def run(target, cores, s=None):
""" Genetic Hill Climbing Algorithm run here.
Stores the image results in RESULTS
directory, after every SAVE_PER_GEN
generations .
"""
RESULTS = "results"
# Make RESULTS directory, if not exists.
if not os.path.exists(RESULTS):
os.mkdir(RESULTS)
# Logs file, in RESULTS directory.
f = open(os.path.join(RESULTS, "logs.txt"), "a")
generation = 1
INIT_GENES = 50
parent = Chromosome(target.size, INIT_GENES)
# Load Save file from function parameter.
if s is not None:
generation = parent.load(jsonpickle.decode(s))
# First generation fitness.
score = utils.fitness(parent.draw(), target)
# Create thread pool for each core.
p = multiprocessing.Pool(cores)
# Frequency of saves and number of children
# per generation of Genetic Hill Climbing Algorithm.
SAVE_PER_GEN = 500
CHILDS_PER_GEN = 50
while True:
f.write("Generation {0} Score {1}\n".format(
generation, round(score, 5)))
# Draw the image and save it.
# Also save the logs.
if generation % SAVE_PER_GEN == 0:
print("Generation {0}\tScore {1}".format(
generation, round(score, 5)))
parent.draw().save(os.path.join(
RESULTS, "{0}.png".format(generation)))
save_file = open(os.path.join(
RESULTS, "{0}.txt".format(generation)), "w")
save_file.write(jsonpickle.encode(parent.save(generation)))
save_file.close()
generation += 1
# Mutate the current parent.
try:
results = utils.group_mutate(parent, CHILDS_PER_GEN - 1, p, target)
except KeyboardInterrupt:
print("Exiting program... Bye!")
p.close()
return
# Children for the next generation.
# Includes the current parent in case
# bad mutation.
new_score, new_children = map(list, zip(*results))
new_score.append(score)
new_children.append(parent)
# Choose the fittest child for next generation.
fittest = min(zip(new_children, new_score), key=lambda x: x[1])
parent, score = fittest
