def run_search(self):
print "begin PES:"
try:
del (self.pesobj)
except:
pass
self.pesobj = PES.PES_inplane(
self.X,
self.Y,
self.S,
self.D,
self.lb.flatten(),
self.ub.flatten(),
self.para['kindex'],
self.para['mprior'],
self.para['sprior'],
self.para['axis'],
self.para['value'],
DH_SAMPLES=self.para['DH_SAMPLES'],
DM_SAMPLES=self.para['DM_SAMPLES'],
DM_SUPPORT=self.para['DM_SUPPORT'],
DM_SLICELCBPARA=self.para['DM_SLICELCBPARA'],
mode=self.para['SUPPORT_MODE'],
noS=True)
self.train_costest()
[Qmin, ymin,
ierror] = self.pesobj.search_acq(self.costest,
self.para['sfn'],
volper=self.para['volper'])
return [Qmin, 0., [sp.NaN]]
def PESbsaq(optstate, persist, **para):
para = copy.deepcopy(para)
if persist == None:
persist = {'n': 0, 'd': len(para['ub'])}
n = persist['n']
d = persist['d']
if n < para['nrandinit']:
persist['n'] += 1
para['ev']['xa'] = sp.random.uniform(para['xal'], para['xau'])
return randomaq(optstate, persist, **para)
logger.info('PESssaq')
x = sp.hstack(
[sp.vstack([e['xa'] for e in optstate.ev]),
sp.vstack(optstate.x)])
y = sp.vstack(optstate.y)
s = sp.vstack([e['s'] for e in optstate.ev])
dx = [e['d'] for e in optstate.ev]
print "\n at pesinplane x {} axis 0".format(x)
pesobj = PES.PES_inplane(x,
y,
s,
dx, [para['xal']] + para['lb'],
[para['xau']] + para['ub'],
para['kindex'],
para['mprior'],
para['sprior'],
0,
0,
DH_SAMPLES=para['DH_SAMPLES'],
DM_SAMPLES=para['DM_SAMPLES'],
DM_SUPPORT=para['DM_SUPPORT'],
DM_SLICELCBPARA=para['DM_SLICELCBPARA'],
mode=para['SUPPORT_MODE'])
if para['traincfn']: #
#print "XXXXXXXXXXXXXXx"
cx = sp.vstack([e['xa'] for e in optstate.ev])
cc = sp.vstack([e for e in optstate.c])
#print cx
#print cc
#print optstate.ev
#print optstate.x
cfn = objectives.traincfn(cx, cc)
"""
if len(cc)%5==0:
from matplotlib import pyplot as plt
f,a = plt.subplots(1)
xt = sp.linspace(0,1,100)
m = sp.empty(100)
for i in xrange(100):
m[i]=cfn(0.,**{'xa':xt[i]})
a.plot(xt,m,'b')
for i in xrange(len(optstate.c)):
a.plot(cx[i,0],cc[i,0],'ro')
plt.show()
"""
else:
cfn = para['cfn']
[xmin, ymin, ierror] = pesobj.search_acq(cfn,
lambda s: para['ev']['s'],
volper=para['volper'])
logger.debug([xmin, ymin, ierror])
para['ev']['xa'] = xmin[0]
xout = [i for i in xmin[1:]]
return xout, para['ev'], persist, {
'HYPdraws': [k.hyp for k in pesobj.G.kf],
'mindraws': pesobj.Z,
'DIRECTmessage': ierror,
'PESmin': ymin
}
sp.array([1.5, 0.55, 0.25]))
kindex = GPdc.SQUEXP
mprior = sp.array([0.] + [-1.] * d)
sprior = sp.array([1.] * (d + 1))
axis = 0
value = 0.
pesobj = PES.PES_inplane(X,
Y,
S,
D,
lb,
ub,
kindex,
mprior,
sprior,
axis,
value,
DH_SAMPLES=8,
DM_SAMPLES=8,
DM_SUPPORT=400,
DM_SLICELCBPARA=1.,
AM_POLICY=PES.NOMIN,
mode=ESutils.SUPPORT_SLICEEI)
def cfn(x):
return 1. - (0.6 * x[0])**0.1
def sfn(x):
