def visualize(self):
self.ubi = Ubigraph(self.C)
# Color the input and output neurons
self.ubi.set_properties({
"color": "#1E90FF"
}, self.input_neurons)
self.ubi.set_properties({
"color": "#FFD700"
}, self.output_neurons)
class Brain(object):
"""
The brain class for openbrain.
"""
def __init__(self, num_input, num_hidden, num_output, density=0.5):
"""
Constructs an open brain object.
:param num_input: The number of inputs
:param num_output: The number of outputs
:param num_hidden: The number of hidden neurons.
"""
self.initialize_graph(num_input,num_hidden, num_output, density=0.5)
self.visualize()
def initialize_graph(self, num_input, num_hidden, num_output,density):
"""
Initializes the open brain graph.
:param num_input:
:param num_hidden:
:param num_output:
:param density:
:return:
"""
self.num_neurons = num_input + num_output + num_hidden
self.C = np.zeros((self.num_neurons, self.num_neurons))
adjacency_list = self.define_toplogy(num_input, num_hidden, num_output, density)
for i, adjacent in enumerate(adjacency_list):
for node in adjacent:
self.C[i, node] = np.random.rand(1)*0.9
self.C = gpu.garray(self.C)
def define_toplogy(self, num_input, num_hidden, num_output, density):
"""
Defines the topology of the OpenBrain network.
:param num_input:
:param num_hidden:
:param num_output:
:param density:
:return:
"""
topo = networkx.DiGraph(networkx.watts_strogatz_graph(self.num_neurons, 5, density, seed=None)).to_directed()
adjacency_list = topo.adjacency_list()
# Pick the output neurons to be those with highest in degree
in_deg = np.array([topo.in_degree(x) for x,_ in enumerate(adjacency_list)])
self.output_neurons = np.argpartition(in_deg, -num_output)[-num_output:]
print(self.output_neurons)
print([topo.in_degree(x) for x in self.output_neurons])
# Pick the input neurons to be those with highest out degree
out_deg = np.array([topo.out_degree(x) if x not in self.output_neurons else -1
for x,_ in enumerate(adjacency_list)])
self.input_neurons = np.argpartition(out_deg, -num_input)[-num_input:]
# Output neurons do not fire out.
for adjacent_neurons in adjacency_list:
for out_neuron in self.output_neurons:
if out_neuron in adjacent_neurons:
adjacent_neurons.remove(out_neuron)
# Disconnect input -> output
for out in self.output_neurons:
for inp in self.input_neurons:
if out in adjacency_list[inp]: adjacency_list[inp].remove(out)
if inp in adjacency_list[out]: adjacency_list[out].remove(inp)
for i, adjacent in enumerate(adjacency_list):
if i not in self.input_neurons and i not in self.output_neurons:
for n in adjacent:
if i in adjacency_list[n]:
if np.random.rand(1)>0.5:
adjacent.remove(n)
else:
adjacency_list[n].remove(i)
# Let nothing enter the input neurons
for inp in self.input_neurons:
adjacency_list[inp] = []
return adjacency_list
def visualize(self):
self.ubi = Ubigraph(self.C)
# Color the input and output neurons
self.ubi.set_properties({
"color": "#1E90FF"
}, self.input_neurons)
self.ubi.set_properties({
"color": "#FFD700"
}, self.output_neurons)
