From 1b54365f7cb61c2053d1d718685781c1a9f230e1 Mon Sep 17 00:00:00 2001 From: Ricardo Date: Fri, 13 Sep 2013 12:25:53 +0100 Subject: [PATCH] errors fixed --- GPy/testing/unit_tests.py | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/GPy/testing/unit_tests.py b/GPy/testing/unit_tests.py index 6e504a69..2aea03ac 100644 --- a/GPy/testing/unit_tests.py +++ b/GPy/testing/unit_tests.py @@ -166,10 +166,7 @@ class GradientTests(unittest.TestCase): X = np.hstack([np.random.normal(5, 2, N / 2), np.random.normal(10, 2, N / 2)])[:, None] Y = np.hstack([np.ones(N / 2), np.zeros(N / 2)])[:, None] kernel = GPy.kern.rbf(1) - distribution = GPy.likelihoods.likelihood_functions.Binomial() - likelihood = GPy.likelihoods.EP(Y, distribution) - m = GPy.core.GP(X, likelihood, kernel) - m.ensure_default_constraints() + m = GPy.models.GPClassification(X,Y,kernel=kernel) m.update_likelihood_approximation() self.assertTrue(m.checkgrad()) @@ -179,10 +176,11 @@ class GradientTests(unittest.TestCase): Y = np.hstack([np.ones(N / 2), np.zeros(N / 2)])[:, None] Z = np.linspace(0, 15, 4)[:, None] kernel = GPy.kern.rbf(1) - distribution = GPy.likelihoods.likelihood_functions.Binomial() - likelihood = GPy.likelihoods.EP(Y, distribution) - m = GPy.core.SparseGP(X, likelihood, kernel, Z) - m.ensure_default_constraints() + m = GPy.models.SparseGPClassification(X,Y,kernel=kernel,Z=Z) + #distribution = GPy.likelihoods.likelihood_functions.Binomial() + #likelihood = GPy.likelihoods.EP(Y, distribution) + #m = GPy.core.SparseGP(X, likelihood, kernel, Z) + #m.ensure_default_constraints() m.update_likelihood_approximation() self.assertTrue(m.checkgrad()) @@ -191,7 +189,7 @@ class GradientTests(unittest.TestCase): X = np.hstack([np.random.rand(N / 2) + 1, np.random.rand(N / 2) - 1])[:, None] k = GPy.kern.rbf(1) + GPy.kern.white(1) Y = np.hstack([np.ones(N/2),np.zeros(N/2)])[:,None] - m = GPy.models.FITCClassification(X, Y=Y) + m = GPy.models.FITCClassification(X, Y, kernel = k) m.update_likelihood_approximation() self.assertTrue(m.checkgrad())