artlib.elementary.QuadraticNeuronART
====================================

.. py:module:: artlib.elementary.QuadraticNeuronART

.. autoapi-nested-parse::

   Quadratic Neuron ART :cite:`su2001application`, :cite:`su2005new`.



Classes
-------

.. autoapisummary::

   artlib.elementary.QuadraticNeuronART.QuadraticNeuronART


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

.. py:class:: QuadraticNeuronART(rho: float, s_init: float, lr_b: float, lr_w: float, lr_s: float)

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


   Quadratic Neuron ART for Clustering.

   This module implements Quadratic Neuron ART as first published in:
   :cite:`su2001application`, :cite:`su2005new`.

   .. # Su, M.-C., & Liu, T.-K. (2001).
   .. # Application of neural networks using quadratic junctions in cluster analysis.
   .. # Neurocomputing, 37, 165 – 175. doi:10.1016/S0925-2312(00)00343-X.

   .. # Su, M.-C., & Liu, Y.-C. (2005).
   .. # A new approach to clustering data with arbitrary shapes.
   .. # Pattern Recognition, 38, 1887 – 1901. doi:10.1016/j.patcog.2005.04.010.

   Quadratic Neuron ART clusters data in Hyper-ellipsoid by utilizing a quadratic
   neural network for activation and resonance.



   .. 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, cache used for later processing.
      :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