artlib.common.BaseARTMAP
========================

.. py:module:: artlib.common.BaseARTMAP

.. autoapi-nested-parse::

   Base class for all ARTMAP objects.



Classes
-------

.. autoapisummary::

   artlib.common.BaseARTMAP.BaseARTMAP


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

.. py:class:: BaseARTMAP

   Bases: :py:obj:`sklearn.base.BaseEstimator`, :py:obj:`sklearn.base.ClassifierMixin`, :py:obj:`sklearn.base.ClusterMixin`


   Generic implementation of Adaptive Resonance Theory MAP (ARTMAP)


   .. py:attribute:: map
      :type:  dict[int, int]


   .. py:method:: set_params(**params)

      Set the parameters of this estimator.

      Specific redefinition of `sklearn.BaseEstimator.set_params` for ARTMAP classes.

      :param \*\*params: Estimator parameters.
      :type \*\*params: dict

      :returns: **self** -- Estimator instance.
      :rtype: object



   .. py:method:: map_a2b(y_a: Union[numpy.ndarray, int]) -> Union[numpy.ndarray, int]

      Map an a-side label to a b-side label.

      :param y_a: Side A label(s).
      :type y_a: Union[np.ndarray, int]

      :returns: Side B cluster label(s).
      :rtype: Union[np.ndarray, int]



   .. py:method:: validate_data(X: numpy.ndarray, y: numpy.ndarray)
      :abstractmethod:


      Validate the data prior to clustering.

      :param X: Dataset A.
      :type X: np.ndarray
      :param y: Dataset B.
      :type y: np.ndarray



   .. py:method:: fit(X: numpy.ndarray, y: numpy.ndarray, max_iter=1, match_tracking: Literal['MT+', 'MT-', 'MT0', 'MT1', 'MT~'] = 'MT+', epsilon: float = 1e-10)
      :abstractmethod:


      Fit the model to the data.

      :param X: Dataset A.
      :type X: np.ndarray
      :param y: Dataset B.
      :type y: np.ndarray
      :param max_iter: Number of iterations to fit the model on the same dataset.
      :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 1e-10.
      :type epsilon: float, optional



   .. py:method:: partial_fit(X: numpy.ndarray, y: numpy.ndarray, match_tracking: Literal['MT+', 'MT-', 'MT0', 'MT1', 'MT~'] = 'MT+', epsilon: float = 1e-10)
      :abstractmethod:


      Partial fit the model to the data.

      :param X: Dataset A.
      :type X: np.ndarray
      :param y: Dataset B.
      :type y: np.ndarray
      :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 1e-10.
      :type epsilon: float, optional



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


      Predict labels for the data.

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

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



   .. py:method:: predict_ab(X: numpy.ndarray, clip: bool = False) -> tuple[numpy.ndarray, numpy.ndarray]
      :abstractmethod:


      Predict labels for the data, both A-side and B-side.

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

      :returns: A-side labels for the data, B-side labels for the data.
      :rtype: tuple[np.ndarray, np.ndarray]



   .. py:method:: plot_cluster_bounds(ax: matplotlib.axes.Axes, colors: artlib.common.utils.IndexableOrKeyable, linewidth: int = 1)
      :abstractmethod:


      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



   .. py:method:: visualize(X: numpy.ndarray, y: numpy.ndarray, ax: Optional[matplotlib.axes.Axes] = None, marker_size: int = 10, linewidth: int = 1, colors: Optional[artlib.common.utils.IndexableOrKeyable] = None)
      :abstractmethod:


      Visualize the clustering of the data.

      :param X: Dataset.
      :type X: np.ndarray
      :param y: Sample labels.
      :type y: np.ndarray
      :param ax: Figure axes, by default None.
      :type ax: matplotlib.axes.Axes, optional
      :param marker_size: Size used for data points, by default 10.
      :type marker_size: int, optional
      :param linewidth: Width of boundary line, by default 1.
      :type linewidth: int, optional
      :param colors: Colors to use for each cluster, by default None.
      :type colors: IndexableOrKeyable, optional