def ojf(x, s, d, override=False):
#print "called ojf: "+str(x)
try:
x = x.flatten(0)
except:
pass
xlow = [-2., -2.]
xupp = [2., 2.]
xthis = [
xlow[i] + 0.5 * (xin + 1) * (xupp[i] - xlow[i])
for i, xin in enumerate(x)
]
hyp = [10**i for i in xthis]
print hyp
t0 = time.clock()
llk = sp.clip(
GPdc.GP_LKonly(X, Y, S, D, GPdc.kernel(GPdc.MAT52, 1,
sp.array(hyp))).plk(pm, ps),
-1e60, 1e60)
t1 = time.clock()
if llk < -1.:
out = sp.log(-llk) + 1.
else:
out = -llk
print "--->llk: {0} {1} t: {2}".format(llk, out, t1 - t0)
return [out, t1 - t0]
def llks(self, hy, sub):
t0 = time.clock()
Xs = sp.empty([sub, self.d])
Ys = sp.empty([sub, 1])
Ss = sp.zeros([sub, 1])
Ds = []
ix = npr.choice(range(self.n), size=sub, replace=False)
for i, x in enumerate(ix):
Xs[i, :] = self.X[x, :]
Ys[i, :] = self.Y[x, :]
Ds.append(self.D[x])
t1 = time.clock()
r = GPdc.GP_LKonly(Xs, Ys, Ss, Ds, GPdc.kernel(self.ki, self.d,
hy)).llk()
t2 = time.clock()
#print [t1-t0,t2-t1]
return [r, t2 - t0]
def f(loghyp):
ub = hm + 1.8 * hs
lb = hm - 1.8 * hs
if all(loghyp < ub) and all(loghyp > lb):
r = GPdc.GP_LKonly(
X, Y, S, D, GPdc.kernel(ki, X.shape[1],
[10**i for i in loghyp])).plk(hm, hs)
if sp.isnan(r):
class MJMError(Exception):
pass
print 'nan from GPLKonly with input'
print[X, Y, S, D, ki, hm, hs, n, burn, subsam]
raise MJMError('nan from GPLKonly with input')
else:
r = -1e99
#print [loghyp, r]
return r
def ojfa(x, s, d, override=False):
#print "called ojf: "+str(x)
try:
x = x.flatten(0)
except:
pass
xlow = [-2., -2.]
xupp = [2., 2.]
xthis = [
xlow[i] + 0.5 * (xin + 1) * (xupp[i] - xlow[i])
for i, xin in enumerate(x[1:])
]
hyp = [10**i for i in xthis]
#hyp = [10**i for i in x.flatten()[1:]]
print hyp
t0 = time.clock()
sub = x.flatten()[0]
npts = int((1. - 0.9 * sub) * n)
if override:
npts = n
print "subsampling {0} of {1} at x[0]={2}".format(npts, n, x.flatten()[0])
ps = npr.choice(range(n), size=npts, replace=False)
Xd = sp.vstack([X[i] for i in ps])
Yd = sp.vstack([Y[i] for i in ps])
Sd = sp.vstack([S[i] for i in ps])
Dd = [[sp.NaN]] * npts
llk = GPdc.GP_LKonly(Xd, Yd, Sd, Dd,
GPdc.kernel(GPdc.MAT52, 1,
sp.array(hyp))).plk(pm, ps)
t1 = time.clock()
if llk < -1.:
out = sp.log(-llk) + 1.
else:
out = -llk
print "--->llk: {0} {1} t: {2}".format(llk, out, t1 - t0)
return [out, t1 - t0]
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()
plt.contour(sp.log10(sup), sp.log10(sup), A, 30)
print 'draw hyps 1'
X = ESutils.drawhyp_plk(X, Y, S, D, GPdc.SQUEXP, sp.array([0., -1.]),
sp.array([1., 1.]), 200)
plt.plot(sp.log10(X[:, 1]), sp.log10(X[:, 0]), 'b.')
#lots of points
nt = 100
X = ESutils.draw_support(1, sp.array([-1.]), sp.array([1.]), nt,
ESutils.SUPPORT_UNIFORM)
D = [[sp.NaN]] * (nt)
def llk(self, hy):
t0 = time.clock()
r = GPdc.GP_LKonly(self.X, self.Y, self.S, self.D,
GPdc.kernel(self.ki, self.d, hy)).llk()
t1 = time.clock()
return [r, t1 - t0]
