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