Nettoyage des tutos ce coup ci c'est bon :)
git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@1815 331e1502-861f-0410-8da2-ba01fb791d7f
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jhumeau
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21 changed files with 2697 additions and 2697 deletions
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@ -49,191 +49,191 @@ using namespace std;
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// Indi is the typedef of the solution type like in paradisEO-eo
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typedef eoBit<unsigned int> Indi; // bit string with unsigned fitness type
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// Neighbor is the typedef of the neighbor type,
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// Neighbor = How to compute the neighbor from the solution + information on it (i.e. fitness)
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// Neighbor = How to compute the neighbor from the solution + information on it (i.e. fitness)
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// all classes from paradisEO-mo use this template type
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typedef moBitNeighbor<unsigned int> Neighbor ; // bit string neighbor with unsigned fitness type
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void main_function(int argc, char **argv)
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{
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/* =========================================================
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*
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* Parameters
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*
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* ========================================================= */
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// more information on the input parameters: see EO tutorial lesson 3
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// but don't care at first it just read the parameters of the bit string size and the random seed.
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/* =========================================================
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*
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* Parameters
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*
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* ========================================================= */
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// more information on the input parameters: see EO tutorial lesson 3
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// but don't care at first it just read the parameters of the bit string size and the random seed.
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// First define a parser from the command-line arguments
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eoParser parser(argc, argv);
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// For each parameter, define Parameter, read it through the parser,
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// and assign the value to the variable
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// First define a parser from the command-line arguments
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eoParser parser(argc, argv);
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// random seed parameter
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eoValueParam<uint32_t> seedParam(time(0), "seed", "Random number seed", 'S');
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parser.processParam( seedParam );
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unsigned seed = seedParam.value();
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// length of the bit string
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eoValueParam<unsigned int> vecSizeParam(20, "vecSize", "Genotype size", 'V');
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parser.processParam( vecSizeParam, "Representation" );
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unsigned vecSize = vecSizeParam.value();
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// size of the block
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eoValueParam<unsigned int> blockSizeParam(4, "blockSize", "Block size of the Royal Road", 'k');
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parser.processParam( blockSizeParam, "Representation" );
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unsigned blockSize = blockSizeParam.value();
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// the number of steps of the random walk
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eoValueParam<unsigned int> stepParam(100, "nbStep", "Number of steps of the random walk", 'n');
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parser.processParam( stepParam, "Representation" );
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unsigned nbStep = stepParam.value();
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// the name of the output file
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string str_out = "out.dat"; // default value
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eoValueParam<string> outParam(str_out.c_str(), "out", "Output file of the sampling", 'o');
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parser.processParam(outParam, "Persistence" );
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// the name of the "status" file where all actual parameter values will be saved
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string str_status = parser.ProgramName() + ".status"; // default value
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eoValueParam<string> statusParam(str_status.c_str(), "status", "Status file");
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parser.processParam( statusParam, "Persistence" );
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// do the following AFTER ALL PARAMETERS HAVE BEEN PROCESSED
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// i.e. in case you need parameters somewhere else, postpone these
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if (parser.userNeedsHelp()) {
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parser.printHelp(cout);
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exit(1);
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}
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if (statusParam.value() != "") {
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ofstream os(statusParam.value().c_str());
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os << parser;// and you can use that file as parameter file
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}
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// For each parameter, define Parameter, read it through the parser,
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// and assign the value to the variable
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/* =========================================================
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*
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* Random seed
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*
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* ========================================================= */
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// random seed parameter
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eoValueParam<uint32_t> seedParam(time(0), "seed", "Random number seed", 'S');
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parser.processParam( seedParam );
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unsigned seed = seedParam.value();
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// reproducible random seed: if you don't change SEED above,
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// you'll aways get the same result, NOT a random run
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// more information: see EO tutorial lesson 1 (FirstBitGA.cpp)
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rng.reseed(seed);
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// length of the bit string
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eoValueParam<unsigned int> vecSizeParam(20, "vecSize", "Genotype size", 'V');
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parser.processParam( vecSizeParam, "Representation" );
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unsigned vecSize = vecSizeParam.value();
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/* =========================================================
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*
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* Eval fitness function (full evaluation)
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*
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* ========================================================= */
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// size of the block
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eoValueParam<unsigned int> blockSizeParam(4, "blockSize", "Block size of the Royal Road", 'k');
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parser.processParam( blockSizeParam, "Representation" );
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unsigned blockSize = blockSizeParam.value();
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// the fitness function is the royal function (oneMax is a Royal Road with block of 1)
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RoyalRoadEval<Indi> fullEval(blockSize);
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// the number of steps of the random walk
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eoValueParam<unsigned int> stepParam(100, "nbStep", "Number of steps of the random walk", 'n');
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parser.processParam( stepParam, "Representation" );
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unsigned nbStep = stepParam.value();
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/* =========================================================
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*
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* evaluation of a neighbor solution
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*
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* ========================================================= */
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// the name of the output file
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string str_out = "out.dat"; // default value
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eoValueParam<string> outParam(str_out.c_str(), "out", "Output file of the sampling", 'o');
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parser.processParam(outParam, "Persistence" );
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// Incremental evaluation of the neighbor: fitness is modified by +1 , 0 or -1
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moRoyalRoadIncrEval<Neighbor> neighborEval(fullEval);
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// the name of the "status" file where all actual parameter values will be saved
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string str_status = parser.ProgramName() + ".status"; // default value
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eoValueParam<string> statusParam(str_status.c_str(), "status", "Status file");
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parser.processParam( statusParam, "Persistence" );
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/* =========================================================
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*
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* the neighborhood of a solution
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*
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* ========================================================= */
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// Exploration of the neighborhood in random order
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// at each step one bit is randomly generated
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moRndWithoutReplNeighborhood<Neighbor> neighborhood(vecSize);
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/* =========================================================
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*
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* The sampling of the search space
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*
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* ========================================================= */
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// Initial Solution of the random neutral walk
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Indi initialSol(vecSize, false);
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// Hamming distance
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eoHammingDistance<Indi> distance;
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// sampling object :
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// - random initialization
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// - neighborhood to compute the next step
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// - fitness function
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// - neighbor evaluation
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// - number of steps of the walk
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moNeutralWalkSampling<Neighbor> sampling(initialSol, neighborhood, fullEval, neighborEval, distance, nbStep);
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/* =========================================================
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*
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* execute the sampling
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*
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* ========================================================= */
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// nearly 2 blocks are complete
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for(unsigned i = 0; i < blockSize - 1; i++) {
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initialSol[i] = true;
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initialSol[blockSize + i] = true;
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initialSol[2 * blockSize + i] = true;
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}
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// first block is complete
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initialSol[blockSize - 1] = true;
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fullEval(initialSol);
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std::cout << "Initial Solution: " << initialSol << std::endl;
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// the sampling
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sampling();
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/* =========================================================
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*
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* export the sampling
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*
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* ========================================================= */
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// to export the statistics into file
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sampling.fileExport(str_out);
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// to get the values of statistics
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// so, you can compute some statistics in c++ from the data
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const std::vector<Indi> & solutions = sampling.getSolutions(0);
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std::cout << "First values:" << std::endl;
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std::cout << "Solution " << solutions[0] << std::endl;
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std::cout << "Last values:" << std::endl;
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std::cout << "Solution " << solutions[solutions.size() - 1] << std::endl;
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// export only the solution into file
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sampling.fileExport(0, str_out + "_sol");
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// more basic statistics on the distribution:
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moStatistics statistics;
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vector< vector<double> > dist;
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vector<double> v;
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statistics.distances(solutions, distance, dist);
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for(unsigned i = 0; i < dist.size(); i++) {
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for(unsigned j = 0; j < dist.size(); j++) {
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std::cout << dist[i][j] << " " ;
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if (j < i)
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v.push_back(dist[i][j]);
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// do the following AFTER ALL PARAMETERS HAVE BEEN PROCESSED
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// i.e. in case you need parameters somewhere else, postpone these
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if (parser.userNeedsHelp()) {
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parser.printHelp(cout);
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exit(1);
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}
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if (statusParam.value() != "") {
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ofstream os(statusParam.value().c_str());
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os << parser;// and you can use that file as parameter file
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}
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std::cout << std::endl;
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}
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double min, max, avg, std;
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statistics.basic(v, min, max, avg, std);
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std::cout << "min=" << min << ", max=" << max << ", average=" << avg << ", std dev=" << std << std::endl;
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/* =========================================================
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*
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* Random seed
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*
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* ========================================================= */
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// reproducible random seed: if you don't change SEED above,
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// you'll aways get the same result, NOT a random run
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// more information: see EO tutorial lesson 1 (FirstBitGA.cpp)
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rng.reseed(seed);
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/* =========================================================
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*
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* Eval fitness function (full evaluation)
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*
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* ========================================================= */
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// the fitness function is the royal function (oneMax is a Royal Road with block of 1)
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RoyalRoadEval<Indi> fullEval(blockSize);
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/* =========================================================
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*
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* evaluation of a neighbor solution
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*
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* ========================================================= */
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// Incremental evaluation of the neighbor: fitness is modified by +1 , 0 or -1
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moRoyalRoadIncrEval<Neighbor> neighborEval(fullEval);
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/* =========================================================
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*
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* the neighborhood of a solution
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*
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* ========================================================= */
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// Exploration of the neighborhood in random order
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// at each step one bit is randomly generated
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moRndWithoutReplNeighborhood<Neighbor> neighborhood(vecSize);
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/* =========================================================
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*
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* The sampling of the search space
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*
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* ========================================================= */
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// Initial Solution of the random neutral walk
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Indi initialSol(vecSize, false);
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// Hamming distance
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eoHammingDistance<Indi> distance;
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// sampling object :
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// - random initialization
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// - neighborhood to compute the next step
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// - fitness function
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// - neighbor evaluation
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// - number of steps of the walk
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moNeutralWalkSampling<Neighbor> sampling(initialSol, neighborhood, fullEval, neighborEval, distance, nbStep);
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/* =========================================================
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*
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* execute the sampling
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*
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* ========================================================= */
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// nearly 2 blocks are complete
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for (unsigned i = 0; i < blockSize - 1; i++) {
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initialSol[i] = true;
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initialSol[blockSize + i] = true;
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initialSol[2 * blockSize + i] = true;
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}
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// first block is complete
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initialSol[blockSize - 1] = true;
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fullEval(initialSol);
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std::cout << "Initial Solution: " << initialSol << std::endl;
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// the sampling
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sampling();
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/* =========================================================
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*
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* export the sampling
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*
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* ========================================================= */
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// to export the statistics into file
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sampling.fileExport(str_out);
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// to get the values of statistics
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// so, you can compute some statistics in c++ from the data
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const std::vector<Indi> & solutions = sampling.getSolutions(0);
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std::cout << "First values:" << std::endl;
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std::cout << "Solution " << solutions[0] << std::endl;
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std::cout << "Last values:" << std::endl;
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std::cout << "Solution " << solutions[solutions.size() - 1] << std::endl;
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// export only the solution into file
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sampling.fileExport(0, str_out + "_sol");
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// more basic statistics on the distribution:
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moStatistics statistics;
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vector< vector<double> > dist;
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vector<double> v;
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statistics.distances(solutions, distance, dist);
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for (unsigned i = 0; i < dist.size(); i++) {
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for (unsigned j = 0; j < dist.size(); j++) {
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std::cout << dist[i][j] << " " ;
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if (j < i)
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v.push_back(dist[i][j]);
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}
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std::cout << std::endl;
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}
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double min, max, avg, std;
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statistics.basic(v, min, max, avg, std);
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std::cout << "min=" << min << ", max=" << max << ", average=" << avg << ", std dev=" << std << std::endl;
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}
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// A main that catches the exceptions
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