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							99 lines
						
					
					
						
							3.2 KiB
						
					
					
				| // 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)); | |
|     } | |
|   } | |
| } | |
| 
 |