Vous êtes un professionnel et vous avez besoin d'une formation ? Mise en oeuvre d'IHM
avec Qt et PySide6 Voir le programme détaillé

Module « scipy.sparse.csgraph »

Fonction floyd_warshall - module scipy.sparse.csgraph

Signature de la fonction floyd_warshall

def floyd_warshall(csgraph, directed=True, return_predecessors=False, unweighted=False, overwrite=False) 

Description

help(scipy.sparse.csgraph.floyd_warshall)

    floyd_warshall(csgraph, directed=True, return_predecessors=False,
                   unweighted=False, overwrite=False)

    Compute the shortest path lengths using the Floyd-Warshall algorithm

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : array_like, or sparse array or matrix, 2 dimensions
        The N x N array of distances representing the input graph.
    directed : bool, optional
        If True (default), then find the shortest path on a directed graph:
        only move from point i to point j along paths csgraph[i, j].
        If False, then find the shortest path on an undirected graph: the
        algorithm can progress from point i to j along csgraph[i, j] or
        csgraph[j, i]
    return_predecessors : bool, optional
        If True, return the size (N, N) predecessor matrix.
    unweighted : bool, optional
        If True, then find unweighted distances.  That is, rather than finding
        the path between each point such that the sum of weights is minimized,
        find the path such that the number of edges is minimized.
    overwrite : bool, optional
        If True, overwrite csgraph with the result.  This applies only if
        csgraph is a dense, c-ordered array with dtype=float64.

    Returns
    -------
    dist_matrix : ndarray
        The N x N matrix of distances between graph nodes. dist_matrix[i,j]
        gives the shortest distance from point i to point j along the graph.

    predecessors : ndarray
        Returned only if return_predecessors == True.
        The N x N matrix of predecessors, which can be used to reconstruct
        the shortest paths.  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

    Raises
    ------
    NegativeCycleError:
        if there are negative cycles in the graph

    Notes
    -----
    If multiple valid solutions are possible, output may vary with SciPy and
    Python version.

    Examples
    --------
    >>> from scipy.sparse import csr_array
    >>> from scipy.sparse.csgraph import floyd_warshall

    >>> graph = [
    ... [0, 1, 2, 0],
    ... [0, 0, 0, 1],
    ... [2, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ]
    >>> graph = csr_array(graph)
    >>> print(graph)
    <Compressed Sparse Row sparse array of dtype 'int64'
    	with 5 stored elements and shape (4, 4)>
    	Coords	Values
    	(0, 1)	1
    	(0, 2)	2
    	(1, 3)	1
    	(2, 0)	2
    	(2, 3)	3

    >>> dist_matrix, predecessors = floyd_warshall(csgraph=graph, directed=False, return_predecessors=True)
    >>> dist_matrix
    array([[0., 1., 2., 2.],
           [1., 0., 3., 1.],
           [2., 3., 0., 3.],
           [2., 1., 3., 0.]])
    >>> predecessors
    array([[-9999,     0,     0,     1],
           [    1, -9999,     0,     1],
           [    2,     0, -9999,     2],
           [    1,     3,     3, -9999]], dtype=int32)

    

Vous êtes un professionnel et vous avez besoin d'une formation ? Sensibilisation à
l'Intelligence Artificielle Voir le programme détaillé