199 lines
6.5 KiB
C++
199 lines
6.5 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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#include <sstream>
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#include <iomanip>
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#include <eo>
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#include <mo>
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#include <edo>
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#include "Rosenbrock.h"
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#include "Sphere.h"
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typedef eoReal< eoMinimizingFitness > EOT;
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typedef edoNormalMulti< EOT > Distrib;
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typedef EOT::AtomType AtomType;
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#ifdef WITH_BOOST
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#include <boost/numeric/ublas/vector.hpp>
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#include <boost/numeric/ublas/symmetric.hpp>
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typedef ublas::vector< AtomType > Vector;
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typedef ublas::symmetric_matrix< AtomType, ublas::lower > Matrix;
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#else
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#ifdef WITH_EIGEN
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#include <Eigen/Dense>
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typedef typename edoNormalMulti<EOT>::Vector Vector;
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typedef typename edoNormalMulti<EOT>::Matrix Matrix;
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#endif
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#endif
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int main(int ac, char** av)
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{
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// (0) parser + eo routines
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eoParser parser(ac, av);
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std::string section("Algorithm parameters");
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unsigned int p_size = parser.createParam((unsigned int)100, "popSize", "Population Size", 'P', section).value(); // P
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unsigned int s_size = parser.createParam((unsigned int)2, "dimension-size", "Dimension size", 'd', section).value(); // d
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AtomType mean_value = parser.createParam((AtomType)0, "mean", "Mean value", 'm', section).value(); // m
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AtomType covar1_value = parser.createParam((AtomType)1.0, "covar1", "Covar value 1", '1', section).value();
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AtomType covar2_value = parser.createParam((AtomType)0.5, "covar2", "Covar value 2", '2', section).value();
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AtomType covar3_value = parser.createParam((AtomType)1.0, "covar3", "Covar value 3", '3', section).value();
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std::ostringstream ss;
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ss << p_size << "_" << std::fixed << std::setprecision(1)
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<< mean_value << "_" << covar1_value << "_" << covar2_value << "_"
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<< covar3_value << "_gen";
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std::string gen_filename = ss.str();
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if( parser.userNeedsHelp() ) {
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parser.printHelp(std::cout);
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exit(1);
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}
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make_verbose(parser);
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make_help(parser);
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assert(p_size > 0);
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assert(s_size > 0);
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eoState state;
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// (1) Population init and sampler
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eoRndGenerator< double >* gen = new eoUniformGenerator< double >(-5, 5);
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state.storeFunctor(gen);
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eoInitFixedLength< EOT >* init = new eoInitFixedLength< EOT >( s_size, *gen );
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state.storeFunctor(init);
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// create an empty pop and let the state handle the memory
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// fill population thanks to eoInit instance
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eoPop< EOT >& pop = state.takeOwnership( eoPop< EOT >( p_size, *init ) );
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// (2) distribution initial parameters
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Vector mean( s_size );
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for (unsigned int i = 0; i < s_size; ++i) {
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mean( i ) = mean_value;
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}
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Matrix varcovar( s_size, s_size );
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varcovar( 0, 0 ) = covar1_value;
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varcovar( 0, 1 ) = covar2_value;
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varcovar( 1, 1 ) = covar3_value;
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Distrib distrib( mean, varcovar );
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// (3a) distribution output preparation
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edoDummyContinue< Distrib >* distrib_dummy_continue = new edoDummyContinue< Distrib >();
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state.storeFunctor(distrib_dummy_continue);
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edoCheckPoint< Distrib >* distrib_continue = new edoCheckPoint< Distrib >( *distrib_dummy_continue );
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state.storeFunctor(distrib_continue);
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edoDistribStat< Distrib >* distrib_stat = new edoStatNormalMulti< EOT >();
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state.storeFunctor(distrib_stat);
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distrib_continue->add( *distrib_stat );
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edoFileSnapshot* distrib_file_snapshot = new edoFileSnapshot( "TestResDistrib", 1, gen_filename );
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state.storeFunctor(distrib_file_snapshot);
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distrib_file_snapshot->add(*distrib_stat);
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distrib_continue->add(*distrib_file_snapshot);
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// (3b) distribution output
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(*distrib_continue)( distrib );
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// Prepare bounder class to set bounds of sampling.
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// This is used by edoSampler.
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edoBounder< EOT >* bounder = new edoBounderRng< EOT >(
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EOT(pop[0].size(), -5), EOT(pop[0].size(), 5), *gen
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);
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state.storeFunctor(bounder);
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// Prepare sampler class with a specific distribution
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edoSampler< Distrib >* sampler = new edoSamplerNormalMulti< EOT >( *bounder );
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state.storeFunctor(sampler);
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// (4) sampling phase
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pop.clear();
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for( unsigned int i = 0; i < p_size; ++i ) {
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EOT candidate_solution = (*sampler)( distrib );
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pop.push_back( candidate_solution );
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}
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// (5) population output
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eoContinue< EOT >* pop_cont = new eoGenContinue< EOT >( 2 ); // never reached fitness
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state.storeFunctor(pop_cont);
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eoCheckPoint< EOT >* pop_continue = new eoCheckPoint< EOT >( *pop_cont );
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state.storeFunctor(pop_continue);
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edoPopStat< EOT >* pop_stat = new edoPopStat<EOT>;
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state.storeFunctor(pop_stat);
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pop_continue->add(*pop_stat);
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edoFileSnapshot* pop_file_snapshot = new edoFileSnapshot( "TestResPop", 1, gen_filename );
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state.storeFunctor(pop_file_snapshot);
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pop_file_snapshot->add(*pop_stat);
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pop_continue->add(*pop_file_snapshot);
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(*pop_continue)( pop );
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// (6) estimation phase
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edoEstimator< Distrib >* estimator = new edoEstimatorNormalMulti< EOT >();
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state.storeFunctor(estimator);
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distrib = (*estimator)( pop );
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// (7) distribution output
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(*distrib_continue)( distrib );
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// (8) euclidianne distance estimation
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Vector new_mean = distrib.mean();
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Matrix new_varcovar = distrib.varcovar();
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AtomType distance = 0;
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for( unsigned int d = 0; d < s_size; ++d ) {
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distance += pow( mean[ d ] - new_mean[ d ], 2 );
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}
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distance = sqrt( distance );
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eo::log << eo::logging
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<< "mean: " << mean << std::endl
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<< "new mean: " << new_mean << std::endl
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<< "distance: " << distance << std::endl
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;
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return 0;
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}
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