artlib.elementary.GaussianART
=============================

.. py:module:: artlib.elementary.GaussianART

.. autoapi-nested-parse::

   Gaussian ART :cite:`williamson1996gaussian`.



Classes
-------

.. autoapisummary::

   artlib.elementary.GaussianART.GaussianART


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

.. py:class:: GaussianART(rho: float, sigma_init: numpy.ndarray, alpha: float = 1e-10)

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


   Gaussian ART for Clustering.

   This module implements Gaussian ART as first published in:
   :cite:`williamson1996gaussian`.

   .. # Williamson, J. R. (1996).
   .. # Gaussian ARTMAP: A Neural Network for Fast Incremental Learning of Noisy
   .. # Multidimensional Maps.
   .. # Neural Networks, 9, 881 – 897. doi:10.1016/0893-6080(95)00115-8.

   Guassian ART clusters data in Gaussian Distributions (Hyper-ellipsoids) and is
   similar to :class:`~artlib.elementary.BayesianART.BayesianART` but differs in that
   the hyper-ellipsoid always have their principal axes square to the coordinate
   frame. It is also faster than :class:`~artlib.elementary.BayesianART.BayesianART`.



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


      Validate clustering parameters.

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



   .. 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:: 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: New 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