def HGT_simulations_main__parallel(): common_classes = CommonClassesCreator() length, epsilon, mutation_neighborhood, tolerance = common_classes.get_common_classes( ) number_of_activations, population_factors, HGT_factors, mutation_factors = common_classes.get_simulation_variables( ) number_of_generations_per_factor = list() parallel = Parallel(n_jobs=-1) for HGT_factor in HGT_factors: HGT_process = HGTProcess(HGT_factor, length) avg_number_of_generations = sum( parallel( delayed(compute_part) (length, epsilon, mutation_neighborhood, tolerance, HGT_process, representation_class, number_of_activations / 4, get_number_of_generations_of_single_run) for _ in xrange(4))) / 4 print "number_of_generations for " + str( HGT_factor) + " factor is: " + str(avg_number_of_generations) number_of_generations_per_factor.append(avg_number_of_generations) print "number_of_generations_per_factor: " + str( number_of_generations_per_factor)
def main(): common_classes = CommonClassesCreator(False) length, epsilon, mutation_neighborhood, tolerance = common_classes.get_common_classes() concept_class = MonotoneConjunction(length) performance = common_classes.get_perf_without_precomp(concept_class) mutation_probability = common_classes.get_mutation_probability() mutator = Mutator(mutation_neighborhood, performance, tolerance, mutation_probability, epsilon) algorithm = ConjunctionEvolvabilityAlgorithm(mutator, length, epsilon, performance) hypo = algorithm.learn_ideal_function(epsilon) print "HYPO IS: " + str(hypo)
def recombination_main(): common_classes = CommonClassesCreator() length, epsilon, mutation_neighborhood, tolerance = common_classes.get_common_classes() mutation_factor = 0.1 concept_class = MonotoneConjunction(length) performance = common_classes.get_perf_without_precomp(concept_class) recomb_process = RecombinationProcess() neighborhood = NeighborhoodWithOtherRepresentations(length, mutation_neighborhood, mutation_factor, recomb_process) recombinator = Recombinator(neighborhood, performance, tolerance, epsilon) recombination = RecombinationConjunctionAlgorithm(recombinator, length, epsilon, concept_class) recombination.learn_ideal_function(concept_class)
def recombination_main(): common_classes = CommonClassesCreator() length, epsilon, mutation_neighborhood, tolerance = common_classes.get_common_classes( ) mutation_factor = 0.1 concept_class = MonotoneConjunction(length) performance = common_classes.get_perf_without_precomp(concept_class) recomb_process = RecombinationProcess() neighborhood = NeighborhoodWithOtherRepresentations( length, mutation_neighborhood, mutation_factor, recomb_process) recombinator = Recombinator(neighborhood, performance, tolerance, epsilon) recombination = RecombinationConjunctionAlgorithm(recombinator, length, epsilon, concept_class) recombination.learn_ideal_function(concept_class)
def HGT_main(): common_classes = CommonClassesCreator() length, epsilon, mutation_neighborhood, tolerance = common_classes.get_common_classes() mutation_factor = 0.1 HGT_factor = 1 concept_class = MonotoneConjunction(length) performance = common_classes.get_perf_without_precomp(concept_class) HGT_process = HGTProcess(HGT_factor, length) neighborhood = NeighborhoodWithOtherRepresentations(length, mutation_neighborhood, mutation_factor, HGT_process) HGT_mutator = HGT_Mutator(neighborhood, performance, tolerance, epsilon, HGT_process) mutation = HGTConjunctionAlgorithm(HGT_mutator, length, epsilon, performance) final_population = mutation.learn_ideal_function(concept_class) if len(final_population) <= 30: print final_population
def HGT_simulations_main__parallel(): common_classes = CommonClassesCreator() length, epsilon, mutation_neighborhood, tolerance = common_classes.get_common_classes() number_of_activations, population_factors, HGT_factors, mutation_factors = common_classes.get_simulation_variables() number_of_generations_per_factor = list() parallel = Parallel(n_jobs=-1) for HGT_factor in HGT_factors: HGT_process = HGTProcess(HGT_factor, length) avg_number_of_generations = sum(parallel(delayed(compute_part)(length, epsilon, mutation_neighborhood, tolerance, HGT_process, representation_class, number_of_activations / 4, get_number_of_generations_of_single_run) for _ in xrange(4))) / 4 print "number_of_generations for " + str(HGT_factor) + " factor is: " + str(avg_number_of_generations) number_of_generations_per_factor.append(avg_number_of_generations) print "number_of_generations_per_factor: " + str(number_of_generations_per_factor)
def HGT_main(): common_classes = CommonClassesCreator() length, epsilon, mutation_neighborhood, tolerance = common_classes.get_common_classes( ) mutation_factor = 0.1 HGT_factor = 1 concept_class = MonotoneConjunction(length) performance = common_classes.get_perf_without_precomp(concept_class) HGT_process = HGTProcess(HGT_factor, length) neighborhood = NeighborhoodWithOtherRepresentations( length, mutation_neighborhood, mutation_factor, HGT_process) HGT_mutator = HGT_Mutator(neighborhood, performance, tolerance, epsilon, HGT_process) mutation = HGTConjunctionAlgorithm(HGT_mutator, length, epsilon, performance) final_population = mutation.learn_ideal_function(concept_class) if len(final_population) <= 30: print final_population