o .wi@sddlmZddlmZmZmZddlmZmZddl m Z m Z ddl m Z ddlmZddlmZddlmZmZes?d gZGd d d e Zd S) )Sequence)AnyOptionalUnion)Tensortensor)_minkowski_distance_compute_minkowski_distance_update)Metric)TorchMetricsUserError)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEMinkowskiDistance.plotcseZdZUdZdZeeed<dZeeed<dZ eeed<dZ e ed<e ed <d e d e d d ffdd Zde de d d fddZd e fddZ ddeee ee fdeed efddZZS)MinkowskiDistanceaCompute `Minkowski Distance`_. .. math:: d_{\text{Minkowski}} = \sum_{i}^N (| y_i - \hat{y_i} |^p)^\frac{1}{p} where :math: `y` is a tensor of target values, :math: `\hat{y}` is a tensor of predictions, :math: `\p` is a non-negative integer or floating-point number This metric can be seen as generalized version of the standard euclidean distance which corresponds to minkowski distance with p=2. Args: p: int or float larger than 1, exponent to which the difference between preds and target is to be raised kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torchmetrics.regression import MinkowskiDistance >>> target = tensor([1.0, 2.8, 3.5, 4.5]) >>> preds = tensor([6.1, 2.11, 3.1, 5.6]) >>> minkowski_distance = MinkowskiDistance(3) >>> minkowski_distance(preds, target) tensor(5.1220) Tis_differentiableFhigher_is_betterfull_state_updateplot_lower_boundminkowski_dist_sumpkwargsreturnNc sTtjdi|t|ttfr|dkstd|||_|jdtddddS)Nz>Argument ``p`` must be a float or int greater than 1, but got rrsum)defaultdist_reduce_fx) super__init__ isinstancefloatintr r add_stater)selfrr __class__r^/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/regression/minkowski.pyr As zMinkowskiDistance.__init__predstargetscCs t|||j}|j|7_dS)z*Update state with predictions and targets.N)r rr)r%r)r*rrrr(updateIszMinkowskiDistance.updatecCst|j|jS)zCompute metric.)rrr)r%rrr(computeNszMinkowskiDistance.computevalaxcCs |||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting a single value >>> from torchmetrics.regression import MinkowskiDistance >>> metric = MinkowskiDistance(p=3) >>> metric.update(randn(10,), randn(10,)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting multiple values >>> from torchmetrics.regression import MinkowskiDistance >>> metric = MinkowskiDistance(p=3) >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values) )_plot)r%r-r.rrr(plotRs (r)NN)__name__ __module__ __qualname____doc__rrbool__annotations__rrrr"rrr r+r,rrr rr0 __classcell__rrr&r(rs$  rN)collections.abcrtypingrrrtorchrr,torchmetrics.functional.regression.minkowskirr torchmetrics.metricr !torchmetrics.utilities.exceptionsr torchmetrics.utilities.importsr torchmetrics.utilities.plotr r__doctest_skip__rrrrr(s