def build_NN(self, sample):
nn_sample = []
##initial the three layer network for every sample
input_layer = Layer(lambda x: x, self.input_cells)
hidden_layer = Layer(tanh, self.hidden_cells)
output_layer = Layer(sigmoid, self.output_cells)
# build input layer
input_vec = sample[:3]
input_layer.set_net_vals_from_vec(input_vec)
input_layer.active_cells()
# build hidden layer
hidden_net_vec = numpy.dot(self.w_ih, input_vec)
hidden_layer.set_net_vals_from_vec(hidden_net_vec)
# active hidden layer cells
hidden_layer.active_cells()
hid_out_vec = hidden_layer.get_active_val_as_vec()
# build output layer
output_vec = numpy.dot(self.w_hj, hid_out_vec)
output_layer.set_net_vals_from_vec(output_vec)
# active output layer cells
output_layer.active_cells()
type = sample[3]
base = numpy.zeros(3)
base[type - 1] = 1
base = numpy.asarray(base)
nn_sample.append(input_layer)
nn_sample.append(hidden_layer)
nn_sample.append(output_layer)
nn_sample.append(base)
return nn_sample
