def getClusters(self) :
PASS=1
matrix=csr_matrix(self.similarityMatrixBuilder)
continu=True
realClusters=[]
clusters=[]
nextMatrix=[]
firstRound=True
while continu :
print "-"*40
print "PASS #{0}".format(PASS)
PASS+=1
clustersNumber,clusters=LouvainClusterer.getOnePassLouvainCommunities(matrix)
if firstRound :
firstRound=False
realClusters=clusters
else :
LouvainClusterer.updateRealClusters(realClusters,clusters)
if (clustersNumber==matrix.shape[0]) :
continu=False
else :
nextMatrix=LouvainClusterer.buildNewSimilarityMatrix(matrix,clusters,clustersNumber)
matrix=nextMatrix
return np.array(realClusters)
def getClusters(self):
PASS = 1
matrix = csr_matrix(self.similarityMatrixBuilder)
continu = True
realClusters = []
clusters = []
nextMatrix = []
firstRound = True
while continu:
print "-" * 40
print "PASS #{0}".format(PASS)
PASS += 1
clustersNumber, clusters = LouvainClusterer.getOnePassLouvainCommunities(
matrix)
if firstRound:
firstRound = False
realClusters = clusters
else:
LouvainClusterer.updateRealClusters(realClusters, clusters)
if (clustersNumber == matrix.shape[0]):
continu = False
else:
nextMatrix = LouvainClusterer.buildNewSimilarityMatrix(
matrix, clusters, clustersNumber)
matrix = nextMatrix
return np.array(realClusters)
def getClusters(self):
PASS = 1
matrix = csr_matrix(self.similarityMatrixBuilder)
continu = True
realClusters = []
clusters = []
nextMatrix = []
firstRound = True
while continu:
print "-" * 40
print "PASS #{0}".format(PASS)
PASS += 1
# on recupere le nombre de clusters et le vecteur de mapping obtenus a l'aide de la matrice de similarité
clustersNumber, clusters = LouvainClusterer.getOnePassLouvainCommunities(
matrix)
# on charge nos donnees de clusters dans realClusters
if firstRound:
firstRound = False
realClusters = clusters
else:
LouvainClusterer.updateRealClusters(realClusters, clusters)
# on regarde si on a reussi a reduire le nombre de clusters
# si oui on réitère en modifiant la matrice de travail
# et ce jusqu'à ne plus arriver à réduire le nbr de clusters
if (clustersNumber == matrix.shape[0]):
continu = False
else:
nextMatrix = LouvainClusterer.buildNewSimilarityMatrix(
matrix, clusters, clustersNumber)
matrix = nextMatrix
return np.array(realClusters)
