weight_function = None
if args.random:
print(
"Weight of links between nodes will be set to random values from range [{}; {}]".format(
args.random[0], args.random[1]
)
)
weight_function = lambda: random.uniform(args.random[0], args.random[1])
elif args.zeros:
print("Weight of links between nodes will be set to 0.")
weight_function = lambda: 0.0
network = NetParser(args.network).parse(weight_function)
if args.vector_limits:
input_vector = RandomInputVectorFactory.create_new(
args.vector_limits[0], args.vector_limits[1], network.layers[0].get_nodes_ids()
)
print(
"Generated vector with values from range [{}; {}]:\n{}".format(
args.vector_limits[0], args.vector_limits[1], input_vector
)
)
else:
input_vectors = [InputVectorParser(vector).parse() for vector in args.vector_file]
if args.kohonen:
network.learning_process(input_vectors, args.coefficient, args.coefficient_half_life, args.turns)
if args.backpropagation:
network.backpropagation_learn(input_vectors, args.coefficient, args.turns)
else:
print("Network response is:")
print(network.calculte_answer(input_vectors[0]))
