artlib.optimized.backends.cpp.BinaryFuzzyART
============================================

.. py:module:: artlib.optimized.backends.cpp.BinaryFuzzyART

.. autoapi-nested-parse::

   Fuzzy ART :cite:`carpenter1991fuzzy`.



Classes
-------

.. autoapisummary::

   artlib.optimized.backends.cpp.BinaryFuzzyART.BinaryFuzzyART


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

.. py:class:: BinaryFuzzyART(rho: float)

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


   BinaryFuzzyART for Clustering. optimized with C++

   This module implements BinaryFuzzyART

   FuzzyART is a non-modular clustering model for binary input data which has been
   highly optimized for run-time performance. Fit and predict functions are
   implemented in c++ for efficient execution. This class acts as a wrapper for the
   underlying c++ functions and to provide compatibility with the artlib style and
   usage. Functionally, FuzzyART behaves as a special case of
   :class:`~artlib.elementary.BinaryFuzzyART.BinaryFuzzyART`.



   .. py:method:: _synchronize_cpp_results(labels_a_out: numpy.ndarray, weights_arrays: list[numpy.ndarray], incremental: bool = False)

      Synchronize the python class with the output of the c++ code.

      :param labels_a_out: A 1D numpy array containing the a-side labels from fitting
      :type labels_a_out: np.ndarray
      :param weights_arrays: A 2D numpy array where rows are the Fuzzy ART weights
      :type weights_arrays: np.ndarray
      :param incremental: This flag is set to true when synchronizing after a partial_fit
      :type incremental: bool, default=False



   .. 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, verbose: bool = False, leave_progress_bar: bool = True)

      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 that influences cluster creation.
                               Not used. For compatibility.
      :type match_reset_func: callable, optional
      :param max_iter: Number of iterations to fit the model on the same dataset.
      :type max_iter: int, default=1
      :param match_tracking: Method for resetting match criterion.
                             Not used. For compatibility.
      :type match_tracking: {"MT+", "MT-", "MT0", "MT1", "MT~"}, default="MT+"
      :param epsilon: Epsilon value used for adjusting match criterion.
                      Not used. For compatibility.
      :type epsilon: float, default=0.0
      :param verbose: If True, displays progress of the fitting process.
                      Not used. For compatibility.
      :type verbose: bool, default=False
      :param leave_progress_bar: If True, leaves thge progress of the fitting process. Only used when
                                 verbose=True
                                 Not used. For compatibility.
      :type leave_progress_bar: bool, default=True



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

      Iteratively fit the model to the data.

      :param X: The dataset.
      :type X: np.ndarray
      :param match_reset_func: A callable that influences cluster creation.
                               Not used. For compatibility.
      :type match_reset_func: callable, optional
      :param match_tracking: Method for resetting match criterion.
                             Not used. For compatibility.
      :type match_tracking: {"MT+", "MT-", "MT0", "MT1", "MT~"}, default="MT+"
      :param epsilon: Epsilon value used for adjusting match criterion.
                      Not used. For compatibility.
      :type epsilon: float, default=0.0



   .. py:method:: predict(X: numpy.ndarray, clip: bool = False) -> numpy.ndarray

      Predict labels for the data.

      :param X: Data set A.
      :type X: np.ndarray
      :param clip: clip the input values to be between the previously seen data limits
      :type clip: bool

      :returns: B labels for the data.
      :rtype: np.ndarray