def run_analysis():
population_size = np.arange(start=25, stop=35, step=5)
pixels_percentage_to_change = np.arange(start=0.4, stop=0.5, step=0.1)
max_generation_count = [100]
population_percentage_to_keep = np.arange(start=0.1,stop=0.3, step=0.1)
mutation_prob = np.arange(start=0.4, stop=0.6, step=0.2)
crossover_prob = np.arange(start=0.7, stop=0.8, step=0.1)
all_params_combination = itertools.product(population_size,
pixels_percentage_to_change,
max_generation_count,
population_percentage_to_keep,
mutation_prob,
crossover_prob)
fake_img_generator = FakeImgGenerator(Utils.get_test_image_path(IMG_NAME),
P_MAX,
P_MIN,
FAKE_CLASS_PROB_TO_GET)
results = []
output_dir = Utils.get_new_output_dir()
for i, c in enumerate(all_params_combination):
print("Test generation set number: {}".format(i))
generation_result = fake_img_generator.run(population_size=c[0],
pixels_percentage_to_change=c[1],
max_generations_count=c[2],
population_percentage_to_keep=c[3],
mutation_prob=c[4],
crossover_prob=c[5])
results.append(generation_result)
result_path = Utils.create_child_dir(basepath=output_dir, child_dir="result_{}".format(str(i)))
print("Result probability: {}".format(generation_result.get_last_probability()))
generation_result.save(output_dir=result_path)
_save_results_comparison(results, output_dir)
from src.FakeImgGenerator import FakeImgGenerator
from src.Utils import Utils
from Config import *
if __name__ == '__main__':
img_name = IMG_NAME
img_path = Utils.get_test_image_path(img_name)
p_max = P_MAX
p_min = P_MIN
fake_class_prob_to_get = FAKE_CLASS_PROB_TO_GET
fake_img_generator = FakeImgGenerator(img_path, p_max, p_min,
fake_class_prob_to_get)
population_size = 30
pixels_percentage_to_change = 0.4
max_generations_count = 100
population_percentage_to_keep = 0.1
mutation_prob = 0.6
crossover_prob = 0.7
result = fake_img_generator.run(population_size,
pixels_percentage_to_change,
max_generations_count,
population_percentage_to_keep,
mutation_prob, crossover_prob)
output_dir = Utils.get_new_output_dir()
result.save(output_dir)