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Fixed bernoulli likelihood divide by 0 and log of 0

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This commit is contained in:
Alan Saul 2014-02-12 16:48:57 +00:00
parent c788c463d8
commit 46ce76dee8
5 changed files with 33 additions and 20 deletions
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8
GPy/testing/likelihood_tests.py
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@ -516,9 +516,8 @@ class TestNoiseModels(object):
Y = Y/Y.max()
white_var = 1e-6
kernel = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
laplace_likelihood = GPy.inference.latent_function_inference.LaplaceInference()
laplace_likelihood = GPy.inference.latent_function_inference.Laplace()
m = GPy.core.GP(X.copy(), Y.copy(), kernel, likelihood=model, inference_method=laplace_likelihood)
m.ensure_default_constraints()
m['white'].constrain_fixed(white_var)
#Set constraints
@ -555,7 +554,6 @@ class TestNoiseModels(object):
kernel = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
ep_inf = GPy.inference.latent_function_inference.EP()
m = GPy.core.GP(X.copy(), Y.copy(), kernel=kernel, likelihood=model, inference_method=ep_inf)
m.ensure_default_constraints()
m['white'].constrain_fixed(white_var)
for param_num in range(len(param_names)):
@ -644,13 +642,11 @@ class LaplaceTests(unittest.TestCase):
m1['variance'] = initial_var_guess
m1['variance'].constrain_bounded(1e-4, 10)
m1['rbf'].constrain_bounded(1e-4, 10)
m1.ensure_default_constraints()
m1.randomize()
gauss_distr2 = GPy.likelihoods.Gaussian(variance=initial_var_guess)
laplace_inf = GPy.inference.latent_function_inference.LaplaceInference()
laplace_inf = GPy.inference.latent_function_inference.Laplace()
m2 = GPy.core.GP(X, Y.copy(), kernel=kernel2, likelihood=gauss_distr2, inference_method=laplace_inf)
m2.ensure_default_constraints()
m2['white'].constrain_fixed(1e-6)
m2['rbf'].constrain_bounded(1e-4, 10)
m2['variance'].constrain_bounded(1e-4, 10)