def PESIPS(ojf, lb, ub, ki, b, fname):
sp.random.seed(int(os.urandom(4).encode('hex'), 16))
para = dict()
para['kindex'] = ki[0]
para['mprior'] = ki[1]
para['sprior'] = ki[2]
para['d'] = lb.size + 1
para['ninit'] = 10
para['volper'] = 1e-7
para['DH_SAMPLES'] = 12
para['DM_SAMPLES'] = 12
para['DM_SUPPORT'] = 1200
para['DM_SLICELCBPARA'] = 1.
para['SUPPORT_MODE'] = [
ESutils.SUPPORT_LAPAPR
] #[ESutils.SUPPORT_SLICELCB,ESutils.SUPPORT_SLICEEI]
para['sl'] = 0.
para['su'] = 1.
para['s'] = 0.
para['sfn'] = None
para['axis'] = 0
para['value'] = para['sl']
if os.path.exists(fname):
print "starting from " + str(fname)
OE = OPTutils.PESIPS(ojf,
lb,
ub,
para,
initstate=pickle.load(open(fname, 'rb')))
else:
print "fresh start"
OE = OPTutils.PESIPS(ojf, lb, ub, para)
if sum(OE.C) >= b:
print "no further steps needed"
k = 0
while sum(OE.C[:k]) < b:
k += 1
state = [
OE.X[:k, :], OE.Y[:k, :], OE.S[:k, :], OE.D[:k], OE.R[:k, :],
OE.C[:k], OE.T[:k], OE.Tr[:k], OE.Ymin[:k], OE.Xmin[:k, :],
OE.Yreg[:k, :], OE.Rreg[:k, :]
]
else:
pbar = tqdm(total=(b - sum(OE.C)))
while sum(OE.C) < b:
print "used {0} of {1} eval budget".format(sum(OE.C), b)
pbar.update(OE.C[-1])
OE.step()
state = [
OE.X, OE.Y, OE.S, OE.D, OE.R, OE.C, OE.T, OE.Tr, OE.Ymin, OE.Xmin,
OE.Yreg, OE.Rreg
]
pickle.dump(state, open(fname, 'wb'))
return state
para['d'] = d+1
para['ninit'] = 10
para['volper'] = 1e-7
para['DH_SAMPLES'] = 8
para['DM_SAMPLES'] = 8
para['DM_SUPPORT'] = 800
para['DM_SLICELCBPARA'] = 1.
para['SUPPORT_MODE'] = [ESutils.SUPPORT_SLICELCB]
#para['cfn'] = lambda x,s: sp.exp(-0.5*x.flatten()[0])
para['sl'] = 0.
para['su'] = 1.
para['s'] = 0.
para['sfn'] = None
para['axis'] = 0
para['value'] = para['sl']
OI = OPTutils.PESIPS(ojf,lb,ub,para)
for i in tqdm(xrange(2)):
OI.step()
print OI.X
print OI.R
print OI.Y
print OI.Ymin
print OI.Rreg
#f,a = plt.subplots(7)
OI.plot(sp.hstack([para['sl'],truexmin]),ymin)
#f.savefig("../../figs/testIPopt.pdf")