0.minCoeff()); - VERIFY_IS_APPROX(v0.homogeneous().maxCoeff(), hv0.maxCoeff()); - - hm0 << m0, ones.transpose(); - VERIFY_IS_APPROX(m0.colwise().homogeneous(), hm0); - VERIFY_IS_APPROX(m0, hm0.colwise().hnormalized()); - hm0.row(Size-1).setRandom(); - for(int j=0; j<Size; ++j) - m0.col(j) = hm0.col(j).head(Size) / hm0(Size,j); - VERIFY_IS_APPROX(m0, hm0.colwise().hnormalized()); - - T1MatrixType t1 = T1MatrixType::Random(); - VERIFY_IS_APPROX(t1 * (v0.homogeneous().eval()), t1 * v0.homogeneous()); - VERIFY_IS_APPROX(t1 * (m0.colwise().homogeneous().eval()), t1 * m0.colwise().homogeneous()); - - T2MatrixType t2 = T2MatrixType::Random(); - VERIFY_IS_APPROX(t2 * (v0.homogeneous().eval()), t2 * v0.homogeneous()); - VERIFY_IS_APPROX(t2 * (m0.colwise().homogeneous().eval()), t2 * m0.colwise().homogeneous()); - VERIFY_IS_APPROX(t2 * (v0.homogeneous().asDiagonal()), t2 * hv0.asDiagonal()); - VERIFY_IS_APPROX((v0.homogeneous().asDiagonal()) * t2, hv0.asDiagonal() * t2); - - VERIFY_IS_APPROX((v0.transpose().rowwise().homogeneous().eval()) * t2, - v0.transpose().rowwise().homogeneous() * t2); - VERIFY_IS_APPROX((m0.transpose().rowwise().homogeneous().eval()) * t2, - m0.transpose().rowwise().homogeneous() * t2); - - T3MatrixType t3 = T3MatrixType::Random(); - VERIFY_IS_APPROX((v0.transpose().rowwise().homogeneous().eval()) * t3, - v0.transpose().rowwise().homogeneous() * t3); - VERIFY_IS_APPROX((m0.transpose().rowwise().homogeneous().eval()) * t3, - m0.transpose().rowwise().homogeneous() * t3); - - // test product with a Transform object - Transform<Scalar, Size, Affine> aff; - Transform<Scalar, Size, AffineCompact> caff; - Transform<Scalar, Size, Projective> proj; - Matrix<Scalar, Size, Dynamic> pts; - Matrix<Scalar, Size+1, Dynamic> pts1, pts2; - - aff.affine().setRandom(); - proj = caff = aff; - pts.setRandom(Size,internal::random<int>(1,20)); - - pts1 = pts.colwise().homogeneous(); - VERIFY_IS_APPROX(aff * pts.colwise().homogeneous(), (aff * pts1).colwise().hnormalized()); - VERIFY_IS_APPROX(caff * pts.colwise().homogeneous(), (caff * pts1).colwise().hnormalized()); - VERIFY_IS_APPROX(proj * pts.colwise().homogeneous(), (proj * pts1)); - - VERIFY_IS_APPROX((aff * pts1).colwise().hnormalized(), aff * pts); - VERIFY_IS_APPROX((caff * pts1).colwise().hnormalized(), caff * pts); - - pts2 = pts1; - pts2.row(Size).setRandom(); - VERIFY_IS_APPROX((aff * pts2).colwise().hnormalized(), aff * pts2.colwise().hnormalized()); - VERIFY_IS_APPROX((caff * pts2).colwise().hnormalized(), caff * pts2.colwise().hnormalized()); - VERIFY_IS_APPROX((proj * pts2).colwise().hnormalized(), (proj * pts2.colwise().hnormalized().colwise().homogeneous()).colwise().hnormalized()); - - // Test combination of homogeneous - - VERIFY_IS_APPROX( (t2 * v0.homogeneous()).hnormalized(), - (t2.template topLeftCorner<Size,Size>() * v0 + t2.template topRightCorner<Size,1>()) - / ((t2.template bottomLeftCorner<1,Size>()*v0).value() + t2(Size,Size)) ); - - VERIFY_IS_APPROX( (t2 * pts.colwise().homogeneous()).colwise().hnormalized(), - (Matrix<Scalar, Size+1, Dynamic>(t2 * pts1).colwise().hnormalized()) ); - - VERIFY_IS_APPROX( (t2 .lazyProduct( v0.homogeneous() )).hnormalized(), (t2 * v0.homogeneous()).hnormalized() ); - VERIFY_IS_APPROX( (t2 .lazyProduct ( pts.colwise().homogeneous() )).colwise().hnormalized(), (t2 * pts1).colwise().hnormalized() ); - - VERIFY_IS_APPROX( (v0.transpose().homogeneous() .lazyProduct( t2 )).hnormalized(), (v0.transpose().homogeneous()*t2).hnormalized() ); - VERIFY_IS_APPROX( (pts.transpose().rowwise().homogeneous() .lazyProduct( t2 )).rowwise().hnormalized(), (pts1.transpose()*t2).rowwise().hnormalized() ); - - VERIFY_IS_APPROX( (t2.template triangularView<Lower>() * v0.homogeneous()).eval(), (t2.template triangularView<Lower>()*hv0) ); -} - -void test_geo_homogeneous() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1(( homogeneous<float,1>() )); - CALL_SUBTEST_2(( homogeneous<double,3>() )); - CALL_SUBTEST_3(( homogeneous<double,8>() )); - } -} diff --git a/eigen/test/geo_hyperplane.cpp b/eigen/test/geo_hyperplane.cpp deleted file mode 100644 index b3a48c5..0000000 --- a/eigen/test/geo_hyperplane.cpp +++ /dev/null @@ -1,197 +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) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/Geometry> -#include <Eigen/LU> -#include <Eigen/QR> - -template<typename HyperplaneType> void hyperplane(const HyperplaneType& _plane) -{ - /* this test covers the following files: - Hyperplane.h - */ - using std::abs; - const Index dim = _plane.dim(); - enum { Options = HyperplaneType::Options }; - typedef typename HyperplaneType::Scalar Scalar; - typedef typename HyperplaneType::RealScalar RealScalar; - typedef Matrix<Scalar, HyperplaneType::AmbientDimAtCompileTime, 1> VectorType; - typedef Matrix<Scalar, HyperplaneType::AmbientDimAtCompileTime, - HyperplaneType::AmbientDimAtCompileTime> MatrixType; - - VectorType p0 = VectorType::Random(dim); - VectorType p1 = VectorType::Random(dim); - - VectorType n0 = VectorType::Random(dim).normalized(); - VectorType n1 = VectorType::Random(dim).normalized(); - - HyperplaneType pl0(n0, p0); - HyperplaneType pl1(n1, p1); - HyperplaneType pl2 = pl1; - - Scalar s0 = internal::random<Scalar>(); - Scalar s1 = internal::random<Scalar>(); - - VERIFY_IS_APPROX( n1.dot(n1), Scalar(1) ); - - VERIFY_IS_MUCH_SMALLER_THAN( pl0.absDistance(p0), Scalar(1) ); - if(numext::abs2(s0)>RealScalar(1e-6)) - VERIFY_IS_APPROX( pl1.signedDistance(p1 + n1 * s0), s0); - else - VERIFY_IS_MUCH_SMALLER_THAN( abs(pl1.signedDistance(p1 + n1 * s0) - s0), Scalar(1) ); - VERIFY_IS_MUCH_SMALLER_THAN( pl1.signedDistance(pl1.projection(p0)), Scalar(1) ); - VERIFY_IS_MUCH_SMALLER_THAN( pl1.absDistance(p1 + pl1.normal().unitOrthogonal() * s1), Scalar(1) ); - - // transform - if (!NumTraits<Scalar>::IsComplex) - { - MatrixType rot = MatrixType::Random(dim,dim).householderQr().householderQ(); - DiagonalMatrix<Scalar,HyperplaneType::AmbientDimAtCompileTime> scaling(VectorType::Random()); - Translation<Scalar,HyperplaneType::AmbientDimAtCompileTime> translation(VectorType::Random()); - - while(scaling.diagonal().cwiseAbs().minCoeff()<RealScalar(1e-4)) scaling.diagonal() = VectorType::Random(); - - pl2 = pl1; - VERIFY_IS_MUCH_SMALLER_THAN( pl2.transform(rot).absDistance(rot * p1), Scalar(1) ); - pl2 = pl1; - VERIFY_IS_MUCH_SMALLER_THAN( pl2.transform(rot,Isometry).absDistance(rot * p1), Scalar(1) ); - pl2 = pl1; - VERIFY_IS_MUCH_SMALLER_THAN( pl2.transform(rot*scaling).absDistance((rot*scaling) * p1), Scalar(1) ); - VERIFY_IS_APPROX( pl2.normal().norm(), RealScalar(1) ); - pl2 = pl1; - VERIFY_IS_MUCH_SMALLER_THAN( pl2.transform(rot*scaling*translation) - .absDistance((rot*scaling*translation) * p1), Scalar(1) ); - VERIFY_IS_APPROX( pl2.normal().norm(), RealScalar(1) ); - pl2 = pl1; - VERIFY_IS_MUCH_SMALLER_THAN( pl2.transform(rot*translation,Isometry) - .absDistance((rot*translation) * p1), Scalar(1) ); - VERIFY_IS_APPROX( pl2.normal().norm(), RealScalar(1) ); - } - - // casting - const int Dim = HyperplaneType::AmbientDimAtCompileTime; - typedef typename GetDifferentType<Scalar>::type OtherScalar; - Hyperplane<OtherScalar,Dim,Options> hp1f = pl1.template cast<OtherScalar>(); - VERIFY_IS_APPROX(hp1f.template cast<Scalar>(),pl1); - Hyperplane<Scalar,Dim,Options> hp1d = pl1.template cast<Scalar>(); - VERIFY_IS_APPROX(hp1d.template cast<Scalar>(),pl1); -} - -template<typename Scalar> void lines() -{ - using std::abs; - typedef Hyperplane<Scalar, 2> HLine; - typedef ParametrizedLine<Scalar, 2> PLine; - typedef Matrix<Scalar,2,1> Vector; - typedef Matrix<Scalar,3,1> CoeffsType; - - for(int i = 0; i < 10; i++) - { - Vector center = Vector::Random(); - Vector u = Vector::Random(); - Vector v = Vector::Random(); - Scalar a = internal::random<Scalar>(); - while (abs(a-1) < Scalar(1e-4)) a = internal::random<Scalar>(); - while (u.norm() < Scalar(1e-4)) u = Vector::Random(); - while (v.norm() < Scalar(1e-4)) v = Vector::Random(); - - HLine line_u = HLine::Through(center + u, center + a*u); - HLine line_v = HLine::Through(center + v, center + a*v); - - // the line equations should be normalized so that a^2+b^2=1 - VERIFY_IS_APPROX(line_u.normal().norm(), Scalar(1)); - VERIFY_IS_APPROX(line_v.normal().norm(), Scalar(1)); - - Vector result = line_u.intersection(line_v); - - // the lines should intersect at the point we called "center" - if(abs(a-1) > Scalar(1e-2) && abs(v.normalized().dot(u.normalized()))<Scalar(0.9)) - VERIFY_IS_APPROX(result, center); - - // check conversions between two types of lines - PLine pl(line_u); // gcc 3.3 will commit suicide if we don't name this variable - HLine line_u2(pl); - CoeffsType converted_coeffs = line_u2.coeffs(); - if(line_u2.normal().dot(line_u.normal())<Scalar(0)) - converted_coeffs = -line_u2.coeffs(); - VERIFY(line_u.coeffs().isApprox(converted_coeffs)); - } -} - -template<typename Scalar> void planes() -{ - using std::abs; - typedef Hyperplane<Scalar, 3> Plane; - typedef Matrix<Scalar,3,1> Vector; - - for(int i = 0; i < 10; i++) - { - Vector v0 = Vector::Random(); - Vector v1(v0), v2(v0); - if(internal::random<double>(0,1)>0.25) - v1 += Vector::Random(); - if(internal::random<double>(0,1)>0.25) - v2 += v1 * std::pow(internal::random<Scalar>(0,1),internal::random<int>(1,16)); - if(internal::random<double>(0,1)>0.25) - v2 += Vector::Random() * std::pow(internal::random<Scalar>(0,1),internal::random<int>(1,16)); - - Plane p0 = Plane::Through(v0, v1, v2); - - VERIFY_IS_APPROX(p0.normal().norm(), Scalar(1)); - VERIFY_IS_MUCH_SMALLER_THAN(p0.absDistance(v0), Scalar(1)); - VERIFY_IS_MUCH_SMALLER_THAN(p0.absDistance(v1), Scalar(1)); - VERIFY_IS_MUCH_SMALLER_THAN(p0.absDistance(v2), Scalar(1)); - } -} - -template<typename Scalar> void hyperplane_alignment() -{ - typedef Hyperplane<Scalar,3,AutoAlign> Plane3a; - typedef Hyperplane<Scalar,3,DontAlign> Plane3u; - - EIGEN_ALIGN_MAX Scalar array1[4]; - EIGEN_ALIGN_MAX Scalar array2[4]; - EIGEN_ALIGN_MAX Scalar array3[4+1]; - Scalar* array3u = array3+1; - - Plane3a *p1 = ::new(reinterpret_cast<void*>(array1)) Plane3a; - Plane3u *p2 = ::new(reinterpret_cast<void*>(array2)) Plane3u; - Plane3u *p3 = ::new(reinterpret_cast<void*>(array3u)) Plane3u; - - p1->coeffs().setRandom(); - *p2 = *p1; - *p3 = *p1; - - VERIFY_IS_APPROX(p1->coeffs(), p2->coeffs()); - VERIFY_IS_APPROX(p1->coeffs(), p3->coeffs()); - - #if defined(EIGEN_VECTORIZE) && EIGEN_MAX_STATIC_ALIGN_BYTES > 0 - if(internal::packet_traits<Scalar>::Vectorizable && internal::packet_traits<Scalar>::size<=4) - VERIFY_RAISES_ASSERT((::new(reinterpret_cast<void*>(array3u)) Plane3a)); - #endif -} - - -void test_geo_hyperplane() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( hyperplane(Hyperplane<float,2>()) ); - CALL_SUBTEST_2( hyperplane(Hyperplane<float,3>()) ); - CALL_SUBTEST_2( hyperplane(Hyperplane<float,3,DontAlign>()) ); - CALL_SUBTEST_2( hyperplane_alignment<float>() ); - CALL_SUBTEST_3( hyperplane(Hyperplane<double,4>()) ); - CALL_SUBTEST_4( hyperplane(Hyperplane<std::complex<double>,5>()) ); - CALL_SUBTEST_1( lines<float>() ); - CALL_SUBTEST_3( lines<double>() ); - CALL_SUBTEST_2( planes<float>() ); - CALL_SUBTEST_5( planes<double>() ); - } -} diff --git a/eigen/test/geo_orthomethods.cpp b/eigen/test/geo_orthomethods.cpp deleted file mode 100644 index e178df2..0000000 --- a/eigen/test/geo_orthomethods.cpp +++ /dev/null @@ -1,133 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/Geometry> -#include <Eigen/LU> -#include <Eigen/SVD> - -/* this test covers the following files: - Geometry/OrthoMethods.h -*/ - -template<typename Scalar> void orthomethods_3() -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar,3,3> Matrix3; - typedef Matrix<Scalar,3,1> Vector3; - - typedef Matrix<Scalar,4,1> Vector4; - - Vector3 v0 = Vector3::Random(), - v1 = Vector3::Random(), - v2 = Vector3::Random(); - - // cross product - VERIFY_IS_MUCH_SMALLER_THAN(v1.cross(v2).dot(v1), Scalar(1)); - VERIFY_IS_MUCH_SMALLER_THAN(v1.dot(v1.cross(v2)), Scalar(1)); - VERIFY_IS_MUCH_SMALLER_THAN(v1.cross(v2).dot(v2), Scalar(1)); - VERIFY_IS_MUCH_SMALLER_THAN(v2.dot(v1.cross(v2)), Scalar(1)); - VERIFY_IS_MUCH_SMALLER_THAN(v1.cross(Vector3::Random()).dot(v1), Scalar(1)); - Matrix3 mat3; - mat3 << v0.normalized(), - (v0.cross(v1)).normalized(), - (v0.cross(v1).cross(v0)).normalized(); - VERIFY(mat3.isUnitary()); - - mat3.setRandom(); - VERIFY_IS_APPROX(v0.cross(mat3*v1), -(mat3*v1).cross(v0)); - VERIFY_IS_APPROX(v0.cross(mat3.lazyProduct(v1)), -(mat3.lazyProduct(v1)).cross(v0)); - - // colwise/rowwise cross product - mat3.setRandom(); - Vector3 vec3 = Vector3::Random(); - Matrix3 mcross; - int i = internal::random<int>(0,2); - mcross = mat3.colwise().cross(vec3); - VERIFY_IS_APPROX(mcross.col(i), mat3.col(i).cross(vec3)); - - VERIFY_IS_MUCH_SMALLER_THAN((mat3.adjoint() * mat3.colwise().cross(vec3)).diagonal().cwiseAbs().sum(), Scalar(1)); - VERIFY_IS_MUCH_SMALLER_THAN((mat3.adjoint() * mat3.colwise().cross(Vector3::Random())).diagonal().cwiseAbs().sum(), Scalar(1)); - - VERIFY_IS_MUCH_SMALLER_THAN((vec3.adjoint() * mat3.colwise().cross(vec3)).cwiseAbs().sum(), Scalar(1)); - VERIFY_IS_MUCH_SMALLER_THAN((vec3.adjoint() * Matrix3::Random().colwise().cross(vec3)).cwiseAbs().sum(), Scalar(1)); - - mcross = mat3.rowwise().cross(vec3); - VERIFY_IS_APPROX(mcross.row(i), mat3.row(i).cross(vec3)); - - // cross3 - Vector4 v40 = Vector4::Random(), - v41 = Vector4::Random(), - v42 = Vector4::Random(); - v40.w() = v41.w() = v42.w() = 0; - v42.template head<3>() = v40.template head<3>().cross(v41.template head<3>()); - VERIFY_IS_APPROX(v40.cross3(v41), v42); - VERIFY_IS_MUCH_SMALLER_THAN(v40.cross3(Vector4::Random()).dot(v40), Scalar(1)); - - // check mixed product - typedef Matrix<RealScalar, 3, 1> RealVector3; - RealVector3 rv1 = RealVector3::Random(); - VERIFY_IS_APPROX(v1.cross(rv1.template cast<Scalar>()), v1.cross(rv1)); - VERIFY_IS_APPROX(rv1.template cast<Scalar>().cross(v1), rv1.cross(v1)); -} - -template<typename Scalar, int Size> void orthomethods(int size=Size) -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar,Size,1> VectorType; - typedef Matrix<Scalar,3,Size> Matrix3N; - typedef Matrix<Scalar,Size,3> MatrixN3; - typedef Matrix<Scalar,3,1> Vector3; - - VectorType v0 = VectorType::Random(size); - - // unitOrthogonal - VERIFY_IS_MUCH_SMALLER_THAN(v0.unitOrthogonal().dot(v0), Scalar(1)); - VERIFY_IS_APPROX(v0.unitOrthogonal().norm(), RealScalar(1)); - - if (size>=3) - { - v0.template head<2>().setZero(); - v0.tail(size-2).setRandom(); - - VERIFY_IS_MUCH_SMALLER_THAN(v0.unitOrthogonal().dot(v0), Scalar(1)); - VERIFY_IS_APPROX(v0.unitOrthogonal().norm(), RealScalar(1)); - } - - // colwise/rowwise cross product - Vector3 vec3 = Vector3::Random(); - int i = internal::random<int>(0,size-1); - - Matrix3N mat3N(3,size), mcross3N(3,size); - mat3N.setRandom(); - mcross3N = mat3N.colwise().cross(vec3); - VERIFY_IS_APPROX(mcross3N.col(i), mat3N.col(i).cross(vec3)); - - MatrixN3 matN3(size,3), mcrossN3(size,3); - matN3.setRandom(); - mcrossN3 = matN3.rowwise().cross(vec3); - VERIFY_IS_APPROX(mcrossN3.row(i), matN3.row(i).cross(vec3)); -} - -void test_geo_orthomethods() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( orthomethods_3<float>() ); - CALL_SUBTEST_2( orthomethods_3<double>() ); - CALL_SUBTEST_4( orthomethods_3<std::complex<double> >() ); - CALL_SUBTEST_1( (orthomethods<float,2>()) ); - CALL_SUBTEST_2( (orthomethods<double,2>()) ); - CALL_SUBTEST_1( (orthomethods<float,3>()) ); - CALL_SUBTEST_2( (orthomethods<double,3>()) ); - CALL_SUBTEST_3( (orthomethods<float,7>()) ); - CALL_SUBTEST_4( (orthomethods<std::complex<double>,8>()) ); - CALL_SUBTEST_5( (orthomethods<float,Dynamic>(36)) ); - CALL_SUBTEST_6( (orthomethods<double,Dynamic>(35)) ); - } -} diff --git a/eigen/test/geo_parametrizedline.cpp b/eigen/test/geo_parametrizedline.cpp deleted file mode 100644 index 6a87947..0000000 --- a/eigen/test/geo_parametrizedline.cpp +++ /dev/null @@ -1,103 +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) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/Geometry> -#include <Eigen/LU> -#include <Eigen/QR> - -template<typename LineType> void parametrizedline(const LineType& _line) -{ - /* this test covers the following files: - ParametrizedLine.h - */ - using std::abs; - const Index dim = _line.dim(); - typedef typename LineType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, LineType::AmbientDimAtCompileTime, 1> VectorType; - typedef Hyperplane<Scalar,LineType::AmbientDimAtCompileTime> HyperplaneType; - - VectorType p0 = VectorType::Random(dim); - VectorType p1 = VectorType::Random(dim); - - VectorType d0 = VectorType::Random(dim).normalized(); - - LineType l0(p0, d0); - - Scalar s0 = internal::random<Scalar>(); - Scalar s1 = abs(internal::random<Scalar>()); - - VERIFY_IS_MUCH_SMALLER_THAN( l0.distance(p0), RealScalar(1) ); - VERIFY_IS_MUCH_SMALLER_THAN( l0.distance(p0+s0*d0), RealScalar(1) ); - VERIFY_IS_APPROX( (l0.projection(p1)-p1).norm(), l0.distance(p1) ); - VERIFY_IS_MUCH_SMALLER_THAN( l0.distance(l0.projection(p1)), RealScalar(1) ); - VERIFY_IS_APPROX( Scalar(l0.distance((p0+s0*d0) + d0.unitOrthogonal() * s1)), s1 ); - - // casting - const int Dim = LineType::AmbientDimAtCompileTime; - typedef typename GetDifferentType<Scalar>::type OtherScalar; - ParametrizedLine<OtherScalar,Dim> hp1f = l0.template cast<OtherScalar>(); - VERIFY_IS_APPROX(hp1f.template cast<Scalar>(),l0); - ParametrizedLine<Scalar,Dim> hp1d = l0.template cast<Scalar>(); - VERIFY_IS_APPROX(hp1d.template cast<Scalar>(),l0); - - // intersections - VectorType p2 = VectorType::Random(dim); - VectorType n2 = VectorType::Random(dim).normalized(); - HyperplaneType hp(p2,n2); - Scalar t = l0.intersectionParameter(hp); - VectorType pi = l0.pointAt(t); - VERIFY_IS_MUCH_SMALLER_THAN(hp.signedDistance(pi), RealScalar(1)); - VERIFY_IS_MUCH_SMALLER_THAN(l0.distance(pi), RealScalar(1)); - VERIFY_IS_APPROX(l0.intersectionPoint(hp), pi); -} - -template<typename Scalar> void parametrizedline_alignment() -{ - typedef ParametrizedLine<Scalar,4,AutoAlign> Line4a; - typedef ParametrizedLine<Scalar,4,DontAlign> Line4u; - - EIGEN_ALIGN_MAX Scalar array1[16]; - EIGEN_ALIGN_MAX Scalar array2[16]; - EIGEN_ALIGN_MAX Scalar array3[16+1]; - Scalar* array3u = array3+1; - - Line4a *p1 = ::new(reinterpret_cast<void*>(array1)) Line4a; - Line4u *p2 = ::new(reinterpret_cast<void*>(array2)) Line4u; - Line4u *p3 = ::new(reinterpret_cast<void*>(array3u)) Line4u; - - p1->origin().setRandom(); - p1->direction().setRandom(); - *p2 = *p1; - *p3 = *p1; - - VERIFY_IS_APPROX(p1->origin(), p2->origin()); - VERIFY_IS_APPROX(p1->origin(), p3->origin()); - VERIFY_IS_APPROX(p1->direction(), p2->direction()); - VERIFY_IS_APPROX(p1->direction(), p3->direction()); - - #if defined(EIGEN_VECTORIZE) && EIGEN_MAX_STATIC_ALIGN_BYTES>0 - if(internal::packet_traits<Scalar>::Vectorizable && internal::packet_traits<Scalar>::size<=4) - VERIFY_RAISES_ASSERT((::new(reinterpret_cast<void*>(array3u)) Line4a)); - #endif -} - -void test_geo_parametrizedline() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( parametrizedline(ParametrizedLine<float,2>()) ); - CALL_SUBTEST_2( parametrizedline(ParametrizedLine<float,3>()) ); - CALL_SUBTEST_2( parametrizedline_alignment<float>() ); - CALL_SUBTEST_3( parametrizedline(ParametrizedLine<double,4>()) ); - CALL_SUBTEST_3( parametrizedline_alignment<double>() ); - CALL_SUBTEST_4( parametrizedline(ParametrizedLine<std::complex<double>,5>()) ); - } -} diff --git a/eigen/test/geo_quaternion.cpp b/eigen/test/geo_quaternion.cpp deleted file mode 100644 index 8ee8fdb..0000000 --- a/eigen/test/geo_quaternion.cpp +++ /dev/null @@ -1,302 +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) 2009 Mathieu Gautier <mathieu.gautier@cea.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/Geometry> -#include <Eigen/LU> -#include <Eigen/SVD> - -template<typename T> T bounded_acos(T v) -{ - using std::acos; - using std::min; - using std::max; - return acos((max)(T(-1),(min)(v,T(1)))); -} - -template<typename QuatType> void check_slerp(const QuatType& q0, const QuatType& q1) -{ - using std::abs; - typedef typename QuatType::Scalar Scalar; - typedef AngleAxis<Scalar> AA; - - Scalar largeEps = test_precision<Scalar>(); - - Scalar theta_tot = AA(q1*q0.inverse()).angle(); - if(theta_tot>Scalar(EIGEN_PI)) - theta_tot = Scalar(2.)*Scalar(EIGEN_PI)-theta_tot; - for(Scalar t=0; t<=Scalar(1.001); t+=Scalar(0.1)) - { - QuatType q = q0.slerp(t,q1); - Scalar theta = AA(q*q0.inverse()).angle(); - VERIFY(abs(q.norm() - 1) < largeEps); - if(theta_tot==0) VERIFY(theta_tot==0); - else VERIFY(abs(theta - t * theta_tot) < largeEps); - } -} - -template<typename Scalar, int Options> void quaternion(void) -{ - /* this test covers the following files: - Quaternion.h - */ - using std::abs; - typedef Matrix<Scalar,3,1> Vector3; - typedef Matrix<Scalar,3,3> Matrix3; - typedef Quaternion<Scalar,Options> Quaternionx; - typedef AngleAxis<Scalar> AngleAxisx; - - Scalar largeEps = test_precision<Scalar>(); - if (internal::is_same<Scalar,float>::value) - largeEps = Scalar(1e-3); - - Scalar eps = internal::random<Scalar>() * Scalar(1e-2); - - Vector3 v0 = Vector3::Random(), - v1 = Vector3::Random(), - v2 = Vector3::Random(), - v3 = Vector3::Random(); - - Scalar a = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI)), - b = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI)); - - // Quaternion: Identity(), setIdentity(); - Quaternionx q1, q2; - q2.setIdentity(); - VERIFY_IS_APPROX(Quaternionx(Quaternionx::Identity()).coeffs(), q2.coeffs()); - q1.coeffs().setRandom(); - VERIFY_IS_APPROX(q1.coeffs(), (q1*q2).coeffs()); - - // concatenation - q1 *= q2; - - q1 = AngleAxisx(a, v0.normalized()); - q2 = AngleAxisx(a, v1.normalized()); - - // angular distance - Scalar refangle = abs(AngleAxisx(q1.inverse()*q2).angle()); - if (refangle>Scalar(EIGEN_PI)) - refangle = Scalar(2)*Scalar(EIGEN_PI) - refangle; - - if((q1.coeffs()-q2.coeffs()).norm() > 10*largeEps) - { - VERIFY_IS_MUCH_SMALLER_THAN(abs(q1.angularDistance(q2) - refangle), Scalar(1)); - } - - // rotation matrix conversion - VERIFY_IS_APPROX(q1 * v2, q1.toRotationMatrix() * v2); - VERIFY_IS_APPROX(q1 * q2 * v2, - q1.toRotationMatrix() * q2.toRotationMatrix() * v2); - - VERIFY( (q2*q1).isApprox(q1*q2, largeEps) - || !(q2 * q1 * v2).isApprox(q1.toRotationMatrix() * q2.toRotationMatrix() * v2)); - - q2 = q1.toRotationMatrix(); - VERIFY_IS_APPROX(q1*v1,q2*v1); - - Matrix3 rot1(q1); - VERIFY_IS_APPROX(q1*v1,rot1*v1); - Quaternionx q3(rot1.transpose()*rot1); - VERIFY_IS_APPROX(q3*v1,v1); - - - // angle-axis conversion - AngleAxisx aa = AngleAxisx(q1); - VERIFY_IS_APPROX(q1 * v1, Quaternionx(aa) * v1); - - // Do not execute the test if the rotation angle is almost zero, or - // the rotation axis and v1 are almost parallel. - if (abs(aa.angle()) > 5*test_precision<Scalar>() - && (aa.axis() - v1.normalized()).norm() < Scalar(1.99) - && (aa.axis() + v1.normalized()).norm() < Scalar(1.99)) - { - VERIFY_IS_NOT_APPROX(q1 * v1, Quaternionx(AngleAxisx(aa.angle()*2,aa.axis())) * v1); - } - - // from two vector creation - VERIFY_IS_APPROX( v2.normalized(),(q2.setFromTwoVectors(v1, v2)*v1).normalized()); - VERIFY_IS_APPROX( v1.normalized(),(q2.setFromTwoVectors(v1, v1)*v1).normalized()); - VERIFY_IS_APPROX(-v1.normalized(),(q2.setFromTwoVectors(v1,-v1)*v1).normalized()); - if (internal::is_same<Scalar,double>::value) - { - v3 = (v1.array()+eps).matrix(); - VERIFY_IS_APPROX( v3.normalized(),(q2.setFromTwoVectors(v1, v3)*v1).normalized()); - VERIFY_IS_APPROX(-v3.normalized(),(q2.setFromTwoVectors(v1,-v3)*v1).normalized()); - } - - // from two vector creation static function - VERIFY_IS_APPROX( v2.normalized(),(Quaternionx::FromTwoVectors(v1, v2)*v1).normalized()); - VERIFY_IS_APPROX( v1.normalized(),(Quaternionx::FromTwoVectors(v1, v1)*v1).normalized()); - VERIFY_IS_APPROX(-v1.normalized(),(Quaternionx::FromTwoVectors(v1,-v1)*v1).normalized()); - if (internal::is_same<Scalar,double>::value) - { - v3 = (v1.array()+eps).matrix(); - VERIFY_IS_APPROX( v3.normalized(),(Quaternionx::FromTwoVectors(v1, v3)*v1).normalized()); - VERIFY_IS_APPROX(-v3.normalized(),(Quaternionx::FromTwoVectors(v1,-v3)*v1).normalized()); - } - - // inverse and conjugate - VERIFY_IS_APPROX(q1 * (q1.inverse() * v1), v1); - VERIFY_IS_APPROX(q1 * (q1.conjugate() * v1), v1); - - // test casting - Quaternion<float> q1f = q1.template cast<float>(); - VERIFY_IS_APPROX(q1f.template cast<Scalar>(),q1); - Quaternion<double> q1d = q1.template cast<double>(); - VERIFY_IS_APPROX(q1d.template cast<Scalar>(),q1); - - // test bug 369 - improper alignment. - Quaternionx *q = new Quaternionx; - delete q; - - q1 = Quaternionx::UnitRandom(); - q2 = Quaternionx::UnitRandom(); - check_slerp(q1,q2); - - q1 = AngleAxisx(b, v1.normalized()); - q2 = AngleAxisx(b+Scalar(EIGEN_PI), v1.normalized()); - check_slerp(q1,q2); - - q1 = AngleAxisx(b, v1.normalized()); - q2 = AngleAxisx(-b, -v1.normalized()); - check_slerp(q1,q2); - - q1 = Quaternionx::UnitRandom(); - q2.coeffs() = -q1.coeffs(); - check_slerp(q1,q2); -} - -template<typename Scalar> void mapQuaternion(void){ - typedef Map<Quaternion<Scalar>, Aligned> MQuaternionA; - typedef Map<const Quaternion<Scalar>, Aligned> MCQuaternionA; - typedef Map<Quaternion<Scalar> > MQuaternionUA; - typedef Map<const Quaternion<Scalar> > MCQuaternionUA; - typedef Quaternion<Scalar> Quaternionx; - typedef Matrix<Scalar,3,1> Vector3; - typedef AngleAxis<Scalar> AngleAxisx; - - Vector3 v0 = Vector3::Random(), - v1 = Vector3::Random(); - Scalar a = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI)); - - EIGEN_ALIGN_MAX Scalar array1[4]; - EIGEN_ALIGN_MAX Scalar array2[4]; - EIGEN_ALIGN_MAX Scalar array3[4+1]; - Scalar* array3unaligned = array3+1; - - MQuaternionA mq1(array1); - MCQuaternionA mcq1(array1); - MQuaternionA mq2(array2); - MQuaternionUA mq3(array3unaligned); - MCQuaternionUA mcq3(array3unaligned); - -// std::cerr << array1 << " " << array2 << " " << array3 << "\n"; - mq1 = AngleAxisx(a, v0.normalized()); - mq2 = mq1; - mq3 = mq1; - - Quaternionx q1 = mq1; - Quaternionx q2 = mq2; - Quaternionx q3 = mq3; - Quaternionx q4 = MCQuaternionUA(array3unaligned); - - VERIFY_IS_APPROX(q1.coeffs(), q2.coeffs()); - VERIFY_IS_APPROX(q1.coeffs(), q3.coeffs()); - VERIFY_IS_APPROX(q4.coeffs(), q3.coeffs()); - #ifdef EIGEN_VECTORIZE - if(internal::packet_traits<Scalar>::Vectorizable) - VERIFY_RAISES_ASSERT((MQuaternionA(array3unaligned))); - #endif - - VERIFY_IS_APPROX(mq1 * (mq1.inverse() * v1), v1); - VERIFY_IS_APPROX(mq1 * (mq1.conjugate() * v1), v1); - - VERIFY_IS_APPROX(mcq1 * (mcq1.inverse() * v1), v1); - VERIFY_IS_APPROX(mcq1 * (mcq1.conjugate() * v1), v1); - - VERIFY_IS_APPROX(mq3 * (mq3.inverse() * v1), v1); - VERIFY_IS_APPROX(mq3 * (mq3.conjugate() * v1), v1); - - VERIFY_IS_APPROX(mcq3 * (mcq3.inverse() * v1), v1); - VERIFY_IS_APPROX(mcq3 * (mcq3.conjugate() * v1), v1); - - VERIFY_IS_APPROX(mq1*mq2, q1*q2); - VERIFY_IS_APPROX(mq3*mq2, q3*q2); - VERIFY_IS_APPROX(mcq1*mq2, q1*q2); - VERIFY_IS_APPROX(mcq3*mq2, q3*q2); - - // Bug 1461, compilation issue with Map<const Quat>::w(), and other reference/constness checks: - VERIFY_IS_APPROX(mcq3.coeffs().x() + mcq3.coeffs().y() + mcq3.coeffs().z() + mcq3.coeffs().w(), mcq3.coeffs().sum()); - VERIFY_IS_APPROX(mcq3.x() + mcq3.y() + mcq3.z() + mcq3.w(), mcq3.coeffs().sum()); - mq3.w() = 1; - const Quaternionx& cq3(q3); - VERIFY( &cq3.x() == &q3.x() ); - const MQuaternionUA& cmq3(mq3); - VERIFY( &cmq3.x() == &mq3.x() ); - // FIXME the following should be ok. The problem is that currently the LValueBit flag - // is used to determine wether we can return a coeff by reference or not, which is not enough for Map<const ...>. - //const MCQuaternionUA& cmcq3(mcq3); - //VERIFY( &cmcq3.x() == &mcq3.x() ); -} - -template<typename Scalar> void quaternionAlignment(void){ - typedef Quaternion<Scalar,AutoAlign> QuaternionA; - typedef Quaternion<Scalar,DontAlign> QuaternionUA; - - EIGEN_ALIGN_MAX Scalar array1[4]; - EIGEN_ALIGN_MAX Scalar array2[4]; - EIGEN_ALIGN_MAX Scalar array3[4+1]; - Scalar* arrayunaligned = array3+1; - - QuaternionA *q1 = ::new(reinterpret_cast<void*>(array1)) QuaternionA; - QuaternionUA *q2 = ::new(reinterpret_cast<void*>(array2)) QuaternionUA; - QuaternionUA *q3 = ::new(reinterpret_cast<void*>(arrayunaligned)) QuaternionUA; - - q1->coeffs().setRandom(); - *q2 = *q1; - *q3 = *q1; - - VERIFY_IS_APPROX(q1->coeffs(), q2->coeffs()); - VERIFY_IS_APPROX(q1->coeffs(), q3->coeffs()); - #if defined(EIGEN_VECTORIZE) && EIGEN_MAX_STATIC_ALIGN_BYTES>0 - if(internal::packet_traits<Scalar>::Vectorizable && internal::packet_traits<Scalar>::size<=4) - VERIFY_RAISES_ASSERT((::new(reinterpret_cast<void*>(arrayunaligned)) QuaternionA)); - #endif -} - -template<typename PlainObjectType> void check_const_correctness(const PlainObjectType&) -{ - // there's a lot that we can't test here while still having this test compile! - // the only possible approach would be to run a script trying to compile stuff and checking that it fails. - // CMake can help with that. - - // verify that map-to-const don't have LvalueBit - typedef typename internal::add_const<PlainObjectType>::type ConstPlainObjectType; - VERIFY( !(internal::traits<Map<ConstPlainObjectType> >::Flags & LvalueBit) ); - VERIFY( !(internal::traits<Map<ConstPlainObjectType, Aligned> >::Flags & LvalueBit) ); - VERIFY( !(Map<ConstPlainObjectType>::Flags & LvalueBit) ); - VERIFY( !(Map<ConstPlainObjectType, Aligned>::Flags & LvalueBit) ); -} - -void test_geo_quaternion() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1(( quaternion<float,AutoAlign>() )); - CALL_SUBTEST_1( check_const_correctness(Quaternionf()) ); - CALL_SUBTEST_2(( quaternion<double,AutoAlign>() )); - CALL_SUBTEST_2( check_const_correctness(Quaterniond()) ); - CALL_SUBTEST_3(( quaternion<float,DontAlign>() )); - CALL_SUBTEST_4(( quaternion<double,DontAlign>() )); - CALL_SUBTEST_5(( quaternionAlignment<float>() )); - CALL_SUBTEST_6(( quaternionAlignment<double>() )); - CALL_SUBTEST_1( mapQuaternion<float>() ); - CALL_SUBTEST_2( mapQuaternion<double>() ); - } -} diff --git a/eigen/test/geo_transformations.cpp b/eigen/test/geo_transformations.cpp deleted file mode 100644 index 278e527..0000000 --- a/eigen/test/geo_transformations.cpp +++ /dev/null @@ -1,645 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/Geometry> -#include <Eigen/LU> -#include <Eigen/SVD> - -template<typename T> -Matrix<T,2,1> angleToVec(T a) -{ - return Matrix<T,2,1>(std::cos(a), std::sin(a)); -} - -// This permits to workaround a bug in clang/llvm code generation. -template<typename T> -EIGEN_DONT_INLINE -void dont_over_optimize(T& x) { volatile typename T::Scalar tmp = x(0); x(0) = tmp; } - -template<typename Scalar, int Mode, int Options> void non_projective_only() -{ - /* this test covers the following files: - Cross.h Quaternion.h, Transform.cpp - */ - typedef Matrix<Scalar,3,1> Vector3; - typedef Quaternion<Scalar> Quaternionx; - typedef AngleAxis<Scalar> AngleAxisx; - typedef Transform<Scalar,3,Mode,Options> Transform3; - typedef DiagonalMatrix<Scalar,3> AlignedScaling3; - typedef Translation<Scalar,3> Translation3; - - Vector3 v0 = Vector3::Random(), - v1 = Vector3::Random(); - - Transform3 t0, t1, t2; - - Scalar a = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI)); - - Quaternionx q1, q2; - - q1 = AngleAxisx(a, v0.normalized()); - - t0 = Transform3::Identity(); - VERIFY_IS_APPROX(t0.matrix(), Transform3::MatrixType::Identity()); - - t0.linear() = q1.toRotationMatrix(); - - v0 << 50, 2, 1; - t0.scale(v0); - - VERIFY_IS_APPROX( (t0 * Vector3(1,0,0)).template head<3>().norm(), v0.x()); - - t0.setIdentity(); - t1.setIdentity(); - v1 << 1, 2, 3; - t0.linear() = q1.toRotationMatrix(); - t0.pretranslate(v0); - t0.scale(v1); - t1.linear() = q1.conjugate().toRotationMatrix(); - t1.prescale(v1.cwiseInverse()); - t1.translate(-v0); - - VERIFY((t0 * t1).matrix().isIdentity(test_precision<Scalar>())); - - t1.fromPositionOrientationScale(v0, q1, v1); - VERIFY_IS_APPROX(t1.matrix(), t0.matrix()); - VERIFY_IS_APPROX(t1*v1, t0*v1); - - // translation * vector - t0.setIdentity(); - t0.translate(v0); - VERIFY_IS_APPROX((t0 * v1).template head<3>(), Translation3(v0) * v1); - - // AlignedScaling * vector - t0.setIdentity(); - t0.scale(v0); - VERIFY_IS_APPROX((t0 * v1).template head<3>(), AlignedScaling3(v0) * v1); -} - -template<typename Scalar, int Mode, int Options> void transformations() -{ - /* this test covers the following files: - Cross.h Quaternion.h, Transform.cpp - */ - using std::cos; - using std::abs; - typedef Matrix<Scalar,3,3> Matrix3; - typedef Matrix<Scalar,4,4> Matrix4; - typedef Matrix<Scalar,2,1> Vector2; - typedef Matrix<Scalar,3,1> Vector3; - typedef Matrix<Scalar,4,1> Vector4; - typedef Quaternion<Scalar> Quaternionx; - typedef AngleAxis<Scalar> AngleAxisx; - typedef Transform<Scalar,2,Mode,Options> Transform2; - typedef Transform<Scalar,3,Mode,Options> Transform3; - typedef typename Transform3::MatrixType MatrixType; - typedef DiagonalMatrix<Scalar,3> AlignedScaling3; - typedef Translation<Scalar,2> Translation2; - typedef Translation<Scalar,3> Translation3; - - Vector3 v0 = Vector3::Random(), - v1 = Vector3::Random(); - Matrix3 matrot1, m; - - Scalar a = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI)); - Scalar s0 = internal::random<Scalar>(), s1 = internal::random<Scalar>(); - - while(v0.norm() < test_precision<Scalar>()) v0 = Vector3::Random(); - while(v1.norm() < test_precision<Scalar>()) v1 = Vector3::Random(); - - VERIFY_IS_APPROX(v0, AngleAxisx(a, v0.normalized()) * v0); - VERIFY_IS_APPROX(-v0, AngleAxisx(Scalar(EIGEN_PI), v0.unitOrthogonal()) * v0); - if(abs(cos(a)) > test_precision<Scalar>()) - { - VERIFY_IS_APPROX(cos(a)*v0.squaredNorm(), v0.dot(AngleAxisx(a, v0.unitOrthogonal()) * v0)); - } - m = AngleAxisx(a, v0.normalized()).toRotationMatrix().adjoint(); - VERIFY_IS_APPROX(Matrix3::Identity(), m * AngleAxisx(a, v0.normalized())); - VERIFY_IS_APPROX(Matrix3::Identity(), AngleAxisx(a, v0.normalized()) * m); - - Quaternionx q1, q2; - q1 = AngleAxisx(a, v0.normalized()); - q2 = AngleAxisx(a, v1.normalized()); - - // rotation matrix conversion - matrot1 = AngleAxisx(Scalar(0.1), Vector3::UnitX()) - * AngleAxisx(Scalar(0.2), Vector3::UnitY()) - * AngleAxisx(Scalar(0.3), Vector3::UnitZ()); - VERIFY_IS_APPROX(matrot1 * v1, - AngleAxisx(Scalar(0.1), Vector3(1,0,0)).toRotationMatrix() - * (AngleAxisx(Scalar(0.2), Vector3(0,1,0)).toRotationMatrix() - * (AngleAxisx(Scalar(0.3), Vector3(0,0,1)).toRotationMatrix() * v1))); - - // angle-axis conversion - AngleAxisx aa = AngleAxisx(q1); - VERIFY_IS_APPROX(q1 * v1, Quaternionx(aa) * v1); - - // The following test is stable only if 2*angle != angle and v1 is not colinear with axis - if( (abs(aa.angle()) > test_precision<Scalar>()) && (abs(aa.axis().dot(v1.normalized()))<(Scalar(1)-Scalar(4)*test_precision<Scalar>())) ) - { - VERIFY( !(q1 * v1).isApprox(Quaternionx(AngleAxisx(aa.angle()*2,aa.axis())) * v1) ); - } - - aa.fromRotationMatrix(aa.toRotationMatrix()); - VERIFY_IS_APPROX(q1 * v1, Quaternionx(aa) * v1); - // The following test is stable only if 2*angle != angle and v1 is not colinear with axis - if( (abs(aa.angle()) > test_precision<Scalar>()) && (abs(aa.axis().dot(v1.normalized()))<(Scalar(1)-Scalar(4)*test_precision<Scalar>())) ) - { - VERIFY( !(q1 * v1).isApprox(Quaternionx(AngleAxisx(aa.angle()*2,aa.axis())) * v1) ); - } - - // AngleAxis - VERIFY_IS_APPROX(AngleAxisx(a,v1.normalized()).toRotationMatrix(), - Quaternionx(AngleAxisx(a,v1.normalized())).toRotationMatrix()); - - AngleAxisx aa1; - m = q1.toRotationMatrix(); - aa1 = m; - VERIFY_IS_APPROX(AngleAxisx(m).toRotationMatrix(), - Quaternionx(m).toRotationMatrix()); - - // Transform - // TODO complete the tests ! - a = 0; - while (abs(a)<Scalar(0.1)) - a = internal::random<Scalar>(-Scalar(0.4)*Scalar(EIGEN_PI), Scalar(0.4)*Scalar(EIGEN_PI)); - q1 = AngleAxisx(a, v0.normalized()); - Transform3 t0, t1, t2; - - // first test setIdentity() and Identity() - t0.setIdentity(); - VERIFY_IS_APPROX(t0.matrix(), Transform3::MatrixType::Identity()); - t0.matrix().setZero(); - t0 = Transform3::Identity(); - VERIFY_IS_APPROX(t0.matrix(), Transform3::MatrixType::Identity()); - - t0.setIdentity(); - t1.setIdentity(); - v1 << 1, 2, 3; - t0.linear() = q1.toRotationMatrix(); - t0.pretranslate(v0); - t0.scale(v1); - t1.linear() = q1.conjugate().toRotationMatrix(); - t1.prescale(v1.cwiseInverse()); - t1.translate(-v0); - - VERIFY((t0 * t1).matrix().isIdentity(test_precision<Scalar>())); - - t1.fromPositionOrientationScale(v0, q1, v1); - VERIFY_IS_APPROX(t1.matrix(), t0.matrix()); - - t0.setIdentity(); t0.scale(v0).rotate(q1.toRotationMatrix()); - t1.setIdentity(); t1.scale(v0).rotate(q1); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - t0.setIdentity(); t0.scale(v0).rotate(AngleAxisx(q1)); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - VERIFY_IS_APPROX(t0.scale(a).matrix(), t1.scale(Vector3::Constant(a)).matrix()); - VERIFY_IS_APPROX(t0.prescale(a).matrix(), t1.prescale(Vector3::Constant(a)).matrix()); - - // More transform constructors, operator=, operator*= - - Matrix3 mat3 = Matrix3::Random(); - Matrix4 mat4; - mat4 << mat3 , Vector3::Zero() , Vector4::Zero().transpose(); - Transform3 tmat3(mat3), tmat4(mat4); - if(Mode!=int(AffineCompact)) - tmat4.matrix()(3,3) = Scalar(1); - VERIFY_IS_APPROX(tmat3.matrix(), tmat4.matrix()); - - Scalar a3 = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI)); - Vector3 v3 = Vector3::Random().normalized(); - AngleAxisx aa3(a3, v3); - Transform3 t3(aa3); - Transform3 t4; - t4 = aa3; - VERIFY_IS_APPROX(t3.matrix(), t4.matrix()); - t4.rotate(AngleAxisx(-a3,v3)); - VERIFY_IS_APPROX(t4.matrix(), MatrixType::Identity()); - t4 *= aa3; - VERIFY_IS_APPROX(t3.matrix(), t4.matrix()); - - do { - v3 = Vector3::Random(); - dont_over_optimize(v3); - } while (v3.cwiseAbs().minCoeff()<NumTraits<Scalar>::epsilon()); - Translation3 tv3(v3); - Transform3 t5(tv3); - t4 = tv3; - VERIFY_IS_APPROX(t5.matrix(), t4.matrix()); - t4.translate((-v3).eval()); - VERIFY_IS_APPROX(t4.matrix(), MatrixType::Identity()); - t4 *= tv3; - VERIFY_IS_APPROX(t5.matrix(), t4.matrix()); - - AlignedScaling3 sv3(v3); - Transform3 t6(sv3); - t4 = sv3; - VERIFY_IS_APPROX(t6.matrix(), t4.matrix()); - t4.scale(v3.cwiseInverse()); - VERIFY_IS_APPROX(t4.matrix(), MatrixType::Identity()); - t4 *= sv3; - VERIFY_IS_APPROX(t6.matrix(), t4.matrix()); - - // matrix * transform - VERIFY_IS_APPROX((t3.matrix()*t4).matrix(), (t3*t4).matrix()); - - // chained Transform product - VERIFY_IS_APPROX(((t3*t4)*t5).matrix(), (t3*(t4*t5)).matrix()); - - // check that Transform product doesn't have aliasing problems - t5 = t4; - t5 = t5*t5; - VERIFY_IS_APPROX(t5, t4*t4); - - // 2D transformation - Transform2 t20, t21; - Vector2 v20 = Vector2::Random(); - Vector2 v21 = Vector2::Random(); - for (int k=0; k<2; ++k) - if (abs(v21[k])<Scalar(1e-3)) v21[k] = Scalar(1e-3); - t21.setIdentity(); - t21.linear() = Rotation2D<Scalar>(a).toRotationMatrix(); - VERIFY_IS_APPROX(t20.fromPositionOrientationScale(v20,a,v21).matrix(), - t21.pretranslate(v20).scale(v21).matrix()); - - t21.setIdentity(); - t21.linear() = Rotation2D<Scalar>(-a).toRotationMatrix(); - VERIFY( (t20.fromPositionOrientationScale(v20,a,v21) - * (t21.prescale(v21.cwiseInverse()).translate(-v20))).matrix().isIdentity(test_precision<Scalar>()) ); - - // Transform - new API - // 3D - t0.setIdentity(); - t0.rotate(q1).scale(v0).translate(v0); - // mat * aligned scaling and mat * translation - t1 = (Matrix3(q1) * AlignedScaling3(v0)) * Translation3(v0); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - t1 = (Matrix3(q1) * Eigen::Scaling(v0)) * Translation3(v0); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - t1 = (q1 * Eigen::Scaling(v0)) * Translation3(v0); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - // mat * transformation and aligned scaling * translation - t1 = Matrix3(q1) * (AlignedScaling3(v0) * Translation3(v0)); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - - t0.setIdentity(); - t0.scale(s0).translate(v0); - t1 = Eigen::Scaling(s0) * Translation3(v0); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - t0.prescale(s0); - t1 = Eigen::Scaling(s0) * t1; - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - t0 = t3; - t0.scale(s0); - t1 = t3 * Eigen::Scaling(s0,s0,s0); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - t0.prescale(s0); - t1 = Eigen::Scaling(s0,s0,s0) * t1; - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - t0 = t3; - t0.scale(s0); - t1 = t3 * Eigen::Scaling(s0); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - t0.prescale(s0); - t1 = Eigen::Scaling(s0) * t1; - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - t0.setIdentity(); - t0.prerotate(q1).prescale(v0).pretranslate(v0); - // translation * aligned scaling and transformation * mat - t1 = (Translation3(v0) * AlignedScaling3(v0)) * Transform3(q1); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - // scaling * mat and translation * mat - t1 = Translation3(v0) * (AlignedScaling3(v0) * Transform3(q1)); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - t0.setIdentity(); - t0.scale(v0).translate(v0).rotate(q1); - // translation * mat and aligned scaling * transformation - t1 = AlignedScaling3(v0) * (Translation3(v0) * Transform3(q1)); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - // transformation * aligned scaling - t0.scale(v0); - t1 *= AlignedScaling3(v0); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - t1 = AlignedScaling3(v0) * (Translation3(v0) * Transform3(q1)); - t1 = t1 * v0.asDiagonal(); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - // transformation * translation - t0.translate(v0); - t1 = t1 * Translation3(v0); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - // translation * transformation - t0.pretranslate(v0); - t1 = Translation3(v0) * t1; - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - // transform * quaternion - t0.rotate(q1); - t1 = t1 * q1; - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - // translation * quaternion - t0.translate(v1).rotate(q1); - t1 = t1 * (Translation3(v1) * q1); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - // aligned scaling * quaternion - t0.scale(v1).rotate(q1); - t1 = t1 * (AlignedScaling3(v1) * q1); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - // quaternion * transform - t0.prerotate(q1); - t1 = q1 * t1; - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - // quaternion * translation - t0.rotate(q1).translate(v1); - t1 = t1 * (q1 * Translation3(v1)); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - // quaternion * aligned scaling - t0.rotate(q1).scale(v1); - t1 = t1 * (q1 * AlignedScaling3(v1)); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - // test transform inversion - t0.setIdentity(); - t0.translate(v0); - do { - t0.linear().setRandom(); - } while(t0.linear().jacobiSvd().singularValues()(2)<test_precision<Scalar>()); - Matrix4 t044 = Matrix4::Zero(); - t044(3,3) = 1; - t044.block(0,0,t0.matrix().rows(),4) = t0.matrix(); - VERIFY_IS_APPROX(t0.inverse(Affine).matrix(), t044.inverse().block(0,0,t0.matrix().rows(),4)); - t0.setIdentity(); - t0.translate(v0).rotate(q1); - t044 = Matrix4::Zero(); - t044(3,3) = 1; - t044.block(0,0,t0.matrix().rows(),4) = t0.matrix(); - VERIFY_IS_APPROX(t0.inverse(Isometry).matrix(), t044.inverse().block(0,0,t0.matrix().rows(),4)); - - Matrix3 mat_rotation, mat_scaling; - t0.setIdentity(); - t0.translate(v0).rotate(q1).scale(v1); - t0.computeRotationScaling(&mat_rotation, &mat_scaling); - VERIFY_IS_APPROX(t0.linear(), mat_rotation * mat_scaling); - VERIFY_IS_APPROX(mat_rotation*mat_rotation.adjoint(), Matrix3::Identity()); - VERIFY_IS_APPROX(mat_rotation.determinant(), Scalar(1)); - t0.computeScalingRotation(&mat_scaling, &mat_rotation); - VERIFY_IS_APPROX(t0.linear(), mat_scaling * mat_rotation); - VERIFY_IS_APPROX(mat_rotation*mat_rotation.adjoint(), Matrix3::Identity()); - VERIFY_IS_APPROX(mat_rotation.determinant(), Scalar(1)); - - // test casting - Transform<float,3,Mode> t1f = t1.template cast<float>(); - VERIFY_IS_APPROX(t1f.template cast<Scalar>(),t1); - Transform<double,3,Mode> t1d = t1.template cast<double>(); - VERIFY_IS_APPROX(t1d.template cast<Scalar>(),t1); - - Translation3 tr1(v0); - Translation<float,3> tr1f = tr1.template cast<float>(); - VERIFY_IS_APPROX(tr1f.template cast<Scalar>(),tr1); - Translation<double,3> tr1d = tr1.template cast<double>(); - VERIFY_IS_APPROX(tr1d.template cast<Scalar>(),tr1); - - AngleAxis<float> aa1f = aa1.template cast<float>(); - VERIFY_IS_APPROX(aa1f.template cast<Scalar>(),aa1); - AngleAxis<double> aa1d = aa1.template cast<double>(); - VERIFY_IS_APPROX(aa1d.template cast<Scalar>(),aa1); - - Rotation2D<Scalar> r2d1(internal::random<Scalar>()); - Rotation2D<float> r2d1f = r2d1.template cast<float>(); - VERIFY_IS_APPROX(r2d1f.template cast<Scalar>(),r2d1); - Rotation2D<double> r2d1d = r2d1.template cast<double>(); - VERIFY_IS_APPROX(r2d1d.template cast<Scalar>(),r2d1); - - for(int k=0; k<100; ++k) - { - Scalar angle = internal::random<Scalar>(-100,100); - Rotation2D<Scalar> rot2(angle); - VERIFY( rot2.smallestPositiveAngle() >= 0 ); - VERIFY( rot2.smallestPositiveAngle() <= Scalar(2)*Scalar(EIGEN_PI) ); - VERIFY_IS_APPROX( angleToVec(rot2.smallestPositiveAngle()), angleToVec(rot2.angle()) ); - - VERIFY( rot2.smallestAngle() >= -Scalar(EIGEN_PI) ); - VERIFY( rot2.smallestAngle() <= Scalar(EIGEN_PI) ); - VERIFY_IS_APPROX( angleToVec(rot2.smallestAngle()), angleToVec(rot2.angle()) ); - - Matrix<Scalar,2,2> rot2_as_mat(rot2); - Rotation2D<Scalar> rot3(rot2_as_mat); - VERIFY_IS_APPROX( angleToVec(rot2.smallestAngle()), angleToVec(rot3.angle()) ); - } - - s0 = internal::random<Scalar>(-100,100); - s1 = internal::random<Scalar>(-100,100); - Rotation2D<Scalar> R0(s0), R1(s1); - - t20 = Translation2(v20) * (R0 * Eigen::Scaling(s0)); - t21 = Translation2(v20) * R0 * Eigen::Scaling(s0); - VERIFY_IS_APPROX(t20,t21); - - t20 = Translation2(v20) * (R0 * R0.inverse() * Eigen::Scaling(s0)); - t21 = Translation2(v20) * Eigen::Scaling(s0); - VERIFY_IS_APPROX(t20,t21); - - VERIFY_IS_APPROX(s0, (R0.slerp(0, R1)).angle()); - VERIFY_IS_APPROX( angleToVec(R1.smallestPositiveAngle()), angleToVec((R0.slerp(1, R1)).smallestPositiveAngle()) ); - VERIFY_IS_APPROX(R0.smallestPositiveAngle(), (R0.slerp(0.5, R0)).smallestPositiveAngle()); - - if(std::cos(s0)>0) - VERIFY_IS_MUCH_SMALLER_THAN((R0.slerp(0.5, R0.inverse())).smallestAngle(), Scalar(1)); - else - VERIFY_IS_APPROX(Scalar(EIGEN_PI), (R0.slerp(0.5, R0.inverse())).smallestPositiveAngle()); - - // Check path length - Scalar l = 0; - int path_steps = 100; - for(int k=0; k<path_steps; ++k) - { - Scalar a1 = R0.slerp(Scalar(k)/Scalar(path_steps), R1).angle(); - Scalar a2 = R0.slerp(Scalar(k+1)/Scalar(path_steps), R1).angle(); - l += std::abs(a2-a1); - } - VERIFY(l<=Scalar(EIGEN_PI)*(Scalar(1)+NumTraits<Scalar>::epsilon()*Scalar(path_steps/2))); - - // check basic features - { - Rotation2D<Scalar> r1; // default ctor - r1 = Rotation2D<Scalar>(s0); // copy assignment - VERIFY_IS_APPROX(r1.angle(),s0); - Rotation2D<Scalar> r2(r1); // copy ctor - VERIFY_IS_APPROX(r2.angle(),s0); - } - - { - Transform3 t32(Matrix4::Random()), t33, t34; - t34 = t33 = t32; - t32.scale(v0); - t33*=AlignedScaling3(v0); - VERIFY_IS_APPROX(t32.matrix(), t33.matrix()); - t33 = t34 * AlignedScaling3(v0); - VERIFY_IS_APPROX(t32.matrix(), t33.matrix()); - } - -} - -template<typename A1, typename A2, typename P, typename Q, typename V, typename H> -void transform_associativity_left(const A1& a1, const A2& a2, const P& p, const Q& q, const V& v, const H& h) -{ - VERIFY_IS_APPROX( q*(a1*v), (q*a1)*v ); - VERIFY_IS_APPROX( q*(a2*v), (q*a2)*v ); - VERIFY_IS_APPROX( q*(p*h).hnormalized(), ((q*p)*h).hnormalized() ); -} - -template<typename A1, typename A2, typename P, typename Q, typename V, typename H> -void transform_associativity2(const A1& a1, const A2& a2, const P& p, const Q& q, const V& v, const H& h) -{ - VERIFY_IS_APPROX( a1*(q*v), (a1*q)*v ); - VERIFY_IS_APPROX( a2*(q*v), (a2*q)*v ); - VERIFY_IS_APPROX( p *(q*v).homogeneous(), (p *q)*v.homogeneous() ); - - transform_associativity_left(a1, a2,p, q, v, h); -} - -template<typename Scalar, int Dim, int Options,typename RotationType> -void transform_associativity(const RotationType& R) -{ - typedef Matrix<Scalar,Dim,1> VectorType; - typedef Matrix<Scalar,Dim+1,1> HVectorType; - typedef Matrix<Scalar,Dim,Dim> LinearType; - typedef Matrix<Scalar,Dim+1,Dim+1> MatrixType; - typedef Transform<Scalar,Dim,AffineCompact,Options> AffineCompactType; - typedef Transform<Scalar,Dim,Affine,Options> AffineType; - typedef Transform<Scalar,Dim,Projective,Options> ProjectiveType; - typedef DiagonalMatrix<Scalar,Dim> ScalingType; - typedef Translation<Scalar,Dim> TranslationType; - - AffineCompactType A1c; A1c.matrix().setRandom(); - AffineCompactType A2c; A2c.matrix().setRandom(); - AffineType A1(A1c); - AffineType A2(A2c); - ProjectiveType P1; P1.matrix().setRandom(); - VectorType v1 = VectorType::Random(); - VectorType v2 = VectorType::Random(); - HVectorType h1 = HVectorType::Random(); - Scalar s1 = internal::random<Scalar>(); - LinearType L = LinearType::Random(); - MatrixType M = MatrixType::Random(); - - CALL_SUBTEST( transform_associativity2(A1c, A1, P1, A2, v2, h1) ); - CALL_SUBTEST( transform_associativity2(A1c, A1, P1, A2c, v2, h1) ); - CALL_SUBTEST( transform_associativity2(A1c, A1, P1, v1.asDiagonal(), v2, h1) ); - CALL_SUBTEST( transform_associativity2(A1c, A1, P1, ScalingType(v1), v2, h1) ); - CALL_SUBTEST( transform_associativity2(A1c, A1, P1, Scaling(v1), v2, h1) ); - CALL_SUBTEST( transform_associativity2(A1c, A1, P1, Scaling(s1), v2, h1) ); - CALL_SUBTEST( transform_associativity2(A1c, A1, P1, TranslationType(v1), v2, h1) ); - CALL_SUBTEST( transform_associativity_left(A1c, A1, P1, L, v2, h1) ); - CALL_SUBTEST( transform_associativity2(A1c, A1, P1, R, v2, h1) ); - - VERIFY_IS_APPROX( A1*(M*h1), (A1*M)*h1 ); - VERIFY_IS_APPROX( A1c*(M*h1), (A1c*M)*h1 ); - VERIFY_IS_APPROX( P1*(M*h1), (P1*M)*h1 ); - - VERIFY_IS_APPROX( M*(A1*h1), (M*A1)*h1 ); - VERIFY_IS_APPROX( M*(A1c*h1), (M*A1c)*h1 ); - VERIFY_IS_APPROX( M*(P1*h1), ((M*P1)*h1) ); -} - -template<typename Scalar> void transform_alignment() -{ - typedef Transform<Scalar,3,Projective,AutoAlign> Projective3a; - typedef Transform<Scalar,3,Projective,DontAlign> Projective3u; - - EIGEN_ALIGN_MAX Scalar array1[16]; - EIGEN_ALIGN_MAX Scalar array2[16]; - EIGEN_ALIGN_MAX Scalar array3[16+1]; - Scalar* array3u = array3+1; - - Projective3a *p1 = ::new(reinterpret_cast<void*>(array1)) Projective3a; - Projective3u *p2 = ::new(reinterpret_cast<void*>(array2)) Projective3u; - Projective3u *p3 = ::new(reinterpret_cast<void*>(array3u)) Projective3u; - - p1->matrix().setRandom(); - *p2 = *p1; - *p3 = *p1; - - VERIFY_IS_APPROX(p1->matrix(), p2->matrix()); - VERIFY_IS_APPROX(p1->matrix(), p3->matrix()); - - VERIFY_IS_APPROX( (*p1) * (*p1), (*p2)*(*p3)); - - #if defined(EIGEN_VECTORIZE) && EIGEN_MAX_STATIC_ALIGN_BYTES>0 - if(internal::packet_traits<Scalar>::Vectorizable) - VERIFY_RAISES_ASSERT((::new(reinterpret_cast<void*>(array3u)) Projective3a)); - #endif -} - -template<typename Scalar, int Dim, int Options> void transform_products() -{ - typedef Matrix<Scalar,Dim+1,Dim+1> Mat; - typedef Transform<Scalar,Dim,Projective,Options> Proj; - typedef Transform<Scalar,Dim,Affine,Options> Aff; - typedef Transform<Scalar,Dim,AffineCompact,Options> AffC; - - Proj p; p.matrix().setRandom(); - Aff a; a.linear().setRandom(); a.translation().setRandom(); - AffC ac = a; - - Mat p_m(p.matrix()), a_m(a.matrix()); - - VERIFY_IS_APPROX((p*p).matrix(), p_m*p_m); - VERIFY_IS_APPROX((a*a).matrix(), a_m*a_m); - VERIFY_IS_APPROX((p*a).matrix(), p_m*a_m); - VERIFY_IS_APPROX((a*p).matrix(), a_m*p_m); - VERIFY_IS_APPROX((ac*a).matrix(), a_m*a_m); - VERIFY_IS_APPROX((a*ac).matrix(), a_m*a_m); - VERIFY_IS_APPROX((p*ac).matrix(), p_m*a_m); - VERIFY_IS_APPROX((ac*p).matrix(), a_m*p_m); -} - -void test_geo_transformations() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1(( transformations<double,Affine,AutoAlign>() )); - CALL_SUBTEST_1(( non_projective_only<double,Affine,AutoAlign>() )); - - CALL_SUBTEST_2(( transformations<float,AffineCompact,AutoAlign>() )); - CALL_SUBTEST_2(( non_projective_only<float,AffineCompact,AutoAlign>() )); - CALL_SUBTEST_2(( transform_alignment<float>() )); - - CALL_SUBTEST_3(( transformations<double,Projective,AutoAlign>() )); - CALL_SUBTEST_3(( transformations<double,Projective,DontAlign>() )); - CALL_SUBTEST_3(( transform_alignment<double>() )); - - CALL_SUBTEST_4(( transformations<float,Affine,RowMajor|AutoAlign>() )); - CALL_SUBTEST_4(( non_projective_only<float,Affine,RowMajor>() )); - - CALL_SUBTEST_5(( transformations<double,AffineCompact,RowMajor|AutoAlign>() )); - CALL_SUBTEST_5(( non_projective_only<double,AffineCompact,RowMajor>() )); - - CALL_SUBTEST_6(( transformations<double,Projective,RowMajor|AutoAlign>() )); - CALL_SUBTEST_6(( transformations<double,Projective,RowMajor|DontAlign>() )); - - - CALL_SUBTEST_7(( transform_products<double,3,RowMajor|AutoAlign>() )); - CALL_SUBTEST_7(( transform_products<float,2,AutoAlign>() )); - - CALL_SUBTEST_8(( transform_associativity<double,2,ColMajor>(Rotation2D<double>(internal::random<double>()*double(EIGEN_PI))) )); - CALL_SUBTEST_8(( transform_associativity<double,3,ColMajor>(Quaterniond::UnitRandom()) )); - } -} diff --git a/eigen/test/half_float.cpp b/eigen/test/half_float.cpp deleted file mode 100644 index b37b819..0000000 --- a/eigen/test/half_float.cpp +++ /dev/null @@ -1,268 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include <sstream> - -#include "main.h" - -#include <Eigen/src/Core/arch/CUDA/Half.h> - -#ifdef EIGEN_HAS_CUDA_FP16 -#error "EIGEN_HAS_CUDA_FP16 should not be defined in this CPU unit test" -#endif - -// Make sure it's possible to forward declare Eigen::half -namespace Eigen { -struct half; -} - -using Eigen::half; - -void test_conversion() -{ - using Eigen::half_impl::__half_raw; - - // Conversion from float. - VERIFY_IS_EQUAL(half(1.0f).x, 0x3c00); - VERIFY_IS_EQUAL(half(0.5f).x, 0x3800); - VERIFY_IS_EQUAL(half(0.33333f).x, 0x3555); - VERIFY_IS_EQUAL(half(0.0f).x, 0x0000); - VERIFY_IS_EQUAL(half(-0.0f).x, 0x8000); - VERIFY_IS_EQUAL(half(65504.0f).x, 0x7bff); - VERIFY_IS_EQUAL(half(65536.0f).x, 0x7c00); // Becomes infinity. - - // Denormals. - VERIFY_IS_EQUAL(half(-5.96046e-08f).x, 0x8001); - VERIFY_IS_EQUAL(half(5.96046e-08f).x, 0x0001); - VERIFY_IS_EQUAL(half(1.19209e-07f).x, 0x0002); - - // Verify round-to-nearest-even behavior. - float val1 = float(half(__half_raw(0x3c00))); - float val2 = float(half(__half_raw(0x3c01))); - float val3 = float(half(__half_raw(0x3c02))); - VERIFY_IS_EQUAL(half(0.5f * (val1 + val2)).x, 0x3c00); - VERIFY_IS_EQUAL(half(0.5f * (val2 + val3)).x, 0x3c02); - - // Conversion from int. - VERIFY_IS_EQUAL(half(-1).x, 0xbc00); - VERIFY_IS_EQUAL(half(0).x, 0x0000); - VERIFY_IS_EQUAL(half(1).x, 0x3c00); - VERIFY_IS_EQUAL(half(2).x, 0x4000); - VERIFY_IS_EQUAL(half(3).x, 0x4200); - - // Conversion from bool. - VERIFY_IS_EQUAL(half(false).x, 0x0000); - VERIFY_IS_EQUAL(half(true).x, 0x3c00); - - // Conversion to float. - VERIFY_IS_EQUAL(float(half(__half_raw(0x0000))), 0.0f); - VERIFY_IS_EQUAL(float(half(__half_raw(0x3c00))), 1.0f); - - // Denormals. - VERIFY_IS_APPROX(float(half(__half_raw(0x8001))), -5.96046e-08f); - VERIFY_IS_APPROX(float(half(__half_raw(0x0001))), 5.96046e-08f); - VERIFY_IS_APPROX(float(half(__half_raw(0x0002))), 1.19209e-07f); - - // NaNs and infinities. - VERIFY(!(numext::isinf)(float(half(65504.0f)))); // Largest finite number. - VERIFY(!(numext::isnan)(float(half(0.0f)))); - VERIFY((numext::isinf)(float(half(__half_raw(0xfc00))))); - VERIFY((numext::isnan)(float(half(__half_raw(0xfc01))))); - VERIFY((numext::isinf)(float(half(__half_raw(0x7c00))))); - VERIFY((numext::isnan)(float(half(__half_raw(0x7c01))))); - -#if !EIGEN_COMP_MSVC - // Visual Studio errors out on divisions by 0 - VERIFY((numext::isnan)(float(half(0.0 / 0.0)))); - VERIFY((numext::isinf)(float(half(1.0 / 0.0)))); - VERIFY((numext::isinf)(float(half(-1.0 / 0.0)))); -#endif - - // Exactly same checks as above, just directly on the half representation. - VERIFY(!(numext::isinf)(half(__half_raw(0x7bff)))); - VERIFY(!(numext::isnan)(half(__half_raw(0x0000)))); - VERIFY((numext::isinf)(half(__half_raw(0xfc00)))); - VERIFY((numext::isnan)(half(__half_raw(0xfc01)))); - VERIFY((numext::isinf)(half(__half_raw(0x7c00)))); - VERIFY((numext::isnan)(half(__half_raw(0x7c01)))); - -#if !EIGEN_COMP_MSVC - // Visual Studio errors out on divisions by 0 - VERIFY((numext::isnan)(half(0.0 / 0.0))); - VERIFY((numext::isinf)(half(1.0 / 0.0))); - VERIFY((numext::isinf)(half(-1.0 / 0.0))); -#endif -} - -void test_numtraits() -{ - std::cout << "epsilon = " << NumTraits<half>::epsilon() << " (0x" << std::hex << NumTraits<half>::epsilon().x << ")" << std::endl; - std::cout << "highest = " << NumTraits<half>::highest() << " (0x" << std::hex << NumTraits<half>::highest().x << ")" << std::endl; - std::cout << "lowest = " << NumTraits<half>::lowest() << " (0x" << std::hex << NumTraits<half>::lowest().x << ")" << std::endl; - std::cout << "min = " << (std::numeric_limits<half>::min)() << " (0x" << std::hex << half((std::numeric_limits<half>::min)()).x << ")" << std::endl; - std::cout << "denorm min = " << (std::numeric_limits<half>::denorm_min)() << " (0x" << std::hex << half((std::numeric_limits<half>::denorm_min)()).x << ")" << std::endl; - std::cout << "infinity = " << NumTraits<half>::infinity() << " (0x" << std::hex << NumTraits<half>::infinity().x << ")" << std::endl; - std::cout << "quiet nan = " << NumTraits<half>::quiet_NaN() << " (0x" << std::hex << NumTraits<half>::quiet_NaN().x << ")" << std::endl; - std::cout << "signaling nan = " << std::numeric_limits<half>::signaling_NaN() << " (0x" << std::hex << std::numeric_limits<half>::signaling_NaN().x << ")" << std::endl; - - VERIFY(NumTraits<half>::IsSigned); - - VERIFY_IS_EQUAL( std::numeric_limits<half>::infinity().x, half(std::numeric_limits<float>::infinity()).x ); - VERIFY_IS_EQUAL( std::numeric_limits<half>::quiet_NaN().x, half(std::numeric_limits<float>::quiet_NaN()).x ); - VERIFY_IS_EQUAL( std::numeric_limits<half>::signaling_NaN().x, half(std::numeric_limits<float>::signaling_NaN()).x ); - VERIFY( (std::numeric_limits<half>::min)() > half(0.f) ); - VERIFY( (std::numeric_limits<half>::denorm_min)() > half(0.f) ); - VERIFY( (std::numeric_limits<half>::min)()/half(2) > half(0.f) ); - VERIFY_IS_EQUAL( (std::numeric_limits<half>::denorm_min)()/half(2), half(0.f) ); -} - -void test_arithmetic() -{ - VERIFY_IS_EQUAL(float(half(2) + half(2)), 4); - VERIFY_IS_EQUAL(float(half(2) + half(-2)), 0); - VERIFY_IS_APPROX(float(half(0.33333f) + half(0.66667f)), 1.0f); - VERIFY_IS_EQUAL(float(half(2.0f) * half(-5.5f)), -11.0f); - VERIFY_IS_APPROX(float(half(1.0f) / half(3.0f)), 0.33333f); - VERIFY_IS_EQUAL(float(-half(4096.0f)), -4096.0f); - VERIFY_IS_EQUAL(float(-half(-4096.0f)), 4096.0f); -} - -void test_comparison() -{ - VERIFY(half(1.0f) > half(0.5f)); - VERIFY(half(0.5f) < half(1.0f)); - VERIFY(!(half(1.0f) < half(0.5f))); - VERIFY(!(half(0.5f) > half(1.0f))); - - VERIFY(!(half(4.0f) > half(4.0f))); - VERIFY(!(half(4.0f) < half(4.0f))); - - VERIFY(!(half(0.0f) < half(-0.0f))); - VERIFY(!(half(-0.0f) < half(0.0f))); - VERIFY(!(half(0.0f) > half(-0.0f))); - VERIFY(!(half(-0.0f) > half(0.0f))); - - VERIFY(half(0.2f) > half(-1.0f)); - VERIFY(half(-1.0f) < half(0.2f)); - VERIFY(half(-16.0f) < half(-15.0f)); - - VERIFY(half(1.0f) == half(1.0f)); - VERIFY(half(1.0f) != half(2.0f)); - - // Comparisons with NaNs and infinities. -#if !EIGEN_COMP_MSVC - // Visual Studio errors out on divisions by 0 - VERIFY(!(half(0.0 / 0.0) == half(0.0 / 0.0))); - VERIFY(half(0.0 / 0.0) != half(0.0 / 0.0)); - - VERIFY(!(half(1.0) == half(0.0 / 0.0))); - VERIFY(!(half(1.0) < half(0.0 / 0.0))); - VERIFY(!(half(1.0) > half(0.0 / 0.0))); - VERIFY(half(1.0) != half(0.0 / 0.0)); - - VERIFY(half(1.0) < half(1.0 / 0.0)); - VERIFY(half(1.0) > half(-1.0 / 0.0)); -#endif -} - -void test_basic_functions() -{ - VERIFY_IS_EQUAL(float(numext::abs(half(3.5f))), 3.5f); - VERIFY_IS_EQUAL(float(abs(half(3.5f))), 3.5f); - VERIFY_IS_EQUAL(float(numext::abs(half(-3.5f))), 3.5f); - VERIFY_IS_EQUAL(float(abs(half(-3.5f))), 3.5f); - - VERIFY_IS_EQUAL(float(numext::floor(half(3.5f))), 3.0f); - VERIFY_IS_EQUAL(float(floor(half(3.5f))), 3.0f); - VERIFY_IS_EQUAL(float(numext::floor(half(-3.5f))), -4.0f); - VERIFY_IS_EQUAL(float(floor(half(-3.5f))), -4.0f); - - VERIFY_IS_EQUAL(float(numext::ceil(half(3.5f))), 4.0f); - VERIFY_IS_EQUAL(float(ceil(half(3.5f))), 4.0f); - VERIFY_IS_EQUAL(float(numext::ceil(half(-3.5f))), -3.0f); - VERIFY_IS_EQUAL(float(ceil(half(-3.5f))), -3.0f); - - VERIFY_IS_APPROX(float(numext::sqrt(half(0.0f))), 0.0f); - VERIFY_IS_APPROX(float(sqrt(half(0.0f))), 0.0f); - VERIFY_IS_APPROX(float(numext::sqrt(half(4.0f))), 2.0f); - VERIFY_IS_APPROX(float(sqrt(half(4.0f))), 2.0f); - - VERIFY_IS_APPROX(float(numext::pow(half(0.0f), half(1.0f))), 0.0f); - VERIFY_IS_APPROX(float(pow(half(0.0f), half(1.0f))), 0.0f); - VERIFY_IS_APPROX(float(numext::pow(half(2.0f), half(2.0f))), 4.0f); - VERIFY_IS_APPROX(float(pow(half(2.0f), half(2.0f))), 4.0f); - - VERIFY_IS_EQUAL(float(numext::exp(half(0.0f))), 1.0f); - VERIFY_IS_EQUAL(float(exp(half(0.0f))), 1.0f); - VERIFY_IS_APPROX(float(numext::exp(half(EIGEN_PI))), 20.f + float(EIGEN_PI)); - VERIFY_IS_APPROX(float(exp(half(EIGEN_PI))), 20.f + float(EIGEN_PI)); - - VERIFY_IS_EQUAL(float(numext::log(half(1.0f))), 0.0f); - VERIFY_IS_EQUAL(float(log(half(1.0f))), 0.0f); - VERIFY_IS_APPROX(float(numext::log(half(10.0f))), 2.30273f); - VERIFY_IS_APPROX(float(log(half(10.0f))), 2.30273f); - - VERIFY_IS_EQUAL(float(numext::log1p(half(0.0f))), 0.0f); - VERIFY_IS_EQUAL(float(log1p(half(0.0f))), 0.0f); - VERIFY_IS_APPROX(float(numext::log1p(half(10.0f))), 2.3978953f); - VERIFY_IS_APPROX(float(log1p(half(10.0f))), 2.3978953f); -} - -void test_trigonometric_functions() -{ - VERIFY_IS_APPROX(numext::cos(half(0.0f)), half(cosf(0.0f))); - VERIFY_IS_APPROX(cos(half(0.0f)), half(cosf(0.0f))); - VERIFY_IS_APPROX(numext::cos(half(EIGEN_PI)), half(cosf(EIGEN_PI))); - //VERIFY_IS_APPROX(numext::cos(half(EIGEN_PI/2)), half(cosf(EIGEN_PI/2))); - //VERIFY_IS_APPROX(numext::cos(half(3*EIGEN_PI/2)), half(cosf(3*EIGEN_PI/2))); - VERIFY_IS_APPROX(numext::cos(half(3.5f)), half(cosf(3.5f))); - - VERIFY_IS_APPROX(numext::sin(half(0.0f)), half(sinf(0.0f))); - VERIFY_IS_APPROX(sin(half(0.0f)), half(sinf(0.0f))); - // VERIFY_IS_APPROX(numext::sin(half(EIGEN_PI)), half(sinf(EIGEN_PI))); - VERIFY_IS_APPROX(numext::sin(half(EIGEN_PI/2)), half(sinf(EIGEN_PI/2))); - VERIFY_IS_APPROX(numext::sin(half(3*EIGEN_PI/2)), half(sinf(3*EIGEN_PI/2))); - VERIFY_IS_APPROX(numext::sin(half(3.5f)), half(sinf(3.5f))); - - VERIFY_IS_APPROX(numext::tan(half(0.0f)), half(tanf(0.0f))); - VERIFY_IS_APPROX(tan(half(0.0f)), half(tanf(0.0f))); - // VERIFY_IS_APPROX(numext::tan(half(EIGEN_PI)), half(tanf(EIGEN_PI))); - // VERIFY_IS_APPROX(numext::tan(half(EIGEN_PI/2)), half(tanf(EIGEN_PI/2))); - //VERIFY_IS_APPROX(numext::tan(half(3*EIGEN_PI/2)), half(tanf(3*EIGEN_PI/2))); - VERIFY_IS_APPROX(numext::tan(half(3.5f)), half(tanf(3.5f))); -} - -void test_array() -{ - typedef Array<half,1,Dynamic> ArrayXh; - Index size = internal::random<Index>(1,10); - Index i = internal::random<Index>(0,size-1); - ArrayXh a1 = ArrayXh::Random(size), a2 = ArrayXh::Random(size); - VERIFY_IS_APPROX( a1+a1, half(2)*a1 ); - VERIFY( (a1.abs() >= half(0)).all() ); - VERIFY_IS_APPROX( (a1*a1).sqrt(), a1.abs() ); - - VERIFY( ((a1.min)(a2) <= (a1.max)(a2)).all() ); - a1(i) = half(-10.); - VERIFY_IS_EQUAL( a1.minCoeff(), half(-10.) ); - a1(i) = half(10.); - VERIFY_IS_EQUAL( a1.maxCoeff(), half(10.) ); - - std::stringstream ss; - ss << a1; -} - -void test_half_float() -{ - CALL_SUBTEST(test_conversion()); - CALL_SUBTEST(test_numtraits()); - CALL_SUBTEST(test_arithmetic()); - CALL_SUBTEST(test_comparison()); - CALL_SUBTEST(test_basic_functions()); - CALL_SUBTEST(test_trigonometric_functions()); - CALL_SUBTEST(test_array()); -} diff --git a/eigen/test/hessenberg.cpp b/eigen/test/hessenberg.cpp deleted file mode 100644 index 96bc19e..0000000 --- a/eigen/test/hessenberg.cpp +++ /dev/null @@ -1,62 +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) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/Eigenvalues> - -template<typename Scalar,int Size> void hessenberg(int size = Size) -{ - typedef Matrix<Scalar,Size,Size> MatrixType; - - // Test basic functionality: A = U H U* and H is Hessenberg - for(int counter = 0; counter < g_repeat; ++counter) { - MatrixType m = MatrixType::Random(size,size); - HessenbergDecomposition<MatrixType> hess(m); - MatrixType Q = hess.matrixQ(); - MatrixType H = hess.matrixH(); - VERIFY_IS_APPROX(m, Q * H * Q.adjoint()); - for(int row = 2; row < size; ++row) { - for(int col = 0; col < row-1; ++col) { - VERIFY(H(row,col) == (typename MatrixType::Scalar)0); - } - } - } - - // Test whether compute() and constructor returns same result - MatrixType A = MatrixType::Random(size, size); - HessenbergDecomposition<MatrixType> cs1; - cs1.compute(A); - HessenbergDecomposition<MatrixType> cs2(A); - VERIFY_IS_EQUAL(cs1.matrixH().eval(), cs2.matrixH().eval()); - MatrixType cs1Q = cs1.matrixQ(); - MatrixType cs2Q = cs2.matrixQ(); - VERIFY_IS_EQUAL(cs1Q, cs2Q); - - // Test assertions for when used uninitialized - HessenbergDecomposition<MatrixType> hessUninitialized; - VERIFY_RAISES_ASSERT( hessUninitialized.matrixH() ); - VERIFY_RAISES_ASSERT( hessUninitialized.matrixQ() ); - VERIFY_RAISES_ASSERT( hessUninitialized.householderCoefficients() ); - VERIFY_RAISES_ASSERT( hessUninitialized.packedMatrix() ); - - // TODO: Add tests for packedMatrix() and householderCoefficients() -} - -void test_hessenberg() -{ - CALL_SUBTEST_1(( hessenberg<std::complex<double>,1>() )); - CALL_SUBTEST_2(( hessenberg<std::complex<double>,2>() )); - CALL_SUBTEST_3(( hessenberg<std::complex<float>,4>() )); - CALL_SUBTEST_4(( hessenberg<float,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)) )); - CALL_SUBTEST_5(( hessenberg<std::complex<double>,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)) )); - - // Test problem size constructors - CALL_SUBTEST_6(HessenbergDecomposition<MatrixXf>(10)); -} diff --git a/eigen/test/householder.cpp b/eigen/test/householder.cpp deleted file mode 100644 index e70b7ea..0000000 --- a/eigen/test/householder.cpp +++ /dev/null @@ -1,137 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/QR> - -template<typename MatrixType> void householder(const MatrixType& m) -{ - static bool even = true; - even = !even; - /* this test covers the following files: - Householder.h - */ - Index rows = m.rows(); - Index cols = m.cols(); - - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - typedef Matrix<Scalar, internal::decrement_size<MatrixType::RowsAtCompileTime>::ret, 1> EssentialVectorType; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType; - typedef Matrix<Scalar, Dynamic, MatrixType::ColsAtCompileTime> HBlockMatrixType; - typedef Matrix<Scalar, Dynamic, 1> HCoeffsVectorType; - - typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, MatrixType::RowsAtCompileTime> TMatrixType; - - Matrix<Scalar, EIGEN_SIZE_MAX(MatrixType::RowsAtCompileTime,MatrixType::ColsAtCompileTime), 1> _tmp((std::max)(rows,cols)); - Scalar* tmp = &_tmp.coeffRef(0,0); - - Scalar beta; - RealScalar alpha; - EssentialVectorType essential; - - VectorType v1 = VectorType::Random(rows), v2; - v2 = v1; - v1.makeHouseholder(essential, beta, alpha); - v1.applyHouseholderOnTheLeft(essential,beta,tmp); - VERIFY_IS_APPROX(v1.norm(), v2.norm()); - if(rows>=2) VERIFY_IS_MUCH_SMALLER_THAN(v1.tail(rows-1).norm(), v1.norm()); - v1 = VectorType::Random(rows); - v2 = v1; - v1.applyHouseholderOnTheLeft(essential,beta,tmp); - VERIFY_IS_APPROX(v1.norm(), v2.norm()); - - MatrixType m1(rows, cols), - m2(rows, cols); - - v1 = VectorType::Random(rows); - if(even) v1.tail(rows-1).setZero(); - m1.colwise() = v1; - m2 = m1; - m1.col(0).makeHouseholder(essential, beta, alpha); - m1.applyHouseholderOnTheLeft(essential,beta,tmp); - VERIFY_IS_APPROX(m1.norm(), m2.norm()); - if(rows>=2) VERIFY_IS_MUCH_SMALLER_THAN(m1.block(1,0,rows-1,cols).norm(), m1.norm()); - VERIFY_IS_MUCH_SMALLER_THAN(numext::imag(m1(0,0)), numext::real(m1(0,0))); - VERIFY_IS_APPROX(numext::real(m1(0,0)), alpha); - - v1 = VectorType::Random(rows); - if(even) v1.tail(rows-1).setZero(); - SquareMatrixType m3(rows,rows), m4(rows,rows); - m3.rowwise() = v1.transpose(); - m4 = m3; - m3.row(0).makeHouseholder(essential, beta, alpha); - m3.applyHouseholderOnTheRight(essential,beta,tmp); - VERIFY_IS_APPROX(m3.norm(), m4.norm()); - if(rows>=2) VERIFY_IS_MUCH_SMALLER_THAN(m3.block(0,1,rows,rows-1).norm(), m3.norm()); - VERIFY_IS_MUCH_SMALLER_THAN(numext::imag(m3(0,0)), numext::real(m3(0,0))); - VERIFY_IS_APPROX(numext::real(m3(0,0)), alpha); - - // test householder sequence on the left with a shift - - Index shift = internal::random<Index>(0, std::max<Index>(rows-2,0)); - Index brows = rows - shift; - m1.setRandom(rows, cols); - HBlockMatrixType hbm = m1.block(shift,0,brows,cols); - HouseholderQR<HBlockMatrixType> qr(hbm); - m2 = m1; - m2.block(shift,0,brows,cols) = qr.matrixQR(); - HCoeffsVectorType hc = qr.hCoeffs().conjugate(); - HouseholderSequence<MatrixType, HCoeffsVectorType> hseq(m2, hc); - hseq.setLength(hc.size()).setShift(shift); - VERIFY(hseq.length() == hc.size()); - VERIFY(hseq.shift() == shift); - - MatrixType m5 = m2; - m5.block(shift,0,brows,cols).template triangularView<StrictlyLower>().setZero(); - VERIFY_IS_APPROX(hseq * m5, m1); // test applying hseq directly - m3 = hseq; - VERIFY_IS_APPROX(m3 * m5, m1); // test evaluating hseq to a dense matrix, then applying - - SquareMatrixType hseq_mat = hseq; - SquareMatrixType hseq_mat_conj = hseq.conjugate(); - SquareMatrixType hseq_mat_adj = hseq.adjoint(); - SquareMatrixType hseq_mat_trans = hseq.transpose(); - SquareMatrixType m6 = SquareMatrixType::Random(rows, rows); - VERIFY_IS_APPROX(hseq_mat.adjoint(), hseq_mat_adj); - VERIFY_IS_APPROX(hseq_mat.conjugate(), hseq_mat_conj); - VERIFY_IS_APPROX(hseq_mat.transpose(), hseq_mat_trans); - VERIFY_IS_APPROX(hseq_mat * m6, hseq_mat * m6); - VERIFY_IS_APPROX(hseq_mat.adjoint() * m6, hseq_mat_adj * m6); - VERIFY_IS_APPROX(hseq_mat.conjugate() * m6, hseq_mat_conj * m6); - VERIFY_IS_APPROX(hseq_mat.transpose() * m6, hseq_mat_trans * m6); - VERIFY_IS_APPROX(m6 * hseq_mat, m6 * hseq_mat); - VERIFY_IS_APPROX(m6 * hseq_mat.adjoint(), m6 * hseq_mat_adj); - VERIFY_IS_APPROX(m6 * hseq_mat.conjugate(), m6 * hseq_mat_conj); - VERIFY_IS_APPROX(m6 * hseq_mat.transpose(), m6 * hseq_mat_trans); - - // test householder sequence on the right with a shift - - TMatrixType tm2 = m2.transpose(); - HouseholderSequence<TMatrixType, HCoeffsVectorType, OnTheRight> rhseq(tm2, hc); - rhseq.setLength(hc.size()).setShift(shift); - VERIFY_IS_APPROX(rhseq * m5, m1); // test applying rhseq directly - m3 = rhseq; - VERIFY_IS_APPROX(m3 * m5, m1); // test evaluating rhseq to a dense matrix, then applying -} - -void test_householder() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( householder(Matrix<double,2,2>()) ); - CALL_SUBTEST_2( householder(Matrix<float,2,3>()) ); - CALL_SUBTEST_3( householder(Matrix<double,3,5>()) ); - CALL_SUBTEST_4( householder(Matrix<float,4,4>()) ); - CALL_SUBTEST_5( householder(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_6( householder(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_7( householder(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_8( householder(Matrix<double,1,1>()) ); - } -} diff --git a/eigen/test/incomplete_cholesky.cpp b/eigen/test/incomplete_cholesky.cpp deleted file mode 100644 index 59ffe92..0000000 --- a/eigen/test/incomplete_cholesky.cpp +++ /dev/null @@ -1,65 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015-2016 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. -// #define EIGEN_DONT_VECTORIZE -// #define EIGEN_MAX_ALIGN_BYTES 0 -#include "sparse_solver.h" -#include <Eigen/IterativeLinearSolvers> -#include <unsupported/Eigen/IterativeSolvers> - -template<typename T, typename I> void test_incomplete_cholesky_T() -{ - typedef SparseMatrix<T,0,I> SparseMatrixType; - ConjugateGradient<SparseMatrixType, Lower, IncompleteCholesky<T, Lower, AMDOrdering<I> > > cg_illt_lower_amd; - ConjugateGradient<SparseMatrixType, Lower, IncompleteCholesky<T, Lower, NaturalOrdering<I> > > cg_illt_lower_nat; - ConjugateGradient<SparseMatrixType, Upper, IncompleteCholesky<T, Upper, AMDOrdering<I> > > cg_illt_upper_amd; - ConjugateGradient<SparseMatrixType, Upper, IncompleteCholesky<T, Upper, NaturalOrdering<I> > > cg_illt_upper_nat; - ConjugateGradient<SparseMatrixType, Upper|Lower, IncompleteCholesky<T, Lower, AMDOrdering<I> > > cg_illt_uplo_amd; - - - CALL_SUBTEST( check_sparse_spd_solving(cg_illt_lower_amd) ); - CALL_SUBTEST( check_sparse_spd_solving(cg_illt_lower_nat) ); - CALL_SUBTEST( check_sparse_spd_solving(cg_illt_upper_amd) ); - CALL_SUBTEST( check_sparse_spd_solving(cg_illt_upper_nat) ); - CALL_SUBTEST( check_sparse_spd_solving(cg_illt_uplo_amd) ); -} - -void test_incomplete_cholesky() -{ - CALL_SUBTEST_1(( test_incomplete_cholesky_T<double,int>() )); - CALL_SUBTEST_2(( test_incomplete_cholesky_T<std::complex<double>, int>() )); - CALL_SUBTEST_3(( test_incomplete_cholesky_T<double,long int>() )); - -#ifdef EIGEN_TEST_PART_1 - // regression for bug 1150 - for(int N = 1; N<20; ++N) - { - Eigen::MatrixXd b( N, N ); - b.setOnes(); - - Eigen::SparseMatrix<double> m( N, N ); - m.reserve(Eigen::VectorXi::Constant(N,4)); - for( int i = 0; i < N; ++i ) - { - m.insert( i, i ) = 1; - m.coeffRef( i, i / 2 ) = 2; - m.coeffRef( i, i / 3 ) = 2; - m.coeffRef( i, i / 4 ) = 2; - } - - Eigen::SparseMatrix<double> A; - A = m * m.transpose(); - - Eigen::ConjugateGradient<Eigen::SparseMatrix<double>, - Eigen::Lower | Eigen::Upper, - Eigen::IncompleteCholesky<double> > solver( A ); - VERIFY(solver.preconditioner().info() == Eigen::Success); - VERIFY(solver.info() == Eigen::Success); - } -#endif -} diff --git a/eigen/test/inplace_decomposition.cpp b/eigen/test/inplace_decomposition.cpp deleted file mode 100644 index 92d0d91..0000000 --- a/eigen/test/inplace_decomposition.cpp +++ /dev/null @@ -1,110 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/LU> -#include <Eigen/Cholesky> -#include <Eigen/QR> - -// This file test inplace decomposition through Ref<>, as supported by Cholesky, LU, and QR decompositions. - -template<typename DecType,typename MatrixType> void inplace(bool square = false, bool SPD = false) -{ - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> RhsType; - typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> ResType; - - Index rows = MatrixType::RowsAtCompileTime==Dynamic ? internal::random<Index>(2,EIGEN_TEST_MAX_SIZE/2) : Index(MatrixType::RowsAtCompileTime); - Index cols = MatrixType::ColsAtCompileTime==Dynamic ? (square?rows:internal::random<Index>(2,rows)) : Index(MatrixType::ColsAtCompileTime); - - MatrixType A = MatrixType::Random(rows,cols); - RhsType b = RhsType::Random(rows); - ResType x(cols); - - if(SPD) - { - assert(square); - A.topRows(cols) = A.topRows(cols).adjoint() * A.topRows(cols); - A.diagonal().array() += 1e-3; - } - - MatrixType A0 = A; - MatrixType A1 = A; - - DecType dec(A); - - // Check that the content of A has been modified - VERIFY_IS_NOT_APPROX( A, A0 ); - - // Check that the decomposition is correct: - if(rows==cols) - { - VERIFY_IS_APPROX( A0 * (x = dec.solve(b)), b ); - } - else - { - VERIFY_IS_APPROX( A0.transpose() * A0 * (x = dec.solve(b)), A0.transpose() * b ); - } - - // Check that modifying A breaks the current dec: - A.setRandom(); - if(rows==cols) - { - VERIFY_IS_NOT_APPROX( A0 * (x = dec.solve(b)), b ); - } - else - { - VERIFY_IS_NOT_APPROX( A0.transpose() * A0 * (x = dec.solve(b)), A0.transpose() * b ); - } - - // Check that calling compute(A1) does not modify A1: - A = A0; - dec.compute(A1); - VERIFY_IS_EQUAL(A0,A1); - VERIFY_IS_NOT_APPROX( A, A0 ); - if(rows==cols) - { - VERIFY_IS_APPROX( A0 * (x = dec.solve(b)), b ); - } - else - { - VERIFY_IS_APPROX( A0.transpose() * A0 * (x = dec.solve(b)), A0.transpose() * b ); - } -} - - -void test_inplace_decomposition() -{ - EIGEN_UNUSED typedef Matrix<double,4,3> Matrix43d; - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1(( inplace<LLT<Ref<MatrixXd> >, MatrixXd>(true,true) )); - CALL_SUBTEST_1(( inplace<LLT<Ref<Matrix4d> >, Matrix4d>(true,true) )); - - CALL_SUBTEST_2(( inplace<LDLT<Ref<MatrixXd> >, MatrixXd>(true,true) )); - CALL_SUBTEST_2(( inplace<LDLT<Ref<Matrix4d> >, Matrix4d>(true,true) )); - - CALL_SUBTEST_3(( inplace<PartialPivLU<Ref<MatrixXd> >, MatrixXd>(true,false) )); - CALL_SUBTEST_3(( inplace<PartialPivLU<Ref<Matrix4d> >, Matrix4d>(true,false) )); - - CALL_SUBTEST_4(( inplace<FullPivLU<Ref<MatrixXd> >, MatrixXd>(true,false) )); - CALL_SUBTEST_4(( inplace<FullPivLU<Ref<Matrix4d> >, Matrix4d>(true,false) )); - - CALL_SUBTEST_5(( inplace<HouseholderQR<Ref<MatrixXd> >, MatrixXd>(false,false) )); - CALL_SUBTEST_5(( inplace<HouseholderQR<Ref<Matrix43d> >, Matrix43d>(false,false) )); - - CALL_SUBTEST_6(( inplace<ColPivHouseholderQR<Ref<MatrixXd> >, MatrixXd>(false,false) )); - CALL_SUBTEST_6(( inplace<ColPivHouseholderQR<Ref<Matrix43d> >, Matrix43d>(false,false) )); - - CALL_SUBTEST_7(( inplace<FullPivHouseholderQR<Ref<MatrixXd> >, MatrixXd>(false,false) )); - CALL_SUBTEST_7(( inplace<FullPivHouseholderQR<Ref<Matrix43d> >, Matrix43d>(false,false) )); - - CALL_SUBTEST_8(( inplace<CompleteOrthogonalDecomposition<Ref<MatrixXd> >, MatrixXd>(false,false) )); - CALL_SUBTEST_8(( inplace<CompleteOrthogonalDecomposition<Ref<Matrix43d> >, Matrix43d>(false,false) )); - } -} diff --git a/eigen/test/integer_types.cpp b/eigen/test/integer_types.cpp deleted file mode 100644 index 3629559..0000000 --- a/eigen/test/integer_types.cpp +++ /dev/null @@ -1,167 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_NO_STATIC_ASSERT - -#include "main.h" - -#undef VERIFY_IS_APPROX -#define VERIFY_IS_APPROX(a, b) VERIFY((a)==(b)); -#undef VERIFY_IS_NOT_APPROX -#define VERIFY_IS_NOT_APPROX(a, b) VERIFY((a)!=(b)); - -template<typename MatrixType> void signed_integer_type_tests(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - - enum { is_signed = (Scalar(-1) > Scalar(0)) ? 0 : 1 }; - VERIFY(is_signed == 1); - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1(rows, cols), - m2 = MatrixType::Random(rows, cols), - mzero = MatrixType::Zero(rows, cols); - - do { - m1 = MatrixType::Random(rows, cols); - } while(m1 == mzero || m1 == m2); - - // check linear structure - - Scalar s1; - do { - s1 = internal::random<Scalar>(); - } while(s1 == 0); - - VERIFY_IS_EQUAL(-(-m1), m1); - VERIFY_IS_EQUAL(-m2+m1+m2, m1); - VERIFY_IS_EQUAL((-m1+m2)*s1, -s1*m1+s1*m2); -} - -template<typename MatrixType> void integer_type_tests(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - - VERIFY(NumTraits<Scalar>::IsInteger); - enum { is_signed = (Scalar(-1) > Scalar(0)) ? 0 : 1 }; - VERIFY(int(NumTraits<Scalar>::IsSigned) == is_signed); - - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - - Index rows = m.rows(); - Index cols = m.cols(); - - // this test relies a lot on Random.h, and there's not much more that we can do - // to test it, hence I consider that we will have tested Random.h - MatrixType m1(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols), - mzero = MatrixType::Zero(rows, cols); - - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType; - SquareMatrixType identity = SquareMatrixType::Identity(rows, rows), - square = SquareMatrixType::Random(rows, rows); - VectorType v1(rows), - v2 = VectorType::Random(rows), - vzero = VectorType::Zero(rows); - - do { - m1 = MatrixType::Random(rows, cols); - } while(m1 == mzero || m1 == m2); - - do { - v1 = VectorType::Random(rows); - } while(v1 == vzero || v1 == v2); - - VERIFY_IS_APPROX( v1, v1); - VERIFY_IS_NOT_APPROX( v1, 2*v1); - VERIFY_IS_APPROX( vzero, v1-v1); - VERIFY_IS_APPROX( m1, m1); - VERIFY_IS_NOT_APPROX( m1, 2*m1); - VERIFY_IS_APPROX( mzero, m1-m1); - - VERIFY_IS_APPROX(m3 = m1,m1); - MatrixType m4; - VERIFY_IS_APPROX(m4 = m1,m1); - - m3.real() = m1.real(); - VERIFY_IS_APPROX(static_cast<const MatrixType&>(m3).real(), static_cast<const MatrixType&>(m1).real()); - VERIFY_IS_APPROX(static_cast<const MatrixType&>(m3).real(), m1.real()); - - // check == / != operators - VERIFY(m1==m1); - VERIFY(m1!=m2); - VERIFY(!(m1==m2)); - VERIFY(!(m1!=m1)); - m1 = m2; - VERIFY(m1==m2); - VERIFY(!(m1!=m2)); - - // check linear structure - - Scalar s1; - do { - s1 = internal::random<Scalar>(); - } while(s1 == 0); - - VERIFY_IS_EQUAL(m1+m1, 2*m1); - VERIFY_IS_EQUAL(m1+m2-m1, m2); - VERIFY_IS_EQUAL(m1*s1, s1*m1); - VERIFY_IS_EQUAL((m1+m2)*s1, s1*m1+s1*m2); - m3 = m2; m3 += m1; - VERIFY_IS_EQUAL(m3, m1+m2); - m3 = m2; m3 -= m1; - VERIFY_IS_EQUAL(m3, m2-m1); - m3 = m2; m3 *= s1; - VERIFY_IS_EQUAL(m3, s1*m2); - - // check matrix product. - - VERIFY_IS_APPROX(identity * m1, m1); - VERIFY_IS_APPROX(square * (m1 + m2), square * m1 + square * m2); - VERIFY_IS_APPROX((m1 + m2).transpose() * square, m1.transpose() * square + m2.transpose() * square); - VERIFY_IS_APPROX((m1 * m2.transpose()) * m1, m1 * (m2.transpose() * m1)); -} - -void test_integer_types() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( integer_type_tests(Matrix<unsigned int, 1, 1>()) ); - CALL_SUBTEST_1( integer_type_tests(Matrix<unsigned long, 3, 4>()) ); - - CALL_SUBTEST_2( integer_type_tests(Matrix<long, 2, 2>()) ); - CALL_SUBTEST_2( signed_integer_type_tests(Matrix<long, 2, 2>()) ); - - CALL_SUBTEST_3( integer_type_tests(Matrix<char, 2, Dynamic>(2, 10)) ); - CALL_SUBTEST_3( signed_integer_type_tests(Matrix<signed char, 2, Dynamic>(2, 10)) ); - - CALL_SUBTEST_4( integer_type_tests(Matrix<unsigned char, 3, 3>()) ); - CALL_SUBTEST_4( integer_type_tests(Matrix<unsigned char, Dynamic, Dynamic>(20, 20)) ); - - CALL_SUBTEST_5( integer_type_tests(Matrix<short, Dynamic, 4>(7, 4)) ); - CALL_SUBTEST_5( signed_integer_type_tests(Matrix<short, Dynamic, 4>(7, 4)) ); - - CALL_SUBTEST_6( integer_type_tests(Matrix<unsigned short, 4, 4>()) ); - - CALL_SUBTEST_7( integer_type_tests(Matrix<long long, 11, 13>()) ); - CALL_SUBTEST_7( signed_integer_type_tests(Matrix<long long, 11, 13>()) ); - - CALL_SUBTEST_8( integer_type_tests(Matrix<unsigned long long, Dynamic, 5>(1, 5)) ); - } -#ifdef EIGEN_TEST_PART_9 - VERIFY_IS_EQUAL(internal::scalar_div_cost<int>::value, 8); - VERIFY_IS_EQUAL(internal::scalar_div_cost<unsigned int>::value, 8); - if(sizeof(long)>sizeof(int)) { - VERIFY(int(internal::scalar_div_cost<long>::value) > int(internal::scalar_div_cost<int>::value)); - VERIFY(int(internal::scalar_div_cost<unsigned long>::value) > int(internal::scalar_div_cost<int>::value)); - } -#endif -} diff --git a/eigen/test/inverse.cpp b/eigen/test/inverse.cpp deleted file mode 100644 index be607cc..0000000 --- a/eigen/test/inverse.cpp +++ /dev/null @@ -1,118 +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) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/LU> - -template<typename MatrixType> void inverse(const MatrixType& m) -{ - using std::abs; - /* this test covers the following files: - Inverse.h - */ - Index rows = m.rows(); - Index cols = m.cols(); - - typedef typename MatrixType::Scalar Scalar; - - MatrixType m1(rows, cols), - m2(rows, cols), - identity = MatrixType::Identity(rows, rows); - createRandomPIMatrixOfRank(rows,rows,rows,m1); - m2 = m1.inverse(); - VERIFY_IS_APPROX(m1, m2.inverse() ); - - VERIFY_IS_APPROX((Scalar(2)*m2).inverse(), m2.inverse()*Scalar(0.5)); - - VERIFY_IS_APPROX(identity, m1.inverse() * m1 ); - VERIFY_IS_APPROX(identity, m1 * m1.inverse() ); - - VERIFY_IS_APPROX(m1, m1.inverse().inverse() ); - - // since for the general case we implement separately row-major and col-major, test that - VERIFY_IS_APPROX(MatrixType(m1.transpose().inverse()), MatrixType(m1.inverse().transpose())); - -#if !defined(EIGEN_TEST_PART_5) && !defined(EIGEN_TEST_PART_6) - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> VectorType; - - //computeInverseAndDetWithCheck tests - //First: an invertible matrix - bool invertible; - Scalar det; - - m2.setZero(); - m1.computeInverseAndDetWithCheck(m2, det, invertible); - VERIFY(invertible); - VERIFY_IS_APPROX(identity, m1*m2); - VERIFY_IS_APPROX(det, m1.determinant()); - - m2.setZero(); - m1.computeInverseWithCheck(m2, invertible); - VERIFY(invertible); - VERIFY_IS_APPROX(identity, m1*m2); - - //Second: a rank one matrix (not invertible, except for 1x1 matrices) - VectorType v3 = VectorType::Random(rows); - MatrixType m3 = v3*v3.transpose(), m4(rows,cols); - m3.computeInverseAndDetWithCheck(m4, det, invertible); - VERIFY( rows==1 ? invertible : !invertible ); - VERIFY_IS_MUCH_SMALLER_THAN(abs(det-m3.determinant()), RealScalar(1)); - m3.computeInverseWithCheck(m4, invertible); - VERIFY( rows==1 ? invertible : !invertible ); - - // check with submatrices - { - Matrix<Scalar, MatrixType::RowsAtCompileTime+1, MatrixType::RowsAtCompileTime+1, MatrixType::Options> m5; - m5.setRandom(); - m5.topLeftCorner(rows,rows) = m1; - m2 = m5.template topLeftCorner<MatrixType::RowsAtCompileTime,MatrixType::ColsAtCompileTime>().inverse(); - VERIFY_IS_APPROX( (m5.template topLeftCorner<MatrixType::RowsAtCompileTime,MatrixType::ColsAtCompileTime>()), m2.inverse() ); - } -#endif - - // check in-place inversion - if(MatrixType::RowsAtCompileTime>=2 && MatrixType::RowsAtCompileTime<=4) - { - // in-place is forbidden - VERIFY_RAISES_ASSERT(m1 = m1.inverse()); - } - else - { - m2 = m1.inverse(); - m1 = m1.inverse(); - VERIFY_IS_APPROX(m1,m2); - } -} - -void test_inverse() -{ - int s = 0; - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( inverse(Matrix<double,1,1>()) ); - CALL_SUBTEST_2( inverse(Matrix2d()) ); - CALL_SUBTEST_3( inverse(Matrix3f()) ); - CALL_SUBTEST_4( inverse(Matrix4f()) ); - CALL_SUBTEST_4( inverse(Matrix<float,4,4,DontAlign>()) ); - - s = internal::random<int>(50,320); - CALL_SUBTEST_5( inverse(MatrixXf(s,s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - - s = internal::random<int>(25,100); - CALL_SUBTEST_6( inverse(MatrixXcd(s,s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - - CALL_SUBTEST_7( inverse(Matrix4d()) ); - CALL_SUBTEST_7( inverse(Matrix<double,4,4,DontAlign>()) ); - - CALL_SUBTEST_8( inverse(Matrix4cd()) ); - } -} diff --git a/eigen/test/is_same_dense.cpp b/eigen/test/is_same_dense.cpp deleted file mode 100644 index 2c7838c..0000000 --- a/eigen/test/is_same_dense.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -using internal::is_same_dense; - -void test_is_same_dense() -{ - typedef Matrix<double,Dynamic,Dynamic,ColMajor> ColMatrixXd; - ColMatrixXd m1(10,10); - Ref<ColMatrixXd> ref_m1(m1); - Ref<const ColMatrixXd> const_ref_m1(m1); - VERIFY(is_same_dense(m1,m1)); - VERIFY(is_same_dense(m1,ref_m1)); - VERIFY(is_same_dense(const_ref_m1,m1)); - VERIFY(is_same_dense(const_ref_m1,ref_m1)); - - VERIFY(is_same_dense(m1.block(0,0,m1.rows(),m1.cols()),m1)); - VERIFY(!is_same_dense(m1.row(0),m1.col(0))); - - Ref<const ColMatrixXd> const_ref_m1_row(m1.row(1)); - VERIFY(!is_same_dense(m1.row(1),const_ref_m1_row)); - - Ref<const ColMatrixXd> const_ref_m1_col(m1.col(1)); - VERIFY(is_same_dense(m1.col(1),const_ref_m1_col)); -} diff --git a/eigen/test/jacobi.cpp b/eigen/test/jacobi.cpp deleted file mode 100644 index 319e476..0000000 --- a/eigen/test/jacobi.cpp +++ /dev/null @@ -1,80 +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) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/SVD> - -template<typename MatrixType, typename JacobiScalar> -void jacobi(const MatrixType& m = MatrixType()) -{ - Index rows = m.rows(); - Index cols = m.cols(); - - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime - }; - - typedef Matrix<JacobiScalar, 2, 1> JacobiVector; - - const MatrixType a(MatrixType::Random(rows, cols)); - - JacobiVector v = JacobiVector::Random().normalized(); - JacobiScalar c = v.x(), s = v.y(); - JacobiRotation<JacobiScalar> rot(c, s); - - { - Index p = internal::random<Index>(0, rows-1); - Index q; - do { - q = internal::random<Index>(0, rows-1); - } while (q == p); - - MatrixType b = a; - b.applyOnTheLeft(p, q, rot); - VERIFY_IS_APPROX(b.row(p), c * a.row(p) + numext::conj(s) * a.row(q)); - VERIFY_IS_APPROX(b.row(q), -s * a.row(p) + numext::conj(c) * a.row(q)); - } - - { - Index p = internal::random<Index>(0, cols-1); - Index q; - do { - q = internal::random<Index>(0, cols-1); - } while (q == p); - - MatrixType b = a; - b.applyOnTheRight(p, q, rot); - VERIFY_IS_APPROX(b.col(p), c * a.col(p) - s * a.col(q)); - VERIFY_IS_APPROX(b.col(q), numext::conj(s) * a.col(p) + numext::conj(c) * a.col(q)); - } -} - -void test_jacobi() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1(( jacobi<Matrix3f, float>() )); - CALL_SUBTEST_2(( jacobi<Matrix4d, double>() )); - CALL_SUBTEST_3(( jacobi<Matrix4cf, float>() )); - CALL_SUBTEST_3(( jacobi<Matrix4cf, std::complex<float> >() )); - - int r = internal::random<int>(2, internal::random<int>(1,EIGEN_TEST_MAX_SIZE)/2), - c = internal::random<int>(2, internal::random<int>(1,EIGEN_TEST_MAX_SIZE)/2); - CALL_SUBTEST_4(( jacobi<MatrixXf, float>(MatrixXf(r,c)) )); - CALL_SUBTEST_5(( jacobi<MatrixXcd, double>(MatrixXcd(r,c)) )); - CALL_SUBTEST_5(( jacobi<MatrixXcd, std::complex<double> >(MatrixXcd(r,c)) )); - // complex<float> is really important to test as it is the only way to cover conjugation issues in certain unaligned paths - CALL_SUBTEST_6(( jacobi<MatrixXcf, float>(MatrixXcf(r,c)) )); - CALL_SUBTEST_6(( jacobi<MatrixXcf, std::complex<float> >(MatrixXcf(r,c)) )); - - TEST_SET_BUT_UNUSED_VARIABLE(r); - TEST_SET_BUT_UNUSED_VARIABLE(c); - } -} diff --git a/eigen/test/jacobisvd.cpp b/eigen/test/jacobisvd.cpp deleted file mode 100644 index 64b8663..0000000 --- a/eigen/test/jacobisvd.cpp +++ /dev/null @@ -1,142 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// discard stack allocation as that too bypasses malloc -#define EIGEN_STACK_ALLOCATION_LIMIT 0 -#define EIGEN_RUNTIME_NO_MALLOC -#include "main.h" -#include <Eigen/SVD> - -#define SVD_DEFAULT(M) JacobiSVD<M> -#define SVD_FOR_MIN_NORM(M) JacobiSVD<M,ColPivHouseholderQRPreconditioner> -#include "svd_common.h" - -// Check all variants of JacobiSVD -template<typename MatrixType> -void jacobisvd(const MatrixType& a = MatrixType(), bool pickrandom = true) -{ - MatrixType m = a; - if(pickrandom) - svd_fill_random(m); - - CALL_SUBTEST(( svd_test_all_computation_options<JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner> >(m, true) )); // check full only - CALL_SUBTEST(( svd_test_all_computation_options<JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner> >(m, false) )); - CALL_SUBTEST(( svd_test_all_computation_options<JacobiSVD<MatrixType, HouseholderQRPreconditioner> >(m, false) )); - if(m.rows()==m.cols()) - CALL_SUBTEST(( svd_test_all_computation_options<JacobiSVD<MatrixType, NoQRPreconditioner> >(m, false) )); -} - -template<typename MatrixType> void jacobisvd_verify_assert(const MatrixType& m) -{ - svd_verify_assert<JacobiSVD<MatrixType> >(m); - Index rows = m.rows(); - Index cols = m.cols(); - - enum { - ColsAtCompileTime = MatrixType::ColsAtCompileTime - }; - - - MatrixType a = MatrixType::Zero(rows, cols); - a.setZero(); - - if (ColsAtCompileTime == Dynamic) - { - JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner> svd_fullqr; - VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeFullU|ComputeThinV)) - VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeThinU|ComputeThinV)) - VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeThinU|ComputeFullV)) - } -} - -template<typename MatrixType> -void jacobisvd_method() -{ - enum { Size = MatrixType::RowsAtCompileTime }; - typedef typename MatrixType::RealScalar RealScalar; - typedef Matrix<RealScalar, Size, 1> RealVecType; - MatrixType m = MatrixType::Identity(); - VERIFY_IS_APPROX(m.jacobiSvd().singularValues(), RealVecType::Ones()); - VERIFY_RAISES_ASSERT(m.jacobiSvd().matrixU()); - VERIFY_RAISES_ASSERT(m.jacobiSvd().matrixV()); - VERIFY_IS_APPROX(m.jacobiSvd(ComputeFullU|ComputeFullV).solve(m), m); -} - -namespace Foo { -// older compiler require a default constructor for Bar -// cf: https://stackoverflow.com/questions/7411515/ -class Bar {public: Bar() {}}; -bool operator<(const Bar&, const Bar&) { return true; } -} -// regression test for a very strange MSVC issue for which simply -// including SVDBase.h messes up with std::max and custom scalar type -void msvc_workaround() -{ - const Foo::Bar a; - const Foo::Bar b; - std::max EIGEN_NOT_A_MACRO (a,b); -} - -void test_jacobisvd() -{ - CALL_SUBTEST_3(( jacobisvd_verify_assert(Matrix3f()) )); - CALL_SUBTEST_4(( jacobisvd_verify_assert(Matrix4d()) )); - CALL_SUBTEST_7(( jacobisvd_verify_assert(MatrixXf(10,12)) )); - CALL_SUBTEST_8(( jacobisvd_verify_assert(MatrixXcd(7,5)) )); - - CALL_SUBTEST_11(svd_all_trivial_2x2(jacobisvd<Matrix2cd>)); - CALL_SUBTEST_12(svd_all_trivial_2x2(jacobisvd<Matrix2d>)); - - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_3(( jacobisvd<Matrix3f>() )); - CALL_SUBTEST_4(( jacobisvd<Matrix4d>() )); - CALL_SUBTEST_5(( jacobisvd<Matrix<float,3,5> >() )); - CALL_SUBTEST_6(( jacobisvd<Matrix<double,Dynamic,2> >(Matrix<double,Dynamic,2>(10,2)) )); - - int r = internal::random<int>(1, 30), - c = internal::random<int>(1, 30); - - TEST_SET_BUT_UNUSED_VARIABLE(r) - TEST_SET_BUT_UNUSED_VARIABLE(c) - - CALL_SUBTEST_10(( jacobisvd<MatrixXd>(MatrixXd(r,c)) )); - CALL_SUBTEST_7(( jacobisvd<MatrixXf>(MatrixXf(r,c)) )); - CALL_SUBTEST_8(( jacobisvd<MatrixXcd>(MatrixXcd(r,c)) )); - (void) r; - (void) c; - - // Test on inf/nan matrix - CALL_SUBTEST_7( (svd_inf_nan<JacobiSVD<MatrixXf>, MatrixXf>()) ); - CALL_SUBTEST_10( (svd_inf_nan<JacobiSVD<MatrixXd>, MatrixXd>()) ); - - // bug1395 test compile-time vectors as input - CALL_SUBTEST_13(( jacobisvd_verify_assert(Matrix<double,6,1>()) )); - CALL_SUBTEST_13(( jacobisvd_verify_assert(Matrix<double,1,6>()) )); - CALL_SUBTEST_13(( jacobisvd_verify_assert(Matrix<double,Dynamic,1>(r)) )); - CALL_SUBTEST_13(( jacobisvd_verify_assert(Matrix<double,1,Dynamic>(c)) )); - } - - CALL_SUBTEST_7(( jacobisvd<MatrixXf>(MatrixXf(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2))) )); - CALL_SUBTEST_8(( jacobisvd<MatrixXcd>(MatrixXcd(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/3), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/3))) )); - - // test matrixbase method - CALL_SUBTEST_1(( jacobisvd_method<Matrix2cd>() )); - CALL_SUBTEST_3(( jacobisvd_method<Matrix3f>() )); - - // Test problem size constructors - CALL_SUBTEST_7( JacobiSVD<MatrixXf>(10,10) ); - - // Check that preallocation avoids subsequent mallocs - CALL_SUBTEST_9( svd_preallocate<void>() ); - - CALL_SUBTEST_2( svd_underoverflow<void>() ); - - msvc_workaround(); -} diff --git a/eigen/test/linearstructure.cpp b/eigen/test/linearstructure.cpp deleted file mode 100644 index b6559b2..0000000 --- a/eigen/test/linearstructure.cpp +++ /dev/null @@ -1,148 +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) 2014 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -static bool g_called; -#define EIGEN_SCALAR_BINARY_OP_PLUGIN { g_called |= (!internal::is_same<LhsScalar,RhsScalar>::value); } - -#include "main.h" - -template<typename MatrixType> void linearStructure(const MatrixType& m) -{ - using std::abs; - /* this test covers the following files: - CwiseUnaryOp.h, CwiseBinaryOp.h, SelfCwiseBinaryOp.h - */ - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - - Index rows = m.rows(); - Index cols = m.cols(); - - // this test relies a lot on Random.h, and there's not much more that we can do - // to test it, hence I consider that we will have tested Random.h - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols); - - Scalar s1 = internal::random<Scalar>(); - while (abs(s1)<RealScalar(1e-3)) s1 = internal::random<Scalar>(); - - Index r = internal::random<Index>(0, rows-1), - c = internal::random<Index>(0, cols-1); - - VERIFY_IS_APPROX(-(-m1), m1); - VERIFY_IS_APPROX(m1+m1, 2*m1); - VERIFY_IS_APPROX(m1+m2-m1, m2); - VERIFY_IS_APPROX(-m2+m1+m2, m1); - VERIFY_IS_APPROX(m1*s1, s1*m1); - VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2); - VERIFY_IS_APPROX((-m1+m2)*s1, -s1*m1+s1*m2); - m3 = m2; m3 += m1; - VERIFY_IS_APPROX(m3, m1+m2); - m3 = m2; m3 -= m1; - VERIFY_IS_APPROX(m3, m2-m1); - m3 = m2; m3 *= s1; - VERIFY_IS_APPROX(m3, s1*m2); - if(!NumTraits<Scalar>::IsInteger) - { - m3 = m2; m3 /= s1; - VERIFY_IS_APPROX(m3, m2/s1); - } - - // again, test operator() to check const-qualification - VERIFY_IS_APPROX((-m1)(r,c), -(m1(r,c))); - VERIFY_IS_APPROX((m1-m2)(r,c), (m1(r,c))-(m2(r,c))); - VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c))); - VERIFY_IS_APPROX((s1*m1)(r,c), s1*(m1(r,c))); - VERIFY_IS_APPROX((m1*s1)(r,c), (m1(r,c))*s1); - if(!NumTraits<Scalar>::IsInteger) - VERIFY_IS_APPROX((m1/s1)(r,c), (m1(r,c))/s1); - - // use .block to disable vectorization and compare to the vectorized version - VERIFY_IS_APPROX(m1+m1.block(0,0,rows,cols), m1+m1); - VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0,0,rows,cols)), m1.cwiseProduct(m1)); - VERIFY_IS_APPROX(m1 - m1.block(0,0,rows,cols), m1 - m1); - VERIFY_IS_APPROX(m1.block(0,0,rows,cols) * s1, m1 * s1); -} - -// Make sure that complex * real and real * complex are properly optimized -template<typename MatrixType> void real_complex(DenseIndex rows = MatrixType::RowsAtCompileTime, DenseIndex cols = MatrixType::ColsAtCompileTime) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - - RealScalar s = internal::random<RealScalar>(); - MatrixType m1 = MatrixType::Random(rows, cols); - - g_called = false; - VERIFY_IS_APPROX(s*m1, Scalar(s)*m1); - VERIFY(g_called && "real * matrix<complex> not properly optimized"); - - g_called = false; - VERIFY_IS_APPROX(m1*s, m1*Scalar(s)); - VERIFY(g_called && "matrix<complex> * real not properly optimized"); - - g_called = false; - VERIFY_IS_APPROX(m1/s, m1/Scalar(s)); - VERIFY(g_called && "matrix<complex> / real not properly optimized"); - - g_called = false; - VERIFY_IS_APPROX(s+m1.array(), Scalar(s)+m1.array()); - VERIFY(g_called && "real + matrix<complex> not properly optimized"); - - g_called = false; - VERIFY_IS_APPROX(m1.array()+s, m1.array()+Scalar(s)); - VERIFY(g_called && "matrix<complex> + real not properly optimized"); - - g_called = false; - VERIFY_IS_APPROX(s-m1.array(), Scalar(s)-m1.array()); - VERIFY(g_called && "real - matrix<complex> not properly optimized"); - - g_called = false; - VERIFY_IS_APPROX(m1.array()-s, m1.array()-Scalar(s)); - VERIFY(g_called && "matrix<complex> - real not properly optimized"); -} - -void test_linearstructure() -{ - g_called = true; - VERIFY(g_called); // avoid `unneeded-internal-declaration` warning. - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( linearStructure(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( linearStructure(Matrix2f()) ); - CALL_SUBTEST_3( linearStructure(Vector3d()) ); - CALL_SUBTEST_4( linearStructure(Matrix4d()) ); - CALL_SUBTEST_5( linearStructure(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) ); - CALL_SUBTEST_6( linearStructure(MatrixXf (internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_7( linearStructure(MatrixXi (internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_8( linearStructure(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) ); - CALL_SUBTEST_9( linearStructure(ArrayXXf (internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_10( linearStructure(ArrayXXcf (internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - - CALL_SUBTEST_11( real_complex<Matrix4cd>() ); - CALL_SUBTEST_11( real_complex<MatrixXcf>(10,10) ); - CALL_SUBTEST_11( real_complex<ArrayXXcf>(10,10) ); - } - -#ifdef EIGEN_TEST_PART_4 - { - // make sure that /=scalar and /scalar do not overflow - // rational: 1.0/4.94e-320 overflow, but m/4.94e-320 should not - Matrix4d m2, m3; - m3 = m2 = Matrix4d::Random()*1e-20; - m2 = m2 / 4.9e-320; - VERIFY_IS_APPROX(m2.cwiseQuotient(m2), Matrix4d::Ones()); - m3 /= 4.9e-320; - VERIFY_IS_APPROX(m3.cwiseQuotient(m3), Matrix4d::Ones()); - - - } -#endif -} diff --git a/eigen/test/lscg.cpp b/eigen/test/lscg.cpp deleted file mode 100644 index d49ee00..0000000 --- a/eigen/test/lscg.cpp +++ /dev/null @@ -1,37 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "sparse_solver.h" -#include <Eigen/IterativeLinearSolvers> - -template<typename T> void test_lscg_T() -{ - LeastSquaresConjugateGradient<SparseMatrix<T> > lscg_colmajor_diag; - LeastSquaresConjugateGradient<SparseMatrix<T>, IdentityPreconditioner> lscg_colmajor_I; - LeastSquaresConjugateGradient<SparseMatrix<T,RowMajor> > lscg_rowmajor_diag; - LeastSquaresConjugateGradient<SparseMatrix<T,RowMajor>, IdentityPreconditioner> lscg_rowmajor_I; - - CALL_SUBTEST( check_sparse_square_solving(lscg_colmajor_diag) ); - CALL_SUBTEST( check_sparse_square_solving(lscg_colmajor_I) ); - - CALL_SUBTEST( check_sparse_leastsquare_solving(lscg_colmajor_diag) ); - CALL_SUBTEST( check_sparse_leastsquare_solving(lscg_colmajor_I) ); - - CALL_SUBTEST( check_sparse_square_solving(lscg_rowmajor_diag) ); - CALL_SUBTEST( check_sparse_square_solving(lscg_rowmajor_I) ); - - CALL_SUBTEST( check_sparse_leastsquare_solving(lscg_rowmajor_diag) ); - CALL_SUBTEST( check_sparse_leastsquare_solving(lscg_rowmajor_I) ); -} - -void test_lscg() -{ - CALL_SUBTEST_1(test_lscg_T<double>()); - CALL_SUBTEST_2(test_lscg_T<std::complex<double> >()); -} diff --git a/eigen/test/lu.cpp b/eigen/test/lu.cpp deleted file mode 100644 index 176a2f0..0000000 --- a/eigen/test/lu.cpp +++ /dev/null @@ -1,283 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/LU> -using namespace std; - -template<typename MatrixType> -typename MatrixType::RealScalar matrix_l1_norm(const MatrixType& m) { - return m.cwiseAbs().colwise().sum().maxCoeff(); -} - -template<typename MatrixType> void lu_non_invertible() -{ - typedef typename MatrixType::RealScalar RealScalar; - /* this test covers the following files: - LU.h - */ - Index rows, cols, cols2; - if(MatrixType::RowsAtCompileTime==Dynamic) - { - rows = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE); - } - else - { - rows = MatrixType::RowsAtCompileTime; - } - if(MatrixType::ColsAtCompileTime==Dynamic) - { - cols = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE); - cols2 = internal::random<int>(2,EIGEN_TEST_MAX_SIZE); - } - else - { - cols2 = cols = MatrixType::ColsAtCompileTime; - } - - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime - }; - typedef typename internal::kernel_retval_base<FullPivLU<MatrixType> >::ReturnType KernelMatrixType; - typedef typename internal::image_retval_base<FullPivLU<MatrixType> >::ReturnType ImageMatrixType; - typedef Matrix<typename MatrixType::Scalar, ColsAtCompileTime, ColsAtCompileTime> - CMatrixType; - typedef Matrix<typename MatrixType::Scalar, RowsAtCompileTime, RowsAtCompileTime> - RMatrixType; - - Index rank = internal::random<Index>(1, (std::min)(rows, cols)-1); - - // The image of the zero matrix should consist of a single (zero) column vector - VERIFY((MatrixType::Zero(rows,cols).fullPivLu().image(MatrixType::Zero(rows,cols)).cols() == 1)); - - // The kernel of the zero matrix is the entire space, and thus is an invertible matrix of dimensions cols. - KernelMatrixType kernel = MatrixType::Zero(rows,cols).fullPivLu().kernel(); - VERIFY((kernel.fullPivLu().isInvertible())); - - MatrixType m1(rows, cols), m3(rows, cols2); - CMatrixType m2(cols, cols2); - createRandomPIMatrixOfRank(rank, rows, cols, m1); - - FullPivLU<MatrixType> lu; - - // The special value 0.01 below works well in tests. Keep in mind that we're only computing the rank - // of singular values are either 0 or 1. - // So it's not clear at all that the epsilon should play any role there. - lu.setThreshold(RealScalar(0.01)); - lu.compute(m1); - - MatrixType u(rows,cols); - u = lu.matrixLU().template triangularView<Upper>(); - RMatrixType l = RMatrixType::Identity(rows,rows); - l.block(0,0,rows,(std::min)(rows,cols)).template triangularView<StrictlyLower>() - = lu.matrixLU().block(0,0,rows,(std::min)(rows,cols)); - - VERIFY_IS_APPROX(lu.permutationP() * m1 * lu.permutationQ(), l*u); - - KernelMatrixType m1kernel = lu.kernel(); - ImageMatrixType m1image = lu.image(m1); - - VERIFY_IS_APPROX(m1, lu.reconstructedMatrix()); - VERIFY(rank == lu.rank()); - VERIFY(cols - lu.rank() == lu.dimensionOfKernel()); - VERIFY(!lu.isInjective()); - VERIFY(!lu.isInvertible()); - VERIFY(!lu.isSurjective()); - VERIFY_IS_MUCH_SMALLER_THAN((m1 * m1kernel), m1); - VERIFY(m1image.fullPivLu().rank() == rank); - VERIFY_IS_APPROX(m1 * m1.adjoint() * m1image, m1image); - - m2 = CMatrixType::Random(cols,cols2); - m3 = m1*m2; - m2 = CMatrixType::Random(cols,cols2); - // test that the code, which does resize(), may be applied to an xpr - m2.block(0,0,m2.rows(),m2.cols()) = lu.solve(m3); - VERIFY_IS_APPROX(m3, m1*m2); - - // test solve with transposed - m3 = MatrixType::Random(rows,cols2); - m2 = m1.transpose()*m3; - m3 = MatrixType::Random(rows,cols2); - lu.template _solve_impl_transposed<false>(m2, m3); - VERIFY_IS_APPROX(m2, m1.transpose()*m3); - m3 = MatrixType::Random(rows,cols2); - m3 = lu.transpose().solve(m2); - VERIFY_IS_APPROX(m2, m1.transpose()*m3); - - // test solve with conjugate transposed - m3 = MatrixType::Random(rows,cols2); - m2 = m1.adjoint()*m3; - m3 = MatrixType::Random(rows,cols2); - lu.template _solve_impl_transposed<true>(m2, m3); - VERIFY_IS_APPROX(m2, m1.adjoint()*m3); - m3 = MatrixType::Random(rows,cols2); - m3 = lu.adjoint().solve(m2); - VERIFY_IS_APPROX(m2, m1.adjoint()*m3); -} - -template<typename MatrixType> void lu_invertible() -{ - /* this test covers the following files: - LU.h - */ - typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; - Index size = MatrixType::RowsAtCompileTime; - if( size==Dynamic) - size = internal::random<Index>(1,EIGEN_TEST_MAX_SIZE); - - MatrixType m1(size, size), m2(size, size), m3(size, size); - FullPivLU<MatrixType> lu; - lu.setThreshold(RealScalar(0.01)); - do { - m1 = MatrixType::Random(size,size); - lu.compute(m1); - } while(!lu.isInvertible()); - - VERIFY_IS_APPROX(m1, lu.reconstructedMatrix()); - VERIFY(0 == lu.dimensionOfKernel()); - VERIFY(lu.kernel().cols() == 1); // the kernel() should consist of a single (zero) column vector - VERIFY(size == lu.rank()); - VERIFY(lu.isInjective()); - VERIFY(lu.isSurjective()); - VERIFY(lu.isInvertible()); - VERIFY(lu.image(m1).fullPivLu().isInvertible()); - m3 = MatrixType::Random(size,size); - m2 = lu.solve(m3); - VERIFY_IS_APPROX(m3, m1*m2); - MatrixType m1_inverse = lu.inverse(); - VERIFY_IS_APPROX(m2, m1_inverse*m3); - - RealScalar rcond = (RealScalar(1) / matrix_l1_norm(m1)) / matrix_l1_norm(m1_inverse); - const RealScalar rcond_est = lu.rcond(); - // Verify that the estimated condition number is within a factor of 10 of the - // truth. - VERIFY(rcond_est > rcond / 10 && rcond_est < rcond * 10); - - // test solve with transposed - lu.template _solve_impl_transposed<false>(m3, m2); - VERIFY_IS_APPROX(m3, m1.transpose()*m2); - m3 = MatrixType::Random(size,size); - m3 = lu.transpose().solve(m2); - VERIFY_IS_APPROX(m2, m1.transpose()*m3); - - // test solve with conjugate transposed - lu.template _solve_impl_transposed<true>(m3, m2); - VERIFY_IS_APPROX(m3, m1.adjoint()*m2); - m3 = MatrixType::Random(size,size); - m3 = lu.adjoint().solve(m2); - VERIFY_IS_APPROX(m2, m1.adjoint()*m3); - - // Regression test for Bug 302 - MatrixType m4 = MatrixType::Random(size,size); - VERIFY_IS_APPROX(lu.solve(m3*m4), lu.solve(m3)*m4); -} - -template<typename MatrixType> void lu_partial_piv() -{ - /* this test covers the following files: - PartialPivLU.h - */ - typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; - Index size = internal::random<Index>(1,4); - - MatrixType m1(size, size), m2(size, size), m3(size, size); - m1.setRandom(); - PartialPivLU<MatrixType> plu(m1); - - VERIFY_IS_APPROX(m1, plu.reconstructedMatrix()); - - m3 = MatrixType::Random(size,size); - m2 = plu.solve(m3); - VERIFY_IS_APPROX(m3, m1*m2); - MatrixType m1_inverse = plu.inverse(); - VERIFY_IS_APPROX(m2, m1_inverse*m3); - - RealScalar rcond = (RealScalar(1) / matrix_l1_norm(m1)) / matrix_l1_norm(m1_inverse); - const RealScalar rcond_est = plu.rcond(); - // Verify that the estimate is within a factor of 10 of the truth. - VERIFY(rcond_est > rcond / 10 && rcond_est < rcond * 10); - - // test solve with transposed - plu.template _solve_impl_transposed<false>(m3, m2); - VERIFY_IS_APPROX(m3, m1.transpose()*m2); - m3 = MatrixType::Random(size,size); - m3 = plu.transpose().solve(m2); - VERIFY_IS_APPROX(m2, m1.transpose()*m3); - - // test solve with conjugate transposed - plu.template _solve_impl_transposed<true>(m3, m2); - VERIFY_IS_APPROX(m3, m1.adjoint()*m2); - m3 = MatrixType::Random(size,size); - m3 = plu.adjoint().solve(m2); - VERIFY_IS_APPROX(m2, m1.adjoint()*m3); -} - -template<typename MatrixType> void lu_verify_assert() -{ - MatrixType tmp; - - FullPivLU<MatrixType> lu; - VERIFY_RAISES_ASSERT(lu.matrixLU()) - VERIFY_RAISES_ASSERT(lu.permutationP()) - VERIFY_RAISES_ASSERT(lu.permutationQ()) - VERIFY_RAISES_ASSERT(lu.kernel()) - VERIFY_RAISES_ASSERT(lu.image(tmp)) - VERIFY_RAISES_ASSERT(lu.solve(tmp)) - VERIFY_RAISES_ASSERT(lu.determinant()) - VERIFY_RAISES_ASSERT(lu.rank()) - VERIFY_RAISES_ASSERT(lu.dimensionOfKernel()) - VERIFY_RAISES_ASSERT(lu.isInjective()) - VERIFY_RAISES_ASSERT(lu.isSurjective()) - VERIFY_RAISES_ASSERT(lu.isInvertible()) - VERIFY_RAISES_ASSERT(lu.inverse()) - - PartialPivLU<MatrixType> plu; - VERIFY_RAISES_ASSERT(plu.matrixLU()) - VERIFY_RAISES_ASSERT(plu.permutationP()) - VERIFY_RAISES_ASSERT(plu.solve(tmp)) - VERIFY_RAISES_ASSERT(plu.determinant()) - VERIFY_RAISES_ASSERT(plu.inverse()) -} - -void test_lu() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( lu_non_invertible<Matrix3f>() ); - CALL_SUBTEST_1( lu_invertible<Matrix3f>() ); - CALL_SUBTEST_1( lu_verify_assert<Matrix3f>() ); - - CALL_SUBTEST_2( (lu_non_invertible<Matrix<double, 4, 6> >()) ); - CALL_SUBTEST_2( (lu_verify_assert<Matrix<double, 4, 6> >()) ); - - CALL_SUBTEST_3( lu_non_invertible<MatrixXf>() ); - CALL_SUBTEST_3( lu_invertible<MatrixXf>() ); - CALL_SUBTEST_3( lu_verify_assert<MatrixXf>() ); - - CALL_SUBTEST_4( lu_non_invertible<MatrixXd>() ); - CALL_SUBTEST_4( lu_invertible<MatrixXd>() ); - CALL_SUBTEST_4( lu_partial_piv<MatrixXd>() ); - CALL_SUBTEST_4( lu_verify_assert<MatrixXd>() ); - - CALL_SUBTEST_5( lu_non_invertible<MatrixXcf>() ); - CALL_SUBTEST_5( lu_invertible<MatrixXcf>() ); - CALL_SUBTEST_5( lu_verify_assert<MatrixXcf>() ); - - CALL_SUBTEST_6( lu_non_invertible<MatrixXcd>() ); - CALL_SUBTEST_6( lu_invertible<MatrixXcd>() ); - CALL_SUBTEST_6( lu_partial_piv<MatrixXcd>() ); - CALL_SUBTEST_6( lu_verify_assert<MatrixXcd>() ); - - CALL_SUBTEST_7(( lu_non_invertible<Matrix<float,Dynamic,16> >() )); - - // Test problem size constructors - CALL_SUBTEST_9( PartialPivLU<MatrixXf>(10) ); - CALL_SUBTEST_9( FullPivLU<MatrixXf>(10, 20); ); - } -} diff --git a/eigen/test/main.h b/eigen/test/main.h deleted file mode 100644 index 8c868ee..0000000 --- a/eigen/test/main.h +++ /dev/null @@ -1,803 +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) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include <cstdlib> -#include <cerrno> -#include <ctime> -#include <iostream> -#include <fstream> -#include <string> -#include <sstream> -#include <vector> -#include <typeinfo> - -// The following includes of STL headers have to be done _before_ the -// definition of macros min() and max(). The reason is that many STL -// implementations will not work properly as the min and max symbols collide -// with the STL functions std:min() and std::max(). The STL headers may check -// for the macro definition of min/max and issue a warning or undefine the -// macros. -// -// Still, Windows defines min() and max() in windef.h as part of the regular -// Windows system interfaces and many other Windows APIs depend on these -// macros being available. To prevent the macro expansion of min/max and to -// make Eigen compatible with the Windows environment all function calls of -// std::min() and std::max() have to be written with parenthesis around the -// function name. -// -// All STL headers used by Eigen should be included here. Because main.h is -// included before any Eigen header and because the STL headers are guarded -// against multiple inclusions, no STL header will see our own min/max macro -// definitions. -#include <limits> -#include <algorithm> -#include <complex> -#include <deque> -#include <queue> -#include <cassert> -#include <list> -#if __cplusplus >= 201103L -#include <random> -#ifdef EIGEN_USE_THREADS -#include <future> -#endif -#endif - -// Same for cuda_fp16.h -#if defined(__CUDACC_VER_MAJOR__) && (__CUDACC_VER_MAJOR__ >= 9) -#define EIGEN_TEST_CUDACC_VER ((__CUDACC_VER_MAJOR__ * 10000) + (__CUDACC_VER_MINOR__ * 100)) -#elif defined(__CUDACC_VER__) -#define EIGEN_TEST_CUDACC_VER __CUDACC_VER__ -#else -#define EIGEN_TEST_CUDACC_VER 0 -#endif - -#if EIGEN_TEST_CUDACC_VER >= 70500 -#include <cuda_fp16.h> -#endif - -// To test that all calls from Eigen code to std::min() and std::max() are -// protected by parenthesis against macro expansion, the min()/max() macros -// are defined here and any not-parenthesized min/max call will cause a -// compiler error. -#define min(A,B) please_protect_your_min_with_parentheses -#define max(A,B) please_protect_your_max_with_parentheses -#define isnan(X) please_protect_your_isnan_with_parentheses -#define isinf(X) please_protect_your_isinf_with_parentheses -#define isfinite(X) please_protect_your_isfinite_with_parentheses -#ifdef M_PI -#undef M_PI -#endif -#define M_PI please_use_EIGEN_PI_instead_of_M_PI - -#define FORBIDDEN_IDENTIFIER (this_identifier_is_forbidden_to_avoid_clashes) this_identifier_is_forbidden_to_avoid_clashes -// B0 is defined in POSIX header termios.h -#define B0 FORBIDDEN_IDENTIFIER - -// Unit tests calling Eigen's blas library must preserve the default blocking size -// to avoid troubles. -#ifndef EIGEN_NO_DEBUG_SMALL_PRODUCT_BLOCKS -#define EIGEN_DEBUG_SMALL_PRODUCT_BLOCKS -#endif - -// shuts down ICC's remark #593: variable "XXX" was set but never used -#define TEST_SET_BUT_UNUSED_VARIABLE(X) EIGEN_UNUSED_VARIABLE(X) - -#ifdef TEST_ENABLE_TEMPORARY_TRACKING - -static long int nb_temporaries; -static long int nb_temporaries_on_assert = -1; - -inline void on_temporary_creation(long int size) { - // here's a great place to set a breakpoint when debugging failures in this test! - if(size!=0) nb_temporaries++; - if(nb_temporaries_on_assert>0) assert(nb_temporaries<nb_temporaries_on_assert); -} - -#define EIGEN_DENSE_STORAGE_CTOR_PLUGIN { on_temporary_creation(size); } - -#define VERIFY_EVALUATION_COUNT(XPR,N) {\ - nb_temporaries = 0; \ - XPR; \ - if(nb_temporaries!=N) { std::cerr << "nb_temporaries == " << nb_temporaries << "\n"; }\ - VERIFY( (#XPR) && nb_temporaries==N ); \ - } - -#endif - -// the following file is automatically generated by cmake -#include "split_test_helper.h" - -#ifdef NDEBUG -#undef NDEBUG -#endif - -// On windows CE, NDEBUG is automatically defined <assert.h> if NDEBUG is not defined. -#ifndef DEBUG -#define DEBUG -#endif - -// bounds integer values for AltiVec -#if defined(__ALTIVEC__) || defined(__VSX__) -#define EIGEN_MAKING_DOCS -#endif - -#ifndef EIGEN_TEST_FUNC -#error EIGEN_TEST_FUNC must be defined -#endif - -#define DEFAULT_REPEAT 10 - -namespace Eigen -{ - static std::vector<std::string> g_test_stack; - // level == 0 <=> abort if test fail - // level >= 1 <=> warning message to std::cerr if test fail - static int g_test_level = 0; - static int g_repeat; - static unsigned int g_seed; - static bool g_has_set_repeat, g_has_set_seed; -} - -#define TRACK std::cerr << __FILE__ << " " << __LINE__ << std::endl -// #define TRACK while() - -#define EI_PP_MAKE_STRING2(S) #S -#define EI_PP_MAKE_STRING(S) EI_PP_MAKE_STRING2(S) - -#define EIGEN_DEFAULT_IO_FORMAT IOFormat(4, 0, " ", "\n", "", "", "", "") - -#if (defined(_CPPUNWIND) || defined(__EXCEPTIONS)) && !defined(__CUDA_ARCH__) - #define EIGEN_EXCEPTIONS -#endif - -#ifndef EIGEN_NO_ASSERTION_CHECKING - - namespace Eigen - { - static const bool should_raise_an_assert = false; - - // Used to avoid to raise two exceptions at a time in which - // case the exception is not properly caught. - // This may happen when a second exceptions is triggered in a destructor. - static bool no_more_assert = false; - static bool report_on_cerr_on_assert_failure = true; - - struct eigen_assert_exception - { - eigen_assert_exception(void) {} - ~eigen_assert_exception() { Eigen::no_more_assert = false; } - }; - - struct eigen_static_assert_exception - { - eigen_static_assert_exception(void) {} - ~eigen_static_assert_exception() { Eigen::no_more_assert = false; } - }; - } - // If EIGEN_DEBUG_ASSERTS is defined and if no assertion is triggered while - // one should have been, then the list of excecuted assertions is printed out. - // - // EIGEN_DEBUG_ASSERTS is not enabled by default as it - // significantly increases the compilation time - // and might even introduce side effects that would hide - // some memory errors. - #ifdef EIGEN_DEBUG_ASSERTS - - namespace Eigen - { - namespace internal - { - static bool push_assert = false; - } - static std::vector<std::string> eigen_assert_list; - } - #define eigen_assert(a) \ - if( (!(a)) && (!no_more_assert) ) \ - { \ - if(report_on_cerr_on_assert_failure) \ - std::cerr << #a << " " __FILE__ << "(" << __LINE__ << ")\n"; \ - Eigen::no_more_assert = true; \ - EIGEN_THROW_X(Eigen::eigen_assert_exception()); \ - } \ - else if (Eigen::internal::push_assert) \ - { \ - eigen_assert_list.push_back(std::string(EI_PP_MAKE_STRING(__FILE__) " (" EI_PP_MAKE_STRING(__LINE__) ") : " #a) ); \ - } - - #ifdef EIGEN_EXCEPTIONS - #define VERIFY_RAISES_ASSERT(a) \ - { \ - Eigen::no_more_assert = false; \ - Eigen::eigen_assert_list.clear(); \ - Eigen::internal::push_assert = true; \ - Eigen::report_on_cerr_on_assert_failure = false; \ - try { \ - a; \ - std::cerr << "One of the following asserts should have been triggered:\n"; \ - for (uint ai=0 ; ai<eigen_assert_list.size() ; ++ai) \ - std::cerr << " " << eigen_assert_list[ai] << "\n"; \ - VERIFY(Eigen::should_raise_an_assert && # a); \ - } catch (Eigen::eigen_assert_exception) { \ - Eigen::internal::push_assert = false; VERIFY(true); \ - } \ - Eigen::report_on_cerr_on_assert_failure = true; \ - Eigen::internal::push_assert = false; \ - } - #endif //EIGEN_EXCEPTIONS - - #elif !defined(__CUDACC__) // EIGEN_DEBUG_ASSERTS - // see bug 89. The copy_bool here is working around a bug in gcc <= 4.3 - #define eigen_assert(a) \ - if( (!Eigen::internal::copy_bool(a)) && (!no_more_assert) )\ - { \ - Eigen::no_more_assert = true; \ - if(report_on_cerr_on_assert_failure) \ - eigen_plain_assert(a); \ - else \ - EIGEN_THROW_X(Eigen::eigen_assert_exception()); \ - } - - #ifdef EIGEN_EXCEPTIONS - #define VERIFY_RAISES_ASSERT(a) { \ - Eigen::no_more_assert = false; \ - Eigen::report_on_cerr_on_assert_failure = false; \ - try { \ - a; \ - VERIFY(Eigen::should_raise_an_assert && # a); \ - } \ - catch (Eigen::eigen_assert_exception&) { VERIFY(true); } \ - Eigen::report_on_cerr_on_assert_failure = true; \ - } - #endif // EIGEN_EXCEPTIONS - #endif // EIGEN_DEBUG_ASSERTS - - #if defined(TEST_CHECK_STATIC_ASSERTIONS) && defined(EIGEN_EXCEPTIONS) - #define EIGEN_STATIC_ASSERT(a,MSG) \ - if( (!Eigen::internal::copy_bool(a)) && (!no_more_assert) )\ - { \ - Eigen::no_more_assert = true; \ - if(report_on_cerr_on_assert_failure) \ - eigen_plain_assert((a) && #MSG); \ - else \ - EIGEN_THROW_X(Eigen::eigen_static_assert_exception()); \ - } - #define VERIFY_RAISES_STATIC_ASSERT(a) { \ - Eigen::no_more_assert = false; \ - Eigen::report_on_cerr_on_assert_failure = false; \ - try { \ - a; \ - VERIFY(Eigen::should_raise_an_assert && # a); \ - } \ - catch (Eigen::eigen_static_assert_exception&) { VERIFY(true); } \ - Eigen::report_on_cerr_on_assert_failure = true; \ - } - #endif // TEST_CHECK_STATIC_ASSERTIONS - -#ifndef VERIFY_RAISES_ASSERT - #define VERIFY_RAISES_ASSERT(a) \ - std::cout << "Can't VERIFY_RAISES_ASSERT( " #a " ) with exceptions disabled\n"; -#endif -#ifndef VERIFY_RAISES_STATIC_ASSERT - #define VERIFY_RAISES_STATIC_ASSERT(a) \ - std::cout << "Can't VERIFY_RAISES_STATIC_ASSERT( " #a " ) with exceptions disabled\n"; -#endif - - #if !defined(__CUDACC__) - #define EIGEN_USE_CUSTOM_ASSERT - #endif - -#else // EIGEN_NO_ASSERTION_CHECKING - - #define VERIFY_RAISES_ASSERT(a) {} - #define VERIFY_RAISES_STATIC_ASSERT(a) {} - -#endif // EIGEN_NO_ASSERTION_CHECKING - -#define EIGEN_INTERNAL_DEBUGGING -#include <Eigen/QR> // required for createRandomPIMatrixOfRank - -inline void verify_impl(bool condition, const char *testname, const char *file, int line, const char *condition_as_string) -{ - if (!condition) - { - if(Eigen::g_test_level>0) - std::cerr << "WARNING: "; - std::cerr << "Test " << testname << " failed in " << file << " (" << line << ")" - << std::endl << " " << condition_as_string << std::endl; - std::cerr << "Stack:\n"; - const int test_stack_size = static_cast<int>(Eigen::g_test_stack.size()); - for(int i=test_stack_size-1; i>=0; --i) - std::cerr << " - " << Eigen::g_test_stack[i] << "\n"; - std::cerr << "\n"; - if(Eigen::g_test_level==0) - abort(); - } -} - -#define VERIFY(a) ::verify_impl(a, g_test_stack.back().c_str(), __FILE__, __LINE__, EI_PP_MAKE_STRING(a)) - -#define VERIFY_GE(a, b) ::verify_impl(a >= b, g_test_stack.back().c_str(), __FILE__, __LINE__, EI_PP_MAKE_STRING(a >= b)) -#define VERIFY_LE(a, b) ::verify_impl(a <= b, g_test_stack.back().c_str(), __FILE__, __LINE__, EI_PP_MAKE_STRING(a <= b)) - - -#define VERIFY_IS_EQUAL(a, b) VERIFY(test_is_equal(a, b, true)) -#define VERIFY_IS_NOT_EQUAL(a, b) VERIFY(test_is_equal(a, b, false)) -#define VERIFY_IS_APPROX(a, b) VERIFY(verifyIsApprox(a, b)) -#define VERIFY_IS_NOT_APPROX(a, b) VERIFY(!test_isApprox(a, b)) -#define VERIFY_IS_MUCH_SMALLER_THAN(a, b) VERIFY(test_isMuchSmallerThan(a, b)) -#define VERIFY_IS_NOT_MUCH_SMALLER_THAN(a, b) VERIFY(!test_isMuchSmallerThan(a, b)) -#define VERIFY_IS_APPROX_OR_LESS_THAN(a, b) VERIFY(test_isApproxOrLessThan(a, b)) -#define VERIFY_IS_NOT_APPROX_OR_LESS_THAN(a, b) VERIFY(!test_isApproxOrLessThan(a, b)) - -#define VERIFY_IS_UNITARY(a) VERIFY(test_isUnitary(a)) - -#define CALL_SUBTEST(FUNC) do { \ - g_test_stack.push_back(EI_PP_MAKE_STRING(FUNC)); \ - FUNC; \ - g_test_stack.pop_back(); \ - } while (0) - - -namespace Eigen { - -template<typename T> inline typename NumTraits<T>::Real test_precision() { return NumTraits<T>::dummy_precision(); } -template<> inline float test_precision<float>() { return 1e-3f; } -template<> inline double test_precision<double>() { return 1e-6; } -template<> inline long double test_precision<long double>() { return 1e-6l; } -template<> inline float test_precision<std::complex<float> >() { return test_precision<float>(); } -template<> inline double test_precision<std::complex<double> >() { return test_precision<double>(); } -template<> inline long double test_precision<std::complex<long double> >() { return test_precision<long double>(); } - -inline bool test_isApprox(const short& a, const short& b) -{ return internal::isApprox(a, b, test_precision<short>()); } -inline bool test_isApprox(const unsigned short& a, const unsigned short& b) -{ return internal::isApprox(a, b, test_precision<unsigned short>()); } -inline bool test_isApprox(const unsigned int& a, const unsigned int& b) -{ return internal::isApprox(a, b, test_precision<unsigned int>()); } -inline bool test_isApprox(const long& a, const long& b) -{ return internal::isApprox(a, b, test_precision<long>()); } -inline bool test_isApprox(const unsigned long& a, const unsigned long& b) -{ return internal::isApprox(a, b, test_precision<unsigned long>()); } - -inline bool test_isApprox(const int& a, const int& b) -{ return internal::isApprox(a, b, test_precision<int>()); } -inline bool test_isMuchSmallerThan(const int& a, const int& b) -{ return internal::isMuchSmallerThan(a, b, test_precision<int>()); } -inline bool test_isApproxOrLessThan(const int& a, const int& b) -{ return internal::isApproxOrLessThan(a, b, test_precision<int>()); } - -inline bool test_isApprox(const float& a, const float& b) -{ return internal::isApprox(a, b, test_precision<float>()); } -inline bool test_isMuchSmallerThan(const float& a, const float& b) -{ return internal::isMuchSmallerThan(a, b, test_precision<float>()); } -inline bool test_isApproxOrLessThan(const float& a, const float& b) -{ return internal::isApproxOrLessThan(a, b, test_precision<float>()); } - -inline bool test_isApprox(const double& a, const double& b) -{ return internal::isApprox(a, b, test_precision<double>()); } -inline bool test_isMuchSmallerThan(const double& a, const double& b) -{ return internal::isMuchSmallerThan(a, b, test_precision<double>()); } -inline bool test_isApproxOrLessThan(const double& a, const double& b) -{ return internal::isApproxOrLessThan(a, b, test_precision<double>()); } - -#ifndef EIGEN_TEST_NO_COMPLEX -inline bool test_isApprox(const std::complex<float>& a, const std::complex<float>& b) -{ return internal::isApprox(a, b, test_precision<std::complex<float> >()); } -inline bool test_isMuchSmallerThan(const std::complex<float>& a, const std::complex<float>& b) -{ return internal::isMuchSmallerThan(a, b, test_precision<std::complex<float> >()); } - -inline bool test_isApprox(const std::complex<double>& a, const std::complex<double>& b) -{ return internal::isApprox(a, b, test_precision<std::complex<double> >()); } -inline bool test_isMuchSmallerThan(const std::complex<double>& a, const std::complex<double>& b) -{ return internal::isMuchSmallerThan(a, b, test_precision<std::complex<double> >()); } - -#ifndef EIGEN_TEST_NO_LONGDOUBLE -inline bool test_isApprox(const std::complex<long double>& a, const std::complex<long double>& b) -{ return internal::isApprox(a, b, test_precision<std::complex<long double> >()); } -inline bool test_isMuchSmallerThan(const std::complex<long double>& a, const std::complex<long double>& b) -{ return internal::isMuchSmallerThan(a, b, test_precision<std::complex<long double> >()); } -#endif -#endif - -#ifndef EIGEN_TEST_NO_LONGDOUBLE -inline bool test_isApprox(const long double& a, const long double& b) -{ - bool ret = internal::isApprox(a, b, test_precision<long double>()); - if (!ret) std::cerr - << std::endl << " actual = " << a - << std::endl << " expected = " << b << std::endl << std::endl; - return ret; -} - -inline bool test_isMuchSmallerThan(const long double& a, const long double& b) -{ return internal::isMuchSmallerThan(a, b, test_precision<long double>()); } -inline bool test_isApproxOrLessThan(const long double& a, const long double& b) -{ return internal::isApproxOrLessThan(a, b, test_precision<long double>()); } -#endif // EIGEN_TEST_NO_LONGDOUBLE - -inline bool test_isApprox(const half& a, const half& b) -{ return internal::isApprox(a, b, test_precision<half>()); } -inline bool test_isMuchSmallerThan(const half& a, const half& b) -{ return internal::isMuchSmallerThan(a, b, test_precision<half>()); } -inline bool test_isApproxOrLessThan(const half& a, const half& b) -{ return internal::isApproxOrLessThan(a, b, test_precision<half>()); } - -// test_relative_error returns the relative difference between a and b as a real scalar as used in isApprox. -template<typename T1,typename T2> -typename NumTraits<typename T1::RealScalar>::NonInteger test_relative_error(const EigenBase<T1> &a, const EigenBase<T2> &b) -{ - using std::sqrt; - typedef typename NumTraits<typename T1::RealScalar>::NonInteger RealScalar; - typename internal::nested_eval<T1,2>::type ea(a.derived()); - typename internal::nested_eval<T2,2>::type eb(b.derived()); - return sqrt(RealScalar((ea-eb).cwiseAbs2().sum()) / RealScalar((std::min)(eb.cwiseAbs2().sum(),ea.cwiseAbs2().sum()))); -} - -template<typename T1,typename T2> -typename T1::RealScalar test_relative_error(const T1 &a, const T2 &b, const typename T1::Coefficients* = 0) -{ - return test_relative_error(a.coeffs(), b.coeffs()); -} - -template<typename T1,typename T2> -typename T1::Scalar test_relative_error(const T1 &a, const T2 &b, const typename T1::MatrixType* = 0) -{ - return test_relative_error(a.matrix(), b.matrix()); -} - -template<typename S, int D> -S test_relative_error(const Translation<S,D> &a, const Translation<S,D> &b) -{ - return test_relative_error(a.vector(), b.vector()); -} - -template <typename S, int D, int O> -S test_relative_error(const ParametrizedLine<S,D,O> &a, const ParametrizedLine<S,D,O> &b) -{ - return (std::max)(test_relative_error(a.origin(), b.origin()), test_relative_error(a.origin(), b.origin())); -} - -template <typename S, int D> -S test_relative_error(const AlignedBox<S,D> &a, const AlignedBox<S,D> &b) -{ - return (std::max)(test_relative_error((a.min)(), (b.min)()), test_relative_error((a.max)(), (b.max)())); -} - -template<typename Derived> class SparseMatrixBase; -template<typename T1,typename T2> -typename T1::RealScalar test_relative_error(const MatrixBase<T1> &a, const SparseMatrixBase<T2> &b) -{ - return test_relative_error(a,b.toDense()); -} - -template<typename Derived> class SparseMatrixBase; -template<typename T1,typename T2> -typename T1::RealScalar test_relative_error(const SparseMatrixBase<T1> &a, const MatrixBase<T2> &b) -{ - return test_relative_error(a.toDense(),b); -} - -template<typename Derived> class SparseMatrixBase; -template<typename T1,typename T2> -typename T1::RealScalar test_relative_error(const SparseMatrixBase<T1> &a, const SparseMatrixBase<T2> &b) -{ - return test_relative_error(a.toDense(),b.toDense()); -} - -template<typename T1,typename T2> -typename NumTraits<typename NumTraits<T1>::Real>::NonInteger test_relative_error(const T1 &a, const T2 &b, typename internal::enable_if<internal::is_arithmetic<typename NumTraits<T1>::Real>::value, T1>::type* = 0) -{ - typedef typename NumTraits<typename NumTraits<T1>::Real>::NonInteger RealScalar; - return numext::sqrt(RealScalar(numext::abs2(a-b))/RealScalar((numext::mini)(numext::abs2(a),numext::abs2(b)))); -} - -template<typename T> -T test_relative_error(const Rotation2D<T> &a, const Rotation2D<T> &b) -{ - return test_relative_error(a.angle(), b.angle()); -} - -template<typename T> -T test_relative_error(const AngleAxis<T> &a, const AngleAxis<T> &b) -{ - return (std::max)(test_relative_error(a.angle(), b.angle()), test_relative_error(a.axis(), b.axis())); -} - -template<typename Type1, typename Type2> -inline bool test_isApprox(const Type1& a, const Type2& b, typename Type1::Scalar* = 0) // Enabled for Eigen's type only -{ - return a.isApprox(b, test_precision<typename Type1::Scalar>()); -} - -// get_test_precision is a small wrapper to test_precision allowing to return the scalar precision for either scalars or expressions -template<typename T> -typename NumTraits<typename T::Scalar>::Real get_test_precision(const T&, const typename T::Scalar* = 0) -{ - return test_precision<typename NumTraits<typename T::Scalar>::Real>(); -} - -template<typename T> -typename NumTraits<T>::Real get_test_precision(const T&,typename internal::enable_if<internal::is_arithmetic<typename NumTraits<T>::Real>::value, T>::type* = 0) -{ - return test_precision<typename NumTraits<T>::Real>(); -} - -// verifyIsApprox is a wrapper to test_isApprox that outputs the relative difference magnitude if the test fails. -template<typename Type1, typename Type2> -inline bool verifyIsApprox(const Type1& a, const Type2& b) -{ - bool ret = test_isApprox(a,b); - if(!ret) - { - std::cerr << "Difference too large wrt tolerance " << get_test_precision(a) << ", relative error is: " << test_relative_error(a,b) << std::endl; - } - return ret; -} - -// The idea behind this function is to compare the two scalars a and b where -// the scalar ref is a hint about the expected order of magnitude of a and b. -// WARNING: the scalar a and b must be positive -// Therefore, if for some reason a and b are very small compared to ref, -// we won't issue a false negative. -// This test could be: abs(a-b) <= eps * ref -// However, it seems that simply comparing a+ref and b+ref is more sensitive to true error. -template<typename Scalar,typename ScalarRef> -inline bool test_isApproxWithRef(const Scalar& a, const Scalar& b, const ScalarRef& ref) -{ - return test_isApprox(a+ref, b+ref); -} - -template<typename Derived1, typename Derived2> -inline bool test_isMuchSmallerThan(const MatrixBase<Derived1>& m1, - const MatrixBase<Derived2>& m2) -{ - return m1.isMuchSmallerThan(m2, test_precision<typename internal::traits<Derived1>::Scalar>()); -} - -template<typename Derived> -inline bool test_isMuchSmallerThan(const MatrixBase<Derived>& m, - const typename NumTraits<typename internal::traits<Derived>::Scalar>::Real& s) -{ - return m.isMuchSmallerThan(s, test_precision<typename internal::traits<Derived>::Scalar>()); -} - -template<typename Derived> -inline bool test_isUnitary(const MatrixBase<Derived>& m) -{ - return m.isUnitary(test_precision<typename internal::traits<Derived>::Scalar>()); -} - -// Forward declaration to avoid ICC warning -template<typename T, typename U> -bool test_is_equal(const T& actual, const U& expected, bool expect_equal=true); - -template<typename T, typename U> -bool test_is_equal(const T& actual, const U& expected, bool expect_equal) -{ - if ((actual==expected) == expect_equal) - return true; - // false: - std::cerr - << "\n actual = " << actual - << "\n expected " << (expect_equal ? "= " : "!=") << expected << "\n\n"; - return false; -} - -/** Creates a random Partial Isometry matrix of given rank. - * - * A partial isometry is a matrix all of whose singular values are either 0 or 1. - * This is very useful to test rank-revealing algorithms. - */ -// Forward declaration to avoid ICC warning -template<typename MatrixType> -void createRandomPIMatrixOfRank(Index desired_rank, Index rows, Index cols, MatrixType& m); -template<typename MatrixType> -void createRandomPIMatrixOfRank(Index desired_rank, Index rows, Index cols, MatrixType& m) -{ - typedef typename internal::traits<MatrixType>::Scalar Scalar; - enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime }; - - typedef Matrix<Scalar, Dynamic, 1> VectorType; - typedef Matrix<Scalar, Rows, Rows> MatrixAType; - typedef Matrix<Scalar, Cols, Cols> MatrixBType; - - if(desired_rank == 0) - { - m.setZero(rows,cols); - return; - } - - if(desired_rank == 1) - { - // here we normalize the vectors to get a partial isometry - m = VectorType::Random(rows).normalized() * VectorType::Random(cols).normalized().transpose(); - return; - } - - MatrixAType a = MatrixAType::Random(rows,rows); - MatrixType d = MatrixType::Identity(rows,cols); - MatrixBType b = MatrixBType::Random(cols,cols); - - // set the diagonal such that only desired_rank non-zero entries reamain - const Index diag_size = (std::min)(d.rows(),d.cols()); - if(diag_size != desired_rank) - d.diagonal().segment(desired_rank, diag_size-desired_rank) = VectorType::Zero(diag_size-desired_rank); - - HouseholderQR<MatrixAType> qra(a); - HouseholderQR<MatrixBType> qrb(b); - m = qra.householderQ() * d * qrb.householderQ(); -} - -// Forward declaration to avoid ICC warning -template<typename PermutationVectorType> -void randomPermutationVector(PermutationVectorType& v, Index size); -template<typename PermutationVectorType> -void randomPermutationVector(PermutationVectorType& v, Index size) -{ - typedef typename PermutationVectorType::Scalar Scalar; - v.resize(size); - for(Index i = 0; i < size; ++i) v(i) = Scalar(i); - if(size == 1) return; - for(Index n = 0; n < 3 * size; ++n) - { - Index i = internal::random<Index>(0, size-1); - Index j; - do j = internal::random<Index>(0, size-1); while(j==i); - std::swap(v(i), v(j)); - } -} - -template<typename T> bool isNotNaN(const T& x) -{ - return x==x; -} - -template<typename T> bool isPlusInf(const T& x) -{ - return x > NumTraits<T>::highest(); -} - -template<typename T> bool isMinusInf(const T& x) -{ - return x < NumTraits<T>::lowest(); -} - -} // end namespace Eigen - -template<typename T> struct GetDifferentType; - -template<> struct GetDifferentType<float> { typedef double type; }; -template<> struct GetDifferentType<double> { typedef float type; }; -template<typename T> struct GetDifferentType<std::complex<T> > -{ typedef std::complex<typename GetDifferentType<T>::type> type; }; - -// Forward declaration to avoid ICC warning -template<typename T> std::string type_name(); -template<typename T> std::string type_name() { return "other"; } -template<> std::string type_name<float>() { return "float"; } -template<> std::string type_name<double>() { return "double"; } -template<> std::string type_name<long double>() { return "long double"; } -template<> std::string type_name<int>() { return "int"; } -template<> std::string type_name<std::complex<float> >() { return "complex<float>"; } -template<> std::string type_name<std::complex<double> >() { return "complex<double>"; } -template<> std::string type_name<std::complex<long double> >() { return "complex<long double>"; } -template<> std::string type_name<std::complex<int> >() { return "complex<int>"; } - -// forward declaration of the main test function -void EIGEN_CAT(test_,EIGEN_TEST_FUNC)(); - -using namespace Eigen; - -inline void set_repeat_from_string(const char *str) -{ - errno = 0; - g_repeat = int(strtoul(str, 0, 10)); - if(errno || g_repeat <= 0) - { - std::cout << "Invalid repeat value " << str << std::endl; - exit(EXIT_FAILURE); - } - g_has_set_repeat = true; -} - -inline void set_seed_from_string(const char *str) -{ - errno = 0; - g_seed = int(strtoul(str, 0, 10)); - if(errno || g_seed == 0) - { - std::cout << "Invalid seed value " << str << std::endl; - exit(EXIT_FAILURE); - } - g_has_set_seed = true; -} - -int main(int argc, char *argv[]) -{ - g_has_set_repeat = false; - g_has_set_seed = false; - bool need_help = false; - - for(int i = 1; i < argc; i++) - { - if(argv[i][0] == 'r') - { - if(g_has_set_repeat) - { - std::cout << "Argument " << argv[i] << " conflicting with a former argument" << std::endl; - return 1; - } - set_repeat_from_string(argv[i]+1); - } - else if(argv[i][0] == 's') - { - if(g_has_set_seed) - { - std::cout << "Argument " << argv[i] << " conflicting with a former argument" << std::endl; - return 1; - } - set_seed_from_string(argv[i]+1); - } - else - { - need_help = true; - } - } - - if(need_help) - { - std::cout << "This test application takes the following optional arguments:" << std::endl; - std::cout << " rN Repeat each test N times (default: " << DEFAULT_REPEAT << ")" << std::endl; - std::cout << " sN Use N as seed for random numbers (default: based on current time)" << std::endl; - std::cout << std::endl; - std::cout << "If defined, the environment variables EIGEN_REPEAT and EIGEN_SEED" << std::endl; - std::cout << "will be used as default values for these parameters." << std::endl; - return 1; - } - - char *env_EIGEN_REPEAT = getenv("EIGEN_REPEAT"); - if(!g_has_set_repeat && env_EIGEN_REPEAT) - set_repeat_from_string(env_EIGEN_REPEAT); - char *env_EIGEN_SEED = getenv("EIGEN_SEED"); - if(!g_has_set_seed && env_EIGEN_SEED) - set_seed_from_string(env_EIGEN_SEED); - - if(!g_has_set_seed) g_seed = (unsigned int) time(NULL); - if(!g_has_set_repeat) g_repeat = DEFAULT_REPEAT; - - std::cout << "Initializing random number generator with seed " << g_seed << std::endl; - std::stringstream ss; - ss << "Seed: " << g_seed; - g_test_stack.push_back(ss.str()); - srand(g_seed); - std::cout << "Repeating each test " << g_repeat << " times" << std::endl; - - Eigen::g_test_stack.push_back(std::string(EI_PP_MAKE_STRING(EIGEN_TEST_FUNC))); - - EIGEN_CAT(test_,EIGEN_TEST_FUNC)(); - return 0; -} - -// These warning are disabled here such that they are still ON when parsing Eigen's header files. -#if defined __INTEL_COMPILER - // remark #383: value copied to temporary, reference to temporary used - // -> this warning is raised even for legal usage as: g_test_stack.push_back("foo"); where g_test_stack is a std::vector<std::string> - // remark #1418: external function definition with no prior declaration - // -> this warning is raised for all our test functions. Declaring them static would fix the issue. - // warning #279: controlling expression is constant - // remark #1572: floating-point equality and inequality comparisons are unreliable - #pragma warning disable 279 383 1418 1572 -#endif - -#ifdef _MSC_VER - // 4503 - decorated name length exceeded, name was truncated - #pragma warning( disable : 4503) -#endif diff --git a/eigen/test/mapped_matrix.cpp b/eigen/test/mapped_matrix.cpp deleted file mode 100644 index bc8a694..0000000 --- a/eigen/test/mapped_matrix.cpp +++ /dev/null @@ -1,208 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_NO_STATIC_ASSERT -#define EIGEN_NO_STATIC_ASSERT // turn static asserts into runtime asserts in order to check them -#endif - -#include "main.h" - -#define EIGEN_TESTMAP_MAX_SIZE 256 - -template<typename VectorType> void map_class_vector(const VectorType& m) -{ - typedef typename VectorType::Scalar Scalar; - - Index size = m.size(); - - Scalar* array1 = internal::aligned_new<Scalar>(size); - Scalar* array2 = internal::aligned_new<Scalar>(size); - Scalar* array3 = new Scalar[size+1]; - Scalar* array3unaligned = (internal::UIntPtr(array3)%EIGEN_MAX_ALIGN_BYTES) == 0 ? array3+1 : array3; - Scalar array4[EIGEN_TESTMAP_MAX_SIZE]; - - Map<VectorType, AlignedMax>(array1, size) = VectorType::Random(size); - Map<VectorType, AlignedMax>(array2, size) = Map<VectorType,AlignedMax>(array1, size); - Map<VectorType>(array3unaligned, size) = Map<VectorType>(array1, size); - Map<VectorType>(array4, size) = Map<VectorType,AlignedMax>(array1, size); - VectorType ma1 = Map<VectorType, AlignedMax>(array1, size); - VectorType ma2 = Map<VectorType, AlignedMax>(array2, size); - VectorType ma3 = Map<VectorType>(array3unaligned, size); - VectorType ma4 = Map<VectorType>(array4, size); - VERIFY_IS_EQUAL(ma1, ma2); - VERIFY_IS_EQUAL(ma1, ma3); - VERIFY_IS_EQUAL(ma1, ma4); - #ifdef EIGEN_VECTORIZE - if(internal::packet_traits<Scalar>::Vectorizable && size>=AlignedMax) - VERIFY_RAISES_ASSERT((Map<VectorType,AlignedMax>(array3unaligned, size))) - #endif - - internal::aligned_delete(array1, size); - internal::aligned_delete(array2, size); - delete[] array3; -} - -template<typename MatrixType> void map_class_matrix(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - - Index rows = m.rows(), cols = m.cols(), size = rows*cols; - Scalar s1 = internal::random<Scalar>(); - - // array1 and array2 -> aligned heap allocation - Scalar* array1 = internal::aligned_new<Scalar>(size); - for(int i = 0; i < size; i++) array1[i] = Scalar(1); - Scalar* array2 = internal::aligned_new<Scalar>(size); - for(int i = 0; i < size; i++) array2[i] = Scalar(1); - // array3unaligned -> unaligned pointer to heap - Scalar* array3 = new Scalar[size+1]; - Index sizep1 = size + 1; // <- without this temporary MSVC 2103 generates bad code - for(Index i = 0; i < sizep1; i++) array3[i] = Scalar(1); - Scalar* array3unaligned = (internal::UIntPtr(array3)%EIGEN_MAX_ALIGN_BYTES) == 0 ? array3+1 : array3; - Scalar array4[256]; - if(size<=256) - for(int i = 0; i < size; i++) array4[i] = Scalar(1); - - Map<MatrixType> map1(array1, rows, cols); - Map<MatrixType, AlignedMax> map2(array2, rows, cols); - Map<MatrixType> map3(array3unaligned, rows, cols); - Map<MatrixType> map4(array4, rows, cols); - - VERIFY_IS_EQUAL(map1, MatrixType::Ones(rows,cols)); - VERIFY_IS_EQUAL(map2, MatrixType::Ones(rows,cols)); - VERIFY_IS_EQUAL(map3, MatrixType::Ones(rows,cols)); - map1 = MatrixType::Random(rows,cols); - map2 = map1; - map3 = map1; - MatrixType ma1 = map1; - MatrixType ma2 = map2; - MatrixType ma3 = map3; - VERIFY_IS_EQUAL(map1, map2); - VERIFY_IS_EQUAL(map1, map3); - VERIFY_IS_EQUAL(ma1, ma2); - VERIFY_IS_EQUAL(ma1, ma3); - VERIFY_IS_EQUAL(ma1, map3); - - VERIFY_IS_APPROX(s1*map1, s1*map2); - VERIFY_IS_APPROX(s1*ma1, s1*ma2); - VERIFY_IS_EQUAL(s1*ma1, s1*ma3); - VERIFY_IS_APPROX(s1*map1, s1*map3); - - map2 *= s1; - map3 *= s1; - VERIFY_IS_APPROX(s1*map1, map2); - VERIFY_IS_APPROX(s1*map1, map3); - - if(size<=256) - { - VERIFY_IS_EQUAL(map4, MatrixType::Ones(rows,cols)); - map4 = map1; - MatrixType ma4 = map4; - VERIFY_IS_EQUAL(map1, map4); - VERIFY_IS_EQUAL(ma1, map4); - VERIFY_IS_EQUAL(ma1, ma4); - VERIFY_IS_APPROX(s1*map1, s1*map4); - - map4 *= s1; - VERIFY_IS_APPROX(s1*map1, map4); - } - - internal::aligned_delete(array1, size); - internal::aligned_delete(array2, size); - delete[] array3; -} - -template<typename VectorType> void map_static_methods(const VectorType& m) -{ - typedef typename VectorType::Scalar Scalar; - - Index size = m.size(); - - Scalar* array1 = internal::aligned_new<Scalar>(size); - Scalar* array2 = internal::aligned_new<Scalar>(size); - Scalar* array3 = new Scalar[size+1]; - Scalar* array3unaligned = internal::UIntPtr(array3)%EIGEN_MAX_ALIGN_BYTES == 0 ? array3+1 : array3; - - VectorType::MapAligned(array1, size) = VectorType::Random(size); - VectorType::Map(array2, size) = VectorType::Map(array1, size); - VectorType::Map(array3unaligned, size) = VectorType::Map(array1, size); - VectorType ma1 = VectorType::Map(array1, size); - VectorType ma2 = VectorType::MapAligned(array2, size); - VectorType ma3 = VectorType::Map(array3unaligned, size); - VERIFY_IS_EQUAL(ma1, ma2); - VERIFY_IS_EQUAL(ma1, ma3); - - internal::aligned_delete(array1, size); - internal::aligned_delete(array2, size); - delete[] array3; -} - -template<typename PlainObjectType> void check_const_correctness(const PlainObjectType&) -{ - // there's a lot that we can't test here while still having this test compile! - // the only possible approach would be to run a script trying to compile stuff and checking that it fails. - // CMake can help with that. - - // verify that map-to-const don't have LvalueBit - typedef typename internal::add_const<PlainObjectType>::type ConstPlainObjectType; - VERIFY( !(internal::traits<Map<ConstPlainObjectType> >::Flags & LvalueBit) ); - VERIFY( !(internal::traits<Map<ConstPlainObjectType, AlignedMax> >::Flags & LvalueBit) ); - VERIFY( !(Map<ConstPlainObjectType>::Flags & LvalueBit) ); - VERIFY( !(Map<ConstPlainObjectType, AlignedMax>::Flags & LvalueBit) ); -} - -template<typename Scalar> -void map_not_aligned_on_scalar() -{ - typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType; - Index size = 11; - Scalar* array1 = internal::aligned_new<Scalar>((size+1)*(size+1)+1); - Scalar* array2 = reinterpret_cast<Scalar*>(sizeof(Scalar)/2+std::size_t(array1)); - Map<MatrixType,0,OuterStride<> > map2(array2, size, size, OuterStride<>(size+1)); - MatrixType m2 = MatrixType::Random(size,size); - map2 = m2; - VERIFY_IS_EQUAL(m2, map2); - - typedef Matrix<Scalar,Dynamic,1> VectorType; - Map<VectorType> map3(array2, size); - MatrixType v3 = VectorType::Random(size); - map3 = v3; - VERIFY_IS_EQUAL(v3, map3); - - internal::aligned_delete(array1, (size+1)*(size+1)+1); -} - -void test_mapped_matrix() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( map_class_vector(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_1( check_const_correctness(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( map_class_vector(Vector4d()) ); - CALL_SUBTEST_2( map_class_vector(VectorXd(13)) ); - CALL_SUBTEST_2( check_const_correctness(Matrix4d()) ); - CALL_SUBTEST_3( map_class_vector(RowVector4f()) ); - CALL_SUBTEST_4( map_class_vector(VectorXcf(8)) ); - CALL_SUBTEST_5( map_class_vector(VectorXi(12)) ); - CALL_SUBTEST_5( check_const_correctness(VectorXi(12)) ); - - CALL_SUBTEST_1( map_class_matrix(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( map_class_matrix(Matrix4d()) ); - CALL_SUBTEST_11( map_class_matrix(Matrix<float,3,5>()) ); - CALL_SUBTEST_4( map_class_matrix(MatrixXcf(internal::random<int>(1,10),internal::random<int>(1,10))) ); - CALL_SUBTEST_5( map_class_matrix(MatrixXi(internal::random<int>(1,10),internal::random<int>(1,10))) ); - - CALL_SUBTEST_6( map_static_methods(Matrix<double, 1, 1>()) ); - CALL_SUBTEST_7( map_static_methods(Vector3f()) ); - CALL_SUBTEST_8( map_static_methods(RowVector3d()) ); - CALL_SUBTEST_9( map_static_methods(VectorXcd(8)) ); - CALL_SUBTEST_10( map_static_methods(VectorXf(12)) ); - - CALL_SUBTEST_11( map_not_aligned_on_scalar<double>() ); - } -} diff --git a/eigen/test/mapstaticmethods.cpp b/eigen/test/mapstaticmethods.cpp deleted file mode 100644 index 8156ca9..0000000 --- a/eigen/test/mapstaticmethods.cpp +++ /dev/null @@ -1,173 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -float *ptr; -const float *const_ptr; - -template<typename PlainObjectType, - bool IsDynamicSize = PlainObjectType::SizeAtCompileTime == Dynamic, - bool IsVector = PlainObjectType::IsVectorAtCompileTime -> -struct mapstaticmethods_impl {}; - -template<typename PlainObjectType, bool IsVector> -struct mapstaticmethods_impl<PlainObjectType, false, IsVector> -{ - static void run(const PlainObjectType& m) - { - mapstaticmethods_impl<PlainObjectType, true, IsVector>::run(m); - - int i = internal::random<int>(2,5), j = internal::random<int>(2,5); - - PlainObjectType::Map(ptr).setZero(); - PlainObjectType::MapAligned(ptr).setZero(); - PlainObjectType::Map(const_ptr).sum(); - PlainObjectType::MapAligned(const_ptr).sum(); - - PlainObjectType::Map(ptr, InnerStride<>(i)).setZero(); - PlainObjectType::MapAligned(ptr, InnerStride<>(i)).setZero(); - PlainObjectType::Map(const_ptr, InnerStride<>(i)).sum(); - PlainObjectType::MapAligned(const_ptr, InnerStride<>(i)).sum(); - - PlainObjectType::Map(ptr, InnerStride<2>()).setZero(); - PlainObjectType::MapAligned(ptr, InnerStride<3>()).setZero(); - PlainObjectType::Map(const_ptr, InnerStride<4>()).sum(); - PlainObjectType::MapAligned(const_ptr, InnerStride<5>()).sum(); - - PlainObjectType::Map(ptr, OuterStride<>(i)).setZero(); - PlainObjectType::MapAligned(ptr, OuterStride<>(i)).setZero(); - PlainObjectType::Map(const_ptr, OuterStride<>(i)).sum(); - PlainObjectType::MapAligned(const_ptr, OuterStride<>(i)).sum(); - - PlainObjectType::Map(ptr, OuterStride<2>()).setZero(); - PlainObjectType::MapAligned(ptr, OuterStride<3>()).setZero(); - PlainObjectType::Map(const_ptr, OuterStride<4>()).sum(); - PlainObjectType::MapAligned(const_ptr, OuterStride<5>()).sum(); - - PlainObjectType::Map(ptr, Stride<Dynamic, Dynamic>(i,j)).setZero(); - PlainObjectType::MapAligned(ptr, Stride<2,Dynamic>(2,i)).setZero(); - PlainObjectType::Map(const_ptr, Stride<Dynamic,3>(i,3)).sum(); - PlainObjectType::MapAligned(const_ptr, Stride<Dynamic, Dynamic>(i,j)).sum(); - - PlainObjectType::Map(ptr, Stride<2,3>()).setZero(); - PlainObjectType::MapAligned(ptr, Stride<3,4>()).setZero(); - PlainObjectType::Map(const_ptr, Stride<2,4>()).sum(); - PlainObjectType::MapAligned(const_ptr, Stride<5,3>()).sum(); - } -}; - -template<typename PlainObjectType> -struct mapstaticmethods_impl<PlainObjectType, true, false> -{ - static void run(const PlainObjectType& m) - { - Index rows = m.rows(), cols = m.cols(); - - int i = internal::random<int>(2,5), j = internal::random<int>(2,5); - - PlainObjectType::Map(ptr, rows, cols).setZero(); - PlainObjectType::MapAligned(ptr, rows, cols).setZero(); - PlainObjectType::Map(const_ptr, rows, cols).sum(); - PlainObjectType::MapAligned(const_ptr, rows, cols).sum(); - - PlainObjectType::Map(ptr, rows, cols, InnerStride<>(i)).setZero(); - PlainObjectType::MapAligned(ptr, rows, cols, InnerStride<>(i)).setZero(); - PlainObjectType::Map(const_ptr, rows, cols, InnerStride<>(i)).sum(); - PlainObjectType::MapAligned(const_ptr, rows, cols, InnerStride<>(i)).sum(); - - PlainObjectType::Map(ptr, rows, cols, InnerStride<2>()).setZero(); - PlainObjectType::MapAligned(ptr, rows, cols, InnerStride<3>()).setZero(); - PlainObjectType::Map(const_ptr, rows, cols, InnerStride<4>()).sum(); - PlainObjectType::MapAligned(const_ptr, rows, cols, InnerStride<5>()).sum(); - - PlainObjectType::Map(ptr, rows, cols, OuterStride<>(i)).setZero(); - PlainObjectType::MapAligned(ptr, rows, cols, OuterStride<>(i)).setZero(); - PlainObjectType::Map(const_ptr, rows, cols, OuterStride<>(i)).sum(); - PlainObjectType::MapAligned(const_ptr, rows, cols, OuterStride<>(i)).sum(); - - PlainObjectType::Map(ptr, rows, cols, OuterStride<2>()).setZero(); - PlainObjectType::MapAligned(ptr, rows, cols, OuterStride<3>()).setZero(); - PlainObjectType::Map(const_ptr, rows, cols, OuterStride<4>()).sum(); - PlainObjectType::MapAligned(const_ptr, rows, cols, OuterStride<5>()).sum(); - - PlainObjectType::Map(ptr, rows, cols, Stride<Dynamic, Dynamic>(i,j)).setZero(); - PlainObjectType::MapAligned(ptr, rows, cols, Stride<2,Dynamic>(2,i)).setZero(); - PlainObjectType::Map(const_ptr, rows, cols, Stride<Dynamic,3>(i,3)).sum(); - PlainObjectType::MapAligned(const_ptr, rows, cols, Stride<Dynamic, Dynamic>(i,j)).sum(); - - PlainObjectType::Map(ptr, rows, cols, Stride<2,3>()).setZero(); - PlainObjectType::MapAligned(ptr, rows, cols, Stride<3,4>()).setZero(); - PlainObjectType::Map(const_ptr, rows, cols, Stride<2,4>()).sum(); - PlainObjectType::MapAligned(const_ptr, rows, cols, Stride<5,3>()).sum(); - } -}; - -template<typename PlainObjectType> -struct mapstaticmethods_impl<PlainObjectType, true, true> -{ - static void run(const PlainObjectType& v) - { - Index size = v.size(); - - int i = internal::random<int>(2,5); - - PlainObjectType::Map(ptr, size).setZero(); - PlainObjectType::MapAligned(ptr, size).setZero(); - PlainObjectType::Map(const_ptr, size).sum(); - PlainObjectType::MapAligned(const_ptr, size).sum(); - - PlainObjectType::Map(ptr, size, InnerStride<>(i)).setZero(); - PlainObjectType::MapAligned(ptr, size, InnerStride<>(i)).setZero(); - PlainObjectType::Map(const_ptr, size, InnerStride<>(i)).sum(); - PlainObjectType::MapAligned(const_ptr, size, InnerStride<>(i)).sum(); - - PlainObjectType::Map(ptr, size, InnerStride<2>()).setZero(); - PlainObjectType::MapAligned(ptr, size, InnerStride<3>()).setZero(); - PlainObjectType::Map(const_ptr, size, InnerStride<4>()).sum(); - PlainObjectType::MapAligned(const_ptr, size, InnerStride<5>()).sum(); - } -}; - -template<typename PlainObjectType> -void mapstaticmethods(const PlainObjectType& m) -{ - mapstaticmethods_impl<PlainObjectType>::run(m); - VERIFY(true); // just to avoid 'unused function' warning -} - -void test_mapstaticmethods() -{ - ptr = internal::aligned_new<float>(1000); - for(int i = 0; i < 1000; i++) ptr[i] = float(i); - - const_ptr = ptr; - - CALL_SUBTEST_1(( mapstaticmethods(Matrix<float, 1, 1>()) )); - CALL_SUBTEST_1(( mapstaticmethods(Vector2f()) )); - CALL_SUBTEST_2(( mapstaticmethods(Vector3f()) )); - CALL_SUBTEST_2(( mapstaticmethods(Matrix2f()) )); - CALL_SUBTEST_3(( mapstaticmethods(Matrix4f()) )); - CALL_SUBTEST_3(( mapstaticmethods(Array4f()) )); - CALL_SUBTEST_4(( mapstaticmethods(Array3f()) )); - CALL_SUBTEST_4(( mapstaticmethods(Array33f()) )); - CALL_SUBTEST_5(( mapstaticmethods(Array44f()) )); - CALL_SUBTEST_5(( mapstaticmethods(VectorXf(1)) )); - CALL_SUBTEST_5(( mapstaticmethods(VectorXf(8)) )); - CALL_SUBTEST_6(( mapstaticmethods(MatrixXf(1,1)) )); - CALL_SUBTEST_6(( mapstaticmethods(MatrixXf(5,7)) )); - CALL_SUBTEST_7(( mapstaticmethods(ArrayXf(1)) )); - CALL_SUBTEST_7(( mapstaticmethods(ArrayXf(5)) )); - CALL_SUBTEST_8(( mapstaticmethods(ArrayXXf(1,1)) )); - CALL_SUBTEST_8(( mapstaticmethods(ArrayXXf(8,6)) )); - - internal::aligned_delete(ptr, 1000); -} - diff --git a/eigen/test/mapstride.cpp b/eigen/test/mapstride.cpp deleted file mode 100644 index d785148..0000000 --- a/eigen/test/mapstride.cpp +++ /dev/null @@ -1,234 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<int Alignment,typename VectorType> void map_class_vector(const VectorType& m) -{ - typedef typename VectorType::Scalar Scalar; - - Index size = m.size(); - - VectorType v = VectorType::Random(size); - - Index arraysize = 3*size; - - Scalar* a_array = internal::aligned_new<Scalar>(arraysize+1); - Scalar* array = a_array; - if(Alignment!=Aligned) - array = (Scalar*)(internal::IntPtr(a_array) + (internal::packet_traits<Scalar>::AlignedOnScalar?sizeof(Scalar):sizeof(typename NumTraits<Scalar>::Real))); - - { - Map<VectorType, Alignment, InnerStride<3> > map(array, size); - map = v; - for(int i = 0; i < size; ++i) - { - VERIFY(array[3*i] == v[i]); - VERIFY(map[i] == v[i]); - } - } - - { - Map<VectorType, Unaligned, InnerStride<Dynamic> > map(array, size, InnerStride<Dynamic>(2)); - map = v; - for(int i = 0; i < size; ++i) - { - VERIFY(array[2*i] == v[i]); - VERIFY(map[i] == v[i]); - } - } - - internal::aligned_delete(a_array, arraysize+1); -} - -template<int Alignment,typename MatrixType> void map_class_matrix(const MatrixType& _m) -{ - typedef typename MatrixType::Scalar Scalar; - - Index rows = _m.rows(), cols = _m.cols(); - - MatrixType m = MatrixType::Random(rows,cols); - Scalar s1 = internal::random<Scalar>(); - - Index arraysize = 4*(rows+4)*(cols+4); - - Scalar* a_array1 = internal::aligned_new<Scalar>(arraysize+1); - Scalar* array1 = a_array1; - if(Alignment!=Aligned) - array1 = (Scalar*)(internal::IntPtr(a_array1) + (internal::packet_traits<Scalar>::AlignedOnScalar?sizeof(Scalar):sizeof(typename NumTraits<Scalar>::Real))); - - Scalar a_array2[256]; - Scalar* array2 = a_array2; - if(Alignment!=Aligned) - array2 = (Scalar*)(internal::IntPtr(a_array2) + (internal::packet_traits<Scalar>::AlignedOnScalar?sizeof(Scalar):sizeof(typename NumTraits<Scalar>::Real))); - else - array2 = (Scalar*)(((internal::UIntPtr(a_array2)+EIGEN_MAX_ALIGN_BYTES-1)/EIGEN_MAX_ALIGN_BYTES)*EIGEN_MAX_ALIGN_BYTES); - Index maxsize2 = a_array2 - array2 + 256; - - // test no inner stride and some dynamic outer stride - for(int k=0; k<2; ++k) - { - if(k==1 && (m.innerSize()+1)*m.outerSize() > maxsize2) - break; - Scalar* array = (k==0 ? array1 : array2); - - Map<MatrixType, Alignment, OuterStride<Dynamic> > map(array, rows, cols, OuterStride<Dynamic>(m.innerSize()+1)); - map = m; - VERIFY(map.outerStride() == map.innerSize()+1); - for(int i = 0; i < m.outerSize(); ++i) - for(int j = 0; j < m.innerSize(); ++j) - { - VERIFY(array[map.outerStride()*i+j] == m.coeffByOuterInner(i,j)); - VERIFY(map.coeffByOuterInner(i,j) == m.coeffByOuterInner(i,j)); - } - VERIFY_IS_APPROX(s1*map,s1*m); - map *= s1; - VERIFY_IS_APPROX(map,s1*m); - } - - // test no inner stride and an outer stride of +4. This is quite important as for fixed-size matrices, - // this allows to hit the special case where it's vectorizable. - for(int k=0; k<2; ++k) - { - if(k==1 && (m.innerSize()+4)*m.outerSize() > maxsize2) - break; - Scalar* array = (k==0 ? array1 : array2); - - enum { - InnerSize = MatrixType::InnerSizeAtCompileTime, - OuterStrideAtCompileTime = InnerSize==Dynamic ? Dynamic : InnerSize+4 - }; - Map<MatrixType, Alignment, OuterStride<OuterStrideAtCompileTime> > - map(array, rows, cols, OuterStride<OuterStrideAtCompileTime>(m.innerSize()+4)); - map = m; - VERIFY(map.outerStride() == map.innerSize()+4); - for(int i = 0; i < m.outerSize(); ++i) - for(int j = 0; j < m.innerSize(); ++j) - { - VERIFY(array[map.outerStride()*i+j] == m.coeffByOuterInner(i,j)); - VERIFY(map.coeffByOuterInner(i,j) == m.coeffByOuterInner(i,j)); - } - VERIFY_IS_APPROX(s1*map,s1*m); - map *= s1; - VERIFY_IS_APPROX(map,s1*m); - } - - // test both inner stride and outer stride - for(int k=0; k<2; ++k) - { - if(k==1 && (2*m.innerSize()+1)*(m.outerSize()*2) > maxsize2) - break; - Scalar* array = (k==0 ? array1 : array2); - - Map<MatrixType, Alignment, Stride<Dynamic,Dynamic> > map(array, rows, cols, Stride<Dynamic,Dynamic>(2*m.innerSize()+1, 2)); - map = m; - VERIFY(map.outerStride() == 2*map.innerSize()+1); - VERIFY(map.innerStride() == 2); - for(int i = 0; i < m.outerSize(); ++i) - for(int j = 0; j < m.innerSize(); ++j) - { - VERIFY(array[map.outerStride()*i+map.innerStride()*j] == m.coeffByOuterInner(i,j)); - VERIFY(map.coeffByOuterInner(i,j) == m.coeffByOuterInner(i,j)); - } - VERIFY_IS_APPROX(s1*map,s1*m); - map *= s1; - VERIFY_IS_APPROX(map,s1*m); - } - - // test inner stride and no outer stride - for(int k=0; k<2; ++k) - { - if(k==1 && (m.innerSize()*2)*m.outerSize() > maxsize2) - break; - Scalar* array = (k==0 ? array1 : array2); - - Map<MatrixType, Alignment, InnerStride<Dynamic> > map(array, rows, cols, InnerStride<Dynamic>(2)); - map = m; - VERIFY(map.outerStride() == map.innerSize()*2); - for(int i = 0; i < m.outerSize(); ++i) - for(int j = 0; j < m.innerSize(); ++j) - { - VERIFY(array[map.innerSize()*i*2+j*2] == m.coeffByOuterInner(i,j)); - VERIFY(map.coeffByOuterInner(i,j) == m.coeffByOuterInner(i,j)); - } - VERIFY_IS_APPROX(s1*map,s1*m); - map *= s1; - VERIFY_IS_APPROX(map,s1*m); - } - - internal::aligned_delete(a_array1, arraysize+1); -} - -// Additional tests for inner-stride but no outer-stride -template<int> -void bug1453() -{ - const int data[] = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31}; - typedef Matrix<int,Dynamic,Dynamic,RowMajor> RowMatrixXi; - typedef Matrix<int,2,3,ColMajor> ColMatrix23i; - typedef Matrix<int,3,2,ColMajor> ColMatrix32i; - typedef Matrix<int,2,3,RowMajor> RowMatrix23i; - typedef Matrix<int,3,2,RowMajor> RowMatrix32i; - - VERIFY_IS_APPROX(MatrixXi::Map(data, 2, 3, InnerStride<2>()), MatrixXi::Map(data, 2, 3, Stride<4,2>())); - VERIFY_IS_APPROX(MatrixXi::Map(data, 2, 3, InnerStride<>(2)), MatrixXi::Map(data, 2, 3, Stride<4,2>())); - VERIFY_IS_APPROX(MatrixXi::Map(data, 3, 2, InnerStride<2>()), MatrixXi::Map(data, 3, 2, Stride<6,2>())); - VERIFY_IS_APPROX(MatrixXi::Map(data, 3, 2, InnerStride<>(2)), MatrixXi::Map(data, 3, 2, Stride<6,2>())); - - VERIFY_IS_APPROX(RowMatrixXi::Map(data, 2, 3, InnerStride<2>()), RowMatrixXi::Map(data, 2, 3, Stride<6,2>())); - VERIFY_IS_APPROX(RowMatrixXi::Map(data, 2, 3, InnerStride<>(2)), RowMatrixXi::Map(data, 2, 3, Stride<6,2>())); - VERIFY_IS_APPROX(RowMatrixXi::Map(data, 3, 2, InnerStride<2>()), RowMatrixXi::Map(data, 3, 2, Stride<4,2>())); - VERIFY_IS_APPROX(RowMatrixXi::Map(data, 3, 2, InnerStride<>(2)), RowMatrixXi::Map(data, 3, 2, Stride<4,2>())); - - VERIFY_IS_APPROX(ColMatrix23i::Map(data, InnerStride<2>()), MatrixXi::Map(data, 2, 3, Stride<4,2>())); - VERIFY_IS_APPROX(ColMatrix23i::Map(data, InnerStride<>(2)), MatrixXi::Map(data, 2, 3, Stride<4,2>())); - VERIFY_IS_APPROX(ColMatrix32i::Map(data, InnerStride<2>()), MatrixXi::Map(data, 3, 2, Stride<6,2>())); - VERIFY_IS_APPROX(ColMatrix32i::Map(data, InnerStride<>(2)), MatrixXi::Map(data, 3, 2, Stride<6,2>())); - - VERIFY_IS_APPROX(RowMatrix23i::Map(data, InnerStride<2>()), RowMatrixXi::Map(data, 2, 3, Stride<6,2>())); - VERIFY_IS_APPROX(RowMatrix23i::Map(data, InnerStride<>(2)), RowMatrixXi::Map(data, 2, 3, Stride<6,2>())); - VERIFY_IS_APPROX(RowMatrix32i::Map(data, InnerStride<2>()), RowMatrixXi::Map(data, 3, 2, Stride<4,2>())); - VERIFY_IS_APPROX(RowMatrix32i::Map(data, InnerStride<>(2)), RowMatrixXi::Map(data, 3, 2, Stride<4,2>())); -} - -void test_mapstride() -{ - for(int i = 0; i < g_repeat; i++) { - int maxn = 30; - CALL_SUBTEST_1( map_class_vector<Aligned>(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_1( map_class_vector<Unaligned>(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( map_class_vector<Aligned>(Vector4d()) ); - CALL_SUBTEST_2( map_class_vector<Unaligned>(Vector4d()) ); - CALL_SUBTEST_3( map_class_vector<Aligned>(RowVector4f()) ); - CALL_SUBTEST_3( map_class_vector<Unaligned>(RowVector4f()) ); - CALL_SUBTEST_4( map_class_vector<Aligned>(VectorXcf(internal::random<int>(1,maxn))) ); - CALL_SUBTEST_4( map_class_vector<Unaligned>(VectorXcf(internal::random<int>(1,maxn))) ); - CALL_SUBTEST_5( map_class_vector<Aligned>(VectorXi(internal::random<int>(1,maxn))) ); - CALL_SUBTEST_5( map_class_vector<Unaligned>(VectorXi(internal::random<int>(1,maxn))) ); - - CALL_SUBTEST_1( map_class_matrix<Aligned>(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_1( map_class_matrix<Unaligned>(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( map_class_matrix<Aligned>(Matrix4d()) ); - CALL_SUBTEST_2( map_class_matrix<Unaligned>(Matrix4d()) ); - CALL_SUBTEST_3( map_class_matrix<Aligned>(Matrix<float,3,5>()) ); - CALL_SUBTEST_3( map_class_matrix<Unaligned>(Matrix<float,3,5>()) ); - CALL_SUBTEST_3( map_class_matrix<Aligned>(Matrix<float,4,8>()) ); - CALL_SUBTEST_3( map_class_matrix<Unaligned>(Matrix<float,4,8>()) ); - CALL_SUBTEST_4( map_class_matrix<Aligned>(MatrixXcf(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) ); - CALL_SUBTEST_4( map_class_matrix<Unaligned>(MatrixXcf(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) ); - CALL_SUBTEST_5( map_class_matrix<Aligned>(MatrixXi(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) ); - CALL_SUBTEST_5( map_class_matrix<Unaligned>(MatrixXi(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) ); - CALL_SUBTEST_6( map_class_matrix<Aligned>(MatrixXcd(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) ); - CALL_SUBTEST_6( map_class_matrix<Unaligned>(MatrixXcd(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) ); - - CALL_SUBTEST_5( bug1453<0>() ); - - TEST_SET_BUT_UNUSED_VARIABLE(maxn); - } -} diff --git a/eigen/test/meta.cpp b/eigen/test/meta.cpp deleted file mode 100644 index b8dea68..0000000 --- a/eigen/test/meta.cpp +++ /dev/null @@ -1,97 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename From, typename To> -bool check_is_convertible(const From&, const To&) -{ - return internal::is_convertible<From,To>::value; -} - -void test_meta() -{ - VERIFY((internal::conditional<(3<4),internal::true_type, internal::false_type>::type::value)); - VERIFY(( internal::is_same<float,float>::value)); - VERIFY((!internal::is_same<float,double>::value)); - VERIFY((!internal::is_same<float,float&>::value)); - VERIFY((!internal::is_same<float,const float&>::value)); - - VERIFY(( internal::is_same<float,internal::remove_all<const float&>::type >::value)); - VERIFY(( internal::is_same<float,internal::remove_all<const float*>::type >::value)); - VERIFY(( internal::is_same<float,internal::remove_all<const float*&>::type >::value)); - VERIFY(( internal::is_same<float,internal::remove_all<float**>::type >::value)); - VERIFY(( internal::is_same<float,internal::remove_all<float**&>::type >::value)); - VERIFY(( internal::is_same<float,internal::remove_all<float* const *&>::type >::value)); - VERIFY(( internal::is_same<float,internal::remove_all<float* const>::type >::value)); - - // test add_const - VERIFY(( internal::is_same< internal::add_const<float>::type, const float >::value)); - VERIFY(( internal::is_same< internal::add_const<float*>::type, float* const>::value)); - VERIFY(( internal::is_same< internal::add_const<float const*>::type, float const* const>::value)); - VERIFY(( internal::is_same< internal::add_const<float&>::type, float& >::value)); - - // test remove_const - VERIFY(( internal::is_same< internal::remove_const<float const* const>::type, float const* >::value)); - VERIFY(( internal::is_same< internal::remove_const<float const*>::type, float const* >::value)); - VERIFY(( internal::is_same< internal::remove_const<float* const>::type, float* >::value)); - - // test add_const_on_value_type - VERIFY(( internal::is_same< internal::add_const_on_value_type<float&>::type, float const& >::value)); - VERIFY(( internal::is_same< internal::add_const_on_value_type<float*>::type, float const* >::value)); - - VERIFY(( internal::is_same< internal::add_const_on_value_type<float>::type, const float >::value)); - VERIFY(( internal::is_same< internal::add_const_on_value_type<const float>::type, const float >::value)); - - VERIFY(( internal::is_same< internal::add_const_on_value_type<const float* const>::type, const float* const>::value)); - VERIFY(( internal::is_same< internal::add_const_on_value_type<float* const>::type, const float* const>::value)); - - VERIFY(( internal::is_same<float,internal::remove_reference<float&>::type >::value)); - VERIFY(( internal::is_same<const float,internal::remove_reference<const float&>::type >::value)); - VERIFY(( internal::is_same<float,internal::remove_pointer<float*>::type >::value)); - VERIFY(( internal::is_same<const float,internal::remove_pointer<const float*>::type >::value)); - VERIFY(( internal::is_same<float,internal::remove_pointer<float* const >::type >::value)); - - VERIFY(( internal::is_convertible<float,double>::value )); - VERIFY(( internal::is_convertible<int,double>::value )); - VERIFY(( internal::is_convertible<double,int>::value )); - VERIFY((!internal::is_convertible<std::complex<double>,double>::value )); - VERIFY(( internal::is_convertible<Array33f,Matrix3f>::value )); -// VERIFY((!internal::is_convertible<Matrix3f,Matrix3d>::value )); //does not work because the conversion is prevented by a static assertion - VERIFY((!internal::is_convertible<Array33f,int>::value )); - VERIFY((!internal::is_convertible<MatrixXf,float>::value )); - { - float f; - MatrixXf A, B; - VectorXf a, b; - VERIFY(( check_is_convertible(a.dot(b), f) )); - VERIFY(( check_is_convertible(a.transpose()*b, f) )); - VERIFY((!check_is_convertible(A*B, f) )); - VERIFY(( check_is_convertible(A*B, A) )); - } - - VERIFY(internal::meta_sqrt<1>::ret == 1); - #define VERIFY_META_SQRT(X) VERIFY(internal::meta_sqrt<X>::ret == int(std::sqrt(double(X)))) - VERIFY_META_SQRT(2); - VERIFY_META_SQRT(3); - VERIFY_META_SQRT(4); - VERIFY_META_SQRT(5); - VERIFY_META_SQRT(6); - VERIFY_META_SQRT(8); - VERIFY_META_SQRT(9); - VERIFY_META_SQRT(15); - VERIFY_META_SQRT(16); - VERIFY_META_SQRT(17); - VERIFY_META_SQRT(255); - VERIFY_META_SQRT(256); - VERIFY_META_SQRT(257); - VERIFY_META_SQRT(1023); - VERIFY_META_SQRT(1024); - VERIFY_META_SQRT(1025); -} diff --git a/eigen/test/metis_support.cpp b/eigen/test/metis_support.cpp deleted file mode 100644 index d87c56a..0000000 --- a/eigen/test/metis_support.cpp +++ /dev/null @@ -1,25 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "sparse_solver.h" -#include <Eigen/SparseLU> -#include <Eigen/MetisSupport> -#include <unsupported/Eigen/SparseExtra> - -template<typename T> void test_metis_T() -{ - SparseLU<SparseMatrix<T, ColMajor>, MetisOrdering<int> > sparselu_metis; - - check_sparse_square_solving(sparselu_metis); -} - -void test_metis_support() -{ - CALL_SUBTEST_1(test_metis_T<double>()); -} diff --git a/eigen/test/miscmatrices.cpp b/eigen/test/miscmatrices.cpp deleted file mode 100644 index f17291c..0000000 --- a/eigen/test/miscmatrices.cpp +++ /dev/null @@ -1,46 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename MatrixType> void miscMatrices(const MatrixType& m) -{ - /* this test covers the following files: - DiagonalMatrix.h Ones.h - */ - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - Index rows = m.rows(); - Index cols = m.cols(); - - Index r = internal::random<Index>(0, rows-1), r2 = internal::random<Index>(0, rows-1), c = internal::random<Index>(0, cols-1); - VERIFY_IS_APPROX(MatrixType::Ones(rows,cols)(r,c), static_cast<Scalar>(1)); - MatrixType m1 = MatrixType::Ones(rows,cols); - VERIFY_IS_APPROX(m1(r,c), static_cast<Scalar>(1)); - VectorType v1 = VectorType::Random(rows); - v1[0]; - Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> - square(v1.asDiagonal()); - if(r==r2) VERIFY_IS_APPROX(square(r,r2), v1[r]); - else VERIFY_IS_MUCH_SMALLER_THAN(square(r,r2), static_cast<Scalar>(1)); - square = MatrixType::Zero(rows, rows); - square.diagonal() = VectorType::Ones(rows); - VERIFY_IS_APPROX(square, MatrixType::Identity(rows, rows)); -} - -void test_miscmatrices() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( miscMatrices(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( miscMatrices(Matrix4d()) ); - CALL_SUBTEST_3( miscMatrices(MatrixXcf(3, 3)) ); - CALL_SUBTEST_4( miscMatrices(MatrixXi(8, 12)) ); - CALL_SUBTEST_5( miscMatrices(MatrixXcd(20, 20)) ); - } -} diff --git a/eigen/test/mixingtypes.cpp b/eigen/test/mixingtypes.cpp deleted file mode 100644 index 45d79aa..0000000 --- a/eigen/test/mixingtypes.cpp +++ /dev/null @@ -1,328 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#if defined(EIGEN_TEST_PART_7) - -#ifndef EIGEN_NO_STATIC_ASSERT -#define EIGEN_NO_STATIC_ASSERT // turn static asserts into runtime asserts in order to check them -#endif - -// ignore double-promotion diagnostic for clang and gcc, if we check for static assertion anyway: -// TODO do the same for MSVC? -#if defined(__clang__) -# if (__clang_major__ * 100 + __clang_minor__) >= 308 -# pragma clang diagnostic ignored "-Wdouble-promotion" -# endif -#elif defined(__GNUC__) - // TODO is there a minimal GCC version for this? At least g++-4.7 seems to be fine with this. -# pragma GCC diagnostic ignored "-Wdouble-promotion" -#endif - -#endif - - - -#if defined(EIGEN_TEST_PART_1) || defined(EIGEN_TEST_PART_2) || defined(EIGEN_TEST_PART_3) - -#ifndef EIGEN_DONT_VECTORIZE -#define EIGEN_DONT_VECTORIZE -#endif - -#endif - -static bool g_called; -#define EIGEN_SCALAR_BINARY_OP_PLUGIN { g_called |= (!internal::is_same<LhsScalar,RhsScalar>::value); } - -#include "main.h" - -using namespace std; - -#define VERIFY_MIX_SCALAR(XPR,REF) \ - g_called = false; \ - VERIFY_IS_APPROX(XPR,REF); \ - VERIFY( g_called && #XPR" not properly optimized"); - -template<int SizeAtCompileType> -void raise_assertion(Index size = SizeAtCompileType) -{ - // VERIFY_RAISES_ASSERT(mf+md); // does not even compile - Matrix<float, SizeAtCompileType, 1> vf; vf.setRandom(size); - Matrix<double, SizeAtCompileType, 1> vd; vd.setRandom(size); - VERIFY_RAISES_ASSERT(vf=vd); - VERIFY_RAISES_ASSERT(vf+=vd); - VERIFY_RAISES_ASSERT(vf-=vd); - VERIFY_RAISES_ASSERT(vd=vf); - VERIFY_RAISES_ASSERT(vd+=vf); - VERIFY_RAISES_ASSERT(vd-=vf); - - // vd.asDiagonal() * mf; // does not even compile - // vcd.asDiagonal() * mf; // does not even compile - -#if 0 // we get other compilation errors here than just static asserts - VERIFY_RAISES_ASSERT(vd.dot(vf)); -#endif -} - - -template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType) -{ - typedef std::complex<float> CF; - typedef std::complex<double> CD; - typedef Matrix<float, SizeAtCompileType, SizeAtCompileType> Mat_f; - typedef Matrix<double, SizeAtCompileType, SizeAtCompileType> Mat_d; - typedef Matrix<std::complex<float>, SizeAtCompileType, SizeAtCompileType> Mat_cf; - typedef Matrix<std::complex<double>, SizeAtCompileType, SizeAtCompileType> Mat_cd; - typedef Matrix<float, SizeAtCompileType, 1> Vec_f; - typedef Matrix<double, SizeAtCompileType, 1> Vec_d; - typedef Matrix<std::complex<float>, SizeAtCompileType, 1> Vec_cf; - typedef Matrix<std::complex<double>, SizeAtCompileType, 1> Vec_cd; - - Mat_f mf = Mat_f::Random(size,size); - Mat_d md = mf.template cast<double>(); - //Mat_d rd = md; - Mat_cf mcf = Mat_cf::Random(size,size); - Mat_cd mcd = mcf.template cast<complex<double> >(); - Mat_cd rcd = mcd; - Vec_f vf = Vec_f::Random(size,1); - Vec_d vd = vf.template cast<double>(); - Vec_cf vcf = Vec_cf::Random(size,1); - Vec_cd vcd = vcf.template cast<complex<double> >(); - float sf = internal::random<float>(); - double sd = internal::random<double>(); - complex<float> scf = internal::random<complex<float> >(); - complex<double> scd = internal::random<complex<double> >(); - - mf+mf; - - float epsf = std::sqrt(std::numeric_limits<float> ::min EIGEN_EMPTY ()); - double epsd = std::sqrt(std::numeric_limits<double>::min EIGEN_EMPTY ()); - - while(std::abs(sf )<epsf) sf = internal::random<float>(); - while(std::abs(sd )<epsd) sd = internal::random<double>(); - while(std::abs(scf)<epsf) scf = internal::random<CF>(); - while(std::abs(scd)<epsd) scd = internal::random<CD>(); - - // check scalar products - VERIFY_MIX_SCALAR(vcf * sf , vcf * complex<float>(sf)); - VERIFY_MIX_SCALAR(sd * vcd , complex<double>(sd) * vcd); - VERIFY_MIX_SCALAR(vf * scf , vf.template cast<complex<float> >() * scf); - VERIFY_MIX_SCALAR(scd * vd , scd * vd.template cast<complex<double> >()); - - VERIFY_MIX_SCALAR(vcf * 2 , vcf * complex<float>(2)); - VERIFY_MIX_SCALAR(vcf * 2.1 , vcf * complex<float>(2.1)); - VERIFY_MIX_SCALAR(2 * vcf, vcf * complex<float>(2)); - VERIFY_MIX_SCALAR(2.1 * vcf , vcf * complex<float>(2.1)); - - // check scalar quotients - VERIFY_MIX_SCALAR(vcf / sf , vcf / complex<float>(sf)); - VERIFY_MIX_SCALAR(vf / scf , vf.template cast<complex<float> >() / scf); - VERIFY_MIX_SCALAR(vf.array() / scf, vf.template cast<complex<float> >().array() / scf); - VERIFY_MIX_SCALAR(scd / vd.array() , scd / vd.template cast<complex<double> >().array()); - - // check scalar increment - VERIFY_MIX_SCALAR(vcf.array() + sf , vcf.array() + complex<float>(sf)); - VERIFY_MIX_SCALAR(sd + vcd.array(), complex<double>(sd) + vcd.array()); - VERIFY_MIX_SCALAR(vf.array() + scf, vf.template cast<complex<float> >().array() + scf); - VERIFY_MIX_SCALAR(scd + vd.array() , scd + vd.template cast<complex<double> >().array()); - - // check scalar subtractions - VERIFY_MIX_SCALAR(vcf.array() - sf , vcf.array() - complex<float>(sf)); - VERIFY_MIX_SCALAR(sd - vcd.array(), complex<double>(sd) - vcd.array()); - VERIFY_MIX_SCALAR(vf.array() - scf, vf.template cast<complex<float> >().array() - scf); - VERIFY_MIX_SCALAR(scd - vd.array() , scd - vd.template cast<complex<double> >().array()); - - // check scalar powers - VERIFY_MIX_SCALAR( pow(vcf.array(), sf), Eigen::pow(vcf.array(), complex<float>(sf)) ); - VERIFY_MIX_SCALAR( vcf.array().pow(sf) , Eigen::pow(vcf.array(), complex<float>(sf)) ); - VERIFY_MIX_SCALAR( pow(sd, vcd.array()), Eigen::pow(complex<double>(sd), vcd.array()) ); - VERIFY_MIX_SCALAR( Eigen::pow(vf.array(), scf), Eigen::pow(vf.template cast<complex<float> >().array(), scf) ); - VERIFY_MIX_SCALAR( vf.array().pow(scf) , Eigen::pow(vf.template cast<complex<float> >().array(), scf) ); - VERIFY_MIX_SCALAR( Eigen::pow(scd, vd.array()), Eigen::pow(scd, vd.template cast<complex<double> >().array()) ); - - // check dot product - vf.dot(vf); - VERIFY_IS_APPROX(vcf.dot(vf), vcf.dot(vf.template cast<complex<float> >())); - - // check diagonal product - VERIFY_IS_APPROX(vf.asDiagonal() * mcf, vf.template cast<complex<float> >().asDiagonal() * mcf); - VERIFY_IS_APPROX(vcd.asDiagonal() * md, vcd.asDiagonal() * md.template cast<complex<double> >()); - VERIFY_IS_APPROX(mcf * vf.asDiagonal(), mcf * vf.template cast<complex<float> >().asDiagonal()); - VERIFY_IS_APPROX(md * vcd.asDiagonal(), md.template cast<complex<double> >() * vcd.asDiagonal()); - - // check inner product - VERIFY_IS_APPROX((vf.transpose() * vcf).value(), (vf.template cast<complex<float> >().transpose() * vcf).value()); - - // check outer product - VERIFY_IS_APPROX((vf * vcf.transpose()).eval(), (vf.template cast<complex<float> >() * vcf.transpose()).eval()); - - // coeff wise product - - VERIFY_IS_APPROX((vf * vcf.transpose()).eval(), (vf.template cast<complex<float> >() * vcf.transpose()).eval()); - - Mat_cd mcd2 = mcd; - VERIFY_IS_APPROX(mcd.array() *= md.array(), mcd2.array() *= md.array().template cast<std::complex<double> >()); - - // check matrix-matrix products - VERIFY_IS_APPROX(sd*md*mcd, (sd*md).template cast<CD>().eval()*mcd); - VERIFY_IS_APPROX(sd*mcd*md, sd*mcd*md.template cast<CD>()); - VERIFY_IS_APPROX(scd*md*mcd, scd*md.template cast<CD>().eval()*mcd); - VERIFY_IS_APPROX(scd*mcd*md, scd*mcd*md.template cast<CD>()); - - VERIFY_IS_APPROX(sf*mf*mcf, sf*mf.template cast<CF>()*mcf); - VERIFY_IS_APPROX(sf*mcf*mf, sf*mcf*mf.template cast<CF>()); - VERIFY_IS_APPROX(scf*mf*mcf, scf*mf.template cast<CF>()*mcf); - VERIFY_IS_APPROX(scf*mcf*mf, scf*mcf*mf.template cast<CF>()); - - VERIFY_IS_APPROX(sd*md.adjoint()*mcd, (sd*md).template cast<CD>().eval().adjoint()*mcd); - VERIFY_IS_APPROX(sd*mcd.adjoint()*md, sd*mcd.adjoint()*md.template cast<CD>()); - VERIFY_IS_APPROX(sd*md.adjoint()*mcd.adjoint(), (sd*md).template cast<CD>().eval().adjoint()*mcd.adjoint()); - VERIFY_IS_APPROX(sd*mcd.adjoint()*md.adjoint(), sd*mcd.adjoint()*md.template cast<CD>().adjoint()); - VERIFY_IS_APPROX(sd*md*mcd.adjoint(), (sd*md).template cast<CD>().eval()*mcd.adjoint()); - VERIFY_IS_APPROX(sd*mcd*md.adjoint(), sd*mcd*md.template cast<CD>().adjoint()); - - VERIFY_IS_APPROX(sf*mf.adjoint()*mcf, (sf*mf).template cast<CF>().eval().adjoint()*mcf); - VERIFY_IS_APPROX(sf*mcf.adjoint()*mf, sf*mcf.adjoint()*mf.template cast<CF>()); - VERIFY_IS_APPROX(sf*mf.adjoint()*mcf.adjoint(), (sf*mf).template cast<CF>().eval().adjoint()*mcf.adjoint()); - VERIFY_IS_APPROX(sf*mcf.adjoint()*mf.adjoint(), sf*mcf.adjoint()*mf.template cast<CF>().adjoint()); - VERIFY_IS_APPROX(sf*mf*mcf.adjoint(), (sf*mf).template cast<CF>().eval()*mcf.adjoint()); - VERIFY_IS_APPROX(sf*mcf*mf.adjoint(), sf*mcf*mf.template cast<CF>().adjoint()); - - VERIFY_IS_APPROX(sf*mf*vcf, (sf*mf).template cast<CF>().eval()*vcf); - VERIFY_IS_APPROX(scf*mf*vcf,(scf*mf.template cast<CF>()).eval()*vcf); - VERIFY_IS_APPROX(sf*mcf*vf, sf*mcf*vf.template cast<CF>()); - VERIFY_IS_APPROX(scf*mcf*vf,scf*mcf*vf.template cast<CF>()); - - VERIFY_IS_APPROX(sf*vcf.adjoint()*mf, sf*vcf.adjoint()*mf.template cast<CF>().eval()); - VERIFY_IS_APPROX(scf*vcf.adjoint()*mf, scf*vcf.adjoint()*mf.template cast<CF>().eval()); - VERIFY_IS_APPROX(sf*vf.adjoint()*mcf, sf*vf.adjoint().template cast<CF>().eval()*mcf); - VERIFY_IS_APPROX(scf*vf.adjoint()*mcf, scf*vf.adjoint().template cast<CF>().eval()*mcf); - - VERIFY_IS_APPROX(sd*md*vcd, (sd*md).template cast<CD>().eval()*vcd); - VERIFY_IS_APPROX(scd*md*vcd,(scd*md.template cast<CD>()).eval()*vcd); - VERIFY_IS_APPROX(sd*mcd*vd, sd*mcd*vd.template cast<CD>().eval()); - VERIFY_IS_APPROX(scd*mcd*vd,scd*mcd*vd.template cast<CD>().eval()); - - VERIFY_IS_APPROX(sd*vcd.adjoint()*md, sd*vcd.adjoint()*md.template cast<CD>().eval()); - VERIFY_IS_APPROX(scd*vcd.adjoint()*md, scd*vcd.adjoint()*md.template cast<CD>().eval()); - VERIFY_IS_APPROX(sd*vd.adjoint()*mcd, sd*vd.adjoint().template cast<CD>().eval()*mcd); - VERIFY_IS_APPROX(scd*vd.adjoint()*mcd, scd*vd.adjoint().template cast<CD>().eval()*mcd); - - VERIFY_IS_APPROX( sd*vcd.adjoint()*md.template triangularView<Upper>(), sd*vcd.adjoint()*md.template cast<CD>().eval().template triangularView<Upper>()); - VERIFY_IS_APPROX(scd*vcd.adjoint()*md.template triangularView<Lower>(), scd*vcd.adjoint()*md.template cast<CD>().eval().template triangularView<Lower>()); - VERIFY_IS_APPROX( sd*vcd.adjoint()*md.transpose().template triangularView<Upper>(), sd*vcd.adjoint()*md.transpose().template cast<CD>().eval().template triangularView<Upper>()); - VERIFY_IS_APPROX(scd*vcd.adjoint()*md.transpose().template triangularView<Lower>(), scd*vcd.adjoint()*md.transpose().template cast<CD>().eval().template triangularView<Lower>()); - VERIFY_IS_APPROX( sd*vd.adjoint()*mcd.template triangularView<Lower>(), sd*vd.adjoint().template cast<CD>().eval()*mcd.template triangularView<Lower>()); - VERIFY_IS_APPROX(scd*vd.adjoint()*mcd.template triangularView<Upper>(), scd*vd.adjoint().template cast<CD>().eval()*mcd.template triangularView<Upper>()); - VERIFY_IS_APPROX( sd*vd.adjoint()*mcd.transpose().template triangularView<Lower>(), sd*vd.adjoint().template cast<CD>().eval()*mcd.transpose().template triangularView<Lower>()); - VERIFY_IS_APPROX(scd*vd.adjoint()*mcd.transpose().template triangularView<Upper>(), scd*vd.adjoint().template cast<CD>().eval()*mcd.transpose().template triangularView<Upper>()); - - // Not supported yet: trmm -// VERIFY_IS_APPROX(sd*mcd*md.template triangularView<Lower>(), sd*mcd*md.template cast<CD>().eval().template triangularView<Lower>()); -// VERIFY_IS_APPROX(scd*mcd*md.template triangularView<Upper>(), scd*mcd*md.template cast<CD>().eval().template triangularView<Upper>()); -// VERIFY_IS_APPROX(sd*md*mcd.template triangularView<Lower>(), sd*md.template cast<CD>().eval()*mcd.template triangularView<Lower>()); -// VERIFY_IS_APPROX(scd*md*mcd.template triangularView<Upper>(), scd*md.template cast<CD>().eval()*mcd.template triangularView<Upper>()); - - // Not supported yet: symv -// VERIFY_IS_APPROX(sd*vcd.adjoint()*md.template selfadjointView<Upper>(), sd*vcd.adjoint()*md.template cast<CD>().eval().template selfadjointView<Upper>()); -// VERIFY_IS_APPROX(scd*vcd.adjoint()*md.template selfadjointView<Lower>(), scd*vcd.adjoint()*md.template cast<CD>().eval().template selfadjointView<Lower>()); -// VERIFY_IS_APPROX(sd*vd.adjoint()*mcd.template selfadjointView<Lower>(), sd*vd.adjoint().template cast<CD>().eval()*mcd.template selfadjointView<Lower>()); -// VERIFY_IS_APPROX(scd*vd.adjoint()*mcd.template selfadjointView<Upper>(), scd*vd.adjoint().template cast<CD>().eval()*mcd.template selfadjointView<Upper>()); - - // Not supported yet: symm -// VERIFY_IS_APPROX(sd*vcd.adjoint()*md.template selfadjointView<Upper>(), sd*vcd.adjoint()*md.template cast<CD>().eval().template selfadjointView<Upper>()); -// VERIFY_IS_APPROX(scd*vcd.adjoint()*md.template selfadjointView<Upper>(), scd*vcd.adjoint()*md.template cast<CD>().eval().template selfadjointView<Upper>()); -// VERIFY_IS_APPROX(sd*vd.adjoint()*mcd.template selfadjointView<Upper>(), sd*vd.adjoint().template cast<CD>().eval()*mcd.template selfadjointView<Upper>()); -// VERIFY_IS_APPROX(scd*vd.adjoint()*mcd.template selfadjointView<Upper>(), scd*vd.adjoint().template cast<CD>().eval()*mcd.template selfadjointView<Upper>()); - - rcd.setZero(); - VERIFY_IS_APPROX(Mat_cd(rcd.template triangularView<Upper>() = sd * mcd * md), - Mat_cd((sd * mcd * md.template cast<CD>().eval()).template triangularView<Upper>())); - VERIFY_IS_APPROX(Mat_cd(rcd.template triangularView<Upper>() = sd * md * mcd), - Mat_cd((sd * md.template cast<CD>().eval() * mcd).template triangularView<Upper>())); - VERIFY_IS_APPROX(Mat_cd(rcd.template triangularView<Upper>() = scd * mcd * md), - Mat_cd((scd * mcd * md.template cast<CD>().eval()).template triangularView<Upper>())); - VERIFY_IS_APPROX(Mat_cd(rcd.template triangularView<Upper>() = scd * md * mcd), - Mat_cd((scd * md.template cast<CD>().eval() * mcd).template triangularView<Upper>())); - - - VERIFY_IS_APPROX( md.array() * mcd.array(), md.template cast<CD>().eval().array() * mcd.array() ); - VERIFY_IS_APPROX( mcd.array() * md.array(), mcd.array() * md.template cast<CD>().eval().array() ); - - VERIFY_IS_APPROX( md.array() + mcd.array(), md.template cast<CD>().eval().array() + mcd.array() ); - VERIFY_IS_APPROX( mcd.array() + md.array(), mcd.array() + md.template cast<CD>().eval().array() ); - - VERIFY_IS_APPROX( md.array() - mcd.array(), md.template cast<CD>().eval().array() - mcd.array() ); - VERIFY_IS_APPROX( mcd.array() - md.array(), mcd.array() - md.template cast<CD>().eval().array() ); - - if(mcd.array().abs().minCoeff()>epsd) - { - VERIFY_IS_APPROX( md.array() / mcd.array(), md.template cast<CD>().eval().array() / mcd.array() ); - } - if(md.array().abs().minCoeff()>epsd) - { - VERIFY_IS_APPROX( mcd.array() / md.array(), mcd.array() / md.template cast<CD>().eval().array() ); - } - - if(md.array().abs().minCoeff()>epsd || mcd.array().abs().minCoeff()>epsd) - { - VERIFY_IS_APPROX( md.array().pow(mcd.array()), md.template cast<CD>().eval().array().pow(mcd.array()) ); - VERIFY_IS_APPROX( mcd.array().pow(md.array()), mcd.array().pow(md.template cast<CD>().eval().array()) ); - - VERIFY_IS_APPROX( pow(md.array(),mcd.array()), md.template cast<CD>().eval().array().pow(mcd.array()) ); - VERIFY_IS_APPROX( pow(mcd.array(),md.array()), mcd.array().pow(md.template cast<CD>().eval().array()) ); - } - - rcd = mcd; - VERIFY_IS_APPROX( rcd = md, md.template cast<CD>().eval() ); - rcd = mcd; - VERIFY_IS_APPROX( rcd += md, mcd + md.template cast<CD>().eval() ); - rcd = mcd; - VERIFY_IS_APPROX( rcd -= md, mcd - md.template cast<CD>().eval() ); - rcd = mcd; - VERIFY_IS_APPROX( rcd.array() *= md.array(), mcd.array() * md.template cast<CD>().eval().array() ); - rcd = mcd; - if(md.array().abs().minCoeff()>epsd) - { - VERIFY_IS_APPROX( rcd.array() /= md.array(), mcd.array() / md.template cast<CD>().eval().array() ); - } - - rcd = mcd; - VERIFY_IS_APPROX( rcd.noalias() += md + mcd*md, mcd + (md.template cast<CD>().eval()) + mcd*(md.template cast<CD>().eval())); - - VERIFY_IS_APPROX( rcd.noalias() = md*md, ((md*md).eval().template cast<CD>()) ); - rcd = mcd; - VERIFY_IS_APPROX( rcd.noalias() += md*md, mcd + ((md*md).eval().template cast<CD>()) ); - rcd = mcd; - VERIFY_IS_APPROX( rcd.noalias() -= md*md, mcd - ((md*md).eval().template cast<CD>()) ); - - VERIFY_IS_APPROX( rcd.noalias() = mcd + md*md, mcd + ((md*md).eval().template cast<CD>()) ); - rcd = mcd; - VERIFY_IS_APPROX( rcd.noalias() += mcd + md*md, mcd + mcd + ((md*md).eval().template cast<CD>()) ); - rcd = mcd; - VERIFY_IS_APPROX( rcd.noalias() -= mcd + md*md, - ((md*md).eval().template cast<CD>()) ); -} - -void test_mixingtypes() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1(mixingtypes<3>()); - CALL_SUBTEST_2(mixingtypes<4>()); - CALL_SUBTEST_3(mixingtypes<Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))); - - CALL_SUBTEST_4(mixingtypes<3>()); - CALL_SUBTEST_5(mixingtypes<4>()); - CALL_SUBTEST_6(mixingtypes<Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))); - CALL_SUBTEST_7(raise_assertion<Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))); - } - CALL_SUBTEST_7(raise_assertion<0>()); - CALL_SUBTEST_7(raise_assertion<3>()); - CALL_SUBTEST_7(raise_assertion<4>()); - CALL_SUBTEST_7(raise_assertion<Dynamic>(0)); -} diff --git a/eigen/test/mpl2only.cpp b/eigen/test/mpl2only.cpp deleted file mode 100644 index 7d04d6b..0000000 --- a/eigen/test/mpl2only.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_MPL2_ONLY -#include <Eigen/Dense> -#include <Eigen/SparseCore> -#include <Eigen/SparseLU> -#include <Eigen/SparseQR> -#include <Eigen/Sparse> -#include <Eigen/IterativeLinearSolvers> -#include <Eigen/Eigen> - -int main() -{ - return 0; -} diff --git a/eigen/test/nesting_ops.cpp b/eigen/test/nesting_ops.cpp deleted file mode 100644 index a419b0e..0000000 --- a/eigen/test/nesting_ops.cpp +++ /dev/null @@ -1,107 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com> -// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define TEST_ENABLE_TEMPORARY_TRACKING - -#include "main.h" - -template <int N, typename XprType> -void use_n_times(const XprType &xpr) -{ - typename internal::nested_eval<XprType,N>::type mat(xpr); - typename XprType::PlainObject res(mat.rows(), mat.cols()); - nb_temporaries--; // remove res - res.setZero(); - for(int i=0; i<N; ++i) - res += mat; -} - -template <int N, typename ReferenceType, typename XprType> -bool verify_eval_type(const XprType &, const ReferenceType&) -{ - typedef typename internal::nested_eval<XprType,N>::type EvalType; - return internal::is_same<typename internal::remove_all<EvalType>::type, typename internal::remove_all<ReferenceType>::type>::value; -} - -template <typename MatrixType> void run_nesting_ops_1(const MatrixType& _m) -{ - typename internal::nested_eval<MatrixType,2>::type m(_m); - - // Make really sure that we are in debug mode! - VERIFY_RAISES_ASSERT(eigen_assert(false)); - - // The only intention of these tests is to ensure that this code does - // not trigger any asserts or segmentation faults... more to come. - VERIFY_IS_APPROX( (m.transpose() * m).diagonal().sum(), (m.transpose() * m).diagonal().sum() ); - VERIFY_IS_APPROX( (m.transpose() * m).diagonal().array().abs().sum(), (m.transpose() * m).diagonal().array().abs().sum() ); - - VERIFY_IS_APPROX( (m.transpose() * m).array().abs().sum(), (m.transpose() * m).array().abs().sum() ); -} - -template <typename MatrixType> void run_nesting_ops_2(const MatrixType& _m) -{ - typedef typename MatrixType::Scalar Scalar; - Index rows = _m.rows(); - Index cols = _m.cols(); - MatrixType m1 = MatrixType::Random(rows,cols); - Matrix<Scalar,MatrixType::RowsAtCompileTime,MatrixType::ColsAtCompileTime,ColMajor> m2; - - if((MatrixType::SizeAtCompileTime==Dynamic)) - { - VERIFY_EVALUATION_COUNT( use_n_times<1>(m1 + m1*m1), 1 ); - VERIFY_EVALUATION_COUNT( use_n_times<10>(m1 + m1*m1), 1 ); - - VERIFY_EVALUATION_COUNT( use_n_times<1>(m1.template triangularView<Lower>().solve(m1.col(0))), 1 ); - VERIFY_EVALUATION_COUNT( use_n_times<10>(m1.template triangularView<Lower>().solve(m1.col(0))), 1 ); - - VERIFY_EVALUATION_COUNT( use_n_times<1>(Scalar(2)*m1.template triangularView<Lower>().solve(m1.col(0))), 2 ); // FIXME could be one by applying the scaling in-place on the solve result - VERIFY_EVALUATION_COUNT( use_n_times<1>(m1.col(0)+m1.template triangularView<Lower>().solve(m1.col(0))), 2 ); // FIXME could be one by adding m1.col() inplace - VERIFY_EVALUATION_COUNT( use_n_times<10>(m1.col(0)+m1.template triangularView<Lower>().solve(m1.col(0))), 2 ); - } - - { - VERIFY( verify_eval_type<10>(m1, m1) ); - if(!NumTraits<Scalar>::IsComplex) - { - VERIFY( verify_eval_type<3>(2*m1, 2*m1) ); - VERIFY( verify_eval_type<4>(2*m1, m1) ); - } - else - { - VERIFY( verify_eval_type<2>(2*m1, 2*m1) ); - VERIFY( verify_eval_type<3>(2*m1, m1) ); - } - VERIFY( verify_eval_type<2>(m1+m1, m1+m1) ); - VERIFY( verify_eval_type<3>(m1+m1, m1) ); - VERIFY( verify_eval_type<1>(m1*m1.transpose(), m2) ); - VERIFY( verify_eval_type<1>(m1*(m1+m1).transpose(), m2) ); - VERIFY( verify_eval_type<2>(m1*m1.transpose(), m2) ); - VERIFY( verify_eval_type<1>(m1+m1*m1, m1) ); - - VERIFY( verify_eval_type<1>(m1.template triangularView<Lower>().solve(m1), m1) ); - VERIFY( verify_eval_type<1>(m1+m1.template triangularView<Lower>().solve(m1), m1) ); - } -} - - -void test_nesting_ops() -{ - CALL_SUBTEST_1(run_nesting_ops_1(MatrixXf::Random(25,25))); - CALL_SUBTEST_2(run_nesting_ops_1(MatrixXcd::Random(25,25))); - CALL_SUBTEST_3(run_nesting_ops_1(Matrix4f::Random())); - CALL_SUBTEST_4(run_nesting_ops_1(Matrix2d::Random())); - - Index s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE); - CALL_SUBTEST_1( run_nesting_ops_2(MatrixXf(s,s)) ); - CALL_SUBTEST_2( run_nesting_ops_2(MatrixXcd(s,s)) ); - CALL_SUBTEST_3( run_nesting_ops_2(Matrix4f()) ); - CALL_SUBTEST_4( run_nesting_ops_2(Matrix2d()) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) -} diff --git a/eigen/test/nomalloc.cpp b/eigen/test/nomalloc.cpp deleted file mode 100644 index b7ea4d3..0000000 --- a/eigen/test/nomalloc.cpp +++ /dev/null @@ -1,228 +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) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// discard stack allocation as that too bypasses malloc -#define EIGEN_STACK_ALLOCATION_LIMIT 0 -// heap allocation will raise an assert if enabled at runtime -#define EIGEN_RUNTIME_NO_MALLOC - -#include "main.h" -#include <Eigen/Cholesky> -#include <Eigen/Eigenvalues> -#include <Eigen/LU> -#include <Eigen/QR> -#include <Eigen/SVD> - -template<typename MatrixType> void nomalloc(const MatrixType& m) -{ - /* this test check no dynamic memory allocation are issued with fixed-size matrices - */ - typedef typename MatrixType::Scalar Scalar; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols); - - Scalar s1 = internal::random<Scalar>(); - - Index r = internal::random<Index>(0, rows-1), - c = internal::random<Index>(0, cols-1); - - VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2); - VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c))); - VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0,0,rows,cols)), (m1.array()*m1.array()).matrix()); - VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2)); - - m2.col(0).noalias() = m1 * m1.col(0); - m2.col(0).noalias() -= m1.adjoint() * m1.col(0); - m2.col(0).noalias() -= m1 * m1.row(0).adjoint(); - m2.col(0).noalias() -= m1.adjoint() * m1.row(0).adjoint(); - - m2.row(0).noalias() = m1.row(0) * m1; - m2.row(0).noalias() -= m1.row(0) * m1.adjoint(); - m2.row(0).noalias() -= m1.col(0).adjoint() * m1; - m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint(); - VERIFY_IS_APPROX(m2,m2); - - m2.col(0).noalias() = m1.template triangularView<Upper>() * m1.col(0); - m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.col(0); - m2.col(0).noalias() -= m1.template triangularView<Upper>() * m1.row(0).adjoint(); - m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.row(0).adjoint(); - - m2.row(0).noalias() = m1.row(0) * m1.template triangularView<Upper>(); - m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template triangularView<Upper>(); - m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template triangularView<Upper>(); - m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template triangularView<Upper>(); - VERIFY_IS_APPROX(m2,m2); - - m2.col(0).noalias() = m1.template selfadjointView<Upper>() * m1.col(0); - m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.col(0); - m2.col(0).noalias() -= m1.template selfadjointView<Upper>() * m1.row(0).adjoint(); - m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.row(0).adjoint(); - - m2.row(0).noalias() = m1.row(0) * m1.template selfadjointView<Upper>(); - m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template selfadjointView<Upper>(); - m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template selfadjointView<Upper>(); - m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template selfadjointView<Upper>(); - VERIFY_IS_APPROX(m2,m2); - - m2.template selfadjointView<Lower>().rankUpdate(m1.col(0),-1); - m2.template selfadjointView<Upper>().rankUpdate(m1.row(0),-1); - m2.template selfadjointView<Lower>().rankUpdate(m1.col(0), m1.col(0)); // rank-2 - - // The following fancy matrix-matrix products are not safe yet regarding static allocation - m2.template selfadjointView<Lower>().rankUpdate(m1); - m2 += m2.template triangularView<Upper>() * m1; - m2.template triangularView<Upper>() = m2 * m2; - m1 += m1.template selfadjointView<Lower>() * m2; - VERIFY_IS_APPROX(m2,m2); -} - -template<typename Scalar> -void ctms_decompositions() -{ - const int maxSize = 16; - const int size = 12; - - typedef Eigen::Matrix<Scalar, - Eigen::Dynamic, Eigen::Dynamic, - 0, - maxSize, maxSize> Matrix; - - typedef Eigen::Matrix<Scalar, - Eigen::Dynamic, 1, - 0, - maxSize, 1> Vector; - - typedef Eigen::Matrix<std::complex<Scalar>, - Eigen::Dynamic, Eigen::Dynamic, - 0, - maxSize, maxSize> ComplexMatrix; - - const Matrix A(Matrix::Random(size, size)), B(Matrix::Random(size, size)); - Matrix X(size,size); - const ComplexMatrix complexA(ComplexMatrix::Random(size, size)); - const Matrix saA = A.adjoint() * A; - const Vector b(Vector::Random(size)); - Vector x(size); - - // Cholesky module - Eigen::LLT<Matrix> LLT; LLT.compute(A); - X = LLT.solve(B); - x = LLT.solve(b); - Eigen::LDLT<Matrix> LDLT; LDLT.compute(A); - X = LDLT.solve(B); - x = LDLT.solve(b); - - // Eigenvalues module - Eigen::HessenbergDecomposition<ComplexMatrix> hessDecomp; hessDecomp.compute(complexA); - Eigen::ComplexSchur<ComplexMatrix> cSchur(size); cSchur.compute(complexA); - Eigen::ComplexEigenSolver<ComplexMatrix> cEigSolver; cEigSolver.compute(complexA); - Eigen::EigenSolver<Matrix> eigSolver; eigSolver.compute(A); - Eigen::SelfAdjointEigenSolver<Matrix> saEigSolver(size); saEigSolver.compute(saA); - Eigen::Tridiagonalization<Matrix> tridiag; tridiag.compute(saA); - - // LU module - Eigen::PartialPivLU<Matrix> ppLU; ppLU.compute(A); - X = ppLU.solve(B); - x = ppLU.solve(b); - Eigen::FullPivLU<Matrix> fpLU; fpLU.compute(A); - X = fpLU.solve(B); - x = fpLU.solve(b); - - // QR module - Eigen::HouseholderQR<Matrix> hQR; hQR.compute(A); - X = hQR.solve(B); - x = hQR.solve(b); - Eigen::ColPivHouseholderQR<Matrix> cpQR; cpQR.compute(A); - X = cpQR.solve(B); - x = cpQR.solve(b); - Eigen::FullPivHouseholderQR<Matrix> fpQR; fpQR.compute(A); - // FIXME X = fpQR.solve(B); - x = fpQR.solve(b); - - // SVD module - Eigen::JacobiSVD<Matrix> jSVD; jSVD.compute(A, ComputeFullU | ComputeFullV); -} - -void test_zerosized() { - // default constructors: - Eigen::MatrixXd A; - Eigen::VectorXd v; - // explicit zero-sized: - Eigen::ArrayXXd A0(0,0); - Eigen::ArrayXd v0(0); - - // assigning empty objects to each other: - A=A0; - v=v0; -} - -template<typename MatrixType> void test_reference(const MatrixType& m) { - typedef typename MatrixType::Scalar Scalar; - enum { Flag = MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor}; - enum { TransposeFlag = !MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor}; - typename MatrixType::Index rows = m.rows(), cols=m.cols(); - typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Flag > MatrixX; - typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, TransposeFlag> MatrixXT; - // Dynamic reference: - typedef Eigen::Ref<const MatrixX > Ref; - typedef Eigen::Ref<const MatrixXT > RefT; - - Ref r1(m); - Ref r2(m.block(rows/3, cols/4, rows/2, cols/2)); - RefT r3(m.transpose()); - RefT r4(m.topLeftCorner(rows/2, cols/2).transpose()); - - VERIFY_RAISES_ASSERT(RefT r5(m)); - VERIFY_RAISES_ASSERT(Ref r6(m.transpose())); - VERIFY_RAISES_ASSERT(Ref r7(Scalar(2) * m)); - - // Copy constructors shall also never malloc - Ref r8 = r1; - RefT r9 = r3; - - // Initializing from a compatible Ref shall also never malloc - Eigen::Ref<const MatrixX, Unaligned, Stride<Dynamic, Dynamic> > r10=r8, r11=m; - - // Initializing from an incompatible Ref will malloc: - typedef Eigen::Ref<const MatrixX, Aligned> RefAligned; - VERIFY_RAISES_ASSERT(RefAligned r12=r10); - VERIFY_RAISES_ASSERT(Ref r13=r10); // r10 has more dynamic strides - -} - -void test_nomalloc() -{ - // create some dynamic objects - Eigen::MatrixXd M1 = MatrixXd::Random(3,3); - Ref<const MatrixXd> R1 = 2.0*M1; // Ref requires temporary - - // from here on prohibit malloc: - Eigen::internal::set_is_malloc_allowed(false); - - // check that our operator new is indeed called: - VERIFY_RAISES_ASSERT(MatrixXd dummy(MatrixXd::Random(3,3))); - CALL_SUBTEST_1(nomalloc(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2(nomalloc(Matrix4d()) ); - CALL_SUBTEST_3(nomalloc(Matrix<float,32,32>()) ); - - // Check decomposition modules with dynamic matrices that have a known compile-time max size (ctms) - CALL_SUBTEST_4(ctms_decompositions<float>()); - - CALL_SUBTEST_5(test_zerosized()); - - CALL_SUBTEST_6(test_reference(Matrix<float,32,32>())); - CALL_SUBTEST_7(test_reference(R1)); - CALL_SUBTEST_8(Ref<MatrixXd> R2 = M1.topRows<2>(); test_reference(R2)); -} diff --git a/eigen/test/nullary.cpp b/eigen/test/nullary.cpp deleted file mode 100644 index acd5550..0000000 --- a/eigen/test/nullary.cpp +++ /dev/null @@ -1,304 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010-2011 Jitse Niesen <jitse@maths.leeds.ac.uk> -// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename MatrixType> -bool equalsIdentity(const MatrixType& A) -{ - typedef typename MatrixType::Scalar Scalar; - Scalar zero = static_cast<Scalar>(0); - - bool offDiagOK = true; - for (Index i = 0; i < A.rows(); ++i) { - for (Index j = i+1; j < A.cols(); ++j) { - offDiagOK = offDiagOK && (A(i,j) == zero); - } - } - for (Index i = 0; i < A.rows(); ++i) { - for (Index j = 0; j < (std::min)(i, A.cols()); ++j) { - offDiagOK = offDiagOK && (A(i,j) == zero); - } - } - - bool diagOK = (A.diagonal().array() == 1).all(); - return offDiagOK && diagOK; - -} - -template<typename VectorType> -void check_extremity_accuracy(const VectorType &v, const typename VectorType::Scalar &low, const typename VectorType::Scalar &high) -{ - typedef typename VectorType::Scalar Scalar; - typedef typename VectorType::RealScalar RealScalar; - - RealScalar prec = internal::is_same<RealScalar,float>::value ? NumTraits<RealScalar>::dummy_precision()*10 : NumTraits<RealScalar>::dummy_precision()/10; - Index size = v.size(); - - if(size<20) - return; - - for (int i=0; i<size; ++i) - { - if(i<5 || i>size-6) - { - Scalar ref = (low*RealScalar(size-i-1))/RealScalar(size-1) + (high*RealScalar(i))/RealScalar(size-1); - if(std::abs(ref)>1) - { - if(!internal::isApprox(v(i), ref, prec)) - std::cout << v(i) << " != " << ref << " ; relative error: " << std::abs((v(i)-ref)/ref) << " ; required precision: " << prec << " ; range: " << low << "," << high << " ; i: " << i << "\n"; - VERIFY(internal::isApprox(v(i), (low*RealScalar(size-i-1))/RealScalar(size-1) + (high*RealScalar(i))/RealScalar(size-1), prec)); - } - } - } -} - -template<typename VectorType> -void testVectorType(const VectorType& base) -{ - typedef typename VectorType::Scalar Scalar; - typedef typename VectorType::RealScalar RealScalar; - - const Index size = base.size(); - - Scalar high = internal::random<Scalar>(-500,500); - Scalar low = (size == 1 ? high : internal::random<Scalar>(-500,500)); - if (low>high) std::swap(low,high); - - // check low==high - if(internal::random<float>(0.f,1.f)<0.05f) - low = high; - // check abs(low) >> abs(high) - else if(size>2 && std::numeric_limits<RealScalar>::max_exponent10>0 && internal::random<float>(0.f,1.f)<0.1f) - low = -internal::random<Scalar>(1,2) * RealScalar(std::pow(RealScalar(10),std::numeric_limits<RealScalar>::max_exponent10/2)); - - const Scalar step = ((size == 1) ? 1 : (high-low)/(size-1)); - - // check whether the result yields what we expect it to do - VectorType m(base); - m.setLinSpaced(size,low,high); - - if(!NumTraits<Scalar>::IsInteger) - { - VectorType n(size); - for (int i=0; i<size; ++i) - n(i) = low+i*step; - VERIFY_IS_APPROX(m,n); - - CALL_SUBTEST( check_extremity_accuracy(m, low, high) ); - } - - if((!NumTraits<Scalar>::IsInteger) || ((high-low)>=size && (Index(high-low)%(size-1))==0) || (Index(high-low+1)<size && (size%Index(high-low+1))==0)) - { - VectorType n(size); - if((!NumTraits<Scalar>::IsInteger) || (high-low>=size)) - for (int i=0; i<size; ++i) - n(i) = size==1 ? low : (low + ((high-low)*Scalar(i))/(size-1)); - else - for (int i=0; i<size; ++i) - n(i) = size==1 ? low : low + Scalar((double(high-low+1)*double(i))/double(size)); - VERIFY_IS_APPROX(m,n); - - // random access version - m = VectorType::LinSpaced(size,low,high); - VERIFY_IS_APPROX(m,n); - VERIFY( internal::isApprox(m(m.size()-1),high) ); - VERIFY( size==1 || internal::isApprox(m(0),low) ); - VERIFY_IS_EQUAL(m(m.size()-1) , high); - if(!NumTraits<Scalar>::IsInteger) - CALL_SUBTEST( check_extremity_accuracy(m, low, high) ); - } - - VERIFY( m(m.size()-1) <= high ); - VERIFY( (m.array() <= high).all() ); - VERIFY( (m.array() >= low).all() ); - - - VERIFY( m(m.size()-1) >= low ); - if(size>=1) - { - VERIFY( internal::isApprox(m(0),low) ); - VERIFY_IS_EQUAL(m(0) , low); - } - - // check whether everything works with row and col major vectors - Matrix<Scalar,Dynamic,1> row_vector(size); - Matrix<Scalar,1,Dynamic> col_vector(size); - row_vector.setLinSpaced(size,low,high); - col_vector.setLinSpaced(size,low,high); - // when using the extended precision (e.g., FPU) the relative error might exceed 1 bit - // when computing the squared sum in isApprox, thus the 2x factor. - VERIFY( row_vector.isApprox(col_vector.transpose(), Scalar(2)*NumTraits<Scalar>::epsilon())); - - Matrix<Scalar,Dynamic,1> size_changer(size+50); - size_changer.setLinSpaced(size,low,high); - VERIFY( size_changer.size() == size ); - - typedef Matrix<Scalar,1,1> ScalarMatrix; - ScalarMatrix scalar; - scalar.setLinSpaced(1,low,high); - VERIFY_IS_APPROX( scalar, ScalarMatrix::Constant(high) ); - VERIFY_IS_APPROX( ScalarMatrix::LinSpaced(1,low,high), ScalarMatrix::Constant(high) ); - - // regression test for bug 526 (linear vectorized transversal) - if (size > 1 && (!NumTraits<Scalar>::IsInteger)) { - m.tail(size-1).setLinSpaced(low, high); - VERIFY_IS_APPROX(m(size-1), high); - } - - // regression test for bug 1383 (LinSpaced with empty size/range) - { - Index n0 = VectorType::SizeAtCompileTime==Dynamic ? 0 : VectorType::SizeAtCompileTime; - low = internal::random<Scalar>(); - m = VectorType::LinSpaced(n0,low,low-1); - VERIFY(m.size()==n0); - - if(VectorType::SizeAtCompileTime==Dynamic) - { - VERIFY_IS_EQUAL(VectorType::LinSpaced(n0,0,Scalar(n0-1)).sum(),Scalar(0)); - VERIFY_IS_EQUAL(VectorType::LinSpaced(n0,low,low-1).sum(),Scalar(0)); - } - - m.setLinSpaced(n0,0,Scalar(n0-1)); - VERIFY(m.size()==n0); - m.setLinSpaced(n0,low,low-1); - VERIFY(m.size()==n0); - - // empty range only: - VERIFY_IS_APPROX(VectorType::LinSpaced(size,low,low),VectorType::Constant(size,low)); - m.setLinSpaced(size,low,low); - VERIFY_IS_APPROX(m,VectorType::Constant(size,low)); - - if(NumTraits<Scalar>::IsInteger) - { - VERIFY_IS_APPROX( VectorType::LinSpaced(size,low,Scalar(low+size-1)), VectorType::LinSpaced(size,Scalar(low+size-1),low).reverse() ); - - if(VectorType::SizeAtCompileTime==Dynamic) - { - // Check negative multiplicator path: - for(Index k=1; k<5; ++k) - VERIFY_IS_APPROX( VectorType::LinSpaced(size,low,Scalar(low+(size-1)*k)), VectorType::LinSpaced(size,Scalar(low+(size-1)*k),low).reverse() ); - // Check negative divisor path: - for(Index k=1; k<5; ++k) - VERIFY_IS_APPROX( VectorType::LinSpaced(size*k,low,Scalar(low+size-1)), VectorType::LinSpaced(size*k,Scalar(low+size-1),low).reverse() ); - } - } - } -} - -template<typename MatrixType> -void testMatrixType(const MatrixType& m) -{ - using std::abs; - const Index rows = m.rows(); - const Index cols = m.cols(); - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - - Scalar s1; - do { - s1 = internal::random<Scalar>(); - } while(abs(s1)<RealScalar(1e-5) && (!NumTraits<Scalar>::IsInteger)); - - MatrixType A; - A.setIdentity(rows, cols); - VERIFY(equalsIdentity(A)); - VERIFY(equalsIdentity(MatrixType::Identity(rows, cols))); - - - A = MatrixType::Constant(rows,cols,s1); - Index i = internal::random<Index>(0,rows-1); - Index j = internal::random<Index>(0,cols-1); - VERIFY_IS_APPROX( MatrixType::Constant(rows,cols,s1)(i,j), s1 ); - VERIFY_IS_APPROX( MatrixType::Constant(rows,cols,s1).coeff(i,j), s1 ); - VERIFY_IS_APPROX( A(i,j), s1 ); -} - -void test_nullary() -{ - CALL_SUBTEST_1( testMatrixType(Matrix2d()) ); - CALL_SUBTEST_2( testMatrixType(MatrixXcf(internal::random<int>(1,300),internal::random<int>(1,300))) ); - CALL_SUBTEST_3( testMatrixType(MatrixXf(internal::random<int>(1,300),internal::random<int>(1,300))) ); - - for(int i = 0; i < g_repeat*10; i++) { - CALL_SUBTEST_4( testVectorType(VectorXd(internal::random<int>(1,30000))) ); - CALL_SUBTEST_5( testVectorType(Vector4d()) ); // regression test for bug 232 - CALL_SUBTEST_6( testVectorType(Vector3d()) ); - CALL_SUBTEST_7( testVectorType(VectorXf(internal::random<int>(1,30000))) ); - CALL_SUBTEST_8( testVectorType(Vector3f()) ); - CALL_SUBTEST_8( testVectorType(Vector4f()) ); - CALL_SUBTEST_8( testVectorType(Matrix<float,8,1>()) ); - CALL_SUBTEST_8( testVectorType(Matrix<float,1,1>()) ); - - CALL_SUBTEST_9( testVectorType(VectorXi(internal::random<int>(1,10))) ); - CALL_SUBTEST_9( testVectorType(VectorXi(internal::random<int>(9,300))) ); - CALL_SUBTEST_9( testVectorType(Matrix<int,1,1>()) ); - } - -#ifdef EIGEN_TEST_PART_6 - // Assignment of a RowVectorXd to a MatrixXd (regression test for bug #79). - VERIFY( (MatrixXd(RowVectorXd::LinSpaced(3, 0, 1)) - RowVector3d(0, 0.5, 1)).norm() < std::numeric_limits<double>::epsilon() ); -#endif - -#ifdef EIGEN_TEST_PART_9 - // Check possible overflow issue - { - int n = 60000; - ArrayXi a1(n), a2(n); - a1.setLinSpaced(n, 0, n-1); - for(int i=0; i<n; ++i) - a2(i) = i; - VERIFY_IS_APPROX(a1,a2); - } -#endif - -#ifdef EIGEN_TEST_PART_10 - // check some internal logic - VERIFY(( internal::has_nullary_operator<internal::scalar_constant_op<double> >::value )); - VERIFY(( !internal::has_unary_operator<internal::scalar_constant_op<double> >::value )); - VERIFY(( !internal::has_binary_operator<internal::scalar_constant_op<double> >::value )); - VERIFY(( internal::functor_has_linear_access<internal::scalar_constant_op<double> >::ret )); - - VERIFY(( !internal::has_nullary_operator<internal::scalar_identity_op<double> >::value )); - VERIFY(( !internal::has_unary_operator<internal::scalar_identity_op<double> >::value )); - VERIFY(( internal::has_binary_operator<internal::scalar_identity_op<double> >::value )); - VERIFY(( !internal::functor_has_linear_access<internal::scalar_identity_op<double> >::ret )); - - VERIFY(( !internal::has_nullary_operator<internal::linspaced_op<float,float> >::value )); - VERIFY(( internal::has_unary_operator<internal::linspaced_op<float,float> >::value )); - VERIFY(( !internal::has_binary_operator<internal::linspaced_op<float,float> >::value )); - VERIFY(( internal::functor_has_linear_access<internal::linspaced_op<float,float> >::ret )); - - // Regression unit test for a weird MSVC bug. - // Search "nullary_wrapper_workaround_msvc" in CoreEvaluators.h for the details. - // See also traits<Ref>::match. - { - MatrixXf A = MatrixXf::Random(3,3); - Ref<const MatrixXf> R = 2.0*A; - VERIFY_IS_APPROX(R, A+A); - - Ref<const MatrixXf> R1 = MatrixXf::Random(3,3)+A; - - VectorXi V = VectorXi::Random(3); - Ref<const VectorXi> R2 = VectorXi::LinSpaced(3,1,3)+V; - VERIFY_IS_APPROX(R2, V+Vector3i(1,2,3)); - - VERIFY(( internal::has_nullary_operator<internal::scalar_constant_op<float> >::value )); - VERIFY(( !internal::has_unary_operator<internal::scalar_constant_op<float> >::value )); - VERIFY(( !internal::has_binary_operator<internal::scalar_constant_op<float> >::value )); - VERIFY(( internal::functor_has_linear_access<internal::scalar_constant_op<float> >::ret )); - - VERIFY(( !internal::has_nullary_operator<internal::linspaced_op<int,int> >::value )); - VERIFY(( internal::has_unary_operator<internal::linspaced_op<int,int> >::value )); - VERIFY(( !internal::has_binary_operator<internal::linspaced_op<int,int> >::value )); - VERIFY(( internal::functor_has_linear_access<internal::linspaced_op<int,int> >::ret )); - } -#endif -} diff --git a/eigen/test/numext.cpp b/eigen/test/numext.cpp deleted file mode 100644 index 3de33e2..0000000 --- a/eigen/test/numext.cpp +++ /dev/null @@ -1,53 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename T> -void check_abs() { - typedef typename NumTraits<T>::Real Real; - - if(NumTraits<T>::IsSigned) - VERIFY_IS_EQUAL(numext::abs(-T(1)), T(1)); - VERIFY_IS_EQUAL(numext::abs(T(0)), T(0)); - VERIFY_IS_EQUAL(numext::abs(T(1)), T(1)); - - for(int k=0; k<g_repeat*100; ++k) - { - T x = internal::random<T>(); - if(!internal::is_same<T,bool>::value) - x = x/Real(2); - if(NumTraits<T>::IsSigned) - { - VERIFY_IS_EQUAL(numext::abs(x), numext::abs(-x)); - VERIFY( numext::abs(-x) >= Real(0)); - } - VERIFY( numext::abs(x) >= Real(0)); - VERIFY_IS_APPROX( numext::abs2(x), numext::abs2(numext::abs(x)) ); - } -} - -void test_numext() { - CALL_SUBTEST( check_abs<bool>() ); - CALL_SUBTEST( check_abs<signed char>() ); - CALL_SUBTEST( check_abs<unsigned char>() ); - CALL_SUBTEST( check_abs<short>() ); - CALL_SUBTEST( check_abs<unsigned short>() ); - CALL_SUBTEST( check_abs<int>() ); - CALL_SUBTEST( check_abs<unsigned int>() ); - CALL_SUBTEST( check_abs<long>() ); - CALL_SUBTEST( check_abs<unsigned long>() ); - CALL_SUBTEST( check_abs<half>() ); - CALL_SUBTEST( check_abs<float>() ); - CALL_SUBTEST( check_abs<double>() ); - CALL_SUBTEST( check_abs<long double>() ); - - CALL_SUBTEST( check_abs<std::complex<float> >() ); - CALL_SUBTEST( check_abs<std::complex<double> >() ); -} diff --git a/eigen/test/packetmath.cpp b/eigen/test/packetmath.cpp deleted file mode 100644 index 7821a17..0000000 --- a/eigen/test/packetmath.cpp +++ /dev/null @@ -1,641 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include "unsupported/Eigen/SpecialFunctions" - -#if defined __GNUC__ && __GNUC__>=6 - #pragma GCC diagnostic ignored "-Wignored-attributes" -#endif -// using namespace Eigen; - -#ifdef EIGEN_VECTORIZE_SSE -const bool g_vectorize_sse = true; -#else -const bool g_vectorize_sse = false; -#endif - -namespace Eigen { -namespace internal { -template<typename T> T negate(const T& x) { return -x; } -} -} - -// NOTE: we disbale inlining for this function to workaround a GCC issue when using -O3 and the i387 FPU. -template<typename Scalar> EIGEN_DONT_INLINE -bool isApproxAbs(const Scalar& a, const Scalar& b, const typename NumTraits<Scalar>::Real& refvalue) -{ - return internal::isMuchSmallerThan(a-b, refvalue); -} - -template<typename Scalar> bool areApproxAbs(const Scalar* a, const Scalar* b, int size, const typename NumTraits<Scalar>::Real& refvalue) -{ - for (int i=0; i<size; ++i) - { - if (!isApproxAbs(a[i],b[i],refvalue)) - { - std::cout << "ref: [" << Map<const Matrix<Scalar,1,Dynamic> >(a,size) << "]" << " != vec: [" << Map<const Matrix<Scalar,1,Dynamic> >(b,size) << "]\n"; - return false; - } - } - return true; -} - -template<typename Scalar> bool areApprox(const Scalar* a, const Scalar* b, int size) -{ - for (int i=0; i<size; ++i) - { - if (a[i]!=b[i] && !internal::isApprox(a[i],b[i])) - { - std::cout << "ref: [" << Map<const Matrix<Scalar,1,Dynamic> >(a,size) << "]" << " != vec: [" << Map<const Matrix<Scalar,1,Dynamic> >(b,size) << "]\n"; - return false; - } - } - return true; -} - -#define CHECK_CWISE1(REFOP, POP) { \ - for (int i=0; i<PacketSize; ++i) \ - ref[i] = REFOP(data1[i]); \ - internal::pstore(data2, POP(internal::pload<Packet>(data1))); \ - VERIFY(areApprox(ref, data2, PacketSize) && #POP); \ -} - -template<bool Cond,typename Packet> -struct packet_helper -{ - template<typename T> - inline Packet load(const T* from) const { return internal::pload<Packet>(from); } - - template<typename T> - inline void store(T* to, const Packet& x) const { internal::pstore(to,x); } -}; - -template<typename Packet> -struct packet_helper<false,Packet> -{ - template<typename T> - inline T load(const T* from) const { return *from; } - - template<typename T> - inline void store(T* to, const T& x) const { *to = x; } -}; - -#define CHECK_CWISE1_IF(COND, REFOP, POP) if(COND) { \ - packet_helper<COND,Packet> h; \ - for (int i=0; i<PacketSize; ++i) \ - ref[i] = REFOP(data1[i]); \ - h.store(data2, POP(h.load(data1))); \ - VERIFY(areApprox(ref, data2, PacketSize) && #POP); \ -} - -#define CHECK_CWISE2_IF(COND, REFOP, POP) if(COND) { \ - packet_helper<COND,Packet> h; \ - for (int i=0; i<PacketSize; ++i) \ - ref[i] = REFOP(data1[i], data1[i+PacketSize]); \ - h.store(data2, POP(h.load(data1),h.load(data1+PacketSize))); \ - VERIFY(areApprox(ref, data2, PacketSize) && #POP); \ -} - -#define REF_ADD(a,b) ((a)+(b)) -#define REF_SUB(a,b) ((a)-(b)) -#define REF_MUL(a,b) ((a)*(b)) -#define REF_DIV(a,b) ((a)/(b)) - -template<typename Scalar> void packetmath() -{ - using std::abs; - typedef internal::packet_traits<Scalar> PacketTraits; - typedef typename PacketTraits::type Packet; - const int PacketSize = PacketTraits::size; - typedef typename NumTraits<Scalar>::Real RealScalar; - - const int max_size = PacketSize > 4 ? PacketSize : 4; - const int size = PacketSize*max_size; - EIGEN_ALIGN_MAX Scalar data1[size]; - EIGEN_ALIGN_MAX Scalar data2[size]; - EIGEN_ALIGN_MAX Packet packets[PacketSize*2]; - EIGEN_ALIGN_MAX Scalar ref[size]; - RealScalar refvalue = 0; - for (int i=0; i<size; ++i) - { - data1[i] = internal::random<Scalar>()/RealScalar(PacketSize); - data2[i] = internal::random<Scalar>()/RealScalar(PacketSize); - refvalue = (std::max)(refvalue,abs(data1[i])); - } - - internal::pstore(data2, internal::pload<Packet>(data1)); - VERIFY(areApprox(data1, data2, PacketSize) && "aligned load/store"); - - for (int offset=0; offset<PacketSize; ++offset) - { - internal::pstore(data2, internal::ploadu<Packet>(data1+offset)); - VERIFY(areApprox(data1+offset, data2, PacketSize) && "internal::ploadu"); - } - - for (int offset=0; offset<PacketSize; ++offset) - { - internal::pstoreu(data2+offset, internal::pload<Packet>(data1)); - VERIFY(areApprox(data1, data2+offset, PacketSize) && "internal::pstoreu"); - } - - for (int offset=0; offset<PacketSize; ++offset) - { - packets[0] = internal::pload<Packet>(data1); - packets[1] = internal::pload<Packet>(data1+PacketSize); - if (offset==0) internal::palign<0>(packets[0], packets[1]); - else if (offset==1) internal::palign<1>(packets[0], packets[1]); - else if (offset==2) internal::palign<2>(packets[0], packets[1]); - else if (offset==3) internal::palign<3>(packets[0], packets[1]); - else if (offset==4) internal::palign<4>(packets[0], packets[1]); - else if (offset==5) internal::palign<5>(packets[0], packets[1]); - else if (offset==6) internal::palign<6>(packets[0], packets[1]); - else if (offset==7) internal::palign<7>(packets[0], packets[1]); - else if (offset==8) internal::palign<8>(packets[0], packets[1]); - else if (offset==9) internal::palign<9>(packets[0], packets[1]); - else if (offset==10) internal::palign<10>(packets[0], packets[1]); - else if (offset==11) internal::palign<11>(packets[0], packets[1]); - else if (offset==12) internal::palign<12>(packets[0], packets[1]); - else if (offset==13) internal::palign<13>(packets[0], packets[1]); - else if (offset==14) internal::palign<14>(packets[0], packets[1]); - else if (offset==15) internal::palign<15>(packets[0], packets[1]); - internal::pstore(data2, packets[0]); - - for (int i=0; i<PacketSize; ++i) - ref[i] = data1[i+offset]; - - VERIFY(areApprox(ref, data2, PacketSize) && "internal::palign"); - } - - VERIFY((!PacketTraits::Vectorizable) || PacketTraits::HasAdd); - VERIFY((!PacketTraits::Vectorizable) || PacketTraits::HasSub); - VERIFY((!PacketTraits::Vectorizable) || PacketTraits::HasMul); - VERIFY((!PacketTraits::Vectorizable) || PacketTraits::HasNegate); - VERIFY((internal::is_same<Scalar,int>::value) || (!PacketTraits::Vectorizable) || PacketTraits::HasDiv); - - CHECK_CWISE2_IF(PacketTraits::HasAdd, REF_ADD, internal::padd); - CHECK_CWISE2_IF(PacketTraits::HasSub, REF_SUB, internal::psub); - CHECK_CWISE2_IF(PacketTraits::HasMul, REF_MUL, internal::pmul); - CHECK_CWISE2_IF(PacketTraits::HasDiv, REF_DIV, internal::pdiv); - - CHECK_CWISE1(internal::negate, internal::pnegate); - CHECK_CWISE1(numext::conj, internal::pconj); - - for(int offset=0;offset<3;++offset) - { - for (int i=0; i<PacketSize; ++i) - ref[i] = data1[offset]; - internal::pstore(data2, internal::pset1<Packet>(data1[offset])); - VERIFY(areApprox(ref, data2, PacketSize) && "internal::pset1"); - } - - { - for (int i=0; i<PacketSize*4; ++i) - ref[i] = data1[i/PacketSize]; - Packet A0, A1, A2, A3; - internal::pbroadcast4<Packet>(data1, A0, A1, A2, A3); - internal::pstore(data2+0*PacketSize, A0); - internal::pstore(data2+1*PacketSize, A1); - internal::pstore(data2+2*PacketSize, A2); - internal::pstore(data2+3*PacketSize, A3); - VERIFY(areApprox(ref, data2, 4*PacketSize) && "internal::pbroadcast4"); - } - - { - for (int i=0; i<PacketSize*2; ++i) - ref[i] = data1[i/PacketSize]; - Packet A0, A1; - internal::pbroadcast2<Packet>(data1, A0, A1); - internal::pstore(data2+0*PacketSize, A0); - internal::pstore(data2+1*PacketSize, A1); - VERIFY(areApprox(ref, data2, 2*PacketSize) && "internal::pbroadcast2"); - } - - VERIFY(internal::isApprox(data1[0], internal::pfirst(internal::pload<Packet>(data1))) && "internal::pfirst"); - - if(PacketSize>1) - { - for(int offset=0;offset<4;++offset) - { - for(int i=0;i<PacketSize/2;++i) - ref[2*i+0] = ref[2*i+1] = data1[offset+i]; - internal::pstore(data2,internal::ploaddup<Packet>(data1+offset)); - VERIFY(areApprox(ref, data2, PacketSize) && "ploaddup"); - } - } - - if(PacketSize>2) - { - for(int offset=0;offset<4;++offset) - { - for(int i=0;i<PacketSize/4;++i) - ref[4*i+0] = ref[4*i+1] = ref[4*i+2] = ref[4*i+3] = data1[offset+i]; - internal::pstore(data2,internal::ploadquad<Packet>(data1+offset)); - VERIFY(areApprox(ref, data2, PacketSize) && "ploadquad"); - } - } - - ref[0] = 0; - for (int i=0; i<PacketSize; ++i) - ref[0] += data1[i]; - VERIFY(isApproxAbs(ref[0], internal::predux(internal::pload<Packet>(data1)), refvalue) && "internal::predux"); - - { - for (int i=0; i<4; ++i) - ref[i] = 0; - for (int i=0; i<PacketSize; ++i) - ref[i%4] += data1[i]; - internal::pstore(data2, internal::predux_downto4(internal::pload<Packet>(data1))); - VERIFY(areApprox(ref, data2, PacketSize>4?PacketSize/2:PacketSize) && "internal::predux_downto4"); - } - - ref[0] = 1; - for (int i=0; i<PacketSize; ++i) - ref[0] *= data1[i]; - VERIFY(internal::isApprox(ref[0], internal::predux_mul(internal::pload<Packet>(data1))) && "internal::predux_mul"); - - for (int j=0; j<PacketSize; ++j) - { - ref[j] = 0; - for (int i=0; i<PacketSize; ++i) - ref[j] += data1[i+j*PacketSize]; - packets[j] = internal::pload<Packet>(data1+j*PacketSize); - } - internal::pstore(data2, internal::preduxp(packets)); - VERIFY(areApproxAbs(ref, data2, PacketSize, refvalue) && "internal::preduxp"); - - for (int i=0; i<PacketSize; ++i) - ref[i] = data1[PacketSize-i-1]; - internal::pstore(data2, internal::preverse(internal::pload<Packet>(data1))); - VERIFY(areApprox(ref, data2, PacketSize) && "internal::preverse"); - - internal::PacketBlock<Packet> kernel; - for (int i=0; i<PacketSize; ++i) { - kernel.packet[i] = internal::pload<Packet>(data1+i*PacketSize); - } - ptranspose(kernel); - for (int i=0; i<PacketSize; ++i) { - internal::pstore(data2, kernel.packet[i]); - for (int j = 0; j < PacketSize; ++j) { - VERIFY(isApproxAbs(data2[j], data1[i+j*PacketSize], refvalue) && "ptranspose"); - } - } - - if (PacketTraits::HasBlend) { - Packet thenPacket = internal::pload<Packet>(data1); - Packet elsePacket = internal::pload<Packet>(data2); - EIGEN_ALIGN_MAX internal::Selector<PacketSize> selector; - for (int i = 0; i < PacketSize; ++i) { - selector.select[i] = i; - } - - Packet blend = internal::pblend(selector, thenPacket, elsePacket); - EIGEN_ALIGN_MAX Scalar result[size]; - internal::pstore(result, blend); - for (int i = 0; i < PacketSize; ++i) { - VERIFY(isApproxAbs(result[i], (selector.select[i] ? data1[i] : data2[i]), refvalue)); - } - } - - if (PacketTraits::HasBlend || g_vectorize_sse) { - // pinsertfirst - for (int i=0; i<PacketSize; ++i) - ref[i] = data1[i]; - Scalar s = internal::random<Scalar>(); - ref[0] = s; - internal::pstore(data2, internal::pinsertfirst(internal::pload<Packet>(data1),s)); - VERIFY(areApprox(ref, data2, PacketSize) && "internal::pinsertfirst"); - } - - if (PacketTraits::HasBlend || g_vectorize_sse) { - // pinsertlast - for (int i=0; i<PacketSize; ++i) - ref[i] = data1[i]; - Scalar s = internal::random<Scalar>(); - ref[PacketSize-1] = s; - internal::pstore(data2, internal::pinsertlast(internal::pload<Packet>(data1),s)); - VERIFY(areApprox(ref, data2, PacketSize) && "internal::pinsertlast"); - } -} - -template<typename Scalar> void packetmath_real() -{ - using std::abs; - typedef internal::packet_traits<Scalar> PacketTraits; - typedef typename PacketTraits::type Packet; - const int PacketSize = PacketTraits::size; - - const int size = PacketSize*4; - EIGEN_ALIGN_MAX Scalar data1[PacketTraits::size*4]; - EIGEN_ALIGN_MAX Scalar data2[PacketTraits::size*4]; - EIGEN_ALIGN_MAX Scalar ref[PacketTraits::size*4]; - - for (int i=0; i<size; ++i) - { - data1[i] = internal::random<Scalar>(-1,1) * std::pow(Scalar(10), internal::random<Scalar>(-3,3)); - data2[i] = internal::random<Scalar>(-1,1) * std::pow(Scalar(10), internal::random<Scalar>(-3,3)); - } - CHECK_CWISE1_IF(PacketTraits::HasSin, std::sin, internal::psin); - CHECK_CWISE1_IF(PacketTraits::HasCos, std::cos, internal::pcos); - CHECK_CWISE1_IF(PacketTraits::HasTan, std::tan, internal::ptan); - - CHECK_CWISE1_IF(PacketTraits::HasRound, numext::round, internal::pround); - CHECK_CWISE1_IF(PacketTraits::HasCeil, numext::ceil, internal::pceil); - CHECK_CWISE1_IF(PacketTraits::HasFloor, numext::floor, internal::pfloor); - - for (int i=0; i<size; ++i) - { - data1[i] = internal::random<Scalar>(-1,1); - data2[i] = internal::random<Scalar>(-1,1); - } - CHECK_CWISE1_IF(PacketTraits::HasASin, std::asin, internal::pasin); - CHECK_CWISE1_IF(PacketTraits::HasACos, std::acos, internal::pacos); - - for (int i=0; i<size; ++i) - { - data1[i] = internal::random<Scalar>(-87,88); - data2[i] = internal::random<Scalar>(-87,88); - } - CHECK_CWISE1_IF(PacketTraits::HasExp, std::exp, internal::pexp); - for (int i=0; i<size; ++i) - { - data1[i] = internal::random<Scalar>(-1,1) * std::pow(Scalar(10), internal::random<Scalar>(-6,6)); - data2[i] = internal::random<Scalar>(-1,1) * std::pow(Scalar(10), internal::random<Scalar>(-6,6)); - } - CHECK_CWISE1_IF(PacketTraits::HasTanh, std::tanh, internal::ptanh); - if(PacketTraits::HasExp && PacketTraits::size>=2) - { - data1[0] = std::numeric_limits<Scalar>::quiet_NaN(); - data1[1] = std::numeric_limits<Scalar>::epsilon(); - packet_helper<PacketTraits::HasExp,Packet> h; - h.store(data2, internal::pexp(h.load(data1))); - VERIFY((numext::isnan)(data2[0])); - VERIFY_IS_EQUAL(std::exp(std::numeric_limits<Scalar>::epsilon()), data2[1]); - - data1[0] = -std::numeric_limits<Scalar>::epsilon(); - data1[1] = 0; - h.store(data2, internal::pexp(h.load(data1))); - VERIFY_IS_EQUAL(std::exp(-std::numeric_limits<Scalar>::epsilon()), data2[0]); - VERIFY_IS_EQUAL(std::exp(Scalar(0)), data2[1]); - - data1[0] = (std::numeric_limits<Scalar>::min)(); - data1[1] = -(std::numeric_limits<Scalar>::min)(); - h.store(data2, internal::pexp(h.load(data1))); - VERIFY_IS_EQUAL(std::exp((std::numeric_limits<Scalar>::min)()), data2[0]); - VERIFY_IS_EQUAL(std::exp(-(std::numeric_limits<Scalar>::min)()), data2[1]); - - data1[0] = std::numeric_limits<Scalar>::denorm_min(); - data1[1] = -std::numeric_limits<Scalar>::denorm_min(); - h.store(data2, internal::pexp(h.load(data1))); - VERIFY_IS_EQUAL(std::exp(std::numeric_limits<Scalar>::denorm_min()), data2[0]); - VERIFY_IS_EQUAL(std::exp(-std::numeric_limits<Scalar>::denorm_min()), data2[1]); - } - - if (PacketTraits::HasTanh) { - // NOTE this test migh fail with GCC prior to 6.3, see MathFunctionsImpl.h for details. - data1[0] = std::numeric_limits<Scalar>::quiet_NaN(); - packet_helper<internal::packet_traits<Scalar>::HasTanh,Packet> h; - h.store(data2, internal::ptanh(h.load(data1))); - VERIFY((numext::isnan)(data2[0])); - } - -#if EIGEN_HAS_C99_MATH - { - data1[0] = std::numeric_limits<Scalar>::quiet_NaN(); - packet_helper<internal::packet_traits<Scalar>::HasLGamma,Packet> h; - h.store(data2, internal::plgamma(h.load(data1))); - VERIFY((numext::isnan)(data2[0])); - } - { - data1[0] = std::numeric_limits<Scalar>::quiet_NaN(); - packet_helper<internal::packet_traits<Scalar>::HasErf,Packet> h; - h.store(data2, internal::perf(h.load(data1))); - VERIFY((numext::isnan)(data2[0])); - } - { - data1[0] = std::numeric_limits<Scalar>::quiet_NaN(); - packet_helper<internal::packet_traits<Scalar>::HasErfc,Packet> h; - h.store(data2, internal::perfc(h.load(data1))); - VERIFY((numext::isnan)(data2[0])); - } -#endif // EIGEN_HAS_C99_MATH - - for (int i=0; i<size; ++i) - { - data1[i] = internal::random<Scalar>(0,1) * std::pow(Scalar(10), internal::random<Scalar>(-6,6)); - data2[i] = internal::random<Scalar>(0,1) * std::pow(Scalar(10), internal::random<Scalar>(-6,6)); - } - - if(internal::random<float>(0,1)<0.1f) - data1[internal::random<int>(0, PacketSize)] = 0; - CHECK_CWISE1_IF(PacketTraits::HasSqrt, std::sqrt, internal::psqrt); - CHECK_CWISE1_IF(PacketTraits::HasLog, std::log, internal::plog); -#if EIGEN_HAS_C99_MATH && (__cplusplus > 199711L) - CHECK_CWISE1_IF(PacketTraits::HasLog1p, std::log1p, internal::plog1p); - CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasLGamma, std::lgamma, internal::plgamma); - CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasErf, std::erf, internal::perf); - CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasErfc, std::erfc, internal::perfc); -#endif - - if(PacketTraits::HasLog && PacketTraits::size>=2) - { - data1[0] = std::numeric_limits<Scalar>::quiet_NaN(); - data1[1] = std::numeric_limits<Scalar>::epsilon(); - packet_helper<PacketTraits::HasLog,Packet> h; - h.store(data2, internal::plog(h.load(data1))); - VERIFY((numext::isnan)(data2[0])); - VERIFY_IS_EQUAL(std::log(std::numeric_limits<Scalar>::epsilon()), data2[1]); - - data1[0] = -std::numeric_limits<Scalar>::epsilon(); - data1[1] = 0; - h.store(data2, internal::plog(h.load(data1))); - VERIFY((numext::isnan)(data2[0])); - VERIFY_IS_EQUAL(std::log(Scalar(0)), data2[1]); - - data1[0] = (std::numeric_limits<Scalar>::min)(); - data1[1] = -(std::numeric_limits<Scalar>::min)(); - h.store(data2, internal::plog(h.load(data1))); - VERIFY_IS_EQUAL(std::log((std::numeric_limits<Scalar>::min)()), data2[0]); - VERIFY((numext::isnan)(data2[1])); - - data1[0] = std::numeric_limits<Scalar>::denorm_min(); - data1[1] = -std::numeric_limits<Scalar>::denorm_min(); - h.store(data2, internal::plog(h.load(data1))); - // VERIFY_IS_EQUAL(std::log(std::numeric_limits<Scalar>::denorm_min()), data2[0]); - VERIFY((numext::isnan)(data2[1])); - - data1[0] = Scalar(-1.0f); - h.store(data2, internal::plog(h.load(data1))); - VERIFY((numext::isnan)(data2[0])); - h.store(data2, internal::psqrt(h.load(data1))); - VERIFY((numext::isnan)(data2[0])); - VERIFY((numext::isnan)(data2[1])); - } -} - -template<typename Scalar> void packetmath_notcomplex() -{ - using std::abs; - typedef internal::packet_traits<Scalar> PacketTraits; - typedef typename PacketTraits::type Packet; - const int PacketSize = PacketTraits::size; - - EIGEN_ALIGN_MAX Scalar data1[PacketTraits::size*4]; - EIGEN_ALIGN_MAX Scalar data2[PacketTraits::size*4]; - EIGEN_ALIGN_MAX Scalar ref[PacketTraits::size*4]; - - Array<Scalar,Dynamic,1>::Map(data1, PacketTraits::size*4).setRandom(); - - ref[0] = data1[0]; - for (int i=0; i<PacketSize; ++i) - ref[0] = (std::min)(ref[0],data1[i]); - VERIFY(internal::isApprox(ref[0], internal::predux_min(internal::pload<Packet>(data1))) && "internal::predux_min"); - - VERIFY((!PacketTraits::Vectorizable) || PacketTraits::HasMin); - VERIFY((!PacketTraits::Vectorizable) || PacketTraits::HasMax); - - CHECK_CWISE2_IF(PacketTraits::HasMin, (std::min), internal::pmin); - CHECK_CWISE2_IF(PacketTraits::HasMax, (std::max), internal::pmax); - CHECK_CWISE1(abs, internal::pabs); - - ref[0] = data1[0]; - for (int i=0; i<PacketSize; ++i) - ref[0] = (std::max)(ref[0],data1[i]); - VERIFY(internal::isApprox(ref[0], internal::predux_max(internal::pload<Packet>(data1))) && "internal::predux_max"); - - for (int i=0; i<PacketSize; ++i) - ref[i] = data1[0]+Scalar(i); - internal::pstore(data2, internal::plset<Packet>(data1[0])); - VERIFY(areApprox(ref, data2, PacketSize) && "internal::plset"); -} - -template<typename Scalar,bool ConjLhs,bool ConjRhs> void test_conj_helper(Scalar* data1, Scalar* data2, Scalar* ref, Scalar* pval) -{ - typedef internal::packet_traits<Scalar> PacketTraits; - typedef typename PacketTraits::type Packet; - const int PacketSize = PacketTraits::size; - - internal::conj_if<ConjLhs> cj0; - internal::conj_if<ConjRhs> cj1; - internal::conj_helper<Scalar,Scalar,ConjLhs,ConjRhs> cj; - internal::conj_helper<Packet,Packet,ConjLhs,ConjRhs> pcj; - - for(int i=0;i<PacketSize;++i) - { - ref[i] = cj0(data1[i]) * cj1(data2[i]); - VERIFY(internal::isApprox(ref[i], cj.pmul(data1[i],data2[i])) && "conj_helper pmul"); - } - internal::pstore(pval,pcj.pmul(internal::pload<Packet>(data1),internal::pload<Packet>(data2))); - VERIFY(areApprox(ref, pval, PacketSize) && "conj_helper pmul"); - - for(int i=0;i<PacketSize;++i) - { - Scalar tmp = ref[i]; - ref[i] += cj0(data1[i]) * cj1(data2[i]); - VERIFY(internal::isApprox(ref[i], cj.pmadd(data1[i],data2[i],tmp)) && "conj_helper pmadd"); - } - internal::pstore(pval,pcj.pmadd(internal::pload<Packet>(data1),internal::pload<Packet>(data2),internal::pload<Packet>(pval))); - VERIFY(areApprox(ref, pval, PacketSize) && "conj_helper pmadd"); -} - -template<typename Scalar> void packetmath_complex() -{ - typedef internal::packet_traits<Scalar> PacketTraits; - typedef typename PacketTraits::type Packet; - const int PacketSize = PacketTraits::size; - - const int size = PacketSize*4; - EIGEN_ALIGN_MAX Scalar data1[PacketSize*4]; - EIGEN_ALIGN_MAX Scalar data2[PacketSize*4]; - EIGEN_ALIGN_MAX Scalar ref[PacketSize*4]; - EIGEN_ALIGN_MAX Scalar pval[PacketSize*4]; - - for (int i=0; i<size; ++i) - { - data1[i] = internal::random<Scalar>() * Scalar(1e2); - data2[i] = internal::random<Scalar>() * Scalar(1e2); - } - - test_conj_helper<Scalar,false,false> (data1,data2,ref,pval); - test_conj_helper<Scalar,false,true> (data1,data2,ref,pval); - test_conj_helper<Scalar,true,false> (data1,data2,ref,pval); - test_conj_helper<Scalar,true,true> (data1,data2,ref,pval); - - { - for(int i=0;i<PacketSize;++i) - ref[i] = Scalar(std::imag(data1[i]),std::real(data1[i])); - internal::pstore(pval,internal::pcplxflip(internal::pload<Packet>(data1))); - VERIFY(areApprox(ref, pval, PacketSize) && "pcplxflip"); - } -} - -template<typename Scalar> void packetmath_scatter_gather() -{ - typedef internal::packet_traits<Scalar> PacketTraits; - typedef typename PacketTraits::type Packet; - typedef typename NumTraits<Scalar>::Real RealScalar; - const int PacketSize = PacketTraits::size; - EIGEN_ALIGN_MAX Scalar data1[PacketSize]; - RealScalar refvalue = 0; - for (int i=0; i<PacketSize; ++i) { - data1[i] = internal::random<Scalar>()/RealScalar(PacketSize); - } - - int stride = internal::random<int>(1,20); - - EIGEN_ALIGN_MAX Scalar buffer[PacketSize*20]; - memset(buffer, 0, 20*PacketSize*sizeof(Scalar)); - Packet packet = internal::pload<Packet>(data1); - internal::pscatter<Scalar, Packet>(buffer, packet, stride); - - for (int i = 0; i < PacketSize*20; ++i) { - if ((i%stride) == 0 && i<stride*PacketSize) { - VERIFY(isApproxAbs(buffer[i], data1[i/stride], refvalue) && "pscatter"); - } else { - VERIFY(isApproxAbs(buffer[i], Scalar(0), refvalue) && "pscatter"); - } - } - - for (int i=0; i<PacketSize*7; ++i) { - buffer[i] = internal::random<Scalar>()/RealScalar(PacketSize); - } - packet = internal::pgather<Scalar, Packet>(buffer, 7); - internal::pstore(data1, packet); - for (int i = 0; i < PacketSize; ++i) { - VERIFY(isApproxAbs(data1[i], buffer[i*7], refvalue) && "pgather"); - } -} - -void test_packetmath() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( packetmath<float>() ); - CALL_SUBTEST_2( packetmath<double>() ); - CALL_SUBTEST_3( packetmath<int>() ); - CALL_SUBTEST_4( packetmath<std::complex<float> >() ); - CALL_SUBTEST_5( packetmath<std::complex<double> >() ); - - CALL_SUBTEST_1( packetmath_notcomplex<float>() ); - CALL_SUBTEST_2( packetmath_notcomplex<double>() ); - CALL_SUBTEST_3( packetmath_notcomplex<int>() ); - - CALL_SUBTEST_1( packetmath_real<float>() ); - CALL_SUBTEST_2( packetmath_real<double>() ); - - CALL_SUBTEST_4( packetmath_complex<std::complex<float> >() ); - CALL_SUBTEST_5( packetmath_complex<std::complex<double> >() ); - - CALL_SUBTEST_1( packetmath_scatter_gather<float>() ); - CALL_SUBTEST_2( packetmath_scatter_gather<double>() ); - CALL_SUBTEST_3( packetmath_scatter_gather<int>() ); - CALL_SUBTEST_4( packetmath_scatter_gather<std::complex<float> >() ); - CALL_SUBTEST_5( packetmath_scatter_gather<std::complex<double> >() ); - } -} diff --git a/eigen/test/pardiso_support.cpp b/eigen/test/pardiso_support.cpp deleted file mode 100644 index 67efad6..0000000 --- a/eigen/test/pardiso_support.cpp +++ /dev/null @@ -1,29 +0,0 @@ -/* - Intel Copyright (C) .... -*/ - -#include "sparse_solver.h" -#include <Eigen/PardisoSupport> - -template<typename T> void test_pardiso_T() -{ - PardisoLLT < SparseMatrix<T, RowMajor>, Lower> pardiso_llt_lower; - PardisoLLT < SparseMatrix<T, RowMajor>, Upper> pardiso_llt_upper; - PardisoLDLT < SparseMatrix<T, RowMajor>, Lower> pardiso_ldlt_lower; - PardisoLDLT < SparseMatrix<T, RowMajor>, Upper> pardiso_ldlt_upper; - PardisoLU < SparseMatrix<T, RowMajor> > pardiso_lu; - - check_sparse_spd_solving(pardiso_llt_lower); - check_sparse_spd_solving(pardiso_llt_upper); - check_sparse_spd_solving(pardiso_ldlt_lower); - check_sparse_spd_solving(pardiso_ldlt_upper); - check_sparse_square_solving(pardiso_lu); -} - -void test_pardiso_support() -{ - CALL_SUBTEST_1(test_pardiso_T<float>()); - CALL_SUBTEST_2(test_pardiso_T<double>()); - CALL_SUBTEST_3(test_pardiso_T< std::complex<float> >()); - CALL_SUBTEST_4(test_pardiso_T< std::complex<double> >()); -} diff --git a/eigen/test/pastix_support.cpp b/eigen/test/pastix_support.cpp deleted file mode 100644 index b62f857..0000000 --- a/eigen/test/pastix_support.cpp +++ /dev/null @@ -1,54 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2012 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_NO_DEBUG_SMALL_PRODUCT_BLOCKS -#include "sparse_solver.h" -#include <Eigen/PaStiXSupport> -#include <unsupported/Eigen/SparseExtra> - - -template<typename T> void test_pastix_T() -{ - PastixLLT< SparseMatrix<T, ColMajor>, Eigen::Lower > pastix_llt_lower; - PastixLDLT< SparseMatrix<T, ColMajor>, Eigen::Lower > pastix_ldlt_lower; - PastixLLT< SparseMatrix<T, ColMajor>, Eigen::Upper > pastix_llt_upper; - PastixLDLT< SparseMatrix<T, ColMajor>, Eigen::Upper > pastix_ldlt_upper; - PastixLU< SparseMatrix<T, ColMajor> > pastix_lu; - - check_sparse_spd_solving(pastix_llt_lower); - check_sparse_spd_solving(pastix_ldlt_lower); - check_sparse_spd_solving(pastix_llt_upper); - check_sparse_spd_solving(pastix_ldlt_upper); - check_sparse_square_solving(pastix_lu); - - // Some compilation check: - pastix_llt_lower.iparm(); - pastix_llt_lower.dparm(); - pastix_ldlt_lower.iparm(); - pastix_ldlt_lower.dparm(); - pastix_lu.iparm(); - pastix_lu.dparm(); -} - -// There is no support for selfadjoint matrices with PaStiX. -// Complex symmetric matrices should pass though -template<typename T> void test_pastix_T_LU() -{ - PastixLU< SparseMatrix<T, ColMajor> > pastix_lu; - check_sparse_square_solving(pastix_lu); -} - -void test_pastix_support() -{ - CALL_SUBTEST_1(test_pastix_T<float>()); - CALL_SUBTEST_2(test_pastix_T<double>()); - CALL_SUBTEST_3( (test_pastix_T_LU<std::complex<float> >()) ); - CALL_SUBTEST_4(test_pastix_T_LU<std::complex<double> >()); -} diff --git a/eigen/test/permutationmatrices.cpp b/eigen/test/permutationmatrices.cpp deleted file mode 100644 index e885f0e..0000000 --- a/eigen/test/permutationmatrices.cpp +++ /dev/null @@ -1,167 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define TEST_ENABLE_TEMPORARY_TRACKING - -#include "main.h" - -using namespace std; -template<typename MatrixType> void permutationmatrices(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime, - Options = MatrixType::Options }; - typedef PermutationMatrix<Rows> LeftPermutationType; - typedef Transpositions<Rows> LeftTranspositionsType; - typedef Matrix<int, Rows, 1> LeftPermutationVectorType; - typedef Map<LeftPermutationType> MapLeftPerm; - typedef PermutationMatrix<Cols> RightPermutationType; - typedef Transpositions<Cols> RightTranspositionsType; - typedef Matrix<int, Cols, 1> RightPermutationVectorType; - typedef Map<RightPermutationType> MapRightPerm; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m_original = MatrixType::Random(rows,cols); - LeftPermutationVectorType lv; - randomPermutationVector(lv, rows); - LeftPermutationType lp(lv); - RightPermutationVectorType rv; - randomPermutationVector(rv, cols); - RightPermutationType rp(rv); - LeftTranspositionsType lt(lv); - RightTranspositionsType rt(rv); - MatrixType m_permuted = MatrixType::Random(rows,cols); - - VERIFY_EVALUATION_COUNT(m_permuted = lp * m_original * rp, 1); // 1 temp for sub expression "lp * m_original" - - for (int i=0; i<rows; i++) - for (int j=0; j<cols; j++) - VERIFY_IS_APPROX(m_permuted(lv(i),j), m_original(i,rv(j))); - - Matrix<Scalar,Rows,Rows> lm(lp); - Matrix<Scalar,Cols,Cols> rm(rp); - - VERIFY_IS_APPROX(m_permuted, lm*m_original*rm); - - m_permuted = m_original; - VERIFY_EVALUATION_COUNT(m_permuted = lp * m_permuted * rp, 1); - VERIFY_IS_APPROX(m_permuted, lm*m_original*rm); - - VERIFY_IS_APPROX(lp.inverse()*m_permuted*rp.inverse(), m_original); - VERIFY_IS_APPROX(lv.asPermutation().inverse()*m_permuted*rv.asPermutation().inverse(), m_original); - VERIFY_IS_APPROX(MapLeftPerm(lv.data(),lv.size()).inverse()*m_permuted*MapRightPerm(rv.data(),rv.size()).inverse(), m_original); - - VERIFY((lp*lp.inverse()).toDenseMatrix().isIdentity()); - VERIFY((lv.asPermutation()*lv.asPermutation().inverse()).toDenseMatrix().isIdentity()); - VERIFY((MapLeftPerm(lv.data(),lv.size())*MapLeftPerm(lv.data(),lv.size()).inverse()).toDenseMatrix().isIdentity()); - - LeftPermutationVectorType lv2; - randomPermutationVector(lv2, rows); - LeftPermutationType lp2(lv2); - Matrix<Scalar,Rows,Rows> lm2(lp2); - VERIFY_IS_APPROX((lp*lp2).toDenseMatrix().template cast<Scalar>(), lm*lm2); - VERIFY_IS_APPROX((lv.asPermutation()*lv2.asPermutation()).toDenseMatrix().template cast<Scalar>(), lm*lm2); - VERIFY_IS_APPROX((MapLeftPerm(lv.data(),lv.size())*MapLeftPerm(lv2.data(),lv2.size())).toDenseMatrix().template cast<Scalar>(), lm*lm2); - - LeftPermutationType identityp; - identityp.setIdentity(rows); - VERIFY_IS_APPROX(m_original, identityp*m_original); - - // check inplace permutations - m_permuted = m_original; - VERIFY_EVALUATION_COUNT(m_permuted.noalias()= lp.inverse() * m_permuted, 1); // 1 temp to allocate the mask - VERIFY_IS_APPROX(m_permuted, lp.inverse()*m_original); - - m_permuted = m_original; - VERIFY_EVALUATION_COUNT(m_permuted.noalias() = m_permuted * rp.inverse(), 1); // 1 temp to allocate the mask - VERIFY_IS_APPROX(m_permuted, m_original*rp.inverse()); - - m_permuted = m_original; - VERIFY_EVALUATION_COUNT(m_permuted.noalias() = lp * m_permuted, 1); // 1 temp to allocate the mask - VERIFY_IS_APPROX(m_permuted, lp*m_original); - - m_permuted = m_original; - VERIFY_EVALUATION_COUNT(m_permuted.noalias() = m_permuted * rp, 1); // 1 temp to allocate the mask - VERIFY_IS_APPROX(m_permuted, m_original*rp); - - if(rows>1 && cols>1) - { - lp2 = lp; - Index i = internal::random<Index>(0, rows-1); - Index j; - do j = internal::random<Index>(0, rows-1); while(j==i); - lp2.applyTranspositionOnTheLeft(i, j); - lm = lp; - lm.row(i).swap(lm.row(j)); - VERIFY_IS_APPROX(lm, lp2.toDenseMatrix().template cast<Scalar>()); - - RightPermutationType rp2 = rp; - i = internal::random<Index>(0, cols-1); - do j = internal::random<Index>(0, cols-1); while(j==i); - rp2.applyTranspositionOnTheRight(i, j); - rm = rp; - rm.col(i).swap(rm.col(j)); - VERIFY_IS_APPROX(rm, rp2.toDenseMatrix().template cast<Scalar>()); - } - - { - // simple compilation check - Matrix<Scalar, Cols, Cols> A = rp; - Matrix<Scalar, Cols, Cols> B = rp.transpose(); - VERIFY_IS_APPROX(A, B.transpose()); - } - - m_permuted = m_original; - lp = lt; - rp = rt; - VERIFY_EVALUATION_COUNT(m_permuted = lt * m_permuted * rt, 1); - VERIFY_IS_APPROX(m_permuted, lp*m_original*rp.transpose()); - - VERIFY_IS_APPROX(lt.inverse()*m_permuted*rt.inverse(), m_original); -} - -template<typename T> -void bug890() -{ - typedef Matrix<T, Dynamic, Dynamic> MatrixType; - typedef Matrix<T, Dynamic, 1> VectorType; - typedef Stride<Dynamic,Dynamic> S; - typedef Map<MatrixType, Aligned, S> MapType; - typedef PermutationMatrix<Dynamic> Perm; - - VectorType v1(2), v2(2), op(4), rhs(2); - v1 << 666,667; - op << 1,0,0,1; - rhs << 42,42; - - Perm P(2); - P.indices() << 1, 0; - - MapType(v1.data(),2,1,S(1,1)) = P * MapType(rhs.data(),2,1,S(1,1)); - VERIFY_IS_APPROX(v1, (P * rhs).eval()); - - MapType(v1.data(),2,1,S(1,1)) = P.inverse() * MapType(rhs.data(),2,1,S(1,1)); - VERIFY_IS_APPROX(v1, (P.inverse() * rhs).eval()); -} - -void test_permutationmatrices() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( permutationmatrices(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( permutationmatrices(Matrix3f()) ); - CALL_SUBTEST_3( permutationmatrices(Matrix<double,3,3,RowMajor>()) ); - CALL_SUBTEST_4( permutationmatrices(Matrix4d()) ); - CALL_SUBTEST_5( permutationmatrices(Matrix<double,40,60>()) ); - CALL_SUBTEST_6( permutationmatrices(Matrix<double,Dynamic,Dynamic,RowMajor>(20, 30)) ); - CALL_SUBTEST_7( permutationmatrices(MatrixXcf(15, 10)) ); - } - CALL_SUBTEST_5( bug890<double>() ); -} diff --git a/eigen/test/prec_inverse_4x4.cpp b/eigen/test/prec_inverse_4x4.cpp deleted file mode 100644 index eb6ad18..0000000 --- a/eigen/test/prec_inverse_4x4.cpp +++ /dev/null @@ -1,83 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/LU> -#include <algorithm> - -template<typename MatrixType> void inverse_permutation_4x4() -{ - typedef typename MatrixType::Scalar Scalar; - Vector4i indices(0,1,2,3); - for(int i = 0; i < 24; ++i) - { - MatrixType m = PermutationMatrix<4>(indices); - MatrixType inv = m.inverse(); - double error = double( (m*inv-MatrixType::Identity()).norm() / NumTraits<Scalar>::epsilon() ); - EIGEN_DEBUG_VAR(error) - VERIFY(error == 0.0); - std::next_permutation(indices.data(),indices.data()+4); - } -} - -template<typename MatrixType> void inverse_general_4x4(int repeat) -{ - using std::abs; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - double error_sum = 0., error_max = 0.; - for(int i = 0; i < repeat; ++i) - { - MatrixType m; - RealScalar absdet; - do { - m = MatrixType::Random(); - absdet = abs(m.determinant()); - } while(absdet < NumTraits<Scalar>::epsilon()); - MatrixType inv = m.inverse(); - double error = double( (m*inv-MatrixType::Identity()).norm() * absdet / NumTraits<Scalar>::epsilon() ); - error_sum += error; - error_max = (std::max)(error_max, error); - } - std::cerr << "inverse_general_4x4, Scalar = " << type_name<Scalar>() << std::endl; - double error_avg = error_sum / repeat; - EIGEN_DEBUG_VAR(error_avg); - EIGEN_DEBUG_VAR(error_max); - // FIXME that 1.25 used to be a 1.0 until the NumTraits changes on 28 April 2010, what's going wrong?? - // FIXME that 1.25 used to be 1.2 until we tested gcc 4.1 on 30 June 2010 and got 1.21. - VERIFY(error_avg < (NumTraits<Scalar>::IsComplex ? 8.0 : 1.25)); - VERIFY(error_max < (NumTraits<Scalar>::IsComplex ? 64.0 : 20.0)); - - { - int s = 5;//internal::random<int>(4,10); - int i = 0;//internal::random<int>(0,s-4); - int j = 0;//internal::random<int>(0,s-4); - Matrix<Scalar,5,5> mat(s,s); - mat.setRandom(); - MatrixType submat = mat.template block<4,4>(i,j); - MatrixType mat_inv = mat.template block<4,4>(i,j).inverse(); - VERIFY_IS_APPROX(mat_inv, submat.inverse()); - mat.template block<4,4>(i,j) = submat.inverse(); - VERIFY_IS_APPROX(mat_inv, (mat.template block<4,4>(i,j))); - } -} - -void test_prec_inverse_4x4() -{ - CALL_SUBTEST_1((inverse_permutation_4x4<Matrix4f>())); - CALL_SUBTEST_1(( inverse_general_4x4<Matrix4f>(200000 * g_repeat) )); - CALL_SUBTEST_1(( inverse_general_4x4<Matrix<float,4,4,RowMajor> >(200000 * g_repeat) )); - - CALL_SUBTEST_2((inverse_permutation_4x4<Matrix<double,4,4,RowMajor> >())); - CALL_SUBTEST_2(( inverse_general_4x4<Matrix<double,4,4,ColMajor> >(200000 * g_repeat) )); - CALL_SUBTEST_2(( inverse_general_4x4<Matrix<double,4,4,RowMajor> >(200000 * g_repeat) )); - - CALL_SUBTEST_3((inverse_permutation_4x4<Matrix4cf>())); - CALL_SUBTEST_3((inverse_general_4x4<Matrix4cf>(50000 * g_repeat))); -} diff --git a/eigen/test/product.h b/eigen/test/product.h deleted file mode 100644 index 3b65112..0000000 --- a/eigen/test/product.h +++ /dev/null @@ -1,231 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/QR> - -template<typename Derived1, typename Derived2> -bool areNotApprox(const MatrixBase<Derived1>& m1, const MatrixBase<Derived2>& m2, typename Derived1::RealScalar epsilon = NumTraits<typename Derived1::RealScalar>::dummy_precision()) -{ - return !((m1-m2).cwiseAbs2().maxCoeff() < epsilon * epsilon - * (std::max)(m1.cwiseAbs2().maxCoeff(), m2.cwiseAbs2().maxCoeff())); -} - -template<typename MatrixType> void product(const MatrixType& m) -{ - /* this test covers the following files: - Identity.h Product.h - */ - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> RowVectorType; - typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> ColVectorType; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> RowSquareMatrixType; - typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> ColSquareMatrixType; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime, - MatrixType::Flags&RowMajorBit?ColMajor:RowMajor> OtherMajorMatrixType; - - Index rows = m.rows(); - Index cols = m.cols(); - - // this test relies a lot on Random.h, and there's not much more that we can do - // to test it, hence I consider that we will have tested Random.h - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols); - RowSquareMatrixType - identity = RowSquareMatrixType::Identity(rows, rows), - square = RowSquareMatrixType::Random(rows, rows), - res = RowSquareMatrixType::Random(rows, rows); - ColSquareMatrixType - square2 = ColSquareMatrixType::Random(cols, cols), - res2 = ColSquareMatrixType::Random(cols, cols); - RowVectorType v1 = RowVectorType::Random(rows); - ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols); - OtherMajorMatrixType tm1 = m1; - - Scalar s1 = internal::random<Scalar>(); - - Index r = internal::random<Index>(0, rows-1), - c = internal::random<Index>(0, cols-1), - c2 = internal::random<Index>(0, cols-1); - - // begin testing Product.h: only associativity for now - // (we use Transpose.h but this doesn't count as a test for it) - VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2)); - m3 = m1; - m3 *= m1.transpose() * m2; - VERIFY_IS_APPROX(m3, m1 * (m1.transpose()*m2)); - VERIFY_IS_APPROX(m3, m1 * (m1.transpose()*m2)); - - // continue testing Product.h: distributivity - VERIFY_IS_APPROX(square*(m1 + m2), square*m1+square*m2); - VERIFY_IS_APPROX(square*(m1 - m2), square*m1-square*m2); - - // continue testing Product.h: compatibility with ScalarMultiple.h - VERIFY_IS_APPROX(s1*(square*m1), (s1*square)*m1); - VERIFY_IS_APPROX(s1*(square*m1), square*(m1*s1)); - - // test Product.h together with Identity.h - VERIFY_IS_APPROX(v1, identity*v1); - VERIFY_IS_APPROX(v1.transpose(), v1.transpose() * identity); - // again, test operator() to check const-qualification - VERIFY_IS_APPROX(MatrixType::Identity(rows, cols)(r,c), static_cast<Scalar>(r==c)); - - if (rows!=cols) - VERIFY_RAISES_ASSERT(m3 = m1*m1); - - // test the previous tests were not screwed up because operator* returns 0 - // (we use the more accurate default epsilon) - if (!NumTraits<Scalar>::IsInteger && (std::min)(rows,cols)>1) - { - VERIFY(areNotApprox(m1.transpose()*m2,m2.transpose()*m1)); - } - - // test optimized operator+= path - res = square; - res.noalias() += m1 * m2.transpose(); - VERIFY_IS_APPROX(res, square + m1 * m2.transpose()); - if (!NumTraits<Scalar>::IsInteger && (std::min)(rows,cols)>1) - { - VERIFY(areNotApprox(res,square + m2 * m1.transpose())); - } - vcres = vc2; - vcres.noalias() += m1.transpose() * v1; - VERIFY_IS_APPROX(vcres, vc2 + m1.transpose() * v1); - - // test optimized operator-= path - res = square; - res.noalias() -= m1 * m2.transpose(); - VERIFY_IS_APPROX(res, square - (m1 * m2.transpose())); - if (!NumTraits<Scalar>::IsInteger && (std::min)(rows,cols)>1) - { - VERIFY(areNotApprox(res,square - m2 * m1.transpose())); - } - vcres = vc2; - vcres.noalias() -= m1.transpose() * v1; - VERIFY_IS_APPROX(vcres, vc2 - m1.transpose() * v1); - - // test d ?= a+b*c rules - res.noalias() = square + m1 * m2.transpose(); - VERIFY_IS_APPROX(res, square + m1 * m2.transpose()); - res.noalias() += square + m1 * m2.transpose(); - VERIFY_IS_APPROX(res, 2*(square + m1 * m2.transpose())); - res.noalias() -= square + m1 * m2.transpose(); - VERIFY_IS_APPROX(res, square + m1 * m2.transpose()); - - // test d ?= a-b*c rules - res.noalias() = square - m1 * m2.transpose(); - VERIFY_IS_APPROX(res, square - m1 * m2.transpose()); - res.noalias() += square - m1 * m2.transpose(); - VERIFY_IS_APPROX(res, 2*(square - m1 * m2.transpose())); - res.noalias() -= square - m1 * m2.transpose(); - VERIFY_IS_APPROX(res, square - m1 * m2.transpose()); - - - tm1 = m1; - VERIFY_IS_APPROX(tm1.transpose() * v1, m1.transpose() * v1); - VERIFY_IS_APPROX(v1.transpose() * tm1, v1.transpose() * m1); - - // test submatrix and matrix/vector product - for (int i=0; i<rows; ++i) - res.row(i) = m1.row(i) * m2.transpose(); - VERIFY_IS_APPROX(res, m1 * m2.transpose()); - // the other way round: - for (int i=0; i<rows; ++i) - res.col(i) = m1 * m2.transpose().col(i); - VERIFY_IS_APPROX(res, m1 * m2.transpose()); - - res2 = square2; - res2.noalias() += m1.transpose() * m2; - VERIFY_IS_APPROX(res2, square2 + m1.transpose() * m2); - if (!NumTraits<Scalar>::IsInteger && (std::min)(rows,cols)>1) - { - VERIFY(areNotApprox(res2,square2 + m2.transpose() * m1)); - } - - VERIFY_IS_APPROX(res.col(r).noalias() = square.adjoint() * square.col(r), (square.adjoint() * square.col(r)).eval()); - VERIFY_IS_APPROX(res.col(r).noalias() = square * square.col(r), (square * square.col(r)).eval()); - - // vector at runtime (see bug 1166) - { - RowSquareMatrixType ref(square); - ColSquareMatrixType ref2(square2); - ref = res = square; - VERIFY_IS_APPROX(res.block(0,0,1,rows).noalias() = m1.col(0).transpose() * square.transpose(), (ref.row(0) = m1.col(0).transpose() * square.transpose())); - VERIFY_IS_APPROX(res.block(0,0,1,rows).noalias() = m1.block(0,0,rows,1).transpose() * square.transpose(), (ref.row(0) = m1.col(0).transpose() * square.transpose())); - VERIFY_IS_APPROX(res.block(0,0,1,rows).noalias() = m1.col(0).transpose() * square, (ref.row(0) = m1.col(0).transpose() * square)); - VERIFY_IS_APPROX(res.block(0,0,1,rows).noalias() = m1.block(0,0,rows,1).transpose() * square, (ref.row(0) = m1.col(0).transpose() * square)); - ref2 = res2 = square2; - VERIFY_IS_APPROX(res2.block(0,0,1,cols).noalias() = m1.row(0) * square2.transpose(), (ref2.row(0) = m1.row(0) * square2.transpose())); - VERIFY_IS_APPROX(res2.block(0,0,1,cols).noalias() = m1.block(0,0,1,cols) * square2.transpose(), (ref2.row(0) = m1.row(0) * square2.transpose())); - VERIFY_IS_APPROX(res2.block(0,0,1,cols).noalias() = m1.row(0) * square2, (ref2.row(0) = m1.row(0) * square2)); - VERIFY_IS_APPROX(res2.block(0,0,1,cols).noalias() = m1.block(0,0,1,cols) * square2, (ref2.row(0) = m1.row(0) * square2)); - } - - // vector.block() (see bug 1283) - { - RowVectorType w1(rows); - VERIFY_IS_APPROX(square * v1.block(0,0,rows,1), square * v1); - VERIFY_IS_APPROX(w1.noalias() = square * v1.block(0,0,rows,1), square * v1); - VERIFY_IS_APPROX(w1.block(0,0,rows,1).noalias() = square * v1.block(0,0,rows,1), square * v1); - - Matrix<Scalar,1,MatrixType::ColsAtCompileTime> w2(cols); - VERIFY_IS_APPROX(vc2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2); - VERIFY_IS_APPROX(w2.noalias() = vc2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2); - VERIFY_IS_APPROX(w2.block(0,0,1,cols).noalias() = vc2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2); - - vc2 = square2.block(0,0,1,cols).transpose(); - VERIFY_IS_APPROX(square2.block(0,0,1,cols) * square2, vc2.transpose() * square2); - VERIFY_IS_APPROX(w2.noalias() = square2.block(0,0,1,cols) * square2, vc2.transpose() * square2); - VERIFY_IS_APPROX(w2.block(0,0,1,cols).noalias() = square2.block(0,0,1,cols) * square2, vc2.transpose() * square2); - - vc2 = square2.block(0,0,cols,1); - VERIFY_IS_APPROX(square2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2); - VERIFY_IS_APPROX(w2.noalias() = square2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2); - VERIFY_IS_APPROX(w2.block(0,0,1,cols).noalias() = square2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2); - } - - // inner product - { - Scalar x = square2.row(c) * square2.col(c2); - VERIFY_IS_APPROX(x, square2.row(c).transpose().cwiseProduct(square2.col(c2)).sum()); - } - - // outer product - { - VERIFY_IS_APPROX(m1.col(c) * m1.row(r), m1.block(0,c,rows,1) * m1.block(r,0,1,cols)); - VERIFY_IS_APPROX(m1.row(r).transpose() * m1.col(c).transpose(), m1.block(r,0,1,cols).transpose() * m1.block(0,c,rows,1).transpose()); - VERIFY_IS_APPROX(m1.block(0,c,rows,1) * m1.row(r), m1.block(0,c,rows,1) * m1.block(r,0,1,cols)); - VERIFY_IS_APPROX(m1.col(c) * m1.block(r,0,1,cols), m1.block(0,c,rows,1) * m1.block(r,0,1,cols)); - VERIFY_IS_APPROX(m1.leftCols(1) * m1.row(r), m1.block(0,0,rows,1) * m1.block(r,0,1,cols)); - VERIFY_IS_APPROX(m1.col(c) * m1.topRows(1), m1.block(0,c,rows,1) * m1.block(0,0,1,cols)); - } - - // Aliasing - { - ColVectorType x(cols); x.setRandom(); - ColVectorType z(x); - ColVectorType y(cols); y.setZero(); - ColSquareMatrixType A(cols,cols); A.setRandom(); - // CwiseBinaryOp - VERIFY_IS_APPROX(x = y + A*x, A*z); - x = z; - // CwiseUnaryOp - VERIFY_IS_APPROX(x = Scalar(1.)*(A*x), A*z); - } - - // regression for blas_trais - { - VERIFY_IS_APPROX(square * (square*square).transpose(), square * square.transpose() * square.transpose()); - VERIFY_IS_APPROX(square * (-(square*square)), -square * square * square); - VERIFY_IS_APPROX(square * (s1*(square*square)), s1 * square * square * square); - VERIFY_IS_APPROX(square * (square*square).conjugate(), square * square.conjugate() * square.conjugate()); - } - -} diff --git a/eigen/test/product_extra.cpp b/eigen/test/product_extra.cpp deleted file mode 100644 index de2709d..0000000 --- a/eigen/test/product_extra.cpp +++ /dev/null @@ -1,374 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename MatrixType> void product_extra(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, 1, Dynamic> RowVectorType; - typedef Matrix<Scalar, Dynamic, 1> ColVectorType; - typedef Matrix<Scalar, Dynamic, Dynamic, - MatrixType::Flags&RowMajorBit> OtherMajorMatrixType; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols), - mzero = MatrixType::Zero(rows, cols), - identity = MatrixType::Identity(rows, rows), - square = MatrixType::Random(rows, rows), - res = MatrixType::Random(rows, rows), - square2 = MatrixType::Random(cols, cols), - res2 = MatrixType::Random(cols, cols); - RowVectorType v1 = RowVectorType::Random(rows), vrres(rows); - ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols); - OtherMajorMatrixType tm1 = m1; - - Scalar s1 = internal::random<Scalar>(), - s2 = internal::random<Scalar>(), - s3 = internal::random<Scalar>(); - - VERIFY_IS_APPROX(m3.noalias() = m1 * m2.adjoint(), m1 * m2.adjoint().eval()); - VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * square.adjoint(), m1.adjoint().eval() * square.adjoint().eval()); - VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * m2, m1.adjoint().eval() * m2); - VERIFY_IS_APPROX(m3.noalias() = (s1 * m1.adjoint()) * m2, (s1 * m1.adjoint()).eval() * m2); - VERIFY_IS_APPROX(m3.noalias() = ((s1 * m1).adjoint()) * m2, (numext::conj(s1) * m1.adjoint()).eval() * m2); - VERIFY_IS_APPROX(m3.noalias() = (- m1.adjoint() * s1) * (s3 * m2), (- m1.adjoint() * s1).eval() * (s3 * m2).eval()); - VERIFY_IS_APPROX(m3.noalias() = (s2 * m1.adjoint() * s1) * m2, (s2 * m1.adjoint() * s1).eval() * m2); - VERIFY_IS_APPROX(m3.noalias() = (-m1*s2) * s1*m2.adjoint(), (-m1*s2).eval() * (s1*m2.adjoint()).eval()); - - // a very tricky case where a scale factor has to be automatically conjugated: - VERIFY_IS_APPROX( m1.adjoint() * (s1*m2).conjugate(), (m1.adjoint()).eval() * ((s1*m2).conjugate()).eval()); - - - // test all possible conjugate combinations for the four matrix-vector product cases: - - VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2), - (-m1.conjugate()*s2).eval() * (s1 * vc2).eval()); - VERIFY_IS_APPROX((-m1 * s2) * (s1 * vc2.conjugate()), - (-m1*s2).eval() * (s1 * vc2.conjugate()).eval()); - VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2.conjugate()), - (-m1.conjugate()*s2).eval() * (s1 * vc2.conjugate()).eval()); - - VERIFY_IS_APPROX((s1 * vc2.transpose()) * (-m1.adjoint() * s2), - (s1 * vc2.transpose()).eval() * (-m1.adjoint()*s2).eval()); - VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.transpose() * s2), - (s1 * vc2.adjoint()).eval() * (-m1.transpose()*s2).eval()); - VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.adjoint() * s2), - (s1 * vc2.adjoint()).eval() * (-m1.adjoint()*s2).eval()); - - VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.transpose()), - (-m1.adjoint()*s2).eval() * (s1 * v1.transpose()).eval()); - VERIFY_IS_APPROX((-m1.transpose() * s2) * (s1 * v1.adjoint()), - (-m1.transpose()*s2).eval() * (s1 * v1.adjoint()).eval()); - VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()), - (-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval()); - - VERIFY_IS_APPROX((s1 * v1) * (-m1.conjugate() * s2), - (s1 * v1).eval() * (-m1.conjugate()*s2).eval()); - VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1 * s2), - (s1 * v1.conjugate()).eval() * (-m1*s2).eval()); - VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1.conjugate() * s2), - (s1 * v1.conjugate()).eval() * (-m1.conjugate()*s2).eval()); - - VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()), - (-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval()); - - // test the vector-matrix product with non aligned starts - Index i = internal::random<Index>(0,m1.rows()-2); - Index j = internal::random<Index>(0,m1.cols()-2); - Index r = internal::random<Index>(1,m1.rows()-i); - Index c = internal::random<Index>(1,m1.cols()-j); - Index i2 = internal::random<Index>(0,m1.rows()-1); - Index j2 = internal::random<Index>(0,m1.cols()-1); - - VERIFY_IS_APPROX(m1.col(j2).adjoint() * m1.block(0,j,m1.rows(),c), m1.col(j2).adjoint().eval() * m1.block(0,j,m1.rows(),c).eval()); - VERIFY_IS_APPROX(m1.block(i,0,r,m1.cols()) * m1.row(i2).adjoint(), m1.block(i,0,r,m1.cols()).eval() * m1.row(i2).adjoint().eval()); - - // regression test - MatrixType tmp = m1 * m1.adjoint() * s1; - VERIFY_IS_APPROX(tmp, m1 * m1.adjoint() * s1); - - // regression test for bug 1343, assignment to arrays - Array<Scalar,Dynamic,1> a1 = m1 * vc2; - VERIFY_IS_APPROX(a1.matrix(),m1*vc2); - Array<Scalar,Dynamic,1> a2 = s1 * (m1 * vc2); - VERIFY_IS_APPROX(a2.matrix(),s1*m1*vc2); - Array<Scalar,1,Dynamic> a3 = v1 * m1; - VERIFY_IS_APPROX(a3.matrix(),v1*m1); - Array<Scalar,Dynamic,Dynamic> a4 = m1 * m2.adjoint(); - VERIFY_IS_APPROX(a4.matrix(),m1*m2.adjoint()); -} - -// Regression test for bug reported at http://forum.kde.org/viewtopic.php?f=74&t=96947 -void mat_mat_scalar_scalar_product() -{ - Eigen::Matrix2Xd dNdxy(2, 3); - dNdxy << -0.5, 0.5, 0, - -0.3, 0, 0.3; - double det = 6.0, wt = 0.5; - VERIFY_IS_APPROX(dNdxy.transpose()*dNdxy*det*wt, det*wt*dNdxy.transpose()*dNdxy); -} - -template <typename MatrixType> -void zero_sized_objects(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - const int PacketSize = internal::packet_traits<Scalar>::size; - const int PacketSize1 = PacketSize>1 ? PacketSize-1 : 1; - Index rows = m.rows(); - Index cols = m.cols(); - - { - MatrixType res, a(rows,0), b(0,cols); - VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(rows,cols) ); - VERIFY_IS_APPROX( (res=a*a.transpose()), MatrixType::Zero(rows,rows) ); - VERIFY_IS_APPROX( (res=b.transpose()*b), MatrixType::Zero(cols,cols) ); - VERIFY_IS_APPROX( (res=b.transpose()*a.transpose()), MatrixType::Zero(cols,rows) ); - } - - { - MatrixType res, a(rows,cols), b(cols,0); - res = a*b; - VERIFY(res.rows()==rows && res.cols()==0); - b.resize(0,rows); - res = b*a; - VERIFY(res.rows()==0 && res.cols()==cols); - } - - { - Matrix<Scalar,PacketSize,0> a; - Matrix<Scalar,0,1> b; - Matrix<Scalar,PacketSize,1> res; - VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize,1) ); - VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize,1) ); - } - - { - Matrix<Scalar,PacketSize1,0> a; - Matrix<Scalar,0,1> b; - Matrix<Scalar,PacketSize1,1> res; - VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize1,1) ); - VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize1,1) ); - } - - { - Matrix<Scalar,PacketSize,Dynamic> a(PacketSize,0); - Matrix<Scalar,Dynamic,1> b(0,1); - Matrix<Scalar,PacketSize,1> res; - VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize,1) ); - VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize,1) ); - } - - { - Matrix<Scalar,PacketSize1,Dynamic> a(PacketSize1,0); - Matrix<Scalar,Dynamic,1> b(0,1); - Matrix<Scalar,PacketSize1,1> res; - VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize1,1) ); - VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize1,1) ); - } -} - -template<int> -void bug_127() -{ - // Bug 127 - // - // a product of the form lhs*rhs with - // - // lhs: - // rows = 1, cols = 4 - // RowsAtCompileTime = 1, ColsAtCompileTime = -1 - // MaxRowsAtCompileTime = 1, MaxColsAtCompileTime = 5 - // - // rhs: - // rows = 4, cols = 0 - // RowsAtCompileTime = -1, ColsAtCompileTime = -1 - // MaxRowsAtCompileTime = 5, MaxColsAtCompileTime = 1 - // - // was failing on a runtime assertion, because it had been mis-compiled as a dot product because Product.h was using the - // max-sizes to detect size 1 indicating vectors, and that didn't account for 0-sized object with max-size 1. - - Matrix<float,1,Dynamic,RowMajor,1,5> a(1,4); - Matrix<float,Dynamic,Dynamic,ColMajor,5,1> b(4,0); - a*b; -} - -template<int> void bug_817() -{ - ArrayXXf B = ArrayXXf::Random(10,10), C; - VectorXf x = VectorXf::Random(10); - C = (x.transpose()*B.matrix()); - B = (x.transpose()*B.matrix()); - VERIFY_IS_APPROX(B,C); -} - -template<int> -void unaligned_objects() -{ - // Regression test for the bug reported here: - // http://forum.kde.org/viewtopic.php?f=74&t=107541 - // Recall the matrix*vector kernel avoid unaligned loads by loading two packets and then reassemble then. - // There was a mistake in the computation of the valid range for fully unaligned objects: in some rare cases, - // memory was read outside the allocated matrix memory. Though the values were not used, this might raise segfault. - for(int m=450;m<460;++m) - { - for(int n=8;n<12;++n) - { - MatrixXf M(m, n); - VectorXf v1(n), r1(500); - RowVectorXf v2(m), r2(16); - - M.setRandom(); - v1.setRandom(); - v2.setRandom(); - for(int o=0; o<4; ++o) - { - r1.segment(o,m).noalias() = M * v1; - VERIFY_IS_APPROX(r1.segment(o,m), M * MatrixXf(v1)); - r2.segment(o,n).noalias() = v2 * M; - VERIFY_IS_APPROX(r2.segment(o,n), MatrixXf(v2) * M); - } - } - } -} - -template<typename T> -EIGEN_DONT_INLINE -Index test_compute_block_size(Index m, Index n, Index k) -{ - Index mc(m), nc(n), kc(k); - internal::computeProductBlockingSizes<T,T>(kc, mc, nc); - return kc+mc+nc; -} - -template<typename T> -Index compute_block_size() -{ - Index ret = 0; - ret += test_compute_block_size<T>(0,1,1); - ret += test_compute_block_size<T>(1,0,1); - ret += test_compute_block_size<T>(1,1,0); - ret += test_compute_block_size<T>(0,0,1); - ret += test_compute_block_size<T>(0,1,0); - ret += test_compute_block_size<T>(1,0,0); - ret += test_compute_block_size<T>(0,0,0); - return ret; -} - -template<typename> -void aliasing_with_resize() -{ - Index m = internal::random<Index>(10,50); - Index n = internal::random<Index>(10,50); - MatrixXd A, B, C(m,n), D(m,m); - VectorXd a, b, c(n); - C.setRandom(); - D.setRandom(); - c.setRandom(); - double s = internal::random<double>(1,10); - - A = C; - B = A * A.transpose(); - A = A * A.transpose(); - VERIFY_IS_APPROX(A,B); - - A = C; - B = (A * A.transpose())/s; - A = (A * A.transpose())/s; - VERIFY_IS_APPROX(A,B); - - A = C; - B = (A * A.transpose()) + D; - A = (A * A.transpose()) + D; - VERIFY_IS_APPROX(A,B); - - A = C; - B = D + (A * A.transpose()); - A = D + (A * A.transpose()); - VERIFY_IS_APPROX(A,B); - - A = C; - B = s * (A * A.transpose()); - A = s * (A * A.transpose()); - VERIFY_IS_APPROX(A,B); - - A = C; - a = c; - b = (A * a)/s; - a = (A * a)/s; - VERIFY_IS_APPROX(a,b); -} - -template<int> -void bug_1308() -{ - int n = 10; - MatrixXd r(n,n); - VectorXd v = VectorXd::Random(n); - r = v * RowVectorXd::Ones(n); - VERIFY_IS_APPROX(r, v.rowwise().replicate(n)); - r = VectorXd::Ones(n) * v.transpose(); - VERIFY_IS_APPROX(r, v.rowwise().replicate(n).transpose()); - - Matrix4d ones44 = Matrix4d::Ones(); - Matrix4d m44 = Matrix4d::Ones() * Matrix4d::Ones(); - VERIFY_IS_APPROX(m44,Matrix4d::Constant(4)); - VERIFY_IS_APPROX(m44.noalias()=ones44*Matrix4d::Ones(), Matrix4d::Constant(4)); - VERIFY_IS_APPROX(m44.noalias()=ones44.transpose()*Matrix4d::Ones(), Matrix4d::Constant(4)); - VERIFY_IS_APPROX(m44.noalias()=Matrix4d::Ones()*ones44, Matrix4d::Constant(4)); - VERIFY_IS_APPROX(m44.noalias()=Matrix4d::Ones()*ones44.transpose(), Matrix4d::Constant(4)); - - typedef Matrix<double,4,4,RowMajor> RMatrix4d; - RMatrix4d r44 = Matrix4d::Ones() * Matrix4d::Ones(); - VERIFY_IS_APPROX(r44,Matrix4d::Constant(4)); - VERIFY_IS_APPROX(r44.noalias()=ones44*Matrix4d::Ones(), Matrix4d::Constant(4)); - VERIFY_IS_APPROX(r44.noalias()=ones44.transpose()*Matrix4d::Ones(), Matrix4d::Constant(4)); - VERIFY_IS_APPROX(r44.noalias()=Matrix4d::Ones()*ones44, Matrix4d::Constant(4)); - VERIFY_IS_APPROX(r44.noalias()=Matrix4d::Ones()*ones44.transpose(), Matrix4d::Constant(4)); - VERIFY_IS_APPROX(r44.noalias()=ones44*RMatrix4d::Ones(), Matrix4d::Constant(4)); - VERIFY_IS_APPROX(r44.noalias()=ones44.transpose()*RMatrix4d::Ones(), Matrix4d::Constant(4)); - VERIFY_IS_APPROX(r44.noalias()=RMatrix4d::Ones()*ones44, Matrix4d::Constant(4)); - VERIFY_IS_APPROX(r44.noalias()=RMatrix4d::Ones()*ones44.transpose(), Matrix4d::Constant(4)); - -// RowVector4d r4; - m44.setOnes(); - r44.setZero(); - VERIFY_IS_APPROX(r44.noalias() += m44.row(0).transpose() * RowVector4d::Ones(), ones44); - r44.setZero(); - VERIFY_IS_APPROX(r44.noalias() += m44.col(0) * RowVector4d::Ones(), ones44); - r44.setZero(); - VERIFY_IS_APPROX(r44.noalias() += Vector4d::Ones() * m44.row(0), ones44); - r44.setZero(); - VERIFY_IS_APPROX(r44.noalias() += Vector4d::Ones() * m44.col(0).transpose(), ones44); -} - -void test_product_extra() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( product_extra(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_2( product_extra(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_2( mat_mat_scalar_scalar_product() ); - CALL_SUBTEST_3( product_extra(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) ); - CALL_SUBTEST_4( product_extra(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) ); - CALL_SUBTEST_1( zero_sized_objects(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } - CALL_SUBTEST_5( bug_127<0>() ); - CALL_SUBTEST_5( bug_817<0>() ); - CALL_SUBTEST_5( bug_1308<0>() ); - CALL_SUBTEST_6( unaligned_objects<0>() ); - CALL_SUBTEST_7( compute_block_size<float>() ); - CALL_SUBTEST_7( compute_block_size<double>() ); - CALL_SUBTEST_7( compute_block_size<std::complex<double> >() ); - CALL_SUBTEST_8( aliasing_with_resize<void>() ); - -} diff --git a/eigen/test/product_large.cpp b/eigen/test/product_large.cpp deleted file mode 100644 index 845cd40..0000000 --- a/eigen/test/product_large.cpp +++ /dev/null @@ -1,107 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "product.h" - -template<typename T> -void test_aliasing() -{ - int rows = internal::random<int>(1,12); - int cols = internal::random<int>(1,12); - typedef Matrix<T,Dynamic,Dynamic> MatrixType; - typedef Matrix<T,Dynamic,1> VectorType; - VectorType x(cols); x.setRandom(); - VectorType z(x); - VectorType y(rows); y.setZero(); - MatrixType A(rows,cols); A.setRandom(); - // CwiseBinaryOp - VERIFY_IS_APPROX(x = y + A*x, A*z); // OK because "y + A*x" is marked as "assume-aliasing" - x = z; - // CwiseUnaryOp - VERIFY_IS_APPROX(x = T(1.)*(A*x), A*z); // OK because 1*(A*x) is replaced by (1*A*x) which is a Product<> expression - x = z; - // VERIFY_IS_APPROX(x = y-A*x, -A*z); // Not OK in 3.3 because x is resized before A*x gets evaluated - x = z; -} - -void test_product_large() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( product(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_2( product(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_3( product(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_4( product(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) ); - CALL_SUBTEST_5( product(Matrix<float,Dynamic,Dynamic,RowMajor>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - - CALL_SUBTEST_1( test_aliasing<float>() ); - } - -#if defined EIGEN_TEST_PART_6 - { - // test a specific issue in DiagonalProduct - int N = 1000000; - VectorXf v = VectorXf::Ones(N); - MatrixXf m = MatrixXf::Ones(N,3); - m = (v+v).asDiagonal() * m; - VERIFY_IS_APPROX(m, MatrixXf::Constant(N,3,2)); - } - - { - // test deferred resizing in Matrix::operator= - MatrixXf a = MatrixXf::Random(10,4), b = MatrixXf::Random(4,10), c = a; - VERIFY_IS_APPROX((a = a * b), (c * b).eval()); - } - - { - // check the functions to setup blocking sizes compile and do not segfault - // FIXME check they do what they are supposed to do !! - std::ptrdiff_t l1 = internal::random<int>(10000,20000); - std::ptrdiff_t l2 = internal::random<int>(100000,200000); - std::ptrdiff_t l3 = internal::random<int>(1000000,2000000); - setCpuCacheSizes(l1,l2,l3); - VERIFY(l1==l1CacheSize()); - VERIFY(l2==l2CacheSize()); - std::ptrdiff_t k1 = internal::random<int>(10,100)*16; - std::ptrdiff_t m1 = internal::random<int>(10,100)*16; - std::ptrdiff_t n1 = internal::random<int>(10,100)*16; - // only makes sure it compiles fine - internal::computeProductBlockingSizes<float,float,std::ptrdiff_t>(k1,m1,n1,1); - } - - { - // test regression in row-vector by matrix (bad Map type) - MatrixXf mat1(10,32); mat1.setRandom(); - MatrixXf mat2(32,32); mat2.setRandom(); - MatrixXf r1 = mat1.row(2)*mat2.transpose(); - VERIFY_IS_APPROX(r1, (mat1.row(2)*mat2.transpose()).eval()); - - MatrixXf r2 = mat1.row(2)*mat2; - VERIFY_IS_APPROX(r2, (mat1.row(2)*mat2).eval()); - } - - { - Eigen::MatrixXd A(10,10), B, C; - A.setRandom(); - C = A; - for(int k=0; k<79; ++k) - C = C * A; - B.noalias() = (((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A)) * ((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))) - * (((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A)) * ((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))); - VERIFY_IS_APPROX(B,C); - } -#endif - - // Regression test for bug 714: -#if defined EIGEN_HAS_OPENMP - omp_set_dynamic(1); - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_6( product(Matrix<float,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } -#endif -} diff --git a/eigen/test/product_mmtr.cpp b/eigen/test/product_mmtr.cpp deleted file mode 100644 index d3e24b0..0000000 --- a/eigen/test/product_mmtr.cpp +++ /dev/null @@ -1,96 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010-2017 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#define CHECK_MMTR(DEST, TRI, OP) { \ - ref3 = DEST; \ - ref2 = ref1 = DEST; \ - DEST.template triangularView<TRI>() OP; \ - ref1 OP; \ - ref2.template triangularView<TRI>() \ - = ref1.template triangularView<TRI>(); \ - VERIFY_IS_APPROX(DEST,ref2); \ - \ - DEST = ref3; \ - ref3 = ref2; \ - ref3.diagonal() = DEST.diagonal(); \ - DEST.template triangularView<TRI|ZeroDiag>() OP; \ - VERIFY_IS_APPROX(DEST,ref3); \ - } - -template<typename Scalar> void mmtr(int size) -{ - typedef Matrix<Scalar,Dynamic,Dynamic,ColMajor> MatrixColMaj; - typedef Matrix<Scalar,Dynamic,Dynamic,RowMajor> MatrixRowMaj; - - DenseIndex othersize = internal::random<DenseIndex>(1,200); - - MatrixColMaj matc = MatrixColMaj::Zero(size, size); - MatrixRowMaj matr = MatrixRowMaj::Zero(size, size); - MatrixColMaj ref1(size, size), ref2(size, size), ref3(size,size); - - MatrixColMaj soc(size,othersize); soc.setRandom(); - MatrixColMaj osc(othersize,size); osc.setRandom(); - MatrixRowMaj sor(size,othersize); sor.setRandom(); - MatrixRowMaj osr(othersize,size); osr.setRandom(); - MatrixColMaj sqc(size,size); sqc.setRandom(); - MatrixRowMaj sqr(size,size); sqr.setRandom(); - - Scalar s = internal::random<Scalar>(); - - CHECK_MMTR(matc, Lower, = s*soc*sor.adjoint()); - CHECK_MMTR(matc, Upper, = s*(soc*soc.adjoint())); - CHECK_MMTR(matr, Lower, = s*soc*soc.adjoint()); - CHECK_MMTR(matr, Upper, = soc*(s*sor.adjoint())); - - CHECK_MMTR(matc, Lower, += s*soc*soc.adjoint()); - CHECK_MMTR(matc, Upper, += s*(soc*sor.transpose())); - CHECK_MMTR(matr, Lower, += s*sor*soc.adjoint()); - CHECK_MMTR(matr, Upper, += soc*(s*soc.adjoint())); - - CHECK_MMTR(matc, Lower, -= s*soc*soc.adjoint()); - CHECK_MMTR(matc, Upper, -= s*(osc.transpose()*osc.conjugate())); - CHECK_MMTR(matr, Lower, -= s*soc*soc.adjoint()); - CHECK_MMTR(matr, Upper, -= soc*(s*soc.adjoint())); - - CHECK_MMTR(matc, Lower, -= s*sqr*sqc.template triangularView<Upper>()); - CHECK_MMTR(matc, Upper, = s*sqc*sqr.template triangularView<Upper>()); - CHECK_MMTR(matc, Lower, += s*sqr*sqc.template triangularView<Lower>()); - CHECK_MMTR(matc, Upper, = s*sqc*sqc.template triangularView<Lower>()); - - CHECK_MMTR(matc, Lower, = (s*sqr).template triangularView<Upper>()*sqc); - CHECK_MMTR(matc, Upper, -= (s*sqc).template triangularView<Upper>()*sqc); - CHECK_MMTR(matc, Lower, = (s*sqr).template triangularView<Lower>()*sqc); - CHECK_MMTR(matc, Upper, += (s*sqc).template triangularView<Lower>()*sqc); - - // check aliasing - ref2 = ref1 = matc; - ref1 = sqc.adjoint() * matc * sqc; - ref2.template triangularView<Upper>() = ref1.template triangularView<Upper>(); - matc.template triangularView<Upper>() = sqc.adjoint() * matc * sqc; - VERIFY_IS_APPROX(matc, ref2); - - ref2 = ref1 = matc; - ref1 = sqc * matc * sqc.adjoint(); - ref2.template triangularView<Lower>() = ref1.template triangularView<Lower>(); - matc.template triangularView<Lower>() = sqc * matc * sqc.adjoint(); - VERIFY_IS_APPROX(matc, ref2); -} - -void test_product_mmtr() -{ - for(int i = 0; i < g_repeat ; i++) - { - CALL_SUBTEST_1((mmtr<float>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)))); - CALL_SUBTEST_2((mmtr<double>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)))); - CALL_SUBTEST_3((mmtr<std::complex<float> >(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2)))); - CALL_SUBTEST_4((mmtr<std::complex<double> >(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2)))); - } -} diff --git a/eigen/test/product_notemporary.cpp b/eigen/test/product_notemporary.cpp deleted file mode 100644 index 28865d3..0000000 --- a/eigen/test/product_notemporary.cpp +++ /dev/null @@ -1,159 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define TEST_ENABLE_TEMPORARY_TRACKING - -#include "main.h" - -template<typename MatrixType> void product_notemporary(const MatrixType& m) -{ - /* This test checks the number of temporaries created - * during the evaluation of a complex expression */ - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef Matrix<Scalar, 1, Dynamic> RowVectorType; - typedef Matrix<Scalar, Dynamic, 1> ColVectorType; - typedef Matrix<Scalar, Dynamic, Dynamic, ColMajor> ColMajorMatrixType; - typedef Matrix<Scalar, Dynamic, Dynamic, RowMajor> RowMajorMatrixType; - - Index rows = m.rows(); - Index cols = m.cols(); - - ColMajorMatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols); - RowVectorType rv1 = RowVectorType::Random(rows), rvres(rows); - ColVectorType cv1 = ColVectorType::Random(cols), cvres(cols); - RowMajorMatrixType rm3(rows, cols); - - Scalar s1 = internal::random<Scalar>(), - s2 = internal::random<Scalar>(), - s3 = internal::random<Scalar>(); - - Index c0 = internal::random<Index>(4,cols-8), - c1 = internal::random<Index>(8,cols-c0), - r0 = internal::random<Index>(4,cols-8), - r1 = internal::random<Index>(8,rows-r0); - - VERIFY_EVALUATION_COUNT( m3 = (m1 * m2.adjoint()), 1); - VERIFY_EVALUATION_COUNT( m3 = (m1 * m2.adjoint()).transpose(), 1); - VERIFY_EVALUATION_COUNT( m3.noalias() = m1 * m2.adjoint(), 0); - - VERIFY_EVALUATION_COUNT( m3 = s1 * (m1 * m2.transpose()), 1); -// VERIFY_EVALUATION_COUNT( m3 = m3 + s1 * (m1 * m2.transpose()), 1); - VERIFY_EVALUATION_COUNT( m3.noalias() = s1 * (m1 * m2.transpose()), 0); - - VERIFY_EVALUATION_COUNT( m3 = m3 + (m1 * m2.adjoint()), 1); - VERIFY_EVALUATION_COUNT( m3 = m3 - (m1 * m2.adjoint()), 1); - - VERIFY_EVALUATION_COUNT( m3 = m3 + (m1 * m2.adjoint()).transpose(), 1); - VERIFY_EVALUATION_COUNT( m3.noalias() = m3 + m1 * m2.transpose(), 0); - VERIFY_EVALUATION_COUNT( m3.noalias() += m3 + m1 * m2.transpose(), 0); - VERIFY_EVALUATION_COUNT( m3.noalias() -= m3 + m1 * m2.transpose(), 0); - VERIFY_EVALUATION_COUNT( m3.noalias() = m3 - m1 * m2.transpose(), 0); - VERIFY_EVALUATION_COUNT( m3.noalias() += m3 - m1 * m2.transpose(), 0); - VERIFY_EVALUATION_COUNT( m3.noalias() -= m3 - m1 * m2.transpose(), 0); - - VERIFY_EVALUATION_COUNT( m3.noalias() = s1 * m1 * s2 * m2.adjoint(), 0); - VERIFY_EVALUATION_COUNT( m3.noalias() = s1 * m1 * s2 * (m1*s3+m2*s2).adjoint(), 1); - VERIFY_EVALUATION_COUNT( m3.noalias() = (s1 * m1).adjoint() * s2 * m2, 0); - VERIFY_EVALUATION_COUNT( m3.noalias() += s1 * (-m1*s3).adjoint() * (s2 * m2 * s3), 0); - VERIFY_EVALUATION_COUNT( m3.noalias() -= s1 * (m1.transpose() * m2), 0); - - VERIFY_EVALUATION_COUNT(( m3.block(r0,r0,r1,r1).noalias() += -m1.block(r0,c0,r1,c1) * (s2*m2.block(r0,c0,r1,c1)).adjoint() ), 0); - VERIFY_EVALUATION_COUNT(( m3.block(r0,r0,r1,r1).noalias() -= s1 * m1.block(r0,c0,r1,c1) * m2.block(c0,r0,c1,r1) ), 0); - - // NOTE this is because the Block expression is not handled yet by our expression analyser - VERIFY_EVALUATION_COUNT(( m3.block(r0,r0,r1,r1).noalias() = s1 * m1.block(r0,c0,r1,c1) * (s1*m2).block(c0,r0,c1,r1) ), 1); - - VERIFY_EVALUATION_COUNT( m3.noalias() -= (s1 * m1).template triangularView<Lower>() * m2, 0); - VERIFY_EVALUATION_COUNT( rm3.noalias() = (s1 * m1.adjoint()).template triangularView<Upper>() * (m2+m2), 1); - VERIFY_EVALUATION_COUNT( rm3.noalias() = (s1 * m1.adjoint()).template triangularView<UnitUpper>() * m2.adjoint(), 0); - - VERIFY_EVALUATION_COUNT( m3.template triangularView<Upper>() = (m1 * m2.adjoint()), 0); - VERIFY_EVALUATION_COUNT( m3.template triangularView<Upper>() -= (m1 * m2.adjoint()), 0); - - // NOTE this is because the blas_traits require innerstride==1 to avoid a temporary, but that doesn't seem to be actually needed for the triangular products - VERIFY_EVALUATION_COUNT( rm3.col(c0).noalias() = (s1 * m1.adjoint()).template triangularView<UnitUpper>() * (s2*m2.row(c0)).adjoint(), 1); - - VERIFY_EVALUATION_COUNT( m1.template triangularView<Lower>().solveInPlace(m3), 0); - VERIFY_EVALUATION_COUNT( m1.adjoint().template triangularView<Lower>().solveInPlace(m3.transpose()), 0); - - VERIFY_EVALUATION_COUNT( m3.noalias() -= (s1 * m1).adjoint().template selfadjointView<Lower>() * (-m2*s3).adjoint(), 0); - VERIFY_EVALUATION_COUNT( m3.noalias() = s2 * m2.adjoint() * (s1 * m1.adjoint()).template selfadjointView<Upper>(), 0); - VERIFY_EVALUATION_COUNT( rm3.noalias() = (s1 * m1.adjoint()).template selfadjointView<Lower>() * m2.adjoint(), 0); - - // NOTE this is because the blas_traits require innerstride==1 to avoid a temporary, but that doesn't seem to be actually needed for the triangular products - VERIFY_EVALUATION_COUNT( m3.col(c0).noalias() = (s1 * m1).adjoint().template selfadjointView<Lower>() * (-m2.row(c0)*s3).adjoint(), 1); - VERIFY_EVALUATION_COUNT( m3.col(c0).noalias() -= (s1 * m1).adjoint().template selfadjointView<Upper>() * (-m2.row(c0)*s3).adjoint(), 1); - - VERIFY_EVALUATION_COUNT( m3.block(r0,c0,r1,c1).noalias() += m1.block(r0,r0,r1,r1).template selfadjointView<Upper>() * (s1*m2.block(r0,c0,r1,c1)), 0); - VERIFY_EVALUATION_COUNT( m3.block(r0,c0,r1,c1).noalias() = m1.block(r0,r0,r1,r1).template selfadjointView<Upper>() * m2.block(r0,c0,r1,c1), 0); - - VERIFY_EVALUATION_COUNT( m3.template selfadjointView<Lower>().rankUpdate(m2.adjoint()), 0); - - // Here we will get 1 temporary for each resize operation of the lhs operator; resize(r1,c1) would lead to zero temporaries - m3.resize(1,1); - VERIFY_EVALUATION_COUNT( m3.noalias() = m1.block(r0,r0,r1,r1).template selfadjointView<Lower>() * m2.block(r0,c0,r1,c1), 1); - m3.resize(1,1); - VERIFY_EVALUATION_COUNT( m3.noalias() = m1.block(r0,r0,r1,r1).template triangularView<UnitUpper>() * m2.block(r0,c0,r1,c1), 1); - - // Zero temporaries for lazy products ... - VERIFY_EVALUATION_COUNT( Scalar tmp = 0; tmp += Scalar(RealScalar(1)) / (m3.transpose().lazyProduct(m3)).diagonal().sum(), 0 ); - - // ... and even no temporary for even deeply (>=2) nested products - VERIFY_EVALUATION_COUNT( Scalar tmp = 0; tmp += Scalar(RealScalar(1)) / (m3.transpose() * m3).diagonal().sum(), 0 ); - VERIFY_EVALUATION_COUNT( Scalar tmp = 0; tmp += Scalar(RealScalar(1)) / (m3.transpose() * m3).diagonal().array().abs().sum(), 0 ); - - // Zero temporaries for ... CoeffBasedProductMode - VERIFY_EVALUATION_COUNT( m3.col(0).template head<5>() * m3.col(0).transpose() + m3.col(0).template head<5>() * m3.col(0).transpose(), 0 ); - - // Check matrix * vectors - VERIFY_EVALUATION_COUNT( cvres.noalias() = m1 * cv1, 0 ); - VERIFY_EVALUATION_COUNT( cvres.noalias() -= m1 * cv1, 0 ); - VERIFY_EVALUATION_COUNT( cvres.noalias() -= m1 * m2.col(0), 0 ); - VERIFY_EVALUATION_COUNT( cvres.noalias() -= m1 * rv1.adjoint(), 0 ); - VERIFY_EVALUATION_COUNT( cvres.noalias() -= m1 * m2.row(0).transpose(), 0 ); - - VERIFY_EVALUATION_COUNT( cvres.noalias() = (m1+m1) * cv1, 0 ); - VERIFY_EVALUATION_COUNT( cvres.noalias() = (rm3+rm3) * cv1, 0 ); - VERIFY_EVALUATION_COUNT( cvres.noalias() = (m1+m1) * (m1*cv1), 1 ); - VERIFY_EVALUATION_COUNT( cvres.noalias() = (rm3+rm3) * (m1*cv1), 1 ); - - // Check outer products - m3 = cv1 * rv1; - VERIFY_EVALUATION_COUNT( m3.noalias() = cv1 * rv1, 0 ); - VERIFY_EVALUATION_COUNT( m3.noalias() = (cv1+cv1) * (rv1+rv1), 1 ); - VERIFY_EVALUATION_COUNT( m3.noalias() = (m1*cv1) * (rv1), 1 ); - VERIFY_EVALUATION_COUNT( m3.noalias() += (m1*cv1) * (rv1), 1 ); - VERIFY_EVALUATION_COUNT( rm3.noalias() = (cv1) * (rv1 * m1), 1 ); - VERIFY_EVALUATION_COUNT( rm3.noalias() -= (cv1) * (rv1 * m1), 1 ); - VERIFY_EVALUATION_COUNT( rm3.noalias() = (m1*cv1) * (rv1 * m1), 2 ); - VERIFY_EVALUATION_COUNT( rm3.noalias() += (m1*cv1) * (rv1 * m1), 2 ); - - // Check nested products - VERIFY_EVALUATION_COUNT( cvres.noalias() = m1.adjoint() * m1 * cv1, 1 ); - VERIFY_EVALUATION_COUNT( rvres.noalias() = rv1 * (m1 * m2.adjoint()), 1 ); -} - -void test_product_notemporary() -{ - int s; - for(int i = 0; i < g_repeat; i++) { - s = internal::random<int>(16,EIGEN_TEST_MAX_SIZE); - CALL_SUBTEST_1( product_notemporary(MatrixXf(s, s)) ); - CALL_SUBTEST_2( product_notemporary(MatrixXd(s, s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - - s = internal::random<int>(16,EIGEN_TEST_MAX_SIZE/2); - CALL_SUBTEST_3( product_notemporary(MatrixXcf(s,s)) ); - CALL_SUBTEST_4( product_notemporary(MatrixXcd(s,s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - } -} diff --git a/eigen/test/product_selfadjoint.cpp b/eigen/test/product_selfadjoint.cpp deleted file mode 100644 index 88d6839..0000000 --- a/eigen/test/product_selfadjoint.cpp +++ /dev/null @@ -1,86 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename MatrixType> void product_selfadjoint(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - typedef Matrix<Scalar, 1, MatrixType::RowsAtCompileTime> RowVectorType; - - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, Dynamic, RowMajor> RhsMatrixType; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3; - VectorType v1 = VectorType::Random(rows), - v2 = VectorType::Random(rows), - v3(rows); - RowVectorType r1 = RowVectorType::Random(rows), - r2 = RowVectorType::Random(rows); - RhsMatrixType m4 = RhsMatrixType::Random(rows,10); - - Scalar s1 = internal::random<Scalar>(), - s2 = internal::random<Scalar>(), - s3 = internal::random<Scalar>(); - - m1 = (m1.adjoint() + m1).eval(); - - // rank2 update - m2 = m1.template triangularView<Lower>(); - m2.template selfadjointView<Lower>().rankUpdate(v1,v2); - VERIFY_IS_APPROX(m2, (m1 + v1 * v2.adjoint()+ v2 * v1.adjoint()).template triangularView<Lower>().toDenseMatrix()); - - m2 = m1.template triangularView<Upper>(); - m2.template selfadjointView<Upper>().rankUpdate(-v1,s2*v2,s3); - VERIFY_IS_APPROX(m2, (m1 + (s3*(-v1)*(s2*v2).adjoint()+numext::conj(s3)*(s2*v2)*(-v1).adjoint())).template triangularView<Upper>().toDenseMatrix()); - - m2 = m1.template triangularView<Upper>(); - m2.template selfadjointView<Upper>().rankUpdate(-s2*r1.adjoint(),r2.adjoint()*s3,s1); - VERIFY_IS_APPROX(m2, (m1 + s1*(-s2*r1.adjoint())*(r2.adjoint()*s3).adjoint() + numext::conj(s1)*(r2.adjoint()*s3) * (-s2*r1.adjoint()).adjoint()).template triangularView<Upper>().toDenseMatrix()); - - if (rows>1) - { - m2 = m1.template triangularView<Lower>(); - m2.block(1,1,rows-1,cols-1).template selfadjointView<Lower>().rankUpdate(v1.tail(rows-1),v2.head(cols-1)); - m3 = m1; - m3.block(1,1,rows-1,cols-1) += v1.tail(rows-1) * v2.head(cols-1).adjoint()+ v2.head(cols-1) * v1.tail(rows-1).adjoint(); - VERIFY_IS_APPROX(m2, m3.template triangularView<Lower>().toDenseMatrix()); - } -} - -void test_product_selfadjoint() -{ - int s = 0; - for(int i = 0; i < g_repeat ; i++) { - CALL_SUBTEST_1( product_selfadjoint(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( product_selfadjoint(Matrix<float, 2, 2>()) ); - CALL_SUBTEST_3( product_selfadjoint(Matrix3d()) ); - - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2); - CALL_SUBTEST_4( product_selfadjoint(MatrixXcf(s, s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2); - CALL_SUBTEST_5( product_selfadjoint(MatrixXcd(s,s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE); - CALL_SUBTEST_6( product_selfadjoint(MatrixXd(s,s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE); - CALL_SUBTEST_7( product_selfadjoint(Matrix<float,Dynamic,Dynamic,RowMajor>(s,s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - } -} diff --git a/eigen/test/product_small.cpp b/eigen/test/product_small.cpp deleted file mode 100644 index fdfdd9f..0000000 --- a/eigen/test/product_small.cpp +++ /dev/null @@ -1,293 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_NO_STATIC_ASSERT -#include "product.h" -#include <Eigen/LU> - -// regression test for bug 447 -template<int> -void product1x1() -{ - Matrix<float,1,3> matAstatic; - Matrix<float,3,1> matBstatic; - matAstatic.setRandom(); - matBstatic.setRandom(); - VERIFY_IS_APPROX( (matAstatic * matBstatic).coeff(0,0), - matAstatic.cwiseProduct(matBstatic.transpose()).sum() ); - - MatrixXf matAdynamic(1,3); - MatrixXf matBdynamic(3,1); - matAdynamic.setRandom(); - matBdynamic.setRandom(); - VERIFY_IS_APPROX( (matAdynamic * matBdynamic).coeff(0,0), - matAdynamic.cwiseProduct(matBdynamic.transpose()).sum() ); -} - -template<typename TC, typename TA, typename TB> -const TC& ref_prod(TC &C, const TA &A, const TB &B) -{ - for(Index i=0;i<C.rows();++i) - for(Index j=0;j<C.cols();++j) - for(Index k=0;k<A.cols();++k) - C.coeffRef(i,j) += A.coeff(i,k) * B.coeff(k,j); - return C; -} - -template<typename T, int Rows, int Cols, int Depth, int OC, int OA, int OB> -typename internal::enable_if<! ( (Rows ==1&&Depth!=1&&OA==ColMajor) - || (Depth==1&&Rows !=1&&OA==RowMajor) - || (Cols ==1&&Depth!=1&&OB==RowMajor) - || (Depth==1&&Cols !=1&&OB==ColMajor) - || (Rows ==1&&Cols !=1&&OC==ColMajor) - || (Cols ==1&&Rows !=1&&OC==RowMajor)),void>::type -test_lazy_single(int rows, int cols, int depth) -{ - Matrix<T,Rows,Depth,OA> A(rows,depth); A.setRandom(); - Matrix<T,Depth,Cols,OB> B(depth,cols); B.setRandom(); - Matrix<T,Rows,Cols,OC> C(rows,cols); C.setRandom(); - Matrix<T,Rows,Cols,OC> D(C); - VERIFY_IS_APPROX(C+=A.lazyProduct(B), ref_prod(D,A,B)); -} - -template<typename T, int Rows, int Cols, int Depth, int OC, int OA, int OB> -typename internal::enable_if< ( (Rows ==1&&Depth!=1&&OA==ColMajor) - || (Depth==1&&Rows !=1&&OA==RowMajor) - || (Cols ==1&&Depth!=1&&OB==RowMajor) - || (Depth==1&&Cols !=1&&OB==ColMajor) - || (Rows ==1&&Cols !=1&&OC==ColMajor) - || (Cols ==1&&Rows !=1&&OC==RowMajor)),void>::type -test_lazy_single(int, int, int) -{ -} - -template<typename T, int Rows, int Cols, int Depth> -void test_lazy_all_layout(int rows=Rows, int cols=Cols, int depth=Depth) -{ - CALL_SUBTEST(( test_lazy_single<T,Rows,Cols,Depth,ColMajor,ColMajor,ColMajor>(rows,cols,depth) )); - CALL_SUBTEST(( test_lazy_single<T,Rows,Cols,Depth,RowMajor,ColMajor,ColMajor>(rows,cols,depth) )); - CALL_SUBTEST(( test_lazy_single<T,Rows,Cols,Depth,ColMajor,RowMajor,ColMajor>(rows,cols,depth) )); - CALL_SUBTEST(( test_lazy_single<T,Rows,Cols,Depth,RowMajor,RowMajor,ColMajor>(rows,cols,depth) )); - CALL_SUBTEST(( test_lazy_single<T,Rows,Cols,Depth,ColMajor,ColMajor,RowMajor>(rows,cols,depth) )); - CALL_SUBTEST(( test_lazy_single<T,Rows,Cols,Depth,RowMajor,ColMajor,RowMajor>(rows,cols,depth) )); - CALL_SUBTEST(( test_lazy_single<T,Rows,Cols,Depth,ColMajor,RowMajor,RowMajor>(rows,cols,depth) )); - CALL_SUBTEST(( test_lazy_single<T,Rows,Cols,Depth,RowMajor,RowMajor,RowMajor>(rows,cols,depth) )); -} - -template<typename T> -void test_lazy_l1() -{ - int rows = internal::random<int>(1,12); - int cols = internal::random<int>(1,12); - int depth = internal::random<int>(1,12); - - // Inner - CALL_SUBTEST(( test_lazy_all_layout<T,1,1,1>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,1,1,2>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,1,1,3>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,1,1,8>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,1,1,9>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,1,1,-1>(1,1,depth) )); - - // Outer - CALL_SUBTEST(( test_lazy_all_layout<T,2,1,1>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,1,2,1>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,2,2,1>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,3,3,1>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,4,4,1>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,4,8,1>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,4,-1,1>(4,cols) )); - CALL_SUBTEST(( test_lazy_all_layout<T,7,-1,1>(7,cols) )); - CALL_SUBTEST(( test_lazy_all_layout<T,-1,8,1>(rows) )); - CALL_SUBTEST(( test_lazy_all_layout<T,-1,3,1>(rows) )); - CALL_SUBTEST(( test_lazy_all_layout<T,-1,-1,1>(rows,cols) )); -} - -template<typename T> -void test_lazy_l2() -{ - int rows = internal::random<int>(1,12); - int cols = internal::random<int>(1,12); - int depth = internal::random<int>(1,12); - - // mat-vec - CALL_SUBTEST(( test_lazy_all_layout<T,2,1,2>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,2,1,4>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,4,1,2>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,4,1,4>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,5,1,4>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,4,1,5>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,4,1,6>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,6,1,4>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,8,1,8>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,-1,1,4>(rows) )); - CALL_SUBTEST(( test_lazy_all_layout<T,4,1,-1>(4,1,depth) )); - CALL_SUBTEST(( test_lazy_all_layout<T,-1,1,-1>(rows,1,depth) )); - - // vec-mat - CALL_SUBTEST(( test_lazy_all_layout<T,1,2,2>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,1,2,4>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,1,4,2>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,1,4,4>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,1,5,4>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,1,4,5>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,1,4,6>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,1,6,4>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,1,8,8>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,1,-1, 4>(1,cols) )); - CALL_SUBTEST(( test_lazy_all_layout<T,1, 4,-1>(1,4,depth) )); - CALL_SUBTEST(( test_lazy_all_layout<T,1,-1,-1>(1,cols,depth) )); -} - -template<typename T> -void test_lazy_l3() -{ - int rows = internal::random<int>(1,12); - int cols = internal::random<int>(1,12); - int depth = internal::random<int>(1,12); - // mat-mat - CALL_SUBTEST(( test_lazy_all_layout<T,2,4,2>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,2,6,4>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,4,3,2>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,4,8,4>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,5,6,4>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,4,2,5>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,4,7,6>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,6,8,4>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,8,3,8>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,-1,6,4>(rows) )); - CALL_SUBTEST(( test_lazy_all_layout<T,4,3,-1>(4,3,depth) )); - CALL_SUBTEST(( test_lazy_all_layout<T,-1,6,-1>(rows,6,depth) )); - CALL_SUBTEST(( test_lazy_all_layout<T,8,2,2>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,5,2,4>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,4,4,2>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,8,4,4>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,6,5,4>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,4,4,5>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,3,4,6>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,2,6,4>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,7,8,8>() )); - CALL_SUBTEST(( test_lazy_all_layout<T,8,-1, 4>(8,cols) )); - CALL_SUBTEST(( test_lazy_all_layout<T,3, 4,-1>(3,4,depth) )); - CALL_SUBTEST(( test_lazy_all_layout<T,4,-1,-1>(4,cols,depth) )); -} - -template<typename T,int N,int M,int K> -void test_linear_but_not_vectorizable() -{ - // Check tricky cases for which the result of the product is a vector and thus must exhibit the LinearBit flag, - // but is not vectorizable along the linear dimension. - Index n = N==Dynamic ? internal::random<Index>(1,32) : N; - Index m = M==Dynamic ? internal::random<Index>(1,32) : M; - Index k = K==Dynamic ? internal::random<Index>(1,32) : K; - - { - Matrix<T,N,M+1> A; A.setRandom(n,m+1); - Matrix<T,M*2,K> B; B.setRandom(m*2,k); - Matrix<T,1,K> C; - Matrix<T,1,K> R; - - C.noalias() = A.template topLeftCorner<1,M>() * (B.template topRows<M>()+B.template bottomRows<M>()); - R.noalias() = A.template topLeftCorner<1,M>() * (B.template topRows<M>()+B.template bottomRows<M>()).eval(); - VERIFY_IS_APPROX(C,R); - } - - { - Matrix<T,M+1,N,RowMajor> A; A.setRandom(m+1,n); - Matrix<T,K,M*2,RowMajor> B; B.setRandom(k,m*2); - Matrix<T,K,1> C; - Matrix<T,K,1> R; - - C.noalias() = (B.template leftCols<M>()+B.template rightCols<M>()) * A.template topLeftCorner<M,1>(); - R.noalias() = (B.template leftCols<M>()+B.template rightCols<M>()).eval() * A.template topLeftCorner<M,1>(); - VERIFY_IS_APPROX(C,R); - } -} - -template<int Rows> -void bug_1311() -{ - Matrix< double, Rows, 2 > A; A.setRandom(); - Vector2d b = Vector2d::Random() ; - Matrix<double,Rows,1> res; - res.noalias() = 1. * (A * b); - VERIFY_IS_APPROX(res, A*b); - res.noalias() = 1.*A * b; - VERIFY_IS_APPROX(res, A*b); - res.noalias() = (1.*A).lazyProduct(b); - VERIFY_IS_APPROX(res, A*b); - res.noalias() = (1.*A).lazyProduct(1.*b); - VERIFY_IS_APPROX(res, A*b); - res.noalias() = (A).lazyProduct(1.*b); - VERIFY_IS_APPROX(res, A*b); -} - -void test_product_small() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( product(Matrix<float, 3, 2>()) ); - CALL_SUBTEST_2( product(Matrix<int, 3, 17>()) ); - CALL_SUBTEST_8( product(Matrix<double, 3, 17>()) ); - CALL_SUBTEST_3( product(Matrix3d()) ); - CALL_SUBTEST_4( product(Matrix4d()) ); - CALL_SUBTEST_5( product(Matrix4f()) ); - CALL_SUBTEST_6( product1x1<0>() ); - - CALL_SUBTEST_11( test_lazy_l1<float>() ); - CALL_SUBTEST_12( test_lazy_l2<float>() ); - CALL_SUBTEST_13( test_lazy_l3<float>() ); - - CALL_SUBTEST_21( test_lazy_l1<double>() ); - CALL_SUBTEST_22( test_lazy_l2<double>() ); - CALL_SUBTEST_23( test_lazy_l3<double>() ); - - CALL_SUBTEST_31( test_lazy_l1<std::complex<float> >() ); - CALL_SUBTEST_32( test_lazy_l2<std::complex<float> >() ); - CALL_SUBTEST_33( test_lazy_l3<std::complex<float> >() ); - - CALL_SUBTEST_41( test_lazy_l1<std::complex<double> >() ); - CALL_SUBTEST_42( test_lazy_l2<std::complex<double> >() ); - CALL_SUBTEST_43( test_lazy_l3<std::complex<double> >() ); - - CALL_SUBTEST_7(( test_linear_but_not_vectorizable<float,2,1,Dynamic>() )); - CALL_SUBTEST_7(( test_linear_but_not_vectorizable<float,3,1,Dynamic>() )); - CALL_SUBTEST_7(( test_linear_but_not_vectorizable<float,2,1,16>() )); - - CALL_SUBTEST_6( bug_1311<3>() ); - CALL_SUBTEST_6( bug_1311<5>() ); - } - -#ifdef EIGEN_TEST_PART_6 - { - // test compilation of (outer_product) * vector - Vector3f v = Vector3f::Random(); - VERIFY_IS_APPROX( (v * v.transpose()) * v, (v * v.transpose()).eval() * v); - } - - { - // regression test for pull-request #93 - Eigen::Matrix<double, 1, 1> A; A.setRandom(); - Eigen::Matrix<double, 18, 1> B; B.setRandom(); - Eigen::Matrix<double, 1, 18> C; C.setRandom(); - VERIFY_IS_APPROX(B * A.inverse(), B * A.inverse()[0]); - VERIFY_IS_APPROX(A.inverse() * C, A.inverse()[0] * C); - } - - { - Eigen::Matrix<double, 10, 10> A, B, C; - A.setRandom(); - C = A; - for(int k=0; k<79; ++k) - C = C * A; - B.noalias() = (((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A)) * ((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))) - * (((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A)) * ((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))); - VERIFY_IS_APPROX(B,C); - } -#endif -} diff --git a/eigen/test/product_symm.cpp b/eigen/test/product_symm.cpp deleted file mode 100644 index 7d1042a..0000000 --- a/eigen/test/product_symm.cpp +++ /dev/null @@ -1,111 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename Scalar, int Size, int OtherSize> void symm(int size = Size, int othersize = OtherSize) -{ - typedef Matrix<Scalar, Size, Size> MatrixType; - typedef Matrix<Scalar, Size, OtherSize> Rhs1; - typedef Matrix<Scalar, OtherSize, Size> Rhs2; - enum { order = OtherSize==1 ? 0 : RowMajor }; - typedef Matrix<Scalar, Size, OtherSize,order> Rhs3; - - Index rows = size; - Index cols = size; - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), m3; - - m1 = (m1+m1.adjoint()).eval(); - - Rhs1 rhs1 = Rhs1::Random(cols, othersize), rhs12(cols, othersize), rhs13(cols, othersize); - Rhs2 rhs2 = Rhs2::Random(othersize, rows), rhs22(othersize, rows), rhs23(othersize, rows); - Rhs3 rhs3 = Rhs3::Random(cols, othersize), rhs32(cols, othersize), rhs33(cols, othersize); - - Scalar s1 = internal::random<Scalar>(), - s2 = internal::random<Scalar>(); - - m2 = m1.template triangularView<Lower>(); - m3 = m2.template selfadjointView<Lower>(); - VERIFY_IS_EQUAL(m1, m3); - VERIFY_IS_APPROX(rhs12 = (s1*m2).template selfadjointView<Lower>() * (s2*rhs1), - rhs13 = (s1*m1) * (s2*rhs1)); - - VERIFY_IS_APPROX(rhs12 = (s1*m2).transpose().template selfadjointView<Upper>() * (s2*rhs1), - rhs13 = (s1*m1.transpose()) * (s2*rhs1)); - - VERIFY_IS_APPROX(rhs12 = (s1*m2).template selfadjointView<Lower>().transpose() * (s2*rhs1), - rhs13 = (s1*m1.transpose()) * (s2*rhs1)); - - VERIFY_IS_APPROX(rhs12 = (s1*m2).conjugate().template selfadjointView<Lower>() * (s2*rhs1), - rhs13 = (s1*m1).conjugate() * (s2*rhs1)); - - VERIFY_IS_APPROX(rhs12 = (s1*m2).template selfadjointView<Lower>().conjugate() * (s2*rhs1), - rhs13 = (s1*m1).conjugate() * (s2*rhs1)); - - VERIFY_IS_APPROX(rhs12 = (s1*m2).adjoint().template selfadjointView<Upper>() * (s2*rhs1), - rhs13 = (s1*m1).adjoint() * (s2*rhs1)); - - VERIFY_IS_APPROX(rhs12 = (s1*m2).template selfadjointView<Lower>().adjoint() * (s2*rhs1), - rhs13 = (s1*m1).adjoint() * (s2*rhs1)); - - m2 = m1.template triangularView<Upper>(); rhs12.setRandom(); rhs13 = rhs12; - m3 = m2.template selfadjointView<Upper>(); - VERIFY_IS_EQUAL(m1, m3); - VERIFY_IS_APPROX(rhs12 += (s1*m2).template selfadjointView<Upper>() * (s2*rhs1), - rhs13 += (s1*m1) * (s2*rhs1)); - - m2 = m1.template triangularView<Lower>(); - VERIFY_IS_APPROX(rhs12 = (s1*m2).template selfadjointView<Lower>() * (s2*rhs2.adjoint()), - rhs13 = (s1*m1) * (s2*rhs2.adjoint())); - - m2 = m1.template triangularView<Upper>(); - VERIFY_IS_APPROX(rhs12 = (s1*m2).template selfadjointView<Upper>() * (s2*rhs2.adjoint()), - rhs13 = (s1*m1) * (s2*rhs2.adjoint())); - - m2 = m1.template triangularView<Upper>(); - VERIFY_IS_APPROX(rhs12 = (s1*m2.adjoint()).template selfadjointView<Lower>() * (s2*rhs2.adjoint()), - rhs13 = (s1*m1.adjoint()) * (s2*rhs2.adjoint())); - - // test row major = <...> - m2 = m1.template triangularView<Lower>(); rhs12.setRandom(); rhs13 = rhs12; - VERIFY_IS_APPROX(rhs12 -= (s1*m2).template selfadjointView<Lower>() * (s2*rhs3), - rhs13 -= (s1*m1) * (s2 * rhs3)); - - m2 = m1.template triangularView<Upper>(); - VERIFY_IS_APPROX(rhs12 = (s1*m2.adjoint()).template selfadjointView<Lower>() * (s2*rhs3).conjugate(), - rhs13 = (s1*m1.adjoint()) * (s2*rhs3).conjugate()); - - - m2 = m1.template triangularView<Upper>(); rhs13 = rhs12; - VERIFY_IS_APPROX(rhs12.noalias() += s1 * ((m2.adjoint()).template selfadjointView<Lower>() * (s2*rhs3).conjugate()), - rhs13 += (s1*m1.adjoint()) * (s2*rhs3).conjugate()); - - m2 = m1.template triangularView<Lower>(); - VERIFY_IS_APPROX(rhs22 = (rhs2) * (m2).template selfadjointView<Lower>(), rhs23 = (rhs2) * (m1)); - VERIFY_IS_APPROX(rhs22 = (s2*rhs2) * (s1*m2).template selfadjointView<Lower>(), rhs23 = (s2*rhs2) * (s1*m1)); - -} - -void test_product_symm() -{ - for(int i = 0; i < g_repeat ; i++) - { - CALL_SUBTEST_1(( symm<float,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE)) )); - CALL_SUBTEST_2(( symm<double,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE)) )); - CALL_SUBTEST_3(( symm<std::complex<float>,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2),internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2)) )); - CALL_SUBTEST_4(( symm<std::complex<double>,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2),internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2)) )); - - CALL_SUBTEST_5(( symm<float,Dynamic,1>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)) )); - CALL_SUBTEST_6(( symm<double,Dynamic,1>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)) )); - CALL_SUBTEST_7(( symm<std::complex<float>,Dynamic,1>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)) )); - CALL_SUBTEST_8(( symm<std::complex<double>,Dynamic,1>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)) )); - } -} diff --git a/eigen/test/product_syrk.cpp b/eigen/test/product_syrk.cpp deleted file mode 100644 index 3ebbe14..0000000 --- a/eigen/test/product_syrk.cpp +++ /dev/null @@ -1,135 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename MatrixType> void syrk(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime, RowMajor> RMatrixType; - typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, Dynamic> Rhs1; - typedef Matrix<Scalar, Dynamic, MatrixType::RowsAtCompileTime> Rhs2; - typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, Dynamic,RowMajor> Rhs3; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3 = MatrixType::Random(rows, cols); - RMatrixType rm2 = MatrixType::Random(rows, cols); - - Rhs1 rhs1 = Rhs1::Random(internal::random<int>(1,320), cols); Rhs1 rhs11 = Rhs1::Random(rhs1.rows(), cols); - Rhs2 rhs2 = Rhs2::Random(rows, internal::random<int>(1,320)); Rhs2 rhs22 = Rhs2::Random(rows, rhs2.cols()); - Rhs3 rhs3 = Rhs3::Random(internal::random<int>(1,320), rows); - - Scalar s1 = internal::random<Scalar>(); - - Index c = internal::random<Index>(0,cols-1); - - m2.setZero(); - VERIFY_IS_APPROX((m2.template selfadjointView<Lower>().rankUpdate(rhs2,s1)._expression()), - ((s1 * rhs2 * rhs2.adjoint()).eval().template triangularView<Lower>().toDenseMatrix())); - m2.setZero(); - VERIFY_IS_APPROX(((m2.template triangularView<Lower>() += s1 * rhs2 * rhs22.adjoint()).nestedExpression()), - ((s1 * rhs2 * rhs22.adjoint()).eval().template triangularView<Lower>().toDenseMatrix())); - - - m2.setZero(); - VERIFY_IS_APPROX(m2.template selfadjointView<Upper>().rankUpdate(rhs2,s1)._expression(), - (s1 * rhs2 * rhs2.adjoint()).eval().template triangularView<Upper>().toDenseMatrix()); - m2.setZero(); - VERIFY_IS_APPROX((m2.template triangularView<Upper>() += s1 * rhs22 * rhs2.adjoint()).nestedExpression(), - (s1 * rhs22 * rhs2.adjoint()).eval().template triangularView<Upper>().toDenseMatrix()); - - - m2.setZero(); - VERIFY_IS_APPROX(m2.template selfadjointView<Lower>().rankUpdate(rhs1.adjoint(),s1)._expression(), - (s1 * rhs1.adjoint() * rhs1).eval().template triangularView<Lower>().toDenseMatrix()); - m2.setZero(); - VERIFY_IS_APPROX((m2.template triangularView<Lower>() += s1 * rhs11.adjoint() * rhs1).nestedExpression(), - (s1 * rhs11.adjoint() * rhs1).eval().template triangularView<Lower>().toDenseMatrix()); - - - m2.setZero(); - VERIFY_IS_APPROX(m2.template selfadjointView<Upper>().rankUpdate(rhs1.adjoint(),s1)._expression(), - (s1 * rhs1.adjoint() * rhs1).eval().template triangularView<Upper>().toDenseMatrix()); - VERIFY_IS_APPROX((m2.template triangularView<Upper>() = s1 * rhs1.adjoint() * rhs11).nestedExpression(), - (s1 * rhs1.adjoint() * rhs11).eval().template triangularView<Upper>().toDenseMatrix()); - - - m2.setZero(); - VERIFY_IS_APPROX(m2.template selfadjointView<Lower>().rankUpdate(rhs3.adjoint(),s1)._expression(), - (s1 * rhs3.adjoint() * rhs3).eval().template triangularView<Lower>().toDenseMatrix()); - - m2.setZero(); - VERIFY_IS_APPROX(m2.template selfadjointView<Upper>().rankUpdate(rhs3.adjoint(),s1)._expression(), - (s1 * rhs3.adjoint() * rhs3).eval().template triangularView<Upper>().toDenseMatrix()); - - m2.setZero(); - VERIFY_IS_APPROX((m2.template selfadjointView<Lower>().rankUpdate(m1.col(c),s1)._expression()), - ((s1 * m1.col(c) * m1.col(c).adjoint()).eval().template triangularView<Lower>().toDenseMatrix())); - - m2.setZero(); - VERIFY_IS_APPROX((m2.template selfadjointView<Upper>().rankUpdate(m1.col(c),s1)._expression()), - ((s1 * m1.col(c) * m1.col(c).adjoint()).eval().template triangularView<Upper>().toDenseMatrix())); - rm2.setZero(); - VERIFY_IS_APPROX((rm2.template selfadjointView<Upper>().rankUpdate(m1.col(c),s1)._expression()), - ((s1 * m1.col(c) * m1.col(c).adjoint()).eval().template triangularView<Upper>().toDenseMatrix())); - m2.setZero(); - VERIFY_IS_APPROX((m2.template triangularView<Upper>() += s1 * m3.col(c) * m1.col(c).adjoint()).nestedExpression(), - ((s1 * m3.col(c) * m1.col(c).adjoint()).eval().template triangularView<Upper>().toDenseMatrix())); - rm2.setZero(); - VERIFY_IS_APPROX((rm2.template triangularView<Upper>() += s1 * m1.col(c) * m3.col(c).adjoint()).nestedExpression(), - ((s1 * m1.col(c) * m3.col(c).adjoint()).eval().template triangularView<Upper>().toDenseMatrix())); - - m2.setZero(); - VERIFY_IS_APPROX((m2.template selfadjointView<Lower>().rankUpdate(m1.col(c).conjugate(),s1)._expression()), - ((s1 * m1.col(c).conjugate() * m1.col(c).conjugate().adjoint()).eval().template triangularView<Lower>().toDenseMatrix())); - - m2.setZero(); - VERIFY_IS_APPROX((m2.template selfadjointView<Upper>().rankUpdate(m1.col(c).conjugate(),s1)._expression()), - ((s1 * m1.col(c).conjugate() * m1.col(c).conjugate().adjoint()).eval().template triangularView<Upper>().toDenseMatrix())); - - - m2.setZero(); - VERIFY_IS_APPROX((m2.template selfadjointView<Lower>().rankUpdate(m1.row(c),s1)._expression()), - ((s1 * m1.row(c).transpose() * m1.row(c).transpose().adjoint()).eval().template triangularView<Lower>().toDenseMatrix())); - rm2.setZero(); - VERIFY_IS_APPROX((rm2.template selfadjointView<Lower>().rankUpdate(m1.row(c),s1)._expression()), - ((s1 * m1.row(c).transpose() * m1.row(c).transpose().adjoint()).eval().template triangularView<Lower>().toDenseMatrix())); - m2.setZero(); - VERIFY_IS_APPROX((m2.template triangularView<Lower>() += s1 * m3.row(c).transpose() * m1.row(c).transpose().adjoint()).nestedExpression(), - ((s1 * m3.row(c).transpose() * m1.row(c).transpose().adjoint()).eval().template triangularView<Lower>().toDenseMatrix())); - rm2.setZero(); - VERIFY_IS_APPROX((rm2.template triangularView<Lower>() += s1 * m3.row(c).transpose() * m1.row(c).transpose().adjoint()).nestedExpression(), - ((s1 * m3.row(c).transpose() * m1.row(c).transpose().adjoint()).eval().template triangularView<Lower>().toDenseMatrix())); - - - m2.setZero(); - VERIFY_IS_APPROX((m2.template selfadjointView<Upper>().rankUpdate(m1.row(c).adjoint(),s1)._expression()), - ((s1 * m1.row(c).adjoint() * m1.row(c).adjoint().adjoint()).eval().template triangularView<Upper>().toDenseMatrix())); -} - -void test_product_syrk() -{ - for(int i = 0; i < g_repeat ; i++) - { - int s; - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE); - CALL_SUBTEST_1( syrk(MatrixXf(s, s)) ); - CALL_SUBTEST_2( syrk(MatrixXd(s, s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2); - CALL_SUBTEST_3( syrk(MatrixXcf(s, s)) ); - CALL_SUBTEST_4( syrk(MatrixXcd(s, s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - } -} diff --git a/eigen/test/product_trmm.cpp b/eigen/test/product_trmm.cpp deleted file mode 100644 index e08d9f3..0000000 --- a/eigen/test/product_trmm.cpp +++ /dev/null @@ -1,127 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename T> -int get_random_size() -{ - const int factor = NumTraits<T>::ReadCost; - const int max_test_size = EIGEN_TEST_MAX_SIZE>2*factor ? EIGEN_TEST_MAX_SIZE/factor : EIGEN_TEST_MAX_SIZE; - return internal::random<int>(1,max_test_size); -} - -template<typename Scalar, int Mode, int TriOrder, int OtherOrder, int ResOrder, int OtherCols> -void trmm(int rows=get_random_size<Scalar>(), - int cols=get_random_size<Scalar>(), - int otherCols = OtherCols==Dynamic?get_random_size<Scalar>():OtherCols) -{ - typedef Matrix<Scalar,Dynamic,Dynamic,TriOrder> TriMatrix; - typedef Matrix<Scalar,Dynamic,OtherCols,OtherCols==1?ColMajor:OtherOrder> OnTheRight; - typedef Matrix<Scalar,OtherCols,Dynamic,OtherCols==1?RowMajor:OtherOrder> OnTheLeft; - - typedef Matrix<Scalar,Dynamic,OtherCols,OtherCols==1?ColMajor:ResOrder> ResXS; - typedef Matrix<Scalar,OtherCols,Dynamic,OtherCols==1?RowMajor:ResOrder> ResSX; - - TriMatrix mat(rows,cols), tri(rows,cols), triTr(cols,rows), s1tri(rows,cols), s1triTr(cols,rows); - - OnTheRight ge_right(cols,otherCols); - OnTheLeft ge_left(otherCols,rows); - ResSX ge_sx, ge_sx_save; - ResXS ge_xs, ge_xs_save; - - Scalar s1 = internal::random<Scalar>(), - s2 = internal::random<Scalar>(); - - mat.setRandom(); - tri = mat.template triangularView<Mode>(); - triTr = mat.transpose().template triangularView<Mode>(); - s1tri = (s1*mat).template triangularView<Mode>(); - s1triTr = (s1*mat).transpose().template triangularView<Mode>(); - ge_right.setRandom(); - ge_left.setRandom(); - - VERIFY_IS_APPROX( ge_xs = mat.template triangularView<Mode>() * ge_right, tri * ge_right); - VERIFY_IS_APPROX( ge_sx = ge_left * mat.template triangularView<Mode>(), ge_left * tri); - - VERIFY_IS_APPROX( ge_xs.noalias() = mat.template triangularView<Mode>() * ge_right, tri * ge_right); - VERIFY_IS_APPROX( ge_sx.noalias() = ge_left * mat.template triangularView<Mode>(), ge_left * tri); - - if((Mode&UnitDiag)==0) - VERIFY_IS_APPROX( ge_xs.noalias() = (s1*mat.adjoint()).template triangularView<Mode>() * (s2*ge_left.transpose()), s1*triTr.conjugate() * (s2*ge_left.transpose())); - - VERIFY_IS_APPROX( ge_xs.noalias() = (s1*mat.transpose()).template triangularView<Mode>() * (s2*ge_left.transpose()), s1triTr * (s2*ge_left.transpose())); - VERIFY_IS_APPROX( ge_sx.noalias() = (s2*ge_left) * (s1*mat).template triangularView<Mode>(), (s2*ge_left)*s1tri); - - VERIFY_IS_APPROX( ge_sx.noalias() = ge_right.transpose() * mat.adjoint().template triangularView<Mode>(), ge_right.transpose() * triTr.conjugate()); - VERIFY_IS_APPROX( ge_sx.noalias() = ge_right.adjoint() * mat.adjoint().template triangularView<Mode>(), ge_right.adjoint() * triTr.conjugate()); - - ge_xs_save = ge_xs; - if((Mode&UnitDiag)==0) - VERIFY_IS_APPROX( (ge_xs_save + s1*triTr.conjugate() * (s2*ge_left.adjoint())).eval(), ge_xs.noalias() += (s1*mat.adjoint()).template triangularView<Mode>() * (s2*ge_left.adjoint()) ); - ge_xs_save = ge_xs; - VERIFY_IS_APPROX( (ge_xs_save + s1triTr * (s2*ge_left.adjoint())).eval(), ge_xs.noalias() += (s1*mat.transpose()).template triangularView<Mode>() * (s2*ge_left.adjoint()) ); - ge_sx.setRandom(); - ge_sx_save = ge_sx; - if((Mode&UnitDiag)==0) - VERIFY_IS_APPROX( ge_sx_save - (ge_right.adjoint() * (-s1 * triTr).conjugate()).eval(), ge_sx.noalias() -= (ge_right.adjoint() * (-s1 * mat).adjoint().template triangularView<Mode>()).eval()); - - if((Mode&UnitDiag)==0) - VERIFY_IS_APPROX( ge_xs = (s1*mat).adjoint().template triangularView<Mode>() * ge_left.adjoint(), numext::conj(s1) * triTr.conjugate() * ge_left.adjoint()); - VERIFY_IS_APPROX( ge_xs = (s1*mat).transpose().template triangularView<Mode>() * ge_left.adjoint(), s1triTr * ge_left.adjoint()); - - - // TODO check with sub-matrix expressions ? -} - -template<typename Scalar, int Mode, int TriOrder> -void trmv(int rows=get_random_size<Scalar>(), int cols=get_random_size<Scalar>()) -{ - trmm<Scalar,Mode,TriOrder,ColMajor,ColMajor,1>(rows,cols,1); -} - -template<typename Scalar, int Mode, int TriOrder, int OtherOrder, int ResOrder> -void trmm(int rows=get_random_size<Scalar>(), int cols=get_random_size<Scalar>(), int otherCols = get_random_size<Scalar>()) -{ - trmm<Scalar,Mode,TriOrder,OtherOrder,ResOrder,Dynamic>(rows,cols,otherCols); -} - -#define CALL_ALL_ORDERS(NB,SCALAR,MODE) \ - EIGEN_CAT(CALL_SUBTEST_,NB)((trmm<SCALAR, MODE, ColMajor,ColMajor,ColMajor>())); \ - EIGEN_CAT(CALL_SUBTEST_,NB)((trmm<SCALAR, MODE, ColMajor,ColMajor,RowMajor>())); \ - EIGEN_CAT(CALL_SUBTEST_,NB)((trmm<SCALAR, MODE, ColMajor,RowMajor,ColMajor>())); \ - EIGEN_CAT(CALL_SUBTEST_,NB)((trmm<SCALAR, MODE, ColMajor,RowMajor,RowMajor>())); \ - EIGEN_CAT(CALL_SUBTEST_,NB)((trmm<SCALAR, MODE, RowMajor,ColMajor,ColMajor>())); \ - EIGEN_CAT(CALL_SUBTEST_,NB)((trmm<SCALAR, MODE, RowMajor,ColMajor,RowMajor>())); \ - EIGEN_CAT(CALL_SUBTEST_,NB)((trmm<SCALAR, MODE, RowMajor,RowMajor,ColMajor>())); \ - EIGEN_CAT(CALL_SUBTEST_,NB)((trmm<SCALAR, MODE, RowMajor,RowMajor,RowMajor>())); \ - \ - EIGEN_CAT(CALL_SUBTEST_1,NB)((trmv<SCALAR, MODE, ColMajor>())); \ - EIGEN_CAT(CALL_SUBTEST_1,NB)((trmv<SCALAR, MODE, RowMajor>())); - - -#define CALL_ALL(NB,SCALAR) \ - CALL_ALL_ORDERS(EIGEN_CAT(1,NB),SCALAR,Upper) \ - CALL_ALL_ORDERS(EIGEN_CAT(2,NB),SCALAR,UnitUpper) \ - CALL_ALL_ORDERS(EIGEN_CAT(3,NB),SCALAR,StrictlyUpper) \ - CALL_ALL_ORDERS(EIGEN_CAT(1,NB),SCALAR,Lower) \ - CALL_ALL_ORDERS(EIGEN_CAT(2,NB),SCALAR,UnitLower) \ - CALL_ALL_ORDERS(EIGEN_CAT(3,NB),SCALAR,StrictlyLower) - - -void test_product_trmm() -{ - for(int i = 0; i < g_repeat ; i++) - { - CALL_ALL(1,float); // EIGEN_SUFFIXES;11;111;21;121;31;131 - CALL_ALL(2,double); // EIGEN_SUFFIXES;12;112;22;122;32;132 - CALL_ALL(3,std::complex<float>); // EIGEN_SUFFIXES;13;113;23;123;33;133 - CALL_ALL(4,std::complex<double>); // EIGEN_SUFFIXES;14;114;24;124;34;134 - } -} diff --git a/eigen/test/product_trmv.cpp b/eigen/test/product_trmv.cpp deleted file mode 100644 index 65d66e5..0000000 --- a/eigen/test/product_trmv.cpp +++ /dev/null @@ -1,90 +0,0 @@ -// This file is triangularView of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename MatrixType> void trmv(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - - RealScalar largerEps = 10*test_precision<RealScalar>(); - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m3(rows, cols); - VectorType v1 = VectorType::Random(rows); - - Scalar s1 = internal::random<Scalar>(); - - m1 = MatrixType::Random(rows, cols); - - // check with a column-major matrix - m3 = m1.template triangularView<Eigen::Lower>(); - VERIFY((m3 * v1).isApprox(m1.template triangularView<Eigen::Lower>() * v1, largerEps)); - m3 = m1.template triangularView<Eigen::Upper>(); - VERIFY((m3 * v1).isApprox(m1.template triangularView<Eigen::Upper>() * v1, largerEps)); - m3 = m1.template triangularView<Eigen::UnitLower>(); - VERIFY((m3 * v1).isApprox(m1.template triangularView<Eigen::UnitLower>() * v1, largerEps)); - m3 = m1.template triangularView<Eigen::UnitUpper>(); - VERIFY((m3 * v1).isApprox(m1.template triangularView<Eigen::UnitUpper>() * v1, largerEps)); - - // check conjugated and scalar multiple expressions (col-major) - m3 = m1.template triangularView<Eigen::Lower>(); - VERIFY(((s1*m3).conjugate() * v1).isApprox((s1*m1).conjugate().template triangularView<Eigen::Lower>() * v1, largerEps)); - m3 = m1.template triangularView<Eigen::Upper>(); - VERIFY((m3.conjugate() * v1.conjugate()).isApprox(m1.conjugate().template triangularView<Eigen::Upper>() * v1.conjugate(), largerEps)); - - // check with a row-major matrix - m3 = m1.template triangularView<Eigen::Upper>(); - VERIFY((m3.transpose() * v1).isApprox(m1.transpose().template triangularView<Eigen::Lower>() * v1, largerEps)); - m3 = m1.template triangularView<Eigen::Lower>(); - VERIFY((m3.transpose() * v1).isApprox(m1.transpose().template triangularView<Eigen::Upper>() * v1, largerEps)); - m3 = m1.template triangularView<Eigen::UnitUpper>(); - VERIFY((m3.transpose() * v1).isApprox(m1.transpose().template triangularView<Eigen::UnitLower>() * v1, largerEps)); - m3 = m1.template triangularView<Eigen::UnitLower>(); - VERIFY((m3.transpose() * v1).isApprox(m1.transpose().template triangularView<Eigen::UnitUpper>() * v1, largerEps)); - - // check conjugated and scalar multiple expressions (row-major) - m3 = m1.template triangularView<Eigen::Upper>(); - VERIFY((m3.adjoint() * v1).isApprox(m1.adjoint().template triangularView<Eigen::Lower>() * v1, largerEps)); - m3 = m1.template triangularView<Eigen::Lower>(); - VERIFY((m3.adjoint() * (s1*v1.conjugate())).isApprox(m1.adjoint().template triangularView<Eigen::Upper>() * (s1*v1.conjugate()), largerEps)); - m3 = m1.template triangularView<Eigen::UnitUpper>(); - - // check transposed cases: - m3 = m1.template triangularView<Eigen::Lower>(); - VERIFY((v1.transpose() * m3).isApprox(v1.transpose() * m1.template triangularView<Eigen::Lower>(), largerEps)); - VERIFY((v1.adjoint() * m3).isApprox(v1.adjoint() * m1.template triangularView<Eigen::Lower>(), largerEps)); - VERIFY((v1.adjoint() * m3.adjoint()).isApprox(v1.adjoint() * m1.template triangularView<Eigen::Lower>().adjoint(), largerEps)); - - // TODO check with sub-matrices -} - -void test_product_trmv() -{ - int s = 0; - for(int i = 0; i < g_repeat ; i++) { - CALL_SUBTEST_1( trmv(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( trmv(Matrix<float, 2, 2>()) ); - CALL_SUBTEST_3( trmv(Matrix3d()) ); - - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2); - CALL_SUBTEST_4( trmv(MatrixXcf(s,s)) ); - CALL_SUBTEST_5( trmv(MatrixXcd(s,s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE); - CALL_SUBTEST_6( trmv(Matrix<float,Dynamic,Dynamic,RowMajor>(s, s)) ); - TEST_SET_BUT_UNUSED_VARIABLE(s) - } -} diff --git a/eigen/test/product_trsolve.cpp b/eigen/test/product_trsolve.cpp deleted file mode 100644 index 4b97fa9..0000000 --- a/eigen/test/product_trsolve.cpp +++ /dev/null @@ -1,101 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#define VERIFY_TRSM(TRI,XB) { \ - (XB).setRandom(); ref = (XB); \ - (TRI).solveInPlace(XB); \ - VERIFY_IS_APPROX((TRI).toDenseMatrix() * (XB), ref); \ - (XB).setRandom(); ref = (XB); \ - (XB) = (TRI).solve(XB); \ - VERIFY_IS_APPROX((TRI).toDenseMatrix() * (XB), ref); \ - } - -#define VERIFY_TRSM_ONTHERIGHT(TRI,XB) { \ - (XB).setRandom(); ref = (XB); \ - (TRI).transpose().template solveInPlace<OnTheRight>(XB.transpose()); \ - VERIFY_IS_APPROX((XB).transpose() * (TRI).transpose().toDenseMatrix(), ref.transpose()); \ - (XB).setRandom(); ref = (XB); \ - (XB).transpose() = (TRI).transpose().template solve<OnTheRight>(XB.transpose()); \ - VERIFY_IS_APPROX((XB).transpose() * (TRI).transpose().toDenseMatrix(), ref.transpose()); \ - } - -template<typename Scalar,int Size, int Cols> void trsolve(int size=Size,int cols=Cols) -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - - Matrix<Scalar,Size,Size,ColMajor> cmLhs(size,size); - Matrix<Scalar,Size,Size,RowMajor> rmLhs(size,size); - - enum { colmajor = Size==1 ? RowMajor : ColMajor, - rowmajor = Cols==1 ? ColMajor : RowMajor }; - Matrix<Scalar,Size,Cols,colmajor> cmRhs(size,cols); - Matrix<Scalar,Size,Cols,rowmajor> rmRhs(size,cols); - Matrix<Scalar,Dynamic,Dynamic,colmajor> ref(size,cols); - - cmLhs.setRandom(); cmLhs *= static_cast<RealScalar>(0.1); cmLhs.diagonal().array() += static_cast<RealScalar>(1); - rmLhs.setRandom(); rmLhs *= static_cast<RealScalar>(0.1); rmLhs.diagonal().array() += static_cast<RealScalar>(1); - - VERIFY_TRSM(cmLhs.conjugate().template triangularView<Lower>(), cmRhs); - VERIFY_TRSM(cmLhs.adjoint() .template triangularView<Lower>(), cmRhs); - VERIFY_TRSM(cmLhs .template triangularView<Upper>(), cmRhs); - VERIFY_TRSM(cmLhs .template triangularView<Lower>(), rmRhs); - VERIFY_TRSM(cmLhs.conjugate().template triangularView<Upper>(), rmRhs); - VERIFY_TRSM(cmLhs.adjoint() .template triangularView<Upper>(), rmRhs); - - VERIFY_TRSM(cmLhs.conjugate().template triangularView<UnitLower>(), cmRhs); - VERIFY_TRSM(cmLhs .template triangularView<UnitUpper>(), rmRhs); - - VERIFY_TRSM(rmLhs .template triangularView<Lower>(), cmRhs); - VERIFY_TRSM(rmLhs.conjugate().template triangularView<UnitUpper>(), rmRhs); - - - VERIFY_TRSM_ONTHERIGHT(cmLhs.conjugate().template triangularView<Lower>(), cmRhs); - VERIFY_TRSM_ONTHERIGHT(cmLhs .template triangularView<Upper>(), cmRhs); - VERIFY_TRSM_ONTHERIGHT(cmLhs .template triangularView<Lower>(), rmRhs); - VERIFY_TRSM_ONTHERIGHT(cmLhs.conjugate().template triangularView<Upper>(), rmRhs); - - VERIFY_TRSM_ONTHERIGHT(cmLhs.conjugate().template triangularView<UnitLower>(), cmRhs); - VERIFY_TRSM_ONTHERIGHT(cmLhs .template triangularView<UnitUpper>(), rmRhs); - - VERIFY_TRSM_ONTHERIGHT(rmLhs .template triangularView<Lower>(), cmRhs); - VERIFY_TRSM_ONTHERIGHT(rmLhs.conjugate().template triangularView<UnitUpper>(), rmRhs); - - int c = internal::random<int>(0,cols-1); - VERIFY_TRSM(rmLhs.template triangularView<Lower>(), rmRhs.col(c)); - VERIFY_TRSM(cmLhs.template triangularView<Lower>(), rmRhs.col(c)); -} - -void test_product_trsolve() -{ - for(int i = 0; i < g_repeat ; i++) - { - // matrices - CALL_SUBTEST_1((trsolve<float,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE)))); - CALL_SUBTEST_2((trsolve<double,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE)))); - CALL_SUBTEST_3((trsolve<std::complex<float>,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2),internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2)))); - CALL_SUBTEST_4((trsolve<std::complex<double>,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2),internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2)))); - - // vectors - CALL_SUBTEST_5((trsolve<float,Dynamic,1>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)))); - CALL_SUBTEST_6((trsolve<double,Dynamic,1>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)))); - CALL_SUBTEST_7((trsolve<std::complex<float>,Dynamic,1>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)))); - CALL_SUBTEST_8((trsolve<std::complex<double>,Dynamic,1>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)))); - - // meta-unrollers - CALL_SUBTEST_9((trsolve<float,4,1>())); - CALL_SUBTEST_10((trsolve<double,4,1>())); - CALL_SUBTEST_11((trsolve<std::complex<float>,4,1>())); - CALL_SUBTEST_12((trsolve<float,1,1>())); - CALL_SUBTEST_13((trsolve<float,1,2>())); - CALL_SUBTEST_14((trsolve<float,3,1>())); - - } -} diff --git a/eigen/test/qr.cpp b/eigen/test/qr.cpp deleted file mode 100644 index 5688460..0000000 --- a/eigen/test/qr.cpp +++ /dev/null @@ -1,130 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/QR> - -template<typename MatrixType> void qr(const MatrixType& m) -{ - Index rows = m.rows(); - Index cols = m.cols(); - - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> MatrixQType; - - MatrixType a = MatrixType::Random(rows,cols); - HouseholderQR<MatrixType> qrOfA(a); - - MatrixQType q = qrOfA.householderQ(); - VERIFY_IS_UNITARY(q); - - MatrixType r = qrOfA.matrixQR().template triangularView<Upper>(); - VERIFY_IS_APPROX(a, qrOfA.householderQ() * r); -} - -template<typename MatrixType, int Cols2> void qr_fixedsize() -{ - enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime }; - typedef typename MatrixType::Scalar Scalar; - Matrix<Scalar,Rows,Cols> m1 = Matrix<Scalar,Rows,Cols>::Random(); - HouseholderQR<Matrix<Scalar,Rows,Cols> > qr(m1); - - Matrix<Scalar,Rows,Cols> r = qr.matrixQR(); - // FIXME need better way to construct trapezoid - for(int i = 0; i < Rows; i++) for(int j = 0; j < Cols; j++) if(i>j) r(i,j) = Scalar(0); - - VERIFY_IS_APPROX(m1, qr.householderQ() * r); - - Matrix<Scalar,Cols,Cols2> m2 = Matrix<Scalar,Cols,Cols2>::Random(Cols,Cols2); - Matrix<Scalar,Rows,Cols2> m3 = m1*m2; - m2 = Matrix<Scalar,Cols,Cols2>::Random(Cols,Cols2); - m2 = qr.solve(m3); - VERIFY_IS_APPROX(m3, m1*m2); -} - -template<typename MatrixType> void qr_invertible() -{ - using std::log; - using std::abs; - using std::pow; - using std::max; - typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; - typedef typename MatrixType::Scalar Scalar; - - int size = internal::random<int>(10,50); - - MatrixType m1(size, size), m2(size, size), m3(size, size); - m1 = MatrixType::Random(size,size); - - if (internal::is_same<RealScalar,float>::value) - { - // let's build a matrix more stable to inverse - MatrixType a = MatrixType::Random(size,size*4); - m1 += a * a.adjoint(); - } - - HouseholderQR<MatrixType> qr(m1); - m3 = MatrixType::Random(size,size); - m2 = qr.solve(m3); - VERIFY_IS_APPROX(m3, m1*m2); - - // now construct a matrix with prescribed determinant - m1.setZero(); - for(int i = 0; i < size; i++) m1(i,i) = internal::random<Scalar>(); - RealScalar absdet = abs(m1.diagonal().prod()); - m3 = qr.householderQ(); // get a unitary - m1 = m3 * m1 * m3; - qr.compute(m1); - VERIFY_IS_APPROX(log(absdet), qr.logAbsDeterminant()); - // This test is tricky if the determinant becomes too small. - // Since we generate random numbers with magnitude rrange [0,1], the average determinant is 0.5^size - VERIFY_IS_MUCH_SMALLER_THAN( abs(absdet-qr.absDeterminant()), numext::maxi(RealScalar(pow(0.5,size)),numext::maxi<RealScalar>(abs(absdet),abs(qr.absDeterminant()))) ); - -} - -template<typename MatrixType> void qr_verify_assert() -{ - MatrixType tmp; - - HouseholderQR<MatrixType> qr; - VERIFY_RAISES_ASSERT(qr.matrixQR()) - VERIFY_RAISES_ASSERT(qr.solve(tmp)) - VERIFY_RAISES_ASSERT(qr.householderQ()) - VERIFY_RAISES_ASSERT(qr.absDeterminant()) - VERIFY_RAISES_ASSERT(qr.logAbsDeterminant()) -} - -void test_qr() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( qr(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_2( qr(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2),internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) ); - CALL_SUBTEST_3(( qr_fixedsize<Matrix<float,3,4>, 2 >() )); - CALL_SUBTEST_4(( qr_fixedsize<Matrix<double,6,2>, 4 >() )); - CALL_SUBTEST_5(( qr_fixedsize<Matrix<double,2,5>, 7 >() )); - CALL_SUBTEST_11( qr(Matrix<float,1,1>()) ); - } - - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( qr_invertible<MatrixXf>() ); - CALL_SUBTEST_6( qr_invertible<MatrixXd>() ); - CALL_SUBTEST_7( qr_invertible<MatrixXcf>() ); - CALL_SUBTEST_8( qr_invertible<MatrixXcd>() ); - } - - CALL_SUBTEST_9(qr_verify_assert<Matrix3f>()); - CALL_SUBTEST_10(qr_verify_assert<Matrix3d>()); - CALL_SUBTEST_1(qr_verify_assert<MatrixXf>()); - CALL_SUBTEST_6(qr_verify_assert<MatrixXd>()); - CALL_SUBTEST_7(qr_verify_assert<MatrixXcf>()); - CALL_SUBTEST_8(qr_verify_assert<MatrixXcd>()); - - // Test problem size constructors - CALL_SUBTEST_12(HouseholderQR<MatrixXf>(10, 20)); -} diff --git a/eigen/test/qr_colpivoting.cpp b/eigen/test/qr_colpivoting.cpp deleted file mode 100644 index 96c0bad..0000000 --- a/eigen/test/qr_colpivoting.cpp +++ /dev/null @@ -1,338 +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) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/QR> -#include <Eigen/SVD> - -template <typename MatrixType> -void cod() { - Index rows = internal::random<Index>(2, EIGEN_TEST_MAX_SIZE); - Index cols = internal::random<Index>(2, EIGEN_TEST_MAX_SIZE); - Index cols2 = internal::random<Index>(2, EIGEN_TEST_MAX_SIZE); - Index rank = internal::random<Index>(1, (std::min)(rows, cols) - 1); - - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, - MatrixType::RowsAtCompileTime> - MatrixQType; - MatrixType matrix; - createRandomPIMatrixOfRank(rank, rows, cols, matrix); - CompleteOrthogonalDecomposition<MatrixType> cod(matrix); - VERIFY(rank == cod.rank()); - VERIFY(cols - cod.rank() == cod.dimensionOfKernel()); - VERIFY(!cod.isInjective()); - VERIFY(!cod.isInvertible()); - VERIFY(!cod.isSurjective()); - - MatrixQType q = cod.householderQ(); - VERIFY_IS_UNITARY(q); - - MatrixType z = cod.matrixZ(); - VERIFY_IS_UNITARY(z); - - MatrixType t; - t.setZero(rows, cols); - t.topLeftCorner(rank, rank) = - cod.matrixT().topLeftCorner(rank, rank).template triangularView<Upper>(); - - MatrixType c = q * t * z * cod.colsPermutation().inverse(); - VERIFY_IS_APPROX(matrix, c); - - MatrixType exact_solution = MatrixType::Random(cols, cols2); - MatrixType rhs = matrix * exact_solution; - MatrixType cod_solution = cod.solve(rhs); - VERIFY_IS_APPROX(rhs, matrix * cod_solution); - - // Verify that we get the same minimum-norm solution as the SVD. - JacobiSVD<MatrixType> svd(matrix, ComputeThinU | ComputeThinV); - MatrixType svd_solution = svd.solve(rhs); - VERIFY_IS_APPROX(cod_solution, svd_solution); - - MatrixType pinv = cod.pseudoInverse(); - VERIFY_IS_APPROX(cod_solution, pinv * rhs); -} - -template <typename MatrixType, int Cols2> -void cod_fixedsize() { - enum { - Rows = MatrixType::RowsAtCompileTime, - Cols = MatrixType::ColsAtCompileTime - }; - typedef typename MatrixType::Scalar Scalar; - int rank = internal::random<int>(1, (std::min)(int(Rows), int(Cols)) - 1); - Matrix<Scalar, Rows, Cols> matrix; - createRandomPIMatrixOfRank(rank, Rows, Cols, matrix); - CompleteOrthogonalDecomposition<Matrix<Scalar, Rows, Cols> > cod(matrix); - VERIFY(rank == cod.rank()); - VERIFY(Cols - cod.rank() == cod.dimensionOfKernel()); - VERIFY(cod.isInjective() == (rank == Rows)); - VERIFY(cod.isSurjective() == (rank == Cols)); - VERIFY(cod.isInvertible() == (cod.isInjective() && cod.isSurjective())); - - Matrix<Scalar, Cols, Cols2> exact_solution; - exact_solution.setRandom(Cols, Cols2); - Matrix<Scalar, Rows, Cols2> rhs = matrix * exact_solution; - Matrix<Scalar, Cols, Cols2> cod_solution = cod.solve(rhs); - VERIFY_IS_APPROX(rhs, matrix * cod_solution); - - // Verify that we get the same minimum-norm solution as the SVD. - JacobiSVD<MatrixType> svd(matrix, ComputeFullU | ComputeFullV); - Matrix<Scalar, Cols, Cols2> svd_solution = svd.solve(rhs); - VERIFY_IS_APPROX(cod_solution, svd_solution); -} - -template<typename MatrixType> void qr() -{ - using std::sqrt; - - Index rows = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE), cols = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE), cols2 = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE); - Index rank = internal::random<Index>(1, (std::min)(rows, cols)-1); - - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> MatrixQType; - MatrixType m1; - createRandomPIMatrixOfRank(rank,rows,cols,m1); - ColPivHouseholderQR<MatrixType> qr(m1); - VERIFY_IS_EQUAL(rank, qr.rank()); - VERIFY_IS_EQUAL(cols - qr.rank(), qr.dimensionOfKernel()); - VERIFY(!qr.isInjective()); - VERIFY(!qr.isInvertible()); - VERIFY(!qr.isSurjective()); - - MatrixQType q = qr.householderQ(); - VERIFY_IS_UNITARY(q); - - MatrixType r = qr.matrixQR().template triangularView<Upper>(); - MatrixType c = q * r * qr.colsPermutation().inverse(); - VERIFY_IS_APPROX(m1, c); - - // Verify that the absolute value of the diagonal elements in R are - // non-increasing until they reach the singularity threshold. - RealScalar threshold = - sqrt(RealScalar(rows)) * numext::abs(r(0, 0)) * NumTraits<Scalar>::epsilon(); - for (Index i = 0; i < (std::min)(rows, cols) - 1; ++i) { - RealScalar x = numext::abs(r(i, i)); - RealScalar y = numext::abs(r(i + 1, i + 1)); - if (x < threshold && y < threshold) continue; - if (!test_isApproxOrLessThan(y, x)) { - for (Index j = 0; j < (std::min)(rows, cols); ++j) { - std::cout << "i = " << j << ", |r_ii| = " << numext::abs(r(j, j)) << std::endl; - } - std::cout << "Failure at i=" << i << ", rank=" << rank - << ", threshold=" << threshold << std::endl; - } - VERIFY_IS_APPROX_OR_LESS_THAN(y, x); - } - - MatrixType m2 = MatrixType::Random(cols,cols2); - MatrixType m3 = m1*m2; - m2 = MatrixType::Random(cols,cols2); - m2 = qr.solve(m3); - VERIFY_IS_APPROX(m3, m1*m2); - - { - Index size = rows; - do { - m1 = MatrixType::Random(size,size); - qr.compute(m1); - } while(!qr.isInvertible()); - MatrixType m1_inv = qr.inverse(); - m3 = m1 * MatrixType::Random(size,cols2); - m2 = qr.solve(m3); - VERIFY_IS_APPROX(m2, m1_inv*m3); - } -} - -template<typename MatrixType, int Cols2> void qr_fixedsize() -{ - using std::sqrt; - using std::abs; - enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime }; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - int rank = internal::random<int>(1, (std::min)(int(Rows), int(Cols))-1); - Matrix<Scalar,Rows,Cols> m1; - createRandomPIMatrixOfRank(rank,Rows,Cols,m1); - ColPivHouseholderQR<Matrix<Scalar,Rows,Cols> > qr(m1); - VERIFY_IS_EQUAL(rank, qr.rank()); - VERIFY_IS_EQUAL(Cols - qr.rank(), qr.dimensionOfKernel()); - VERIFY_IS_EQUAL(qr.isInjective(), (rank == Rows)); - VERIFY_IS_EQUAL(qr.isSurjective(), (rank == Cols)); - VERIFY_IS_EQUAL(qr.isInvertible(), (qr.isInjective() && qr.isSurjective())); - - Matrix<Scalar,Rows,Cols> r = qr.matrixQR().template triangularView<Upper>(); - Matrix<Scalar,Rows,Cols> c = qr.householderQ() * r * qr.colsPermutation().inverse(); - VERIFY_IS_APPROX(m1, c); - - Matrix<Scalar,Cols,Cols2> m2 = Matrix<Scalar,Cols,Cols2>::Random(Cols,Cols2); - Matrix<Scalar,Rows,Cols2> m3 = m1*m2; - m2 = Matrix<Scalar,Cols,Cols2>::Random(Cols,Cols2); - m2 = qr.solve(m3); - VERIFY_IS_APPROX(m3, m1*m2); - // Verify that the absolute value of the diagonal elements in R are - // non-increasing until they reache the singularity threshold. - RealScalar threshold = - sqrt(RealScalar(Rows)) * (std::abs)(r(0, 0)) * NumTraits<Scalar>::epsilon(); - for (Index i = 0; i < (std::min)(int(Rows), int(Cols)) - 1; ++i) { - RealScalar x = numext::abs(r(i, i)); - RealScalar y = numext::abs(r(i + 1, i + 1)); - if (x < threshold && y < threshold) continue; - if (!test_isApproxOrLessThan(y, x)) { - for (Index j = 0; j < (std::min)(int(Rows), int(Cols)); ++j) { - std::cout << "i = " << j << ", |r_ii| = " << numext::abs(r(j, j)) << std::endl; - } - std::cout << "Failure at i=" << i << ", rank=" << rank - << ", threshold=" << threshold << std::endl; - } - VERIFY_IS_APPROX_OR_LESS_THAN(y, x); - } -} - -// This test is meant to verify that pivots are chosen such that -// even for a graded matrix, the diagonal of R falls of roughly -// monotonically until it reaches the threshold for singularity. -// We use the so-called Kahan matrix, which is a famous counter-example -// for rank-revealing QR. See -// http://www.netlib.org/lapack/lawnspdf/lawn176.pdf -// page 3 for more detail. -template<typename MatrixType> void qr_kahan_matrix() -{ - using std::sqrt; - using std::abs; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - - Index rows = 300, cols = rows; - - MatrixType m1; - m1.setZero(rows,cols); - RealScalar s = std::pow(NumTraits<RealScalar>::epsilon(), 1.0 / rows); - RealScalar c = std::sqrt(1 - s*s); - RealScalar pow_s_i(1.0); // pow(s,i) - for (Index i = 0; i < rows; ++i) { - m1(i, i) = pow_s_i; - m1.row(i).tail(rows - i - 1) = -pow_s_i * c * MatrixType::Ones(1, rows - i - 1); - pow_s_i *= s; - } - m1 = (m1 + m1.transpose()).eval(); - ColPivHouseholderQR<MatrixType> qr(m1); - MatrixType r = qr.matrixQR().template triangularView<Upper>(); - - RealScalar threshold = - std::sqrt(RealScalar(rows)) * numext::abs(r(0, 0)) * NumTraits<Scalar>::epsilon(); - for (Index i = 0; i < (std::min)(rows, cols) - 1; ++i) { - RealScalar x = numext::abs(r(i, i)); - RealScalar y = numext::abs(r(i + 1, i + 1)); - if (x < threshold && y < threshold) continue; - if (!test_isApproxOrLessThan(y, x)) { - for (Index j = 0; j < (std::min)(rows, cols); ++j) { - std::cout << "i = " << j << ", |r_ii| = " << numext::abs(r(j, j)) << std::endl; - } - std::cout << "Failure at i=" << i << ", rank=" << qr.rank() - << ", threshold=" << threshold << std::endl; - } - VERIFY_IS_APPROX_OR_LESS_THAN(y, x); - } -} - -template<typename MatrixType> void qr_invertible() -{ - using std::log; - using std::abs; - typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; - typedef typename MatrixType::Scalar Scalar; - - int size = internal::random<int>(10,50); - - MatrixType m1(size, size), m2(size, size), m3(size, size); - m1 = MatrixType::Random(size,size); - - if (internal::is_same<RealScalar,float>::value) - { - // let's build a matrix more stable to inverse - MatrixType a = MatrixType::Random(size,size*2); - m1 += a * a.adjoint(); - } - - ColPivHouseholderQR<MatrixType> qr(m1); - m3 = MatrixType::Random(size,size); - m2 = qr.solve(m3); - //VERIFY_IS_APPROX(m3, m1*m2); - - // now construct a matrix with prescribed determinant - m1.setZero(); - for(int i = 0; i < size; i++) m1(i,i) = internal::random<Scalar>(); - RealScalar absdet = abs(m1.diagonal().prod()); - m3 = qr.householderQ(); // get a unitary - m1 = m3 * m1 * m3; - qr.compute(m1); - VERIFY_IS_APPROX(absdet, qr.absDeterminant()); - VERIFY_IS_APPROX(log(absdet), qr.logAbsDeterminant()); -} - -template<typename MatrixType> void qr_verify_assert() -{ - MatrixType tmp; - - ColPivHouseholderQR<MatrixType> qr; - VERIFY_RAISES_ASSERT(qr.matrixQR()) - VERIFY_RAISES_ASSERT(qr.solve(tmp)) - VERIFY_RAISES_ASSERT(qr.householderQ()) - VERIFY_RAISES_ASSERT(qr.dimensionOfKernel()) - VERIFY_RAISES_ASSERT(qr.isInjective()) - VERIFY_RAISES_ASSERT(qr.isSurjective()) - VERIFY_RAISES_ASSERT(qr.isInvertible()) - VERIFY_RAISES_ASSERT(qr.inverse()) - VERIFY_RAISES_ASSERT(qr.absDeterminant()) - VERIFY_RAISES_ASSERT(qr.logAbsDeterminant()) -} - -void test_qr_colpivoting() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( qr<MatrixXf>() ); - CALL_SUBTEST_2( qr<MatrixXd>() ); - CALL_SUBTEST_3( qr<MatrixXcd>() ); - CALL_SUBTEST_4(( qr_fixedsize<Matrix<float,3,5>, 4 >() )); - CALL_SUBTEST_5(( qr_fixedsize<Matrix<double,6,2>, 3 >() )); - CALL_SUBTEST_5(( qr_fixedsize<Matrix<double,1,1>, 1 >() )); - } - - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( cod<MatrixXf>() ); - CALL_SUBTEST_2( cod<MatrixXd>() ); - CALL_SUBTEST_3( cod<MatrixXcd>() ); - CALL_SUBTEST_4(( cod_fixedsize<Matrix<float,3,5>, 4 >() )); - CALL_SUBTEST_5(( cod_fixedsize<Matrix<double,6,2>, 3 >() )); - CALL_SUBTEST_5(( cod_fixedsize<Matrix<double,1,1>, 1 >() )); - } - - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( qr_invertible<MatrixXf>() ); - CALL_SUBTEST_2( qr_invertible<MatrixXd>() ); - CALL_SUBTEST_6( qr_invertible<MatrixXcf>() ); - CALL_SUBTEST_3( qr_invertible<MatrixXcd>() ); - } - - CALL_SUBTEST_7(qr_verify_assert<Matrix3f>()); - CALL_SUBTEST_8(qr_verify_assert<Matrix3d>()); - CALL_SUBTEST_1(qr_verify_assert<MatrixXf>()); - CALL_SUBTEST_2(qr_verify_assert<MatrixXd>()); - CALL_SUBTEST_6(qr_verify_assert<MatrixXcf>()); - CALL_SUBTEST_3(qr_verify_assert<MatrixXcd>()); - - // Test problem size constructors - CALL_SUBTEST_9(ColPivHouseholderQR<MatrixXf>(10, 20)); - - CALL_SUBTEST_1( qr_kahan_matrix<MatrixXf>() ); - CALL_SUBTEST_2( qr_kahan_matrix<MatrixXd>() ); -} diff --git a/eigen/test/qr_fullpivoting.cpp b/eigen/test/qr_fullpivoting.cpp deleted file mode 100644 index 4d8ef68..0000000 --- a/eigen/test/qr_fullpivoting.cpp +++ /dev/null @@ -1,157 +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) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/QR> - -template<typename MatrixType> void qr() -{ - Index max_size = EIGEN_TEST_MAX_SIZE; - Index min_size = numext::maxi(1,EIGEN_TEST_MAX_SIZE/10); - Index rows = internal::random<Index>(min_size,max_size), - cols = internal::random<Index>(min_size,max_size), - cols2 = internal::random<Index>(min_size,max_size), - rank = internal::random<Index>(1, (std::min)(rows, cols)-1); - - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> MatrixQType; - MatrixType m1; - createRandomPIMatrixOfRank(rank,rows,cols,m1); - FullPivHouseholderQR<MatrixType> qr(m1); - VERIFY_IS_EQUAL(rank, qr.rank()); - VERIFY_IS_EQUAL(cols - qr.rank(), qr.dimensionOfKernel()); - VERIFY(!qr.isInjective()); - VERIFY(!qr.isInvertible()); - VERIFY(!qr.isSurjective()); - - MatrixType r = qr.matrixQR(); - - MatrixQType q = qr.matrixQ(); - VERIFY_IS_UNITARY(q); - - // FIXME need better way to construct trapezoid - for(int i = 0; i < rows; i++) for(int j = 0; j < cols; j++) if(i>j) r(i,j) = Scalar(0); - - MatrixType c = qr.matrixQ() * r * qr.colsPermutation().inverse(); - - VERIFY_IS_APPROX(m1, c); - - // stress the ReturnByValue mechanism - MatrixType tmp; - VERIFY_IS_APPROX(tmp.noalias() = qr.matrixQ() * r, (qr.matrixQ() * r).eval()); - - MatrixType m2 = MatrixType::Random(cols,cols2); - MatrixType m3 = m1*m2; - m2 = MatrixType::Random(cols,cols2); - m2 = qr.solve(m3); - VERIFY_IS_APPROX(m3, m1*m2); - - { - Index size = rows; - do { - m1 = MatrixType::Random(size,size); - qr.compute(m1); - } while(!qr.isInvertible()); - MatrixType m1_inv = qr.inverse(); - m3 = m1 * MatrixType::Random(size,cols2); - m2 = qr.solve(m3); - VERIFY_IS_APPROX(m2, m1_inv*m3); - } -} - -template<typename MatrixType> void qr_invertible() -{ - using std::log; - using std::abs; - typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; - typedef typename MatrixType::Scalar Scalar; - - Index max_size = numext::mini(50,EIGEN_TEST_MAX_SIZE); - Index min_size = numext::maxi(1,EIGEN_TEST_MAX_SIZE/10); - Index size = internal::random<Index>(min_size,max_size); - - MatrixType m1(size, size), m2(size, size), m3(size, size); - m1 = MatrixType::Random(size,size); - - if (internal::is_same<RealScalar,float>::value) - { - // let's build a matrix more stable to inverse - MatrixType a = MatrixType::Random(size,size*2); - m1 += a * a.adjoint(); - } - - FullPivHouseholderQR<MatrixType> qr(m1); - VERIFY(qr.isInjective()); - VERIFY(qr.isInvertible()); - VERIFY(qr.isSurjective()); - - m3 = MatrixType::Random(size,size); - m2 = qr.solve(m3); - VERIFY_IS_APPROX(m3, m1*m2); - - // now construct a matrix with prescribed determinant - m1.setZero(); - for(int i = 0; i < size; i++) m1(i,i) = internal::random<Scalar>(); - RealScalar absdet = abs(m1.diagonal().prod()); - m3 = qr.matrixQ(); // get a unitary - m1 = m3 * m1 * m3; - qr.compute(m1); - VERIFY_IS_APPROX(absdet, qr.absDeterminant()); - VERIFY_IS_APPROX(log(absdet), qr.logAbsDeterminant()); -} - -template<typename MatrixType> void qr_verify_assert() -{ - MatrixType tmp; - - FullPivHouseholderQR<MatrixType> qr; - VERIFY_RAISES_ASSERT(qr.matrixQR()) - VERIFY_RAISES_ASSERT(qr.solve(tmp)) - VERIFY_RAISES_ASSERT(qr.matrixQ()) - VERIFY_RAISES_ASSERT(qr.dimensionOfKernel()) - VERIFY_RAISES_ASSERT(qr.isInjective()) - VERIFY_RAISES_ASSERT(qr.isSurjective()) - VERIFY_RAISES_ASSERT(qr.isInvertible()) - VERIFY_RAISES_ASSERT(qr.inverse()) - VERIFY_RAISES_ASSERT(qr.absDeterminant()) - VERIFY_RAISES_ASSERT(qr.logAbsDeterminant()) -} - -void test_qr_fullpivoting() -{ - for(int i = 0; i < 1; i++) { - // FIXME : very weird bug here -// CALL_SUBTEST(qr(Matrix2f()) ); - CALL_SUBTEST_1( qr<MatrixXf>() ); - CALL_SUBTEST_2( qr<MatrixXd>() ); - CALL_SUBTEST_3( qr<MatrixXcd>() ); - } - - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( qr_invertible<MatrixXf>() ); - CALL_SUBTEST_2( qr_invertible<MatrixXd>() ); - CALL_SUBTEST_4( qr_invertible<MatrixXcf>() ); - CALL_SUBTEST_3( qr_invertible<MatrixXcd>() ); - } - - CALL_SUBTEST_5(qr_verify_assert<Matrix3f>()); - CALL_SUBTEST_6(qr_verify_assert<Matrix3d>()); - CALL_SUBTEST_1(qr_verify_assert<MatrixXf>()); - CALL_SUBTEST_2(qr_verify_assert<MatrixXd>()); - CALL_SUBTEST_4(qr_verify_assert<MatrixXcf>()); - CALL_SUBTEST_3(qr_verify_assert<MatrixXcd>()); - - // Test problem size constructors - CALL_SUBTEST_7(FullPivHouseholderQR<MatrixXf>(10, 20)); - CALL_SUBTEST_7((FullPivHouseholderQR<Matrix<float,10,20> >(10,20))); - CALL_SUBTEST_7((FullPivHouseholderQR<Matrix<float,10,20> >(Matrix<float,10,20>::Random()))); - CALL_SUBTEST_7((FullPivHouseholderQR<Matrix<float,20,10> >(20,10))); - CALL_SUBTEST_7((FullPivHouseholderQR<Matrix<float,20,10> >(Matrix<float,20,10>::Random()))); -} diff --git a/eigen/test/qtvector.cpp b/eigen/test/qtvector.cpp deleted file mode 100644 index 22df0d5..0000000 --- a/eigen/test/qtvector.cpp +++ /dev/null @@ -1,156 +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) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_WORK_AROUND_QT_BUG_CALLING_WRONG_OPERATOR_NEW_FIXED_IN_QT_4_5 - -#include "main.h" -#include <QtCore/QVector> -#include <Eigen/Geometry> -#include <Eigen/QtAlignedMalloc> - -template<typename MatrixType> -void check_qtvector_matrix(const MatrixType& m) -{ - Index rows = m.rows(); - Index cols = m.cols(); - MatrixType x = MatrixType::Random(rows,cols), y = MatrixType::Random(rows,cols); - QVector<MatrixType> v(10, MatrixType(rows,cols)), w(20, y); - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(w[i], y); - } - v[5] = x; - w[6] = v[5]; - VERIFY_IS_APPROX(w[6], v[5]); - v = w; - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(w[i], v[i]); - } - - v.resize(21); - v[20] = x; - VERIFY_IS_APPROX(v[20], x); - v.fill(y,22); - VERIFY_IS_APPROX(v[21], y); - v.push_back(x); - VERIFY_IS_APPROX(v[22], x); - VERIFY((size_t)&(v[22]) == (size_t)&(v[21]) + sizeof(MatrixType)); - - // do a lot of push_back such that the vector gets internally resized - // (with memory reallocation) - MatrixType* ref = &w[0]; - for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i) - v.push_back(w[i%w.size()]); - for(int i=23; i<v.size(); ++i) - { - VERIFY(v[i]==w[(i-23)%w.size()]); - } -} - -template<typename TransformType> -void check_qtvector_transform(const TransformType&) -{ - typedef typename TransformType::MatrixType MatrixType; - TransformType x(MatrixType::Random()), y(MatrixType::Random()); - QVector<TransformType> v(10), w(20, y); - v[5] = x; - w[6] = v[5]; - VERIFY_IS_APPROX(w[6], v[5]); - v = w; - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(w[i], v[i]); - } - - v.resize(21); - v[20] = x; - VERIFY_IS_APPROX(v[20], x); - v.fill(y,22); - VERIFY_IS_APPROX(v[21], y); - v.push_back(x); - VERIFY_IS_APPROX(v[22], x); - VERIFY((size_t)&(v[22]) == (size_t)&(v[21]) + sizeof(TransformType)); - - // do a lot of push_back such that the vector gets internally resized - // (with memory reallocation) - TransformType* ref = &w[0]; - for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i) - v.push_back(w[i%w.size()]); - for(unsigned int i=23; int(i)<v.size(); ++i) - { - VERIFY(v[i].matrix()==w[(i-23)%w.size()].matrix()); - } -} - -template<typename QuaternionType> -void check_qtvector_quaternion(const QuaternionType&) -{ - typedef typename QuaternionType::Coefficients Coefficients; - QuaternionType x(Coefficients::Random()), y(Coefficients::Random()); - QVector<QuaternionType> v(10), w(20, y); - v[5] = x; - w[6] = v[5]; - VERIFY_IS_APPROX(w[6], v[5]); - v = w; - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(w[i], v[i]); - } - - v.resize(21); - v[20] = x; - VERIFY_IS_APPROX(v[20], x); - v.fill(y,22); - VERIFY_IS_APPROX(v[21], y); - v.push_back(x); - VERIFY_IS_APPROX(v[22], x); - VERIFY((size_t)&(v[22]) == (size_t)&(v[21]) + sizeof(QuaternionType)); - - // do a lot of push_back such that the vector gets internally resized - // (with memory reallocation) - QuaternionType* ref = &w[0]; - for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i) - v.push_back(w[i%w.size()]); - for(unsigned int i=23; int(i)<v.size(); ++i) - { - VERIFY(v[i].coeffs()==w[(i-23)%w.size()].coeffs()); - } -} - -void test_qtvector() -{ - // some non vectorizable fixed sizes - CALL_SUBTEST(check_qtvector_matrix(Vector2f())); - CALL_SUBTEST(check_qtvector_matrix(Matrix3f())); - CALL_SUBTEST(check_qtvector_matrix(Matrix3d())); - - // some vectorizable fixed sizes - CALL_SUBTEST(check_qtvector_matrix(Matrix2f())); - CALL_SUBTEST(check_qtvector_matrix(Vector4f())); - CALL_SUBTEST(check_qtvector_matrix(Matrix4f())); - CALL_SUBTEST(check_qtvector_matrix(Matrix4d())); - - // some dynamic sizes - CALL_SUBTEST(check_qtvector_matrix(MatrixXd(1,1))); - CALL_SUBTEST(check_qtvector_matrix(VectorXd(20))); - CALL_SUBTEST(check_qtvector_matrix(RowVectorXf(20))); - CALL_SUBTEST(check_qtvector_matrix(MatrixXcf(10,10))); - - // some Transform - CALL_SUBTEST(check_qtvector_transform(Affine2f())); - CALL_SUBTEST(check_qtvector_transform(Affine3f())); - CALL_SUBTEST(check_qtvector_transform(Affine3d())); - //CALL_SUBTEST(check_qtvector_transform(Transform4d())); - - // some Quaternion - CALL_SUBTEST(check_qtvector_quaternion(Quaternionf())); - CALL_SUBTEST(check_qtvector_quaternion(Quaternionf())); -} diff --git a/eigen/test/rand.cpp b/eigen/test/rand.cpp deleted file mode 100644 index 51cf017..0000000 --- a/eigen/test/rand.cpp +++ /dev/null @@ -1,118 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -typedef long long int64; - -template<typename Scalar> Scalar check_in_range(Scalar x, Scalar y) -{ - Scalar r = internal::random<Scalar>(x,y); - VERIFY(r>=x); - if(y>=x) - { - VERIFY(r<=y); - } - return r; -} - -template<typename Scalar> void check_all_in_range(Scalar x, Scalar y) -{ - Array<int,1,Dynamic> mask(y-x+1); - mask.fill(0); - long n = (y-x+1)*32; - for(long k=0; k<n; ++k) - { - mask( check_in_range(x,y)-x )++; - } - for(Index i=0; i<mask.size(); ++i) - if(mask(i)==0) - std::cout << "WARNING: value " << x+i << " not reached." << std::endl; - VERIFY( (mask>0).all() ); -} - -template<typename Scalar> void check_histogram(Scalar x, Scalar y, int bins) -{ - Array<int,1,Dynamic> hist(bins); - hist.fill(0); - int f = 100000; - int n = bins*f; - int64 range = int64(y)-int64(x); - int divisor = int((range+1)/bins); - assert(((range+1)%bins)==0); - for(int k=0; k<n; ++k) - { - Scalar r = check_in_range(x,y); - hist( int((int64(r)-int64(x))/divisor) )++; - } - VERIFY( (((hist.cast<double>()/double(f))-1.0).abs()<0.02).all() ); -} - -void test_rand() -{ - long long_ref = NumTraits<long>::highest()/10; - signed char char_offset = (std::min)(g_repeat,64); - signed char short_offset = (std::min)(g_repeat,16000); - - for(int i = 0; i < g_repeat*10000; i++) { - CALL_SUBTEST(check_in_range<float>(10,11)); - CALL_SUBTEST(check_in_range<float>(1.24234523,1.24234523)); - CALL_SUBTEST(check_in_range<float>(-1,1)); - CALL_SUBTEST(check_in_range<float>(-1432.2352,-1432.2352)); - - CALL_SUBTEST(check_in_range<double>(10,11)); - CALL_SUBTEST(check_in_range<double>(1.24234523,1.24234523)); - CALL_SUBTEST(check_in_range<double>(-1,1)); - CALL_SUBTEST(check_in_range<double>(-1432.2352,-1432.2352)); - - CALL_SUBTEST(check_in_range<int>(0,-1)); - CALL_SUBTEST(check_in_range<short>(0,-1)); - CALL_SUBTEST(check_in_range<long>(0,-1)); - CALL_SUBTEST(check_in_range<int>(-673456,673456)); - CALL_SUBTEST(check_in_range<int>(-RAND_MAX+10,RAND_MAX-10)); - CALL_SUBTEST(check_in_range<short>(-24345,24345)); - CALL_SUBTEST(check_in_range<long>(-long_ref,long_ref)); - } - - CALL_SUBTEST(check_all_in_range<signed char>(11,11)); - CALL_SUBTEST(check_all_in_range<signed char>(11,11+char_offset)); - CALL_SUBTEST(check_all_in_range<signed char>(-5,5)); - CALL_SUBTEST(check_all_in_range<signed char>(-11-char_offset,-11)); - CALL_SUBTEST(check_all_in_range<signed char>(-126,-126+char_offset)); - CALL_SUBTEST(check_all_in_range<signed char>(126-char_offset,126)); - CALL_SUBTEST(check_all_in_range<signed char>(-126,126)); - - CALL_SUBTEST(check_all_in_range<short>(11,11)); - CALL_SUBTEST(check_all_in_range<short>(11,11+short_offset)); - CALL_SUBTEST(check_all_in_range<short>(-5,5)); - CALL_SUBTEST(check_all_in_range<short>(-11-short_offset,-11)); - CALL_SUBTEST(check_all_in_range<short>(-24345,-24345+short_offset)); - CALL_SUBTEST(check_all_in_range<short>(24345,24345+short_offset)); - - CALL_SUBTEST(check_all_in_range<int>(11,11)); - CALL_SUBTEST(check_all_in_range<int>(11,11+g_repeat)); - CALL_SUBTEST(check_all_in_range<int>(-5,5)); - CALL_SUBTEST(check_all_in_range<int>(-11-g_repeat,-11)); - CALL_SUBTEST(check_all_in_range<int>(-673456,-673456+g_repeat)); - CALL_SUBTEST(check_all_in_range<int>(673456,673456+g_repeat)); - - CALL_SUBTEST(check_all_in_range<long>(11,11)); - CALL_SUBTEST(check_all_in_range<long>(11,11+g_repeat)); - CALL_SUBTEST(check_all_in_range<long>(-5,5)); - CALL_SUBTEST(check_all_in_range<long>(-11-g_repeat,-11)); - CALL_SUBTEST(check_all_in_range<long>(-long_ref,-long_ref+g_repeat)); - CALL_SUBTEST(check_all_in_range<long>( long_ref, long_ref+g_repeat)); - - CALL_SUBTEST(check_histogram<int>(-5,5,11)); - int bins = 100; - CALL_SUBTEST(check_histogram<int>(-3333,-3333+bins*(3333/bins)-1,bins)); - bins = 1000; - CALL_SUBTEST(check_histogram<int>(-RAND_MAX+10,-RAND_MAX+10+bins*(RAND_MAX/bins)-1,bins)); - CALL_SUBTEST(check_histogram<int>(-RAND_MAX+10,-int64(RAND_MAX)+10+bins*(2*int64(RAND_MAX)/bins)-1,bins)); -} diff --git a/eigen/test/real_qz.cpp b/eigen/test/real_qz.cpp deleted file mode 100644 index 3c1492e..0000000 --- a/eigen/test/real_qz.cpp +++ /dev/null @@ -1,94 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Alexey Korepanov <kaikaikai@yandex.ru> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_RUNTIME_NO_MALLOC -#include "main.h" -#include <limits> -#include <Eigen/Eigenvalues> - -template<typename MatrixType> void real_qz(const MatrixType& m) -{ - /* this test covers the following files: - RealQZ.h - */ - using std::abs; - typedef typename MatrixType::Scalar Scalar; - - Index dim = m.cols(); - - MatrixType A = MatrixType::Random(dim,dim), - B = MatrixType::Random(dim,dim); - - - // Regression test for bug 985: Randomly set rows or columns to zero - Index k=internal::random<Index>(0, dim-1); - switch(internal::random<int>(0,10)) { - case 0: - A.row(k).setZero(); break; - case 1: - A.col(k).setZero(); break; - case 2: - B.row(k).setZero(); break; - case 3: - B.col(k).setZero(); break; - default: - break; - } - - RealQZ<MatrixType> qz(dim); - // TODO enable full-prealocation of required memory, this probably requires an in-place mode for HessenbergDecomposition - //Eigen::internal::set_is_malloc_allowed(false); - qz.compute(A,B); - //Eigen::internal::set_is_malloc_allowed(true); - - VERIFY_IS_EQUAL(qz.info(), Success); - // check for zeros - bool all_zeros = true; - for (Index i=0; i<A.cols(); i++) - for (Index j=0; j<i; j++) { - if (abs(qz.matrixT()(i,j))!=Scalar(0.0)) - { - std::cerr << "Error: T(" << i << "," << j << ") = " << qz.matrixT()(i,j) << std::endl; - all_zeros = false; - } - if (j<i-1 && abs(qz.matrixS()(i,j))!=Scalar(0.0)) - { - std::cerr << "Error: S(" << i << "," << j << ") = " << qz.matrixS()(i,j) << std::endl; - all_zeros = false; - } - if (j==i-1 && j>0 && abs(qz.matrixS()(i,j))!=Scalar(0.0) && abs(qz.matrixS()(i-1,j-1))!=Scalar(0.0)) - { - std::cerr << "Error: S(" << i << "," << j << ") = " << qz.matrixS()(i,j) << " && S(" << i-1 << "," << j-1 << ") = " << qz.matrixS()(i-1,j-1) << std::endl; - all_zeros = false; - } - } - VERIFY_IS_EQUAL(all_zeros, true); - VERIFY_IS_APPROX(qz.matrixQ()*qz.matrixS()*qz.matrixZ(), A); - VERIFY_IS_APPROX(qz.matrixQ()*qz.matrixT()*qz.matrixZ(), B); - VERIFY_IS_APPROX(qz.matrixQ()*qz.matrixQ().adjoint(), MatrixType::Identity(dim,dim)); - VERIFY_IS_APPROX(qz.matrixZ()*qz.matrixZ().adjoint(), MatrixType::Identity(dim,dim)); -} - -void test_real_qz() -{ - int s = 0; - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( real_qz(Matrix4f()) ); - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4); - CALL_SUBTEST_2( real_qz(MatrixXd(s,s)) ); - - // some trivial but implementation-wise tricky cases - CALL_SUBTEST_2( real_qz(MatrixXd(1,1)) ); - CALL_SUBTEST_2( real_qz(MatrixXd(2,2)) ); - CALL_SUBTEST_3( real_qz(Matrix<double,1,1>()) ); - CALL_SUBTEST_4( real_qz(Matrix2d()) ); - } - - TEST_SET_BUT_UNUSED_VARIABLE(s) -} diff --git a/eigen/test/redux.cpp b/eigen/test/redux.cpp deleted file mode 100644 index 213f080..0000000 --- a/eigen/test/redux.cpp +++ /dev/null @@ -1,178 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define TEST_ENABLE_TEMPORARY_TRACKING -#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8 -// ^^ see bug 1449 - -#include "main.h" - -template<typename MatrixType> void matrixRedux(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols); - - // The entries of m1 are uniformly distributed in [0,1], so m1.prod() is very small. This may lead to test - // failures if we underflow into denormals. Thus, we scale so that entries are close to 1. - MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + RealScalar(0.2) * m1; - - VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1)); - VERIFY_IS_APPROX(MatrixType::Ones(rows, cols).sum(), Scalar(float(rows*cols))); // the float() here to shut up excessive MSVC warning about int->complex conversion being lossy - Scalar s(0), p(1), minc(numext::real(m1.coeff(0))), maxc(numext::real(m1.coeff(0))); - for(int j = 0; j < cols; j++) - for(int i = 0; i < rows; i++) - { - s += m1(i,j); - p *= m1_for_prod(i,j); - minc = (std::min)(numext::real(minc), numext::real(m1(i,j))); - maxc = (std::max)(numext::real(maxc), numext::real(m1(i,j))); - } - const Scalar mean = s/Scalar(RealScalar(rows*cols)); - - VERIFY_IS_APPROX(m1.sum(), s); - VERIFY_IS_APPROX(m1.mean(), mean); - VERIFY_IS_APPROX(m1_for_prod.prod(), p); - VERIFY_IS_APPROX(m1.real().minCoeff(), numext::real(minc)); - VERIFY_IS_APPROX(m1.real().maxCoeff(), numext::real(maxc)); - - // test slice vectorization assuming assign is ok - Index r0 = internal::random<Index>(0,rows-1); - Index c0 = internal::random<Index>(0,cols-1); - Index r1 = internal::random<Index>(r0+1,rows)-r0; - Index c1 = internal::random<Index>(c0+1,cols)-c0; - VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).sum(), m1.block(r0,c0,r1,c1).eval().sum()); - VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).mean(), m1.block(r0,c0,r1,c1).eval().mean()); - VERIFY_IS_APPROX(m1_for_prod.block(r0,c0,r1,c1).prod(), m1_for_prod.block(r0,c0,r1,c1).eval().prod()); - VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().minCoeff(), m1.block(r0,c0,r1,c1).real().eval().minCoeff()); - VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().maxCoeff(), m1.block(r0,c0,r1,c1).real().eval().maxCoeff()); - - // regression for bug 1090 - const int R1 = MatrixType::RowsAtCompileTime>=2 ? MatrixType::RowsAtCompileTime/2 : 6; - const int C1 = MatrixType::ColsAtCompileTime>=2 ? MatrixType::ColsAtCompileTime/2 : 6; - if(R1<=rows-r0 && C1<=cols-c0) - { - VERIFY_IS_APPROX( (m1.template block<R1,C1>(r0,c0).sum()), m1.block(r0,c0,R1,C1).sum() ); - } - - // test empty objects - VERIFY_IS_APPROX(m1.block(r0,c0,0,0).sum(), Scalar(0)); - VERIFY_IS_APPROX(m1.block(r0,c0,0,0).prod(), Scalar(1)); - - // test nesting complex expression - VERIFY_EVALUATION_COUNT( (m1.matrix()*m1.matrix().transpose()).sum(), (MatrixType::IsVectorAtCompileTime && MatrixType::SizeAtCompileTime!=1 ? 0 : 1) ); - Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> m2(rows,rows); - m2.setRandom(); - VERIFY_EVALUATION_COUNT( ((m1.matrix()*m1.matrix().transpose())+m2).sum(),(MatrixType::IsVectorAtCompileTime && MatrixType::SizeAtCompileTime!=1 ? 0 : 1)); -} - -template<typename VectorType> void vectorRedux(const VectorType& w) -{ - using std::abs; - typedef typename VectorType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - Index size = w.size(); - - VectorType v = VectorType::Random(size); - VectorType v_for_prod = VectorType::Ones(size) + Scalar(0.2) * v; // see comment above declaration of m1_for_prod - - for(int i = 1; i < size; i++) - { - Scalar s(0), p(1); - RealScalar minc(numext::real(v.coeff(0))), maxc(numext::real(v.coeff(0))); - for(int j = 0; j < i; j++) - { - s += v[j]; - p *= v_for_prod[j]; - minc = (std::min)(minc, numext::real(v[j])); - maxc = (std::max)(maxc, numext::real(v[j])); - } - VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.head(i).sum()), Scalar(1)); - VERIFY_IS_APPROX(p, v_for_prod.head(i).prod()); - VERIFY_IS_APPROX(minc, v.real().head(i).minCoeff()); - VERIFY_IS_APPROX(maxc, v.real().head(i).maxCoeff()); - } - - for(int i = 0; i < size-1; i++) - { - Scalar s(0), p(1); - RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i))); - for(int j = i; j < size; j++) - { - s += v[j]; - p *= v_for_prod[j]; - minc = (std::min)(minc, numext::real(v[j])); - maxc = (std::max)(maxc, numext::real(v[j])); - } - VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.tail(size-i).sum()), Scalar(1)); - VERIFY_IS_APPROX(p, v_for_prod.tail(size-i).prod()); - VERIFY_IS_APPROX(minc, v.real().tail(size-i).minCoeff()); - VERIFY_IS_APPROX(maxc, v.real().tail(size-i).maxCoeff()); - } - - for(int i = 0; i < size/2; i++) - { - Scalar s(0), p(1); - RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i))); - for(int j = i; j < size-i; j++) - { - s += v[j]; - p *= v_for_prod[j]; - minc = (std::min)(minc, numext::real(v[j])); - maxc = (std::max)(maxc, numext::real(v[j])); - } - VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.segment(i, size-2*i).sum()), Scalar(1)); - VERIFY_IS_APPROX(p, v_for_prod.segment(i, size-2*i).prod()); - VERIFY_IS_APPROX(minc, v.real().segment(i, size-2*i).minCoeff()); - VERIFY_IS_APPROX(maxc, v.real().segment(i, size-2*i).maxCoeff()); - } - - // test empty objects - VERIFY_IS_APPROX(v.head(0).sum(), Scalar(0)); - VERIFY_IS_APPROX(v.tail(0).prod(), Scalar(1)); - VERIFY_RAISES_ASSERT(v.head(0).mean()); - VERIFY_RAISES_ASSERT(v.head(0).minCoeff()); - VERIFY_RAISES_ASSERT(v.head(0).maxCoeff()); -} - -void test_redux() -{ - // the max size cannot be too large, otherwise reduxion operations obviously generate large errors. - int maxsize = (std::min)(100,EIGEN_TEST_MAX_SIZE); - TEST_SET_BUT_UNUSED_VARIABLE(maxsize); - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( matrixRedux(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_1( matrixRedux(Array<float, 1, 1>()) ); - CALL_SUBTEST_2( matrixRedux(Matrix2f()) ); - CALL_SUBTEST_2( matrixRedux(Array2f()) ); - CALL_SUBTEST_2( matrixRedux(Array22f()) ); - CALL_SUBTEST_3( matrixRedux(Matrix4d()) ); - CALL_SUBTEST_3( matrixRedux(Array4d()) ); - CALL_SUBTEST_3( matrixRedux(Array44d()) ); - CALL_SUBTEST_4( matrixRedux(MatrixXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); - CALL_SUBTEST_4( matrixRedux(ArrayXXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); - CALL_SUBTEST_5( matrixRedux(MatrixXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); - CALL_SUBTEST_5( matrixRedux(ArrayXXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); - CALL_SUBTEST_6( matrixRedux(MatrixXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); - CALL_SUBTEST_6( matrixRedux(ArrayXXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); - } - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_7( vectorRedux(Vector4f()) ); - CALL_SUBTEST_7( vectorRedux(Array4f()) ); - CALL_SUBTEST_5( vectorRedux(VectorXd(internal::random<int>(1,maxsize))) ); - CALL_SUBTEST_5( vectorRedux(ArrayXd(internal::random<int>(1,maxsize))) ); - CALL_SUBTEST_8( vectorRedux(VectorXf(internal::random<int>(1,maxsize))) ); - CALL_SUBTEST_8( vectorRedux(ArrayXf(internal::random<int>(1,maxsize))) ); - } -} diff --git a/eigen/test/ref.cpp b/eigen/test/ref.cpp deleted file mode 100644 index 704495a..0000000 --- a/eigen/test/ref.cpp +++ /dev/null @@ -1,290 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 20013 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// This unit test cannot be easily written to work with EIGEN_DEFAULT_TO_ROW_MAJOR -#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR -#undef EIGEN_DEFAULT_TO_ROW_MAJOR -#endif - -#define TEST_ENABLE_TEMPORARY_TRACKING -#define TEST_CHECK_STATIC_ASSERTIONS -#include "main.h" - -// test Ref.h - -// Deal with i387 extended precision -#if EIGEN_ARCH_i386 && !(EIGEN_ARCH_x86_64) - -#if EIGEN_COMP_GNUC_STRICT && EIGEN_GNUC_AT_LEAST(4,4) -#pragma GCC optimize ("-ffloat-store") -#else -#undef VERIFY_IS_EQUAL -#define VERIFY_IS_EQUAL(X,Y) VERIFY_IS_APPROX(X,Y) -#endif - -#endif - -template<typename MatrixType> void ref_matrix(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef Matrix<Scalar,Dynamic,Dynamic,MatrixType::Options> DynMatrixType; - typedef Matrix<RealScalar,Dynamic,Dynamic,MatrixType::Options> RealDynMatrixType; - - typedef Ref<MatrixType> RefMat; - typedef Ref<DynMatrixType> RefDynMat; - typedef Ref<const DynMatrixType> ConstRefDynMat; - typedef Ref<RealDynMatrixType , 0, Stride<Dynamic,Dynamic> > RefRealMatWithStride; - - Index rows = m.rows(), cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = m1; - - Index i = internal::random<Index>(0,rows-1); - Index j = internal::random<Index>(0,cols-1); - Index brows = internal::random<Index>(1,rows-i); - Index bcols = internal::random<Index>(1,cols-j); - - RefMat rm0 = m1; - VERIFY_IS_EQUAL(rm0, m1); - RefDynMat rm1 = m1; - VERIFY_IS_EQUAL(rm1, m1); - RefDynMat rm2 = m1.block(i,j,brows,bcols); - VERIFY_IS_EQUAL(rm2, m1.block(i,j,brows,bcols)); - rm2.setOnes(); - m2.block(i,j,brows,bcols).setOnes(); - VERIFY_IS_EQUAL(m1, m2); - - m2.block(i,j,brows,bcols).setRandom(); - rm2 = m2.block(i,j,brows,bcols); - VERIFY_IS_EQUAL(m1, m2); - - ConstRefDynMat rm3 = m1.block(i,j,brows,bcols); - m1.block(i,j,brows,bcols) *= 2; - m2.block(i,j,brows,bcols) *= 2; - VERIFY_IS_EQUAL(rm3, m2.block(i,j,brows,bcols)); - RefRealMatWithStride rm4 = m1.real(); - VERIFY_IS_EQUAL(rm4, m2.real()); - rm4.array() += 1; - m2.real().array() += 1; - VERIFY_IS_EQUAL(m1, m2); -} - -template<typename VectorType> void ref_vector(const VectorType& m) -{ - typedef typename VectorType::Scalar Scalar; - typedef typename VectorType::RealScalar RealScalar; - typedef Matrix<Scalar,Dynamic,1,VectorType::Options> DynMatrixType; - typedef Matrix<Scalar,Dynamic,Dynamic,ColMajor> MatrixType; - typedef Matrix<RealScalar,Dynamic,1,VectorType::Options> RealDynMatrixType; - - typedef Ref<VectorType> RefMat; - typedef Ref<DynMatrixType> RefDynMat; - typedef Ref<const DynMatrixType> ConstRefDynMat; - typedef Ref<RealDynMatrixType , 0, InnerStride<> > RefRealMatWithStride; - typedef Ref<DynMatrixType , 0, InnerStride<> > RefMatWithStride; - - Index size = m.size(); - - VectorType v1 = VectorType::Random(size), - v2 = v1; - MatrixType mat1 = MatrixType::Random(size,size), - mat2 = mat1, - mat3 = MatrixType::Random(size,size); - - Index i = internal::random<Index>(0,size-1); - Index bsize = internal::random<Index>(1,size-i); - - RefMat rm0 = v1; - VERIFY_IS_EQUAL(rm0, v1); - RefDynMat rv1 = v1; - VERIFY_IS_EQUAL(rv1, v1); - RefDynMat rv2 = v1.segment(i,bsize); - VERIFY_IS_EQUAL(rv2, v1.segment(i,bsize)); - rv2.setOnes(); - v2.segment(i,bsize).setOnes(); - VERIFY_IS_EQUAL(v1, v2); - - v2.segment(i,bsize).setRandom(); - rv2 = v2.segment(i,bsize); - VERIFY_IS_EQUAL(v1, v2); - - ConstRefDynMat rm3 = v1.segment(i,bsize); - v1.segment(i,bsize) *= 2; - v2.segment(i,bsize) *= 2; - VERIFY_IS_EQUAL(rm3, v2.segment(i,bsize)); - - RefRealMatWithStride rm4 = v1.real(); - VERIFY_IS_EQUAL(rm4, v2.real()); - rm4.array() += 1; - v2.real().array() += 1; - VERIFY_IS_EQUAL(v1, v2); - - RefMatWithStride rm5 = mat1.row(i).transpose(); - VERIFY_IS_EQUAL(rm5, mat1.row(i).transpose()); - rm5.array() += 1; - mat2.row(i).array() += 1; - VERIFY_IS_EQUAL(mat1, mat2); - rm5.noalias() = rm4.transpose() * mat3; - mat2.row(i) = v2.real().transpose() * mat3; - VERIFY_IS_APPROX(mat1, mat2); -} - -template<typename PlainObjectType> void check_const_correctness(const PlainObjectType&) -{ - // verify that ref-to-const don't have LvalueBit - typedef typename internal::add_const<PlainObjectType>::type ConstPlainObjectType; - VERIFY( !(internal::traits<Ref<ConstPlainObjectType> >::Flags & LvalueBit) ); - VERIFY( !(internal::traits<Ref<ConstPlainObjectType, Aligned> >::Flags & LvalueBit) ); - VERIFY( !(Ref<ConstPlainObjectType>::Flags & LvalueBit) ); - VERIFY( !(Ref<ConstPlainObjectType, Aligned>::Flags & LvalueBit) ); -} - -template<typename B> -EIGEN_DONT_INLINE void call_ref_1(Ref<VectorXf> a, const B &b) { VERIFY_IS_EQUAL(a,b); } -template<typename B> -EIGEN_DONT_INLINE void call_ref_2(const Ref<const VectorXf>& a, const B &b) { VERIFY_IS_EQUAL(a,b); } -template<typename B> -EIGEN_DONT_INLINE void call_ref_3(Ref<VectorXf,0,InnerStride<> > a, const B &b) { VERIFY_IS_EQUAL(a,b); } -template<typename B> -EIGEN_DONT_INLINE void call_ref_4(const Ref<const VectorXf,0,InnerStride<> >& a, const B &b) { VERIFY_IS_EQUAL(a,b); } -template<typename B> -EIGEN_DONT_INLINE void call_ref_5(Ref<MatrixXf,0,OuterStride<> > a, const B &b) { VERIFY_IS_EQUAL(a,b); } -template<typename B> -EIGEN_DONT_INLINE void call_ref_6(const Ref<const MatrixXf,0,OuterStride<> >& a, const B &b) { VERIFY_IS_EQUAL(a,b); } -template<typename B> -EIGEN_DONT_INLINE void call_ref_7(Ref<Matrix<float,Dynamic,3> > a, const B &b) { VERIFY_IS_EQUAL(a,b); } - -void call_ref() -{ - VectorXcf ca = VectorXcf::Random(10); - VectorXf a = VectorXf::Random(10); - RowVectorXf b = RowVectorXf::Random(10); - MatrixXf A = MatrixXf::Random(10,10); - RowVector3f c = RowVector3f::Random(); - const VectorXf& ac(a); - VectorBlock<VectorXf> ab(a,0,3); - const VectorBlock<VectorXf> abc(a,0,3); - - - VERIFY_EVALUATION_COUNT( call_ref_1(a,a), 0); - VERIFY_EVALUATION_COUNT( call_ref_1(b,b.transpose()), 0); -// call_ref_1(ac,a<c); // does not compile because ac is const - VERIFY_EVALUATION_COUNT( call_ref_1(ab,ab), 0); - VERIFY_EVALUATION_COUNT( call_ref_1(a.head(4),a.head(4)), 0); - VERIFY_EVALUATION_COUNT( call_ref_1(abc,abc), 0); - VERIFY_EVALUATION_COUNT( call_ref_1(A.col(3),A.col(3)), 0); -// call_ref_1(A.row(3),A.row(3)); // does not compile because innerstride!=1 - VERIFY_EVALUATION_COUNT( call_ref_3(A.row(3),A.row(3).transpose()), 0); - VERIFY_EVALUATION_COUNT( call_ref_4(A.row(3),A.row(3).transpose()), 0); -// call_ref_1(a+a, a+a); // does not compile for obvious reason - - MatrixXf tmp = A*A.col(1); - VERIFY_EVALUATION_COUNT( call_ref_2(A*A.col(1), tmp), 1); // evaluated into a temp - VERIFY_EVALUATION_COUNT( call_ref_2(ac.head(5),ac.head(5)), 0); - VERIFY_EVALUATION_COUNT( call_ref_2(ac,ac), 0); - VERIFY_EVALUATION_COUNT( call_ref_2(a,a), 0); - VERIFY_EVALUATION_COUNT( call_ref_2(ab,ab), 0); - VERIFY_EVALUATION_COUNT( call_ref_2(a.head(4),a.head(4)), 0); - tmp = a+a; - VERIFY_EVALUATION_COUNT( call_ref_2(a+a,tmp), 1); // evaluated into a temp - VERIFY_EVALUATION_COUNT( call_ref_2(ca.imag(),ca.imag()), 1); // evaluated into a temp - - VERIFY_EVALUATION_COUNT( call_ref_4(ac.head(5),ac.head(5)), 0); - tmp = a+a; - VERIFY_EVALUATION_COUNT( call_ref_4(a+a,tmp), 1); // evaluated into a temp - VERIFY_EVALUATION_COUNT( call_ref_4(ca.imag(),ca.imag()), 0); - - VERIFY_EVALUATION_COUNT( call_ref_5(a,a), 0); - VERIFY_EVALUATION_COUNT( call_ref_5(a.head(3),a.head(3)), 0); - VERIFY_EVALUATION_COUNT( call_ref_5(A,A), 0); -// call_ref_5(A.transpose(),A.transpose()); // does not compile because storage order does not match - VERIFY_EVALUATION_COUNT( call_ref_5(A.block(1,1,2,2),A.block(1,1,2,2)), 0); - VERIFY_EVALUATION_COUNT( call_ref_5(b,b), 0); // storage order do not match, but this is a degenerate case that should work - VERIFY_EVALUATION_COUNT( call_ref_5(a.row(3),a.row(3)), 0); - - VERIFY_EVALUATION_COUNT( call_ref_6(a,a), 0); - VERIFY_EVALUATION_COUNT( call_ref_6(a.head(3),a.head(3)), 0); - VERIFY_EVALUATION_COUNT( call_ref_6(A.row(3),A.row(3)), 1); // evaluated into a temp thouth it could be avoided by viewing it as a 1xn matrix - tmp = A+A; - VERIFY_EVALUATION_COUNT( call_ref_6(A+A,tmp), 1); // evaluated into a temp - VERIFY_EVALUATION_COUNT( call_ref_6(A,A), 0); - VERIFY_EVALUATION_COUNT( call_ref_6(A.transpose(),A.transpose()), 1); // evaluated into a temp because the storage orders do not match - VERIFY_EVALUATION_COUNT( call_ref_6(A.block(1,1,2,2),A.block(1,1,2,2)), 0); - - VERIFY_EVALUATION_COUNT( call_ref_7(c,c), 0); -} - -typedef Matrix<double,Dynamic,Dynamic,RowMajor> RowMatrixXd; -int test_ref_overload_fun1(Ref<MatrixXd> ) { return 1; } -int test_ref_overload_fun1(Ref<RowMatrixXd> ) { return 2; } -int test_ref_overload_fun1(Ref<MatrixXf> ) { return 3; } - -int test_ref_overload_fun2(Ref<const MatrixXd> ) { return 4; } -int test_ref_overload_fun2(Ref<const MatrixXf> ) { return 5; } - -void test_ref_ambiguous(const Ref<const ArrayXd> &A, Ref<ArrayXd> B) -{ - B = A; - B = A - A; -} - -// See also bug 969 -void test_ref_overloads() -{ - MatrixXd Ad, Bd; - RowMatrixXd rAd, rBd; - VERIFY( test_ref_overload_fun1(Ad)==1 ); - VERIFY( test_ref_overload_fun1(rAd)==2 ); - - MatrixXf Af, Bf; - VERIFY( test_ref_overload_fun2(Ad)==4 ); - VERIFY( test_ref_overload_fun2(Ad+Bd)==4 ); - VERIFY( test_ref_overload_fun2(Af+Bf)==5 ); - - ArrayXd A, B; - test_ref_ambiguous(A, B); -} - -void test_ref_fixed_size_assert() -{ - Vector4f v4; - VectorXf vx(10); - VERIFY_RAISES_STATIC_ASSERT( Ref<Vector3f> y = v4; (void)y; ); - VERIFY_RAISES_STATIC_ASSERT( Ref<Vector3f> y = vx.head<4>(); (void)y; ); - VERIFY_RAISES_STATIC_ASSERT( Ref<const Vector3f> y = v4; (void)y; ); - VERIFY_RAISES_STATIC_ASSERT( Ref<const Vector3f> y = vx.head<4>(); (void)y; ); - VERIFY_RAISES_STATIC_ASSERT( Ref<const Vector3f> y = 2*v4; (void)y; ); -} - -void test_ref() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( ref_vector(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_1( check_const_correctness(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( ref_vector(Vector4d()) ); - CALL_SUBTEST_2( check_const_correctness(Matrix4d()) ); - CALL_SUBTEST_3( ref_vector(Vector4cf()) ); - CALL_SUBTEST_4( ref_vector(VectorXcf(8)) ); - CALL_SUBTEST_5( ref_vector(VectorXi(12)) ); - CALL_SUBTEST_5( check_const_correctness(VectorXi(12)) ); - - CALL_SUBTEST_1( ref_matrix(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( ref_matrix(Matrix4d()) ); - CALL_SUBTEST_1( ref_matrix(Matrix<float,3,5>()) ); - CALL_SUBTEST_4( ref_matrix(MatrixXcf(internal::random<int>(1,10),internal::random<int>(1,10))) ); - CALL_SUBTEST_4( ref_matrix(Matrix<std::complex<double>,10,15>()) ); - CALL_SUBTEST_5( ref_matrix(MatrixXi(internal::random<int>(1,10),internal::random<int>(1,10))) ); - CALL_SUBTEST_6( call_ref() ); - } - - CALL_SUBTEST_7( test_ref_overloads() ); - CALL_SUBTEST_7( test_ref_fixed_size_assert() ); -} diff --git a/eigen/test/resize.cpp b/eigen/test/resize.cpp deleted file mode 100644 index 4adaafe..0000000 --- a/eigen/test/resize.cpp +++ /dev/null @@ -1,41 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Keir Mierle <mierle@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<DenseIndex rows, DenseIndex cols> -void resizeLikeTest() -{ - MatrixXf A(rows, cols); - MatrixXf B; - Matrix<double, rows, cols> C; - B.resizeLike(A); - C.resizeLike(B); // Shouldn't crash. - VERIFY(B.rows() == rows && B.cols() == cols); - - VectorXf x(rows); - RowVectorXf y; - y.resizeLike(x); - VERIFY(y.rows() == 1 && y.cols() == rows); - - y.resize(cols); - x.resizeLike(y); - VERIFY(x.rows() == cols && x.cols() == 1); -} - -void resizeLikeTest12() { resizeLikeTest<1,2>(); } -void resizeLikeTest1020() { resizeLikeTest<10,20>(); } -void resizeLikeTest31() { resizeLikeTest<3,1>(); } - -void test_resize() -{ - CALL_SUBTEST(resizeLikeTest12() ); - CALL_SUBTEST(resizeLikeTest1020() ); - CALL_SUBTEST(resizeLikeTest31() ); -} diff --git a/eigen/test/rvalue_types.cpp b/eigen/test/rvalue_types.cpp deleted file mode 100644 index 8887f1b..0000000 --- a/eigen/test/rvalue_types.cpp +++ /dev/null @@ -1,64 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2013 Hauke Heibel <hauke.heibel@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/Core> - -using internal::UIntPtr; - -#if EIGEN_HAS_RVALUE_REFERENCES -template <typename MatrixType> -void rvalue_copyassign(const MatrixType& m) -{ - - typedef typename internal::traits<MatrixType>::Scalar Scalar; - - // create a temporary which we are about to destroy by moving - MatrixType tmp = m; - UIntPtr src_address = reinterpret_cast<UIntPtr>(tmp.data()); - - // move the temporary to n - MatrixType n = std::move(tmp); - UIntPtr dst_address = reinterpret_cast<UIntPtr>(n.data()); - - if (MatrixType::RowsAtCompileTime==Dynamic|| MatrixType::ColsAtCompileTime==Dynamic) - { - // verify that we actually moved the guts - VERIFY_IS_EQUAL(src_address, dst_address); - } - - // verify that the content did not change - Scalar abs_diff = (m-n).array().abs().sum(); - VERIFY_IS_EQUAL(abs_diff, Scalar(0)); -} -#else -template <typename MatrixType> -void rvalue_copyassign(const MatrixType&) {} -#endif - -void test_rvalue_types() -{ - CALL_SUBTEST_1(rvalue_copyassign( MatrixXf::Random(50,50).eval() )); - CALL_SUBTEST_1(rvalue_copyassign( ArrayXXf::Random(50,50).eval() )); - - CALL_SUBTEST_1(rvalue_copyassign( Matrix<float,1,Dynamic>::Random(50).eval() )); - CALL_SUBTEST_1(rvalue_copyassign( Array<float,1,Dynamic>::Random(50).eval() )); - - CALL_SUBTEST_1(rvalue_copyassign( Matrix<float,Dynamic,1>::Random(50).eval() )); - CALL_SUBTEST_1(rvalue_copyassign( Array<float,Dynamic,1>::Random(50).eval() )); - - CALL_SUBTEST_2(rvalue_copyassign( Array<float,2,1>::Random().eval() )); - CALL_SUBTEST_2(rvalue_copyassign( Array<float,3,1>::Random().eval() )); - CALL_SUBTEST_2(rvalue_copyassign( Array<float,4,1>::Random().eval() )); - - CALL_SUBTEST_2(rvalue_copyassign( Array<float,2,2>::Random().eval() )); - CALL_SUBTEST_2(rvalue_copyassign( Array<float,3,3>::Random().eval() )); - CALL_SUBTEST_2(rvalue_copyassign( Array<float,4,4>::Random().eval() )); -} diff --git a/eigen/test/schur_complex.cpp b/eigen/test/schur_complex.cpp deleted file mode 100644 index deb78e4..0000000 --- a/eigen/test/schur_complex.cpp +++ /dev/null @@ -1,91 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <limits> -#include <Eigen/Eigenvalues> - -template<typename MatrixType> void schur(int size = MatrixType::ColsAtCompileTime) -{ - typedef typename ComplexSchur<MatrixType>::ComplexScalar ComplexScalar; - typedef typename ComplexSchur<MatrixType>::ComplexMatrixType ComplexMatrixType; - - // Test basic functionality: T is triangular and A = U T U* - for(int counter = 0; counter < g_repeat; ++counter) { - MatrixType A = MatrixType::Random(size, size); - ComplexSchur<MatrixType> schurOfA(A); - VERIFY_IS_EQUAL(schurOfA.info(), Success); - ComplexMatrixType U = schurOfA.matrixU(); - ComplexMatrixType T = schurOfA.matrixT(); - for(int row = 1; row < size; ++row) { - for(int col = 0; col < row; ++col) { - VERIFY(T(row,col) == (typename MatrixType::Scalar)0); - } - } - VERIFY_IS_APPROX(A.template cast<ComplexScalar>(), U * T * U.adjoint()); - } - - // Test asserts when not initialized - ComplexSchur<MatrixType> csUninitialized; - VERIFY_RAISES_ASSERT(csUninitialized.matrixT()); - VERIFY_RAISES_ASSERT(csUninitialized.matrixU()); - VERIFY_RAISES_ASSERT(csUninitialized.info()); - - // Test whether compute() and constructor returns same result - MatrixType A = MatrixType::Random(size, size); - ComplexSchur<MatrixType> cs1; - cs1.compute(A); - ComplexSchur<MatrixType> cs2(A); - VERIFY_IS_EQUAL(cs1.info(), Success); - VERIFY_IS_EQUAL(cs2.info(), Success); - VERIFY_IS_EQUAL(cs1.matrixT(), cs2.matrixT()); - VERIFY_IS_EQUAL(cs1.matrixU(), cs2.matrixU()); - - // Test maximum number of iterations - ComplexSchur<MatrixType> cs3; - cs3.setMaxIterations(ComplexSchur<MatrixType>::m_maxIterationsPerRow * size).compute(A); - VERIFY_IS_EQUAL(cs3.info(), Success); - VERIFY_IS_EQUAL(cs3.matrixT(), cs1.matrixT()); - VERIFY_IS_EQUAL(cs3.matrixU(), cs1.matrixU()); - cs3.setMaxIterations(1).compute(A); - VERIFY_IS_EQUAL(cs3.info(), size > 1 ? NoConvergence : Success); - VERIFY_IS_EQUAL(cs3.getMaxIterations(), 1); - - MatrixType Atriangular = A; - Atriangular.template triangularView<StrictlyLower>().setZero(); - cs3.setMaxIterations(1).compute(Atriangular); // triangular matrices do not need any iterations - VERIFY_IS_EQUAL(cs3.info(), Success); - VERIFY_IS_EQUAL(cs3.matrixT(), Atriangular.template cast<ComplexScalar>()); - VERIFY_IS_EQUAL(cs3.matrixU(), ComplexMatrixType::Identity(size, size)); - - // Test computation of only T, not U - ComplexSchur<MatrixType> csOnlyT(A, false); - VERIFY_IS_EQUAL(csOnlyT.info(), Success); - VERIFY_IS_EQUAL(cs1.matrixT(), csOnlyT.matrixT()); - VERIFY_RAISES_ASSERT(csOnlyT.matrixU()); - - if (size > 1 && size < 20) - { - // Test matrix with NaN - A(0,0) = std::numeric_limits<typename MatrixType::RealScalar>::quiet_NaN(); - ComplexSchur<MatrixType> csNaN(A); - VERIFY_IS_EQUAL(csNaN.info(), NoConvergence); - } -} - -void test_schur_complex() -{ - CALL_SUBTEST_1(( schur<Matrix4cd>() )); - CALL_SUBTEST_2(( schur<MatrixXcf>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4)) )); - CALL_SUBTEST_3(( schur<Matrix<std::complex<float>, 1, 1> >() )); - CALL_SUBTEST_4(( schur<Matrix<float, 3, 3, Eigen::RowMajor> >() )); - - // Test problem size constructors - CALL_SUBTEST_5(ComplexSchur<MatrixXf>(10)); -} diff --git a/eigen/test/schur_real.cpp b/eigen/test/schur_real.cpp deleted file mode 100644 index e5229e6..0000000 --- a/eigen/test/schur_real.cpp +++ /dev/null @@ -1,110 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <limits> -#include <Eigen/Eigenvalues> - -template<typename MatrixType> void verifyIsQuasiTriangular(const MatrixType& T) -{ - const Index size = T.cols(); - typedef typename MatrixType::Scalar Scalar; - - // Check T is lower Hessenberg - for(int row = 2; row < size; ++row) { - for(int col = 0; col < row - 1; ++col) { - VERIFY(T(row,col) == Scalar(0)); - } - } - - // Check that any non-zero on the subdiagonal is followed by a zero and is - // part of a 2x2 diagonal block with imaginary eigenvalues. - for(int row = 1; row < size; ++row) { - if (T(row,row-1) != Scalar(0)) { - VERIFY(row == size-1 || T(row+1,row) == 0); - Scalar tr = T(row-1,row-1) + T(row,row); - Scalar det = T(row-1,row-1) * T(row,row) - T(row-1,row) * T(row,row-1); - VERIFY(4 * det > tr * tr); - } - } -} - -template<typename MatrixType> void schur(int size = MatrixType::ColsAtCompileTime) -{ - // Test basic functionality: T is quasi-triangular and A = U T U* - for(int counter = 0; counter < g_repeat; ++counter) { - MatrixType A = MatrixType::Random(size, size); - RealSchur<MatrixType> schurOfA(A); - VERIFY_IS_EQUAL(schurOfA.info(), Success); - MatrixType U = schurOfA.matrixU(); - MatrixType T = schurOfA.matrixT(); - verifyIsQuasiTriangular(T); - VERIFY_IS_APPROX(A, U * T * U.transpose()); - } - - // Test asserts when not initialized - RealSchur<MatrixType> rsUninitialized; - VERIFY_RAISES_ASSERT(rsUninitialized.matrixT()); - VERIFY_RAISES_ASSERT(rsUninitialized.matrixU()); - VERIFY_RAISES_ASSERT(rsUninitialized.info()); - - // Test whether compute() and constructor returns same result - MatrixType A = MatrixType::Random(size, size); - RealSchur<MatrixType> rs1; - rs1.compute(A); - RealSchur<MatrixType> rs2(A); - VERIFY_IS_EQUAL(rs1.info(), Success); - VERIFY_IS_EQUAL(rs2.info(), Success); - VERIFY_IS_EQUAL(rs1.matrixT(), rs2.matrixT()); - VERIFY_IS_EQUAL(rs1.matrixU(), rs2.matrixU()); - - // Test maximum number of iterations - RealSchur<MatrixType> rs3; - rs3.setMaxIterations(RealSchur<MatrixType>::m_maxIterationsPerRow * size).compute(A); - VERIFY_IS_EQUAL(rs3.info(), Success); - VERIFY_IS_EQUAL(rs3.matrixT(), rs1.matrixT()); - VERIFY_IS_EQUAL(rs3.matrixU(), rs1.matrixU()); - if (size > 2) { - rs3.setMaxIterations(1).compute(A); - VERIFY_IS_EQUAL(rs3.info(), NoConvergence); - VERIFY_IS_EQUAL(rs3.getMaxIterations(), 1); - } - - MatrixType Atriangular = A; - Atriangular.template triangularView<StrictlyLower>().setZero(); - rs3.setMaxIterations(1).compute(Atriangular); // triangular matrices do not need any iterations - VERIFY_IS_EQUAL(rs3.info(), Success); - VERIFY_IS_APPROX(rs3.matrixT(), Atriangular); // approx because of scaling... - VERIFY_IS_EQUAL(rs3.matrixU(), MatrixType::Identity(size, size)); - - // Test computation of only T, not U - RealSchur<MatrixType> rsOnlyT(A, false); - VERIFY_IS_EQUAL(rsOnlyT.info(), Success); - VERIFY_IS_EQUAL(rs1.matrixT(), rsOnlyT.matrixT()); - VERIFY_RAISES_ASSERT(rsOnlyT.matrixU()); - - if (size > 2 && size < 20) - { - // Test matrix with NaN - A(0,0) = std::numeric_limits<typename MatrixType::Scalar>::quiet_NaN(); - RealSchur<MatrixType> rsNaN(A); - VERIFY_IS_EQUAL(rsNaN.info(), NoConvergence); - } -} - -void test_schur_real() -{ - CALL_SUBTEST_1(( schur<Matrix4f>() )); - CALL_SUBTEST_2(( schur<MatrixXd>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4)) )); - CALL_SUBTEST_3(( schur<Matrix<float, 1, 1> >() )); - CALL_SUBTEST_4(( schur<Matrix<double, 3, 3, Eigen::RowMajor> >() )); - - // Test problem size constructors - CALL_SUBTEST_5(RealSchur<MatrixXf>(10)); -} diff --git a/eigen/test/selfadjoint.cpp b/eigen/test/selfadjoint.cpp deleted file mode 100644 index bb11cc3..0000000 --- a/eigen/test/selfadjoint.cpp +++ /dev/null @@ -1,75 +0,0 @@ -// This file is triangularView of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define TEST_CHECK_STATIC_ASSERTIONS -#include "main.h" - -// This file tests the basic selfadjointView API, -// the related products and decompositions are tested in specific files. - -template<typename MatrixType> void selfadjoint(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols), - m4(rows, cols); - - m1.diagonal() = m1.diagonal().real().template cast<Scalar>(); - - // check selfadjoint to dense - m3 = m1.template selfadjointView<Upper>(); - VERIFY_IS_APPROX(MatrixType(m3.template triangularView<Upper>()), MatrixType(m1.template triangularView<Upper>())); - VERIFY_IS_APPROX(m3, m3.adjoint()); - - m3 = m1.template selfadjointView<Lower>(); - VERIFY_IS_APPROX(MatrixType(m3.template triangularView<Lower>()), MatrixType(m1.template triangularView<Lower>())); - VERIFY_IS_APPROX(m3, m3.adjoint()); - - m3 = m1.template selfadjointView<Upper>(); - m4 = m2; - m4 += m1.template selfadjointView<Upper>(); - VERIFY_IS_APPROX(m4, m2+m3); - - m3 = m1.template selfadjointView<Lower>(); - m4 = m2; - m4 -= m1.template selfadjointView<Lower>(); - VERIFY_IS_APPROX(m4, m2-m3); - - VERIFY_RAISES_STATIC_ASSERT(m2.template selfadjointView<StrictlyUpper>()); - VERIFY_RAISES_STATIC_ASSERT(m2.template selfadjointView<UnitLower>()); -} - -void bug_159() -{ - Matrix3d m = Matrix3d::Random().selfadjointView<Lower>(); - EIGEN_UNUSED_VARIABLE(m) -} - -void test_selfadjoint() -{ - for(int i = 0; i < g_repeat ; i++) - { - int s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE); - - CALL_SUBTEST_1( selfadjoint(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( selfadjoint(Matrix<float, 2, 2>()) ); - CALL_SUBTEST_3( selfadjoint(Matrix3cf()) ); - CALL_SUBTEST_4( selfadjoint(MatrixXcd(s,s)) ); - CALL_SUBTEST_5( selfadjoint(Matrix<float,Dynamic,Dynamic,RowMajor>(s, s)) ); - - TEST_SET_BUT_UNUSED_VARIABLE(s) - } - - CALL_SUBTEST_1( bug_159() ); -} diff --git a/eigen/test/simplicial_cholesky.cpp b/eigen/test/simplicial_cholesky.cpp deleted file mode 100644 index 649c817..0000000 --- a/eigen/test/simplicial_cholesky.cpp +++ /dev/null @@ -1,47 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "sparse_solver.h" - -template<typename T, typename I> void test_simplicial_cholesky_T() -{ - typedef SparseMatrix<T,0,I> SparseMatrixType; - SimplicialCholesky<SparseMatrixType, Lower> chol_colmajor_lower_amd; - SimplicialCholesky<SparseMatrixType, Upper> chol_colmajor_upper_amd; - SimplicialLLT< SparseMatrixType, Lower> llt_colmajor_lower_amd; - SimplicialLLT< SparseMatrixType, Upper> llt_colmajor_upper_amd; - SimplicialLDLT< SparseMatrixType, Lower> ldlt_colmajor_lower_amd; - SimplicialLDLT< SparseMatrixType, Upper> ldlt_colmajor_upper_amd; - SimplicialLDLT< SparseMatrixType, Lower, NaturalOrdering<I> > ldlt_colmajor_lower_nat; - SimplicialLDLT< SparseMatrixType, Upper, NaturalOrdering<I> > ldlt_colmajor_upper_nat; - - check_sparse_spd_solving(chol_colmajor_lower_amd); - check_sparse_spd_solving(chol_colmajor_upper_amd); - check_sparse_spd_solving(llt_colmajor_lower_amd); - check_sparse_spd_solving(llt_colmajor_upper_amd); - check_sparse_spd_solving(ldlt_colmajor_lower_amd); - check_sparse_spd_solving(ldlt_colmajor_upper_amd); - - check_sparse_spd_determinant(chol_colmajor_lower_amd); - check_sparse_spd_determinant(chol_colmajor_upper_amd); - check_sparse_spd_determinant(llt_colmajor_lower_amd); - check_sparse_spd_determinant(llt_colmajor_upper_amd); - check_sparse_spd_determinant(ldlt_colmajor_lower_amd); - check_sparse_spd_determinant(ldlt_colmajor_upper_amd); - - check_sparse_spd_solving(ldlt_colmajor_lower_nat, 300, 1000); - check_sparse_spd_solving(ldlt_colmajor_upper_nat, 300, 1000); -} - -void test_simplicial_cholesky() -{ - CALL_SUBTEST_1(( test_simplicial_cholesky_T<double,int>() )); - CALL_SUBTEST_2(( test_simplicial_cholesky_T<std::complex<double>, int>() )); - CALL_SUBTEST_3(( test_simplicial_cholesky_T<double,long int>() )); -} diff --git a/eigen/test/sizeof.cpp b/eigen/test/sizeof.cpp deleted file mode 100644 index 03ad204..0000000 --- a/eigen/test/sizeof.cpp +++ /dev/null @@ -1,47 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename MatrixType> void verifySizeOf(const MatrixType&) -{ - typedef typename MatrixType::Scalar Scalar; - if (MatrixType::RowsAtCompileTime!=Dynamic && MatrixType::ColsAtCompileTime!=Dynamic) - VERIFY_IS_EQUAL(std::ptrdiff_t(sizeof(MatrixType)),std::ptrdiff_t(sizeof(Scalar))*std::ptrdiff_t(MatrixType::SizeAtCompileTime)); - else - VERIFY_IS_EQUAL(sizeof(MatrixType),sizeof(Scalar*) + 2 * sizeof(typename MatrixType::Index)); -} - -void test_sizeof() -{ - CALL_SUBTEST(verifySizeOf(Matrix<float, 1, 1>()) ); - CALL_SUBTEST(verifySizeOf(Array<float, 2, 1>()) ); - CALL_SUBTEST(verifySizeOf(Array<float, 3, 1>()) ); - CALL_SUBTEST(verifySizeOf(Array<float, 4, 1>()) ); - CALL_SUBTEST(verifySizeOf(Array<float, 5, 1>()) ); - CALL_SUBTEST(verifySizeOf(Array<float, 6, 1>()) ); - CALL_SUBTEST(verifySizeOf(Array<float, 7, 1>()) ); - CALL_SUBTEST(verifySizeOf(Array<float, 8, 1>()) ); - CALL_SUBTEST(verifySizeOf(Array<float, 9, 1>()) ); - CALL_SUBTEST(verifySizeOf(Array<float, 10, 1>()) ); - CALL_SUBTEST(verifySizeOf(Array<float, 11, 1>()) ); - CALL_SUBTEST(verifySizeOf(Array<float, 12, 1>()) ); - CALL_SUBTEST(verifySizeOf(Vector2d()) ); - CALL_SUBTEST(verifySizeOf(Vector4f()) ); - CALL_SUBTEST(verifySizeOf(Matrix4d()) ); - CALL_SUBTEST(verifySizeOf(Matrix<double, 4, 2>()) ); - CALL_SUBTEST(verifySizeOf(Matrix<bool, 7, 5>()) ); - CALL_SUBTEST(verifySizeOf(MatrixXcf(3, 3)) ); - CALL_SUBTEST(verifySizeOf(MatrixXi(8, 12)) ); - CALL_SUBTEST(verifySizeOf(MatrixXcd(20, 20)) ); - CALL_SUBTEST(verifySizeOf(Matrix<float, 100, 100>()) ); - - VERIFY(sizeof(std::complex<float>) == 2*sizeof(float)); - VERIFY(sizeof(std::complex<double>) == 2*sizeof(double)); -} diff --git a/eigen/test/sizeoverflow.cpp b/eigen/test/sizeoverflow.cpp deleted file mode 100644 index 240d222..0000000 --- a/eigen/test/sizeoverflow.cpp +++ /dev/null @@ -1,64 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#define VERIFY_THROWS_BADALLOC(a) { \ - bool threw = false; \ - try { \ - a; \ - } \ - catch (std::bad_alloc&) { threw = true; } \ - VERIFY(threw && "should have thrown bad_alloc: " #a); \ - } - -template<typename MatrixType> -void triggerMatrixBadAlloc(Index rows, Index cols) -{ - VERIFY_THROWS_BADALLOC( MatrixType m(rows, cols) ); - VERIFY_THROWS_BADALLOC( MatrixType m; m.resize(rows, cols) ); - VERIFY_THROWS_BADALLOC( MatrixType m; m.conservativeResize(rows, cols) ); -} - -template<typename VectorType> -void triggerVectorBadAlloc(Index size) -{ - VERIFY_THROWS_BADALLOC( VectorType v(size) ); - VERIFY_THROWS_BADALLOC( VectorType v; v.resize(size) ); - VERIFY_THROWS_BADALLOC( VectorType v; v.conservativeResize(size) ); -} - -void test_sizeoverflow() -{ - // there are 2 levels of overflow checking. first in PlainObjectBase.h we check for overflow in rows*cols computations. - // this is tested in tests of the form times_itself_gives_0 * times_itself_gives_0 - // Then in Memory.h we check for overflow in size * sizeof(T) computations. - // this is tested in tests of the form times_4_gives_0 * sizeof(float) - - size_t times_itself_gives_0 = size_t(1) << (8 * sizeof(Index) / 2); - VERIFY(times_itself_gives_0 * times_itself_gives_0 == 0); - - size_t times_4_gives_0 = size_t(1) << (8 * sizeof(Index) - 2); - VERIFY(times_4_gives_0 * 4 == 0); - - size_t times_8_gives_0 = size_t(1) << (8 * sizeof(Index) - 3); - VERIFY(times_8_gives_0 * 8 == 0); - - triggerMatrixBadAlloc<MatrixXf>(times_itself_gives_0, times_itself_gives_0); - triggerMatrixBadAlloc<MatrixXf>(times_itself_gives_0 / 4, times_itself_gives_0); - triggerMatrixBadAlloc<MatrixXf>(times_4_gives_0, 1); - - triggerMatrixBadAlloc<MatrixXd>(times_itself_gives_0, times_itself_gives_0); - triggerMatrixBadAlloc<MatrixXd>(times_itself_gives_0 / 8, times_itself_gives_0); - triggerMatrixBadAlloc<MatrixXd>(times_8_gives_0, 1); - - triggerVectorBadAlloc<VectorXf>(times_4_gives_0); - - triggerVectorBadAlloc<VectorXd>(times_8_gives_0); -} diff --git a/eigen/test/smallvectors.cpp b/eigen/test/smallvectors.cpp deleted file mode 100644 index 7815113..0000000 --- a/eigen/test/smallvectors.cpp +++ /dev/null @@ -1,67 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_NO_STATIC_ASSERT -#include "main.h" - -template<typename Scalar> void smallVectors() -{ - typedef Matrix<Scalar, 1, 2> V2; - typedef Matrix<Scalar, 3, 1> V3; - typedef Matrix<Scalar, 1, 4> V4; - typedef Matrix<Scalar, Dynamic, 1> VX; - Scalar x1 = internal::random<Scalar>(), - x2 = internal::random<Scalar>(), - x3 = internal::random<Scalar>(), - x4 = internal::random<Scalar>(); - V2 v2(x1, x2); - V3 v3(x1, x2, x3); - V4 v4(x1, x2, x3, x4); - VERIFY_IS_APPROX(x1, v2.x()); - VERIFY_IS_APPROX(x1, v3.x()); - VERIFY_IS_APPROX(x1, v4.x()); - VERIFY_IS_APPROX(x2, v2.y()); - VERIFY_IS_APPROX(x2, v3.y()); - VERIFY_IS_APPROX(x2, v4.y()); - VERIFY_IS_APPROX(x3, v3.z()); - VERIFY_IS_APPROX(x3, v4.z()); - VERIFY_IS_APPROX(x4, v4.w()); - - if (!NumTraits<Scalar>::IsInteger) - { - VERIFY_RAISES_ASSERT(V3(2, 1)) - VERIFY_RAISES_ASSERT(V3(3, 2)) - VERIFY_RAISES_ASSERT(V3(Scalar(3), 1)) - VERIFY_RAISES_ASSERT(V3(3, Scalar(1))) - VERIFY_RAISES_ASSERT(V3(Scalar(3), Scalar(1))) - VERIFY_RAISES_ASSERT(V3(Scalar(123), Scalar(123))) - - VERIFY_RAISES_ASSERT(V4(1, 3)) - VERIFY_RAISES_ASSERT(V4(2, 4)) - VERIFY_RAISES_ASSERT(V4(1, Scalar(4))) - VERIFY_RAISES_ASSERT(V4(Scalar(1), 4)) - VERIFY_RAISES_ASSERT(V4(Scalar(1), Scalar(4))) - VERIFY_RAISES_ASSERT(V4(Scalar(123), Scalar(123))) - - VERIFY_RAISES_ASSERT(VX(3, 2)) - VERIFY_RAISES_ASSERT(VX(Scalar(3), 1)) - VERIFY_RAISES_ASSERT(VX(3, Scalar(1))) - VERIFY_RAISES_ASSERT(VX(Scalar(3), Scalar(1))) - VERIFY_RAISES_ASSERT(VX(Scalar(123), Scalar(123))) - } -} - -void test_smallvectors() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST(smallVectors<int>() ); - CALL_SUBTEST(smallVectors<float>() ); - CALL_SUBTEST(smallVectors<double>() ); - } -} diff --git a/eigen/test/sparse.h b/eigen/test/sparse.h deleted file mode 100644 index 9912e1e..0000000 --- a/eigen/test/sparse.h +++ /dev/null @@ -1,210 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_TESTSPARSE_H -#define EIGEN_TESTSPARSE_H - -#define EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET - -#include "main.h" - -#if EIGEN_GNUC_AT_LEAST(4,0) && !defined __ICC && !defined(__clang__) - -#ifdef min -#undef min -#endif - -#ifdef max -#undef max -#endif - -#include <tr1/unordered_map> -#define EIGEN_UNORDERED_MAP_SUPPORT -namespace std { - using std::tr1::unordered_map; -} -#endif - -#ifdef EIGEN_GOOGLEHASH_SUPPORT - #include <google/sparse_hash_map> -#endif - -#include <Eigen/Cholesky> -#include <Eigen/LU> -#include <Eigen/Sparse> - -enum { - ForceNonZeroDiag = 1, - MakeLowerTriangular = 2, - MakeUpperTriangular = 4, - ForceRealDiag = 8 -}; - -/* Initializes both a sparse and dense matrix with same random values, - * and a ratio of \a density non zero entries. - * \param flags is a union of ForceNonZeroDiag, MakeLowerTriangular and MakeUpperTriangular - * allowing to control the shape of the matrix. - * \param zeroCoords and nonzeroCoords allows to get the coordinate lists of the non zero, - * and zero coefficients respectively. - */ -template<typename Scalar,int Opt1,int Opt2,typename StorageIndex> void -initSparse(double density, - Matrix<Scalar,Dynamic,Dynamic,Opt1>& refMat, - SparseMatrix<Scalar,Opt2,StorageIndex>& sparseMat, - int flags = 0, - std::vector<Matrix<StorageIndex,2,1> >* zeroCoords = 0, - std::vector<Matrix<StorageIndex,2,1> >* nonzeroCoords = 0) -{ - enum { IsRowMajor = SparseMatrix<Scalar,Opt2,StorageIndex>::IsRowMajor }; - sparseMat.setZero(); - //sparseMat.reserve(int(refMat.rows()*refMat.cols()*density)); - sparseMat.reserve(VectorXi::Constant(IsRowMajor ? refMat.rows() : refMat.cols(), int((1.5*density)*(IsRowMajor?refMat.cols():refMat.rows())))); - - for(Index j=0; j<sparseMat.outerSize(); j++) - { - //sparseMat.startVec(j); - for(Index i=0; i<sparseMat.innerSize(); i++) - { - Index ai(i), aj(j); - if(IsRowMajor) - std::swap(ai,aj); - Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0); - if ((flags&ForceNonZeroDiag) && (i==j)) - { - // FIXME: the following is too conservative - v = internal::random<Scalar>()*Scalar(3.); - v = v*v; - if(numext::real(v)>0) v += Scalar(5); - else v -= Scalar(5); - } - if ((flags & MakeLowerTriangular) && aj>ai) - v = Scalar(0); - else if ((flags & MakeUpperTriangular) && aj<ai) - v = Scalar(0); - - if ((flags&ForceRealDiag) && (i==j)) - v = numext::real(v); - - if (v!=Scalar(0)) - { - //sparseMat.insertBackByOuterInner(j,i) = v; - sparseMat.insertByOuterInner(j,i) = v; - if (nonzeroCoords) - nonzeroCoords->push_back(Matrix<StorageIndex,2,1> (ai,aj)); - } - else if (zeroCoords) - { - zeroCoords->push_back(Matrix<StorageIndex,2,1> (ai,aj)); - } - refMat(ai,aj) = v; - } - } - //sparseMat.finalize(); -} - -template<typename Scalar,int Opt1,int Opt2,typename Index> void -initSparse(double density, - Matrix<Scalar,Dynamic,Dynamic, Opt1>& refMat, - DynamicSparseMatrix<Scalar, Opt2, Index>& sparseMat, - int flags = 0, - std::vector<Matrix<Index,2,1> >* zeroCoords = 0, - std::vector<Matrix<Index,2,1> >* nonzeroCoords = 0) -{ - enum { IsRowMajor = DynamicSparseMatrix<Scalar,Opt2,Index>::IsRowMajor }; - sparseMat.setZero(); - sparseMat.reserve(int(refMat.rows()*refMat.cols()*density)); - for(int j=0; j<sparseMat.outerSize(); j++) - { - sparseMat.startVec(j); // not needed for DynamicSparseMatrix - for(int i=0; i<sparseMat.innerSize(); i++) - { - int ai(i), aj(j); - if(IsRowMajor) - std::swap(ai,aj); - Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0); - if ((flags&ForceNonZeroDiag) && (i==j)) - { - v = internal::random<Scalar>()*Scalar(3.); - v = v*v + Scalar(5.); - } - if ((flags & MakeLowerTriangular) && aj>ai) - v = Scalar(0); - else if ((flags & MakeUpperTriangular) && aj<ai) - v = Scalar(0); - - if ((flags&ForceRealDiag) && (i==j)) - v = numext::real(v); - - if (v!=Scalar(0)) - { - sparseMat.insertBackByOuterInner(j,i) = v; - if (nonzeroCoords) - nonzeroCoords->push_back(Matrix<Index,2,1> (ai,aj)); - } - else if (zeroCoords) - { - zeroCoords->push_back(Matrix<Index,2,1> (ai,aj)); - } - refMat(ai,aj) = v; - } - } - sparseMat.finalize(); -} - -template<typename Scalar,int Options,typename Index> void -initSparse(double density, - Matrix<Scalar,Dynamic,1>& refVec, - SparseVector<Scalar,Options,Index>& sparseVec, - std::vector<int>* zeroCoords = 0, - std::vector<int>* nonzeroCoords = 0) -{ - sparseVec.reserve(int(refVec.size()*density)); - sparseVec.setZero(); - for(int i=0; i<refVec.size(); i++) - { - Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0); - if (v!=Scalar(0)) - { - sparseVec.insertBack(i) = v; - if (nonzeroCoords) - nonzeroCoords->push_back(i); - } - else if (zeroCoords) - zeroCoords->push_back(i); - refVec[i] = v; - } -} - -template<typename Scalar,int Options,typename Index> void -initSparse(double density, - Matrix<Scalar,1,Dynamic>& refVec, - SparseVector<Scalar,Options,Index>& sparseVec, - std::vector<int>* zeroCoords = 0, - std::vector<int>* nonzeroCoords = 0) -{ - sparseVec.reserve(int(refVec.size()*density)); - sparseVec.setZero(); - for(int i=0; i<refVec.size(); i++) - { - Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0); - if (v!=Scalar(0)) - { - sparseVec.insertBack(i) = v; - if (nonzeroCoords) - nonzeroCoords->push_back(i); - } - else if (zeroCoords) - zeroCoords->push_back(i); - refVec[i] = v; - } -} - - -#include <unsupported/Eigen/SparseExtra> -#endif // EIGEN_TESTSPARSE_H diff --git a/eigen/test/sparseLM.cpp b/eigen/test/sparseLM.cpp deleted file mode 100644 index 8e148f9..0000000 --- a/eigen/test/sparseLM.cpp +++ /dev/null @@ -1,176 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr> -// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. -#include <iostream> -#include <fstream> -#include <iomanip> - -#include "main.h" -#include <Eigen/LevenbergMarquardt> - -using namespace std; -using namespace Eigen; - -template <typename Scalar> -struct sparseGaussianTest : SparseFunctor<Scalar, int> -{ - typedef Matrix<Scalar,Dynamic,1> VectorType; - typedef SparseFunctor<Scalar,int> Base; - typedef typename Base::JacobianType JacobianType; - sparseGaussianTest(int inputs, int values) : SparseFunctor<Scalar,int>(inputs,values) - { } - - VectorType model(const VectorType& uv, VectorType& x) - { - VectorType y; //Change this to use expression template - int m = Base::values(); - int n = Base::inputs(); - eigen_assert(uv.size()%2 == 0); - eigen_assert(uv.size() == n); - eigen_assert(x.size() == m); - y.setZero(m); - int half = n/2; - VectorBlock<const VectorType> u(uv, 0, half); - VectorBlock<const VectorType> v(uv, half, half); - Scalar coeff; - for (int j = 0; j < m; j++) - { - for (int i = 0; i < half; i++) - { - coeff = (x(j)-i)/v(i); - coeff *= coeff; - if (coeff < 1. && coeff > 0.) - y(j) += u(i)*std::pow((1-coeff), 2); - } - } - return y; - } - void initPoints(VectorType& uv_ref, VectorType& x) - { - m_x = x; - m_y = this->model(uv_ref,x); - } - int operator()(const VectorType& uv, VectorType& fvec) - { - int m = Base::values(); - int n = Base::inputs(); - eigen_assert(uv.size()%2 == 0); - eigen_assert(uv.size() == n); - int half = n/2; - VectorBlock<const VectorType> u(uv, 0, half); - VectorBlock<const VectorType> v(uv, half, half); - fvec = m_y; - Scalar coeff; - for (int j = 0; j < m; j++) - { - for (int i = 0; i < half; i++) - { - coeff = (m_x(j)-i)/v(i); - coeff *= coeff; - if (coeff < 1. && coeff > 0.) - fvec(j) -= u(i)*std::pow((1-coeff), 2); - } - } - return 0; - } - - int df(const VectorType& uv, JacobianType& fjac) - { - int m = Base::values(); - int n = Base::inputs(); - eigen_assert(n == uv.size()); - eigen_assert(fjac.rows() == m); - eigen_assert(fjac.cols() == n); - int half = n/2; - VectorBlock<const VectorType> u(uv, 0, half); - VectorBlock<const VectorType> v(uv, half, half); - Scalar coeff; - - //Derivatives with respect to u - for (int col = 0; col < half; col++) - { - for (int row = 0; row < m; row++) - { - coeff = (m_x(row)-col)/v(col); - coeff = coeff*coeff; - if(coeff < 1. && coeff > 0.) - { - fjac.coeffRef(row,col) = -(1-coeff)*(1-coeff); - } - } - } - //Derivatives with respect to v - for (int col = 0; col < half; col++) - { - for (int row = 0; row < m; row++) - { - coeff = (m_x(row)-col)/v(col); - coeff = coeff*coeff; - if(coeff < 1. && coeff > 0.) - { - fjac.coeffRef(row,col+half) = -4 * (u(col)/v(col))*coeff*(1-coeff); - } - } - } - return 0; - } - - VectorType m_x, m_y; //Data points -}; - - -template<typename T> -void test_sparseLM_T() -{ - typedef Matrix<T,Dynamic,1> VectorType; - - int inputs = 10; - int values = 2000; - sparseGaussianTest<T> sparse_gaussian(inputs, values); - VectorType uv(inputs),uv_ref(inputs); - VectorType x(values); - // Generate the reference solution - uv_ref << -2, 1, 4 ,8, 6, 1.8, 1.2, 1.1, 1.9 , 3; - //Generate the reference data points - x.setRandom(); - x = 10*x; - x.array() += 10; - sparse_gaussian.initPoints(uv_ref, x); - - - // Generate the initial parameters - VectorBlock<VectorType> u(uv, 0, inputs/2); - VectorBlock<VectorType> v(uv, inputs/2, inputs/2); - v.setOnes(); - //Generate u or Solve for u from v - u.setOnes(); - - // Solve the optimization problem - LevenbergMarquardt<sparseGaussianTest<T> > lm(sparse_gaussian); - int info; -// info = lm.minimize(uv); - - VERIFY_IS_EQUAL(info,1); - // Do a step by step solution and save the residual - int maxiter = 200; - int iter = 0; - MatrixXd Err(values, maxiter); - MatrixXd Mod(values, maxiter); - LevenbergMarquardtSpace::Status status; - status = lm.minimizeInit(uv); - if (status==LevenbergMarquardtSpace::ImproperInputParameters) - return ; - -} -void test_sparseLM() -{ - CALL_SUBTEST_1(test_sparseLM_T<double>()); - - // CALL_SUBTEST_2(test_sparseLM_T<std::complex<double>()); -} diff --git a/eigen/test/sparse_basic.cpp b/eigen/test/sparse_basic.cpp deleted file mode 100644 index d0ef722..0000000 --- a/eigen/test/sparse_basic.cpp +++ /dev/null @@ -1,689 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@gmail.com> -// Copyright (C) 2013 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -static long g_realloc_count = 0; -#define EIGEN_SPARSE_COMPRESSED_STORAGE_REALLOCATE_PLUGIN g_realloc_count++; - -#include "sparse.h" - -template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& ref) -{ - typedef typename SparseMatrixType::StorageIndex StorageIndex; - typedef Matrix<StorageIndex,2,1> Vector2; - - const Index rows = ref.rows(); - const Index cols = ref.cols(); - //const Index inner = ref.innerSize(); - //const Index outer = ref.outerSize(); - - typedef typename SparseMatrixType::Scalar Scalar; - typedef typename SparseMatrixType::RealScalar RealScalar; - enum { Flags = SparseMatrixType::Flags }; - - double density = (std::max)(8./(rows*cols), 0.01); - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - typedef Matrix<Scalar,Dynamic,1> DenseVector; - Scalar eps = 1e-6; - - Scalar s1 = internal::random<Scalar>(); - { - SparseMatrixType m(rows, cols); - DenseMatrix refMat = DenseMatrix::Zero(rows, cols); - DenseVector vec1 = DenseVector::Random(rows); - - std::vector<Vector2> zeroCoords; - std::vector<Vector2> nonzeroCoords; - initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords); - - // test coeff and coeffRef - for (std::size_t i=0; i<zeroCoords.size(); ++i) - { - VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps ); - if(internal::is_same<SparseMatrixType,SparseMatrix<Scalar,Flags> >::value) - VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[i].x(),zeroCoords[i].y()) = 5 ); - } - VERIFY_IS_APPROX(m, refMat); - - if(!nonzeroCoords.empty()) { - m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5); - refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5); - } - - VERIFY_IS_APPROX(m, refMat); - - // test assertion - VERIFY_RAISES_ASSERT( m.coeffRef(-1,1) = 0 ); - VERIFY_RAISES_ASSERT( m.coeffRef(0,m.cols()) = 0 ); - } - - // test insert (inner random) - { - DenseMatrix m1(rows,cols); - m1.setZero(); - SparseMatrixType m2(rows,cols); - bool call_reserve = internal::random<int>()%2; - Index nnz = internal::random<int>(1,int(rows)/2); - if(call_reserve) - { - if(internal::random<int>()%2) - m2.reserve(VectorXi::Constant(m2.outerSize(), int(nnz))); - else - m2.reserve(m2.outerSize() * nnz); - } - g_realloc_count = 0; - for (Index j=0; j<cols; ++j) - { - for (Index k=0; k<nnz; ++k) - { - Index i = internal::random<Index>(0,rows-1); - if (m1.coeff(i,j)==Scalar(0)) - m2.insert(i,j) = m1(i,j) = internal::random<Scalar>(); - } - } - - if(call_reserve && !SparseMatrixType::IsRowMajor) - { - VERIFY(g_realloc_count==0); - } - - m2.finalize(); - VERIFY_IS_APPROX(m2,m1); - } - - // test insert (fully random) - { - DenseMatrix m1(rows,cols); - m1.setZero(); - SparseMatrixType m2(rows,cols); - if(internal::random<int>()%2) - m2.reserve(VectorXi::Constant(m2.outerSize(), 2)); - for (int k=0; k<rows*cols; ++k) - { - Index i = internal::random<Index>(0,rows-1); - Index j = internal::random<Index>(0,cols-1); - if ((m1.coeff(i,j)==Scalar(0)) && (internal::random<int>()%2)) - m2.insert(i,j) = m1(i,j) = internal::random<Scalar>(); - else - { - Scalar v = internal::random<Scalar>(); - m2.coeffRef(i,j) += v; - m1(i,j) += v; - } - } - VERIFY_IS_APPROX(m2,m1); - } - - // test insert (un-compressed) - for(int mode=0;mode<4;++mode) - { - DenseMatrix m1(rows,cols); - m1.setZero(); - SparseMatrixType m2(rows,cols); - VectorXi r(VectorXi::Constant(m2.outerSize(), ((mode%2)==0) ? int(m2.innerSize()) : std::max<int>(1,int(m2.innerSize())/8))); - m2.reserve(r); - for (Index k=0; k<rows*cols; ++k) - { - Index i = internal::random<Index>(0,rows-1); - Index j = internal::random<Index>(0,cols-1); - if (m1.coeff(i,j)==Scalar(0)) - m2.insert(i,j) = m1(i,j) = internal::random<Scalar>(); - if(mode==3) - m2.reserve(r); - } - if(internal::random<int>()%2) - m2.makeCompressed(); - VERIFY_IS_APPROX(m2,m1); - } - - // test basic computations - { - DenseMatrix refM1 = DenseMatrix::Zero(rows, cols); - DenseMatrix refM2 = DenseMatrix::Zero(rows, cols); - DenseMatrix refM3 = DenseMatrix::Zero(rows, cols); - DenseMatrix refM4 = DenseMatrix::Zero(rows, cols); - SparseMatrixType m1(rows, cols); - SparseMatrixType m2(rows, cols); - SparseMatrixType m3(rows, cols); - SparseMatrixType m4(rows, cols); - initSparse<Scalar>(density, refM1, m1); - initSparse<Scalar>(density, refM2, m2); - initSparse<Scalar>(density, refM3, m3); - initSparse<Scalar>(density, refM4, m4); - - if(internal::random<bool>()) - m1.makeCompressed(); - - Index m1_nnz = m1.nonZeros(); - - VERIFY_IS_APPROX(m1*s1, refM1*s1); - VERIFY_IS_APPROX(m1+m2, refM1+refM2); - VERIFY_IS_APPROX(m1+m2+m3, refM1+refM2+refM3); - VERIFY_IS_APPROX(m3.cwiseProduct(m1+m2), refM3.cwiseProduct(refM1+refM2)); - VERIFY_IS_APPROX(m1*s1-m2, refM1*s1-refM2); - VERIFY_IS_APPROX(m4=m1/s1, refM1/s1); - VERIFY_IS_EQUAL(m4.nonZeros(), m1_nnz); - - if(SparseMatrixType::IsRowMajor) - VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.row(0)), refM1.row(0).dot(refM2.row(0))); - else - VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.col(0)), refM1.col(0).dot(refM2.col(0))); - - DenseVector rv = DenseVector::Random(m1.cols()); - DenseVector cv = DenseVector::Random(m1.rows()); - Index r = internal::random<Index>(0,m1.rows()-2); - Index c = internal::random<Index>(0,m1.cols()-1); - VERIFY_IS_APPROX(( m1.template block<1,Dynamic>(r,0,1,m1.cols()).dot(rv)) , refM1.row(r).dot(rv)); - VERIFY_IS_APPROX(m1.row(r).dot(rv), refM1.row(r).dot(rv)); - VERIFY_IS_APPROX(m1.col(c).dot(cv), refM1.col(c).dot(cv)); - - VERIFY_IS_APPROX(m1.conjugate(), refM1.conjugate()); - VERIFY_IS_APPROX(m1.real(), refM1.real()); - - refM4.setRandom(); - // sparse cwise* dense - VERIFY_IS_APPROX(m3.cwiseProduct(refM4), refM3.cwiseProduct(refM4)); - // dense cwise* sparse - VERIFY_IS_APPROX(refM4.cwiseProduct(m3), refM4.cwiseProduct(refM3)); -// VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4); - - VERIFY_IS_APPROX(refM4 + m3, refM4 + refM3); - VERIFY_IS_APPROX(m3 + refM4, refM3 + refM4); - VERIFY_IS_APPROX(refM4 - m3, refM4 - refM3); - VERIFY_IS_APPROX(m3 - refM4, refM3 - refM4); - VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3); - VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3*RealScalar(0.5)).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3); - VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3.cwiseProduct(m3)).eval(), RealScalar(0.5)*refM4 + refM3.cwiseProduct(refM3)); - - VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3); - VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3*RealScalar(0.5)).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3); - VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (m3+m3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3)); - VERIFY_IS_APPROX(((refM3+m3)+RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM3 + (refM3+refM3)); - VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (refM3+m3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3)); - VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (m3+refM3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3)); - - - VERIFY_IS_APPROX(m1.sum(), refM1.sum()); - - m4 = m1; refM4 = m4; - - VERIFY_IS_APPROX(m1*=s1, refM1*=s1); - VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz); - VERIFY_IS_APPROX(m1/=s1, refM1/=s1); - VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz); - - VERIFY_IS_APPROX(m1+=m2, refM1+=refM2); - VERIFY_IS_APPROX(m1-=m2, refM1-=refM2); - - if (rows>=2 && cols>=2) - { - VERIFY_RAISES_ASSERT( m1 += m1.innerVector(0) ); - VERIFY_RAISES_ASSERT( m1 -= m1.innerVector(0) ); - VERIFY_RAISES_ASSERT( refM1 -= m1.innerVector(0) ); - VERIFY_RAISES_ASSERT( refM1 += m1.innerVector(0) ); - } - m1 = m4; refM1 = refM4; - - // test aliasing - VERIFY_IS_APPROX((m1 = -m1), (refM1 = -refM1)); - VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz); - m1 = m4; refM1 = refM4; - VERIFY_IS_APPROX((m1 = m1.transpose()), (refM1 = refM1.transpose().eval())); - VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz); - m1 = m4; refM1 = refM4; - VERIFY_IS_APPROX((m1 = -m1.transpose()), (refM1 = -refM1.transpose().eval())); - VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz); - m1 = m4; refM1 = refM4; - VERIFY_IS_APPROX((m1 += -m1), (refM1 += -refM1)); - VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz); - m1 = m4; refM1 = refM4; - - if(m1.isCompressed()) - { - VERIFY_IS_APPROX(m1.coeffs().sum(), m1.sum()); - m1.coeffs() += s1; - for(Index j = 0; j<m1.outerSize(); ++j) - for(typename SparseMatrixType::InnerIterator it(m1,j); it; ++it) - refM1(it.row(), it.col()) += s1; - VERIFY_IS_APPROX(m1, refM1); - } - - // and/or - { - typedef SparseMatrix<bool, SparseMatrixType::Options, typename SparseMatrixType::StorageIndex> SpBool; - SpBool mb1 = m1.real().template cast<bool>(); - SpBool mb2 = m2.real().template cast<bool>(); - VERIFY_IS_EQUAL(mb1.template cast<int>().sum(), refM1.real().template cast<bool>().count()); - VERIFY_IS_EQUAL((mb1 && mb2).template cast<int>().sum(), (refM1.real().template cast<bool>() && refM2.real().template cast<bool>()).count()); - VERIFY_IS_EQUAL((mb1 || mb2).template cast<int>().sum(), (refM1.real().template cast<bool>() || refM2.real().template cast<bool>()).count()); - SpBool mb3 = mb1 && mb2; - if(mb1.coeffs().all() && mb2.coeffs().all()) - { - VERIFY_IS_EQUAL(mb3.nonZeros(), (refM1.real().template cast<bool>() && refM2.real().template cast<bool>()).count()); - } - } - } - - // test reverse iterators - { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols); - SparseMatrixType m2(rows, cols); - initSparse<Scalar>(density, refMat2, m2); - std::vector<Scalar> ref_value(m2.innerSize()); - std::vector<Index> ref_index(m2.innerSize()); - if(internal::random<bool>()) - m2.makeCompressed(); - for(Index j = 0; j<m2.outerSize(); ++j) - { - Index count_forward = 0; - - for(typename SparseMatrixType::InnerIterator it(m2,j); it; ++it) - { - ref_value[ref_value.size()-1-count_forward] = it.value(); - ref_index[ref_index.size()-1-count_forward] = it.index(); - count_forward++; - } - Index count_reverse = 0; - for(typename SparseMatrixType::ReverseInnerIterator it(m2,j); it; --it) - { - VERIFY_IS_APPROX( std::abs(ref_value[ref_value.size()-count_forward+count_reverse])+1, std::abs(it.value())+1); - VERIFY_IS_EQUAL( ref_index[ref_index.size()-count_forward+count_reverse] , it.index()); - count_reverse++; - } - VERIFY_IS_EQUAL(count_forward, count_reverse); - } - } - - // test transpose - { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols); - SparseMatrixType m2(rows, cols); - initSparse<Scalar>(density, refMat2, m2); - VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval()); - VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose()); - - VERIFY_IS_APPROX(SparseMatrixType(m2.adjoint()), refMat2.adjoint()); - - // check isApprox handles opposite storage order - typename Transpose<SparseMatrixType>::PlainObject m3(m2); - VERIFY(m2.isApprox(m3)); - } - - // test prune - { - SparseMatrixType m2(rows, cols); - DenseMatrix refM2(rows, cols); - refM2.setZero(); - int countFalseNonZero = 0; - int countTrueNonZero = 0; - m2.reserve(VectorXi::Constant(m2.outerSize(), int(m2.innerSize()))); - for (Index j=0; j<m2.cols(); ++j) - { - for (Index i=0; i<m2.rows(); ++i) - { - float x = internal::random<float>(0,1); - if (x<0.1f) - { - // do nothing - } - else if (x<0.5f) - { - countFalseNonZero++; - m2.insert(i,j) = Scalar(0); - } - else - { - countTrueNonZero++; - m2.insert(i,j) = Scalar(1); - refM2(i,j) = Scalar(1); - } - } - } - if(internal::random<bool>()) - m2.makeCompressed(); - VERIFY(countFalseNonZero+countTrueNonZero == m2.nonZeros()); - if(countTrueNonZero>0) - VERIFY_IS_APPROX(m2, refM2); - m2.prune(Scalar(1)); - VERIFY(countTrueNonZero==m2.nonZeros()); - VERIFY_IS_APPROX(m2, refM2); - } - - // test setFromTriplets - { - typedef Triplet<Scalar,StorageIndex> TripletType; - std::vector<TripletType> triplets; - Index ntriplets = rows*cols; - triplets.reserve(ntriplets); - DenseMatrix refMat_sum = DenseMatrix::Zero(rows,cols); - DenseMatrix refMat_prod = DenseMatrix::Zero(rows,cols); - DenseMatrix refMat_last = DenseMatrix::Zero(rows,cols); - - for(Index i=0;i<ntriplets;++i) - { - StorageIndex r = internal::random<StorageIndex>(0,StorageIndex(rows-1)); - StorageIndex c = internal::random<StorageIndex>(0,StorageIndex(cols-1)); - Scalar v = internal::random<Scalar>(); - triplets.push_back(TripletType(r,c,v)); - refMat_sum(r,c) += v; - if(std::abs(refMat_prod(r,c))==0) - refMat_prod(r,c) = v; - else - refMat_prod(r,c) *= v; - refMat_last(r,c) = v; - } - SparseMatrixType m(rows,cols); - m.setFromTriplets(triplets.begin(), triplets.end()); - VERIFY_IS_APPROX(m, refMat_sum); - - m.setFromTriplets(triplets.begin(), triplets.end(), std::multiplies<Scalar>()); - VERIFY_IS_APPROX(m, refMat_prod); -#if (defined(__cplusplus) && __cplusplus >= 201103L) - m.setFromTriplets(triplets.begin(), triplets.end(), [] (Scalar,Scalar b) { return b; }); - VERIFY_IS_APPROX(m, refMat_last); -#endif - } - - // test Map - { - DenseMatrix refMat2(rows, cols), refMat3(rows, cols); - SparseMatrixType m2(rows, cols), m3(rows, cols); - initSparse<Scalar>(density, refMat2, m2); - initSparse<Scalar>(density, refMat3, m3); - { - Map<SparseMatrixType> mapMat2(m2.rows(), m2.cols(), m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr()); - Map<SparseMatrixType> mapMat3(m3.rows(), m3.cols(), m3.nonZeros(), m3.outerIndexPtr(), m3.innerIndexPtr(), m3.valuePtr(), m3.innerNonZeroPtr()); - VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3); - VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3); - } - { - MappedSparseMatrix<Scalar,SparseMatrixType::Options,StorageIndex> mapMat2(m2.rows(), m2.cols(), m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr()); - MappedSparseMatrix<Scalar,SparseMatrixType::Options,StorageIndex> mapMat3(m3.rows(), m3.cols(), m3.nonZeros(), m3.outerIndexPtr(), m3.innerIndexPtr(), m3.valuePtr(), m3.innerNonZeroPtr()); - VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3); - VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3); - } - - Index i = internal::random<Index>(0,rows-1); - Index j = internal::random<Index>(0,cols-1); - m2.coeffRef(i,j) = 123; - if(internal::random<bool>()) - m2.makeCompressed(); - Map<SparseMatrixType> mapMat2(rows, cols, m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr()); - VERIFY_IS_EQUAL(m2.coeff(i,j),Scalar(123)); - VERIFY_IS_EQUAL(mapMat2.coeff(i,j),Scalar(123)); - mapMat2.coeffRef(i,j) = -123; - VERIFY_IS_EQUAL(m2.coeff(i,j),Scalar(-123)); - } - - // test triangularView - { - DenseMatrix refMat2(rows, cols), refMat3(rows, cols); - SparseMatrixType m2(rows, cols), m3(rows, cols); - initSparse<Scalar>(density, refMat2, m2); - refMat3 = refMat2.template triangularView<Lower>(); - m3 = m2.template triangularView<Lower>(); - VERIFY_IS_APPROX(m3, refMat3); - - refMat3 = refMat2.template triangularView<Upper>(); - m3 = m2.template triangularView<Upper>(); - VERIFY_IS_APPROX(m3, refMat3); - - { - refMat3 = refMat2.template triangularView<UnitUpper>(); - m3 = m2.template triangularView<UnitUpper>(); - VERIFY_IS_APPROX(m3, refMat3); - - refMat3 = refMat2.template triangularView<UnitLower>(); - m3 = m2.template triangularView<UnitLower>(); - VERIFY_IS_APPROX(m3, refMat3); - } - - refMat3 = refMat2.template triangularView<StrictlyUpper>(); - m3 = m2.template triangularView<StrictlyUpper>(); - VERIFY_IS_APPROX(m3, refMat3); - - refMat3 = refMat2.template triangularView<StrictlyLower>(); - m3 = m2.template triangularView<StrictlyLower>(); - VERIFY_IS_APPROX(m3, refMat3); - - // check sparse-triangular to dense - refMat3 = m2.template triangularView<StrictlyUpper>(); - VERIFY_IS_APPROX(refMat3, DenseMatrix(refMat2.template triangularView<StrictlyUpper>())); - } - - // test selfadjointView - if(!SparseMatrixType::IsRowMajor) - { - DenseMatrix refMat2(rows, rows), refMat3(rows, rows); - SparseMatrixType m2(rows, rows), m3(rows, rows); - initSparse<Scalar>(density, refMat2, m2); - refMat3 = refMat2.template selfadjointView<Lower>(); - m3 = m2.template selfadjointView<Lower>(); - VERIFY_IS_APPROX(m3, refMat3); - - refMat3 += refMat2.template selfadjointView<Lower>(); - m3 += m2.template selfadjointView<Lower>(); - VERIFY_IS_APPROX(m3, refMat3); - - refMat3 -= refMat2.template selfadjointView<Lower>(); - m3 -= m2.template selfadjointView<Lower>(); - VERIFY_IS_APPROX(m3, refMat3); - - // selfadjointView only works for square matrices: - SparseMatrixType m4(rows, rows+1); - VERIFY_RAISES_ASSERT(m4.template selfadjointView<Lower>()); - VERIFY_RAISES_ASSERT(m4.template selfadjointView<Upper>()); - } - - // test sparseView - { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows); - SparseMatrixType m2(rows, rows); - initSparse<Scalar>(density, refMat2, m2); - VERIFY_IS_APPROX(m2.eval(), refMat2.sparseView().eval()); - - // sparse view on expressions: - VERIFY_IS_APPROX((s1*m2).eval(), (s1*refMat2).sparseView().eval()); - VERIFY_IS_APPROX((m2+m2).eval(), (refMat2+refMat2).sparseView().eval()); - VERIFY_IS_APPROX((m2*m2).eval(), (refMat2.lazyProduct(refMat2)).sparseView().eval()); - VERIFY_IS_APPROX((m2*m2).eval(), (refMat2*refMat2).sparseView().eval()); - } - - // test diagonal - { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols); - SparseMatrixType m2(rows, cols); - initSparse<Scalar>(density, refMat2, m2); - VERIFY_IS_APPROX(m2.diagonal(), refMat2.diagonal().eval()); - DenseVector d = m2.diagonal(); - VERIFY_IS_APPROX(d, refMat2.diagonal().eval()); - d = m2.diagonal().array(); - VERIFY_IS_APPROX(d, refMat2.diagonal().eval()); - VERIFY_IS_APPROX(const_cast<const SparseMatrixType&>(m2).diagonal(), refMat2.diagonal().eval()); - - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag); - m2.diagonal() += refMat2.diagonal(); - refMat2.diagonal() += refMat2.diagonal(); - VERIFY_IS_APPROX(m2, refMat2); - } - - // test diagonal to sparse - { - DenseVector d = DenseVector::Random(rows); - DenseMatrix refMat2 = d.asDiagonal(); - SparseMatrixType m2(rows, rows); - m2 = d.asDiagonal(); - VERIFY_IS_APPROX(m2, refMat2); - SparseMatrixType m3(d.asDiagonal()); - VERIFY_IS_APPROX(m3, refMat2); - refMat2 += d.asDiagonal(); - m2 += d.asDiagonal(); - VERIFY_IS_APPROX(m2, refMat2); - } - - // test conservative resize - { - std::vector< std::pair<StorageIndex,StorageIndex> > inc; - if(rows > 3 && cols > 2) - inc.push_back(std::pair<StorageIndex,StorageIndex>(-3,-2)); - inc.push_back(std::pair<StorageIndex,StorageIndex>(0,0)); - inc.push_back(std::pair<StorageIndex,StorageIndex>(3,2)); - inc.push_back(std::pair<StorageIndex,StorageIndex>(3,0)); - inc.push_back(std::pair<StorageIndex,StorageIndex>(0,3)); - - for(size_t i = 0; i< inc.size(); i++) { - StorageIndex incRows = inc[i].first; - StorageIndex incCols = inc[i].second; - SparseMatrixType m1(rows, cols); - DenseMatrix refMat1 = DenseMatrix::Zero(rows, cols); - initSparse<Scalar>(density, refMat1, m1); - - m1.conservativeResize(rows+incRows, cols+incCols); - refMat1.conservativeResize(rows+incRows, cols+incCols); - if (incRows > 0) refMat1.bottomRows(incRows).setZero(); - if (incCols > 0) refMat1.rightCols(incCols).setZero(); - - VERIFY_IS_APPROX(m1, refMat1); - - // Insert new values - if (incRows > 0) - m1.insert(m1.rows()-1, 0) = refMat1(refMat1.rows()-1, 0) = 1; - if (incCols > 0) - m1.insert(0, m1.cols()-1) = refMat1(0, refMat1.cols()-1) = 1; - - VERIFY_IS_APPROX(m1, refMat1); - - - } - } - - // test Identity matrix - { - DenseMatrix refMat1 = DenseMatrix::Identity(rows, rows); - SparseMatrixType m1(rows, rows); - m1.setIdentity(); - VERIFY_IS_APPROX(m1, refMat1); - for(int k=0; k<rows*rows/4; ++k) - { - Index i = internal::random<Index>(0,rows-1); - Index j = internal::random<Index>(0,rows-1); - Scalar v = internal::random<Scalar>(); - m1.coeffRef(i,j) = v; - refMat1.coeffRef(i,j) = v; - VERIFY_IS_APPROX(m1, refMat1); - if(internal::random<Index>(0,10)<2) - m1.makeCompressed(); - } - m1.setIdentity(); - refMat1.setIdentity(); - VERIFY_IS_APPROX(m1, refMat1); - } - - // test array/vector of InnerIterator - { - typedef typename SparseMatrixType::InnerIterator IteratorType; - - DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols); - SparseMatrixType m2(rows, cols); - initSparse<Scalar>(density, refMat2, m2); - IteratorType static_array[2]; - static_array[0] = IteratorType(m2,0); - static_array[1] = IteratorType(m2,m2.outerSize()-1); - VERIFY( static_array[0] || m2.innerVector(static_array[0].outer()).nonZeros() == 0 ); - VERIFY( static_array[1] || m2.innerVector(static_array[1].outer()).nonZeros() == 0 ); - if(static_array[0] && static_array[1]) - { - ++(static_array[1]); - static_array[1] = IteratorType(m2,0); - VERIFY( static_array[1] ); - VERIFY( static_array[1].index() == static_array[0].index() ); - VERIFY( static_array[1].outer() == static_array[0].outer() ); - VERIFY( static_array[1].value() == static_array[0].value() ); - } - - std::vector<IteratorType> iters(2); - iters[0] = IteratorType(m2,0); - iters[1] = IteratorType(m2,m2.outerSize()-1); - } -} - - -template<typename SparseMatrixType> -void big_sparse_triplet(Index rows, Index cols, double density) { - typedef typename SparseMatrixType::StorageIndex StorageIndex; - typedef typename SparseMatrixType::Scalar Scalar; - typedef Triplet<Scalar,Index> TripletType; - std::vector<TripletType> triplets; - double nelements = density * rows*cols; - VERIFY(nelements>=0 && nelements < NumTraits<StorageIndex>::highest()); - Index ntriplets = Index(nelements); - triplets.reserve(ntriplets); - Scalar sum = Scalar(0); - for(Index i=0;i<ntriplets;++i) - { - Index r = internal::random<Index>(0,rows-1); - Index c = internal::random<Index>(0,cols-1); - // use positive values to prevent numerical cancellation errors in sum - Scalar v = numext::abs(internal::random<Scalar>()); - triplets.push_back(TripletType(r,c,v)); - sum += v; - } - SparseMatrixType m(rows,cols); - m.setFromTriplets(triplets.begin(), triplets.end()); - VERIFY(m.nonZeros() <= ntriplets); - VERIFY_IS_APPROX(sum, m.sum()); -} - - -void test_sparse_basic() -{ - for(int i = 0; i < g_repeat; i++) { - int r = Eigen::internal::random<int>(1,200), c = Eigen::internal::random<int>(1,200); - if(Eigen::internal::random<int>(0,4) == 0) { - r = c; // check square matrices in 25% of tries - } - EIGEN_UNUSED_VARIABLE(r+c); - CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(1, 1)) )); - CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(8, 8)) )); - CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, ColMajor>(r, c)) )); - CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, RowMajor>(r, c)) )); - CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(r, c)) )); - CALL_SUBTEST_5(( sparse_basic(SparseMatrix<double,ColMajor,long int>(r, c)) )); - CALL_SUBTEST_5(( sparse_basic(SparseMatrix<double,RowMajor,long int>(r, c)) )); - - r = Eigen::internal::random<int>(1,100); - c = Eigen::internal::random<int>(1,100); - if(Eigen::internal::random<int>(0,4) == 0) { - r = c; // check square matrices in 25% of tries - } - - CALL_SUBTEST_6(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(r), short(c))) )); - CALL_SUBTEST_6(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(r), short(c))) )); - } - - // Regression test for bug 900: (manually insert higher values here, if you have enough RAM): - CALL_SUBTEST_3((big_sparse_triplet<SparseMatrix<float, RowMajor, int> >(10000, 10000, 0.125))); - CALL_SUBTEST_4((big_sparse_triplet<SparseMatrix<double, ColMajor, long int> >(10000, 10000, 0.125))); - - // Regression test for bug 1105 -#ifdef EIGEN_TEST_PART_7 - { - int n = Eigen::internal::random<int>(200,600); - SparseMatrix<std::complex<double>,0, long> mat(n, n); - std::complex<double> val; - - for(int i=0; i<n; ++i) - { - mat.coeffRef(i, i%(n/10)) = val; - VERIFY(mat.data().allocatedSize()<20*n); - } - } -#endif -} diff --git a/eigen/test/sparse_block.cpp b/eigen/test/sparse_block.cpp deleted file mode 100644 index 2a0b3b6..0000000 --- a/eigen/test/sparse_block.cpp +++ /dev/null @@ -1,317 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "sparse.h" - -template<typename T> -typename Eigen::internal::enable_if<(T::Flags&RowMajorBit)==RowMajorBit, typename T::RowXpr>::type -innervec(T& A, Index i) -{ - return A.row(i); -} - -template<typename T> -typename Eigen::internal::enable_if<(T::Flags&RowMajorBit)==0, typename T::ColXpr>::type -innervec(T& A, Index i) -{ - return A.col(i); -} - -template<typename SparseMatrixType> void sparse_block(const SparseMatrixType& ref) -{ - const Index rows = ref.rows(); - const Index cols = ref.cols(); - const Index inner = ref.innerSize(); - const Index outer = ref.outerSize(); - - typedef typename SparseMatrixType::Scalar Scalar; - typedef typename SparseMatrixType::StorageIndex StorageIndex; - - double density = (std::max)(8./(rows*cols), 0.01); - typedef Matrix<Scalar,Dynamic,Dynamic,SparseMatrixType::IsRowMajor?RowMajor:ColMajor> DenseMatrix; - typedef Matrix<Scalar,Dynamic,1> DenseVector; - typedef Matrix<Scalar,1,Dynamic> RowDenseVector; - typedef SparseVector<Scalar> SparseVectorType; - - Scalar s1 = internal::random<Scalar>(); - { - SparseMatrixType m(rows, cols); - DenseMatrix refMat = DenseMatrix::Zero(rows, cols); - initSparse<Scalar>(density, refMat, m); - - VERIFY_IS_APPROX(m, refMat); - - // test InnerIterators and Block expressions - for (int t=0; t<10; ++t) - { - Index j = internal::random<Index>(0,cols-2); - Index i = internal::random<Index>(0,rows-2); - Index w = internal::random<Index>(1,cols-j); - Index h = internal::random<Index>(1,rows-i); - - VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w)); - for(Index c=0; c<w; c++) - { - VERIFY_IS_APPROX(m.block(i,j,h,w).col(c), refMat.block(i,j,h,w).col(c)); - for(Index r=0; r<h; r++) - { - VERIFY_IS_APPROX(m.block(i,j,h,w).col(c).coeff(r), refMat.block(i,j,h,w).col(c).coeff(r)); - VERIFY_IS_APPROX(m.block(i,j,h,w).coeff(r,c), refMat.block(i,j,h,w).coeff(r,c)); - } - } - for(Index r=0; r<h; r++) - { - VERIFY_IS_APPROX(m.block(i,j,h,w).row(r), refMat.block(i,j,h,w).row(r)); - for(Index c=0; c<w; c++) - { - VERIFY_IS_APPROX(m.block(i,j,h,w).row(r).coeff(c), refMat.block(i,j,h,w).row(r).coeff(c)); - VERIFY_IS_APPROX(m.block(i,j,h,w).coeff(r,c), refMat.block(i,j,h,w).coeff(r,c)); - } - } - - VERIFY_IS_APPROX(m.middleCols(j,w), refMat.middleCols(j,w)); - VERIFY_IS_APPROX(m.middleRows(i,h), refMat.middleRows(i,h)); - for(Index r=0; r<h; r++) - { - VERIFY_IS_APPROX(m.middleCols(j,w).row(r), refMat.middleCols(j,w).row(r)); - VERIFY_IS_APPROX(m.middleRows(i,h).row(r), refMat.middleRows(i,h).row(r)); - for(Index c=0; c<w; c++) - { - VERIFY_IS_APPROX(m.col(c).coeff(r), refMat.col(c).coeff(r)); - VERIFY_IS_APPROX(m.row(r).coeff(c), refMat.row(r).coeff(c)); - - VERIFY_IS_APPROX(m.middleCols(j,w).coeff(r,c), refMat.middleCols(j,w).coeff(r,c)); - VERIFY_IS_APPROX(m.middleRows(i,h).coeff(r,c), refMat.middleRows(i,h).coeff(r,c)); - if(m.middleCols(j,w).coeff(r,c) != Scalar(0)) - { - VERIFY_IS_APPROX(m.middleCols(j,w).coeffRef(r,c), refMat.middleCols(j,w).coeff(r,c)); - } - if(m.middleRows(i,h).coeff(r,c) != Scalar(0)) - { - VERIFY_IS_APPROX(m.middleRows(i,h).coeff(r,c), refMat.middleRows(i,h).coeff(r,c)); - } - } - } - for(Index c=0; c<w; c++) - { - VERIFY_IS_APPROX(m.middleCols(j,w).col(c), refMat.middleCols(j,w).col(c)); - VERIFY_IS_APPROX(m.middleRows(i,h).col(c), refMat.middleRows(i,h).col(c)); - } - } - - for(Index c=0; c<cols; c++) - { - VERIFY_IS_APPROX(m.col(c) + m.col(c), (m + m).col(c)); - VERIFY_IS_APPROX(m.col(c) + m.col(c), refMat.col(c) + refMat.col(c)); - } - - for(Index r=0; r<rows; r++) - { - VERIFY_IS_APPROX(m.row(r) + m.row(r), (m + m).row(r)); - VERIFY_IS_APPROX(m.row(r) + m.row(r), refMat.row(r) + refMat.row(r)); - } - } - - // test innerVector() - { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols); - SparseMatrixType m2(rows, cols); - initSparse<Scalar>(density, refMat2, m2); - Index j0 = internal::random<Index>(0,outer-1); - Index j1 = internal::random<Index>(0,outer-1); - Index r0 = internal::random<Index>(0,rows-1); - Index c0 = internal::random<Index>(0,cols-1); - - VERIFY_IS_APPROX(m2.innerVector(j0), innervec(refMat2,j0)); - VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), innervec(refMat2,j0)+innervec(refMat2,j1)); - - m2.innerVector(j0) *= Scalar(2); - innervec(refMat2,j0) *= Scalar(2); - VERIFY_IS_APPROX(m2, refMat2); - - m2.row(r0) *= Scalar(3); - refMat2.row(r0) *= Scalar(3); - VERIFY_IS_APPROX(m2, refMat2); - - m2.col(c0) *= Scalar(4); - refMat2.col(c0) *= Scalar(4); - VERIFY_IS_APPROX(m2, refMat2); - - m2.row(r0) /= Scalar(3); - refMat2.row(r0) /= Scalar(3); - VERIFY_IS_APPROX(m2, refMat2); - - m2.col(c0) /= Scalar(4); - refMat2.col(c0) /= Scalar(4); - VERIFY_IS_APPROX(m2, refMat2); - - SparseVectorType v1; - VERIFY_IS_APPROX(v1 = m2.col(c0) * 4, refMat2.col(c0)*4); - VERIFY_IS_APPROX(v1 = m2.row(r0) * 4, refMat2.row(r0).transpose()*4); - - SparseMatrixType m3(rows,cols); - m3.reserve(VectorXi::Constant(outer,int(inner/2))); - for(Index j=0; j<outer; ++j) - for(Index k=0; k<(std::min)(j,inner); ++k) - m3.insertByOuterInner(j,k) = internal::convert_index<StorageIndex>(k+1); - for(Index j=0; j<(std::min)(outer, inner); ++j) - { - VERIFY(j==numext::real(m3.innerVector(j).nonZeros())); - if(j>0) - VERIFY(j==numext::real(m3.innerVector(j).lastCoeff())); - } - m3.makeCompressed(); - for(Index j=0; j<(std::min)(outer, inner); ++j) - { - VERIFY(j==numext::real(m3.innerVector(j).nonZeros())); - if(j>0) - VERIFY(j==numext::real(m3.innerVector(j).lastCoeff())); - } - - VERIFY(m3.innerVector(j0).nonZeros() == m3.transpose().innerVector(j0).nonZeros()); - -// m2.innerVector(j0) = 2*m2.innerVector(j1); -// refMat2.col(j0) = 2*refMat2.col(j1); -// VERIFY_IS_APPROX(m2, refMat2); - } - - // test innerVectors() - { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols); - SparseMatrixType m2(rows, cols); - initSparse<Scalar>(density, refMat2, m2); - if(internal::random<float>(0,1)>0.5f) m2.makeCompressed(); - Index j0 = internal::random<Index>(0,outer-2); - Index j1 = internal::random<Index>(0,outer-2); - Index n0 = internal::random<Index>(1,outer-(std::max)(j0,j1)); - if(SparseMatrixType::IsRowMajor) - VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(j0,0,n0,cols)); - else - VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(0,j0,rows,n0)); - if(SparseMatrixType::IsRowMajor) - VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0), - refMat2.middleRows(j0,n0)+refMat2.middleRows(j1,n0)); - else - VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0), - refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0)); - - VERIFY_IS_APPROX(m2, refMat2); - - VERIFY(m2.innerVectors(j0,n0).nonZeros() == m2.transpose().innerVectors(j0,n0).nonZeros()); - - m2.innerVectors(j0,n0) = m2.innerVectors(j0,n0) + m2.innerVectors(j1,n0); - if(SparseMatrixType::IsRowMajor) - refMat2.middleRows(j0,n0) = (refMat2.middleRows(j0,n0) + refMat2.middleRows(j1,n0)).eval(); - else - refMat2.middleCols(j0,n0) = (refMat2.middleCols(j0,n0) + refMat2.middleCols(j1,n0)).eval(); - - VERIFY_IS_APPROX(m2, refMat2); - } - - // test generic blocks - { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols); - SparseMatrixType m2(rows, cols); - initSparse<Scalar>(density, refMat2, m2); - Index j0 = internal::random<Index>(0,outer-2); - Index j1 = internal::random<Index>(0,outer-2); - Index n0 = internal::random<Index>(1,outer-(std::max)(j0,j1)); - if(SparseMatrixType::IsRowMajor) - VERIFY_IS_APPROX(m2.block(j0,0,n0,cols), refMat2.block(j0,0,n0,cols)); - else - VERIFY_IS_APPROX(m2.block(0,j0,rows,n0), refMat2.block(0,j0,rows,n0)); - - if(SparseMatrixType::IsRowMajor) - VERIFY_IS_APPROX(m2.block(j0,0,n0,cols)+m2.block(j1,0,n0,cols), - refMat2.block(j0,0,n0,cols)+refMat2.block(j1,0,n0,cols)); - else - VERIFY_IS_APPROX(m2.block(0,j0,rows,n0)+m2.block(0,j1,rows,n0), - refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0)); - - Index i = internal::random<Index>(0,m2.outerSize()-1); - if(SparseMatrixType::IsRowMajor) { - m2.innerVector(i) = m2.innerVector(i) * s1; - refMat2.row(i) = refMat2.row(i) * s1; - VERIFY_IS_APPROX(m2,refMat2); - } else { - m2.innerVector(i) = m2.innerVector(i) * s1; - refMat2.col(i) = refMat2.col(i) * s1; - VERIFY_IS_APPROX(m2,refMat2); - } - - Index r0 = internal::random<Index>(0,rows-2); - Index c0 = internal::random<Index>(0,cols-2); - Index r1 = internal::random<Index>(1,rows-r0); - Index c1 = internal::random<Index>(1,cols-c0); - - VERIFY_IS_APPROX(DenseVector(m2.col(c0)), refMat2.col(c0)); - VERIFY_IS_APPROX(m2.col(c0), refMat2.col(c0)); - - VERIFY_IS_APPROX(RowDenseVector(m2.row(r0)), refMat2.row(r0)); - VERIFY_IS_APPROX(m2.row(r0), refMat2.row(r0)); - - VERIFY_IS_APPROX(m2.block(r0,c0,r1,c1), refMat2.block(r0,c0,r1,c1)); - VERIFY_IS_APPROX((2*m2).block(r0,c0,r1,c1), (2*refMat2).block(r0,c0,r1,c1)); - - if(m2.nonZeros()>0) - { - VERIFY_IS_APPROX(m2, refMat2); - SparseMatrixType m3(rows, cols); - DenseMatrix refMat3(rows, cols); refMat3.setZero(); - Index n = internal::random<Index>(1,10); - for(Index k=0; k<n; ++k) - { - Index o1 = internal::random<Index>(0,outer-1); - Index o2 = internal::random<Index>(0,outer-1); - if(SparseMatrixType::IsRowMajor) - { - m3.innerVector(o1) = m2.row(o2); - refMat3.row(o1) = refMat2.row(o2); - } - else - { - m3.innerVector(o1) = m2.col(o2); - refMat3.col(o1) = refMat2.col(o2); - } - if(internal::random<bool>()) - m3.makeCompressed(); - } - if(m3.nonZeros()>0) - VERIFY_IS_APPROX(m3, refMat3); - } - } -} - -void test_sparse_block() -{ - for(int i = 0; i < g_repeat; i++) { - int r = Eigen::internal::random<int>(1,200), c = Eigen::internal::random<int>(1,200); - if(Eigen::internal::random<int>(0,4) == 0) { - r = c; // check square matrices in 25% of tries - } - EIGEN_UNUSED_VARIABLE(r+c); - CALL_SUBTEST_1(( sparse_block(SparseMatrix<double>(1, 1)) )); - CALL_SUBTEST_1(( sparse_block(SparseMatrix<double>(8, 8)) )); - CALL_SUBTEST_1(( sparse_block(SparseMatrix<double>(r, c)) )); - CALL_SUBTEST_2(( sparse_block(SparseMatrix<std::complex<double>, ColMajor>(r, c)) )); - CALL_SUBTEST_2(( sparse_block(SparseMatrix<std::complex<double>, RowMajor>(r, c)) )); - - CALL_SUBTEST_3(( sparse_block(SparseMatrix<double,ColMajor,long int>(r, c)) )); - CALL_SUBTEST_3(( sparse_block(SparseMatrix<double,RowMajor,long int>(r, c)) )); - - r = Eigen::internal::random<int>(1,100); - c = Eigen::internal::random<int>(1,100); - if(Eigen::internal::random<int>(0,4) == 0) { - r = c; // check square matrices in 25% of tries - } - - CALL_SUBTEST_4(( sparse_block(SparseMatrix<double,ColMajor,short int>(short(r), short(c))) )); - CALL_SUBTEST_4(( sparse_block(SparseMatrix<double,RowMajor,short int>(short(r), short(c))) )); - } -} diff --git a/eigen/test/sparse_permutations.cpp b/eigen/test/sparse_permutations.cpp deleted file mode 100644 index b82ccef..0000000 --- a/eigen/test/sparse_permutations.cpp +++ /dev/null @@ -1,236 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011-2015 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - - -static long int nb_transposed_copies; -#define EIGEN_SPARSE_TRANSPOSED_COPY_PLUGIN {nb_transposed_copies++;} -#define VERIFY_TRANSPOSITION_COUNT(XPR,N) {\ - nb_transposed_copies = 0; \ - XPR; \ - if(nb_transposed_copies!=N) std::cerr << "nb_transposed_copies == " << nb_transposed_copies << "\n"; \ - VERIFY( (#XPR) && nb_transposed_copies==N ); \ - } - -#include "sparse.h" - -template<typename T> -bool is_sorted(const T& mat) { - for(Index k = 0; k<mat.outerSize(); ++k) - { - Index prev = -1; - for(typename T::InnerIterator it(mat,k); it; ++it) - { - if(prev>=it.index()) - return false; - prev = it.index(); - } - } - return true; -} - -template<typename T> -typename internal::nested_eval<T,1>::type eval(const T &xpr) -{ - VERIFY( int(internal::nested_eval<T,1>::type::Flags&RowMajorBit) == int(internal::evaluator<T>::Flags&RowMajorBit) ); - return xpr; -} - -template<int OtherStorage, typename SparseMatrixType> void sparse_permutations(const SparseMatrixType& ref) -{ - const Index rows = ref.rows(); - const Index cols = ref.cols(); - typedef typename SparseMatrixType::Scalar Scalar; - typedef typename SparseMatrixType::StorageIndex StorageIndex; - typedef SparseMatrix<Scalar, OtherStorage, StorageIndex> OtherSparseMatrixType; - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - typedef Matrix<StorageIndex,Dynamic,1> VectorI; -// bool IsRowMajor1 = SparseMatrixType::IsRowMajor; -// bool IsRowMajor2 = OtherSparseMatrixType::IsRowMajor; - - double density = (std::max)(8./(rows*cols), 0.01); - - SparseMatrixType mat(rows, cols), up(rows,cols), lo(rows,cols); - OtherSparseMatrixType res; - DenseMatrix mat_d = DenseMatrix::Zero(rows, cols), up_sym_d, lo_sym_d, res_d; - - initSparse<Scalar>(density, mat_d, mat, 0); - - up = mat.template triangularView<Upper>(); - lo = mat.template triangularView<Lower>(); - - up_sym_d = mat_d.template selfadjointView<Upper>(); - lo_sym_d = mat_d.template selfadjointView<Lower>(); - - VERIFY_IS_APPROX(mat, mat_d); - VERIFY_IS_APPROX(up, DenseMatrix(mat_d.template triangularView<Upper>())); - VERIFY_IS_APPROX(lo, DenseMatrix(mat_d.template triangularView<Lower>())); - - PermutationMatrix<Dynamic> p, p_null; - VectorI pi; - randomPermutationVector(pi, cols); - p.indices() = pi; - - VERIFY( is_sorted( ::eval(mat*p) )); - VERIFY( is_sorted( res = mat*p )); - VERIFY_TRANSPOSITION_COUNT( ::eval(mat*p), 0); - //VERIFY_TRANSPOSITION_COUNT( res = mat*p, IsRowMajor ? 1 : 0 ); - res_d = mat_d*p; - VERIFY(res.isApprox(res_d) && "mat*p"); - - VERIFY( is_sorted( ::eval(p*mat) )); - VERIFY( is_sorted( res = p*mat )); - VERIFY_TRANSPOSITION_COUNT( ::eval(p*mat), 0); - res_d = p*mat_d; - VERIFY(res.isApprox(res_d) && "p*mat"); - - VERIFY( is_sorted( (mat*p).eval() )); - VERIFY( is_sorted( res = mat*p.inverse() )); - VERIFY_TRANSPOSITION_COUNT( ::eval(mat*p.inverse()), 0); - res_d = mat*p.inverse(); - VERIFY(res.isApprox(res_d) && "mat*inv(p)"); - - VERIFY( is_sorted( (p*mat+p*mat).eval() )); - VERIFY( is_sorted( res = p.inverse()*mat )); - VERIFY_TRANSPOSITION_COUNT( ::eval(p.inverse()*mat), 0); - res_d = p.inverse()*mat_d; - VERIFY(res.isApprox(res_d) && "inv(p)*mat"); - - VERIFY( is_sorted( (p * mat * p.inverse()).eval() )); - VERIFY( is_sorted( res = mat.twistedBy(p) )); - VERIFY_TRANSPOSITION_COUNT( ::eval(p * mat * p.inverse()), 0); - res_d = (p * mat_d) * p.inverse(); - VERIFY(res.isApprox(res_d) && "p*mat*inv(p)"); - - - VERIFY( is_sorted( res = mat.template selfadjointView<Upper>().twistedBy(p_null) )); - res_d = up_sym_d; - VERIFY(res.isApprox(res_d) && "full selfadjoint upper to full"); - - VERIFY( is_sorted( res = mat.template selfadjointView<Lower>().twistedBy(p_null) )); - res_d = lo_sym_d; - VERIFY(res.isApprox(res_d) && "full selfadjoint lower to full"); - - - VERIFY( is_sorted( res = up.template selfadjointView<Upper>().twistedBy(p_null) )); - res_d = up_sym_d; - VERIFY(res.isApprox(res_d) && "upper selfadjoint to full"); - - VERIFY( is_sorted( res = lo.template selfadjointView<Lower>().twistedBy(p_null) )); - res_d = lo_sym_d; - VERIFY(res.isApprox(res_d) && "lower selfadjoint full"); - - - VERIFY( is_sorted( res = mat.template selfadjointView<Upper>() )); - res_d = up_sym_d; - VERIFY(res.isApprox(res_d) && "full selfadjoint upper to full"); - - VERIFY( is_sorted( res = mat.template selfadjointView<Lower>() )); - res_d = lo_sym_d; - VERIFY(res.isApprox(res_d) && "full selfadjoint lower to full"); - - VERIFY( is_sorted( res = up.template selfadjointView<Upper>() )); - res_d = up_sym_d; - VERIFY(res.isApprox(res_d) && "upper selfadjoint to full"); - - VERIFY( is_sorted( res = lo.template selfadjointView<Lower>() )); - res_d = lo_sym_d; - VERIFY(res.isApprox(res_d) && "lower selfadjoint full"); - - - res.template selfadjointView<Upper>() = mat.template selfadjointView<Upper>(); - res_d = up_sym_d.template triangularView<Upper>(); - VERIFY(res.isApprox(res_d) && "full selfadjoint upper to upper"); - - res.template selfadjointView<Lower>() = mat.template selfadjointView<Upper>(); - res_d = up_sym_d.template triangularView<Lower>(); - VERIFY(res.isApprox(res_d) && "full selfadjoint upper to lower"); - - res.template selfadjointView<Upper>() = mat.template selfadjointView<Lower>(); - res_d = lo_sym_d.template triangularView<Upper>(); - VERIFY(res.isApprox(res_d) && "full selfadjoint lower to upper"); - - res.template selfadjointView<Lower>() = mat.template selfadjointView<Lower>(); - res_d = lo_sym_d.template triangularView<Lower>(); - VERIFY(res.isApprox(res_d) && "full selfadjoint lower to lower"); - - - - res.template selfadjointView<Upper>() = mat.template selfadjointView<Upper>().twistedBy(p); - res_d = ((p * up_sym_d) * p.inverse()).eval().template triangularView<Upper>(); - VERIFY(res.isApprox(res_d) && "full selfadjoint upper twisted to upper"); - - res.template selfadjointView<Upper>() = mat.template selfadjointView<Lower>().twistedBy(p); - res_d = ((p * lo_sym_d) * p.inverse()).eval().template triangularView<Upper>(); - VERIFY(res.isApprox(res_d) && "full selfadjoint lower twisted to upper"); - - res.template selfadjointView<Lower>() = mat.template selfadjointView<Lower>().twistedBy(p); - res_d = ((p * lo_sym_d) * p.inverse()).eval().template triangularView<Lower>(); - VERIFY(res.isApprox(res_d) && "full selfadjoint lower twisted to lower"); - - res.template selfadjointView<Lower>() = mat.template selfadjointView<Upper>().twistedBy(p); - res_d = ((p * up_sym_d) * p.inverse()).eval().template triangularView<Lower>(); - VERIFY(res.isApprox(res_d) && "full selfadjoint upper twisted to lower"); - - - res.template selfadjointView<Upper>() = up.template selfadjointView<Upper>().twistedBy(p); - res_d = ((p * up_sym_d) * p.inverse()).eval().template triangularView<Upper>(); - VERIFY(res.isApprox(res_d) && "upper selfadjoint twisted to upper"); - - res.template selfadjointView<Upper>() = lo.template selfadjointView<Lower>().twistedBy(p); - res_d = ((p * lo_sym_d) * p.inverse()).eval().template triangularView<Upper>(); - VERIFY(res.isApprox(res_d) && "lower selfadjoint twisted to upper"); - - res.template selfadjointView<Lower>() = lo.template selfadjointView<Lower>().twistedBy(p); - res_d = ((p * lo_sym_d) * p.inverse()).eval().template triangularView<Lower>(); - VERIFY(res.isApprox(res_d) && "lower selfadjoint twisted to lower"); - - res.template selfadjointView<Lower>() = up.template selfadjointView<Upper>().twistedBy(p); - res_d = ((p * up_sym_d) * p.inverse()).eval().template triangularView<Lower>(); - VERIFY(res.isApprox(res_d) && "upper selfadjoint twisted to lower"); - - - VERIFY( is_sorted( res = mat.template selfadjointView<Upper>().twistedBy(p) )); - res_d = (p * up_sym_d) * p.inverse(); - VERIFY(res.isApprox(res_d) && "full selfadjoint upper twisted to full"); - - VERIFY( is_sorted( res = mat.template selfadjointView<Lower>().twistedBy(p) )); - res_d = (p * lo_sym_d) * p.inverse(); - VERIFY(res.isApprox(res_d) && "full selfadjoint lower twisted to full"); - - VERIFY( is_sorted( res = up.template selfadjointView<Upper>().twistedBy(p) )); - res_d = (p * up_sym_d) * p.inverse(); - VERIFY(res.isApprox(res_d) && "upper selfadjoint twisted to full"); - - VERIFY( is_sorted( res = lo.template selfadjointView<Lower>().twistedBy(p) )); - res_d = (p * lo_sym_d) * p.inverse(); - VERIFY(res.isApprox(res_d) && "lower selfadjoint twisted to full"); -} - -template<typename Scalar> void sparse_permutations_all(int size) -{ - CALL_SUBTEST(( sparse_permutations<ColMajor>(SparseMatrix<Scalar, ColMajor>(size,size)) )); - CALL_SUBTEST(( sparse_permutations<ColMajor>(SparseMatrix<Scalar, RowMajor>(size,size)) )); - CALL_SUBTEST(( sparse_permutations<RowMajor>(SparseMatrix<Scalar, ColMajor>(size,size)) )); - CALL_SUBTEST(( sparse_permutations<RowMajor>(SparseMatrix<Scalar, RowMajor>(size,size)) )); -} - -void test_sparse_permutations() -{ - for(int i = 0; i < g_repeat; i++) { - int s = Eigen::internal::random<int>(1,50); - CALL_SUBTEST_1(( sparse_permutations_all<double>(s) )); - CALL_SUBTEST_2(( sparse_permutations_all<std::complex<double> >(s) )); - } - - VERIFY((internal::is_same<internal::permutation_matrix_product<SparseMatrix<double>,OnTheRight,false,SparseShape>::ReturnType, - internal::nested_eval<Product<SparseMatrix<double>,PermutationMatrix<Dynamic,Dynamic>,AliasFreeProduct>,1>::type>::value)); - - VERIFY((internal::is_same<internal::permutation_matrix_product<SparseMatrix<double>,OnTheLeft,false,SparseShape>::ReturnType, - internal::nested_eval<Product<PermutationMatrix<Dynamic,Dynamic>,SparseMatrix<double>,AliasFreeProduct>,1>::type>::value)); -} diff --git a/eigen/test/sparse_product.cpp b/eigen/test/sparse_product.cpp deleted file mode 100644 index 7f77bb7..0000000 --- a/eigen/test/sparse_product.cpp +++ /dev/null @@ -1,475 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#if defined(_MSC_VER) && (_MSC_VER==1800) -// This unit test takes forever to compile in Release mode with MSVC 2013, -// multiple hours. So let's switch off optimization for this one. -#pragma optimize("",off) -#endif - -static long int nb_temporaries; - -inline void on_temporary_creation() { - // here's a great place to set a breakpoint when debugging failures in this test! - nb_temporaries++; -} - -#define EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN { on_temporary_creation(); } - -#include "sparse.h" - -#define VERIFY_EVALUATION_COUNT(XPR,N) {\ - nb_temporaries = 0; \ - CALL_SUBTEST( XPR ); \ - if(nb_temporaries!=N) std::cerr << "nb_temporaries == " << nb_temporaries << "\n"; \ - VERIFY( (#XPR) && nb_temporaries==N ); \ - } - - - -template<typename SparseMatrixType> void sparse_product() -{ - typedef typename SparseMatrixType::StorageIndex StorageIndex; - Index n = 100; - const Index rows = internal::random<Index>(1,n); - const Index cols = internal::random<Index>(1,n); - const Index depth = internal::random<Index>(1,n); - typedef typename SparseMatrixType::Scalar Scalar; - enum { Flags = SparseMatrixType::Flags }; - - double density = (std::max)(8./(rows*cols), 0.2); - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - typedef Matrix<Scalar,Dynamic,1> DenseVector; - typedef Matrix<Scalar,1,Dynamic> RowDenseVector; - typedef SparseVector<Scalar,0,StorageIndex> ColSpVector; - typedef SparseVector<Scalar,RowMajor,StorageIndex> RowSpVector; - - Scalar s1 = internal::random<Scalar>(); - Scalar s2 = internal::random<Scalar>(); - - // test matrix-matrix product - { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, depth); - DenseMatrix refMat2t = DenseMatrix::Zero(depth, rows); - DenseMatrix refMat3 = DenseMatrix::Zero(depth, cols); - DenseMatrix refMat3t = DenseMatrix::Zero(cols, depth); - DenseMatrix refMat4 = DenseMatrix::Zero(rows, cols); - DenseMatrix refMat4t = DenseMatrix::Zero(cols, rows); - DenseMatrix refMat5 = DenseMatrix::Random(depth, cols); - DenseMatrix refMat6 = DenseMatrix::Random(rows, rows); - DenseMatrix dm4 = DenseMatrix::Zero(rows, rows); -// DenseVector dv1 = DenseVector::Random(rows); - SparseMatrixType m2 (rows, depth); - SparseMatrixType m2t(depth, rows); - SparseMatrixType m3 (depth, cols); - SparseMatrixType m3t(cols, depth); - SparseMatrixType m4 (rows, cols); - SparseMatrixType m4t(cols, rows); - SparseMatrixType m6(rows, rows); - initSparse(density, refMat2, m2); - initSparse(density, refMat2t, m2t); - initSparse(density, refMat3, m3); - initSparse(density, refMat3t, m3t); - initSparse(density, refMat4, m4); - initSparse(density, refMat4t, m4t); - initSparse(density, refMat6, m6); - -// int c = internal::random<int>(0,depth-1); - - // sparse * sparse - VERIFY_IS_APPROX(m4=m2*m3, refMat4=refMat2*refMat3); - VERIFY_IS_APPROX(m4=m2t.transpose()*m3, refMat4=refMat2t.transpose()*refMat3); - VERIFY_IS_APPROX(m4=m2t.transpose()*m3t.transpose(), refMat4=refMat2t.transpose()*refMat3t.transpose()); - VERIFY_IS_APPROX(m4=m2*m3t.transpose(), refMat4=refMat2*refMat3t.transpose()); - - VERIFY_IS_APPROX(m4 = m2*m3/s1, refMat4 = refMat2*refMat3/s1); - VERIFY_IS_APPROX(m4 = m2*m3*s1, refMat4 = refMat2*refMat3*s1); - VERIFY_IS_APPROX(m4 = s2*m2*m3*s1, refMat4 = s2*refMat2*refMat3*s1); - VERIFY_IS_APPROX(m4 = (m2+m2)*m3, refMat4 = (refMat2+refMat2)*refMat3); - VERIFY_IS_APPROX(m4 = m2*m3.leftCols(cols/2), refMat4 = refMat2*refMat3.leftCols(cols/2)); - VERIFY_IS_APPROX(m4 = m2*(m3+m3).leftCols(cols/2), refMat4 = refMat2*(refMat3+refMat3).leftCols(cols/2)); - - VERIFY_IS_APPROX(m4=(m2*m3).pruned(0), refMat4=refMat2*refMat3); - VERIFY_IS_APPROX(m4=(m2t.transpose()*m3).pruned(0), refMat4=refMat2t.transpose()*refMat3); - VERIFY_IS_APPROX(m4=(m2t.transpose()*m3t.transpose()).pruned(0), refMat4=refMat2t.transpose()*refMat3t.transpose()); - VERIFY_IS_APPROX(m4=(m2*m3t.transpose()).pruned(0), refMat4=refMat2*refMat3t.transpose()); - - // make sure the right product implementation is called: - if((!SparseMatrixType::IsRowMajor) && m2.rows()<=m3.cols()) - { - VERIFY_EVALUATION_COUNT(m4 = m2*m3, 3); // 1 temp for the result + 2 for transposing and get a sorted result. - VERIFY_EVALUATION_COUNT(m4 = (m2*m3).pruned(0), 1); - VERIFY_EVALUATION_COUNT(m4 = (m2*m3).eval().pruned(0), 4); - } - - // and that pruning is effective: - { - DenseMatrix Ad(2,2); - Ad << -1, 1, 1, 1; - SparseMatrixType As(Ad.sparseView()), B(2,2); - VERIFY_IS_EQUAL( (As*As.transpose()).eval().nonZeros(), 4); - VERIFY_IS_EQUAL( (Ad*Ad.transpose()).eval().sparseView().eval().nonZeros(), 2); - VERIFY_IS_EQUAL( (As*As.transpose()).pruned(1e-6).eval().nonZeros(), 2); - } - - // dense ?= sparse * sparse - VERIFY_IS_APPROX(dm4 =m2*m3, refMat4 =refMat2*refMat3); - VERIFY_IS_APPROX(dm4+=m2*m3, refMat4+=refMat2*refMat3); - VERIFY_IS_APPROX(dm4-=m2*m3, refMat4-=refMat2*refMat3); - VERIFY_IS_APPROX(dm4 =m2t.transpose()*m3, refMat4 =refMat2t.transpose()*refMat3); - VERIFY_IS_APPROX(dm4+=m2t.transpose()*m3, refMat4+=refMat2t.transpose()*refMat3); - VERIFY_IS_APPROX(dm4-=m2t.transpose()*m3, refMat4-=refMat2t.transpose()*refMat3); - VERIFY_IS_APPROX(dm4 =m2t.transpose()*m3t.transpose(), refMat4 =refMat2t.transpose()*refMat3t.transpose()); - VERIFY_IS_APPROX(dm4+=m2t.transpose()*m3t.transpose(), refMat4+=refMat2t.transpose()*refMat3t.transpose()); - VERIFY_IS_APPROX(dm4-=m2t.transpose()*m3t.transpose(), refMat4-=refMat2t.transpose()*refMat3t.transpose()); - VERIFY_IS_APPROX(dm4 =m2*m3t.transpose(), refMat4 =refMat2*refMat3t.transpose()); - VERIFY_IS_APPROX(dm4+=m2*m3t.transpose(), refMat4+=refMat2*refMat3t.transpose()); - VERIFY_IS_APPROX(dm4-=m2*m3t.transpose(), refMat4-=refMat2*refMat3t.transpose()); - VERIFY_IS_APPROX(dm4 = m2*m3*s1, refMat4 = refMat2*refMat3*s1); - - // test aliasing - m4 = m2; refMat4 = refMat2; - VERIFY_IS_APPROX(m4=m4*m3, refMat4=refMat4*refMat3); - - // sparse * dense matrix - VERIFY_IS_APPROX(dm4=m2*refMat3, refMat4=refMat2*refMat3); - VERIFY_IS_APPROX(dm4=m2*refMat3t.transpose(), refMat4=refMat2*refMat3t.transpose()); - VERIFY_IS_APPROX(dm4=m2t.transpose()*refMat3, refMat4=refMat2t.transpose()*refMat3); - VERIFY_IS_APPROX(dm4=m2t.transpose()*refMat3t.transpose(), refMat4=refMat2t.transpose()*refMat3t.transpose()); - - VERIFY_IS_APPROX(dm4=m2*refMat3, refMat4=refMat2*refMat3); - VERIFY_IS_APPROX(dm4=dm4+m2*refMat3, refMat4=refMat4+refMat2*refMat3); - VERIFY_IS_APPROX(dm4+=m2*refMat3, refMat4+=refMat2*refMat3); - VERIFY_IS_APPROX(dm4-=m2*refMat3, refMat4-=refMat2*refMat3); - VERIFY_IS_APPROX(dm4.noalias()+=m2*refMat3, refMat4+=refMat2*refMat3); - VERIFY_IS_APPROX(dm4.noalias()-=m2*refMat3, refMat4-=refMat2*refMat3); - VERIFY_IS_APPROX(dm4=m2*(refMat3+refMat3), refMat4=refMat2*(refMat3+refMat3)); - VERIFY_IS_APPROX(dm4=m2t.transpose()*(refMat3+refMat5)*0.5, refMat4=refMat2t.transpose()*(refMat3+refMat5)*0.5); - - // sparse * dense vector - VERIFY_IS_APPROX(dm4.col(0)=m2*refMat3.col(0), refMat4.col(0)=refMat2*refMat3.col(0)); - VERIFY_IS_APPROX(dm4.col(0)=m2*refMat3t.transpose().col(0), refMat4.col(0)=refMat2*refMat3t.transpose().col(0)); - VERIFY_IS_APPROX(dm4.col(0)=m2t.transpose()*refMat3.col(0), refMat4.col(0)=refMat2t.transpose()*refMat3.col(0)); - VERIFY_IS_APPROX(dm4.col(0)=m2t.transpose()*refMat3t.transpose().col(0), refMat4.col(0)=refMat2t.transpose()*refMat3t.transpose().col(0)); - - // dense * sparse - VERIFY_IS_APPROX(dm4=refMat2*m3, refMat4=refMat2*refMat3); - VERIFY_IS_APPROX(dm4=dm4+refMat2*m3, refMat4=refMat4+refMat2*refMat3); - VERIFY_IS_APPROX(dm4+=refMat2*m3, refMat4+=refMat2*refMat3); - VERIFY_IS_APPROX(dm4-=refMat2*m3, refMat4-=refMat2*refMat3); - VERIFY_IS_APPROX(dm4.noalias()+=refMat2*m3, refMat4+=refMat2*refMat3); - VERIFY_IS_APPROX(dm4.noalias()-=refMat2*m3, refMat4-=refMat2*refMat3); - VERIFY_IS_APPROX(dm4=refMat2*m3t.transpose(), refMat4=refMat2*refMat3t.transpose()); - VERIFY_IS_APPROX(dm4=refMat2t.transpose()*m3, refMat4=refMat2t.transpose()*refMat3); - VERIFY_IS_APPROX(dm4=refMat2t.transpose()*m3t.transpose(), refMat4=refMat2t.transpose()*refMat3t.transpose()); - - // sparse * dense and dense * sparse outer product - { - Index c = internal::random<Index>(0,depth-1); - Index r = internal::random<Index>(0,rows-1); - Index c1 = internal::random<Index>(0,cols-1); - Index r1 = internal::random<Index>(0,depth-1); - DenseMatrix dm5 = DenseMatrix::Random(depth, cols); - - VERIFY_IS_APPROX( m4=m2.col(c)*dm5.col(c1).transpose(), refMat4=refMat2.col(c)*dm5.col(c1).transpose()); - VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count()); - VERIFY_IS_APPROX( m4=m2.middleCols(c,1)*dm5.col(c1).transpose(), refMat4=refMat2.col(c)*dm5.col(c1).transpose()); - VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count()); - VERIFY_IS_APPROX(dm4=m2.col(c)*dm5.col(c1).transpose(), refMat4=refMat2.col(c)*dm5.col(c1).transpose()); - - VERIFY_IS_APPROX(m4=dm5.col(c1)*m2.col(c).transpose(), refMat4=dm5.col(c1)*refMat2.col(c).transpose()); - VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count()); - VERIFY_IS_APPROX(m4=dm5.col(c1)*m2.middleCols(c,1).transpose(), refMat4=dm5.col(c1)*refMat2.col(c).transpose()); - VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count()); - VERIFY_IS_APPROX(dm4=dm5.col(c1)*m2.col(c).transpose(), refMat4=dm5.col(c1)*refMat2.col(c).transpose()); - - VERIFY_IS_APPROX( m4=dm5.row(r1).transpose()*m2.col(c).transpose(), refMat4=dm5.row(r1).transpose()*refMat2.col(c).transpose()); - VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count()); - VERIFY_IS_APPROX(dm4=dm5.row(r1).transpose()*m2.col(c).transpose(), refMat4=dm5.row(r1).transpose()*refMat2.col(c).transpose()); - - VERIFY_IS_APPROX( m4=m2.row(r).transpose()*dm5.col(c1).transpose(), refMat4=refMat2.row(r).transpose()*dm5.col(c1).transpose()); - VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count()); - VERIFY_IS_APPROX( m4=m2.middleRows(r,1).transpose()*dm5.col(c1).transpose(), refMat4=refMat2.row(r).transpose()*dm5.col(c1).transpose()); - VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count()); - VERIFY_IS_APPROX(dm4=m2.row(r).transpose()*dm5.col(c1).transpose(), refMat4=refMat2.row(r).transpose()*dm5.col(c1).transpose()); - - VERIFY_IS_APPROX( m4=dm5.col(c1)*m2.row(r), refMat4=dm5.col(c1)*refMat2.row(r)); - VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count()); - VERIFY_IS_APPROX( m4=dm5.col(c1)*m2.middleRows(r,1), refMat4=dm5.col(c1)*refMat2.row(r)); - VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count()); - VERIFY_IS_APPROX(dm4=dm5.col(c1)*m2.row(r), refMat4=dm5.col(c1)*refMat2.row(r)); - - VERIFY_IS_APPROX( m4=dm5.row(r1).transpose()*m2.row(r), refMat4=dm5.row(r1).transpose()*refMat2.row(r)); - VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count()); - VERIFY_IS_APPROX(dm4=dm5.row(r1).transpose()*m2.row(r), refMat4=dm5.row(r1).transpose()*refMat2.row(r)); - } - - VERIFY_IS_APPROX(m6=m6*m6, refMat6=refMat6*refMat6); - - // sparse matrix * sparse vector - ColSpVector cv0(cols), cv1; - DenseVector dcv0(cols), dcv1; - initSparse(2*density,dcv0, cv0); - - RowSpVector rv0(depth), rv1; - RowDenseVector drv0(depth), drv1(rv1); - initSparse(2*density,drv0, rv0); - - VERIFY_IS_APPROX(cv1=m3*cv0, dcv1=refMat3*dcv0); - VERIFY_IS_APPROX(rv1=rv0*m3, drv1=drv0*refMat3); - VERIFY_IS_APPROX(cv1=m3t.adjoint()*cv0, dcv1=refMat3t.adjoint()*dcv0); - VERIFY_IS_APPROX(cv1=rv0*m3, dcv1=drv0*refMat3); - VERIFY_IS_APPROX(rv1=m3*cv0, drv1=refMat3*dcv0); - } - - // test matrix - diagonal product - { - DenseMatrix refM2 = DenseMatrix::Zero(rows, cols); - DenseMatrix refM3 = DenseMatrix::Zero(rows, cols); - DenseMatrix d3 = DenseMatrix::Zero(rows, cols); - DiagonalMatrix<Scalar,Dynamic> d1(DenseVector::Random(cols)); - DiagonalMatrix<Scalar,Dynamic> d2(DenseVector::Random(rows)); - SparseMatrixType m2(rows, cols); - SparseMatrixType m3(rows, cols); - initSparse<Scalar>(density, refM2, m2); - initSparse<Scalar>(density, refM3, m3); - VERIFY_IS_APPROX(m3=m2*d1, refM3=refM2*d1); - VERIFY_IS_APPROX(m3=m2.transpose()*d2, refM3=refM2.transpose()*d2); - VERIFY_IS_APPROX(m3=d2*m2, refM3=d2*refM2); - VERIFY_IS_APPROX(m3=d1*m2.transpose(), refM3=d1*refM2.transpose()); - - // also check with a SparseWrapper: - DenseVector v1 = DenseVector::Random(cols); - DenseVector v2 = DenseVector::Random(rows); - DenseVector v3 = DenseVector::Random(rows); - VERIFY_IS_APPROX(m3=m2*v1.asDiagonal(), refM3=refM2*v1.asDiagonal()); - VERIFY_IS_APPROX(m3=m2.transpose()*v2.asDiagonal(), refM3=refM2.transpose()*v2.asDiagonal()); - VERIFY_IS_APPROX(m3=v2.asDiagonal()*m2, refM3=v2.asDiagonal()*refM2); - VERIFY_IS_APPROX(m3=v1.asDiagonal()*m2.transpose(), refM3=v1.asDiagonal()*refM2.transpose()); - - VERIFY_IS_APPROX(m3=v2.asDiagonal()*m2*v1.asDiagonal(), refM3=v2.asDiagonal()*refM2*v1.asDiagonal()); - - VERIFY_IS_APPROX(v2=m2*v1.asDiagonal()*v1, refM2*v1.asDiagonal()*v1); - VERIFY_IS_APPROX(v3=v2.asDiagonal()*m2*v1, v2.asDiagonal()*refM2*v1); - - // evaluate to a dense matrix to check the .row() and .col() iterator functions - VERIFY_IS_APPROX(d3=m2*d1, refM3=refM2*d1); - VERIFY_IS_APPROX(d3=m2.transpose()*d2, refM3=refM2.transpose()*d2); - VERIFY_IS_APPROX(d3=d2*m2, refM3=d2*refM2); - VERIFY_IS_APPROX(d3=d1*m2.transpose(), refM3=d1*refM2.transpose()); - } - - // test self-adjoint and triangular-view products - { - DenseMatrix b = DenseMatrix::Random(rows, rows); - DenseMatrix x = DenseMatrix::Random(rows, rows); - DenseMatrix refX = DenseMatrix::Random(rows, rows); - DenseMatrix refUp = DenseMatrix::Zero(rows, rows); - DenseMatrix refLo = DenseMatrix::Zero(rows, rows); - DenseMatrix refS = DenseMatrix::Zero(rows, rows); - DenseMatrix refA = DenseMatrix::Zero(rows, rows); - SparseMatrixType mUp(rows, rows); - SparseMatrixType mLo(rows, rows); - SparseMatrixType mS(rows, rows); - SparseMatrixType mA(rows, rows); - initSparse<Scalar>(density, refA, mA); - do { - initSparse<Scalar>(density, refUp, mUp, ForceRealDiag|/*ForceNonZeroDiag|*/MakeUpperTriangular); - } while (refUp.isZero()); - refLo = refUp.adjoint(); - mLo = mUp.adjoint(); - refS = refUp + refLo; - refS.diagonal() *= 0.5; - mS = mUp + mLo; - // TODO be able to address the diagonal.... - for (int k=0; k<mS.outerSize(); ++k) - for (typename SparseMatrixType::InnerIterator it(mS,k); it; ++it) - if (it.index() == k) - it.valueRef() *= Scalar(0.5); - - VERIFY_IS_APPROX(refS.adjoint(), refS); - VERIFY_IS_APPROX(mS.adjoint(), mS); - VERIFY_IS_APPROX(mS, refS); - VERIFY_IS_APPROX(x=mS*b, refX=refS*b); - - // sparse selfadjointView with dense matrices - VERIFY_IS_APPROX(x=mUp.template selfadjointView<Upper>()*b, refX=refS*b); - VERIFY_IS_APPROX(x=mLo.template selfadjointView<Lower>()*b, refX=refS*b); - VERIFY_IS_APPROX(x=mS.template selfadjointView<Upper|Lower>()*b, refX=refS*b); - - VERIFY_IS_APPROX(x=b * mUp.template selfadjointView<Upper>(), refX=b*refS); - VERIFY_IS_APPROX(x=b * mLo.template selfadjointView<Lower>(), refX=b*refS); - VERIFY_IS_APPROX(x=b * mS.template selfadjointView<Upper|Lower>(), refX=b*refS); - - VERIFY_IS_APPROX(x.noalias()+=mUp.template selfadjointView<Upper>()*b, refX+=refS*b); - VERIFY_IS_APPROX(x.noalias()-=mLo.template selfadjointView<Lower>()*b, refX-=refS*b); - VERIFY_IS_APPROX(x.noalias()+=mS.template selfadjointView<Upper|Lower>()*b, refX+=refS*b); - - // sparse selfadjointView with sparse matrices - SparseMatrixType mSres(rows,rows); - VERIFY_IS_APPROX(mSres = mLo.template selfadjointView<Lower>()*mS, - refX = refLo.template selfadjointView<Lower>()*refS); - VERIFY_IS_APPROX(mSres = mS * mLo.template selfadjointView<Lower>(), - refX = refS * refLo.template selfadjointView<Lower>()); - - // sparse triangularView with dense matrices - VERIFY_IS_APPROX(x=mA.template triangularView<Upper>()*b, refX=refA.template triangularView<Upper>()*b); - VERIFY_IS_APPROX(x=mA.template triangularView<Lower>()*b, refX=refA.template triangularView<Lower>()*b); - VERIFY_IS_APPROX(x=b*mA.template triangularView<Upper>(), refX=b*refA.template triangularView<Upper>()); - VERIFY_IS_APPROX(x=b*mA.template triangularView<Lower>(), refX=b*refA.template triangularView<Lower>()); - - // sparse triangularView with sparse matrices - VERIFY_IS_APPROX(mSres = mA.template triangularView<Lower>()*mS, refX = refA.template triangularView<Lower>()*refS); - VERIFY_IS_APPROX(mSres = mS * mA.template triangularView<Lower>(), refX = refS * refA.template triangularView<Lower>()); - VERIFY_IS_APPROX(mSres = mA.template triangularView<Upper>()*mS, refX = refA.template triangularView<Upper>()*refS); - VERIFY_IS_APPROX(mSres = mS * mA.template triangularView<Upper>(), refX = refS * refA.template triangularView<Upper>()); - } -} - -// New test for Bug in SparseTimeDenseProduct -template<typename SparseMatrixType, typename DenseMatrixType> void sparse_product_regression_test() -{ - // This code does not compile with afflicted versions of the bug - SparseMatrixType sm1(3,2); - DenseMatrixType m2(2,2); - sm1.setZero(); - m2.setZero(); - - DenseMatrixType m3 = sm1*m2; - - - // This code produces a segfault with afflicted versions of another SparseTimeDenseProduct - // bug - - SparseMatrixType sm2(20000,2); - sm2.setZero(); - DenseMatrixType m4(sm2*m2); - - VERIFY_IS_APPROX( m4(0,0), 0.0 ); -} - -template<typename Scalar> -void bug_942() -{ - typedef Matrix<Scalar, Dynamic, 1> Vector; - typedef SparseMatrix<Scalar, ColMajor> ColSpMat; - typedef SparseMatrix<Scalar, RowMajor> RowSpMat; - ColSpMat cmA(1,1); - cmA.insert(0,0) = 1; - - RowSpMat rmA(1,1); - rmA.insert(0,0) = 1; - - Vector d(1); - d[0] = 2; - - double res = 2; - - VERIFY_IS_APPROX( ( cmA*d.asDiagonal() ).eval().coeff(0,0), res ); - VERIFY_IS_APPROX( ( d.asDiagonal()*rmA ).eval().coeff(0,0), res ); - VERIFY_IS_APPROX( ( rmA*d.asDiagonal() ).eval().coeff(0,0), res ); - VERIFY_IS_APPROX( ( d.asDiagonal()*cmA ).eval().coeff(0,0), res ); -} - -template<typename Real> -void test_mixing_types() -{ - typedef std::complex<Real> Cplx; - typedef SparseMatrix<Real> SpMatReal; - typedef SparseMatrix<Cplx> SpMatCplx; - typedef SparseMatrix<Cplx,RowMajor> SpRowMatCplx; - typedef Matrix<Real,Dynamic,Dynamic> DenseMatReal; - typedef Matrix<Cplx,Dynamic,Dynamic> DenseMatCplx; - - Index n = internal::random<Index>(1,100); - double density = (std::max)(8./(n*n), 0.2); - - SpMatReal sR1(n,n); - SpMatCplx sC1(n,n), sC2(n,n), sC3(n,n); - SpRowMatCplx sCR(n,n); - DenseMatReal dR1(n,n); - DenseMatCplx dC1(n,n), dC2(n,n), dC3(n,n); - - initSparse<Real>(density, dR1, sR1); - initSparse<Cplx>(density, dC1, sC1); - initSparse<Cplx>(density, dC2, sC2); - - VERIFY_IS_APPROX( sC2 = (sR1 * sC1), dC3 = dR1.template cast<Cplx>() * dC1 ); - VERIFY_IS_APPROX( sC2 = (sC1 * sR1), dC3 = dC1 * dR1.template cast<Cplx>() ); - VERIFY_IS_APPROX( sC2 = (sR1.transpose() * sC1), dC3 = dR1.template cast<Cplx>().transpose() * dC1 ); - VERIFY_IS_APPROX( sC2 = (sC1.transpose() * sR1), dC3 = dC1.transpose() * dR1.template cast<Cplx>() ); - VERIFY_IS_APPROX( sC2 = (sR1 * sC1.transpose()), dC3 = dR1.template cast<Cplx>() * dC1.transpose() ); - VERIFY_IS_APPROX( sC2 = (sC1 * sR1.transpose()), dC3 = dC1 * dR1.template cast<Cplx>().transpose() ); - VERIFY_IS_APPROX( sC2 = (sR1.transpose() * sC1.transpose()), dC3 = dR1.template cast<Cplx>().transpose() * dC1.transpose() ); - VERIFY_IS_APPROX( sC2 = (sC1.transpose() * sR1.transpose()), dC3 = dC1.transpose() * dR1.template cast<Cplx>().transpose() ); - - VERIFY_IS_APPROX( sCR = (sR1 * sC1), dC3 = dR1.template cast<Cplx>() * dC1 ); - VERIFY_IS_APPROX( sCR = (sC1 * sR1), dC3 = dC1 * dR1.template cast<Cplx>() ); - VERIFY_IS_APPROX( sCR = (sR1.transpose() * sC1), dC3 = dR1.template cast<Cplx>().transpose() * dC1 ); - VERIFY_IS_APPROX( sCR = (sC1.transpose() * sR1), dC3 = dC1.transpose() * dR1.template cast<Cplx>() ); - VERIFY_IS_APPROX( sCR = (sR1 * sC1.transpose()), dC3 = dR1.template cast<Cplx>() * dC1.transpose() ); - VERIFY_IS_APPROX( sCR = (sC1 * sR1.transpose()), dC3 = dC1 * dR1.template cast<Cplx>().transpose() ); - VERIFY_IS_APPROX( sCR = (sR1.transpose() * sC1.transpose()), dC3 = dR1.template cast<Cplx>().transpose() * dC1.transpose() ); - VERIFY_IS_APPROX( sCR = (sC1.transpose() * sR1.transpose()), dC3 = dC1.transpose() * dR1.template cast<Cplx>().transpose() ); - - - VERIFY_IS_APPROX( sC2 = (sR1 * sC1).pruned(), dC3 = dR1.template cast<Cplx>() * dC1 ); - VERIFY_IS_APPROX( sC2 = (sC1 * sR1).pruned(), dC3 = dC1 * dR1.template cast<Cplx>() ); - VERIFY_IS_APPROX( sC2 = (sR1.transpose() * sC1).pruned(), dC3 = dR1.template cast<Cplx>().transpose() * dC1 ); - VERIFY_IS_APPROX( sC2 = (sC1.transpose() * sR1).pruned(), dC3 = dC1.transpose() * dR1.template cast<Cplx>() ); - VERIFY_IS_APPROX( sC2 = (sR1 * sC1.transpose()).pruned(), dC3 = dR1.template cast<Cplx>() * dC1.transpose() ); - VERIFY_IS_APPROX( sC2 = (sC1 * sR1.transpose()).pruned(), dC3 = dC1 * dR1.template cast<Cplx>().transpose() ); - VERIFY_IS_APPROX( sC2 = (sR1.transpose() * sC1.transpose()).pruned(), dC3 = dR1.template cast<Cplx>().transpose() * dC1.transpose() ); - VERIFY_IS_APPROX( sC2 = (sC1.transpose() * sR1.transpose()).pruned(), dC3 = dC1.transpose() * dR1.template cast<Cplx>().transpose() ); - - VERIFY_IS_APPROX( sCR = (sR1 * sC1).pruned(), dC3 = dR1.template cast<Cplx>() * dC1 ); - VERIFY_IS_APPROX( sCR = (sC1 * sR1).pruned(), dC3 = dC1 * dR1.template cast<Cplx>() ); - VERIFY_IS_APPROX( sCR = (sR1.transpose() * sC1).pruned(), dC3 = dR1.template cast<Cplx>().transpose() * dC1 ); - VERIFY_IS_APPROX( sCR = (sC1.transpose() * sR1).pruned(), dC3 = dC1.transpose() * dR1.template cast<Cplx>() ); - VERIFY_IS_APPROX( sCR = (sR1 * sC1.transpose()).pruned(), dC3 = dR1.template cast<Cplx>() * dC1.transpose() ); - VERIFY_IS_APPROX( sCR = (sC1 * sR1.transpose()).pruned(), dC3 = dC1 * dR1.template cast<Cplx>().transpose() ); - VERIFY_IS_APPROX( sCR = (sR1.transpose() * sC1.transpose()).pruned(), dC3 = dR1.template cast<Cplx>().transpose() * dC1.transpose() ); - VERIFY_IS_APPROX( sCR = (sC1.transpose() * sR1.transpose()).pruned(), dC3 = dC1.transpose() * dR1.template cast<Cplx>().transpose() ); - - - VERIFY_IS_APPROX( dC2 = (sR1 * sC1), dC3 = dR1.template cast<Cplx>() * dC1 ); - VERIFY_IS_APPROX( dC2 = (sC1 * sR1), dC3 = dC1 * dR1.template cast<Cplx>() ); - VERIFY_IS_APPROX( dC2 = (sR1.transpose() * sC1), dC3 = dR1.template cast<Cplx>().transpose() * dC1 ); - VERIFY_IS_APPROX( dC2 = (sC1.transpose() * sR1), dC3 = dC1.transpose() * dR1.template cast<Cplx>() ); - VERIFY_IS_APPROX( dC2 = (sR1 * sC1.transpose()), dC3 = dR1.template cast<Cplx>() * dC1.transpose() ); - VERIFY_IS_APPROX( dC2 = (sC1 * sR1.transpose()), dC3 = dC1 * dR1.template cast<Cplx>().transpose() ); - VERIFY_IS_APPROX( dC2 = (sR1.transpose() * sC1.transpose()), dC3 = dR1.template cast<Cplx>().transpose() * dC1.transpose() ); - VERIFY_IS_APPROX( dC2 = (sC1.transpose() * sR1.transpose()), dC3 = dC1.transpose() * dR1.template cast<Cplx>().transpose() ); - - - VERIFY_IS_APPROX( dC2 = dR1 * sC1, dC3 = dR1.template cast<Cplx>() * sC1 ); - VERIFY_IS_APPROX( dC2 = sR1 * dC1, dC3 = sR1.template cast<Cplx>() * dC1 ); - VERIFY_IS_APPROX( dC2 = dC1 * sR1, dC3 = dC1 * sR1.template cast<Cplx>() ); - VERIFY_IS_APPROX( dC2 = sC1 * dR1, dC3 = sC1 * dR1.template cast<Cplx>() ); - - VERIFY_IS_APPROX( dC2 = dR1.row(0) * sC1, dC3 = dR1.template cast<Cplx>().row(0) * sC1 ); - VERIFY_IS_APPROX( dC2 = sR1 * dC1.col(0), dC3 = sR1.template cast<Cplx>() * dC1.col(0) ); - VERIFY_IS_APPROX( dC2 = dC1.row(0) * sR1, dC3 = dC1.row(0) * sR1.template cast<Cplx>() ); - VERIFY_IS_APPROX( dC2 = sC1 * dR1.col(0), dC3 = sC1 * dR1.template cast<Cplx>().col(0) ); -} - -void test_sparse_product() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( (sparse_product<SparseMatrix<double,ColMajor> >()) ); - CALL_SUBTEST_1( (sparse_product<SparseMatrix<double,RowMajor> >()) ); - CALL_SUBTEST_1( (bug_942<double>()) ); - CALL_SUBTEST_2( (sparse_product<SparseMatrix<std::complex<double>, ColMajor > >()) ); - CALL_SUBTEST_2( (sparse_product<SparseMatrix<std::complex<double>, RowMajor > >()) ); - CALL_SUBTEST_3( (sparse_product<SparseMatrix<float,ColMajor,long int> >()) ); - CALL_SUBTEST_4( (sparse_product_regression_test<SparseMatrix<double,RowMajor>, Matrix<double, Dynamic, Dynamic, RowMajor> >()) ); - - CALL_SUBTEST_5( (test_mixing_types<float>()) ); - } -} diff --git a/eigen/test/sparse_ref.cpp b/eigen/test/sparse_ref.cpp deleted file mode 100644 index 5e96072..0000000 --- a/eigen/test/sparse_ref.cpp +++ /dev/null @@ -1,139 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 20015 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// This unit test cannot be easily written to work with EIGEN_DEFAULT_TO_ROW_MAJOR -#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR -#undef EIGEN_DEFAULT_TO_ROW_MAJOR -#endif - -static long int nb_temporaries; - -inline void on_temporary_creation() { - // here's a great place to set a breakpoint when debugging failures in this test! - nb_temporaries++; -} - -#define EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN { on_temporary_creation(); } - -#include "main.h" -#include <Eigen/SparseCore> - -#define VERIFY_EVALUATION_COUNT(XPR,N) {\ - nb_temporaries = 0; \ - CALL_SUBTEST( XPR ); \ - if(nb_temporaries!=N) std::cerr << "nb_temporaries == " << nb_temporaries << "\n"; \ - VERIFY( (#XPR) && nb_temporaries==N ); \ - } - -template<typename PlainObjectType> void check_const_correctness(const PlainObjectType&) -{ - // verify that ref-to-const don't have LvalueBit - typedef typename internal::add_const<PlainObjectType>::type ConstPlainObjectType; - VERIFY( !(internal::traits<Ref<ConstPlainObjectType> >::Flags & LvalueBit) ); - VERIFY( !(internal::traits<Ref<ConstPlainObjectType, Aligned> >::Flags & LvalueBit) ); - VERIFY( !(Ref<ConstPlainObjectType>::Flags & LvalueBit) ); - VERIFY( !(Ref<ConstPlainObjectType, Aligned>::Flags & LvalueBit) ); -} - -template<typename B> -EIGEN_DONT_INLINE void call_ref_1(Ref<SparseMatrix<float> > a, const B &b) { VERIFY_IS_EQUAL(a.toDense(),b.toDense()); } - -template<typename B> -EIGEN_DONT_INLINE void call_ref_2(const Ref<const SparseMatrix<float> >& a, const B &b) { VERIFY_IS_EQUAL(a.toDense(),b.toDense()); } - -template<typename B> -EIGEN_DONT_INLINE void call_ref_3(const Ref<const SparseMatrix<float>, StandardCompressedFormat>& a, const B &b) { - VERIFY(a.isCompressed()); - VERIFY_IS_EQUAL(a.toDense(),b.toDense()); -} - -template<typename B> -EIGEN_DONT_INLINE void call_ref_4(Ref<SparseVector<float> > a, const B &b) { VERIFY_IS_EQUAL(a.toDense(),b.toDense()); } - -template<typename B> -EIGEN_DONT_INLINE void call_ref_5(const Ref<const SparseVector<float> >& a, const B &b) { VERIFY_IS_EQUAL(a.toDense(),b.toDense()); } - -void call_ref() -{ - SparseMatrix<float> A = MatrixXf::Random(10,10).sparseView(0.5,1); - SparseMatrix<float,RowMajor> B = MatrixXf::Random(10,10).sparseView(0.5,1); - SparseMatrix<float> C = MatrixXf::Random(10,10).sparseView(0.5,1); - C.reserve(VectorXi::Constant(C.outerSize(), 2)); - const SparseMatrix<float>& Ac(A); - Block<SparseMatrix<float> > Ab(A,0,1, 3,3); - const Block<SparseMatrix<float> > Abc(A,0,1,3,3); - SparseVector<float> vc = VectorXf::Random(10).sparseView(0.5,1); - SparseVector<float,RowMajor> vr = VectorXf::Random(10).sparseView(0.5,1); - SparseMatrix<float> AA = A*A; - - - VERIFY_EVALUATION_COUNT( call_ref_1(A, A), 0); -// VERIFY_EVALUATION_COUNT( call_ref_1(Ac, Ac), 0); // does not compile on purpose - VERIFY_EVALUATION_COUNT( call_ref_2(A, A), 0); - VERIFY_EVALUATION_COUNT( call_ref_3(A, A), 0); - VERIFY_EVALUATION_COUNT( call_ref_2(A.transpose(), A.transpose()), 1); - VERIFY_EVALUATION_COUNT( call_ref_3(A.transpose(), A.transpose()), 1); - VERIFY_EVALUATION_COUNT( call_ref_2(Ac,Ac), 0); - VERIFY_EVALUATION_COUNT( call_ref_3(Ac,Ac), 0); - VERIFY_EVALUATION_COUNT( call_ref_2(A+A,2*Ac), 1); - VERIFY_EVALUATION_COUNT( call_ref_3(A+A,2*Ac), 1); - VERIFY_EVALUATION_COUNT( call_ref_2(B, B), 1); - VERIFY_EVALUATION_COUNT( call_ref_3(B, B), 1); - VERIFY_EVALUATION_COUNT( call_ref_2(B.transpose(), B.transpose()), 0); - VERIFY_EVALUATION_COUNT( call_ref_3(B.transpose(), B.transpose()), 0); - VERIFY_EVALUATION_COUNT( call_ref_2(A*A, AA), 3); - VERIFY_EVALUATION_COUNT( call_ref_3(A*A, AA), 3); - - VERIFY(!C.isCompressed()); - VERIFY_EVALUATION_COUNT( call_ref_3(C, C), 1); - - Ref<SparseMatrix<float> > Ar(A); - VERIFY_IS_APPROX(Ar+Ar, A+A); - VERIFY_EVALUATION_COUNT( call_ref_1(Ar, A), 0); - VERIFY_EVALUATION_COUNT( call_ref_2(Ar, A), 0); - - Ref<SparseMatrix<float,RowMajor> > Br(B); - VERIFY_EVALUATION_COUNT( call_ref_1(Br.transpose(), Br.transpose()), 0); - VERIFY_EVALUATION_COUNT( call_ref_2(Br, Br), 1); - VERIFY_EVALUATION_COUNT( call_ref_2(Br.transpose(), Br.transpose()), 0); - - Ref<const SparseMatrix<float> > Arc(A); -// VERIFY_EVALUATION_COUNT( call_ref_1(Arc, Arc), 0); // does not compile on purpose - VERIFY_EVALUATION_COUNT( call_ref_2(Arc, Arc), 0); - - VERIFY_EVALUATION_COUNT( call_ref_2(A.middleCols(1,3), A.middleCols(1,3)), 0); - - VERIFY_EVALUATION_COUNT( call_ref_2(A.col(2), A.col(2)), 0); - VERIFY_EVALUATION_COUNT( call_ref_2(vc, vc), 0); - VERIFY_EVALUATION_COUNT( call_ref_2(vr.transpose(), vr.transpose()), 0); - VERIFY_EVALUATION_COUNT( call_ref_2(vr, vr.transpose()), 0); - - VERIFY_EVALUATION_COUNT( call_ref_2(A.block(1,1,3,3), A.block(1,1,3,3)), 1); // should be 0 (allocate starts/nnz only) - - VERIFY_EVALUATION_COUNT( call_ref_4(vc, vc), 0); - VERIFY_EVALUATION_COUNT( call_ref_4(vr, vr.transpose()), 0); - VERIFY_EVALUATION_COUNT( call_ref_5(vc, vc), 0); - VERIFY_EVALUATION_COUNT( call_ref_5(vr, vr.transpose()), 0); - VERIFY_EVALUATION_COUNT( call_ref_4(A.col(2), A.col(2)), 0); - VERIFY_EVALUATION_COUNT( call_ref_5(A.col(2), A.col(2)), 0); - // VERIFY_EVALUATION_COUNT( call_ref_4(A.row(2), A.row(2).transpose()), 1); // does not compile on purpose - VERIFY_EVALUATION_COUNT( call_ref_5(A.row(2), A.row(2).transpose()), 1); -} - -void test_sparse_ref() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( check_const_correctness(SparseMatrix<float>()) ); - CALL_SUBTEST_1( check_const_correctness(SparseMatrix<double,RowMajor>()) ); - CALL_SUBTEST_2( call_ref() ); - - CALL_SUBTEST_3( check_const_correctness(SparseVector<float>()) ); - CALL_SUBTEST_3( check_const_correctness(SparseVector<double,RowMajor>()) ); - } -} diff --git a/eigen/test/sparse_solver.h b/eigen/test/sparse_solver.h deleted file mode 100644 index 5145bc3..0000000 --- a/eigen/test/sparse_solver.h +++ /dev/null @@ -1,565 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "sparse.h" -#include <Eigen/SparseCore> -#include <sstream> - -template<typename Solver, typename Rhs, typename Guess,typename Result> -void solve_with_guess(IterativeSolverBase<Solver>& solver, const MatrixBase<Rhs>& b, const Guess& g, Result &x) { - if(internal::random<bool>()) - { - // With a temporary through evaluator<SolveWithGuess> - x = solver.derived().solveWithGuess(b,g) + Result::Zero(x.rows(), x.cols()); - } - else - { - // direct evaluation within x through Assignment<Result,SolveWithGuess> - x = solver.derived().solveWithGuess(b.derived(),g); - } -} - -template<typename Solver, typename Rhs, typename Guess,typename Result> -void solve_with_guess(SparseSolverBase<Solver>& solver, const MatrixBase<Rhs>& b, const Guess& , Result& x) { - if(internal::random<bool>()) - x = solver.derived().solve(b) + Result::Zero(x.rows(), x.cols()); - else - x = solver.derived().solve(b); -} - -template<typename Solver, typename Rhs, typename Guess,typename Result> -void solve_with_guess(SparseSolverBase<Solver>& solver, const SparseMatrixBase<Rhs>& b, const Guess& , Result& x) { - x = solver.derived().solve(b); -} - -template<typename Solver, typename Rhs, typename DenseMat, typename DenseRhs> -void check_sparse_solving(Solver& solver, const typename Solver::MatrixType& A, const Rhs& b, const DenseMat& dA, const DenseRhs& db) -{ - typedef typename Solver::MatrixType Mat; - typedef typename Mat::Scalar Scalar; - typedef typename Mat::StorageIndex StorageIndex; - - DenseRhs refX = dA.householderQr().solve(db); - { - Rhs x(A.cols(), b.cols()); - Rhs oldb = b; - - solver.compute(A); - if (solver.info() != Success) - { - std::cerr << "ERROR | sparse solver testing, factorization failed (" << typeid(Solver).name() << ")\n"; - VERIFY(solver.info() == Success); - } - x = solver.solve(b); - if (solver.info() != Success) - { - std::cerr << "WARNING | sparse solver testing: solving failed (" << typeid(Solver).name() << ")\n"; - return; - } - VERIFY(oldb.isApprox(b) && "sparse solver testing: the rhs should not be modified!"); - VERIFY(x.isApprox(refX,test_precision<Scalar>())); - - x.setZero(); - solve_with_guess(solver, b, x, x); - VERIFY(solver.info() == Success && "solving failed when using analyzePattern/factorize API"); - VERIFY(oldb.isApprox(b) && "sparse solver testing: the rhs should not be modified!"); - VERIFY(x.isApprox(refX,test_precision<Scalar>())); - - x.setZero(); - // test the analyze/factorize API - solver.analyzePattern(A); - solver.factorize(A); - VERIFY(solver.info() == Success && "factorization failed when using analyzePattern/factorize API"); - x = solver.solve(b); - VERIFY(solver.info() == Success && "solving failed when using analyzePattern/factorize API"); - VERIFY(oldb.isApprox(b) && "sparse solver testing: the rhs should not be modified!"); - VERIFY(x.isApprox(refX,test_precision<Scalar>())); - - x.setZero(); - // test with Map - MappedSparseMatrix<Scalar,Mat::Options,StorageIndex> Am(A.rows(), A.cols(), A.nonZeros(), const_cast<StorageIndex*>(A.outerIndexPtr()), const_cast<StorageIndex*>(A.innerIndexPtr()), const_cast<Scalar*>(A.valuePtr())); - solver.compute(Am); - VERIFY(solver.info() == Success && "factorization failed when using Map"); - DenseRhs dx(refX); - dx.setZero(); - Map<DenseRhs> xm(dx.data(), dx.rows(), dx.cols()); - Map<const DenseRhs> bm(db.data(), db.rows(), db.cols()); - xm = solver.solve(bm); - VERIFY(solver.info() == Success && "solving failed when using Map"); - VERIFY(oldb.isApprox(bm) && "sparse solver testing: the rhs should not be modified!"); - VERIFY(xm.isApprox(refX,test_precision<Scalar>())); - } - - // if not too large, do some extra check: - if(A.rows()<2000) - { - // test initialization ctor - { - Rhs x(b.rows(), b.cols()); - Solver solver2(A); - VERIFY(solver2.info() == Success); - x = solver2.solve(b); - VERIFY(x.isApprox(refX,test_precision<Scalar>())); - } - - // test dense Block as the result and rhs: - { - DenseRhs x(refX.rows(), refX.cols()); - DenseRhs oldb(db); - x.setZero(); - x.block(0,0,x.rows(),x.cols()) = solver.solve(db.block(0,0,db.rows(),db.cols())); - VERIFY(oldb.isApprox(db) && "sparse solver testing: the rhs should not be modified!"); - VERIFY(x.isApprox(refX,test_precision<Scalar>())); - } - - // test uncompressed inputs - { - Mat A2 = A; - A2.reserve((ArrayXf::Random(A.outerSize())+2).template cast<typename Mat::StorageIndex>().eval()); - solver.compute(A2); - Rhs x = solver.solve(b); - VERIFY(x.isApprox(refX,test_precision<Scalar>())); - } - - // test expression as input - { - solver.compute(0.5*(A+A)); - Rhs x = solver.solve(b); - VERIFY(x.isApprox(refX,test_precision<Scalar>())); - - Solver solver2(0.5*(A+A)); - Rhs x2 = solver2.solve(b); - VERIFY(x2.isApprox(refX,test_precision<Scalar>())); - } - } -} - -template<typename Solver, typename Rhs> -void check_sparse_solving_real_cases(Solver& solver, const typename Solver::MatrixType& A, const Rhs& b, const typename Solver::MatrixType& fullA, const Rhs& refX) -{ - typedef typename Solver::MatrixType Mat; - typedef typename Mat::Scalar Scalar; - typedef typename Mat::RealScalar RealScalar; - - Rhs x(A.cols(), b.cols()); - - solver.compute(A); - if (solver.info() != Success) - { - std::cerr << "ERROR | sparse solver testing, factorization failed (" << typeid(Solver).name() << ")\n"; - VERIFY(solver.info() == Success); - } - x = solver.solve(b); - - if (solver.info() != Success) - { - std::cerr << "WARNING | sparse solver testing, solving failed (" << typeid(Solver).name() << ")\n"; - return; - } - - RealScalar res_error = (fullA*x-b).norm()/b.norm(); - VERIFY( (res_error <= test_precision<Scalar>() ) && "sparse solver failed without noticing it"); - - - if(refX.size() != 0 && (refX - x).norm()/refX.norm() > test_precision<Scalar>()) - { - std::cerr << "WARNING | found solution is different from the provided reference one\n"; - } - -} -template<typename Solver, typename DenseMat> -void check_sparse_determinant(Solver& solver, const typename Solver::MatrixType& A, const DenseMat& dA) -{ - typedef typename Solver::MatrixType Mat; - typedef typename Mat::Scalar Scalar; - - solver.compute(A); - if (solver.info() != Success) - { - std::cerr << "WARNING | sparse solver testing: factorization failed (check_sparse_determinant)\n"; - return; - } - - Scalar refDet = dA.determinant(); - VERIFY_IS_APPROX(refDet,solver.determinant()); -} -template<typename Solver, typename DenseMat> -void check_sparse_abs_determinant(Solver& solver, const typename Solver::MatrixType& A, const DenseMat& dA) -{ - using std::abs; - typedef typename Solver::MatrixType Mat; - typedef typename Mat::Scalar Scalar; - - solver.compute(A); - if (solver.info() != Success) - { - std::cerr << "WARNING | sparse solver testing: factorization failed (check_sparse_abs_determinant)\n"; - return; - } - - Scalar refDet = abs(dA.determinant()); - VERIFY_IS_APPROX(refDet,solver.absDeterminant()); -} - -template<typename Solver, typename DenseMat> -int generate_sparse_spd_problem(Solver& , typename Solver::MatrixType& A, typename Solver::MatrixType& halfA, DenseMat& dA, int maxSize = 300) -{ - typedef typename Solver::MatrixType Mat; - typedef typename Mat::Scalar Scalar; - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - - int size = internal::random<int>(1,maxSize); - double density = (std::max)(8./(size*size), 0.01); - - Mat M(size, size); - DenseMatrix dM(size, size); - - initSparse<Scalar>(density, dM, M, ForceNonZeroDiag); - - A = M * M.adjoint(); - dA = dM * dM.adjoint(); - - halfA.resize(size,size); - if(Solver::UpLo==(Lower|Upper)) - halfA = A; - else - halfA.template selfadjointView<Solver::UpLo>().rankUpdate(M); - - return size; -} - - -#ifdef TEST_REAL_CASES -template<typename Scalar> -inline std::string get_matrixfolder() -{ - std::string mat_folder = TEST_REAL_CASES; - if( internal::is_same<Scalar, std::complex<float> >::value || internal::is_same<Scalar, std::complex<double> >::value ) - mat_folder = mat_folder + static_cast<std::string>("/complex/"); - else - mat_folder = mat_folder + static_cast<std::string>("/real/"); - return mat_folder; -} -std::string sym_to_string(int sym) -{ - if(sym==Symmetric) return "Symmetric "; - if(sym==SPD) return "SPD "; - return ""; -} -template<typename Derived> -std::string solver_stats(const IterativeSolverBase<Derived> &solver) -{ - std::stringstream ss; - ss << solver.iterations() << " iters, error: " << solver.error(); - return ss.str(); -} -template<typename Derived> -std::string solver_stats(const SparseSolverBase<Derived> &/*solver*/) -{ - return ""; -} -#endif - -template<typename Solver> void check_sparse_spd_solving(Solver& solver, int maxSize = 300, int maxRealWorldSize = 100000) -{ - typedef typename Solver::MatrixType Mat; - typedef typename Mat::Scalar Scalar; - typedef typename Mat::StorageIndex StorageIndex; - typedef SparseMatrix<Scalar,ColMajor, StorageIndex> SpMat; - typedef SparseVector<Scalar, 0, StorageIndex> SpVec; - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - typedef Matrix<Scalar,Dynamic,1> DenseVector; - - // generate the problem - Mat A, halfA; - DenseMatrix dA; - for (int i = 0; i < g_repeat; i++) { - int size = generate_sparse_spd_problem(solver, A, halfA, dA, maxSize); - - // generate the right hand sides - int rhsCols = internal::random<int>(1,16); - double density = (std::max)(8./(size*rhsCols), 0.1); - SpMat B(size,rhsCols); - DenseVector b = DenseVector::Random(size); - DenseMatrix dB(size,rhsCols); - initSparse<Scalar>(density, dB, B, ForceNonZeroDiag); - SpVec c = B.col(0); - DenseVector dc = dB.col(0); - - CALL_SUBTEST( check_sparse_solving(solver, A, b, dA, b) ); - CALL_SUBTEST( check_sparse_solving(solver, halfA, b, dA, b) ); - CALL_SUBTEST( check_sparse_solving(solver, A, dB, dA, dB) ); - CALL_SUBTEST( check_sparse_solving(solver, halfA, dB, dA, dB) ); - CALL_SUBTEST( check_sparse_solving(solver, A, B, dA, dB) ); - CALL_SUBTEST( check_sparse_solving(solver, halfA, B, dA, dB) ); - CALL_SUBTEST( check_sparse_solving(solver, A, c, dA, dc) ); - CALL_SUBTEST( check_sparse_solving(solver, halfA, c, dA, dc) ); - - // check only once - if(i==0) - { - b = DenseVector::Zero(size); - check_sparse_solving(solver, A, b, dA, b); - } - } - - // First, get the folder -#ifdef TEST_REAL_CASES - // Test real problems with double precision only - if (internal::is_same<typename NumTraits<Scalar>::Real, double>::value) - { - std::string mat_folder = get_matrixfolder<Scalar>(); - MatrixMarketIterator<Scalar> it(mat_folder); - for (; it; ++it) - { - if (it.sym() == SPD){ - A = it.matrix(); - if(A.diagonal().size() <= maxRealWorldSize) - { - DenseVector b = it.rhs(); - DenseVector refX = it.refX(); - PermutationMatrix<Dynamic, Dynamic, StorageIndex> pnull; - halfA.resize(A.rows(), A.cols()); - if(Solver::UpLo == (Lower|Upper)) - halfA = A; - else - halfA.template selfadjointView<Solver::UpLo>() = A.template triangularView<Eigen::Lower>().twistedBy(pnull); - - std::cout << "INFO | Testing " << sym_to_string(it.sym()) << "sparse problem " << it.matname() - << " (" << A.rows() << "x" << A.cols() << ") using " << typeid(Solver).name() << "..." << std::endl; - CALL_SUBTEST( check_sparse_solving_real_cases(solver, A, b, A, refX) ); - std::string stats = solver_stats(solver); - if(stats.size()>0) - std::cout << "INFO | " << stats << std::endl; - CALL_SUBTEST( check_sparse_solving_real_cases(solver, halfA, b, A, refX) ); - } - else - { - std::cout << "INFO | Skip sparse problem \"" << it.matname() << "\" (too large)" << std::endl; - } - } - } - } -#else - EIGEN_UNUSED_VARIABLE(maxRealWorldSize); -#endif -} - -template<typename Solver> void check_sparse_spd_determinant(Solver& solver) -{ - typedef typename Solver::MatrixType Mat; - typedef typename Mat::Scalar Scalar; - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - - // generate the problem - Mat A, halfA; - DenseMatrix dA; - generate_sparse_spd_problem(solver, A, halfA, dA, 30); - - for (int i = 0; i < g_repeat; i++) { - check_sparse_determinant(solver, A, dA); - check_sparse_determinant(solver, halfA, dA ); - } -} - -template<typename Solver, typename DenseMat> -Index generate_sparse_square_problem(Solver&, typename Solver::MatrixType& A, DenseMat& dA, int maxSize = 300, int options = ForceNonZeroDiag) -{ - typedef typename Solver::MatrixType Mat; - typedef typename Mat::Scalar Scalar; - - Index size = internal::random<int>(1,maxSize); - double density = (std::max)(8./(size*size), 0.01); - - A.resize(size,size); - dA.resize(size,size); - - initSparse<Scalar>(density, dA, A, options); - - return size; -} - - -struct prune_column { - Index m_col; - prune_column(Index col) : m_col(col) {} - template<class Scalar> - bool operator()(Index, Index col, const Scalar&) const { - return col != m_col; - } -}; - - -template<typename Solver> void check_sparse_square_solving(Solver& solver, int maxSize = 300, int maxRealWorldSize = 100000, bool checkDeficient = false) -{ - typedef typename Solver::MatrixType Mat; - typedef typename Mat::Scalar Scalar; - typedef SparseMatrix<Scalar,ColMajor, typename Mat::StorageIndex> SpMat; - typedef SparseVector<Scalar, 0, typename Mat::StorageIndex> SpVec; - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - typedef Matrix<Scalar,Dynamic,1> DenseVector; - - int rhsCols = internal::random<int>(1,16); - - Mat A; - DenseMatrix dA; - for (int i = 0; i < g_repeat; i++) { - Index size = generate_sparse_square_problem(solver, A, dA, maxSize); - - A.makeCompressed(); - DenseVector b = DenseVector::Random(size); - DenseMatrix dB(size,rhsCols); - SpMat B(size,rhsCols); - double density = (std::max)(8./(size*rhsCols), 0.1); - initSparse<Scalar>(density, dB, B, ForceNonZeroDiag); - B.makeCompressed(); - SpVec c = B.col(0); - DenseVector dc = dB.col(0); - CALL_SUBTEST(check_sparse_solving(solver, A, b, dA, b)); - CALL_SUBTEST(check_sparse_solving(solver, A, dB, dA, dB)); - CALL_SUBTEST(check_sparse_solving(solver, A, B, dA, dB)); - CALL_SUBTEST(check_sparse_solving(solver, A, c, dA, dc)); - - // check only once - if(i==0) - { - b = DenseVector::Zero(size); - check_sparse_solving(solver, A, b, dA, b); - } - // regression test for Bug 792 (structurally rank deficient matrices): - if(checkDeficient && size>1) { - Index col = internal::random<int>(0,int(size-1)); - A.prune(prune_column(col)); - solver.compute(A); - VERIFY_IS_EQUAL(solver.info(), NumericalIssue); - } - } - - // First, get the folder -#ifdef TEST_REAL_CASES - // Test real problems with double precision only - if (internal::is_same<typename NumTraits<Scalar>::Real, double>::value) - { - std::string mat_folder = get_matrixfolder<Scalar>(); - MatrixMarketIterator<Scalar> it(mat_folder); - for (; it; ++it) - { - A = it.matrix(); - if(A.diagonal().size() <= maxRealWorldSize) - { - DenseVector b = it.rhs(); - DenseVector refX = it.refX(); - std::cout << "INFO | Testing " << sym_to_string(it.sym()) << "sparse problem " << it.matname() - << " (" << A.rows() << "x" << A.cols() << ") using " << typeid(Solver).name() << "..." << std::endl; - CALL_SUBTEST(check_sparse_solving_real_cases(solver, A, b, A, refX)); - std::string stats = solver_stats(solver); - if(stats.size()>0) - std::cout << "INFO | " << stats << std::endl; - } - else - { - std::cout << "INFO | SKIP sparse problem \"" << it.matname() << "\" (too large)" << std::endl; - } - } - } -#else - EIGEN_UNUSED_VARIABLE(maxRealWorldSize); -#endif - -} - -template<typename Solver> void check_sparse_square_determinant(Solver& solver) -{ - typedef typename Solver::MatrixType Mat; - typedef typename Mat::Scalar Scalar; - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - - for (int i = 0; i < g_repeat; i++) { - // generate the problem - Mat A; - DenseMatrix dA; - - int size = internal::random<int>(1,30); - dA.setRandom(size,size); - - dA = (dA.array().abs()<0.3).select(0,dA); - dA.diagonal() = (dA.diagonal().array()==0).select(1,dA.diagonal()); - A = dA.sparseView(); - A.makeCompressed(); - - check_sparse_determinant(solver, A, dA); - } -} - -template<typename Solver> void check_sparse_square_abs_determinant(Solver& solver) -{ - typedef typename Solver::MatrixType Mat; - typedef typename Mat::Scalar Scalar; - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - - for (int i = 0; i < g_repeat; i++) { - // generate the problem - Mat A; - DenseMatrix dA; - generate_sparse_square_problem(solver, A, dA, 30); - A.makeCompressed(); - check_sparse_abs_determinant(solver, A, dA); - } -} - -template<typename Solver, typename DenseMat> -void generate_sparse_leastsquare_problem(Solver&, typename Solver::MatrixType& A, DenseMat& dA, int maxSize = 300, int options = ForceNonZeroDiag) -{ - typedef typename Solver::MatrixType Mat; - typedef typename Mat::Scalar Scalar; - - int rows = internal::random<int>(1,maxSize); - int cols = internal::random<int>(1,rows); - double density = (std::max)(8./(rows*cols), 0.01); - - A.resize(rows,cols); - dA.resize(rows,cols); - - initSparse<Scalar>(density, dA, A, options); -} - -template<typename Solver> void check_sparse_leastsquare_solving(Solver& solver) -{ - typedef typename Solver::MatrixType Mat; - typedef typename Mat::Scalar Scalar; - typedef SparseMatrix<Scalar,ColMajor, typename Mat::StorageIndex> SpMat; - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - typedef Matrix<Scalar,Dynamic,1> DenseVector; - - int rhsCols = internal::random<int>(1,16); - - Mat A; - DenseMatrix dA; - for (int i = 0; i < g_repeat; i++) { - generate_sparse_leastsquare_problem(solver, A, dA); - - A.makeCompressed(); - DenseVector b = DenseVector::Random(A.rows()); - DenseMatrix dB(A.rows(),rhsCols); - SpMat B(A.rows(),rhsCols); - double density = (std::max)(8./(A.rows()*rhsCols), 0.1); - initSparse<Scalar>(density, dB, B, ForceNonZeroDiag); - B.makeCompressed(); - check_sparse_solving(solver, A, b, dA, b); - check_sparse_solving(solver, A, dB, dA, dB); - check_sparse_solving(solver, A, B, dA, dB); - - // check only once - if(i==0) - { - b = DenseVector::Zero(A.rows()); - check_sparse_solving(solver, A, b, dA, b); - } - } -} diff --git a/eigen/test/sparse_solvers.cpp b/eigen/test/sparse_solvers.cpp deleted file mode 100644 index 3a8873d..0000000 --- a/eigen/test/sparse_solvers.cpp +++ /dev/null @@ -1,112 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "sparse.h" - -template<typename Scalar> void -initSPD(double density, - Matrix<Scalar,Dynamic,Dynamic>& refMat, - SparseMatrix<Scalar>& sparseMat) -{ - Matrix<Scalar,Dynamic,Dynamic> aux(refMat.rows(),refMat.cols()); - initSparse(density,refMat,sparseMat); - refMat = refMat * refMat.adjoint(); - for (int k=0; k<2; ++k) - { - initSparse(density,aux,sparseMat,ForceNonZeroDiag); - refMat += aux * aux.adjoint(); - } - sparseMat.setZero(); - for (int j=0 ; j<sparseMat.cols(); ++j) - for (int i=j ; i<sparseMat.rows(); ++i) - if (refMat(i,j)!=Scalar(0)) - sparseMat.insert(i,j) = refMat(i,j); - sparseMat.finalize(); -} - -template<typename Scalar> void sparse_solvers(int rows, int cols) -{ - double density = (std::max)(8./(rows*cols), 0.01); - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - typedef Matrix<Scalar,Dynamic,1> DenseVector; - // Scalar eps = 1e-6; - - DenseVector vec1 = DenseVector::Random(rows); - - std::vector<Vector2i> zeroCoords; - std::vector<Vector2i> nonzeroCoords; - - // test triangular solver - { - DenseVector vec2 = vec1, vec3 = vec1; - SparseMatrix<Scalar> m2(rows, cols); - DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols); - - // lower - dense - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular, &zeroCoords, &nonzeroCoords); - VERIFY_IS_APPROX(refMat2.template triangularView<Lower>().solve(vec2), - m2.template triangularView<Lower>().solve(vec3)); - - // upper - dense - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular, &zeroCoords, &nonzeroCoords); - VERIFY_IS_APPROX(refMat2.template triangularView<Upper>().solve(vec2), - m2.template triangularView<Upper>().solve(vec3)); - VERIFY_IS_APPROX(refMat2.conjugate().template triangularView<Upper>().solve(vec2), - m2.conjugate().template triangularView<Upper>().solve(vec3)); - { - SparseMatrix<Scalar> cm2(m2); - //Index rows, Index cols, Index nnz, Index* outerIndexPtr, Index* innerIndexPtr, Scalar* valuePtr - MappedSparseMatrix<Scalar> mm2(rows, cols, cm2.nonZeros(), cm2.outerIndexPtr(), cm2.innerIndexPtr(), cm2.valuePtr()); - VERIFY_IS_APPROX(refMat2.conjugate().template triangularView<Upper>().solve(vec2), - mm2.conjugate().template triangularView<Upper>().solve(vec3)); - } - - // lower - transpose - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular, &zeroCoords, &nonzeroCoords); - VERIFY_IS_APPROX(refMat2.transpose().template triangularView<Upper>().solve(vec2), - m2.transpose().template triangularView<Upper>().solve(vec3)); - - // upper - transpose - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular, &zeroCoords, &nonzeroCoords); - VERIFY_IS_APPROX(refMat2.transpose().template triangularView<Lower>().solve(vec2), - m2.transpose().template triangularView<Lower>().solve(vec3)); - - SparseMatrix<Scalar> matB(rows, rows); - DenseMatrix refMatB = DenseMatrix::Zero(rows, rows); - - // lower - sparse - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular); - initSparse<Scalar>(density, refMatB, matB); - refMat2.template triangularView<Lower>().solveInPlace(refMatB); - m2.template triangularView<Lower>().solveInPlace(matB); - VERIFY_IS_APPROX(matB.toDense(), refMatB); - - // upper - sparse - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular); - initSparse<Scalar>(density, refMatB, matB); - refMat2.template triangularView<Upper>().solveInPlace(refMatB); - m2.template triangularView<Upper>().solveInPlace(matB); - VERIFY_IS_APPROX(matB, refMatB); - - // test deprecated API - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular, &zeroCoords, &nonzeroCoords); - VERIFY_IS_APPROX(refMat2.template triangularView<Lower>().solve(vec2), - m2.template triangularView<Lower>().solve(vec3)); - } -} - -void test_sparse_solvers() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1(sparse_solvers<double>(8, 8) ); - int s = internal::random<int>(1,300); - CALL_SUBTEST_2(sparse_solvers<std::complex<double> >(s,s) ); - CALL_SUBTEST_1(sparse_solvers<double>(s,s) ); - } -} diff --git a/eigen/test/sparse_vector.cpp b/eigen/test/sparse_vector.cpp deleted file mode 100644 index b3e1dda..0000000 --- a/eigen/test/sparse_vector.cpp +++ /dev/null @@ -1,163 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "sparse.h" - -template<typename Scalar,typename StorageIndex> void sparse_vector(int rows, int cols) -{ - double densityMat = (std::max)(8./(rows*cols), 0.01); - double densityVec = (std::max)(8./(rows), 0.1); - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - typedef Matrix<Scalar,Dynamic,1> DenseVector; - typedef SparseVector<Scalar,0,StorageIndex> SparseVectorType; - typedef SparseMatrix<Scalar,0,StorageIndex> SparseMatrixType; - Scalar eps = 1e-6; - - SparseMatrixType m1(rows,rows); - SparseVectorType v1(rows), v2(rows), v3(rows); - DenseMatrix refM1 = DenseMatrix::Zero(rows, rows); - DenseVector refV1 = DenseVector::Random(rows), - refV2 = DenseVector::Random(rows), - refV3 = DenseVector::Random(rows); - - std::vector<int> zerocoords, nonzerocoords; - initSparse<Scalar>(densityVec, refV1, v1, &zerocoords, &nonzerocoords); - initSparse<Scalar>(densityMat, refM1, m1); - - initSparse<Scalar>(densityVec, refV2, v2); - initSparse<Scalar>(densityVec, refV3, v3); - - Scalar s1 = internal::random<Scalar>(); - - // test coeff and coeffRef - for (unsigned int i=0; i<zerocoords.size(); ++i) - { - VERIFY_IS_MUCH_SMALLER_THAN( v1.coeff(zerocoords[i]), eps ); - //VERIFY_RAISES_ASSERT( v1.coeffRef(zerocoords[i]) = 5 ); - } - { - VERIFY(int(nonzerocoords.size()) == v1.nonZeros()); - int j=0; - for (typename SparseVectorType::InnerIterator it(v1); it; ++it,++j) - { - VERIFY(nonzerocoords[j]==it.index()); - VERIFY(it.value()==v1.coeff(it.index())); - VERIFY(it.value()==refV1.coeff(it.index())); - } - } - VERIFY_IS_APPROX(v1, refV1); - - // test coeffRef with reallocation - { - SparseVectorType v4(rows); - DenseVector v5 = DenseVector::Zero(rows); - for(int k=0; k<rows; ++k) - { - int i = internal::random<int>(0,rows-1); - Scalar v = internal::random<Scalar>(); - v4.coeffRef(i) += v; - v5.coeffRef(i) += v; - } - VERIFY_IS_APPROX(v4,v5); - } - - v1.coeffRef(nonzerocoords[0]) = Scalar(5); - refV1.coeffRef(nonzerocoords[0]) = Scalar(5); - VERIFY_IS_APPROX(v1, refV1); - - VERIFY_IS_APPROX(v1+v2, refV1+refV2); - VERIFY_IS_APPROX(v1+v2+v3, refV1+refV2+refV3); - - VERIFY_IS_APPROX(v1*s1-v2, refV1*s1-refV2); - - VERIFY_IS_APPROX(v1*=s1, refV1*=s1); - VERIFY_IS_APPROX(v1/=s1, refV1/=s1); - - VERIFY_IS_APPROX(v1+=v2, refV1+=refV2); - VERIFY_IS_APPROX(v1-=v2, refV1-=refV2); - - VERIFY_IS_APPROX(v1.dot(v2), refV1.dot(refV2)); - VERIFY_IS_APPROX(v1.dot(refV2), refV1.dot(refV2)); - - VERIFY_IS_APPROX(m1*v2, refM1*refV2); - VERIFY_IS_APPROX(v1.dot(m1*v2), refV1.dot(refM1*refV2)); - { - int i = internal::random<int>(0,rows-1); - VERIFY_IS_APPROX(v1.dot(m1.col(i)), refV1.dot(refM1.col(i))); - } - - - VERIFY_IS_APPROX(v1.squaredNorm(), refV1.squaredNorm()); - - VERIFY_IS_APPROX(v1.blueNorm(), refV1.blueNorm()); - - // test aliasing - VERIFY_IS_APPROX((v1 = -v1), (refV1 = -refV1)); - VERIFY_IS_APPROX((v1 = v1.transpose()), (refV1 = refV1.transpose().eval())); - VERIFY_IS_APPROX((v1 += -v1), (refV1 += -refV1)); - - // sparse matrix to sparse vector - SparseMatrixType mv1; - VERIFY_IS_APPROX((mv1=v1),v1); - VERIFY_IS_APPROX(mv1,(v1=mv1)); - VERIFY_IS_APPROX(mv1,(v1=mv1.transpose())); - - // check copy to dense vector with transpose - refV3.resize(0); - VERIFY_IS_APPROX(refV3 = v1.transpose(),v1.toDense()); - VERIFY_IS_APPROX(DenseVector(v1),v1.toDense()); - - // test conservative resize - { - std::vector<StorageIndex> inc; - if(rows > 3) - inc.push_back(-3); - inc.push_back(0); - inc.push_back(3); - inc.push_back(1); - inc.push_back(10); - - for(std::size_t i = 0; i< inc.size(); i++) { - StorageIndex incRows = inc[i]; - SparseVectorType vec1(rows); - DenseVector refVec1 = DenseVector::Zero(rows); - initSparse<Scalar>(densityVec, refVec1, vec1); - - vec1.conservativeResize(rows+incRows); - refVec1.conservativeResize(rows+incRows); - if (incRows > 0) refVec1.tail(incRows).setZero(); - - VERIFY_IS_APPROX(vec1, refVec1); - - // Insert new values - if (incRows > 0) - vec1.insert(vec1.rows()-1) = refVec1(refVec1.rows()-1) = 1; - - VERIFY_IS_APPROX(vec1, refVec1); - } - } - -} - -void test_sparse_vector() -{ - for(int i = 0; i < g_repeat; i++) { - int r = Eigen::internal::random<int>(1,500), c = Eigen::internal::random<int>(1,500); - if(Eigen::internal::random<int>(0,4) == 0) { - r = c; // check square matrices in 25% of tries - } - EIGEN_UNUSED_VARIABLE(r+c); - - CALL_SUBTEST_1(( sparse_vector<double,int>(8, 8) )); - CALL_SUBTEST_2(( sparse_vector<std::complex<double>, int>(r, c) )); - CALL_SUBTEST_1(( sparse_vector<double,long int>(r, c) )); - CALL_SUBTEST_1(( sparse_vector<double,short>(r, c) )); - } -} - diff --git a/eigen/test/sparselu.cpp b/eigen/test/sparselu.cpp deleted file mode 100644 index bd000ba..0000000 --- a/eigen/test/sparselu.cpp +++ /dev/null @@ -1,45 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// SparseLU solve does not accept column major matrices for the destination. -// However, as expected, the generic check_sparse_square_solving routines produces row-major -// rhs and destination matrices when compiled with EIGEN_DEFAULT_TO_ROW_MAJOR - -#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR -#undef EIGEN_DEFAULT_TO_ROW_MAJOR -#endif - -#include "sparse_solver.h" -#include <Eigen/SparseLU> -#include <unsupported/Eigen/SparseExtra> - -template<typename T> void test_sparselu_T() -{ - SparseLU<SparseMatrix<T, ColMajor> /*, COLAMDOrdering<int>*/ > sparselu_colamd; // COLAMDOrdering is the default - SparseLU<SparseMatrix<T, ColMajor>, AMDOrdering<int> > sparselu_amd; - SparseLU<SparseMatrix<T, ColMajor, long int>, NaturalOrdering<long int> > sparselu_natural; - - check_sparse_square_solving(sparselu_colamd, 300, 100000, true); - check_sparse_square_solving(sparselu_amd, 300, 10000, true); - check_sparse_square_solving(sparselu_natural, 300, 2000, true); - - check_sparse_square_abs_determinant(sparselu_colamd); - check_sparse_square_abs_determinant(sparselu_amd); - - check_sparse_square_determinant(sparselu_colamd); - check_sparse_square_determinant(sparselu_amd); -} - -void test_sparselu() -{ - CALL_SUBTEST_1(test_sparselu_T<float>()); - CALL_SUBTEST_2(test_sparselu_T<double>()); - CALL_SUBTEST_3(test_sparselu_T<std::complex<float> >()); - CALL_SUBTEST_4(test_sparselu_T<std::complex<double> >()); -} diff --git a/eigen/test/sparseqr.cpp b/eigen/test/sparseqr.cpp deleted file mode 100644 index f0e721f..0000000 --- a/eigen/test/sparseqr.cpp +++ /dev/null @@ -1,128 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Desire Nuentsa Wakam <desire.nuentsa_wakam@inria.fr> -// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -#include "sparse.h" -#include <Eigen/SparseQR> - -template<typename MatrixType,typename DenseMat> -int generate_sparse_rectangular_problem(MatrixType& A, DenseMat& dA, int maxRows = 300, int maxCols = 150) -{ - eigen_assert(maxRows >= maxCols); - typedef typename MatrixType::Scalar Scalar; - int rows = internal::random<int>(1,maxRows); - int cols = internal::random<int>(1,maxCols); - double density = (std::max)(8./(rows*cols), 0.01); - - A.resize(rows,cols); - dA.resize(rows,cols); - initSparse<Scalar>(density, dA, A,ForceNonZeroDiag); - A.makeCompressed(); - int nop = internal::random<int>(0, internal::random<double>(0,1) > 0.5 ? cols/2 : 0); - for(int k=0; k<nop; ++k) - { - int j0 = internal::random<int>(0,cols-1); - int j1 = internal::random<int>(0,cols-1); - Scalar s = internal::random<Scalar>(); - A.col(j0) = s * A.col(j1); - dA.col(j0) = s * dA.col(j1); - } - -// if(rows<cols) { -// A.conservativeResize(cols,cols); -// dA.conservativeResize(cols,cols); -// dA.bottomRows(cols-rows).setZero(); -// } - - return rows; -} - -template<typename Scalar> void test_sparseqr_scalar() -{ - typedef SparseMatrix<Scalar,ColMajor> MatrixType; - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMat; - typedef Matrix<Scalar,Dynamic,1> DenseVector; - MatrixType A; - DenseMat dA; - DenseVector refX,x,b; - SparseQR<MatrixType, COLAMDOrdering<int> > solver; - generate_sparse_rectangular_problem(A,dA); - - b = dA * DenseVector::Random(A.cols()); - solver.compute(A); - - // Q should be MxM - VERIFY_IS_EQUAL(solver.matrixQ().rows(), A.rows()); - VERIFY_IS_EQUAL(solver.matrixQ().cols(), A.rows()); - - // R should be MxN - VERIFY_IS_EQUAL(solver.matrixR().rows(), A.rows()); - VERIFY_IS_EQUAL(solver.matrixR().cols(), A.cols()); - - // Q and R can be multiplied - DenseMat recoveredA = solver.matrixQ() - * DenseMat(solver.matrixR().template triangularView<Upper>()) - * solver.colsPermutation().transpose(); - VERIFY_IS_EQUAL(recoveredA.rows(), A.rows()); - VERIFY_IS_EQUAL(recoveredA.cols(), A.cols()); - - // and in the full rank case the original matrix is recovered - if (solver.rank() == A.cols()) - { - VERIFY_IS_APPROX(A, recoveredA); - } - - if(internal::random<float>(0,1)>0.5f) - solver.factorize(A); // this checks that calling analyzePattern is not needed if the pattern do not change. - if (solver.info() != Success) - { - std::cerr << "sparse QR factorization failed\n"; - exit(0); - return; - } - x = solver.solve(b); - if (solver.info() != Success) - { - std::cerr << "sparse QR factorization failed\n"; - exit(0); - return; - } - - VERIFY_IS_APPROX(A * x, b); - - //Compare with a dense QR solver - ColPivHouseholderQR<DenseMat> dqr(dA); - refX = dqr.solve(b); - - VERIFY_IS_EQUAL(dqr.rank(), solver.rank()); - if(solver.rank()==A.cols()) // full rank - VERIFY_IS_APPROX(x, refX); -// else -// VERIFY((dA * refX - b).norm() * 2 > (A * x - b).norm() ); - - // Compute explicitly the matrix Q - MatrixType Q, QtQ, idM; - Q = solver.matrixQ(); - //Check ||Q' * Q - I || - QtQ = Q * Q.adjoint(); - idM.resize(Q.rows(), Q.rows()); idM.setIdentity(); - VERIFY(idM.isApprox(QtQ)); - - // Q to dense - DenseMat dQ; - dQ = solver.matrixQ(); - VERIFY_IS_APPROX(Q, dQ); -} -void test_sparseqr() -{ - for(int i=0; i<g_repeat; ++i) - { - CALL_SUBTEST_1(test_sparseqr_scalar<double>()); - CALL_SUBTEST_2(test_sparseqr_scalar<std::complex<double> >()); - } -} - diff --git a/eigen/test/special_numbers.cpp b/eigen/test/special_numbers.cpp deleted file mode 100644 index 2f1b704..0000000 --- a/eigen/test/special_numbers.cpp +++ /dev/null @@ -1,58 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2013 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename Scalar> void special_numbers() -{ - typedef Matrix<Scalar, Dynamic,Dynamic> MatType; - int rows = internal::random<int>(1,300); - int cols = internal::random<int>(1,300); - - Scalar nan = std::numeric_limits<Scalar>::quiet_NaN(); - Scalar inf = std::numeric_limits<Scalar>::infinity(); - Scalar s1 = internal::random<Scalar>(); - - MatType m1 = MatType::Random(rows,cols), - mnan = MatType::Random(rows,cols), - minf = MatType::Random(rows,cols), - mboth = MatType::Random(rows,cols); - - int n = internal::random<int>(1,10); - for(int k=0; k<n; ++k) - { - mnan(internal::random<int>(0,rows-1), internal::random<int>(0,cols-1)) = nan; - minf(internal::random<int>(0,rows-1), internal::random<int>(0,cols-1)) = inf; - } - mboth = mnan + minf; - - VERIFY(!m1.hasNaN()); - VERIFY(m1.allFinite()); - - VERIFY(mnan.hasNaN()); - VERIFY((s1*mnan).hasNaN()); - VERIFY(!minf.hasNaN()); - VERIFY(!(2*minf).hasNaN()); - VERIFY(mboth.hasNaN()); - VERIFY(mboth.array().hasNaN()); - - VERIFY(!mnan.allFinite()); - VERIFY(!minf.allFinite()); - VERIFY(!(minf-mboth).allFinite()); - VERIFY(!mboth.allFinite()); - VERIFY(!mboth.array().allFinite()); -} - -void test_special_numbers() -{ - for(int i = 0; i < 10*g_repeat; i++) { - CALL_SUBTEST_1( special_numbers<float>() ); - CALL_SUBTEST_1( special_numbers<double>() ); - } -} diff --git a/eigen/test/spqr_support.cpp b/eigen/test/spqr_support.cpp deleted file mode 100644 index 81e63b6..0000000 --- a/eigen/test/spqr_support.cpp +++ /dev/null @@ -1,64 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Desire Nuentsa Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed - -#define EIGEN_NO_DEBUG_SMALL_PRODUCT_BLOCKS -#include "sparse.h" -#include <Eigen/SPQRSupport> - - -template<typename MatrixType,typename DenseMat> -int generate_sparse_rectangular_problem(MatrixType& A, DenseMat& dA, int maxRows = 300, int maxCols = 300) -{ - eigen_assert(maxRows >= maxCols); - typedef typename MatrixType::Scalar Scalar; - int rows = internal::random<int>(1,maxRows); - int cols = internal::random<int>(1,rows); - double density = (std::max)(8./(rows*cols), 0.01); - - A.resize(rows,cols); - dA.resize(rows,cols); - initSparse<Scalar>(density, dA, A,ForceNonZeroDiag); - A.makeCompressed(); - return rows; -} - -template<typename Scalar> void test_spqr_scalar() -{ - typedef SparseMatrix<Scalar,ColMajor> MatrixType; - MatrixType A; - Matrix<Scalar,Dynamic,Dynamic> dA; - typedef Matrix<Scalar,Dynamic,1> DenseVector; - DenseVector refX,x,b; - SPQR<MatrixType> solver; - generate_sparse_rectangular_problem(A,dA); - - Index m = A.rows(); - b = DenseVector::Random(m); - solver.compute(A); - if (solver.info() != Success) - { - std::cerr << "sparse QR factorization failed\n"; - exit(0); - return; - } - x = solver.solve(b); - if (solver.info() != Success) - { - std::cerr << "sparse QR factorization failed\n"; - exit(0); - return; - } - //Compare with a dense solver - refX = dA.colPivHouseholderQr().solve(b); - VERIFY(x.isApprox(refX,test_precision<Scalar>())); -} -void test_spqr_support() -{ - CALL_SUBTEST_1(test_spqr_scalar<double>()); - CALL_SUBTEST_2(test_spqr_scalar<std::complex<double> >()); -} diff --git a/eigen/test/stable_norm.cpp b/eigen/test/stable_norm.cpp deleted file mode 100644 index ac8b129..0000000 --- a/eigen/test/stable_norm.cpp +++ /dev/null @@ -1,201 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009-2014 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename T> EIGEN_DONT_INLINE T copy(const T& x) -{ - return x; -} - -template<typename MatrixType> void stable_norm(const MatrixType& m) -{ - /* this test covers the following files: - StableNorm.h - */ - using std::sqrt; - using std::abs; - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - bool complex_real_product_ok = true; - - // Check the basic machine-dependent constants. - { - int ibeta, it, iemin, iemax; - - 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 - - VERIFY( (!(iemin > 1 - 2*it || 1+it>iemax || (it==2 && ibeta<5) || (it<=4 && ibeta <= 3 ) || it<2)) - && "the stable norm algorithm cannot be guaranteed on this computer"); - - Scalar inf = std::numeric_limits<RealScalar>::infinity(); - if(NumTraits<Scalar>::IsComplex && (numext::isnan)(inf*RealScalar(1)) ) - { - complex_real_product_ok = false; - static bool first = true; - if(first) - std::cerr << "WARNING: compiler mess up complex*real product, " << inf << " * " << 1.0 << " = " << inf*RealScalar(1) << std::endl; - first = false; - } - } - - - Index rows = m.rows(); - Index cols = m.cols(); - - // get a non-zero random factor - Scalar factor = internal::random<Scalar>(); - while(numext::abs2(factor)<RealScalar(1e-4)) - factor = internal::random<Scalar>(); - Scalar big = factor * ((std::numeric_limits<RealScalar>::max)() * RealScalar(1e-4)); - - factor = internal::random<Scalar>(); - while(numext::abs2(factor)<RealScalar(1e-4)) - factor = internal::random<Scalar>(); - Scalar small = factor * ((std::numeric_limits<RealScalar>::min)() * RealScalar(1e4)); - - Scalar one(1); - - MatrixType vzero = MatrixType::Zero(rows, cols), - vrand = MatrixType::Random(rows, cols), - vbig(rows, cols), - vsmall(rows,cols); - - vbig.fill(big); - vsmall.fill(small); - - VERIFY_IS_MUCH_SMALLER_THAN(vzero.norm(), static_cast<RealScalar>(1)); - VERIFY_IS_APPROX(vrand.stableNorm(), vrand.norm()); - VERIFY_IS_APPROX(vrand.blueNorm(), vrand.norm()); - VERIFY_IS_APPROX(vrand.hypotNorm(), vrand.norm()); - - // test with expressions as input - VERIFY_IS_APPROX((one*vrand).stableNorm(), vrand.norm()); - VERIFY_IS_APPROX((one*vrand).blueNorm(), vrand.norm()); - VERIFY_IS_APPROX((one*vrand).hypotNorm(), vrand.norm()); - VERIFY_IS_APPROX((one*vrand+one*vrand-one*vrand).stableNorm(), vrand.norm()); - VERIFY_IS_APPROX((one*vrand+one*vrand-one*vrand).blueNorm(), vrand.norm()); - VERIFY_IS_APPROX((one*vrand+one*vrand-one*vrand).hypotNorm(), vrand.norm()); - - RealScalar size = static_cast<RealScalar>(m.size()); - - // test numext::isfinite - VERIFY(!(numext::isfinite)( std::numeric_limits<RealScalar>::infinity())); - VERIFY(!(numext::isfinite)(sqrt(-abs(big)))); - - // test overflow - VERIFY((numext::isfinite)(sqrt(size)*abs(big))); - VERIFY_IS_NOT_APPROX(sqrt(copy(vbig.squaredNorm())), abs(sqrt(size)*big)); // here the default norm must fail - VERIFY_IS_APPROX(vbig.stableNorm(), sqrt(size)*abs(big)); - VERIFY_IS_APPROX(vbig.blueNorm(), sqrt(size)*abs(big)); - VERIFY_IS_APPROX(vbig.hypotNorm(), sqrt(size)*abs(big)); - - // test underflow - VERIFY((numext::isfinite)(sqrt(size)*abs(small))); - VERIFY_IS_NOT_APPROX(sqrt(copy(vsmall.squaredNorm())), abs(sqrt(size)*small)); // here the default norm must fail - VERIFY_IS_APPROX(vsmall.stableNorm(), sqrt(size)*abs(small)); - VERIFY_IS_APPROX(vsmall.blueNorm(), sqrt(size)*abs(small)); - VERIFY_IS_APPROX(vsmall.hypotNorm(), sqrt(size)*abs(small)); - - // Test compilation of cwise() version - VERIFY_IS_APPROX(vrand.colwise().stableNorm(), vrand.colwise().norm()); - VERIFY_IS_APPROX(vrand.colwise().blueNorm(), vrand.colwise().norm()); - VERIFY_IS_APPROX(vrand.colwise().hypotNorm(), vrand.colwise().norm()); - VERIFY_IS_APPROX(vrand.rowwise().stableNorm(), vrand.rowwise().norm()); - VERIFY_IS_APPROX(vrand.rowwise().blueNorm(), vrand.rowwise().norm()); - VERIFY_IS_APPROX(vrand.rowwise().hypotNorm(), vrand.rowwise().norm()); - - // test NaN, +inf, -inf - MatrixType v; - Index i = internal::random<Index>(0,rows-1); - Index j = internal::random<Index>(0,cols-1); - - // NaN - { - v = vrand; - v(i,j) = std::numeric_limits<RealScalar>::quiet_NaN(); - VERIFY(!(numext::isfinite)(v.squaredNorm())); VERIFY((numext::isnan)(v.squaredNorm())); - VERIFY(!(numext::isfinite)(v.norm())); VERIFY((numext::isnan)(v.norm())); - VERIFY(!(numext::isfinite)(v.stableNorm())); VERIFY((numext::isnan)(v.stableNorm())); - VERIFY(!(numext::isfinite)(v.blueNorm())); VERIFY((numext::isnan)(v.blueNorm())); - VERIFY(!(numext::isfinite)(v.hypotNorm())); VERIFY((numext::isnan)(v.hypotNorm())); - } - - // +inf - { - v = vrand; - v(i,j) = std::numeric_limits<RealScalar>::infinity(); - VERIFY(!(numext::isfinite)(v.squaredNorm())); VERIFY(isPlusInf(v.squaredNorm())); - VERIFY(!(numext::isfinite)(v.norm())); VERIFY(isPlusInf(v.norm())); - VERIFY(!(numext::isfinite)(v.stableNorm())); - if(complex_real_product_ok){ - VERIFY(isPlusInf(v.stableNorm())); - } - VERIFY(!(numext::isfinite)(v.blueNorm())); VERIFY(isPlusInf(v.blueNorm())); - VERIFY(!(numext::isfinite)(v.hypotNorm())); VERIFY(isPlusInf(v.hypotNorm())); - } - - // -inf - { - v = vrand; - v(i,j) = -std::numeric_limits<RealScalar>::infinity(); - VERIFY(!(numext::isfinite)(v.squaredNorm())); VERIFY(isPlusInf(v.squaredNorm())); - VERIFY(!(numext::isfinite)(v.norm())); VERIFY(isPlusInf(v.norm())); - VERIFY(!(numext::isfinite)(v.stableNorm())); - if(complex_real_product_ok) { - VERIFY(isPlusInf(v.stableNorm())); - } - VERIFY(!(numext::isfinite)(v.blueNorm())); VERIFY(isPlusInf(v.blueNorm())); - VERIFY(!(numext::isfinite)(v.hypotNorm())); VERIFY(isPlusInf(v.hypotNorm())); - } - - // mix - { - Index i2 = internal::random<Index>(0,rows-1); - Index j2 = internal::random<Index>(0,cols-1); - v = vrand; - v(i,j) = -std::numeric_limits<RealScalar>::infinity(); - v(i2,j2) = std::numeric_limits<RealScalar>::quiet_NaN(); - VERIFY(!(numext::isfinite)(v.squaredNorm())); VERIFY((numext::isnan)(v.squaredNorm())); - VERIFY(!(numext::isfinite)(v.norm())); VERIFY((numext::isnan)(v.norm())); - VERIFY(!(numext::isfinite)(v.stableNorm())); VERIFY((numext::isnan)(v.stableNorm())); - VERIFY(!(numext::isfinite)(v.blueNorm())); VERIFY((numext::isnan)(v.blueNorm())); - VERIFY(!(numext::isfinite)(v.hypotNorm())); VERIFY((numext::isnan)(v.hypotNorm())); - } - - // stableNormalize[d] - { - VERIFY_IS_APPROX(vrand.stableNormalized(), vrand.normalized()); - MatrixType vcopy(vrand); - vcopy.stableNormalize(); - VERIFY_IS_APPROX(vcopy, vrand.normalized()); - VERIFY_IS_APPROX((vrand.stableNormalized()).norm(), RealScalar(1)); - VERIFY_IS_APPROX(vcopy.norm(), RealScalar(1)); - VERIFY_IS_APPROX((vbig.stableNormalized()).norm(), RealScalar(1)); - VERIFY_IS_APPROX((vsmall.stableNormalized()).norm(), RealScalar(1)); - RealScalar big_scaling = ((std::numeric_limits<RealScalar>::max)() * RealScalar(1e-4)); - VERIFY_IS_APPROX(vbig/big_scaling, (vbig.stableNorm() * vbig.stableNormalized()).eval()/big_scaling); - VERIFY_IS_APPROX(vsmall, vsmall.stableNorm() * vsmall.stableNormalized()); - } -} - -void test_stable_norm() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( stable_norm(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( stable_norm(Vector4d()) ); - CALL_SUBTEST_3( stable_norm(VectorXd(internal::random<int>(10,2000))) ); - CALL_SUBTEST_4( stable_norm(VectorXf(internal::random<int>(10,2000))) ); - CALL_SUBTEST_5( stable_norm(VectorXcd(internal::random<int>(10,2000))) ); - } -} diff --git a/eigen/test/stddeque.cpp b/eigen/test/stddeque.cpp deleted file mode 100644 index b511c4e..0000000 --- a/eigen/test/stddeque.cpp +++ /dev/null @@ -1,130 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/StdDeque> -#include <Eigen/Geometry> - -template<typename MatrixType> -void check_stddeque_matrix(const MatrixType& m) -{ - Index rows = m.rows(); - Index cols = m.cols(); - MatrixType x = MatrixType::Random(rows,cols), y = MatrixType::Random(rows,cols); - std::deque<MatrixType,Eigen::aligned_allocator<MatrixType> > v(10, MatrixType(rows,cols)), w(20, y); - v.front() = x; - w.front() = w.back(); - VERIFY_IS_APPROX(w.front(), w.back()); - v = w; - - typename std::deque<MatrixType,Eigen::aligned_allocator<MatrixType> >::iterator vi = v.begin(); - typename std::deque<MatrixType,Eigen::aligned_allocator<MatrixType> >::iterator wi = w.begin(); - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(*vi, *wi); - ++vi; - ++wi; - } - - v.resize(21); - v.back() = x; - VERIFY_IS_APPROX(v.back(), x); - v.resize(22,y); - VERIFY_IS_APPROX(v.back(), y); - v.push_back(x); - VERIFY_IS_APPROX(v.back(), x); -} - -template<typename TransformType> -void check_stddeque_transform(const TransformType&) -{ - typedef typename TransformType::MatrixType MatrixType; - TransformType x(MatrixType::Random()), y(MatrixType::Random()); - std::deque<TransformType,Eigen::aligned_allocator<TransformType> > v(10), w(20, y); - v.front() = x; - w.front() = w.back(); - VERIFY_IS_APPROX(w.front(), w.back()); - v = w; - - typename std::deque<TransformType,Eigen::aligned_allocator<TransformType> >::iterator vi = v.begin(); - typename std::deque<TransformType,Eigen::aligned_allocator<TransformType> >::iterator wi = w.begin(); - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(*vi, *wi); - ++vi; - ++wi; - } - - v.resize(21); - v.back() = x; - VERIFY_IS_APPROX(v.back(), x); - v.resize(22,y); - VERIFY_IS_APPROX(v.back(), y); - v.push_back(x); - VERIFY_IS_APPROX(v.back(), x); -} - -template<typename QuaternionType> -void check_stddeque_quaternion(const QuaternionType&) -{ - typedef typename QuaternionType::Coefficients Coefficients; - QuaternionType x(Coefficients::Random()), y(Coefficients::Random()); - std::deque<QuaternionType,Eigen::aligned_allocator<QuaternionType> > v(10), w(20, y); - v.front() = x; - w.front() = w.back(); - VERIFY_IS_APPROX(w.front(), w.back()); - v = w; - - typename std::deque<QuaternionType,Eigen::aligned_allocator<QuaternionType> >::iterator vi = v.begin(); - typename std::deque<QuaternionType,Eigen::aligned_allocator<QuaternionType> >::iterator wi = w.begin(); - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(*vi, *wi); - ++vi; - ++wi; - } - - v.resize(21); - v.back() = x; - VERIFY_IS_APPROX(v.back(), x); - v.resize(22,y); - VERIFY_IS_APPROX(v.back(), y); - v.push_back(x); - VERIFY_IS_APPROX(v.back(), x); -} - -void test_stddeque() -{ - // some non vectorizable fixed sizes - CALL_SUBTEST_1(check_stddeque_matrix(Vector2f())); - CALL_SUBTEST_1(check_stddeque_matrix(Matrix3f())); - CALL_SUBTEST_2(check_stddeque_matrix(Matrix3d())); - - // some vectorizable fixed sizes - CALL_SUBTEST_1(check_stddeque_matrix(Matrix2f())); - CALL_SUBTEST_1(check_stddeque_matrix(Vector4f())); - CALL_SUBTEST_1(check_stddeque_matrix(Matrix4f())); - CALL_SUBTEST_2(check_stddeque_matrix(Matrix4d())); - - // some dynamic sizes - CALL_SUBTEST_3(check_stddeque_matrix(MatrixXd(1,1))); - CALL_SUBTEST_3(check_stddeque_matrix(VectorXd(20))); - CALL_SUBTEST_3(check_stddeque_matrix(RowVectorXf(20))); - CALL_SUBTEST_3(check_stddeque_matrix(MatrixXcf(10,10))); - - // some Transform - CALL_SUBTEST_4(check_stddeque_transform(Affine2f())); - CALL_SUBTEST_4(check_stddeque_transform(Affine3f())); - CALL_SUBTEST_4(check_stddeque_transform(Affine3d())); - - // some Quaternion - CALL_SUBTEST_5(check_stddeque_quaternion(Quaternionf())); - CALL_SUBTEST_5(check_stddeque_quaternion(Quaterniond())); -} diff --git a/eigen/test/stddeque_overload.cpp b/eigen/test/stddeque_overload.cpp deleted file mode 100644 index 4da618b..0000000 --- a/eigen/test/stddeque_overload.cpp +++ /dev/null @@ -1,158 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/StdDeque> -#include <Eigen/Geometry> - -EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(Vector4f) - -EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(Matrix2f) -EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(Matrix4f) -EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(Matrix4d) - -EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(Affine3f) -EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(Affine3d) - -EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(Quaternionf) -EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(Quaterniond) - -template<typename MatrixType> -void check_stddeque_matrix(const MatrixType& m) -{ - typename MatrixType::Index rows = m.rows(); - typename MatrixType::Index cols = m.cols(); - MatrixType x = MatrixType::Random(rows,cols), y = MatrixType::Random(rows,cols); - std::deque<MatrixType> v(10, MatrixType(rows,cols)), w(20, y); - v[5] = x; - w[6] = v[5]; - VERIFY_IS_APPROX(w[6], v[5]); - v = w; - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(w[i], v[i]); - } - - v.resize(21); - v[20] = x; - VERIFY_IS_APPROX(v[20], x); - v.resize(22,y); - VERIFY_IS_APPROX(v[21], y); - v.push_back(x); - VERIFY_IS_APPROX(v[22], x); - - // do a lot of push_back such that the deque gets internally resized - // (with memory reallocation) - MatrixType* ref = &w[0]; - for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i) - v.push_back(w[i%w.size()]); - for(unsigned int i=23; i<v.size(); ++i) - { - VERIFY(v[i]==w[(i-23)%w.size()]); - } -} - -template<typename TransformType> -void check_stddeque_transform(const TransformType&) -{ - typedef typename TransformType::MatrixType MatrixType; - TransformType x(MatrixType::Random()), y(MatrixType::Random()); - std::deque<TransformType> v(10), w(20, y); - v[5] = x; - w[6] = v[5]; - VERIFY_IS_APPROX(w[6], v[5]); - v = w; - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(w[i], v[i]); - } - - v.resize(21); - v[20] = x; - VERIFY_IS_APPROX(v[20], x); - v.resize(22,y); - VERIFY_IS_APPROX(v[21], y); - v.push_back(x); - VERIFY_IS_APPROX(v[22], x); - - // do a lot of push_back such that the deque gets internally resized - // (with memory reallocation) - TransformType* ref = &w[0]; - for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i) - v.push_back(w[i%w.size()]); - for(unsigned int i=23; i<v.size(); ++i) - { - VERIFY(v[i].matrix()==w[(i-23)%w.size()].matrix()); - } -} - -template<typename QuaternionType> -void check_stddeque_quaternion(const QuaternionType&) -{ - typedef typename QuaternionType::Coefficients Coefficients; - QuaternionType x(Coefficients::Random()), y(Coefficients::Random()); - std::deque<QuaternionType> v(10), w(20, y); - v[5] = x; - w[6] = v[5]; - VERIFY_IS_APPROX(w[6], v[5]); - v = w; - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(w[i], v[i]); - } - - v.resize(21); - v[20] = x; - VERIFY_IS_APPROX(v[20], x); - v.resize(22,y); - VERIFY_IS_APPROX(v[21], y); - v.push_back(x); - VERIFY_IS_APPROX(v[22], x); - - // do a lot of push_back such that the deque gets internally resized - // (with memory reallocation) - QuaternionType* ref = &w[0]; - for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i) - v.push_back(w[i%w.size()]); - for(unsigned int i=23; i<v.size(); ++i) - { - VERIFY(v[i].coeffs()==w[(i-23)%w.size()].coeffs()); - } -} - -void test_stddeque_overload() -{ - // some non vectorizable fixed sizes - CALL_SUBTEST_1(check_stddeque_matrix(Vector2f())); - CALL_SUBTEST_1(check_stddeque_matrix(Matrix3f())); - CALL_SUBTEST_2(check_stddeque_matrix(Matrix3d())); - - // some vectorizable fixed sizes - CALL_SUBTEST_1(check_stddeque_matrix(Matrix2f())); - CALL_SUBTEST_1(check_stddeque_matrix(Vector4f())); - CALL_SUBTEST_1(check_stddeque_matrix(Matrix4f())); - CALL_SUBTEST_2(check_stddeque_matrix(Matrix4d())); - - // some dynamic sizes - CALL_SUBTEST_3(check_stddeque_matrix(MatrixXd(1,1))); - CALL_SUBTEST_3(check_stddeque_matrix(VectorXd(20))); - CALL_SUBTEST_3(check_stddeque_matrix(RowVectorXf(20))); - CALL_SUBTEST_3(check_stddeque_matrix(MatrixXcf(10,10))); - - // some Transform - CALL_SUBTEST_4(check_stddeque_transform(Affine2f())); // does not need the specialization (2+1)^2 = 9 - CALL_SUBTEST_4(check_stddeque_transform(Affine3f())); - CALL_SUBTEST_4(check_stddeque_transform(Affine3d())); - - // some Quaternion - CALL_SUBTEST_5(check_stddeque_quaternion(Quaternionf())); - CALL_SUBTEST_5(check_stddeque_quaternion(Quaterniond())); -} diff --git a/eigen/test/stdlist.cpp b/eigen/test/stdlist.cpp deleted file mode 100644 index 23cbe90..0000000 --- a/eigen/test/stdlist.cpp +++ /dev/null @@ -1,130 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/StdList> -#include <Eigen/Geometry> - -template<typename MatrixType> -void check_stdlist_matrix(const MatrixType& m) -{ - Index rows = m.rows(); - Index cols = m.cols(); - MatrixType x = MatrixType::Random(rows,cols), y = MatrixType::Random(rows,cols); - std::list<MatrixType,Eigen::aligned_allocator<MatrixType> > v(10, MatrixType(rows,cols)), w(20, y); - v.front() = x; - w.front() = w.back(); - VERIFY_IS_APPROX(w.front(), w.back()); - v = w; - - typename std::list<MatrixType,Eigen::aligned_allocator<MatrixType> >::iterator vi = v.begin(); - typename std::list<MatrixType,Eigen::aligned_allocator<MatrixType> >::iterator wi = w.begin(); - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(*vi, *wi); - ++vi; - ++wi; - } - - v.resize(21); - v.back() = x; - VERIFY_IS_APPROX(v.back(), x); - v.resize(22,y); - VERIFY_IS_APPROX(v.back(), y); - v.push_back(x); - VERIFY_IS_APPROX(v.back(), x); -} - -template<typename TransformType> -void check_stdlist_transform(const TransformType&) -{ - typedef typename TransformType::MatrixType MatrixType; - TransformType x(MatrixType::Random()), y(MatrixType::Random()); - std::list<TransformType,Eigen::aligned_allocator<TransformType> > v(10), w(20, y); - v.front() = x; - w.front() = w.back(); - VERIFY_IS_APPROX(w.front(), w.back()); - v = w; - - typename std::list<TransformType,Eigen::aligned_allocator<TransformType> >::iterator vi = v.begin(); - typename std::list<TransformType,Eigen::aligned_allocator<TransformType> >::iterator wi = w.begin(); - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(*vi, *wi); - ++vi; - ++wi; - } - - v.resize(21); - v.back() = x; - VERIFY_IS_APPROX(v.back(), x); - v.resize(22,y); - VERIFY_IS_APPROX(v.back(), y); - v.push_back(x); - VERIFY_IS_APPROX(v.back(), x); -} - -template<typename QuaternionType> -void check_stdlist_quaternion(const QuaternionType&) -{ - typedef typename QuaternionType::Coefficients Coefficients; - QuaternionType x(Coefficients::Random()), y(Coefficients::Random()); - std::list<QuaternionType,Eigen::aligned_allocator<QuaternionType> > v(10), w(20, y); - v.front() = x; - w.front() = w.back(); - VERIFY_IS_APPROX(w.front(), w.back()); - v = w; - - typename std::list<QuaternionType,Eigen::aligned_allocator<QuaternionType> >::iterator vi = v.begin(); - typename std::list<QuaternionType,Eigen::aligned_allocator<QuaternionType> >::iterator wi = w.begin(); - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(*vi, *wi); - ++vi; - ++wi; - } - - v.resize(21); - v.back() = x; - VERIFY_IS_APPROX(v.back(), x); - v.resize(22,y); - VERIFY_IS_APPROX(v.back(), y); - v.push_back(x); - VERIFY_IS_APPROX(v.back(), x); -} - -void test_stdlist() -{ - // some non vectorizable fixed sizes - CALL_SUBTEST_1(check_stdlist_matrix(Vector2f())); - CALL_SUBTEST_1(check_stdlist_matrix(Matrix3f())); - CALL_SUBTEST_2(check_stdlist_matrix(Matrix3d())); - - // some vectorizable fixed sizes - CALL_SUBTEST_1(check_stdlist_matrix(Matrix2f())); - CALL_SUBTEST_1(check_stdlist_matrix(Vector4f())); - CALL_SUBTEST_1(check_stdlist_matrix(Matrix4f())); - CALL_SUBTEST_2(check_stdlist_matrix(Matrix4d())); - - // some dynamic sizes - CALL_SUBTEST_3(check_stdlist_matrix(MatrixXd(1,1))); - CALL_SUBTEST_3(check_stdlist_matrix(VectorXd(20))); - CALL_SUBTEST_3(check_stdlist_matrix(RowVectorXf(20))); - CALL_SUBTEST_3(check_stdlist_matrix(MatrixXcf(10,10))); - - // some Transform - CALL_SUBTEST_4(check_stdlist_transform(Affine2f())); - CALL_SUBTEST_4(check_stdlist_transform(Affine3f())); - CALL_SUBTEST_4(check_stdlist_transform(Affine3d())); - - // some Quaternion - CALL_SUBTEST_5(check_stdlist_quaternion(Quaternionf())); - CALL_SUBTEST_5(check_stdlist_quaternion(Quaterniond())); -} diff --git a/eigen/test/stdlist_overload.cpp b/eigen/test/stdlist_overload.cpp deleted file mode 100644 index bb910bd..0000000 --- a/eigen/test/stdlist_overload.cpp +++ /dev/null @@ -1,192 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/StdList> -#include <Eigen/Geometry> - -EIGEN_DEFINE_STL_LIST_SPECIALIZATION(Vector4f) - -EIGEN_DEFINE_STL_LIST_SPECIALIZATION(Matrix2f) -EIGEN_DEFINE_STL_LIST_SPECIALIZATION(Matrix4f) -EIGEN_DEFINE_STL_LIST_SPECIALIZATION(Matrix4d) - -EIGEN_DEFINE_STL_LIST_SPECIALIZATION(Affine3f) -EIGEN_DEFINE_STL_LIST_SPECIALIZATION(Affine3d) - -EIGEN_DEFINE_STL_LIST_SPECIALIZATION(Quaternionf) -EIGEN_DEFINE_STL_LIST_SPECIALIZATION(Quaterniond) - -template <class Container, class Position> -typename Container::iterator get(Container & c, Position position) -{ - typename Container::iterator it = c.begin(); - std::advance(it, position); - return it; -} - -template <class Container, class Position, class Value> -void set(Container & c, Position position, const Value & value) -{ - typename Container::iterator it = c.begin(); - std::advance(it, position); - *it = value; -} - -template<typename MatrixType> -void check_stdlist_matrix(const MatrixType& m) -{ - typename MatrixType::Index rows = m.rows(); - typename MatrixType::Index cols = m.cols(); - MatrixType x = MatrixType::Random(rows,cols), y = MatrixType::Random(rows,cols); - std::list<MatrixType> v(10, MatrixType(rows,cols)), w(20, y); - typename std::list<MatrixType>::iterator itv = get(v, 5); - typename std::list<MatrixType>::iterator itw = get(w, 6); - *itv = x; - *itw = *itv; - VERIFY_IS_APPROX(*itw, *itv); - v = w; - itv = v.begin(); - itw = w.begin(); - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(*itw, *itv); - ++itv; - ++itw; - } - - v.resize(21); - set(v, 20, x); - VERIFY_IS_APPROX(*get(v, 20), x); - v.resize(22,y); - VERIFY_IS_APPROX(*get(v, 21), y); - v.push_back(x); - VERIFY_IS_APPROX(*get(v, 22), x); - - // do a lot of push_back such that the list gets internally resized - // (with memory reallocation) - MatrixType* ref = &(*get(w, 0)); - for(int i=0; i<30 || ((ref==&(*get(w, 0))) && i<300); ++i) - v.push_back(*get(w, i%w.size())); - for(unsigned int i=23; i<v.size(); ++i) - { - VERIFY((*get(v, i))==(*get(w, (i-23)%w.size()))); - } -} - -template<typename TransformType> -void check_stdlist_transform(const TransformType&) -{ - typedef typename TransformType::MatrixType MatrixType; - TransformType x(MatrixType::Random()), y(MatrixType::Random()); - std::list<TransformType> v(10), w(20, y); - typename std::list<TransformType>::iterator itv = get(v, 5); - typename std::list<TransformType>::iterator itw = get(w, 6); - *itv = x; - *itw = *itv; - VERIFY_IS_APPROX(*itw, *itv); - v = w; - itv = v.begin(); - itw = w.begin(); - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(*itw, *itv); - ++itv; - ++itw; - } - - v.resize(21); - set(v, 20, x); - VERIFY_IS_APPROX(*get(v, 20), x); - v.resize(22,y); - VERIFY_IS_APPROX(*get(v, 21), y); - v.push_back(x); - VERIFY_IS_APPROX(*get(v, 22), x); - - // do a lot of push_back such that the list gets internally resized - // (with memory reallocation) - TransformType* ref = &(*get(w, 0)); - for(int i=0; i<30 || ((ref==&(*get(w, 0))) && i<300); ++i) - v.push_back(*get(w, i%w.size())); - for(unsigned int i=23; i<v.size(); ++i) - { - VERIFY(get(v, i)->matrix()==get(w, (i-23)%w.size())->matrix()); - } -} - -template<typename QuaternionType> -void check_stdlist_quaternion(const QuaternionType&) -{ - typedef typename QuaternionType::Coefficients Coefficients; - QuaternionType x(Coefficients::Random()), y(Coefficients::Random()); - std::list<QuaternionType> v(10), w(20, y); - typename std::list<QuaternionType>::iterator itv = get(v, 5); - typename std::list<QuaternionType>::iterator itw = get(w, 6); - *itv = x; - *itw = *itv; - VERIFY_IS_APPROX(*itw, *itv); - v = w; - itv = v.begin(); - itw = w.begin(); - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(*itw, *itv); - ++itv; - ++itw; - } - - v.resize(21); - set(v, 20, x); - VERIFY_IS_APPROX(*get(v, 20), x); - v.resize(22,y); - VERIFY_IS_APPROX(*get(v, 21), y); - v.push_back(x); - VERIFY_IS_APPROX(*get(v, 22), x); - - // do a lot of push_back such that the list gets internally resized - // (with memory reallocation) - QuaternionType* ref = &(*get(w, 0)); - for(int i=0; i<30 || ((ref==&(*get(w, 0))) && i<300); ++i) - v.push_back(*get(w, i%w.size())); - for(unsigned int i=23; i<v.size(); ++i) - { - VERIFY(get(v, i)->coeffs()==get(w, (i-23)%w.size())->coeffs()); - } -} - -void test_stdlist_overload() -{ - // some non vectorizable fixed sizes - CALL_SUBTEST_1(check_stdlist_matrix(Vector2f())); - CALL_SUBTEST_1(check_stdlist_matrix(Matrix3f())); - CALL_SUBTEST_2(check_stdlist_matrix(Matrix3d())); - - // some vectorizable fixed sizes - CALL_SUBTEST_1(check_stdlist_matrix(Matrix2f())); - CALL_SUBTEST_1(check_stdlist_matrix(Vector4f())); - CALL_SUBTEST_1(check_stdlist_matrix(Matrix4f())); - CALL_SUBTEST_2(check_stdlist_matrix(Matrix4d())); - - // some dynamic sizes - CALL_SUBTEST_3(check_stdlist_matrix(MatrixXd(1,1))); - CALL_SUBTEST_3(check_stdlist_matrix(VectorXd(20))); - CALL_SUBTEST_3(check_stdlist_matrix(RowVectorXf(20))); - CALL_SUBTEST_3(check_stdlist_matrix(MatrixXcf(10,10))); - - // some Transform - CALL_SUBTEST_4(check_stdlist_transform(Affine2f())); // does not need the specialization (2+1)^2 = 9 - CALL_SUBTEST_4(check_stdlist_transform(Affine3f())); - CALL_SUBTEST_4(check_stdlist_transform(Affine3d())); - - // some Quaternion - CALL_SUBTEST_5(check_stdlist_quaternion(Quaternionf())); - CALL_SUBTEST_5(check_stdlist_quaternion(Quaterniond())); -} diff --git a/eigen/test/stdvector.cpp b/eigen/test/stdvector.cpp deleted file mode 100644 index fa928ea..0000000 --- a/eigen/test/stdvector.cpp +++ /dev/null @@ -1,158 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/StdVector> -#include <Eigen/Geometry> - -template<typename MatrixType> -void check_stdvector_matrix(const MatrixType& m) -{ - typename MatrixType::Index rows = m.rows(); - typename MatrixType::Index cols = m.cols(); - MatrixType x = MatrixType::Random(rows,cols), y = MatrixType::Random(rows,cols); - std::vector<MatrixType,Eigen::aligned_allocator<MatrixType> > v(10, MatrixType(rows,cols)), w(20, y); - v[5] = x; - w[6] = v[5]; - VERIFY_IS_APPROX(w[6], v[5]); - v = w; - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(w[i], v[i]); - } - - v.resize(21); - v[20] = x; - VERIFY_IS_APPROX(v[20], x); - v.resize(22,y); - VERIFY_IS_APPROX(v[21], y); - v.push_back(x); - VERIFY_IS_APPROX(v[22], x); - VERIFY((internal::UIntPtr)&(v[22]) == (internal::UIntPtr)&(v[21]) + sizeof(MatrixType)); - - // do a lot of push_back such that the vector gets internally resized - // (with memory reallocation) - MatrixType* ref = &w[0]; - for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i) - v.push_back(w[i%w.size()]); - for(unsigned int i=23; i<v.size(); ++i) - { - VERIFY(v[i]==w[(i-23)%w.size()]); - } -} - -template<typename TransformType> -void check_stdvector_transform(const TransformType&) -{ - typedef typename TransformType::MatrixType MatrixType; - TransformType x(MatrixType::Random()), y(MatrixType::Random()); - std::vector<TransformType,Eigen::aligned_allocator<TransformType> > v(10), w(20, y); - v[5] = x; - w[6] = v[5]; - VERIFY_IS_APPROX(w[6], v[5]); - v = w; - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(w[i], v[i]); - } - - v.resize(21); - v[20] = x; - VERIFY_IS_APPROX(v[20], x); - v.resize(22,y); - VERIFY_IS_APPROX(v[21], y); - v.push_back(x); - VERIFY_IS_APPROX(v[22], x); - VERIFY((internal::UIntPtr)&(v[22]) == (internal::UIntPtr)&(v[21]) + sizeof(TransformType)); - - // do a lot of push_back such that the vector gets internally resized - // (with memory reallocation) - TransformType* ref = &w[0]; - for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i) - v.push_back(w[i%w.size()]); - for(unsigned int i=23; i<v.size(); ++i) - { - VERIFY(v[i].matrix()==w[(i-23)%w.size()].matrix()); - } -} - -template<typename QuaternionType> -void check_stdvector_quaternion(const QuaternionType&) -{ - typedef typename QuaternionType::Coefficients Coefficients; - QuaternionType x(Coefficients::Random()), y(Coefficients::Random()); - std::vector<QuaternionType,Eigen::aligned_allocator<QuaternionType> > v(10), w(20, y); - v[5] = x; - w[6] = v[5]; - VERIFY_IS_APPROX(w[6], v[5]); - v = w; - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(w[i], v[i]); - } - - v.resize(21); - v[20] = x; - VERIFY_IS_APPROX(v[20], x); - v.resize(22,y); - VERIFY_IS_APPROX(v[21], y); - v.push_back(x); - VERIFY_IS_APPROX(v[22], x); - VERIFY((internal::UIntPtr)&(v[22]) == (internal::UIntPtr)&(v[21]) + sizeof(QuaternionType)); - - // do a lot of push_back such that the vector gets internally resized - // (with memory reallocation) - QuaternionType* ref = &w[0]; - for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i) - v.push_back(w[i%w.size()]); - for(unsigned int i=23; i<v.size(); ++i) - { - VERIFY(v[i].coeffs()==w[(i-23)%w.size()].coeffs()); - } -} - -// the code below triggered an invalid warning with gcc >= 7 -// eigen/Eigen/src/Core/util/Memory.h:189:12: warning: argument 1 value '18446744073709551612' exceeds maximum object size 9223372036854775807 -// This has been reported to gcc there: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=87544 -void std_vector_gcc_warning() -{ - typedef Eigen::Vector3f T; - std::vector<T, Eigen::aligned_allocator<T> > v; - v.push_back(T()); -} - -void test_stdvector() -{ - // some non vectorizable fixed sizes - CALL_SUBTEST_1(check_stdvector_matrix(Vector2f())); - CALL_SUBTEST_1(check_stdvector_matrix(Matrix3f())); - CALL_SUBTEST_2(check_stdvector_matrix(Matrix3d())); - - // some vectorizable fixed sizes - CALL_SUBTEST_1(check_stdvector_matrix(Matrix2f())); - CALL_SUBTEST_1(check_stdvector_matrix(Vector4f())); - CALL_SUBTEST_1(check_stdvector_matrix(Matrix4f())); - CALL_SUBTEST_2(check_stdvector_matrix(Matrix4d())); - - // some dynamic sizes - CALL_SUBTEST_3(check_stdvector_matrix(MatrixXd(1,1))); - CALL_SUBTEST_3(check_stdvector_matrix(VectorXd(20))); - CALL_SUBTEST_3(check_stdvector_matrix(RowVectorXf(20))); - CALL_SUBTEST_3(check_stdvector_matrix(MatrixXcf(10,10))); - - // some Transform - CALL_SUBTEST_4(check_stdvector_transform(Projective2f())); - CALL_SUBTEST_4(check_stdvector_transform(Projective3f())); - CALL_SUBTEST_4(check_stdvector_transform(Projective3d())); - //CALL_SUBTEST(heck_stdvector_transform(Projective4d())); - - // some Quaternion - CALL_SUBTEST_5(check_stdvector_quaternion(Quaternionf())); - CALL_SUBTEST_5(check_stdvector_quaternion(Quaterniond())); -} diff --git a/eigen/test/stdvector_overload.cpp b/eigen/test/stdvector_overload.cpp deleted file mode 100644 index 9596659..0000000 --- a/eigen/test/stdvector_overload.cpp +++ /dev/null @@ -1,161 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/StdVector> -#include <Eigen/Geometry> - -EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(Vector4f) - -EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(Matrix2f) -EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(Matrix4f) -EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(Matrix4d) - -EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(Affine3f) -EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(Affine3d) - -EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(Quaternionf) -EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(Quaterniond) - -template<typename MatrixType> -void check_stdvector_matrix(const MatrixType& m) -{ - typename MatrixType::Index rows = m.rows(); - typename MatrixType::Index cols = m.cols(); - MatrixType x = MatrixType::Random(rows,cols), y = MatrixType::Random(rows,cols); - std::vector<MatrixType> v(10, MatrixType(rows,cols)), w(20, y); - v[5] = x; - w[6] = v[5]; - VERIFY_IS_APPROX(w[6], v[5]); - v = w; - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(w[i], v[i]); - } - - v.resize(21); - v[20] = x; - VERIFY_IS_APPROX(v[20], x); - v.resize(22,y); - VERIFY_IS_APPROX(v[21], y); - v.push_back(x); - VERIFY_IS_APPROX(v[22], x); - VERIFY((internal::UIntPtr)&(v[22]) == (internal::UIntPtr)&(v[21]) + sizeof(MatrixType)); - - // do a lot of push_back such that the vector gets internally resized - // (with memory reallocation) - MatrixType* ref = &w[0]; - for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i) - v.push_back(w[i%w.size()]); - for(unsigned int i=23; i<v.size(); ++i) - { - VERIFY(v[i]==w[(i-23)%w.size()]); - } -} - -template<typename TransformType> -void check_stdvector_transform(const TransformType&) -{ - typedef typename TransformType::MatrixType MatrixType; - TransformType x(MatrixType::Random()), y(MatrixType::Random()); - std::vector<TransformType> v(10), w(20, y); - v[5] = x; - w[6] = v[5]; - VERIFY_IS_APPROX(w[6], v[5]); - v = w; - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(w[i], v[i]); - } - - v.resize(21); - v[20] = x; - VERIFY_IS_APPROX(v[20], x); - v.resize(22,y); - VERIFY_IS_APPROX(v[21], y); - v.push_back(x); - VERIFY_IS_APPROX(v[22], x); - VERIFY((internal::UIntPtr)&(v[22]) == (internal::UIntPtr)&(v[21]) + sizeof(TransformType)); - - // do a lot of push_back such that the vector gets internally resized - // (with memory reallocation) - TransformType* ref = &w[0]; - for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i) - v.push_back(w[i%w.size()]); - for(unsigned int i=23; i<v.size(); ++i) - { - VERIFY(v[i].matrix()==w[(i-23)%w.size()].matrix()); - } -} - -template<typename QuaternionType> -void check_stdvector_quaternion(const QuaternionType&) -{ - typedef typename QuaternionType::Coefficients Coefficients; - QuaternionType x(Coefficients::Random()), y(Coefficients::Random()); - std::vector<QuaternionType> v(10), w(20, y); - v[5] = x; - w[6] = v[5]; - VERIFY_IS_APPROX(w[6], v[5]); - v = w; - for(int i = 0; i < 20; i++) - { - VERIFY_IS_APPROX(w[i], v[i]); - } - - v.resize(21); - v[20] = x; - VERIFY_IS_APPROX(v[20], x); - v.resize(22,y); - VERIFY_IS_APPROX(v[21], y); - v.push_back(x); - VERIFY_IS_APPROX(v[22], x); - VERIFY((internal::UIntPtr)&(v[22]) == (internal::UIntPtr)&(v[21]) + sizeof(QuaternionType)); - - // do a lot of push_back such that the vector gets internally resized - // (with memory reallocation) - QuaternionType* ref = &w[0]; - for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i) - v.push_back(w[i%w.size()]); - for(unsigned int i=23; i<v.size(); ++i) - { - VERIFY(v[i].coeffs()==w[(i-23)%w.size()].coeffs()); - } -} - -void test_stdvector_overload() -{ - // some non vectorizable fixed sizes - CALL_SUBTEST_1(check_stdvector_matrix(Vector2f())); - CALL_SUBTEST_1(check_stdvector_matrix(Matrix3f())); - CALL_SUBTEST_2(check_stdvector_matrix(Matrix3d())); - - // some vectorizable fixed sizes - CALL_SUBTEST_1(check_stdvector_matrix(Matrix2f())); - CALL_SUBTEST_1(check_stdvector_matrix(Vector4f())); - CALL_SUBTEST_1(check_stdvector_matrix(Matrix4f())); - CALL_SUBTEST_2(check_stdvector_matrix(Matrix4d())); - - // some dynamic sizes - CALL_SUBTEST_3(check_stdvector_matrix(MatrixXd(1,1))); - CALL_SUBTEST_3(check_stdvector_matrix(VectorXd(20))); - CALL_SUBTEST_3(check_stdvector_matrix(RowVectorXf(20))); - CALL_SUBTEST_3(check_stdvector_matrix(MatrixXcf(10,10))); - - // some Transform - CALL_SUBTEST_4(check_stdvector_transform(Affine2f())); // does not need the specialization (2+1)^2 = 9 - CALL_SUBTEST_4(check_stdvector_transform(Affine3f())); - CALL_SUBTEST_4(check_stdvector_transform(Affine3d())); - - // some Quaternion - CALL_SUBTEST_5(check_stdvector_quaternion(Quaternionf())); - CALL_SUBTEST_5(check_stdvector_quaternion(Quaterniond())); -} diff --git a/eigen/test/superlu_support.cpp b/eigen/test/superlu_support.cpp deleted file mode 100644 index 98a7bc5..0000000 --- a/eigen/test/superlu_support.cpp +++ /dev/null @@ -1,23 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_NO_DEBUG_SMALL_PRODUCT_BLOCKS -#include "sparse_solver.h" - -#include <Eigen/SuperLUSupport> - -void test_superlu_support() -{ - SuperLU<SparseMatrix<double> > superlu_double_colmajor; - SuperLU<SparseMatrix<std::complex<double> > > superlu_cplxdouble_colmajor; - CALL_SUBTEST_1( check_sparse_square_solving(superlu_double_colmajor) ); - CALL_SUBTEST_2( check_sparse_square_solving(superlu_cplxdouble_colmajor) ); - CALL_SUBTEST_1( check_sparse_square_determinant(superlu_double_colmajor) ); - CALL_SUBTEST_2( check_sparse_square_determinant(superlu_cplxdouble_colmajor) ); -} diff --git a/eigen/test/svd_common.h b/eigen/test/svd_common.h deleted file mode 100644 index cba0665..0000000 --- a/eigen/test/svd_common.h +++ /dev/null @@ -1,478 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef SVD_DEFAULT -#error a macro SVD_DEFAULT(MatrixType) must be defined prior to including svd_common.h -#endif - -#ifndef SVD_FOR_MIN_NORM -#error a macro SVD_FOR_MIN_NORM(MatrixType) must be defined prior to including svd_common.h -#endif - -#include "svd_fill.h" - -// Check that the matrix m is properly reconstructed and that the U and V factors are unitary -// The SVD must have already been computed. -template<typename SvdType, typename MatrixType> -void svd_check_full(const MatrixType& m, const SvdType& svd) -{ - Index rows = m.rows(); - Index cols = m.cols(); - - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime - }; - - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime> MatrixUType; - typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime> MatrixVType; - - MatrixType sigma = MatrixType::Zero(rows,cols); - sigma.diagonal() = svd.singularValues().template cast<Scalar>(); - MatrixUType u = svd.matrixU(); - MatrixVType v = svd.matrixV(); - RealScalar scaling = m.cwiseAbs().maxCoeff(); - if(scaling<(std::numeric_limits<RealScalar>::min)()) - { - VERIFY(sigma.cwiseAbs().maxCoeff() <= (std::numeric_limits<RealScalar>::min)()); - } - else - { - VERIFY_IS_APPROX(m/scaling, u * (sigma/scaling) * v.adjoint()); - } - VERIFY_IS_UNITARY(u); - VERIFY_IS_UNITARY(v); -} - -// Compare partial SVD defined by computationOptions to a full SVD referenceSvd -template<typename SvdType, typename MatrixType> -void svd_compare_to_full(const MatrixType& m, - unsigned int computationOptions, - const SvdType& referenceSvd) -{ - typedef typename MatrixType::RealScalar RealScalar; - Index rows = m.rows(); - Index cols = m.cols(); - Index diagSize = (std::min)(rows, cols); - RealScalar prec = test_precision<RealScalar>(); - - SvdType svd(m, computationOptions); - - VERIFY_IS_APPROX(svd.singularValues(), referenceSvd.singularValues()); - - if(computationOptions & (ComputeFullV|ComputeThinV)) - { - VERIFY( (svd.matrixV().adjoint()*svd.matrixV()).isIdentity(prec) ); - VERIFY_IS_APPROX( svd.matrixV().leftCols(diagSize) * svd.singularValues().asDiagonal() * svd.matrixV().leftCols(diagSize).adjoint(), - referenceSvd.matrixV().leftCols(diagSize) * referenceSvd.singularValues().asDiagonal() * referenceSvd.matrixV().leftCols(diagSize).adjoint()); - } - - if(computationOptions & (ComputeFullU|ComputeThinU)) - { - VERIFY( (svd.matrixU().adjoint()*svd.matrixU()).isIdentity(prec) ); - VERIFY_IS_APPROX( svd.matrixU().leftCols(diagSize) * svd.singularValues().cwiseAbs2().asDiagonal() * svd.matrixU().leftCols(diagSize).adjoint(), - referenceSvd.matrixU().leftCols(diagSize) * referenceSvd.singularValues().cwiseAbs2().asDiagonal() * referenceSvd.matrixU().leftCols(diagSize).adjoint()); - } - - // The following checks are not critical. - // For instance, with Dived&Conquer SVD, if only the factor 'V' is computedt then different matrix-matrix product implementation will be used - // and the resulting 'V' factor might be significantly different when the SVD decomposition is not unique, especially with single precision float. - ++g_test_level; - if(computationOptions & ComputeFullU) VERIFY_IS_APPROX(svd.matrixU(), referenceSvd.matrixU()); - if(computationOptions & ComputeThinU) VERIFY_IS_APPROX(svd.matrixU(), referenceSvd.matrixU().leftCols(diagSize)); - if(computationOptions & ComputeFullV) VERIFY_IS_APPROX(svd.matrixV().cwiseAbs(), referenceSvd.matrixV().cwiseAbs()); - if(computationOptions & ComputeThinV) VERIFY_IS_APPROX(svd.matrixV(), referenceSvd.matrixV().leftCols(diagSize)); - --g_test_level; -} - -// -template<typename SvdType, typename MatrixType> -void svd_least_square(const MatrixType& m, unsigned int computationOptions) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - Index rows = m.rows(); - Index cols = m.cols(); - - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime - }; - - typedef Matrix<Scalar, RowsAtCompileTime, Dynamic> RhsType; - typedef Matrix<Scalar, ColsAtCompileTime, Dynamic> SolutionType; - - RhsType rhs = RhsType::Random(rows, internal::random<Index>(1, cols)); - SvdType svd(m, computationOptions); - - if(internal::is_same<RealScalar,double>::value) svd.setThreshold(1e-8); - else if(internal::is_same<RealScalar,float>::value) svd.setThreshold(2e-4); - - SolutionType x = svd.solve(rhs); - - RealScalar residual = (m*x-rhs).norm(); - RealScalar rhs_norm = rhs.norm(); - if(!test_isMuchSmallerThan(residual,rhs.norm())) - { - // ^^^ If the residual is very small, then we have an exact solution, so we are already good. - - // evaluate normal equation which works also for least-squares solutions - if(internal::is_same<RealScalar,double>::value || svd.rank()==m.diagonal().size()) - { - using std::sqrt; - // This test is not stable with single precision. - // This is probably because squaring m signicantly affects the precision. - if(internal::is_same<RealScalar,float>::value) ++g_test_level; - - VERIFY_IS_APPROX(m.adjoint()*(m*x),m.adjoint()*rhs); - - if(internal::is_same<RealScalar,float>::value) --g_test_level; - } - - // Check that there is no significantly better solution in the neighborhood of x - for(Index k=0;k<x.rows();++k) - { - using std::abs; - - SolutionType y(x); - y.row(k) = (RealScalar(1)+2*NumTraits<RealScalar>::epsilon())*x.row(k); - RealScalar residual_y = (m*y-rhs).norm(); - VERIFY( test_isMuchSmallerThan(abs(residual_y-residual), rhs_norm) || residual < residual_y ); - if(internal::is_same<RealScalar,float>::value) ++g_test_level; - VERIFY( test_isApprox(residual_y,residual) || residual < residual_y ); - if(internal::is_same<RealScalar,float>::value) --g_test_level; - - y.row(k) = (RealScalar(1)-2*NumTraits<RealScalar>::epsilon())*x.row(k); - residual_y = (m*y-rhs).norm(); - VERIFY( test_isMuchSmallerThan(abs(residual_y-residual), rhs_norm) || residual < residual_y ); - if(internal::is_same<RealScalar,float>::value) ++g_test_level; - VERIFY( test_isApprox(residual_y,residual) || residual < residual_y ); - if(internal::is_same<RealScalar,float>::value) --g_test_level; - } - } -} - -// check minimal norm solutions, the inoput matrix m is only used to recover problem size -template<typename MatrixType> -void svd_min_norm(const MatrixType& m, unsigned int computationOptions) -{ - typedef typename MatrixType::Scalar Scalar; - Index cols = m.cols(); - - enum { - ColsAtCompileTime = MatrixType::ColsAtCompileTime - }; - - typedef Matrix<Scalar, ColsAtCompileTime, Dynamic> SolutionType; - - // generate a full-rank m x n problem with m<n - enum { - RankAtCompileTime2 = ColsAtCompileTime==Dynamic ? Dynamic : (ColsAtCompileTime)/2+1, - RowsAtCompileTime3 = ColsAtCompileTime==Dynamic ? Dynamic : ColsAtCompileTime+1 - }; - typedef Matrix<Scalar, RankAtCompileTime2, ColsAtCompileTime> MatrixType2; - typedef Matrix<Scalar, RankAtCompileTime2, 1> RhsType2; - typedef Matrix<Scalar, ColsAtCompileTime, RankAtCompileTime2> MatrixType2T; - Index rank = RankAtCompileTime2==Dynamic ? internal::random<Index>(1,cols) : Index(RankAtCompileTime2); - MatrixType2 m2(rank,cols); - int guard = 0; - do { - m2.setRandom(); - } while(SVD_FOR_MIN_NORM(MatrixType2)(m2).setThreshold(test_precision<Scalar>()).rank()!=rank && (++guard)<10); - VERIFY(guard<10); - - RhsType2 rhs2 = RhsType2::Random(rank); - // use QR to find a reference minimal norm solution - HouseholderQR<MatrixType2T> qr(m2.adjoint()); - Matrix<Scalar,Dynamic,1> tmp = qr.matrixQR().topLeftCorner(rank,rank).template triangularView<Upper>().adjoint().solve(rhs2); - tmp.conservativeResize(cols); - tmp.tail(cols-rank).setZero(); - SolutionType x21 = qr.householderQ() * tmp; - // now check with SVD - SVD_FOR_MIN_NORM(MatrixType2) svd2(m2, computationOptions); - SolutionType x22 = svd2.solve(rhs2); - VERIFY_IS_APPROX(m2*x21, rhs2); - VERIFY_IS_APPROX(m2*x22, rhs2); - VERIFY_IS_APPROX(x21, x22); - - // Now check with a rank deficient matrix - typedef Matrix<Scalar, RowsAtCompileTime3, ColsAtCompileTime> MatrixType3; - typedef Matrix<Scalar, RowsAtCompileTime3, 1> RhsType3; - Index rows3 = RowsAtCompileTime3==Dynamic ? internal::random<Index>(rank+1,2*cols) : Index(RowsAtCompileTime3); - Matrix<Scalar,RowsAtCompileTime3,Dynamic> C = Matrix<Scalar,RowsAtCompileTime3,Dynamic>::Random(rows3,rank); - MatrixType3 m3 = C * m2; - RhsType3 rhs3 = C * rhs2; - SVD_FOR_MIN_NORM(MatrixType3) svd3(m3, computationOptions); - SolutionType x3 = svd3.solve(rhs3); - VERIFY_IS_APPROX(m3*x3, rhs3); - VERIFY_IS_APPROX(m3*x21, rhs3); - VERIFY_IS_APPROX(m2*x3, rhs2); - VERIFY_IS_APPROX(x21, x3); -} - -// Check full, compare_to_full, least_square, and min_norm for all possible compute-options -template<typename SvdType, typename MatrixType> -void svd_test_all_computation_options(const MatrixType& m, bool full_only) -{ -// if (QRPreconditioner == NoQRPreconditioner && m.rows() != m.cols()) -// return; - SvdType fullSvd(m, ComputeFullU|ComputeFullV); - CALL_SUBTEST(( svd_check_full(m, fullSvd) )); - CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeFullU | ComputeFullV) )); - CALL_SUBTEST(( svd_min_norm(m, ComputeFullU | ComputeFullV) )); - - #if defined __INTEL_COMPILER - // remark #111: statement is unreachable - #pragma warning disable 111 - #endif - if(full_only) - return; - - CALL_SUBTEST(( svd_compare_to_full(m, ComputeFullU, fullSvd) )); - CALL_SUBTEST(( svd_compare_to_full(m, ComputeFullV, fullSvd) )); - CALL_SUBTEST(( svd_compare_to_full(m, 0, fullSvd) )); - - if (MatrixType::ColsAtCompileTime == Dynamic) { - // thin U/V are only available with dynamic number of columns - CALL_SUBTEST(( svd_compare_to_full(m, ComputeFullU|ComputeThinV, fullSvd) )); - CALL_SUBTEST(( svd_compare_to_full(m, ComputeThinV, fullSvd) )); - CALL_SUBTEST(( svd_compare_to_full(m, ComputeThinU|ComputeFullV, fullSvd) )); - CALL_SUBTEST(( svd_compare_to_full(m, ComputeThinU , fullSvd) )); - CALL_SUBTEST(( svd_compare_to_full(m, ComputeThinU|ComputeThinV, fullSvd) )); - - CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeFullU | ComputeThinV) )); - CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeThinU | ComputeFullV) )); - CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeThinU | ComputeThinV) )); - - CALL_SUBTEST(( svd_min_norm(m, ComputeFullU | ComputeThinV) )); - CALL_SUBTEST(( svd_min_norm(m, ComputeThinU | ComputeFullV) )); - CALL_SUBTEST(( svd_min_norm(m, ComputeThinU | ComputeThinV) )); - - // test reconstruction - Index diagSize = (std::min)(m.rows(), m.cols()); - SvdType svd(m, ComputeThinU | ComputeThinV); - VERIFY_IS_APPROX(m, svd.matrixU().leftCols(diagSize) * svd.singularValues().asDiagonal() * svd.matrixV().leftCols(diagSize).adjoint()); - } -} - - -// work around stupid msvc error when constructing at compile time an expression that involves -// a division by zero, even if the numeric type has floating point -template<typename Scalar> -EIGEN_DONT_INLINE Scalar zero() { return Scalar(0); } - -// workaround aggressive optimization in ICC -template<typename T> EIGEN_DONT_INLINE T sub(T a, T b) { return a - b; } - -// all this function does is verify we don't iterate infinitely on nan/inf values -template<typename SvdType, typename MatrixType> -void svd_inf_nan() -{ - SvdType svd; - typedef typename MatrixType::Scalar Scalar; - Scalar some_inf = Scalar(1) / zero<Scalar>(); - VERIFY(sub(some_inf, some_inf) != sub(some_inf, some_inf)); - svd.compute(MatrixType::Constant(10,10,some_inf), ComputeFullU | ComputeFullV); - - Scalar nan = std::numeric_limits<Scalar>::quiet_NaN(); - VERIFY(nan != nan); - svd.compute(MatrixType::Constant(10,10,nan), ComputeFullU | ComputeFullV); - - MatrixType m = MatrixType::Zero(10,10); - m(internal::random<int>(0,9), internal::random<int>(0,9)) = some_inf; - svd.compute(m, ComputeFullU | ComputeFullV); - - m = MatrixType::Zero(10,10); - m(internal::random<int>(0,9), internal::random<int>(0,9)) = nan; - svd.compute(m, ComputeFullU | ComputeFullV); - - // regression test for bug 791 - m.resize(3,3); - m << 0, 2*NumTraits<Scalar>::epsilon(), 0.5, - 0, -0.5, 0, - nan, 0, 0; - svd.compute(m, ComputeFullU | ComputeFullV); - - m.resize(4,4); - m << 1, 0, 0, 0, - 0, 3, 1, 2e-308, - 1, 0, 1, nan, - 0, nan, nan, 0; - svd.compute(m, ComputeFullU | ComputeFullV); -} - -// Regression test for bug 286: JacobiSVD loops indefinitely with some -// matrices containing denormal numbers. -template<typename> -void svd_underoverflow() -{ -#if defined __INTEL_COMPILER -// shut up warning #239: floating point underflow -#pragma warning push -#pragma warning disable 239 -#endif - Matrix2d M; - M << -7.90884e-313, -4.94e-324, - 0, 5.60844e-313; - SVD_DEFAULT(Matrix2d) svd; - svd.compute(M,ComputeFullU|ComputeFullV); - CALL_SUBTEST( svd_check_full(M,svd) ); - - // Check all 2x2 matrices made with the following coefficients: - VectorXd value_set(9); - value_set << 0, 1, -1, 5.60844e-313, -5.60844e-313, 4.94e-324, -4.94e-324, -4.94e-223, 4.94e-223; - Array4i id(0,0,0,0); - int k = 0; - do - { - M << value_set(id(0)), value_set(id(1)), value_set(id(2)), value_set(id(3)); - svd.compute(M,ComputeFullU|ComputeFullV); - CALL_SUBTEST( svd_check_full(M,svd) ); - - id(k)++; - if(id(k)>=value_set.size()) - { - while(k<3 && id(k)>=value_set.size()) id(++k)++; - id.head(k).setZero(); - k=0; - } - - } while((id<int(value_set.size())).all()); - -#if defined __INTEL_COMPILER -#pragma warning pop -#endif - - // Check for overflow: - Matrix3d M3; - M3 << 4.4331978442502944e+307, -5.8585363752028680e+307, 6.4527017443412964e+307, - 3.7841695601406358e+307, 2.4331702789740617e+306, -3.5235707140272905e+307, - -8.7190887618028355e+307, -7.3453213709232193e+307, -2.4367363684472105e+307; - - SVD_DEFAULT(Matrix3d) svd3; - svd3.compute(M3,ComputeFullU|ComputeFullV); // just check we don't loop indefinitely - CALL_SUBTEST( svd_check_full(M3,svd3) ); -} - -// void jacobisvd(const MatrixType& a = MatrixType(), bool pickrandom = true) - -template<typename MatrixType> -void svd_all_trivial_2x2( void (*cb)(const MatrixType&,bool) ) -{ - MatrixType M; - VectorXd value_set(3); - value_set << 0, 1, -1; - Array4i id(0,0,0,0); - int k = 0; - do - { - M << value_set(id(0)), value_set(id(1)), value_set(id(2)), value_set(id(3)); - - cb(M,false); - - id(k)++; - if(id(k)>=value_set.size()) - { - while(k<3 && id(k)>=value_set.size()) id(++k)++; - id.head(k).setZero(); - k=0; - } - - } while((id<int(value_set.size())).all()); -} - -template<typename> -void svd_preallocate() -{ - Vector3f v(3.f, 2.f, 1.f); - MatrixXf m = v.asDiagonal(); - - internal::set_is_malloc_allowed(false); - VERIFY_RAISES_ASSERT(VectorXf tmp(10);) - SVD_DEFAULT(MatrixXf) svd; - internal::set_is_malloc_allowed(true); - svd.compute(m); - VERIFY_IS_APPROX(svd.singularValues(), v); - - SVD_DEFAULT(MatrixXf) svd2(3,3); - internal::set_is_malloc_allowed(false); - svd2.compute(m); - internal::set_is_malloc_allowed(true); - VERIFY_IS_APPROX(svd2.singularValues(), v); - VERIFY_RAISES_ASSERT(svd2.matrixU()); - VERIFY_RAISES_ASSERT(svd2.matrixV()); - svd2.compute(m, ComputeFullU | ComputeFullV); - VERIFY_IS_APPROX(svd2.matrixU(), Matrix3f::Identity()); - VERIFY_IS_APPROX(svd2.matrixV(), Matrix3f::Identity()); - internal::set_is_malloc_allowed(false); - svd2.compute(m); - internal::set_is_malloc_allowed(true); - - SVD_DEFAULT(MatrixXf) svd3(3,3,ComputeFullU|ComputeFullV); - internal::set_is_malloc_allowed(false); - svd2.compute(m); - internal::set_is_malloc_allowed(true); - VERIFY_IS_APPROX(svd2.singularValues(), v); - VERIFY_IS_APPROX(svd2.matrixU(), Matrix3f::Identity()); - VERIFY_IS_APPROX(svd2.matrixV(), Matrix3f::Identity()); - internal::set_is_malloc_allowed(false); - svd2.compute(m, ComputeFullU|ComputeFullV); - internal::set_is_malloc_allowed(true); -} - -template<typename SvdType,typename MatrixType> -void svd_verify_assert(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - Index rows = m.rows(); - Index cols = m.cols(); - - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime - }; - - typedef Matrix<Scalar, RowsAtCompileTime, 1> RhsType; - RhsType rhs(rows); - SvdType svd; - VERIFY_RAISES_ASSERT(svd.matrixU()) - VERIFY_RAISES_ASSERT(svd.singularValues()) - VERIFY_RAISES_ASSERT(svd.matrixV()) - VERIFY_RAISES_ASSERT(svd.solve(rhs)) - MatrixType a = MatrixType::Zero(rows, cols); - a.setZero(); - svd.compute(a, 0); - VERIFY_RAISES_ASSERT(svd.matrixU()) - VERIFY_RAISES_ASSERT(svd.matrixV()) - svd.singularValues(); - VERIFY_RAISES_ASSERT(svd.solve(rhs)) - - if (ColsAtCompileTime == Dynamic) - { - svd.compute(a, ComputeThinU); - svd.matrixU(); - VERIFY_RAISES_ASSERT(svd.matrixV()) - VERIFY_RAISES_ASSERT(svd.solve(rhs)) - svd.compute(a, ComputeThinV); - svd.matrixV(); - VERIFY_RAISES_ASSERT(svd.matrixU()) - VERIFY_RAISES_ASSERT(svd.solve(rhs)) - } - else - { - VERIFY_RAISES_ASSERT(svd.compute(a, ComputeThinU)) - VERIFY_RAISES_ASSERT(svd.compute(a, ComputeThinV)) - } -} - -#undef SVD_DEFAULT -#undef SVD_FOR_MIN_NORM diff --git a/eigen/test/svd_fill.h b/eigen/test/svd_fill.h deleted file mode 100644 index d68647e..0000000 --- a/eigen/test/svd_fill.h +++ /dev/null @@ -1,118 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014-2015 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -template<typename T> -Array<T,4,1> four_denorms(); - -template<> -Array4f four_denorms() { return Array4f(5.60844e-39f, -5.60844e-39f, 4.94e-44f, -4.94e-44f); } -template<> -Array4d four_denorms() { return Array4d(5.60844e-313, -5.60844e-313, 4.94e-324, -4.94e-324); } -template<typename T> -Array<T,4,1> four_denorms() { return four_denorms<double>().cast<T>(); } - -template<typename MatrixType> -void svd_fill_random(MatrixType &m, int Option = 0) -{ - using std::pow; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - Index diagSize = (std::min)(m.rows(), m.cols()); - RealScalar s = std::numeric_limits<RealScalar>::max_exponent10/4; - s = internal::random<RealScalar>(1,s); - Matrix<RealScalar,Dynamic,1> d = Matrix<RealScalar,Dynamic,1>::Random(diagSize); - for(Index k=0; k<diagSize; ++k) - d(k) = d(k)*pow(RealScalar(10),internal::random<RealScalar>(-s,s)); - - bool dup = internal::random<int>(0,10) < 3; - bool unit_uv = internal::random<int>(0,10) < (dup?7:3); // if we duplicate some diagonal entries, then increase the chance to preserve them using unitary U and V factors - - // duplicate some singular values - if(dup) - { - Index n = internal::random<Index>(0,d.size()-1); - for(Index i=0; i<n; ++i) - d(internal::random<Index>(0,d.size()-1)) = d(internal::random<Index>(0,d.size()-1)); - } - - Matrix<Scalar,Dynamic,Dynamic> U(m.rows(),diagSize); - Matrix<Scalar,Dynamic,Dynamic> VT(diagSize,m.cols()); - if(unit_uv) - { - // in very rare cases let's try with a pure diagonal matrix - if(internal::random<int>(0,10) < 1) - { - U.setIdentity(); - VT.setIdentity(); - } - else - { - createRandomPIMatrixOfRank(diagSize,U.rows(), U.cols(), U); - createRandomPIMatrixOfRank(diagSize,VT.rows(), VT.cols(), VT); - } - } - else - { - U.setRandom(); - VT.setRandom(); - } - - Matrix<Scalar,Dynamic,1> samples(9); - samples << 0, four_denorms<RealScalar>(), - -RealScalar(1)/NumTraits<RealScalar>::highest(), RealScalar(1)/NumTraits<RealScalar>::highest(), (std::numeric_limits<RealScalar>::min)(), pow((std::numeric_limits<RealScalar>::min)(),0.8); - - if(Option==Symmetric) - { - m = U * d.asDiagonal() * U.transpose(); - - // randomly nullify some rows/columns - { - Index count = internal::random<Index>(-diagSize,diagSize); - for(Index k=0; k<count; ++k) - { - Index i = internal::random<Index>(0,diagSize-1); - m.row(i).setZero(); - m.col(i).setZero(); - } - if(count<0) - // (partly) cancel some coeffs - if(!(dup && unit_uv)) - { - - Index n = internal::random<Index>(0,m.size()-1); - for(Index k=0; k<n; ++k) - { - Index i = internal::random<Index>(0,m.rows()-1); - Index j = internal::random<Index>(0,m.cols()-1); - m(j,i) = m(i,j) = samples(internal::random<Index>(0,samples.size()-1)); - if(NumTraits<Scalar>::IsComplex) - *(&numext::real_ref(m(j,i))+1) = *(&numext::real_ref(m(i,j))+1) = samples.real()(internal::random<Index>(0,samples.size()-1)); - } - } - } - } - else - { - m = U * d.asDiagonal() * VT; - // (partly) cancel some coeffs - if(!(dup && unit_uv)) - { - Index n = internal::random<Index>(0,m.size()-1); - for(Index k=0; k<n; ++k) - { - Index i = internal::random<Index>(0,m.rows()-1); - Index j = internal::random<Index>(0,m.cols()-1); - m(i,j) = samples(internal::random<Index>(0,samples.size()-1)); - if(NumTraits<Scalar>::IsComplex) - *(&numext::real_ref(m(i,j))+1) = samples.real()(internal::random<Index>(0,samples.size()-1)); - } - } - } -} - diff --git a/eigen/test/swap.cpp b/eigen/test/swap.cpp deleted file mode 100644 index f76e362..0000000 --- a/eigen/test/swap.cpp +++ /dev/null @@ -1,94 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_NO_STATIC_ASSERT -#include "main.h" - -template<typename T> -struct other_matrix_type -{ - typedef int type; -}; - -template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols> -struct other_matrix_type<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > -{ - typedef Matrix<_Scalar, _Rows, _Cols, _Options^RowMajor, _MaxRows, _MaxCols> type; -}; - -template<typename MatrixType> void swap(const MatrixType& m) -{ - typedef typename other_matrix_type<MatrixType>::type OtherMatrixType; - typedef typename MatrixType::Scalar Scalar; - - eigen_assert((!internal::is_same<MatrixType,OtherMatrixType>::value)); - typename MatrixType::Index rows = m.rows(); - typename MatrixType::Index cols = m.cols(); - - // construct 3 matrix guaranteed to be distinct - MatrixType m1 = MatrixType::Random(rows,cols); - MatrixType m2 = MatrixType::Random(rows,cols) + Scalar(100) * MatrixType::Identity(rows,cols); - OtherMatrixType m3 = OtherMatrixType::Random(rows,cols) + Scalar(200) * OtherMatrixType::Identity(rows,cols); - - MatrixType m1_copy = m1; - MatrixType m2_copy = m2; - OtherMatrixType m3_copy = m3; - - // test swapping 2 matrices of same type - Scalar *d1=m1.data(), *d2=m2.data(); - m1.swap(m2); - VERIFY_IS_APPROX(m1,m2_copy); - VERIFY_IS_APPROX(m2,m1_copy); - if(MatrixType::SizeAtCompileTime==Dynamic) - { - VERIFY(m1.data()==d2); - VERIFY(m2.data()==d1); - } - m1 = m1_copy; - m2 = m2_copy; - - // test swapping 2 matrices of different types - m1.swap(m3); - VERIFY_IS_APPROX(m1,m3_copy); - VERIFY_IS_APPROX(m3,m1_copy); - m1 = m1_copy; - m3 = m3_copy; - - // test swapping matrix with expression - m1.swap(m2.block(0,0,rows,cols)); - VERIFY_IS_APPROX(m1,m2_copy); - VERIFY_IS_APPROX(m2,m1_copy); - m1 = m1_copy; - m2 = m2_copy; - - // test swapping two expressions of different types - m1.transpose().swap(m3.transpose()); - VERIFY_IS_APPROX(m1,m3_copy); - VERIFY_IS_APPROX(m3,m1_copy); - m1 = m1_copy; - m3 = m3_copy; - - if(m1.rows()>1) - { - // test assertion on mismatching size -- matrix case - VERIFY_RAISES_ASSERT(m1.swap(m1.row(0))); - // test assertion on mismatching size -- xpr case - VERIFY_RAISES_ASSERT(m1.row(0).swap(m1)); - } -} - -void test_swap() -{ - int s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE); - CALL_SUBTEST_1( swap(Matrix3f()) ); // fixed size, no vectorization - CALL_SUBTEST_2( swap(Matrix4d()) ); // fixed size, possible vectorization - CALL_SUBTEST_3( swap(MatrixXd(s,s)) ); // dyn size, no vectorization - CALL_SUBTEST_4( swap(MatrixXf(s,s)) ); // dyn size, possible vectorization - TEST_SET_BUT_UNUSED_VARIABLE(s) -} diff --git a/eigen/test/triangular.cpp b/eigen/test/triangular.cpp deleted file mode 100644 index 328eef4..0000000 --- a/eigen/test/triangular.cpp +++ /dev/null @@ -1,246 +0,0 @@ -// This file is triangularView of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - - - -template<typename MatrixType> void triangular_square(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - - RealScalar largerEps = 10*test_precision<RealScalar>(); - - typename MatrixType::Index rows = m.rows(); - typename MatrixType::Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols), - m4(rows, cols), - r1(rows, cols), - r2(rows, cols); - VectorType v2 = VectorType::Random(rows); - - MatrixType m1up = m1.template triangularView<Upper>(); - MatrixType m2up = m2.template triangularView<Upper>(); - - if (rows*cols>1) - { - VERIFY(m1up.isUpperTriangular()); - VERIFY(m2up.transpose().isLowerTriangular()); - VERIFY(!m2.isLowerTriangular()); - } - -// VERIFY_IS_APPROX(m1up.transpose() * m2, m1.upper().transpose().lower() * m2); - - // test overloaded operator+= - r1.setZero(); - r2.setZero(); - r1.template triangularView<Upper>() += m1; - r2 += m1up; - VERIFY_IS_APPROX(r1,r2); - - // test overloaded operator= - m1.setZero(); - m1.template triangularView<Upper>() = m2.transpose() + m2; - m3 = m2.transpose() + m2; - VERIFY_IS_APPROX(m3.template triangularView<Lower>().transpose().toDenseMatrix(), m1); - - // test overloaded operator= - m1.setZero(); - m1.template triangularView<Lower>() = m2.transpose() + m2; - VERIFY_IS_APPROX(m3.template triangularView<Lower>().toDenseMatrix(), m1); - - VERIFY_IS_APPROX(m3.template triangularView<Lower>().conjugate().toDenseMatrix(), - m3.conjugate().template triangularView<Lower>().toDenseMatrix()); - - m1 = MatrixType::Random(rows, cols); - for (int i=0; i<rows; ++i) - while (numext::abs2(m1(i,i))<RealScalar(1e-1)) m1(i,i) = internal::random<Scalar>(); - - Transpose<MatrixType> trm4(m4); - // test back and forward subsitution with a vector as the rhs - m3 = m1.template triangularView<Upper>(); - VERIFY(v2.isApprox(m3.adjoint() * (m1.adjoint().template triangularView<Lower>().solve(v2)), largerEps)); - m3 = m1.template triangularView<Lower>(); - VERIFY(v2.isApprox(m3.transpose() * (m1.transpose().template triangularView<Upper>().solve(v2)), largerEps)); - m3 = m1.template triangularView<Upper>(); - VERIFY(v2.isApprox(m3 * (m1.template triangularView<Upper>().solve(v2)), largerEps)); - m3 = m1.template triangularView<Lower>(); - VERIFY(v2.isApprox(m3.conjugate() * (m1.conjugate().template triangularView<Lower>().solve(v2)), largerEps)); - - // test back and forward substitution with a matrix as the rhs - m3 = m1.template triangularView<Upper>(); - VERIFY(m2.isApprox(m3.adjoint() * (m1.adjoint().template triangularView<Lower>().solve(m2)), largerEps)); - m3 = m1.template triangularView<Lower>(); - VERIFY(m2.isApprox(m3.transpose() * (m1.transpose().template triangularView<Upper>().solve(m2)), largerEps)); - m3 = m1.template triangularView<Upper>(); - VERIFY(m2.isApprox(m3 * (m1.template triangularView<Upper>().solve(m2)), largerEps)); - m3 = m1.template triangularView<Lower>(); - VERIFY(m2.isApprox(m3.conjugate() * (m1.conjugate().template triangularView<Lower>().solve(m2)), largerEps)); - - // check M * inv(L) using in place API - m4 = m3; - m1.transpose().template triangularView<Eigen::Upper>().solveInPlace(trm4); - VERIFY_IS_APPROX(m4 * m1.template triangularView<Eigen::Lower>(), m3); - - // check M * inv(U) using in place API - m3 = m1.template triangularView<Upper>(); - m4 = m3; - m3.transpose().template triangularView<Eigen::Lower>().solveInPlace(trm4); - VERIFY_IS_APPROX(m4 * m1.template triangularView<Eigen::Upper>(), m3); - - // check solve with unit diagonal - m3 = m1.template triangularView<UnitUpper>(); - VERIFY(m2.isApprox(m3 * (m1.template triangularView<UnitUpper>().solve(m2)), largerEps)); - -// VERIFY(( m1.template triangularView<Upper>() -// * m2.template triangularView<Upper>()).isUpperTriangular()); - - // test swap - m1.setOnes(); - m2.setZero(); - m2.template triangularView<Upper>().swap(m1); - m3.setZero(); - m3.template triangularView<Upper>().setOnes(); - VERIFY_IS_APPROX(m2,m3); - - m1.setRandom(); - m3 = m1.template triangularView<Upper>(); - Matrix<Scalar, MatrixType::ColsAtCompileTime, Dynamic> m5(cols, internal::random<int>(1,20)); m5.setRandom(); - Matrix<Scalar, Dynamic, MatrixType::RowsAtCompileTime> m6(internal::random<int>(1,20), rows); m6.setRandom(); - VERIFY_IS_APPROX(m1.template triangularView<Upper>() * m5, m3*m5); - VERIFY_IS_APPROX(m6*m1.template triangularView<Upper>(), m6*m3); - - m1up = m1.template triangularView<Upper>(); - VERIFY_IS_APPROX(m1.template selfadjointView<Upper>().template triangularView<Upper>().toDenseMatrix(), m1up); - VERIFY_IS_APPROX(m1up.template selfadjointView<Upper>().template triangularView<Upper>().toDenseMatrix(), m1up); - VERIFY_IS_APPROX(m1.template selfadjointView<Upper>().template triangularView<Lower>().toDenseMatrix(), m1up.adjoint()); - VERIFY_IS_APPROX(m1up.template selfadjointView<Upper>().template triangularView<Lower>().toDenseMatrix(), m1up.adjoint()); - - VERIFY_IS_APPROX(m1.template selfadjointView<Upper>().diagonal(), m1.diagonal()); - -} - - -template<typename MatrixType> void triangular_rect(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime }; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols), - m4(rows, cols), - r1(rows, cols), - r2(rows, cols); - - MatrixType m1up = m1.template triangularView<Upper>(); - MatrixType m2up = m2.template triangularView<Upper>(); - - if (rows>1 && cols>1) - { - VERIFY(m1up.isUpperTriangular()); - VERIFY(m2up.transpose().isLowerTriangular()); - VERIFY(!m2.isLowerTriangular()); - } - - // test overloaded operator+= - r1.setZero(); - r2.setZero(); - r1.template triangularView<Upper>() += m1; - r2 += m1up; - VERIFY_IS_APPROX(r1,r2); - - // test overloaded operator= - m1.setZero(); - m1.template triangularView<Upper>() = 3 * m2; - m3 = 3 * m2; - VERIFY_IS_APPROX(m3.template triangularView<Upper>().toDenseMatrix(), m1); - - - m1.setZero(); - m1.template triangularView<Lower>() = 3 * m2; - VERIFY_IS_APPROX(m3.template triangularView<Lower>().toDenseMatrix(), m1); - - m1.setZero(); - m1.template triangularView<StrictlyUpper>() = 3 * m2; - VERIFY_IS_APPROX(m3.template triangularView<StrictlyUpper>().toDenseMatrix(), m1); - - - m1.setZero(); - m1.template triangularView<StrictlyLower>() = 3 * m2; - VERIFY_IS_APPROX(m3.template triangularView<StrictlyLower>().toDenseMatrix(), m1); - m1.setRandom(); - m2 = m1.template triangularView<Upper>(); - VERIFY(m2.isUpperTriangular()); - VERIFY(!m2.isLowerTriangular()); - m2 = m1.template triangularView<StrictlyUpper>(); - VERIFY(m2.isUpperTriangular()); - VERIFY(m2.diagonal().isMuchSmallerThan(RealScalar(1))); - m2 = m1.template triangularView<UnitUpper>(); - VERIFY(m2.isUpperTriangular()); - m2.diagonal().array() -= Scalar(1); - VERIFY(m2.diagonal().isMuchSmallerThan(RealScalar(1))); - m2 = m1.template triangularView<Lower>(); - VERIFY(m2.isLowerTriangular()); - VERIFY(!m2.isUpperTriangular()); - m2 = m1.template triangularView<StrictlyLower>(); - VERIFY(m2.isLowerTriangular()); - VERIFY(m2.diagonal().isMuchSmallerThan(RealScalar(1))); - m2 = m1.template triangularView<UnitLower>(); - VERIFY(m2.isLowerTriangular()); - m2.diagonal().array() -= Scalar(1); - VERIFY(m2.diagonal().isMuchSmallerThan(RealScalar(1))); - // test swap - m1.setOnes(); - m2.setZero(); - m2.template triangularView<Upper>().swap(m1); - m3.setZero(); - m3.template triangularView<Upper>().setOnes(); - VERIFY_IS_APPROX(m2,m3); -} - -void bug_159() -{ - Matrix3d m = Matrix3d::Random().triangularView<Lower>(); - EIGEN_UNUSED_VARIABLE(m) -} - -void test_triangular() -{ - int maxsize = (std::min)(EIGEN_TEST_MAX_SIZE,20); - for(int i = 0; i < g_repeat ; i++) - { - int r = internal::random<int>(2,maxsize); TEST_SET_BUT_UNUSED_VARIABLE(r) - int c = internal::random<int>(2,maxsize); TEST_SET_BUT_UNUSED_VARIABLE(c) - - CALL_SUBTEST_1( triangular_square(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( triangular_square(Matrix<float, 2, 2>()) ); - CALL_SUBTEST_3( triangular_square(Matrix3d()) ); - CALL_SUBTEST_4( triangular_square(Matrix<std::complex<float>,8, 8>()) ); - CALL_SUBTEST_5( triangular_square(MatrixXcd(r,r)) ); - CALL_SUBTEST_6( triangular_square(Matrix<float,Dynamic,Dynamic,RowMajor>(r, r)) ); - - CALL_SUBTEST_7( triangular_rect(Matrix<float, 4, 5>()) ); - CALL_SUBTEST_8( triangular_rect(Matrix<double, 6, 2>()) ); - CALL_SUBTEST_9( triangular_rect(MatrixXcf(r, c)) ); - CALL_SUBTEST_5( triangular_rect(MatrixXcd(r, c)) ); - CALL_SUBTEST_6( triangular_rect(Matrix<float,Dynamic,Dynamic,RowMajor>(r, c)) ); - } - - CALL_SUBTEST_1( bug_159() ); -} diff --git a/eigen/test/umeyama.cpp b/eigen/test/umeyama.cpp deleted file mode 100644 index 2e80924..0000000 --- a/eigen/test/umeyama.cpp +++ /dev/null @@ -1,183 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Hauke Heibel <hauke.heibel@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/Core> -#include <Eigen/Geometry> - -#include <Eigen/LU> // required for MatrixBase::determinant -#include <Eigen/SVD> // required for SVD - -using namespace Eigen; - -// Constructs a random matrix from the unitary group U(size). -template <typename T> -Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> randMatrixUnitary(int size) -{ - typedef T Scalar; - typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> MatrixType; - - MatrixType Q; - - int max_tries = 40; - double is_unitary = false; - - while (!is_unitary && max_tries > 0) - { - // initialize random matrix - Q = MatrixType::Random(size, size); - - // orthogonalize columns using the Gram-Schmidt algorithm - for (int col = 0; col < size; ++col) - { - typename MatrixType::ColXpr colVec = Q.col(col); - for (int prevCol = 0; prevCol < col; ++prevCol) - { - typename MatrixType::ColXpr prevColVec = Q.col(prevCol); - colVec -= colVec.dot(prevColVec)*prevColVec; - } - Q.col(col) = colVec.normalized(); - } - - // this additional orthogonalization is not necessary in theory but should enhance - // the numerical orthogonality of the matrix - for (int row = 0; row < size; ++row) - { - typename MatrixType::RowXpr rowVec = Q.row(row); - for (int prevRow = 0; prevRow < row; ++prevRow) - { - typename MatrixType::RowXpr prevRowVec = Q.row(prevRow); - rowVec -= rowVec.dot(prevRowVec)*prevRowVec; - } - Q.row(row) = rowVec.normalized(); - } - - // final check - is_unitary = Q.isUnitary(); - --max_tries; - } - - if (max_tries == 0) - eigen_assert(false && "randMatrixUnitary: Could not construct unitary matrix!"); - - return Q; -} - -// Constructs a random matrix from the special unitary group SU(size). -template <typename T> -Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> randMatrixSpecialUnitary(int size) -{ - typedef T Scalar; - - typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> MatrixType; - - // initialize unitary matrix - MatrixType Q = randMatrixUnitary<Scalar>(size); - - // tweak the first column to make the determinant be 1 - Q.col(0) *= numext::conj(Q.determinant()); - - return Q; -} - -template <typename MatrixType> -void run_test(int dim, int num_elements) -{ - using std::abs; - typedef typename internal::traits<MatrixType>::Scalar Scalar; - typedef Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> MatrixX; - typedef Matrix<Scalar, Eigen::Dynamic, 1> VectorX; - - // MUST be positive because in any other case det(cR_t) may become negative for - // odd dimensions! - const Scalar c = abs(internal::random<Scalar>()); - - MatrixX R = randMatrixSpecialUnitary<Scalar>(dim); - VectorX t = Scalar(50)*VectorX::Random(dim,1); - - MatrixX cR_t = MatrixX::Identity(dim+1,dim+1); - cR_t.block(0,0,dim,dim) = c*R; - cR_t.block(0,dim,dim,1) = t; - - MatrixX src = MatrixX::Random(dim+1, num_elements); - src.row(dim) = Matrix<Scalar, 1, Dynamic>::Constant(num_elements, Scalar(1)); - - MatrixX dst = cR_t*src; - - MatrixX cR_t_umeyama = umeyama(src.block(0,0,dim,num_elements), dst.block(0,0,dim,num_elements)); - - const Scalar error = ( cR_t_umeyama*src - dst ).norm() / dst.norm(); - VERIFY(error < Scalar(40)*std::numeric_limits<Scalar>::epsilon()); -} - -template<typename Scalar, int Dimension> -void run_fixed_size_test(int num_elements) -{ - using std::abs; - typedef Matrix<Scalar, Dimension+1, Dynamic> MatrixX; - typedef Matrix<Scalar, Dimension+1, Dimension+1> HomMatrix; - typedef Matrix<Scalar, Dimension, Dimension> FixedMatrix; - typedef Matrix<Scalar, Dimension, 1> FixedVector; - - const int dim = Dimension; - - // MUST be positive because in any other case det(cR_t) may become negative for - // odd dimensions! - // Also if c is to small compared to t.norm(), problem is ill-posed (cf. Bug 744) - const Scalar c = internal::random<Scalar>(0.5, 2.0); - - FixedMatrix R = randMatrixSpecialUnitary<Scalar>(dim); - FixedVector t = Scalar(32)*FixedVector::Random(dim,1); - - HomMatrix cR_t = HomMatrix::Identity(dim+1,dim+1); - cR_t.block(0,0,dim,dim) = c*R; - cR_t.block(0,dim,dim,1) = t; - - MatrixX src = MatrixX::Random(dim+1, num_elements); - src.row(dim) = Matrix<Scalar, 1, Dynamic>::Constant(num_elements, Scalar(1)); - - MatrixX dst = cR_t*src; - - Block<MatrixX, Dimension, Dynamic> src_block(src,0,0,dim,num_elements); - Block<MatrixX, Dimension, Dynamic> dst_block(dst,0,0,dim,num_elements); - - HomMatrix cR_t_umeyama = umeyama(src_block, dst_block); - - const Scalar error = ( cR_t_umeyama*src - dst ).squaredNorm(); - - VERIFY(error < Scalar(16)*std::numeric_limits<Scalar>::epsilon()); -} - -void test_umeyama() -{ - for (int i=0; i<g_repeat; ++i) - { - const int num_elements = internal::random<int>(40,500); - - // works also for dimensions bigger than 3... - for (int dim=2; dim<8; ++dim) - { - CALL_SUBTEST_1(run_test<MatrixXd>(dim, num_elements)); - CALL_SUBTEST_2(run_test<MatrixXf>(dim, num_elements)); - } - - CALL_SUBTEST_3((run_fixed_size_test<float, 2>(num_elements))); - CALL_SUBTEST_4((run_fixed_size_test<float, 3>(num_elements))); - CALL_SUBTEST_5((run_fixed_size_test<float, 4>(num_elements))); - - CALL_SUBTEST_6((run_fixed_size_test<double, 2>(num_elements))); - CALL_SUBTEST_7((run_fixed_size_test<double, 3>(num_elements))); - CALL_SUBTEST_8((run_fixed_size_test<double, 4>(num_elements))); - } - - // Those two calls don't compile and result in meaningful error messages! - // umeyama(MatrixXcf(),MatrixXcf()); - // umeyama(MatrixXcd(),MatrixXcd()); -} diff --git a/eigen/test/umfpack_support.cpp b/eigen/test/umfpack_support.cpp deleted file mode 100644 index 37ab11f..0000000 --- a/eigen/test/umfpack_support.cpp +++ /dev/null @@ -1,32 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_NO_DEBUG_SMALL_PRODUCT_BLOCKS -#include "sparse_solver.h" - -#include <Eigen/UmfPackSupport> - -template<typename T> void test_umfpack_support_T() -{ - UmfPackLU<SparseMatrix<T, ColMajor> > umfpack_colmajor; - UmfPackLU<SparseMatrix<T, RowMajor> > umfpack_rowmajor; - - check_sparse_square_solving(umfpack_colmajor); - check_sparse_square_solving(umfpack_rowmajor); - - check_sparse_square_determinant(umfpack_colmajor); - check_sparse_square_determinant(umfpack_rowmajor); -} - -void test_umfpack_support() -{ - CALL_SUBTEST_1(test_umfpack_support_T<double>()); - CALL_SUBTEST_2(test_umfpack_support_T<std::complex<double> >()); -} - diff --git a/eigen/test/unalignedassert.cpp b/eigen/test/unalignedassert.cpp deleted file mode 100644 index 731a089..0000000 --- a/eigen/test/unalignedassert.cpp +++ /dev/null @@ -1,180 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#if defined(EIGEN_TEST_PART_1) - // default -#elif defined(EIGEN_TEST_PART_2) - #define EIGEN_MAX_STATIC_ALIGN_BYTES 16 - #define EIGEN_MAX_ALIGN_BYTES 16 -#elif defined(EIGEN_TEST_PART_3) - #define EIGEN_MAX_STATIC_ALIGN_BYTES 32 - #define EIGEN_MAX_ALIGN_BYTES 32 -#elif defined(EIGEN_TEST_PART_4) - #define EIGEN_MAX_STATIC_ALIGN_BYTES 64 - #define EIGEN_MAX_ALIGN_BYTES 64 -#endif - -#include "main.h" - -typedef Matrix<float, 6,1> Vector6f; -typedef Matrix<float, 8,1> Vector8f; -typedef Matrix<float, 12,1> Vector12f; - -typedef Matrix<double, 5,1> Vector5d; -typedef Matrix<double, 6,1> Vector6d; -typedef Matrix<double, 7,1> Vector7d; -typedef Matrix<double, 8,1> Vector8d; -typedef Matrix<double, 9,1> Vector9d; -typedef Matrix<double,10,1> Vector10d; -typedef Matrix<double,12,1> Vector12d; - -struct TestNew1 -{ - MatrixXd m; // good: m will allocate its own array, taking care of alignment. - TestNew1() : m(20,20) {} -}; - -struct TestNew2 -{ - Matrix3d m; // good: m's size isn't a multiple of 16 bytes, so m doesn't have to be 16-byte aligned, - // 8-byte alignment is good enough here, which we'll get automatically -}; - -struct TestNew3 -{ - Vector2f m; // good: m's size isn't a multiple of 16 bytes, so m doesn't have to be 16-byte aligned -}; - -struct TestNew4 -{ - EIGEN_MAKE_ALIGNED_OPERATOR_NEW - Vector2d m; - float f; // make the struct have sizeof%16!=0 to make it a little more tricky when we allow an array of 2 such objects -}; - -struct TestNew5 -{ - EIGEN_MAKE_ALIGNED_OPERATOR_NEW - float f; // try the f at first -- the EIGEN_ALIGN_MAX attribute of m should make that still work - Matrix4f m; -}; - -struct TestNew6 -{ - Matrix<float,2,2,DontAlign> m; // good: no alignment requested - float f; -}; - -template<bool Align> struct Depends -{ - EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(Align) - Vector2d m; - float f; -}; - -template<typename T> -void check_unalignedassert_good() -{ - T *x, *y; - x = new T; - delete x; - y = new T[2]; - delete[] y; -} - -#if EIGEN_MAX_STATIC_ALIGN_BYTES>0 -template<typename T> -void construct_at_boundary(int boundary) -{ - char buf[sizeof(T)+256]; - size_t _buf = reinterpret_cast<internal::UIntPtr>(buf); - _buf += (EIGEN_MAX_ALIGN_BYTES - (_buf % EIGEN_MAX_ALIGN_BYTES)); // make 16/32/...-byte aligned - _buf += boundary; // make exact boundary-aligned - T *x = ::new(reinterpret_cast<void*>(_buf)) T; - x[0].setZero(); // just in order to silence warnings - x->~T(); -} -#endif - -void unalignedassert() -{ -#if EIGEN_MAX_STATIC_ALIGN_BYTES>0 - construct_at_boundary<Vector2f>(4); - construct_at_boundary<Vector3f>(4); - construct_at_boundary<Vector4f>(16); - construct_at_boundary<Vector6f>(4); - construct_at_boundary<Vector8f>(EIGEN_MAX_ALIGN_BYTES); - construct_at_boundary<Vector12f>(16); - construct_at_boundary<Matrix2f>(16); - construct_at_boundary<Matrix3f>(4); - construct_at_boundary<Matrix4f>(EIGEN_MAX_ALIGN_BYTES); - - construct_at_boundary<Vector2d>(16); - construct_at_boundary<Vector3d>(4); - construct_at_boundary<Vector4d>(EIGEN_MAX_ALIGN_BYTES); - construct_at_boundary<Vector5d>(4); - construct_at_boundary<Vector6d>(16); - construct_at_boundary<Vector7d>(4); - construct_at_boundary<Vector8d>(EIGEN_MAX_ALIGN_BYTES); - construct_at_boundary<Vector9d>(4); - construct_at_boundary<Vector10d>(16); - construct_at_boundary<Vector12d>(EIGEN_MAX_ALIGN_BYTES); - construct_at_boundary<Matrix2d>(EIGEN_MAX_ALIGN_BYTES); - construct_at_boundary<Matrix3d>(4); - construct_at_boundary<Matrix4d>(EIGEN_MAX_ALIGN_BYTES); - - construct_at_boundary<Vector2cf>(16); - construct_at_boundary<Vector3cf>(4); - construct_at_boundary<Vector2cd>(EIGEN_MAX_ALIGN_BYTES); - construct_at_boundary<Vector3cd>(16); -#endif - - check_unalignedassert_good<TestNew1>(); - check_unalignedassert_good<TestNew2>(); - check_unalignedassert_good<TestNew3>(); - - check_unalignedassert_good<TestNew4>(); - check_unalignedassert_good<TestNew5>(); - check_unalignedassert_good<TestNew6>(); - check_unalignedassert_good<Depends<true> >(); - -#if EIGEN_MAX_STATIC_ALIGN_BYTES>0 - if(EIGEN_MAX_ALIGN_BYTES>=16) - { - VERIFY_RAISES_ASSERT(construct_at_boundary<Vector4f>(8)); - VERIFY_RAISES_ASSERT(construct_at_boundary<Vector8f>(8)); - VERIFY_RAISES_ASSERT(construct_at_boundary<Vector12f>(8)); - VERIFY_RAISES_ASSERT(construct_at_boundary<Vector2d>(8)); - VERIFY_RAISES_ASSERT(construct_at_boundary<Vector4d>(8)); - VERIFY_RAISES_ASSERT(construct_at_boundary<Vector6d>(8)); - VERIFY_RAISES_ASSERT(construct_at_boundary<Vector8d>(8)); - VERIFY_RAISES_ASSERT(construct_at_boundary<Vector10d>(8)); - VERIFY_RAISES_ASSERT(construct_at_boundary<Vector12d>(8)); - // Complexes are disabled because the compiler might aggressively vectorize - // the initialization of complex coeffs to 0 before we can check for alignedness - //VERIFY_RAISES_ASSERT(construct_at_boundary<Vector2cf>(8)); - VERIFY_RAISES_ASSERT(construct_at_boundary<Vector4i>(8)); - } - for(int b=8; b<EIGEN_MAX_ALIGN_BYTES; b+=8) - { - if(b<32) VERIFY_RAISES_ASSERT(construct_at_boundary<Vector8f>(b)); - if(b<64) VERIFY_RAISES_ASSERT(construct_at_boundary<Matrix4f>(b)); - if(b<32) VERIFY_RAISES_ASSERT(construct_at_boundary<Vector4d>(b)); - if(b<32) VERIFY_RAISES_ASSERT(construct_at_boundary<Matrix2d>(b)); - if(b<128) VERIFY_RAISES_ASSERT(construct_at_boundary<Matrix4d>(b)); - //if(b<32) VERIFY_RAISES_ASSERT(construct_at_boundary<Vector2cd>(b)); - } -#endif -} - -void test_unalignedassert() -{ - CALL_SUBTEST(unalignedassert()); -} diff --git a/eigen/test/unalignedcount.cpp b/eigen/test/unalignedcount.cpp deleted file mode 100644 index d6ffeaf..0000000 --- a/eigen/test/unalignedcount.cpp +++ /dev/null @@ -1,53 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -static int nb_load; -static int nb_loadu; -static int nb_store; -static int nb_storeu; - -#define EIGEN_DEBUG_ALIGNED_LOAD { nb_load++; } -#define EIGEN_DEBUG_UNALIGNED_LOAD { nb_loadu++; } -#define EIGEN_DEBUG_ALIGNED_STORE { nb_store++; } -#define EIGEN_DEBUG_UNALIGNED_STORE { nb_storeu++; } - -#define VERIFY_ALIGNED_UNALIGNED_COUNT(XPR,AL,UL,AS,US) {\ - nb_load = nb_loadu = nb_store = nb_storeu = 0; \ - XPR; \ - if(!(nb_load==AL && nb_loadu==UL && nb_store==AS && nb_storeu==US)) \ - std::cerr << " >> " << nb_load << ", " << nb_loadu << ", " << nb_store << ", " << nb_storeu << "\n"; \ - VERIFY( (#XPR) && nb_load==AL && nb_loadu==UL && nb_store==AS && nb_storeu==US ); \ - } - - -#include "main.h" - -void test_unalignedcount() -{ - #if defined(EIGEN_VECTORIZE_AVX) - VectorXf a(40), b(40); - VERIFY_ALIGNED_UNALIGNED_COUNT(a += b, 10, 0, 5, 0); - VERIFY_ALIGNED_UNALIGNED_COUNT(a.segment(0,40) += b.segment(0,40), 5, 5, 5, 0); - VERIFY_ALIGNED_UNALIGNED_COUNT(a.segment(0,40) -= b.segment(0,40), 5, 5, 5, 0); - VERIFY_ALIGNED_UNALIGNED_COUNT(a.segment(0,40) *= 3.5, 5, 0, 5, 0); - VERIFY_ALIGNED_UNALIGNED_COUNT(a.segment(0,40) /= 3.5, 5, 0, 5, 0); - #elif defined(EIGEN_VECTORIZE_SSE) - VectorXf a(40), b(40); - VERIFY_ALIGNED_UNALIGNED_COUNT(a += b, 20, 0, 10, 0); - VERIFY_ALIGNED_UNALIGNED_COUNT(a.segment(0,40) += b.segment(0,40), 10, 10, 10, 0); - VERIFY_ALIGNED_UNALIGNED_COUNT(a.segment(0,40) -= b.segment(0,40), 10, 10, 10, 0); - VERIFY_ALIGNED_UNALIGNED_COUNT(a.segment(0,40) *= 3.5, 10, 0, 10, 0); - VERIFY_ALIGNED_UNALIGNED_COUNT(a.segment(0,40) /= 3.5, 10, 0, 10, 0); - #else - // The following line is to eliminate "variable not used" warnings - nb_load = nb_loadu = nb_store = nb_storeu = 0; - int a(0), b(0); - VERIFY(a==b); - #endif -} diff --git a/eigen/test/upperbidiagonalization.cpp b/eigen/test/upperbidiagonalization.cpp deleted file mode 100644 index 847b34b..0000000 --- a/eigen/test/upperbidiagonalization.cpp +++ /dev/null @@ -1,43 +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> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/SVD> - -template<typename MatrixType> void upperbidiag(const MatrixType& m) -{ - const typename MatrixType::Index rows = m.rows(); - const typename MatrixType::Index cols = m.cols(); - - typedef Matrix<typename MatrixType::RealScalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime> RealMatrixType; - typedef Matrix<typename MatrixType::Scalar, MatrixType::ColsAtCompileTime, MatrixType::RowsAtCompileTime> TransposeMatrixType; - - MatrixType a = MatrixType::Random(rows,cols); - internal::UpperBidiagonalization<MatrixType> ubd(a); - RealMatrixType b(rows, cols); - b.setZero(); - b.block(0,0,cols,cols) = ubd.bidiagonal(); - MatrixType c = ubd.householderU() * b * ubd.householderV().adjoint(); - VERIFY_IS_APPROX(a,c); - TransposeMatrixType d = ubd.householderV() * b.adjoint() * ubd.householderU().adjoint(); - VERIFY_IS_APPROX(a.adjoint(),d); -} - -void test_upperbidiagonalization() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( upperbidiag(MatrixXf(3,3)) ); - CALL_SUBTEST_2( upperbidiag(MatrixXd(17,12)) ); - CALL_SUBTEST_3( upperbidiag(MatrixXcf(20,20)) ); - CALL_SUBTEST_4( upperbidiag(Matrix<std::complex<double>,Dynamic,Dynamic,RowMajor>(16,15)) ); - CALL_SUBTEST_5( upperbidiag(Matrix<float,6,4>()) ); - CALL_SUBTEST_6( upperbidiag(Matrix<float,5,5>()) ); - CALL_SUBTEST_7( upperbidiag(Matrix<double,4,3>()) ); - } -} diff --git a/eigen/test/vectorization_logic.cpp b/eigen/test/vectorization_logic.cpp deleted file mode 100644 index 37e7495..0000000 --- a/eigen/test/vectorization_logic.cpp +++ /dev/null @@ -1,425 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifdef EIGEN_TEST_PART_1 -#define EIGEN_UNALIGNED_VECTORIZE 1 -#endif - -#ifdef EIGEN_TEST_PART_2 -#define EIGEN_UNALIGNED_VECTORIZE 0 -#endif - -#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR -#undef EIGEN_DEFAULT_TO_ROW_MAJOR -#endif -#define EIGEN_DEBUG_ASSIGN -#include "main.h" -#include <typeinfo> - -using internal::demangle_flags; -using internal::demangle_traversal; -using internal::demangle_unrolling; - -template<typename Dst, typename Src> -bool test_assign(const Dst&, const Src&, int traversal, int unrolling) -{ - typedef internal::copy_using_evaluator_traits<internal::evaluator<Dst>,internal::evaluator<Src>, internal::assign_op<typename Dst::Scalar,typename Src::Scalar> > traits; - bool res = traits::Traversal==traversal; - if(unrolling==InnerUnrolling+CompleteUnrolling) - res = res && (int(traits::Unrolling)==InnerUnrolling || int(traits::Unrolling)==CompleteUnrolling); - else - res = res && int(traits::Unrolling)==unrolling; - if(!res) - { - std::cerr << "Src: " << demangle_flags(Src::Flags) << std::endl; - std::cerr << " " << demangle_flags(internal::evaluator<Src>::Flags) << std::endl; - std::cerr << "Dst: " << demangle_flags(Dst::Flags) << std::endl; - std::cerr << " " << demangle_flags(internal::evaluator<Dst>::Flags) << std::endl; - traits::debug(); - std::cerr << " Expected Traversal == " << demangle_traversal(traversal) - << " got " << demangle_traversal(traits::Traversal) << "\n"; - std::cerr << " Expected Unrolling == " << demangle_unrolling(unrolling) - << " got " << demangle_unrolling(traits::Unrolling) << "\n"; - } - return res; -} - -template<typename Dst, typename Src> -bool test_assign(int traversal, int unrolling) -{ - typedef internal::copy_using_evaluator_traits<internal::evaluator<Dst>,internal::evaluator<Src>, internal::assign_op<typename Dst::Scalar,typename Src::Scalar> > traits; - bool res = traits::Traversal==traversal && traits::Unrolling==unrolling; - if(!res) - { - std::cerr << "Src: " << demangle_flags(Src::Flags) << std::endl; - std::cerr << " " << demangle_flags(internal::evaluator<Src>::Flags) << std::endl; - std::cerr << "Dst: " << demangle_flags(Dst::Flags) << std::endl; - std::cerr << " " << demangle_flags(internal::evaluator<Dst>::Flags) << std::endl; - traits::debug(); - std::cerr << " Expected Traversal == " << demangle_traversal(traversal) - << " got " << demangle_traversal(traits::Traversal) << "\n"; - std::cerr << " Expected Unrolling == " << demangle_unrolling(unrolling) - << " got " << demangle_unrolling(traits::Unrolling) << "\n"; - } - return res; -} - -template<typename Xpr> -bool test_redux(const Xpr&, int traversal, int unrolling) -{ - typedef typename Xpr::Scalar Scalar; - typedef internal::redux_traits<internal::scalar_sum_op<Scalar,Scalar>,internal::redux_evaluator<Xpr> > traits; - - bool res = traits::Traversal==traversal && traits::Unrolling==unrolling; - if(!res) - { - std::cerr << demangle_flags(Xpr::Flags) << std::endl; - std::cerr << demangle_flags(internal::evaluator<Xpr>::Flags) << std::endl; - traits::debug(); - - std::cerr << " Expected Traversal == " << demangle_traversal(traversal) - << " got " << demangle_traversal(traits::Traversal) << "\n"; - std::cerr << " Expected Unrolling == " << demangle_unrolling(unrolling) - << " got " << demangle_unrolling(traits::Unrolling) << "\n"; - } - return res; -} - -template<typename Scalar, bool Enable = internal::packet_traits<Scalar>::Vectorizable> -struct vectorization_logic -{ - typedef internal::packet_traits<Scalar> PacketTraits; - - typedef typename internal::packet_traits<Scalar>::type PacketType; - typedef typename internal::unpacket_traits<PacketType>::half HalfPacketType; - enum { - PacketSize = internal::unpacket_traits<PacketType>::size, - HalfPacketSize = internal::unpacket_traits<HalfPacketType>::size - }; - static void run() - { - - typedef Matrix<Scalar,PacketSize,1> Vector1; - typedef Matrix<Scalar,Dynamic,1> VectorX; - typedef Matrix<Scalar,Dynamic,Dynamic> MatrixXX; - typedef Matrix<Scalar,PacketSize,PacketSize> Matrix11; - typedef Matrix<Scalar,2*PacketSize,2*PacketSize> Matrix22; - typedef Matrix<Scalar,(Matrix11::Flags&RowMajorBit)?16:4*PacketSize,(Matrix11::Flags&RowMajorBit)?4*PacketSize:16> Matrix44; - typedef Matrix<Scalar,(Matrix11::Flags&RowMajorBit)?16:4*PacketSize,(Matrix11::Flags&RowMajorBit)?4*PacketSize:16,DontAlign|EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION> Matrix44u; - typedef Matrix<Scalar,4*PacketSize,4*PacketSize,ColMajor> Matrix44c; - typedef Matrix<Scalar,4*PacketSize,4*PacketSize,RowMajor> Matrix44r; - - typedef Matrix<Scalar, - (PacketSize==8 ? 4 : PacketSize==4 ? 2 : PacketSize==2 ? 1 : /*PacketSize==1 ?*/ 1), - (PacketSize==8 ? 2 : PacketSize==4 ? 2 : PacketSize==2 ? 2 : /*PacketSize==1 ?*/ 1) - > Matrix1; - - typedef Matrix<Scalar, - (PacketSize==8 ? 4 : PacketSize==4 ? 2 : PacketSize==2 ? 1 : /*PacketSize==1 ?*/ 1), - (PacketSize==8 ? 2 : PacketSize==4 ? 2 : PacketSize==2 ? 2 : /*PacketSize==1 ?*/ 1), - DontAlign|((Matrix1::Flags&RowMajorBit)?RowMajor:ColMajor)> Matrix1u; - - // this type is made such that it can only be vectorized when viewed as a linear 1D vector - typedef Matrix<Scalar, - (PacketSize==8 ? 4 : PacketSize==4 ? 6 : PacketSize==2 ? ((Matrix11::Flags&RowMajorBit)?2:3) : /*PacketSize==1 ?*/ 1), - (PacketSize==8 ? 6 : PacketSize==4 ? 2 : PacketSize==2 ? ((Matrix11::Flags&RowMajorBit)?3:2) : /*PacketSize==1 ?*/ 3) - > Matrix3; - - #if !EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT - VERIFY(test_assign(Vector1(),Vector1(), - InnerVectorizedTraversal,CompleteUnrolling)); - VERIFY(test_assign(Vector1(),Vector1()+Vector1(), - InnerVectorizedTraversal,CompleteUnrolling)); - VERIFY(test_assign(Vector1(),Vector1().cwiseProduct(Vector1()), - InnerVectorizedTraversal,CompleteUnrolling)); - VERIFY(test_assign(Vector1(),Vector1().template cast<Scalar>(), - InnerVectorizedTraversal,CompleteUnrolling)); - - - VERIFY(test_assign(Vector1(),Vector1(), - InnerVectorizedTraversal,CompleteUnrolling)); - VERIFY(test_assign(Vector1(),Vector1()+Vector1(), - InnerVectorizedTraversal,CompleteUnrolling)); - VERIFY(test_assign(Vector1(),Vector1().cwiseProduct(Vector1()), - InnerVectorizedTraversal,CompleteUnrolling)); - - VERIFY(test_assign(Matrix44(),Matrix44()+Matrix44(), - InnerVectorizedTraversal,InnerUnrolling)); - - VERIFY(test_assign(Matrix44u(),Matrix44()+Matrix44(), - EIGEN_UNALIGNED_VECTORIZE ? InnerVectorizedTraversal : LinearTraversal, - EIGEN_UNALIGNED_VECTORIZE ? InnerUnrolling : NoUnrolling)); - - VERIFY(test_assign(Matrix1(),Matrix1()+Matrix1(), - (Matrix1::InnerSizeAtCompileTime % PacketSize)==0 ? InnerVectorizedTraversal : LinearVectorizedTraversal, - CompleteUnrolling)); - - VERIFY(test_assign(Matrix1u(),Matrix1()+Matrix1(), - EIGEN_UNALIGNED_VECTORIZE ? ((Matrix1::InnerSizeAtCompileTime % PacketSize)==0 ? InnerVectorizedTraversal : LinearVectorizedTraversal) - : LinearTraversal, CompleteUnrolling)); - - VERIFY(test_assign(Matrix44c().col(1),Matrix44c().col(2)+Matrix44c().col(3), - InnerVectorizedTraversal,CompleteUnrolling)); - - VERIFY(test_assign(Matrix44r().row(2),Matrix44r().row(1)+Matrix44r().row(1), - InnerVectorizedTraversal,CompleteUnrolling)); - - if(PacketSize>1) - { - typedef Matrix<Scalar,3,3,ColMajor> Matrix33c; - typedef Matrix<Scalar,3,1,ColMajor> Vector3; - VERIFY(test_assign(Matrix33c().row(2),Matrix33c().row(1)+Matrix33c().row(1), - LinearTraversal,CompleteUnrolling)); - VERIFY(test_assign(Vector3(),Vector3()+Vector3(), - EIGEN_UNALIGNED_VECTORIZE ? (HalfPacketSize==1 ? InnerVectorizedTraversal : LinearVectorizedTraversal) : (HalfPacketSize==1 ? InnerVectorizedTraversal : LinearTraversal), CompleteUnrolling)); - VERIFY(test_assign(Matrix33c().col(0),Matrix33c().col(1)+Matrix33c().col(1), - EIGEN_UNALIGNED_VECTORIZE ? (HalfPacketSize==1 ? InnerVectorizedTraversal : LinearVectorizedTraversal) : (HalfPacketSize==1 ? SliceVectorizedTraversal : LinearTraversal), - ((!EIGEN_UNALIGNED_VECTORIZE) && HalfPacketSize==1) ? NoUnrolling : CompleteUnrolling)); - - VERIFY(test_assign(Matrix3(),Matrix3().cwiseProduct(Matrix3()), - LinearVectorizedTraversal,CompleteUnrolling)); - - VERIFY(test_assign(Matrix<Scalar,17,17>(),Matrix<Scalar,17,17>()+Matrix<Scalar,17,17>(), - HalfPacketSize==1 ? InnerVectorizedTraversal : - EIGEN_UNALIGNED_VECTORIZE ? LinearVectorizedTraversal : - LinearTraversal, - NoUnrolling)); - - VERIFY(test_assign(Matrix11(), Matrix11()+Matrix11(),InnerVectorizedTraversal,CompleteUnrolling)); - - - VERIFY(test_assign(Matrix11(),Matrix<Scalar,17,17>().template block<PacketSize,PacketSize>(2,3)+Matrix<Scalar,17,17>().template block<PacketSize,PacketSize>(8,4), - (EIGEN_UNALIGNED_VECTORIZE) ? InnerVectorizedTraversal : DefaultTraversal, CompleteUnrolling|InnerUnrolling)); - - VERIFY(test_assign(Vector1(),Matrix11()*Vector1(), - InnerVectorizedTraversal,CompleteUnrolling)); - - VERIFY(test_assign(Matrix11(),Matrix11().lazyProduct(Matrix11()), - InnerVectorizedTraversal,InnerUnrolling+CompleteUnrolling)); - } - - VERIFY(test_redux(Vector1(), - LinearVectorizedTraversal,CompleteUnrolling)); - - VERIFY(test_redux(Vector1().array()*Vector1().array(), - LinearVectorizedTraversal,CompleteUnrolling)); - - VERIFY(test_redux((Vector1().array()*Vector1().array()).col(0), - LinearVectorizedTraversal,CompleteUnrolling)); - - VERIFY(test_redux(Matrix<Scalar,PacketSize,3>(), - LinearVectorizedTraversal,CompleteUnrolling)); - - VERIFY(test_redux(Matrix3(), - LinearVectorizedTraversal,CompleteUnrolling)); - - VERIFY(test_redux(Matrix44(), - LinearVectorizedTraversal,NoUnrolling)); - - VERIFY(test_redux(Matrix44().template block<(Matrix1::Flags&RowMajorBit)?4:PacketSize,(Matrix1::Flags&RowMajorBit)?PacketSize:4>(1,2), - DefaultTraversal,CompleteUnrolling)); - - VERIFY(test_redux(Matrix44c().template block<2*PacketSize,1>(1,2), - LinearVectorizedTraversal,CompleteUnrolling)); - - VERIFY(test_redux(Matrix44r().template block<1,2*PacketSize>(2,1), - LinearVectorizedTraversal,CompleteUnrolling)); - - VERIFY((test_assign< - Map<Matrix22, AlignedMax, OuterStride<3*PacketSize> >, - Matrix22 - >(InnerVectorizedTraversal,CompleteUnrolling))); - - VERIFY((test_assign< - Map<Matrix<Scalar,EIGEN_PLAIN_ENUM_MAX(2,PacketSize),EIGEN_PLAIN_ENUM_MAX(2,PacketSize)>, AlignedMax, InnerStride<3*PacketSize> >, - Matrix<Scalar,EIGEN_PLAIN_ENUM_MAX(2,PacketSize),EIGEN_PLAIN_ENUM_MAX(2,PacketSize)> - >(DefaultTraversal,PacketSize>=8?InnerUnrolling:CompleteUnrolling))); - - VERIFY((test_assign(Matrix11(), Matrix<Scalar,PacketSize,EIGEN_PLAIN_ENUM_MIN(2,PacketSize)>()*Matrix<Scalar,EIGEN_PLAIN_ENUM_MIN(2,PacketSize),PacketSize>(), - InnerVectorizedTraversal, CompleteUnrolling))); - #endif - - VERIFY(test_assign(MatrixXX(10,10),MatrixXX(20,20).block(10,10,2,3), - SliceVectorizedTraversal,NoUnrolling)); - - VERIFY(test_redux(VectorX(10), - LinearVectorizedTraversal,NoUnrolling)); - } -}; - -template<typename Scalar> struct vectorization_logic<Scalar,false> -{ - static void run() {} -}; - -template<typename Scalar, bool Enable = !internal::is_same<typename internal::unpacket_traits<typename internal::packet_traits<Scalar>::type>::half, - typename internal::packet_traits<Scalar>::type>::value > -struct vectorization_logic_half -{ - typedef internal::packet_traits<Scalar> PacketTraits; - typedef typename internal::unpacket_traits<typename internal::packet_traits<Scalar>::type>::half PacketType; - enum { - PacketSize = internal::unpacket_traits<PacketType>::size - }; - static void run() - { - - typedef Matrix<Scalar,PacketSize,1> Vector1; - typedef Matrix<Scalar,PacketSize,PacketSize> Matrix11; - typedef Matrix<Scalar,5*PacketSize,7,ColMajor> Matrix57; - typedef Matrix<Scalar,3*PacketSize,5,ColMajor> Matrix35; - typedef Matrix<Scalar,5*PacketSize,7,DontAlign|ColMajor> Matrix57u; -// typedef Matrix<Scalar,(Matrix11::Flags&RowMajorBit)?16:4*PacketSize,(Matrix11::Flags&RowMajorBit)?4*PacketSize:16> Matrix44; -// typedef Matrix<Scalar,(Matrix11::Flags&RowMajorBit)?16:4*PacketSize,(Matrix11::Flags&RowMajorBit)?4*PacketSize:16,DontAlign|EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION> Matrix44u; -// typedef Matrix<Scalar,4*PacketSize,4*PacketSize,ColMajor> Matrix44c; -// typedef Matrix<Scalar,4*PacketSize,4*PacketSize,RowMajor> Matrix44r; - - typedef Matrix<Scalar, - (PacketSize==8 ? 4 : PacketSize==4 ? 2 : PacketSize==2 ? 1 : /*PacketSize==1 ?*/ 1), - (PacketSize==8 ? 2 : PacketSize==4 ? 2 : PacketSize==2 ? 2 : /*PacketSize==1 ?*/ 1) - > Matrix1; - - typedef Matrix<Scalar, - (PacketSize==8 ? 4 : PacketSize==4 ? 2 : PacketSize==2 ? 1 : /*PacketSize==1 ?*/ 1), - (PacketSize==8 ? 2 : PacketSize==4 ? 2 : PacketSize==2 ? 2 : /*PacketSize==1 ?*/ 1), - DontAlign|((Matrix1::Flags&RowMajorBit)?RowMajor:ColMajor)> Matrix1u; - - // this type is made such that it can only be vectorized when viewed as a linear 1D vector - typedef Matrix<Scalar, - (PacketSize==8 ? 4 : PacketSize==4 ? 6 : PacketSize==2 ? ((Matrix11::Flags&RowMajorBit)?2:3) : /*PacketSize==1 ?*/ 1), - (PacketSize==8 ? 6 : PacketSize==4 ? 2 : PacketSize==2 ? ((Matrix11::Flags&RowMajorBit)?3:2) : /*PacketSize==1 ?*/ 3) - > Matrix3; - - #if !EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT - VERIFY(test_assign(Vector1(),Vector1(), - InnerVectorizedTraversal,CompleteUnrolling)); - VERIFY(test_assign(Vector1(),Vector1()+Vector1(), - InnerVectorizedTraversal,CompleteUnrolling)); - VERIFY(test_assign(Vector1(),Vector1().template segment<PacketSize>(0).derived(), - EIGEN_UNALIGNED_VECTORIZE ? InnerVectorizedTraversal : LinearVectorizedTraversal,CompleteUnrolling)); - VERIFY(test_assign(Vector1(),Scalar(2.1)*Vector1()-Vector1(), - InnerVectorizedTraversal,CompleteUnrolling)); - VERIFY(test_assign(Vector1(),(Scalar(2.1)*Vector1().template segment<PacketSize>(0)-Vector1().template segment<PacketSize>(0)).derived(), - EIGEN_UNALIGNED_VECTORIZE ? InnerVectorizedTraversal : LinearVectorizedTraversal,CompleteUnrolling)); - VERIFY(test_assign(Vector1(),Vector1().cwiseProduct(Vector1()), - InnerVectorizedTraversal,CompleteUnrolling)); - VERIFY(test_assign(Vector1(),Vector1().template cast<Scalar>(), - InnerVectorizedTraversal,CompleteUnrolling)); - - - VERIFY(test_assign(Vector1(),Vector1(), - InnerVectorizedTraversal,CompleteUnrolling)); - VERIFY(test_assign(Vector1(),Vector1()+Vector1(), - InnerVectorizedTraversal,CompleteUnrolling)); - VERIFY(test_assign(Vector1(),Vector1().cwiseProduct(Vector1()), - InnerVectorizedTraversal,CompleteUnrolling)); - - VERIFY(test_assign(Matrix57(),Matrix57()+Matrix57(), - InnerVectorizedTraversal,InnerUnrolling)); - - VERIFY(test_assign(Matrix57u(),Matrix57()+Matrix57(), - EIGEN_UNALIGNED_VECTORIZE ? InnerVectorizedTraversal : LinearTraversal, - EIGEN_UNALIGNED_VECTORIZE ? InnerUnrolling : NoUnrolling)); - - VERIFY(test_assign(Matrix1u(),Matrix1()+Matrix1(), - EIGEN_UNALIGNED_VECTORIZE ? ((Matrix1::InnerSizeAtCompileTime % PacketSize)==0 ? InnerVectorizedTraversal : LinearVectorizedTraversal) : LinearTraversal,CompleteUnrolling)); - - if(PacketSize>1) - { - typedef Matrix<Scalar,3,3,ColMajor> Matrix33c; - VERIFY(test_assign(Matrix33c().row(2),Matrix33c().row(1)+Matrix33c().row(1), - LinearTraversal,CompleteUnrolling)); - VERIFY(test_assign(Matrix33c().col(0),Matrix33c().col(1)+Matrix33c().col(1), - EIGEN_UNALIGNED_VECTORIZE ? (PacketSize==1 ? InnerVectorizedTraversal : LinearVectorizedTraversal) : LinearTraversal,CompleteUnrolling)); - - VERIFY(test_assign(Matrix3(),Matrix3().cwiseQuotient(Matrix3()), - PacketTraits::HasDiv ? LinearVectorizedTraversal : LinearTraversal,CompleteUnrolling)); - - VERIFY(test_assign(Matrix<Scalar,17,17>(),Matrix<Scalar,17,17>()+Matrix<Scalar,17,17>(), - EIGEN_UNALIGNED_VECTORIZE ? (PacketSize==1 ? InnerVectorizedTraversal : LinearVectorizedTraversal) : LinearTraversal, - NoUnrolling)); - - VERIFY(test_assign(Matrix11(),Matrix<Scalar,17,17>().template block<PacketSize,PacketSize>(2,3)+Matrix<Scalar,17,17>().template block<PacketSize,PacketSize>(8,4), - EIGEN_UNALIGNED_VECTORIZE ? InnerVectorizedTraversal : DefaultTraversal,PacketSize>4?InnerUnrolling:CompleteUnrolling)); - - VERIFY(test_assign(Vector1(),Matrix11()*Vector1(), - InnerVectorizedTraversal,CompleteUnrolling)); - - VERIFY(test_assign(Matrix11(),Matrix11().lazyProduct(Matrix11()), - InnerVectorizedTraversal,InnerUnrolling+CompleteUnrolling)); - } - - VERIFY(test_redux(Vector1(), - LinearVectorizedTraversal,CompleteUnrolling)); - - VERIFY(test_redux(Matrix<Scalar,PacketSize,3>(), - LinearVectorizedTraversal,CompleteUnrolling)); - - VERIFY(test_redux(Matrix3(), - LinearVectorizedTraversal,CompleteUnrolling)); - - VERIFY(test_redux(Matrix35(), - LinearVectorizedTraversal,CompleteUnrolling)); - - VERIFY(test_redux(Matrix57().template block<PacketSize,3>(1,0), - DefaultTraversal,CompleteUnrolling)); - - VERIFY((test_assign< - Map<Matrix<Scalar,EIGEN_PLAIN_ENUM_MAX(2,PacketSize),EIGEN_PLAIN_ENUM_MAX(2,PacketSize)>, AlignedMax, InnerStride<3*PacketSize> >, - Matrix<Scalar,EIGEN_PLAIN_ENUM_MAX(2,PacketSize),EIGEN_PLAIN_ENUM_MAX(2,PacketSize)> - >(DefaultTraversal,CompleteUnrolling))); - - VERIFY((test_assign(Matrix57(), Matrix<Scalar,5*PacketSize,3>()*Matrix<Scalar,3,7>(), - InnerVectorizedTraversal, InnerUnrolling|CompleteUnrolling))); - #endif - } -}; - -template<typename Scalar> struct vectorization_logic_half<Scalar,false> -{ - static void run() {} -}; - -void test_vectorization_logic() -{ - -#ifdef EIGEN_VECTORIZE - - CALL_SUBTEST( vectorization_logic<int>::run() ); - CALL_SUBTEST( vectorization_logic<float>::run() ); - CALL_SUBTEST( vectorization_logic<double>::run() ); - CALL_SUBTEST( vectorization_logic<std::complex<float> >::run() ); - CALL_SUBTEST( vectorization_logic<std::complex<double> >::run() ); - - CALL_SUBTEST( vectorization_logic_half<int>::run() ); - CALL_SUBTEST( vectorization_logic_half<float>::run() ); - CALL_SUBTEST( vectorization_logic_half<double>::run() ); - CALL_SUBTEST( vectorization_logic_half<std::complex<float> >::run() ); - CALL_SUBTEST( vectorization_logic_half<std::complex<double> >::run() ); - - if(internal::packet_traits<float>::Vectorizable) - { - VERIFY(test_assign(Matrix<float,3,3>(),Matrix<float,3,3>()+Matrix<float,3,3>(), - EIGEN_UNALIGNED_VECTORIZE ? LinearVectorizedTraversal : LinearTraversal,CompleteUnrolling)); - - VERIFY(test_redux(Matrix<float,5,2>(), - EIGEN_UNALIGNED_VECTORIZE ? LinearVectorizedTraversal : DefaultTraversal,CompleteUnrolling)); - } - - if(internal::packet_traits<double>::Vectorizable) - { - VERIFY(test_assign(Matrix<double,3,3>(),Matrix<double,3,3>()+Matrix<double,3,3>(), - EIGEN_UNALIGNED_VECTORIZE ? LinearVectorizedTraversal : LinearTraversal,CompleteUnrolling)); - - VERIFY(test_redux(Matrix<double,7,3>(), - EIGEN_UNALIGNED_VECTORIZE ? LinearVectorizedTraversal : DefaultTraversal,CompleteUnrolling)); - } -#endif // EIGEN_VECTORIZE - -} diff --git a/eigen/test/vectorwiseop.cpp b/eigen/test/vectorwiseop.cpp deleted file mode 100644 index a099d17..0000000 --- a/eigen/test/vectorwiseop.cpp +++ /dev/null @@ -1,250 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define TEST_ENABLE_TEMPORARY_TRACKING -#define EIGEN_NO_STATIC_ASSERT - -#include "main.h" - -template<typename ArrayType> void vectorwiseop_array(const ArrayType& m) -{ - typedef typename ArrayType::Scalar Scalar; - typedef Array<Scalar, ArrayType::RowsAtCompileTime, 1> ColVectorType; - typedef Array<Scalar, 1, ArrayType::ColsAtCompileTime> RowVectorType; - - Index rows = m.rows(); - Index cols = m.cols(); - Index r = internal::random<Index>(0, rows-1), - c = internal::random<Index>(0, cols-1); - - ArrayType m1 = ArrayType::Random(rows, cols), - m2(rows, cols), - m3(rows, cols); - - ColVectorType colvec = ColVectorType::Random(rows); - RowVectorType rowvec = RowVectorType::Random(cols); - - // test addition - - m2 = m1; - m2.colwise() += colvec; - VERIFY_IS_APPROX(m2, m1.colwise() + colvec); - VERIFY_IS_APPROX(m2.col(c), m1.col(c) + colvec); - - VERIFY_RAISES_ASSERT(m2.colwise() += colvec.transpose()); - VERIFY_RAISES_ASSERT(m1.colwise() + colvec.transpose()); - - m2 = m1; - m2.rowwise() += rowvec; - VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec); - VERIFY_IS_APPROX(m2.row(r), m1.row(r) + rowvec); - - VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose()); - VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose()); - - // test substraction - - m2 = m1; - m2.colwise() -= colvec; - VERIFY_IS_APPROX(m2, m1.colwise() - colvec); - VERIFY_IS_APPROX(m2.col(c), m1.col(c) - colvec); - - VERIFY_RAISES_ASSERT(m2.colwise() -= colvec.transpose()); - VERIFY_RAISES_ASSERT(m1.colwise() - colvec.transpose()); - - m2 = m1; - m2.rowwise() -= rowvec; - VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec); - VERIFY_IS_APPROX(m2.row(r), m1.row(r) - rowvec); - - VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose()); - VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose()); - - // test multiplication - - m2 = m1; - m2.colwise() *= colvec; - VERIFY_IS_APPROX(m2, m1.colwise() * colvec); - VERIFY_IS_APPROX(m2.col(c), m1.col(c) * colvec); - - VERIFY_RAISES_ASSERT(m2.colwise() *= colvec.transpose()); - VERIFY_RAISES_ASSERT(m1.colwise() * colvec.transpose()); - - m2 = m1; - m2.rowwise() *= rowvec; - VERIFY_IS_APPROX(m2, m1.rowwise() * rowvec); - VERIFY_IS_APPROX(m2.row(r), m1.row(r) * rowvec); - - VERIFY_RAISES_ASSERT(m2.rowwise() *= rowvec.transpose()); - VERIFY_RAISES_ASSERT(m1.rowwise() * rowvec.transpose()); - - // test quotient - - m2 = m1; - m2.colwise() /= colvec; - VERIFY_IS_APPROX(m2, m1.colwise() / colvec); - VERIFY_IS_APPROX(m2.col(c), m1.col(c) / colvec); - - VERIFY_RAISES_ASSERT(m2.colwise() /= colvec.transpose()); - VERIFY_RAISES_ASSERT(m1.colwise() / colvec.transpose()); - - m2 = m1; - m2.rowwise() /= rowvec; - VERIFY_IS_APPROX(m2, m1.rowwise() / rowvec); - VERIFY_IS_APPROX(m2.row(r), m1.row(r) / rowvec); - - VERIFY_RAISES_ASSERT(m2.rowwise() /= rowvec.transpose()); - VERIFY_RAISES_ASSERT(m1.rowwise() / rowvec.transpose()); - - m2 = m1; - // yes, there might be an aliasing issue there but ".rowwise() /=" - // is supposed to evaluate " m2.colwise().sum()" into a temporary to avoid - // evaluating the reduction multiple times - if(ArrayType::RowsAtCompileTime>2 || ArrayType::RowsAtCompileTime==Dynamic) - { - m2.rowwise() /= m2.colwise().sum(); - VERIFY_IS_APPROX(m2, m1.rowwise() / m1.colwise().sum()); - } - - // all/any - Array<bool,Dynamic,Dynamic> mb(rows,cols); - mb = (m1.real()<=0.7).colwise().all(); - VERIFY( (mb.col(c) == (m1.real().col(c)<=0.7).all()).all() ); - mb = (m1.real()<=0.7).rowwise().all(); - VERIFY( (mb.row(r) == (m1.real().row(r)<=0.7).all()).all() ); - - mb = (m1.real()>=0.7).colwise().any(); - VERIFY( (mb.col(c) == (m1.real().col(c)>=0.7).any()).all() ); - mb = (m1.real()>=0.7).rowwise().any(); - VERIFY( (mb.row(r) == (m1.real().row(r)>=0.7).any()).all() ); -} - -template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVectorType; - typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType; - typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealColVectorType; - typedef Matrix<RealScalar, 1, MatrixType::ColsAtCompileTime> RealRowVectorType; - - Index rows = m.rows(); - Index cols = m.cols(); - Index r = internal::random<Index>(0, rows-1), - c = internal::random<Index>(0, cols-1); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2(rows, cols), - m3(rows, cols); - - ColVectorType colvec = ColVectorType::Random(rows); - RowVectorType rowvec = RowVectorType::Random(cols); - RealColVectorType rcres; - RealRowVectorType rrres; - - // test addition - - m2 = m1; - m2.colwise() += colvec; - VERIFY_IS_APPROX(m2, m1.colwise() + colvec); - VERIFY_IS_APPROX(m2.col(c), m1.col(c) + colvec); - - if(rows>1) - { - VERIFY_RAISES_ASSERT(m2.colwise() += colvec.transpose()); - VERIFY_RAISES_ASSERT(m1.colwise() + colvec.transpose()); - } - - m2 = m1; - m2.rowwise() += rowvec; - VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec); - VERIFY_IS_APPROX(m2.row(r), m1.row(r) + rowvec); - - if(cols>1) - { - VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose()); - VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose()); - } - - // test substraction - - m2 = m1; - m2.colwise() -= colvec; - VERIFY_IS_APPROX(m2, m1.colwise() - colvec); - VERIFY_IS_APPROX(m2.col(c), m1.col(c) - colvec); - - if(rows>1) - { - VERIFY_RAISES_ASSERT(m2.colwise() -= colvec.transpose()); - VERIFY_RAISES_ASSERT(m1.colwise() - colvec.transpose()); - } - - m2 = m1; - m2.rowwise() -= rowvec; - VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec); - VERIFY_IS_APPROX(m2.row(r), m1.row(r) - rowvec); - - if(cols>1) - { - VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose()); - VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose()); - } - - // test norm - rrres = m1.colwise().norm(); - VERIFY_IS_APPROX(rrres(c), m1.col(c).norm()); - rcres = m1.rowwise().norm(); - VERIFY_IS_APPROX(rcres(r), m1.row(r).norm()); - - VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum(), m1.colwise().template lpNorm<1>()); - VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().sum(), m1.rowwise().template lpNorm<1>()); - VERIFY_IS_APPROX(m1.cwiseAbs().colwise().maxCoeff(), m1.colwise().template lpNorm<Infinity>()); - VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().maxCoeff(), m1.rowwise().template lpNorm<Infinity>()); - - // regression for bug 1158 - VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum().x(), m1.col(0).cwiseAbs().sum()); - - // test normalized - m2 = m1.colwise().normalized(); - VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized()); - m2 = m1.rowwise().normalized(); - VERIFY_IS_APPROX(m2.row(r), m1.row(r).normalized()); - - // test normalize - m2 = m1; - m2.colwise().normalize(); - VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized()); - m2 = m1; - m2.rowwise().normalize(); - VERIFY_IS_APPROX(m2.row(r), m1.row(r).normalized()); - - // test with partial reduction of products - Matrix<Scalar,MatrixType::RowsAtCompileTime,MatrixType::RowsAtCompileTime> m1m1 = m1 * m1.transpose(); - VERIFY_IS_APPROX( (m1 * m1.transpose()).colwise().sum(), m1m1.colwise().sum()); - Matrix<Scalar,1,MatrixType::RowsAtCompileTime> tmp(rows); - VERIFY_EVALUATION_COUNT( tmp = (m1 * m1.transpose()).colwise().sum(), 1); - - m2 = m1.rowwise() - (m1.colwise().sum()/RealScalar(m1.rows())).eval(); - m1 = m1.rowwise() - (m1.colwise().sum()/RealScalar(m1.rows())); - VERIFY_IS_APPROX( m1, m2 ); - VERIFY_EVALUATION_COUNT( m2 = (m1.rowwise() - m1.colwise().sum()/RealScalar(m1.rows())), (MatrixType::RowsAtCompileTime!=1 ? 1 : 0) ); -} - -void test_vectorwiseop() -{ - CALL_SUBTEST_1( vectorwiseop_array(Array22cd()) ); - CALL_SUBTEST_2( vectorwiseop_array(Array<double, 3, 2>()) ); - CALL_SUBTEST_3( vectorwiseop_array(ArrayXXf(3, 4)) ); - CALL_SUBTEST_4( vectorwiseop_matrix(Matrix4cf()) ); - CALL_SUBTEST_5( vectorwiseop_matrix(Matrix<float,4,5>()) ); - CALL_SUBTEST_6( vectorwiseop_matrix(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_7( vectorwiseop_matrix(VectorXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_7( vectorwiseop_matrix(RowVectorXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); -} diff --git a/eigen/test/visitor.cpp b/eigen/test/visitor.cpp deleted file mode 100644 index 7f4efab..0000000 --- a/eigen/test/visitor.cpp +++ /dev/null @@ -1,133 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -template<typename MatrixType> void matrixVisitor(const MatrixType& p) -{ - typedef typename MatrixType::Scalar Scalar; - - Index rows = p.rows(); - Index cols = p.cols(); - - // construct a random matrix where all coefficients are different - MatrixType m; - m = MatrixType::Random(rows, cols); - for(Index i = 0; i < m.size(); i++) - for(Index i2 = 0; i2 < i; i2++) - while(m(i) == m(i2)) // yes, == - m(i) = internal::random<Scalar>(); - - Scalar minc = Scalar(1000), maxc = Scalar(-1000); - Index minrow=0,mincol=0,maxrow=0,maxcol=0; - for(Index j = 0; j < cols; j++) - for(Index i = 0; i < rows; i++) - { - if(m(i,j) < minc) - { - minc = m(i,j); - minrow = i; - mincol = j; - } - if(m(i,j) > maxc) - { - maxc = m(i,j); - maxrow = i; - maxcol = j; - } - } - Index eigen_minrow, eigen_mincol, eigen_maxrow, eigen_maxcol; - Scalar eigen_minc, eigen_maxc; - eigen_minc = m.minCoeff(&eigen_minrow,&eigen_mincol); - eigen_maxc = m.maxCoeff(&eigen_maxrow,&eigen_maxcol); - VERIFY(minrow == eigen_minrow); - VERIFY(maxrow == eigen_maxrow); - VERIFY(mincol == eigen_mincol); - VERIFY(maxcol == eigen_maxcol); - VERIFY_IS_APPROX(minc, eigen_minc); - VERIFY_IS_APPROX(maxc, eigen_maxc); - VERIFY_IS_APPROX(minc, m.minCoeff()); - VERIFY_IS_APPROX(maxc, m.maxCoeff()); - - eigen_maxc = (m.adjoint()*m).maxCoeff(&eigen_maxrow,&eigen_maxcol); - eigen_maxc = (m.adjoint()*m).eval().maxCoeff(&maxrow,&maxcol); - VERIFY(maxrow == eigen_maxrow); - VERIFY(maxcol == eigen_maxcol); -} - -template<typename VectorType> void vectorVisitor(const VectorType& w) -{ - typedef typename VectorType::Scalar Scalar; - - Index size = w.size(); - - // construct a random vector where all coefficients are different - VectorType v; - v = VectorType::Random(size); - for(Index i = 0; i < size; i++) - for(Index i2 = 0; i2 < i; i2++) - while(v(i) == v(i2)) // yes, == - v(i) = internal::random<Scalar>(); - - Scalar minc = v(0), maxc = v(0); - Index minidx=0, maxidx=0; - for(Index i = 0; i < size; i++) - { - if(v(i) < minc) - { - minc = v(i); - minidx = i; - } - if(v(i) > maxc) - { - maxc = v(i); - maxidx = i; - } - } - Index eigen_minidx, eigen_maxidx; - Scalar eigen_minc, eigen_maxc; - eigen_minc = v.minCoeff(&eigen_minidx); - eigen_maxc = v.maxCoeff(&eigen_maxidx); - VERIFY(minidx == eigen_minidx); - VERIFY(maxidx == eigen_maxidx); - VERIFY_IS_APPROX(minc, eigen_minc); - VERIFY_IS_APPROX(maxc, eigen_maxc); - VERIFY_IS_APPROX(minc, v.minCoeff()); - VERIFY_IS_APPROX(maxc, v.maxCoeff()); - - Index idx0 = internal::random<Index>(0,size-1); - Index idx1 = eigen_minidx; - Index idx2 = eigen_maxidx; - VectorType v1(v), v2(v); - v1(idx0) = v1(idx1); - v2(idx0) = v2(idx2); - v1.minCoeff(&eigen_minidx); - v2.maxCoeff(&eigen_maxidx); - VERIFY(eigen_minidx == (std::min)(idx0,idx1)); - VERIFY(eigen_maxidx == (std::min)(idx0,idx2)); -} - -void test_visitor() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( matrixVisitor(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( matrixVisitor(Matrix2f()) ); - CALL_SUBTEST_3( matrixVisitor(Matrix4d()) ); - CALL_SUBTEST_4( matrixVisitor(MatrixXd(8, 12)) ); - CALL_SUBTEST_5( matrixVisitor(Matrix<double,Dynamic,Dynamic,RowMajor>(20, 20)) ); - CALL_SUBTEST_6( matrixVisitor(MatrixXi(8, 12)) ); - } - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_7( vectorVisitor(Vector4f()) ); - CALL_SUBTEST_7( vectorVisitor(Matrix<int,12,1>()) ); - CALL_SUBTEST_8( vectorVisitor(VectorXd(10)) ); - CALL_SUBTEST_9( vectorVisitor(RowVectorXd(10)) ); - CALL_SUBTEST_10( vectorVisitor(VectorXf(33)) ); - } -} diff --git a/eigen/test/zerosized.cpp b/eigen/test/zerosized.cpp deleted file mode 100644 index 477ff00..0000000 --- a/eigen/test/zerosized.cpp +++ /dev/null @@ -1,102 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - - -template<typename MatrixType> void zeroReduction(const MatrixType& m) { - // Reductions that must hold for zero sized objects - VERIFY(m.all()); - VERIFY(!m.any()); - VERIFY(m.prod()==1); - VERIFY(m.sum()==0); - VERIFY(m.count()==0); - VERIFY(m.allFinite()); - VERIFY(!m.hasNaN()); -} - - -template<typename MatrixType> void zeroSizedMatrix() -{ - MatrixType t1; - typedef typename MatrixType::Scalar Scalar; - - if (MatrixType::SizeAtCompileTime == Dynamic || MatrixType::SizeAtCompileTime == 0) - { - zeroReduction(t1); - if (MatrixType::RowsAtCompileTime == Dynamic) - VERIFY(t1.rows() == 0); - if (MatrixType::ColsAtCompileTime == Dynamic) - VERIFY(t1.cols() == 0); - - if (MatrixType::RowsAtCompileTime == Dynamic && MatrixType::ColsAtCompileTime == Dynamic) - { - - MatrixType t2(0, 0), t3(t1); - VERIFY(t2.rows() == 0); - VERIFY(t2.cols() == 0); - - zeroReduction(t2); - VERIFY(t1==t2); - } - } - - if(MatrixType::MaxColsAtCompileTime!=0 && MatrixType::MaxRowsAtCompileTime!=0) - { - Index rows = MatrixType::RowsAtCompileTime==Dynamic ? internal::random<Index>(1,10) : Index(MatrixType::RowsAtCompileTime); - Index cols = MatrixType::ColsAtCompileTime==Dynamic ? internal::random<Index>(1,10) : Index(MatrixType::ColsAtCompileTime); - MatrixType m(rows,cols); - zeroReduction(m.template block<0,MatrixType::ColsAtCompileTime>(0,0,0,cols)); - zeroReduction(m.template block<MatrixType::RowsAtCompileTime,0>(0,0,rows,0)); - zeroReduction(m.template block<0,1>(0,0)); - zeroReduction(m.template block<1,0>(0,0)); - Matrix<Scalar,Dynamic,Dynamic> prod = m.template block<MatrixType::RowsAtCompileTime,0>(0,0,rows,0) * m.template block<0,MatrixType::ColsAtCompileTime>(0,0,0,cols); - VERIFY(prod.rows()==rows && prod.cols()==cols); - VERIFY(prod.isZero()); - prod = m.template block<1,0>(0,0) * m.template block<0,1>(0,0); - VERIFY(prod.size()==1); - VERIFY(prod.isZero()); - } -} - -template<typename VectorType> void zeroSizedVector() -{ - VectorType t1; - - if (VectorType::SizeAtCompileTime == Dynamic || VectorType::SizeAtCompileTime==0) - { - zeroReduction(t1); - VERIFY(t1.size() == 0); - VectorType t2(DenseIndex(0)); // DenseIndex disambiguates with 0-the-null-pointer (error with gcc 4.4 and MSVC8) - VERIFY(t2.size() == 0); - zeroReduction(t2); - - VERIFY(t1==t2); - } -} - -void test_zerosized() -{ - zeroSizedMatrix<Matrix2d>(); - zeroSizedMatrix<Matrix3i>(); - zeroSizedMatrix<Matrix<float, 2, Dynamic> >(); - zeroSizedMatrix<MatrixXf>(); - zeroSizedMatrix<Matrix<float, 0, 0> >(); - zeroSizedMatrix<Matrix<float, Dynamic, 0, 0, 0, 0> >(); - zeroSizedMatrix<Matrix<float, 0, Dynamic, 0, 0, 0> >(); - zeroSizedMatrix<Matrix<float, Dynamic, Dynamic, 0, 0, 0> >(); - zeroSizedMatrix<Matrix<float, 0, 4> >(); - zeroSizedMatrix<Matrix<float, 4, 0> >(); - - zeroSizedVector<Vector2d>(); - zeroSizedVector<Vector3i>(); - zeroSizedVector<VectorXf>(); - zeroSizedVector<Matrix<float, 0, 1> >(); - zeroSizedVector<Matrix<float, 1, 0> >(); -} |