Ytestn_cur /= Ystd_cur
#cur.Ymean = Ymean_cur
#cur.Ystd = Ystd_cur
# As above but for the labels
#Lmean_cur = L_cur.mean()
#Ln_cur = L_cur - Lmean_cur
#Lstd_cur = Ln_cur.std()
#Ln_cur /= Lstd_cur
#Ltestn_cur = Ltest_cur - Lmean_cur
#Ltestn_cur /= Lstd_cur
cur.X = None
cur.Y = None
cur.Y = {'Y': Yn_cur}
cur.Ytestn = {'Ytest': Ytestn_cur}
cur.Ltest = {'Ltest': Ltest_cur}
print
fname = modelList[i]
if Q > 100:
#one could parse and execute the string kernelStr for kernel instead of line below
kernel = GPy.kern.RBF(Q,
ARD=False) + GPy.kern.Bias(Q) + GPy.kern.White(Q)
else:
kernel = None
# Simulate the function of storing a collection of events
cur.SAMObject.store(observed=cur.Y,
inputs=cur.X,
Q=Q,
Ytestn_cur = Ytest_cur - Ymean_cur
Ytestn_cur /= Ystd_cur
cur.Ymean = Ymean_cur
cur.Ystd = Ystd_cur
# As above but for the labels
#Lmean_cur = L_cur.mean()
#Ln_cur = L_cur - Lmean_cur
#Lstd_cur = Ln_cur.std()
#Ln_cur /= Lstd_cur
#Ltestn_cur = Ltest_cur - Lmean_cur
#Ltestn_cur /= Lstd_cur
cur.X=None
cur.Y = {'Y':Yn_cur}
cur.Ytestn = {'Ytest':Ytestn_cur}
cur.Ltest = {'Ltest':Ltest_cur}
fname_cur = fname + '_L' + str(i)
cur.training(model_num_inducing, model_num_iterations, model_init_iterations, fname_cur, save_model, economy_save)
mm.append(cur)
ss = [];
sstest = [];
for i in range(len(Lunique)):
for j in range(len(Lunique)):
ss = mm[i].SAMObject.familiarity(mm[j].Y['Y'])
print('Familiarity of model ' + participantList[i] + ' given label: ' + participantList[j] + ' using training data is: ' + str(ss))
print("")
print("")
print("")
