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#!/usr/bin/python
from fann2 import libfann
connection_rate = 1
learning_rate = 0.7
num_input = 2
num_neurons_hidden = 4
num_output = 1
desired_error = 0.0001
max_iterations = 100000
iterations_between_reports = 1000
ann = libfann.neural_net()
ann.create_sparse_array(connection_rate, (num_input, num_neurons_hidden, num_output))
ann.set_learning_rate(learning_rate)
ann.set_activation_function_output(libfann.SIGMOID_SYMMETRIC_STEPWISE)
ann.train_on_file("xor.data", max_iterations, iterations_between_reports, desired_error)
ann.save("xor_float.net")
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