def __init__(self, is_neigh_precomp=True, is_using_output_one_neigh=True):
self.length = 100
self.epsilon = Fraction(2**-55)
self.number_of_activations = 20
self.number_of_mutations_from_mutator = 50
self.population_size = 75
self.tolerance = ConjunctionTolerance(self.length, self.epsilon)
self.tau = 0 # (self.epsilon / Decimal(self.length))**Decimal(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)
elif is_using_output_one_neigh:
self.mutation_neighborhood = ConjunctionNeighborhoodOutputOne()
else:
self.mutation_neighborhood = MonotoneConjunctionNeighborhood()
self.mutation_probability = ConjunctionMutationProbability(
self.mutation_neighborhood)
self.recombination_factor = 1
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 setUp(self):
self.neighbors_finder = Mock()
self.conjunction_mutation_probability = ConjunctionMutationProbability(self.neighbors_finder)
