spateo.tools.cluster.leiden
===========================

.. py:module:: spateo.tools.cluster.leiden


Functions
---------

.. autoapisummary::

   spateo.tools.cluster.leiden.distance_knn_graph
   spateo.tools.cluster.leiden.embedding_knn_graph
   spateo.tools.cluster.leiden.adj_to_igraph
   spateo.tools.cluster.leiden.calculate_leiden_partition
   spateo.tools.cluster.leiden.calculate_louvain_partition


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

.. py:function:: distance_knn_graph(dist: numpy.ndarray, num_neighbors: int)

   Construct a k-nearest neighbor graph from a distance matrix.

   :param dist: Pairwise distance matrix
   :param num_neighbors: Number of nearest neighbors

   :returns: K-nearest neighbor graph
   :rtype: G


.. py:function:: embedding_knn_graph(X: numpy.ndarray, num_neighbors: int)

   Construct a k-nearest neighbor graph from an arbitrary array, of shape [n_samples, n_features]

   :param X: Embedding matrix
   :param num_neighbors: Number of nearest neighbors

   :returns: K-nearest neighbor graph
   :rtype: G


.. py:function:: adj_to_igraph(adj: numpy.ndarray)

   Convert an adjacency matrix to an igraph graph.


.. py:function:: calculate_leiden_partition(adj: Optional[Union[scipy.sparse.spmatrix, numpy.ndarray]] = None, input_mat: Optional[numpy.ndarray] = None, num_neighbors: int = 10, graph_type: Literal['distance', 'embedding'] = 'distance', resolution: float = 1.0, n_iterations: int = -1) -> numpy.ndarray

   Performs Leiden clustering on a given dataset.

   :param adj: Optional precomputed adjacency matrix
   :param input_mat: Optional, will be used only if 'adj' is not given. The input data, will be interepreted as either a
                     distance matrix (if :param `graph_type` is "distance" or an embedding matrix (if :param `graph_type` is
                     "embedding")
   :param num_neighbors: Only used if 'adj' is not given- the number of nearest neighbors for constructing the graph
   :param graph_type: Only used if 'adj' is not given- specifies the input type, either 'distance' or 'embedding'
   :param resolution: The resolution parameter for the Leiden algorithm
   :param n_iterations: The number of iterations for the Leiden algorithm (-1 for unlimited iterations)

   :returns: Array containing cluster assignments
   :rtype: clusters


.. py:function:: calculate_louvain_partition(adj: Optional[Union[scipy.sparse.spmatrix, numpy.ndarray]] = None, input_mat: Optional[numpy.ndarray] = None, num_neighbors: int = 10, graph_type: Literal['distance', 'embedding'] = 'distance', resolution: float = 1.0, n_iterations: int = -1) -> numpy.ndarray

   Performs Louvain clustering on a given dataset.

   :param adj: Optional precomputed adjacency matrix
   :param input_mat: Optional, will be used only if 'adj' is not given. The input data, will be interepreted as either a
                     distance matrix (if :param `graph_type` is "distance" or an embedding matrix (if :param `graph_type` is
                     "embedding")
   :param num_neighbors: Only used if 'adj' is not given- the number of nearest neighbors for constructing the graph
   :param graph_type: Only used if 'adj' is not given- specifies the input type, either 'distance' or 'embedding'
   :param resolution: The resolution parameter for the Louvain algorithm
   :param n_iterations: The number of iterations for the Louvain algorithm (-1 for unlimited iterations)

   :returns: Array containing cluster assignments
   :rtype: clusters