87 lines
2.8 KiB
C++
87 lines
2.8 KiB
C++
/*
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The Evolving Distribution Objects framework (EDO) is a template-based,
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ANSI-C++ evolutionary computation library which helps you to write your
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own estimation of distribution algorithms.
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This library is free software; you can redistribute it and/or
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modify it under the terms of the GNU Lesser General Public
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License as published by the Free Software Foundation; either
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version 2.1 of the License, or (at your option) any later version.
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This library is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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Lesser General Public License for more details.
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You should have received a copy of the GNU Lesser General Public
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License along with this library; if not, write to the Free Software
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Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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Copyright (C) 2010 Thales group
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*/
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/*
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Authors:
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Johann Dréo <johann.dreo@thalesgroup.com>
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Caner Candan <caner.candan@thalesgroup.com>
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*/
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#ifndef _edoSamplerNormalMulti_h
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#define _edoSamplerNormalMulti_h
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#include <cmath>
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#include <edoSampler.h>
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#include <boost/numeric/ublas/lu.hpp>
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#include <boost/numeric/ublas/symmetric.hpp>
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/** Sample points in a multi-normal law defined by a mean vector and a covariance matrix.
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*
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* Given M the mean vector and V the covariance matrix, of order n:
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* - draw a vector T in N(0,I) (i.e. each value is drawn in a normal law with mean=0 an stddev=1)
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* - compute the Cholesky decomposition L of V (i.e. such as V=LL*)
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* - return X = M + LT
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*/
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template< class EOT, typename EOD = edoNormalMulti< EOT > >
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class edoSamplerNormalMulti : public edoSampler< EOD >
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{
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public:
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typedef typename EOT::AtomType AtomType;
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edoSamplerNormalMulti( edoRepairer<EOT> & repairer, typename Cholesky::Method use = Cholesky::absolute )
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: edoSampler< EOD >( repairer), _cholesky(use)
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{}
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EOT sample( EOD& distrib )
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{
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unsigned int size = distrib.size();
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assert(size > 0);
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// L = cholesky decomposition of varcovar
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const typename Cholesky::FactorMat& L = _cholesky( distrib.varcovar() );
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// T = vector of size elements drawn in N(0,1)
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ublas::vector< AtomType > T( size );
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for ( unsigned int i = 0; i < size; ++i ) {
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T( i ) = rng.normal();
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}
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// LT = L * T
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ublas::vector< AtomType > LT = ublas::prod( L, T );
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// solution = means + LT
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ublas::vector< AtomType > mean = distrib.mean();
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ublas::vector< AtomType > ublas_solution = mean + LT;
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EOT solution( size );
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std::copy( ublas_solution.begin(), ublas_solution.end(), solution.begin() );
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return solution;
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}
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protected:
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Cholesky _cholesky;
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};
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#endif // !_edoSamplerNormalMulti_h
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