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Sparse GP with EP is working now

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This commit is contained in:
Ricardo Andrade 2013-03-06 15:43:58 +00:00
parent f2ce47d96e
commit c6f2082839
4 changed files with 78 additions and 46 deletions
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20
GPy/testing/unit_tests.py
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@ -157,13 +157,29 @@ class GradientTests(unittest.TestCase):
def test_GP_EP_probit(self):
N = 20
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.repeat(-1,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.probit()
likelihood = GPy.likelihoods.EP(Y, distribution)
m = GPy.models.GP(X, likelihood, kernel)
m.ensure_default_constraints()
self.assertTrue(m.EPEM)
m.update_likelihood_approximation()
self.assertTrue(m.checkgrad())
#self.assertTrue(m.EPEM)
def test_sparse_EP_DTC_probit(self):
N = 20
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]
Z = np.linspace(0,15,4)[:,None]
kernel = GPy.kern.rbf(1)
distribution = GPy.likelihoods.likelihood_functions.probit()
likelihood = GPy.likelihoods.EP(Y, distribution)
m = GPy.models.sparse_GP(X, likelihood, kernel,Z)
m.ensure_default_constraints()
m.update_likelihood_approximation()
self.assertTrue(m.checkgrad())
@unittest.skip("FITC will be broken for a while")
def test_generalized_FITC(self):