Source code for ignite.contrib.metrics.regression.manhattan_distance
from __future__ import division
import torch
from ignite.contrib.metrics.regression._base import _BaseRegression
[docs]class ManhattanDistance(_BaseRegression):
r"""
Calculates the Manhattan Distance:
:math:`\text{MD} = \sum_{j=1}^n (A_j - P_j)`,
where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value.
More details can be found in `Botchkarev 2018`__.
- `update` must receive output of the form `(y_pred, y)` or `{'y_pred': y_pred, 'y': y}`.
- `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.
__ https://arxiv.org/abs/1809.03006
"""
def reset(self):
self._sum_of_errors = 0.0
def _update(self, output):
y_pred, y = output
errors = y.view_as(y_pred) - y_pred
self._sum_of_errors += torch.sum(errors).item()
def compute(self):
return self._sum_of_errors