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 learn_DNF__HGT():
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()
natural_process = HGTProcess(0.35, length)
neighborhood = NeighborhoodWithOtherRepresentations(length, mutation_neighborhood,
0.1, natural_process)
HGT_mutator = HGT_Mutator(neighborhood, performance_oracle, tolerance, epsilon, natural_process)
algorithm = ConstantPopulationMutationConjunctionAlgorithm(HGT_mutator, length, epsilon, performance_oracle, 75)
print algorithm.learn_ideal_function_until_match()
def setUp(self):
self.neighbors_finder = MonotoneConjunctionNeighborhood()
class TestNeighborsFinder(unittest.TestCase):
def setUp(self):
self.neighbors_finder = MonotoneConjunctionNeighborhood()
def test_find_for_zero(self):
neighbors = set()
neighbors.add((0, 0, 0, 0, 0))
neighbors.add((1, 0, 0, 0, 0))
neighbors.add((0, 1, 0, 0, 0))
neighbors.add((0, 0, 1, 0, 0))
neighbors.add((0, 0, 0, 1, 0))
neighbors.add((0, 0, 0, 0, 1))
self.assertEqual(
neighbors,
self.neighbors_finder.get_neighborhood_of_rep((0, 0, 0, 0, 0)))
def test_find_for_other(self):
neighbors = set()
neighbors.add((0, 0, 0, 0, 0))
neighbors.add((1, 0, 0, 0, 0))
neighbors.add((0, 1, 0, 0, 0))
neighbors.add((0, 0, 1, 0, 0))
neighbors.add((0, 0, 0, 1, 0))
neighbors.add((0, 0, 0, 0, 1))
neighbors.add((1, 0, 1, 0, 0))
neighbors.add((0, 1, 1, 0, 0))
neighbors.add((0, 0, 1, 1, 0))
neighbors.add((0, 0, 1, 0, 1))
self.assertEqual(
neighbors,
self.neighbors_finder.get_neighborhood_of_rep((0, 0, 1, 0, 0)))
def test_find_for_another_one(self):
neighbors = set()
neighbors.add((1, 1, 1, 1, 0))
neighbors.add((1, 1, 1, 0, 1))
neighbors.add((1, 1, 0, 1, 1))
neighbors.add((1, 0, 1, 1, 1))
neighbors.add((0, 1, 1, 1, 1))
neighbors.add((1, 1, 1, 0, 0))
neighbors.add((1, 1, 0, 1, 0))
neighbors.add((1, 0, 1, 1, 0))
neighbors.add((0, 1, 1, 1, 0))
neighbors.add((1, 1, 1, 1, 1))
self.assertEqual(
neighbors,
self.neighbors_finder.get_neighborhood_of_rep((1, 1, 1, 1, 0)))
def test_find_for_unique(self):
neighbors = set()
neighbors.add((1, 0))
neighbors.add((0, 1))
neighbors.add((0, 0))
neighbors.add((1, 1))
self.assertEqual(neighbors,
self.neighbors_finder.get_neighborhood_of_rep((0, 1)))
def setUp(self):
self.neighbors_finder = MonotoneConjunctionNeighborhood()
class TestNeighborsFinder(unittest.TestCase):
def setUp(self):
self.neighbors_finder = MonotoneConjunctionNeighborhood()
def test_find_for_zero(self):
neighbors = set()
neighbors.add((0, 0, 0, 0, 0))
neighbors.add((1, 0, 0, 0, 0))
neighbors.add((0, 1, 0, 0, 0))
neighbors.add((0, 0, 1, 0, 0))
neighbors.add((0, 0, 0, 1, 0))
neighbors.add((0, 0, 0, 0, 1))
self.assertEqual(neighbors, self.neighbors_finder.get_neighborhood_of_rep((0, 0, 0, 0, 0)))
def test_find_for_other(self):
neighbors = set()
neighbors.add((0, 0, 0, 0, 0))
neighbors.add((1, 0, 0, 0, 0))
neighbors.add((0, 1, 0, 0, 0))
neighbors.add((0, 0, 1, 0, 0))
neighbors.add((0, 0, 0, 1, 0))
neighbors.add((0, 0, 0, 0, 1))
neighbors.add((1, 0, 1, 0, 0))
neighbors.add((0, 1, 1, 0, 0))
neighbors.add((0, 0, 1, 1, 0))
neighbors.add((0, 0, 1, 0, 1))
self.assertEqual(neighbors, self.neighbors_finder.get_neighborhood_of_rep((0, 0, 1, 0, 0)))
def test_find_for_another_one(self):
neighbors = set()
neighbors.add((1, 1, 1, 1, 0))
neighbors.add((1, 1, 1, 0, 1))
neighbors.add((1, 1, 0, 1, 1))
neighbors.add((1, 0, 1, 1, 1))
neighbors.add((0, 1, 1, 1, 1))
neighbors.add((1, 1, 1, 0, 0))
neighbors.add((1, 1, 0, 1, 0))
neighbors.add((1, 0, 1, 1, 0))
neighbors.add((0, 1, 1, 1, 0))
neighbors.add((1, 1, 1, 1, 1))
self.assertEqual(neighbors, self.neighbors_finder.get_neighborhood_of_rep((1, 1, 1, 1, 0)))
def test_find_for_unique(self):
neighbors = set()
neighbors.add((1, 0))
neighbors.add((0, 1))
neighbors.add((0, 0))
neighbors.add((1, 1))
self.assertEqual(neighbors, self.neighbors_finder.get_neighborhood_of_rep((0, 1)))