mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-21 07:19:46 +08:00
100 lines
3.2 KiB
C++
100 lines
3.2 KiB
C++
// 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 MatrixType>
|
|
void svd_fill_random(MatrixType &m, int Option = 0)
|
|
{
|
|
typedef typename MatrixType::Scalar Scalar;
|
|
typedef typename MatrixType::RealScalar RealScalar;
|
|
typedef typename MatrixType::Index Index;
|
|
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)*std::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(7);
|
|
samples << 0, 5.60844e-313, -5.60844e-313, 4.94e-324, -4.94e-324, -1./NumTraits<RealScalar>::highest(), 1./NumTraits<RealScalar>::highest();
|
|
|
|
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));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
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 i=0; i<n; ++i)
|
|
m(internal::random<Index>(0,m.rows()-1), internal::random<Index>(0,m.cols()-1)) = samples(internal::random<Index>(0,samples.size()-1));
|
|
}
|
|
}
|
|
}
|
|
|