diff options
Diffstat (limited to 'test')
180 files changed, 8939 insertions, 8432 deletions
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index f5f208a37..2141d07c2 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -1,22 +1,38 @@ - -# generate split test header file -message(STATUS ${CMAKE_CURRENT_BINARY_DIR}) -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" +# 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() + 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) +endif() # configure blas/lapack (use Eigen's ones) -set(BLAS_FOUND TRUE) -set(LAPACK_FOUND TRUE) -set(BLAS_LIBRARIES eigen_blas) -set(LAPACK_LIBRARIES eigen_lapack) +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) @@ -31,33 +47,33 @@ endif(EIGEN_TEST_MATRIX_DIR) set(SPARSE_LIBS " ") find_package(Cholmod) -if(CHOLMOD_FOUND AND BLAS_FOUND AND LAPACK_FOUND) +if(CHOLMOD_FOUND) add_definitions("-DEIGEN_CHOLMOD_SUPPORT") include_directories(${CHOLMOD_INCLUDES}) - set(SPARSE_LIBS ${SPARSE_LIBS} ${CHOLMOD_LIBRARIES} ${BLAS_LIBRARIES} ${LAPACK_LIBRARIES}) - set(CHOLMOD_ALL_LIBS ${CHOLMOD_LIBRARIES} ${BLAS_LIBRARIES} ${LAPACK_LIBRARIES}) + set(SPARSE_LIBS ${SPARSE_LIBS} ${CHOLMOD_LIBRARIES} ${EIGEN_BLAS_LIBRARIES} ${EIGEN_LAPACK_LIBRARIES}) + set(CHOLMOD_ALL_LIBS ${CHOLMOD_LIBRARIES} ${EIGEN_BLAS_LIBRARIES} ${EIGEN_LAPACK_LIBRARIES}) ei_add_property(EIGEN_TESTED_BACKENDS "Cholmod, ") else() ei_add_property(EIGEN_MISSING_BACKENDS "Cholmod, ") endif() find_package(Umfpack) -if(UMFPACK_FOUND AND BLAS_FOUND) +if(UMFPACK_FOUND) add_definitions("-DEIGEN_UMFPACK_SUPPORT") include_directories(${UMFPACK_INCLUDES}) - set(SPARSE_LIBS ${SPARSE_LIBS} ${UMFPACK_LIBRARIES} ${BLAS_LIBRARIES}) - set(UMFPACK_ALL_LIBS ${UMFPACK_LIBRARIES} ${BLAS_LIBRARIES}) + set(SPARSE_LIBS ${SPARSE_LIBS} ${UMFPACK_LIBRARIES} ${EIGEN_BLAS_LIBRARIES}) + set(UMFPACK_ALL_LIBS ${UMFPACK_LIBRARIES} ${EIGEN_BLAS_LIBRARIES}) ei_add_property(EIGEN_TESTED_BACKENDS "UmfPack, ") else() ei_add_property(EIGEN_MISSING_BACKENDS "UmfPack, ") endif() -find_package(SuperLU) -if(SUPERLU_FOUND AND BLAS_FOUND) +find_package(SuperLU 4.0) +if(SUPERLU_FOUND) add_definitions("-DEIGEN_SUPERLU_SUPPORT") include_directories(${SUPERLU_INCLUDES}) - set(SPARSE_LIBS ${SPARSE_LIBS} ${SUPERLU_LIBRARIES} ${BLAS_LIBRARIES}) - set(SUPERLU_ALL_LIBS ${SUPERLU_LIBRARIES} ${BLAS_LIBRARIES}) + set(SPARSE_LIBS ${SPARSE_LIBS} ${SUPERLU_LIBRARIES} ${EIGEN_BLAS_LIBRARIES}) + set(SUPERLU_ALL_LIBS ${SUPERLU_LIBRARIES} ${EIGEN_BLAS_LIBRARIES}) ei_add_property(EIGEN_TESTED_BACKENDS "SuperLU, ") else() ei_add_property(EIGEN_MISSING_BACKENDS "SuperLU, ") @@ -67,7 +83,7 @@ endif() find_package(Pastix) find_package(Scotch) find_package(Metis 5.0 REQUIRED) -if(PASTIX_FOUND AND BLAS_FOUND) +if(PASTIX_FOUND) add_definitions("-DEIGEN_PASTIX_SUPPORT") include_directories(${PASTIX_INCLUDES}) if(SCOTCH_FOUND) @@ -79,8 +95,8 @@ if(PASTIX_FOUND AND BLAS_FOUND) else(SCOTCH_FOUND) ei_add_property(EIGEN_MISSING_BACKENDS "PaStiX, ") endif(SCOTCH_FOUND) - set(SPARSE_LIBS ${SPARSE_LIBS} ${PASTIX_LIBRARIES} ${ORDERING_LIBRARIES} ${BLAS_LIBRARIES}) - set(PASTIX_ALL_LIBS ${PASTIX_LIBRARIES} ${BLAS_LIBRARIES}) + set(SPARSE_LIBS ${SPARSE_LIBS} ${PASTIX_LIBRARIES} ${ORDERING_LIBRARIES} ${EIGEN_BLAS_LIBRARIES}) + set(PASTIX_ALL_LIBS ${PASTIX_LIBRARIES} ${EIGEN_BLAS_LIBRARIES}) ei_add_property(EIGEN_TESTED_BACKENDS "PaStiX, ") else() ei_add_property(EIGEN_MISSING_BACKENDS "PaStiX, ") @@ -95,16 +111,14 @@ else() endif() find_package(SPQR) -if(SPQR_FOUND AND BLAS_FOUND AND LAPACK_FOUND) - if(CHOLMOD_FOUND) - add_definitions("-DEIGEN_SPQR_SUPPORT") - include_directories(${SPQR_INCLUDES}) - set(SPQR_ALL_LIBS ${SPQR_LIBRARIES} ${CHOLMOD_LIBRARIES} ${LAPACK_LIBRARIES} ${BLAS_LIBRARIES}) - set(SPARSE_LIBS ${SPARSE_LIBS} ${SPQR_ALL_LIBS}) - ei_add_property(EIGEN_TESTED_BACKENDS "SPQR, ") - else(CHOLMOD_FOUND) - ei_add_property(EIGEN_MISSING_BACKENDS "SPQR, ") - endif(CHOLMOD_FOUND) +if(SPQR_FOUND AND CHOLMOD_FOUND AND (EIGEN_Fortran_COMPILER_WORKS OR LAPACK_FOUND) ) + add_definitions("-DEIGEN_SPQR_SUPPORT") + include_directories(${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) @@ -125,25 +139,32 @@ 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(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) +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(cwiseop) ei_add_test(unalignedcount) -ei_add_test(exceptions) +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) @@ -161,6 +182,7 @@ 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) @@ -172,6 +194,7 @@ 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) @@ -191,52 +214,75 @@ 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_homogeneous) ei_add_test(geo_quaternion) -ei_add_test(geo_transformations) ei_add_test(geo_eulerangles) -ei_add_test(geo_hyperplane) 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(resize) -ei_add_test(sparse_vector) +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(umeyama) -ei_add_test(householder) -ei_add_test(swap) -ei_add_test(conservative_resize) -ei_add_test(permutationmatrices) ei_add_test(sparse_permutations) -ei_add_test(nullary) +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(sizeoverflow) +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) -ei_add_test(simplicial_cholesky) -ei_add_test(conjugate_gradient) -ei_add_test(bicgstab) -ei_add_test(sparselu) -ei_add_test(sparseqr) +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(denseLM) +ei_add_test(fastmath " ${EIGEN_FASTMATH_FLAGS} ") + +# # ei_add_test(denseLM) if(QT4_FOUND) ei_add_test(qtvector "" "${QT_QTCORE_LIBRARY}") endif(QT4_FOUND) -ei_add_test(eigen2support) - if(UMFPACK_FOUND) ei_add_test(umfpack_support "" "${UMFPACK_ALL_LIBS}") endif() @@ -281,9 +327,54 @@ ei_add_property(EIGEN_TESTING_SUMMARY "Sparse lib flags: ${SPARSE_LIBS}\n") option(EIGEN_TEST_EIGEN2 "Run whole Eigen2 test suite against EIGEN2_SUPPORT" OFF) mark_as_advanced(EIGEN_TEST_EIGEN2) if(EIGEN_TEST_EIGEN2) - add_subdirectory(eigen2) + message(WARNING "The Eigen2 test suite has been removed") +endif() + +# boost MP unit test +find_package(Boost) +if(Boost_FOUND) + include_directories(${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) diff --git a/test/adjoint.cpp b/test/adjoint.cpp index ea36f7841..bdea51c10 100644 --- a/test/adjoint.cpp +++ b/test/adjoint.cpp @@ -42,6 +42,17 @@ template<> struct adjoint_specific<false> { 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())); @@ -64,6 +75,7 @@ template<typename MatrixType> void adjoint(const MatrixType& m) 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(); @@ -108,6 +120,17 @@ template<typename MatrixType> void adjoint(const MatrixType& m) 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; @@ -129,14 +152,24 @@ void test_adjoint() 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_4 +#ifdef EIGEN_TEST_PART_13 { MatrixXcf a(10,10), b(10,10); VERIFY_RAISES_ASSERT(a = a.transpose()); @@ -154,6 +187,13 @@ void test_adjoint() 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/test/array.cpp b/test/array.cpp index 68f6b3d7a..15c3266a9 100644 --- a/test/array.cpp +++ b/test/array.cpp @@ -13,6 +13,7 @@ template<typename ArrayType> void array(const ArrayType& m) { typedef typename ArrayType::Index Index; typedef typename ArrayType::Scalar Scalar; + typedef typename ArrayType::RealScalar RealScalar; typedef Array<Scalar, ArrayType::RowsAtCompileTime, 1> ColVectorType; typedef Array<Scalar, 1, ArrayType::ColsAtCompileTime> RowVectorType; @@ -22,6 +23,8 @@ template<typename ArrayType> void array(const ArrayType& m) 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); @@ -70,7 +73,7 @@ template<typename ArrayType> void array(const ArrayType& m) 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>())); + VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar,Scalar>())); // vector-wise ops m3 = m1; @@ -81,6 +84,47 @@ template<typename ArrayType> void array(const ArrayType& m) 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) @@ -97,8 +141,11 @@ template<typename ArrayType> void comparisons(const ArrayType& m) c = internal::random<Index>(0, cols-1); ArrayType m1 = ArrayType::Random(rows, cols), - m2 = ArrayType::Random(rows, cols), - m3(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()); @@ -112,11 +159,17 @@ template<typename ArrayType> void comparisons(const ArrayType& m) VERIFY(!(m1 > m2 && m1 < m2).any()); VERIFY((m1 <= m2 || m1 >= m2).all()); - // comparisons to scalar + // 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() ); + 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) ); @@ -164,21 +217,69 @@ template<typename ArrayType> void array_real(const ArrayType& m) ArrayType m1 = ArrayType::Random(rows, cols), m2 = ArrayType::Random(rows, cols), - m3(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. + // 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.tan(), tan(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)); + - VERIFY_IS_APPROX(m1.abs().sqrt(), sqrt(abs(m1))); - VERIFY_IS_APPROX(m1.abs(), sqrt(numext::abs2(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)); @@ -187,52 +288,138 @@ template<typename ArrayType> void array_real(const ArrayType& m) // shift argument of logarithm so that it is not zero Scalar smallNumber = NumTraits<Scalar>::dummy_precision(); - VERIFY_IS_APPROX((m1.abs() + smallNumber).log() , log(abs(m1) + smallNumber)); + 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(m1.pow(2), m1.square()); - VERIFY_IS_APPROX(pow(m1,2), m1.square()); - - ArrayType exponents = ArrayType::Constant(rows, cols, RealScalar(2)); - VERIFY_IS_APPROX(Eigen::pow(m1,exponents), m1.square()); - - m3 = m1.abs(); 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()); + VERIFY_IS_APPROX(m3, m1.transpose()); m3.transposeInPlace(); - VERIFY_IS_APPROX(m3,m1); + VERIFY_IS_APPROX(m3, m1); } template<typename ArrayType> void array_complex(const ArrayType& m) { typedef typename ArrayType::Index Index; + 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); + 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)); - VERIFY_IS_APPROX(m1.sqrt(), m2); - VERIFY_IS_APPROX(m1.sqrt(), Eigen::sqrt(m1)); + // 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) @@ -301,7 +488,7 @@ void test_array() 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_sum_op<double>, ArrayXd > Xpr; + 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/test/array_for_matrix.cpp b/test/array_for_matrix.cpp index 9667e1f14..c1501947b 100644 --- a/test/array_for_matrix.cpp +++ b/test/array_for_matrix.cpp @@ -45,7 +45,7 @@ template<typename MatrixType> void array_for_matrix(const MatrixType& m) 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>())); + VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar,Scalar>())); // vector-wise ops m3 = m1; @@ -68,6 +68,16 @@ template<typename MatrixType> void array_for_matrix(const MatrixType& m) 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) @@ -124,6 +134,12 @@ template<typename MatrixType> void comparisons(const MatrixType& m) // 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 @@ -134,9 +150,21 @@ template<typename MatrixType> void comparisons(const MatrixType& m) template<typename VectorType> void lpNorm(const VectorType& v) { using std::sqrt; + typedef typename VectorType::RealScalar RealScalar; VectorType u = VectorType::Random(v.size()); - VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), u.cwiseAbs().maxCoeff()); + 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()); @@ -245,6 +273,8 @@ void test_array_for_matrix() 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))) ); diff --git a/test/array_of_string.cpp b/test/array_of_string.cpp new file mode 100644 index 000000000..e23b7c59e --- /dev/null +++ b/test/array_of_string.cpp @@ -0,0 +1,32 @@ +// 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/test/array_replicate.cpp b/test/array_replicate.cpp index f412d1aed..779c8fc2f 100644 --- a/test/array_replicate.cpp +++ b/test/array_replicate.cpp @@ -44,6 +44,19 @@ template<typename MatrixType> void replicate(const MatrixType& m) 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) diff --git a/test/array_reverse.cpp b/test/array_reverse.cpp index fbe7a9901..c9d9f90c3 100644 --- a/test/array_reverse.cpp +++ b/test/array_reverse.cpp @@ -24,7 +24,7 @@ template<typename MatrixType> void reverse(const MatrixType& m) // 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); + MatrixType m1 = MatrixType::Random(rows, cols), m2; VectorType v1 = VectorType::Random(rows); MatrixType m1_r = m1.reverse(); @@ -96,14 +96,32 @@ template<typename MatrixType> void reverse(const MatrixType& m) 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() @@ -113,11 +131,11 @@ void test_array_reverse() CALL_SUBTEST_2( reverse(Matrix2f()) ); CALL_SUBTEST_3( reverse(Matrix4f()) ); CALL_SUBTEST_4( reverse(Matrix4d()) ); - CALL_SUBTEST_5( reverse(MatrixXcf(3, 3)) ); - CALL_SUBTEST_6( reverse(MatrixXi(6, 3)) ); - CALL_SUBTEST_7( reverse(MatrixXcd(20, 20)) ); + 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>(6,3)) ); + 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; diff --git a/test/bandmatrix.cpp b/test/bandmatrix.cpp index 5e4e8e07b..f8c38f7c3 100644 --- a/test/bandmatrix.cpp +++ b/test/bandmatrix.cpp @@ -11,7 +11,6 @@ template<typename MatrixType> void bandmatrix(const MatrixType& _m) { - typedef typename MatrixType::Index Index; typedef typename MatrixType::Scalar Scalar; typedef typename NumTraits<Scalar>::Real RealScalar; typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrixType; @@ -62,8 +61,6 @@ using Eigen::internal::BandMatrix; void test_bandmatrix() { - typedef BandMatrix<float>::Index Index; - for(int i = 0; i < 10*g_repeat ; i++) { Index rows = internal::random<Index>(1,10); Index cols = internal::random<Index>(1,10); diff --git a/test/basicstuff.cpp b/test/basicstuff.cpp index 8c0621ecd..99d91f9da 100644 --- a/test/basicstuff.cpp +++ b/test/basicstuff.cpp @@ -126,6 +126,20 @@ template<typename MatrixType> void basicStuff(const MatrixType& m) 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) @@ -180,15 +194,64 @@ void casting() template <typename Scalar> void fixedSizeMatrixConstruction() { - const Scalar raw[3] = {1,2,3}; - Matrix<Scalar,3,1> m(raw); - Array<Scalar,3,1> a(raw); - VERIFY(m(0) == 1); - VERIFY(m(1) == 2); - VERIFY(m(2) == 3); - VERIFY(a(0) == 1); - VERIFY(a(1) == 2); - VERIFY(a(2) == 3); + 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() @@ -207,8 +270,11 @@ void test_basicstuff() } CALL_SUBTEST_1(fixedSizeMatrixConstruction<unsigned char>()); + CALL_SUBTEST_1(fixedSizeMatrixConstruction<float>()); CALL_SUBTEST_1(fixedSizeMatrixConstruction<double>()); - 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/test/bdcsvd.cpp b/test/bdcsvd.cpp new file mode 100644 index 000000000..f9f687aac --- /dev/null +++ b/test/bdcsvd.cpp @@ -0,0 +1,111 @@ +// 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 + CALL_SUBTEST_9( svd_preallocate<void>() ); + + CALL_SUBTEST_2( svd_underoverflow<void>() ); +} + diff --git a/test/bicgstab.cpp b/test/bicgstab.cpp index f327e2fac..4cc0dd31c 100644 --- a/test/bicgstab.cpp +++ b/test/bicgstab.cpp @@ -10,13 +10,16 @@ #include "sparse_solver.h" #include <Eigen/IterativeLinearSolvers> -template<typename T> void test_bicgstab_T() +template<typename T, typename I> void test_bicgstab_T() { - BiCGSTAB<SparseMatrix<T>, DiagonalPreconditioner<T> > bicgstab_colmajor_diag; - BiCGSTAB<SparseMatrix<T>, IdentityPreconditioner > bicgstab_colmajor_I; - BiCGSTAB<SparseMatrix<T>, IncompleteLUT<T> > bicgstab_colmajor_ilut; + 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) ); @@ -25,6 +28,7 @@ template<typename T> void test_bicgstab_T() void test_bicgstab() { - CALL_SUBTEST_1(test_bicgstab_T<double>()); - CALL_SUBTEST_2(test_bicgstab_T<std::complex<double> >()); + 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/test/block.cpp b/test/block.cpp index 9ed5d7bc5..39565af83 100644 --- a/test/block.cpp +++ b/test/block.cpp @@ -130,6 +130,14 @@ template<typename MatrixType> void block(const MatrixType& m) VERIFY(numext::real(ones.col(c1).dot(ones.col(c2))) == RealScalar(rows)); VERIFY(numext::real(ones.row(r1).dot(ones.row(r2))) == RealScalar(cols)); + + // chekc 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. @@ -141,11 +149,11 @@ template<typename MatrixType> void block(const MatrixType& m) 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_EQUAL( ((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_EQUAL( ((m1+m2).block(r1,c1,r2-r1+1,c2-c1+1).row(0)) , ((m1+m2).row(r1).segment(c1,c2-c1+1)) ); - VERIFY_IS_EQUAL( ((m1+m2).block(r1,c1,r2-r1+1,c2-c1+1).col(0)) , ((m1+m2).col(c1).segment(r1,r2-r1+1)) ); - VERIFY_IS_EQUAL( ((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_EQUAL( ((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+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() ); // evaluation into plain matrices from expressions with direct access (stress MapBase) DynamicMatrixType dm; @@ -173,6 +181,19 @@ template<typename MatrixType> void block(const MatrixType& m) 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() ); + } } diff --git a/test/boostmultiprec.cpp b/test/boostmultiprec.cpp new file mode 100644 index 000000000..e06e9bdaf --- /dev/null +++ b/test/boostmultiprec.cpp @@ -0,0 +1,201 @@ +// 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/test/bug1213.cpp b/test/bug1213.cpp new file mode 100644 index 000000000..581760c1a --- /dev/null +++ b/test/bug1213.cpp @@ -0,0 +1,13 @@ + +// 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/test/bug1213.h b/test/bug1213.h new file mode 100644 index 000000000..040e5a470 --- /dev/null +++ b/test/bug1213.h @@ -0,0 +1,8 @@ + +#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/test/bug1213_main.cpp b/test/bug1213_main.cpp new file mode 100644 index 000000000..4802c0003 --- /dev/null +++ b/test/bug1213_main.cpp @@ -0,0 +1,18 @@ + +// 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/test/cholesky.cpp b/test/cholesky.cpp index 56885deb7..8ad5ac639 100644 --- a/test/cholesky.cpp +++ b/test/cholesky.cpp @@ -11,20 +11,17 @@ #define EIGEN_NO_ASSERTION_CHECKING #endif -static int nb_temporaries; - -#define EIGEN_DENSE_STORAGE_CTOR_PLUGIN { if(size!=0) nb_temporaries++; } +#define TEST_ENABLE_TEMPORARY_TRACKING #include "main.h" #include <Eigen/Cholesky> #include <Eigen/QR> -#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 ); \ - } +template<typename MatrixType, int UpLo> +typename MatrixType::RealScalar matrix_l1_norm(const MatrixType& m) { + 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) { @@ -83,14 +80,10 @@ template<typename MatrixType> void cholesky(const MatrixType& m) symm += a1 * a1.adjoint(); } - // to test if really Cholesky only uses the upper triangular part, uncomment the following - // FIXME: currently that fails !! - //symm.template part<StrictlyLower>().setZero(); - { 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); @@ -98,6 +91,14 @@ template<typename MatrixType> void cholesky(const MatrixType& m) 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()); @@ -106,6 +107,15 @@ template<typename MatrixType> void cholesky(const MatrixType& m) 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(chollo.info()==NumericalIssue); @@ -114,7 +124,7 @@ template<typename MatrixType> void cholesky(const MatrixType& m) 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; @@ -144,19 +154,38 @@ template<typename MatrixType> void cholesky(const MatrixType& m) 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())); @@ -185,7 +214,7 @@ template<typename MatrixType> void cholesky(const MatrixType& m) if(rows>=3) { SquareMatrixType A = symm; - int c = internal::random<int>(0,rows-2); + Index c = internal::random<Index>(0,rows-2); A.bottomRightCorner(c,c).setZero(); // Make sure a solution exists: vecX.setRandom(); @@ -196,11 +225,11 @@ template<typename MatrixType> void cholesky(const MatrixType& m) vecX = ldltlo.solve(vecB); VERIFY_IS_APPROX(A * vecX, vecB); } - + // check non-full rank matrices if(rows>=3) { - int r = internal::random<int>(1,rows-1); + 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: @@ -212,15 +241,17 @@ template<typename MatrixType> void cholesky(const MatrixType& m) 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(int k=0; k<rows; ++k) - d(k) = d(k)*std::pow(RealScalar(10),internal::random<RealScalar>(-s,s)); + 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(); @@ -229,7 +260,20 @@ template<typename MatrixType> void cholesky(const MatrixType& m) ldltlo.compute(A); VERIFY_IS_APPROX(A, ldltlo.reconstructedMatrix()); vecX = ldltlo.solve(vecB); - VERIFY_IS_APPROX(A * vecX, 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; + } } } @@ -289,6 +333,7 @@ template<typename MatrixType> void cholesky_cplx(const MatrixType& m) 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); @@ -314,46 +359,101 @@ template<typename MatrixType> void cholesky_bug241(const MatrixType& m) } // 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. +// 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()); } { mat << 1, 2, 2, 1; ldlt.compute(mat); + VERIFY(ldlt.info()==Success); VERIFY(!ldlt.isNegative()); VERIFY(!ldlt.isPositive()); } { mat << 0, 0, 0, 0; ldlt.compute(mat); + VERIFY(ldlt.info()==Success); VERIFY(ldlt.isNegative()); VERIFY(ldlt.isPositive()); } { mat << 0, 0, 0, 1; ldlt.compute(mat); + VERIFY(ldlt.info()==Success); VERIFY(!ldlt.isNegative()); VERIFY(ldlt.isPositive()); } { mat << -1, 0, 0, 0; ldlt.compute(mat); + VERIFY(ldlt.info()==Success); VERIFY(ldlt.isNegative()); VERIFY(!ldlt.isPositive()); } } +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()); + } +} + template<typename MatrixType> void cholesky_verify_assert() { MatrixType tmp; @@ -384,10 +484,14 @@ void test_cholesky() 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) } CALL_SUBTEST_4( cholesky_verify_assert<Matrix3f>() ); @@ -398,7 +502,8 @@ void test_cholesky() // Test problem size constructors CALL_SUBTEST_9( LLT<MatrixXf>(10) ); CALL_SUBTEST_9( LDLT<MatrixXf>(10) ); - - TEST_SET_BUT_UNUSED_VARIABLE(s) + + CALL_SUBTEST_2( cholesky_faillure_cases<void>() ); + TEST_SET_BUT_UNUSED_VARIABLE(nb_temporaries) } diff --git a/test/cholmod_support.cpp b/test/cholmod_support.cpp index 8f8be3c0e..a7eda28f7 100644 --- a/test/cholmod_support.cpp +++ b/test/cholmod_support.cpp @@ -7,6 +7,7 @@ // 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> @@ -40,13 +41,13 @@ template<typename T> void test_cholmod_T() 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); + + 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() diff --git a/test/commainitializer.cpp b/test/commainitializer.cpp index 99102b966..9844adbd2 100644 --- a/test/commainitializer.cpp +++ b/test/commainitializer.cpp @@ -9,6 +9,62 @@ #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; @@ -43,4 +99,8 @@ void test_commainitializer() 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/test/conjugate_gradient.cpp b/test/conjugate_gradient.cpp index 019cc4d64..9622fd86d 100644 --- a/test/conjugate_gradient.cpp +++ b/test/conjugate_gradient.cpp @@ -10,13 +10,14 @@ #include "sparse_solver.h" #include <Eigen/IterativeLinearSolvers> -template<typename T> void test_conjugate_gradient_T() +template<typename T, typename I> void test_conjugate_gradient_T() { - ConjugateGradient<SparseMatrix<T>, Lower > cg_colmajor_lower_diag; - ConjugateGradient<SparseMatrix<T>, Upper > cg_colmajor_upper_diag; - ConjugateGradient<SparseMatrix<T>, Lower|Upper> cg_colmajor_loup_diag; - ConjugateGradient<SparseMatrix<T>, Lower, IdentityPreconditioner> cg_colmajor_lower_I; - ConjugateGradient<SparseMatrix<T>, Upper, IdentityPreconditioner> cg_colmajor_upper_I; + 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) ); @@ -27,6 +28,7 @@ template<typename T> void test_conjugate_gradient_T() void test_conjugate_gradient() { - CALL_SUBTEST_1(test_conjugate_gradient_T<double>()); - CALL_SUBTEST_2(test_conjugate_gradient_T<std::complex<double> >()); + 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/test/constructor.cpp b/test/constructor.cpp new file mode 100644 index 000000000..eec9e2192 --- /dev/null +++ b/test/constructor.cpp @@ -0,0 +1,84 @@ +// 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/test/ctorleak.cpp b/test/ctorleak.cpp new file mode 100644 index 000000000..c158f5e4e --- /dev/null +++ b/test/ctorleak.cpp @@ -0,0 +1,69 @@ +#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/test/cuda_basic.cu b/test/cuda_basic.cu new file mode 100644 index 000000000..cb2e4167a --- /dev/null +++ b/test/cuda_basic.cu @@ -0,0 +1,173 @@ +// 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> +#if defined __CUDACC_VER__ && __CUDACC_VER__ >= 70500 +#include <cuda_fp16.h> +#endif +#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/test/cuda_common.h b/test/cuda_common.h new file mode 100644 index 000000000..9737693ac --- /dev/null +++ b/test/cuda_common.h @@ -0,0 +1,101 @@ + +#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/test/cwiseop.cpp b/test/cwiseop.cpp deleted file mode 100644 index e3361da17..000000000 --- a/test/cwiseop.cpp +++ /dev/null @@ -1,184 +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/. - -#define EIGEN2_SUPPORT -#define EIGEN_NO_EIGEN2_DEPRECATED_WARNING - -#define EIGEN_NO_STATIC_ASSERT -#include "main.h" -#include <functional> - -#ifdef min -#undef min -#endif - -#ifdef max -#undef max -#endif - -using namespace std; - -template<typename Scalar> struct AddIfNull { - const Scalar operator() (const Scalar a, const Scalar b) const {return a<=1e-3 ? b : a;} - enum { Cost = NumTraits<Scalar>::AddCost }; -}; - -template<typename MatrixType> -typename Eigen::internal::enable_if<!NumTraits<typename MatrixType::Scalar>::IsInteger,typename MatrixType::Scalar>::type -cwiseops_real_only(MatrixType& m1, MatrixType& m2, MatrixType& m3, MatrixType& mones) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - VERIFY_IS_APPROX(m1.cwise() / m2, m1.cwise() * (m2.cwise().inverse())); - m3 = m1.cwise().abs().cwise().sqrt(); - VERIFY_IS_APPROX(m3.cwise().square(), m1.cwise().abs()); - VERIFY_IS_APPROX(m1.cwise().square().cwise().sqrt(), m1.cwise().abs()); - VERIFY_IS_APPROX(m1.cwise().abs().cwise().log().cwise().exp() , m1.cwise().abs()); - - VERIFY_IS_APPROX(m1.cwise().pow(2), m1.cwise().square()); - m3 = (m1.cwise().abs().cwise()<=RealScalar(0.01)).select(mones,m1); - VERIFY_IS_APPROX(m3.cwise().pow(-1), m3.cwise().inverse()); - m3 = m1.cwise().abs(); - VERIFY_IS_APPROX(m3.cwise().pow(RealScalar(0.5)), m3.cwise().sqrt()); - -// VERIFY_IS_APPROX(m1.cwise().tan(), m1.cwise().sin().cwise() / m1.cwise().cos()); - VERIFY_IS_APPROX(mones, m1.cwise().sin().cwise().square() + m1.cwise().cos().cwise().square()); - m3 = m1; - m3.cwise() /= m2; - VERIFY_IS_APPROX(m3, m1.cwise() / m2); - - return Scalar(0); -} - -template<typename MatrixType> -typename Eigen::internal::enable_if<NumTraits<typename MatrixType::Scalar>::IsInteger,typename MatrixType::Scalar>::type -cwiseops_real_only(MatrixType& , MatrixType& , MatrixType& , MatrixType& ) -{ - return 0; -} - -template<typename MatrixType> void cwiseops(const MatrixType& m) -{ - typedef typename MatrixType::Index Index; - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - - Index rows = m.rows(); - Index cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m1bis = m1, - m2 = MatrixType::Random(rows, cols), - m3(rows, cols), - m4(rows, cols), - mzero = MatrixType::Zero(rows, cols), - mones = MatrixType::Ones(rows, cols), - identity = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> - ::Identity(rows, rows); - VectorType vzero = VectorType::Zero(rows), - vones = VectorType::Ones(rows), - v3(rows); - - Index r = internal::random<Index>(0, rows-1), - c = internal::random<Index>(0, cols-1); - - Scalar s1 = internal::random<Scalar>(); - - // test Zero, Ones, Constant, and the set* variants - m3 = MatrixType::Constant(rows, cols, s1); - for (int j=0; j<cols; ++j) - for (int i=0; i<rows; ++i) - { - VERIFY_IS_APPROX(mzero(i,j), Scalar(0)); - VERIFY_IS_APPROX(mones(i,j), Scalar(1)); - VERIFY_IS_APPROX(m3(i,j), s1); - } - VERIFY(mzero.isZero()); - VERIFY(mones.isOnes()); - VERIFY(m3.isConstant(s1)); - VERIFY(identity.isIdentity()); - VERIFY_IS_APPROX(m4.setConstant(s1), m3); - VERIFY_IS_APPROX(m4.setConstant(rows,cols,s1), m3); - VERIFY_IS_APPROX(m4.setZero(), mzero); - VERIFY_IS_APPROX(m4.setZero(rows,cols), mzero); - VERIFY_IS_APPROX(m4.setOnes(), mones); - VERIFY_IS_APPROX(m4.setOnes(rows,cols), mones); - m4.fill(s1); - VERIFY_IS_APPROX(m4, m3); - - VERIFY_IS_APPROX(v3.setConstant(rows, s1), VectorType::Constant(rows,s1)); - VERIFY_IS_APPROX(v3.setZero(rows), vzero); - VERIFY_IS_APPROX(v3.setOnes(rows), vones); - - m2 = m2.template binaryExpr<AddIfNull<Scalar> >(mones); - - VERIFY_IS_APPROX(m1.cwise().pow(2), m1.cwise().abs2()); - VERIFY_IS_APPROX(m1.cwise().pow(2), m1.cwise().square()); - VERIFY_IS_APPROX(m1.cwise().pow(3), m1.cwise().cube()); - - VERIFY_IS_APPROX(m1 + mones, m1.cwise()+Scalar(1)); - VERIFY_IS_APPROX(m1 - mones, m1.cwise()-Scalar(1)); - m3 = m1; m3.cwise() += 1; - VERIFY_IS_APPROX(m1 + mones, m3); - m3 = m1; m3.cwise() -= 1; - VERIFY_IS_APPROX(m1 - mones, m3); - - VERIFY_IS_APPROX(m2, m2.cwise() * mones); - VERIFY_IS_APPROX(m1.cwise() * m2, m2.cwise() * m1); - m3 = m1; - m3.cwise() *= m2; - VERIFY_IS_APPROX(m3, m1.cwise() * m2); - - VERIFY_IS_APPROX(mones, m2.cwise()/m2); - - // check min - VERIFY_IS_APPROX( m1.cwise().min(m2), m2.cwise().min(m1) ); - VERIFY_IS_APPROX( m1.cwise().min(m1+mones), m1 ); - VERIFY_IS_APPROX( m1.cwise().min(m1-mones), m1-mones ); - - // check max - VERIFY_IS_APPROX( m1.cwise().max(m2), m2.cwise().max(m1) ); - VERIFY_IS_APPROX( m1.cwise().max(m1-mones), m1 ); - VERIFY_IS_APPROX( m1.cwise().max(m1+mones), m1+mones ); - - VERIFY( (m1.cwise() == m1).all() ); - VERIFY( (m1.cwise() != m2).any() ); - VERIFY(!(m1.cwise() == (m1+mones)).any() ); - if (rows*cols>1) - { - m3 = m1; - m3(r,c) += 1; - VERIFY( (m1.cwise() == m3).any() ); - VERIFY( !(m1.cwise() == m3).all() ); - } - VERIFY( (m1.cwise().min(m2).cwise() <= m2).all() ); - VERIFY( (m1.cwise().max(m2).cwise() >= m2).all() ); - VERIFY( (m1.cwise().min(m2).cwise() < (m1+mones)).all() ); - VERIFY( (m1.cwise().max(m2).cwise() > (m1-mones)).all() ); - - VERIFY( (m1.cwise()<m1.unaryExpr(bind2nd(plus<Scalar>(), Scalar(1)))).all() ); - VERIFY( !(m1.cwise()<m1bis.unaryExpr(bind2nd(minus<Scalar>(), Scalar(1)))).all() ); - VERIFY( !(m1.cwise()>m1bis.unaryExpr(bind2nd(plus<Scalar>(), Scalar(1)))).any() ); - - cwiseops_real_only(m1, m2, m3, mones); -} - -void test_cwiseop() -{ - for(int i = 0; i < g_repeat ; i++) { - CALL_SUBTEST_1( cwiseops(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( cwiseops(Matrix4d()) ); - CALL_SUBTEST_3( cwiseops(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_4( cwiseops(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_5( cwiseops(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - CALL_SUBTEST_6( cwiseops(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); - } -} diff --git a/test/dense_storage.cpp b/test/dense_storage.cpp new file mode 100644 index 000000000..e63712b1a --- /dev/null +++ b/test/dense_storage.cpp @@ -0,0 +1,76 @@ +// 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/test/diagonal.cpp b/test/diagonal.cpp index 53814a588..c1546e97d 100644 --- a/test/diagonal.cpp +++ b/test/diagonal.cpp @@ -20,6 +20,8 @@ template<typename MatrixType> void diagonal(const MatrixType& m) 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(); @@ -58,6 +60,26 @@ template<typename MatrixType> void diagonal(const MatrixType& m) 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); + } +} + +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() ); } } @@ -74,4 +96,6 @@ void test_diagonal() 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/test/diagonalmatrices.cpp b/test/diagonalmatrices.cpp index 149f1db2f..cd6dc8cf0 100644 --- a/test/diagonalmatrices.cpp +++ b/test/diagonalmatrices.cpp @@ -17,6 +17,7 @@ template<typename MatrixType> void diagonalmatrices(const MatrixType& m) 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; @@ -64,6 +65,13 @@ template<typename MatrixType> void diagonalmatrices(const MatrixType& m) 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); @@ -84,6 +92,24 @@ template<typename MatrixType> void diagonalmatrices(const MatrixType& m) 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() ); +} + +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() @@ -99,4 +125,5 @@ void test_diagonalmatrices() 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_10( bug987<0>() ); } diff --git a/test/dynalloc.cpp b/test/dynalloc.cpp index 7e41bfa97..f1cc70bee 100644 --- a/test/dynalloc.cpp +++ b/test/dynalloc.cpp @@ -9,18 +9,20 @@ #include "main.h" -#if EIGEN_ALIGN -#define ALIGNMENT 16 +#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(size_t(p)%ALIGNMENT==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; internal::handmade_aligned_free(p); @@ -29,10 +31,10 @@ void check_handmade_aligned_malloc() void check_aligned_malloc() { - for(int i = 1; i < 1000; i++) + for(int i = ALIGNMENT; i < 1000; i++) { char *p = (char*)internal::aligned_malloc(i); - VERIFY(size_t(p)%ALIGNMENT==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; internal::aligned_free(p); @@ -41,10 +43,10 @@ void check_aligned_malloc() void check_aligned_new() { - for(int i = 1; i < 1000; i++) + for(int i = ALIGNMENT; i < 1000; i++) { float *p = internal::aligned_new<float>(i); - VERIFY(size_t(p)%ALIGNMENT==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; internal::aligned_delete(p,i); @@ -53,10 +55,10 @@ void check_aligned_new() void check_aligned_stack_alloc() { - for(int i = 1; i < 400; i++) + for(int i = ALIGNMENT; i < 400; i++) { ei_declare_aligned_stack_constructed_variable(float,p,i,0); - VERIFY(size_t(p)%ALIGNMENT==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; } @@ -68,7 +70,7 @@ struct MyStruct { EIGEN_MAKE_ALIGNED_OPERATOR_NEW char dummychar; - Vector4f avec; + Vector8f avec; }; class MyClassA @@ -76,15 +78,45 @@ class MyClassA public: EIGEN_MAKE_ALIGNED_OPERATOR_NEW char dummychar; - Vector4f avec; + Vector8f avec; }; template<typename T> void check_dynaligned() { - T* obj = new T; - VERIFY(T::NeedsToAlign==1); - VERIFY(size_t(obj)%ALIGNMENT==0); - delete obj; + // 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() @@ -97,25 +129,34 @@ void test_dynalloc() 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>() ); } - - // check static allocation, who knows ? - #if EIGEN_ALIGN_STATICALLY + { - MyStruct foo0; VERIFY(size_t(foo0.avec.data())%ALIGNMENT==0); - MyClassA fooA; VERIFY(size_t(fooA.avec.data())%ALIGNMENT==0); + 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(size_t(foo0->avec.data())%ALIGNMENT==0); - MyClassA *fooA = new MyClassA(); VERIFY(size_t(fooA->avec.data())%ALIGNMENT==0); + 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; } @@ -124,8 +165,8 @@ void test_dynalloc() const int N = 10; for (int i=0; i<g_repeat*100; ++i) { - MyStruct *foo0 = new MyStruct[N]; VERIFY(size_t(foo0->avec.data())%ALIGNMENT==0); - MyClassA *fooA = new MyClassA[N]; VERIFY(size_t(fooA->avec.data())%ALIGNMENT==0); + 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; } diff --git a/test/eigen2/CMakeLists.txt b/test/eigen2/CMakeLists.txt deleted file mode 100644 index 9615a6026..000000000 --- a/test/eigen2/CMakeLists.txt +++ /dev/null @@ -1,61 +0,0 @@ -add_custom_target(eigen2_buildtests) -add_custom_target(eigen2_check COMMAND "ctest -R eigen2") -add_dependencies(eigen2_check eigen2_buildtests) -add_dependencies(buildtests eigen2_buildtests) - -add_definitions("-DEIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API") -add_definitions("-DEIGEN_NO_EIGEN2_DEPRECATED_WARNING") - -ei_add_test(eigen2_meta) -ei_add_test(eigen2_sizeof) -ei_add_test(eigen2_dynalloc) -ei_add_test(eigen2_nomalloc) -#ei_add_test(eigen2_first_aligned) -ei_add_test(eigen2_mixingtypes) -#ei_add_test(eigen2_packetmath) -ei_add_test(eigen2_unalignedassert) -#ei_add_test(eigen2_vectorization_logic) -ei_add_test(eigen2_basicstuff) -ei_add_test(eigen2_linearstructure) -ei_add_test(eigen2_cwiseop) -ei_add_test(eigen2_sum) -ei_add_test(eigen2_product_small) -ei_add_test(eigen2_product_large ${EI_OFLAG}) -ei_add_test(eigen2_adjoint) -ei_add_test(eigen2_submatrices) -ei_add_test(eigen2_miscmatrices) -ei_add_test(eigen2_commainitializer) -ei_add_test(eigen2_smallvectors) -ei_add_test(eigen2_map) -ei_add_test(eigen2_array) -ei_add_test(eigen2_triangular) -ei_add_test(eigen2_cholesky " " "${GSL_LIBRARIES}") -ei_add_test(eigen2_lu ${EI_OFLAG}) -ei_add_test(eigen2_determinant ${EI_OFLAG}) -ei_add_test(eigen2_inverse) -ei_add_test(eigen2_qr) -ei_add_test(eigen2_eigensolver " " "${GSL_LIBRARIES}") -ei_add_test(eigen2_svd) -ei_add_test(eigen2_geometry) -ei_add_test(eigen2_geometry_with_eigen2_prefix) -ei_add_test(eigen2_hyperplane) -ei_add_test(eigen2_parametrizedline) -ei_add_test(eigen2_alignedbox) -ei_add_test(eigen2_regression) -ei_add_test(eigen2_stdvector) -ei_add_test(eigen2_newstdvector) -if(QT4_FOUND) - ei_add_test(eigen2_qtvector " " "${QT_QTCORE_LIBRARY}") -endif(QT4_FOUND) -# no support for eigen2 sparse module -# if(NOT EIGEN_DEFAULT_TO_ROW_MAJOR) -# ei_add_test(eigen2_sparse_vector) -# ei_add_test(eigen2_sparse_basic) -# ei_add_test(eigen2_sparse_solvers " " "${SPARSE_LIBS}") -# ei_add_test(eigen2_sparse_product) -# endif() -ei_add_test(eigen2_swap) -ei_add_test(eigen2_visitor) -ei_add_test(eigen2_bug_132) - -ei_add_test(eigen2_prec_inverse_4x4 ${EI_OFLAG}) diff --git a/test/eigen2/eigen2_adjoint.cpp b/test/eigen2/eigen2_adjoint.cpp deleted file mode 100644 index c0f811459..000000000 --- a/test/eigen2/eigen2_adjoint.cpp +++ /dev/null @@ -1,99 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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 adjoint(const MatrixType& m) -{ - /* this test covers the following files: - Transpose.h Conjugate.h Dot.h - */ - - 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; - int rows = m.rows(); - int cols = m.cols(); - - RealScalar largerEps = test_precision<RealScalar>(); - if (ei_is_same_type<RealScalar,float>::ret) - largerEps = RealScalar(1e-3f); - - 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 = ei_random<Scalar>(), - s2 = ei_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(), ei_conj(s1) * m1.adjoint()); - - // check basic properties of dot, norm, norm2 - typedef typename NumTraits<Scalar>::Real RealScalar; - VERIFY(ei_isApprox((s1 * v1 + s2 * v2).eigen2_dot(v3), s1 * v1.eigen2_dot(v3) + s2 * v2.eigen2_dot(v3), largerEps)); - VERIFY(ei_isApprox(v3.eigen2_dot(s1 * v1 + s2 * v2), ei_conj(s1)*v3.eigen2_dot(v1)+ei_conj(s2)*v3.eigen2_dot(v2), largerEps)); - VERIFY_IS_APPROX(ei_conj(v1.eigen2_dot(v2)), v2.eigen2_dot(v1)); - VERIFY_IS_APPROX(ei_real(v1.eigen2_dot(v1)), v1.squaredNorm()); - if(NumTraits<Scalar>::HasFloatingPoint) - VERIFY_IS_APPROX(v1.squaredNorm(), v1.norm() * v1.norm()); - VERIFY_IS_MUCH_SMALLER_THAN(ei_abs(vzero.eigen2_dot(v1)), static_cast<RealScalar>(1)); - if(NumTraits<Scalar>::HasFloatingPoint) - VERIFY_IS_MUCH_SMALLER_THAN(vzero.norm(), static_cast<RealScalar>(1)); - - // check compatibility of dot and adjoint - VERIFY(ei_isApprox(v1.eigen2_dot(square * v2), (square.adjoint() * v1).eigen2_dot(v2), largerEps)); - - // like in testBasicStuff, test operator() to check const-qualification - int r = ei_random<int>(0, rows-1), - c = ei_random<int>(0, cols-1); - VERIFY_IS_APPROX(m1.conjugate()(r,c), ei_conj(m1(r,c))); - VERIFY_IS_APPROX(m1.adjoint()(c,r), ei_conj(m1(r,c))); - - if(NumTraits<Scalar>::HasFloatingPoint) - { - // 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(VectorType::Random(rows).normalized().norm(), RealScalar(1)); - } - - // check inplace transpose - m3 = m1; - m3.transposeInPlace(); - VERIFY_IS_APPROX(m3,m1.transpose()); - m3.transposeInPlace(); - VERIFY_IS_APPROX(m3,m1); - -} - -void test_eigen2_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(4, 4)) ); - CALL_SUBTEST_5( adjoint(MatrixXi(8, 12)) ); - CALL_SUBTEST_6( adjoint(MatrixXf(21, 21)) ); - } - // test a large matrix only once - CALL_SUBTEST_7( adjoint(Matrix<float, 100, 100>()) ); -} - diff --git a/test/eigen2/eigen2_alignedbox.cpp b/test/eigen2/eigen2_alignedbox.cpp deleted file mode 100644 index 35043b958..000000000 --- a/test/eigen2/eigen2_alignedbox.cpp +++ /dev/null @@ -1,60 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// 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 BoxType> void alignedbox(const BoxType& _box) -{ - /* this test covers the following files: - AlignedBox.h - */ - - const int dim = _box.dim(); - typedef typename BoxType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, BoxType::AmbientDimAtCompileTime, 1> VectorType; - - VectorType p0 = VectorType::Random(dim); - VectorType p1 = VectorType::Random(dim); - RealScalar s1 = ei_random<RealScalar>(0,1); - - BoxType b0(dim); - BoxType b1(VectorType::Random(dim),VectorType::Random(dim)); - BoxType b2; - - b0.extend(p0); - b0.extend(p1); - VERIFY(b0.contains(p0*s1+(Scalar(1)-s1)*p1)); - VERIFY(!b0.contains(p0 + (1+s1)*(p1-p0))); - - (b2 = b0).extend(b1); - VERIFY(b2.contains(b0)); - VERIFY(b2.contains(b1)); - VERIFY_IS_APPROX(b2.clamp(b0), b0); - - // casting - 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 test_eigen2_alignedbox() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( alignedbox(AlignedBox<float,2>()) ); - CALL_SUBTEST_2( alignedbox(AlignedBox<float,3>()) ); - CALL_SUBTEST_3( alignedbox(AlignedBox<double,4>()) ); - } -} diff --git a/test/eigen2/eigen2_array.cpp b/test/eigen2/eigen2_array.cpp deleted file mode 100644 index c1ff40ce7..000000000 --- a/test/eigen2/eigen2_array.cpp +++ /dev/null @@ -1,142 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// 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/Array> - -template<typename MatrixType> void array(const MatrixType& m) -{ - /* this test covers the following files: - Array.cpp - */ - - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - - int rows = m.rows(); - int cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols); - - Scalar s1 = ei_random<Scalar>(), - s2 = ei_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); - - // reductions - VERIFY_IS_APPROX(m1.colwise().sum().sum(), m1.sum()); - VERIFY_IS_APPROX(m1.rowwise().sum().sum(), m1.sum()); - if (!ei_isApprox(m1.sum(), (m1+m2).sum())) - 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>())); -} - -template<typename MatrixType> void comparisons(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - - int rows = m.rows(); - int cols = m.cols(); - - int r = ei_random<int>(0, rows-1), - c = ei_random<int>(0, cols-1); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols); - - VERIFY(((m1.cwise() + Scalar(1)).cwise() > m1).all()); - VERIFY(((m1.cwise() - Scalar(1)).cwise() < m1).all()); - if (rows*cols>1) - { - m3 = m1; - m3(r,c) += 1; - VERIFY(! (m1.cwise() < m3).all() ); - VERIFY(! (m1.cwise() > m3).all() ); - } - - // comparisons to scalar - VERIFY( (m1.cwise() != (m1(r,c)+1) ).any() ); - VERIFY( (m1.cwise() > (m1(r,c)-1) ).any() ); - VERIFY( (m1.cwise() < (m1(r,c)+1) ).any() ); - VERIFY( (m1.cwise() == m1(r,c) ).any() ); - - // test Select - VERIFY_IS_APPROX( (m1.cwise()<m2).select(m1,m2), m1.cwise().min(m2) ); - VERIFY_IS_APPROX( (m1.cwise()>m2).select(m1,m2), m1.cwise().max(m2) ); - Scalar mid = (m1.cwise().abs().minCoeff() + m1.cwise().abs().maxCoeff())/Scalar(2); - for (int j=0; j<cols; ++j) - for (int i=0; i<rows; ++i) - m3(i,j) = ei_abs(m1(i,j))<mid ? 0 : m1(i,j); - VERIFY_IS_APPROX( (m1.cwise().abs().cwise()<MatrixType::Constant(rows,cols,mid)) - .select(MatrixType::Zero(rows,cols),m1), m3); - // shorter versions: - VERIFY_IS_APPROX( (m1.cwise().abs().cwise()<MatrixType::Constant(rows,cols,mid)) - .select(0,m1), m3); - VERIFY_IS_APPROX( (m1.cwise().abs().cwise()>=MatrixType::Constant(rows,cols,mid)) - .select(m1,0), m3); - // even shorter version: - VERIFY_IS_APPROX( (m1.cwise().abs().cwise()<mid).select(0,m1), m3); - - // count - VERIFY(((m1.cwise().abs().cwise()+1).cwise()>RealScalar(0.1)).count() == rows*cols); - VERIFY_IS_APPROX(((m1.cwise().abs().cwise()+1).cwise()>RealScalar(0.1)).colwise().count().template cast<int>(), RowVectorXi::Constant(cols,rows)); - VERIFY_IS_APPROX(((m1.cwise().abs().cwise()+1).cwise()>RealScalar(0.1)).rowwise().count().template cast<int>(), VectorXi::Constant(rows, cols)); -} - -template<typename VectorType> void lpNorm(const VectorType& v) -{ - VectorType u = VectorType::Random(v.size()); - - VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), u.cwise().abs().maxCoeff()); - VERIFY_IS_APPROX(u.template lpNorm<1>(), u.cwise().abs().sum()); - VERIFY_IS_APPROX(u.template lpNorm<2>(), ei_sqrt(u.cwise().abs().cwise().square().sum())); - VERIFY_IS_APPROX(ei_pow(u.template lpNorm<5>(), typename VectorType::RealScalar(5)), u.cwise().abs().cwise().pow(5).sum()); -} - -void test_eigen2_array() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( array(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( array(Matrix2f()) ); - CALL_SUBTEST_3( array(Matrix4d()) ); - CALL_SUBTEST_4( array(MatrixXcf(3, 3)) ); - CALL_SUBTEST_5( array(MatrixXf(8, 12)) ); - CALL_SUBTEST_6( array(MatrixXi(8, 12)) ); - } - 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(8, 12)) ); - CALL_SUBTEST_6( comparisons(MatrixXi(8, 12)) ); - } - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( lpNorm(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( lpNorm(Vector2f()) ); - CALL_SUBTEST_3( lpNorm(Vector3d()) ); - CALL_SUBTEST_4( lpNorm(Vector4f()) ); - CALL_SUBTEST_5( lpNorm(VectorXf(16)) ); - CALL_SUBTEST_7( lpNorm(VectorXcd(10)) ); - } -} diff --git a/test/eigen2/eigen2_basicstuff.cpp b/test/eigen2/eigen2_basicstuff.cpp deleted file mode 100644 index dd2dec1ef..000000000 --- a/test/eigen2/eigen2_basicstuff.cpp +++ /dev/null @@ -1,105 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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 basicStuff(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - - int rows = m.rows(); - int 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); - - Scalar x = ei_random<Scalar>(); - - int r = ei_random<int>(0, rows-1), - c = ei_random<int>(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); - if(NumTraits<Scalar>::HasFloatingPoint) - VERIFY_IS_MUCH_SMALLER_THAN( vzero, v1.norm()); - 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))); - } - - VERIFY_IS_APPROX(m3 = m1,m1); - MatrixType m4; - VERIFY_IS_APPROX(m4 = m1,m1); - - // test swap - m3 = m1; - m1.swap(m2); - VERIFY_IS_APPROX(m3, m2); - if(rows*cols>=3) - { - VERIFY_IS_NOT_APPROX(m3, m1); - } -} - -void test_eigen2_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(3, 3)) ); - CALL_SUBTEST_4( basicStuff(MatrixXi(8, 12)) ); - CALL_SUBTEST_5( basicStuff(MatrixXcd(20, 20)) ); - CALL_SUBTEST_6( basicStuff(Matrix<float, 100, 100>()) ); - CALL_SUBTEST_7( basicStuff(Matrix<long double,Dynamic,Dynamic>(10,10)) ); - } -} diff --git a/test/eigen2/eigen2_bug_132.cpp b/test/eigen2/eigen2_bug_132.cpp deleted file mode 100644 index 7fe3610ce..000000000 --- a/test/eigen2/eigen2_bug_132.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// 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" - -void test_eigen2_bug_132() { - int size = 100; - MatrixXd A(size, size); - VectorXd b(size), c(size); - { - VectorXd y = A.transpose() * (b-c); // bug 132: infinite recursion in coeffRef - VectorXd z = (b-c).transpose() * A; // bug 132: infinite recursion in coeffRef - } - - // the following ones weren't failing, but let's include them for completeness: - { - VectorXd y = A * (b-c); - VectorXd z = (b-c).transpose() * A.transpose(); - } -} diff --git a/test/eigen2/eigen2_cholesky.cpp b/test/eigen2/eigen2_cholesky.cpp deleted file mode 100644 index 9c4b6f561..000000000 --- a/test/eigen2/eigen2_cholesky.cpp +++ /dev/null @@ -1,113 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// 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_ASSERTION_CHECKING -#include "main.h" -#include <Eigen/Cholesky> -#include <Eigen/LU> - -#ifdef HAS_GSL -#include "gsl_helper.h" -#endif - -template<typename MatrixType> void cholesky(const MatrixType& m) -{ - /* this test covers the following files: - LLT.h LDLT.h - */ - int rows = m.rows(); - int 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 - MatrixType a1 = MatrixType::Random(rows,cols); - symm += a1 * a1.adjoint(); - - #ifdef HAS_GSL - if (ei_is_same_type<RealScalar,double>::ret) - { - typedef GslTraits<Scalar> Gsl; - typename Gsl::Matrix gMatA=0, gSymm=0; - typename Gsl::Vector gVecB=0, gVecX=0; - convert<MatrixType>(symm, gSymm); - convert<MatrixType>(symm, gMatA); - convert<VectorType>(vecB, gVecB); - convert<VectorType>(vecB, gVecX); - Gsl::cholesky(gMatA); - Gsl::cholesky_solve(gMatA, gVecB, gVecX); - VectorType vecX(rows), _vecX, _vecB; - convert(gVecX, _vecX); - symm.llt().solve(vecB, &vecX); - Gsl::prod(gSymm, gVecX, gVecB); - convert(gVecB, _vecB); - // test gsl itself ! - VERIFY_IS_APPROX(vecB, _vecB); - VERIFY_IS_APPROX(vecX, _vecX); - - Gsl::free(gMatA); - Gsl::free(gSymm); - Gsl::free(gVecB); - Gsl::free(gVecX); - } - #endif - - { - LDLT<SquareMatrixType> ldlt(symm); - VERIFY(ldlt.isPositiveDefinite()); - // in eigen3, LDLT is pivoting - //VERIFY_IS_APPROX(symm, ldlt.matrixL() * ldlt.vectorD().asDiagonal() * ldlt.matrixL().adjoint()); - ldlt.solve(vecB, &vecX); - VERIFY_IS_APPROX(symm * vecX, vecB); - ldlt.solve(matB, &matX); - VERIFY_IS_APPROX(symm * matX, matB); - } - - { - LLT<SquareMatrixType> chol(symm); - VERIFY(chol.isPositiveDefinite()); - VERIFY_IS_APPROX(symm, chol.matrixL() * chol.matrixL().adjoint()); - chol.solve(vecB, &vecX); - VERIFY_IS_APPROX(symm * vecX, vecB); - chol.solve(matB, &matX); - VERIFY_IS_APPROX(symm * matX, matB); - } - -#if 0 // cholesky is not rank-revealing anyway - // test isPositiveDefinite on non definite matrix - if (rows>4) - { - SquareMatrixType symm = a0.block(0,0,rows,cols-4) * a0.block(0,0,rows,cols-4).adjoint(); - LLT<SquareMatrixType> chol(symm); - VERIFY(!chol.isPositiveDefinite()); - LDLT<SquareMatrixType> cholnosqrt(symm); - VERIFY(!cholnosqrt.isPositiveDefinite()); - } -#endif -} - -void test_eigen2_cholesky() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( cholesky(Matrix<double,1,1>()) ); - CALL_SUBTEST_2( cholesky(Matrix2d()) ); - CALL_SUBTEST_3( cholesky(Matrix3f()) ); - CALL_SUBTEST_4( cholesky(Matrix4d()) ); - CALL_SUBTEST_5( cholesky(MatrixXcd(7,7)) ); - CALL_SUBTEST_6( cholesky(MatrixXf(17,17)) ); - CALL_SUBTEST_7( cholesky(MatrixXd(33,33)) ); - } -} diff --git a/test/eigen2/eigen2_commainitializer.cpp b/test/eigen2/eigen2_commainitializer.cpp deleted file mode 100644 index e0f901e0b..000000000 --- a/test/eigen2/eigen2_commainitializer.cpp +++ /dev/null @@ -1,46 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// 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_eigen2_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); -} diff --git a/test/eigen2/eigen2_cwiseop.cpp b/test/eigen2/eigen2_cwiseop.cpp deleted file mode 100644 index 22e1cc342..000000000 --- a/test/eigen2/eigen2_cwiseop.cpp +++ /dev/null @@ -1,155 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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 <functional> -#include <Eigen/Array> - -using namespace std; - -template<typename Scalar> struct AddIfNull { - const Scalar operator() (const Scalar a, const Scalar b) const {return a<=1e-3 ? b : a;} - enum { Cost = NumTraits<Scalar>::AddCost }; -}; - -template<typename MatrixType> void cwiseops(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - - int rows = m.rows(); - int cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols), - m4(rows, cols), - mzero = MatrixType::Zero(rows, cols), - mones = MatrixType::Ones(rows, cols), - identity = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> - ::Identity(rows, rows); - VectorType vzero = VectorType::Zero(rows), - vones = VectorType::Ones(rows), - v3(rows); - - int r = ei_random<int>(0, rows-1), - c = ei_random<int>(0, cols-1); - - Scalar s1 = ei_random<Scalar>(); - - // test Zero, Ones, Constant, and the set* variants - m3 = MatrixType::Constant(rows, cols, s1); - for (int j=0; j<cols; ++j) - for (int i=0; i<rows; ++i) - { - VERIFY_IS_APPROX(mzero(i,j), Scalar(0)); - VERIFY_IS_APPROX(mones(i,j), Scalar(1)); - VERIFY_IS_APPROX(m3(i,j), s1); - } - VERIFY(mzero.isZero()); - VERIFY(mones.isOnes()); - VERIFY(m3.isConstant(s1)); - VERIFY(identity.isIdentity()); - VERIFY_IS_APPROX(m4.setConstant(s1), m3); - VERIFY_IS_APPROX(m4.setConstant(rows,cols,s1), m3); - VERIFY_IS_APPROX(m4.setZero(), mzero); - VERIFY_IS_APPROX(m4.setZero(rows,cols), mzero); - VERIFY_IS_APPROX(m4.setOnes(), mones); - VERIFY_IS_APPROX(m4.setOnes(rows,cols), mones); - m4.fill(s1); - VERIFY_IS_APPROX(m4, m3); - - VERIFY_IS_APPROX(v3.setConstant(rows, s1), VectorType::Constant(rows,s1)); - VERIFY_IS_APPROX(v3.setZero(rows), vzero); - VERIFY_IS_APPROX(v3.setOnes(rows), vones); - - m2 = m2.template binaryExpr<AddIfNull<Scalar> >(mones); - - VERIFY_IS_APPROX(m1.cwise().pow(2), m1.cwise().abs2()); - VERIFY_IS_APPROX(m1.cwise().pow(2), m1.cwise().square()); - VERIFY_IS_APPROX(m1.cwise().pow(3), m1.cwise().cube()); - - VERIFY_IS_APPROX(m1 + mones, m1.cwise()+Scalar(1)); - VERIFY_IS_APPROX(m1 - mones, m1.cwise()-Scalar(1)); - m3 = m1; m3.cwise() += 1; - VERIFY_IS_APPROX(m1 + mones, m3); - m3 = m1; m3.cwise() -= 1; - VERIFY_IS_APPROX(m1 - mones, m3); - - VERIFY_IS_APPROX(m2, m2.cwise() * mones); - VERIFY_IS_APPROX(m1.cwise() * m2, m2.cwise() * m1); - m3 = m1; - m3.cwise() *= m2; - VERIFY_IS_APPROX(m3, m1.cwise() * m2); - - VERIFY_IS_APPROX(mones, m2.cwise()/m2); - if(NumTraits<Scalar>::HasFloatingPoint) - { - VERIFY_IS_APPROX(m1.cwise() / m2, m1.cwise() * (m2.cwise().inverse())); - m3 = m1.cwise().abs().cwise().sqrt(); - VERIFY_IS_APPROX(m3.cwise().square(), m1.cwise().abs()); - VERIFY_IS_APPROX(m1.cwise().square().cwise().sqrt(), m1.cwise().abs()); - VERIFY_IS_APPROX(m1.cwise().abs().cwise().log().cwise().exp() , m1.cwise().abs()); - - VERIFY_IS_APPROX(m1.cwise().pow(2), m1.cwise().square()); - m3 = (m1.cwise().abs().cwise()<=RealScalar(0.01)).select(mones,m1); - VERIFY_IS_APPROX(m3.cwise().pow(-1), m3.cwise().inverse()); - m3 = m1.cwise().abs(); - VERIFY_IS_APPROX(m3.cwise().pow(RealScalar(0.5)), m3.cwise().sqrt()); - -// VERIFY_IS_APPROX(m1.cwise().tan(), m1.cwise().sin().cwise() / m1.cwise().cos()); - VERIFY_IS_APPROX(mones, m1.cwise().sin().cwise().square() + m1.cwise().cos().cwise().square()); - m3 = m1; - m3.cwise() /= m2; - VERIFY_IS_APPROX(m3, m1.cwise() / m2); - } - - // check min - VERIFY_IS_APPROX( m1.cwise().min(m2), m2.cwise().min(m1) ); - VERIFY_IS_APPROX( m1.cwise().min(m1+mones), m1 ); - VERIFY_IS_APPROX( m1.cwise().min(m1-mones), m1-mones ); - - // check max - VERIFY_IS_APPROX( m1.cwise().max(m2), m2.cwise().max(m1) ); - VERIFY_IS_APPROX( m1.cwise().max(m1-mones), m1 ); - VERIFY_IS_APPROX( m1.cwise().max(m1+mones), m1+mones ); - - VERIFY( (m1.cwise() == m1).all() ); - VERIFY( (m1.cwise() != m2).any() ); - VERIFY(!(m1.cwise() == (m1+mones)).any() ); - if (rows*cols>1) - { - m3 = m1; - m3(r,c) += 1; - VERIFY( (m1.cwise() == m3).any() ); - VERIFY( !(m1.cwise() == m3).all() ); - } - VERIFY( (m1.cwise().min(m2).cwise() <= m2).all() ); - VERIFY( (m1.cwise().max(m2).cwise() >= m2).all() ); - VERIFY( (m1.cwise().min(m2).cwise() < (m1+mones)).all() ); - VERIFY( (m1.cwise().max(m2).cwise() > (m1-mones)).all() ); - - VERIFY( (m1.cwise()<m1.unaryExpr(bind2nd(plus<Scalar>(), Scalar(1)))).all() ); - VERIFY( !(m1.cwise()<m1.unaryExpr(bind2nd(minus<Scalar>(), Scalar(1)))).all() ); - VERIFY( !(m1.cwise()>m1.unaryExpr(bind2nd(plus<Scalar>(), Scalar(1)))).any() ); -} - -void test_eigen2_cwiseop() -{ - for(int i = 0; i < g_repeat ; i++) { - CALL_SUBTEST_1( cwiseops(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( cwiseops(Matrix4d()) ); - CALL_SUBTEST_3( cwiseops(MatrixXf(3, 3)) ); - CALL_SUBTEST_3( cwiseops(MatrixXf(22, 22)) ); - CALL_SUBTEST_4( cwiseops(MatrixXi(8, 12)) ); - CALL_SUBTEST_5( cwiseops(MatrixXd(20, 20)) ); - } -} diff --git a/test/eigen2/eigen2_determinant.cpp b/test/eigen2/eigen2_determinant.cpp deleted file mode 100644 index c7b4ad053..000000000 --- a/test/eigen2/eigen2_determinant.cpp +++ /dev/null @@ -1,61 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// 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 - */ - int size = m.rows(); - - MatrixType m1(size, size), m2(size, size); - m1.setRandom(); - m2.setRandom(); - typedef typename MatrixType::Scalar Scalar; - Scalar x = ei_random<Scalar>(); - VERIFY_IS_APPROX(MatrixType::Identity(size, size).determinant(), Scalar(1)); - VERIFY_IS_APPROX((m1*m2).determinant(), m1.determinant() * m2.determinant()); - if(size==1) return; - int i = ei_random<int>(0, size-1); - int j; - do { - j = ei_random<int>(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(ei_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); -} - -void test_eigen2_determinant() -{ - for(int i = 0; i < g_repeat; i++) { - 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>()) ); - CALL_SUBTEST_6( determinant(MatrixXd(20, 20)) ); - } - CALL_SUBTEST_6( determinant(MatrixXd(200, 200)) ); -} diff --git a/test/eigen2/eigen2_dynalloc.cpp b/test/eigen2/eigen2_dynalloc.cpp deleted file mode 100644 index 1891a9e33..000000000 --- a/test/eigen2/eigen2_dynalloc.cpp +++ /dev/null @@ -1,131 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// 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_ARCH_WANTS_ALIGNMENT -#define ALIGNMENT 16 -#else -#define ALIGNMENT 1 -#endif - -void check_handmade_aligned_malloc() -{ - for(int i = 1; i < 1000; i++) - { - char *p = (char*)ei_handmade_aligned_malloc(i); - VERIFY(std::size_t(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; - ei_handmade_aligned_free(p); - } -} - -void check_aligned_malloc() -{ - for(int i = 1; i < 1000; i++) - { - char *p = (char*)ei_aligned_malloc(i); - VERIFY(std::size_t(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; - ei_aligned_free(p); - } -} - -void check_aligned_new() -{ - for(int i = 1; i < 1000; i++) - { - float *p = ei_aligned_new<float>(i); - VERIFY(std::size_t(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; - ei_aligned_delete(p,i); - } -} - -void check_aligned_stack_alloc() -{ - for(int i = 1; i < 1000; i++) - { - ei_declare_aligned_stack_constructed_variable(float, p, i, 0); - VERIFY(std::size_t(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; - Vector4f avec; -}; - -class MyClassA -{ - public: - EIGEN_MAKE_ALIGNED_OPERATOR_NEW - char dummychar; - Vector4f avec; -}; - -template<typename T> void check_dynaligned() -{ - T* obj = new T; - VERIFY(std::size_t(obj)%ALIGNMENT==0); - delete obj; -} - -void test_eigen2_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_dynaligned<Vector4f>() ); - CALL_SUBTEST( check_dynaligned<Vector2d>() ); - CALL_SUBTEST( check_dynaligned<Matrix4f>() ); - CALL_SUBTEST( check_dynaligned<Vector4d>() ); - CALL_SUBTEST( check_dynaligned<Vector4i>() ); - } - - // check static allocation, who knows ? - { - MyStruct foo0; VERIFY(std::size_t(foo0.avec.data())%ALIGNMENT==0); - MyClassA fooA; VERIFY(std::size_t(fooA.avec.data())%ALIGNMENT==0); - } - - // dynamic allocation, single object - for (int i=0; i<g_repeat*100; ++i) - { - MyStruct *foo0 = new MyStruct(); VERIFY(std::size_t(foo0->avec.data())%ALIGNMENT==0); - MyClassA *fooA = new MyClassA(); VERIFY(std::size_t(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(std::size_t(foo0->avec.data())%ALIGNMENT==0); - MyClassA *fooA = new MyClassA[N]; VERIFY(std::size_t(fooA->avec.data())%ALIGNMENT==0); - delete[] foo0; - delete[] fooA; - } - -} diff --git a/test/eigen2/eigen2_eigensolver.cpp b/test/eigen2/eigen2_eigensolver.cpp deleted file mode 100644 index 48b4ace43..000000000 --- a/test/eigen2/eigen2_eigensolver.cpp +++ /dev/null @@ -1,146 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// 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> - -#ifdef HAS_GSL -#include "gsl_helper.h" -#endif - -template<typename MatrixType> void selfadjointeigensolver(const MatrixType& m) -{ - /* this test covers the following files: - EigenSolver.h, SelfAdjointEigenSolver.h (and indirectly: Tridiagonalization.h) - */ - int rows = m.rows(); - int cols = m.cols(); - - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealVectorType; - typedef typename std::complex<typename NumTraits<typename MatrixType::Scalar>::Real> Complex; - - 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 b = MatrixType::Random(rows,cols); - MatrixType b1 = MatrixType::Random(rows,cols); - MatrixType symmB = b.adjoint() * b + b1.adjoint() * b1; - - SelfAdjointEigenSolver<MatrixType> eiSymm(symmA); - // generalized eigen pb - SelfAdjointEigenSolver<MatrixType> eiSymmGen(symmA, symmB); - - #ifdef HAS_GSL - if (ei_is_same_type<RealScalar,double>::ret) - { - typedef GslTraits<Scalar> Gsl; - typename Gsl::Matrix gEvec=0, gSymmA=0, gSymmB=0; - typename GslTraits<RealScalar>::Vector gEval=0; - RealVectorType _eval; - MatrixType _evec; - convert<MatrixType>(symmA, gSymmA); - convert<MatrixType>(symmB, gSymmB); - convert<MatrixType>(symmA, gEvec); - gEval = GslTraits<RealScalar>::createVector(rows); - - Gsl::eigen_symm(gSymmA, gEval, gEvec); - convert(gEval, _eval); - convert(gEvec, _evec); - - // test gsl itself ! - VERIFY((symmA * _evec).isApprox(_evec * _eval.asDiagonal(), largerEps)); - - // compare with eigen - VERIFY_IS_APPROX(_eval, eiSymm.eigenvalues()); - VERIFY_IS_APPROX(_evec.cwise().abs(), eiSymm.eigenvectors().cwise().abs()); - - // generalized pb - Gsl::eigen_symm_gen(gSymmA, gSymmB, gEval, gEvec); - convert(gEval, _eval); - convert(gEvec, _evec); - // test GSL itself: - VERIFY((symmA * _evec).isApprox(symmB * (_evec * _eval.asDiagonal()), largerEps)); - - // compare with eigen - MatrixType normalized_eivec = eiSymmGen.eigenvectors()*eiSymmGen.eigenvectors().colwise().norm().asDiagonal().inverse(); - VERIFY_IS_APPROX(_eval, eiSymmGen.eigenvalues()); - VERIFY_IS_APPROX(_evec.cwiseAbs(), normalized_eivec.cwiseAbs()); - - Gsl::free(gSymmA); - Gsl::free(gSymmB); - GslTraits<RealScalar>::free(gEval); - Gsl::free(gEvec); - } - #endif - - VERIFY((symmA * eiSymm.eigenvectors()).isApprox( - eiSymm.eigenvectors() * eiSymm.eigenvalues().asDiagonal(), largerEps)); - - // generalized eigen problem Ax = lBx - VERIFY((symmA * eiSymmGen.eigenvectors()).isApprox( - symmB * (eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps)); - - MatrixType sqrtSymmA = eiSymm.operatorSqrt(); - VERIFY_IS_APPROX(symmA, sqrtSymmA*sqrtSymmA); - VERIFY_IS_APPROX(sqrtSymmA, symmA*eiSymm.operatorInverseSqrt()); -} - -template<typename MatrixType> void eigensolver(const MatrixType& m) -{ - /* this test covers the following files: - EigenSolver.h - */ - int rows = m.rows(); - int cols = m.cols(); - - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealVectorType; - typedef typename std::complex<typename NumTraits<typename MatrixType::Scalar>::Real> Complex; - - // 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; - - EigenSolver<MatrixType> ei0(symmA); - 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_APPROX(a * ei1.pseudoEigenvectors(), ei1.pseudoEigenvectors() * ei1.pseudoEigenvalueMatrix()); - VERIFY_IS_APPROX(a.template cast<Complex>() * ei1.eigenvectors(), - ei1.eigenvectors() * ei1.eigenvalues().asDiagonal()); - -} - -void test_eigen2_eigensolver() -{ - for(int i = 0; i < g_repeat; i++) { - // very important to test a 3x3 matrix since we provide a special path for it - CALL_SUBTEST_1( selfadjointeigensolver(Matrix3f()) ); - CALL_SUBTEST_2( selfadjointeigensolver(Matrix4d()) ); - CALL_SUBTEST_3( selfadjointeigensolver(MatrixXf(7,7)) ); - CALL_SUBTEST_4( selfadjointeigensolver(MatrixXcd(5,5)) ); - CALL_SUBTEST_5( selfadjointeigensolver(MatrixXd(19,19)) ); - - CALL_SUBTEST_6( eigensolver(Matrix4f()) ); - CALL_SUBTEST_5( eigensolver(MatrixXd(17,17)) ); - } -} - diff --git a/test/eigen2/eigen2_first_aligned.cpp b/test/eigen2/eigen2_first_aligned.cpp deleted file mode 100644 index 51bb3cad1..000000000 --- a/test/eigen2/eigen2_first_aligned.cpp +++ /dev/null @@ -1,49 +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_eigen2_first_aligned_helper(Scalar *array, int size) -{ - const int packet_size = sizeof(Scalar) * ei_packet_traits<Scalar>::size; - VERIFY(((std::size_t(array) + sizeof(Scalar) * ei_alignmentOffset(array, size)) % packet_size) == 0); -} - -template<typename Scalar> -void test_eigen2_none_aligned_helper(Scalar *array, int size) -{ - VERIFY(ei_packet_traits<Scalar>::size == 1 || ei_alignmentOffset(array, size) == size); -} - -struct some_non_vectorizable_type { float x; }; - -void test_eigen2_first_aligned() -{ - EIGEN_ALIGN_128 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_ALIGN_128 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*)(std::size_t(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/test/eigen2/eigen2_geometry.cpp b/test/eigen2/eigen2_geometry.cpp deleted file mode 100644 index 514040774..000000000 --- a/test/eigen2/eigen2_geometry.cpp +++ /dev/null @@ -1,432 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// 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 geometry(void) -{ - /* this test covers the following files: - Cross.h Quaternion.h, Transform.cpp - */ - - typedef Matrix<Scalar,2,2> Matrix2; - 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> Transform2; - typedef Transform<Scalar,3> Transform3; - typedef Scaling<Scalar,2> Scaling2; - typedef Scaling<Scalar,3> Scaling3; - typedef Translation<Scalar,2> Translation2; - typedef Translation<Scalar,3> Translation3; - - Scalar largeEps = test_precision<Scalar>(); - if (ei_is_same_type<Scalar,float>::ret) - largeEps = 1e-2f; - - Vector3 v0 = Vector3::Random(), - v1 = Vector3::Random(), - v2 = Vector3::Random(); - Vector2 u0 = Vector2::Random(); - Matrix3 matrot1; - - Scalar a = ei_random<Scalar>(-Scalar(M_PI), Scalar(M_PI)); - - // cross product - VERIFY_IS_MUCH_SMALLER_THAN(v1.cross(v2).eigen2_dot(v1), Scalar(1)); - Matrix3 m; - m << v0.normalized(), - (v0.cross(v1)).normalized(), - (v0.cross(v1).cross(v0)).normalized(); - VERIFY(m.isUnitary()); - - // 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()); - - // unitOrthogonal - VERIFY_IS_MUCH_SMALLER_THAN(u0.unitOrthogonal().eigen2_dot(u0), Scalar(1)); - VERIFY_IS_MUCH_SMALLER_THAN(v0.unitOrthogonal().eigen2_dot(v0), Scalar(1)); - VERIFY_IS_APPROX(u0.unitOrthogonal().norm(), Scalar(1)); - VERIFY_IS_APPROX(v0.unitOrthogonal().norm(), Scalar(1)); - - - VERIFY_IS_APPROX(v0, AngleAxisx(a, v0.normalized()) * v0); - VERIFY_IS_APPROX(-v0, AngleAxisx(Scalar(M_PI), v0.unitOrthogonal()) * v0); - VERIFY_IS_APPROX(ei_cos(a)*v0.squaredNorm(), v0.eigen2_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); - - q1 = AngleAxisx(a, v0.normalized()); - q2 = AngleAxisx(a, v1.normalized()); - - // angular distance - Scalar refangle = ei_abs(AngleAxisx(q1.inverse()*q2).angle()); - if (refangle>Scalar(M_PI)) - refangle = Scalar(2)*Scalar(M_PI) - refangle; - - if((q1.coeffs()-q2.coeffs()).norm() > 10*largeEps) - { - VERIFY(ei_isApprox(q1.angularDistance(q2), refangle, largeEps)); - } - - // 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); - - 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 = q1; - VERIFY_IS_APPROX(q1 * v1, Quaternionx(aa) * v1); - 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(v2.normalized(),(q2.setFromTwoVectors(v1,v2)*v1).normalized()); - - // inverse and conjugate - VERIFY_IS_APPROX(q1 * (q1.inverse() * v1), v1); - VERIFY_IS_APPROX(q1 * (q1.conjugate() * v1), 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 (ei_abs(a)<Scalar(0.1)) - a = ei_random<Scalar>(-Scalar(0.4)*Scalar(M_PI), Scalar(0.4)*Scalar(M_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.linear() = q1.toRotationMatrix(); - t1.setIdentity(); - t1.linear() = q1.toRotationMatrix(); - - v0 << 50, 2, 1;//= ei_random_matrix<Vector3>().cwiseProduct(Vector3(10,2,0.5)); - t0.scale(v0); - t1.prescale(v0); - - VERIFY_IS_APPROX( (t0 * Vector3(1,0,0)).norm(), v0.x()); - //VERIFY(!ei_isApprox((t1 * Vector3(1,0,0)).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.cwise().inverse()); - t1.translate(-v0); - - VERIFY((t0.matrix() * 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); - - 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); - tmat4.matrix()(3,3) = Scalar(1); - VERIFY_IS_APPROX(tmat3.matrix(), tmat4.matrix()); - - Scalar a3 = ei_random<Scalar>(-Scalar(M_PI), Scalar(M_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(), Matrix4::Identity()); - t4 *= aa3; - VERIFY_IS_APPROX(t3.matrix(), t4.matrix()); - - v3 = Vector3::Random(); - Translation3 tv3(v3); - Transform3 t5(tv3); - t4 = tv3; - VERIFY_IS_APPROX(t5.matrix(), t4.matrix()); - t4.translate(-v3); - VERIFY_IS_APPROX(t4.matrix(), Matrix4::Identity()); - t4 *= tv3; - VERIFY_IS_APPROX(t5.matrix(), t4.matrix()); - - Scaling3 sv3(v3); - Transform3 t6(sv3); - t4 = sv3; - VERIFY_IS_APPROX(t6.matrix(), t4.matrix()); - t4.scale(v3.cwise().inverse()); - VERIFY_IS_APPROX(t4.matrix(), Matrix4::Identity()); - t4 *= sv3; - VERIFY_IS_APPROX(t6.matrix(), t4.matrix()); - - // matrix * transform - VERIFY_IS_APPROX(Transform3(t3.matrix()*t4).matrix(), Transform3(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 (ei_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.cwise().inverse()).translate(-v20))).matrix().isIdentity(test_precision<Scalar>()) ); - - // Transform - new API - // 3D - t0.setIdentity(); - t0.rotate(q1).scale(v0).translate(v0); - // mat * scaling and mat * translation - t1 = (Matrix3(q1) * Scaling3(v0)) * Translation3(v0); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - // mat * transformation and scaling * translation - t1 = Matrix3(q1) * (Scaling3(v0) * Translation3(v0)); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - t0.setIdentity(); - t0.prerotate(q1).prescale(v0).pretranslate(v0); - // translation * scaling and transformation * mat - t1 = (Translation3(v0) * Scaling3(v0)) * Matrix3(q1); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - // scaling * mat and translation * mat - t1 = Translation3(v0) * (Scaling3(v0) * Matrix3(q1)); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - t0.setIdentity(); - t0.scale(v0).translate(v0).rotate(q1); - // translation * mat and scaling * transformation - t1 = Scaling3(v0) * (Translation3(v0) * Matrix3(q1)); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - // transformation * scaling - t0.scale(v0); - t1 = t1 * Scaling3(v0); - 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()); - - // scaling * quaternion - t0.scale(v1).rotate(q1); - t1 = t1 * (Scaling3(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 * scaling - t0.rotate(q1).scale(v1); - t1 = t1 * (q1 * Scaling3(v1)); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - // translation * vector - t0.setIdentity(); - t0.translate(v0); - VERIFY_IS_APPROX(t0 * v1, Translation3(v0) * v1); - - // scaling * vector - t0.setIdentity(); - t0.scale(v0); - VERIFY_IS_APPROX(t0 * v1, Scaling3(v0) * v1); - - // test transform inversion - t0.setIdentity(); - t0.translate(v0); - t0.linear().setRandom(); - VERIFY_IS_APPROX(t0.inverse(Affine), t0.matrix().inverse()); - t0.setIdentity(); - t0.translate(v0).rotate(q1); - VERIFY_IS_APPROX(t0.inverse(Isometry), t0.matrix().inverse()); - - // test extract rotation and scaling - t0.setIdentity(); - t0.translate(v0).rotate(q1).scale(v1); - VERIFY_IS_APPROX(t0.rotation() * v1, Matrix3(q1) * v1); - - 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> t1f = t1.template cast<float>(); - VERIFY_IS_APPROX(t1f.template cast<Scalar>(),t1); - Transform<double,3> 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); - - Scaling3 sc1(v0); - Scaling<float,3> sc1f = sc1.template cast<float>(); - VERIFY_IS_APPROX(sc1f.template cast<Scalar>(),sc1); - Scaling<double,3> sc1d = sc1.template cast<double>(); - VERIFY_IS_APPROX(sc1d.template cast<Scalar>(),sc1); - - 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); - - 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(ei_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); - - m = q1; -// m.col(1) = Vector3(0,ei_random<Scalar>(),ei_random<Scalar>()).normalized(); -// m.col(0) = Vector3(-1,0,0).normalized(); -// m.col(2) = m.col(0).cross(m.col(1)); - #define VERIFY_EULER(I,J,K, X,Y,Z) { \ - Vector3 ea = m.eulerAngles(I,J,K); \ - Matrix3 m1 = Matrix3(AngleAxisx(ea[0], Vector3::Unit##X()) * AngleAxisx(ea[1], Vector3::Unit##Y()) * AngleAxisx(ea[2], Vector3::Unit##Z())); \ - VERIFY_IS_APPROX(m, m1); \ - VERIFY_IS_APPROX(m, Matrix3(AngleAxisx(ea[0], Vector3::Unit##X()) * AngleAxisx(ea[1], Vector3::Unit##Y()) * AngleAxisx(ea[2], Vector3::Unit##Z()))); \ - } - VERIFY_EULER(0,1,2, X,Y,Z); - VERIFY_EULER(0,1,0, X,Y,X); - VERIFY_EULER(0,2,1, X,Z,Y); - VERIFY_EULER(0,2,0, X,Z,X); - - VERIFY_EULER(1,2,0, Y,Z,X); - VERIFY_EULER(1,2,1, Y,Z,Y); - VERIFY_EULER(1,0,2, Y,X,Z); - VERIFY_EULER(1,0,1, Y,X,Y); - - VERIFY_EULER(2,0,1, Z,X,Y); - VERIFY_EULER(2,0,2, Z,X,Z); - VERIFY_EULER(2,1,0, Z,Y,X); - VERIFY_EULER(2,1,2, Z,Y,Z); - - // colwise/rowwise cross product - mat3.setRandom(); - Vector3 vec3 = Vector3::Random(); - Matrix3 mcross; - int i = ei_random<int>(0,2); - mcross = mat3.colwise().cross(vec3); - VERIFY_IS_APPROX(mcross.col(i), mat3.col(i).cross(vec3)); - mcross = mat3.rowwise().cross(vec3); - VERIFY_IS_APPROX(mcross.row(i), mat3.row(i).cross(vec3)); - - -} - -void test_eigen2_geometry() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( geometry<float>() ); - CALL_SUBTEST_2( geometry<double>() ); - } -} diff --git a/test/eigen2/eigen2_geometry_with_eigen2_prefix.cpp b/test/eigen2/eigen2_geometry_with_eigen2_prefix.cpp deleted file mode 100644 index 12d4a71c3..000000000 --- a/test/eigen2/eigen2_geometry_with_eigen2_prefix.cpp +++ /dev/null @@ -1,435 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// 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_STAGE15_RESOLVE_API_CONFLICTS_WARN - -#include "main.h" -#include <Eigen/Geometry> -#include <Eigen/LU> -#include <Eigen/SVD> - -template<typename Scalar> void geometry(void) -{ - /* this test covers the following files: - Cross.h Quaternion.h, Transform.cpp - */ - - typedef Matrix<Scalar,2,2> Matrix2; - 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 eigen2_Quaternion<Scalar> Quaternionx; - typedef eigen2_AngleAxis<Scalar> AngleAxisx; - typedef eigen2_Transform<Scalar,2> Transform2; - typedef eigen2_Transform<Scalar,3> Transform3; - typedef eigen2_Scaling<Scalar,2> Scaling2; - typedef eigen2_Scaling<Scalar,3> Scaling3; - typedef eigen2_Translation<Scalar,2> Translation2; - typedef eigen2_Translation<Scalar,3> Translation3; - - Scalar largeEps = test_precision<Scalar>(); - if (ei_is_same_type<Scalar,float>::ret) - largeEps = 1e-2f; - - Vector3 v0 = Vector3::Random(), - v1 = Vector3::Random(), - v2 = Vector3::Random(); - Vector2 u0 = Vector2::Random(); - Matrix3 matrot1; - - Scalar a = ei_random<Scalar>(-Scalar(M_PI), Scalar(M_PI)); - - // cross product - VERIFY_IS_MUCH_SMALLER_THAN(v1.cross(v2).eigen2_dot(v1), Scalar(1)); - Matrix3 m; - m << v0.normalized(), - (v0.cross(v1)).normalized(), - (v0.cross(v1).cross(v0)).normalized(); - VERIFY(m.isUnitary()); - - // 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()); - - // unitOrthogonal - VERIFY_IS_MUCH_SMALLER_THAN(u0.unitOrthogonal().eigen2_dot(u0), Scalar(1)); - VERIFY_IS_MUCH_SMALLER_THAN(v0.unitOrthogonal().eigen2_dot(v0), Scalar(1)); - VERIFY_IS_APPROX(u0.unitOrthogonal().norm(), Scalar(1)); - VERIFY_IS_APPROX(v0.unitOrthogonal().norm(), Scalar(1)); - - - VERIFY_IS_APPROX(v0, AngleAxisx(a, v0.normalized()) * v0); - VERIFY_IS_APPROX(-v0, AngleAxisx(Scalar(M_PI), v0.unitOrthogonal()) * v0); - VERIFY_IS_APPROX(ei_cos(a)*v0.squaredNorm(), v0.eigen2_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); - - q1 = AngleAxisx(a, v0.normalized()); - q2 = AngleAxisx(a, v1.normalized()); - - // angular distance - Scalar refangle = ei_abs(AngleAxisx(q1.inverse()*q2).angle()); - if (refangle>Scalar(M_PI)) - refangle = Scalar(2)*Scalar(M_PI) - refangle; - - if((q1.coeffs()-q2.coeffs()).norm() > 10*largeEps) - { - VERIFY(ei_isApprox(q1.angularDistance(q2), refangle, largeEps)); - } - - // 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); - - 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 = q1; - VERIFY_IS_APPROX(q1 * v1, Quaternionx(aa) * v1); - 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(v2.normalized(),(q2.setFromTwoVectors(v1,v2)*v1).normalized()); - - // inverse and conjugate - VERIFY_IS_APPROX(q1 * (q1.inverse() * v1), v1); - VERIFY_IS_APPROX(q1 * (q1.conjugate() * v1), 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 (ei_abs(a)<Scalar(0.1)) - a = ei_random<Scalar>(-Scalar(0.4)*Scalar(M_PI), Scalar(0.4)*Scalar(M_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.linear() = q1.toRotationMatrix(); - t1.setIdentity(); - t1.linear() = q1.toRotationMatrix(); - - v0 << 50, 2, 1;//= ei_random_matrix<Vector3>().cwiseProduct(Vector3(10,2,0.5)); - t0.scale(v0); - t1.prescale(v0); - - VERIFY_IS_APPROX( (t0 * Vector3(1,0,0)).norm(), v0.x()); - //VERIFY(!ei_isApprox((t1 * Vector3(1,0,0)).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.cwise().inverse()); - t1.translate(-v0); - - VERIFY((t0.matrix() * 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); - - 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); - tmat4.matrix()(3,3) = Scalar(1); - VERIFY_IS_APPROX(tmat3.matrix(), tmat4.matrix()); - - Scalar a3 = ei_random<Scalar>(-Scalar(M_PI), Scalar(M_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(), Matrix4::Identity()); - t4 *= aa3; - VERIFY_IS_APPROX(t3.matrix(), t4.matrix()); - - v3 = Vector3::Random(); - Translation3 tv3(v3); - Transform3 t5(tv3); - t4 = tv3; - VERIFY_IS_APPROX(t5.matrix(), t4.matrix()); - t4.translate(-v3); - VERIFY_IS_APPROX(t4.matrix(), Matrix4::Identity()); - t4 *= tv3; - VERIFY_IS_APPROX(t5.matrix(), t4.matrix()); - - Scaling3 sv3(v3); - Transform3 t6(sv3); - t4 = sv3; - VERIFY_IS_APPROX(t6.matrix(), t4.matrix()); - t4.scale(v3.cwise().inverse()); - VERIFY_IS_APPROX(t4.matrix(), Matrix4::Identity()); - t4 *= sv3; - VERIFY_IS_APPROX(t6.matrix(), t4.matrix()); - - // matrix * transform - VERIFY_IS_APPROX(Transform3(t3.matrix()*t4).matrix(), Transform3(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 (ei_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.cwise().inverse()).translate(-v20))).matrix().isIdentity(test_precision<Scalar>()) ); - - // Transform - new API - // 3D - t0.setIdentity(); - t0.rotate(q1).scale(v0).translate(v0); - // mat * scaling and mat * translation - t1 = (Matrix3(q1) * Scaling3(v0)) * Translation3(v0); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - // mat * transformation and scaling * translation - t1 = Matrix3(q1) * (Scaling3(v0) * Translation3(v0)); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - t0.setIdentity(); - t0.prerotate(q1).prescale(v0).pretranslate(v0); - // translation * scaling and transformation * mat - t1 = (Translation3(v0) * Scaling3(v0)) * Matrix3(q1); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - // scaling * mat and translation * mat - t1 = Translation3(v0) * (Scaling3(v0) * Matrix3(q1)); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - t0.setIdentity(); - t0.scale(v0).translate(v0).rotate(q1); - // translation * mat and scaling * transformation - t1 = Scaling3(v0) * (Translation3(v0) * Matrix3(q1)); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - // transformation * scaling - t0.scale(v0); - t1 = t1 * Scaling3(v0); - 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()); - - // scaling * quaternion - t0.scale(v1).rotate(q1); - t1 = t1 * (Scaling3(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 * scaling - t0.rotate(q1).scale(v1); - t1 = t1 * (q1 * Scaling3(v1)); - VERIFY_IS_APPROX(t0.matrix(), t1.matrix()); - - // translation * vector - t0.setIdentity(); - t0.translate(v0); - VERIFY_IS_APPROX(t0 * v1, Translation3(v0) * v1); - - // scaling * vector - t0.setIdentity(); - t0.scale(v0); - VERIFY_IS_APPROX(t0 * v1, Scaling3(v0) * v1); - - // test transform inversion - t0.setIdentity(); - t0.translate(v0); - t0.linear().setRandom(); - VERIFY_IS_APPROX(t0.inverse(Affine), t0.matrix().inverse()); - t0.setIdentity(); - t0.translate(v0).rotate(q1); - VERIFY_IS_APPROX(t0.inverse(Isometry), t0.matrix().inverse()); - - // test extract rotation and scaling - t0.setIdentity(); - t0.translate(v0).rotate(q1).scale(v1); - VERIFY_IS_APPROX(t0.rotation() * v1, Matrix3(q1) * v1); - - 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 - eigen2_Transform<float,3> t1f = t1.template cast<float>(); - VERIFY_IS_APPROX(t1f.template cast<Scalar>(),t1); - eigen2_Transform<double,3> t1d = t1.template cast<double>(); - VERIFY_IS_APPROX(t1d.template cast<Scalar>(),t1); - - Translation3 tr1(v0); - eigen2_Translation<float,3> tr1f = tr1.template cast<float>(); - VERIFY_IS_APPROX(tr1f.template cast<Scalar>(),tr1); - eigen2_Translation<double,3> tr1d = tr1.template cast<double>(); - VERIFY_IS_APPROX(tr1d.template cast<Scalar>(),tr1); - - Scaling3 sc1(v0); - eigen2_Scaling<float,3> sc1f = sc1.template cast<float>(); - VERIFY_IS_APPROX(sc1f.template cast<Scalar>(),sc1); - eigen2_Scaling<double,3> sc1d = sc1.template cast<double>(); - VERIFY_IS_APPROX(sc1d.template cast<Scalar>(),sc1); - - eigen2_Quaternion<float> q1f = q1.template cast<float>(); - VERIFY_IS_APPROX(q1f.template cast<Scalar>(),q1); - eigen2_Quaternion<double> q1d = q1.template cast<double>(); - VERIFY_IS_APPROX(q1d.template cast<Scalar>(),q1); - - eigen2_AngleAxis<float> aa1f = aa1.template cast<float>(); - VERIFY_IS_APPROX(aa1f.template cast<Scalar>(),aa1); - eigen2_AngleAxis<double> aa1d = aa1.template cast<double>(); - VERIFY_IS_APPROX(aa1d.template cast<Scalar>(),aa1); - - eigen2_Rotation2D<Scalar> r2d1(ei_random<Scalar>()); - eigen2_Rotation2D<float> r2d1f = r2d1.template cast<float>(); - VERIFY_IS_APPROX(r2d1f.template cast<Scalar>(),r2d1); - eigen2_Rotation2D<double> r2d1d = r2d1.template cast<double>(); - VERIFY_IS_APPROX(r2d1d.template cast<Scalar>(),r2d1); - - m = q1; -// m.col(1) = Vector3(0,ei_random<Scalar>(),ei_random<Scalar>()).normalized(); -// m.col(0) = Vector3(-1,0,0).normalized(); -// m.col(2) = m.col(0).cross(m.col(1)); - #define VERIFY_EULER(I,J,K, X,Y,Z) { \ - Vector3 ea = m.eulerAngles(I,J,K); \ - Matrix3 m1 = Matrix3(AngleAxisx(ea[0], Vector3::Unit##X()) * AngleAxisx(ea[1], Vector3::Unit##Y()) * AngleAxisx(ea[2], Vector3::Unit##Z())); \ - VERIFY_IS_APPROX(m, m1); \ - VERIFY_IS_APPROX(m, Matrix3(AngleAxisx(ea[0], Vector3::Unit##X()) * AngleAxisx(ea[1], Vector3::Unit##Y()) * AngleAxisx(ea[2], Vector3::Unit##Z()))); \ - } - VERIFY_EULER(0,1,2, X,Y,Z); - VERIFY_EULER(0,1,0, X,Y,X); - VERIFY_EULER(0,2,1, X,Z,Y); - VERIFY_EULER(0,2,0, X,Z,X); - - VERIFY_EULER(1,2,0, Y,Z,X); - VERIFY_EULER(1,2,1, Y,Z,Y); - VERIFY_EULER(1,0,2, Y,X,Z); - VERIFY_EULER(1,0,1, Y,X,Y); - - VERIFY_EULER(2,0,1, Z,X,Y); - VERIFY_EULER(2,0,2, Z,X,Z); - VERIFY_EULER(2,1,0, Z,Y,X); - VERIFY_EULER(2,1,2, Z,Y,Z); - - // colwise/rowwise cross product - mat3.setRandom(); - Vector3 vec3 = Vector3::Random(); - Matrix3 mcross; - int i = ei_random<int>(0,2); - mcross = mat3.colwise().cross(vec3); - VERIFY_IS_APPROX(mcross.col(i), mat3.col(i).cross(vec3)); - mcross = mat3.rowwise().cross(vec3); - VERIFY_IS_APPROX(mcross.row(i), mat3.row(i).cross(vec3)); - - -} - -void test_eigen2_geometry_with_eigen2_prefix() -{ - std::cout << "eigen2 support: " << EIGEN2_SUPPORT_STAGE << std::endl; - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( geometry<float>() ); - CALL_SUBTEST_2( geometry<double>() ); - } -} diff --git a/test/eigen2/eigen2_hyperplane.cpp b/test/eigen2/eigen2_hyperplane.cpp deleted file mode 100644 index f3f85e14d..000000000 --- a/test/eigen2/eigen2_hyperplane.cpp +++ /dev/null @@ -1,126 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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 - */ - - const int dim = _plane.dim(); - typedef typename HyperplaneType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real 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 = ei_random<Scalar>(); - Scalar s1 = ei_random<Scalar>(); - - VERIFY_IS_APPROX( n1.eigen2_dot(n1), Scalar(1) ); - - VERIFY_IS_MUCH_SMALLER_THAN( pl0.absDistance(p0), Scalar(1) ); - VERIFY_IS_APPROX( pl1.signedDistance(p1 + n1 * s0), s0 ); - 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).qr().matrixQ(); - Scaling<Scalar,HyperplaneType::AmbientDimAtCompileTime> scaling(VectorType::Random()); - Translation<Scalar,HyperplaneType::AmbientDimAtCompileTime> translation(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) ); - pl2 = pl1; - VERIFY_IS_MUCH_SMALLER_THAN( pl2.transform(rot*scaling*translation) - .absDistance((rot*scaling*translation) * p1), Scalar(1) ); - pl2 = pl1; - VERIFY_IS_MUCH_SMALLER_THAN( pl2.transform(rot*translation,Isometry) - .absDistance((rot*translation) * p1), Scalar(1) ); - } - - // casting - const int Dim = HyperplaneType::AmbientDimAtCompileTime; - typedef typename GetDifferentType<Scalar>::type OtherScalar; - Hyperplane<OtherScalar,Dim> hp1f = pl1.template cast<OtherScalar>(); - VERIFY_IS_APPROX(hp1f.template cast<Scalar>(),pl1); - Hyperplane<Scalar,Dim> hp1d = pl1.template cast<Scalar>(); - VERIFY_IS_APPROX(hp1d.template cast<Scalar>(),pl1); -} - -template<typename Scalar> void lines() -{ - 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 = ei_random<Scalar>(); - while (ei_abs(a-1) < 1e-4) a = ei_random<Scalar>(); - while (u.norm() < 1e-4) u = Vector::Random(); - while (v.norm() < 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" - 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 - CoeffsType converted_coeffs(HLine(pl).coeffs()); - converted_coeffs *= line_u.coeffs()(0)/converted_coeffs(0); - VERIFY(line_u.coeffs().isApprox(converted_coeffs)); - } -} - -void test_eigen2_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_3( hyperplane(Hyperplane<double,4>()) ); - CALL_SUBTEST_4( hyperplane(Hyperplane<std::complex<double>,5>()) ); - CALL_SUBTEST_5( lines<float>() ); - CALL_SUBTEST_6( lines<double>() ); - } -} diff --git a/test/eigen2/eigen2_inverse.cpp b/test/eigen2/eigen2_inverse.cpp deleted file mode 100644 index ccd24a194..000000000 --- a/test/eigen2/eigen2_inverse.cpp +++ /dev/null @@ -1,62 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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) -{ - /* this test covers the following files: - Inverse.h - */ - int rows = m.rows(); - int cols = m.cols(); - - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> VectorType; - - MatrixType m1 = MatrixType::Random(rows, cols), - m2(rows, cols), - identity = MatrixType::Identity(rows, rows); - - while(ei_abs(m1.determinant()) < RealScalar(0.1) && rows <= 8) - { - m1 = MatrixType::Random(rows, cols); - } - - m2 = m1.inverse(); - VERIFY_IS_APPROX(m1, m2.inverse() ); - - m1.computeInverse(&m2); - 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(m1.transpose().inverse(), m1.inverse().transpose()); -} - -void test_eigen2_inverse() -{ - 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_5( inverse(MatrixXf(8,8)) ); - CALL_SUBTEST_6( inverse(MatrixXcd(7,7)) ); - } -} diff --git a/test/eigen2/eigen2_linearstructure.cpp b/test/eigen2/eigen2_linearstructure.cpp deleted file mode 100644 index 488f4c485..000000000 --- a/test/eigen2/eigen2_linearstructure.cpp +++ /dev/null @@ -1,83 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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 linearStructure(const MatrixType& m) -{ - /* this test covers the following files: - Sum.h Difference.h Opposite.h ScalarMultiple.h - */ - - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - - int rows = m.rows(); - int 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 = ei_random<Scalar>(); - while (ei_abs(s1)<1e-3) s1 = ei_random<Scalar>(); - - int r = ei_random<int>(0, rows-1), - c = ei_random<int>(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>::HasFloatingPoint) - { - 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>::HasFloatingPoint) - 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.cwise() * m1.block(0,0,rows,cols), m1.cwise() * 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); -} - -void test_eigen2_linearstructure() -{ - 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(3, 3)) ); - CALL_SUBTEST_6( linearStructure(MatrixXf(8, 12)) ); - CALL_SUBTEST_7( linearStructure(MatrixXi(8, 12)) ); - CALL_SUBTEST_8( linearStructure(MatrixXcd(20, 20)) ); - } -} diff --git a/test/eigen2/eigen2_lu.cpp b/test/eigen2/eigen2_lu.cpp deleted file mode 100644 index e993b1c7c..000000000 --- a/test/eigen2/eigen2_lu.cpp +++ /dev/null @@ -1,122 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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 Derived> -void doSomeRankPreservingOperations(Eigen::MatrixBase<Derived>& m) -{ - typedef typename Derived::RealScalar RealScalar; - for(int a = 0; a < 3*(m.rows()+m.cols()); a++) - { - RealScalar d = Eigen::ei_random<RealScalar>(-1,1); - int i = Eigen::ei_random<int>(0,m.rows()-1); // i is a random row number - int j; - do { - j = Eigen::ei_random<int>(0,m.rows()-1); - } while (i==j); // j is another one (must be different) - m.row(i) += d * m.row(j); - - i = Eigen::ei_random<int>(0,m.cols()-1); // i is a random column number - do { - j = Eigen::ei_random<int>(0,m.cols()-1); - } while (i==j); // j is another one (must be different) - m.col(i) += d * m.col(j); - } -} - -template<typename MatrixType> void lu_non_invertible() -{ - /* this test covers the following files: - LU.h - */ - // NOTE there seems to be a problem with too small sizes -- could easily lie in the doSomeRankPreservingOperations function - int rows = ei_random<int>(20,200), cols = ei_random<int>(20,200), cols2 = ei_random<int>(20,200); - int rank = ei_random<int>(1, std::min(rows, cols)-1); - - MatrixType m1(rows, cols), m2(cols, cols2), m3(rows, cols2), k(1,1); - m1 = MatrixType::Random(rows,cols); - if(rows <= cols) - for(int i = rank; i < rows; i++) m1.row(i).setZero(); - else - for(int i = rank; i < cols; i++) m1.col(i).setZero(); - doSomeRankPreservingOperations(m1); - - LU<MatrixType> lu(m1); - typename LU<MatrixType>::KernelResultType m1kernel = lu.kernel(); - typename LU<MatrixType>::ImageResultType m1image = lu.image(); - - VERIFY(rank == lu.rank()); - VERIFY(cols - lu.rank() == lu.dimensionOfKernel()); - VERIFY(!lu.isInjective()); - VERIFY(!lu.isInvertible()); - VERIFY(lu.isSurjective() == (lu.rank() == rows)); - VERIFY((m1 * m1kernel).isMuchSmallerThan(m1)); - VERIFY(m1image.lu().rank() == rank); - MatrixType sidebyside(m1.rows(), m1.cols() + m1image.cols()); - sidebyside << m1, m1image; - VERIFY(sidebyside.lu().rank() == rank); - m2 = MatrixType::Random(cols,cols2); - m3 = m1*m2; - m2 = MatrixType::Random(cols,cols2); - lu.solve(m3, &m2); - VERIFY_IS_APPROX(m3, m1*m2); - /* solve now always returns true - m3 = MatrixType::Random(rows,cols2); - VERIFY(!lu.solve(m3, &m2)); - */ -} - -template<typename MatrixType> void lu_invertible() -{ - /* this test covers the following files: - LU.h - */ - typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; - int size = ei_random<int>(10,200); - - MatrixType m1(size, size), m2(size, size), m3(size, size); - m1 = MatrixType::Random(size,size); - - if (ei_is_same_type<RealScalar,float>::ret) - { - // let's build a matrix more stable to inverse - MatrixType a = MatrixType::Random(size,size*2); - m1 += a * a.adjoint(); - } - - LU<MatrixType> lu(m1); - VERIFY(0 == lu.dimensionOfKernel()); - VERIFY(size == lu.rank()); - VERIFY(lu.isInjective()); - VERIFY(lu.isSurjective()); - VERIFY(lu.isInvertible()); - VERIFY(lu.image().lu().isInvertible()); - m3 = MatrixType::Random(size,size); - lu.solve(m3, &m2); - VERIFY_IS_APPROX(m3, m1*m2); - VERIFY_IS_APPROX(m2, lu.inverse()*m3); - m3 = MatrixType::Random(size,size); - VERIFY(lu.solve(m3, &m2)); -} - -void test_eigen2_lu() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( lu_non_invertible<MatrixXf>() ); - CALL_SUBTEST_2( lu_non_invertible<MatrixXd>() ); - CALL_SUBTEST_3( lu_non_invertible<MatrixXcf>() ); - CALL_SUBTEST_4( lu_non_invertible<MatrixXcd>() ); - CALL_SUBTEST_1( lu_invertible<MatrixXf>() ); - CALL_SUBTEST_2( lu_invertible<MatrixXd>() ); - CALL_SUBTEST_3( lu_invertible<MatrixXcf>() ); - CALL_SUBTEST_4( lu_invertible<MatrixXcd>() ); - } -} diff --git a/test/eigen2/eigen2_map.cpp b/test/eigen2/eigen2_map.cpp deleted file mode 100644 index 4a1c4e11a..000000000 --- a/test/eigen2/eigen2_map.cpp +++ /dev/null @@ -1,114 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2007-2010 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 VectorType> void map_class_vector(const VectorType& m) -{ - typedef typename VectorType::Scalar Scalar; - - int size = m.size(); - - // test Map.h - Scalar* array1 = ei_aligned_new<Scalar>(size); - Scalar* array2 = ei_aligned_new<Scalar>(size); - Scalar* array3 = new Scalar[size+1]; - Scalar* array3unaligned = std::size_t(array3)%16 == 0 ? array3+1 : array3; - - Map<VectorType, Aligned>(array1, size) = VectorType::Random(size); - Map<VectorType>(array2, size) = Map<VectorType>(array1, size); - Map<VectorType>(array3unaligned, size) = Map<VectorType>((const Scalar*)array1, size); // test non-const-correctness support in eigen2 - VectorType ma1 = Map<VectorType>(array1, size); - VectorType ma2 = Map<VectorType, Aligned>(array2, size); - VectorType ma3 = Map<VectorType>(array3unaligned, size); - VERIFY_IS_APPROX(ma1, ma2); - VERIFY_IS_APPROX(ma1, ma3); - - ei_aligned_delete(array1, size); - ei_aligned_delete(array2, size); - delete[] array3; -} - -template<typename MatrixType> void map_class_matrix(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - - int rows = m.rows(), cols = m.cols(), size = rows*cols; - - // test Map.h - Scalar* array1 = ei_aligned_new<Scalar>(size); - for(int i = 0; i < size; i++) array1[i] = Scalar(1); - Scalar* array2 = ei_aligned_new<Scalar>(size); - for(int i = 0; i < size; i++) array2[i] = Scalar(1); - Scalar* array3 = new Scalar[size+1]; - for(int i = 0; i < size+1; i++) array3[i] = Scalar(1); - Scalar* array3unaligned = std::size_t(array3)%16 == 0 ? array3+1 : array3; - Map<MatrixType, Aligned>(array1, rows, cols) = MatrixType::Ones(rows,cols); - Map<MatrixType>(array2, rows, cols) = Map<MatrixType>((const Scalar*)array1, rows, cols); // test non-const-correctness support in eigen2 - Map<MatrixType>(array3unaligned, rows, cols) = Map<MatrixType>(array1, rows, cols); - MatrixType ma1 = Map<MatrixType>(array1, rows, cols); - MatrixType ma2 = Map<MatrixType, Aligned>(array2, rows, cols); - VERIFY_IS_APPROX(ma1, ma2); - MatrixType ma3 = Map<MatrixType>(array3unaligned, rows, cols); - VERIFY_IS_APPROX(ma1, ma3); - - ei_aligned_delete(array1, size); - ei_aligned_delete(array2, size); - delete[] array3; -} - -template<typename VectorType> void map_static_methods(const VectorType& m) -{ - typedef typename VectorType::Scalar Scalar; - - int size = m.size(); - - // test Map.h - Scalar* array1 = ei_aligned_new<Scalar>(size); - Scalar* array2 = ei_aligned_new<Scalar>(size); - Scalar* array3 = new Scalar[size+1]; - Scalar* array3unaligned = std::size_t(array3)%16 == 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_APPROX(ma1, ma2); - VERIFY_IS_APPROX(ma1, ma3); - - ei_aligned_delete(array1, size); - ei_aligned_delete(array2, size); - delete[] array3; -} - - -void test_eigen2_map() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( map_class_vector(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( map_class_vector(Vector4d()) ); - 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_1( map_class_matrix(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( map_class_matrix(Matrix4d()) ); - CALL_SUBTEST_6( map_class_matrix(Matrix<float,3,5>()) ); - CALL_SUBTEST_4( map_class_matrix(MatrixXcf(ei_random<int>(1,10),ei_random<int>(1,10))) ); - CALL_SUBTEST_5( map_class_matrix(MatrixXi(ei_random<int>(1,10),ei_random<int>(1,10))) ); - - CALL_SUBTEST_1( map_static_methods(Matrix<double, 1, 1>()) ); - CALL_SUBTEST_2( map_static_methods(Vector3f()) ); - CALL_SUBTEST_7( map_static_methods(RowVector3d()) ); - CALL_SUBTEST_4( map_static_methods(VectorXcd(8)) ); - CALL_SUBTEST_5( map_static_methods(VectorXf(12)) ); - } -} diff --git a/test/eigen2/eigen2_meta.cpp b/test/eigen2/eigen2_meta.cpp deleted file mode 100644 index 1d01bd84d..000000000 --- a/test/eigen2/eigen2_meta.cpp +++ /dev/null @@ -1,60 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// 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_eigen2_meta() -{ - typedef float & FloatRef; - typedef const float & ConstFloatRef; - - VERIFY((ei_meta_if<(3<4),ei_meta_true, ei_meta_false>::ret::ret)); - VERIFY(( ei_is_same_type<float,float>::ret)); - VERIFY((!ei_is_same_type<float,double>::ret)); - VERIFY((!ei_is_same_type<float,float&>::ret)); - VERIFY((!ei_is_same_type<float,const float&>::ret)); - - VERIFY(( ei_is_same_type<float,ei_cleantype<const float&>::type >::ret)); - VERIFY(( ei_is_same_type<float,ei_cleantype<const float*>::type >::ret)); - VERIFY(( ei_is_same_type<float,ei_cleantype<const float*&>::type >::ret)); - VERIFY(( ei_is_same_type<float,ei_cleantype<float**>::type >::ret)); - VERIFY(( ei_is_same_type<float,ei_cleantype<float**&>::type >::ret)); - VERIFY(( ei_is_same_type<float,ei_cleantype<float* const *&>::type >::ret)); - VERIFY(( ei_is_same_type<float,ei_cleantype<float* const>::type >::ret)); - - VERIFY(( ei_is_same_type<float*,ei_unconst<const float*>::type >::ret)); - VERIFY(( ei_is_same_type<float&,ei_unconst<const float&>::type >::ret)); - VERIFY(( ei_is_same_type<float&,ei_unconst<ConstFloatRef>::type >::ret)); - - VERIFY(( ei_is_same_type<float&,ei_unconst<float&>::type >::ret)); - VERIFY(( ei_is_same_type<float,ei_unref<float&>::type >::ret)); - VERIFY(( ei_is_same_type<const float,ei_unref<const float&>::type >::ret)); - VERIFY(( ei_is_same_type<float,ei_unpointer<float*>::type >::ret)); - VERIFY(( ei_is_same_type<const float,ei_unpointer<const float*>::type >::ret)); - VERIFY(( ei_is_same_type<float,ei_unpointer<float* const >::type >::ret)); - - VERIFY(ei_meta_sqrt<1>::ret == 1); - #define VERIFY_META_SQRT(X) VERIFY(ei_meta_sqrt<X>::ret == int(ei_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/test/eigen2/eigen2_miscmatrices.cpp b/test/eigen2/eigen2_miscmatrices.cpp deleted file mode 100644 index 8bbb20cc8..000000000 --- a/test/eigen2/eigen2_miscmatrices.cpp +++ /dev/null @@ -1,48 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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; - typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType; - int rows = m.rows(); - int cols = m.cols(); - - int r = ei_random<int>(0, rows-1), r2 = ei_random<int>(0, rows-1), c = ei_random<int>(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_eigen2_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/test/eigen2/eigen2_mixingtypes.cpp b/test/eigen2/eigen2_mixingtypes.cpp deleted file mode 100644 index fb5ac5dda..000000000 --- a/test/eigen2/eigen2_mixingtypes.cpp +++ /dev/null @@ -1,77 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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 - -#ifndef EIGEN_DONT_VECTORIZE -#define EIGEN_DONT_VECTORIZE // SSE intrinsics aren't designed to allow mixing types -#endif - -#include "main.h" - - -template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType) -{ - 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(size,size); - Mat_d md(size,size); - Mat_cf mcf(size,size); - Mat_cd mcd(size,size); - Vec_f vf(size,1); - Vec_d vd(size,1); - Vec_cf vcf(size,1); - Vec_cd vcd(size,1); - - mf+mf; - VERIFY_RAISES_ASSERT(mf+md); - VERIFY_RAISES_ASSERT(mf+mcf); - VERIFY_RAISES_ASSERT(vf=vd); - VERIFY_RAISES_ASSERT(vf+=vd); - VERIFY_RAISES_ASSERT(mcd=md); - - mf*mf; - md*mcd; - mcd*md; - mf*vcf; - mcf*vf; - mcf *= mf; - vcd = md*vcd; - vcf = mcf*vf; -#if 0 - // these are know generating hard build errors in eigen3 - VERIFY_RAISES_ASSERT(mf*md); - VERIFY_RAISES_ASSERT(mcf*mcd); - VERIFY_RAISES_ASSERT(mcf*vcd); - VERIFY_RAISES_ASSERT(vcf = mf*vf); - - vf.eigen2_dot(vf); - VERIFY_RAISES_ASSERT(vd.eigen2_dot(vf)); - VERIFY_RAISES_ASSERT(vcf.eigen2_dot(vf)); // yeah eventually we should allow this but i'm too lazy to make that change now in Dot.h - // especially as that might be rewritten as cwise product .sum() which would make that automatic. -#endif -} - -void test_eigen2_mixingtypes() -{ - // check that our operator new is indeed called: - CALL_SUBTEST_1(mixingtypes<3>()); - CALL_SUBTEST_2(mixingtypes<4>()); - CALL_SUBTEST_3(mixingtypes<Dynamic>(20)); -} diff --git a/test/eigen2/eigen2_nomalloc.cpp b/test/eigen2/eigen2_nomalloc.cpp deleted file mode 100644 index d34a69999..000000000 --- a/test/eigen2/eigen2_nomalloc.cpp +++ /dev/null @@ -1,53 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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 hack is needed to make this file compiles with -pedantic (gcc) -#ifdef __GNUC__ -#define throw(X) -#endif -// discard stack allocation as that too bypasses malloc -#define EIGEN_STACK_ALLOCATION_LIMIT 0 -// any heap allocation will raise an assert -#define EIGEN_NO_MALLOC - -#include "main.h" - -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; - - int rows = m.rows(); - int cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols); - - Scalar s1 = ei_random<Scalar>(); - - int r = ei_random<int>(0, rows-1), - c = ei_random<int>(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.cwise() * m1.block(0,0,rows,cols), m1.cwise() * m1); - VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2)); -} - -void test_eigen2_nomalloc() -{ - // 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>()) ); -} diff --git a/test/eigen2/eigen2_packetmath.cpp b/test/eigen2/eigen2_packetmath.cpp deleted file mode 100644 index b1f325fe7..000000000 --- a/test/eigen2/eigen2_packetmath.cpp +++ /dev/null @@ -1,132 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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 Eigen; - -template<typename Scalar> bool areApprox(const Scalar* a, const Scalar* b, int size) -{ - for (int i=0; i<size; ++i) - if (!ei_isApprox(a[i],b[i])) return false; - return true; -} - -#define CHECK_CWISE(REFOP, POP) { \ - for (int i=0; i<PacketSize; ++i) \ - ref[i] = REFOP(data1[i], data1[i+PacketSize]); \ - ei_pstore(data2, POP(ei_pload(data1), ei_pload(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)) - -namespace std { - -template<> const complex<float>& min(const complex<float>& a, const complex<float>& b) -{ return a.real() < b.real() ? a : b; } - -template<> const complex<float>& max(const complex<float>& a, const complex<float>& b) -{ return a.real() < b.real() ? b : a; } - -} - -template<typename Scalar> void packetmath() -{ - typedef typename ei_packet_traits<Scalar>::type Packet; - const int PacketSize = ei_packet_traits<Scalar>::size; - - const int size = PacketSize*4; - EIGEN_ALIGN_128 Scalar data1[ei_packet_traits<Scalar>::size*4]; - EIGEN_ALIGN_128 Scalar data2[ei_packet_traits<Scalar>::size*4]; - EIGEN_ALIGN_128 Packet packets[PacketSize*2]; - EIGEN_ALIGN_128 Scalar ref[ei_packet_traits<Scalar>::size*4]; - for (int i=0; i<size; ++i) - { - data1[i] = ei_random<Scalar>(); - data2[i] = ei_random<Scalar>(); - } - - ei_pstore(data2, ei_pload(data1)); - VERIFY(areApprox(data1, data2, PacketSize) && "aligned load/store"); - - for (int offset=0; offset<PacketSize; ++offset) - { - ei_pstore(data2, ei_ploadu(data1+offset)); - VERIFY(areApprox(data1+offset, data2, PacketSize) && "ei_ploadu"); - } - - for (int offset=0; offset<PacketSize; ++offset) - { - ei_pstoreu(data2+offset, ei_pload(data1)); - VERIFY(areApprox(data1, data2+offset, PacketSize) && "ei_pstoreu"); - } - - for (int offset=0; offset<PacketSize; ++offset) - { - packets[0] = ei_pload(data1); - packets[1] = ei_pload(data1+PacketSize); - if (offset==0) ei_palign<0>(packets[0], packets[1]); - else if (offset==1) ei_palign<1>(packets[0], packets[1]); - else if (offset==2) ei_palign<2>(packets[0], packets[1]); - else if (offset==3) ei_palign<3>(packets[0], packets[1]); - ei_pstore(data2, packets[0]); - - for (int i=0; i<PacketSize; ++i) - ref[i] = data1[i+offset]; - - typedef Matrix<Scalar, PacketSize, 1> Vector; - VERIFY(areApprox(ref, data2, PacketSize) && "ei_palign"); - } - - CHECK_CWISE(REF_ADD, ei_padd); - CHECK_CWISE(REF_SUB, ei_psub); - CHECK_CWISE(REF_MUL, ei_pmul); - #ifndef EIGEN_VECTORIZE_ALTIVEC - if (!ei_is_same_type<Scalar,int>::ret) - CHECK_CWISE(REF_DIV, ei_pdiv); - #endif - CHECK_CWISE(std::min, ei_pmin); - CHECK_CWISE(std::max, ei_pmax); - - for (int i=0; i<PacketSize; ++i) - ref[i] = data1[0]; - ei_pstore(data2, ei_pset1(data1[0])); - VERIFY(areApprox(ref, data2, PacketSize) && "ei_pset1"); - - VERIFY(ei_isApprox(data1[0], ei_pfirst(ei_pload(data1))) && "ei_pfirst"); - - ref[0] = 0; - for (int i=0; i<PacketSize; ++i) - ref[0] += data1[i]; - VERIFY(ei_isApprox(ref[0], ei_predux(ei_pload(data1))) && "ei_predux"); - - 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] = ei_pload(data1+j*PacketSize); - } - ei_pstore(data2, ei_preduxp(packets)); - VERIFY(areApprox(ref, data2, PacketSize) && "ei_preduxp"); -} - -void test_eigen2_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> >() ); - } -} diff --git a/test/eigen2/eigen2_parametrizedline.cpp b/test/eigen2/eigen2_parametrizedline.cpp deleted file mode 100644 index 814728870..000000000 --- a/test/eigen2/eigen2_parametrizedline.cpp +++ /dev/null @@ -1,62 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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 - */ - - const int dim = _line.dim(); - typedef typename LineType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar, LineType::AmbientDimAtCompileTime, 1> VectorType; - typedef Matrix<Scalar, LineType::AmbientDimAtCompileTime, - LineType::AmbientDimAtCompileTime> MatrixType; - - VectorType p0 = VectorType::Random(dim); - VectorType p1 = VectorType::Random(dim); - - VectorType d0 = VectorType::Random(dim).normalized(); - - LineType l0(p0, d0); - - Scalar s0 = ei_random<Scalar>(); - Scalar s1 = ei_abs(ei_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); -} - -void test_eigen2_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_3( parametrizedline(ParametrizedLine<double,4>()) ); - CALL_SUBTEST_4( parametrizedline(ParametrizedLine<std::complex<double>,5>()) ); - } -} diff --git a/test/eigen2/eigen2_prec_inverse_4x4.cpp b/test/eigen2/eigen2_prec_inverse_4x4.cpp deleted file mode 100644 index 8bfa55694..000000000 --- a/test/eigen2/eigen2_prec_inverse_4x4.cpp +++ /dev/null @@ -1,84 +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 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<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<int> >() { return "complex<int>"; } - -#define EIGEN_DEBUG_VAR(x) std::cerr << #x << " = " << x << std::endl; - -template<typename T> inline typename NumTraits<T>::Real epsilon() -{ - return std::numeric_limits<typename NumTraits<T>::Real>::epsilon(); -} - -template<typename MatrixType> void inverse_permutation_4x4() -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - Vector4i indices(0,1,2,3); - for(int i = 0; i < 24; ++i) - { - MatrixType m = MatrixType::Zero(); - m(indices(0),0) = 1; - m(indices(1),1) = 1; - m(indices(2),2) = 1; - m(indices(3),3) = 1; - MatrixType inv = m.inverse(); - double error = double( (m*inv-MatrixType::Identity()).norm() / epsilon<Scalar>() ); - VERIFY(error == 0.0); - std::next_permutation(indices.data(),indices.data()+4); - } -} - -template<typename MatrixType> void inverse_general_4x4(int repeat) -{ - 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 = ei_abs(m.determinant()); - } while(absdet < 10 * epsilon<Scalar>()); - MatrixType inv = m.inverse(); - double error = double( (m*inv-MatrixType::Identity()).norm() * absdet / epsilon<Scalar>() ); - 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); - VERIFY(error_avg < (NumTraits<Scalar>::IsComplex ? 8.0 : 1.25)); - VERIFY(error_max < (NumTraits<Scalar>::IsComplex ? 64.0 : 20.0)); -} - -void test_eigen2_prec_inverse_4x4() -{ - CALL_SUBTEST_1((inverse_permutation_4x4<Matrix4f>())); - CALL_SUBTEST_1(( inverse_general_4x4<Matrix4f>(200000 * g_repeat) )); - - CALL_SUBTEST_2((inverse_permutation_4x4<Matrix<double,4,4,RowMajor> >())); - 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/test/eigen2/eigen2_product_large.cpp b/test/eigen2/eigen2_product_large.cpp deleted file mode 100644 index 5149ef748..000000000 --- a/test/eigen2/eigen2_product_large.cpp +++ /dev/null @@ -1,45 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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" - -void test_eigen2_product_large() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( product(MatrixXf(ei_random<int>(1,320), ei_random<int>(1,320))) ); - CALL_SUBTEST_2( product(MatrixXd(ei_random<int>(1,320), ei_random<int>(1,320))) ); - CALL_SUBTEST_3( product(MatrixXi(ei_random<int>(1,320), ei_random<int>(1,320))) ); - CALL_SUBTEST_4( product(MatrixXcf(ei_random<int>(1,50), ei_random<int>(1,50))) ); - CALL_SUBTEST_5( product(Matrix<float,Dynamic,Dynamic,RowMajor>(ei_random<int>(1,320), ei_random<int>(1,320))) ); - } - -#ifdef 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()); - } - - { - MatrixXf mat1(10,10); mat1.setRandom(); - MatrixXf mat2(32,10); mat2.setRandom(); - MatrixXf result = mat1.row(2)*mat2.transpose(); - VERIFY_IS_APPROX(result, (mat1.row(2)*mat2.transpose()).eval()); - } -#endif -} diff --git a/test/eigen2/eigen2_product_small.cpp b/test/eigen2/eigen2_product_small.cpp deleted file mode 100644 index 4cd8c102f..000000000 --- a/test/eigen2/eigen2_product_small.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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" - -void test_eigen2_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, 5>()) ); - CALL_SUBTEST_3( product(Matrix3d()) ); - CALL_SUBTEST_4( product(Matrix4d()) ); - CALL_SUBTEST_5( product(Matrix4f()) ); - } -} diff --git a/test/eigen2/eigen2_qr.cpp b/test/eigen2/eigen2_qr.cpp deleted file mode 100644 index 76977e4c1..000000000 --- a/test/eigen2/eigen2_qr.cpp +++ /dev/null @@ -1,69 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// 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) -{ - /* this test covers the following files: - QR.h - */ - int rows = m.rows(); - int cols = m.cols(); - - typedef typename MatrixType::Scalar Scalar; - typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> SquareMatrixType; - typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> VectorType; - - MatrixType a = MatrixType::Random(rows,cols); - QR<MatrixType> qrOfA(a); - VERIFY_IS_APPROX(a, qrOfA.matrixQ() * qrOfA.matrixR()); - VERIFY_IS_NOT_APPROX(a+MatrixType::Identity(rows, cols), qrOfA.matrixQ() * qrOfA.matrixR()); - - #if 0 // eigenvalues module not yet ready - SquareMatrixType b = a.adjoint() * a; - - // check tridiagonalization - Tridiagonalization<SquareMatrixType> tridiag(b); - VERIFY_IS_APPROX(b, tridiag.matrixQ() * tridiag.matrixT() * tridiag.matrixQ().adjoint()); - - // check hessenberg decomposition - HessenbergDecomposition<SquareMatrixType> hess(b); - VERIFY_IS_APPROX(b, hess.matrixQ() * hess.matrixH() * hess.matrixQ().adjoint()); - VERIFY_IS_APPROX(tridiag.matrixT(), hess.matrixH()); - b = SquareMatrixType::Random(cols,cols); - hess.compute(b); - VERIFY_IS_APPROX(b, hess.matrixQ() * hess.matrixH() * hess.matrixQ().adjoint()); - #endif -} - -void test_eigen2_qr() -{ - for(int i = 0; i < 1; i++) { - CALL_SUBTEST_1( qr(Matrix2f()) ); - CALL_SUBTEST_2( qr(Matrix4d()) ); - CALL_SUBTEST_3( qr(MatrixXf(12,8)) ); - CALL_SUBTEST_4( qr(MatrixXcd(5,5)) ); - CALL_SUBTEST_4( qr(MatrixXcd(7,3)) ); - } - -#ifdef EIGEN_TEST_PART_5 - // small isFullRank test - { - Matrix3d mat; - mat << 1, 45, 1, 2, 2, 2, 1, 2, 3; - VERIFY(mat.qr().isFullRank()); - mat << 1, 1, 1, 2, 2, 2, 1, 2, 3; - //always returns true in eigen2support - //VERIFY(!mat.qr().isFullRank()); - } - -#endif -} diff --git a/test/eigen2/eigen2_qtvector.cpp b/test/eigen2/eigen2_qtvector.cpp deleted file mode 100644 index 6cfb58a26..000000000 --- a/test/eigen2/eigen2_qtvector.cpp +++ /dev/null @@ -1,158 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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 <Eigen/Geometry> -#include <Eigen/QtAlignedMalloc> - -#include <QtCore/QVector> - -template<typename MatrixType> -void check_qtvector_matrix(const MatrixType& m) -{ - int rows = m.rows(); - int 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_eigen2_qtvector() -{ - // some non vectorizable fixed sizes - CALL_SUBTEST_1(check_qtvector_matrix(Vector2f())); - CALL_SUBTEST_1(check_qtvector_matrix(Matrix3f())); - CALL_SUBTEST_1(check_qtvector_matrix(Matrix3d())); - - // some vectorizable fixed sizes - CALL_SUBTEST_2(check_qtvector_matrix(Matrix2f())); - CALL_SUBTEST_2(check_qtvector_matrix(Vector4f())); - CALL_SUBTEST_2(check_qtvector_matrix(Matrix4f())); - CALL_SUBTEST_2(check_qtvector_matrix(Matrix4d())); - - // some dynamic sizes - CALL_SUBTEST_3(check_qtvector_matrix(MatrixXd(1,1))); - CALL_SUBTEST_3(check_qtvector_matrix(VectorXd(20))); - CALL_SUBTEST_3(check_qtvector_matrix(RowVectorXf(20))); - CALL_SUBTEST_3(check_qtvector_matrix(MatrixXcf(10,10))); - - // some Transform - CALL_SUBTEST_4(check_qtvector_transform(Transform2f())); - CALL_SUBTEST_4(check_qtvector_transform(Transform3f())); - CALL_SUBTEST_4(check_qtvector_transform(Transform3d())); - //CALL_SUBTEST_4(check_qtvector_transform(Transform4d())); - - // some Quaternion - CALL_SUBTEST_5(check_qtvector_quaternion(Quaternionf())); - CALL_SUBTEST_5(check_qtvector_quaternion(Quaternionf())); -} diff --git a/test/eigen2/eigen2_regression.cpp b/test/eigen2/eigen2_regression.cpp deleted file mode 100644 index c68b58da8..000000000 --- a/test/eigen2/eigen2_regression.cpp +++ /dev/null @@ -1,136 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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/LeastSquares> - -template<typename VectorType, - typename HyperplaneType> -void makeNoisyCohyperplanarPoints(int numPoints, - VectorType **points, - HyperplaneType *hyperplane, - typename VectorType::Scalar noiseAmplitude) -{ - typedef typename VectorType::Scalar Scalar; - const int size = points[0]->size(); - // pick a random hyperplane, store the coefficients of its equation - hyperplane->coeffs().resize(size + 1); - for(int j = 0; j < size + 1; j++) - { - do { - hyperplane->coeffs().coeffRef(j) = ei_random<Scalar>(); - } while(ei_abs(hyperplane->coeffs().coeff(j)) < 0.5); - } - - // now pick numPoints random points on this hyperplane - for(int i = 0; i < numPoints; i++) - { - VectorType& cur_point = *(points[i]); - do - { - cur_point = VectorType::Random(size)/*.normalized()*/; - // project cur_point onto the hyperplane - Scalar x = - (hyperplane->coeffs().start(size).cwise()*cur_point).sum(); - cur_point *= hyperplane->coeffs().coeff(size) / x; - } while( cur_point.norm() < 0.5 - || cur_point.norm() > 2.0 ); - } - - // add some noise to these points - for(int i = 0; i < numPoints; i++ ) - *(points[i]) += noiseAmplitude * VectorType::Random(size); -} - -template<typename VectorType> -void check_linearRegression(int numPoints, - VectorType **points, - const VectorType& original, - typename VectorType::Scalar tolerance) -{ - int size = points[0]->size(); - assert(size==2); - VectorType result(size); - linearRegression(numPoints, points, &result, 1); - typename VectorType::Scalar error = (result - original).norm() / original.norm(); - VERIFY(ei_abs(error) < ei_abs(tolerance)); -} - -template<typename VectorType, - typename HyperplaneType> -void check_fitHyperplane(int numPoints, - VectorType **points, - const HyperplaneType& original, - typename VectorType::Scalar tolerance) -{ - int size = points[0]->size(); - HyperplaneType result(size); - fitHyperplane(numPoints, points, &result); - result.coeffs() *= original.coeffs().coeff(size)/result.coeffs().coeff(size); - typename VectorType::Scalar error = (result.coeffs() - original.coeffs()).norm() / original.coeffs().norm(); - std::cout << ei_abs(error) << " xxx " << ei_abs(tolerance) << std::endl; - VERIFY(ei_abs(error) < ei_abs(tolerance)); -} - -void test_eigen2_regression() -{ - for(int i = 0; i < g_repeat; i++) - { -#ifdef EIGEN_TEST_PART_1 - { - Vector2f points2f [1000]; - Vector2f *points2f_ptrs [1000]; - for(int i = 0; i < 1000; i++) points2f_ptrs[i] = &(points2f[i]); - Vector2f coeffs2f; - Hyperplane<float,2> coeffs3f; - makeNoisyCohyperplanarPoints(1000, points2f_ptrs, &coeffs3f, 0.01f); - coeffs2f[0] = -coeffs3f.coeffs()[0]/coeffs3f.coeffs()[1]; - coeffs2f[1] = -coeffs3f.coeffs()[2]/coeffs3f.coeffs()[1]; - CALL_SUBTEST(check_linearRegression(10, points2f_ptrs, coeffs2f, 0.05f)); - CALL_SUBTEST(check_linearRegression(100, points2f_ptrs, coeffs2f, 0.01f)); - CALL_SUBTEST(check_linearRegression(1000, points2f_ptrs, coeffs2f, 0.002f)); - } -#endif -#ifdef EIGEN_TEST_PART_2 - { - Vector2f points2f [1000]; - Vector2f *points2f_ptrs [1000]; - for(int i = 0; i < 1000; i++) points2f_ptrs[i] = &(points2f[i]); - Hyperplane<float,2> coeffs3f; - makeNoisyCohyperplanarPoints(1000, points2f_ptrs, &coeffs3f, 0.01f); - CALL_SUBTEST(check_fitHyperplane(10, points2f_ptrs, coeffs3f, 0.05f)); - CALL_SUBTEST(check_fitHyperplane(100, points2f_ptrs, coeffs3f, 0.01f)); - CALL_SUBTEST(check_fitHyperplane(1000, points2f_ptrs, coeffs3f, 0.002f)); - } -#endif -#ifdef EIGEN_TEST_PART_3 - { - Vector4d points4d [1000]; - Vector4d *points4d_ptrs [1000]; - for(int i = 0; i < 1000; i++) points4d_ptrs[i] = &(points4d[i]); - Hyperplane<double,4> coeffs5d; - makeNoisyCohyperplanarPoints(1000, points4d_ptrs, &coeffs5d, 0.01); - CALL_SUBTEST(check_fitHyperplane(10, points4d_ptrs, coeffs5d, 0.05)); - CALL_SUBTEST(check_fitHyperplane(100, points4d_ptrs, coeffs5d, 0.01)); - CALL_SUBTEST(check_fitHyperplane(1000, points4d_ptrs, coeffs5d, 0.002)); - } -#endif -#ifdef EIGEN_TEST_PART_4 - { - VectorXcd *points11cd_ptrs[1000]; - for(int i = 0; i < 1000; i++) points11cd_ptrs[i] = new VectorXcd(11); - Hyperplane<std::complex<double>,Dynamic> *coeffs12cd = new Hyperplane<std::complex<double>,Dynamic>(11); - makeNoisyCohyperplanarPoints(1000, points11cd_ptrs, coeffs12cd, 0.01); - CALL_SUBTEST(check_fitHyperplane(100, points11cd_ptrs, *coeffs12cd, 0.025)); - CALL_SUBTEST(check_fitHyperplane(1000, points11cd_ptrs, *coeffs12cd, 0.006)); - delete coeffs12cd; - for(int i = 0; i < 1000; i++) delete points11cd_ptrs[i]; - } -#endif - } -} diff --git a/test/eigen2/eigen2_sizeof.cpp b/test/eigen2/eigen2_sizeof.cpp deleted file mode 100644 index ec1af5a06..000000000 --- a/test/eigen2/eigen2_sizeof.cpp +++ /dev/null @@ -1,31 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// 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(sizeof(MatrixType)==sizeof(Scalar)*MatrixType::SizeAtCompileTime); - else - VERIFY(sizeof(MatrixType)==sizeof(Scalar*) + 2 * sizeof(typename MatrixType::Index)); -} - -void test_eigen2_sizeof() -{ - CALL_SUBTEST( verifySizeOf(Matrix<float, 1, 1>()) ); - 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>()) ); -} diff --git a/test/eigen2/eigen2_smallvectors.cpp b/test/eigen2/eigen2_smallvectors.cpp deleted file mode 100644 index 03962b17d..000000000 --- a/test/eigen2/eigen2_smallvectors.cpp +++ /dev/null @@ -1,42 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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 smallVectors() -{ - typedef Matrix<Scalar, 1, 2> V2; - typedef Matrix<Scalar, 3, 1> V3; - typedef Matrix<Scalar, 1, 4> V4; - Scalar x1 = ei_random<Scalar>(), - x2 = ei_random<Scalar>(), - x3 = ei_random<Scalar>(), - x4 = ei_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()); -} - -void test_eigen2_smallvectors() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST( smallVectors<int>() ); - CALL_SUBTEST( smallVectors<float>() ); - CALL_SUBTEST( smallVectors<double>() ); - } -} diff --git a/test/eigen2/eigen2_sparse_basic.cpp b/test/eigen2/eigen2_sparse_basic.cpp deleted file mode 100644 index 049077670..000000000 --- a/test/eigen2/eigen2_sparse_basic.cpp +++ /dev/null @@ -1,317 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@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 "sparse.h" - -template<typename SetterType,typename DenseType, typename Scalar, int Options> -bool test_random_setter(SparseMatrix<Scalar,Options>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords) -{ - typedef SparseMatrix<Scalar,Options> SparseType; - { - sm.setZero(); - SetterType w(sm); - std::vector<Vector2i> remaining = nonzeroCoords; - while(!remaining.empty()) - { - int i = ei_random<int>(0,remaining.size()-1); - w(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y()); - remaining[i] = remaining.back(); - remaining.pop_back(); - } - } - return sm.isApprox(ref); -} - -template<typename SetterType,typename DenseType, typename T> -bool test_random_setter(DynamicSparseMatrix<T>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords) -{ - sm.setZero(); - std::vector<Vector2i> remaining = nonzeroCoords; - while(!remaining.empty()) - { - int i = ei_random<int>(0,remaining.size()-1); - sm.coeffRef(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y()); - remaining[i] = remaining.back(); - remaining.pop_back(); - } - return sm.isApprox(ref); -} - -template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& ref) -{ - const int rows = ref.rows(); - const int cols = ref.cols(); - typedef typename SparseMatrixType::Scalar Scalar; - 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; - - SparseMatrixType m(rows, cols); - DenseMatrix refMat = DenseMatrix::Zero(rows, cols); - DenseVector vec1 = DenseVector::Random(rows); - Scalar s1 = ei_random<Scalar>(); - - std::vector<Vector2i> zeroCoords; - std::vector<Vector2i> nonzeroCoords; - initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords); - - if (zeroCoords.size()==0 || nonzeroCoords.size()==0) - return; - - // test coeff and coeffRef - for (int i=0; i<(int)zeroCoords.size(); ++i) - { - VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps ); - if(ei_is_same_type<SparseMatrixType,SparseMatrix<Scalar,Flags> >::ret) - VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[0].x(),zeroCoords[0].y()) = 5 ); - } - VERIFY_IS_APPROX(m, refMat); - - 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 InnerIterators and Block expressions - for (int t=0; t<10; ++t) - { - int j = ei_random<int>(0,cols-1); - int i = ei_random<int>(0,rows-1); - int w = ei_random<int>(1,cols-j-1); - int h = ei_random<int>(1,rows-i-1); - -// VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w)); - for(int 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(int 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)); - } - } -// for(int 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(int 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)); -// } -// } - } - - for(int 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(int 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 SparseSetters - // coherent setter - // TODO extend the MatrixSetter -// { -// m.setZero(); -// VERIFY_IS_NOT_APPROX(m, refMat); -// SparseSetter<SparseMatrixType, FullyCoherentAccessPattern> w(m); -// for (int i=0; i<nonzeroCoords.size(); ++i) -// { -// w->coeffRef(nonzeroCoords[i].x(),nonzeroCoords[i].y()) = refMat.coeff(nonzeroCoords[i].x(),nonzeroCoords[i].y()); -// } -// } -// VERIFY_IS_APPROX(m, refMat); - - // random setter -// { -// m.setZero(); -// VERIFY_IS_NOT_APPROX(m, refMat); -// SparseSetter<SparseMatrixType, RandomAccessPattern> w(m); -// std::vector<Vector2i> remaining = nonzeroCoords; -// while(!remaining.empty()) -// { -// int i = ei_random<int>(0,remaining.size()-1); -// w->coeffRef(remaining[i].x(),remaining[i].y()) = refMat.coeff(remaining[i].x(),remaining[i].y()); -// remaining[i] = remaining.back(); -// remaining.pop_back(); -// } -// } -// VERIFY_IS_APPROX(m, refMat); - - VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdMapTraits> >(m,refMat,nonzeroCoords) )); - #ifdef EIGEN_UNORDERED_MAP_SUPPORT - VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdUnorderedMapTraits> >(m,refMat,nonzeroCoords) )); - #endif - #ifdef _DENSE_HASH_MAP_H_ - VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleDenseHashMapTraits> >(m,refMat,nonzeroCoords) )); - #endif - #ifdef _SPARSE_HASH_MAP_H_ - VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleSparseHashMapTraits> >(m,refMat,nonzeroCoords) )); - #endif - - // test fillrand - { - DenseMatrix m1(rows,cols); - m1.setZero(); - SparseMatrixType m2(rows,cols); - m2.startFill(); - for (int j=0; j<cols; ++j) - { - for (int k=0; k<rows/2; ++k) - { - int i = ei_random<int>(0,rows-1); - if (m1.coeff(i,j)==Scalar(0)) - m2.fillrand(i,j) = m1(i,j) = ei_random<Scalar>(); - } - } - m2.endFill(); - VERIFY_IS_APPROX(m2,m1); - } - - // test RandomSetter - /*{ - SparseMatrixType m1(rows,cols), m2(rows,cols); - DenseMatrix refM1 = DenseMatrix::Zero(rows, rows); - initSparse<Scalar>(density, refM1, m1); - { - Eigen::RandomSetter<SparseMatrixType > setter(m2); - for (int j=0; j<m1.outerSize(); ++j) - for (typename SparseMatrixType::InnerIterator i(m1,j); i; ++i) - setter(i.index(), j) = i.value(); - } - VERIFY_IS_APPROX(m1, m2); - }*/ -// std::cerr << m.transpose() << "\n\n" << refMat.transpose() << "\n\n"; -// VERIFY_IS_APPROX(m, refMat); - - // test basic computations - { - DenseMatrix refM1 = DenseMatrix::Zero(rows, rows); - DenseMatrix refM2 = DenseMatrix::Zero(rows, rows); - DenseMatrix refM3 = DenseMatrix::Zero(rows, rows); - DenseMatrix refM4 = DenseMatrix::Zero(rows, rows); - SparseMatrixType m1(rows, rows); - SparseMatrixType m2(rows, rows); - SparseMatrixType m3(rows, rows); - SparseMatrixType m4(rows, rows); - initSparse<Scalar>(density, refM1, m1); - initSparse<Scalar>(density, refM2, m2); - initSparse<Scalar>(density, refM3, m3); - initSparse<Scalar>(density, refM4, m4); - - VERIFY_IS_APPROX(m1+m2, refM1+refM2); - VERIFY_IS_APPROX(m1+m2+m3, refM1+refM2+refM3); - VERIFY_IS_APPROX(m3.cwise()*(m1+m2), refM3.cwise()*(refM1+refM2)); - VERIFY_IS_APPROX(m1*s1-m2, refM1*s1-refM2); - - VERIFY_IS_APPROX(m1*=s1, refM1*=s1); - VERIFY_IS_APPROX(m1/=s1, refM1/=s1); - - VERIFY_IS_APPROX(m1+=m2, refM1+=refM2); - VERIFY_IS_APPROX(m1-=m2, refM1-=refM2); - - VERIFY_IS_APPROX(m1.col(0).eigen2_dot(refM2.row(0)), refM1.col(0).eigen2_dot(refM2.row(0))); - - refM4.setRandom(); - // sparse cwise* dense - VERIFY_IS_APPROX(m3.cwise()*refM4, refM3.cwise()*refM4); -// VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4); - } - - // test innerVector() - { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows); - SparseMatrixType m2(rows, rows); - initSparse<Scalar>(density, refMat2, m2); - int j0 = ei_random(0,rows-1); - int j1 = ei_random(0,rows-1); - VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.col(j0)); - VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.col(j0)+refMat2.col(j1)); - //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, rows); - SparseMatrixType m2(rows, rows); - initSparse<Scalar>(density, refMat2, m2); - int j0 = ei_random(0,rows-2); - int j1 = ei_random(0,rows-2); - int n0 = ei_random<int>(1,rows-std::max(j0,j1)); - VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(0,j0,rows,n0)); - VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0), - refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0)); - //m2.innerVectors(j0,n0) = m2.innerVectors(j0,n0) + m2.innerVectors(j1,n0); - //refMat2.block(0,j0,rows,n0) = refMat2.block(0,j0,rows,n0) + refMat2.block(0,j1,rows,n0); - } - - // test transpose - { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows); - SparseMatrixType m2(rows, rows); - initSparse<Scalar>(density, refMat2, m2); - VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval()); - VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose()); - } - - // test prune - { - SparseMatrixType m2(rows, rows); - DenseMatrix refM2(rows, rows); - refM2.setZero(); - int countFalseNonZero = 0; - int countTrueNonZero = 0; - m2.startFill(); - for (int j=0; j<m2.outerSize(); ++j) - for (int i=0; i<m2.innerSize(); ++i) - { - float x = ei_random<float>(0,1); - if (x<0.1) - { - // do nothing - } - else if (x<0.5) - { - countFalseNonZero++; - m2.fill(i,j) = Scalar(0); - } - else - { - countTrueNonZero++; - m2.fill(i,j) = refM2(i,j) = Scalar(1); - } - } - m2.endFill(); - VERIFY(countFalseNonZero+countTrueNonZero == m2.nonZeros()); - VERIFY_IS_APPROX(m2, refM2); - m2.prune(1); - VERIFY(countTrueNonZero==m2.nonZeros()); - VERIFY_IS_APPROX(m2, refM2); - } -} - -void test_eigen2_sparse_basic() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( sparse_basic(SparseMatrix<double>(8, 8)) ); - CALL_SUBTEST_2( sparse_basic(SparseMatrix<std::complex<double> >(16, 16)) ); - CALL_SUBTEST_1( sparse_basic(SparseMatrix<double>(33, 33)) ); - - CALL_SUBTEST_3( sparse_basic(DynamicSparseMatrix<double>(8, 8)) ); - } -} diff --git a/test/eigen2/eigen2_sparse_product.cpp b/test/eigen2/eigen2_sparse_product.cpp deleted file mode 100644 index d28e76dff..000000000 --- a/test/eigen2/eigen2_sparse_product.cpp +++ /dev/null @@ -1,115 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@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 "sparse.h" - -template<typename SparseMatrixType> void sparse_product(const SparseMatrixType& ref) -{ - const int rows = ref.rows(); - const int cols = ref.cols(); - typedef typename SparseMatrixType::Scalar Scalar; - 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; - - // test matrix-matrix product - { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows); - DenseMatrix refMat3 = DenseMatrix::Zero(rows, rows); - DenseMatrix refMat4 = DenseMatrix::Zero(rows, rows); - DenseMatrix dm4 = DenseMatrix::Zero(rows, rows); - SparseMatrixType m2(rows, rows); - SparseMatrixType m3(rows, rows); - SparseMatrixType m4(rows, rows); - initSparse<Scalar>(density, refMat2, m2); - initSparse<Scalar>(density, refMat3, m3); - initSparse<Scalar>(density, refMat4, m4); - VERIFY_IS_APPROX(m4=m2*m3, refMat4=refMat2*refMat3); - VERIFY_IS_APPROX(m4=m2.transpose()*m3, refMat4=refMat2.transpose()*refMat3); - VERIFY_IS_APPROX(m4=m2.transpose()*m3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose()); - VERIFY_IS_APPROX(m4=m2*m3.transpose(), refMat4=refMat2*refMat3.transpose()); - - // sparse * dense - VERIFY_IS_APPROX(dm4=m2*refMat3, refMat4=refMat2*refMat3); - VERIFY_IS_APPROX(dm4=m2*refMat3.transpose(), refMat4=refMat2*refMat3.transpose()); - VERIFY_IS_APPROX(dm4=m2.transpose()*refMat3, refMat4=refMat2.transpose()*refMat3); - VERIFY_IS_APPROX(dm4=m2.transpose()*refMat3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose()); - - // dense * sparse - VERIFY_IS_APPROX(dm4=refMat2*m3, refMat4=refMat2*refMat3); - VERIFY_IS_APPROX(dm4=refMat2*m3.transpose(), refMat4=refMat2*refMat3.transpose()); - VERIFY_IS_APPROX(dm4=refMat2.transpose()*m3, refMat4=refMat2.transpose()*refMat3); - VERIFY_IS_APPROX(dm4=refMat2.transpose()*m3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose()); - - VERIFY_IS_APPROX(m3=m3*m3, refMat3=refMat3*refMat3); - } - - // test matrix - diagonal product - if(false) // it compiles, but the precision is terrible. probably doesn't matter in this branch.... - { - DenseMatrix refM2 = DenseMatrix::Zero(rows, rows); - DenseMatrix refM3 = DenseMatrix::Zero(rows, rows); - DiagonalMatrix<DenseVector> d1(DenseVector::Random(rows)); - SparseMatrixType m2(rows, rows); - SparseMatrixType m3(rows, rows); - 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()*d1, refM3=refM2.transpose()*d1); - VERIFY_IS_APPROX(m3=d1*m2, refM3=d1*refM2); - VERIFY_IS_APPROX(m3=d1*m2.transpose(), refM3=d1 * refM2.transpose()); - } - - // test self adjoint 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); - SparseMatrixType mUp(rows, rows); - SparseMatrixType mLo(rows, rows); - SparseMatrixType mS(rows, rows); - do { - initSparse<Scalar>(density, refUp, mUp, ForceRealDiag|/*ForceNonZeroDiag|*/MakeUpperTriangular); - } while (refUp.isZero()); - refLo = refUp.transpose().conjugate(); - mLo = mUp.transpose().conjugate(); - refS = refUp + refLo; - refS.diagonal() *= 0.5; - mS = mUp + mLo; - for (int k=0; k<mS.outerSize(); ++k) - for (typename SparseMatrixType::InnerIterator it(mS,k); it; ++it) - if (it.index() == k) - it.valueRef() *= 0.5; - - VERIFY_IS_APPROX(refS.adjoint(), refS); - VERIFY_IS_APPROX(mS.transpose().conjugate(), mS); - VERIFY_IS_APPROX(mS, refS); - VERIFY_IS_APPROX(x=mS*b, refX=refS*b); - VERIFY_IS_APPROX(x=mUp.template marked<UpperTriangular|SelfAdjoint>()*b, refX=refS*b); - VERIFY_IS_APPROX(x=mLo.template marked<LowerTriangular|SelfAdjoint>()*b, refX=refS*b); - VERIFY_IS_APPROX(x=mS.template marked<SelfAdjoint>()*b, refX=refS*b); - } - -} - -void test_eigen2_sparse_product() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( sparse_product(SparseMatrix<double>(8, 8)) ); - CALL_SUBTEST_2( sparse_product(SparseMatrix<std::complex<double> >(16, 16)) ); - CALL_SUBTEST_1( sparse_product(SparseMatrix<double>(33, 33)) ); - - CALL_SUBTEST_3( sparse_product(DynamicSparseMatrix<double>(8, 8)) ); - } -} diff --git a/test/eigen2/eigen2_sparse_solvers.cpp b/test/eigen2/eigen2_sparse_solvers.cpp deleted file mode 100644 index 3aef27ab4..000000000 --- a/test/eigen2/eigen2_sparse_solvers.cpp +++ /dev/null @@ -1,200 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@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 "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.startFill(); - for (int j=0 ; j<sparseMat.cols(); ++j) - for (int i=j ; i<sparseMat.rows(); ++i) - if (refMat(i,j)!=Scalar(0)) - sparseMat.fill(i,j) = refMat(i,j); - sparseMat.endFill(); -} - -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 - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular, &zeroCoords, &nonzeroCoords); - VERIFY_IS_APPROX(refMat2.template marked<LowerTriangular>().solveTriangular(vec2), - m2.template marked<LowerTriangular>().solveTriangular(vec3)); - - // lower - transpose - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular, &zeroCoords, &nonzeroCoords); - VERIFY_IS_APPROX(refMat2.template marked<LowerTriangular>().transpose().solveTriangular(vec2), - m2.template marked<LowerTriangular>().transpose().solveTriangular(vec3)); - - // upper - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular, &zeroCoords, &nonzeroCoords); - VERIFY_IS_APPROX(refMat2.template marked<UpperTriangular>().solveTriangular(vec2), - m2.template marked<UpperTriangular>().solveTriangular(vec3)); - - // upper - transpose - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular, &zeroCoords, &nonzeroCoords); - VERIFY_IS_APPROX(refMat2.template marked<UpperTriangular>().transpose().solveTriangular(vec2), - m2.template marked<UpperTriangular>().transpose().solveTriangular(vec3)); - } - - // test LLT - { - // TODO fix the issue with complex (see SparseLLT::solveInPlace) - SparseMatrix<Scalar> m2(rows, cols); - DenseMatrix refMat2(rows, cols); - - DenseVector b = DenseVector::Random(cols); - DenseVector refX(cols), x(cols); - - initSPD(density, refMat2, m2); - - refMat2.llt().solve(b, &refX); - typedef SparseMatrix<Scalar,LowerTriangular|SelfAdjoint> SparseSelfAdjointMatrix; - if (!NumTraits<Scalar>::IsComplex) - { - x = b; - SparseLLT<SparseSelfAdjointMatrix> (m2).solveInPlace(x); - VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LLT: default"); - } - #ifdef EIGEN_CHOLMOD_SUPPORT - x = b; - SparseLLT<SparseSelfAdjointMatrix,Cholmod>(m2).solveInPlace(x); - VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LLT: cholmod"); - #endif - if (!NumTraits<Scalar>::IsComplex) - { - #ifdef EIGEN_TAUCS_SUPPORT - x = b; - SparseLLT<SparseSelfAdjointMatrix,Taucs>(m2,IncompleteFactorization).solveInPlace(x); - VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LLT: taucs (IncompleteFactorization)"); - x = b; - SparseLLT<SparseSelfAdjointMatrix,Taucs>(m2,SupernodalMultifrontal).solveInPlace(x); - VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LLT: taucs (SupernodalMultifrontal)"); - x = b; - SparseLLT<SparseSelfAdjointMatrix,Taucs>(m2,SupernodalLeftLooking).solveInPlace(x); - VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LLT: taucs (SupernodalLeftLooking)"); - #endif - } - } - - // test LDLT - if (!NumTraits<Scalar>::IsComplex) - { - // TODO fix the issue with complex (see SparseLDLT::solveInPlace) - SparseMatrix<Scalar> m2(rows, cols); - DenseMatrix refMat2(rows, cols); - - DenseVector b = DenseVector::Random(cols); - DenseVector refX(cols), x(cols); - - //initSPD(density, refMat2, m2); - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular, 0, 0); - refMat2 += refMat2.adjoint(); - refMat2.diagonal() *= 0.5; - - refMat2.ldlt().solve(b, &refX); - typedef SparseMatrix<Scalar,UpperTriangular|SelfAdjoint> SparseSelfAdjointMatrix; - x = b; - SparseLDLT<SparseSelfAdjointMatrix> ldlt(m2); - if (ldlt.succeeded()) - ldlt.solveInPlace(x); - VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LDLT: default"); - } - - // test LU - { - static int count = 0; - SparseMatrix<Scalar> m2(rows, cols); - DenseMatrix refMat2(rows, cols); - - DenseVector b = DenseVector::Random(cols); - DenseVector refX(cols), x(cols); - - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag, &zeroCoords, &nonzeroCoords); - - LU<DenseMatrix> refLu(refMat2); - refLu.solve(b, &refX); - #if defined(EIGEN_SUPERLU_SUPPORT) || defined(EIGEN_UMFPACK_SUPPORT) - Scalar refDet = refLu.determinant(); - #endif - x.setZero(); - // // SparseLU<SparseMatrix<Scalar> > (m2).solve(b,&x); - // // VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LU: default"); - #ifdef EIGEN_SUPERLU_SUPPORT - { - x.setZero(); - SparseLU<SparseMatrix<Scalar>,SuperLU> slu(m2); - if (slu.succeeded()) - { - if (slu.solve(b,&x)) { - VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LU: SuperLU"); - } - // std::cerr << refDet << " == " << slu.determinant() << "\n"; - if (count==0) { - VERIFY_IS_APPROX(refDet,slu.determinant()); // FIXME det is not very stable for complex - } - } - } - #endif - #ifdef EIGEN_UMFPACK_SUPPORT - { - // check solve - x.setZero(); - SparseLU<SparseMatrix<Scalar>,UmfPack> slu(m2); - if (slu.succeeded()) { - if (slu.solve(b,&x)) { - if (count==0) { - VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LU: umfpack"); // FIXME solve is not very stable for complex - } - } - VERIFY_IS_APPROX(refDet,slu.determinant()); - // TODO check the extracted data - //std::cerr << slu.matrixL() << "\n"; - } - } - #endif - count++; - } - -} - -void test_eigen2_sparse_solvers() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( sparse_solvers<double>(8, 8) ); - CALL_SUBTEST_2( sparse_solvers<std::complex<double> >(16, 16) ); - CALL_SUBTEST_1( sparse_solvers<double>(101, 101) ); - } -} diff --git a/test/eigen2/eigen2_sparse_vector.cpp b/test/eigen2/eigen2_sparse_vector.cpp deleted file mode 100644 index e6d2d77a1..000000000 --- a/test/eigen2/eigen2_sparse_vector.cpp +++ /dev/null @@ -1,84 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@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 "sparse.h" - -template<typename Scalar> void sparse_vector(int rows, int cols) -{ - double densityMat = std::max(8./(rows*cols), 0.01); - double densityVec = std::max(8./float(rows), 0.1); - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - typedef Matrix<Scalar,Dynamic,1> DenseVector; - typedef SparseVector<Scalar> SparseVectorType; - typedef SparseMatrix<Scalar> SparseMatrixType; - Scalar eps = 1e-6; - - SparseMatrixType m1(rows,cols); - SparseVectorType v1(rows), v2(rows), v3(rows); - DenseMatrix refM1 = DenseMatrix::Zero(rows, cols); - 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 = ei_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); - - 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.eigen2_dot(v2), refV1.eigen2_dot(refV2)); - VERIFY_IS_APPROX(v1.eigen2_dot(refV2), refV1.eigen2_dot(refV2)); - -} - -void test_eigen2_sparse_vector() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( sparse_vector<double>(8, 8) ); - CALL_SUBTEST_2( sparse_vector<std::complex<double> >(16, 16) ); - CALL_SUBTEST_1( sparse_vector<double>(299, 535) ); - } -} - diff --git a/test/eigen2/eigen2_stdvector.cpp b/test/eigen2/eigen2_stdvector.cpp deleted file mode 100644 index 6ab05c20a..000000000 --- a/test/eigen2/eigen2_stdvector.cpp +++ /dev/null @@ -1,148 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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 <Eigen/StdVector> -#include "main.h" -#include <Eigen/Geometry> - -template<typename MatrixType> -void check_stdvector_matrix(const MatrixType& m) -{ - int rows = m.rows(); - int cols = m.cols(); - MatrixType x = MatrixType::Random(rows,cols), y = MatrixType::Random(rows,cols); - std::vector<MatrixType, 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((std::size_t)&(v[22]) == (std::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(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, 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((std::size_t)&(v[22]) == (std::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; 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, 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((std::size_t)&(v[22]) == (std::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; i<v.size(); ++i) - { - VERIFY(v[i].coeffs()==w[(i-23)%w.size()].coeffs()); - } -} - -void test_eigen2_stdvector() -{ - // some non vectorizable fixed sizes - CALL_SUBTEST_1(check_stdvector_matrix(Vector2f())); - CALL_SUBTEST_1(check_stdvector_matrix(Matrix3f())); - CALL_SUBTEST_1(check_stdvector_matrix(Matrix3d())); - - // some vectorizable fixed sizes - CALL_SUBTEST_2(check_stdvector_matrix(Matrix2f())); - CALL_SUBTEST_2(check_stdvector_matrix(Vector4f())); - CALL_SUBTEST_2(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(Transform2f())); - CALL_SUBTEST_4(check_stdvector_transform(Transform3f())); - CALL_SUBTEST_4(check_stdvector_transform(Transform3d())); - //CALL_SUBTEST_4(check_stdvector_transform(Transform4d())); - - // some Quaternion - CALL_SUBTEST_5(check_stdvector_quaternion(Quaternionf())); - CALL_SUBTEST_5(check_stdvector_quaternion(Quaternionf())); -} diff --git a/test/eigen2/eigen2_submatrices.cpp b/test/eigen2/eigen2_submatrices.cpp deleted file mode 100644 index dee970b63..000000000 --- a/test/eigen2/eigen2_submatrices.cpp +++ /dev/null @@ -1,142 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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" - -// check minor separately in order to avoid the possible creation of a zero-sized -// array. Comes from a compilation error with gcc-3.4 or gcc-4 with -ansi -pedantic. -// Another solution would be to declare the array like this: T m_data[Size==0?1:Size]; in ei_matrix_storage -// but this is probably not bad to raise such an error at compile time... -template<typename Scalar, int _Rows, int _Cols> struct CheckMinor -{ - typedef Matrix<Scalar, _Rows, _Cols> MatrixType; - CheckMinor(MatrixType& m1, int r1, int c1) - { - int rows = m1.rows(); - int cols = m1.cols(); - - Matrix<Scalar, Dynamic, Dynamic> mi = m1.minor(0,0).eval(); - VERIFY_IS_APPROX(mi, m1.block(1,1,rows-1,cols-1)); - mi = m1.minor(r1,c1); - VERIFY_IS_APPROX(mi.transpose(), m1.transpose().minor(c1,r1)); - //check operator(), both constant and non-constant, on minor() - m1.minor(r1,c1)(0,0) = m1.minor(0,0)(0,0); - } -}; - -template<typename Scalar> struct CheckMinor<Scalar,1,1> -{ - typedef Matrix<Scalar, 1, 1> MatrixType; - CheckMinor(MatrixType&, int, int) {} -}; - -template<typename MatrixType> void submatrices(const MatrixType& m) -{ - /* this test covers the following files: - Row.h Column.h Block.h Minor.h DiagonalCoeffs.h - */ - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; - typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType; - int rows = m.rows(); - int cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols), - m2 = MatrixType::Random(rows, cols), - m3(rows, cols), - ones = MatrixType::Ones(rows, cols), - square = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> - ::Random(rows, rows); - VectorType v1 = VectorType::Random(rows); - - Scalar s1 = ei_random<Scalar>(); - - int r1 = ei_random<int>(0,rows-1); - int r2 = ei_random<int>(r1,rows-1); - int c1 = ei_random<int>(0,cols-1); - int c2 = ei_random<int>(c1,cols-1); - - //check row() and col() - VERIFY_IS_APPROX(m1.col(c1).transpose(), m1.transpose().row(c1)); - VERIFY_IS_APPROX(square.row(r1).eigen2_dot(m1.col(c1)), (square.lazy() * m1.conjugate())(r1,c1)); - //check operator(), both constant and non-constant, on row() and col() - m1.row(r1) += s1 * m1.row(r2); - m1.col(c1) += s1 * m1.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_APPROX(b1, m1.block(r1,c1,1,1)); - VERIFY_IS_APPROX(m1.row(r1), br1); - VERIFY_IS_APPROX(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); - - //check minor() - CheckMinor<Scalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime> checkminor(m1,r1,c1); - - //check diagonal() - VERIFY_IS_APPROX(m1.diagonal(), m1.transpose().diagonal()); - m2.diagonal() = 2 * m1.diagonal(); - m2.diagonal()[0] *= 3; - VERIFY_IS_APPROX(m2.diagonal()[0], static_cast<Scalar>(6) * m1.diagonal()[0]); - - enum { - BlockRows = EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::RowsAtCompileTime,2), - BlockCols = EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::ColsAtCompileTime,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_APPROX(b, m1.block(3,3,BlockRows,BlockCols)); - } - - if (rows>2) - { - // test sub vectors - VERIFY_IS_APPROX(v1.template start<2>(), v1.block(0,0,2,1)); - VERIFY_IS_APPROX(v1.template start<2>(), v1.start(2)); - VERIFY_IS_APPROX(v1.template start<2>(), v1.segment(0,2)); - VERIFY_IS_APPROX(v1.template start<2>(), v1.template segment<2>(0)); - int i = rows-2; - VERIFY_IS_APPROX(v1.template end<2>(), v1.block(i,0,2,1)); - VERIFY_IS_APPROX(v1.template end<2>(), v1.end(2)); - VERIFY_IS_APPROX(v1.template end<2>(), v1.segment(i,2)); - VERIFY_IS_APPROX(v1.template end<2>(), v1.template segment<2>(i)); - i = ei_random(0,rows-2); - VERIFY_IS_APPROX(v1.segment(i,2), v1.template segment<2>(i)); - } - - // stress some basic stuffs with block matrices - VERIFY(ei_real(ones.col(c1).sum()) == RealScalar(rows)); - VERIFY(ei_real(ones.row(r1).sum()) == RealScalar(cols)); - - VERIFY(ei_real(ones.col(c1).eigen2_dot(ones.col(c2))) == RealScalar(rows)); - VERIFY(ei_real(ones.row(r1).eigen2_dot(ones.row(r2))) == RealScalar(cols)); -} - -void test_eigen2_submatrices() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( submatrices(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( submatrices(Matrix4d()) ); - CALL_SUBTEST_3( submatrices(MatrixXcf(3, 3)) ); - CALL_SUBTEST_4( submatrices(MatrixXi(8, 12)) ); - CALL_SUBTEST_5( submatrices(MatrixXcd(20, 20)) ); - CALL_SUBTEST_6( submatrices(MatrixXf(20, 20)) ); - } -} diff --git a/test/eigen2/eigen2_sum.cpp b/test/eigen2/eigen2_sum.cpp deleted file mode 100644 index b47057caa..000000000 --- a/test/eigen2/eigen2_sum.cpp +++ /dev/null @@ -1,71 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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 matrixSum(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - - int rows = m.rows(); - int cols = m.cols(); - - MatrixType m1 = MatrixType::Random(rows, cols); - - 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 x = Scalar(0); - for(int i = 0; i < rows; i++) for(int j = 0; j < cols; j++) x += m1(i,j); - VERIFY_IS_APPROX(m1.sum(), x); -} - -template<typename VectorType> void vectorSum(const VectorType& w) -{ - typedef typename VectorType::Scalar Scalar; - int size = w.size(); - - VectorType v = VectorType::Random(size); - for(int i = 1; i < size; i++) - { - Scalar s = Scalar(0); - for(int j = 0; j < i; j++) s += v[j]; - VERIFY_IS_APPROX(s, v.start(i).sum()); - } - - for(int i = 0; i < size-1; i++) - { - Scalar s = Scalar(0); - for(int j = i; j < size; j++) s += v[j]; - VERIFY_IS_APPROX(s, v.end(size-i).sum()); - } - - for(int i = 0; i < size/2; i++) - { - Scalar s = Scalar(0); - for(int j = i; j < size-i; j++) s += v[j]; - VERIFY_IS_APPROX(s, v.segment(i, size-2*i).sum()); - } -} - -void test_eigen2_sum() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( matrixSum(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( matrixSum(Matrix2f()) ); - CALL_SUBTEST_3( matrixSum(Matrix4d()) ); - CALL_SUBTEST_4( matrixSum(MatrixXcf(3, 3)) ); - CALL_SUBTEST_5( matrixSum(MatrixXf(8, 12)) ); - CALL_SUBTEST_6( matrixSum(MatrixXi(8, 12)) ); - } - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_5( vectorSum(VectorXf(5)) ); - CALL_SUBTEST_7( vectorSum(VectorXd(10)) ); - CALL_SUBTEST_5( vectorSum(VectorXf(33)) ); - } -} diff --git a/test/eigen2/eigen2_svd.cpp b/test/eigen2/eigen2_svd.cpp deleted file mode 100644 index d4689a56f..000000000 --- a/test/eigen2/eigen2_svd.cpp +++ /dev/null @@ -1,87 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// 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 svd(const MatrixType& m) -{ - /* this test covers the following files: - SVD.h - */ - int rows = m.rows(); - int cols = m.cols(); - - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - MatrixType a = MatrixType::Random(rows,cols); - Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> b = - Matrix<Scalar, MatrixType::RowsAtCompileTime, 1>::Random(rows,1); - Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> x(cols,1), x2(cols,1); - - RealScalar largerEps = test_precision<RealScalar>(); - if (ei_is_same_type<RealScalar,float>::ret) - largerEps = 1e-3f; - - { - SVD<MatrixType> svd(a); - MatrixType sigma = MatrixType::Zero(rows,cols); - MatrixType matU = MatrixType::Zero(rows,rows); - sigma.block(0,0,cols,cols) = svd.singularValues().asDiagonal(); - matU.block(0,0,rows,cols) = svd.matrixU(); - VERIFY_IS_APPROX(a, matU * sigma * svd.matrixV().transpose()); - } - - - if (rows==cols) - { - if (ei_is_same_type<RealScalar,float>::ret) - { - MatrixType a1 = MatrixType::Random(rows,cols); - a += a * a.adjoint() + a1 * a1.adjoint(); - } - SVD<MatrixType> svd(a); - svd.solve(b, &x); - VERIFY_IS_APPROX(a * x,b); - } - - - if(rows==cols) - { - SVD<MatrixType> svd(a); - MatrixType unitary, positive; - svd.computeUnitaryPositive(&unitary, &positive); - VERIFY_IS_APPROX(unitary * unitary.adjoint(), MatrixType::Identity(unitary.rows(),unitary.rows())); - VERIFY_IS_APPROX(positive, positive.adjoint()); - for(int i = 0; i < rows; i++) VERIFY(positive.diagonal()[i] >= 0); // cheap necessary (not sufficient) condition for positivity - VERIFY_IS_APPROX(unitary*positive, a); - - svd.computePositiveUnitary(&positive, &unitary); - VERIFY_IS_APPROX(unitary * unitary.adjoint(), MatrixType::Identity(unitary.rows(),unitary.rows())); - VERIFY_IS_APPROX(positive, positive.adjoint()); - for(int i = 0; i < rows; i++) VERIFY(positive.diagonal()[i] >= 0); // cheap necessary (not sufficient) condition for positivity - VERIFY_IS_APPROX(positive*unitary, a); - } -} - -void test_eigen2_svd() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( svd(Matrix3f()) ); - CALL_SUBTEST_2( svd(Matrix4d()) ); - CALL_SUBTEST_3( svd(MatrixXf(7,7)) ); - CALL_SUBTEST_4( svd(MatrixXd(14,7)) ); - // complex are not implemented yet -// CALL_SUBTEST( svd(MatrixXcd(6,6)) ); -// CALL_SUBTEST( svd(MatrixXcf(3,3)) ); - SVD<MatrixXf> s; - MatrixXf m = MatrixXf::Random(10,1); - s.compute(m); - } -} diff --git a/test/eigen2/eigen2_swap.cpp b/test/eigen2/eigen2_swap.cpp deleted file mode 100644 index f3a8846d9..000000000 --- a/test/eigen2/eigen2_swap.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/. - -#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; - - ei_assert((!ei_is_same_type<MatrixType,OtherMatrixType>::ret)); - int rows = m.rows(); - int 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 - m1.swap(m2); - VERIFY_IS_APPROX(m1,m2_copy); - VERIFY_IS_APPROX(m2,m1_copy); - 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; - - // 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_eigen2_swap() -{ - CALL_SUBTEST_1( swap(Matrix3f()) ); // fixed size, no vectorization - CALL_SUBTEST_1( swap(Matrix4d()) ); // fixed size, possible vectorization - CALL_SUBTEST_1( swap(MatrixXd(3,3)) ); // dyn size, no vectorization - CALL_SUBTEST_1( swap(MatrixXf(30,30)) ); // dyn size, possible vectorization -} diff --git a/test/eigen2/eigen2_triangular.cpp b/test/eigen2/eigen2_triangular.cpp deleted file mode 100644 index 6f17b7dff..000000000 --- a/test/eigen2/eigen2_triangular.cpp +++ /dev/null @@ -1,148 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@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 triangular(const MatrixType& m) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - RealScalar largerEps = 10*test_precision<RealScalar>(); - - int rows = m.rows(); - int 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 part<Eigen::UpperTriangular>(); - MatrixType m2up = m2.template part<Eigen::UpperTriangular>(); - - 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 part<Eigen::UpperTriangular>() += m1; - r2 += m1up; - VERIFY_IS_APPROX(r1,r2); - - // test overloaded operator= - m1.setZero(); - m1.template part<Eigen::UpperTriangular>() = (m2.transpose() * m2).lazy(); - m3 = m2.transpose() * m2; - VERIFY_IS_APPROX(m3.template part<Eigen::LowerTriangular>().transpose(), m1); - - // test overloaded operator= - m1.setZero(); - m1.template part<Eigen::LowerTriangular>() = (m2.transpose() * m2).lazy(); - VERIFY_IS_APPROX(m3.template part<Eigen::LowerTriangular>(), m1); - - VERIFY_IS_APPROX(m3.template part<Diagonal>(), m3.diagonal().asDiagonal()); - - m1 = MatrixType::Random(rows, cols); - for (int i=0; i<rows; ++i) - while (ei_abs2(m1(i,i))<1e-3) m1(i,i) = ei_random<Scalar>(); - - Transpose<MatrixType> trm4(m4); - // test back and forward subsitution - m3 = m1.template part<Eigen::LowerTriangular>(); - VERIFY(m3.template marked<Eigen::LowerTriangular>().solveTriangular(m3).cwise().abs().isIdentity(test_precision<RealScalar>())); - VERIFY(m3.transpose().template marked<Eigen::UpperTriangular>() - .solveTriangular(m3.transpose()).cwise().abs().isIdentity(test_precision<RealScalar>())); - // check M * inv(L) using in place API - m4 = m3; - m3.transpose().template marked<Eigen::UpperTriangular>().solveTriangularInPlace(trm4); - VERIFY(m4.cwise().abs().isIdentity(test_precision<RealScalar>())); - - m3 = m1.template part<Eigen::UpperTriangular>(); - VERIFY(m3.template marked<Eigen::UpperTriangular>().solveTriangular(m3).cwise().abs().isIdentity(test_precision<RealScalar>())); - VERIFY(m3.transpose().template marked<Eigen::LowerTriangular>() - .solveTriangular(m3.transpose()).cwise().abs().isIdentity(test_precision<RealScalar>())); - // check M * inv(U) using in place API - m4 = m3; - m3.transpose().template marked<Eigen::LowerTriangular>().solveTriangularInPlace(trm4); - VERIFY(m4.cwise().abs().isIdentity(test_precision<RealScalar>())); - - m3 = m1.template part<Eigen::UpperTriangular>(); - VERIFY(m2.isApprox(m3 * (m3.template marked<Eigen::UpperTriangular>().solveTriangular(m2)), largerEps)); - m3 = m1.template part<Eigen::LowerTriangular>(); - VERIFY(m2.isApprox(m3 * (m3.template marked<Eigen::LowerTriangular>().solveTriangular(m2)), largerEps)); - - VERIFY((m1.template part<Eigen::UpperTriangular>() * m2.template part<Eigen::UpperTriangular>()).isUpperTriangular()); - - // test swap - m1.setOnes(); - m2.setZero(); - m2.template part<Eigen::UpperTriangular>().swap(m1); - m3.setZero(); - m3.template part<Eigen::UpperTriangular>().setOnes(); - VERIFY_IS_APPROX(m2,m3); - -} - -void selfadjoint() -{ - Matrix2i m; - m << 1, 2, - 3, 4; - - Matrix2i m1 = Matrix2i::Zero(); - m1.part<SelfAdjoint>() = m; - Matrix2i ref1; - ref1 << 1, 2, - 2, 4; - VERIFY(m1 == ref1); - - Matrix2i m2 = Matrix2i::Zero(); - m2.part<SelfAdjoint>() = m.part<UpperTriangular>(); - Matrix2i ref2; - ref2 << 1, 2, - 2, 4; - VERIFY(m2 == ref2); - - Matrix2i m3 = Matrix2i::Zero(); - m3.part<SelfAdjoint>() = m.part<LowerTriangular>(); - Matrix2i ref3; - ref3 << 1, 0, - 0, 4; - VERIFY(m3 == ref3); - - // example inspired from bug 159 - int array[] = {1, 2, 3, 4}; - Matrix2i::Map(array).part<SelfAdjoint>() = Matrix2i::Random().part<LowerTriangular>(); - - std::cout << "hello\n" << array << std::endl; -} - -void test_eigen2_triangular() -{ - CALL_SUBTEST_8( selfadjoint() ); - for(int i = 0; i < g_repeat ; i++) { - CALL_SUBTEST_1( triangular(Matrix<float, 1, 1>()) ); - CALL_SUBTEST_2( triangular(Matrix<float, 2, 2>()) ); - CALL_SUBTEST_3( triangular(Matrix3d()) ); - CALL_SUBTEST_4( triangular(MatrixXcf(4, 4)) ); - CALL_SUBTEST_5( triangular(Matrix<std::complex<float>,8, 8>()) ); - CALL_SUBTEST_6( triangular(MatrixXd(17,17)) ); - CALL_SUBTEST_7( triangular(Matrix<float,Dynamic,Dynamic,RowMajor>(5, 5)) ); - } -} diff --git a/test/eigen2/eigen2_unalignedassert.cpp b/test/eigen2/eigen2_unalignedassert.cpp deleted file mode 100644 index d10b6f538..000000000 --- a/test/eigen2/eigen2_unalignedassert.cpp +++ /dev/null @@ -1,116 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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" - -struct Good1 -{ - MatrixXd m; // good: m will allocate its own array, taking care of alignment. - Good1() : m(20,20) {} -}; - -struct Good2 -{ - Matrix3d m; // good: m's size isn't a multiple of 16 bytes, so m doesn't have to be aligned -}; - -struct Good3 -{ - Vector2f m; // good: same reason -}; - -struct Bad4 -{ - Vector2d m; // bad: sizeof(m)%16==0 so alignment is required -}; - -struct Bad5 -{ - Matrix<float, 2, 6> m; // bad: same reason -}; - -struct Bad6 -{ - Matrix<double, 3, 4> m; // bad: same reason -}; - -struct Good7 -{ - 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 Good8 -{ - EIGEN_MAKE_ALIGNED_OPERATOR_NEW - float f; // try the f at first -- the EIGEN_ALIGN_128 attribute of m should make that still work - Matrix4f m; -}; - -struct Good9 -{ - 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_ARCH_WANTS_ALIGNMENT -template<typename T> -void check_unalignedassert_bad() -{ - float buf[sizeof(T)+16]; - float *unaligned = buf; - while((reinterpret_cast<std::size_t>(unaligned)&0xf)==0) ++unaligned; // make sure unaligned is really unaligned - T *x = ::new(static_cast<void*>(unaligned)) T; - x->~T(); -} -#endif - -void unalignedassert() -{ - check_unalignedassert_good<Good1>(); - check_unalignedassert_good<Good2>(); - check_unalignedassert_good<Good3>(); -#if EIGEN_ARCH_WANTS_ALIGNMENT - VERIFY_RAISES_ASSERT(check_unalignedassert_bad<Bad4>()); - VERIFY_RAISES_ASSERT(check_unalignedassert_bad<Bad5>()); - VERIFY_RAISES_ASSERT(check_unalignedassert_bad<Bad6>()); -#endif - - check_unalignedassert_good<Good7>(); - check_unalignedassert_good<Good8>(); - check_unalignedassert_good<Good9>(); - check_unalignedassert_good<Depends<true> >(); - -#if EIGEN_ARCH_WANTS_ALIGNMENT - VERIFY_RAISES_ASSERT(check_unalignedassert_bad<Depends<false> >()); -#endif -} - -void test_eigen2_unalignedassert() -{ - CALL_SUBTEST(unalignedassert()); -} diff --git a/test/eigen2/eigen2_visitor.cpp b/test/eigen2/eigen2_visitor.cpp deleted file mode 100644 index 4781991de..000000000 --- a/test/eigen2/eigen2_visitor.cpp +++ /dev/null @@ -1,116 +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; - - int rows = p.rows(); - int cols = p.cols(); - - // construct a random matrix where all coefficients are different - MatrixType m; - m = MatrixType::Random(rows, cols); - for(int i = 0; i < m.size(); i++) - for(int i2 = 0; i2 < i; i2++) - while(m(i) == m(i2)) // yes, == - m(i) = ei_random<Scalar>(); - - Scalar minc = Scalar(1000), maxc = Scalar(-1000); - int minrow=0,mincol=0,maxrow=0,maxcol=0; - for(int j = 0; j < cols; j++) - for(int 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; - } - } - int 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()); -} - -template<typename VectorType> void vectorVisitor(const VectorType& w) -{ - typedef typename VectorType::Scalar Scalar; - - int size = w.size(); - - // construct a random vector where all coefficients are different - VectorType v; - v = VectorType::Random(size); - for(int i = 0; i < size; i++) - for(int i2 = 0; i2 < i; i2++) - while(v(i) == v(i2)) // yes, == - v(i) = ei_random<Scalar>(); - - Scalar minc = Scalar(1000), maxc = Scalar(-1000); - int minidx=0,maxidx=0; - for(int i = 0; i < size; i++) - { - if(v(i) < minc) - { - minc = v(i); - minidx = i; - } - if(v(i) > maxc) - { - maxc = v(i); - maxidx = i; - } - } - int 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()); -} - -void test_eigen2_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_4( vectorVisitor(VectorXd(10)) ); - CALL_SUBTEST_4( vectorVisitor(RowVectorXd(10)) ); - CALL_SUBTEST_8( vectorVisitor(VectorXf(33)) ); - } -} diff --git a/test/eigen2/gsl_helper.h b/test/eigen2/gsl_helper.h deleted file mode 100644 index d1d854533..000000000 --- a/test/eigen2/gsl_helper.h +++ /dev/null @@ -1,175 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// 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_GSL_HELPER -#define EIGEN_GSL_HELPER - -#include <Eigen/Core> - -#include <gsl/gsl_blas.h> -#include <gsl/gsl_multifit.h> -#include <gsl/gsl_eigen.h> -#include <gsl/gsl_linalg.h> -#include <gsl/gsl_complex.h> -#include <gsl/gsl_complex_math.h> - -namespace Eigen { - -template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex> struct GslTraits -{ - typedef gsl_matrix* Matrix; - typedef gsl_vector* Vector; - static Matrix createMatrix(int rows, int cols) { return gsl_matrix_alloc(rows,cols); } - static Vector createVector(int size) { return gsl_vector_alloc(size); } - static void free(Matrix& m) { gsl_matrix_free(m); m=0; } - static void free(Vector& m) { gsl_vector_free(m); m=0; } - static void prod(const Matrix& m, const Vector& v, Vector& x) { gsl_blas_dgemv(CblasNoTrans,1,m,v,0,x); } - static void cholesky(Matrix& m) { gsl_linalg_cholesky_decomp(m); } - static void cholesky_solve(const Matrix& m, const Vector& b, Vector& x) { gsl_linalg_cholesky_solve(m,b,x); } - static void eigen_symm(const Matrix& m, Vector& eval, Matrix& evec) - { - gsl_eigen_symmv_workspace * w = gsl_eigen_symmv_alloc(m->size1); - Matrix a = createMatrix(m->size1, m->size2); - gsl_matrix_memcpy(a, m); - gsl_eigen_symmv(a,eval,evec,w); - gsl_eigen_symmv_sort(eval, evec, GSL_EIGEN_SORT_VAL_ASC); - gsl_eigen_symmv_free(w); - free(a); - } - static void eigen_symm_gen(const Matrix& m, const Matrix& _b, Vector& eval, Matrix& evec) - { - gsl_eigen_gensymmv_workspace * w = gsl_eigen_gensymmv_alloc(m->size1); - Matrix a = createMatrix(m->size1, m->size2); - Matrix b = createMatrix(_b->size1, _b->size2); - gsl_matrix_memcpy(a, m); - gsl_matrix_memcpy(b, _b); - gsl_eigen_gensymmv(a,b,eval,evec,w); - gsl_eigen_symmv_sort(eval, evec, GSL_EIGEN_SORT_VAL_ASC); - gsl_eigen_gensymmv_free(w); - free(a); - } -}; - -template<typename Scalar> struct GslTraits<Scalar,true> -{ - typedef gsl_matrix_complex* Matrix; - typedef gsl_vector_complex* Vector; - static Matrix createMatrix(int rows, int cols) { return gsl_matrix_complex_alloc(rows,cols); } - static Vector createVector(int size) { return gsl_vector_complex_alloc(size); } - static void free(Matrix& m) { gsl_matrix_complex_free(m); m=0; } - static void free(Vector& m) { gsl_vector_complex_free(m); m=0; } - static void cholesky(Matrix& m) { gsl_linalg_complex_cholesky_decomp(m); } - static void cholesky_solve(const Matrix& m, const Vector& b, Vector& x) { gsl_linalg_complex_cholesky_solve(m,b,x); } - static void prod(const Matrix& m, const Vector& v, Vector& x) - { gsl_blas_zgemv(CblasNoTrans,gsl_complex_rect(1,0),m,v,gsl_complex_rect(0,0),x); } - static void eigen_symm(const Matrix& m, gsl_vector* &eval, Matrix& evec) - { - gsl_eigen_hermv_workspace * w = gsl_eigen_hermv_alloc(m->size1); - Matrix a = createMatrix(m->size1, m->size2); - gsl_matrix_complex_memcpy(a, m); - gsl_eigen_hermv(a,eval,evec,w); - gsl_eigen_hermv_sort(eval, evec, GSL_EIGEN_SORT_VAL_ASC); - gsl_eigen_hermv_free(w); - free(a); - } - static void eigen_symm_gen(const Matrix& m, const Matrix& _b, gsl_vector* &eval, Matrix& evec) - { - gsl_eigen_genhermv_workspace * w = gsl_eigen_genhermv_alloc(m->size1); - Matrix a = createMatrix(m->size1, m->size2); - Matrix b = createMatrix(_b->size1, _b->size2); - gsl_matrix_complex_memcpy(a, m); - gsl_matrix_complex_memcpy(b, _b); - gsl_eigen_genhermv(a,b,eval,evec,w); - gsl_eigen_hermv_sort(eval, evec, GSL_EIGEN_SORT_VAL_ASC); - gsl_eigen_genhermv_free(w); - free(a); - } -}; - -template<typename MatrixType> -void convert(const MatrixType& m, gsl_matrix* &res) -{ -// if (res) -// gsl_matrix_free(res); - res = gsl_matrix_alloc(m.rows(), m.cols()); - for (int i=0 ; i<m.rows() ; ++i) - for (int j=0 ; j<m.cols(); ++j) - gsl_matrix_set(res, i, j, m(i,j)); -} - -template<typename MatrixType> -void convert(const gsl_matrix* m, MatrixType& res) -{ - res.resize(int(m->size1), int(m->size2)); - for (int i=0 ; i<res.rows() ; ++i) - for (int j=0 ; j<res.cols(); ++j) - res(i,j) = gsl_matrix_get(m,i,j); -} - -template<typename VectorType> -void convert(const VectorType& m, gsl_vector* &res) -{ - if (res) gsl_vector_free(res); - res = gsl_vector_alloc(m.size()); - for (int i=0 ; i<m.size() ; ++i) - gsl_vector_set(res, i, m[i]); -} - -template<typename VectorType> -void convert(const gsl_vector* m, VectorType& res) -{ - res.resize (m->size); - for (int i=0 ; i<res.rows() ; ++i) - res[i] = gsl_vector_get(m, i); -} - -template<typename MatrixType> -void convert(const MatrixType& m, gsl_matrix_complex* &res) -{ - res = gsl_matrix_complex_alloc(m.rows(), m.cols()); - for (int i=0 ; i<m.rows() ; ++i) - for (int j=0 ; j<m.cols(); ++j) - { - gsl_matrix_complex_set(res, i, j, - gsl_complex_rect(m(i,j).real(), m(i,j).imag())); - } -} - -template<typename MatrixType> -void convert(const gsl_matrix_complex* m, MatrixType& res) -{ - res.resize(int(m->size1), int(m->size2)); - for (int i=0 ; i<res.rows() ; ++i) - for (int j=0 ; j<res.cols(); ++j) - res(i,j) = typename MatrixType::Scalar( - GSL_REAL(gsl_matrix_complex_get(m,i,j)), - GSL_IMAG(gsl_matrix_complex_get(m,i,j))); -} - -template<typename VectorType> -void convert(const VectorType& m, gsl_vector_complex* &res) -{ - res = gsl_vector_complex_alloc(m.size()); - for (int i=0 ; i<m.size() ; ++i) - gsl_vector_complex_set(res, i, gsl_complex_rect(m[i].real(), m[i].imag())); -} - -template<typename VectorType> -void convert(const gsl_vector_complex* m, VectorType& res) -{ - res.resize(m->size); - for (int i=0 ; i<res.rows() ; ++i) - res[i] = typename VectorType::Scalar( - GSL_REAL(gsl_vector_complex_get(m, i)), - GSL_IMAG(gsl_vector_complex_get(m, i))); -} - -} - -#endif // EIGEN_GSL_HELPER diff --git a/test/eigen2/main.h b/test/eigen2/main.h deleted file mode 100644 index ad2ba1994..000000000 --- a/test/eigen2/main.h +++ /dev/null @@ -1,399 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2008 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 <cstdlib> -#include <ctime> -#include <iostream> -#include <string> -#include <vector> - -#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; - static int g_repeat; -} - -#define EI_PP_MAKE_STRING2(S) #S -#define EI_PP_MAKE_STRING(S) EI_PP_MAKE_STRING2(S) - -#define EI_PP_CAT2(a,b) a ## b -#define EI_PP_CAT(a,b) EI_PP_CAT2(a,b) - -#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 raise in a destructor. - static bool no_more_assert = false; - - struct eigen_assert_exception - { - eigen_assert_exception(void) {} - ~eigen_assert_exception() { Eigen::no_more_assert = false; } - }; - } - - // If EIGEN_DEBUG_ASSERTS is defined and if no assertion is raised 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 - { - static bool ei_push_assert = false; - static std::vector<std::string> eigen_assert_list; - } - - #define eigen_assert(a) \ - if( (!(a)) && (!no_more_assert) ) \ - { \ - Eigen::no_more_assert = true; \ - throw Eigen::eigen_assert_exception(); \ - } \ - else if (Eigen::ei_push_assert) \ - { \ - eigen_assert_list.push_back(std::string(EI_PP_MAKE_STRING(__FILE__)" ("EI_PP_MAKE_STRING(__LINE__)") : "#a) ); \ - } - - #define VERIFY_RAISES_ASSERT(a) \ - { \ - Eigen::no_more_assert = false; \ - try { \ - Eigen::eigen_assert_list.clear(); \ - Eigen::ei_push_assert = true; \ - a; \ - Eigen::ei_push_assert = false; \ - std::cerr << "One of the following asserts should have been raised:\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 e) { \ - Eigen::ei_push_assert = false; VERIFY(true); \ - } \ - } - - #else // EIGEN_DEBUG_ASSERTS - - #undef eigen_assert - - // 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; \ - throw Eigen::eigen_assert_exception(); \ - } - - #define VERIFY_RAISES_ASSERT(a) { \ - Eigen::no_more_assert = false; \ - try { a; VERIFY(Eigen::should_raise_an_assert && # a); } \ - catch (Eigen::eigen_assert_exception e) { VERIFY(true); } \ - } - - #endif // EIGEN_DEBUG_ASSERTS - - #define EIGEN_USE_CUSTOM_ASSERT - -#else // EIGEN_NO_ASSERTION_CHECKING - - #define VERIFY_RAISES_ASSERT(a) {} - -#endif // EIGEN_NO_ASSERTION_CHECKING - - -#define EIGEN_INTERNAL_DEBUGGING -#define EIGEN_NICE_RANDOM -#include <Eigen/Array> - - -#define VERIFY(a) do { if (!(a)) { \ - std::cerr << "Test " << g_test_stack.back() << " failed in "EI_PP_MAKE_STRING(__FILE__) << " (" << EI_PP_MAKE_STRING(__LINE__) << ")" \ - << std::endl << " " << EI_PP_MAKE_STRING(a) << std::endl << std::endl; \ - abort(); \ - } } while (0) - -#define VERIFY_IS_APPROX(a, b) VERIFY(test_ei_isApprox(a, b)) -#define VERIFY_IS_NOT_APPROX(a, b) VERIFY(!test_ei_isApprox(a, b)) -#define VERIFY_IS_MUCH_SMALLER_THAN(a, b) VERIFY(test_ei_isMuchSmallerThan(a, b)) -#define VERIFY_IS_NOT_MUCH_SMALLER_THAN(a, b) VERIFY(!test_ei_isMuchSmallerThan(a, b)) -#define VERIFY_IS_APPROX_OR_LESS_THAN(a, b) VERIFY(test_ei_isApproxOrLessThan(a, b)) -#define VERIFY_IS_NOT_APPROX_OR_LESS_THAN(a, b) VERIFY(!test_ei_isApproxOrLessThan(a, b)) - -#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(); -template<> inline int test_precision<int>() { return 0; } -template<> inline float test_precision<float>() { return 1e-3f; } -template<> inline double test_precision<double>() { return 1e-6; } -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<long double>() { return 1e-6; } - -inline bool test_ei_isApprox(const int& a, const int& b) -{ return ei_isApprox(a, b, test_precision<int>()); } -inline bool test_ei_isMuchSmallerThan(const int& a, const int& b) -{ return ei_isMuchSmallerThan(a, b, test_precision<int>()); } -inline bool test_ei_isApproxOrLessThan(const int& a, const int& b) -{ return ei_isApproxOrLessThan(a, b, test_precision<int>()); } - -inline bool test_ei_isApprox(const float& a, const float& b) -{ return ei_isApprox(a, b, test_precision<float>()); } -inline bool test_ei_isMuchSmallerThan(const float& a, const float& b) -{ return ei_isMuchSmallerThan(a, b, test_precision<float>()); } -inline bool test_ei_isApproxOrLessThan(const float& a, const float& b) -{ return ei_isApproxOrLessThan(a, b, test_precision<float>()); } - -inline bool test_ei_isApprox(const double& a, const double& b) -{ return ei_isApprox(a, b, test_precision<double>()); } -inline bool test_ei_isMuchSmallerThan(const double& a, const double& b) -{ return ei_isMuchSmallerThan(a, b, test_precision<double>()); } -inline bool test_ei_isApproxOrLessThan(const double& a, const double& b) -{ return ei_isApproxOrLessThan(a, b, test_precision<double>()); } - -inline bool test_ei_isApprox(const std::complex<float>& a, const std::complex<float>& b) -{ return ei_isApprox(a, b, test_precision<std::complex<float> >()); } -inline bool test_ei_isMuchSmallerThan(const std::complex<float>& a, const std::complex<float>& b) -{ return ei_isMuchSmallerThan(a, b, test_precision<std::complex<float> >()); } - -inline bool test_ei_isApprox(const std::complex<double>& a, const std::complex<double>& b) -{ return ei_isApprox(a, b, test_precision<std::complex<double> >()); } -inline bool test_ei_isMuchSmallerThan(const std::complex<double>& a, const std::complex<double>& b) -{ return ei_isMuchSmallerThan(a, b, test_precision<std::complex<double> >()); } - -inline bool test_ei_isApprox(const long double& a, const long double& b) -{ return ei_isApprox(a, b, test_precision<long double>()); } -inline bool test_ei_isMuchSmallerThan(const long double& a, const long double& b) -{ return ei_isMuchSmallerThan(a, b, test_precision<long double>()); } -inline bool test_ei_isApproxOrLessThan(const long double& a, const long double& b) -{ return ei_isApproxOrLessThan(a, b, test_precision<long double>()); } - -template<typename Type1, typename Type2> -inline bool test_ei_isApprox(const Type1& a, const Type2& b) -{ - return a.isApprox(b, test_precision<typename Type1::Scalar>()); -} - -template<typename Derived1, typename Derived2> -inline bool test_ei_isMuchSmallerThan(const MatrixBase<Derived1>& m1, - const MatrixBase<Derived2>& m2) -{ - return m1.isMuchSmallerThan(m2, test_precision<typename ei_traits<Derived1>::Scalar>()); -} - -template<typename Derived> -inline bool test_ei_isMuchSmallerThan(const MatrixBase<Derived>& m, - const typename NumTraits<typename ei_traits<Derived>::Scalar>::Real& s) -{ - return m.isMuchSmallerThan(s, test_precision<typename ei_traits<Derived>::Scalar>()); -} - -} // 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 of the main test function -void EI_PP_CAT(test_,EIGEN_TEST_FUNC)(); - -using namespace Eigen; - -#ifdef EIGEN_TEST_PART_1 -#define CALL_SUBTEST_1(FUNC) CALL_SUBTEST(FUNC) -#else -#define CALL_SUBTEST_1(FUNC) -#endif - -#ifdef EIGEN_TEST_PART_2 -#define CALL_SUBTEST_2(FUNC) CALL_SUBTEST(FUNC) -#else -#define CALL_SUBTEST_2(FUNC) -#endif - -#ifdef EIGEN_TEST_PART_3 -#define CALL_SUBTEST_3(FUNC) CALL_SUBTEST(FUNC) -#else -#define CALL_SUBTEST_3(FUNC) -#endif - -#ifdef EIGEN_TEST_PART_4 -#define CALL_SUBTEST_4(FUNC) CALL_SUBTEST(FUNC) -#else -#define CALL_SUBTEST_4(FUNC) -#endif - -#ifdef EIGEN_TEST_PART_5 -#define CALL_SUBTEST_5(FUNC) CALL_SUBTEST(FUNC) -#else -#define CALL_SUBTEST_5(FUNC) -#endif - -#ifdef EIGEN_TEST_PART_6 -#define CALL_SUBTEST_6(FUNC) CALL_SUBTEST(FUNC) -#else -#define CALL_SUBTEST_6(FUNC) -#endif - -#ifdef EIGEN_TEST_PART_7 -#define CALL_SUBTEST_7(FUNC) CALL_SUBTEST(FUNC) -#else -#define CALL_SUBTEST_7(FUNC) -#endif - -#ifdef EIGEN_TEST_PART_8 -#define CALL_SUBTEST_8(FUNC) CALL_SUBTEST(FUNC) -#else -#define CALL_SUBTEST_8(FUNC) -#endif - -#ifdef EIGEN_TEST_PART_9 -#define CALL_SUBTEST_9(FUNC) CALL_SUBTEST(FUNC) -#else -#define CALL_SUBTEST_9(FUNC) -#endif - -#ifdef EIGEN_TEST_PART_10 -#define CALL_SUBTEST_10(FUNC) CALL_SUBTEST(FUNC) -#else -#define CALL_SUBTEST_10(FUNC) -#endif - -#ifdef EIGEN_TEST_PART_11 -#define CALL_SUBTEST_11(FUNC) CALL_SUBTEST(FUNC) -#else -#define CALL_SUBTEST_11(FUNC) -#endif - -#ifdef EIGEN_TEST_PART_12 -#define CALL_SUBTEST_12(FUNC) CALL_SUBTEST(FUNC) -#else -#define CALL_SUBTEST_12(FUNC) -#endif - -#ifdef EIGEN_TEST_PART_13 -#define CALL_SUBTEST_13(FUNC) CALL_SUBTEST(FUNC) -#else -#define CALL_SUBTEST_13(FUNC) -#endif - -#ifdef EIGEN_TEST_PART_14 -#define CALL_SUBTEST_14(FUNC) CALL_SUBTEST(FUNC) -#else -#define CALL_SUBTEST_14(FUNC) -#endif - -#ifdef EIGEN_TEST_PART_15 -#define CALL_SUBTEST_15(FUNC) CALL_SUBTEST(FUNC) -#else -#define CALL_SUBTEST_15(FUNC) -#endif - -#ifdef EIGEN_TEST_PART_16 -#define CALL_SUBTEST_16(FUNC) CALL_SUBTEST(FUNC) -#else -#define CALL_SUBTEST_16(FUNC) -#endif - - - -int main(int argc, char *argv[]) -{ - bool has_set_repeat = false; - bool has_set_seed = false; - bool need_help = false; - unsigned int seed = 0; - int repeat = DEFAULT_REPEAT; - - for(int i = 1; i < argc; i++) - { - if(argv[i][0] == 'r') - { - if(has_set_repeat) - { - std::cout << "Argument " << argv[i] << " conflicting with a former argument" << std::endl; - return 1; - } - repeat = std::atoi(argv[i]+1); - has_set_repeat = true; - if(repeat <= 0) - { - std::cout << "Invalid \'repeat\' value " << argv[i]+1 << std::endl; - return 1; - } - } - else if(argv[i][0] == 's') - { - if(has_set_seed) - { - std::cout << "Argument " << argv[i] << " conflicting with a former argument" << std::endl; - return 1; - } - seed = int(std::strtoul(argv[i]+1, 0, 10)); - has_set_seed = true; - bool ok = seed!=0; - if(!ok) - { - std::cout << "Invalid \'seed\' value " << argv[i]+1 << std::endl; - return 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; - return 1; - } - - if(!has_set_seed) seed = (unsigned int) std::time(NULL); - if(!has_set_repeat) repeat = DEFAULT_REPEAT; - - std::cout << "Initializing random number generator with seed " << seed << std::endl; - std::srand(seed); - std::cout << "Repeating each test " << repeat << " times" << std::endl; - - Eigen::g_repeat = repeat; - Eigen::g_test_stack.push_back(EI_PP_MAKE_STRING(EIGEN_TEST_FUNC)); - - EI_PP_CAT(test_,EIGEN_TEST_FUNC)(); - return 0; -} - - - diff --git a/test/eigen2/product.h b/test/eigen2/product.h deleted file mode 100644 index ae1b4bae4..000000000 --- a/test/eigen2/product.h +++ /dev/null @@ -1,129 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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/Array> -#include <Eigen/QR> - -template<typename Derived1, typename Derived2> -bool areNotApprox(const MatrixBase<Derived1>& m1, const MatrixBase<Derived2>& m2, typename Derived1::RealScalar epsilon = precision<typename Derived1::RealScalar>()) -{ - return !((m1-m2).cwise().abs2().maxCoeff() < epsilon * epsilon - * std::max(m1.cwise().abs2().maxCoeff(), m2.cwise().abs2().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 typename NumTraits<Scalar>::FloatingPoint FloatingPoint; - 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::Options^RowMajor> OtherMajorMatrixType; - - int rows = m.rows(); - int 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 = ei_random<Scalar>(); - - int r = ei_random<int>(0, rows-1), - c = ei_random<int>(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.lazy() * (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)); - - // again, test operator() to check const-qualification - s1 += (square.lazy() * m1)(r,c); - - // 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>::HasFloatingPoint && std::min(rows,cols)>1) - { - VERIFY(areNotApprox(m1.transpose()*m2,m2.transpose()*m1)); - } - - // test optimized operator+= path - res = square; - res += (m1 * m2.transpose()).lazy(); - VERIFY_IS_APPROX(res, square + m1 * m2.transpose()); - if (NumTraits<Scalar>::HasFloatingPoint && std::min(rows,cols)>1) - { - VERIFY(areNotApprox(res,square + m2 * m1.transpose())); - } - vcres = vc2; - vcres += (m1.transpose() * v1).lazy(); - VERIFY_IS_APPROX(vcres, vc2 + m1.transpose() * v1); - 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 += (m1.transpose() * m2).lazy(); - VERIFY_IS_APPROX(res2, square2 + m1.transpose() * m2); - - if (NumTraits<Scalar>::HasFloatingPoint && std::min(rows,cols)>1) - { - VERIFY(areNotApprox(res2,square2 + m2.transpose() * m1)); - } -} - diff --git a/test/eigen2/runtest.sh b/test/eigen2/runtest.sh deleted file mode 100755 index bc693af13..000000000 --- a/test/eigen2/runtest.sh +++ /dev/null @@ -1,28 +0,0 @@ -#!/bin/bash - -black='\E[30m' -red='\E[31m' -green='\E[32m' -yellow='\E[33m' -blue='\E[34m' -magenta='\E[35m' -cyan='\E[36m' -white='\E[37m' - -if make test_$1 > /dev/null 2> .runtest.log ; then - if ! ./test_$1 r20 > /dev/null 2> .runtest.log ; then - echo -e $red Test $1 failed: $black - echo -e $blue - cat .runtest.log - echo -e $black - exit 1 - else - echo -e $green Test $1 passed$black - fi -else - echo -e $red Build of target $1 failed: $black - echo -e $blue - cat .runtest.log - echo -e $black - exit 1 -fi diff --git a/test/eigen2/sparse.h b/test/eigen2/sparse.h deleted file mode 100644 index e12f89990..000000000 --- a/test/eigen2/sparse.h +++ /dev/null @@ -1,154 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@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_TESTSPARSE_H - -#include "main.h" - -#if EIGEN_GNUC_AT_LEAST(4,0) && !defined __ICC -#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> void -initSparse(double density, - Matrix<Scalar,Dynamic,Dynamic>& refMat, - SparseMatrix<Scalar>& sparseMat, - int flags = 0, - std::vector<Vector2i>* zeroCoords = 0, - std::vector<Vector2i>* nonzeroCoords = 0) -{ - sparseMat.startFill(int(refMat.rows()*refMat.cols()*density)); - for(int j=0; j<refMat.cols(); j++) - { - for(int i=0; i<refMat.rows(); i++) - { - Scalar v = (ei_random<double>(0,1) < density) ? ei_random<Scalar>() : Scalar(0); - if ((flags&ForceNonZeroDiag) && (i==j)) - { - v = ei_random<Scalar>()*Scalar(3.); - v = v*v + Scalar(5.); - } - if ((flags & MakeLowerTriangular) && j>i) - v = Scalar(0); - else if ((flags & MakeUpperTriangular) && j<i) - v = Scalar(0); - - if ((flags&ForceRealDiag) && (i==j)) - v = ei_real(v); - - if (v!=Scalar(0)) - { - sparseMat.fill(i,j) = v; - if (nonzeroCoords) - nonzeroCoords->push_back(Vector2i(i,j)); - } - else if (zeroCoords) - { - zeroCoords->push_back(Vector2i(i,j)); - } - refMat(i,j) = v; - } - } - sparseMat.endFill(); -} - -template<typename Scalar> void -initSparse(double density, - Matrix<Scalar,Dynamic,Dynamic>& refMat, - DynamicSparseMatrix<Scalar>& sparseMat, - int flags = 0, - std::vector<Vector2i>* zeroCoords = 0, - std::vector<Vector2i>* nonzeroCoords = 0) -{ - sparseMat.startFill(int(refMat.rows()*refMat.cols()*density)); - for(int j=0; j<refMat.cols(); j++) - { - for(int i=0; i<refMat.rows(); i++) - { - Scalar v = (ei_random<double>(0,1) < density) ? ei_random<Scalar>() : Scalar(0); - if ((flags&ForceNonZeroDiag) && (i==j)) - { - v = ei_random<Scalar>()*Scalar(3.); - v = v*v + Scalar(5.); - } - if ((flags & MakeLowerTriangular) && j>i) - v = Scalar(0); - else if ((flags & MakeUpperTriangular) && j<i) - v = Scalar(0); - - if ((flags&ForceRealDiag) && (i==j)) - v = ei_real(v); - - if (v!=Scalar(0)) - { - sparseMat.fill(i,j) = v; - if (nonzeroCoords) - nonzeroCoords->push_back(Vector2i(i,j)); - } - else if (zeroCoords) - { - zeroCoords->push_back(Vector2i(i,j)); - } - refMat(i,j) = v; - } - } - sparseMat.endFill(); -} - -template<typename Scalar> void -initSparse(double density, - Matrix<Scalar,Dynamic,1>& refVec, - SparseVector<Scalar>& 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 = (ei_random<double>(0,1) < density) ? ei_random<Scalar>() : Scalar(0); - if (v!=Scalar(0)) - { - sparseVec.fill(i) = v; - if (nonzeroCoords) - nonzeroCoords->push_back(i); - } - else if (zeroCoords) - zeroCoords->push_back(i); - refVec[i] = v; - } -} - -#endif // EIGEN_TESTSPARSE_H diff --git a/test/eigen2/testsuite.cmake b/test/eigen2/testsuite.cmake deleted file mode 100644 index 12b6bfa2e..000000000 --- a/test/eigen2/testsuite.cmake +++ /dev/null @@ -1,197 +0,0 @@ - -#################################################################### -# -# Usage: -# - create a new folder, let's call it cdash -# - in that folder, do: -# ctest -S path/to/eigen2/test/testsuite.cmake[,option1=value1[,option2=value2]] -# -# Options: -# - EIGEN_CXX: compiler, eg.: g++-4.2 -# default: default c++ compiler -# - EIGEN_SITE: eg, INRIA-Bdx_pc-gael, or the name of the contributor, etc. -# default: hostname -# - EIGEN_BUILD_STRING: a string which identify the system/compiler. It should be formed like that: -# <OS_name>-<OS_version>-<arch>-<compiler-version> -# with: -# <OS_name> = opensuse, debian, osx, windows, cygwin, freebsd, solaris, etc. -# <OS_version> = 11.1, XP, vista, leopard, etc. -# <arch> = i386, x86_64, ia64, powerpc, etc. -# <compiler-version> = gcc-4.3.2, icc-11.0, MSVC-2008, etc. -# - EIGEN_EXPLICIT_VECTORIZATION: novec, SSE2, Altivec -# default: SSE2 for x86_64 systems, novec otherwise -# Its value is automatically appended to EIGEN_BUILD_STRING -# - EIGEN_CMAKE_DIR: path to cmake executable -# - EIGEN_MODE: dashboard model, can be Experimental, Nightly, or Continuous -# default: Nightly -# - EIGEN_WORK_DIR: directory used to download the source files and make the builds -# default: folder which contains this script -# - EIGEN_CMAKE_ARGS: additional arguments passed to cmake -# - CTEST_SOURCE_DIRECTORY: path to eigen's src (use a new and empty folder, not the one you are working on) -# default: <EIGEN_WORK_DIR>/src -# - CTEST_BINARY_DIRECTORY: build directory -# default: <EIGEN_WORK_DIR>/nightly-<EIGEN_CXX> -# -# Here is an example running several compilers on a linux system: -# #!/bin/bash -# ARCH=`uname -m` -# SITE=`hostname` -# VERSION=opensuse-11.1 -# WORK_DIR=/home/gael/Coding/eigen2/cdash -# # get the last version of the script -# wget http://bitbucket.org/eigen/eigen/raw/tip/test/testsuite.cmake -o $WORK_DIR/testsuite.cmake -# COMMON="ctest -S $WORK_DIR/testsuite.cmake,EIGEN_WORK_DIR=$WORK_DIR,EIGEN_SITE=$SITE,EIGEN_MODE=$1,EIGEN_BUILD_STRING=$OS_VERSION-$ARCH" -# $COMMON-gcc-3.4.6,EIGEN_CXX=g++-3.4 -# $COMMON-gcc-4.0.1,EIGEN_CXX=g++-4.0.1 -# $COMMON-gcc-4.3.2,EIGEN_CXX=g++-4.3,EIGEN_EXPLICIT_VECTORIZATION=novec -# $COMMON-gcc-4.3.2,EIGEN_CXX=g++-4.3,EIGEN_EXPLICIT_VECTORIZATION=SSE2 -# $COMMON-icc-11.0,EIGEN_CXX=icpc -# -#################################################################### - -# process the arguments - -set(ARGLIST ${CTEST_SCRIPT_ARG}) -while(${ARGLIST} MATCHES ".+.*") - - # pick first - string(REGEX MATCH "([^,]*)(,.*)?" DUMMY ${ARGLIST}) - SET(TOP ${CMAKE_MATCH_1}) - - # remove first - string(REGEX MATCHALL "[^,]*,(.*)" DUMMY ${ARGLIST}) - SET(ARGLIST ${CMAKE_MATCH_1}) - - # decompose as a pair key=value - string(REGEX MATCH "([^=]*)(=.*)?" DUMMY ${TOP}) - SET(KEY ${CMAKE_MATCH_1}) - - string(REGEX MATCH "[^=]*=(.*)" DUMMY ${TOP}) - SET(VALUE ${CMAKE_MATCH_1}) - - # set the variable to the specified value - if(VALUE) - SET(${KEY} ${VALUE}) - else(VALUE) - SET(${KEY} ON) - endif(VALUE) - -endwhile(${ARGLIST} MATCHES ".+.*") - -#################################################################### -# Automatically set some user variables if they have not been defined manually -#################################################################### -cmake_minimum_required(VERSION 2.6 FATAL_ERROR) - -if(NOT EIGEN_SITE) - site_name(EIGEN_SITE) -endif(NOT EIGEN_SITE) - -if(NOT EIGEN_CMAKE_DIR) - SET(EIGEN_CMAKE_DIR "") -endif(NOT EIGEN_CMAKE_DIR) - -if(NOT EIGEN_BUILD_STRING) - - # let's try to find all information we need to make the build string ourself - - # OS - build_name(EIGEN_OS_VERSION) - - # arch - set(EIGEN_ARCH ${CMAKE_SYSTEM_PROCESSOR}) - if(WIN32) - set(EIGEN_ARCH $ENV{PROCESSOR_ARCHITECTURE}) - else(WIN32) - execute_process(COMMAND uname -m OUTPUT_VARIABLE EIGEN_ARCH OUTPUT_STRIP_TRAILING_WHITESPACE) - endif(WIN32) - - set(EIGEN_BUILD_STRING ${EIGEN_OS_VERSION}${EIGEN_ARCH}-${EIGEN_CXX}) - -endif(NOT EIGEN_BUILD_STRING) - -if(DEFINED EIGEN_EXPLICIT_VECTORIZATION) - set(EIGEN_BUILD_STRING ${EIGEN_BUILD_STRING}-${EIGEN_EXPLICIT_VECTORIZATION}) -endif(DEFINED EIGEN_EXPLICIT_VECTORIZATION) - -if(NOT EIGEN_WORK_DIR) - set(EIGEN_WORK_DIR ${CTEST_SCRIPT_DIRECTORY}) -endif(NOT EIGEN_WORK_DIR) - -if(NOT CTEST_SOURCE_DIRECTORY) - SET (CTEST_SOURCE_DIRECTORY "${EIGEN_WORK_DIR}/src") -endif(NOT CTEST_SOURCE_DIRECTORY) - -if(NOT CTEST_BINARY_DIRECTORY) - SET (CTEST_BINARY_DIRECTORY "${EIGEN_WORK_DIR}/nightly_${EIGEN_CXX}") -endif(NOT CTEST_BINARY_DIRECTORY) - -if(NOT EIGEN_MODE) - set(EIGEN_MODE Nightly) -endif(NOT EIGEN_MODE) - -## mandatory variables (the default should be ok in most cases): - -SET (CTEST_CVS_COMMAND "hg") -SET (CTEST_CVS_CHECKOUT "${CTEST_CVS_COMMAND} clone -r 2.0 http://bitbucket.org/eigen/eigen \"${CTEST_SOURCE_DIRECTORY}\"") - -# which ctest command to use for running the dashboard -SET (CTEST_COMMAND "${EIGEN_CMAKE_DIR}ctest -D ${EIGEN_MODE}") - -# what cmake command to use for configuring this dashboard -SET (CTEST_CMAKE_COMMAND "${EIGEN_CMAKE_DIR}cmake -DEIGEN_BUILD_TESTS=on ") - -#################################################################### -# The values in this section are optional you can either -# have them or leave them commented out -#################################################################### - -# this make sure we get consistent outputs -SET($ENV{LC_MESSAGES} "en_EN") - -# should ctest wipe the binary tree before running -SET(CTEST_START_WITH_EMPTY_BINARY_DIRECTORY TRUE) -SET(CTEST_BACKUP_AND_RESTORE TRUE) - -# this is the initial cache to use for the binary tree, be careful to escape -# any quotes inside of this string if you use it -if(WIN32 AND NOT UNIX) - #message(SEND_ERROR "win32") - set(CTEST_CMAKE_COMMAND "${CTEST_CMAKE_COMMAND} -G \"NMake Makefiles\" -DCMAKE_MAKE_PROGRAM=nmake") - SET (CTEST_INITIAL_CACHE " - MAKECOMMAND:STRING=nmake -i - CMAKE_MAKE_PROGRAM:FILEPATH=nmake - CMAKE_GENERATOR:INTERNAL=NMake Makefiles - BUILDNAME:STRING=${EIGEN_BUILD_STRING} - SITE:STRING=${EIGEN_SITE} - ") -else(WIN32 AND NOT UNIX) - SET (CTEST_INITIAL_CACHE " - BUILDNAME:STRING=${EIGEN_BUILD_STRING} - SITE:STRING=${EIGEN_SITE} - ") -endif(WIN32 AND NOT UNIX) - -# set any extra environment variables to use during the execution of the script here: - -if(EIGEN_CXX) - set(CTEST_ENVIRONMENT "CXX=${EIGEN_CXX}") -endif(EIGEN_CXX) - -if(DEFINED EIGEN_EXPLICIT_VECTORIZATION) - if(EIGEN_EXPLICIT_VECTORIZATION MATCHES SSE2) - set(CTEST_CMAKE_COMMAND "${CTEST_CMAKE_COMMAND} -DEIGEN_TEST_SSE2=ON") - elseif(EIGEN_EXPLICIT_VECTORIZATION MATCHES SSE3) - set(CTEST_CMAKE_COMMAND "${CTEST_CMAKE_COMMAND} -DEIGEN_TEST_SSE2=ON -DEIGEN_TEST_SSE3=ON") - elseif(EIGEN_EXPLICIT_VECTORIZATION MATCHES Altivec) - set(CTEST_CMAKE_COMMAND "${CTEST_CMAKE_COMMAND} -DEIGEN_TEST_ALTIVEC=ON") - elseif(EIGEN_EXPLICIT_VECTORIZATION MATCHES novec) - set(CTEST_CMAKE_COMMAND "${CTEST_CMAKE_COMMAND} -DEIGEN_TEST_NO_EXPLICIT_VECTORIZATION=ON") - else(EIGEN_EXPLICIT_VECTORIZATION MATCHES SSE2) - message(FATAL_ERROR "Invalid value for EIGEN_EXPLICIT_VECTORIZATION (${EIGEN_EXPLICIT_VECTORIZATION}), must be: novec, SSE2, SSE3, Altivec") - endif(EIGEN_EXPLICIT_VECTORIZATION MATCHES SSE2) -endif(DEFINED EIGEN_EXPLICIT_VECTORIZATION) - -if(DEFINED EIGEN_CMAKE_ARGS) - set(CTEST_CMAKE_COMMAND "${CTEST_CMAKE_COMMAND} ${EIGEN_CMAKE_ARGS}") -endif(DEFINED EIGEN_CMAKE_ARGS) diff --git a/test/eigen2support.cpp b/test/eigen2support.cpp index 1fa49a8c8..ad1d98091 100644 --- a/test/eigen2support.cpp +++ b/test/eigen2support.cpp @@ -8,7 +8,6 @@ // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. #define EIGEN2_SUPPORT -#define EIGEN_NO_EIGEN2_DEPRECATED_WARNING #include "main.h" diff --git a/test/eigensolver_complex.cpp b/test/eigensolver_complex.cpp index c9d8c0877..293b1b265 100644 --- a/test/eigensolver_complex.cpp +++ b/test/eigensolver_complex.cpp @@ -13,20 +13,59 @@ #include <Eigen/Eigenvalues> #include <Eigen/LU> -/* Check that two column vectors are approximately equal upto permutations, - by checking that the k-th power sums are equal for k = 1, ..., vec1.rows() */ +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 NumTraits<typename VectorType::Scalar>::Real RealScalar; + typedef typename VectorType::Scalar Scalar; + typedef typename NumTraits<Scalar>::Real RealScalar; VERIFY(vec1.cols() == 1); VERIFY(vec2.cols() == 1); VERIFY(vec1.rows() == vec2.rows()); - for (int k = 1; k <= vec1.rows(); ++k) - { - VERIFY_IS_APPROX(vec1.array().pow(RealScalar(k)).sum(), vec2.array().pow(RealScalar(k)).sum()); - } + + 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) ); } @@ -79,13 +118,28 @@ template<typename MatrixType> void eigensolver(const MatrixType& m) MatrixType id = MatrixType::Identity(rows, cols); VERIFY_IS_APPROX(id.operatorNorm(), RealScalar(1)); - if (rows > 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) @@ -108,6 +162,7 @@ void test_eigensolver_complex() 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); diff --git a/test/eigensolver_generalized_real.cpp b/test/eigensolver_generalized_real.cpp index 566a4bdc6..9c0838ba4 100644 --- a/test/eigensolver_generalized_real.cpp +++ b/test/eigensolver_generalized_real.cpp @@ -1,15 +1,17 @@ // This file is part of Eigen, a lightweight C++ template library // for linear algebra. // -// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr> +// 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) { @@ -21,6 +23,7 @@ template<typename MatrixType> void generalized_eigensolver_real(const MatrixType 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); @@ -31,14 +34,49 @@ template<typename MatrixType> void generalized_eigensolver_real(const MatrixType MatrixType spdB = b.adjoint() * b + b1.adjoint() * b1; // lets compare to GeneralizedSelfAdjointEigenSolver - GeneralizedSelfAdjointEigenSolver<MatrixType> symmEig(spdA, spdB); - GeneralizedEigenSolver<MatrixType> eig(spdA, spdB); + { + GeneralizedSelfAdjointEigenSolver<MatrixType> symmEig(spdA, spdB); + GeneralizedEigenSolver<MatrixType> eig(spdA, spdB); - VERIFY_IS_EQUAL(eig.eigenvalues().imag().cwiseAbs().maxCoeff(), 0); + 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()); + 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); + } } void test_eigensolver_generalized_real() @@ -49,7 +87,7 @@ void test_eigensolver_generalized_real() s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4); CALL_SUBTEST_2( generalized_eigensolver_real(MatrixXd(s,s)) ); - // some trivial but implementation-wise tricky cases + // 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>()) ); diff --git a/test/eigensolver_generic.cpp b/test/eigensolver_generic.cpp index 005af81eb..d0e644d4b 100644 --- a/test/eigensolver_generic.cpp +++ b/test/eigensolver_generic.cpp @@ -63,13 +63,28 @@ template<typename MatrixType> void eigensolver(const MatrixType& m) MatrixType id = MatrixType::Identity(rows, cols); VERIFY_IS_APPROX(id.operatorNorm(), RealScalar(1)); - if (rows > 2) + 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) @@ -93,6 +108,7 @@ void test_eigensolver_generic() 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)) ); @@ -114,12 +130,37 @@ void test_eigensolver_generic() CALL_SUBTEST_2( { MatrixXd A(1,1); - A(0,0) = std::sqrt(-1.); + A(0,0) = std::sqrt(-1.); // is Not-a-Number Eigen::EigenSolver<MatrixXd> solver(A); - MatrixXd V(1, 1); - V(0,0) = solver.eigenvectors()(0,0).real(); + 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/test/eigensolver_selfadjoint.cpp b/test/eigensolver_selfadjoint.cpp index 38689cfbf..39ad4130e 100644 --- a/test/eigensolver_selfadjoint.cpp +++ b/test/eigensolver_selfadjoint.cpp @@ -9,8 +9,62 @@ // 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) { @@ -31,17 +85,8 @@ template<typename MatrixType> void selfadjointeigensolver(const MatrixType& m) MatrixType symmA = a.adjoint() * a + a1.adjoint() * a1; MatrixType symmC = symmA; - // randomly nullify some rows/columns - { - Index count = 1;//internal::random<Index>(-cols,cols); - for(Index k=0; k<count; ++k) - { - Index i = internal::random<Index>(0,cols-1); - symmA.row(i).setZero(); - symmA.col(i).setZero(); - } - } - + svd_fill_random(symmA,Symmetric); + symmA.template triangularView<StrictlyUpper>().setZero(); symmC.template triangularView<StrictlyUpper>().setZero(); @@ -49,23 +94,13 @@ template<typename MatrixType> void selfadjointeigensolver(const MatrixType& m) 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); - SelfAdjointEigenSolver<MatrixType> eiDirect; - eiDirect.computeDirect(symmA); // generalized eigen pb GeneralizedSelfAdjointEigenSolver<MatrixType> eiSymmGen(symmC, symmB); - VERIFY_IS_EQUAL(eiSymm.info(), Success); - VERIFY((symmA.template selfadjointView<Lower>() * eiSymm.eigenvectors()).isApprox( - eiSymm.eigenvectors() * eiSymm.eigenvalues().asDiagonal(), largerEps)); - VERIFY_IS_APPROX(symmA.template selfadjointView<Lower>().eigenvalues(), eiSymm.eigenvalues()); - - VERIFY_IS_EQUAL(eiDirect.info(), Success); - VERIFY((symmA.template selfadjointView<Lower>() * eiDirect.eigenvectors()).isApprox( - eiDirect.eigenvectors() * eiDirect.eigenvalues().asDiagonal(), largerEps)); - VERIFY_IS_APPROX(symmA.template selfadjointView<Lower>().eigenvalues(), eiDirect.eigenvalues()); - SelfAdjointEigenSolver<MatrixType> eiSymmNoEivecs(symmA, false); VERIFY_IS_EQUAL(eiSymmNoEivecs.info(), Success); VERIFY_IS_APPROX(eiSymm.eigenvalues(), eiSymmNoEivecs.eigenvalues()); @@ -111,37 +146,111 @@ template<typename MatrixType> void selfadjointeigensolver(const MatrixType& m) // test Tridiagonalization's methods Tridiagonalization<MatrixType> tridiag(symmC); - // FIXME tridiag.matrixQ().adjoint() does not work + 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()); - if (rows > 1) + // 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_1( selfadjointeigensolver(Matrix2f()) ); - CALL_SUBTEST_1( selfadjointeigensolver(Matrix2d()) ); - CALL_SUBTEST_1( selfadjointeigensolver(Matrix3f()) ); - CALL_SUBTEST_1( selfadjointeigensolver(Matrix3d()) ); + 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)) ); - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4); CALL_SUBTEST_4( selfadjointeigensolver(MatrixXd(s,s)) ); - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4); CALL_SUBTEST_5( selfadjointeigensolver(MatrixXcd(s,s)) ); - - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4); 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)) ); @@ -149,6 +258,11 @@ void test_eigensolver_selfadjoint() 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); diff --git a/test/evaluator_common.h b/test/evaluator_common.h new file mode 100644 index 000000000..e69de29bb --- /dev/null +++ b/test/evaluator_common.h diff --git a/test/evaluators.cpp b/test/evaluators.cpp new file mode 100644 index 000000000..aed5a05a7 --- /dev/null +++ b/test/evaluators.cpp @@ -0,0 +1,499 @@ + +#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/test/fastmath.cpp b/test/fastmath.cpp new file mode 100644 index 000000000..cc5db0746 --- /dev/null +++ b/test/fastmath.cpp @@ -0,0 +1,99 @@ +// 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/test/first_aligned.cpp b/test/first_aligned.cpp index 467f94510..ae2d4bc42 100644 --- a/test/first_aligned.cpp +++ b/test/first_aligned.cpp @@ -13,7 +13,7 @@ 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_aligned(array, size)) % packet_size) == 0); + VERIFY(((size_t(array) + sizeof(Scalar) * internal::first_default_aligned(array, size)) % packet_size) == 0); } template<typename Scalar> @@ -21,7 +21,7 @@ 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_aligned(array, size) == size); + VERIFY(internal::packet_traits<Scalar>::size == 1 || internal::first_default_aligned(array, size) == size); } struct some_non_vectorizable_type { float x; }; @@ -41,7 +41,7 @@ void test_first_aligned() test_first_aligned_helper(array_double+1, 50); test_first_aligned_helper(array_double+2, 50); - double *array_double_plus_4_bytes = (double*)(size_t(array_double)+4); + 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); diff --git a/test/geo_alignedbox.cpp b/test/geo_alignedbox.cpp index 8e36adbe3..d2339a651 100644 --- a/test/geo_alignedbox.cpp +++ b/test/geo_alignedbox.cpp @@ -16,7 +16,7 @@ using namespace std; template<typename T> EIGEN_DONT_INLINE -void kill_extra_precision(T& x) { eigen_assert(&x != 0); } +void kill_extra_precision(T& x) { eigen_assert((void*)(&x) != (void*)0); } template<typename BoxType> void alignedbox(const BoxType& _box) @@ -48,12 +48,21 @@ template<typename BoxType> void alignedbox(const BoxType& _box) 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); @@ -172,6 +181,8 @@ void test_geo_alignedbox() 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/test/geo_eulerangles.cpp b/test/geo_eulerangles.cpp index b4830bd41..932ebe773 100644 --- a/test/geo_eulerangles.cpp +++ b/test/geo_eulerangles.cpp @@ -26,16 +26,16 @@ void verify_euler(const Matrix<Scalar,3,1>& ea, int i, int j, int 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(M_PI/2),test_precision<Scalar>())) ) + 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(M_PI)); - VERIFY_IS_APPROX_OR_LESS_THAN(-Scalar(M_PI), eabis[1]); - VERIFY_IS_APPROX_OR_LESS_THAN(eabis[1], Scalar(M_PI)); - VERIFY_IS_APPROX_OR_LESS_THAN(-Scalar(M_PI), eabis[2]); - VERIFY_IS_APPROX_OR_LESS_THAN(eabis[2], Scalar(M_PI)); + 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) @@ -64,7 +64,7 @@ template<typename Scalar> void eulerangles() typedef Quaternion<Scalar> Quaternionx; typedef AngleAxis<Scalar> AngleAxisx; - Scalar a = internal::random<Scalar>(-Scalar(M_PI), Scalar(M_PI)); + Scalar a = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI)); Quaternionx q1; q1 = AngleAxisx(a, Vector3::Random().normalized()); Matrix3 m; @@ -84,13 +84,13 @@ template<typename Scalar> void eulerangles() 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(M_PI)*Array3(0.5,1,1); + 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(M_PI)); + ea[2] = ea[0] = internal::random<Scalar>(0,Scalar(EIGEN_PI)); check_all_var(ea); - ea[0] = ea[1] = internal::random<Scalar>(0,Scalar(M_PI)); + ea[0] = ea[1] = internal::random<Scalar>(0,Scalar(EIGEN_PI)); check_all_var(ea); ea[1] = 0; diff --git a/test/geo_homogeneous.cpp b/test/geo_homogeneous.cpp index c91bde819..2187c7bf9 100644 --- a/test/geo_homogeneous.cpp +++ b/test/geo_homogeneous.cpp @@ -38,6 +38,10 @@ template<typename Scalar,int Size> void homogeneous(void) 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); @@ -54,10 +58,11 @@ template<typename Scalar,int Size> void homogeneous(void) 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); - m0.transpose().rowwise().homogeneous().eval(); VERIFY_IS_APPROX((m0.transpose().rowwise().homogeneous().eval()) * t2, m0.transpose().rowwise().homogeneous() * t2); @@ -82,7 +87,7 @@ template<typename Scalar,int Size> void homogeneous(void) 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); @@ -91,6 +96,23 @@ template<typename Scalar,int Size> void homogeneous(void) 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() diff --git a/test/geo_hyperplane.cpp b/test/geo_hyperplane.cpp index 327537801..27892850d 100644 --- a/test/geo_hyperplane.cpp +++ b/test/geo_hyperplane.cpp @@ -18,10 +18,12 @@ template<typename HyperplaneType> void hyperplane(const HyperplaneType& _plane) /* this test covers the following files: Hyperplane.h */ + using std::abs; typedef typename HyperplaneType::Index Index; 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; @@ -42,7 +44,10 @@ template<typename HyperplaneType> void hyperplane(const HyperplaneType& _plane) VERIFY_IS_APPROX( n1.dot(n1), Scalar(1) ); VERIFY_IS_MUCH_SMALLER_THAN( pl0.absDistance(p0), Scalar(1) ); - VERIFY_IS_APPROX( pl1.signedDistance(p1 + n1 * s0), s0 ); + 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) ); @@ -52,6 +57,8 @@ template<typename HyperplaneType> void hyperplane(const HyperplaneType& _plane) 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) ); @@ -59,12 +66,15 @@ template<typename HyperplaneType> void hyperplane(const HyperplaneType& _plane) 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) ); + .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 @@ -90,9 +100,9 @@ template<typename Scalar> void lines() Vector u = Vector::Random(); Vector v = Vector::Random(); Scalar a = internal::random<Scalar>(); - while (abs(a-1) < 1e-4) a = internal::random<Scalar>(); - while (u.norm() < 1e-4) u = Vector::Random(); - while (v.norm() < 1e-4) v = Vector::Random(); + 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); @@ -104,12 +114,15 @@ template<typename Scalar> void lines() Vector result = line_u.intersection(line_v); // the lines should intersect at the point we called "center" - VERIFY_IS_APPROX(result, 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 - CoeffsType converted_coeffs = HLine(pl).coeffs(); - converted_coeffs *= (line_u.coeffs()[0])/(converted_coeffs[0]); + 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)); } } @@ -145,9 +158,9 @@ template<typename Scalar> void hyperplane_alignment() typedef Hyperplane<Scalar,3,AutoAlign> Plane3a; typedef Hyperplane<Scalar,3,DontAlign> Plane3u; - EIGEN_ALIGN16 Scalar array1[4]; - EIGEN_ALIGN16 Scalar array2[4]; - EIGEN_ALIGN16 Scalar array3[4+1]; + 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; @@ -161,8 +174,8 @@ template<typename Scalar> void hyperplane_alignment() VERIFY_IS_APPROX(p1->coeffs(), p2->coeffs()); VERIFY_IS_APPROX(p1->coeffs(), p3->coeffs()); - #if defined(EIGEN_VECTORIZE) && EIGEN_ALIGN_STATICALLY - if(internal::packet_traits<Scalar>::Vectorizable) + #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 } diff --git a/test/geo_orthomethods.cpp b/test/geo_orthomethods.cpp index c836dae40..e178df257 100644 --- a/test/geo_orthomethods.cpp +++ b/test/geo_orthomethods.cpp @@ -33,12 +33,16 @@ template<typename Scalar> void orthomethods_3() 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(); @@ -47,6 +51,13 @@ template<typename Scalar> void orthomethods_3() 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)); @@ -57,6 +68,7 @@ template<typename Scalar> void orthomethods_3() 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; diff --git a/test/geo_parametrizedline.cpp b/test/geo_parametrizedline.cpp index f0462d40a..9bf5f3c1d 100644 --- a/test/geo_parametrizedline.cpp +++ b/test/geo_parametrizedline.cpp @@ -66,9 +66,9 @@ template<typename Scalar> void parametrizedline_alignment() typedef ParametrizedLine<Scalar,4,AutoAlign> Line4a; typedef ParametrizedLine<Scalar,4,DontAlign> Line4u; - EIGEN_ALIGN16 Scalar array1[8]; - EIGEN_ALIGN16 Scalar array2[8]; - EIGEN_ALIGN16 Scalar array3[8+1]; + 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; @@ -85,8 +85,8 @@ template<typename Scalar> void parametrizedline_alignment() VERIFY_IS_APPROX(p1->direction(), p2->direction()); VERIFY_IS_APPROX(p1->direction(), p3->direction()); - #if defined(EIGEN_VECTORIZE) && EIGEN_ALIGN_STATICALLY - if(internal::packet_traits<Scalar>::Vectorizable) + #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 } diff --git a/test/geo_quaternion.cpp b/test/geo_quaternion.cpp index 1694b32c7..96889e722 100644 --- a/test/geo_quaternion.cpp +++ b/test/geo_quaternion.cpp @@ -30,8 +30,8 @@ template<typename QuatType> void check_slerp(const QuatType& q0, const QuatType& Scalar largeEps = test_precision<Scalar>(); Scalar theta_tot = AA(q1*q0.inverse()).angle(); - if(theta_tot>M_PI) - theta_tot = Scalar(2.*M_PI)-theta_tot; + 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); @@ -49,13 +49,13 @@ template<typename Scalar, int Options> void quaternion(void) */ using std::abs; typedef Matrix<Scalar,3,1> Vector3; - typedef Matrix<Scalar,4,1> Vector4; + 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 = 1e-3f; + largeEps = Scalar(1e-3); Scalar eps = internal::random<Scalar>() * Scalar(1e-2); @@ -64,8 +64,8 @@ template<typename Scalar, int Options> void quaternion(void) v2 = Vector3::Random(), v3 = Vector3::Random(); - Scalar a = internal::random<Scalar>(-Scalar(M_PI), Scalar(M_PI)), - b = internal::random<Scalar>(-Scalar(M_PI), Scalar(M_PI)); + 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; @@ -82,8 +82,8 @@ template<typename Scalar, int Options> void quaternion(void) // angular distance Scalar refangle = abs(AngleAxisx(q1.inverse()*q2).angle()); - if (refangle>Scalar(M_PI)) - refangle = Scalar(2)*Scalar(M_PI) - refangle; + if (refangle>Scalar(EIGEN_PI)) + refangle = Scalar(2)*Scalar(EIGEN_PI) - refangle; if((q1.coeffs()-q2.coeffs()).norm() > 10*largeEps) { @@ -101,6 +101,11 @@ template<typename Scalar, int Options> void quaternion(void) 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); @@ -109,8 +114,8 @@ template<typename Scalar, int Options> void quaternion(void) // 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() < 1.99 - && (aa.axis() + v1.normalized()).norm() < 1.99) + && (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); } @@ -151,19 +156,19 @@ template<typename Scalar, int Options> void quaternion(void) Quaternionx *q = new Quaternionx; delete q; - q1 = AngleAxisx(a, v0.normalized()); - q2 = AngleAxisx(b, v1.normalized()); + q1 = Quaternionx::UnitRandom(); + q2 = Quaternionx::UnitRandom(); check_slerp(q1,q2); q1 = AngleAxisx(b, v1.normalized()); - q2 = AngleAxisx(b+Scalar(M_PI), 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.coeffs() = Vector4::Random().normalized(); + q1 = Quaternionx::UnitRandom(); q2.coeffs() = -q1.coeffs(); check_slerp(q1,q2); } @@ -179,11 +184,11 @@ template<typename Scalar> void mapQuaternion(void){ Vector3 v0 = Vector3::Random(), v1 = Vector3::Random(); - Scalar a = internal::random<Scalar>(-Scalar(M_PI), Scalar(M_PI)); + Scalar a = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI)); - EIGEN_ALIGN16 Scalar array1[4]; - EIGEN_ALIGN16 Scalar array2[4]; - EIGEN_ALIGN16 Scalar array3[4+1]; + 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); @@ -232,9 +237,9 @@ template<typename Scalar> void quaternionAlignment(void){ typedef Quaternion<Scalar,AutoAlign> QuaternionA; typedef Quaternion<Scalar,DontAlign> QuaternionUA; - EIGEN_ALIGN16 Scalar array1[4]; - EIGEN_ALIGN16 Scalar array2[4]; - EIGEN_ALIGN16 Scalar array3[4+1]; + 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; @@ -247,8 +252,8 @@ template<typename Scalar> void quaternionAlignment(void){ VERIFY_IS_APPROX(q1->coeffs(), q2->coeffs()); VERIFY_IS_APPROX(q1->coeffs(), q3->coeffs()); - #if defined(EIGEN_VECTORIZE) && EIGEN_ALIGN_STATICALLY - if(internal::packet_traits<Scalar>::Vectorizable) + #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 } diff --git a/test/geo_transformations.cpp b/test/geo_transformations.cpp index 4ad3793d8..278e527c2 100644..100755 --- a/test/geo_transformations.cpp +++ b/test/geo_transformations.cpp @@ -12,6 +12,17 @@ #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: @@ -29,7 +40,7 @@ template<typename Scalar, int Mode, int Options> void non_projective_only() Transform3 t0, t1, t2; - Scalar a = internal::random<Scalar>(-Scalar(M_PI), Scalar(M_PI)); + Scalar a = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI)); Quaternionx q1, q2; @@ -97,16 +108,14 @@ template<typename Scalar, int Mode, int Options> void transformations() v1 = Vector3::Random(); Matrix3 matrot1, m; - Scalar a = internal::random<Scalar>(-Scalar(M_PI), Scalar(M_PI)); - Scalar s0 = internal::random<Scalar>(), - s1 = internal::random<Scalar>(); + 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(M_PI), v0.unitOrthogonal()) * 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)); @@ -132,14 +141,16 @@ template<typename Scalar, int Mode, int Options> void transformations() AngleAxisx aa = AngleAxisx(q1); VERIFY_IS_APPROX(q1 * v1, Quaternionx(aa) * v1); - if(abs(aa.angle()) > NumTraits<Scalar>::dummy_precision()) + // 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); - if(abs(aa.angle()) > NumTraits<Scalar>::dummy_precision()) + // 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) ); } @@ -158,7 +169,7 @@ template<typename Scalar, int Mode, int Options> void transformations() // TODO complete the tests ! a = 0; while (abs(a)<Scalar(0.1)) - a = internal::random<Scalar>(-Scalar(0.4)*Scalar(M_PI), Scalar(0.4)*Scalar(M_PI)); + 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; @@ -204,7 +215,7 @@ template<typename Scalar, int Mode, int Options> void transformations() tmat4.matrix()(3,3) = Scalar(1); VERIFY_IS_APPROX(tmat3.matrix(), tmat4.matrix()); - Scalar a3 = internal::random<Scalar>(-Scalar(M_PI), Scalar(M_PI)); + Scalar a3 = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI)); Vector3 v3 = Vector3::Random().normalized(); AngleAxisx aa3(a3, v3); Transform3 t3(aa3); @@ -216,12 +227,15 @@ template<typename Scalar, int Mode, int Options> void transformations() t4 *= aa3; VERIFY_IS_APPROX(t3.matrix(), t4.matrix()); - v3 = Vector3::Random(); + 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); + t4.translate((-v3).eval()); VERIFY_IS_APPROX(t4.matrix(), MatrixType::Identity()); t4 *= tv3; VERIFY_IS_APPROX(t5.matrix(), t4.matrix()); @@ -413,12 +427,28 @@ template<typename Scalar, int Mode, int Options> void transformations() VERIFY_IS_APPROX(r2d1f.template cast<Scalar>(),r2d1); Rotation2D<double> r2d1d = r2d1.template cast<double>(); VERIFY_IS_APPROX(r2d1d.template cast<Scalar>(),r2d1); - - t20 = Translation2(v20) * (Rotation2D<Scalar>(s0) * Eigen::Scaling(s0)); - t21 = Translation2(v20) * Rotation2D<Scalar>(s0) * Eigen::Scaling(s0); - VERIFY_IS_APPROX(t20,t21); + 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); @@ -428,9 +458,24 @@ template<typename Scalar, int Mode, int Options> void transformations() VERIFY_IS_APPROX(t20,t21); VERIFY_IS_APPROX(s0, (R0.slerp(0, R1)).angle()); - VERIFY_IS_APPROX(s1, (R0.slerp(1, R1)).angle()); - VERIFY_IS_APPROX(s0, (R0.slerp(0.5, R0)).angle()); - VERIFY_IS_APPROX(Scalar(0), (R0.slerp(0.5, R0.inverse())).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 { @@ -520,9 +565,9 @@ template<typename Scalar> void transform_alignment() typedef Transform<Scalar,3,Projective,AutoAlign> Projective3a; typedef Transform<Scalar,3,Projective,DontAlign> Projective3u; - EIGEN_ALIGN16 Scalar array1[16]; - EIGEN_ALIGN16 Scalar array2[16]; - EIGEN_ALIGN16 Scalar array3[16+1]; + 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; @@ -538,7 +583,7 @@ template<typename Scalar> void transform_alignment() VERIFY_IS_APPROX( (*p1) * (*p1), (*p2)*(*p3)); - #if defined(EIGEN_VECTORIZE) && EIGEN_ALIGN_STATICALLY + #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 diff --git a/test/half_float.cpp b/test/half_float.cpp new file mode 100644 index 000000000..f8d438e2f --- /dev/null +++ b/test/half_float.cpp @@ -0,0 +1,252 @@ +// 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> + +// 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; + + // 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(0x3c00))); + float val2 = float(half(__half(0x3c01))); + float val3 = float(half(__half(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(0x0000))), 0.0f); + VERIFY_IS_EQUAL(float(half(__half(0x3c00))), 1.0f); + + // Denormals. + VERIFY_IS_APPROX(float(half(__half(0x8001))), -5.96046e-08f); + VERIFY_IS_APPROX(float(half(__half(0x0001))), 5.96046e-08f); + VERIFY_IS_APPROX(float(half(__half(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(0xfc00))))); + VERIFY((numext::isnan)(float(half(__half(0xfc01))))); + VERIFY((numext::isinf)(float(half(__half(0x7c00))))); + VERIFY((numext::isnan)(float(half(__half(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(0x7bff)))); + VERIFY(!(numext::isnan)(half(__half(0x0000)))); + VERIFY((numext::isinf)(half(__half(0xfc00)))); + VERIFY((numext::isnan)(half(__half(0xfc01)))); + VERIFY((numext::isinf)(half(__half(0x7c00)))); + VERIFY((numext::isnan)(half(__half(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() << std::endl; + std::cout << "highest = " << NumTraits<half>::highest() << std::endl; + std::cout << "lowest = " << NumTraits<half>::lowest() << std::endl; + std::cout << "inifinty = " << NumTraits<half>::infinity() << std::endl; + std::cout << "nan = " << NumTraits<half>::quiet_NaN() << std::endl; + +} + +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/test/incomplete_cholesky.cpp b/test/incomplete_cholesky.cpp new file mode 100644 index 000000000..59ffe9259 --- /dev/null +++ b/test/incomplete_cholesky.cpp @@ -0,0 +1,65 @@ +// 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/test/inplace_decomposition.cpp b/test/inplace_decomposition.cpp new file mode 100644 index 000000000..92d0d91b6 --- /dev/null +++ b/test/inplace_decomposition.cpp @@ -0,0 +1,110 @@ +// 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/test/integer_types.cpp b/test/integer_types.cpp index 950f8e9be..a21f73a81 100644 --- a/test/integer_types.cpp +++ b/test/integer_types.cpp @@ -158,4 +158,12 @@ void test_integer_types() 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(internal::scalar_div_cost<long>::value > internal::scalar_div_cost<int>::value); + VERIFY(internal::scalar_div_cost<unsigned long>::value > internal::scalar_div_cost<int>::value); + } +#endif } diff --git a/test/inverse.cpp b/test/inverse.cpp index 8187b088d..5c6777a18 100644 --- a/test/inverse.cpp +++ b/test/inverse.cpp @@ -68,6 +68,15 @@ template<typename MatrixType> void inverse(const MatrixType& m) 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 @@ -93,12 +102,16 @@ void test_inverse() 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>()) ); } - TEST_SET_BUT_UNUSED_VARIABLE(s) } diff --git a/test/is_same_dense.cpp b/test/is_same_dense.cpp new file mode 100644 index 000000000..2c7838ce9 --- /dev/null +++ b/test/is_same_dense.cpp @@ -0,0 +1,33 @@ +// 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/test/jacobisvd.cpp b/test/jacobisvd.cpp index 12c556e59..7f5f71562 100644 --- a/test/jacobisvd.cpp +++ b/test/jacobisvd.cpp @@ -1,7 +1,7 @@ // This file is part of Eigen, a lightweight C++ template library // for linear algebra. // -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> +// Copyright (C) 2008-2014 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 @@ -14,279 +14,47 @@ #include "main.h" #include <Eigen/SVD> -template<typename MatrixType, int QRPreconditioner> -void jacobisvd_check_full(const MatrixType& m, const JacobiSVD<MatrixType, QRPreconditioner>& svd) -{ - typedef typename MatrixType::Index Index; - Index rows = m.rows(); - Index cols = m.cols(); - - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime - }; - - typedef typename MatrixType::Scalar Scalar; - 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(); - - VERIFY_IS_APPROX(m, u * sigma * v.adjoint()); - VERIFY_IS_UNITARY(u); - VERIFY_IS_UNITARY(v); -} - -template<typename MatrixType, int QRPreconditioner> -void jacobisvd_compare_to_full(const MatrixType& m, - unsigned int computationOptions, - const JacobiSVD<MatrixType, QRPreconditioner>& referenceSvd) -{ - typedef typename MatrixType::Index Index; - Index rows = m.rows(); - Index cols = m.cols(); - Index diagSize = (std::min)(rows, cols); - - JacobiSVD<MatrixType, QRPreconditioner> svd(m, computationOptions); - - VERIFY_IS_APPROX(svd.singularValues(), referenceSvd.singularValues()); - 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(), referenceSvd.matrixV()); - if(computationOptions & ComputeThinV) - VERIFY_IS_APPROX(svd.matrixV(), referenceSvd.matrixV().leftCols(diagSize)); -} - -template<typename MatrixType, int QRPreconditioner> -void jacobisvd_solve(const MatrixType& m, unsigned int computationOptions) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - 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)); - JacobiSVD<MatrixType, QRPreconditioner> svd(m, computationOptions); - - if(internal::is_same<RealScalar,double>::value) svd.setThreshold(1e-8); - else if(internal::is_same<RealScalar,float>::value) svd.setThreshold(1e-4); - - SolutionType x = svd.solve(rhs); - - RealScalar residual = (m*x-rhs).norm(); - // Check that there is no significantly better solution in the neighborhood of x - if(!test_isMuchSmallerThan(residual,rhs.norm())) - { - // If the residual is very small, then we have an exact solution, so we are already good. - for(int k=0;k<x.rows();++k) - { - SolutionType y(x); - y.row(k).array() += 2*NumTraits<RealScalar>::epsilon(); - RealScalar residual_y = (m*y-rhs).norm(); - VERIFY( test_isApprox(residual_y,residual) || residual < residual_y ); - - y.row(k) = x.row(k).array() - 2*NumTraits<RealScalar>::epsilon(); - residual_y = (m*y-rhs).norm(); - VERIFY( test_isApprox(residual_y,residual) || residual < residual_y ); - } - } - - // evaluate normal equation which works also for least-squares solutions - if(internal::is_same<RealScalar,double>::value) - { - // This test is not stable with single precision. - // This is probably because squaring m signicantly affects the precision. - VERIFY_IS_APPROX(m.adjoint()*m*x,m.adjoint()*rhs); - } - - // check minimal norm solutions - { - // 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(m2.jacobiSvd().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 - JacobiSVD<MatrixType2, ColPivHouseholderQRPreconditioner> 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; - JacobiSVD<MatrixType3, ColPivHouseholderQRPreconditioner> svd3(m3, computationOptions); - SolutionType x3 = svd3.solve(rhs3); - if(svd3.rank()!=rank) { - std::cout << m3 << "\n\n"; - std::cout << svd3.singularValues().transpose() << "\n"; - std::cout << svd3.rank() << " == " << rank << "\n"; - std::cout << x21.norm() << " == " << x3.norm() << "\n"; - } -// VERIFY_IS_APPROX(m3*x3, rhs3); - VERIFY_IS_APPROX(m3*x21, rhs3); - VERIFY_IS_APPROX(m2*x3, rhs2); - - VERIFY_IS_APPROX(x21, x3); - } -} - -template<typename MatrixType, int QRPreconditioner> -void jacobisvd_test_all_computation_options(const MatrixType& m) -{ - if (QRPreconditioner == NoQRPreconditioner && m.rows() != m.cols()) - return; - JacobiSVD<MatrixType, QRPreconditioner> fullSvd(m, ComputeFullU|ComputeFullV); - CALL_SUBTEST(( jacobisvd_check_full(m, fullSvd) )); - CALL_SUBTEST(( jacobisvd_solve<MatrixType, QRPreconditioner>(m, ComputeFullU | ComputeFullV) )); - - #if defined __INTEL_COMPILER - // remark #111: statement is unreachable - #pragma warning disable 111 - #endif - if(QRPreconditioner == FullPivHouseholderQRPreconditioner) - return; - - CALL_SUBTEST(( jacobisvd_compare_to_full(m, ComputeFullU, fullSvd) )); - CALL_SUBTEST(( jacobisvd_compare_to_full(m, ComputeFullV, fullSvd) )); - CALL_SUBTEST(( jacobisvd_compare_to_full(m, 0, fullSvd) )); - - if (MatrixType::ColsAtCompileTime == Dynamic) { - // thin U/V are only available with dynamic number of columns - CALL_SUBTEST(( jacobisvd_compare_to_full(m, ComputeFullU|ComputeThinV, fullSvd) )); - CALL_SUBTEST(( jacobisvd_compare_to_full(m, ComputeThinV, fullSvd) )); - CALL_SUBTEST(( jacobisvd_compare_to_full(m, ComputeThinU|ComputeFullV, fullSvd) )); - CALL_SUBTEST(( jacobisvd_compare_to_full(m, ComputeThinU , fullSvd) )); - CALL_SUBTEST(( jacobisvd_compare_to_full(m, ComputeThinU|ComputeThinV, fullSvd) )); - CALL_SUBTEST(( jacobisvd_solve<MatrixType, QRPreconditioner>(m, ComputeFullU | ComputeThinV) )); - CALL_SUBTEST(( jacobisvd_solve<MatrixType, QRPreconditioner>(m, ComputeThinU | ComputeFullV) )); - CALL_SUBTEST(( jacobisvd_solve<MatrixType, QRPreconditioner>(m, ComputeThinU | ComputeThinV) )); - - // test reconstruction - typedef typename MatrixType::Index Index; - Index diagSize = (std::min)(m.rows(), m.cols()); - JacobiSVD<MatrixType, QRPreconditioner> svd(m, ComputeThinU | ComputeThinV); - VERIFY_IS_APPROX(m, svd.matrixU().leftCols(diagSize) * svd.singularValues().asDiagonal() * svd.matrixV().leftCols(diagSize).adjoint()); - } -} +#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) - { - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - Index diagSize = (std::min)(a.rows(), a.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)*std::pow(RealScalar(10),internal::random<RealScalar>(-s,s)); - m = Matrix<Scalar,Dynamic,Dynamic>::Random(a.rows(),diagSize) * d.asDiagonal() * Matrix<Scalar,Dynamic,Dynamic>::Random(diagSize,a.cols()); - // cancel some coeffs - Index n = internal::random<Index>(0,m.size()-1); - for(Index i=0; i<n; ++i) - m(internal::random<Index>(0,m.rows()-1), internal::random<Index>(0,m.cols()-1)) = Scalar(0); - } + svd_fill_random(m); - CALL_SUBTEST(( jacobisvd_test_all_computation_options<MatrixType, FullPivHouseholderQRPreconditioner>(m) )); - CALL_SUBTEST(( jacobisvd_test_all_computation_options<MatrixType, ColPivHouseholderQRPreconditioner>(m) )); - CALL_SUBTEST(( jacobisvd_test_all_computation_options<MatrixType, HouseholderQRPreconditioner>(m) )); - CALL_SUBTEST(( jacobisvd_test_all_computation_options<MatrixType, NoQRPreconditioner>(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) { - typedef typename MatrixType::Scalar Scalar; + svd_verify_assert<JacobiSVD<MatrixType> >(m); typedef typename MatrixType::Index Index; Index rows = m.rows(); Index cols = m.cols(); enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, ColsAtCompileTime = MatrixType::ColsAtCompileTime }; - typedef Matrix<Scalar, RowsAtCompileTime, 1> RhsType; - - RhsType rhs(rows); - - JacobiSVD<MatrixType> 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)) - 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)) } - else - { - VERIFY_RAISES_ASSERT(svd.compute(a, ComputeThinU)) - VERIFY_RAISES_ASSERT(svd.compute(a, ComputeThinV)) - } } template<typename MatrixType> @@ -302,126 +70,17 @@ void jacobisvd_method() VERIFY_IS_APPROX(m.jacobiSvd(ComputeFullU|ComputeFullV).solve(m), m); } -// 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; } - -template<typename MatrixType> -void jacobisvd_inf_nan() -{ - // all this function does is verify we don't iterate infinitely on nan/inf values - - JacobiSVD<MatrixType> 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); -} - -// Regression test for bug 286: JacobiSVD loops indefinitely with some -// matrices containing denormal numbers. -void jacobisvd_bug286() -{ -#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; -#if defined __INTEL_COMPILER -#pragma warning pop -#endif - JacobiSVD<Matrix2d> svd; - svd.compute(M); // just check we don't loop indefinitely -} - -void jacobisvd_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);) - JacobiSVD<MatrixXf> svd; - internal::set_is_malloc_allowed(true); - svd.compute(m); - VERIFY_IS_APPROX(svd.singularValues(), v); - - JacobiSVD<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); - - JacobiSVD<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); -} - 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++) { - Matrix2cd m; - m << 0, 1, - 0, 1; - CALL_SUBTEST_1(( jacobisvd(m, false) )); - m << 1, 0, - 1, 0; - CALL_SUBTEST_1(( jacobisvd(m, false) )); - - Matrix2d n; - n << 0, 0, - 0, 0; - CALL_SUBTEST_2(( jacobisvd(n, false) )); - n << 0, 0, - 0, 1; - CALL_SUBTEST_2(( jacobisvd(n, false) )); - CALL_SUBTEST_3(( jacobisvd<Matrix3f>() )); CALL_SUBTEST_4(( jacobisvd<Matrix4d>() )); CALL_SUBTEST_5(( jacobisvd<Matrix<float,3,5> >() )); @@ -440,8 +99,14 @@ void test_jacobisvd() (void) c; // Test on inf/nan matrix - CALL_SUBTEST_7( jacobisvd_inf_nan<MatrixXf>() ); - CALL_SUBTEST_10( jacobisvd_inf_nan<MatrixXd>() ); + 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))) )); @@ -455,8 +120,7 @@ void test_jacobisvd() CALL_SUBTEST_7( JacobiSVD<MatrixXf>(10,10) ); // Check that preallocation avoids subsequent mallocs - CALL_SUBTEST_9( jacobisvd_preallocate() ); + CALL_SUBTEST_9( svd_preallocate<void>() ); - // Regression check for bug 286 - CALL_SUBTEST_2( jacobisvd_bug286() ); + CALL_SUBTEST_2( svd_underoverflow<void>() ); } diff --git a/test/linearstructure.cpp b/test/linearstructure.cpp index 618984d5c..17474af10 100644 --- a/test/linearstructure.cpp +++ b/test/linearstructure.cpp @@ -2,11 +2,15 @@ // 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) @@ -17,6 +21,7 @@ template<typename MatrixType> void linearStructure(const MatrixType& m) */ typedef typename MatrixType::Index Index; typedef typename MatrixType::Scalar Scalar; + typedef typename MatrixType::RealScalar RealScalar; Index rows = m.rows(); Index cols = m.cols(); @@ -28,7 +33,7 @@ template<typename MatrixType> void linearStructure(const MatrixType& m) m3(rows, cols); Scalar s1 = internal::random<Scalar>(); - while (abs(s1)<1e-3) 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); @@ -68,8 +73,48 @@ template<typename MatrixType> void linearStructure(const MatrixType& m) 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()) ); @@ -80,5 +125,25 @@ void test_linearstructure() 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/test/lscg.cpp b/test/lscg.cpp new file mode 100644 index 000000000..daa62a954 --- /dev/null +++ b/test/lscg.cpp @@ -0,0 +1,29 @@ +// 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; + + 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) ); +} + +void test_lscg() +{ + CALL_SUBTEST_1(test_lscg_T<double>()); + CALL_SUBTEST_2(test_lscg_T<std::complex<double> >()); +} diff --git a/test/lu.cpp b/test/lu.cpp index 374652694..9787f4d86 100644 --- a/test/lu.cpp +++ b/test/lu.cpp @@ -11,6 +11,11 @@ #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::Index Index; @@ -92,6 +97,26 @@ template<typename MatrixType> void lu_non_invertible() // 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() @@ -100,9 +125,9 @@ template<typename MatrixType> void lu_invertible() LU.h */ typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; - DenseIndex size = MatrixType::RowsAtCompileTime; + Index size = MatrixType::RowsAtCompileTime; if( size==Dynamic) - size = internal::random<DenseIndex>(1,EIGEN_TEST_MAX_SIZE); + size = internal::random<Index>(1,EIGEN_TEST_MAX_SIZE); MatrixType m1(size, size), m2(size, size), m3(size, size); FullPivLU<MatrixType> lu; @@ -123,7 +148,28 @@ template<typename MatrixType> void lu_invertible() m3 = MatrixType::Random(size,size); m2 = lu.solve(m3); VERIFY_IS_APPROX(m3, m1*m2); - VERIFY_IS_APPROX(m2, lu.inverse()*m3); + 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); @@ -136,14 +182,39 @@ template<typename MatrixType> void lu_partial_piv() PartialPivLU.h */ typedef typename MatrixType::Index Index; - Index rows = internal::random<Index>(1,4); - Index cols = rows; + typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; + Index size = internal::random<Index>(1,4); - MatrixType m1(cols, rows); + 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() diff --git a/test/main.h b/test/main.h index 664204866..25d2dcf43 100644 --- a/test/main.h +++ b/test/main.h @@ -41,7 +41,14 @@ #include <complex> #include <deque> #include <queue> +#include <cassert> #include <list> +#if __cplusplus >= 201103L +#include <random> +#ifdef EIGEN_USE_THREADS +#include <future> +#endif +#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 @@ -49,14 +56,48 @@ // 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) X = X + 0; +#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" @@ -71,7 +112,7 @@ #endif // bounds integer values for AltiVec -#ifdef __ALTIVEC__ +#if defined(__ALTIVEC__) || defined(__VSX__) #define EIGEN_MAKING_DOCS #endif @@ -84,16 +125,26 @@ 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 @@ -135,33 +186,35 @@ namespace Eigen if(report_on_cerr_on_assert_failure) \ std::cerr << #a << " " __FILE__ << "(" << __LINE__ << ")\n"; \ Eigen::no_more_assert = true; \ - throw Eigen::eigen_assert_exception(); \ + 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::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"; \ + 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); \ + } 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; \ + Eigen::internal::push_assert = false; \ } + #endif //EIGEN_EXCEPTIONS - #else // EIGEN_DEBUG_ASSERTS + #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) )\ @@ -170,22 +223,30 @@ namespace Eigen if(report_on_cerr_on_assert_failure) \ eigen_plain_assert(a); \ else \ - throw Eigen::eigen_assert_exception(); \ + EIGEN_THROW_X(Eigen::eigen_assert_exception()); \ } - #define VERIFY_RAISES_ASSERT(a) { \ + #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); } \ + catch (Eigen::eigen_assert_exception&) { VERIFY(true); } \ Eigen::report_on_cerr_on_assert_failure = true; \ } - + #endif //EIGEN_EXCEPTIONS #endif // EIGEN_DEBUG_ASSERTS +#ifndef VERIFY_RAISES_ASSERT + #define VERIFY_RAISES_ASSERT(a) \ + std::cout << "Can't VERIFY_RAISES_ASSERT( " #a " ) with exceptions disabled\n"; +#endif + + #if !defined(__CUDACC__) #define EIGEN_USE_CUSTOM_ASSERT + #endif #else // EIGEN_NO_ASSERTION_CHECKING @@ -201,6 +262,8 @@ inline void verify_impl(bool condition, const char *testname, const char *file, { 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"; @@ -208,14 +271,20 @@ inline void verify_impl(bool condition, const char *testname, const char *file, for(int i=test_stack_size-1; i>=0; --i) std::cerr << " - " << Eigen::g_test_stack[i] << "\n"; std::cerr << "\n"; - abort(); + 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_IS_EQUAL(a, b) VERIFY(test_is_equal(a, b)) -#define VERIFY_IS_APPROX(a, b) VERIFY(test_isApprox(a, b)) +#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)) @@ -236,9 +305,10 @@ 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<long double>() { return 1e-6; } +template<> inline long double test_precision<std::complex<long double> >() { return test_precision<long double>(); } inline bool test_isApprox(const int& a, const int& b) { return internal::isApprox(a, b, test_precision<int>()); } @@ -253,14 +323,15 @@ 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) @@ -271,6 +342,15 @@ inline bool test_isApprox(const std::complex<double>& a, const std::complex<doub 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>()); @@ -284,13 +364,127 @@ 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) +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 @@ -326,17 +520,17 @@ inline bool test_isUnitary(const MatrixBase<Derived>& m) // Forward declaration to avoid ICC warning template<typename T, typename U> -bool test_is_equal(const T& actual, const U& expected); +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 test_is_equal(const T& actual, const U& expected, bool expect_equal) { - if (actual==expected) + if ((actual==expected) == expect_equal) return true; // false: std::cerr - << std::endl << " actual = " << actual - << std::endl << " expected = " << expected << std::endl << std::endl; + << "\n actual = " << actual + << "\n expected " << (expect_equal ? "= " : "!=") << expected << "\n\n"; return false; } @@ -347,11 +541,10 @@ bool test_is_equal(const T& actual, const U& expected) */ // Forward declaration to avoid ICC warning template<typename MatrixType> -void createRandomPIMatrixOfRank(typename MatrixType::Index desired_rank, typename MatrixType::Index rows, typename MatrixType::Index cols, MatrixType& m); +void createRandomPIMatrixOfRank(Index desired_rank, Index rows, Index cols, MatrixType& m); template<typename MatrixType> -void createRandomPIMatrixOfRank(typename MatrixType::Index desired_rank, typename MatrixType::Index rows, typename MatrixType::Index cols, MatrixType& m) +void createRandomPIMatrixOfRank(Index desired_rank, Index rows, Index cols, MatrixType& m) { - typedef typename internal::traits<MatrixType>::Index Index; typedef typename internal::traits<MatrixType>::Scalar Scalar; enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime }; @@ -388,11 +581,10 @@ void createRandomPIMatrixOfRank(typename MatrixType::Index desired_rank, typenam // Forward declaration to avoid ICC warning template<typename PermutationVectorType> -void randomPermutationVector(PermutationVectorType& v, typename PermutationVectorType::Index size); +void randomPermutationVector(PermutationVectorType& v, Index size); template<typename PermutationVectorType> -void randomPermutationVector(PermutationVectorType& v, typename PermutationVectorType::Index size) +void randomPermutationVector(PermutationVectorType& v, Index size) { - typedef typename PermutationVectorType::Index Index; typedef typename PermutationVectorType::Scalar Scalar; v.resize(size); for(Index i = 0; i < size; ++i) v(i) = Scalar(i); @@ -411,12 +603,7 @@ template<typename T> bool isNotNaN(const T& x) return x==x; } -template<typename T> bool isNaN(const T& x) -{ - return x!=x; -} - -template<typename T> bool isInf(const T& x) +template<typename T> bool isPlusInf(const T& x) { return x > NumTraits<T>::highest(); } @@ -437,13 +624,15 @@ template<typename T> struct GetDifferentType<std::complex<T> > // 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<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<int> >() { return "complex<int>"; } +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)(); @@ -550,3 +739,8 @@ int main(int argc, char *argv[]) // 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/test/mapped_matrix.cpp b/test/mapped_matrix.cpp index 58904fa37..6a84c5897 100644 --- a/test/mapped_matrix.cpp +++ b/test/mapped_matrix.cpp @@ -13,6 +13,8 @@ #include "main.h" +#define EIGEN_TESTMAP_MAX_SIZE 256 + template<typename VectorType> void map_class_vector(const VectorType& m) { typedef typename VectorType::Index Index; @@ -20,23 +22,26 @@ template<typename VectorType> void map_class_vector(const VectorType& m) Index size = m.size(); - // test Map.h Scalar* array1 = internal::aligned_new<Scalar>(size); Scalar* array2 = internal::aligned_new<Scalar>(size); Scalar* array3 = new Scalar[size+1]; - Scalar* array3unaligned = size_t(array3)%16 == 0 ? array3+1 : array3; + Scalar* array3unaligned = (internal::UIntPtr(array3)%EIGEN_MAX_ALIGN_BYTES) == 0 ? array3+1 : array3; + Scalar array4[EIGEN_TESTMAP_MAX_SIZE]; - Map<VectorType, Aligned>(array1, size) = VectorType::Random(size); - Map<VectorType, Aligned>(array2, size) = Map<VectorType,Aligned>(array1, 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); - VectorType ma1 = Map<VectorType, Aligned>(array1, size); - VectorType ma2 = Map<VectorType, Aligned>(array2, 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) - VERIFY_RAISES_ASSERT((Map<VectorType,Aligned>(array3unaligned, size))) + if(internal::packet_traits<Scalar>::Vectorizable && size>=AlignedMax) + VERIFY_RAISES_ASSERT((Map<VectorType,AlignedMax>(array3unaligned, size))) #endif internal::aligned_delete(array1, size); @@ -50,23 +55,64 @@ 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>(); - // test Map.h + // 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]; for(int i = 0; i < size+1; i++) array3[i] = Scalar(1); - Scalar* array3unaligned = size_t(array3)%16 == 0 ? array3+1 : array3; - Map<MatrixType, Aligned>(array1, rows, cols) = MatrixType::Ones(rows,cols); - Map<MatrixType>(array2, rows, cols) = Map<MatrixType>(array1, rows, cols); - Map<MatrixType>(array3unaligned, rows, cols) = Map<MatrixType>(array1, rows, cols); - MatrixType ma1 = Map<MatrixType>(array1, rows, cols); - MatrixType ma2 = Map<MatrixType, Aligned>(array2, rows, cols); + 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); - MatrixType ma3 = Map<MatrixType>(array3unaligned, rows, cols); 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); @@ -80,11 +126,10 @@ template<typename VectorType> void map_static_methods(const VectorType& m) Index size = m.size(); - // test Map.h Scalar* array1 = internal::aligned_new<Scalar>(size); Scalar* array2 = internal::aligned_new<Scalar>(size); Scalar* array3 = new Scalar[size+1]; - Scalar* array3unaligned = size_t(array3)%16 == 0 ? array3+1 : array3; + 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); @@ -109,9 +154,9 @@ template<typename PlainObjectType> void check_const_correctness(const PlainObjec // 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( !(internal::traits<Map<ConstPlainObjectType, AlignedMax> >::Flags & LvalueBit) ); VERIFY( !(Map<ConstPlainObjectType>::Flags & LvalueBit) ); - VERIFY( !(Map<ConstPlainObjectType, Aligned>::Flags & LvalueBit) ); + VERIFY( !(Map<ConstPlainObjectType, AlignedMax>::Flags & LvalueBit) ); } template<typename Scalar> @@ -142,6 +187,7 @@ void test_mapped_matrix() 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)) ); diff --git a/test/mapstaticmethods.cpp b/test/mapstaticmethods.cpp index 5b512bde4..06272d106 100644 --- a/test/mapstaticmethods.cpp +++ b/test/mapstaticmethods.cpp @@ -69,7 +69,8 @@ struct mapstaticmethods_impl<PlainObjectType, true, false> { static void run(const PlainObjectType& m) { - int rows = m.rows(), cols = m.cols(); + typedef typename PlainObjectType::Index Index; + Index rows = m.rows(), cols = m.cols(); int i = internal::random<int>(2,5), j = internal::random<int>(2,5); @@ -115,7 +116,8 @@ struct mapstaticmethods_impl<PlainObjectType, true, true> { static void run(const PlainObjectType& v) { - int size = v.size(); + typedef typename PlainObjectType::Index Index; + Index size = v.size(); int i = internal::random<int>(2,5); diff --git a/test/mapstride.cpp b/test/mapstride.cpp index b1dc9de2a..4858f8fea 100644 --- a/test/mapstride.cpp +++ b/test/mapstride.cpp @@ -23,7 +23,7 @@ template<int Alignment,typename VectorType> void map_class_vector(const VectorTy Scalar* a_array = internal::aligned_new<Scalar>(arraysize+1); Scalar* array = a_array; if(Alignment!=Aligned) - array = (Scalar*)(ptrdiff_t(a_array) + (internal::packet_traits<Scalar>::AlignedOnScalar?sizeof(Scalar):sizeof(typename NumTraits<Scalar>::Real))); + 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); @@ -56,16 +56,30 @@ template<int Alignment,typename MatrixType> void map_class_matrix(const MatrixTy Index rows = _m.rows(), cols = _m.cols(); MatrixType m = MatrixType::Random(rows,cols); + Scalar s1 = internal::random<Scalar>(); Index arraysize = 2*(rows+4)*(cols+4); - Scalar* a_array = internal::aligned_new<Scalar>(arraysize+1); - Scalar* array = a_array; + Scalar* a_array1 = internal::aligned_new<Scalar>(arraysize+1); + Scalar* array1 = a_array1; if(Alignment!=Aligned) - array = (Scalar*)(ptrdiff_t(a_array) + (internal::packet_traits<Scalar>::AlignedOnScalar?sizeof(Scalar):sizeof(typename NumTraits<Scalar>::Real))); + 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); @@ -75,11 +89,19 @@ template<int Alignment,typename MatrixType> void map_class_matrix(const MatrixTy 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 @@ -94,10 +116,18 @@ template<int Alignment,typename MatrixType> void map_class_matrix(const MatrixTy 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); @@ -108,9 +138,12 @@ template<int Alignment,typename MatrixType> void map_class_matrix(const MatrixTy 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); } - internal::aligned_delete(a_array, arraysize+1); + internal::aligned_delete(a_array1, arraysize+1); } void test_mapstride() diff --git a/test/meta.cpp b/test/meta.cpp index 3302c5887..b8dea68e8 100644 --- a/test/meta.cpp +++ b/test/meta.cpp @@ -9,6 +9,12 @@ #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)); @@ -52,6 +58,24 @@ void test_meta() 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); diff --git a/test/metis_support.cpp b/test/metis_support.cpp index 932b04074..d87c56a13 100644 --- a/test/metis_support.cpp +++ b/test/metis_support.cpp @@ -3,24 +3,10 @@ // // Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> // -// Eigen is free software; you can redistribute it and/or -// modify it under the terms of the GNU Lesser General Public -// License as published by the Free Software Foundation; either -// version 3 of the License, or (at your option) any later version. -// -// Alternatively, you can redistribute it and/or -// modify it under the terms of the GNU General Public License as -// published by the Free Software Foundation; either version 2 of -// the License, or (at your option) any later version. -// -// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY -// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS -// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the -// GNU General Public License for more details. -// -// You should have received a copy of the GNU Lesser General Public -// License and a copy of the GNU General Public License along with -// Eigen. If not, see <http://www.gnu.org/licenses/>. +// 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> diff --git a/test/mixingtypes.cpp b/test/mixingtypes.cpp index 6c2f74875..ad9c2c652 100644 --- a/test/mixingtypes.cpp +++ b/test/mixingtypes.cpp @@ -1,7 +1,7 @@ // This file is part of Eigen, a lightweight C++ template library // for linear algebra. // -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> +// Copyright (C) 2008-2015 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 @@ -15,14 +15,26 @@ #define EIGEN_NO_STATIC_ASSERT // turn static asserts into runtime asserts in order to check them #endif -// #ifndef EIGEN_DONT_VECTORIZE -// #define EIGEN_DONT_VECTORIZE // SSE intrinsics aren't designed to allow mixing types -// #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 mixingtypes(int size = SizeAtCompileType) { typedef std::complex<float> CF; @@ -38,8 +50,10 @@ template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType) 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); @@ -49,19 +63,59 @@ template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType) complex<float> scf = internal::random<complex<float> >(); complex<double> scd = internal::random<complex<double> >(); - mf+mf; - VERIFY_RAISES_ASSERT(mf+md); - VERIFY_RAISES_ASSERT(mf+mcf); + + 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) sf = internal::random<double>(); + while(std::abs(scf)<epsf) scf = internal::random<CF>(); + while(std::abs(scd)<epsd) scd = internal::random<CD>(); + +// VERIFY_RAISES_ASSERT(mf+md); // does not even compile + +#ifdef EIGEN_DONT_VECTORIZE VERIFY_RAISES_ASSERT(vf=vd); VERIFY_RAISES_ASSERT(vf+=vd); - VERIFY_RAISES_ASSERT(mcd=md); - +#endif + // check scalar products - VERIFY_IS_APPROX(vcf * sf , vcf * complex<float>(sf)); - VERIFY_IS_APPROX(sd * vcd, complex<double>(sd) * vcd); - VERIFY_IS_APPROX(vf * scf , vf.template cast<complex<float> >() * scf); - VERIFY_IS_APPROX(scd * vd, scd * vd.template cast<complex<double> >()); + 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); @@ -75,6 +129,7 @@ template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType) 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()); + // vd.asDiagonal() * mf; // does not even compile // vcd.asDiagonal() * mf; // does not even compile @@ -92,7 +147,6 @@ template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType) 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); @@ -103,6 +157,20 @@ template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType) 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>()); @@ -122,11 +190,111 @@ template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType) 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() { - CALL_SUBTEST_1(mixingtypes<3>()); - CALL_SUBTEST_2(mixingtypes<4>()); - CALL_SUBTEST_3(mixingtypes<Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))); + 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))); + } } diff --git a/test/mpl2only.cpp b/test/mpl2only.cpp new file mode 100644 index 000000000..7d04d6bba --- /dev/null +++ b/test/mpl2only.cpp @@ -0,0 +1,22 @@ +// 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/test/nesting_ops.cpp b/test/nesting_ops.cpp index 1e8523283..a419b0e44 100644 --- a/test/nesting_ops.cpp +++ b/test/nesting_ops.cpp @@ -2,16 +2,37 @@ // 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 <typename MatrixType> void run_nesting_ops(const MatrixType& _m) +template <int N, typename XprType> +void use_n_times(const XprType &xpr) { - typename MatrixType::Nested m(_m); + 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)); @@ -24,10 +45,63 @@ template <typename MatrixType> void run_nesting_ops(const MatrixType& _m) 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(MatrixXf::Random(25,25))); - CALL_SUBTEST_2(run_nesting_ops(MatrixXd::Random(25,25))); - CALL_SUBTEST_3(run_nesting_ops(Matrix4f::Random())); - CALL_SUBTEST_4(run_nesting_ops(Matrix4d::Random())); + 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/test/nomalloc.cpp b/test/nomalloc.cpp index 8e0402358..50756c2fb 100644 --- a/test/nomalloc.cpp +++ b/test/nomalloc.cpp @@ -8,20 +8,10 @@ // 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 hack is needed to make this file compiles with -pedantic (gcc) -#ifdef __GNUC__ -#define throw(X) -#endif - -#ifdef __INTEL_COMPILER - // disable "warning #76: argument to macro is empty" produced by the above hack - #pragma warning disable 76 -#endif - // discard stack allocation as that too bypasses malloc #define EIGEN_STACK_ALLOCATION_LIMIT 0 -// any heap allocation will raise an assert -#define EIGEN_NO_MALLOC +// heap allocation will raise an assert if enabled at runtime +#define EIGEN_RUNTIME_NO_MALLOC #include "main.h" #include <Eigen/Cholesky> @@ -88,14 +78,15 @@ template<typename MatrixType> void nomalloc(const MatrixType& m) VERIFY_IS_APPROX(m2,m2); m2.template selfadjointView<Lower>().rankUpdate(m1.col(0),-1); - m2.template selfadjointView<Lower>().rankUpdate(m1.row(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 -// m1 += m1.template triangularView<Upper>() * m2.col(; -// m1.template selfadjointView<Lower>().rankUpdate(m2); -// m1 += m1.template triangularView<Upper>() * m2; -// m1 += m1.template selfadjointView<Lower>() * m2; -// VERIFY_IS_APPROX(m1,m1); + 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> @@ -171,7 +162,7 @@ void test_zerosized() { Eigen::VectorXd v; // explicit zero-sized: Eigen::ArrayXXd A0(0,0); - Eigen::ArrayXd v0(std::ptrdiff_t(0)); // FIXME ArrayXd(0) is ambiguous + Eigen::ArrayXd v0(0); // assigning empty objects to each other: A=A0; @@ -183,9 +174,11 @@ template<typename MatrixType> void test_reference(const MatrixType& m) { 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 Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Flag > > Ref; - typedef Eigen::Ref<const Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, TransposeFlag> > RefT; + 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)); @@ -195,10 +188,30 @@ template<typename MatrixType> void test_reference(const MatrixType& m) { 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>()) ); @@ -207,6 +220,10 @@ void test_nomalloc() // 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/test/nullary.cpp b/test/nullary.cpp index fbc721a1a..acd55506e 100644 --- a/test/nullary.cpp +++ b/test/nullary.cpp @@ -2,6 +2,7 @@ // 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 @@ -12,7 +13,6 @@ template<typename MatrixType> bool equalsIdentity(const MatrixType& A) { - typedef typename MatrixType::Index Index; typedef typename MatrixType::Scalar Scalar; Scalar zero = static_cast<Scalar>(0); @@ -30,13 +30,41 @@ bool equalsIdentity(const MatrixType& A) 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 internal::traits<VectorType>::Index Index; - typedef typename internal::traits<VectorType>::Scalar Scalar; + typedef typename VectorType::Scalar Scalar; + typedef typename VectorType::RealScalar RealScalar; const Index size = base.size(); @@ -44,36 +72,61 @@ void testVectorType(const VectorType& base) 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); - VectorType n(size); - for (int i=0; i<size; ++i) - n(i) = low+i*step; + if(!NumTraits<Scalar>::IsInteger) + { + VectorType n(size); + for (int i=0; i<size; ++i) + n(i) = low+i*step; + VERIFY_IS_APPROX(m,n); - VERIFY_IS_APPROX(m,n); + CALL_SUBTEST( check_extremity_accuracy(m, low, high) ); + } - // random access version - m = VectorType::LinSpaced(size,low,high); - VERIFY_IS_APPROX(m,n); + 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); - // 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<Scalar>::epsilon() ); + // 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) ); + } - // These guys sometimes fail! This is not good. Any ideas how to fix them!? - //VERIFY( m(m.size()-1) == high ); - //VERIFY( m(0) == low ); + VERIFY( m(m.size()-1) <= high ); + VERIFY( (m.array() <= high).all() ); + VERIFY( (m.array() >= low).all() ); - // sequential access version - m = VectorType::LinSpaced(Sequential,size,low,high); - VERIFY_IS_APPROX(m,n); - // These guys sometimes fail! This is not good. Any ideas how to fix them!? - //VERIFY( m(m.size()-1) == high ); - //VERIFY( m(0) == low ); + 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); @@ -95,23 +148,77 @@ void testVectorType(const VectorType& base) VERIFY_IS_APPROX( ScalarMatrix::LinSpaced(1,low,high), ScalarMatrix::Constant(high) ); // regression test for bug 526 (linear vectorized transversal) - if (size > 1) { + 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) { - typedef typename MatrixType::Index Index; + 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() @@ -120,12 +227,78 @@ void test_nullary() 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; i++) { - CALL_SUBTEST_4( testVectorType(VectorXd(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>(1,300))) ); + 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/test/packetmath.cpp b/test/packetmath.cpp index 38aa256ce..7821a1738 100644 --- a/test/packetmath.cpp +++ b/test/packetmath.cpp @@ -9,16 +9,28 @@ // 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; } } } -template<typename Scalar> bool isApproxAbs(const Scalar& a, const Scalar& b, const typename NumTraits<Scalar>::Real& refvalue) +// 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); } @@ -29,7 +41,7 @@ template<typename Scalar> bool areApproxAbs(const Scalar* a, const Scalar* b, in { if (!isApproxAbs(a[i],b[i],refvalue)) { - std::cout << "[" << Map<const Matrix<Scalar,1,Dynamic> >(a,size) << "]" << " != " << Map<const Matrix<Scalar,1,Dynamic> >(b,size) << "\n"; + std::cout << "ref: [" << Map<const Matrix<Scalar,1,Dynamic> >(a,size) << "]" << " != vec: [" << Map<const Matrix<Scalar,1,Dynamic> >(b,size) << "]\n"; return false; } } @@ -42,21 +54,13 @@ template<typename Scalar> bool areApprox(const Scalar* a, const Scalar* b, int s { if (a[i]!=b[i] && !internal::isApprox(a[i],b[i])) { - std::cout << "[" << Map<const Matrix<Scalar,1,Dynamic> >(a,size) << "]" << " != " << Map<const Matrix<Scalar,1,Dynamic> >(b,size) << "\n"; + 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_CWISE2(REFOP, POP) { \ - for (int i=0; i<PacketSize; ++i) \ - ref[i] = REFOP(data1[i], data1[i+PacketSize]); \ - internal::pstore(data2, POP(internal::pload<Packet>(data1), internal::pload<Packet>(data1+PacketSize))); \ - VERIFY(areApprox(ref, data2, PacketSize) && #POP); \ -} - #define CHECK_CWISE1(REFOP, POP) { \ for (int i=0; i<PacketSize; ++i) \ ref[i] = REFOP(data1[i]); \ @@ -92,6 +96,14 @@ struct packet_helper<false,Packet> 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)) @@ -100,15 +112,17 @@ struct packet_helper<false,Packet> template<typename Scalar> void packetmath() { using std::abs; - typedef typename internal::packet_traits<Scalar>::type Packet; - const int PacketSize = internal::packet_traits<Scalar>::size; + typedef internal::packet_traits<Scalar> PacketTraits; + typedef typename PacketTraits::type Packet; + const int PacketSize = PacketTraits::size; typedef typename NumTraits<Scalar>::Real RealScalar; - const int size = PacketSize*4; - EIGEN_ALIGN16 Scalar data1[internal::packet_traits<Scalar>::size*4]; - EIGEN_ALIGN16 Scalar data2[internal::packet_traits<Scalar>::size*4]; - EIGEN_ALIGN16 Packet packets[PacketSize*2]; - EIGEN_ALIGN16 Scalar ref[internal::packet_traits<Scalar>::size*4]; + 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) { @@ -140,6 +154,18 @@ template<typename Scalar> void packetmath() 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) @@ -148,13 +174,17 @@ template<typename Scalar> void packetmath() VERIFY(areApprox(ref, data2, PacketSize) && "internal::palign"); } - CHECK_CWISE2(REF_ADD, internal::padd); - CHECK_CWISE2(REF_SUB, internal::psub); - CHECK_CWISE2(REF_MUL, internal::pmul); - #ifndef EIGEN_VECTORIZE_ALTIVEC - if (!internal::is_same<Scalar,int>::value) - CHECK_CWISE2(REF_DIV, internal::pdiv); - #endif + 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); @@ -165,9 +195,31 @@ template<typename Scalar> void packetmath() 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) @@ -179,11 +231,31 @@ template<typename Scalar> void packetmath() } } + 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]; @@ -203,116 +275,258 @@ template<typename Scalar> void packetmath() 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 typename internal::packet_traits<Scalar>::type Packet; - const int PacketSize = internal::packet_traits<Scalar>::size; + typedef internal::packet_traits<Scalar> PacketTraits; + typedef typename PacketTraits::type Packet; + const int PacketSize = PacketTraits::size; const int size = PacketSize*4; - EIGEN_ALIGN16 Scalar data1[internal::packet_traits<Scalar>::size*4]; - EIGEN_ALIGN16 Scalar data2[internal::packet_traits<Scalar>::size*4]; - EIGEN_ALIGN16 Scalar ref[internal::packet_traits<Scalar>::size*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(internal::packet_traits<Scalar>::HasSin, std::sin, internal::psin); - CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasCos, std::cos, internal::pcos); - CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasTan, std::tan, internal::ptan); - + 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(internal::packet_traits<Scalar>::HasASin, std::asin, internal::pasin); - CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasACos, std::acos, internal::pacos); + 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(internal::packet_traits<Scalar>::HasExp, std::exp, internal::pexp); + 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>::HasExp,Packet> h; - h.store(data2, internal::pexp(h.load(data1))); - VERIFY(isNaN(data2[0])); + 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)<0.1) + + if(internal::random<float>(0,1)<0.1f) data1[internal::random<int>(0, PacketSize)] = 0; - CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasSqrt, std::sqrt, internal::psqrt); - CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasLog, std::log, internal::plog); + 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(); - packet_helper<internal::packet_traits<Scalar>::HasLog,Packet> h; + data1[1] = std::numeric_limits<Scalar>::epsilon(); + packet_helper<PacketTraits::HasLog,Packet> h; h.store(data2, internal::plog(h.load(data1))); - VERIFY(isNaN(data2[0])); - data1[0] = -1.0f; + 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(isNaN(data2[0])); -#if !EIGEN_FAST_MATH + VERIFY((numext::isnan)(data2[0])); h.store(data2, internal::psqrt(h.load(data1))); - VERIFY(isNaN(data2[0])); - VERIFY(isNaN(data2[1])); -#endif + VERIFY((numext::isnan)(data2[0])); + VERIFY((numext::isnan)(data2[1])); } } template<typename Scalar> void packetmath_notcomplex() { using std::abs; - typedef typename internal::packet_traits<Scalar>::type Packet; - const int PacketSize = internal::packet_traits<Scalar>::size; + 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]; - EIGEN_ALIGN16 Scalar data1[internal::packet_traits<Scalar>::size*4]; - EIGEN_ALIGN16 Scalar data2[internal::packet_traits<Scalar>::size*4]; - EIGEN_ALIGN16 Scalar ref[internal::packet_traits<Scalar>::size*4]; - - Array<Scalar,Dynamic,1>::Map(data1, internal::packet_traits<Scalar>::size*4).setRandom(); + 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"); - CHECK_CWISE2((std::min), internal::pmin); - CHECK_CWISE2((std::max), internal::pmax); + 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(data1[0])); + 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 typename internal::packet_traits<Scalar>::type Packet; - const int PacketSize = internal::packet_traits<Scalar>::size; - + 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]); @@ -320,7 +534,7 @@ template<typename Scalar,bool ConjLhs,bool ConjRhs> void test_conj_helper(Scalar } 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]; @@ -333,34 +547,70 @@ template<typename Scalar,bool ConjLhs,bool ConjRhs> void test_conj_helper(Scalar template<typename Scalar> void packetmath_complex() { - typedef typename internal::packet_traits<Scalar>::type Packet; - const int PacketSize = internal::packet_traits<Scalar>::size; + typedef internal::packet_traits<Scalar> PacketTraits; + typedef typename PacketTraits::type Packet; + const int PacketSize = PacketTraits::size; const int size = PacketSize*4; - EIGEN_ALIGN16 Scalar data1[PacketSize*4]; - EIGEN_ALIGN16 Scalar data2[PacketSize*4]; - EIGEN_ALIGN16 Scalar ref[PacketSize*4]; - EIGEN_ALIGN16 Scalar pval[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() @@ -369,17 +619,23 @@ void test_packetmath() CALL_SUBTEST_1( packetmath<float>() ); CALL_SUBTEST_2( packetmath<double>() ); CALL_SUBTEST_3( packetmath<int>() ); - CALL_SUBTEST_1( packetmath<std::complex<float> >() ); - CALL_SUBTEST_2( packetmath<std::complex<double> >() ); + 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_1( packetmath_complex<std::complex<float> >() ); - CALL_SUBTEST_2( packetmath_complex<std::complex<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/test/pastix_support.cpp b/test/pastix_support.cpp index 14da0944b..b62f85739 100644 --- a/test/pastix_support.cpp +++ b/test/pastix_support.cpp @@ -7,6 +7,8 @@ // 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> @@ -25,6 +27,14 @@ template<typename T> void test_pastix_T() 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. diff --git a/test/permutationmatrices.cpp b/test/permutationmatrices.cpp index 7b0dbc763..db1266579 100644 --- a/test/permutationmatrices.cpp +++ b/test/permutationmatrices.cpp @@ -7,6 +7,8 @@ // 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; @@ -33,7 +35,9 @@ template<typename MatrixType> void permutationmatrices(const MatrixType& m) RightPermutationVectorType rv; randomPermutationVector(rv, cols); RightPermutationType rp(rv); - MatrixType m_permuted = lp * m_original * rp; + 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++) @@ -43,7 +47,11 @@ template<typename MatrixType> void permutationmatrices(const MatrixType& m) 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); @@ -63,22 +71,22 @@ template<typename MatrixType> void permutationmatrices(const MatrixType& m) LeftPermutationType identityp; identityp.setIdentity(rows); VERIFY_IS_APPROX(m_original, identityp*m_original); - + // check inplace permutations m_permuted = m_original; - m_permuted = lp.inverse() * m_permuted; + 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; - m_permuted = m_permuted * rp.inverse(); + 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; - m_permuted = lp * m_permuted; + 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; - m_permuted = m_permuted * rp; + 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) @@ -99,7 +107,38 @@ template<typename MatrixType> void permutationmatrices(const MatrixType& m) 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()); + } +} + +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() @@ -113,4 +152,5 @@ void test_permutationmatrices() 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/test/prec_inverse_4x4.cpp b/test/prec_inverse_4x4.cpp index c4ef2d4bd..eb6ad18c9 100644 --- a/test/prec_inverse_4x4.cpp +++ b/test/prec_inverse_4x4.cpp @@ -53,14 +53,29 @@ template<typename MatrixType> void inverse_general_4x4(int repeat) // 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>())); diff --git a/test/product.h b/test/product.h index 0b3abe402..3b6511270 100644 --- a/test/product.h +++ b/test/product.h @@ -22,7 +22,6 @@ template<typename MatrixType> void product(const MatrixType& m) /* this test covers the following files: Identity.h Product.h */ - typedef typename MatrixType::Index Index; typedef typename MatrixType::Scalar Scalar; typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> RowVectorType; typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> ColVectorType; @@ -112,6 +111,23 @@ template<typename MatrixType> void product(const MatrixType& m) 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); @@ -136,15 +152,80 @@ template<typename MatrixType> void product(const MatrixType& m) 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()); - + { + 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)); + { + 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/test/product_extra.cpp b/test/product_extra.cpp index ea2486937..e2b855bff 100644 --- a/test/product_extra.cpp +++ b/test/product_extra.cpp @@ -98,6 +98,16 @@ template<typename MatrixType> void product_extra(const MatrixType& m) // 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 @@ -116,8 +126,8 @@ 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; - DenseIndex rows = m.rows(); - DenseIndex cols = m.cols(); + Index rows = m.rows(); + Index cols = m.cols(); { MatrixType res, a(rows,0), b(0,cols); @@ -169,6 +179,7 @@ void zero_sized_objects(const MatrixType& m) } } +template<int> void bug_127() { // Bug 127 @@ -193,6 +204,16 @@ void bug_127() 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: @@ -222,6 +243,116 @@ void unaligned_objects() } } +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++) { @@ -232,6 +363,13 @@ void test_product_extra() 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() ); - CALL_SUBTEST_6( unaligned_objects() ); + 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/test/product_large.cpp b/test/product_large.cpp index 03d7bd8ed..845cd40ca 100644 --- a/test/product_large.cpp +++ b/test/product_large.cpp @@ -9,6 +9,27 @@ #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++) { @@ -17,6 +38,8 @@ void test_product_large() 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 @@ -39,15 +62,16 @@ void test_product_large() // 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>(1000000,2000000); - setCpuCacheSizes(l1,l2); + 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>(k1,m1,n1); + internal::computeProductBlockingSizes<float,float,std::ptrdiff_t>(k1,m1,n1,1); } { @@ -60,5 +84,24 @@ void test_product_large() 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/test/product_mmtr.cpp b/test/product_mmtr.cpp index aeba009f4..f6e4bb1ae 100644 --- a/test/product_mmtr.cpp +++ b/test/product_mmtr.cpp @@ -13,7 +13,8 @@ ref2 = ref1 = DEST; \ DEST.template triangularView<TRI>() OP; \ ref1 OP; \ - ref2.template triangularView<TRI>() = ref1; \ + ref2.template triangularView<TRI>() \ + = ref1.template triangularView<TRI>(); \ VERIFY_IS_APPROX(DEST,ref2); \ } @@ -32,6 +33,8 @@ template<typename Scalar> void mmtr(int size) 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>(); @@ -49,6 +52,29 @@ template<typename Scalar> void mmtr(int size) 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() diff --git a/test/product_notemporary.cpp b/test/product_notemporary.cpp index 258d238e2..8bf71b4f2 100644 --- a/test/product_notemporary.cpp +++ b/test/product_notemporary.cpp @@ -7,25 +7,10 @@ // 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_temporaries; - -inline void on_temporary_creation(int size) { - // here's a great place to set a breakpoint when debugging failures in this test! - if(size!=0) nb_temporaries++; -} - - -#define EIGEN_DENSE_STORAGE_CTOR_PLUGIN { on_temporary_creation(size); } +#define TEST_ENABLE_TEMPORARY_TRACKING #include "main.h" -#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 ); \ - } - template<typename MatrixType> void product_notemporary(const MatrixType& m) { /* This test checks the number of temporaries created @@ -58,10 +43,23 @@ template<typename MatrixType> void product_notemporary(const MatrixType& m) 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()).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); @@ -77,7 +75,7 @@ template<typename MatrixType> void product_notemporary(const MatrixType& m) 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); @@ -114,8 +112,7 @@ template<typename MatrixType> void product_notemporary(const MatrixType& m) VERIFY_EVALUATION_COUNT( Scalar tmp = 0; tmp += Scalar(RealScalar(1)) / (m3.transpose() * m3).diagonal().array().abs().sum(), 0 ); // Zero temporaries for ... CoeffBasedProductMode - // - does not work with GCC because of the <..>, we'ld need variadic macros ... - //VERIFY_EVALUATION_COUNT( m3.col(0).head<5>() * m3.col(0).transpose() + m3.col(0).head<5>() * m3.col(0).transpose(), 0 ); + 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 ); @@ -123,6 +120,26 @@ template<typename MatrixType> void product_notemporary(const MatrixType& m) 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() @@ -131,11 +148,12 @@ void test_product_notemporary() 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)) ); - s = internal::random<int>(16,EIGEN_TEST_MAX_SIZE); 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)) ); - s = internal::random<int>(16,EIGEN_TEST_MAX_SIZE/2); CALL_SUBTEST_4( product_notemporary(MatrixXcd(s,s)) ); + TEST_SET_BUT_UNUSED_VARIABLE(s) } } diff --git a/test/product_selfadjoint.cpp b/test/product_selfadjoint.cpp index 374e2393b..3d768aa7e 100644 --- a/test/product_selfadjoint.cpp +++ b/test/product_selfadjoint.cpp @@ -67,14 +67,21 @@ void test_product_selfadjoint() 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) } - TEST_SET_BUT_UNUSED_VARIABLE(s) } diff --git a/test/product_small.cpp b/test/product_small.cpp index 8b132abb6..fdfdd9f6c 100644 --- a/test/product_small.cpp +++ b/test/product_small.cpp @@ -9,8 +9,10 @@ #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; @@ -28,16 +30,237 @@ void product1x1() 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, 5>()) ); + 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() ); + 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 @@ -46,5 +269,25 @@ void test_product_small() 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/test/product_symm.cpp b/test/product_symm.cpp index 74d7329b1..8c44383f9 100644 --- a/test/product_symm.cpp +++ b/test/product_symm.cpp @@ -39,6 +39,24 @@ template<typename Scalar, int Size, int OtherSize> void symm(int size = Size, in 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); diff --git a/test/product_syrk.cpp b/test/product_syrk.cpp index 73c95000c..e10f0f2f2 100644 --- a/test/product_syrk.cpp +++ b/test/product_syrk.cpp @@ -125,11 +125,12 @@ void test_product_syrk() int s; s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE); CALL_SUBTEST_1( syrk(MatrixXf(s, s)) ); - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE); 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)) ); - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2); CALL_SUBTEST_4( syrk(MatrixXcd(s, s)) ); + TEST_SET_BUT_UNUSED_VARIABLE(s) } } diff --git a/test/product_trmm.cpp b/test/product_trmm.cpp index d715b9a36..12e554410 100644 --- a/test/product_trmm.cpp +++ b/test/product_trmm.cpp @@ -9,10 +9,18 @@ #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=internal::random<int>(1,EIGEN_TEST_MAX_SIZE), - int cols=internal::random<int>(1,EIGEN_TEST_MAX_SIZE), - int otherCols = OtherCols==Dynamic?internal::random<int>(1,EIGEN_TEST_MAX_SIZE):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; @@ -42,13 +50,13 @@ void trmm(int rows=internal::random<int>(1,EIGEN_TEST_MAX_SIZE), 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); - + 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_sx.noalias() = ge_right.transpose() * mat.adjoint().template triangularView<Mode>(), ge_right.transpose() * triTr.conjugate()); VERIFY_IS_APPROX( ge_xs.noalias() = (s1*mat.adjoint()).template triangularView<Mode>() * (s2*ge_left.adjoint()), s1*triTr.conjugate() * (s2*ge_left.adjoint())); VERIFY_IS_APPROX( ge_sx.noalias() = ge_right.adjoint() * mat.adjoint().template triangularView<Mode>(), ge_right.adjoint() * triTr.conjugate()); - + ge_xs_save = ge_xs; 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_sx.setRandom(); @@ -61,13 +69,13 @@ void trmm(int rows=internal::random<int>(1,EIGEN_TEST_MAX_SIZE), } template<typename Scalar, int Mode, int TriOrder> -void trmv(int rows=internal::random<int>(1,EIGEN_TEST_MAX_SIZE), int cols=internal::random<int>(1,EIGEN_TEST_MAX_SIZE)) +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=internal::random<int>(1,EIGEN_TEST_MAX_SIZE), int cols=internal::random<int>(1,EIGEN_TEST_MAX_SIZE), int otherCols = internal::random<int>(1,EIGEN_TEST_MAX_SIZE)) +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); } diff --git a/test/product_trmv.cpp b/test/product_trmv.cpp index 4c3c435c2..57a202afc 100644 --- a/test/product_trmv.cpp +++ b/test/product_trmv.cpp @@ -78,12 +78,14 @@ void test_product_trmv() 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)) ); - s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2); 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) } - TEST_SET_BUT_UNUSED_VARIABLE(s); } diff --git a/test/product_trsolve.cpp b/test/product_trsolve.cpp index 69892b3a8..4b97fa9d6 100644 --- a/test/product_trsolve.cpp +++ b/test/product_trsolve.cpp @@ -84,10 +84,18 @@ void test_product_trsolve() 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_1((trsolve<float,Dynamic,1>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)))); - CALL_SUBTEST_5((trsolve<std::complex<double>,Dynamic,1>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)))); - CALL_SUBTEST_6((trsolve<float,1,1>())); - CALL_SUBTEST_7((trsolve<float,1,2>())); - CALL_SUBTEST_8((trsolve<std::complex<float>,4,1>())); + 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/test/qr.cpp b/test/qr.cpp index a79e0dd34..dfcc1e8f9 100644 --- a/test/qr.cpp +++ b/test/qr.cpp @@ -54,6 +54,8 @@ 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; @@ -65,7 +67,7 @@ template<typename MatrixType> void qr_invertible() if (internal::is_same<RealScalar,float>::value) { // let's build a matrix more stable to inverse - MatrixType a = MatrixType::Random(size,size*2); + MatrixType a = MatrixType::Random(size,size*4); m1 += a * a.adjoint(); } @@ -81,8 +83,11 @@ template<typename MatrixType> void qr_invertible() 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()); + // 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() diff --git a/test/qr_colpivoting.cpp b/test/qr_colpivoting.cpp index eb3feac01..26ed27f5c 100644 --- a/test/qr_colpivoting.cpp +++ b/test/qr_colpivoting.cpp @@ -10,21 +10,103 @@ #include "main.h" #include <Eigen/QR> +#include <Eigen/SVD> + +template <typename MatrixType> +void cod() { + typedef typename MatrixType::Index Index; + + 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; typedef typename MatrixType::Index Index; 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(rank == qr.rank()); - VERIFY(cols - qr.rank() == qr.dimensionOfKernel()); + VERIFY_IS_EQUAL(rank, qr.rank()); + VERIFY_IS_EQUAL(cols - qr.rank(), qr.dimensionOfKernel()); VERIFY(!qr.isInjective()); VERIFY(!qr.isInvertible()); VERIFY(!qr.isSurjective()); @@ -36,26 +118,59 @@ template<typename MatrixType> void qr() 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(rank == qr.rank()); - VERIFY(Cols - qr.rank() == qr.dimensionOfKernel()); - VERIFY(qr.isInjective() == (rank == Rows)); - VERIFY(qr.isSurjective() == (rank == Cols)); - VERIFY(qr.isInvertible() == (qr.isInjective() && qr.isSurjective())); + 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(); @@ -66,6 +181,71 @@ template<typename MatrixType, int Cols2> void qr_fixedsize() 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::Index Index; + 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() @@ -132,6 +312,15 @@ void test_qr_colpivoting() } 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>() ); @@ -147,4 +336,7 @@ void test_qr_colpivoting() // 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/test/qr_fullpivoting.cpp b/test/qr_fullpivoting.cpp index 511f2473f..70e89c198 100644 --- a/test/qr_fullpivoting.cpp +++ b/test/qr_fullpivoting.cpp @@ -15,16 +15,20 @@ template<typename MatrixType> void qr() { typedef typename MatrixType::Index Index; - Index rows = internal::random<Index>(20,200), cols = internal::random<int>(20,200), cols2 = internal::random<int>(20,200); - Index rank = internal::random<Index>(1, (std::min)(rows, cols)-1); + 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(rank == qr.rank()); - VERIFY(cols - qr.rank() == qr.dimensionOfKernel()); + VERIFY_IS_EQUAL(rank, qr.rank()); + VERIFY_IS_EQUAL(cols - qr.rank(), qr.dimensionOfKernel()); VERIFY(!qr.isInjective()); VERIFY(!qr.isInvertible()); VERIFY(!qr.isSurjective()); @@ -40,12 +44,28 @@ template<typename MatrixType> void qr() 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() @@ -55,7 +75,9 @@ template<typename MatrixType> void qr_invertible() typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; typedef typename MatrixType::Scalar Scalar; - int size = internal::random<int>(10,50); + 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); diff --git a/test/rand.cpp b/test/rand.cpp new file mode 100644 index 000000000..51cf01773 --- /dev/null +++ b/test/rand.cpp @@ -0,0 +1,118 @@ +// 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/test/real_qz.cpp b/test/real_qz.cpp index a1766c6d9..99ac31235 100644 --- a/test/real_qz.cpp +++ b/test/real_qz.cpp @@ -7,6 +7,7 @@ // 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> @@ -41,7 +42,11 @@ template<typename MatrixType> void real_qz(const MatrixType& m) break; } - RealQZ<MatrixType> qz(A,B); + 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 @@ -49,11 +54,20 @@ template<typename MatrixType> void real_qz(const MatrixType& m) 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); diff --git a/test/redux.cpp b/test/redux.cpp index 0d176e500..989e1057b 100644 --- a/test/redux.cpp +++ b/test/redux.cpp @@ -2,11 +2,14 @@ // 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 + #include "main.h" template<typename MatrixType> void matrixRedux(const MatrixType& m) @@ -21,7 +24,7 @@ template<typename MatrixType> void matrixRedux(const MatrixType& m) 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 entires are close to 1. + // 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)); @@ -53,10 +56,24 @@ template<typename MatrixType> void matrixRedux(const MatrixType& m) 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) @@ -139,8 +156,10 @@ void test_redux() 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))) ); diff --git a/test/ref.cpp b/test/ref.cpp index 44bbd3bf1..769db0414 100644 --- a/test/ref.cpp +++ b/test/ref.cpp @@ -12,27 +12,23 @@ #undef EIGEN_DEFAULT_TO_ROW_MAJOR #endif -static int nb_temporaries; - -inline void on_temporary_creation(int) { - // here's a great place to set a breakpoint when debugging failures in this test! - nb_temporaries++; -} - - -#define EIGEN_DENSE_STORAGE_CTOR_PLUGIN { on_temporary_creation(size); } +#define TEST_ENABLE_TEMPORARY_TRACKING #include "main.h" -#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 ); \ - } +// test Ref.h +// Deal with i387 extended precision +#if EIGEN_ARCH_i386 && !(EIGEN_ARCH_x86_64) -// test Ref.h +#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) { @@ -71,7 +67,6 @@ template<typename MatrixType> void ref_matrix(const MatrixType& m) 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; @@ -237,6 +232,12 @@ 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() { @@ -249,6 +250,9 @@ void test_ref_overloads() 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() diff --git a/test/runtest.sh b/test/runtest.sh deleted file mode 100755 index 2be250819..000000000 --- a/test/runtest.sh +++ /dev/null @@ -1,20 +0,0 @@ -#!/bin/bash - -black='\E[30m' -red='\E[31m' -green='\E[32m' -yellow='\E[33m' -blue='\E[34m' -magenta='\E[35m' -cyan='\E[36m' -white='\E[37m' - -if ! ./$1 > /dev/null 2> .runtest.log ; then - echo -e $red Test $1 failed: $black - echo -e $blue - cat .runtest.log - echo -e $black - exit 1 -else -echo -e $green Test $1 passed$black -fi diff --git a/test/rvalue_types.cpp b/test/rvalue_types.cpp new file mode 100644 index 000000000..8887f1b1b --- /dev/null +++ b/test/rvalue_types.cpp @@ -0,0 +1,64 @@ +// 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/test/schur_complex.cpp b/test/schur_complex.cpp index 5e869790f..deb78e44e 100644 --- a/test/schur_complex.cpp +++ b/test/schur_complex.cpp @@ -25,7 +25,7 @@ template<typename MatrixType> void schur(int size = MatrixType::ColsAtCompileTim 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(T(row,col) == (typename MatrixType::Scalar)0); } } VERIFY_IS_APPROX(A.template cast<ComplexScalar>(), U * T * U.adjoint()); @@ -70,7 +70,7 @@ template<typename MatrixType> void schur(int size = MatrixType::ColsAtCompileTim VERIFY_IS_EQUAL(cs1.matrixT(), csOnlyT.matrixT()); VERIFY_RAISES_ASSERT(csOnlyT.matrixU()); - if (size > 1) + if (size > 1 && size < 20) { // Test matrix with NaN A(0,0) = std::numeric_limits<typename MatrixType::RealScalar>::quiet_NaN(); diff --git a/test/schur_real.cpp b/test/schur_real.cpp index 36b9c24d1..4aede87df 100644 --- a/test/schur_real.cpp +++ b/test/schur_real.cpp @@ -82,7 +82,7 @@ template<typename MatrixType> void schur(int size = MatrixType::ColsAtCompileTim 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_EQUAL(rs3.matrixT(), Atriangular); + 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 @@ -91,7 +91,7 @@ template<typename MatrixType> void schur(int size = MatrixType::ColsAtCompileTim VERIFY_IS_EQUAL(rs1.matrixT(), rsOnlyT.matrixT()); VERIFY_RAISES_ASSERT(rsOnlyT.matrixU()); - if (size > 2) + if (size > 2 && size < 20) { // Test matrix with NaN A(0,0) = std::numeric_limits<typename MatrixType::Scalar>::quiet_NaN(); diff --git a/test/selfadjoint.cpp b/test/selfadjoint.cpp index 76dab6d64..92401e506 100644 --- a/test/selfadjoint.cpp +++ b/test/selfadjoint.cpp @@ -21,7 +21,9 @@ template<typename MatrixType> void selfadjoint(const MatrixType& m) Index cols = m.cols(); MatrixType m1 = MatrixType::Random(rows, cols), - m3(rows, cols); + m2 = MatrixType::Random(rows, cols), + m3(rows, cols), + m4(rows, cols); m1.diagonal() = m1.diagonal().real().template cast<Scalar>(); @@ -30,10 +32,19 @@ template<typename MatrixType> void selfadjoint(const MatrixType& m) 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); } void bug_159() diff --git a/test/simplicial_cholesky.cpp b/test/simplicial_cholesky.cpp index 786468421..649c817b4 100644 --- a/test/simplicial_cholesky.cpp +++ b/test/simplicial_cholesky.cpp @@ -9,16 +9,17 @@ #include "sparse_solver.h" -template<typename T> void test_simplicial_cholesky_T() +template<typename T, typename I> void test_simplicial_cholesky_T() { - SimplicialCholesky<SparseMatrix<T>, Lower> chol_colmajor_lower_amd; - SimplicialCholesky<SparseMatrix<T>, Upper> chol_colmajor_upper_amd; - SimplicialLLT<SparseMatrix<T>, Lower> llt_colmajor_lower_amd; - SimplicialLLT<SparseMatrix<T>, Upper> llt_colmajor_upper_amd; - SimplicialLDLT<SparseMatrix<T>, Lower> ldlt_colmajor_lower_amd; - SimplicialLDLT<SparseMatrix<T>, Upper> ldlt_colmajor_upper_amd; - SimplicialLDLT<SparseMatrix<T>, Lower, NaturalOrdering<int> > ldlt_colmajor_lower_nat; - SimplicialLDLT<SparseMatrix<T>, Upper, NaturalOrdering<int> > ldlt_colmajor_upper_nat; + 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); @@ -34,12 +35,13 @@ template<typename T> void test_simplicial_cholesky_T() check_sparse_spd_determinant(ldlt_colmajor_lower_amd); check_sparse_spd_determinant(ldlt_colmajor_upper_amd); - check_sparse_spd_solving(ldlt_colmajor_lower_nat); - check_sparse_spd_solving(ldlt_colmajor_upper_nat); + 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>()); - CALL_SUBTEST_2(test_simplicial_cholesky_T<std::complex<double> >()); + 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/test/sizeof.cpp b/test/sizeof.cpp index d9ad35620..03ad20453 100644 --- a/test/sizeof.cpp +++ b/test/sizeof.cpp @@ -13,14 +13,27 @@ template<typename MatrixType> void verifySizeOf(const MatrixType&) { typedef typename MatrixType::Scalar Scalar; if (MatrixType::RowsAtCompileTime!=Dynamic && MatrixType::ColsAtCompileTime!=Dynamic) - VERIFY(std::ptrdiff_t(sizeof(MatrixType))==std::ptrdiff_t(sizeof(Scalar))*std::ptrdiff_t(MatrixType::SizeAtCompileTime)); + VERIFY_IS_EQUAL(std::ptrdiff_t(sizeof(MatrixType)),std::ptrdiff_t(sizeof(Scalar))*std::ptrdiff_t(MatrixType::SizeAtCompileTime)); else - VERIFY(sizeof(MatrixType)==sizeof(Scalar*) + 2 * sizeof(typename MatrixType::Index)); + 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>()) ); diff --git a/test/sizeoverflow.cpp b/test/sizeoverflow.cpp index 16d6f8d04..240d22294 100644 --- a/test/sizeoverflow.cpp +++ b/test/sizeoverflow.cpp @@ -18,8 +18,6 @@ VERIFY(threw && "should have thrown bad_alloc: " #a); \ } -typedef DenseIndex Index; - template<typename MatrixType> void triggerMatrixBadAlloc(Index rows, Index cols) { diff --git a/test/sparse.h b/test/sparse.h index e19a76316..9912e1e24 100644 --- a/test/sparse.h +++ b/test/sparse.h @@ -53,15 +53,15 @@ enum { * \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 Index> void +template<typename Scalar,int Opt1,int Opt2,typename StorageIndex> void initSparse(double density, Matrix<Scalar,Dynamic,Dynamic,Opt1>& refMat, - SparseMatrix<Scalar,Opt2,Index>& sparseMat, + SparseMatrix<Scalar,Opt2,StorageIndex>& sparseMat, int flags = 0, - std::vector<Matrix<Index,2,1> >* zeroCoords = 0, - std::vector<Matrix<Index,2,1> >* nonzeroCoords = 0) + std::vector<Matrix<StorageIndex,2,1> >* zeroCoords = 0, + std::vector<Matrix<StorageIndex,2,1> >* nonzeroCoords = 0) { - enum { IsRowMajor = SparseMatrix<Scalar,Opt2,Index>::IsRowMajor }; + 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())))); @@ -71,14 +71,17 @@ initSparse(double density, //sparseMat.startVec(j); for(Index i=0; i<sparseMat.innerSize(); i++) { - int ai(i), aj(j); + 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 + Scalar(5.); + v = v*v; + if(numext::real(v)>0) v += Scalar(5); + else v -= Scalar(5); } if ((flags & MakeLowerTriangular) && aj>ai) v = Scalar(0); @@ -93,11 +96,11 @@ initSparse(double density, //sparseMat.insertBackByOuterInner(j,i) = v; sparseMat.insertByOuterInner(j,i) = v; if (nonzeroCoords) - nonzeroCoords->push_back(Matrix<Index,2,1> (ai,aj)); + nonzeroCoords->push_back(Matrix<StorageIndex,2,1> (ai,aj)); } else if (zeroCoords) { - zeroCoords->push_back(Matrix<Index,2,1> (ai,aj)); + zeroCoords->push_back(Matrix<StorageIndex,2,1> (ai,aj)); } refMat(ai,aj) = v; } @@ -163,7 +166,7 @@ initSparse(double density, { sparseVec.reserve(int(refVec.size()*density)); sparseVec.setZero(); - for(Index i=0; i<refVec.size(); i++) + 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)) diff --git a/test/sparse_basic.cpp b/test/sparse_basic.cpp index ce41d713c..384985028 100644 --- a/test/sparse_basic.cpp +++ b/test/sparse_basic.cpp @@ -9,22 +9,28 @@ // 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::Index Index; - typedef Matrix<Index,2,1> Vector2; + 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; - typedef Matrix<Scalar,1,Dynamic> RowDenseVector; Scalar eps = 1e-6; Scalar s1 = internal::random<Scalar>(); @@ -37,94 +43,22 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re std::vector<Vector2> nonzeroCoords; initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords); - if (zeroCoords.size()==0 || nonzeroCoords.size()==0) - return; - // test coeff and coeffRef - for (int i=0; i<(int)zeroCoords.size(); ++i) + 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[0].x(),zeroCoords[0].y()) = 5 ); + VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[i].x(),zeroCoords[i].y()) = 5 ); } VERIFY_IS_APPROX(m, refMat); - m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5); - refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5); + 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 InnerIterators and Block expressions - for (int t=0; t<10; ++t) - { - int j = internal::random<int>(0,cols-1); - int i = internal::random<int>(0,rows-1); - int w = internal::random<int>(1,cols-j-1); - int h = internal::random<int>(1,rows-i-1); - VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w)); - for(int 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(int 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(int 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(int 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(int 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(int 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(int 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(int 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(int 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 assertion VERIFY_RAISES_ASSERT( m.coeffRef(-1,1) = 0 ); VERIFY_RAISES_ASSERT( m.coeffRef(0,m.cols()) = 0 ); @@ -135,17 +69,31 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re DenseMatrix m1(rows,cols); m1.setZero(); SparseMatrixType m2(rows,cols); - if(internal::random<int>()%2) - m2.reserve(VectorXi::Constant(m2.outerSize(), 2)); + 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<rows/2; ++k) + 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); } @@ -179,9 +127,9 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re DenseMatrix m1(rows,cols); m1.setZero(); SparseMatrixType m2(rows,cols); - VectorXi r(VectorXi::Constant(m2.outerSize(), ((mode%2)==0) ? m2.innerSize() : std::max<int>(1,m2.innerSize()/8))); + VectorXi r(VectorXi::Constant(m2.outerSize(), ((mode%2)==0) ? int(m2.innerSize()) : std::max<int>(1,int(m2.innerSize())/8))); m2.reserve(r); - for (int k=0; k<rows*cols; ++k) + for (Index k=0; k<rows*cols; ++k) { Index i = internal::random<Index>(0,rows-1); Index j = internal::random<Index>(0,cols-1); @@ -195,110 +143,46 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re VERIFY_IS_APPROX(m2,m1); } - // test innerVector() - { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows); - SparseMatrixType m2(rows, rows); - initSparse<Scalar>(density, refMat2, m2); - Index j0 = internal::random<Index>(0,rows-1); - Index j1 = internal::random<Index>(0,rows-1); - if(SparseMatrixType::IsRowMajor) - VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.row(j0)); - else - VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.col(j0)); - - if(SparseMatrixType::IsRowMajor) - VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.row(j0)+refMat2.row(j1)); - else - VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.col(j0)+refMat2.col(j1)); - - SparseMatrixType m3(rows,rows); - m3.reserve(VectorXi::Constant(rows,rows/2)); - for(Index j=0; j<rows; ++j) - for(Index k=0; k<j; ++k) - m3.insertByOuterInner(j,k) = k+1; - for(Index j=0; j<rows; ++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<rows; ++j) - { - VERIFY(j==numext::real(m3.innerVector(j).nonZeros())); - if(j>0) - VERIFY(j==numext::real(m3.innerVector(j).lastCoeff())); - } - - //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, rows); - SparseMatrixType m2(rows, rows); - initSparse<Scalar>(density, refMat2, m2); - if(internal::random<float>(0,1)>0.5) m2.makeCompressed(); - - Index j0 = internal::random<Index>(0,rows-2); - Index j1 = internal::random<Index>(0,rows-2); - Index n0 = internal::random<Index>(1,rows-(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); - - 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 basic computations { - DenseMatrix refM1 = DenseMatrix::Zero(rows, rows); - DenseMatrix refM2 = DenseMatrix::Zero(rows, rows); - DenseMatrix refM3 = DenseMatrix::Zero(rows, rows); - DenseMatrix refM4 = DenseMatrix::Zero(rows, rows); - SparseMatrixType m1(rows, rows); - SparseMatrixType m2(rows, rows); - SparseMatrixType m3(rows, rows); - SparseMatrixType m4(rows, rows); + 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(m1*=s1, refM1*=s1); - VERIFY_IS_APPROX(m1/=s1, refM1/=s1); - - VERIFY_IS_APPROX(m1+=m2, refM1+=refM2); - VERIFY_IS_APPROX(m1-=m2, refM1-=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.row(0)), refM1.col(0).dot(refM2.row(0))); + 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()); @@ -306,105 +190,167 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re 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 transpose + // test reverse iterators { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows); - SparseMatrixType m2(rows, rows); + 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()); + 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; - VERIFY_IS_APPROX(SparseMatrixType(m2.adjoint()), refMat2.adjoint()); + 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 generic blocks + // test transpose { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows); - SparseMatrixType m2(rows, rows); + DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols); + SparseMatrixType m2(rows, cols); initSparse<Scalar>(density, refMat2, m2); - Index j0 = internal::random<Index>(0,rows-2); - Index j1 = internal::random<Index>(0,rows-2); - Index n0 = internal::random<Index>(1,rows-(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); - } - - VERIFY_IS_APPROX(DenseVector(m2.col(j0)), refMat2.col(j0)); - VERIFY_IS_APPROX(m2.col(j0), refMat2.col(j0)); - - VERIFY_IS_APPROX(RowDenseVector(m2.row(j0)), refMat2.row(j0)); - VERIFY_IS_APPROX(m2.row(j0), refMat2.row(j0)); + VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval()); + VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose()); + + VERIFY_IS_APPROX(SparseMatrixType(m2.adjoint()), refMat2.adjoint()); - VERIFY_IS_APPROX(m2.block(j0,j1,n0,n0), refMat2.block(j0,j1,n0,n0)); - VERIFY_IS_APPROX((2*m2).block(j0,j1,n0,n0), (2*refMat2).block(j0,j1,n0,n0)); + // check isApprox handles opposite storage order + typename Transpose<SparseMatrixType>::PlainObject m3(m2); + VERIFY(m2.isApprox(m3)); } // test prune { - SparseMatrixType m2(rows, rows); - DenseMatrix refM2(rows, rows); + SparseMatrixType m2(rows, cols); + DenseMatrix refM2(rows, cols); refM2.setZero(); int countFalseNonZero = 0; int countTrueNonZero = 0; - for (Index j=0; j<m2.outerSize(); ++j) + m2.reserve(VectorXi::Constant(m2.outerSize(), int(m2.innerSize()))); + for (Index j=0; j<m2.cols(); ++j) { - m2.startVec(j); - for (Index i=0; i<m2.innerSize(); ++i) + for (Index i=0; i<m2.rows(); ++i) { float x = internal::random<float>(0,1); - if (x<0.1) + if (x<0.1f) { // do nothing } - else if (x<0.5) + else if (x<0.5f) { countFalseNonZero++; - m2.insertBackByOuterInner(j,i) = Scalar(0); + m2.insert(i,j) = Scalar(0); } else { countTrueNonZero++; - m2.insertBackByOuterInner(j,i) = Scalar(1); - if(SparseMatrixType::IsRowMajor) - refM2(j,i) = Scalar(1); - else - refM2(i,j) = Scalar(1); + m2.insert(i,j) = Scalar(1); + refM2(i,j) = Scalar(1); } } } - m2.finalize(); + if(internal::random<bool>()) + m2.makeCompressed(); VERIFY(countFalseNonZero+countTrueNonZero == m2.nonZeros()); - VERIFY_IS_APPROX(m2, refM2); + if(countTrueNonZero>0) + VERIFY_IS_APPROX(m2, refM2); m2.prune(Scalar(1)); VERIFY(countTrueNonZero==m2.nonZeros()); VERIFY_IS_APPROX(m2, refM2); @@ -412,29 +358,74 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re // test setFromTriplets { - typedef Triplet<Scalar,Index> TripletType; + typedef Triplet<Scalar,StorageIndex> TripletType; std::vector<TripletType> triplets; - int ntriplets = rows*cols; + Index ntriplets = rows*cols; triplets.reserve(ntriplets); - DenseMatrix refMat(rows,cols); - refMat.setZero(); - for(int i=0;i<ntriplets;++i) + 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) { - Index r = internal::random<Index>(0,rows-1); - Index c = internal::random<Index>(0,cols-1); + 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(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); + 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, rows), refMat3(rows, rows); - SparseMatrixType m2(rows, rows), m3(rows, rows); + 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>(); @@ -444,13 +435,15 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re 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<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<UnitLower>(); + m3 = m2.template triangularView<UnitLower>(); + VERIFY_IS_APPROX(m3, refMat3); + } refMat3 = refMat2.template triangularView<StrictlyUpper>(); m3 = m2.template triangularView<StrictlyUpper>(); @@ -459,6 +452,10 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re 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 @@ -470,6 +467,19 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re 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 @@ -478,28 +488,59 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re 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, rows); - SparseMatrixType m2(rows, rows); + 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<Index,Index> > inc; - inc.push_back(std::pair<Index,Index>(-3,-2)); - inc.push_back(std::pair<Index,Index>(0,0)); - inc.push_back(std::pair<Index,Index>(3,2)); - inc.push_back(std::pair<Index,Index>(3,0)); - inc.push_back(std::pair<Index,Index>(0,3)); + 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++) { - Index incRows = inc[i].first; - Index incCols = inc[i].second; + 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); @@ -529,22 +570,119 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re 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); + Scalar v = 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 s = Eigen::internal::random<int>(1,50); - EIGEN_UNUSED_VARIABLE(s); + 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>(s, s)) )); - CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, RowMajor>(s, s)) )); - CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(s, s)) )); - CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,long int>(s, s)) )); - CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,long int>(s, s)) )); + 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)) )); - CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(s), short(s))) )); - CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(s), short(s))) )); + 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/test/sparse_block.cpp b/test/sparse_block.cpp new file mode 100644 index 000000000..2a0b3b617 --- /dev/null +++ b/test/sparse_block.cpp @@ -0,0 +1,317 @@ +// 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/test/sparse_permutations.cpp b/test/sparse_permutations.cpp index e4ce1d679..b82cceff8 100644 --- a/test/sparse_permutations.cpp +++ b/test/sparse_permutations.cpp @@ -1,25 +1,57 @@ // This file is part of Eigen, a lightweight C++ template library // for linear algebra. // -// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr> +// 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<int OtherStorage, typename SparseMatrixType> void sparse_permutations(const SparseMatrixType& ref) +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) { - typedef typename SparseMatrixType::Index Index; + 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::Index Index; - typedef SparseMatrix<Scalar, OtherStorage, Index> OtherSparseMatrixType; + typedef typename SparseMatrixType::StorageIndex StorageIndex; + typedef SparseMatrix<Scalar, OtherStorage, StorageIndex> OtherSparseMatrixType; typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - typedef Matrix<Index,Dynamic,1> VectorI; + typedef Matrix<StorageIndex,Dynamic,1> VectorI; +// bool IsRowMajor1 = SparseMatrixType::IsRowMajor; +// bool IsRowMajor2 = OtherSparseMatrixType::IsRowMajor; double density = (std::max)(8./(rows*cols), 0.01); @@ -44,58 +76,69 @@ template<int OtherStorage, typename SparseMatrixType> void sparse_permutations(c randomPermutationVector(pi, cols); p.indices() = pi; - res = mat*p; + 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"); - res = p*mat; + 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"); - res = mat*p.inverse(); + 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)"); - res = p.inverse()*mat; + 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"); - res = mat.twistedBy(p); + 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)"); - res = mat.template selfadjointView<Upper>().twistedBy(p_null); + 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"); - res = mat.template selfadjointView<Lower>().twistedBy(p_null); + 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"); - res = up.template selfadjointView<Upper>().twistedBy(p_null); + 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"); - res = lo.template selfadjointView<Lower>().twistedBy(p_null); + VERIFY( is_sorted( res = lo.template selfadjointView<Lower>().twistedBy(p_null) )); res_d = lo_sym_d; VERIFY(res.isApprox(res_d) && "lower selfadjoint full"); - res = mat.template selfadjointView<Upper>(); + VERIFY( is_sorted( res = mat.template selfadjointView<Upper>() )); res_d = up_sym_d; VERIFY(res.isApprox(res_d) && "full selfadjoint upper to full"); - res = mat.template selfadjointView<Lower>(); + VERIFY( is_sorted( res = mat.template selfadjointView<Lower>() )); res_d = lo_sym_d; VERIFY(res.isApprox(res_d) && "full selfadjoint lower to full"); - res = up.template selfadjointView<Upper>(); + VERIFY( is_sorted( res = up.template selfadjointView<Upper>() )); res_d = up_sym_d; VERIFY(res.isApprox(res_d) && "upper selfadjoint to full"); - res = lo.template selfadjointView<Lower>(); + VERIFY( is_sorted( res = lo.template selfadjointView<Lower>() )); res_d = lo_sym_d; VERIFY(res.isApprox(res_d) && "lower selfadjoint full"); @@ -152,19 +195,19 @@ template<int OtherStorage, typename SparseMatrixType> void sparse_permutations(c VERIFY(res.isApprox(res_d) && "upper selfadjoint twisted to lower"); - res = mat.template selfadjointView<Upper>().twistedBy(p); + 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"); - res = mat.template selfadjointView<Lower>().twistedBy(p); + 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"); - res = up.template selfadjointView<Upper>().twistedBy(p); + 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"); - res = lo.template selfadjointView<Lower>().twistedBy(p); + 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"); } @@ -184,4 +227,10 @@ void test_sparse_permutations() 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/test/sparse_product.cpp b/test/sparse_product.cpp index a2ea9d5b7..c1edd26e3 100644 --- a/test/sparse_product.cpp +++ b/test/sparse_product.cpp @@ -7,37 +7,29 @@ // 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" +static long int nb_temporaries; -template<typename SparseMatrixType, typename DenseMatrix, bool IsRowMajor=SparseMatrixType::IsRowMajor> struct test_outer; +inline void on_temporary_creation() { + // here's a great place to set a breakpoint when debugging failures in this test! + nb_temporaries++; +} -template<typename SparseMatrixType, typename DenseMatrix> struct test_outer<SparseMatrixType,DenseMatrix,false> { - static void run(SparseMatrixType& m2, SparseMatrixType& m4, DenseMatrix& refMat2, DenseMatrix& refMat4) { - typedef typename SparseMatrixType::Index Index; - Index c = internal::random<Index>(0,m2.cols()-1); - Index c1 = internal::random<Index>(0,m2.cols()-1); - VERIFY_IS_APPROX(m4=m2.col(c)*refMat2.col(c1).transpose(), refMat4=refMat2.col(c)*refMat2.col(c1).transpose()); - VERIFY_IS_APPROX(m4=refMat2.col(c1)*m2.col(c).transpose(), refMat4=refMat2.col(c1)*refMat2.col(c).transpose()); - } -}; - -template<typename SparseMatrixType, typename DenseMatrix> struct test_outer<SparseMatrixType,DenseMatrix,true> { - static void run(SparseMatrixType& m2, SparseMatrixType& m4, DenseMatrix& refMat2, DenseMatrix& refMat4) { - typedef typename SparseMatrixType::Index Index; - Index r = internal::random<Index>(0,m2.rows()-1); - Index c1 = internal::random<Index>(0,m2.cols()-1); - VERIFY_IS_APPROX(m4=m2.row(r).transpose()*refMat2.col(c1).transpose(), refMat4=refMat2.row(r).transpose()*refMat2.col(c1).transpose()); - VERIFY_IS_APPROX(m4=refMat2.col(c1)*m2.row(r), refMat4=refMat2.col(c1)*refMat2.row(r)); +#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 ); \ } -}; -// (m2,m4,refMat2,refMat4,dv1); -// VERIFY_IS_APPROX(m4=m2.innerVector(c)*dv1.transpose(), refMat4=refMat2.colVector(c)*dv1.transpose()); -// VERIFY_IS_APPROX(m4=dv1*mcm.col(c).transpose(), refMat4=dv1*refMat2.col(c).transpose()); + template<typename SparseMatrixType> void sparse_product() { - typedef typename SparseMatrixType::Index Index; + typedef typename SparseMatrixType::StorageIndex StorageIndex; Index n = 100; const Index rows = internal::random<Index>(1,n); const Index cols = internal::random<Index>(1,n); @@ -45,12 +37,12 @@ template<typename SparseMatrixType> void sparse_product() typedef typename SparseMatrixType::Scalar Scalar; enum { Flags = SparseMatrixType::Flags }; - double density = (std::max)(8./(rows*cols), 0.1); + 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,Index> ColSpVector; - typedef SparseVector<Scalar,RowMajor,Index> RowSpVector; + typedef SparseVector<Scalar,0,StorageIndex> ColSpVector; + typedef SparseVector<Scalar,RowMajor,StorageIndex> RowSpVector; Scalar s1 = internal::random<Scalar>(); Scalar s2 = internal::random<Scalar>(); @@ -93,33 +85,124 @@ template<typename SparseMatrixType> void sparse_product() 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 + // 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 - test_outer<SparseMatrixType,DenseMatrix>::run(m2,m4,refMat2,refMat4); + { + 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); @@ -131,11 +214,11 @@ template<typename SparseMatrixType> void sparse_product() RowSpVector rv0(depth), rv1; RowDenseVector drv0(depth), drv1(rv1); initSparse(2*density,drv0, rv0); - - VERIFY_IS_APPROX(cv1=rv0*m3, dcv1=drv0*refMat3); + + VERIFY_IS_APPROX(cv1=m3*cv0, dcv1=refMat3*dcv0); VERIFY_IS_APPROX(rv1=rv0*m3, drv1=drv0*refMat3); - VERIFY_IS_APPROX(cv1=m3*cv0, dcv1=refMat3*dcv0); 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); } @@ -158,12 +241,16 @@ template<typename SparseMatrixType> void sparse_product() // 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); @@ -172,7 +259,7 @@ template<typename SparseMatrixType> void sparse_product() VERIFY_IS_APPROX(d3=d1*m2.transpose(), refM3=d1*refM2.transpose()); } - // test self adjoint products + // test self-adjoint and triangular-view products { DenseMatrix b = DenseMatrix::Random(rows, rows); DenseMatrix x = DenseMatrix::Random(rows, rows); @@ -180,9 +267,12 @@ template<typename SparseMatrixType> void sparse_product() 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()); @@ -195,26 +285,41 @@ template<typename SparseMatrixType> void sparse_product() for (int k=0; k<mS.outerSize(); ++k) for (typename SparseMatrixType::InnerIterator it(mS,k); it; ++it) if (it.index() == k) - it.valueRef() *= 0.5; + 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.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 * sparse + // sparse selfadjointView with sparse matrices SparseMatrixType mSres(rows,rows); VERIFY_IS_APPROX(mSres = mLo.template selfadjointView<Lower>()*mS, refX = refLo.template selfadjointView<Lower>()*refS); - // sparse * sparse selfadjointview 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 @@ -239,11 +344,35 @@ template<typename SparseMatrixType, typename DenseMatrixType> void sparse_produc 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 ); +} + 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> >()) ); diff --git a/test/sparse_ref.cpp b/test/sparse_ref.cpp new file mode 100644 index 000000000..5e9607234 --- /dev/null +++ b/test/sparse_ref.cpp @@ -0,0 +1,139 @@ +// 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/test/sparse_solver.h b/test/sparse_solver.h index 59d77daa2..5145bc3eb 100644 --- a/test/sparse_solver.h +++ b/test/sparse_solver.h @@ -9,102 +9,167 @@ #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.lu().solve(db); + DenseRhs refX = dA.householderQr().solve(db); { - Rhs x(b.rows(), b.cols()); + Rhs x(A.cols(), b.cols()); Rhs oldb = b; solver.compute(A); if (solver.info() != Success) { - std::cerr << "sparse solver testing: factorization failed (check_sparse_solving)\n"; - exit(0); - return; + 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 << "sparse solver testing: solving failed\n"; + 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); - if (solver.info() != Success) - { - std::cerr << "sparse solver testing: factorization failed (check_sparse_solving)\n"; - exit(0); - return; - } + VERIFY(solver.info() == Success && "factorization failed when using analyzePattern/factorize API"); x = solver.solve(b); - if (solver.info() != Success) - { - std::cerr << "sparse solver testing: solving failed\n"; - return; - } + 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>())); } - // test dense Block as the result and rhs: + // if not too large, do some extra check: + if(A.rows()<2000) { - DenseRhs x(db.rows(), db.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 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 Rhs& refX) +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(b.rows(), b.cols()); - + Rhs x(A.cols(), b.cols()); + solver.compute(A); if (solver.info() != Success) { - std::cerr << "sparse solver testing: factorization failed (check_sparse_solving_real_cases)\n"; - exit(0); - return; + 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 << "sparse solver testing: solving failed\n"; + std::cerr << "WARNING | sparse solver testing, solving failed (" << typeid(Solver).name() << ")\n"; return; } - RealScalar res_error; - // Compute the norm of the relative error - if(refX.size() != 0) - res_error = (refX - x).norm()/refX.norm(); - else - { - // Compute the relative residual norm - res_error = (b - A * x).norm()/b.norm(); - } - if (res_error > test_precision<Scalar>() ){ - std::cerr << "Test " << g_test_stack.back() << " failed in "EI_PP_MAKE_STRING(__FILE__) - << " (" << EI_PP_MAKE_STRING(__LINE__) << ")" << std::endl << std::endl; - abort(); + 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"; } } @@ -117,7 +182,7 @@ void check_sparse_determinant(Solver& solver, const typename Solver::MatrixType& solver.compute(A); if (solver.info() != Success) { - std::cerr << "sparse solver testing: factorization failed (check_sparse_determinant)\n"; + std::cerr << "WARNING | sparse solver testing: factorization failed (check_sparse_determinant)\n"; return; } @@ -134,7 +199,7 @@ void check_sparse_abs_determinant(Solver& solver, const typename Solver::MatrixT solver.compute(A); if (solver.info() != Success) { - std::cerr << "sparse solver testing: factorization failed (check_sparse_abs_determinant)\n"; + std::cerr << "WARNING | sparse solver testing: factorization failed (check_sparse_abs_determinant)\n"; return; } @@ -181,13 +246,33 @@ inline std::string get_matrixfolder() 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) +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 SparseMatrix<Scalar,ColMajor> SpMat; + 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; @@ -195,7 +280,7 @@ template<typename Solver> void check_sparse_spd_solving(Solver& solver) Mat A, halfA; DenseMatrix dA; for (int i = 0; i < g_repeat; i++) { - int size = generate_sparse_spd_problem(solver, A, halfA, dA); + int size = generate_sparse_spd_problem(solver, A, halfA, dA, maxSize); // generate the right hand sides int rhsCols = internal::random<int>(1,16); @@ -204,13 +289,17 @@ template<typename Solver> void check_sparse_spd_solving(Solver& solver) 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); - check_sparse_solving(solver, A, b, dA, b); - check_sparse_solving(solver, halfA, b, dA, b); - check_sparse_solving(solver, A, dB, dA, dB); - check_sparse_solving(solver, halfA, dB, dA, dB); - check_sparse_solving(solver, A, B, dA, dB); - check_sparse_solving(solver, halfA, B, dA, dB); + 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) @@ -221,25 +310,44 @@ template<typename Solver> void check_sparse_spd_solving(Solver& solver) } // First, get the folder -#ifdef TEST_REAL_CASES - if (internal::is_same<Scalar, float>::value - || internal::is_same<Scalar, std::complex<float> >::value) - return ; - - std::string mat_folder = get_matrixfolder<Scalar>(); - MatrixMarketIterator<Scalar> it(mat_folder); - for (; it; ++it) +#ifdef TEST_REAL_CASES + // Test real problems with double precision only + if (internal::is_same<typename NumTraits<Scalar>::Real, double>::value) { - if (it.sym() == SPD){ - Mat halfA; - PermutationMatrix<Dynamic, Dynamic, Index> pnull; - halfA.template selfadjointView<Solver::UpLo>() = it.matrix().template triangularView<Eigen::Lower>().twistedBy(pnull); - - std::cout<< " ==== SOLVING WITH MATRIX " << it.matname() << " ==== \n"; - check_sparse_solving_real_cases(solver, it.matrix(), it.rhs(), it.refX()); - check_sparse_solving_real_cases(solver, halfA, it.rhs(), it.refX()); + 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 } @@ -261,27 +369,39 @@ template<typename Solver> void check_sparse_spd_determinant(Solver& solver) } template<typename Solver, typename DenseMat> -int generate_sparse_square_problem(Solver&, typename Solver::MatrixType& A, DenseMat& dA, int maxSize = 300) +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; - int size = internal::random<int>(1,maxSize); + 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, ForceNonZeroDiag); + initSparse<Scalar>(density, dA, A, options); return size; } -template<typename Solver> void check_sparse_square_solving(Solver& solver) + +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> SpMat; + 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; @@ -290,7 +410,7 @@ template<typename Solver> void check_sparse_square_solving(Solver& solver) Mat A; DenseMatrix dA; for (int i = 0; i < g_repeat; i++) { - int size = generate_sparse_square_problem(solver, A, dA); + Index size = generate_sparse_square_problem(solver, A, dA, maxSize); A.makeCompressed(); DenseVector b = DenseVector::Random(size); @@ -299,9 +419,12 @@ template<typename Solver> void check_sparse_square_solving(Solver& solver) double density = (std::max)(8./(size*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); + 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) @@ -309,21 +432,44 @@ template<typename Solver> void check_sparse_square_solving(Solver& solver) 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 - if (internal::is_same<Scalar, float>::value - || internal::is_same<Scalar, std::complex<float> >::value) - return ; - - std::string mat_folder = get_matrixfolder<Scalar>(); - MatrixMarketIterator<Scalar> it(mat_folder); - for (; it; ++it) + // Test real problems with double precision only + if (internal::is_same<typename NumTraits<Scalar>::Real, double>::value) { - std::cout<< " ==== SOLVING WITH MATRIX " << it.matname() << " ==== \n"; - check_sparse_solving_real_cases(solver, it.matrix(), it.rhs(), it.refX()); + 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 } @@ -333,13 +479,20 @@ 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; - - // generate the problem - Mat A; - DenseMatrix dA; - generate_sparse_square_problem(solver, A, dA, 30); - A.makeCompressed(); + 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); } } @@ -350,13 +503,63 @@ template<typename Solver> void check_sparse_square_abs_determinant(Solver& solve typedef typename Mat::Scalar Scalar; typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - // generate the problem - Mat A; - DenseMatrix dA; - generate_sparse_square_problem(solver, A, dA, 30); - A.makeCompressed(); 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/test/sparse_vector.cpp b/test/sparse_vector.cpp index 0c9476803..b3e1dda25 100644 --- a/test/sparse_vector.cpp +++ b/test/sparse_vector.cpp @@ -9,22 +9,22 @@ #include "sparse.h" -template<typename Scalar,typename Index> void sparse_vector(int rows, int cols) +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./float(rows), 0.1); + double densityVec = (std::max)(8./(rows), 0.1); typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; typedef Matrix<Scalar,Dynamic,1> DenseVector; - typedef SparseVector<Scalar,0,Index> SparseVectorType; - typedef SparseMatrix<Scalar,0,Index> SparseMatrixType; + 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); + refV2 = DenseVector::Random(rows), + refV3 = DenseVector::Random(rows); std::vector<int> zerocoords, nonzerocoords; initSparse<Scalar>(densityVec, refV1, v1, &zerocoords, &nonzerocoords); @@ -52,6 +52,20 @@ template<typename Scalar,typename Index> void sparse_vector(int rows, int cols) } } 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); @@ -71,9 +85,12 @@ template<typename Scalar,typename Index> void sparse_vector(int rows, int cols) 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))); + { + 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()); @@ -96,15 +113,51 @@ template<typename Scalar,typename Index> void sparse_vector(int rows, int cols) 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>(16, 16) )); - CALL_SUBTEST_1(( sparse_vector<double,long int>(299, 535) )); - CALL_SUBTEST_1(( sparse_vector<double,short>(299, 535) )); + 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/test/sparselu.cpp b/test/sparselu.cpp index 37eb069a9..bd000baf1 100644 --- a/test/sparselu.cpp +++ b/test/sparselu.cpp @@ -3,25 +3,9 @@ // // Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> // -// Eigen is free software; you can redistribute it and/or -// modify it under the terms of the GNU Lesser General Public -// License as published by the Free Software Foundation; either -// version 3 of the License, or (at your option) any later version. -// -// Alternatively, you can redistribute it and/or -// modify it under the terms of the GNU General Public License as -// published by the Free Software Foundation; either version 2 of -// the License, or (at your option) any later version. -// -// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY -// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS -// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the -// GNU General Public License for more details. -// -// You should have received a copy of the GNU Lesser General Public -// License and a copy of the GNU General Public License along with -// Eigen. If not, see <http://www.gnu.org/licenses/>. - +// 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 @@ -41,9 +25,9 @@ template<typename T> void test_sparselu_T() 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); - check_sparse_square_solving(sparselu_amd); - check_sparse_square_solving(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); diff --git a/test/sparseqr.cpp b/test/sparseqr.cpp index 451c0e7f8..e8605fd21 100644 --- a/test/sparseqr.cpp +++ b/test/sparseqr.cpp @@ -10,11 +10,12 @@ #include <Eigen/SparseQR> template<typename MatrixType,typename DenseMat> -int generate_sparse_rectangular_problem(MatrixType& A, DenseMat& dA, int maxRows = 300) +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,rows); + int cols = internal::random<int>(1,maxCols); double density = (std::max)(8./(rows*cols), 0.01); A.resize(rows,cols); @@ -53,7 +54,7 @@ template<typename Scalar> void test_sparseqr_scalar() b = dA * DenseVector::Random(A.cols()); solver.compute(A); - if(internal::random<float>(0,1)>0.5) + 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) { @@ -88,6 +89,11 @@ template<typename Scalar> void test_sparseqr_scalar() 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() { diff --git a/test/spqr_support.cpp b/test/spqr_support.cpp index b8980e081..81e63b6a5 100644 --- a/test/spqr_support.cpp +++ b/test/spqr_support.cpp @@ -5,6 +5,8 @@ // // 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> @@ -18,8 +20,8 @@ int generate_sparse_rectangular_problem(MatrixType& A, DenseMat& dA, int maxRows int cols = internal::random<int>(1,rows); double density = (std::max)(8./(rows*cols), 0.01); - A.resize(rows,rows); - dA.resize(rows,rows); + A.resize(rows,cols); + dA.resize(rows,cols); initSparse<Scalar>(density, dA, A,ForceNonZeroDiag); A.makeCompressed(); return rows; @@ -35,7 +37,7 @@ template<typename Scalar> void test_spqr_scalar() SPQR<MatrixType> solver; generate_sparse_rectangular_problem(A,dA); - int m = A.rows(); + Index m = A.rows(); b = DenseVector::Random(m); solver.compute(A); if (solver.info() != Success) diff --git a/test/stable_norm.cpp b/test/stable_norm.cpp index 231dd9189..c3eb5ff31 100644 --- a/test/stable_norm.cpp +++ b/test/stable_norm.cpp @@ -1,7 +1,7 @@ // This file is part of Eigen, a lightweight C++ template library // for linear algebra. // -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> +// Copyright (C) 2009-2014 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 @@ -9,14 +9,6 @@ #include "main.h" -// workaround aggressive optimization in ICC -template<typename T> EIGEN_DONT_INLINE T sub(T a, T b) { return a - b; } - -template<typename T> bool isFinite(const T& x) -{ - return isNotNaN(sub(x,x)); -} - template<typename T> EIGEN_DONT_INLINE T copy(const T& x) { return x; @@ -32,6 +24,8 @@ template<typename MatrixType> void stable_norm(const MatrixType& m) typedef typename MatrixType::Index Index; typedef typename MatrixType::Scalar Scalar; typedef typename NumTraits<Scalar>::Real RealScalar; + + bool complex_real_product_ok = true; // Check the basic machine-dependent constants. { @@ -44,6 +38,16 @@ template<typename MatrixType> void stable_norm(const MatrixType& m) 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; + } } @@ -76,19 +80,19 @@ template<typename MatrixType> void stable_norm(const MatrixType& m) RealScalar size = static_cast<RealScalar>(m.size()); - // test isFinite - VERIFY(!isFinite( std::numeric_limits<RealScalar>::infinity())); - VERIFY(!isFinite(sqrt(-abs(big)))); + // test numext::isfinite + VERIFY(!(numext::isfinite)( std::numeric_limits<RealScalar>::infinity())); + VERIFY(!(numext::isfinite)(sqrt(-abs(big)))); // test overflow - VERIFY(isFinite(sqrt(size)*abs(big))); + 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(isFinite(sqrt(size)*abs(small))); + 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)); @@ -101,6 +105,79 @@ template<typename MatrixType> void stable_norm(const MatrixType& m) 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() diff --git a/test/eigen2/eigen2_newstdvector.cpp b/test/stddeque_overload.cpp index 5f9009901..4da618bbf 100644 --- a/test/eigen2/eigen2_newstdvector.cpp +++ b/test/stddeque_overload.cpp @@ -2,23 +2,36 @@ // 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/. -#define EIGEN_USE_NEW_STDVECTOR #include "main.h" -#include <Eigen/StdVector> + +#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_stdvector_matrix(const MatrixType& m) +void check_stddeque_matrix(const MatrixType& m) { - int rows = m.rows(); - int cols = m.cols(); + 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); + 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]); @@ -35,9 +48,8 @@ void check_stdvector_matrix(const MatrixType& m) VERIFY_IS_APPROX(v[21], y); v.push_back(x); VERIFY_IS_APPROX(v[22], x); - VERIFY((std::size_t)&(v[22]) == (std::size_t)&(v[21]) + sizeof(MatrixType)); - // do a lot of push_back such that the vector gets internally resized + // 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) @@ -49,11 +61,11 @@ void check_stdvector_matrix(const MatrixType& m) } template<typename TransformType> -void check_stdvector_transform(const TransformType&) +void check_stddeque_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); + std::deque<TransformType> v(10), w(20, y); v[5] = x; w[6] = v[5]; VERIFY_IS_APPROX(w[6], v[5]); @@ -70,9 +82,8 @@ void check_stdvector_transform(const TransformType&) VERIFY_IS_APPROX(v[21], y); v.push_back(x); VERIFY_IS_APPROX(v[22], x); - VERIFY((std::size_t)&(v[22]) == (std::size_t)&(v[21]) + sizeof(TransformType)); - // do a lot of push_back such that the vector gets internally resized + // 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) @@ -84,11 +95,11 @@ void check_stdvector_transform(const TransformType&) } template<typename QuaternionType> -void check_stdvector_quaternion(const QuaternionType&) +void check_stddeque_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); + std::deque<QuaternionType> v(10), w(20, y); v[5] = x; w[6] = v[5]; VERIFY_IS_APPROX(w[6], v[5]); @@ -105,9 +116,8 @@ void check_stdvector_quaternion(const QuaternionType&) VERIFY_IS_APPROX(v[21], y); v.push_back(x); VERIFY_IS_APPROX(v[22], x); - VERIFY((std::size_t)&(v[22]) == (std::size_t)&(v[21]) + sizeof(QuaternionType)); - // do a lot of push_back such that the vector gets internally resized + // 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) @@ -118,32 +128,31 @@ void check_stdvector_quaternion(const QuaternionType&) } } -void test_eigen2_newstdvector() +void test_stddeque_overload() { // some non vectorizable fixed sizes - CALL_SUBTEST_1(check_stdvector_matrix(Vector2f())); - CALL_SUBTEST_1(check_stdvector_matrix(Matrix3f())); - CALL_SUBTEST_1(check_stdvector_matrix(Matrix3d())); + 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_2(check_stdvector_matrix(Matrix2f())); - CALL_SUBTEST_2(check_stdvector_matrix(Vector4f())); - CALL_SUBTEST_2(check_stdvector_matrix(Matrix4f())); - CALL_SUBTEST_2(check_stdvector_matrix(Matrix4d())); + 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_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))); + 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_stdvector_transform(Transform2f())); - CALL_SUBTEST_4(check_stdvector_transform(Transform3f())); - CALL_SUBTEST_4(check_stdvector_transform(Transform3d())); - //CALL_SUBTEST(check_stdvector_transform(Transform4d())); + 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_stdvector_quaternion(Quaternionf())); - CALL_SUBTEST_5(check_stdvector_quaternion(Quaterniond())); + CALL_SUBTEST_5(check_stddeque_quaternion(Quaternionf())); + CALL_SUBTEST_5(check_stddeque_quaternion(Quaterniond())); } diff --git a/test/stdlist_overload.cpp b/test/stdlist_overload.cpp new file mode 100644 index 000000000..bb910bd43 --- /dev/null +++ b/test/stdlist_overload.cpp @@ -0,0 +1,192 @@ +// 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/test/stdvector.cpp b/test/stdvector.cpp index 6e173c678..50cb3341d 100644 --- a/test/stdvector.cpp +++ b/test/stdvector.cpp @@ -34,7 +34,7 @@ void check_stdvector_matrix(const MatrixType& m) 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)); + 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) @@ -69,7 +69,7 @@ void check_stdvector_transform(const TransformType&) 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)); + 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) @@ -104,7 +104,7 @@ void check_stdvector_quaternion(const QuaternionType&) 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)); + 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) diff --git a/test/stdvector_overload.cpp b/test/stdvector_overload.cpp index 736ff0ee7..959665954 100644 --- a/test/stdvector_overload.cpp +++ b/test/stdvector_overload.cpp @@ -48,7 +48,7 @@ void check_stdvector_matrix(const MatrixType& m) 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)); + 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) @@ -83,7 +83,7 @@ void check_stdvector_transform(const TransformType&) 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)); + 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) @@ -118,7 +118,7 @@ void check_stdvector_quaternion(const QuaternionType&) 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)); + 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) diff --git a/test/superlu_support.cpp b/test/superlu_support.cpp index 3b16135bc..98a7bc5c8 100644 --- a/test/superlu_support.cpp +++ b/test/superlu_support.cpp @@ -7,6 +7,7 @@ // 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> diff --git a/test/svd_common.h b/test/svd_common.h new file mode 100644 index 000000000..605d5dfef --- /dev/null +++ b/test/svd_common.h @@ -0,0 +1,483 @@ +// 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) +{ + typedef typename MatrixType::Index Index; + 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; + typedef typename MatrixType::Index Index; + 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; + typedef typename MatrixType::Index Index; + 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 + typedef typename MatrixType::Index Index; + 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; + typedef typename MatrixType::Index Index; + 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/test/svd_fill.h b/test/svd_fill.h new file mode 100644 index 000000000..3877c0c7e --- /dev/null +++ b/test/svd_fill.h @@ -0,0 +1,119 @@ +// 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; + typedef typename MatrixType::Index Index; + 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/test/swap.cpp b/test/swap.cpp index 36b353148..f76e3624d 100644 --- a/test/swap.cpp +++ b/test/swap.cpp @@ -41,9 +41,15 @@ template<typename MatrixType> void swap(const MatrixType& m) 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; @@ -68,16 +74,21 @@ template<typename MatrixType> void swap(const MatrixType& m) m1 = m1_copy; m3 = m3_copy; - // 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)); + 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(3,3)) ); // dyn size, no vectorization - CALL_SUBTEST_4( swap(MatrixXf(30,30)) ); // dyn 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/test/testsuite.cmake b/test/testsuite.cmake deleted file mode 100644 index 3bec56b3f..000000000 --- a/test/testsuite.cmake +++ /dev/null @@ -1,229 +0,0 @@ - -#################################################################### -# -# Usage: -# - create a new folder, let's call it cdash -# - in that folder, do: -# ctest -S path/to/eigen/test/testsuite.cmake[,option1=value1[,option2=value2]] -# -# Options: -# - EIGEN_CXX: compiler, eg.: g++-4.2 -# default: default c++ compiler -# - EIGEN_SITE: eg, INRIA-Bdx_pc-gael, or the name of the contributor, etc. -# default: hostname -# - EIGEN_BUILD_STRING: a string which identify the system/compiler. It should be formed like that: -# <OS_name>-<OS_version>-<arch>-<compiler-version> -# with: -# <OS_name> = opensuse, debian, osx, windows, cygwin, freebsd, solaris, etc. -# <OS_version> = 11.1, XP, vista, leopard, etc. -# <arch> = i386, x86_64, ia64, powerpc, etc. -# <compiler-version> = gcc-4.3.2, icc-11.0, MSVC-2008, etc. -# - EIGEN_EXPLICIT_VECTORIZATION: novec, SSE2, Altivec -# default: SSE2 for x86_64 systems, novec otherwise -# Its value is automatically appended to EIGEN_BUILD_STRING -# - EIGEN_CMAKE_DIR: path to cmake executable -# - EIGEN_MODE: dashboard model, can be Experimental, Nightly, or Continuous -# default: Nightly -# - EIGEN_WORK_DIR: directory used to download the source files and make the builds -# default: folder which contains this script -# - EIGEN_CMAKE_ARGS: additional arguments passed to cmake -# - EIGEN_GENERATOR_TYPE: allows to overwrite the generator type -# default: nmake (windows -# See http://www.cmake.org/cmake/help/cmake2.6docs.html#section_Generators for a complete -# list of supported generators. -# - EIGEN_NO_UPDATE: allows to submit dash boards from local repositories -# This might be interesting in case you want to submit dashboards -# including local changes. -# - CTEST_SOURCE_DIRECTORY: path to eigen's src (use a new and empty folder, not the one you are working on) -# default: <EIGEN_WORK_DIR>/src -# - CTEST_BINARY_DIRECTORY: build directory -# default: <EIGEN_WORK_DIR>/nightly-<EIGEN_CXX> -# -# Here is an example running several compilers on a linux system: -# #!/bin/bash -# ARCH=`uname -m` -# SITE=`hostname` -# VERSION=opensuse-11.1 -# WORK_DIR=/home/gael/Coding/eigen/cdash -# # get the last version of the script -# wget http://bitbucket.org/eigen/eigen/raw/tip/test/testsuite.cmake -o $WORK_DIR/testsuite.cmake -# COMMON="ctest -S $WORK_DIR/testsuite.cmake,EIGEN_WORK_DIR=$WORK_DIR,EIGEN_SITE=$SITE,EIGEN_MODE=$1,EIGEN_BUILD_STRING=$OS_VERSION-$ARCH" -# $COMMON-gcc-3.4.6,EIGEN_CXX=g++-3.4 -# $COMMON-gcc-4.0.1,EIGEN_CXX=g++-4.0.1 -# $COMMON-gcc-4.3.2,EIGEN_CXX=g++-4.3,EIGEN_EXPLICIT_VECTORIZATION=novec -# $COMMON-gcc-4.3.2,EIGEN_CXX=g++-4.3,EIGEN_EXPLICIT_VECTORIZATION=SSE2 -# $COMMON-icc-11.0,EIGEN_CXX=icpc -# -#################################################################### - -# process the arguments - -set(ARGLIST ${CTEST_SCRIPT_ARG}) -while(${ARGLIST} MATCHES ".+.*") - - # pick first - string(REGEX MATCH "([^,]*)(,.*)?" DUMMY ${ARGLIST}) - SET(TOP ${CMAKE_MATCH_1}) - - # remove first - string(REGEX MATCHALL "[^,]*,(.*)" DUMMY ${ARGLIST}) - SET(ARGLIST ${CMAKE_MATCH_1}) - - # decompose as a pair key=value - string(REGEX MATCH "([^=]*)(=.*)?" DUMMY ${TOP}) - SET(KEY ${CMAKE_MATCH_1}) - - string(REGEX MATCH "[^=]*=(.*)" DUMMY ${TOP}) - SET(VALUE ${CMAKE_MATCH_1}) - - # set the variable to the specified value - if(VALUE) - SET(${KEY} ${VALUE}) - else(VALUE) - SET(${KEY} ON) - endif(VALUE) - -endwhile(${ARGLIST} MATCHES ".+.*") - -#################################################################### -# Automatically set some user variables if they have not been defined manually -#################################################################### -cmake_minimum_required(VERSION 2.6 FATAL_ERROR) - -if(NOT EIGEN_SITE) - site_name(EIGEN_SITE) -endif(NOT EIGEN_SITE) - -if(NOT EIGEN_CMAKE_DIR) - SET(EIGEN_CMAKE_DIR "") -endif(NOT EIGEN_CMAKE_DIR) - -if(NOT EIGEN_BUILD_STRING) - - # let's try to find all information we need to make the build string ourself - - # OS - build_name(EIGEN_OS_VERSION) - - # arch - set(EIGEN_ARCH ${CMAKE_SYSTEM_PROCESSOR}) - if(WIN32) - set(EIGEN_ARCH $ENV{PROCESSOR_ARCHITECTURE}) - else(WIN32) - execute_process(COMMAND uname -m OUTPUT_VARIABLE EIGEN_ARCH OUTPUT_STRIP_TRAILING_WHITESPACE) - endif(WIN32) - - set(EIGEN_BUILD_STRING ${EIGEN_OS_VERSION}${EIGEN_ARCH}-${EIGEN_CXX}) - -endif(NOT EIGEN_BUILD_STRING) - -if(DEFINED EIGEN_EXPLICIT_VECTORIZATION) - set(EIGEN_BUILD_STRING ${EIGEN_BUILD_STRING}-${EIGEN_EXPLICIT_VECTORIZATION}) -endif(DEFINED EIGEN_EXPLICIT_VECTORIZATION) - -if(NOT EIGEN_WORK_DIR) - set(EIGEN_WORK_DIR ${CTEST_SCRIPT_DIRECTORY}) -endif(NOT EIGEN_WORK_DIR) - -if(NOT CTEST_SOURCE_DIRECTORY) - SET (CTEST_SOURCE_DIRECTORY "${EIGEN_WORK_DIR}/src") -endif(NOT CTEST_SOURCE_DIRECTORY) - -if(NOT CTEST_BINARY_DIRECTORY) - SET (CTEST_BINARY_DIRECTORY "${EIGEN_WORK_DIR}/nightly_${EIGEN_CXX}") -endif(NOT CTEST_BINARY_DIRECTORY) - -if(NOT EIGEN_MODE) - set(EIGEN_MODE Nightly) -endif(NOT EIGEN_MODE) - -## mandatory variables (the default should be ok in most cases): - -if(NOT EIGEN_NO_UPDATE) - SET (CTEST_CVS_COMMAND "hg") - SET (CTEST_CVS_CHECKOUT "${CTEST_CVS_COMMAND} clone http://bitbucket.org/eigen/eigen \"${CTEST_SOURCE_DIRECTORY}\"") - SET(CTEST_BACKUP_AND_RESTORE TRUE) # the backup is CVS related ... -endif(NOT EIGEN_NO_UPDATE) - -# which ctest command to use for running the dashboard -SET (CTEST_COMMAND "${EIGEN_CMAKE_DIR}ctest -D ${EIGEN_MODE} --no-compress-output") -if($ENV{EIGEN_CTEST_ARGS}) -SET (CTEST_COMMAND "${CTEST_COMMAND} $ENV{EIGEN_CTEST_ARGS}") -endif($ENV{EIGEN_CTEST_ARGS}) -# what cmake command to use for configuring this dashboard -SET (CTEST_CMAKE_COMMAND "${EIGEN_CMAKE_DIR}cmake -DEIGEN_LEAVE_TEST_IN_ALL_TARGET=ON") - -#################################################################### -# The values in this section are optional you can either -# have them or leave them commented out -#################################################################### - -# this make sure we get consistent outputs -SET($ENV{LC_MESSAGES} "en_EN") - -# should ctest wipe the binary tree before running -SET(CTEST_START_WITH_EMPTY_BINARY_DIRECTORY TRUE) - -# raise the warning/error limit -set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_WARNINGS "33331") -set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_ERRORS "33331") - -# this is the initial cache to use for the binary tree, be careful to escape -# any quotes inside of this string if you use it -if(WIN32 AND NOT UNIX) - #message(SEND_ERROR "win32") - if(EIGEN_GENERATOR_TYPE) - set(CTEST_CMAKE_COMMAND "${CTEST_CMAKE_COMMAND} -G \"${EIGEN_GENERATOR_TYPE}\"") - SET (CTEST_INITIAL_CACHE " - CMAKE_BUILD_TYPE:STRING=Release - BUILDNAME:STRING=${EIGEN_BUILD_STRING} - SITE:STRING=${EIGEN_SITE} - ") - else(EIGEN_GENERATOR_TYPE) - set(CTEST_CMAKE_COMMAND "${CTEST_CMAKE_COMMAND} -G \"NMake Makefiles\" -DCMAKE_MAKE_PROGRAM=nmake") - SET (CTEST_INITIAL_CACHE " - MAKECOMMAND:STRING=nmake /i - CMAKE_MAKE_PROGRAM:FILEPATH=nmake - CMAKE_GENERATOR:INTERNAL=NMake Makefiles - CMAKE_BUILD_TYPE:STRING=Release - BUILDNAME:STRING=${EIGEN_BUILD_STRING} - SITE:STRING=${EIGEN_SITE} - ") - endif(EIGEN_GENERATOR_TYPE) -else(WIN32 AND NOT UNIX) - SET (CTEST_INITIAL_CACHE " - BUILDNAME:STRING=${EIGEN_BUILD_STRING} - SITE:STRING=${EIGEN_SITE} - ") -endif(WIN32 AND NOT UNIX) - -# set any extra environment variables to use during the execution of the script here: -# setting this variable on windows machines causes trouble ... - -if(EIGEN_CXX AND NOT WIN32) - set(CTEST_ENVIRONMENT "CXX=${EIGEN_CXX}") -endif(EIGEN_CXX AND NOT WIN32) - -if(DEFINED EIGEN_EXPLICIT_VECTORIZATION) - if(EIGEN_EXPLICIT_VECTORIZATION MATCHES SSE2) - set(CTEST_CMAKE_COMMAND "${CTEST_CMAKE_COMMAND} -DEIGEN_TEST_SSE2=ON") - elseif(EIGEN_EXPLICIT_VECTORIZATION MATCHES SSE3) - set(CTEST_CMAKE_COMMAND "${CTEST_CMAKE_COMMAND} -DEIGEN_TEST_SSE2=ON -DEIGEN_TEST_SSE3=ON") - elseif(EIGEN_EXPLICIT_VECTORIZATION MATCHES SSSE3) - set(CTEST_CMAKE_COMMAND "${CTEST_CMAKE_COMMAND} -DEIGEN_TEST_SSE2=ON -DEIGEN_TEST_SSE3=ON -DEIGEN_TEST_SSSE3=ON") - elseif(EIGEN_EXPLICIT_VECTORIZATION MATCHES SSE4_1) - set(CTEST_CMAKE_COMMAND "${CTEST_CMAKE_COMMAND} -DEIGEN_TEST_SSE2=ON -DEIGEN_TEST_SSE3=ON -DEIGEN_TEST_SSSE3=ON -DEIGEN_TEST_SSE4_1=ON") - elseif(EIGEN_EXPLICIT_VECTORIZATION MATCHES SSE4_2) - set(CTEST_CMAKE_COMMAND "${CTEST_CMAKE_COMMAND} -DEIGEN_TEST_SSE2=ON -DEIGEN_TEST_SSE3=ON -DEIGEN_TEST_SSSE3=ON -DEIGEN_TEST_SSE4_1=ON -DEIGEN_TEST_SSE4_2=ON") - elseif(EIGEN_EXPLICIT_VECTORIZATION MATCHES Altivec) - set(CTEST_CMAKE_COMMAND "${CTEST_CMAKE_COMMAND} -DEIGEN_TEST_ALTIVEC=ON") - elseif(EIGEN_EXPLICIT_VECTORIZATION MATCHES novec) - set(CTEST_CMAKE_COMMAND "${CTEST_CMAKE_COMMAND} -DEIGEN_TEST_NO_EXPLICIT_VECTORIZATION=ON") - else(EIGEN_EXPLICIT_VECTORIZATION MATCHES SSE2) - message(FATAL_ERROR "Invalid value for EIGEN_EXPLICIT_VECTORIZATION (${EIGEN_EXPLICIT_VECTORIZATION}), must be: novec, SSE2, SSE3, Altivec") - endif(EIGEN_EXPLICIT_VECTORIZATION MATCHES SSE2) -endif(DEFINED EIGEN_EXPLICIT_VECTORIZATION) - -if(DEFINED EIGEN_CMAKE_ARGS) - set(CTEST_CMAKE_COMMAND "${CTEST_CMAKE_COMMAND} ${EIGEN_CMAKE_ARGS}") -endif(DEFINED EIGEN_CMAKE_ARGS) diff --git a/test/triangular.cpp b/test/triangular.cpp index 54320390b..b96856486 100644 --- a/test/triangular.cpp +++ b/test/triangular.cpp @@ -65,7 +65,7 @@ template<typename MatrixType> void triangular_square(const MatrixType& m) m1 = MatrixType::Random(rows, cols); for (int i=0; i<rows; ++i) - while (numext::abs2(m1(i,i))<1e-1) m1(i,i) = internal::random<Scalar>(); + 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 @@ -78,7 +78,7 @@ template<typename MatrixType> void triangular_square(const MatrixType& m) m3 = m1.template triangularView<Lower>(); VERIFY(v2.isApprox(m3.conjugate() * (m1.conjugate().template triangularView<Lower>().solve(v2)), largerEps)); - // test back and forward subsitution with a matrix as the rhs + // 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>(); @@ -113,6 +113,21 @@ template<typename MatrixType> void triangular_square(const MatrixType& m) 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()); } diff --git a/test/umfpack_support.cpp b/test/umfpack_support.cpp index 9eb84c14b..37ab11f0b 100644 --- a/test/umfpack_support.cpp +++ b/test/umfpack_support.cpp @@ -7,6 +7,7 @@ // 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> diff --git a/test/unalignedassert.cpp b/test/unalignedassert.cpp index 601dbf214..731a08977 100644 --- a/test/unalignedassert.cpp +++ b/test/unalignedassert.cpp @@ -2,13 +2,39 @@ // 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. @@ -36,7 +62,7 @@ struct TestNew4 struct TestNew5 { EIGEN_MAKE_ALIGNED_OPERATOR_NEW - float f; // try the f at first -- the EIGEN_ALIGN16 attribute of m should make that still work + float f; // try the f at first -- the EIGEN_ALIGN_MAX attribute of m should make that still work Matrix4f m; }; @@ -63,13 +89,13 @@ void check_unalignedassert_good() delete[] y; } -#if EIGEN_ALIGN_STATICALLY +#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<size_t>(buf); - _buf += (16 - (_buf % 16)); // make 16-byte aligned + 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 @@ -79,26 +105,36 @@ void construct_at_boundary(int boundary) void unalignedassert() { - #if EIGEN_ALIGN_STATICALLY +#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>(16); + construct_at_boundary<Matrix4f>(EIGEN_MAX_ALIGN_BYTES); construct_at_boundary<Vector2d>(16); construct_at_boundary<Vector3d>(4); - construct_at_boundary<Vector4d>(16); - construct_at_boundary<Matrix2d>(16); + 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>(16); + construct_at_boundary<Matrix4d>(EIGEN_MAX_ALIGN_BYTES); construct_at_boundary<Vector2cf>(16); construct_at_boundary<Vector3cf>(4); - construct_at_boundary<Vector2cd>(16); + construct_at_boundary<Vector2cd>(EIGEN_MAX_ALIGN_BYTES); construct_at_boundary<Vector3cd>(16); - #endif +#endif check_unalignedassert_good<TestNew1>(); check_unalignedassert_good<TestNew2>(); @@ -109,15 +145,32 @@ void unalignedassert() check_unalignedassert_good<TestNew6>(); check_unalignedassert_good<Depends<true> >(); -#if EIGEN_ALIGN_STATICALLY - VERIFY_RAISES_ASSERT(construct_at_boundary<Vector4f>(8)); - VERIFY_RAISES_ASSERT(construct_at_boundary<Matrix4f>(8)); - VERIFY_RAISES_ASSERT(construct_at_boundary<Vector2d>(8)); - VERIFY_RAISES_ASSERT(construct_at_boundary<Vector4d>(8)); - VERIFY_RAISES_ASSERT(construct_at_boundary<Matrix2d>(8)); - VERIFY_RAISES_ASSERT(construct_at_boundary<Matrix4d>(8)); - VERIFY_RAISES_ASSERT(construct_at_boundary<Vector2cf>(8)); - VERIFY_RAISES_ASSERT(construct_at_boundary<Vector2cd>(8)); +#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 } diff --git a/test/unalignedcount.cpp b/test/unalignedcount.cpp index ca7e159f3..d6ffeafdf 100644 --- a/test/unalignedcount.cpp +++ b/test/unalignedcount.cpp @@ -30,7 +30,14 @@ static int nb_storeu; void test_unalignedcount() { - #ifdef EIGEN_VECTORIZE_SSE + #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); diff --git a/test/upperbidiagonalization.cpp b/test/upperbidiagonalization.cpp index d15bf588b..847b34b55 100644 --- a/test/upperbidiagonalization.cpp +++ b/test/upperbidiagonalization.cpp @@ -35,7 +35,7 @@ void test_upperbidiagonalization() 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(MatrixXcd(16,15)) ); + 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/test/vectorization_logic.cpp b/test/vectorization_logic.cpp index aee68a87f..83c1439ad 100644 --- a/test/vectorization_logic.cpp +++ b/test/vectorization_logic.cpp @@ -1,45 +1,51 @@ // 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) 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> -std::string demangle_traversal(int t) -{ - if(t==DefaultTraversal) return "DefaultTraversal"; - if(t==LinearTraversal) return "LinearTraversal"; - if(t==InnerVectorizedTraversal) return "InnerVectorizedTraversal"; - if(t==LinearVectorizedTraversal) return "LinearVectorizedTraversal"; - if(t==SliceVectorizedTraversal) return "SliceVectorizedTraversal"; - return "?"; -} -std::string demangle_unrolling(int t) -{ - if(t==NoUnrolling) return "NoUnrolling"; - if(t==InnerUnrolling) return "InnerUnrolling"; - if(t==CompleteUnrolling) return "CompleteUnrolling"; - return "?"; -} +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) { - internal::assign_traits<Dst,Src>::debug(); - bool res = internal::assign_traits<Dst,Src>::Traversal==traversal - && internal::assign_traits<Dst,Src>::Unrolling==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(internal::assign_traits<Dst,Src>::Traversal) << "\n"; + << " got " << demangle_traversal(traits::Traversal) << "\n"; std::cerr << " Expected Unrolling == " << demangle_unrolling(unrolling) - << " got " << demangle_unrolling(internal::assign_traits<Dst,Src>::Unrolling) << "\n"; + << " got " << demangle_unrolling(traits::Unrolling) << "\n"; } return res; } @@ -47,15 +53,19 @@ bool test_assign(const Dst&, const Src&, int traversal, int unrolling) template<typename Dst, typename Src> bool test_assign(int traversal, int unrolling) { - internal::assign_traits<Dst,Src>::debug(); - bool res = internal::assign_traits<Dst,Src>::Traversal==traversal - && internal::assign_traits<Dst,Src>::Unrolling==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(internal::assign_traits<Dst,Src>::Traversal) << "\n"; + << " got " << demangle_traversal(traits::Traversal) << "\n"; std::cerr << " Expected Unrolling == " << demangle_unrolling(unrolling) - << " got " << demangle_unrolling(internal::assign_traits<Dst,Src>::Unrolling) << "\n"; + << " got " << demangle_unrolling(traits::Unrolling) << "\n"; } return res; } @@ -63,10 +73,16 @@ bool test_assign(int traversal, int unrolling) template<typename Xpr> bool test_redux(const Xpr&, int traversal, int unrolling) { - typedef internal::redux_traits<internal::scalar_sum_op<typename Xpr::Scalar>,Xpr> traits; + 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) @@ -75,10 +91,16 @@ bool test_redux(const Xpr&, int traversal, int unrolling) return res; } -template<typename Scalar, bool Enable = internal::packet_traits<Scalar>::Vectorizable> struct vectorization_logic +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::packet_traits<Scalar>::size + PacketSize = internal::unpacket_traits<PacketType>::size, + HalfPacketSize = internal::unpacket_traits<HalfPacketType>::size }; static void run() { @@ -90,8 +112,8 @@ template<typename Scalar, bool Enable = internal::packet_traits<Scalar>::Vectori 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,16,ColMajor> Matrix44c; - typedef Matrix<Scalar,4*PacketSize,16,RowMajor> Matrix44r; + 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), @@ -131,35 +153,63 @@ template<typename Scalar, bool Enable = internal::packet_traits<Scalar>::Vectori InnerVectorizedTraversal,InnerUnrolling)); VERIFY(test_assign(Matrix44u(),Matrix44()+Matrix44(), - LinearTraversal,NoUnrolling)); + 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(), - LinearTraversal,CompleteUnrolling)); + 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), - LinearTraversal,CompleteUnrolling)); - - VERIFY(test_assign(Matrix3(),Matrix3().cwiseQuotient(Matrix3()), + 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>(), - LinearTraversal,NoUnrolling)); + HalfPacketSize==1 ? InnerVectorizedTraversal : + EIGEN_UNALIGNED_VECTORIZE ? 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>(10,4), - DefaultTraversal,CompleteUnrolling)); + 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(Matrix<Scalar,PacketSize,3>(), + LinearVectorizedTraversal,CompleteUnrolling)); + VERIFY(test_redux(Matrix3(), LinearVectorizedTraversal,CompleteUnrolling)); @@ -174,18 +224,19 @@ template<typename Scalar, bool Enable = internal::packet_traits<Scalar>::Vectori VERIFY(test_redux(Matrix44r().template block<1,2*PacketSize>(2,1), LinearVectorizedTraversal,CompleteUnrolling)); - + VERIFY((test_assign< - Map<Matrix22, Aligned, OuterStride<3*PacketSize> >, + Map<Matrix22, AlignedMax, OuterStride<3*PacketSize> >, Matrix22 >(InnerVectorizedTraversal,CompleteUnrolling))); VERIFY((test_assign< - Map<Matrix22, Aligned, InnerStride<3*PacketSize> >, - Matrix22 - >(DefaultTraversal,CompleteUnrolling))); + 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(), Matrix11()*Matrix11(), InnerVectorizedTraversal, 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), @@ -193,12 +244,138 @@ template<typename Scalar, bool Enable = internal::packet_traits<Scalar>::Vectori 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<Scalar,false> +template<typename Scalar> struct vectorization_logic_half<Scalar,false> { static void run() {} }; @@ -208,27 +385,34 @@ 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>(), - LinearTraversal,CompleteUnrolling)); + EIGEN_UNALIGNED_VECTORIZE ? LinearVectorizedTraversal : LinearTraversal,CompleteUnrolling)); VERIFY(test_redux(Matrix<float,5,2>(), - DefaultTraversal,CompleteUnrolling)); + 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>(), - LinearTraversal,CompleteUnrolling)); + EIGEN_UNALIGNED_VECTORIZE ? LinearVectorizedTraversal : LinearTraversal,CompleteUnrolling)); VERIFY(test_redux(Matrix<double,7,3>(), - DefaultTraversal,CompleteUnrolling)); + EIGEN_UNALIGNED_VECTORIZE ? LinearVectorizedTraversal : DefaultTraversal,CompleteUnrolling)); } #endif // EIGEN_VECTORIZE diff --git a/test/vectorwiseop.cpp b/test/vectorwiseop.cpp index 6cd1acdda..f3ab561ee 100644 --- a/test/vectorwiseop.cpp +++ b/test/vectorwiseop.cpp @@ -2,11 +2,13 @@ // 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" @@ -101,16 +103,28 @@ template<typename ArrayType> void vectorwiseop_array(const ArrayType& m) 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 suppposed to evaluate " m2.colwise().sum()" into to temporary to avoid - // evaluating the reducions multiple times + // 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) @@ -144,16 +158,22 @@ template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m) 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()); + 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); - VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose()); - VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose()); + if(cols>1) + { + VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose()); + VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose()); + } // test substraction @@ -162,29 +182,43 @@ template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m) 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()); + 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); - VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose()); - VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose()); - + 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(); @@ -192,14 +226,27 @@ template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m) 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(7,2))); + 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/test/visitor.cpp b/test/visitor.cpp index 39a5d6b5f..844170ec6 100644 --- a/test/visitor.cpp +++ b/test/visitor.cpp @@ -55,6 +55,11 @@ template<typename MatrixType> void matrixVisitor(const MatrixType& p) 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) diff --git a/test/zerosized.cpp b/test/zerosized.cpp index da7dd0481..477ff0070 100644 --- a/test/zerosized.cpp +++ b/test/zerosized.cpp @@ -25,6 +25,7 @@ template<typename MatrixType> void zeroReduction(const MatrixType& m) { template<typename MatrixType> void zeroSizedMatrix() { MatrixType t1; + typedef typename MatrixType::Scalar Scalar; if (MatrixType::SizeAtCompileTime == Dynamic || MatrixType::SizeAtCompileTime == 0) { @@ -37,7 +38,7 @@ template<typename MatrixType> void zeroSizedMatrix() if (MatrixType::RowsAtCompileTime == Dynamic && MatrixType::ColsAtCompileTime == Dynamic) { - MatrixType t2(0, 0); + MatrixType t2(0, 0), t3(t1); VERIFY(t2.rows() == 0); VERIFY(t2.cols() == 0); @@ -45,6 +46,23 @@ template<typename MatrixType> void zeroSizedMatrix() 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() |