AFQ.recognition.sparse_decisions
================================

.. py:module:: AFQ.recognition.sparse_decisions


Functions
---------

.. autoapisummary::

   AFQ.recognition.sparse_decisions.compute_sparse_decisions
   AFQ.recognition.sparse_decisions.get_conflict_count
   AFQ.recognition.sparse_decisions.remove_conflicts


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

.. py:function:: compute_sparse_decisions(bundles_being_recognized, n_streamlines)

   
   Compute a sparse matrix of distances to ROIs for the streamlines that are
   currently being recognized. This can be used to weight decisions by distance
   to ROIs, without having to create a dense matrix of distances for all
   streamlines and all bundles.


   :Parameters:

       **bundles_being_recognized** : dict
           A dictionary of SlsBeingRecognized objects, keyed by bundle name.

       **n_streamlines** : int
           The total number of streamlines in the original tractogram.

   :Returns:

       csr_matrix
           A sparse matrix of shape (number of bundles being recognized, n_streamlines),
           where the entry (i, j) is a score:
               bundles with ROIs result in weights [2.0 to 3.0] with higher scores
               for streamlines closer to ROIs
               Non-ROI bundles result in weight 1.0
               Everything else is 0.0 (implicit in sparse matrices)













   ..
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.. py:function:: get_conflict_count(sparse_scores)

   
   Count how many streamlines are being considered for more than one bundle
















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.. py:function:: remove_conflicts(sparse_scores, bundles_being_recognized)

   
   Returns a dictionary of {bundle_name: np.array(accepted_indices)}
















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