def learn_xnes_batch():
x0 = np.array([0.0, 0.0, 0.0])
l = opt.XNES(objf_batch, x0)
l.minimize = True
l.mustMinimize = True
l.verbose = False
l.maxLearningSteps = int(args["<n>"])
l.batchSize = 25
r = l.learn()
return r
def learn_xnes(x0=np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0])):
try:
l = opt.XNES(objf, x0)
l.minimize = True
l.mustMinimize = True
l.verbose = False
l.maxLearningSteps = int(args["<n>"])
l.batchSize = 25
r = l.learn()
return r
except:
print("errrrrrrrrrrror single learning")
p.wait()
objs = [float(x.stdout.read()) for x in processes]
sum_objs = sum(objs)
return (sum_objs)
print("blablabla")
for f in args["<swf_file>"]:
print(type(f))
print("performance of optimized policy on training set:")
#x0 = array([0.0 for e in norm[0]])
x0 = array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
import pybrain.optimization as opt
lopt = [
("xnes", opt.XNES(objf, x0)),
# ("r1",opt.Rank1NES(objf, x0)),
# ("snes",opt.SNES(objf, x0)),
# ("fem",opt.FEM(objf, x0)),
# ("ves",opt.VanillaGradientEvolutionStrategies(objf, x0)),
# ("memetic",opt.MemeticSearch(objf, x0)),
# ("inmemetic",opt.InnerMemeticSearch(objf, x0)),
# ("imemetic",opt.InverseMemeticSearch(objf, x0)),
# ("inimemetic",opt.InnerInverseMemeticSearch(objf, x0)),
# ("pso",opt.ParticleSwarmOptimizer(objf, x0)),
# ("ga",opt.GA(objf, x0)),
# ("spsa",opt.SimpleSPSA(objf, x0)),
# ("pgpe",opt.PGPE(objf, x0)),
# ("es",opt.ES(objf, x0))
]
for desc, l in lopt:
def objf(x):
processes = [
subprocess.Popen(getperf(f, x), stdout=subprocess.PIPE, shell=True)
for f in args["<swf_file>"]
]
for p in processes:
if p.poll() is None:
p.wait()
objs = [float(x.stdout.read()) for x in processes]
return (sum(objs))
print("performance of optimized policy on training set:")
x0 = array([0.0, 0.0])
import pybrain.optimization as opt
lopt = [("xnes", opt.XNES(objf, x0))]
# ("snes",opt.SNES(objf, x0)),
# ("fem",opt.FEM(objf, x0)),
# ("memetic",opt.MemeticSearch(objf, x0)),
# ("inmemetic",opt.InnerMemeticSearch(objf, x0)),
# ("imemetic",opt.InverseMemeticSearch(objf, x0)),
# ("inimemetic",opt.InnerInverseMemeticSearch(objf, x0)),
# ("spsa",opt.SimpleSPSA(objf, x0)),
# ("pgpe",opt.PGPE(objf, x0)),
# ("es",opt.ES(objf, x0))]
for desc, l in lopt:
l.minimize = True
l.mustMinimize = True
l.maxEvaluations = int(args["<n>"])
r = l.learn()
print(desc)