def test_setBias(self):
biasDict = {}
for i in range(21):
biasDict['bias' + str(i)] = np.random.normal()
model = MoodNeuralNetwork()
self.assertNotEqual(biasDict, model.getBiases())
self.assertTrue(model.setBias(biasDict))
self.assertEqual(biasDict, model.getBiases())
def test_getBiases(self):
weightDict, biasDict = {}, {}
for i in range(208):
weightDict['weight' + str(i)] = np.random.normal()
if i < 21:
biasDict['bias' + str(i)] = np.random.normal()
model = MoodNeuralNetwork(weights=weightDict, biases=biasDict)
self.assertEqual(biasDict, model.getBiases())
self.assertTrue(model.getBiases())
def setWeightsBias(self, biases_list=False):
if biases_list:
if len(biases_list) != 21:
return False
self.bias_int_list = ",".join(str(x) for x in biases_list)
else:
model = MoodNeuralNetwork()
biasDict = model.getBiases()
biases = []
for i in range(len(biasDict)):
biases.append(biasDict["bias" + str(i)])
self.setWeightsBias(biases)
return True
def retrain(self):
weightDict, biasDict = self.getWeightBiasDictionaries()
model = MoodNeuralNetwork(weights=weightDict, biases=biasDict)
input_data, mood_data = self.transformUserData(7)
model.train(input_data, mood_data)
weightDict = model.getWeights()
weights = []
for i in range(len(weightDict)):
weights.append(weightDict["weight" + str(i)])
self.setWeightsWeights(weights)
biasDict = model.getBiases()
biases = []
for i in range(len(biasDict)):
biases.append(biasDict["bias" + str(i)])
self.setWeightsBias(biases)
return True
