artlib.elementary.BayesianART
=============================

.. py:module:: artlib.elementary.BayesianART

.. autoapi-nested-parse::

   Bayesian ART :cite:`vigdor2007bayesian`.



Classes
-------

.. autoapisummary::

   artlib.elementary.BayesianART.BayesianART


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

.. py:class:: BayesianART(rho: float, cov_init: numpy.ndarray)

   Bases: :py:obj:`artlib.common.BaseART.BaseART`


   Bayesian ART for Clustering.

   This module implements Bayesian ART as first published in:
   :cite:`vigdor2007bayesian`.

    .. # Vigdor, B., & Lerner, B. (2007).
   .. # The Bayesian ARTMAP.
   .. # IEEE Transactions on Neural
   .. # Networks, 18, 1628–1644. doi:10.1109/TNN.2007.900234.

   Bayesian ART clusters data in Bayesian Distributions (Hyper-ellipsoids) and is
   similar to :class:`~artlib.elementary.GaussianART.GaussianART` but differs in that
   it allows arbitrary rotation of the hyper-ellipsoid.



   .. py:attribute:: pi2


   .. py:method:: validate_params(params: dict)
      :staticmethod:


      Validate clustering parameters.

      :param params: Dictionary containing parameters for the algorithm.
      :type params: dict



   .. py:method:: check_dimensions(X: numpy.ndarray)

      Check that the data has the correct dimensions.

      :param X: The dataset.
      :type X: np.ndarray



   .. py:method:: category_choice(i: numpy.ndarray, w: numpy.ndarray, params: dict) -> tuple[float, Optional[dict]]

      Get the activation of the cluster.

      :param i: Data sample.
      :type i: np.ndarray
      :param w: Cluster weight or information.
      :type w: np.ndarray
      :param params: Dictionary containing parameters for the algorithm.
      :type params: dict

      :returns: * *float* -- Cluster activation.
                * *dict, optional* -- Cache used for later processing.



   .. py:method:: match_criterion(i: numpy.ndarray, w: numpy.ndarray, params: dict, cache: Optional[dict] = None) -> Tuple[float, Optional[Dict]]

      Get the match criterion of the cluster.

      :param i: Data sample.
      :type i: np.ndarray
      :param w: Cluster weight or information.
      :type w: np.ndarray
      :param params: Dictionary containing parameters for the algorithm.
      :type params: dict
      :param cache: Cache containing values from previous calculations.
      :type cache: dict, optional

      :returns: * *float* -- Cluster match criterion.
                * *dict* -- Cache used for later processing.



   .. py:method:: match_criterion_bin(i: numpy.ndarray, w: numpy.ndarray, params: dict, cache: Optional[dict] = None, op: Callable = operator.ge) -> tuple[bool, dict]

      Get the binary match criterion of the cluster.

      :param i: Data sample.
      :type i: np.ndarray
      :param w: Cluster weight or information.
      :type w: np.ndarray
      :param params: Dictionary containing parameters for the algorithm.
      :type params: dict
      :param cache: Cache containing values from previous calculations.
      :type cache: dict, optional
      :param op: Operator for comparison, by default operator.ge.
      :type op: callable, optional

      :returns: * *bool* -- Binary match criterion.
                * *dict* -- Cache used for later processing.



   .. py:method:: _match_tracking(cache: Union[List[Dict], Dict], epsilon: float, params: Union[List[Dict], Dict], method: Literal['MT+', 'MT-', 'MT0', 'MT1', 'MT~']) -> bool

      Adjust match tracking based on the method and epsilon value.

      :param cache: Cache containing intermediate results, including the match criterion.
      :type cache: dict
      :param epsilon: Adjustment factor for the match criterion.
      :type epsilon: float
      :param params: Dictionary containing algorithm parameters.
      :type params: dict
      :param method: Match tracking method to use.
      :type method: {"MT+", "MT-", "MT0", "MT1", "MT~"}

      :returns: True if match tracking continues, False otherwise.
      :rtype: bool



   .. py:method:: update(i: numpy.ndarray, w: numpy.ndarray, params: dict, cache: Optional[dict] = None) -> numpy.ndarray

      Get the updated cluster weight.

      :param i: Data sample.
      :type i: np.ndarray
      :param w: Cluster weight or information.
      :type w: np.ndarray
      :param params: Dictionary containing parameters for the algorithm.
      :type params: dict
      :param cache: Cache containing values from previous calculations.
      :type cache: dict, optional

      :returns: Updated cluster weight.
      :rtype: np.ndarray



   .. py:method:: new_weight(i: numpy.ndarray, params: dict) -> numpy.ndarray

      Generate a new cluster weight.

      :param i: Data sample.
      :type i: np.ndarray
      :param params: Dictionary containing parameters for the algorithm.
      :type params: dict

      :returns: Updated cluster weight.
      :rtype: np.ndarray



   .. py:method:: get_cluster_centers() -> List[numpy.ndarray]

      Get the centers of each cluster, used for regression.

      :returns: Cluster centroids.
      :rtype: list of np.ndarray



   .. py:method:: plot_cluster_bounds(ax: matplotlib.axes.Axes, colors: Iterable, linewidth: int = 1)

      Visualize the bounds of each cluster.

      :param ax: Figure axes.
      :type ax: matplotlib.axes.Axes
      :param colors: Colors to use for each cluster.
      :type colors: IndexableOrKeyable
      :param linewidth: Width of boundary line, by default 1.
      :type linewidth: int, optional