python类shortest_path_length()的实例源码

def dist_between(x, a, b):
    """ Returns the geodesic distance between nodes in nanometers.

    Parameters
    ----------
    x :             {CatmaidNeuron, CatmaidNeuronList}
                    Neuron containing the nodes
    a, b :          treenode IDs
                    Treenodes to check.

    Returns
    -------
    int
                    distance in nm

    See Also
    --------
    :func:`~pymaid.distal_to`
        Check if a node A is distal to node B.
    :func:`~pymaid.geodesic_matrix`
        Get all-by-all geodesic distance matrix.

    """

    if isinstance( x, core.CatmaidNeuronList ):
        if len(x) == 1:
            x = x[0]
        else:
            raise ValueError('Need a single CatmaidNeuron')
    elif isinstance( x, core.CatmaidNeuron ):
        pass
    else:
        raise ValueError('Unable to process data of type {0}'.format(type(x)))

    try:
        _ = int(a)
        _ = int(b)
    except:
        raise ValueError('a, b need to be treenode IDs')  


    return int( nx.algorithms.shortest_path_length( g.to_undirected(as_view=True), 
                                                    a, b, 
                                                    weight='weight') )

def sp_kernel(g1, g2=None):
    if g2 is not None:
        graphs = []
        for g in g1:
            graphs.append(g)
        for g in g2:
            graphs.append(g)
    else:
        graphs = g1

    sp_lengths = []

    for graph in graphs:
        sp_lengths.append(nx.shortest_path_length(graph))

    N = len(graphs)
    all_paths = {}
    sp_counts = {}
    for i in range(N):
        sp_counts[i] = {}
        nodes = graphs[i].nodes()
        for v1 in nodes:
            for v2 in nodes:
                if v2 in sp_lengths[i][v1]:
                    label = sp_lengths[i][v1][v2]
                    if label in sp_counts[i]:
                        sp_counts[i][label] += 1
                    else:
                        sp_counts[i][label] = 1

                    if label not in all_paths:
                        all_paths[label] = len(all_paths)

    phi = np.zeros((N, len(all_paths)))

    for i in range(N):
        for label in sp_counts[i]:
            phi[i, all_paths[label]] = sp_counts[i][label]

    if g2 is not None:
        K = np.dot(phi[:len(g1), :], phi[len(g1):, :].T)
    else:
        K = np.dot(phi, phi.T)

    return K

def sp_kernel(g1, g2=None):
    with Timer("SP kernel"):
        if g2 is not None:
            graphs = []
            for g in g1:
                graphs.append(g)
            for g in g2:
                graphs.append(g)
        else:
            graphs = g1

        sp_lengths = []

        for graph in graphs:
            sp_lengths.append(nx.shortest_path_length(graph))

        N = len(graphs)
        all_paths = {}
        sp_counts = {}
        for i in range(N):
            sp_counts[i] = {}
            nodes = graphs[i].nodes()
            for v1 in nodes:
                for v2 in nodes:
                    if v2 in sp_lengths[i][v1]:
                        label = tuple(
                            sorted([graphs[i].node[v1]['label'], graphs[i].node[v2]['label']]) + [
                                sp_lengths[i][v1][v2]])
                        if label in sp_counts[i]:
                            sp_counts[i][label] += 1
                        else:
                            sp_counts[i][label] = 1

                        if label not in all_paths:
                            all_paths[label] = len(all_paths)

        phi = lil_matrix((N, len(all_paths)))

        for i in range(N):
            for label in sp_counts[i]:
                phi[i, all_paths[label]] = sp_counts[i][label]

        if g2 is not None:
            K = np.dot(phi[:len(g1), :], phi[len(g1):, :].T)
        else:
            K = np.dot(phi, phi.T)

    return K.todense()