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_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_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_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)