:py:mod:`anomalib.models.components.stats.kde`
==============================================

.. py:module:: anomalib.models.components.stats.kde

.. autoapi-nested-parse::

   Gaussian Kernel Density Estimation.



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   anomalib.models.components.stats.kde.GaussianKDE




.. py:class:: GaussianKDE(dataset: Optional[torch.Tensor] = None)

   Bases: :py:obj:`anomalib.models.components.base.DynamicBufferModule`

   Gaussian Kernel Density Estimation.

   :param dataset: Dataset on which to fit the KDE model. Defaults to None.
   :type dataset: Optional[Tensor], optional

   .. py:method:: forward(self, features: torch.Tensor) -> torch.Tensor

      Get the KDE estimates from the feature map.

      :param features: Feature map extracted from the CNN
      :type features: Tensor

      Returns: KDE Estimates


   .. py:method:: fit(self, dataset: torch.Tensor) -> None

      Fit a KDE model to the input dataset.

      :param dataset: Input dataset.
      :type dataset: Tensor

      :returns: None


   .. py:method:: cov(tensor: torch.Tensor) -> torch.Tensor
      :staticmethod:

      Calculate the unbiased covariance matrix.

      :param tensor: Input tensor from which covariance matrix is computed.
      :type tensor: Tensor

      :returns: Output covariance matrix.