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110 lines
4.0 KiB
C++
110 lines
4.0 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
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// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
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//
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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
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// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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#include "main.h"
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#include <Eigen/SVD>
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#include <Eigen/LU>
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template<typename MatrixType, unsigned int Options> void svd(const MatrixType& m = MatrixType(), bool pickrandom = true)
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{
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int rows = m.rows();
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int cols = m.cols();
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enum {
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RowsAtCompileTime = MatrixType::RowsAtCompileTime,
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ColsAtCompileTime = MatrixType::ColsAtCompileTime
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};
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typedef typename MatrixType::Scalar Scalar;
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typedef typename NumTraits<Scalar>::Real RealScalar;
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typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime> MatrixUType;
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typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime> MatrixVType;
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typedef Matrix<Scalar, RowsAtCompileTime, 1> ColVectorType;
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typedef Matrix<Scalar, ColsAtCompileTime, 1> InputVectorType;
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MatrixType a;
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if(pickrandom) a = MatrixType::Random(rows,cols);
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else a = m;
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JacobiSVD<MatrixType,Options> svd(a);
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MatrixType sigma = MatrixType::Zero(rows,cols);
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sigma.diagonal() = svd.singularValues().template cast<Scalar>();
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MatrixUType u = svd.matrixU();
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MatrixVType v = svd.matrixV();
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//std::cout << "a\n" << a << std::endl;
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//std::cout << "b\n" << u * sigma * v.adjoint() << std::endl;
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VERIFY_IS_APPROX(a, u * sigma * v.adjoint());
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VERIFY_IS_UNITARY(u);
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VERIFY_IS_UNITARY(v);
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}
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template<typename MatrixType> void svd_verify_assert()
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{
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MatrixType tmp;
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SVD<MatrixType> svd;
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//VERIFY_RAISES_ASSERT(svd.solve(tmp, &tmp))
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VERIFY_RAISES_ASSERT(svd.matrixU())
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VERIFY_RAISES_ASSERT(svd.singularValues())
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VERIFY_RAISES_ASSERT(svd.matrixV())
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/*VERIFY_RAISES_ASSERT(svd.computeUnitaryPositive(&tmp,&tmp))
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VERIFY_RAISES_ASSERT(svd.computePositiveUnitary(&tmp,&tmp))
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VERIFY_RAISES_ASSERT(svd.computeRotationScaling(&tmp,&tmp))
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VERIFY_RAISES_ASSERT(svd.computeScalingRotation(&tmp,&tmp))*/
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}
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void test_jacobisvd()
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{
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for(int i = 0; i < g_repeat; i++) {
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Matrix2cd m;
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m << 0, 1,
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0, 1;
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CALL_SUBTEST_1(( svd<Matrix2cd,0>(m, false) ));
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m << 1, 0,
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1, 0;
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CALL_SUBTEST_1(( svd<Matrix2cd,0>(m, false) ));
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Matrix2d n;
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n << 1, 1,
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1, -1;
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CALL_SUBTEST_2(( svd<Matrix2d,0>(n, false) ));
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CALL_SUBTEST_3(( svd<Matrix3f,0>() ));
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CALL_SUBTEST_4(( svd<Matrix4d,Square>() ));
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CALL_SUBTEST_5(( svd<Matrix<float,3,5> , AtLeastAsManyColsAsRows>() ));
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CALL_SUBTEST_6(( svd<Matrix<double,Dynamic,2> , AtLeastAsManyRowsAsCols>(Matrix<double,Dynamic,2>(10,2)) ));
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CALL_SUBTEST_7(( svd<MatrixXf,Square>(MatrixXf(50,50)) ));
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CALL_SUBTEST_8(( svd<MatrixXcd,AtLeastAsManyRowsAsCols>(MatrixXcd(14,7)) ));
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}
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CALL_SUBTEST_9(( svd<MatrixXf,0>(MatrixXf(300,200)) ));
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CALL_SUBTEST_10(( svd<MatrixXcd,AtLeastAsManyColsAsRows>(MatrixXcd(100,150)) ));
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CALL_SUBTEST_3(( svd_verify_assert<Matrix3f>() ));
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CALL_SUBTEST_3(( svd_verify_assert<Matrix3d>() ));
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CALL_SUBTEST_9(( svd_verify_assert<MatrixXf>() ));
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CALL_SUBTEST_11(( svd_verify_assert<MatrixXd>() ));
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}
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