def testfunc(x):
return 10 * sin(7.8 * log(1 + x)) / (1 + 0.1 * x**2)
Ntr = 500
Xtr = 10 * random.random((Ntr, 1))
Ytr = 5 + testfunc(Xtr) + 0.1 * random.normal(0, 1, (Ntr, 1)) * Xtr
# initialize the LWPR model
model = LWPR(1, 1)
model.init_D = 20 * eye(1)
model.update_D = True
model.init_alpha = 40 * eye(1)
model.meta = False
model.penalty = 1e-4
model.diag_only = True
# train the model
for k in range(20):
ind = random.permutation(Ntr)
mse = 0
for i in range(Ntr):
yp = model.update(Xtr[ind[i]], Ytr[ind[i]])
mse = mse + (Ytr[ind[i], :] - yp)**2
nMSE = mse / Ntr / var(Ytr)
print "#Data: %5i #RFs: %3i nMSE=%5.3f" % (model.n_data, model.num_rfs,
nMSE)
# test the model with unseen data
