def __init__()

in multiple_futures_prediction/dataset_ngsim.py [0:0]

• 36 lines of code
• 5 McCabe index (conditional complexity)

  def __init__(self, mat_file:str, t_h:int=30, t_f:int=50, d_s:int = 2, 
   	                enc_size:int=64, use_gru:bool=False, self_norm:bool=False, 
                    data_aug:bool=False, use_context:bool=False, nbr_search_depth:int= 3, 
                    ds_seed:int=1234) -> None:
    self.D = scp.loadmat(mat_file)['traj']
    self.T = scp.loadmat(mat_file)['tracks']
    self.t_h = t_h  # length of track history
    self.t_f = t_f  # length of predicted trajectory
    self.d_s = d_s  # down sampling rate of all sequences
    self.enc_size = enc_size # size of encoder LSTM
    self.grid_size = (13,3) # size of context grid
    self.enc_fac = 2 if use_gru else 1
    self.self_norm = self_norm
    self.data_aug = data_aug
    self.noise = np.array([[0.5, 2.0]])
    self.dt = 0.1*self.d_s
    self.ft_to_m = 0.3048
    self.use_context = use_context
    if self.use_context:
      self.maps = pickle.load(open('data/maps.pkl', 'rb'))
    self.nbr_search_depth = nbr_search_depth

    cache_file = 'multiple_futures_prediction/ngsim_data/NgsimIndex_%s.p'%os.path.basename(mat_file) 
    #build index of [dataset (0 based), veh_id_0b, frame(time)] into a dictionary
    if not os.path.exists(cache_file):          
      self.Index = {}
      print('building index...')
      for i, row in enumerate(self.D):
        key = (int(row[0]-1), int(row[1]-1), int(row[2]))
        self.Index[key] = i
      print('build index done')
      pickle.dump( self.Index, open(cache_file,'wb'))
    else:
      self.Index = pickle.load( open(cache_file,'rb'))

    self.ind_random = np.arange(len(self.D))
    self.seed = ds_seed
    np.random.seed(self.seed)
    np.random.shuffle(self.ind_random)