test support for Eigen implementations
This commit is contained in:
nojhan
parent
661ef08e44
commit
f0564c233e
2 changed files with 146 additions and 203 deletions
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@ -40,22 +40,29 @@ 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 Eigen::Matrix< AtomType, Eigen::Dynamic, 1> Vector;
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typedef Eigen::Matrix< AtomType, Eigen::Dynamic, Eigen::Dynamic> 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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//-----------------------------------------------------
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// (0) parser + eo routines
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//-----------------------------------------------------
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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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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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@ -66,29 +73,20 @@ int main(int ac, char** av)
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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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{
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parser.printHelp(std::cout);
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exit(1);
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}
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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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//-----------------------------------------------------
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//-----------------------------------------------------
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// (1) Population init and sampler
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//-----------------------------------------------------
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eoRndGenerator< double >* gen = new eoUniformGenerator< double >(-5, 5);
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state.storeFunctor(gen);
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@ -99,18 +97,14 @@ int main(int ac, char** av)
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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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//-----------------------------------------------------
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//-----------------------------------------------------------------------------
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// (2) distribution initial parameters
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//-----------------------------------------------------------------------------
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Vector mean( s_size );
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ublas::vector< AtomType > 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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for (unsigned int i = 0; i < s_size; ++i) { mean( i ) = mean_value; }
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ublas::symmetric_matrix< AtomType, ublas::lower > varcovar( s_size, s_size );
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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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@ -118,13 +112,7 @@ int main(int ac, char** av)
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Distrib distrib( mean, varcovar );
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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// (3a) distribution output preparation
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//-----------------------------------------------------------------------------
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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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@ -141,60 +129,29 @@ int main(int ac, char** av)
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distrib_file_snapshot->add(*distrib_stat);
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distrib_continue->add(*distrib_file_snapshot);
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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// (3b) distribution output
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//-----------------------------------------------------------------------------
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(*distrib_continue)( distrib );
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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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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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//-----------------------------------------------------------------------------
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edoBounder< EOT >* bounder = new edoBounderRng< EOT >(EOT(pop[0].size(), -5),
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EOT(pop[0].size(), 5),
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*gen);
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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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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// Prepare sampler class with a specific distribution
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//-----------------------------------------------------------------------------
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edoSampler< Distrib >* sampler = new edoSamplerNormalMulti< EOT >( *bounder );
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state.storeFunctor(sampler);
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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// (4) sampling phase
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//-----------------------------------------------------------------------------
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pop.clear();
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for (unsigned int i = 0; i < p_size; ++i)
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{
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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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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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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// (5) population output
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//-----------------------------------------------------------------------------
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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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@ -212,53 +169,31 @@ int main(int ac, char** av)
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(*pop_continue)( pop );
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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// (6) estimation phase
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//-----------------------------------------------------------------------------
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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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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// (7) distribution output
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//-----------------------------------------------------------------------------
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(*distrib_continue)( distrib );
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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// (8) euclidianne distance estimation
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//-----------------------------------------------------------------------------
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ublas::vector< AtomType > new_mean = distrib.mean();
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ublas::symmetric_matrix< AtomType, ublas::lower > new_varcovar = distrib.varcovar();
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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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{
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distance += pow( mean[ d ] - new_mean[ d ], 2 );
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}
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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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//-----------------------------------------------------------------------------
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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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@ -37,8 +37,6 @@ Authors:
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#include <edo>
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#include <boost/numeric/ublas/vector.hpp>
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#include <boost/numeric/ublas/symmetric.hpp>
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#include "Rosenbrock.h"
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#include "Sphere.h"
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@ -47,15 +45,25 @@ 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 Eigen::Matrix< AtomType, Eigen::Dynamic, 1> Vector;
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typedef Eigen::Matrix< AtomType, Eigen::Dynamic, Eigen::Dynamic> 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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//-----------------------------------------------------
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// (0) parser + eo routines
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//-----------------------------------------------------
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eoParser parser(ac, av);
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std::string section("Algorithm parameters");
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std::string section("Algorithm parameters");
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unsigned int r_max = parser.createParam((unsigned int)100, "run-number", "Number of run", 'r', section).value(); // r
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unsigned int p_min = parser.createParam((unsigned int)10, "population-min", "Population min", 'p', section).value(); // p
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@ -72,15 +80,15 @@ int main(int ac, char** av)
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std::string files_description = parser.createParam((std::string)"files_description.txt", "files-description", "Files description", 'F', section).value(); // F
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if (parser.userNeedsHelp())
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{
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parser.printHelp(std::cout);
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exit(1);
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}
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{
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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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//-----------------------------------------------------
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assert(r_max >= 1);
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assert(s_size >= 2);
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@ -90,139 +98,139 @@ int main(int ac, char** av)
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::mkdir( results_directory.c_str(), 0755 );
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for ( unsigned int p_size = p_min; p_size <= p_max; p_size += p_step )
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{
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assert(p_size >= p_min);
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{
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assert(p_size >= p_min);
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std::ostringstream desc_file;
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desc_file << results_directory << "/" << files_description;
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std::ostringstream desc_file;
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desc_file << results_directory << "/" << files_description;
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std::ostringstream cur_file;
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cur_file << results_directory << "/pop_" << p_size << ".txt";
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std::ostringstream cur_file;
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cur_file << results_directory << "/pop_" << p_size << ".txt";
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eo::log << eo::file( desc_file.str() ) << cur_file.str().c_str() << std::endl;
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eo::log << eo::file( desc_file.str() ) << cur_file.str().c_str() << std::endl;
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eo::log << eo::file( cur_file.str() );
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eo::log << eo::file( cur_file.str() );
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eo::log << eo::logging << "run_number p_size s_size mean(0) mean(1) new-mean(0) new-mean(1) distance" << std::endl;
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eo::log << eo::logging << "run_number p_size s_size mean(0) mean(1) new-mean(0) new-mean(1) distance" << std::endl;
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eo::log << eo::quiet;
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eo::log << eo::quiet;
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for ( unsigned int r = 1; r <= r_max; ++r)
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{
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for ( unsigned int r = 1; r <= r_max; ++r)
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{
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eoState state;
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eoState state;
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//-----------------------------------------------------
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// (1) Population init and sampler
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//-----------------------------------------------------
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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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//-----------------------------------------------------
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// (1) Population init and sampler
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//-----------------------------------------------------------------------------
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// (2) distribution initial parameters
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//-----------------------------------------------------------------------------
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eoRndGenerator< double >* gen = new eoUniformGenerator< double >(-5, 5);
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state.storeFunctor(gen);
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ublas::vector< AtomType > mean( s_size, mean_value );
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ublas::symmetric_matrix< AtomType, ublas::lower > varcovar( s_size, s_size );
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eoInitFixedLength< EOT >* init = new eoInitFixedLength< EOT >( s_size, *gen );
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state.storeFunctor(init);
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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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//-----------------------------------------------------------------------------
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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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//-----------------------------------------------------------------------------
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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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//-----------------------------------------------------------------------------
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edoBounder< EOT >* bounder = new edoBounderRng< EOT >(EOT(pop[0].size(), -5),
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EOT(pop[0].size(), 5),
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*gen);
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state.storeFunctor(bounder);
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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// Prepare sampler class with a specific distribution
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//-----------------------------------------------------------------------------
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edoSampler< Distrib >* sampler = new edoSamplerNormalMulti< EOT >( *bounder );
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state.storeFunctor(sampler);
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//-----------------------------------------------------------------------------
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// (2) distribution initial parameters
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//-----------------------------------------------------------------------------
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// (4) sampling phase
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//-----------------------------------------------------------------------------
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Vector mean( s_size, mean_value );
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Matrix varcovar( s_size, s_size );
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pop.clear();
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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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for (unsigned int i = 0; i < p_size; ++i)
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{
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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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//-----------------------------------------------------------------------------
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Distrib distrib( mean, varcovar );
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//-----------------------------------------------------------------------------
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// (6) estimation phase
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//-----------------------------------------------------------------------------
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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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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// (8) euclidianne distance estimation
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//-----------------------------------------------------------------------------
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ublas::vector< AtomType > new_mean = distrib.mean();
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ublas::symmetric_matrix< AtomType, ublas::lower > new_varcovar = distrib.varcovar();
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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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AtomType distance = 0;
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for ( unsigned int d = 0; d < s_size; ++d )
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{
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distance += pow( mean[ d ] - new_mean[ d ], 2 );
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}
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edoBounder< EOT >* bounder = new edoBounderRng< EOT >(EOT(pop[0].size(), -5),
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EOT(pop[0].size(), 5),
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*gen);
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state.storeFunctor(bounder);
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distance = sqrt( distance );
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eo::log << r << " " << p_size << " " << s_size << " "
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<< mean(0) << " " << mean(1) << " "
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<< new_mean(0) << " " << new_mean(1) << " "
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<< distance << std::endl
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;
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//-----------------------------------------------------------------------------
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}
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}
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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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{
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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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// (6) estimation phase
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edoEstimator< Distrib >* estimator = new edoEstimatorNormalMulti< EOT >();
|
||||
state.storeFunctor(estimator);
|
||||
|
||||
distrib = (*estimator)( pop );
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
// (8) euclidianne distance estimation
|
||||
|
||||
|
||||
Vector new_mean = distrib.mean();
|
||||
Matrix new_varcovar = distrib.varcovar();
|
||||
|
||||
AtomType distance = 0;
|
||||
|
||||
for ( unsigned int d = 0; d < s_size; ++d )
|
||||
{
|
||||
distance += pow( mean[ d ] - new_mean[ d ], 2 );
|
||||
}
|
||||
|
||||
distance = sqrt( distance );
|
||||
|
||||
eo::log << r << " " << p_size << " " << s_size << " "
|
||||
<< mean(0) << " " << mean(1) << " "
|
||||
<< new_mean(0) << " " << new_mean(1) << " "
|
||||
<< distance << std::endl
|
||||
;
|
||||
|
||||
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
|
|
|||
Reference in a new issue