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							87 lines
						
					
					
						
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							87 lines
						
					
					
						
							2.9 KiB
						
					
					
				
								// This file is part of Eigen, a lightweight C++ template library
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								// for linear algebra. Eigen itself is part of the KDE project.
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								//
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								// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
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								//
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								// This Source Code Form is subject to the terms of the Mozilla
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								// Public License v. 2.0. If a copy of the MPL was not distributed
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								// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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								#include "main.h"
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								#include <Eigen/SVD>
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								template<typename MatrixType> void svd(const MatrixType& m)
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								{
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								  /* this test covers the following files:
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								     SVD.h
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								  */
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								  int rows = m.rows();
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								  int cols = m.cols();
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								  typedef typename MatrixType::Scalar Scalar;
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								  typedef typename NumTraits<Scalar>::Real RealScalar;
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								  MatrixType a = MatrixType::Random(rows,cols);
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								  Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> b =
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								    Matrix<Scalar, MatrixType::RowsAtCompileTime, 1>::Random(rows,1);
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								  Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> x(cols,1), x2(cols,1);
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								  RealScalar largerEps = test_precision<RealScalar>();
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								  if (ei_is_same_type<RealScalar,float>::ret)
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								    largerEps = 1e-3f;
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								  {
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								    SVD<MatrixType> svd(a);
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								    MatrixType sigma = MatrixType::Zero(rows,cols);
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								    MatrixType matU  = MatrixType::Zero(rows,rows);
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								    sigma.block(0,0,cols,cols) = svd.singularValues().asDiagonal();
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								    matU.block(0,0,rows,cols) = svd.matrixU();
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								    VERIFY_IS_APPROX(a, matU * sigma * svd.matrixV().transpose());
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								  }
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								  if (rows==cols)
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								  {
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								    if (ei_is_same_type<RealScalar,float>::ret)
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								    {
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								      MatrixType a1 = MatrixType::Random(rows,cols);
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								      a += a * a.adjoint() + a1 * a1.adjoint();
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								    }
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								    SVD<MatrixType> svd(a);
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								    svd.solve(b, &x);
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								    VERIFY_IS_APPROX(a * x,b);
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								  }
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								  if(rows==cols)
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								  {
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								    SVD<MatrixType> svd(a);
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								    MatrixType unitary, positive;
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								    svd.computeUnitaryPositive(&unitary, &positive);
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								    VERIFY_IS_APPROX(unitary * unitary.adjoint(), MatrixType::Identity(unitary.rows(),unitary.rows()));
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								    VERIFY_IS_APPROX(positive, positive.adjoint());
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								    for(int i = 0; i < rows; i++) VERIFY(positive.diagonal()[i] >= 0); // cheap necessary (not sufficient) condition for positivity
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								    VERIFY_IS_APPROX(unitary*positive, a);
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								    svd.computePositiveUnitary(&positive, &unitary);
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								    VERIFY_IS_APPROX(unitary * unitary.adjoint(), MatrixType::Identity(unitary.rows(),unitary.rows()));
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								    VERIFY_IS_APPROX(positive, positive.adjoint());
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								    for(int i = 0; i < rows; i++) VERIFY(positive.diagonal()[i] >= 0); // cheap necessary (not sufficient) condition for positivity
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								    VERIFY_IS_APPROX(positive*unitary, a);
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								  }
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								}
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								void test_eigen2_svd()
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								{
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								  for(int i = 0; i < g_repeat; i++) {
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								    CALL_SUBTEST_1( svd(Matrix3f()) );
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								    CALL_SUBTEST_2( svd(Matrix4d()) );
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								    CALL_SUBTEST_3( svd(MatrixXf(7,7)) );
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								    CALL_SUBTEST_4( svd(MatrixXd(14,7)) );
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								    // complex are not implemented yet
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								//     CALL_SUBTEST( svd(MatrixXcd(6,6)) );
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								//     CALL_SUBTEST( svd(MatrixXcf(3,3)) );
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								    SVD<MatrixXf> s;
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								    MatrixXf m = MatrixXf::Random(10,1);
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								    s.compute(m);
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								  }
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								}
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