152 lines
4.7 KiB
C++
152 lines
4.7 KiB
C++
// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
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//-----------------------------------------------------------------------------
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// eoEsChromInit.h
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// (c) Maarten Keijzer 2000, GeNeura Team, 1998 - EEAAX 1999
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/*
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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 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., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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Contact: todos@geneura.ugr.es, http://geneura.ugr.es
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Marc.Schoenauer@polytechnique.fr
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mak@dhi.dk
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*/
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//-----------------------------------------------------------------------------
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#ifndef _eoEsChromInit_H
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#define _eoEsChromInit_H
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#include <cmath>
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#include <es/eoRealInitBounded.h>
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#include <es/eoEsSimple.h>
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#include <es/eoEsStdev.h>
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#include <es/eoEsFull.h>
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#ifndef M_PI
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#define M_PI 3.1415926535897932384626433832795
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#endif
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/**
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\ingroup EvolutionStrategies
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Random Es-chromosome initializer (therefore derived from eoInit)
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This class can initialize four types of real-valued genotypes
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thanks to tempate specialization of private method create
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eoReal just an eoVector<double>
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eoEsSimple + one self-adapting single sigma for all variables
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eoEsStdev a whole std::vector of self-adapting sigmas
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eoEsFull a full self-adapting correlation matrix
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@see eoReal eoEsSimple eoEsStdev eoEsFull eoInit
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*/
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template <class EOT>
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class eoEsChromInit : public eoRealInitBounded<EOT>
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{
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public:
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using eoEsChromInit< EOT >::size;
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using eoEsChromInit< EOT >::theBounds;
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typedef typename EOT::Fitness FitT;
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/** Ctor:
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@param eoRealVectorBounds& _bounds : bounds for uniform initialization
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@param _sigma : initial value for the stddev
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@param _to_scale : wether sigma should be multiplied by the range of each variable
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added December 2004 - MS (together with the whole comment :-)
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*/
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eoEsChromInit(eoRealVectorBounds& _bounds, double _sigma = 0.3, bool _to_scale=false)
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: eoRealInitBounded<EOT>(_bounds)
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{
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// a bit of pre-computations, to save time later (even if some are useless)
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// first, the case of one unique sigma
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if (_to_scale) // sigma is scaled by the average range (if that means anything!)
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{
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double scaleUnique = 0;
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for (unsigned i=0; i<size(); i++)
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scaleUnique += theBounds().range(i);
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scaleUnique /= size();
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uniqueSigma = _sigma * scaleUnique;
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}
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else
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uniqueSigma = _sigma;
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// now the case of a vector of sigmas
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// first allocate
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lesSigmas.resize(size()); // size() is the size of the bounds (see eoRealInitBounded)
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for (unsigned i=0; i<size(); i++)
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if (_to_scale) // each sigma is scaled by the range of the corresponding variable
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{
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lesSigmas[i] = _sigma * theBounds().range(i);
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}
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else
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lesSigmas[i] = _sigma;
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}
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void operator()(EOT& _eo)
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{
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eoRealInitBounded<EOT>::operator()(_eo);
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create_self_adapt(_eo);
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_eo.invalidate(); // was MISSING!!!!
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}
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// accessor to sigma
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// double sigmaInit() {return sigma;}
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private :
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// No adaptive mutation at all
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void create_self_adapt(eoReal<FitT>&)// nothing to do here ...
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{ }
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// Adaptive mutation through a unique sigma
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void create_self_adapt(eoEsSimple<FitT>& result)
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{
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// pre-computed in the Ctor
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result.stdev = uniqueSigma;
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}
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// Adaptive mutation through a std::vector of sigmas
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void create_self_adapt(eoEsStdev<FitT>& result)
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{
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result.stdevs = lesSigmas;
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}
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// Adaptive mutation through a whole correlation matrix
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void create_self_adapt(eoEsFull<FitT>& result)
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{
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// first the stdevs (pre-computed in the Ctor)
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result.stdevs = lesSigmas;
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unsigned int theSize = size();
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// nb of rotation angles: N*(N-1)/2 (in general!)
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result.correlations.resize(theSize*(theSize - 1) / 2);
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for (unsigned i=0; i<result.correlations.size(); ++i)
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{
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// uniform in [-PI, PI)
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result.correlations[i] = rng.uniform(2 * M_PI) - M_PI;
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}
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}
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// the DATA
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double uniqueSigma; // initial value in case of a unique sigma
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std::vector<double> lesSigmas; // initial values in case of a vector fo sigmas
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};
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#endif
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