testing_set = generate_cases(50)
def error_function(program, debug=False):
errors = []
for case in training_set:
interpreter = PushInterpreter()
result = interpreter.run(program, [case[0]], ['_vector_integer'],
debug)[0]
if result is None:
errors.append(1e5)
else:
errors.append(levenshtein_distance(case[1], result))
return errors
atom_generators = list(
merge_sets(get_instructions_by_pysh_type("_integer"),
get_instructions_by_pysh_type("_boolean"),
get_instructions_by_pysh_type("_vector_integer"),
get_instructions_by_pysh_type("_exec"),
[lambda: 0, lambda: PushVector([], int)]))
if __name__ == "__main__":
evo = SimplePushGPEvolver(n_jobs=-1,
verbose=1,
atom_generators=atom_generators,
initial_max_genome_size=400,
selection_method='lexicase')
evo.fit(error_function, 1, ['_vector_integer'])
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'])
def error_function(program, debug=False):
errors = []
for case in training_set:
interpreter = PushInterpreter()
interpreter.run(program, [case[0]], [], debug)
result = interpreter.state.stdout
if result is None:
errors.append(1e5)
else:
errors.append(levenshtein_distance(case[1], result))
return errors
atom_generators = list(merge_sets(
get_instructions_by_pysh_type("_integer"),
get_instructions_by_pysh_type("_boolean"),
get_instructions_by_pysh_type("_string"),
get_instructions_by_pysh_type("_char"),
get_instructions_by_pysh_type("_exec"),
get_instructions_by_pysh_type("_print"),
[lambda: Character('!')]
))
if __name__ == "__main__":
evo = SimplePushGPEvolver(n_jobs=-1, verbose=1,
atom_generators=atom_generators,
initial_max_genome_size=400,
max_generations=300)
evo.fit(error_function, 1, [])
for case in cases:
interpreter = PushInterpreter()
output = interpreter.run(program, [case], ['_vector_boolean'],
debug)[0]
target = prepend_zero(case)
if output is None:
errors.append(1e5)
if len(output) != len(target):
errors.append(1e4)
else:
errors.append(levenshtein_distance(output, target))
return errors
atom_generators = list(
merge_sets(get_instructions_by_pysh_type('_boolean'),
get_instructions_by_pysh_type('_vector'),
get_instructions_by_pysh_type('_exec')))
mut = UniformMutation(rate=0.01)
alt = Alternation(rate=0.01, 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)
def get_instructions_by_pysh_type_invd(self):
lst = ri.get_instructions_by_pysh_type('_foo_bar')
self.assertEqual(lst, [])
def get_instructions_by_pysh_type_strd(self):
lst = ri.get_instructions_by_pysh_type('_boolean')
self.assertIsInstance(lst, list)
self.assertTrue(len(lst) > 0)
self.assertIsInstance(lst[0], Instruction)
interpreter = PushInterpreter()
output = interpreter.run(program, [case], ['_vector_integer'])[0]
target = [x - 1 for x in case]
if output is None:
errors.append(1e5)
elif len(output) != len(target):
errors.append(1e4)
else:
rmse = np.linalg.norm(np.array(output) -
np.array(target)) / np.sqrt(len(output))
errors.append(rmse)
return errors
atom_generators = list(
merge_sets(get_instructions_by_pysh_type('_integer'),
get_instructions_by_pysh_type('_vector'),
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,
selection_method='epsilon_lexicase',
initial_max_genome_size=300,
population_size=500,
max_generations=300,
training_set = generate_cases(50)
testing_set = generate_cases(50)
def error_function(program, debug=False):
errors = []
for case in training_set:
interpreter = PushInterpreter()
result = interpreter.run(program, [case[0]], ['_float'], debug)[0]
if result is None:
errors.append(1e5)
else:
errors.append((result - case[1])**2)
return errors
atom_generators = list(
merge_sets(get_instructions_by_pysh_type("_integer"),
get_instructions_by_pysh_type("_float"),
get_instructions_by_pysh_type("_vector_float"),
get_instructions_by_pysh_type("_exec"),
[lambda: PushVector([], float)]))
if __name__ == "__main__":
evo = SimplePushGPEvolver(n_jobs=-1,
verbose=1,
atom_generators=atom_generators,
initial_max_genome_size=400)
evo.fit(error_function, 1, ['_float'])
