119 lines
3.5 KiB
C++
119 lines
3.5 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.hpp
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* \brief Definition of the type SymbRegEvalOp.
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* \author Christian Gagne
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* \author Marc Parizeau
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* $Revision: 1.5.2.1 $
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* $Date: 2007/05/09 01:51:24 $
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*/
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/*!
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* \defgroup SymbReg Symbolic Regression Example
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* \brief Symbolic regression (symbreg): A simple GP example with Open BEAGLE.
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*
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* \par Objective
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* Find a function of one independent variable and one dependent variable, in
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* symbolic form, that fits a given sample of 20 \f$(x_i,y_i)\f$ data points,
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* where the target function is the quadratic polynomial \f$x^4 + x^3 + x^2 + x\f$.
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*
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* \par Terminal set
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* - X (the independent variable)
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* - PI
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* - Ephemeral constants randomly generated in [-1,1]
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*
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* \par Function set
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* - +
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* - -
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* - *
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* - / (protected division)
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* - SIN
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* - COS
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* - EXP
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* - LOG (protected logarithm)
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*
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* \par Fitness cases
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* The given sample of 20 data points \f$(x_i,y_i)\f$, randomly chosen within
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* interval [-1,1].
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*
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* \par Fitness
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* \f$\frac{1.}{1.+RMSE}\f$ where RMSE is the Root Mean Square Error on the
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* fitness cases.
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*
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* \par Stopping criteria
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* When the evolution reaches the maximum number of generations.
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*
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* \par Reference
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* John R. Koza, "Genetic Programming: On the Programming of Computers by Means
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* of Natural Selection", MIT Press, 1992, pages 162-169.
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*
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*/
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#ifndef SymbRegEvalOp_hpp
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#define SymbRegEvalOp_hpp
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#include "beagle/GP.hpp"
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#include <string>
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#include <vector>
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/*!
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* \class SymbRegEvalOp SymbRegEvalOp.hpp "SymbRegEvalOp.hpp"
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* \brief The individual evaluation class operator for the problem of symbolic regression.
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* \ingroup SymbReg
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*/
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class SymbRegEvalOp : public Beagle::GP::EvaluationOp {
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public:
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//! SymbRegEvalOp allocator type.
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typedef Beagle::AllocatorT<SymbRegEvalOp,Beagle::GP::EvaluationOp::Alloc>
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Alloc;
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//!< SymbRegEvalOp handle type.
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typedef Beagle::PointerT<SymbRegEvalOp,Beagle::GP::EvaluationOp::Handle>
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Handle;
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//!< SymbRegEvalOp bag type.
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typedef Beagle::ContainerT<SymbRegEvalOp,Beagle::GP::EvaluationOp::Bag>
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Bag;
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explicit SymbRegEvalOp(std::string inName="SymbRegEvalOp");
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virtual Beagle::Fitness::Handle evaluate(Beagle::GP::Individual& inIndividual,
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Beagle::GP::Context& ioContext);
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virtual void postInit(Beagle::System& ioSystem);
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protected:
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std::vector<Beagle::Double> mX;
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std::vector<Beagle::Double> mY;
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};
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#endif // SymbRegEvalOp_hpp
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