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)