93 lines
2.8 KiB
C++
93 lines
2.8 KiB
C++
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/*
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* Open BEAGLE
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* Copyright (C) 2001-2007 by Christian Gagne and Marc Parizeau
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*
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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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*
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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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*
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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., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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*
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* Contact:
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* Laboratoire de Vision et Systemes Numeriques
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* Departement de genie electrique et de genie informatique
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* Universite Laval, Quebec, Canada, G1K 7P4
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* http://vision.gel.ulaval.ca
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*
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*/
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/*!
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* \file SymbRegEvalOp.cpp
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* \brief Implementation of the class SymbRegEvalOp.
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* \author Christian Gagne
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* \author Marc Parizeau
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* $Revision: 1.7.2.1 $
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* $Date: 2007/05/09 01:51:24 $
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*/
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#include "SymbRegEvalOp.hpp"
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#include <cmath>
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using namespace Beagle;
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/*!
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* \brief Construct a new symbolic regression evaluation operator.
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* \param inName Name of the evaluation operator.
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*/
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SymbRegEvalOp::SymbRegEvalOp(std::string inName) :
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GP::EvaluationOp(inName.c_str()),
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mX(0),
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mY(0)
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{ }
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/*!
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* \brief Evaluate the individual fitness for the symbolic regression problem.
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* \param inIndividual Individual to evaluate.
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* \param ioContext Evolutionary context.
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* \return Handle to the fitness measure,
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*/
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Fitness::Handle SymbRegEvalOp::evaluate(GP::Individual& inIndividual, GP::Context& ioContext)
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{
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using namespace std;
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cout << "Evaluating individual's fitness:" << endl;
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double lSquareError = 0.0;
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for(unsigned int i=0; i<mX.size(); i++) {
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setValue("X", mX[i], ioContext);
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Double lResult;
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inIndividual.run(lResult, ioContext);
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cout << " Result = " << lResult << endl;
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double lError = mY[i]-lResult;
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lSquareError += (lError*lError);
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}
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double lMSE = lSquareError / mX.size();
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double lRMSE = sqrt(lMSE);
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double lFitness = (1.0 / (lRMSE + 1.0));
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return new FitnessSimple(lFitness);
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}
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/*!
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* \brief Post-initialize the operator by sampling the function to regress.
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* \param ioSystem System to use to sample.
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*/
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void SymbRegEvalOp::postInit(System& ioSystem)
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{
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GP::EvaluationOp::postInit(ioSystem);
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for(unsigned int i=0; i<1; i++) {
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mX.push_back(ioSystem.getRandomizer().rollUniform(-1.0,1.0));
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std::cout << "case #" << i << ": X = " << mX.back() << std::endl;
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mY.push_back(mX[i]*(mX[i]*(mX[i]*(mX[i]+1.0)+1.0)+1.0));
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}
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}
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