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 solve_onmax_2d(args):
"""Solve the onemax problem
"""
population_shape, generations = args
population = randint_population(population_shape, 1)
inputs = Input(population_shape)
x = RandomMutations2D(max_gene_value=1)(inputs)
outputs = UniformCrossover2D()(x)
gf = EvoFlow(inputs, outputs, debug=0)
fitness_function = Sum(max_sum_value=10000000)
evolution_strategy = SelectFittest()
gf.compile(evolution_strategy, fitness_function)
gf.evolve(population, generations=generations, verbose=0)
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_mutation2d_graph_mode():
"make sure the boxing / unboxing works in graph mode"
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)
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._call_from_graph(population)
assert B.is_tensor(population)
assert population.shape == pop_shape