def test_setWeights(self):
weightDict = {}
for i in range(208):
weightDict['weight' + str(i)] = np.random.normal()
model = MoodNeuralNetwork()
self.assertNotEqual(weightDict, model.getWeights())
self.assertTrue(model.setWeights(weightDict))
self.assertEqual(weightDict, model.getWeights())
def test_getWeights(self):
weightDict, biasDict = {}, {}
for i in range(208):
weightDict['weight' + str(i)] = np.random.normal()
if i < 21:
biasDict['weight' + str(i)] = np.random.normal()
model = MoodNeuralNetwork(weights=weightDict, biases=biasDict)
self.assertEqual(weightDict, model.getWeights())
self.assertTrue(model.getWeights())
def setWeightsWeights(self, weights_list=False):
if weights_list:
if len(weights_list) != 208:
return False
self.weights_int_list = ",".join(str(x) for x in weights_list)
else:
model = MoodNeuralNetwork()
weightDict = model.getWeights()
weights = []
for i in range(len(weightDict)):
weights.append(weightDict["weight" + str(i)])
self.setWeightsWeights(weights)
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