int main()
{
std::cout << "[moeoNSGAII]" << std::endl;
TestEval eval;
eoPopLoopEval <Solution> popEval(eval);
eoQuadCloneOp < Solution > xover;
eoUniformMutation < Solution > mutation(0.05);
eoRealVectorBounds bounds(1, 1.0, 2.0);
eoRealInitBounded < Solution > init(bounds);
eoPop < Solution > pop(20, init);
eoQuadGenOp <Solution> genOp(xover);
eoSGATransform < Solution > transform(xover, 0.1, mutation, 0.1);
eoGenContinue <Solution > continuator(10);
// build NSGA-II
moeoNSGAII < Solution > algo(20, eval, xover, 1.0, mutation, 1.0);
moeoNSGAII < Solution > algo2(continuator, eval, genOp);
moeoNSGAII < Solution > algo3(continuator, popEval, genOp);
moeoNSGAII < Solution > algo4(continuator, eval, transform);
moeoNSGAII < Solution > algo5(continuator, popEval, transform);
// run the algo
algo(pop);
// final pop
std::cout << "Final population" << std::endl;
std::cout << pop << std::endl;
std::cout << "[moeoNSGAII] OK" << std::endl;
return EXIT_SUCCESS;
}
int main()
{
std::cout << "[moeoSEEA]" << std::endl;
TestEval eval;
eoQuadCloneOp < Solution > xover;
eoUniformMutation < Solution > mutation(0.05);
eoRealVectorBounds bounds(2, 1.0, 2.0);
eoRealInitBounded < Solution > init(bounds);
eoPop < Solution > pop(20, init);
eoQuadGenOp <Solution> genOp(xover);
eoSGATransform < Solution > transform(xover, 0.1, mutation, 0.1);
eoGenContinue <Solution > continuator(20);
moeoUnboundedArchive < Solution > archive;
eoPopLoopEval <Solution> loopEval(eval);
eoPopEvalFunc <Solution>& popEval(loopEval);
// build SEEA
moeoSEEA < Solution > algo1(20, eval, xover, 1.0, mutation, 1.0, archive);
moeoSEEA < Solution > algo2(continuator, eval, genOp, archive);
moeoSEEA < Solution > algo3(continuator, popEval, genOp, archive);
moeoSEEA < Solution > algo4(continuator, eval, transform, archive);
moeoSEEA < Solution > algo5(continuator, popEval, transform, archive);
// run the algo
algo5(pop);
// final archive
std::cout << "Final archive" << std::endl;
std::cout << archive << std::endl;
std::cout << "[moeoSEEA] OK" << std::endl;
return EXIT_SUCCESS;
}
void main_function()
{
const unsigned SIZE = 8;
unsigned i, j;
eoBooleanGenerator gen;
Chrom chrom(SIZE), chrom2;
chrom.fitness(binary_value(chrom)); chrom2.fitness(binary_value(chrom2));
std::cout << "chrom: " << chrom << std::endl;
chrom[0] = chrom[SIZE - 1] = true; chrom.fitness(binary_value(chrom));
std::cout << "chrom: " << chrom << std::endl;
chrom[0] = chrom[SIZE - 1] = false; chrom.fitness(binary_value(chrom));
std::cout << "chrom: " << chrom << std::endl;
chrom[0] = chrom[SIZE - 1] = true; chrom.fitness(binary_value(chrom));
std::cout << "chrom.className() = " << chrom.className() << std::endl;
std::cout << "chrom: " << chrom << std::endl
<< "chrom2: " << chrom2 << std::endl;
std::ostringstream os;
os << chrom;
std::istringstream is(os.str());
is >> chrom2; chrom.fitness(binary_value(chrom2));
std::cout << "\nTesting reading, writing\n";
std::cout << "chrom: " << chrom << "\nchrom2: " << chrom2 << '\n';
std::fill(chrom.begin(), chrom.end(), false);
std::cout << "--------------------------------------------------"
<< std::endl << "eoMonOp's aplied to .......... " << chrom << std::endl;
eoInitFixedLength<Chrom>
random(chrom.size(), gen);
random(chrom); chrom.fitness(binary_value(chrom));
std::cout << "after eoBinRandom ............ " << chrom << std::endl;
eoOneBitFlip<Chrom> bitflip;
bitflip(chrom); chrom.fitness(binary_value(chrom));
std::cout << "after eoBitFlip .............. " << chrom << std::endl;
eoBitMutation<Chrom> mutation(0.5);
mutation(chrom); chrom.fitness(binary_value(chrom));
std::cout << "after eoBinMutation(0.5) ..... " << chrom << std::endl;
eoBitInversion<Chrom> inversion;
inversion(chrom); chrom.fitness(binary_value(chrom));
std::cout << "after eoBinInversion ......... " << chrom << std::endl;
eoBitNext<Chrom> next;
next(chrom); chrom.fitness(binary_value(chrom));
std::cout << "after eoBinNext .............. " << chrom << std::endl;
eoBitPrev<Chrom> prev;
prev(chrom); chrom.fitness(binary_value(chrom));
std::cout << "after eoBinPrev .............. " << chrom << std::endl;
std::fill(chrom.begin(), chrom.end(), false); chrom.fitness(binary_value(chrom));
std::fill(chrom2.begin(), chrom2.end(), true); chrom2.fitness(binary_value(chrom2));
std::cout << "--------------------------------------------------"
<< std::endl << "eoBinOp's aplied to ... "
<< chrom << " " << chrom2 << std::endl;
eo1PtBitXover<Chrom> xover;
std::fill(chrom.begin(), chrom.end(), false);
std::fill(chrom2.begin(), chrom2.end(), true);
xover(chrom, chrom2);
chrom.fitness(binary_value(chrom)); chrom2.fitness(binary_value(chrom2));
std::cout << "eoBinCrossover ........ " << chrom << " " << chrom2 << std::endl;
for (i = 1; i < SIZE; i++)
{
eoNPtsBitXover<Chrom> nxover(i);
std::fill(chrom.begin(), chrom.end(), false);
std::fill(chrom2.begin(), chrom2.end(), true);
nxover(chrom, chrom2);
chrom.fitness(binary_value(chrom)); chrom2.fitness(binary_value(chrom2));
std::cout << "eoBinNxOver(" << i << ") ........ "
<< chrom << " " << chrom2 << std::endl;
}
for (i = 1; i < SIZE / 2; i++)
for (j = 1; j < SIZE / 2; j++)
{
eoBitGxOver<Chrom> gxover(i, j);
std::fill(chrom.begin(), chrom.end(), false);
std::fill(chrom2.begin(), chrom2.end(), true);
gxover(chrom, chrom2);
chrom.fitness(binary_value(chrom)); chrom2.fitness(binary_value(chrom2));
std::cout << "eoBinGxOver(" << i << ", " << j << ") ..... "
<< chrom << " " << chrom2 << std::endl;
}
// test SGA algorithm
eoGenContinue<Chrom> continuator1(50);
eoFitContinue<Chrom> continuator2(65535.f);
eoCombinedContinue<Chrom> continuator(continuator1, continuator2);
eoCheckPoint<Chrom> checkpoint(continuator);
eoStdoutMonitor monitor;
checkpoint.add(monitor);
eoSecondMomentStats<Chrom> stats;
monitor.add(stats);
checkpoint.add(stats);
eoProportionalSelect<Chrom> select;
eoEvalFuncPtr<Chrom> eval(binary_value);
eoSGA<Chrom> sga(select, xover, 0.8f, bitflip, 0.1f, eval, checkpoint);
eoInitFixedLength<Chrom> init(16, gen);
eoPop<Chrom> pop(100, init);
apply<Chrom>(eval, pop);
sga(pop);
pop.sort();
std::cout << "Population " << pop << std::endl;
std::cout << "\nBest: " << pop[0].fitness() << '\n';
/*
Commented this out, waiting for a definite decision what to do with the mOp's
// Check multiOps
eoMultiMonOp<Chrom> mOp( &next );
mOp.adOp( &bitflip );
std::cout << "before multiMonOp............ " << chrom << std::endl;
mOp( chrom );
std::cout << "after multiMonOp .............. " << chrom << std::endl;
eoBinGxOver<Chrom> gxover(2, 4);
eoMultiBinOp<Chrom> mbOp( &gxover );
mOp.adOp( &bitflip );
std::cout << "before multiBinOp............ " << chrom << " " << chrom2 << std::endl;
mbOp( chrom, chrom2 );
std::cout << "after multiBinOp .............. " << chrom << " " << chrom2 <<std::endl;
*/
}
int
main(int argc, char** argv)
{
const unsigned int T_SIZE = 3; // size for tournament selection
const unsigned int VEC_SIZE = 3; // Number of object variables in genotypes
const unsigned int POP_SIZE = 10; // Size of population
const unsigned int MAX_GEN = 1000; // Maximum number of generation before STOP
const unsigned int MIN_GEN = 10; // Minimum number of generation before ...
const unsigned int STEADY_GEN = 10; // stop after STEADY_GEN gen. without improvement
const float P_CROSS = 0.8; // Crossover probability
const float P_MUT = 0.5; // mutation probability
const double EPSILON = 0.01; // range for real uniform mutation
double SIGMA = 0.3; // std dev. for normal mutation
// some parameters for chosing among different operators
const double hypercubeRate = 0.5; // relative weight for hypercube Xover
const double segmentRate = 0.5; // relative weight for segment Xover
const double uniformMutRate = 0.5; // relative weight for uniform mutation
const double detMutRate = 0.5; // relative weight for det-uniform mutation
const double normalMutRate = 0.5; // relative weight for normal mutation
const unsigned int SEED = 42; // seed for random number generator
po::options_description desc;
std::string matrixdir, model, nullmodel, trainingset, testingset, lastrun;
desc.add_options()
("matrixdir", po::value<std::string>(&matrixdir),
"The directory set up by SOFT2Matrix")
("model", po::value<std::string>(&model),
"The gene regulatory network model")
("nullmodel", po::value<std::string>(&nullmodel),
"The gene regulatory network null (scrambled) model")
("trainingset", po::value<std::string>(&trainingset),
"The list of arrays which have been selected for inclusion in the training set")
("testingset", po::value<std::string>(&testingset),
"The list of arrays which have been selected for inclusion in the testing set")
("lastrun", po::value<std::string>(&lastrun),
"The output file from the last run, to re-use scores from (optional)")
;
po::variables_map vm;
po::store(po::parse_command_line(argc, argv, desc), vm);
po::notify(vm);
std::string wrong;
if (!vm.count("help"))
{
if (!vm.count("matrixdir"))
wrong = "matrixdir";
else if (!vm.count("model"))
wrong = "model";
else if (!vm.count("trainingset"))
wrong = "trainingset";
else if (!vm.count("testingset"))
wrong = "testingset";
else if (!vm.count("nullmodel"))
wrong = "nullmodel";
}
if (wrong != "")
std::cerr << "Missing option: " << wrong << std::endl;
if (vm.count("help") || wrong != "")
{
std::cout << desc << std::endl;
return 1;
}
if (!fs::is_directory(matrixdir))
{
std::cout << "Matrix directory doesn't exist."
<< std::endl;
return 1;
}
if (!fs::is_regular(model))
{
std::cout << "Model file doesn't exist or not regular file."
<< std::endl;
return 1;
}
if (!fs::is_regular(nullmodel))
{
std::cout << "Null model file doesn't exist or not regular file."
<< std::endl;
return 1;
}
if (!fs::is_regular(trainingset))
{
std::cout << "Training set file doesn't exist or not regular file."
<< std::endl;
return 1;
}
if (!fs::is_regular(testingset))
{
std::cout << "Testing set file doesn't exist or not regular file."
<< std::endl;
return 1;
}
std::list<double> lastRun;
if (fs::is_regular(lastrun))
{
std::ifstream flastrun(lastrun.c_str());
static const boost::regex prev(".*SVM Result: .* Result \\(([^\\)]+)\\).*");
while (flastrun.good())
{
std::string l;
std::getline(flastrun, l);
boost::smatch m;
if (!boost::regex_match(l, m, prev))
{
continue;
}
lastRun.push_back(strtod(m[1].str().c_str(), NULL));
}
}
ExpressionMatrixProcessor emp(matrixdir);
GRNModel m(model, emp, 30);
GRNModel m2(nullmodel, emp, 30);
std::list<std::string> trainingArrays, testingArrays;
m.loadArraySet(trainingset, trainingArrays);
m.loadArraySet(testingset, testingArrays);
m.loadSVMTrainingData(trainingArrays);
m2.loadArraySet(trainingset, trainingArrays);
m2.loadArraySet(testingset, testingArrays);
m2.loadSVMTrainingData(trainingArrays);
// We seed it just so we can restart if need be.
rng.reseed(SEED);
EvaluateSVMFit eval(m, m2, trainingArrays, lastRun, 30, testingArrays.size());
std::vector<double> minVals, maxVals;
// log(gamma)
minVals.push_back(-15);
maxVals.push_back(15);
// log(C)
minVals.push_back(-15);
maxVals.push_back(2);
// nu
minVals.push_back(0);
maxVals.push_back(1);
eoRealVectorBounds rvb(minVals, maxVals);
eoRealInitBounded<Indi> random(rvb);
eoPop<Indi> pop(POP_SIZE, random);
apply<Indi>(eval, pop);
pop.sort();
std::cout << "Initial Population" << std::endl;
std::cout << pop;
eoDetTournamentSelect<Indi> selectOne(T_SIZE);
eoSelectPerc<Indi> select(selectOne);// by default rate==1
eoGenerationalReplacement<Indi> replace;
eoSegmentCrossover<Indi> xoverS;
eoHypercubeCrossover<Indi> xoverA;
eoPropCombinedQuadOp<Indi> xover(xoverS, segmentRate);
xover.add(xoverA, hypercubeRate, true);
eoUniformMutation<Indi> mutationU(EPSILON);
eoDetUniformMutation<Indi> mutationD(EPSILON);
eoNormalMutation<Indi> mutationN(SIGMA);
eoPropCombinedMonOp<Indi> mutation(mutationU, uniformMutRate);
mutation.add(mutationD, detMutRate);
mutation.add(mutationN, normalMutRate, true);
eoGenContinue<Indi> genCont(MAX_GEN);
eoSteadyFitContinue<Indi> steadyCont(MIN_GEN, STEADY_GEN);
eoCombinedContinue<Indi> continuator(genCont);
continuator.add(steadyCont);
eoSGATransform<Indi> transform(xover, P_CROSS, mutation, P_MUT);
eoEasyEA<Indi> gga(continuator, eval, select, transform, replace);
gga(pop);
pop.sort();
std::cout << "Final Population:"
<< std::endl << pop << std::endl;
return 0;
}
int main()
{
const unsigned POP_SIZE = 10, CHROM_SIZE = 12;
unsigned i;
eoBooleanGenerator gen;
// the populations:
eoPop<Chrom> pop;
// Evaluation
RoyalRoad<Chrom> rr( 8 );
eoEvalFuncCounter<Chrom> eval( rr );
eoInitVirus<float> random(CHROM_SIZE, gen);
for (i = 0; i < POP_SIZE; ++i) {
Chrom chrom;
random(chrom);
eval(chrom);
pop.push_back(chrom);
}
std::cout << "population:" << std::endl;
for (i = 0; i < pop.size(); ++i)
std::cout << "\t" << pop[i] << " " << pop[i].fitness() << std::endl;
// selection
eoStochTournamentSelect<Chrom> lottery(0.9 );
// breeder
eoOneBitFlip<Chrom> vm;
eoUBitXover<Chrom> xover;
eoProportionalOp<Chrom> propSel;
eoGeneralBreeder<Chrom> breeder( lottery, propSel );
propSel.add(vm, 0.2);
propSel.add(xover, 0.8);
// Replace a single one
eoCommaReplacement<Chrom> replace;
// Terminators
eoGenContinue<Chrom> continuator1(10);
eoFitContinue<Chrom> continuator2(CHROM_SIZE);
eoCombinedContinue<Chrom> continuator(continuator1, continuator2);
eoCheckPoint<Chrom> checkpoint(continuator);
eoStdoutMonitor monitor;
checkpoint.add(monitor);
eoSecondMomentStats<Chrom> stats;
eoPopStat<Chrom> dumper( 10 );
monitor.add(stats);
checkpoint.add(dumper);
checkpoint.add(stats);
// GA generation
eoEasyEA<Chrom> ea(checkpoint, eval, breeder, replace );
// evolution
try
{
ea(pop);
}
catch (std::exception& e)
{
std::cout << "exception: " << e.what() << std::endl;;
exit(EXIT_FAILURE);
}
std::cout << "pop" << std::endl;
for (i = 0; i < pop.size(); ++i)
std::cout << "\t" << pop[i] << " " << pop[i].fitness() << std::endl;
std::cout << "\n --> Number of Evaluations = " << eval.getValue() << std::endl;
return 0;
}
