def test_indexer():
indexer0 = Indexer(0)
assert indexer0.get_next() == 0
assert indexer0.get_next() == 1
assert indexer0.get_next() == 2
indexer17 = Indexer(17)
assert indexer17.get_next() == 17
assert indexer17.get_next() == 18
assert indexer17.get_next() == 19
class Neuron(object):
""" A simple sigmoidal neuron """
# TODO: Get rid of these global indexers.
indexer = Indexer(0)
def __init__(self, neuron_type, ID, bias, response, activation_type):
assert neuron_type in ('INPUT', 'OUTPUT', 'HIDDEN')
self.type = neuron_type
self.ID = self.indexer.next(ID)
self.bias = bias
self.response = response
self.activation = activation_functions.get(activation_type)
self._synapses = []
self.output = 0.0 # for recurrent networks all neurons must have an "initial state"
def activate(self):
"""Activates the neuron"""
assert self.type is not 'INPUT'
z = self.bias + self.response * self._update_activation()
return self.activation(z)
def _update_activation(self):
soma = 0.0
for s in self._synapses:
soma += s.incoming()
return soma
def create_synapse(self, s):
self._synapses.append(s)
def __repr__(self):
return '{0:d} {1!s}'.format(self.ID, self.type)
def __init__(self, config, reporters, stagnation):
self.elitism = int(config.get('elitism'))
self.survival_threshold = float(config.get('survival_threshold'))
self.reporters = reporters
self.genome_indexer = Indexer(1)
self.stagnation = stagnation
self.ancestors = {}
def get_new_node_key(self, node_dict):
if not hasattr(self, 'node_indexer'):
self.node_indexer = Indexer(max(list(iterkeys(node_dict))) + 1)
new_id = self.node_indexer.get_next()
assert new_id not in node_dict
return new_id
def test_indexer():
indexer = Indexer(0)
assert indexer.get_next() == 0
assert indexer.get_next() == 1
# TODO: Why doesn't Indexer remember its starting value given in the ctor?
indexer.clear()
assert indexer.get_next() == 1
assert indexer.get_next() == 2
def __init__(self, config, reporters):
self.config = config
params = config.get_type_config(self)
self.elitism = int(params.get('elitism'))
self.survival_threshold = float(params.get('survival_threshold'))
self.reporters = reporters
self.genome_indexer = Indexer(1)
self.stagnation = config.stagnation_type(config, reporters)
def __init__(self, config, initial_population=None):
"""
:param config: Either a config.Config object or path to a configuration file.
:param initial_population: Either an initial set of Genome instances to be used
as the initial population, or None, in which case a randomized set of Genomes
will be created automatically based on the configuration parameters.
"""
# If config is not a Config object, assume it is a path to the config file.
if not isinstance(config, Config):
config = Config(config)
# Configure statistics and reporting as requested by the user.
self.reporters = ReporterSet()
if config.collect_statistics:
self.statistics = StatisticsReporter()
self.add_reporter(self.statistics)
else:
self.statistics = None
if config.report:
self.add_reporter(StdOutReporter())
self.config = config
self.species_indexer = Indexer(1)
self.genome_indexer = Indexer(1)
self.innovation_indexer = InnovationIndexer(0)
self.reproduction = config.reproduction_type(self.config,
self.reporters,
self.genome_indexer,
self.innovation_indexer)
self.species = []
self.generation = -1
self.total_evaluations = 0
# Create a population if one is not given, then partition into species.
if initial_population is None:
initial_population = self._create_population()
self._speciate(initial_population)
def __init__(self, config):
self.indexer = Indexer(1)
self.species = {}
self.to_species = {}
def __init__(self, config):
self.config = config
self.indexer = Indexer(1)
self.species = []
def __init__(self, config, reporters):
self.reporters = reporters
self.indexer = Indexer(1)
self.species = {}
self.genome_to_species = {}
