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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.
*********************************************************************/
#include <iostream>
#include "EvolutionStrategy.h"
namespace ES
{
Individual::Individual(unsigned cutCount, unsigned dataSize, double beta):
classifier(cutCount, dataSize, beta),
r_w(1),
r_b(1),
r_v(1),
r_v_b(1)
{}
void Individual::mutate(double dataAvrSd)
{
assert(classifier.w.rows() == classifier.b.size());
assert(classifier.w.rows() == classifier.v.size());
// mutate rate
r_w *= (rand()%2 == 0) ? 1.25 : 0.8;
r_b *= (rand()%2 == 0) ? 1.25 : 0.8;
r_v *= (rand()%2 == 0) ? 1.25 : 0.8;
r_v_b *= (rand()%2 == 0) ? 1.25 : 0.8;
// mutate using rate
const double aprioriRate(0.05);
// w
for (int i = 0; i < classifier.w.rows(); ++i)
{
for (int j = 0; j < classifier.w.cols(); ++j)
classifier.w(i,j) += gaussianRand(0, aprioriRate*r_w);
classifier.w.row(i) /= classifier.w.row(i).norm();
}
// b
for (int i = 0; i < classifier.b.size(); ++i)
classifier.b(i) += gaussianRand(0, dataAvrSd*aprioriRate*r_b);
// v
for (int i = 0; i < classifier.v.size(); ++i)
classifier.v(i) += gaussianRand(0, aprioriRate*r_v);
classifier.v /= classifier.v.norm();
// v_b
classifier.v_b += gaussianRand(0, double(classifier.v.size())*aprioriRate*r_v_b);
}
Individual Individual::createChild(double dataAvrSd) const
{
Individual child(*this);
child.mutate(dataAvrSd);
return child;
}
Individual Individual::createRandom(unsigned cutCount, unsigned dataSize, double dataAvrSd, double beta)
{
Individual ind(cutCount, dataSize, beta);
Classifier& classifier(ind.classifier);
classifier.setRandom(dataAvrSd);
return ind;
}
Population::Population(unsigned cutCount, unsigned dataSize, double dataAvrSd, double beta, unsigned indPerDim):
vector<Individual>((((cutCount*(dataSize+1)+1)*indPerDim)/4)*4)
{
// create initial population
for (iterator it(begin()); it != end(); ++it)
*it = Individual::createRandom(cutCount, dataSize, dataAvrSd, beta);
}
Population::ErrorPair Population::evolveOneGen(const VectorXd& y, const MatrixXd& x, double dataAvrSd)
{
assert(y.size() == x.rows());
typedef std::multimap<double, Individual> EvaluationMap;
typedef EvaluationMap::iterator EvaluationMapIterator;
EvaluationMap evalutationMap;
// evaluation
double totalError(0);
for (const_iterator it(begin()); it != end(); ++it)
{
const Individual& ind(*it);
const double error(ind.classifier.sumSquareError(y, x));
totalError += error;
evalutationMap.insert(std::make_pair(error, ind));
}
const double averageError(totalError / double(size()));
// selection
assert((size() / 4) * 4 == size());
size_t ind = 0;
for (EvaluationMapIterator it = evalutationMap.begin(); ind < size() / 4; ++it, ++ind)
{
//cout << "S " << it->first << "\n";
(*this)[ind * 4] = it->second;
(*this)[ind * 4 + 1] = it->second.createChild(dataAvrSd);
(*this)[ind * 4 + 2] = it->second.createChild(dataAvrSd);
(*this)[ind * 4 + 3] = it->second.createChild(dataAvrSd);
}
// return statistics
return ErrorPair(evalutationMap.begin()->first, averageError);
}
Classifier Population::optimise(const VectorXd& y, const MatrixXd& x, double dataAvrSd, size_t genCount)
{
// optimise
for (size_t g = 0; g < genCount; ++g)
{
const ErrorPair e = evolveOneGen(y, x, dataAvrSd);
std::cout << g << " : " << e.first << ", " << e.second << ", ";
// compute number of missclassified
unsigned missClassified(0);
for (int sample = 0; sample < y.size(); ++sample)
missClassified += fabs(sgn((*this)[0].classifier.eval(x.row(sample))) - y(sample)) / 2;
std::cout << missClassified << std::endl;
}
return (*this)[0].classifier;
}
} // namespace ES
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