original_docstring:  Calculate rate per iteration for all sessions.
  
          Returns
          -------
          :class:`numpy.ndarray`
              The training rate for each iteration in all sessions
  
          Notes
          -----
          For totals, gaps between sessions can be large so the time difference has to be reset for
          each session's rate calculation.
          
docstring: Calculate rate per iteration for all sessions.
  Returns
docstring_tokens: Calculate rate per iteration for all sessions . Returns
code: def _calc_rate_total(cls):
          """ Calculate rate per iteration for all sessions.
  
          Returns
          -------
          :class:`numpy.ndarray`
              The training rate for each iteration in all sessions
  
          Notes
          -----
          For totals, gaps between sessions can be large so the time difference has to be reset for
          each session's rate calculation.
          """
          logger.debug("Calculating totals rate")
          batchsizes = Session.batch_sizes
          total_timestamps = Session.get_timestamps(None)
          rate = list()
          for sess_id in sorted(total_timestamps.keys()):
              batchsize = batchsizes[sess_id]
              timestamps = total_timestamps[sess_id]
              rate.extend((batchsize * 2) / np.diff(timestamps))
          retval = np.array(rate)
          logger.debug("Calculated totals rate: Item_count: %s", len(retval))
          return retval
code_tokens: def _calc_rate_total ( cls ) : logger . debug ( "Calculating totals rate" ) batchsizes = Session . batch_sizes total_timestamps = Session . get_timestamps ( None ) rate = list ( ) for sess_id in sorted ( total_timestamps . keys ( ) ) : batchsize = batchsizes [ sess_id ] timestamps = total_timestamps [ sess_id ] rate . extend ( ( batchsize * 2 ) / np . diff ( timestamps ) ) retval = np . array ( rate ) logger . debug ( "Calculated totals rate: Item_count: %s" , len ( retval ) ) return retval
short_docstring: Calculate rate per iteration for all sessions.
short_docstring_tokens: Calculate rate per iteration for all sessions .
comment: """ Calculate rate per iteration for all sessions.
  
          Returns
          -------
          :class:`numpy.ndarray`
              The training rate for each iteration in all sessions
  
          Notes
          -----
          For totals, gaps between sessions can be large so the time difference has to be reset for
          each session's rate calculation.
          """