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 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 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 makedata(self, n, d, ki, hy, lb, ub):
self.n = n
self.d = d
self.ki = ki
self.hy = hy
self.nhy = hy.size
self.lb = lb
self.ub = ub
t0 = time.time()
[X, Y, S, D] = ESutils.gen_dataset(n, d, lb, ub, ki, hy, s=hy[-1])
t1 = time.time()
print "Setuptime: " + str(t1 - t0)
S = sp.zeros(S.shape)
self.X = X
self.Y = Y
self.S = S
self.D = D
return
def makedata(self,n,d,ki,hy,lb,ub):
self.n=n
self.d=d
self.ki=ki
self.hy=hy
self.nhy = hy.size
self.lb=lb
self.ub=ub
t0 = time.time()
[X,Y,S,D] = ESutils.gen_dataset(n,d,lb,ub,ki,hy,s=hy[-1])
t1 = time.time()
print "Setuptime: "+str(t1-t0)
S = sp.zeros(S.shape)
self.X=X
self.Y=Y
self.S=S
self.D=D
return
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]
import ESutils
import scipy as sp
from scipy import linalg as spl
from scipy import stats as sps
from matplotlib import pyplot as plt
import GPdc
import PES
#-------------------------------------------------------------------------
#2d
nt = 30
d = 2
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, 0.35, 0.30]))
kindex = GPdc.SQUEXP
mprior = sp.array([0.] + [-1.] * d)
sprior = sp.array([1.] * (d + 1))
pesobj = PES.PES(X,
Y,
S,
D,
lb,
ub,
kindex,
mprior,
sprior,
DH_SAMPLES=8,
from scipy import stats as sps
from scipy import linalg as spl
import scipy as sp
from matplotlib import pyplot as plt
import ESutils
import GPdc
nt = 22
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([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.])
import scipy as sp
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:],
import ESutils
import scipy as sp
from scipy import linalg as spl
from scipy import stats as sps
from matplotlib import pyplot as plt
import GPdc
import PES
#-------------------------------------------------------------------------
#2d
nt=30
d=2
lb = sp.array([0.]+[-1.]*(d-1))
ub = sp.array([1.]*d)
[X,Y,S,D] = ESutils.gen_dataset(nt,d,lb,ub,GPdc.SQUEXP,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):
return 1e-4
[xmin,ymin,ierror] = pesobj.search_acq(cfn,sfn)
# 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 ESutils
import GPdc
nt=80
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([0.9,0.25]),s=0.)
S*=0.
for i in xrange(nt):
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])
import scipy as sp
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=[], algmethod=1, maxf=12000, logfilename='/dev/null')
print "DIRECT found: " +str([xm,ym,ierror])
return xm
mintrue = opt_ip(0.)
minaug = sp.hstack([[0.],mintrue])
# 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. import scipy as sp from scipy import linalg as spl from scipy import stats as sps from matplotlib import pyplot as plt import GPdc import ESutils nt=2 d=1 lb = sp.array([-1.]) ub = sp.array([1.]) [X,Y,S,D] = ESutils.gen_dataset(nt,d,lb,ub,GPdc.SQUEXP,sp.array([1.5,0.35]),s=1e-3) g = GPdc.GPcore(X,Y,S,D,GPdc.kernel(GPdc.SQUEXP,1,sp.array([1.5,0.35]))) 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) f,a = plt.subplots(3) 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.')
