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* cma-sa name has been replace by eda-sa in the project

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This commit is contained in:
Caner Candan 2010-08-18 18:39:58 +02:00
commit a84417d349
9 changed files with 17 additions and 17 deletions
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2
application/CMakeLists.txt
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@ -2,6 +2,6 @@
### 1) Where do we go now ?!? ### 1) Where do we go now ?!?
###################################################################################### ######################################################################################
ADD_SUBDIRECTORY(cma_sa) ADD_SUBDIRECTORY(eda_sa)
###################################################################################### ######################################################################################

28
application/eda_sa/CMakeLists.txt Normal file
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PROJECT(eda_sa)
FIND_PACKAGE(Boost 1.33.0)
INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
INCLUDE_DIRECTORIES(${Boost_INCLUDE_DIRS})
LINK_DIRECTORIES(${Boost_LIBRARY_DIRS})
SET(RESOURCES
eda_sa.param
plot.py
)
FOREACH(file ${RESOURCES})
EXECUTE_PROCESS(
COMMAND ${CMAKE_COMMAND} -E copy_if_different
${CMAKE_CURRENT_SOURCE_DIR}/${file}
${DO_BINARY_DIR}/${file}
)
ENDFOREACH(file)
FILE(GLOB SOURCES *.cpp)
SET(EXECUTABLE_OUTPUT_PATH ${DO_BINARY_DIR})
ADD_EXECUTABLE(${PROJECT_NAME} ${SOURCES})
TARGET_LINK_LIBRARIES(${PROJECT_NAME} do ${EO_LIBRARIES} ${MO_LIBRARIES} ${Boost_LIBRARIES})

42
application/eda_sa/Rosenbrock.h Normal file
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#ifndef _Rosenbrock_h
#define _Rosenbrock_h
#include <eo>
#include <es.h>
#include <es/eoRealInitBounded.h>
#include <es/eoRealOp.h>
#include <es/eoEsChromInit.h>
#include <es/eoRealOp.h>
#include <es/make_real.h>
#include <apply.h>
#include <eoProportionalCombinedOp.h>
template < typename EOT >
class Rosenbrock : public eoEvalFunc< EOT >
{
public:
typedef typename EOT::AtomType AtomType;
virtual void operator()( EOT& p )
{
if (!p.invalid())
return;
p.fitness( _evaluate( p ) );
}
private:
AtomType _evaluate( EOT& p )
{
AtomType r = 0.0;
for (unsigned int i = 0; i < p.size() - 1; ++i)
{
r += p[i] * p[i];
}
return r;
}
};
#endif // !_Rosenbrock_h

42
application/eda_sa/Sphere.h Normal file
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#ifndef _Sphere_h
#define _Sphere_h
#include <eo>
#include <es.h>
#include <es/eoRealInitBounded.h>
#include <es/eoRealOp.h>
#include <es/eoEsChromInit.h>
#include <es/eoRealOp.h>
#include <es/make_real.h>
#include <apply.h>
#include <eoProportionalCombinedOp.h>
template < typename EOT >
class Sphere : public eoEvalFunc< EOT >
{
public:
typedef typename EOT::AtomType AtomType;
virtual void operator()( EOT& p )
{
if (!p.invalid())
return;
p.fitness( _evaluate( p ) );
}
private:
AtomType _evaluate( EOT& p )
{
AtomType r = 0.0;
for (unsigned int i = 0; i < p.size() - 1; ++i)
{
r += p[i] * p[i];
}
return r;
}
};
#endif // !_Sphere_h

7
application/eda_sa/eda_sa.param Normal file
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--rho=0 # -p : <etropolis sample size
--alpha=0 # -a : Temperature dicrease rate
--threshold=0.1 # -t : Temperature threshold stopping criteria
--sample-size=10 # -P : Sample size
--dimension-size=10 # -d : Dimension size
--temperature=100 # -T : Initial temperature
#--verbose # Enable verbose mode

243
application/eda_sa/main.cpp Normal file
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#include <eo>
#include <mo>
#include <utils/eoLogger.h>
#include <utils/eoParserLogger.h>
#include <do/make_pop.h>
#include <do/make_run.h>
#include <do/make_continue.h>
#include <do/make_checkpoint.h>
#include <do>
#include "Rosenbrock.h"
#include "Sphere.h"
typedef eoReal<eoMinimizingFitness> EOT;
//typedef doUniform< EOT > Distrib;
//typedef doNormalMono< EOT > Distrib;
typedef doNormalMulti< EOT > Distrib;
int main(int ac, char** av)
{
eoParserLogger parser(ac, av);
// Letters used by the following declarations :
// a d i p t
std::string section("Algorithm parameters");
// FIXME: a verifier la valeur par defaut
double initial_temperature = parser.createParam((double)10e5, "temperature", "Initial temperature", 'i', section).value(); // i
eoState state;
//-----------------------------------------------------------------------------
// Instantiate all need parameters for EDASA algorithm
//-----------------------------------------------------------------------------
eoSelect< EOT >* selector = new eoDetSelect< EOT >(0.5);
state.storeFunctor(selector);
doEstimator< Distrib >* estimator =
//new doEstimatorUniform< EOT >();
//new doEstimatorNormalMono< EOT >();
new doEstimatorNormalMulti< EOT >();
state.storeFunctor(estimator);
eoSelectOne< EOT >* selectone = new eoDetTournamentSelect< EOT >( 2 );
state.storeFunctor(selectone);
doModifierMass< Distrib >* modifier =
//new doUniformCenter< EOT >();
//new doNormalMonoCenter< EOT >();
new doNormalMultiCenter< EOT >();
state.storeFunctor(modifier);
eoEvalFunc< EOT >* plainEval =
//new BopoRosenbrock< EOT, typename EOT::AtomType, const EOT& >();
new Sphere< EOT >();
state.storeFunctor(plainEval);
unsigned long max_eval = parser.getORcreateParam((unsigned long)0, "maxEval", "Maximum number of evaluations (0 = none)", 'E', "Stopping criterion").value(); // E
eoEvalFuncCounter< EOT > eval(*plainEval, max_eval);
eoRndGenerator< double >* gen = new eoUniformGenerator< double >(-5, 5);
//eoRndGenerator< double >* gen = new eoNormalGenerator< double >(0, 1);
state.storeFunctor(gen);
unsigned int dimension_size = parser.createParam((unsigned int)10, "dimension-size", "Dimension size", 'd', section).value(); // d
eoInitFixedLength< EOT >* init = new eoInitFixedLength< EOT >( dimension_size, *gen );
state.storeFunctor(init);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// (1) Population init and sampler
//-----------------------------------------------------------------------------
// Generation of population from do_make_pop (creates parameter, manages persistance and so on...)
// ... and creates the parameter letters: L P r S
// this first sampler creates a uniform distribution independently of our distribution (it doesnot use doUniform).
eoPop< EOT >& pop = do_make_pop(parser, state, *init);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// (2) First evaluation before starting the research algorithm
//-----------------------------------------------------------------------------
apply(eval, pop);
//-----------------------------------------------------------------------------
//doBounder< EOT >* bounder = new doBounderNo< EOT >();
doBounder< EOT >* bounder = new doBounderRng< EOT >(EOT(pop[0].size(), -5),
EOT(pop[0].size(), 5),
*gen);
state.storeFunctor(bounder);
doSampler< Distrib >* sampler =
//new doSamplerUniform< EOT >();
//new doSamplerNormalMono< EOT >( *bounder );
new doSamplerNormalMulti< EOT >( *bounder );
state.storeFunctor(sampler);
unsigned int rho = parser.createParam((unsigned int)0, "rho", "Rho: metropolis sample size", 'p', section).value(); // p
moGenSolContinue< EOT >* sa_continue = new moGenSolContinue< EOT >(rho);
state.storeFunctor(sa_continue);
double threshold = parser.createParam((double)0.1, "threshold", "Threshold: temperature threshold stopping criteria", 't', section).value(); // t
double alpha = parser.createParam((double)0.1, "alpha", "Alpha: temperature dicrease rate", 'a', section).value(); // a
moCoolingSchedule* cooling_schedule = new moGeometricCoolingSchedule(threshold, alpha);
state.storeFunctor(cooling_schedule);
// stopping criteria
// ... and creates the parameter letters: C E g G s T
eoContinue< EOT >& eo_continue = do_make_continue(parser, state, eval);
// output
eoCheckPoint< EOT >& monitoring_continue = do_make_checkpoint(parser, state, eval, eo_continue);
// eoPopStat< EOT >* popStat = new eoPopStat<EOT>;
// state.storeFunctor(popStat);
// checkpoint.add(*popStat);
// eoGnuplot1DMonitor* gnuplot = new eoGnuplot1DMonitor("gnuplot.txt");
// state.storeFunctor(gnuplot);
// gnuplot->add(eval);
// gnuplot->add(*popStat);
//gnuplot->gnuplotCommand("set yrange [0:500]");
// checkpoint.add(*gnuplot);
// eoMonitor* fileSnapshot = new doFileSnapshot< std::vector< std::string > >("ResPop");
// state.storeFunctor(fileSnapshot);
// fileSnapshot->add(*popStat);
// checkpoint.add(*fileSnapshot);
doDummyContinue< Distrib >* dummy_continue = new doDummyContinue< Distrib >();
state.storeFunctor(dummy_continue);
doCheckPoint< Distrib >* distribution_continue = new doCheckPoint< Distrib >( *dummy_continue );
state.storeFunctor(distribution_continue);
doDistribStat< Distrib >* distrib_stat = new doStatNormalMulti< EOT >();
state.storeFunctor(distrib_stat);
distribution_continue->add( *distrib_stat );
// eoMonitor* stdout_monitor = new eoStdoutMonitor();
// state.storeFunctor(stdout_monitor);
// stdout_monitor->add(*distrib_stat);
// distribution_continue->add( *stdout_monitor );
eoFileMonitor* file_monitor = new eoFileMonitor("distribution_bounds.txt");
state.storeFunctor(file_monitor);
file_monitor->add(*distrib_stat);
distribution_continue->add( *file_monitor );
//-----------------------------------------------------------------------------
// eoEPRemplacement causes the using of the current and previous
// sample for sampling.
//-----------------------------------------------------------------------------
eoReplacement< EOT >* replacor = new eoEPReplacement< EOT >(pop.size());
// Below, use eoGenerationalReplacement to sample only on the current sample
//eoReplacement< EOT >* replacor = new eoGenerationalReplacement< EOT >(); // FIXME: to define the size
state.storeFunctor(replacor);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// EDASA algorithm configuration
//-----------------------------------------------------------------------------
doAlgo< Distrib >* algo = new doEDASA< Distrib >
(*selector, *estimator, *selectone, *modifier, *sampler,
monitoring_continue, *distribution_continue,
eval, *sa_continue, *cooling_schedule,
initial_temperature, *replacor);
//-----------------------------------------------------------------------------
// state.storeFunctor(algo);
if (parser.userNeedsHelp())
{
parser.printHelp(std::cout);
exit(1);
}
// Help + Verbose routines
make_verbose(parser);
make_help(parser);
//-----------------------------------------------------------------------------
// Beginning of the algorithm call
//-----------------------------------------------------------------------------
try
{
do_run(*algo, pop);
}
catch (eoReachedThresholdException& e)
{
eo::log << eo::warnings << e.what() << std::endl;
}
catch (std::exception& e)
{
eo::log << eo::errors << "exception: " << e.what() << std::endl;
exit(EXIT_FAILURE);
}
//-----------------------------------------------------------------------------
return 0;
}

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application/eda_sa/plot.py Normal file
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#!/usr/bin/env python
"""plot.py -- Plot EDA-SA results file"""
import os, time, math, tempfile
import numpy
try:
import Gnuplot, Gnuplot.PlotItems, Gnuplot.funcutils
except ImportError:
# kludge in case Gnuplot hasn't been installed as a module yet:
import __init__
Gnuplot = __init__
import PlotItems
Gnuplot.PlotItems = PlotItems
import funcutils
Gnuplot.funcutils = funcutils
def wait(str=None, prompt='Press return to show results...\n'):
if str is not None:
print str
raw_input(prompt)
def draw2DRect(min=(0,0), max=(1,1), color='black', state=None, g=None):
if g == None: g = Gnuplot.Gnuplot()
if state != None: state.append(g)
xmin, ymin = min
xmax, ymax = max
cmd = 'set arrow from %s,%s to %s,%s nohead lc rgb "%s"'
g(cmd % (xmin, ymin, xmin, ymax, color))
g(cmd % (xmin, ymax, xmax, ymax, color))
g(cmd % (xmax, ymax, xmax, ymin, color))
g(cmd % (xmax, ymin, xmin, ymin, color))
return g
def draw3DRect(min=(0,0,0), max=(1,1,1), state=None, g=None):
if g == None: g = Gnuplot.Gnuplot()
if state != None: state.append(g)
# TODO
return g
def getSortedFiles(path):
assert path != None
filelist = os.listdir(path)
filelist.sort()
return filelist
def plotXPointYFitness(path, fields='3:1', state=None, g=None):
if g == None: g = Gnuplot.Gnuplot()
if state != None: state.append(g)
g.title('Fitness observation')
g.xlabel('Coordinates')
g.ylabel('Fitness (Quality)')
files=[]
for filename in getSortedFiles(path):
files.append(Gnuplot.File(path + '/' + filename, using=fields,
with_='points',
title='distribution \'' + filename + '\''))
g.plot(*files)
return g
def plotXYPointZFitness(path, fields='4:3:1', state=None, g=None):
if g == None: g = Gnuplot.Gnuplot()
if state != None: state.append(g)
g.title('Fitness observation in 3-D')
g.xlabel('x-axes')
g.ylabel('y-axes')
g.zlabel('Fitness (Quality)')
files=[]
for filename in getSortedFiles(path):
files.append(Gnuplot.File(path + '/' + filename, using=fields,
with_='points',
title='distribution \'' + filename + '\''))
g.splot(*files)
return g
def plotXYPoint(path, fields='3:4', state=None, g=None):
if g == None: g = Gnuplot.Gnuplot()
if state != None: state.append(g)
g.title('Points observation in 2-D')
g.xlabel('x-axes')
g.ylabel('y-axes')
files=[]
for filename in getSortedFiles(path):
files.append(Gnuplot.File(path + '/' + filename, using=fields,
with_='points',
title='distribution \'' + filename + '\''))
g.plot(*files)
return g
def plotXYZPoint(path, fields='3:4:5', state=None, g=None):
if g == None: g = Gnuplot.Gnuplot()
if state != None: state.append(g)
g.title('Points observation in 3-D')
g.xlabel('x-axes')
g.ylabel('y-axes')
g.zlabel('z-axes')
files=[]
for filename in getSortedFiles(path):
files.append(Gnuplot.File(path + '/' + filename, using=fields,
with_='points',
title='distribution \'' + filename + '\''))
g.splot(*files)
return g
def plotParams(path, field='1', state=None, g=None):
if g == None: g = Gnuplot.Gnuplot()
if state != None: state.append(g)
g.title('Hyper-volume comparaison through all dimensions')
g.xlabel('Iterations')
g.ylabel('Hyper-volume')
g.plot(Gnuplot.File(path, with_='lines', using=field,
title='multivariate distribution narrowing'))
return g
def plot2DRectFromFiles(path, state=None, g=None, plot=True):
if g == None: g = Gnuplot.Gnuplot()
if state != None: state.append(g)
g.title('Rectangle drawing observation')
g.xlabel('x-axes')
g.ylabel('y-axes')
x1,x2,y1,y2 = 0,0,0,0
colors = ['red', 'orange', 'blue', 'green', 'gold', 'yellow', 'gray']
#colors = open('rgb.txt', 'r').readlines()
colors_size = len(colors)
i = 0 # for color
for filename in getSortedFiles(path):
line = open(path + '/' + filename, 'r').readline()
fields = line.split(' ')
if not fields[0] == '2':
print 'plot2DRectFromFiles: higher than 2 dimensions not possible to draw'
return
xmin,ymin,xmax,ymax = fields[1:5]
#print xmin,ymin,xmax,ymax
cur_color = colors[i % colors_size]
draw2DRect((xmin,ymin), (xmax,ymax), cur_color, g=g)
g('set obj rect from %s,%s to %s,%s back lw 1.0 fc rgb "%s" fillstyle solid 1.00 border -1'
% (xmin,ymin,xmax,ymax,cur_color)
)
if plot:
if float(xmin) < x1: x1 = float(xmin)
if float(ymin) < y1: y1 = float(ymin)
if float(xmax) > x2: x2 = float(xmax)
if float(ymax) > y2: y2 = float(ymax)
#print x1,y1,x2,y2
i += 1
#print x1,y1,x2,y2
if plot:
g.plot('[%s:%s][%s:%s] -9999 notitle' % (x1, x2, y1, y2))
return g
def main(n):
gstate = []
if n >= 1:
plotXPointYFitness('./ResPop', state=gstate)
if n >= 2:
plotXPointYFitness('./ResPop', '4:1', state=gstate)
if n >= 2:
plotXYPointZFitness('./ResPop', state=gstate)
if n >= 3:
plotXYZPoint('./ResPop', state=gstate)
if n >= 1:
plotParams('./ResParams.txt', state=gstate)
if n >= 2:
plot2DRectFromFiles('./ResBounds', state=gstate)
plotXYPoint('./ResPop', state=gstate)
g = plot2DRectFromFiles('./ResBounds', state=gstate, plot=False)
plotXYPoint('./ResPop', g=g)
wait(prompt='Press return to end the plot.\n')
pass
# when executed, just run main():
if __name__ == '__main__':
from sys import argv, exit
if len(argv) < 2:
print 'Usage: plot [dimension]'
exit()
main(int(argv[1]))

2
src/do
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@ -9,7 +9,7 @@
#define _do_ #define _do_
#include "doAlgo.h" #include "doAlgo.h"
#include "doCMASA.h" #include "doEDASA.h"
#include "doDistrib.h" #include "doDistrib.h"
#include "doUniform.h" #include "doUniform.h"

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src/doEDASA.h Normal file
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// (c) Thales group, 2010
/*
Authors:
Johann Dreo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _doEDASA_h
#define _doEDASA_h
//-----------------------------------------------------------------------------
// Temporary solution to store populations state at each iteration for plotting.
//-----------------------------------------------------------------------------
// system header inclusion
#include <cstdlib>
// end system header inclusion
// mkdir headers inclusion
#include <sys/stat.h>
#include <sys/types.h>
// end mkdir headers inclusion
#include <fstream>
#include <numeric>
#include <limits>
#include <boost/numeric/ublas/vector.hpp>
#include <boost/numeric/ublas/io.hpp>
//-----------------------------------------------------------------------------
#include <eo>
#include <mo>
#include <utils/eoRNG.h>
#include "doAlgo.h"
#include "doEstimator.h"
#include "doModifierMass.h"
#include "doSampler.h"
#include "doHyperVolume.h"
#include "doStat.h"
#include "doContinue.h"
using namespace boost::numeric::ublas;
template < typename D >
class doEDASA : public doAlgo< D >
{
public:
//! Alias for the type EOT
typedef typename D::EOType EOT;
//! Alias for the atom type
typedef typename EOT::AtomType AtomType;
//! Alias for the fitness
typedef typename EOT::Fitness Fitness;
public:
//! doEDASA constructor
/*!
All the boxes used by a EDASA need to be given.
\param selector The EOT selector
\param estomator The EOT selector
\param selectone SelectOne
\param modifier The D modifier
\param sampler The D sampler
\param evaluation The evaluation function.
\param continue The stopping criterion.
\param cooling_schedule The cooling schedule, describes how the temperature is modified.
\param initial_temperature The initial temperature.
\param replacor The EOT replacor
*/
doEDASA (eoSelect< EOT > & selector,
doEstimator< D > & estimator,
eoSelectOne< EOT > & selectone,
doModifierMass< D > & modifier,
doSampler< D > & sampler,
eoContinue< EOT > & monitoring_continue,
doContinue< D > & distribution_continue,
eoEvalFunc < EOT > & evaluation,
moSolContinue < EOT > & sa_continue,
moCoolingSchedule & cooling_schedule,
double initial_temperature,
eoReplacement< EOT > & replacor
)
: _selector(selector),
_estimator(estimator),
_selectone(selectone),
_modifier(modifier),
_sampler(sampler),
_monitoring_continue(monitoring_continue),
_distribution_continue(distribution_continue),
_evaluation(evaluation),
_sa_continue(sa_continue),
_cooling_schedule(cooling_schedule),
_initial_temperature(initial_temperature),
_replacor(replacor),
_pop_results_destination("ResPop")//,
// directory where populations state are going to be stored.
// _ofs_params("ResParams.txt"),
// _ofs_params_var("ResVars.txt"),
// _bounds_results_destination("ResBounds")
{
//-------------------------------------------------------------
// Temporary solution to store populations state at each
// iteration for plotting.
//-------------------------------------------------------------
{
std::stringstream os;
os << "rm -rf " << _pop_results_destination;
::system(os.str().c_str());
}
::mkdir(_pop_results_destination.c_str(), 0755); // create a first time
//-------------------------------------------------------------
//-------------------------------------------------------------
// Temporary solution to store bounds values for each distribution.
//-------------------------------------------------------------
// {
// std::stringstream os;
// os << "rm -rf " << _bounds_results_destination;
// ::system(os.str().c_str());
// }
//::mkdir(_bounds_results_destination.c_str(), 0755); // create once directory
//-------------------------------------------------------------
}
//! function that launches the EDASA algorithm.
/*!
As a moTS or a moHC, the EDASA can be used for HYBRIDATION in an evolutionary algorithm.
\param pop A population to improve.
\return TRUE.
*/
//bool operator ()(eoPop< EOT > & pop)
void operator ()(eoPop< EOT > & pop)
{
assert(pop.size() > 0);
double temperature = _initial_temperature;
eoPop< EOT > current_pop = pop;
eoPop< EOT > selected_pop;
//-------------------------------------------------------------
// Temporary solution used by plot to enumerate iterations in
// several files.
//-------------------------------------------------------------
int number_of_iterations = 0;
//-------------------------------------------------------------
//-------------------------------------------------------------
// Estimating a first time the distribution parameter thanks
// to population.
//-------------------------------------------------------------
D distrib = _estimator(pop);
double size = distrib.size();
assert(size > 0);
//-------------------------------------------------------------
// {
// D distrib = _estimator(pop);
// double size = distrib.size();
// assert(size > 0);
// doHyperVolume< EOT > hv;
// for (int i = 0; i < size; ++i)
// {
// //hv.update( distrib.varcovar()[i] );
// }
// // _ofs_params_var << hv << std::endl;
// }
do
{
if (pop != current_pop)
{
_replacor(pop, current_pop);
}
current_pop.clear();
selected_pop.clear();
//-------------------------------------------------------------
// (3) Selection of the best points in the population
//-------------------------------------------------------------
_selector(pop, selected_pop);
assert( selected_pop.size() > 0 );
//-------------------------------------------------------------
_sa_continue.init();
//-------------------------------------------------------------
// (4) Estimation of the distribution parameters
//-------------------------------------------------------------
distrib = _estimator(selected_pop);
//-------------------------------------------------------------
// TODO: utiliser selected_pop ou pop ???
assert(selected_pop.size() > 0);
//-------------------------------------------------------------
// Init of a variable contening a point with the bestest fitnesses
//-------------------------------------------------------------
EOT current_solution = _selectone(selected_pop);
//-------------------------------------------------------------
//-------------------------------------------------------------
// Fit the current solution with the distribution parameters (bounds)
//-------------------------------------------------------------
// FIXME: si besoin de modifier la dispersion de la distribution
// _modifier_dispersion(distribution, selected_pop);
_modifier(distrib, current_solution);
//-------------------------------------------------------------
//-------------------------------------------------------------
// Building of the sampler in current_pop
//-------------------------------------------------------------
do
{
EOT candidate_solution = _sampler(distrib);
EOT& e1 = candidate_solution;
_evaluation( e1 );
EOT& e2 = current_solution;
_evaluation( e2 );
// TODO: verifier le critere d'acceptation
if ( e1.fitness() < e2.fitness() ||
rng.uniform() < exp( ::fabs(e1.fitness() - e2.fitness()) / temperature ) )
{
current_pop.push_back(candidate_solution);
current_solution = candidate_solution;
}
}
while ( _sa_continue( current_solution) );
//-------------------------------------------------------------
// Temporary solution to store populations state
// at each iteration for plotting.
//-------------------------------------------------------------
{
std::ostringstream os;
os << _pop_results_destination << "/" << number_of_iterations;
std::ofstream ofs(os.str().c_str());
ofs << current_pop;
}
//-------------------------------------------------------------
//-------------------------------------------------------------
// Temporary solution used by plot to enumerate iterations in
// several files.
//-------------------------------------------------------------
// {
// double size = distrib.size();
// assert(size > 0);
// std::stringstream os;
// os << _bounds_results_destination << "/" << number_of_iterations;
// std::ofstream ofs(os.str().c_str());
// ofs << size << " ";
//ublas::vector< AtomType > mean = distrib.mean();
//std::copy(mean.begin(), mean.end(), std::ostream_iterator< double >(ofs, " "));
//std::copy(distrib.varcovar().begin(), distrib.varcovar().end(), std::ostream_iterator< double >(ofs, " "));
// ofs << std::endl;
// }
//-------------------------------------------------------------
//-------------------------------------------------------------
// Temporary saving to get a proof for distribution bounds
// dicreasement
//-------------------------------------------------------------
// {
// double size = distrib.size();
// assert(size > 0);
// vector< double > vmin(size);
// vector< double > vmax(size);
// std::copy(distrib.param(0).begin(), distrib.param(0).end(), vmin.begin());
// std::copy(distrib.param(1).begin(), distrib.param(1).end(), vmax.begin());
// vector< double > vrange = vmax - vmin;
// doHyperVolume hv;
// for (int i = 0, size = vrange.size(); i < size; ++i)
// {
// hv.update( vrange(i) );
// }
// ofs_params << hv << std::endl;
// }
//-------------------------------------------------------------
//-------------------------------------------------------------
// Temporary saving to get a proof for distribution bounds
// dicreasement
//-------------------------------------------------------------
// {
// double size = distrib.size();
// assert(size > 0);
// doHyperVolume< EOT > hv;
// for (int i = 0; i < size; ++i)
// {
// //hv.update( distrib.varcovar()[i] );
// }
// //_ofs_params_var << hv << std::endl;
// }
//-------------------------------------------------------------
++number_of_iterations;
}
while ( _cooling_schedule( temperature ) &&
_distribution_continue( distrib ) &&
_monitoring_continue( selected_pop )
);
}
private:
//! A EOT selector
eoSelect < EOT > & _selector;
//! A EOT estimator. It is going to estimate distribution parameters.
doEstimator< D > & _estimator;
//! SelectOne
eoSelectOne< EOT > & _selectone;
//! A D modifier
doModifierMass< D > & _modifier;
//! A D sampler
doSampler< D > & _sampler;
//! A EOT monitoring continuator
eoContinue < EOT > & _monitoring_continue;
//! A D continuator
doContinue < D > & _distribution_continue;
//! A full evaluation function.
eoEvalFunc < EOT > & _evaluation;
//! Stopping criterion before temperature update
moSolContinue < EOT > & _sa_continue;
//! The cooling schedule
moCoolingSchedule & _cooling_schedule;
//! Initial temperature
double _initial_temperature;
//! A EOT replacor
eoReplacement < EOT > & _replacor;
//-------------------------------------------------------------
// Temporary solution to store populations state at each
// iteration for plotting.
//-------------------------------------------------------------
std::string _pop_results_destination;
// std::ofstream _ofs_params;
// std::ofstream _ofs_params_var;
//-------------------------------------------------------------
//-------------------------------------------------------------
// Temporary solution to store bounds values for each distribution.
//-------------------------------------------------------------
// std::string _bounds_results_destination;
//-------------------------------------------------------------
};
#endif // !_doEDASA_h