original_docstring: 
      Converts ndarray into timeseries format and supervised data format. Takes first TIME_STEPS
      number of rows as input and sets the TIME_STEPS+1th data as corresponding output and so on.
      :param mat: ndarray which holds the dataset
      :param y_col_index: index of column which acts as output
      :return: returns two ndarrays-- input and output in format suitable to feed
      to LSTM.
      
docstring: Converts ndarray into timeseries format and supervised data format. Takes first TIME_STEPS
  number of rows as input and sets the TIME_STEPS+1th data as corresponding output and so on.
docstring_tokens: Converts ndarray into timeseries format and supervised data format . Takes first TIME_STEPS number of rows as input and sets the TIME_STEPS + 1th data as corresponding output and so on .
code: def build_timeseries(mat, y_col_index):
      """
      Converts ndarray into timeseries format and supervised data format. Takes first TIME_STEPS
      number of rows as input and sets the TIME_STEPS+1th data as corresponding output and so on.
      :param mat: ndarray which holds the dataset
      :param y_col_index: index of column which acts as output
      :return: returns two ndarrays-- input and output in format suitable to feed
      to LSTM.
      """
      # total number of time-series samples would be len(mat) - TIME_STEPS
      dim_0 = mat.shape[0] - TIME_STEPS
      dim_1 = mat.shape[1]
      x = np.zeros((dim_0, TIME_STEPS, dim_1))
      y = np.zeros((dim_0,))
      print("dim_0",dim_0)
      for i in tqdm_notebook(range(dim_0)):
          x[i] = mat[i:TIME_STEPS+i]
          y[i] = mat[TIME_STEPS+i, y_col_index]
  #         if i < 10:
  #           print(i,"-->", x[i,-1,:], y[i])
      print("length of time-series i/o",x.shape,y.shape)
      return x, y
code_tokens: def build_timeseries ( mat , y_col_index ) : dim_0 = mat . shape [ 0 ] - TIME_STEPS dim_1 = mat . shape [ 1 ] x = np . zeros ( ( dim_0 , TIME_STEPS , dim_1 ) ) y = np . zeros ( ( dim_0 , ) ) print ( "dim_0" , dim_0 ) for i in tqdm_notebook ( range ( dim_0 ) ) : x [ i ] = mat [ i : TIME_STEPS + i ] y [ i ] = mat [ TIME_STEPS + i , y_col_index ] print ( "length of time-series i/o" , x . shape , y . shape ) return x , y
short_docstring: Converts ndarray into timeseries format and supervised data format.
short_docstring_tokens: Converts ndarray into timeseries format and supervised data format .
comment: """
      Converts ndarray into timeseries format and supervised data format. Takes first TIME_STEPS
      number of rows as input and sets the TIME_STEPS+1th data as corresponding output and so on.
      :param mat: ndarray which holds the dataset
      :param y_col_index: index of column which acts as output
      :return: returns two ndarrays-- input and output in format suitable to feed
      to LSTM.
      """$SEP$# total number of time-series samples would be len(mat) - TIME_STEPS$SEP$#         if i < 10:$SEP$#           print(i,"-->", x[i,-1,:], y[i])