artlib.cvi.iCVIFuzzyArt
=======================

.. py:module:: artlib.cvi.iCVIFuzzyArt

.. autoapi-nested-parse::

   iCVI Fuzzy ART

   .. # da Silva, Leonardo Enzo Brito, Nagasharath Rayapati, and Donald C. Wunsch.
   .. # "iCVI-ARTMAP: using incremental cluster validity indices and adaptive resonance
   .. # theory reset mechanism to accelerate validation and achieve multiprototype
   .. # unsupervised representations."
   .. # IEEE Transactions on Neural Networks and Learning Systems 34.12 (2022): 9757-9770.

   The original matlab code can be found at
   https://github.com/ACIL-Group/iCVI-toolbox/tree/master
    The formulation is available at
   scholarsmine.mst.edu/cgi/viewcontent.cgi?article=3833&context=doctoral_dissertations
    Pages 314-316 and 319-320 Extended icvi offline mode can be found at
   https://ieeexplore.ieee.org/document/9745260

   .. bibliography:: ../../references.bib
      :filter: citation_key == "da2022icvi"



Classes
-------

.. autoapisummary::

   artlib.cvi.iCVIFuzzyArt.iCVIFuzzyART


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

.. py:class:: iCVIFuzzyART(rho: float, alpha: float, beta: float, validity: int, offline: bool = True)

   Bases: :py:obj:`artlib.elementary.FuzzyART.FuzzyART`


   ICVI Fuzzy Art For Clustering.

   .. # da Silva, Leonardo Enzo Brito, Nagasharath Rayapati, and Donald C. Wunsch.
   .. # "iCVI-ARTMAP: using incremental cluster validity indices and adaptive resonance
   .. # theory reset mechanism to accelerate validation and achieve multiprototype
   .. # unsupervised representations."
   .. # IEEE Transactions on Neural Networks and Learning Systems
   .. # 34.12 (2022): 9757-9770.

   .. bibliography:: ../../references.bib
      :filter: citation_key == "da2022icvi"



   .. py:attribute:: CALINSKIHARABASZ
      :value: 1



   .. py:attribute:: offline
      :value: True



   .. py:method:: iCVI_match(x, w, c_, params, cache)

      Apply iCVI (incremental Cluster Validity Index) matching criteria.

      :param x: Data sample.
      :type x: np.ndarray
      :param w: Cluster weight.
      :type w: np.ndarray
      :param c_: Cluster index.
      :type c_: int
      :param params: Dictionary containing algorithm parameters.
      :type params: dict
      :param cache: Cache used for storing intermediate results.
      :type cache: dict

      :returns: True if the new criterion value is better than the previous one,
                False otherwise.
      :rtype: bool



   .. py:method:: fit(X: numpy.ndarray, y: Optional[numpy.ndarray] = None, match_reset_func: Optional[Callable] = None, max_iter=1, match_tracking: Literal['MT+', 'MT-', 'MT0', 'MT1', 'MT~'] = 'MT+', epsilon: float = 0.0)

      Fit the model to the data.

      :param X: The dataset.
      :type X: np.ndarray
      :param y: Not used. For compatibility.
      :type y: np.ndarray, optional
      :param match_reset_func: A callable accepting the data sample, a cluster weight, the params dict,
                               and the cache dict.
                               Returns True if the cluster is valid for the sample, False otherwise.
      :type match_reset_func: callable, optional
      :param max_iter: Number of iterations to fit the model on the same dataset, by default 1.
      :type max_iter: int, optional
      :param match_tracking: Method for resetting match criterion.
      :type match_tracking: {"MT+", "MT-", "MT0", "MT1", "MT~"}, optional
      :param epsilon: Epsilon value used for adjusting match criterion, by default 0.0.
      :type epsilon: float, optional