Ajout FDC

git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@1789 331e1502-861f-0410-8da2-ba01fb791d7f
This commit is contained in:
verel 2010-05-06 14:04:31 +00:00
commit 5afed6591e
4 changed files with 291 additions and 3 deletions

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@ -36,7 +36,6 @@
#define moDensityOfStatesSampling_h
#include <eoInit.h>
#include <eval/moEval.h>
#include <eoEvalFunc.h>
#include <algo/moRandomSearch.h>
#include <continuator/moFitnessStat.h>
@ -59,8 +58,8 @@ public:
/**
* Default Constructor
* @param _init initialisation method of the solution
* @param _neighborhood neighborhood giving neighbor in random order
* @param _nbStep Number of steps of the random walk
* @param _fullEval Fitness function, full evaluation function
* @param _nbSol Number of solutions in the sample
*/
moDensityOfStatesSampling(eoInit<EOT> & _init,
eoEvalFunc<EOT>& _fullEval,

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@ -0,0 +1,95 @@
/*
<moFDCsampling.h>
Copyright (C) DOLPHIN Project-Team, INRIA Lille - Nord Europe, 2006-2010
Sebastien Verel, Arnaud Liefooghe, Jeremie Humeau
This software is governed by the CeCILL license under French law and
abiding by the rules of distribution of free software. You can use,
modify and/ or redistribute the software under the terms of the CeCILL
license as circulated by CEA, CNRS and INRIA at the following URL
"http://www.cecill.info".
As a counterpart to the access to the source code and rights to copy,
modify and redistribute granted by the license, users are provided only
with a limited warranty and the software's author, the holder of the
economic rights, and the successive licensors have only limited liability.
In this respect, the user's attention is drawn to the risks associated
with loading, using, modifying and/or developing or reproducing the
software by the user in light of its specific status of free software,
that may mean that it is complicated to manipulate, and that also
therefore means that it is reserved for developers and experienced
professionals having in-depth computer knowledge. Users are therefore
encouraged to load and test the software's suitability as regards their
requirements in conditions enabling the security of their systems and/or
data to be ensured and, more generally, to use and operate it in the
same conditions as regards security.
The fact that you are presently reading this means that you have had
knowledge of the CeCILL license and that you accept its terms.
ParadisEO WebSite : http://paradiseo.gforge.inria.fr
Contact: paradiseo-help@lists.gforge.inria.fr
*/
#ifndef moFDCsampling_h
#define moFDCsampling_h
#include <eoInit.h>
#include <eval/moEval.h>
#include <eoEvalFunc.h>
#include <algo/moRandomSearch.h>
#include <continuator/moFitnessStat.h>
#include <utils/eoDistance.h>
#include <continuator/moDistanceStat.h>
#include <sampling/moSampling.h>
/**
* To compute the fitness distance correlation:
* Sample the fitness and the distance from a particular solution of random solution in the search space
* The fitness values and distances of solutions are collected during the random search
* Then the correlation between the fitness and the distance can be computed
*
*/
template <class Neighbor>
class moFDCsampling : public moSampling<Neighbor>
{
public:
typedef typename Neighbor::EOT EOT ;
using moSampling<Neighbor>::localSearch;
/**
* Default Constructor
* @param _init initialisation method of the solution
* @param _neighborhood neighborhood giving neighbor in random order
* @param _dist the distance function between solution
* @param _refSol the reference solution to compute the distance (think of global optimum when possible)
* @param _nbSol Number of solutions of the sample
*/
moFDCsampling(eoInit<EOT> & _init,
eoEvalFunc<EOT>& _fullEval,
eoDistance<EOT>& _dist,
EOT& _refSol,
unsigned int _nbSol) :
moSampling<Neighbor>(_init, * new moRandomSearch<Neighbor>(_init, _fullEval, _nbSol), fitnessStat),
distStat(_dist, _refSol)
{
add(distStat);
}
/**
* default destructor
*/
~moFDCsampling() {
// delete the pointer on the local search which has been constructed in the constructor
delete &localSearch;
}
protected:
moFitnessStat<EOT> fitnessStat;
moDistanceStat<EOT> distStat;
};
#endif

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@ -13,6 +13,7 @@ ADD_EXECUTABLE(sampling sampling.cpp)
ADD_EXECUTABLE(densityOfStates densityOfStates.cpp)
ADD_EXECUTABLE(autocorrelation autocorrelation.cpp)
ADD_EXECUTABLE(adaptiveWalks adaptiveWalks.cpp)
ADD_EXECUTABLE(fdc fdc.cpp)
TARGET_LINK_LIBRARIES(testRandomWalk eoutils ga eo)
TARGET_LINK_LIBRARIES(testMetropolisHasting eoutils ga eo)
@ -21,3 +22,4 @@ TARGET_LINK_LIBRARIES(sampling eoutils ga eo)
TARGET_LINK_LIBRARIES(densityOfStates eoutils ga eo)
TARGET_LINK_LIBRARIES(autocorrelation eoutils ga eo)
TARGET_LINK_LIBRARIES(adaptiveWalks eoutils ga eo)
TARGET_LINK_LIBRARIES(fdc eoutils ga eo)

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@ -0,0 +1,192 @@
//-----------------------------------------------------------------------------
/** fdc.cpp
*
* SV - 06/05/10
*
*/
//-----------------------------------------------------------------------------
// standard includes
#define HAVE_SSTREAM
#include <stdexcept> // runtime_error
#include <iostream> // cout
#include <sstream> // ostrstream, istrstream
#include <fstream>
#include <string.h>
// the general include for eo
#include <eo>
// declaration of the namespace
using namespace std;
//-----------------------------------------------------------------------------
// representation of solutions, and neighbors
#include <ga/eoBit.h> // bit string : see also EO tutorial lesson 1: FirstBitGA.cpp
#include <problems/bitString/moBitNeighbor.h> // neighbor of bit string
//-----------------------------------------------------------------------------
// fitness function, and evaluation of neighbors
#include <eval/oneMaxEval.h>
//-----------------------------------------------------------------------------
// the distance defined over the search space
#include <utils/eoDistance.h>
//-----------------------------------------------------------------------------
// the sampling class
#include <sampling/moFDCsampling.h>
// Declaration of types
//-----------------------------------------------------------------------------
// Indi is the typedef of the solution type like in paradisEO-eo
typedef eoBit<unsigned int> Indi; // bit string with unsigned fitness type
// Neighbor is the typedef of the neighbor type,
// Neighbor = How to compute the neighbor from the solution + information on it (i.e. fitness)
// all classes from paradisEO-mo use this template type
typedef moBitNeighbor<unsigned int> Neighbor ; // bit string neighbor with unsigned fitness type
void main_function(int argc, char **argv)
{
/* =========================================================
*
* Parameters
*
* ========================================================= */
// more information on the input parameters: see EO tutorial lesson 3
// but don't care at first it just read the parameters of the bit string size and the random seed.
// First define a parser from the command-line arguments
eoParser parser(argc, argv);
// For each parameter, define Parameter, read it through the parser,
// and assign the value to the variable
// random seed parameter
eoValueParam<uint32_t> seedParam(time(0), "seed", "Random number seed", 'S');
parser.processParam( seedParam );
unsigned seed = seedParam.value();
// length of the bit string
eoValueParam<unsigned int> vecSizeParam(20, "vecSize", "Genotype size", 'V');
parser.processParam( vecSizeParam, "Representation" );
unsigned vecSize = vecSizeParam.value();
// the number of solution sampled
eoValueParam<unsigned int> solParam(100, "nbSol", "Number of random solution", 'n');
parser.processParam( solParam, "Representation" );
unsigned nbSol = solParam.value();
// the name of the output file
string str_out = "out.dat"; // default value
eoValueParam<string> outParam(str_out.c_str(), "out", "Output file of the sampling", 'o');
parser.processParam(outParam, "Persistence" );
// the name of the "status" file where all actual parameter values will be saved
string str_status = parser.ProgramName() + ".status"; // default value
eoValueParam<string> statusParam(str_status.c_str(), "status", "Status file");
parser.processParam( statusParam, "Persistence" );
// do the following AFTER ALL PARAMETERS HAVE BEEN PROCESSED
// i.e. in case you need parameters somewhere else, postpone these
if (parser.userNeedsHelp()) {
parser.printHelp(cout);
exit(1);
}
if (statusParam.value() != "") {
ofstream os(statusParam.value().c_str());
os << parser;// and you can use that file as parameter file
}
/* =========================================================
*
* Random seed
*
* ========================================================= */
// reproducible random seed: if you don't change SEED above,
// you'll aways get the same result, NOT a random run
// more information: see EO tutorial lesson 1 (FirstBitGA.cpp)
rng.reseed(seed);
/* =========================================================
*
* Initialization of the solution
*
* ========================================================= */
// a Indi random initializer: each bit is random
// more information: see EO tutorial lesson 1 (FirstBitGA.cpp)
eoUniformGenerator<bool> uGen;
eoInitFixedLength<Indi> random(vecSize, uGen);
/* =========================================================
*
* Eval fitness function (full evaluation)
*
* ========================================================= */
// the fitness function is just the number of 1 in the bit string
oneMaxEval<Indi> fullEval;
/* =========================================================
*
* The sampling of the search space
*
* ========================================================= */
// Hamming distance to the global optimum
eoHammingDistance<Indi> distance; // Hamming distance
Indi bestSolution(vecSize, true); // global optimum
// sampling object :
// - random initialization
// - fitness function
// - number of solutions to sample
moFDCsampling<Neighbor> sampling(random, fullEval, distance, bestSolution, nbSol);
/* =========================================================
*
* execute the sampling
*
* ========================================================= */
sampling();
/* =========================================================
*
* export the sampling
*
* ========================================================= */
// to export the statistics into file
sampling.fileExport(str_out);
// to get the values of statistics
// so, you can compute some statistics in c++ from the data
const std::vector<double> & fitnessValues = sampling.getValues(0);
const std::vector<double> & distValues = sampling.getValues(1);
std::cout << "First values:" << std::endl;
std::cout << "Fitness " << fitnessValues[0] << std::endl;
std::cout << "Distance " << distValues[0] << std::endl;
std::cout << "Last values:" << std::endl;
std::cout << "Fitness " << fitnessValues[fitnessValues.size() - 1] << std::endl;
std::cout << "Distance " << distValues[distValues.size() - 1] << std::endl;
}
// A main that catches the exceptions
int main(int argc, char **argv)
{
try {
main_function(argc, argv);
}
catch (exception& e) {
cout << "Exception: " << e.what() << '\n';
}
return 1;
}