def learn(existing_brain, eval_function):
input_size = brain.Mutation_params().input_count
output_size = brain.Mutation_params().output_count
if existing_brain != None:
best_brain = existing_brain
else:
best_brain = brain.Brain()
benchmark_instance = copy.deepcopy(best_brain)
best_score = eval_function(benchmark_instance, visualization.Visualization_flags.VISUALIZATION_OFF)
print('NEW BEST SCORE: ' + str(best_score))
counter = 0
average = 0
while best_score < 100:
counter += 1
score = 0
mutant = copy.deepcopy(best_brain)
for i in range(1):
mutant.default_mutation(input_size,output_size)
test_instance = copy.deepcopy(mutant)
score = eval_function(test_instance, visualization.Visualization_flags.VISUALIZATION_OFF)
average += score
if ((counter % 100) == 0):
print ('LAST 100 AVERAGE: ' + str(average/100))
average = 0
if score >= best_score:
print('NEW BEST SCORE: ' + str(score))
brain.print_brain_to_file(mutant)
best_score = score
best_brain = copy.deepcopy(mutant)
return best_brain
def learn(eval_function):
input_size = brain.Mutation_params().input_count
output_size = brain.Mutation_params().output_count
best_brain = brain.Brain(1)
best_score = 0
counter = 0
average = 0
while best_score < 100:
counter += 1
score = 0
mutant = copy.deepcopy(best_brain)
for i in range(1):
mutant.default_mutation(input_size, output_size)
test_instance = copy.deepcopy(mutant)
score = eval_function(test_instance, Learning_flags.VISUALIZATION_OFF)
#print(score)
average += score
if ((counter % 100) == 0):
print('LAST 100 AVERAGE: ' + str(average / 100))
average = 0
if score >= best_score:
print('NEW BEST SCORE: ' + str(score))
brain.print_brain_to_file(mutant)
best_score = score
best_brain = copy.deepcopy(mutant)
return best_brain
def impatient_learn(existing_brain, eval_function):
input_size = brain.Mutation_params().input_count
output_size = brain.Mutation_params().output_count
if existing_brain != None:
best_brain = existing_brain
else:
best_brain = brain.Brain()
benchmark_instance = copy.deepcopy(best_brain)
best_score = eval_function(benchmark_instance, visualization.Visualization_flags.VISUALIZATION_OFF)
print('NEW BEST SCORE: ' + str(best_score))
counter = 0
average = 0
chaos = 1.0
chaos_ceiling = 5.0
while best_score < 100:
counter += 1
score = 0
mutant = copy.deepcopy(best_brain)
for i in range(1):
mutant.default_mutation(input_size,output_size)
test_instance = copy.deepcopy(mutant)
score = eval_function(test_instance, visualization.Visualization_flags.VISUALIZATION_OFF)
average += score
if ((counter % 100) == 0):
print ('LAST 100 AVERAGE: ' + str(average/100))
print('CHAOS: ' + str(chaos))
#if uniform(0,1) > .5:
# print('SUPRESSING MUTATION')
# brain.Mutation_params().supress_mutation()
# else:
# print('AMPLIFYING MUTATION')
# brain.Mutation_params().amplify_mutation()
average = 0
if score >= best_score:
chaos = 1
print('NEW BEST SCORE: ' + str(score))
brain.print_brain_to_file(mutant)
best_score = score
best_brain = copy.deepcopy(mutant)
elif chaos < chaos_ceiling:
chaos += .01
brain.Mutation_params().mutation_cycles = randrange(int(chaos)) + 1
return best_brain