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Module « scipy.sparse.csgraph »

Fonction construct_dist_matrix - module scipy.sparse.csgraph

Signature de la fonction construct_dist_matrix

def construct_dist_matrix(graph, predecessors, directed=True, null_value=inf) 

Description

help(scipy.sparse.csgraph.construct_dist_matrix)

    construct_dist_matrix(graph, predecessors, directed=True, null_value=np.inf)

    Construct distance matrix from a predecessor matrix

    .. versionadded:: 0.11.0

    Parameters
    ----------
    graph : array_like or sparse
        The N x N matrix representation of a directed or undirected graph.
        If dense, then non-edges are indicated by zeros or infinities.
    predecessors : array_like
        The N x N matrix of predecessors of each node (see Notes below).
    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].
    null_value : bool, optional
        value to use for distances between unconnected nodes.  Default is
        np.inf

    Returns
    -------
    dist_matrix : ndarray
        The N x N matrix of distances between nodes along the path specified
        by the predecessor matrix.  If no path exists, the distance is zero.

    Notes
    -----
    The predecessor matrix is of the form returned by
    `shortest_path`.  Row i of the predecessor matrix contains
    information on the shortest paths from point i: each entry
    predecessors[i, j] gives the index of the previous node in the path from
    point i to point j.  If no path exists between point i and j, then
    predecessors[i, j] = -9999

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

    >>> 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, 2],
    ...                  [1, -9999, 0, 1],
    ...                  [2, 0, -9999, 2],
    ...                  [1, 3, 3, -9999]], dtype=np.int32)

    >>> construct_dist_matrix(graph=graph, predecessors=pred, directed=False)
    array([[0., 1., 2., 5.],
           [1., 0., 3., 1.],
           [2., 3., 0., 3.],
           [2., 1., 3., 0.]])

    

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