Cg = freeform(P)
Cn = freeform(P)
Cg1 = freeform(P)
Cn1 = freeform(P)
ipdb.set_trace()
if 1:
# compare with mtSet implementation
params = {}
params['Cg'] = SP.randn(int(0.5*P*(P+1)))
params['Cn'] = SP.randn(int(0.5*P*(P+1)))
print "check gradient with gp2kronSum"
gp = gp2kronSum(mu,Cg,Cn,XX)
gp.setParams(params)
if 0:
gp.set_reml(False)
print "test optimization"
start = TIME.time()
conv,info = OPT.opt_hyper(gp,params,factr=1e3)
print 'Reml GP:', TIME.time()-start
if mtSet_present:
params1 = copy.copy(params)
params1['mean'] = SP.zeros(mu1.getParams().shape[0])
gp1 = gp2kronSumMtSet(mu1,Cg1,Cn1,XX)
gp1.setParams(params1)
# define covariance matrices
Cg = freeform(P)
Cn = freeform(P)
Cg1 = freeform(P)
Cn1 = freeform(P)
# compare with mtSet implementation
params = {}
params['Cg'] = SP.randn(int(0.5 * P * (P + 1)))
params['Cn'] = SP.randn(int(0.5 * P * (P + 1)))
print "creating gp2kronSum object"
XX = SP.dot(X, X.T)
XX /= XX.diagonal().mean()
gp = gp2kronSum(mu, Cg, Cn, XX)
gp.setParams(params)
if "ML" in sys.argv:
print "ML estimation"
gp.set_reml(False)
else:
print "REML estimation"
print "optimization of GP parameters"
start = TIME.time()
conv, info = OPT.opt_hyper(gp, params, factr=1e3)
print 'time for fitting GP:', TIME.time() - start
print conv