o yi @s<ddlmZddlmZddlmZmZGdddeZdS))Tensor)_concordance_corrcoef_compute)PearsonCorrCoef_final_aggregationc@seZdZdZdefddZdS)ConcordanceCorrCoefaComputes concordance correlation coefficient that measures the agreement between two variables. It is defined as. .. math:: \rho_c = \frac{2 \rho \sigma_x \sigma_y}{\sigma_x^2 + \sigma_y^2 + (\mu_x - \mu_y)^2} where :math:`\mu_x, \mu_y` is the means for the two variables, :math:`\sigma_x^2, \sigma_y^2` are the corresponding variances and \rho is the pearson correlation coefficient between the two variables. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): either single output float tensor with shape ``(N,)`` or multioutput float tensor of shape ``(N,d)`` - ``target`` (:class:`~torch.Tensor`): either single output float tensor with shape ``(N,)`` or multioutput float tensor of shape ``(N,d)`` As output of ``forward`` and ``compute`` the metric returns the following output: - ``concordance`` (:class:`~torch.Tensor`): A scalar float tensor with the concordance coefficient(s) for non-multioutput input or a float tensor with shape ``(d,)`` for multioutput input 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 ConcordanceCorrCoef >>> import torch >>> target = torch.tensor([3, -0.5, 2, 7]) >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> concordance = ConcordanceCorrCoef() >>> concordance(preds, target) tensor(0.9777) Example (multi output regression): >>> from torchmetrics import ConcordanceCorrCoef >>> import torch >>> target = torch.tensor([[3, -0.5], [2, 7]]) >>> preds = torch.tensor([[2.5, 0.0], [2, 8]]) >>> concordance = ConcordanceCorrCoef(num_outputs=2) >>> concordance(preds, target) tensor([0.7273, 0.9887]) returncCs|jdkr |jdks|jdkr-|jjdkr-t|j|j|j|j|j|j \}}}}}}n|j}|j}|j}|j}|j}|j }t ||||||S)zFComputes final concordance correlation coefficient over metric states.) num_outputsmean_xnumelndimrmean_yvar_xvar_ycorr_xyn_totalr)selfr r rrrrrW/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/regression/concordance.pycomputeAs.zConcordanceCorrCoef.computeN)__name__ __module__ __qualname____doc__rrrrrrrs,rN)torchr.torchmetrics.functional.regression.concordancertorchmetrics.regression.pearsonrrrrrrrs