artlib.optimized.backends.torch.BinaryFuzzyARTMAP
=================================================

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

.. autoapi-nested-parse::

   Binary Fuzzy ARTMAP (Torch-accelerated backend)



Classes
-------

.. autoapisummary::

   artlib.optimized.backends.torch.BinaryFuzzyARTMAP._TorchBinaryFuzzyARTMAPConfig
   artlib.optimized.backends.torch.BinaryFuzzyARTMAP._TorchBinaryFuzzyARTMAP
   artlib.optimized.backends.torch.BinaryFuzzyARTMAP.BinaryFuzzyARTMAP


Functions
---------

.. autoapisummary::

   artlib.optimized.backends.torch.BinaryFuzzyARTMAP._to_device
   artlib.optimized.backends.torch.BinaryFuzzyARTMAP._complement_code_any


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

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

.. py:function:: _complement_code_any(x: torch.Tensor) -> torch.Tensor

   Complement-code that works for float or bool tensors.


.. py:class:: _TorchBinaryFuzzyARTMAPConfig

   .. py:attribute:: input_dim
      :type:  int


   .. py:attribute:: alpha
      :type:  float
      :value: 1e-10



   .. py:attribute:: rho
      :type:  float
      :value: 0.75



   .. py:attribute:: epsilon
      :type:  float
      :value: 1e-07



   .. py:attribute:: match_tracking
      :type:  bool
      :value: True



   .. py:attribute:: device
      :type:  str
      :value: 'cuda'



   .. py:attribute:: dtype
      :type:  torch.dtype
      :value: Ellipsis



   .. py:attribute:: clamp_inputs
      :type:  bool
      :value: True



   .. py:attribute:: fallback_to_choice_on_fail
      :type:  bool
      :value: True



.. py:class:: _TorchBinaryFuzzyARTMAP(cfg: _TorchBinaryFuzzyARTMAPConfig)

   Torch-accelerated Binary Fuzzy ARTMAP with export hooks for artlib sync.


   .. py:attribute:: cfg


   .. py:attribute:: device


   .. py:attribute:: dtype


   .. py:attribute:: input_dim


   .. py:attribute:: code_dim


   .. py:attribute:: W
      :type:  Optional[torch.Tensor]
      :value: None



   .. py:attribute:: map_y
      :type:  Optional[torch.Tensor]
      :value: None



   .. py:attribute:: _lower_bounds
      :type:  Optional[torch.Tensor]
      :value: None



   .. py:attribute:: _upper_bounds
      :type:  Optional[torch.Tensor]
      :value: None



   .. py:attribute:: _prep_tol
      :type:  float
      :value: 1e-06



   .. py:property:: n_cat
      :type: int



   .. py:method:: _ensure_capacity()


   .. py:method:: set_data_bounds(lower: Union[torch.Tensor, numpy.ndarray], upper: Union[torch.Tensor, numpy.ndarray])


   .. py:method:: prepare_data(X: Union[torch.Tensor, numpy.ndarray]) -> torch.Tensor

      Optional helper: normalize -> complement-code -> binarize -> bool.
      If your inputs are already binary and complement-coded, skip this and
      pass them straight to partial_fit_and_export/predict_*.



   .. py:method:: _validate_prepared(X: torch.Tensor)


   .. py:method:: _free_mem_bytes() -> int


   .. py:method:: _choice_and_match(I: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]

      For a single prepared binary input I (bool), return:

      T: [K] choice values
      m: [K] match values
      w_sum: [K] |w| (as float64)




   .. py:method:: _commit_new_category(I: torch.Tensor, y: int)


   .. py:method:: partial_fit_and_export(X_prepared: Union[torch.Tensor, numpy.ndarray], y: Union[torch.Tensor, numpy.ndarray], epsilon: float = 1e-10, match_tracking: Literal['MT+', 'MT-', 'MT0', 'MT1', 'MT~'] = 'MT+') -> Tuple[numpy.ndarray, list[numpy.ndarray], numpy.ndarray]

      Incremental training on already-prepared, binary, complement-coded inputs.

      :returns: (N,) chosen A-side category indices (int)
                weights_arrays: list of K arrays, each weight as uint8
                cluster_labels_out: (K,) map from A categories to B labels (int)
      :rtype: labels_a_out



   .. py:method:: predict_ab_prepared(X_prepared: Union[torch.Tensor, numpy.ndarray]) -> Tuple[numpy.ndarray, numpy.ndarray]


.. py:class:: BinaryFuzzyARTMAP(rho: float, alpha: float, input_dim: Optional[int] = None, device: str = 'cuda')

   Bases: :py:obj:`artlib.optimized.backends.torch._TorchSimpleARTMAP._TorchSimpleARTMAP`


   BinaryFuzzyARTMAP for Classification. optimized with torch.

   This module implements BinaryFuzzyARTMAP

   BinaryFuzzyARTMAP is a non-modular classification model which has been highly
   optimized for run-time performance. Fit and predict functions are implemented in
   torch for efficient execution. This class acts as a wrapper for the underlying torch
   functions and to provide compatibility with the artlib style and usage.
   Functionally, BinaryFuzzyARTMAP behaves as a special case of
   :class:`~artlib.supervised.SimpleARTMAP.SimpleARTMAP` instantiated with
   :class:`~artlib.elementary.BinaryFuzzyART.BinaryFuzzyART`.



   .. py:attribute:: _device
      :value: 'cuda'



   .. py:attribute:: _backend
      :type:  Optional[_TorchBinaryFuzzyARTMAP]
      :value: None



   .. py:attribute:: _declared_input_dim
      :value: None



   .. py:method:: _ensure_backend(X: numpy.ndarray)

      Initialize backend using prepared X to infer raw dimension when needed.