def eval_error(output): if isinstance(output, dict): return optimbox(output).error() elif isinstance(output, (float, int)): return float(abs(output)) elif isinstance(output, tuple): return average([ eval_error(elt) for elt in output ]) elif hasattr(output, '__iter__'): return mae(output) else: raise Exception('output must be based on optimbox, float, tuple or list/array')
def eval_error(output): if isinstance(output, dict): return optimbox(output).error() elif isinstance(output, (float, int)): return float(abs(output)) elif isinstance(output, tuple): return average([eval_error(elt) for elt in output]) elif hasattr(output, '__iter__'): return mae(output) else: raise Exception( 'output must be based on optimbox, float, tuple or list/array' )
def mean(self, x): return mae(x)
def mean(self, x): return mae(x)