Gemfury

stream / scikit-learn python

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scikit-learn / utils / sparsetools / _traversal.so
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    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 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 predecesor array (see below).

    Returns
    -------
    node_array : ndarray, one dimension
        The breadth-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 breadth-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.
    
    breadth_first_order(csgraph, i_start, directed=True, return_predecessors=True)

    Return a breadth-first ordering starting with specified node.

    Note that a breadth-first order is not unique, but the tree which it
    generates is unique.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : array_like or sparse 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 predecesor array (see below).

    Returns
    -------
    node_array : ndarray, one dimension
        The breadth-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 breadth-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.
    
    depth_first_tree(csgraph, i_start, directed=True)

    Return a tree generated by a depth-first search.

    Note that a tree generated by a depth-first search is not unique:
    it depends on the order that the children of each node are searched.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : array_like or sparse matrix
        The N x N matrix representing the 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].

    Returns
    -------
    cstree : csr matrix
        The N x N directed compressed-sparse representation of the depth-
        first tree drawn from csgraph, starting at the specified node.

    Examples
    --------
    The following example shows the computation of a depth-first tree
    over a simple four-component graph, starting at node 0::

         input graph           depth first tree from (0)

             (0)                         (0)
            /   \                           \
           3     8                           8
          /       \                           \
        (3)---5---(1)               (3)       (1)
          \       /                   \       /
           6     2                     6     2
            \   /                       \   /
             (2)                         (2)

    In compressed sparse representation, the solution looks like this:

    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import depth_first_tree
    >>> X = csr_matrix([[0, 8, 0, 3],
    ...                 [0, 0, 2, 5],
    ...                 [0, 0, 0, 6],
    ...                 [0, 0, 0, 0]])
    >>> Tcsr = depth_first_tree(X, 0, directed=False)
    >>> Tcsr.toarray().astype(int)
    array([[0, 8, 0, 0],
           [0, 0, 2, 0],
           [0, 0, 0, 6],
           [0, 0, 0, 0]])

    Note that the resulting graph is a Directed Acyclic Graph which spans
    the graph.  Unlike a breadth-first tree, a depth-first tree of a given
    graph is not unique if the graph contains cycles.  If the above solution
    had begun with the edge connecting nodes 0 and 3, the result would have
    been different.
    
    breadth_first_tree(csgraph, i_start, directed=True)

    Return the tree generated by a breadth-first search

    Note that a breadth-first tree from a specified node is unique.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : array_like or sparse matrix
        The N x N matrix representing the 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].

    Returns
    -------
    cstree : csr matrix
        The N x N directed compressed-sparse representation of the breadth-
        first tree drawn from csgraph, starting at the specified node.

    Examples
    --------
    The following example shows the computation of a depth-first tree
    over a simple four-component graph, starting at node 0::

         input graph          breadth first tree from (0)

             (0)                         (0)
            /   \                       /   \
           3     8                     3     8
          /       \                   /       \
        (3)---5---(1)               (3)       (1)
          \       /                           /
           6     2                           2
            \   /                           /
             (2)                         (2)

    In compressed sparse representation, the solution looks like this:

    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import breadth_first_tree
    >>> X = csr_matrix([[0, 8, 0, 3],
    ...                 [0, 0, 2, 5],
    ...                 [0, 0, 0, 6],
    ...                 [0, 0, 0, 0]])
    >>> Tcsr = breadth_first_tree(X, 0, directed=False)
    >>> Tcsr.toarray().astype(int)
    array([[0, 8, 0, 3],
           [0, 0, 2, 0],
           [0, 0, 0, 0],
           [0, 0, 0, 0]])

    Note that the resulting graph is a Directed Acyclic Graph which spans
    the graph.  A breadth-first tree from a given node is unique.
    
    connected_components(csgraph, directed=True, connection='weak',
                         return_labels=True)

    Analyze the connected components of a sparse graph

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : array_like or sparse matrix
        The N x N matrix representing the compressed sparse graph.  The input
        csgraph will be converted to csr format for the calculation.
    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].
    connection : str, optional
        ['weak'|'strong'].  For directed graphs, the type of connection to
        use.  Nodes i and j are strongly connected if a path exists both
        from i to j and from j to i.  Nodes i and j are weakly connected if
        only one of these paths exists.  If directed == False, this keyword
        is not referenced.
    return_labels : str, optional
        If True (default), then return the labels for each of the connected
        components.

    Returns
    -------
    n_components: int
        The number of connected components.
    labels: ndarray
        The length-N array of labels of the connected components.
    return_predecessorsbreadth_first_orderbreadth_first_treedepth_first_order_graph_validationreconstruct_pathdepth_first_treevalidate_graphisspmatrix_csrisspmatrix_cscreturn_labelspredecessorsn_componentsdense_output_graph_toolsRuntimeErrorisspmatrixcsr_matrixconnectionValueErrorroot_listnode_listcsgraph_Tdirected__test____main__indicesi_startfloat64csgraphstronglengthlabelsindptrzerostocsrshaperangenumpylowerint32emptydtypeITYPEDTYPEweakflagfillnpZgZfZdqlihgfdbTQONLIHB
    depth_first_tree(csgraph, i_start, directed=True)

    Return a tree generated by a depth-first search.

    Note that a tree generated by a depth-first search is not unique:
    it depends on the order that the children of each node are searched.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : array_like or sparse matrix
        The N x N matrix representing the 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].

    Returns
    -------
    cstree : csr matrix
        The N x N directed compressed-sparse representation of the depth-
        first tree drawn from csgraph, starting at the specified node.

    Examples
    --------
    The following example shows the computation of a depth-first tree
    over a simple four-component graph, starting at node 0::

         input graph           depth first tree from (0)

             (0)                         (0)
            /   \                           \
           3     8                           8
          /       \                           \
        (3)---5---(1)               (3)       (1)
          \       /                   \       /
           6     2                     6     2
            \   /                       \   /
             (2)                         (2)

    In compressed sparse representation, the solution looks like this:

    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import depth_first_tree
    >>> X = csr_matrix([[0, 8, 0, 3],
    ...                 [0, 0, 2, 5],
    ...                 [0, 0, 0, 6],
    ...                 [0, 0, 0, 0]])
    >>> Tcsr = depth_first_tree(X, 0, directed=False)
    >>> Tcsr.toarray().astype(int)
    array([[0, 8, 0, 0],
           [0, 0, 2, 0],
           [0, 0, 0, 6],
           [0, 0, 0, 0]])

    Note that the resulting graph is a Directed Acyclic Graph which spans
    the graph.  Unlike a breadth-first tree, a depth-first tree of a given
    graph is not unique if the graph contains cycles.  If the above solution
    had begun with the edge connecting nodes 0 and 3, the result would have
    been different.
    depth_first_tree (line 169)
    breadth_first_tree(csgraph, i_start, directed=True)

    Return the tree generated by a breadth-first search

    Note that a breadth-first tree from a specified node is unique.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : array_like or sparse matrix
        The N x N matrix representing the 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].

    Returns
    -------
    cstree : csr matrix
        The N x N directed compressed-sparse representation of the breadth-
        first tree drawn from csgraph, starting at the specified node.

    Examples
    --------
    The following example shows the computation of a depth-first tree
    over a simple four-component graph, starting at node 0::

         input graph          breadth first tree from (0)

             (0)                         (0)
            /   \                       /   \
           3     8                     3     8
          /       \                   /       \
        (3)---5---(1)               (3)       (1)
          \       /                           /
           6     2                           2
            \   /                           /
             (2)                         (2)

    In compressed sparse representation, the solution looks like this:

    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import breadth_first_tree
    >>> X = csr_matrix([[0, 8, 0, 3],
    ...                 [0, 0, 2, 5],
    ...                 [0, 0, 0, 6],
    ...                 [0, 0, 0, 0]])
    >>> Tcsr = breadth_first_tree(X, 0, directed=False)
    >>> Tcsr.toarray().astype(int)
    array([[0, 8, 0, 3],
           [0, 0, 2, 0],
           [0, 0, 0, 0],
           [0, 0, 0, 0]])

    Note that the resulting graph is a Directed Acyclic Graph which spans
    the graph.  A breadth-first tree from a given node is unique.
    breadth_first_tree (line 99)sklearn.utils.mst._traversal/home/x006/Code/scikit-learn/sklearn/utils/mst/_traversal.pyxconnected_componentsscipy.sparse
Routines for traversing graphs in compressed sparse format
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