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 __init__(self, is_neigh_precomp=True): self.length = 40 self.epsilon = 2**-31 self.number_of_activations = 10 self.number_of_mutations_from_mutator = 40 self.population_size = 200 self.tolerance = ConjunctionTolerance(self.length, self.epsilon) self.tau = (self.epsilon / self.length)**3 * log(1/self.epsilon) self.representation_class = None if is_neigh_precomp: self.representation_class = get_set_of_all_representations_with_length(self.length) self.mutation_neighborhood = PrecompMonotoneConjunctionNeighborhood(self.representation_class) else: self.mutation_neighborhood = MonotoneConjunctionNeighborhood() self.mutation_probability = ConjunctionMutationProbability(self.mutation_neighborhood)
def setUp(self): self.neighbors_finder = Mock() self.conjunction_mutation_probability = ConjunctionMutationProbability( self.neighbors_finder)