def type2(self, era_one, era_two):
for objective in self.get_objectives():
era_one_objective = []
era_two_objective = []
for i in xrange(0, len(era_one)):
era_one_objective.append(objective(era_one[i]))
era_two_objective.append(objective(era_two[i]))
if (a12(era_one_objective, era_two_objective) > 0.56):
return 5
return -1
def type2(self, era_one, era_two):
for objective in self.get_objectives():
era_one_objective = []
era_two_objective = []
for i in xrange(0, len(era_one)):
era_one_objective.append(objective(era_one[i]))
era_two_objective.append(objective(era_two[i]))
if (a12(era_one_objective, era_two_objective) > 0.56):
return 5
return -1
def run(self):
max_tries = 100
max_changes = 50
self.model.resetBaselines()
best_state = self.model.get_random_state()
output = str()
lives = 5
ERA_LENGTH = 25
current_era = []
eras = []
for _ in range(0, max_tries):
current_state = self.model.get_random_state()
for i in range(0, max_changes):
if self.model.energy(current_state) < self.threshold:
return current_state
else:
new_state = self.retry(current_state)
operation = ""
if self.model.energy(new_state) > self.model.energy(best_state):
best_state = new_state
operation = "!"
elif self.model.energy(new_state) > self.model.energy(current_state):
operation = "+"
elif self.model.energy(new_state) < self.model.energy(current_state):
operation = "."
output += operation
current_era.append(self.model.normalize_energy(self.model.energy(current_state), self.model.baseline_low, self.model.baseline_high))
if len(current_era) == ERA_LENGTH and len(eras) > 0:
if a12(current_era, eras[-1]) < 0.56:
lives -= 1
#print "reducing the life by 1 lives: " + str(lives)
else:
lives = 5
if lives <= 0:
print "Early termination"
return
if len(current_era) == ERA_LENGTH:
eras.append(current_era)
current_era = []
if len(output) == 50:
print "Lives: " + str(lives) + " " + output + " current best state energy(normalized) = " + str(self.model.energy(best_state)) + " Evaluations: " + str(self.evals)
output = ""
print output + " current best state energy(normalized) = " + str(self.model.aggregate_energy(best_state)) + " Evaluations: " + str(self.evals)
return best_state
def same(): return a12(new, old)<=0.56 if self.less:
def run(self):
kmax = 1000
max_e = -0.1
output = ''
current_s = self.model.get_random_state()
best_s = current_s
current_e = self.model.normalize_energy(self.model.energy(current_s), self.model.baseline_low, self.model.baseline_high)
best_e = current_e
k = 1
current_era = []
eras = []
lives = 5
ERA_LENGTH = 10
while k < kmax and current_e > max_e:
neighbor_s = self.model.get_random_state()
# print 'Neighbor: ' + str(neighbor_s)
neighbor_e = self.model.normalize_energy(self.model.energy(neighbor_s), self.model.baseline_low, self.model.baseline_high)
# print current_e, neighbor_e, tmp
if neighbor_e < best_e:
best_s, best_e = neighbor_s, neighbor_e
output += ' !'
if neighbor_e < current_e:
current_s, current_e = neighbor_s, neighbor_e
output += ' +'
elif self.P(current_e, neighbor_e, (1-(float(k)/kmax))**5) > random.random():
current_s, current_e = neighbor_s, neighbor_e
output += ' ?'
else:
output += ' .'
current_era.append(self.model.normalize_energy(current_e, self.model.baseline_low, self.model.baseline_high))
if len(current_era) == ERA_LENGTH and len(eras) > 0:
if a12(current_era, eras[-1]) < 0.56:
lives -= 1
print "Lives:" + str(lives) + " a12 statistic:" + str(a12(current_era, eras[-1])),
else:
lives = 5
if len(current_era) == ERA_LENGTH:
eras.append(current_era)
current_era = []
if lives <= 0:
print "Early termination"
break
if k % 25 == 0:
print ', %4d, : %d, %25s' % (k, self.model.denormalize_energy(best_e), output)
output = ''
k += 1
print
print 'Best State Found: ' + str(best_s)
print 'Energy At Best State: ' + str(self.model.denormalize_energy(best_e))
print
return best_e
def run(self):
kmax = 1000
max_e = -0.1
output = ''
current_s = self.model.get_random_state()
best_s = current_s
current_e = self.model.normalize_energy(self.model.energy(current_s),
self.model.baseline_low,
self.model.baseline_high)
best_e = current_e
k = 1
current_era = []
eras = []
lives = 5
ERA_LENGTH = 10
while k < kmax and current_e > max_e:
neighbor_s = self.model.get_random_state()
# print 'Neighbor: ' + str(neighbor_s)
neighbor_e = self.model.normalize_energy(
self.model.energy(neighbor_s), self.model.baseline_low,
self.model.baseline_high)
# print current_e, neighbor_e, tmp
if neighbor_e < best_e:
best_s, best_e = neighbor_s, neighbor_e
output += ' !'
if neighbor_e < current_e:
current_s, current_e = neighbor_s, neighbor_e
output += ' +'
elif self.P(current_e, neighbor_e,
(1 - (float(k) / kmax))**5) > random.random():
current_s, current_e = neighbor_s, neighbor_e
output += ' ?'
else:
output += ' .'
current_era.append(
self.model.normalize_energy(current_e, self.model.baseline_low,
self.model.baseline_high))
if len(current_era) == ERA_LENGTH and len(eras) > 0:
if a12(current_era, eras[-1]) < 0.56:
lives -= 1
print "Lives:" + str(lives) + " a12 statistic:" + str(
a12(current_era, eras[-1])),
else:
lives = 5
if len(current_era) == ERA_LENGTH:
eras.append(current_era)
current_era = []
if lives <= 0:
print "Early termination"
break
if k % 25 == 0:
print ', %4d, : %d, %25s' % (
k, self.model.denormalize_energy(best_e), output)
output = ''
k += 1
print
print 'Best State Found: ' + str(best_s)
print 'Energy At Best State: ' + str(
self.model.denormalize_energy(best_e))
print
return best_e
