def action(model):
start_time = time.time()
# generating the 10k random solutions
candidates = list()
for _ in range(10000):
ran_dec = [random.random() for _ in range(model.decsNum)]
can = model.Individual(ran_dec)
candidates.append(can)
print('random sol created.')
for can in candidates:
model.eval(can)
print('finish evaluating.')
res = emo.sortNondominated(candidates, len(candidates), True)
print('finish selection.')
finish_time = time.time()
with open(request_new_file('./tse_rs/god', model.name), 'w') as f:
f.write('T:' + str(start_time) + '\n~~~\n')
f.write('T:' + str(finish_time) + '\n')
for front in res[0]:
f.write(' '.join(map(str, front.fitness.values)))
f.write('\n')
f.write('~~~\n')
return res
from __future__ import division
from Benchmarks.POM3 import pre_defined
from Algorithms.NSGA2 import action
from repeats import request_new_file
import time
import pdb
if __name__ == '__main__':
for x in [0, 1, 2]:
model = pre_defined()[x]
start_time = time.time()
res = action(model, mu=300, ngen=10000 // 100, cxpb=0.9, mutpb=0.15)
finish_time = time.time()
with open(request_new_file('./tse_rs/nsga2', model.name), 'w') as f:
f.write('T:' + str(start_time) + '\n~~~\n')
f.write('T:' + str(finish_time) + '\n')
for i in res:
f.write(' '.join(map(str, i.fitness.values)))
f.write('\n')
candidates = list()
for _ in range(10000):
ran_dec = [random.random() for _ in range(model.decsNum)]
can = model.Individual(ran_dec)
candidates.append(can)
global M
M = model
res = sway(candidates, model.eval, where, comparing)
return res
if __name__ == '__main__':
for repeat in range(1):
ii = [0, 1, 2, 3]
for i in ii:
XOMO_model = pre_defined()[i]
start_time = time.time()
res = get_sway_res(XOMO_model)
finish_time = time.time()
print(finish_time - start_time)
# save the results
with open(request_new_file('./tse_rs/sway', XOMO_model.name), 'w') as f:
f.write('T:' + str(start_time) + '\n~~~\n')
f.write('T:' + str(finish_time) + '\n')
for i in res:
f.write(' '.join(map(str, i.fitness.values)))
f.write('\n')
print('****** ' + str(repeat) + ' ******')
# for l in f:
# can = model.Individual(l.strip('\n'))
# candidates.append(can)
candidates = sat_gen_valid_pop(model, 10000)
res = sway(candidates, model.eval,
partial(split_products, groupC=min(15, model.featureNum // 7)),
comparing)
return res
if __name__ == '__main__':
# models = ['eshop']
#models = [ 'fiasco', 'freebsd', 'linux']
models = ['webportal']
for repeat in range(1):
for name in models:
print(name)
model = DimacsModel(name)
start_time = time.time()
res = get_sway_res(model)
finish_time = time.time()
# save the results
with open(request_new_file('./tse_rs/sway', name), 'w') as f:
f.write('T:' + str(start_time) + '\n~~~\n')
f.write('T:' + str(finish_time) + '\n')
for i in res:
f.write(' '.join(map(str, i.fitness.values)))
f.write('\n')
import sys
if __name__ == '__main__':
# save_stdout = sys.stdout
models = ['webportal', 'eshop', 'fiasco', 'freebsd', 'linux']
# models = ['webportal']
# for repeat in range(20):
for name in models:
fm = DimacsModel(name)
start_at = time.time()
res = SATIBEA.action(fm)
# sys.stdout = save_stdout
print(time.time() - start_at)
# save the results
with open(request_new_file('./tse_rs/satibea', name), 'w') as f:
f.write('T:' + str(start_at) + '\n')
f.write('~~~\n')
for log in res[1]:
gen = log['gen']
fitness = log['fitness']
at = log['time']
if fitness == 'pass':
continue
f.write('T:' + str(at) + '\n')
f.write('Gen: ' + str(gen) + '\n')
for i in fitness:
f.write(' '.join(map(str, i)))
def action(fm):
# get the 10k sat solutions
def sat_gen_valid_pop(n):
pops = list()
cnf = copy.deepcopy(fm.cnfs)
while len(pops) < n:
for index, sol in enumerate(pycosat.itersolve(cnf)):
new_ind = fm.Individual(''.join(
['1' if i > 0 else '0' for i in sol]))
pops.append(new_ind)
if index > 20:
break
for x in cnf:
random.shuffle(x)
random.shuffle(cnf)
# for c in range(n):
# if c % 1 == 0: print(c)
# for sol in pycosat.itersolve(cnf):
# new_ind = fm.Individual(''.join(['1' if i > 0 else '0' for i in sol]))
# print 'x'
#
# sol = pycosat.solve(cnf, vars=fm.featureNum)
# # ground_sol = copy.deepcopy(sol)
# if isinstance(sol, list):
# new_ind = fm.Individual(''.join(['1' if i > 0 else '0' for i in sol]))
# pops.append(new_ind)
# cnf.append([-x for x in sol])
# if c % 100 == 0:
# for x in cnf:
# random.shuffle(x)
# random.shuffle(cnf)
# else:
# break
random.shuffle(pops)
return pops
start_time = time.time()
print('start gen ' + fm.name)
pops = sat_gen_valid_pop(10000)
print('finish gen ' + fm.name)
candidates = [fm.Individual(i) for i in pops]
# pdb.set_trace()
# with open('/Users/jianfeng/Desktop/tse_rs/' + fm.name + '.txt', 'r') as f:
# for l in f:
# can = fm.Individual(l.strip('\n'))
# candidates.append(can)
# evaluate all
for can in candidates:
fm.eval(can)
res = emo.sortNondominated(candidates, len(candidates), True)
finish_time = time.time()
with open(request_new_file('./tse_rs/god', fm.name), 'w') as f:
f.write('T:' + str(start_time) + '\n~~~\n')
f.write('T:' + str(finish_time) + '\n')
for front in res[0]:
f.write(' '.join(map(str, front.fitness.values)))
f.write('\n')
f.write('~~~\n')