nojhan
38e3f40bad
- Change the ill-condition continuator to use eigen decomposition of the covariance matrix, just like in the adaptive estimator. - Add a warning message in adaptive sampler.
112 lines
3.8 KiB
C++
112 lines
3.8 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) 2020 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 _edoContAdaptiveIllCovar_h
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#define _edoContAdaptiveIllCovar_h
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#ifdef WITH_EIGEN
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#include<Eigen/Dense>
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#include "edoContinue.h"
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/** A continuator that check if the covariance matrix
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* of an edoNormalAdaptive distribution is ill-conditioned.
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*
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* If the condition number of the covariance matrix
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* is strictly greater than the threshold given at construction,
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* it will ask for a stop.
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*
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* @ingroup Continuators
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*/
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template<class D>
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class edoContAdaptiveIllCovar : public edoContinue<D>
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{
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public:
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using EOType = typename D::EOType;
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using Matrix = typename D::Matrix;
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using Vector = typename D::Vector;
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edoContAdaptiveIllCovar( double threshold = 1e6) :
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_threshold(threshold)
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{ }
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bool operator()(const D& d)
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{
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Eigen::SelfAdjointEigenSolver<Matrix> eigensolver( d.covar() );
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auto info = eigensolver.info();
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if(info == Eigen::ComputationInfo::NumericalIssue) {
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eo::log << eo::warnings << "WARNING: the eigen decomposition of the covariance matrix"
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<< " did not satisfy the prerequisites." << std::endl;
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} else if(info == Eigen::ComputationInfo::NoConvergence) {
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eo::log << eo::warnings << "WARNING: the eigen decomposition of the covariance matrix"
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<< " did not converged." << std::endl;
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} else if(info == Eigen::ComputationInfo::InvalidInput) {
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eo::log << eo::warnings << "WARNING: the eigen decomposition of the covariance matrix"
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<< " had invalid inputs." << std::endl;
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}
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if(info != Eigen::ComputationInfo::Success) {
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eo::log << eo::progress << "STOP because the covariance matrix"
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<< " cannot be decomposed" << std::endl;
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#ifndef NDEBUG
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eo::log << eo::xdebug
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<< "mean:\n" << d.mean() << std::endl
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<< "sigma:" << d.sigma() << std::endl
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<< "coord_sys:\n" << d.coord_sys() << std::endl
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<< "scaling:\n" << d.scaling() << std::endl;
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#endif
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return false;
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}else {
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Matrix EV = eigensolver.eigenvalues();
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double condition = EV.maxCoeff() / EV.minCoeff();
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if( not std::isfinite(condition) ) {
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eo::log << eo::progress << "STOP because the covariance matrix"
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<< " condition is not finite." << std::endl;
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return false;
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} else if( condition >= _threshold ) {
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eo::log << eo::progress << "STOP because the covariance matrix"
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<< " is ill-conditionned (condition number: " << condition << ")" << std::endl;
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return false;
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} else {
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return true;
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}
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}
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}
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virtual std::string className() const { return "edoContAdaptiveIllCovar"; }
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protected:
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const double _threshold;
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};
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#endif // WITH_EIGEN
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#endif
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