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)
##----------------------------------------------------------##
## 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]
hyp2.cov = np.array([-1.0,0.0])
hyp2.mean = np.array([0.5,1.0])
hyp2.lik = np.array([np.log(0.1)])
### PREDICTION
import time
t0 = time.time()
print 'prediction'
vargout = gp(hyp2,inffunc,meanfunc,covfunc,likfunc,x,y,xstar)
ym = vargout[0]; ys2 = vargout[1]; m = vargout[2]; s2 = vargout[3]
print time.time() - t0
### PLOT results
plotter(xstar,ym,ys2,x,y,[-2, 2, -0.9, 3.9])
### GET negative log marginal likelihood
[nlml, post] = gp(hyp2,inffunc,meanfunc,covfunc,likfunc,x,y,None,None,False)
print "nlml2 = ", nlml
###----------------------------------------------------------###
### STANDARD GP (example 3) ###
###----------------------------------------------------------###
### TRAINING: OPTIMIZE hyperparameters
[hyp2_opt, fopt, gopt, funcCalls] = min_wrapper(hyp2,gp,'CG',inffunc,meanfunc,covfunc,likfunc,x,y,None,None,True)
print "nlml_opt = ", fopt
#[hyp2_opt, fopt, gopt, funcCalls] = min_wrapper(hyp2,gp,'BFGS',inffunc,meanfunc,covfunc,likfunc,x,y,None,None,True)
#print fopt
hyp = hyperParameters()
hyp.cov = np.array([np.log(0.25),np.log(1.0),np.log(3.0)])
hyp.mean = np.array([0.5,1.0])
sn = 0.1; hyp.lik = np.array([np.log(sn)])
#_________________________________
# STANDARD GP:
## PREDICTION
vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,None,None,False)
print "nlml = ",vargout[0]
vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,z)
ym = vargout[0]; ys2 = vargout[1]
m = vargout[2]; s2 = vargout[3]
## Plot results
plotter(z,ym,ys2,x,y,[-1.9, 1.9, -0.9, 3.9])
print "pre" + str(x)
x = np.delete(x, 0, 0)
x = np.delete(x, 5, 0)
print "post" + str(x)
y = np.delete(y, 0, 0)
y = np.delete(y, 5, 0)
vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,None,None,False)
print "nlml = ",vargout[0]
vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,z)
ym = vargout[0]; ys2 = vargout[1]
m = vargout[2]; s2 = vargout[3]
## Plot results
plotter(z,ym,ys2,x,y,[-1.9, 1.9, -0.9, 3.9])
hyp = hyperParameters()
hyp.cov = np.array([np.log(0.25),np.log(1.0),np.log(3.0)])
hyp.mean = np.array([0.5,1.0])
sn = 0.1; hyp.lik = np.array([np.log(sn)])
#_________________________________
# STANDARD GP:
## PREDICTION
vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,None,None,False)
print "nlml = ",vargout[0]
vargout = gp(hyp,inffunc,meanfunc,covfunc,likfunc,x,y,z)
ym = vargout[0]; ys2 = vargout[1]
m = vargout[2]; s2 = vargout[3]
## Plot results
plotter(z,ym,ys2,x,y,[-1.9, 1.9, -0.9, 3.9])
###########################################################
covfunc = [ ['kernels.covSEiso'] ]
## SET (hyper)parameters
hyp2 = hyperParameters()
hyp2.cov = np.array([-1.0,0.0])
hyp2.lik = np.array([np.log(0.1)])
vargout = min_wrapper(hyp2,gp,'SCG',inffunc,[],covfunc,likfunc,x,y,None,None,True)
hyp2 = vargout[0]
#hyp2.cov = np.array([-0.993396880620537,0.685943441677086])
#hyp2.lik = np.array([-1.902546786026883])
vargout = gp(hyp2,inffunc,[],covfunc,likfunc,x,y,None,None,False)
print "nlml2 = ",vargout[0]