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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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14
GPy/examples/classification.py
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@ -87,18 +87,22 @@ def toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=
Y = data['Y'][:, 0:1]
Y[Y.flatten() == -1] = 0
bern_noise_model = GPy.likelihoods.bernoulli()
laplace_likelihood = GPy.likelihoods.Laplace(Y.copy(), bern_noise_model)
likelihood = GPy.likelihoods.Bernoulli()
laplace_inf = GPy.inference.latent_function_inference.Laplace()
kernel = GPy.kern.rbf(1)
# Model definition
m = GPy.models.GPClassification(data['X'], Y, likelihood=laplace_likelihood)
print m
m = GPy.core.GP(data['X'], Y, kernel=kernel, likelihood=likelihood, inference_method=laplace_inf)
# Optimize
if optimize:
#m.update_likelihood_approximation()
# Parameters optimization:
m.optimize('bfgs', messages=1)
try:
m.optimize('scg', messages=1)
except Exception as e:
return m
#m.pseudo_EM()
# Plot