def test_trend_comparison(self):
# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)
# Model selection
model = epd.SIRModel(g)
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.001)
cfg.add_model_parameter('gamma', 0.02)
cfg.add_model_parameter("percentage_infected", 0.01)
model.set_initial_status(cfg)
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
model1 = epd.SIModel(g)
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.001)
cfg.add_model_parameter("percentage_infected", 0.01)
model1.set_initial_status(cfg)
iterations = model1.iteration_bunch(200)
trends1 = model1.build_trends(iterations)
viz = DiffusionTrendComparison([model, model1], [trends, trends1])
viz.plot("trend_comparison.pdf")
os.remove("trend_comparison.pdf")
def test_trend_comparison(self):
# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)
# Model selection
model = sir.SIRModel(g)
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.001)
cfg.add_model_parameter('gamma', 0.02)
cfg.add_model_parameter("percentage_infected", 0.01)
model.set_initial_status(cfg)
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
model1 = si.SIModel(g)
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.001)
cfg.add_model_parameter("percentage_infected", 0.01)
model1.set_initial_status(cfg)
iterations = model1.iteration_bunch(200)
trends1 = model1.build_trends(iterations)
viz = DiffusionTrendComparison([model, model1], [trends, trends1])
viz.plot("trend_comparison.pdf")
os.remove("trend_comparison.pdf")
cfg.add_model_parameter('beta', b)
infected_nodes3 = [37]
cfg.add_model_initial_configuration("Infected", infected_nodes3)
model2.set_initial_status(cfg)
# 3° Simulation execution
iterations = model2.iteration_bunch(it)
trends3 = model2.build_trends(iterations)
#trends3 = multi_runs(model2, execution_number=ex, iteration_number=it, nprocesses=4)
#--------------------------------------------------------------
# In[2]:
from ndlib.viz.mpl.TrendComparison import DiffusionTrendComparison
viz = DiffusionTrendComparison([model, model1, model2], [trends1, trends2,trends3], statuses=['Infected'])
viz.plot(percentile=90)
#viz.plot("img1.png",percentile=90)
# In[3]:
from ndlib.viz.mpl.PrevalenceComparison import DiffusionPrevalenceComparison
viz = DiffusionPrevalenceComparison([model, model1, model2], [trends1, trends2, trends3], statuses=['Infected'])
viz.plot(percentile=90)
#viz.plot("img2.png",percentile=90)
nx.barabasi_albert_graph(1000, 8, seed=0),
nx.newman_watts_strogatz_graph(1000, k=4, p=0.12, seed=0),
nx.fast_gnp_random_graph(n=1000, p=0.20)
]
model_list = []
iteration_list = []
trend_list = []
for graph in G_list:
model = ep.SIRModel(graph)
model_list.append(model)
config = mc.Configuration()
config.add_model_parameter('beta', 0.1)
config.add_model_parameter('gamma', 0.1)
config.add_model_parameter('fraction_infected', 0.01)
model.set_initial_status(config)
for i in range(3):
iterations = model_list[i].iteration_bunch(100)
iteration_list.append(iterations)
trends = model.build_trends(iterations)
trend_list.append(trends)
visual = DiffusionTrendComparison(
[model_list[0], model_list[1], model_list[2]],
[trend_list[0], trend_list[1], trend_list[2]],
statuses=["Susceptible", "Infected", "Removed"])
plt.title(" BA vs SW vs Random")
result = visual.plot()