def anywhere3_selector(problem, individuals, Configurations, values_to_be_passed):
new_population = []
mutated_population = []
new_population.extend(individuals)
# The population explodes by Configurations["STORM"]["STORM_EXPLOSION"] times
for count in xrange(Configurations["STORM"]["STORM_EXPLOSION"]):
new_population.extend([extrapolate(problem, individuals, individual, Configurations["STORM"]["F"],
Configurations["STORM"]["CF"]) for individual in individuals])
stars = find_poles3(problem, new_population, Configurations)
# remove population from new population TODO: Clean up
raw_poles = []
for s in stars:
for star in [s.east, s.west]:
if look_for_duplicates(star, raw_poles) is False:
star.correct_pole = s.id # added to keep in sync with the mutated_population
star.anyscore = 1e32 # added to keep in sync with the mutated_population
raw_poles.append(star)
else: continue
# Remove the star from the new_population
new_population = [pop for pop in new_population if pop != star]
poles_distribution_list = []
for individual in new_population:
# correct_poles, individual = scores(individual, stars, Configurations)
correct_poles_scores, individual = scores2(individual, stars, Configurations)
correct_poles = [x[0] for x in correct_poles_scores]
increments = [x[-1] for x in correct_poles_scores]
# distribution_list would be used to help in the culling process
poles_distribution_list.extend(correct_poles)
for poles, increment in zip(correct_poles, increments):
temp_individual = nudge(problem, individual, stars[poles].east, increment, Configurations)
temp_individual.anyscore = increment
temp_individual.correct_pole = poles
mutated_population.append(temp_individual)
mutated_population.extend(raw_poles) # adding the poles to the population
# from Helper.culling import culling
# after_culling_population = culling(poles_distribution_list, mutated_population, Configurations)
# return after_culling_population, sum([1 if x.fitness.fitness is not None else 0 for x in after_culling_population])
distribution_list = list(set(poles_distribution_list))
return_population = []
# print "Length of the new_population: ", len(new_population)
# print "Length of the mutated population: ", len(mutated_population)
# print len([x for x in mutated_population if x.fitness.fitness is not None])
for point in distribution_list:
temp_list = [individual for individual in mutated_population if individual.correct_pole == point]
return_population += sorted(temp_list, key=lambda x:x.anyscore, reverse=True)[:(len(temp_list) * 100)/len(mutated_population)]
howmany = Configurations["Universal"]["Population_Size"] - len(return_population)
for _ in xrange(howmany):
return_population.append(jmoo_individual(problem, problem.generateInput(), None))
# print "Length of return population: ", len(return_population)
# print "length of stars: ", len(stars)
return return_population, sum([1 if x.fitness.fitness != None else 0 for x in return_population])
def anywhere_selector(problem, individuals, configurations):
print "=" * 10
new_population = []
mutated_population = []
new_population.extend(individuals)
for count in xrange(jmoo_properties.ANYWHERE_EXPLOSION):
new_population.extend([extrapolate(problem, individuals, individual, jmoo_properties.F, jmoo_properties.CF)
for individual in individuals])
import pdb
pdb.set_trace()
stars = find_poles2(problem, new_population)
# print ">> ", len([x.fitness.fitness for x in new_population if x.fitness.fitness is not None]), len(new_population), jmoo_properties.ANYWHERE_POLES
# exit()
# remove population from new population TODO: Clean up
raw_poles = []
for s in stars:
for star in [s.east, s.west]:
if look_for_duplicates(star, raw_poles) is False:
star.correct_pole = s.id # added to keep in sync with the mutated_population
star.anyscore = 1e32 # added to keep in sync with the mutated_population
raw_poles.append(star)
else: continue
for pop in new_population:
if star. decisionValues == pop.decisionValues:
new_population.remove(pop)
distribution_list = []
for individual in new_population:
correct_poles, individual = scores2(individual, stars)
distribution_list.extend(correct_poles)
for poles in correct_poles:
temp_individual = nudge(problem, individual, stars[poles].east, individual.anyscore)
temp_individual.anyscore = individual.anyscore
temp_individual.correct_pole = poles
mutated_population.append(temp_individual)
mutated_population.extend(raw_poles) # adding the poles to the population
distribution_list = list(set(distribution_list))
return_population = []
# print "Length of the new_population: ", len(new_population)
# print "Length of the mutated population: ", len(mutated_population)
# print len([x for x in mutated_population if x.fitness.fitness is not None])
for point in distribution_list:
temp_list = [individual for individual in mutated_population if individual.correct_pole == point]
return_population += sorted(temp_list, key=lambda x:x.anyscore, reverse=True)[:(len(temp_list) * 100)/len(mutated_population)]
howmany = jmoo_properties.MU - len(return_population)
for _ in xrange(howmany):
return_population.append(jmoo_individual(problem, problem.generateInput(), None))
# print "Length of return population: ", len(return_population)
# print "length of stars: ", len(stars)
return return_population, sum([1 if x.fitness.fitness != None else 0 for x in return_population])
def anywhere_selector(problem, individuals, configurations):
print "=" * 10
new_population = []
mutated_population = []
new_population.extend(individuals)
for count in xrange(jmoo_properties.ANYWHERE_EXPLOSION):
new_population.extend([
extrapolate(problem, individuals, individual, jmoo_properties.F,
jmoo_properties.CF) for individual in individuals
])
import pdb
pdb.set_trace()
stars = find_poles2(problem, new_population)
# print ">> ", len([x.fitness.fitness for x in new_population if x.fitness.fitness is not None]), len(new_population), jmoo_properties.ANYWHERE_POLES
# exit()
# remove population from new population TODO: Clean up
raw_poles = []
for s in stars:
for star in [s.east, s.west]:
if look_for_duplicates(star, raw_poles) is False:
star.correct_pole = s.id # added to keep in sync with the mutated_population
star.anyscore = 1e32 # added to keep in sync with the mutated_population
raw_poles.append(star)
else:
continue
for pop in new_population:
if star.decisionValues == pop.decisionValues:
new_population.remove(pop)
distribution_list = []
for individual in new_population:
correct_poles, individual = scores2(individual, stars)
distribution_list.extend(correct_poles)
for poles in correct_poles:
temp_individual = nudge(problem, individual, stars[poles].east,
individual.anyscore)
temp_individual.anyscore = individual.anyscore
temp_individual.correct_pole = poles
mutated_population.append(temp_individual)
mutated_population.extend(raw_poles) # adding the poles to the population
distribution_list = list(set(distribution_list))
return_population = []
# print "Length of the new_population: ", len(new_population)
# print "Length of the mutated population: ", len(mutated_population)
# print len([x for x in mutated_population if x.fitness.fitness is not None])
for point in distribution_list:
temp_list = [
individual for individual in mutated_population
if individual.correct_pole == point
]
return_population += sorted(temp_list,
key=lambda x: x.anyscore,
reverse=True)[:(len(temp_list) * 100) /
len(mutated_population)]
howmany = jmoo_properties.MU - len(return_population)
for _ in xrange(howmany):
return_population.append(
jmoo_individual(problem, problem.generateInput(), None))
# print "Length of return population: ", len(return_population)
# print "length of stars: ", len(stars)
return return_population, sum(
[1 if x.fitness.fitness != None else 0 for x in return_population])
def anywhere3_selector(problem, individuals, Configurations,
values_to_be_passed):
new_population = []
mutated_population = []
new_population.extend(individuals)
# The population explodes by Configurations["STORM"]["STORM_EXPLOSION"] times
for count in xrange(Configurations["STORM"]["STORM_EXPLOSION"]):
new_population.extend([
extrapolate(problem, individuals, individual,
Configurations["STORM"]["F"],
Configurations["STORM"]["CF"])
for individual in individuals
])
stars = find_poles3(problem, new_population, Configurations)
# remove population from new population TODO: Clean up
raw_poles = []
for s in stars:
for star in [s.east, s.west]:
if look_for_duplicates(star, raw_poles) is False:
star.correct_pole = s.id # added to keep in sync with the mutated_population
star.anyscore = 1e32 # added to keep in sync with the mutated_population
raw_poles.append(star)
else:
continue
# Remove the star from the new_population
new_population = [pop for pop in new_population if pop != star]
poles_distribution_list = []
for individual in new_population:
# correct_poles, individual = scores(individual, stars, Configurations)
correct_poles_scores, individual = scores2(individual, stars,
Configurations)
correct_poles = [x[0] for x in correct_poles_scores]
increments = [x[-1] for x in correct_poles_scores]
# distribution_list would be used to help in the culling process
poles_distribution_list.extend(correct_poles)
for poles, increment in zip(correct_poles, increments):
temp_individual = nudge(problem, individual, stars[poles].east,
increment, Configurations)
temp_individual.anyscore = increment
temp_individual.correct_pole = poles
mutated_population.append(temp_individual)
mutated_population.extend(raw_poles) # adding the poles to the population
# from Helper.culling import culling
# after_culling_population = culling(poles_distribution_list, mutated_population, Configurations)
# return after_culling_population, sum([1 if x.fitness.fitness is not None else 0 for x in after_culling_population])
distribution_list = list(set(poles_distribution_list))
return_population = []
# print "Length of the new_population: ", len(new_population)
# print "Length of the mutated population: ", len(mutated_population)
# print len([x for x in mutated_population if x.fitness.fitness is not None])
for point in distribution_list:
temp_list = [
individual for individual in mutated_population
if individual.correct_pole == point
]
return_population += sorted(temp_list,
key=lambda x: x.anyscore,
reverse=True)[:(len(temp_list) * 100) /
len(mutated_population)]
howmany = Configurations["Universal"]["Population_Size"] - len(
return_population)
for _ in xrange(howmany):
return_population.append(
jmoo_individual(problem, problem.generateInput(), None))
# print "Length of return population: ", len(return_population)
# print "length of stars: ", len(stars)
return return_population, sum(
[1 if x.fitness.fitness != None else 0 for x in return_population])