3067f3f8e4
Refactor distribution, sampler and estimator related to the multi-binomial distribution. This introduce tomic methods which may be overloaded for data structures more complex than eoReal of vector of bool (the default implentation).
114 lines
3.6 KiB
C++
114 lines
3.6 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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#ifndef _edoEstimatorBinomialMulti_h
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#define _edoEstimatorBinomialMulti_h
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#include "edoBinomialMulti.h"
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#include "edoEstimator.h"
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#ifdef WITH_EIGEN
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#include <Eigen/Dense>
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/** An estimator for edoBinomialMulti
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*
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* @ingroup Estimators
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* @ingroup Binomial
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*/
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template< class EOT, class D = edoBinomialMulti<EOT> >
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class edoEstimatorBinomialMulti : public edoEstimator<D>
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{
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protected:
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/** Decide whether a given element of the distribution is true or false.
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*
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* The default implementation is to set the item to the value of the atom itself
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* (which is a boolean in the basic version).
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* If you have a more complex data structure, you can just overload this.
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*/
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virtual void make( D & to, unsigned int i, unsigned int j, typename EOT::AtomType::const_iterator iatom )
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{
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to(i,j) = *iatom;
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}
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/** Transliterate a EOT in a boolean matrix
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*/
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D eot2d( const EOT & from, unsigned int rows, unsigned int cols ) // FIXME maybe more elegant with Eigen::Map?
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{
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assert( rows > 0 );
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assert( from.size() == rows );
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assert( cols > 0 );
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D to( Eigen::MatrixXd(rows, cols) );
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unsigned int i=0;
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for( typename EOT::const_iterator irow = from.begin(), end=from.end(); irow != end; ++irow ) {
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assert( irow->size() == cols );
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unsigned int j=0;
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for( typename EOT::AtomType::const_iterator icol = irow->begin(), end=irow->end(); icol != end; ++icol ) {
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make( to, i, j, icol );
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j++;
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}
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i++;
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}
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return to;
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}
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public:
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/** The expected EOT interface is the same as an Eigen3::MatrixXd.
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*/
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D operator()( eoPop<EOT>& pop )
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{
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unsigned int popsize = pop.size();
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assert(popsize > 0);
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unsigned int rows = pop.begin()->size();
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assert( rows > 0 );
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unsigned int cols = pop.begin()->begin()->size();
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assert( cols > 0 );
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D probas( D::Zero(rows, cols) );
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// We still need a loop over pop, because it is an eoVector
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for (unsigned int i = 0; i < popsize; ++i) {
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D indiv = eot2d( pop[i], rows, cols );
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assert( indiv.rows() == rows && indiv.cols() == cols );
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// the EOT matrix should be filled with 1 or 0 only
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assert( indiv.sum() <= popsize );
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probas += indiv / popsize;
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// sum and scalar product, no size pb expected
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assert( probas.rows() == rows && probas.cols() == cols );
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}
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return probas;
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}
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};
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#endif // WITH_EIGEN
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#endif // !_edoEstimatorBinomialMulti_h
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