artlib.elementary.EllipsoidART
==============================

.. py:module:: artlib.elementary.EllipsoidART

.. autoapi-nested-parse::

   Ellipsoid ART :cite:`anagnostopoulos2001a`, :cite:`anagnostopoulos2001b`.



Classes
-------

.. autoapisummary::

   artlib.elementary.EllipsoidART.EllipsoidART


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

.. py:class:: EllipsoidART(rho: float, alpha: float, beta: float, mu: float, r_hat: float)

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


   Ellipsoid ART for Clustering.

   This module implements Ellipsoid ART as first published in:
   :cite:`anagnostopoulos2001a`, :cite:`anagnostopoulos2001b`.

   .. # Anagnostopoulos, G. C., & Georgiopoulos, M. (2001a).
   .. # Ellipsoid ART and ARTMAP for incremental clustering and classification.
   .. # In Proc. IEEE International Joint Conference on Neural Networks (IJCNN)
   .. # (pp. 1221–1226). volume 2. doi:10.1109/IJCNN.2001.939535.

   .. # Anagnostopoulos, G. C., & Georgiopoulos, M. (2001b).
   .. # Ellipsoid ART and ARTMAP for incremental unsupervised and supervised learning.
   .. # In Aerospace/Defense Sensing, Simulation, and Controls (pp. 293– 304).
   .. # International Society for Optics and Photonics. doi:10.1117/12.421180.

   Ellipsoid ART clusters data in Hyper-ellipsoids. It is highly sensitive to sample
   presentation order as the second sample will determine the orientation of the
   principal axes.



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


      Validate clustering parameters.

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



   .. py:method:: category_distance(i: numpy.ndarray, centroid: numpy.ndarray, major_axis: numpy.ndarray, params: dict) -> float
      :staticmethod:


      Calculate the distance between a sample and the cluster centroid.

      :param i: Data sample.
      :type i: np.ndarray
      :param centroid: Centroid of the cluster.
      :type centroid: np.ndarray
      :param major_axis: Major axis of the cluster.
      :type major_axis: np.ndarray
      :param params: Dictionary containing parameters for the algorithm.
      :type params: dict

      :returns: Distance between the sample and the cluster centroid.
      :rtype: float



   .. 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_2d_ellipsoids() -> List[Tuple[numpy.ndarray, float, float, float]]

      Get the 2D ellipsoids for visualization.

      :returns: Each tuple contains the centroid, width, height, and angle of an ellipsoid.
      :rtype: list of tuple



   .. 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: artlib.common.utils.IndexableOrKeyable, 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