CMAExperiment.cpp

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/*!
 * 
 *
 * \brief       Example of an experiment using the CMA-ES on several benchmark functions
 *
 * \author      O.Krause
 * \date        2014
 *
 * \par Copyright 1995-2017 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <https://shark-ml.github.io/Shark/>
 * 
 * Shark is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published 
 * by the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 * 
 * Shark is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Lesser General Public License for more details.
 * 
 * You should have received a copy of the GNU Lesser General Public License
 * along with Shark.  If not, see <http://www.gnu.org/licenses/>.
 *
 */
// Implementation of the CMA-ES
#include <shark/Algorithms/DirectSearch/CMA.h>
// Access to benchmark functions
#include <shark/ObjectiveFunctions/Benchmarks/Rosenbrock.h>
#include <shark/ObjectiveFunctions/Benchmarks/Cigar.h>
#include <shark/ObjectiveFunctions/Benchmarks/Discus.h>
#include <shark/ObjectiveFunctions/Benchmarks/Ellipsoid.h>
        
using namespace shark;
 
 
int main( int argc, char ** argv ) {
 
    std::size_t numDimensions = 10; //dimensions of the objective functions
    std::size_t numTrials = 100; // how often the optimization is repeated
    std::size_t recordingInterval = 20; //we want to record after some multiple of this
    std::size_t numIterations = 20*recordingInterval; //number of iterations to perform
    
    //assortment of test functions
    typedef boost::shared_ptr<SingleObjectiveFunction > Function;
    std::vector<Function > functions;
    functions.push_back(Function(new benchmarks::Rosenbrock(numDimensions)));
    functions.push_back(Function(new benchmarks::Cigar(numDimensions)));
    functions.push_back(Function(new benchmarks::Discus(numDimensions)));
    functions.push_back(Function(new benchmarks::Ellipsoid(numDimensions)));
    
    RealMatrix meanPerformance(functions.size(), numIterations/recordingInterval+1,0.0);
    for(std::size_t f = 0; f != functions.size(); ++f){
        for(std::size_t trial = 0; trial != numTrials; ++trial){
            //print progress
            std::cout<<"\r" <<functions[f]->name() <<": "<<trial<<"/"<<numTrials<<std::flush;
            //create and initialize the optimizer
            CMA cma;
            functions[f]->init();
            cma.init( *functions[f] );
            
            //record value
            meanPerformance(f,0) += cma.solution().value; 
            
            //optimize
            for(std::size_t i = 1; i <= numIterations; ++i){
                cma.step(*functions[f]);
                if(i % recordingInterval == 0){
                    meanPerformance(f,i / recordingInterval) += cma.solution().value; 
                }
            }
        }
    }
    meanPerformance /= numTrials;
 
    std::cout<<"\r# Iteration ";
    for(std::size_t f = 0; f != functions.size(); ++f)
        std::cout<<functions[f]->name()<<" ";
    std::cout<<"\n";
    
    std::cout.precision( 7 );
    for(std::size_t i = 0; i != meanPerformance.size2();++i){
        std::cout<< i*recordingInterval<<" ";
        for(std::size_t f = 0; f != functions.size(); ++f){
            std::cout<<meanPerformance(f,i)<<" ";
        }
        std::cout<<"\n";
    }
}