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 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
