76 lines
2.3 KiB
C++
76 lines
2.3 KiB
C++
/*
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The Evolving Distribution Objects framework (EDO) is a template-based,
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ANSI-C++ evolutionary computation library which helps you to write your
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own estimation of distribution algorithms.
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This library is free software; you can redistribute it and/or
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modify it under the terms of the GNU Lesser General Public
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License as published by the Free Software Foundation; either
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version 2.1 of the License, or (at your option) any later version.
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This library is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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Lesser General Public License for more details.
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You should have received a copy of the GNU Lesser General Public
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License along with this library; if not, write to the Free Software
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Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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Copyright (C) 2010 Thales group
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*/
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/*
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Authors:
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Johann Dréo <johann.dreo@thalesgroup.com>
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Caner Candan <caner.candan@thalesgroup.com>
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*/
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#ifndef _edoSamplerNormalMono_h
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#define _edoSamplerNormalMono_h
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#include <cmath>
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#include <utils/eoRNG.h>
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#include "edoSampler.h"
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#include "edoNormalMono.h"
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#include "edoBounder.h"
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/**
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* edoSamplerNormalMono
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* This class uses the NormalMono distribution parameters (bounds) to return
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* a random position used for population sampling.
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*/
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template < typename EOT, typename D = edoNormalMono< EOT > >
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class edoSamplerNormalMono : public edoSampler< D >
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{
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public:
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typedef typename EOT::AtomType AtomType;
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edoSamplerNormalMono( edoRepairer<EOT> & repairer ) : edoSampler< D >( repairer) {}
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EOT sample( edoNormalMono<EOT>& distrib )
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{
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unsigned int size = distrib.size();
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assert(size > 0);
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// The point we want to draw
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// (coordinates in n dimension)
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// x = {x1, x2, ..., xn}
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EOT solution;
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// Sampling all dimensions
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for (unsigned int i = 0; i < size; ++i) {
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AtomType mean = distrib.mean()[i];
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AtomType variance = distrib.variance()[i];
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// should use the standard deviation, which have the same scale than the mean
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AtomType random = rng.normal(mean, sqrt(variance) );
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solution.push_back(random);
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}
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return solution;
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}
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};
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#endif // !_edoSamplerNormalMono_h
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