| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2014-2015 Gael Guennebaud <gael.guennebaud@inria.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/. |
| |
| template<typename T> |
| Array<T,4,1> four_denorms(); |
| |
| template<> |
| Array4f four_denorms() { return Array4f(5.60844e-39f, -5.60844e-39f, 4.94e-44f, -4.94e-44f); } |
| template<> |
| Array4d four_denorms() { return Array4d(5.60844e-313, -5.60844e-313, 4.94e-324, -4.94e-324); } |
| template<typename T> |
| Array<T,4,1> four_denorms() { return four_denorms<double>().cast<T>(); } |
| |
| template<typename MatrixType> |
| void svd_fill_random(MatrixType &m, int Option = 0) |
| { |
| using std::pow; |
| typedef typename MatrixType::Scalar Scalar; |
| typedef typename MatrixType::RealScalar RealScalar; |
| Index diagSize = (std::min)(m.rows(), m.cols()); |
| RealScalar s = std::numeric_limits<RealScalar>::max_exponent10/4; |
| s = internal::random<RealScalar>(1,s); |
| Matrix<RealScalar,Dynamic,1> d = Matrix<RealScalar,Dynamic,1>::Random(diagSize); |
| for(Index k=0; k<diagSize; ++k) |
| d(k) = d(k)*pow(RealScalar(10),internal::random<RealScalar>(-s,s)); |
| |
| bool dup = internal::random<int>(0,10) < 3; |
| bool unit_uv = internal::random<int>(0,10) < (dup?7:3); // if we duplicate some diagonal entries, then increase the chance to preserve them using unitary U and V factors |
| |
| // duplicate some singular values |
| if(dup) |
| { |
| Index n = internal::random<Index>(0,d.size()-1); |
| for(Index i=0; i<n; ++i) |
| d(internal::random<Index>(0,d.size()-1)) = d(internal::random<Index>(0,d.size()-1)); |
| } |
| |
| Matrix<Scalar,Dynamic,Dynamic> U(m.rows(),diagSize); |
| Matrix<Scalar,Dynamic,Dynamic> VT(diagSize,m.cols()); |
| if(unit_uv) |
| { |
| // in very rare cases let's try with a pure diagonal matrix |
| if(internal::random<int>(0,10) < 1) |
| { |
| U.setIdentity(); |
| VT.setIdentity(); |
| } |
| else |
| { |
| createRandomPIMatrixOfRank(diagSize,U.rows(), U.cols(), U); |
| createRandomPIMatrixOfRank(diagSize,VT.rows(), VT.cols(), VT); |
| } |
| } |
| else |
| { |
| U.setRandom(); |
| VT.setRandom(); |
| } |
| |
| Matrix<Scalar,Dynamic,1> samples(9); |
| samples << Scalar(0), four_denorms<RealScalar>(), |
| -RealScalar(1)/NumTraits<RealScalar>::highest(), |
| RealScalar(1)/NumTraits<RealScalar>::highest(), |
| (std::numeric_limits<RealScalar>::min)(), |
| pow((std::numeric_limits<RealScalar>::min)(), RealScalar(0.8)); |
| |
| if(Option==Symmetric) |
| { |
| m = U * d.asDiagonal() * U.transpose(); |
| |
| // randomly nullify some rows/columns |
| { |
| Index count = internal::random<Index>(-diagSize,diagSize); |
| for(Index k=0; k<count; ++k) |
| { |
| Index i = internal::random<Index>(0,diagSize-1); |
| m.row(i).setZero(); |
| m.col(i).setZero(); |
| } |
| if(count<0) |
| // (partly) cancel some coeffs |
| if(!(dup && unit_uv)) |
| { |
| |
| Index n = internal::random<Index>(0,m.size()-1); |
| for(Index k=0; k<n; ++k) |
| { |
| Index i = internal::random<Index>(0,m.rows()-1); |
| Index j = internal::random<Index>(0,m.cols()-1); |
| m(j,i) = m(i,j) = samples(internal::random<Index>(0,samples.size()-1)); |
| if(NumTraits<Scalar>::IsComplex) |
| *(&numext::real_ref(m(j,i))+1) = *(&numext::real_ref(m(i,j))+1) = samples.real()(internal::random<Index>(0,samples.size()-1)); |
| } |
| } |
| } |
| } |
| else |
| { |
| m = U * d.asDiagonal() * VT; |
| // (partly) cancel some coeffs |
| if(!(dup && unit_uv)) |
| { |
| Index n = internal::random<Index>(0,m.size()-1); |
| for(Index k=0; k<n; ++k) |
| { |
| Index i = internal::random<Index>(0,m.rows()-1); |
| Index j = internal::random<Index>(0,m.cols()-1); |
| m(i,j) = samples(internal::random<Index>(0,samples.size()-1)); |
| if(NumTraits<Scalar>::IsComplex) |
| *(&numext::real_ref(m(i,j))+1) = samples.real()(internal::random<Index>(0,samples.size()-1)); |
| } |
| } |
| } |
| } |
| |