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Module « scipy.sparse.csgraph »

Fonction depth_first_order - module scipy.sparse.csgraph

Signature de la fonction depth_first_order

def depth_first_order(csgraph, i_start, directed=True, return_predecessors=True) 

Description

help(scipy.sparse.csgraph.depth_first_order)

    depth_first_order(csgraph, i_start, directed=True, return_predecessors=True)

    Return a depth-first ordering starting with specified node.

    Note that a depth-first order is not unique.  Furthermore, for graphs
    with cycles, the tree generated by a depth-first search is not
    unique either.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : array_like or sparse array or matrix
        The N x N compressed sparse graph.  The input csgraph will be
        converted to csr format for the calculation.
    i_start : int
        The index of starting node.
    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 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 (default), then return the predecessor array (see below).

    Returns
    -------
    node_array : ndarray, one dimension
        The depth-first list of nodes, starting with specified node.  The
        length of node_array is the number of nodes reachable from the
        specified node.
    predecessors : ndarray, one dimension
        Returned only if return_predecessors is True.
        The length-N list of predecessors of each node in a depth-first
        tree.  If node i is in the tree, then its parent is given by
        predecessors[i]. If node i is not in the tree (and for the parent
        node) then predecessors[i] = -9999.

    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 depth_first_order

    >>> 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

    >>> depth_first_order(graph,0)
    (array([0, 1, 3, 2], dtype=int32), array([-9999,     0,     0,     1], dtype=int32))

    

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