Gemfury

steminc / scikit-learn python

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scikit-learn / utils / sparsetools / _graph_tools.so
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    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
    :func:`graph_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
    
    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 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.
    
    csgraph_to_masked(csgraph)

    Convert a sparse graph representation to a masked array representation

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : csr_matrix, csc_matrix, or lil_matrix
        Sparse representation of a graph.

    Returns
    -------
    graph : MaskedArray
        The masked dense representation of the sparse graph.
    
    csgraph_to_dense(csgraph, null_value=0)

    Convert a sparse graph representation to a dense representation

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : csr_matrix, csc_matrix, or lil_matrix
        Sparse representation of a graph.
    null_value : float, optional
        The value used to indicate null edges in the dense representation.
        Default is 0.

    Returns
    -------
    graph : ndarray
        The dense representation of the sparse graph.

    Notes
    -----
    For normal sparse graph representations, calling csgraph_to_dense with
    null_value=0 produces an equivalent result to using dense format
    conversions in the main sparse package.  When the sparse representations
    have repeated values, however, the results will differ.  The tools in
    scipy.sparse will add repeating values to obtain a final value.  This
    function will select the minimum among repeating values to obtain a
    final value.  For example, here we'll create a two-node directed sparse
    graph with multiple edges from node 0 to node 1, of weights 2 and 3.
    This illustrates the difference in behavior:

    >>> from scipy.sparse import csr_matrix
    >>> data = np.array([2, 3])
    >>> indices = np.array([1, 1])
    >>> indptr = np.array([0, 2, 2])
    >>> M = csr_matrix((data, indices, indptr), shape=(2, 2))
    >>> M.toarray()
    array([[0, 5],
           [0, 0]])
    >>> csgraph_to_dense(M)
    array([[0, 2],
           [0, 0]])

    The reason for this difference is to allow a compressed sparse graph to
    represent multiple edges between any two nodes.  As most sparse graph
    algorithms are concerned with the single lowest-cost edge between any
    two nodes, the default scipy.sparse behavior of summming multiple weights
    does not make sense in this context.

    The other reason for using this routine is to allow for graphs with
    zero-weight edges.  Let's look at the example of a two-node directed
    graph, connected by an edge of weight zero:

    >>> from scipy.sparse import csr_matrix
    >>> data = np.array([0.0])
    >>> indices = np.array([1])
    >>> indptr = np.array([0, 2, 2])
    >>> M = csr_matrix((data, indices, indptr), shape=(2, 2))
    >>> M.toarray()
    array([[0, 0],
           [0, 0]])
    >>> csgraph_to_dense(M, np.inf)
    array([[ Inf,   0.],
           [ Inf,  Inf]])

    In the first case, the zero-weight edge gets lost in the dense
    representation.  In the second case, we can choose a different null value
    and see the true form of the graph.
    
    csgraph_from_dense(graph, null_value=0, nan_null=True, infinity_null=True)

    Construct a CSR-format sparse graph from a dense matrix.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    graph : array_like
        Input graph.  Shape should be (n_nodes, n_nodes).
    null_value : float or None (optional)
        Value that denotes non-edges in the graph.  Default is zero.
    infinity_null : bool
        If True (default), then infinite entries (both positive and negative)
        are treated as null edges.
    nan_null : bool
        If True (default), then NaN entries are treated as non-edges

    Returns
    -------
    csgraph : csr_matrix
        Compressed sparse representation of graph, 
    
    csgraph_masked_from_dense(graph, null_value=0, nan_null=True,
                              infinity_null=True, copy=True)

    Construct a masked array graph representation from a dense matrix.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    graph : array_like
        Input graph.  Shape should be (n_nodes, n_nodes).
    null_value : float or None (optional)
        Value that denotes non-edges in the graph.  Default is zero.
    infinity_null : bool
        If True (default), then infinite entries (both positive and negative)
        are treated as null edges.
    nan_null : bool
        If True (default), then NaN entries are treated as non-edges

    Returns
    -------
    csgraph : MaskedArray
        masked array representation of graph
    
    csgraph_from_masked(graph)

    Construct a CSR-format graph from a masked array.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    graph : MaskedArray
        Input graph.  Shape should be (n_nodes, n_nodes).

    Returns
    -------
    csgraph : csr_matrix
        Compressed sparse representation of graph, 
    csgraph_from_maskedcsgraph_from_densecsgraph_to_maskedreconstruct_pathcsgraph_to_densevalidate_graphmasked_invalidisspmatrix_lilisspmatrix_csrisspmatrix_cscmasked_valuesinfinity_nullcopy_if_densesearchsortedpredecessorsmasked_arraydense_outputRuntimeErrordist_matrix_validationnull_valueisspmatrixcsr_outputcsr_matrixcompressedValueErroris_maskednan_nullidx_griddirected__test____main__minimumindicesfloat64csgraphasarrayargsortindptrcumsumastypearangezerostocsrshaperavelrangeordernumpynnullisnanisinfint32graphemptydtypedata2data1arrayITYPEDTYPEpindonesndimmaskfilldatacopyboolsumnaninfnpmaZgZfZdqlihgfdcbQONLIHCB
    csgraph_to_dense(csgraph, null_value=0)

    Convert a sparse graph representation to a dense representation

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : csr_matrix, csc_matrix, or lil_matrix
        Sparse representation of a graph.
    null_value : float, optional
        The value used to indicate null edges in the dense representation.
        Default is 0.

    Returns
    -------
    graph : ndarray
        The dense representation of the sparse graph.

    Notes
    -----
    For normal sparse graph representations, calling csgraph_to_dense with
    null_value=0 produces an equivalent result to using dense format
    conversions in the main sparse package.  When the sparse representations
    have repeated values, however, the results will differ.  The tools in
    scipy.sparse will add repeating values to obtain a final value.  This
    function will select the minimum among repeating values to obtain a
    final value.  For example, here we'll create a two-node directed sparse
    graph with multiple edges from node 0 to node 1, of weights 2 and 3.
    This illustrates the difference in behavior:

    >>> from scipy.sparse import csr_matrix
    >>> data = np.array([2, 3])
    >>> indices = np.array([1, 1])
    >>> indptr = np.array([0, 2, 2])
    >>> M = csr_matrix((data, indices, indptr), shape=(2, 2))
    >>> M.toarray()
    array([[0, 5],
           [0, 0]])
    >>> csgraph_to_dense(M)
    array([[0, 2],
           [0, 0]])

    The reason for this difference is to allow a compressed sparse graph to
    represent multiple edges between any two nodes.  As most sparse graph
    algorithms are concerned with the single lowest-cost edge between any
    two nodes, the default scipy.sparse behavior of summming multiple weights
    does not make sense in this context.

    The other reason for using this routine is to allow for graphs with
    zero-weight edges.  Let's look at the example of a two-node directed
    graph, connected by an edge of weight zero:

    >>> from scipy.sparse import csr_matrix
    >>> data = np.array([0.0])
    >>> indices = np.array([1])
    >>> indptr = np.array([0, 2, 2])
    >>> M = csr_matrix((data, indices, indptr), shape=(2, 2))
    >>> M.toarray()
    array([[0, 0],
           [0, 0]])
    >>> csgraph_to_dense(M, np.inf)
    array([[ Inf,   0.],
           [ Inf,  Inf]])

    In the first case, the zero-weight edge gets lost in the dense
    representation.  In the second case, we can choose a different null value
    and see the true form of the graph.
    csgraph_to_dense (line 172)construct_dist_matrixsklearn.utils._graph_tools/home/x006/Code/scikit-learn/sklearn/utils/_graph_tools.pyxscipy.sparse
Tools and utilities for working with compressed sparse graphs
Format string allocated too short.Format string allocated too short, see comment in numpy.pxdunknown dtype code in numpy.pxd (%d)Non-native byte order not supportedndarray is not Fortran contiguousndarray is not C contiguousgraph and predecessors must have the same shapecsgraph should be a square matrixcsgraph must be lil, csr, or csc formatcsgraph_masked_from_densegraph should be a square arraygraph should have two dimensionsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿXµ!´!°´!0µ!øµ!Xµ!´!°´!0µ!èµ!´!èµ!h´!µ!Xµ!h´!µ!´!·!  !!·!П!·! !(ø¶!`!"ð¶! !0è¶!ð!à¶!!"ض!!$ж!@!%ȶ!ð!6!!<¸¶!!#°¶!\!
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