plots = SP.int_(SP.sqrt(24) + 1)
PL.figure()
for i, BP in enumerate(x1[0,:]):
#PL.subplot(plots,plots,i+1)
_hyper = copy.deepcopy(opt_model_params)
_logtheta = _hyper['covar']
_logtheta = SP.concatenate((_logtheta, [BP, 10]))#SP.var(y[:,i])]))
_hyper['covar'] = _logtheta
priors_BP[3] = [lnpriors.lnGauss, [BP, 3]]
# [opt_model_params,opt_lml] = opt_hyper(gpr_BP,_hyper,priors=priors_BP,gradcheck=False,Ifilter=Ifilter_BP)
#break_lml.append(opt_lml)
try:
break_lml.append(gpr_BP.LML(_hyper, priors_BP))
print "Variance: %s" % (_logtheta)
# PL.figure()
# [M, S] = gpr_BP.predict(_hyper, X)
# gpr_plot.plot_sausage(X, M, SP.sqrt(S))
# gpr_plot.plot_training_data(x1, C[1], replicate_indices=x1_rep.reshape(-1))
# gpr_plot.plot_training_data(x2, T[1], replicate_indices=x2_rep.reshape(-1))
except:
break_lml.append(0)
# PL.plot(C[0].transpose(),C[1].transpose(),'+b',markersize=10)
# PL.plot(T[0].transpose(),T[1].transpose(),'+r',markersize=10)
# [M,S] = gpr_BP.predict(opt_model_params,X)
# gpr_plot.plot_sausage(X,M,SP.sqrt(S),format_fill={'alpha':0.1,'facecolor':'k'})
