def gpmapasrecc(optstate, **para):
if para["onlyafter"] > len(optstate.y) or not len(optstate.y) % para["everyn"] == 0:
return [sp.NaN for i in para["lb"]], {"didnotrun": True}
logger.info("gpmapas reccomender")
d = len(para["lb"])
x = sp.hstack([sp.vstack(optstate.x), sp.vstack([e["xa"] for e in optstate.ev])])
y = sp.vstack(optstate.y)
s = sp.vstack([e["s"] for e in optstate.ev])
dx = [e["d"] for e in optstate.ev]
MAP = GPdc.searchMAPhyp(x, y, s, dx, para["mprior"], para["sprior"], para["kindex"])
logger.info("MAPHYP {}".format(MAP))
G = GPdc.GPcore(x, y, s, dx, GPdc.kernel(para["kindex"], d + 1, MAP))
def directwrap(xq, y):
xq.resize([1, d])
xe = sp.hstack([xq, sp.array([[0.0]])])
# print xe
a = G.infer_m(xe, [[sp.NaN]])
return (a[0, 0], 0)
[xmin, ymin, ierror] = DIRECT.solve(
directwrap, para["lb"], para["ub"], user_data=[], algmethod=1, volper=para["volper"], logfilename="/dev/null"
)
logger.info("reccsearchresult: {}".format([xmin, ymin, ierror]))
return [i for i in xmin], {"MAPHYP": MAP, "ymin": ymin}
def gpmapasrecc(optstate, **para):
if para['onlyafter'] > len(
optstate.y) or not len(optstate.y) % para['everyn'] == 0:
return [sp.NaN for i in para['lb']], {'didnotrun': True}
logger.info('gpmapas reccomender')
d = len(para['lb'])
x = sp.hstack(
[sp.vstack(optstate.x),
sp.vstack([e['xa'] for e in optstate.ev])])
y = sp.vstack(optstate.y)
s = sp.vstack([e['s'] for e in optstate.ev])
dx = [e['d'] for e in optstate.ev]
MAP = GPdc.searchMAPhyp(x, y, s, dx, para['mprior'], para['sprior'],
para['kindex'])
logger.info('MAPHYP {}'.format(MAP))
G = GPdc.GPcore(x, y, s, dx, GPdc.kernel(para['kindex'], d + 1, MAP))
def directwrap(xq, y):
xq.resize([1, d])
xe = sp.hstack([xq, sp.array([[0.]])])
#print xe
a = G.infer_m(xe, [[sp.NaN]])
return (a[0, 0], 0)
[xmin, ymin, ierror] = DIRECT.solve(directwrap,
para['lb'],
para['ub'],
user_data=[],
algmethod=1,
volper=para['volper'],
logfilename='/dev/null')
logger.info('reccsearchresult: {}'.format([xmin, ymin, ierror]))
return [i for i in xmin], {'MAPHYP': MAP, 'ymin': ymin}
def EIMAPaq(optstate,persist,ev=None, ub = None, lb=None, nrandinit=None, mprior=None,sprior=None,kindex = None,directmaxiter=None):
para = copy.deepcopy(para)
if persist==None:
persist = {'n':0,'d':len(ub)}
n = persist['n']
d = persist['d']
if n<nrandinit:
persist['n']+=1
return randomaq(optstate,persist,ev=ev,lb=lb,ub=ub)
logger.info('EIMAPaq')
#logger.debug(sp.vstack([e[0] for e in optstate.ev]))
#raise
x=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]
MAP = GPdc.searchMAPhyp(x,y,s,dx,mprior,sprior, kindex)
logger.info('MAPHYP {}'.format(MAP))
G = GPdc.GPcore(x,y,s,dx,GPdc.kernel(kindex,d,MAP))
def directwrap(xq,y):
xq.resize([1,d])
a = G.infer_lEI(xq,[ev['d']])
return (-a[0,0],0)
[xmin,ymin,ierror] = DIRECT.solve(directwrap,lb,ub,user_data=[], algmethod=0, maxf = directmaxiter, logfilename='/dev/null')
#logger.debug([xmin,ymin,ierror])
persist['n']+=1
return [i for i in xmin],ev,persist,{'MAPHYP':MAP,'logEImin':ymin,'DIRECTmessage':ierror}
def run_search(self):
MAP = GPdc.searchMAPhyp(self.X, self.Y, self.S, self.D, self.mprior,
self.sprior, self.kindex)
try:
del (self.G)
except:
pass
self.G = GPdc.GPcore(self.X, self.Y, self.S, self.D,
GPdc.kernel(self.kindex, self.d, MAP))
def directwrap(x, y):
x.resize([1, self.d])
a = self.G.infer_lEI(x, [[sp.NaN]])
#print [x,a]
#print G.infer_diag_post(x,[[sp.NaN]])
return (-a[0, 0], 0)
[xmin, ymin, ierror] = DIRECT.solve(directwrap,
self.lb,
self.ub,
user_data=[],
algmethod=1,
volper=self.volper,
logfilename='/dev/null')
return [xmin, self.s, [sp.NaN]]
def EIMAPaq(optstate,
persist,
ev=None,
ub=None,
lb=None,
nrandinit=None,
mprior=None,
sprior=None,
kindex=None,
directmaxiter=None):
para = copy.deepcopy(para)
if persist == None:
persist = {'n': 0, 'd': len(ub)}
n = persist['n']
d = persist['d']
if n < nrandinit:
persist['n'] += 1
return randomaq(optstate, persist, ev=ev, lb=lb, ub=ub)
logger.info('EIMAPaq')
#logger.debug(sp.vstack([e[0] for e in optstate.ev]))
#raise
x = 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]
MAP = GPdc.searchMAPhyp(x, y, s, dx, mprior, sprior, kindex)
logger.info('MAPHYP {}'.format(MAP))
G = GPdc.GPcore(x, y, s, dx, GPdc.kernel(kindex, d, MAP))
def directwrap(xq, y):
xq.resize([1, d])
a = G.infer_lEI(xq, [ev['d']])
return (-a[0, 0], 0)
[xmin, ymin, ierror] = DIRECT.solve(directwrap,
lb,
ub,
user_data=[],
algmethod=0,
maxf=directmaxiter,
logfilename='/dev/null')
#logger.debug([xmin,ymin,ierror])
persist['n'] += 1
return [i for i in xmin], ev, persist, {
'MAPHYP': MAP,
'logEImin': ymin,
'DIRECTmessage': ierror
}
def run_search(self):
MAP = GPdc.searchMAPhyp(self.X,self.Y,self.S,self.D,self.mprior,self.sprior, self.kindex)
try:
del(self.G)
except:
pass
self.G = GPdc.GPcore(self.X,self.Y,self.S,self.D,GPdc.kernel(self.kindex,self.d,MAP))
def directwrap(x,y):
x.resize([1,self.d])
a = self.G.infer_LCB(x,[[sp.NaN]],1.)[0,0]
return (a,0)
[xmin,ymin,ierror] = DIRECT.solve(directwrap,self.lb,self.ub,user_data=[], algmethod=1, volper=self.volper, logfilename='/dev/null')
return [xmin,self.s,[sp.NaN]]
GPdc.SQUEXP,
sp.array([0.9, 0.25]),
s=1e-8)
S *= 0.
f0 = plt.figure()
a0 = plt.subplot(111)
a0.plot(sp.array(X[:, 0]).flatten(), Y, 'g.')
lb = sp.array([-2., -2., -9])
ub = sp.array([2., 2., -1])
MLEH = GPdc.searchMLEhyp(X, Y, S, D, lb, ub, GPdc.SQUEXPCS, mx=10000)
mprior = sp.array([0., -1., -5.])
sprior = sp.array([1., 1., 3.])
MAPH = GPdc.searchMAPhyp(X, Y, S, D, mprior, sprior, GPdc.SQUEXPCS, mx=10000)
print "MLEH: " + str(MLEH)
print "MAPH: " + str(MAPH)
G = GPdc.GPcore(X, Y, S, D, GPdc.kernel(GPdc.SQUEXPCS, 1, sp.array(MLEH)))
print G.llk()
np = 180
sup = sp.linspace(-1, 1, np)
Dp = [[sp.NaN]] * np
Xp = sp.vstack([sp.array([i]) for i in sup])
[m, v] = G.infer_diag(Xp, Dp)
a0.plot(sup, m.flatten())
sq = sp.sqrt(v)
# To change this license header, choose License Headers in Project Properties. # To change this template file, choose Tools | Templates # and open the template in the editor. from scipy import stats as sps from scipy import linalg as spl import scipy as sp from matplotlib import pyplot as plt import GPdc ni = 100 kf = GPdc.kernel(GPdc.SQUEXP,2,sp.array([1.3,0.3,0.2])) X = sp.random.uniform(-1,1,size=[ni,2]) D = [[sp.NaN]]*ni Kxx = GPdc.buildKsym_d(kf,X,D) s = 1e-2 Y = spl.cholesky(Kxx,lower=True)*sp.matrix(sps.norm.rvs(0,1.,ni)).T+sp.matrix(sps.norm.rvs(0,s,ni)).T S = sp.ones(ni)*s print Y MLEHYP = GPdc.searchMLEhyp(X,Y,S,D,sp.array([2.,2.,2.]),sp.array([-2.,-2.,-2.]), GPdc.SQUEXP) print MLEHYP MAPHYP = GPdc.searchMAPhyp(X,Y,S,D,sp.array([0.,0.,0.]),sp.array([1.,1.,1.]), GPdc.SQUEXP) print MAPHYP
x = X[i,0]
s = -(1e-2)*(x-1.)*(x+1.1)
Y[i,0]+= sps.norm.rvs(0,sp.sqrt(s))
f0 = plt.figure()
a0 = plt.subplot(111)
a0.plot(sp.array(X[:,0]).flatten(),Y,'g.')
lb = sp.array([-2.,-2.,-9,-2.,-2.])
ub = sp.array([2.,2.,-1,2.,2.])
MLEH = GPdc.searchMLEhyp(X,Y,S,D,lb,ub,GPdc.SQUEXPPS,mx=20000)
mprior = sp.array([0.,-1.,-5.,-0.5,0.5])
sprior = sp.array([1.,1.,3.,1.,1.])
MAPH = GPdc.searchMAPhyp(X,Y,S,D,mprior,sprior,GPdc.SQUEXPPS,mx=20000)
print "MLEH: "+str(MLEH)
print "MAPH: "+str(MAPH)
G = GPdc.GPcore(X,Y,S,D,GPdc.kernel(GPdc.SQUEXPPS,1,sp.array(MAPH)))
print G.llk()
np=180
sup = sp.linspace(-1,1,np)
Dp = [[sp.NaN]]*np
Xp = sp.vstack([sp.array([i]) for i in sup])
[m,v] = G.infer_diag(Xp,Dp)
sq = sp.sqrt(v)
# To change this license header, choose License Headers in Project Properties.
# To change this template file, choose Tools | Templates
# and open the template in the editor.
from scipy import stats as sps
from scipy import linalg as spl
import scipy as sp
from matplotlib import pyplot as plt
import GPdc
ni = 100
kf = GPdc.kernel(GPdc.SQUEXP, 2, sp.array([1.3, 0.3, 0.2]))
X = sp.random.uniform(-1, 1, size=[ni, 2])
D = [[sp.NaN]] * ni
Kxx = GPdc.buildKsym_d(kf, X, D)
s = 1e-2
Y = spl.cholesky(Kxx, lower=True) * sp.matrix(sps.norm.rvs(
0, 1., ni)).T + sp.matrix(sps.norm.rvs(0, s, ni)).T
S = sp.ones(ni) * s
print Y
MLEHYP = GPdc.searchMLEhyp(X, Y, S, D, sp.array([2., 2., 2.]),
sp.array([-2., -2., -2.]), GPdc.SQUEXP)
print MLEHYP
MAPHYP = GPdc.searchMAPhyp(X, Y, S, D, sp.array([0., 0., 0.]),
sp.array([1., 1., 1.]), GPdc.SQUEXP)
print MAPHYP
