o .wi@sddlmZddlmZmZmZmZddlmZddl m Z ddl m Z ddl mZddlmZddlmZmZes=d gZGd d d e Zd S) )Sequence)AnyListOptionalUnion)Tensor) dunn_index)Metric) dim_zero_cat)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEDunnIndex.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<e e ed <e e ed <dd e d e ddffdd Zd e d e ddfddZde fddZddee ee dfdeedefddZZS) DunnIndexa Compute `Dunn Index`_. .. math:: DI_m = \frac{\min_{1\leq i>> import torch >>> from torchmetrics.clustering import DunnIndex >>> data = torch.tensor([[0, 0], [0.5, 0], [1, 0], [0.5, 1]]) >>> labels = torch.tensor([0, 0, 0, 1]) >>> dunn_index = DunnIndex(p=2) >>> dunn_index(data, labels) tensor(2.) Tis_differentiablehigher_is_betterFfull_state_updategplot_lower_bounddatalabelspkwargsreturnNc s<tjdi|||_|jdgdd|jdgdddS)Nrcat)defaultdist_reduce_fxr)super__init__r add_state)selfrr __class__r_/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/clustering/dunn_index.pyrIszDunnIndex.__init__cCs|j||j|dS)z*Update state with predictions and targets.N)rappendr)r!rrrrr$updatePs zDunnIndex.updatecCstt|jt|j|jS)z&Compute mutual information over state.)rr rrr)r!rrr$computeUszDunnIndex.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 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.clustering import DunnIndex >>> data = torch.tensor([[0, 0], [0.5, 0], [1, 0], [0.5, 1]]) >>> labels = torch.tensor([0, 0, 0, 1]) >>> metric = DunnIndex(p=2) >>> metric.update(data, labels) >>> fig_, ax_ = metric.plot(metric.compute()) .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.clustering import DunnIndex >>> metric = DunnIndex(p=2) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.randn(50, 3), torch.randint(0, 2, (50,)))) >>> fig_, ax_ = metric.plot(values) )_plot)r!r(r)rrr$plotYs (r)r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrrr&r'rrrr r r+ __classcell__rrr"r$rs  $     2rN)collections.abcrtypingrrrrtorchr-torchmetrics.functional.clustering.dunn_indexrtorchmetrics.metricr torchmetrics.utilities.datar torchmetrics.utilities.importsr torchmetrics.utilities.plotr r __doctest_skip__rrrrr$s