def test_loss(self):
network = MoodNeuralNetwork()
sample_data = np.array(list(range(100)))
sample_real = [x + 0.1 for x in list(range(0,100))]
sample_real = np.array(sample_real)
self.assertEqual(0.009999999999999724, network.loss(sample_data, sample_real))
sample_real = list(range(0,100))
self.assertEqual(0, network.loss(sample_data, sample_real))
def test_train(self):
weightDict, biasDict = {}, {}
for i in range(208):
weightDict['weight' + str(i)] = i
if i < 21:
biasDict['bias' + str(i)] = i
network = MoodNeuralNetwork(weights=weightDict, biases=biasDict)
sample_data = np.array([[2,1,4,5,6,2,6,7,3,6,6]])
true = np.array([30])
prediction = network.feedforward(sample_data[0])
loss1 = network.loss(true, prediction)
network.train(sample_data, true)
prediction = network.feedforward(sample_data[0])
loss2 = network.loss(true, network.roundClass(prediction))
self.assertTrue(loss2 < loss1)