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test support for Eigen implementations

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nojhan 2012-07-09 22:37:40 +02:00
commit f0564c233e
2 changed files with 146 additions and 203 deletions
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149
edo/test/t-edoEstimatorNormalMulti.cpp
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@ -40,22 +40,29 @@ typedef eoReal< eoMinimizingFitness > EOT;
typedef edoNormalMulti< EOT > Distrib;
typedef EOT::AtomType AtomType;
#ifdef WITH_BOOST
#include <boost/numeric/ublas/vector.hpp>
#include <boost/numeric/ublas/symmetric.hpp>
typedef ublas::vector< AtomType > Vector;
typedef ublas::symmetric_matrix< AtomType, ublas::lower > Matrix;
#else
#ifdef WITH_EIGEN
#include <Eigen/Dense>
typedef Eigen::Matrix< AtomType, Eigen::Dynamic, 1> Vector;
typedef Eigen::Matrix< AtomType, Eigen::Dynamic, Eigen::Dynamic> Matrix;
#endif
#endif
int main(int ac, char** av)
{
//-----------------------------------------------------
// (0) parser + eo routines
//-----------------------------------------------------
eoParser parser(ac, av);
std::string section("Algorithm parameters");
unsigned int p_size = parser.createParam((unsigned int)100, "popSize", "Population Size", 'P', section).value(); // P
unsigned int s_size = parser.createParam((unsigned int)2, "dimension-size", "Dimension size", 'd', section).value(); // d
AtomType mean_value = parser.createParam((AtomType)0, "mean", "Mean value", 'm', section).value(); // m
std::string section("Algorithm parameters");
unsigned int p_size = parser.createParam((unsigned int)100, "popSize", "Population Size", 'P', section).value(); // P
unsigned int s_size = parser.createParam((unsigned int)2, "dimension-size", "Dimension size", 'd', section).value(); // d
AtomType mean_value = parser.createParam((AtomType)0, "mean", "Mean value", 'm', section).value(); // m
AtomType covar1_value = parser.createParam((AtomType)1.0, "covar1", "Covar value 1", '1', section).value();
AtomType covar2_value = parser.createParam((AtomType)0.5, "covar2", "Covar value 2", '2', section).value();
AtomType covar3_value = parser.createParam((AtomType)1.0, "covar3", "Covar value 3", '3', section).value();
@ -66,29 +73,20 @@ int main(int ac, char** av)
<< covar3_value << "_gen";
std::string gen_filename = ss.str();
if (parser.userNeedsHelp())
{
parser.printHelp(std::cout);
exit(1);
}
if( parser.userNeedsHelp() ) {
parser.printHelp(std::cout);
exit(1);
}
make_verbose(parser);
make_help(parser);
assert(p_size > 0);
assert(s_size > 0);
eoState state;
//-----------------------------------------------------
//-----------------------------------------------------
// (1) Population init and sampler
//-----------------------------------------------------
eoRndGenerator< double >* gen = new eoUniformGenerator< double >(-5, 5);
state.storeFunctor(gen);
@ -99,18 +97,14 @@ int main(int ac, char** av)
// fill population thanks to eoInit instance
eoPop< EOT >& pop = state.takeOwnership( eoPop< EOT >( p_size, *init ) );
//-----------------------------------------------------
//-----------------------------------------------------------------------------
// (2) distribution initial parameters
//-----------------------------------------------------------------------------
Vector mean( s_size );
ublas::vector< AtomType > mean( s_size );
for (unsigned int i = 0; i < s_size; ++i) {
mean( i ) = mean_value;
}
for (unsigned int i = 0; i < s_size; ++i) { mean( i ) = mean_value; }
ublas::symmetric_matrix< AtomType, ublas::lower > varcovar( s_size, s_size );
Matrix varcovar( s_size, s_size );
varcovar( 0, 0 ) = covar1_value;
varcovar( 0, 1 ) = covar2_value;
@ -118,13 +112,7 @@ int main(int ac, char** av)
Distrib distrib( mean, varcovar );
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// (3a) distribution output preparation
//-----------------------------------------------------------------------------
edoDummyContinue< Distrib >* distrib_dummy_continue = new edoDummyContinue< Distrib >();
state.storeFunctor(distrib_dummy_continue);
@ -141,60 +129,29 @@ int main(int ac, char** av)
distrib_file_snapshot->add(*distrib_stat);
distrib_continue->add(*distrib_file_snapshot);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// (3b) distribution output
//-----------------------------------------------------------------------------
(*distrib_continue)( distrib );
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// Prepare bounder class to set bounds of sampling.
// This is used by edoSampler.
//-----------------------------------------------------------------------------
edoBounder< EOT >* bounder = new edoBounderRng< EOT >(EOT(pop[0].size(), -5),
EOT(pop[0].size(), 5),
*gen);
edoBounder< EOT >* bounder = new edoBounderRng< EOT >(
EOT(pop[0].size(), -5), EOT(pop[0].size(), 5), *gen
);
state.storeFunctor(bounder);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// Prepare sampler class with a specific distribution
//-----------------------------------------------------------------------------
edoSampler< Distrib >* sampler = new edoSamplerNormalMulti< EOT >( *bounder );
state.storeFunctor(sampler);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// (4) sampling phase
//-----------------------------------------------------------------------------
pop.clear();
for (unsigned int i = 0; i < p_size; ++i)
{
EOT candidate_solution = (*sampler)( distrib );
pop.push_back( candidate_solution );
}
for( unsigned int i = 0; i < p_size; ++i ) {
EOT candidate_solution = (*sampler)( distrib );
pop.push_back( candidate_solution );
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// (5) population output
//-----------------------------------------------------------------------------
eoContinue< EOT >* pop_cont = new eoGenContinue< EOT >( 2 ); // never reached fitness
state.storeFunctor(pop_cont);
@ -212,53 +169,31 @@ int main(int ac, char** av)
(*pop_continue)( pop );
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// (6) estimation phase
//-----------------------------------------------------------------------------
edoEstimator< Distrib >* estimator = new edoEstimatorNormalMulti< EOT >();
state.storeFunctor(estimator);
distrib = (*estimator)( pop );
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// (7) distribution output
//-----------------------------------------------------------------------------
(*distrib_continue)( distrib );
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// (8) euclidianne distance estimation
//-----------------------------------------------------------------------------
ublas::vector< AtomType > new_mean = distrib.mean();
ublas::symmetric_matrix< AtomType, ublas::lower > new_varcovar = distrib.varcovar();
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 );
}
for( unsigned int d = 0; d < s_size; ++d ) {
distance += pow( mean[ d ] - new_mean[ d ], 2 );
}
distance = sqrt( distance );
eo::log << eo::logging
<< "mean: " << mean << std::endl
<< "new mean: " << new_mean << std::endl
<< "distance: " << distance << std::endl
;
//-----------------------------------------------------------------------------
<< "mean: " << mean << std::endl
<< "new mean: " << new_mean << std::endl
<< "distance: " << distance << std::endl
;
return 0;
}