lenskit.data.matrix
===================

.. py:module:: lenskit.data.matrix

.. autoapi-nested-parse::

   Classes for working with matrix data.



Attributes
----------

.. autoapisummary::

   lenskit.data.matrix.t
   lenskit.data.matrix.M
   lenskit.data.matrix.SPARSE_IDX_EXT_NAME
   lenskit.data.matrix.SPARSE_IDX_LIST_EXT_NAME
   lenskit.data.matrix.SPARSE_ROW_EXT_NAME


Classes
-------

.. toctree::
   :hidden:

   /api/lenskit/data/matrix/CSRStructure
   /api/lenskit/data/matrix/COOStructure
   /api/lenskit/data/matrix/SparseIndexType
   /api/lenskit/data/matrix/SparseIndexListType
   /api/lenskit/data/matrix/SparseRowType
   /api/lenskit/data/matrix/SparseRowArray

.. autoapisummary::

   lenskit.data.matrix.CSRStructure
   lenskit.data.matrix.COOStructure
   lenskit.data.matrix.SparseIndexType
   lenskit.data.matrix.SparseIndexListType
   lenskit.data.matrix.SparseRowType
   lenskit.data.matrix.SparseRowArray


Functions
---------

.. autoapisummary::

   lenskit.data.matrix.fast_col_cooc
   lenskit.data.matrix.normalize_matrix



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

.. py:data:: t

.. py:data:: M

.. py:data:: SPARSE_IDX_EXT_NAME
   :value: 'lenskit.sparse_index'


.. py:data:: SPARSE_IDX_LIST_EXT_NAME
   :value: 'lenskit.sparse_index_list'


.. py:data:: SPARSE_ROW_EXT_NAME
   :value: 'lenskit.sparse_row'


.. py:function:: fast_col_cooc(rows: lenskit.data.types.NPVector[numpy.int32] | pyarrow.Int32Array, cols: lenskit.data.types.NPVector[numpy.int32] | pyarrow.Int32Array, shape: tuple[int, int], *, progress: lenskit.logging.Progress | None = None, include_diagonal: bool = True, ordered: bool = False, dense: Literal[True]) -> numpy.ndarray[tuple[int, int], numpy.dtype[numpy.float32]]
                 fast_col_cooc(rows: lenskit.data.types.NPVector[numpy.int32] | pyarrow.Int32Array, cols: lenskit.data.types.NPVector[numpy.int32] | pyarrow.Int32Array, shape: tuple[int, int], *, progress: lenskit.logging.Progress | None = None, include_diagonal: bool = True, ordered: bool = False, dense: Literal[False] = False) -> scipy.sparse.coo_array
                 fast_col_cooc(rows: lenskit.data.types.NPVector[numpy.int32] | pyarrow.Int32Array, cols: lenskit.data.types.NPVector[numpy.int32] | pyarrow.Int32Array, shape: tuple[int, int], *, progress: lenskit.logging.Progress | None = None, include_diagonal: bool = True, ordered: bool = False, dense: bool = False) -> Any

   Compute column co-occurrances (:math:`M^{\mathrm{T}}M`) efficiently.


.. py:function:: normalize_matrix(matrix, normalize)

   Normalize rows of a matrix.

   :param matrix: Sparse or dense matrix to normalize
   :param normalize: Normalization mode ("unit" for L2, "distribution" for L1)

   :returns: Normalized matrix



Exported Aliases
----------------

.. py:class:: lenskit.data.matrix.Progress

    Re-exported alias for :py:class:`lenskit.logging.Progress`.

.. py:data:: lenskit.data.matrix.NPVector

    Re-exported alias for :py:data:`lenskit.data.types.NPVector`.