Python DiGraph.node[n]['r']方法代码示例

# 需要导入模块: from networkx.classes.digraph import DiGraph [as 别名]
# 或者: from networkx.classes.digraph.DiGraph import node[n]['r'] [as 别名]
def get_example_5():
    g = DiGraph()
    g.add_edges_from([(0, 1), (0, 2), (1, 3), (1, 4),
                      (2, 4), (2, 5), (2, 6)])
    for s, t in g.edges():
        g[s][t]['c'] = 1
    g[1][4]['c'] = 0
    g[2][4]['c'] = 0
    g[2][6]['c'] = 3
    for n in g.nodes():
        g.node[n]['r'] = 1
    g.node[3]['r'] = 10
    g.node[4]['r'] = 100
    g.node[5]['r'] = 11

    U = [10]
    # sub-optimal answer actually
    expected_edge_set = [[(0, 2), (2, 4), (2, 5), (2, 6)]]
    return (g, U, expected_edge_set)

# 需要导入模块: from networkx.classes.digraph import DiGraph [as 别名]
# 或者: from networkx.classes.digraph.DiGraph import node[n]['r'] [as 别名]
def get_example_4():
    g = DiGraph()
    g.add_edges_from([(0, 1), (1, 2), (2, 3), (2, 14),  # tree 1
                      (2, 15), (3, 16), (3, 17),
                      (0, 4), (4, 5), (4, 6),  # tree 2
                      (5, 11), (6, 11), (6, 12), (6, 13),
                      (0, 7), (7, 8), (7, 9),  # tree 3
                      (8, 10), (8, 11), (9, 12), (9, 13)])
    for s, t in g.edges():
        g[s][t]['c'] = 1
    for n in g.nodes():
        g.node[n]['r'] = 1
    g.node[10]['r'] = 2

    U = [7]
    expected_edge_set = [
        [(0, 7), (7, 8), (7, 9),  # tree 3
         (8, 10), (8, 11), (9, 12), (9, 13)]
    ]
    return (g, U, expected_edge_set)

# 需要导入模块: from networkx.classes.digraph import DiGraph [as 别名]
# 或者: from networkx.classes.digraph.DiGraph import node[n]['r'] [as 别名]
def get_example_6():
    # IN-OPTIMAL CASE
    g = DiGraph()
    g.add_edges_from([(0, 1), (0, 2), (1, 3),
                      (1, 4), (2, 4), (2, 5)])
    for s, t in g.edges():
        g[s][t]['c'] = 0
    g[1][3]['c'] = 4
    g[1][4]['c'] = 4
    g[2][4]['c'] = 2
    g[2][5]['c'] = 1
    for n in g.nodes():
        g.node[n]['r'] = 0
    g.node[3]['r'] = 1
    g.node[4]['r'] = 100
    g.node[5]['r'] = 1

    U = [7]
    # sub-optimal answer actually
    expected_edge_set = [[(0, 2), (2, 4), (2, 5)]]
    return (g, U, expected_edge_set)

# 需要导入模块: from networkx.classes.digraph import DiGraph [as 别名]
# 或者: from networkx.classes.digraph.DiGraph import node[n]['r'] [as 别名]
def test_variance_based_cost():
    D = {'u': {}, 'v': {10: {'x', 'dum'}}, 'w': {12: {'y'}}}
    G = DiGraph()
    G.add_edges_from([('u', 'v'),
                      ('u', 'w'),
                      ('v', 'dum'),
                      ('dum', 'x'),
                      ('w', 'y')])
    G.node['dum']['dummy'] = True
    reprs = np.array([[0, 1],
                      [1, 0],
                      [0, 1],
                      [1, 1],
                      [0, 0]])
    real_nodes = ['u', 'v', 'w', 'x', 'y']
    for r, n in zip(reprs, real_nodes):
        G.node[n]['r'] = r
    n = 'u'
    children = [(10, 'v'),
                (12, 'w')]

    actual = get_all_nodes(G, n, D, children, ignore_dummy=True)
    expected = real_nodes
    assert_equal(sorted(expected), sorted(actual))

    cost_func = make_variance_cost_func(euclidean, 'r')
    
    actual = cost_func(n, D, G,
                       children)
    mean_vec = np.mean(reprs, axis=0)
    expected = np.sum([euclidean(mean_vec, v)
                       for v in reprs])
    np.testing.assert_almost_equal(expected, actual)

    # with fixed_point
    cost_func_fp = make_variance_cost_func(euclidean, 'r', fixed_point=2)
    actual = cost_func_fp(n, D, G,
                          children)
    assert_equal(int(expected*100), actual)

# 需要导入模块: from networkx.classes.digraph import DiGraph [as 别名]
# 或者: from networkx.classes.digraph.DiGraph import node[n]['r'] [as 别名]
def get_example_3():
    """get a binarized example, whose original graph is
    more complicated than the above example
    """
    g = DiGraph()
    g.add_nodes_from(range(1, 10))
    g.add_edges_from([(1, 2), (1, 3), (1, 7),
                      (2, 4), (2, 5), (2, 6),
                      (2, 7), (3, 8), (3, 9)])
    rewards = range(1, 10)
    for r, n in zip(rewards, g.nodes()):
        g.node[n]['r'] = r
        
    # all edges have cost 2 except 1 -> 2 and 1 -> 3(cost 1)
    for s, t in g.edges():
        g[s][t]['c'] = 2
    g[1][2]['c'] = 1
    g[1][3]['c'] = 1
    
    g = binarize_dag(g,
                     vertex_weight_key='r',
                     edge_weight_key='c',
                     dummy_node_name_prefix='d_')
    
    # parameters and expected output
    U = [0, 2, 3, 4, 100]
    expected_edges_set = [
        [],
        [(1, 7)],
        [(1, 'd_1'), ('d_1', 3), (3, 9)],
        [(1, 'd_1'), ('d_1', 3), (3, 9), ('d_1', 2)],
        # (1, 7) removed to make it a tree
        list(set(g.edges()) - set([(1, 7)]))
    ]
    
    return (g, U, expected_edges_set)

# 需要导入模块: from networkx.classes.digraph import DiGraph [as 别名]
# 或者: from networkx.classes.digraph.DiGraph import node[n]['r'] [as 别名]
def get_variance_example_1():
    g = DiGraph()
    g.add_edges_from([
        (0, 1), (0, 2),
        (2, 3), (3, 4),
        (2, 'dummy'),
        ('dummy', 5)
    ])
    g.node['dummy']['dummy'] = True

    for n in (0, 1, 2, 5):  # topic 1
        g.node[n]['repr'] = np.array([0, 0])
    for n in (3, 4):  # topic 2
        g.node[n]['repr'] = np.array([1, 1])
    for n in g.nodes_iter():
        g.node[n]['r'] = 1

    # correct is (0, 1, 2, 5) for cost 0
    U = [0, 42]
    expected_edge_set = [
        set(g.edges()) - {(2, 3), (3, 4)},
        set(g.edges())
    ]
    return (g, U, expected_edge_set)