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/*********************************************************************
MLDemos: A User-Friendly visualization toolkit for machine learning
Copyright (C) 2010 Basilio Noris
Contact: mldemos@b4silio.com
Mixture of Logisitics Regression
Copyright (C) 2011 Stephane Magnenat
Contact: stephane at magnenat dot net
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public License,
version 3 as published by the Free Software Foundation.
This library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free
Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*********************************************************************/
#ifndef _MLR_EVOLUTION_STRATEGY_H
#define _MLR_EVOLUTION_STRATEGY_H
#include <map>
#include "MixtureLogisticRegression.h"
namespace ES
{
using namespace MLR;
typedef MLR::Classifier Classifier;
struct Individual
{
Classifier classifier;
double r_w;
double r_b;
double r_v;
double r_v_b;
Individual(unsigned cutCount = 0, unsigned dataSize = 0, double beta = 1);
void mutate(double dataAvrSd);
Individual createChild(double dataAvrSd) const;
static Individual createRandom(unsigned cutCount, unsigned dataSize, double dataAvrSd, double beta);
};
struct Population: protected std::vector<Individual>
{
typedef std::pair<double, double> ErrorPair;
Population(unsigned cutCount, unsigned dataSize, double dataAvrSd, double beta, unsigned indPerDim);
ErrorPair evolveOneGen(const VectorXd& y, const MatrixXd& x, double dataAvrSd);
Classifier optimise(const VectorXd& y, const MatrixXd& x, double dataAvrSd, size_t genCount);
};
} // namespace ES
#endif // _EVOLUTION_STRATEGY_H
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