def GraphModelWithoutOverlaps(mu=0.2):
''' Trochu propojene komunity: mu = 0.2 '''
K = 3
omega = np.array([[1 - mu, mu, 0], [mu, 1 - 2 * mu, mu], [0, mu, 1 - mu]])
N = 100
membership = [np.random.randint(K) for _ in range(N)]
G = np.array([[int(membership[n] == k) for n in range(N)] for k in range(K)])
model = Model(G, omega)
return model
def GrafWithoutOverlaps(mu=0.2):
''' Trochu propojene komunity: mu = 0.2 '''
K = 3
omega = np.array([[1 - mu, mu, 0], [mu, 1 - 2 * mu, mu], [0, mu, 1 - mu]])
N = 100
membership = [np.random.randint(K) for _ in range(N)]
G = np.array([[int(membership[n] == k) for n in range(N)]
for k in range(K)])
model = Model(G, omega)
generateDetectAndSave(model, 'bezPrekryvu.gexf')
def GrafBezPrekryvuIzolovane():
''' komunity jsou komponenty souvislosti: mu = 0 '''
K = 3
omega = np.eye(K, K)
N = 100
membership = [np.random.randint(K) for _ in range(N)]
G = np.array([[int(membership[n] == k) for n in range(N)]
for k in range(K)])
model = Model(G, omega)
generateDetectAndSave(model, 'bezPrekryvuIzolovane.gexf')
def generateDetectAndSave(model: Model, fileName):
graph = Generator(model).next()
olapSBMmax, olapSBM = service.olapSBM(graph, 6, 50)
memberships = dict(bigClam=service.bigClam(graph),
louvain=service.louvain(graph),
olapSBMmax=olapSBMmax,
olapSBM=olapSBM,
biSBM=service.biSBM(graph))
evaluations = dict()
for method in memberships:
appendMemberships(graph, memberships[method], method)
evaluations[method] = Evaluator(model.getMemberships(),
memberships[method]).evaluate()
record = dict(graph=fileName, evaluations=evaluations)
recordEvaluation(record)
nx.write_gexf(graph, output(fileName))
def getModel(self) -> Model:
G = self._getGroupMatrix()
omega = self._getCommunityMatrix()
model = Model(G, omega)
return model
def getComsBiNum(model: Model):
if isinstance(model, BipartitniModel):
return (model.get_num_coms_type_a(), model.get_num_coms_type_b())
else: return (model.get_num_coms(), 0)