@classmethod
def _prepare_data(cls, data_x, input_shape, data_y, number_of_classes, normalize):
    '\n        Prepare the data for the training, turning it into a numpy array.\n        :param list data_x: data that will be used to train.\n        :param tuple input_shape: the input shape that the data must have to be used as training data.\n        :param list data_y: the labels related to the data used to train.\n        :param int number_of_classes: number of classes of the problem.\n        :param bool normalize: if the data should be normalized (True) or not (False).\n        :return np.array: the data processed.\n        '
    if (len(input_shape) == 2):
        data_x = np.asarray(data_x).reshape((- 1), input_shape[0], input_shape[1])
    elif (len(input_shape) == 3):
        data_x = np.asarray(data_x).reshape((- 1), input_shape[0], input_shape[1], input_shape[2])
    else:
        raise Exception('Only inputs of two and three dimensions were implemented.')
    if normalize:
        data_x = (data_x / np.amax(data_x))
    data_y = keras.utils.to_categorical(data_y).reshape((- 1), number_of_classes)
    return (data_x, data_y)
Prepare the data for the training, turning it into a numpy array.
:param list data_x: data that will be used to train.
:param tuple input_shape: the input shape that the data must have to be used as training data.
:param list data_y: the labels related to the data used to train.
:param int number_of_classes: number of classes of the problem.
:param bool normalize: if the data should be normalized (True) or not (False).
:return np.array: the data processed.
@classmethod
def _prepare_data(cls, data_x, input_shape, data_y, number_of_classes, normalize):
    '\n        Prepare the data for the training, turning it into a numpy array.\n        :param list data_x: data that will be used to train.\n        :param tuple input_shape: the input shape that the data must have to be used as training data.\n        :param list data_y: the labels related to the data used to train.\n        :param int number_of_classes: number of classes of the problem.\n        :param bool normalize: if the data should be normalized (True) or not (False).\n        :return np.array: the data processed.\n        '
    if (len(input_shape) == 2):
        data_x = np.asarray(data_x).reshape((- 1), input_shape[0], input_shape[1])
    elif (len(input_shape) == 3):
        data_x = np.asarray(data_x).reshape((- 1), input_shape[0], input_shape[1], input_shape[2])
    else:
        raise Exception('Only inputs of two and three dimensions were implemented.')
    if normalize:
        data_x = (data_x / np.amax(data_x))
    data_y = keras.utils.to_categorical(data_y).reshape((- 1), number_of_classes)
    return (data_x, data_y)