From 19598cf8073a94687d8829cd79b1f877f628cb10 Mon Sep 17 00:00:00 2001 From: Akash Kumar Dhaka Date: Mon, 26 Jun 2017 17:33:57 +0300 Subject: [PATCH] fixing a typo-bug in the last commit for ep test case --- GPy/testing/ep_likelihood_tests.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/GPy/testing/ep_likelihood_tests.py b/GPy/testing/ep_likelihood_tests.py index 2e1072fa..ca202b4a 100644 --- a/GPy/testing/ep_likelihood_tests.py +++ b/GPy/testing/ep_likelihood_tests.py @@ -31,7 +31,7 @@ class TestObservationModels(unittest.TestCase): self.Y_noisy[75:80] += 1.3 self.init_var = 0.3 - self.deg_free = 5. + self.deg_free = 4. censored = np.zeros_like(self.Y) random_inds = np.random.choice(self.N, int(self.N / 2), replace=True) censored[random_inds] = 1 @@ -83,7 +83,7 @@ class TestObservationModels(unittest.TestCase): # taking laplace predictions as the ground truth probs_mean_lap, probs_var_lap = m1.predict(self.X) probs_mean_ep_alt, probs_var_ep_alt = m2.predict(self.X) - probs_mean_ep_nested, probs_var_ep_nested = m2.predict(self.X) + probs_mean_ep_nested, probs_var_ep_nested = m3.predict(self.X) # for simple single dimension data , marginal likelihood for laplace and EP approximations should not be so far apart. self.assertAlmostEqual(m1.log_likelihood(), m2.log_likelihood(),delta=1) @@ -125,6 +125,7 @@ class TestObservationModels(unittest.TestCase): optimizer='bfgs' m1.optimize(optimizer=optimizer,max_iters=400) m2.optimize(optimizer=optimizer, max_iters=500) + # m3.optimize(optimizer=optimizer, max_iters=500) self.assertAlmostEqual(m1.log_likelihood(), m2.log_likelihood(),delta=10) # self.assertAlmostEqual(m1.log_likelihood(), m3.log_likelihood(), 3) @@ -132,12 +133,12 @@ class TestObservationModels(unittest.TestCase): preds_mean_lap, preds_var_lap = m1.predict(self.X) preds_mean_alt, preds_var_alt = m2.predict(self.X) # preds_mean_nested, preds_var_nested = m3.predict(self.X) - rmse_lap = self.rmse(preds_mean_lap, self.Y_noisy) - rmse_alt = self.rmse(preds_mean_alt, self.Y_noisy) + rmse_lap = self.rmse(preds_mean_lap, self.Y) + rmse_alt = self.rmse(preds_mean_alt, self.Y) # rmse_nested = self.rmse(preds_mean_nested, self.Y_noisy) - if rmse_alt > rmse_alt: - self.assertAlmostEqual(rmse_lap, rmse_alt, delta=1.) + if rmse_alt > rmse_lap: + self.assertAlmostEqual(rmse_lap, rmse_alt, delta=1.5) # m3.optimize(optimizer=optimizer, max_iters=500)