def mutate(self, nb_intermediate_neuron:int, nb_total_neuron:int,
neuron_types:iter, edges:iter):
"""Return the data received in input, modified according to
mutation settings.
nb_intermediate_neuron: integer equal to number of intermediate neuron.
nb_total_neuron: integer equal to total number of neuron.
neuron_types: iterable of NeuronType.
edges: iterable of 2-tuple describing links between neurons.
"""
MUTATION_RATE = self.mutation_rate
mutation = lambda: random() <= MUTATION_RATE
mutate_nb_neuron = mutation(), mutation()
mutate_neuron_type = mutation(), mutation()
mutate_edges = mutation(), mutation()
del mutation
if any(mutate_nb_neuron):
add, rmv = mutate_nb_neuron
neuron_types = list(neuron_types) # allow modifications
if add:
nb_total_neuron += 1
nb_intermediate_neuron += 1
neuron_types.append(random_choice(NeuronType.xano()))
if commons.log_level() >= logging.INFO:
LOGGER.info('Mutator ' + str(self)
+ ' add new neuron of type '
+ neuron_types[-1].name + '.')
if rmv and nb_intermediate_neuron > 0:
nb_total_neuron -= 1
nb_intermediate_neuron -= 1
target_idx = randrange(0, len(neuron_types))
if commons.log_level() >= logging.INFO:
LOGGER.info('Mutator ' + str(self)
+ ' remove neuron ' + str(target_idx) + ' ('
+ neuron_types[target_idx].name + ').')
del neuron_types[target_idx]
if any(mutate_neuron_type):
modify, swap = mutate_neuron_type
neuron_types = list(neuron_types) # allow modifications
if modify: # modify just one type
target_idx = randrange(0, len(neuron_types))
new_type = random_choice(NeuronType.xano())
if commons.log_level() >= logging.INFO:
LOGGER.info('Mutator ' + str(self) + ' modify type of '
+ str(target_idx) + ' from '
+ neuron_types[target_idx].name + ' to '
+ new_type.name + '.'
)
neuron_types[target_idx] = new_type
if swap: # swap two types in the list
target1_idx = randrange(0, len(neuron_types))
target2_idx = randrange(0, len(neuron_types))
neuron_types[target1_idx], neuron_types[target2_idx] = (
neuron_types[target2_idx], neuron_types[target1_idx]
)
if commons.log_level() >= logging.INFO:
LOGGER.info('Mutator ' + str(self) + ' swaps '
+ str(target1_idx) + ' ('
+ neuron_types[target2_idx].name + ') and '
+ str(target1_idx) + ' ('
+ neuron_types[target2_idx].name + ').'
)
if any(mutate_edges):
add, rmv = mutate_edges
edges = list(edges) # allow modifications
if add: # add one new edge
target1_idx = randrange(1, nb_total_neuron + 1)
target2_idx = randrange(1, nb_total_neuron + 1)
edges.append((target1_idx, target2_idx))
if commons.log_level() >= logging.INFO:
LOGGER.info('Mutator ' + str(self) + ' get an edge '
+ str((target1_idx, target2_idx)) + '.')
if rmv: # remove one existing edge
idx = randrange(0, len(edges))
if commons.log_level() >= logging.INFO:
LOGGER.info('Mutator ' + str(self) + ' lose its edge '
+ str(edges[idx]) + '.')
neuron_types = tuple(neuron_types)
edges = tuple(edges)
return nb_intermediate_neuron, neuron_types, edges
except (KeyboardInterrupt, EOFError):
LOGGER.info('Treatment loop finished through keyboard interruption.')
e.world.deinit()
LOGGER.info('Deinitialization of World')
def run_individual(config):
"""Run an individual simulation"""
from neural_world.incubator import Incubator
individual = Incubator(config).spawn()
print(individual.network_dict)
print('\n'.join(individual.prettyfied_neural_network))
if __name__ == '__main__':
# CLI arguments handling
args = docopt.docopt(__doc__, version=VERSION)
commons.log_level(level=args['--log-level'])
render_png = bool(int(args['--render-png']))
# Configuration
config = Configuration()
assert config.is_valid()
# Run
if args['simulation']:
run_simulation(config, render_png)
elif args['individual']:
run_individual(config)
except (KeyboardInterrupt, EOFError):
LOGGER.info('Treatment loop finished through keyboard interruption.')
e.world.deinit()
LOGGER.info('Deinitialization of World')
def run_individual(config):
"""Run an individual simulation"""
from neural_world.incubator import Incubator
individual = Incubator(config).spawn()
print(individual.network_dict)
print('\n'.join(individual.prettyfied_neural_network))
if __name__ == '__main__':
# CLI arguments handling
args = docopt.docopt(__doc__, version=VERSION)
commons.log_level(level=args['--log-level'])
render_png = bool(int(args['--render-png']))
# Configuration
config = Configuration()
assert config.is_valid()
# Run
if args['simulation']:
run_simulation(config, render_png)
elif args['individual']:
run_individual(config)
