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some noodling around in the likelihoods

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James Hensman 2013-12-10 14:51:11 -08:00
parent d8a627c1d8
commit 9011d8fe2f
2 changed files with 22 additions and 54 deletions
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16
GPy/likelihoods/gaussian.py
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@ -1,4 +1,4 @@
# Copyright (c) 2012, 2013 Ricardo Andrade
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
@ -30,18 +30,22 @@ class Gaussian(Likelihood):
analytical_variance = False
analytical_mean = False
super(Gaussian, self).__init__(gp_link, analytical_mean, analytical_variance, name=name)
super(Gaussian, self).__init__(gp_link, name=name)
self.variance = Param('variance', variance)
self.add_parameter(self.variance)
if isinstance(gp_link , link_functions.Identity):
if isinstance(gp_link, link_functions.Identity):
self.log_concave = True
def covariance_matrix(self, Y, Y_metadata=None):
return np.eye(Y.shape[0]) * self.variance
def _gradients(self, partial):
"""
Return the derivative of the log marginal likelihood wrt self.variance,
given the appropriate partial derivative
"""
return np.sum(partial)
def _preprocess_values(self, Y):
@ -51,7 +55,7 @@ class Gaussian(Likelihood):
"""
return Y
def _moments_match_analytical(self, data_i, tau_i, v_i):
def _moments_match_ep(self, data_i, tau_i, v_i):
"""
Moments match of the marginal approximation in EP algorithm
@ -65,11 +69,11 @@ 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)
return Z_hat, mu_hat, sigma2_hat
def _predictive_mean_analytical(self, mu, sigma):
def _predictive_mean(self, mu, sigma):
new_sigma2 = self.predictive_variance(mu, sigma)
return new_sigma2*(mu/sigma**2 + self.gp_link.transf(mu)/self.variance)
def _predictive_variance_analytical(self, mu, sigma, predictive_mean=None):
def _predictive_variance(self, mu, sigma, predictive_mean=None):
return 1./(1./self.variance + 1./sigma**2)
def pdf_link(self, link_f, y, extra_data=None):