def test_vs_single_layer(self):
lik = Gaussian()
lik_var = 0.01
lik.variance = lik_var
N, Ns, D_Y, D_X = self.X.shape[0], self.Xs.shape[
0], self.D_Y, self.X.shape[1]
Y = np.random.randn(N, D_Y)
Ys = np.random.randn(Ns, D_Y)
kern = Matern52(self.X.shape[1], lengthscales=0.5)
# mf = Linear(A=np.random.randn(D_X, D_Y), b=np.random.randn(D_Y))
mf = Zero()
m_gpr = GPR(self.X, Y, kern, mean_function=mf)
m_gpr.likelihood.variance = lik_var
mean_gpr, var_gpr = m_gpr.predict_y(self.Xs)
test_lik_gpr = m_gpr.predict_density(self.Xs, Ys)
pred_m_gpr, pred_v_gpr = m_gpr.predict_f(self.Xs)
pred_mfull_gpr, pred_vfull_gpr = m_gpr.predict_f_full_cov(self.Xs)
kerns = []
kerns.append(
Matern52(self.X.shape[1], lengthscales=0.5, variance=1e-1))
kerns.append(kern)
layer0 = GPMC_Layer(kerns[0], self.X.copy(), D_X, Identity())
layer1 = GPR_Layer(kerns[1], mf, D_Y)
m_dgp = DGP_Heinonen(self.X, Y, lik, [layer0, layer1])
mean_dgp, var_dgp = m_dgp.predict_y(self.Xs, 1)
test_lik_dgp = m_dgp.predict_density(self.Xs, Ys, 1)
pred_m_dgp, pred_v_dgp = m_dgp.predict_f(self.Xs, 1)
pred_mfull_dgp, pred_vfull_dgp = m_dgp.predict_f_full_cov(
self.Xs, 1)
tol = 1e-4
assert_allclose(mean_dgp[0], mean_gpr, atol=tol, rtol=tol)
assert_allclose(test_lik_dgp, test_lik_gpr, atol=tol, rtol=tol)
assert_allclose(pred_m_dgp[0], pred_m_gpr, atol=tol, rtol=tol)
assert_allclose(pred_mfull_dgp[0],
pred_mfull_gpr,
atol=tol,
rtol=tol)
assert_allclose(pred_vfull_dgp[0],
pred_vfull_gpr,
atol=tol,
rtol=tol)
def test_single_layer(self):
kern = RBF(1, lengthscales=0.1)
layers = init_layers_linear(self.X, self.Y, self.X, [kern])
lik = Gaussian()
lik.variance = self.lik_var
last_layer = SGPR_Layer(layers[-1].kern,
layers[-1].feature.Z.read_value(), self.D_Y,
layers[-1].mean_function)
layers = layers[:-1] + [last_layer]
m_dgp = DGP_Collapsed(self.X, self.Y, lik, layers)
L_dgp = m_dgp.compute_log_likelihood()
mean_dgp, var_dgp = m_dgp.predict_f_full_cov(self.Xs, 1)
m_exact = GPR(self.X, self.Y, kern)
m_exact.likelihood.variance = self.lik_var
L_exact = m_exact.compute_log_likelihood()
mean_exact, var_exact = m_exact.predict_f_full_cov(self.Xs)
assert_allclose(L_dgp, L_exact, atol=1e-5, rtol=1e-5)
assert_allclose(mean_dgp[0], mean_exact, atol=1e-5, rtol=1e-5)
assert_allclose(var_dgp[0], var_exact, atol=1e-5, rtol=1e-5)