fname = Fname(origin=fname_origin, log=fname_log)
return Param(IND_SIZE=IND_SIZE, n=n, k=k, fname=fname)
if __name__ == "__main__":
if len(sys.argv) != 3 or not sys.argv[1].isdecimal(
) or not sys.argv[2].isdecimal():
print("usage: {0} <IND_SIZE> <k>".format(sys.argv[0]))
sys.exit()
param = get_param(int(sys.argv[1]), int(sys.argv[2])) # input IND_SIZE, k
model_origin = problem.nk_landscape.from_setting(param.n, param.IND_SIZE,
param.k)
ga.to_pkl(model_origin, param.fname.origin)
for i in range(100):
random.seed(i)
model = copy.deepcopy(model_origin)
model, logbook = ga.run(model, ngen=3000)
to_csv(param.fname.log.format(i=i), logbook, exist_header=True)
# calculate information
total_time = sum(logbook.select('time'))
maxidx = numpy.argmax(logbook.select('max'))
maxval = logbook.select('max')[maxidx]
print("idx: {0}, best_fit: {1}, time: {2}".format(
) or not sys.argv[2].isdecimal():
print("usage: {0} <IND_SIZE> <k>".format(sys.argv[0]))
sys.exit()
param = get_param(int(sys.argv[1]), int(sys.argv[2])) # input IND_SIZE, k
if not os.path.exists(param.fname.tar):
print("requirment: python -O experiment_nk_landscape <IND_SIZE> <k>")
sys.exit()
model_tar, _ = ga.from_pkl(param.fname.tar)
model_ori = problem.good_basis.from_setting(param.n, model_tar,
param.IND_SIZE)
model_ori, logbook = ga.run(model_ori, param.IND_SIZE * 4)
ga.to_pkl(model_ori, param.fname.ori)
to_csv(param.fname.log_ori, logbook, exist_header=True)
ind = model_ori.halloffame[0]
""" only load
model_tar, _ = ga.from_pkl(param.fname.tar)
model_ori, _ = ga.from_pkl(param.fname.ori)
ind = model_ori.halloffame[0]
"""
for i in range(100):
random.seed(i)
model = ind.change_basis(model_tar)
model, logbook = ga.run(model, ngen=3000)
to_csv(param.fname.log_tar.format(i=i), logbook, exist_header=True)
with open(fname, "wb") as fout:
pickle.dump(basis, fout)
return
if __name__ == "__main__":
if len(sys.argv) != 2 or not sys.argv[1].isdecimal():
print("usage: {0} <IND_SIZE>".format(sys.argv[0]))
sys.exit()
param = get_param(int(sys.argv[1])) # input IND_SIZE
save_basis(param.basis, param.fname.basis)
model_origin = problem.v_onemax.from_setting(param.n, param.IND_SIZE,
param.basis)
ga.to_pkl(model_origin, param.fname.origin) #, with_rndstate=True)
for i in range(100):
random.seed(i)
model = copy.deepcopy(model_origin)
model, logbook = ga.run_target(model,
ngen=100,
fit_val_target=(param.IND_SIZE, ),
nloop=100)
to_csv(param.fname.log.format(i=i), logbook, exist_header=True)
# calculate information
total_time = sum(logbook.select('time'))
maxidx = numpy.argmax(logbook.select('max'))