def get_performance_rate_of_single_run(common_classes,
population_factor=1,
mutation_factor=0.1):
length, epsilon, mutation_neighborhood, tolerance = common_classes.get_common_classes(
)
natural_process = common_classes.get_natural_process()
concept_class = MonotoneConjunction(length)
performance_oracle = common_classes.get_perf(concept_class)
neighborhood = common_classes.get_neighborhood_with_other_representations(
mutation_factor)
HGT_mutator = HGT_Mutator(neighborhood, performance_oracle, tolerance,
epsilon, natural_process)
algorithm = HGTConjunctionAlgorithm(HGT_mutator, length, epsilon,
performance_oracle, population_factor)
return algorithm.get_learning_rate()
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