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108 lines
3.6 KiB
C++
108 lines
3.6 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) 2014-2015 Gael Guennebaud <gael.guennebaud@inria.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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template<typename MatrixType>
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void svd_fill_random(MatrixType &m, int Option = 0)
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{
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typedef typename MatrixType::Scalar Scalar;
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typedef typename MatrixType::RealScalar RealScalar;
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typedef typename MatrixType::Index Index;
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Index diagSize = (std::min)(m.rows(), m.cols());
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RealScalar s = std::numeric_limits<RealScalar>::max_exponent10/4;
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s = internal::random<RealScalar>(1,s);
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Matrix<RealScalar,Dynamic,1> d = Matrix<RealScalar,Dynamic,1>::Random(diagSize);
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for(Index k=0; k<diagSize; ++k)
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d(k) = d(k)*std::pow(RealScalar(10),internal::random<RealScalar>(-s,s));
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bool dup = internal::random<int>(0,10) < 3;
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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
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// duplicate some singular values
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if(dup)
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{
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Index n = internal::random<Index>(0,d.size()-1);
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for(Index i=0; i<n; ++i)
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d(internal::random<Index>(0,d.size()-1)) = d(internal::random<Index>(0,d.size()-1));
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}
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Matrix<Scalar,Dynamic,Dynamic> U(m.rows(),diagSize);
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Matrix<Scalar,Dynamic,Dynamic> VT(diagSize,m.cols());
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if(unit_uv)
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{
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// in very rare cases let's try with a pure diagonal matrix
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if(internal::random<int>(0,10) < 1)
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{
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U.setIdentity();
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VT.setIdentity();
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}
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else
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{
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createRandomPIMatrixOfRank(diagSize,U.rows(), U.cols(), U);
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createRandomPIMatrixOfRank(diagSize,VT.rows(), VT.cols(), VT);
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}
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}
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else
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{
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U.setRandom();
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VT.setRandom();
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}
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Matrix<Scalar,Dynamic,1> samples(7);
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samples << 0, 5.60844e-313, -5.60844e-313, 4.94e-324, -4.94e-324, -RealScalar(1)/NumTraits<RealScalar>::highest(), RealScalar(1)/NumTraits<RealScalar>::highest();
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if(Option==Symmetric)
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{
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m = U * d.asDiagonal() * U.transpose();
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// randomly nullify some rows/columns
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{
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Index count = internal::random<Index>(-diagSize,diagSize);
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for(Index k=0; k<count; ++k)
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{
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Index i = internal::random<Index>(0,diagSize-1);
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m.row(i).setZero();
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m.col(i).setZero();
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}
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if(count<0)
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// (partly) cancel some coeffs
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if(!(dup && unit_uv))
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{
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Index n = internal::random<Index>(0,m.size()-1);
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for(Index k=0; k<n; ++k)
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{
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Index i = internal::random<Index>(0,m.rows()-1);
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Index j = internal::random<Index>(0,m.cols()-1);
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m(j,i) = m(i,j) = samples(internal::random<Index>(0,samples.size()-1));
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if(NumTraits<Scalar>::IsComplex)
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*(&numext::real_ref(m(j,i))+1) = *(&numext::real_ref(m(i,j))+1) = samples.real()(internal::random<Index>(0,samples.size()-1));
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}
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}
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}
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}
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else
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{
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m = U * d.asDiagonal() * VT;
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// (partly) cancel some coeffs
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if(!(dup && unit_uv))
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{
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Index n = internal::random<Index>(0,m.size()-1);
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for(Index k=0; k<n; ++k)
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{
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Index i = internal::random<Index>(0,m.rows()-1);
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Index j = internal::random<Index>(0,m.cols()-1);
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m(i,j) = samples(internal::random<Index>(0,samples.size()-1));
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if(NumTraits<Scalar>::IsComplex)
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*(&numext::real_ref(m(i,j))+1) = samples.real()(internal::random<Index>(0,samples.size()-1));
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}
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}
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}
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}
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