o y“©ij ã @s¨ddlmZddlZddlmZddlmZddlmZdededeeeffd d „Zdeded e deeeffd d „Z dededefdd„Z dededefdd„Z dS)é)ÚTupleN)ÚTensor)Ú _check_data_shape_to_num_outputs)Ú_check_same_shapeÚpredsÚtargetÚreturncCs&|jdkr ||fS| d¡| d¡fS)Néé)ÚndimÚ unsqueeze)rr©r ú_/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/regression/log_cosh.pyÚ_unsqueeze_tensorss rÚ num_outputscCstt||ƒt|||ƒt||ƒ\}}||}t t |¡t | ¡d¡ d¡ ¡}tj|j d|j d}||fS)aUpdates and returns variables required to compute LogCosh error. Checks for same shape of input tensors. Args: preds: Predicted tensor target: Ground truth tensor Return: Sum of LogCosh error over examples, and total number of examples r r)Údevice) rrrÚtorchÚlogÚexpÚsumÚsqueezeÚtensorÚshaper)rrrÚdiffÚsum_log_cosh_errorÚn_obsr r rÚ_log_cosh_error_updates *rrrcCs || ¡S)z¨Computes Mean Squared Error. Args: sum_squared_error: Sum of LogCosh errors over all observations n_obs: Number of predictions or observations )r)rrr r rÚ_log_cosh_error_compute3s rcCs0t|||jdkr dn|jdd\}}t||ƒS)aCCompute 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. Args: preds: estimated labels with shape ``(batch_size,)`` or `(batch_size, num_outputs)`` target: ground truth labels with shape ``(batch_size,)`` or `(batch_size, num_outputs)`` Return: Tensor with LogCosh error Example (single output regression):: >>> from torchmetrics.functional import log_cosh_error >>> 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(preds, target) tensor(0.3523) Example (multi output regression):: >>> from torchmetrics.functional import log_cosh_error >>> 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(preds, target) tensor([0.9176, 0.4277, 0.2194]) r éÿÿÿÿ)r)rr rr)rrrrr r rÚlog_cosh_error=s ÿ r) ÚtypingrrrÚ(torchmetrics.functional.regression.utilsrÚtorchmetrics.utilities.checksrrÚintrrrr r r rÚs    "