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

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

.. autoapi-nested-parse::

   Statistical functions.



Submodules
----------
.. toctree::
   :titlesonly:
   :maxdepth: 1

   kde/index.rst
   multi_variate_gaussian/index.rst


Package Contents
----------------

Classes
~~~~~~~

.. autoapisummary::

   anomalib.models.components.stats.GaussianKDE
   anomalib.models.components.stats.MultiVariateGaussian




.. 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(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(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.



.. py:class:: MultiVariateGaussian(n_features, n_patches)

   Bases: :py:obj:`torch.nn.Module`

   Multi Variate Gaussian Distribution.

   .. py:method:: _cov(observations: torch.Tensor, rowvar: bool = False, bias: bool = False, ddof: Optional[int] = None, aweights: torch.Tensor = None) -> torch.Tensor
      :staticmethod:

      Estimates covariance matrix like numpy.cov.

      :param observations: A 1-D or 2-D array containing multiple variables and observations.
                           Each row of `m` represents a variable, and each column a single
                           observation of all those variables. Also see `rowvar` below.
      :type observations: Tensor
      :param rowvar: If `rowvar` is True (default), then each row represents a
                     variable, with observations in the columns. Otherwise, the relationship
                     is transposed: each column represents a variable, while the rows
                     contain observations. Defaults to False.
      :type rowvar: bool
      :param bias: Default normalization (False) is by ``(N - 1)``, where ``N`` is the
                   number of observations given (unbiased estimate). If `bias` is True,
                   then normalization is by ``N``. These values can be overridden by using
                   the keyword ``ddof`` in numpy versions >= 1.5. Defaults to False
      :type bias: bool
      :param ddof: If not ``None`` the default value implied by `bias` is overridden.
                   Note that ``ddof=1`` will return the unbiased estimate, even if both
                   `fweights` and `aweights` are specified, and ``ddof=0`` will return
                   the simple average. See the notes for the details. The default value
                   is ``None``.
      :type ddof: Optional, int
      :param aweights: 1-D array of observation vector weights. These relative weights are
                       typically large for observations considered "important" and smaller for
                       observations considered less "important". If ``ddof=0`` the array of
                       weights can be used to assign probabilities to observation vectors. (Default value = None)
      :type aweights: Tensor

      :returns: The covariance matrix of the variables.


   .. py:method:: forward(embedding: torch.Tensor) -> List[torch.Tensor]

      Calculate multivariate Gaussian distribution.

      :param embedding: CNN features whose dimensionality is reduced via either random sampling or PCA.
      :type embedding: Tensor

      :returns: mean and inverse covariance of the multi-variate gaussian distribution that fits the features.


   .. py:method:: fit(embedding: torch.Tensor) -> List[torch.Tensor]

      Fit multi-variate gaussian distribution to the input embedding.

      :param embedding: Embedding vector extracted from CNN.
      :type embedding: Tensor

      :returns: Mean and the covariance of the embedding.