Python networkx.dijkstra_path_length方法代码示例

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dijkstra_path_length [as 别名]
def test_weight_function(self):
        """Tests for computing the length of the shortest path using
        Dijkstra's algorithm with a user-defined weight function.

        """
        # Create a triangle in which the edge from node 0 to node 2 has
        # a large weight and the other two edges have a small weight.
        G = nx.complete_graph(3)
        G.adj[0][2]['weight'] = 10
        G.adj[0][1]['weight'] = 1
        G.adj[1][2]['weight'] = 1
        # The weight function will take the multiplicative inverse of
        # the weights on the edges. This way, weights that were large
        # before now become small and vice versa.

        def weight(u, v, d): return 1 / d['weight']
        # The shortest path from 0 to 2 using the actual weights on the
        # edges should be [0, 1, 2]. However, with the above weight
        # function, the shortest path should be [0, 2], since that has a
        # very small weight.
        length = nx.dijkstra_path_length(G, 0, 2, weight=weight)
        assert_equal(length, 1 / 10) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dijkstra_path_length [as 别名]
def test_weight_function(self):
        """Tests for computing the length of the shortest path using
        Dijkstra's algorithm with a user-defined weight function.

        """
        # Create a triangle in which the edge from node 0 to node 2 has
        # a large weight and the other two edges have a small weight.
        G = nx.complete_graph(3)
        G.adj[0][2]['weight'] = 10
        G.adj[0][1]['weight'] = 1
        G.adj[1][2]['weight'] = 1
        # The weight function will take the multiplicative inverse of
        # the weights on the edges. This way, weights that were large
        # before now become small and vice versa.
        weight = lambda u, v, d: 1 / d['weight']
        # The shortest path from 0 to 2 using the actual weights on the
        # edges should be [0, 1, 2]. However, with the above weight
        # function, the shortest path should be [0, 2], since that has a
        # very small weight.
        length = nx.dijkstra_path_length(G, 0, 2, weight=weight)
        assert_equal(length, 1 / 10) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dijkstra_path_length [as 别名]
def get_shortest_paths_distances(graph, pairs, edge_weight_name='distance'):
    """
    Calculate shortest distance between each pair of nodes in a graph

    Args:
        graph (networkx graph)
        pairs (list[2tuple]): List of length 2 tuples containing node pairs to calculate shortest path between
        edge_weight_name (str): edge attribute used for distance calculation

    Returns:
        dict: mapping each pair in `pairs` to the shortest path using `edge_weight_name` between them.
    """
    distances = {}
    for pair in pairs:
        distances[pair] = nx.dijkstra_path_length(graph, pair[0], pair[1], weight=edge_weight_name)
    return distances 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dijkstra_path_length [as 别名]
def add_augmenting_path_to_graph(graph, min_weight_pairs, edge_weight_name='weight'):
    """
    Add the min weight matching edges to the original graph
    Note the resulting graph could (and likely will) have edges that didn't exist on the original graph.  To get the
    true circuit, we must breakdown these augmented edges into the shortest path through the edges that do exist.  This
    is done with `create_eulerian_circuit`.

    Args:
        graph (networkx graph):
        min_weight_pairs (list[2tuples): output of `dedupe_matching` specifying the odd degree nodes to link together
        edge_weight_name (str): edge attribute used for distance calculation

    Returns:
        networkx graph: `graph` augmented with edges between the odd nodes specified in `min_weight_pairs`
    """
    graph_aug = graph.copy()  # so we don't mess with the original graph
    for pair in min_weight_pairs:
        graph_aug.add_edge(pair[0],
                           pair[1],
                           **{'distance': nx.dijkstra_path_length(graph, pair[0], pair[1], weight=edge_weight_name),
                              'augmented': True}
                           )
    return graph_aug 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dijkstra_path_length [as 别名]
def test_absent_source(self):
        # the check is in _dijkstra_multisource, but this will provide
        # regression testing against later changes to any of the "client"
        # Dijkstra or Bellman-Ford functions
        G = nx.path_graph(2)
        for fn in (nx.dijkstra_path,
                   nx.dijkstra_path_length,
                   nx.single_source_dijkstra_path,
                   nx.single_source_dijkstra_path_length,
                   nx.single_source_dijkstra,
                   nx.dijkstra_predecessor_and_distance,):
            assert_raises(nx.NodeNotFound, fn, G, 3, 0) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dijkstra_path_length [as 别名]
def dijkstra_path_length(G, source, target, weight='weight'):
    """Returns the shortest path length from source to target
    in a weighted graph.

    Parameters
    ----------
    G : NetworkX graph

    source : node label
       starting node for path

    target : node label
       ending node for path

    weight: string, optional (default='weight')
       Edge data key corresponding to the edge weight

    Returns
    -------
    length : number
        Shortest path length.

    Raises
    ------
    NetworkXNoPath
        If no path exists between source and target.

    Examples
    --------
    >>> G=nx.path_graph(5)
    >>> print(nx.dijkstra_path_length(G,0,4))
    4

    Notes
    -----
    Edge weight attributes must be numerical.
    Distances are calculated as sums of weighted edges traversed.

    See Also
    --------
    bidirectional_dijkstra()
    """
    length = single_source_dijkstra_path_length(G, source, weight=weight)
    try:
        return length[target]
    except KeyError:
        raise nx.NetworkXNoPath(
            "node %s not reachable from %s" % (source, target)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dijkstra_path_length [as 别名]
def test_dijkstra(self):
        (D, P) = nx.single_source_dijkstra(self.XG, 's')
        validate_path(self.XG, 's', 'v', 9, P['v'])
        assert_equal(D['v'], 9)

        validate_path(
            self.XG, 's', 'v', 9, nx.single_source_dijkstra_path(self.XG, 's')['v'])
        assert_equal(
            nx.single_source_dijkstra_path_length(self.XG, 's')['v'], 9)

        validate_path(
            self.XG, 's', 'v', 9, nx.single_source_dijkstra(self.XG, 's')[1]['v'])
        validate_path(
            self.MXG, 's', 'v', 9, nx.single_source_dijkstra_path(self.MXG, 's')['v'])

        GG = self.XG.to_undirected()
        # make sure we get lower weight
        # to_undirected might choose either edge with weight 2 or weight 3
        GG['u']['x']['weight'] = 2
        (D, P) = nx.single_source_dijkstra(GG, 's')
        validate_path(GG, 's', 'v', 8, P['v'])
        assert_equal(D['v'], 8)     # uses lower weight of 2 on u<->x edge
        validate_path(GG, 's', 'v', 8, nx.dijkstra_path(GG, 's', 'v'))
        assert_equal(nx.dijkstra_path_length(GG, 's', 'v'), 8)

        validate_path(self.XG2, 1, 3, 4, nx.dijkstra_path(self.XG2, 1, 3))
        validate_path(self.XG3, 0, 3, 15, nx.dijkstra_path(self.XG3, 0, 3))
        assert_equal(nx.dijkstra_path_length(self.XG3, 0, 3), 15)
        validate_path(self.XG4, 0, 2, 4, nx.dijkstra_path(self.XG4, 0, 2))
        assert_equal(nx.dijkstra_path_length(self.XG4, 0, 2), 4)
        validate_path(self.MXG4, 0, 2, 4, nx.dijkstra_path(self.MXG4, 0, 2))
        validate_path(
            self.G, 's', 'v', 2, nx.single_source_dijkstra(self.G, 's', 'v')[1]['v'])
        validate_path(
            self.G, 's', 'v', 2, nx.single_source_dijkstra(self.G, 's')[1]['v'])

        validate_path(self.G, 's', 'v', 2, nx.dijkstra_path(self.G, 's', 'v'))
        assert_equal(nx.dijkstra_path_length(self.G, 's', 'v'), 2)

        # NetworkXError: node s not reachable from moon
        assert_raises(nx.NetworkXNoPath, nx.dijkstra_path, self.G, 's', 'moon')
        assert_raises(
            nx.NetworkXNoPath, nx.dijkstra_path_length, self.G, 's', 'moon')

        validate_path(self.cycle, 0, 3, 3, nx.dijkstra_path(self.cycle, 0, 3))
        validate_path(self.cycle, 0, 4, 3, nx.dijkstra_path(self.cycle, 0, 4))

        assert_equal(
            nx.single_source_dijkstra(self.cycle, 0, 0), ({0: 0}, {0: [0]})) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dijkstra_path_length [as 别名]
def bellman_ford_path_length(G, source, target, weight='weight'):
    """Returns the shortest path length from source to target
    in a weighted graph.

    Parameters
    ----------
    G : NetworkX graph

    source : node label
       starting node for path

    target : node label
       ending node for path

    weight: string, optional (default='weight')
       Edge data key corresponding to the edge weight

    Returns
    -------
    length : number
        Shortest path length.

    Raises
    ------
    NodeNotFound
        If `source` is not in `G`.

    NetworkXNoPath
        If no path exists between source and target.

    Examples
    --------
    >>> G=nx.path_graph(5)
    >>> print(nx.bellman_ford_path_length(G,0,4))
    4

    Notes
    -----
    Edge weight attributes must be numerical.
    Distances are calculated as sums of weighted edges traversed.

    See Also
    --------
    dijkstra_path_length(), bellman_ford_path()
    """
    if source == target:
        return 0

    weight = _weight_function(G, weight)

    length = _bellman_ford(G, [source], weight, target=target)

    try:
        return length[target]
    except KeyError:
        raise nx.NetworkXNoPath(
            "node %s not reachable from %s" % (source, target)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dijkstra_path_length [as 别名]
def test_dijkstra(self):
        (D, P) = nx.single_source_dijkstra(self.XG, 's')
        validate_path(self.XG, 's', 'v', 9, P['v'])
        assert_equal(D['v'], 9)

        validate_path(
            self.XG, 's', 'v', 9, nx.single_source_dijkstra_path(self.XG, 's')['v'])
        assert_equal(dict(
            nx.single_source_dijkstra_path_length(self.XG, 's'))['v'], 9)

        validate_path(
            self.XG, 's', 'v', 9, nx.single_source_dijkstra(self.XG, 's')[1]['v'])
        validate_path(
            self.MXG, 's', 'v', 9, nx.single_source_dijkstra_path(self.MXG, 's')['v'])

        GG = self.XG.to_undirected()
        # make sure we get lower weight
        # to_undirected might choose either edge with weight 2 or weight 3
        GG['u']['x']['weight'] = 2
        (D, P) = nx.single_source_dijkstra(GG, 's')
        validate_path(GG, 's', 'v', 8, P['v'])
        assert_equal(D['v'], 8)     # uses lower weight of 2 on u<->x edge
        validate_path(GG, 's', 'v', 8, nx.dijkstra_path(GG, 's', 'v'))
        assert_equal(nx.dijkstra_path_length(GG, 's', 'v'), 8)

        validate_path(self.XG2, 1, 3, 4, nx.dijkstra_path(self.XG2, 1, 3))
        validate_path(self.XG3, 0, 3, 15, nx.dijkstra_path(self.XG3, 0, 3))
        assert_equal(nx.dijkstra_path_length(self.XG3, 0, 3), 15)
        validate_path(self.XG4, 0, 2, 4, nx.dijkstra_path(self.XG4, 0, 2))
        assert_equal(nx.dijkstra_path_length(self.XG4, 0, 2), 4)
        validate_path(self.MXG4, 0, 2, 4, nx.dijkstra_path(self.MXG4, 0, 2))
        validate_path(
            self.G, 's', 'v', 2, nx.single_source_dijkstra(self.G, 's', 'v')[1])
        validate_path(
            self.G, 's', 'v', 2, nx.single_source_dijkstra(self.G, 's')[1]['v'])

        validate_path(self.G, 's', 'v', 2, nx.dijkstra_path(self.G, 's', 'v'))
        assert_equal(nx.dijkstra_path_length(self.G, 's', 'v'), 2)

        # NetworkXError: node s not reachable from moon
        assert_raises(nx.NetworkXNoPath, nx.dijkstra_path, self.G, 's', 'moon')
        assert_raises(
            nx.NetworkXNoPath, nx.dijkstra_path_length, self.G, 's', 'moon')

        validate_path(self.cycle, 0, 3, 3, nx.dijkstra_path(self.cycle, 0, 3))
        validate_path(self.cycle, 0, 4, 3, nx.dijkstra_path(self.cycle, 0, 4))

        assert_equal(nx.single_source_dijkstra(self.cycle, 0, 0), (0, [0])) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dijkstra_path_length [as 别名]
def bellman_ford_path_length(G, source, target, weight='weight'):
    """Returns the shortest path length from source to target
    in a weighted graph.

    Parameters
    ----------
    G : NetworkX graph

    source : node label
       starting node for path

    target : node label
       ending node for path

    weight: string, optional (default='weight')
       Edge data key corresponding to the edge weight

    Returns
    -------
    length : number
        Shortest path length.

    Raises
    ------
    NetworkXNoPath
        If no path exists between source and target.

    Examples
    --------
    >>> G=nx.path_graph(5)
    >>> print(nx.bellman_ford_path_length(G,0,4))
    4

    Notes
    -----
    Edge weight attributes must be numerical.
    Distances are calculated as sums of weighted edges traversed.

    See Also
    --------
    dijkstra_path_length(), bellman_ford_path()
    """
    if source == target:
        return 0

    weight = _weight_function(G, weight)

    length = _bellman_ford(G, [source], weight, target=target)

    try:
        return length[target]
    except KeyError:
        raise nx.NetworkXNoPath(
            "node %s not reachable from %s" % (source, target))