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 = 300
GENOME_SHAPE = (20, 4, 4)
population = B.randint(0, 1024, GENOME_SHAPE)
population_fraction = 1
crossover_probability = (0.5, 0.5)
OP = RandomMutations2D(population_fraction, crossover_probability)
# diff matrix
previous_population = B.copy(population)
population = OP(population)
diff = B.clip(abs(population - previous_population), 0, 1)
for _ in range(NUM_ITERATIONS - 1):
previous_population = B.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) > NUM_ITERATIONS // 15
assert B.max(c) < NUM_ITERATIONS // 2
assert NUM_ITERATIONS // 8 < B.mean(c) < NUM_ITERATIONS // 2
def test_uniformpop():
shape = (100, 100)
pop = uniform_population(shape)
assert pop.shape == shape
assert B.max(pop) == 99
assert B.min(pop) == 0
for chrm in pop:
_, _, count = B.unique_with_counts(chrm)
assert B.max(count) == 1
def test_binary_val_default_params():
pop_shape = (6, 4)
population = randint_population(pop_shape, 2)
population = RandomMutations1D(max_gene_value=1, debug=1)(population)
print(population)
assert B.max(population) == 1
assert not B.min(population)
def test_randintpop():
shape = (100, 100, 10)
pop = randint_population(shape, 42, 1)
assert pop.shape == shape
assert B.max(pop) == 42
assert B.min(pop) == 1
assert pop.dtype == B.intx()
def test_mutation2d_eager():
pop_shape = (10, 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 = randint_population(pop_shape, max_gene_value)
# save original
original_population = B.copy(population)
cprint('[Initial genepool]', 'blue')
cprint(original_population, 'blue')
for _ in range(3):
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
ok = 0
for chromosome in diff:
if B.sum(chromosome) >= 2:
ok += 1
if (ok / len(diff)) > 0.5:
break
assert (ok / len(diff)) > 0.5
def test_1D_tensor_values_maintained():
POPULATION_SHAPE = (6, 8)
# need a uniform population otherwise its not testable.
population = uniform_population(POPULATION_SHAPE)
population_fraction = 0.5
max_reverse_probability = 0.2
cprint(population, 'cyan')
population = Reverse1D(population_fraction,
max_reverse_probability,
debug=1)(population)
cprint(population, 'yellow')
for chrm in population:
_, _, count = B.unique_with_counts(chrm)
cprint(count, 'green')
assert B.max(count) == 1
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 = 0.0
mc = 0.0
for r in c:
s = B.cast(B.sum(r), B.floatx())
if s:
mr += 1
mc += s
assert abs(mutated_rows - mr) <= 2
assert abs(B.cast(mutated_cells, B.floatx()) -
(mc / mutated_rows)) <= 3.0 # 2.5
def test_clip():
pop_shape = (100, 100)
population = randint_population(pop_shape, 100)
population = RandomMutations1D(max_gene_value=100, debug=1)(population)
assert B.max(population) <= 100
assert not B.min(population)
population = UniformCrossover2D(population_fraction,
crossover_probability)(population)
print(population)
# diff matrix
diff = B.clip(abs(population - original_population), 0, 1)
print(diff)
quit()
# 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')
pop_shape = (6, 4, 4)
max_gene_value = 10
min_gene_value = 0
population_fraction = 0.5
mutations_probability = (0.5, 0.5)
min_mutation_value = 1
max_mutation_value = 1
population = B.randint(0, max_gene_value, pop_shape)
OP = 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)
chromosomes_sav = copy(population)
cprint('[Initial genepool]', 'blue')
cprint(chromosomes_sav, 'blue')
population = OP(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