97 lines
2.3 KiB
C++
97 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 _edoEstimatorNormalMono_h
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#define _edoEstimatorNormalMono_h
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#include "edoEstimator.h"
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#include "edoNormalMono.h"
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template < typename EOT >
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class edoEstimatorNormalMono : public edoEstimator< edoNormalMono< EOT > >
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{
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public:
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typedef typename EOT::AtomType AtomType;
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class Variance
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{
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public:
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Variance() : _sumvar(0){}
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void update(AtomType v)
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{
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_n++;
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AtomType d = v - _mean;
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_mean += 1 / _n * d;
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_sumvar += (_n - 1) / _n * d * d;
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}
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AtomType get_mean() const {return _mean;}
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AtomType get_var() const {return _sumvar / (_n - 1);}
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AtomType get_std() const {return sqrt( get_var() );}
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private:
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AtomType _n;
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AtomType _mean;
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AtomType _sumvar;
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};
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public:
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edoNormalMono< EOT > operator()(eoPop<EOT>& pop)
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{
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unsigned int popsize = pop.size();
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assert(popsize > 0);
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unsigned int dimsize = pop[0].size();
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assert(dimsize > 0);
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std::vector< Variance > var( dimsize );
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for (unsigned int i = 0; i < popsize; ++i)
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{
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for (unsigned int d = 0; d < dimsize; ++d)
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{
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var[d].update( pop[i][d] );
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}
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}
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EOT mean( dimsize );
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EOT variance( dimsize );
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for (unsigned int d = 0; d < dimsize; ++d)
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{
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mean[d] = var[d].get_mean();
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variance[d] = var[d].get_var();
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}
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return edoNormalMono< EOT >( mean, variance );
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}
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};
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#endif // !_edoEstimatorNormalMono_h
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