Python networkx.complete_graph方法代碼示例

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import complete_graph [as 別名]
def test_networkx2igraph(self):
        import networkx as nx
        ng = nx.complete_graph(3)
        [x, y] = [int(x) for x in nx.__version__.split('.')]
        if x == 1:
            nx.set_node_attributes(ng, 'vattrib', 0)
            nx.set_edge_attributes(ng, 'eattrib', 1)
        else:
            nx.set_node_attributes(ng, 0, 'vattrib')
            nx.set_edge_attributes(ng, 1, 'eattrib')
        (e, n) = graphistry.bind(source='src', destination='dst').networkx2pandas(ng)

        edges = pd.DataFrame({
            'dst': {0: 1, 1: 2, 2: 2},
            'src': {0: 0, 1: 0, 2: 1},
            'eattrib': {0: 1, 1: 1, 2: 1}
        })
        nodes = pd.DataFrame({
            '__nodeid__': {0: 0, 1: 1, 2: 2},
            'vattrib': {0: 0, 1: 0, 2: 0}
        })

        assertFrameEqual(e, edges)
        assertFrameEqual(n, nodes) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import complete_graph [as 別名]
def test_nx_pfs_deep_search(self):
        pfs = EnergyImpactDecomposer._pfs_nodes

        # two K4 graphs connected with one edge
        graph = nx.complete_graph(4)
        graph.add_edges_from(nx.complete_graph(range(4,8)).edges)
        graph.add_edge(1, 4)

        # use node index for weight/priority
        priority = lambda node: node

        # make sure once the second cluster is within reach, we deplete it
        self.assertEqual(set(pfs(graph, 3, 2, priority)), set([2, 3]))
        self.assertEqual(set(pfs(graph, 3, 3, priority)), set([1, 2, 3]))
        self.assertEqual(set(pfs(graph, 3, 4, priority)), set([1, 2, 3, 4]))
        self.assertEqual(set(pfs(graph, 3, 7, priority)), set([1, 2, 3, 4, 5, 6, 7]))
        self.assertEqual(set(pfs(graph, 3, 8, priority)), set([0, 1, 2, 3, 4, 5, 6, 7])) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import complete_graph [as 別名]
def is_clique(graph: nx.Graph) -> bool:
    """Determines if the input graph is a clique. A clique of :math:`n` nodes has exactly :math:`n(
    n-1)/2` edges.

    **Example usage:**

    >>> graph = nx.complete_graph(10)
    >>> is_clique(graph)
    True

    Args:
        graph (nx.Graph): the input graph

    Returns:
        bool: ``True`` if input graph is a clique and ``False`` otherwise
    """
    edges = graph.edges
    nodes = graph.order()

    return len(edges) == nodes * (nodes - 1) / 2 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import complete_graph [as 別名]
def test_tie(self, monkeypatch):
        """Test if function correctly settles ties with random selection. This test starts with
        the problem of a 6-dimensional complete graph and a 4-node subgraph, with the objective
        of shrinking down to 3 nodes. In this case, any of the 4 nodes in the subgraph is an
        equally good candidate for removal since all nodes have the same degree. The test
        monkeypatches the ``np.random.choice`` function to simply choose a fixed element,
        e.g. the element 2 of the list [0, 1, 2, 3] will be 2. Using this we expect the node to
        be removed by ``resize`` in this case to have label equal to the element."""
        dim = 6
        g = nx.complete_graph(dim)
        s = [0, 1, 2, 3]

        for i in range(4):
            choice_i = functools.partial(self.choice, element=i)
            with monkeypatch.context() as m:
                m.setattr(np.random, "choice", choice_i)
                resized = subgraph.resize(s, g, min_size=3, max_size=3)
                resized_subgraph = resized[3]
                removed_node = list(set(s) - set(resized_subgraph))[0]
                assert removed_node == i 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import complete_graph [as 別名]
def test_weight_and_degree_ties(self, dim, monkeypatch, elem):
        """Test if function correctly resizes on a fixed example where the ideal resizing is
        known and node-weight based selection is used to settle ties, but with all node weights
        equal so that they must be settled uniformly at random. The example is a complete
        graph with a starting subgraph of the first ``dim - 2`` nodes. The task is to resize to
        one node smaller and larger. This test monkeypatches the ``np.random.choice`` call used in
        the function so that instead it returns a fixed element. This element is set to 0 or 1 in
        the test, resulting in different nodes being added and removed from the starting
        subgraph."""
        g = nx.complete_graph(dim)
        s = list(range(dim - 2))
        min_size = dim - 3
        max_size = dim - 1
        w = [1] * dim

        choice_elem = functools.partial(self.choice, element=elem)

        ideal = {dim - 2: s, dim - 1: s + [dim - 2 + elem], dim - 3: list(set(s) - {elem})}
        with monkeypatch.context() as m:
            m.setattr(np.random, "choice", choice_elem)
            resized = subgraph.resize(s, g, min_size, max_size, node_select=w)

        assert resized == ideal 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import complete_graph [as 別名]
def test_dead_end(self, monkeypatch):
        """Test if function stops when in a dead end (i.e., ``grow == swap``) such that no
        swapping is possible. This is achieved by monkeypatching ``grow`` and
        ``swap`` so that they simply return the same clique as fed in, which should cause
        the local search algorithm to conclude that it is already in a dead end and return the
        same clique."""

        graph = nx.complete_graph(5)
        c = [0, 1, 2]

        with monkeypatch.context() as m:
            m.setattr(clique, "grow", patch_resize)
            m.setattr(clique, "swap", patch_resize)
            result = clique.search(c, graph, iterations=100)

        assert result == c 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import complete_graph [as 別名]
def test_grow_maximal_degree_tie(self, dim, monkeypatch):
        """Test if function grows using randomness to break ties during degree-based node
        selection. The chosen graph is a fully connected graph with the ``dim - 2`` and ``dim -
        1`` nodes then disconnected. Starting from the first ``dim - 3`` nodes, one can add
        either of the ``dim - 2`` and ``dim - 1`` nodes. As they have the same degree, they should
        be selected randomly with equal probability. This function monkeypatches the
        ``np.random.choice`` call to guarantee that one of the nodes is picked during one run of
        ``grow`` and the other node is picked during the next run."""
        graph = nx.complete_graph(dim)
        graph.remove_edge(dim - 2, dim - 1)
        s = set(range(dim - 2))

        patch_random_choice_1 = functools.partial(patch_random_choice, element=0)
        patch_random_choice_2 = functools.partial(patch_random_choice, element=1)

        with monkeypatch.context() as m:
            m.setattr(np.random, "choice", patch_random_choice_1)
            c1 = clique.grow(s, graph, node_select="degree")

        with monkeypatch.context() as m:
            m.setattr(np.random, "choice", patch_random_choice_2)
            c2 = clique.grow(s, graph, node_select="degree")

        assert c1 != c2 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import complete_graph [as 別名]
def test_grow_maximal_weight(self, dim):
        """Test if function grows to expected maximal graph when weight-based node selection is
        used. The chosen graph is a fully connected graph where the final three nodes have
        subsequently been disconnected from each other, but remain connected to all the other
        nodes. We then start from the clique composed of all but the final three nodes and seek
        to grow. In this construction, we can add just one of the final three nodes. This test
        gives the final node the largest weight, so we expect that one to be added."""
        graph = nx.complete_graph(dim)
        s = graph.subgraph([dim - 3, dim - 2, dim - 1])
        for e in s.edges():
            graph.remove_edge(*e)

        s = set(range(dim - 3))
        weights = list(range(dim))
        target = s | {dim - 1}
        assert set(clique.grow(s, graph, node_select=weights)) == target 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import complete_graph [as 別名]
def test_correct_vector_returned(self, monkeypatch):
        """Test if function correctly constructs the feature vector. The ``prob_orbit_exact``
         and ``prob_orbit_mc`` function called within ``feature_vector_orbits`` are
         monkeypatched to return hard-coded outputs that depend only on the orbit."""

        with monkeypatch.context() as m:
            m.setattr(
                similarity,
                "prob_orbit_mc",
                lambda _graph, orbit, n_mean, samples, loss: 1.0 / sum(orbit),
            )
            graph = nx.complete_graph(8)
            assert similarity.feature_vector_orbits(graph, [[1, 1], [2, 1, 1]], samples=1) == [
                0.5,
                0.25,
            ]

        with monkeypatch.context() as m:
            m.setattr(
                similarity,
                "prob_orbit_exact",
                lambda _graph, orbit, n_mean, loss: 0.5 * (1.0 / sum(orbit)),
            )
            graph = nx.complete_graph(8)
            assert similarity.feature_vector_orbits(graph, [[1, 1], [2, 1, 1]]) == [0.25, 0.125] 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import complete_graph [as 別名]
def test_networkx_vice_versa_convert():
    G = nx.complete_graph(5)
    assert G.is_directed() is False
    data = from_networkx(G)
    assert data.is_directed() is False
    G = to_networkx(data)
    assert G.is_directed() is True
    G = nx.to_undirected(G)
    assert G.is_directed() is False 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import complete_graph [as 別名]
def test_subgraph_convert():
    G = nx.complete_graph(5)

    edge_index = from_networkx(G).edge_index
    sub_edge_index_1, _ = subgraph([0, 1, 3, 4], edge_index,
                                   relabel_nodes=True)

    sub_edge_index_2 = from_networkx(G.subgraph([0, 1, 3, 4])).edge_index

    assert sub_edge_index_1.tolist() == sub_edge_index_2.tolist() 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import complete_graph [as 別名]
def _gen_clique(self, n):
        for _ in range(n):
            num_v = np.random.randint(self.min_num_v, self.max_num_v)
            g = nx.complete_graph(num_v)
            self.graphs.append(g)
            self.labels.append(6) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import complete_graph [as 別名]
def c_0(clique: list, graph: nx.Graph):
    """Generates the set :math:`C_0` of nodes that are connected to all nodes in the input
    clique subgraph.

    The set :math:`C_0` is defined in :cite:`pullan2006phased` and is used to determine nodes
    that can be added to the current clique to grow it into a larger one.

    **Example usage:**

    >>> graph = nx.complete_graph(10)
    >>> clique = [0, 1, 2, 3, 4]
    >>> c_0(clique, graph)
    [5, 6, 7, 8, 9]

    Args:
        clique (list[int]): a subgraph specified by a list of nodes; the subgraph must be a clique
        graph (nx.Graph): the input graph

    Returns:
        list[int]: a list containing the :math:`C_0` nodes for the clique

    """
    if not is_clique(graph.subgraph(clique)):
        raise ValueError("Input subgraph is not a clique")

    clique = set(clique)
    c_0_nodes = []
    non_clique_nodes = set(graph.nodes) - clique

    for i in non_clique_nodes:
        if clique.issubset(graph.neighbors(i)):
            c_0_nodes.append(i)

    return c_0_nodes 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import complete_graph [as 別名]
def test_complete_graph(self, dim):
        """Tests that nodes in a complete graph are located in the unit circle when using the
        Kamada-Kawai layout"""
        graph = nx.complete_graph(dim)
        layout = nx.kamada_kawai_layout(graph)
        coords = plot._node_coords(graph, layout)
        x = coords["x"]
        y = coords["y"]
        radii = [np.sqrt(x[i] ** 2 + y[i] ** 2) for i in range(dim)]

        assert np.allclose(radii, np.ones(dim), atol=1e-5) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import complete_graph [as 別名]
def test_custom_layout(self):
        """Tests that nodes in a complete graph are correctly located based on a custom layout"""
        graph = nx.complete_graph(4)
        layout = {0: [1, 1], 1: [1, -1], 2: [-1, 1], 3: [-1, -1]}
        coords = plot._node_coords(graph, layout)
        x = coords["x"]
        y = coords["y"]

        assert x == [1, 1, -1, -1]
        assert y == [1, -1, 1, -1]