def drawmins(G,n,lb,ub,SUPPORT=300,mode = [ESutils.SUPPORT_SLICELCB],SLICELCB_PARA=1.):
#draw support points
W = sp.vstack([ESutils.draw_support(G, lb,ub,SUPPORT/len(mode),m, para = SLICELCB_PARA) for m in mode])
if False:
print 'how did I get here'
plt.figure()
plt.plot(W[:,0],W[:,1],'g.')
plt.show()
#draw in samples on the support
print "drawing mins from support"
R = ESutils.draw_min(G,W,n)
if False:
plt.figure()
plt.plot(R[:,0],R[:,1],'r.')
plt.show()
#plt.show()
return R
# 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 ESutils
import scipy as sp
from scipy import linalg as spl
from scipy import stats as sps
from matplotlib import pyplot as plt
#uniform test
import GPdc
X = ESutils.draw_support(2, sp.array([-2,-1]),sp.array([0,3]),500,ESutils.SUPPORT_UNIFORM)
for i in xrange(X.shape[0]):
plt.plot(X[i,0],X[i,1],'r.')
plt.axis([-5,5,-5,5])
#2d gp test
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
#!/usr/bin/env python2
#encoding: UTF-8
#Draw 200 hyperparameters from the posterior, plot the draws on a countour plot, first for very few points, then a large number
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
#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):
self.D = D
return
X = ESutils.draw_support_inplane(bob(2), sp.array([-2, -1]), sp.array([0, 3]),
500, ESutils.SUPPORT_UNIFORM, 1, 0.5)
for i in xrange(X.shape[0]):
plt.plot(X[i, 0], X[i, 1], 'r.')
plt.axis([-5, 5, -5, 5])
#2d gp test
import GPdc
nt = 34
X = ESutils.draw_support(bob(2), sp.array([-1., -1.]), sp.array([1., 1.]), nt,
ESutils.SUPPORT_UNIFORM)
D = [[sp.NaN]] * (nt)
hyp = sp.array([1.5, 0.15, 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
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)
self.D=D
return
X = ESutils.draw_support_inplane(bob(2), sp.array([-2,-1]),sp.array([0,3]),500,ESutils.SUPPORT_UNIFORM,1,0.5)
for i in xrange(X.shape[0]):
plt.plot(X[i,0],X[i,1],'r.')
plt.axis([-5,5,-5,5])
#2d gp test
import GPdc
nt=34
X = ESutils.draw_support(bob(2), sp.array([-1.,-1.]),sp.array([1.,1.]),nt,ESutils.SUPPORT_UNIFORM)
D = [[sp.NaN]]*(nt)
hyp = sp.array([1.5,0.15,0.15])
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
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])
#!/usr/bin/env python2
#encoding: UTF-8
#Draw 200 hyperparameters from the posterior, plot the draws on a countour plot, first for very few points, then a large number
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
#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):
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.')
ns = 500
R = ESutils.draw_support(g, lb, ub, ns, ESutils.SUPPORT_UNIFORM)
w = sp.ones(ns)
R2 = ESutils.draw_support(g, lb, ub, ns, ESutils.SUPPORT_SLICELCB, para=2.)
w2 = sp.exp(-g.infer_LCB_post(R2, [[sp.NaN]] * ns, 2.))
a[0].twinx().plot(R2.flatten(), w2.flatten(), 'rx')
[m0, V0] = g.infer_full_post(R, [[sp.NaN]] * ns)
ndr = 10000
Z = g.draw_post(R, [[sp.NaN]] * ns, ndr)
Z2 = g.draw_post(R2, [[sp.NaN]] * ns, ndr)
H = sp.zeros(ns)
H2 = sp.zeros(ns)
for i in xrange(ndr):
H[sp.argmin(Z[i, :])] += 1
H2[sp.argmin(Z2[i, :])] += 1
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.')
ns=500
R = ESutils.draw_support(g,lb,ub,ns,ESutils.SUPPORT_UNIFORM)
w = sp.ones(ns)
R2 = ESutils.draw_support(g,lb,ub,ns,ESutils.SUPPORT_SLICELCB,para=2.)
w2 = sp.exp(-g.infer_LCB_post(R2,[[sp.NaN]]*ns,2.))
a[0].twinx().plot(R2.flatten(),w2.flatten(),'rx')
[m0,V0] = g.infer_full_post(R,[[sp.NaN]]*ns)
ndr=10000
Z = g.draw_post(R, [[sp.NaN]]*ns,ndr)
Z2 = g.draw_post(R2, [[sp.NaN]]*ns,ndr)
H = sp.zeros(ns)
H2 = sp.zeros(ns)
for i in xrange(ndr):
H[sp.argmin(Z[i,:])]+=1
H2[sp.argmin(Z2[i,:])]+=1
