Type objective_function<Type>::operator() ()
{
DATA_VECTOR(observed);
PARAMETER_VECTOR(population);
PARAMETER(log_process_error);
PARAMETER(log_obs_error);
Type process_error = exp(log_process_error);
Type obs_error = exp(log_obs_error);
int n_obs = observed.size(); // number of observations
Type nll = 0; // negative log likelihood
// likelihood for state transitions
for(int y=1; y<n_obs; y++){
Type m = population[y-1];
nll -= dnorm(population(y), m, process_error, true);
}
// likelihood for observations
for(int y=0; y<n_obs; y++){
nll -= dnorm(observed(y), population(y), obs_error, true);
}
ADREPORT(process_error);
ADREPORT(obs_error);
return nll;
}
// Connects stub to the workflow
void GaRDGAStub::Connected()
{
GaBasicStub::Connected();
GaCachedPopulation population( GetWorkflowStorage(), _populationID );
// register statistics trackers required by RDGA
population.GetData().RegisterTracker( Population::GaPopulationSizeTracker::TRACKER_ID, &_sizeTracker );
// create data objects required by the RDGA and insert them to workflow data storage
Population::GaChromosomeGroup* selectionOutput = new Population::GaChromosomeGroup();
Population::GaChromosomeGroup* coulingOutput = new Population::GaChromosomeGroup();
Common::Workflows::GaDataStorage* bgStorage = _brachGroup->GetData();
bgStorage->AddData( new Common::Workflows::GaDataEntry<Population::GaChromosomeGroup>( GADID_SELECTION_OUTPUT, selectionOutput ), Common::Workflows::GADSL_BRANCH_GROUP );
bgStorage->AddData( new Common::Workflows::GaDataEntry<Population::GaChromosomeGroup>( GADID_COUPLING_OUTPUT, coulingOutput ), Common::Workflows::GADSL_BRANCH_GROUP );
// create flow steps that
_checkStep = new GaCheckPopulationStep( GetWorkflowStorage(), _populationID );
_initStep = new Common::Workflows::GaSimpleMethodExecStep<Population::GaPopulation, Common::Workflows::GaMethodExecIgnoreBranch<Population::GaPopulation> >
( &Population::GaPopulation::Initialize, GetWorkflowStorage(), _populationID );
_selectionStep = new Population::GaSelectionStep(
Population::GaSelectionSetup( &_selectionOperation, &_selectionParameters, &Population::SelectionOperations::GaTournamentSelectionConfig(
Fitness::GaFitnessComparatorSetup( &_scaledFitnessComparator, &Fitness::Comparators::GaSimpleComparatorParams(), NULL ), _mating ) ),
GetWorkflowStorage(), _populationID, bgStorage, GADID_SELECTION_OUTPUT );
_couplingStep = new Population::GaCouplingStep( Population::GaCouplingSetup( &_couplingOperation, &_couplingParameters, &Population::GaCouplingConfig( _mating ) ),
bgStorage, GADID_SELECTION_OUTPUT, bgStorage, GADID_COUPLING_OUTPUT );
_nopStep = new Common::Workflows::GaNopStep();
_fitnessStep = new Population::GaPopulationFitnessStep( GetWorkflowStorage(), _populationID );
_replacementStep = new Population::GaReplacementStep( Population::GaReplacementSetup( &_rdgaOperation, &_rdgaParameters, &Multiobjective::RDGA::GaRDGAConfig( *_grid ) ),
bgStorage, GADID_COUPLING_OUTPUT, GetWorkflowStorage(), _populationID );
_nextGenStep = new Common::Workflows::GaSimpleMethodExecStep<Population::GaPopulation>( &Population::GaPopulation::NextGeneration, GetWorkflowStorage(), _populationID );
Common::Workflows::GaBranchGroupFlow* flow = _brachGroup->GetBranchGroupFlow();
// connect created flow steps
flow->SetFirstStep( _checkStep );
flow->ConnectSteps( _checkStep, _selectionStep, 1 );
flow->ConnectSteps( _selectionStep, _couplingStep, 0 );
flow->ConnectSteps( _couplingStep, _nopStep, 0 );
flow->ConnectSteps( _fitnessStep, _replacementStep, 0 );
flow->ConnectSteps( _replacementStep, _nextGenStep, 0 );
// do not use fitness step if population does not have to be re-evaluated in each generation
_fitnessConnection = UseFitnessStep() ? flow->ConnectSteps( _nopStep, _fitnessStep, 0 ) : flow->ConnectSteps( _nopStep, _replacementStep, 0 );
flow->ConnectSteps( _checkStep, _initStep, 0 );
flow->ConnectSteps( _initStep, _nopStep, 0 );
}
State MiPlusLambdaStrategy::operator () (const int mi, const int lambda, const int iterations, const int p,const int threads, const std::vector<State*>& initialStates) const{
std::vector<State*> results;
#pragma omp parallel for shared(results) num_threads(threads)
for (int pi =0; pi <p; ++pi)
{
int iter=0;
//Inicjacja
std::vector<State*> population (initialStates);
while (iter<iterations) {
//Reprodukcja
for (int i =0; i <lambda; ++i){
population.push_back(new State(population[rand()%mi]));
}
//Krzyżowanie
for (int i =0; i <lambda/2; ++i){
population[mi+2*i]->crossover(population[mi+2*i+1]);
}
//Mutacja
for (int i =0; i <lambda; ++i){
population[mi+i]->mutate();
}
//Ocena i wybor kolejnej populacji
sort(population.begin(),population.end(),compare);
population.resize(mi);
++iter;
}
results.push_back(new State(population[0]));
}
sort(results.begin(),results.end(),compare);
return results[0];
// return initialStates[0];
}
template <typename T2> array<T2> flatten()
{
array<T2> result;
//allocate
result.allocate(population());
//copy
int index=0;
for(int i=0; i<count(); i++)
{
auto& current=at(i);
for(int j=0; j<current.count(); j++)
{
result[index]=current[j];
//next
index++;
}
}
//consistent
check(index==result.count());
return result;
}
// Disconnects stub from the workflow
void GaRDGAStub::Disconnecting()
{
Common::Workflows::GaBranchGroupFlow* flow = _brachGroup->GetBranchGroupFlow();
// disconnect and destroy flow steps created by the stub
flow->RemoveStep( _checkStep, true, true );
flow->RemoveStep( _initStep, true, true );
flow->RemoveStep( _selectionStep, true, true );
flow->RemoveStep( _couplingStep, true, true );
flow->RemoveStep( _replacementStep, true, true );
flow->RemoveStep( _nopStep, true, true );
flow->RemoveStep( _fitnessStep, true, true );
flow->RemoveStep( _nextGenStep, true, true );
// clear internal bookkeeping
Clear();
// remove data from the workflow storage
Common::Workflows::GaDataStorage* bgStorage = _brachGroup->GetData();
bgStorage->RemoveData( GADID_SELECTION_OUTPUT, Common::Workflows::GADSL_BRANCH_GROUP );
bgStorage->RemoveData( GADID_COUPLING_OUTPUT, Common::Workflows::GADSL_BRANCH_GROUP );
GaCachedPopulation population( GetWorkflowStorage(), _populationID );
// remove statistics trackers
population.GetData().UnregisterTracker( Population::GaPopulationSizeTracker::TRACKER_ID );
population.GetData().UnregisterTracker( Population::GaScaledFitnessTracker::TRACKER_ID );
population.Clear();
GaBasicStub::Disconnecting();
}
MMKPSolution MMKP_BBA::run(std::vector<MMKPSolution> initialPopulation){
//init initial parameters
//init MMKPBatSolution vector
std::vector<MMKPBatSolution> population(initialPopulation.size());
for(int i=0;i<initialPopulation.size();i++){
population[i].solution = initialPopulation[i];
}
MMKP_BBA::initBatParemeters(population);
this->currentGeneration = 0;
bool terminationCriterion = false;
this->convergenceData.empty();
this->convergenceIteration = 0;
currentFuncEvals = 0;
MMKP_BBA::quickSort(population,0,(population.size()-1));
MMKPSolution bestSolution;
for(int i=0;i<population.size();i++){
if(this->dataSet.isFeasible(population[i].solution)){
bestSolution = population[i].solution;
break;
}
}
std::tuple<int,float> temp(currentFuncEvals,bestSolution.getProfit());
this->convergenceData.push_back(temp);
//main loop
while(!terminationCriterion){
MMKP_BBA::globalSearch(population);
MMKP_BBA::quickSort(population,0,(population.size()-1));
for(int i=0;i<population.size();i++){
if(this->dataSet.isFeasible(population[i].solution)){
if(population[i].solution.getProfit() > bestSolution.getProfit()){
bestSolution = population[i].solution;
this->convergenceIteration = currentGeneration;
break;
}
}
}
std::tuple<int,float> temp(currentFuncEvals,bestSolution.getProfit());
this->convergenceData.push_back(temp);
if(currentGeneration >= this->parameters.numberOfGenerations){
terminationCriterion = true;
}
this->currentGeneration++;
}
return bestSolution;
}
void MainWindow::startEvolve()
{
this->dateback_sbox->setDisabled( true );
this->dateback_btn->setDisabled( true );
nth_gen_indi.clear();
select_operator_indi.clear();
crossover_operator_indi.clear();
mutation_operator_indi.clear();
Population population( this->individual_num_input->value(), this->crossover_rate_input->value(), mutation_rate_input->value(),
this->range_min_input->value(), this->range_max_input->value(), min_btn->isChecked() ? SELECT_MIN : SELECT_MAX);
qint16 gene_num = generation_num_input->value();
population.calculate_fitness();
for( qint16 i = 0; i < gene_num; i++)
{
saveNthSample( population );
populateNthPlot( i, nth_gen_indi.at( i ) );
population.select_operator_elitist_strategy();
//population.select_operator_roulette();
saveSelectSample( population );
populateSelectPlot( select_operator_indi.at( i ) );
population.crossover_operator();
population.calculate_fitness();
saveCrossSample( population );
populateCrossPlot( crossover_operator_indi.at( i ) );
population.mutate_operator();
population.calculate_fitness();
saveMutationSample( population );
populateMutationPlot( mutation_operator_indi.at( i ) );
}
start_evolve ->setDisabled( true );
dateback_btn->setDisabled( false );
dateback_sbox->setDisabled( false );
dateback_sbox->setRange( 1, gene_num );
qint16 best_indi_index = population.find_best_individual_index();
qreal best_x_value = population.getValueAt( best_indi_index );
qreal best_y_value = UtilityFunction::f( population.getValueAt( best_indi_index ) );
QString x_value, y_value, result_string;
x_value.setNum(best_x_value, 'f', 5);
y_value.setNum(best_y_value, 'f', 5);
result_string.append( QString("当x=") ).append( x_value ).append(QString("时 ")).append( min_btn->isChecked() ? QString( "最小值y=") : QString("最大值y=") ).append( y_value);
result_label->setVisible( true );
result_label->setText( result_string );
}
void Simulation::run() {
_timer.start();
Population population(_problem);
if (solve(population) == true)
_display.basicLine("Solution found in " + std::to_string(_currentGeneration) + " generations(s) ");
else
_display.basicLine("Solution not found, best candidate is: ");
_problem->print(population.best());
_timer.stop();
_problem->giveBestSolution(population.best());
if (config.verbose)
_display.basicLine("Simulation took " + std::to_string(_timer.getTime()) + "s");
}
int main()
{
GaInitialize();
{
Chromosome::GaMatingConfig matingConfiguration(
Chromosome::GaCrossoverSetup( &crossover, &Chromosome::GaCrossoverParams( 0.8f, 2 ), NULL ),
Chromosome::GaMutationSetup( &mutation, &Chromosome::GaMutationParams( 0.03f, false ), NULL ) );
float gridSize[] = { 0.5, 0.5 };
Algorithm::Stubs::GaPAESStub paesStub( WDID_POPULATION, WDID_POPULATION_STATS,
Chromosome::GaInitializatorSetup( &initializator, NULL, &Chromosome::GaInitializatorConfig() ),
Population::GaPopulationFitnessOperationSetup( &populationFitnessOperation, NULL, &Fitness::GaFitnessOperationConfig( &Fitness::Representation::GaMVFitnessParams( 2 ) ) ),
Fitness::GaFitnessComparatorSetup( &fitnessComparator, &Fitness::Comparators::GaSimpleComparatorParams( Fitness::Comparators::GACT_MINIMIZE_ALL ), NULL ),
Population::GaPopulationParams( 33, 1 ),
Chromosome::GaMutationSetup( &mutation, &Chromosome::GaMutationParams( 0.03f, false ), NULL ),
Multiobjective::PAES::GaPAESSelectionParams( PTID_CURRENT_SOLUTION, PTID_CROSSOVER_BUFFER),
Multiobjective::PAES::GaPAESParams( CTID_DOMINANCE, CTID_HYPERBOX, CTID_HYPERBOX_INFO, PTID_HYPERBOX_INFO_BUFFER, PTID_CURRENT_SOLUTION, PTID_CROWDING_STORAGE ),
Common::Grid::GaHyperGrid<Fitness::GaFitness, float, Multiobjective::GaFitnessCoordiantesGetter<float> >( gridSize, 2 ) );
Common::Workflows::GaWorkflow workflow( NULL );
workflow.RemoveConnection( *workflow.GetFirstStep()->GetOutboundConnections().begin(), true );
Common::Workflows::GaWorkflowBarrier* br1 = new Common::Workflows::GaWorkflowBarrier();
paesStub.Connect( workflow.GetFirstStep(), br1 );
Common::Workflows::GaBranchGroup* bg1 = (Common::Workflows::GaBranchGroup*)workflow.ConnectSteps( br1, workflow.GetLastStep(), 0 );
Algorithm::StopCriteria::GaStopCriterionStep* stopStep = new Algorithm::StopCriteria::GaStopCriterionStep(
Algorithm::StopCriteria::GaStopCriterionSetup( &stopCriterion, &Algorithm::StopCriteria::GaGenerationCriterionParams( 1000 ), NULL ),
workflow.GetWorkflowData(), WDID_POPULATION_STATS );
Common::Workflows::GaBranchGroupTransition* bt1 = new Common::Workflows::GaBranchGroupTransition();
bg1->GetBranchGroupFlow()->SetFirstStep( stopStep );
bg1->GetBranchGroupFlow()->ConnectSteps( stopStep, bt1, 0 );
workflow.ConnectSteps( bt1, paesStub.GetStubFlow().GetFirstStep(), 1 );
Common::Workflows::GaDataCache<Population::GaPopulation> population( workflow.GetWorkflowData(), WDID_POPULATION );
population.GetData().GetEventManager().AddEventHandler( Population::GaPopulation::GAPE_NEW_GENERATION, &newGenHandler );
workflow.Start();
workflow.Wait();
}
GaFinalize();
}
int main()
{
GaInitialize();
{
Chromosome::GaMatingConfig matingConfiguration(
Chromosome::GaCrossoverSetup( &crossover, &Chromosome::GaCrossoverParams( 0.8f, 2 ), NULL ),
Chromosome::GaMutationSetup( &mutation, &Chromosome::GaMutationParams( 0.03f, false ), NULL ) );
Algorithm::Stubs::GaSPEAStub speaStub( WDID_POPULATION, WDID_POPULATION_STATS,
Chromosome::GaInitializatorSetup( &initializator, NULL, &Chromosome::GaInitializatorConfig() ),
Population::GaPopulationFitnessOperationSetup( &populationFitnessOperation, NULL, &Fitness::GaFitnessOperationConfig( &Fitness::Representation::GaMVFitnessParams( 2 ) ) ),
Fitness::GaFitnessComparatorSetup( &fitnessComparator, &Fitness::Comparators::GaSimpleComparatorParams( Fitness::Comparators::GACT_MINIMIZE_ALL ), NULL ),
Population::GaPopulationParams( 64, 0 ),
Chromosome::GaMatingSetup( &mating, NULL, &matingConfiguration ),
Population::GaCouplingSetup(),
Population::SelectionOperations::GaTournamentSelectionParams( 32, PTID_CROSSOVER_BUFFER, -1, 2,
Population::SelectionOperations::GaTournamentSelectionParams::GATST_ROULETTE_WHEEL_SELECTION ),
Multiobjective::SPEA::GaSPEAParams( CTID_DOMINANCE_LIST, CTID_STRENGTH, CTID_DOMINATED, PTID_CLUSTER_STORAGE ) );
Common::Workflows::GaWorkflow workflow( NULL );
workflow.RemoveConnection( *workflow.GetFirstStep()->GetOutboundConnections().begin(), true );
Common::Workflows::GaWorkflowBarrier* br1 = new Common::Workflows::GaWorkflowBarrier();
speaStub.Connect( workflow.GetFirstStep(), br1 );
Common::Workflows::GaBranchGroup* bg1 = (Common::Workflows::GaBranchGroup*)workflow.ConnectSteps( br1, workflow.GetLastStep(), 0 );
Algorithm::StopCriteria::GaStopCriterionStep* stopStep = new Algorithm::StopCriteria::GaStopCriterionStep(
Algorithm::StopCriteria::GaStopCriterionSetup( &stopCriterion, &Algorithm::StopCriteria::GaGenerationCriterionParams( 100 ), NULL ),
workflow.GetWorkflowData(), WDID_POPULATION_STATS );
Common::Workflows::GaBranchGroupTransition* bt1 = new Common::Workflows::GaBranchGroupTransition();
bg1->GetBranchGroupFlow()->SetFirstStep( stopStep );
bg1->GetBranchGroupFlow()->ConnectSteps( stopStep, bt1, 0 );
workflow.ConnectSteps( bt1, speaStub.GetStubFlow().GetFirstStep(), 1 );
Common::Workflows::GaDataCache<Population::GaPopulation> population( workflow.GetWorkflowData(), WDID_POPULATION );
population.GetData().GetEventManager().AddEventHandler( Population::GaPopulation::GAPE_NEW_GENERATION, &newGenHandler );
workflow.Start();
workflow.Wait();
}
GaFinalize();
}
float chronoSelection(ParametersMap* parametersMap, unsigned repetitions)
{
AuxTask auxTask;
Individual* example = auxTask.getExample(parametersMap);
unsigned populationSize = parametersMap->getNumber(Population::SIZE);
Population population(&auxTask, example, populationSize, 5);
population.setParams(parametersMap);
START_CHRONO
population.nextGeneration();
STOP_CHRONO
return chrono.getSeconds();
}
int main(int argc, char* argv[]) {
const size_t N_total(8636);
const double obsTime(50.0);
Parameters parms;
AIPopulation population(8636,50.0,"/home/stsiab/AI/trunk/MCMC_input/kieran");
AIModel model(&parms, &population);
population.loadEpiData("/home/stsiab/AI/trunk/MCMC_input/ss.18.0.1.ipt");
return EXIT_SUCCESS;
}
void MainWindow::checkOriginal()
{
Population population( this->individual_num_input->value(), this->crossover_rate_input->value(), mutation_rate_input->value(),
this->range_min_input->value(), this->range_max_input->value(), min_btn->isChecked() ? SELECT_MIN : SELECT_MAX);
qint16 size = population.getIndividualNum();
QPolygonF samples;
for( qint16 i = 0; i < size; i ++)
{
samples += QPointF( population.getValueAt( i ), UtilityFunction::f( population.getValueAt( i ) ) );
}
original_plot->setSamples( samples );
check_original_fun->setDisabled( true );
}
int main(int argc, char** argv) {
//parparser parser(argc, argv);
//long inititalPosition = parser.get("x").asLong();
srand(time(0));
//-----------------------------------------------------------------------------
MPICHECK(MPI_Init(&argc, &argv));
//-----------------------------------------------------------------------------
int commSize = 0;
int rank = 0;
MPICHECK(MPI_Comm_size(MPI_COMM_WORLD, &commSize));
MPICHECK(MPI_Comm_rank(MPI_COMM_WORLD, &rank));
const int nextProc = rank == commSize - 1 ? 0 : rank + 1;
const int prevProc = rank == 0 ? commSize - 1 : rank - 1;
vector<Individ> population(islandSize);
for (int i = 0; i < islandSize; ++i) {
population[i].init(problemSize);
}
for (int i = 1; i <= iterationsThreshold; ++i) {
select(population);
crossoverPopulation(population);
mutatePopulation(population);
if (i % migrationFreq == 0) {
migrate(population, nextProc, prevProc);
}
cout << getBestFitness(population, rank, commSize) << "\n";
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
MPICHECK(MPI_Finalize());
return 0;
}
int main(){
MiniMap<int> population(100);
population.set("Manhattan", 1626000);
population.set("Brooklyn", 2592000);
population.set("Queens", 2296000);
population.set("Bronx", 1419000);
population.set("Staten Island", 472621);
std::cout << "Population of Manhattan: " << *population.get("Manhattan") << "\n";
if (population.get("Brooklyn") == nullptr) std::cout << "Population of Brooklyn: " << "undefined\n";
else std::cout << "Population of Brooklyn: " << *population.get("Brooklyn") << "\n";
population.remove("Brooklyn");
if (population.get("Brooklyn") == nullptr) std::cout << "Population of Brooklyn: " << "undefined\n";
else std::cout << "Population of Brooklyn: " << *population.get("Brooklyn") << "\n";
std::cout << population.load() << "\n";
return 0;
}
EndCriteria::Type DifferentialEvolution::minimize(Problem& p, const EndCriteria& endCriteria) {
EndCriteria::Type ecType;
upperBound_ = p.constraint().upperBound(p.currentValue());
lowerBound_ = p.constraint().lowerBound(p.currentValue());
currGenSizeWeights_ = Array(configuration().populationMembers,
configuration().stepsizeWeight);
currGenCrossover_ = Array(configuration().populationMembers,
configuration().crossoverProbability);
std::vector<Candidate> population(configuration().populationMembers,
Candidate(p.currentValue().size()));
fillInitialPopulation(population, p);
std::partial_sort(population.begin(), population.begin() + 1, population.end(),
sort_by_cost());
bestMemberEver_ = population.front();
Real fxOld = population.front().cost;
Size iteration = 0, stationaryPointIteration = 0;
// main loop - calculate consecutive emerging populations
while (!endCriteria.checkMaxIterations(iteration++, ecType)) {
calculateNextGeneration(population, p.costFunction());
std::partial_sort(population.begin(), population.begin() + 1, population.end(),
sort_by_cost());
if (population.front().cost < bestMemberEver_.cost)
bestMemberEver_ = population.front();
Real fxNew = population.front().cost;
if (endCriteria.checkStationaryFunctionValue(fxOld, fxNew, stationaryPointIteration,
ecType))
break;
fxOld = fxNew;
};
p.setCurrentValue(bestMemberEver_.values);
p.setFunctionValue(bestMemberEver_.cost);
return ecType;
}
void TestSetupSolveException() throw (Exception)
{
// First set up SimulationTime (this is usually handled by a simulation object)
SimulationTime::Instance()->SetEndTimeAndNumberOfTimeSteps(1.0, 1);
// Create a SimpleTargetAreaModifier
MAKE_PTR(SimpleTargetAreaModifier<2>, p_modifier);
// Create a cell population whose type should not be used with a SimpleTargetAreaModifier
HoneycombMeshGenerator generator(4, 4, 0);
MutableMesh<2,2>* p_mesh = generator.GetMesh();
std::vector<CellPtr> cells;
CellsGenerator<FixedDurationGenerationBasedCellCycleModel, 2> cells_generator;
cells_generator.GenerateBasic(cells, p_mesh->GetNumNodes());
MeshBasedCellPopulation<2> population(*p_mesh, cells);
// Test that the correct exception is thrown if we try to call UpdateTargetAreas() on the population
TS_ASSERT_THROWS_THIS(p_modifier->SetupSolve(population, "unused_argument"),
"AbstractTargetAreaModifiers are to be used with a VertexBasedCellPopulation only");
CellBasedEventHandler::Reset(); // Otherwise logging has been started but not stopped due to exception above.
}
int main()
{
int year;
printf("Enter a year after 1990> ");
scanf("%d", &year);
FILE*pop;
pop = fopen("CHRISpop.txt", "w");
fprintf(pop, "Predicted Gotham City population for %d (in thousands): %.3lf\n" , year, population(year));
return 0;
}//main
int main( int argc , char *argv[] )
{
rng_type rng;
trainings_data_type c = gpcxx::generate_evenly_spaced_test_data< 3 >( -5.0 , 5.0 + 0.1 , 0.4 , []( double x1 , double x2 , double x3 ) {
return 1.0 / ( 1.0 + pow( x1 , -4.0 ) ) + 1.0 / ( 1.0 + pow( x2 , -4.0 ) ) + 1.0 / ( 1.0 + pow( x3 , -4.0 ) ); } );
gpcxx::normalize( c.y );
std::ofstream fout1( "testdata.dat" );
for( size_t i=0 ; i<c.x[0].size() ; ++i )
fout1 << c.y[i] << " " << c.x[0][i] << " " << c.x[1][i] << " " << c.x[2][i] << "\n";
fout1.close();
auto eval = gpcxx::make_static_eval< value_type , symbol_type , eval_context_type >(
fusion::make_vector(
fusion::make_vector( 'x' , []( eval_context_type const& t ) { return t[0]; } )
, fusion::make_vector( 'y' , []( eval_context_type const& t ) { return t[1]; } )
, fusion::make_vector( 'z' , []( eval_context_type const& t ) { return t[2]; } )
) ,
fusion::make_vector(
fusion::make_vector( 's' , []( double v ) -> double { return std::sin( v ); } )
, fusion::make_vector( 'c' , []( double v ) -> double { return std::cos( v ); } )
, fusion::make_vector( 'e' , []( double v ) -> double { return std::exp( v ); } )
, fusion::make_vector( 'l' , []( double v ) -> double { return ( std::abs( v ) < 1.0e-20 ) ? log( 1.0e-20 ) : std::log( std::abs( v ) ); } )
) ,
fusion::make_vector(
fusion::make_vector( '+' , std::plus< double >() )
, fusion::make_vector( '-' , std::minus< double >() )
, fusion::make_vector( '*' , std::multiplies< double >() )
, fusion::make_vector( '/' , std::divides< double >() )
) );
typedef decltype( eval ) eval_type;
typedef eval_type::node_attribute_type node_attribute_type;
typedef gpcxx::basic_tree< node_attribute_type > tree_type;
typedef std::vector< tree_type > population_type;
typedef gpcxx::static_pipeline< population_type , fitness_type , rng_type > evolver_type;
size_t population_size = 1000;
size_t generation_size = 20;
double number_elite = 1;
double mutation_rate = 0.0;
double crossover_rate = 0.6;
double reproduction_rate = 0.3;
size_t min_tree_height = 8 , max_tree_height = 8;
size_t tournament_size = 15;
// generators< rng_type > gen( rng );
auto terminal_gen = eval.get_terminal_symbol_distribution();
auto unary_gen = eval.get_unary_symbol_distribution();
auto binary_gen = eval.get_binary_symbol_distribution();
gpcxx::node_generator< node_attribute_type , rng_type , 3 > node_generator {
{ 2.0 * double( terminal_gen.num_symbols() ) , 0 , terminal_gen } ,
{ double( unary_gen.num_symbols() ) , 1 , unary_gen } ,
{ double( binary_gen.num_symbols() ) , 2 , binary_gen } };
auto tree_generator = gpcxx::make_ramp( rng , node_generator , min_tree_height , max_tree_height , 0.5 );
evolver_type evolver( number_elite , mutation_rate , crossover_rate , reproduction_rate , rng );
std::vector< double > fitness( population_size , 0.0 );
std::vector< tree_type > population( population_size );
auto fitness_f = gpcxx::regression_fitness< eval_type >( eval );
evolver.mutation_function() = gpcxx::make_mutation(
gpcxx::make_simple_mutation_strategy( rng , node_generator ) ,
gpcxx::make_tournament_selector( rng , tournament_size ) );
evolver.crossover_function() = gpcxx::make_crossover(
gpcxx::make_one_point_crossover_strategy( rng , max_tree_height ) ,
gpcxx::make_tournament_selector( rng , tournament_size ) );
evolver.reproduction_function() = gpcxx::make_reproduce( gpcxx::make_tournament_selector( rng , tournament_size ) );
gpcxx::timer timer;
// initialize population with random trees and evaluate fitness
timer.restart();
for( size_t i=0 ; i<population.size() ; ++i )
{
tree_generator( population[i] );
fitness[i] = fitness_f( population[i] , c );
}
std::cout << gpcxx::indent( 0 ) << "Generation time " << timer.seconds() << std::endl;
std::cout << gpcxx::indent( 1 ) << "Best individuals" << std::endl << gpcxx::best_individuals( population , fitness , 1 , 10 ) << std::endl;
std::cout << gpcxx::indent( 1 ) << "Statistics : " << gpcxx::calc_population_statistics( population ) << std::endl;
std::cout << gpcxx::indent( 1 ) << std::endl << std::endl;
timer.restart();
for( size_t generation=1 ; generation<=generation_size ; ++generation )
{
gpcxx::timer iteration_timer;
iteration_timer.restart();
evolver.next_generation( population , fitness );
double evolve_time = iteration_timer.seconds();
iteration_timer.restart();
std::transform( population.begin() , population.end() , fitness.begin() , [&]( tree_type const &t ) { return fitness_f( t , c ); } );
double eval_time = iteration_timer.seconds();
std::cout << gpcxx::indent( 0 ) << "Generation " << generation << std::endl;
std::cout << gpcxx::indent( 1 ) << "Evolve time " << evolve_time << std::endl;
std::cout << gpcxx::indent( 1 ) << "Eval time " << eval_time << std::endl;
std::cout << gpcxx::indent( 1 ) << "Best individuals" << std::endl << gpcxx::best_individuals( population , fitness , 2 , 10 ) << std::endl;
std::cout << gpcxx::indent( 1 ) << "Statistics : " << gpcxx::calc_population_statistics( population ) << std::endl << std::endl;
}
std::cout << "Overall time : " << timer.seconds() << std::endl;
return 0;
}
int main(int argc, char* argv[])
{
srand (time(0));
if(argc < 4)
{
cerr << "ERROR: The number of arguments is incorrect" << endl << "Enter:\targ_1 = user_definition_file\t arg_2 = genetic_encoding_file\t arg_3 = port_number";
return -1;
}
if(system((char*)"mkdir -p NEAT_organisms") == -1)
{
cerr << "TRAIN ERROR:\tFailed to create folder 'NEAT_organisms'" << endl;
}
if(system("rm -f NEAT_organisms/*") == -1)
{
cerr << "TRAIN ERROR:\tFailed to remove files inside of 'NEAT_organisms'" << endl;
}
SimFiles * simfile = new SimFiles();
Fitness * fitness = new Fitness();
RobotVREP * vrep = new RobotVREP(false, atoi(argv[3]));
// ============= VREP INITIALIZATIONS ============= //
Joint * rightWheel = new Joint((char*)"SCALE", (char*)"motor_der");
Joint * leftWheel = new Joint((char*)"SCALE", (char*)"motor_izq");
vrep->addJoint(rightWheel);
vrep->addJoint(leftWheel);
VisionSensor * visionSensor = new VisionSensor((char*)"VisionSensor");
vrep->addVisionSensor(visionSensor);
Object * centerDummy = new Object((char*)"modi_dummy");
Object * Modi = new Object((char*)"MODI");
vrep->addObject(centerDummy);
vrep->addObject(Modi);
CollisionObject * chasis = new CollisionObject((char*)"Collision_MODI_1#");
CollisionObject * rueda1 = new CollisionObject((char*)"Collision_MODI_2#");
CollisionObject * rueda2 = new CollisionObject((char*)"Collision_MODI_3#");
vrep->addCollisionObject(chasis);
vrep->addCollisionObject(rueda1);
vrep->addCollisionObject(rueda2);
vector < CollisionObject * > structure = {chasis, rueda1, rueda2};
vector < Object * > cubes;
// Set random position of Obstacles
double y0 = -2;
for(int cp_y = 0; cp_y < 9; cp_y++)
{
double x0 = -2 + 0.25*(cp_y%2);
for(int cp_x = 0; cp_x < 8 + (cp_y + 1)%2; cp_x++)
{
if(9*cp_y + cp_x != 40)
{
stringstream sstm1;
sstm1 << "Obstacle" << 9*cp_y+cp_x<< "#";
Object * obstacle = new Object((char*)sstm1.str().c_str());
vrep->addObject(obstacle);
double rand1 = rand()%201 - 100;
double rand2 = rand()%201 - 100;
vector < double > position;
position.push_back(x0 + rand1/100*.10);
position.push_back(y0 + rand2/100*.10);
position.push_back(0.05);
vrep->setObjectPosition(obstacle, position);
cubes.push_back(obstacle);
}
x0 = x0 + 0.5;
}
y0 = y0 + 0.5;
}
// ================================================ //
// ========== NEAT INITIALIZATIONS =========== //
vector < double > output(2,0.0);
vector < double > input(NX*NY+2,0.0);
Population population(argv[1], argv[2], (char *)"NEAT_RIAR", (char *)"./NEAT_organisms");
// ================================================ //
namedWindow( "Display window", WINDOW_AUTOSIZE ); // Create a window for display.
int finalChampionGeneration = 0;
int finalChampionPopulation = 0;
double finalChampionFitness = 0.0;
for(int g = 0; g < population.GENERATIONS; g++)
{
fitness->resetGenerationValues();
int generationChampionPopulation = 0;
double generationChampionFitness = 0.0;
for(int p = 0; p < population.POPULATION_MAX; p++)
{
fitness->resetPopulationValues();
int sim_time = 0;
bool flag = true;
int flag_times = 0;
double rightVel = 0.0;
double leftVel = 0.0;
vrep->setJointTargetVelocity(rightWheel, 0.0);
vrep->setJointTargetVelocity(leftWheel, 0.0);
double rand1 = rand()%201 - 100;
double rand2 = rand()%201 - 100;
double rand3 = rand()%201 - 100;
vector < double > position, orientation;
position.push_back(rand1/100*.10);
position.push_back(rand2/100*.10);
position.push_back(0.03011);
orientation.push_back(0);
orientation.push_back(0);
orientation.push_back(rand3/100*M_PI);
vrep->setObjectPosition(Modi, position);
vrep->setObjectOrientation(Modi, orientation);
unsigned char * image;
Mat frameRGB = Mat(NX, NY, CV_8UC3);
Mat frameGRAY = Mat(NX, NY, CV_8UC1);
stringstream message1, message2, video_name;
message1 << "Generation " << g << " Population " << p;
vrep->addStatusbarMessage((char*)message1.str().c_str());
video_name << "G" << g << "P" << p;
//vrep->changeVideoName((char *)video_name.str().c_str(), simx_opmode_oneshot_wait);
simfile->openRobotMovementFile(g, p);
simfile->openRobotMotorVelocityFile(g, p);
clog << "======= G" << g << " P" << p << " ======= " << endl;
vrep->startSimulation(simx_opmode_oneshot_wait);
timeval tv1, tv2;
gettimeofday(&tv1, NULL);
while(sim_time < TIME_SIMULATION && flag)
{
image = vrep->getVisionSensorImage(visionSensor);
frameRGB.data = image;
flip(frameRGB, frameRGB, 0);
cvtColor(frameRGB,frameGRAY,CV_BGR2GRAY);
Mat tmp = frameGRAY;
Mat frame = tmp;
resize(tmp, frame, Size(0,0) , 6.0, 6.0, (int)INTER_NEAREST );
imshow( "Display window", frame );
waitKey(10);
for(int i = 0; i < NY; i++)
{
for(int j = 0;j < NX; j++)
{
input.at(i*NX + j) = (double)frame.at<uchar>(i,j)/255*2-1;
}
}
input.at(NX*NY) = (double)((2.0/(MAX_VEL - MIN_VEL))*(rightVel - MIN_VEL) - 1.0);
input.at(NX*NY + 1) = (double)((2.0/(MAX_VEL - MIN_VEL))*(leftVel - MIN_VEL) - 1.0);
output = population.organisms.at(p).eval(input);
rightVel = output.at(0) + rightVel;
leftVel = output.at(1) + leftVel;
if(rightVel > MAX_VEL) rightVel = MAX_VEL;
else if(rightVel < MIN_VEL) rightVel = MIN_VEL;
if(leftVel > MAX_VEL) leftVel = MAX_VEL;
else if(leftVel < MIN_VEL) leftVel = MIN_VEL;
vrep->setJointTargetVelocity(rightWheel,-rightVel);
vrep->setJointTargetVelocity(leftWheel,leftVel);
if(sim_time > TIME_INIT_MEASURING)
{
position = vrep->getObjectPosition(centerDummy);
orientation = vrep->getObjectOrientation(centerDummy);
simfile->addRobotMovementFile((double)sim_time/1000000.0, position, orientation.at(2));
simfile->addRobotMotorVelocityFile((double)sim_time/1000000.0, rightVel, leftVel);
fitness->measuringValues(position, rightVel, leftVel, vrep->readCollision(structure));
if (abs(orientation.at(0)) > 0.78 || abs(orientation.at(1)) > 0.78)
{
flag_times++;
if(flag_times > 10) flag = false;
}else
flag_times = 0;
}
usleep(DELTA_TIME - EXECUTION_TIME);
sim_time += DELTA_TIME;
}
vrep->stopSimulation(simx_opmode_oneshot_wait);
gettimeofday(&tv2, NULL);
long int simulationtime = ((tv2.tv_sec - tv1.tv_sec)*1000000L + tv2.tv_usec) - tv1.tv_usec;
simfile->closeRobotMovementFile();
simfile->closeRobotMotorVelocityFile();
if (flag)
{
population.organisms.at(p).fitness = fitness->calculateFitness();
simfile->addFileResults(fitness->getFitness(), g, p);
clog << "Fitness:\t" << fitness->getFitness() << endl;
clog << "Distance:\t" << fitness->getDistance() << endl;
clog << "Tiempo de simulación:\t" << (double)simulationtime/1000000 << endl;
clog << endl;
message2 << "FITNESS : " << fitness->getFitness();
vrep->addStatusbarMessage((char*)message2.str().c_str());
if(generationChampionFitness < fitness->getFitness())
{
generationChampionPopulation = p;
generationChampionFitness = fitness->getFitness();
}
}
else
{
clog << "OVERTURNING! The simulation has stopped" << endl;
population.organisms.at(p).fitness = FAILED_FITNESS;
}
}
simfile->addFileChampion(generationChampionFitness, g, generationChampionPopulation);
simfile->addFileFitness(fitness->getGenerationFitness(), g);
//////////////////////////// SAVE CHAMPION FILES /////////////////////////////////
stringstream generation_champion_filename;
generation_champion_filename << "NEAT_organisms/Champion_G" << g << "P" << generationChampionPopulation << ".txt";
population.organisms.at(generationChampionPopulation).save((char *)generation_champion_filename.str().c_str());
stringstream cp_gen_champion_movement, cp_gen_champion_motorVelocity;
cp_gen_champion_movement << "cp simulation_files/movement/movement_G" << g << "P" << generationChampionPopulation << ".txt ./simulation_files/movement/Champion_G" << g << "P" << generationChampionPopulation << ".txt";
cp_gen_champion_motorVelocity << "cp simulation_files/motorVelocity/motorVelocity_G" << g << "P" << generationChampionPopulation << ".txt ./simulation_files/motorVelocity/Champion_G" << g << "P" << generationChampionPopulation << ".txt";
if(system((char*)cp_gen_champion_movement.str().c_str()) == -1)
{
cerr << "TRAIN ERROR:\tFailed to copy the Champion movement File" << endl;
}
else
{
if(system("rm -f ./simulation_files/movement/movement_G*.txt") == -1)
{
cerr << "TRAIN ERROR:\tFailed to remove useless files" << endl;
}
}
if(system((char*)cp_gen_champion_motorVelocity.str().c_str()) == -1)
{
cerr << "TRAIN ERROR:\tFailed to copy the Champion motor velocity File" << endl;
}
else
{
if(system("rm -f ./simulation_files/motorVelocity/motorVelocity_G*.txt") == -1)
{
cerr << "TRAIN ERROR:\tFailed to remove useless files" << endl;
}
}
///////////////////////////////////////////////////////////////////////////////////
population.epoch();
if(finalChampionFitness < generationChampionFitness)
{
finalChampionGeneration = g;
finalChampionPopulation = generationChampionPopulation;
finalChampionFitness = generationChampionFitness;
}
}
//////////////////////////// SAVE CHAMPION FILES /////////////////////////////////
stringstream cp_champion_organism, cp_champion_movement, cp_champion_motorVelocity;
cp_champion_organism << "cp NEAT_organisms/Champion_G" << finalChampionGeneration << "P" << finalChampionPopulation << ".txt ./NEAT_organisms/Champion.txt";
cp_champion_movement << "cp simulation_files/movement/Champion_G" << finalChampionGeneration << "P" << finalChampionPopulation << ".txt ./simulation_files/movement/Champion.txt";
cp_champion_motorVelocity << "cp simulation_files/motorVelocity/Champion_G" << finalChampionGeneration << "P" << finalChampionPopulation << ".txt ./simulation_files/motorVelocity/Champion.txt";
if(system((char*)cp_champion_organism.str().c_str()) == -1)
{
cerr << "TRAIN ERROR:\tFailed to copy the Champion Organism File" << endl;
}
if(system((char*)cp_champion_movement.str().c_str()) == -1)
{
cerr << "TRAIN ERROR:\tFailed to copy the Champion Movement File" << endl;
}
if(system((char*)cp_champion_motorVelocity.str().c_str()) == -1)
{
cerr << "TRAIN ERROR:\tFailed to copy the Champion Motor Velocity File" << endl;
}
///////////////////////////////////////////////////////////////////////////////////
clog << "Fitness champion: " << finalChampionFitness << "\n\n"<< endl;
delete(vrep);
delete(simfile);
delete(fitness);
return(0);
}
CRAlgorithmStatistics CRGenetics::execute()
{
CRAlgorithmStatistics ret;
solved = false;
// Load some needed stuff
if (config->debug) {
cout << "CRGenetics::run --> Loading system data.\n";
}
GeneticConfig &config = dynamic_cast<GeneticConfig&>(* (this->config));
iterations = 0;
struct timeval t1, t2;
gettimeofday(&t1, NULL);
if (!ret.getError()) {
// Make a first simulation to check if there exist some collisions in the system
if (config.debug) {
cout << "CRGenetics::run --> Simulating system in order to check for collisions...\n";
}
bool collision;
float min_distance;
if ( sim->run(collision) ) { // Simulating the whole system saving trajectory.
if (!collision) {
cout << "CRGenetics::run --> No collisions found. It is not necessary to run GA algorithm.\n";
// cout << "CRGenetics::run --> Min distance = " << min_distance << endl;
ret.setSolved(false);
ret.setCollisionDetected(false);
} else {
ret.setCollisionDetected(true);
cout << "CRGenetics::run --> Collisions found, initiating the CR Algorithm\n";
// cout << "CRGenetics::run --> Min distance = " << min_distance << endl;
}
} else {
cerr << "CRGenetics::run --> An error was found while simulating the system. Aborting.\n";
ret.setError(true);
}
}
if ( !ret.getError() && ret.getCollisionDetected() ) {
// Beggining of the algorithm.
// Associate the objetive and population initializer functions to genome and population
if (config.debug) {
cout << "CRGenetics::run --> Associating the proper functions to genome algorithm\n";
}
if (bounds == NULL) {
cerr << "CRGenetics::execute() --> This should not happen: bounds = NULL\n";
return ret;
}
GARealGenome genome(*bounds, CRALgorithmObjective, (void *)this);
setGeneticOperators(genome);
// The same process has to be applied to the population
GAPopulation population(genome, config.population);
if (config.initializer_type == "Deterministic") {
population.initializer(GAPopulationInitializer);
}
// Define parameters. There is an option to do this from file.
population.userData((void *) this);
if (config.debug) {
cout << "CRGenetics::run --> Creating the algorithm.\n";
}
delete algorithm;
algorithm = new GASimpleGA(population);
cout << "CRGenetics::run --> Setting the GAAlgorithm parameters.\n";
setGeneticParameters();
if (!config.custom_evolution) {
if (config.debug) {
cout << "CRGenetics::run --> Letting the algorithm evolve.\n";
}
algorithm->evolve();
} else {
customEvolution(ret, t1, true);
}
// Calculate spended time and show it
gettimeofday(&t2,NULL);
cout << "\n\n CRGenetics::run --> Spended time = " << functions::showTime(t1,t2) << endl;
ret.setExecutionTime(functions::calculateLapseTime(t1, t2));
// Get best flight plan
GARealGenome &best = (GARealGenome &) algorithm->statistics().bestIndividual();
vector<FlightPlan> best_wp(getGeneticFlightPlan(best));
// Get the minimum objetive value and store it for statistics purposes...
ret.setMinObjetive( best.score() );
// Show the best flight plan
cout << "CRGenetics::run --> Evolution finished. Best flight plan:\n";
cout << functions::printToStringVector(best_wp) << endl;
// Show genetic statistics
cout << algorithm->statistics() << endl;
// Check for collisions in the solution
bool collision;
// float min_d;
bool ok;
try {
ok = simulateSystem(best_wp, collision);
} catch(...) {
ok = false;
}
if (collision || !ok) {
if (!ok) {
cerr << "CRGenetics::run --> Error while simulating the best flight plan\n";
}
ret.setSolved(false);
cout << "CRGenetics::run --> The problem was not solved.\n"; // Min distance = " << min_d << "\n";
} else {
cout << "CRGenetics::run --> The problem was solved successfully.\n";// Min distance = " << min_d << "\n";
if (config.export_trajectories) {
if (sim->exportTrajectory(config.trajectory_filename)) {
cout << "Trajectories exported successfully.\n";
}
}
if (config.export_solution) {
if (exportSolution(config.solution_filename, best_wp)) {
cout << "CRGenetics::execute() --> Solution flight plans exported successfully.\n";
}
}
if (config.export_evolution) {
}
ret.setSolved(true);
}
}
return ret;
}
CRAlgorithmStatistics CRGenetics::execute_one_vs_all(std::vector<std::vector<functions::RealVector> > &traj, bool initialize) {
functions::FormattedTime t1, t2;
struct timeval t;
gettimeofday(&t, NULL);
t1.getTime();
CRAlgorithmStatistics ret;
GeneticConfig &config = dynamic_cast<GeneticConfig&>(* (this->config));
sim->setTrajectories(traj);
if (config.debug) {
cout << "CRGenetics::run --> Associating the proper functions to genome algorithm\n";
}
if (bounds == NULL) {
cerr << "CRGenetics::execute_one_vs_all() --> This should not happen: bounds = NULL\n";
return ret;
}
if (initialize) {
GARealGenome genome(*bounds, CRALgorithmOneObjective, (void *)this);
setGeneticOperators(genome);
// The same process has to be applied to the population
GAPopulation population(genome, config.population);
if (config.initializer_type == "Deterministic") {
population.initializer(GAPopulationInitializer);
}
delete algorithm;
algorithm = new GASimpleGA(population);
setGeneticParameters();
}
// Run genetics
if (!config.custom_evolution) {
if (config.debug) {
cout << "CRGenetics::run --> Letting the algorithm evolve.\n";
}
algorithm->evolve();
} else {
customEvolution(ret, t, initialize);
}
// Calculate spended time and show it
t2.getTime();
ret.setExecutionTime(t2 - t1);
// Get best flight plan
GARealGenome &best = (GARealGenome &) algorithm->statistics().bestIndividual();
FlightPlan best_wp(get1vsAllFlightPlan(geneToVector(best)));
// Get the minimum objetive value and store it for statistics purposes...
ret.setMinObjetive( best.score() );
// Show the best flight plan
cout << "CRGenetics::run --> Evolution finished. Best flight plan:\n";
cout << best_wp.toString() << endl;
sim->getParticle(0)->getController()->setFlightPlan(best_wp);
if (config.export_trajectories) {
if (sim->exportTrajectory(config.trajectory_filename)) {
cout << "Trajectories exported successfully.\n";
}
}
return ret;
}
int main( int argc , char *argv[] )
{
rng_type rng;
trainings_data_type c;
generate_test_data( c , -5.0 , 5.0 + 0.1 , 0.4 , []( double x1 , double x2 , double x3 ) {
return 1.0 / ( 1.0 + pow( x1 , -4.0 ) ) + 1.0 / ( 1.0 + pow( x2 , -4.0 ) ) + 1.0 / ( 1.0 + pow( x3 , -4.0 ) ); } );
gpcxx::normalize( c.y );
std::ofstream fout1( "testdata.dat" );
for( size_t i=0 ; i<c.x[0].size() ; ++i )
fout1 << c.y[i] << " " << c.x[0][i] << " " << c.x[1][i] << " " << c.x[2][i] << "\n";
fout1.close();
gpcxx::uniform_symbol< node_type > terminal_gen { std::vector< node_type >{
node_type { gpcxx::array_terminal< 0 >{} , "x" } ,
node_type { gpcxx::array_terminal< 1 >{} , "y" } ,
node_type { gpcxx::array_terminal< 2 >{} , "z" } } };
gpcxx::uniform_symbol< node_type > unary_gen { std::vector< node_type >{
node_type { gpcxx::sin_func{} , "s" } ,
node_type { gpcxx::cos_func{} , "c" } ,
node_type { gpcxx::exp_func{} , "e" } ,
node_type { gpcxx::log_func{} , "l" } } };
gpcxx::uniform_symbol< node_type > binary_gen { std::vector< node_type > {
node_type { gpcxx::plus_func{} , "+" } ,
node_type { gpcxx::minus_func{} , "-" } ,
node_type { gpcxx::multiplies_func{} , "*" } ,
node_type { gpcxx::divides_func{} , "/" }
} };
gpcxx::node_generator< node_type , rng_type , 3 > node_generator {
{ double ( terminal_gen.num_symbols() ) , 0 , terminal_gen } ,
{ double ( unary_gen.num_symbols() ) , 1 , unary_gen } ,
{ double ( binary_gen.num_symbols() ) , 2 , binary_gen } };
size_t population_size = 512;
size_t generation_size = 20;
double number_elite = 1;
double mutation_rate = 0.0;
double crossover_rate = 0.6;
double reproduction_rate = 0.3;
size_t max_tree_height = 8;
size_t tournament_size = 15;
auto tree_generator = gpcxx::make_basic_generate_strategy( rng , node_generator , max_tree_height , max_tree_height );
// size_t min_tree_height = 1
// auto new_tree_generator = gpcxx::make_ramp( rng , node_generator , min_tree_height , max_tree_height , 0.5 );
typedef gpcxx::static_pipeline< population_type , fitness_type , rng_type > evolver_type;
evolver_type evolver( number_elite , mutation_rate , crossover_rate , reproduction_rate , rng );
std::vector< double > fitness( population_size , 0.0 );
std::vector< tree_type > population( population_size );
evolver.mutation_function() = gpcxx::make_mutation(
gpcxx::make_simple_mutation_strategy( rng , node_generator ) ,
gpcxx::make_tournament_selector( rng , tournament_size ) );
evolver.crossover_function() = gpcxx::make_crossover(
gpcxx::make_one_point_crossover_strategy( rng , max_tree_height ) ,
gpcxx::make_tournament_selector( rng , tournament_size ) );
evolver.reproduction_function() = gpcxx::make_reproduce(
gpcxx::make_tournament_selector( rng , tournament_size ) );
gpcxx::timer timer;
auto fitness_f = gpcxx::make_regression_fitness( evaluator() );
// initialize population with random trees and evaluate fitness
timer.restart();
for( size_t i=0 ; i<population.size() ; ++i )
{
tree_generator( population[i] );
fitness[i] = fitness_f( population[i] , c );
}
std::cout << gpcxx::indent( 0 ) << "Generation time " << timer.seconds() << std::endl;
std::cout << gpcxx::indent( 1 ) << "Best individuals" << std::endl << gpcxx::best_individuals( population , fitness , 1 , 10 ) << std::endl;
std::cout << gpcxx::indent( 1 ) << "Statistics : " << gpcxx::calc_population_statistics( population ) << std::endl;
std::cout << gpcxx::indent( 1 ) << std::endl << std::endl;
write_height_hist( population , "initial_height.hist" );
write_size_hist( population , "initial_size.hist" );
timer.restart();
for( size_t generation=1 ; generation<=generation_size ; ++generation )
{
gpcxx::timer iteration_timer;
iteration_timer.restart();
evolver.next_generation( population , fitness );
double evolve_time = iteration_timer.seconds();
iteration_timer.restart();
std::transform( population.begin() , population.end() , fitness.begin() , [&]( tree_type const &t ) { return fitness_f( t , c ); } );
double eval_time = iteration_timer.seconds();
write_height_hist( population , "height_" + std::to_string( generation ) + ".hist" );
write_size_hist( population , "size_" + std::to_string( generation ) + ".hist" );
std::cout << gpcxx::indent( 0 ) << "Generation " << generation << std::endl;
std::cout << gpcxx::indent( 1 ) << "Evolve time " << evolve_time << std::endl;
std::cout << gpcxx::indent( 1 ) << "Eval time " << eval_time << std::endl;
std::cout << gpcxx::indent( 1 ) << "Best individuals" << std::endl << gpcxx::best_individuals( population , fitness , 2 , 10 ) << std::endl;
std::cout << gpcxx::indent( 1 ) << "Statistics : " << gpcxx::calc_population_statistics( population ) << std::endl << std::endl;
}
std::cout << "Overall time : " << timer.seconds() << std::endl;
// generate_data();
// init_constants();
// init_node_types(); // init generators
// init_population();
// evolve();
// report();
return 0;
}
bool EpidemicDataSet::loadNodePopulationFile(const char * filename)
{
// make sure we have appropriate number of stratifications
if(stratifications_.size() < 2)
{
put_flog(LOG_ERROR, "need at least 2 stratification, got %i", stratifications_.size());
return false;
}
std::ifstream in(filename);
if(in.is_open() != true)
{
put_flog(LOG_ERROR, "could not load file %s", filename);
return false;
}
// full shape of population variable: [time][node][stratifications...]
blitz::TinyVector<int, 2+NUM_STRATIFICATION_DIMENSIONS> shape;
shape(0) = 1; // one time step
shape(1) = numNodes_;
for(int j=0; j<NUM_STRATIFICATION_DIMENSIONS; j++)
{
shape(2 + j) = stratifications_[j].size();
}
blitz::Array<float, 2+NUM_STRATIFICATION_DIMENSIONS> population(shape);
population = 0.;
// the data file contains population data stratified only by the first two stratifications
// stratification values of 0 are assumed for all other stratifications
// use boost tokenizer to parse the file
typedef boost::tokenizer< boost::escaped_list_separator<char> > Tokenizer;
std::vector<std::string> vec;
std::string line;
// read (and ignore) header
getline(in, line);
while(getline(in, line))
{
Tokenizer tok(line);
vec.assign(tok.begin(), tok.end());
// each line is: county, fips, <population data>...
if(vec.size() != 2+stratifications_[0].size()*stratifications_[1].size())
{
put_flog(LOG_ERROR, "number of values != %i, == %i", 2+stratifications_[0].size()*stratifications_[1].size(), vec.size());
return false;
}
int time = 0;
int nodeId = atoi(vec[1].c_str());
if(nodeIdToIndex_.count(nodeId) == 0)
{
put_flog(LOG_ERROR, "could not map nodeId %i to an index", nodeId);
return false;
}
int nodeIndex = nodeIdToIndex_[nodeId];
// the CSV orders by the second stratification, then first stratification... (first stratification varies fastest)
for(int j=0; j<(int)stratifications_[1].size(); j++)
{
for(int i=0; i<(int)stratifications_[0].size(); i++)
{
// array position; all indices initialized to 0
blitz::TinyVector<int, 2+NUM_STRATIFICATION_DIMENSIONS> index(0);
index(0) = time;
index(1) = nodeIndex;
index(2) = i;
index(3) = j;
// all other stratification indices are zero
population(index) = atof(vec[2+j*(int)stratifications_[0].size() + i].c_str());
}
}
}
// add to regular variables map
variables_["population"].reference(population);
return true;
}
int main()
{
GaInitialize();
{
int values[ Problems::TNG::TNG_NUMBER_COUNT ];
values[ 0 ] = GaGlobalRandomIntegerGenerator->Generate( 1, 9 );
values[ 1 ] = GaGlobalRandomIntegerGenerator->Generate( 1, 9 );
values[ 2 ] = GaGlobalRandomIntegerGenerator->Generate( 1, 9 );
values[ 3 ] = GaGlobalRandomIntegerGenerator->Generate( 1, 9 );
values[ 4 ] = ( 10 + GaGlobalRandomIntegerGenerator->Generate( 0, 2 ) * 5 );
values[ 5 ] = ( 25 + GaGlobalRandomIntegerGenerator->Generate( 0, 3 ) * 25 );
int target = ( GaGlobalRandomIntegerGenerator->Generate( 100, 999 ) );
Chromosome::GaMatingConfig matingConfiguration(
Chromosome::GaCrossoverSetup( &crossover, &Chromosome::GaCrossoverPointParams( 0.8f, 2, 1 ), NULL ),
Chromosome::GaMutationSetup( &mutation, &Chromosome::GaMutationSizeParams( 0.3f, true, 2L ), NULL ) );
Chromosome::GaInitializatorSetup initializatorSetup( &initializator, NULL, &Chromosome::GaInitializatorConfig(
&Problems::TNG::TngConfigBlock( values, target, &Chromosome::Representation::GaBinaryChromosomeParams() ) ) );
Fitness::GaFitnessComparatorSetup fitnessComparatorSetup( &fitnessComparator, &Fitness::Comparators::GaSimpleComparatorParams( Fitness::Comparators::GACT_MAXIMIZE_ALL ), NULL );
Algorithm::Stubs::GaSimpleGAStub::GaStatTrackersCollection trackers;
trackers[ Population::GaPopulationSizeTracker::TRACKER_ID ] = &sizeTracker;
trackers[ Population::GaRawFitnessTracker::TRACKER_ID ] = &rawTracker;
trackers[ Population::GaScaledFitnessTracker::TRACKER_ID ] = &scaledTracker;
Population::GaSelectionSetup selectionSetup( &selection, &Population::SelectionOperations::GaDuplicatesSelectionParams( 8, 1, 2 ),
&Population::GaCouplingConfig( Chromosome::GaMatingSetup( &mating, NULL, &matingConfiguration ) ) );
Population::GaReplacementSetup replacementSetup( &replacement, &Population::ReplacementOperations::GaRandomReplacementParams( 8, 4, 3 ),
&Population::GaReplacementConfig( Chromosome::GaChromosomeComparatorSetup( &chromosomeComparator, NULL, NULL ) ) );
Population::GaScalingSetup scalingSetup( &scaling, &Population::ScalingOperations::GaScalingFactorParams( 2.0f )/*&Population::ScalingOperations::GaShareFitnessParams( 0.2f, 1.0f, 4 )*/,
&Population::ScalingOperations::GaShareFitnessScalingConfig( NULL, Chromosome::GaChromosomeComparatorSetup( &chromosomeComparator, NULL, NULL ) ) );
/*
Population::GaPopulation* population = new Population::GaPopulation( Population::GaPopulationParams( 32, 0, Population::GaPopulationParams::GAPFO_FILL_ON_INIT ),
Chromosome::GaInitializatorSetup( &initializator, NULL, &Chromosome::GaInitializatorConfig(
&Problems::TNG::TngConfigBlock( values, target, &Chromosome::Representation::GaBinaryChromosomeParams() ) ) ),
Population::GaPopulationFitnessOperationSetup( &populationFitnessOperation, NULL, &Fitness::GaFitnessOperationConfig() ),
fitnessComparatorSetup );
*/
Algorithm::Stubs::GaSimpleGAStub simpleGA( WDID_POPULATION, WDID_POPULATION_STATS,
initializatorSetup,
Population::GaPopulationFitnessOperationSetup( &populationFitnessOperation, NULL, &Fitness::GaFitnessOperationConfig() ),
fitnessComparatorSetup,
Population::GaPopulationParams( 32, 0, Population::GaPopulationParams::GAPFO_FILL_ON_INIT ),
trackers,
Chromosome::GaMatingSetup(),
selectionSetup,
Population::GaCouplingSetup(),
replacementSetup,
scalingSetup,
Population::GaFitnessComparatorSortingCriteria( fitnessComparatorSetup, Population::GaChromosomeStorage::GAFT_RAW ) );
/*
Common::Workflows::GaWorkflow workflow( NULL );
Common::Workflows::GaDataStorage* storage = workflow.GetWorkflowData();
storage->AddData( new Common::Workflows::GaDataEntry<Population::GaPopulation>( 1, population ), Common::Workflows::GADSL_WORKFLOW );
storage->AddData( new Common::Workflows::GaDataEntry<Statistics::GaStatistics>( 2, Common::Memory::GaAutoPtr<Statistics::GaStatistics>( &population->GetStatistics(),
Common::Memory::GaNoDeletionPolicy<Statistics::GaStatistics>::GetInstance() ) ), Common::Workflows::GADSL_WORKFLOW );
storage->AddData( new Common::Workflows::GaDataEntry<Population::GaChromosomeGroup>( 3, new Population::GaChromosomeGroup( false ) ), Common::Workflows::GADSL_WORKFLOW );
Common::Workflows::GaWorkflowBarrier* br1 = new Common::Workflows::GaWorkflowBarrier();
Common::Workflows::GaWorkflowBarrier* br2 = new Common::Workflows::GaWorkflowBarrier();
Common::Workflows::GaBranchGroup* bg1 = (Common::Workflows::GaBranchGroup*)workflow.ConnectSteps( workflow.GetFirstStep(), br1, 0 );
Common::Workflows::GaBranchGroup* bg2 = (Common::Workflows::GaBranchGroup*)workflow.ConnectSteps( br1, br2, 0 );
Common::Workflows::GaBranchGroup* bg3 = (Common::Workflows::GaBranchGroup*)workflow.ConnectSteps( br2, workflow.GetLastStep(), 0 );
Common::Workflows::GaFlowStep* initStep =
new Common::Workflows::GaSimpleMethodExecStep<Population::GaPopulation, Common::Workflows::GaMethodExecIgnoreBranch<Population::GaPopulation> >(
&Population::GaPopulation::Initialize, storage, 1 );
bg1->GetBranchGroupFlow()->SetFirstStep( initStep );
Common::Workflows::GaFlowStep* sortStep = new Population::GaSortPopulationStep<Population::GaFitnessSortingCriteria>( storage, 1, Population::GaFitnessSortingCriteria( population ) );
Common::Workflows::GaFlowStep* scalingStep = new Population::GaScalingStep(
Population::GaScalingSetup( &scaling, &Population::ScalingOperations::GaScalingFactorParams( 2.0f ), //&Population::ScalingOperations::GaShareFitnessParams( 0.2f, 1.0f, 4 ),
&Population::ScalingOperations::GaShareFitnessScalingConfig( NULL, Chromosome::GaChromosomeComparatorSetup( &chromosomeComparator, NULL, NULL ) ) ), storage, 1 );
Chromosome::GaMatingConfig matingConfiguration(
Chromosome::GaCrossoverSetup( &crossover, &Chromosome::GaCrossoverPointParams( 0.8f, 2, 1 ), NULL ),
Chromosome::GaMutationSetup( &mutation, &Chromosome::GaMutationSizeParams( 0.3f, true, 2L ), NULL ) );
Common::Workflows::GaFlowStep* selectionStep = new Population::GaSelectionStep(
Population::GaSelectionSetup( &selection, &Population::SelectionOperations::GaDuplicatesSelectionParams( 8, 1, 2 ),
&Population::GaCouplingConfig( Chromosome::GaMatingSetup( &mating, NULL, &matingConfiguration ) ) ), storage, 1, storage, 3 );
Common::Workflows::GaFlowStep* replacementStep = new Population::GaReplacementStep(
Population::GaReplacementSetup( &replacement, &Population::ReplacementOperations::GaRandomReplacementParams( 8, 4, 3 ),
&Population::GaReplacementConfig( Chromosome::GaChromosomeComparatorSetup( &chromosomeComparator, NULL, NULL ) ) ),
storage, 3, storage, 1 );
Common::Workflows::GaFlowStep* nextGenStep = new Common::Workflows::GaSimpleMethodExecStep<Population::GaPopulation>( &Population::GaPopulation::NextGeneration, storage, 1 );
Common::Workflows::GaBranchGroupFlow* flow2 = bg2->GetBranchGroupFlow();
flow2->SetFirstStep( sortStep );
flow2->ConnectSteps( sortStep, scalingStep, 0 );
flow2->ConnectSteps( scalingStep, nextGenStep, 0 );
flow2->ConnectSteps( selectionStep, replacementStep, 0 );
flow2->ConnectSteps( replacementStep, sortStep, 0 );
Fitness::Representation::GaSVFitness<float> targetFitness( 1.0f, Common::Memory::GaSmartPtr<const Fitness::GaFitnessParams>::NullPtr );
Algorithm::StopCriteria::GaStopCriterionStep* stopStep = new Algorithm::StopCriteria::GaStopCriterionStep(
Algorithm::StopCriteria::GaStopCriterionSetup( &stopCriterion,
&Algorithm::StopCriteria::GaStatsCriterionParams<Fitness::GaFitness>(
Population::GADV_BEST_FITNESS, targetFitness, Algorithm::StopCriteria::GAST_STOP_IF_EQUAL_TO,
Algorithm::StopCriteria::GaStatsCriterionComparator<Fitness::GaFitness>( fitnessComparatorSetup ) ), NULL ), storage, 2 );
Common::Workflows::GaBranchGroupTransition* bt32 = new Common::Workflows::GaBranchGroupTransition();
Common::Workflows::GaBranchGroupFlow* flow3 = bg3->GetBranchGroupFlow();
flow3->SetFirstStep( stopStep );
flow3->ConnectSteps( stopStep, bt32, 0 );
workflow.ConnectSteps( bt32, selectionStep, 1 );
population->GetEventManager().AddEventHandler( Population::GaPopulation::GAPE_NEW_GENERATION, &newGenHandler );
workflow.Start();
workflow.Wait();
*/
Common::Workflows::GaWorkflow workflow( NULL );
workflow.RemoveConnection( *workflow.GetFirstStep()->GetOutboundConnections().begin(), true );
Common::Workflows::GaWorkflowBarrier* br1 = new Common::Workflows::GaWorkflowBarrier();
simpleGA.Connect( workflow.GetFirstStep(), br1 );
Common::Workflows::GaBranchGroup* bg1 = (Common::Workflows::GaBranchGroup*)workflow.ConnectSteps( br1, workflow.GetLastStep(), 0 );
Fitness::Representation::GaSVFitness<float> targetFitness( 1.0f, Common::Memory::GaSmartPtr<const Fitness::GaFitnessParams>::NullPtr );
Algorithm::StopCriteria::GaStopCriterionStep* stopStep = new Algorithm::StopCriteria::GaStopCriterionStep(
Algorithm::StopCriteria::GaStopCriterionSetup( &stopCriterion,
&Algorithm::StopCriteria::GaStatsCriterionParams<Fitness::GaFitness>(
Population::GADV_BEST_FITNESS, targetFitness, Algorithm::StopCriteria::GAST_STOP_IF_EQUAL_TO,
Algorithm::StopCriteria::GaStatsCriterionComparator<Fitness::GaFitness>( fitnessComparatorSetup ) ), NULL ), workflow.GetWorkflowData(), WDID_POPULATION_STATS );
Common::Workflows::GaBranchGroupTransition* bt1 = new Common::Workflows::GaBranchGroupTransition();
bg1->GetBranchGroupFlow()->SetFirstStep( stopStep );
bg1->GetBranchGroupFlow()->ConnectSteps( stopStep, bt1, 0 );
workflow.ConnectSteps( bt1, simpleGA.GetStubFlow().GetFirstStep(), 1 );
Common::Workflows::GaDataCache<Population::GaPopulation> population( workflow.GetWorkflowData(), WDID_POPULATION );
population.GetData().GetEventManager().AddEventHandler( Population::GaPopulation::GAPE_NEW_GENERATION, &newGenHandler );
workflow.Start();
workflow.Wait();
}
GaFinalize();
return 0;
}
int main( int argc , char** argv )
{
auto options = dynsys::get_options();
auto positional_options = dynsys::get_positional_options();
po::options_description cmdline_options;
cmdline_options.add( options ).add( positional_options.second );
po::variables_map vm;
try
{
po::store( po::command_line_parser( argc , argv ).options( cmdline_options ).positional( positional_options.first ).run() , vm );
po::notify( vm );
}
catch( std::exception& e )
{
std::cerr << "Error " << e.what() << "\n\n";
std::cerr << cmdline_options << "\n";
return -1;
}
dynsys::rng_type rng;
//[ create_lorenz_data
auto training_data = dynsys::generate_data();
std::array< std::pair< double , double > , dynsys::dim > xstat , ystat;
for( size_t i=0 ; i<dynsys::dim ; ++i )
{
xstat[i].first = ystat[i].first = 0.0;
xstat[i].second = ystat[i].second = 1.0;
}
if( vm.count( "normalize" ) )
{
xstat = dynsys::normalize_data( training_data.first );
ystat = dynsys::normalize_data( training_data.second );
}
// plot_data( training_data );
//]
//[ lorenz_define_node_generator
auto node_generator = dynsys::create_node_generator();
//]
//[ define_gp_parameters
// size_t population_size = 256;
// size_t generation_size = 20;
size_t population_size = 512 * 32;
size_t generation_size = 2000;
size_t number_elite = 1;
double mutation_rate = 0.2;
double crossover_rate = 0.6;
double reproduction_rate = 0.3;
size_t min_tree_height = 1 , max_tree_height = 8;
size_t tournament_size = 15;
//]
//[ define_population_and_fitness
using population_type = std::vector< dynsys::tree_type >;
using fitness_type = std::vector< double >;
//]
//[ define_evolution
using evolver_type = gpcxx::dynamic_pipeline< population_type , fitness_type , dynsys::rng_type >;
evolver_type evolver( rng , number_elite );
//]
//[define_evaluator
using evaluator = struct {
using context_type = gpcxx::regression_context< double , dynsys::dim >;
using value_type = double;
value_type operator()( dynsys::tree_type const& t , context_type const& c ) const {
return t.root()->eval( c );
} };
//]
//[define_genetic_operators
auto tree_generator = gpcxx::make_ramp( rng , node_generator , min_tree_height , max_tree_height , 0.5 );
auto fitness_f = gpcxx::make_regression_fitness( evaluator {} );
evolver.add_operator(
gpcxx::make_mutation(
gpcxx::make_point_mutation( rng , tree_generator , max_tree_height , 20 ) ,
gpcxx::make_tournament_selector( rng , tournament_size )
) , mutation_rate );
evolver.add_operator(
gpcxx::make_crossover(
gpcxx::make_one_point_crossover_strategy( rng , 10 ) ,
gpcxx::make_tournament_selector( rng , tournament_size )
) , crossover_rate );
evolver.add_operator(
gpcxx::make_reproduce(
gpcxx::make_tournament_selector( rng , tournament_size )
) , reproduction_rate );
//]
std::ofstream fout { vm[ "evolution" ].as< std::string >() };
std::ofstream fout2 { vm[ "result" ].as< std::string >() };
fout2.precision( 14 );
for( size_t j=0 ; j<dynsys::dim ; ++j )
fout2 << xstat[j].first << " " << xstat[j].second << " " << ystat[j].first << " " << ystat[j].second << "\n";
fout2 << std::endl << std::endl;
std::array< dynsys::tree_type , dynsys::dim > winner;
for( size_t dimension = 0 ; dimension < dynsys::dim ; ++dimension )
{
fitness_type fitness ( population_size , 0.0 );
population_type population ( population_size );
gpcxx::regression_training_data< double , dynsys::dim > problem;
for( size_t i=0 ; i<training_data.first.size() ; ++i )
{
problem.y.push_back( training_data.second[i][ dimension ] );
for( size_t j=0 ; j<dynsys::dim ; ++j )
problem.x[j].push_back( training_data.first[i][j] );
}
//[init_population
for( size_t i=0 ; i<population.size() ; ++i )
{
tree_generator( population[i] );
fitness[i] = fitness_f( population[i] , problem );
}
std::cout << "Best individuals" << std::endl << gpcxx::best_individuals( population , fitness ) << std::endl;
std::cout << "Statistics : " << gpcxx::calc_population_statistics( population ) << std::endl;
std::cout << std::endl << std::endl;
//]
//[main_loop
for( size_t i=0 ; i<generation_size ; ++i )
{
evolver.next_generation( population , fitness );
for( size_t i=0 ; i<population.size() ; ++i )
fitness[i] = fitness_f( population[i] , problem );
std::cout << "Iteration " << i << std::endl;
std::cout << "Best individuals" << std::endl << gpcxx::best_individuals( population , fitness , 1 ) << std::endl;
std::cout << "Statistics : " << gpcxx::calc_population_statistics( population ) << std::endl << std::endl;
fout << "Iteration " << i << std::endl;
fout << "Best individuals" << std::endl << gpcxx::best_individuals( population , fitness , 1 ) << std::endl;
fout << "Statistics : " << gpcxx::calc_population_statistics( population ) << std::endl << std::endl;
auto min_fitness = * ( std::min_element( fitness.begin() , fitness.end() ) );
if( std::abs( min_fitness ) < 1.0e-7 )
{
std::cout << "Minimal fitness is small then 1.0e-7. Stopping now." << std::endl << std::endl << std::endl << std::endl;
fout << "Minimal fitness is small then 1.0e-7. Stopping now." << std::endl << std::endl << std::endl << std::endl;
break;
}
}
//]
fout2 << gpcxx::best_individuals( population , fitness , 0 ) << std::endl << std::endl;
std::vector< size_t > idx;
gpcxx::sort_indices( fitness , idx );
winner[dimension] = population[ idx[0] ];
}
std::ofstream winner_out { vm[ "winner" ].as< std::string >() };
winner_out << dynsys::serialize_winner( winner , xstat , ystat ) << "\n";
return 0;
}
void PlayTest_Tune(unsigned int rollbacks, unsigned int plays, unsigned int population_size, unsigned int tournament_size, unsigned int latency) {
std::mt19937 rng(RandomSeed());
// create initial population
std::vector<TuneElement> population(population_size);
for(unsigned int i = 0; i < population_size; ++i) {
GetDefaultHeuristicParameters(&population[i].m_parameters);
population[i].m_score = 20000; // guess, should be relatively low
}
// simulate plays
std::vector<TuneElement> history(plays);
std::vector<std::future<unsigned int> > futures(latency);
for(unsigned int p = 0; p < plays + latency; ++p) {
std::cout << "Tune progress: " << 100 * p / (plays + latency) << "%" << std::endl;
// add completed play to the population
if(p >= latency) {
history[p - latency].m_score = futures[p % latency].get();
population[(p - latency) % population_size] = history[p - latency];
}
// tournament selection
TuneElement best1, best2;
GetDefaultHeuristicParameters(&best1.m_parameters);
GetDefaultHeuristicParameters(&best2.m_parameters);
best1.m_score = 0;
best2.m_score = 0;
for(unsigned int t = 0; t < tournament_size; ++t) {
unsigned int sel1 = rng() % population_size;
if(population[sel1].m_score > best1.m_score)
best1 = population[sel1];
unsigned int sel2 = rng() % population_size;
if(population[sel2].m_score > best2.m_score)
best2 = population[sel2];
}
// create winner
HeuristicParameters winner;
std::cout << "Winner (" << best1.m_score << "|" << best2.m_score << "): ";
for(unsigned int i = 0; i < PARAM_COUNT; ++i) {
winner.m_values[i] = (best1.m_parameters.m_values[i] + best2.m_parameters.m_values[i] + (rng() & 1)) / 2;
std::cout << winner.m_values[i] << " ";
}
std::cout << std::endl;
if(p < plays) {
// do some mutations
for(unsigned int i = 0; i < PARAM_COUNT; ++i) {
winner.m_values[i] = Mutate(winner.m_values[i], PARAMETERS_MIN[i], PARAMETERS_MAX[i], PARAMETERS_STEP[i], rng);
}
// start the job
history[p].m_parameters = winner;
futures[p % latency] = StartJob(PlayTest, winner, rollbacks, (BoardDB*) NULL, (std::mutex*) NULL);
}
}
std::cout << "scores = array([\n\t";
for(unsigned int p = 0; p < plays; ++p) {
std::cout << history[p].m_score;
if(p != plays - 1) {
if(p % 20 == 19)
std::cout << ",\n\t";
else
std::cout << ", ";
}
}
std::cout << "])" << std::endl;
// calculate population average
HeuristicParameters population_average;
std::cout << "Population average: ";
for(unsigned int i = 0; i < PARAM_COUNT; ++i) {
population_average.m_values[i] = 0;
for(unsigned int p = 0; p < population_size; ++p) {
population_average.m_values[i] += population[p].m_parameters.m_values[i];
}
population_average.m_values[i] = (population_average.m_values[i] + population_size / 2) / population_size;
std::cout << population_average.m_values[i] << " ";
}
std::cout << std::endl;
}
/**
* Run the co-evolution algorithm
*
* @param[in,out] pop input/output pagmo::population to be evolved.
*/
void cstrs_co_evolution::evolve(population &pop) const
{
// Let's store some useful variables.
const problem::base &prob = pop.problem();
const population::size_type pop_size = pop.size();
const problem::base::size_type prob_dimension = prob.get_dimension();
// get the constraints dimension
problem::base::c_size_type prob_c_dimension = prob.get_c_dimension();
//We perform some checks to determine wether the problem/population are suitable for co-evolution
if(prob_c_dimension < 1) {
pagmo_throw(value_error,"The problem is not constrained and co-evolution is not suitable to solve it");
}
if(prob.get_f_dimension() != 1) {
pagmo_throw(value_error,"The problem is multiobjective and co-evolution is not suitable to solve it");
}
// Get out if there is nothing to do.
if(pop_size == 0) {
return;
}
//get the dimension of the chromosome of P2
unsigned int pop_2_dim = 0;
switch(m_method)
{
case algorithm::cstrs_co_evolution::SIMPLE:
{
pop_2_dim = 2;
break;
}
case algorithm::cstrs_co_evolution::SPLIT_NEQ_EQ:
{
pop_2_dim = 4;
break;
}
case algorithm::cstrs_co_evolution::SPLIT_CONSTRAINTS:
pop_2_dim = 2*prob.get_c_dimension();
break;
default:
pagmo_throw(value_error,"The constraints co-evolutionary method is not valid.");
break;
}
// split the population into two populations
// the population P1 associated to the modified problem with penalized fitness
// and population P2 encoding the penalty weights
// Populations size
population::size_type pop_1_size = pop_size;
population::size_type pop_2_size = m_pop_penalties_size;
//Creates problem associated to P2
problem::cstrs_co_evolution_penalty prob_2(prob,pop_2_dim,pop_2_size);
prob_2.set_bounds(m_pen_lower_bound,m_pen_upper_bound);
//random initialization of the P2 chromosome (needed for the fist generation)
std::vector<decision_vector> pop_2_x(pop_2_size);
std::vector<fitness_vector> pop_2_f(pop_2_size);
for(population::size_type j=0; j<pop_2_size; j++) {
pop_2_x[j] = decision_vector(pop_2_dim,0.);
// choose random coefficients between lower bound and upper bound
for(population::size_type i=0; i<pop_2_dim;i++) {
pop_2_x[j][i] = boost::uniform_real<double>(m_pen_lower_bound,m_pen_upper_bound)(m_drng);
}
}
//vector of the population P1. Initialized with clones of the original population
std::vector<population> pop_1_vector;
for(population::size_type i=0; i<pop_2_size; i++){
pop_1_vector.push_back(population(pop));
}
// Main Co-Evolution loop
for(int k=0; k<m_gen; k++) {
// for each individuals of pop 2, evolve the current population,
// and store the position of the feasible idx
for(population::size_type j=0; j<pop_2_size; j++) {
problem::cstrs_co_evolution prob_1(prob, pop_1_vector.at(j), m_method);
// modify the problem by setting decision vector encoding penalty
// coefficients w1 and w2 in prob 1
prob_1.set_penalty_coeff(pop_2_x.at(j));
// creating the POPULATION 1 instance based on the
// updated prob 1
// prob_1 is a BASE_META???? THE CLONE OF prob_1 IN POP_1 IS AT THE LEVEL OF
// THE BASE CLASS AND NOT AT THE LEVEL OF THE BASE_META, NO?!?
population pop_1(prob_1,0);
// initialize P1 chromosomes. The fitnesses related to problem 1 are computed
for(population::size_type i=0; i<pop_1_size; i++) {
pop_1.push_back(pop_1_vector.at(j).get_individual(i).cur_x);
}
// evolve the P1 instance
m_original_algo->evolve(pop_1);
//updating the original problem population (computation of fitness and constraints)
pop_1_vector.at(j).clear();
for(population::size_type i=0; i<pop_1_size; i++){
pop_1_vector.at(j).push_back(pop_1.get_individual(i).cur_x);
}
// set up penalization variables needs for the population 2
// the constraints has not been evaluated yet.
prob_2.update_penalty_coeff(j,pop_2_x.at(j),pop_1_vector.at(j));
}
// creating the POPULATION 2 instance based on the
// updated prob 2
population pop_2(prob_2,0);
// compute the fitness values of the second population
for(population::size_type i=0; i<pop_2_size; i++) {
pop_2.push_back(pop_2_x[i]);
}
m_original_algo_penalties->evolve(pop_2);
// store the new chromosomes
for(population::size_type i=0; i<pop_2_size; i++) {
pop_2_x[i] = pop_2.get_individual(i).cur_x;
pop_2_f[i] = pop_2.get_individual(i).cur_f;
}
// Check the exit conditions (every 40 generations, just as DE)
if(k % 40 == 0) {
// finds the best population
population::size_type best_idx = 0;
for(population::size_type j=1; j<pop_2_size; j++) {
if(pop_2_f[j][0] < pop_2_f[best_idx][0]) {
best_idx = j;
}
}
const population ¤t_population = pop_1_vector.at(best_idx);
decision_vector tmp(prob_dimension);
double dx = 0;
for(decision_vector::size_type i=0; i<prob_dimension; i++) {
tmp[i] = current_population.get_individual(current_population.get_worst_idx()).best_x[i] -
current_population.get_individual(current_population.get_best_idx()).best_x[i];
dx += std::fabs(tmp[i]);
}
if(dx < m_xtol ) {
if (m_screen_output) {
std::cout << "Exit condition -- xtol < " << m_xtol << std::endl;
}
break;
}
double mah = std::fabs(current_population.get_individual(current_population.get_worst_idx()).best_f[0] -
current_population.get_individual(current_population.get_best_idx()).best_f[0]);
if(mah < m_ftol) {
if(m_screen_output) {
std::cout << "Exit condition -- ftol < " << m_ftol << std::endl;
}
break;
}
// outputs current values
if(m_screen_output) {
std::cout << "Generation " << k << " ***" << std::endl;
std::cout << " Best global fitness: " << current_population.champion().f << std::endl;
std::cout << " xtol: " << dx << ", ftol: " << mah << std::endl;
std::cout << " xtol: " << dx << ", ftol: " << mah << std::endl;
}
}
}
// find the best fitness population in the final pop
population::size_type best_idx = 0;
for(population::size_type j=1; j<pop_2_size; j++) {
if(pop_2_f[j][0] < pop_2_f[best_idx][0]) {
best_idx = j;
}
}
// store the final population in the main population
// can't avoid to recompute the vectors here, otherwise we
// clone the problem stored in the population with the
// pop = operator!
pop.clear();
for(population::size_type i=0; i<pop_1_size; i++) {
pop.push_back(pop_1_vector.at(best_idx).get_individual(i).cur_x);
}
}
int main(int argc, char **argv)
{
gsl_rng_env_setup();
T = gsl_rng_default;
r = gsl_rng_alloc (T);
gsl_rng_set(r, time(NULL));
Graph *g = new Graph();
g->addVertex(Vertex(1, "v1", 0, 0, 0, 1.0, 1.0, 1.0));
g->addVertex(Vertex(2, "v2", 0, 0, 0, 1.0, 0.2, 0.2));
g->addVertex(Vertex(3, "v3", 0, 0, 0, 0.2, 1.0, 0.2));
g->addVertex(Vertex(4, "v4", 0, 0, 0, 0.2, 0.2, 1.0));
g->addEdge(1, 2);
g->addEdge(1, 3);
g->addEdge(3, 4);
ptr population(new std::vector<Graph>(4, *g));
/*
std::cout << "Population" << std::endl;
printgraph(population);
ptr reproduced = reproduce(population);
std::cout << "Reproduced" << std::endl;
printgraph(reproduced);
ptr offsprings = genetic(reproduced);
std::cout << "Offsprings" << std::endl;
printgraph(offsprings);
population = succession(population, offsprings);
std::cout << "After one iteration" << std::endl;
printgraph(population);
*/
int iterationcount = 0;
double bestv = best(population);
double bestvold = bestv * 2;
std::cout << "best " << bestv << "; bestvold " << bestvold << std::endl;
while (fabs(bestvold - bestv)/bestvold > 0.00000001)
{
++iterationcount;
ptr offsprings = genetic(reproduce(population));
population = succession(population, offsprings);
bestvold = bestv;
bestv = best(population);
}
std::cout << "Possible result found after " << iterationcount << "iterations: " << std::endl;
printgraph(population);
const Graph *bestgraph = bestg(population);
for (Graph::vertices_const_iterator i = bestgraph->vertices.begin(); i != bestgraph->vertices.end(); ++i)
{
const Vertex &v = i->second;
std::cout << "\"" << v.name << "\"";
std::cout << "\t@" << v.x << " " << v.y << " " << v.z;
std::cout << "\tcolor" << v.r << " " << v.g << " " << v.b;
std::cout << "\tfrozen light size 1.0" << std::endl;
}
gsl_rng_free (r);
return 0;
}