artlib.elementary.ART2
======================

.. py:module:: artlib.elementary.ART2

.. autoapi-nested-parse::

   ART2 :cite:`carpenter1987art`, :cite:`carpenter1991art`.

   ::

       ==================================================================
       DISCLAIMER: DO NOT USE ART2!!!
       IT DOES NOT WORK
       It is provided for completeness only.
       Stephan Grossberg himself has said ART2 does not work.
       ==================================================================



Classes
-------

.. autoapisummary::

   artlib.elementary.ART2.ART2A


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

.. py:class:: ART2A(rho: float, alpha: float, beta: float)

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


   ART2-A for Clustering.

   This module implements ART2-A as first published in:
   :cite:`carpenter1987art`, :cite:`carpenter1991art`


   .. # Carpenter, G. A., & Grossberg, S. (1987b).
   .. # ART 2: self-organization of stable category recognition codes for analog input
   .. # patterns.
   .. # Appl. Opt., 26, 4919–4930. doi:10.1364/AO.26.004919.

   .. # Carpenter, G. A., Grossberg, S., & Rosen, D. B. (1991b).
   .. # ART 2-A: An adaptive resonance algorithm for rapid category learning and
   .. # recognition.
   .. # Neural Networks, 4, 493 – 504. doi:10.1016/0893-6080(91) 90045-7.


   ART2-A is similar to :class:`~artlib.elementary.ART1.ART1` but designed for analog
   data. This method is implemented for historical purposes and is not recommended
   for use.



   .. 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:: 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