hyp.mean = np.array([])
sn = 0.1
hyp.lik = np.array([np.log(sn)])
##----------------------------------------------------------##
## STANDARD GP (prediction) ##
##----------------------------------------------------------##
xs = np.arange(2004+1./24.,2024-1./24.,1./12.) # TEST POINTS
xs = xs.reshape(len(xs),1)
vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,xs)
ym = vargout[0]; ys2 = vargout[1]
m = vargout[2]; s2 = vargout[3]
plotter(xs,ym,ys2,x,y)#,[1955, 2030, 310, 420])
##----------------------------------------------------------##
## STANDARD GP (training) ##
## OPTIMIZE HYPERPARAMETERS ##
##----------------------------------------------------------##
## -> parameter training using (off the shelf) conjugent gradient (CG) optimization (NOTE: SCG is faster)
from time import clock
t0 = clock()
vargout = min_wrapper(hyp,gp,'SCG',inffunc,meanfunc,covfunc,likfunc,x,y,None,None,True)
t1 = clock()
hyp = vargout[0]
vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,xs)
ym = vargout[0]; ys2 = vargout[1]
m = vargout[2]; s2 = vargout[3]
sn = 0.1
hyp.lik = np.array([np.log(sn)])
##----------------------------------------------------------##
## STANDARD GP (prediction) ##
##----------------------------------------------------------##
xs = np.arange(2004 + 1. / 24., 2024 - 1. / 24., 1. / 12.) # TEST POINTS
xs = xs.reshape(len(xs), 1)
vargout = gp(hyp, inffunc, meanfunc, covfunc, likfunc, x, y, xs)
ym = vargout[0]
ys2 = vargout[1]
m = vargout[2]
s2 = vargout[3]
plotter(xs, ym, ys2, x, y) #,[1955, 2030, 310, 420])
##----------------------------------------------------------##
## STANDARD GP (training) ##
## OPTIMIZE HYPERPARAMETERS ##
##----------------------------------------------------------##
## -> parameter training using (off the shelf) conjugent gradient (CG) optimization (NOTE: SCG is faster)
from time import clock
t0 = clock()
vargout = min_wrapper(hyp, gp, 'SCG', inffunc, meanfunc, covfunc, likfunc,
x, y, None, None, True)
t1 = clock()
hyp = vargout[0]
vargout = gp(hyp, inffunc, meanfunc, covfunc, likfunc, x, y, xs)
ym = vargout[0]
ys2 = vargout[1]
##----------------------------------------------------------##
## STANDARD GP (example 1) ##
##----------------------------------------------------------##
print '...example 1: prediction...'
## PREDICTION
t0 = clock()
vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,xstar)
t1 = clock()
ym = vargout[0]; ys2 = vargout[1]; m = vargout[2]; s2 = vargout[3]
print 'Time for prediction =',t1-t0
## PLOT results
if PLOT:
plotter(xstar,ym,s2,x,y,[-2, 2, -0.9, 3.9])
## GET negative log marginal likelihood
[nlml, post] = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,None,None,False)
print "nlml =", nlml
##----------------------------------------------------------##
## STANDARD GP (example 2) ##
##----------------------------------------------------------##
print '...example 2: prediction...'
## USE another covariance function -> for use of composite covariance functions see demoMaunaLoa.py
covfunc = [ ['kernels.covSEiso'] ]
### SET (hyper)parameters
hyp2 = hyperParameters()
##----------------------------------------------------------##
print '...example 1: prediction...'
## PREDICTION
t0 = clock()
vargout = gp(hyp, inffunc, meanfunc, covfunc, likfunc, x, y, xstar)
t1 = clock()
ym = vargout[0]
ys2 = vargout[1]
m = vargout[2]
s2 = vargout[3]
print 'Time for prediction =', t1 - t0
## PLOT results
if PLOT:
plotter(xstar, ym, s2, x, y, [-2, 2, -0.9, 3.9])
## GET negative log marginal likelihood
[nlml, post] = gp(hyp, inffunc, meanfunc, covfunc, likfunc, x, y, None,
None, False)
print "nlml =", nlml
##----------------------------------------------------------##
## STANDARD GP (example 2) ##
##----------------------------------------------------------##
print '...example 2: prediction...'
## USE another covariance function -> for use of composite covariance functions see demoMaunaLoa.py
covfunc = [['kernels.covSEiso']]
### SET (hyper)parameters
hyp2 = hyperParameters()
