Python networkx.erdos_renyi_graph函数代码示例

    def test_multi(self):

        vm = MultiPlot()

        g = nx.erdos_renyi_graph(1000, 0.1)
        model = epd.SIRModel(g)
        config = mc.Configuration()
        config.add_model_parameter('beta', 0.001)
        config.add_model_parameter('gamma', 0.01)
        config.add_model_parameter("percentage_infected", 0.05)
        model.set_initial_status(config)
        iterations = model.iteration_bunch(200)
        trends = model.build_trends(iterations)
        viz = DiffusionTrend(model, trends)
        p = viz.plot()

        vm.add_plot(p)

        g = nx.erdos_renyi_graph(1000, 0.1)
        model = epd.SIRModel(g)
        config = mc.Configuration()
        config.add_model_parameter('beta', 0.001)
        config.add_model_parameter('gamma', 0.01)
        config.add_model_parameter("percentage_infected", 0.05)
        model.set_initial_status(config)
        iterations = model.iteration_bunch(200)
        trends = model.build_trends(iterations)
        viz = DiffusionPrevalence(model, trends)
        p1 = viz.plot()

        vm.add_plot(p1)
        m = vm.plot()
        self.assertIsInstance(m, Column)

def synthetic_three_level(n,p1,p2,p3,J_isolates=False,F_isolates=False,D_isolates=False):#,isolate_up=True,isolate_down=True):
    
    k=n

    J=nx.erdos_renyi_graph(n,p1) #The first layer graph
    Jis = nx.isolates(J)
    F=nx.erdos_renyi_graph(n,p2) #The second layer graph
    Fis = nx.isolates(F)
    D=nx.erdos_renyi_graph(n,p3) #The third layer graph
    Dis = nx.isolates(D)

    def translation_graph(J,F,D):
        H1=nx.Graph()
        H2=nx.Graph()
        for i in range(n):
            H1.add_edges_from([(J.nodes()[i],F.nodes()[i])])
            H2.add_edges_from([(F.nodes()[i],D.nodes()[i])])
        return H1, H2

    Jed = set(J.edges())
    Fed = set(F.edges())
    Ded = set(D.edges())
    l=[Jed,Fed,Ded]
    lu = list(set.union(*l))
    JFD=nx.Graph()
    JFD.add_edges_from(lu)

    G=nx.Graph()  #The synthetic two-layer graph
    
    # Relabing nodes maps
    
    mappingF={}
    for i in range(2*n):
        mappingF[i]=n+i
    FF=nx.relabel_nodes(F,mappingF,copy=True)
    
    mappingD={}
    for i in range(2*n):
        if i >n-1:
            mappingD[i]=i-n
        else:
            mappingD[i]=2*n+i
    DD=nx.relabel_nodes(D,mappingD,copy=True)
    
    H1, HH2 = translation_graph(J,FF,DD)
    
    G.add_edges_from(J.edges())
    G.add_edges_from(H1.edges())
    G.add_edges_from(DD.edges())
    G.add_edges_from(HH2.edges())
    G.add_edges_from(FF.edges())

    edgeList = []
    for e in H1.edges():
        edgeList.append(e)
    for e in HH2.edges():
        edgeList.append(e)
    
    return G, J, FF, DD, JFD, edgeList  

def test_strong_product():
    null=nx.null_graph()
    empty1=nx.empty_graph(1)
    empty10=nx.empty_graph(10)
    K2=nx.complete_graph(2)
    K3=nx.complete_graph(3)
    K5=nx.complete_graph(5)
    K10=nx.complete_graph(10)
    P2=nx.path_graph(2)
    P3=nx.path_graph(3)
    P5=nx.path_graph(5)
    P10=nx.path_graph(10)
    # null graph
    G=strong_product(null,null)
    assert_true(nx.is_isomorphic(G,null))
    # null_graph X anything = null_graph and v.v.
    G=strong_product(null,empty10)
    assert_true(nx.is_isomorphic(G,null))
    G=strong_product(null,K3)
    assert_true(nx.is_isomorphic(G,null))
    G=strong_product(null,K10)
    assert_true(nx.is_isomorphic(G,null))
    G=strong_product(null,P3)
    assert_true(nx.is_isomorphic(G,null))
    G=strong_product(null,P10)
    assert_true(nx.is_isomorphic(G,null))
    G=strong_product(empty10,null)
    assert_true(nx.is_isomorphic(G,null))
    G=strong_product(K3,null)
    assert_true(nx.is_isomorphic(G,null))
    G=strong_product(K10,null)
    assert_true(nx.is_isomorphic(G,null))
    G=strong_product(P3,null)
    assert_true(nx.is_isomorphic(G,null))
    G=strong_product(P10,null)
    assert_true(nx.is_isomorphic(G,null))

    G=strong_product(P5,K3)
    assert_equal(nx.number_of_nodes(G),5*3)
    G=strong_product(K3,K5)
    assert_equal(nx.number_of_nodes(G),3*5)

    #No classic easily found classic results for strong product

    G = nx.erdos_renyi_graph(10,2/10.)
    H = nx.erdos_renyi_graph(10,2/10.)
    GH = strong_product(G,H)

    for (u_G,u_H) in GH.nodes_iter():
        for (v_G,v_H) in GH.nodes_iter():
            if (u_G==v_G and H.has_edge(u_H,v_H)) or \
               (u_H==v_H and G.has_edge(u_G,v_G)) or \
               (G.has_edge(u_G,v_G) and H.has_edge(u_H,v_H)):
                assert_true(GH.has_edge((u_G,u_H),(v_G,v_H)))
            else:
                assert_true(not GH.has_edge((u_G,u_H),(v_G,v_H)))

def test_tensor_product_random():
    G = nx.erdos_renyi_graph(10,2/10.)
    H = nx.erdos_renyi_graph(10,2/10.)
    GH = tensor_product(G,H)

    for (u_G,u_H) in GH.nodes_iter():
        for (v_G,v_H) in GH.nodes_iter():
            if H.has_edge(u_H,v_H) and G.has_edge(u_G,v_G):
                assert_true(GH.has_edge((u_G,u_H),(v_G,v_H)))
            else:
                assert_true(not GH.has_edge((u_G,u_H),(v_G,v_H)))

def create_list_of_adj_mats(n_runs):
    adj_mats = []
    for i in xrange(n_runs):
        # Creating connected adj matrix
        net = nx.erdos_renyi_graph(N, link_density)
        while len(list(nx.connected_components(net))) > 1:
            print "Network has isolated components. Try again!"
            net = nx.erdos_renyi_graph(N, link_density)
        adj_mats.append(nx.adj_matrix(net).toarray())

    return adj_mats

def test_lexicographic_product_random():
    G = nx.erdos_renyi_graph(10,2/10.)
    H = nx.erdos_renyi_graph(10,2/10.)
    GH = lexicographic_product(G,H)

    for (u_G,u_H) in GH.nodes_iter():
        for (v_G,v_H) in GH.nodes_iter():
            if G.has_edge(u_G,v_G) or (u_G==v_G and H.has_edge(u_H,v_H)):
                assert_true(GH.has_edge((u_G,u_H),(v_G,v_H)))
            else:
                assert_true(not GH.has_edge((u_G,u_H),(v_G,v_H)))

def test_cartesian_product_random():
    G = nx.erdos_renyi_graph(10,2/10.)
    H = nx.erdos_renyi_graph(10,2/10.)
    GH = cartesian_product(G,H)

    for (u_G,u_H) in GH.nodes_iter():
        for (v_G,v_H) in GH.nodes_iter():
            if (u_G==v_G and H.has_edge(u_H,v_H)) or \
               (u_H==v_H and G.has_edge(u_G,v_G)):
                assert_true(GH.has_edge((u_G,u_H),(v_G,v_H)))
            else:
                assert_true(not GH.has_edge((u_G,u_H),(v_G,v_H)))

 def __init__(self, name, para1, para2):
     if name == "PAM":
         # nodes, edges, and nodes > edges
         self.G = nx.barabasi_albert_graph(para1, para2)
         # nodes, prob
     elif name == "ER":
         self.G = nx.erdos_renyi_graph(para1, para2)

def generate_graph(n, expected_degree, model="ba"):
    """
    Generates a graph with a given model and expected_mean
    degree

    :param n: int Number of nodes of the graph
    :param expected_degree: int Expected mean degree
    :param model: string Model (ba, er, or ws)
    :return: networkx graph
    """

    global m
    global ws_p

    g = None
    if model == "ba":
        # BA expected avg. degree? m = ba_mean_degrees()
        if m is None:
            m = ba_mean_degrees(n, expected_degree)
        g = nx.barabasi_albert_graph(n, m, seed=None)
    if model == "er":
        # ER expected avg. degree: d = p*(n-1)
        p = float(expected_degree) / float(n - 1)
        g = nx.erdos_renyi_graph(n, p, seed=None, directed=False)
    if model == "ws":
        # WS expected degree == k
        g = nx.watts_strogatz_graph(n, expected_degree, ws_p)

    return g

    def __init__(self, size, probability):
        self.size = size
        self.probability = probability
        self.network = nx.erdos_renyi_graph(self.size, self.probability, directed=False)

        # Network stats object
        self.networkStats = Networkstats(self.network, self.size)

    def test_dyn_node_stochastic(self):

        dg = dn.DynGraph()

        for t in past.builtins.xrange(0, 3):
            g = nx.erdos_renyi_graph(200, 0.05)
            dg.add_interactions_from(g.edges(), t)

        model = gc.DynamicCompositeModel(dg)

        model.add_status("Susceptible")
        model.add_status("Infected")
        model.add_status("Removed")

        c1 = cpm.NodeStochastic(0.02, "Infected")
        c2 = cpm.NodeStochastic(0.01)
        c3 = cpm.NodeStochastic(0.5)

        model.add_rule("Susceptible", "Infected", c1)
        model.add_rule("Infected", "Removed", c2)
        model.add_rule("Infected", "Susceptible", c3)

        config = mc.Configuration()
        config.add_model_parameter('percentage_infected', 0.1)

        model.set_initial_status(config)
        iterations = model.execute_snapshots()
        self.assertEqual(len(iterations), 3)

        iterations = model.execute_iterations()
        trends = model.build_trends(iterations)
        self.assertEqual(len(trends[0]['trends']['status_delta'][1]),
                         len([x for x in dg.stream_interactions() if x[2] == "+"]))

def test_directed_havel_hakimi():
    # Test range of valid directed degree sequences
    n, r = 100, 10
    p = 1.0 / r
    for i in range(r):
        G1 = nx.erdos_renyi_graph(n, p * (i + 1), None, True)
        din1 = list(d for n, d in G1.in_degree())
        dout1 = list(d for n, d in G1.out_degree())
        G2 = nx.directed_havel_hakimi_graph(din1, dout1)
        din2 = list(d for n, d in G2.in_degree())
        dout2 = list(d for n, d in G2.out_degree())
        assert_equal(sorted(din1), sorted(din2))
        assert_equal(sorted(dout1), sorted(dout2))

    # Test non-graphical sequence
    dout = [1000, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
    din = [103, 102, 102, 102, 102, 102, 102, 102, 102, 102]
    assert_raises(nx.exception.NetworkXError,
                  nx.directed_havel_hakimi_graph, din, dout)
    # Test valid sequences
    dout = [1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
    din = [2, 2, 2, 2, 2, 2, 2, 2, 0, 2]
    G2 = nx.directed_havel_hakimi_graph(din, dout)
    dout2 = (d for n, d in G2.out_degree())
    din2 = (d for n, d in G2.in_degree())
    assert_equal(sorted(dout), sorted(dout2))
    assert_equal(sorted(din), sorted(din2))
    # Test unequal sums
    din = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
    assert_raises(nx.exception.NetworkXError,
                  nx.directed_havel_hakimi_graph, din, dout)
    # Test for negative values
    din = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, -2]
    assert_raises(nx.exception.NetworkXError,
                  nx.directed_havel_hakimi_graph, din, dout)

def spread_size_distribution_vs_probability():
    probabilities = [float(i) / 100. for i in range(100)]
    t = 1

    graph = sm.SISModel.get_opera_graph()
    seed = sm.SISModel.get_random_seed(graph, 100)
    print(seed)
    o_sizes = []

    for i in xrange(len(probabilities)):
        print(probabilities[i])
        o_sizes.append(sm.SISModel(graph, seed, probabilities[i], t).spread())
    print(o_sizes)

    graph = nx.barabasi_albert_graph(4604, 11)
    seed = sm.SISModel.get_random_seed(graph, 100)
    ba_sizes = []
    for i in xrange(len(probabilities)):
        ba_sizes.append(sm.SISModel(graph, seed, probabilities[i], t).spread())
    print(ba_sizes)

    graph = nx.erdos_renyi_graph(4604, 0.0047)
    seed = sm.SISModel.get_random_seed(graph, 100)
    er_sizes = []
    for i in xrange(len(probabilities)):
        er_sizes.append(sm.SISModel(graph, seed, probabilities[i], t).spread())
    print(er_sizes)

    sm.SISModel.plot_spread_size_distribution(
        probabilities,
        [o_sizes, ba_sizes, er_sizes],
        ['blue', 'black', 'red'],
        sm.SISModel.get_data_dir() + sm.SISModel.RESULT_DIR + 'spread_size_distribution.png',
        'p'
    )

 def test_bad_graph_input(self) :
     """modularity is only defined with undirected graph"""
     g = nx.erdos_renyi_graph(50, 0.1, directed=True)
     part = dict([])
     for node in g :
         part[node] = 0
     self.assertRaises(TypeError, co.modularity, part, g)

    def __init__(self, num_nodes=10, avg_node_degree=3, initial_outbreak_size=1, virus_spread_chance=0.4,
                virus_check_frequency=0.4, recovery_chance=0.3, gain_resistance_chance=0.5):

        self.num_nodes = num_nodes
        prob = avg_node_degree / self.num_nodes
        self.G = nx.erdos_renyi_graph(n=self.num_nodes, p=prob)
        self.grid = NetworkGrid(self.G)
        self.schedule = RandomActivation(self)
        self.initial_outbreak_size = initial_outbreak_size if initial_outbreak_size <= num_nodes else num_nodes
        self.virus_spread_chance = virus_spread_chance
        self.virus_check_frequency = virus_check_frequency
        self.recovery_chance = recovery_chance
        self.gain_resistance_chance = gain_resistance_chance

        self.datacollector = DataCollector({"Infected": number_infected,
                                            "Susceptible": number_susceptible,
                                            "Resistant": number_resistant})

        # Create agents
        for i, node in enumerate(self.G.nodes()):
            a = VirusAgent(i, self, State.SUSCEPTIBLE, self.virus_spread_chance, self.virus_check_frequency,
                           self.recovery_chance, self.gain_resistance_chance)
            self.schedule.add(a)
            # Add the agent to the node
            self.grid.place_agent(a, node)

        # Infect some nodes
        infected_nodes = self.random.sample(self.G.nodes(), self.initial_outbreak_size)
        for a in self.grid.get_cell_list_contents(infected_nodes):
            a.state = State.INFECTED

        self.running = True
        self.datacollector.collect(self)