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test_crossover_mutation.py
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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_crossover_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,
crossover_probability=None,
mutation_probability=None,
crossover_type=None,
mutation_type=None):
def fitness_func(ga, solution, idx):
return random.random()
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,
crossover_probability=crossover_probability,
mutation_probability=mutation_probability,
crossover_type=crossover_type,
mutation_type=mutation_type,
suppress_warnings=True,
random_seed=1)
ga_instance.run()
comparison_result = []
for solution_idx, solution in enumerate(ga_instance.population):
if list(solution) in ga_instance.initial_population.tolist():
comparison_result.append(True)
else:
comparison_result.append(False)
comparison_result = numpy.array(comparison_result)
result = numpy.all(comparison_result == True)
print("Comparison result is {result}".format(result=result))
return result, ga_instance
def test_no_crossover_no_mutation():
result, ga_instance = output_crossover_mutation()
assert result == True
def test_no_crossover_no_mutation_gene_space():
result, ga_instance = output_crossover_mutation(gene_space=range(10))
assert result == True
def test_no_crossover_no_mutation_int_gene_type():
result, ga_instance = output_crossover_mutation(gene_type=int)
assert result == True
def test_no_crossover_no_mutation_gene_space_gene_type():
result, ga_instance = output_crossover_mutation(gene_space={"low": 0, "high": 10},
gene_type=[float, 2])
assert result == True
def test_no_crossover_no_mutation_nested_gene_space():
result, ga_instance = output_crossover_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_no_crossover_no_mutation_nested_gene_type():
result, ga_instance = output_crossover_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_no_crossover_no_mutation_nested_gene_space_nested_gene_type():
result, ga_instance = output_crossover_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_no_crossover_no_mutation_initial_population():
global initial_population
result, ga_instance = output_crossover_mutation(initial_population=initial_population)
assert result == True
def test_no_crossover_no_mutation_initial_population_nested_gene_type():
global initial_population
result, ga_instance = output_crossover_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_crossover_no_mutation_zero_crossover_probability():
global initial_population
result, ga_instance = output_crossover_mutation(crossover_type="single_point",
crossover_probability=0.0)
assert result == True
def test_zero_crossover_probability_zero_mutation_probability():
global initial_population
result, ga_instance = output_crossover_mutation(crossover_type="single_point",
crossover_probability=0.0,
mutation_type="random",
mutation_probability=0.0)
assert result == True
if __name__ == "__main__":
print()
test_no_crossover_no_mutation()
print()
test_no_crossover_no_mutation_int_gene_type()
print()
test_no_crossover_no_mutation_gene_space()
print()
test_no_crossover_no_mutation_gene_space_gene_type()
print()
test_no_crossover_no_mutation_nested_gene_space()
print()
test_no_crossover_no_mutation_nested_gene_type()
print()
test_no_crossover_no_mutation_initial_population()
print()
test_no_crossover_no_mutation_initial_population_nested_gene_type()
print()
test_crossover_no_mutation_zero_crossover_probability()
print()
test_zero_crossover_probability_zero_mutation_probability()
print()