def run(self,mod):
frontier = []
for _ in range(self.candidates):
frontier.append(mod.generate())
generations = 0
lives = 5
baseline = frontier[:]
prev_era = self.fitting(mod,baseline)
for i in range(self.generations):
to_select_sample = self.select(mod,frontier) # binary denomination score of all pairs
sorted_sample = OrderedDict(sorted(to_select_sample.items(), key=lambda t: t[1],reverse=True)) # sorted by the scores
selected_sample = Utility.take(40, sorted_sample.iteritems()) # select 40% of the population
selection_pool = []
for key, value in selected_sample:
selection_pool.append(frontier[key])
new_frontier = self.get_new_frontier(mod,selection_pool) # perform crossover
mutated_frontier = self.do_mutation(mod,new_frontier) # mutated some candidates
cur_era = self.fitting(mod,mutated_frontier) #average
generations += 1
ret_val = Utility.better(mod,prev_era,cur_era)
if ret_val > 0:
prev_era = cur_era[:]
lives += ret_val
if lives is 0:
return prev_era
#print "Generations : ", generations
#print "Best Solution : ", sum(cur_era)
return cur_era
def mws(mod):
max_tries = 100
max_changes = 100
threshold = 0.001
p = 0.05
lives = 3
best_sn = mod.generate()
norm = mod.baseline_study()
norm, best_sc = mod.get_energy(best_sn,norm)
prev = mod.default_objs()
k = 1
Utility.say(str(k)+"|")
for i in range(max_tries):
sn = mod.generate()
cur = []
for j in range(max_changes):
norm,score = mod.get_energy(sn,norm)
if score < threshold:
f1,f2 = mod.objs(sn)
norm,score = mod.get_energy(sn,norm)
best_sn = sn[:]
best_sc = score
Utility.printOutput("Success", f1,f2,best_sn,best_sc)
return best_sn
limit = len(mod.decisons)-1 if len(mod.decisons) > 0 else 0
c = random.randint(0,limit)
if p < random.random():
temp = mutate(mod,sn,c)
if mod.checkconstraint(temp):
sn = temp[:]
Utility.say('?')
else:
Utility.say('.')
else:
changed, local_sn = maximizesolution(mod,sn,c,norm)
if changed:
norm,local_sc = mod.get_energy(local_sn,norm)
if local_sc < best_sc:
best_sc = local_sc
best_sn = local_sn[:]
sn = local_sn[:]
Utility.say('+')
else:
Utility.say('.')
cur.append(mod.objs(sn))
cur = map(Utility.mean, zip(*cur))
if Utility.better(prev,cur):
lives -= 1
if lives is 0:
break
else:
prev = cur[:]
k += max_changes
Utility.say("\n"+str(int(k))+"|")
f1,f2 = mod.objs(sn)
Utility.printOutput('Failure',f1,f2,best_sn,best_sc)
return best_sn
def sa(mod):
s = mod.generate()
norm = mod.baseline_study()
norm,e = mod.get_energy(s,norm)
sb = s[:]
eb = e
k = 1.0
kmax = 1499.0
lives = 5
Utility.say("1|")
prev = mod.default_objs()
cur = []
status = "Success"
while k < kmax:
sn = mod.generate()
norm, en = mod.get_energy(sn,norm)
if en < eb:
sb = sn[:]
eb = en
Utility.say("!")
if en < e:
s = sn[:]
e = en
Utility.say("+")
elif mod.prob(e, en, k*7/(kmax)) < random.random():
s = sn
e = en
Utility.say("?")
Utility.say(".")
cur.append(mod.objs(sb))
k = k + 1
if k % 100 == 0:
cur = map(Utility.mean, zip(*cur))
if Utility.better(prev,cur):
lives -= 1
if lives is 0:
status = "Terminated"
break
else:
prev = cur[:]
cur = []
Utility.say("\n"+str(int(k))+"|")
f1,f2 = mod.objs(sb)
Utility.printOutput(status,f1,f2,sb,f1+f2)
return sb
def run(self, mod):
frontier = []
for _ in range(self.candidates):
frontier.append(mod.generate())
generations = 0
lives = 5
baseline = frontier[:]
prev_era = self.fitting(mod, baseline)
#print "Optimizer"
for i in range(self.generations):
#print "Start generation"
to_select_sample = self.select(
mod, frontier) # binary denomination score of all pairs
sorted_sample = OrderedDict(
sorted(to_select_sample.items(),
key=lambda t: t[1],
reverse=True)) # sorted by the scores
slice = int(0.5 * self.candidates)
selected_sample = Utility.take(
slice,
sorted_sample.iteritems()) # select 50% of the population
selection_pool = []
for key, value in selected_sample:
selection_pool.append(frontier[key])
new_frontier = self.get_new_frontier(
mod, selection_pool) # perform crossover
mutated_frontier = self.do_mutation(
mod, new_frontier) # mutated some candidates
cur_era = self.fitting(mod, mutated_frontier) #average
generations += 1
if Utility.better(mod, prev_era, cur_era):
prev_era = cur_era[:]
lives += 0
else:
lives += -1
if lives is 0:
return prev_era
#print "Generations : ", generations
#print "Best Solution : ", sum(cur_era)
return cur_era
