def sway(model, order=4, use_bin_instead_of_con=False, applyseed=False, granularity=10):
global skiptime
skiptime = 0
from SAT_Guide.sway import bin_dominate, cont_dominate, sway, evalCount
cmpr = bin_dominate if use_bin_instead_of_con else cont_dominate
mins = [0, 0, 0, 0, 0]
maxs = [model.cnfNum, model.featureNum, model.featureNum, sum(model.defects), sum(model.cost)]
# generating the random seed
pop = []
tx = time.time()
if applyseed:
with open('/Users/jianfeng/git/SPL/j_res/seeding/'+model.name+'_SEEDs_10000_1.txt', 'r') as f:
lines = f.readlines()
lines = lines[:9500]
lines = map(lambda x:x.strip(), lines)
random.shuffle(lines)
else:
p = subprocess.Popen(
['java', '-jar', 'minisat.jar', model.name, str(model.featureNum), 'positive', str(int(10**order/3))],
stdout=subprocess.PIPE)
xs1 = p.communicate()[0].split('\n')[:-1]
p = subprocess.Popen(
['java', '-jar', 'minisat.jar', model.name, str(model.featureNum), 'negative', str(int(10 ** order / 3))],
stdout=subprocess.PIPE)
xs2 = p.communicate()[0].split('\n')[:-1]
p = subprocess.Popen(
['java', '-jar', 'minisat.jar', model.name, str(model.featureNum), 'random', str(int(10 ** order / 3))],
stdout=subprocess.PIPE)
xs3 = p.communicate()[0].split('\n')[:-1]
lines = xs1 + xs2 + xs3
# lines = xs3
random.shuffle(lines)
lines = list(set(lines))
for l in lines:
pop.append(model.Individual(l))
# for p in pop:
# model.eval(p)
# return pop, 10000, time.time()-tx
# final_pop = pop[:1000]
final_pop, evalC = sway(pop, ms=mins, Ms=maxs, evalfunc=model.eval, better=cmpr,granularity=granularity)
print evalC,
# if len(final_pop) > 500:
# final_pop = list(final_pop)
# random.shuffle(final_pop)
# final_pop = final_pop[:500]
for p in final_pop:
if not p.fitness.valid:
model.eval(p)
return final_pop, evalC
def running(model_name):
model = DimacsModel(model_name)
for i in [8, 32, 64, 128]:
print i
for repeat in range(1,8):
t1 = time.time()
rr, eval_count = sway(model, use_bin_instead_of_con=True, applyseed=True, granularity=i)
runtime = time.time() - t1
alg = 'SWAY4_D'
# with open("../j_res/{1}/{0}_{1}_{2}_{3}.txt".format(model_name, alg, eval_count, i), "w") as f:
with open("../j_res/e.txt", 'w') as f:
for r in rr:
f.write(r)
f.write('\n')
f.write('~~~\n')
for r in rr:
f.write(' '.join(map(str, r.fitness.values)))
f.write('\n')
f.write('~~~\n')
f.write(str(runtime+skiptime))
f.write('\n')
print get_stats('eshop', '/Users/jianfeng/git/SPL/j_res/e.txt')
