def predict_extended(self, inputs): """ Predicts targets for given data set. @param data_set: data Set inheriting AbstractDataSet :return: List of predictions, i.e. output of this net for each observation in the data set. """ data_set = NumericalDataSet(inputs) predictions = [] # loop through dataset for observation, _ in data_set.gen_observations(): # make sure it is a numpy array input_arr = np.array(observation) outputs = self.feedforward(input_arr) predictions.append(outputs[-1]) return predictions
def predict_extended(self, inputs): """ Predicts targets for given data set. @param data_set: data Set inheriting AbstractDataSet :return: List of predictions, i.e. output of this net for each observation in the data set. """ data_set = NumericalDataSet(inputs) predictions = [] # loop through dataset for observation, _ in data_set.gen_observations( ): # make sure it is a numpy array input_arr = np.array(observation) outputs = self.feedforward(input_arr) predictions.append(outputs[-1]) return predictions
def test_gen_observations(self): ''' Test generator getting all observations as (1,x) numpy array from input dataset ''' inputs = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4]]) labels = np.array([[0], [1], [2], [3], [4]]) dataSet = NumericalDataSet(inputs, labels) i = 0 for inputs, labels in dataSet.gen_observations(): assert_equal(inputs, i * np.ones((1, 3)), 'wrong input at observation %d' % i) assert_equal(labels, i * np.ones((1, 1)), 'wrong label at observation %d' % i) i = i + 1