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some work in the Gaussian likelihood

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James Hensman 2013-12-11 16:03:36 -08:00
parent f1a08fbfdb
commit d703afd96a
1 changed files with 18 additions and 5 deletions
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23
GPy/likelihoods/gaussian.py
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@ -1,6 +1,17 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt). # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt) # Licensed under the BSD 3-clause license (see LICENSE.txt)
#TODO
"""
A lot of this code assumes that the link functio nis the identity.
I think laplace code is okay, but I'm quite sure that the EP moments will only work if the link is identity.
Furthermore, exact Guassian inference can only be done for the identity link, so we should be asserting so for all calls which relate to that.
James 11/12/13
"""
import numpy as np import numpy as np
from scipy import stats, special from scipy import stats, special
from GPy.util.univariate_Gaussian import std_norm_pdf, std_norm_cdf from GPy.util.univariate_Gaussian import std_norm_pdf, std_norm_cdf
@ -69,12 +80,14 @@ class Gaussian(Likelihood):
Z_hat = 1./np.sqrt(2.*np.pi*sum_var)*np.exp(-.5*(data_i - v_i/tau_i)**2./sum_var) Z_hat = 1./np.sqrt(2.*np.pi*sum_var)*np.exp(-.5*(data_i - v_i/tau_i)**2./sum_var)
return Z_hat, mu_hat, sigma2_hat return Z_hat, mu_hat, sigma2_hat
def _predictive_mean(self, mu, sigma): def predictive_mean(self, mu, sigma):
new_sigma2 = self.predictive_variance(mu, sigma) #new_sigma2 = self.predictive_variance(mu, sigma)
return new_sigma2*(mu/sigma**2 + self.gp_link.transf(mu)/self.variance) #return new_sigma2*(mu/sigma**2 + self.gp_link.transf(mu)/self.variance)
return mu
def _predictive_variance(self, mu, sigma, predictive_mean=None): def predictive_variance(self, mu, sigma, predictive_mean=None):
return 1./(1./self.variance + 1./sigma**2) #what on earth was the sum of precisions doing here? JH
return self.variance + sigma**2
def pdf_link(self, link_f, y, extra_data=None): def pdf_link(self, link_f, y, extra_data=None):
""" """