Add moRandomWalk.h, update lesson 6 sampling.cpp
git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@1775 331e1502-861f-0410-8da2-ba01fb791d7f
This commit is contained in:
verel
parent
984872b03d
commit
d05c43ea3a
5 changed files with 303 additions and 168 deletions
|
|
@ -11,193 +11,228 @@
|
|||
|
||||
#include <stdexcept> // runtime_error
|
||||
#include <iostream> // cout
|
||||
#include <sstream> // ostrstream, istrstream
|
||||
#include <sstream> // ostrstream, istrstream
|
||||
#include <fstream>
|
||||
#include <string.h>
|
||||
|
||||
// the general include for eo
|
||||
#include <eo>
|
||||
#include <ga.h>
|
||||
|
||||
// declaration of the namespace
|
||||
using namespace std;
|
||||
|
||||
//-----------------------------------------------------------------------------
|
||||
// fitness function
|
||||
#include <eval/oneMaxEval.h>
|
||||
#include <problems/bitString/moBitNeighbor.h>
|
||||
#include <eoInt.h>
|
||||
#include <neighborhood/moRndWithReplNeighborhood.h>
|
||||
// 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>
|
||||
#include <problems/eval/moOneMaxIncrEval.h>
|
||||
#include <eval/moFullEvalByModif.h>
|
||||
#include <eval/moFullEvalByCopy.h>
|
||||
#include <continuator/moTrueContinuator.h>
|
||||
#include <algo/moLocalSearch.h>
|
||||
#include <explorer/moRandomWalkExplorer.h>
|
||||
#include <continuator/moCheckpoint.h>
|
||||
|
||||
//-----------------------------------------------------------------------------
|
||||
// neighborhood description
|
||||
#include <neighborhood/moRndWithReplNeighborhood.h> // visit one random neighbor possibly the same one several times
|
||||
|
||||
//-----------------------------------------------------------------------------
|
||||
// the random walk local search: heuristic to sample the search space
|
||||
#include <algo/moRandomWalk.h>
|
||||
|
||||
//-----------------------------------------------------------------------------
|
||||
// the statistics to compute during the sampling
|
||||
#include <continuator/moFitnessStat.h>
|
||||
#include <continuator/moSolutionStat.h>
|
||||
#include <utils/eoDistance.h>
|
||||
#include <continuator/moDistanceStat.h>
|
||||
|
||||
#include <utils/eoFileMonitor.h>
|
||||
#include <utils/eoUpdater.h>
|
||||
|
||||
//-----------------------------------------------------------------------------
|
||||
// the sampling class
|
||||
#include <sampling/moSampling.h>
|
||||
|
||||
// REPRESENTATION
|
||||
|
||||
// Declaration of types
|
||||
//-----------------------------------------------------------------------------
|
||||
typedef eoBit<unsigned> Indi;
|
||||
typedef moBitNeighbor<unsigned int> Neighbor ; // incremental evaluation
|
||||
typedef moRndWithReplNeighborhood<Neighbor> Neighborhood ;
|
||||
// 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
|
||||
*
|
||||
* ========================================================= */
|
||||
/* =========================================================
|
||||
*
|
||||
* 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);
|
||||
// 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
|
||||
|
||||
// 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 steps of the random walk
|
||||
eoValueParam<unsigned int> stepParam(100, "nbStep", "Number of steps of the random walk", 'n');
|
||||
parser.processParam( stepParam, "Representation" );
|
||||
unsigned nbStep = stepParam.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
|
||||
}
|
||||
|
||||
eoValueParam<uint32_t> seedParam(time(0), "seed", "Random number seed", 'S');
|
||||
parser.processParam( seedParam );
|
||||
unsigned seed = seedParam.value();
|
||||
/* =========================================================
|
||||
*
|
||||
* Random seed
|
||||
*
|
||||
* ========================================================= */
|
||||
|
||||
// description of genotype
|
||||
eoValueParam<unsigned int> vecSizeParam(8, "vecSize", "Genotype size", 'V');
|
||||
parser.processParam( vecSizeParam, "Representation" );
|
||||
unsigned vecSize = vecSizeParam.value();
|
||||
// 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);
|
||||
|
||||
eoValueParam<unsigned int> stepParam(10, "nbStep", "Number of steps of the random walk", 'n');
|
||||
parser.processParam( stepParam, "Representation" );
|
||||
unsigned nbStep = stepParam.value();
|
||||
/* =========================================================
|
||||
*
|
||||
* Initialization of the solution
|
||||
*
|
||||
* ========================================================= */
|
||||
|
||||
// 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" );
|
||||
// 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);
|
||||
|
||||
// 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" );
|
||||
/* =========================================================
|
||||
*
|
||||
* Eval fitness function (full evaluation)
|
||||
*
|
||||
* ========================================================= */
|
||||
|
||||
// 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
|
||||
}
|
||||
// the fitness function is just the number of 1 in the bit string
|
||||
oneMaxEval<Indi> fullEval;
|
||||
|
||||
/* =========================================================
|
||||
*
|
||||
* Random seed
|
||||
*
|
||||
* ========================================================= */
|
||||
/* =========================================================
|
||||
*
|
||||
* evaluation of a neighbor solution
|
||||
*
|
||||
* ========================================================= */
|
||||
|
||||
//reproducible random seed: if you don't change SEED above,
|
||||
// you'll aways get the same result, NOT a random run
|
||||
rng.reseed(seed);
|
||||
// Use it if there is no incremental evaluation: a neighbor is evaluated by the full evaluation of a solution
|
||||
// moFullEvalByModif<Neighbor> neighborEval(fullEval);
|
||||
|
||||
// Incremental evaluation of the neighbor: fitness is modified by +/- 1
|
||||
moOneMaxIncrEval<Neighbor> neighborEval;
|
||||
|
||||
/* =========================================================
|
||||
*
|
||||
* Eval fitness function
|
||||
*
|
||||
* ========================================================= */
|
||||
/* =========================================================
|
||||
*
|
||||
* the neighborhood of a solution
|
||||
*
|
||||
* ========================================================= */
|
||||
|
||||
oneMaxEval<Indi> eval;
|
||||
// Exploration of the neighborhood in random order
|
||||
// at each step one bit is randomly generated
|
||||
moRndWithReplNeighborhood<Neighbor> neighborhood(vecSize);
|
||||
|
||||
/* =========================================================
|
||||
*
|
||||
* the local search algorithm to sample the search space
|
||||
*
|
||||
* ========================================================= */
|
||||
|
||||
/* =========================================================
|
||||
*
|
||||
* Initilisation of the solution
|
||||
*
|
||||
* ========================================================= */
|
||||
moRandomWalk<Neighbor> walk(neighborhood, fullEval, neighborEval, nbStep);
|
||||
|
||||
// a Indi random initializer
|
||||
eoUniformGenerator<bool> uGen;
|
||||
eoInitFixedLength<Indi> random(vecSize, uGen);
|
||||
/* =========================================================
|
||||
*
|
||||
* the statistics to compute
|
||||
*
|
||||
* ========================================================= */
|
||||
|
||||
// fitness of the solution at each step
|
||||
moFitnessStat<Indi, unsigned> fStat;
|
||||
|
||||
// Hamming distance to the global optimum
|
||||
eoHammingDistance<Indi> distance; // Hamming distance
|
||||
Indi bestSolution(vecSize, true); // global optimum
|
||||
|
||||
/* =========================================================
|
||||
*
|
||||
* evaluation of a neighbor solution
|
||||
*
|
||||
* ========================================================= */
|
||||
moDistanceStat<Indi, unsigned> distStat(distance, bestSolution); // statistic
|
||||
|
||||
moFullEvalByModif<Neighbor> nhEval(eval);
|
||||
/* =========================================================
|
||||
*
|
||||
* The sampling of the search space
|
||||
*
|
||||
* ========================================================= */
|
||||
|
||||
// sampling object :
|
||||
// - random initialization
|
||||
// - local search to sample the search space
|
||||
// - one statistic to compute
|
||||
moSampling<Neighbor> sampling(random, walk, fStat);
|
||||
|
||||
// to add another statistics
|
||||
sampling.add(distStat);
|
||||
|
||||
//An eval by copy can be used instead of the eval by modif
|
||||
//moFullEvalByCopy<Neighbor> nhEval(eval);
|
||||
/* =========================================================
|
||||
*
|
||||
* 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.getVector(0);
|
||||
const std::vector<double> & distValues = sampling.getVector(1);
|
||||
|
||||
/* =========================================================
|
||||
*
|
||||
* the neighborhood of a solution
|
||||
*
|
||||
* ========================================================= */
|
||||
|
||||
Neighborhood neighborhood(vecSize);
|
||||
|
||||
|
||||
/* =========================================================
|
||||
*
|
||||
* a neighborhood explorer solution
|
||||
*
|
||||
* ========================================================= */
|
||||
|
||||
moRandomWalkExplorer<Neighbor> explorer(neighborhood, nhEval, nbStep);
|
||||
|
||||
|
||||
/* =========================================================
|
||||
*
|
||||
* the continuator and the checkpoint
|
||||
*
|
||||
* ========================================================= */
|
||||
|
||||
moTrueContinuator<Neighbor> continuator;//always continue
|
||||
|
||||
moFitnessStat<Indi, unsigned> fStat;
|
||||
eoHammingDistance<Indi> distance;
|
||||
Indi bestSolution(vecSize, true);
|
||||
moDistanceStat<Indi, unsigned> distStat(distance, bestSolution);
|
||||
|
||||
/* =========================================================
|
||||
*
|
||||
* the local search algorithm
|
||||
*
|
||||
* ========================================================= */
|
||||
|
||||
moLocalSearch<Neighbor> localSearch(explorer, continuator, eval);
|
||||
|
||||
/* =========================================================
|
||||
*
|
||||
* The sampling of the search space
|
||||
*
|
||||
* ========================================================= */
|
||||
|
||||
moSampling<Neighbor> sampling(random, localSearch, fStat);
|
||||
|
||||
/* =========================================================
|
||||
*
|
||||
* execute the sampling
|
||||
*
|
||||
* ========================================================= */
|
||||
|
||||
sampling();
|
||||
|
||||
sampling.exportFile(str_out);
|
||||
std::cout << "First values:" << std::endl;
|
||||
std::cout << "Fitness " << fitnessValues[0] << std::endl;
|
||||
std::cout << "Distance " << distValues[0] << std::endl << 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
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue