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Fix EP for non-zero mean GP priors

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Moreno 2017-11-16 15:18:29 +00:00
parent 83c3ed44e2
commit 33aabdea90
2 changed files with 60 additions and 38 deletions
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11
GPy/testing/inference_tests.py
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@ -86,11 +86,11 @@ class InferenceGPEP(unittest.TestCase):
inference_method=inf,
likelihood=lik)
K = self.model.kern.K(X)
post_params, ga_approx, cav_params, log_Z_tilde = self.model.inference_method.expectation_propagation(K, ObsAr(Y), lik, None)
mean_prior = np.zeros(K.shape[0])
post_params, ga_approx, cav_params, log_Z_tilde = self.model.inference_method.expectation_propagation(mean_prior, K, ObsAr(Y), lik, None)
mu_tilde = ga_approx.v / ga_approx.tau.astype(float)
p, m, d = self.model.inference_method._inference(Y, K, ga_approx, cav_params, lik, Y_metadata=None, Z_tilde=log_Z_tilde)
p, m, d = self.model.inference_method._inference(Y, mean_prior, K, ga_approx, cav_params, lik, Y_metadata=None, Z_tilde=log_Z_tilde)
p0, m0, d0 = super(GPy.inference.latent_function_inference.expectation_propagation.EP, inf).inference(k, X,lik ,mu_tilde[:,None], mean_function=None, variance=1./ga_approx.tau, K=K, Z_tilde=log_Z_tilde + np.sum(- 0.5*np.log(ga_approx.tau) + 0.5*(ga_approx.v*ga_approx.v*1./ga_approx.tau)))
assert (np.sum(np.array([m - m0,
@ -120,10 +120,11 @@ class InferenceGPEP(unittest.TestCase):
# ep_inf_nested = GPy.inference.latent_function_inference.expectation_propagation.EP(ep_mode='nested', max_iters=100, delta=0.5)
m = GPy.core.GP(X=X,Y=Y_extra_noisy,kernel=k,likelihood=lik_studentT,inference_method=ep_inf_alt)
K = m.kern.K(X)
post_params, ga_approx, cav_params, log_Z_tilde = m.inference_method.expectation_propagation(K, ObsAr(Y_extra_noisy), lik_studentT, None)
mean_prior = np.zeros(K.shape[0])
post_params, ga_approx, cav_params, log_Z_tilde = m.inference_method.expectation_propagation(mean_prior, K, ObsAr(Y_extra_noisy), lik_studentT, None)
mu_tilde = ga_approx.v / ga_approx.tau.astype(float)
p, m, d = m.inference_method._inference(Y_extra_noisy, K, ga_approx, cav_params, lik_studentT, Y_metadata=None, Z_tilde=log_Z_tilde)
p, m, d = m.inference_method._inference(Y_extra_noisy, mean_prior, K, ga_approx, cav_params, lik_studentT, Y_metadata=None, Z_tilde=log_Z_tilde)
p0, m0, d0 = super(GPy.inference.latent_function_inference.expectation_propagation.EP, ep_inf_alt).inference(k, X,lik_studentT ,mu_tilde[:,None], mean_function=None, variance=1./ga_approx.tau, K=K, Z_tilde=log_Z_tilde + np.sum(- 0.5*np.log(ga_approx.tau) + 0.5*(ga_approx.v*ga_approx.v*1./ga_approx.tau)))
assert (np.sum(np.array([m - m0,