nojhan
38e3f40bad
- Change the ill-condition continuator to use eigen decomposition of the covariance matrix, just like in the adaptive estimator. - Add a warning message in adaptive sampler.
112 lines
3.6 KiB
C++
112 lines
3.6 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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Pierre Savéant <pierre.saveant@thalesgroup.com>
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*/
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#ifndef _edoSamplerNormalAdaptive_h
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#define _edoSamplerNormalAdaptive_h
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#include <cmath>
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#include <limits>
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#include <edoSampler.h>
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/** Sample points in a multi-normal law defined by a mean vector, a covariance matrix, a sigma scale factor and
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* evolution paths. This is a step of the CMA-ES algorithm.
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*
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* @ingroup Samplers
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* @ingroup CMAES
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* @ingroup Adaptivenormal
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*/
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#ifdef WITH_EIGEN
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template< class EOT, typename D = edoNormalAdaptive< EOT > >
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class edoSamplerNormalAdaptive : public edoSampler< D >
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{
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public:
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typedef typename EOT::AtomType AtomType;
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typedef typename D::Vector Vector;
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typedef typename D::Matrix Matrix;
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edoSamplerNormalAdaptive( edoRepairer<EOT> & repairer )
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: edoSampler< D >( repairer)
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{}
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EOT sample( D& distrib )
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{
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unsigned int N = distrib.size();
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assert( N > 0);
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// T = vector of size elements drawn in N(0,1)
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Vector T( N );
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for ( unsigned int i = 0; i < N; ++i ) {
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T( i ) = rng.normal();
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}
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assert(T.innerSize() == N );
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assert(T.outerSize() == 1);
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// mean(N,1) + sigma * B(N,N) * ( D(N,1) .* T(N,1) )
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Vector sol = distrib.mean()
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+ distrib.sigma()
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* distrib.coord_sys()
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* (distrib.scaling().cwiseProduct(T) ); // C * T = B * (D .* T)
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assert( sol.size() == N );
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/*Vector sol = distrib.mean() + distrib.sigma()
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* distrib.coord_sys().dot( distrib.scaling().dot( T ) );*/
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#ifndef NDEBUG
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bool is_finite = true;
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for(long i=0; i<sol.size(); ++i) {
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if(not std::isfinite(sol(i))) {
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is_finite = false;
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}
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}
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if(not is_finite) {
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eo::log << eo::warnings << "WARNING: sampled solution is not finite"
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<< " (the search should stop after this warning)" << std::endl;
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eo::log << eo::debug << sol << std::endl;
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eo::log << eo::xdebug
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<< "mean:\n" << distrib.mean() << std::endl
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<< "sigma:" << distrib.sigma() << std::endl
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<< "coord_sys:\n" << distrib.coord_sys() << std::endl
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<< "scaling:\n" << distrib.scaling() << std::endl;
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}
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// assert(is_finite);
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#endif
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// copy in the EOT structure (more probably a vector)
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EOT solution( N );
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for( unsigned int i = 0; i < N; i++ ) {
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solution[i]= sol(i);
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}
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return solution;
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}
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};
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#endif // WITH_EIGEN
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#endif // !_edoSamplerNormalAdaptive_h
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