def test_adjust_weight(self):
backpropagator = Backpropagator()
output = ReceiveAllNeuron()
hidden_1 = ReceiveAllNeuron()
hidden_2 = ReceiveAllNeuron()
hidden_1.connect_to(output)
hidden_1.out_connections_list[0].weight = -0.75
hidden_2.connect_to(output)
hidden_2.out_connections_list[0].weight = -0.25
hidden_1.receive_signal(0.434719)
hidden_2.receive_signal(0.434719)
output.error = 0.09754925
hidden_1.error = -0.01745285
hidden_2.error = -0.00581762
connection_1 = hidden_1.out_connections_list[0]
backpropagator.adjust_weight(connection_1, 1.1)
# -0.75 + (1.1 * 0.09754925 * -0.45525)
self.assertAlmostEqual(connection_1.weight, -0.68486637)
connection_2 = hidden_2.out_connections_list[0]
backpropagator.adjust_weight(connection_2, 1.1)
# -0.25 + (1.1 * 0.09754925 * -0.15175)
self.assertAlmostEqual(connection_2.weight, -0.18486637)
def test_hiddenError(self):
backpropagator = Backpropagator()
output = ReceiveAllNeuron()
hidden_1 = ReceiveAllNeuron()
hidden_2 = ReceiveAllNeuron()
hidden_1.connect_to(output)
hidden_1.out_connections_list[0].weight = -0.75
hidden_2.connect_to(output)
hidden_2.out_connections_list[0].weight = -0.25
# sigmoid(0.434719) == 0.6070000
hidden_1.receive_signal(0.434719)
hidden_2.receive_signal(0.434719)
# sigmoid(0.434719) * -0.75 + sigmoid(0.434719) * -0.25 == -0.607
# sigmoid(-0.607) == 0.352744
# 0.78 - 0.352744 == 0.3527438
output.error = backpropagator.output_error(output, 0.78)
self.assertAlmostEqual(output.error, 0.09754925)
hidden_1.error = backpropagator.hidden_error(hidden_1)
hidden_2.error = backpropagator.hidden_error(hidden_2)
# 0.6070000 * (1 - 0.6070000)*(-0.75 * 0.09754925)
self.assertAlmostEqual(hidden_1.error, -0.01745285)
# 0.6070000 * (1 - 0.6070000)*(-0.25 * 0.09754925)
self.assertAlmostEqual(hidden_2.error, -0.00581762)
