braninmin = 0.
kindex = GPdc.MAT52
prior = sp.array([0.]+[-1.]*d)
sprior = sp.array([1.]*(d+1))
kernel = [kindex,prior,sprior]
nreps = 2
bd = 4*60
slist = [1e-9]
print 'start'
f,a = plt.subplots(3)
import os
for s in slist:
names = ["../cache/mnist/EIMLE_5mnist"+str(int(100*sp.log10(s)))+"_"+"_"+str(i)+".p" for i in xrange(nreps)]
results = search.multiMLEFS(ojf,lb,ub,kernel,s,bd,names)
yr = [r[11].flatten() for r in results]
C = [r[5] for r in results]
for i in xrange(nreps):
m = yr[i]
a[2].plot([sum(C[i][:j]) for j in xrange(len(C[i]))],m.flatten(),'x-')
a[1].plot(C[i],'r')
print "reccomend: "+str([r[4][-1,:] for r in results])
f.savefig("../figs/braninnoise.png")
plt.show()
pwr = 0.2
cfn = lambda s:((1e-6)/s)**pwr
ojf = OPTutils.genbanana(cfn=cfn)
kindex = GPdc.MAT52
prior = sp.array([0.]+[-1.]*d)
sprior = sp.array([1.]*(d+1))
kernel = [kindex,prior,sprior]
nreps = 5
bd = 15
slist = [1e-4,1e-6,1e-8]
f,a = plt.subplots(2)
for s in slist:
names = ["../cache/rosennoise/EIMLE_"+str(int(100*sp.log10(s)))+"_"+str(pwr)+"_"+str(i)+".p" for i in xrange(nreps)]
results = search.multiMLEFS(ojf,lb,ub,kernel,s,bd,names)
yr = [r[11].flatten() for r in results]
C = results[0][5]
names = ["../cache/rosennoise/PESFS_"+str(int(100*sp.log10(s)))+"_"+str(pwr)+"_"+str(i)+".p" for i in xrange(nreps)]
results = search.multiPESFS(ojf,lb,ub,kernel,s,bd,names)
zr = [r[11].flatten() for r in results]
C = results[0][5]
Z = sp.vstack(yr)
m = sp.mean(sp.log10(Z),axis=0)
v = sp.var(sp.log10(Z),axis=0)
sq = sp.sqrt(v)
a[1].fill_between(sp.array([sum(C[:j]) for j in xrange(len(C))]),(m-sq).flatten(),(m+sq).flatten(),facecolor='lightblue',edgecolor='lightblue',alpha=0.5)
d = 2
kindex = GPdc.MAT52CS
prior = sp.array([0.] + [-1.] * d + [-2.])
sprior = sp.array([1.] + [1.] * d + [2.])
kernel = [kindex, prior, sprior]
#lets start with EI
lb = sp.array([[-1] * d])
ub = sp.array([[1] * d])
budget = 20
fnames = ['../cache/fith/EI{}.p'.format(i) for i in xrange(5)]
statesEI = search.multiMLEFS(ojf, lb, ub, kernel, 1., budget, fnames)
fnames = ['../cache/fith/PE{}.p'.format(i) for i in xrange(5)]
statesPE = search.multiPESFS(ojf, lb, ub, kernel, 1., budget, fnames)
kindex = GPdc.MAT52CS
prior = sp.array([0.] + [-1.] * (d + 1) + [-2.])
sprior = sp.array([1.] * (d + 2) + [2.])
kernel = [kindex, prior, sprior]
fnames = ["../cache/fith/PI{}.p".format(i) for i in xrange(5)]
statesPI = search.multiPESIPS(ojfa, lb, ub, kernel, 10, fnames)
x = []
y = []
for stateEI in statesEI:
d=2
kindex = GPdc.MAT52CS
prior = sp.array([0.]+[-1.]*d+[-2.])
sprior = sp.array([1.]+[1.]*d+[2.])
kernel = [kindex,prior,sprior]
#lets start with EI
lb = sp.array([[-1]*d])
ub = sp.array([[1]*d])
budget = 20
fnames = ['../cache/fith/EI{}.p'.format(i) for i in xrange(5)]
statesEI=search.multiMLEFS(ojf,lb,ub,kernel,1.,budget,fnames)
fnames = ['../cache/fith/PE{}.p'.format(i) for i in xrange(5)]
statesPE=search.multiPESFS(ojf,lb,ub,kernel,1.,budget,fnames)
kindex = GPdc.MAT52CS
prior = sp.array([0.]+[-1.]*(d+1)+[-2.])
sprior = sp.array([1.]*(d+2)+[2.])
kernel = [kindex,prior,sprior]
fnames = ["../cache/fith/PI{}.p".format(i) for i in xrange(5)]
statesPI = search.multiPESIPS(ojfa,lb,ub,kernel,10,fnames)