parameters: (self)
return_statement: return self._abs_error_sum / self._samples_num
docstring: Computes the mean absolute error(MAE).
  
          Returns:
              numpy.float64. The computed result.
  
          Raises:
              RuntimeError: If the total number of samples is 0.
docstring_summary: Computes the mean absolute error(MAE).
docstring_tokens: Computes the mean absolute error ( MAE ) .
function: def eval(self):
          """
          Computes the mean absolute error(MAE).
  
          Returns:
              numpy.float64. The computed result.
  
          Raises:
              RuntimeError: If the total number of samples is 0.
          """
          if self._samples_num == 0:
              raise RuntimeError("The 'MAE' can not be calculated, because the number of samples is 0, "
                                 "please check whether your inputs(predicted value, true value) are empty, "
                                 "or has called update method before calling eval method.")
          return self._abs_error_sum / self._samples_num
function_tokens: def eval ( self ) : if self . _samples_num == 0 : raise RuntimeError ( "The 'MAE' can not be calculated, because the number of samples is 0, " "please check whether your inputs(predicted value, true value) are empty, " "or has called update method before calling eval method." ) return self . _abs_error_sum / self . _samples_num