def delta(self, n, o, d):
dK = 0
dW = {o:0, d:0}
nE = set()
dE = set()
for v in self.neighbors(n):
w = self[n][v]['weight']
e = self[n][v]['edge']
p = self.node[v]['partition']
if p == d:
dK -= w
dW[d] += w
dE.add(e)
elif p == o:
dK += w
dW[o] -= w
nE.add(e)
W = {p: self.W[p] for p in self.W}
W[o] += dW[o]
W[d] += dW[d]
dB = max(W.values()) * self.k / float(sum(W.values())) - self.eval['to']['balance']
Wop = self.W[o] + dW[o]
Wdp = self.W[d] + dW[d]
Ko = self.K[o] * self.W[d] * Wop * Wdp
Kd = self.K[d] * self.W[o] * Wop * Wdp
Kop = (self.K[o] + dK) * self.W[o] * self.W[d] * Wdp
Kdp = (self.K[d] + dK) * self.W[o] * self.W[d] * Wop
Dn = self.W[o] * self.W[d] * Wop * Wdp
dR = division(Kop + Kdp - Ko - Kd , Dn)
return {'+e': nE, '-e': dE, 'dK': dK, 'dW': dW,
'cut': dK, 'balance': dB, 'ratio': dR}
def partition(self, k=2, depth=None, theta=None, preemption=['ratio'], replays=0, rand=False, log=Stub()):
eval = preemption[0]
# Get P, E and C from a greedy solution
super(Local, self).partition(k, select='weight', rand=rand)
self.eval = {'from': {}, 'to': {}}
self.eval['from']['cut'] = self.cut()
self.eval['to']['cut'] = self.eval['from']['cut']
self.eval['from']['balance'] = self.balance()
self.eval['to']['balance'] = self.eval['from']['balance']
self.eval['from']['ratio'] = sum([division(self.K[p], self.W[p]) for p in range(k)])
self.eval['to']['ratio'] = self.eval['from']['ratio']
min = {e: self.eval['to'][e] for e in self.eval['to']}
iter = 0
path = []
best = []
done = set()
pool = [self.set_pool(eval, done)]
move = Depth()
step = 0
try:
while iter >= 0:
while len(pool[iter]) != 0:
label = pool[iter].pop()
delta = label.delta[eval]
self.do(label)
if not self.broke(label, move.value, eval=eval, depth=depth, theta=theta):
step += 1
path.append(label)
log.write('Iteration ' + str(step) + '\n')
log.write('Eval(' + eval + ') ' + str(self.eval['to'][eval]) + '\n')
if compare(self.eval['to'], min, preemption) < 0:
min = {e: self.eval['to'][e] for e in self.eval['to']}
best = [l for l in path]
move.set(label=label)
move.forward(label, eval=eval)
pool.append(self.set_pool(eval, done, label=label))
iter += 1
else:
self.undo(label)
if iter != 0:
label = path.pop()
self.undo(label)
move.backward(label, eval=eval)
if self.broke(label, move.value, eval=eval, back=True, depth=depth, theta=theta):
break
pool.pop()
iter -= 1
while len(path) != 0:
label = path.pop()
self.undo(label)
for label in best:
self.do(label)
if compare(self.eval['to'], self.best['to'], preemption) < 0:
P = [set() for p in range(self.k)]
for n in self.nodes_iter():
P[self.node[n]['partition']].add(n)
self.best['state'] = P
self.best['step'] = step
self.best['from'] = {e: self.eval['from'][e] for e in self.eval['from']}
self.best['to'] = {e: self.eval['to'][e] for e in self.eval['to']}
log.write('Operation success in ' + str(step) + ' iterations\n')
log.write(str(self) + '\n\n')
except KeyboardInterrupt:
if compare(self.eval['to'], self.best['to'], preemption) < 0:
self.best['state'] = P
self.best['step'] = step
self.best['from'] = {e: self.eval['from'][e] for e in self.eval['from']}
self.best['to'] = {e: self.eval['to'][e] for e in self.eval['to']}
eval = 'Operation interrupted during process\n'
eval += 'Iteration ' + str(self.best['step']) + '\n'
eval += 'Best solution found:\n'
eval += str(self) + '\n'
raise KeyboardInterrupt(eval)
if replays > 0:
self.partition(k=k, depth=depth, theta=theta, preemption=preemption, replays=replays - 1, rand=True, log=log)
else:
self.apply(self.best)
