def show(title, a, b, c, d):
n = Neuron(0.0, a, b, c, d)
spike_train = []
for i in range(1000):
n.current = 0.0 if i < 100 or i > 800 else 10.0
spike_train.append((1.0 * i, n.current, n.v, n.u))
print('{0:d}\t{1:f}\t{2:f}\t{3:f}'.format(i, n.current, n.v, n.u))
n.advance()
visualize.plot_spikes(spike_train, view=False, title=title)
def show(title, a, b, c, d):
n = Neuron(0.0, a, b, c, d)
spike_train = []
for i in range(1000):
n.current = 0.0 if i < 100 or i > 800 else 10.0
spike_train.append((1.0 * i, n.current, n.v, n.u))
print('%d\t%f\t%f\t%f' % (i, n.current, n.v, n.u))
n.advance()
visualize.plot_spikes(spike_train, view=True, title=title)
def show(title, a, b, c, d):
n = Neuron(0.0, a, b, c, d)
spike_train = []
for i in range(1000):
n.current = 0.0 if i < 100 or i > 800 else 10.0
spike_train.append((1.0 * i, n.current, n.v, n.u))
print('{0:d}\t{1:f}\t{2:f}\t{3:f}'.format(i, n.current, n.v, n.u))
n.advance()
visualize.plot_spikes(spike_train, view=False, title=title)
self.__has_fired = False
self.current = self.__bias
potential = property(lambda self: self.__v, doc = 'Membrane potential')
has_fired = property(lambda self: self.__has_fired,
doc = 'Indicates whether the neuron has fired')
class Synapse:
""" A synapse indicates the connection strength between two neurons (or itself) """
def __init__(self, source, dest, weight):
self.__weight = weight
self.__source = source
self.__dest = dest
def advance(self):
'Advances time in 1 ms.'
if self.__source.has_fired:
self.__dest.current += self.__weight # dest.current or dest.__v ?
if __name__ == '__main__':
from neat import visualize
n = Neuron(10)
neuron_membrane = []
for i in range(1000):
neuron_membrane.append(n.potential)
#print '%d\t%f' % (i, n.potential)
n.advance()
visualize.plot_spikes(neuron_membrane)
def create_phenotype(chromosome):
""" Receives a chromosome and returns its phenotype (a neural network) """
neurons = {}
input_neurons = []
output_neurons = []
for ng in chromosome.node_genes:
neurons[ng.id] = Neuron(ng.bias)
if ng.type == 'INPUT':
input_neurons.append(neurons[ng.id])
elif ng.type == 'OUTPUT':
output_neurons.append(neurons[ng.id])
synapses = [Synapse(neurons[cg.innodeid], neurons[cg.outnodeid], cg.weight) \
for cg in chromosome.conn_genes if cg.enabled]
return Network(neurons, input_neurons, output_neurons, synapses)
if __name__ == '__main__':
from neat import visualize
n = Neuron(10)
spike_train = []
for i in range(1000):
spike_train.append(n.potential)
#print '%d\t%f' % (i, n.potential)
n.advance()
visualize.plot_spikes(spike_train)
neurons = property(lambda self: self.__neurons.values())
def create_phenotype(chromosome):
""" Receives a chromosome and returns its phenotype (a neural network) """
neurons = {}
input_neurons = []
output_neurons = []
for ng in chromosome.node_genes:
neurons[ng.id] = Neuron(ng.bias)
if ng.type == 'INPUT':
input_neurons.append(neurons[ng.id])
elif ng.type == 'OUTPUT':
output_neurons.append(neurons[ng.id])
synapses = [Synapse(neurons[cg.innodeid], neurons[cg.outnodeid], cg.weight) \
for cg in chromosome.conn_genes if cg.enabled]
return Network(neurons, input_neurons, output_neurons, synapses)
if __name__ == '__main__':
from neat import visualize
n = Neuron(10)
spike_train = []
for i in range(1000):
spike_train.append(n.potential)
print '%d\t%f' % (i, n.potential)
n.advance()
visualize.plot_spikes(spike_train)