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test_adaptive_mutation.py
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222 lines (166 loc) · 8.53 KB
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import pygad
import random
import numpy
num_generations = 1
initial_population = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]
def output_adaptive_mutation(gene_space=None,
gene_type=float,
num_genes=10,
mutation_by_replacement=False,
random_mutation_min_val=-1,
random_mutation_max_val=1,
init_range_low=-4,
init_range_high=4,
initial_population=None,
mutation_probability=[0.2, 0.1],
fitness_batch_size=None,
mutation_type="adaptive"):
def fitness_func_single(ga, solution, idx):
return random.random()
def fitness_func_batch(ga, soluions, idxs):
return numpy.random.uniform(size=len(soluions))
if fitness_batch_size in [1, None]:
fitness_func = fitness_func_single
else:
fitness_func = fitness_func_batch
ga_instance = pygad.GA(num_generations=num_generations,
num_parents_mating=5,
fitness_func=fitness_func,
sol_per_pop=10,
num_genes=num_genes,
gene_space=gene_space,
gene_type=gene_type,
initial_population=initial_population,
init_range_low=init_range_low,
init_range_high=init_range_high,
random_mutation_min_val=random_mutation_min_val,
random_mutation_max_val=random_mutation_max_val,
allow_duplicate_genes=True,
mutation_by_replacement=mutation_by_replacement,
save_solutions=True,
mutation_probability=mutation_probability,
mutation_type=mutation_type,
suppress_warnings=True,
fitness_batch_size=fitness_batch_size,
random_seed=1)
ga_instance.run()
return None, ga_instance
def test_adaptive_mutation():
result, ga_instance = output_adaptive_mutation()
# assert result == True
def test_adaptive_mutation_gene_space():
result, ga_instance = output_adaptive_mutation(gene_space=range(10))
# assert result == True
def test_adaptive_mutation_int_gene_type():
result, ga_instance = output_adaptive_mutation(gene_type=int)
# assert result == True
def test_adaptive_mutation_gene_space_gene_type():
result, ga_instance = output_adaptive_mutation(gene_space={"low": 0, "high": 10},
gene_type=[float, 2])
# assert result == True
def test_adaptive_mutation_nested_gene_space():
result, ga_instance = output_adaptive_mutation(gene_space=[[0, 1, 2, 3, 4],
numpy.arange(5, 10),
range(10, 15),
{"low": 15, "high": 20},
{"low": 20, "high": 30, "step": 2},
None,
numpy.arange(30, 35),
numpy.arange(35, 40),
numpy.arange(40, 45),
[45, 46, 47, 48, 49]])
# assert result == True
def test_adaptive_mutation_nested_gene_type():
result, ga_instance = output_adaptive_mutation(gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
# assert result == True
def test_adaptive_mutation_nested_gene_space_nested_gene_type():
result, ga_instance = output_adaptive_mutation(gene_space=[[0, 1, 2, 3, 4],
numpy.arange(5, 10),
range(10, 15),
{"low": 15, "high": 20},
{"low": 20, "high": 30, "step": 2},
None,
numpy.arange(30, 35),
numpy.arange(35, 40),
numpy.arange(40, 45),
[45, 46, 47, 48, 49]],
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
# assert result == True
def test_adaptive_mutation_initial_population():
global initial_population
result, ga_instance = output_adaptive_mutation(initial_population=initial_population)
# assert result == True
def test_adaptive_mutation_initial_population_nested_gene_type():
global initial_population
result, ga_instance = output_adaptive_mutation(initial_population=initial_population,
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
# assert result == True
def test_adaptive_mutation_fitness_batch_size_1():
result, ga_instance = output_adaptive_mutation(fitness_batch_size=1)
def test_adaptive_mutation_fitness_batch_size_2():
result, ga_instance = output_adaptive_mutation(fitness_batch_size=2)
def test_adaptive_mutation_fitness_batch_size_3():
result, ga_instance = output_adaptive_mutation(fitness_batch_size=3)
def test_adaptive_mutation_fitness_batch_size_4():
result, ga_instance = output_adaptive_mutation(fitness_batch_size=4)
def test_adaptive_mutation_fitness_batch_size_5():
result, ga_instance = output_adaptive_mutation(fitness_batch_size=5)
def test_adaptive_mutation_fitness_batch_size_6():
result, ga_instance = output_adaptive_mutation(fitness_batch_size=6)
def test_adaptive_mutation_fitness_batch_size_7():
result, ga_instance = output_adaptive_mutation(fitness_batch_size=7)
def test_adaptive_mutation_fitness_batch_size_8():
result, ga_instance = output_adaptive_mutation(fitness_batch_size=8)
def test_adaptive_mutation_fitness_batch_size_9():
result, ga_instance = output_adaptive_mutation(fitness_batch_size=9)
def test_adaptive_mutation_fitness_batch_size_10():
result, ga_instance = output_adaptive_mutation(fitness_batch_size=10)
if __name__ == "__main__":
print()
test_adaptive_mutation()
print()
test_adaptive_mutation_int_gene_type()
print()
test_adaptive_mutation_gene_space()
print()
test_adaptive_mutation_gene_space_gene_type()
print()
test_adaptive_mutation_nested_gene_space()
print()
test_adaptive_mutation_nested_gene_type()
print()
test_adaptive_mutation_initial_population()
print()
test_adaptive_mutation_initial_population_nested_gene_type()
print()
test_adaptive_mutation_fitness_batch_size_1()
print()
test_adaptive_mutation_fitness_batch_size_1()
print()
test_adaptive_mutation_fitness_batch_size_2()
print()
test_adaptive_mutation_fitness_batch_size_3()
print()
test_adaptive_mutation_fitness_batch_size_4()
print()
test_adaptive_mutation_fitness_batch_size_5()
print()
test_adaptive_mutation_fitness_batch_size_6()
print()
test_adaptive_mutation_fitness_batch_size_7()
print()
test_adaptive_mutation_fitness_batch_size_8()
print()
test_adaptive_mutation_fitness_batch_size_9()
print()
test_adaptive_mutation_fitness_batch_size_10()
print()