apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-06 10:32:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

predictive_mean and predictive_variance now use gp_var as a parameter, rather than gp_std

Browse source
This commit is contained in:
Ricardo 2013-11-08 17:39:52 +00:00
parent ae6648e0cf
commit c3d84f1d9d
1 changed files with 8 additions and 4 deletions
Download patch file Download diff file Expand all files Collapse all files

12
GPy/likelihoods/noise_models/bernoulli_noise.py
View file

@ -71,15 +71,19 @@ class Bernoulli(NoiseDistribution):
return Z_hat, mu_hat, sigma2_hat return Z_hat, mu_hat, sigma2_hat
def _predictive_mean_analytical(self,mu,sigma): def _predictive_mean_analytical(self,mu,variance):
if isinstance(self.gp_link,gp_transformations.Probit): if isinstance(self.gp_link,gp_transformations.Probit):
return stats.norm.cdf(mu/np.sqrt(1+sigma**2)) return stats.norm.cdf(mu/np.sqrt(1+variance))
elif isinstance(self.gp_link,gp_transformations.Heaviside): elif isinstance(self.gp_link,gp_transformations.Heaviside):
return stats.norm.cdf(mu/sigma) return stats.norm.cdf(mu/np.sqrt(variance))
else: else:
raise NotImplementedError raise NotImplementedError
def _predictive_variance_analytical(self,mu,sigma, pred_mean): def _predictive_variance_analytical(self,mu,variance, pred_mean):
if isinstance(self.gp_link,gp_transformations.Heaviside): if isinstance(self.gp_link,gp_transformations.Heaviside):
return 0. return 0.
else: else: