def getOnePassLouvainCommunities(matrix) :
ITER=1
matrixSize=matrix.shape[0]
sumsOfWeights=np.array([sum(matrix.getrow(i).data)+sum(matrix.getcol(i).data) for i in range(matrixSize)])
totalSumOfWeight=sum(sumsOfWeights)
clusters=np.array(range(0,matrixSize))
modified=True
while modified :
print " ITER #{0}".format(ITER)
ITER+=1
modified=False
for i in range(0,matrixSize) :
newCluster=LouvainClusterer.getArgMaxModularity(matrix,clusters,sumsOfWeights,totalSumOfWeight,i)
if (newCluster!=-1) :
modified=True
clusters[i]=newCluster
newClusterIdentifiers={}
clustersNumber=0
for i in range(0,len(clusters)) :
if (clusters[i] in newClusterIdentifiers) :
clusters[i]=newClusterIdentifiers[clusters[i]]
else :
newClusterIdentifiers[clusters[i]]=clustersNumber
clusters[i]=clustersNumber
clustersNumber+=1
print "End of a Pass"
print "-"*40
return clustersNumber, clusters
def getOnePassLouvainCommunities(matrix):
ITER = 1
matrixSize = matrix.shape[0]
sumsOfWeights = np.array([
sum(matrix.getrow(i).data) + sum(matrix.getcol(i).data)
for i in range(matrixSize)
])
totalSumOfWeight = sum(sumsOfWeights)
clusters = np.array(range(0, matrixSize))
modified = True
while modified:
print " ITER #{0}".format(ITER)
ITER += 1
modified = False
for i in range(0, matrixSize):
newCluster = LouvainClusterer.getArgMaxModularity(
matrix, clusters, sumsOfWeights, totalSumOfWeight, i)
if (newCluster != -1):
modified = True
clusters[i] = newCluster
newClusterIdentifiers = {}
clustersNumber = 0
for i in range(0, len(clusters)):
if (clusters[i] in newClusterIdentifiers):
clusters[i] = newClusterIdentifiers[clusters[i]]
else:
newClusterIdentifiers[clusters[i]] = clustersNumber
clusters[i] = clustersNumber
clustersNumber += 1
print "End of a Pass"
print "-" * 40
return clustersNumber, clusters
def getOnePassLouvainCommunities(matrix):
ITER = 1
matrixSize = matrix.shape[0]
# on somme les valeurs en colonnes et en lignes pour chaque cluster de la matrice
# sumsOfWeights -> liste de matrixSize valeurs
sumsOfWeights = np.array([
sum(matrix.getrow(i).data) + sum(matrix.getcol(i).data)
for i in range(matrixSize)
])
# totalSumOfWeight -> somme de toutes les valeurs
totalSumOfWeight = sum(sumsOfWeights)
# on crée une liste de matrixSize valeurs pour representer les clusters
# au début on a autant de clusteur que d'elements de la matrice
clusters = np.array(range(0, matrixSize))
modified = True
# boucle pour agglomérer les elements entre eux
while modified:
print " ITER #{0}".format(ITER)
ITER += 1
modified = False
for i in range(0, matrixSize):
newCluster = LouvainClusterer.getArgMaxModularity(
matrix, clusters, sumsOfWeights, totalSumOfWeight, i)
if (newCluster != -1):
modified = True
clusters[i] = newCluster
# on aplique un reduce sur le vecteur clusters pour avoir les vrais clusters
# newClusterIdentifiers est une map d'association entre les clusters du vecteur cluster et les nouveaux clusters
newClusterIdentifiers = {}
clustersNumber = 0
for i in range(0, len(clusters)):
if (clusters[i] in newClusterIdentifiers):
clusters[i] = newClusterIdentifiers[clusters[i]]
else:
newClusterIdentifiers[clusters[i]] = clustersNumber
clusters[i] = clustersNumber
clustersNumber += 1
print "End of a Pass"
print "-" * 40
return clustersNumber, clusters
