def bounds(Xs, Ys, ns=100):
#use a gp to infer mean and bounds on sets of x/y data that have diffent x
#f,a = plt.subplots(2)
#for i in xrange(len(Ys)):
# a[0].plot(Xs[i],Ys[i])
X = sp.hstack(Xs)
np = X.size
Y = sp.hstack(Ys)
X.resize([np, 1])
Y.resize([np, 1])
#a[1].plot(X,Y,'r.')
np = X.size
S = sp.zeros(np)
D = [[sp.NaN]] * np
ki = GPdc.MAT52CS
mprior = sp.array([1., 2., 1.])
sprior = sp.array([2., 2., 2.])
#MAPH = GPdc.searchMAPhyp(X,Y,S,D,mprior,sprior, ki,mx=500)
MAPH = sp.array([0.5, 5., 0.3])
g = GPdc.GPcore(X, Y, S, D, GPdc.kernel(ki, 1, MAPH))
sup = sp.linspace(min(X), max(X), ns)
[m, V] = g.infer_diag_post(sup, [[sp.NaN]] * ns)
std = sp.sqrt(V + MAPH[2])
#plt.fill_between(sup.flatten(),(m-std).flatten(),(m+std).flatten(),facecolor='lightblue',edgecolor='lightblue',alpha=0.5)
#a[1].plot(sup,m.flatten(),'b')
return [sup, m, std]
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 genmat52ojf(d, lb, ub):
from ESutils import gen_dataset
nt = 14
[X, Y, S, D] = gen_dataset(nt, d, lb, ub, GPdc.MAT52,
sp.array([1.5] + [0.55] * d))
G = GPdc.GPcore(X, Y, S, D,
GPdc.kernel(GPdc.MAT52, d, sp.array([1.5] + [0.55] * d)))
def ojf(x, **ev):
dx = ev['d']
s = ev['s']
if ev['s'] > 0:
noise = sp.random.normal(scale=sp.sqrt(ev['s']))
else:
noise = 0
return G.infer_m(sp.array(x), [dx])[0, 0] + noise, 1., dict()
def dirwrap(x, y):
z = G.infer_m(x, [[sp.NaN]])[0, 0]
#z = obj(x,0.,[sp.NaN])
return (z, 0)
[xmin, ymin, ierror] = DIRECT.solve(dirwrap,
lb,
ub,
user_data=[],
algmethod=1,
maxf=89000,
logfilename='/dev/null')
logger.info('generated function xmin {} ymin {} {}'.format(
xmin, ymin, ierror))
return ojf, xmin, ymin
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 gensquexpdraw(d, lb, ub, ignores=-1):
nt = 14
[X, Y, S, D] = ESutils.gen_dataset(nt, d, lb, ub, GPdc.SQUEXP,
sp.array([1.5] + [0.30] * d))
G = GPdc.GPcore(X, Y, S, D,
GPdc.kernel(GPdc.SQUEXP, d, sp.array([1.5] + [0.30] * d)))
def obj(x, s, d, override=False):
#print [x,s,d]
if ignores > 0:
s = ignores
if s == 0. or override:
noise = 0.
else:
noise = sp.random.normal(scale=sp.sqrt(s))
print "EVAL WITH NOISE: " + str(noise) + "FROM S= " + str(s)
return [G.infer_m(x, [d])[0, 0] + noise, 1.]
def dirwrap(x, y):
z = G.infer_m(x, [[sp.NaN]])[0, 0]
#z = obj(x,0.,[sp.NaN])
return (z, 0)
[xmin, ymin, ierror] = DIRECT.solve(dirwrap,
lb,
ub,
user_data=[],
algmethod=1,
maxf=89000,
logfilename='/dev/null')
return [obj, xmin, ymin]
def makeG(X,Y,S,D,kindex,mprior,sprior,nh):
#draw hyps based on plk
#print "RRRRRRRRRRRRRR"+str([X,Y,S,D,kindex,mprior,sprior,nh])
H = ESutils.drawhyp_plk(X,Y,S,D,kindex,mprior,sprior,nh)
G = GPdc.GPcore(X,Y,S,D,[GPdc.kernel(kindex,X.shape[1],i) for i in H])
return G
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 gensquexpIPdraw(d, lb, ub, sl, su, sfn, sls, cfn):
#axis = 0 value = sl
#d dimensional objective +1 for s
nt = 25
#print sp.hstack([sp.array([[sl]]),lb])
#print sp.hstack([sp.array([[su]]),ub])
[X, Y,
S, D] = ESutils.gen_dataset(nt, d + 1,
sp.hstack([sp.array([[sl]]), lb]).flatten(),
sp.hstack([sp.array([[su]]), ub]).flatten(),
GPdc.SQUEXP,
sp.array([1.5] + [sls] + [0.30] * d))
G = GPdc.GPcore(
X, Y, S, D,
GPdc.kernel(GPdc.SQUEXP, d + 1, sp.array([1.5] + [sls] + [0.30] * d)))
def obj(x, s, d, override=False):
x = x.flatten()
if sfn(x) == 0. or override:
noise = 0.
else:
noise = sp.random.normal(scale=sp.sqrt(sfn(x)))
return [G.infer_m(x, [d])[0, 0] + noise, cfn(x)]
def dirwrap(x, y):
z = obj(sp.array([[sl] + [i for i in x]]), sl, [sp.NaN], override=True)
return (z, 0)
[xmin0, ymin0, ierror] = DIRECT.solve(dirwrap,
lb,
ub,
user_data=[],
algmethod=1,
maxf=89000,
logfilename='/dev/null')
lb2 = xmin0 - sp.ones(d) * 1e-4
ub2 = xmin0 + sp.ones(d) * 1e-4
[xmin, ymin, ierror] = DIRECT.solve(dirwrap,
lb2,
ub2,
user_data=[],
algmethod=1,
maxf=89000,
logfilename='/dev/null')
#print "RRRRR"+str([xmin0,xmin,ymin0,ymin,xmin0-xmin,ymin0-ymin])
return [obj, xmin, ymin]
def __init__(self,X,Y,S,D,lb,ub,kindex,mprior,sprior,DH_SAMPLES=8,DM_SAMPLES=8, DM_SUPPORT=400,DM_SLICELCBPARA=1.,mode=ESutils.SUPPORT_SLICELCB,noS=False):
print "PES init:"
self.lb=lb
self.ub=ub
self.noS=noS
if noS:
S=sp.zeros(S.shape)
self.G = makeG(X,Y,S,D,kindex,mprior,sprior,DH_SAMPLES)
HS = sp.vstack([k.hyp for k in self.G.kf])
print "\nhyp mean: "+str(sp.mean(HS,axis=0))
print "hyp std: "+str(sp.sqrt(sp.mean(HS,axis=0)))
self.Z = drawmins(self.G,DM_SAMPLES,lb,ub,SUPPORT=DM_SUPPORT,SLICELCB_PARA=DM_SLICELCBPARA,mode=mode)
print "mindraws: "+str(self.Z)
self.Ga = [GPdc.GPcore(*addmins(self.G,X,Y,S,D,self.Z[i,:])+[self.G.kf]) for i in xrange(DM_SAMPLES)]
def __init__(self,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=NOMIN,mode=ESutils.SUPPORT_SLICELCB,noS=False):
print "PES init:"
self.lb=lb
self.ub=ub
self.noS=noS
self.X=X
if noS:
S=sp.zeros(S.shape)
print [X.shape,Y.shape,mprior,sprior]
self.G = makeG(X,Y,S,D,kindex,mprior,sprior,DH_SAMPLES)
HS = sp.vstack([k.hyp for k in self.G.kf])
print "\nhyp mean: "+str(sp.mean(HS,axis=0))
print "hyp std: "+str(sp.sqrt(sp.mean(HS,axis=0)))
self.Z = drawmins_inplane(self.G,DM_SAMPLES,lb,ub,axis=axis,value=value,SUPPORT=DM_SUPPORT,SLICELCB_PARA=DM_SLICELCBPARA,mode=mode)
print "mindraws:\n"+str(self.Z)
self.Ga = [GPdc.GPcore(*addmins_inplane(self.G,X,Y,S,D,self.Z[i,:],axis=axis,value=value,MINPOLICY=AM_POLICY)+[self.G.kf]) for i in xrange(DM_SAMPLES)]
return
def train_costest(self):
print self.X[:, 0]
X = self.X[:, 0].copy().reshape([len(self.C), 1])
C = sp.array(self.C)
D = [[sp.NaN]] * len(self.C)
S = sp.zeros([len(self.C), 1])
lbc = sp.array([-3., -2., -6.])
ubc = sp.array([3., 2., 1.])
MLEC = GPdc.searchMLEhyp(X,
sp.log(C),
S,
D,
lbc,
ubc,
GPdc.SQUEXPCS,
mx=10000)
print "MLEC " + str(MLEC)
self.ce = GPdc.GPcore(X.copy(), sp.log(C), S, D,
GPdc.kernel(GPdc.SQUEXPCS, 1, sp.array(MLEC)))
#self.ce = GPdc.GPcore(X,C,S,D,GPdc.kernel(GPdc.SQUEXPCS,1,sp.array([1.,0.3,1e-3])))
#self.ce.printc()
return
def genbiasedmat52ojf(d, lb, ub, sls):
#s normalised to 0 exact, 1
from ESutils import gen_dataset
nt = 20
[X, Y, S, D] = gen_dataset(nt, d + 1, [0.] + lb, [1.] + ub, GPdc.MAT52,
sp.array([1.5] + [0.30] * d + [sls]))
G = GPdc.GPcore(
X, Y, S, D,
GPdc.kernel(GPdc.MAT52, d + 1, sp.array([1.5] + [0.30] * d + [sls])))
def ojf(x, **ev):
#print "\nojfinput: {} : {}".format(x,ev)
dx = ev['d']
s = ev['s']
if ev['s'] > 0:
noise = sp.random.normal(scale=sp.sqrt(ev['s']))
else:
noise = 0
xa = ev['xa']
x = sp.array(x)
xfull = sp.hstack([x, xa])
return G.infer_m(xfull, [dx])[0, 0] + noise, 1., dict()
def dirwrap(x, y):
z = G.infer_m(sp.hstack(sp.array(x) + [0.]), [[sp.NaN]])[0, 0]
return (z, 0)
[xmin, ymin, ierror] = DIRECT.solve(dirwrap,
lb,
ub,
user_data=[],
algmethod=1,
maxf=89000,
logfilename='/dev/null')
logger.info('generated function xmin {} ymin {}'.format(xmin, ymin))
return ojf, xmin, ymin
nt=34 X = ESutils.draw_support(2, sp.array([-1.,-1.]),sp.array([1.,1.]),nt,ESutils.SUPPORT_UNIFORM) D = [[sp.NaN]]*(nt) hyp = sp.array([1.5,0.25,0.25]) kf = GPdc.gen_sqexp_k_d(hyp) Kxx = GPdc.buildKsym_d(kf,X,D) Y = spl.cholesky(Kxx,lower=True)*sp.matrix(sps.norm.rvs(0,1.,nt)).T+sp.matrix(sps.norm.rvs(0,1e-3,nt)).T S = sp.matrix([1e-6]*nt).T lb = sp.array([-2.,-2.,-2.]) ub = sp.array([2.,2.,2.]) #MLEH = GPdc.searchMLEhyp(X,Y,S,D,lb,ub,GPdc.SQUEXP,mx=10000) G = GPdc.GPcore(X,Y,S,D,GPdc.kernel(GPdc.SQUEXP,2,sp.array([1.5,0.15,0.15]))) #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) #sq = sp.sqrt(v) #a0.fill_between(sup, sp.array(m-2.*sq).flatten(), sp.array(m+2.*sq).flatten(), facecolor='lightblue',edgecolor='lightblue') Z = ESutils.draw_support(G, sp.array([-1.,-1.]),sp.array([1.,1.]),500,ESutils.SUPPORT_SLICEEI) np=30 R = ESutils.draw_min(G,Z,500) Z2 = ESutils.draw_support(G, sp.array([-1.,-1.]),sp.array([1.,1.]),500,ESutils.SUPPORT_LAPAPR,para=np) R2 = ESutils.draw_min(G,Z2,500)
from scipy import linalg as spl
from matplotlib import pyplot as plt
import GPdc
import OPTutils
import ESutils
import DIRECT
#base dimension
d = 2
kindex = GPdc.MAT52
nt = 34
lb = sp.array([0.] + [-1.] * d)
ub = sp.array([5.] + [1.] * d)
Htrue = sp.array([1.4, 4.] + [0.25] * d)
[X, Y, S, D] = ESutils.gen_dataset(nt, d + 1, lb, ub, kindex, Htrue, s=1e-8)
G = GPdc.GPcore(X, Y, S, D, GPdc.kernel(kindex, d + 1, Htrue))
def ojfaugnn(x):
return G.infer_m(x, [[sp.NaN]])[0, 0]
def opt_ip(s):
def dwrap(x, y):
X = sp.hstack([[s], x])
return (ojfaugnn(X), 0)
[xm, ym, ierror] = DIRECT.solve(dwrap,
lb[1:],
ub[1:],
user_data=[],
#lb = sp.array([0.,0.,-4.,-0.5*float(nf),float(nf)])
#ub = sp.array([4.,3.,3.,0.,1.5*float(nf)])
#MLEH = GPdc.searchMLEhyp(X,H,sp.zeros([np,1]),[[sp.NaN]]*(np),lb,ub,GPdc.SQUEXPPS,mx=10000)
#G = GPdc.GPcore(X.copy(),H,sp.zeros([np,1]),[[sp.NaN]]*(np),GPdc.kernel(GPdc.SQUEXPPS,1,sp.array(MLEH)))
lb = sp.array([0., 0., -4., -1., -3.])
ub = sp.array([4., 3., 3., 0.5, 3.])
MLEH = GPdc.searchMLEhyp(1. / float(nf) * X,
H,
sp.zeros([np, 1]), [[sp.NaN]] * (np),
lb,
ub,
GPdc.SQUEXPBS,
mx=10000)
G = GPdc.GPcore(1. / float(nf) * X.copy(), H, sp.zeros([np,
1]), [[sp.NaN]] * (np),
GPdc.kernel(GPdc.SQUEXPBS, 1, sp.array(MLEH)))
[m, v] = G.infer_diag(1. / float(nf) * X, [[sp.NaN]] * (np))
S = sp.empty([np, 1])
for i in xrange(np):
S[i, 0] = MLEH[2] * ((1. / float(nf) * X[i, 0])**(MLEH[3] * MLEH[4])) * (
(1. - 1. / float(nf) * X[i, 0])**(MLEH[3] * (1. - MLEH[4])))
lbc = sp.array([-4., 0., -6.])
ubc = sp.array([2., 3., 0.])
MLEC = GPdc.searchMLEhyp(X,
sp.log(T),
sp.zeros([np, 1]), [[sp.NaN]] * (np),
lbc,
D = [[sp.NaN], [sp.NaN], [0], [0, 0]]
a[0].plot([-0.8, -0.8], [0.3 - sp.sqrt(1e-1), 0.3 + sp.sqrt(1e-1)], 'r')
a[0].plot([-0.8], [0.3], 'ro')
a[0].plot([-0.25], [-0.2], 'ro')
a[1].plot([0.25], [2.5], 'ro')
a[2].plot([0.8], [50], 'ro')
k = GPdc.kernel(GPdc.SQUEXP, 1, sp.array([0.5, 0.2]))
K = sp.empty([4, 4])
for i in xrange(4):
for j in xrange(i, 4):
K[i, j] = K[j, i] = k(X[i, :], X[j, :], D[i], D[j])
K[i, i] += 1e-6
g = GPdc.GPcore(X, Y, S, D, GPdc.kernel(GPdc.SQUEXP, 1, sp.array([0.5, 0.2])))
#g.printc()
C = g.get_cho()
print C
print spl.cho_solve((C, True), sp.eye(4)).dot(K)
for i, d in enumerate([[sp.NaN], [0], [0, 0]]):
m, v = g.infer_diag(sup, [d] * ns)
vl = (m - sp.sqrt(v)).flatten()
vu = (m + sp.sqrt(v)).flatten()
a[i].plot(sup, m.flatten())
a[i].fill_between(sup,
vl,
vu,
facecolor='LightBlue',
edgecolor='LightBlue')
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)
S = sp.empty([np,1])
for i in xrange(np):
S[i,0] = -MAPH[2]*(Xp[i,0]-MAPH[3])*(Xp[i,0]-MAPH[4])
def traincfn(x, c):
n = x.size
g = GPdc.GPcore(x, c, sp.array([1e-1] * n), [[sp.NaN]] * n,
GPdc.kernel(GPdc.MAT52, 1, [1., 0.2]))
return cfnobj(g)
from matplotlib import pyplot as plt
import GPdc
#test on a single point
nt = 3
X = ESutils.draw_support(1, sp.array([-1.]), sp.array([1.]), nt,
ESutils.SUPPORT_UNIFORM)
D = [[sp.NaN]] * (nt)
hyp = sp.array([1.5, 0.15])
kf = GPdc.gen_sqexp_k_d(hyp)
Kxx = GPdc.buildKsym_d(kf, X, D)
Y = spl.cholesky(Kxx, lower=True) * sp.matrix(sps.norm.rvs(
0, 1., nt)).T + sp.matrix(sps.norm.rvs(0, 1e-3, nt)).T
S = sp.matrix([1e-6] * nt).T
G = GPdc.GPcore(X, Y, S, D, GPdc.kernel(GPdc.SQUEXP, 2, hyp))
ng = 40
A = sp.empty([ng, ng])
print 'startimage1'
sup = sp.logspace(-3, 2, ng)
for i, hi in enumerate(sup):
for j, hj in enumerate(sup):
A[i,
j] = GPdc.GP_LKonly(X, Y, S, D,
GPdc.kernel(GPdc.SQUEXP, 2,
sp.array([hi, hj]))).plk(
sp.array([0., -1.]),
sp.array([1., 1.]))
A = -sp.log10(-A + sp.amax(A) + 1.)
plt.figure()
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)
a0.fill_between(sup,
sp.array(m - 1. * sq).flatten(),
sp.array(m + 1. * sq).flatten(),
ax[i].plot(sup,sps.norm.pdf(sup,loc=m[i],scale=sp.sqrt(v[i,i])),'b')
ax[i].plot(sup,sps.norm.pdf(sup,loc=mu[i],scale=sp.sqrt(vu[i,i])),'r')
ax[i].twinx().plot(sup,sps.norm.cdf(Z[i]*(sup-Y[i])/max(F[i],1e-20))*sps.norm.pdf(sup,loc=m[i],scale=sp.sqrt(v[i,i])),'g')
#est on a gp
import ESutils
import GPdc
nt=5
X = sp.matrix(sp.linspace(-1,1,nt)).T
D = [[sp.NaN]]*(nt)
hyp = sp.array([1.5,0.15])
kf = GPdc.kernel(GPdc.SQUEXP,1,hyp)
Kxx = GPdc.buildKsym_d(kf,X,D)
Y = spl.cholesky(Kxx,lower=True)*sp.matrix(sps.norm.rvs(0,1.,nt)).T+sp.matrix(sps.norm.rvs(0,1e-3,nt)).T
S = sp.matrix([1e-2]*nt).T
g = GPdc.GPcore(X,Y,S,D,kf)
f,a = plt.subplots(2)
Xe = sp.array([-0.25,0.25])
De = [[sp.NaN]]*2
[m0,V0] = g.infer_full(Xe,De)
Ye = sp.array([2.,-2.])
Ze = sp.array([1.,-1.])
Fe = sp.array([(1e-6)**2,(1e-6)**2])
mt,vt = eprop.expectation_prop(m0,V0,Ye,Ze,Fe,20)
print D+De
g2 = GPdc.GPcore(sp.vstack([X,sp.array([Xe]).T]),sp.vstack([Y,sp.array([Ye]).T]),sp.vstack([S,sp.array([Fe]).T]),D+De,kf)
ESutils.plot_gp(g,a[0],sp.linspace(-1,1,100),[[sp.NaN]]*100)
ESutils.plot_gp(g2,a[1],sp.linspace(-1,1,100),[[sp.NaN]]*100)
from scipy import linalg as spl from scipy import stats as sps from matplotlib import pyplot as plt import GPdc import PES nt=12 d=1 lb = sp.array([-1.]*d) ub = sp.array([1.]*d) [X,Y,S,D] = ESutils.gen_dataset(nt,d,lb,ub,GPdc.SQUEXP,sp.array([1.5,0.15])) G = PES.makeG(X,Y,S,D,GPdc.SQUEXP,sp.array([0.,-1.]),sp.array([1.,1.]),12) Z=PES.drawmins(G,8,sp.array([-1.]),sp.array([1.]),SUPPORT=400,SLICELCB_PARA=1.) Ga = GPdc.GPcore(*PES.addmins(G,X,Y,S,D,Z[0,:])+[G.kf]) np=100 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_post(Xp,Dp) [mp,Vp] = Ga.infer_diag_post(Xp,Dp) f,a = plt.subplots(2) s = sp.sqrt(V[0,:]) a[0].fill_between(sup,sp.array(m[0,:]-2.*s).flatten(),sp.array(m[0,:]+2.*s).flatten(),facecolor='lightblue',edgecolor='lightblue') a[0].plot(sup,m[0,:].flatten()) a[0].plot(sp.array(X[:,0]).flatten(),Y,'g.')
#X = sp.matrix([[0.,-9.],[0.2,5.],[0.4,12.],[0.6,3.],[0.8,9.]])
#Y = sp.matrix([0.2,0.1,0.,-0.15,-0.3]).T
D = [[sp.NaN]] * (X.shape[0])
S = sp.matrix([0.005**2] * X.shape[0]).T
f0 = plt.figure()
a0 = plt.subplot(111)
a0.plot(sp.array(X[:, 0]).flatten(), Y, 'g.')
lb = sp.array([-2., -1., -2.])
ub = sp.array([2., 1., 2.])
MLEH = GPdc.searchMLEhyp(X, Y, S, D, lb, ub, GPdc.LIN1, mx=10000)
print MLEH
G = GPdc.GPcore(X, Y, S, D, GPdc.kernel(GPdc.LIN1, 2, MLEH))
print G.llk()
np = 180
sup = sp.linspace(-1, 1, np)
Dp = [[sp.NaN]] * np
Xp = sp.vstack([sp.array([i, 1.]) for i in sup])
[m, v] = G.infer_diag(Xp, Dp)
a0.plot(sup, m.flatten())
sq = sp.sqrt(v)
a0.fill_between(sup,
sp.array(m - 1. * sq).flatten(),
sp.array(m + 1. * sq).flatten(),
facecolor='lightblue',
Kxx = GPdc.buildKsym_d(kf, X, D)
Y = spl.cholesky(Kxx, lower=True) * sp.matrix(sps.norm.rvs(
0, 1., nt)).T + sp.matrix(sps.norm.rvs(0, 1e-3, nt)).T
S = sp.matrix([1e-6] * nt).T
f0 = plt.figure()
a0 = plt.subplot(111)
a0.plot(sp.array(X[:, 0]).flatten(), Y, 'g.')
lb = sp.array([-2., -2.])
ub = sp.array([2., 2.])
MLEH = GPdc.searchMLEhyp(X, Y, S, D, lb, ub, GPdc.SQUEXP, mx=10000)
print MLEH
G = GPdc.GPcore(X, Y, S, D, GPdc.kernel(GPdc.SQUEXP, 1, 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)
a0.fill_between(sup,
sp.array(m - 2. * sq).flatten(),
sp.array(m + 2. * sq).flatten(),
D = [[sp.NaN]] * (nt)
hyp0 = sp.array([1.5, 0.15])
hyp1 = sp.array([1.5, 0.05])
hyp2 = sp.array([1.5, 0.20])
kf = GPdc.kernel(GPdc.SQUEXP, 1, hyp0)
Kxx = GPdc.buildKsym_d(kf, X, D)
Y = spl.cholesky(Kxx, lower=True) * sp.matrix(sps.norm.rvs(
0, 1., nt)).T + sp.matrix(sps.norm.rvs(0, 1e-3, nt)).T
S = sp.matrix([1e-6] * nt).T
G = GPdc.GPcore(X, Y, S, D, [
GPdc.kernel(GPdc.SQUEXP, 1, hyp0),
GPdc.kernel(GPdc.SQUEXP, 1, hyp1),
GPdc.kernel(GPdc.SQUEXP, 1, hyp2),
GPdc.kernel(GPdc.SQUEXP, 1, hyp0)
])
#G.printc()
np = 100
sup = sp.linspace(-1, 1, np)
Dp = [[sp.NaN]] * np
Xp = sp.vstack([sp.array([i]) for i in sup])
m = G.infer_m(Xp, Dp)
f, a = plt.subplots(m.shape[0])
for i in xrange(m.shape[0]):
a[i].plot(sup, m[i, :].flatten())
a[i].plot(sp.array(X[:, 0]).flatten(), Y, 'g.')
D = [[sp.NaN]]*X.shape[0] S = sp.matrix([0.005**2]*X.shape[0]).T f0 = plt.figure() a0 = plt.subplot(111) a0.plot(sp.array(X[:,0]).flatten(),Y,'g.') lb = sp.array([-2.,-1.,-2.,-2.,-2.,-2.,-2.]) ub = sp.array([2.,1.,2.,2.,2.,2.,2.]) MLEH = GPdc.searchMLEhyp(X,Y,S,D,lb,ub,GPdc.LINSQUEXPXSQUEXP,mx=10000) print "xxx" GPdc.kernel(GPdc.LINSQUEXPXSQUEXP,2,MLEH) print "yyyy" print MLEH G = GPdc.GPcore(X,Y,S,D,GPdc.kernel(GPdc.LINSQUEXPXSQUEXP,2,MLEH)) print G.llk() print "zzz" np=180 sup = sp.linspace(-1,1,np) Dp = [[sp.NaN]]*np Xp = sp.vstack([sp.array([i,0.2]) for i in sup]) [m,v] = G.infer_diag(Xp,Dp) print "wwww" a0.plot(sup,m) sq = sp.sqrt(v) a0.fill_between(sup, sp.array(m-1.*sq).flatten(), sp.array(m+1.*sq).flatten(), facecolor='lightblue',edgecolor='lightblue')
G = PES.makeG(X, Y, S, D, GPdc.SQUEXP, sp.array([0., -1., -1.]),
sp.array([1., 1., 1.]), 6)
nz = 8
Z = PES.drawmins_inplane(G,
nz,
sp.array([-1.] * d),
sp.array([1.] * d),
axis=0,
value=0.,
SUPPORT=500,
SLICELCB_PARA=1.)
print Z
Ga = [
GPdc.GPcore(*PES.addmins_inplane(
G, X, Y, S, D, Z[i, :], axis=0, value=0., MINPOLICY=PES.NOMIN) +
[G.kf]) for i in xrange(nz)
]
np = 220
sup = sp.linspace(-1, 1, np)
Dp = [[sp.NaN]] * np
Xp0 = sp.vstack([sp.array([i, Z[0, 1]]) for i in sup])
Xp1 = sp.vstack([sp.array([Z[0, 0], i]) for i in sup])
[mp0, Vp0] = Ga[0].infer_diag_post(Xp0, Dp)
[mp1, Vp1] = Ga[0].infer_diag_post(Xp1, Dp)
f, a = plt.subplots(d)
s = sp.sqrt(Vp0[0, :])
a[0].fill_between(sup,
