def test_binary_val_default_params():
pop_shape = (6, 4)
population = B.randint(0, 2, pop_shape)
population = RandomMutations1D(max_gene_value=1, debug=1)(population)
print(population)
assert B.max(population) == 1
assert not B.min(population)
def test_uniform_distribution():
"""check that every gene of the tensor are going to be flipped equally
Note:
# ! We need enough iterations and chromosomes to reduce collision
# ! and ensure numerical stability
"""
NUM_ITERATIONS = 1000
GENOME_SHAPE = (20, 4, 4)
population = B.randint(0, 1024, GENOME_SHAPE)
population_fraction = 1
crossover_probability = (0.5, 0.5)
# each gene proba of being mutated 0.5*0.5 > 0.25
# each chromosome proba of being mutated 1
# => gene average hit rate: 1000 / (1/4) ~250
MIN_DIFF_BOUND = 200
MAX_DIFF_BOUND = 300
OP = RandomMutations2D(population_fraction, crossover_probability)
# diff matrix
previous_population = copy(population)
population = OP(population)
diff = B.clip(abs(population - previous_population), 0, 1)
for _ in range(NUM_ITERATIONS - 1):
previous_population = copy(population)
population = OP(population)
curr_diff = B.clip(abs(population - previous_population), 0, 1)
# acumulating diff matrix
diff += curr_diff
print(curr_diff)
for c in diff:
print(c)
print('mean', B.mean(c), 'min', B.min(c), 'max', B.max(c))
assert B.min(c) > MIN_DIFF_BOUND
assert B.max(c) < MAX_DIFF_BOUND
assert MIN_DIFF_BOUND < B.mean(c) < MAX_DIFF_BOUND
def test_dualcrossover2d_distribution():
"""check that every gene of the tensor are going to be flipped equally
Note:
# ! We need enough iterations and chromosomes to reduce collision
# ! and ensure numerical stability
"""
NUM_ITERATIONS = 1000
GENOME_SHAPE = (100, 4, 4)
population = B.randint(0, 1024, GENOME_SHAPE)
population_fraction = 1
crossover_probability = (0.5, 0.5)
OP = DualCrossover2D(population_fraction, crossover_probability)
# diff matrix
previous_population = copy(population)
population = OP(population)
diff = B.clip(abs(population - previous_population), 0, 1)
print(diff)
for _ in range(NUM_ITERATIONS - 1):
previous_population = copy(population)
population = OP(population)
curr_diff = B.clip(abs(population - previous_population), 0, 1)
# acumulating diff matrix
diff += curr_diff
# print(curr_diff)
for c in diff:
print(c)
print('mean', B.mean(c), 'min', B.min(c), 'max', B.max(c))
assert B.min(c) > 50
assert B.max(c) < NUM_ITERATIONS / 2
assert 200 < B.mean(c) < NUM_ITERATIONS / 2
def test_mutation2d_eager():
pop_shape = (2, 4, 4)
max_gene_value = 10
min_gene_value = 0
population_fraction = 1
mutations_probability = (0.5, 0.5)
min_mutation_value = 1
max_mutation_value = 1
population = B.randint(0, max_gene_value, pop_shape)
# save original
original_population = copy(population)
cprint('[Initial genepool]', 'blue')
cprint(original_population, 'blue')
RM = RandomMutations2D(population_fraction=population_fraction,
mutations_probability=mutations_probability,
min_gene_value=min_gene_value,
max_gene_value=max_gene_value,
min_mutation_value=min_mutation_value,
max_mutation_value=max_mutation_value,
debug=True)
population = RM(population)
cprint('\n[Mutated genepool]', 'yellow')
cprint(population, 'yellow')
cprint('\n[Diff]', 'magenta')
diff = population - original_population
cprint(diff, 'magenta')
assert B.is_tensor(population)
assert population.shape == pop_shape
assert B.max(diff) <= max_mutation_value
for chromosome in diff:
assert B.sum(chromosome) == 4
def test_uniform_2Dcrossover_randomness_shape():
GENOME_SHAPE = (10, 4, 4)
population = B.randint(0, 1024, GENOME_SHAPE)
population_fraction = 0.5
crossover_probability = (0.5, 0.5)
original_population = copy(population)
OP = UniformCrossover2D(population_fraction, crossover_probability)
population = OP(population)
diff = B.clip(abs(population - original_population), 0, 1)
print(diff)
expected_mutations = original_population.shape[0] * population_fraction
mutated_chromosomes = []
for c in diff:
if B.max(c):
mutated_chromosomes.append(c)
num_mutations = len(mutated_chromosomes)
# sometime we have a collision so we use a delta
assert abs(num_mutations - expected_mutations) < 2
mutated_rows = crossover_probability[0] * GENOME_SHAPE[1]
mutated_cells = crossover_probability[0] * GENOME_SHAPE[2]
for cidx, c in enumerate(mutated_chromosomes):
mr = 0
mc = 0
for r in c:
s = B.sum(r)
if s:
mr += 1
mc += s
assert abs(mutated_rows - mr) < 2
assert abs(mutated_cells - (mc // mutated_rows)) < 2
original_population = copy(population)
population = UniformCrossover2D(population_fraction,
crossover_probability)(population)
# diff matrix
diff = B.clip(abs(population - original_population), 0, 1)
print(diff)
# expected mutated chromosomes
expected_mutated = population.shape[0] * population_fraction
cprint(
"Expected mutated chromosome:%d (+/- 1 due to collision)" %
(expected_mutated), 'cyan')
# select mutated chromosomes
mutated_chromosomes = []
for c in diff:
if B.max(c):
mutated_chromosomes.append(c)
cprint("mutated chromosome:%d" % (len(mutated_chromosomes)), 'magenta')
cprint("[example of mutated chromosome]", 'yellow')
cprint(mutated_chromosomes[0], 'cyan')
OP = UniformCrossover2D(population_fraction, crossover_probability)
res = OP(population)
for _ in range(100):
prev = copy(res)
res = OP(population)
# acumulating diff matrix
diff += B.clip(abs(res - prev), 0, 1)
cprint("[cumulative diff matrix 100 runs]", 'yellow')
max_gene_value = 10
min_gene_value = 0
population_fraction = 1
mutations_probability = (0.5, 0.5)
min_mutation_value = 1
max_mutation_value = 1
population = B.randint(0, max_gene_value, pop_shape)
RM = RandomMutations2D(population_fraction=population_fraction,
mutations_probability=mutations_probability,
min_gene_value=min_gene_value,
max_gene_value=max_gene_value,
min_mutation_value=min_mutation_value,
max_mutation_value=max_mutation_value,
debug=True)
RM(population)
chromosomes_sav = copy(population)
cprint('[Initial genepool]', 'blue')
cprint(chromosomes_sav, 'blue')
population = RM(population)
cprint('\n[Mutated genepool]', 'yellow')
cprint(population, 'yellow')
cprint('\n[Diff]', 'magenta')
diff = population - chromosomes_sav
cprint(diff, 'magenta')
assert B.max(diff) <= max_mutation_value
def test_randintpop():
shape = (100, 100, 10)
pop = genRandIntPopulation(shape, 42, 1)
assert pop.shape == shape
assert B.max(pop) == 42
assert B.min(pop) == 1