+ boxplot.py: script to generate graphic with boxplot to illustrate distances between theorical and visual means for each population value
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Caner Candan
parent
b70a60bc59
commit
ba6770df43
3 changed files with 123 additions and 78 deletions
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@ -34,6 +34,7 @@ INCLUDE_DIRECTORIES(${CMAKE_SOURCE_DIR}/application/eda_sa)
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SET(SOURCES
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t-doEstimatorNormalMulti
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t-mean-distance
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)
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FOREACH(current ${SOURCES})
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19
test/boxplot.py
Executable file
19
test/boxplot.py
Executable file
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@ -0,0 +1,19 @@
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#!/usr/bin/env python
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from pylab import *
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FILE_LOCATIONS = 'means_distances_results/files_description.txt'
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data = []
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locations = [ line.split()[0] for line in open( FILE_LOCATIONS ) ]
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for cur_file in locations:
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data.append( [ float(line.split()[7]) for line in open( cur_file ).readlines() ] )
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print locations
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#print data
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boxplot( data )
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show()
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@ -1,3 +1,6 @@
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#include <sys/stat.h>
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#include <sys/types.h>
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#include <sstream>
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#include <iomanip>
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#include <fstream>
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@ -27,9 +30,10 @@ int main(int ac, char** av)
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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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unsigned int p_max = parser.createParam((unsigned int)10000, "population-max", "Population max", 'P', section).value(); // P
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unsigned int p_step = parser.createParam((unsigned int)10, "population-step", "Population step", 't', section).value(); // t
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unsigned int p_max = parser.createParam((unsigned int)1000, "population-max", "Population max", 'P', section).value(); // P
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unsigned int p_step = parser.createParam((unsigned int)50, "population-step", "Population step", 't', section).value(); // t
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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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@ -37,6 +41,9 @@ int main(int ac, char** av)
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AtomType covar2_value = parser.createParam((AtomType)0.5, "covar2", "Covar value 2", '2', section).value(); // 2
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AtomType covar3_value = parser.createParam((AtomType)1.0, "covar3", "Covar value 3", '3', section).value(); // 3
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std::string results_directory = parser.createParam((std::string)"means_distances_results", "results-directory", "Results directory", 'R', section).value(); // R
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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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@ -48,128 +55,146 @@ int main(int ac, char** av)
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//-----------------------------------------------------
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assert(r_max >= 1);
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assert(s_size >= 2);
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eo::log << eo::debug << "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::logging;
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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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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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eoState state;
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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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//-----------------------------------------------------
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// (1) Population init and sampler
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//-----------------------------------------------------
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eo::log << eo::file( desc_file.str() ) << cur_file.str().c_str() << std::endl;
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eoRndGenerator< double >* gen = new eoUniformGenerator< double >(-5, 5);
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state.storeFunctor(gen);
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eo::log << eo::file( cur_file.str() );
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eoInitFixedLength< EOT >* init = new eoInitFixedLength< EOT >( s_size, *gen );
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state.storeFunctor(init);
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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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// 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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eo::log << eo::quiet;
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//-----------------------------------------------------
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//-----------------------------------------------------------------------------
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// (2) distribution initial parameters
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//-----------------------------------------------------------------------------
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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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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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//-----------------------------------------------------------------------------
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// Prepare bounder class to set bounds of sampling.
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// This is used by doSampler.
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//-----------------------------------------------------------------------------
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doBounder< EOT >* bounder = new doBounderRng< 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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doSampler< Distrib >* sampler = new doSamplerNormalMulti< 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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for ( unsigned int r = 1; r <= r_max; ++r)
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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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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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//-----------------------------------------------------------------------------
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// (2) distribution initial parameters
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//-----------------------------------------------------------------------------
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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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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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//-----------------------------------------------------------------------------
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// Prepare bounder class to set bounds of sampling.
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// This is used by doSampler.
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//-----------------------------------------------------------------------------
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doBounder< EOT >* bounder = new doBounderRng< 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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doSampler< Distrib >* sampler = new doSamplerNormalMulti< 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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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// (6) estimation phase
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//-----------------------------------------------------------------------------
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doEstimator< Distrib >* estimator = new doEstimatorNormalMulti< 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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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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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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//-----------------------------------------------------------------------------
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// (6) estimation phase
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//-----------------------------------------------------------------------------
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doEstimator< Distrib >* estimator = new doEstimatorNormalMulti< 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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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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distance = sqrt( distance );
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eo::log << 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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return 0;
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