// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com> // Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr> // Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr> // Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr> // // This Source Code Form is subject to the terms of the Mozilla // Public License v. 2.0. If a copy of the MPL was not distributed // with this file, You can obtain one at http://mozilla.org/MPL/2.0/ // discard stack allocation as that too bypasses malloc #define EIGEN_STACK_ALLOCATION_LIMIT 0 #define EIGEN_RUNTIME_NO_MALLOC #include "main.h" #include <Eigen/SVD> #include <iostream> #include <Eigen/LU> #define SVD_DEFAULT(M) BDCSVD<M> #define SVD_FOR_MIN_NORM(M) BDCSVD<M> #include "svd_common.h" // Check all variants of JacobiSVD template<typename MatrixType> void bdcsvd(const MatrixType& a = MatrixType(), bool pickrandom = true) { MatrixType m = a; if(pickrandom) svd_fill_random(m); CALL_SUBTEST(( svd_test_all_computation_options<BDCSVD<MatrixType> >(m, false) )); } template<typename MatrixType> void bdcsvd_method() { enum { Size = MatrixType::RowsAtCompileTime }; typedef typename MatrixType::RealScalar RealScalar; typedef Matrix<RealScalar, Size, 1> RealVecType; MatrixType m = MatrixType::Identity(); VERIFY_IS_APPROX(m.bdcSvd().singularValues(), RealVecType::Ones()); VERIFY_RAISES_ASSERT(m.bdcSvd().matrixU()); VERIFY_RAISES_ASSERT(m.bdcSvd().matrixV()); VERIFY_IS_APPROX(m.bdcSvd(ComputeFullU|ComputeFullV).solve(m), m); } // compare the Singular values returned with Jacobi and Bdc template<typename MatrixType> void compare_bdc_jacobi(const MatrixType& a = MatrixType(), unsigned int computationOptions = 0) { MatrixType m = MatrixType::Random(a.rows(), a.cols()); BDCSVD<MatrixType> bdc_svd(m); JacobiSVD<MatrixType> jacobi_svd(m); VERIFY_IS_APPROX(bdc_svd.singularValues(), jacobi_svd.singularValues()); if(computationOptions & ComputeFullU) VERIFY_IS_APPROX(bdc_svd.matrixU(), jacobi_svd.matrixU()); if(computationOptions & ComputeThinU) VERIFY_IS_APPROX(bdc_svd.matrixU(), jacobi_svd.matrixU()); if(computationOptions & ComputeFullV) VERIFY_IS_APPROX(bdc_svd.matrixV(), jacobi_svd.matrixV()); if(computationOptions & ComputeThinV) VERIFY_IS_APPROX(bdc_svd.matrixV(), jacobi_svd.matrixV()); } void test_bdcsvd() { CALL_SUBTEST_3(( svd_verify_assert<BDCSVD<Matrix3f> >(Matrix3f()) )); CALL_SUBTEST_4(( svd_verify_assert<BDCSVD<Matrix4d> >(Matrix4d()) )); CALL_SUBTEST_7(( svd_verify_assert<BDCSVD<MatrixXf> >(MatrixXf(10,12)) )); CALL_SUBTEST_8(( svd_verify_assert<BDCSVD<MatrixXcd> >(MatrixXcd(7,5)) )); CALL_SUBTEST_101(( svd_all_trivial_2x2(bdcsvd<Matrix2cd>) )); CALL_SUBTEST_102(( svd_all_trivial_2x2(bdcsvd<Matrix2d>) )); for(int i = 0; i < g_repeat; i++) { CALL_SUBTEST_3(( bdcsvd<Matrix3f>() )); CALL_SUBTEST_4(( bdcsvd<Matrix4d>() )); CALL_SUBTEST_5(( bdcsvd<Matrix<float,3,5> >() )); int r = internal::random<int>(1, EIGEN_TEST_MAX_SIZE/2), c = internal::random<int>(1, EIGEN_TEST_MAX_SIZE/2); TEST_SET_BUT_UNUSED_VARIABLE(r) TEST_SET_BUT_UNUSED_VARIABLE(c) CALL_SUBTEST_6(( bdcsvd(Matrix<double,Dynamic,2>(r,2)) )); CALL_SUBTEST_7(( bdcsvd(MatrixXf(r,c)) )); CALL_SUBTEST_7(( compare_bdc_jacobi(MatrixXf(r,c)) )); CALL_SUBTEST_10(( bdcsvd(MatrixXd(r,c)) )); CALL_SUBTEST_10(( compare_bdc_jacobi(MatrixXd(r,c)) )); CALL_SUBTEST_8(( bdcsvd(MatrixXcd(r,c)) )); CALL_SUBTEST_8(( compare_bdc_jacobi(MatrixXcd(r,c)) )); // Test on inf/nan matrix CALL_SUBTEST_7( (svd_inf_nan<BDCSVD<MatrixXf>, MatrixXf>()) ); CALL_SUBTEST_10( (svd_inf_nan<BDCSVD<MatrixXd>, MatrixXd>()) ); } // test matrixbase method CALL_SUBTEST_1(( bdcsvd_method<Matrix2cd>() )); CALL_SUBTEST_3(( bdcsvd_method<Matrix3f>() )); // Test problem size constructors CALL_SUBTEST_7( BDCSVD<MatrixXf>(10,10) ); // Check that preallocation avoids subsequent mallocs CALL_SUBTEST_9( svd_preallocate<void>() ); CALL_SUBTEST_2( svd_underoverflow<void>() ); }