示例#1
0
    def test_seis_model(self):
        g = nx.erdos_renyi_graph(1000, 0.1)
        model = epd.SEISModel(g)
        config = mc.Configuration()
        config.add_model_parameter('beta', 0.5)
        config.add_model_parameter('lambda', 0.2)
        config.add_model_parameter('alpha', 0.05)
        config.add_model_parameter("fraction_infected", 0.1)
        model.set_initial_status(config)
        iterations = model.iteration_bunch(10)
        self.assertEqual(len(iterations), 10)

        g = g.to_directed()
        model = epd.SEISModel(g)
        config = mc.Configuration()
        config.add_model_parameter('beta', 0.5)
        config.add_model_parameter('lambda', 0.8)
        config.add_model_parameter('alpha', 0.5)
        config.add_model_parameter("fraction_infected", 0.1)
        model.set_initial_status(config)
        iterations = model.iteration_bunch(10, node_status=False)
        self.assertEqual(len(iterations), 10)
示例#2
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    def test_seis_model(self):
        for g in get_graph(True):
            model = epd.SEISModel(g)
            config = mc.Configuration()
            config.add_model_parameter('beta', 0.5)
            config.add_model_parameter('lambda', 0.2)
            config.add_model_parameter('alpha', 0.05)
            config.add_model_parameter("fraction_infected", 0.1)
            model.set_initial_status(config)
            iterations = model.iteration_bunch(10)
            self.assertEqual(len(iterations), 10)

        for g in get_directed_graph(True):
            model = epd.SEISModel(g)
            config = mc.Configuration()
            config.add_model_parameter('beta', 0.5)
            config.add_model_parameter('lambda', 0.8)
            config.add_model_parameter('alpha', 0.5)
            config.add_model_parameter("fraction_infected", 0.1)
            model.set_initial_status(config)
            iterations = model.iteration_bunch(10, node_status=False)
            self.assertEqual(len(iterations), 10)
示例#3
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    ###############################################################

    SIModel = ep.SIModel(g.copy())
    SIModel.set_initial_status(get_si_params())
    SI_iterations = SIModel.iteration_bunch(num_iterations)
    SI_trends = SIModel.build_trends(SI_iterations)
    visualize(SIModel, SI_trends, sub_dir='epidemics')

    SISModel = ep.SISModel(g.copy())
    SISModel.set_initial_status(get_sis_params())
    SIS_iterations = SISModel.iteration_bunch(num_iterations)
    SIS_trends = SISModel.build_trends(SIS_iterations)
    visualize(SISModel, SIS_trends, sub_dir='epidemics')

    SEISModel = ep.SEISModel(g.copy())
    SEISModel.set_initial_status(get_seis_params())
    SEIS_iterations = SEISModel.iteration_bunch(num_iterations)
    SEIS_trends = SEISModel.build_trends(SEIS_iterations)
    visualize(SEISModel, SEIS_trends, sub_dir='epidemics')

    SEIRModel = ep.SEIRModel(g.copy())
    SEIRModel.set_initial_status(get_seir_params())
    SEIR_iterations = SEIRModel.iteration_bunch(num_iterations)
    SEIR_trends = SEIRModel.build_trends(SEIR_iterations)
    visualize(SEIRModel, SEIR_trends, sub_dir='epidemics')

    ###############################################################

    voter_model = op.VoterModel(g.copy())
    voter_model.set_initial_status(get_voter_params())