def __init__(self, in_n, out_n, hidden):
self.result = {}
self.answers = None
self.timing_controller = TimingController()
self.input_neurons = make_neurons(in_n, self)
self.output_neurons = make_neurons(out_n, self)
self.hidden_neurons = make_neurons(hidden, self)
self.timing_controller.set_neurons(self.input_neurons + self.output_neurons + self.hidden_neurons)
# now go through and connect them all...
connections = make_connections(self.input_neurons, self.hidden_neurons)
connections = connections + make_connections(self.hidden_neurons, self.output_neurons)
connections = connections + make_connections(self.output_neurons, [self])
self.timing_controller.set_connections(connections)
self.done_processing = False
class FeedForward(object):
inputs = []
def __init__(self, in_n, out_n, hidden):
self.result = {}
self.answers = None
self.timing_controller = TimingController()
self.input_neurons = make_neurons(in_n, self)
self.output_neurons = make_neurons(out_n, self)
self.hidden_neurons = make_neurons(hidden, self)
self.timing_controller.set_neurons(self.input_neurons + self.output_neurons + self.hidden_neurons)
# now go through and connect them all...
connections = make_connections(self.input_neurons, self.hidden_neurons)
connections = connections + make_connections(self.hidden_neurons, self.output_neurons)
connections = connections + make_connections(self.output_neurons, [self])
self.timing_controller.set_connections(connections)
self.done_processing = False
def process_input(self, i, a):
# inputs should be the same dimension as our input_neuron array
if len(i) != len(self.input_neurons):
raise IndexError("Input does not have the same number of items as the net has input neurons.")
if len(a) != len(self.output_neurons):
raise IndexError("Answer does not have the same number of items as the net has output neurons.")
# Set our answers
self.answers = a
# proecess our inputs
for x in range(len(i)):
value = i[x]
neuron = self.input_neurons[x]
neuron.receive_signal(signal=value, sender=self)
self.timing_controller.start()
def receive_signal(self, sender=None, signal=None):
# on output, the output neuron should call this with the result.
self.result[sender] = signal
if len(self.result) == len(self.answers):
print "Guesses for answers", self.answers
for x in range(len(self.output_neurons)):
neuron = self.output_neurons[x]
answer = self.answers[x]
# let's get a percentage of error.
# if it's positive, we're under, if it ended up negative, we were over.
correction = 1
if answer == 0:
error_percent = self.result[neuron]
else:
error_percent = answer / self.result[neuron]
if answer <= self.result[neuron]:
correction = 1 - (error_percent / 10)
else:
correction = 1 + (error_percent / 10)
print "Answer was: %s, correct answer was: %s, error was: %s" % (
self.result[neuron],
answer,
error_percent,
)
print correction
neuron.learn(correction)
# we're done, break the while loop in process_input
self.timing_controller.stop()
self.result = {}
