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 setUp(self): self.neighborhood = Mock() self.performance = Mock() self.tolerance = Mock() self.mutation_probability = Mock() self.mutation_probability.get_relational_probability.return_value = 0.2 epsilon = 1 self.selection_size = 1 self.mutator = Mutator(self.neighborhood, self.performance, self.tolerance, self.mutation_probability, epsilon) self.tolerance.get_tolerance.return_value = 2**-5
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()