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Made sampling default for non-gaussian likelihoods as a quick fix to allow plotting again for likelihoods without predictive values

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Alan Saul 2014-03-05 14:16:53 +00:00
parent e7b601b424
commit 2f5d5dd3bf
3 changed files with 16 additions and 17 deletions
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2
GPy/examples/regression.py
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@ -284,7 +284,7 @@ def toy_poisson_rbf_1d_laplace(optimize=True, plot=True):
kern = GPy.kern.RBF(1) kern = GPy.kern.RBF(1)
poisson_lik = GPy.likelihoods.Poisson() poisson_lik = GPy.likelihoods.Poisson()
laplace_inf = GPy.inference.latent_function_inference.LaplaceInference() laplace_inf = GPy.inference.latent_function_inference.Laplace()
# create simple GP Model # create simple GP Model
m = GPy.core.GP(X, Y, kernel=kern, likelihood=poisson_lik, inference_method=laplace_inf) m = GPy.core.GP(X, Y, kernel=kern, likelihood=poisson_lik, inference_method=laplace_inf)

1
GPy/kern/_src/sympykern.py
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@ -5,7 +5,6 @@ try:
from sympy.utilities.lambdify import lambdify from sympy.utilities.lambdify import lambdify
except ImportError: except ImportError:
sympy_available=False sympy_available=False
exit()
import numpy as np import numpy as np
from kern import Kern from kern import Kern

2
GPy/likelihoods/likelihood.py
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@ -358,7 +358,7 @@ class Likelihood(Parameterized):
return dlogpdf_dtheta, dlogpdf_df_dtheta, d2logpdf_df2_dtheta return dlogpdf_dtheta, dlogpdf_df_dtheta, d2logpdf_df2_dtheta
def predictive_values(self, mu, var, full_cov=False, sampling=False, num_samples=10000): def predictive_values(self, mu, var, full_cov=False, sampling=True, num_samples=10000):
""" """
Compute mean, variance and conficence interval (percentiles 5 and 95) of the prediction. Compute mean, variance and conficence interval (percentiles 5 and 95) of the prediction.