208 lines
5.7 KiB
C++
208 lines
5.7 KiB
C++
//
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/* (c) Maarten Keijzer 2000, GeNeura Team, 1998 - EEAAX 1999
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This library is free software; you can redistribute it and/or modify it under
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the terms of the GNU Lesser General Public License as published by the Free
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Software Foundation; either version 2 of the License, or (at your option) any
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later version.
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This library is distributed in the hope that it will be useful, but WITHOUT ANY
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WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
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PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.
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You should have received a copy of the GNU Lesser General Public License along
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with this library; if not, write to the Free Software Foundation, Inc., 59
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Temple Place, Suite 330, Boston, MA 02111-1307 USA
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Contact: http://eodev.sourceforge.net
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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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#ifndef _eoEsChromInit_H
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#define _eoEsChromInit_H
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#include <algorithm>
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#include <cassert>
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#include <cmath>
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#include <vector>
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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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/** Random Es-chromosome initializer (therefore derived from eoInit)
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@ingroup Real
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@ingroup Initializators
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This class can initialize four types of real-valued genotypes thanks
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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 eoRealInitBounded<EOT>::size;
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using eoRealInitBounded<EOT>::theBounds;
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typedef typename EOT::Fitness FitT;
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/** Constructor
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@param _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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//
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// first, in the case of one unique sigma
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// sigma is scaled by the average range (if that means anything!)
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if (_to_scale)
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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 first allocate space according
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// to the size of the bounds (see eoRealInitBounded)
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vecSigma.resize(size());
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// each sigma is scaled by the range of the corresponding variable
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for(unsigned i=0; i<size(); i++)
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if(_to_scale)
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vecSigma[i] = _sigma * theBounds().range(i);
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else
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vecSigma[i] = _sigma;
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}
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/** Constructor
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@overload
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Specify individual initial sigmas for each variable.
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@param _bounds bounds for uniform initialization
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@param _vecSigma initial value for the stddev
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*/
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eoEsChromInit(eoRealVectorBounds& _bounds, const std::vector<double>& _vecSigma)
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: eoRealInitBounded<EOT>(_bounds), uniqueSigma(_vecSigma[0]), vecSigma(_vecSigma)
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{
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assert(_bounds.size() == size());
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assert(_vecSigma.size() == size());
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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();
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}
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private:
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/** Create intializer
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No adaptive mutation at all
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*/
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void create_self_adapt(eoReal<FitT>&)
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{}
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/** Create intializer
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@overload
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Adaptive mutation through a unique sigma
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*/
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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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/** Create intializer
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@overload
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Adaptive mutation through a std::vector of sigmas
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@todo Should we scale sigmas to the corresponding object variable range?
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*/
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void create_self_adapt(eoEsStdev<FitT>& result)
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{
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// pre-computed in the constructor
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result.stdevs = vecSigma;
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}
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/** Create intializer
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@overload
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Adaptive mutation through a whole correlation matrix
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*/
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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 = vecSigma;
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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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/** Initial value in case of a unique sigma */
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double uniqueSigma;
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/** Initial values in case of a vector of sigmas */
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std::vector<double> vecSigma;
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};
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#endif
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// Local Variables:
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// coding: iso-8859-1
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// mode:C++
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// c-file-style: "Stroustrup"
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// comment-column: 35
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// fill-column: 80
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// End:
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