163 lines
4.6 KiB
C++
163 lines
4.6 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) 2010 Thales group
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*/
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/*
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Authors:
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Johann Dreo <johann.dreo@thalesgroup.com>
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Caner Candan <caner.candan@thalesgroup.com>
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*/
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#ifndef _edoNormalMulti_h
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#define _edoNormalMulti_h
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#include "edoDistrib.h"
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#ifdef WITH_BOOST
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#include <boost/numeric/ublas/symmetric.hpp>
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#include <boost/numeric/ublas/lu.hpp>
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namespace ublas = boost::numeric::ublas;
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#else
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#ifdef WITH_EIGEN
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#include <Eigen/Dense>
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#endif // WITH_EIGEN
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#endif // WITH_BOOST
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/** @defgroup EMNA
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*
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* Estimation of Multivariate Normal Algorithm (EMNA) is a stochastic,
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* derivative-free methods for numerical optimization of non-linear or
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* non-convex continuous optimization problems.
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*
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* @ingroup Algorithms
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*/
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/** @defgroup Multinormal Multivariate normal
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*
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* Distribution that model co-variances between variables.
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*
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* @ingroup Distributions
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*/
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/** A multi-normal distribution, that models co-variances.
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*
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* Defines a mean vector and a co-variances matrix.
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*
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* Exists in two implementations, using either
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* <a href="http://www.boost.org/doc/libs/1_50_0/libs/numeric/ublas/doc/index.htm">Boost::uBLAS</a> (if compiled WITH_BOOST)
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* or <a href="http://eigen.tuxfamily.org">Eigen3</a> (WITH_EIGEN).
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*
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* @ingroup Distributions
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* @ingroup EMNA
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* @ingroup Multinormal
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*/
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template < typename EOT >
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class edoNormalMulti : public edoDistrib< EOT >
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{
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#ifdef WITH_BOOST
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public:
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typedef typename EOT::AtomType AtomType;
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edoNormalMulti( unsigned int dim = 1 ) :
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_mean( const ublas::vector<AtomType>(0,dim) ),
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_varcovar( const ublas::identity_matrix<AtomType>(dim) )
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{
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assert(_mean.size() > 0);
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assert(_mean.size() == _varcovar.size1());
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assert(_mean.size() == _varcovar.size2());
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}
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edoNormalMulti
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(
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const ublas::vector< AtomType >& mean,
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const ublas::symmetric_matrix< AtomType, ublas::lower >& varcovar
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)
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: _mean(mean), _varcovar(varcovar)
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{
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assert(_mean.size() > 0);
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assert(_mean.size() == _varcovar.size1());
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assert(_mean.size() == _varcovar.size2());
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}
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unsigned int size()
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{
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assert(_mean.size() == _varcovar.size1());
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assert(_mean.size() == _varcovar.size2());
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return _mean.size();
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}
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ublas::vector< AtomType > mean() const {return _mean;}
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ublas::symmetric_matrix< AtomType, ublas::lower > varcovar() const {return _varcovar;}
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private:
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ublas::vector< AtomType > _mean;
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ublas::symmetric_matrix< AtomType, ublas::lower > _varcovar;
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#else
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#ifdef WITH_EIGEN
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public:
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typedef typename EOT::AtomType AtomType;
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typedef Eigen::Matrix< AtomType, Eigen::Dynamic, 1> Vector;
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typedef Eigen::Matrix< AtomType, Eigen::Dynamic, Eigen::Dynamic> Matrix;
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edoNormalMulti( unsigned int dim = 1 ) :
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_mean( Vector::Zero(dim) ),
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_varcovar( Matrix::Identity(dim,dim) )
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{
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assert(_mean.size() > 0);
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assert(_mean.innerSize() == _varcovar.innerSize());
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assert(_mean.innerSize() == _varcovar.outerSize());
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}
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edoNormalMulti(
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const Vector & mean,
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const Matrix & varcovar
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)
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: _mean(mean), _varcovar(varcovar)
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{
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assert(_mean.innerSize() > 0);
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assert(_mean.innerSize() == _varcovar.innerSize());
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assert(_mean.innerSize() == _varcovar.outerSize());
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}
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unsigned int size()
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{
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assert(_mean.innerSize() == _varcovar.innerSize());
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assert(_mean.innerSize() == _varcovar.outerSize());
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return _mean.innerSize();
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}
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Vector mean() const {return _mean;}
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Matrix varcovar() const {return _varcovar;}
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private:
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Vector _mean;
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Matrix _varcovar;
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#endif // WITH_EIGEN
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#endif // WITH_BOOST
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}; // class edoNormalMulti
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#endif // !_edoNormalMulti_h
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