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)
