def HGT_simulations_main__population_factor():
common_classes = CommonClassesCreator()
number_of_activations, population_factors, \
HGT_factors, mutation_factors = common_classes.get_simulation_variables()
HGT_factor = 0.2
number_of_generations_per_factor = list()
for population_factor in population_factors:
generation_number = 0
common_classes.create_next_HGT_process(HGT_factor)
for _ in xrange(number_of_activations):
generation_number += get_number_of_generations_of_single_run(
common_classes, population_factor)
avg_number_of_generations = float(
generation_number) / number_of_activations
print "number_of_generations for " + str(
population_factor) + " population is: " + str(
avg_number_of_generations)
number_of_generations_per_factor.append(avg_number_of_generations)
print "number_of_generations_per_factor: " + str(
number_of_generations_per_factor)
def HGT_simulations_main__performance_rate():
common_classes = CommonClassesCreator()
number_of_activations, population_factors, \
HGT_factors, mutation_factors = common_classes.get_simulation_variables()
HGT_factor = 0.5
avg_performance_rate = list()
for i in xrange(1000):
avg_performance_rate.append((0, [0] * i))
common_classes.create_next_HGT_process(HGT_factor)
for _ in xrange(number_of_activations):
current_performance_rate = get_performance_rate_of_single_run(
common_classes)
performance_rate_with_same_generation = avg_performance_rate[len(
current_performance_rate)]
avg_performance_rate[len(current_performance_rate)] = (
performance_rate_with_same_generation[0] + 1, [
a + b for a, b in zip(performance_rate_with_same_generation[1],
current_performance_rate)
])
for i in xrange(len(avg_performance_rate)):
if avg_performance_rate[i][0] is not 0:
average_rate_for_generation_i = [
avg_rate / avg_performance_rate[i][0]
for avg_rate in avg_performance_rate[i][1]
]
print "performance rate for " + str(i) + " generation is: " + str(
average_rate_for_generation_i)
def HGT_simulations_main__parallel():
common_classes = CommonClassesCreator()
length, epsilon, mutation_neighborhood, tolerance = common_classes.get_common_classes(
)
number_of_activations, population_factors, HGT_factors, mutation_factors = common_classes.get_simulation_variables(
)
number_of_generations_per_factor = list()
parallel = Parallel(n_jobs=-1)
for HGT_factor in HGT_factors:
HGT_process = HGTProcess(HGT_factor, length)
avg_number_of_generations = sum(
parallel(
delayed(compute_part)
(length, epsilon, mutation_neighborhood, tolerance,
HGT_process, representation_class, number_of_activations /
4, get_number_of_generations_of_single_run)
for _ in xrange(4))) / 4
print "number_of_generations for " + str(
HGT_factor) + " factor is: " + str(avg_number_of_generations)
number_of_generations_per_factor.append(avg_number_of_generations)
print "number_of_generations_per_factor: " + str(
number_of_generations_per_factor)
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 recombination_main():
common_classes = CommonClassesCreator()
length, epsilon, mutation_neighborhood, tolerance = common_classes.get_common_classes()
mutation_factor = 0.1
concept_class = MonotoneConjunction(length)
performance = common_classes.get_perf_without_precomp(concept_class)
recomb_process = RecombinationProcess()
neighborhood = NeighborhoodWithOtherRepresentations(length, mutation_neighborhood,
mutation_factor, recomb_process)
recombinator = Recombinator(neighborhood, performance, tolerance, epsilon)
recombination = RecombinationConjunctionAlgorithm(recombinator, length, epsilon, concept_class)
recombination.learn_ideal_function(concept_class)
def HGT_simulations_main__HGT_factor():
common_classes = CommonClassesCreator()
number_of_activations, population_factors, \
HGT_factors, mutation_factors = common_classes.get_simulation_variables()
number_of_generations_per_factor = list()
for HGT_factor in HGT_factors:
generation_number = 0
common_classes.create_next_HGT_process(HGT_factor)
for _ in xrange(number_of_activations):
generation_number += get_number_of_generations_of_single_run(common_classes)
avg_number_of_generations = float(generation_number) / number_of_activations
print "number_of_generations for " + str(HGT_factor) + " factor is: " + str(avg_number_of_generations)
number_of_generations_per_factor.append(avg_number_of_generations)
print "number_of_generations_per_factor: " + str(number_of_generations_per_factor)
def recombination_main():
common_classes = CommonClassesCreator()
length, epsilon, mutation_neighborhood, tolerance = common_classes.get_common_classes(
)
mutation_factor = 0.1
concept_class = MonotoneConjunction(length)
performance = common_classes.get_perf_without_precomp(concept_class)
recomb_process = RecombinationProcess()
neighborhood = NeighborhoodWithOtherRepresentations(
length, mutation_neighborhood, mutation_factor, recomb_process)
recombinator = Recombinator(neighborhood, performance, tolerance, epsilon)
recombination = RecombinationConjunctionAlgorithm(recombinator, length,
epsilon, concept_class)
recombination.learn_ideal_function(concept_class)
def HGT_main():
common_classes = CommonClassesCreator()
length, epsilon, mutation_neighborhood, tolerance = common_classes.get_common_classes()
mutation_factor = 0.1
HGT_factor = 1
concept_class = MonotoneConjunction(length)
performance = common_classes.get_perf_without_precomp(concept_class)
HGT_process = HGTProcess(HGT_factor, length)
neighborhood = NeighborhoodWithOtherRepresentations(length, mutation_neighborhood,
mutation_factor, HGT_process)
HGT_mutator = HGT_Mutator(neighborhood, performance, tolerance, epsilon, HGT_process)
mutation = HGTConjunctionAlgorithm(HGT_mutator, length, epsilon, performance)
final_population = mutation.learn_ideal_function(concept_class)
if len(final_population) <= 30:
print final_population
def HGT_simulations_main__parallel():
common_classes = CommonClassesCreator()
length, epsilon, mutation_neighborhood, tolerance = common_classes.get_common_classes()
number_of_activations, population_factors, HGT_factors, mutation_factors = common_classes.get_simulation_variables()
number_of_generations_per_factor = list()
parallel = Parallel(n_jobs=-1)
for HGT_factor in HGT_factors:
HGT_process = HGTProcess(HGT_factor, length)
avg_number_of_generations = sum(parallel(delayed(compute_part)(length, epsilon, mutation_neighborhood,
tolerance, HGT_process, representation_class,
number_of_activations / 4,
get_number_of_generations_of_single_run)
for _ in xrange(4))) / 4
print "number_of_generations for " + str(HGT_factor) + " factor is: " + str(avg_number_of_generations)
number_of_generations_per_factor.append(avg_number_of_generations)
print "number_of_generations_per_factor: " + str(number_of_generations_per_factor)
def run_models_without_precomp():
pr = cProfile.Profile()
pr.enable()
common_classes = CommonClassesCreator(False)
compare_with_HGT_factors(common_classes)
compare_with_mutation_factors(common_classes)
compare_with_population_factors(common_classes)
pr.disable()
pstats.Stats(pr).sort_stats("time").print_stats()
def HGT_simulations_main__performance_rate():
common_classes = CommonClassesCreator()
number_of_activations, population_factors, \
HGT_factors, mutation_factors = common_classes.get_simulation_variables()
HGT_factor = 0.5
avg_performance_rate = list()
for i in xrange(1000):
avg_performance_rate.append((0, [0]*i))
common_classes.create_next_HGT_process(HGT_factor)
for _ in xrange(number_of_activations):
current_performance_rate = get_performance_rate_of_single_run(common_classes)
performance_rate_with_same_generation = avg_performance_rate[len(current_performance_rate)]
avg_performance_rate[len(current_performance_rate)] = (performance_rate_with_same_generation[0]+1,
[a+b for a, b in zip(performance_rate_with_same_generation[1],current_performance_rate)])
for i in xrange(len(avg_performance_rate)):
if avg_performance_rate[i][0] is not 0:
average_rate_for_generation_i = [avg_rate / avg_performance_rate[i][0] for avg_rate in avg_performance_rate[i][1]]
print "performance rate for " + str(i) + " generation is: " + str(average_rate_for_generation_i)
def HGT_main():
common_classes = CommonClassesCreator()
length, epsilon, mutation_neighborhood, tolerance = common_classes.get_common_classes(
)
mutation_factor = 0.1
HGT_factor = 1
concept_class = MonotoneConjunction(length)
performance = common_classes.get_perf_without_precomp(concept_class)
HGT_process = HGTProcess(HGT_factor, length)
neighborhood = NeighborhoodWithOtherRepresentations(
length, mutation_neighborhood, mutation_factor, HGT_process)
HGT_mutator = HGT_Mutator(neighborhood, performance, tolerance, epsilon,
HGT_process)
mutation = HGTConjunctionAlgorithm(HGT_mutator, length, epsilon,
performance)
final_population = mutation.learn_ideal_function(concept_class)
if len(final_population) <= 30:
print final_population
