def test_roundClass(self):
network = MoodNeuralNetwork()
self.assertEqual(0, int(network.roundClass(0)))
self.assertEqual(41, int(network.roundClass(0.999)))
self.assertEqual(8, int(network.roundClass(0.2)))
self.assertEqual(12, int(network.roundClass(0.3)))
self.assertEqual(20, int(network.roundClass(0.5)))
self.assertEqual(29, int(network.roundClass(0.7)))
def predict(self):
weightDict, biasDict = self.getWeightBiasDictionaries()
model = MoodNeuralNetwork(weights=weightDict, biases=biasDict)
input_data, mood_data = self.transformUserData(1)
output = model.feedforward(input_data[0])
mood = model.roundClass(output)
return mood
def test_feedforward(self):
weightDict, biasDict = {}, {}
for i in range(208):
weightDict['weight' + str(i)] = i/8
if i < 21:
biasDict['bias' + str(i)] = i/8
network = MoodNeuralNetwork(weights=weightDict, biases=biasDict)
sample_data = [2,1,4,5,6,2,6,7,3,6,6]
self.assertEqual(41, network.roundClass(network.feedforward(sample_data)))
weightDict, biasDict = {}, {}
for i in range(208):
weightDict['weight' + str(i)] = i/208
if i < 21:
biasDict['bias' + str(i)] = i/208
network = MoodNeuralNetwork(weights=weightDict, biases=biasDict)
sample_data = list(range(11))
self.assertEqual(39, network.roundClass(network.feedforward(sample_data)))
network = MoodNeuralNetwork()
sample_data = [4.51,0,0,0,0,0,0,0,-1,8,35]
self.assertEqual(25, network.roundClass(network.feedforward(sample_data)))
sample_data = [15,25,50,0,0,0,0,0,-1,-100,0]
self.assertEqual(33, network.roundClass(network.feedforward(sample_data)))
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)
def predict(self):
weightDict, biasDict = self.getWeightBiasDictionaries()
model = MoodNeuralNetwork(weights=weightDict, biases=biasDict)
input_data, mood_data = self.transformUserData(1)
try:
output = model.feedforward(input_data[0])
except:
return -1, 0
mood = model.roundClass(output)
self.updateMoodPrediction(mood)
profile = self.user.profile
obs = Observation.objects.filter(
user__user__username=profile.user.username)
print("\n\n\n\n", obs, "\n\n\n\n")
return mood, len(obs)