def load_preprocessed_data(self):
    '\n        raw_data is a list that has three components\n        component1) trajectory data for training\n        component2) trajectory data for validation and visualization\n        '
    f = open(self.dataset_path, 'rb')
    raw_data = pickle.load(f)
    f.close()
    counter = 0
    self.train_data = []
    for data in raw_data[0]:
        scaled_data = np.copy(data)
        self.train_data.append(scaled_data)
        counter += int((len(scaled_data) - self.seq_length))
    self.num_batches = int((counter / self.batch_size))
    self.valid_data = []
    for data in raw_data[1]:
        scaled_data = np.copy(data)
        self.valid_data.append(scaled_data)
    self.map = []
    for data in raw_data[2]:
        self.map.append(data)
    self.map_info = []
    for data in raw_data[3]:
        self.map_info.append(data)
raw_data is a list that has three components
component1) trajectory data for training
component2) trajectory data for validation and visualization
def load_preprocessed_data(self):
    '\n        raw_data is a list that has three components\n        component1) trajectory data for training\n        component2) trajectory data for validation and visualization\n        '
    f = open(self.dataset_path, 'rb')
    raw_data = pickle.load(f)
    f.close()
    counter = 0
    self.train_data = []
    for data in raw_data[0]:
        scaled_data = np.copy(data)
        self.train_data.append(scaled_data)
        counter += int((len(scaled_data) - self.seq_length))
    self.num_batches = int((counter / self.batch_size))
    self.valid_data = []
    for data in raw_data[1]:
        scaled_data = np.copy(data)
        self.valid_data.append(scaled_data)
    self.map = []
    for data in raw_data[2]:
        self.map.append(data)
    self.map_info = []
    for data in raw_data[3]:
        self.map_info.append(data)