##############
Y_cur = Yall[L_index[i],:].copy()
Ytest_cur = YtestAll[L_index[i],:].copy()
L_cur = Lall[L_index[i],:].copy()
Ltest_cur = LtestAll[L_index[i],:].copy()
# Center data to zero mean and 1 std
Ymean_cur = Y_cur.mean()
Yn_cur = Y_cur - Ymean_cur
Ystd_cur = Yn_cur.std()
Yn_cur /= Ystd_cur
# Normalise test data similarly to training data
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)
startIDx = Ntest*i;
endIDx = (Ntest*(i+1))
Ytest_cur = YtestAll[startIDx:endIDx,:].copy()
Ltest_cur = LtestAll[startIDx:endIDx,:].copy()
# Center data to zero mean and 1 std
Ymean_cur = Y_cur.mean()
Yn_cur = Y_cur - Ymean_cur
Ystd_cur = Yn_cur.std()
Yn_cur /= Ystd_cur
# Normalise test data similarly to training data
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}
print 'training data' + str(cur.Y['Y'].shape)
print 'testing data' + str(cur.Ytestn['Ytest'].shape)
