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// 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() );}
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