Python Examples of networkx.erdos_renyi

Source File: test_seir.py From epydemic with GNU General Public License v3.0

6 votes

def setUp( self ):
        '''Set up the experimental parameters and experiment.'''
        
        # single experiment
        self._params = dict()
        self._params[SEIR.P_INFECT_SYMPTOMATIC] = 0.2
        self._params[SEIR.P_INFECT_ASYMPTOMATIC] = 0.1
        self._params[SEIR.P_EXPOSED] = 0.01
        self._params[SEIR.P_SYMPTOMS] = 0.05
        self._params[SEIR.P_REMOVE] = 0.05
        self._network = networkx.erdos_renyi_graph(1000, 0.005)

        # lab run
        self._lab = epyc.Lab()
        self._lab[SEIR.P_INFECT_SYMPTOMATIC] = 0.1
        self._lab[SEIR.P_INFECT_ASYMPTOMATIC] = [ 0.1,  0.3 ]
        self._lab[SEIR.P_EXPOSED] = 0.01
        self._lab[SEIR.P_SYMPTOMS] = [ 0.05, 1 ]
        self._lab[SEIR.P_REMOVE] = 0.05

        # model
        self._model = SEIR()

Source File: test_nn.py From dgl with Apache License 2.0

6 votes

def test_gat_conv():
    ctx = F.ctx()

    g = dgl.DGLGraph(nx.erdos_renyi_graph(20, 0.3))
    gat = nn.GATConv(10, 20, 5) # n_heads = 5
    gat.initialize(ctx=ctx)
    print(gat)

    # test#1: basic
    feat = F.randn((20, 10))
    h = gat(g, feat)
    assert h.shape == (20, 5, 20)

    # test#2: bipartite
    g = dgl.bipartite(sp.sparse.random(100, 200, density=0.1))
    gat = nn.GATConv((5, 10), 2, 4)
    gat.initialize(ctx=ctx)
    feat = (F.randn((100, 5)), F.randn((200, 10)))
    h = gat(g, feat)
    assert h.shape == (200, 4, 2)

Source File: test_transform.py From dgl with Apache License 2.0

6 votes

def test_laplacian_lambda_max():
    N = 20
    eps = 1e-6
    # test DGLGraph
    g = dgl.DGLGraph(nx.erdos_renyi_graph(N, 0.3))
    l_max = dgl.laplacian_lambda_max(g)
    assert (l_max[0] < 2 + eps)
    # test batched DGLGraph
    N_arr = [20, 30, 10, 12]
    bg = dgl.batch([
        dgl.DGLGraph(nx.erdos_renyi_graph(N, 0.3))
        for N in N_arr
    ])
    l_max_arr = dgl.laplacian_lambda_max(bg)
    assert len(l_max_arr) == len(N_arr)
    for l_max in l_max_arr:
        assert l_max < 2 + eps

Source File: test_nn.py From dgl with Apache License 2.0

6 votes

def test_gin_conv():
    g = dgl.DGLGraph(nx.erdos_renyi_graph(20, 0.3))
    ctx = F.ctx()

    gin_conv = nn.GINConv(lambda x: x, 'mean', 0.1)
    gin_conv.initialize(ctx=ctx)
    print(gin_conv)

    # test #1: basic
    feat = F.randn((g.number_of_nodes(), 5))
    h = gin_conv(g, feat)
    assert h.shape == (20, 5)

    # test #2: bipartite
    g = dgl.bipartite(sp.sparse.random(100, 200, density=0.1))
    feat = (F.randn((100, 5)), F.randn((200, 5)))
    h = gin_conv(g, feat)
    return h.shape == (20, 5)

Source File: sample.py From strawberryfields with Apache License 2.0

6 votes

def seed(value: Optional[int]) -> None:
    """Seed for random number generators.

    Wrapper function for `numpy.random.seed <https://docs.scipy.org/doc/numpy//reference/generated
    /numpy.random.seed.html>`_ to seed all NumPy-based random number generators. This allows for
    repeatable sampling.

    **Example usage:**

    >>> g = nx.erdos_renyi_graph(5, 0.7)
    >>> a = nx.to_numpy_array(g)
    >>> seed(1967)
    >>> sample(a, 3, 4)
    [[1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 0, 1, 0, 1], [0, 0, 0, 0, 0]]
    >>> seed(1967)
    >>> sample(a, 3, 4)
    [[1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 0, 1, 0, 1], [0, 0, 0, 0, 0]]

    Args:
        value (int): random seed
    """
    np.random.seed(value)

Source File: test_mpl_viz.py From ndlib with BSD 2-Clause "Simplified" License

6 votes

def test_visualize_dynamic(self):
        dg = dn.DynGraph()

        for t in past.builtins.xrange(0, 4):
            g = nx.erdos_renyi_graph(200, 0.05)
            dg.add_interactions_from(g.edges(), t)

        model = dyn.DynSIModel(dg)
        config = mc.Configuration()
        config.add_model_parameter('beta', 0.1)
        config.add_model_parameter("fraction_infected", 0.1)
        model.set_initial_status(config)
        iterations = model.execute_snapshots()
        trends = model.build_trends(iterations)

        # Visualization
        viz = DiffusionPrevalence(model, trends)
        viz.plot("prevd.pdf")
        os.remove("prevd.pdf")