def learn_single_time__classic_model(common_classes):
length, epsilon, mutation_neighborhood, tolerance = common_classes.get_common_classes()
mutation_probability = common_classes.get_mutation_probability()
concept_class = MonotoneConjunction(length)
performance = common_classes.get_perf_without_precomp(concept_class)
mutator = Mutator(mutation_neighborhood, performance, tolerance, mutation_probability, epsilon)
algorithm = ConjunctionEvolvabilityAlgorithm(mutator, length, epsilon, performance)
return algorithm.learn_ideal_function_until_match()
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 learn_DNF__basic_model():
dnf = DNF()
length = 58
epsilon = 2 ** -53
tau = (epsilon / length) ** 3 * log(1 / epsilon)
tolerance = ConjunctionTolerance(length, epsilon)
conjunction_performance_oracle = OneSidedPerformanceOracleWithTolerance(dnf, tau)
performance_oracle = DNFOneSidePerformanceOracleWithTolerance(dnf, tau, epsilon, conjunction_performance_oracle)
mutation_neighborhood = MonotoneConjunctionNeighborhood()
mutation_probability = ConjunctionMutationProbability(mutation_neighborhood)
mutator = Mutator(mutation_neighborhood, performance_oracle, tolerance, mutation_probability, epsilon)
algorithm = ConjunctionEvolvabilityAlgorithm(mutator, length, epsilon, performance_oracle)
print algorithm.learn_ideal_function_until_match()
def learn_DNF__basic_model():
dnf = DNF()
length = 58
epsilon = (2**-53)
tau = (epsilon / length)**3 * log(1/epsilon)
tolerance = ConjunctionTolerance(length, epsilon)
conjunction_performance_oracle = OneSidedPerformanceOracleWithTolerance(dnf, tau)
performance_oracle = DNFOneSidePerformanceOracleWithTolerance(dnf, tau, epsilon,
conjunction_performance_oracle)
mutation_neighborhood = MonotoneConjunctionNeighborhood()
mutation_probability = ConjunctionMutationProbability(mutation_neighborhood)
mutator = Mutator(mutation_neighborhood, performance_oracle, tolerance, mutation_probability, epsilon)
algorithm = ConjunctionEvolvabilityAlgorithm(mutator, length, epsilon, performance_oracle)
print algorithm.learn_ideal_function_until_match()
