nojhan
b06250dc39
So as to model vector<vector<bool>> individuals with 2D binomial distributions (as Eigen matrix).
87 lines
2.9 KiB
C++
87 lines
2.9 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) 2013 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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*/
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#include <vector>
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#include <iostream>
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#include <string>
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#include <cmath>
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#include <eo>
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#include <edo>
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#include <ga.h> // for Bools
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#ifdef WITH_EIGEN
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#include <Eigen/Dense>
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// NOTE: a typedef on eoVector does not work, because of readFrom on a vector AtomType
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// typedef eoVector<eoMinimizingFitness, std::vector<bool> > Bools;
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class Bools : public std::vector<std::vector<bool> >, public EO<double>
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{
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public:
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typedef std::vector<bool> AtomType;
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};
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int main(int ac, char** av)
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{
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eoParser parser(ac, av);
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std::string section("Algorithm parameters");
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unsigned int popsize = parser.createParam((unsigned int)100000, "popSize", "Population Size", 'P', section).value(); // P
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unsigned int rows = parser.createParam((unsigned int)2, "lines", "Lines number", 'l', section).value(); // l
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unsigned int cols = parser.createParam((unsigned int)3, "columns", "Columns number", 'c', section).value(); // c
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double proba = parser.createParam((double)0.5, "proba", "Probability to estimate", 'b', section).value(); // b
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if( parser.userNeedsHelp() ) {
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parser.printHelp(std::cout);
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exit(1);
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}
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make_help(parser);
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std::cout << "Init distrib" << std::endl;
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Eigen::MatrixXd initd = Eigen::MatrixXd::Constant(rows,cols,proba);
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edoBinomialMulti<Bools> distrib( initd );
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std::cout << distrib << std::endl;
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edoEstimatorBinomialMulti<Bools> estimate;
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edoSamplerBinomialMulti<Bools> sample;
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std::cout << "Sample a pop from the init distrib" << std::endl;
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eoPop<Bools> pop; pop.reserve(popsize);
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for( unsigned int i=0; i < popsize; ++i ) {
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pop.push_back( sample( distrib ) );
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}
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std::cout << "Estimate a distribution from the sampled pop" << std::endl;
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distrib = estimate( pop );
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std::cout << distrib << std::endl;
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std::cout << "Estimated initial proba = " << distrib.mean() << std::endl;
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}
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#endif // WITH_EIGEN
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