1*bf2c3715SXin Li // This file is part of Eigen, a lightweight C++ template library
2*bf2c3715SXin Li // for linear algebra.
3*bf2c3715SXin Li //
4*bf2c3715SXin Li // Copyright (C) 2014-2015 Gael Guennebaud <[email protected]>
5*bf2c3715SXin Li //
6*bf2c3715SXin Li // This Source Code Form is subject to the terms of the Mozilla
7*bf2c3715SXin Li // Public License v. 2.0. If a copy of the MPL was not distributed
8*bf2c3715SXin Li // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9*bf2c3715SXin Li
10*bf2c3715SXin Li template<typename T>
11*bf2c3715SXin Li Array<T,4,1> four_denorms();
12*bf2c3715SXin Li
13*bf2c3715SXin Li template<>
four_denorms()14*bf2c3715SXin Li Array4f four_denorms() { return Array4f(5.60844e-39f, -5.60844e-39f, 4.94e-44f, -4.94e-44f); }
15*bf2c3715SXin Li template<>
four_denorms()16*bf2c3715SXin Li Array4d four_denorms() { return Array4d(5.60844e-313, -5.60844e-313, 4.94e-324, -4.94e-324); }
17*bf2c3715SXin Li template<typename T>
four_denorms()18*bf2c3715SXin Li Array<T,4,1> four_denorms() { return four_denorms<double>().cast<T>(); }
19*bf2c3715SXin Li
20*bf2c3715SXin Li template<typename MatrixType>
21*bf2c3715SXin Li void svd_fill_random(MatrixType &m, int Option = 0)
22*bf2c3715SXin Li {
23*bf2c3715SXin Li using std::pow;
24*bf2c3715SXin Li typedef typename MatrixType::Scalar Scalar;
25*bf2c3715SXin Li typedef typename MatrixType::RealScalar RealScalar;
26*bf2c3715SXin Li Index diagSize = (std::min)(m.rows(), m.cols());
27*bf2c3715SXin Li RealScalar s = std::numeric_limits<RealScalar>::max_exponent10/4;
28*bf2c3715SXin Li s = internal::random<RealScalar>(1,s);
29*bf2c3715SXin Li Matrix<RealScalar,Dynamic,1> d = Matrix<RealScalar,Dynamic,1>::Random(diagSize);
30*bf2c3715SXin Li for(Index k=0; k<diagSize; ++k)
31*bf2c3715SXin Li d(k) = d(k)*pow(RealScalar(10),internal::random<RealScalar>(-s,s));
32*bf2c3715SXin Li
33*bf2c3715SXin Li bool dup = internal::random<int>(0,10) < 3;
34*bf2c3715SXin Li 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
35*bf2c3715SXin Li
36*bf2c3715SXin Li // duplicate some singular values
37*bf2c3715SXin Li if(dup)
38*bf2c3715SXin Li {
39*bf2c3715SXin Li Index n = internal::random<Index>(0,d.size()-1);
40*bf2c3715SXin Li for(Index i=0; i<n; ++i)
41*bf2c3715SXin Li d(internal::random<Index>(0,d.size()-1)) = d(internal::random<Index>(0,d.size()-1));
42*bf2c3715SXin Li }
43*bf2c3715SXin Li
44*bf2c3715SXin Li Matrix<Scalar,Dynamic,Dynamic> U(m.rows(),diagSize);
45*bf2c3715SXin Li Matrix<Scalar,Dynamic,Dynamic> VT(diagSize,m.cols());
46*bf2c3715SXin Li if(unit_uv)
47*bf2c3715SXin Li {
48*bf2c3715SXin Li // in very rare cases let's try with a pure diagonal matrix
49*bf2c3715SXin Li if(internal::random<int>(0,10) < 1)
50*bf2c3715SXin Li {
51*bf2c3715SXin Li U.setIdentity();
52*bf2c3715SXin Li VT.setIdentity();
53*bf2c3715SXin Li }
54*bf2c3715SXin Li else
55*bf2c3715SXin Li {
56*bf2c3715SXin Li createRandomPIMatrixOfRank(diagSize,U.rows(), U.cols(), U);
57*bf2c3715SXin Li createRandomPIMatrixOfRank(diagSize,VT.rows(), VT.cols(), VT);
58*bf2c3715SXin Li }
59*bf2c3715SXin Li }
60*bf2c3715SXin Li else
61*bf2c3715SXin Li {
62*bf2c3715SXin Li U.setRandom();
63*bf2c3715SXin Li VT.setRandom();
64*bf2c3715SXin Li }
65*bf2c3715SXin Li
66*bf2c3715SXin Li Matrix<Scalar,Dynamic,1> samples(9);
67*bf2c3715SXin Li samples << 0, four_denorms<RealScalar>(),
68*bf2c3715SXin Li -RealScalar(1)/NumTraits<RealScalar>::highest(), RealScalar(1)/NumTraits<RealScalar>::highest(), (std::numeric_limits<RealScalar>::min)(), pow((std::numeric_limits<RealScalar>::min)(),0.8);
69*bf2c3715SXin Li
70*bf2c3715SXin Li if(Option==Symmetric)
71*bf2c3715SXin Li {
72*bf2c3715SXin Li m = U * d.asDiagonal() * U.transpose();
73*bf2c3715SXin Li
74*bf2c3715SXin Li // randomly nullify some rows/columns
75*bf2c3715SXin Li {
76*bf2c3715SXin Li Index count = internal::random<Index>(-diagSize,diagSize);
77*bf2c3715SXin Li for(Index k=0; k<count; ++k)
78*bf2c3715SXin Li {
79*bf2c3715SXin Li Index i = internal::random<Index>(0,diagSize-1);
80*bf2c3715SXin Li m.row(i).setZero();
81*bf2c3715SXin Li m.col(i).setZero();
82*bf2c3715SXin Li }
83*bf2c3715SXin Li if(count<0)
84*bf2c3715SXin Li // (partly) cancel some coeffs
85*bf2c3715SXin Li if(!(dup && unit_uv))
86*bf2c3715SXin Li {
87*bf2c3715SXin Li
88*bf2c3715SXin Li Index n = internal::random<Index>(0,m.size()-1);
89*bf2c3715SXin Li for(Index k=0; k<n; ++k)
90*bf2c3715SXin Li {
91*bf2c3715SXin Li Index i = internal::random<Index>(0,m.rows()-1);
92*bf2c3715SXin Li Index j = internal::random<Index>(0,m.cols()-1);
93*bf2c3715SXin Li m(j,i) = m(i,j) = samples(internal::random<Index>(0,samples.size()-1));
94*bf2c3715SXin Li if(NumTraits<Scalar>::IsComplex)
95*bf2c3715SXin Li *(&numext::real_ref(m(j,i))+1) = *(&numext::real_ref(m(i,j))+1) = samples.real()(internal::random<Index>(0,samples.size()-1));
96*bf2c3715SXin Li }
97*bf2c3715SXin Li }
98*bf2c3715SXin Li }
99*bf2c3715SXin Li }
100*bf2c3715SXin Li else
101*bf2c3715SXin Li {
102*bf2c3715SXin Li m = U * d.asDiagonal() * VT;
103*bf2c3715SXin Li // (partly) cancel some coeffs
104*bf2c3715SXin Li if(!(dup && unit_uv))
105*bf2c3715SXin Li {
106*bf2c3715SXin Li Index n = internal::random<Index>(0,m.size()-1);
107*bf2c3715SXin Li for(Index k=0; k<n; ++k)
108*bf2c3715SXin Li {
109*bf2c3715SXin Li Index i = internal::random<Index>(0,m.rows()-1);
110*bf2c3715SXin Li Index j = internal::random<Index>(0,m.cols()-1);
111*bf2c3715SXin Li m(i,j) = samples(internal::random<Index>(0,samples.size()-1));
112*bf2c3715SXin Li if(NumTraits<Scalar>::IsComplex)
113*bf2c3715SXin Li *(&numext::real_ref(m(i,j))+1) = samples.real()(internal::random<Index>(0,samples.size()-1));
114*bf2c3715SXin Li }
115*bf2c3715SXin Li }
116*bf2c3715SXin Li }
117*bf2c3715SXin Li }
118*bf2c3715SXin Li
119