From 4af1f017ec9ea7cb9fc70a914810e0670c7e7070 Mon Sep 17 00:00:00 2001 From: Joachim van der Herten Date: Sat, 15 Jul 2017 00:35:39 +0200 Subject: [PATCH] Solved incorrect parameter assignments (causing test faillure) --- GPy/inference/latent_function_inference/posterior.py | 1 - GPy/testing/gp_tests.py | 1 - GPy/testing/tp_tests.py | 6 +++--- 3 files changed, 3 insertions(+), 5 deletions(-) diff --git a/GPy/inference/latent_function_inference/posterior.py b/GPy/inference/latent_function_inference/posterior.py index 964ead7a..96042c6f 100644 --- a/GPy/inference/latent_function_inference/posterior.py +++ b/GPy/inference/latent_function_inference/posterior.py @@ -318,7 +318,6 @@ class StudentTPosterior(PosteriorExact): self.nu = deg_free def _raw_predict(self, kern, Xnew, pred_var, full_cov=False): - print(self.nu) mu, var = super(StudentTPosterior, self)._raw_predict(kern, Xnew, pred_var, full_cov) beta = np.sum(self.woodbury_vector * self.mean) N = self.woodbury_vector.shape[0] diff --git a/GPy/testing/gp_tests.py b/GPy/testing/gp_tests.py index 54f24fed..97e3718d 100644 --- a/GPy/testing/gp_tests.py +++ b/GPy/testing/gp_tests.py @@ -92,7 +92,6 @@ class Test(unittest.TestCase): Y = p.f(X) + np.random.multivariate_normal(np.zeros(X.shape[0]), k.K(X)+np.eye(X.shape[0])*1e-8)[:,None] + np.random.normal(0, .1, (X.shape[0], 1)) m = GPy.models.GPRegression(X, Y, mean_function=p) m.randomize() - print(m) assert(m.checkgrad()) _ = m.predict(m.X) diff --git a/GPy/testing/tp_tests.py b/GPy/testing/tp_tests.py index 620ae791..643d67e0 100644 --- a/GPy/testing/tp_tests.py +++ b/GPy/testing/tp_tests.py @@ -131,11 +131,11 @@ class Test(unittest.TestCase): k = GPy.kern.RBF(1) m = GPy.models.GPRegression(self.X, self.Y, kernel=k) m.optimize() - - k1 = GPy.kern.RBF(1, variance=k.variance, lengthscale=k.lengthscale) + mu1, var1 = m.predict(self.X) + k1 = GPy.kern.RBF(1) + k1[:] = k[:] k2 = GPy.kern.White(1, variance=m.likelihood.variance) m2 = GPy.models.TPRegression(self.X, self.Y, kernel=k1 + k2, deg_free=1e6) - mu1, var1 = m.predict(self.X) mu2, var2 = m2.predict(self.X) np.testing.assert_allclose(mu1, mu2) np.testing.assert_allclose(var1, var2)