def test_config(self):
for g in get_graph(True):
model = epd.ThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
config.add_model_initial_configuration("Infected", [1, 2, 3])
config.add_node_set_configuration("partial", {1: 1, 2: 2})
try:
model.set_initial_status(config)
except:
pass
if isinstance(g, nx.Graph):
edges = list(g.edges)
nodes = list(g.nodes)
else:
edges = [(g.vs[e.tuple[0]]['name'], g.vs[e.tuple[1]]['name'])
for e in g.es]
nodes = g.vs['name']
config.add_edge_set_configuration("partial",
{e: 1
for e in edges[:10]})
try:
model.set_initial_status(config)
except:
pass
config.add_node_set_configuration("partial", {n: 1 for n in nodes})
config.add_edge_set_configuration("partial", {e: 1 for e in edges})
model.set_initial_status(config)
for g in get_graph():
model = opn.MajorityRuleModel(g)
config = mc.Configuration()
config.add_model_parameter("percentage_infected", 0.2)
try:
model.set_initial_status(config)
except:
pass
for g in get_graph(True):
model = epd.IndependentCascadesModel(g)
config = mc.Configuration()
config.add_model_parameter('percentage_infected', 0.1)
try:
model.set_initial_status(config)
except:
pass
for g in get_graph(True):
model = epd.ThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('percentage_infected', 0.1)
try:
model.set_initial_status(config)
except:
pass
def test_majorityrule_model(self):
for g in get_graph():
model = opn.MajorityRuleModel(g)
config = mc.Configuration()
config.add_model_parameter("q", 3)
config.add_model_parameter("fraction_infected", 0.2)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
iterations = model.iteration_bunch(10, node_status=False)
self.assertEqual(len(iterations), 10)
def test_majorityrule_model(self):
g = nx.complete_graph(100)
model = opn.MajorityRuleModel(g)
config = mc.Configuration()
config.add_model_parameter("q", 3)
config.add_model_parameter("percentage_infected", 0.2)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
iterations = model.iteration_bunch(10, node_status=False)
self.assertEqual(len(iterations), 10)
def test_config(self):
g = nx.erdos_renyi_graph(99, 0.1)
model = epd.ThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
config.add_model_initial_configuration("Infected", [1, 2, 3])
config.add_node_set_configuration("partial", {1: 1, 2: 2})
try:
model.set_initial_status(config)
except:
pass
config.add_edge_set_configuration("partial",
{e: 1
for e in list(g.edges)[:10]})
try:
model.set_initial_status(config)
except:
pass
config.add_node_set_configuration("partial", {n: 1 for n in g.nodes})
config.add_edge_set_configuration("partial", {e: 1 for e in g.edges})
model.set_initial_status(config)
g = nx.complete_graph(100)
model = opn.MajorityRuleModel(g)
config = mc.Configuration()
config.add_model_parameter("fraction_infected", 0.2)
try:
model.set_initial_status(config)
except:
pass
g = nx.erdos_renyi_graph(1000, 0.1)
model = epd.IndependentCascadesModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
try:
model.set_initial_status(config)
except:
pass
g = nx.erdos_renyi_graph(1000, 0.1)
model = epd.ThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
try:
model.set_initial_status(config)
except:
pass
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())
voter_iterations = voter_model.iteration_bunch(num_iterations)
voter_trends = voter_model.build_trends(voter_iterations)
visualize(voter_model, voter_trends, sub_dir='opinions')
QVoter_model = op.QVoterModel(g.copy())
QVoter_model.set_initial_status(get_qvoter_params())
QVoter_iterations = QVoter_model.iteration_bunch(num_iterations)
QVoter_trends = QVoter_model.build_trends(QVoter_iterations)
visualize(QVoter_model, QVoter_trends, sub_dir='opinions')
majority_rule_model = op.MajorityRuleModel(g.copy())
majority_rule_model.set_initial_status(get_majority_rules_params())
majority_rule_iterations = majority_rule_model.iteration_bunch(
num_iterations)
majority_rule_trends = majority_rule_model.build_trends(
majority_rule_iterations)
visualize(majority_rule_model, majority_rule_trends, sub_dir='opinions')
sznajd_model = op.SznajdModel(g.copy())
sznajd_model.set_initial_status(get_sznajd_params())
sznajd_iterations = sznajd_model.iteration_bunch(num_iterations)
sznajd_trends = sznajd_model.build_trends(sznajd_iterations)
visualize(sznajd_model, sznajd_trends, sub_dir='opinions')
