o yiI@sPddlmZddlZddlmZddlmZmZddlmZGdddeZ dS))AnyN)Tensor)_log_cosh_error_compute_log_cosh_error_update)MetriccsreZdZUdZdZdZdZeed<eed<dde de d d ffd d Z d eded d fddZ d efddZ ZS) LogCoshErrora8Compute the `LogCosh Error`_. .. math:: \text{LogCoshError} = \log\left(\frac{\exp(\hat{y} - y) + \exp(\hat{y - y})}{2}\right) Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): Estimated labels with shape ``(batch_size,)`` or ``(batch_size, num_outputs)`` - ``target`` (:class:`~torch.Tensor`): Ground truth labels with shape ``(batch_size,)`` or ``(batch_size, num_outputs)`` As output of ``forward`` and ``compute`` the metric returns the following output: - ``log_cosh_error`` (:class:`~torch.Tensor`): A tensor with the log cosh error Args: num_outputs: Number of outputs in multioutput setting kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example (single output regression):: >>> from torchmetrics import LogCoshError >>> preds = torch.tensor([3.0, 5.0, 2.5, 7.0]) >>> target = torch.tensor([2.5, 5.0, 4.0, 8.0]) >>> log_cosh_error = LogCoshError() >>> log_cosh_error(preds, target) tensor(0.3523) Example (multi output regression):: >>> from torchmetrics import LogCoshError >>> preds = torch.tensor([[3.0, 5.0, 1.2], [-2.1, 2.5, 7.0]]) >>> target = torch.tensor([[2.5, 5.0, 1.3], [0.3, 4.0, 8.0]]) >>> log_cosh_error = LogCoshError(num_outputs=3) >>> log_cosh_error(preds, target) tensor([0.9176, 0.4277, 0.2194]) TFsum_log_cosh_errortotal num_outputskwargsreturnNc shtjdi|t|ts|dkrtd|||_|jdt|dd|jdt ddddS) Nr zDExpected argument `num_outputs` to be an int larger than 0, but got rsum)defaultdist_reduce_fxr r) super__init__ isinstanceint ValueErrorr add_statetorchzerostensor)selfr r  __class__rT/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/regression/log_cosh.pyrDs zLogCoshError.__init__predstargetcCs2t|||j\}}|j|7_|j|7_dS)zUpdate state with predictions and targets. Raises: ValueError: If ``preds`` or ``target`` has multiple outputs when ``num_outputs=1`` N)rr rr )rrr rn_obsrrrupdateMszLogCoshError.updatecCst|j|jS)z!Compute LogCosh error over state.)rrr )rrrrcomputeXszLogCoshError.compute)r )__name__ __module__ __qualname____doc__is_differentiablehigher_is_betterfull_state_updater__annotations__rrrr"r# __classcell__rrrrrs &  r) typingrrr+torchmetrics.functional.regression.log_coshrrtorchmetrics.metricrrrrrrs