a  d@sddlmZmZmZmZddlmZmZddlm Z m Z ddl m Z ddl mZddlmZddlmZmZesvdgZGd d d e Zd S) )AnyOptionalSequenceUnion)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 <e e d d fd d Ze e d dddZe dddZdeee ee feeedddZZS)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_sumN)pkwargsreturnc sTtjfi|t|ttfr(|dks6td|||_|jdtddddS)Nz>Argument ``p`` must be a float or int greater than 1, but got rrsum)defaultZdist_reduce_fx) super__init__ isinstancefloatintr rZ add_stater)selfrr __class__j/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/regression/minkowski.pyr@s zMinkowskiDistance.__init__)predstargetsrcCs t|||j}|j|7_dS)z*Update state with predictions and targets.N)r rr)r"r'r(rr%r%r&updateHszMinkowskiDistance.update)rcCst|j|jS)zCompute metric.)rrr)r"r%r%r&computeMszMinkowskiDistance.compute)valaxrcCs |||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) )Z_plot)r"r+r,r%r%r&plotQs(r)NN)__name__ __module__ __qualname____doc__rrbool__annotations__rrrr rrrr)r*rrr rr- __classcell__r%r%r#r&rs  rN)typingrrrrZtorchrrZ,torchmetrics.functional.regression.minkowskirr Ztorchmetrics.metricr Z!torchmetrics.utilities.exceptionsr Ztorchmetrics.utilities.importsr Ztorchmetrics.utilities.plotr rZ__doctest_skip__rr%r%r%r&s