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