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):
"""
:param config: Either a config.Config object or path to a configuration file.
"""
# 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
print(self.config.genotype.__name__)
## Check if we have a society directory defined and may be this a continuation on an existing run.
if self.config.society_directory != None:
## Check if latest society file is available
if os.path.isdir(self.config.society_directory):
print("Society Directory")
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.
self.population = self._create_population()
self._speciate(self.population)
