| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> |
| // Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> |
| // |
| // This Source Code Form is subject to the terms of the Mozilla |
| // Public License v. 2.0. If a copy of the MPL was not distributed |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| |
| #include "svd_common.h" |
| |
| template<typename MatrixType, int QRPreconditioner> |
| void jacobisvd_check_full(const MatrixType& m, const JacobiSVD<MatrixType, QRPreconditioner>& svd) |
| { |
| svd_check_full<MatrixType, JacobiSVD<MatrixType, QRPreconditioner > >(m, svd); |
| } |
| |
| template<typename MatrixType, int QRPreconditioner> |
| void jacobisvd_compare_to_full(const MatrixType& m, |
| unsigned int computationOptions, |
| const JacobiSVD<MatrixType, QRPreconditioner>& referenceSvd) |
| { |
| svd_compare_to_full<MatrixType, JacobiSVD<MatrixType, QRPreconditioner> >(m, computationOptions, referenceSvd); |
| } |
| |
| |
| template<typename MatrixType, int QRPreconditioner> |
| void jacobisvd_solve(const MatrixType& m, unsigned int computationOptions) |
| { |
| svd_solve< MatrixType, JacobiSVD< MatrixType, QRPreconditioner > >(m, computationOptions); |
| } |
| |
| |
| |
| template<typename MatrixType, int QRPreconditioner> |
| void jacobisvd_test_all_computation_options(const MatrixType& m) |
| { |
| |
| if (QRPreconditioner == NoQRPreconditioner && m.rows() != m.cols()) |
| return; |
| |
| JacobiSVD< MatrixType, QRPreconditioner > fullSvd(m, ComputeFullU|ComputeFullV); |
| svd_test_computation_options_1< MatrixType, JacobiSVD< MatrixType, QRPreconditioner > >(m, fullSvd); |
| |
| if(QRPreconditioner == FullPivHouseholderQRPreconditioner) |
| return; |
| svd_test_computation_options_2< MatrixType, JacobiSVD< MatrixType, QRPreconditioner > >(m, fullSvd); |
| |
| } |
| |
| template<typename MatrixType> |
| void jacobisvd(const MatrixType& a = MatrixType(), bool pickrandom = true) |
| { |
| MatrixType m = pickrandom ? MatrixType::Random(a.rows(), a.cols()) : a; |
| |
| jacobisvd_test_all_computation_options<MatrixType, FullPivHouseholderQRPreconditioner>(m); |
| jacobisvd_test_all_computation_options<MatrixType, ColPivHouseholderQRPreconditioner>(m); |
| jacobisvd_test_all_computation_options<MatrixType, HouseholderQRPreconditioner>(m); |
| jacobisvd_test_all_computation_options<MatrixType, NoQRPreconditioner>(m); |
| } |
| |
| |
| template<typename MatrixType> |
| void jacobisvd_verify_assert(const MatrixType& m) |
| { |
| |
| svd_verify_assert<MatrixType, JacobiSVD< MatrixType > >(m); |
| |
| typedef typename MatrixType::Index Index; |
| Index rows = m.rows(); |
| Index cols = m.cols(); |
| |
| enum { |
| RowsAtCompileTime = MatrixType::RowsAtCompileTime, |
| ColsAtCompileTime = MatrixType::ColsAtCompileTime |
| }; |
| |
| MatrixType a = MatrixType::Zero(rows, cols); |
| a.setZero(); |
| |
| if (ColsAtCompileTime == Dynamic) |
| { |
| JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner> svd_fullqr; |
| VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeFullU|ComputeThinV)) |
| VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeThinU|ComputeThinV)) |
| VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeThinU|ComputeFullV)) |
| } |
| } |
| |
| template<typename MatrixType> |
| void jacobisvd_method() |
| { |
| enum { Size = MatrixType::RowsAtCompileTime }; |
| typedef typename MatrixType::RealScalar RealScalar; |
| typedef Matrix<RealScalar, Size, 1> RealVecType; |
| MatrixType m = MatrixType::Identity(); |
| VERIFY_IS_APPROX(m.jacobiSvd().singularValues(), RealVecType::Ones()); |
| VERIFY_RAISES_ASSERT(m.jacobiSvd().matrixU()); |
| VERIFY_RAISES_ASSERT(m.jacobiSvd().matrixV()); |
| VERIFY_IS_APPROX(m.jacobiSvd(ComputeFullU|ComputeFullV).solve(m), m); |
| } |
| |
| |
| |
| template<typename MatrixType> |
| void jacobisvd_inf_nan() |
| { |
| svd_inf_nan<MatrixType, JacobiSVD< MatrixType > >(); |
| } |
| |
| |
| // Regression test for bug 286: JacobiSVD loops indefinitely with some |
| // matrices containing denormal numbers. |
| void jacobisvd_bug286() |
| { |
| #if defined __INTEL_COMPILER |
| // shut up warning #239: floating point underflow |
| #pragma warning push |
| #pragma warning disable 239 |
| #endif |
| Matrix2d M; |
| M << -7.90884e-313, -4.94e-324, |
| 0, 5.60844e-313; |
| #if defined __INTEL_COMPILER |
| #pragma warning pop |
| #endif |
| JacobiSVD<Matrix2d> svd; |
| svd.compute(M); // just check we don't loop indefinitely |
| } |
| |
| |
| void jacobisvd_preallocate() |
| { |
| svd_preallocate< JacobiSVD <MatrixXf> >(); |
| } |
| |
| void test_jacobisvd() |
| { |
| CALL_SUBTEST_11(( jacobisvd<Matrix<double,Dynamic,Dynamic> > |
| (Matrix<double,Dynamic,Dynamic>(16, 6)) )); |
| |
| CALL_SUBTEST_3(( jacobisvd_verify_assert(Matrix3f()) )); |
| CALL_SUBTEST_4(( jacobisvd_verify_assert(Matrix4d()) )); |
| CALL_SUBTEST_7(( jacobisvd_verify_assert(MatrixXf(10,12)) )); |
| CALL_SUBTEST_8(( jacobisvd_verify_assert(MatrixXcd(7,5)) )); |
| |
| for(int i = 0; i < g_repeat; i++) { |
| Matrix2cd m; |
| m << 0, 1, |
| 0, 1; |
| CALL_SUBTEST_1(( jacobisvd(m, false) )); |
| m << 1, 0, |
| 1, 0; |
| CALL_SUBTEST_1(( jacobisvd(m, false) )); |
| |
| Matrix2d n; |
| n << 0, 0, |
| 0, 0; |
| CALL_SUBTEST_2(( jacobisvd(n, false) )); |
| n << 0, 0, |
| 0, 1; |
| CALL_SUBTEST_2(( jacobisvd(n, false) )); |
| |
| CALL_SUBTEST_3(( jacobisvd<Matrix3f>() )); |
| CALL_SUBTEST_4(( jacobisvd<Matrix4d>() )); |
| CALL_SUBTEST_5(( jacobisvd<Matrix<float,3,5> >() )); |
| CALL_SUBTEST_6(( jacobisvd<Matrix<double,Dynamic,2> >(Matrix<double,Dynamic,2>(10,2)) )); |
| |
| int r = internal::random<int>(1, 30), |
| c = internal::random<int>(1, 30); |
| CALL_SUBTEST_7(( jacobisvd<MatrixXf>(MatrixXf(r,c)) )); |
| CALL_SUBTEST_8(( jacobisvd<MatrixXcd>(MatrixXcd(r,c)) )); |
| (void) r; |
| (void) c; |
| |
| // Test on inf/nan matrix |
| CALL_SUBTEST_7( jacobisvd_inf_nan<MatrixXf>() ); |
| } |
| |
| CALL_SUBTEST_7(( jacobisvd<MatrixXf>(MatrixXf(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2))) )); |
| CALL_SUBTEST_8(( jacobisvd<MatrixXcd>(MatrixXcd(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/3), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/3))) )); |
| |
| |
| // test matrixbase method |
| CALL_SUBTEST_1(( jacobisvd_method<Matrix2cd>() )); |
| CALL_SUBTEST_3(( jacobisvd_method<Matrix3f>() )); |
| |
| |
| // Test problem size constructors |
| CALL_SUBTEST_7( JacobiSVD<MatrixXf>(10,10) ); |
| |
| // Check that preallocation avoids subsequent mallocs |
| CALL_SUBTEST_9( jacobisvd_preallocate() ); |
| |
| // Regression check for bug 286 |
| CALL_SUBTEST_2( jacobisvd_bug286() ); |
| } |