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Module « scipy.sparse.csgraph »

Fonction reconstruct_path - module scipy.sparse.csgraph

Signature de la fonction reconstruct_path

def reconstruct_path(csgraph, predecessors, directed=True) 

Description

help(scipy.sparse.csgraph.reconstruct_path)

    reconstruct_path(csgraph, predecessors, directed=True)

    Construct a tree from a graph and a predecessor list.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : array_like or sparse array or matrix
        The N x N matrix representing the directed or undirected graph
        from which the predecessors are drawn.
    predecessors : array_like, one dimension
        The length-N array of indices of predecessors for the tree.  The
        index of the parent of node i is given by predecessors[i].
    directed : bool, optional
        If True (default), then operate on a directed graph: only move from
        point i to point j along paths csgraph[i, j].
        If False, then operate on an undirected graph: the algorithm can
        progress from point i to j along csgraph[i, j] or csgraph[j, i].

    Returns
    -------
    cstree : csr matrix
        The N x N directed compressed-sparse representation of the tree drawn
        from csgraph which is encoded by the predecessor list.

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.sparse import csr_array
    >>> from scipy.sparse.csgraph import reconstruct_path

    >>> graph = [
    ... [0, 1, 2, 0],
    ... [0, 0, 0, 1],
    ... [0, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ]
    >>> graph = csr_array(graph)
    >>> print(graph)
    <Compressed Sparse Row sparse array of dtype 'int64'
    	with 4 stored elements and shape (4, 4)>
    	Coords	Values
    	(0, 1)	1
    	(0, 2)	2
    	(1, 3)	1
    	(2, 3)	3

    >>> pred = np.array([-9999, 0, 0, 1], dtype=np.int32)

    >>> cstree = reconstruct_path(csgraph=graph, predecessors=pred, directed=False)
    >>> cstree.todense()
    array([[0., 1., 2., 0.],
           [0., 0., 0., 1.],
           [0., 0., 0., 0.],
           [0., 0., 0., 0.]])

    

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