diff options
| author | Stanislaw Halik <sthalik@misaki.pl> | 2019-03-03 21:09:10 +0100 |
|---|---|---|
| committer | Stanislaw Halik <sthalik@misaki.pl> | 2019-03-03 21:10:13 +0100 |
| commit | f0238cfb6997c4acfc2bd200de7295f3fa36968f (patch) | |
| tree | b215183760e4f615b9c1dabc1f116383b72a1b55 /eigen/unsupported/test | |
| parent | 543edd372a5193d04b3de9f23c176ab439e51b31 (diff) | |
don't index Eigen
Diffstat (limited to 'eigen/unsupported/test')
100 files changed, 0 insertions, 25752 deletions
diff --git a/eigen/unsupported/test/BVH.cpp b/eigen/unsupported/test/BVH.cpp deleted file mode 100644 index ff5b329..0000000 --- a/eigen/unsupported/test/BVH.cpp +++ /dev/null @@ -1,222 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Ilya Baran <ibaran@mit.edu> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <Eigen/StdVector> -#include <Eigen/Geometry> -#include <unsupported/Eigen/BVH> - -namespace Eigen { - -template<typename Scalar, int Dim> AlignedBox<Scalar, Dim> bounding_box(const Matrix<Scalar, Dim, 1> &v) { return AlignedBox<Scalar, Dim>(v); } - -} - - -template<int Dim> -struct Ball -{ -EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(double, Dim) - - typedef Matrix<double, Dim, 1> VectorType; - - Ball() {} - Ball(const VectorType &c, double r) : center(c), radius(r) {} - - VectorType center; - double radius; -}; -template<int Dim> AlignedBox<double, Dim> bounding_box(const Ball<Dim> &b) -{ return AlignedBox<double, Dim>(b.center.array() - b.radius, b.center.array() + b.radius); } - -inline double SQR(double x) { return x * x; } - -template<int Dim> -struct BallPointStuff //this class provides functions to be both an intersector and a minimizer, both for a ball and a point and for two trees -{ - typedef double Scalar; - typedef Matrix<double, Dim, 1> VectorType; - typedef Ball<Dim> BallType; - typedef AlignedBox<double, Dim> BoxType; - - BallPointStuff() : calls(0), count(0) {} - BallPointStuff(const VectorType &inP) : p(inP), calls(0), count(0) {} - - - bool intersectVolume(const BoxType &r) { ++calls; return r.contains(p); } - bool intersectObject(const BallType &b) { - ++calls; - if((b.center - p).squaredNorm() < SQR(b.radius)) - ++count; - return false; //continue - } - - bool intersectVolumeVolume(const BoxType &r1, const BoxType &r2) { ++calls; return !(r1.intersection(r2)).isNull(); } - bool intersectVolumeObject(const BoxType &r, const BallType &b) { ++calls; return r.squaredExteriorDistance(b.center) < SQR(b.radius); } - bool intersectObjectVolume(const BallType &b, const BoxType &r) { ++calls; return r.squaredExteriorDistance(b.center) < SQR(b.radius); } - bool intersectObjectObject(const BallType &b1, const BallType &b2){ - ++calls; - if((b1.center - b2.center).norm() < b1.radius + b2.radius) - ++count; - return false; - } - bool intersectVolumeObject(const BoxType &r, const VectorType &v) { ++calls; return r.contains(v); } - bool intersectObjectObject(const BallType &b, const VectorType &v){ - ++calls; - if((b.center - v).squaredNorm() < SQR(b.radius)) - ++count; - return false; - } - - double minimumOnVolume(const BoxType &r) { ++calls; return r.squaredExteriorDistance(p); } - double minimumOnObject(const BallType &b) { ++calls; return (std::max)(0., (b.center - p).squaredNorm() - SQR(b.radius)); } - double minimumOnVolumeVolume(const BoxType &r1, const BoxType &r2) { ++calls; return r1.squaredExteriorDistance(r2); } - double minimumOnVolumeObject(const BoxType &r, const BallType &b) { ++calls; return SQR((std::max)(0., r.exteriorDistance(b.center) - b.radius)); } - double minimumOnObjectVolume(const BallType &b, const BoxType &r) { ++calls; return SQR((std::max)(0., r.exteriorDistance(b.center) - b.radius)); } - double minimumOnObjectObject(const BallType &b1, const BallType &b2){ ++calls; return SQR((std::max)(0., (b1.center - b2.center).norm() - b1.radius - b2.radius)); } - double minimumOnVolumeObject(const BoxType &r, const VectorType &v) { ++calls; return r.squaredExteriorDistance(v); } - double minimumOnObjectObject(const BallType &b, const VectorType &v){ ++calls; return SQR((std::max)(0., (b.center - v).norm() - b.radius)); } - - VectorType p; - int calls; - int count; -}; - - -template<int Dim> -struct TreeTest -{ - typedef Matrix<double, Dim, 1> VectorType; - typedef std::vector<VectorType, aligned_allocator<VectorType> > VectorTypeList; - typedef Ball<Dim> BallType; - typedef std::vector<BallType, aligned_allocator<BallType> > BallTypeList; - typedef AlignedBox<double, Dim> BoxType; - - void testIntersect1() - { - BallTypeList b; - for(int i = 0; i < 500; ++i) { - b.push_back(BallType(VectorType::Random(), 0.5 * internal::random(0., 1.))); - } - KdBVH<double, Dim, BallType> tree(b.begin(), b.end()); - - VectorType pt = VectorType::Random(); - BallPointStuff<Dim> i1(pt), i2(pt); - - for(int i = 0; i < (int)b.size(); ++i) - i1.intersectObject(b[i]); - - BVIntersect(tree, i2); - - VERIFY(i1.count == i2.count); - } - - void testMinimize1() - { - BallTypeList b; - for(int i = 0; i < 500; ++i) { - b.push_back(BallType(VectorType::Random(), 0.01 * internal::random(0., 1.))); - } - KdBVH<double, Dim, BallType> tree(b.begin(), b.end()); - - VectorType pt = VectorType::Random(); - BallPointStuff<Dim> i1(pt), i2(pt); - - double m1 = (std::numeric_limits<double>::max)(), m2 = m1; - - for(int i = 0; i < (int)b.size(); ++i) - m1 = (std::min)(m1, i1.minimumOnObject(b[i])); - - m2 = BVMinimize(tree, i2); - - VERIFY_IS_APPROX(m1, m2); - } - - void testIntersect2() - { - BallTypeList b; - VectorTypeList v; - - for(int i = 0; i < 50; ++i) { - b.push_back(BallType(VectorType::Random(), 0.5 * internal::random(0., 1.))); - for(int j = 0; j < 3; ++j) - v.push_back(VectorType::Random()); - } - - KdBVH<double, Dim, BallType> tree(b.begin(), b.end()); - KdBVH<double, Dim, VectorType> vTree(v.begin(), v.end()); - - BallPointStuff<Dim> i1, i2; - - for(int i = 0; i < (int)b.size(); ++i) - for(int j = 0; j < (int)v.size(); ++j) - i1.intersectObjectObject(b[i], v[j]); - - BVIntersect(tree, vTree, i2); - - VERIFY(i1.count == i2.count); - } - - void testMinimize2() - { - BallTypeList b; - VectorTypeList v; - - for(int i = 0; i < 50; ++i) { - b.push_back(BallType(VectorType::Random(), 1e-7 + 1e-6 * internal::random(0., 1.))); - for(int j = 0; j < 3; ++j) - v.push_back(VectorType::Random()); - } - - KdBVH<double, Dim, BallType> tree(b.begin(), b.end()); - KdBVH<double, Dim, VectorType> vTree(v.begin(), v.end()); - - BallPointStuff<Dim> i1, i2; - - double m1 = (std::numeric_limits<double>::max)(), m2 = m1; - - for(int i = 0; i < (int)b.size(); ++i) - for(int j = 0; j < (int)v.size(); ++j) - m1 = (std::min)(m1, i1.minimumOnObjectObject(b[i], v[j])); - - m2 = BVMinimize(tree, vTree, i2); - - VERIFY_IS_APPROX(m1, m2); - } -}; - - -void test_BVH() -{ - for(int i = 0; i < g_repeat; i++) { -#ifdef EIGEN_TEST_PART_1 - TreeTest<2> test2; - CALL_SUBTEST(test2.testIntersect1()); - CALL_SUBTEST(test2.testMinimize1()); - CALL_SUBTEST(test2.testIntersect2()); - CALL_SUBTEST(test2.testMinimize2()); -#endif - -#ifdef EIGEN_TEST_PART_2 - TreeTest<3> test3; - CALL_SUBTEST(test3.testIntersect1()); - CALL_SUBTEST(test3.testMinimize1()); - CALL_SUBTEST(test3.testIntersect2()); - CALL_SUBTEST(test3.testMinimize2()); -#endif - -#ifdef EIGEN_TEST_PART_3 - TreeTest<4> test4; - CALL_SUBTEST(test4.testIntersect1()); - CALL_SUBTEST(test4.testMinimize1()); - CALL_SUBTEST(test4.testIntersect2()); - CALL_SUBTEST(test4.testMinimize2()); -#endif - } -} diff --git a/eigen/unsupported/test/CMakeLists.txt b/eigen/unsupported/test/CMakeLists.txt deleted file mode 100644 index 3a8775a..0000000 --- a/eigen/unsupported/test/CMakeLists.txt +++ /dev/null @@ -1,263 +0,0 @@ -# generate split test header file only if it does not yet exist -# in order to prevent a rebuild everytime cmake is configured -if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/split_test_helper.h) - file(WRITE ${CMAKE_CURRENT_BINARY_DIR}/split_test_helper.h "") - foreach(i RANGE 1 999) - file(APPEND ${CMAKE_CURRENT_BINARY_DIR}/split_test_helper.h - "#ifdef EIGEN_TEST_PART_${i}\n" - "#define CALL_SUBTEST_${i}(FUNC) CALL_SUBTEST(FUNC)\n" - "#else\n" - "#define CALL_SUBTEST_${i}(FUNC)\n" - "#endif\n\n" - ) - endforeach() -endif() - -set_property(GLOBAL PROPERTY EIGEN_CURRENT_SUBPROJECT "Unsupported") -add_custom_target(BuildUnsupported) - -include_directories(../../test ../../unsupported ../../Eigen - ${CMAKE_CURRENT_BINARY_DIR}/../../test) - -find_package (Threads) - -find_package(GoogleHash) -if(GOOGLEHASH_FOUND) - add_definitions("-DEIGEN_GOOGLEHASH_SUPPORT") - include_directories(${GOOGLEHASH_INCLUDES}) - ei_add_property(EIGEN_TESTED_BACKENDS "GoogleHash, ") -else(GOOGLEHASH_FOUND) - ei_add_property(EIGEN_MISSING_BACKENDS "GoogleHash, ") -endif(GOOGLEHASH_FOUND) - - -find_package(Adolc) -if(ADOLC_FOUND) - include_directories(${ADOLC_INCLUDES}) - ei_add_property(EIGEN_TESTED_BACKENDS "Adolc, ") - if(EIGEN_TEST_CXX11) - ei_add_test(forward_adolc "" ${ADOLC_LIBRARIES}) - else() - message(STATUS "Adolc found, but tests require C++11 mode") - endif() -else(ADOLC_FOUND) - ei_add_property(EIGEN_MISSING_BACKENDS "Adolc, ") -endif(ADOLC_FOUND) - -# this test seems to never have been successful on x87, so is considered to contain a FP-related bug. -# see thread: "non-linear optimization test summary" -ei_add_test(NonLinearOptimization) - -ei_add_test(NumericalDiff) -ei_add_test(autodiff_scalar) -ei_add_test(autodiff) - -if (NOT CMAKE_CXX_COMPILER MATCHES "clang\\+\\+$") -ei_add_test(BVH) -endif() - -ei_add_test(matrix_exponential) -ei_add_test(matrix_function) -ei_add_test(matrix_power) -ei_add_test(matrix_square_root) -ei_add_test(alignedvector3) - -ei_add_test(FFT) - -ei_add_test(EulerAngles) - -find_package(MPFR 2.3.0) -find_package(GMP) -if(MPFR_FOUND AND EIGEN_COMPILER_SUPPORT_CPP11) - include_directories(${MPFR_INCLUDES} ./mpreal) - ei_add_property(EIGEN_TESTED_BACKENDS "MPFR C++, ") - set(EIGEN_MPFR_TEST_LIBRARIES ${MPFR_LIBRARIES} ${GMP_LIBRARIES}) - ei_add_test(mpreal_support "-std=c++11" "${EIGEN_MPFR_TEST_LIBRARIES}" ) -else() - ei_add_property(EIGEN_MISSING_BACKENDS "MPFR C++, ") -endif() - -ei_add_test(sparse_extra "" "") - -find_package(FFTW) -if(FFTW_FOUND) - ei_add_property(EIGEN_TESTED_BACKENDS "fftw, ") - include_directories( ${FFTW_INCLUDES} ) - if(FFTWL_LIB) - ei_add_test(FFTW "-DEIGEN_FFTW_DEFAULT -DEIGEN_HAS_FFTWL" "${FFTW_LIBRARIES}" ) - else() - ei_add_test(FFTW "-DEIGEN_FFTW_DEFAULT" "${FFTW_LIBRARIES}" ) - endif() -else() - ei_add_property(EIGEN_MISSING_BACKENDS "fftw, ") -endif() - -option(EIGEN_TEST_NO_OPENGL "Disable OpenGL support in unit tests" OFF) -if(NOT EIGEN_TEST_NO_OPENGL) - find_package(OpenGL) - find_package(GLUT) - find_package(GLEW) - if(OPENGL_FOUND AND GLUT_FOUND AND GLEW_FOUND) - include_directories(${OPENGL_INCLUDE_DIR} ${GLUT_INCLUDE_DIR} ${GLEW_INCLUDE_DIRS}) - ei_add_property(EIGEN_TESTED_BACKENDS "OpenGL, ") - set(EIGEN_GL_LIB ${GLUT_LIBRARIES} ${GLEW_LIBRARIES} ${OPENGL_LIBRARIES}) - ei_add_test(openglsupport "" "${EIGEN_GL_LIB}" ) - else() - ei_add_property(EIGEN_MISSING_BACKENDS "OpenGL, ") - endif() -else() - ei_add_property(EIGEN_MISSING_BACKENDS "OpenGL, ") -endif() - -ei_add_test(polynomialsolver) -ei_add_test(polynomialutils) -ei_add_test(splines) -ei_add_test(gmres) -ei_add_test(minres) -ei_add_test(levenberg_marquardt) -ei_add_test(kronecker_product) -ei_add_test(special_functions) - -# TODO: The following test names are prefixed with the cxx11 string, since historically -# the tests depended on c++11. This isn't the case anymore so we ought to rename them. -# FIXME: Old versions of MSVC fail to compile this code, so we just disable these tests -# when using visual studio. We should make the check more strict to enable the tests for -# newer versions of MSVC. -if (NOT CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") -ei_add_test(cxx11_tensor_dimension) -ei_add_test(cxx11_tensor_map) -ei_add_test(cxx11_tensor_assign) -ei_add_test(cxx11_tensor_comparisons) -ei_add_test(cxx11_tensor_forced_eval) -ei_add_test(cxx11_tensor_math) -ei_add_test(cxx11_tensor_const) -ei_add_test(cxx11_tensor_intdiv) -ei_add_test(cxx11_tensor_casts) -ei_add_test(cxx11_tensor_empty) -ei_add_test(cxx11_tensor_sugar) -ei_add_test(cxx11_tensor_roundings) -ei_add_test(cxx11_tensor_layout_swap) -ei_add_test(cxx11_tensor_io) -if("${CMAKE_SIZEOF_VOID_P}" EQUAL "8") - # This test requires __uint128_t which is only available on 64bit systems - ei_add_test(cxx11_tensor_uint128) -endif() -endif() - -if(EIGEN_TEST_CXX11) - if(EIGEN_TEST_SYCL) - ei_add_test_sycl(cxx11_tensor_sycl "-std=c++11") - ei_add_test_sycl(cxx11_tensor_forced_eval_sycl "-std=c++11") - ei_add_test_sycl(cxx11_tensor_broadcast_sycl "-std=c++11") - ei_add_test_sycl(cxx11_tensor_device_sycl "-std=c++11") - ei_add_test_sycl(cxx11_tensor_reduction_sycl "-std=c++11") - endif(EIGEN_TEST_SYCL) - # It should be safe to always run these tests as there is some fallback code for - # older compiler that don't support cxx11. - # This is already set if EIGEN_TEST_CXX11 is enabled: - # set(CMAKE_CXX_STANDARD 11) - - ei_add_test(cxx11_eventcount "-pthread" "${CMAKE_THREAD_LIBS_INIT}") - ei_add_test(cxx11_runqueue "-pthread" "${CMAKE_THREAD_LIBS_INIT}") - ei_add_test(cxx11_non_blocking_thread_pool "-pthread" "${CMAKE_THREAD_LIBS_INIT}") - - ei_add_test(cxx11_meta) - ei_add_test(cxx11_tensor_simple) -# ei_add_test(cxx11_tensor_symmetry) - ei_add_test(cxx11_tensor_index_list) - ei_add_test(cxx11_tensor_mixed_indices) - ei_add_test(cxx11_tensor_contraction) - ei_add_test(cxx11_tensor_convolution) - ei_add_test(cxx11_tensor_expr) - ei_add_test(cxx11_tensor_fixed_size) - ei_add_test(cxx11_tensor_of_const_values) - ei_add_test(cxx11_tensor_of_complex) - ei_add_test(cxx11_tensor_of_strings) - ei_add_test(cxx11_tensor_lvalue) - ei_add_test(cxx11_tensor_broadcasting) - ei_add_test(cxx11_tensor_chipping) - ei_add_test(cxx11_tensor_concatenation) - ei_add_test(cxx11_tensor_inflation) - ei_add_test(cxx11_tensor_morphing) - ei_add_test(cxx11_tensor_padding) - ei_add_test(cxx11_tensor_patch) - ei_add_test(cxx11_tensor_image_patch) - ei_add_test(cxx11_tensor_volume_patch) - ei_add_test(cxx11_tensor_reduction) - ei_add_test(cxx11_tensor_argmax) - ei_add_test(cxx11_tensor_shuffling) - ei_add_test(cxx11_tensor_striding) - ei_add_test(cxx11_tensor_notification "-pthread" "${CMAKE_THREAD_LIBS_INIT}") - ei_add_test(cxx11_tensor_thread_pool "-pthread" "${CMAKE_THREAD_LIBS_INIT}") - ei_add_test(cxx11_tensor_ref) - ei_add_test(cxx11_tensor_random) - ei_add_test(cxx11_tensor_generator) - ei_add_test(cxx11_tensor_custom_op) - ei_add_test(cxx11_tensor_custom_index) - ei_add_test(cxx11_tensor_fft) - ei_add_test(cxx11_tensor_ifft) - ei_add_test(cxx11_tensor_scan) - -endif() - -# These tests needs nvcc -find_package(CUDA 7.0) -if(CUDA_FOUND AND EIGEN_TEST_CUDA) - # Make sure to compile without the -pedantic, -Wundef, -Wnon-virtual-dtor - # and -fno-check-new flags since they trigger thousands of compilation warnings - # in the CUDA runtime - # Also remove -ansi that is incompatible with std=c++11. - string(REPLACE "-pedantic" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}") - string(REPLACE "-Wundef" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}") - string(REPLACE "-Wnon-virtual-dtor" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}") - string(REPLACE "-fno-check-new" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}") - string(REPLACE "-ansi" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}") - - message(STATUS "Flags used to compile cuda code: " ${CMAKE_CXX_FLAGS}) - - if("${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_${EIGEN_CUDA_COMPUTE_ARCH}") - endif() - - set(EIGEN_CUDA_RELAXED_CONSTEXPR "--expt-relaxed-constexpr") - if (${CUDA_VERSION} STREQUAL "7.0") - set(EIGEN_CUDA_RELAXED_CONSTEXPR "--relaxed-constexpr") - endif() - - if( (NOT EIGEN_TEST_CXX11) OR (CMAKE_VERSION VERSION_LESS 3.3)) - set(EIGEN_CUDA_CXX11_FLAG "-std=c++11") - else() - # otherwise the flag has already been added because of the above set(CMAKE_CXX_STANDARD 11) - set(EIGEN_CUDA_CXX11_FLAG "") - endif() - - set(CUDA_NVCC_FLAGS "${EIGEN_CUDA_CXX11_FLAG} ${EIGEN_CUDA_RELAXED_CONSTEXPR} -arch compute_${EIGEN_CUDA_COMPUTE_ARCH} -Xcudafe \"--display_error_number\" ${CUDA_NVCC_FLAGS}") - cuda_include_directories("${CMAKE_CURRENT_BINARY_DIR}" "${CUDA_TOOLKIT_ROOT_DIR}/include") - set(EIGEN_ADD_TEST_FILENAME_EXTENSION "cu") - - ei_add_test(cxx11_tensor_complex_cuda) - ei_add_test(cxx11_tensor_complex_cwise_ops_cuda) - ei_add_test(cxx11_tensor_reduction_cuda) - ei_add_test(cxx11_tensor_argmax_cuda) - ei_add_test(cxx11_tensor_cast_float16_cuda) - ei_add_test(cxx11_tensor_scan_cuda) - - # Contractions require arch 3.0 or higher - if (${EIGEN_CUDA_COMPUTE_ARCH} GREATER 29) - ei_add_test(cxx11_tensor_device) - ei_add_test(cxx11_tensor_cuda) - ei_add_test(cxx11_tensor_contract_cuda) - ei_add_test(cxx11_tensor_of_float16_cuda) - endif() - - # The random number generation code requires arch 3.5 or greater. - if (${EIGEN_CUDA_COMPUTE_ARCH} GREATER 34) - ei_add_test(cxx11_tensor_random_cuda) - endif() - - - unset(EIGEN_ADD_TEST_FILENAME_EXTENSION) -endif() diff --git a/eigen/unsupported/test/EulerAngles.cpp b/eigen/unsupported/test/EulerAngles.cpp deleted file mode 100644 index a8cb528..0000000 --- a/eigen/unsupported/test/EulerAngles.cpp +++ /dev/null @@ -1,208 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Tal Hadad <tal_hd@hotmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <unsupported/Eigen/EulerAngles> - -using namespace Eigen; - -template<typename EulerSystem, typename Scalar> -void verify_euler_ranged(const Matrix<Scalar,3,1>& ea, - bool positiveRangeAlpha, bool positiveRangeBeta, bool positiveRangeGamma) -{ - typedef EulerAngles<Scalar, EulerSystem> EulerAnglesType; - typedef Matrix<Scalar,3,3> Matrix3; - typedef Matrix<Scalar,3,1> Vector3; - typedef Quaternion<Scalar> QuaternionType; - typedef AngleAxis<Scalar> AngleAxisType; - using std::abs; - - Scalar alphaRangeStart, alphaRangeEnd; - Scalar betaRangeStart, betaRangeEnd; - Scalar gammaRangeStart, gammaRangeEnd; - - if (positiveRangeAlpha) - { - alphaRangeStart = Scalar(0); - alphaRangeEnd = Scalar(2 * EIGEN_PI); - } - else - { - alphaRangeStart = -Scalar(EIGEN_PI); - alphaRangeEnd = Scalar(EIGEN_PI); - } - - if (positiveRangeBeta) - { - betaRangeStart = Scalar(0); - betaRangeEnd = Scalar(2 * EIGEN_PI); - } - else - { - betaRangeStart = -Scalar(EIGEN_PI); - betaRangeEnd = Scalar(EIGEN_PI); - } - - if (positiveRangeGamma) - { - gammaRangeStart = Scalar(0); - gammaRangeEnd = Scalar(2 * EIGEN_PI); - } - else - { - gammaRangeStart = -Scalar(EIGEN_PI); - gammaRangeEnd = Scalar(EIGEN_PI); - } - - const int i = EulerSystem::AlphaAxisAbs - 1; - const int j = EulerSystem::BetaAxisAbs - 1; - const int k = EulerSystem::GammaAxisAbs - 1; - - const int iFactor = EulerSystem::IsAlphaOpposite ? -1 : 1; - const int jFactor = EulerSystem::IsBetaOpposite ? -1 : 1; - const int kFactor = EulerSystem::IsGammaOpposite ? -1 : 1; - - const Vector3 I = EulerAnglesType::AlphaAxisVector(); - const Vector3 J = EulerAnglesType::BetaAxisVector(); - const Vector3 K = EulerAnglesType::GammaAxisVector(); - - EulerAnglesType e(ea[0], ea[1], ea[2]); - - Matrix3 m(e); - Vector3 eabis = EulerAnglesType(m, positiveRangeAlpha, positiveRangeBeta, positiveRangeGamma).angles(); - - // Check that eabis in range - VERIFY(alphaRangeStart <= eabis[0] && eabis[0] <= alphaRangeEnd); - VERIFY(betaRangeStart <= eabis[1] && eabis[1] <= betaRangeEnd); - VERIFY(gammaRangeStart <= eabis[2] && eabis[2] <= gammaRangeEnd); - - Vector3 eabis2 = m.eulerAngles(i, j, k); - - // Invert the relevant axes - eabis2[0] *= iFactor; - eabis2[1] *= jFactor; - eabis2[2] *= kFactor; - - // Saturate the angles to the correct range - if (positiveRangeAlpha && (eabis2[0] < 0)) - eabis2[0] += Scalar(2 * EIGEN_PI); - if (positiveRangeBeta && (eabis2[1] < 0)) - eabis2[1] += Scalar(2 * EIGEN_PI); - if (positiveRangeGamma && (eabis2[2] < 0)) - eabis2[2] += Scalar(2 * EIGEN_PI); - - VERIFY_IS_APPROX(eabis, eabis2);// Verify that our estimation is the same as m.eulerAngles() is - - Matrix3 mbis(AngleAxisType(eabis[0], I) * AngleAxisType(eabis[1], J) * AngleAxisType(eabis[2], K)); - VERIFY_IS_APPROX(m, mbis); - - // Tests that are only relevant for no possitive range - if (!(positiveRangeAlpha || positiveRangeBeta || positiveRangeGamma)) - { - /* If I==K, and ea[1]==0, then there no unique solution. */ - /* The remark apply in the case where I!=K, and |ea[1]| is close to pi/2. */ - if( (i!=k || ea[1]!=0) && (i==k || !internal::isApprox(abs(ea[1]),Scalar(EIGEN_PI/2),test_precision<Scalar>())) ) - VERIFY((ea-eabis).norm() <= test_precision<Scalar>()); - - // approx_or_less_than does not work for 0 - VERIFY(0 < eabis[0] || test_isMuchSmallerThan(eabis[0], Scalar(1))); - } - - // Quaternions - QuaternionType q(e); - eabis = EulerAnglesType(q, positiveRangeAlpha, positiveRangeBeta, positiveRangeGamma).angles(); - VERIFY_IS_APPROX(eabis, eabis2);// Verify that the euler angles are still the same -} - -template<typename EulerSystem, typename Scalar> -void verify_euler(const Matrix<Scalar,3,1>& ea) -{ - verify_euler_ranged<EulerSystem>(ea, false, false, false); - verify_euler_ranged<EulerSystem>(ea, false, false, true); - verify_euler_ranged<EulerSystem>(ea, false, true, false); - verify_euler_ranged<EulerSystem>(ea, false, true, true); - verify_euler_ranged<EulerSystem>(ea, true, false, false); - verify_euler_ranged<EulerSystem>(ea, true, false, true); - verify_euler_ranged<EulerSystem>(ea, true, true, false); - verify_euler_ranged<EulerSystem>(ea, true, true, true); -} - -template<typename Scalar> void check_all_var(const Matrix<Scalar,3,1>& ea) -{ - verify_euler<EulerSystemXYZ>(ea); - verify_euler<EulerSystemXYX>(ea); - verify_euler<EulerSystemXZY>(ea); - verify_euler<EulerSystemXZX>(ea); - - verify_euler<EulerSystemYZX>(ea); - verify_euler<EulerSystemYZY>(ea); - verify_euler<EulerSystemYXZ>(ea); - verify_euler<EulerSystemYXY>(ea); - - verify_euler<EulerSystemZXY>(ea); - verify_euler<EulerSystemZXZ>(ea); - verify_euler<EulerSystemZYX>(ea); - verify_euler<EulerSystemZYZ>(ea); -} - -template<typename Scalar> void eulerangles() -{ - typedef Matrix<Scalar,3,3> Matrix3; - typedef Matrix<Scalar,3,1> Vector3; - typedef Array<Scalar,3,1> Array3; - typedef Quaternion<Scalar> Quaternionx; - typedef AngleAxis<Scalar> AngleAxisType; - - Scalar a = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI)); - Quaternionx q1; - q1 = AngleAxisType(a, Vector3::Random().normalized()); - Matrix3 m; - m = q1; - - Vector3 ea = m.eulerAngles(0,1,2); - check_all_var(ea); - ea = m.eulerAngles(0,1,0); - check_all_var(ea); - - // Check with purely random Quaternion: - q1.coeffs() = Quaternionx::Coefficients::Random().normalized(); - m = q1; - ea = m.eulerAngles(0,1,2); - check_all_var(ea); - ea = m.eulerAngles(0,1,0); - check_all_var(ea); - - // Check with random angles in range [0:pi]x[-pi:pi]x[-pi:pi]. - ea = (Array3::Random() + Array3(1,0,0))*Scalar(EIGEN_PI)*Array3(0.5,1,1); - check_all_var(ea); - - ea[2] = ea[0] = internal::random<Scalar>(0,Scalar(EIGEN_PI)); - check_all_var(ea); - - ea[0] = ea[1] = internal::random<Scalar>(0,Scalar(EIGEN_PI)); - check_all_var(ea); - - ea[1] = 0; - check_all_var(ea); - - ea.head(2).setZero(); - check_all_var(ea); - - ea.setZero(); - check_all_var(ea); -} - -void test_EulerAngles() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( eulerangles<float>() ); - CALL_SUBTEST_2( eulerangles<double>() ); - } -} diff --git a/eigen/unsupported/test/FFT.cpp b/eigen/unsupported/test/FFT.cpp deleted file mode 100644 index 45c87f5..0000000 --- a/eigen/unsupported/test/FFT.cpp +++ /dev/null @@ -1,2 +0,0 @@ -#define test_FFTW test_FFT -#include "FFTW.cpp" diff --git a/eigen/unsupported/test/FFTW.cpp b/eigen/unsupported/test/FFTW.cpp deleted file mode 100644 index 8b7528f..0000000 --- a/eigen/unsupported/test/FFTW.cpp +++ /dev/null @@ -1,262 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Mark Borgerding mark a borgerding net -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <unsupported/Eigen/FFT> - -template <typename T> -std::complex<T> RandomCpx() { return std::complex<T>( (T)(rand()/(T)RAND_MAX - .5), (T)(rand()/(T)RAND_MAX - .5) ); } - -using namespace std; -using namespace Eigen; - - -template < typename T> -complex<long double> promote(complex<T> x) { return complex<long double>((long double)x.real(),(long double)x.imag()); } - -complex<long double> promote(float x) { return complex<long double>((long double)x); } -complex<long double> promote(double x) { return complex<long double>((long double)x); } -complex<long double> promote(long double x) { return complex<long double>((long double)x); } - - - template <typename VT1,typename VT2> - long double fft_rmse( const VT1 & fftbuf,const VT2 & timebuf) - { - long double totalpower=0; - long double difpower=0; - long double pi = acos((long double)-1 ); - for (size_t k0=0;k0<(size_t)fftbuf.size();++k0) { - complex<long double> acc = 0; - long double phinc = (long double)(-2.)*k0* pi / timebuf.size(); - for (size_t k1=0;k1<(size_t)timebuf.size();++k1) { - acc += promote( timebuf[k1] ) * exp( complex<long double>(0,k1*phinc) ); - } - totalpower += numext::abs2(acc); - complex<long double> x = promote(fftbuf[k0]); - complex<long double> dif = acc - x; - difpower += numext::abs2(dif); - //cerr << k0 << "\t" << acc << "\t" << x << "\t" << sqrt(numext::abs2(dif)) << endl; - } - cerr << "rmse:" << sqrt(difpower/totalpower) << endl; - return sqrt(difpower/totalpower); - } - - template <typename VT1,typename VT2> - long double dif_rmse( const VT1 buf1,const VT2 buf2) - { - long double totalpower=0; - long double difpower=0; - size_t n = (min)( buf1.size(),buf2.size() ); - for (size_t k=0;k<n;++k) { - totalpower += (long double)((numext::abs2( buf1[k] ) + numext::abs2(buf2[k]) )/2); - difpower += (long double)(numext::abs2(buf1[k] - buf2[k])); - } - return sqrt(difpower/totalpower); - } - -enum { StdVectorContainer, EigenVectorContainer }; - -template<int Container, typename Scalar> struct VectorType; - -template<typename Scalar> struct VectorType<StdVectorContainer,Scalar> -{ - typedef vector<Scalar> type; -}; - -template<typename Scalar> struct VectorType<EigenVectorContainer,Scalar> -{ - typedef Matrix<Scalar,Dynamic,1> type; -}; - -template <int Container, typename T> -void test_scalar_generic(int nfft) -{ - typedef typename FFT<T>::Complex Complex; - typedef typename FFT<T>::Scalar Scalar; - typedef typename VectorType<Container,Scalar>::type ScalarVector; - typedef typename VectorType<Container,Complex>::type ComplexVector; - - FFT<T> fft; - ScalarVector tbuf(nfft); - ComplexVector freqBuf; - for (int k=0;k<nfft;++k) - tbuf[k]= (T)( rand()/(double)RAND_MAX - .5); - - // make sure it DOESN'T give the right full spectrum answer - // if we've asked for half-spectrum - fft.SetFlag(fft.HalfSpectrum ); - fft.fwd( freqBuf,tbuf); - VERIFY((size_t)freqBuf.size() == (size_t)( (nfft>>1)+1) ); - VERIFY( T(fft_rmse(freqBuf,tbuf)) < test_precision<T>() );// gross check - - fft.ClearFlag(fft.HalfSpectrum ); - fft.fwd( freqBuf,tbuf); - VERIFY( (size_t)freqBuf.size() == (size_t)nfft); - VERIFY( T(fft_rmse(freqBuf,tbuf)) < test_precision<T>() );// gross check - - if (nfft&1) - return; // odd FFTs get the wrong size inverse FFT - - ScalarVector tbuf2; - fft.inv( tbuf2 , freqBuf); - VERIFY( T(dif_rmse(tbuf,tbuf2)) < test_precision<T>() );// gross check - - - // verify that the Unscaled flag takes effect - ScalarVector tbuf3; - fft.SetFlag(fft.Unscaled); - - fft.inv( tbuf3 , freqBuf); - - for (int k=0;k<nfft;++k) - tbuf3[k] *= T(1./nfft); - - - //for (size_t i=0;i<(size_t) tbuf.size();++i) - // cout << "freqBuf=" << freqBuf[i] << " in2=" << tbuf3[i] << " - in=" << tbuf[i] << " => " << (tbuf3[i] - tbuf[i] ) << endl; - - VERIFY( T(dif_rmse(tbuf,tbuf3)) < test_precision<T>() );// gross check - - // verify that ClearFlag works - fft.ClearFlag(fft.Unscaled); - fft.inv( tbuf2 , freqBuf); - VERIFY( T(dif_rmse(tbuf,tbuf2)) < test_precision<T>() );// gross check -} - -template <typename T> -void test_scalar(int nfft) -{ - test_scalar_generic<StdVectorContainer,T>(nfft); - //test_scalar_generic<EigenVectorContainer,T>(nfft); -} - - -template <int Container, typename T> -void test_complex_generic(int nfft) -{ - typedef typename FFT<T>::Complex Complex; - typedef typename VectorType<Container,Complex>::type ComplexVector; - - FFT<T> fft; - - ComplexVector inbuf(nfft); - ComplexVector outbuf; - ComplexVector buf3; - for (int k=0;k<nfft;++k) - inbuf[k]= Complex( (T)(rand()/(double)RAND_MAX - .5), (T)(rand()/(double)RAND_MAX - .5) ); - fft.fwd( outbuf , inbuf); - - VERIFY( T(fft_rmse(outbuf,inbuf)) < test_precision<T>() );// gross check - fft.inv( buf3 , outbuf); - - VERIFY( T(dif_rmse(inbuf,buf3)) < test_precision<T>() );// gross check - - // verify that the Unscaled flag takes effect - ComplexVector buf4; - fft.SetFlag(fft.Unscaled); - fft.inv( buf4 , outbuf); - for (int k=0;k<nfft;++k) - buf4[k] *= T(1./nfft); - VERIFY( T(dif_rmse(inbuf,buf4)) < test_precision<T>() );// gross check - - // verify that ClearFlag works - fft.ClearFlag(fft.Unscaled); - fft.inv( buf3 , outbuf); - VERIFY( T(dif_rmse(inbuf,buf3)) < test_precision<T>() );// gross check -} - -template <typename T> -void test_complex(int nfft) -{ - test_complex_generic<StdVectorContainer,T>(nfft); - test_complex_generic<EigenVectorContainer,T>(nfft); -} -/* -template <typename T,int nrows,int ncols> -void test_complex2d() -{ - typedef typename Eigen::FFT<T>::Complex Complex; - FFT<T> fft; - Eigen::Matrix<Complex,nrows,ncols> src,src2,dst,dst2; - - src = Eigen::Matrix<Complex,nrows,ncols>::Random(); - //src = Eigen::Matrix<Complex,nrows,ncols>::Identity(); - - for (int k=0;k<ncols;k++) { - Eigen::Matrix<Complex,nrows,1> tmpOut; - fft.fwd( tmpOut,src.col(k) ); - dst2.col(k) = tmpOut; - } - - for (int k=0;k<nrows;k++) { - Eigen::Matrix<Complex,1,ncols> tmpOut; - fft.fwd( tmpOut, dst2.row(k) ); - dst2.row(k) = tmpOut; - } - - fft.fwd2(dst.data(),src.data(),ncols,nrows); - fft.inv2(src2.data(),dst.data(),ncols,nrows); - VERIFY( (src-src2).norm() < test_precision<T>() ); - VERIFY( (dst-dst2).norm() < test_precision<T>() ); -} -*/ - - -void test_return_by_value(int len) -{ - VectorXf in; - VectorXf in1; - in.setRandom( len ); - VectorXcf out1,out2; - FFT<float> fft; - - fft.SetFlag(fft.HalfSpectrum ); - - fft.fwd(out1,in); - out2 = fft.fwd(in); - VERIFY( (out1-out2).norm() < test_precision<float>() ); - in1 = fft.inv(out1); - VERIFY( (in1-in).norm() < test_precision<float>() ); -} - -void test_FFTW() -{ - CALL_SUBTEST( test_return_by_value(32) ); - //CALL_SUBTEST( ( test_complex2d<float,4,8> () ) ); CALL_SUBTEST( ( test_complex2d<double,4,8> () ) ); - //CALL_SUBTEST( ( test_complex2d<long double,4,8> () ) ); - CALL_SUBTEST( test_complex<float>(32) ); CALL_SUBTEST( test_complex<double>(32) ); - CALL_SUBTEST( test_complex<float>(256) ); CALL_SUBTEST( test_complex<double>(256) ); - CALL_SUBTEST( test_complex<float>(3*8) ); CALL_SUBTEST( test_complex<double>(3*8) ); - CALL_SUBTEST( test_complex<float>(5*32) ); CALL_SUBTEST( test_complex<double>(5*32) ); - CALL_SUBTEST( test_complex<float>(2*3*4) ); CALL_SUBTEST( test_complex<double>(2*3*4) ); - CALL_SUBTEST( test_complex<float>(2*3*4*5) ); CALL_SUBTEST( test_complex<double>(2*3*4*5) ); - CALL_SUBTEST( test_complex<float>(2*3*4*5*7) ); CALL_SUBTEST( test_complex<double>(2*3*4*5*7) ); - - CALL_SUBTEST( test_scalar<float>(32) ); CALL_SUBTEST( test_scalar<double>(32) ); - CALL_SUBTEST( test_scalar<float>(45) ); CALL_SUBTEST( test_scalar<double>(45) ); - CALL_SUBTEST( test_scalar<float>(50) ); CALL_SUBTEST( test_scalar<double>(50) ); - CALL_SUBTEST( test_scalar<float>(256) ); CALL_SUBTEST( test_scalar<double>(256) ); - CALL_SUBTEST( test_scalar<float>(2*3*4*5*7) ); CALL_SUBTEST( test_scalar<double>(2*3*4*5*7) ); - - #ifdef EIGEN_HAS_FFTWL - CALL_SUBTEST( test_complex<long double>(32) ); - CALL_SUBTEST( test_complex<long double>(256) ); - CALL_SUBTEST( test_complex<long double>(3*8) ); - CALL_SUBTEST( test_complex<long double>(5*32) ); - CALL_SUBTEST( test_complex<long double>(2*3*4) ); - CALL_SUBTEST( test_complex<long double>(2*3*4*5) ); - CALL_SUBTEST( test_complex<long double>(2*3*4*5*7) ); - - CALL_SUBTEST( test_scalar<long double>(32) ); - CALL_SUBTEST( test_scalar<long double>(45) ); - CALL_SUBTEST( test_scalar<long double>(50) ); - CALL_SUBTEST( test_scalar<long double>(256) ); - CALL_SUBTEST( test_scalar<long double>(2*3*4*5*7) ); - #endif -} diff --git a/eigen/unsupported/test/NonLinearOptimization.cpp b/eigen/unsupported/test/NonLinearOptimization.cpp deleted file mode 100644 index f0c336c..0000000 --- a/eigen/unsupported/test/NonLinearOptimization.cpp +++ /dev/null @@ -1,1878 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org> - -#include <stdio.h> - -#include "main.h" -#include <unsupported/Eigen/NonLinearOptimization> - -// This disables some useless Warnings on MSVC. -// It is intended to be done for this test only. -#include <Eigen/src/Core/util/DisableStupidWarnings.h> - -// tolerance for chekcing number of iterations -#define LM_EVAL_COUNT_TOL 4/3 - -int fcn_chkder(const VectorXd &x, VectorXd &fvec, MatrixXd &fjac, int iflag) -{ - /* subroutine fcn for chkder example. */ - - int i; - assert(15 == fvec.size()); - assert(3 == x.size()); - double tmp1, tmp2, tmp3, tmp4; - static const double y[15]={1.4e-1, 1.8e-1, 2.2e-1, 2.5e-1, 2.9e-1, 3.2e-1, 3.5e-1, - 3.9e-1, 3.7e-1, 5.8e-1, 7.3e-1, 9.6e-1, 1.34, 2.1, 4.39}; - - - if (iflag == 0) - return 0; - - if (iflag != 2) - for (i=0; i<15; i++) { - tmp1 = i+1; - tmp2 = 16-i-1; - tmp3 = tmp1; - if (i >= 8) tmp3 = tmp2; - fvec[i] = y[i] - (x[0] + tmp1/(x[1]*tmp2 + x[2]*tmp3)); - } - else { - for (i = 0; i < 15; i++) { - tmp1 = i+1; - tmp2 = 16-i-1; - - /* error introduced into next statement for illustration. */ - /* corrected statement should read tmp3 = tmp1 . */ - - tmp3 = tmp2; - if (i >= 8) tmp3 = tmp2; - tmp4 = (x[1]*tmp2 + x[2]*tmp3); tmp4=tmp4*tmp4; - fjac(i,0) = -1.; - fjac(i,1) = tmp1*tmp2/tmp4; - fjac(i,2) = tmp1*tmp3/tmp4; - } - } - return 0; -} - - -void testChkder() -{ - const int m=15, n=3; - VectorXd x(n), fvec(m), xp, fvecp(m), err; - MatrixXd fjac(m,n); - VectorXi ipvt; - - /* the following values should be suitable for */ - /* checking the jacobian matrix. */ - x << 9.2e-1, 1.3e-1, 5.4e-1; - - internal::chkder(x, fvec, fjac, xp, fvecp, 1, err); - fcn_chkder(x, fvec, fjac, 1); - fcn_chkder(x, fvec, fjac, 2); - fcn_chkder(xp, fvecp, fjac, 1); - internal::chkder(x, fvec, fjac, xp, fvecp, 2, err); - - fvecp -= fvec; - - // check those - VectorXd fvec_ref(m), fvecp_ref(m), err_ref(m); - fvec_ref << - -1.181606, -1.429655, -1.606344, - -1.745269, -1.840654, -1.921586, - -1.984141, -2.022537, -2.468977, - -2.827562, -3.473582, -4.437612, - -6.047662, -9.267761, -18.91806; - fvecp_ref << - -7.724666e-09, -3.432406e-09, -2.034843e-10, - 2.313685e-09, 4.331078e-09, 5.984096e-09, - 7.363281e-09, 8.53147e-09, 1.488591e-08, - 2.33585e-08, 3.522012e-08, 5.301255e-08, - 8.26666e-08, 1.419747e-07, 3.19899e-07; - err_ref << - 0.1141397, 0.09943516, 0.09674474, - 0.09980447, 0.1073116, 0.1220445, - 0.1526814, 1, 1, - 1, 1, 1, - 1, 1, 1; - - VERIFY_IS_APPROX(fvec, fvec_ref); - VERIFY_IS_APPROX(fvecp, fvecp_ref); - VERIFY_IS_APPROX(err, err_ref); -} - -// Generic functor -template<typename _Scalar, int NX=Dynamic, int NY=Dynamic> -struct Functor -{ - typedef _Scalar Scalar; - enum { - InputsAtCompileTime = NX, - ValuesAtCompileTime = NY - }; - typedef Matrix<Scalar,InputsAtCompileTime,1> InputType; - typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType; - typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType; - - const int m_inputs, m_values; - - Functor() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {} - Functor(int inputs, int values) : m_inputs(inputs), m_values(values) {} - - int inputs() const { return m_inputs; } - int values() const { return m_values; } - - // you should define that in the subclass : -// void operator() (const InputType& x, ValueType* v, JacobianType* _j=0) const; -}; - -struct lmder_functor : Functor<double> -{ - lmder_functor(void): Functor<double>(3,15) {} - int operator()(const VectorXd &x, VectorXd &fvec) const - { - double tmp1, tmp2, tmp3; - static const double y[15] = {1.4e-1, 1.8e-1, 2.2e-1, 2.5e-1, 2.9e-1, 3.2e-1, 3.5e-1, - 3.9e-1, 3.7e-1, 5.8e-1, 7.3e-1, 9.6e-1, 1.34, 2.1, 4.39}; - - for (int i = 0; i < values(); i++) - { - tmp1 = i+1; - tmp2 = 16 - i - 1; - tmp3 = (i>=8)? tmp2 : tmp1; - fvec[i] = y[i] - (x[0] + tmp1/(x[1]*tmp2 + x[2]*tmp3)); - } - return 0; - } - - int df(const VectorXd &x, MatrixXd &fjac) const - { - double tmp1, tmp2, tmp3, tmp4; - for (int i = 0; i < values(); i++) - { - tmp1 = i+1; - tmp2 = 16 - i - 1; - tmp3 = (i>=8)? tmp2 : tmp1; - tmp4 = (x[1]*tmp2 + x[2]*tmp3); tmp4 = tmp4*tmp4; - fjac(i,0) = -1; - fjac(i,1) = tmp1*tmp2/tmp4; - fjac(i,2) = tmp1*tmp3/tmp4; - } - return 0; - } -}; - -void testLmder1() -{ - int n=3, info; - - VectorXd x; - - /* the following starting values provide a rough fit. */ - x.setConstant(n, 1.); - - // do the computation - lmder_functor functor; - LevenbergMarquardt<lmder_functor> lm(functor); - info = lm.lmder1(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 6); - VERIFY_IS_EQUAL(lm.njev, 5); - - // check norm - VERIFY_IS_APPROX(lm.fvec.blueNorm(), 0.09063596); - - // check x - VectorXd x_ref(n); - x_ref << 0.08241058, 1.133037, 2.343695; - VERIFY_IS_APPROX(x, x_ref); -} - -void testLmder() -{ - const int m=15, n=3; - int info; - double fnorm, covfac; - VectorXd x; - - /* the following starting values provide a rough fit. */ - x.setConstant(n, 1.); - - // do the computation - lmder_functor functor; - LevenbergMarquardt<lmder_functor> lm(functor); - info = lm.minimize(x); - - // check return values - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 6); - VERIFY_IS_EQUAL(lm.njev, 5); - - // check norm - fnorm = lm.fvec.blueNorm(); - VERIFY_IS_APPROX(fnorm, 0.09063596); - - // check x - VectorXd x_ref(n); - x_ref << 0.08241058, 1.133037, 2.343695; - VERIFY_IS_APPROX(x, x_ref); - - // check covariance - covfac = fnorm*fnorm/(m-n); - internal::covar(lm.fjac, lm.permutation.indices()); // TODO : move this as a function of lm - - MatrixXd cov_ref(n,n); - cov_ref << - 0.0001531202, 0.002869941, -0.002656662, - 0.002869941, 0.09480935, -0.09098995, - -0.002656662, -0.09098995, 0.08778727; - -// std::cout << fjac*covfac << std::endl; - - MatrixXd cov; - cov = covfac*lm.fjac.topLeftCorner<n,n>(); - VERIFY_IS_APPROX( cov, cov_ref); - // TODO: why isn't this allowed ? : - // VERIFY_IS_APPROX( covfac*fjac.topLeftCorner<n,n>() , cov_ref); -} - -struct hybrj_functor : Functor<double> -{ - hybrj_functor(void) : Functor<double>(9,9) {} - - int operator()(const VectorXd &x, VectorXd &fvec) - { - double temp, temp1, temp2; - const VectorXd::Index n = x.size(); - assert(fvec.size()==n); - for (VectorXd::Index k = 0; k < n; k++) - { - temp = (3. - 2.*x[k])*x[k]; - temp1 = 0.; - if (k) temp1 = x[k-1]; - temp2 = 0.; - if (k != n-1) temp2 = x[k+1]; - fvec[k] = temp - temp1 - 2.*temp2 + 1.; - } - return 0; - } - int df(const VectorXd &x, MatrixXd &fjac) - { - const VectorXd::Index n = x.size(); - assert(fjac.rows()==n); - assert(fjac.cols()==n); - for (VectorXd::Index k = 0; k < n; k++) - { - for (VectorXd::Index j = 0; j < n; j++) - fjac(k,j) = 0.; - fjac(k,k) = 3.- 4.*x[k]; - if (k) fjac(k,k-1) = -1.; - if (k != n-1) fjac(k,k+1) = -2.; - } - return 0; - } -}; - - -void testHybrj1() -{ - const int n=9; - int info; - VectorXd x(n); - - /* the following starting values provide a rough fit. */ - x.setConstant(n, -1.); - - // do the computation - hybrj_functor functor; - HybridNonLinearSolver<hybrj_functor> solver(functor); - info = solver.hybrj1(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(solver.nfev, 11); - VERIFY_IS_EQUAL(solver.njev, 1); - - // check norm - VERIFY_IS_APPROX(solver.fvec.blueNorm(), 1.192636e-08); - - -// check x - VectorXd x_ref(n); - x_ref << - -0.5706545, -0.6816283, -0.7017325, - -0.7042129, -0.701369, -0.6918656, - -0.665792, -0.5960342, -0.4164121; - VERIFY_IS_APPROX(x, x_ref); -} - -void testHybrj() -{ - const int n=9; - int info; - VectorXd x(n); - - /* the following starting values provide a rough fit. */ - x.setConstant(n, -1.); - - - // do the computation - hybrj_functor functor; - HybridNonLinearSolver<hybrj_functor> solver(functor); - solver.diag.setConstant(n, 1.); - solver.useExternalScaling = true; - info = solver.solve(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(solver.nfev, 11); - VERIFY_IS_EQUAL(solver.njev, 1); - - // check norm - VERIFY_IS_APPROX(solver.fvec.blueNorm(), 1.192636e-08); - - -// check x - VectorXd x_ref(n); - x_ref << - -0.5706545, -0.6816283, -0.7017325, - -0.7042129, -0.701369, -0.6918656, - -0.665792, -0.5960342, -0.4164121; - VERIFY_IS_APPROX(x, x_ref); - -} - -struct hybrd_functor : Functor<double> -{ - hybrd_functor(void) : Functor<double>(9,9) {} - int operator()(const VectorXd &x, VectorXd &fvec) const - { - double temp, temp1, temp2; - const VectorXd::Index n = x.size(); - - assert(fvec.size()==n); - for (VectorXd::Index k=0; k < n; k++) - { - temp = (3. - 2.*x[k])*x[k]; - temp1 = 0.; - if (k) temp1 = x[k-1]; - temp2 = 0.; - if (k != n-1) temp2 = x[k+1]; - fvec[k] = temp - temp1 - 2.*temp2 + 1.; - } - return 0; - } -}; - -void testHybrd1() -{ - int n=9, info; - VectorXd x(n); - - /* the following starting values provide a rough solution. */ - x.setConstant(n, -1.); - - // do the computation - hybrd_functor functor; - HybridNonLinearSolver<hybrd_functor> solver(functor); - info = solver.hybrd1(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(solver.nfev, 20); - - // check norm - VERIFY_IS_APPROX(solver.fvec.blueNorm(), 1.192636e-08); - - // check x - VectorXd x_ref(n); - x_ref << -0.5706545, -0.6816283, -0.7017325, -0.7042129, -0.701369, -0.6918656, -0.665792, -0.5960342, -0.4164121; - VERIFY_IS_APPROX(x, x_ref); -} - -void testHybrd() -{ - const int n=9; - int info; - VectorXd x; - - /* the following starting values provide a rough fit. */ - x.setConstant(n, -1.); - - // do the computation - hybrd_functor functor; - HybridNonLinearSolver<hybrd_functor> solver(functor); - solver.parameters.nb_of_subdiagonals = 1; - solver.parameters.nb_of_superdiagonals = 1; - solver.diag.setConstant(n, 1.); - solver.useExternalScaling = true; - info = solver.solveNumericalDiff(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(solver.nfev, 14); - - // check norm - VERIFY_IS_APPROX(solver.fvec.blueNorm(), 1.192636e-08); - - // check x - VectorXd x_ref(n); - x_ref << - -0.5706545, -0.6816283, -0.7017325, - -0.7042129, -0.701369, -0.6918656, - -0.665792, -0.5960342, -0.4164121; - VERIFY_IS_APPROX(x, x_ref); -} - -struct lmstr_functor : Functor<double> -{ - lmstr_functor(void) : Functor<double>(3,15) {} - int operator()(const VectorXd &x, VectorXd &fvec) - { - /* subroutine fcn for lmstr1 example. */ - double tmp1, tmp2, tmp3; - static const double y[15]={1.4e-1, 1.8e-1, 2.2e-1, 2.5e-1, 2.9e-1, 3.2e-1, 3.5e-1, - 3.9e-1, 3.7e-1, 5.8e-1, 7.3e-1, 9.6e-1, 1.34, 2.1, 4.39}; - - assert(15==fvec.size()); - assert(3==x.size()); - - for (int i=0; i<15; i++) - { - tmp1 = i+1; - tmp2 = 16 - i - 1; - tmp3 = (i>=8)? tmp2 : tmp1; - fvec[i] = y[i] - (x[0] + tmp1/(x[1]*tmp2 + x[2]*tmp3)); - } - return 0; - } - int df(const VectorXd &x, VectorXd &jac_row, VectorXd::Index rownb) - { - assert(x.size()==3); - assert(jac_row.size()==x.size()); - double tmp1, tmp2, tmp3, tmp4; - - VectorXd::Index i = rownb-2; - tmp1 = i+1; - tmp2 = 16 - i - 1; - tmp3 = (i>=8)? tmp2 : tmp1; - tmp4 = (x[1]*tmp2 + x[2]*tmp3); tmp4 = tmp4*tmp4; - jac_row[0] = -1; - jac_row[1] = tmp1*tmp2/tmp4; - jac_row[2] = tmp1*tmp3/tmp4; - return 0; - } -}; - -void testLmstr1() -{ - const int n=3; - int info; - - VectorXd x(n); - - /* the following starting values provide a rough fit. */ - x.setConstant(n, 1.); - - // do the computation - lmstr_functor functor; - LevenbergMarquardt<lmstr_functor> lm(functor); - info = lm.lmstr1(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 6); - VERIFY_IS_EQUAL(lm.njev, 5); - - // check norm - VERIFY_IS_APPROX(lm.fvec.blueNorm(), 0.09063596); - - // check x - VectorXd x_ref(n); - x_ref << 0.08241058, 1.133037, 2.343695 ; - VERIFY_IS_APPROX(x, x_ref); -} - -void testLmstr() -{ - const int n=3; - int info; - double fnorm; - VectorXd x(n); - - /* the following starting values provide a rough fit. */ - x.setConstant(n, 1.); - - // do the computation - lmstr_functor functor; - LevenbergMarquardt<lmstr_functor> lm(functor); - info = lm.minimizeOptimumStorage(x); - - // check return values - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 6); - VERIFY_IS_EQUAL(lm.njev, 5); - - // check norm - fnorm = lm.fvec.blueNorm(); - VERIFY_IS_APPROX(fnorm, 0.09063596); - - // check x - VectorXd x_ref(n); - x_ref << 0.08241058, 1.133037, 2.343695; - VERIFY_IS_APPROX(x, x_ref); - -} - -struct lmdif_functor : Functor<double> -{ - lmdif_functor(void) : Functor<double>(3,15) {} - int operator()(const VectorXd &x, VectorXd &fvec) const - { - int i; - double tmp1,tmp2,tmp3; - static const double y[15]={1.4e-1,1.8e-1,2.2e-1,2.5e-1,2.9e-1,3.2e-1,3.5e-1,3.9e-1, - 3.7e-1,5.8e-1,7.3e-1,9.6e-1,1.34e0,2.1e0,4.39e0}; - - assert(x.size()==3); - assert(fvec.size()==15); - for (i=0; i<15; i++) - { - tmp1 = i+1; - tmp2 = 15 - i; - tmp3 = tmp1; - - if (i >= 8) tmp3 = tmp2; - fvec[i] = y[i] - (x[0] + tmp1/(x[1]*tmp2 + x[2]*tmp3)); - } - return 0; - } -}; - -void testLmdif1() -{ - const int n=3; - int info; - - VectorXd x(n), fvec(15); - - /* the following starting values provide a rough fit. */ - x.setConstant(n, 1.); - - // do the computation - lmdif_functor functor; - DenseIndex nfev = -1; // initialize to avoid maybe-uninitialized warning - info = LevenbergMarquardt<lmdif_functor>::lmdif1(functor, x, &nfev); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(nfev, 26); - - // check norm - functor(x, fvec); - VERIFY_IS_APPROX(fvec.blueNorm(), 0.09063596); - - // check x - VectorXd x_ref(n); - x_ref << 0.0824106, 1.1330366, 2.3436947; - VERIFY_IS_APPROX(x, x_ref); - -} - -void testLmdif() -{ - const int m=15, n=3; - int info; - double fnorm, covfac; - VectorXd x(n); - - /* the following starting values provide a rough fit. */ - x.setConstant(n, 1.); - - // do the computation - lmdif_functor functor; - NumericalDiff<lmdif_functor> numDiff(functor); - LevenbergMarquardt<NumericalDiff<lmdif_functor> > lm(numDiff); - info = lm.minimize(x); - - // check return values - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 26); - - // check norm - fnorm = lm.fvec.blueNorm(); - VERIFY_IS_APPROX(fnorm, 0.09063596); - - // check x - VectorXd x_ref(n); - x_ref << 0.08241058, 1.133037, 2.343695; - VERIFY_IS_APPROX(x, x_ref); - - // check covariance - covfac = fnorm*fnorm/(m-n); - internal::covar(lm.fjac, lm.permutation.indices()); // TODO : move this as a function of lm - - MatrixXd cov_ref(n,n); - cov_ref << - 0.0001531202, 0.002869942, -0.002656662, - 0.002869942, 0.09480937, -0.09098997, - -0.002656662, -0.09098997, 0.08778729; - -// std::cout << fjac*covfac << std::endl; - - MatrixXd cov; - cov = covfac*lm.fjac.topLeftCorner<n,n>(); - VERIFY_IS_APPROX( cov, cov_ref); - // TODO: why isn't this allowed ? : - // VERIFY_IS_APPROX( covfac*fjac.topLeftCorner<n,n>() , cov_ref); -} - -struct chwirut2_functor : Functor<double> -{ - chwirut2_functor(void) : Functor<double>(3,54) {} - static const double m_x[54]; - static const double m_y[54]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - int i; - - assert(b.size()==3); - assert(fvec.size()==54); - for(i=0; i<54; i++) { - double x = m_x[i]; - fvec[i] = exp(-b[0]*x)/(b[1]+b[2]*x) - m_y[i]; - } - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==3); - assert(fjac.rows()==54); - assert(fjac.cols()==3); - for(int i=0; i<54; i++) { - double x = m_x[i]; - double factor = 1./(b[1]+b[2]*x); - double e = exp(-b[0]*x); - fjac(i,0) = -x*e*factor; - fjac(i,1) = -e*factor*factor; - fjac(i,2) = -x*e*factor*factor; - } - return 0; - } -}; -const double chwirut2_functor::m_x[54] = { 0.500E0, 1.000E0, 1.750E0, 3.750E0, 5.750E0, 0.875E0, 2.250E0, 3.250E0, 5.250E0, 0.750E0, 1.750E0, 2.750E0, 4.750E0, 0.625E0, 1.250E0, 2.250E0, 4.250E0, .500E0, 3.000E0, .750E0, 3.000E0, 1.500E0, 6.000E0, 3.000E0, 6.000E0, 1.500E0, 3.000E0, .500E0, 2.000E0, 4.000E0, .750E0, 2.000E0, 5.000E0, .750E0, 2.250E0, 3.750E0, 5.750E0, 3.000E0, .750E0, 2.500E0, 4.000E0, .750E0, 2.500E0, 4.000E0, .750E0, 2.500E0, 4.000E0, .500E0, 6.000E0, 3.000E0, .500E0, 2.750E0, .500E0, 1.750E0}; -const double chwirut2_functor::m_y[54] = { 92.9000E0 ,57.1000E0 ,31.0500E0 ,11.5875E0 ,8.0250E0 ,63.6000E0 ,21.4000E0 ,14.2500E0 ,8.4750E0 ,63.8000E0 ,26.8000E0 ,16.4625E0 ,7.1250E0 ,67.3000E0 ,41.0000E0 ,21.1500E0 ,8.1750E0 ,81.5000E0 ,13.1200E0 ,59.9000E0 ,14.6200E0 ,32.9000E0 ,5.4400E0 ,12.5600E0 ,5.4400E0 ,32.0000E0 ,13.9500E0 ,75.8000E0 ,20.0000E0 ,10.4200E0 ,59.5000E0 ,21.6700E0 ,8.5500E0 ,62.0000E0 ,20.2000E0 ,7.7600E0 ,3.7500E0 ,11.8100E0 ,54.7000E0 ,23.7000E0 ,11.5500E0 ,61.3000E0 ,17.7000E0 ,8.7400E0 ,59.2000E0 ,16.3000E0 ,8.6200E0 ,81.0000E0 ,4.8700E0 ,14.6200E0 ,81.7000E0 ,17.1700E0 ,81.3000E0 ,28.9000E0 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/chwirut2.shtml -void testNistChwirut2(void) -{ - const int n=3; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 0.1, 0.01, 0.02; - // do the computation - chwirut2_functor functor; - LevenbergMarquardt<chwirut2_functor> lm(functor); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 10); - VERIFY_IS_EQUAL(lm.njev, 8); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02); - // check x - VERIFY_IS_APPROX(x[0], 1.6657666537E-01); - VERIFY_IS_APPROX(x[1], 5.1653291286E-03); - VERIFY_IS_APPROX(x[2], 1.2150007096E-02); - - /* - * Second try - */ - x<< 0.15, 0.008, 0.010; - // do the computation - lm.resetParameters(); - lm.parameters.ftol = 1.E6*NumTraits<double>::epsilon(); - lm.parameters.xtol = 1.E6*NumTraits<double>::epsilon(); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 7); - VERIFY_IS_EQUAL(lm.njev, 6); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02); - // check x - VERIFY_IS_APPROX(x[0], 1.6657666537E-01); - VERIFY_IS_APPROX(x[1], 5.1653291286E-03); - VERIFY_IS_APPROX(x[2], 1.2150007096E-02); -} - - -struct misra1a_functor : Functor<double> -{ - misra1a_functor(void) : Functor<double>(2,14) {} - static const double m_x[14]; - static const double m_y[14]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==2); - assert(fvec.size()==14); - for(int i=0; i<14; i++) { - fvec[i] = b[0]*(1.-exp(-b[1]*m_x[i])) - m_y[i] ; - } - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==2); - assert(fjac.rows()==14); - assert(fjac.cols()==2); - for(int i=0; i<14; i++) { - fjac(i,0) = (1.-exp(-b[1]*m_x[i])); - fjac(i,1) = (b[0]*m_x[i]*exp(-b[1]*m_x[i])); - } - return 0; - } -}; -const double misra1a_functor::m_x[14] = { 77.6E0, 114.9E0, 141.1E0, 190.8E0, 239.9E0, 289.0E0, 332.8E0, 378.4E0, 434.8E0, 477.3E0, 536.8E0, 593.1E0, 689.1E0, 760.0E0}; -const double misra1a_functor::m_y[14] = { 10.07E0, 14.73E0, 17.94E0, 23.93E0, 29.61E0, 35.18E0, 40.02E0, 44.82E0, 50.76E0, 55.05E0, 61.01E0, 66.40E0, 75.47E0, 81.78E0}; - -// http://www.itl.nist.gov/div898/strd/nls/data/misra1a.shtml -void testNistMisra1a(void) -{ - const int n=2; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 500., 0.0001; - // do the computation - misra1a_functor functor; - LevenbergMarquardt<misra1a_functor> lm(functor); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 19); - VERIFY_IS_EQUAL(lm.njev, 15); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01); - // check x - VERIFY_IS_APPROX(x[0], 2.3894212918E+02); - VERIFY_IS_APPROX(x[1], 5.5015643181E-04); - - /* - * Second try - */ - x<< 250., 0.0005; - // do the computation - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 5); - VERIFY_IS_EQUAL(lm.njev, 4); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01); - // check x - VERIFY_IS_APPROX(x[0], 2.3894212918E+02); - VERIFY_IS_APPROX(x[1], 5.5015643181E-04); -} - -struct hahn1_functor : Functor<double> -{ - hahn1_functor(void) : Functor<double>(7,236) {} - static const double m_x[236]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - static const double m_y[236] = { .591E0 , 1.547E0 , 2.902E0 , 2.894E0 , 4.703E0 , 6.307E0 , 7.03E0 , 7.898E0 , 9.470E0 , 9.484E0 , 10.072E0 , 10.163E0 , 11.615E0 , 12.005E0 , 12.478E0 , 12.982E0 , 12.970E0 , 13.926E0 , 14.452E0 , 14.404E0 , 15.190E0 , 15.550E0 , 15.528E0 , 15.499E0 , 16.131E0 , 16.438E0 , 16.387E0 , 16.549E0 , 16.872E0 , 16.830E0 , 16.926E0 , 16.907E0 , 16.966E0 , 17.060E0 , 17.122E0 , 17.311E0 , 17.355E0 , 17.668E0 , 17.767E0 , 17.803E0 , 17.765E0 , 17.768E0 , 17.736E0 , 17.858E0 , 17.877E0 , 17.912E0 , 18.046E0 , 18.085E0 , 18.291E0 , 18.357E0 , 18.426E0 , 18.584E0 , 18.610E0 , 18.870E0 , 18.795E0 , 19.111E0 , .367E0 , .796E0 , 0.892E0 , 1.903E0 , 2.150E0 , 3.697E0 , 5.870E0 , 6.421E0 , 7.422E0 , 9.944E0 , 11.023E0 , 11.87E0 , 12.786E0 , 14.067E0 , 13.974E0 , 14.462E0 , 14.464E0 , 15.381E0 , 15.483E0 , 15.59E0 , 16.075E0 , 16.347E0 , 16.181E0 , 16.915E0 , 17.003E0 , 16.978E0 , 17.756E0 , 17.808E0 , 17.868E0 , 18.481E0 , 18.486E0 , 19.090E0 , 16.062E0 , 16.337E0 , 16.345E0 , - 16.388E0 , 17.159E0 , 17.116E0 , 17.164E0 , 17.123E0 , 17.979E0 , 17.974E0 , 18.007E0 , 17.993E0 , 18.523E0 , 18.669E0 , 18.617E0 , 19.371E0 , 19.330E0 , 0.080E0 , 0.248E0 , 1.089E0 , 1.418E0 , 2.278E0 , 3.624E0 , 4.574E0 , 5.556E0 , 7.267E0 , 7.695E0 , 9.136E0 , 9.959E0 , 9.957E0 , 11.600E0 , 13.138E0 , 13.564E0 , 13.871E0 , 13.994E0 , 14.947E0 , 15.473E0 , 15.379E0 , 15.455E0 , 15.908E0 , 16.114E0 , 17.071E0 , 17.135E0 , 17.282E0 , 17.368E0 , 17.483E0 , 17.764E0 , 18.185E0 , 18.271E0 , 18.236E0 , 18.237E0 , 18.523E0 , 18.627E0 , 18.665E0 , 19.086E0 , 0.214E0 , 0.943E0 , 1.429E0 , 2.241E0 , 2.951E0 , 3.782E0 , 4.757E0 , 5.602E0 , 7.169E0 , 8.920E0 , 10.055E0 , 12.035E0 , 12.861E0 , 13.436E0 , 14.167E0 , 14.755E0 , 15.168E0 , 15.651E0 , 15.746E0 , 16.216E0 , 16.445E0 , 16.965E0 , 17.121E0 , 17.206E0 , 17.250E0 , 17.339E0 , 17.793E0 , 18.123E0 , 18.49E0 , 18.566E0 , 18.645E0 , 18.706E0 , 18.924E0 , 19.1E0 , 0.375E0 , 0.471E0 , 1.504E0 , 2.204E0 , 2.813E0 , 4.765E0 , 9.835E0 , 10.040E0 , 11.946E0 , 12.596E0 , -13.303E0 , 13.922E0 , 14.440E0 , 14.951E0 , 15.627E0 , 15.639E0 , 15.814E0 , 16.315E0 , 16.334E0 , 16.430E0 , 16.423E0 , 17.024E0 , 17.009E0 , 17.165E0 , 17.134E0 , 17.349E0 , 17.576E0 , 17.848E0 , 18.090E0 , 18.276E0 , 18.404E0 , 18.519E0 , 19.133E0 , 19.074E0 , 19.239E0 , 19.280E0 , 19.101E0 , 19.398E0 , 19.252E0 , 19.89E0 , 20.007E0 , 19.929E0 , 19.268E0 , 19.324E0 , 20.049E0 , 20.107E0 , 20.062E0 , 20.065E0 , 19.286E0 , 19.972E0 , 20.088E0 , 20.743E0 , 20.83E0 , 20.935E0 , 21.035E0 , 20.93E0 , 21.074E0 , 21.085E0 , 20.935E0 }; - - // int called=0; printf("call hahn1_functor with iflag=%d, called=%d\n", iflag, called); if (iflag==1) called++; - - assert(b.size()==7); - assert(fvec.size()==236); - for(int i=0; i<236; i++) { - double x=m_x[i], xx=x*x, xxx=xx*x; - fvec[i] = (b[0]+b[1]*x+b[2]*xx+b[3]*xxx) / (1.+b[4]*x+b[5]*xx+b[6]*xxx) - m_y[i]; - } - return 0; - } - - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==7); - assert(fjac.rows()==236); - assert(fjac.cols()==7); - for(int i=0; i<236; i++) { - double x=m_x[i], xx=x*x, xxx=xx*x; - double fact = 1./(1.+b[4]*x+b[5]*xx+b[6]*xxx); - fjac(i,0) = 1.*fact; - fjac(i,1) = x*fact; - fjac(i,2) = xx*fact; - fjac(i,3) = xxx*fact; - fact = - (b[0]+b[1]*x+b[2]*xx+b[3]*xxx) * fact * fact; - fjac(i,4) = x*fact; - fjac(i,5) = xx*fact; - fjac(i,6) = xxx*fact; - } - return 0; - } -}; -const double hahn1_functor::m_x[236] = { 24.41E0 , 34.82E0 , 44.09E0 , 45.07E0 , 54.98E0 , 65.51E0 , 70.53E0 , 75.70E0 , 89.57E0 , 91.14E0 , 96.40E0 , 97.19E0 , 114.26E0 , 120.25E0 , 127.08E0 , 133.55E0 , 133.61E0 , 158.67E0 , 172.74E0 , 171.31E0 , 202.14E0 , 220.55E0 , 221.05E0 , 221.39E0 , 250.99E0 , 268.99E0 , 271.80E0 , 271.97E0 , 321.31E0 , 321.69E0 , 330.14E0 , 333.03E0 , 333.47E0 , 340.77E0 , 345.65E0 , 373.11E0 , 373.79E0 , 411.82E0 , 419.51E0 , 421.59E0 , 422.02E0 , 422.47E0 , 422.61E0 , 441.75E0 , 447.41E0 , 448.7E0 , 472.89E0 , 476.69E0 , 522.47E0 , 522.62E0 , 524.43E0 , 546.75E0 , 549.53E0 , 575.29E0 , 576.00E0 , 625.55E0 , 20.15E0 , 28.78E0 , 29.57E0 , 37.41E0 , 39.12E0 , 50.24E0 , 61.38E0 , 66.25E0 , 73.42E0 , 95.52E0 , 107.32E0 , 122.04E0 , 134.03E0 , 163.19E0 , 163.48E0 , 175.70E0 , 179.86E0 , 211.27E0 , 217.78E0 , 219.14E0 , 262.52E0 , 268.01E0 , 268.62E0 , 336.25E0 , 337.23E0 , 339.33E0 , 427.38E0 , 428.58E0 , 432.68E0 , 528.99E0 , 531.08E0 , 628.34E0 , 253.24E0 , 273.13E0 , 273.66E0 , -282.10E0 , 346.62E0 , 347.19E0 , 348.78E0 , 351.18E0 , 450.10E0 , 450.35E0 , 451.92E0 , 455.56E0 , 552.22E0 , 553.56E0 , 555.74E0 , 652.59E0 , 656.20E0 , 14.13E0 , 20.41E0 , 31.30E0 , 33.84E0 , 39.70E0 , 48.83E0 , 54.50E0 , 60.41E0 , 72.77E0 , 75.25E0 , 86.84E0 , 94.88E0 , 96.40E0 , 117.37E0 , 139.08E0 , 147.73E0 , 158.63E0 , 161.84E0 , 192.11E0 , 206.76E0 , 209.07E0 , 213.32E0 , 226.44E0 , 237.12E0 , 330.90E0 , 358.72E0 , 370.77E0 , 372.72E0 , 396.24E0 , 416.59E0 , 484.02E0 , 495.47E0 , 514.78E0 , 515.65E0 , 519.47E0 , 544.47E0 , 560.11E0 , 620.77E0 , 18.97E0 , 28.93E0 , 33.91E0 , 40.03E0 , 44.66E0 , 49.87E0 , 55.16E0 , 60.90E0 , 72.08E0 , 85.15E0 , 97.06E0 , 119.63E0 , 133.27E0 , 143.84E0 , 161.91E0 , 180.67E0 , 198.44E0 , 226.86E0 , 229.65E0 , 258.27E0 , 273.77E0 , 339.15E0 , 350.13E0 , 362.75E0 , 371.03E0 , 393.32E0 , 448.53E0 , 473.78E0 , 511.12E0 , 524.70E0 , 548.75E0 , 551.64E0 , 574.02E0 , 623.86E0 , 21.46E0 , 24.33E0 , 33.43E0 , 39.22E0 , 44.18E0 , 55.02E0 , 94.33E0 , 96.44E0 , 118.82E0 , 128.48E0 , -141.94E0 , 156.92E0 , 171.65E0 , 190.00E0 , 223.26E0 , 223.88E0 , 231.50E0 , 265.05E0 , 269.44E0 , 271.78E0 , 273.46E0 , 334.61E0 , 339.79E0 , 349.52E0 , 358.18E0 , 377.98E0 , 394.77E0 , 429.66E0 , 468.22E0 , 487.27E0 , 519.54E0 , 523.03E0 , 612.99E0 , 638.59E0 , 641.36E0 , 622.05E0 , 631.50E0 , 663.97E0 , 646.9E0 , 748.29E0 , 749.21E0 , 750.14E0 , 647.04E0 , 646.89E0 , 746.9E0 , 748.43E0 , 747.35E0 , 749.27E0 , 647.61E0 , 747.78E0 , 750.51E0 , 851.37E0 , 845.97E0 , 847.54E0 , 849.93E0 , 851.61E0 , 849.75E0 , 850.98E0 , 848.23E0}; - -// http://www.itl.nist.gov/div898/strd/nls/data/hahn1.shtml -void testNistHahn1(void) -{ - const int n=7; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 10., -1., .05, -.00001, -.05, .001, -.000001; - // do the computation - hahn1_functor functor; - LevenbergMarquardt<hahn1_functor> lm(functor); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 11); - VERIFY_IS_EQUAL(lm.njev, 10); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00); - // check x - VERIFY_IS_APPROX(x[0], 1.0776351733E+00); - VERIFY_IS_APPROX(x[1],-1.2269296921E-01); - VERIFY_IS_APPROX(x[2], 4.0863750610E-03); - VERIFY_IS_APPROX(x[3],-1.426264e-06); // shoulde be : -1.4262662514E-06 - VERIFY_IS_APPROX(x[4],-5.7609940901E-03); - VERIFY_IS_APPROX(x[5], 2.4053735503E-04); - VERIFY_IS_APPROX(x[6],-1.2314450199E-07); - - /* - * Second try - */ - x<< .1, -.1, .005, -.000001, -.005, .0001, -.0000001; - // do the computation - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 11); - VERIFY_IS_EQUAL(lm.njev, 10); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00); - // check x - VERIFY_IS_APPROX(x[0], 1.077640); // should be : 1.0776351733E+00 - VERIFY_IS_APPROX(x[1], -0.1226933); // should be : -1.2269296921E-01 - VERIFY_IS_APPROX(x[2], 0.004086383); // should be : 4.0863750610E-03 - VERIFY_IS_APPROX(x[3], -1.426277e-06); // shoulde be : -1.4262662514E-06 - VERIFY_IS_APPROX(x[4],-5.7609940901E-03); - VERIFY_IS_APPROX(x[5], 0.00024053772); // should be : 2.4053735503E-04 - VERIFY_IS_APPROX(x[6], -1.231450e-07); // should be : -1.2314450199E-07 - -} - -struct misra1d_functor : Functor<double> -{ - misra1d_functor(void) : Functor<double>(2,14) {} - static const double x[14]; - static const double y[14]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==2); - assert(fvec.size()==14); - for(int i=0; i<14; i++) { - fvec[i] = b[0]*b[1]*x[i]/(1.+b[1]*x[i]) - y[i]; - } - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==2); - assert(fjac.rows()==14); - assert(fjac.cols()==2); - for(int i=0; i<14; i++) { - double den = 1.+b[1]*x[i]; - fjac(i,0) = b[1]*x[i] / den; - fjac(i,1) = b[0]*x[i]*(den-b[1]*x[i])/den/den; - } - return 0; - } -}; -const double misra1d_functor::x[14] = { 77.6E0, 114.9E0, 141.1E0, 190.8E0, 239.9E0, 289.0E0, 332.8E0, 378.4E0, 434.8E0, 477.3E0, 536.8E0, 593.1E0, 689.1E0, 760.0E0}; -const double misra1d_functor::y[14] = { 10.07E0, 14.73E0, 17.94E0, 23.93E0, 29.61E0, 35.18E0, 40.02E0, 44.82E0, 50.76E0, 55.05E0, 61.01E0, 66.40E0, 75.47E0, 81.78E0}; - -// http://www.itl.nist.gov/div898/strd/nls/data/misra1d.shtml -void testNistMisra1d(void) -{ - const int n=2; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 500., 0.0001; - // do the computation - misra1d_functor functor; - LevenbergMarquardt<misra1d_functor> lm(functor); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 3); - VERIFY_IS_EQUAL(lm.nfev, 9); - VERIFY_IS_EQUAL(lm.njev, 7); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02); - // check x - VERIFY_IS_APPROX(x[0], 4.3736970754E+02); - VERIFY_IS_APPROX(x[1], 3.0227324449E-04); - - /* - * Second try - */ - x<< 450., 0.0003; - // do the computation - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 4); - VERIFY_IS_EQUAL(lm.njev, 3); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02); - // check x - VERIFY_IS_APPROX(x[0], 4.3736970754E+02); - VERIFY_IS_APPROX(x[1], 3.0227324449E-04); -} - - -struct lanczos1_functor : Functor<double> -{ - lanczos1_functor(void) : Functor<double>(6,24) {} - static const double x[24]; - static const double y[24]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==6); - assert(fvec.size()==24); - for(int i=0; i<24; i++) - fvec[i] = b[0]*exp(-b[1]*x[i]) + b[2]*exp(-b[3]*x[i]) + b[4]*exp(-b[5]*x[i]) - y[i]; - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==6); - assert(fjac.rows()==24); - assert(fjac.cols()==6); - for(int i=0; i<24; i++) { - fjac(i,0) = exp(-b[1]*x[i]); - fjac(i,1) = -b[0]*x[i]*exp(-b[1]*x[i]); - fjac(i,2) = exp(-b[3]*x[i]); - fjac(i,3) = -b[2]*x[i]*exp(-b[3]*x[i]); - fjac(i,4) = exp(-b[5]*x[i]); - fjac(i,5) = -b[4]*x[i]*exp(-b[5]*x[i]); - } - return 0; - } -}; -const double lanczos1_functor::x[24] = { 0.000000000000E+00, 5.000000000000E-02, 1.000000000000E-01, 1.500000000000E-01, 2.000000000000E-01, 2.500000000000E-01, 3.000000000000E-01, 3.500000000000E-01, 4.000000000000E-01, 4.500000000000E-01, 5.000000000000E-01, 5.500000000000E-01, 6.000000000000E-01, 6.500000000000E-01, 7.000000000000E-01, 7.500000000000E-01, 8.000000000000E-01, 8.500000000000E-01, 9.000000000000E-01, 9.500000000000E-01, 1.000000000000E+00, 1.050000000000E+00, 1.100000000000E+00, 1.150000000000E+00 }; -const double lanczos1_functor::y[24] = { 2.513400000000E+00 ,2.044333373291E+00 ,1.668404436564E+00 ,1.366418021208E+00 ,1.123232487372E+00 ,9.268897180037E-01 ,7.679338563728E-01 ,6.388775523106E-01 ,5.337835317402E-01 ,4.479363617347E-01 ,3.775847884350E-01 ,3.197393199326E-01 ,2.720130773746E-01 ,2.324965529032E-01 ,1.996589546065E-01 ,1.722704126914E-01 ,1.493405660168E-01 ,1.300700206922E-01 ,1.138119324644E-01 ,1.000415587559E-01 ,8.833209084540E-02 ,7.833544019350E-02 ,6.976693743449E-02 ,6.239312536719E-02 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/lanczos1.shtml -void testNistLanczos1(void) -{ - const int n=6; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 1.2, 0.3, 5.6, 5.5, 6.5, 7.6; - // do the computation - lanczos1_functor functor; - LevenbergMarquardt<lanczos1_functor> lm(functor); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 2); - VERIFY_IS_EQUAL(lm.nfev, 79); - VERIFY_IS_EQUAL(lm.njev, 72); - // check norm^2 - std::cout.precision(30); - std::cout << lm.fvec.squaredNorm() << "\n"; - VERIFY(lm.fvec.squaredNorm() <= 1.4307867721E-25); - // check x - VERIFY_IS_APPROX(x[0], 9.5100000027E-02); - VERIFY_IS_APPROX(x[1], 1.0000000001E+00); - VERIFY_IS_APPROX(x[2], 8.6070000013E-01); - VERIFY_IS_APPROX(x[3], 3.0000000002E+00); - VERIFY_IS_APPROX(x[4], 1.5575999998E+00); - VERIFY_IS_APPROX(x[5], 5.0000000001E+00); - - /* - * Second try - */ - x<< 0.5, 0.7, 3.6, 4.2, 4., 6.3; - // do the computation - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 2); - VERIFY_IS_EQUAL(lm.nfev, 9); - VERIFY_IS_EQUAL(lm.njev, 8); - // check norm^2 - VERIFY(lm.fvec.squaredNorm() <= 1.4307867721E-25); - // check x - VERIFY_IS_APPROX(x[0], 9.5100000027E-02); - VERIFY_IS_APPROX(x[1], 1.0000000001E+00); - VERIFY_IS_APPROX(x[2], 8.6070000013E-01); - VERIFY_IS_APPROX(x[3], 3.0000000002E+00); - VERIFY_IS_APPROX(x[4], 1.5575999998E+00); - VERIFY_IS_APPROX(x[5], 5.0000000001E+00); - -} - -struct rat42_functor : Functor<double> -{ - rat42_functor(void) : Functor<double>(3,9) {} - static const double x[9]; - static const double y[9]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==3); - assert(fvec.size()==9); - for(int i=0; i<9; i++) { - fvec[i] = b[0] / (1.+exp(b[1]-b[2]*x[i])) - y[i]; - } - return 0; - } - - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==3); - assert(fjac.rows()==9); - assert(fjac.cols()==3); - for(int i=0; i<9; i++) { - double e = exp(b[1]-b[2]*x[i]); - fjac(i,0) = 1./(1.+e); - fjac(i,1) = -b[0]*e/(1.+e)/(1.+e); - fjac(i,2) = +b[0]*e*x[i]/(1.+e)/(1.+e); - } - return 0; - } -}; -const double rat42_functor::x[9] = { 9.000E0, 14.000E0, 21.000E0, 28.000E0, 42.000E0, 57.000E0, 63.000E0, 70.000E0, 79.000E0 }; -const double rat42_functor::y[9] = { 8.930E0 ,10.800E0 ,18.590E0 ,22.330E0 ,39.350E0 ,56.110E0 ,61.730E0 ,64.620E0 ,67.080E0 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/ratkowsky2.shtml -void testNistRat42(void) -{ - const int n=3; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 100., 1., 0.1; - // do the computation - rat42_functor functor; - LevenbergMarquardt<rat42_functor> lm(functor); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 10); - VERIFY_IS_EQUAL(lm.njev, 8); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.0565229338E+00); - // check x - VERIFY_IS_APPROX(x[0], 7.2462237576E+01); - VERIFY_IS_APPROX(x[1], 2.6180768402E+00); - VERIFY_IS_APPROX(x[2], 6.7359200066E-02); - - /* - * Second try - */ - x<< 75., 2.5, 0.07; - // do the computation - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 6); - VERIFY_IS_EQUAL(lm.njev, 5); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.0565229338E+00); - // check x - VERIFY_IS_APPROX(x[0], 7.2462237576E+01); - VERIFY_IS_APPROX(x[1], 2.6180768402E+00); - VERIFY_IS_APPROX(x[2], 6.7359200066E-02); -} - -struct MGH10_functor : Functor<double> -{ - MGH10_functor(void) : Functor<double>(3,16) {} - static const double x[16]; - static const double y[16]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==3); - assert(fvec.size()==16); - for(int i=0; i<16; i++) - fvec[i] = b[0] * exp(b[1]/(x[i]+b[2])) - y[i]; - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==3); - assert(fjac.rows()==16); - assert(fjac.cols()==3); - for(int i=0; i<16; i++) { - double factor = 1./(x[i]+b[2]); - double e = exp(b[1]*factor); - fjac(i,0) = e; - fjac(i,1) = b[0]*factor*e; - fjac(i,2) = -b[1]*b[0]*factor*factor*e; - } - return 0; - } -}; -const double MGH10_functor::x[16] = { 5.000000E+01, 5.500000E+01, 6.000000E+01, 6.500000E+01, 7.000000E+01, 7.500000E+01, 8.000000E+01, 8.500000E+01, 9.000000E+01, 9.500000E+01, 1.000000E+02, 1.050000E+02, 1.100000E+02, 1.150000E+02, 1.200000E+02, 1.250000E+02 }; -const double MGH10_functor::y[16] = { 3.478000E+04, 2.861000E+04, 2.365000E+04, 1.963000E+04, 1.637000E+04, 1.372000E+04, 1.154000E+04, 9.744000E+03, 8.261000E+03, 7.030000E+03, 6.005000E+03, 5.147000E+03, 4.427000E+03, 3.820000E+03, 3.307000E+03, 2.872000E+03 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/mgh10.shtml -void testNistMGH10(void) -{ - const int n=3; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 2., 400000., 25000.; - // do the computation - MGH10_functor functor; - LevenbergMarquardt<MGH10_functor> lm(functor); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 2); - VERIFY_IS_EQUAL(lm.nfev, 284 ); - VERIFY_IS_EQUAL(lm.njev, 249 ); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7945855171E+01); - // check x - VERIFY_IS_APPROX(x[0], 5.6096364710E-03); - VERIFY_IS_APPROX(x[1], 6.1813463463E+03); - VERIFY_IS_APPROX(x[2], 3.4522363462E+02); - - /* - * Second try - */ - x<< 0.02, 4000., 250.; - // do the computation - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 3); - VERIFY_IS_EQUAL(lm.nfev, 126); - VERIFY_IS_EQUAL(lm.njev, 116); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7945855171E+01); - // check x - VERIFY_IS_APPROX(x[0], 5.6096364710E-03); - VERIFY_IS_APPROX(x[1], 6.1813463463E+03); - VERIFY_IS_APPROX(x[2], 3.4522363462E+02); -} - - -struct BoxBOD_functor : Functor<double> -{ - BoxBOD_functor(void) : Functor<double>(2,6) {} - static const double x[6]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - static const double y[6] = { 109., 149., 149., 191., 213., 224. }; - assert(b.size()==2); - assert(fvec.size()==6); - for(int i=0; i<6; i++) - fvec[i] = b[0]*(1.-exp(-b[1]*x[i])) - y[i]; - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==2); - assert(fjac.rows()==6); - assert(fjac.cols()==2); - for(int i=0; i<6; i++) { - double e = exp(-b[1]*x[i]); - fjac(i,0) = 1.-e; - fjac(i,1) = b[0]*x[i]*e; - } - return 0; - } -}; -const double BoxBOD_functor::x[6] = { 1., 2., 3., 5., 7., 10. }; - -// http://www.itl.nist.gov/div898/strd/nls/data/boxbod.shtml -void testNistBoxBOD(void) -{ - const int n=2; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 1., 1.; - // do the computation - BoxBOD_functor functor; - LevenbergMarquardt<BoxBOD_functor> lm(functor); - lm.parameters.ftol = 1.E6*NumTraits<double>::epsilon(); - lm.parameters.xtol = 1.E6*NumTraits<double>::epsilon(); - lm.parameters.factor = 10.; - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY(lm.nfev < 31); // 31 - VERIFY(lm.njev < 25); // 25 - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.1680088766E+03); - // check x - VERIFY_IS_APPROX(x[0], 2.1380940889E+02); - VERIFY_IS_APPROX(x[1], 5.4723748542E-01); - - /* - * Second try - */ - x<< 100., 0.75; - // do the computation - lm.resetParameters(); - lm.parameters.ftol = NumTraits<double>::epsilon(); - lm.parameters.xtol = NumTraits<double>::epsilon(); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 15 ); - VERIFY_IS_EQUAL(lm.njev, 14 ); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.1680088766E+03); - // check x - VERIFY_IS_APPROX(x[0], 2.1380940889E+02); - VERIFY_IS_APPROX(x[1], 5.4723748542E-01); -} - -struct MGH17_functor : Functor<double> -{ - MGH17_functor(void) : Functor<double>(5,33) {} - static const double x[33]; - static const double y[33]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==5); - assert(fvec.size()==33); - for(int i=0; i<33; i++) - fvec[i] = b[0] + b[1]*exp(-b[3]*x[i]) + b[2]*exp(-b[4]*x[i]) - y[i]; - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==5); - assert(fjac.rows()==33); - assert(fjac.cols()==5); - for(int i=0; i<33; i++) { - fjac(i,0) = 1.; - fjac(i,1) = exp(-b[3]*x[i]); - fjac(i,2) = exp(-b[4]*x[i]); - fjac(i,3) = -x[i]*b[1]*exp(-b[3]*x[i]); - fjac(i,4) = -x[i]*b[2]*exp(-b[4]*x[i]); - } - return 0; - } -}; -const double MGH17_functor::x[33] = { 0.000000E+00, 1.000000E+01, 2.000000E+01, 3.000000E+01, 4.000000E+01, 5.000000E+01, 6.000000E+01, 7.000000E+01, 8.000000E+01, 9.000000E+01, 1.000000E+02, 1.100000E+02, 1.200000E+02, 1.300000E+02, 1.400000E+02, 1.500000E+02, 1.600000E+02, 1.700000E+02, 1.800000E+02, 1.900000E+02, 2.000000E+02, 2.100000E+02, 2.200000E+02, 2.300000E+02, 2.400000E+02, 2.500000E+02, 2.600000E+02, 2.700000E+02, 2.800000E+02, 2.900000E+02, 3.000000E+02, 3.100000E+02, 3.200000E+02 }; -const double MGH17_functor::y[33] = { 8.440000E-01, 9.080000E-01, 9.320000E-01, 9.360000E-01, 9.250000E-01, 9.080000E-01, 8.810000E-01, 8.500000E-01, 8.180000E-01, 7.840000E-01, 7.510000E-01, 7.180000E-01, 6.850000E-01, 6.580000E-01, 6.280000E-01, 6.030000E-01, 5.800000E-01, 5.580000E-01, 5.380000E-01, 5.220000E-01, 5.060000E-01, 4.900000E-01, 4.780000E-01, 4.670000E-01, 4.570000E-01, 4.480000E-01, 4.380000E-01, 4.310000E-01, 4.240000E-01, 4.200000E-01, 4.140000E-01, 4.110000E-01, 4.060000E-01 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/mgh17.shtml -void testNistMGH17(void) -{ - const int n=5; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 50., 150., -100., 1., 2.; - // do the computation - MGH17_functor functor; - LevenbergMarquardt<MGH17_functor> lm(functor); - lm.parameters.ftol = NumTraits<double>::epsilon(); - lm.parameters.xtol = NumTraits<double>::epsilon(); - lm.parameters.maxfev = 1000; - info = lm.minimize(x); - - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.4648946975E-05); - // check x - VERIFY_IS_APPROX(x[0], 3.7541005211E-01); - VERIFY_IS_APPROX(x[1], 1.9358469127E+00); - VERIFY_IS_APPROX(x[2], -1.4646871366E+00); - VERIFY_IS_APPROX(x[3], 1.2867534640E-02); - VERIFY_IS_APPROX(x[4], 2.2122699662E-02); - - // check return value - VERIFY_IS_EQUAL(info, 2); - ++g_test_level; - VERIFY_IS_EQUAL(lm.nfev, 602); // 602 - VERIFY_IS_EQUAL(lm.njev, 545); // 545 - --g_test_level; - VERIFY(lm.nfev < 602 * LM_EVAL_COUNT_TOL); - VERIFY(lm.njev < 545 * LM_EVAL_COUNT_TOL); - - /* - * Second try - */ - x<< 0.5 ,1.5 ,-1 ,0.01 ,0.02; - // do the computation - lm.resetParameters(); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 18); - VERIFY_IS_EQUAL(lm.njev, 15); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.4648946975E-05); - // check x - VERIFY_IS_APPROX(x[0], 3.7541005211E-01); - VERIFY_IS_APPROX(x[1], 1.9358469127E+00); - VERIFY_IS_APPROX(x[2], -1.4646871366E+00); - VERIFY_IS_APPROX(x[3], 1.2867534640E-02); - VERIFY_IS_APPROX(x[4], 2.2122699662E-02); -} - -struct MGH09_functor : Functor<double> -{ - MGH09_functor(void) : Functor<double>(4,11) {} - static const double _x[11]; - static const double y[11]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==4); - assert(fvec.size()==11); - for(int i=0; i<11; i++) { - double x = _x[i], xx=x*x; - fvec[i] = b[0]*(xx+x*b[1])/(xx+x*b[2]+b[3]) - y[i]; - } - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==4); - assert(fjac.rows()==11); - assert(fjac.cols()==4); - for(int i=0; i<11; i++) { - double x = _x[i], xx=x*x; - double factor = 1./(xx+x*b[2]+b[3]); - fjac(i,0) = (xx+x*b[1]) * factor; - fjac(i,1) = b[0]*x* factor; - fjac(i,2) = - b[0]*(xx+x*b[1]) * x * factor * factor; - fjac(i,3) = - b[0]*(xx+x*b[1]) * factor * factor; - } - return 0; - } -}; -const double MGH09_functor::_x[11] = { 4., 2., 1., 5.E-1 , 2.5E-01, 1.670000E-01, 1.250000E-01, 1.E-01, 8.330000E-02, 7.140000E-02, 6.250000E-02 }; -const double MGH09_functor::y[11] = { 1.957000E-01, 1.947000E-01, 1.735000E-01, 1.600000E-01, 8.440000E-02, 6.270000E-02, 4.560000E-02, 3.420000E-02, 3.230000E-02, 2.350000E-02, 2.460000E-02 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/mgh09.shtml -void testNistMGH09(void) -{ - const int n=4; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 25., 39, 41.5, 39.; - // do the computation - MGH09_functor functor; - LevenbergMarquardt<MGH09_functor> lm(functor); - lm.parameters.maxfev = 1000; - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 490 ); - VERIFY_IS_EQUAL(lm.njev, 376 ); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 3.0750560385E-04); - // check x - VERIFY_IS_APPROX(x[0], 0.1928077089); // should be 1.9280693458E-01 - VERIFY_IS_APPROX(x[1], 0.19126423573); // should be 1.9128232873E-01 - VERIFY_IS_APPROX(x[2], 0.12305309914); // should be 1.2305650693E-01 - VERIFY_IS_APPROX(x[3], 0.13605395375); // should be 1.3606233068E-01 - - /* - * Second try - */ - x<< 0.25, 0.39, 0.415, 0.39; - // do the computation - lm.resetParameters(); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 18); - VERIFY_IS_EQUAL(lm.njev, 16); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 3.0750560385E-04); - // check x - VERIFY_IS_APPROX(x[0], 0.19280781); // should be 1.9280693458E-01 - VERIFY_IS_APPROX(x[1], 0.19126265); // should be 1.9128232873E-01 - VERIFY_IS_APPROX(x[2], 0.12305280); // should be 1.2305650693E-01 - VERIFY_IS_APPROX(x[3], 0.13605322); // should be 1.3606233068E-01 -} - - - -struct Bennett5_functor : Functor<double> -{ - Bennett5_functor(void) : Functor<double>(3,154) {} - static const double x[154]; - static const double y[154]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==3); - assert(fvec.size()==154); - for(int i=0; i<154; i++) - fvec[i] = b[0]* pow(b[1]+x[i],-1./b[2]) - y[i]; - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==3); - assert(fjac.rows()==154); - assert(fjac.cols()==3); - for(int i=0; i<154; i++) { - double e = pow(b[1]+x[i],-1./b[2]); - fjac(i,0) = e; - fjac(i,1) = - b[0]*e/b[2]/(b[1]+x[i]); - fjac(i,2) = b[0]*e*log(b[1]+x[i])/b[2]/b[2]; - } - return 0; - } -}; -const double Bennett5_functor::x[154] = { 7.447168E0, 8.102586E0, 8.452547E0, 8.711278E0, 8.916774E0, 9.087155E0, 9.232590E0, 9.359535E0, 9.472166E0, 9.573384E0, 9.665293E0, 9.749461E0, 9.827092E0, 9.899128E0, 9.966321E0, 10.029280E0, 10.088510E0, 10.144430E0, 10.197380E0, 10.247670E0, 10.295560E0, 10.341250E0, 10.384950E0, 10.426820E0, 10.467000E0, 10.505640E0, 10.542830E0, 10.578690E0, 10.613310E0, 10.646780E0, 10.679150E0, 10.710520E0, 10.740920E0, 10.770440E0, 10.799100E0, 10.826970E0, 10.854080E0, 10.880470E0, 10.906190E0, 10.931260E0, 10.955720E0, 10.979590E0, 11.002910E0, 11.025700E0, 11.047980E0, 11.069770E0, 11.091100E0, 11.111980E0, 11.132440E0, 11.152480E0, 11.172130E0, 11.191410E0, 11.210310E0, 11.228870E0, 11.247090E0, 11.264980E0, 11.282560E0, 11.299840E0, 11.316820E0, 11.333520E0, 11.349940E0, 11.366100E0, 11.382000E0, 11.397660E0, 11.413070E0, 11.428240E0, 11.443200E0, 11.457930E0, 11.472440E0, 11.486750E0, 11.500860E0, 11.514770E0, 11.528490E0, 11.542020E0, 11.555380E0, 11.568550E0, -11.581560E0, 11.594420E0, 11.607121E0, 11.619640E0, 11.632000E0, 11.644210E0, 11.656280E0, 11.668200E0, 11.679980E0, 11.691620E0, 11.703130E0, 11.714510E0, 11.725760E0, 11.736880E0, 11.747890E0, 11.758780E0, 11.769550E0, 11.780200E0, 11.790730E0, 11.801160E0, 11.811480E0, 11.821700E0, 11.831810E0, 11.841820E0, 11.851730E0, 11.861550E0, 11.871270E0, 11.880890E0, 11.890420E0, 11.899870E0, 11.909220E0, 11.918490E0, 11.927680E0, 11.936780E0, 11.945790E0, 11.954730E0, 11.963590E0, 11.972370E0, 11.981070E0, 11.989700E0, 11.998260E0, 12.006740E0, 12.015150E0, 12.023490E0, 12.031760E0, 12.039970E0, 12.048100E0, 12.056170E0, 12.064180E0, 12.072120E0, 12.080010E0, 12.087820E0, 12.095580E0, 12.103280E0, 12.110920E0, 12.118500E0, 12.126030E0, 12.133500E0, 12.140910E0, 12.148270E0, 12.155570E0, 12.162830E0, 12.170030E0, 12.177170E0, 12.184270E0, 12.191320E0, 12.198320E0, 12.205270E0, 12.212170E0, 12.219030E0, 12.225840E0, 12.232600E0, 12.239320E0, 12.245990E0, 12.252620E0, 12.259200E0, 12.265750E0, 12.272240E0 }; -const double Bennett5_functor::y[154] = { -34.834702E0 ,-34.393200E0 ,-34.152901E0 ,-33.979099E0 ,-33.845901E0 ,-33.732899E0 ,-33.640301E0 ,-33.559200E0 ,-33.486801E0 ,-33.423100E0 ,-33.365101E0 ,-33.313000E0 ,-33.260899E0 ,-33.217400E0 ,-33.176899E0 ,-33.139198E0 ,-33.101601E0 ,-33.066799E0 ,-33.035000E0 ,-33.003101E0 ,-32.971298E0 ,-32.942299E0 ,-32.916302E0 ,-32.890202E0 ,-32.864101E0 ,-32.841000E0 ,-32.817799E0 ,-32.797501E0 ,-32.774300E0 ,-32.757000E0 ,-32.733799E0 ,-32.716400E0 ,-32.699100E0 ,-32.678799E0 ,-32.661400E0 ,-32.644001E0 ,-32.626701E0 ,-32.612202E0 ,-32.597698E0 ,-32.583199E0 ,-32.568699E0 ,-32.554298E0 ,-32.539799E0 ,-32.525299E0 ,-32.510799E0 ,-32.499199E0 ,-32.487598E0 ,-32.473202E0 ,-32.461601E0 ,-32.435501E0 ,-32.435501E0 ,-32.426800E0 ,-32.412300E0 ,-32.400799E0 ,-32.392101E0 ,-32.380501E0 ,-32.366001E0 ,-32.357300E0 ,-32.348598E0 ,-32.339901E0 ,-32.328400E0 ,-32.319698E0 ,-32.311001E0 ,-32.299400E0 ,-32.290699E0 ,-32.282001E0 ,-32.273300E0 ,-32.264599E0 ,-32.256001E0 ,-32.247299E0 -,-32.238602E0 ,-32.229900E0 ,-32.224098E0 ,-32.215401E0 ,-32.203800E0 ,-32.198002E0 ,-32.189400E0 ,-32.183601E0 ,-32.174900E0 ,-32.169102E0 ,-32.163300E0 ,-32.154598E0 ,-32.145901E0 ,-32.140099E0 ,-32.131401E0 ,-32.125599E0 ,-32.119801E0 ,-32.111198E0 ,-32.105400E0 ,-32.096699E0 ,-32.090900E0 ,-32.088001E0 ,-32.079300E0 ,-32.073502E0 ,-32.067699E0 ,-32.061901E0 ,-32.056099E0 ,-32.050301E0 ,-32.044498E0 ,-32.038799E0 ,-32.033001E0 ,-32.027199E0 ,-32.024300E0 ,-32.018501E0 ,-32.012699E0 ,-32.004002E0 ,-32.001099E0 ,-31.995300E0 ,-31.989500E0 ,-31.983700E0 ,-31.977900E0 ,-31.972099E0 ,-31.969299E0 ,-31.963501E0 ,-31.957701E0 ,-31.951900E0 ,-31.946100E0 ,-31.940300E0 ,-31.937401E0 ,-31.931601E0 ,-31.925800E0 ,-31.922899E0 ,-31.917101E0 ,-31.911301E0 ,-31.908400E0 ,-31.902599E0 ,-31.896900E0 ,-31.893999E0 ,-31.888201E0 ,-31.885300E0 ,-31.882401E0 ,-31.876600E0 ,-31.873699E0 ,-31.867901E0 ,-31.862101E0 ,-31.859200E0 ,-31.856300E0 ,-31.850500E0 ,-31.844700E0 ,-31.841801E0 ,-31.838900E0 ,-31.833099E0 ,-31.830200E0 , --31.827299E0 ,-31.821600E0 ,-31.818701E0 ,-31.812901E0 ,-31.809999E0 ,-31.807100E0 ,-31.801300E0 ,-31.798401E0 ,-31.795500E0 ,-31.789700E0 ,-31.786800E0 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/bennett5.shtml -void testNistBennett5(void) -{ - const int n=3; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< -2000., 50., 0.8; - // do the computation - Bennett5_functor functor; - LevenbergMarquardt<Bennett5_functor> lm(functor); - lm.parameters.maxfev = 1000; - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 758); - VERIFY_IS_EQUAL(lm.njev, 744); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.2404744073E-04); - // check x - VERIFY_IS_APPROX(x[0], -2.5235058043E+03); - VERIFY_IS_APPROX(x[1], 4.6736564644E+01); - VERIFY_IS_APPROX(x[2], 9.3218483193E-01); - /* - * Second try - */ - x<< -1500., 45., 0.85; - // do the computation - lm.resetParameters(); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 203); - VERIFY_IS_EQUAL(lm.njev, 192); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.2404744073E-04); - // check x - VERIFY_IS_APPROX(x[0], -2523.3007865); // should be -2.5235058043E+03 - VERIFY_IS_APPROX(x[1], 46.735705771); // should be 4.6736564644E+01); - VERIFY_IS_APPROX(x[2], 0.93219881891); // should be 9.3218483193E-01); -} - -struct thurber_functor : Functor<double> -{ - thurber_functor(void) : Functor<double>(7,37) {} - static const double _x[37]; - static const double _y[37]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - // int called=0; printf("call hahn1_functor with iflag=%d, called=%d\n", iflag, called); if (iflag==1) called++; - assert(b.size()==7); - assert(fvec.size()==37); - for(int i=0; i<37; i++) { - double x=_x[i], xx=x*x, xxx=xx*x; - fvec[i] = (b[0]+b[1]*x+b[2]*xx+b[3]*xxx) / (1.+b[4]*x+b[5]*xx+b[6]*xxx) - _y[i]; - } - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==7); - assert(fjac.rows()==37); - assert(fjac.cols()==7); - for(int i=0; i<37; i++) { - double x=_x[i], xx=x*x, xxx=xx*x; - double fact = 1./(1.+b[4]*x+b[5]*xx+b[6]*xxx); - fjac(i,0) = 1.*fact; - fjac(i,1) = x*fact; - fjac(i,2) = xx*fact; - fjac(i,3) = xxx*fact; - fact = - (b[0]+b[1]*x+b[2]*xx+b[3]*xxx) * fact * fact; - fjac(i,4) = x*fact; - fjac(i,5) = xx*fact; - fjac(i,6) = xxx*fact; - } - return 0; - } -}; -const double thurber_functor::_x[37] = { -3.067E0, -2.981E0, -2.921E0, -2.912E0, -2.840E0, -2.797E0, -2.702E0, -2.699E0, -2.633E0, -2.481E0, -2.363E0, -2.322E0, -1.501E0, -1.460E0, -1.274E0, -1.212E0, -1.100E0, -1.046E0, -0.915E0, -0.714E0, -0.566E0, -0.545E0, -0.400E0, -0.309E0, -0.109E0, -0.103E0, 0.010E0, 0.119E0, 0.377E0, 0.790E0, 0.963E0, 1.006E0, 1.115E0, 1.572E0, 1.841E0, 2.047E0, 2.200E0 }; -const double thurber_functor::_y[37] = { 80.574E0, 84.248E0, 87.264E0, 87.195E0, 89.076E0, 89.608E0, 89.868E0, 90.101E0, 92.405E0, 95.854E0, 100.696E0, 101.060E0, 401.672E0, 390.724E0, 567.534E0, 635.316E0, 733.054E0, 759.087E0, 894.206E0, 990.785E0, 1090.109E0, 1080.914E0, 1122.643E0, 1178.351E0, 1260.531E0, 1273.514E0, 1288.339E0, 1327.543E0, 1353.863E0, 1414.509E0, 1425.208E0, 1421.384E0, 1442.962E0, 1464.350E0, 1468.705E0, 1447.894E0, 1457.628E0}; - -// http://www.itl.nist.gov/div898/strd/nls/data/thurber.shtml -void testNistThurber(void) -{ - const int n=7; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 1000 ,1000 ,400 ,40 ,0.7,0.3,0.0 ; - // do the computation - thurber_functor functor; - LevenbergMarquardt<thurber_functor> lm(functor); - lm.parameters.ftol = 1.E4*NumTraits<double>::epsilon(); - lm.parameters.xtol = 1.E4*NumTraits<double>::epsilon(); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 39); - VERIFY_IS_EQUAL(lm.njev, 36); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6427082397E+03); - // check x - VERIFY_IS_APPROX(x[0], 1.2881396800E+03); - VERIFY_IS_APPROX(x[1], 1.4910792535E+03); - VERIFY_IS_APPROX(x[2], 5.8323836877E+02); - VERIFY_IS_APPROX(x[3], 7.5416644291E+01); - VERIFY_IS_APPROX(x[4], 9.6629502864E-01); - VERIFY_IS_APPROX(x[5], 3.9797285797E-01); - VERIFY_IS_APPROX(x[6], 4.9727297349E-02); - - /* - * Second try - */ - x<< 1300 ,1500 ,500 ,75 ,1 ,0.4 ,0.05 ; - // do the computation - lm.resetParameters(); - lm.parameters.ftol = 1.E4*NumTraits<double>::epsilon(); - lm.parameters.xtol = 1.E4*NumTraits<double>::epsilon(); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 29); - VERIFY_IS_EQUAL(lm.njev, 28); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6427082397E+03); - // check x - VERIFY_IS_APPROX(x[0], 1.2881396800E+03); - VERIFY_IS_APPROX(x[1], 1.4910792535E+03); - VERIFY_IS_APPROX(x[2], 5.8323836877E+02); - VERIFY_IS_APPROX(x[3], 7.5416644291E+01); - VERIFY_IS_APPROX(x[4], 9.6629502864E-01); - VERIFY_IS_APPROX(x[5], 3.9797285797E-01); - VERIFY_IS_APPROX(x[6], 4.9727297349E-02); -} - -struct rat43_functor : Functor<double> -{ - rat43_functor(void) : Functor<double>(4,15) {} - static const double x[15]; - static const double y[15]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==4); - assert(fvec.size()==15); - for(int i=0; i<15; i++) - fvec[i] = b[0] * pow(1.+exp(b[1]-b[2]*x[i]),-1./b[3]) - y[i]; - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==4); - assert(fjac.rows()==15); - assert(fjac.cols()==4); - for(int i=0; i<15; i++) { - double e = exp(b[1]-b[2]*x[i]); - double power = -1./b[3]; - fjac(i,0) = pow(1.+e, power); - fjac(i,1) = power*b[0]*e*pow(1.+e, power-1.); - fjac(i,2) = -power*b[0]*e*x[i]*pow(1.+e, power-1.); - fjac(i,3) = b[0]*power*power*log(1.+e)*pow(1.+e, power); - } - return 0; - } -}; -const double rat43_functor::x[15] = { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15. }; -const double rat43_functor::y[15] = { 16.08, 33.83, 65.80, 97.20, 191.55, 326.20, 386.87, 520.53, 590.03, 651.92, 724.93, 699.56, 689.96, 637.56, 717.41 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/ratkowsky3.shtml -void testNistRat43(void) -{ - const int n=4; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 100., 10., 1., 1.; - // do the computation - rat43_functor functor; - LevenbergMarquardt<rat43_functor> lm(functor); - lm.parameters.ftol = 1.E6*NumTraits<double>::epsilon(); - lm.parameters.xtol = 1.E6*NumTraits<double>::epsilon(); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 27); - VERIFY_IS_EQUAL(lm.njev, 20); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7864049080E+03); - // check x - VERIFY_IS_APPROX(x[0], 6.9964151270E+02); - VERIFY_IS_APPROX(x[1], 5.2771253025E+00); - VERIFY_IS_APPROX(x[2], 7.5962938329E-01); - VERIFY_IS_APPROX(x[3], 1.2792483859E+00); - - /* - * Second try - */ - x<< 700., 5., 0.75, 1.3; - // do the computation - lm.resetParameters(); - lm.parameters.ftol = 1.E5*NumTraits<double>::epsilon(); - lm.parameters.xtol = 1.E5*NumTraits<double>::epsilon(); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 9); - VERIFY_IS_EQUAL(lm.njev, 8); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7864049080E+03); - // check x - VERIFY_IS_APPROX(x[0], 6.9964151270E+02); - VERIFY_IS_APPROX(x[1], 5.2771253025E+00); - VERIFY_IS_APPROX(x[2], 7.5962938329E-01); - VERIFY_IS_APPROX(x[3], 1.2792483859E+00); -} - - - -struct eckerle4_functor : Functor<double> -{ - eckerle4_functor(void) : Functor<double>(3,35) {} - static const double x[35]; - static const double y[35]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==3); - assert(fvec.size()==35); - for(int i=0; i<35; i++) - fvec[i] = b[0]/b[1] * exp(-0.5*(x[i]-b[2])*(x[i]-b[2])/(b[1]*b[1])) - y[i]; - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==3); - assert(fjac.rows()==35); - assert(fjac.cols()==3); - for(int i=0; i<35; i++) { - double b12 = b[1]*b[1]; - double e = exp(-0.5*(x[i]-b[2])*(x[i]-b[2])/b12); - fjac(i,0) = e / b[1]; - fjac(i,1) = ((x[i]-b[2])*(x[i]-b[2])/b12-1.) * b[0]*e/b12; - fjac(i,2) = (x[i]-b[2])*e*b[0]/b[1]/b12; - } - return 0; - } -}; -const double eckerle4_functor::x[35] = { 400.0, 405.0, 410.0, 415.0, 420.0, 425.0, 430.0, 435.0, 436.5, 438.0, 439.5, 441.0, 442.5, 444.0, 445.5, 447.0, 448.5, 450.0, 451.5, 453.0, 454.5, 456.0, 457.5, 459.0, 460.5, 462.0, 463.5, 465.0, 470.0, 475.0, 480.0, 485.0, 490.0, 495.0, 500.0}; -const double eckerle4_functor::y[35] = { 0.0001575, 0.0001699, 0.0002350, 0.0003102, 0.0004917, 0.0008710, 0.0017418, 0.0046400, 0.0065895, 0.0097302, 0.0149002, 0.0237310, 0.0401683, 0.0712559, 0.1264458, 0.2073413, 0.2902366, 0.3445623, 0.3698049, 0.3668534, 0.3106727, 0.2078154, 0.1164354, 0.0616764, 0.0337200, 0.0194023, 0.0117831, 0.0074357, 0.0022732, 0.0008800, 0.0004579, 0.0002345, 0.0001586, 0.0001143, 0.0000710 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/eckerle4.shtml -void testNistEckerle4(void) -{ - const int n=3; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 1., 10., 500.; - // do the computation - eckerle4_functor functor; - LevenbergMarquardt<eckerle4_functor> lm(functor); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 18); - VERIFY_IS_EQUAL(lm.njev, 15); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.4635887487E-03); - // check x - VERIFY_IS_APPROX(x[0], 1.5543827178); - VERIFY_IS_APPROX(x[1], 4.0888321754); - VERIFY_IS_APPROX(x[2], 4.5154121844E+02); - - /* - * Second try - */ - x<< 1.5, 5., 450.; - // do the computation - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev, 7); - VERIFY_IS_EQUAL(lm.njev, 6); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.4635887487E-03); - // check x - VERIFY_IS_APPROX(x[0], 1.5543827178); - VERIFY_IS_APPROX(x[1], 4.0888321754); - VERIFY_IS_APPROX(x[2], 4.5154121844E+02); -} - -void test_NonLinearOptimization() -{ - // Tests using the examples provided by (c)minpack - CALL_SUBTEST/*_1*/(testChkder()); - CALL_SUBTEST/*_1*/(testLmder1()); - CALL_SUBTEST/*_1*/(testLmder()); - CALL_SUBTEST/*_2*/(testHybrj1()); - CALL_SUBTEST/*_2*/(testHybrj()); - CALL_SUBTEST/*_2*/(testHybrd1()); - CALL_SUBTEST/*_2*/(testHybrd()); - CALL_SUBTEST/*_3*/(testLmstr1()); - CALL_SUBTEST/*_3*/(testLmstr()); - CALL_SUBTEST/*_3*/(testLmdif1()); - CALL_SUBTEST/*_3*/(testLmdif()); - - // NIST tests, level of difficulty = "Lower" - CALL_SUBTEST/*_4*/(testNistMisra1a()); - CALL_SUBTEST/*_4*/(testNistChwirut2()); - - // NIST tests, level of difficulty = "Average" - CALL_SUBTEST/*_5*/(testNistHahn1()); - CALL_SUBTEST/*_6*/(testNistMisra1d()); - CALL_SUBTEST/*_7*/(testNistMGH17()); - CALL_SUBTEST/*_8*/(testNistLanczos1()); - -// // NIST tests, level of difficulty = "Higher" - CALL_SUBTEST/*_9*/(testNistRat42()); -// CALL_SUBTEST/*_10*/(testNistMGH10()); - CALL_SUBTEST/*_11*/(testNistBoxBOD()); -// CALL_SUBTEST/*_12*/(testNistMGH09()); - CALL_SUBTEST/*_13*/(testNistBennett5()); - CALL_SUBTEST/*_14*/(testNistThurber()); - CALL_SUBTEST/*_15*/(testNistRat43()); - CALL_SUBTEST/*_16*/(testNistEckerle4()); -} - -/* - * Can be useful for debugging... - printf("info, nfev : %d, %d\n", info, lm.nfev); - printf("info, nfev, njev : %d, %d, %d\n", info, solver.nfev, solver.njev); - printf("info, nfev : %d, %d\n", info, solver.nfev); - printf("x[0] : %.32g\n", x[0]); - printf("x[1] : %.32g\n", x[1]); - printf("x[2] : %.32g\n", x[2]); - printf("x[3] : %.32g\n", x[3]); - printf("fvec.blueNorm() : %.32g\n", solver.fvec.blueNorm()); - printf("fvec.blueNorm() : %.32g\n", lm.fvec.blueNorm()); - - printf("info, nfev, njev : %d, %d, %d\n", info, lm.nfev, lm.njev); - printf("fvec.squaredNorm() : %.13g\n", lm.fvec.squaredNorm()); - std::cout << x << std::endl; - std::cout.precision(9); - std::cout << x[0] << std::endl; - std::cout << x[1] << std::endl; - std::cout << x[2] << std::endl; - std::cout << x[3] << std::endl; -*/ - diff --git a/eigen/unsupported/test/NumericalDiff.cpp b/eigen/unsupported/test/NumericalDiff.cpp deleted file mode 100644 index 27d8880..0000000 --- a/eigen/unsupported/test/NumericalDiff.cpp +++ /dev/null @@ -1,114 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org> - -#include <stdio.h> - -#include "main.h" -#include <unsupported/Eigen/NumericalDiff> - -// Generic functor -template<typename _Scalar, int NX=Dynamic, int NY=Dynamic> -struct Functor -{ - typedef _Scalar Scalar; - enum { - InputsAtCompileTime = NX, - ValuesAtCompileTime = NY - }; - typedef Matrix<Scalar,InputsAtCompileTime,1> InputType; - typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType; - typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType; - - int m_inputs, m_values; - - Functor() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {} - Functor(int inputs, int values) : m_inputs(inputs), m_values(values) {} - - int inputs() const { return m_inputs; } - int values() const { return m_values; } - -}; - -struct my_functor : Functor<double> -{ - my_functor(void): Functor<double>(3,15) {} - int operator()(const VectorXd &x, VectorXd &fvec) const - { - double tmp1, tmp2, tmp3; - double y[15] = {1.4e-1, 1.8e-1, 2.2e-1, 2.5e-1, 2.9e-1, 3.2e-1, 3.5e-1, - 3.9e-1, 3.7e-1, 5.8e-1, 7.3e-1, 9.6e-1, 1.34, 2.1, 4.39}; - - for (int i = 0; i < values(); i++) - { - tmp1 = i+1; - tmp2 = 16 - i - 1; - tmp3 = (i>=8)? tmp2 : tmp1; - fvec[i] = y[i] - (x[0] + tmp1/(x[1]*tmp2 + x[2]*tmp3)); - } - return 0; - } - - int actual_df(const VectorXd &x, MatrixXd &fjac) const - { - double tmp1, tmp2, tmp3, tmp4; - for (int i = 0; i < values(); i++) - { - tmp1 = i+1; - tmp2 = 16 - i - 1; - tmp3 = (i>=8)? tmp2 : tmp1; - tmp4 = (x[1]*tmp2 + x[2]*tmp3); tmp4 = tmp4*tmp4; - fjac(i,0) = -1; - fjac(i,1) = tmp1*tmp2/tmp4; - fjac(i,2) = tmp1*tmp3/tmp4; - } - return 0; - } -}; - -void test_forward() -{ - VectorXd x(3); - MatrixXd jac(15,3); - MatrixXd actual_jac(15,3); - my_functor functor; - - x << 0.082, 1.13, 2.35; - - // real one - functor.actual_df(x, actual_jac); -// std::cout << actual_jac << std::endl << std::endl; - - // using NumericalDiff - NumericalDiff<my_functor> numDiff(functor); - numDiff.df(x, jac); -// std::cout << jac << std::endl; - - VERIFY_IS_APPROX(jac, actual_jac); -} - -void test_central() -{ - VectorXd x(3); - MatrixXd jac(15,3); - MatrixXd actual_jac(15,3); - my_functor functor; - - x << 0.082, 1.13, 2.35; - - // real one - functor.actual_df(x, actual_jac); - - // using NumericalDiff - NumericalDiff<my_functor,Central> numDiff(functor); - numDiff.df(x, jac); - - VERIFY_IS_APPROX(jac, actual_jac); -} - -void test_NumericalDiff() -{ - CALL_SUBTEST(test_forward()); - CALL_SUBTEST(test_central()); -} diff --git a/eigen/unsupported/test/alignedvector3.cpp b/eigen/unsupported/test/alignedvector3.cpp deleted file mode 100644 index 252cb1d..0000000 --- a/eigen/unsupported/test/alignedvector3.cpp +++ /dev/null @@ -1,84 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 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 <unsupported/Eigen/AlignedVector3> - -namespace Eigen { - -template<typename T,typename Derived> -T test_relative_error(const AlignedVector3<T> &a, const MatrixBase<Derived> &b) -{ - return test_relative_error(a.coeffs().template head<3>(), b); -} - -} - -template<typename Scalar> -void alignedvector3() -{ - Scalar s1 = internal::random<Scalar>(); - Scalar s2 = internal::random<Scalar>(); - typedef Matrix<Scalar,3,1> RefType; - typedef Matrix<Scalar,3,3> Mat33; - typedef AlignedVector3<Scalar> FastType; - RefType r1(RefType::Random()), r2(RefType::Random()), r3(RefType::Random()), - r4(RefType::Random()), r5(RefType::Random()); - FastType f1(r1), f2(r2), f3(r3), f4(r4), f5(r5); - Mat33 m1(Mat33::Random()); - - VERIFY_IS_APPROX(f1,r1); - VERIFY_IS_APPROX(f4,r4); - - VERIFY_IS_APPROX(f4+f1,r4+r1); - VERIFY_IS_APPROX(f4-f1,r4-r1); - VERIFY_IS_APPROX(f4+f1-f2,r4+r1-r2); - VERIFY_IS_APPROX(f4+=f3,r4+=r3); - VERIFY_IS_APPROX(f4-=f5,r4-=r5); - VERIFY_IS_APPROX(f4-=f5+f1,r4-=r5+r1); - VERIFY_IS_APPROX(f5+f1-s1*f2,r5+r1-s1*r2); - VERIFY_IS_APPROX(f5+f1/s2-s1*f2,r5+r1/s2-s1*r2); - - VERIFY_IS_APPROX(m1*f4,m1*r4); - VERIFY_IS_APPROX(f4.transpose()*m1,r4.transpose()*m1); - - VERIFY_IS_APPROX(f2.dot(f3),r2.dot(r3)); - VERIFY_IS_APPROX(f2.cross(f3),r2.cross(r3)); - VERIFY_IS_APPROX(f2.norm(),r2.norm()); - - VERIFY_IS_APPROX(f2.normalized(),r2.normalized()); - - VERIFY_IS_APPROX((f2+f1).normalized(),(r2+r1).normalized()); - - f2.normalize(); - r2.normalize(); - VERIFY_IS_APPROX(f2,r2); - - { - FastType f6 = RefType::Zero(); - FastType f7 = FastType::Zero(); - VERIFY_IS_APPROX(f6,f7); - f6 = r4+r1; - VERIFY_IS_APPROX(f6,r4+r1); - f6 -= Scalar(2)*r4; - VERIFY_IS_APPROX(f6,r1-r4); - } - - std::stringstream ss1, ss2; - ss1 << f1; - ss2 << r1; - VERIFY(ss1.str()==ss2.str()); -} - -void test_alignedvector3() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST( alignedvector3<float>() ); - } -} diff --git a/eigen/unsupported/test/autodiff.cpp b/eigen/unsupported/test/autodiff.cpp deleted file mode 100644 index 1c5e0dc..0000000 --- a/eigen/unsupported/test/autodiff.cpp +++ /dev/null @@ -1,371 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 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 <unsupported/Eigen/AutoDiff> - -template<typename Scalar> -EIGEN_DONT_INLINE Scalar foo(const Scalar& x, const Scalar& y) -{ - using namespace std; -// return x+std::sin(y); - EIGEN_ASM_COMMENT("mybegin"); - // pow(float, int) promotes to pow(double, double) - return x*2 - 1 + static_cast<Scalar>(pow(1+x,2)) + 2*sqrt(y*y+0) - 4 * sin(0+x) + 2 * cos(y+0) - exp(Scalar(-0.5)*x*x+0); - //return x+2*y*x;//x*2 -std::pow(x,2);//(2*y/x);// - y*2; - EIGEN_ASM_COMMENT("myend"); -} - -template<typename Vector> -EIGEN_DONT_INLINE typename Vector::Scalar foo(const Vector& p) -{ - typedef typename Vector::Scalar Scalar; - return (p-Vector(Scalar(-1),Scalar(1.))).norm() + (p.array() * p.array()).sum() + p.dot(p); -} - -template<typename _Scalar, int NX=Dynamic, int NY=Dynamic> -struct TestFunc1 -{ - typedef _Scalar Scalar; - enum { - InputsAtCompileTime = NX, - ValuesAtCompileTime = NY - }; - typedef Matrix<Scalar,InputsAtCompileTime,1> InputType; - typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType; - typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType; - - int m_inputs, m_values; - - TestFunc1() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {} - TestFunc1(int inputs, int values) : m_inputs(inputs), m_values(values) {} - - int inputs() const { return m_inputs; } - int values() const { return m_values; } - - template<typename T> - void operator() (const Matrix<T,InputsAtCompileTime,1>& x, Matrix<T,ValuesAtCompileTime,1>* _v) const - { - Matrix<T,ValuesAtCompileTime,1>& v = *_v; - - v[0] = 2 * x[0] * x[0] + x[0] * x[1]; - v[1] = 3 * x[1] * x[0] + 0.5 * x[1] * x[1]; - if(inputs()>2) - { - v[0] += 0.5 * x[2]; - v[1] += x[2]; - } - if(values()>2) - { - v[2] = 3 * x[1] * x[0] * x[0]; - } - if (inputs()>2 && values()>2) - v[2] *= x[2]; - } - - void operator() (const InputType& x, ValueType* v, JacobianType* _j) const - { - (*this)(x, v); - - if(_j) - { - JacobianType& j = *_j; - - j(0,0) = 4 * x[0] + x[1]; - j(1,0) = 3 * x[1]; - - j(0,1) = x[0]; - j(1,1) = 3 * x[0] + 2 * 0.5 * x[1]; - - if (inputs()>2) - { - j(0,2) = 0.5; - j(1,2) = 1; - } - if(values()>2) - { - j(2,0) = 3 * x[1] * 2 * x[0]; - j(2,1) = 3 * x[0] * x[0]; - } - if (inputs()>2 && values()>2) - { - j(2,0) *= x[2]; - j(2,1) *= x[2]; - - j(2,2) = 3 * x[1] * x[0] * x[0]; - j(2,2) = 3 * x[1] * x[0] * x[0]; - } - } - } -}; - - -#if EIGEN_HAS_VARIADIC_TEMPLATES -/* Test functor for the C++11 features. */ -template <typename Scalar> -struct integratorFunctor -{ - typedef Matrix<Scalar, 2, 1> InputType; - typedef Matrix<Scalar, 2, 1> ValueType; - - /* - * Implementation starts here. - */ - integratorFunctor(const Scalar gain) : _gain(gain) {} - integratorFunctor(const integratorFunctor& f) : _gain(f._gain) {} - const Scalar _gain; - - template <typename T1, typename T2> - void operator() (const T1 &input, T2 *output, const Scalar dt) const - { - T2 &o = *output; - - /* Integrator to test the AD. */ - o[0] = input[0] + input[1] * dt * _gain; - o[1] = input[1] * _gain; - } - - /* Only needed for the test */ - template <typename T1, typename T2, typename T3> - void operator() (const T1 &input, T2 *output, T3 *jacobian, const Scalar dt) const - { - T2 &o = *output; - - /* Integrator to test the AD. */ - o[0] = input[0] + input[1] * dt * _gain; - o[1] = input[1] * _gain; - - if (jacobian) - { - T3 &j = *jacobian; - - j(0, 0) = 1; - j(0, 1) = dt * _gain; - j(1, 0) = 0; - j(1, 1) = _gain; - } - } - -}; - -template<typename Func> void forward_jacobian_cpp11(const Func& f) -{ - typedef typename Func::ValueType::Scalar Scalar; - typedef typename Func::ValueType ValueType; - typedef typename Func::InputType InputType; - typedef typename AutoDiffJacobian<Func>::JacobianType JacobianType; - - InputType x = InputType::Random(InputType::RowsAtCompileTime); - ValueType y, yref; - JacobianType j, jref; - - const Scalar dt = internal::random<double>(); - - jref.setZero(); - yref.setZero(); - f(x, &yref, &jref, dt); - - //std::cerr << "y, yref, jref: " << "\n"; - //std::cerr << y.transpose() << "\n\n"; - //std::cerr << yref << "\n\n"; - //std::cerr << jref << "\n\n"; - - AutoDiffJacobian<Func> autoj(f); - autoj(x, &y, &j, dt); - - //std::cerr << "y j (via autodiff): " << "\n"; - //std::cerr << y.transpose() << "\n\n"; - //std::cerr << j << "\n\n"; - - VERIFY_IS_APPROX(y, yref); - VERIFY_IS_APPROX(j, jref); -} -#endif - -template<typename Func> void forward_jacobian(const Func& f) -{ - typename Func::InputType x = Func::InputType::Random(f.inputs()); - typename Func::ValueType y(f.values()), yref(f.values()); - typename Func::JacobianType j(f.values(),f.inputs()), jref(f.values(),f.inputs()); - - jref.setZero(); - yref.setZero(); - f(x,&yref,&jref); -// std::cerr << y.transpose() << "\n\n";; -// std::cerr << j << "\n\n";; - - j.setZero(); - y.setZero(); - AutoDiffJacobian<Func> autoj(f); - autoj(x, &y, &j); -// std::cerr << y.transpose() << "\n\n";; -// std::cerr << j << "\n\n";; - - VERIFY_IS_APPROX(y, yref); - VERIFY_IS_APPROX(j, jref); -} - -// TODO also check actual derivatives! -template <int> -void test_autodiff_scalar() -{ - Vector2f p = Vector2f::Random(); - typedef AutoDiffScalar<Vector2f> AD; - AD ax(p.x(),Vector2f::UnitX()); - AD ay(p.y(),Vector2f::UnitY()); - AD res = foo<AD>(ax,ay); - VERIFY_IS_APPROX(res.value(), foo(p.x(),p.y())); -} - - -// TODO also check actual derivatives! -template <int> -void test_autodiff_vector() -{ - Vector2f p = Vector2f::Random(); - typedef AutoDiffScalar<Vector2f> AD; - typedef Matrix<AD,2,1> VectorAD; - VectorAD ap = p.cast<AD>(); - ap.x().derivatives() = Vector2f::UnitX(); - ap.y().derivatives() = Vector2f::UnitY(); - - AD res = foo<VectorAD>(ap); - VERIFY_IS_APPROX(res.value(), foo(p)); -} - -template <int> -void test_autodiff_jacobian() -{ - CALL_SUBTEST(( forward_jacobian(TestFunc1<double,2,2>()) )); - CALL_SUBTEST(( forward_jacobian(TestFunc1<double,2,3>()) )); - CALL_SUBTEST(( forward_jacobian(TestFunc1<double,3,2>()) )); - CALL_SUBTEST(( forward_jacobian(TestFunc1<double,3,3>()) )); - CALL_SUBTEST(( forward_jacobian(TestFunc1<double>(3,3)) )); -#if EIGEN_HAS_VARIADIC_TEMPLATES - CALL_SUBTEST(( forward_jacobian_cpp11(integratorFunctor<double>(10)) )); -#endif -} - - -template <int> -void test_autodiff_hessian() -{ - typedef AutoDiffScalar<VectorXd> AD; - typedef Matrix<AD,Eigen::Dynamic,1> VectorAD; - typedef AutoDiffScalar<VectorAD> ADD; - typedef Matrix<ADD,Eigen::Dynamic,1> VectorADD; - VectorADD x(2); - double s1 = internal::random<double>(), s2 = internal::random<double>(), s3 = internal::random<double>(), s4 = internal::random<double>(); - x(0).value()=s1; - x(1).value()=s2; - - //set unit vectors for the derivative directions (partial derivatives of the input vector) - x(0).derivatives().resize(2); - x(0).derivatives().setZero(); - x(0).derivatives()(0)= 1; - x(1).derivatives().resize(2); - x(1).derivatives().setZero(); - x(1).derivatives()(1)=1; - - //repeat partial derivatives for the inner AutoDiffScalar - x(0).value().derivatives() = VectorXd::Unit(2,0); - x(1).value().derivatives() = VectorXd::Unit(2,1); - - //set the hessian matrix to zero - for(int idx=0; idx<2; idx++) { - x(0).derivatives()(idx).derivatives() = VectorXd::Zero(2); - x(1).derivatives()(idx).derivatives() = VectorXd::Zero(2); - } - - ADD y = sin(AD(s3)*x(0) + AD(s4)*x(1)); - - VERIFY_IS_APPROX(y.value().derivatives()(0), y.derivatives()(0).value()); - VERIFY_IS_APPROX(y.value().derivatives()(1), y.derivatives()(1).value()); - VERIFY_IS_APPROX(y.value().derivatives()(0), s3*std::cos(s1*s3+s2*s4)); - VERIFY_IS_APPROX(y.value().derivatives()(1), s4*std::cos(s1*s3+s2*s4)); - VERIFY_IS_APPROX(y.derivatives()(0).derivatives(), -std::sin(s1*s3+s2*s4)*Vector2d(s3*s3,s4*s3)); - VERIFY_IS_APPROX(y.derivatives()(1).derivatives(), -std::sin(s1*s3+s2*s4)*Vector2d(s3*s4,s4*s4)); - - ADD z = x(0)*x(1); - VERIFY_IS_APPROX(z.derivatives()(0).derivatives(), Vector2d(0,1)); - VERIFY_IS_APPROX(z.derivatives()(1).derivatives(), Vector2d(1,0)); -} - -double bug_1222() { - typedef Eigen::AutoDiffScalar<Eigen::Vector3d> AD; - const double _cv1_3 = 1.0; - const AD chi_3 = 1.0; - // this line did not work, because operator+ returns ADS<DerType&>, which then cannot be converted to ADS<DerType> - const AD denom = chi_3 + _cv1_3; - return denom.value(); -} - -#ifdef EIGEN_TEST_PART_5 - -double bug_1223() { - using std::min; - typedef Eigen::AutoDiffScalar<Eigen::Vector3d> AD; - - const double _cv1_3 = 1.0; - const AD chi_3 = 1.0; - const AD denom = 1.0; - - // failed because implementation of min attempts to construct ADS<DerType&> via constructor AutoDiffScalar(const Real& value) - // without initializing m_derivatives (which is a reference in this case) - #define EIGEN_TEST_SPACE - const AD t = min EIGEN_TEST_SPACE (denom / chi_3, 1.0); - - const AD t2 = min EIGEN_TEST_SPACE (denom / (chi_3 * _cv1_3), 1.0); - - return t.value() + t2.value(); -} - -// regression test for some compilation issues with specializations of ScalarBinaryOpTraits -void bug_1260() { - Matrix4d A = Matrix4d::Ones(); - Vector4d v = Vector4d::Ones(); - A*v; -} - -// check a compilation issue with numext::max -double bug_1261() { - typedef AutoDiffScalar<Matrix2d> AD; - typedef Matrix<AD,2,1> VectorAD; - - VectorAD v(0.,0.); - const AD maxVal = v.maxCoeff(); - const AD minVal = v.minCoeff(); - return maxVal.value() + minVal.value(); -} - -double bug_1264() { - typedef AutoDiffScalar<Vector2d> AD; - const AD s = 0.; - const Matrix<AD, 3, 1> v1(0.,0.,0.); - const Matrix<AD, 3, 1> v2 = (s + 3.0) * v1; - return v2(0).value(); -} - -#endif - -void test_autodiff() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( test_autodiff_scalar<1>() ); - CALL_SUBTEST_2( test_autodiff_vector<1>() ); - CALL_SUBTEST_3( test_autodiff_jacobian<1>() ); - CALL_SUBTEST_4( test_autodiff_hessian<1>() ); - } - - CALL_SUBTEST_5( bug_1222() ); - CALL_SUBTEST_5( bug_1223() ); - CALL_SUBTEST_5( bug_1260() ); - CALL_SUBTEST_5( bug_1261() ); -} - diff --git a/eigen/unsupported/test/autodiff_scalar.cpp b/eigen/unsupported/test/autodiff_scalar.cpp deleted file mode 100644 index a917ec3..0000000 --- a/eigen/unsupported/test/autodiff_scalar.cpp +++ /dev/null @@ -1,101 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2013 Christoph Hertzberg <chtz@informatik.uni-bremen.de> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <unsupported/Eigen/AutoDiff> - -/* - * In this file scalar derivations are tested for correctness. - * TODO add more tests! - */ - -template<typename Scalar> void check_atan2() -{ - typedef Matrix<Scalar, 1, 1> Deriv1; - typedef AutoDiffScalar<Deriv1> AD; - - AD x(internal::random<Scalar>(-3.0, 3.0), Deriv1::UnitX()); - - using std::exp; - Scalar r = exp(internal::random<Scalar>(-10, 10)); - - AD s = sin(x), c = cos(x); - AD res = atan2(r*s, r*c); - - VERIFY_IS_APPROX(res.value(), x.value()); - VERIFY_IS_APPROX(res.derivatives(), x.derivatives()); - - res = atan2(r*s+0, r*c+0); - VERIFY_IS_APPROX(res.value(), x.value()); - VERIFY_IS_APPROX(res.derivatives(), x.derivatives()); -} - -template<typename Scalar> void check_hyperbolic_functions() -{ - using std::sinh; - using std::cosh; - using std::tanh; - typedef Matrix<Scalar, 1, 1> Deriv1; - typedef AutoDiffScalar<Deriv1> AD; - Deriv1 p = Deriv1::Random(); - AD val(p.x(),Deriv1::UnitX()); - - Scalar cosh_px = std::cosh(p.x()); - AD res1 = tanh(val); - VERIFY_IS_APPROX(res1.value(), std::tanh(p.x())); - VERIFY_IS_APPROX(res1.derivatives().x(), Scalar(1.0) / (cosh_px * cosh_px)); - - AD res2 = sinh(val); - VERIFY_IS_APPROX(res2.value(), std::sinh(p.x())); - VERIFY_IS_APPROX(res2.derivatives().x(), cosh_px); - - AD res3 = cosh(val); - VERIFY_IS_APPROX(res3.value(), cosh_px); - VERIFY_IS_APPROX(res3.derivatives().x(), std::sinh(p.x())); - - // Check constant values. - const Scalar sample_point = Scalar(1) / Scalar(3); - val = AD(sample_point,Deriv1::UnitX()); - res1 = tanh(val); - VERIFY_IS_APPROX(res1.derivatives().x(), Scalar(0.896629559604914)); - - res2 = sinh(val); - VERIFY_IS_APPROX(res2.derivatives().x(), Scalar(1.056071867829939)); - - res3 = cosh(val); - VERIFY_IS_APPROX(res3.derivatives().x(), Scalar(0.339540557256150)); -} - -template <typename Scalar> -void check_limits_specialization() -{ - typedef Eigen::Matrix<Scalar, 1, 1> Deriv; - typedef Eigen::AutoDiffScalar<Deriv> AD; - - typedef std::numeric_limits<AD> A; - typedef std::numeric_limits<Scalar> B; - - // workaround "unsed typedef" warning: - VERIFY(!bool(internal::is_same<B, A>::value)); - -#if EIGEN_HAS_CXX11 - VERIFY(bool(std::is_base_of<B, A>::value)); -#endif -} - -void test_autodiff_scalar() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( check_atan2<float>() ); - CALL_SUBTEST_2( check_atan2<double>() ); - CALL_SUBTEST_3( check_hyperbolic_functions<float>() ); - CALL_SUBTEST_4( check_hyperbolic_functions<double>() ); - CALL_SUBTEST_5( check_limits_specialization<double>()); - } -} diff --git a/eigen/unsupported/test/cxx11_eventcount.cpp b/eigen/unsupported/test/cxx11_eventcount.cpp deleted file mode 100644 index 3b598bf..0000000 --- a/eigen/unsupported/test/cxx11_eventcount.cpp +++ /dev/null @@ -1,142 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 Dmitry Vyukov <dvyukov@google.com> -// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_USE_THREADS -#include "main.h" -#include <Eigen/CXX11/ThreadPool> - -// Visual studio doesn't implement a rand_r() function since its -// implementation of rand() is already thread safe -int rand_reentrant(unsigned int* s) { -#ifdef EIGEN_COMP_MSVC_STRICT - EIGEN_UNUSED_VARIABLE(s); - return rand(); -#else - return rand_r(s); -#endif -} - -static void test_basic_eventcount() -{ - MaxSizeVector<EventCount::Waiter> waiters(1); - waiters.resize(1); - EventCount ec(waiters); - EventCount::Waiter& w = waiters[0]; - ec.Notify(false); - ec.Prewait(&w); - ec.Notify(true); - ec.CommitWait(&w); - ec.Prewait(&w); - ec.CancelWait(&w); -} - -// Fake bounded counter-based queue. -struct TestQueue { - std::atomic<int> val_; - static const int kQueueSize = 10; - - TestQueue() : val_() {} - - ~TestQueue() { VERIFY_IS_EQUAL(val_.load(), 0); } - - bool Push() { - int val = val_.load(std::memory_order_relaxed); - for (;;) { - VERIFY_GE(val, 0); - VERIFY_LE(val, kQueueSize); - if (val == kQueueSize) return false; - if (val_.compare_exchange_weak(val, val + 1, std::memory_order_relaxed)) - return true; - } - } - - bool Pop() { - int val = val_.load(std::memory_order_relaxed); - for (;;) { - VERIFY_GE(val, 0); - VERIFY_LE(val, kQueueSize); - if (val == 0) return false; - if (val_.compare_exchange_weak(val, val - 1, std::memory_order_relaxed)) - return true; - } - } - - bool Empty() { return val_.load(std::memory_order_relaxed) == 0; } -}; - -const int TestQueue::kQueueSize; - -// A number of producers send messages to a set of consumers using a set of -// fake queues. Ensure that it does not crash, consumers don't deadlock and -// number of blocked and unblocked threads match. -static void test_stress_eventcount() -{ - const int kThreads = std::thread::hardware_concurrency(); - static const int kEvents = 1 << 16; - static const int kQueues = 10; - - MaxSizeVector<EventCount::Waiter> waiters(kThreads); - waiters.resize(kThreads); - EventCount ec(waiters); - TestQueue queues[kQueues]; - - std::vector<std::unique_ptr<std::thread>> producers; - for (int i = 0; i < kThreads; i++) { - producers.emplace_back(new std::thread([&ec, &queues]() { - unsigned int rnd = static_cast<unsigned int>(std::hash<std::thread::id>()(std::this_thread::get_id())); - for (int j = 0; j < kEvents; j++) { - unsigned idx = rand_reentrant(&rnd) % kQueues; - if (queues[idx].Push()) { - ec.Notify(false); - continue; - } - EIGEN_THREAD_YIELD(); - j--; - } - })); - } - - std::vector<std::unique_ptr<std::thread>> consumers; - for (int i = 0; i < kThreads; i++) { - consumers.emplace_back(new std::thread([&ec, &queues, &waiters, i]() { - EventCount::Waiter& w = waiters[i]; - unsigned int rnd = static_cast<unsigned int>(std::hash<std::thread::id>()(std::this_thread::get_id())); - for (int j = 0; j < kEvents; j++) { - unsigned idx = rand_reentrant(&rnd) % kQueues; - if (queues[idx].Pop()) continue; - j--; - ec.Prewait(&w); - bool empty = true; - for (int q = 0; q < kQueues; q++) { - if (!queues[q].Empty()) { - empty = false; - break; - } - } - if (!empty) { - ec.CancelWait(&w); - continue; - } - ec.CommitWait(&w); - } - })); - } - - for (int i = 0; i < kThreads; i++) { - producers[i]->join(); - consumers[i]->join(); - } -} - -void test_cxx11_eventcount() -{ - CALL_SUBTEST(test_basic_eventcount()); - CALL_SUBTEST(test_stress_eventcount()); -} diff --git a/eigen/unsupported/test/cxx11_meta.cpp b/eigen/unsupported/test/cxx11_meta.cpp deleted file mode 100644 index 8911c59..0000000 --- a/eigen/unsupported/test/cxx11_meta.cpp +++ /dev/null @@ -1,357 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2013 Christian Seiler <christian@iwakd.de> -// -// 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 <array> -#include <Eigen/CXX11/src/util/CXX11Meta.h> - -using Eigen::internal::is_same; -using Eigen::internal::type_list; -using Eigen::internal::numeric_list; -using Eigen::internal::gen_numeric_list; -using Eigen::internal::gen_numeric_list_reversed; -using Eigen::internal::gen_numeric_list_swapped_pair; -using Eigen::internal::gen_numeric_list_repeated; -using Eigen::internal::concat; -using Eigen::internal::mconcat; -using Eigen::internal::take; -using Eigen::internal::skip; -using Eigen::internal::slice; -using Eigen::internal::get; -using Eigen::internal::id_numeric; -using Eigen::internal::id_type; -using Eigen::internal::is_same_gf; -using Eigen::internal::apply_op_from_left; -using Eigen::internal::apply_op_from_right; -using Eigen::internal::contained_in_list; -using Eigen::internal::contained_in_list_gf; -using Eigen::internal::arg_prod; -using Eigen::internal::arg_sum; -using Eigen::internal::sum_op; -using Eigen::internal::product_op; -using Eigen::internal::array_reverse; -using Eigen::internal::array_sum; -using Eigen::internal::array_prod; -using Eigen::internal::array_reduce; -using Eigen::internal::array_zip; -using Eigen::internal::array_zip_and_reduce; -using Eigen::internal::array_apply; -using Eigen::internal::array_apply_and_reduce; -using Eigen::internal::repeat; -using Eigen::internal::instantiate_by_c_array; - -struct dummy_a {}; -struct dummy_b {}; -struct dummy_c {}; -struct dummy_d {}; -struct dummy_e {}; - -// dummy operation for testing apply -template<typename A, typename B> struct dummy_op; -template<> struct dummy_op<dummy_a, dummy_b> { typedef dummy_c type; }; -template<> struct dummy_op<dummy_b, dummy_a> { typedef dummy_d type; }; -template<> struct dummy_op<dummy_b, dummy_c> { typedef dummy_a type; }; -template<> struct dummy_op<dummy_c, dummy_b> { typedef dummy_d type; }; -template<> struct dummy_op<dummy_c, dummy_a> { typedef dummy_b type; }; -template<> struct dummy_op<dummy_a, dummy_c> { typedef dummy_d type; }; -template<> struct dummy_op<dummy_a, dummy_a> { typedef dummy_e type; }; -template<> struct dummy_op<dummy_b, dummy_b> { typedef dummy_e type; }; -template<> struct dummy_op<dummy_c, dummy_c> { typedef dummy_e type; }; - -template<typename A, typename B> struct dummy_test { constexpr static bool value = false; constexpr static int global_flags = 0; }; -template<> struct dummy_test<dummy_a, dummy_a> { constexpr static bool value = true; constexpr static int global_flags = 1; }; -template<> struct dummy_test<dummy_b, dummy_b> { constexpr static bool value = true; constexpr static int global_flags = 2; }; -template<> struct dummy_test<dummy_c, dummy_c> { constexpr static bool value = true; constexpr static int global_flags = 4; }; - -struct times2_op { template<typename A> static A run(A v) { return v * 2; } }; - -struct dummy_inst -{ - int c; - - dummy_inst() : c(0) {} - explicit dummy_inst(int) : c(1) {} - dummy_inst(int, int) : c(2) {} - dummy_inst(int, int, int) : c(3) {} - dummy_inst(int, int, int, int) : c(4) {} - dummy_inst(int, int, int, int, int) : c(5) {} -}; - -static void test_gen_numeric_list() -{ - VERIFY((is_same<typename gen_numeric_list<int, 0>::type, numeric_list<int>>::value)); - VERIFY((is_same<typename gen_numeric_list<int, 1>::type, numeric_list<int, 0>>::value)); - VERIFY((is_same<typename gen_numeric_list<int, 2>::type, numeric_list<int, 0, 1>>::value)); - VERIFY((is_same<typename gen_numeric_list<int, 5>::type, numeric_list<int, 0, 1, 2, 3, 4>>::value)); - VERIFY((is_same<typename gen_numeric_list<int, 10>::type, numeric_list<int, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9>>::value)); - - VERIFY((is_same<typename gen_numeric_list<int, 0, 42>::type, numeric_list<int>>::value)); - VERIFY((is_same<typename gen_numeric_list<int, 1, 42>::type, numeric_list<int, 42>>::value)); - VERIFY((is_same<typename gen_numeric_list<int, 2, 42>::type, numeric_list<int, 42, 43>>::value)); - VERIFY((is_same<typename gen_numeric_list<int, 5, 42>::type, numeric_list<int, 42, 43, 44, 45, 46>>::value)); - VERIFY((is_same<typename gen_numeric_list<int, 10, 42>::type, numeric_list<int, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51>>::value)); - - VERIFY((is_same<typename gen_numeric_list_reversed<int, 0>::type, numeric_list<int>>::value)); - VERIFY((is_same<typename gen_numeric_list_reversed<int, 1>::type, numeric_list<int, 0>>::value)); - VERIFY((is_same<typename gen_numeric_list_reversed<int, 2>::type, numeric_list<int, 1, 0>>::value)); - VERIFY((is_same<typename gen_numeric_list_reversed<int, 5>::type, numeric_list<int, 4, 3, 2, 1, 0>>::value)); - VERIFY((is_same<typename gen_numeric_list_reversed<int, 10>::type, numeric_list<int, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0>>::value)); - - VERIFY((is_same<typename gen_numeric_list_reversed<int, 0, 42>::type, numeric_list<int>>::value)); - VERIFY((is_same<typename gen_numeric_list_reversed<int, 1, 42>::type, numeric_list<int, 42>>::value)); - VERIFY((is_same<typename gen_numeric_list_reversed<int, 2, 42>::type, numeric_list<int, 43, 42>>::value)); - VERIFY((is_same<typename gen_numeric_list_reversed<int, 5, 42>::type, numeric_list<int, 46, 45, 44, 43, 42>>::value)); - VERIFY((is_same<typename gen_numeric_list_reversed<int, 10, 42>::type, numeric_list<int, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42>>::value)); - - VERIFY((is_same<typename gen_numeric_list_swapped_pair<int, 0, 2, 3>::type, numeric_list<int>>::value)); - VERIFY((is_same<typename gen_numeric_list_swapped_pair<int, 1, 2, 3>::type, numeric_list<int, 0>>::value)); - VERIFY((is_same<typename gen_numeric_list_swapped_pair<int, 2, 2, 3>::type, numeric_list<int, 0, 1>>::value)); - VERIFY((is_same<typename gen_numeric_list_swapped_pair<int, 5, 2, 3>::type, numeric_list<int, 0, 1, 3, 2, 4>>::value)); - VERIFY((is_same<typename gen_numeric_list_swapped_pair<int, 10, 2, 3>::type, numeric_list<int, 0, 1, 3, 2, 4, 5, 6, 7, 8, 9>>::value)); - - VERIFY((is_same<typename gen_numeric_list_swapped_pair<int, 0, 44, 45, 42>::type, numeric_list<int>>::value)); - VERIFY((is_same<typename gen_numeric_list_swapped_pair<int, 1, 44, 45, 42>::type, numeric_list<int, 42>>::value)); - VERIFY((is_same<typename gen_numeric_list_swapped_pair<int, 2, 44, 45, 42>::type, numeric_list<int, 42, 43>>::value)); - VERIFY((is_same<typename gen_numeric_list_swapped_pair<int, 5, 44, 45, 42>::type, numeric_list<int, 42, 43, 45, 44, 46>>::value)); - VERIFY((is_same<typename gen_numeric_list_swapped_pair<int, 10, 44, 45, 42>::type, numeric_list<int, 42, 43, 45, 44, 46, 47, 48, 49, 50, 51>>::value)); - - VERIFY((is_same<typename gen_numeric_list_repeated<int, 0, 0>::type, numeric_list<int>>::value)); - VERIFY((is_same<typename gen_numeric_list_repeated<int, 1, 0>::type, numeric_list<int, 0>>::value)); - VERIFY((is_same<typename gen_numeric_list_repeated<int, 2, 0>::type, numeric_list<int, 0, 0>>::value)); - VERIFY((is_same<typename gen_numeric_list_repeated<int, 5, 0>::type, numeric_list<int, 0, 0, 0, 0, 0>>::value)); - VERIFY((is_same<typename gen_numeric_list_repeated<int, 10, 0>::type, numeric_list<int, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>>::value)); -} - -static void test_concat() -{ - VERIFY((is_same<typename concat<type_list<dummy_a, dummy_a>, type_list<>>::type, type_list<dummy_a, dummy_a>>::value)); - VERIFY((is_same<typename concat<type_list<>, type_list<dummy_a, dummy_a>>::type, type_list<dummy_a, dummy_a>>::value)); - VERIFY((is_same<typename concat<type_list<dummy_a, dummy_a>, type_list<dummy_a, dummy_a>>::type, type_list<dummy_a, dummy_a, dummy_a, dummy_a>>::value)); - VERIFY((is_same<typename concat<type_list<dummy_a, dummy_a>, type_list<dummy_b, dummy_c>>::type, type_list<dummy_a, dummy_a, dummy_b, dummy_c>>::value)); - VERIFY((is_same<typename concat<type_list<dummy_a>, type_list<dummy_b, dummy_c>>::type, type_list<dummy_a, dummy_b, dummy_c>>::value)); - - VERIFY((is_same<typename concat<numeric_list<int, 0, 0>, numeric_list<int>>::type, numeric_list<int, 0, 0>>::value)); - VERIFY((is_same<typename concat<numeric_list<int>, numeric_list<int, 0, 0>>::type, numeric_list<int, 0, 0>>::value)); - VERIFY((is_same<typename concat<numeric_list<int, 0, 0>, numeric_list<int, 0, 0>>::type, numeric_list<int, 0, 0, 0, 0>>::value)); - VERIFY((is_same<typename concat<numeric_list<int, 0, 0>, numeric_list<int, 1, 2>>::type, numeric_list<int, 0, 0, 1, 2>>::value)); - VERIFY((is_same<typename concat<numeric_list<int, 0>, numeric_list<int, 1, 2>>::type, numeric_list<int, 0, 1, 2>>::value)); - - VERIFY((is_same<typename mconcat<type_list<dummy_a>>::type, type_list<dummy_a>>::value)); - VERIFY((is_same<typename mconcat<type_list<dummy_a>, type_list<dummy_b>>::type, type_list<dummy_a, dummy_b>>::value)); - VERIFY((is_same<typename mconcat<type_list<dummy_a>, type_list<dummy_b>, type_list<dummy_c>>::type, type_list<dummy_a, dummy_b, dummy_c>>::value)); - VERIFY((is_same<typename mconcat<type_list<dummy_a>, type_list<dummy_b, dummy_c>>::type, type_list<dummy_a, dummy_b, dummy_c>>::value)); - VERIFY((is_same<typename mconcat<type_list<dummy_a, dummy_b>, type_list<dummy_c>>::type, type_list<dummy_a, dummy_b, dummy_c>>::value)); - - VERIFY((is_same<typename mconcat<numeric_list<int, 0>>::type, numeric_list<int, 0>>::value)); - VERIFY((is_same<typename mconcat<numeric_list<int, 0>, numeric_list<int, 1>>::type, numeric_list<int, 0, 1>>::value)); - VERIFY((is_same<typename mconcat<numeric_list<int, 0>, numeric_list<int, 1>, numeric_list<int, 2>>::type, numeric_list<int, 0, 1, 2>>::value)); - VERIFY((is_same<typename mconcat<numeric_list<int, 0>, numeric_list<int, 1, 2>>::type, numeric_list<int, 0, 1, 2>>::value)); - VERIFY((is_same<typename mconcat<numeric_list<int, 0, 1>, numeric_list<int, 2>>::type, numeric_list<int, 0, 1, 2>>::value)); -} - -static void test_slice() -{ - typedef type_list<dummy_a, dummy_a, dummy_b, dummy_b, dummy_c, dummy_c> tl; - typedef numeric_list<int, 0, 1, 2, 3, 4, 5> il; - - VERIFY((is_same<typename take<0, tl>::type, type_list<>>::value)); - VERIFY((is_same<typename take<1, tl>::type, type_list<dummy_a>>::value)); - VERIFY((is_same<typename take<2, tl>::type, type_list<dummy_a, dummy_a>>::value)); - VERIFY((is_same<typename take<3, tl>::type, type_list<dummy_a, dummy_a, dummy_b>>::value)); - VERIFY((is_same<typename take<4, tl>::type, type_list<dummy_a, dummy_a, dummy_b, dummy_b>>::value)); - VERIFY((is_same<typename take<5, tl>::type, type_list<dummy_a, dummy_a, dummy_b, dummy_b, dummy_c>>::value)); - VERIFY((is_same<typename take<6, tl>::type, type_list<dummy_a, dummy_a, dummy_b, dummy_b, dummy_c, dummy_c>>::value)); - - VERIFY((is_same<typename take<0, il>::type, numeric_list<int>>::value)); - VERIFY((is_same<typename take<1, il>::type, numeric_list<int, 0>>::value)); - VERIFY((is_same<typename take<2, il>::type, numeric_list<int, 0, 1>>::value)); - VERIFY((is_same<typename take<3, il>::type, numeric_list<int, 0, 1, 2>>::value)); - VERIFY((is_same<typename take<4, il>::type, numeric_list<int, 0, 1, 2, 3>>::value)); - VERIFY((is_same<typename take<5, il>::type, numeric_list<int, 0, 1, 2, 3, 4>>::value)); - VERIFY((is_same<typename take<6, il>::type, numeric_list<int, 0, 1, 2, 3, 4, 5>>::value)); - - VERIFY((is_same<typename skip<0, tl>::type, type_list<dummy_a, dummy_a, dummy_b, dummy_b, dummy_c, dummy_c>>::value)); - VERIFY((is_same<typename skip<1, tl>::type, type_list<dummy_a, dummy_b, dummy_b, dummy_c, dummy_c>>::value)); - VERIFY((is_same<typename skip<2, tl>::type, type_list<dummy_b, dummy_b, dummy_c, dummy_c>>::value)); - VERIFY((is_same<typename skip<3, tl>::type, type_list<dummy_b, dummy_c, dummy_c>>::value)); - VERIFY((is_same<typename skip<4, tl>::type, type_list<dummy_c, dummy_c>>::value)); - VERIFY((is_same<typename skip<5, tl>::type, type_list<dummy_c>>::value)); - VERIFY((is_same<typename skip<6, tl>::type, type_list<>>::value)); - - VERIFY((is_same<typename skip<0, il>::type, numeric_list<int, 0, 1, 2, 3, 4, 5>>::value)); - VERIFY((is_same<typename skip<1, il>::type, numeric_list<int, 1, 2, 3, 4, 5>>::value)); - VERIFY((is_same<typename skip<2, il>::type, numeric_list<int, 2, 3, 4, 5>>::value)); - VERIFY((is_same<typename skip<3, il>::type, numeric_list<int, 3, 4, 5>>::value)); - VERIFY((is_same<typename skip<4, il>::type, numeric_list<int, 4, 5>>::value)); - VERIFY((is_same<typename skip<5, il>::type, numeric_list<int, 5>>::value)); - VERIFY((is_same<typename skip<6, il>::type, numeric_list<int>>::value)); - - VERIFY((is_same<typename slice<0, 3, tl>::type, typename take<3, tl>::type>::value)); - VERIFY((is_same<typename slice<0, 3, il>::type, typename take<3, il>::type>::value)); - VERIFY((is_same<typename slice<1, 3, tl>::type, type_list<dummy_a, dummy_b, dummy_b>>::value)); - VERIFY((is_same<typename slice<1, 3, il>::type, numeric_list<int, 1, 2, 3>>::value)); -} - -static void test_get() -{ - typedef type_list<dummy_a, dummy_a, dummy_b, dummy_b, dummy_c, dummy_c> tl; - typedef numeric_list<int, 4, 8, 15, 16, 23, 42> il; - - VERIFY((is_same<typename get<0, tl>::type, dummy_a>::value)); - VERIFY((is_same<typename get<1, tl>::type, dummy_a>::value)); - VERIFY((is_same<typename get<2, tl>::type, dummy_b>::value)); - VERIFY((is_same<typename get<3, tl>::type, dummy_b>::value)); - VERIFY((is_same<typename get<4, tl>::type, dummy_c>::value)); - VERIFY((is_same<typename get<5, tl>::type, dummy_c>::value)); - - VERIFY_IS_EQUAL(((int)get<0, il>::value), 4); - VERIFY_IS_EQUAL(((int)get<1, il>::value), 8); - VERIFY_IS_EQUAL(((int)get<2, il>::value), 15); - VERIFY_IS_EQUAL(((int)get<3, il>::value), 16); - VERIFY_IS_EQUAL(((int)get<4, il>::value), 23); - VERIFY_IS_EQUAL(((int)get<5, il>::value), 42); -} - -static void test_id_helper(dummy_a a, dummy_a b, dummy_a c) -{ - (void)a; - (void)b; - (void)c; -} - -template<int... ii> -static void test_id_numeric() -{ - test_id_helper(typename id_numeric<int, ii, dummy_a>::type()...); -} - -template<typename... tt> -static void test_id_type() -{ - test_id_helper(typename id_type<tt, dummy_a>::type()...); -} - -static void test_id() -{ - // don't call VERIFY here, just assume it works if it compiles - // (otherwise it will complain that it can't find the function) - test_id_numeric<1, 4, 6>(); - test_id_type<dummy_a, dummy_b, dummy_c>(); -} - -static void test_is_same_gf() -{ - VERIFY((!is_same_gf<dummy_a, dummy_b>::value)); - VERIFY((!!is_same_gf<dummy_a, dummy_a>::value)); - VERIFY_IS_EQUAL((!!is_same_gf<dummy_a, dummy_b>::global_flags), false); - VERIFY_IS_EQUAL((!!is_same_gf<dummy_a, dummy_a>::global_flags), false); -} - -static void test_apply_op() -{ - typedef type_list<dummy_a, dummy_b, dummy_c> tl; - VERIFY((!!is_same<typename apply_op_from_left<dummy_op, dummy_a, tl>::type, type_list<dummy_e, dummy_c, dummy_d>>::value)); - VERIFY((!!is_same<typename apply_op_from_right<dummy_op, dummy_a, tl>::type, type_list<dummy_e, dummy_d, dummy_b>>::value)); -} - -static void test_contained_in_list() -{ - typedef type_list<dummy_a, dummy_b, dummy_c> tl; - - VERIFY((!!contained_in_list<is_same, dummy_a, tl>::value)); - VERIFY((!!contained_in_list<is_same, dummy_b, tl>::value)); - VERIFY((!!contained_in_list<is_same, dummy_c, tl>::value)); - VERIFY((!contained_in_list<is_same, dummy_d, tl>::value)); - VERIFY((!contained_in_list<is_same, dummy_e, tl>::value)); - - VERIFY((!!contained_in_list_gf<dummy_test, dummy_a, tl>::value)); - VERIFY((!!contained_in_list_gf<dummy_test, dummy_b, tl>::value)); - VERIFY((!!contained_in_list_gf<dummy_test, dummy_c, tl>::value)); - VERIFY((!contained_in_list_gf<dummy_test, dummy_d, tl>::value)); - VERIFY((!contained_in_list_gf<dummy_test, dummy_e, tl>::value)); - - VERIFY_IS_EQUAL(((int)contained_in_list_gf<dummy_test, dummy_a, tl>::global_flags), 1); - VERIFY_IS_EQUAL(((int)contained_in_list_gf<dummy_test, dummy_b, tl>::global_flags), 2); - VERIFY_IS_EQUAL(((int)contained_in_list_gf<dummy_test, dummy_c, tl>::global_flags), 4); - VERIFY_IS_EQUAL(((int)contained_in_list_gf<dummy_test, dummy_d, tl>::global_flags), 0); - VERIFY_IS_EQUAL(((int)contained_in_list_gf<dummy_test, dummy_e, tl>::global_flags), 0); -} - -static void test_arg_reductions() -{ - VERIFY_IS_EQUAL(arg_sum(1,2,3,4), 10); - VERIFY_IS_EQUAL(arg_prod(1,2,3,4), 24); - VERIFY_IS_APPROX(arg_sum(0.5, 2, 5), 7.5); - VERIFY_IS_APPROX(arg_prod(0.5, 2, 5), 5.0); -} - -static void test_array_reverse_and_reduce() -{ - array<int, 6> a{{4, 8, 15, 16, 23, 42}}; - array<int, 6> b{{42, 23, 16, 15, 8, 4}}; - - // there is no operator<< for std::array, so VERIFY_IS_EQUAL will - // not compile - VERIFY((array_reverse(a) == b)); - VERIFY((array_reverse(b) == a)); - VERIFY_IS_EQUAL((array_sum(a)), 108); - VERIFY_IS_EQUAL((array_sum(b)), 108); - VERIFY_IS_EQUAL((array_prod(a)), 7418880); - VERIFY_IS_EQUAL((array_prod(b)), 7418880); -} - -static void test_array_zip_and_apply() -{ - array<int, 6> a{{4, 8, 15, 16, 23, 42}}; - array<int, 6> b{{0, 1, 2, 3, 4, 5}}; - array<int, 6> c{{4, 9, 17, 19, 27, 47}}; - array<int, 6> d{{0, 8, 30, 48, 92, 210}}; - array<int, 6> e{{0, 2, 4, 6, 8, 10}}; - - VERIFY((array_zip<sum_op>(a, b) == c)); - VERIFY((array_zip<product_op>(a, b) == d)); - VERIFY((array_apply<times2_op>(b) == e)); - VERIFY_IS_EQUAL((array_apply_and_reduce<sum_op, times2_op>(a)), 216); - VERIFY_IS_EQUAL((array_apply_and_reduce<sum_op, times2_op>(b)), 30); - VERIFY_IS_EQUAL((array_zip_and_reduce<product_op, sum_op>(a, b)), 14755932); - VERIFY_IS_EQUAL((array_zip_and_reduce<sum_op, product_op>(a, b)), 388); -} - -static void test_array_misc() -{ - array<int, 3> a3{{1, 1, 1}}; - array<int, 6> a6{{2, 2, 2, 2, 2, 2}}; - VERIFY((repeat<3, int>(1) == a3)); - VERIFY((repeat<6, int>(2) == a6)); - - int data[5] = { 0, 1, 2, 3, 4 }; - VERIFY_IS_EQUAL((instantiate_by_c_array<dummy_inst, int, 0>(data).c), 0); - VERIFY_IS_EQUAL((instantiate_by_c_array<dummy_inst, int, 1>(data).c), 1); - VERIFY_IS_EQUAL((instantiate_by_c_array<dummy_inst, int, 2>(data).c), 2); - VERIFY_IS_EQUAL((instantiate_by_c_array<dummy_inst, int, 3>(data).c), 3); - VERIFY_IS_EQUAL((instantiate_by_c_array<dummy_inst, int, 4>(data).c), 4); - VERIFY_IS_EQUAL((instantiate_by_c_array<dummy_inst, int, 5>(data).c), 5); -} - -void test_cxx11_meta() -{ - CALL_SUBTEST(test_gen_numeric_list()); - CALL_SUBTEST(test_concat()); - CALL_SUBTEST(test_slice()); - CALL_SUBTEST(test_get()); - CALL_SUBTEST(test_id()); - CALL_SUBTEST(test_is_same_gf()); - CALL_SUBTEST(test_apply_op()); - CALL_SUBTEST(test_contained_in_list()); - CALL_SUBTEST(test_arg_reductions()); - CALL_SUBTEST(test_array_reverse_and_reduce()); - CALL_SUBTEST(test_array_zip_and_apply()); - CALL_SUBTEST(test_array_misc()); -} diff --git a/eigen/unsupported/test/cxx11_non_blocking_thread_pool.cpp b/eigen/unsupported/test/cxx11_non_blocking_thread_pool.cpp deleted file mode 100644 index 5f9bb93..0000000 --- a/eigen/unsupported/test/cxx11_non_blocking_thread_pool.cpp +++ /dev/null @@ -1,107 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 Dmitry Vyukov <dvyukov@google.com> -// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_USE_THREADS -#include "main.h" -#include "Eigen/CXX11/ThreadPool" - -static void test_create_destroy_empty_pool() -{ - // Just create and destroy the pool. This will wind up and tear down worker - // threads. Ensure there are no issues in that logic. - for (int i = 0; i < 16; ++i) { - NonBlockingThreadPool tp(i); - } -} - - -static void test_parallelism() -{ - // Test we never-ever fail to match available tasks with idle threads. - const int kThreads = 16; // code below expects that this is a multiple of 4 - NonBlockingThreadPool tp(kThreads); - VERIFY_IS_EQUAL(tp.NumThreads(), kThreads); - VERIFY_IS_EQUAL(tp.CurrentThreadId(), -1); - for (int iter = 0; iter < 100; ++iter) { - std::atomic<int> running(0); - std::atomic<int> done(0); - std::atomic<int> phase(0); - // Schedule kThreads tasks and ensure that they all are running. - for (int i = 0; i < kThreads; ++i) { - tp.Schedule([&]() { - const int thread_id = tp.CurrentThreadId(); - VERIFY_GE(thread_id, 0); - VERIFY_LE(thread_id, kThreads - 1); - running++; - while (phase < 1) { - } - done++; - }); - } - while (running != kThreads) { - } - running = 0; - phase = 1; - // Now, while the previous tasks exit, schedule another kThreads tasks and - // ensure that they are running. - for (int i = 0; i < kThreads; ++i) { - tp.Schedule([&, i]() { - running++; - while (phase < 2) { - } - // When all tasks are running, half of tasks exit, quarter of tasks - // continue running and quarter of tasks schedule another 2 tasks each. - // Concurrently main thread schedules another quarter of tasks. - // This gives us another kThreads tasks and we ensure that they all - // are running. - if (i < kThreads / 2) { - } else if (i < 3 * kThreads / 4) { - running++; - while (phase < 3) { - } - done++; - } else { - for (int j = 0; j < 2; ++j) { - tp.Schedule([&]() { - running++; - while (phase < 3) { - } - done++; - }); - } - } - done++; - }); - } - while (running != kThreads) { - } - running = 0; - phase = 2; - for (int i = 0; i < kThreads / 4; ++i) { - tp.Schedule([&]() { - running++; - while (phase < 3) { - } - done++; - }); - } - while (running != kThreads) { - } - phase = 3; - while (done != 3 * kThreads) { - } - } -} - -void test_cxx11_non_blocking_thread_pool() -{ - CALL_SUBTEST(test_create_destroy_empty_pool()); - CALL_SUBTEST(test_parallelism()); -} diff --git a/eigen/unsupported/test/cxx11_runqueue.cpp b/eigen/unsupported/test/cxx11_runqueue.cpp deleted file mode 100644 index 91f6901..0000000 --- a/eigen/unsupported/test/cxx11_runqueue.cpp +++ /dev/null @@ -1,235 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 Dmitry Vyukov <dvyukov@google.com> -// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_USE_THREADS -#include <cstdlib> -#include "main.h" -#include <Eigen/CXX11/ThreadPool> - - -// Visual studio doesn't implement a rand_r() function since its -// implementation of rand() is already thread safe -int rand_reentrant(unsigned int* s) { -#ifdef EIGEN_COMP_MSVC_STRICT - EIGEN_UNUSED_VARIABLE(s); - return rand(); -#else - return rand_r(s); -#endif -} - -void test_basic_runqueue() -{ - RunQueue<int, 4> q; - // Check empty state. - VERIFY(q.Empty()); - VERIFY_IS_EQUAL(0u, q.Size()); - VERIFY_IS_EQUAL(0, q.PopFront()); - std::vector<int> stolen; - VERIFY_IS_EQUAL(0u, q.PopBackHalf(&stolen)); - VERIFY_IS_EQUAL(0u, stolen.size()); - // Push one front, pop one front. - VERIFY_IS_EQUAL(0, q.PushFront(1)); - VERIFY_IS_EQUAL(1u, q.Size()); - VERIFY_IS_EQUAL(1, q.PopFront()); - VERIFY_IS_EQUAL(0u, q.Size()); - // Push front to overflow. - VERIFY_IS_EQUAL(0, q.PushFront(2)); - VERIFY_IS_EQUAL(1u, q.Size()); - VERIFY_IS_EQUAL(0, q.PushFront(3)); - VERIFY_IS_EQUAL(2u, q.Size()); - VERIFY_IS_EQUAL(0, q.PushFront(4)); - VERIFY_IS_EQUAL(3u, q.Size()); - VERIFY_IS_EQUAL(0, q.PushFront(5)); - VERIFY_IS_EQUAL(4u, q.Size()); - VERIFY_IS_EQUAL(6, q.PushFront(6)); - VERIFY_IS_EQUAL(4u, q.Size()); - VERIFY_IS_EQUAL(5, q.PopFront()); - VERIFY_IS_EQUAL(3u, q.Size()); - VERIFY_IS_EQUAL(4, q.PopFront()); - VERIFY_IS_EQUAL(2u, q.Size()); - VERIFY_IS_EQUAL(3, q.PopFront()); - VERIFY_IS_EQUAL(1u, q.Size()); - VERIFY_IS_EQUAL(2, q.PopFront()); - VERIFY_IS_EQUAL(0u, q.Size()); - VERIFY_IS_EQUAL(0, q.PopFront()); - // Push one back, pop one back. - VERIFY_IS_EQUAL(0, q.PushBack(7)); - VERIFY_IS_EQUAL(1u, q.Size()); - VERIFY_IS_EQUAL(1u, q.PopBackHalf(&stolen)); - VERIFY_IS_EQUAL(1u, stolen.size()); - VERIFY_IS_EQUAL(7, stolen[0]); - VERIFY_IS_EQUAL(0u, q.Size()); - stolen.clear(); - // Push back to overflow. - VERIFY_IS_EQUAL(0, q.PushBack(8)); - VERIFY_IS_EQUAL(1u, q.Size()); - VERIFY_IS_EQUAL(0, q.PushBack(9)); - VERIFY_IS_EQUAL(2u, q.Size()); - VERIFY_IS_EQUAL(0, q.PushBack(10)); - VERIFY_IS_EQUAL(3u, q.Size()); - VERIFY_IS_EQUAL(0, q.PushBack(11)); - VERIFY_IS_EQUAL(4u, q.Size()); - VERIFY_IS_EQUAL(12, q.PushBack(12)); - VERIFY_IS_EQUAL(4u, q.Size()); - // Pop back in halves. - VERIFY_IS_EQUAL(2u, q.PopBackHalf(&stolen)); - VERIFY_IS_EQUAL(2u, stolen.size()); - VERIFY_IS_EQUAL(10, stolen[0]); - VERIFY_IS_EQUAL(11, stolen[1]); - VERIFY_IS_EQUAL(2u, q.Size()); - stolen.clear(); - VERIFY_IS_EQUAL(1u, q.PopBackHalf(&stolen)); - VERIFY_IS_EQUAL(1u, stolen.size()); - VERIFY_IS_EQUAL(9, stolen[0]); - VERIFY_IS_EQUAL(1u, q.Size()); - stolen.clear(); - VERIFY_IS_EQUAL(1u, q.PopBackHalf(&stolen)); - VERIFY_IS_EQUAL(1u, stolen.size()); - VERIFY_IS_EQUAL(8, stolen[0]); - stolen.clear(); - VERIFY_IS_EQUAL(0u, q.PopBackHalf(&stolen)); - VERIFY_IS_EQUAL(0u, stolen.size()); - // Empty again. - VERIFY(q.Empty()); - VERIFY_IS_EQUAL(0u, q.Size()); - VERIFY_IS_EQUAL(0, q.PushFront(1)); - VERIFY_IS_EQUAL(0, q.PushFront(2)); - VERIFY_IS_EQUAL(0, q.PushFront(3)); - VERIFY_IS_EQUAL(1, q.PopBack()); - VERIFY_IS_EQUAL(2, q.PopBack()); - VERIFY_IS_EQUAL(3, q.PopBack()); - VERIFY(q.Empty()); - VERIFY_IS_EQUAL(0u, q.Size()); -} - -// Empty tests that the queue is not claimed to be empty when is is in fact not. -// Emptiness property is crucial part of thread pool blocking scheme, -// so we go to great effort to ensure this property. We create a queue with -// 1 element and then push 1 element (either front or back at random) and pop -// 1 element (either front or back at random). So queue always contains at least -// 1 element, but otherwise changes chaotically. Another thread constantly tests -// that the queue is not claimed to be empty. -void test_empty_runqueue() -{ - RunQueue<int, 4> q; - q.PushFront(1); - std::atomic<bool> done(false); - std::thread mutator([&q, &done]() { - unsigned rnd = 0; - std::vector<int> stolen; - for (int i = 0; i < 1 << 18; i++) { - if (rand_reentrant(&rnd) % 2) - VERIFY_IS_EQUAL(0, q.PushFront(1)); - else - VERIFY_IS_EQUAL(0, q.PushBack(1)); - if (rand_reentrant(&rnd) % 2) - VERIFY_IS_EQUAL(1, q.PopFront()); - else { - for (;;) { - if (q.PopBackHalf(&stolen) == 1) { - stolen.clear(); - break; - } - VERIFY_IS_EQUAL(0u, stolen.size()); - } - } - } - done = true; - }); - while (!done) { - VERIFY(!q.Empty()); - int size = q.Size(); - VERIFY_GE(size, 1); - VERIFY_LE(size, 2); - } - VERIFY_IS_EQUAL(1, q.PopFront()); - mutator.join(); -} - -// Stress is a chaotic random test. -// One thread (owner) calls PushFront/PopFront, other threads call PushBack/ -// PopBack. Ensure that we don't crash, deadlock, and all sanity checks pass. -void test_stress_runqueue() -{ - static const int kEvents = 1 << 18; - RunQueue<int, 8> q; - std::atomic<int> total(0); - std::vector<std::unique_ptr<std::thread>> threads; - threads.emplace_back(new std::thread([&q, &total]() { - int sum = 0; - int pushed = 1; - int popped = 1; - while (pushed < kEvents || popped < kEvents) { - if (pushed < kEvents) { - if (q.PushFront(pushed) == 0) { - sum += pushed; - pushed++; - } - } - if (popped < kEvents) { - int v = q.PopFront(); - if (v != 0) { - sum -= v; - popped++; - } - } - } - total += sum; - })); - for (int i = 0; i < 2; i++) { - threads.emplace_back(new std::thread([&q, &total]() { - int sum = 0; - for (int j = 1; j < kEvents; j++) { - if (q.PushBack(j) == 0) { - sum += j; - continue; - } - EIGEN_THREAD_YIELD(); - j--; - } - total += sum; - })); - threads.emplace_back(new std::thread([&q, &total]() { - int sum = 0; - std::vector<int> stolen; - for (int j = 1; j < kEvents;) { - if (q.PopBackHalf(&stolen) == 0) { - EIGEN_THREAD_YIELD(); - continue; - } - while (stolen.size() && j < kEvents) { - int v = stolen.back(); - stolen.pop_back(); - VERIFY_IS_NOT_EQUAL(v, 0); - sum += v; - j++; - } - } - while (stolen.size()) { - int v = stolen.back(); - stolen.pop_back(); - VERIFY_IS_NOT_EQUAL(v, 0); - while ((v = q.PushBack(v)) != 0) EIGEN_THREAD_YIELD(); - } - total -= sum; - })); - } - for (size_t i = 0; i < threads.size(); i++) threads[i]->join(); - VERIFY(q.Empty()); - VERIFY(total.load() == 0); -} - -void test_cxx11_runqueue() -{ - CALL_SUBTEST_1(test_basic_runqueue()); - CALL_SUBTEST_2(test_empty_runqueue()); - CALL_SUBTEST_3(test_stress_runqueue()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_argmax.cpp b/eigen/unsupported/test/cxx11_tensor_argmax.cpp deleted file mode 100644 index 0377672..0000000 --- a/eigen/unsupported/test/cxx11_tensor_argmax.cpp +++ /dev/null @@ -1,294 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Eugene Brevdo <ebrevdo@google.com> -// Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::array; -using Eigen::Tuple; - -template <int DataLayout> -static void test_simple_index_tuples() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - tensor = (tensor + tensor.constant(0.5)).log(); - - Tensor<Tuple<DenseIndex, float>, 4, DataLayout> index_tuples(2,3,5,7); - index_tuples = tensor.index_tuples(); - - for (DenseIndex n = 0; n < 2*3*5*7; ++n) { - const Tuple<DenseIndex, float>& v = index_tuples.coeff(n); - VERIFY_IS_EQUAL(v.first, n); - VERIFY_IS_EQUAL(v.second, tensor.coeff(n)); - } -} - -template <int DataLayout> -static void test_index_tuples_dim() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - tensor = (tensor + tensor.constant(0.5)).log(); - - Tensor<Tuple<DenseIndex, float>, 4, DataLayout> index_tuples(2,3,5,7); - - index_tuples = tensor.index_tuples(); - - for (Eigen::DenseIndex n = 0; n < tensor.size(); ++n) { - const Tuple<DenseIndex, float>& v = index_tuples(n); //(i, j, k, l); - VERIFY_IS_EQUAL(v.first, n); - VERIFY_IS_EQUAL(v.second, tensor(n)); - } -} - -template <int DataLayout> -static void test_argmax_tuple_reducer() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - tensor = (tensor + tensor.constant(0.5)).log(); - - Tensor<Tuple<DenseIndex, float>, 4, DataLayout> index_tuples(2,3,5,7); - index_tuples = tensor.index_tuples(); - - Tensor<Tuple<DenseIndex, float>, 0, DataLayout> reduced; - DimensionList<DenseIndex, 4> dims; - reduced = index_tuples.reduce( - dims, internal::ArgMaxTupleReducer<Tuple<DenseIndex, float> >()); - - Tensor<float, 0, DataLayout> maxi = tensor.maximum(); - - VERIFY_IS_EQUAL(maxi(), reduced(0).second); - - array<DenseIndex, 3> reduce_dims; - for (int d = 0; d < 3; ++d) reduce_dims[d] = d; - Tensor<Tuple<DenseIndex, float>, 1, DataLayout> reduced_by_dims(7); - reduced_by_dims = index_tuples.reduce( - reduce_dims, internal::ArgMaxTupleReducer<Tuple<DenseIndex, float> >()); - - Tensor<float, 1, DataLayout> max_by_dims = tensor.maximum(reduce_dims); - - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(max_by_dims(l), reduced_by_dims(l).second); - } -} - -template <int DataLayout> -static void test_argmin_tuple_reducer() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - tensor = (tensor + tensor.constant(0.5)).log(); - - Tensor<Tuple<DenseIndex, float>, 4, DataLayout> index_tuples(2,3,5,7); - index_tuples = tensor.index_tuples(); - - Tensor<Tuple<DenseIndex, float>, 0, DataLayout> reduced; - DimensionList<DenseIndex, 4> dims; - reduced = index_tuples.reduce( - dims, internal::ArgMinTupleReducer<Tuple<DenseIndex, float> >()); - - Tensor<float, 0, DataLayout> mini = tensor.minimum(); - - VERIFY_IS_EQUAL(mini(), reduced(0).second); - - array<DenseIndex, 3> reduce_dims; - for (int d = 0; d < 3; ++d) reduce_dims[d] = d; - Tensor<Tuple<DenseIndex, float>, 1, DataLayout> reduced_by_dims(7); - reduced_by_dims = index_tuples.reduce( - reduce_dims, internal::ArgMinTupleReducer<Tuple<DenseIndex, float> >()); - - Tensor<float, 1, DataLayout> min_by_dims = tensor.minimum(reduce_dims); - - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(min_by_dims(l), reduced_by_dims(l).second); - } -} - -template <int DataLayout> -static void test_simple_argmax() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - tensor = (tensor + tensor.constant(0.5)).log(); - tensor(0,0,0,0) = 10.0; - - Tensor<DenseIndex, 0, DataLayout> tensor_argmax; - - tensor_argmax = tensor.argmax(); - - VERIFY_IS_EQUAL(tensor_argmax(0), 0); - - tensor(1,2,4,6) = 20.0; - - tensor_argmax = tensor.argmax(); - - VERIFY_IS_EQUAL(tensor_argmax(0), 2*3*5*7 - 1); -} - -template <int DataLayout> -static void test_simple_argmin() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - tensor = (tensor + tensor.constant(0.5)).log(); - tensor(0,0,0,0) = -10.0; - - Tensor<DenseIndex, 0, DataLayout> tensor_argmin; - - tensor_argmin = tensor.argmin(); - - VERIFY_IS_EQUAL(tensor_argmin(0), 0); - - tensor(1,2,4,6) = -20.0; - - tensor_argmin = tensor.argmin(); - - VERIFY_IS_EQUAL(tensor_argmin(0), 2*3*5*7 - 1); -} - -template <int DataLayout> -static void test_argmax_dim() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - std::vector<int> dims {2, 3, 5, 7}; - - for (int dim = 0; dim < 4; ++dim) { - tensor.setRandom(); - tensor = (tensor + tensor.constant(0.5)).log(); - - Tensor<DenseIndex, 3, DataLayout> tensor_argmax; - array<DenseIndex, 4> ix; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l; - if (ix[dim] != 0) continue; - // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = 10.0 - tensor(ix) = 10.0; - } - } - } - } - - tensor_argmax = tensor.argmax(dim); - - VERIFY_IS_EQUAL(tensor_argmax.size(), - ptrdiff_t(2*3*5*7 / tensor.dimension(dim))); - for (ptrdiff_t n = 0; n < tensor_argmax.size(); ++n) { - // Expect max to be in the first index of the reduced dimension - VERIFY_IS_EQUAL(tensor_argmax.data()[n], 0); - } - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l; - if (ix[dim] != tensor.dimension(dim) - 1) continue; - // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0 - tensor(ix) = 20.0; - } - } - } - } - - tensor_argmax = tensor.argmax(dim); - - VERIFY_IS_EQUAL(tensor_argmax.size(), - ptrdiff_t(2*3*5*7 / tensor.dimension(dim))); - for (ptrdiff_t n = 0; n < tensor_argmax.size(); ++n) { - // Expect max to be in the last index of the reduced dimension - VERIFY_IS_EQUAL(tensor_argmax.data()[n], tensor.dimension(dim) - 1); - } - } -} - -template <int DataLayout> -static void test_argmin_dim() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - std::vector<int> dims {2, 3, 5, 7}; - - for (int dim = 0; dim < 4; ++dim) { - tensor.setRandom(); - tensor = (tensor + tensor.constant(0.5)).log(); - - Tensor<DenseIndex, 3, DataLayout> tensor_argmin; - array<DenseIndex, 4> ix; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l; - if (ix[dim] != 0) continue; - // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = -10.0 - tensor(ix) = -10.0; - } - } - } - } - - tensor_argmin = tensor.argmin(dim); - - VERIFY_IS_EQUAL(tensor_argmin.size(), - ptrdiff_t(2*3*5*7 / tensor.dimension(dim))); - for (ptrdiff_t n = 0; n < tensor_argmin.size(); ++n) { - // Expect min to be in the first index of the reduced dimension - VERIFY_IS_EQUAL(tensor_argmin.data()[n], 0); - } - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l; - if (ix[dim] != tensor.dimension(dim) - 1) continue; - // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = -20.0 - tensor(ix) = -20.0; - } - } - } - } - - tensor_argmin = tensor.argmin(dim); - - VERIFY_IS_EQUAL(tensor_argmin.size(), - ptrdiff_t(2*3*5*7 / tensor.dimension(dim))); - for (ptrdiff_t n = 0; n < tensor_argmin.size(); ++n) { - // Expect min to be in the last index of the reduced dimension - VERIFY_IS_EQUAL(tensor_argmin.data()[n], tensor.dimension(dim) - 1); - } - } -} - -void test_cxx11_tensor_argmax() -{ - CALL_SUBTEST(test_simple_index_tuples<RowMajor>()); - CALL_SUBTEST(test_simple_index_tuples<ColMajor>()); - CALL_SUBTEST(test_index_tuples_dim<RowMajor>()); - CALL_SUBTEST(test_index_tuples_dim<ColMajor>()); - CALL_SUBTEST(test_argmax_tuple_reducer<RowMajor>()); - CALL_SUBTEST(test_argmax_tuple_reducer<ColMajor>()); - CALL_SUBTEST(test_argmin_tuple_reducer<RowMajor>()); - CALL_SUBTEST(test_argmin_tuple_reducer<ColMajor>()); - CALL_SUBTEST(test_simple_argmax<RowMajor>()); - CALL_SUBTEST(test_simple_argmax<ColMajor>()); - CALL_SUBTEST(test_simple_argmin<RowMajor>()); - CALL_SUBTEST(test_simple_argmin<ColMajor>()); - CALL_SUBTEST(test_argmax_dim<RowMajor>()); - CALL_SUBTEST(test_argmax_dim<ColMajor>()); - CALL_SUBTEST(test_argmin_dim<RowMajor>()); - CALL_SUBTEST(test_argmin_dim<ColMajor>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_argmax_cuda.cu b/eigen/unsupported/test/cxx11_tensor_argmax_cuda.cu deleted file mode 100644 index 3d73d49..0000000 --- a/eigen/unsupported/test/cxx11_tensor_argmax_cuda.cu +++ /dev/null @@ -1,251 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - - -#define EIGEN_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_FUNC cxx11_tensor_cuda -#define EIGEN_USE_GPU - -#include "main.h" -#include <unsupported/Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -template <int Layout> -void test_cuda_simple_argmax() -{ - Tensor<double, 3, Layout> in(Eigen::array<DenseIndex, 3>(72,53,97)); - Tensor<DenseIndex, 1, Layout> out_max(Eigen::array<DenseIndex, 1>(1)); - Tensor<DenseIndex, 1, Layout> out_min(Eigen::array<DenseIndex, 1>(1)); - in.setRandom(); - in *= in.constant(100.0); - in(0, 0, 0) = -1000.0; - in(71, 52, 96) = 1000.0; - - std::size_t in_bytes = in.size() * sizeof(double); - std::size_t out_bytes = out_max.size() * sizeof(DenseIndex); - - double* d_in; - DenseIndex* d_out_max; - DenseIndex* d_out_min; - cudaMalloc((void**)(&d_in), in_bytes); - cudaMalloc((void**)(&d_out_max), out_bytes); - cudaMalloc((void**)(&d_out_min), out_bytes); - - cudaMemcpy(d_in, in.data(), in_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<double, 3, Layout>, Aligned > gpu_in(d_in, Eigen::array<DenseIndex, 3>(72,53,97)); - Eigen::TensorMap<Eigen::Tensor<DenseIndex, 1, Layout>, Aligned > gpu_out_max(d_out_max, Eigen::array<DenseIndex, 1>(1)); - Eigen::TensorMap<Eigen::Tensor<DenseIndex, 1, Layout>, Aligned > gpu_out_min(d_out_min, Eigen::array<DenseIndex, 1>(1)); - - gpu_out_max.device(gpu_device) = gpu_in.argmax(); - gpu_out_min.device(gpu_device) = gpu_in.argmin(); - - assert(cudaMemcpyAsync(out_max.data(), d_out_max, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaMemcpyAsync(out_min.data(), d_out_min, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - VERIFY_IS_EQUAL(out_max(Eigen::array<DenseIndex, 1>(0)), 72*53*97 - 1); - VERIFY_IS_EQUAL(out_min(Eigen::array<DenseIndex, 1>(0)), 0); - - cudaFree(d_in); - cudaFree(d_out_max); - cudaFree(d_out_min); -} - -template <int DataLayout> -void test_cuda_argmax_dim() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - std::vector<int> dims; - dims.push_back(2); dims.push_back(3); dims.push_back(5); dims.push_back(7); - - for (int dim = 0; dim < 4; ++dim) { - tensor.setRandom(); - tensor = (tensor + tensor.constant(0.5)).log(); - - array<DenseIndex, 3> out_shape; - for (int d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d+1]; - - Tensor<DenseIndex, 3, DataLayout> tensor_arg(out_shape); - - array<DenseIndex, 4> ix; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l; - if (ix[dim] != 0) continue; - // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = 10.0 - tensor(ix) = 10.0; - } - } - } - } - - std::size_t in_bytes = tensor.size() * sizeof(float); - std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex); - - float* d_in; - DenseIndex* d_out; - cudaMalloc((void**)(&d_in), in_bytes); - cudaMalloc((void**)(&d_out), out_bytes); - - cudaMemcpy(d_in, tensor.data(), in_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout>, Aligned > gpu_in(d_in, Eigen::array<DenseIndex, 4>(2, 3, 5, 7)); - Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout>, Aligned > gpu_out(d_out, out_shape); - - gpu_out.device(gpu_device) = gpu_in.argmax(dim); - - assert(cudaMemcpyAsync(tensor_arg.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - VERIFY_IS_EQUAL(tensor_arg.size(), - size_t(2*3*5*7 / tensor.dimension(dim))); - - for (DenseIndex n = 0; n < tensor_arg.size(); ++n) { - // Expect max to be in the first index of the reduced dimension - VERIFY_IS_EQUAL(tensor_arg.data()[n], 0); - } - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l; - if (ix[dim] != tensor.dimension(dim) - 1) continue; - // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0 - tensor(ix) = 20.0; - } - } - } - } - - cudaMemcpy(d_in, tensor.data(), in_bytes, cudaMemcpyHostToDevice); - - gpu_out.device(gpu_device) = gpu_in.argmax(dim); - - assert(cudaMemcpyAsync(tensor_arg.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (DenseIndex n = 0; n < tensor_arg.size(); ++n) { - // Expect max to be in the last index of the reduced dimension - VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1); - } - - cudaFree(d_in); - cudaFree(d_out); - } -} - -template <int DataLayout> -void test_cuda_argmin_dim() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - std::vector<int> dims; - dims.push_back(2); dims.push_back(3); dims.push_back(5); dims.push_back(7); - - for (int dim = 0; dim < 4; ++dim) { - tensor.setRandom(); - tensor = (tensor + tensor.constant(0.5)).log(); - - array<DenseIndex, 3> out_shape; - for (int d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d+1]; - - Tensor<DenseIndex, 3, DataLayout> tensor_arg(out_shape); - - array<DenseIndex, 4> ix; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l; - if (ix[dim] != 0) continue; - // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = 10.0 - tensor(ix) = -10.0; - } - } - } - } - - std::size_t in_bytes = tensor.size() * sizeof(float); - std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex); - - float* d_in; - DenseIndex* d_out; - cudaMalloc((void**)(&d_in), in_bytes); - cudaMalloc((void**)(&d_out), out_bytes); - - cudaMemcpy(d_in, tensor.data(), in_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout>, Aligned > gpu_in(d_in, Eigen::array<DenseIndex, 4>(2, 3, 5, 7)); - Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout>, Aligned > gpu_out(d_out, out_shape); - - gpu_out.device(gpu_device) = gpu_in.argmin(dim); - - assert(cudaMemcpyAsync(tensor_arg.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - VERIFY_IS_EQUAL(tensor_arg.size(), - 2*3*5*7 / tensor.dimension(dim)); - - for (DenseIndex n = 0; n < tensor_arg.size(); ++n) { - // Expect min to be in the first index of the reduced dimension - VERIFY_IS_EQUAL(tensor_arg.data()[n], 0); - } - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l; - if (ix[dim] != tensor.dimension(dim) - 1) continue; - // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0 - tensor(ix) = -20.0; - } - } - } - } - - cudaMemcpy(d_in, tensor.data(), in_bytes, cudaMemcpyHostToDevice); - - gpu_out.device(gpu_device) = gpu_in.argmin(dim); - - assert(cudaMemcpyAsync(tensor_arg.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (DenseIndex n = 0; n < tensor_arg.size(); ++n) { - // Expect max to be in the last index of the reduced dimension - VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1); - } - - cudaFree(d_in); - cudaFree(d_out); - } -} - -void test_cxx11_tensor_cuda() -{ - CALL_SUBTEST_1(test_cuda_simple_argmax<RowMajor>()); - CALL_SUBTEST_1(test_cuda_simple_argmax<ColMajor>()); - CALL_SUBTEST_2(test_cuda_argmax_dim<RowMajor>()); - CALL_SUBTEST_2(test_cuda_argmax_dim<ColMajor>()); - CALL_SUBTEST_3(test_cuda_argmin_dim<RowMajor>()); - CALL_SUBTEST_3(test_cuda_argmin_dim<ColMajor>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_assign.cpp b/eigen/unsupported/test/cxx11_tensor_assign.cpp deleted file mode 100644 index 8fe85d8..0000000 --- a/eigen/unsupported/test/cxx11_tensor_assign.cpp +++ /dev/null @@ -1,370 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::RowMajor; - -static void test_1d() -{ - Tensor<int, 1> vec1(6); - Tensor<int, 1, RowMajor> vec2(6); - vec1(0) = 4; vec2(0) = 0; - vec1(1) = 8; vec2(1) = 1; - vec1(2) = 15; vec2(2) = 2; - vec1(3) = 16; vec2(3) = 3; - vec1(4) = 23; vec2(4) = 4; - vec1(5) = 42; vec2(5) = 5; - - int col_major[6]; - int row_major[6]; - memset(col_major, 0, 6*sizeof(int)); - memset(row_major, 0, 6*sizeof(int)); - TensorMap<Tensor<int, 1> > vec3(col_major, 6); - TensorMap<Tensor<int, 1, RowMajor> > vec4(row_major, 6); - - vec3 = vec1; - vec4 = vec2; - - VERIFY_IS_EQUAL(vec3(0), 4); - VERIFY_IS_EQUAL(vec3(1), 8); - VERIFY_IS_EQUAL(vec3(2), 15); - VERIFY_IS_EQUAL(vec3(3), 16); - VERIFY_IS_EQUAL(vec3(4), 23); - VERIFY_IS_EQUAL(vec3(5), 42); - - VERIFY_IS_EQUAL(vec4(0), 0); - VERIFY_IS_EQUAL(vec4(1), 1); - VERIFY_IS_EQUAL(vec4(2), 2); - VERIFY_IS_EQUAL(vec4(3), 3); - VERIFY_IS_EQUAL(vec4(4), 4); - VERIFY_IS_EQUAL(vec4(5), 5); - - vec1.setZero(); - vec2.setZero(); - vec1 = vec3; - vec2 = vec4; - - VERIFY_IS_EQUAL(vec1(0), 4); - VERIFY_IS_EQUAL(vec1(1), 8); - VERIFY_IS_EQUAL(vec1(2), 15); - VERIFY_IS_EQUAL(vec1(3), 16); - VERIFY_IS_EQUAL(vec1(4), 23); - VERIFY_IS_EQUAL(vec1(5), 42); - - VERIFY_IS_EQUAL(vec2(0), 0); - VERIFY_IS_EQUAL(vec2(1), 1); - VERIFY_IS_EQUAL(vec2(2), 2); - VERIFY_IS_EQUAL(vec2(3), 3); - VERIFY_IS_EQUAL(vec2(4), 4); - VERIFY_IS_EQUAL(vec2(5), 5); -} - -static void test_2d() -{ - Tensor<int, 2> mat1(2,3); - Tensor<int, 2, RowMajor> mat2(2,3); - - mat1(0,0) = 0; - mat1(0,1) = 1; - mat1(0,2) = 2; - mat1(1,0) = 3; - mat1(1,1) = 4; - mat1(1,2) = 5; - - mat2(0,0) = 0; - mat2(0,1) = 1; - mat2(0,2) = 2; - mat2(1,0) = 3; - mat2(1,1) = 4; - mat2(1,2) = 5; - - int col_major[6]; - int row_major[6]; - memset(col_major, 0, 6*sizeof(int)); - memset(row_major, 0, 6*sizeof(int)); - TensorMap<Tensor<int, 2> > mat3(row_major, 2, 3); - TensorMap<Tensor<int, 2, RowMajor> > mat4(col_major, 2, 3); - - mat3 = mat1; - mat4 = mat2; - - VERIFY_IS_EQUAL(mat3(0,0), 0); - VERIFY_IS_EQUAL(mat3(0,1), 1); - VERIFY_IS_EQUAL(mat3(0,2), 2); - VERIFY_IS_EQUAL(mat3(1,0), 3); - VERIFY_IS_EQUAL(mat3(1,1), 4); - VERIFY_IS_EQUAL(mat3(1,2), 5); - - VERIFY_IS_EQUAL(mat4(0,0), 0); - VERIFY_IS_EQUAL(mat4(0,1), 1); - VERIFY_IS_EQUAL(mat4(0,2), 2); - VERIFY_IS_EQUAL(mat4(1,0), 3); - VERIFY_IS_EQUAL(mat4(1,1), 4); - VERIFY_IS_EQUAL(mat4(1,2), 5); - - mat1.setZero(); - mat2.setZero(); - mat1 = mat3; - mat2 = mat4; - - VERIFY_IS_EQUAL(mat1(0,0), 0); - VERIFY_IS_EQUAL(mat1(0,1), 1); - VERIFY_IS_EQUAL(mat1(0,2), 2); - VERIFY_IS_EQUAL(mat1(1,0), 3); - VERIFY_IS_EQUAL(mat1(1,1), 4); - VERIFY_IS_EQUAL(mat1(1,2), 5); - - VERIFY_IS_EQUAL(mat2(0,0), 0); - VERIFY_IS_EQUAL(mat2(0,1), 1); - VERIFY_IS_EQUAL(mat2(0,2), 2); - VERIFY_IS_EQUAL(mat2(1,0), 3); - VERIFY_IS_EQUAL(mat2(1,1), 4); - VERIFY_IS_EQUAL(mat2(1,2), 5); -} - -static void test_3d() -{ - Tensor<int, 3> mat1(2,3,7); - Tensor<int, 3, RowMajor> mat2(2,3,7); - - int val = 0; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - mat1(i,j,k) = val; - mat2(i,j,k) = val; - val++; - } - } - } - - int col_major[2*3*7]; - int row_major[2*3*7]; - memset(col_major, 0, 2*3*7*sizeof(int)); - memset(row_major, 0, 2*3*7*sizeof(int)); - TensorMap<Tensor<int, 3> > mat3(col_major, 2, 3, 7); - TensorMap<Tensor<int, 3, RowMajor> > mat4(row_major, 2, 3, 7); - - mat3 = mat1; - mat4 = mat2; - - val = 0; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL(mat3(i,j,k), val); - VERIFY_IS_EQUAL(mat4(i,j,k), val); - val++; - } - } - } - - mat1.setZero(); - mat2.setZero(); - mat1 = mat3; - mat2 = mat4; - - val = 0; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL(mat1(i,j,k), val); - VERIFY_IS_EQUAL(mat2(i,j,k), val); - val++; - } - } - } -} - -static void test_same_type() -{ - Tensor<int, 1> orig_tensor(5); - Tensor<int, 1> dest_tensor(5); - orig_tensor.setRandom(); - dest_tensor.setRandom(); - int* orig_data = orig_tensor.data(); - int* dest_data = dest_tensor.data(); - dest_tensor = orig_tensor; - VERIFY_IS_EQUAL(orig_tensor.data(), orig_data); - VERIFY_IS_EQUAL(dest_tensor.data(), dest_data); - for (int i = 0; i < 5; ++i) { - VERIFY_IS_EQUAL(dest_tensor(i), orig_tensor(i)); - } - - TensorFixedSize<int, Sizes<5> > orig_array; - TensorFixedSize<int, Sizes<5> > dest_array; - orig_array.setRandom(); - dest_array.setRandom(); - orig_data = orig_array.data(); - dest_data = dest_array.data(); - dest_array = orig_array; - VERIFY_IS_EQUAL(orig_array.data(), orig_data); - VERIFY_IS_EQUAL(dest_array.data(), dest_data); - for (int i = 0; i < 5; ++i) { - VERIFY_IS_EQUAL(dest_array(i), orig_array(i)); - } - - int orig[5] = {1, 2, 3, 4, 5}; - int dest[5] = {6, 7, 8, 9, 10}; - TensorMap<Tensor<int, 1> > orig_map(orig, 5); - TensorMap<Tensor<int, 1> > dest_map(dest, 5); - orig_data = orig_map.data(); - dest_data = dest_map.data(); - dest_map = orig_map; - VERIFY_IS_EQUAL(orig_map.data(), orig_data); - VERIFY_IS_EQUAL(dest_map.data(), dest_data); - for (int i = 0; i < 5; ++i) { - VERIFY_IS_EQUAL(dest[i], i+1); - } -} - -static void test_auto_resize() -{ - Tensor<int, 1> tensor1; - Tensor<int, 1> tensor2(3); - Tensor<int, 1> tensor3(5); - Tensor<int, 1> tensor4(7); - - Tensor<int, 1> new_tensor(5); - new_tensor.setRandom(); - - tensor1 = tensor2 = tensor3 = tensor4 = new_tensor; - - VERIFY_IS_EQUAL(tensor1.dimension(0), new_tensor.dimension(0)); - VERIFY_IS_EQUAL(tensor2.dimension(0), new_tensor.dimension(0)); - VERIFY_IS_EQUAL(tensor3.dimension(0), new_tensor.dimension(0)); - VERIFY_IS_EQUAL(tensor4.dimension(0), new_tensor.dimension(0)); - for (int i = 0; i < new_tensor.dimension(0); ++i) { - VERIFY_IS_EQUAL(tensor1(i), new_tensor(i)); - VERIFY_IS_EQUAL(tensor2(i), new_tensor(i)); - VERIFY_IS_EQUAL(tensor3(i), new_tensor(i)); - VERIFY_IS_EQUAL(tensor4(i), new_tensor(i)); - } -} - - -static void test_compound_assign() -{ - Tensor<int, 1> start_tensor(10); - Tensor<int, 1> offset_tensor(10); - start_tensor.setRandom(); - offset_tensor.setRandom(); - - Tensor<int, 1> tensor = start_tensor; - tensor += offset_tensor; - for (int i = 0; i < 10; ++i) { - VERIFY_IS_EQUAL(tensor(i), start_tensor(i) + offset_tensor(i)); - } - - tensor = start_tensor; - tensor -= offset_tensor; - for (int i = 0; i < 10; ++i) { - VERIFY_IS_EQUAL(tensor(i), start_tensor(i) - offset_tensor(i)); - } - - tensor = start_tensor; - tensor *= offset_tensor; - for (int i = 0; i < 10; ++i) { - VERIFY_IS_EQUAL(tensor(i), start_tensor(i) * offset_tensor(i)); - } - - tensor = start_tensor; - tensor /= offset_tensor; - for (int i = 0; i < 10; ++i) { - VERIFY_IS_EQUAL(tensor(i), start_tensor(i) / offset_tensor(i)); - } -} - -static void test_std_initializers_tensor() { -#if EIGEN_HAS_VARIADIC_TEMPLATES - Tensor<int, 1> a(3); - a.setValues({0, 1, 2}); - VERIFY_IS_EQUAL(a(0), 0); - VERIFY_IS_EQUAL(a(1), 1); - VERIFY_IS_EQUAL(a(2), 2); - - // It fills the top-left slice. - a.setValues({10, 20}); - VERIFY_IS_EQUAL(a(0), 10); - VERIFY_IS_EQUAL(a(1), 20); - VERIFY_IS_EQUAL(a(2), 2); - - // Chaining. - Tensor<int, 1> a2(3); - a2 = a.setValues({100, 200, 300}); - VERIFY_IS_EQUAL(a(0), 100); - VERIFY_IS_EQUAL(a(1), 200); - VERIFY_IS_EQUAL(a(2), 300); - VERIFY_IS_EQUAL(a2(0), 100); - VERIFY_IS_EQUAL(a2(1), 200); - VERIFY_IS_EQUAL(a2(2), 300); - - Tensor<int, 2> b(2, 3); - b.setValues({{0, 1, 2}, {3, 4, 5}}); - VERIFY_IS_EQUAL(b(0, 0), 0); - VERIFY_IS_EQUAL(b(0, 1), 1); - VERIFY_IS_EQUAL(b(0, 2), 2); - VERIFY_IS_EQUAL(b(1, 0), 3); - VERIFY_IS_EQUAL(b(1, 1), 4); - VERIFY_IS_EQUAL(b(1, 2), 5); - - // It fills the top-left slice. - b.setValues({{10, 20}, {30}}); - VERIFY_IS_EQUAL(b(0, 0), 10); - VERIFY_IS_EQUAL(b(0, 1), 20); - VERIFY_IS_EQUAL(b(0, 2), 2); - VERIFY_IS_EQUAL(b(1, 0), 30); - VERIFY_IS_EQUAL(b(1, 1), 4); - VERIFY_IS_EQUAL(b(1, 2), 5); - - Eigen::Tensor<int, 3> c(3, 2, 4); - c.setValues({{{0, 1, 2, 3}, {4, 5, 6, 7}}, - {{10, 11, 12, 13}, {14, 15, 16, 17}}, - {{20, 21, 22, 23}, {24, 25, 26, 27}}}); - VERIFY_IS_EQUAL(c(0, 0, 0), 0); - VERIFY_IS_EQUAL(c(0, 0, 1), 1); - VERIFY_IS_EQUAL(c(0, 0, 2), 2); - VERIFY_IS_EQUAL(c(0, 0, 3), 3); - VERIFY_IS_EQUAL(c(0, 1, 0), 4); - VERIFY_IS_EQUAL(c(0, 1, 1), 5); - VERIFY_IS_EQUAL(c(0, 1, 2), 6); - VERIFY_IS_EQUAL(c(0, 1, 3), 7); - VERIFY_IS_EQUAL(c(1, 0, 0), 10); - VERIFY_IS_EQUAL(c(1, 0, 1), 11); - VERIFY_IS_EQUAL(c(1, 0, 2), 12); - VERIFY_IS_EQUAL(c(1, 0, 3), 13); - VERIFY_IS_EQUAL(c(1, 1, 0), 14); - VERIFY_IS_EQUAL(c(1, 1, 1), 15); - VERIFY_IS_EQUAL(c(1, 1, 2), 16); - VERIFY_IS_EQUAL(c(1, 1, 3), 17); - VERIFY_IS_EQUAL(c(2, 0, 0), 20); - VERIFY_IS_EQUAL(c(2, 0, 1), 21); - VERIFY_IS_EQUAL(c(2, 0, 2), 22); - VERIFY_IS_EQUAL(c(2, 0, 3), 23); - VERIFY_IS_EQUAL(c(2, 1, 0), 24); - VERIFY_IS_EQUAL(c(2, 1, 1), 25); - VERIFY_IS_EQUAL(c(2, 1, 2), 26); - VERIFY_IS_EQUAL(c(2, 1, 3), 27); -#endif // EIGEN_HAS_VARIADIC_TEMPLATES -} - -void test_cxx11_tensor_assign() -{ - CALL_SUBTEST(test_1d()); - CALL_SUBTEST(test_2d()); - CALL_SUBTEST(test_3d()); - CALL_SUBTEST(test_same_type()); - CALL_SUBTEST(test_auto_resize()); - CALL_SUBTEST(test_compound_assign()); - CALL_SUBTEST(test_std_initializers_tensor()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp b/eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp deleted file mode 100644 index 7201bfe..0000000 --- a/eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp +++ /dev/null @@ -1,74 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 -// Mehdi Goli Codeplay Software Ltd. -// Ralph Potter Codeplay Software Ltd. -// Luke Iwanski Codeplay Software Ltd. -// Contact: <eigen@codeplay.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_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_NO_COMPLEX -#define EIGEN_TEST_FUNC cxx11_tensor_broadcast_sycl -#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int -#define EIGEN_USE_SYCL - -#include "main.h" -#include <unsupported/Eigen/CXX11/Tensor> - -using Eigen::array; -using Eigen::SyclDevice; -using Eigen::Tensor; -using Eigen::TensorMap; - -static void test_broadcast_sycl(const Eigen::SyclDevice &sycl_device){ - - // BROADCAST test: - array<int, 4> in_range = {{2, 3, 5, 7}}; - array<int, 4> broadcasts = {{2, 3, 1, 4}}; - array<int, 4> out_range; // = in_range * broadcasts - for (size_t i = 0; i < out_range.size(); ++i) - out_range[i] = in_range[i] * broadcasts[i]; - - Tensor<float, 4> input(in_range); - Tensor<float, 4> out(out_range); - - for (size_t i = 0; i < in_range.size(); ++i) - VERIFY_IS_EQUAL(out.dimension(i), out_range[i]); - - - for (int i = 0; i < input.size(); ++i) - input(i) = static_cast<float>(i); - - float * gpu_in_data = static_cast<float*>(sycl_device.allocate(input.dimensions().TotalSize()*sizeof(float))); - float * gpu_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float))); - - TensorMap<Tensor<float, 4>> gpu_in(gpu_in_data, in_range); - TensorMap<Tensor<float, 4>> gpu_out(gpu_out_data, out_range); - sycl_device.memcpyHostToDevice(gpu_in_data, input.data(),(input.dimensions().TotalSize())*sizeof(float)); - gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts); - sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); - - for (int i = 0; i < 4; ++i) { - for (int j = 0; j < 9; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 28; ++l) { - VERIFY_IS_APPROX(input(i%2,j%3,k%5,l%7), out(i,j,k,l)); - } - } - } - } - printf("Broadcast Test Passed\n"); - sycl_device.deallocate(gpu_in_data); - sycl_device.deallocate(gpu_out_data); -} - -void test_cxx11_tensor_broadcast_sycl() { - cl::sycl::gpu_selector s; - Eigen::SyclDevice sycl_device(s); - CALL_SUBTEST(test_broadcast_sycl(sycl_device)); -} diff --git a/eigen/unsupported/test/cxx11_tensor_broadcasting.cpp b/eigen/unsupported/test/cxx11_tensor_broadcasting.cpp deleted file mode 100644 index 5c0ea58..0000000 --- a/eigen/unsupported/test/cxx11_tensor_broadcasting.cpp +++ /dev/null @@ -1,194 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -template <int DataLayout> -static void test_simple_broadcasting() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - array<ptrdiff_t, 4> broadcasts; - broadcasts[0] = 1; - broadcasts[1] = 1; - broadcasts[2] = 1; - broadcasts[3] = 1; - - Tensor<float, 4, DataLayout> no_broadcast; - no_broadcast = tensor.broadcast(broadcasts); - - VERIFY_IS_EQUAL(no_broadcast.dimension(0), 2); - VERIFY_IS_EQUAL(no_broadcast.dimension(1), 3); - VERIFY_IS_EQUAL(no_broadcast.dimension(2), 5); - VERIFY_IS_EQUAL(no_broadcast.dimension(3), 7); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(tensor(i,j,k,l), no_broadcast(i,j,k,l)); - } - } - } - } - - broadcasts[0] = 2; - broadcasts[1] = 3; - broadcasts[2] = 1; - broadcasts[3] = 4; - Tensor<float, 4, DataLayout> broadcast; - broadcast = tensor.broadcast(broadcasts); - - VERIFY_IS_EQUAL(broadcast.dimension(0), 4); - VERIFY_IS_EQUAL(broadcast.dimension(1), 9); - VERIFY_IS_EQUAL(broadcast.dimension(2), 5); - VERIFY_IS_EQUAL(broadcast.dimension(3), 28); - - for (int i = 0; i < 4; ++i) { - for (int j = 0; j < 9; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 28; ++l) { - VERIFY_IS_EQUAL(tensor(i%2,j%3,k%5,l%7), broadcast(i,j,k,l)); - } - } - } - } -} - - -template <int DataLayout> -static void test_vectorized_broadcasting() -{ - Tensor<float, 3, DataLayout> tensor(8,3,5); - tensor.setRandom(); - array<ptrdiff_t, 3> broadcasts; - broadcasts[0] = 2; - broadcasts[1] = 3; - broadcasts[2] = 4; - - Tensor<float, 3, DataLayout> broadcast; - broadcast = tensor.broadcast(broadcasts); - - VERIFY_IS_EQUAL(broadcast.dimension(0), 16); - VERIFY_IS_EQUAL(broadcast.dimension(1), 9); - VERIFY_IS_EQUAL(broadcast.dimension(2), 20); - - for (int i = 0; i < 16; ++i) { - for (int j = 0; j < 9; ++j) { - for (int k = 0; k < 20; ++k) { - VERIFY_IS_EQUAL(tensor(i%8,j%3,k%5), broadcast(i,j,k)); - } - } - } - - tensor.resize(11,3,5); - tensor.setRandom(); - broadcast = tensor.broadcast(broadcasts); - - VERIFY_IS_EQUAL(broadcast.dimension(0), 22); - VERIFY_IS_EQUAL(broadcast.dimension(1), 9); - VERIFY_IS_EQUAL(broadcast.dimension(2), 20); - - for (int i = 0; i < 22; ++i) { - for (int j = 0; j < 9; ++j) { - for (int k = 0; k < 20; ++k) { - VERIFY_IS_EQUAL(tensor(i%11,j%3,k%5), broadcast(i,j,k)); - } - } - } -} - - -template <int DataLayout> -static void test_static_broadcasting() -{ - Tensor<float, 3, DataLayout> tensor(8,3,5); - tensor.setRandom(); - -#if EIGEN_HAS_CONSTEXPR - Eigen::IndexList<Eigen::type2index<2>, Eigen::type2index<3>, Eigen::type2index<4>> broadcasts; -#else - Eigen::array<int, 3> broadcasts; - broadcasts[0] = 2; - broadcasts[1] = 3; - broadcasts[2] = 4; -#endif - - Tensor<float, 3, DataLayout> broadcast; - broadcast = tensor.broadcast(broadcasts); - - VERIFY_IS_EQUAL(broadcast.dimension(0), 16); - VERIFY_IS_EQUAL(broadcast.dimension(1), 9); - VERIFY_IS_EQUAL(broadcast.dimension(2), 20); - - for (int i = 0; i < 16; ++i) { - for (int j = 0; j < 9; ++j) { - for (int k = 0; k < 20; ++k) { - VERIFY_IS_EQUAL(tensor(i%8,j%3,k%5), broadcast(i,j,k)); - } - } - } - - tensor.resize(11,3,5); - tensor.setRandom(); - broadcast = tensor.broadcast(broadcasts); - - VERIFY_IS_EQUAL(broadcast.dimension(0), 22); - VERIFY_IS_EQUAL(broadcast.dimension(1), 9); - VERIFY_IS_EQUAL(broadcast.dimension(2), 20); - - for (int i = 0; i < 22; ++i) { - for (int j = 0; j < 9; ++j) { - for (int k = 0; k < 20; ++k) { - VERIFY_IS_EQUAL(tensor(i%11,j%3,k%5), broadcast(i,j,k)); - } - } - } -} - - -template <int DataLayout> -static void test_fixed_size_broadcasting() -{ - // Need to add a [] operator to the Size class for this to work -#if 0 - Tensor<float, 1, DataLayout> t1(10); - t1.setRandom(); - TensorFixedSize<float, Sizes<1>, DataLayout> t2; - t2 = t2.constant(20.0f); - - Tensor<float, 1, DataLayout> t3 = t1 + t2.broadcast(Eigen::array<int, 1>{{10}}); - for (int i = 0; i < 10; ++i) { - VERIFY_IS_APPROX(t3(i), t1(i) + t2(0)); - } - - TensorMap<TensorFixedSize<float, Sizes<1>, DataLayout> > t4(t2.data(), {{1}}); - Tensor<float, 1, DataLayout> t5 = t1 + t4.broadcast(Eigen::array<int, 1>{{10}}); - for (int i = 0; i < 10; ++i) { - VERIFY_IS_APPROX(t5(i), t1(i) + t2(0)); - } -#endif -} - - -void test_cxx11_tensor_broadcasting() -{ - CALL_SUBTEST(test_simple_broadcasting<ColMajor>()); - CALL_SUBTEST(test_simple_broadcasting<RowMajor>()); - CALL_SUBTEST(test_vectorized_broadcasting<ColMajor>()); - CALL_SUBTEST(test_vectorized_broadcasting<RowMajor>()); - CALL_SUBTEST(test_static_broadcasting<ColMajor>()); - CALL_SUBTEST(test_static_broadcasting<RowMajor>()); - CALL_SUBTEST(test_fixed_size_broadcasting<ColMajor>()); - CALL_SUBTEST(test_fixed_size_broadcasting<RowMajor>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_cast_float16_cuda.cu b/eigen/unsupported/test/cxx11_tensor_cast_float16_cuda.cu deleted file mode 100644 index 816e032..0000000 --- a/eigen/unsupported/test/cxx11_tensor_cast_float16_cuda.cu +++ /dev/null @@ -1,79 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_NO_COMPLEX -#define EIGEN_TEST_FUNC cxx11_tensor_cast_float16_cuda -#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int -#define EIGEN_USE_GPU - -#include "main.h" -#include <unsupported/Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -void test_cuda_conversion() { - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - int num_elem = 101; - - Tensor<float, 1> floats(num_elem); - floats.setRandom(); - - float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); - Eigen::half* d_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); - float* d_conv = (float*)gpu_device.allocate(num_elem * sizeof(float)); - - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float( - d_float, num_elem); - Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_half( - d_half, num_elem); - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_conv( - d_conv, num_elem); - - gpu_device.memcpyHostToDevice(d_float, floats.data(), num_elem*sizeof(float)); - - gpu_half.device(gpu_device) = gpu_float.cast<Eigen::half>(); - gpu_conv.device(gpu_device) = gpu_half.cast<float>(); - - Tensor<float, 1> initial(num_elem); - Tensor<float, 1> final(num_elem); - gpu_device.memcpyDeviceToHost(initial.data(), d_float, num_elem*sizeof(float)); - gpu_device.memcpyDeviceToHost(final.data(), d_conv, num_elem*sizeof(float)); - gpu_device.synchronize(); - - for (int i = 0; i < num_elem; ++i) { - VERIFY_IS_APPROX(initial(i), final(i)); - } - - gpu_device.deallocate(d_float); - gpu_device.deallocate(d_half); - gpu_device.deallocate(d_conv); -} - - -void test_fallback_conversion() { - int num_elem = 101; - Tensor<float, 1> floats(num_elem); - floats.setRandom(); - - Eigen::Tensor<Eigen::half, 1> halfs = floats.cast<Eigen::half>(); - Eigen::Tensor<float, 1> conv = halfs.cast<float>(); - - for (int i = 0; i < num_elem; ++i) { - VERIFY_IS_APPROX(floats(i), conv(i)); - } -} - - -void test_cxx11_tensor_cast_float16_cuda() -{ - CALL_SUBTEST(test_cuda_conversion()); - CALL_SUBTEST(test_fallback_conversion()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_casts.cpp b/eigen/unsupported/test/cxx11_tensor_casts.cpp deleted file mode 100644 index 3c6d0d2..0000000 --- a/eigen/unsupported/test/cxx11_tensor_casts.cpp +++ /dev/null @@ -1,115 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::array; - -static void test_simple_cast() -{ - Tensor<float, 2> ftensor(20,30); - ftensor = ftensor.random() * 100.f; - Tensor<char, 2> chartensor(20,30); - chartensor.setRandom(); - Tensor<std::complex<float>, 2> cplextensor(20,30); - cplextensor.setRandom(); - - chartensor = ftensor.cast<char>(); - cplextensor = ftensor.cast<std::complex<float> >(); - - for (int i = 0; i < 20; ++i) { - for (int j = 0; j < 30; ++j) { - VERIFY_IS_EQUAL(chartensor(i,j), static_cast<char>(ftensor(i,j))); - VERIFY_IS_EQUAL(cplextensor(i,j), static_cast<std::complex<float> >(ftensor(i,j))); - } - } -} - - -static void test_vectorized_cast() -{ - Tensor<int, 2> itensor(20,30); - itensor = itensor.random() / 1000; - Tensor<float, 2> ftensor(20,30); - ftensor.setRandom(); - Tensor<double, 2> dtensor(20,30); - dtensor.setRandom(); - - ftensor = itensor.cast<float>(); - dtensor = itensor.cast<double>(); - - for (int i = 0; i < 20; ++i) { - for (int j = 0; j < 30; ++j) { - VERIFY_IS_EQUAL(itensor(i,j), static_cast<int>(ftensor(i,j))); - VERIFY_IS_EQUAL(dtensor(i,j), static_cast<double>(ftensor(i,j))); - } - } -} - - -static void test_float_to_int_cast() -{ - Tensor<float, 2> ftensor(20,30); - ftensor = ftensor.random() * 1000.0f; - Tensor<double, 2> dtensor(20,30); - dtensor = dtensor.random() * 1000.0; - - Tensor<int, 2> i1tensor = ftensor.cast<int>(); - Tensor<int, 2> i2tensor = dtensor.cast<int>(); - - for (int i = 0; i < 20; ++i) { - for (int j = 0; j < 30; ++j) { - VERIFY_IS_EQUAL(i1tensor(i,j), static_cast<int>(ftensor(i,j))); - VERIFY_IS_EQUAL(i2tensor(i,j), static_cast<int>(dtensor(i,j))); - } - } -} - - -static void test_big_to_small_type_cast() -{ - Tensor<double, 2> dtensor(20, 30); - dtensor.setRandom(); - Tensor<float, 2> ftensor(20, 30); - ftensor = dtensor.cast<float>(); - - for (int i = 0; i < 20; ++i) { - for (int j = 0; j < 30; ++j) { - VERIFY_IS_APPROX(dtensor(i,j), static_cast<double>(ftensor(i,j))); - } - } -} - - -static void test_small_to_big_type_cast() -{ - Tensor<float, 2> ftensor(20, 30); - ftensor.setRandom(); - Tensor<double, 2> dtensor(20, 30); - dtensor = ftensor.cast<double>(); - - for (int i = 0; i < 20; ++i) { - for (int j = 0; j < 30; ++j) { - VERIFY_IS_APPROX(dtensor(i,j), static_cast<double>(ftensor(i,j))); - } - } -} - - -void test_cxx11_tensor_casts() -{ - CALL_SUBTEST(test_simple_cast()); - CALL_SUBTEST(test_vectorized_cast()); - CALL_SUBTEST(test_float_to_int_cast()); - CALL_SUBTEST(test_big_to_small_type_cast()); - CALL_SUBTEST(test_small_to_big_type_cast()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_chipping.cpp b/eigen/unsupported/test/cxx11_tensor_chipping.cpp deleted file mode 100644 index 1832dec..0000000 --- a/eigen/unsupported/test/cxx11_tensor_chipping.cpp +++ /dev/null @@ -1,425 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -template<int DataLayout> -static void test_simple_chip() -{ - Tensor<float, 5, DataLayout> tensor(2,3,5,7,11); - tensor.setRandom(); - - Tensor<float, 4, DataLayout> chip1; - chip1 = tensor.template chip<0>(1); - - VERIFY_IS_EQUAL(chip1.dimension(0), 3); - VERIFY_IS_EQUAL(chip1.dimension(1), 5); - VERIFY_IS_EQUAL(chip1.dimension(2), 7); - VERIFY_IS_EQUAL(chip1.dimension(3), 11); - - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 5; ++j) { - for (int k = 0; k < 7; ++k) { - for (int l = 0; l < 11; ++l) { - VERIFY_IS_EQUAL(chip1(i,j,k,l), tensor(1,i,j,k,l)); - } - } - } - } - - Tensor<float, 4, DataLayout> chip2 = tensor.template chip<1>(1); - VERIFY_IS_EQUAL(chip2.dimension(0), 2); - VERIFY_IS_EQUAL(chip2.dimension(1), 5); - VERIFY_IS_EQUAL(chip2.dimension(2), 7); - VERIFY_IS_EQUAL(chip2.dimension(3), 11); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - for (int l = 0; l < 11; ++l) { - VERIFY_IS_EQUAL(chip2(i,j,k,l), tensor(i,1,j,k,l)); - } - } - } - } - - Tensor<float, 4, DataLayout> chip3 = tensor.template chip<2>(2); - VERIFY_IS_EQUAL(chip3.dimension(0), 2); - VERIFY_IS_EQUAL(chip3.dimension(1), 3); - VERIFY_IS_EQUAL(chip3.dimension(2), 7); - VERIFY_IS_EQUAL(chip3.dimension(3), 11); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - for (int l = 0; l < 11; ++l) { - VERIFY_IS_EQUAL(chip3(i,j,k,l), tensor(i,j,2,k,l)); - } - } - } - } - - Tensor<float, 4, DataLayout> chip4(tensor.template chip<3>(5)); - VERIFY_IS_EQUAL(chip4.dimension(0), 2); - VERIFY_IS_EQUAL(chip4.dimension(1), 3); - VERIFY_IS_EQUAL(chip4.dimension(2), 5); - VERIFY_IS_EQUAL(chip4.dimension(3), 11); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(chip4(i,j,k,l), tensor(i,j,k,5,l)); - } - } - } - } - - Tensor<float, 4, DataLayout> chip5(tensor.template chip<4>(7)); - VERIFY_IS_EQUAL(chip5.dimension(0), 2); - VERIFY_IS_EQUAL(chip5.dimension(1), 3); - VERIFY_IS_EQUAL(chip5.dimension(2), 5); - VERIFY_IS_EQUAL(chip5.dimension(3), 7); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(chip5(i,j,k,l), tensor(i,j,k,l,7)); - } - } - } - } -} - -template<int DataLayout> -static void test_dynamic_chip() -{ - Tensor<float, 5, DataLayout> tensor(2,3,5,7,11); - tensor.setRandom(); - - Tensor<float, 4, DataLayout> chip1; - chip1 = tensor.chip(1, 0); - VERIFY_IS_EQUAL(chip1.dimension(0), 3); - VERIFY_IS_EQUAL(chip1.dimension(1), 5); - VERIFY_IS_EQUAL(chip1.dimension(2), 7); - VERIFY_IS_EQUAL(chip1.dimension(3), 11); - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 5; ++j) { - for (int k = 0; k < 7; ++k) { - for (int l = 0; l < 11; ++l) { - VERIFY_IS_EQUAL(chip1(i,j,k,l), tensor(1,i,j,k,l)); - } - } - } - } - - Tensor<float, 4, DataLayout> chip2 = tensor.chip(1, 1); - VERIFY_IS_EQUAL(chip2.dimension(0), 2); - VERIFY_IS_EQUAL(chip2.dimension(1), 5); - VERIFY_IS_EQUAL(chip2.dimension(2), 7); - VERIFY_IS_EQUAL(chip2.dimension(3), 11); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - for (int l = 0; l < 11; ++l) { - VERIFY_IS_EQUAL(chip2(i,j,k,l), tensor(i,1,j,k,l)); - } - } - } - } - - Tensor<float, 4, DataLayout> chip3 = tensor.chip(2, 2); - VERIFY_IS_EQUAL(chip3.dimension(0), 2); - VERIFY_IS_EQUAL(chip3.dimension(1), 3); - VERIFY_IS_EQUAL(chip3.dimension(2), 7); - VERIFY_IS_EQUAL(chip3.dimension(3), 11); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - for (int l = 0; l < 11; ++l) { - VERIFY_IS_EQUAL(chip3(i,j,k,l), tensor(i,j,2,k,l)); - } - } - } - } - - Tensor<float, 4, DataLayout> chip4(tensor.chip(5, 3)); - VERIFY_IS_EQUAL(chip4.dimension(0), 2); - VERIFY_IS_EQUAL(chip4.dimension(1), 3); - VERIFY_IS_EQUAL(chip4.dimension(2), 5); - VERIFY_IS_EQUAL(chip4.dimension(3), 11); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(chip4(i,j,k,l), tensor(i,j,k,5,l)); - } - } - } - } - - Tensor<float, 4, DataLayout> chip5(tensor.chip(7, 4)); - VERIFY_IS_EQUAL(chip5.dimension(0), 2); - VERIFY_IS_EQUAL(chip5.dimension(1), 3); - VERIFY_IS_EQUAL(chip5.dimension(2), 5); - VERIFY_IS_EQUAL(chip5.dimension(3), 7); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(chip5(i,j,k,l), tensor(i,j,k,l,7)); - } - } - } - } -} - -template<int DataLayout> -static void test_chip_in_expr() { - Tensor<float, 5, DataLayout> input1(2,3,5,7,11); - input1.setRandom(); - Tensor<float, 4, DataLayout> input2(3,5,7,11); - input2.setRandom(); - - Tensor<float, 4, DataLayout> result = input1.template chip<0>(0) + input2; - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 5; ++j) { - for (int k = 0; k < 7; ++k) { - for (int l = 0; l < 11; ++l) { - float expected = input1(0,i,j,k,l) + input2(i,j,k,l); - VERIFY_IS_EQUAL(result(i,j,k,l), expected); - } - } - } - } - - Tensor<float, 3, DataLayout> input3(3,7,11); - input3.setRandom(); - Tensor<float, 3, DataLayout> result2 = input1.template chip<0>(0).template chip<1>(2) + input3; - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 7; ++j) { - for (int k = 0; k < 11; ++k) { - float expected = input1(0,i,2,j,k) + input3(i,j,k); - VERIFY_IS_EQUAL(result2(i,j,k), expected); - } - } - } -} - -template<int DataLayout> -static void test_chip_as_lvalue() -{ - Tensor<float, 5, DataLayout> input1(2,3,5,7,11); - input1.setRandom(); - - Tensor<float, 4, DataLayout> input2(3,5,7,11); - input2.setRandom(); - Tensor<float, 5, DataLayout> tensor = input1; - tensor.template chip<0>(1) = input2; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - for (int m = 0; m < 11; ++m) { - if (i != 1) { - VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m)); - } else { - VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input2(j,k,l,m)); - } - } - } - } - } - } - - Tensor<float, 4, DataLayout> input3(2,5,7,11); - input3.setRandom(); - tensor = input1; - tensor.template chip<1>(1) = input3; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - for (int m = 0; m < 11; ++m) { - if (j != 1) { - VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m)); - } else { - VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input3(i,k,l,m)); - } - } - } - } - } - } - - Tensor<float, 4, DataLayout> input4(2,3,7,11); - input4.setRandom(); - tensor = input1; - tensor.template chip<2>(3) = input4; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - for (int m = 0; m < 11; ++m) { - if (k != 3) { - VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m)); - } else { - VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input4(i,j,l,m)); - } - } - } - } - } - } - - Tensor<float, 4, DataLayout> input5(2,3,5,11); - input5.setRandom(); - tensor = input1; - tensor.template chip<3>(4) = input5; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - for (int m = 0; m < 11; ++m) { - if (l != 4) { - VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m)); - } else { - VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input5(i,j,k,m)); - } - } - } - } - } - } - - Tensor<float, 4, DataLayout> input6(2,3,5,7); - input6.setRandom(); - tensor = input1; - tensor.template chip<4>(5) = input6; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - for (int m = 0; m < 11; ++m) { - if (m != 5) { - VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m)); - } else { - VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input6(i,j,k,l)); - } - } - } - } - } - } - - Tensor<float, 5, DataLayout> input7(2,3,5,7,11); - input7.setRandom(); - tensor = input1; - tensor.chip(0, 0) = input7.chip(0, 0); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - for (int m = 0; m < 11; ++m) { - if (i != 0) { - VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m)); - } else { - VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input7(i,j,k,l,m)); - } - } - } - } - } - } -} - -static void test_chip_raw_data_col_major() -{ - Tensor<float, 5, ColMajor> tensor(2,3,5,7,11); - tensor.setRandom(); - - typedef TensorEvaluator<decltype(tensor.chip<4>(3)), DefaultDevice> Evaluator4; - auto chip = Evaluator4(tensor.chip<4>(3), DefaultDevice()); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - int chip_index = i + 2 * (j + 3 * (k + 5 * l)); - VERIFY_IS_EQUAL(chip.data()[chip_index], tensor(i,j,k,l,3)); - } - } - } - } - - typedef TensorEvaluator<decltype(tensor.chip<0>(0)), DefaultDevice> Evaluator0; - auto chip0 = Evaluator0(tensor.chip<0>(0), DefaultDevice()); - VERIFY_IS_EQUAL(chip0.data(), static_cast<float*>(0)); - - typedef TensorEvaluator<decltype(tensor.chip<1>(0)), DefaultDevice> Evaluator1; - auto chip1 = Evaluator1(tensor.chip<1>(0), DefaultDevice()); - VERIFY_IS_EQUAL(chip1.data(), static_cast<float*>(0)); - - typedef TensorEvaluator<decltype(tensor.chip<2>(0)), DefaultDevice> Evaluator2; - auto chip2 = Evaluator2(tensor.chip<2>(0), DefaultDevice()); - VERIFY_IS_EQUAL(chip2.data(), static_cast<float*>(0)); - - typedef TensorEvaluator<decltype(tensor.chip<3>(0)), DefaultDevice> Evaluator3; - auto chip3 = Evaluator3(tensor.chip<3>(0), DefaultDevice()); - VERIFY_IS_EQUAL(chip3.data(), static_cast<float*>(0)); -} - -static void test_chip_raw_data_row_major() -{ - Tensor<float, 5, RowMajor> tensor(11,7,5,3,2); - tensor.setRandom(); - - typedef TensorEvaluator<decltype(tensor.chip<0>(3)), DefaultDevice> Evaluator0; - auto chip = Evaluator0(tensor.chip<0>(3), DefaultDevice()); - for (int i = 0; i < 7; ++i) { - for (int j = 0; j < 5; ++j) { - for (int k = 0; k < 3; ++k) { - for (int l = 0; l < 2; ++l) { - int chip_index = l + 2 * (k + 3 * (j + 5 * i)); - VERIFY_IS_EQUAL(chip.data()[chip_index], tensor(3,i,j,k,l)); - } - } - } - } - - typedef TensorEvaluator<decltype(tensor.chip<1>(0)), DefaultDevice> Evaluator1; - auto chip1 = Evaluator1(tensor.chip<1>(0), DefaultDevice()); - VERIFY_IS_EQUAL(chip1.data(), static_cast<float*>(0)); - - typedef TensorEvaluator<decltype(tensor.chip<2>(0)), DefaultDevice> Evaluator2; - auto chip2 = Evaluator2(tensor.chip<2>(0), DefaultDevice()); - VERIFY_IS_EQUAL(chip2.data(), static_cast<float*>(0)); - - typedef TensorEvaluator<decltype(tensor.chip<3>(0)), DefaultDevice> Evaluator3; - auto chip3 = Evaluator3(tensor.chip<3>(0), DefaultDevice()); - VERIFY_IS_EQUAL(chip3.data(), static_cast<float*>(0)); - - typedef TensorEvaluator<decltype(tensor.chip<4>(0)), DefaultDevice> Evaluator4; - auto chip4 = Evaluator4(tensor.chip<4>(0), DefaultDevice()); - VERIFY_IS_EQUAL(chip4.data(), static_cast<float*>(0)); -} - -void test_cxx11_tensor_chipping() -{ - CALL_SUBTEST(test_simple_chip<ColMajor>()); - CALL_SUBTEST(test_simple_chip<RowMajor>()); - CALL_SUBTEST(test_dynamic_chip<ColMajor>()); - CALL_SUBTEST(test_dynamic_chip<RowMajor>()); - CALL_SUBTEST(test_chip_in_expr<ColMajor>()); - CALL_SUBTEST(test_chip_in_expr<RowMajor>()); - CALL_SUBTEST(test_chip_as_lvalue<ColMajor>()); - CALL_SUBTEST(test_chip_as_lvalue<RowMajor>()); - CALL_SUBTEST(test_chip_raw_data_col_major()); - CALL_SUBTEST(test_chip_raw_data_row_major()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_comparisons.cpp b/eigen/unsupported/test/cxx11_tensor_comparisons.cpp deleted file mode 100644 index b1ff8ae..0000000 --- a/eigen/unsupported/test/cxx11_tensor_comparisons.cpp +++ /dev/null @@ -1,84 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::RowMajor; - -static void test_orderings() -{ - Tensor<float, 3> mat1(2,3,7); - Tensor<float, 3> mat2(2,3,7); - Tensor<bool, 3> lt(2,3,7); - Tensor<bool, 3> le(2,3,7); - Tensor<bool, 3> gt(2,3,7); - Tensor<bool, 3> ge(2,3,7); - - mat1.setRandom(); - mat2.setRandom(); - - lt = mat1 < mat2; - le = mat1 <= mat2; - gt = mat1 > mat2; - ge = mat1 >= mat2; - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL(lt(i,j,k), mat1(i,j,k) < mat2(i,j,k)); - VERIFY_IS_EQUAL(le(i,j,k), mat1(i,j,k) <= mat2(i,j,k)); - VERIFY_IS_EQUAL(gt(i,j,k), mat1(i,j,k) > mat2(i,j,k)); - VERIFY_IS_EQUAL(ge(i,j,k), mat1(i,j,k) >= mat2(i,j,k)); - } - } - } -} - - -static void test_equality() -{ - Tensor<float, 3> mat1(2,3,7); - Tensor<float, 3> mat2(2,3,7); - - mat1.setRandom(); - mat2.setRandom(); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - if (internal::random<bool>()) { - mat2(i,j,k) = mat1(i,j,k); - } - } - } - } - - Tensor<bool, 3> eq(2,3,7); - Tensor<bool, 3> ne(2,3,7); - eq = (mat1 == mat2); - ne = (mat1 != mat2); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL(eq(i,j,k), mat1(i,j,k) == mat2(i,j,k)); - VERIFY_IS_EQUAL(ne(i,j,k), mat1(i,j,k) != mat2(i,j,k)); - } - } - } -} - - -void test_cxx11_tensor_comparisons() -{ - CALL_SUBTEST(test_orderings()); - CALL_SUBTEST(test_equality()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_complex_cuda.cu b/eigen/unsupported/test/cxx11_tensor_complex_cuda.cu deleted file mode 100644 index 916f12a..0000000 --- a/eigen/unsupported/test/cxx11_tensor_complex_cuda.cu +++ /dev/null @@ -1,150 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_FUNC cxx11_tensor_complex -#define EIGEN_USE_GPU - -#include "main.h" -#include <unsupported/Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -void test_cuda_nullary() { - Tensor<std::complex<float>, 1, 0, int> in1(2); - Tensor<std::complex<float>, 1, 0, int> in2(2); - in1.setRandom(); - in2.setRandom(); - - std::size_t float_bytes = in1.size() * sizeof(float); - std::size_t complex_bytes = in1.size() * sizeof(std::complex<float>); - - std::complex<float>* d_in1; - std::complex<float>* d_in2; - float* d_out2; - cudaMalloc((void**)(&d_in1), complex_bytes); - cudaMalloc((void**)(&d_in2), complex_bytes); - cudaMalloc((void**)(&d_out2), float_bytes); - cudaMemcpy(d_in1, in1.data(), complex_bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_in2, in2.data(), complex_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<std::complex<float>, 1, 0, int>, Eigen::Aligned> gpu_in1( - d_in1, 2); - Eigen::TensorMap<Eigen::Tensor<std::complex<float>, 1, 0, int>, Eigen::Aligned> gpu_in2( - d_in2, 2); - Eigen::TensorMap<Eigen::Tensor<float, 1, 0, int>, Eigen::Aligned> gpu_out2( - d_out2, 2); - - gpu_in1.device(gpu_device) = gpu_in1.constant(std::complex<float>(3.14f, 2.7f)); - gpu_out2.device(gpu_device) = gpu_in2.abs(); - - Tensor<std::complex<float>, 1, 0, int> new1(2); - Tensor<float, 1, 0, int> new2(2); - - assert(cudaMemcpyAsync(new1.data(), d_in1, complex_bytes, cudaMemcpyDeviceToHost, - gpu_device.stream()) == cudaSuccess); - assert(cudaMemcpyAsync(new2.data(), d_out2, float_bytes, cudaMemcpyDeviceToHost, - gpu_device.stream()) == cudaSuccess); - - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 2; ++i) { - VERIFY_IS_APPROX(new1(i), std::complex<float>(3.14f, 2.7f)); - VERIFY_IS_APPROX(new2(i), std::abs(in2(i))); - } - - cudaFree(d_in1); - cudaFree(d_in2); - cudaFree(d_out2); -} - - -static void test_cuda_sum_reductions() { - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - const int num_rows = internal::random<int>(1024, 5*1024); - const int num_cols = internal::random<int>(1024, 5*1024); - - Tensor<std::complex<float>, 2> in(num_rows, num_cols); - in.setRandom(); - - Tensor<std::complex<float>, 0> full_redux; - full_redux = in.sum(); - - std::size_t in_bytes = in.size() * sizeof(std::complex<float>); - std::size_t out_bytes = full_redux.size() * sizeof(std::complex<float>); - std::complex<float>* gpu_in_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(in_bytes)); - std::complex<float>* gpu_out_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(out_bytes)); - gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes); - - TensorMap<Tensor<std::complex<float>, 2> > in_gpu(gpu_in_ptr, num_rows, num_cols); - TensorMap<Tensor<std::complex<float>, 0> > out_gpu(gpu_out_ptr); - - out_gpu.device(gpu_device) = in_gpu.sum(); - - Tensor<std::complex<float>, 0> full_redux_gpu; - gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes); - gpu_device.synchronize(); - - // Check that the CPU and GPU reductions return the same result. - VERIFY_IS_APPROX(full_redux(), full_redux_gpu()); - - gpu_device.deallocate(gpu_in_ptr); - gpu_device.deallocate(gpu_out_ptr); -} - - -static void test_cuda_product_reductions() { - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - const int num_rows = internal::random<int>(1024, 5*1024); - const int num_cols = internal::random<int>(1024, 5*1024); - - Tensor<std::complex<float>, 2> in(num_rows, num_cols); - in.setRandom(); - - Tensor<std::complex<float>, 0> full_redux; - full_redux = in.prod(); - - std::size_t in_bytes = in.size() * sizeof(std::complex<float>); - std::size_t out_bytes = full_redux.size() * sizeof(std::complex<float>); - std::complex<float>* gpu_in_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(in_bytes)); - std::complex<float>* gpu_out_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(out_bytes)); - gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes); - - TensorMap<Tensor<std::complex<float>, 2> > in_gpu(gpu_in_ptr, num_rows, num_cols); - TensorMap<Tensor<std::complex<float>, 0> > out_gpu(gpu_out_ptr); - - out_gpu.device(gpu_device) = in_gpu.prod(); - - Tensor<std::complex<float>, 0> full_redux_gpu; - gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes); - gpu_device.synchronize(); - - // Check that the CPU and GPU reductions return the same result. - VERIFY_IS_APPROX(full_redux(), full_redux_gpu()); - - gpu_device.deallocate(gpu_in_ptr); - gpu_device.deallocate(gpu_out_ptr); -} - - -void test_cxx11_tensor_complex() -{ - CALL_SUBTEST(test_cuda_nullary()); - CALL_SUBTEST(test_cuda_sum_reductions()); - CALL_SUBTEST(test_cuda_product_reductions()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_complex_cwise_ops_cuda.cu b/eigen/unsupported/test/cxx11_tensor_complex_cwise_ops_cuda.cu deleted file mode 100644 index aac7809..0000000 --- a/eigen/unsupported/test/cxx11_tensor_complex_cwise_ops_cuda.cu +++ /dev/null @@ -1,94 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_FUNC cxx11_tensor_complex_cwise_ops -#define EIGEN_USE_GPU - -#include "main.h" -#include <unsupported/Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -template<typename T> -void test_cuda_complex_cwise_ops() { - const int kNumItems = 2; - std::size_t complex_bytes = kNumItems * sizeof(std::complex<T>); - - std::complex<T>* d_in1; - std::complex<T>* d_in2; - std::complex<T>* d_out; - cudaMalloc((void**)(&d_in1), complex_bytes); - cudaMalloc((void**)(&d_in2), complex_bytes); - cudaMalloc((void**)(&d_out), complex_bytes); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<std::complex<T>, 1, 0, int>, Eigen::Aligned> gpu_in1( - d_in1, kNumItems); - Eigen::TensorMap<Eigen::Tensor<std::complex<T>, 1, 0, int>, Eigen::Aligned> gpu_in2( - d_in2, kNumItems); - Eigen::TensorMap<Eigen::Tensor<std::complex<T>, 1, 0, int>, Eigen::Aligned> gpu_out( - d_out, kNumItems); - - const std::complex<T> a(3.14f, 2.7f); - const std::complex<T> b(-10.6f, 1.4f); - - gpu_in1.device(gpu_device) = gpu_in1.constant(a); - gpu_in2.device(gpu_device) = gpu_in2.constant(b); - - enum CwiseOp { - Add = 0, - Sub, - Mul, - Div - }; - - Tensor<std::complex<T>, 1, 0, int> actual(kNumItems); - for (int op = Add; op <= Div; op++) { - std::complex<T> expected; - switch (static_cast<CwiseOp>(op)) { - case Add: - gpu_out.device(gpu_device) = gpu_in1 + gpu_in2; - expected = a + b; - break; - case Sub: - gpu_out.device(gpu_device) = gpu_in1 - gpu_in2; - expected = a - b; - break; - case Mul: - gpu_out.device(gpu_device) = gpu_in1 * gpu_in2; - expected = a * b; - break; - case Div: - gpu_out.device(gpu_device) = gpu_in1 / gpu_in2; - expected = a / b; - break; - } - assert(cudaMemcpyAsync(actual.data(), d_out, complex_bytes, cudaMemcpyDeviceToHost, - gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < kNumItems; ++i) { - VERIFY_IS_APPROX(actual(i), expected); - } - } - - cudaFree(d_in1); - cudaFree(d_in2); - cudaFree(d_out); -} - - -void test_cxx11_tensor_complex_cwise_ops() -{ - CALL_SUBTEST(test_cuda_complex_cwise_ops<float>()); - CALL_SUBTEST(test_cuda_complex_cwise_ops<double>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_concatenation.cpp b/eigen/unsupported/test/cxx11_tensor_concatenation.cpp deleted file mode 100644 index 03ef12e..0000000 --- a/eigen/unsupported/test/cxx11_tensor_concatenation.cpp +++ /dev/null @@ -1,137 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -template<int DataLayout> -static void test_dimension_failures() -{ - Tensor<int, 3, DataLayout> left(2, 3, 1); - Tensor<int, 3, DataLayout> right(3, 3, 1); - left.setRandom(); - right.setRandom(); - - // Okay; other dimensions are equal. - Tensor<int, 3, DataLayout> concatenation = left.concatenate(right, 0); - - // Dimension mismatches. - VERIFY_RAISES_ASSERT(concatenation = left.concatenate(right, 1)); - VERIFY_RAISES_ASSERT(concatenation = left.concatenate(right, 2)); - - // Axis > NumDims or < 0. - VERIFY_RAISES_ASSERT(concatenation = left.concatenate(right, 3)); - VERIFY_RAISES_ASSERT(concatenation = left.concatenate(right, -1)); -} - -template<int DataLayout> -static void test_static_dimension_failure() -{ - Tensor<int, 2, DataLayout> left(2, 3); - Tensor<int, 3, DataLayout> right(2, 3, 1); - -#ifdef CXX11_TENSOR_CONCATENATION_STATIC_DIMENSION_FAILURE - // Technically compatible, but we static assert that the inputs have same - // NumDims. - Tensor<int, 3, DataLayout> concatenation = left.concatenate(right, 0); -#endif - - // This can be worked around in this case. - Tensor<int, 3, DataLayout> concatenation = left - .reshape(Tensor<int, 3>::Dimensions(2, 3, 1)) - .concatenate(right, 0); - Tensor<int, 2, DataLayout> alternative = left - .concatenate(right.reshape(Tensor<int, 2>::Dimensions{{{2, 3}}}), 0); -} - -template<int DataLayout> -static void test_simple_concatenation() -{ - Tensor<int, 3, DataLayout> left(2, 3, 1); - Tensor<int, 3, DataLayout> right(2, 3, 1); - left.setRandom(); - right.setRandom(); - - Tensor<int, 3, DataLayout> concatenation = left.concatenate(right, 0); - VERIFY_IS_EQUAL(concatenation.dimension(0), 4); - VERIFY_IS_EQUAL(concatenation.dimension(1), 3); - VERIFY_IS_EQUAL(concatenation.dimension(2), 1); - for (int j = 0; j < 3; ++j) { - for (int i = 0; i < 2; ++i) { - VERIFY_IS_EQUAL(concatenation(i, j, 0), left(i, j, 0)); - } - for (int i = 2; i < 4; ++i) { - VERIFY_IS_EQUAL(concatenation(i, j, 0), right(i - 2, j, 0)); - } - } - - concatenation = left.concatenate(right, 1); - VERIFY_IS_EQUAL(concatenation.dimension(0), 2); - VERIFY_IS_EQUAL(concatenation.dimension(1), 6); - VERIFY_IS_EQUAL(concatenation.dimension(2), 1); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - VERIFY_IS_EQUAL(concatenation(i, j, 0), left(i, j, 0)); - } - for (int j = 3; j < 6; ++j) { - VERIFY_IS_EQUAL(concatenation(i, j, 0), right(i, j - 3, 0)); - } - } - - concatenation = left.concatenate(right, 2); - VERIFY_IS_EQUAL(concatenation.dimension(0), 2); - VERIFY_IS_EQUAL(concatenation.dimension(1), 3); - VERIFY_IS_EQUAL(concatenation.dimension(2), 2); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - VERIFY_IS_EQUAL(concatenation(i, j, 0), left(i, j, 0)); - VERIFY_IS_EQUAL(concatenation(i, j, 1), right(i, j, 0)); - } - } -} - - -// TODO(phli): Add test once we have a real vectorized implementation. -// static void test_vectorized_concatenation() {} - -static void test_concatenation_as_lvalue() -{ - Tensor<int, 2> t1(2, 3); - Tensor<int, 2> t2(2, 3); - t1.setRandom(); - t2.setRandom(); - - Tensor<int, 2> result(4, 3); - result.setRandom(); - t1.concatenate(t2, 0) = result; - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - VERIFY_IS_EQUAL(t1(i, j), result(i, j)); - VERIFY_IS_EQUAL(t2(i, j), result(i+2, j)); - } - } -} - - -void test_cxx11_tensor_concatenation() -{ - CALL_SUBTEST(test_dimension_failures<ColMajor>()); - CALL_SUBTEST(test_dimension_failures<RowMajor>()); - CALL_SUBTEST(test_static_dimension_failure<ColMajor>()); - CALL_SUBTEST(test_static_dimension_failure<RowMajor>()); - CALL_SUBTEST(test_simple_concatenation<ColMajor>()); - CALL_SUBTEST(test_simple_concatenation<RowMajor>()); - // CALL_SUBTEST(test_vectorized_concatenation()); - CALL_SUBTEST(test_concatenation_as_lvalue()); - -} diff --git a/eigen/unsupported/test/cxx11_tensor_const.cpp b/eigen/unsupported/test/cxx11_tensor_const.cpp deleted file mode 100644 index ad9c9da..0000000 --- a/eigen/unsupported/test/cxx11_tensor_const.cpp +++ /dev/null @@ -1,62 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> -using Eigen::Tensor; - - -static void test_simple_assign() -{ - Tensor<int, 3> random(2,3,7); - random.setRandom(); - - TensorMap<Tensor<const int, 3> > constant(random.data(), 2, 3, 7); - Tensor<int, 3> result(2,3,7); - result = constant; - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL((result(i,j,k)), random(i,j,k)); - } - } - } -} - - -static void test_assign_of_const_tensor() -{ - Tensor<int, 3> random(2,3,7); - random.setRandom(); - - TensorMap<Tensor<const int, 3> > constant1(random.data(), 2, 3, 7); - TensorMap<const Tensor<int, 3> > constant2(random.data(), 2, 3, 7); - const TensorMap<Tensor<int, 3> > constant3(random.data(), 2, 3, 7); - - Tensor<int, 2> result1 = constant1.chip(0, 2); - Tensor<int, 2> result2 = constant2.chip(0, 2); - Tensor<int, 2> result3 = constant3.chip(0, 2); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - VERIFY_IS_EQUAL((result1(i,j)), random(i,j,0)); - VERIFY_IS_EQUAL((result2(i,j)), random(i,j,0)); - VERIFY_IS_EQUAL((result3(i,j)), random(i,j,0)); - } - } -} - - -void test_cxx11_tensor_const() -{ - CALL_SUBTEST(test_simple_assign()); - CALL_SUBTEST(test_assign_of_const_tensor()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_contract_cuda.cu b/eigen/unsupported/test/cxx11_tensor_contract_cuda.cu deleted file mode 100644 index e821ccf..0000000 --- a/eigen/unsupported/test/cxx11_tensor_contract_cuda.cu +++ /dev/null @@ -1,213 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// Copyright (C) 2014 Navdeep Jaitly <ndjaitly@google.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_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_NO_COMPLEX -#define EIGEN_TEST_FUNC cxx11_tensor_cuda -#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int -#define EIGEN_USE_GPU - -#include "main.h" -#include <unsupported/Eigen/CXX11/Tensor> - -using Eigen::Tensor; -typedef Tensor<float, 1>::DimensionPair DimPair; - -template<int DataLayout> -void test_cuda_contraction(int m_size, int k_size, int n_size) -{ - std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << ")" << std::endl; - // with these dimensions, the output has 300 * 140 elements, which is - // more than 30 * 1024, which is the number of threads in blocks on - // a 15 SM GK110 GPU - Tensor<float, 2, DataLayout> t_left(m_size, k_size); - Tensor<float, 2, DataLayout> t_right(k_size, n_size); - Tensor<float, 2, DataLayout> t_result(m_size, n_size); - Tensor<float, 2, DataLayout> t_result_gpu(m_size, n_size); - Eigen::array<DimPair, 1> dims(DimPair(1, 0)); - - t_left.setRandom(); - t_right.setRandom(); - - std::size_t t_left_bytes = t_left.size() * sizeof(float); - std::size_t t_right_bytes = t_right.size() * sizeof(float); - std::size_t t_result_bytes = t_result.size() * sizeof(float); - - float* d_t_left; - float* d_t_right; - float* d_t_result; - - cudaMalloc((void**)(&d_t_left), t_left_bytes); - cudaMalloc((void**)(&d_t_right), t_right_bytes); - cudaMalloc((void**)(&d_t_result), t_result_bytes); - - cudaMemcpy(d_t_left, t_left.data(), t_left_bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_t_right, t_right.data(), t_right_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > - gpu_t_left(d_t_left, Eigen::array<int, 2>(m_size, k_size)); - Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > - gpu_t_right(d_t_right, Eigen::array<int, 2>(k_size, n_size)); - Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > - gpu_t_result(d_t_result, Eigen::array<int, 2>(m_size, n_size)); - - - gpu_t_result.device(gpu_device) = gpu_t_left.contract(gpu_t_right, dims); - t_result = t_left.contract(t_right, dims); - - cudaMemcpy(t_result_gpu.data(), d_t_result, t_result_bytes, cudaMemcpyDeviceToHost); - for (DenseIndex i = 0; i < t_result.size(); i++) { - if (fabs(t_result(i) - t_result_gpu(i)) < 1e-4f) { - continue; - } - if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), 1e-4f)) { - continue; - } - std::cout << "mismatch detected at index " << i << ": " << t_result(i) - << " vs " << t_result_gpu(i) << std::endl; - assert(false); - } - - cudaFree((void*)d_t_left); - cudaFree((void*)d_t_right); - cudaFree((void*)d_t_result); -} - - -template<int DataLayout> -void test_scalar(int m_size, int k_size, int n_size) -{ - std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << ")" << std::endl; - // with these dimensions, the output has 300 * 140 elements, which is - // more than 30 * 1024, which is the number of threads in blocks on - // a 15 SM GK110 GPU - Tensor<float, 2, DataLayout> t_left(m_size, k_size); - Tensor<float, 2, DataLayout> t_right(k_size, n_size); - Tensor<float, 0, DataLayout> t_result; - Tensor<float, 0, DataLayout> t_result_gpu; - Eigen::array<DimPair, 2> dims(DimPair(0, 0), DimPair(1, 1)); - - t_left.setRandom(); - t_right.setRandom(); - - std::size_t t_left_bytes = t_left.size() * sizeof(float); - std::size_t t_right_bytes = t_right.size() * sizeof(float); - std::size_t t_result_bytes = sizeof(float); - - float* d_t_left; - float* d_t_right; - float* d_t_result; - - cudaMalloc((void**)(&d_t_left), t_left_bytes); - cudaMalloc((void**)(&d_t_right), t_right_bytes); - cudaMalloc((void**)(&d_t_result), t_result_bytes); - - cudaMemcpy(d_t_left, t_left.data(), t_left_bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_t_right, t_right.data(), t_right_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > - gpu_t_left(d_t_left, m_size, k_size); - Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > - gpu_t_right(d_t_right, k_size, n_size); - Eigen::TensorMap<Eigen::Tensor<float, 0, DataLayout> > - gpu_t_result(d_t_result); - - gpu_t_result.device(gpu_device) = gpu_t_left.contract(gpu_t_right, dims); - t_result = t_left.contract(t_right, dims); - - cudaMemcpy(t_result_gpu.data(), d_t_result, t_result_bytes, cudaMemcpyDeviceToHost); - if (fabs(t_result() - t_result_gpu()) > 1e-4f && - !Eigen::internal::isApprox(t_result(), t_result_gpu(), 1e-4f)) { - std::cout << "mismatch detected: " << t_result() - << " vs " << t_result_gpu() << std::endl; - assert(false); - } - - cudaFree((void*)d_t_left); - cudaFree((void*)d_t_right); - cudaFree((void*)d_t_result); -} - - -template<int DataLayout> -void test_cuda_contraction_m() { - for (int k = 32; k < 256; k++) { - test_cuda_contraction<ColMajor>(k, 128, 128); - test_cuda_contraction<RowMajor>(k, 128, 128); - } -} - -template<int DataLayout> -void test_cuda_contraction_k() { - for (int k = 32; k < 256; k++) { - test_cuda_contraction<ColMajor>(128, k, 128); - test_cuda_contraction<RowMajor>(128, k, 128); - } -} - -template<int DataLayout> -void test_cuda_contraction_n() { - for (int k = 32; k < 256; k++) { - test_cuda_contraction<ColMajor>(128, 128, k); - test_cuda_contraction<RowMajor>(128, 128, k); - } -} - - -template<int DataLayout> -void test_cuda_contraction_sizes() { - int m_sizes[] = { 31, 39, 63, 64, 65, - 127, 129, 255, 257 , 511, - 512, 513, 1023, 1024, 1025}; - - int n_sizes[] = { 31, 39, 63, 64, 65, - 127, 129, 255, 257, 511, - 512, 513, 1023, 1024, 1025}; - - int k_sizes[] = { 31, 39, 63, 64, 65, - 95, 96, 127, 129, 255, - 257, 511, 512, 513, 1023, - 1024, 1025}; - - for (int i = 0; i < 15; i++) { - for (int j = 0; j < 15; j++) { - for (int k = 0; k < 17; k++) { - test_cuda_contraction<DataLayout>(m_sizes[i], n_sizes[j], k_sizes[k]); - } - } - } -} - -void test_cxx11_tensor_cuda() -{ - CALL_SUBTEST_1(test_cuda_contraction<ColMajor>(128, 128, 128)); - CALL_SUBTEST_1(test_cuda_contraction<RowMajor>(128, 128, 128)); - - CALL_SUBTEST_1(test_scalar<ColMajor>(128, 128, 128)); - CALL_SUBTEST_1(test_scalar<RowMajor>(128, 128, 128)); - - CALL_SUBTEST_2(test_cuda_contraction_m<ColMajor>()); - CALL_SUBTEST_3(test_cuda_contraction_m<RowMajor>()); - - CALL_SUBTEST_4(test_cuda_contraction_k<ColMajor>()); - CALL_SUBTEST_5(test_cuda_contraction_k<RowMajor>()); - - CALL_SUBTEST_6(test_cuda_contraction_n<ColMajor>()); - CALL_SUBTEST_7(test_cuda_contraction_n<RowMajor>()); - - CALL_SUBTEST_8(test_cuda_contraction_sizes<ColMajor>()); - CALL_SUBTEST_9(test_cuda_contraction_sizes<RowMajor>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_contraction.cpp b/eigen/unsupported/test/cxx11_tensor_contraction.cpp deleted file mode 100644 index ace9705..0000000 --- a/eigen/unsupported/test/cxx11_tensor_contraction.cpp +++ /dev/null @@ -1,545 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::DefaultDevice; -using Eigen::Tensor; - -typedef Tensor<float, 1>::DimensionPair DimPair; - -template<int DataLayout> -static void test_evals() -{ - Tensor<float, 2, DataLayout> mat1(2, 3); - Tensor<float, 2, DataLayout> mat2(2, 3); - Tensor<float, 2, DataLayout> mat3(3, 2); - - mat1.setRandom(); - mat2.setRandom(); - mat3.setRandom(); - - Tensor<float, 2, DataLayout> mat4(3,3); - mat4.setZero(); - Eigen::array<DimPair, 1> dims3 = {{DimPair(0, 0)}}; - typedef TensorEvaluator<decltype(mat1.contract(mat2, dims3)), DefaultDevice> Evaluator; - Evaluator eval(mat1.contract(mat2, dims3), DefaultDevice()); - eval.evalTo(mat4.data()); - EIGEN_STATIC_ASSERT(Evaluator::NumDims==2ul, YOU_MADE_A_PROGRAMMING_MISTAKE); - VERIFY_IS_EQUAL(eval.dimensions()[0], 3); - VERIFY_IS_EQUAL(eval.dimensions()[1], 3); - - VERIFY_IS_APPROX(mat4(0,0), mat1(0,0)*mat2(0,0) + mat1(1,0)*mat2(1,0)); - VERIFY_IS_APPROX(mat4(0,1), mat1(0,0)*mat2(0,1) + mat1(1,0)*mat2(1,1)); - VERIFY_IS_APPROX(mat4(0,2), mat1(0,0)*mat2(0,2) + mat1(1,0)*mat2(1,2)); - VERIFY_IS_APPROX(mat4(1,0), mat1(0,1)*mat2(0,0) + mat1(1,1)*mat2(1,0)); - VERIFY_IS_APPROX(mat4(1,1), mat1(0,1)*mat2(0,1) + mat1(1,1)*mat2(1,1)); - VERIFY_IS_APPROX(mat4(1,2), mat1(0,1)*mat2(0,2) + mat1(1,1)*mat2(1,2)); - VERIFY_IS_APPROX(mat4(2,0), mat1(0,2)*mat2(0,0) + mat1(1,2)*mat2(1,0)); - VERIFY_IS_APPROX(mat4(2,1), mat1(0,2)*mat2(0,1) + mat1(1,2)*mat2(1,1)); - VERIFY_IS_APPROX(mat4(2,2), mat1(0,2)*mat2(0,2) + mat1(1,2)*mat2(1,2)); - - Tensor<float, 2, DataLayout> mat5(2,2); - mat5.setZero(); - Eigen::array<DimPair, 1> dims4 = {{DimPair(1, 1)}}; - typedef TensorEvaluator<decltype(mat1.contract(mat2, dims4)), DefaultDevice> Evaluator2; - Evaluator2 eval2(mat1.contract(mat2, dims4), DefaultDevice()); - eval2.evalTo(mat5.data()); - EIGEN_STATIC_ASSERT(Evaluator2::NumDims==2ul, YOU_MADE_A_PROGRAMMING_MISTAKE); - VERIFY_IS_EQUAL(eval2.dimensions()[0], 2); - VERIFY_IS_EQUAL(eval2.dimensions()[1], 2); - - VERIFY_IS_APPROX(mat5(0,0), mat1(0,0)*mat2(0,0) + mat1(0,1)*mat2(0,1) + mat1(0,2)*mat2(0,2)); - VERIFY_IS_APPROX(mat5(0,1), mat1(0,0)*mat2(1,0) + mat1(0,1)*mat2(1,1) + mat1(0,2)*mat2(1,2)); - VERIFY_IS_APPROX(mat5(1,0), mat1(1,0)*mat2(0,0) + mat1(1,1)*mat2(0,1) + mat1(1,2)*mat2(0,2)); - VERIFY_IS_APPROX(mat5(1,1), mat1(1,0)*mat2(1,0) + mat1(1,1)*mat2(1,1) + mat1(1,2)*mat2(1,2)); - - Tensor<float, 2, DataLayout> mat6(2,2); - mat6.setZero(); - Eigen::array<DimPair, 1> dims6 = {{DimPair(1, 0)}}; - typedef TensorEvaluator<decltype(mat1.contract(mat3, dims6)), DefaultDevice> Evaluator3; - Evaluator3 eval3(mat1.contract(mat3, dims6), DefaultDevice()); - eval3.evalTo(mat6.data()); - EIGEN_STATIC_ASSERT(Evaluator3::NumDims==2ul, YOU_MADE_A_PROGRAMMING_MISTAKE); - VERIFY_IS_EQUAL(eval3.dimensions()[0], 2); - VERIFY_IS_EQUAL(eval3.dimensions()[1], 2); - - VERIFY_IS_APPROX(mat6(0,0), mat1(0,0)*mat3(0,0) + mat1(0,1)*mat3(1,0) + mat1(0,2)*mat3(2,0)); - VERIFY_IS_APPROX(mat6(0,1), mat1(0,0)*mat3(0,1) + mat1(0,1)*mat3(1,1) + mat1(0,2)*mat3(2,1)); - VERIFY_IS_APPROX(mat6(1,0), mat1(1,0)*mat3(0,0) + mat1(1,1)*mat3(1,0) + mat1(1,2)*mat3(2,0)); - VERIFY_IS_APPROX(mat6(1,1), mat1(1,0)*mat3(0,1) + mat1(1,1)*mat3(1,1) + mat1(1,2)*mat3(2,1)); -} - -template<int DataLayout> -static void test_scalar() -{ - Tensor<float, 1, DataLayout> vec1({6}); - Tensor<float, 1, DataLayout> vec2({6}); - - vec1.setRandom(); - vec2.setRandom(); - - Eigen::array<DimPair, 1> dims = {{DimPair(0, 0)}}; - Tensor<float, 0, DataLayout> scalar = vec1.contract(vec2, dims); - - float expected = 0.0f; - for (int i = 0; i < 6; ++i) { - expected += vec1(i) * vec2(i); - } - VERIFY_IS_APPROX(scalar(), expected); -} - -template<int DataLayout> -static void test_multidims() -{ - Tensor<float, 3, DataLayout> mat1(2, 2, 2); - Tensor<float, 4, DataLayout> mat2(2, 2, 2, 2); - - mat1.setRandom(); - mat2.setRandom(); - - Tensor<float, 3, DataLayout> mat3(2, 2, 2); - mat3.setZero(); - Eigen::array<DimPair, 2> dims = {{DimPair(1, 2), DimPair(2, 3)}}; - typedef TensorEvaluator<decltype(mat1.contract(mat2, dims)), DefaultDevice> Evaluator; - Evaluator eval(mat1.contract(mat2, dims), DefaultDevice()); - eval.evalTo(mat3.data()); - EIGEN_STATIC_ASSERT(Evaluator::NumDims==3ul, YOU_MADE_A_PROGRAMMING_MISTAKE); - VERIFY_IS_EQUAL(eval.dimensions()[0], 2); - VERIFY_IS_EQUAL(eval.dimensions()[1], 2); - VERIFY_IS_EQUAL(eval.dimensions()[2], 2); - - VERIFY_IS_APPROX(mat3(0,0,0), mat1(0,0,0)*mat2(0,0,0,0) + mat1(0,1,0)*mat2(0,0,1,0) + - mat1(0,0,1)*mat2(0,0,0,1) + mat1(0,1,1)*mat2(0,0,1,1)); - VERIFY_IS_APPROX(mat3(0,0,1), mat1(0,0,0)*mat2(0,1,0,0) + mat1(0,1,0)*mat2(0,1,1,0) + - mat1(0,0,1)*mat2(0,1,0,1) + mat1(0,1,1)*mat2(0,1,1,1)); - VERIFY_IS_APPROX(mat3(0,1,0), mat1(0,0,0)*mat2(1,0,0,0) + mat1(0,1,0)*mat2(1,0,1,0) + - mat1(0,0,1)*mat2(1,0,0,1) + mat1(0,1,1)*mat2(1,0,1,1)); - VERIFY_IS_APPROX(mat3(0,1,1), mat1(0,0,0)*mat2(1,1,0,0) + mat1(0,1,0)*mat2(1,1,1,0) + - mat1(0,0,1)*mat2(1,1,0,1) + mat1(0,1,1)*mat2(1,1,1,1)); - VERIFY_IS_APPROX(mat3(1,0,0), mat1(1,0,0)*mat2(0,0,0,0) + mat1(1,1,0)*mat2(0,0,1,0) + - mat1(1,0,1)*mat2(0,0,0,1) + mat1(1,1,1)*mat2(0,0,1,1)); - VERIFY_IS_APPROX(mat3(1,0,1), mat1(1,0,0)*mat2(0,1,0,0) + mat1(1,1,0)*mat2(0,1,1,0) + - mat1(1,0,1)*mat2(0,1,0,1) + mat1(1,1,1)*mat2(0,1,1,1)); - VERIFY_IS_APPROX(mat3(1,1,0), mat1(1,0,0)*mat2(1,0,0,0) + mat1(1,1,0)*mat2(1,0,1,0) + - mat1(1,0,1)*mat2(1,0,0,1) + mat1(1,1,1)*mat2(1,0,1,1)); - VERIFY_IS_APPROX(mat3(1,1,1), mat1(1,0,0)*mat2(1,1,0,0) + mat1(1,1,0)*mat2(1,1,1,0) + - mat1(1,0,1)*mat2(1,1,0,1) + mat1(1,1,1)*mat2(1,1,1,1)); - - Tensor<float, 2, DataLayout> mat4(2, 2); - Tensor<float, 3, DataLayout> mat5(2, 2, 2); - - mat4.setRandom(); - mat5.setRandom(); - - Tensor<float, 1, DataLayout> mat6(2); - mat6.setZero(); - Eigen::array<DimPair, 2> dims2({{DimPair(0, 1), DimPair(1, 0)}}); - typedef TensorEvaluator<decltype(mat4.contract(mat5, dims2)), DefaultDevice> Evaluator2; - Evaluator2 eval2(mat4.contract(mat5, dims2), DefaultDevice()); - eval2.evalTo(mat6.data()); - EIGEN_STATIC_ASSERT(Evaluator2::NumDims==1ul, YOU_MADE_A_PROGRAMMING_MISTAKE); - VERIFY_IS_EQUAL(eval2.dimensions()[0], 2); - - VERIFY_IS_APPROX(mat6(0), mat4(0,0)*mat5(0,0,0) + mat4(1,0)*mat5(0,1,0) + - mat4(0,1)*mat5(1,0,0) + mat4(1,1)*mat5(1,1,0)); - VERIFY_IS_APPROX(mat6(1), mat4(0,0)*mat5(0,0,1) + mat4(1,0)*mat5(0,1,1) + - mat4(0,1)*mat5(1,0,1) + mat4(1,1)*mat5(1,1,1)); -} - -template<int DataLayout> -static void test_holes() { - Tensor<float, 4, DataLayout> t1(2, 5, 7, 3); - Tensor<float, 5, DataLayout> t2(2, 7, 11, 13, 3); - t1.setRandom(); - t2.setRandom(); - - Eigen::array<DimPair, 2> dims = {{DimPair(0, 0), DimPair(3, 4)}}; - Tensor<float, 5, DataLayout> result = t1.contract(t2, dims); - VERIFY_IS_EQUAL(result.dimension(0), 5); - VERIFY_IS_EQUAL(result.dimension(1), 7); - VERIFY_IS_EQUAL(result.dimension(2), 7); - VERIFY_IS_EQUAL(result.dimension(3), 11); - VERIFY_IS_EQUAL(result.dimension(4), 13); - - for (int i = 0; i < 5; ++i) { - for (int j = 0; j < 5; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 5; ++l) { - for (int m = 0; m < 5; ++m) { - VERIFY_IS_APPROX(result(i, j, k, l, m), - t1(0, i, j, 0) * t2(0, k, l, m, 0) + - t1(1, i, j, 0) * t2(1, k, l, m, 0) + - t1(0, i, j, 1) * t2(0, k, l, m, 1) + - t1(1, i, j, 1) * t2(1, k, l, m, 1) + - t1(0, i, j, 2) * t2(0, k, l, m, 2) + - t1(1, i, j, 2) * t2(1, k, l, m, 2)); - } - } - } - } - } -} - -template<int DataLayout> -static void test_full_redux() -{ - Tensor<float, 2, DataLayout> t1(2, 2); - Tensor<float, 3, DataLayout> t2(2, 2, 2); - t1.setRandom(); - t2.setRandom(); - - Eigen::array<DimPair, 2> dims = {{DimPair(0, 0), DimPair(1, 1)}}; - Tensor<float, 1, DataLayout> result = t1.contract(t2, dims); - VERIFY_IS_EQUAL(result.dimension(0), 2); - VERIFY_IS_APPROX(result(0), t1(0, 0) * t2(0, 0, 0) + t1(1, 0) * t2(1, 0, 0) - + t1(0, 1) * t2(0, 1, 0) + t1(1, 1) * t2(1, 1, 0)); - VERIFY_IS_APPROX(result(1), t1(0, 0) * t2(0, 0, 1) + t1(1, 0) * t2(1, 0, 1) - + t1(0, 1) * t2(0, 1, 1) + t1(1, 1) * t2(1, 1, 1)); - - dims[0] = DimPair(1, 0); - dims[1] = DimPair(2, 1); - result = t2.contract(t1, dims); - VERIFY_IS_EQUAL(result.dimension(0), 2); - VERIFY_IS_APPROX(result(0), t1(0, 0) * t2(0, 0, 0) + t1(1, 0) * t2(0, 1, 0) - + t1(0, 1) * t2(0, 0, 1) + t1(1, 1) * t2(0, 1, 1)); - VERIFY_IS_APPROX(result(1), t1(0, 0) * t2(1, 0, 0) + t1(1, 0) * t2(1, 1, 0) - + t1(0, 1) * t2(1, 0, 1) + t1(1, 1) * t2(1, 1, 1)); -} - -template<int DataLayout> -static void test_contraction_of_contraction() -{ - Tensor<float, 2, DataLayout> t1(2, 2); - Tensor<float, 2, DataLayout> t2(2, 2); - Tensor<float, 2, DataLayout> t3(2, 2); - Tensor<float, 2, DataLayout> t4(2, 2); - t1.setRandom(); - t2.setRandom(); - t3.setRandom(); - t4.setRandom(); - - Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}}; - auto contract1 = t1.contract(t2, dims); - auto diff = t3 - contract1; - auto contract2 = t1.contract(t4, dims); - Tensor<float, 2, DataLayout> result = contract2.contract(diff, dims); - - VERIFY_IS_EQUAL(result.dimension(0), 2); - VERIFY_IS_EQUAL(result.dimension(1), 2); - - Eigen::Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> - m1(t1.data(), 2, 2), m2(t2.data(), 2, 2), m3(t3.data(), 2, 2), - m4(t4.data(), 2, 2); - Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> - expected = (m1 * m4) * (m3 - m1 * m2); - - VERIFY_IS_APPROX(result(0, 0), expected(0, 0)); - VERIFY_IS_APPROX(result(0, 1), expected(0, 1)); - VERIFY_IS_APPROX(result(1, 0), expected(1, 0)); - VERIFY_IS_APPROX(result(1, 1), expected(1, 1)); -} - -template<int DataLayout> -static void test_expr() -{ - Tensor<float, 2, DataLayout> mat1(2, 3); - Tensor<float, 2, DataLayout> mat2(3, 2); - mat1.setRandom(); - mat2.setRandom(); - - Tensor<float, 2, DataLayout> mat3(2,2); - - Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}}; - mat3 = mat1.contract(mat2, dims); - - VERIFY_IS_APPROX(mat3(0,0), mat1(0,0)*mat2(0,0) + mat1(0,1)*mat2(1,0) + mat1(0,2)*mat2(2,0)); - VERIFY_IS_APPROX(mat3(0,1), mat1(0,0)*mat2(0,1) + mat1(0,1)*mat2(1,1) + mat1(0,2)*mat2(2,1)); - VERIFY_IS_APPROX(mat3(1,0), mat1(1,0)*mat2(0,0) + mat1(1,1)*mat2(1,0) + mat1(1,2)*mat2(2,0)); - VERIFY_IS_APPROX(mat3(1,1), mat1(1,0)*mat2(0,1) + mat1(1,1)*mat2(1,1) + mat1(1,2)*mat2(2,1)); -} - -template<int DataLayout> -static void test_out_of_order_contraction() -{ - Tensor<float, 3, DataLayout> mat1(2, 2, 2); - Tensor<float, 3, DataLayout> mat2(2, 2, 2); - - mat1.setRandom(); - mat2.setRandom(); - - Tensor<float, 2, DataLayout> mat3(2, 2); - - Eigen::array<DimPair, 2> dims = {{DimPair(2, 0), DimPair(0, 2)}}; - mat3 = mat1.contract(mat2, dims); - - VERIFY_IS_APPROX(mat3(0, 0), - mat1(0,0,0)*mat2(0,0,0) + mat1(1,0,0)*mat2(0,0,1) + - mat1(0,0,1)*mat2(1,0,0) + mat1(1,0,1)*mat2(1,0,1)); - VERIFY_IS_APPROX(mat3(1, 0), - mat1(0,1,0)*mat2(0,0,0) + mat1(1,1,0)*mat2(0,0,1) + - mat1(0,1,1)*mat2(1,0,0) + mat1(1,1,1)*mat2(1,0,1)); - VERIFY_IS_APPROX(mat3(0, 1), - mat1(0,0,0)*mat2(0,1,0) + mat1(1,0,0)*mat2(0,1,1) + - mat1(0,0,1)*mat2(1,1,0) + mat1(1,0,1)*mat2(1,1,1)); - VERIFY_IS_APPROX(mat3(1, 1), - mat1(0,1,0)*mat2(0,1,0) + mat1(1,1,0)*mat2(0,1,1) + - mat1(0,1,1)*mat2(1,1,0) + mat1(1,1,1)*mat2(1,1,1)); - - Eigen::array<DimPair, 2> dims2 = {{DimPair(0, 2), DimPair(2, 0)}}; - mat3 = mat1.contract(mat2, dims2); - - VERIFY_IS_APPROX(mat3(0, 0), - mat1(0,0,0)*mat2(0,0,0) + mat1(1,0,0)*mat2(0,0,1) + - mat1(0,0,1)*mat2(1,0,0) + mat1(1,0,1)*mat2(1,0,1)); - VERIFY_IS_APPROX(mat3(1, 0), - mat1(0,1,0)*mat2(0,0,0) + mat1(1,1,0)*mat2(0,0,1) + - mat1(0,1,1)*mat2(1,0,0) + mat1(1,1,1)*mat2(1,0,1)); - VERIFY_IS_APPROX(mat3(0, 1), - mat1(0,0,0)*mat2(0,1,0) + mat1(1,0,0)*mat2(0,1,1) + - mat1(0,0,1)*mat2(1,1,0) + mat1(1,0,1)*mat2(1,1,1)); - VERIFY_IS_APPROX(mat3(1, 1), - mat1(0,1,0)*mat2(0,1,0) + mat1(1,1,0)*mat2(0,1,1) + - mat1(0,1,1)*mat2(1,1,0) + mat1(1,1,1)*mat2(1,1,1)); - -} - -template<int DataLayout> -static void test_consistency() -{ - // this does something like testing (A*B)^T = (B^T * A^T) - - Tensor<float, 3, DataLayout> mat1(4, 3, 5); - Tensor<float, 5, DataLayout> mat2(3, 2, 1, 5, 4); - mat1.setRandom(); - mat2.setRandom(); - - Tensor<float, 4, DataLayout> mat3(5, 2, 1, 5); - Tensor<float, 4, DataLayout> mat4(2, 1, 5, 5); - - // contract on dimensions of size 4 and 3 - Eigen::array<DimPair, 2> dims1 = {{DimPair(0, 4), DimPair(1, 0)}}; - Eigen::array<DimPair, 2> dims2 = {{DimPair(4, 0), DimPair(0, 1)}}; - - mat3 = mat1.contract(mat2, dims1); - mat4 = mat2.contract(mat1, dims2); - - // check that these are equal except for ordering of dimensions - if (DataLayout == ColMajor) { - for (size_t i = 0; i < 5; i++) { - for (size_t j = 0; j < 10; j++) { - VERIFY_IS_APPROX(mat3.data()[i + 5 * j], mat4.data()[j + 10 * i]); - } - } - } else { - // Row major - for (size_t i = 0; i < 5; i++) { - for (size_t j = 0; j < 10; j++) { - VERIFY_IS_APPROX(mat3.data()[10 * i + j], mat4.data()[i + 5 * j]); - } - } - } -} - -template<int DataLayout> -static void test_large_contraction() -{ - Tensor<float, 4, DataLayout> t_left(30, 50, 8, 31); - Tensor<float, 5, DataLayout> t_right(8, 31, 7, 20, 10); - Tensor<float, 5, DataLayout> t_result(30, 50, 7, 20, 10); - - t_left.setRandom(); - t_right.setRandom(); - - // Add a little offset so that the results won't be close to zero. - t_left += t_left.constant(1.0f); - t_right += t_right.constant(1.0f); - - typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf; - MapXf m_left(t_left.data(), 1500, 248); - MapXf m_right(t_right.data(), 248, 1400); - Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(1500, 1400); - - // this contraction should be equivalent to a single matrix multiplication - Eigen::array<DimPair, 2> dims = {{DimPair(2, 0), DimPair(3, 1)}}; - - // compute results by separate methods - t_result = t_left.contract(t_right, dims); - m_result = m_left * m_right; - - for (int i = 0; i < t_result.dimensions().TotalSize(); i++) { - VERIFY(&t_result.data()[i] != &m_result.data()[i]); - VERIFY_IS_APPROX(t_result.data()[i], m_result.data()[i]); - } -} - -template<int DataLayout> -static void test_matrix_vector() -{ - Tensor<float, 2, DataLayout> t_left(30, 50); - Tensor<float, 1, DataLayout> t_right(50); - Tensor<float, 1, DataLayout> t_result(30); - - t_left.setRandom(); - t_right.setRandom(); - - typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf; - MapXf m_left(t_left.data(), 30, 50); - MapXf m_right(t_right.data(), 50, 1); - Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(30, 1); - - // this contraction should be equivalent to a single matrix multiplication - Eigen::array<DimPair, 1> dims{{DimPair(1, 0)}}; - - // compute results by separate methods - t_result = t_left.contract(t_right, dims); - m_result = m_left * m_right; - - for (int i = 0; i < t_result.dimensions().TotalSize(); i++) { - VERIFY(internal::isApprox(t_result(i), m_result(i, 0), 1)); - } -} - - -template<int DataLayout> -static void test_tensor_vector() -{ - Tensor<float, 3, DataLayout> t_left(7, 13, 17); - Tensor<float, 2, DataLayout> t_right(1, 7); - - t_left.setRandom(); - t_right.setRandom(); - - typedef typename Tensor<float, 1, DataLayout>::DimensionPair DimensionPair; - Eigen::array<DimensionPair, 1> dim_pair01{{{0, 1}}}; - Tensor<float, 3, DataLayout> t_result = t_left.contract(t_right, dim_pair01); - - typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf; - MapXf m_left(t_left.data(), 7, 13*17); - MapXf m_right(t_right.data(), 1, 7); - Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result = m_left.transpose() * m_right.transpose(); - - for (int i = 0; i < t_result.dimensions().TotalSize(); i++) { - VERIFY(internal::isApprox(t_result(i), m_result(i, 0), 1)); - } -} - - -template<int DataLayout> -static void test_small_blocking_factors() -{ - Tensor<float, 4, DataLayout> t_left(30, 5, 3, 31); - Tensor<float, 5, DataLayout> t_right(3, 31, 7, 20, 1); - t_left.setRandom(); - t_right.setRandom(); - - // Add a little offset so that the results won't be close to zero. - t_left += t_left.constant(1.0f); - t_right += t_right.constant(1.0f); - - // Force the cache sizes, which results in smaller blocking factors. - Eigen::setCpuCacheSizes(896, 1920, 2944); - - // this contraction should be equivalent to a single matrix multiplication - Eigen::array<DimPair, 2> dims = {{DimPair(2, 0), DimPair(3, 1)}}; - Tensor<float, 5, DataLayout> t_result; - t_result = t_left.contract(t_right, dims); - - // compute result using a simple eigen matrix product - Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> m_left(t_left.data(), 150, 93); - Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> m_right(t_right.data(), 93, 140); - Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result = m_left * m_right; - - for (int i = 0; i < t_result.dimensions().TotalSize(); i++) { - VERIFY_IS_APPROX(t_result.data()[i], m_result.data()[i]); - } -} - -template<int DataLayout> -static void test_tensor_product() -{ - Tensor<float, 2, DataLayout> mat1(2, 3); - Tensor<float, 2, DataLayout> mat2(4, 1); - mat1.setRandom(); - mat2.setRandom(); - - Tensor<float, 4, DataLayout> result = mat1.contract(mat2, Eigen::array<DimPair, 0>{{}}); - - VERIFY_IS_EQUAL(result.dimension(0), 2); - VERIFY_IS_EQUAL(result.dimension(1), 3); - VERIFY_IS_EQUAL(result.dimension(2), 4); - VERIFY_IS_EQUAL(result.dimension(3), 1); - for (int i = 0; i < result.dimension(0); ++i) { - for (int j = 0; j < result.dimension(1); ++j) { - for (int k = 0; k < result.dimension(2); ++k) { - for (int l = 0; l < result.dimension(3); ++l) { - VERIFY_IS_APPROX(result(i, j, k, l), mat1(i, j) * mat2(k, l) ); - } - } - } - } -} - - -template<int DataLayout> -static void test_const_inputs() -{ - Tensor<float, 2, DataLayout> in1(2, 3); - Tensor<float, 2, DataLayout> in2(3, 2); - in1.setRandom(); - in2.setRandom(); - - TensorMap<Tensor<const float, 2, DataLayout> > mat1(in1.data(), 2, 3); - TensorMap<Tensor<const float, 2, DataLayout> > mat2(in2.data(), 3, 2); - Tensor<float, 2, DataLayout> mat3(2,2); - - Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}}; - mat3 = mat1.contract(mat2, dims); - - VERIFY_IS_APPROX(mat3(0,0), mat1(0,0)*mat2(0,0) + mat1(0,1)*mat2(1,0) + mat1(0,2)*mat2(2,0)); - VERIFY_IS_APPROX(mat3(0,1), mat1(0,0)*mat2(0,1) + mat1(0,1)*mat2(1,1) + mat1(0,2)*mat2(2,1)); - VERIFY_IS_APPROX(mat3(1,0), mat1(1,0)*mat2(0,0) + mat1(1,1)*mat2(1,0) + mat1(1,2)*mat2(2,0)); - VERIFY_IS_APPROX(mat3(1,1), mat1(1,0)*mat2(0,1) + mat1(1,1)*mat2(1,1) + mat1(1,2)*mat2(2,1)); -} - -void test_cxx11_tensor_contraction() -{ - CALL_SUBTEST(test_evals<ColMajor>()); - CALL_SUBTEST(test_evals<RowMajor>()); - CALL_SUBTEST(test_scalar<ColMajor>()); - CALL_SUBTEST(test_scalar<RowMajor>()); - CALL_SUBTEST(test_multidims<ColMajor>()); - CALL_SUBTEST(test_multidims<RowMajor>()); - CALL_SUBTEST(test_holes<ColMajor>()); - CALL_SUBTEST(test_holes<RowMajor>()); - CALL_SUBTEST(test_full_redux<ColMajor>()); - CALL_SUBTEST(test_full_redux<RowMajor>()); - CALL_SUBTEST(test_contraction_of_contraction<ColMajor>()); - CALL_SUBTEST(test_contraction_of_contraction<RowMajor>()); - CALL_SUBTEST(test_expr<ColMajor>()); - CALL_SUBTEST(test_expr<RowMajor>()); - CALL_SUBTEST(test_out_of_order_contraction<ColMajor>()); - CALL_SUBTEST(test_out_of_order_contraction<RowMajor>()); - CALL_SUBTEST(test_consistency<ColMajor>()); - CALL_SUBTEST(test_consistency<RowMajor>()); - CALL_SUBTEST(test_large_contraction<ColMajor>()); - CALL_SUBTEST(test_large_contraction<RowMajor>()); - CALL_SUBTEST(test_matrix_vector<ColMajor>()); - CALL_SUBTEST(test_matrix_vector<RowMajor>()); - CALL_SUBTEST(test_tensor_vector<ColMajor>()); - CALL_SUBTEST(test_tensor_vector<RowMajor>()); - CALL_SUBTEST(test_small_blocking_factors<ColMajor>()); - CALL_SUBTEST(test_small_blocking_factors<RowMajor>()); - CALL_SUBTEST(test_tensor_product<ColMajor>()); - CALL_SUBTEST(test_tensor_product<RowMajor>()); - CALL_SUBTEST(test_const_inputs<ColMajor>()); - CALL_SUBTEST(test_const_inputs<RowMajor>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_convolution.cpp b/eigen/unsupported/test/cxx11_tensor_convolution.cpp deleted file mode 100644 index e3d4675..0000000 --- a/eigen/unsupported/test/cxx11_tensor_convolution.cpp +++ /dev/null @@ -1,149 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::DefaultDevice; - -template <int DataLayout> -static void test_evals() -{ - Tensor<float, 2, DataLayout> input(3, 3); - Tensor<float, 1, DataLayout> kernel(2); - - input.setRandom(); - kernel.setRandom(); - - Tensor<float, 2, DataLayout> result(2,3); - result.setZero(); - Eigen::array<Tensor<float, 2>::Index, 1> dims3{{0}}; - - typedef TensorEvaluator<decltype(input.convolve(kernel, dims3)), DefaultDevice> Evaluator; - Evaluator eval(input.convolve(kernel, dims3), DefaultDevice()); - eval.evalTo(result.data()); - EIGEN_STATIC_ASSERT(Evaluator::NumDims==2ul, YOU_MADE_A_PROGRAMMING_MISTAKE); - VERIFY_IS_EQUAL(eval.dimensions()[0], 2); - VERIFY_IS_EQUAL(eval.dimensions()[1], 3); - - VERIFY_IS_APPROX(result(0,0), input(0,0)*kernel(0) + input(1,0)*kernel(1)); // index 0 - VERIFY_IS_APPROX(result(0,1), input(0,1)*kernel(0) + input(1,1)*kernel(1)); // index 2 - VERIFY_IS_APPROX(result(0,2), input(0,2)*kernel(0) + input(1,2)*kernel(1)); // index 4 - VERIFY_IS_APPROX(result(1,0), input(1,0)*kernel(0) + input(2,0)*kernel(1)); // index 1 - VERIFY_IS_APPROX(result(1,1), input(1,1)*kernel(0) + input(2,1)*kernel(1)); // index 3 - VERIFY_IS_APPROX(result(1,2), input(1,2)*kernel(0) + input(2,2)*kernel(1)); // index 5 -} - -template <int DataLayout> -static void test_expr() -{ - Tensor<float, 2, DataLayout> input(3, 3); - Tensor<float, 2, DataLayout> kernel(2, 2); - input.setRandom(); - kernel.setRandom(); - - Tensor<float, 2, DataLayout> result(2,2); - Eigen::array<ptrdiff_t, 2> dims; - dims[0] = 0; - dims[1] = 1; - result = input.convolve(kernel, dims); - - VERIFY_IS_APPROX(result(0,0), input(0,0)*kernel(0,0) + input(0,1)*kernel(0,1) + - input(1,0)*kernel(1,0) + input(1,1)*kernel(1,1)); - VERIFY_IS_APPROX(result(0,1), input(0,1)*kernel(0,0) + input(0,2)*kernel(0,1) + - input(1,1)*kernel(1,0) + input(1,2)*kernel(1,1)); - VERIFY_IS_APPROX(result(1,0), input(1,0)*kernel(0,0) + input(1,1)*kernel(0,1) + - input(2,0)*kernel(1,0) + input(2,1)*kernel(1,1)); - VERIFY_IS_APPROX(result(1,1), input(1,1)*kernel(0,0) + input(1,2)*kernel(0,1) + - input(2,1)*kernel(1,0) + input(2,2)*kernel(1,1)); -} - -template <int DataLayout> -static void test_modes() { - Tensor<float, 1, DataLayout> input(3); - Tensor<float, 1, DataLayout> kernel(3); - input(0) = 1.0f; - input(1) = 2.0f; - input(2) = 3.0f; - kernel(0) = 0.5f; - kernel(1) = 1.0f; - kernel(2) = 0.0f; - - Eigen::array<ptrdiff_t, 1> dims; - dims[0] = 0; - Eigen::array<std::pair<ptrdiff_t, ptrdiff_t>, 1> padding; - - // Emulate VALID mode (as defined in - // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html). - padding[0] = std::make_pair(0, 0); - Tensor<float, 1, DataLayout> valid(1); - valid = input.pad(padding).convolve(kernel, dims); - VERIFY_IS_EQUAL(valid.dimension(0), 1); - VERIFY_IS_APPROX(valid(0), 2.5f); - - // Emulate SAME mode (as defined in - // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html). - padding[0] = std::make_pair(1, 1); - Tensor<float, 1, DataLayout> same(3); - same = input.pad(padding).convolve(kernel, dims); - VERIFY_IS_EQUAL(same.dimension(0), 3); - VERIFY_IS_APPROX(same(0), 1.0f); - VERIFY_IS_APPROX(same(1), 2.5f); - VERIFY_IS_APPROX(same(2), 4.0f); - - // Emulate FULL mode (as defined in - // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html). - padding[0] = std::make_pair(2, 2); - Tensor<float, 1, DataLayout> full(5); - full = input.pad(padding).convolve(kernel, dims); - VERIFY_IS_EQUAL(full.dimension(0), 5); - VERIFY_IS_APPROX(full(0), 0.0f); - VERIFY_IS_APPROX(full(1), 1.0f); - VERIFY_IS_APPROX(full(2), 2.5f); - VERIFY_IS_APPROX(full(3), 4.0f); - VERIFY_IS_APPROX(full(4), 1.5f); -} - -template <int DataLayout> -static void test_strides() { - Tensor<float, 1, DataLayout> input(13); - Tensor<float, 1, DataLayout> kernel(3); - input.setRandom(); - kernel.setRandom(); - - Eigen::array<ptrdiff_t, 1> dims; - dims[0] = 0; - Eigen::array<ptrdiff_t, 1> stride_of_3; - stride_of_3[0] = 3; - Eigen::array<ptrdiff_t, 1> stride_of_2; - stride_of_2[0] = 2; - - Tensor<float, 1, DataLayout> result; - result = input.stride(stride_of_3).convolve(kernel, dims).stride(stride_of_2); - - VERIFY_IS_EQUAL(result.dimension(0), 2); - VERIFY_IS_APPROX(result(0), (input(0)*kernel(0) + input(3)*kernel(1) + - input(6)*kernel(2))); - VERIFY_IS_APPROX(result(1), (input(6)*kernel(0) + input(9)*kernel(1) + - input(12)*kernel(2))); -} - -void test_cxx11_tensor_convolution() -{ - CALL_SUBTEST(test_evals<ColMajor>()); - CALL_SUBTEST(test_evals<RowMajor>()); - CALL_SUBTEST(test_expr<ColMajor>()); - CALL_SUBTEST(test_expr<RowMajor>()); - CALL_SUBTEST(test_modes<ColMajor>()); - CALL_SUBTEST(test_modes<RowMajor>()); - CALL_SUBTEST(test_strides<ColMajor>()); - CALL_SUBTEST(test_strides<RowMajor>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_cuda.cu b/eigen/unsupported/test/cxx11_tensor_cuda.cu deleted file mode 100644 index 9584a53..0000000 --- a/eigen/unsupported/test/cxx11_tensor_cuda.cu +++ /dev/null @@ -1,1284 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_NO_COMPLEX -#define EIGEN_TEST_FUNC cxx11_tensor_cuda -#define EIGEN_USE_GPU - -#include "main.h" -#include <unsupported/Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -void test_cuda_nullary() { - Tensor<float, 1, 0, int> in1(2); - Tensor<float, 1, 0, int> in2(2); - in1.setRandom(); - in2.setRandom(); - - std::size_t tensor_bytes = in1.size() * sizeof(float); - - float* d_in1; - float* d_in2; - cudaMalloc((void**)(&d_in1), tensor_bytes); - cudaMalloc((void**)(&d_in2), tensor_bytes); - cudaMemcpy(d_in1, in1.data(), tensor_bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_in2, in2.data(), tensor_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 1, 0, int>, Eigen::Aligned> gpu_in1( - d_in1, 2); - Eigen::TensorMap<Eigen::Tensor<float, 1, 0, int>, Eigen::Aligned> gpu_in2( - d_in2, 2); - - gpu_in1.device(gpu_device) = gpu_in1.constant(3.14f); - gpu_in2.device(gpu_device) = gpu_in2.random(); - - Tensor<float, 1, 0, int> new1(2); - Tensor<float, 1, 0, int> new2(2); - - assert(cudaMemcpyAsync(new1.data(), d_in1, tensor_bytes, cudaMemcpyDeviceToHost, - gpu_device.stream()) == cudaSuccess); - assert(cudaMemcpyAsync(new2.data(), d_in2, tensor_bytes, cudaMemcpyDeviceToHost, - gpu_device.stream()) == cudaSuccess); - - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 2; ++i) { - VERIFY_IS_APPROX(new1(i), 3.14f); - VERIFY_IS_NOT_EQUAL(new2(i), in2(i)); - } - - cudaFree(d_in1); - cudaFree(d_in2); -} - -void test_cuda_elementwise_small() { - Tensor<float, 1> in1(Eigen::array<Eigen::DenseIndex, 1>(2)); - Tensor<float, 1> in2(Eigen::array<Eigen::DenseIndex, 1>(2)); - Tensor<float, 1> out(Eigen::array<Eigen::DenseIndex, 1>(2)); - in1.setRandom(); - in2.setRandom(); - - std::size_t in1_bytes = in1.size() * sizeof(float); - std::size_t in2_bytes = in2.size() * sizeof(float); - std::size_t out_bytes = out.size() * sizeof(float); - - float* d_in1; - float* d_in2; - float* d_out; - cudaMalloc((void**)(&d_in1), in1_bytes); - cudaMalloc((void**)(&d_in2), in2_bytes); - cudaMalloc((void**)(&d_out), out_bytes); - - cudaMemcpy(d_in1, in1.data(), in1_bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_in2, in2.data(), in2_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_in1( - d_in1, Eigen::array<Eigen::DenseIndex, 1>(2)); - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_in2( - d_in2, Eigen::array<Eigen::DenseIndex, 1>(2)); - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_out( - d_out, Eigen::array<Eigen::DenseIndex, 1>(2)); - - gpu_out.device(gpu_device) = gpu_in1 + gpu_in2; - - assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, - gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 2; ++i) { - VERIFY_IS_APPROX( - out(Eigen::array<Eigen::DenseIndex, 1>(i)), - in1(Eigen::array<Eigen::DenseIndex, 1>(i)) + in2(Eigen::array<Eigen::DenseIndex, 1>(i))); - } - - cudaFree(d_in1); - cudaFree(d_in2); - cudaFree(d_out); -} - -void test_cuda_elementwise() -{ - Tensor<float, 3> in1(Eigen::array<Eigen::DenseIndex, 3>(72,53,97)); - Tensor<float, 3> in2(Eigen::array<Eigen::DenseIndex, 3>(72,53,97)); - Tensor<float, 3> in3(Eigen::array<Eigen::DenseIndex, 3>(72,53,97)); - Tensor<float, 3> out(Eigen::array<Eigen::DenseIndex, 3>(72,53,97)); - in1.setRandom(); - in2.setRandom(); - in3.setRandom(); - - std::size_t in1_bytes = in1.size() * sizeof(float); - std::size_t in2_bytes = in2.size() * sizeof(float); - std::size_t in3_bytes = in3.size() * sizeof(float); - std::size_t out_bytes = out.size() * sizeof(float); - - float* d_in1; - float* d_in2; - float* d_in3; - float* d_out; - cudaMalloc((void**)(&d_in1), in1_bytes); - cudaMalloc((void**)(&d_in2), in2_bytes); - cudaMalloc((void**)(&d_in3), in3_bytes); - cudaMalloc((void**)(&d_out), out_bytes); - - cudaMemcpy(d_in1, in1.data(), in1_bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_in2, in2.data(), in2_bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_in3, in3.data(), in3_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in1(d_in1, Eigen::array<Eigen::DenseIndex, 3>(72,53,97)); - Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in2(d_in2, Eigen::array<Eigen::DenseIndex, 3>(72,53,97)); - Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in3(d_in3, Eigen::array<Eigen::DenseIndex, 3>(72,53,97)); - Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_out(d_out, Eigen::array<Eigen::DenseIndex, 3>(72,53,97)); - - gpu_out.device(gpu_device) = gpu_in1 + gpu_in2 * gpu_in3; - - assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 72; ++i) { - for (int j = 0; j < 53; ++j) { - for (int k = 0; k < 97; ++k) { - VERIFY_IS_APPROX(out(Eigen::array<Eigen::DenseIndex, 3>(i,j,k)), in1(Eigen::array<Eigen::DenseIndex, 3>(i,j,k)) + in2(Eigen::array<Eigen::DenseIndex, 3>(i,j,k)) * in3(Eigen::array<Eigen::DenseIndex, 3>(i,j,k))); - } - } - } - - cudaFree(d_in1); - cudaFree(d_in2); - cudaFree(d_in3); - cudaFree(d_out); -} - -void test_cuda_props() { - Tensor<float, 1> in1(200); - Tensor<bool, 1> out(200); - in1.setRandom(); - - std::size_t in1_bytes = in1.size() * sizeof(float); - std::size_t out_bytes = out.size() * sizeof(bool); - - float* d_in1; - bool* d_out; - cudaMalloc((void**)(&d_in1), in1_bytes); - cudaMalloc((void**)(&d_out), out_bytes); - - cudaMemcpy(d_in1, in1.data(), in1_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_in1( - d_in1, 200); - Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_out( - d_out, 200); - - gpu_out.device(gpu_device) = (gpu_in1.isnan)(); - - assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, - gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 200; ++i) { - VERIFY_IS_EQUAL(out(i), (std::isnan)(in1(i))); - } - - cudaFree(d_in1); - cudaFree(d_out); -} - -void test_cuda_reduction() -{ - Tensor<float, 4> in1(72,53,97,113); - Tensor<float, 2> out(72,97); - in1.setRandom(); - - std::size_t in1_bytes = in1.size() * sizeof(float); - std::size_t out_bytes = out.size() * sizeof(float); - - float* d_in1; - float* d_out; - cudaMalloc((void**)(&d_in1), in1_bytes); - cudaMalloc((void**)(&d_out), out_bytes); - - cudaMemcpy(d_in1, in1.data(), in1_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 4> > gpu_in1(d_in1, 72,53,97,113); - Eigen::TensorMap<Eigen::Tensor<float, 2> > gpu_out(d_out, 72,97); - - array<Eigen::DenseIndex, 2> reduction_axis; - reduction_axis[0] = 1; - reduction_axis[1] = 3; - - gpu_out.device(gpu_device) = gpu_in1.maximum(reduction_axis); - - assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 72; ++i) { - for (int j = 0; j < 97; ++j) { - float expected = 0; - for (int k = 0; k < 53; ++k) { - for (int l = 0; l < 113; ++l) { - expected = - std::max<float>(expected, in1(i, k, j, l)); - } - } - VERIFY_IS_APPROX(out(i,j), expected); - } - } - - cudaFree(d_in1); - cudaFree(d_out); -} - -template<int DataLayout> -void test_cuda_contraction() -{ - // with these dimensions, the output has 300 * 140 elements, which is - // more than 30 * 1024, which is the number of threads in blocks on - // a 15 SM GK110 GPU - Tensor<float, 4, DataLayout> t_left(6, 50, 3, 31); - Tensor<float, 5, DataLayout> t_right(Eigen::array<Eigen::DenseIndex, 5>(3, 31, 7, 20, 1)); - Tensor<float, 5, DataLayout> t_result(Eigen::array<Eigen::DenseIndex, 5>(6, 50, 7, 20, 1)); - - t_left.setRandom(); - t_right.setRandom(); - - std::size_t t_left_bytes = t_left.size() * sizeof(float); - std::size_t t_right_bytes = t_right.size() * sizeof(float); - std::size_t t_result_bytes = t_result.size() * sizeof(float); - - float* d_t_left; - float* d_t_right; - float* d_t_result; - - cudaMalloc((void**)(&d_t_left), t_left_bytes); - cudaMalloc((void**)(&d_t_right), t_right_bytes); - cudaMalloc((void**)(&d_t_result), t_result_bytes); - - cudaMemcpy(d_t_left, t_left.data(), t_left_bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_t_right, t_right.data(), t_right_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_t_left(d_t_left, 6, 50, 3, 31); - Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> > gpu_t_right(d_t_right, 3, 31, 7, 20, 1); - Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> > gpu_t_result(d_t_result, 6, 50, 7, 20, 1); - - typedef Eigen::Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> > MapXf; - MapXf m_left(t_left.data(), 300, 93); - MapXf m_right(t_right.data(), 93, 140); - Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(300, 140); - - typedef Tensor<float, 1>::DimensionPair DimPair; - Eigen::array<DimPair, 2> dims; - dims[0] = DimPair(2, 0); - dims[1] = DimPair(3, 1); - - m_result = m_left * m_right; - gpu_t_result.device(gpu_device) = gpu_t_left.contract(gpu_t_right, dims); - - cudaMemcpy(t_result.data(), d_t_result, t_result_bytes, cudaMemcpyDeviceToHost); - - for (DenseIndex i = 0; i < t_result.size(); i++) { - if (fabs(t_result.data()[i] - m_result.data()[i]) >= 1e-4f) { - std::cout << "mismatch detected at index " << i << ": " << t_result.data()[i] << " vs " << m_result.data()[i] << std::endl; - assert(false); - } - } - - cudaFree(d_t_left); - cudaFree(d_t_right); - cudaFree(d_t_result); -} - -template<int DataLayout> -void test_cuda_convolution_1d() -{ - Tensor<float, 4, DataLayout> input(74,37,11,137); - Tensor<float, 1, DataLayout> kernel(4); - Tensor<float, 4, DataLayout> out(74,34,11,137); - input = input.constant(10.0f) + input.random(); - kernel = kernel.constant(7.0f) + kernel.random(); - - std::size_t input_bytes = input.size() * sizeof(float); - std::size_t kernel_bytes = kernel.size() * sizeof(float); - std::size_t out_bytes = out.size() * sizeof(float); - - float* d_input; - float* d_kernel; - float* d_out; - cudaMalloc((void**)(&d_input), input_bytes); - cudaMalloc((void**)(&d_kernel), kernel_bytes); - cudaMalloc((void**)(&d_out), out_bytes); - - cudaMemcpy(d_input, input.data(), input_bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_kernel, kernel.data(), kernel_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_input(d_input, 74,37,11,137); - Eigen::TensorMap<Eigen::Tensor<float, 1, DataLayout> > gpu_kernel(d_kernel, 4); - Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_out(d_out, 74,34,11,137); - - Eigen::array<Eigen::DenseIndex, 1> dims(1); - gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims); - - assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 74; ++i) { - for (int j = 0; j < 34; ++j) { - for (int k = 0; k < 11; ++k) { - for (int l = 0; l < 137; ++l) { - const float result = out(i,j,k,l); - const float expected = input(i,j+0,k,l) * kernel(0) + input(i,j+1,k,l) * kernel(1) + - input(i,j+2,k,l) * kernel(2) + input(i,j+3,k,l) * kernel(3); - VERIFY_IS_APPROX(result, expected); - } - } - } - } - - cudaFree(d_input); - cudaFree(d_kernel); - cudaFree(d_out); -} - -void test_cuda_convolution_inner_dim_col_major_1d() -{ - Tensor<float, 4, ColMajor> input(74,9,11,7); - Tensor<float, 1, ColMajor> kernel(4); - Tensor<float, 4, ColMajor> out(71,9,11,7); - input = input.constant(10.0f) + input.random(); - kernel = kernel.constant(7.0f) + kernel.random(); - - std::size_t input_bytes = input.size() * sizeof(float); - std::size_t kernel_bytes = kernel.size() * sizeof(float); - std::size_t out_bytes = out.size() * sizeof(float); - - float* d_input; - float* d_kernel; - float* d_out; - cudaMalloc((void**)(&d_input), input_bytes); - cudaMalloc((void**)(&d_kernel), kernel_bytes); - cudaMalloc((void**)(&d_out), out_bytes); - - cudaMemcpy(d_input, input.data(), input_bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_kernel, kernel.data(), kernel_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 4, ColMajor> > gpu_input(d_input,74,9,11,7); - Eigen::TensorMap<Eigen::Tensor<float, 1, ColMajor> > gpu_kernel(d_kernel,4); - Eigen::TensorMap<Eigen::Tensor<float, 4, ColMajor> > gpu_out(d_out,71,9,11,7); - - Eigen::array<Eigen::DenseIndex, 1> dims(0); - gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims); - - assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 71; ++i) { - for (int j = 0; j < 9; ++j) { - for (int k = 0; k < 11; ++k) { - for (int l = 0; l < 7; ++l) { - const float result = out(i,j,k,l); - const float expected = input(i+0,j,k,l) * kernel(0) + input(i+1,j,k,l) * kernel(1) + - input(i+2,j,k,l) * kernel(2) + input(i+3,j,k,l) * kernel(3); - VERIFY_IS_APPROX(result, expected); - } - } - } - } - - cudaFree(d_input); - cudaFree(d_kernel); - cudaFree(d_out); -} - -void test_cuda_convolution_inner_dim_row_major_1d() -{ - Tensor<float, 4, RowMajor> input(7,9,11,74); - Tensor<float, 1, RowMajor> kernel(4); - Tensor<float, 4, RowMajor> out(7,9,11,71); - input = input.constant(10.0f) + input.random(); - kernel = kernel.constant(7.0f) + kernel.random(); - - std::size_t input_bytes = input.size() * sizeof(float); - std::size_t kernel_bytes = kernel.size() * sizeof(float); - std::size_t out_bytes = out.size() * sizeof(float); - - float* d_input; - float* d_kernel; - float* d_out; - cudaMalloc((void**)(&d_input), input_bytes); - cudaMalloc((void**)(&d_kernel), kernel_bytes); - cudaMalloc((void**)(&d_out), out_bytes); - - cudaMemcpy(d_input, input.data(), input_bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_kernel, kernel.data(), kernel_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 4, RowMajor> > gpu_input(d_input, 7,9,11,74); - Eigen::TensorMap<Eigen::Tensor<float, 1, RowMajor> > gpu_kernel(d_kernel, 4); - Eigen::TensorMap<Eigen::Tensor<float, 4, RowMajor> > gpu_out(d_out, 7,9,11,71); - - Eigen::array<Eigen::DenseIndex, 1> dims(3); - gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims); - - assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 7; ++i) { - for (int j = 0; j < 9; ++j) { - for (int k = 0; k < 11; ++k) { - for (int l = 0; l < 71; ++l) { - const float result = out(i,j,k,l); - const float expected = input(i,j,k,l+0) * kernel(0) + input(i,j,k,l+1) * kernel(1) + - input(i,j,k,l+2) * kernel(2) + input(i,j,k,l+3) * kernel(3); - VERIFY_IS_APPROX(result, expected); - } - } - } - } - - cudaFree(d_input); - cudaFree(d_kernel); - cudaFree(d_out); -} - -template<int DataLayout> -void test_cuda_convolution_2d() -{ - Tensor<float, 4, DataLayout> input(74,37,11,137); - Tensor<float, 2, DataLayout> kernel(3,4); - Tensor<float, 4, DataLayout> out(74,35,8,137); - input = input.constant(10.0f) + input.random(); - kernel = kernel.constant(7.0f) + kernel.random(); - - std::size_t input_bytes = input.size() * sizeof(float); - std::size_t kernel_bytes = kernel.size() * sizeof(float); - std::size_t out_bytes = out.size() * sizeof(float); - - float* d_input; - float* d_kernel; - float* d_out; - cudaMalloc((void**)(&d_input), input_bytes); - cudaMalloc((void**)(&d_kernel), kernel_bytes); - cudaMalloc((void**)(&d_out), out_bytes); - - cudaMemcpy(d_input, input.data(), input_bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_kernel, kernel.data(), kernel_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_input(d_input,74,37,11,137); - Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_kernel(d_kernel,3,4); - Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_out(d_out,74,35,8,137); - - Eigen::array<Eigen::DenseIndex, 2> dims(1,2); - gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims); - - assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 74; ++i) { - for (int j = 0; j < 35; ++j) { - for (int k = 0; k < 8; ++k) { - for (int l = 0; l < 137; ++l) { - const float result = out(i,j,k,l); - const float expected = input(i,j+0,k+0,l) * kernel(0,0) + - input(i,j+1,k+0,l) * kernel(1,0) + - input(i,j+2,k+0,l) * kernel(2,0) + - input(i,j+0,k+1,l) * kernel(0,1) + - input(i,j+1,k+1,l) * kernel(1,1) + - input(i,j+2,k+1,l) * kernel(2,1) + - input(i,j+0,k+2,l) * kernel(0,2) + - input(i,j+1,k+2,l) * kernel(1,2) + - input(i,j+2,k+2,l) * kernel(2,2) + - input(i,j+0,k+3,l) * kernel(0,3) + - input(i,j+1,k+3,l) * kernel(1,3) + - input(i,j+2,k+3,l) * kernel(2,3); - VERIFY_IS_APPROX(result, expected); - } - } - } - } - - cudaFree(d_input); - cudaFree(d_kernel); - cudaFree(d_out); -} - -template<int DataLayout> -void test_cuda_convolution_3d() -{ - Tensor<float, 5, DataLayout> input(Eigen::array<Eigen::DenseIndex, 5>(74,37,11,137,17)); - Tensor<float, 3, DataLayout> kernel(3,4,2); - Tensor<float, 5, DataLayout> out(Eigen::array<Eigen::DenseIndex, 5>(74,35,8,136,17)); - input = input.constant(10.0f) + input.random(); - kernel = kernel.constant(7.0f) + kernel.random(); - - std::size_t input_bytes = input.size() * sizeof(float); - std::size_t kernel_bytes = kernel.size() * sizeof(float); - std::size_t out_bytes = out.size() * sizeof(float); - - float* d_input; - float* d_kernel; - float* d_out; - cudaMalloc((void**)(&d_input), input_bytes); - cudaMalloc((void**)(&d_kernel), kernel_bytes); - cudaMalloc((void**)(&d_out), out_bytes); - - cudaMemcpy(d_input, input.data(), input_bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_kernel, kernel.data(), kernel_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> > gpu_input(d_input,74,37,11,137,17); - Eigen::TensorMap<Eigen::Tensor<float, 3, DataLayout> > gpu_kernel(d_kernel,3,4,2); - Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> > gpu_out(d_out,74,35,8,136,17); - - Eigen::array<Eigen::DenseIndex, 3> dims(1,2,3); - gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims); - - assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 74; ++i) { - for (int j = 0; j < 35; ++j) { - for (int k = 0; k < 8; ++k) { - for (int l = 0; l < 136; ++l) { - for (int m = 0; m < 17; ++m) { - const float result = out(i,j,k,l,m); - const float expected = input(i,j+0,k+0,l+0,m) * kernel(0,0,0) + - input(i,j+1,k+0,l+0,m) * kernel(1,0,0) + - input(i,j+2,k+0,l+0,m) * kernel(2,0,0) + - input(i,j+0,k+1,l+0,m) * kernel(0,1,0) + - input(i,j+1,k+1,l+0,m) * kernel(1,1,0) + - input(i,j+2,k+1,l+0,m) * kernel(2,1,0) + - input(i,j+0,k+2,l+0,m) * kernel(0,2,0) + - input(i,j+1,k+2,l+0,m) * kernel(1,2,0) + - input(i,j+2,k+2,l+0,m) * kernel(2,2,0) + - input(i,j+0,k+3,l+0,m) * kernel(0,3,0) + - input(i,j+1,k+3,l+0,m) * kernel(1,3,0) + - input(i,j+2,k+3,l+0,m) * kernel(2,3,0) + - input(i,j+0,k+0,l+1,m) * kernel(0,0,1) + - input(i,j+1,k+0,l+1,m) * kernel(1,0,1) + - input(i,j+2,k+0,l+1,m) * kernel(2,0,1) + - input(i,j+0,k+1,l+1,m) * kernel(0,1,1) + - input(i,j+1,k+1,l+1,m) * kernel(1,1,1) + - input(i,j+2,k+1,l+1,m) * kernel(2,1,1) + - input(i,j+0,k+2,l+1,m) * kernel(0,2,1) + - input(i,j+1,k+2,l+1,m) * kernel(1,2,1) + - input(i,j+2,k+2,l+1,m) * kernel(2,2,1) + - input(i,j+0,k+3,l+1,m) * kernel(0,3,1) + - input(i,j+1,k+3,l+1,m) * kernel(1,3,1) + - input(i,j+2,k+3,l+1,m) * kernel(2,3,1); - VERIFY_IS_APPROX(result, expected); - } - } - } - } - } - - cudaFree(d_input); - cudaFree(d_kernel); - cudaFree(d_out); -} - - -template <typename Scalar> -void test_cuda_lgamma(const Scalar stddev) -{ - Tensor<Scalar, 2> in(72,97); - in.setRandom(); - in *= in.constant(stddev); - Tensor<Scalar, 2> out(72,97); - out.setZero(); - - std::size_t bytes = in.size() * sizeof(Scalar); - - Scalar* d_in; - Scalar* d_out; - cudaMalloc((void**)(&d_in), bytes); - cudaMalloc((void**)(&d_out), bytes); - - cudaMemcpy(d_in, in.data(), bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_in(d_in, 72, 97); - Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_out(d_out, 72, 97); - - gpu_out.device(gpu_device) = gpu_in.lgamma(); - - assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 72; ++i) { - for (int j = 0; j < 97; ++j) { - VERIFY_IS_APPROX(out(i,j), (std::lgamma)(in(i,j))); - } - } - - cudaFree(d_in); - cudaFree(d_out); -} - -template <typename Scalar> -void test_cuda_digamma() -{ - Tensor<Scalar, 1> in(7); - Tensor<Scalar, 1> out(7); - Tensor<Scalar, 1> expected_out(7); - out.setZero(); - - in(0) = Scalar(1); - in(1) = Scalar(1.5); - in(2) = Scalar(4); - in(3) = Scalar(-10.5); - in(4) = Scalar(10000.5); - in(5) = Scalar(0); - in(6) = Scalar(-1); - - expected_out(0) = Scalar(-0.5772156649015329); - expected_out(1) = Scalar(0.03648997397857645); - expected_out(2) = Scalar(1.2561176684318); - expected_out(3) = Scalar(2.398239129535781); - expected_out(4) = Scalar(9.210340372392849); - expected_out(5) = std::numeric_limits<Scalar>::infinity(); - expected_out(6) = std::numeric_limits<Scalar>::infinity(); - - std::size_t bytes = in.size() * sizeof(Scalar); - - Scalar* d_in; - Scalar* d_out; - cudaMalloc((void**)(&d_in), bytes); - cudaMalloc((void**)(&d_out), bytes); - - cudaMemcpy(d_in, in.data(), bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in(d_in, 7); - Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 7); - - gpu_out.device(gpu_device) = gpu_in.digamma(); - - assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 5; ++i) { - VERIFY_IS_APPROX(out(i), expected_out(i)); - } - for (int i = 5; i < 7; ++i) { - VERIFY_IS_EQUAL(out(i), expected_out(i)); - } - - cudaFree(d_in); - cudaFree(d_out); -} - -template <typename Scalar> -void test_cuda_zeta() -{ - Tensor<Scalar, 1> in_x(6); - Tensor<Scalar, 1> in_q(6); - Tensor<Scalar, 1> out(6); - Tensor<Scalar, 1> expected_out(6); - out.setZero(); - - in_x(0) = Scalar(1); - in_x(1) = Scalar(1.5); - in_x(2) = Scalar(4); - in_x(3) = Scalar(-10.5); - in_x(4) = Scalar(10000.5); - in_x(5) = Scalar(3); - - in_q(0) = Scalar(1.2345); - in_q(1) = Scalar(2); - in_q(2) = Scalar(1.5); - in_q(3) = Scalar(3); - in_q(4) = Scalar(1.0001); - in_q(5) = Scalar(-2.5); - - expected_out(0) = std::numeric_limits<Scalar>::infinity(); - expected_out(1) = Scalar(1.61237534869); - expected_out(2) = Scalar(0.234848505667); - expected_out(3) = Scalar(1.03086757337e-5); - expected_out(4) = Scalar(0.367879440865); - expected_out(5) = Scalar(0.054102025820864097); - - std::size_t bytes = in_x.size() * sizeof(Scalar); - - Scalar* d_in_x; - Scalar* d_in_q; - Scalar* d_out; - cudaMalloc((void**)(&d_in_x), bytes); - cudaMalloc((void**)(&d_in_q), bytes); - cudaMalloc((void**)(&d_out), bytes); - - cudaMemcpy(d_in_x, in_x.data(), bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_in_q, in_q.data(), bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_x(d_in_x, 6); - Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_q(d_in_q, 6); - Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 6); - - gpu_out.device(gpu_device) = gpu_in_x.zeta(gpu_in_q); - - assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - VERIFY_IS_EQUAL(out(0), expected_out(0)); - VERIFY((std::isnan)(out(3))); - - for (int i = 1; i < 6; ++i) { - if (i != 3) { - VERIFY_IS_APPROX(out(i), expected_out(i)); - } - } - - cudaFree(d_in_x); - cudaFree(d_in_q); - cudaFree(d_out); -} - -template <typename Scalar> -void test_cuda_polygamma() -{ - Tensor<Scalar, 1> in_x(7); - Tensor<Scalar, 1> in_n(7); - Tensor<Scalar, 1> out(7); - Tensor<Scalar, 1> expected_out(7); - out.setZero(); - - in_n(0) = Scalar(1); - in_n(1) = Scalar(1); - in_n(2) = Scalar(1); - in_n(3) = Scalar(17); - in_n(4) = Scalar(31); - in_n(5) = Scalar(28); - in_n(6) = Scalar(8); - - in_x(0) = Scalar(2); - in_x(1) = Scalar(3); - in_x(2) = Scalar(25.5); - in_x(3) = Scalar(4.7); - in_x(4) = Scalar(11.8); - in_x(5) = Scalar(17.7); - in_x(6) = Scalar(30.2); - - expected_out(0) = Scalar(0.644934066848); - expected_out(1) = Scalar(0.394934066848); - expected_out(2) = Scalar(0.0399946696496); - expected_out(3) = Scalar(293.334565435); - expected_out(4) = Scalar(0.445487887616); - expected_out(5) = Scalar(-2.47810300902e-07); - expected_out(6) = Scalar(-8.29668781082e-09); - - std::size_t bytes = in_x.size() * sizeof(Scalar); - - Scalar* d_in_x; - Scalar* d_in_n; - Scalar* d_out; - cudaMalloc((void**)(&d_in_x), bytes); - cudaMalloc((void**)(&d_in_n), bytes); - cudaMalloc((void**)(&d_out), bytes); - - cudaMemcpy(d_in_x, in_x.data(), bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_in_n, in_n.data(), bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_x(d_in_x, 7); - Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_n(d_in_n, 7); - Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 7); - - gpu_out.device(gpu_device) = gpu_in_n.polygamma(gpu_in_x); - - assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 7; ++i) { - VERIFY_IS_APPROX(out(i), expected_out(i)); - } - - cudaFree(d_in_x); - cudaFree(d_in_n); - cudaFree(d_out); -} - -template <typename Scalar> -void test_cuda_igamma() -{ - Tensor<Scalar, 2> a(6, 6); - Tensor<Scalar, 2> x(6, 6); - Tensor<Scalar, 2> out(6, 6); - out.setZero(); - - Scalar a_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)}; - Scalar x_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)}; - - for (int i = 0; i < 6; ++i) { - for (int j = 0; j < 6; ++j) { - a(i, j) = a_s[i]; - x(i, j) = x_s[j]; - } - } - - Scalar nan = std::numeric_limits<Scalar>::quiet_NaN(); - Scalar igamma_s[][6] = {{0.0, nan, nan, nan, nan, nan}, - {0.0, 0.6321205588285578, 0.7768698398515702, - 0.9816843611112658, 9.999500016666262e-05, 1.0}, - {0.0, 0.4275932955291202, 0.608374823728911, - 0.9539882943107686, 7.522076445089201e-07, 1.0}, - {0.0, 0.01898815687615381, 0.06564245437845008, - 0.5665298796332909, 4.166333347221828e-18, 1.0}, - {0.0, 0.9999780593618628, 0.9999899967080838, - 0.9999996219837988, 0.9991370418689945, 1.0}, - {0.0, 0.0, 0.0, 0.0, 0.0, 0.5042041932513908}}; - - - - std::size_t bytes = a.size() * sizeof(Scalar); - - Scalar* d_a; - Scalar* d_x; - Scalar* d_out; - assert(cudaMalloc((void**)(&d_a), bytes) == cudaSuccess); - assert(cudaMalloc((void**)(&d_x), bytes) == cudaSuccess); - assert(cudaMalloc((void**)(&d_out), bytes) == cudaSuccess); - - cudaMemcpy(d_a, a.data(), bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_x, x.data(), bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_a(d_a, 6, 6); - Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_x(d_x, 6, 6); - Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_out(d_out, 6, 6); - - gpu_out.device(gpu_device) = gpu_a.igamma(gpu_x); - - assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 6; ++i) { - for (int j = 0; j < 6; ++j) { - if ((std::isnan)(igamma_s[i][j])) { - VERIFY((std::isnan)(out(i, j))); - } else { - VERIFY_IS_APPROX(out(i, j), igamma_s[i][j]); - } - } - } - - cudaFree(d_a); - cudaFree(d_x); - cudaFree(d_out); -} - -template <typename Scalar> -void test_cuda_igammac() -{ - Tensor<Scalar, 2> a(6, 6); - Tensor<Scalar, 2> x(6, 6); - Tensor<Scalar, 2> out(6, 6); - out.setZero(); - - Scalar a_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)}; - Scalar x_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)}; - - for (int i = 0; i < 6; ++i) { - for (int j = 0; j < 6; ++j) { - a(i, j) = a_s[i]; - x(i, j) = x_s[j]; - } - } - - Scalar nan = std::numeric_limits<Scalar>::quiet_NaN(); - Scalar igammac_s[][6] = {{nan, nan, nan, nan, nan, nan}, - {1.0, 0.36787944117144233, 0.22313016014842982, - 0.018315638888734182, 0.9999000049998333, 0.0}, - {1.0, 0.5724067044708798, 0.3916251762710878, - 0.04601170568923136, 0.9999992477923555, 0.0}, - {1.0, 0.9810118431238462, 0.9343575456215499, - 0.4334701203667089, 1.0, 0.0}, - {1.0, 2.1940638138146658e-05, 1.0003291916285e-05, - 3.7801620118431334e-07, 0.0008629581310054535, - 0.0}, - {1.0, 1.0, 1.0, 1.0, 1.0, 0.49579580674813944}}; - - std::size_t bytes = a.size() * sizeof(Scalar); - - Scalar* d_a; - Scalar* d_x; - Scalar* d_out; - cudaMalloc((void**)(&d_a), bytes); - cudaMalloc((void**)(&d_x), bytes); - cudaMalloc((void**)(&d_out), bytes); - - cudaMemcpy(d_a, a.data(), bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_x, x.data(), bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_a(d_a, 6, 6); - Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_x(d_x, 6, 6); - Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_out(d_out, 6, 6); - - gpu_out.device(gpu_device) = gpu_a.igammac(gpu_x); - - assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 6; ++i) { - for (int j = 0; j < 6; ++j) { - if ((std::isnan)(igammac_s[i][j])) { - VERIFY((std::isnan)(out(i, j))); - } else { - VERIFY_IS_APPROX(out(i, j), igammac_s[i][j]); - } - } - } - - cudaFree(d_a); - cudaFree(d_x); - cudaFree(d_out); -} - -template <typename Scalar> -void test_cuda_erf(const Scalar stddev) -{ - Tensor<Scalar, 2> in(72,97); - in.setRandom(); - in *= in.constant(stddev); - Tensor<Scalar, 2> out(72,97); - out.setZero(); - - std::size_t bytes = in.size() * sizeof(Scalar); - - Scalar* d_in; - Scalar* d_out; - assert(cudaMalloc((void**)(&d_in), bytes) == cudaSuccess); - assert(cudaMalloc((void**)(&d_out), bytes) == cudaSuccess); - - cudaMemcpy(d_in, in.data(), bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_in(d_in, 72, 97); - Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_out(d_out, 72, 97); - - gpu_out.device(gpu_device) = gpu_in.erf(); - - assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 72; ++i) { - for (int j = 0; j < 97; ++j) { - VERIFY_IS_APPROX(out(i,j), (std::erf)(in(i,j))); - } - } - - cudaFree(d_in); - cudaFree(d_out); -} - -template <typename Scalar> -void test_cuda_erfc(const Scalar stddev) -{ - Tensor<Scalar, 2> in(72,97); - in.setRandom(); - in *= in.constant(stddev); - Tensor<Scalar, 2> out(72,97); - out.setZero(); - - std::size_t bytes = in.size() * sizeof(Scalar); - - Scalar* d_in; - Scalar* d_out; - cudaMalloc((void**)(&d_in), bytes); - cudaMalloc((void**)(&d_out), bytes); - - cudaMemcpy(d_in, in.data(), bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_in(d_in, 72, 97); - Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_out(d_out, 72, 97); - - gpu_out.device(gpu_device) = gpu_in.erfc(); - - assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 0; i < 72; ++i) { - for (int j = 0; j < 97; ++j) { - VERIFY_IS_APPROX(out(i,j), (std::erfc)(in(i,j))); - } - } - - cudaFree(d_in); - cudaFree(d_out); -} - -template <typename Scalar> -void test_cuda_betainc() -{ - Tensor<Scalar, 1> in_x(125); - Tensor<Scalar, 1> in_a(125); - Tensor<Scalar, 1> in_b(125); - Tensor<Scalar, 1> out(125); - Tensor<Scalar, 1> expected_out(125); - out.setZero(); - - Scalar nan = std::numeric_limits<Scalar>::quiet_NaN(); - - Array<Scalar, 1, Dynamic> x(125); - Array<Scalar, 1, Dynamic> a(125); - Array<Scalar, 1, Dynamic> b(125); - Array<Scalar, 1, Dynamic> v(125); - - a << 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, - 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, - 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999, - 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, - 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, - 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999; - - b << 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999, - 0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999, - 999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999, 0.0, 0.0, - 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999, - 0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999, - 999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999, 0.0, 0.0, - 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999, - 0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999, - 999.999, 999.999, 999.999; - - x << -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, - 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, - 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, - 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, - 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, - -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, - 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, - 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, - 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1; - - v << nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, - nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, - nan, nan, 0.47972119876364683, 0.5, 0.5202788012363533, nan, nan, - 0.9518683957740043, 0.9789663010413743, 0.9931729188073435, nan, nan, - 0.999995949033062, 0.9999999999993698, 0.9999999999999999, nan, nan, - 0.9999999999999999, 0.9999999999999999, 0.9999999999999999, nan, nan, nan, - nan, nan, nan, nan, 0.006827081192655869, 0.0210336989586256, - 0.04813160422599567, nan, nan, 0.20014344256217678, 0.5000000000000001, - 0.7998565574378232, nan, nan, 0.9991401428435834, 0.999999999698403, - 0.9999999999999999, nan, nan, 0.9999999999999999, 0.9999999999999999, - 0.9999999999999999, nan, nan, nan, nan, nan, nan, nan, - 1.0646600232370887e-25, 6.301722877826246e-13, 4.050966937974938e-06, nan, - nan, 7.864342668429763e-23, 3.015969667594166e-10, 0.0008598571564165444, - nan, nan, 6.031987710123844e-08, 0.5000000000000007, 0.9999999396801229, - nan, nan, 0.9999999999999999, 0.9999999999999999, 0.9999999999999999, nan, - nan, nan, nan, nan, nan, nan, 0.0, 7.029920380986636e-306, - 2.2450728208591345e-101, nan, nan, 0.0, 9.275871147869727e-302, - 1.2232913026152827e-97, nan, nan, 0.0, 3.0891393081932924e-252, - 2.9303043666183996e-60, nan, nan, 2.248913486879199e-196, - 0.5000000000004947, 0.9999999999999999, nan; - - for (int i = 0; i < 125; ++i) { - in_x(i) = x(i); - in_a(i) = a(i); - in_b(i) = b(i); - expected_out(i) = v(i); - } - - std::size_t bytes = in_x.size() * sizeof(Scalar); - - Scalar* d_in_x; - Scalar* d_in_a; - Scalar* d_in_b; - Scalar* d_out; - cudaMalloc((void**)(&d_in_x), bytes); - cudaMalloc((void**)(&d_in_a), bytes); - cudaMalloc((void**)(&d_in_b), bytes); - cudaMalloc((void**)(&d_out), bytes); - - cudaMemcpy(d_in_x, in_x.data(), bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_in_a, in_a.data(), bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_in_b, in_b.data(), bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_x(d_in_x, 125); - Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_a(d_in_a, 125); - Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_b(d_in_b, 125); - Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 125); - - gpu_out.device(gpu_device) = betainc(gpu_in_a, gpu_in_b, gpu_in_x); - - assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - for (int i = 1; i < 125; ++i) { - if ((std::isnan)(expected_out(i))) { - VERIFY((std::isnan)(out(i))); - } else { - VERIFY_IS_APPROX(out(i), expected_out(i)); - } - } - - cudaFree(d_in_x); - cudaFree(d_in_a); - cudaFree(d_in_b); - cudaFree(d_out); -} - - -void test_cxx11_tensor_cuda() -{ - CALL_SUBTEST_1(test_cuda_nullary()); - CALL_SUBTEST_1(test_cuda_elementwise_small()); - CALL_SUBTEST_1(test_cuda_elementwise()); - CALL_SUBTEST_1(test_cuda_props()); - CALL_SUBTEST_1(test_cuda_reduction()); - CALL_SUBTEST_2(test_cuda_contraction<ColMajor>()); - CALL_SUBTEST_2(test_cuda_contraction<RowMajor>()); - CALL_SUBTEST_3(test_cuda_convolution_1d<ColMajor>()); - CALL_SUBTEST_3(test_cuda_convolution_1d<RowMajor>()); - CALL_SUBTEST_3(test_cuda_convolution_inner_dim_col_major_1d()); - CALL_SUBTEST_3(test_cuda_convolution_inner_dim_row_major_1d()); - CALL_SUBTEST_3(test_cuda_convolution_2d<ColMajor>()); - CALL_SUBTEST_3(test_cuda_convolution_2d<RowMajor>()); - CALL_SUBTEST_3(test_cuda_convolution_3d<ColMajor>()); - CALL_SUBTEST_3(test_cuda_convolution_3d<RowMajor>()); - -#if __cplusplus > 199711L - // std::erf, std::erfc, and so on where only added in c++11. We use them - // as a golden reference to validate the results produced by Eigen. Therefore - // we can only run these tests if we use a c++11 compiler. - CALL_SUBTEST_4(test_cuda_lgamma<float>(1.0f)); - CALL_SUBTEST_4(test_cuda_lgamma<float>(100.0f)); - CALL_SUBTEST_4(test_cuda_lgamma<float>(0.01f)); - CALL_SUBTEST_4(test_cuda_lgamma<float>(0.001f)); - - CALL_SUBTEST_4(test_cuda_lgamma<double>(1.0)); - CALL_SUBTEST_4(test_cuda_lgamma<double>(100.0)); - CALL_SUBTEST_4(test_cuda_lgamma<double>(0.01)); - CALL_SUBTEST_4(test_cuda_lgamma<double>(0.001)); - - CALL_SUBTEST_4(test_cuda_erf<float>(1.0f)); - CALL_SUBTEST_4(test_cuda_erf<float>(100.0f)); - CALL_SUBTEST_4(test_cuda_erf<float>(0.01f)); - CALL_SUBTEST_4(test_cuda_erf<float>(0.001f)); - - CALL_SUBTEST_4(test_cuda_erfc<float>(1.0f)); - // CALL_SUBTEST(test_cuda_erfc<float>(100.0f)); - CALL_SUBTEST_4(test_cuda_erfc<float>(5.0f)); // CUDA erfc lacks precision for large inputs - CALL_SUBTEST_4(test_cuda_erfc<float>(0.01f)); - CALL_SUBTEST_4(test_cuda_erfc<float>(0.001f)); - - CALL_SUBTEST_4(test_cuda_erf<double>(1.0)); - CALL_SUBTEST_4(test_cuda_erf<double>(100.0)); - CALL_SUBTEST_4(test_cuda_erf<double>(0.01)); - CALL_SUBTEST_4(test_cuda_erf<double>(0.001)); - - CALL_SUBTEST_4(test_cuda_erfc<double>(1.0)); - // CALL_SUBTEST(test_cuda_erfc<double>(100.0)); - CALL_SUBTEST_4(test_cuda_erfc<double>(5.0)); // CUDA erfc lacks precision for large inputs - CALL_SUBTEST_4(test_cuda_erfc<double>(0.01)); - CALL_SUBTEST_4(test_cuda_erfc<double>(0.001)); - - CALL_SUBTEST_5(test_cuda_digamma<float>()); - CALL_SUBTEST_5(test_cuda_digamma<double>()); - - CALL_SUBTEST_5(test_cuda_polygamma<float>()); - CALL_SUBTEST_5(test_cuda_polygamma<double>()); - - CALL_SUBTEST_5(test_cuda_zeta<float>()); - CALL_SUBTEST_5(test_cuda_zeta<double>()); - - CALL_SUBTEST_5(test_cuda_igamma<float>()); - CALL_SUBTEST_5(test_cuda_igammac<float>()); - - CALL_SUBTEST_5(test_cuda_igamma<double>()); - CALL_SUBTEST_5(test_cuda_igammac<double>()); - - CALL_SUBTEST_6(test_cuda_betainc<float>()); - CALL_SUBTEST_6(test_cuda_betainc<double>()); -#endif -} diff --git a/eigen/unsupported/test/cxx11_tensor_custom_index.cpp b/eigen/unsupported/test/cxx11_tensor_custom_index.cpp deleted file mode 100644 index 4528cc1..0000000 --- a/eigen/unsupported/test/cxx11_tensor_custom_index.cpp +++ /dev/null @@ -1,100 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <limits> -#include <map> - -#include <Eigen/Dense> -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - - -template <int DataLayout> -static void test_map_as_index() -{ -#ifdef EIGEN_HAS_SFINAE - Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7); - tensor.setRandom(); - - using NormalIndex = DSizes<ptrdiff_t, 4>; - using CustomIndex = std::map<ptrdiff_t, ptrdiff_t>; - CustomIndex coeffC; - coeffC[0] = 1; - coeffC[1] = 2; - coeffC[2] = 4; - coeffC[3] = 1; - NormalIndex coeff(1,2,4,1); - - VERIFY_IS_EQUAL(tensor.coeff(coeffC), tensor.coeff(coeff)); - VERIFY_IS_EQUAL(tensor.coeffRef(coeffC), tensor.coeffRef(coeff)); -#endif -} - - -template <int DataLayout> -static void test_matrix_as_index() -{ -#ifdef EIGEN_HAS_SFINAE - Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7); - tensor.setRandom(); - - using NormalIndex = DSizes<ptrdiff_t, 4>; - using CustomIndex = Matrix<unsigned int, 4, 1>; - CustomIndex coeffC(1,2,4,1); - NormalIndex coeff(1,2,4,1); - - VERIFY_IS_EQUAL(tensor.coeff(coeffC), tensor.coeff(coeff)); - VERIFY_IS_EQUAL(tensor.coeffRef(coeffC), tensor.coeffRef(coeff)); -#endif -} - - -template <int DataLayout> -static void test_varlist_as_index() -{ -#ifdef EIGEN_HAS_SFINAE - Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7); - tensor.setRandom(); - - DSizes<ptrdiff_t, 4> coeff(1,2,4,1); - - VERIFY_IS_EQUAL(tensor.coeff({1,2,4,1}), tensor.coeff(coeff)); - VERIFY_IS_EQUAL(tensor.coeffRef({1,2,4,1}), tensor.coeffRef(coeff)); -#endif -} - - -template <int DataLayout> -static void test_sizes_as_index() -{ -#ifdef EIGEN_HAS_SFINAE - Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7); - tensor.setRandom(); - - DSizes<ptrdiff_t, 4> coeff(1,2,4,1); - Sizes<1,2,4,1> coeffC; - - VERIFY_IS_EQUAL(tensor.coeff(coeffC), tensor.coeff(coeff)); - VERIFY_IS_EQUAL(tensor.coeffRef(coeffC), tensor.coeffRef(coeff)); -#endif -} - - -void test_cxx11_tensor_custom_index() { - test_map_as_index<ColMajor>(); - test_map_as_index<RowMajor>(); - test_matrix_as_index<ColMajor>(); - test_matrix_as_index<RowMajor>(); - test_varlist_as_index<ColMajor>(); - test_varlist_as_index<RowMajor>(); - test_sizes_as_index<ColMajor>(); - test_sizes_as_index<RowMajor>(); -} diff --git a/eigen/unsupported/test/cxx11_tensor_custom_op.cpp b/eigen/unsupported/test/cxx11_tensor_custom_op.cpp deleted file mode 100644 index 8baa477..0000000 --- a/eigen/unsupported/test/cxx11_tensor_custom_op.cpp +++ /dev/null @@ -1,111 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - - -struct InsertZeros { - DSizes<DenseIndex, 2> dimensions(const Tensor<float, 2>& input) const { - DSizes<DenseIndex, 2> result; - result[0] = input.dimension(0) * 2; - result[1] = input.dimension(1) * 2; - return result; - } - - template <typename Output, typename Device> - void eval(const Tensor<float, 2>& input, Output& output, const Device& device) const - { - array<DenseIndex, 2> strides; - strides[0] = 2; - strides[1] = 2; - output.stride(strides).device(device) = input; - - Eigen::DSizes<DenseIndex, 2> offsets(1,1); - Eigen::DSizes<DenseIndex, 2> extents(output.dimension(0)-1, output.dimension(1)-1); - output.slice(offsets, extents).stride(strides).device(device) = input.constant(0.0f); - } -}; - -static void test_custom_unary_op() -{ - Tensor<float, 2> tensor(3,5); - tensor.setRandom(); - - Tensor<float, 2> result = tensor.customOp(InsertZeros()); - VERIFY_IS_EQUAL(result.dimension(0), 6); - VERIFY_IS_EQUAL(result.dimension(1), 10); - - for (int i = 0; i < 6; i+=2) { - for (int j = 0; j < 10; j+=2) { - VERIFY_IS_EQUAL(result(i, j), tensor(i/2, j/2)); - } - } - for (int i = 1; i < 6; i+=2) { - for (int j = 1; j < 10; j+=2) { - VERIFY_IS_EQUAL(result(i, j), 0); - } - } -} - - -struct BatchMatMul { - DSizes<DenseIndex, 3> dimensions(const Tensor<float, 3>& input1, const Tensor<float, 3>& input2) const { - DSizes<DenseIndex, 3> result; - result[0] = input1.dimension(0); - result[1] = input2.dimension(1); - result[2] = input2.dimension(2); - return result; - } - - template <typename Output, typename Device> - void eval(const Tensor<float, 3>& input1, const Tensor<float, 3>& input2, - Output& output, const Device& device) const - { - typedef Tensor<float, 3>::DimensionPair DimPair; - array<DimPair, 1> dims; - dims[0] = DimPair(1, 0); - for (int i = 0; i < output.dimension(2); ++i) { - output.template chip<2>(i).device(device) = input1.chip<2>(i).contract(input2.chip<2>(i), dims); - } - } -}; - - -static void test_custom_binary_op() -{ - Tensor<float, 3> tensor1(2,3,5); - tensor1.setRandom(); - Tensor<float, 3> tensor2(3,7,5); - tensor2.setRandom(); - - Tensor<float, 3> result = tensor1.customOp(tensor2, BatchMatMul()); - for (int i = 0; i < 5; ++i) { - typedef Tensor<float, 3>::DimensionPair DimPair; - array<DimPair, 1> dims; - dims[0] = DimPair(1, 0); - Tensor<float, 2> reference = tensor1.chip<2>(i).contract(tensor2.chip<2>(i), dims); - TensorRef<Tensor<float, 2> > val = result.chip<2>(i); - for (int j = 0; j < 2; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_APPROX(val(j, k), reference(j, k)); - } - } - } -} - - -void test_cxx11_tensor_custom_op() -{ - CALL_SUBTEST(test_custom_unary_op()); - CALL_SUBTEST(test_custom_binary_op()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_device.cu b/eigen/unsupported/test/cxx11_tensor_device.cu deleted file mode 100644 index cbb43e2..0000000 --- a/eigen/unsupported/test/cxx11_tensor_device.cu +++ /dev/null @@ -1,387 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_NO_COMPLEX -#define EIGEN_TEST_FUNC cxx11_tensor_device -#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int -#define EIGEN_USE_GPU - -#include "main.h" -#include <unsupported/Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::RowMajor; - -// Context for evaluation on cpu -struct CPUContext { - CPUContext(const Eigen::Tensor<float, 3>& in1, Eigen::Tensor<float, 3>& in2, Eigen::Tensor<float, 3>& out) : in1_(in1), in2_(in2), out_(out), kernel_1d_(2), kernel_2d_(2,2), kernel_3d_(2,2,2) { - kernel_1d_(0) = 3.14f; - kernel_1d_(1) = 2.7f; - - kernel_2d_(0,0) = 3.14f; - kernel_2d_(1,0) = 2.7f; - kernel_2d_(0,1) = 0.2f; - kernel_2d_(1,1) = 7.0f; - - kernel_3d_(0,0,0) = 3.14f; - kernel_3d_(0,1,0) = 2.7f; - kernel_3d_(0,0,1) = 0.2f; - kernel_3d_(0,1,1) = 7.0f; - kernel_3d_(1,0,0) = -1.0f; - kernel_3d_(1,1,0) = -0.3f; - kernel_3d_(1,0,1) = -0.7f; - kernel_3d_(1,1,1) = -0.5f; - } - - const Eigen::DefaultDevice& device() const { return cpu_device_; } - - const Eigen::Tensor<float, 3>& in1() const { return in1_; } - const Eigen::Tensor<float, 3>& in2() const { return in2_; } - Eigen::Tensor<float, 3>& out() { return out_; } - const Eigen::Tensor<float, 1>& kernel1d() const { return kernel_1d_; } - const Eigen::Tensor<float, 2>& kernel2d() const { return kernel_2d_; } - const Eigen::Tensor<float, 3>& kernel3d() const { return kernel_3d_; } - - private: - const Eigen::Tensor<float, 3>& in1_; - const Eigen::Tensor<float, 3>& in2_; - Eigen::Tensor<float, 3>& out_; - - Eigen::Tensor<float, 1> kernel_1d_; - Eigen::Tensor<float, 2> kernel_2d_; - Eigen::Tensor<float, 3> kernel_3d_; - - Eigen::DefaultDevice cpu_device_; -}; - - -// Context for evaluation on GPU -struct GPUContext { - GPUContext(const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in1, Eigen::TensorMap<Eigen::Tensor<float, 3> >& in2, Eigen::TensorMap<Eigen::Tensor<float, 3> >& out) : in1_(in1), in2_(in2), out_(out), gpu_device_(&stream_) { - assert(cudaMalloc((void**)(&kernel_1d_), 2*sizeof(float)) == cudaSuccess); - float kernel_1d_val[] = {3.14f, 2.7f}; - assert(cudaMemcpy(kernel_1d_, kernel_1d_val, 2*sizeof(float), cudaMemcpyHostToDevice) == cudaSuccess); - - assert(cudaMalloc((void**)(&kernel_2d_), 4*sizeof(float)) == cudaSuccess); - float kernel_2d_val[] = {3.14f, 2.7f, 0.2f, 7.0f}; - assert(cudaMemcpy(kernel_2d_, kernel_2d_val, 4*sizeof(float), cudaMemcpyHostToDevice) == cudaSuccess); - - assert(cudaMalloc((void**)(&kernel_3d_), 8*sizeof(float)) == cudaSuccess); - float kernel_3d_val[] = {3.14f, -1.0f, 2.7f, -0.3f, 0.2f, -0.7f, 7.0f, -0.5f}; - assert(cudaMemcpy(kernel_3d_, kernel_3d_val, 8*sizeof(float), cudaMemcpyHostToDevice) == cudaSuccess); - } - ~GPUContext() { - assert(cudaFree(kernel_1d_) == cudaSuccess); - assert(cudaFree(kernel_2d_) == cudaSuccess); - assert(cudaFree(kernel_3d_) == cudaSuccess); - } - - const Eigen::GpuDevice& device() const { return gpu_device_; } - - const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in1() const { return in1_; } - const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in2() const { return in2_; } - Eigen::TensorMap<Eigen::Tensor<float, 3> >& out() { return out_; } - Eigen::TensorMap<Eigen::Tensor<float, 1> > kernel1d() const { return Eigen::TensorMap<Eigen::Tensor<float, 1> >(kernel_1d_, 2); } - Eigen::TensorMap<Eigen::Tensor<float, 2> > kernel2d() const { return Eigen::TensorMap<Eigen::Tensor<float, 2> >(kernel_2d_, 2, 2); } - Eigen::TensorMap<Eigen::Tensor<float, 3> > kernel3d() const { return Eigen::TensorMap<Eigen::Tensor<float, 3> >(kernel_3d_, 2, 2, 2); } - - private: - const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in1_; - const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in2_; - Eigen::TensorMap<Eigen::Tensor<float, 3> >& out_; - - float* kernel_1d_; - float* kernel_2d_; - float* kernel_3d_; - - Eigen::CudaStreamDevice stream_; - Eigen::GpuDevice gpu_device_; -}; - - -// The actual expression to evaluate -template <typename Context> -void test_contextual_eval(Context* context) -{ - context->out().device(context->device()) = context->in1() + context->in2() * 3.14f + context->in1().constant(2.718f); -} - -template <typename Context> -void test_forced_contextual_eval(Context* context) -{ - context->out().device(context->device()) = (context->in1() + context->in2()).eval() * 3.14f + context->in1().constant(2.718f); -} - -template <typename Context> -void test_compound_assignment(Context* context) -{ - context->out().device(context->device()) = context->in1().constant(2.718f); - context->out().device(context->device()) += context->in1() + context->in2() * 3.14f; -} - - -template <typename Context> -void test_contraction(Context* context) -{ - Eigen::array<std::pair<int, int>, 2> dims; - dims[0] = std::make_pair(1, 1); - dims[1] = std::make_pair(2, 2); - - Eigen::array<int, 2> shape(40, 50*70); - - Eigen::DSizes<int, 2> indices(0,0); - Eigen::DSizes<int, 2> sizes(40,40); - - context->out().reshape(shape).slice(indices, sizes).device(context->device()) = context->in1().contract(context->in2(), dims); -} - - -template <typename Context> -void test_1d_convolution(Context* context) -{ - Eigen::DSizes<int, 3> indices(0,0,0); - Eigen::DSizes<int, 3> sizes(40,49,70); - - Eigen::array<int, 1> dims(1); - context->out().slice(indices, sizes).device(context->device()) = context->in1().convolve(context->kernel1d(), dims); -} - -template <typename Context> -void test_2d_convolution(Context* context) -{ - Eigen::DSizes<int, 3> indices(0,0,0); - Eigen::DSizes<int, 3> sizes(40,49,69); - - Eigen::array<int, 2> dims(1,2); - context->out().slice(indices, sizes).device(context->device()) = context->in1().convolve(context->kernel2d(), dims); -} - -template <typename Context> -void test_3d_convolution(Context* context) -{ - Eigen::DSizes<int, 3> indices(0,0,0); - Eigen::DSizes<int, 3> sizes(39,49,69); - - Eigen::array<int, 3> dims(0,1,2); - context->out().slice(indices, sizes).device(context->device()) = context->in1().convolve(context->kernel3d(), dims); -} - - -void test_cpu() { - Eigen::Tensor<float, 3> in1(40,50,70); - Eigen::Tensor<float, 3> in2(40,50,70); - Eigen::Tensor<float, 3> out(40,50,70); - - in1 = in1.random() + in1.constant(10.0f); - in2 = in2.random() + in2.constant(10.0f); - - CPUContext context(in1, in2, out); - test_contextual_eval(&context); - for (int i = 0; i < 40; ++i) { - for (int j = 0; j < 50; ++j) { - for (int k = 0; k < 70; ++k) { - VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) + in2(i,j,k) * 3.14f + 2.718f); - } - } - } - - test_forced_contextual_eval(&context); - for (int i = 0; i < 40; ++i) { - for (int j = 0; j < 50; ++j) { - for (int k = 0; k < 70; ++k) { - VERIFY_IS_APPROX(out(i,j,k), (in1(i,j,k) + in2(i,j,k)) * 3.14f + 2.718f); - } - } - } - - test_compound_assignment(&context); - for (int i = 0; i < 40; ++i) { - for (int j = 0; j < 50; ++j) { - for (int k = 0; k < 70; ++k) { - VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) + in2(i,j,k) * 3.14f + 2.718f); - } - } - } - - test_contraction(&context); - for (int i = 0; i < 40; ++i) { - for (int j = 0; j < 40; ++j) { - const float result = out(i,j,0); - float expected = 0; - for (int k = 0; k < 50; ++k) { - for (int l = 0; l < 70; ++l) { - expected += in1(i, k, l) * in2(j, k, l); - } - } - VERIFY_IS_APPROX(expected, result); - } - } - - test_1d_convolution(&context); - for (int i = 0; i < 40; ++i) { - for (int j = 0; j < 49; ++j) { - for (int k = 0; k < 70; ++k) { - VERIFY_IS_APPROX(out(i,j,k), (in1(i,j,k) * 3.14f + in1(i,j+1,k) * 2.7f)); - } - } - } - - test_2d_convolution(&context); - for (int i = 0; i < 40; ++i) { - for (int j = 0; j < 49; ++j) { - for (int k = 0; k < 69; ++k) { - const float result = out(i,j,k); - const float expected = (in1(i,j,k) * 3.14f + in1(i,j+1,k) * 2.7f) + - (in1(i,j,k+1) * 0.2f + in1(i,j+1,k+1) * 7.0f); - if (fabs(expected) < 1e-4f && fabs(result) < 1e-4f) { - continue; - } - VERIFY_IS_APPROX(expected, result); - } - } - } - - test_3d_convolution(&context); - for (int i = 0; i < 39; ++i) { - for (int j = 0; j < 49; ++j) { - for (int k = 0; k < 69; ++k) { - const float result = out(i,j,k); - const float expected = (in1(i,j,k) * 3.14f + in1(i,j+1,k) * 2.7f + - in1(i,j,k+1) * 0.2f + in1(i,j+1,k+1) * 7.0f) + - (in1(i+1,j,k) * -1.0f + in1(i+1,j+1,k) * -0.3f + - in1(i+1,j,k+1) * -0.7f + in1(i+1,j+1,k+1) * -0.5f); - if (fabs(expected) < 1e-4f && fabs(result) < 1e-4f) { - continue; - } - VERIFY_IS_APPROX(expected, result); - } - } - } -} - -void test_gpu() { - Eigen::Tensor<float, 3> in1(40,50,70); - Eigen::Tensor<float, 3> in2(40,50,70); - Eigen::Tensor<float, 3> out(40,50,70); - in1 = in1.random() + in1.constant(10.0f); - in2 = in2.random() + in2.constant(10.0f); - - std::size_t in1_bytes = in1.size() * sizeof(float); - std::size_t in2_bytes = in2.size() * sizeof(float); - std::size_t out_bytes = out.size() * sizeof(float); - - float* d_in1; - float* d_in2; - float* d_out; - cudaMalloc((void**)(&d_in1), in1_bytes); - cudaMalloc((void**)(&d_in2), in2_bytes); - cudaMalloc((void**)(&d_out), out_bytes); - - cudaMemcpy(d_in1, in1.data(), in1_bytes, cudaMemcpyHostToDevice); - cudaMemcpy(d_in2, in2.data(), in2_bytes, cudaMemcpyHostToDevice); - - Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in1(d_in1, 40,50,70); - Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in2(d_in2, 40,50,70); - Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_out(d_out, 40,50,70); - - GPUContext context(gpu_in1, gpu_in2, gpu_out); - test_contextual_eval(&context); - assert(cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost) == cudaSuccess); - for (int i = 0; i < 40; ++i) { - for (int j = 0; j < 50; ++j) { - for (int k = 0; k < 70; ++k) { - VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) + in2(i,j,k) * 3.14f + 2.718f); - } - } - } - - test_forced_contextual_eval(&context); - assert(cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost) == cudaSuccess); - for (int i = 0; i < 40; ++i) { - for (int j = 0; j < 50; ++j) { - for (int k = 0; k < 70; ++k) { - VERIFY_IS_APPROX(out(i,j,k), (in1(i,j,k) + in2(i,j,k)) * 3.14f + 2.718f); - } - } - } - - test_compound_assignment(&context); - assert(cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost) == cudaSuccess); - for (int i = 0; i < 40; ++i) { - for (int j = 0; j < 50; ++j) { - for (int k = 0; k < 70; ++k) { - VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) + in2(i,j,k) * 3.14f + 2.718f); - } - } - } - - test_contraction(&context); - assert(cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost) == cudaSuccess); - for (int i = 0; i < 40; ++i) { - for (int j = 0; j < 40; ++j) { - const float result = out(i,j,0); - float expected = 0; - for (int k = 0; k < 50; ++k) { - for (int l = 0; l < 70; ++l) { - expected += in1(i, k, l) * in2(j, k, l); - } - } - VERIFY_IS_APPROX(expected, result); - } - } - - test_1d_convolution(&context); - assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, context.device().stream()) == cudaSuccess); - assert(cudaStreamSynchronize(context.device().stream()) == cudaSuccess); - for (int i = 0; i < 40; ++i) { - for (int j = 0; j < 49; ++j) { - for (int k = 0; k < 70; ++k) { - VERIFY_IS_APPROX(out(i,j,k), (in1(i,j,k) * 3.14f + in1(i,j+1,k) * 2.7f)); - } - } - } - - test_2d_convolution(&context); - assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, context.device().stream()) == cudaSuccess); - assert(cudaStreamSynchronize(context.device().stream()) == cudaSuccess); - for (int i = 0; i < 40; ++i) { - for (int j = 0; j < 49; ++j) { - for (int k = 0; k < 69; ++k) { - const float result = out(i,j,k); - const float expected = (in1(i,j,k) * 3.14f + in1(i,j+1,k) * 2.7f + - in1(i,j,k+1) * 0.2f + in1(i,j+1,k+1) * 7.0f); - VERIFY_IS_APPROX(expected, result); - } - } - } - - test_3d_convolution(&context); - assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, context.device().stream()) == cudaSuccess); - assert(cudaStreamSynchronize(context.device().stream()) == cudaSuccess); - for (int i = 0; i < 39; ++i) { - for (int j = 0; j < 49; ++j) { - for (int k = 0; k < 69; ++k) { - const float result = out(i,j,k); - const float expected = (in1(i,j,k) * 3.14f + in1(i,j+1,k) * 2.7f + - in1(i,j,k+1) * 0.2f + in1(i,j+1,k+1) * 7.0f + - in1(i+1,j,k) * -1.0f + in1(i+1,j+1,k) * -0.3f + - in1(i+1,j,k+1) * -0.7f + in1(i+1,j+1,k+1) * -0.5f); - VERIFY_IS_APPROX(expected, result); - } - } - } -} - - -void test_cxx11_tensor_device() -{ - CALL_SUBTEST_1(test_cpu()); - CALL_SUBTEST_2(test_gpu()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_device_sycl.cpp b/eigen/unsupported/test/cxx11_tensor_device_sycl.cpp deleted file mode 100644 index 7f79753..0000000 --- a/eigen/unsupported/test/cxx11_tensor_device_sycl.cpp +++ /dev/null @@ -1,31 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 -// Mehdi Goli Codeplay Software Ltd. -// Ralph Potter Codeplay Software Ltd. -// Luke Iwanski Codeplay Software Ltd. -// Contact: <eigen@codeplay.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_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_NO_COMPLEX -#define EIGEN_TEST_FUNC cxx11_tensor_device_sycl -#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int -#define EIGEN_USE_SYCL - -#include "main.h" -#include <unsupported/Eigen/CXX11/Tensor> - -void test_device_sycl(const Eigen::SyclDevice &sycl_device) { - std::cout <<"Helo from ComputeCpp: the requested device exists and the device name is : " - << sycl_device.m_queue.get_device(). template get_info<cl::sycl::info::device::name>() <<std::endl;; -} -void test_cxx11_tensor_device_sycl() { - cl::sycl::gpu_selector s; - Eigen::SyclDevice sycl_device(s); - CALL_SUBTEST(test_device_sycl(sycl_device)); -} diff --git a/eigen/unsupported/test/cxx11_tensor_dimension.cpp b/eigen/unsupported/test/cxx11_tensor_dimension.cpp deleted file mode 100644 index 16f168e..0000000 --- a/eigen/unsupported/test/cxx11_tensor_dimension.cpp +++ /dev/null @@ -1,69 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - - -static void test_dynamic_size() -{ - Eigen::DSizes<int, 3> dimensions(2,3,7); - - VERIFY_IS_EQUAL((int)Eigen::internal::array_get<0>(dimensions), 2); - VERIFY_IS_EQUAL((int)Eigen::internal::array_get<1>(dimensions), 3); - VERIFY_IS_EQUAL((int)Eigen::internal::array_get<2>(dimensions), 7); - VERIFY_IS_EQUAL((int)dimensions.TotalSize(), 2*3*7); - VERIFY_IS_EQUAL((int)dimensions[0], 2); - VERIFY_IS_EQUAL((int)dimensions[1], 3); - VERIFY_IS_EQUAL((int)dimensions[2], 7); -} - -static void test_fixed_size() -{ - Eigen::Sizes<2,3,7> dimensions; - - VERIFY_IS_EQUAL((int)Eigen::internal::array_get<0>(dimensions), 2); - VERIFY_IS_EQUAL((int)Eigen::internal::array_get<1>(dimensions), 3); - VERIFY_IS_EQUAL((int)Eigen::internal::array_get<2>(dimensions), 7); - VERIFY_IS_EQUAL((int)dimensions.TotalSize(), 2*3*7); -} - -static void test_match() -{ - Eigen::DSizes<unsigned int, 3> dyn((unsigned int)2,(unsigned int)3,(unsigned int)7); - Eigen::Sizes<2,3,7> stat; - VERIFY_IS_EQUAL(Eigen::dimensions_match(dyn, stat), true); - - Eigen::DSizes<int, 3> dyn1(2,3,7); - Eigen::DSizes<int, 2> dyn2(2,3); - VERIFY_IS_EQUAL(Eigen::dimensions_match(dyn1, dyn2), false); -} - -static void test_rank_zero() -{ - Eigen::Sizes<> scalar; - VERIFY_IS_EQUAL((int)scalar.TotalSize(), 1); - VERIFY_IS_EQUAL((int)scalar.rank(), 0); - VERIFY_IS_EQUAL((int)internal::array_prod(scalar), 1); - - Eigen::DSizes<ptrdiff_t, 0> dscalar; - VERIFY_IS_EQUAL((int)dscalar.TotalSize(), 1); - VERIFY_IS_EQUAL((int)dscalar.rank(), 0); -} - -void test_cxx11_tensor_dimension() -{ - CALL_SUBTEST(test_dynamic_size()); - CALL_SUBTEST(test_fixed_size()); - CALL_SUBTEST(test_match()); - CALL_SUBTEST(test_rank_zero()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_empty.cpp b/eigen/unsupported/test/cxx11_tensor_empty.cpp deleted file mode 100644 index d7eea42..0000000 --- a/eigen/unsupported/test/cxx11_tensor_empty.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - - -static void test_empty_tensor() -{ - Tensor<float, 2> source; - Tensor<float, 2> tgt1 = source; - Tensor<float, 2> tgt2(source); - Tensor<float, 2> tgt3; - tgt3 = tgt1; - tgt3 = tgt2; -} - -static void test_empty_fixed_size_tensor() -{ - TensorFixedSize<float, Sizes<0> > source; - TensorFixedSize<float, Sizes<0> > tgt1 = source; - TensorFixedSize<float, Sizes<0> > tgt2(source); - TensorFixedSize<float, Sizes<0> > tgt3; - tgt3 = tgt1; - tgt3 = tgt2; -} - - -void test_cxx11_tensor_empty() -{ - CALL_SUBTEST(test_empty_tensor()); - CALL_SUBTEST(test_empty_fixed_size_tensor()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_expr.cpp b/eigen/unsupported/test/cxx11_tensor_expr.cpp deleted file mode 100644 index 77e24cb..0000000 --- a/eigen/unsupported/test/cxx11_tensor_expr.cpp +++ /dev/null @@ -1,314 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::RowMajor; - -static void test_1d() -{ - Tensor<float, 1> vec1(6); - Tensor<float, 1, RowMajor> vec2(6); - - vec1(0) = 4.0; vec2(0) = 0.0; - vec1(1) = 8.0; vec2(1) = 1.0; - vec1(2) = 15.0; vec2(2) = 2.0; - vec1(3) = 16.0; vec2(3) = 3.0; - vec1(4) = 23.0; vec2(4) = 4.0; - vec1(5) = 42.0; vec2(5) = 5.0; - - float data3[6]; - TensorMap<Tensor<float, 1>> vec3(data3, 6); - vec3 = vec1.sqrt(); - float data4[6]; - TensorMap<Tensor<float, 1, RowMajor>> vec4(data4, 6); - vec4 = vec2.square(); - float data5[6]; - TensorMap<Tensor<float, 1, RowMajor>> vec5(data5, 6); - vec5 = vec2.cube(); - - VERIFY_IS_APPROX(vec3(0), sqrtf(4.0)); - VERIFY_IS_APPROX(vec3(1), sqrtf(8.0)); - VERIFY_IS_APPROX(vec3(2), sqrtf(15.0)); - VERIFY_IS_APPROX(vec3(3), sqrtf(16.0)); - VERIFY_IS_APPROX(vec3(4), sqrtf(23.0)); - VERIFY_IS_APPROX(vec3(5), sqrtf(42.0)); - - VERIFY_IS_APPROX(vec4(0), 0.0f); - VERIFY_IS_APPROX(vec4(1), 1.0f); - VERIFY_IS_APPROX(vec4(2), 2.0f * 2.0f); - VERIFY_IS_APPROX(vec4(3), 3.0f * 3.0f); - VERIFY_IS_APPROX(vec4(4), 4.0f * 4.0f); - VERIFY_IS_APPROX(vec4(5), 5.0f * 5.0f); - - VERIFY_IS_APPROX(vec5(0), 0.0f); - VERIFY_IS_APPROX(vec5(1), 1.0f); - VERIFY_IS_APPROX(vec5(2), 2.0f * 2.0f * 2.0f); - VERIFY_IS_APPROX(vec5(3), 3.0f * 3.0f * 3.0f); - VERIFY_IS_APPROX(vec5(4), 4.0f * 4.0f * 4.0f); - VERIFY_IS_APPROX(vec5(5), 5.0f * 5.0f * 5.0f); - - vec3 = vec1 + vec2; - VERIFY_IS_APPROX(vec3(0), 4.0f + 0.0f); - VERIFY_IS_APPROX(vec3(1), 8.0f + 1.0f); - VERIFY_IS_APPROX(vec3(2), 15.0f + 2.0f); - VERIFY_IS_APPROX(vec3(3), 16.0f + 3.0f); - VERIFY_IS_APPROX(vec3(4), 23.0f + 4.0f); - VERIFY_IS_APPROX(vec3(5), 42.0f + 5.0f); -} - -static void test_2d() -{ - float data1[6]; - TensorMap<Tensor<float, 2>> mat1(data1, 2, 3); - float data2[6]; - TensorMap<Tensor<float, 2, RowMajor>> mat2(data2, 2, 3); - - mat1(0,0) = 0.0; - mat1(0,1) = 1.0; - mat1(0,2) = 2.0; - mat1(1,0) = 3.0; - mat1(1,1) = 4.0; - mat1(1,2) = 5.0; - - mat2(0,0) = -0.0; - mat2(0,1) = -1.0; - mat2(0,2) = -2.0; - mat2(1,0) = -3.0; - mat2(1,1) = -4.0; - mat2(1,2) = -5.0; - - Tensor<float, 2> mat3(2,3); - Tensor<float, 2, RowMajor> mat4(2,3); - mat3 = mat1.abs(); - mat4 = mat2.abs(); - - VERIFY_IS_APPROX(mat3(0,0), 0.0f); - VERIFY_IS_APPROX(mat3(0,1), 1.0f); - VERIFY_IS_APPROX(mat3(0,2), 2.0f); - VERIFY_IS_APPROX(mat3(1,0), 3.0f); - VERIFY_IS_APPROX(mat3(1,1), 4.0f); - VERIFY_IS_APPROX(mat3(1,2), 5.0f); - - VERIFY_IS_APPROX(mat4(0,0), 0.0f); - VERIFY_IS_APPROX(mat4(0,1), 1.0f); - VERIFY_IS_APPROX(mat4(0,2), 2.0f); - VERIFY_IS_APPROX(mat4(1,0), 3.0f); - VERIFY_IS_APPROX(mat4(1,1), 4.0f); - VERIFY_IS_APPROX(mat4(1,2), 5.0f); -} - -static void test_3d() -{ - Tensor<float, 3> mat1(2,3,7); - Tensor<float, 3, RowMajor> mat2(2,3,7); - - float val = 1.0f; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - mat1(i,j,k) = val; - mat2(i,j,k) = val; - val += 1.0f; - } - } - } - - Tensor<float, 3> mat3(2,3,7); - mat3 = mat1 + mat1; - Tensor<float, 3, RowMajor> mat4(2,3,7); - mat4 = mat2 * 3.14f; - Tensor<float, 3> mat5(2,3,7); - mat5 = mat1.inverse().log(); - Tensor<float, 3, RowMajor> mat6(2,3,7); - mat6 = mat2.pow(0.5f) * 3.14f; - Tensor<float, 3> mat7(2,3,7); - mat7 = mat1.cwiseMax(mat5 * 2.0f).exp(); - Tensor<float, 3, RowMajor> mat8(2,3,7); - mat8 = (-mat2).exp() * 3.14f; - Tensor<float, 3, RowMajor> mat9(2,3,7); - mat9 = mat2 + 3.14f; - Tensor<float, 3, RowMajor> mat10(2,3,7); - mat10 = mat2 - 3.14f; - Tensor<float, 3, RowMajor> mat11(2,3,7); - mat11 = mat2 / 3.14f; - - val = 1.0f; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_APPROX(mat3(i,j,k), val + val); - VERIFY_IS_APPROX(mat4(i,j,k), val * 3.14f); - VERIFY_IS_APPROX(mat5(i,j,k), logf(1.0f/val)); - VERIFY_IS_APPROX(mat6(i,j,k), sqrtf(val) * 3.14f); - VERIFY_IS_APPROX(mat7(i,j,k), expf((std::max)(val, mat5(i,j,k) * 2.0f))); - VERIFY_IS_APPROX(mat8(i,j,k), expf(-val) * 3.14f); - VERIFY_IS_APPROX(mat9(i,j,k), val + 3.14f); - VERIFY_IS_APPROX(mat10(i,j,k), val - 3.14f); - VERIFY_IS_APPROX(mat11(i,j,k), val / 3.14f); - val += 1.0f; - } - } - } -} - -static void test_constants() -{ - Tensor<float, 3> mat1(2,3,7); - Tensor<float, 3> mat2(2,3,7); - Tensor<float, 3> mat3(2,3,7); - - float val = 1.0f; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - mat1(i,j,k) = val; - val += 1.0f; - } - } - } - mat2 = mat1.constant(3.14f); - mat3 = mat1.cwiseMax(7.3f).exp(); - - val = 1.0f; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_APPROX(mat2(i,j,k), 3.14f); - VERIFY_IS_APPROX(mat3(i,j,k), expf((std::max)(val, 7.3f))); - val += 1.0f; - } - } - } -} - -static void test_boolean() -{ - Tensor<int, 1> vec(6); - std::copy_n(std::begin({0, 1, 2, 3, 4, 5}), 6, vec.data()); - - // Test ||. - Tensor<bool, 1> bool1 = vec < vec.constant(1) || vec > vec.constant(4); - VERIFY_IS_EQUAL(bool1[0], true); - VERIFY_IS_EQUAL(bool1[1], false); - VERIFY_IS_EQUAL(bool1[2], false); - VERIFY_IS_EQUAL(bool1[3], false); - VERIFY_IS_EQUAL(bool1[4], false); - VERIFY_IS_EQUAL(bool1[5], true); - - // Test &&, including cast of operand vec. - Tensor<bool, 1> bool2 = vec.cast<bool>() && vec < vec.constant(4); - VERIFY_IS_EQUAL(bool2[0], false); - VERIFY_IS_EQUAL(bool2[1], true); - VERIFY_IS_EQUAL(bool2[2], true); - VERIFY_IS_EQUAL(bool2[3], true); - VERIFY_IS_EQUAL(bool2[4], false); - VERIFY_IS_EQUAL(bool2[5], false); - - // Compilation tests: - // Test Tensor<bool> against results of cast or comparison; verifies that - // CoeffReturnType is set to match Op return type of bool for Unary and Binary - // Ops. - Tensor<bool, 1> bool3 = vec.cast<bool>() && bool2; - bool3 = vec < vec.constant(4) && bool2; -} - -static void test_functors() -{ - Tensor<float, 3> mat1(2,3,7); - Tensor<float, 3> mat2(2,3,7); - Tensor<float, 3> mat3(2,3,7); - - float val = 1.0f; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - mat1(i,j,k) = val; - val += 1.0f; - } - } - } - mat2 = mat1.inverse().unaryExpr(&asinf); - mat3 = mat1.unaryExpr(&tanhf); - - val = 1.0f; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_APPROX(mat2(i,j,k), asinf(1.0f / mat1(i,j,k))); - VERIFY_IS_APPROX(mat3(i,j,k), tanhf(mat1(i,j,k))); - val += 1.0f; - } - } - } -} - -static void test_type_casting() -{ - Tensor<bool, 3> mat1(2,3,7); - Tensor<float, 3> mat2(2,3,7); - Tensor<double, 3> mat3(2,3,7); - mat1.setRandom(); - mat2.setRandom(); - - mat3 = mat1.cast<double>(); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_APPROX(mat3(i,j,k), mat1(i,j,k) ? 1.0 : 0.0); - } - } - } - - mat3 = mat2.cast<double>(); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_APPROX(mat3(i,j,k), static_cast<double>(mat2(i,j,k))); - } - } - } -} - -static void test_select() -{ - Tensor<float, 3> selector(2,3,7); - Tensor<float, 3> mat1(2,3,7); - Tensor<float, 3> mat2(2,3,7); - Tensor<float, 3> result(2,3,7); - - selector.setRandom(); - mat1.setRandom(); - mat2.setRandom(); - result = (selector > selector.constant(0.5f)).select(mat1, mat2); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_APPROX(result(i,j,k), (selector(i,j,k) > 0.5f) ? mat1(i,j,k) : mat2(i,j,k)); - } - } - } -} - - -void test_cxx11_tensor_expr() -{ - CALL_SUBTEST(test_1d()); - CALL_SUBTEST(test_2d()); - CALL_SUBTEST(test_3d()); - CALL_SUBTEST(test_constants()); - CALL_SUBTEST(test_boolean()); - CALL_SUBTEST(test_functors()); - CALL_SUBTEST(test_type_casting()); - CALL_SUBTEST(test_select()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_fft.cpp b/eigen/unsupported/test/cxx11_tensor_fft.cpp deleted file mode 100644 index 2f14ebc..0000000 --- a/eigen/unsupported/test/cxx11_tensor_fft.cpp +++ /dev/null @@ -1,273 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Jianwei Cui <thucjw@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/CXX11/Tensor> - -using Eigen::Tensor; - -template <int DataLayout> -static void test_fft_2D_golden() { - Tensor<float, 2, DataLayout> input(2, 3); - input(0, 0) = 1; - input(0, 1) = 2; - input(0, 2) = 3; - input(1, 0) = 4; - input(1, 1) = 5; - input(1, 2) = 6; - - array<ptrdiff_t, 2> fft; - fft[0] = 0; - fft[1] = 1; - - Tensor<std::complex<float>, 2, DataLayout> output = input.template fft<Eigen::BothParts, Eigen::FFT_FORWARD>(fft); - - std::complex<float> output_golden[6]; // in ColMajor order - output_golden[0] = std::complex<float>(21, 0); - output_golden[1] = std::complex<float>(-9, 0); - output_golden[2] = std::complex<float>(-3, 1.73205); - output_golden[3] = std::complex<float>( 0, 0); - output_golden[4] = std::complex<float>(-3, -1.73205); - output_golden[5] = std::complex<float>(0 ,0); - - std::complex<float> c_offset = std::complex<float>(1.0, 1.0); - - if (DataLayout == ColMajor) { - VERIFY_IS_APPROX(output(0) + c_offset, output_golden[0] + c_offset); - VERIFY_IS_APPROX(output(1) + c_offset, output_golden[1] + c_offset); - VERIFY_IS_APPROX(output(2) + c_offset, output_golden[2] + c_offset); - VERIFY_IS_APPROX(output(3) + c_offset, output_golden[3] + c_offset); - VERIFY_IS_APPROX(output(4) + c_offset, output_golden[4] + c_offset); - VERIFY_IS_APPROX(output(5) + c_offset, output_golden[5] + c_offset); - } - else { - VERIFY_IS_APPROX(output(0)+ c_offset, output_golden[0]+ c_offset); - VERIFY_IS_APPROX(output(1)+ c_offset, output_golden[2]+ c_offset); - VERIFY_IS_APPROX(output(2)+ c_offset, output_golden[4]+ c_offset); - VERIFY_IS_APPROX(output(3)+ c_offset, output_golden[1]+ c_offset); - VERIFY_IS_APPROX(output(4)+ c_offset, output_golden[3]+ c_offset); - VERIFY_IS_APPROX(output(5)+ c_offset, output_golden[5]+ c_offset); - } -} - -static void test_fft_complex_input_golden() { - Tensor<std::complex<float>, 1, ColMajor> input(5); - input(0) = std::complex<float>(1, 1); - input(1) = std::complex<float>(2, 2); - input(2) = std::complex<float>(3, 3); - input(3) = std::complex<float>(4, 4); - input(4) = std::complex<float>(5, 5); - - array<ptrdiff_t, 1> fft; - fft[0] = 0; - - Tensor<std::complex<float>, 1, ColMajor> forward_output_both_parts = input.fft<BothParts, FFT_FORWARD>(fft); - Tensor<std::complex<float>, 1, ColMajor> reverse_output_both_parts = input.fft<BothParts, FFT_REVERSE>(fft); - - Tensor<float, 1, ColMajor> forward_output_real_part = input.fft<RealPart, FFT_FORWARD>(fft); - Tensor<float, 1, ColMajor> reverse_output_real_part = input.fft<RealPart, FFT_REVERSE>(fft); - - Tensor<float, 1, ColMajor> forward_output_imag_part = input.fft<ImagPart, FFT_FORWARD>(fft); - Tensor<float, 1, ColMajor> reverse_output_imag_part = input.fft<ImagPart, FFT_REVERSE>(fft); - - VERIFY_IS_EQUAL(forward_output_both_parts.dimension(0), input.dimension(0)); - VERIFY_IS_EQUAL(reverse_output_both_parts.dimension(0), input.dimension(0)); - - VERIFY_IS_EQUAL(forward_output_real_part.dimension(0), input.dimension(0)); - VERIFY_IS_EQUAL(reverse_output_real_part.dimension(0), input.dimension(0)); - - VERIFY_IS_EQUAL(forward_output_imag_part.dimension(0), input.dimension(0)); - VERIFY_IS_EQUAL(reverse_output_imag_part.dimension(0), input.dimension(0)); - - std::complex<float> forward_golden_result[5]; - std::complex<float> reverse_golden_result[5]; - - forward_golden_result[0] = std::complex<float>(15.000000000000000,+15.000000000000000); - forward_golden_result[1] = std::complex<float>(-5.940954801177935, +0.940954801177934); - forward_golden_result[2] = std::complex<float>(-3.312299240582266, -1.687700759417735); - forward_golden_result[3] = std::complex<float>(-1.687700759417735, -3.312299240582266); - forward_golden_result[4] = std::complex<float>( 0.940954801177934, -5.940954801177935); - - reverse_golden_result[0] = std::complex<float>( 3.000000000000000, + 3.000000000000000); - reverse_golden_result[1] = std::complex<float>( 0.188190960235587, - 1.188190960235587); - reverse_golden_result[2] = std::complex<float>(-0.337540151883547, - 0.662459848116453); - reverse_golden_result[3] = std::complex<float>(-0.662459848116453, - 0.337540151883547); - reverse_golden_result[4] = std::complex<float>(-1.188190960235587, + 0.188190960235587); - - for(int i = 0; i < 5; ++i) { - VERIFY_IS_APPROX(forward_output_both_parts(i), forward_golden_result[i]); - VERIFY_IS_APPROX(forward_output_real_part(i), forward_golden_result[i].real()); - VERIFY_IS_APPROX(forward_output_imag_part(i), forward_golden_result[i].imag()); - } - - for(int i = 0; i < 5; ++i) { - VERIFY_IS_APPROX(reverse_output_both_parts(i), reverse_golden_result[i]); - VERIFY_IS_APPROX(reverse_output_real_part(i), reverse_golden_result[i].real()); - VERIFY_IS_APPROX(reverse_output_imag_part(i), reverse_golden_result[i].imag()); - } -} - -static void test_fft_real_input_golden() { - Tensor<float, 1, ColMajor> input(5); - input(0) = 1.0; - input(1) = 2.0; - input(2) = 3.0; - input(3) = 4.0; - input(4) = 5.0; - - array<ptrdiff_t, 1> fft; - fft[0] = 0; - - Tensor<std::complex<float>, 1, ColMajor> forward_output_both_parts = input.fft<BothParts, FFT_FORWARD>(fft); - Tensor<std::complex<float>, 1, ColMajor> reverse_output_both_parts = input.fft<BothParts, FFT_REVERSE>(fft); - - Tensor<float, 1, ColMajor> forward_output_real_part = input.fft<RealPart, FFT_FORWARD>(fft); - Tensor<float, 1, ColMajor> reverse_output_real_part = input.fft<RealPart, FFT_REVERSE>(fft); - - Tensor<float, 1, ColMajor> forward_output_imag_part = input.fft<ImagPart, FFT_FORWARD>(fft); - Tensor<float, 1, ColMajor> reverse_output_imag_part = input.fft<ImagPart, FFT_REVERSE>(fft); - - VERIFY_IS_EQUAL(forward_output_both_parts.dimension(0), input.dimension(0)); - VERIFY_IS_EQUAL(reverse_output_both_parts.dimension(0), input.dimension(0)); - - VERIFY_IS_EQUAL(forward_output_real_part.dimension(0), input.dimension(0)); - VERIFY_IS_EQUAL(reverse_output_real_part.dimension(0), input.dimension(0)); - - VERIFY_IS_EQUAL(forward_output_imag_part.dimension(0), input.dimension(0)); - VERIFY_IS_EQUAL(reverse_output_imag_part.dimension(0), input.dimension(0)); - - std::complex<float> forward_golden_result[5]; - std::complex<float> reverse_golden_result[5]; - - - forward_golden_result[0] = std::complex<float>( 15, 0); - forward_golden_result[1] = std::complex<float>(-2.5, +3.44095480117793); - forward_golden_result[2] = std::complex<float>(-2.5, +0.81229924058227); - forward_golden_result[3] = std::complex<float>(-2.5, -0.81229924058227); - forward_golden_result[4] = std::complex<float>(-2.5, -3.44095480117793); - - reverse_golden_result[0] = std::complex<float>( 3.0, 0); - reverse_golden_result[1] = std::complex<float>(-0.5, -0.688190960235587); - reverse_golden_result[2] = std::complex<float>(-0.5, -0.162459848116453); - reverse_golden_result[3] = std::complex<float>(-0.5, +0.162459848116453); - reverse_golden_result[4] = std::complex<float>(-0.5, +0.688190960235587); - - std::complex<float> c_offset(1.0, 1.0); - float r_offset = 1.0; - - for(int i = 0; i < 5; ++i) { - VERIFY_IS_APPROX(forward_output_both_parts(i) + c_offset, forward_golden_result[i] + c_offset); - VERIFY_IS_APPROX(forward_output_real_part(i) + r_offset, forward_golden_result[i].real() + r_offset); - VERIFY_IS_APPROX(forward_output_imag_part(i) + r_offset, forward_golden_result[i].imag() + r_offset); - } - - for(int i = 0; i < 5; ++i) { - VERIFY_IS_APPROX(reverse_output_both_parts(i) + c_offset, reverse_golden_result[i] + c_offset); - VERIFY_IS_APPROX(reverse_output_real_part(i) + r_offset, reverse_golden_result[i].real() + r_offset); - VERIFY_IS_APPROX(reverse_output_imag_part(i) + r_offset, reverse_golden_result[i].imag() + r_offset); - } -} - - -template <int DataLayout, typename RealScalar, bool isComplexInput, int FFTResultType, int FFTDirection, int TensorRank> -static void test_fft_real_input_energy() { - - Eigen::DSizes<ptrdiff_t, TensorRank> dimensions; - ptrdiff_t total_size = 1; - for (int i = 0; i < TensorRank; ++i) { - dimensions[i] = rand() % 20 + 1; - total_size *= dimensions[i]; - } - const DSizes<ptrdiff_t, TensorRank> arr = dimensions; - - typedef typename internal::conditional<isComplexInput == true, std::complex<RealScalar>, RealScalar>::type InputScalar; - - Tensor<InputScalar, TensorRank, DataLayout> input; - input.resize(arr); - input.setRandom(); - - array<ptrdiff_t, TensorRank> fft; - for (int i = 0; i < TensorRank; ++i) { - fft[i] = i; - } - - typedef typename internal::conditional<FFTResultType == Eigen::BothParts, std::complex<RealScalar>, RealScalar>::type OutputScalar; - Tensor<OutputScalar, TensorRank, DataLayout> output; - output = input.template fft<FFTResultType, FFTDirection>(fft); - - for (int i = 0; i < TensorRank; ++i) { - VERIFY_IS_EQUAL(output.dimension(i), input.dimension(i)); - } - - RealScalar energy_original = 0.0; - RealScalar energy_after_fft = 0.0; - - for (int i = 0; i < total_size; ++i) { - energy_original += numext::abs2(input(i)); - } - - for (int i = 0; i < total_size; ++i) { - energy_after_fft += numext::abs2(output(i)); - } - - if(FFTDirection == FFT_FORWARD) { - VERIFY_IS_APPROX(energy_original, energy_after_fft / total_size); - } - else { - VERIFY_IS_APPROX(energy_original, energy_after_fft * total_size); - } -} - -void test_cxx11_tensor_fft() { - test_fft_complex_input_golden(); - test_fft_real_input_golden(); - - test_fft_2D_golden<ColMajor>(); - test_fft_2D_golden<RowMajor>(); - - test_fft_real_input_energy<ColMajor, float, true, Eigen::BothParts, FFT_FORWARD, 1>(); - test_fft_real_input_energy<ColMajor, double, true, Eigen::BothParts, FFT_FORWARD, 1>(); - test_fft_real_input_energy<ColMajor, float, false, Eigen::BothParts, FFT_FORWARD, 1>(); - test_fft_real_input_energy<ColMajor, double, false, Eigen::BothParts, FFT_FORWARD, 1>(); - - test_fft_real_input_energy<ColMajor, float, true, Eigen::BothParts, FFT_FORWARD, 2>(); - test_fft_real_input_energy<ColMajor, double, true, Eigen::BothParts, FFT_FORWARD, 2>(); - test_fft_real_input_energy<ColMajor, float, false, Eigen::BothParts, FFT_FORWARD, 2>(); - test_fft_real_input_energy<ColMajor, double, false, Eigen::BothParts, FFT_FORWARD, 2>(); - - test_fft_real_input_energy<ColMajor, float, true, Eigen::BothParts, FFT_FORWARD, 3>(); - test_fft_real_input_energy<ColMajor, double, true, Eigen::BothParts, FFT_FORWARD, 3>(); - test_fft_real_input_energy<ColMajor, float, false, Eigen::BothParts, FFT_FORWARD, 3>(); - test_fft_real_input_energy<ColMajor, double, false, Eigen::BothParts, FFT_FORWARD, 3>(); - - test_fft_real_input_energy<ColMajor, float, true, Eigen::BothParts, FFT_FORWARD, 4>(); - test_fft_real_input_energy<ColMajor, double, true, Eigen::BothParts, FFT_FORWARD, 4>(); - test_fft_real_input_energy<ColMajor, float, false, Eigen::BothParts, FFT_FORWARD, 4>(); - test_fft_real_input_energy<ColMajor, double, false, Eigen::BothParts, FFT_FORWARD, 4>(); - - test_fft_real_input_energy<RowMajor, float, true, Eigen::BothParts, FFT_FORWARD, 1>(); - test_fft_real_input_energy<RowMajor, double, true, Eigen::BothParts, FFT_FORWARD, 1>(); - test_fft_real_input_energy<RowMajor, float, false, Eigen::BothParts, FFT_FORWARD, 1>(); - test_fft_real_input_energy<RowMajor, double, false, Eigen::BothParts, FFT_FORWARD, 1>(); - - test_fft_real_input_energy<RowMajor, float, true, Eigen::BothParts, FFT_FORWARD, 2>(); - test_fft_real_input_energy<RowMajor, double, true, Eigen::BothParts, FFT_FORWARD, 2>(); - test_fft_real_input_energy<RowMajor, float, false, Eigen::BothParts, FFT_FORWARD, 2>(); - test_fft_real_input_energy<RowMajor, double, false, Eigen::BothParts, FFT_FORWARD, 2>(); - - test_fft_real_input_energy<RowMajor, float, true, Eigen::BothParts, FFT_FORWARD, 3>(); - test_fft_real_input_energy<RowMajor, double, true, Eigen::BothParts, FFT_FORWARD, 3>(); - test_fft_real_input_energy<RowMajor, float, false, Eigen::BothParts, FFT_FORWARD, 3>(); - test_fft_real_input_energy<RowMajor, double, false, Eigen::BothParts, FFT_FORWARD, 3>(); - - test_fft_real_input_energy<RowMajor, float, true, Eigen::BothParts, FFT_FORWARD, 4>(); - test_fft_real_input_energy<RowMajor, double, true, Eigen::BothParts, FFT_FORWARD, 4>(); - test_fft_real_input_energy<RowMajor, float, false, Eigen::BothParts, FFT_FORWARD, 4>(); - test_fft_real_input_energy<RowMajor, double, false, Eigen::BothParts, FFT_FORWARD, 4>(); -} diff --git a/eigen/unsupported/test/cxx11_tensor_fixed_size.cpp b/eigen/unsupported/test/cxx11_tensor_fixed_size.cpp deleted file mode 100644 index 4c660de..0000000 --- a/eigen/unsupported/test/cxx11_tensor_fixed_size.cpp +++ /dev/null @@ -1,261 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::RowMajor; - - -static void test_0d() -{ - TensorFixedSize<float, Sizes<> > scalar1; - TensorFixedSize<float, Sizes<>, RowMajor> scalar2; - VERIFY_IS_EQUAL(scalar1.rank(), 0); - VERIFY_IS_EQUAL(scalar1.size(), 1); - VERIFY_IS_EQUAL(array_prod(scalar1.dimensions()), 1); - - scalar1() = 7.0; - scalar2() = 13.0; - - // Test against shallow copy. - TensorFixedSize<float, Sizes<> > copy = scalar1; - VERIFY_IS_NOT_EQUAL(scalar1.data(), copy.data()); - VERIFY_IS_APPROX(scalar1(), copy()); - copy = scalar1; - VERIFY_IS_NOT_EQUAL(scalar1.data(), copy.data()); - VERIFY_IS_APPROX(scalar1(), copy()); - - TensorFixedSize<float, Sizes<> > scalar3 = scalar1.sqrt(); - TensorFixedSize<float, Sizes<>, RowMajor> scalar4 = scalar2.sqrt(); - VERIFY_IS_EQUAL(scalar3.rank(), 0); - VERIFY_IS_APPROX(scalar3(), sqrtf(7.0)); - VERIFY_IS_APPROX(scalar4(), sqrtf(13.0)); - - scalar3 = scalar1 + scalar2; - VERIFY_IS_APPROX(scalar3(), 7.0f + 13.0f); -} - -static void test_1d() -{ - TensorFixedSize<float, Sizes<6> > vec1; - TensorFixedSize<float, Sizes<6>, RowMajor> vec2; - - VERIFY_IS_EQUAL((vec1.size()), 6); - // VERIFY_IS_EQUAL((vec1.dimensions()[0]), 6); - // VERIFY_IS_EQUAL((vec1.dimension(0)), 6); - - vec1(0) = 4.0; vec2(0) = 0.0; - vec1(1) = 8.0; vec2(1) = 1.0; - vec1(2) = 15.0; vec2(2) = 2.0; - vec1(3) = 16.0; vec2(3) = 3.0; - vec1(4) = 23.0; vec2(4) = 4.0; - vec1(5) = 42.0; vec2(5) = 5.0; - - // Test against shallow copy. - TensorFixedSize<float, Sizes<6> > copy = vec1; - VERIFY_IS_NOT_EQUAL(vec1.data(), copy.data()); - for (int i = 0; i < 6; ++i) { - VERIFY_IS_APPROX(vec1(i), copy(i)); - } - copy = vec1; - VERIFY_IS_NOT_EQUAL(vec1.data(), copy.data()); - for (int i = 0; i < 6; ++i) { - VERIFY_IS_APPROX(vec1(i), copy(i)); - } - - TensorFixedSize<float, Sizes<6> > vec3 = vec1.sqrt(); - TensorFixedSize<float, Sizes<6>, RowMajor> vec4 = vec2.sqrt(); - - VERIFY_IS_EQUAL((vec3.size()), 6); - VERIFY_IS_EQUAL(vec3.rank(), 1); - // VERIFY_IS_EQUAL((vec3.dimensions()[0]), 6); - // VERIFY_IS_EQUAL((vec3.dimension(0)), 6); - - VERIFY_IS_APPROX(vec3(0), sqrtf(4.0)); - VERIFY_IS_APPROX(vec3(1), sqrtf(8.0)); - VERIFY_IS_APPROX(vec3(2), sqrtf(15.0)); - VERIFY_IS_APPROX(vec3(3), sqrtf(16.0)); - VERIFY_IS_APPROX(vec3(4), sqrtf(23.0)); - VERIFY_IS_APPROX(vec3(5), sqrtf(42.0)); - - VERIFY_IS_APPROX(vec4(0), sqrtf(0.0)); - VERIFY_IS_APPROX(vec4(1), sqrtf(1.0)); - VERIFY_IS_APPROX(vec4(2), sqrtf(2.0)); - VERIFY_IS_APPROX(vec4(3), sqrtf(3.0)); - VERIFY_IS_APPROX(vec4(4), sqrtf(4.0)); - VERIFY_IS_APPROX(vec4(5), sqrtf(5.0)); - - vec3 = vec1 + vec2; - VERIFY_IS_APPROX(vec3(0), 4.0f + 0.0f); - VERIFY_IS_APPROX(vec3(1), 8.0f + 1.0f); - VERIFY_IS_APPROX(vec3(2), 15.0f + 2.0f); - VERIFY_IS_APPROX(vec3(3), 16.0f + 3.0f); - VERIFY_IS_APPROX(vec3(4), 23.0f + 4.0f); - VERIFY_IS_APPROX(vec3(5), 42.0f + 5.0f); -} - -static void test_tensor_map() -{ - TensorFixedSize<float, Sizes<6> > vec1; - TensorFixedSize<float, Sizes<6>, RowMajor> vec2; - - vec1(0) = 4.0; vec2(0) = 0.0; - vec1(1) = 8.0; vec2(1) = 1.0; - vec1(2) = 15.0; vec2(2) = 2.0; - vec1(3) = 16.0; vec2(3) = 3.0; - vec1(4) = 23.0; vec2(4) = 4.0; - vec1(5) = 42.0; vec2(5) = 5.0; - - float data3[6]; - TensorMap<TensorFixedSize<float, Sizes<6> > > vec3(data3, 6); - vec3 = vec1.sqrt() + vec2; - - VERIFY_IS_APPROX(vec3(0), sqrtf(4.0)); - VERIFY_IS_APPROX(vec3(1), sqrtf(8.0) + 1.0f); - VERIFY_IS_APPROX(vec3(2), sqrtf(15.0) + 2.0f); - VERIFY_IS_APPROX(vec3(3), sqrtf(16.0) + 3.0f); - VERIFY_IS_APPROX(vec3(4), sqrtf(23.0) + 4.0f); - VERIFY_IS_APPROX(vec3(5), sqrtf(42.0) + 5.0f); -} - -static void test_2d() -{ - float data1[6]; - TensorMap<TensorFixedSize<float, Sizes<2, 3> > > mat1(data1,2,3); - float data2[6]; - TensorMap<TensorFixedSize<float, Sizes<2, 3>, RowMajor> > mat2(data2,2,3); - - VERIFY_IS_EQUAL((mat1.size()), 2*3); - VERIFY_IS_EQUAL(mat1.rank(), 2); - // VERIFY_IS_EQUAL((mat1.dimension(0)), 2); - // VERIFY_IS_EQUAL((mat1.dimension(1)), 3); - - mat1(0,0) = 0.0; - mat1(0,1) = 1.0; - mat1(0,2) = 2.0; - mat1(1,0) = 3.0; - mat1(1,1) = 4.0; - mat1(1,2) = 5.0; - - mat2(0,0) = -0.0; - mat2(0,1) = -1.0; - mat2(0,2) = -2.0; - mat2(1,0) = -3.0; - mat2(1,1) = -4.0; - mat2(1,2) = -5.0; - - TensorFixedSize<float, Sizes<2, 3> > mat3; - TensorFixedSize<float, Sizes<2, 3>, RowMajor> mat4; - mat3 = mat1.abs(); - mat4 = mat2.abs(); - - VERIFY_IS_EQUAL((mat3.size()), 2*3); - // VERIFY_IS_EQUAL((mat3.dimension(0)), 2); - // VERIFY_IS_EQUAL((mat3.dimension(1)), 3); - - VERIFY_IS_APPROX(mat3(0,0), 0.0f); - VERIFY_IS_APPROX(mat3(0,1), 1.0f); - VERIFY_IS_APPROX(mat3(0,2), 2.0f); - VERIFY_IS_APPROX(mat3(1,0), 3.0f); - VERIFY_IS_APPROX(mat3(1,1), 4.0f); - VERIFY_IS_APPROX(mat3(1,2), 5.0f); - - VERIFY_IS_APPROX(mat4(0,0), 0.0f); - VERIFY_IS_APPROX(mat4(0,1), 1.0f); - VERIFY_IS_APPROX(mat4(0,2), 2.0f); - VERIFY_IS_APPROX(mat4(1,0), 3.0f); - VERIFY_IS_APPROX(mat4(1,1), 4.0f); - VERIFY_IS_APPROX(mat4(1,2), 5.0f); -} - -static void test_3d() -{ - TensorFixedSize<float, Sizes<2, 3, 7> > mat1; - TensorFixedSize<float, Sizes<2, 3, 7>, RowMajor> mat2; - - VERIFY_IS_EQUAL((mat1.size()), 2*3*7); - VERIFY_IS_EQUAL(mat1.rank(), 3); - // VERIFY_IS_EQUAL((mat1.dimension(0)), 2); - // VERIFY_IS_EQUAL((mat1.dimension(1)), 3); - // VERIFY_IS_EQUAL((mat1.dimension(2)), 7); - - float val = 0.0f; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - mat1(i,j,k) = val; - mat2(i,j,k) = val; - val += 1.0f; - } - } - } - - TensorFixedSize<float, Sizes<2, 3, 7> > mat3; - mat3 = mat1.sqrt(); - TensorFixedSize<float, Sizes<2, 3, 7>, RowMajor> mat4; - mat4 = mat2.sqrt(); - - VERIFY_IS_EQUAL((mat3.size()), 2*3*7); - // VERIFY_IS_EQUAL((mat3.dimension(0)), 2); - // VERIFY_IS_EQUAL((mat3.dimension(1)), 3); - // VERIFY_IS_EQUAL((mat3.dimension(2)), 7); - - - val = 0.0f; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_APPROX(mat3(i,j,k), sqrtf(val)); - VERIFY_IS_APPROX(mat4(i,j,k), sqrtf(val)); - val += 1.0f; - } - } - } -} - - -static void test_array() -{ - TensorFixedSize<float, Sizes<2, 3, 7> > mat1; - float val = 0.0f; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - mat1(i,j,k) = val; - val += 1.0f; - } - } - } - - TensorFixedSize<float, Sizes<2, 3, 7> > mat3; - mat3 = mat1.pow(3.5f); - - val = 0.0f; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_APPROX(mat3(i,j,k), powf(val, 3.5f)); - val += 1.0f; - } - } - } -} - -void test_cxx11_tensor_fixed_size() -{ - CALL_SUBTEST(test_0d()); - CALL_SUBTEST(test_1d()); - CALL_SUBTEST(test_tensor_map()); - CALL_SUBTEST(test_2d()); - CALL_SUBTEST(test_3d()); - CALL_SUBTEST(test_array()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_forced_eval.cpp b/eigen/unsupported/test/cxx11_tensor_forced_eval.cpp deleted file mode 100644 index 45d7345..0000000 --- a/eigen/unsupported/test/cxx11_tensor_forced_eval.cpp +++ /dev/null @@ -1,79 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/Core> -#include <Eigen/CXX11/Tensor> - -using Eigen::MatrixXf; -using Eigen::Tensor; - -static void test_simple() -{ - MatrixXf m1(3,3); - MatrixXf m2(3,3); - m1.setRandom(); - m2.setRandom(); - - TensorMap<Tensor<float, 2> > mat1(m1.data(), 3,3); - TensorMap<Tensor<float, 2> > mat2(m2.data(), 3,3); - - Tensor<float, 2> mat3(3,3); - mat3 = mat1; - - typedef Tensor<float, 1>::DimensionPair DimPair; - Eigen::array<DimPair, 1> dims; - dims[0] = DimPair(1, 0); - - mat3 = mat3.contract(mat2, dims).eval(); - - VERIFY_IS_APPROX(mat3(0, 0), (m1*m2).eval()(0,0)); - VERIFY_IS_APPROX(mat3(0, 1), (m1*m2).eval()(0,1)); - VERIFY_IS_APPROX(mat3(0, 2), (m1*m2).eval()(0,2)); - VERIFY_IS_APPROX(mat3(1, 0), (m1*m2).eval()(1,0)); - VERIFY_IS_APPROX(mat3(1, 1), (m1*m2).eval()(1,1)); - VERIFY_IS_APPROX(mat3(1, 2), (m1*m2).eval()(1,2)); - VERIFY_IS_APPROX(mat3(2, 0), (m1*m2).eval()(2,0)); - VERIFY_IS_APPROX(mat3(2, 1), (m1*m2).eval()(2,1)); - VERIFY_IS_APPROX(mat3(2, 2), (m1*m2).eval()(2,2)); -} - - -static void test_const() -{ - MatrixXf input(3,3); - input.setRandom(); - MatrixXf output = input; - output.rowwise() -= input.colwise().maxCoeff(); - - Eigen::array<int, 1> depth_dim; - depth_dim[0] = 0; - Tensor<float, 2>::Dimensions dims2d; - dims2d[0] = 1; - dims2d[1] = 3; - Eigen::array<int, 2> bcast; - bcast[0] = 3; - bcast[1] = 1; - const TensorMap<Tensor<const float, 2> > input_tensor(input.data(), 3, 3); - Tensor<float, 2> output_tensor= (input_tensor - input_tensor.maximum(depth_dim).eval().reshape(dims2d).broadcast(bcast)); - - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 3; ++j) { - VERIFY_IS_APPROX(output(i, j), output_tensor(i, j)); - } - } -} - - -void test_cxx11_tensor_forced_eval() -{ - CALL_SUBTEST(test_simple()); - CALL_SUBTEST(test_const()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp b/eigen/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp deleted file mode 100644 index 5690da7..0000000 --- a/eigen/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp +++ /dev/null @@ -1,70 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 -// Mehdi Goli Codeplay Software Ltd. -// Ralph Potter Codeplay Software Ltd. -// Luke Iwanski Codeplay Software Ltd. -// Contact: <eigen@codeplay.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_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_NO_COMPLEX -#define EIGEN_TEST_FUNC cxx11_tensor_forced_eval_sycl -#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int -#define EIGEN_USE_SYCL - -#include "main.h" -#include <unsupported/Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -void test_forced_eval_sycl(const Eigen::SyclDevice &sycl_device) { - - int sizeDim1 = 100; - int sizeDim2 = 200; - int sizeDim3 = 200; - Eigen::array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}}; - Eigen::Tensor<float, 3> in1(tensorRange); - Eigen::Tensor<float, 3> in2(tensorRange); - Eigen::Tensor<float, 3> out(tensorRange); - - float * gpu_in1_data = static_cast<float*>(sycl_device.allocate(in1.dimensions().TotalSize()*sizeof(float))); - float * gpu_in2_data = static_cast<float*>(sycl_device.allocate(in2.dimensions().TotalSize()*sizeof(float))); - float * gpu_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float))); - - in1 = in1.random() + in1.constant(10.0f); - in2 = in2.random() + in2.constant(10.0f); - - // creating TensorMap from tensor - Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in1(gpu_in1_data, tensorRange); - Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in2(gpu_in2_data, tensorRange); - Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_out(gpu_out_data, tensorRange); - sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(),(in1.dimensions().TotalSize())*sizeof(float)); - sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in1.dimensions().TotalSize())*sizeof(float)); - /// c=(a+b)*b - gpu_out.device(sycl_device) =(gpu_in1 + gpu_in2).eval() * gpu_in2; - sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); - for (int i = 0; i < sizeDim1; ++i) { - for (int j = 0; j < sizeDim2; ++j) { - for (int k = 0; k < sizeDim3; ++k) { - VERIFY_IS_APPROX(out(i, j, k), - (in1(i, j, k) + in2(i, j, k)) * in2(i, j, k)); - } - } - } - printf("(a+b)*b Test Passed\n"); - sycl_device.deallocate(gpu_in1_data); - sycl_device.deallocate(gpu_in2_data); - sycl_device.deallocate(gpu_out_data); - -} - -void test_cxx11_tensor_forced_eval_sycl() { - cl::sycl::gpu_selector s; - Eigen::SyclDevice sycl_device(s); - CALL_SUBTEST(test_forced_eval_sycl(sycl_device)); -} diff --git a/eigen/unsupported/test/cxx11_tensor_generator.cpp b/eigen/unsupported/test/cxx11_tensor_generator.cpp deleted file mode 100644 index dcb9287..0000000 --- a/eigen/unsupported/test/cxx11_tensor_generator.cpp +++ /dev/null @@ -1,91 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -struct Generator1D { - Generator1D() { } - - float operator()(const array<Eigen::DenseIndex, 1>& coordinates) const { - return coordinates[0]; - } -}; - -template <int DataLayout> -static void test_1D() -{ - Tensor<float, 1> vec(6); - Tensor<float, 1> result = vec.generate(Generator1D()); - - for (int i = 0; i < 6; ++i) { - VERIFY_IS_EQUAL(result(i), i); - } -} - - -struct Generator2D { - Generator2D() { } - - float operator()(const array<Eigen::DenseIndex, 2>& coordinates) const { - return 3 * coordinates[0] + 11 * coordinates[1]; - } -}; - -template <int DataLayout> -static void test_2D() -{ - Tensor<float, 2> matrix(5, 7); - Tensor<float, 2> result = matrix.generate(Generator2D()); - - for (int i = 0; i < 5; ++i) { - for (int j = 0; j < 5; ++j) { - VERIFY_IS_EQUAL(result(i, j), 3*i + 11*j); - } - } -} - - -template <int DataLayout> -static void test_gaussian() -{ - int rows = 32; - int cols = 48; - array<float, 2> means; - means[0] = rows / 2.0f; - means[1] = cols / 2.0f; - array<float, 2> std_devs; - std_devs[0] = 3.14f; - std_devs[1] = 2.7f; - internal::GaussianGenerator<float, Eigen::DenseIndex, 2> gaussian_gen(means, std_devs); - - Tensor<float, 2> matrix(rows, cols); - Tensor<float, 2> result = matrix.generate(gaussian_gen); - - for (int i = 0; i < rows; ++i) { - for (int j = 0; j < cols; ++j) { - float g_rows = powf(rows/2.0f - i, 2) / (3.14f * 3.14f) * 0.5f; - float g_cols = powf(cols/2.0f - j, 2) / (2.7f * 2.7f) * 0.5f; - float gaussian = expf(-g_rows - g_cols); - VERIFY_IS_EQUAL(result(i, j), gaussian); - } - } -} - - -void test_cxx11_tensor_generator() -{ - CALL_SUBTEST(test_1D<ColMajor>()); - CALL_SUBTEST(test_1D<RowMajor>()); - CALL_SUBTEST(test_2D<ColMajor>()); - CALL_SUBTEST(test_2D<RowMajor>()); - CALL_SUBTEST(test_gaussian<ColMajor>()); - CALL_SUBTEST(test_gaussian<RowMajor>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_ifft.cpp b/eigen/unsupported/test/cxx11_tensor_ifft.cpp deleted file mode 100644 index 5fd88fa..0000000 --- a/eigen/unsupported/test/cxx11_tensor_ifft.cpp +++ /dev/null @@ -1,154 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Jianwei Cui <thucjw@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 <complex> -#include <cmath> -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -template <int DataLayout> -static void test_1D_fft_ifft_invariant(int sequence_length) { - Tensor<double, 1, DataLayout> tensor(sequence_length); - tensor.setRandom(); - - array<int, 1> fft; - fft[0] = 0; - - Tensor<std::complex<double>, 1, DataLayout> tensor_after_fft; - Tensor<std::complex<double>, 1, DataLayout> tensor_after_fft_ifft; - - tensor_after_fft = tensor.template fft<Eigen::BothParts, Eigen::FFT_FORWARD>(fft); - tensor_after_fft_ifft = tensor_after_fft.template fft<Eigen::BothParts, Eigen::FFT_REVERSE>(fft); - - VERIFY_IS_EQUAL(tensor_after_fft.dimension(0), sequence_length); - VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(0), sequence_length); - - for (int i = 0; i < sequence_length; ++i) { - VERIFY_IS_APPROX(static_cast<float>(tensor(i)), static_cast<float>(std::real(tensor_after_fft_ifft(i)))); - } -} - -template <int DataLayout> -static void test_2D_fft_ifft_invariant(int dim0, int dim1) { - Tensor<double, 2, DataLayout> tensor(dim0, dim1); - tensor.setRandom(); - - array<int, 2> fft; - fft[0] = 0; - fft[1] = 1; - - Tensor<std::complex<double>, 2, DataLayout> tensor_after_fft; - Tensor<std::complex<double>, 2, DataLayout> tensor_after_fft_ifft; - - tensor_after_fft = tensor.template fft<Eigen::BothParts, Eigen::FFT_FORWARD>(fft); - tensor_after_fft_ifft = tensor_after_fft.template fft<Eigen::BothParts, Eigen::FFT_REVERSE>(fft); - - VERIFY_IS_EQUAL(tensor_after_fft.dimension(0), dim0); - VERIFY_IS_EQUAL(tensor_after_fft.dimension(1), dim1); - VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(0), dim0); - VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(1), dim1); - - for (int i = 0; i < dim0; ++i) { - for (int j = 0; j < dim1; ++j) { - //std::cout << "[" << i << "][" << j << "]" << " Original data: " << tensor(i,j) << " Transformed data:" << tensor_after_fft_ifft(i,j) << std::endl; - VERIFY_IS_APPROX(static_cast<float>(tensor(i,j)), static_cast<float>(std::real(tensor_after_fft_ifft(i,j)))); - } - } -} - -template <int DataLayout> -static void test_3D_fft_ifft_invariant(int dim0, int dim1, int dim2) { - Tensor<double, 3, DataLayout> tensor(dim0, dim1, dim2); - tensor.setRandom(); - - array<int, 3> fft; - fft[0] = 0; - fft[1] = 1; - fft[2] = 2; - - Tensor<std::complex<double>, 3, DataLayout> tensor_after_fft; - Tensor<std::complex<double>, 3, DataLayout> tensor_after_fft_ifft; - - tensor_after_fft = tensor.template fft<Eigen::BothParts, Eigen::FFT_FORWARD>(fft); - tensor_after_fft_ifft = tensor_after_fft.template fft<Eigen::BothParts, Eigen::FFT_REVERSE>(fft); - - VERIFY_IS_EQUAL(tensor_after_fft.dimension(0), dim0); - VERIFY_IS_EQUAL(tensor_after_fft.dimension(1), dim1); - VERIFY_IS_EQUAL(tensor_after_fft.dimension(2), dim2); - VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(0), dim0); - VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(1), dim1); - VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(2), dim2); - - for (int i = 0; i < dim0; ++i) { - for (int j = 0; j < dim1; ++j) { - for (int k = 0; k < dim2; ++k) { - VERIFY_IS_APPROX(static_cast<float>(tensor(i,j,k)), static_cast<float>(std::real(tensor_after_fft_ifft(i,j,k)))); - } - } - } -} - -template <int DataLayout> -static void test_sub_fft_ifft_invariant(int dim0, int dim1, int dim2, int dim3) { - Tensor<double, 4, DataLayout> tensor(dim0, dim1, dim2, dim3); - tensor.setRandom(); - - array<int, 2> fft; - fft[0] = 2; - fft[1] = 0; - - Tensor<std::complex<double>, 4, DataLayout> tensor_after_fft; - Tensor<double, 4, DataLayout> tensor_after_fft_ifft; - - tensor_after_fft = tensor.template fft<Eigen::BothParts, Eigen::FFT_FORWARD>(fft); - tensor_after_fft_ifft = tensor_after_fft.template fft<Eigen::RealPart, Eigen::FFT_REVERSE>(fft); - - VERIFY_IS_EQUAL(tensor_after_fft.dimension(0), dim0); - VERIFY_IS_EQUAL(tensor_after_fft.dimension(1), dim1); - VERIFY_IS_EQUAL(tensor_after_fft.dimension(2), dim2); - VERIFY_IS_EQUAL(tensor_after_fft.dimension(3), dim3); - VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(0), dim0); - VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(1), dim1); - VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(2), dim2); - VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(3), dim3); - - for (int i = 0; i < dim0; ++i) { - for (int j = 0; j < dim1; ++j) { - for (int k = 0; k < dim2; ++k) { - for (int l = 0; l < dim3; ++l) { - VERIFY_IS_APPROX(static_cast<float>(tensor(i,j,k,l)), static_cast<float>(tensor_after_fft_ifft(i,j,k,l))); - } - } - } - } -} - -void test_cxx11_tensor_ifft() { - CALL_SUBTEST(test_1D_fft_ifft_invariant<ColMajor>(4)); - CALL_SUBTEST(test_1D_fft_ifft_invariant<ColMajor>(16)); - CALL_SUBTEST(test_1D_fft_ifft_invariant<ColMajor>(32)); - CALL_SUBTEST(test_1D_fft_ifft_invariant<ColMajor>(1024*1024)); - - CALL_SUBTEST(test_2D_fft_ifft_invariant<ColMajor>(4,4)); - CALL_SUBTEST(test_2D_fft_ifft_invariant<ColMajor>(8,16)); - CALL_SUBTEST(test_2D_fft_ifft_invariant<ColMajor>(16,32)); - CALL_SUBTEST(test_2D_fft_ifft_invariant<ColMajor>(1024,1024)); - - CALL_SUBTEST(test_3D_fft_ifft_invariant<ColMajor>(4,4,4)); - CALL_SUBTEST(test_3D_fft_ifft_invariant<ColMajor>(8,16,32)); - CALL_SUBTEST(test_3D_fft_ifft_invariant<ColMajor>(16,4,8)); - CALL_SUBTEST(test_3D_fft_ifft_invariant<ColMajor>(256,256,256)); - - CALL_SUBTEST(test_sub_fft_ifft_invariant<ColMajor>(4,4,4,4)); - CALL_SUBTEST(test_sub_fft_ifft_invariant<ColMajor>(8,16,32,64)); - CALL_SUBTEST(test_sub_fft_ifft_invariant<ColMajor>(16,4,8,12)); - CALL_SUBTEST(test_sub_fft_ifft_invariant<ColMajor>(64,64,64,64)); -} diff --git a/eigen/unsupported/test/cxx11_tensor_image_patch.cpp b/eigen/unsupported/test/cxx11_tensor_image_patch.cpp deleted file mode 100644 index 475c596..0000000 --- a/eigen/unsupported/test/cxx11_tensor_image_patch.cpp +++ /dev/null @@ -1,757 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -void test_simple_patch() -{ - Tensor<float, 4> tensor(2,3,5,7); - tensor.setRandom(); - Tensor<float, 4, RowMajor> tensor_row_major = tensor.swap_layout(); - VERIFY_IS_EQUAL(tensor.dimension(0), tensor_row_major.dimension(3)); - VERIFY_IS_EQUAL(tensor.dimension(1), tensor_row_major.dimension(2)); - VERIFY_IS_EQUAL(tensor.dimension(2), tensor_row_major.dimension(1)); - VERIFY_IS_EQUAL(tensor.dimension(3), tensor_row_major.dimension(0)); - - // Single pixel patch: ColMajor - Tensor<float, 5> single_pixel_patch; - single_pixel_patch = tensor.extract_image_patches(1, 1); - VERIFY_IS_EQUAL(single_pixel_patch.dimension(0), 2); - VERIFY_IS_EQUAL(single_pixel_patch.dimension(1), 1); - VERIFY_IS_EQUAL(single_pixel_patch.dimension(2), 1); - VERIFY_IS_EQUAL(single_pixel_patch.dimension(3), 3*5); - VERIFY_IS_EQUAL(single_pixel_patch.dimension(4), 7); - - // Single pixel patch: RowMajor - Tensor<float, 5, RowMajor> single_pixel_patch_row_major; - single_pixel_patch_row_major = tensor_row_major.extract_image_patches(1, 1); - VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(0), 7); - VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(1), 3*5); - VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(2), 1); - VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(3), 1); - VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(4), 2); - - for (int i = 0; i < tensor.size(); ++i) { - // ColMajor - if (tensor.data()[i] != single_pixel_patch.data()[i]) { - std::cout << "Mismatch detected at index " << i << " : " - << tensor.data()[i] << " vs " << single_pixel_patch.data()[i] - << std::endl; - } - VERIFY_IS_EQUAL(single_pixel_patch.data()[i], tensor.data()[i]); - // RowMajor - if (tensor_row_major.data()[i] != single_pixel_patch_row_major.data()[i]) { - std::cout << "Mismatch detected at index " << i << " : " - << tensor.data()[i] << " vs " - << single_pixel_patch_row_major.data()[i] << std::endl; - } - VERIFY_IS_EQUAL(single_pixel_patch_row_major.data()[i], - tensor_row_major.data()[i]); - VERIFY_IS_EQUAL(tensor.data()[i], tensor_row_major.data()[i]); - VERIFY_IS_EQUAL(single_pixel_patch.data()[i], - single_pixel_patch_row_major.data()[i]); - } - - // Entire image patch: ColMajor - Tensor<float, 5> entire_image_patch; - entire_image_patch = tensor.extract_image_patches(3, 5); - VERIFY_IS_EQUAL(entire_image_patch.dimension(0), 2); - VERIFY_IS_EQUAL(entire_image_patch.dimension(1), 3); - VERIFY_IS_EQUAL(entire_image_patch.dimension(2), 5); - VERIFY_IS_EQUAL(entire_image_patch.dimension(3), 3*5); - VERIFY_IS_EQUAL(entire_image_patch.dimension(4), 7); - - // Entire image patch: RowMajor - Tensor<float, 5, RowMajor> entire_image_patch_row_major; - entire_image_patch_row_major = tensor_row_major.extract_image_patches(3, 5); - VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(0), 7); - VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(1), 3*5); - VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(2), 5); - VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(3), 3); - VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(4), 2); - - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 5; ++j) { - int patchId = i+3*j; - for (int r = 0; r < 3; ++r) { - for (int c = 0; c < 5; ++c) { - for (int d = 0; d < 2; ++d) { - for (int b = 0; b < 7; ++b) { - float expected = 0.0f; - float expected_row_major = 0.0f; - if (r-1+i >= 0 && c-2+j >= 0 && r-1+i < 3 && c-2+j < 5) { - expected = tensor(d, r-1+i, c-2+j, b); - expected_row_major = tensor_row_major(b, c-2+j, r-1+i, d); - } - // ColMajor - if (entire_image_patch(d, r, c, patchId, b) != expected) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; - } - VERIFY_IS_EQUAL(entire_image_patch(d, r, c, patchId, b), expected); - // RowMajor - if (entire_image_patch_row_major(b, patchId, c, r, d) != - expected_row_major) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j - << " r=" << r << " c=" << c << " d=" << d << " b=" << b - << std::endl; - } - VERIFY_IS_EQUAL(entire_image_patch_row_major(b, patchId, c, r, d), - expected_row_major); - // Check that ColMajor and RowMajor agree. - VERIFY_IS_EQUAL(expected, expected_row_major); - } - } - } - } - } - } - - // 2D patch: ColMajor - Tensor<float, 5> twod_patch; - twod_patch = tensor.extract_image_patches(2, 2); - VERIFY_IS_EQUAL(twod_patch.dimension(0), 2); - VERIFY_IS_EQUAL(twod_patch.dimension(1), 2); - VERIFY_IS_EQUAL(twod_patch.dimension(2), 2); - VERIFY_IS_EQUAL(twod_patch.dimension(3), 3*5); - VERIFY_IS_EQUAL(twod_patch.dimension(4), 7); - - // 2D patch: RowMajor - Tensor<float, 5, RowMajor> twod_patch_row_major; - twod_patch_row_major = tensor_row_major.extract_image_patches(2, 2); - VERIFY_IS_EQUAL(twod_patch_row_major.dimension(0), 7); - VERIFY_IS_EQUAL(twod_patch_row_major.dimension(1), 3*5); - VERIFY_IS_EQUAL(twod_patch_row_major.dimension(2), 2); - VERIFY_IS_EQUAL(twod_patch_row_major.dimension(3), 2); - VERIFY_IS_EQUAL(twod_patch_row_major.dimension(4), 2); - - - // Based on the calculation described in TensorTraits.h, padding happens to be 0. - int row_padding = 0; - int col_padding = 0; - int stride = 1; - - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 5; ++j) { - int patchId = i+3*j; - for (int r = 0; r < 2; ++r) { - for (int c = 0; c < 2; ++c) { - for (int d = 0; d < 2; ++d) { - for (int b = 0; b < 7; ++b) { - float expected = 0.0f; - float expected_row_major = 0.0f; - int row_offset = r*stride + i - row_padding; - int col_offset = c*stride + j - col_padding; - // ColMajor - if (row_offset >= 0 && col_offset >= 0 && row_offset < tensor.dimension(1) && col_offset < tensor.dimension(2)) { - expected = tensor(d, row_offset, col_offset, b); - } - if (twod_patch(d, r, c, patchId, b) != expected) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; - } - VERIFY_IS_EQUAL(twod_patch(d, r, c, patchId, b), expected); - - // RowMajor - if (row_offset >= 0 && col_offset >= 0 && row_offset < tensor_row_major.dimension(2) && col_offset < tensor_row_major.dimension(1)) { - expected_row_major = tensor_row_major(b, col_offset, row_offset, d); - - } - if (twod_patch_row_major(b, patchId, c, r, d) != expected_row_major) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; - } - VERIFY_IS_EQUAL(twod_patch_row_major(b, patchId, c, r, d), expected_row_major); - // Check that ColMajor and RowMajor agree. - VERIFY_IS_EQUAL(expected, expected_row_major); - } - } - } - } - } - } -} - -// Verifies VALID padding (no padding) with incrementing values. -void test_patch_padding_valid() -{ - int input_depth = 3; - int input_rows = 3; - int input_cols = 3; - int input_batches = 1; - int ksize = 2; // Corresponds to the Rows and Cols for tensor.extract_image_patches<>. - int stride = 2; // Only same stride is supported. - Tensor<float, 4> tensor(input_depth, input_rows, input_cols, input_batches); - // Initializes tensor with incrementing numbers. - for (int i = 0; i < tensor.size(); ++i) { - tensor.data()[i] = i + 1; - } - // ColMajor - Tensor<float, 5> result = tensor.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID); - - VERIFY_IS_EQUAL(result.dimension(0), input_depth); // depth - VERIFY_IS_EQUAL(result.dimension(1), ksize); // kernel rows - VERIFY_IS_EQUAL(result.dimension(2), ksize); // kernel cols - VERIFY_IS_EQUAL(result.dimension(3), 1); // number of patches - VERIFY_IS_EQUAL(result.dimension(4), input_batches); // number of batches - - // RowMajor - Tensor<float, 4, RowMajor> tensor_row_major = tensor.swap_layout(); - VERIFY_IS_EQUAL(tensor.dimension(0), tensor_row_major.dimension(3)); - VERIFY_IS_EQUAL(tensor.dimension(1), tensor_row_major.dimension(2)); - VERIFY_IS_EQUAL(tensor.dimension(2), tensor_row_major.dimension(1)); - VERIFY_IS_EQUAL(tensor.dimension(3), tensor_row_major.dimension(0)); - - Tensor<float, 5, RowMajor> result_row_major = tensor_row_major.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID); - VERIFY_IS_EQUAL(result.dimension(0), result_row_major.dimension(4)); - VERIFY_IS_EQUAL(result.dimension(1), result_row_major.dimension(3)); - VERIFY_IS_EQUAL(result.dimension(2), result_row_major.dimension(2)); - VERIFY_IS_EQUAL(result.dimension(3), result_row_major.dimension(1)); - VERIFY_IS_EQUAL(result.dimension(4), result_row_major.dimension(0)); - - // No padding is carried out. - int row_padding = 0; - int col_padding = 0; - - for (int i = 0; (i+stride+ksize-1) < input_rows; i += stride) { // input rows - for (int j = 0; (j+stride+ksize-1) < input_cols; j += stride) { // input cols - int patchId = i+input_rows*j; - for (int r = 0; r < ksize; ++r) { // patch rows - for (int c = 0; c < ksize; ++c) { // patch cols - for (int d = 0; d < input_depth; ++d) { // depth - for (int b = 0; b < input_batches; ++b) { // batch - float expected = 0.0f; - float expected_row_major = 0.0f; - int row_offset = r + i - row_padding; - int col_offset = c + j - col_padding; - if (row_offset >= 0 && col_offset >= 0 && row_offset < input_rows && col_offset < input_cols) { - expected = tensor(d, row_offset, col_offset, b); - expected_row_major = tensor_row_major(b, col_offset, row_offset, d); - } - // ColMajor - if (result(d, r, c, patchId, b) != expected) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; - } - VERIFY_IS_EQUAL(result(d, r, c, patchId, b), expected); - // RowMajor - if (result_row_major(b, patchId, c, r, d) != expected_row_major) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; - } - VERIFY_IS_EQUAL(result_row_major(b, patchId, c, r, d), expected_row_major); - // Check that ColMajor and RowMajor agree. - VERIFY_IS_EQUAL(expected, expected_row_major); - } - } - } - } - } - } -} - -// Verifies VALID padding (no padding) with the same value. -void test_patch_padding_valid_same_value() -{ - int input_depth = 1; - int input_rows = 5; - int input_cols = 5; - int input_batches = 2; - int ksize = 3; // Corresponds to the Rows and Cols for tensor.extract_image_patches<>. - int stride = 2; // Only same stride is supported. - // ColMajor - Tensor<float, 4> tensor(input_depth, input_rows, input_cols, input_batches); - tensor = tensor.constant(11.0f); - Tensor<float, 5> result = tensor.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID); - - VERIFY_IS_EQUAL(result.dimension(0), input_depth); // depth - VERIFY_IS_EQUAL(result.dimension(1), ksize); // kernel rows - VERIFY_IS_EQUAL(result.dimension(2), ksize); // kernel cols - VERIFY_IS_EQUAL(result.dimension(3), 4); // number of patches - VERIFY_IS_EQUAL(result.dimension(4), input_batches); // number of batches - - // RowMajor - Tensor<float, 4, RowMajor> tensor_row_major = tensor.swap_layout(); - VERIFY_IS_EQUAL(tensor.dimension(0), tensor_row_major.dimension(3)); - VERIFY_IS_EQUAL(tensor.dimension(1), tensor_row_major.dimension(2)); - VERIFY_IS_EQUAL(tensor.dimension(2), tensor_row_major.dimension(1)); - VERIFY_IS_EQUAL(tensor.dimension(3), tensor_row_major.dimension(0)); - - Tensor<float, 5, RowMajor> result_row_major = tensor_row_major.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID); - VERIFY_IS_EQUAL(result.dimension(0), result_row_major.dimension(4)); - VERIFY_IS_EQUAL(result.dimension(1), result_row_major.dimension(3)); - VERIFY_IS_EQUAL(result.dimension(2), result_row_major.dimension(2)); - VERIFY_IS_EQUAL(result.dimension(3), result_row_major.dimension(1)); - VERIFY_IS_EQUAL(result.dimension(4), result_row_major.dimension(0)); - - // No padding is carried out. - int row_padding = 0; - int col_padding = 0; - - for (int i = 0; (i+stride+ksize-1) <= input_rows; i += stride) { // input rows - for (int j = 0; (j+stride+ksize-1) <= input_cols; j += stride) { // input cols - int patchId = i+input_rows*j; - for (int r = 0; r < ksize; ++r) { // patch rows - for (int c = 0; c < ksize; ++c) { // patch cols - for (int d = 0; d < input_depth; ++d) { // depth - for (int b = 0; b < input_batches; ++b) { // batch - float expected = 0.0f; - float expected_row_major = 0.0f; - int row_offset = r + i - row_padding; - int col_offset = c + j - col_padding; - if (row_offset >= 0 && col_offset >= 0 && row_offset < input_rows && col_offset < input_cols) { - expected = tensor(d, row_offset, col_offset, b); - expected_row_major = tensor_row_major(b, col_offset, row_offset, d); - } - // ColMajor - if (result(d, r, c, patchId, b) != expected) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; - } - VERIFY_IS_EQUAL(result(d, r, c, patchId, b), expected); - // RowMajor - if (result_row_major(b, patchId, c, r, d) != expected_row_major) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; - } - VERIFY_IS_EQUAL(result_row_major(b, patchId, c, r, d), expected_row_major); - // Check that ColMajor and RowMajor agree. - VERIFY_IS_EQUAL(expected, expected_row_major); - } - } - } - } - } - } -} - -// Verifies SAME padding. -void test_patch_padding_same() -{ - int input_depth = 3; - int input_rows = 4; - int input_cols = 2; - int input_batches = 1; - int ksize = 2; // Corresponds to the Rows and Cols for tensor.extract_image_patches<>. - int stride = 2; // Only same stride is supported. - // ColMajor - Tensor<float, 4> tensor(input_depth, input_rows, input_cols, input_batches); - // Initializes tensor with incrementing numbers. - for (int i = 0; i < tensor.size(); ++i) { - tensor.data()[i] = i + 1; - } - Tensor<float, 5> result = tensor.extract_image_patches(ksize, ksize, stride, stride, PADDING_SAME); - - VERIFY_IS_EQUAL(result.dimension(0), input_depth); // depth - VERIFY_IS_EQUAL(result.dimension(1), ksize); // kernel rows - VERIFY_IS_EQUAL(result.dimension(2), ksize); // kernel cols - VERIFY_IS_EQUAL(result.dimension(3), 2); // number of patches - VERIFY_IS_EQUAL(result.dimension(4), input_batches); // number of batches - - // RowMajor - Tensor<float, 4, RowMajor> tensor_row_major = tensor.swap_layout(); - VERIFY_IS_EQUAL(tensor.dimension(0), tensor_row_major.dimension(3)); - VERIFY_IS_EQUAL(tensor.dimension(1), tensor_row_major.dimension(2)); - VERIFY_IS_EQUAL(tensor.dimension(2), tensor_row_major.dimension(1)); - VERIFY_IS_EQUAL(tensor.dimension(3), tensor_row_major.dimension(0)); - - Tensor<float, 5, RowMajor> result_row_major = tensor_row_major.extract_image_patches(ksize, ksize, stride, stride, PADDING_SAME); - VERIFY_IS_EQUAL(result.dimension(0), result_row_major.dimension(4)); - VERIFY_IS_EQUAL(result.dimension(1), result_row_major.dimension(3)); - VERIFY_IS_EQUAL(result.dimension(2), result_row_major.dimension(2)); - VERIFY_IS_EQUAL(result.dimension(3), result_row_major.dimension(1)); - VERIFY_IS_EQUAL(result.dimension(4), result_row_major.dimension(0)); - - // Based on the calculation described in TensorTraits.h, padding happens to be - // 0. - int row_padding = 0; - int col_padding = 0; - - for (int i = 0; (i+stride+ksize-1) <= input_rows; i += stride) { // input rows - for (int j = 0; (j+stride+ksize-1) <= input_cols; j += stride) { // input cols - int patchId = i+input_rows*j; - for (int r = 0; r < ksize; ++r) { // patch rows - for (int c = 0; c < ksize; ++c) { // patch cols - for (int d = 0; d < input_depth; ++d) { // depth - for (int b = 0; b < input_batches; ++b) { // batch - float expected = 0.0f; - float expected_row_major = 0.0f; - int row_offset = r*stride + i - row_padding; - int col_offset = c*stride + j - col_padding; - if (row_offset >= 0 && col_offset >= 0 && row_offset < input_rows && col_offset < input_cols) { - expected = tensor(d, row_offset, col_offset, b); - expected_row_major = tensor_row_major(b, col_offset, row_offset, d); - } - // ColMajor - if (result(d, r, c, patchId, b) != expected) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; - } - VERIFY_IS_EQUAL(result(d, r, c, patchId, b), expected); - // RowMajor - if (result_row_major(b, patchId, c, r, d) != expected_row_major) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; - } - VERIFY_IS_EQUAL(result_row_major(b, patchId, c, r, d), expected_row_major); - // Check that ColMajor and RowMajor agree. - VERIFY_IS_EQUAL(expected, expected_row_major); - } - } - } - } - } - } -} - -void test_patch_no_extra_dim() -{ - Tensor<float, 3> tensor(2,3,5); - tensor.setRandom(); - Tensor<float, 3, RowMajor> tensor_row_major = tensor.swap_layout(); - VERIFY_IS_EQUAL(tensor.dimension(0), tensor_row_major.dimension(2)); - VERIFY_IS_EQUAL(tensor.dimension(1), tensor_row_major.dimension(1)); - VERIFY_IS_EQUAL(tensor.dimension(2), tensor_row_major.dimension(0)); - - // Single pixel patch: ColMajor - Tensor<float, 4> single_pixel_patch; - single_pixel_patch = tensor.extract_image_patches(1, 1); - VERIFY_IS_EQUAL(single_pixel_patch.dimension(0), 2); - VERIFY_IS_EQUAL(single_pixel_patch.dimension(1), 1); - VERIFY_IS_EQUAL(single_pixel_patch.dimension(2), 1); - VERIFY_IS_EQUAL(single_pixel_patch.dimension(3), 3*5); - - // Single pixel patch: RowMajor - Tensor<float, 4, RowMajor> single_pixel_patch_row_major; - single_pixel_patch_row_major = tensor_row_major.extract_image_patches(1, 1); - VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(0), 3*5); - VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(1), 1); - VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(2), 1); - VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(3), 2); - - for (int i = 0; i < tensor.size(); ++i) { - // ColMajor - if (tensor.data()[i] != single_pixel_patch.data()[i]) { - std::cout << "Mismatch detected at index " << i << " : " << tensor.data()[i] << " vs " << single_pixel_patch.data()[i] << std::endl; - } - VERIFY_IS_EQUAL(single_pixel_patch.data()[i], tensor.data()[i]); - // RowMajor - if (tensor_row_major.data()[i] != single_pixel_patch_row_major.data()[i]) { - std::cout << "Mismatch detected at index " << i << " : " - << tensor.data()[i] << " vs " - << single_pixel_patch_row_major.data()[i] << std::endl; - } - VERIFY_IS_EQUAL(single_pixel_patch_row_major.data()[i], - tensor_row_major.data()[i]); - VERIFY_IS_EQUAL(tensor.data()[i], tensor_row_major.data()[i]); - VERIFY_IS_EQUAL(single_pixel_patch.data()[i], - single_pixel_patch_row_major.data()[i]); - } - - // Entire image patch: ColMajor - Tensor<float, 4> entire_image_patch; - entire_image_patch = tensor.extract_image_patches(3, 5); - VERIFY_IS_EQUAL(entire_image_patch.dimension(0), 2); - VERIFY_IS_EQUAL(entire_image_patch.dimension(1), 3); - VERIFY_IS_EQUAL(entire_image_patch.dimension(2), 5); - VERIFY_IS_EQUAL(entire_image_patch.dimension(3), 3*5); - - // Entire image patch: RowMajor - Tensor<float, 4, RowMajor> entire_image_patch_row_major; - entire_image_patch_row_major = tensor_row_major.extract_image_patches(3, 5); - VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(0), 3*5); - VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(1), 5); - VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(2), 3); - VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(3), 2); - - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 5; ++j) { - int patchId = i+3*j; - for (int r = 0; r < 3; ++r) { - for (int c = 0; c < 5; ++c) { - for (int d = 0; d < 2; ++d) { - float expected = 0.0f; - float expected_row_major = 0.0f; - if (r-1+i >= 0 && c-2+j >= 0 && r-1+i < 3 && c-2+j < 5) { - expected = tensor(d, r-1+i, c-2+j); - expected_row_major = tensor_row_major(c-2+j, r-1+i, d); - } - // ColMajor - if (entire_image_patch(d, r, c, patchId) != expected) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << std::endl; - } - VERIFY_IS_EQUAL(entire_image_patch(d, r, c, patchId), expected); - // RowMajor - if (entire_image_patch_row_major(patchId, c, r, d) != - expected_row_major) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << std::endl; - } - VERIFY_IS_EQUAL(entire_image_patch_row_major(patchId, c, r, d), - expected_row_major); - // Check that ColMajor and RowMajor agree. - VERIFY_IS_EQUAL(expected, expected_row_major); - } - } - } - } - } - - // 2D patch: ColMajor - Tensor<float, 4> twod_patch; - twod_patch = tensor.extract_image_patches(2, 2); - VERIFY_IS_EQUAL(twod_patch.dimension(0), 2); - VERIFY_IS_EQUAL(twod_patch.dimension(1), 2); - VERIFY_IS_EQUAL(twod_patch.dimension(2), 2); - VERIFY_IS_EQUAL(twod_patch.dimension(3), 3*5); - - // 2D patch: RowMajor - Tensor<float, 4, RowMajor> twod_patch_row_major; - twod_patch_row_major = tensor_row_major.extract_image_patches(2, 2); - VERIFY_IS_EQUAL(twod_patch_row_major.dimension(0), 3*5); - VERIFY_IS_EQUAL(twod_patch_row_major.dimension(1), 2); - VERIFY_IS_EQUAL(twod_patch_row_major.dimension(2), 2); - VERIFY_IS_EQUAL(twod_patch_row_major.dimension(3), 2); - - // Based on the calculation described in TensorTraits.h, padding happens to be 0. - int row_padding = 0; - int col_padding = 0; - int stride = 1; - - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 5; ++j) { - int patchId = i+3*j; - for (int r = 0; r < 2; ++r) { - for (int c = 0; c < 2; ++c) { - for (int d = 0; d < 2; ++d) { - float expected = 0.0f; - float expected_row_major = 0.0f; - int row_offset = r*stride + i - row_padding; - int col_offset = c*stride + j - col_padding; - // ColMajor - if (row_offset >= 0 && col_offset >= 0 && row_offset < tensor.dimension(1) && col_offset < tensor.dimension(2)) { - expected = tensor(d, row_offset, col_offset); - } - if (twod_patch(d, r, c, patchId) != expected) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << std::endl; - } - VERIFY_IS_EQUAL(twod_patch(d, r, c, patchId), expected); - // RowMajor - if (row_offset >= 0 && col_offset >= 0 && row_offset < tensor_row_major.dimension(1) && col_offset < tensor_row_major.dimension(0)) { - expected_row_major = tensor_row_major(col_offset, row_offset, d); - } - if (twod_patch_row_major(patchId, c, r, d) != expected_row_major) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << std::endl; - } - VERIFY_IS_EQUAL(twod_patch_row_major(patchId, c, r, d), expected_row_major); - // Check that ColMajor and RowMajor agree. - VERIFY_IS_EQUAL(expected, expected_row_major); - } - } - } - } - } -} - -void test_imagenet_patches() -{ - // Test the code on typical configurations used by the 'imagenet' benchmarks at - // https://github.com/soumith/convnet-benchmarks - // ColMajor - Tensor<float, 4> l_in(3, 128, 128, 16); - l_in.setRandom(); - Tensor<float, 5> l_out = l_in.extract_image_patches(11, 11); - VERIFY_IS_EQUAL(l_out.dimension(0), 3); - VERIFY_IS_EQUAL(l_out.dimension(1), 11); - VERIFY_IS_EQUAL(l_out.dimension(2), 11); - VERIFY_IS_EQUAL(l_out.dimension(3), 128*128); - VERIFY_IS_EQUAL(l_out.dimension(4), 16); - - // RowMajor - Tensor<float, 5, RowMajor> l_out_row_major = l_in.swap_layout().extract_image_patches(11, 11); - VERIFY_IS_EQUAL(l_out_row_major.dimension(0), 16); - VERIFY_IS_EQUAL(l_out_row_major.dimension(1), 128*128); - VERIFY_IS_EQUAL(l_out_row_major.dimension(2), 11); - VERIFY_IS_EQUAL(l_out_row_major.dimension(3), 11); - VERIFY_IS_EQUAL(l_out_row_major.dimension(4), 3); - - for (int b = 0; b < 16; ++b) { - for (int i = 0; i < 128; ++i) { - for (int j = 0; j < 128; ++j) { - int patchId = i+128*j; - for (int c = 0; c < 11; ++c) { - for (int r = 0; r < 11; ++r) { - for (int d = 0; d < 3; ++d) { - float expected = 0.0f; - if (r-5+i >= 0 && c-5+j >= 0 && r-5+i < 128 && c-5+j < 128) { - expected = l_in(d, r-5+i, c-5+j, b); - } - // ColMajor - if (l_out(d, r, c, patchId, b) != expected) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; - } - VERIFY_IS_EQUAL(l_out(d, r, c, patchId, b), expected); - // RowMajor - if (l_out_row_major(b, patchId, c, r, d) != - expected) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j - << " r=" << r << " c=" << c << " d=" << d << " b=" << b - << std::endl; - } - VERIFY_IS_EQUAL(l_out_row_major(b, patchId, c, r, d), - expected); - } - } - } - } - } - } - - // ColMajor - l_in.resize(16, 64, 64, 32); - l_in.setRandom(); - l_out = l_in.extract_image_patches(9, 9); - VERIFY_IS_EQUAL(l_out.dimension(0), 16); - VERIFY_IS_EQUAL(l_out.dimension(1), 9); - VERIFY_IS_EQUAL(l_out.dimension(2), 9); - VERIFY_IS_EQUAL(l_out.dimension(3), 64*64); - VERIFY_IS_EQUAL(l_out.dimension(4), 32); - - // RowMajor - l_out_row_major = l_in.swap_layout().extract_image_patches(9, 9); - VERIFY_IS_EQUAL(l_out_row_major.dimension(0), 32); - VERIFY_IS_EQUAL(l_out_row_major.dimension(1), 64*64); - VERIFY_IS_EQUAL(l_out_row_major.dimension(2), 9); - VERIFY_IS_EQUAL(l_out_row_major.dimension(3), 9); - VERIFY_IS_EQUAL(l_out_row_major.dimension(4), 16); - - for (int b = 0; b < 32; ++b) { - for (int i = 0; i < 64; ++i) { - for (int j = 0; j < 64; ++j) { - int patchId = i+64*j; - for (int c = 0; c < 9; ++c) { - for (int r = 0; r < 9; ++r) { - for (int d = 0; d < 16; ++d) { - float expected = 0.0f; - if (r-4+i >= 0 && c-4+j >= 0 && r-4+i < 64 && c-4+j < 64) { - expected = l_in(d, r-4+i, c-4+j, b); - } - // ColMajor - if (l_out(d, r, c, patchId, b) != expected) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; - } - VERIFY_IS_EQUAL(l_out(d, r, c, patchId, b), expected); - // RowMajor - if (l_out_row_major(b, patchId, c, r, d) != expected) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; - } - VERIFY_IS_EQUAL(l_out_row_major(b, patchId, c, r, d), expected); - } - } - } - } - } - } - - // ColMajor - l_in.resize(32, 16, 16, 32); - l_in.setRandom(); - l_out = l_in.extract_image_patches(7, 7); - VERIFY_IS_EQUAL(l_out.dimension(0), 32); - VERIFY_IS_EQUAL(l_out.dimension(1), 7); - VERIFY_IS_EQUAL(l_out.dimension(2), 7); - VERIFY_IS_EQUAL(l_out.dimension(3), 16*16); - VERIFY_IS_EQUAL(l_out.dimension(4), 32); - - // RowMajor - l_out_row_major = l_in.swap_layout().extract_image_patches(7, 7); - VERIFY_IS_EQUAL(l_out_row_major.dimension(0), 32); - VERIFY_IS_EQUAL(l_out_row_major.dimension(1), 16*16); - VERIFY_IS_EQUAL(l_out_row_major.dimension(2), 7); - VERIFY_IS_EQUAL(l_out_row_major.dimension(3), 7); - VERIFY_IS_EQUAL(l_out_row_major.dimension(4), 32); - - for (int b = 0; b < 32; ++b) { - for (int i = 0; i < 16; ++i) { - for (int j = 0; j < 16; ++j) { - int patchId = i+16*j; - for (int c = 0; c < 7; ++c) { - for (int r = 0; r < 7; ++r) { - for (int d = 0; d < 32; ++d) { - float expected = 0.0f; - if (r-3+i >= 0 && c-3+j >= 0 && r-3+i < 16 && c-3+j < 16) { - expected = l_in(d, r-3+i, c-3+j, b); - } - // ColMajor - if (l_out(d, r, c, patchId, b) != expected) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; - } - VERIFY_IS_EQUAL(l_out(d, r, c, patchId, b), expected); - // RowMajor - if (l_out_row_major(b, patchId, c, r, d) != expected) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; - } - VERIFY_IS_EQUAL(l_out_row_major(b, patchId, c, r, d), expected); - } - } - } - } - } - } - - // ColMajor - l_in.resize(64, 13, 13, 32); - l_in.setRandom(); - l_out = l_in.extract_image_patches(3, 3); - VERIFY_IS_EQUAL(l_out.dimension(0), 64); - VERIFY_IS_EQUAL(l_out.dimension(1), 3); - VERIFY_IS_EQUAL(l_out.dimension(2), 3); - VERIFY_IS_EQUAL(l_out.dimension(3), 13*13); - VERIFY_IS_EQUAL(l_out.dimension(4), 32); - - // RowMajor - l_out_row_major = l_in.swap_layout().extract_image_patches(3, 3); - VERIFY_IS_EQUAL(l_out_row_major.dimension(0), 32); - VERIFY_IS_EQUAL(l_out_row_major.dimension(1), 13*13); - VERIFY_IS_EQUAL(l_out_row_major.dimension(2), 3); - VERIFY_IS_EQUAL(l_out_row_major.dimension(3), 3); - VERIFY_IS_EQUAL(l_out_row_major.dimension(4), 64); - - for (int b = 0; b < 32; ++b) { - for (int i = 0; i < 13; ++i) { - for (int j = 0; j < 13; ++j) { - int patchId = i+13*j; - for (int c = 0; c < 3; ++c) { - for (int r = 0; r < 3; ++r) { - for (int d = 0; d < 64; ++d) { - float expected = 0.0f; - if (r-1+i >= 0 && c-1+j >= 0 && r-1+i < 13 && c-1+j < 13) { - expected = l_in(d, r-1+i, c-1+j, b); - } - // ColMajor - if (l_out(d, r, c, patchId, b) != expected) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; - } - VERIFY_IS_EQUAL(l_out(d, r, c, patchId, b), expected); - // RowMajor - if (l_out_row_major(b, patchId, c, r, d) != expected) { - std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; - } - VERIFY_IS_EQUAL(l_out_row_major(b, patchId, c, r, d), expected); - } - } - } - } - } - } -} - -void test_cxx11_tensor_image_patch() -{ - CALL_SUBTEST_1(test_simple_patch()); - CALL_SUBTEST_2(test_patch_no_extra_dim()); - CALL_SUBTEST_3(test_patch_padding_valid()); - CALL_SUBTEST_4(test_patch_padding_valid_same_value()); - CALL_SUBTEST_5(test_patch_padding_same()); - CALL_SUBTEST_6(test_imagenet_patches()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_index_list.cpp b/eigen/unsupported/test/cxx11_tensor_index_list.cpp deleted file mode 100644 index 4cf5df6..0000000 --- a/eigen/unsupported/test/cxx11_tensor_index_list.cpp +++ /dev/null @@ -1,386 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -#ifdef EIGEN_HAS_INDEX_LIST - -static void test_static_index_list() -{ - Tensor<float, 4> tensor(2,3,5,7); - tensor.setRandom(); - - constexpr auto reduction_axis = make_index_list(0, 1, 2); - VERIFY_IS_EQUAL(internal::array_get<0>(reduction_axis), 0); - VERIFY_IS_EQUAL(internal::array_get<1>(reduction_axis), 1); - VERIFY_IS_EQUAL(internal::array_get<2>(reduction_axis), 2); - VERIFY_IS_EQUAL(static_cast<DenseIndex>(reduction_axis[0]), 0); - VERIFY_IS_EQUAL(static_cast<DenseIndex>(reduction_axis[1]), 1); - VERIFY_IS_EQUAL(static_cast<DenseIndex>(reduction_axis[2]), 2); - - EIGEN_STATIC_ASSERT((internal::array_get<0>(reduction_axis) == 0), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::array_get<1>(reduction_axis) == 1), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::array_get<2>(reduction_axis) == 2), YOU_MADE_A_PROGRAMMING_MISTAKE); - - Tensor<float, 1> result = tensor.sum(reduction_axis); - for (int i = 0; i < result.size(); ++i) { - float expected = 0.0f; - for (int j = 0; j < 2; ++j) { - for (int k = 0; k < 3; ++k) { - for (int l = 0; l < 5; ++l) { - expected += tensor(j,k,l,i); - } - } - } - VERIFY_IS_APPROX(result(i), expected); - } -} - - -static void test_type2index_list() -{ - Tensor<float, 5> tensor(2,3,5,7,11); - tensor.setRandom(); - tensor += tensor.constant(10.0f); - - typedef Eigen::IndexList<Eigen::type2index<0>> Dims0; - typedef Eigen::IndexList<Eigen::type2index<0>, Eigen::type2index<1>> Dims1; - typedef Eigen::IndexList<Eigen::type2index<0>, Eigen::type2index<1>, Eigen::type2index<2>> Dims2; - typedef Eigen::IndexList<Eigen::type2index<0>, Eigen::type2index<1>, Eigen::type2index<2>, Eigen::type2index<3>> Dims3; - typedef Eigen::IndexList<Eigen::type2index<0>, Eigen::type2index<1>, Eigen::type2index<2>, Eigen::type2index<3>, Eigen::type2index<4>> Dims4; - -#if 0 - EIGEN_STATIC_ASSERT((internal::indices_statically_known_to_increase<Dims0>() == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::indices_statically_known_to_increase<Dims1>() == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::indices_statically_known_to_increase<Dims2>() == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::indices_statically_known_to_increase<Dims3>() == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::indices_statically_known_to_increase<Dims4>() == true), YOU_MADE_A_PROGRAMMING_MISTAKE); -#endif - - EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims0, 1, ColMajor>::value == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims1, 2, ColMajor>::value == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims2, 3, ColMajor>::value == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims3, 4, ColMajor>::value == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims4, 5, ColMajor>::value == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - - EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims0, 1, RowMajor>::value == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims1, 2, RowMajor>::value == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims2, 3, RowMajor>::value == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims3, 4, RowMajor>::value == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims4, 5, RowMajor>::value == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - - const Dims0 reduction_axis0; - Tensor<float, 4> result0 = tensor.sum(reduction_axis0); - for (int m = 0; m < 11; ++m) { - for (int l = 0; l < 7; ++l) { - for (int k = 0; k < 5; ++k) { - for (int j = 0; j < 3; ++j) { - float expected = 0.0f; - for (int i = 0; i < 2; ++i) { - expected += tensor(i,j,k,l,m); - } - VERIFY_IS_APPROX(result0(j,k,l,m), expected); - } - } - } - } - - const Dims1 reduction_axis1; - Tensor<float, 3> result1 = tensor.sum(reduction_axis1); - for (int m = 0; m < 11; ++m) { - for (int l = 0; l < 7; ++l) { - for (int k = 0; k < 5; ++k) { - float expected = 0.0f; - for (int j = 0; j < 3; ++j) { - for (int i = 0; i < 2; ++i) { - expected += tensor(i,j,k,l,m); - } - } - VERIFY_IS_APPROX(result1(k,l,m), expected); - } - } - } - - const Dims2 reduction_axis2; - Tensor<float, 2> result2 = tensor.sum(reduction_axis2); - for (int m = 0; m < 11; ++m) { - for (int l = 0; l < 7; ++l) { - float expected = 0.0f; - for (int k = 0; k < 5; ++k) { - for (int j = 0; j < 3; ++j) { - for (int i = 0; i < 2; ++i) { - expected += tensor(i,j,k,l,m); - } - } - } - VERIFY_IS_APPROX(result2(l,m), expected); - } - } - - const Dims3 reduction_axis3; - Tensor<float, 1> result3 = tensor.sum(reduction_axis3); - for (int m = 0; m < 11; ++m) { - float expected = 0.0f; - for (int l = 0; l < 7; ++l) { - for (int k = 0; k < 5; ++k) { - for (int j = 0; j < 3; ++j) { - for (int i = 0; i < 2; ++i) { - expected += tensor(i,j,k,l,m); - } - } - } - } - VERIFY_IS_APPROX(result3(m), expected); - } - - const Dims4 reduction_axis4; - Tensor<float, 0> result4 = tensor.sum(reduction_axis4); - float expected = 0.0f; - for (int m = 0; m < 11; ++m) { - for (int l = 0; l < 7; ++l) { - for (int k = 0; k < 5; ++k) { - for (int j = 0; j < 3; ++j) { - for (int i = 0; i < 2; ++i) { - expected += tensor(i,j,k,l,m); - } - } - } - } - } - VERIFY_IS_APPROX(result4(), expected); -} - - -static void test_type2indexpair_list() -{ - Tensor<float, 5> tensor(2,3,5,7,11); - tensor.setRandom(); - tensor += tensor.constant(10.0f); - - typedef Eigen::IndexPairList<Eigen::type2indexpair<0,10>> Dims0; - typedef Eigen::IndexPairList<Eigen::type2indexpair<0,10>, Eigen::type2indexpair<1,11>, Eigen::type2indexpair<2,12>> Dims2_a; - typedef Eigen::IndexPairList<Eigen::type2indexpair<0,10>, Eigen::IndexPair<DenseIndex>, Eigen::type2indexpair<2,12>> Dims2_b; - typedef Eigen::IndexPairList<Eigen::IndexPair<DenseIndex>, Eigen::type2indexpair<1,11>, Eigen::IndexPair<DenseIndex>> Dims2_c; - - Dims0 d0; - Dims2_a d2_a; - - Dims2_b d2_b; - d2_b.set(1, Eigen::IndexPair<DenseIndex>(1,11)); - - Dims2_c d2_c; - d2_c.set(0, Eigen::IndexPair<DenseIndex>(Eigen::IndexPair<DenseIndex>(0,10))); - d2_c.set(1, Eigen::IndexPair<DenseIndex>(1,11)); // setting type2indexpair to correct value. - d2_c.set(2, Eigen::IndexPair<DenseIndex>(2,12)); - - VERIFY_IS_EQUAL(d2_a[0].first, 0); - VERIFY_IS_EQUAL(d2_a[0].second, 10); - VERIFY_IS_EQUAL(d2_a[1].first, 1); - VERIFY_IS_EQUAL(d2_a[1].second, 11); - VERIFY_IS_EQUAL(d2_a[2].first, 2); - VERIFY_IS_EQUAL(d2_a[2].second, 12); - - VERIFY_IS_EQUAL(d2_b[0].first, 0); - VERIFY_IS_EQUAL(d2_b[0].second, 10); - VERIFY_IS_EQUAL(d2_b[1].first, 1); - VERIFY_IS_EQUAL(d2_b[1].second, 11); - VERIFY_IS_EQUAL(d2_b[2].first, 2); - VERIFY_IS_EQUAL(d2_b[2].second, 12); - - VERIFY_IS_EQUAL(d2_c[0].first, 0); - VERIFY_IS_EQUAL(d2_c[0].second, 10); - VERIFY_IS_EQUAL(d2_c[1].first, 1); - VERIFY_IS_EQUAL(d2_c[1].second, 11); - VERIFY_IS_EQUAL(d2_c[2].first, 2); - VERIFY_IS_EQUAL(d2_c[2].second, 12); - - EIGEN_STATIC_ASSERT((d2_a.value_known_statically(0) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((d2_a.value_known_statically(1) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((d2_a.value_known_statically(2) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - - EIGEN_STATIC_ASSERT((d2_b.value_known_statically(0) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((d2_b.value_known_statically(1) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((d2_b.value_known_statically(2) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - - EIGEN_STATIC_ASSERT((d2_c.value_known_statically(0) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((d2_c.value_known_statically(1) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((d2_c.value_known_statically(2) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims0>(0, 0) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims0>(0, 1) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_a>(0, 0) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_a>(0, 1) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_a>(1, 1) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_a>(1, 2) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_a>(2, 2) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_a>(2, 3) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_b>(0, 0) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_b>(0, 1) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_b>(1, 1) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_b>(1, 2) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_b>(2, 2) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_b>(2, 3) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_c>(0, 0) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_c>(0, 1) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_c>(1, 1) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_c>(1, 2) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_c>(2, 2) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_c>(2, 3) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims0>(0, 10) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims0>(0, 11) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_a>(0, 10) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_a>(0, 11) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_a>(1, 11) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_a>(1, 12) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_a>(2, 12) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_a>(2, 13) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_b>(0, 10) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_b>(0, 11) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_b>(1, 11) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_b>(1, 12) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_b>(2, 12) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_b>(2, 13) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_c>(0, 10) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_c>(0, 11) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_c>(1, 11) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_c>(1, 12) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_c>(2, 12) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_c>(2, 13) == false), YOU_MADE_A_PROGRAMMING_MISTAKE); -} - - -static void test_dynamic_index_list() -{ - Tensor<float, 4> tensor(2,3,5,7); - tensor.setRandom(); - - int dim1 = 2; - int dim2 = 1; - int dim3 = 0; - - auto reduction_axis = make_index_list(dim1, dim2, dim3); - - VERIFY_IS_EQUAL(internal::array_get<0>(reduction_axis), 2); - VERIFY_IS_EQUAL(internal::array_get<1>(reduction_axis), 1); - VERIFY_IS_EQUAL(internal::array_get<2>(reduction_axis), 0); - VERIFY_IS_EQUAL(static_cast<DenseIndex>(reduction_axis[0]), 2); - VERIFY_IS_EQUAL(static_cast<DenseIndex>(reduction_axis[1]), 1); - VERIFY_IS_EQUAL(static_cast<DenseIndex>(reduction_axis[2]), 0); - - Tensor<float, 1> result = tensor.sum(reduction_axis); - for (int i = 0; i < result.size(); ++i) { - float expected = 0.0f; - for (int j = 0; j < 2; ++j) { - for (int k = 0; k < 3; ++k) { - for (int l = 0; l < 5; ++l) { - expected += tensor(j,k,l,i); - } - } - } - VERIFY_IS_APPROX(result(i), expected); - } -} - -static void test_mixed_index_list() -{ - Tensor<float, 4> tensor(2,3,5,7); - tensor.setRandom(); - - int dim2 = 1; - int dim4 = 3; - - auto reduction_axis = make_index_list(0, dim2, 2, dim4); - - VERIFY_IS_EQUAL(internal::array_get<0>(reduction_axis), 0); - VERIFY_IS_EQUAL(internal::array_get<1>(reduction_axis), 1); - VERIFY_IS_EQUAL(internal::array_get<2>(reduction_axis), 2); - VERIFY_IS_EQUAL(internal::array_get<3>(reduction_axis), 3); - VERIFY_IS_EQUAL(static_cast<DenseIndex>(reduction_axis[0]), 0); - VERIFY_IS_EQUAL(static_cast<DenseIndex>(reduction_axis[1]), 1); - VERIFY_IS_EQUAL(static_cast<DenseIndex>(reduction_axis[2]), 2); - VERIFY_IS_EQUAL(static_cast<DenseIndex>(reduction_axis[3]), 3); - - typedef IndexList<type2index<0>, int, type2index<2>, int> ReductionIndices; - ReductionIndices reduction_indices; - reduction_indices.set(1, 1); - reduction_indices.set(3, 3); - EIGEN_STATIC_ASSERT((internal::array_get<0>(reduction_indices) == 0), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::array_get<2>(reduction_indices) == 2), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::index_known_statically<ReductionIndices>(0) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::index_known_statically<ReductionIndices>(2) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::index_statically_eq<ReductionIndices>(0, 0) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::index_statically_eq<ReductionIndices>(2, 2) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); -#if 0 - EIGEN_STATIC_ASSERT((internal::all_indices_known_statically<ReductionIndices>() == false), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::indices_statically_known_to_increase<ReductionIndices>() == false), YOU_MADE_A_PROGRAMMING_MISTAKE); -#endif - - typedef IndexList<type2index<0>, type2index<1>, type2index<2>, type2index<3>> ReductionList; - ReductionList reduction_list; - EIGEN_STATIC_ASSERT((internal::index_statically_eq<ReductionList>(0, 0) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::index_statically_eq<ReductionList>(1, 1) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::index_statically_eq<ReductionList>(2, 2) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::index_statically_eq<ReductionList>(3, 3) == true), YOU_MADE_A_PROGRAMMING_MISTAKE); -#if 0 - EIGEN_STATIC_ASSERT((internal::all_indices_known_statically<ReductionList>() == true), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::indices_statically_known_to_increase<ReductionList>() == true), YOU_MADE_A_PROGRAMMING_MISTAKE); -#endif - - Tensor<float, 0> result1 = tensor.sum(reduction_axis); - Tensor<float, 0> result2 = tensor.sum(reduction_indices); - Tensor<float, 0> result3 = tensor.sum(reduction_list); - - float expected = 0.0f; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - expected += tensor(i,j,k,l); - } - } - } - } - VERIFY_IS_APPROX(result1(), expected); - VERIFY_IS_APPROX(result2(), expected); - VERIFY_IS_APPROX(result3(), expected); -} - - -static void test_dim_check() -{ - Eigen::IndexList<Eigen::type2index<1>, int> dim1; - dim1.set(1, 2); - Eigen::IndexList<Eigen::type2index<1>, int> dim2; - dim2.set(1, 2); - VERIFY(dimensions_match(dim1, dim2)); -} - - -#endif - -void test_cxx11_tensor_index_list() -{ -#ifdef EIGEN_HAS_INDEX_LIST - CALL_SUBTEST(test_static_index_list()); - CALL_SUBTEST(test_type2index_list()); - CALL_SUBTEST(test_type2indexpair_list()); - CALL_SUBTEST(test_dynamic_index_list()); - CALL_SUBTEST(test_mixed_index_list()); - CALL_SUBTEST(test_dim_check()); -#endif -} diff --git a/eigen/unsupported/test/cxx11_tensor_inflation.cpp b/eigen/unsupported/test/cxx11_tensor_inflation.cpp deleted file mode 100644 index 4997935..0000000 --- a/eigen/unsupported/test/cxx11_tensor_inflation.cpp +++ /dev/null @@ -1,81 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Ke Yang <yangke@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/CXX11/Tensor> - -using Eigen::Tensor; - -template<int DataLayout> -static void test_simple_inflation() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - array<ptrdiff_t, 4> strides; - - strides[0] = 1; - strides[1] = 1; - strides[2] = 1; - strides[3] = 1; - - Tensor<float, 4, DataLayout> no_stride; - no_stride = tensor.inflate(strides); - - VERIFY_IS_EQUAL(no_stride.dimension(0), 2); - VERIFY_IS_EQUAL(no_stride.dimension(1), 3); - VERIFY_IS_EQUAL(no_stride.dimension(2), 5); - VERIFY_IS_EQUAL(no_stride.dimension(3), 7); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(tensor(i,j,k,l), no_stride(i,j,k,l)); - } - } - } - } - - strides[0] = 2; - strides[1] = 4; - strides[2] = 2; - strides[3] = 3; - Tensor<float, 4, DataLayout> inflated; - inflated = tensor.inflate(strides); - - VERIFY_IS_EQUAL(inflated.dimension(0), 3); - VERIFY_IS_EQUAL(inflated.dimension(1), 9); - VERIFY_IS_EQUAL(inflated.dimension(2), 9); - VERIFY_IS_EQUAL(inflated.dimension(3), 19); - - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 9; ++j) { - for (int k = 0; k < 9; ++k) { - for (int l = 0; l < 19; ++l) { - if (i % 2 == 0 && - j % 4 == 0 && - k % 2 == 0 && - l % 3 == 0) { - VERIFY_IS_EQUAL(inflated(i,j,k,l), - tensor(i/2, j/4, k/2, l/3)); - } else { - VERIFY_IS_EQUAL(0, inflated(i,j,k,l)); - } - } - } - } - } -} - -void test_cxx11_tensor_inflation() -{ - CALL_SUBTEST(test_simple_inflation<ColMajor>()); - CALL_SUBTEST(test_simple_inflation<RowMajor>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_intdiv.cpp b/eigen/unsupported/test/cxx11_tensor_intdiv.cpp deleted file mode 100644 index 8e2b70b..0000000 --- a/eigen/unsupported/test/cxx11_tensor_intdiv.cpp +++ /dev/null @@ -1,147 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014-2015 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - - -void test_signed_32bit() -{ - // Divide by one - const Eigen::internal::TensorIntDivisor<int32_t, false> div_by_one(1); - - for (int32_t j = 0; j < 25000; ++j) { - const int32_t fast_div = j / div_by_one; - const int32_t slow_div = j / 1; - VERIFY_IS_EQUAL(fast_div, slow_div); - } - - // Standard divide by 2 or more - for (int32_t i = 2; i < 25000; ++i) { - const Eigen::internal::TensorIntDivisor<int32_t, false> div(i); - - for (int32_t j = 0; j < 25000; ++j) { - const int32_t fast_div = j / div; - const int32_t slow_div = j / i; - VERIFY_IS_EQUAL(fast_div, slow_div); - } - } - - // Optimized divide by 2 or more - for (int32_t i = 2; i < 25000; ++i) { - const Eigen::internal::TensorIntDivisor<int32_t, true> div(i); - - for (int32_t j = 0; j < 25000; ++j) { - const int32_t fast_div = j / div; - const int32_t slow_div = j / i; - VERIFY_IS_EQUAL(fast_div, slow_div); - } - } -} - - -void test_unsigned_32bit() -{ - for (uint32_t i = 1; i < 25000; ++i) { - const Eigen::internal::TensorIntDivisor<uint32_t> div(i); - - for (uint32_t j = 0; j < 25000; ++j) { - const uint32_t fast_div = j / div; - const uint32_t slow_div = j / i; - VERIFY_IS_EQUAL(fast_div, slow_div); - } - } -} - - -void test_signed_64bit() -{ - for (int64_t i = 1; i < 25000; ++i) { - const Eigen::internal::TensorIntDivisor<int64_t> div(i); - - for (int64_t j = 0; j < 25000; ++j) { - const int64_t fast_div = j / div; - const int64_t slow_div = j / i; - VERIFY_IS_EQUAL(fast_div, slow_div); - } - } -} - - -void test_unsigned_64bit() -{ - for (uint64_t i = 1; i < 25000; ++i) { - const Eigen::internal::TensorIntDivisor<uint64_t> div(i); - - for (uint64_t j = 0; j < 25000; ++j) { - const uint64_t fast_div = j / div; - const uint64_t slow_div = j / i; - VERIFY_IS_EQUAL(fast_div, slow_div); - } - } -} - -void test_powers_32bit() { - for (int expon = 1; expon < 31; expon++) { - int32_t div = (1 << expon); - for (int num_expon = 0; num_expon < 32; num_expon++) { - int32_t start_num = (1 << num_expon) - 100; - int32_t end_num = (1 << num_expon) + 100; - if (start_num < 0) - start_num = 0; - for (int32_t num = start_num; num < end_num; num++) { - Eigen::internal::TensorIntDivisor<int32_t> divider = - Eigen::internal::TensorIntDivisor<int32_t>(div); - int32_t result = num/div; - int32_t result_op = divider.divide(num); - VERIFY_IS_EQUAL(result_op, result); - } - } - } -} - -void test_powers_64bit() { - for (int expon = 0; expon < 63; expon++) { - int64_t div = (1ull << expon); - for (int num_expon = 0; num_expon < 63; num_expon++) { - int64_t start_num = (1ull << num_expon) - 10; - int64_t end_num = (1ull << num_expon) + 10; - if (start_num < 0) - start_num = 0; - for (int64_t num = start_num; num < end_num; num++) { - Eigen::internal::TensorIntDivisor<int64_t> divider(div); - int64_t result = num/div; - int64_t result_op = divider.divide(num); - VERIFY_IS_EQUAL(result_op, result); - } - } - } -} - -void test_specific() { - // A particular combination that was previously failing - int64_t div = 209715200; - int64_t num = 3238002688ll; - Eigen::internal::TensorIntDivisor<int64_t> divider(div); - int64_t result = num/div; - int64_t result_op = divider.divide(num); - VERIFY_IS_EQUAL(result, result_op); -} - -void test_cxx11_tensor_intdiv() -{ - CALL_SUBTEST_1(test_signed_32bit()); - CALL_SUBTEST_2(test_unsigned_32bit()); - CALL_SUBTEST_3(test_signed_64bit()); - CALL_SUBTEST_4(test_unsigned_64bit()); - CALL_SUBTEST_5(test_powers_32bit()); - CALL_SUBTEST_6(test_powers_64bit()); - CALL_SUBTEST_7(test_specific()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_io.cpp b/eigen/unsupported/test/cxx11_tensor_io.cpp deleted file mode 100644 index 4899605..0000000 --- a/eigen/unsupported/test/cxx11_tensor_io.cpp +++ /dev/null @@ -1,136 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <sstream> -#include <string> -#include <Eigen/CXX11/Tensor> - - -template<int DataLayout> -static void test_output_0d() -{ - Tensor<int, 0, DataLayout> tensor; - tensor() = 123; - - std::stringstream os; - os << tensor; - - std::string expected("123"); - VERIFY_IS_EQUAL(std::string(os.str()), expected); -} - - -template<int DataLayout> -static void test_output_1d() -{ - Tensor<int, 1, DataLayout> tensor(5); - for (int i = 0; i < 5; ++i) { - tensor(i) = i; - } - - std::stringstream os; - os << tensor; - - std::string expected("0\n1\n2\n3\n4"); - VERIFY_IS_EQUAL(std::string(os.str()), expected); - - Eigen::Tensor<double,1,DataLayout> empty_tensor(0); - std::stringstream empty_os; - empty_os << empty_tensor; - std::string empty_string; - VERIFY_IS_EQUAL(std::string(empty_os.str()), empty_string); -} - - -template<int DataLayout> -static void test_output_2d() -{ - Tensor<int, 2, DataLayout> tensor(5, 3); - for (int i = 0; i < 5; ++i) { - for (int j = 0; j < 3; ++j) { - tensor(i, j) = i*j; - } - } - - std::stringstream os; - os << tensor; - - std::string expected("0 0 0\n0 1 2\n0 2 4\n0 3 6\n0 4 8"); - VERIFY_IS_EQUAL(std::string(os.str()), expected); -} - - -template<int DataLayout> -static void test_output_expr() -{ - Tensor<int, 1, DataLayout> tensor1(5); - Tensor<int, 1, DataLayout> tensor2(5); - for (int i = 0; i < 5; ++i) { - tensor1(i) = i; - tensor2(i) = 7; - } - - std::stringstream os; - os << tensor1 + tensor2; - - std::string expected(" 7\n 8\n 9\n10\n11"); - VERIFY_IS_EQUAL(std::string(os.str()), expected); -} - - -template<int DataLayout> -static void test_output_string() -{ - Tensor<std::string, 2, DataLayout> tensor(5, 3); - tensor.setConstant(std::string("foo")); - - std::cout << tensor << std::endl; - - std::stringstream os; - os << tensor; - - std::string expected("foo foo foo\nfoo foo foo\nfoo foo foo\nfoo foo foo\nfoo foo foo"); - VERIFY_IS_EQUAL(std::string(os.str()), expected); -} - - -template<int DataLayout> -static void test_output_const() -{ - Tensor<int, 1, DataLayout> tensor(5); - for (int i = 0; i < 5; ++i) { - tensor(i) = i; - } - - TensorMap<Tensor<const int, 1, DataLayout> > tensor_map(tensor.data(), 5); - - std::stringstream os; - os << tensor_map; - - std::string expected("0\n1\n2\n3\n4"); - VERIFY_IS_EQUAL(std::string(os.str()), expected); -} - - -void test_cxx11_tensor_io() -{ - CALL_SUBTEST(test_output_0d<ColMajor>()); - CALL_SUBTEST(test_output_0d<RowMajor>()); - CALL_SUBTEST(test_output_1d<ColMajor>()); - CALL_SUBTEST(test_output_1d<RowMajor>()); - CALL_SUBTEST(test_output_2d<ColMajor>()); - CALL_SUBTEST(test_output_2d<RowMajor>()); - CALL_SUBTEST(test_output_expr<ColMajor>()); - CALL_SUBTEST(test_output_expr<RowMajor>()); - CALL_SUBTEST(test_output_string<ColMajor>()); - CALL_SUBTEST(test_output_string<RowMajor>()); - CALL_SUBTEST(test_output_const<ColMajor>()); - CALL_SUBTEST(test_output_const<RowMajor>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_layout_swap.cpp b/eigen/unsupported/test/cxx11_tensor_layout_swap.cpp deleted file mode 100644 index ae297a9..0000000 --- a/eigen/unsupported/test/cxx11_tensor_layout_swap.cpp +++ /dev/null @@ -1,61 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -static void test_simple_swap() -{ - Tensor<float, 3, ColMajor> tensor(2,3,7); - tensor.setRandom(); - - Tensor<float, 3, RowMajor> tensor2 = tensor.swap_layout(); - VERIFY_IS_EQUAL(tensor.dimension(0), tensor2.dimension(2)); - VERIFY_IS_EQUAL(tensor.dimension(1), tensor2.dimension(1)); - VERIFY_IS_EQUAL(tensor.dimension(2), tensor2.dimension(0)); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL(tensor(i,j,k), tensor2(k,j,i)); - } - } - } -} - - -static void test_swap_as_lvalue() -{ - Tensor<float, 3, ColMajor> tensor(2,3,7); - tensor.setRandom(); - - Tensor<float, 3, RowMajor> tensor2(7,3,2); - tensor2.swap_layout() = tensor; - VERIFY_IS_EQUAL(tensor.dimension(0), tensor2.dimension(2)); - VERIFY_IS_EQUAL(tensor.dimension(1), tensor2.dimension(1)); - VERIFY_IS_EQUAL(tensor.dimension(2), tensor2.dimension(0)); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL(tensor(i,j,k), tensor2(k,j,i)); - } - } - } -} - - -void test_cxx11_tensor_layout_swap() -{ - CALL_SUBTEST(test_simple_swap()); - CALL_SUBTEST(test_swap_as_lvalue()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_lvalue.cpp b/eigen/unsupported/test/cxx11_tensor_lvalue.cpp deleted file mode 100644 index 071f5b4..0000000 --- a/eigen/unsupported/test/cxx11_tensor_lvalue.cpp +++ /dev/null @@ -1,42 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::RowMajor; - - -static void test_compound_assignment() -{ - Tensor<float, 3> mat1(2,3,7); - Tensor<float, 3> mat2(2,3,7); - Tensor<float, 3> mat3(2,3,7); - - mat1.setRandom(); - mat2.setRandom(); - mat3 = mat1; - mat3 += mat2; - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_APPROX(mat3(i,j,k), mat1(i,j,k) + mat2(i,j,k)); - } - } - } -} - - -void test_cxx11_tensor_lvalue() -{ - CALL_SUBTEST(test_compound_assignment()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_map.cpp b/eigen/unsupported/test/cxx11_tensor_map.cpp deleted file mode 100644 index 3db0ee7..0000000 --- a/eigen/unsupported/test/cxx11_tensor_map.cpp +++ /dev/null @@ -1,277 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::RowMajor; - -static void test_0d() -{ - Tensor<int, 0> scalar1; - Tensor<int, 0, RowMajor> scalar2; - - TensorMap<Tensor<const int, 0> > scalar3(scalar1.data()); - TensorMap<Tensor<const int, 0, RowMajor> > scalar4(scalar2.data()); - - scalar1() = 7; - scalar2() = 13; - - VERIFY_IS_EQUAL(scalar1.rank(), 0); - VERIFY_IS_EQUAL(scalar1.size(), 1); - - VERIFY_IS_EQUAL(scalar3(), 7); - VERIFY_IS_EQUAL(scalar4(), 13); -} - -static void test_1d() -{ - Tensor<int, 1> vec1(6); - Tensor<int, 1, RowMajor> vec2(6); - - TensorMap<Tensor<const int, 1> > vec3(vec1.data(), 6); - TensorMap<Tensor<const int, 1, RowMajor> > vec4(vec2.data(), 6); - - vec1(0) = 4; vec2(0) = 0; - vec1(1) = 8; vec2(1) = 1; - vec1(2) = 15; vec2(2) = 2; - vec1(3) = 16; vec2(3) = 3; - vec1(4) = 23; vec2(4) = 4; - vec1(5) = 42; vec2(5) = 5; - - VERIFY_IS_EQUAL(vec1.rank(), 1); - VERIFY_IS_EQUAL(vec1.size(), 6); - VERIFY_IS_EQUAL(vec1.dimension(0), 6); - - VERIFY_IS_EQUAL(vec3(0), 4); - VERIFY_IS_EQUAL(vec3(1), 8); - VERIFY_IS_EQUAL(vec3(2), 15); - VERIFY_IS_EQUAL(vec3(3), 16); - VERIFY_IS_EQUAL(vec3(4), 23); - VERIFY_IS_EQUAL(vec3(5), 42); - - VERIFY_IS_EQUAL(vec4(0), 0); - VERIFY_IS_EQUAL(vec4(1), 1); - VERIFY_IS_EQUAL(vec4(2), 2); - VERIFY_IS_EQUAL(vec4(3), 3); - VERIFY_IS_EQUAL(vec4(4), 4); - VERIFY_IS_EQUAL(vec4(5), 5); -} - -static void test_2d() -{ - Tensor<int, 2> mat1(2,3); - Tensor<int, 2, RowMajor> mat2(2,3); - - mat1(0,0) = 0; - mat1(0,1) = 1; - mat1(0,2) = 2; - mat1(1,0) = 3; - mat1(1,1) = 4; - mat1(1,2) = 5; - - mat2(0,0) = 0; - mat2(0,1) = 1; - mat2(0,2) = 2; - mat2(1,0) = 3; - mat2(1,1) = 4; - mat2(1,2) = 5; - - TensorMap<Tensor<const int, 2> > mat3(mat1.data(), 2, 3); - TensorMap<Tensor<const int, 2, RowMajor> > mat4(mat2.data(), 2, 3); - - VERIFY_IS_EQUAL(mat3.rank(), 2); - VERIFY_IS_EQUAL(mat3.size(), 6); - VERIFY_IS_EQUAL(mat3.dimension(0), 2); - VERIFY_IS_EQUAL(mat3.dimension(1), 3); - - VERIFY_IS_EQUAL(mat4.rank(), 2); - VERIFY_IS_EQUAL(mat4.size(), 6); - VERIFY_IS_EQUAL(mat4.dimension(0), 2); - VERIFY_IS_EQUAL(mat4.dimension(1), 3); - - VERIFY_IS_EQUAL(mat3(0,0), 0); - VERIFY_IS_EQUAL(mat3(0,1), 1); - VERIFY_IS_EQUAL(mat3(0,2), 2); - VERIFY_IS_EQUAL(mat3(1,0), 3); - VERIFY_IS_EQUAL(mat3(1,1), 4); - VERIFY_IS_EQUAL(mat3(1,2), 5); - - VERIFY_IS_EQUAL(mat4(0,0), 0); - VERIFY_IS_EQUAL(mat4(0,1), 1); - VERIFY_IS_EQUAL(mat4(0,2), 2); - VERIFY_IS_EQUAL(mat4(1,0), 3); - VERIFY_IS_EQUAL(mat4(1,1), 4); - VERIFY_IS_EQUAL(mat4(1,2), 5); -} - -static void test_3d() -{ - Tensor<int, 3> mat1(2,3,7); - Tensor<int, 3, RowMajor> mat2(2,3,7); - - int val = 0; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - mat1(i,j,k) = val; - mat2(i,j,k) = val; - val++; - } - } - } - - TensorMap<Tensor<const int, 3> > mat3(mat1.data(), 2, 3, 7); - TensorMap<Tensor<const int, 3, RowMajor> > mat4(mat2.data(), 2, 3, 7); - - VERIFY_IS_EQUAL(mat3.rank(), 3); - VERIFY_IS_EQUAL(mat3.size(), 2*3*7); - VERIFY_IS_EQUAL(mat3.dimension(0), 2); - VERIFY_IS_EQUAL(mat3.dimension(1), 3); - VERIFY_IS_EQUAL(mat3.dimension(2), 7); - - VERIFY_IS_EQUAL(mat4.rank(), 3); - VERIFY_IS_EQUAL(mat4.size(), 2*3*7); - VERIFY_IS_EQUAL(mat4.dimension(0), 2); - VERIFY_IS_EQUAL(mat4.dimension(1), 3); - VERIFY_IS_EQUAL(mat4.dimension(2), 7); - - val = 0; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL(mat3(i,j,k), val); - VERIFY_IS_EQUAL(mat4(i,j,k), val); - val++; - } - } - } -} - - -static void test_from_tensor() -{ - Tensor<int, 3> mat1(2,3,7); - Tensor<int, 3, RowMajor> mat2(2,3,7); - - int val = 0; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - mat1(i,j,k) = val; - mat2(i,j,k) = val; - val++; - } - } - } - - TensorMap<Tensor<int, 3> > mat3(mat1); - TensorMap<Tensor<int, 3, RowMajor> > mat4(mat2); - - VERIFY_IS_EQUAL(mat3.rank(), 3); - VERIFY_IS_EQUAL(mat3.size(), 2*3*7); - VERIFY_IS_EQUAL(mat3.dimension(0), 2); - VERIFY_IS_EQUAL(mat3.dimension(1), 3); - VERIFY_IS_EQUAL(mat3.dimension(2), 7); - - VERIFY_IS_EQUAL(mat4.rank(), 3); - VERIFY_IS_EQUAL(mat4.size(), 2*3*7); - VERIFY_IS_EQUAL(mat4.dimension(0), 2); - VERIFY_IS_EQUAL(mat4.dimension(1), 3); - VERIFY_IS_EQUAL(mat4.dimension(2), 7); - - val = 0; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL(mat3(i,j,k), val); - VERIFY_IS_EQUAL(mat4(i,j,k), val); - val++; - } - } - } - - TensorFixedSize<int, Sizes<2,3,7> > mat5; - - val = 0; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - array<ptrdiff_t, 3> coords; - coords[0] = i; - coords[1] = j; - coords[2] = k; - mat5(coords) = val; - val++; - } - } - } - - TensorMap<TensorFixedSize<int, Sizes<2,3,7> > > mat6(mat5); - - VERIFY_IS_EQUAL(mat6.rank(), 3); - VERIFY_IS_EQUAL(mat6.size(), 2*3*7); - VERIFY_IS_EQUAL(mat6.dimension(0), 2); - VERIFY_IS_EQUAL(mat6.dimension(1), 3); - VERIFY_IS_EQUAL(mat6.dimension(2), 7); - - val = 0; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL(mat6(i,j,k), val); - val++; - } - } - } -} - - -static int f(const TensorMap<Tensor<int, 3> >& tensor) { - // Size<0> empty; - EIGEN_STATIC_ASSERT((internal::array_size<Sizes<> >::value == 0), YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT((internal::array_size<DSizes<int, 0> >::value == 0), YOU_MADE_A_PROGRAMMING_MISTAKE); - Tensor<int, 0> result = tensor.sum(); - return result(); -} - -static void test_casting() -{ - Tensor<int, 3> tensor(2,3,7); - - int val = 0; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - tensor(i,j,k) = val; - val++; - } - } - } - - TensorMap<Tensor<int, 3> > map(tensor); - int sum1 = f(map); - int sum2 = f(tensor); - - VERIFY_IS_EQUAL(sum1, sum2); - VERIFY_IS_EQUAL(sum1, 861); -} - -void test_cxx11_tensor_map() -{ - CALL_SUBTEST(test_0d()); - CALL_SUBTEST(test_1d()); - CALL_SUBTEST(test_2d()); - CALL_SUBTEST(test_3d()); - - CALL_SUBTEST(test_from_tensor()); - CALL_SUBTEST(test_casting()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_math.cpp b/eigen/unsupported/test/cxx11_tensor_math.cpp deleted file mode 100644 index 61c742a..0000000 --- a/eigen/unsupported/test/cxx11_tensor_math.cpp +++ /dev/null @@ -1,46 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::RowMajor; - -static void test_tanh() -{ - Tensor<float, 1> vec1(6); - vec1.setRandom(); - - Tensor<float, 1> vec2 = vec1.tanh(); - - for (int i = 0; i < 6; ++i) { - VERIFY_IS_APPROX(vec2(i), tanhf(vec1(i))); - } -} - -static void test_sigmoid() -{ - Tensor<float, 1> vec1(6); - vec1.setRandom(); - - Tensor<float, 1> vec2 = vec1.sigmoid(); - - for (int i = 0; i < 6; ++i) { - VERIFY_IS_APPROX(vec2(i), 1.0f / (1.0f + std::exp(-vec1(i)))); - } -} - - -void test_cxx11_tensor_math() -{ - CALL_SUBTEST(test_tanh()); - CALL_SUBTEST(test_sigmoid()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_mixed_indices.cpp b/eigen/unsupported/test/cxx11_tensor_mixed_indices.cpp deleted file mode 100644 index 4fba6fd..0000000 --- a/eigen/unsupported/test/cxx11_tensor_mixed_indices.cpp +++ /dev/null @@ -1,53 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - - -static void test_simple() -{ - Tensor<float, 1, ColMajor> vec1(6); - Tensor<float, 1, ColMajor, int> vec2(6); - - vec1(0) = 4.0; vec2(0) = 0.0; - vec1(1) = 8.0; vec2(1) = 1.0; - vec1(2) = 15.0; vec2(2) = 2.0; - vec1(3) = 16.0; vec2(3) = 3.0; - vec1(4) = 23.0; vec2(4) = 4.0; - vec1(5) = 42.0; vec2(5) = 5.0; - - float data3[6]; - TensorMap<Tensor<float, 1, ColMajor>> vec3(data3, 6); - vec3 = vec1.sqrt(); - float data4[6]; - TensorMap<Tensor<float, 1, ColMajor, int>> vec4(data4, 6); - vec4 = vec2.square(); - - VERIFY_IS_APPROX(vec3(0), sqrtf(4.0)); - VERIFY_IS_APPROX(vec3(1), sqrtf(8.0)); - VERIFY_IS_APPROX(vec3(2), sqrtf(15.0)); - VERIFY_IS_APPROX(vec3(3), sqrtf(16.0)); - VERIFY_IS_APPROX(vec3(4), sqrtf(23.0)); - VERIFY_IS_APPROX(vec3(5), sqrtf(42.0)); - - VERIFY_IS_APPROX(vec4(0), 0.0f); - VERIFY_IS_APPROX(vec4(1), 1.0f); - VERIFY_IS_APPROX(vec4(2), 2.0f * 2.0f); - VERIFY_IS_APPROX(vec4(3), 3.0f * 3.0f); - VERIFY_IS_APPROX(vec4(4), 4.0f * 4.0f); - VERIFY_IS_APPROX(vec4(5), 5.0f * 5.0f); -} - - -void test_cxx11_tensor_mixed_indices() -{ - CALL_SUBTEST(test_simple()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_morphing.cpp b/eigen/unsupported/test/cxx11_tensor_morphing.cpp deleted file mode 100644 index f7de431..0000000 --- a/eigen/unsupported/test/cxx11_tensor_morphing.cpp +++ /dev/null @@ -1,485 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -template<typename> -static void test_simple_reshape() -{ - Tensor<float, 5> tensor1(2,3,1,7,1); - tensor1.setRandom(); - - Tensor<float, 3> tensor2(2,3,7); - Tensor<float, 2> tensor3(6,7); - Tensor<float, 2> tensor4(2,21); - - Tensor<float, 3>::Dimensions dim1(2,3,7); - tensor2 = tensor1.reshape(dim1); - Tensor<float, 2>::Dimensions dim2(6,7); - tensor3 = tensor1.reshape(dim2); - Tensor<float, 2>::Dimensions dim3(2,21); - tensor4 = tensor1.reshape(dim1).reshape(dim3); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor2(i,j,k)); - VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor3(i+2*j,k)); - VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor4(i,j+3*k)); - } - } - } -} - -template<typename> -static void test_reshape_in_expr() { - MatrixXf m1(2,3*5*7*11); - MatrixXf m2(3*5*7*11,13); - m1.setRandom(); - m2.setRandom(); - MatrixXf m3 = m1 * m2; - - TensorMap<Tensor<float, 5>> tensor1(m1.data(), 2,3,5,7,11); - TensorMap<Tensor<float, 5>> tensor2(m2.data(), 3,5,7,11,13); - Tensor<float, 2>::Dimensions newDims1(2,3*5*7*11); - Tensor<float, 2>::Dimensions newDims2(3*5*7*11,13); - typedef Tensor<float, 1>::DimensionPair DimPair; - array<DimPair, 1> contract_along{{DimPair(1, 0)}}; - Tensor<float, 2> tensor3(2,13); - tensor3 = tensor1.reshape(newDims1).contract(tensor2.reshape(newDims2), contract_along); - - Map<MatrixXf> res(tensor3.data(), 2, 13); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 13; ++j) { - VERIFY_IS_APPROX(res(i,j), m3(i,j)); - } - } -} - -template<typename> -static void test_reshape_as_lvalue() -{ - Tensor<float, 3> tensor(2,3,7); - tensor.setRandom(); - - Tensor<float, 2> tensor2d(6,7); - Tensor<float, 3>::Dimensions dim(2,3,7); - tensor2d.reshape(dim) = tensor; - - float scratch[2*3*1*7*1]; - TensorMap<Tensor<float, 5>> tensor5d(scratch, 2,3,1,7,1); - tensor5d.reshape(dim).device(Eigen::DefaultDevice()) = tensor; - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL(tensor2d(i+2*j,k), tensor(i,j,k)); - VERIFY_IS_EQUAL(tensor5d(i,j,0,k,0), tensor(i,j,k)); - } - } - } -} - -template<int DataLayout> -static void test_simple_slice() -{ - Tensor<float, 5, DataLayout> tensor(2,3,5,7,11); - tensor.setRandom(); - - Tensor<float, 5, DataLayout> slice1(1,1,1,1,1); - Eigen::DSizes<ptrdiff_t, 5> indices(1,2,3,4,5); - Eigen::DSizes<ptrdiff_t, 5> sizes(1,1,1,1,1); - slice1 = tensor.slice(indices, sizes); - VERIFY_IS_EQUAL(slice1(0,0,0,0,0), tensor(1,2,3,4,5)); - - Tensor<float, 5, DataLayout> slice2(1,1,2,2,3); - Eigen::DSizes<ptrdiff_t, 5> indices2(1,1,3,4,5); - Eigen::DSizes<ptrdiff_t, 5> sizes2(1,1,2,2,3); - slice2 = tensor.slice(indices2, sizes2); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 2; ++j) { - for (int k = 0; k < 3; ++k) { - VERIFY_IS_EQUAL(slice2(0,0,i,j,k), tensor(1,1,3+i,4+j,5+k)); - } - } - } -} - -template<typename=void> -static void test_const_slice() -{ - const float b[1] = {42}; - TensorMap<Tensor<const float, 1> > m(b, 1); - DSizes<DenseIndex, 1> offsets; - offsets[0] = 0; - TensorRef<Tensor<const float, 1> > slice_ref(m.slice(offsets, m.dimensions())); - VERIFY_IS_EQUAL(slice_ref(0), 42); -} - -template<int DataLayout> -static void test_slice_in_expr() { - typedef Matrix<float, Dynamic, Dynamic, DataLayout> Mtx; - Mtx m1(7,7); - Mtx m2(3,3); - m1.setRandom(); - m2.setRandom(); - - Mtx m3 = m1.block(1, 2, 3, 3) * m2.block(0, 2, 3, 1); - - TensorMap<Tensor<float, 2, DataLayout>> tensor1(m1.data(), 7, 7); - TensorMap<Tensor<float, 2, DataLayout>> tensor2(m2.data(), 3, 3); - Tensor<float, 2, DataLayout> tensor3(3,1); - typedef Tensor<float, 1>::DimensionPair DimPair; - array<DimPair, 1> contract_along{{DimPair(1, 0)}}; - - Eigen::DSizes<ptrdiff_t, 2> indices1(1,2); - Eigen::DSizes<ptrdiff_t, 2> sizes1(3,3); - Eigen::DSizes<ptrdiff_t, 2> indices2(0,2); - Eigen::DSizes<ptrdiff_t, 2> sizes2(3,1); - tensor3 = tensor1.slice(indices1, sizes1).contract(tensor2.slice(indices2, sizes2), contract_along); - - Map<Mtx> res(tensor3.data(), 3, 1); - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 1; ++j) { - VERIFY_IS_APPROX(res(i,j), m3(i,j)); - } - } - - // Take an arbitrary slice of an arbitrarily sized tensor. - TensorMap<Tensor<const float, 2, DataLayout>> tensor4(m1.data(), 7, 7); - Tensor<float, 1, DataLayout> tensor6 = tensor4.reshape(DSizes<ptrdiff_t, 1>(7*7)).exp().slice(DSizes<ptrdiff_t, 1>(0), DSizes<ptrdiff_t, 1>(35)); - for (int i = 0; i < 35; ++i) { - VERIFY_IS_APPROX(tensor6(i), expf(tensor4.data()[i])); - } -} - -template<int DataLayout> -static void test_slice_as_lvalue() -{ - Tensor<float, 3, DataLayout> tensor1(2,2,7); - tensor1.setRandom(); - Tensor<float, 3, DataLayout> tensor2(2,2,7); - tensor2.setRandom(); - Tensor<float, 3, DataLayout> tensor3(4,3,5); - tensor3.setRandom(); - Tensor<float, 3, DataLayout> tensor4(4,3,2); - tensor4.setRandom(); - Tensor<float, 3, DataLayout> tensor5(10,13,12); - tensor5.setRandom(); - - Tensor<float, 3, DataLayout> result(4,5,7); - Eigen::DSizes<ptrdiff_t, 3> sizes12(2,2,7); - Eigen::DSizes<ptrdiff_t, 3> first_slice(0,0,0); - result.slice(first_slice, sizes12) = tensor1; - Eigen::DSizes<ptrdiff_t, 3> second_slice(2,0,0); - result.slice(second_slice, sizes12).device(Eigen::DefaultDevice()) = tensor2; - - Eigen::DSizes<ptrdiff_t, 3> sizes3(4,3,5); - Eigen::DSizes<ptrdiff_t, 3> third_slice(0,2,0); - result.slice(third_slice, sizes3) = tensor3; - - Eigen::DSizes<ptrdiff_t, 3> sizes4(4,3,2); - Eigen::DSizes<ptrdiff_t, 3> fourth_slice(0,2,5); - result.slice(fourth_slice, sizes4) = tensor4; - - for (int j = 0; j < 2; ++j) { - for (int k = 0; k < 7; ++k) { - for (int i = 0; i < 2; ++i) { - VERIFY_IS_EQUAL(result(i,j,k), tensor1(i,j,k)); - VERIFY_IS_EQUAL(result(i+2,j,k), tensor2(i,j,k)); - } - } - } - for (int i = 0; i < 4; ++i) { - for (int j = 2; j < 5; ++j) { - for (int k = 0; k < 5; ++k) { - VERIFY_IS_EQUAL(result(i,j,k), tensor3(i,j-2,k)); - } - for (int k = 5; k < 7; ++k) { - VERIFY_IS_EQUAL(result(i,j,k), tensor4(i,j-2,k-5)); - } - } - } - - Eigen::DSizes<ptrdiff_t, 3> sizes5(4,5,7); - Eigen::DSizes<ptrdiff_t, 3> fifth_slice(0,0,0); - result.slice(fifth_slice, sizes5) = tensor5.slice(fifth_slice, sizes5); - for (int i = 0; i < 4; ++i) { - for (int j = 2; j < 5; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL(result(i,j,k), tensor5(i,j,k)); - } - } - } -} - -template<int DataLayout> -static void test_slice_raw_data() -{ - Tensor<float, 4, DataLayout> tensor(3,5,7,11); - tensor.setRandom(); - - Eigen::DSizes<ptrdiff_t, 4> offsets(1,2,3,4); - Eigen::DSizes<ptrdiff_t, 4> extents(1,1,1,1); - typedef TensorEvaluator<decltype(tensor.slice(offsets, extents)), DefaultDevice> SliceEvaluator; - auto slice1 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice()); - VERIFY_IS_EQUAL(slice1.dimensions().TotalSize(), 1); - VERIFY_IS_EQUAL(slice1.data()[0], tensor(1,2,3,4)); - - if (DataLayout == ColMajor) { - extents = Eigen::DSizes<ptrdiff_t, 4>(2,1,1,1); - auto slice2 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice()); - VERIFY_IS_EQUAL(slice2.dimensions().TotalSize(), 2); - VERIFY_IS_EQUAL(slice2.data()[0], tensor(1,2,3,4)); - VERIFY_IS_EQUAL(slice2.data()[1], tensor(2,2,3,4)); - } else { - extents = Eigen::DSizes<ptrdiff_t, 4>(1,1,1,2); - auto slice2 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice()); - VERIFY_IS_EQUAL(slice2.dimensions().TotalSize(), 2); - VERIFY_IS_EQUAL(slice2.data()[0], tensor(1,2,3,4)); - VERIFY_IS_EQUAL(slice2.data()[1], tensor(1,2,3,5)); - } - - extents = Eigen::DSizes<ptrdiff_t, 4>(1,2,1,1); - auto slice3 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice()); - VERIFY_IS_EQUAL(slice3.dimensions().TotalSize(), 2); - VERIFY_IS_EQUAL(slice3.data(), static_cast<float*>(0)); - - if (DataLayout == ColMajor) { - offsets = Eigen::DSizes<ptrdiff_t, 4>(0,2,3,4); - extents = Eigen::DSizes<ptrdiff_t, 4>(3,2,1,1); - auto slice4 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice()); - VERIFY_IS_EQUAL(slice4.dimensions().TotalSize(), 6); - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 2; ++j) { - VERIFY_IS_EQUAL(slice4.data()[i+3*j], tensor(i,2+j,3,4)); - } - } - } else { - offsets = Eigen::DSizes<ptrdiff_t, 4>(1,2,3,0); - extents = Eigen::DSizes<ptrdiff_t, 4>(1,1,2,11); - auto slice4 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice()); - VERIFY_IS_EQUAL(slice4.dimensions().TotalSize(), 22); - for (int l = 0; l < 11; ++l) { - for (int k = 0; k < 2; ++k) { - VERIFY_IS_EQUAL(slice4.data()[l+11*k], tensor(1,2,3+k,l)); - } - } - } - - if (DataLayout == ColMajor) { - offsets = Eigen::DSizes<ptrdiff_t, 4>(0,0,0,4); - extents = Eigen::DSizes<ptrdiff_t, 4>(3,5,7,2); - auto slice5 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice()); - VERIFY_IS_EQUAL(slice5.dimensions().TotalSize(), 210); - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 5; ++j) { - for (int k = 0; k < 7; ++k) { - for (int l = 0; l < 2; ++l) { - int slice_index = i + 3 * (j + 5 * (k + 7 * l)); - VERIFY_IS_EQUAL(slice5.data()[slice_index], tensor(i,j,k,l+4)); - } - } - } - } - } else { - offsets = Eigen::DSizes<ptrdiff_t, 4>(1,0,0,0); - extents = Eigen::DSizes<ptrdiff_t, 4>(2,5,7,11); - auto slice5 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice()); - VERIFY_IS_EQUAL(slice5.dimensions().TotalSize(), 770); - for (int l = 0; l < 11; ++l) { - for (int k = 0; k < 7; ++k) { - for (int j = 0; j < 5; ++j) { - for (int i = 0; i < 2; ++i) { - int slice_index = l + 11 * (k + 7 * (j + 5 * i)); - VERIFY_IS_EQUAL(slice5.data()[slice_index], tensor(i+1,j,k,l)); - } - } - } - } - - } - - offsets = Eigen::DSizes<ptrdiff_t, 4>(0,0,0,0); - extents = Eigen::DSizes<ptrdiff_t, 4>(3,5,7,11); - auto slice6 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice()); - VERIFY_IS_EQUAL(slice6.dimensions().TotalSize(), 3*5*7*11); - VERIFY_IS_EQUAL(slice6.data(), tensor.data()); -} - - -template<int DataLayout> -static void test_strided_slice() -{ - typedef Tensor<float, 5, DataLayout> Tensor5f; - typedef Eigen::DSizes<Eigen::DenseIndex, 5> Index5; - typedef Tensor<float, 2, DataLayout> Tensor2f; - typedef Eigen::DSizes<Eigen::DenseIndex, 2> Index2; - Tensor<float, 5, DataLayout> tensor(2,3,5,7,11); - Tensor<float, 2, DataLayout> tensor2(7,11); - tensor.setRandom(); - tensor2.setRandom(); - - if (true) { - Tensor2f slice(2,3); - Index2 strides(-2,-1); - Index2 indicesStart(5,7); - Index2 indicesStop(0,4); - slice = tensor2.stridedSlice(indicesStart, indicesStop, strides); - for (int j = 0; j < 2; ++j) { - for (int k = 0; k < 3; ++k) { - VERIFY_IS_EQUAL(slice(j,k), tensor2(5-2*j,7-k)); - } - } - } - - if(true) { - Tensor2f slice(0,1); - Index2 strides(1,1); - Index2 indicesStart(5,4); - Index2 indicesStop(5,5); - slice = tensor2.stridedSlice(indicesStart, indicesStop, strides); - } - - if(true) { // test clamped degenerate interavls - Tensor2f slice(7,11); - Index2 strides(1,-1); - Index2 indicesStart(-3,20); // should become 0,10 - Index2 indicesStop(20,-11); // should become 11, -1 - slice = tensor2.stridedSlice(indicesStart, indicesStop, strides); - for (int j = 0; j < 7; ++j) { - for (int k = 0; k < 11; ++k) { - VERIFY_IS_EQUAL(slice(j,k), tensor2(j,10-k)); - } - } - } - - if(true) { - Tensor5f slice1(1,1,1,1,1); - Eigen::DSizes<Eigen::DenseIndex, 5> indicesStart(1, 2, 3, 4, 5); - Eigen::DSizes<Eigen::DenseIndex, 5> indicesStop(2, 3, 4, 5, 6); - Eigen::DSizes<Eigen::DenseIndex, 5> strides(1, 1, 1, 1, 1); - slice1 = tensor.stridedSlice(indicesStart, indicesStop, strides); - VERIFY_IS_EQUAL(slice1(0,0,0,0,0), tensor(1,2,3,4,5)); - } - - if(true) { - Tensor5f slice(1,1,2,2,3); - Index5 start(1, 1, 3, 4, 5); - Index5 stop(2, 2, 5, 6, 8); - Index5 strides(1, 1, 1, 1, 1); - slice = tensor.stridedSlice(start, stop, strides); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 2; ++j) { - for (int k = 0; k < 3; ++k) { - VERIFY_IS_EQUAL(slice(0,0,i,j,k), tensor(1,1,3+i,4+j,5+k)); - } - } - } - } - - if(true) { - Tensor5f slice(1,1,2,2,3); - Index5 strides3(1, 1, -2, 1, -1); - Index5 indices3Start(1, 1, 4, 4, 7); - Index5 indices3Stop(2, 2, 0, 6, 4); - slice = tensor.stridedSlice(indices3Start, indices3Stop, strides3); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 2; ++j) { - for (int k = 0; k < 3; ++k) { - VERIFY_IS_EQUAL(slice(0,0,i,j,k), tensor(1,1,4-2*i,4+j,7-k)); - } - } - } - } - - if(false) { // tests degenerate interval - Tensor5f slice(1,1,2,2,3); - Index5 strides3(1, 1, 2, 1, 1); - Index5 indices3Start(1, 1, 4, 4, 7); - Index5 indices3Stop(2, 2, 0, 6, 4); - slice = tensor.stridedSlice(indices3Start, indices3Stop, strides3); - } -} - -template<int DataLayout> -static void test_strided_slice_write() -{ - typedef Tensor<float, 2, DataLayout> Tensor2f; - typedef Eigen::DSizes<Eigen::DenseIndex, 2> Index2; - - Tensor<float, 2, DataLayout> tensor(7,11),tensor2(7,11); - tensor.setRandom(); - tensor2=tensor; - Tensor2f slice(2,3); - - slice.setRandom(); - - Index2 strides(1,1); - Index2 indicesStart(3,4); - Index2 indicesStop(5,7); - Index2 lengths(2,3); - - tensor.slice(indicesStart,lengths)=slice; - tensor2.stridedSlice(indicesStart,indicesStop,strides)=slice; - - for(int i=0;i<7;i++) for(int j=0;j<11;j++){ - VERIFY_IS_EQUAL(tensor(i,j), tensor2(i,j)); - } -} - - -template<int DataLayout> -static void test_composition() -{ - Eigen::Tensor<float, 2, DataLayout> matrix(7, 11); - matrix.setRandom(); - - const DSizes<ptrdiff_t, 3> newDims(1, 1, 11); - Eigen::Tensor<float, 3, DataLayout> tensor = - matrix.slice(DSizes<ptrdiff_t, 2>(2, 0), DSizes<ptrdiff_t, 2>(1, 11)).reshape(newDims); - - VERIFY_IS_EQUAL(tensor.dimensions().TotalSize(), 11); - VERIFY_IS_EQUAL(tensor.dimension(0), 1); - VERIFY_IS_EQUAL(tensor.dimension(1), 1); - VERIFY_IS_EQUAL(tensor.dimension(2), 11); - for (int i = 0; i < 11; ++i) { - VERIFY_IS_EQUAL(tensor(0,0,i), matrix(2,i)); - } -} - - -void test_cxx11_tensor_morphing() -{ - CALL_SUBTEST_1(test_simple_reshape<void>()); - CALL_SUBTEST_1(test_reshape_in_expr<void>()); - CALL_SUBTEST_1(test_reshape_as_lvalue<void>()); - - CALL_SUBTEST_1(test_simple_slice<ColMajor>()); - CALL_SUBTEST_1(test_simple_slice<RowMajor>()); - CALL_SUBTEST_1(test_const_slice()); - CALL_SUBTEST_2(test_slice_in_expr<ColMajor>()); - CALL_SUBTEST_3(test_slice_in_expr<RowMajor>()); - CALL_SUBTEST_4(test_slice_as_lvalue<ColMajor>()); - CALL_SUBTEST_4(test_slice_as_lvalue<RowMajor>()); - CALL_SUBTEST_5(test_slice_raw_data<ColMajor>()); - CALL_SUBTEST_5(test_slice_raw_data<RowMajor>()); - - CALL_SUBTEST_6(test_strided_slice_write<ColMajor>()); - CALL_SUBTEST_6(test_strided_slice<ColMajor>()); - CALL_SUBTEST_6(test_strided_slice_write<RowMajor>()); - CALL_SUBTEST_6(test_strided_slice<RowMajor>()); - - CALL_SUBTEST_7(test_composition<ColMajor>()); - CALL_SUBTEST_7(test_composition<RowMajor>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_notification.cpp b/eigen/unsupported/test/cxx11_tensor_notification.cpp deleted file mode 100644 index c946007..0000000 --- a/eigen/unsupported/test/cxx11_tensor_notification.cpp +++ /dev/null @@ -1,81 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Vijay Vasudevan <vrv@google.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_THREADS - -#include <stdlib.h> -#include "main.h" -#include <Eigen/CXX11/Tensor> - -#if EIGEN_OS_WIN || EIGEN_OS_WIN64 -#include <windows.h> -void sleep(int seconds) { - Sleep(seconds*1000); -} -#else -#include <unistd.h> -#endif - - -namespace { - -void WaitAndAdd(Eigen::Notification* n, int* counter) { - n->Wait(); - *counter = *counter + 1; -} - -} // namespace - -static void test_notification_single() -{ - ThreadPool thread_pool(1); - - int counter = 0; - Eigen::Notification n; - std::function<void()> func = std::bind(&WaitAndAdd, &n, &counter); - thread_pool.Schedule(func); - sleep(1); - - // The thread should be waiting for the notification. - VERIFY_IS_EQUAL(counter, 0); - - // Unblock the thread - n.Notify(); - - sleep(1); - - // Verify the counter has been incremented - VERIFY_IS_EQUAL(counter, 1); -} - -// Like test_notification_single() but enqueues multiple threads to -// validate that all threads get notified by Notify(). -static void test_notification_multiple() -{ - ThreadPool thread_pool(1); - - int counter = 0; - Eigen::Notification n; - std::function<void()> func = std::bind(&WaitAndAdd, &n, &counter); - thread_pool.Schedule(func); - thread_pool.Schedule(func); - thread_pool.Schedule(func); - thread_pool.Schedule(func); - sleep(1); - VERIFY_IS_EQUAL(counter, 0); - n.Notify(); - sleep(1); - VERIFY_IS_EQUAL(counter, 4); -} - -void test_cxx11_tensor_notification() -{ - CALL_SUBTEST(test_notification_single()); - CALL_SUBTEST(test_notification_multiple()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_of_complex.cpp b/eigen/unsupported/test/cxx11_tensor_of_complex.cpp deleted file mode 100644 index e9d1b2d..0000000 --- a/eigen/unsupported/test/cxx11_tensor_of_complex.cpp +++ /dev/null @@ -1,103 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::TensorMap; - - - -static void test_additions() -{ - Tensor<std::complex<float>, 1> data1(3); - Tensor<std::complex<float>, 1> data2(3); - for (int i = 0; i < 3; ++i) { - data1(i) = std::complex<float>(i, -i); - data2(i) = std::complex<float>(i, 7 * i); - } - - Tensor<std::complex<float>, 1> sum = data1 + data2; - for (int i = 0; i < 3; ++i) { - VERIFY_IS_EQUAL(sum(i), std::complex<float>(2*i, 6*i)); - } -} - - -static void test_abs() -{ - Tensor<std::complex<float>, 1> data1(3); - Tensor<std::complex<double>, 1> data2(3); - data1.setRandom(); - data2.setRandom(); - - Tensor<float, 1> abs1 = data1.abs(); - Tensor<double, 1> abs2 = data2.abs(); - for (int i = 0; i < 3; ++i) { - VERIFY_IS_APPROX(abs1(i), std::abs(data1(i))); - VERIFY_IS_APPROX(abs2(i), std::abs(data2(i))); - } -} - - -static void test_conjugate() -{ - Tensor<std::complex<float>, 1> data1(3); - Tensor<std::complex<double>, 1> data2(3); - Tensor<int, 1> data3(3); - data1.setRandom(); - data2.setRandom(); - data3.setRandom(); - - Tensor<std::complex<float>, 1> conj1 = data1.conjugate(); - Tensor<std::complex<double>, 1> conj2 = data2.conjugate(); - Tensor<int, 1> conj3 = data3.conjugate(); - for (int i = 0; i < 3; ++i) { - VERIFY_IS_APPROX(conj1(i), std::conj(data1(i))); - VERIFY_IS_APPROX(conj2(i), std::conj(data2(i))); - VERIFY_IS_APPROX(conj3(i), data3(i)); - } -} - -static void test_contractions() -{ - Tensor<std::complex<float>, 4> t_left(30, 50, 8, 31); - Tensor<std::complex<float>, 5> t_right(8, 31, 7, 20, 10); - Tensor<std::complex<float>, 5> t_result(30, 50, 7, 20, 10); - - t_left.setRandom(); - t_right.setRandom(); - - typedef Map<Matrix<std::complex<float>, Dynamic, Dynamic>> MapXcf; - MapXcf m_left(t_left.data(), 1500, 248); - MapXcf m_right(t_right.data(), 248, 1400); - Matrix<std::complex<float>, Dynamic, Dynamic> m_result(1500, 1400); - - // This contraction should be equivalent to a regular matrix multiplication - typedef Tensor<float, 1>::DimensionPair DimPair; - Eigen::array<DimPair, 2> dims; - dims[0] = DimPair(2, 0); - dims[1] = DimPair(3, 1); - t_result = t_left.contract(t_right, dims); - m_result = m_left * m_right; - for (int i = 0; i < t_result.dimensions().TotalSize(); i++) { - VERIFY_IS_APPROX(t_result.data()[i], m_result.data()[i]); - } -} - - -void test_cxx11_tensor_of_complex() -{ - CALL_SUBTEST(test_additions()); - CALL_SUBTEST(test_abs()); - CALL_SUBTEST(test_conjugate()); - CALL_SUBTEST(test_contractions()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_of_const_values.cpp b/eigen/unsupported/test/cxx11_tensor_of_const_values.cpp deleted file mode 100644 index f179a0c..0000000 --- a/eigen/unsupported/test/cxx11_tensor_of_const_values.cpp +++ /dev/null @@ -1,105 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::RowMajor; - -static void test_assign() -{ - float data1[6]; - TensorMap<Tensor<const float, 2>> mat1(data1, 2, 3); - float data2[6]; - const TensorMap<Tensor<float, 2>> mat2(data2, 2, 3); - - for (int i = 0; i < 6; ++i) { - data1[i] = i; - data2[i] = -i; - } - - Tensor<float, 2> rslt1; - rslt1 = mat1; - Tensor<float, 2> rslt2; - rslt2 = mat2; - - Tensor<float, 2> rslt3 = mat1; - Tensor<float, 2> rslt4 = mat2; - - Tensor<float, 2> rslt5(mat1); - Tensor<float, 2> rslt6(mat2); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - VERIFY_IS_APPROX(rslt1(i,j), static_cast<float>(i + 2*j)); - VERIFY_IS_APPROX(rslt2(i,j), static_cast<float>(-i - 2*j)); - VERIFY_IS_APPROX(rslt3(i,j), static_cast<float>(i + 2*j)); - VERIFY_IS_APPROX(rslt4(i,j), static_cast<float>(-i - 2*j)); - VERIFY_IS_APPROX(rslt5(i,j), static_cast<float>(i + 2*j)); - VERIFY_IS_APPROX(rslt6(i,j), static_cast<float>(-i - 2*j)); - } - } -} - - -static void test_plus() -{ - float data1[6]; - TensorMap<Tensor<const float, 2>> mat1(data1, 2, 3); - float data2[6]; - TensorMap<Tensor<float, 2>> mat2(data2, 2, 3); - - for (int i = 0; i < 6; ++i) { - data1[i] = i; - data2[i] = -i; - } - - Tensor<float, 2> sum1; - sum1 = mat1 + mat2; - Tensor<float, 2> sum2; - sum2 = mat2 + mat1; - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - VERIFY_IS_APPROX(sum1(i,j), 0.0f); - VERIFY_IS_APPROX(sum2(i,j), 0.0f); - } - } -} - - -static void test_plus_equal() -{ - float data1[6]; - TensorMap<Tensor<const float, 2>> mat1(data1, 2, 3); - float data2[6]; - TensorMap<Tensor<float, 2>> mat2(data2, 2, 3); - - for (int i = 0; i < 6; ++i) { - data1[i] = i; - data2[i] = -i; - } - mat2 += mat1; - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - VERIFY_IS_APPROX(mat2(i,j), 0.0f); - } - } -} - - -void test_cxx11_tensor_of_const_values() -{ - CALL_SUBTEST(test_assign()); - CALL_SUBTEST(test_plus()); - CALL_SUBTEST(test_plus_equal()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_of_float16_cuda.cu b/eigen/unsupported/test/cxx11_tensor_of_float16_cuda.cu deleted file mode 100644 index e296bf9..0000000 --- a/eigen/unsupported/test/cxx11_tensor_of_float16_cuda.cu +++ /dev/null @@ -1,491 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_NO_COMPLEX -#define EIGEN_TEST_FUNC cxx11_tensor_of_float16_cuda -#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int -#define EIGEN_USE_GPU - -#include "main.h" -#include <unsupported/Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -template<typename> -void test_cuda_numext() { - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - int num_elem = 101; - - float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); - bool* d_res_half = (bool*)gpu_device.allocate(num_elem * sizeof(bool)); - bool* d_res_float = (bool*)gpu_device.allocate(num_elem * sizeof(bool)); - - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float( - d_float, num_elem); - Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_res_half( - d_res_half, num_elem); - Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_res_float( - d_res_float, num_elem); - - gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f); - gpu_res_float.device(gpu_device) = gpu_float.unaryExpr(Eigen::internal::scalar_isnan_op<float>()); - gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().unaryExpr(Eigen::internal::scalar_isnan_op<Eigen::half>()); - - Tensor<bool, 1> half_prec(num_elem); - Tensor<bool, 1> full_prec(num_elem); - gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(bool)); - gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(bool)); - gpu_device.synchronize(); - - for (int i = 0; i < num_elem; ++i) { - std::cout << "Checking numext " << i << std::endl; - VERIFY_IS_EQUAL(full_prec(i), half_prec(i)); - } - - gpu_device.deallocate(d_float); - gpu_device.deallocate(d_res_half); - gpu_device.deallocate(d_res_float); -} - - -#ifdef EIGEN_HAS_CUDA_FP16 - -template<typename> -void test_cuda_conversion() { - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - int num_elem = 101; - - float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); - Eigen::half* d_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); - float* d_conv = (float*)gpu_device.allocate(num_elem * sizeof(float)); - - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float( - d_float, num_elem); - Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_half( - d_half, num_elem); - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_conv( - d_conv, num_elem); - - gpu_float.device(gpu_device) = gpu_float.random(); - gpu_half.device(gpu_device) = gpu_float.cast<Eigen::half>(); - gpu_conv.device(gpu_device) = gpu_half.cast<float>(); - - Tensor<float, 1> initial(num_elem); - Tensor<float, 1> final(num_elem); - gpu_device.memcpyDeviceToHost(initial.data(), d_float, num_elem*sizeof(float)); - gpu_device.memcpyDeviceToHost(final.data(), d_conv, num_elem*sizeof(float)); - - for (int i = 0; i < num_elem; ++i) { - VERIFY_IS_APPROX(initial(i), final(i)); - } - - gpu_device.deallocate(d_float); - gpu_device.deallocate(d_half); - gpu_device.deallocate(d_conv); -} - -template<typename> -void test_cuda_unary() { - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - int num_elem = 101; - - float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); - float* d_res_half = (float*)gpu_device.allocate(num_elem * sizeof(float)); - float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); - - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float( - d_float, num_elem); - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half( - d_res_half, num_elem); - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float( - d_res_float, num_elem); - - gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f); - gpu_res_float.device(gpu_device) = gpu_float.abs(); - gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().abs().cast<float>(); - - Tensor<float, 1> half_prec(num_elem); - Tensor<float, 1> full_prec(num_elem); - gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(float)); - gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(float)); - gpu_device.synchronize(); - - for (int i = 0; i < num_elem; ++i) { - std::cout << "Checking unary " << i << std::endl; - VERIFY_IS_APPROX(full_prec(i), half_prec(i)); - } - - gpu_device.deallocate(d_float); - gpu_device.deallocate(d_res_half); - gpu_device.deallocate(d_res_float); -} - -template<typename> -void test_cuda_elementwise() { - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - int num_elem = 101; - - float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); - float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); - float* d_res_half = (float*)gpu_device.allocate(num_elem * sizeof(float)); - float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); - - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1( - d_float1, num_elem); - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float2( - d_float2, num_elem); - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half( - d_res_half, num_elem); - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float( - d_res_float, num_elem); - - gpu_float1.device(gpu_device) = gpu_float1.random(); - gpu_float2.device(gpu_device) = gpu_float2.random(); - gpu_res_float.device(gpu_device) = (gpu_float1 + gpu_float2) * gpu_float1; - gpu_res_half.device(gpu_device) = ((gpu_float1.cast<Eigen::half>() + gpu_float2.cast<Eigen::half>()) * gpu_float1.cast<Eigen::half>()).cast<float>(); - - Tensor<float, 1> half_prec(num_elem); - Tensor<float, 1> full_prec(num_elem); - gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(float)); - gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(float)); - gpu_device.synchronize(); - - for (int i = 0; i < num_elem; ++i) { - std::cout << "Checking elemwise " << i << ": full prec = " << full_prec(i) << " vs half prec = " << half_prec(i) << std::endl; - VERIFY_IS_APPROX(static_cast<Eigen::half>(full_prec(i)), static_cast<Eigen::half>(half_prec(i))); - } - - gpu_device.deallocate(d_float1); - gpu_device.deallocate(d_float2); - gpu_device.deallocate(d_res_half); - gpu_device.deallocate(d_res_float); -} - -template<typename> -void test_cuda_trancendental() { - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - int num_elem = 101; - - float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); - float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); - float* d_float3 = (float*)gpu_device.allocate(num_elem * sizeof(float)); - Eigen::half* d_res1_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); - Eigen::half* d_res1_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); - Eigen::half* d_res2_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); - Eigen::half* d_res2_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); - Eigen::half* d_res3_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); - Eigen::half* d_res3_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); - - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1(d_float1, num_elem); - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float2(d_float2, num_elem); - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float3(d_float3, num_elem); - Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_half(d_res1_half, num_elem); - Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_float(d_res1_float, num_elem); - Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_half(d_res2_half, num_elem); - Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_float(d_res2_float, num_elem); - Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res3_half(d_res3_half, num_elem); - Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res3_float(d_res3_float, num_elem); - - gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f); - gpu_float2.device(gpu_device) = gpu_float2.random() + gpu_float1.constant(0.5f); - gpu_float3.device(gpu_device) = gpu_float3.random(); - gpu_res1_float.device(gpu_device) = gpu_float1.exp().cast<Eigen::half>(); - gpu_res2_float.device(gpu_device) = gpu_float2.log().cast<Eigen::half>(); - gpu_res3_float.device(gpu_device) = gpu_float3.log1p().cast<Eigen::half>(); - - gpu_res1_half.device(gpu_device) = gpu_float1.cast<Eigen::half>(); - gpu_res1_half.device(gpu_device) = gpu_res1_half.exp(); - - gpu_res2_half.device(gpu_device) = gpu_float2.cast<Eigen::half>(); - gpu_res2_half.device(gpu_device) = gpu_res2_half.log(); - - gpu_res3_half.device(gpu_device) = gpu_float3.cast<Eigen::half>(); - gpu_res3_half.device(gpu_device) = gpu_res3_half.log1p(); - - Tensor<float, 1> input1(num_elem); - Tensor<Eigen::half, 1> half_prec1(num_elem); - Tensor<Eigen::half, 1> full_prec1(num_elem); - Tensor<float, 1> input2(num_elem); - Tensor<Eigen::half, 1> half_prec2(num_elem); - Tensor<Eigen::half, 1> full_prec2(num_elem); - Tensor<float, 1> input3(num_elem); - Tensor<Eigen::half, 1> half_prec3(num_elem); - Tensor<Eigen::half, 1> full_prec3(num_elem); - gpu_device.memcpyDeviceToHost(input1.data(), d_float1, num_elem*sizeof(float)); - gpu_device.memcpyDeviceToHost(input2.data(), d_float2, num_elem*sizeof(float)); - gpu_device.memcpyDeviceToHost(input3.data(), d_float3, num_elem*sizeof(float)); - gpu_device.memcpyDeviceToHost(half_prec1.data(), d_res1_half, num_elem*sizeof(Eigen::half)); - gpu_device.memcpyDeviceToHost(full_prec1.data(), d_res1_float, num_elem*sizeof(Eigen::half)); - gpu_device.memcpyDeviceToHost(half_prec2.data(), d_res2_half, num_elem*sizeof(Eigen::half)); - gpu_device.memcpyDeviceToHost(full_prec2.data(), d_res2_float, num_elem*sizeof(Eigen::half)); - gpu_device.memcpyDeviceToHost(half_prec3.data(), d_res3_half, num_elem*sizeof(Eigen::half)); - gpu_device.memcpyDeviceToHost(full_prec3.data(), d_res3_float, num_elem*sizeof(Eigen::half)); - gpu_device.synchronize(); - - for (int i = 0; i < num_elem; ++i) { - std::cout << "Checking elemwise exp " << i << " input = " << input1(i) << " full = " << full_prec1(i) << " half = " << half_prec1(i) << std::endl; - VERIFY_IS_APPROX(full_prec1(i), half_prec1(i)); - } - for (int i = 0; i < num_elem; ++i) { - std::cout << "Checking elemwise log " << i << " input = " << input2(i) << " full = " << full_prec2(i) << " half = " << half_prec2(i) << std::endl; - if(std::abs(input2(i)-1.f)<0.05f) // log lacks accurary nearby 1 - VERIFY_IS_APPROX(full_prec2(i)+Eigen::half(0.1f), half_prec2(i)+Eigen::half(0.1f)); - else - VERIFY_IS_APPROX(full_prec2(i), half_prec2(i)); - } - for (int i = 0; i < num_elem; ++i) { - std::cout << "Checking elemwise plog1 " << i << " input = " << input3(i) << " full = " << full_prec3(i) << " half = " << half_prec3(i) << std::endl; - VERIFY_IS_APPROX(full_prec3(i), half_prec3(i)); - } - gpu_device.deallocate(d_float1); - gpu_device.deallocate(d_float2); - gpu_device.deallocate(d_float3); - gpu_device.deallocate(d_res1_half); - gpu_device.deallocate(d_res1_float); - gpu_device.deallocate(d_res2_half); - gpu_device.deallocate(d_res2_float); - gpu_device.deallocate(d_res3_float); - gpu_device.deallocate(d_res3_half); -} - -template<typename> -void test_cuda_contractions() { - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - int rows = 23; - int cols = 23; - int num_elem = rows*cols; - - float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); - float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); - Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); - Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); - - Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float1( - d_float1, rows, cols); - Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float2( - d_float2, rows, cols); - Eigen::TensorMap<Eigen::Tensor<Eigen::half, 2>, Eigen::Aligned> gpu_res_half( - d_res_half, rows, cols); - Eigen::TensorMap<Eigen::Tensor<Eigen::half, 2>, Eigen::Aligned> gpu_res_float( - d_res_float, rows, cols); - - gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f); - gpu_float2.device(gpu_device) = gpu_float2.random() - gpu_float2.constant(0.5f); - - typedef Tensor<float, 2>::DimensionPair DimPair; - Eigen::array<DimPair, 1> dims(DimPair(1, 0)); - gpu_res_float.device(gpu_device) = gpu_float1.contract(gpu_float2, dims).cast<Eigen::half>(); - gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().contract(gpu_float2.cast<Eigen::half>(), dims); - - Tensor<Eigen::half, 2> half_prec(rows, cols); - Tensor<Eigen::half, 2> full_prec(rows, cols); - gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(Eigen::half)); - gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(Eigen::half)); - gpu_device.synchronize(); - - for (int i = 0; i < rows; ++i) { - for (int j = 0; j < cols; ++j) { - std::cout << "Checking contract " << i << " " << j << full_prec(i, j) << " " << half_prec(i, j) << std::endl; - if (numext::abs(full_prec(i, j) - half_prec(i, j)) > Eigen::half(1e-2f)) { - VERIFY_IS_APPROX(full_prec(i, j), half_prec(i, j)); - } - } - } - - gpu_device.deallocate(d_float1); - gpu_device.deallocate(d_float2); - gpu_device.deallocate(d_res_half); - gpu_device.deallocate(d_res_float); -} - -template<typename> -void test_cuda_reductions(int size1, int size2, int redux) { - - std::cout << "Reducing " << size1 << " by " << size2 - << " tensor along dim " << redux << std::endl; - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - int num_elem = size1*size2; - int result_size = (redux == 1 ? size1 : size2); - - float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); - float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); - Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(result_size * sizeof(Eigen::half)); - Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(result_size * sizeof(Eigen::half)); - - Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float1( - d_float1, size1, size2); - Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float2( - d_float2, size1, size2); - Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res_half( - d_res_half, result_size); - Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res_float( - d_res_float, result_size); - - gpu_float1.device(gpu_device) = gpu_float1.random() * 2.0f; - gpu_float2.device(gpu_device) = gpu_float2.random() * 2.0f; - - Eigen::array<int, 1> redux_dim = {{redux}}; - gpu_res_float.device(gpu_device) = gpu_float1.sum(redux_dim).cast<Eigen::half>(); - gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().sum(redux_dim); - - Tensor<Eigen::half, 1> half_prec(result_size); - Tensor<Eigen::half, 1> full_prec(result_size); - gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, result_size*sizeof(Eigen::half)); - gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, result_size*sizeof(Eigen::half)); - gpu_device.synchronize(); - - for (int i = 0; i < result_size; ++i) { - std::cout << "EXPECTED " << full_prec(i) << " GOT " << half_prec(i) << std::endl; - VERIFY_IS_APPROX(full_prec(i), half_prec(i)); - } - - gpu_device.deallocate(d_float1); - gpu_device.deallocate(d_float2); - gpu_device.deallocate(d_res_half); - gpu_device.deallocate(d_res_float); -} - -template<typename> -void test_cuda_reductions() { - test_cuda_reductions<void>(13, 13, 0); - test_cuda_reductions<void>(13, 13, 1); - - test_cuda_reductions<void>(35, 36, 0); - test_cuda_reductions<void>(35, 36, 1); - - test_cuda_reductions<void>(36, 35, 0); - test_cuda_reductions<void>(36, 35, 1); -} - -template<typename> -void test_cuda_full_reductions() { - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - int size = 13; - int num_elem = size*size; - - float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); - float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); - Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(1 * sizeof(Eigen::half)); - Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(1 * sizeof(Eigen::half)); - - Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float1( - d_float1, size, size); - Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float2( - d_float2, size, size); - Eigen::TensorMap<Eigen::Tensor<Eigen::half, 0>, Eigen::Aligned> gpu_res_half( - d_res_half); - Eigen::TensorMap<Eigen::Tensor<Eigen::half, 0>, Eigen::Aligned> gpu_res_float( - d_res_float); - - gpu_float1.device(gpu_device) = gpu_float1.random(); - gpu_float2.device(gpu_device) = gpu_float2.random(); - - gpu_res_float.device(gpu_device) = gpu_float1.sum().cast<Eigen::half>(); - gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().sum(); - - Tensor<Eigen::half, 0> half_prec; - Tensor<Eigen::half, 0> full_prec; - gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, sizeof(Eigen::half)); - gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::half)); - gpu_device.synchronize(); - - VERIFY_IS_APPROX(full_prec(), half_prec()); - - gpu_res_float.device(gpu_device) = gpu_float1.maximum().cast<Eigen::half>(); - gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().maximum(); - gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, sizeof(Eigen::half)); - gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::half)); - gpu_device.synchronize(); - - VERIFY_IS_APPROX(full_prec(), half_prec()); - - gpu_device.deallocate(d_float1); - gpu_device.deallocate(d_float2); - gpu_device.deallocate(d_res_half); - gpu_device.deallocate(d_res_float); -} - -template<typename> -void test_cuda_forced_evals() { - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - int num_elem = 101; - - float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); - float* d_res_half1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); - float* d_res_half2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); - float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); - - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float( - d_float, num_elem); - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half1( - d_res_half1, num_elem); - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Unaligned> gpu_res_half2( - d_res_half2, num_elem); - Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float( - d_res_float, num_elem); - - Eigen::array<int, 1> no_bcast; - no_bcast[0] = 1; - - gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f); - gpu_res_float.device(gpu_device) = gpu_float.abs(); - gpu_res_half1.device(gpu_device) = gpu_float.cast<Eigen::half>().abs().eval().cast<float>(); - gpu_res_half2.device(gpu_device) = gpu_float.cast<Eigen::half>().abs().broadcast(no_bcast).eval().cast<float>(); - - Tensor<float, 1> half_prec1(num_elem); - Tensor<float, 1> half_prec2(num_elem); - Tensor<float, 1> full_prec(num_elem); - gpu_device.memcpyDeviceToHost(half_prec1.data(), d_res_half1, num_elem*sizeof(float)); - gpu_device.memcpyDeviceToHost(half_prec2.data(), d_res_half1, num_elem*sizeof(float)); - gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(float)); - gpu_device.synchronize(); - - for (int i = 0; i < num_elem; ++i) { - std::cout << "Checking forced eval " << i << full_prec(i) << " vs " << half_prec1(i) << " vs " << half_prec2(i) << std::endl; - VERIFY_IS_APPROX(full_prec(i), half_prec1(i)); - VERIFY_IS_APPROX(full_prec(i), half_prec2(i)); - } - - gpu_device.deallocate(d_float); - gpu_device.deallocate(d_res_half1); - gpu_device.deallocate(d_res_half2); - gpu_device.deallocate(d_res_float); -} -#endif - - -void test_cxx11_tensor_of_float16_cuda() -{ - CALL_SUBTEST_1(test_cuda_numext<void>()); - -#ifdef EIGEN_HAS_CUDA_FP16 - CALL_SUBTEST_1(test_cuda_conversion<void>()); - CALL_SUBTEST_1(test_cuda_unary<void>()); - CALL_SUBTEST_1(test_cuda_elementwise<void>()); - CALL_SUBTEST_1(test_cuda_trancendental<void>()); - CALL_SUBTEST_2(test_cuda_contractions<void>()); - CALL_SUBTEST_3(test_cuda_reductions<void>()); - CALL_SUBTEST_4(test_cuda_full_reductions<void>()); - CALL_SUBTEST_5(test_cuda_forced_evals<void>()); -#else - std::cout << "Half floats are not supported by this version of cuda: skipping the test" << std::endl; -#endif -} diff --git a/eigen/unsupported/test/cxx11_tensor_of_strings.cpp b/eigen/unsupported/test/cxx11_tensor_of_strings.cpp deleted file mode 100644 index 4ef9aed..0000000 --- a/eigen/unsupported/test/cxx11_tensor_of_strings.cpp +++ /dev/null @@ -1,152 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::TensorMap; - -static void test_assign() -{ - std::string data1[6]; - TensorMap<Tensor<std::string, 2>> mat1(data1, 2, 3); - std::string data2[6]; - const TensorMap<Tensor<const std::string, 2>> mat2(data2, 2, 3); - - for (int i = 0; i < 6; ++i) { - std::ostringstream s1; - s1 << "abc" << i*3; - data1[i] = s1.str(); - std::ostringstream s2; - s2 << "def" << i*5; - data2[i] = s2.str(); - } - - Tensor<std::string, 2> rslt1; - rslt1 = mat1; - Tensor<std::string, 2> rslt2; - rslt2 = mat2; - - Tensor<std::string, 2> rslt3 = mat1; - Tensor<std::string, 2> rslt4 = mat2; - - Tensor<std::string, 2> rslt5(mat1); - Tensor<std::string, 2> rslt6(mat2); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - VERIFY_IS_EQUAL(rslt1(i,j), data1[i+2*j]); - VERIFY_IS_EQUAL(rslt2(i,j), data2[i+2*j]); - VERIFY_IS_EQUAL(rslt3(i,j), data1[i+2*j]); - VERIFY_IS_EQUAL(rslt4(i,j), data2[i+2*j]); - VERIFY_IS_EQUAL(rslt5(i,j), data1[i+2*j]); - VERIFY_IS_EQUAL(rslt6(i,j), data2[i+2*j]); - } - } -} - - -static void test_concat() -{ - Tensor<std::string, 2> t1(2, 3); - Tensor<std::string, 2> t2(2, 3); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - std::ostringstream s1; - s1 << "abc" << i + j*2; - t1(i, j) = s1.str(); - std::ostringstream s2; - s2 << "def" << i*5 + j*32; - t2(i, j) = s2.str(); - } - } - - Tensor<std::string, 2> result = t1.concatenate(t2, 1); - VERIFY_IS_EQUAL(result.dimension(0), 2); - VERIFY_IS_EQUAL(result.dimension(1), 6); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - VERIFY_IS_EQUAL(result(i, j), t1(i, j)); - VERIFY_IS_EQUAL(result(i, j+3), t2(i, j)); - } - } -} - - -static void test_slices() -{ - Tensor<std::string, 2> data(2, 6); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - std::ostringstream s1; - s1 << "abc" << i + j*2; - data(i, j) = s1.str(); - } - } - - const Eigen::DSizes<ptrdiff_t, 2> half_size(2, 3); - const Eigen::DSizes<ptrdiff_t, 2> first_half(0, 0); - const Eigen::DSizes<ptrdiff_t, 2> second_half(0, 3); - - Tensor<std::string, 2> t1 = data.slice(first_half, half_size); - Tensor<std::string, 2> t2 = data.slice(second_half, half_size); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - VERIFY_IS_EQUAL(data(i, j), t1(i, j)); - VERIFY_IS_EQUAL(data(i, j+3), t2(i, j)); - } - } -} - - -static void test_additions() -{ - Tensor<std::string, 1> data1(3); - Tensor<std::string, 1> data2(3); - for (int i = 0; i < 3; ++i) { - data1(i) = "abc"; - std::ostringstream s1; - s1 << i; - data2(i) = s1.str(); - } - - Tensor<std::string, 1> sum = data1 + data2; - for (int i = 0; i < 3; ++i) { - std::ostringstream concat; - concat << "abc" << i; - std::string expected = concat.str(); - VERIFY_IS_EQUAL(sum(i), expected); - } -} - - -static void test_initialization() -{ - Tensor<std::string, 2> a(2, 3); - a.setConstant(std::string("foo")); - for (int i = 0; i < 2*3; ++i) { - VERIFY_IS_EQUAL(a(i), std::string("foo")); - } -} - - -void test_cxx11_tensor_of_strings() -{ - // Beware: none of this is likely to ever work on a GPU. - CALL_SUBTEST(test_assign()); - CALL_SUBTEST(test_concat()); - CALL_SUBTEST(test_slices()); - CALL_SUBTEST(test_additions()); - CALL_SUBTEST(test_initialization()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_padding.cpp b/eigen/unsupported/test/cxx11_tensor_padding.cpp deleted file mode 100644 index ffa1989..0000000 --- a/eigen/unsupported/test/cxx11_tensor_padding.cpp +++ /dev/null @@ -1,93 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -template<int DataLayout> -static void test_simple_padding() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - - array<std::pair<ptrdiff_t, ptrdiff_t>, 4> paddings; - paddings[0] = std::make_pair(0, 0); - paddings[1] = std::make_pair(2, 1); - paddings[2] = std::make_pair(3, 4); - paddings[3] = std::make_pair(0, 0); - - Tensor<float, 4, DataLayout> padded; - padded = tensor.pad(paddings); - - VERIFY_IS_EQUAL(padded.dimension(0), 2+0); - VERIFY_IS_EQUAL(padded.dimension(1), 3+3); - VERIFY_IS_EQUAL(padded.dimension(2), 5+7); - VERIFY_IS_EQUAL(padded.dimension(3), 7+0); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 6; ++j) { - for (int k = 0; k < 12; ++k) { - for (int l = 0; l < 7; ++l) { - if (j >= 2 && j < 5 && k >= 3 && k < 8) { - VERIFY_IS_EQUAL(padded(i,j,k,l), tensor(i,j-2,k-3,l)); - } else { - VERIFY_IS_EQUAL(padded(i,j,k,l), 0.0f); - } - } - } - } - } -} - -template<int DataLayout> -static void test_padded_expr() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - - array<std::pair<ptrdiff_t, ptrdiff_t>, 4> paddings; - paddings[0] = std::make_pair(0, 0); - paddings[1] = std::make_pair(2, 1); - paddings[2] = std::make_pair(3, 4); - paddings[3] = std::make_pair(0, 0); - - Eigen::DSizes<ptrdiff_t, 2> reshape_dims; - reshape_dims[0] = 12; - reshape_dims[1] = 84; - - Tensor<float, 2, DataLayout> result; - result = tensor.pad(paddings).reshape(reshape_dims); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 6; ++j) { - for (int k = 0; k < 12; ++k) { - for (int l = 0; l < 7; ++l) { - const float result_value = DataLayout == ColMajor ? - result(i+2*j,k+12*l) : result(j+6*i,l+7*k); - if (j >= 2 && j < 5 && k >= 3 && k < 8) { - VERIFY_IS_EQUAL(result_value, tensor(i,j-2,k-3,l)); - } else { - VERIFY_IS_EQUAL(result_value, 0.0f); - } - } - } - } - } -} - -void test_cxx11_tensor_padding() -{ - CALL_SUBTEST(test_simple_padding<ColMajor>()); - CALL_SUBTEST(test_simple_padding<RowMajor>()); - CALL_SUBTEST(test_padded_expr<ColMajor>()); - CALL_SUBTEST(test_padded_expr<RowMajor>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_patch.cpp b/eigen/unsupported/test/cxx11_tensor_patch.cpp deleted file mode 100644 index 4343597..0000000 --- a/eigen/unsupported/test/cxx11_tensor_patch.cpp +++ /dev/null @@ -1,172 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -template<int DataLayout> -static void test_simple_patch() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - array<ptrdiff_t, 4> patch_dims; - - patch_dims[0] = 1; - patch_dims[1] = 1; - patch_dims[2] = 1; - patch_dims[3] = 1; - - Tensor<float, 5, DataLayout> no_patch; - no_patch = tensor.extract_patches(patch_dims); - - if (DataLayout == ColMajor) { - VERIFY_IS_EQUAL(no_patch.dimension(0), 1); - VERIFY_IS_EQUAL(no_patch.dimension(1), 1); - VERIFY_IS_EQUAL(no_patch.dimension(2), 1); - VERIFY_IS_EQUAL(no_patch.dimension(3), 1); - VERIFY_IS_EQUAL(no_patch.dimension(4), tensor.size()); - } else { - VERIFY_IS_EQUAL(no_patch.dimension(0), tensor.size()); - VERIFY_IS_EQUAL(no_patch.dimension(1), 1); - VERIFY_IS_EQUAL(no_patch.dimension(2), 1); - VERIFY_IS_EQUAL(no_patch.dimension(3), 1); - VERIFY_IS_EQUAL(no_patch.dimension(4), 1); - } - - for (int i = 0; i < tensor.size(); ++i) { - VERIFY_IS_EQUAL(tensor.data()[i], no_patch.data()[i]); - } - - patch_dims[0] = 2; - patch_dims[1] = 3; - patch_dims[2] = 5; - patch_dims[3] = 7; - Tensor<float, 5, DataLayout> single_patch; - single_patch = tensor.extract_patches(patch_dims); - - if (DataLayout == ColMajor) { - VERIFY_IS_EQUAL(single_patch.dimension(0), 2); - VERIFY_IS_EQUAL(single_patch.dimension(1), 3); - VERIFY_IS_EQUAL(single_patch.dimension(2), 5); - VERIFY_IS_EQUAL(single_patch.dimension(3), 7); - VERIFY_IS_EQUAL(single_patch.dimension(4), 1); - } else { - VERIFY_IS_EQUAL(single_patch.dimension(0), 1); - VERIFY_IS_EQUAL(single_patch.dimension(1), 2); - VERIFY_IS_EQUAL(single_patch.dimension(2), 3); - VERIFY_IS_EQUAL(single_patch.dimension(3), 5); - VERIFY_IS_EQUAL(single_patch.dimension(4), 7); - } - - for (int i = 0; i < tensor.size(); ++i) { - VERIFY_IS_EQUAL(tensor.data()[i], single_patch.data()[i]); - } - - patch_dims[0] = 1; - patch_dims[1] = 2; - patch_dims[2] = 2; - patch_dims[3] = 1; - Tensor<float, 5, DataLayout> twod_patch; - twod_patch = tensor.extract_patches(patch_dims); - - if (DataLayout == ColMajor) { - VERIFY_IS_EQUAL(twod_patch.dimension(0), 1); - VERIFY_IS_EQUAL(twod_patch.dimension(1), 2); - VERIFY_IS_EQUAL(twod_patch.dimension(2), 2); - VERIFY_IS_EQUAL(twod_patch.dimension(3), 1); - VERIFY_IS_EQUAL(twod_patch.dimension(4), 2*2*4*7); - } else { - VERIFY_IS_EQUAL(twod_patch.dimension(0), 2*2*4*7); - VERIFY_IS_EQUAL(twod_patch.dimension(1), 1); - VERIFY_IS_EQUAL(twod_patch.dimension(2), 2); - VERIFY_IS_EQUAL(twod_patch.dimension(3), 2); - VERIFY_IS_EQUAL(twod_patch.dimension(4), 1); - } - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 2; ++j) { - for (int k = 0; k < 4; ++k) { - for (int l = 0; l < 7; ++l) { - int patch_loc; - if (DataLayout == ColMajor) { - patch_loc = i + 2 * (j + 2 * (k + 4 * l)); - } else { - patch_loc = l + 7 * (k + 4 * (j + 2 * i)); - } - for (int x = 0; x < 2; ++x) { - for (int y = 0; y < 2; ++y) { - if (DataLayout == ColMajor) { - VERIFY_IS_EQUAL(tensor(i,j+x,k+y,l), twod_patch(0,x,y,0,patch_loc)); - } else { - VERIFY_IS_EQUAL(tensor(i,j+x,k+y,l), twod_patch(patch_loc,0,x,y,0)); - } - } - } - } - } - } - } - - patch_dims[0] = 1; - patch_dims[1] = 2; - patch_dims[2] = 3; - patch_dims[3] = 5; - Tensor<float, 5, DataLayout> threed_patch; - threed_patch = tensor.extract_patches(patch_dims); - - if (DataLayout == ColMajor) { - VERIFY_IS_EQUAL(threed_patch.dimension(0), 1); - VERIFY_IS_EQUAL(threed_patch.dimension(1), 2); - VERIFY_IS_EQUAL(threed_patch.dimension(2), 3); - VERIFY_IS_EQUAL(threed_patch.dimension(3), 5); - VERIFY_IS_EQUAL(threed_patch.dimension(4), 2*2*3*3); - } else { - VERIFY_IS_EQUAL(threed_patch.dimension(0), 2*2*3*3); - VERIFY_IS_EQUAL(threed_patch.dimension(1), 1); - VERIFY_IS_EQUAL(threed_patch.dimension(2), 2); - VERIFY_IS_EQUAL(threed_patch.dimension(3), 3); - VERIFY_IS_EQUAL(threed_patch.dimension(4), 5); - } - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 2; ++j) { - for (int k = 0; k < 3; ++k) { - for (int l = 0; l < 3; ++l) { - int patch_loc; - if (DataLayout == ColMajor) { - patch_loc = i + 2 * (j + 2 * (k + 3 * l)); - } else { - patch_loc = l + 3 * (k + 3 * (j + 2 * i)); - } - for (int x = 0; x < 2; ++x) { - for (int y = 0; y < 3; ++y) { - for (int z = 0; z < 5; ++z) { - if (DataLayout == ColMajor) { - VERIFY_IS_EQUAL(tensor(i,j+x,k+y,l+z), threed_patch(0,x,y,z,patch_loc)); - } else { - VERIFY_IS_EQUAL(tensor(i,j+x,k+y,l+z), threed_patch(patch_loc,0,x,y,z)); - } - } - } - } - } - } - } - } -} - -void test_cxx11_tensor_patch() -{ - CALL_SUBTEST(test_simple_patch<ColMajor>()); - CALL_SUBTEST(test_simple_patch<RowMajor>()); - // CALL_SUBTEST(test_expr_shuffling()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_random.cpp b/eigen/unsupported/test/cxx11_tensor_random.cpp deleted file mode 100644 index 0f3dc57..0000000 --- a/eigen/unsupported/test/cxx11_tensor_random.cpp +++ /dev/null @@ -1,78 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -static void test_default() -{ - Tensor<float, 1> vec(6); - vec.setRandom(); - - // Fixme: we should check that the generated numbers follow a uniform - // distribution instead. - for (int i = 1; i < 6; ++i) { - VERIFY_IS_NOT_EQUAL(vec(i), vec(i-1)); - } -} - -static void test_normal() -{ - Tensor<float, 1> vec(6); - vec.setRandom<Eigen::internal::NormalRandomGenerator<float>>(); - - // Fixme: we should check that the generated numbers follow a gaussian - // distribution instead. - for (int i = 1; i < 6; ++i) { - VERIFY_IS_NOT_EQUAL(vec(i), vec(i-1)); - } -} - - -struct MyGenerator { - MyGenerator() { } - MyGenerator(const MyGenerator&) { } - - // Return a random value to be used. "element_location" is the - // location of the entry to set in the tensor, it can typically - // be ignored. - int operator()(Eigen::DenseIndex element_location, Eigen::DenseIndex /*unused*/ = 0) const { - return static_cast<int>(3 * element_location); - } - - // Same as above but generates several numbers at a time. - internal::packet_traits<int>::type packetOp( - Eigen::DenseIndex packet_location, Eigen::DenseIndex /*unused*/ = 0) const { - const int packetSize = internal::packet_traits<int>::size; - EIGEN_ALIGN_MAX int values[packetSize]; - for (int i = 0; i < packetSize; ++i) { - values[i] = static_cast<int>(3 * (packet_location + i)); - } - return internal::pload<typename internal::packet_traits<int>::type>(values); - } -}; - - -static void test_custom() -{ - Tensor<int, 1> vec(6); - vec.setRandom<MyGenerator>(); - - for (int i = 0; i < 6; ++i) { - VERIFY_IS_EQUAL(vec(i), 3*i); - } -} - -void test_cxx11_tensor_random() -{ - CALL_SUBTEST(test_default()); - CALL_SUBTEST(test_normal()); - CALL_SUBTEST(test_custom()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_random_cuda.cu b/eigen/unsupported/test/cxx11_tensor_random_cuda.cu deleted file mode 100644 index fa1a467..0000000 --- a/eigen/unsupported/test/cxx11_tensor_random_cuda.cu +++ /dev/null @@ -1,85 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_NO_COMPLEX -#define EIGEN_TEST_FUNC cxx11_tensor_random_cuda -#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int -#define EIGEN_USE_GPU - -#include "main.h" -#include <Eigen/CXX11/Tensor> - - -void test_cuda_random_uniform() -{ - Tensor<float, 2> out(72,97); - out.setZero(); - - std::size_t out_bytes = out.size() * sizeof(float); - - float* d_out; - cudaMalloc((void**)(&d_out), out_bytes); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 2> > gpu_out(d_out, 72,97); - - gpu_out.device(gpu_device) = gpu_out.random(); - - assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); - - // For now we just check thes code doesn't crash. - // TODO: come up with a valid test of randomness -} - - -void test_cuda_random_normal() -{ - Tensor<float, 2> out(72,97); - out.setZero(); - - std::size_t out_bytes = out.size() * sizeof(float); - - float* d_out; - cudaMalloc((void**)(&d_out), out_bytes); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 2> > gpu_out(d_out, 72,97); - - Eigen::internal::NormalRandomGenerator<float> gen(true); - gpu_out.device(gpu_device) = gpu_out.random(gen); - - assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); - assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); -} - -static void test_complex() -{ - Tensor<std::complex<float>, 1> vec(6); - vec.setRandom(); - - // Fixme: we should check that the generated numbers follow a uniform - // distribution instead. - for (int i = 1; i < 6; ++i) { - VERIFY_IS_NOT_EQUAL(vec(i), vec(i-1)); - } -} - - -void test_cxx11_tensor_random_cuda() -{ - CALL_SUBTEST(test_cuda_random_uniform()); - CALL_SUBTEST(test_cuda_random_normal()); - CALL_SUBTEST(test_complex()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_reduction.cpp b/eigen/unsupported/test/cxx11_tensor_reduction.cpp deleted file mode 100644 index 1490ec3..0000000 --- a/eigen/unsupported/test/cxx11_tensor_reduction.cpp +++ /dev/null @@ -1,508 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <limits> -#include <numeric> -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -template <int DataLayout> -static void test_trivial_reductions() { - { - Tensor<float, 0, DataLayout> tensor; - tensor.setRandom(); - array<ptrdiff_t, 0> reduction_axis; - - Tensor<float, 0, DataLayout> result = tensor.sum(reduction_axis); - VERIFY_IS_EQUAL(result(), tensor()); - } - - { - Tensor<float, 1, DataLayout> tensor(7); - tensor.setRandom(); - array<ptrdiff_t, 0> reduction_axis; - - Tensor<float, 1, DataLayout> result = tensor.sum(reduction_axis); - VERIFY_IS_EQUAL(result.dimension(0), 7); - for (int i = 0; i < 7; ++i) { - VERIFY_IS_EQUAL(result(i), tensor(i)); - } - } - - { - Tensor<float, 2, DataLayout> tensor(2, 3); - tensor.setRandom(); - array<ptrdiff_t, 0> reduction_axis; - - Tensor<float, 2, DataLayout> result = tensor.sum(reduction_axis); - VERIFY_IS_EQUAL(result.dimension(0), 2); - VERIFY_IS_EQUAL(result.dimension(1), 3); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - VERIFY_IS_EQUAL(result(i, j), tensor(i, j)); - } - } - } -} - -template <int DataLayout> -static void test_simple_reductions() { - Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7); - tensor.setRandom(); - array<ptrdiff_t, 2> reduction_axis2; - reduction_axis2[0] = 1; - reduction_axis2[1] = 3; - - Tensor<float, 2, DataLayout> result = tensor.sum(reduction_axis2); - VERIFY_IS_EQUAL(result.dimension(0), 2); - VERIFY_IS_EQUAL(result.dimension(1), 5); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 5; ++j) { - float sum = 0.0f; - for (int k = 0; k < 3; ++k) { - for (int l = 0; l < 7; ++l) { - sum += tensor(i, k, j, l); - } - } - VERIFY_IS_APPROX(result(i, j), sum); - } - } - - { - Tensor<float, 0, DataLayout> sum1 = tensor.sum(); - VERIFY_IS_EQUAL(sum1.rank(), 0); - - array<ptrdiff_t, 4> reduction_axis4; - reduction_axis4[0] = 0; - reduction_axis4[1] = 1; - reduction_axis4[2] = 2; - reduction_axis4[3] = 3; - Tensor<float, 0, DataLayout> sum2 = tensor.sum(reduction_axis4); - VERIFY_IS_EQUAL(sum2.rank(), 0); - - VERIFY_IS_APPROX(sum1(), sum2()); - } - - reduction_axis2[0] = 0; - reduction_axis2[1] = 2; - result = tensor.prod(reduction_axis2); - VERIFY_IS_EQUAL(result.dimension(0), 3); - VERIFY_IS_EQUAL(result.dimension(1), 7); - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 7; ++j) { - float prod = 1.0f; - for (int k = 0; k < 2; ++k) { - for (int l = 0; l < 5; ++l) { - prod *= tensor(k, i, l, j); - } - } - VERIFY_IS_APPROX(result(i, j), prod); - } - } - - { - Tensor<float, 0, DataLayout> prod1 = tensor.prod(); - VERIFY_IS_EQUAL(prod1.rank(), 0); - - array<ptrdiff_t, 4> reduction_axis4; - reduction_axis4[0] = 0; - reduction_axis4[1] = 1; - reduction_axis4[2] = 2; - reduction_axis4[3] = 3; - Tensor<float, 0, DataLayout> prod2 = tensor.prod(reduction_axis4); - VERIFY_IS_EQUAL(prod2.rank(), 0); - - VERIFY_IS_APPROX(prod1(), prod2()); - } - - reduction_axis2[0] = 0; - reduction_axis2[1] = 2; - result = tensor.maximum(reduction_axis2); - VERIFY_IS_EQUAL(result.dimension(0), 3); - VERIFY_IS_EQUAL(result.dimension(1), 7); - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 7; ++j) { - float max_val = std::numeric_limits<float>::lowest(); - for (int k = 0; k < 2; ++k) { - for (int l = 0; l < 5; ++l) { - max_val = (std::max)(max_val, tensor(k, i, l, j)); - } - } - VERIFY_IS_APPROX(result(i, j), max_val); - } - } - - { - Tensor<float, 0, DataLayout> max1 = tensor.maximum(); - VERIFY_IS_EQUAL(max1.rank(), 0); - - array<ptrdiff_t, 4> reduction_axis4; - reduction_axis4[0] = 0; - reduction_axis4[1] = 1; - reduction_axis4[2] = 2; - reduction_axis4[3] = 3; - Tensor<float, 0, DataLayout> max2 = tensor.maximum(reduction_axis4); - VERIFY_IS_EQUAL(max2.rank(), 0); - - VERIFY_IS_APPROX(max1(), max2()); - } - - reduction_axis2[0] = 0; - reduction_axis2[1] = 1; - result = tensor.minimum(reduction_axis2); - VERIFY_IS_EQUAL(result.dimension(0), 5); - VERIFY_IS_EQUAL(result.dimension(1), 7); - for (int i = 0; i < 5; ++i) { - for (int j = 0; j < 7; ++j) { - float min_val = (std::numeric_limits<float>::max)(); - for (int k = 0; k < 2; ++k) { - for (int l = 0; l < 3; ++l) { - min_val = (std::min)(min_val, tensor(k, l, i, j)); - } - } - VERIFY_IS_APPROX(result(i, j), min_val); - } - } - - { - Tensor<float, 0, DataLayout> min1 = tensor.minimum(); - VERIFY_IS_EQUAL(min1.rank(), 0); - - array<ptrdiff_t, 4> reduction_axis4; - reduction_axis4[0] = 0; - reduction_axis4[1] = 1; - reduction_axis4[2] = 2; - reduction_axis4[3] = 3; - Tensor<float, 0, DataLayout> min2 = tensor.minimum(reduction_axis4); - VERIFY_IS_EQUAL(min2.rank(), 0); - - VERIFY_IS_APPROX(min1(), min2()); - } - - reduction_axis2[0] = 0; - reduction_axis2[1] = 1; - result = tensor.mean(reduction_axis2); - VERIFY_IS_EQUAL(result.dimension(0), 5); - VERIFY_IS_EQUAL(result.dimension(1), 7); - for (int i = 0; i < 5; ++i) { - for (int j = 0; j < 7; ++j) { - float sum = 0.0f; - int count = 0; - for (int k = 0; k < 2; ++k) { - for (int l = 0; l < 3; ++l) { - sum += tensor(k, l, i, j); - ++count; - } - } - VERIFY_IS_APPROX(result(i, j), sum / count); - } - } - - { - Tensor<float, 0, DataLayout> mean1 = tensor.mean(); - VERIFY_IS_EQUAL(mean1.rank(), 0); - - array<ptrdiff_t, 4> reduction_axis4; - reduction_axis4[0] = 0; - reduction_axis4[1] = 1; - reduction_axis4[2] = 2; - reduction_axis4[3] = 3; - Tensor<float, 0, DataLayout> mean2 = tensor.mean(reduction_axis4); - VERIFY_IS_EQUAL(mean2.rank(), 0); - - VERIFY_IS_APPROX(mean1(), mean2()); - } - - { - Tensor<int, 1> ints(10); - std::iota(ints.data(), ints.data() + ints.dimension(0), 0); - - TensorFixedSize<bool, Sizes<> > all; - all = ints.all(); - VERIFY(!all()); - all = (ints >= ints.constant(0)).all(); - VERIFY(all()); - - TensorFixedSize<bool, Sizes<> > any; - any = (ints > ints.constant(10)).any(); - VERIFY(!any()); - any = (ints < ints.constant(1)).any(); - VERIFY(any()); - } -} - - -template <int DataLayout> -static void test_reductions_in_expr() { - Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7); - tensor.setRandom(); - array<ptrdiff_t, 2> reduction_axis2; - reduction_axis2[0] = 1; - reduction_axis2[1] = 3; - - Tensor<float, 2, DataLayout> result(2, 5); - result = result.constant(1.0f) - tensor.sum(reduction_axis2); - VERIFY_IS_EQUAL(result.dimension(0), 2); - VERIFY_IS_EQUAL(result.dimension(1), 5); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 5; ++j) { - float sum = 0.0f; - for (int k = 0; k < 3; ++k) { - for (int l = 0; l < 7; ++l) { - sum += tensor(i, k, j, l); - } - } - VERIFY_IS_APPROX(result(i, j), 1.0f - sum); - } - } -} - - -template <int DataLayout> -static void test_full_reductions() { - Tensor<float, 2, DataLayout> tensor(2, 3); - tensor.setRandom(); - array<ptrdiff_t, 2> reduction_axis; - reduction_axis[0] = 0; - reduction_axis[1] = 1; - - Tensor<float, 0, DataLayout> result = tensor.sum(reduction_axis); - VERIFY_IS_EQUAL(result.rank(), 0); - - float sum = 0.0f; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - sum += tensor(i, j); - } - } - VERIFY_IS_APPROX(result(0), sum); - - result = tensor.square().sum(reduction_axis).sqrt(); - VERIFY_IS_EQUAL(result.rank(), 0); - - sum = 0.0f; - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - sum += tensor(i, j) * tensor(i, j); - } - } - VERIFY_IS_APPROX(result(), sqrtf(sum)); -} - -struct UserReducer { - static const bool PacketAccess = false; - UserReducer(float offset) : offset_(offset) {} - void reduce(const float val, float* accum) { *accum += val * val; } - float initialize() const { return 0; } - float finalize(const float accum) const { return 1.0f / (accum + offset_); } - - private: - const float offset_; -}; - -template <int DataLayout> -static void test_user_defined_reductions() { - Tensor<float, 2, DataLayout> tensor(5, 7); - tensor.setRandom(); - array<ptrdiff_t, 1> reduction_axis; - reduction_axis[0] = 1; - - UserReducer reducer(10.0f); - Tensor<float, 1, DataLayout> result = tensor.reduce(reduction_axis, reducer); - VERIFY_IS_EQUAL(result.dimension(0), 5); - for (int i = 0; i < 5; ++i) { - float expected = 10.0f; - for (int j = 0; j < 7; ++j) { - expected += tensor(i, j) * tensor(i, j); - } - expected = 1.0f / expected; - VERIFY_IS_APPROX(result(i), expected); - } -} - -template <int DataLayout> -static void test_tensor_maps() { - int inputs[2 * 3 * 5 * 7]; - TensorMap<Tensor<int, 4, DataLayout> > tensor_map(inputs, 2, 3, 5, 7); - TensorMap<Tensor<const int, 4, DataLayout> > tensor_map_const(inputs, 2, 3, 5, - 7); - const TensorMap<Tensor<const int, 4, DataLayout> > tensor_map_const_const( - inputs, 2, 3, 5, 7); - - tensor_map.setRandom(); - array<ptrdiff_t, 2> reduction_axis; - reduction_axis[0] = 1; - reduction_axis[1] = 3; - - Tensor<int, 2, DataLayout> result = tensor_map.sum(reduction_axis); - Tensor<int, 2, DataLayout> result2 = tensor_map_const.sum(reduction_axis); - Tensor<int, 2, DataLayout> result3 = - tensor_map_const_const.sum(reduction_axis); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 5; ++j) { - int sum = 0; - for (int k = 0; k < 3; ++k) { - for (int l = 0; l < 7; ++l) { - sum += tensor_map(i, k, j, l); - } - } - VERIFY_IS_EQUAL(result(i, j), sum); - VERIFY_IS_EQUAL(result2(i, j), sum); - VERIFY_IS_EQUAL(result3(i, j), sum); - } - } -} - -template <int DataLayout> -static void test_static_dims() { - Tensor<float, 4, DataLayout> in(72, 53, 97, 113); - Tensor<float, 2, DataLayout> out(72, 97); - in.setRandom(); - -#if !EIGEN_HAS_CONSTEXPR - array<int, 2> reduction_axis; - reduction_axis[0] = 1; - reduction_axis[1] = 3; -#else - Eigen::IndexList<Eigen::type2index<1>, Eigen::type2index<3> > reduction_axis; -#endif - - out = in.maximum(reduction_axis); - - for (int i = 0; i < 72; ++i) { - for (int j = 0; j < 97; ++j) { - float expected = -1e10f; - for (int k = 0; k < 53; ++k) { - for (int l = 0; l < 113; ++l) { - expected = (std::max)(expected, in(i, k, j, l)); - } - } - VERIFY_IS_APPROX(out(i, j), expected); - } - } -} - -template <int DataLayout> -static void test_innermost_last_dims() { - Tensor<float, 4, DataLayout> in(72, 53, 97, 113); - Tensor<float, 2, DataLayout> out(97, 113); - in.setRandom(); - -// Reduce on the innermost dimensions. -#if !EIGEN_HAS_CONSTEXPR - array<int, 2> reduction_axis; - reduction_axis[0] = 0; - reduction_axis[1] = 1; -#else - // This triggers the use of packets for ColMajor. - Eigen::IndexList<Eigen::type2index<0>, Eigen::type2index<1> > reduction_axis; -#endif - - out = in.maximum(reduction_axis); - - for (int i = 0; i < 97; ++i) { - for (int j = 0; j < 113; ++j) { - float expected = -1e10f; - for (int k = 0; k < 53; ++k) { - for (int l = 0; l < 72; ++l) { - expected = (std::max)(expected, in(l, k, i, j)); - } - } - VERIFY_IS_APPROX(out(i, j), expected); - } - } -} - -template <int DataLayout> -static void test_innermost_first_dims() { - Tensor<float, 4, DataLayout> in(72, 53, 97, 113); - Tensor<float, 2, DataLayout> out(72, 53); - in.setRandom(); - -// Reduce on the innermost dimensions. -#if !EIGEN_HAS_CONSTEXPR - array<int, 2> reduction_axis; - reduction_axis[0] = 2; - reduction_axis[1] = 3; -#else - // This triggers the use of packets for RowMajor. - Eigen::IndexList<Eigen::type2index<2>, Eigen::type2index<3>> reduction_axis; -#endif - - out = in.maximum(reduction_axis); - - for (int i = 0; i < 72; ++i) { - for (int j = 0; j < 53; ++j) { - float expected = -1e10f; - for (int k = 0; k < 97; ++k) { - for (int l = 0; l < 113; ++l) { - expected = (std::max)(expected, in(i, j, k, l)); - } - } - VERIFY_IS_APPROX(out(i, j), expected); - } - } -} - -template <int DataLayout> -static void test_reduce_middle_dims() { - Tensor<float, 4, DataLayout> in(72, 53, 97, 113); - Tensor<float, 2, DataLayout> out(72, 53); - in.setRandom(); - -// Reduce on the innermost dimensions. -#if !EIGEN_HAS_CONSTEXPR - array<int, 2> reduction_axis; - reduction_axis[0] = 1; - reduction_axis[1] = 2; -#else - // This triggers the use of packets for RowMajor. - Eigen::IndexList<Eigen::type2index<1>, Eigen::type2index<2>> reduction_axis; -#endif - - out = in.maximum(reduction_axis); - - for (int i = 0; i < 72; ++i) { - for (int j = 0; j < 113; ++j) { - float expected = -1e10f; - for (int k = 0; k < 53; ++k) { - for (int l = 0; l < 97; ++l) { - expected = (std::max)(expected, in(i, k, l, j)); - } - } - VERIFY_IS_APPROX(out(i, j), expected); - } - } -} - -void test_cxx11_tensor_reduction() { - CALL_SUBTEST(test_trivial_reductions<ColMajor>()); - CALL_SUBTEST(test_trivial_reductions<RowMajor>()); - CALL_SUBTEST(test_simple_reductions<ColMajor>()); - CALL_SUBTEST(test_simple_reductions<RowMajor>()); - CALL_SUBTEST(test_reductions_in_expr<ColMajor>()); - CALL_SUBTEST(test_reductions_in_expr<RowMajor>()); - CALL_SUBTEST(test_full_reductions<ColMajor>()); - CALL_SUBTEST(test_full_reductions<RowMajor>()); - CALL_SUBTEST(test_user_defined_reductions<ColMajor>()); - CALL_SUBTEST(test_user_defined_reductions<RowMajor>()); - CALL_SUBTEST(test_tensor_maps<ColMajor>()); - CALL_SUBTEST(test_tensor_maps<RowMajor>()); - CALL_SUBTEST(test_static_dims<ColMajor>()); - CALL_SUBTEST(test_static_dims<RowMajor>()); - CALL_SUBTEST(test_innermost_last_dims<ColMajor>()); - CALL_SUBTEST(test_innermost_last_dims<RowMajor>()); - CALL_SUBTEST(test_innermost_first_dims<ColMajor>()); - CALL_SUBTEST(test_innermost_first_dims<RowMajor>()); - CALL_SUBTEST(test_reduce_middle_dims<ColMajor>()); - CALL_SUBTEST(test_reduce_middle_dims<RowMajor>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_reduction_cuda.cu b/eigen/unsupported/test/cxx11_tensor_reduction_cuda.cu deleted file mode 100644 index ec06697..0000000 --- a/eigen/unsupported/test/cxx11_tensor_reduction_cuda.cu +++ /dev/null @@ -1,154 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_NO_COMPLEX -#define EIGEN_TEST_FUNC cxx11_tensor_reduction_cuda -#define EIGEN_USE_GPU - -#include "main.h" -#include <unsupported/Eigen/CXX11/Tensor> - - -template<typename Type, int DataLayout> -static void test_full_reductions() { - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - const int num_rows = internal::random<int>(1024, 5*1024); - const int num_cols = internal::random<int>(1024, 5*1024); - - Tensor<Type, 2, DataLayout> in(num_rows, num_cols); - in.setRandom(); - - Tensor<Type, 0, DataLayout> full_redux; - full_redux = in.sum(); - - std::size_t in_bytes = in.size() * sizeof(Type); - std::size_t out_bytes = full_redux.size() * sizeof(Type); - Type* gpu_in_ptr = static_cast<Type*>(gpu_device.allocate(in_bytes)); - Type* gpu_out_ptr = static_cast<Type*>(gpu_device.allocate(out_bytes)); - gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes); - - TensorMap<Tensor<Type, 2, DataLayout> > in_gpu(gpu_in_ptr, num_rows, num_cols); - TensorMap<Tensor<Type, 0, DataLayout> > out_gpu(gpu_out_ptr); - - out_gpu.device(gpu_device) = in_gpu.sum(); - - Tensor<Type, 0, DataLayout> full_redux_gpu; - gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes); - gpu_device.synchronize(); - - // Check that the CPU and GPU reductions return the same result. - VERIFY_IS_APPROX(full_redux(), full_redux_gpu()); - - gpu_device.deallocate(gpu_in_ptr); - gpu_device.deallocate(gpu_out_ptr); -} - -template<typename Type, int DataLayout> -static void test_first_dim_reductions() { - int dim_x = 33; - int dim_y = 1; - int dim_z = 128; - - Tensor<Type, 3, DataLayout> in(dim_x, dim_y, dim_z); - in.setRandom(); - - Eigen::array<int, 1> red_axis; - red_axis[0] = 0; - Tensor<Type, 2, DataLayout> redux = in.sum(red_axis); - - // Create device - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice dev(&stream); - - // Create data(T) - Type* in_data = (Type*)dev.allocate(dim_x*dim_y*dim_z*sizeof(Type)); - Type* out_data = (Type*)dev.allocate(dim_z*dim_y*sizeof(Type)); - Eigen::TensorMap<Eigen::Tensor<Type, 3, DataLayout> > gpu_in(in_data, dim_x, dim_y, dim_z); - Eigen::TensorMap<Eigen::Tensor<Type, 2, DataLayout> > gpu_out(out_data, dim_y, dim_z); - - // Perform operation - dev.memcpyHostToDevice(in_data, in.data(), in.size()*sizeof(Type)); - gpu_out.device(dev) = gpu_in.sum(red_axis); - gpu_out.device(dev) += gpu_in.sum(red_axis); - Tensor<Type, 2, DataLayout> redux_gpu(dim_y, dim_z); - dev.memcpyDeviceToHost(redux_gpu.data(), out_data, gpu_out.size()*sizeof(Type)); - dev.synchronize(); - - // Check that the CPU and GPU reductions return the same result. - for (int i = 0; i < gpu_out.size(); ++i) { - VERIFY_IS_APPROX(2*redux(i), redux_gpu(i)); - } - - dev.deallocate(in_data); - dev.deallocate(out_data); -} - -template<typename Type, int DataLayout> -static void test_last_dim_reductions() { - int dim_x = 128; - int dim_y = 1; - int dim_z = 33; - - Tensor<Type, 3, DataLayout> in(dim_x, dim_y, dim_z); - in.setRandom(); - - Eigen::array<int, 1> red_axis; - red_axis[0] = 2; - Tensor<Type, 2, DataLayout> redux = in.sum(red_axis); - - // Create device - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice dev(&stream); - - // Create data - Type* in_data = (Type*)dev.allocate(dim_x*dim_y*dim_z*sizeof(Type)); - Type* out_data = (Type*)dev.allocate(dim_x*dim_y*sizeof(Type)); - Eigen::TensorMap<Eigen::Tensor<Type, 3, DataLayout> > gpu_in(in_data, dim_x, dim_y, dim_z); - Eigen::TensorMap<Eigen::Tensor<Type, 2, DataLayout> > gpu_out(out_data, dim_x, dim_y); - - // Perform operation - dev.memcpyHostToDevice(in_data, in.data(), in.size()*sizeof(Type)); - gpu_out.device(dev) = gpu_in.sum(red_axis); - gpu_out.device(dev) += gpu_in.sum(red_axis); - Tensor<Type, 2, DataLayout> redux_gpu(dim_x, dim_y); - dev.memcpyDeviceToHost(redux_gpu.data(), out_data, gpu_out.size()*sizeof(Type)); - dev.synchronize(); - - // Check that the CPU and GPU reductions return the same result. - for (int i = 0; i < gpu_out.size(); ++i) { - VERIFY_IS_APPROX(2*redux(i), redux_gpu(i)); - } - - dev.deallocate(in_data); - dev.deallocate(out_data); -} - - -void test_cxx11_tensor_reduction_cuda() { - CALL_SUBTEST_1((test_full_reductions<float, ColMajor>())); - CALL_SUBTEST_1((test_full_reductions<double, ColMajor>())); - CALL_SUBTEST_2((test_full_reductions<float, RowMajor>())); - CALL_SUBTEST_2((test_full_reductions<double, RowMajor>())); - - CALL_SUBTEST_3((test_first_dim_reductions<float, ColMajor>())); - CALL_SUBTEST_3((test_first_dim_reductions<double, ColMajor>())); - CALL_SUBTEST_4((test_first_dim_reductions<float, RowMajor>())); -// Outer reductions of doubles aren't supported just yet. -// CALL_SUBTEST_4((test_first_dim_reductions<double, RowMajor>())) - - CALL_SUBTEST_5((test_last_dim_reductions<float, ColMajor>())); -// Outer reductions of doubles aren't supported just yet. -// CALL_SUBTEST_5((test_last_dim_reductions<double, ColMajor>())); - CALL_SUBTEST_6((test_last_dim_reductions<float, RowMajor>())); - CALL_SUBTEST_6((test_last_dim_reductions<double, RowMajor>())); -} diff --git a/eigen/unsupported/test/cxx11_tensor_reduction_sycl.cpp b/eigen/unsupported/test/cxx11_tensor_reduction_sycl.cpp deleted file mode 100644 index a9ef829..0000000 --- a/eigen/unsupported/test/cxx11_tensor_reduction_sycl.cpp +++ /dev/null @@ -1,138 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 -// Mehdi Goli Codeplay Software Ltd. -// Ralph Potter Codeplay Software Ltd. -// Luke Iwanski Codeplay Software Ltd. -// Contact: <eigen@codeplay.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_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_NO_COMPLEX -#define EIGEN_TEST_FUNC cxx11_tensor_reduction_sycl -#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int -#define EIGEN_USE_SYCL - -#include "main.h" -#include <unsupported/Eigen/CXX11/Tensor> - - - -static void test_full_reductions_sycl(const Eigen::SyclDevice& sycl_device) { - - const int num_rows = 452; - const int num_cols = 765; - array<int, 2> tensorRange = {{num_rows, num_cols}}; - - Tensor<float, 2> in(tensorRange); - Tensor<float, 0> full_redux; - Tensor<float, 0> full_redux_gpu; - - in.setRandom(); - - full_redux = in.sum(); - - float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); - float* gpu_out_data =(float*)sycl_device.allocate(sizeof(float)); - - TensorMap<Tensor<float, 2> > in_gpu(gpu_in_data, tensorRange); - TensorMap<Tensor<float, 0> > out_gpu(gpu_out_data); - - sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); - out_gpu.device(sycl_device) = in_gpu.sum(); - sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(float)); - // Check that the CPU and GPU reductions return the same result. - VERIFY_IS_APPROX(full_redux_gpu(), full_redux()); - - sycl_device.deallocate(gpu_in_data); - sycl_device.deallocate(gpu_out_data); -} - -static void test_first_dim_reductions_sycl(const Eigen::SyclDevice& sycl_device) { - - int dim_x = 145; - int dim_y = 1; - int dim_z = 67; - - array<int, 3> tensorRange = {{dim_x, dim_y, dim_z}}; - Eigen::array<int, 1> red_axis; - red_axis[0] = 0; - array<int, 2> reduced_tensorRange = {{dim_y, dim_z}}; - - Tensor<float, 3> in(tensorRange); - Tensor<float, 2> redux(reduced_tensorRange); - Tensor<float, 2> redux_gpu(reduced_tensorRange); - - in.setRandom(); - - redux= in.sum(red_axis); - - float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); - float* gpu_out_data = static_cast<float*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(float))); - - TensorMap<Tensor<float, 3> > in_gpu(gpu_in_data, tensorRange); - TensorMap<Tensor<float, 2> > out_gpu(gpu_out_data, reduced_tensorRange); - - sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); - out_gpu.device(sycl_device) = in_gpu.sum(red_axis); - sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(float)); - - // Check that the CPU and GPU reductions return the same result. - for(int j=0; j<reduced_tensorRange[0]; j++ ) - for(int k=0; k<reduced_tensorRange[1]; k++ ) - VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k)); - - sycl_device.deallocate(gpu_in_data); - sycl_device.deallocate(gpu_out_data); -} - -static void test_last_dim_reductions_sycl(const Eigen::SyclDevice &sycl_device) { - - int dim_x = 567; - int dim_y = 1; - int dim_z = 47; - - array<int, 3> tensorRange = {{dim_x, dim_y, dim_z}}; - Eigen::array<int, 1> red_axis; - red_axis[0] = 2; - array<int, 2> reduced_tensorRange = {{dim_x, dim_y}}; - - Tensor<float, 3> in(tensorRange); - Tensor<float, 2> redux(reduced_tensorRange); - Tensor<float, 2> redux_gpu(reduced_tensorRange); - - in.setRandom(); - - redux= in.sum(red_axis); - - float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); - float* gpu_out_data = static_cast<float*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(float))); - - TensorMap<Tensor<float, 3> > in_gpu(gpu_in_data, tensorRange); - TensorMap<Tensor<float, 2> > out_gpu(gpu_out_data, reduced_tensorRange); - - sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); - out_gpu.device(sycl_device) = in_gpu.sum(red_axis); - sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(float)); - // Check that the CPU and GPU reductions return the same result. - for(int j=0; j<reduced_tensorRange[0]; j++ ) - for(int k=0; k<reduced_tensorRange[1]; k++ ) - VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k)); - - sycl_device.deallocate(gpu_in_data); - sycl_device.deallocate(gpu_out_data); - -} - -void test_cxx11_tensor_reduction_sycl() { - cl::sycl::gpu_selector s; - Eigen::SyclDevice sycl_device(s); - CALL_SUBTEST((test_full_reductions_sycl(sycl_device))); - CALL_SUBTEST((test_first_dim_reductions_sycl(sycl_device))); - CALL_SUBTEST((test_last_dim_reductions_sycl(sycl_device))); - -} diff --git a/eigen/unsupported/test/cxx11_tensor_ref.cpp b/eigen/unsupported/test/cxx11_tensor_ref.cpp deleted file mode 100644 index c8f105e..0000000 --- a/eigen/unsupported/test/cxx11_tensor_ref.cpp +++ /dev/null @@ -1,248 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::RowMajor; - -static void test_simple_lvalue_ref() -{ - Tensor<int, 1> input(6); - input.setRandom(); - - TensorRef<Tensor<int, 1>> ref3(input); - TensorRef<Tensor<int, 1>> ref4 = input; - - VERIFY_IS_EQUAL(ref3.data(), input.data()); - VERIFY_IS_EQUAL(ref4.data(), input.data()); - - for (int i = 0; i < 6; ++i) { - VERIFY_IS_EQUAL(ref3(i), input(i)); - VERIFY_IS_EQUAL(ref4(i), input(i)); - } - - for (int i = 0; i < 6; ++i) { - ref3.coeffRef(i) = i; - } - for (int i = 0; i < 6; ++i) { - VERIFY_IS_EQUAL(input(i), i); - } - for (int i = 0; i < 6; ++i) { - ref4.coeffRef(i) = -i * 2; - } - for (int i = 0; i < 6; ++i) { - VERIFY_IS_EQUAL(input(i), -i*2); - } -} - - -static void test_simple_rvalue_ref() -{ - Tensor<int, 1> input1(6); - input1.setRandom(); - Tensor<int, 1> input2(6); - input2.setRandom(); - - TensorRef<Tensor<int, 1>> ref3(input1 + input2); - TensorRef<Tensor<int, 1>> ref4 = input1 + input2; - - VERIFY_IS_NOT_EQUAL(ref3.data(), input1.data()); - VERIFY_IS_NOT_EQUAL(ref4.data(), input1.data()); - VERIFY_IS_NOT_EQUAL(ref3.data(), input2.data()); - VERIFY_IS_NOT_EQUAL(ref4.data(), input2.data()); - - for (int i = 0; i < 6; ++i) { - VERIFY_IS_EQUAL(ref3(i), input1(i) + input2(i)); - VERIFY_IS_EQUAL(ref4(i), input1(i) + input2(i)); - } -} - - -static void test_multiple_dims() -{ - Tensor<float, 3> input(3,5,7); - input.setRandom(); - - TensorRef<Tensor<float, 3>> ref(input); - VERIFY_IS_EQUAL(ref.data(), input.data()); - VERIFY_IS_EQUAL(ref.dimension(0), 3); - VERIFY_IS_EQUAL(ref.dimension(1), 5); - VERIFY_IS_EQUAL(ref.dimension(2), 7); - - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 5; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL(ref(i,j,k), input(i,j,k)); - } - } - } -} - - -static void test_slice() -{ - Tensor<float, 5> tensor(2,3,5,7,11); - tensor.setRandom(); - - Eigen::DSizes<ptrdiff_t, 5> indices(1,2,3,4,5); - Eigen::DSizes<ptrdiff_t, 5> sizes(1,1,1,1,1); - TensorRef<Tensor<float, 5>> slice = tensor.slice(indices, sizes); - VERIFY_IS_EQUAL(slice(0,0,0,0,0), tensor(1,2,3,4,5)); - - Eigen::DSizes<ptrdiff_t, 5> indices2(1,1,3,4,5); - Eigen::DSizes<ptrdiff_t, 5> sizes2(1,1,2,2,3); - slice = tensor.slice(indices2, sizes2); - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 2; ++j) { - for (int k = 0; k < 3; ++k) { - VERIFY_IS_EQUAL(slice(0,0,i,j,k), tensor(1,1,3+i,4+j,5+k)); - } - } - } - - Eigen::DSizes<ptrdiff_t, 5> indices3(0,0,0,0,0); - Eigen::DSizes<ptrdiff_t, 5> sizes3(2,3,1,1,1); - slice = tensor.slice(indices3, sizes3); - VERIFY_IS_EQUAL(slice.data(), tensor.data()); -} - - -static void test_ref_of_ref() -{ - Tensor<float, 3> input(3,5,7); - input.setRandom(); - - TensorRef<Tensor<float, 3>> ref(input); - TensorRef<Tensor<float, 3>> ref_of_ref(ref); - TensorRef<Tensor<float, 3>> ref_of_ref2; - ref_of_ref2 = ref; - - VERIFY_IS_EQUAL(ref_of_ref.data(), input.data()); - VERIFY_IS_EQUAL(ref_of_ref.dimension(0), 3); - VERIFY_IS_EQUAL(ref_of_ref.dimension(1), 5); - VERIFY_IS_EQUAL(ref_of_ref.dimension(2), 7); - - VERIFY_IS_EQUAL(ref_of_ref2.data(), input.data()); - VERIFY_IS_EQUAL(ref_of_ref2.dimension(0), 3); - VERIFY_IS_EQUAL(ref_of_ref2.dimension(1), 5); - VERIFY_IS_EQUAL(ref_of_ref2.dimension(2), 7); - - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 5; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL(ref_of_ref(i,j,k), input(i,j,k)); - VERIFY_IS_EQUAL(ref_of_ref2(i,j,k), input(i,j,k)); - } - } - } -} - - -static void test_ref_in_expr() -{ - Tensor<float, 3> input(3,5,7); - input.setRandom(); - TensorRef<Tensor<float, 3>> input_ref(input); - - Tensor<float, 3> result(3,5,7); - result.setRandom(); - TensorRef<Tensor<float, 3>> result_ref(result); - - Tensor<float, 3> bias(3,5,7); - bias.setRandom(); - - result_ref = input_ref + bias; - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 5; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL(result_ref(i,j,k), input(i,j,k) + bias(i,j,k)); - VERIFY_IS_NOT_EQUAL(result(i,j,k), input(i,j,k) + bias(i,j,k)); - } - } - } - - result = result_ref; - for (int i = 0; i < 3; ++i) { - for (int j = 0; j < 5; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_EQUAL(result(i,j,k), input(i,j,k) + bias(i,j,k)); - } - } - } -} - - -static void test_coeff_ref() -{ - Tensor<float, 5> tensor(2,3,5,7,11); - tensor.setRandom(); - Tensor<float, 5> original = tensor; - - TensorRef<Tensor<float, 4>> slice = tensor.chip(7, 4); - slice.coeffRef(0, 0, 0, 0) = 1.0f; - slice.coeffRef(1, 0, 0, 0) += 2.0f; - - VERIFY_IS_EQUAL(tensor(0,0,0,0,7), 1.0f); - VERIFY_IS_EQUAL(tensor(1,0,0,0,7), original(1,0,0,0,7) + 2.0f); -} - - -static void test_nested_ops_with_ref() -{ - Tensor<float, 4> t(2, 3, 5, 7); - t.setRandom(); - TensorMap<Tensor<const float, 4> > m(t.data(), 2, 3, 5, 7); - array<std::pair<ptrdiff_t, ptrdiff_t>, 4> paddings; - paddings[0] = std::make_pair(0, 0); - paddings[1] = std::make_pair(2, 1); - paddings[2] = std::make_pair(3, 4); - paddings[3] = std::make_pair(0, 0); - DSizes<Eigen::DenseIndex, 4> shuffle_dims(0, 1, 2, 3); - TensorRef<Tensor<const float, 4> > ref(m.pad(paddings)); - array<std::pair<ptrdiff_t, ptrdiff_t>, 4> trivial; - trivial[0] = std::make_pair(0, 0); - trivial[1] = std::make_pair(0, 0); - trivial[2] = std::make_pair(0, 0); - trivial[3] = std::make_pair(0, 0); - Tensor<float, 4> padded = ref.shuffle(shuffle_dims).pad(trivial); - VERIFY_IS_EQUAL(padded.dimension(0), 2+0); - VERIFY_IS_EQUAL(padded.dimension(1), 3+3); - VERIFY_IS_EQUAL(padded.dimension(2), 5+7); - VERIFY_IS_EQUAL(padded.dimension(3), 7+0); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 6; ++j) { - for (int k = 0; k < 12; ++k) { - for (int l = 0; l < 7; ++l) { - if (j >= 2 && j < 5 && k >= 3 && k < 8) { - VERIFY_IS_EQUAL(padded(i,j,k,l), t(i,j-2,k-3,l)); - } else { - VERIFY_IS_EQUAL(padded(i,j,k,l), 0.0f); - } - } - } - } - } -} - - -void test_cxx11_tensor_ref() -{ - CALL_SUBTEST(test_simple_lvalue_ref()); - CALL_SUBTEST(test_simple_rvalue_ref()); - CALL_SUBTEST(test_multiple_dims()); - CALL_SUBTEST(test_slice()); - CALL_SUBTEST(test_ref_of_ref()); - CALL_SUBTEST(test_ref_in_expr()); - CALL_SUBTEST(test_coeff_ref()); - CALL_SUBTEST(test_nested_ops_with_ref()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_reverse.cpp b/eigen/unsupported/test/cxx11_tensor_reverse.cpp deleted file mode 100644 index b35b8d2..0000000 --- a/eigen/unsupported/test/cxx11_tensor_reverse.cpp +++ /dev/null @@ -1,190 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Navdeep Jaitly <ndjaitly@google.com and -// Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::array; - -template <int DataLayout> -static void test_simple_reverse() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - - array<bool, 4> dim_rev; - dim_rev[0] = false; - dim_rev[1] = true; - dim_rev[2] = true; - dim_rev[3] = false; - - Tensor<float, 4, DataLayout> reversed_tensor; - reversed_tensor = tensor.reverse(dim_rev); - - VERIFY_IS_EQUAL(reversed_tensor.dimension(0), 2); - VERIFY_IS_EQUAL(reversed_tensor.dimension(1), 3); - VERIFY_IS_EQUAL(reversed_tensor.dimension(2), 5); - VERIFY_IS_EQUAL(reversed_tensor.dimension(3), 7); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(i,2-j,4-k,l)); - } - } - } - } - - dim_rev[0] = true; - dim_rev[1] = false; - dim_rev[2] = false; - dim_rev[3] = false; - - reversed_tensor = tensor.reverse(dim_rev); - - VERIFY_IS_EQUAL(reversed_tensor.dimension(0), 2); - VERIFY_IS_EQUAL(reversed_tensor.dimension(1), 3); - VERIFY_IS_EQUAL(reversed_tensor.dimension(2), 5); - VERIFY_IS_EQUAL(reversed_tensor.dimension(3), 7); - - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(1-i,j,k,l)); - } - } - } - } - - dim_rev[0] = true; - dim_rev[1] = false; - dim_rev[2] = false; - dim_rev[3] = true; - - reversed_tensor = tensor.reverse(dim_rev); - - VERIFY_IS_EQUAL(reversed_tensor.dimension(0), 2); - VERIFY_IS_EQUAL(reversed_tensor.dimension(1), 3); - VERIFY_IS_EQUAL(reversed_tensor.dimension(2), 5); - VERIFY_IS_EQUAL(reversed_tensor.dimension(3), 7); - - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(1-i,j,k,6-l)); - } - } - } - } -} - - -template <int DataLayout> -static void test_expr_reverse(bool LValue) -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - - array<bool, 4> dim_rev; - dim_rev[0] = false; - dim_rev[1] = true; - dim_rev[2] = false; - dim_rev[3] = true; - - Tensor<float, 4, DataLayout> expected(2, 3, 5, 7); - if (LValue) { - expected.reverse(dim_rev) = tensor; - } else { - expected = tensor.reverse(dim_rev); - } - - Tensor<float, 4, DataLayout> result(2,3,5,7); - - array<ptrdiff_t, 4> src_slice_dim; - src_slice_dim[0] = 2; - src_slice_dim[1] = 3; - src_slice_dim[2] = 1; - src_slice_dim[3] = 7; - array<ptrdiff_t, 4> src_slice_start; - src_slice_start[0] = 0; - src_slice_start[1] = 0; - src_slice_start[2] = 0; - src_slice_start[3] = 0; - array<ptrdiff_t, 4> dst_slice_dim = src_slice_dim; - array<ptrdiff_t, 4> dst_slice_start = src_slice_start; - - for (int i = 0; i < 5; ++i) { - if (LValue) { - result.slice(dst_slice_start, dst_slice_dim).reverse(dim_rev) = - tensor.slice(src_slice_start, src_slice_dim); - } else { - result.slice(dst_slice_start, dst_slice_dim) = - tensor.slice(src_slice_start, src_slice_dim).reverse(dim_rev); - } - src_slice_start[2] += 1; - dst_slice_start[2] += 1; - } - - VERIFY_IS_EQUAL(result.dimension(0), 2); - VERIFY_IS_EQUAL(result.dimension(1), 3); - VERIFY_IS_EQUAL(result.dimension(2), 5); - VERIFY_IS_EQUAL(result.dimension(3), 7); - - for (int i = 0; i < expected.dimension(0); ++i) { - for (int j = 0; j < expected.dimension(1); ++j) { - for (int k = 0; k < expected.dimension(2); ++k) { - for (int l = 0; l < expected.dimension(3); ++l) { - VERIFY_IS_EQUAL(result(i,j,k,l), expected(i,j,k,l)); - } - } - } - } - - dst_slice_start[2] = 0; - result.setRandom(); - for (int i = 0; i < 5; ++i) { - if (LValue) { - result.slice(dst_slice_start, dst_slice_dim).reverse(dim_rev) = - tensor.slice(dst_slice_start, dst_slice_dim); - } else { - result.slice(dst_slice_start, dst_slice_dim) = - tensor.reverse(dim_rev).slice(dst_slice_start, dst_slice_dim); - } - dst_slice_start[2] += 1; - } - - for (int i = 0; i < expected.dimension(0); ++i) { - for (int j = 0; j < expected.dimension(1); ++j) { - for (int k = 0; k < expected.dimension(2); ++k) { - for (int l = 0; l < expected.dimension(3); ++l) { - VERIFY_IS_EQUAL(result(i,j,k,l), expected(i,j,k,l)); - } - } - } - } -} - - -void test_cxx11_tensor_reverse() -{ - CALL_SUBTEST(test_simple_reverse<ColMajor>()); - CALL_SUBTEST(test_simple_reverse<RowMajor>()); - CALL_SUBTEST(test_expr_reverse<ColMajor>(true)); - CALL_SUBTEST(test_expr_reverse<RowMajor>(true)); - CALL_SUBTEST(test_expr_reverse<ColMajor>(false)); - CALL_SUBTEST(test_expr_reverse<RowMajor>(false)); -} diff --git a/eigen/unsupported/test/cxx11_tensor_roundings.cpp b/eigen/unsupported/test/cxx11_tensor_roundings.cpp deleted file mode 100644 index 2c26151..0000000 --- a/eigen/unsupported/test/cxx11_tensor_roundings.cpp +++ /dev/null @@ -1,62 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - - -static void test_float_rounding() -{ - Tensor<float, 2> ftensor(20,30); - ftensor = ftensor.random() * 100.f; - - Tensor<float, 2> result = ftensor.round(); - - for (int i = 0; i < 20; ++i) { - for (int j = 0; j < 30; ++j) { - VERIFY_IS_EQUAL(result(i,j), numext::round(ftensor(i,j))); - } - } -} - -static void test_float_flooring() -{ - Tensor<float, 2> ftensor(20,30); - ftensor = ftensor.random() * 100.f; - - Tensor<float, 2> result = ftensor.floor(); - - for (int i = 0; i < 20; ++i) { - for (int j = 0; j < 30; ++j) { - VERIFY_IS_EQUAL(result(i,j), numext::floor(ftensor(i,j))); - } - } -} - -static void test_float_ceiling() -{ - Tensor<float, 2> ftensor(20,30); - ftensor = ftensor.random() * 100.f; - - Tensor<float, 2> result = ftensor.ceil(); - - for (int i = 0; i < 20; ++i) { - for (int j = 0; j < 30; ++j) { - VERIFY_IS_EQUAL(result(i,j), numext::ceil(ftensor(i,j))); - } - } -} - -void test_cxx11_tensor_roundings() -{ - CALL_SUBTEST(test_float_rounding()); - CALL_SUBTEST(test_float_ceiling()); - CALL_SUBTEST(test_float_flooring()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_scan.cpp b/eigen/unsupported/test/cxx11_tensor_scan.cpp deleted file mode 100644 index af59aa3..0000000 --- a/eigen/unsupported/test/cxx11_tensor_scan.cpp +++ /dev/null @@ -1,110 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 Igor Babuschkin <igor@babuschk.in> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <limits> -#include <numeric> -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -template <int DataLayout, typename Type=float, bool Exclusive = false> -static void test_1d_scan() -{ - int size = 50; - Tensor<Type, 1, DataLayout> tensor(size); - tensor.setRandom(); - Tensor<Type, 1, DataLayout> result = tensor.cumsum(0, Exclusive); - - VERIFY_IS_EQUAL(tensor.dimension(0), result.dimension(0)); - - float accum = 0; - for (int i = 0; i < size; i++) { - if (Exclusive) { - VERIFY_IS_EQUAL(result(i), accum); - accum += tensor(i); - } else { - accum += tensor(i); - VERIFY_IS_EQUAL(result(i), accum); - } - } - - accum = 1; - result = tensor.cumprod(0, Exclusive); - for (int i = 0; i < size; i++) { - if (Exclusive) { - VERIFY_IS_EQUAL(result(i), accum); - accum *= tensor(i); - } else { - accum *= tensor(i); - VERIFY_IS_EQUAL(result(i), accum); - } - } -} - -template <int DataLayout, typename Type=float> -static void test_4d_scan() -{ - int size = 5; - Tensor<Type, 4, DataLayout> tensor(size, size, size, size); - tensor.setRandom(); - - Tensor<Type, 4, DataLayout> result(size, size, size, size); - - result = tensor.cumsum(0); - float accum = 0; - for (int i = 0; i < size; i++) { - accum += tensor(i, 1, 2, 3); - VERIFY_IS_EQUAL(result(i, 1, 2, 3), accum); - } - result = tensor.cumsum(1); - accum = 0; - for (int i = 0; i < size; i++) { - accum += tensor(1, i, 2, 3); - VERIFY_IS_EQUAL(result(1, i, 2, 3), accum); - } - result = tensor.cumsum(2); - accum = 0; - for (int i = 0; i < size; i++) { - accum += tensor(1, 2, i, 3); - VERIFY_IS_EQUAL(result(1, 2, i, 3), accum); - } - result = tensor.cumsum(3); - accum = 0; - for (int i = 0; i < size; i++) { - accum += tensor(1, 2, 3, i); - VERIFY_IS_EQUAL(result(1, 2, 3, i), accum); - } -} - -template <int DataLayout> -static void test_tensor_maps() { - int inputs[20]; - TensorMap<Tensor<int, 1, DataLayout> > tensor_map(inputs, 20); - tensor_map.setRandom(); - - Tensor<int, 1, DataLayout> result = tensor_map.cumsum(0); - - int accum = 0; - for (int i = 0; i < 20; ++i) { - accum += tensor_map(i); - VERIFY_IS_EQUAL(result(i), accum); - } -} - -void test_cxx11_tensor_scan() { - CALL_SUBTEST((test_1d_scan<ColMajor, float, true>())); - CALL_SUBTEST((test_1d_scan<ColMajor, float, false>())); - CALL_SUBTEST((test_1d_scan<RowMajor, float, true>())); - CALL_SUBTEST((test_1d_scan<RowMajor, float, false>())); - CALL_SUBTEST(test_4d_scan<ColMajor>()); - CALL_SUBTEST(test_4d_scan<RowMajor>()); - CALL_SUBTEST(test_tensor_maps<ColMajor>()); - CALL_SUBTEST(test_tensor_maps<RowMajor>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_scan_cuda.cu b/eigen/unsupported/test/cxx11_tensor_scan_cuda.cu deleted file mode 100644 index de1c0ac..0000000 --- a/eigen/unsupported/test/cxx11_tensor_scan_cuda.cu +++ /dev/null @@ -1,76 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_NO_COMPLEX -#define EIGEN_TEST_FUNC cxx11_tensor_scan_cuda -#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int -#define EIGEN_USE_GPU - -#include "main.h" -#include <unsupported/Eigen/CXX11/Tensor> - -using Eigen::Tensor; -typedef Tensor<float, 1>::DimensionPair DimPair; - -template<int DataLayout> -void test_cuda_cumsum(int m_size, int k_size, int n_size) -{ - std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << ")" << std::endl; - Tensor<float, 3, DataLayout> t_input(m_size, k_size, n_size); - Tensor<float, 3, DataLayout> t_result(m_size, k_size, n_size); - Tensor<float, 3, DataLayout> t_result_gpu(m_size, k_size, n_size); - - t_input.setRandom(); - - std::size_t t_input_bytes = t_input.size() * sizeof(float); - std::size_t t_result_bytes = t_result.size() * sizeof(float); - - float* d_t_input; - float* d_t_result; - - cudaMalloc((void**)(&d_t_input), t_input_bytes); - cudaMalloc((void**)(&d_t_result), t_result_bytes); - - cudaMemcpy(d_t_input, t_input.data(), t_input_bytes, cudaMemcpyHostToDevice); - - Eigen::CudaStreamDevice stream; - Eigen::GpuDevice gpu_device(&stream); - - Eigen::TensorMap<Eigen::Tensor<float, 3, DataLayout> > - gpu_t_input(d_t_input, Eigen::array<int, 3>(m_size, k_size, n_size)); - Eigen::TensorMap<Eigen::Tensor<float, 3, DataLayout> > - gpu_t_result(d_t_result, Eigen::array<int, 3>(m_size, k_size, n_size)); - - gpu_t_result.device(gpu_device) = gpu_t_input.cumsum(1); - t_result = t_input.cumsum(1); - - cudaMemcpy(t_result_gpu.data(), d_t_result, t_result_bytes, cudaMemcpyDeviceToHost); - for (DenseIndex i = 0; i < t_result.size(); i++) { - if (fabs(t_result(i) - t_result_gpu(i)) < 1e-4f) { - continue; - } - if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), 1e-4f)) { - continue; - } - std::cout << "mismatch detected at index " << i << ": " << t_result(i) - << " vs " << t_result_gpu(i) << std::endl; - assert(false); - } - - cudaFree((void*)d_t_input); - cudaFree((void*)d_t_result); -} - - -void test_cxx11_tensor_scan_cuda() -{ - CALL_SUBTEST_1(test_cuda_cumsum<ColMajor>(128, 128, 128)); - CALL_SUBTEST_2(test_cuda_cumsum<RowMajor>(128, 128, 128)); -} diff --git a/eigen/unsupported/test/cxx11_tensor_shuffling.cpp b/eigen/unsupported/test/cxx11_tensor_shuffling.cpp deleted file mode 100644 index d11444a..0000000 --- a/eigen/unsupported/test/cxx11_tensor_shuffling.cpp +++ /dev/null @@ -1,228 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::array; - -template <int DataLayout> -static void test_simple_shuffling() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - array<ptrdiff_t, 4> shuffles; - shuffles[0] = 0; - shuffles[1] = 1; - shuffles[2] = 2; - shuffles[3] = 3; - - Tensor<float, 4, DataLayout> no_shuffle; - no_shuffle = tensor.shuffle(shuffles); - - VERIFY_IS_EQUAL(no_shuffle.dimension(0), 2); - VERIFY_IS_EQUAL(no_shuffle.dimension(1), 3); - VERIFY_IS_EQUAL(no_shuffle.dimension(2), 5); - VERIFY_IS_EQUAL(no_shuffle.dimension(3), 7); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(tensor(i,j,k,l), no_shuffle(i,j,k,l)); - } - } - } - } - - shuffles[0] = 2; - shuffles[1] = 3; - shuffles[2] = 1; - shuffles[3] = 0; - Tensor<float, 4, DataLayout> shuffle; - shuffle = tensor.shuffle(shuffles); - - VERIFY_IS_EQUAL(shuffle.dimension(0), 5); - VERIFY_IS_EQUAL(shuffle.dimension(1), 7); - VERIFY_IS_EQUAL(shuffle.dimension(2), 3); - VERIFY_IS_EQUAL(shuffle.dimension(3), 2); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(tensor(i,j,k,l), shuffle(k,l,j,i)); - } - } - } - } -} - - -template <int DataLayout> -static void test_expr_shuffling() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - - array<ptrdiff_t, 4> shuffles; - shuffles[0] = 2; - shuffles[1] = 3; - shuffles[2] = 1; - shuffles[3] = 0; - Tensor<float, 4, DataLayout> expected; - expected = tensor.shuffle(shuffles); - - Tensor<float, 4, DataLayout> result(5,7,3,2); - - array<int, 4> src_slice_dim{{2,3,1,7}}; - array<int, 4> src_slice_start{{0,0,0,0}}; - array<int, 4> dst_slice_dim{{1,7,3,2}}; - array<int, 4> dst_slice_start{{0,0,0,0}}; - - for (int i = 0; i < 5; ++i) { - result.slice(dst_slice_start, dst_slice_dim) = - tensor.slice(src_slice_start, src_slice_dim).shuffle(shuffles); - src_slice_start[2] += 1; - dst_slice_start[0] += 1; - } - - VERIFY_IS_EQUAL(result.dimension(0), 5); - VERIFY_IS_EQUAL(result.dimension(1), 7); - VERIFY_IS_EQUAL(result.dimension(2), 3); - VERIFY_IS_EQUAL(result.dimension(3), 2); - - for (int i = 0; i < expected.dimension(0); ++i) { - for (int j = 0; j < expected.dimension(1); ++j) { - for (int k = 0; k < expected.dimension(2); ++k) { - for (int l = 0; l < expected.dimension(3); ++l) { - VERIFY_IS_EQUAL(result(i,j,k,l), expected(i,j,k,l)); - } - } - } - } - - dst_slice_start[0] = 0; - result.setRandom(); - for (int i = 0; i < 5; ++i) { - result.slice(dst_slice_start, dst_slice_dim) = - tensor.shuffle(shuffles).slice(dst_slice_start, dst_slice_dim); - dst_slice_start[0] += 1; - } - - for (int i = 0; i < expected.dimension(0); ++i) { - for (int j = 0; j < expected.dimension(1); ++j) { - for (int k = 0; k < expected.dimension(2); ++k) { - for (int l = 0; l < expected.dimension(3); ++l) { - VERIFY_IS_EQUAL(result(i,j,k,l), expected(i,j,k,l)); - } - } - } - } -} - - -template <int DataLayout> -static void test_shuffling_as_value() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - array<ptrdiff_t, 4> shuffles; - shuffles[2] = 0; - shuffles[3] = 1; - shuffles[1] = 2; - shuffles[0] = 3; - Tensor<float, 4, DataLayout> shuffle(5,7,3,2); - shuffle.shuffle(shuffles) = tensor; - - VERIFY_IS_EQUAL(shuffle.dimension(0), 5); - VERIFY_IS_EQUAL(shuffle.dimension(1), 7); - VERIFY_IS_EQUAL(shuffle.dimension(2), 3); - VERIFY_IS_EQUAL(shuffle.dimension(3), 2); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(tensor(i,j,k,l), shuffle(k,l,j,i)); - } - } - } - } - - array<ptrdiff_t, 4> no_shuffle; - no_shuffle[0] = 0; - no_shuffle[1] = 1; - no_shuffle[2] = 2; - no_shuffle[3] = 3; - Tensor<float, 4, DataLayout> shuffle2(5,7,3,2); - shuffle2.shuffle(shuffles) = tensor.shuffle(no_shuffle); - for (int i = 0; i < 5; ++i) { - for (int j = 0; j < 7; ++j) { - for (int k = 0; k < 3; ++k) { - for (int l = 0; l < 2; ++l) { - VERIFY_IS_EQUAL(shuffle2(i,j,k,l), shuffle(i,j,k,l)); - } - } - } - } -} - - -template <int DataLayout> -static void test_shuffle_unshuffle() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - - // Choose a random permutation. - array<ptrdiff_t, 4> shuffles; - for (int i = 0; i < 4; ++i) { - shuffles[i] = i; - } - array<ptrdiff_t, 4> shuffles_inverse; - for (int i = 0; i < 4; ++i) { - const ptrdiff_t index = internal::random<ptrdiff_t>(i, 3); - shuffles_inverse[shuffles[index]] = i; - std::swap(shuffles[i], shuffles[index]); - } - - Tensor<float, 4, DataLayout> shuffle; - shuffle = tensor.shuffle(shuffles).shuffle(shuffles_inverse); - - VERIFY_IS_EQUAL(shuffle.dimension(0), 2); - VERIFY_IS_EQUAL(shuffle.dimension(1), 3); - VERIFY_IS_EQUAL(shuffle.dimension(2), 5); - VERIFY_IS_EQUAL(shuffle.dimension(3), 7); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(tensor(i,j,k,l), shuffle(i,j,k,l)); - } - } - } - } -} - - -void test_cxx11_tensor_shuffling() -{ - CALL_SUBTEST(test_simple_shuffling<ColMajor>()); - CALL_SUBTEST(test_simple_shuffling<RowMajor>()); - CALL_SUBTEST(test_expr_shuffling<ColMajor>()); - CALL_SUBTEST(test_expr_shuffling<RowMajor>()); - CALL_SUBTEST(test_shuffling_as_value<ColMajor>()); - CALL_SUBTEST(test_shuffling_as_value<RowMajor>()); - CALL_SUBTEST(test_shuffle_unshuffle<ColMajor>()); - CALL_SUBTEST(test_shuffle_unshuffle<RowMajor>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_simple.cpp b/eigen/unsupported/test/cxx11_tensor_simple.cpp deleted file mode 100644 index 5a0d339..0000000 --- a/eigen/unsupported/test/cxx11_tensor_simple.cpp +++ /dev/null @@ -1,327 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2013 Christian Seiler <christian@iwakd.de> -// -// 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/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::RowMajor; - -static void test_0d() -{ - Tensor<int, 0> scalar1; - Tensor<int, 0, RowMajor> scalar2; - Tensor<int, 0> scalar3; - Tensor<int, 0, RowMajor> scalar4; - - scalar3.resize(); - scalar4.resize(); - - scalar1() = 7; - scalar2() = 13; - scalar3.setValues(17); - scalar4.setZero(); - - VERIFY_IS_EQUAL(scalar1.rank(), 0); - VERIFY_IS_EQUAL(scalar1.size(), 1); - - VERIFY_IS_EQUAL(scalar1(), 7); - VERIFY_IS_EQUAL(scalar2(), 13); - VERIFY_IS_EQUAL(scalar3(), 17); - VERIFY_IS_EQUAL(scalar4(), 0); - - Tensor<int, 0> scalar5(scalar1); - - VERIFY_IS_EQUAL(scalar5(), 7); - VERIFY_IS_EQUAL(scalar5.data()[0], 7); -} - -static void test_1d() -{ - Tensor<int, 1> vec1(6); - Tensor<int, 1, RowMajor> vec2(6); - Tensor<int, 1> vec3; - Tensor<int, 1, RowMajor> vec4; - - vec3.resize(6); - vec4.resize(6); - - vec1(0) = 4; vec2(0) = 0; vec3(0) = 5; - vec1(1) = 8; vec2(1) = 1; vec3(1) = 4; - vec1(2) = 15; vec2(2) = 2; vec3(2) = 3; - vec1(3) = 16; vec2(3) = 3; vec3(3) = 2; - vec1(4) = 23; vec2(4) = 4; vec3(4) = 1; - vec1(5) = 42; vec2(5) = 5; vec3(5) = 0; - vec4.setZero(); - - VERIFY_IS_EQUAL((vec1.rank()), 1); - VERIFY_IS_EQUAL((vec1.size()), 6); - VERIFY_IS_EQUAL((vec1.dimensions()[0]), 6); - - VERIFY_IS_EQUAL((vec1[0]), 4); - VERIFY_IS_EQUAL((vec1[1]), 8); - VERIFY_IS_EQUAL((vec1[2]), 15); - VERIFY_IS_EQUAL((vec1[3]), 16); - VERIFY_IS_EQUAL((vec1[4]), 23); - VERIFY_IS_EQUAL((vec1[5]), 42); - - VERIFY_IS_EQUAL((vec2[0]), 0); - VERIFY_IS_EQUAL((vec2[1]), 1); - VERIFY_IS_EQUAL((vec2[2]), 2); - VERIFY_IS_EQUAL((vec2[3]), 3); - VERIFY_IS_EQUAL((vec2[4]), 4); - VERIFY_IS_EQUAL((vec2[5]), 5); - - VERIFY_IS_EQUAL((vec3[0]), 5); - VERIFY_IS_EQUAL((vec3[1]), 4); - VERIFY_IS_EQUAL((vec3[2]), 3); - VERIFY_IS_EQUAL((vec3[3]), 2); - VERIFY_IS_EQUAL((vec3[4]), 1); - VERIFY_IS_EQUAL((vec3[5]), 0); - - VERIFY_IS_EQUAL((vec4[0]), 0); - VERIFY_IS_EQUAL((vec4[1]), 0); - VERIFY_IS_EQUAL((vec4[2]), 0); - VERIFY_IS_EQUAL((vec4[3]), 0); - VERIFY_IS_EQUAL((vec4[4]), 0); - VERIFY_IS_EQUAL((vec4[5]), 0); - - Tensor<int, 1> vec5(vec1); - - VERIFY_IS_EQUAL((vec5(0)), 4); - VERIFY_IS_EQUAL((vec5(1)), 8); - VERIFY_IS_EQUAL((vec5(2)), 15); - VERIFY_IS_EQUAL((vec5(3)), 16); - VERIFY_IS_EQUAL((vec5(4)), 23); - VERIFY_IS_EQUAL((vec5(5)), 42); - - VERIFY_IS_EQUAL((vec5.data()[0]), 4); - VERIFY_IS_EQUAL((vec5.data()[1]), 8); - VERIFY_IS_EQUAL((vec5.data()[2]), 15); - VERIFY_IS_EQUAL((vec5.data()[3]), 16); - VERIFY_IS_EQUAL((vec5.data()[4]), 23); - VERIFY_IS_EQUAL((vec5.data()[5]), 42); -} - -static void test_2d() -{ - Tensor<int, 2> mat1(2,3); - Tensor<int, 2, RowMajor> mat2(2,3); - - mat1(0,0) = 0; - mat1(0,1) = 1; - mat1(0,2) = 2; - mat1(1,0) = 3; - mat1(1,1) = 4; - mat1(1,2) = 5; - - mat2(0,0) = 0; - mat2(0,1) = 1; - mat2(0,2) = 2; - mat2(1,0) = 3; - mat2(1,1) = 4; - mat2(1,2) = 5; - - VERIFY_IS_EQUAL((mat1.rank()), 2); - VERIFY_IS_EQUAL((mat1.size()), 6); - VERIFY_IS_EQUAL((mat1.dimensions()[0]), 2); - VERIFY_IS_EQUAL((mat1.dimensions()[1]), 3); - - VERIFY_IS_EQUAL((mat2.rank()), 2); - VERIFY_IS_EQUAL((mat2.size()), 6); - VERIFY_IS_EQUAL((mat2.dimensions()[0]), 2); - VERIFY_IS_EQUAL((mat2.dimensions()[1]), 3); - - VERIFY_IS_EQUAL((mat1.data()[0]), 0); - VERIFY_IS_EQUAL((mat1.data()[1]), 3); - VERIFY_IS_EQUAL((mat1.data()[2]), 1); - VERIFY_IS_EQUAL((mat1.data()[3]), 4); - VERIFY_IS_EQUAL((mat1.data()[4]), 2); - VERIFY_IS_EQUAL((mat1.data()[5]), 5); - - VERIFY_IS_EQUAL((mat2.data()[0]), 0); - VERIFY_IS_EQUAL((mat2.data()[1]), 1); - VERIFY_IS_EQUAL((mat2.data()[2]), 2); - VERIFY_IS_EQUAL((mat2.data()[3]), 3); - VERIFY_IS_EQUAL((mat2.data()[4]), 4); - VERIFY_IS_EQUAL((mat2.data()[5]), 5); -} - -static void test_3d() -{ - Tensor<int, 3> epsilon(3,3,3); - epsilon.setZero(); - epsilon(0,1,2) = epsilon(2,0,1) = epsilon(1,2,0) = 1; - epsilon(2,1,0) = epsilon(0,2,1) = epsilon(1,0,2) = -1; - - VERIFY_IS_EQUAL((epsilon.size()), 27); - VERIFY_IS_EQUAL((epsilon.dimensions()[0]), 3); - VERIFY_IS_EQUAL((epsilon.dimensions()[1]), 3); - VERIFY_IS_EQUAL((epsilon.dimensions()[2]), 3); - - VERIFY_IS_EQUAL((epsilon(0,0,0)), 0); - VERIFY_IS_EQUAL((epsilon(0,0,1)), 0); - VERIFY_IS_EQUAL((epsilon(0,0,2)), 0); - VERIFY_IS_EQUAL((epsilon(0,1,0)), 0); - VERIFY_IS_EQUAL((epsilon(0,1,1)), 0); - VERIFY_IS_EQUAL((epsilon(0,2,0)), 0); - VERIFY_IS_EQUAL((epsilon(0,2,2)), 0); - VERIFY_IS_EQUAL((epsilon(1,0,0)), 0); - VERIFY_IS_EQUAL((epsilon(1,0,1)), 0); - VERIFY_IS_EQUAL((epsilon(1,1,0)), 0); - VERIFY_IS_EQUAL((epsilon(1,1,1)), 0); - VERIFY_IS_EQUAL((epsilon(1,1,2)), 0); - VERIFY_IS_EQUAL((epsilon(1,2,1)), 0); - VERIFY_IS_EQUAL((epsilon(1,2,2)), 0); - VERIFY_IS_EQUAL((epsilon(2,0,0)), 0); - VERIFY_IS_EQUAL((epsilon(2,0,2)), 0); - VERIFY_IS_EQUAL((epsilon(2,1,1)), 0); - VERIFY_IS_EQUAL((epsilon(2,1,2)), 0); - VERIFY_IS_EQUAL((epsilon(2,2,0)), 0); - VERIFY_IS_EQUAL((epsilon(2,2,1)), 0); - VERIFY_IS_EQUAL((epsilon(2,2,2)), 0); - - VERIFY_IS_EQUAL((epsilon(0,1,2)), 1); - VERIFY_IS_EQUAL((epsilon(2,0,1)), 1); - VERIFY_IS_EQUAL((epsilon(1,2,0)), 1); - VERIFY_IS_EQUAL((epsilon(2,1,0)), -1); - VERIFY_IS_EQUAL((epsilon(0,2,1)), -1); - VERIFY_IS_EQUAL((epsilon(1,0,2)), -1); - - array<Eigen::DenseIndex, 3> dims; - dims[0] = 2; - dims[1] = 3; - dims[2] = 4; - Tensor<int, 3> t1(dims); - Tensor<int, 3, RowMajor> t2(dims); - - VERIFY_IS_EQUAL((t1.size()), 24); - VERIFY_IS_EQUAL((t1.dimensions()[0]), 2); - VERIFY_IS_EQUAL((t1.dimensions()[1]), 3); - VERIFY_IS_EQUAL((t1.dimensions()[2]), 4); - - VERIFY_IS_EQUAL((t2.size()), 24); - VERIFY_IS_EQUAL((t2.dimensions()[0]), 2); - VERIFY_IS_EQUAL((t2.dimensions()[1]), 3); - VERIFY_IS_EQUAL((t2.dimensions()[2]), 4); - - for (int i = 0; i < 2; i++) { - for (int j = 0; j < 3; j++) { - for (int k = 0; k < 4; k++) { - t1(i, j, k) = 100 * i + 10 * j + k; - t2(i, j, k) = 100 * i + 10 * j + k; - } - } - } - - VERIFY_IS_EQUAL((t1.data()[0]), 0); - VERIFY_IS_EQUAL((t1.data()[1]), 100); - VERIFY_IS_EQUAL((t1.data()[2]), 10); - VERIFY_IS_EQUAL((t1.data()[3]), 110); - VERIFY_IS_EQUAL((t1.data()[4]), 20); - VERIFY_IS_EQUAL((t1.data()[5]), 120); - VERIFY_IS_EQUAL((t1.data()[6]), 1); - VERIFY_IS_EQUAL((t1.data()[7]), 101); - VERIFY_IS_EQUAL((t1.data()[8]), 11); - VERIFY_IS_EQUAL((t1.data()[9]), 111); - VERIFY_IS_EQUAL((t1.data()[10]), 21); - VERIFY_IS_EQUAL((t1.data()[11]), 121); - VERIFY_IS_EQUAL((t1.data()[12]), 2); - VERIFY_IS_EQUAL((t1.data()[13]), 102); - VERIFY_IS_EQUAL((t1.data()[14]), 12); - VERIFY_IS_EQUAL((t1.data()[15]), 112); - VERIFY_IS_EQUAL((t1.data()[16]), 22); - VERIFY_IS_EQUAL((t1.data()[17]), 122); - VERIFY_IS_EQUAL((t1.data()[18]), 3); - VERIFY_IS_EQUAL((t1.data()[19]), 103); - VERIFY_IS_EQUAL((t1.data()[20]), 13); - VERIFY_IS_EQUAL((t1.data()[21]), 113); - VERIFY_IS_EQUAL((t1.data()[22]), 23); - VERIFY_IS_EQUAL((t1.data()[23]), 123); - - VERIFY_IS_EQUAL((t2.data()[0]), 0); - VERIFY_IS_EQUAL((t2.data()[1]), 1); - VERIFY_IS_EQUAL((t2.data()[2]), 2); - VERIFY_IS_EQUAL((t2.data()[3]), 3); - VERIFY_IS_EQUAL((t2.data()[4]), 10); - VERIFY_IS_EQUAL((t2.data()[5]), 11); - VERIFY_IS_EQUAL((t2.data()[6]), 12); - VERIFY_IS_EQUAL((t2.data()[7]), 13); - VERIFY_IS_EQUAL((t2.data()[8]), 20); - VERIFY_IS_EQUAL((t2.data()[9]), 21); - VERIFY_IS_EQUAL((t2.data()[10]), 22); - VERIFY_IS_EQUAL((t2.data()[11]), 23); - VERIFY_IS_EQUAL((t2.data()[12]), 100); - VERIFY_IS_EQUAL((t2.data()[13]), 101); - VERIFY_IS_EQUAL((t2.data()[14]), 102); - VERIFY_IS_EQUAL((t2.data()[15]), 103); - VERIFY_IS_EQUAL((t2.data()[16]), 110); - VERIFY_IS_EQUAL((t2.data()[17]), 111); - VERIFY_IS_EQUAL((t2.data()[18]), 112); - VERIFY_IS_EQUAL((t2.data()[19]), 113); - VERIFY_IS_EQUAL((t2.data()[20]), 120); - VERIFY_IS_EQUAL((t2.data()[21]), 121); - VERIFY_IS_EQUAL((t2.data()[22]), 122); - VERIFY_IS_EQUAL((t2.data()[23]), 123); -} - -static void test_simple_assign() -{ - Tensor<int, 3> epsilon(3,3,3); - epsilon.setZero(); - epsilon(0,1,2) = epsilon(2,0,1) = epsilon(1,2,0) = 1; - epsilon(2,1,0) = epsilon(0,2,1) = epsilon(1,0,2) = -1; - - Tensor<int, 3> e2(3,3,3); - e2.setZero(); - VERIFY_IS_EQUAL((e2(1,2,0)), 0); - - e2 = epsilon; - VERIFY_IS_EQUAL((e2(1,2,0)), 1); - VERIFY_IS_EQUAL((e2(0,1,2)), 1); - VERIFY_IS_EQUAL((e2(2,0,1)), 1); - VERIFY_IS_EQUAL((e2(2,1,0)), -1); - VERIFY_IS_EQUAL((e2(0,2,1)), -1); - VERIFY_IS_EQUAL((e2(1,0,2)), -1); -} - -static void test_resize() -{ - Tensor<int, 3> epsilon; - epsilon.resize(2,3,7); - VERIFY_IS_EQUAL(epsilon.dimension(0), 2); - VERIFY_IS_EQUAL(epsilon.dimension(1), 3); - VERIFY_IS_EQUAL(epsilon.dimension(2), 7); - VERIFY_IS_EQUAL(epsilon.size(), 2*3*7); - - const int* old_data = epsilon.data(); - epsilon.resize(3,2,7); - VERIFY_IS_EQUAL(epsilon.dimension(0), 3); - VERIFY_IS_EQUAL(epsilon.dimension(1), 2); - VERIFY_IS_EQUAL(epsilon.dimension(2), 7); - VERIFY_IS_EQUAL(epsilon.size(), 2*3*7); - VERIFY_IS_EQUAL(epsilon.data(), old_data); - - epsilon.resize(3,5,7); - VERIFY_IS_EQUAL(epsilon.dimension(0), 3); - VERIFY_IS_EQUAL(epsilon.dimension(1), 5); - VERIFY_IS_EQUAL(epsilon.dimension(2), 7); - VERIFY_IS_EQUAL(epsilon.size(), 3*5*7); -} - -void test_cxx11_tensor_simple() -{ - CALL_SUBTEST(test_0d()); - CALL_SUBTEST(test_1d()); - CALL_SUBTEST(test_2d()); - CALL_SUBTEST(test_3d()); - CALL_SUBTEST(test_simple_assign()); - CALL_SUBTEST(test_resize()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_striding.cpp b/eigen/unsupported/test/cxx11_tensor_striding.cpp deleted file mode 100644 index 935b908..0000000 --- a/eigen/unsupported/test/cxx11_tensor_striding.cpp +++ /dev/null @@ -1,119 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -template<int DataLayout> -static void test_simple_striding() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - array<ptrdiff_t, 4> strides; - strides[0] = 1; - strides[1] = 1; - strides[2] = 1; - strides[3] = 1; - - Tensor<float, 4, DataLayout> no_stride; - no_stride = tensor.stride(strides); - - VERIFY_IS_EQUAL(no_stride.dimension(0), 2); - VERIFY_IS_EQUAL(no_stride.dimension(1), 3); - VERIFY_IS_EQUAL(no_stride.dimension(2), 5); - VERIFY_IS_EQUAL(no_stride.dimension(3), 7); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(tensor(i,j,k,l), no_stride(i,j,k,l)); - } - } - } - } - - strides[0] = 2; - strides[1] = 4; - strides[2] = 2; - strides[3] = 3; - Tensor<float, 4, DataLayout> stride; - stride = tensor.stride(strides); - - VERIFY_IS_EQUAL(stride.dimension(0), 1); - VERIFY_IS_EQUAL(stride.dimension(1), 1); - VERIFY_IS_EQUAL(stride.dimension(2), 3); - VERIFY_IS_EQUAL(stride.dimension(3), 3); - - for (int i = 0; i < 1; ++i) { - for (int j = 0; j < 1; ++j) { - for (int k = 0; k < 3; ++k) { - for (int l = 0; l < 3; ++l) { - VERIFY_IS_EQUAL(tensor(2*i,4*j,2*k,3*l), stride(i,j,k,l)); - } - } - } - } -} - - -template<int DataLayout> -static void test_striding_as_lvalue() -{ - Tensor<float, 4, DataLayout> tensor(2,3,5,7); - tensor.setRandom(); - array<ptrdiff_t, 4> strides; - strides[0] = 2; - strides[1] = 4; - strides[2] = 2; - strides[3] = 3; - - Tensor<float, 4, DataLayout> result(3, 12, 10, 21); - result.stride(strides) = tensor; - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(tensor(i,j,k,l), result(2*i,4*j,2*k,3*l)); - } - } - } - } - - array<ptrdiff_t, 4> no_strides; - no_strides[0] = 1; - no_strides[1] = 1; - no_strides[2] = 1; - no_strides[3] = 1; - Tensor<float, 4, DataLayout> result2(3, 12, 10, 21); - result2.stride(strides) = tensor.stride(no_strides); - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 5; ++k) { - for (int l = 0; l < 7; ++l) { - VERIFY_IS_EQUAL(tensor(i,j,k,l), result2(2*i,4*j,2*k,3*l)); - } - } - } - } -} - - -void test_cxx11_tensor_striding() -{ - CALL_SUBTEST(test_simple_striding<ColMajor>()); - CALL_SUBTEST(test_simple_striding<RowMajor>()); - CALL_SUBTEST(test_striding_as_lvalue<ColMajor>()); - CALL_SUBTEST(test_striding_as_lvalue<RowMajor>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_sugar.cpp b/eigen/unsupported/test/cxx11_tensor_sugar.cpp deleted file mode 100644 index 2f56eb4..0000000 --- a/eigen/unsupported/test/cxx11_tensor_sugar.cpp +++ /dev/null @@ -1,81 +0,0 @@ -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; -using Eigen::RowMajor; - -static void test_comparison_sugar() { - // we already trust comparisons between tensors, we're simply checking that - // the sugared versions are doing the same thing - Tensor<int, 3> t(6, 7, 5); - - t.setRandom(); - // make sure we have at least one value == 0 - t(0,0,0) = 0; - - Tensor<bool,0> b; - -#define TEST_TENSOR_EQUAL(e1, e2) \ - b = ((e1) == (e2)).all(); \ - VERIFY(b()) - -#define TEST_OP(op) TEST_TENSOR_EQUAL(t op 0, t op t.constant(0)) - - TEST_OP(==); - TEST_OP(!=); - TEST_OP(<=); - TEST_OP(>=); - TEST_OP(<); - TEST_OP(>); -#undef TEST_OP -#undef TEST_TENSOR_EQUAL -} - - -static void test_scalar_sugar_add_mul() { - Tensor<float, 3> A(6, 7, 5); - Tensor<float, 3> B(6, 7, 5); - A.setRandom(); - B.setRandom(); - - const float alpha = 0.43f; - const float beta = 0.21f; - const float gamma = 0.14f; - - Tensor<float, 3> R = A.constant(gamma) + A * A.constant(alpha) + B * B.constant(beta); - Tensor<float, 3> S = A * alpha + B * beta + gamma; - Tensor<float, 3> T = gamma + alpha * A + beta * B; - - for (int i = 0; i < 6*7*5; ++i) { - VERIFY_IS_APPROX(R(i), S(i)); - VERIFY_IS_APPROX(R(i), T(i)); - } -} - -static void test_scalar_sugar_sub_div() { - Tensor<float, 3> A(6, 7, 5); - Tensor<float, 3> B(6, 7, 5); - A.setRandom(); - B.setRandom(); - - const float alpha = 0.43f; - const float beta = 0.21f; - const float gamma = 0.14f; - const float delta = 0.32f; - - Tensor<float, 3> R = A.constant(gamma) - A / A.constant(alpha) - - B.constant(beta) / B - A.constant(delta); - Tensor<float, 3> S = gamma - A / alpha - beta / B - delta; - - for (int i = 0; i < 6*7*5; ++i) { - VERIFY_IS_APPROX(R(i), S(i)); - } -} - -void test_cxx11_tensor_sugar() -{ - CALL_SUBTEST(test_comparison_sugar()); - CALL_SUBTEST(test_scalar_sugar_add_mul()); - CALL_SUBTEST(test_scalar_sugar_sub_div()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_sycl.cpp b/eigen/unsupported/test/cxx11_tensor_sycl.cpp deleted file mode 100644 index 6a9c334..0000000 --- a/eigen/unsupported/test/cxx11_tensor_sycl.cpp +++ /dev/null @@ -1,159 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 -// Mehdi Goli Codeplay Software Ltd. -// Ralph Potter Codeplay Software Ltd. -// Luke Iwanski Codeplay Software Ltd. -// Contact: <eigen@codeplay.com> -// Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - - -#define EIGEN_TEST_NO_LONGDOUBLE -#define EIGEN_TEST_NO_COMPLEX -#define EIGEN_TEST_FUNC cxx11_tensor_sycl -#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int -#define EIGEN_USE_SYCL - -#include "main.h" -#include <unsupported/Eigen/CXX11/Tensor> - -using Eigen::array; -using Eigen::SyclDevice; -using Eigen::Tensor; -using Eigen::TensorMap; - -void test_sycl_cpu(const Eigen::SyclDevice &sycl_device) { - - int sizeDim1 = 100; - int sizeDim2 = 100; - int sizeDim3 = 100; - array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}}; - Tensor<float, 3> in1(tensorRange); - Tensor<float, 3> in2(tensorRange); - Tensor<float, 3> in3(tensorRange); - Tensor<float, 3> out(tensorRange); - - in2 = in2.random(); - in3 = in3.random(); - - float * gpu_in1_data = static_cast<float*>(sycl_device.allocate(in1.dimensions().TotalSize()*sizeof(float))); - float * gpu_in2_data = static_cast<float*>(sycl_device.allocate(in2.dimensions().TotalSize()*sizeof(float))); - float * gpu_in3_data = static_cast<float*>(sycl_device.allocate(in3.dimensions().TotalSize()*sizeof(float))); - float * gpu_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float))); - - TensorMap<Tensor<float, 3>> gpu_in1(gpu_in1_data, tensorRange); - TensorMap<Tensor<float, 3>> gpu_in2(gpu_in2_data, tensorRange); - TensorMap<Tensor<float, 3>> gpu_in3(gpu_in3_data, tensorRange); - TensorMap<Tensor<float, 3>> gpu_out(gpu_out_data, tensorRange); - - /// a=1.2f - gpu_in1.device(sycl_device) = gpu_in1.constant(1.2f); - sycl_device.memcpyDeviceToHost(in1.data(), gpu_in1_data ,(in1.dimensions().TotalSize())*sizeof(float)); - for (int i = 0; i < sizeDim1; ++i) { - for (int j = 0; j < sizeDim2; ++j) { - for (int k = 0; k < sizeDim3; ++k) { - VERIFY_IS_APPROX(in1(i,j,k), 1.2f); - } - } - } - printf("a=1.2f Test passed\n"); - - /// a=b*1.2f - gpu_out.device(sycl_device) = gpu_in1 * 1.2f; - sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data ,(out.dimensions().TotalSize())*sizeof(float)); - for (int i = 0; i < sizeDim1; ++i) { - for (int j = 0; j < sizeDim2; ++j) { - for (int k = 0; k < sizeDim3; ++k) { - VERIFY_IS_APPROX(out(i,j,k), - in1(i,j,k) * 1.2f); - } - } - } - printf("a=b*1.2f Test Passed\n"); - - /// c=a*b - sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in2.dimensions().TotalSize())*sizeof(float)); - gpu_out.device(sycl_device) = gpu_in1 * gpu_in2; - sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); - for (int i = 0; i < sizeDim1; ++i) { - for (int j = 0; j < sizeDim2; ++j) { - for (int k = 0; k < sizeDim3; ++k) { - VERIFY_IS_APPROX(out(i,j,k), - in1(i,j,k) * - in2(i,j,k)); - } - } - } - printf("c=a*b Test Passed\n"); - - /// c=a+b - gpu_out.device(sycl_device) = gpu_in1 + gpu_in2; - sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); - for (int i = 0; i < sizeDim1; ++i) { - for (int j = 0; j < sizeDim2; ++j) { - for (int k = 0; k < sizeDim3; ++k) { - VERIFY_IS_APPROX(out(i,j,k), - in1(i,j,k) + - in2(i,j,k)); - } - } - } - printf("c=a+b Test Passed\n"); - - /// c=a*a - gpu_out.device(sycl_device) = gpu_in1 * gpu_in1; - sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); - for (int i = 0; i < sizeDim1; ++i) { - for (int j = 0; j < sizeDim2; ++j) { - for (int k = 0; k < sizeDim3; ++k) { - VERIFY_IS_APPROX(out(i,j,k), - in1(i,j,k) * - in1(i,j,k)); - } - } - } - printf("c= a*a Test Passed\n"); - - //a*3.14f + b*2.7f - gpu_out.device(sycl_device) = gpu_in1 * gpu_in1.constant(3.14f) + gpu_in2 * gpu_in2.constant(2.7f); - sycl_device.memcpyDeviceToHost(out.data(),gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); - for (int i = 0; i < sizeDim1; ++i) { - for (int j = 0; j < sizeDim2; ++j) { - for (int k = 0; k < sizeDim3; ++k) { - VERIFY_IS_APPROX(out(i,j,k), - in1(i,j,k) * 3.14f - + in2(i,j,k) * 2.7f); - } - } - } - printf("a*3.14f + b*2.7f Test Passed\n"); - - ///d= (a>0.5? b:c) - sycl_device.memcpyHostToDevice(gpu_in3_data, in3.data(),(in3.dimensions().TotalSize())*sizeof(float)); - gpu_out.device(sycl_device) =(gpu_in1 > gpu_in1.constant(0.5f)).select(gpu_in2, gpu_in3); - sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); - for (int i = 0; i < sizeDim1; ++i) { - for (int j = 0; j < sizeDim2; ++j) { - for (int k = 0; k < sizeDim3; ++k) { - VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) > 0.5f) - ? in2(i, j, k) - : in3(i, j, k)); - } - } - } - printf("d= (a>0.5? b:c) Test Passed\n"); - sycl_device.deallocate(gpu_in1_data); - sycl_device.deallocate(gpu_in2_data); - sycl_device.deallocate(gpu_in3_data); - sycl_device.deallocate(gpu_out_data); -} -void test_cxx11_tensor_sycl() { - cl::sycl::gpu_selector s; - Eigen::SyclDevice sycl_device(s); - CALL_SUBTEST(test_sycl_cpu(sycl_device)); -} diff --git a/eigen/unsupported/test/cxx11_tensor_symmetry.cpp b/eigen/unsupported/test/cxx11_tensor_symmetry.cpp deleted file mode 100644 index d680e9b..0000000 --- a/eigen/unsupported/test/cxx11_tensor_symmetry.cpp +++ /dev/null @@ -1,818 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2013 Christian Seiler <christian@iwakd.de> -// -// 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/CXX11/Tensor> -#include <Eigen/CXX11/TensorSymmetry> - -#include <map> -#include <set> - -using Eigen::Tensor; -using Eigen::SGroup; -using Eigen::DynamicSGroup; -using Eigen::StaticSGroup; -using Eigen::Symmetry; -using Eigen::AntiSymmetry; -using Eigen::Hermiticity; -using Eigen::AntiHermiticity; - -using Eigen::NegationFlag; -using Eigen::ConjugationFlag; -using Eigen::GlobalZeroFlag; -using Eigen::GlobalRealFlag; -using Eigen::GlobalImagFlag; - -// helper function to determine if the compiler intantiated a static -// or dynamic symmetry group -template<typename... Sym> -bool isDynGroup(StaticSGroup<Sym...> const& dummy) -{ - (void)dummy; - return false; -} - -bool isDynGroup(DynamicSGroup const& dummy) -{ - (void)dummy; - return true; -} - -// helper class for checking that the symmetry groups are correct -struct checkIdx { - template<typename ArrType> - static inline int doCheck_(ArrType e, int flags, int dummy, std::set<uint64_t>& found, std::map<uint64_t, int> const& expected) - { - // use decimal representation of value - uint64_t value = e[0]; - for (std::size_t i = 1; i < e.size(); i++) - value = value * 10 + e[i]; - - // we want to make sure that we find each element - auto it = expected.find(value); - VERIFY((it != expected.end())); - VERIFY_IS_EQUAL(it->second, flags); - - // we want to make sure we only have each element once; - // set::insert returns true for the second part of the pair - // if the element was really inserted and not already there - auto p = found.insert(value); - VERIFY((p.second)); - - return dummy; - } - - static inline int run(std::vector<int> e, int flags, int dummy, std::set<uint64_t>& found, std::map<uint64_t, int> const& expected) - { - return doCheck_(e, flags, dummy, found, expected); - } - - template<std::size_t N> - static inline int run(std::array<int, N> e, int flags, int dummy, std::set<uint64_t>& found, std::map<uint64_t, int> const& expected) - { - return doCheck_(e, flags, dummy, found, expected); - } -}; - -static void test_symgroups_static() -{ - std::array<int, 7> identity{{0,1,2,3,4,5,6}}; - - // Simple static symmetry group - StaticSGroup< - AntiSymmetry<0,1>, - Hermiticity<0,2> - > group; - - std::set<uint64_t> found; - std::map<uint64_t, int> expected; - expected[ 123456] = 0; - expected[1023456] = NegationFlag; - expected[2103456] = ConjugationFlag; - expected[1203456] = ConjugationFlag | NegationFlag; - expected[2013456] = ConjugationFlag | NegationFlag; - expected[ 213456] = ConjugationFlag; - - VERIFY_IS_EQUAL(group.size(), 6u); - VERIFY_IS_EQUAL(group.globalFlags(), GlobalImagFlag); - group.apply<checkIdx, int>(identity, 0, found, expected); - VERIFY_IS_EQUAL(found.size(), 6u); -} - -static void test_symgroups_dynamic() -{ - std::vector<int> identity; - for (int i = 0; i <= 6; i++) - identity.push_back(i); - - // Simple dynamic symmetry group - DynamicSGroup group; - group.add(0,1,NegationFlag); - group.add(0,2,ConjugationFlag); - - VERIFY_IS_EQUAL(group.size(), 6u); - VERIFY_IS_EQUAL(group.globalFlags(), GlobalImagFlag); - - std::set<uint64_t> found; - std::map<uint64_t, int> expected; - expected[ 123456] = 0; - expected[1023456] = NegationFlag; - expected[2103456] = ConjugationFlag; - expected[1203456] = ConjugationFlag | NegationFlag; - expected[2013456] = ConjugationFlag | NegationFlag; - expected[ 213456] = ConjugationFlag; - - VERIFY_IS_EQUAL(group.size(), 6u); - VERIFY_IS_EQUAL(group.globalFlags(), GlobalImagFlag); - group.apply<checkIdx, int>(identity, 0, found, expected); - VERIFY_IS_EQUAL(found.size(), 6u); -} - -static void test_symgroups_selection() -{ - std::array<int, 7> identity7{{0,1,2,3,4,5,6}}; - std::array<int, 10> identity10{{0,1,2,3,4,5,6,7,8,9}}; - - { - // Do the same test as in test_symgroups_static but - // require selection via SGroup - SGroup< - AntiSymmetry<0,1>, - Hermiticity<0,2> - > group; - - std::set<uint64_t> found; - std::map<uint64_t, int> expected; - expected[ 123456] = 0; - expected[1023456] = NegationFlag; - expected[2103456] = ConjugationFlag; - expected[1203456] = ConjugationFlag | NegationFlag; - expected[2013456] = ConjugationFlag | NegationFlag; - expected[ 213456] = ConjugationFlag; - - VERIFY(!isDynGroup(group)); - VERIFY_IS_EQUAL(group.size(), 6u); - VERIFY_IS_EQUAL(group.globalFlags(), GlobalImagFlag); - group.apply<checkIdx, int>(identity7, 0, found, expected); - VERIFY_IS_EQUAL(found.size(), 6u); - } - - { - // simple factorizing group: 5 generators, 2^5 = 32 elements - // selection should make this dynamic, although static group - // can still be reasonably generated - SGroup< - Symmetry<0,1>, - Symmetry<2,3>, - Symmetry<4,5>, - Symmetry<6,7>, - Symmetry<8,9> - > group; - - std::set<uint64_t> found; - std::map<uint64_t, int> expected; - expected[ 123456789] = 0; expected[ 123456798] = 0; expected[ 123457689] = 0; expected[ 123457698] = 0; - expected[ 123546789] = 0; expected[ 123546798] = 0; expected[ 123547689] = 0; expected[ 123547698] = 0; - expected[ 132456789] = 0; expected[ 132456798] = 0; expected[ 132457689] = 0; expected[ 132457698] = 0; - expected[ 132546789] = 0; expected[ 132546798] = 0; expected[ 132547689] = 0; expected[ 132547698] = 0; - expected[1023456789] = 0; expected[1023456798] = 0; expected[1023457689] = 0; expected[1023457698] = 0; - expected[1023546789] = 0; expected[1023546798] = 0; expected[1023547689] = 0; expected[1023547698] = 0; - expected[1032456789] = 0; expected[1032456798] = 0; expected[1032457689] = 0; expected[1032457698] = 0; - expected[1032546789] = 0; expected[1032546798] = 0; expected[1032547689] = 0; expected[1032547698] = 0; - - VERIFY(isDynGroup(group)); - VERIFY_IS_EQUAL(group.size(), 32u); - VERIFY_IS_EQUAL(group.globalFlags(), 0); - group.apply<checkIdx, int>(identity10, 0, found, expected); - VERIFY_IS_EQUAL(found.size(), 32u); - - // no verify that we could also generate a static group - // with these generators - found.clear(); - StaticSGroup< - Symmetry<0,1>, - Symmetry<2,3>, - Symmetry<4,5>, - Symmetry<6,7>, - Symmetry<8,9> - > group_static; - VERIFY_IS_EQUAL(group_static.size(), 32u); - VERIFY_IS_EQUAL(group_static.globalFlags(), 0); - group_static.apply<checkIdx, int>(identity10, 0, found, expected); - VERIFY_IS_EQUAL(found.size(), 32u); - } - - { - // try to create a HUGE group - SGroup< - Symmetry<0,1>, - Symmetry<1,2>, - Symmetry<2,3>, - Symmetry<3,4>, - Symmetry<4,5>, - Symmetry<5,6> - > group; - - std::set<uint64_t> found; - uint64_t pre_expected[5040] = { - 123456, 1023456, 213456, 2013456, 1203456, 2103456, 132456, 1032456, 312456, 3012456, 1302456, 3102456, - 231456, 2031456, 321456, 3021456, 2301456, 3201456, 1230456, 2130456, 1320456, 3120456, 2310456, 3210456, - 124356, 1024356, 214356, 2014356, 1204356, 2104356, 142356, 1042356, 412356, 4012356, 1402356, 4102356, - 241356, 2041356, 421356, 4021356, 2401356, 4201356, 1240356, 2140356, 1420356, 4120356, 2410356, 4210356, - 134256, 1034256, 314256, 3014256, 1304256, 3104256, 143256, 1043256, 413256, 4013256, 1403256, 4103256, - 341256, 3041256, 431256, 4031256, 3401256, 4301256, 1340256, 3140256, 1430256, 4130256, 3410256, 4310256, - 234156, 2034156, 324156, 3024156, 2304156, 3204156, 243156, 2043156, 423156, 4023156, 2403156, 4203156, - 342156, 3042156, 432156, 4032156, 3402156, 4302156, 2340156, 3240156, 2430156, 4230156, 3420156, 4320156, - 1234056, 2134056, 1324056, 3124056, 2314056, 3214056, 1243056, 2143056, 1423056, 4123056, 2413056, 4213056, - 1342056, 3142056, 1432056, 4132056, 3412056, 4312056, 2341056, 3241056, 2431056, 4231056, 3421056, 4321056, - 123546, 1023546, 213546, 2013546, 1203546, 2103546, 132546, 1032546, 312546, 3012546, 1302546, 3102546, - 231546, 2031546, 321546, 3021546, 2301546, 3201546, 1230546, 2130546, 1320546, 3120546, 2310546, 3210546, - 125346, 1025346, 215346, 2015346, 1205346, 2105346, 152346, 1052346, 512346, 5012346, 1502346, 5102346, - 251346, 2051346, 521346, 5021346, 2501346, 5201346, 1250346, 2150346, 1520346, 5120346, 2510346, 5210346, - 135246, 1035246, 315246, 3015246, 1305246, 3105246, 153246, 1053246, 513246, 5013246, 1503246, 5103246, - 351246, 3051246, 531246, 5031246, 3501246, 5301246, 1350246, 3150246, 1530246, 5130246, 3510246, 5310246, - 235146, 2035146, 325146, 3025146, 2305146, 3205146, 253146, 2053146, 523146, 5023146, 2503146, 5203146, - 352146, 3052146, 532146, 5032146, 3502146, 5302146, 2350146, 3250146, 2530146, 5230146, 3520146, 5320146, - 1235046, 2135046, 1325046, 3125046, 2315046, 3215046, 1253046, 2153046, 1523046, 5123046, 2513046, 5213046, - 1352046, 3152046, 1532046, 5132046, 3512046, 5312046, 2351046, 3251046, 2531046, 5231046, 3521046, 5321046, - 124536, 1024536, 214536, 2014536, 1204536, 2104536, 142536, 1042536, 412536, 4012536, 1402536, 4102536, - 241536, 2041536, 421536, 4021536, 2401536, 4201536, 1240536, 2140536, 1420536, 4120536, 2410536, 4210536, - 125436, 1025436, 215436, 2015436, 1205436, 2105436, 152436, 1052436, 512436, 5012436, 1502436, 5102436, - 251436, 2051436, 521436, 5021436, 2501436, 5201436, 1250436, 2150436, 1520436, 5120436, 2510436, 5210436, - 145236, 1045236, 415236, 4015236, 1405236, 4105236, 154236, 1054236, 514236, 5014236, 1504236, 5104236, - 451236, 4051236, 541236, 5041236, 4501236, 5401236, 1450236, 4150236, 1540236, 5140236, 4510236, 5410236, - 245136, 2045136, 425136, 4025136, 2405136, 4205136, 254136, 2054136, 524136, 5024136, 2504136, 5204136, - 452136, 4052136, 542136, 5042136, 4502136, 5402136, 2450136, 4250136, 2540136, 5240136, 4520136, 5420136, - 1245036, 2145036, 1425036, 4125036, 2415036, 4215036, 1254036, 2154036, 1524036, 5124036, 2514036, 5214036, - 1452036, 4152036, 1542036, 5142036, 4512036, 5412036, 2451036, 4251036, 2541036, 5241036, 4521036, 5421036, - 134526, 1034526, 314526, 3014526, 1304526, 3104526, 143526, 1043526, 413526, 4013526, 1403526, 4103526, - 341526, 3041526, 431526, 4031526, 3401526, 4301526, 1340526, 3140526, 1430526, 4130526, 3410526, 4310526, - 135426, 1035426, 315426, 3015426, 1305426, 3105426, 153426, 1053426, 513426, 5013426, 1503426, 5103426, - 351426, 3051426, 531426, 5031426, 3501426, 5301426, 1350426, 3150426, 1530426, 5130426, 3510426, 5310426, - 145326, 1045326, 415326, 4015326, 1405326, 4105326, 154326, 1054326, 514326, 5014326, 1504326, 5104326, - 451326, 4051326, 541326, 5041326, 4501326, 5401326, 1450326, 4150326, 1540326, 5140326, 4510326, 5410326, - 345126, 3045126, 435126, 4035126, 3405126, 4305126, 354126, 3054126, 534126, 5034126, 3504126, 5304126, - 453126, 4053126, 543126, 5043126, 4503126, 5403126, 3450126, 4350126, 3540126, 5340126, 4530126, 5430126, - 1345026, 3145026, 1435026, 4135026, 3415026, 4315026, 1354026, 3154026, 1534026, 5134026, 3514026, 5314026, - 1453026, 4153026, 1543026, 5143026, 4513026, 5413026, 3451026, 4351026, 3541026, 5341026, 4531026, 5431026, - 234516, 2034516, 324516, 3024516, 2304516, 3204516, 243516, 2043516, 423516, 4023516, 2403516, 4203516, - 342516, 3042516, 432516, 4032516, 3402516, 4302516, 2340516, 3240516, 2430516, 4230516, 3420516, 4320516, - 235416, 2035416, 325416, 3025416, 2305416, 3205416, 253416, 2053416, 523416, 5023416, 2503416, 5203416, - 352416, 3052416, 532416, 5032416, 3502416, 5302416, 2350416, 3250416, 2530416, 5230416, 3520416, 5320416, - 245316, 2045316, 425316, 4025316, 2405316, 4205316, 254316, 2054316, 524316, 5024316, 2504316, 5204316, - 452316, 4052316, 542316, 5042316, 4502316, 5402316, 2450316, 4250316, 2540316, 5240316, 4520316, 5420316, - 345216, 3045216, 435216, 4035216, 3405216, 4305216, 354216, 3054216, 534216, 5034216, 3504216, 5304216, - 453216, 4053216, 543216, 5043216, 4503216, 5403216, 3450216, 4350216, 3540216, 5340216, 4530216, 5430216, - 2345016, 3245016, 2435016, 4235016, 3425016, 4325016, 2354016, 3254016, 2534016, 5234016, 3524016, 5324016, - 2453016, 4253016, 2543016, 5243016, 4523016, 5423016, 3452016, 4352016, 3542016, 5342016, 4532016, 5432016, - 1234506, 2134506, 1324506, 3124506, 2314506, 3214506, 1243506, 2143506, 1423506, 4123506, 2413506, 4213506, - 1342506, 3142506, 1432506, 4132506, 3412506, 4312506, 2341506, 3241506, 2431506, 4231506, 3421506, 4321506, - 1235406, 2135406, 1325406, 3125406, 2315406, 3215406, 1253406, 2153406, 1523406, 5123406, 2513406, 5213406, - 1352406, 3152406, 1532406, 5132406, 3512406, 5312406, 2351406, 3251406, 2531406, 5231406, 3521406, 5321406, - 1245306, 2145306, 1425306, 4125306, 2415306, 4215306, 1254306, 2154306, 1524306, 5124306, 2514306, 5214306, - 1452306, 4152306, 1542306, 5142306, 4512306, 5412306, 2451306, 4251306, 2541306, 5241306, 4521306, 5421306, - 1345206, 3145206, 1435206, 4135206, 3415206, 4315206, 1354206, 3154206, 1534206, 5134206, 3514206, 5314206, - 1453206, 4153206, 1543206, 5143206, 4513206, 5413206, 3451206, 4351206, 3541206, 5341206, 4531206, 5431206, - 2345106, 3245106, 2435106, 4235106, 3425106, 4325106, 2354106, 3254106, 2534106, 5234106, 3524106, 5324106, - 2453106, 4253106, 2543106, 5243106, 4523106, 5423106, 3452106, 4352106, 3542106, 5342106, 4532106, 5432106, - 123465, 1023465, 213465, 2013465, 1203465, 2103465, 132465, 1032465, 312465, 3012465, 1302465, 3102465, - 231465, 2031465, 321465, 3021465, 2301465, 3201465, 1230465, 2130465, 1320465, 3120465, 2310465, 3210465, - 124365, 1024365, 214365, 2014365, 1204365, 2104365, 142365, 1042365, 412365, 4012365, 1402365, 4102365, - 241365, 2041365, 421365, 4021365, 2401365, 4201365, 1240365, 2140365, 1420365, 4120365, 2410365, 4210365, - 134265, 1034265, 314265, 3014265, 1304265, 3104265, 143265, 1043265, 413265, 4013265, 1403265, 4103265, - 341265, 3041265, 431265, 4031265, 3401265, 4301265, 1340265, 3140265, 1430265, 4130265, 3410265, 4310265, - 234165, 2034165, 324165, 3024165, 2304165, 3204165, 243165, 2043165, 423165, 4023165, 2403165, 4203165, - 342165, 3042165, 432165, 4032165, 3402165, 4302165, 2340165, 3240165, 2430165, 4230165, 3420165, 4320165, - 1234065, 2134065, 1324065, 3124065, 2314065, 3214065, 1243065, 2143065, 1423065, 4123065, 2413065, 4213065, - 1342065, 3142065, 1432065, 4132065, 3412065, 4312065, 2341065, 3241065, 2431065, 4231065, 3421065, 4321065, - 123645, 1023645, 213645, 2013645, 1203645, 2103645, 132645, 1032645, 312645, 3012645, 1302645, 3102645, - 231645, 2031645, 321645, 3021645, 2301645, 3201645, 1230645, 2130645, 1320645, 3120645, 2310645, 3210645, - 126345, 1026345, 216345, 2016345, 1206345, 2106345, 162345, 1062345, 612345, 6012345, 1602345, 6102345, - 261345, 2061345, 621345, 6021345, 2601345, 6201345, 1260345, 2160345, 1620345, 6120345, 2610345, 6210345, - 136245, 1036245, 316245, 3016245, 1306245, 3106245, 163245, 1063245, 613245, 6013245, 1603245, 6103245, - 361245, 3061245, 631245, 6031245, 3601245, 6301245, 1360245, 3160245, 1630245, 6130245, 3610245, 6310245, - 236145, 2036145, 326145, 3026145, 2306145, 3206145, 263145, 2063145, 623145, 6023145, 2603145, 6203145, - 362145, 3062145, 632145, 6032145, 3602145, 6302145, 2360145, 3260145, 2630145, 6230145, 3620145, 6320145, - 1236045, 2136045, 1326045, 3126045, 2316045, 3216045, 1263045, 2163045, 1623045, 6123045, 2613045, 6213045, - 1362045, 3162045, 1632045, 6132045, 3612045, 6312045, 2361045, 3261045, 2631045, 6231045, 3621045, 6321045, - 124635, 1024635, 214635, 2014635, 1204635, 2104635, 142635, 1042635, 412635, 4012635, 1402635, 4102635, - 241635, 2041635, 421635, 4021635, 2401635, 4201635, 1240635, 2140635, 1420635, 4120635, 2410635, 4210635, - 126435, 1026435, 216435, 2016435, 1206435, 2106435, 162435, 1062435, 612435, 6012435, 1602435, 6102435, - 261435, 2061435, 621435, 6021435, 2601435, 6201435, 1260435, 2160435, 1620435, 6120435, 2610435, 6210435, - 146235, 1046235, 416235, 4016235, 1406235, 4106235, 164235, 1064235, 614235, 6014235, 1604235, 6104235, - 461235, 4061235, 641235, 6041235, 4601235, 6401235, 1460235, 4160235, 1640235, 6140235, 4610235, 6410235, - 246135, 2046135, 426135, 4026135, 2406135, 4206135, 264135, 2064135, 624135, 6024135, 2604135, 6204135, - 462135, 4062135, 642135, 6042135, 4602135, 6402135, 2460135, 4260135, 2640135, 6240135, 4620135, 6420135, - 1246035, 2146035, 1426035, 4126035, 2416035, 4216035, 1264035, 2164035, 1624035, 6124035, 2614035, 6214035, - 1462035, 4162035, 1642035, 6142035, 4612035, 6412035, 2461035, 4261035, 2641035, 6241035, 4621035, 6421035, - 134625, 1034625, 314625, 3014625, 1304625, 3104625, 143625, 1043625, 413625, 4013625, 1403625, 4103625, - 341625, 3041625, 431625, 4031625, 3401625, 4301625, 1340625, 3140625, 1430625, 4130625, 3410625, 4310625, - 136425, 1036425, 316425, 3016425, 1306425, 3106425, 163425, 1063425, 613425, 6013425, 1603425, 6103425, - 361425, 3061425, 631425, 6031425, 3601425, 6301425, 1360425, 3160425, 1630425, 6130425, 3610425, 6310425, - 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1256430, 2156430, 1526430, 5126430, 2516430, 5216430, 1265430, 2165430, 1625430, 6125430, 2615430, 6215430, - 1562430, 5162430, 1652430, 6152430, 5612430, 6512430, 2561430, 5261430, 2651430, 6251430, 5621430, 6521430, - 1456230, 4156230, 1546230, 5146230, 4516230, 5416230, 1465230, 4165230, 1645230, 6145230, 4615230, 6415230, - 1564230, 5164230, 1654230, 6154230, 5614230, 6514230, 4561230, 5461230, 4651230, 6451230, 5641230, 6541230, - 2456130, 4256130, 2546130, 5246130, 4526130, 5426130, 2465130, 4265130, 2645130, 6245130, 4625130, 6425130, - 2564130, 5264130, 2654130, 6254130, 5624130, 6524130, 4562130, 5462130, 4652130, 6452130, 5642130, 6542130, - 1345620, 3145620, 1435620, 4135620, 3415620, 4315620, 1354620, 3154620, 1534620, 5134620, 3514620, 5314620, - 1453620, 4153620, 1543620, 5143620, 4513620, 5413620, 3451620, 4351620, 3541620, 5341620, 4531620, 5431620, - 1346520, 3146520, 1436520, 4136520, 3416520, 4316520, 1364520, 3164520, 1634520, 6134520, 3614520, 6314520, - 1463520, 4163520, 1643520, 6143520, 4613520, 6413520, 3461520, 4361520, 3641520, 6341520, 4631520, 6431520, - 1356420, 3156420, 1536420, 5136420, 3516420, 5316420, 1365420, 3165420, 1635420, 6135420, 3615420, 6315420, - 1563420, 5163420, 1653420, 6153420, 5613420, 6513420, 3561420, 5361420, 3651420, 6351420, 5631420, 6531420, - 1456320, 4156320, 1546320, 5146320, 4516320, 5416320, 1465320, 4165320, 1645320, 6145320, 4615320, 6415320, - 1564320, 5164320, 1654320, 6154320, 5614320, 6514320, 4561320, 5461320, 4651320, 6451320, 5641320, 6541320, - 3456120, 4356120, 3546120, 5346120, 4536120, 5436120, 3465120, 4365120, 3645120, 6345120, 4635120, 6435120, - 3564120, 5364120, 3654120, 6354120, 5634120, 6534120, 4563120, 5463120, 4653120, 6453120, 5643120, 6543120, - 2345610, 3245610, 2435610, 4235610, 3425610, 4325610, 2354610, 3254610, 2534610, 5234610, 3524610, 5324610, - 2453610, 4253610, 2543610, 5243610, 4523610, 5423610, 3452610, 4352610, 3542610, 5342610, 4532610, 5432610, - 2346510, 3246510, 2436510, 4236510, 3426510, 4326510, 2364510, 3264510, 2634510, 6234510, 3624510, 6324510, - 2463510, 4263510, 2643510, 6243510, 4623510, 6423510, 3462510, 4362510, 3642510, 6342510, 4632510, 6432510, - 2356410, 3256410, 2536410, 5236410, 3526410, 5326410, 2365410, 3265410, 2635410, 6235410, 3625410, 6325410, - 2563410, 5263410, 2653410, 6253410, 5623410, 6523410, 3562410, 5362410, 3652410, 6352410, 5632410, 6532410, - 2456310, 4256310, 2546310, 5246310, 4526310, 5426310, 2465310, 4265310, 2645310, 6245310, 4625310, 6425310, - 2564310, 5264310, 2654310, 6254310, 5624310, 6524310, 4562310, 5462310, 4652310, 6452310, 5642310, 6542310, - 3456210, 4356210, 3546210, 5346210, 4536210, 5436210, 3465210, 4365210, 3645210, 6345210, 4635210, 6435210, - 3564210, 5364210, 3654210, 6354210, 5634210, 6534210, 4563210, 5463210, 4653210, 6453210, 5643210, 6543210 - }; - std::map<uint64_t, int> expected; - for (std::size_t i = 0; i < 5040; i++) - expected[pre_expected[i]] = 0; // flags are 0, everything is symmetric here - - VERIFY(isDynGroup(group)); - VERIFY_IS_EQUAL(group.size(), 5040u); - VERIFY_IS_EQUAL(group.globalFlags(), 0); - group.apply<checkIdx, int>(identity7, 0, found, expected); - VERIFY_IS_EQUAL(found.size(), 5040u); - } -} - -static void test_tensor_epsilon() -{ - SGroup<AntiSymmetry<0,1>, AntiSymmetry<1,2>> sym; - Tensor<int, 3> epsilon(3,3,3); - - epsilon.setZero(); - sym(epsilon, 0, 1, 2) = 1; - - for (int i = 0; i < 3; i++) { - for (int j = 0; j < 3; j++) { - for (int k = 0; k < 3; k++) { - VERIFY_IS_EQUAL((epsilon(i,j,k)), (- (j - i) * (k - j) * (i - k) / 2) ); - } - } - } -} - -static void test_tensor_sym() -{ - SGroup<Symmetry<0,1>, Symmetry<2,3>> sym; - Tensor<int, 4> t(10,10,10,10); - - t.setZero(); - - for (int l = 0; l < 10; l++) { - for (int k = l; k < 10; k++) { - for (int j = 0; j < 10; j++) { - for (int i = j; i < 10; i++) { - sym(t, i, j, k, l) = (i + j) * (k + l); - } - } - } - } - - for (int l = 0; l < 10; l++) { - for (int k = 0; k < 10; k++) { - for (int j = 0; j < 10; j++) { - for (int i = 0; i < 10; i++) { - VERIFY_IS_EQUAL((t(i, j, k, l)), ((i + j) * (k + l))); - } - } - } - } - -} - -static void test_tensor_asym() -{ - SGroup<AntiSymmetry<0,1>, AntiSymmetry<2,3>> sym; - Tensor<int, 4> t(10,10,10,10); - - t.setZero(); - - for (int l = 0; l < 10; l++) { - for (int k = l + 1; k < 10; k++) { - for (int j = 0; j < 10; j++) { - for (int i = j + 1; i < 10; i++) { - sym(t, i, j, k, l) = ((i * j) + (k * l)); - } - } - } - } - - for (int l = 0; l < 10; l++) { - for (int k = 0; k < 10; k++) { - for (int j = 0; j < 10; j++) { - for (int i = 0; i < 10; i++) { - if (i < j && k < l) - VERIFY_IS_EQUAL((t(i, j, k, l)), (((i * j) + (k * l)))); - else if (i > j && k > l) - VERIFY_IS_EQUAL((t(i, j, k, l)), (((i * j) + (k * l)))); - else if (i < j && k > l) - VERIFY_IS_EQUAL((t(i, j, k, l)), (- ((i * j) + (k * l)))); - else if (i > j && k < l) - VERIFY_IS_EQUAL((t(i, j, k, l)), (- ((i * j) + (k * l)))); - else - VERIFY_IS_EQUAL((t(i, j, k, l)), 0); - } - } - } - } -} - -static void test_tensor_dynsym() -{ - DynamicSGroup sym; - sym.addSymmetry(0,1); - sym.addSymmetry(2,3); - Tensor<int, 4> t(10,10,10,10); - - t.setZero(); - - for (int l = 0; l < 10; l++) { - for (int k = l; k < 10; k++) { - for (int j = 0; j < 10; j++) { - for (int i = j; i < 10; i++) { - sym(t, i, j, k, l) = (i + j) * (k + l); - } - } - } - } - - for (int l = 0; l < 10; l++) { - for (int k = 0; k < 10; k++) { - for (int j = 0; j < 10; j++) { - for (int i = 0; i < 10; i++) { - VERIFY_IS_EQUAL((t(i, j, k, l)), ((i + j) * (k + l))); - } - } - } - } -} - -static void test_tensor_randacc() -{ - SGroup<Symmetry<0,1>, Symmetry<2,3>> sym; - Tensor<int, 4> t(10,10,10,10); - - t.setZero(); - - // set elements 1 million times, that way we access the - // entire matrix - for (int n = 0; n < 1000000; n++) { - int i = rand() % 10; - int j = rand() % 10; - int k = rand() % 10; - int l = rand() % 10; - // only access those indices in a given order - if (i < j) - std::swap(i, j); - if (k < l) - std::swap(k, l); - sym(t, i, j, k, l) = (i + j) * (k + l); - } - - for (int l = 0; l < 10; l++) { - for (int k = 0; k < 10; k++) { - for (int j = 0; j < 10; j++) { - for (int i = 0; i < 10; i++) { - VERIFY_IS_EQUAL((t(i, j, k, l)), ((i + j) * (k + l))); - } - } - } - } -} - -void test_cxx11_tensor_symmetry() -{ - CALL_SUBTEST(test_symgroups_static()); - CALL_SUBTEST(test_symgroups_dynamic()); - CALL_SUBTEST(test_symgroups_selection()); - CALL_SUBTEST(test_tensor_epsilon()); - CALL_SUBTEST(test_tensor_sym()); - CALL_SUBTEST(test_tensor_asym()); - CALL_SUBTEST(test_tensor_dynsym()); - CALL_SUBTEST(test_tensor_randacc()); -} - -/* - * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle; - */ diff --git a/eigen/unsupported/test/cxx11_tensor_thread_pool.cpp b/eigen/unsupported/test/cxx11_tensor_thread_pool.cpp deleted file mode 100644 index 2ef665f..0000000 --- a/eigen/unsupported/test/cxx11_tensor_thread_pool.cpp +++ /dev/null @@ -1,373 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#define EIGEN_USE_THREADS - - -#include "main.h" -#include <iostream> -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - - -void test_multithread_elementwise() -{ - Tensor<float, 3> in1(2,3,7); - Tensor<float, 3> in2(2,3,7); - Tensor<float, 3> out(2,3,7); - - in1.setRandom(); - in2.setRandom(); - - Eigen::ThreadPool tp(internal::random<int>(3, 11)); - Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(3, 11)); - out.device(thread_pool_device) = in1 + in2 * 3.14f; - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) + in2(i,j,k) * 3.14f); - } - } - } -} - - -void test_multithread_compound_assignment() -{ - Tensor<float, 3> in1(2,3,7); - Tensor<float, 3> in2(2,3,7); - Tensor<float, 3> out(2,3,7); - - in1.setRandom(); - in2.setRandom(); - - Eigen::ThreadPool tp(internal::random<int>(3, 11)); - Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(3, 11)); - out.device(thread_pool_device) = in1; - out.device(thread_pool_device) += in2 * 3.14f; - - for (int i = 0; i < 2; ++i) { - for (int j = 0; j < 3; ++j) { - for (int k = 0; k < 7; ++k) { - VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) + in2(i,j,k) * 3.14f); - } - } - } -} - -template<int DataLayout> -void test_multithread_contraction() -{ - Tensor<float, 4, DataLayout> t_left(30, 50, 37, 31); - Tensor<float, 5, DataLayout> t_right(37, 31, 70, 2, 10); - Tensor<float, 5, DataLayout> t_result(30, 50, 70, 2, 10); - - t_left.setRandom(); - t_right.setRandom(); - - // this contraction should be equivalent to a single matrix multiplication - typedef Tensor<float, 1>::DimensionPair DimPair; - Eigen::array<DimPair, 2> dims({{DimPair(2, 0), DimPair(3, 1)}}); - - typedef Map<Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf; - MapXf m_left(t_left.data(), 1500, 1147); - MapXf m_right(t_right.data(), 1147, 1400); - Matrix<float, Dynamic, Dynamic, DataLayout> m_result(1500, 1400); - - Eigen::ThreadPool tp(4); - Eigen::ThreadPoolDevice thread_pool_device(&tp, 4); - - // compute results by separate methods - t_result.device(thread_pool_device) = t_left.contract(t_right, dims); - m_result = m_left * m_right; - - for (ptrdiff_t i = 0; i < t_result.size(); i++) { - VERIFY(&t_result.data()[i] != &m_result.data()[i]); - if (fabsf(t_result(i) - m_result(i)) < 1e-4f) { - continue; - } - if (Eigen::internal::isApprox(t_result(i), m_result(i), 1e-4f)) { - continue; - } - std::cout << "mismatch detected at index " << i << ": " << t_result(i) - << " vs " << m_result(i) << std::endl; - assert(false); - } -} - -template<int DataLayout> -void test_contraction_corner_cases() -{ - Tensor<float, 2, DataLayout> t_left(32, 500); - Tensor<float, 2, DataLayout> t_right(32, 28*28); - Tensor<float, 2, DataLayout> t_result(500, 28*28); - - t_left = (t_left.constant(-0.5f) + t_left.random()) * 2.0f; - t_right = (t_right.constant(-0.6f) + t_right.random()) * 2.0f; - t_result = t_result.constant(NAN); - - // this contraction should be equivalent to a single matrix multiplication - typedef Tensor<float, 1>::DimensionPair DimPair; - Eigen::array<DimPair, 1> dims{{DimPair(0, 0)}}; - - typedef Map<Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf; - MapXf m_left(t_left.data(), 32, 500); - MapXf m_right(t_right.data(), 32, 28*28); - Matrix<float, Dynamic, Dynamic, DataLayout> m_result(500, 28*28); - - Eigen::ThreadPool tp(12); - Eigen::ThreadPoolDevice thread_pool_device(&tp, 12); - - // compute results by separate methods - t_result.device(thread_pool_device) = t_left.contract(t_right, dims); - m_result = m_left.transpose() * m_right; - - for (ptrdiff_t i = 0; i < t_result.size(); i++) { - assert(!(numext::isnan)(t_result.data()[i])); - if (fabsf(t_result.data()[i] - m_result.data()[i]) >= 1e-4f) { - std::cout << "mismatch detected at index " << i << " : " << t_result.data()[i] << " vs " << m_result.data()[i] << std::endl; - assert(false); - } - } - - t_left.resize(32, 1); - t_left = (t_left.constant(-0.5f) + t_left.random()) * 2.0f; - t_result.resize (1, 28*28); - t_result = t_result.constant(NAN); - t_result.device(thread_pool_device) = t_left.contract(t_right, dims); - new(&m_left) MapXf(t_left.data(), 32, 1); - m_result = m_left.transpose() * m_right; - for (ptrdiff_t i = 0; i < t_result.size(); i++) { - assert(!(numext::isnan)(t_result.data()[i])); - if (fabsf(t_result.data()[i] - m_result.data()[i]) >= 1e-4f) { - std::cout << "mismatch detected: " << t_result.data()[i] << " vs " << m_result.data()[i] << std::endl; - assert(false); - } - } - - t_left.resize(32, 500); - t_right.resize(32, 4); - t_left = (t_left.constant(-0.5f) + t_left.random()) * 2.0f; - t_right = (t_right.constant(-0.6f) + t_right.random()) * 2.0f; - t_result.resize (500, 4); - t_result = t_result.constant(NAN); - t_result.device(thread_pool_device) = t_left.contract(t_right, dims); - new(&m_left) MapXf(t_left.data(), 32, 500); - new(&m_right) MapXf(t_right.data(), 32, 4); - m_result = m_left.transpose() * m_right; - for (ptrdiff_t i = 0; i < t_result.size(); i++) { - assert(!(numext::isnan)(t_result.data()[i])); - if (fabsf(t_result.data()[i] - m_result.data()[i]) >= 1e-4f) { - std::cout << "mismatch detected: " << t_result.data()[i] << " vs " << m_result.data()[i] << std::endl; - assert(false); - } - } - - t_left.resize(32, 1); - t_right.resize(32, 4); - t_left = (t_left.constant(-0.5f) + t_left.random()) * 2.0f; - t_right = (t_right.constant(-0.6f) + t_right.random()) * 2.0f; - t_result.resize (1, 4); - t_result = t_result.constant(NAN); - t_result.device(thread_pool_device) = t_left.contract(t_right, dims); - new(&m_left) MapXf(t_left.data(), 32, 1); - new(&m_right) MapXf(t_right.data(), 32, 4); - m_result = m_left.transpose() * m_right; - for (ptrdiff_t i = 0; i < t_result.size(); i++) { - assert(!(numext::isnan)(t_result.data()[i])); - if (fabsf(t_result.data()[i] - m_result.data()[i]) >= 1e-4f) { - std::cout << "mismatch detected: " << t_result.data()[i] << " vs " << m_result.data()[i] << std::endl; - assert(false); - } - } -} - -template<int DataLayout> -void test_multithread_contraction_agrees_with_singlethread() { - int contract_size = internal::random<int>(1, 5000); - - Tensor<float, 3, DataLayout> left(internal::random<int>(1, 80), - contract_size, - internal::random<int>(1, 100)); - - Tensor<float, 4, DataLayout> right(internal::random<int>(1, 25), - internal::random<int>(1, 37), - contract_size, - internal::random<int>(1, 51)); - - left.setRandom(); - right.setRandom(); - - // add constants to shift values away from 0 for more precision - left += left.constant(1.5f); - right += right.constant(1.5f); - - typedef Tensor<float, 1>::DimensionPair DimPair; - Eigen::array<DimPair, 1> dims({{DimPair(1, 2)}}); - - Eigen::ThreadPool tp(internal::random<int>(2, 11)); - Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(2, 11)); - - Tensor<float, 5, DataLayout> st_result; - st_result = left.contract(right, dims); - - Tensor<float, 5, DataLayout> tp_result(st_result.dimensions()); - tp_result.device(thread_pool_device) = left.contract(right, dims); - - VERIFY(dimensions_match(st_result.dimensions(), tp_result.dimensions())); - for (ptrdiff_t i = 0; i < st_result.size(); i++) { - // if both of the values are very small, then do nothing (because the test will fail - // due to numerical precision issues when values are small) - if (numext::abs(st_result.data()[i] - tp_result.data()[i]) >= 1e-4f) { - VERIFY_IS_APPROX(st_result.data()[i], tp_result.data()[i]); - } - } -} - - -template<int DataLayout> -void test_full_contraction() { - int contract_size1 = internal::random<int>(1, 500); - int contract_size2 = internal::random<int>(1, 500); - - Tensor<float, 2, DataLayout> left(contract_size1, - contract_size2); - Tensor<float, 2, DataLayout> right(contract_size1, - contract_size2); - left.setRandom(); - right.setRandom(); - - // add constants to shift values away from 0 for more precision - left += left.constant(1.5f); - right += right.constant(1.5f); - - typedef Tensor<float, 2>::DimensionPair DimPair; - Eigen::array<DimPair, 2> dims({{DimPair(0, 0), DimPair(1, 1)}}); - - Eigen::ThreadPool tp(internal::random<int>(2, 11)); - Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(2, 11)); - - Tensor<float, 0, DataLayout> st_result; - st_result = left.contract(right, dims); - - Tensor<float, 0, DataLayout> tp_result; - tp_result.device(thread_pool_device) = left.contract(right, dims); - - VERIFY(dimensions_match(st_result.dimensions(), tp_result.dimensions())); - // if both of the values are very small, then do nothing (because the test will fail - // due to numerical precision issues when values are small) - if (numext::abs(st_result() - tp_result()) >= 1e-4f) { - VERIFY_IS_APPROX(st_result(), tp_result()); - } -} - -template<int DataLayout> -void test_multithreaded_reductions() { - const int num_threads = internal::random<int>(3, 11); - ThreadPool thread_pool(num_threads); - Eigen::ThreadPoolDevice thread_pool_device(&thread_pool, num_threads); - - const int num_rows = internal::random<int>(13, 732); - const int num_cols = internal::random<int>(13, 732); - Tensor<float, 2, DataLayout> t1(num_rows, num_cols); - t1.setRandom(); - - Tensor<float, 0, DataLayout> full_redux; - full_redux = t1.sum(); - - Tensor<float, 0, DataLayout> full_redux_tp; - full_redux_tp.device(thread_pool_device) = t1.sum(); - - // Check that the single threaded and the multi threaded reductions return - // the same result. - VERIFY_IS_APPROX(full_redux(), full_redux_tp()); -} - - -void test_memcpy() { - - for (int i = 0; i < 5; ++i) { - const int num_threads = internal::random<int>(3, 11); - Eigen::ThreadPool tp(num_threads); - Eigen::ThreadPoolDevice thread_pool_device(&tp, num_threads); - - const int size = internal::random<int>(13, 7632); - Tensor<float, 1> t1(size); - t1.setRandom(); - std::vector<float> result(size); - thread_pool_device.memcpy(&result[0], t1.data(), size*sizeof(float)); - for (int j = 0; j < size; j++) { - VERIFY_IS_EQUAL(t1(j), result[j]); - } - } -} - - -void test_multithread_random() -{ - Eigen::ThreadPool tp(2); - Eigen::ThreadPoolDevice device(&tp, 2); - Tensor<float, 1> t(1 << 20); - t.device(device) = t.random<Eigen::internal::NormalRandomGenerator<float>>(); -} - -template<int DataLayout> -void test_multithread_shuffle() -{ - Tensor<float, 4, DataLayout> tensor(17,5,7,11); - tensor.setRandom(); - - const int num_threads = internal::random<int>(2, 11); - ThreadPool threads(num_threads); - Eigen::ThreadPoolDevice device(&threads, num_threads); - - Tensor<float, 4, DataLayout> shuffle(7,5,11,17); - array<ptrdiff_t, 4> shuffles = {{2,1,3,0}}; - shuffle.device(device) = tensor.shuffle(shuffles); - - for (int i = 0; i < 17; ++i) { - for (int j = 0; j < 5; ++j) { - for (int k = 0; k < 7; ++k) { - for (int l = 0; l < 11; ++l) { - VERIFY_IS_EQUAL(tensor(i,j,k,l), shuffle(k,j,l,i)); - } - } - } - } -} - - -void test_cxx11_tensor_thread_pool() -{ - CALL_SUBTEST_1(test_multithread_elementwise()); - CALL_SUBTEST_1(test_multithread_compound_assignment()); - - CALL_SUBTEST_2(test_multithread_contraction<ColMajor>()); - CALL_SUBTEST_2(test_multithread_contraction<RowMajor>()); - - CALL_SUBTEST_3(test_multithread_contraction_agrees_with_singlethread<ColMajor>()); - CALL_SUBTEST_3(test_multithread_contraction_agrees_with_singlethread<RowMajor>()); - - // Exercise various cases that have been problematic in the past. - CALL_SUBTEST_4(test_contraction_corner_cases<ColMajor>()); - CALL_SUBTEST_4(test_contraction_corner_cases<RowMajor>()); - - CALL_SUBTEST_4(test_full_contraction<ColMajor>()); - CALL_SUBTEST_4(test_full_contraction<RowMajor>()); - - CALL_SUBTEST_5(test_multithreaded_reductions<ColMajor>()); - CALL_SUBTEST_5(test_multithreaded_reductions<RowMajor>()); - - CALL_SUBTEST_6(test_memcpy()); - CALL_SUBTEST_6(test_multithread_random()); - CALL_SUBTEST_6(test_multithread_shuffle<ColMajor>()); - CALL_SUBTEST_6(test_multithread_shuffle<RowMajor>()); -} diff --git a/eigen/unsupported/test/cxx11_tensor_uint128.cpp b/eigen/unsupported/test/cxx11_tensor_uint128.cpp deleted file mode 100644 index d2a1e86..0000000 --- a/eigen/unsupported/test/cxx11_tensor_uint128.cpp +++ /dev/null @@ -1,160 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" - -#include <Eigen/CXX11/Tensor> - - -#if EIGEN_COMP_MSVC -#define EIGEN_NO_INT128 -#else -typedef __uint128_t uint128_t; -#endif - -// Only run the test on compilers that support 128bit integers natively -#ifndef EIGEN_NO_INT128 - -using Eigen::internal::TensorUInt128; -using Eigen::internal::static_val; - -void VERIFY_EQUAL(TensorUInt128<uint64_t, uint64_t> actual, uint128_t expected) { - bool matchl = actual.lower() == static_cast<uint64_t>(expected); - bool matchh = actual.upper() == static_cast<uint64_t>(expected >> 64); - if (!matchl || !matchh) { - const char* testname = g_test_stack.back().c_str(); - std::cerr << "Test " << testname << " failed in " << __FILE__ - << " (" << __LINE__ << ")" - << std::endl; - abort(); - } -} - - -void test_add() { - uint64_t incr = internal::random<uint64_t>(1, 9999999999); - for (uint64_t i1 = 0; i1 < 100; ++i1) { - for (uint64_t i2 = 1; i2 < 100 * incr; i2 += incr) { - TensorUInt128<uint64_t, uint64_t> i(i1, i2); - uint128_t a = (static_cast<uint128_t>(i1) << 64) + static_cast<uint128_t>(i2); - for (uint64_t j1 = 0; j1 < 100; ++j1) { - for (uint64_t j2 = 1; j2 < 100 * incr; j2 += incr) { - TensorUInt128<uint64_t, uint64_t> j(j1, j2); - uint128_t b = (static_cast<uint128_t>(j1) << 64) + static_cast<uint128_t>(j2); - TensorUInt128<uint64_t, uint64_t> actual = i + j; - uint128_t expected = a + b; - VERIFY_EQUAL(actual, expected); - } - } - } - } -} - -void test_sub() { - uint64_t incr = internal::random<uint64_t>(1, 9999999999); - for (uint64_t i1 = 0; i1 < 100; ++i1) { - for (uint64_t i2 = 1; i2 < 100 * incr; i2 += incr) { - TensorUInt128<uint64_t, uint64_t> i(i1, i2); - uint128_t a = (static_cast<uint128_t>(i1) << 64) + static_cast<uint128_t>(i2); - for (uint64_t j1 = 0; j1 < 100; ++j1) { - for (uint64_t j2 = 1; j2 < 100 * incr; j2 += incr) { - TensorUInt128<uint64_t, uint64_t> j(j1, j2); - uint128_t b = (static_cast<uint128_t>(j1) << 64) + static_cast<uint128_t>(j2); - TensorUInt128<uint64_t, uint64_t> actual = i - j; - uint128_t expected = a - b; - VERIFY_EQUAL(actual, expected); - } - } - } - } -} - -void test_mul() { - uint64_t incr = internal::random<uint64_t>(1, 9999999999); - for (uint64_t i1 = 0; i1 < 100; ++i1) { - for (uint64_t i2 = 1; i2 < 100 * incr; i2 += incr) { - TensorUInt128<uint64_t, uint64_t> i(i1, i2); - uint128_t a = (static_cast<uint128_t>(i1) << 64) + static_cast<uint128_t>(i2); - for (uint64_t j1 = 0; j1 < 100; ++j1) { - for (uint64_t j2 = 1; j2 < 100 * incr; j2 += incr) { - TensorUInt128<uint64_t, uint64_t> j(j1, j2); - uint128_t b = (static_cast<uint128_t>(j1) << 64) + static_cast<uint128_t>(j2); - TensorUInt128<uint64_t, uint64_t> actual = i * j; - uint128_t expected = a * b; - VERIFY_EQUAL(actual, expected); - } - } - } - } -} - -void test_div() { - uint64_t incr = internal::random<uint64_t>(1, 9999999999); - for (uint64_t i1 = 0; i1 < 100; ++i1) { - for (uint64_t i2 = 1; i2 < 100 * incr; i2 += incr) { - TensorUInt128<uint64_t, uint64_t> i(i1, i2); - uint128_t a = (static_cast<uint128_t>(i1) << 64) + static_cast<uint128_t>(i2); - for (uint64_t j1 = 0; j1 < 100; ++j1) { - for (uint64_t j2 = 1; j2 < 100 * incr; j2 += incr) { - TensorUInt128<uint64_t, uint64_t> j(j1, j2); - uint128_t b = (static_cast<uint128_t>(j1) << 64) + static_cast<uint128_t>(j2); - TensorUInt128<uint64_t, uint64_t> actual = i / j; - uint128_t expected = a / b; - VERIFY_EQUAL(actual, expected); - } - } - } - } -} - -void test_misc1() { - uint64_t incr = internal::random<uint64_t>(1, 9999999999); - for (uint64_t i2 = 1; i2 < 100 * incr; i2 += incr) { - TensorUInt128<static_val<0>, uint64_t> i(0, i2); - uint128_t a = static_cast<uint128_t>(i2); - for (uint64_t j2 = 1; j2 < 100 * incr; j2 += incr) { - TensorUInt128<static_val<0>, uint64_t> j(0, j2); - uint128_t b = static_cast<uint128_t>(j2); - uint64_t actual = (i * j).upper(); - uint64_t expected = (a * b) >> 64; - VERIFY_IS_EQUAL(actual, expected); - } - } -} - -void test_misc2() { - int64_t incr = internal::random<int64_t>(1, 100); - for (int64_t log_div = 0; log_div < 63; ++log_div) { - for (int64_t divider = 1; divider <= 1000000 * incr; divider += incr) { - uint64_t expected = (static_cast<uint128_t>(1) << (64+log_div)) / static_cast<uint128_t>(divider) - (static_cast<uint128_t>(1) << 64) + 1; - uint64_t shift = 1ULL << log_div; - - TensorUInt128<uint64_t, uint64_t> result = (TensorUInt128<uint64_t, static_val<0> >(shift, 0) / TensorUInt128<static_val<0>, uint64_t>(divider) - TensorUInt128<static_val<1>, static_val<0> >(1, 0) + TensorUInt128<static_val<0>, static_val<1> >(1)); - uint64_t actual = static_cast<uint64_t>(result); - VERIFY_IS_EQUAL(actual, expected); - } - } -} -#endif - - -void test_cxx11_tensor_uint128() -{ -#ifdef EIGEN_NO_INT128 - // Skip the test on compilers that don't support 128bit integers natively - return; -#else - CALL_SUBTEST_1(test_add()); - CALL_SUBTEST_2(test_sub()); - CALL_SUBTEST_3(test_mul()); - CALL_SUBTEST_4(test_div()); - CALL_SUBTEST_5(test_misc1()); - CALL_SUBTEST_6(test_misc2()); -#endif -} diff --git a/eigen/unsupported/test/cxx11_tensor_volume_patch.cpp b/eigen/unsupported/test/cxx11_tensor_volume_patch.cpp deleted file mode 100644 index ca6840f..0000000 --- a/eigen/unsupported/test/cxx11_tensor_volume_patch.cpp +++ /dev/null @@ -1,112 +0,0 @@ -#include "main.h" - -#include <Eigen/CXX11/Tensor> - -using Eigen::Tensor; - -static void test_single_voxel_patch() -{ - Tensor<float, 5> tensor(4,2,3,5,7); - tensor.setRandom(); - Tensor<float, 5, RowMajor> tensor_row_major = tensor.swap_layout(); - - Tensor<float, 6> single_voxel_patch; - single_voxel_patch = tensor.extract_volume_patches(1, 1, 1); - VERIFY_IS_EQUAL(single_voxel_patch.dimension(0), 4); - VERIFY_IS_EQUAL(single_voxel_patch.dimension(1), 1); - VERIFY_IS_EQUAL(single_voxel_patch.dimension(2), 1); - VERIFY_IS_EQUAL(single_voxel_patch.dimension(3), 1); - VERIFY_IS_EQUAL(single_voxel_patch.dimension(4), 2 * 3 * 5); - VERIFY_IS_EQUAL(single_voxel_patch.dimension(5), 7); - - Tensor<float, 6, RowMajor> single_voxel_patch_row_major; - single_voxel_patch_row_major = tensor_row_major.extract_volume_patches(1, 1, 1); - VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(0), 7); - VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(1), 2 * 3 * 5); - VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(2), 1); - VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(3), 1); - VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(4), 1); - VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(5), 4); - - for (int i = 0; i < tensor.size(); ++i) { - VERIFY_IS_EQUAL(tensor.data()[i], single_voxel_patch.data()[i]); - VERIFY_IS_EQUAL(tensor_row_major.data()[i], single_voxel_patch_row_major.data()[i]); - VERIFY_IS_EQUAL(tensor.data()[i], tensor_row_major.data()[i]); - } -} - - -static void test_entire_volume_patch() -{ - const int depth = 4; - const int patch_z = 2; - const int patch_y = 3; - const int patch_x = 5; - const int batch = 7; - - Tensor<float, 5> tensor(depth, patch_z, patch_y, patch_x, batch); - tensor.setRandom(); - Tensor<float, 5, RowMajor> tensor_row_major = tensor.swap_layout(); - - Tensor<float, 6> entire_volume_patch; - entire_volume_patch = tensor.extract_volume_patches(patch_z, patch_y, patch_x); - VERIFY_IS_EQUAL(entire_volume_patch.dimension(0), depth); - VERIFY_IS_EQUAL(entire_volume_patch.dimension(1), patch_z); - VERIFY_IS_EQUAL(entire_volume_patch.dimension(2), patch_y); - VERIFY_IS_EQUAL(entire_volume_patch.dimension(3), patch_x); - VERIFY_IS_EQUAL(entire_volume_patch.dimension(4), patch_z * patch_y * patch_x); - VERIFY_IS_EQUAL(entire_volume_patch.dimension(5), batch); - - Tensor<float, 6, RowMajor> entire_volume_patch_row_major; - entire_volume_patch_row_major = tensor_row_major.extract_volume_patches(patch_z, patch_y, patch_x); - VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(0), batch); - VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(1), patch_z * patch_y * patch_x); - VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(2), patch_x); - VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(3), patch_y); - VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(4), patch_z); - VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(5), depth); - - const int dz = patch_z - 1; - const int dy = patch_y - 1; - const int dx = patch_x - 1; - - const int forward_pad_z = dz - dz / 2; - const int forward_pad_y = dy - dy / 2; - const int forward_pad_x = dx - dx / 2; - - for (int pz = 0; pz < patch_z; pz++) { - for (int py = 0; py < patch_y; py++) { - for (int px = 0; px < patch_x; px++) { - const int patchId = pz + patch_z * (py + px * patch_y); - for (int z = 0; z < patch_z; z++) { - for (int y = 0; y < patch_y; y++) { - for (int x = 0; x < patch_x; x++) { - for (int b = 0; b < batch; b++) { - for (int d = 0; d < depth; d++) { - float expected = 0.0f; - float expected_row_major = 0.0f; - const int eff_z = z - forward_pad_z + pz; - const int eff_y = y - forward_pad_y + py; - const int eff_x = x - forward_pad_x + px; - if (eff_z >= 0 && eff_y >= 0 && eff_x >= 0 && - eff_z < patch_z && eff_y < patch_y && eff_x < patch_x) { - expected = tensor(d, eff_z, eff_y, eff_x, b); - expected_row_major = tensor_row_major(b, eff_x, eff_y, eff_z, d); - } - VERIFY_IS_EQUAL(entire_volume_patch(d, z, y, x, patchId, b), expected); - VERIFY_IS_EQUAL(entire_volume_patch_row_major(b, patchId, x, y, z, d), expected_row_major); - } - } - } - } - } - } - } - } -} - -void test_cxx11_tensor_volume_patch() -{ - CALL_SUBTEST(test_single_voxel_patch()); - CALL_SUBTEST(test_entire_volume_patch()); -} diff --git a/eigen/unsupported/test/dgmres.cpp b/eigen/unsupported/test/dgmres.cpp deleted file mode 100644 index 2b11807..0000000 --- a/eigen/unsupported/test/dgmres.cpp +++ /dev/null @@ -1,31 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr> -// Copyright (C) 2012 desire Nuentsa <desire.nuentsa_wakam@inria.fr -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "../../test/sparse_solver.h" -#include <Eigen/src/IterativeSolvers/DGMRES.h> - -template<typename T> void test_dgmres_T() -{ - DGMRES<SparseMatrix<T>, DiagonalPreconditioner<T> > dgmres_colmajor_diag; - DGMRES<SparseMatrix<T>, IdentityPreconditioner > dgmres_colmajor_I; - DGMRES<SparseMatrix<T>, IncompleteLUT<T> > dgmres_colmajor_ilut; - //GMRES<SparseMatrix<T>, SSORPreconditioner<T> > dgmres_colmajor_ssor; - - CALL_SUBTEST( check_sparse_square_solving(dgmres_colmajor_diag) ); -// CALL_SUBTEST( check_sparse_square_solving(dgmres_colmajor_I) ); - CALL_SUBTEST( check_sparse_square_solving(dgmres_colmajor_ilut) ); - //CALL_SUBTEST( check_sparse_square_solving(dgmres_colmajor_ssor) ); -} - -void test_dgmres() -{ - CALL_SUBTEST_1(test_dgmres_T<double>()); - CALL_SUBTEST_2(test_dgmres_T<std::complex<double> >()); -} diff --git a/eigen/unsupported/test/forward_adolc.cpp b/eigen/unsupported/test/forward_adolc.cpp deleted file mode 100644 index 866db8e..0000000 --- a/eigen/unsupported/test/forward_adolc.cpp +++ /dev/null @@ -1,141 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// 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/Dense> - -#define NUMBER_DIRECTIONS 16 -#include <unsupported/Eigen/AdolcForward> - -template<typename Vector> -EIGEN_DONT_INLINE typename Vector::Scalar foo(const Vector& p) -{ - typedef typename Vector::Scalar Scalar; - return (p-Vector(Scalar(-1),Scalar(1.))).norm() + (p.array().sqrt().abs() * p.array().sin()).sum() + p.dot(p); -} - -template<typename _Scalar, int NX=Dynamic, int NY=Dynamic> -struct TestFunc1 -{ - typedef _Scalar Scalar; - enum { - InputsAtCompileTime = NX, - ValuesAtCompileTime = NY - }; - typedef Matrix<Scalar,InputsAtCompileTime,1> InputType; - typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType; - typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType; - - int m_inputs, m_values; - - TestFunc1() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {} - TestFunc1(int inputs, int values) : m_inputs(inputs), m_values(values) {} - - int inputs() const { return m_inputs; } - int values() const { return m_values; } - - template<typename T> - void operator() (const Matrix<T,InputsAtCompileTime,1>& x, Matrix<T,ValuesAtCompileTime,1>* _v) const - { - Matrix<T,ValuesAtCompileTime,1>& v = *_v; - - v[0] = 2 * x[0] * x[0] + x[0] * x[1]; - v[1] = 3 * x[1] * x[0] + 0.5 * x[1] * x[1]; - if(inputs()>2) - { - v[0] += 0.5 * x[2]; - v[1] += x[2]; - } - if(values()>2) - { - v[2] = 3 * x[1] * x[0] * x[0]; - } - if (inputs()>2 && values()>2) - v[2] *= x[2]; - } - - void operator() (const InputType& x, ValueType* v, JacobianType* _j) const - { - (*this)(x, v); - - if(_j) - { - JacobianType& j = *_j; - - j(0,0) = 4 * x[0] + x[1]; - j(1,0) = 3 * x[1]; - - j(0,1) = x[0]; - j(1,1) = 3 * x[0] + 2 * 0.5 * x[1]; - - if (inputs()>2) - { - j(0,2) = 0.5; - j(1,2) = 1; - } - if(values()>2) - { - j(2,0) = 3 * x[1] * 2 * x[0]; - j(2,1) = 3 * x[0] * x[0]; - } - if (inputs()>2 && values()>2) - { - j(2,0) *= x[2]; - j(2,1) *= x[2]; - - j(2,2) = 3 * x[1] * x[0] * x[0]; - j(2,2) = 3 * x[1] * x[0] * x[0]; - } - } - } -}; - -template<typename Func> void adolc_forward_jacobian(const Func& f) -{ - typename Func::InputType x = Func::InputType::Random(f.inputs()); - typename Func::ValueType y(f.values()), yref(f.values()); - typename Func::JacobianType j(f.values(),f.inputs()), jref(f.values(),f.inputs()); - - jref.setZero(); - yref.setZero(); - f(x,&yref,&jref); -// std::cerr << y.transpose() << "\n\n";; -// std::cerr << j << "\n\n";; - - j.setZero(); - y.setZero(); - AdolcForwardJacobian<Func> autoj(f); - autoj(x, &y, &j); -// std::cerr << y.transpose() << "\n\n";; -// std::cerr << j << "\n\n";; - - VERIFY_IS_APPROX(y, yref); - VERIFY_IS_APPROX(j, jref); -} - -void test_forward_adolc() -{ - adtl::setNumDir(NUMBER_DIRECTIONS); - - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,2,2>()) )); - CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,2,3>()) )); - CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,3,2>()) )); - CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,3,3>()) )); - CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double>(3,3)) )); - } - - { - // simple instanciation tests - Matrix<adtl::adouble,2,1> x; - foo(x); - Matrix<adtl::adouble,Dynamic,Dynamic> A(4,4);; - A.selfadjointView<Lower>().eigenvalues(); - } -} diff --git a/eigen/unsupported/test/gmres.cpp b/eigen/unsupported/test/gmres.cpp deleted file mode 100644 index f296911..0000000 --- a/eigen/unsupported/test/gmres.cpp +++ /dev/null @@ -1,31 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr> -// Copyright (C) 2012 Kolja Brix <brix@igpm.rwth-aaachen.de> -// -// 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 "../../test/sparse_solver.h" -#include <Eigen/IterativeSolvers> - -template<typename T> void test_gmres_T() -{ - GMRES<SparseMatrix<T>, DiagonalPreconditioner<T> > gmres_colmajor_diag; - GMRES<SparseMatrix<T>, IdentityPreconditioner > gmres_colmajor_I; - GMRES<SparseMatrix<T>, IncompleteLUT<T> > gmres_colmajor_ilut; - //GMRES<SparseMatrix<T>, SSORPreconditioner<T> > gmres_colmajor_ssor; - - CALL_SUBTEST( check_sparse_square_solving(gmres_colmajor_diag) ); -// CALL_SUBTEST( check_sparse_square_solving(gmres_colmajor_I) ); - CALL_SUBTEST( check_sparse_square_solving(gmres_colmajor_ilut) ); - //CALL_SUBTEST( check_sparse_square_solving(gmres_colmajor_ssor) ); -} - -void test_gmres() -{ - CALL_SUBTEST_1(test_gmres_T<double>()); - CALL_SUBTEST_2(test_gmres_T<std::complex<double> >()); -} diff --git a/eigen/unsupported/test/kronecker_product.cpp b/eigen/unsupported/test/kronecker_product.cpp deleted file mode 100644 index e770049..0000000 --- a/eigen/unsupported/test/kronecker_product.cpp +++ /dev/null @@ -1,252 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Kolja Brix <brix@igpm.rwth-aachen.de> -// Copyright (C) 2011 Andreas Platen <andiplaten@gmx.de> -// Copyright (C) 2012 Chen-Pang He <jdh8@ms63.hinet.net> -// -// 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 - -#include "sparse.h" -#include <Eigen/SparseExtra> -#include <Eigen/KroneckerProduct> - -template<typename MatrixType> -void check_dimension(const MatrixType& ab, const int rows, const int cols) -{ - VERIFY_IS_EQUAL(ab.rows(), rows); - VERIFY_IS_EQUAL(ab.cols(), cols); -} - - -template<typename MatrixType> -void check_kronecker_product(const MatrixType& ab) -{ - VERIFY_IS_EQUAL(ab.rows(), 6); - VERIFY_IS_EQUAL(ab.cols(), 6); - VERIFY_IS_EQUAL(ab.nonZeros(), 36); - VERIFY_IS_APPROX(ab.coeff(0,0), -0.4017367630386106); - VERIFY_IS_APPROX(ab.coeff(0,1), 0.1056863433932735); - VERIFY_IS_APPROX(ab.coeff(0,2), -0.7255206194554212); - VERIFY_IS_APPROX(ab.coeff(0,3), 0.1908653336744706); - VERIFY_IS_APPROX(ab.coeff(0,4), 0.350864567234111); - VERIFY_IS_APPROX(ab.coeff(0,5), -0.0923032108308013); - VERIFY_IS_APPROX(ab.coeff(1,0), 0.415417514804677); - VERIFY_IS_APPROX(ab.coeff(1,1), -0.2369227701722048); - VERIFY_IS_APPROX(ab.coeff(1,2), 0.7502275131458511); - VERIFY_IS_APPROX(ab.coeff(1,3), -0.4278731019742696); - VERIFY_IS_APPROX(ab.coeff(1,4), -0.3628129162264507); - VERIFY_IS_APPROX(ab.coeff(1,5), 0.2069210808481275); - VERIFY_IS_APPROX(ab.coeff(2,0), 0.05465890160863986); - VERIFY_IS_APPROX(ab.coeff(2,1), -0.2634092511419858); - VERIFY_IS_APPROX(ab.coeff(2,2), 0.09871180285793758); - VERIFY_IS_APPROX(ab.coeff(2,3), -0.4757066334017702); - VERIFY_IS_APPROX(ab.coeff(2,4), -0.04773740823058334); - VERIFY_IS_APPROX(ab.coeff(2,5), 0.2300535609645254); - VERIFY_IS_APPROX(ab.coeff(3,0), -0.8172945853260133); - VERIFY_IS_APPROX(ab.coeff(3,1), 0.2150086428359221); - VERIFY_IS_APPROX(ab.coeff(3,2), 0.5825113847292743); - VERIFY_IS_APPROX(ab.coeff(3,3), -0.1532433770097174); - VERIFY_IS_APPROX(ab.coeff(3,4), -0.329383387282399); - VERIFY_IS_APPROX(ab.coeff(3,5), 0.08665207912033064); - VERIFY_IS_APPROX(ab.coeff(4,0), 0.8451267514863225); - VERIFY_IS_APPROX(ab.coeff(4,1), -0.481996458918977); - VERIFY_IS_APPROX(ab.coeff(4,2), -0.6023482390791535); - VERIFY_IS_APPROX(ab.coeff(4,3), 0.3435339347164565); - VERIFY_IS_APPROX(ab.coeff(4,4), 0.3406002157428891); - VERIFY_IS_APPROX(ab.coeff(4,5), -0.1942526344200915); - VERIFY_IS_APPROX(ab.coeff(5,0), 0.1111982482925399); - VERIFY_IS_APPROX(ab.coeff(5,1), -0.5358806424754169); - VERIFY_IS_APPROX(ab.coeff(5,2), -0.07925446559335647); - VERIFY_IS_APPROX(ab.coeff(5,3), 0.3819388757769038); - VERIFY_IS_APPROX(ab.coeff(5,4), 0.04481475387219876); - VERIFY_IS_APPROX(ab.coeff(5,5), -0.2159688616158057); -} - - -template<typename MatrixType> -void check_sparse_kronecker_product(const MatrixType& ab) -{ - VERIFY_IS_EQUAL(ab.rows(), 12); - VERIFY_IS_EQUAL(ab.cols(), 10); - VERIFY_IS_EQUAL(ab.nonZeros(), 3*2); - VERIFY_IS_APPROX(ab.coeff(3,0), -0.04); - VERIFY_IS_APPROX(ab.coeff(5,1), 0.05); - VERIFY_IS_APPROX(ab.coeff(0,6), -0.08); - VERIFY_IS_APPROX(ab.coeff(2,7), 0.10); - VERIFY_IS_APPROX(ab.coeff(6,8), 0.12); - VERIFY_IS_APPROX(ab.coeff(8,9), -0.15); -} - - -void test_kronecker_product() -{ - // DM = dense matrix; SM = sparse matrix - - Matrix<double, 2, 3> DM_a; - SparseMatrix<double> SM_a(2,3); - SM_a.insert(0,0) = DM_a.coeffRef(0,0) = -0.4461540300782201; - SM_a.insert(0,1) = DM_a.coeffRef(0,1) = -0.8057364375283049; - SM_a.insert(0,2) = DM_a.coeffRef(0,2) = 0.3896572459516341; - SM_a.insert(1,0) = DM_a.coeffRef(1,0) = -0.9076572187376921; - SM_a.insert(1,1) = DM_a.coeffRef(1,1) = 0.6469156566545853; - SM_a.insert(1,2) = DM_a.coeffRef(1,2) = -0.3658010398782789; - - MatrixXd DM_b(3,2); - SparseMatrix<double> SM_b(3,2); - SM_b.insert(0,0) = DM_b.coeffRef(0,0) = 0.9004440976767099; - SM_b.insert(0,1) = DM_b.coeffRef(0,1) = -0.2368830858139832; - SM_b.insert(1,0) = DM_b.coeffRef(1,0) = -0.9311078389941825; - SM_b.insert(1,1) = DM_b.coeffRef(1,1) = 0.5310335762980047; - SM_b.insert(2,0) = DM_b.coeffRef(2,0) = -0.1225112806872035; - SM_b.insert(2,1) = DM_b.coeffRef(2,1) = 0.5903998022741264; - - SparseMatrix<double,RowMajor> SM_row_a(SM_a), SM_row_b(SM_b); - - // test DM_fixedSize = kroneckerProduct(DM_block,DM) - Matrix<double, 6, 6> DM_fix_ab = kroneckerProduct(DM_a.topLeftCorner<2,3>(),DM_b); - - CALL_SUBTEST(check_kronecker_product(DM_fix_ab)); - CALL_SUBTEST(check_kronecker_product(kroneckerProduct(DM_a.topLeftCorner<2,3>(),DM_b))); - - for(int i=0;i<DM_fix_ab.rows();++i) - for(int j=0;j<DM_fix_ab.cols();++j) - VERIFY_IS_APPROX(kroneckerProduct(DM_a,DM_b).coeff(i,j), DM_fix_ab(i,j)); - - // test DM_block = kroneckerProduct(DM,DM) - MatrixXd DM_block_ab(10,15); - DM_block_ab.block<6,6>(2,5) = kroneckerProduct(DM_a,DM_b); - CALL_SUBTEST(check_kronecker_product(DM_block_ab.block<6,6>(2,5))); - - // test DM = kroneckerProduct(DM,DM) - MatrixXd DM_ab = kroneckerProduct(DM_a,DM_b); - CALL_SUBTEST(check_kronecker_product(DM_ab)); - CALL_SUBTEST(check_kronecker_product(kroneckerProduct(DM_a,DM_b))); - - // test SM = kroneckerProduct(SM,DM) - SparseMatrix<double> SM_ab = kroneckerProduct(SM_a,DM_b); - CALL_SUBTEST(check_kronecker_product(SM_ab)); - SparseMatrix<double,RowMajor> SM_ab2 = kroneckerProduct(SM_a,DM_b); - CALL_SUBTEST(check_kronecker_product(SM_ab2)); - CALL_SUBTEST(check_kronecker_product(kroneckerProduct(SM_a,DM_b))); - - // test SM = kroneckerProduct(DM,SM) - SM_ab.setZero(); - SM_ab.insert(0,0)=37.0; - SM_ab = kroneckerProduct(DM_a,SM_b); - CALL_SUBTEST(check_kronecker_product(SM_ab)); - SM_ab2.setZero(); - SM_ab2.insert(0,0)=37.0; - SM_ab2 = kroneckerProduct(DM_a,SM_b); - CALL_SUBTEST(check_kronecker_product(SM_ab2)); - CALL_SUBTEST(check_kronecker_product(kroneckerProduct(DM_a,SM_b))); - - // test SM = kroneckerProduct(SM,SM) - SM_ab.resize(2,33); - SM_ab.insert(0,0)=37.0; - SM_ab = kroneckerProduct(SM_a,SM_b); - CALL_SUBTEST(check_kronecker_product(SM_ab)); - SM_ab2.resize(5,11); - SM_ab2.insert(0,0)=37.0; - SM_ab2 = kroneckerProduct(SM_a,SM_b); - CALL_SUBTEST(check_kronecker_product(SM_ab2)); - CALL_SUBTEST(check_kronecker_product(kroneckerProduct(SM_a,SM_b))); - - // test SM = kroneckerProduct(SM,SM) with sparse pattern - SM_a.resize(4,5); - SM_b.resize(3,2); - SM_a.resizeNonZeros(0); - SM_b.resizeNonZeros(0); - SM_a.insert(1,0) = -0.1; - SM_a.insert(0,3) = -0.2; - SM_a.insert(2,4) = 0.3; - SM_a.finalize(); - - SM_b.insert(0,0) = 0.4; - SM_b.insert(2,1) = -0.5; - SM_b.finalize(); - SM_ab.resize(1,1); - SM_ab.insert(0,0)=37.0; - SM_ab = kroneckerProduct(SM_a,SM_b); - CALL_SUBTEST(check_sparse_kronecker_product(SM_ab)); - - // test dimension of result of DM = kroneckerProduct(DM,DM) - MatrixXd DM_a2(2,1); - MatrixXd DM_b2(5,4); - MatrixXd DM_ab2 = kroneckerProduct(DM_a2,DM_b2); - CALL_SUBTEST(check_dimension(DM_ab2,2*5,1*4)); - DM_a2.resize(10,9); - DM_b2.resize(4,8); - DM_ab2 = kroneckerProduct(DM_a2,DM_b2); - CALL_SUBTEST(check_dimension(DM_ab2,10*4,9*8)); - - for(int i = 0; i < g_repeat; i++) - { - double density = Eigen::internal::random<double>(0.01,0.5); - int ra = Eigen::internal::random<int>(1,50); - int ca = Eigen::internal::random<int>(1,50); - int rb = Eigen::internal::random<int>(1,50); - int cb = Eigen::internal::random<int>(1,50); - SparseMatrix<float,ColMajor> sA(ra,ca), sB(rb,cb), sC; - SparseMatrix<float,RowMajor> sC2; - MatrixXf dA(ra,ca), dB(rb,cb), dC; - initSparse(density, dA, sA); - initSparse(density, dB, sB); - - sC = kroneckerProduct(sA,sB); - dC = kroneckerProduct(dA,dB); - VERIFY_IS_APPROX(MatrixXf(sC),dC); - - sC = kroneckerProduct(sA.transpose(),sB); - dC = kroneckerProduct(dA.transpose(),dB); - VERIFY_IS_APPROX(MatrixXf(sC),dC); - - sC = kroneckerProduct(sA.transpose(),sB.transpose()); - dC = kroneckerProduct(dA.transpose(),dB.transpose()); - VERIFY_IS_APPROX(MatrixXf(sC),dC); - - sC = kroneckerProduct(sA,sB.transpose()); - dC = kroneckerProduct(dA,dB.transpose()); - VERIFY_IS_APPROX(MatrixXf(sC),dC); - - sC2 = kroneckerProduct(sA,sB); - dC = kroneckerProduct(dA,dB); - VERIFY_IS_APPROX(MatrixXf(sC2),dC); - - sC2 = kroneckerProduct(dA,sB); - dC = kroneckerProduct(dA,dB); - VERIFY_IS_APPROX(MatrixXf(sC2),dC); - - sC2 = kroneckerProduct(sA,dB); - dC = kroneckerProduct(dA,dB); - VERIFY_IS_APPROX(MatrixXf(sC2),dC); - - sC2 = kroneckerProduct(2*sA,sB); - dC = kroneckerProduct(2*dA,dB); - VERIFY_IS_APPROX(MatrixXf(sC2),dC); - } -} - -#endif - -#ifdef EIGEN_TEST_PART_2 - -// simply check that for a dense kronecker product, sparse module is not needed - -#include "main.h" -#include <Eigen/KroneckerProduct> - -void test_kronecker_product() -{ - MatrixXd a(2,2), b(3,3), c; - a.setRandom(); - b.setRandom(); - c = kroneckerProduct(a,b); - VERIFY_IS_APPROX(c.block(3,3,3,3), a(1,1)*b); -} - -#endif diff --git a/eigen/unsupported/test/levenberg_marquardt.cpp b/eigen/unsupported/test/levenberg_marquardt.cpp deleted file mode 100644 index 64f168c..0000000 --- a/eigen/unsupported/test/levenberg_marquardt.cpp +++ /dev/null @@ -1,1477 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org> -// Copyright (C) 2012 desire Nuentsa <desire.nuentsa_wakam@inria.fr -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - - -// FIXME: These tests all check for hard-coded values. Ideally, parameters and start estimates should be randomized. - - -#include <stdio.h> - -#include "main.h" -#include <unsupported/Eigen/LevenbergMarquardt> - -// This disables some useless Warnings on MSVC. -// It is intended to be done for this test only. -#include <Eigen/src/Core/util/DisableStupidWarnings.h> - -using std::sqrt; - -// tolerance for chekcing number of iterations -#define LM_EVAL_COUNT_TOL 4/3 - -struct lmder_functor : DenseFunctor<double> -{ - lmder_functor(void): DenseFunctor<double>(3,15) {} - int operator()(const VectorXd &x, VectorXd &fvec) const - { - double tmp1, tmp2, tmp3; - static const double y[15] = {1.4e-1, 1.8e-1, 2.2e-1, 2.5e-1, 2.9e-1, 3.2e-1, 3.5e-1, - 3.9e-1, 3.7e-1, 5.8e-1, 7.3e-1, 9.6e-1, 1.34, 2.1, 4.39}; - - for (int i = 0; i < values(); i++) - { - tmp1 = i+1; - tmp2 = 16 - i - 1; - tmp3 = (i>=8)? tmp2 : tmp1; - fvec[i] = y[i] - (x[0] + tmp1/(x[1]*tmp2 + x[2]*tmp3)); - } - return 0; - } - - int df(const VectorXd &x, MatrixXd &fjac) const - { - double tmp1, tmp2, tmp3, tmp4; - for (int i = 0; i < values(); i++) - { - tmp1 = i+1; - tmp2 = 16 - i - 1; - tmp3 = (i>=8)? tmp2 : tmp1; - tmp4 = (x[1]*tmp2 + x[2]*tmp3); tmp4 = tmp4*tmp4; - fjac(i,0) = -1; - fjac(i,1) = tmp1*tmp2/tmp4; - fjac(i,2) = tmp1*tmp3/tmp4; - } - return 0; - } -}; - -void testLmder1() -{ - int n=3, info; - - VectorXd x; - - /* the following starting values provide a rough fit. */ - x.setConstant(n, 1.); - - // do the computation - lmder_functor functor; - LevenbergMarquardt<lmder_functor> lm(functor); - info = lm.lmder1(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 6); - VERIFY_IS_EQUAL(lm.njev(), 5); - - // check norm - VERIFY_IS_APPROX(lm.fvec().blueNorm(), 0.09063596); - - // check x - VectorXd x_ref(n); - x_ref << 0.08241058, 1.133037, 2.343695; - VERIFY_IS_APPROX(x, x_ref); -} - -void testLmder() -{ - const int m=15, n=3; - int info; - double fnorm, covfac; - VectorXd x; - - /* the following starting values provide a rough fit. */ - x.setConstant(n, 1.); - - // do the computation - lmder_functor functor; - LevenbergMarquardt<lmder_functor> lm(functor); - info = lm.minimize(x); - - // check return values - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 6); - VERIFY_IS_EQUAL(lm.njev(), 5); - - // check norm - fnorm = lm.fvec().blueNorm(); - VERIFY_IS_APPROX(fnorm, 0.09063596); - - // check x - VectorXd x_ref(n); - x_ref << 0.08241058, 1.133037, 2.343695; - VERIFY_IS_APPROX(x, x_ref); - - // check covariance - covfac = fnorm*fnorm/(m-n); - internal::covar(lm.matrixR(), lm.permutation().indices()); // TODO : move this as a function of lm - - MatrixXd cov_ref(n,n); - cov_ref << - 0.0001531202, 0.002869941, -0.002656662, - 0.002869941, 0.09480935, -0.09098995, - -0.002656662, -0.09098995, 0.08778727; - -// std::cout << fjac*covfac << std::endl; - - MatrixXd cov; - cov = covfac*lm.matrixR().topLeftCorner<n,n>(); - VERIFY_IS_APPROX( cov, cov_ref); - // TODO: why isn't this allowed ? : - // VERIFY_IS_APPROX( covfac*fjac.topLeftCorner<n,n>() , cov_ref); -} - -struct lmdif_functor : DenseFunctor<double> -{ - lmdif_functor(void) : DenseFunctor<double>(3,15) {} - int operator()(const VectorXd &x, VectorXd &fvec) const - { - int i; - double tmp1,tmp2,tmp3; - static const double y[15]={1.4e-1,1.8e-1,2.2e-1,2.5e-1,2.9e-1,3.2e-1,3.5e-1,3.9e-1, - 3.7e-1,5.8e-1,7.3e-1,9.6e-1,1.34e0,2.1e0,4.39e0}; - - assert(x.size()==3); - assert(fvec.size()==15); - for (i=0; i<15; i++) - { - tmp1 = i+1; - tmp2 = 15 - i; - tmp3 = tmp1; - - if (i >= 8) tmp3 = tmp2; - fvec[i] = y[i] - (x[0] + tmp1/(x[1]*tmp2 + x[2]*tmp3)); - } - return 0; - } -}; - -void testLmdif1() -{ - const int n=3; - int info; - - VectorXd x(n), fvec(15); - - /* the following starting values provide a rough fit. */ - x.setConstant(n, 1.); - - // do the computation - lmdif_functor functor; - DenseIndex nfev; - info = LevenbergMarquardt<lmdif_functor>::lmdif1(functor, x, &nfev); - - // check return value - VERIFY_IS_EQUAL(info, 1); -// VERIFY_IS_EQUAL(nfev, 26); - - // check norm - functor(x, fvec); - VERIFY_IS_APPROX(fvec.blueNorm(), 0.09063596); - - // check x - VectorXd x_ref(n); - x_ref << 0.0824106, 1.1330366, 2.3436947; - VERIFY_IS_APPROX(x, x_ref); - -} - -void testLmdif() -{ - const int m=15, n=3; - int info; - double fnorm, covfac; - VectorXd x(n); - - /* the following starting values provide a rough fit. */ - x.setConstant(n, 1.); - - // do the computation - lmdif_functor functor; - NumericalDiff<lmdif_functor> numDiff(functor); - LevenbergMarquardt<NumericalDiff<lmdif_functor> > lm(numDiff); - info = lm.minimize(x); - - // check return values - VERIFY_IS_EQUAL(info, 1); -// VERIFY_IS_EQUAL(lm.nfev(), 26); - - // check norm - fnorm = lm.fvec().blueNorm(); - VERIFY_IS_APPROX(fnorm, 0.09063596); - - // check x - VectorXd x_ref(n); - x_ref << 0.08241058, 1.133037, 2.343695; - VERIFY_IS_APPROX(x, x_ref); - - // check covariance - covfac = fnorm*fnorm/(m-n); - internal::covar(lm.matrixR(), lm.permutation().indices()); // TODO : move this as a function of lm - - MatrixXd cov_ref(n,n); - cov_ref << - 0.0001531202, 0.002869942, -0.002656662, - 0.002869942, 0.09480937, -0.09098997, - -0.002656662, -0.09098997, 0.08778729; - -// std::cout << fjac*covfac << std::endl; - - MatrixXd cov; - cov = covfac*lm.matrixR().topLeftCorner<n,n>(); - VERIFY_IS_APPROX( cov, cov_ref); - // TODO: why isn't this allowed ? : - // VERIFY_IS_APPROX( covfac*fjac.topLeftCorner<n,n>() , cov_ref); -} - -struct chwirut2_functor : DenseFunctor<double> -{ - chwirut2_functor(void) : DenseFunctor<double>(3,54) {} - static const double m_x[54]; - static const double m_y[54]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - int i; - - assert(b.size()==3); - assert(fvec.size()==54); - for(i=0; i<54; i++) { - double x = m_x[i]; - fvec[i] = exp(-b[0]*x)/(b[1]+b[2]*x) - m_y[i]; - } - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==3); - assert(fjac.rows()==54); - assert(fjac.cols()==3); - for(int i=0; i<54; i++) { - double x = m_x[i]; - double factor = 1./(b[1]+b[2]*x); - double e = exp(-b[0]*x); - fjac(i,0) = -x*e*factor; - fjac(i,1) = -e*factor*factor; - fjac(i,2) = -x*e*factor*factor; - } - return 0; - } -}; -const double chwirut2_functor::m_x[54] = { 0.500E0, 1.000E0, 1.750E0, 3.750E0, 5.750E0, 0.875E0, 2.250E0, 3.250E0, 5.250E0, 0.750E0, 1.750E0, 2.750E0, 4.750E0, 0.625E0, 1.250E0, 2.250E0, 4.250E0, .500E0, 3.000E0, .750E0, 3.000E0, 1.500E0, 6.000E0, 3.000E0, 6.000E0, 1.500E0, 3.000E0, .500E0, 2.000E0, 4.000E0, .750E0, 2.000E0, 5.000E0, .750E0, 2.250E0, 3.750E0, 5.750E0, 3.000E0, .750E0, 2.500E0, 4.000E0, .750E0, 2.500E0, 4.000E0, .750E0, 2.500E0, 4.000E0, .500E0, 6.000E0, 3.000E0, .500E0, 2.750E0, .500E0, 1.750E0}; -const double chwirut2_functor::m_y[54] = { 92.9000E0 ,57.1000E0 ,31.0500E0 ,11.5875E0 ,8.0250E0 ,63.6000E0 ,21.4000E0 ,14.2500E0 ,8.4750E0 ,63.8000E0 ,26.8000E0 ,16.4625E0 ,7.1250E0 ,67.3000E0 ,41.0000E0 ,21.1500E0 ,8.1750E0 ,81.5000E0 ,13.1200E0 ,59.9000E0 ,14.6200E0 ,32.9000E0 ,5.4400E0 ,12.5600E0 ,5.4400E0 ,32.0000E0 ,13.9500E0 ,75.8000E0 ,20.0000E0 ,10.4200E0 ,59.5000E0 ,21.6700E0 ,8.5500E0 ,62.0000E0 ,20.2000E0 ,7.7600E0 ,3.7500E0 ,11.8100E0 ,54.7000E0 ,23.7000E0 ,11.5500E0 ,61.3000E0 ,17.7000E0 ,8.7400E0 ,59.2000E0 ,16.3000E0 ,8.6200E0 ,81.0000E0 ,4.8700E0 ,14.6200E0 ,81.7000E0 ,17.1700E0 ,81.3000E0 ,28.9000E0 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/chwirut2.shtml -void testNistChwirut2(void) -{ - const int n=3; - LevenbergMarquardtSpace::Status info; - - VectorXd x(n); - - /* - * First try - */ - x<< 0.1, 0.01, 0.02; - // do the computation - chwirut2_functor functor; - LevenbergMarquardt<chwirut2_functor> lm(functor); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); -// VERIFY_IS_EQUAL(lm.nfev(), 10); - VERIFY_IS_EQUAL(lm.njev(), 8); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.1304802941E+02); - // check x - VERIFY_IS_APPROX(x[0], 1.6657666537E-01); - VERIFY_IS_APPROX(x[1], 5.1653291286E-03); - VERIFY_IS_APPROX(x[2], 1.2150007096E-02); - - /* - * Second try - */ - x<< 0.15, 0.008, 0.010; - // do the computation - lm.resetParameters(); - lm.setFtol(1.E6*NumTraits<double>::epsilon()); - lm.setXtol(1.E6*NumTraits<double>::epsilon()); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); -// VERIFY_IS_EQUAL(lm.nfev(), 7); - VERIFY_IS_EQUAL(lm.njev(), 6); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.1304802941E+02); - // check x - VERIFY_IS_APPROX(x[0], 1.6657666537E-01); - VERIFY_IS_APPROX(x[1], 5.1653291286E-03); - VERIFY_IS_APPROX(x[2], 1.2150007096E-02); -} - - -struct misra1a_functor : DenseFunctor<double> -{ - misra1a_functor(void) : DenseFunctor<double>(2,14) {} - static const double m_x[14]; - static const double m_y[14]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==2); - assert(fvec.size()==14); - for(int i=0; i<14; i++) { - fvec[i] = b[0]*(1.-exp(-b[1]*m_x[i])) - m_y[i] ; - } - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==2); - assert(fjac.rows()==14); - assert(fjac.cols()==2); - for(int i=0; i<14; i++) { - fjac(i,0) = (1.-exp(-b[1]*m_x[i])); - fjac(i,1) = (b[0]*m_x[i]*exp(-b[1]*m_x[i])); - } - return 0; - } -}; -const double misra1a_functor::m_x[14] = { 77.6E0, 114.9E0, 141.1E0, 190.8E0, 239.9E0, 289.0E0, 332.8E0, 378.4E0, 434.8E0, 477.3E0, 536.8E0, 593.1E0, 689.1E0, 760.0E0}; -const double misra1a_functor::m_y[14] = { 10.07E0, 14.73E0, 17.94E0, 23.93E0, 29.61E0, 35.18E0, 40.02E0, 44.82E0, 50.76E0, 55.05E0, 61.01E0, 66.40E0, 75.47E0, 81.78E0}; - -// http://www.itl.nist.gov/div898/strd/nls/data/misra1a.shtml -void testNistMisra1a(void) -{ - const int n=2; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 500., 0.0001; - // do the computation - misra1a_functor functor; - LevenbergMarquardt<misra1a_functor> lm(functor); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 19); - VERIFY_IS_EQUAL(lm.njev(), 15); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.2455138894E-01); - // check x - VERIFY_IS_APPROX(x[0], 2.3894212918E+02); - VERIFY_IS_APPROX(x[1], 5.5015643181E-04); - - /* - * Second try - */ - x<< 250., 0.0005; - // do the computation - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 5); - VERIFY_IS_EQUAL(lm.njev(), 4); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.2455138894E-01); - // check x - VERIFY_IS_APPROX(x[0], 2.3894212918E+02); - VERIFY_IS_APPROX(x[1], 5.5015643181E-04); -} - -struct hahn1_functor : DenseFunctor<double> -{ - hahn1_functor(void) : DenseFunctor<double>(7,236) {} - static const double m_x[236]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - static const double m_y[236] = { .591E0 , 1.547E0 , 2.902E0 , 2.894E0 , 4.703E0 , 6.307E0 , 7.03E0 , 7.898E0 , 9.470E0 , 9.484E0 , 10.072E0 , 10.163E0 , 11.615E0 , 12.005E0 , 12.478E0 , 12.982E0 , 12.970E0 , 13.926E0 , 14.452E0 , 14.404E0 , 15.190E0 , 15.550E0 , 15.528E0 , 15.499E0 , 16.131E0 , 16.438E0 , 16.387E0 , 16.549E0 , 16.872E0 , 16.830E0 , 16.926E0 , 16.907E0 , 16.966E0 , 17.060E0 , 17.122E0 , 17.311E0 , 17.355E0 , 17.668E0 , 17.767E0 , 17.803E0 , 17.765E0 , 17.768E0 , 17.736E0 , 17.858E0 , 17.877E0 , 17.912E0 , 18.046E0 , 18.085E0 , 18.291E0 , 18.357E0 , 18.426E0 , 18.584E0 , 18.610E0 , 18.870E0 , 18.795E0 , 19.111E0 , .367E0 , .796E0 , 0.892E0 , 1.903E0 , 2.150E0 , 3.697E0 , 5.870E0 , 6.421E0 , 7.422E0 , 9.944E0 , 11.023E0 , 11.87E0 , 12.786E0 , 14.067E0 , 13.974E0 , 14.462E0 , 14.464E0 , 15.381E0 , 15.483E0 , 15.59E0 , 16.075E0 , 16.347E0 , 16.181E0 , 16.915E0 , 17.003E0 , 16.978E0 , 17.756E0 , 17.808E0 , 17.868E0 , 18.481E0 , 18.486E0 , 19.090E0 , 16.062E0 , 16.337E0 , 16.345E0 , - 16.388E0 , 17.159E0 , 17.116E0 , 17.164E0 , 17.123E0 , 17.979E0 , 17.974E0 , 18.007E0 , 17.993E0 , 18.523E0 , 18.669E0 , 18.617E0 , 19.371E0 , 19.330E0 , 0.080E0 , 0.248E0 , 1.089E0 , 1.418E0 , 2.278E0 , 3.624E0 , 4.574E0 , 5.556E0 , 7.267E0 , 7.695E0 , 9.136E0 , 9.959E0 , 9.957E0 , 11.600E0 , 13.138E0 , 13.564E0 , 13.871E0 , 13.994E0 , 14.947E0 , 15.473E0 , 15.379E0 , 15.455E0 , 15.908E0 , 16.114E0 , 17.071E0 , 17.135E0 , 17.282E0 , 17.368E0 , 17.483E0 , 17.764E0 , 18.185E0 , 18.271E0 , 18.236E0 , 18.237E0 , 18.523E0 , 18.627E0 , 18.665E0 , 19.086E0 , 0.214E0 , 0.943E0 , 1.429E0 , 2.241E0 , 2.951E0 , 3.782E0 , 4.757E0 , 5.602E0 , 7.169E0 , 8.920E0 , 10.055E0 , 12.035E0 , 12.861E0 , 13.436E0 , 14.167E0 , 14.755E0 , 15.168E0 , 15.651E0 , 15.746E0 , 16.216E0 , 16.445E0 , 16.965E0 , 17.121E0 , 17.206E0 , 17.250E0 , 17.339E0 , 17.793E0 , 18.123E0 , 18.49E0 , 18.566E0 , 18.645E0 , 18.706E0 , 18.924E0 , 19.1E0 , 0.375E0 , 0.471E0 , 1.504E0 , 2.204E0 , 2.813E0 , 4.765E0 , 9.835E0 , 10.040E0 , 11.946E0 , -12.596E0 , -13.303E0 , 13.922E0 , 14.440E0 , 14.951E0 , 15.627E0 , 15.639E0 , 15.814E0 , 16.315E0 , 16.334E0 , 16.430E0 , 16.423E0 , 17.024E0 , 17.009E0 , 17.165E0 , 17.134E0 , 17.349E0 , 17.576E0 , 17.848E0 , 18.090E0 , 18.276E0 , 18.404E0 , 18.519E0 , 19.133E0 , 19.074E0 , 19.239E0 , 19.280E0 , 19.101E0 , 19.398E0 , 19.252E0 , 19.89E0 , 20.007E0 , 19.929E0 , 19.268E0 , 19.324E0 , 20.049E0 , 20.107E0 , 20.062E0 , 20.065E0 , 19.286E0 , 19.972E0 , 20.088E0 , 20.743E0 , 20.83E0 , 20.935E0 , 21.035E0 , 20.93E0 , 21.074E0 , 21.085E0 , 20.935E0 }; - - // int called=0; printf("call hahn1_functor with iflag=%d, called=%d\n", iflag, called); if (iflag==1) called++; - - assert(b.size()==7); - assert(fvec.size()==236); - for(int i=0; i<236; i++) { - double x=m_x[i], xx=x*x, xxx=xx*x; - fvec[i] = (b[0]+b[1]*x+b[2]*xx+b[3]*xxx) / (1.+b[4]*x+b[5]*xx+b[6]*xxx) - m_y[i]; - } - return 0; - } - - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==7); - assert(fjac.rows()==236); - assert(fjac.cols()==7); - for(int i=0; i<236; i++) { - double x=m_x[i], xx=x*x, xxx=xx*x; - double fact = 1./(1.+b[4]*x+b[5]*xx+b[6]*xxx); - fjac(i,0) = 1.*fact; - fjac(i,1) = x*fact; - fjac(i,2) = xx*fact; - fjac(i,3) = xxx*fact; - fact = - (b[0]+b[1]*x+b[2]*xx+b[3]*xxx) * fact * fact; - fjac(i,4) = x*fact; - fjac(i,5) = xx*fact; - fjac(i,6) = xxx*fact; - } - return 0; - } -}; -const double hahn1_functor::m_x[236] = { 24.41E0 , 34.82E0 , 44.09E0 , 45.07E0 , 54.98E0 , 65.51E0 , 70.53E0 , 75.70E0 , 89.57E0 , 91.14E0 , 96.40E0 , 97.19E0 , 114.26E0 , 120.25E0 , 127.08E0 , 133.55E0 , 133.61E0 , 158.67E0 , 172.74E0 , 171.31E0 , 202.14E0 , 220.55E0 , 221.05E0 , 221.39E0 , 250.99E0 , 268.99E0 , 271.80E0 , 271.97E0 , 321.31E0 , 321.69E0 , 330.14E0 , 333.03E0 , 333.47E0 , 340.77E0 , 345.65E0 , 373.11E0 , 373.79E0 , 411.82E0 , 419.51E0 , 421.59E0 , 422.02E0 , 422.47E0 , 422.61E0 , 441.75E0 , 447.41E0 , 448.7E0 , 472.89E0 , 476.69E0 , 522.47E0 , 522.62E0 , 524.43E0 , 546.75E0 , 549.53E0 , 575.29E0 , 576.00E0 , 625.55E0 , 20.15E0 , 28.78E0 , 29.57E0 , 37.41E0 , 39.12E0 , 50.24E0 , 61.38E0 , 66.25E0 , 73.42E0 , 95.52E0 , 107.32E0 , 122.04E0 , 134.03E0 , 163.19E0 , 163.48E0 , 175.70E0 , 179.86E0 , 211.27E0 , 217.78E0 , 219.14E0 , 262.52E0 , 268.01E0 , 268.62E0 , 336.25E0 , 337.23E0 , 339.33E0 , 427.38E0 , 428.58E0 , 432.68E0 , 528.99E0 , 531.08E0 , 628.34E0 , 253.24E0 , 273.13E0 , 273.66E0 , -282.10E0 , 346.62E0 , 347.19E0 , 348.78E0 , 351.18E0 , 450.10E0 , 450.35E0 , 451.92E0 , 455.56E0 , 552.22E0 , 553.56E0 , 555.74E0 , 652.59E0 , 656.20E0 , 14.13E0 , 20.41E0 , 31.30E0 , 33.84E0 , 39.70E0 , 48.83E0 , 54.50E0 , 60.41E0 , 72.77E0 , 75.25E0 , 86.84E0 , 94.88E0 , 96.40E0 , 117.37E0 , 139.08E0 , 147.73E0 , 158.63E0 , 161.84E0 , 192.11E0 , 206.76E0 , 209.07E0 , 213.32E0 , 226.44E0 , 237.12E0 , 330.90E0 , 358.72E0 , 370.77E0 , 372.72E0 , 396.24E0 , 416.59E0 , 484.02E0 , 495.47E0 , 514.78E0 , 515.65E0 , 519.47E0 , 544.47E0 , 560.11E0 , 620.77E0 , 18.97E0 , 28.93E0 , 33.91E0 , 40.03E0 , 44.66E0 , 49.87E0 , 55.16E0 , 60.90E0 , 72.08E0 , 85.15E0 , 97.06E0 , 119.63E0 , 133.27E0 , 143.84E0 , 161.91E0 , 180.67E0 , 198.44E0 , 226.86E0 , 229.65E0 , 258.27E0 , 273.77E0 , 339.15E0 , 350.13E0 , 362.75E0 , 371.03E0 , 393.32E0 , 448.53E0 , 473.78E0 , 511.12E0 , 524.70E0 , 548.75E0 , 551.64E0 , 574.02E0 , 623.86E0 , 21.46E0 , 24.33E0 , 33.43E0 , 39.22E0 , 44.18E0 , 55.02E0 , 94.33E0 , 96.44E0 , 118.82E0 , 128.48E0 , -141.94E0 , 156.92E0 , 171.65E0 , 190.00E0 , 223.26E0 , 223.88E0 , 231.50E0 , 265.05E0 , 269.44E0 , 271.78E0 , 273.46E0 , 334.61E0 , 339.79E0 , 349.52E0 , 358.18E0 , 377.98E0 , 394.77E0 , 429.66E0 , 468.22E0 , 487.27E0 , 519.54E0 , 523.03E0 , 612.99E0 , 638.59E0 , 641.36E0 , 622.05E0 , 631.50E0 , 663.97E0 , 646.9E0 , 748.29E0 , 749.21E0 , 750.14E0 , 647.04E0 , 646.89E0 , 746.9E0 , 748.43E0 , 747.35E0 , 749.27E0 , 647.61E0 , 747.78E0 , 750.51E0 , 851.37E0 , 845.97E0 , 847.54E0 , 849.93E0 , 851.61E0 , 849.75E0 , 850.98E0 , 848.23E0}; - -// http://www.itl.nist.gov/div898/strd/nls/data/hahn1.shtml -void testNistHahn1(void) -{ - const int n=7; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 10., -1., .05, -.00001, -.05, .001, -.000001; - // do the computation - hahn1_functor functor; - LevenbergMarquardt<hahn1_functor> lm(functor); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 11); - VERIFY_IS_EQUAL(lm.njev(), 10); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.5324382854E+00); - // check x - VERIFY_IS_APPROX(x[0], 1.0776351733E+00); - VERIFY_IS_APPROX(x[1],-1.2269296921E-01); - VERIFY_IS_APPROX(x[2], 4.0863750610E-03); - VERIFY_IS_APPROX(x[3],-1.426264e-06); // shoulde be : -1.4262662514E-06 - VERIFY_IS_APPROX(x[4],-5.7609940901E-03); - VERIFY_IS_APPROX(x[5], 2.4053735503E-04); - VERIFY_IS_APPROX(x[6],-1.2314450199E-07); - - /* - * Second try - */ - x<< .1, -.1, .005, -.000001, -.005, .0001, -.0000001; - // do the computation - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); -// VERIFY_IS_EQUAL(lm.nfev(), 11); - VERIFY_IS_EQUAL(lm.njev(), 10); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.5324382854E+00); - // check x - VERIFY_IS_APPROX(x[0], 1.077640); // should be : 1.0776351733E+00 - VERIFY_IS_APPROX(x[1], -0.1226933); // should be : -1.2269296921E-01 - VERIFY_IS_APPROX(x[2], 0.004086383); // should be : 4.0863750610E-03 - VERIFY_IS_APPROX(x[3], -1.426277e-06); // shoulde be : -1.4262662514E-06 - VERIFY_IS_APPROX(x[4],-5.7609940901E-03); - VERIFY_IS_APPROX(x[5], 0.00024053772); // should be : 2.4053735503E-04 - VERIFY_IS_APPROX(x[6], -1.231450e-07); // should be : -1.2314450199E-07 - -} - -struct misra1d_functor : DenseFunctor<double> -{ - misra1d_functor(void) : DenseFunctor<double>(2,14) {} - static const double x[14]; - static const double y[14]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==2); - assert(fvec.size()==14); - for(int i=0; i<14; i++) { - fvec[i] = b[0]*b[1]*x[i]/(1.+b[1]*x[i]) - y[i]; - } - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==2); - assert(fjac.rows()==14); - assert(fjac.cols()==2); - for(int i=0; i<14; i++) { - double den = 1.+b[1]*x[i]; - fjac(i,0) = b[1]*x[i] / den; - fjac(i,1) = b[0]*x[i]*(den-b[1]*x[i])/den/den; - } - return 0; - } -}; -const double misra1d_functor::x[14] = { 77.6E0, 114.9E0, 141.1E0, 190.8E0, 239.9E0, 289.0E0, 332.8E0, 378.4E0, 434.8E0, 477.3E0, 536.8E0, 593.1E0, 689.1E0, 760.0E0}; -const double misra1d_functor::y[14] = { 10.07E0, 14.73E0, 17.94E0, 23.93E0, 29.61E0, 35.18E0, 40.02E0, 44.82E0, 50.76E0, 55.05E0, 61.01E0, 66.40E0, 75.47E0, 81.78E0}; - -// http://www.itl.nist.gov/div898/strd/nls/data/misra1d.shtml -void testNistMisra1d(void) -{ - const int n=2; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 500., 0.0001; - // do the computation - misra1d_functor functor; - LevenbergMarquardt<misra1d_functor> lm(functor); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 9); - VERIFY_IS_EQUAL(lm.njev(), 7); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.6419295283E-02); - // check x - VERIFY_IS_APPROX(x[0], 4.3736970754E+02); - VERIFY_IS_APPROX(x[1], 3.0227324449E-04); - - /* - * Second try - */ - x<< 450., 0.0003; - // do the computation - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 4); - VERIFY_IS_EQUAL(lm.njev(), 3); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.6419295283E-02); - // check x - VERIFY_IS_APPROX(x[0], 4.3736970754E+02); - VERIFY_IS_APPROX(x[1], 3.0227324449E-04); -} - - -struct lanczos1_functor : DenseFunctor<double> -{ - lanczos1_functor(void) : DenseFunctor<double>(6,24) {} - static const double x[24]; - static const double y[24]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==6); - assert(fvec.size()==24); - for(int i=0; i<24; i++) - fvec[i] = b[0]*exp(-b[1]*x[i]) + b[2]*exp(-b[3]*x[i]) + b[4]*exp(-b[5]*x[i]) - y[i]; - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==6); - assert(fjac.rows()==24); - assert(fjac.cols()==6); - for(int i=0; i<24; i++) { - fjac(i,0) = exp(-b[1]*x[i]); - fjac(i,1) = -b[0]*x[i]*exp(-b[1]*x[i]); - fjac(i,2) = exp(-b[3]*x[i]); - fjac(i,3) = -b[2]*x[i]*exp(-b[3]*x[i]); - fjac(i,4) = exp(-b[5]*x[i]); - fjac(i,5) = -b[4]*x[i]*exp(-b[5]*x[i]); - } - return 0; - } -}; -const double lanczos1_functor::x[24] = { 0.000000000000E+00, 5.000000000000E-02, 1.000000000000E-01, 1.500000000000E-01, 2.000000000000E-01, 2.500000000000E-01, 3.000000000000E-01, 3.500000000000E-01, 4.000000000000E-01, 4.500000000000E-01, 5.000000000000E-01, 5.500000000000E-01, 6.000000000000E-01, 6.500000000000E-01, 7.000000000000E-01, 7.500000000000E-01, 8.000000000000E-01, 8.500000000000E-01, 9.000000000000E-01, 9.500000000000E-01, 1.000000000000E+00, 1.050000000000E+00, 1.100000000000E+00, 1.150000000000E+00 }; -const double lanczos1_functor::y[24] = { 2.513400000000E+00 ,2.044333373291E+00 ,1.668404436564E+00 ,1.366418021208E+00 ,1.123232487372E+00 ,9.268897180037E-01 ,7.679338563728E-01 ,6.388775523106E-01 ,5.337835317402E-01 ,4.479363617347E-01 ,3.775847884350E-01 ,3.197393199326E-01 ,2.720130773746E-01 ,2.324965529032E-01 ,1.996589546065E-01 ,1.722704126914E-01 ,1.493405660168E-01 ,1.300700206922E-01 ,1.138119324644E-01 ,1.000415587559E-01 ,8.833209084540E-02 ,7.833544019350E-02 ,6.976693743449E-02 ,6.239312536719E-02 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/lanczos1.shtml -void testNistLanczos1(void) -{ - const int n=6; - LevenbergMarquardtSpace::Status info; - - VectorXd x(n); - - /* - * First try - */ - x<< 1.2, 0.3, 5.6, 5.5, 6.5, 7.6; - // do the computation - lanczos1_functor functor; - LevenbergMarquardt<lanczos1_functor> lm(functor); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, LevenbergMarquardtSpace::RelativeErrorTooSmall); - VERIFY_IS_EQUAL(lm.nfev(), 79); - VERIFY_IS_EQUAL(lm.njev(), 72); - // check norm^2 - VERIFY(lm.fvec().squaredNorm() <= 1.4307867721E-25); - // check x - VERIFY_IS_APPROX(x[0], 9.5100000027E-02); - VERIFY_IS_APPROX(x[1], 1.0000000001E+00); - VERIFY_IS_APPROX(x[2], 8.6070000013E-01); - VERIFY_IS_APPROX(x[3], 3.0000000002E+00); - VERIFY_IS_APPROX(x[4], 1.5575999998E+00); - VERIFY_IS_APPROX(x[5], 5.0000000001E+00); - - /* - * Second try - */ - x<< 0.5, 0.7, 3.6, 4.2, 4., 6.3; - // do the computation - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, LevenbergMarquardtSpace::RelativeErrorTooSmall); - VERIFY_IS_EQUAL(lm.nfev(), 9); - VERIFY_IS_EQUAL(lm.njev(), 8); - // check norm^2 - VERIFY(lm.fvec().squaredNorm() <= 1.4307867721E-25); - // check x - VERIFY_IS_APPROX(x[0], 9.5100000027E-02); - VERIFY_IS_APPROX(x[1], 1.0000000001E+00); - VERIFY_IS_APPROX(x[2], 8.6070000013E-01); - VERIFY_IS_APPROX(x[3], 3.0000000002E+00); - VERIFY_IS_APPROX(x[4], 1.5575999998E+00); - VERIFY_IS_APPROX(x[5], 5.0000000001E+00); - -} - -struct rat42_functor : DenseFunctor<double> -{ - rat42_functor(void) : DenseFunctor<double>(3,9) {} - static const double x[9]; - static const double y[9]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==3); - assert(fvec.size()==9); - for(int i=0; i<9; i++) { - fvec[i] = b[0] / (1.+exp(b[1]-b[2]*x[i])) - y[i]; - } - return 0; - } - - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==3); - assert(fjac.rows()==9); - assert(fjac.cols()==3); - for(int i=0; i<9; i++) { - double e = exp(b[1]-b[2]*x[i]); - fjac(i,0) = 1./(1.+e); - fjac(i,1) = -b[0]*e/(1.+e)/(1.+e); - fjac(i,2) = +b[0]*e*x[i]/(1.+e)/(1.+e); - } - return 0; - } -}; -const double rat42_functor::x[9] = { 9.000E0, 14.000E0, 21.000E0, 28.000E0, 42.000E0, 57.000E0, 63.000E0, 70.000E0, 79.000E0 }; -const double rat42_functor::y[9] = { 8.930E0 ,10.800E0 ,18.590E0 ,22.330E0 ,39.350E0 ,56.110E0 ,61.730E0 ,64.620E0 ,67.080E0 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/ratkowsky2.shtml -void testNistRat42(void) -{ - const int n=3; - LevenbergMarquardtSpace::Status info; - - VectorXd x(n); - - /* - * First try - */ - x<< 100., 1., 0.1; - // do the computation - rat42_functor functor; - LevenbergMarquardt<rat42_functor> lm(functor); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, LevenbergMarquardtSpace::RelativeReductionTooSmall); - VERIFY_IS_EQUAL(lm.nfev(), 10); - VERIFY_IS_EQUAL(lm.njev(), 8); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 8.0565229338E+00); - // check x - VERIFY_IS_APPROX(x[0], 7.2462237576E+01); - VERIFY_IS_APPROX(x[1], 2.6180768402E+00); - VERIFY_IS_APPROX(x[2], 6.7359200066E-02); - - /* - * Second try - */ - x<< 75., 2.5, 0.07; - // do the computation - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, LevenbergMarquardtSpace::RelativeReductionTooSmall); - VERIFY_IS_EQUAL(lm.nfev(), 6); - VERIFY_IS_EQUAL(lm.njev(), 5); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 8.0565229338E+00); - // check x - VERIFY_IS_APPROX(x[0], 7.2462237576E+01); - VERIFY_IS_APPROX(x[1], 2.6180768402E+00); - VERIFY_IS_APPROX(x[2], 6.7359200066E-02); -} - -struct MGH10_functor : DenseFunctor<double> -{ - MGH10_functor(void) : DenseFunctor<double>(3,16) {} - static const double x[16]; - static const double y[16]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==3); - assert(fvec.size()==16); - for(int i=0; i<16; i++) - fvec[i] = b[0] * exp(b[1]/(x[i]+b[2])) - y[i]; - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==3); - assert(fjac.rows()==16); - assert(fjac.cols()==3); - for(int i=0; i<16; i++) { - double factor = 1./(x[i]+b[2]); - double e = exp(b[1]*factor); - fjac(i,0) = e; - fjac(i,1) = b[0]*factor*e; - fjac(i,2) = -b[1]*b[0]*factor*factor*e; - } - return 0; - } -}; -const double MGH10_functor::x[16] = { 5.000000E+01, 5.500000E+01, 6.000000E+01, 6.500000E+01, 7.000000E+01, 7.500000E+01, 8.000000E+01, 8.500000E+01, 9.000000E+01, 9.500000E+01, 1.000000E+02, 1.050000E+02, 1.100000E+02, 1.150000E+02, 1.200000E+02, 1.250000E+02 }; -const double MGH10_functor::y[16] = { 3.478000E+04, 2.861000E+04, 2.365000E+04, 1.963000E+04, 1.637000E+04, 1.372000E+04, 1.154000E+04, 9.744000E+03, 8.261000E+03, 7.030000E+03, 6.005000E+03, 5.147000E+03, 4.427000E+03, 3.820000E+03, 3.307000E+03, 2.872000E+03 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/mgh10.shtml -void testNistMGH10(void) -{ - const int n=3; - LevenbergMarquardtSpace::Status info; - - VectorXd x(n); - - /* - * First try - */ - x<< 2., 400000., 25000.; - // do the computation - MGH10_functor functor; - LevenbergMarquardt<MGH10_functor> lm(functor); - info = lm.minimize(x); - ++g_test_level; - VERIFY_IS_EQUAL(info, LevenbergMarquardtSpace::RelativeReductionTooSmall); - --g_test_level; - // was: VERIFY_IS_EQUAL(info, 1); - - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 8.7945855171E+01); - // check x - VERIFY_IS_APPROX(x[0], 5.6096364710E-03); - VERIFY_IS_APPROX(x[1], 6.1813463463E+03); - VERIFY_IS_APPROX(x[2], 3.4522363462E+02); - - // check return value - - ++g_test_level; - VERIFY_IS_EQUAL(lm.nfev(), 284 ); - VERIFY_IS_EQUAL(lm.njev(), 249 ); - --g_test_level; - VERIFY(lm.nfev() < 284 * LM_EVAL_COUNT_TOL); - VERIFY(lm.njev() < 249 * LM_EVAL_COUNT_TOL); - - /* - * Second try - */ - x<< 0.02, 4000., 250.; - // do the computation - info = lm.minimize(x); - ++g_test_level; - VERIFY_IS_EQUAL(info, LevenbergMarquardtSpace::RelativeReductionTooSmall); - // was: VERIFY_IS_EQUAL(info, 1); - --g_test_level; - - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 8.7945855171E+01); - // check x - VERIFY_IS_APPROX(x[0], 5.6096364710E-03); - VERIFY_IS_APPROX(x[1], 6.1813463463E+03); - VERIFY_IS_APPROX(x[2], 3.4522363462E+02); - - // check return value - ++g_test_level; - VERIFY_IS_EQUAL(lm.nfev(), 126); - VERIFY_IS_EQUAL(lm.njev(), 116); - --g_test_level; - VERIFY(lm.nfev() < 126 * LM_EVAL_COUNT_TOL); - VERIFY(lm.njev() < 116 * LM_EVAL_COUNT_TOL); -} - - -struct BoxBOD_functor : DenseFunctor<double> -{ - BoxBOD_functor(void) : DenseFunctor<double>(2,6) {} - static const double x[6]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - static const double y[6] = { 109., 149., 149., 191., 213., 224. }; - assert(b.size()==2); - assert(fvec.size()==6); - for(int i=0; i<6; i++) - fvec[i] = b[0]*(1.-exp(-b[1]*x[i])) - y[i]; - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==2); - assert(fjac.rows()==6); - assert(fjac.cols()==2); - for(int i=0; i<6; i++) { - double e = exp(-b[1]*x[i]); - fjac(i,0) = 1.-e; - fjac(i,1) = b[0]*x[i]*e; - } - return 0; - } -}; -const double BoxBOD_functor::x[6] = { 1., 2., 3., 5., 7., 10. }; - -// http://www.itl.nist.gov/div898/strd/nls/data/boxbod.shtml -void testNistBoxBOD(void) -{ - const int n=2; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 1., 1.; - // do the computation - BoxBOD_functor functor; - LevenbergMarquardt<BoxBOD_functor> lm(functor); - lm.setFtol(1.E6*NumTraits<double>::epsilon()); - lm.setXtol(1.E6*NumTraits<double>::epsilon()); - lm.setFactor(10); - info = lm.minimize(x); - - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.1680088766E+03); - // check x - VERIFY_IS_APPROX(x[0], 2.1380940889E+02); - VERIFY_IS_APPROX(x[1], 5.4723748542E-01); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY(lm.nfev() < 31); // 31 - VERIFY(lm.njev() < 25); // 25 - - /* - * Second try - */ - x<< 100., 0.75; - // do the computation - lm.resetParameters(); - lm.setFtol(NumTraits<double>::epsilon()); - lm.setXtol( NumTraits<double>::epsilon()); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - ++g_test_level; - VERIFY_IS_EQUAL(lm.nfev(), 16 ); - VERIFY_IS_EQUAL(lm.njev(), 15 ); - --g_test_level; - VERIFY(lm.nfev() < 16 * LM_EVAL_COUNT_TOL); - VERIFY(lm.njev() < 15 * LM_EVAL_COUNT_TOL); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.1680088766E+03); - // check x - VERIFY_IS_APPROX(x[0], 2.1380940889E+02); - VERIFY_IS_APPROX(x[1], 5.4723748542E-01); -} - -struct MGH17_functor : DenseFunctor<double> -{ - MGH17_functor(void) : DenseFunctor<double>(5,33) {} - static const double x[33]; - static const double y[33]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==5); - assert(fvec.size()==33); - for(int i=0; i<33; i++) - fvec[i] = b[0] + b[1]*exp(-b[3]*x[i]) + b[2]*exp(-b[4]*x[i]) - y[i]; - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==5); - assert(fjac.rows()==33); - assert(fjac.cols()==5); - for(int i=0; i<33; i++) { - fjac(i,0) = 1.; - fjac(i,1) = exp(-b[3]*x[i]); - fjac(i,2) = exp(-b[4]*x[i]); - fjac(i,3) = -x[i]*b[1]*exp(-b[3]*x[i]); - fjac(i,4) = -x[i]*b[2]*exp(-b[4]*x[i]); - } - return 0; - } -}; -const double MGH17_functor::x[33] = { 0.000000E+00, 1.000000E+01, 2.000000E+01, 3.000000E+01, 4.000000E+01, 5.000000E+01, 6.000000E+01, 7.000000E+01, 8.000000E+01, 9.000000E+01, 1.000000E+02, 1.100000E+02, 1.200000E+02, 1.300000E+02, 1.400000E+02, 1.500000E+02, 1.600000E+02, 1.700000E+02, 1.800000E+02, 1.900000E+02, 2.000000E+02, 2.100000E+02, 2.200000E+02, 2.300000E+02, 2.400000E+02, 2.500000E+02, 2.600000E+02, 2.700000E+02, 2.800000E+02, 2.900000E+02, 3.000000E+02, 3.100000E+02, 3.200000E+02 }; -const double MGH17_functor::y[33] = { 8.440000E-01, 9.080000E-01, 9.320000E-01, 9.360000E-01, 9.250000E-01, 9.080000E-01, 8.810000E-01, 8.500000E-01, 8.180000E-01, 7.840000E-01, 7.510000E-01, 7.180000E-01, 6.850000E-01, 6.580000E-01, 6.280000E-01, 6.030000E-01, 5.800000E-01, 5.580000E-01, 5.380000E-01, 5.220000E-01, 5.060000E-01, 4.900000E-01, 4.780000E-01, 4.670000E-01, 4.570000E-01, 4.480000E-01, 4.380000E-01, 4.310000E-01, 4.240000E-01, 4.200000E-01, 4.140000E-01, 4.110000E-01, 4.060000E-01 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/mgh17.shtml -void testNistMGH17(void) -{ - const int n=5; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 50., 150., -100., 1., 2.; - // do the computation - MGH17_functor functor; - LevenbergMarquardt<MGH17_functor> lm(functor); - lm.setFtol(NumTraits<double>::epsilon()); - lm.setXtol(NumTraits<double>::epsilon()); - lm.setMaxfev(1000); - info = lm.minimize(x); - - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.4648946975E-05); - // check x - VERIFY_IS_APPROX(x[0], 3.7541005211E-01); - VERIFY_IS_APPROX(x[1], 1.9358469127E+00); - VERIFY_IS_APPROX(x[2], -1.4646871366E+00); - VERIFY_IS_APPROX(x[3], 1.2867534640E-02); - VERIFY_IS_APPROX(x[4], 2.2122699662E-02); - - // check return value -// VERIFY_IS_EQUAL(info, 2); //FIXME Use (lm.info() == Success) - VERIFY(lm.nfev() < 700 ); // 602 - VERIFY(lm.njev() < 600 ); // 545 - - /* - * Second try - */ - x<< 0.5 ,1.5 ,-1 ,0.01 ,0.02; - // do the computation - lm.resetParameters(); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 18); - VERIFY_IS_EQUAL(lm.njev(), 15); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.4648946975E-05); - // check x - VERIFY_IS_APPROX(x[0], 3.7541005211E-01); - VERIFY_IS_APPROX(x[1], 1.9358469127E+00); - VERIFY_IS_APPROX(x[2], -1.4646871366E+00); - VERIFY_IS_APPROX(x[3], 1.2867534640E-02); - VERIFY_IS_APPROX(x[4], 2.2122699662E-02); -} - -struct MGH09_functor : DenseFunctor<double> -{ - MGH09_functor(void) : DenseFunctor<double>(4,11) {} - static const double _x[11]; - static const double y[11]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==4); - assert(fvec.size()==11); - for(int i=0; i<11; i++) { - double x = _x[i], xx=x*x; - fvec[i] = b[0]*(xx+x*b[1])/(xx+x*b[2]+b[3]) - y[i]; - } - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==4); - assert(fjac.rows()==11); - assert(fjac.cols()==4); - for(int i=0; i<11; i++) { - double x = _x[i], xx=x*x; - double factor = 1./(xx+x*b[2]+b[3]); - fjac(i,0) = (xx+x*b[1]) * factor; - fjac(i,1) = b[0]*x* factor; - fjac(i,2) = - b[0]*(xx+x*b[1]) * x * factor * factor; - fjac(i,3) = - b[0]*(xx+x*b[1]) * factor * factor; - } - return 0; - } -}; -const double MGH09_functor::_x[11] = { 4., 2., 1., 5.E-1 , 2.5E-01, 1.670000E-01, 1.250000E-01, 1.E-01, 8.330000E-02, 7.140000E-02, 6.250000E-02 }; -const double MGH09_functor::y[11] = { 1.957000E-01, 1.947000E-01, 1.735000E-01, 1.600000E-01, 8.440000E-02, 6.270000E-02, 4.560000E-02, 3.420000E-02, 3.230000E-02, 2.350000E-02, 2.460000E-02 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/mgh09.shtml -void testNistMGH09(void) -{ - const int n=4; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 25., 39, 41.5, 39.; - // do the computation - MGH09_functor functor; - LevenbergMarquardt<MGH09_functor> lm(functor); - lm.setMaxfev(1000); - info = lm.minimize(x); - - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 3.0750560385E-04); - // check x - VERIFY_IS_APPROX(x[0], 0.1928077089); // should be 1.9280693458E-01 - VERIFY_IS_APPROX(x[1], 0.19126423573); // should be 1.9128232873E-01 - VERIFY_IS_APPROX(x[2], 0.12305309914); // should be 1.2305650693E-01 - VERIFY_IS_APPROX(x[3], 0.13605395375); // should be 1.3606233068E-01 - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY(lm.nfev() < 510 ); // 490 - VERIFY(lm.njev() < 400 ); // 376 - - /* - * Second try - */ - x<< 0.25, 0.39, 0.415, 0.39; - // do the computation - lm.resetParameters(); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 18); - VERIFY_IS_EQUAL(lm.njev(), 16); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 3.0750560385E-04); - // check x - VERIFY_IS_APPROX(x[0], 0.19280781); // should be 1.9280693458E-01 - VERIFY_IS_APPROX(x[1], 0.19126265); // should be 1.9128232873E-01 - VERIFY_IS_APPROX(x[2], 0.12305280); // should be 1.2305650693E-01 - VERIFY_IS_APPROX(x[3], 0.13605322); // should be 1.3606233068E-01 -} - - - -struct Bennett5_functor : DenseFunctor<double> -{ - Bennett5_functor(void) : DenseFunctor<double>(3,154) {} - static const double x[154]; - static const double y[154]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==3); - assert(fvec.size()==154); - for(int i=0; i<154; i++) - fvec[i] = b[0]* pow(b[1]+x[i],-1./b[2]) - y[i]; - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==3); - assert(fjac.rows()==154); - assert(fjac.cols()==3); - for(int i=0; i<154; i++) { - double e = pow(b[1]+x[i],-1./b[2]); - fjac(i,0) = e; - fjac(i,1) = - b[0]*e/b[2]/(b[1]+x[i]); - fjac(i,2) = b[0]*e*log(b[1]+x[i])/b[2]/b[2]; - } - return 0; - } -}; -const double Bennett5_functor::x[154] = { 7.447168E0, 8.102586E0, 8.452547E0, 8.711278E0, 8.916774E0, 9.087155E0, 9.232590E0, 9.359535E0, 9.472166E0, 9.573384E0, 9.665293E0, 9.749461E0, 9.827092E0, 9.899128E0, 9.966321E0, 10.029280E0, 10.088510E0, 10.144430E0, 10.197380E0, 10.247670E0, 10.295560E0, 10.341250E0, 10.384950E0, 10.426820E0, 10.467000E0, 10.505640E0, 10.542830E0, 10.578690E0, 10.613310E0, 10.646780E0, 10.679150E0, 10.710520E0, 10.740920E0, 10.770440E0, 10.799100E0, 10.826970E0, 10.854080E0, 10.880470E0, 10.906190E0, 10.931260E0, 10.955720E0, 10.979590E0, 11.002910E0, 11.025700E0, 11.047980E0, 11.069770E0, 11.091100E0, 11.111980E0, 11.132440E0, 11.152480E0, 11.172130E0, 11.191410E0, 11.210310E0, 11.228870E0, 11.247090E0, 11.264980E0, 11.282560E0, 11.299840E0, 11.316820E0, 11.333520E0, 11.349940E0, 11.366100E0, 11.382000E0, 11.397660E0, 11.413070E0, 11.428240E0, 11.443200E0, 11.457930E0, 11.472440E0, 11.486750E0, 11.500860E0, 11.514770E0, 11.528490E0, 11.542020E0, 11.555380E0, 11.568550E0, -11.581560E0, 11.594420E0, 11.607121E0, 11.619640E0, 11.632000E0, 11.644210E0, 11.656280E0, 11.668200E0, 11.679980E0, 11.691620E0, 11.703130E0, 11.714510E0, 11.725760E0, 11.736880E0, 11.747890E0, 11.758780E0, 11.769550E0, 11.780200E0, 11.790730E0, 11.801160E0, 11.811480E0, 11.821700E0, 11.831810E0, 11.841820E0, 11.851730E0, 11.861550E0, 11.871270E0, 11.880890E0, 11.890420E0, 11.899870E0, 11.909220E0, 11.918490E0, 11.927680E0, 11.936780E0, 11.945790E0, 11.954730E0, 11.963590E0, 11.972370E0, 11.981070E0, 11.989700E0, 11.998260E0, 12.006740E0, 12.015150E0, 12.023490E0, 12.031760E0, 12.039970E0, 12.048100E0, 12.056170E0, 12.064180E0, 12.072120E0, 12.080010E0, 12.087820E0, 12.095580E0, 12.103280E0, 12.110920E0, 12.118500E0, 12.126030E0, 12.133500E0, 12.140910E0, 12.148270E0, 12.155570E0, 12.162830E0, 12.170030E0, 12.177170E0, 12.184270E0, 12.191320E0, 12.198320E0, 12.205270E0, 12.212170E0, 12.219030E0, 12.225840E0, 12.232600E0, 12.239320E0, 12.245990E0, 12.252620E0, 12.259200E0, 12.265750E0, 12.272240E0 }; -const double Bennett5_functor::y[154] = { -34.834702E0 ,-34.393200E0 ,-34.152901E0 ,-33.979099E0 ,-33.845901E0 ,-33.732899E0 ,-33.640301E0 ,-33.559200E0 ,-33.486801E0 ,-33.423100E0 ,-33.365101E0 ,-33.313000E0 ,-33.260899E0 ,-33.217400E0 ,-33.176899E0 ,-33.139198E0 ,-33.101601E0 ,-33.066799E0 ,-33.035000E0 ,-33.003101E0 ,-32.971298E0 ,-32.942299E0 ,-32.916302E0 ,-32.890202E0 ,-32.864101E0 ,-32.841000E0 ,-32.817799E0 ,-32.797501E0 ,-32.774300E0 ,-32.757000E0 ,-32.733799E0 ,-32.716400E0 ,-32.699100E0 ,-32.678799E0 ,-32.661400E0 ,-32.644001E0 ,-32.626701E0 ,-32.612202E0 ,-32.597698E0 ,-32.583199E0 ,-32.568699E0 ,-32.554298E0 ,-32.539799E0 ,-32.525299E0 ,-32.510799E0 ,-32.499199E0 ,-32.487598E0 ,-32.473202E0 ,-32.461601E0 ,-32.435501E0 ,-32.435501E0 ,-32.426800E0 ,-32.412300E0 ,-32.400799E0 ,-32.392101E0 ,-32.380501E0 ,-32.366001E0 ,-32.357300E0 ,-32.348598E0 ,-32.339901E0 ,-32.328400E0 ,-32.319698E0 ,-32.311001E0 ,-32.299400E0 ,-32.290699E0 ,-32.282001E0 ,-32.273300E0 ,-32.264599E0 ,-32.256001E0 ,-32.247299E0 -,-32.238602E0 ,-32.229900E0 ,-32.224098E0 ,-32.215401E0 ,-32.203800E0 ,-32.198002E0 ,-32.189400E0 ,-32.183601E0 ,-32.174900E0 ,-32.169102E0 ,-32.163300E0 ,-32.154598E0 ,-32.145901E0 ,-32.140099E0 ,-32.131401E0 ,-32.125599E0 ,-32.119801E0 ,-32.111198E0 ,-32.105400E0 ,-32.096699E0 ,-32.090900E0 ,-32.088001E0 ,-32.079300E0 ,-32.073502E0 ,-32.067699E0 ,-32.061901E0 ,-32.056099E0 ,-32.050301E0 ,-32.044498E0 ,-32.038799E0 ,-32.033001E0 ,-32.027199E0 ,-32.024300E0 ,-32.018501E0 ,-32.012699E0 ,-32.004002E0 ,-32.001099E0 ,-31.995300E0 ,-31.989500E0 ,-31.983700E0 ,-31.977900E0 ,-31.972099E0 ,-31.969299E0 ,-31.963501E0 ,-31.957701E0 ,-31.951900E0 ,-31.946100E0 ,-31.940300E0 ,-31.937401E0 ,-31.931601E0 ,-31.925800E0 ,-31.922899E0 ,-31.917101E0 ,-31.911301E0 ,-31.908400E0 ,-31.902599E0 ,-31.896900E0 ,-31.893999E0 ,-31.888201E0 ,-31.885300E0 ,-31.882401E0 ,-31.876600E0 ,-31.873699E0 ,-31.867901E0 ,-31.862101E0 ,-31.859200E0 ,-31.856300E0 ,-31.850500E0 ,-31.844700E0 ,-31.841801E0 ,-31.838900E0 ,-31.833099E0 ,-31.830200E0 , --31.827299E0 ,-31.821600E0 ,-31.818701E0 ,-31.812901E0 ,-31.809999E0 ,-31.807100E0 ,-31.801300E0 ,-31.798401E0 ,-31.795500E0 ,-31.789700E0 ,-31.786800E0 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/bennett5.shtml -void testNistBennett5(void) -{ - const int n=3; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< -2000., 50., 0.8; - // do the computation - Bennett5_functor functor; - LevenbergMarquardt<Bennett5_functor> lm(functor); - lm.setMaxfev(1000); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 758); - VERIFY_IS_EQUAL(lm.njev(), 744); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.2404744073E-04); - // check x - VERIFY_IS_APPROX(x[0], -2.5235058043E+03); - VERIFY_IS_APPROX(x[1], 4.6736564644E+01); - VERIFY_IS_APPROX(x[2], 9.3218483193E-01); - /* - * Second try - */ - x<< -1500., 45., 0.85; - // do the computation - lm.resetParameters(); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 203); - VERIFY_IS_EQUAL(lm.njev(), 192); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.2404744073E-04); - // check x - VERIFY_IS_APPROX(x[0], -2523.3007865); // should be -2.5235058043E+03 - VERIFY_IS_APPROX(x[1], 46.735705771); // should be 4.6736564644E+01); - VERIFY_IS_APPROX(x[2], 0.93219881891); // should be 9.3218483193E-01); -} - -struct thurber_functor : DenseFunctor<double> -{ - thurber_functor(void) : DenseFunctor<double>(7,37) {} - static const double _x[37]; - static const double _y[37]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - // int called=0; printf("call hahn1_functor with iflag=%d, called=%d\n", iflag, called); if (iflag==1) called++; - assert(b.size()==7); - assert(fvec.size()==37); - for(int i=0; i<37; i++) { - double x=_x[i], xx=x*x, xxx=xx*x; - fvec[i] = (b[0]+b[1]*x+b[2]*xx+b[3]*xxx) / (1.+b[4]*x+b[5]*xx+b[6]*xxx) - _y[i]; - } - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==7); - assert(fjac.rows()==37); - assert(fjac.cols()==7); - for(int i=0; i<37; i++) { - double x=_x[i], xx=x*x, xxx=xx*x; - double fact = 1./(1.+b[4]*x+b[5]*xx+b[6]*xxx); - fjac(i,0) = 1.*fact; - fjac(i,1) = x*fact; - fjac(i,2) = xx*fact; - fjac(i,3) = xxx*fact; - fact = - (b[0]+b[1]*x+b[2]*xx+b[3]*xxx) * fact * fact; - fjac(i,4) = x*fact; - fjac(i,5) = xx*fact; - fjac(i,6) = xxx*fact; - } - return 0; - } -}; -const double thurber_functor::_x[37] = { -3.067E0, -2.981E0, -2.921E0, -2.912E0, -2.840E0, -2.797E0, -2.702E0, -2.699E0, -2.633E0, -2.481E0, -2.363E0, -2.322E0, -1.501E0, -1.460E0, -1.274E0, -1.212E0, -1.100E0, -1.046E0, -0.915E0, -0.714E0, -0.566E0, -0.545E0, -0.400E0, -0.309E0, -0.109E0, -0.103E0, 0.010E0, 0.119E0, 0.377E0, 0.790E0, 0.963E0, 1.006E0, 1.115E0, 1.572E0, 1.841E0, 2.047E0, 2.200E0 }; -const double thurber_functor::_y[37] = { 80.574E0, 84.248E0, 87.264E0, 87.195E0, 89.076E0, 89.608E0, 89.868E0, 90.101E0, 92.405E0, 95.854E0, 100.696E0, 101.060E0, 401.672E0, 390.724E0, 567.534E0, 635.316E0, 733.054E0, 759.087E0, 894.206E0, 990.785E0, 1090.109E0, 1080.914E0, 1122.643E0, 1178.351E0, 1260.531E0, 1273.514E0, 1288.339E0, 1327.543E0, 1353.863E0, 1414.509E0, 1425.208E0, 1421.384E0, 1442.962E0, 1464.350E0, 1468.705E0, 1447.894E0, 1457.628E0}; - -// http://www.itl.nist.gov/div898/strd/nls/data/thurber.shtml -void testNistThurber(void) -{ - const int n=7; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 1000 ,1000 ,400 ,40 ,0.7,0.3,0.0 ; - // do the computation - thurber_functor functor; - LevenbergMarquardt<thurber_functor> lm(functor); - lm.setFtol(1.E4*NumTraits<double>::epsilon()); - lm.setXtol(1.E4*NumTraits<double>::epsilon()); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 39); - VERIFY_IS_EQUAL(lm.njev(), 36); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.6427082397E+03); - // check x - VERIFY_IS_APPROX(x[0], 1.2881396800E+03); - VERIFY_IS_APPROX(x[1], 1.4910792535E+03); - VERIFY_IS_APPROX(x[2], 5.8323836877E+02); - VERIFY_IS_APPROX(x[3], 7.5416644291E+01); - VERIFY_IS_APPROX(x[4], 9.6629502864E-01); - VERIFY_IS_APPROX(x[5], 3.9797285797E-01); - VERIFY_IS_APPROX(x[6], 4.9727297349E-02); - - /* - * Second try - */ - x<< 1300 ,1500 ,500 ,75 ,1 ,0.4 ,0.05 ; - // do the computation - lm.resetParameters(); - lm.setFtol(1.E4*NumTraits<double>::epsilon()); - lm.setXtol(1.E4*NumTraits<double>::epsilon()); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 29); - VERIFY_IS_EQUAL(lm.njev(), 28); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.6427082397E+03); - // check x - VERIFY_IS_APPROX(x[0], 1.2881396800E+03); - VERIFY_IS_APPROX(x[1], 1.4910792535E+03); - VERIFY_IS_APPROX(x[2], 5.8323836877E+02); - VERIFY_IS_APPROX(x[3], 7.5416644291E+01); - VERIFY_IS_APPROX(x[4], 9.6629502864E-01); - VERIFY_IS_APPROX(x[5], 3.9797285797E-01); - VERIFY_IS_APPROX(x[6], 4.9727297349E-02); -} - -struct rat43_functor : DenseFunctor<double> -{ - rat43_functor(void) : DenseFunctor<double>(4,15) {} - static const double x[15]; - static const double y[15]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==4); - assert(fvec.size()==15); - for(int i=0; i<15; i++) - fvec[i] = b[0] * pow(1.+exp(b[1]-b[2]*x[i]),-1./b[3]) - y[i]; - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==4); - assert(fjac.rows()==15); - assert(fjac.cols()==4); - for(int i=0; i<15; i++) { - double e = exp(b[1]-b[2]*x[i]); - double power = -1./b[3]; - fjac(i,0) = pow(1.+e, power); - fjac(i,1) = power*b[0]*e*pow(1.+e, power-1.); - fjac(i,2) = -power*b[0]*e*x[i]*pow(1.+e, power-1.); - fjac(i,3) = b[0]*power*power*log(1.+e)*pow(1.+e, power); - } - return 0; - } -}; -const double rat43_functor::x[15] = { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15. }; -const double rat43_functor::y[15] = { 16.08, 33.83, 65.80, 97.20, 191.55, 326.20, 386.87, 520.53, 590.03, 651.92, 724.93, 699.56, 689.96, 637.56, 717.41 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/ratkowsky3.shtml -void testNistRat43(void) -{ - const int n=4; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 100., 10., 1., 1.; - // do the computation - rat43_functor functor; - LevenbergMarquardt<rat43_functor> lm(functor); - lm.setFtol(1.E6*NumTraits<double>::epsilon()); - lm.setXtol(1.E6*NumTraits<double>::epsilon()); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 27); - VERIFY_IS_EQUAL(lm.njev(), 20); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 8.7864049080E+03); - // check x - VERIFY_IS_APPROX(x[0], 6.9964151270E+02); - VERIFY_IS_APPROX(x[1], 5.2771253025E+00); - VERIFY_IS_APPROX(x[2], 7.5962938329E-01); - VERIFY_IS_APPROX(x[3], 1.2792483859E+00); - - /* - * Second try - */ - x<< 700., 5., 0.75, 1.3; - // do the computation - lm.resetParameters(); - lm.setFtol(1.E5*NumTraits<double>::epsilon()); - lm.setXtol(1.E5*NumTraits<double>::epsilon()); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 9); - VERIFY_IS_EQUAL(lm.njev(), 8); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 8.7864049080E+03); - // check x - VERIFY_IS_APPROX(x[0], 6.9964151270E+02); - VERIFY_IS_APPROX(x[1], 5.2771253025E+00); - VERIFY_IS_APPROX(x[2], 7.5962938329E-01); - VERIFY_IS_APPROX(x[3], 1.2792483859E+00); -} - - - -struct eckerle4_functor : DenseFunctor<double> -{ - eckerle4_functor(void) : DenseFunctor<double>(3,35) {} - static const double x[35]; - static const double y[35]; - int operator()(const VectorXd &b, VectorXd &fvec) - { - assert(b.size()==3); - assert(fvec.size()==35); - for(int i=0; i<35; i++) - fvec[i] = b[0]/b[1] * exp(-0.5*(x[i]-b[2])*(x[i]-b[2])/(b[1]*b[1])) - y[i]; - return 0; - } - int df(const VectorXd &b, MatrixXd &fjac) - { - assert(b.size()==3); - assert(fjac.rows()==35); - assert(fjac.cols()==3); - for(int i=0; i<35; i++) { - double b12 = b[1]*b[1]; - double e = exp(-0.5*(x[i]-b[2])*(x[i]-b[2])/b12); - fjac(i,0) = e / b[1]; - fjac(i,1) = ((x[i]-b[2])*(x[i]-b[2])/b12-1.) * b[0]*e/b12; - fjac(i,2) = (x[i]-b[2])*e*b[0]/b[1]/b12; - } - return 0; - } -}; -const double eckerle4_functor::x[35] = { 400.0, 405.0, 410.0, 415.0, 420.0, 425.0, 430.0, 435.0, 436.5, 438.0, 439.5, 441.0, 442.5, 444.0, 445.5, 447.0, 448.5, 450.0, 451.5, 453.0, 454.5, 456.0, 457.5, 459.0, 460.5, 462.0, 463.5, 465.0, 470.0, 475.0, 480.0, 485.0, 490.0, 495.0, 500.0}; -const double eckerle4_functor::y[35] = { 0.0001575, 0.0001699, 0.0002350, 0.0003102, 0.0004917, 0.0008710, 0.0017418, 0.0046400, 0.0065895, 0.0097302, 0.0149002, 0.0237310, 0.0401683, 0.0712559, 0.1264458, 0.2073413, 0.2902366, 0.3445623, 0.3698049, 0.3668534, 0.3106727, 0.2078154, 0.1164354, 0.0616764, 0.0337200, 0.0194023, 0.0117831, 0.0074357, 0.0022732, 0.0008800, 0.0004579, 0.0002345, 0.0001586, 0.0001143, 0.0000710 }; - -// http://www.itl.nist.gov/div898/strd/nls/data/eckerle4.shtml -void testNistEckerle4(void) -{ - const int n=3; - int info; - - VectorXd x(n); - - /* - * First try - */ - x<< 1., 10., 500.; - // do the computation - eckerle4_functor functor; - LevenbergMarquardt<eckerle4_functor> lm(functor); - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 18); - VERIFY_IS_EQUAL(lm.njev(), 15); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.4635887487E-03); - // check x - VERIFY_IS_APPROX(x[0], 1.5543827178); - VERIFY_IS_APPROX(x[1], 4.0888321754); - VERIFY_IS_APPROX(x[2], 4.5154121844E+02); - - /* - * Second try - */ - x<< 1.5, 5., 450.; - // do the computation - info = lm.minimize(x); - - // check return value - VERIFY_IS_EQUAL(info, 1); - VERIFY_IS_EQUAL(lm.nfev(), 7); - VERIFY_IS_EQUAL(lm.njev(), 6); - // check norm^2 - VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.4635887487E-03); - // check x - VERIFY_IS_APPROX(x[0], 1.5543827178); - VERIFY_IS_APPROX(x[1], 4.0888321754); - VERIFY_IS_APPROX(x[2], 4.5154121844E+02); -} - -void test_levenberg_marquardt() -{ - // Tests using the examples provided by (c)minpack - CALL_SUBTEST(testLmder1()); - CALL_SUBTEST(testLmder()); - CALL_SUBTEST(testLmdif1()); -// CALL_SUBTEST(testLmstr1()); -// CALL_SUBTEST(testLmstr()); - CALL_SUBTEST(testLmdif()); - - // NIST tests, level of difficulty = "Lower" - CALL_SUBTEST(testNistMisra1a()); - CALL_SUBTEST(testNistChwirut2()); - - // NIST tests, level of difficulty = "Average" - CALL_SUBTEST(testNistHahn1()); - CALL_SUBTEST(testNistMisra1d()); - CALL_SUBTEST(testNistMGH17()); - CALL_SUBTEST(testNistLanczos1()); - -// // NIST tests, level of difficulty = "Higher" - CALL_SUBTEST(testNistRat42()); - CALL_SUBTEST(testNistMGH10()); - CALL_SUBTEST(testNistBoxBOD()); -// CALL_SUBTEST(testNistMGH09()); - CALL_SUBTEST(testNistBennett5()); - CALL_SUBTEST(testNistThurber()); - CALL_SUBTEST(testNistRat43()); - CALL_SUBTEST(testNistEckerle4()); -} diff --git a/eigen/unsupported/test/matrix_exponential.cpp b/eigen/unsupported/test/matrix_exponential.cpp deleted file mode 100644 index 50dec08..0000000 --- a/eigen/unsupported/test/matrix_exponential.cpp +++ /dev/null @@ -1,141 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "matrix_functions.h" - -double binom(int n, int k) -{ - double res = 1; - for (int i=0; i<k; i++) - res = res * (n-k+i+1) / (i+1); - return res; -} - -template <typename T> -T expfn(T x, int) -{ - return std::exp(x); -} - -template <typename T> -void test2dRotation(double tol) -{ - Matrix<T,2,2> A, B, C; - T angle; - - A << 0, 1, -1, 0; - for (int i=0; i<=20; i++) - { - angle = static_cast<T>(pow(10, i / 5. - 2)); - B << std::cos(angle), std::sin(angle), -std::sin(angle), std::cos(angle); - - C = (angle*A).matrixFunction(expfn); - std::cout << "test2dRotation: i = " << i << " error funm = " << relerr(C, B); - VERIFY(C.isApprox(B, static_cast<T>(tol))); - - C = (angle*A).exp(); - std::cout << " error expm = " << relerr(C, B) << "\n"; - VERIFY(C.isApprox(B, static_cast<T>(tol))); - } -} - -template <typename T> -void test2dHyperbolicRotation(double tol) -{ - Matrix<std::complex<T>,2,2> A, B, C; - std::complex<T> imagUnit(0,1); - T angle, ch, sh; - - for (int i=0; i<=20; i++) - { - angle = static_cast<T>((i-10) / 2.0); - ch = std::cosh(angle); - sh = std::sinh(angle); - A << 0, angle*imagUnit, -angle*imagUnit, 0; - B << ch, sh*imagUnit, -sh*imagUnit, ch; - - C = A.matrixFunction(expfn); - std::cout << "test2dHyperbolicRotation: i = " << i << " error funm = " << relerr(C, B); - VERIFY(C.isApprox(B, static_cast<T>(tol))); - - C = A.exp(); - std::cout << " error expm = " << relerr(C, B) << "\n"; - VERIFY(C.isApprox(B, static_cast<T>(tol))); - } -} - -template <typename T> -void testPascal(double tol) -{ - for (int size=1; size<20; size++) - { - Matrix<T,Dynamic,Dynamic> A(size,size), B(size,size), C(size,size); - A.setZero(); - for (int i=0; i<size-1; i++) - A(i+1,i) = static_cast<T>(i+1); - B.setZero(); - for (int i=0; i<size; i++) - for (int j=0; j<=i; j++) - B(i,j) = static_cast<T>(binom(i,j)); - - C = A.matrixFunction(expfn); - std::cout << "testPascal: size = " << size << " error funm = " << relerr(C, B); - VERIFY(C.isApprox(B, static_cast<T>(tol))); - - C = A.exp(); - std::cout << " error expm = " << relerr(C, B) << "\n"; - VERIFY(C.isApprox(B, static_cast<T>(tol))); - } -} - -template<typename MatrixType> -void randomTest(const MatrixType& m, double tol) -{ - /* this test covers the following files: - Inverse.h - */ - typename MatrixType::Index rows = m.rows(); - typename MatrixType::Index cols = m.cols(); - MatrixType m1(rows, cols), m2(rows, cols), identity = MatrixType::Identity(rows, cols); - - typedef typename NumTraits<typename internal::traits<MatrixType>::Scalar>::Real RealScalar; - - for(int i = 0; i < g_repeat; i++) { - m1 = MatrixType::Random(rows, cols); - - m2 = m1.matrixFunction(expfn) * (-m1).matrixFunction(expfn); - std::cout << "randomTest: error funm = " << relerr(identity, m2); - VERIFY(identity.isApprox(m2, static_cast<RealScalar>(tol))); - - m2 = m1.exp() * (-m1).exp(); - std::cout << " error expm = " << relerr(identity, m2) << "\n"; - VERIFY(identity.isApprox(m2, static_cast<RealScalar>(tol))); - } -} - -void test_matrix_exponential() -{ - CALL_SUBTEST_2(test2dRotation<double>(1e-13)); - CALL_SUBTEST_1(test2dRotation<float>(2e-5)); // was 1e-5, relaxed for clang 2.8 / linux / x86-64 - CALL_SUBTEST_8(test2dRotation<long double>(1e-13)); - CALL_SUBTEST_2(test2dHyperbolicRotation<double>(1e-14)); - CALL_SUBTEST_1(test2dHyperbolicRotation<float>(1e-5)); - CALL_SUBTEST_8(test2dHyperbolicRotation<long double>(1e-14)); - CALL_SUBTEST_6(testPascal<float>(1e-6)); - CALL_SUBTEST_5(testPascal<double>(1e-15)); - CALL_SUBTEST_2(randomTest(Matrix2d(), 1e-13)); - CALL_SUBTEST_7(randomTest(Matrix<double,3,3,RowMajor>(), 1e-13)); - CALL_SUBTEST_3(randomTest(Matrix4cd(), 1e-13)); - CALL_SUBTEST_4(randomTest(MatrixXd(8,8), 1e-13)); - CALL_SUBTEST_1(randomTest(Matrix2f(), 1e-4)); - CALL_SUBTEST_5(randomTest(Matrix3cf(), 1e-4)); - CALL_SUBTEST_1(randomTest(Matrix4f(), 1e-4)); - CALL_SUBTEST_6(randomTest(MatrixXf(8,8), 1e-4)); - CALL_SUBTEST_9(randomTest(Matrix<long double,Dynamic,Dynamic>(7,7), 1e-13)); -} diff --git a/eigen/unsupported/test/matrix_function.cpp b/eigen/unsupported/test/matrix_function.cpp deleted file mode 100644 index 6a2b219..0000000 --- a/eigen/unsupported/test/matrix_function.cpp +++ /dev/null @@ -1,189 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <unsupported/Eigen/MatrixFunctions> - -// Variant of VERIFY_IS_APPROX which uses absolute error instead of -// relative error. -#define VERIFY_IS_APPROX_ABS(a, b) VERIFY(test_isApprox_abs(a, b)) - -template<typename Type1, typename Type2> -inline bool test_isApprox_abs(const Type1& a, const Type2& b) -{ - return ((a-b).array().abs() < test_precision<typename Type1::RealScalar>()).all(); -} - - -// Returns a matrix with eigenvalues clustered around 0, 1 and 2. -template<typename MatrixType> -MatrixType randomMatrixWithRealEivals(const typename MatrixType::Index size) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - MatrixType diag = MatrixType::Zero(size, size); - for (Index i = 0; i < size; ++i) { - diag(i, i) = Scalar(RealScalar(internal::random<int>(0,2))) - + internal::random<Scalar>() * Scalar(RealScalar(0.01)); - } - MatrixType A = MatrixType::Random(size, size); - HouseholderQR<MatrixType> QRofA(A); - return QRofA.householderQ().inverse() * diag * QRofA.householderQ(); -} - -template <typename MatrixType, int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex> -struct randomMatrixWithImagEivals -{ - // Returns a matrix with eigenvalues clustered around 0 and +/- i. - static MatrixType run(const typename MatrixType::Index size); -}; - -// Partial specialization for real matrices -template<typename MatrixType> -struct randomMatrixWithImagEivals<MatrixType, 0> -{ - static MatrixType run(const typename MatrixType::Index size) - { - typedef typename MatrixType::Scalar Scalar; - MatrixType diag = MatrixType::Zero(size, size); - Index i = 0; - while (i < size) { - Index randomInt = internal::random<Index>(-1, 1); - if (randomInt == 0 || i == size-1) { - diag(i, i) = internal::random<Scalar>() * Scalar(0.01); - ++i; - } else { - Scalar alpha = Scalar(randomInt) + internal::random<Scalar>() * Scalar(0.01); - diag(i, i+1) = alpha; - diag(i+1, i) = -alpha; - i += 2; - } - } - MatrixType A = MatrixType::Random(size, size); - HouseholderQR<MatrixType> QRofA(A); - return QRofA.householderQ().inverse() * diag * QRofA.householderQ(); - } -}; - -// Partial specialization for complex matrices -template<typename MatrixType> -struct randomMatrixWithImagEivals<MatrixType, 1> -{ - static MatrixType run(const typename MatrixType::Index size) - { - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - const Scalar imagUnit(0, 1); - MatrixType diag = MatrixType::Zero(size, size); - for (Index i = 0; i < size; ++i) { - diag(i, i) = Scalar(RealScalar(internal::random<Index>(-1, 1))) * imagUnit - + internal::random<Scalar>() * Scalar(RealScalar(0.01)); - } - MatrixType A = MatrixType::Random(size, size); - HouseholderQR<MatrixType> QRofA(A); - return QRofA.householderQ().inverse() * diag * QRofA.householderQ(); - } -}; - - -template<typename MatrixType> -void testMatrixExponential(const MatrixType& A) -{ - typedef typename internal::traits<MatrixType>::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef std::complex<RealScalar> ComplexScalar; - - VERIFY_IS_APPROX(A.exp(), A.matrixFunction(internal::stem_function_exp<ComplexScalar>)); -} - -template<typename MatrixType> -void testMatrixLogarithm(const MatrixType& A) -{ - typedef typename internal::traits<MatrixType>::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - MatrixType scaledA; - RealScalar maxImagPartOfSpectrum = A.eigenvalues().imag().cwiseAbs().maxCoeff(); - if (maxImagPartOfSpectrum >= RealScalar(0.9L * EIGEN_PI)) - scaledA = A * RealScalar(0.9L * EIGEN_PI) / maxImagPartOfSpectrum; - else - scaledA = A; - - // identity X.exp().log() = X only holds if Im(lambda) < pi for all eigenvalues of X - MatrixType expA = scaledA.exp(); - MatrixType logExpA = expA.log(); - VERIFY_IS_APPROX(logExpA, scaledA); -} - -template<typename MatrixType> -void testHyperbolicFunctions(const MatrixType& A) -{ - // Need to use absolute error because of possible cancellation when - // adding/subtracting expA and expmA. - VERIFY_IS_APPROX_ABS(A.sinh(), (A.exp() - (-A).exp()) / 2); - VERIFY_IS_APPROX_ABS(A.cosh(), (A.exp() + (-A).exp()) / 2); -} - -template<typename MatrixType> -void testGonioFunctions(const MatrixType& A) -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef std::complex<RealScalar> ComplexScalar; - typedef Matrix<ComplexScalar, MatrixType::RowsAtCompileTime, - MatrixType::ColsAtCompileTime, MatrixType::Options> ComplexMatrix; - - ComplexScalar imagUnit(0,1); - ComplexScalar two(2,0); - - ComplexMatrix Ac = A.template cast<ComplexScalar>(); - - ComplexMatrix exp_iA = (imagUnit * Ac).exp(); - ComplexMatrix exp_miA = (-imagUnit * Ac).exp(); - - ComplexMatrix sinAc = A.sin().template cast<ComplexScalar>(); - VERIFY_IS_APPROX_ABS(sinAc, (exp_iA - exp_miA) / (two*imagUnit)); - - ComplexMatrix cosAc = A.cos().template cast<ComplexScalar>(); - VERIFY_IS_APPROX_ABS(cosAc, (exp_iA + exp_miA) / 2); -} - -template<typename MatrixType> -void testMatrix(const MatrixType& A) -{ - testMatrixExponential(A); - testMatrixLogarithm(A); - testHyperbolicFunctions(A); - testGonioFunctions(A); -} - -template<typename MatrixType> -void testMatrixType(const MatrixType& m) -{ - // Matrices with clustered eigenvalue lead to different code paths - // in MatrixFunction.h and are thus useful for testing. - - const Index size = m.rows(); - for (int i = 0; i < g_repeat; i++) { - testMatrix(MatrixType::Random(size, size).eval()); - testMatrix(randomMatrixWithRealEivals<MatrixType>(size)); - testMatrix(randomMatrixWithImagEivals<MatrixType>::run(size)); - } -} - -void test_matrix_function() -{ - CALL_SUBTEST_1(testMatrixType(Matrix<float,1,1>())); - CALL_SUBTEST_2(testMatrixType(Matrix3cf())); - CALL_SUBTEST_3(testMatrixType(MatrixXf(8,8))); - CALL_SUBTEST_4(testMatrixType(Matrix2d())); - CALL_SUBTEST_5(testMatrixType(Matrix<double,5,5,RowMajor>())); - CALL_SUBTEST_6(testMatrixType(Matrix4cd())); - CALL_SUBTEST_7(testMatrixType(MatrixXd(13,13))); -} diff --git a/eigen/unsupported/test/matrix_functions.h b/eigen/unsupported/test/matrix_functions.h deleted file mode 100644 index 4e26364..0000000 --- a/eigen/unsupported/test/matrix_functions.h +++ /dev/null @@ -1,67 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009-2011 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <unsupported/Eigen/MatrixFunctions> - -// For complex matrices, any matrix is fine. -template<typename MatrixType, int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex> -struct processTriangularMatrix -{ - static void run(MatrixType&, MatrixType&, const MatrixType&) - { } -}; - -// For real matrices, make sure none of the eigenvalues are negative. -template<typename MatrixType> -struct processTriangularMatrix<MatrixType,0> -{ - static void run(MatrixType& m, MatrixType& T, const MatrixType& U) - { - const Index size = m.cols(); - - for (Index i=0; i < size; ++i) { - if (i == size - 1 || T.coeff(i+1,i) == 0) - T.coeffRef(i,i) = std::abs(T.coeff(i,i)); - else - ++i; - } - m = U * T * U.transpose(); - } -}; - -template <typename MatrixType, int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex> -struct generateTestMatrix; - -template <typename MatrixType> -struct generateTestMatrix<MatrixType,0> -{ - static void run(MatrixType& result, typename MatrixType::Index size) - { - result = MatrixType::Random(size, size); - RealSchur<MatrixType> schur(result); - MatrixType T = schur.matrixT(); - processTriangularMatrix<MatrixType>::run(result, T, schur.matrixU()); - } -}; - -template <typename MatrixType> -struct generateTestMatrix<MatrixType,1> -{ - static void run(MatrixType& result, typename MatrixType::Index size) - { - result = MatrixType::Random(size, size); - } -}; - -template <typename Derived, typename OtherDerived> -typename Derived::RealScalar relerr(const MatrixBase<Derived>& A, const MatrixBase<OtherDerived>& B) -{ - return std::sqrt((A - B).cwiseAbs2().sum() / (std::min)(A.cwiseAbs2().sum(), B.cwiseAbs2().sum())); -} diff --git a/eigen/unsupported/test/matrix_power.cpp b/eigen/unsupported/test/matrix_power.cpp deleted file mode 100644 index 7ccfacf..0000000 --- a/eigen/unsupported/test/matrix_power.cpp +++ /dev/null @@ -1,204 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012, 2013 Chen-Pang He <jdh8@ms63.hinet.net> -// -// 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 "matrix_functions.h" - -template<typename T> -void test2dRotation(const T& tol) -{ - Matrix<T,2,2> A, B, C; - T angle, c, s; - - A << 0, 1, -1, 0; - MatrixPower<Matrix<T,2,2> > Apow(A); - - for (int i=0; i<=20; ++i) { - angle = std::pow(T(10), (i-10) / T(5.)); - c = std::cos(angle); - s = std::sin(angle); - B << c, s, -s, c; - - C = Apow(std::ldexp(angle,1) / T(EIGEN_PI)); - std::cout << "test2dRotation: i = " << i << " error powerm = " << relerr(C,B) << '\n'; - VERIFY(C.isApprox(B, tol)); - } -} - -template<typename T> -void test2dHyperbolicRotation(const T& tol) -{ - Matrix<std::complex<T>,2,2> A, B, C; - T angle, ch = std::cosh((T)1); - std::complex<T> ish(0, std::sinh((T)1)); - - A << ch, ish, -ish, ch; - MatrixPower<Matrix<std::complex<T>,2,2> > Apow(A); - - for (int i=0; i<=20; ++i) { - angle = std::ldexp(static_cast<T>(i-10), -1); - ch = std::cosh(angle); - ish = std::complex<T>(0, std::sinh(angle)); - B << ch, ish, -ish, ch; - - C = Apow(angle); - std::cout << "test2dHyperbolicRotation: i = " << i << " error powerm = " << relerr(C,B) << '\n'; - VERIFY(C.isApprox(B, tol)); - } -} - -template<typename T> -void test3dRotation(const T& tol) -{ - Matrix<T,3,1> v; - T angle; - - for (int i=0; i<=20; ++i) { - v = Matrix<T,3,1>::Random(); - v.normalize(); - angle = std::pow(T(10), (i-10) / T(5.)); - VERIFY(AngleAxis<T>(angle, v).matrix().isApprox(AngleAxis<T>(1,v).matrix().pow(angle), tol)); - } -} - -template<typename MatrixType> -void testGeneral(const MatrixType& m, const typename MatrixType::RealScalar& tol) -{ - typedef typename MatrixType::RealScalar RealScalar; - MatrixType m1, m2, m3, m4, m5; - RealScalar x, y; - - for (int i=0; i < g_repeat; ++i) { - generateTestMatrix<MatrixType>::run(m1, m.rows()); - MatrixPower<MatrixType> mpow(m1); - - x = internal::random<RealScalar>(); - y = internal::random<RealScalar>(); - m2 = mpow(x); - m3 = mpow(y); - - m4 = mpow(x+y); - m5.noalias() = m2 * m3; - VERIFY(m4.isApprox(m5, tol)); - - m4 = mpow(x*y); - m5 = m2.pow(y); - VERIFY(m4.isApprox(m5, tol)); - - m4 = (std::abs(x) * m1).pow(y); - m5 = std::pow(std::abs(x), y) * m3; - VERIFY(m4.isApprox(m5, tol)); - } -} - -template<typename MatrixType> -void testSingular(const MatrixType& m_const, const typename MatrixType::RealScalar& tol) -{ - // we need to pass by reference in order to prevent errors with - // MSVC for aligned data types ... - MatrixType& m = const_cast<MatrixType&>(m_const); - - const int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex; - typedef typename internal::conditional<IsComplex, TriangularView<MatrixType,Upper>, const MatrixType&>::type TriangularType; - typename internal::conditional< IsComplex, ComplexSchur<MatrixType>, RealSchur<MatrixType> >::type schur; - MatrixType T; - - for (int i=0; i < g_repeat; ++i) { - m.setRandom(); - m.col(0).fill(0); - - schur.compute(m); - T = schur.matrixT(); - const MatrixType& U = schur.matrixU(); - processTriangularMatrix<MatrixType>::run(m, T, U); - MatrixPower<MatrixType> mpow(m); - - T = T.sqrt(); - VERIFY(mpow(0.5L).isApprox(U * (TriangularType(T) * U.adjoint()), tol)); - - T = T.sqrt(); - VERIFY(mpow(0.25L).isApprox(U * (TriangularType(T) * U.adjoint()), tol)); - - T = T.sqrt(); - VERIFY(mpow(0.125L).isApprox(U * (TriangularType(T) * U.adjoint()), tol)); - } -} - -template<typename MatrixType> -void testLogThenExp(const MatrixType& m_const, const typename MatrixType::RealScalar& tol) -{ - // we need to pass by reference in order to prevent errors with - // MSVC for aligned data types ... - MatrixType& m = const_cast<MatrixType&>(m_const); - - typedef typename MatrixType::Scalar Scalar; - Scalar x; - - for (int i=0; i < g_repeat; ++i) { - generateTestMatrix<MatrixType>::run(m, m.rows()); - x = internal::random<Scalar>(); - VERIFY(m.pow(x).isApprox((x * m.log()).exp(), tol)); - } -} - -typedef Matrix<double,3,3,RowMajor> Matrix3dRowMajor; -typedef Matrix<long double,3,3> Matrix3e; -typedef Matrix<long double,Dynamic,Dynamic> MatrixXe; - -void test_matrix_power() -{ - CALL_SUBTEST_2(test2dRotation<double>(1e-13)); - CALL_SUBTEST_1(test2dRotation<float>(2e-5)); // was 1e-5, relaxed for clang 2.8 / linux / x86-64 - CALL_SUBTEST_9(test2dRotation<long double>(1e-13L)); - CALL_SUBTEST_2(test2dHyperbolicRotation<double>(1e-14)); - CALL_SUBTEST_1(test2dHyperbolicRotation<float>(1e-5)); - CALL_SUBTEST_9(test2dHyperbolicRotation<long double>(1e-14L)); - - CALL_SUBTEST_10(test3dRotation<double>(1e-13)); - CALL_SUBTEST_11(test3dRotation<float>(1e-5)); - CALL_SUBTEST_12(test3dRotation<long double>(1e-13L)); - - CALL_SUBTEST_2(testGeneral(Matrix2d(), 1e-13)); - CALL_SUBTEST_7(testGeneral(Matrix3dRowMajor(), 1e-13)); - CALL_SUBTEST_3(testGeneral(Matrix4cd(), 1e-13)); - CALL_SUBTEST_4(testGeneral(MatrixXd(8,8), 2e-12)); - CALL_SUBTEST_1(testGeneral(Matrix2f(), 1e-4)); - CALL_SUBTEST_5(testGeneral(Matrix3cf(), 1e-4)); - CALL_SUBTEST_8(testGeneral(Matrix4f(), 1e-4)); - CALL_SUBTEST_6(testGeneral(MatrixXf(2,2), 1e-3)); // see bug 614 - CALL_SUBTEST_9(testGeneral(MatrixXe(7,7), 1e-13L)); - CALL_SUBTEST_10(testGeneral(Matrix3d(), 1e-13)); - CALL_SUBTEST_11(testGeneral(Matrix3f(), 1e-4)); - CALL_SUBTEST_12(testGeneral(Matrix3e(), 1e-13L)); - - CALL_SUBTEST_2(testSingular(Matrix2d(), 1e-13)); - CALL_SUBTEST_7(testSingular(Matrix3dRowMajor(), 1e-13)); - CALL_SUBTEST_3(testSingular(Matrix4cd(), 1e-13)); - CALL_SUBTEST_4(testSingular(MatrixXd(8,8), 2e-12)); - CALL_SUBTEST_1(testSingular(Matrix2f(), 1e-4)); - CALL_SUBTEST_5(testSingular(Matrix3cf(), 1e-4)); - CALL_SUBTEST_8(testSingular(Matrix4f(), 1e-4)); - CALL_SUBTEST_6(testSingular(MatrixXf(2,2), 1e-3)); - CALL_SUBTEST_9(testSingular(MatrixXe(7,7), 1e-13L)); - CALL_SUBTEST_10(testSingular(Matrix3d(), 1e-13)); - CALL_SUBTEST_11(testSingular(Matrix3f(), 1e-4)); - CALL_SUBTEST_12(testSingular(Matrix3e(), 1e-13L)); - - CALL_SUBTEST_2(testLogThenExp(Matrix2d(), 1e-13)); - CALL_SUBTEST_7(testLogThenExp(Matrix3dRowMajor(), 1e-13)); - CALL_SUBTEST_3(testLogThenExp(Matrix4cd(), 1e-13)); - CALL_SUBTEST_4(testLogThenExp(MatrixXd(8,8), 2e-12)); - CALL_SUBTEST_1(testLogThenExp(Matrix2f(), 1e-4)); - CALL_SUBTEST_5(testLogThenExp(Matrix3cf(), 1e-4)); - CALL_SUBTEST_8(testLogThenExp(Matrix4f(), 1e-4)); - CALL_SUBTEST_6(testLogThenExp(MatrixXf(2,2), 1e-3)); - CALL_SUBTEST_9(testLogThenExp(MatrixXe(7,7), 1e-13L)); - CALL_SUBTEST_10(testLogThenExp(Matrix3d(), 1e-13)); - CALL_SUBTEST_11(testLogThenExp(Matrix3f(), 1e-4)); - CALL_SUBTEST_12(testLogThenExp(Matrix3e(), 1e-13L)); -} diff --git a/eigen/unsupported/test/matrix_square_root.cpp b/eigen/unsupported/test/matrix_square_root.cpp deleted file mode 100644 index ea541e1..0000000 --- a/eigen/unsupported/test/matrix_square_root.cpp +++ /dev/null @@ -1,31 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "matrix_functions.h" - -template<typename MatrixType> -void testMatrixSqrt(const MatrixType& m) -{ - MatrixType A; - generateTestMatrix<MatrixType>::run(A, m.rows()); - MatrixType sqrtA = A.sqrt(); - VERIFY_IS_APPROX(sqrtA * sqrtA, A); -} - -void test_matrix_square_root() -{ - for (int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1(testMatrixSqrt(Matrix3cf())); - CALL_SUBTEST_2(testMatrixSqrt(MatrixXcd(12,12))); - CALL_SUBTEST_3(testMatrixSqrt(Matrix4f())); - CALL_SUBTEST_4(testMatrixSqrt(Matrix<double,Dynamic,Dynamic,RowMajor>(9, 9))); - CALL_SUBTEST_5(testMatrixSqrt(Matrix<float,1,1>())); - CALL_SUBTEST_5(testMatrixSqrt(Matrix<std::complex<float>,1,1>())); - } -} diff --git a/eigen/unsupported/test/minres.cpp b/eigen/unsupported/test/minres.cpp deleted file mode 100644 index 8b300b7..0000000 --- a/eigen/unsupported/test/minres.cpp +++ /dev/null @@ -1,44 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Giacomo Po <gpo@ucla.edu> -// 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 <cmath> - -#include "../../test/sparse_solver.h" -#include <Eigen/IterativeSolvers> - -template<typename T> void test_minres_T() -{ - // Identity preconditioner - MINRES<SparseMatrix<T>, Lower, IdentityPreconditioner > minres_colmajor_lower_I; - MINRES<SparseMatrix<T>, Upper, IdentityPreconditioner > minres_colmajor_upper_I; - - // Diagonal preconditioner - MINRES<SparseMatrix<T>, Lower, DiagonalPreconditioner<T> > minres_colmajor_lower_diag; - MINRES<SparseMatrix<T>, Upper, DiagonalPreconditioner<T> > minres_colmajor_upper_diag; - MINRES<SparseMatrix<T>, Lower|Upper, DiagonalPreconditioner<T> > minres_colmajor_uplo_diag; - - // call tests for SPD matrix - CALL_SUBTEST( check_sparse_spd_solving(minres_colmajor_lower_I) ); - CALL_SUBTEST( check_sparse_spd_solving(minres_colmajor_upper_I) ); - - CALL_SUBTEST( check_sparse_spd_solving(minres_colmajor_lower_diag) ); - CALL_SUBTEST( check_sparse_spd_solving(minres_colmajor_upper_diag) ); - CALL_SUBTEST( check_sparse_spd_solving(minres_colmajor_uplo_diag) ); - - // TO DO: symmetric semi-definite matrix - // TO DO: symmetric indefinite matrix - -} - -void test_minres() -{ - CALL_SUBTEST_1(test_minres_T<double>()); -// CALL_SUBTEST_2(test_minres_T<std::compex<double> >()); - -} diff --git a/eigen/unsupported/test/mpreal/mpreal.h b/eigen/unsupported/test/mpreal/mpreal.h deleted file mode 100644 index 8404f1f..0000000 --- a/eigen/unsupported/test/mpreal/mpreal.h +++ /dev/null @@ -1,3104 +0,0 @@ -/*
- MPFR C++: Multi-precision floating point number class for C++.
- Based on MPFR library: http://mpfr.org
-
- Project homepage: http://www.holoborodko.com/pavel/mpfr
- Contact e-mail: pavel@holoborodko.com
-
- Copyright (c) 2008-2015 Pavel Holoborodko
-
- Contributors:
- Dmitriy Gubanov, Konstantin Holoborodko, Brian Gladman,
- Helmut Jarausch, Fokko Beekhof, Ulrich Mutze, Heinz van Saanen,
- Pere Constans, Peter van Hoof, Gael Guennebaud, Tsai Chia Cheng,
- Alexei Zubanov, Jauhien Piatlicki, Victor Berger, John Westwood,
- Petr Aleksandrov, Orion Poplawski, Charles Karney, Arash Partow,
- Rodney James, Jorge Leitao.
-
- Licensing:
- (A) MPFR C++ is under GNU General Public License ("GPL").
-
- (B) Non-free licenses may also be purchased from the author, for users who
- do not want their programs protected by the GPL.
-
- The non-free licenses are for users that wish to use MPFR C++ in
- their products but are unwilling to release their software
- under the GPL (which would require them to release source code
- and allow free redistribution).
-
- Such users can purchase an unlimited-use license from the author.
- Contact us for more details.
-
- GNU General Public License ("GPL") copyright permissions statement:
- **************************************************************************
- This program is free software: 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 3 of the License, or
- (at your option) any later version.
-
- This program 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 General Public License for more details.
-
- You should have received a copy of the GNU General Public License
- along with this program. If not, see <http://www.gnu.org/licenses/>.
-*/
-
-#ifndef __MPREAL_H__
-#define __MPREAL_H__
-
-#include <string>
-#include <iostream>
-#include <sstream>
-#include <stdexcept>
-#include <cfloat>
-#include <cmath>
-#include <cstring>
-#include <limits>
-#include <complex>
-#include <algorithm>
-
-// Options
-#define MPREAL_HAVE_MSVC_DEBUGVIEW // Enable Debugger Visualizer for "Debug" builds in MSVC.
-#define MPREAL_HAVE_DYNAMIC_STD_NUMERIC_LIMITS // Enable extended std::numeric_limits<mpfr::mpreal> specialization.
- // Meaning that "digits", "round_style" and similar members are defined as functions, not constants.
- // See std::numeric_limits<mpfr::mpreal> at the end of the file for more information.
-
-// Library version
-#define MPREAL_VERSION_MAJOR 3
-#define MPREAL_VERSION_MINOR 6
-#define MPREAL_VERSION_PATCHLEVEL 2
-#define MPREAL_VERSION_STRING "3.6.2"
-
-// Detect compiler using signatures from http://predef.sourceforge.net/
-#if defined(__GNUC__)
- #define IsInf(x) (isinf)(x) // GNU C++/Intel ICC compiler on Linux
-#elif defined(_MSC_VER) // Microsoft Visual C++
- #define IsInf(x) (!_finite(x))
-#else
- #define IsInf(x) (std::isinf)(x) // GNU C/C++ (and/or other compilers), just hope for C99 conformance
-#endif
-
-// A Clang feature extension to determine compiler features.
-#ifndef __has_feature
- #define __has_feature(x) 0
-#endif
-
-// Detect support for r-value references (move semantic). Borrowed from Eigen.
-#if (__has_feature(cxx_rvalue_references) || \
- defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L || \
- (defined(_MSC_VER) && _MSC_VER >= 1600))
-
- #define MPREAL_HAVE_MOVE_SUPPORT
-
- // Use fields in mpfr_t structure to check if it was initialized / set dummy initialization
- #define mpfr_is_initialized(x) (0 != (x)->_mpfr_d)
- #define mpfr_set_uninitialized(x) ((x)->_mpfr_d = 0 )
-#endif
-
-// Detect support for explicit converters.
-#if (__has_feature(cxx_explicit_conversions) || \
- (defined(__GXX_EXPERIMENTAL_CXX0X__) && __GNUC_MINOR__ >= 5) || __cplusplus >= 201103L || \
- (defined(_MSC_VER) && _MSC_VER >= 1800))
-
- #define MPREAL_HAVE_EXPLICIT_CONVERTERS
-#endif
-
-#define MPFR_USE_INTMAX_T // Enable 64-bit integer types - should be defined before mpfr.h
-
-#if defined(MPREAL_HAVE_MSVC_DEBUGVIEW) && defined(_MSC_VER) && defined(_DEBUG)
- #define MPREAL_MSVC_DEBUGVIEW_CODE DebugView = toString();
- #define MPREAL_MSVC_DEBUGVIEW_DATA std::string DebugView;
-#else
- #define MPREAL_MSVC_DEBUGVIEW_CODE
- #define MPREAL_MSVC_DEBUGVIEW_DATA
-#endif
-
-#include <mpfr.h>
-
-#if (MPFR_VERSION < MPFR_VERSION_NUM(3,0,0))
- #include <cstdlib> // Needed for random()
-#endif
-
-// Less important options
-#define MPREAL_DOUBLE_BITS_OVERFLOW -1 // Triggers overflow exception during conversion to double if mpreal
- // cannot fit in MPREAL_DOUBLE_BITS_OVERFLOW bits
- // = -1 disables overflow checks (default)
-
-// Fast replacement for mpfr_set_zero(x, +1):
-// (a) uses low-level data members, might not be compatible with new versions of MPFR
-// (b) sign is not set, add (x)->_mpfr_sign = 1;
-#define mpfr_set_zero_fast(x) ((x)->_mpfr_exp = __MPFR_EXP_ZERO)
-
-#if defined(__GNUC__)
- #define MPREAL_PERMISSIVE_EXPR __extension__
-#else
- #define MPREAL_PERMISSIVE_EXPR
-#endif
-
-namespace mpfr {
-
-class mpreal {
-private:
- mpfr_t mp;
-
-public:
-
- // Get default rounding mode & precision
- inline static mp_rnd_t get_default_rnd() { return (mp_rnd_t)(mpfr_get_default_rounding_mode()); }
- inline static mp_prec_t get_default_prec() { return mpfr_get_default_prec(); }
-
- // Constructors && type conversions
- mpreal();
- mpreal(const mpreal& u);
- mpreal(const mpf_t u);
- mpreal(const mpz_t u, mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
- mpreal(const mpq_t u, mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
- mpreal(const double u, mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
- mpreal(const long double u, mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
- mpreal(const unsigned long long int u, mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
- mpreal(const long long int u, mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
- mpreal(const unsigned long int u, mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
- mpreal(const unsigned int u, mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
- mpreal(const long int u, mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
- mpreal(const int u, mp_prec_t prec = mpreal::get_default_prec(), mp_rnd_t mode = mpreal::get_default_rnd());
-
- // Construct mpreal from mpfr_t structure.
- // shared = true allows to avoid deep copy, so that mpreal and 'u' share the same data & pointers.
- mpreal(const mpfr_t u, bool shared = false);
-
- mpreal(const char* s, mp_prec_t prec = mpreal::get_default_prec(), int base = 10, mp_rnd_t mode = mpreal::get_default_rnd());
- mpreal(const std::string& s, mp_prec_t prec = mpreal::get_default_prec(), int base = 10, mp_rnd_t mode = mpreal::get_default_rnd());
-
- ~mpreal();
-
-#ifdef MPREAL_HAVE_MOVE_SUPPORT
- mpreal& operator=(mpreal&& v);
- mpreal(mpreal&& u);
-#endif
-
- // Operations
- // =
- // +, -, *, /, ++, --, <<, >>
- // *=, +=, -=, /=,
- // <, >, ==, <=, >=
-
- // =
- mpreal& operator=(const mpreal& v);
- mpreal& operator=(const mpf_t v);
- mpreal& operator=(const mpz_t v);
- mpreal& operator=(const mpq_t v);
- mpreal& operator=(const long double v);
- mpreal& operator=(const double v);
- mpreal& operator=(const unsigned long int v);
- mpreal& operator=(const unsigned long long int v);
- mpreal& operator=(const long long int v);
- mpreal& operator=(const unsigned int v);
- mpreal& operator=(const long int v);
- mpreal& operator=(const int v);
- mpreal& operator=(const char* s);
- mpreal& operator=(const std::string& s);
- template <typename real_t> mpreal& operator= (const std::complex<real_t>& z);
-
- // +
- mpreal& operator+=(const mpreal& v);
- mpreal& operator+=(const mpf_t v);
- mpreal& operator+=(const mpz_t v);
- mpreal& operator+=(const mpq_t v);
- mpreal& operator+=(const long double u);
- mpreal& operator+=(const double u);
- mpreal& operator+=(const unsigned long int u);
- mpreal& operator+=(const unsigned int u);
- mpreal& operator+=(const long int u);
- mpreal& operator+=(const int u);
-
- mpreal& operator+=(const long long int u);
- mpreal& operator+=(const unsigned long long int u);
- mpreal& operator-=(const long long int u);
- mpreal& operator-=(const unsigned long long int u);
- mpreal& operator*=(const long long int u);
- mpreal& operator*=(const unsigned long long int u);
- mpreal& operator/=(const long long int u);
- mpreal& operator/=(const unsigned long long int u);
-
- const mpreal operator+() const;
- mpreal& operator++ ();
- const mpreal operator++ (int);
-
- // -
- mpreal& operator-=(const mpreal& v);
- mpreal& operator-=(const mpz_t v);
- mpreal& operator-=(const mpq_t v);
- mpreal& operator-=(const long double u);
- mpreal& operator-=(const double u);
- mpreal& operator-=(const unsigned long int u);
- mpreal& operator-=(const unsigned int u);
- mpreal& operator-=(const long int u);
- mpreal& operator-=(const int u);
- const mpreal operator-() const;
- friend const mpreal operator-(const unsigned long int b, const mpreal& a);
- friend const mpreal operator-(const unsigned int b, const mpreal& a);
- friend const mpreal operator-(const long int b, const mpreal& a);
- friend const mpreal operator-(const int b, const mpreal& a);
- friend const mpreal operator-(const double b, const mpreal& a);
- mpreal& operator-- ();
- const mpreal operator-- (int);
-
- // *
- mpreal& operator*=(const mpreal& v);
- mpreal& operator*=(const mpz_t v);
- mpreal& operator*=(const mpq_t v);
- mpreal& operator*=(const long double v);
- mpreal& operator*=(const double v);
- mpreal& operator*=(const unsigned long int v);
- mpreal& operator*=(const unsigned int v);
- mpreal& operator*=(const long int v);
- mpreal& operator*=(const int v);
-
- // /
- mpreal& operator/=(const mpreal& v);
- mpreal& operator/=(const mpz_t v);
- mpreal& operator/=(const mpq_t v);
- mpreal& operator/=(const long double v);
- mpreal& operator/=(const double v);
- mpreal& operator/=(const unsigned long int v);
- mpreal& operator/=(const unsigned int v);
- mpreal& operator/=(const long int v);
- mpreal& operator/=(const int v);
- friend const mpreal operator/(const unsigned long int b, const mpreal& a);
- friend const mpreal operator/(const unsigned int b, const mpreal& a);
- friend const mpreal operator/(const long int b, const mpreal& a);
- friend const mpreal operator/(const int b, const mpreal& a);
- friend const mpreal operator/(const double b, const mpreal& a);
-
- //<<= Fast Multiplication by 2^u
- mpreal& operator<<=(const unsigned long int u);
- mpreal& operator<<=(const unsigned int u);
- mpreal& operator<<=(const long int u);
- mpreal& operator<<=(const int u);
-
- //>>= Fast Division by 2^u
- mpreal& operator>>=(const unsigned long int u);
- mpreal& operator>>=(const unsigned int u);
- mpreal& operator>>=(const long int u);
- mpreal& operator>>=(const int u);
-
- // Type Conversion operators
- bool toBool ( ) const;
- long toLong (mp_rnd_t mode = GMP_RNDZ) const;
- unsigned long toULong (mp_rnd_t mode = GMP_RNDZ) const;
- long long toLLong (mp_rnd_t mode = GMP_RNDZ) const;
- unsigned long long toULLong (mp_rnd_t mode = GMP_RNDZ) const;
- float toFloat (mp_rnd_t mode = GMP_RNDN) const;
- double toDouble (mp_rnd_t mode = GMP_RNDN) const;
- long double toLDouble (mp_rnd_t mode = GMP_RNDN) const;
-
-#if defined (MPREAL_HAVE_EXPLICIT_CONVERTERS)
- explicit operator bool () const { return toBool(); }
- explicit operator int () const { return int(toLong()); }
- explicit operator long () const { return toLong(); }
- explicit operator long long () const { return toLLong(); }
- explicit operator unsigned () const { return unsigned(toULong()); }
- explicit operator unsigned long () const { return toULong(); }
- explicit operator unsigned long long () const { return toULLong(); }
- explicit operator float () const { return toFloat(); }
- explicit operator double () const { return toDouble(); }
- explicit operator long double () const { return toLDouble(); }
-#endif
-
- // Get raw pointers so that mpreal can be directly used in raw mpfr_* functions
- ::mpfr_ptr mpfr_ptr();
- ::mpfr_srcptr mpfr_ptr() const;
- ::mpfr_srcptr mpfr_srcptr() const;
-
- // Convert mpreal to string with n significant digits in base b
- // n = -1 -> convert with the maximum available digits
- std::string toString(int n = -1, int b = 10, mp_rnd_t mode = mpreal::get_default_rnd()) const;
-
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
- std::string toString(const std::string& format) const;
-#endif
-
- std::ostream& output(std::ostream& os) const;
-
- // Math Functions
- friend const mpreal sqr (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal sqrt(const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal sqrt(const unsigned long int v, mp_rnd_t rnd_mode);
- friend const mpreal cbrt(const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal root(const mpreal& v, unsigned long int k, mp_rnd_t rnd_mode);
- friend const mpreal pow (const mpreal& a, const mpreal& b, mp_rnd_t rnd_mode);
- friend const mpreal pow (const mpreal& a, const mpz_t b, mp_rnd_t rnd_mode);
- friend const mpreal pow (const mpreal& a, const unsigned long int b, mp_rnd_t rnd_mode);
- friend const mpreal pow (const mpreal& a, const long int b, mp_rnd_t rnd_mode);
- friend const mpreal pow (const unsigned long int a, const mpreal& b, mp_rnd_t rnd_mode);
- friend const mpreal pow (const unsigned long int a, const unsigned long int b, mp_rnd_t rnd_mode);
- friend const mpreal fabs(const mpreal& v, mp_rnd_t rnd_mode);
-
- friend const mpreal abs(const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal dim(const mpreal& a, const mpreal& b, mp_rnd_t rnd_mode);
- friend inline const mpreal mul_2ui(const mpreal& v, unsigned long int k, mp_rnd_t rnd_mode);
- friend inline const mpreal mul_2si(const mpreal& v, long int k, mp_rnd_t rnd_mode);
- friend inline const mpreal div_2ui(const mpreal& v, unsigned long int k, mp_rnd_t rnd_mode);
- friend inline const mpreal div_2si(const mpreal& v, long int k, mp_rnd_t rnd_mode);
- friend int cmpabs(const mpreal& a,const mpreal& b);
-
- friend const mpreal log (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal log2 (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal logb (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal log10(const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal exp (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal exp2 (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal exp10(const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal log1p(const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal expm1(const mpreal& v, mp_rnd_t rnd_mode);
-
- friend const mpreal cos(const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal sin(const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal tan(const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal sec(const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal csc(const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal cot(const mpreal& v, mp_rnd_t rnd_mode);
- friend int sin_cos(mpreal& s, mpreal& c, const mpreal& v, mp_rnd_t rnd_mode);
-
- friend const mpreal acos (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal asin (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal atan (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal atan2 (const mpreal& y, const mpreal& x, mp_rnd_t rnd_mode);
- friend const mpreal acot (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal asec (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal acsc (const mpreal& v, mp_rnd_t rnd_mode);
-
- friend const mpreal cosh (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal sinh (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal tanh (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal sech (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal csch (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal coth (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal acosh (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal asinh (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal atanh (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal acoth (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal asech (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal acsch (const mpreal& v, mp_rnd_t rnd_mode);
-
- friend const mpreal hypot (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode);
-
- friend const mpreal fac_ui (unsigned long int v, mp_prec_t prec, mp_rnd_t rnd_mode);
- friend const mpreal eint (const mpreal& v, mp_rnd_t rnd_mode);
-
- friend const mpreal gamma (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal tgamma (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal lngamma (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal lgamma (const mpreal& v, int *signp, mp_rnd_t rnd_mode);
- friend const mpreal zeta (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal erf (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal erfc (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal besselj0 (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal besselj1 (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal besseljn (long n, const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal bessely0 (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal bessely1 (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal besselyn (long n, const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal fma (const mpreal& v1, const mpreal& v2, const mpreal& v3, mp_rnd_t rnd_mode);
- friend const mpreal fms (const mpreal& v1, const mpreal& v2, const mpreal& v3, mp_rnd_t rnd_mode);
- friend const mpreal agm (const mpreal& v1, const mpreal& v2, mp_rnd_t rnd_mode);
- friend const mpreal sum (const mpreal tab[], const unsigned long int n, int& status, mp_rnd_t rnd_mode);
- friend int sgn(const mpreal& v); // returns -1 or +1
-
-// MPFR 2.4.0 Specifics
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
- friend int sinh_cosh (mpreal& s, mpreal& c, const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal li2 (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal fmod (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode);
- friend const mpreal rec_sqrt (const mpreal& v, mp_rnd_t rnd_mode);
-
- // MATLAB's semantic equivalents
- friend const mpreal rem (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode); // Remainder after division
- friend const mpreal mod (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode); // Modulus after division
-#endif
-
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,0,0))
- friend const mpreal digamma (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal ai (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal urandom (gmp_randstate_t& state, mp_rnd_t rnd_mode); // use gmp_randinit_default() to init state, gmp_randclear() to clear
-#endif
-
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,1,0))
- friend const mpreal grandom (gmp_randstate_t& state, mp_rnd_t rnd_mode); // use gmp_randinit_default() to init state, gmp_randclear() to clear
- friend const mpreal grandom (unsigned int seed);
-#endif
-
- // Uniformly distributed random number generation in [0,1] using
- // Mersenne-Twister algorithm by default.
- // Use parameter to setup seed, e.g.: random((unsigned)time(NULL))
- // Check urandom() for more precise control.
- friend const mpreal random(unsigned int seed);
-
- // Splits mpreal value into fractional and integer parts.
- // Returns fractional part and stores integer part in n.
- friend const mpreal modf(const mpreal& v, mpreal& n);
-
- // Constants
- // don't forget to call mpfr_free_cache() for every thread where you are using const-functions
- friend const mpreal const_log2 (mp_prec_t prec, mp_rnd_t rnd_mode);
- friend const mpreal const_pi (mp_prec_t prec, mp_rnd_t rnd_mode);
- friend const mpreal const_euler (mp_prec_t prec, mp_rnd_t rnd_mode);
- friend const mpreal const_catalan (mp_prec_t prec, mp_rnd_t rnd_mode);
-
- // returns +inf iff sign>=0 otherwise -inf
- friend const mpreal const_infinity(int sign, mp_prec_t prec);
-
- // Output/ Input
- friend std::ostream& operator<<(std::ostream& os, const mpreal& v);
- friend std::istream& operator>>(std::istream& is, mpreal& v);
-
- // Integer Related Functions
- friend const mpreal rint (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal ceil (const mpreal& v);
- friend const mpreal floor(const mpreal& v);
- friend const mpreal round(const mpreal& v);
- friend const mpreal trunc(const mpreal& v);
- friend const mpreal rint_ceil (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal rint_floor (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal rint_round (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal rint_trunc (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal frac (const mpreal& v, mp_rnd_t rnd_mode);
- friend const mpreal remainder ( const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode);
- friend const mpreal remquo (long* q, const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode);
-
- // Miscellaneous Functions
- friend const mpreal nexttoward (const mpreal& x, const mpreal& y);
- friend const mpreal nextabove (const mpreal& x);
- friend const mpreal nextbelow (const mpreal& x);
-
- // use gmp_randinit_default() to init state, gmp_randclear() to clear
- friend const mpreal urandomb (gmp_randstate_t& state);
-
-// MPFR < 2.4.2 Specifics
-#if (MPFR_VERSION <= MPFR_VERSION_NUM(2,4,2))
- friend const mpreal random2 (mp_size_t size, mp_exp_t exp);
-#endif
-
- // Instance Checkers
- friend bool (isnan) (const mpreal& v);
- friend bool (isinf) (const mpreal& v);
- friend bool (isfinite) (const mpreal& v);
-
- friend bool isnum (const mpreal& v);
- friend bool iszero (const mpreal& v);
- friend bool isint (const mpreal& v);
-
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,0,0))
- friend bool isregular(const mpreal& v);
-#endif
-
- // Set/Get instance properties
- inline mp_prec_t get_prec() const;
- inline void set_prec(mp_prec_t prec, mp_rnd_t rnd_mode = get_default_rnd()); // Change precision with rounding mode
-
- // Aliases for get_prec(), set_prec() - needed for compatibility with std::complex<mpreal> interface
- inline mpreal& setPrecision(int Precision, mp_rnd_t RoundingMode = get_default_rnd());
- inline int getPrecision() const;
-
- // Set mpreal to +/- inf, NaN, +/-0
- mpreal& setInf (int Sign = +1);
- mpreal& setNan ();
- mpreal& setZero (int Sign = +1);
- mpreal& setSign (int Sign, mp_rnd_t RoundingMode = get_default_rnd());
-
- //Exponent
- mp_exp_t get_exp();
- int set_exp(mp_exp_t e);
- int check_range (int t, mp_rnd_t rnd_mode = get_default_rnd());
- int subnormalize (int t, mp_rnd_t rnd_mode = get_default_rnd());
-
- // Inexact conversion from float
- inline bool fits_in_bits(double x, int n);
-
- // Set/Get global properties
- static void set_default_prec(mp_prec_t prec);
- static void set_default_rnd(mp_rnd_t rnd_mode);
-
- static mp_exp_t get_emin (void);
- static mp_exp_t get_emax (void);
- static mp_exp_t get_emin_min (void);
- static mp_exp_t get_emin_max (void);
- static mp_exp_t get_emax_min (void);
- static mp_exp_t get_emax_max (void);
- static int set_emin (mp_exp_t exp);
- static int set_emax (mp_exp_t exp);
-
- // Efficient swapping of two mpreal values - needed for std algorithms
- friend void swap(mpreal& x, mpreal& y);
-
- friend const mpreal fmax(const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode);
- friend const mpreal fmin(const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode);
-
-private:
- // Human friendly Debug Preview in Visual Studio.
- // Put one of these lines:
- //
- // mpfr::mpreal=<DebugView> ; Show value only
- // mpfr::mpreal=<DebugView>, <mp[0]._mpfr_prec,u>bits ; Show value & precision
- //
- // at the beginning of
- // [Visual Studio Installation Folder]\Common7\Packages\Debugger\autoexp.dat
- MPREAL_MSVC_DEBUGVIEW_DATA
-
- // "Smart" resources deallocation. Checks if instance initialized before deletion.
- void clear(::mpfr_ptr);
-};
-
-//////////////////////////////////////////////////////////////////////////
-// Exceptions
-class conversion_overflow : public std::exception {
-public:
- std::string why() { return "inexact conversion from floating point"; }
-};
-
-//////////////////////////////////////////////////////////////////////////
-// Constructors & converters
-// Default constructor: creates mp number and initializes it to 0.
-inline mpreal::mpreal()
-{
- mpfr_init2(mpfr_ptr(), mpreal::get_default_prec());
- mpfr_set_zero_fast(mpfr_ptr());
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline mpreal::mpreal(const mpreal& u)
-{
- mpfr_init2(mpfr_ptr(),mpfr_get_prec(u.mpfr_srcptr()));
- mpfr_set (mpfr_ptr(),u.mpfr_srcptr(),mpreal::get_default_rnd());
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-#ifdef MPREAL_HAVE_MOVE_SUPPORT
-inline mpreal::mpreal(mpreal&& other)
-{
- mpfr_set_uninitialized(mpfr_ptr()); // make sure "other" holds no pointer to actual data
- mpfr_swap(mpfr_ptr(), other.mpfr_ptr());
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline mpreal& mpreal::operator=(mpreal&& other)
-{
- mpfr_swap(mpfr_ptr(), other.mpfr_ptr());
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-#endif
-
-inline mpreal::mpreal(const mpfr_t u, bool shared)
-{
- if(shared)
- {
- std::memcpy(mpfr_ptr(), u, sizeof(mpfr_t));
- }
- else
- {
- mpfr_init2(mpfr_ptr(), mpfr_get_prec(u));
- mpfr_set (mpfr_ptr(), u, mpreal::get_default_rnd());
- }
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline mpreal::mpreal(const mpf_t u)
-{
- mpfr_init2(mpfr_ptr(),(mp_prec_t) mpf_get_prec(u)); // (gmp: mp_bitcnt_t) unsigned long -> long (mpfr: mp_prec_t)
- mpfr_set_f(mpfr_ptr(),u,mpreal::get_default_rnd());
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline mpreal::mpreal(const mpz_t u, mp_prec_t prec, mp_rnd_t mode)
-{
- mpfr_init2(mpfr_ptr(), prec);
- mpfr_set_z(mpfr_ptr(), u, mode);
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline mpreal::mpreal(const mpq_t u, mp_prec_t prec, mp_rnd_t mode)
-{
- mpfr_init2(mpfr_ptr(), prec);
- mpfr_set_q(mpfr_ptr(), u, mode);
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline mpreal::mpreal(const double u, mp_prec_t prec, mp_rnd_t mode)
-{
- mpfr_init2(mpfr_ptr(), prec);
-
-#if (MPREAL_DOUBLE_BITS_OVERFLOW > -1)
- if(fits_in_bits(u, MPREAL_DOUBLE_BITS_OVERFLOW))
- {
- mpfr_set_d(mpfr_ptr(), u, mode);
- }else
- throw conversion_overflow();
-#else
- mpfr_set_d(mpfr_ptr(), u, mode);
-#endif
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline mpreal::mpreal(const long double u, mp_prec_t prec, mp_rnd_t mode)
-{
- mpfr_init2 (mpfr_ptr(), prec);
- mpfr_set_ld(mpfr_ptr(), u, mode);
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline mpreal::mpreal(const unsigned long long int u, mp_prec_t prec, mp_rnd_t mode)
-{
- mpfr_init2 (mpfr_ptr(), prec);
- mpfr_set_uj(mpfr_ptr(), u, mode);
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline mpreal::mpreal(const long long int u, mp_prec_t prec, mp_rnd_t mode)
-{
- mpfr_init2 (mpfr_ptr(), prec);
- mpfr_set_sj(mpfr_ptr(), u, mode);
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline mpreal::mpreal(const unsigned long int u, mp_prec_t prec, mp_rnd_t mode)
-{
- mpfr_init2 (mpfr_ptr(), prec);
- mpfr_set_ui(mpfr_ptr(), u, mode);
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline mpreal::mpreal(const unsigned int u, mp_prec_t prec, mp_rnd_t mode)
-{
- mpfr_init2 (mpfr_ptr(), prec);
- mpfr_set_ui(mpfr_ptr(), u, mode);
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline mpreal::mpreal(const long int u, mp_prec_t prec, mp_rnd_t mode)
-{
- mpfr_init2 (mpfr_ptr(), prec);
- mpfr_set_si(mpfr_ptr(), u, mode);
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline mpreal::mpreal(const int u, mp_prec_t prec, mp_rnd_t mode)
-{
- mpfr_init2 (mpfr_ptr(), prec);
- mpfr_set_si(mpfr_ptr(), u, mode);
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline mpreal::mpreal(const char* s, mp_prec_t prec, int base, mp_rnd_t mode)
-{
- mpfr_init2 (mpfr_ptr(), prec);
- mpfr_set_str(mpfr_ptr(), s, base, mode);
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline mpreal::mpreal(const std::string& s, mp_prec_t prec, int base, mp_rnd_t mode)
-{
- mpfr_init2 (mpfr_ptr(), prec);
- mpfr_set_str(mpfr_ptr(), s.c_str(), base, mode);
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline void mpreal::clear(::mpfr_ptr x)
-{
-#ifdef MPREAL_HAVE_MOVE_SUPPORT
- if(mpfr_is_initialized(x))
-#endif
- mpfr_clear(x);
-}
-
-inline mpreal::~mpreal()
-{
- clear(mpfr_ptr());
-}
-
-// internal namespace needed for template magic
-namespace internal{
-
- // Use SFINAE to restrict arithmetic operations instantiation only for numeric types
- // This is needed for smooth integration with libraries based on expression templates, like Eigen.
- // TODO: Do the same for boolean operators.
- template <typename ArgumentType> struct result_type {};
-
- template <> struct result_type<mpreal> {typedef mpreal type;};
- template <> struct result_type<mpz_t> {typedef mpreal type;};
- template <> struct result_type<mpq_t> {typedef mpreal type;};
- template <> struct result_type<long double> {typedef mpreal type;};
- template <> struct result_type<double> {typedef mpreal type;};
- template <> struct result_type<unsigned long int> {typedef mpreal type;};
- template <> struct result_type<unsigned int> {typedef mpreal type;};
- template <> struct result_type<long int> {typedef mpreal type;};
- template <> struct result_type<int> {typedef mpreal type;};
- template <> struct result_type<long long> {typedef mpreal type;};
- template <> struct result_type<unsigned long long> {typedef mpreal type;};
-}
-
-// + Addition
-template <typename Rhs>
-inline const typename internal::result_type<Rhs>::type
- operator+(const mpreal& lhs, const Rhs& rhs){ return mpreal(lhs) += rhs; }
-
-template <typename Lhs>
-inline const typename internal::result_type<Lhs>::type
- operator+(const Lhs& lhs, const mpreal& rhs){ return mpreal(rhs) += lhs; }
-
-// - Subtraction
-template <typename Rhs>
-inline const typename internal::result_type<Rhs>::type
- operator-(const mpreal& lhs, const Rhs& rhs){ return mpreal(lhs) -= rhs; }
-
-template <typename Lhs>
-inline const typename internal::result_type<Lhs>::type
- operator-(const Lhs& lhs, const mpreal& rhs){ return mpreal(lhs) -= rhs; }
-
-// * Multiplication
-template <typename Rhs>
-inline const typename internal::result_type<Rhs>::type
- operator*(const mpreal& lhs, const Rhs& rhs){ return mpreal(lhs) *= rhs; }
-
-template <typename Lhs>
-inline const typename internal::result_type<Lhs>::type
- operator*(const Lhs& lhs, const mpreal& rhs){ return mpreal(rhs) *= lhs; }
-
-// / Division
-template <typename Rhs>
-inline const typename internal::result_type<Rhs>::type
- operator/(const mpreal& lhs, const Rhs& rhs){ return mpreal(lhs) /= rhs; }
-
-template <typename Lhs>
-inline const typename internal::result_type<Lhs>::type
- operator/(const Lhs& lhs, const mpreal& rhs){ return mpreal(lhs) /= rhs; }
-
-//////////////////////////////////////////////////////////////////////////
-// sqrt
-const mpreal sqrt(const unsigned int v, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal sqrt(const long int v, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal sqrt(const int v, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal sqrt(const long double v, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal sqrt(const double v, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-
-// abs
-inline const mpreal abs(const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd());
-
-//////////////////////////////////////////////////////////////////////////
-// pow
-const mpreal pow(const mpreal& a, const unsigned int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const mpreal& a, const int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const mpreal& a, const long double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const mpreal& a, const double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-
-const mpreal pow(const unsigned int a, const mpreal& b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const long int a, const mpreal& b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const int a, const mpreal& b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const long double a, const mpreal& b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const double a, const mpreal& b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-
-const mpreal pow(const unsigned long int a, const unsigned int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const unsigned long int a, const long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const unsigned long int a, const int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const unsigned long int a, const long double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const unsigned long int a, const double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-
-const mpreal pow(const unsigned int a, const unsigned long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const unsigned int a, const unsigned int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const unsigned int a, const long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const unsigned int a, const int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const unsigned int a, const long double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const unsigned int a, const double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-
-const mpreal pow(const long int a, const unsigned long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const long int a, const unsigned int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const long int a, const long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const long int a, const int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const long int a, const long double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const long int a, const double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-
-const mpreal pow(const int a, const unsigned long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const int a, const unsigned int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const int a, const long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const int a, const int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const int a, const long double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const int a, const double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-
-const mpreal pow(const long double a, const long double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const long double a, const unsigned long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const long double a, const unsigned int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const long double a, const long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const long double a, const int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-
-const mpreal pow(const double a, const double b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const double a, const unsigned long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const double a, const unsigned int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const double a, const long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-const mpreal pow(const double a, const int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-
-inline const mpreal mul_2ui(const mpreal& v, unsigned long int k, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-inline const mpreal mul_2si(const mpreal& v, long int k, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-inline const mpreal div_2ui(const mpreal& v, unsigned long int k, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-inline const mpreal div_2si(const mpreal& v, long int k, mp_rnd_t rnd_mode = mpreal::get_default_rnd());
-
-//////////////////////////////////////////////////////////////////////////
-// Estimate machine epsilon for the given precision
-// Returns smallest eps such that 1.0 + eps != 1.0
-inline mpreal machine_epsilon(mp_prec_t prec = mpreal::get_default_prec());
-
-// Returns smallest eps such that x + eps != x (relative machine epsilon)
-inline mpreal machine_epsilon(const mpreal& x);
-
-// Gives max & min values for the required precision,
-// minval is 'safe' meaning 1 / minval does not overflow
-// maxval is 'safe' meaning 1 / maxval does not underflow
-inline mpreal minval(mp_prec_t prec = mpreal::get_default_prec());
-inline mpreal maxval(mp_prec_t prec = mpreal::get_default_prec());
-
-// 'Dirty' equality check 1: |a-b| < min{|a|,|b|} * eps
-inline bool isEqualFuzzy(const mpreal& a, const mpreal& b, const mpreal& eps);
-
-// 'Dirty' equality check 2: |a-b| < min{|a|,|b|} * eps( min{|a|,|b|} )
-inline bool isEqualFuzzy(const mpreal& a, const mpreal& b);
-
-// 'Bitwise' equality check
-// maxUlps - a and b can be apart by maxUlps binary numbers.
-inline bool isEqualUlps(const mpreal& a, const mpreal& b, int maxUlps);
-
-//////////////////////////////////////////////////////////////////////////
-// Convert precision in 'bits' to decimal digits and vice versa.
-// bits = ceil(digits*log[2](10))
-// digits = floor(bits*log[10](2))
-
-inline mp_prec_t digits2bits(int d);
-inline int bits2digits(mp_prec_t b);
-
-//////////////////////////////////////////////////////////////////////////
-// min, max
-const mpreal (max)(const mpreal& x, const mpreal& y);
-const mpreal (min)(const mpreal& x, const mpreal& y);
-
-//////////////////////////////////////////////////////////////////////////
-// Implementation
-//////////////////////////////////////////////////////////////////////////
-
-//////////////////////////////////////////////////////////////////////////
-// Operators - Assignment
-inline mpreal& mpreal::operator=(const mpreal& v)
-{
- if (this != &v)
- {
- mp_prec_t tp = mpfr_get_prec( mpfr_srcptr());
- mp_prec_t vp = mpfr_get_prec(v.mpfr_srcptr());
-
- if(tp != vp){
- clear(mpfr_ptr());
- mpfr_init2(mpfr_ptr(), vp);
- }
-
- mpfr_set(mpfr_ptr(), v.mpfr_srcptr(), mpreal::get_default_rnd());
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
- }
- return *this;
-}
-
-inline mpreal& mpreal::operator=(const mpf_t v)
-{
- mpfr_set_f(mpfr_ptr(), v, mpreal::get_default_rnd());
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator=(const mpz_t v)
-{
- mpfr_set_z(mpfr_ptr(), v, mpreal::get_default_rnd());
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator=(const mpq_t v)
-{
- mpfr_set_q(mpfr_ptr(), v, mpreal::get_default_rnd());
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator=(const long double v)
-{
- mpfr_set_ld(mpfr_ptr(), v, mpreal::get_default_rnd());
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator=(const double v)
-{
-#if (MPREAL_DOUBLE_BITS_OVERFLOW > -1)
- if(fits_in_bits(v, MPREAL_DOUBLE_BITS_OVERFLOW))
- {
- mpfr_set_d(mpfr_ptr(),v,mpreal::get_default_rnd());
- }else
- throw conversion_overflow();
-#else
- mpfr_set_d(mpfr_ptr(),v,mpreal::get_default_rnd());
-#endif
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator=(const unsigned long int v)
-{
- mpfr_set_ui(mpfr_ptr(), v, mpreal::get_default_rnd());
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator=(const unsigned int v)
-{
- mpfr_set_ui(mpfr_ptr(), v, mpreal::get_default_rnd());
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator=(const unsigned long long int v)
-{
- mpfr_set_uj(mpfr_ptr(), v, mpreal::get_default_rnd());
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator=(const long long int v)
-{
- mpfr_set_sj(mpfr_ptr(), v, mpreal::get_default_rnd());
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator=(const long int v)
-{
- mpfr_set_si(mpfr_ptr(), v, mpreal::get_default_rnd());
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator=(const int v)
-{
- mpfr_set_si(mpfr_ptr(), v, mpreal::get_default_rnd());
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator=(const char* s)
-{
- // Use other converters for more precise control on base & precision & rounding:
- //
- // mpreal(const char* s, mp_prec_t prec, int base, mp_rnd_t mode)
- // mpreal(const std::string& s,mp_prec_t prec, int base, mp_rnd_t mode)
- //
- // Here we assume base = 10 and we use precision of target variable.
-
- mpfr_t t;
-
- mpfr_init2(t, mpfr_get_prec(mpfr_srcptr()));
-
- if(0 == mpfr_set_str(t, s, 10, mpreal::get_default_rnd()))
- {
- mpfr_set(mpfr_ptr(), t, mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- }
-
- clear(t);
- return *this;
-}
-
-inline mpreal& mpreal::operator=(const std::string& s)
-{
- // Use other converters for more precise control on base & precision & rounding:
- //
- // mpreal(const char* s, mp_prec_t prec, int base, mp_rnd_t mode)
- // mpreal(const std::string& s,mp_prec_t prec, int base, mp_rnd_t mode)
- //
- // Here we assume base = 10 and we use precision of target variable.
-
- mpfr_t t;
-
- mpfr_init2(t, mpfr_get_prec(mpfr_srcptr()));
-
- if(0 == mpfr_set_str(t, s.c_str(), 10, mpreal::get_default_rnd()))
- {
- mpfr_set(mpfr_ptr(), t, mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- }
-
- clear(t);
- return *this;
-}
-
-template <typename real_t>
-inline mpreal& mpreal::operator= (const std::complex<real_t>& z)
-{
- return *this = z.real();
-}
-
-//////////////////////////////////////////////////////////////////////////
-// + Addition
-inline mpreal& mpreal::operator+=(const mpreal& v)
-{
- mpfr_add(mpfr_ptr(), mpfr_srcptr(), v.mpfr_srcptr(), mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator+=(const mpf_t u)
-{
- *this += mpreal(u);
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator+=(const mpz_t u)
-{
- mpfr_add_z(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator+=(const mpq_t u)
-{
- mpfr_add_q(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator+= (const long double u)
-{
- *this += mpreal(u);
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator+= (const double u)
-{
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
- mpfr_add_d(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
-#else
- *this += mpreal(u);
-#endif
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator+=(const unsigned long int u)
-{
- mpfr_add_ui(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator+=(const unsigned int u)
-{
- mpfr_add_ui(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator+=(const long int u)
-{
- mpfr_add_si(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator+=(const int u)
-{
- mpfr_add_si(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator+=(const long long int u) { *this += mpreal(u); MPREAL_MSVC_DEBUGVIEW_CODE; return *this; }
-inline mpreal& mpreal::operator+=(const unsigned long long int u){ *this += mpreal(u); MPREAL_MSVC_DEBUGVIEW_CODE; return *this; }
-inline mpreal& mpreal::operator-=(const long long int u) { *this -= mpreal(u); MPREAL_MSVC_DEBUGVIEW_CODE; return *this; }
-inline mpreal& mpreal::operator-=(const unsigned long long int u){ *this -= mpreal(u); MPREAL_MSVC_DEBUGVIEW_CODE; return *this; }
-inline mpreal& mpreal::operator*=(const long long int u) { *this *= mpreal(u); MPREAL_MSVC_DEBUGVIEW_CODE; return *this; }
-inline mpreal& mpreal::operator*=(const unsigned long long int u){ *this *= mpreal(u); MPREAL_MSVC_DEBUGVIEW_CODE; return *this; }
-inline mpreal& mpreal::operator/=(const long long int u) { *this /= mpreal(u); MPREAL_MSVC_DEBUGVIEW_CODE; return *this; }
-inline mpreal& mpreal::operator/=(const unsigned long long int u){ *this /= mpreal(u); MPREAL_MSVC_DEBUGVIEW_CODE; return *this; }
-
-inline const mpreal mpreal::operator+()const { return mpreal(*this); }
-
-inline const mpreal operator+(const mpreal& a, const mpreal& b)
-{
- mpreal c(0, (std::max)(mpfr_get_prec(a.mpfr_ptr()), mpfr_get_prec(b.mpfr_ptr())));
- mpfr_add(c.mpfr_ptr(), a.mpfr_srcptr(), b.mpfr_srcptr(), mpreal::get_default_rnd());
- return c;
-}
-
-inline mpreal& mpreal::operator++()
-{
- return *this += 1;
-}
-
-inline const mpreal mpreal::operator++ (int)
-{
- mpreal x(*this);
- *this += 1;
- return x;
-}
-
-inline mpreal& mpreal::operator--()
-{
- return *this -= 1;
-}
-
-inline const mpreal mpreal::operator-- (int)
-{
- mpreal x(*this);
- *this -= 1;
- return x;
-}
-
-//////////////////////////////////////////////////////////////////////////
-// - Subtraction
-inline mpreal& mpreal::operator-=(const mpreal& v)
-{
- mpfr_sub(mpfr_ptr(),mpfr_srcptr(),v.mpfr_srcptr(),mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator-=(const mpz_t v)
-{
- mpfr_sub_z(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator-=(const mpq_t v)
-{
- mpfr_sub_q(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator-=(const long double v)
-{
- *this -= mpreal(v);
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator-=(const double v)
-{
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
- mpfr_sub_d(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
-#else
- *this -= mpreal(v);
-#endif
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator-=(const unsigned long int v)
-{
- mpfr_sub_ui(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator-=(const unsigned int v)
-{
- mpfr_sub_ui(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator-=(const long int v)
-{
- mpfr_sub_si(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator-=(const int v)
-{
- mpfr_sub_si(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline const mpreal mpreal::operator-()const
-{
- mpreal u(*this);
- mpfr_neg(u.mpfr_ptr(),u.mpfr_srcptr(),mpreal::get_default_rnd());
- return u;
-}
-
-inline const mpreal operator-(const mpreal& a, const mpreal& b)
-{
- mpreal c(0, (std::max)(mpfr_get_prec(a.mpfr_ptr()), mpfr_get_prec(b.mpfr_ptr())));
- mpfr_sub(c.mpfr_ptr(), a.mpfr_srcptr(), b.mpfr_srcptr(), mpreal::get_default_rnd());
- return c;
-}
-
-inline const mpreal operator-(const double b, const mpreal& a)
-{
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
- mpreal x(0, mpfr_get_prec(a.mpfr_ptr()));
- mpfr_d_sub(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
- return x;
-#else
- mpreal x(b, mpfr_get_prec(a.mpfr_ptr()));
- x -= a;
- return x;
-#endif
-}
-
-inline const mpreal operator-(const unsigned long int b, const mpreal& a)
-{
- mpreal x(0, mpfr_get_prec(a.mpfr_ptr()));
- mpfr_ui_sub(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
- return x;
-}
-
-inline const mpreal operator-(const unsigned int b, const mpreal& a)
-{
- mpreal x(0, mpfr_get_prec(a.mpfr_ptr()));
- mpfr_ui_sub(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
- return x;
-}
-
-inline const mpreal operator-(const long int b, const mpreal& a)
-{
- mpreal x(0, mpfr_get_prec(a.mpfr_ptr()));
- mpfr_si_sub(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
- return x;
-}
-
-inline const mpreal operator-(const int b, const mpreal& a)
-{
- mpreal x(0, mpfr_get_prec(a.mpfr_ptr()));
- mpfr_si_sub(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
- return x;
-}
-
-//////////////////////////////////////////////////////////////////////////
-// * Multiplication
-inline mpreal& mpreal::operator*= (const mpreal& v)
-{
- mpfr_mul(mpfr_ptr(),mpfr_srcptr(),v.mpfr_srcptr(),mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator*=(const mpz_t v)
-{
- mpfr_mul_z(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator*=(const mpq_t v)
-{
- mpfr_mul_q(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator*=(const long double v)
-{
- *this *= mpreal(v);
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator*=(const double v)
-{
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
- mpfr_mul_d(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
-#else
- *this *= mpreal(v);
-#endif
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator*=(const unsigned long int v)
-{
- mpfr_mul_ui(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator*=(const unsigned int v)
-{
- mpfr_mul_ui(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator*=(const long int v)
-{
- mpfr_mul_si(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator*=(const int v)
-{
- mpfr_mul_si(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline const mpreal operator*(const mpreal& a, const mpreal& b)
-{
- mpreal c(0, (std::max)(mpfr_get_prec(a.mpfr_ptr()), mpfr_get_prec(b.mpfr_ptr())));
- mpfr_mul(c.mpfr_ptr(), a.mpfr_srcptr(), b.mpfr_srcptr(), mpreal::get_default_rnd());
- return c;
-}
-
-//////////////////////////////////////////////////////////////////////////
-// / Division
-inline mpreal& mpreal::operator/=(const mpreal& v)
-{
- mpfr_div(mpfr_ptr(),mpfr_srcptr(),v.mpfr_srcptr(),mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator/=(const mpz_t v)
-{
- mpfr_div_z(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator/=(const mpq_t v)
-{
- mpfr_div_q(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator/=(const long double v)
-{
- *this /= mpreal(v);
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator/=(const double v)
-{
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
- mpfr_div_d(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
-#else
- *this /= mpreal(v);
-#endif
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator/=(const unsigned long int v)
-{
- mpfr_div_ui(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator/=(const unsigned int v)
-{
- mpfr_div_ui(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator/=(const long int v)
-{
- mpfr_div_si(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator/=(const int v)
-{
- mpfr_div_si(mpfr_ptr(),mpfr_srcptr(),v,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline const mpreal operator/(const mpreal& a, const mpreal& b)
-{
- mpreal c(0, (std::max)(mpfr_get_prec(a.mpfr_srcptr()), mpfr_get_prec(b.mpfr_srcptr())));
- mpfr_div(c.mpfr_ptr(), a.mpfr_srcptr(), b.mpfr_srcptr(), mpreal::get_default_rnd());
- return c;
-}
-
-inline const mpreal operator/(const unsigned long int b, const mpreal& a)
-{
- mpreal x(0, mpfr_get_prec(a.mpfr_srcptr()));
- mpfr_ui_div(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
- return x;
-}
-
-inline const mpreal operator/(const unsigned int b, const mpreal& a)
-{
- mpreal x(0, mpfr_get_prec(a.mpfr_srcptr()));
- mpfr_ui_div(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
- return x;
-}
-
-inline const mpreal operator/(const long int b, const mpreal& a)
-{
- mpreal x(0, mpfr_get_prec(a.mpfr_srcptr()));
- mpfr_si_div(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
- return x;
-}
-
-inline const mpreal operator/(const int b, const mpreal& a)
-{
- mpreal x(0, mpfr_get_prec(a.mpfr_srcptr()));
- mpfr_si_div(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
- return x;
-}
-
-inline const mpreal operator/(const double b, const mpreal& a)
-{
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
- mpreal x(0, mpfr_get_prec(a.mpfr_srcptr()));
- mpfr_d_div(x.mpfr_ptr(), b, a.mpfr_srcptr(), mpreal::get_default_rnd());
- return x;
-#else
- mpreal x(0, mpfr_get_prec(a.mpfr_ptr()));
- x /= a;
- return x;
-#endif
-}
-
-//////////////////////////////////////////////////////////////////////////
-// Shifts operators - Multiplication/Division by power of 2
-inline mpreal& mpreal::operator<<=(const unsigned long int u)
-{
- mpfr_mul_2ui(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator<<=(const unsigned int u)
-{
- mpfr_mul_2ui(mpfr_ptr(),mpfr_srcptr(),static_cast<unsigned long int>(u),mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator<<=(const long int u)
-{
- mpfr_mul_2si(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator<<=(const int u)
-{
- mpfr_mul_2si(mpfr_ptr(),mpfr_srcptr(),static_cast<long int>(u),mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator>>=(const unsigned long int u)
-{
- mpfr_div_2ui(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator>>=(const unsigned int u)
-{
- mpfr_div_2ui(mpfr_ptr(),mpfr_srcptr(),static_cast<unsigned long int>(u),mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator>>=(const long int u)
-{
- mpfr_div_2si(mpfr_ptr(),mpfr_srcptr(),u,mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::operator>>=(const int u)
-{
- mpfr_div_2si(mpfr_ptr(),mpfr_srcptr(),static_cast<long int>(u),mpreal::get_default_rnd());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline const mpreal operator<<(const mpreal& v, const unsigned long int k)
-{
- return mul_2ui(v,k);
-}
-
-inline const mpreal operator<<(const mpreal& v, const unsigned int k)
-{
- return mul_2ui(v,static_cast<unsigned long int>(k));
-}
-
-inline const mpreal operator<<(const mpreal& v, const long int k)
-{
- return mul_2si(v,k);
-}
-
-inline const mpreal operator<<(const mpreal& v, const int k)
-{
- return mul_2si(v,static_cast<long int>(k));
-}
-
-inline const mpreal operator>>(const mpreal& v, const unsigned long int k)
-{
- return div_2ui(v,k);
-}
-
-inline const mpreal operator>>(const mpreal& v, const long int k)
-{
- return div_2si(v,k);
-}
-
-inline const mpreal operator>>(const mpreal& v, const unsigned int k)
-{
- return div_2ui(v,static_cast<unsigned long int>(k));
-}
-
-inline const mpreal operator>>(const mpreal& v, const int k)
-{
- return div_2si(v,static_cast<long int>(k));
-}
-
-// mul_2ui
-inline const mpreal mul_2ui(const mpreal& v, unsigned long int k, mp_rnd_t rnd_mode)
-{
- mpreal x(v);
- mpfr_mul_2ui(x.mpfr_ptr(),v.mpfr_srcptr(),k,rnd_mode);
- return x;
-}
-
-// mul_2si
-inline const mpreal mul_2si(const mpreal& v, long int k, mp_rnd_t rnd_mode)
-{
- mpreal x(v);
- mpfr_mul_2si(x.mpfr_ptr(),v.mpfr_srcptr(),k,rnd_mode);
- return x;
-}
-
-inline const mpreal div_2ui(const mpreal& v, unsigned long int k, mp_rnd_t rnd_mode)
-{
- mpreal x(v);
- mpfr_div_2ui(x.mpfr_ptr(),v.mpfr_srcptr(),k,rnd_mode);
- return x;
-}
-
-inline const mpreal div_2si(const mpreal& v, long int k, mp_rnd_t rnd_mode)
-{
- mpreal x(v);
- mpfr_div_2si(x.mpfr_ptr(),v.mpfr_srcptr(),k,rnd_mode);
- return x;
-}
-
-//////////////////////////////////////////////////////////////////////////
-//Relational operators
-
-// WARNING:
-//
-// Please note that following checks for double-NaN are guaranteed to work only in IEEE math mode:
-//
-// isnan(b) = (b != b)
-// isnan(b) = !(b == b) (we use in code below)
-//
-// Be cautions if you use compiler options which break strict IEEE compliance (e.g. -ffast-math in GCC).
-// Use std::isnan instead (C++11).
-
-inline bool operator > (const mpreal& a, const mpreal& b ){ return (mpfr_greater_p(a.mpfr_srcptr(),b.mpfr_srcptr()) != 0 ); }
-inline bool operator > (const mpreal& a, const unsigned long int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) > 0 ); }
-inline bool operator > (const mpreal& a, const unsigned int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) > 0 ); }
-inline bool operator > (const mpreal& a, const long int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) > 0 ); }
-inline bool operator > (const mpreal& a, const int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) > 0 ); }
-inline bool operator > (const mpreal& a, const long double b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (b == b) && (mpfr_cmp_ld(a.mpfr_srcptr(),b) > 0 ); }
-inline bool operator > (const mpreal& a, const double b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (b == b) && (mpfr_cmp_d (a.mpfr_srcptr(),b) > 0 ); }
-
-inline bool operator >= (const mpreal& a, const mpreal& b ){ return (mpfr_greaterequal_p(a.mpfr_srcptr(),b.mpfr_srcptr()) != 0 ); }
-inline bool operator >= (const mpreal& a, const unsigned long int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) >= 0 ); }
-// inline bool operator >= (const mpreal& a, const unsigned int b ){ return !isnan EIGEN_NOT_A_MACRO (isnan()a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) >= 0 ); }
-inline bool operator >= (const mpreal& a, const long int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) >= 0 ); }
-inline bool operator >= (const mpreal& a, const int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) >= 0 ); }
-inline bool operator >= (const mpreal& a, const long double b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (b == b) && (mpfr_cmp_ld(a.mpfr_srcptr(),b) >= 0 ); }
-inline bool operator >= (const mpreal& a, const double b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (b == b) && (mpfr_cmp_d (a.mpfr_srcptr(),b) >= 0 ); }
-
-inline bool operator < (const mpreal& a, const mpreal& b ){ return (mpfr_less_p(a.mpfr_srcptr(),b.mpfr_srcptr()) != 0 ); }
-inline bool operator < (const mpreal& a, const unsigned long int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) < 0 ); }
-inline bool operator < (const mpreal& a, const unsigned int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) < 0 ); }
-inline bool operator < (const mpreal& a, const long int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) < 0 ); }
-inline bool operator < (const mpreal& a, const int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) < 0 ); }
-inline bool operator < (const mpreal& a, const long double b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (b == b) && (mpfr_cmp_ld(a.mpfr_srcptr(),b) < 0 ); }
-inline bool operator < (const mpreal& a, const double b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (b == b) && (mpfr_cmp_d (a.mpfr_srcptr(),b) < 0 ); }
-
-inline bool operator <= (const mpreal& a, const mpreal& b ){ return (mpfr_lessequal_p(a.mpfr_srcptr(),b.mpfr_srcptr()) != 0 ); }
-inline bool operator <= (const mpreal& a, const unsigned long int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) <= 0 ); }
-inline bool operator <= (const mpreal& a, const unsigned int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) <= 0 ); }
-inline bool operator <= (const mpreal& a, const long int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) <= 0 ); }
-inline bool operator <= (const mpreal& a, const int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) <= 0 ); }
-inline bool operator <= (const mpreal& a, const long double b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (b == b) && (mpfr_cmp_ld(a.mpfr_srcptr(),b) <= 0 ); }
-inline bool operator <= (const mpreal& a, const double b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (b == b) && (mpfr_cmp_d (a.mpfr_srcptr(),b) <= 0 ); }
-
-inline bool operator == (const mpreal& a, const mpreal& b ){ return (mpfr_equal_p(a.mpfr_srcptr(),b.mpfr_srcptr()) != 0 ); }
-inline bool operator == (const mpreal& a, const unsigned long int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) == 0 ); }
-inline bool operator == (const mpreal& a, const unsigned int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_ui(a.mpfr_srcptr(),b) == 0 ); }
-inline bool operator == (const mpreal& a, const long int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) == 0 ); }
-inline bool operator == (const mpreal& a, const int b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (mpfr_cmp_si(a.mpfr_srcptr(),b) == 0 ); }
-inline bool operator == (const mpreal& a, const long double b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (b == b) && (mpfr_cmp_ld(a.mpfr_srcptr(),b) == 0 ); }
-inline bool operator == (const mpreal& a, const double b ){ return !isnan EIGEN_NOT_A_MACRO (a) && (b == b) && (mpfr_cmp_d (a.mpfr_srcptr(),b) == 0 ); }
-
-inline bool operator != (const mpreal& a, const mpreal& b ){ return !(a == b); }
-inline bool operator != (const mpreal& a, const unsigned long int b ){ return !(a == b); }
-inline bool operator != (const mpreal& a, const unsigned int b ){ return !(a == b); }
-inline bool operator != (const mpreal& a, const long int b ){ return !(a == b); }
-inline bool operator != (const mpreal& a, const int b ){ return !(a == b); }
-inline bool operator != (const mpreal& a, const long double b ){ return !(a == b); }
-inline bool operator != (const mpreal& a, const double b ){ return !(a == b); }
-
-inline bool (isnan) (const mpreal& op){ return (mpfr_nan_p (op.mpfr_srcptr()) != 0 ); }
-inline bool (isinf) (const mpreal& op){ return (mpfr_inf_p (op.mpfr_srcptr()) != 0 ); }
-inline bool (isfinite) (const mpreal& op){ return (mpfr_number_p (op.mpfr_srcptr()) != 0 ); }
-inline bool iszero (const mpreal& op){ return (mpfr_zero_p (op.mpfr_srcptr()) != 0 ); }
-inline bool isint (const mpreal& op){ return (mpfr_integer_p(op.mpfr_srcptr()) != 0 ); }
-
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,0,0))
-inline bool isregular(const mpreal& op){ return (mpfr_regular_p(op.mpfr_srcptr()));}
-#endif
-
-//////////////////////////////////////////////////////////////////////////
-// Type Converters
-inline bool mpreal::toBool ( ) const { return mpfr_zero_p (mpfr_srcptr()) == 0; }
-inline long mpreal::toLong (mp_rnd_t mode) const { return mpfr_get_si (mpfr_srcptr(), mode); }
-inline unsigned long mpreal::toULong (mp_rnd_t mode) const { return mpfr_get_ui (mpfr_srcptr(), mode); }
-inline float mpreal::toFloat (mp_rnd_t mode) const { return mpfr_get_flt(mpfr_srcptr(), mode); }
-inline double mpreal::toDouble (mp_rnd_t mode) const { return mpfr_get_d (mpfr_srcptr(), mode); }
-inline long double mpreal::toLDouble(mp_rnd_t mode) const { return mpfr_get_ld (mpfr_srcptr(), mode); }
-inline long long mpreal::toLLong (mp_rnd_t mode) const { return mpfr_get_sj (mpfr_srcptr(), mode); }
-inline unsigned long long mpreal::toULLong (mp_rnd_t mode) const { return mpfr_get_uj (mpfr_srcptr(), mode); }
-
-inline ::mpfr_ptr mpreal::mpfr_ptr() { return mp; }
-inline ::mpfr_srcptr mpreal::mpfr_ptr() const { return mp; }
-inline ::mpfr_srcptr mpreal::mpfr_srcptr() const { return mp; }
-
-template <class T>
-inline std::string toString(T t, std::ios_base & (*f)(std::ios_base&))
-{
- std::ostringstream oss;
- oss << f << t;
- return oss.str();
-}
-
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
-
-inline std::string mpreal::toString(const std::string& format) const
-{
- char *s = NULL;
- std::string out;
-
- if( !format.empty() )
- {
- if(!(mpfr_asprintf(&s, format.c_str(), mpfr_srcptr()) < 0))
- {
- out = std::string(s);
-
- mpfr_free_str(s);
- }
- }
-
- return out;
-}
-
-#endif
-
-inline std::string mpreal::toString(int n, int b, mp_rnd_t mode) const
-{
- // TODO: Add extended format specification (f, e, rounding mode) as it done in output operator
- (void)b;
- (void)mode;
-
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
-
- std::ostringstream format;
-
- int digits = (n >= 0) ? n : 1 + bits2digits(mpfr_get_prec(mpfr_srcptr()));
-
- format << "%." << digits << "RNg";
-
- return toString(format.str());
-
-#else
-
- char *s, *ns = NULL;
- size_t slen, nslen;
- mp_exp_t exp;
- std::string out;
-
- if(mpfr_inf_p(mp))
- {
- if(mpfr_sgn(mp)>0) return "+Inf";
- else return "-Inf";
- }
-
- if(mpfr_zero_p(mp)) return "0";
- if(mpfr_nan_p(mp)) return "NaN";
-
- s = mpfr_get_str(NULL, &exp, b, 0, mp, mode);
- ns = mpfr_get_str(NULL, &exp, b, (std::max)(0,n), mp, mode);
-
- if(s!=NULL && ns!=NULL)
- {
- slen = strlen(s);
- nslen = strlen(ns);
- if(nslen<=slen)
- {
- mpfr_free_str(s);
- s = ns;
- slen = nslen;
- }
- else {
- mpfr_free_str(ns);
- }
-
- // Make human eye-friendly formatting if possible
- if (exp>0 && static_cast<size_t>(exp)<slen)
- {
- if(s[0]=='-')
- {
- // Remove zeros starting from right end
- char* ptr = s+slen-1;
- while (*ptr=='0' && ptr>s+exp) ptr--;
-
- if(ptr==s+exp) out = std::string(s,exp+1);
- else out = std::string(s,exp+1)+'.'+std::string(s+exp+1,ptr-(s+exp+1)+1);
-
- //out = string(s,exp+1)+'.'+string(s+exp+1);
- }
- else
- {
- // Remove zeros starting from right end
- char* ptr = s+slen-1;
- while (*ptr=='0' && ptr>s+exp-1) ptr--;
-
- if(ptr==s+exp-1) out = std::string(s,exp);
- else out = std::string(s,exp)+'.'+std::string(s+exp,ptr-(s+exp)+1);
-
- //out = string(s,exp)+'.'+string(s+exp);
- }
-
- }else{ // exp<0 || exp>slen
- if(s[0]=='-')
- {
- // Remove zeros starting from right end
- char* ptr = s+slen-1;
- while (*ptr=='0' && ptr>s+1) ptr--;
-
- if(ptr==s+1) out = std::string(s,2);
- else out = std::string(s,2)+'.'+std::string(s+2,ptr-(s+2)+1);
-
- //out = string(s,2)+'.'+string(s+2);
- }
- else
- {
- // Remove zeros starting from right end
- char* ptr = s+slen-1;
- while (*ptr=='0' && ptr>s) ptr--;
-
- if(ptr==s) out = std::string(s,1);
- else out = std::string(s,1)+'.'+std::string(s+1,ptr-(s+1)+1);
-
- //out = string(s,1)+'.'+string(s+1);
- }
-
- // Make final string
- if(--exp)
- {
- if(exp>0) out += "e+"+mpfr::toString<mp_exp_t>(exp,std::dec);
- else out += "e"+mpfr::toString<mp_exp_t>(exp,std::dec);
- }
- }
-
- mpfr_free_str(s);
- return out;
- }else{
- return "conversion error!";
- }
-#endif
-}
-
-
-//////////////////////////////////////////////////////////////////////////
-// I/O
-inline std::ostream& mpreal::output(std::ostream& os) const
-{
- std::ostringstream format;
- const std::ios::fmtflags flags = os.flags();
-
- format << ((flags & std::ios::showpos) ? "%+" : "%");
- if (os.precision() >= 0)
- format << '.' << os.precision() << "R*"
- << ((flags & std::ios::floatfield) == std::ios::fixed ? 'f' :
- (flags & std::ios::floatfield) == std::ios::scientific ? 'e' :
- 'g');
- else
- format << "R*e";
-
- char *s = NULL;
- if(!(mpfr_asprintf(&s, format.str().c_str(),
- mpfr::mpreal::get_default_rnd(),
- mpfr_srcptr())
- < 0))
- {
- os << std::string(s);
- mpfr_free_str(s);
- }
- return os;
-}
-
-inline std::ostream& operator<<(std::ostream& os, const mpreal& v)
-{
- return v.output(os);
-}
-
-inline std::istream& operator>>(std::istream &is, mpreal& v)
-{
- // TODO: use cout::hexfloat and other flags to setup base
- std::string tmp;
- is >> tmp;
- mpfr_set_str(v.mpfr_ptr(), tmp.c_str(), 10, mpreal::get_default_rnd());
- return is;
-}
-
-//////////////////////////////////////////////////////////////////////////
-// Bits - decimal digits relation
-// bits = ceil(digits*log[2](10))
-// digits = floor(bits*log[10](2))
-
-inline mp_prec_t digits2bits(int d)
-{
- const double LOG2_10 = 3.3219280948873624;
-
- return mp_prec_t(std::ceil( d * LOG2_10 ));
-}
-
-inline int bits2digits(mp_prec_t b)
-{
- const double LOG10_2 = 0.30102999566398119;
-
- return int(std::floor( b * LOG10_2 ));
-}
-
-//////////////////////////////////////////////////////////////////////////
-// Set/Get number properties
-inline int sgn(const mpreal& op)
-{
- return mpfr_sgn(op.mpfr_srcptr());
-}
-
-inline mpreal& mpreal::setSign(int sign, mp_rnd_t RoundingMode)
-{
- mpfr_setsign(mpfr_ptr(), mpfr_srcptr(), (sign < 0 ? 1 : 0), RoundingMode);
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline int mpreal::getPrecision() const
-{
- return int(mpfr_get_prec(mpfr_srcptr()));
-}
-
-inline mpreal& mpreal::setPrecision(int Precision, mp_rnd_t RoundingMode)
-{
- mpfr_prec_round(mpfr_ptr(), Precision, RoundingMode);
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::setInf(int sign)
-{
- mpfr_set_inf(mpfr_ptr(), sign);
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::setNan()
-{
- mpfr_set_nan(mpfr_ptr());
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mpreal& mpreal::setZero(int sign)
-{
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,0,0))
- mpfr_set_zero(mpfr_ptr(), sign);
-#else
- mpfr_set_si(mpfr_ptr(), 0, (mpfr_get_default_rounding_mode)());
- setSign(sign);
-#endif
-
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return *this;
-}
-
-inline mp_prec_t mpreal::get_prec() const
-{
- return mpfr_get_prec(mpfr_srcptr());
-}
-
-inline void mpreal::set_prec(mp_prec_t prec, mp_rnd_t rnd_mode)
-{
- mpfr_prec_round(mpfr_ptr(),prec,rnd_mode);
- MPREAL_MSVC_DEBUGVIEW_CODE;
-}
-
-inline mp_exp_t mpreal::get_exp ()
-{
- return mpfr_get_exp(mpfr_srcptr());
-}
-
-inline int mpreal::set_exp (mp_exp_t e)
-{
- int x = mpfr_set_exp(mpfr_ptr(), e);
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return x;
-}
-
-inline const mpreal frexp(const mpreal& x, mp_exp_t* exp, mp_rnd_t mode = mpreal::get_default_rnd())
-{
- mpreal y(x);
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,1,0))
- mpfr_frexp(exp,y.mpfr_ptr(),x.mpfr_srcptr(),mode);
-#else
- *exp = mpfr_get_exp(y.mpfr_srcptr());
- mpfr_set_exp(y.mpfr_ptr(),0);
-#endif
- return y;
-}
-
-inline const mpreal ldexp(const mpreal& v, mp_exp_t exp)
-{
- mpreal x(v);
-
- // rounding is not important since we are just increasing the exponent (= exact operation)
- mpfr_mul_2si(x.mpfr_ptr(), x.mpfr_srcptr(), exp, mpreal::get_default_rnd());
- return x;
-}
-
-inline const mpreal scalbn(const mpreal& v, mp_exp_t exp)
-{
- return ldexp(v, exp);
-}
-
-inline mpreal machine_epsilon(mp_prec_t prec)
-{
- /* the smallest eps such that 1 + eps != 1 */
- return machine_epsilon(mpreal(1, prec));
-}
-
-inline mpreal machine_epsilon(const mpreal& x)
-{
- /* the smallest eps such that x + eps != x */
- if( x < 0)
- {
- return nextabove(-x) + x;
- }else{
- return nextabove( x) - x;
- }
-}
-
-// minval is 'safe' meaning 1 / minval does not overflow
-inline mpreal minval(mp_prec_t prec)
-{
- /* min = 1/2 * 2^emin = 2^(emin - 1) */
- return mpreal(1, prec) << mpreal::get_emin()-1;
-}
-
-// maxval is 'safe' meaning 1 / maxval does not underflow
-inline mpreal maxval(mp_prec_t prec)
-{
- /* max = (1 - eps) * 2^emax, eps is machine epsilon */
- return (mpreal(1, prec) - machine_epsilon(prec)) << mpreal::get_emax();
-}
-
-inline bool isEqualUlps(const mpreal& a, const mpreal& b, int maxUlps)
-{
- return abs(a - b) <= machine_epsilon((max)(abs(a), abs(b))) * maxUlps;
-}
-
-inline bool isEqualFuzzy(const mpreal& a, const mpreal& b, const mpreal& eps)
-{
- return abs(a - b) <= eps;
-}
-
-inline bool isEqualFuzzy(const mpreal& a, const mpreal& b)
-{
- return isEqualFuzzy(a, b, machine_epsilon((max)(1, (min)(abs(a), abs(b)))));
-}
-
-//////////////////////////////////////////////////////////////////////////
-// C++11 sign functions.
-inline mpreal copysign(const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal rop(0, mpfr_get_prec(x.mpfr_ptr()));
- mpfr_setsign(rop.mpfr_ptr(), x.mpfr_srcptr(), mpfr_signbit(y.mpfr_srcptr()), rnd_mode);
- return rop;
-}
-
-inline bool signbit(const mpreal& x)
-{
- return mpfr_signbit(x.mpfr_srcptr());
-}
-
-inline const mpreal modf(const mpreal& v, mpreal& n)
-{
- mpreal f(v);
-
- // rounding is not important since we are using the same number
- mpfr_frac (f.mpfr_ptr(),f.mpfr_srcptr(),mpreal::get_default_rnd());
- mpfr_trunc(n.mpfr_ptr(),v.mpfr_srcptr());
- return f;
-}
-
-inline int mpreal::check_range (int t, mp_rnd_t rnd_mode)
-{
- return mpfr_check_range(mpfr_ptr(),t,rnd_mode);
-}
-
-inline int mpreal::subnormalize (int t,mp_rnd_t rnd_mode)
-{
- int r = mpfr_subnormalize(mpfr_ptr(),t,rnd_mode);
- MPREAL_MSVC_DEBUGVIEW_CODE;
- return r;
-}
-
-inline mp_exp_t mpreal::get_emin (void)
-{
- return mpfr_get_emin();
-}
-
-inline int mpreal::set_emin (mp_exp_t exp)
-{
- return mpfr_set_emin(exp);
-}
-
-inline mp_exp_t mpreal::get_emax (void)
-{
- return mpfr_get_emax();
-}
-
-inline int mpreal::set_emax (mp_exp_t exp)
-{
- return mpfr_set_emax(exp);
-}
-
-inline mp_exp_t mpreal::get_emin_min (void)
-{
- return mpfr_get_emin_min();
-}
-
-inline mp_exp_t mpreal::get_emin_max (void)
-{
- return mpfr_get_emin_max();
-}
-
-inline mp_exp_t mpreal::get_emax_min (void)
-{
- return mpfr_get_emax_min();
-}
-
-inline mp_exp_t mpreal::get_emax_max (void)
-{
- return mpfr_get_emax_max();
-}
-
-//////////////////////////////////////////////////////////////////////////
-// Mathematical Functions
-//////////////////////////////////////////////////////////////////////////
-#define MPREAL_UNARY_MATH_FUNCTION_BODY(f) \
- mpreal y(0, mpfr_get_prec(x.mpfr_srcptr())); \
- mpfr_##f(y.mpfr_ptr(), x.mpfr_srcptr(), r); \
- return y;
-
-inline const mpreal sqr (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd())
-{ MPREAL_UNARY_MATH_FUNCTION_BODY(sqr ); }
-
-inline const mpreal sqrt (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd())
-{ MPREAL_UNARY_MATH_FUNCTION_BODY(sqrt); }
-
-inline const mpreal sqrt(const unsigned long int x, mp_rnd_t r)
-{
- mpreal y;
- mpfr_sqrt_ui(y.mpfr_ptr(), x, r);
- return y;
-}
-
-inline const mpreal sqrt(const unsigned int v, mp_rnd_t rnd_mode)
-{
- return sqrt(static_cast<unsigned long int>(v),rnd_mode);
-}
-
-inline const mpreal sqrt(const long int v, mp_rnd_t rnd_mode)
-{
- if (v>=0) return sqrt(static_cast<unsigned long int>(v),rnd_mode);
- else return mpreal().setNan(); // NaN
-}
-
-inline const mpreal sqrt(const int v, mp_rnd_t rnd_mode)
-{
- if (v>=0) return sqrt(static_cast<unsigned long int>(v),rnd_mode);
- else return mpreal().setNan(); // NaN
-}
-
-inline const mpreal root(const mpreal& x, unsigned long int k, mp_rnd_t r = mpreal::get_default_rnd())
-{
- mpreal y(0, mpfr_get_prec(x.mpfr_srcptr()));
- mpfr_root(y.mpfr_ptr(), x.mpfr_srcptr(), k, r);
- return y;
-}
-
-inline const mpreal dim(const mpreal& a, const mpreal& b, mp_rnd_t r = mpreal::get_default_rnd())
-{
- mpreal y(0, mpfr_get_prec(a.mpfr_srcptr()));
- mpfr_dim(y.mpfr_ptr(), a.mpfr_srcptr(), b.mpfr_srcptr(), r);
- return y;
-}
-
-inline int cmpabs(const mpreal& a,const mpreal& b)
-{
- return mpfr_cmpabs(a.mpfr_ptr(), b.mpfr_srcptr());
-}
-
-inline int sin_cos(mpreal& s, mpreal& c, const mpreal& v, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- return mpfr_sin_cos(s.mpfr_ptr(), c.mpfr_ptr(), v.mpfr_srcptr(), rnd_mode);
-}
-
-inline const mpreal sqrt (const long double v, mp_rnd_t rnd_mode) { return sqrt(mpreal(v),rnd_mode); }
-inline const mpreal sqrt (const double v, mp_rnd_t rnd_mode) { return sqrt(mpreal(v),rnd_mode); }
-
-inline const mpreal cbrt (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(cbrt ); }
-inline const mpreal fabs (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(abs ); }
-inline const mpreal abs (const mpreal& x, mp_rnd_t r) { MPREAL_UNARY_MATH_FUNCTION_BODY(abs ); }
-inline const mpreal log (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(log ); }
-inline const mpreal log2 (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(log2 ); }
-inline const mpreal log10 (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(log10); }
-inline const mpreal exp (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(exp ); }
-inline const mpreal exp2 (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(exp2 ); }
-inline const mpreal exp10 (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(exp10); }
-inline const mpreal cos (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(cos ); }
-inline const mpreal sin (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(sin ); }
-inline const mpreal tan (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(tan ); }
-inline const mpreal sec (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(sec ); }
-inline const mpreal csc (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(csc ); }
-inline const mpreal cot (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(cot ); }
-inline const mpreal acos (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(acos ); }
-inline const mpreal asin (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(asin ); }
-inline const mpreal atan (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(atan ); }
-
-inline const mpreal logb (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { return log2 (abs(x),r); }
-
-inline const mpreal acot (const mpreal& v, mp_rnd_t r = mpreal::get_default_rnd()) { return atan (1/v, r); }
-inline const mpreal asec (const mpreal& v, mp_rnd_t r = mpreal::get_default_rnd()) { return acos (1/v, r); }
-inline const mpreal acsc (const mpreal& v, mp_rnd_t r = mpreal::get_default_rnd()) { return asin (1/v, r); }
-inline const mpreal acoth (const mpreal& v, mp_rnd_t r = mpreal::get_default_rnd()) { return atanh(1/v, r); }
-inline const mpreal asech (const mpreal& v, mp_rnd_t r = mpreal::get_default_rnd()) { return acosh(1/v, r); }
-inline const mpreal acsch (const mpreal& v, mp_rnd_t r = mpreal::get_default_rnd()) { return asinh(1/v, r); }
-
-inline const mpreal cosh (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(cosh ); }
-inline const mpreal sinh (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(sinh ); }
-inline const mpreal tanh (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(tanh ); }
-inline const mpreal sech (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(sech ); }
-inline const mpreal csch (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(csch ); }
-inline const mpreal coth (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(coth ); }
-inline const mpreal acosh (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(acosh); }
-inline const mpreal asinh (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(asinh); }
-inline const mpreal atanh (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(atanh); }
-
-inline const mpreal log1p (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(log1p ); }
-inline const mpreal expm1 (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(expm1 ); }
-inline const mpreal eint (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(eint ); }
-inline const mpreal gamma (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(gamma ); }
-inline const mpreal tgamma (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(gamma ); }
-inline const mpreal lngamma (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(lngamma); }
-inline const mpreal zeta (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(zeta ); }
-inline const mpreal erf (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(erf ); }
-inline const mpreal erfc (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(erfc ); }
-inline const mpreal besselj0(const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(j0 ); }
-inline const mpreal besselj1(const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(j1 ); }
-inline const mpreal bessely0(const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(y0 ); }
-inline const mpreal bessely1(const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(y1 ); }
-
-inline const mpreal atan2 (const mpreal& y, const mpreal& x, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal a(0,(std::max)(y.getPrecision(), x.getPrecision()));
- mpfr_atan2(a.mpfr_ptr(), y.mpfr_srcptr(), x.mpfr_srcptr(), rnd_mode);
- return a;
-}
-
-inline const mpreal hypot (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal a(0,(std::max)(y.getPrecision(), x.getPrecision()));
- mpfr_hypot(a.mpfr_ptr(), x.mpfr_srcptr(), y.mpfr_srcptr(), rnd_mode);
- return a;
-}
-
-inline const mpreal remainder (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal a(0,(std::max)(y.getPrecision(), x.getPrecision()));
- mpfr_remainder(a.mpfr_ptr(), x.mpfr_srcptr(), y.mpfr_srcptr(), rnd_mode);
- return a;
-}
-
-inline const mpreal remquo (long* q, const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal a(0,(std::max)(y.getPrecision(), x.getPrecision()));
- mpfr_remquo(a.mpfr_ptr(),q, x.mpfr_srcptr(), y.mpfr_srcptr(), rnd_mode);
- return a;
-}
-
-inline const mpreal fac_ui (unsigned long int v, mp_prec_t prec = mpreal::get_default_prec(),
- mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal x(0, prec);
- mpfr_fac_ui(x.mpfr_ptr(),v,rnd_mode);
- return x;
-}
-
-
-inline const mpreal lgamma (const mpreal& v, int *signp = 0, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal x(v);
- int tsignp;
-
- if(signp) mpfr_lgamma(x.mpfr_ptr(), signp,v.mpfr_srcptr(),rnd_mode);
- else mpfr_lgamma(x.mpfr_ptr(),&tsignp,v.mpfr_srcptr(),rnd_mode);
-
- return x;
-}
-
-
-inline const mpreal besseljn (long n, const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd())
-{
- mpreal y(0, x.getPrecision());
- mpfr_jn(y.mpfr_ptr(), n, x.mpfr_srcptr(), r);
- return y;
-}
-
-inline const mpreal besselyn (long n, const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd())
-{
- mpreal y(0, x.getPrecision());
- mpfr_yn(y.mpfr_ptr(), n, x.mpfr_srcptr(), r);
- return y;
-}
-
-inline const mpreal fma (const mpreal& v1, const mpreal& v2, const mpreal& v3, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal a;
- mp_prec_t p1, p2, p3;
-
- p1 = v1.get_prec();
- p2 = v2.get_prec();
- p3 = v3.get_prec();
-
- a.set_prec(p3>p2?(p3>p1?p3:p1):(p2>p1?p2:p1));
-
- mpfr_fma(a.mp,v1.mp,v2.mp,v3.mp,rnd_mode);
- return a;
-}
-
-inline const mpreal fms (const mpreal& v1, const mpreal& v2, const mpreal& v3, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal a;
- mp_prec_t p1, p2, p3;
-
- p1 = v1.get_prec();
- p2 = v2.get_prec();
- p3 = v3.get_prec();
-
- a.set_prec(p3>p2?(p3>p1?p3:p1):(p2>p1?p2:p1));
-
- mpfr_fms(a.mp,v1.mp,v2.mp,v3.mp,rnd_mode);
- return a;
-}
-
-inline const mpreal agm (const mpreal& v1, const mpreal& v2, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal a;
- mp_prec_t p1, p2;
-
- p1 = v1.get_prec();
- p2 = v2.get_prec();
-
- a.set_prec(p1>p2?p1:p2);
-
- mpfr_agm(a.mp, v1.mp, v2.mp, rnd_mode);
-
- return a;
-}
-
-inline const mpreal sum (const mpreal tab[], const unsigned long int n, int& status, mp_rnd_t mode = mpreal::get_default_rnd())
-{
- mpfr_srcptr *p = new mpfr_srcptr[n];
-
- for (unsigned long int i = 0; i < n; i++)
- p[i] = tab[i].mpfr_srcptr();
-
- mpreal x;
- status = mpfr_sum(x.mpfr_ptr(), (mpfr_ptr*)p, n, mode);
-
- delete [] p;
- return x;
-}
-
-//////////////////////////////////////////////////////////////////////////
-// MPFR 2.4.0 Specifics
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(2,4,0))
-
-inline int sinh_cosh(mpreal& s, mpreal& c, const mpreal& v, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- return mpfr_sinh_cosh(s.mp,c.mp,v.mp,rnd_mode);
-}
-
-inline const mpreal li2 (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd())
-{
- MPREAL_UNARY_MATH_FUNCTION_BODY(li2);
-}
-
-inline const mpreal rem (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- /* R = rem(X,Y) if Y != 0, returns X - n * Y where n = trunc(X/Y). */
- return fmod(x, y, rnd_mode);
-}
-
-inline const mpreal mod (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- (void)rnd_mode;
-
- /*
-
- m = mod(x,y) if y != 0, returns x - n*y where n = floor(x/y)
-
- The following are true by convention:
- - mod(x,0) is x
- - mod(x,x) is 0
- - mod(x,y) for x != y and y != 0 has the same sign as y.
-
- */
-
- if(iszero(y)) return x;
- if(x == y) return 0;
-
- mpreal m = x - floor(x / y) * y;
-
- m.setSign(sgn(y)); // make sure result has the same sign as Y
-
- return m;
-}
-
-inline const mpreal fmod (const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal a;
- mp_prec_t yp, xp;
-
- yp = y.get_prec();
- xp = x.get_prec();
-
- a.set_prec(yp>xp?yp:xp);
-
- mpfr_fmod(a.mp, x.mp, y.mp, rnd_mode);
-
- return a;
-}
-
-inline const mpreal rec_sqrt(const mpreal& v, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal x(v);
- mpfr_rec_sqrt(x.mp,v.mp,rnd_mode);
- return x;
-}
-#endif // MPFR 2.4.0 Specifics
-
-//////////////////////////////////////////////////////////////////////////
-// MPFR 3.0.0 Specifics
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,0,0))
-inline const mpreal digamma (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(digamma); }
-inline const mpreal ai (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(ai); }
-#endif // MPFR 3.0.0 Specifics
-
-//////////////////////////////////////////////////////////////////////////
-// Constants
-inline const mpreal const_log2 (mp_prec_t p = mpreal::get_default_prec(), mp_rnd_t r = mpreal::get_default_rnd())
-{
- mpreal x(0, p);
- mpfr_const_log2(x.mpfr_ptr(), r);
- return x;
-}
-
-inline const mpreal const_pi (mp_prec_t p = mpreal::get_default_prec(), mp_rnd_t r = mpreal::get_default_rnd())
-{
- mpreal x(0, p);
- mpfr_const_pi(x.mpfr_ptr(), r);
- return x;
-}
-
-inline const mpreal const_euler (mp_prec_t p = mpreal::get_default_prec(), mp_rnd_t r = mpreal::get_default_rnd())
-{
- mpreal x(0, p);
- mpfr_const_euler(x.mpfr_ptr(), r);
- return x;
-}
-
-inline const mpreal const_catalan (mp_prec_t p = mpreal::get_default_prec(), mp_rnd_t r = mpreal::get_default_rnd())
-{
- mpreal x(0, p);
- mpfr_const_catalan(x.mpfr_ptr(), r);
- return x;
-}
-
-inline const mpreal const_infinity (int sign = 1, mp_prec_t p = mpreal::get_default_prec())
-{
- mpreal x(0, p);
- mpfr_set_inf(x.mpfr_ptr(), sign);
- return x;
-}
-
-//////////////////////////////////////////////////////////////////////////
-// Integer Related Functions
-inline const mpreal ceil(const mpreal& v)
-{
- mpreal x(v);
- mpfr_ceil(x.mp,v.mp);
- return x;
-}
-
-inline const mpreal floor(const mpreal& v)
-{
- mpreal x(v);
- mpfr_floor(x.mp,v.mp);
- return x;
-}
-
-inline const mpreal round(const mpreal& v)
-{
- mpreal x(v);
- mpfr_round(x.mp,v.mp);
- return x;
-}
-
-inline const mpreal trunc(const mpreal& v)
-{
- mpreal x(v);
- mpfr_trunc(x.mp,v.mp);
- return x;
-}
-
-inline const mpreal rint (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(rint ); }
-inline const mpreal rint_ceil (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(rint_ceil ); }
-inline const mpreal rint_floor (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(rint_floor); }
-inline const mpreal rint_round (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(rint_round); }
-inline const mpreal rint_trunc (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(rint_trunc); }
-inline const mpreal frac (const mpreal& x, mp_rnd_t r = mpreal::get_default_rnd()) { MPREAL_UNARY_MATH_FUNCTION_BODY(frac ); }
-
-//////////////////////////////////////////////////////////////////////////
-// Miscellaneous Functions
-inline void swap (mpreal& a, mpreal& b) { mpfr_swap(a.mp,b.mp); }
-inline const mpreal (max)(const mpreal& x, const mpreal& y){ return (x>y?x:y); }
-inline const mpreal (min)(const mpreal& x, const mpreal& y){ return (x<y?x:y); }
-
-inline const mpreal fmax(const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal a;
- mpfr_max(a.mp,x.mp,y.mp,rnd_mode);
- return a;
-}
-
-inline const mpreal fmin(const mpreal& x, const mpreal& y, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal a;
- mpfr_min(a.mp,x.mp,y.mp,rnd_mode);
- return a;
-}
-
-inline const mpreal nexttoward (const mpreal& x, const mpreal& y)
-{
- mpreal a(x);
- mpfr_nexttoward(a.mp,y.mp);
- return a;
-}
-
-inline const mpreal nextabove (const mpreal& x)
-{
- mpreal a(x);
- mpfr_nextabove(a.mp);
- return a;
-}
-
-inline const mpreal nextbelow (const mpreal& x)
-{
- mpreal a(x);
- mpfr_nextbelow(a.mp);
- return a;
-}
-
-inline const mpreal urandomb (gmp_randstate_t& state)
-{
- mpreal x;
- mpfr_urandomb(x.mpfr_ptr(),state);
- return x;
-}
-
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,0,0))
-inline const mpreal urandom (gmp_randstate_t& state, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal x;
- mpfr_urandom(x.mpfr_ptr(), state, rnd_mode);
- return x;
-}
-#endif
-
-#if (MPFR_VERSION <= MPFR_VERSION_NUM(2,4,2))
-inline const mpreal random2 (mp_size_t size, mp_exp_t exp)
-{
- mpreal x;
- mpfr_random2(x.mpfr_ptr(),size,exp);
- return x;
-}
-#endif
-
-// Uniformly distributed random number generation
-// a = random(seed); <- initialization & first random number generation
-// a = random(); <- next random numbers generation
-// seed != 0
-inline const mpreal random(unsigned int seed = 0)
-{
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,0,0))
- static gmp_randstate_t state;
- static bool initialize = true;
-
- if(initialize)
- {
- gmp_randinit_default(state);
- gmp_randseed_ui(state,0);
- initialize = false;
- }
-
- if(seed != 0) gmp_randseed_ui(state,seed);
-
- return mpfr::urandom(state);
-#else
- if(seed != 0) std::srand(seed);
- return mpfr::mpreal(std::rand()/(double)RAND_MAX);
-#endif
-
-}
-
-#if (MPFR_VERSION >= MPFR_VERSION_NUM(3,1,0))
-
-inline const mpreal grandom (gmp_randstate_t& state, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal x;
- mpfr_grandom(x.mpfr_ptr(), NULL, state, rnd_mode);
- return x;
-}
-
-inline const mpreal grandom(unsigned int seed = 0)
-{
- static gmp_randstate_t state;
- static bool initialize = true;
-
- if(initialize)
- {
- gmp_randinit_default(state);
- gmp_randseed_ui(state,0);
- initialize = false;
- }
-
- if(seed != 0) gmp_randseed_ui(state,seed);
-
- return mpfr::grandom(state);
-}
-#endif
-
-//////////////////////////////////////////////////////////////////////////
-// Set/Get global properties
-inline void mpreal::set_default_prec(mp_prec_t prec)
-{
- mpfr_set_default_prec(prec);
-}
-
-inline void mpreal::set_default_rnd(mp_rnd_t rnd_mode)
-{
- mpfr_set_default_rounding_mode(rnd_mode);
-}
-
-inline bool mpreal::fits_in_bits(double x, int n)
-{
- int i;
- double t;
- return IsInf(x) || (std::modf ( std::ldexp ( std::frexp ( x, &i ), n ), &t ) == 0.0);
-}
-
-inline const mpreal pow(const mpreal& a, const mpreal& b, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal x(a);
- mpfr_pow(x.mp,x.mp,b.mp,rnd_mode);
- return x;
-}
-
-inline const mpreal pow(const mpreal& a, const mpz_t b, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal x(a);
- mpfr_pow_z(x.mp,x.mp,b,rnd_mode);
- return x;
-}
-
-inline const mpreal pow(const mpreal& a, const unsigned long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal x(a);
- mpfr_pow_ui(x.mp,x.mp,b,rnd_mode);
- return x;
-}
-
-inline const mpreal pow(const mpreal& a, const unsigned int b, mp_rnd_t rnd_mode)
-{
- return pow(a,static_cast<unsigned long int>(b),rnd_mode);
-}
-
-inline const mpreal pow(const mpreal& a, const long int b, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal x(a);
- mpfr_pow_si(x.mp,x.mp,b,rnd_mode);
- return x;
-}
-
-inline const mpreal pow(const mpreal& a, const int b, mp_rnd_t rnd_mode)
-{
- return pow(a,static_cast<long int>(b),rnd_mode);
-}
-
-inline const mpreal pow(const mpreal& a, const long double b, mp_rnd_t rnd_mode)
-{
- return pow(a,mpreal(b),rnd_mode);
-}
-
-inline const mpreal pow(const mpreal& a, const double b, mp_rnd_t rnd_mode)
-{
- return pow(a,mpreal(b),rnd_mode);
-}
-
-inline const mpreal pow(const unsigned long int a, const mpreal& b, mp_rnd_t rnd_mode = mpreal::get_default_rnd())
-{
- mpreal x(a);
- mpfr_ui_pow(x.mp,a,b.mp,rnd_mode);
- return x;
-}
-
-inline const mpreal pow(const unsigned int a, const mpreal& b, mp_rnd_t rnd_mode)
-{
- return pow(static_cast<unsigned long int>(a),b,rnd_mode);
-}
-
-inline const mpreal pow(const long int a, const mpreal& b, mp_rnd_t rnd_mode)
-{
- if (a>=0) return pow(static_cast<unsigned long int>(a),b,rnd_mode);
- else return pow(mpreal(a),b,rnd_mode);
-}
-
-inline const mpreal pow(const int a, const mpreal& b, mp_rnd_t rnd_mode)
-{
- if (a>=0) return pow(static_cast<unsigned long int>(a),b,rnd_mode);
- else return pow(mpreal(a),b,rnd_mode);
-}
-
-inline const mpreal pow(const long double a, const mpreal& b, mp_rnd_t rnd_mode)
-{
- return pow(mpreal(a),b,rnd_mode);
-}
-
-inline const mpreal pow(const double a, const mpreal& b, mp_rnd_t rnd_mode)
-{
- return pow(mpreal(a),b,rnd_mode);
-}
-
-// pow unsigned long int
-inline const mpreal pow(const unsigned long int a, const unsigned long int b, mp_rnd_t rnd_mode)
-{
- mpreal x(a);
- mpfr_ui_pow_ui(x.mp,a,b,rnd_mode);
- return x;
-}
-
-inline const mpreal pow(const unsigned long int a, const unsigned int b, mp_rnd_t rnd_mode)
-{
- return pow(a,static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
-}
-
-inline const mpreal pow(const unsigned long int a, const long int b, mp_rnd_t rnd_mode)
-{
- if(b>0) return pow(a,static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
- else return pow(a,mpreal(b),rnd_mode); //mpfr_ui_pow
-}
-
-inline const mpreal pow(const unsigned long int a, const int b, mp_rnd_t rnd_mode)
-{
- if(b>0) return pow(a,static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
- else return pow(a,mpreal(b),rnd_mode); //mpfr_ui_pow
-}
-
-inline const mpreal pow(const unsigned long int a, const long double b, mp_rnd_t rnd_mode)
-{
- return pow(a,mpreal(b),rnd_mode); //mpfr_ui_pow
-}
-
-inline const mpreal pow(const unsigned long int a, const double b, mp_rnd_t rnd_mode)
-{
- return pow(a,mpreal(b),rnd_mode); //mpfr_ui_pow
-}
-
-// pow unsigned int
-inline const mpreal pow(const unsigned int a, const unsigned long int b, mp_rnd_t rnd_mode)
-{
- return pow(static_cast<unsigned long int>(a),b,rnd_mode); //mpfr_ui_pow_ui
-}
-
-inline const mpreal pow(const unsigned int a, const unsigned int b, mp_rnd_t rnd_mode)
-{
- return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
-}
-
-inline const mpreal pow(const unsigned int a, const long int b, mp_rnd_t rnd_mode)
-{
- if(b>0) return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
- else return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
-}
-
-inline const mpreal pow(const unsigned int a, const int b, mp_rnd_t rnd_mode)
-{
- if(b>0) return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
- else return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
-}
-
-inline const mpreal pow(const unsigned int a, const long double b, mp_rnd_t rnd_mode)
-{
- return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
-}
-
-inline const mpreal pow(const unsigned int a, const double b, mp_rnd_t rnd_mode)
-{
- return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
-}
-
-// pow long int
-inline const mpreal pow(const long int a, const unsigned long int b, mp_rnd_t rnd_mode)
-{
- if (a>0) return pow(static_cast<unsigned long int>(a),b,rnd_mode); //mpfr_ui_pow_ui
- else return pow(mpreal(a),b,rnd_mode); //mpfr_pow_ui
-}
-
-inline const mpreal pow(const long int a, const unsigned int b, mp_rnd_t rnd_mode)
-{
- if (a>0) return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
- else return pow(mpreal(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_pow_ui
-}
-
-inline const mpreal pow(const long int a, const long int b, mp_rnd_t rnd_mode)
-{
- if (a>0)
- {
- if(b>0) return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
- else return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
- }else{
- return pow(mpreal(a),b,rnd_mode); // mpfr_pow_si
- }
-}
-
-inline const mpreal pow(const long int a, const int b, mp_rnd_t rnd_mode)
-{
- if (a>0)
- {
- if(b>0) return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
- else return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
- }else{
- return pow(mpreal(a),static_cast<long int>(b),rnd_mode); // mpfr_pow_si
- }
-}
-
-inline const mpreal pow(const long int a, const long double b, mp_rnd_t rnd_mode)
-{
- if (a>=0) return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
- else return pow(mpreal(a),mpreal(b),rnd_mode); //mpfr_pow
-}
-
-inline const mpreal pow(const long int a, const double b, mp_rnd_t rnd_mode)
-{
- if (a>=0) return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
- else return pow(mpreal(a),mpreal(b),rnd_mode); //mpfr_pow
-}
-
-// pow int
-inline const mpreal pow(const int a, const unsigned long int b, mp_rnd_t rnd_mode)
-{
- if (a>0) return pow(static_cast<unsigned long int>(a),b,rnd_mode); //mpfr_ui_pow_ui
- else return pow(mpreal(a),b,rnd_mode); //mpfr_pow_ui
-}
-
-inline const mpreal pow(const int a, const unsigned int b, mp_rnd_t rnd_mode)
-{
- if (a>0) return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
- else return pow(mpreal(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_pow_ui
-}
-
-inline const mpreal pow(const int a, const long int b, mp_rnd_t rnd_mode)
-{
- if (a>0)
- {
- if(b>0) return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
- else return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
- }else{
- return pow(mpreal(a),b,rnd_mode); // mpfr_pow_si
- }
-}
-
-inline const mpreal pow(const int a, const int b, mp_rnd_t rnd_mode)
-{
- if (a>0)
- {
- if(b>0) return pow(static_cast<unsigned long int>(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_ui_pow_ui
- else return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
- }else{
- return pow(mpreal(a),static_cast<long int>(b),rnd_mode); // mpfr_pow_si
- }
-}
-
-inline const mpreal pow(const int a, const long double b, mp_rnd_t rnd_mode)
-{
- if (a>=0) return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
- else return pow(mpreal(a),mpreal(b),rnd_mode); //mpfr_pow
-}
-
-inline const mpreal pow(const int a, const double b, mp_rnd_t rnd_mode)
-{
- if (a>=0) return pow(static_cast<unsigned long int>(a),mpreal(b),rnd_mode); //mpfr_ui_pow
- else return pow(mpreal(a),mpreal(b),rnd_mode); //mpfr_pow
-}
-
-// pow long double
-inline const mpreal pow(const long double a, const long double b, mp_rnd_t rnd_mode)
-{
- return pow(mpreal(a),mpreal(b),rnd_mode);
-}
-
-inline const mpreal pow(const long double a, const unsigned long int b, mp_rnd_t rnd_mode)
-{
- return pow(mpreal(a),b,rnd_mode); //mpfr_pow_ui
-}
-
-inline const mpreal pow(const long double a, const unsigned int b, mp_rnd_t rnd_mode)
-{
- return pow(mpreal(a),static_cast<unsigned long int>(b),rnd_mode); //mpfr_pow_ui
-}
-
-inline const mpreal pow(const long double a, const long int b, mp_rnd_t rnd_mode)
-{
- return pow(mpreal(a),b,rnd_mode); // mpfr_pow_si
-}
-
-inline const mpreal pow(const long double a, const int b, mp_rnd_t rnd_mode)
-{
- return pow(mpreal(a),static_cast<long int>(b),rnd_mode); // mpfr_pow_si
-}
-
-inline const mpreal pow(const double a, const double b, mp_rnd_t rnd_mode)
-{
- return pow(mpreal(a),mpreal(b),rnd_mode);
-}
-
-inline const mpreal pow(const double a, const unsigned long int b, mp_rnd_t rnd_mode)
-{
- return pow(mpreal(a),b,rnd_mode); // mpfr_pow_ui
-}
-
-inline const mpreal pow(const double a, const unsigned int b, mp_rnd_t rnd_mode)
-{
- return pow(mpreal(a),static_cast<unsigned long int>(b),rnd_mode); // mpfr_pow_ui
-}
-
-inline const mpreal pow(const double a, const long int b, mp_rnd_t rnd_mode)
-{
- return pow(mpreal(a),b,rnd_mode); // mpfr_pow_si
-}
-
-inline const mpreal pow(const double a, const int b, mp_rnd_t rnd_mode)
-{
- return pow(mpreal(a),static_cast<long int>(b),rnd_mode); // mpfr_pow_si
-}
-} // End of mpfr namespace
-
-// Explicit specialization of std::swap for mpreal numbers
-// Thus standard algorithms will use efficient version of swap (due to Koenig lookup)
-// Non-throwing swap C++ idiom: http://en.wikibooks.org/wiki/More_C%2B%2B_Idioms/Non-throwing_swap
-namespace std
-{
- // we are allowed to extend namespace std with specializations only
- template <>
- inline void swap(mpfr::mpreal& x, mpfr::mpreal& y)
- {
- return mpfr::swap(x, y);
- }
-
- template<>
- class numeric_limits<mpfr::mpreal>
- {
- public:
- static const bool is_specialized = true;
- static const bool is_signed = true;
- static const bool is_integer = false;
- static const bool is_exact = false;
- static const int radix = 2;
-
- static const bool has_infinity = true;
- static const bool has_quiet_NaN = true;
- static const bool has_signaling_NaN = true;
-
- static const bool is_iec559 = true; // = IEEE 754
- static const bool is_bounded = true;
- static const bool is_modulo = false;
- static const bool traps = true;
- static const bool tinyness_before = true;
-
- static const float_denorm_style has_denorm = denorm_absent;
-
- inline static mpfr::mpreal (min) (mp_prec_t precision = mpfr::mpreal::get_default_prec()) { return mpfr::minval(precision); }
- inline static mpfr::mpreal (max) (mp_prec_t precision = mpfr::mpreal::get_default_prec()) { return mpfr::maxval(precision); }
- inline static mpfr::mpreal lowest (mp_prec_t precision = mpfr::mpreal::get_default_prec()) { return -mpfr::maxval(precision); }
-
- // Returns smallest eps such that 1 + eps != 1 (classic machine epsilon)
- inline static mpfr::mpreal epsilon(mp_prec_t precision = mpfr::mpreal::get_default_prec()) { return mpfr::machine_epsilon(precision); }
-
- // Returns smallest eps such that x + eps != x (relative machine epsilon)
- inline static mpfr::mpreal epsilon(const mpfr::mpreal& x) { return mpfr::machine_epsilon(x); }
-
- inline static mpfr::mpreal round_error(mp_prec_t precision = mpfr::mpreal::get_default_prec())
- {
- mp_rnd_t r = mpfr::mpreal::get_default_rnd();
-
- if(r == GMP_RNDN) return mpfr::mpreal(0.5, precision);
- else return mpfr::mpreal(1.0, precision);
- }
-
- inline static const mpfr::mpreal infinity() { return mpfr::const_infinity(); }
- inline static const mpfr::mpreal quiet_NaN() { return mpfr::mpreal().setNan(); }
- inline static const mpfr::mpreal signaling_NaN() { return mpfr::mpreal().setNan(); }
- inline static const mpfr::mpreal denorm_min() { return (min)(); }
-
- // Please note, exponent range is not fixed in MPFR
- static const int min_exponent = MPFR_EMIN_DEFAULT;
- static const int max_exponent = MPFR_EMAX_DEFAULT;
- MPREAL_PERMISSIVE_EXPR static const int min_exponent10 = (int) (MPFR_EMIN_DEFAULT * 0.3010299956639811);
- MPREAL_PERMISSIVE_EXPR static const int max_exponent10 = (int) (MPFR_EMAX_DEFAULT * 0.3010299956639811);
-
-#ifdef MPREAL_HAVE_DYNAMIC_STD_NUMERIC_LIMITS
-
- // Following members should be constant according to standard, but they can be variable in MPFR
- // So we define them as functions here.
- //
- // This is preferable way for std::numeric_limits<mpfr::mpreal> specialization.
- // But it is incompatible with standard std::numeric_limits and might not work with other libraries, e.g. boost.
- // See below for compatible implementation.
- inline static float_round_style round_style()
- {
- mp_rnd_t r = mpfr::mpreal::get_default_rnd();
-
- switch (r)
- {
- case GMP_RNDN: return round_to_nearest;
- case GMP_RNDZ: return round_toward_zero;
- case GMP_RNDU: return round_toward_infinity;
- case GMP_RNDD: return round_toward_neg_infinity;
- default: return round_indeterminate;
- }
- }
-
- inline static int digits() { return int(mpfr::mpreal::get_default_prec()); }
- inline static int digits(const mpfr::mpreal& x) { return x.getPrecision(); }
-
- inline static int digits10(mp_prec_t precision = mpfr::mpreal::get_default_prec())
- {
- return mpfr::bits2digits(precision);
- }
-
- inline static int digits10(const mpfr::mpreal& x)
- {
- return mpfr::bits2digits(x.getPrecision());
- }
-
- inline static int max_digits10(mp_prec_t precision = mpfr::mpreal::get_default_prec())
- {
- return digits10(precision);
- }
-#else
- // Digits and round_style are NOT constants when it comes to mpreal.
- // If possible, please use functions digits() and round_style() defined above.
- //
- // These (default) values are preserved for compatibility with existing libraries, e.g. boost.
- // Change them accordingly to your application.
- //
- // For example, if you use 256 bits of precision uniformly in your program, then:
- // digits = 256
- // digits10 = 77
- // max_digits10 = 78
- //
- // Approximate formula for decimal digits is: digits10 = floor(log10(2) * digits). See bits2digits() for more details.
-
- static const std::float_round_style round_style = round_to_nearest;
- static const int digits = 53;
- static const int digits10 = 15;
- static const int max_digits10 = 16;
-#endif
- };
-
-}
-
-#endif /* __MPREAL_H__ */
diff --git a/eigen/unsupported/test/mpreal_support.cpp b/eigen/unsupported/test/mpreal_support.cpp deleted file mode 100644 index 685e7ea..0000000 --- a/eigen/unsupported/test/mpreal_support.cpp +++ /dev/null @@ -1,65 +0,0 @@ -#include "main.h" -#include <Eigen/MPRealSupport> -#include <Eigen/LU> -#include <Eigen/Eigenvalues> -#include <sstream> - -using namespace mpfr; -using namespace Eigen; - -void test_mpreal_support() -{ - // set precision to 256 bits (double has only 53 bits) - mpreal::set_default_prec(256); - typedef Matrix<mpreal,Eigen::Dynamic,Eigen::Dynamic> MatrixXmp; - typedef Matrix<std::complex<mpreal>,Eigen::Dynamic,Eigen::Dynamic> MatrixXcmp; - - std::cerr << "epsilon = " << NumTraits<mpreal>::epsilon() << "\n"; - std::cerr << "dummy_precision = " << NumTraits<mpreal>::dummy_precision() << "\n"; - std::cerr << "highest = " << NumTraits<mpreal>::highest() << "\n"; - std::cerr << "lowest = " << NumTraits<mpreal>::lowest() << "\n"; - std::cerr << "digits10 = " << NumTraits<mpreal>::digits10() << "\n"; - - for(int i = 0; i < g_repeat; i++) { - int s = Eigen::internal::random<int>(1,100); - MatrixXmp A = MatrixXmp::Random(s,s); - MatrixXmp B = MatrixXmp::Random(s,s); - MatrixXmp S = A.adjoint() * A; - MatrixXmp X; - MatrixXcmp Ac = MatrixXcmp::Random(s,s); - MatrixXcmp Bc = MatrixXcmp::Random(s,s); - MatrixXcmp Sc = Ac.adjoint() * Ac; - MatrixXcmp Xc; - - // Basic stuffs - VERIFY_IS_APPROX(A.real(), A); - VERIFY(Eigen::internal::isApprox(A.array().abs2().sum(), A.squaredNorm())); - VERIFY_IS_APPROX(A.array().exp(), exp(A.array())); - VERIFY_IS_APPROX(A.array().abs2().sqrt(), A.array().abs()); - VERIFY_IS_APPROX(A.array().sin(), sin(A.array())); - VERIFY_IS_APPROX(A.array().cos(), cos(A.array())); - - // Cholesky - X = S.selfadjointView<Lower>().llt().solve(B); - VERIFY_IS_APPROX((S.selfadjointView<Lower>()*X).eval(),B); - - Xc = Sc.selfadjointView<Lower>().llt().solve(Bc); - VERIFY_IS_APPROX((Sc.selfadjointView<Lower>()*Xc).eval(),Bc); - - // partial LU - X = A.lu().solve(B); - VERIFY_IS_APPROX((A*X).eval(),B); - - // symmetric eigenvalues - SelfAdjointEigenSolver<MatrixXmp> eig(S); - VERIFY_IS_EQUAL(eig.info(), Success); - VERIFY( (S.selfadjointView<Lower>() * eig.eigenvectors()).isApprox(eig.eigenvectors() * eig.eigenvalues().asDiagonal(), NumTraits<mpreal>::dummy_precision()*1e3) ); - } - - { - MatrixXmp A(8,3); A.setRandom(); - // test output (interesting things happen in this code) - std::stringstream stream; - stream << A; - } -} diff --git a/eigen/unsupported/test/openglsupport.cpp b/eigen/unsupported/test/openglsupport.cpp deleted file mode 100644 index 5f63434..0000000 --- a/eigen/unsupported/test/openglsupport.cpp +++ /dev/null @@ -1,333 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include <main.h> -#include <iostream> -#include <GL/glew.h> -#include <Eigen/OpenGLSupport> -#include <GL/glut.h> -using namespace Eigen; - - - - -#define VERIFY_MATRIX(CODE,REF) { \ - glLoadIdentity(); \ - CODE; \ - Matrix<float,4,4,ColMajor> m; m.setZero(); \ - glGet(GL_MODELVIEW_MATRIX, m); \ - if(!(REF).cast<float>().isApprox(m)) { \ - std::cerr << "Expected:\n" << ((REF).cast<float>()) << "\n" << "got\n" << m << "\n\n"; \ - } \ - VERIFY_IS_APPROX((REF).cast<float>(), m); \ - } - -#define VERIFY_UNIFORM(SUFFIX,NAME,TYPE) { \ - TYPE value; value.setRandom(); \ - TYPE data; \ - int loc = glGetUniformLocation(prg_id, #NAME); \ - VERIFY((loc!=-1) && "uniform not found"); \ - glUniform(loc,value); \ - EIGEN_CAT(glGetUniform,SUFFIX)(prg_id,loc,data.data()); \ - if(!value.isApprox(data)) { \ - std::cerr << "Expected:\n" << value << "\n" << "got\n" << data << "\n\n"; \ - } \ - VERIFY_IS_APPROX(value, data); \ - } - -#define VERIFY_UNIFORMi(NAME,TYPE) { \ - TYPE value = TYPE::Random().eval().cast<float>().cast<TYPE::Scalar>(); \ - TYPE data; \ - int loc = glGetUniformLocation(prg_id, #NAME); \ - VERIFY((loc!=-1) && "uniform not found"); \ - glUniform(loc,value); \ - glGetUniformiv(prg_id,loc,(GLint*)data.data()); \ - if(!value.isApprox(data)) { \ - std::cerr << "Expected:\n" << value << "\n" << "got\n" << data << "\n\n"; \ - } \ - VERIFY_IS_APPROX(value, data); \ - } - -void printInfoLog(GLuint objectID) -{ - int infologLength, charsWritten; - GLchar *infoLog; - glGetProgramiv(objectID,GL_INFO_LOG_LENGTH, &infologLength); - if(infologLength > 0) - { - infoLog = new GLchar[infologLength]; - glGetProgramInfoLog(objectID, infologLength, &charsWritten, infoLog); - if (charsWritten>0) - std::cerr << "Shader info : \n" << infoLog << std::endl; - delete[] infoLog; - } -} - -GLint createShader(const char* vtx, const char* frg) -{ - GLint prg_id = glCreateProgram(); - GLint vtx_id = glCreateShader(GL_VERTEX_SHADER); - GLint frg_id = glCreateShader(GL_FRAGMENT_SHADER); - GLint ok; - - glShaderSource(vtx_id, 1, &vtx, 0); - glCompileShader(vtx_id); - glGetShaderiv(vtx_id,GL_COMPILE_STATUS,&ok); - if(!ok) - { - std::cerr << "vtx compilation failed\n"; - } - - glShaderSource(frg_id, 1, &frg, 0); - glCompileShader(frg_id); - glGetShaderiv(frg_id,GL_COMPILE_STATUS,&ok); - if(!ok) - { - std::cerr << "frg compilation failed\n"; - } - - glAttachShader(prg_id, vtx_id); - glAttachShader(prg_id, frg_id); - glLinkProgram(prg_id); - glGetProgramiv(prg_id,GL_LINK_STATUS,&ok); - if(!ok) - { - std::cerr << "linking failed\n"; - } - printInfoLog(prg_id); - - glUseProgram(prg_id); - return prg_id; -} - -void test_openglsupport() -{ - int argc = 0; - glutInit(&argc, 0); - glutInitDisplayMode(GLUT_DOUBLE | GLUT_RGB | GLUT_DEPTH); - glutInitWindowPosition (0,0); - glutInitWindowSize(10, 10); - - if(glutCreateWindow("Eigen") <= 0) - { - std::cerr << "Error: Unable to create GLUT Window.\n"; - exit(1); - } - - glewExperimental = GL_TRUE; - if(glewInit() != GLEW_OK) - { - std::cerr << "Warning: Failed to initialize GLEW\n"; - } - - Vector3f v3f; - Matrix3f rot; - glBegin(GL_POINTS); - - glVertex(v3f); - glVertex(2*v3f+v3f); - glVertex(rot*v3f); - - glEnd(); - - // 4x4 matrices - Matrix4f mf44; mf44.setRandom(); - VERIFY_MATRIX(glLoadMatrix(mf44), mf44); - VERIFY_MATRIX(glMultMatrix(mf44), mf44); - Matrix4d md44; md44.setRandom(); - VERIFY_MATRIX(glLoadMatrix(md44), md44); - VERIFY_MATRIX(glMultMatrix(md44), md44); - - // Quaternion - Quaterniond qd(AngleAxisd(internal::random<double>(), Vector3d::Random())); - VERIFY_MATRIX(glRotate(qd), Projective3d(qd).matrix()); - - Quaternionf qf(AngleAxisf(internal::random<double>(), Vector3f::Random())); - VERIFY_MATRIX(glRotate(qf), Projective3f(qf).matrix()); - - // 3D Transform - Transform<float,3,AffineCompact> acf3; acf3.matrix().setRandom(); - VERIFY_MATRIX(glLoadMatrix(acf3), Projective3f(acf3).matrix()); - VERIFY_MATRIX(glMultMatrix(acf3), Projective3f(acf3).matrix()); - - Transform<float,3,Affine> af3(acf3); - VERIFY_MATRIX(glLoadMatrix(af3), Projective3f(af3).matrix()); - VERIFY_MATRIX(glMultMatrix(af3), Projective3f(af3).matrix()); - - Transform<float,3,Projective> pf3; pf3.matrix().setRandom(); - VERIFY_MATRIX(glLoadMatrix(pf3), Projective3f(pf3).matrix()); - VERIFY_MATRIX(glMultMatrix(pf3), Projective3f(pf3).matrix()); - - Transform<double,3,AffineCompact> acd3; acd3.matrix().setRandom(); - VERIFY_MATRIX(glLoadMatrix(acd3), Projective3d(acd3).matrix()); - VERIFY_MATRIX(glMultMatrix(acd3), Projective3d(acd3).matrix()); - - Transform<double,3,Affine> ad3(acd3); - VERIFY_MATRIX(glLoadMatrix(ad3), Projective3d(ad3).matrix()); - VERIFY_MATRIX(glMultMatrix(ad3), Projective3d(ad3).matrix()); - - Transform<double,3,Projective> pd3; pd3.matrix().setRandom(); - VERIFY_MATRIX(glLoadMatrix(pd3), Projective3d(pd3).matrix()); - VERIFY_MATRIX(glMultMatrix(pd3), Projective3d(pd3).matrix()); - - // translations (2D and 3D) - { - Vector2f vf2; vf2.setRandom(); Vector3f vf23; vf23 << vf2, 0; - VERIFY_MATRIX(glTranslate(vf2), Projective3f(Translation3f(vf23)).matrix()); - Vector2d vd2; vd2.setRandom(); Vector3d vd23; vd23 << vd2, 0; - VERIFY_MATRIX(glTranslate(vd2), Projective3d(Translation3d(vd23)).matrix()); - - Vector3f vf3; vf3.setRandom(); - VERIFY_MATRIX(glTranslate(vf3), Projective3f(Translation3f(vf3)).matrix()); - Vector3d vd3; vd3.setRandom(); - VERIFY_MATRIX(glTranslate(vd3), Projective3d(Translation3d(vd3)).matrix()); - - Translation<float,3> tf3; tf3.vector().setRandom(); - VERIFY_MATRIX(glTranslate(tf3), Projective3f(tf3).matrix()); - - Translation<double,3> td3; td3.vector().setRandom(); - VERIFY_MATRIX(glTranslate(td3), Projective3d(td3).matrix()); - } - - // scaling (2D and 3D) - { - Vector2f vf2; vf2.setRandom(); Vector3f vf23; vf23 << vf2, 1; - VERIFY_MATRIX(glScale(vf2), Projective3f(Scaling(vf23)).matrix()); - Vector2d vd2; vd2.setRandom(); Vector3d vd23; vd23 << vd2, 1; - VERIFY_MATRIX(glScale(vd2), Projective3d(Scaling(vd23)).matrix()); - - Vector3f vf3; vf3.setRandom(); - VERIFY_MATRIX(glScale(vf3), Projective3f(Scaling(vf3)).matrix()); - Vector3d vd3; vd3.setRandom(); - VERIFY_MATRIX(glScale(vd3), Projective3d(Scaling(vd3)).matrix()); - - UniformScaling<float> usf(internal::random<float>()); - VERIFY_MATRIX(glScale(usf), Projective3f(usf).matrix()); - - UniformScaling<double> usd(internal::random<double>()); - VERIFY_MATRIX(glScale(usd), Projective3d(usd).matrix()); - } - - // uniform - { - const char* vtx = "void main(void) { gl_Position = gl_Vertex; }\n"; - - if(GLEW_VERSION_2_0) - { - #ifdef GL_VERSION_2_0 - const char* frg = "" - "uniform vec2 v2f;\n" - "uniform vec3 v3f;\n" - "uniform vec4 v4f;\n" - "uniform ivec2 v2i;\n" - "uniform ivec3 v3i;\n" - "uniform ivec4 v4i;\n" - "uniform mat2 m2f;\n" - "uniform mat3 m3f;\n" - "uniform mat4 m4f;\n" - "void main(void) { gl_FragColor = vec4(v2f[0]+v3f[0]+v4f[0])+vec4(v2i[0]+v3i[0]+v4i[0])+vec4(m2f[0][0]+m3f[0][0]+m4f[0][0]); }\n"; - - GLint prg_id = createShader(vtx,frg); - - VERIFY_UNIFORM(fv,v2f, Vector2f); - VERIFY_UNIFORM(fv,v3f, Vector3f); - VERIFY_UNIFORM(fv,v4f, Vector4f); - VERIFY_UNIFORMi(v2i, Vector2i); - VERIFY_UNIFORMi(v3i, Vector3i); - VERIFY_UNIFORMi(v4i, Vector4i); - VERIFY_UNIFORM(fv,m2f, Matrix2f); - VERIFY_UNIFORM(fv,m3f, Matrix3f); - VERIFY_UNIFORM(fv,m4f, Matrix4f); - #endif - } - else - std::cerr << "Warning: opengl 2.0 was not tested\n"; - - if(GLEW_VERSION_2_1) - { - #ifdef GL_VERSION_2_1 - const char* frg = "#version 120\n" - "uniform mat2x3 m23f;\n" - "uniform mat3x2 m32f;\n" - "uniform mat2x4 m24f;\n" - "uniform mat4x2 m42f;\n" - "uniform mat3x4 m34f;\n" - "uniform mat4x3 m43f;\n" - "void main(void) { gl_FragColor = vec4(m23f[0][0]+m32f[0][0]+m24f[0][0]+m42f[0][0]+m34f[0][0]+m43f[0][0]); }\n"; - - GLint prg_id = createShader(vtx,frg); - - typedef Matrix<float,2,3> Matrix23f; - typedef Matrix<float,3,2> Matrix32f; - typedef Matrix<float,2,4> Matrix24f; - typedef Matrix<float,4,2> Matrix42f; - typedef Matrix<float,3,4> Matrix34f; - typedef Matrix<float,4,3> Matrix43f; - - VERIFY_UNIFORM(fv,m23f, Matrix23f); - VERIFY_UNIFORM(fv,m32f, Matrix32f); - VERIFY_UNIFORM(fv,m24f, Matrix24f); - VERIFY_UNIFORM(fv,m42f, Matrix42f); - VERIFY_UNIFORM(fv,m34f, Matrix34f); - VERIFY_UNIFORM(fv,m43f, Matrix43f); - #endif - } - else - std::cerr << "Warning: opengl 2.1 was not tested\n"; - - if(GLEW_VERSION_3_0) - { - #ifdef GL_VERSION_3_0 - const char* frg = "#version 150\n" - "uniform uvec2 v2ui;\n" - "uniform uvec3 v3ui;\n" - "uniform uvec4 v4ui;\n" - "out vec4 data;\n" - "void main(void) { data = vec4(v2ui[0]+v3ui[0]+v4ui[0]); }\n"; - - GLint prg_id = createShader(vtx,frg); - - typedef Matrix<unsigned int,2,1> Vector2ui; - typedef Matrix<unsigned int,3,1> Vector3ui; - typedef Matrix<unsigned int,4,1> Vector4ui; - - VERIFY_UNIFORMi(v2ui, Vector2ui); - VERIFY_UNIFORMi(v3ui, Vector3ui); - VERIFY_UNIFORMi(v4ui, Vector4ui); - #endif - } - else - std::cerr << "Warning: opengl 3.0 was not tested\n"; - - #ifdef GLEW_ARB_gpu_shader_fp64 - if(GLEW_ARB_gpu_shader_fp64) - { - #ifdef GL_ARB_gpu_shader_fp64 - const char* frg = "#version 150\n" - "uniform dvec2 v2d;\n" - "uniform dvec3 v3d;\n" - "uniform dvec4 v4d;\n" - "out vec4 data;\n" - "void main(void) { data = vec4(v2d[0]+v3d[0]+v4d[0]); }\n"; - - GLint prg_id = createShader(vtx,frg); - - VERIFY_UNIFORM(dv,v2d, Vector2d); - VERIFY_UNIFORM(dv,v3d, Vector3d); - VERIFY_UNIFORM(dv,v4d, Vector4d); - #endif - } - else - std::cerr << "Warning: GLEW_ARB_gpu_shader_fp64 was not tested\n"; - #else - std::cerr << "Warning: GLEW_ARB_gpu_shader_fp64 was not tested\n"; - #endif - } - -} diff --git a/eigen/unsupported/test/polynomialsolver.cpp b/eigen/unsupported/test/polynomialsolver.cpp deleted file mode 100644 index 4cfc46b..0000000 --- a/eigen/unsupported/test/polynomialsolver.cpp +++ /dev/null @@ -1,216 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Manuel Yguel <manuel.yguel@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <unsupported/Eigen/Polynomials> -#include <iostream> -#include <algorithm> - -using namespace std; - -namespace Eigen { -namespace internal { -template<int Size> -struct increment_if_fixed_size -{ - enum { - ret = (Size == Dynamic) ? Dynamic : Size+1 - }; -}; -} -} - - -template<int Deg, typename POLYNOMIAL, typename SOLVER> -bool aux_evalSolver( const POLYNOMIAL& pols, SOLVER& psolve ) -{ - typedef typename POLYNOMIAL::Scalar Scalar; - - typedef typename SOLVER::RootsType RootsType; - typedef Matrix<Scalar,Deg,1> EvalRootsType; - - const Index deg = pols.size()-1; - - // Test template constructor from coefficient vector - SOLVER solve_constr (pols); - - psolve.compute( pols ); - const RootsType& roots( psolve.roots() ); - EvalRootsType evr( deg ); - for( int i=0; i<roots.size(); ++i ){ - evr[i] = std::abs( poly_eval( pols, roots[i] ) ); } - - bool evalToZero = evr.isZero( test_precision<Scalar>() ); - if( !evalToZero ) - { - cerr << "WRONG root: " << endl; - cerr << "Polynomial: " << pols.transpose() << endl; - cerr << "Roots found: " << roots.transpose() << endl; - cerr << "Abs value of the polynomial at the roots: " << evr.transpose() << endl; - cerr << endl; - } - - std::vector<Scalar> rootModuli( roots.size() ); - Map< EvalRootsType > aux( &rootModuli[0], roots.size() ); - aux = roots.array().abs(); - std::sort( rootModuli.begin(), rootModuli.end() ); - bool distinctModuli=true; - for( size_t i=1; i<rootModuli.size() && distinctModuli; ++i ) - { - if( internal::isApprox( rootModuli[i], rootModuli[i-1] ) ){ - distinctModuli = false; } - } - VERIFY( evalToZero || !distinctModuli ); - - return distinctModuli; -} - - - - - - - -template<int Deg, typename POLYNOMIAL> -void evalSolver( const POLYNOMIAL& pols ) -{ - typedef typename POLYNOMIAL::Scalar Scalar; - - typedef PolynomialSolver<Scalar, Deg > PolynomialSolverType; - - PolynomialSolverType psolve; - aux_evalSolver<Deg, POLYNOMIAL, PolynomialSolverType>( pols, psolve ); -} - - - - -template< int Deg, typename POLYNOMIAL, typename ROOTS, typename REAL_ROOTS > -void evalSolverSugarFunction( const POLYNOMIAL& pols, const ROOTS& roots, const REAL_ROOTS& real_roots ) -{ - using std::sqrt; - typedef typename POLYNOMIAL::Scalar Scalar; - - typedef PolynomialSolver<Scalar, Deg > PolynomialSolverType; - - PolynomialSolverType psolve; - if( aux_evalSolver<Deg, POLYNOMIAL, PolynomialSolverType>( pols, psolve ) ) - { - //It is supposed that - // 1) the roots found are correct - // 2) the roots have distinct moduli - - typedef typename REAL_ROOTS::Scalar Real; - - //Test realRoots - std::vector< Real > calc_realRoots; - psolve.realRoots( calc_realRoots ); - VERIFY( calc_realRoots.size() == (size_t)real_roots.size() ); - - const Scalar psPrec = sqrt( test_precision<Scalar>() ); - - for( size_t i=0; i<calc_realRoots.size(); ++i ) - { - bool found = false; - for( size_t j=0; j<calc_realRoots.size()&& !found; ++j ) - { - if( internal::isApprox( calc_realRoots[i], real_roots[j], psPrec ) ){ - found = true; } - } - VERIFY( found ); - } - - //Test greatestRoot - VERIFY( internal::isApprox( roots.array().abs().maxCoeff(), - abs( psolve.greatestRoot() ), psPrec ) ); - - //Test smallestRoot - VERIFY( internal::isApprox( roots.array().abs().minCoeff(), - abs( psolve.smallestRoot() ), psPrec ) ); - - bool hasRealRoot; - //Test absGreatestRealRoot - Real r = psolve.absGreatestRealRoot( hasRealRoot ); - VERIFY( hasRealRoot == (real_roots.size() > 0 ) ); - if( hasRealRoot ){ - VERIFY( internal::isApprox( real_roots.array().abs().maxCoeff(), abs(r), psPrec ) ); } - - //Test absSmallestRealRoot - r = psolve.absSmallestRealRoot( hasRealRoot ); - VERIFY( hasRealRoot == (real_roots.size() > 0 ) ); - if( hasRealRoot ){ - VERIFY( internal::isApprox( real_roots.array().abs().minCoeff(), abs( r ), psPrec ) ); } - - //Test greatestRealRoot - r = psolve.greatestRealRoot( hasRealRoot ); - VERIFY( hasRealRoot == (real_roots.size() > 0 ) ); - if( hasRealRoot ){ - VERIFY( internal::isApprox( real_roots.array().maxCoeff(), r, psPrec ) ); } - - //Test smallestRealRoot - r = psolve.smallestRealRoot( hasRealRoot ); - VERIFY( hasRealRoot == (real_roots.size() > 0 ) ); - if( hasRealRoot ){ - VERIFY( internal::isApprox( real_roots.array().minCoeff(), r, psPrec ) ); } - } -} - - -template<typename _Scalar, int _Deg> -void polynomialsolver(int deg) -{ - typedef internal::increment_if_fixed_size<_Deg> Dim; - typedef Matrix<_Scalar,Dim::ret,1> PolynomialType; - typedef Matrix<_Scalar,_Deg,1> EvalRootsType; - - cout << "Standard cases" << endl; - PolynomialType pols = PolynomialType::Random(deg+1); - evalSolver<_Deg,PolynomialType>( pols ); - - cout << "Hard cases" << endl; - _Scalar multipleRoot = internal::random<_Scalar>(); - EvalRootsType allRoots = EvalRootsType::Constant(deg,multipleRoot); - roots_to_monicPolynomial( allRoots, pols ); - evalSolver<_Deg,PolynomialType>( pols ); - - cout << "Test sugar" << endl; - EvalRootsType realRoots = EvalRootsType::Random(deg); - roots_to_monicPolynomial( realRoots, pols ); - evalSolverSugarFunction<_Deg>( - pols, - realRoots.template cast < - std::complex< - typename NumTraits<_Scalar>::Real - > - >(), - realRoots ); -} - -void test_polynomialsolver() -{ - for(int i = 0; i < g_repeat; i++) - { - CALL_SUBTEST_1( (polynomialsolver<float,1>(1)) ); - CALL_SUBTEST_2( (polynomialsolver<double,2>(2)) ); - CALL_SUBTEST_3( (polynomialsolver<double,3>(3)) ); - CALL_SUBTEST_4( (polynomialsolver<float,4>(4)) ); - CALL_SUBTEST_5( (polynomialsolver<double,5>(5)) ); - CALL_SUBTEST_6( (polynomialsolver<float,6>(6)) ); - CALL_SUBTEST_7( (polynomialsolver<float,7>(7)) ); - CALL_SUBTEST_8( (polynomialsolver<double,8>(8)) ); - - CALL_SUBTEST_9( (polynomialsolver<float,Dynamic>( - internal::random<int>(9,13) - )) ); - CALL_SUBTEST_10((polynomialsolver<double,Dynamic>( - internal::random<int>(9,13) - )) ); - CALL_SUBTEST_11((polynomialsolver<float,Dynamic>(1)) ); - } -} diff --git a/eigen/unsupported/test/polynomialutils.cpp b/eigen/unsupported/test/polynomialutils.cpp deleted file mode 100644 index 5fc9684..0000000 --- a/eigen/unsupported/test/polynomialutils.cpp +++ /dev/null @@ -1,113 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Manuel Yguel <manuel.yguel@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include <unsupported/Eigen/Polynomials> -#include <iostream> - -using namespace std; - -namespace Eigen { -namespace internal { -template<int Size> -struct increment_if_fixed_size -{ - enum { - ret = (Size == Dynamic) ? Dynamic : Size+1 - }; -}; -} -} - -template<typename _Scalar, int _Deg> -void realRoots_to_monicPolynomial_test(int deg) -{ - typedef internal::increment_if_fixed_size<_Deg> Dim; - typedef Matrix<_Scalar,Dim::ret,1> PolynomialType; - typedef Matrix<_Scalar,_Deg,1> EvalRootsType; - - PolynomialType pols(deg+1); - EvalRootsType roots = EvalRootsType::Random(deg); - roots_to_monicPolynomial( roots, pols ); - - EvalRootsType evr( deg ); - for( int i=0; i<roots.size(); ++i ){ - evr[i] = std::abs( poly_eval( pols, roots[i] ) ); } - - bool evalToZero = evr.isZero( test_precision<_Scalar>() ); - if( !evalToZero ){ - cerr << evr.transpose() << endl; } - VERIFY( evalToZero ); -} - -template<typename _Scalar> void realRoots_to_monicPolynomial_scalar() -{ - CALL_SUBTEST_2( (realRoots_to_monicPolynomial_test<_Scalar,2>(2)) ); - CALL_SUBTEST_3( (realRoots_to_monicPolynomial_test<_Scalar,3>(3)) ); - CALL_SUBTEST_4( (realRoots_to_monicPolynomial_test<_Scalar,4>(4)) ); - CALL_SUBTEST_5( (realRoots_to_monicPolynomial_test<_Scalar,5>(5)) ); - CALL_SUBTEST_6( (realRoots_to_monicPolynomial_test<_Scalar,6>(6)) ); - CALL_SUBTEST_7( (realRoots_to_monicPolynomial_test<_Scalar,7>(7)) ); - CALL_SUBTEST_8( (realRoots_to_monicPolynomial_test<_Scalar,17>(17)) ); - - CALL_SUBTEST_9( (realRoots_to_monicPolynomial_test<_Scalar,Dynamic>( - internal::random<int>(18,26) )) ); -} - - - - -template<typename _Scalar, int _Deg> -void CauchyBounds(int deg) -{ - typedef internal::increment_if_fixed_size<_Deg> Dim; - typedef Matrix<_Scalar,Dim::ret,1> PolynomialType; - typedef Matrix<_Scalar,_Deg,1> EvalRootsType; - - PolynomialType pols(deg+1); - EvalRootsType roots = EvalRootsType::Random(deg); - roots_to_monicPolynomial( roots, pols ); - _Scalar M = cauchy_max_bound( pols ); - _Scalar m = cauchy_min_bound( pols ); - _Scalar Max = roots.array().abs().maxCoeff(); - _Scalar min = roots.array().abs().minCoeff(); - bool eval = (M >= Max) && (m <= min); - if( !eval ) - { - cerr << "Roots: " << roots << endl; - cerr << "Bounds: (" << m << ", " << M << ")" << endl; - cerr << "Min,Max: (" << min << ", " << Max << ")" << endl; - } - VERIFY( eval ); -} - -template<typename _Scalar> void CauchyBounds_scalar() -{ - CALL_SUBTEST_2( (CauchyBounds<_Scalar,2>(2)) ); - CALL_SUBTEST_3( (CauchyBounds<_Scalar,3>(3)) ); - CALL_SUBTEST_4( (CauchyBounds<_Scalar,4>(4)) ); - CALL_SUBTEST_5( (CauchyBounds<_Scalar,5>(5)) ); - CALL_SUBTEST_6( (CauchyBounds<_Scalar,6>(6)) ); - CALL_SUBTEST_7( (CauchyBounds<_Scalar,7>(7)) ); - CALL_SUBTEST_8( (CauchyBounds<_Scalar,17>(17)) ); - - CALL_SUBTEST_9( (CauchyBounds<_Scalar,Dynamic>( - internal::random<int>(18,26) )) ); -} - -void test_polynomialutils() -{ - for(int i = 0; i < g_repeat; i++) - { - realRoots_to_monicPolynomial_scalar<double>(); - realRoots_to_monicPolynomial_scalar<float>(); - CauchyBounds_scalar<double>(); - CauchyBounds_scalar<float>(); - } -} diff --git a/eigen/unsupported/test/sparse_extra.cpp b/eigen/unsupported/test/sparse_extra.cpp deleted file mode 100644 index 7a049c8..0000000 --- a/eigen/unsupported/test/sparse_extra.cpp +++ /dev/null @@ -1,147 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 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/. - - -// import basic and product tests for deprecated DynamicSparseMatrix -#define EIGEN_NO_DEPRECATED_WARNING -#include "sparse_product.cpp" -#include "sparse_basic.cpp" -#include <Eigen/SparseExtra> - -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) -{ - { - sm.setZero(); - SetterType w(sm); - std::vector<Vector2i> remaining = nonzeroCoords; - while(!remaining.empty()) - { - int i = internal::random<int>(0,static_cast<int>(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 = internal::random<int>(0,static_cast<int>(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_extra(const SparseMatrixType& ref) -{ - const Index rows = ref.rows(); - const Index 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); - - 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(internal::is_same<SparseMatrixType,SparseMatrix<Scalar,Flags> >::value) - 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); - - // 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 = internal::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 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); - }*/ - - -} - -void test_sparse_extra() -{ - for(int i = 0; i < g_repeat; i++) { - int s = Eigen::internal::random<int>(1,50); - CALL_SUBTEST_1( sparse_extra(SparseMatrix<double>(8, 8)) ); - CALL_SUBTEST_2( sparse_extra(SparseMatrix<std::complex<double> >(s, s)) ); - CALL_SUBTEST_1( sparse_extra(SparseMatrix<double>(s, s)) ); - - CALL_SUBTEST_3( sparse_extra(DynamicSparseMatrix<double>(s, s)) ); -// CALL_SUBTEST_3(( sparse_basic(DynamicSparseMatrix<double>(s, s)) )); -// CALL_SUBTEST_3(( sparse_basic(DynamicSparseMatrix<double,ColMajor,long int>(s, s)) )); - - CALL_SUBTEST_3( (sparse_product<DynamicSparseMatrix<float, ColMajor> >()) ); - CALL_SUBTEST_3( (sparse_product<DynamicSparseMatrix<float, RowMajor> >()) ); - } -} diff --git a/eigen/unsupported/test/special_functions.cpp b/eigen/unsupported/test/special_functions.cpp deleted file mode 100644 index 057fb3e..0000000 --- a/eigen/unsupported/test/special_functions.cpp +++ /dev/null @@ -1,345 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#include "main.h" -#include "../Eigen/SpecialFunctions" - -template<typename X, typename Y> -void verify_component_wise(const X& x, const Y& y) -{ - for(Index i=0; i<x.size(); ++i) - { - if((numext::isfinite)(y(i))) - VERIFY_IS_APPROX( x(i), y(i) ); - else if((numext::isnan)(y(i))) - VERIFY((numext::isnan)(x(i))); - else - VERIFY_IS_EQUAL( x(i), y(i) ); - } -} - -template<typename ArrayType> void array_special_functions() -{ - using std::abs; - using std::sqrt; - typedef typename ArrayType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - Scalar plusinf = std::numeric_limits<Scalar>::infinity(); - Scalar nan = std::numeric_limits<Scalar>::quiet_NaN(); - - Index rows = internal::random<Index>(1,30); - Index cols = 1; - - // API - { - ArrayType m1 = ArrayType::Random(rows,cols); -#if EIGEN_HAS_C99_MATH - VERIFY_IS_APPROX(m1.lgamma(), lgamma(m1)); - VERIFY_IS_APPROX(m1.digamma(), digamma(m1)); - VERIFY_IS_APPROX(m1.erf(), erf(m1)); - VERIFY_IS_APPROX(m1.erfc(), erfc(m1)); -#endif // EIGEN_HAS_C99_MATH - } - - -#if EIGEN_HAS_C99_MATH - // check special functions (comparing against numpy implementation) - if (!NumTraits<Scalar>::IsComplex) - { - - { - ArrayType m1 = ArrayType::Random(rows,cols); - ArrayType m2 = ArrayType::Random(rows,cols); - - // Test various propreties of igamma & igammac. These are normalized - // gamma integrals where - // igammac(a, x) = Gamma(a, x) / Gamma(a) - // igamma(a, x) = gamma(a, x) / Gamma(a) - // where Gamma and gamma are considered the standard unnormalized - // upper and lower incomplete gamma functions, respectively. - ArrayType a = m1.abs() + 2; - ArrayType x = m2.abs() + 2; - ArrayType zero = ArrayType::Zero(rows, cols); - ArrayType one = ArrayType::Constant(rows, cols, Scalar(1.0)); - ArrayType a_m1 = a - one; - ArrayType Gamma_a_x = Eigen::igammac(a, x) * a.lgamma().exp(); - ArrayType Gamma_a_m1_x = Eigen::igammac(a_m1, x) * a_m1.lgamma().exp(); - ArrayType gamma_a_x = Eigen::igamma(a, x) * a.lgamma().exp(); - ArrayType gamma_a_m1_x = Eigen::igamma(a_m1, x) * a_m1.lgamma().exp(); - - // Gamma(a, 0) == Gamma(a) - VERIFY_IS_APPROX(Eigen::igammac(a, zero), one); - - // Gamma(a, x) + gamma(a, x) == Gamma(a) - VERIFY_IS_APPROX(Gamma_a_x + gamma_a_x, a.lgamma().exp()); - - // Gamma(a, x) == (a - 1) * Gamma(a-1, x) + x^(a-1) * exp(-x) - VERIFY_IS_APPROX(Gamma_a_x, (a - 1) * Gamma_a_m1_x + x.pow(a-1) * (-x).exp()); - - // gamma(a, x) == (a - 1) * gamma(a-1, x) - x^(a-1) * exp(-x) - VERIFY_IS_APPROX(gamma_a_x, (a - 1) * gamma_a_m1_x - x.pow(a-1) * (-x).exp()); - } - - { - // Check exact values of igamma and igammac against a third party calculation. - Scalar a_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)}; - Scalar x_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)}; - - // location i*6+j corresponds to a_s[i], x_s[j]. - Scalar igamma_s[][6] = {{0.0, nan, nan, nan, nan, nan}, - {0.0, 0.6321205588285578, 0.7768698398515702, - 0.9816843611112658, 9.999500016666262e-05, 1.0}, - {0.0, 0.4275932955291202, 0.608374823728911, - 0.9539882943107686, 7.522076445089201e-07, 1.0}, - {0.0, 0.01898815687615381, 0.06564245437845008, - 0.5665298796332909, 4.166333347221828e-18, 1.0}, - {0.0, 0.9999780593618628, 0.9999899967080838, - 0.9999996219837988, 0.9991370418689945, 1.0}, - {0.0, 0.0, 0.0, 0.0, 0.0, 0.5042041932513908}}; - Scalar igammac_s[][6] = {{nan, nan, nan, nan, nan, nan}, - {1.0, 0.36787944117144233, 0.22313016014842982, - 0.018315638888734182, 0.9999000049998333, 0.0}, - {1.0, 0.5724067044708798, 0.3916251762710878, - 0.04601170568923136, 0.9999992477923555, 0.0}, - {1.0, 0.9810118431238462, 0.9343575456215499, - 0.4334701203667089, 1.0, 0.0}, - {1.0, 2.1940638138146658e-05, 1.0003291916285e-05, - 3.7801620118431334e-07, 0.0008629581310054535, - 0.0}, - {1.0, 1.0, 1.0, 1.0, 1.0, 0.49579580674813944}}; - for (int i = 0; i < 6; ++i) { - for (int j = 0; j < 6; ++j) { - if ((std::isnan)(igamma_s[i][j])) { - VERIFY((std::isnan)(numext::igamma(a_s[i], x_s[j]))); - } else { - VERIFY_IS_APPROX(numext::igamma(a_s[i], x_s[j]), igamma_s[i][j]); - } - - if ((std::isnan)(igammac_s[i][j])) { - VERIFY((std::isnan)(numext::igammac(a_s[i], x_s[j]))); - } else { - VERIFY_IS_APPROX(numext::igammac(a_s[i], x_s[j]), igammac_s[i][j]); - } - } - } - } - } -#endif // EIGEN_HAS_C99_MATH - - // Check the zeta function against scipy.special.zeta - { - ArrayType x(7), q(7), res(7), ref(7); - x << 1.5, 4, 10.5, 10000.5, 3, 1, 0.9; - q << 2, 1.5, 3, 1.0001, -2.5, 1.2345, 1.2345; - ref << 1.61237534869, 0.234848505667, 1.03086757337e-5, 0.367879440865, 0.054102025820864097, plusinf, nan; - CALL_SUBTEST( verify_component_wise(ref, ref); ); - CALL_SUBTEST( res = x.zeta(q); verify_component_wise(res, ref); ); - CALL_SUBTEST( res = zeta(x,q); verify_component_wise(res, ref); ); - } - - // digamma - { - ArrayType x(7), res(7), ref(7); - x << 1, 1.5, 4, -10.5, 10000.5, 0, -1; - ref << -0.5772156649015329, 0.03648997397857645, 1.2561176684318, 2.398239129535781, 9.210340372392849, plusinf, plusinf; - CALL_SUBTEST( verify_component_wise(ref, ref); ); - - CALL_SUBTEST( res = x.digamma(); verify_component_wise(res, ref); ); - CALL_SUBTEST( res = digamma(x); verify_component_wise(res, ref); ); - } - - -#if EIGEN_HAS_C99_MATH - { - ArrayType n(11), x(11), res(11), ref(11); - n << 1, 1, 1, 1.5, 17, 31, 28, 8, 42, 147, 170; - x << 2, 3, 25.5, 1.5, 4.7, 11.8, 17.7, 30.2, 15.8, 54.1, 64; - ref << 0.644934066848, 0.394934066848, 0.0399946696496, nan, 293.334565435, 0.445487887616, -2.47810300902e-07, -8.29668781082e-09, -0.434562276666, 0.567742190178, -0.0108615497927; - CALL_SUBTEST( verify_component_wise(ref, ref); ); - - if(sizeof(RealScalar)>=8) { // double - // Reason for commented line: http://eigen.tuxfamily.org/bz/show_bug.cgi?id=1232 - // CALL_SUBTEST( res = x.polygamma(n); verify_component_wise(res, ref); ); - CALL_SUBTEST( res = polygamma(n,x); verify_component_wise(res, ref); ); - } - else { - // CALL_SUBTEST( res = x.polygamma(n); verify_component_wise(res.head(8), ref.head(8)); ); - CALL_SUBTEST( res = polygamma(n,x); verify_component_wise(res.head(8), ref.head(8)); ); - } - } -#endif - -#if EIGEN_HAS_C99_MATH - { - // Inputs and ground truth generated with scipy via: - // a = np.logspace(-3, 3, 5) - 1e-3 - // b = np.logspace(-3, 3, 5) - 1e-3 - // x = np.linspace(-0.1, 1.1, 5) - // (full_a, full_b, full_x) = np.vectorize(lambda a, b, x: (a, b, x))(*np.ix_(a, b, x)) - // full_a = full_a.flatten().tolist() # same for full_b, full_x - // v = scipy.special.betainc(full_a, full_b, full_x).flatten().tolist() - // - // Note in Eigen, we call betainc with arguments in the order (x, a, b). - ArrayType a(125); - ArrayType b(125); - ArrayType x(125); - ArrayType v(125); - ArrayType res(125); - - a << 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, - 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, - 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, - 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, - 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, - 999.999, 999.999, 999.999; - - b << 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999, - 0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, - 999.999, 999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, - 0.999, 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999, - 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999, - 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999, - 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, - 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, - 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, - 31.62177660168379, 31.62177660168379, 31.62177660168379, - 31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999, - 999.999, 999.999; - - x << -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, - 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, - 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, - 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, - -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, - 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, - 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, - 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, - 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, - 0.8, 1.1; - - v << nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, - nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, - nan, nan, nan, 0.47972119876364683, 0.5, 0.5202788012363533, nan, nan, - 0.9518683957740043, 0.9789663010413743, 0.9931729188073435, nan, nan, - 0.999995949033062, 0.9999999999993698, 0.9999999999999999, nan, nan, - 0.9999999999999999, 0.9999999999999999, 0.9999999999999999, nan, nan, - nan, nan, nan, nan, nan, 0.006827081192655869, 0.0210336989586256, - 0.04813160422599567, nan, nan, 0.20014344256217678, 0.5000000000000001, - 0.7998565574378232, nan, nan, 0.9991401428435834, 0.999999999698403, - 0.9999999999999999, nan, nan, 0.9999999999999999, 0.9999999999999999, - 0.9999999999999999, nan, nan, nan, nan, nan, nan, nan, - 1.0646600232370887e-25, 6.301722877826246e-13, 4.050966937974938e-06, - nan, nan, 7.864342668429763e-23, 3.015969667594166e-10, - 0.0008598571564165444, nan, nan, 6.031987710123844e-08, - 0.5000000000000007, 0.9999999396801229, nan, nan, 0.9999999999999999, - 0.9999999999999999, 0.9999999999999999, nan, nan, nan, nan, nan, nan, - nan, 0.0, 7.029920380986636e-306, 2.2450728208591345e-101, nan, nan, - 0.0, 9.275871147869727e-302, 1.2232913026152827e-97, nan, nan, 0.0, - 3.0891393081932924e-252, 2.9303043666183996e-60, nan, nan, - 2.248913486879199e-196, 0.5000000000004947, 0.9999999999999999, nan; - - CALL_SUBTEST(res = betainc(a, b, x); - verify_component_wise(res, v);); - } - - // Test various properties of betainc - { - ArrayType m1 = ArrayType::Random(32); - ArrayType m2 = ArrayType::Random(32); - ArrayType m3 = ArrayType::Random(32); - ArrayType one = ArrayType::Constant(32, Scalar(1.0)); - const Scalar eps = std::numeric_limits<Scalar>::epsilon(); - ArrayType a = (m1 * 4.0).exp(); - ArrayType b = (m2 * 4.0).exp(); - ArrayType x = m3.abs(); - - // betainc(a, 1, x) == x**a - CALL_SUBTEST( - ArrayType test = betainc(a, one, x); - ArrayType expected = x.pow(a); - verify_component_wise(test, expected);); - - // betainc(1, b, x) == 1 - (1 - x)**b - CALL_SUBTEST( - ArrayType test = betainc(one, b, x); - ArrayType expected = one - (one - x).pow(b); - verify_component_wise(test, expected);); - - // betainc(a, b, x) == 1 - betainc(b, a, 1-x) - CALL_SUBTEST( - ArrayType test = betainc(a, b, x) + betainc(b, a, one - x); - ArrayType expected = one; - verify_component_wise(test, expected);); - - // betainc(a+1, b, x) = betainc(a, b, x) - x**a * (1 - x)**b / (a * beta(a, b)) - CALL_SUBTEST( - ArrayType num = x.pow(a) * (one - x).pow(b); - ArrayType denom = a * (a.lgamma() + b.lgamma() - (a + b).lgamma()).exp(); - // Add eps to rhs and lhs so that component-wise test doesn't result in - // nans when both outputs are zeros. - ArrayType expected = betainc(a, b, x) - num / denom + eps; - ArrayType test = betainc(a + one, b, x) + eps; - if (sizeof(Scalar) >= 8) { // double - verify_component_wise(test, expected); - } else { - // Reason for limited test: http://eigen.tuxfamily.org/bz/show_bug.cgi?id=1232 - verify_component_wise(test.head(8), expected.head(8)); - }); - - // betainc(a, b+1, x) = betainc(a, b, x) + x**a * (1 - x)**b / (b * beta(a, b)) - CALL_SUBTEST( - // Add eps to rhs and lhs so that component-wise test doesn't result in - // nans when both outputs are zeros. - ArrayType num = x.pow(a) * (one - x).pow(b); - ArrayType denom = b * (a.lgamma() + b.lgamma() - (a + b).lgamma()).exp(); - ArrayType expected = betainc(a, b, x) + num / denom + eps; - ArrayType test = betainc(a, b + one, x) + eps; - verify_component_wise(test, expected);); - } -#endif -} - -void test_special_functions() -{ - CALL_SUBTEST_1(array_special_functions<ArrayXf>()); - CALL_SUBTEST_2(array_special_functions<ArrayXd>()); -} diff --git a/eigen/unsupported/test/splines.cpp b/eigen/unsupported/test/splines.cpp deleted file mode 100644 index 3be0204..0000000 --- a/eigen/unsupported/test/splines.cpp +++ /dev/null @@ -1,281 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010-2011 Hauke Heibel <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 <unsupported/Eigen/Splines> - -namespace Eigen { - - // lets do some explicit instantiations and thus - // force the compilation of all spline functions... - template class Spline<double, 2, Dynamic>; - template class Spline<double, 3, Dynamic>; - - template class Spline<double, 2, 2>; - template class Spline<double, 2, 3>; - template class Spline<double, 2, 4>; - template class Spline<double, 2, 5>; - - template class Spline<float, 2, Dynamic>; - template class Spline<float, 3, Dynamic>; - - template class Spline<float, 3, 2>; - template class Spline<float, 3, 3>; - template class Spline<float, 3, 4>; - template class Spline<float, 3, 5>; - -} - -Spline<double, 2, Dynamic> closed_spline2d() -{ - RowVectorXd knots(12); - knots << 0, - 0, - 0, - 0, - 0.867193179093898, - 1.660330955342408, - 2.605084834823134, - 3.484154586374428, - 4.252699478956276, - 4.252699478956276, - 4.252699478956276, - 4.252699478956276; - - MatrixXd ctrls(8,2); - ctrls << -0.370967741935484, 0.236842105263158, - -0.231401860693277, 0.442245185027632, - 0.344361228532831, 0.773369994120753, - 0.828990216203802, 0.106550882647595, - 0.407270163678382, -1.043452922172848, - -0.488467813584053, -0.390098582530090, - -0.494657189446427, 0.054804824897884, - -0.370967741935484, 0.236842105263158; - ctrls.transposeInPlace(); - - return Spline<double, 2, Dynamic>(knots, ctrls); -} - -/* create a reference spline */ -Spline<double, 3, Dynamic> spline3d() -{ - RowVectorXd knots(11); - knots << 0, - 0, - 0, - 0.118997681558377, - 0.162611735194631, - 0.498364051982143, - 0.655098003973841, - 0.679702676853675, - 1.000000000000000, - 1.000000000000000, - 1.000000000000000; - - MatrixXd ctrls(8,3); - ctrls << 0.959743958516081, 0.340385726666133, 0.585267750979777, - 0.223811939491137, 0.751267059305653, 0.255095115459269, - 0.505957051665142, 0.699076722656686, 0.890903252535799, - 0.959291425205444, 0.547215529963803, 0.138624442828679, - 0.149294005559057, 0.257508254123736, 0.840717255983663, - 0.254282178971531, 0.814284826068816, 0.243524968724989, - 0.929263623187228, 0.349983765984809, 0.196595250431208, - 0.251083857976031, 0.616044676146639, 0.473288848902729; - ctrls.transposeInPlace(); - - return Spline<double, 3, Dynamic>(knots, ctrls); -} - -/* compares evaluations against known results */ -void eval_spline3d() -{ - Spline3d spline = spline3d(); - - RowVectorXd u(10); - u << 0.351659507062997, - 0.830828627896291, - 0.585264091152724, - 0.549723608291140, - 0.917193663829810, - 0.285839018820374, - 0.757200229110721, - 0.753729094278495, - 0.380445846975357, - 0.567821640725221; - - MatrixXd pts(10,3); - pts << 0.707620811535916, 0.510258911240815, 0.417485437023409, - 0.603422256426978, 0.529498282727551, 0.270351549348981, - 0.228364197569334, 0.423745615677815, 0.637687289287490, - 0.275556796335168, 0.350856706427970, 0.684295784598905, - 0.514519311047655, 0.525077224890754, 0.351628308305896, - 0.724152914315666, 0.574461155457304, 0.469860285484058, - 0.529365063753288, 0.613328702656816, 0.237837040141739, - 0.522469395136878, 0.619099658652895, 0.237139665242069, - 0.677357023849552, 0.480655768435853, 0.422227610314397, - 0.247046593173758, 0.380604672404750, 0.670065791405019; - pts.transposeInPlace(); - - for (int i=0; i<u.size(); ++i) - { - Vector3d pt = spline(u(i)); - VERIFY( (pt - pts.col(i)).norm() < 1e-14 ); - } -} - -/* compares evaluations on corner cases */ -void eval_spline3d_onbrks() -{ - Spline3d spline = spline3d(); - - RowVectorXd u = spline.knots(); - - MatrixXd pts(11,3); - pts << 0.959743958516081, 0.340385726666133, 0.585267750979777, - 0.959743958516081, 0.340385726666133, 0.585267750979777, - 0.959743958516081, 0.340385726666133, 0.585267750979777, - 0.430282980289940, 0.713074680056118, 0.720373307943349, - 0.558074875553060, 0.681617921034459, 0.804417124839942, - 0.407076008291750, 0.349707710518163, 0.617275937419545, - 0.240037008286602, 0.738739390398014, 0.324554153129411, - 0.302434111480572, 0.781162443963899, 0.240177089094644, - 0.251083857976031, 0.616044676146639, 0.473288848902729, - 0.251083857976031, 0.616044676146639, 0.473288848902729, - 0.251083857976031, 0.616044676146639, 0.473288848902729; - pts.transposeInPlace(); - - for (int i=0; i<u.size(); ++i) - { - Vector3d pt = spline(u(i)); - VERIFY( (pt - pts.col(i)).norm() < 1e-14 ); - } -} - -void eval_closed_spline2d() -{ - Spline2d spline = closed_spline2d(); - - RowVectorXd u(12); - u << 0, - 0.332457030395796, - 0.356467130532952, - 0.453562180176215, - 0.648017921874804, - 0.973770235555003, - 1.882577647219307, - 2.289408593930498, - 3.511951429883045, - 3.884149321369450, - 4.236261590369414, - 4.252699478956276; - - MatrixXd pts(12,2); - pts << -0.370967741935484, 0.236842105263158, - -0.152576775123250, 0.448975001279334, - -0.133417538277668, 0.461615613865667, - -0.053199060826740, 0.507630360006299, - 0.114249591147281, 0.570414135097409, - 0.377810316891987, 0.560497102875315, - 0.665052120135908, -0.157557441109611, - 0.516006487053228, -0.559763292174825, - -0.379486035348887, -0.331959640488223, - -0.462034726249078, -0.039105670080824, - -0.378730600917982, 0.225127015099919, - -0.370967741935484, 0.236842105263158; - pts.transposeInPlace(); - - for (int i=0; i<u.size(); ++i) - { - Vector2d pt = spline(u(i)); - VERIFY( (pt - pts.col(i)).norm() < 1e-14 ); - } -} - -void check_global_interpolation2d() -{ - typedef Spline2d::PointType PointType; - typedef Spline2d::KnotVectorType KnotVectorType; - typedef Spline2d::ControlPointVectorType ControlPointVectorType; - - ControlPointVectorType points = ControlPointVectorType::Random(2,100); - - KnotVectorType chord_lengths; // knot parameters - Eigen::ChordLengths(points, chord_lengths); - - // interpolation without knot parameters - { - const Spline2d spline = SplineFitting<Spline2d>::Interpolate(points,3); - - for (Eigen::DenseIndex i=0; i<points.cols(); ++i) - { - PointType pt = spline( chord_lengths(i) ); - PointType ref = points.col(i); - VERIFY( (pt - ref).matrix().norm() < 1e-14 ); - } - } - - // interpolation with given knot parameters - { - const Spline2d spline = SplineFitting<Spline2d>::Interpolate(points,3,chord_lengths); - - for (Eigen::DenseIndex i=0; i<points.cols(); ++i) - { - PointType pt = spline( chord_lengths(i) ); - PointType ref = points.col(i); - VERIFY( (pt - ref).matrix().norm() < 1e-14 ); - } - } -} - -void check_global_interpolation_with_derivatives2d() -{ - typedef Spline2d::PointType PointType; - typedef Spline2d::KnotVectorType KnotVectorType; - - const Eigen::DenseIndex numPoints = 100; - const unsigned int dimension = 2; - const unsigned int degree = 3; - - ArrayXXd points = ArrayXXd::Random(dimension, numPoints); - - KnotVectorType knots; - Eigen::ChordLengths(points, knots); - - ArrayXXd derivatives = ArrayXXd::Random(dimension, numPoints); - VectorXd derivativeIndices(numPoints); - - for (Eigen::DenseIndex i = 0; i < numPoints; ++i) - derivativeIndices(i) = static_cast<double>(i); - - const Spline2d spline = SplineFitting<Spline2d>::InterpolateWithDerivatives( - points, derivatives, derivativeIndices, degree); - - for (Eigen::DenseIndex i = 0; i < points.cols(); ++i) - { - PointType point = spline(knots(i)); - PointType referencePoint = points.col(i); - VERIFY_IS_APPROX(point, referencePoint); - PointType derivative = spline.derivatives(knots(i), 1).col(1); - PointType referenceDerivative = derivatives.col(i); - VERIFY_IS_APPROX(derivative, referenceDerivative); - } -} - -void test_splines() -{ - for (int i = 0; i < g_repeat; ++i) - { - CALL_SUBTEST( eval_spline3d() ); - CALL_SUBTEST( eval_spline3d_onbrks() ); - CALL_SUBTEST( eval_closed_spline2d() ); - CALL_SUBTEST( check_global_interpolation2d() ); - CALL_SUBTEST( check_global_interpolation_with_derivatives2d() ); - } -} |