paradiseo/mo/src/sampling/moMHBestFitnessCloudSampling.h
2012-08-30 11:30:11 +02:00

113 lines
4.3 KiB
C++

/*
<moMHBestFitnessCloudSampling.h>
Copyright (C) DOLPHIN Project-Team, INRIA Lille - Nord Europe, 2006-2010
Sebastien Verel, Arnaud Liefooghe, Jeremie Humeau
This software is governed by the CeCILL license under French law and
abiding by the rules of distribution of free software. You can use,
modify and/ or redistribute the software under the terms of the CeCILL
license as circulated by CEA, CNRS and INRIA at the following URL
"http://www.cecill.info".
As a counterpart to the access to the source code and rights to copy,
modify and redistribute granted by the license, users are provided only
with a limited warranty and the software's author, the holder of the
economic rights, and the successive licensors have only limited liability.
In this respect, the user's attention is drawn to the risks associated
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software by the user in light of its specific status of free software,
that may mean that it is complicated to manipulate, and that also
therefore means that it is reserved for developers and experienced
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encouraged to load and test the software's suitability as regards their
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The fact that you are presently reading this means that you have had
knowledge of the CeCILL license and that you accept its terms.
ParadisEO WebSite : http://paradiseo.gforge.inria.fr
Contact: paradiseo-help@lists.gforge.inria.fr
*/
#ifndef moMHBestFitnessCloudSampling_h
#define moMHBestFitnessCloudSampling_h
#include <eoInit.h>
#include <neighborhood/moNeighborhood.h>
#include <eval/moEval.h>
#include <eoEvalFunc.h>
#include <algo/moMetropolisHasting.h>
#include <continuator/moNeighborBestStat.h>
#include <sampling/moFitnessCloudSampling.h>
/**
* To compute an estimation of the fitness cloud,
* i.e. the scatter plot of solution fitness versus neighbor fitness:
*
* Here solution are sampled with Metropolis-Hasting method
*
* Sample the fitness of solutions from Metropolis-Hasting sampling
* and the best fitness of k random neighbor
*
* The values are collected during the Metropolis-Hasting walk
*
*/
template <class Neighbor>
class moMHBestFitnessCloudSampling : public moFitnessCloudSampling<Neighbor>
{
public:
typedef typename Neighbor::EOT EOT ;
using moSampling<Neighbor>::localSearch;
using moSampling<Neighbor>::checkpoint;
using moSampling<Neighbor>::monitorVec;
using moSampling<Neighbor>::continuator;
using moFitnessCloudSampling<Neighbor>::fitnessStat;
/**
* Constructor
* @param _init initialisation method of the solution
* @param _neighborhood neighborhood to get one random neighbor (supposed to be random neighborhood)
* @param _fullEval Fitness function, full evaluation function
* @param _eval neighbor evaluation, incremental evaluation function
* @param _nbStep Number of step of the MH sampling
*/
moMHBestFitnessCloudSampling(eoInit<EOT> & _init,
moNeighborhood<Neighbor> & _neighborhood,
eoEvalFunc<EOT>& _fullEval,
moEval<Neighbor>& _eval,
unsigned int _nbStep) :
moFitnessCloudSampling<Neighbor>(_init, _neighborhood, _fullEval, _eval, _nbStep),
neighborBestStat(_neighborhood, _eval)
{
// delete the dummy local search
delete localSearch;
// Metropolis-Hasting sampling
localSearch = new moMetropolisHasting<Neighbor>(_neighborhood, _fullEval, _eval, _nbStep);
// delete the checkpoint with the wrong continuator
delete checkpoint;
// set the continuator
continuator = localSearch->getContinuator();
// re-construction of the checkpoint
checkpoint = new moCheckpoint<Neighbor>(*continuator);
checkpoint->add(fitnessStat);
checkpoint->add(*monitorVec[0]);
// one random neighbor
this->add(neighborBestStat);
}
protected:
moNeighborBestStat< Neighbor > neighborBestStat;
};
#endif