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predictive_values implemented in EP

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Ricardo Andrade 2013-01-31 15:30:57 +00:00
parent 4211cad89d
commit 2b40ee6f7e
4 changed files with 26 additions and 6 deletions
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8
GPy/likelihoods/likelihood_functions.py
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@ -49,7 +49,7 @@ class probit(likelihood):
def predictive_values(self,mu,var,all=False):
"""
Compute mean, variance, and conficence interval (percentiles 5 and 95) of the prediction
Compute mean, and conficence interval (percentiles 5 and 95) of the prediction
"""
mu = mu.flatten()
var = var.flatten()
@ -57,7 +57,7 @@ class probit(likelihood):
if all:
p_05 = np.zeros([mu.size])
p_95 = np.ones([mu.size])
return mean, mean*(1-mean),p_05,p_95
return mean, p_05, p_95
else:
return mean
@ -136,14 +136,14 @@ class poisson(likelihood):
def predictive_values(self,mu,var,all=False):
"""
Compute mean, variance, and conficence interval (percentiles 5 and 95) of the prediction
Compute mean, and conficence interval (percentiles 5 and 95) of the prediction
"""
mean = np.exp(mu*self.scale + self.location)
if all:
tmp = stats.poisson.ppf(np.array([.05,.95]),mu)
p_05 = tmp[:,0]
p_95 = tmp[:,1]
return mean,mean,p_05,p_95
return mean,p_05,p_95
else:
return mean