def gloutonV2(k, graph, verbose):
g.gloutonWithCut(k, graph, verbose, True)
def kl(graph,verbose):
print "Starting KL version 2.."
startTime = time.time()
## Initialisation ##
# Liste de partitions
partitionsList = []
# Initialisation des listes de sommets à étudier
# (une par partition)
remainingNodesS1 = []
remainingNodesS2 = []
## Déroulement de l'algo ##
# bipartition
partitionsList, graph = g.gloutonWithCut(2,graph,False,False)
if verbose:
print "Partition Init 1: ", partitionsList[0]
print "Partition Init 2: ", partitionsList[1]
deltaTime = -time.time()
ratio = 20
# Mise a jour des listes des sommets à étudier
remainingNodesS1 = list(partitionsList[0])[len(partitionsList[0])*(100-ratio)/100:]
remainingNodesS2 = list(partitionsList[1])[len(partitionsList[1])*(100-ratio)/100:]
# calcul du gain global initial
globalMin = objf.calculateCut(remainingNodesS1,remainingNodesS2,graph)
# Initialisation du gain fictif
GainGlobalFictif = globalMin
if verbose:
print "Global Min Initial:", globalMin
print ""
print "Recherche de sommets uniques à changer..."
nodesList = nx.nodes(graph)
iterations = 100
while iterations > 0 :
improvement = False
localMin = sys.maxint
for node in nodesList:
gain = calculateFictifGainUnique(node, partitionsList[0], partitionsList[1], graph)
# recherche du meilleur gain local
if gain < localMin and gain > 0 :
improvement = True
localMin = gain
s = node
GainGlobalFictif = globalMin - gain
if verbose:
print "Nouveau gain local:", localMin, "Node: ", node
print "Gain Global Fictif =", GainGlobalFictif
if improvement :
nodesList.remove(s)
if (GainGlobalFictif < globalMin):
globalMin = GainGlobalFictif
if verbose:
print "Nouveau gain global:", globalMin, "Node:", s
print "Le noeud",s,"a été échangé de partition"
partitionsList = switchNodesUnique(partitionsList,s)
iterations = iterations - 1
stopTime = time.time()
deltaTime += time.time()
print "Temps d'exécution total:", stopTime-startTime
print "Temps d'exécution KL:", deltaTime
if verbose:
print "Partition Finale 1:", partitionsList[0]
print "Partition Finale 2:", partitionsList[1]
print "Global cut:", objf.calculateCut(partitionsList[0], partitionsList[1], graph)
def recuitSimule(graph, iterations, tempI, remonteeMax,verbose):
print "Starting Recuit Simule with", iterations, "iterations,", tempI, "as initial temperature and", remonteeMax, "as max upgoing movements"
deltaTime0 = -time.time()
## Initialisation ##
# Liste de partitions
partitionsList = []
# Appel du Glouton
partitionsList, graph = g.gloutonWithCut(2,graph,False,False)
P1Final = list(partitionsList[0])
P2Final = list(partitionsList[1])
if verbose :
print "Partition P1 init :", P1Final
print "Partition P2 init :", P2Final
print "Number of nodes in graph:", nx.number_of_nodes(graph)
# calcul du gain global initial
globalMin = objf.calculateCut(partitionsList[0],partitionsList[1],graph)
# Initialisation du gain fictif
GainGlobalFictif = globalMin
print "Global Min Initial:", globalMin
# Temps de départ
deltaTime = -time.time()
# Nombre d'iteration de l'algorithme
iterationsC = 0
# Nombre courant de remontée de temperature consecutives
remontee = 0
# Temperature courante
tempC = tempI
list_pourcentage = [0,10,20,30,40,50,60,70,80,90]
nodesOnLimit,neighborsList = neighbors(partitionsList[0],graph)
# Boucle principale
while iterationsC < iterations :
pourcentage = (iterationsC*100)/iterations
if pourcentage in list_pourcentage:
del(list_pourcentage[0])
print '------------------',pourcentage, "% effectués", '------------------'
change = False
# Candidat aleatoire de la partition P1
nodeA = selectNode(nodesOnLimit)
# Candidat aleatoire de la partition P2
nodeB = selectNode(neighborsList)
# Calcul du gain lié à l'échange de nodeA et nodeB : G(nodeA,nodeB)
gainAB = calculateFictifGain(nodeA, nodeB, partitionsList[0], partitionsList[1], graph)
if verbose:
print "Gain entre",nodeA,"et",nodeB,"=",gainAB
# Amelioration solution locale
if gainAB >= 0 :
change = True
remontee = 0
if verbose :
print "Descente en temperature avec les noeuds", nodeA, "et", nodeB
else:
# Acceptation de la solution dégradante
if random() < exp(gainAB/tempC) and remontee < remonteeMax :
change = True
remontee +=1
if verbose :
print "Remontee en temperature avec les noeuds", nodeA, "et", nodeB
# Changement a effectuer
if change :
# On echange les sommets nodeA et nodeB
partitionsList = switchNodes(partitionsList,nodeA,nodeB)
updateNeighbors(nodeA,nodeB,nodesOnLimit,neighborsList,partitionsList[0],partitionsList[1],graph)
GainGlobalFictif = GainGlobalFictif - gainAB
if verbose:
print "Les noeuds",nodeA,"et",nodeB, "ont été échangés"
print "Gain fictif =", GainGlobalFictif
if GainGlobalFictif < globalMin and GainGlobalFictif > 0:
globalMin = GainGlobalFictif
print "Update globalMin =",globalMin
P1Final = list(partitionsList[0])
P2Final = list(partitionsList[1])
# cas où la remontee de temperature n'a que fait dégrader la solution
if remontee == remonteeMax and not change :
if verbose:
print "Degradation trop élevée --> récupération des anciennes partitions"
# récupération de la meilleure partition
partitionsList[0] = list(P1Final)
partitionsList[1] = list(P2Final)
nodesOnLimit,neighborsList = neighbors(partitionsList[0],graph)
addRandomNode(nodesOnLimit, partitionsList[0])
addRandomNode(neighborsList, partitionsList[1])
iterationsC +=1
tempC = 0.99*tempC
deltaTime += time.time()
deltaTime0 += time.time()
if verbose:
print "Partition Finale 1:", P1Final
print "Partition Finale 2:", P2Final
print "Temps d'exécution recuit-simule:", deltaTime
print "Temps d'exécution global:", deltaTime0
print "Global cut:", globalMin