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							87 lines
						
					
					
						
							2.9 KiB
						
					
					
				| // This file is part of Eigen, a lightweight C++ template library | |
| // for linear algebra. Eigen itself is part of the KDE project. | |
| // | |
| // 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/SVD> | |
|  | |
| template<typename MatrixType> void svd(const MatrixType& m) | |
| { | |
|   /* this test covers the following files: | |
|      SVD.h | |
|   */ | |
|   int rows = m.rows(); | |
|   int cols = m.cols(); | |
| 
 | |
|   typedef typename MatrixType::Scalar Scalar; | |
|   typedef typename NumTraits<Scalar>::Real RealScalar; | |
|   MatrixType a = MatrixType::Random(rows,cols); | |
|   Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> b = | |
|     Matrix<Scalar, MatrixType::RowsAtCompileTime, 1>::Random(rows,1); | |
|   Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> x(cols,1), x2(cols,1); | |
| 
 | |
|   RealScalar largerEps = test_precision<RealScalar>(); | |
|   if (ei_is_same_type<RealScalar,float>::ret) | |
|     largerEps = 1e-3f; | |
| 
 | |
|   { | |
|     SVD<MatrixType> svd(a); | |
|     MatrixType sigma = MatrixType::Zero(rows,cols); | |
|     MatrixType matU  = MatrixType::Zero(rows,rows); | |
|     sigma.block(0,0,cols,cols) = svd.singularValues().asDiagonal(); | |
|     matU.block(0,0,rows,cols) = svd.matrixU(); | |
|     VERIFY_IS_APPROX(a, matU * sigma * svd.matrixV().transpose()); | |
|   } | |
| 
 | |
| 
 | |
|   if (rows==cols) | |
|   { | |
|     if (ei_is_same_type<RealScalar,float>::ret) | |
|     { | |
|       MatrixType a1 = MatrixType::Random(rows,cols); | |
|       a += a * a.adjoint() + a1 * a1.adjoint(); | |
|     } | |
|     SVD<MatrixType> svd(a); | |
|     svd.solve(b, &x); | |
|     VERIFY_IS_APPROX(a * x,b); | |
|   } | |
| 
 | |
| 
 | |
|   if(rows==cols) | |
|   { | |
|     SVD<MatrixType> svd(a); | |
|     MatrixType unitary, positive; | |
|     svd.computeUnitaryPositive(&unitary, &positive); | |
|     VERIFY_IS_APPROX(unitary * unitary.adjoint(), MatrixType::Identity(unitary.rows(),unitary.rows())); | |
|     VERIFY_IS_APPROX(positive, positive.adjoint()); | |
|     for(int i = 0; i < rows; i++) VERIFY(positive.diagonal()[i] >= 0); // cheap necessary (not sufficient) condition for positivity | |
|     VERIFY_IS_APPROX(unitary*positive, a); | |
| 
 | |
|     svd.computePositiveUnitary(&positive, &unitary); | |
|     VERIFY_IS_APPROX(unitary * unitary.adjoint(), MatrixType::Identity(unitary.rows(),unitary.rows())); | |
|     VERIFY_IS_APPROX(positive, positive.adjoint()); | |
|     for(int i = 0; i < rows; i++) VERIFY(positive.diagonal()[i] >= 0); // cheap necessary (not sufficient) condition for positivity | |
|     VERIFY_IS_APPROX(positive*unitary, a); | |
|   } | |
| } | |
| 
 | |
| void test_eigen2_svd() | |
| { | |
|   for(int i = 0; i < g_repeat; i++) { | |
|     CALL_SUBTEST_1( svd(Matrix3f()) ); | |
|     CALL_SUBTEST_2( svd(Matrix4d()) ); | |
|     CALL_SUBTEST_3( svd(MatrixXf(7,7)) ); | |
|     CALL_SUBTEST_4( svd(MatrixXd(14,7)) ); | |
|     // complex are not implemented yet | |
| //     CALL_SUBTEST( svd(MatrixXcd(6,6)) ); | |
| //     CALL_SUBTEST( svd(MatrixXcf(3,3)) ); | |
|     SVD<MatrixXf> s; | |
|     MatrixXf m = MatrixXf::Random(10,1); | |
|     s.compute(m); | |
|   } | |
| }
 |