artlib.optimized.backends.torch._TorchSimpleARTMAP
==================================================

.. py:module:: artlib.optimized.backends.torch._TorchSimpleARTMAP


Classes
-------

.. autoapisummary::

   artlib.optimized.backends.torch._TorchSimpleARTMAP._TorchSimpleARTMAP


Functions
---------

.. autoapisummary::

   artlib.optimized.backends.torch._TorchSimpleARTMAP._to_device


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

.. py:function:: _to_device(x: Union[torch.Tensor, numpy.ndarray], device, dtype=torch.float32) -> torch.Tensor

.. py:class:: _TorchSimpleARTMAP(module_a: artlib.common.BaseART.BaseART)

   Bases: :py:obj:`artlib.supervised.SimpleARTMAP.SimpleARTMAP`


   SimpleARTMAP for Classification.

   This module implements SimpleARTMAP as first published in:
   :cite:`gotarredona1998adaptive`.

   .. # Serrano-Gotarredona, T., Linares-Barranco, B., & Andreou, A. G. (1998).
   .. # Adaptive Resonance Theory Microchips: Circuit Design Techniques.
   .. # Norwell, MA, USA: Kluwer Academic Publishers.

   SimpleARTMAP is a special case of :class:`~artlib.supervised.ARTMAP.ARTMAP`
   specifically for classification. It allows the clustering of data samples while
   enforcing a many-to-one mapping from sample clusters to labels. It accepts an
   instantiated :class:`~artlib.common.BaseART.BaseART` module and dynamically adapts
   the vigilance function to prevent resonance when the many-to-one mapping is
   violated. This enables SimpleARTMAP to identify discrete clusters belonging to
   each category label.



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


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

      Fit the model to the data.

      :param X: Data set A.
      :type X: np.ndarray
      :param y: Data set B.
      :type y: np.ndarray
      :param max_iter: Number of iterations to fit the model on the same data set.
      :type max_iter: int, default=1
      :param match_tracking: Method to reset the match.
      :type match_tracking: Literal, default="MT+"
      :param epsilon: Small value to adjust the vigilance.
      :type epsilon: float, default=1e-10
      :param verbose: non functional. Left for compatibility
      :type verbose: bool, default=False
      :param leave_progress_bar: non functional. Left for compatibility
      :type leave_progress_bar: bool, default=True

      :returns: **self** -- The fitted model.
      :rtype: SimpleARTMAP



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

      Partial fit the model to the data.

      :param X: Data set A.
      :type X: np.ndarray
      :param y: Data set B.
      :type y: np.ndarray
      :param match_tracking: Method to reset the match.
      :type match_tracking: Literal, default="MT+"
      :param epsilon: Small value to adjust the vigilance.
      :type epsilon: float, default=1e-10

      :returns: **self** -- The partially fitted model.
      :rtype: SimpleARTMAP



   .. 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



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

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

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