errors = []
for x in range(10):
# Create the push interpreter and run program
interpreter = PushInterpreter()
y_hat = interpreter.run(program, inputs=[x],
output_types=['_integer'])[0]
# Get output
if y_hat is not None:
# compare to target output
target_int = target_function(x)
# calculate error
errors.append((y_hat - target_int)**2)
else:
errors.append(1e5)
return errors
# Genetic operators
mut = UniformMutation()
alt = Alternation()
ops = [(Alternation(), 0.6), (UniformMutation(), 0.2),
(VariationOperatorPipeline((alt, mut)), 0.2)]
if __name__ == "__main__":
evo = SimplePushGPEvolver(n_jobs=-1,
verbose=1,
operators=ops,
selection_method='epsilon_lexicase',
atom_generators=list(all_instructions))
evo.fit(error_func, 1, ['_integer'])
atom_generators = list(merge_sets(
get_instructions_by_pysh_type("_integer"),
get_instructions_by_pysh_type("_boolean"),
get_instructions_by_pysh_type("_print"),
get_instructions_by_pysh_type("_exec"),
[lambda: random.randint(-100, 100)]
))
alternation = Alternation(rate=0.01, alignment_deviation=5)
mutation = UniformMutation(rate=0.01)
genetic_operators = [
(alternation, 0.2),
(mutation, 0.2),
(PerturbCloseMutation(rate=0.1), 0.1),
(VariationOperatorPipeline((alternation, mutation)), 0.5)
]
if __name__ == "__main__":
evo = SimplePushGPEvolver(n_jobs=-1, verbose=2,
atom_generators=atom_generators,
initial_max_genome_size=400,
operators=genetic_operators,
population_size=1000,
max_generations=200,
selection_method='lexicase')
evo.fit(error_function, 4, [])
e = 0
if output[0] is None:
e += 1000
elif output[0] != targets[0]:
e += 1
if output[1] is None:
e += 1000
elif output[1] != targets[1]:
e += 1
errors.append(e)
return errors
atom_generators = list(merge_sets(get_instructions_by_pysh_type('_boolean'),
get_instructions_by_pysh_type('_exec')))
mut = UniformMutation(rate=0.1)
alt = Alternation(rate=0.1, alignment_deviation=10)
ops = [(alt, 0.2), (mut, 0.3), (VariationOperatorPipeline((mut, alt)), 0.5)]
if __name__ == "__main__":
evo = SimplePushGPEvolver(n_jobs=-1, verbose=1, operators=ops,
atom_generators=atom_generators,
initial_max_genome_size=300,
population_size=500, max_generations=300,
simplification_steps=5000)
evo.fit(error_function, 3, ['_boolean', '_boolean'])