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James' debugging of the EP/GP interface

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It seems that the GP-EP algorithm works now.
This commit is contained in:
Ricardo Andrade 2013-02-01 13:45:55 +00:00
parent eb04cbed63
commit f941d629e6
3 changed files with 11 additions and 8 deletions
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4
GPy/examples/ep_fix.py
View file

@ -4,7 +4,7 @@
"""
Simple Gaussian Processes classification 1D
Probit likelihood
probit likelihood
"""
import pylab as pb
import numpy as np
@ -26,7 +26,7 @@ Y = np.hstack([np.ones(N/2),np.repeat(-1,N/2)])[:,None]
kernel = GPy.kern.rbf(1)
# Define likelihood
distribution = GPy.likelihoods.likelihood_functions.Probit()
distribution = GPy.likelihoods.likelihood_functions.probit()
likelihood_object = GPy.likelihoods.EP(Y,distribution)
# Model definition

11
GPy/likelihoods/EP.py
View file

@ -27,7 +27,7 @@ class EP(likelihood):
#initial values for the GP variables
self.Y = np.zeros((self.N,1))
self.variance = np.zeros((self.N,self.N))#np.eye(self.N)
self.covariance_matrix = np.eye(self.N)
self.Z = 0
self.YYT = None
@ -50,8 +50,9 @@ class EP(likelihood):
mu_diff_2 = (self.v_/self.tau_ - mu_tilde)**2
self.Z = np.sum(np.log(self.Z_hat)) + 0.5*np.sum(np.log(sigma_sum)) + 0.5*np.sum(mu_diff_2/sigma_sum) #Normalization constant, aka Z_ep
self.Y = mu_tilde[:,None]
self.precsion = self.tau_tilde[:,None]
self.Y = mu_tilde[:,None]
self.YYT = np.dot(self.Y,self.Y.T)
self.precision = self.tau_tilde
self.covariance_matrix = np.diag(1./self.precision)
def fit_full(self,K):
@ -61,9 +62,11 @@ class EP(likelihood):
"""
#Prior distribution parameters: p(f|X) = N(f|0,K)
self.tau_tilde = np.zeros(self.N)
self.v_tilde = np.zeros(self.N)
#Initial values - Posterior distribution parameters: q(f|X,Y) = N(f|mu,Sigma)
self.mu = np.zeros(self.N)
self.Sigma = K.copy() - self.variance.copy()
self.Sigma = K.copy()
"""
Initial values - Cavity distribution parameters:

4
GPy/models/GP.py
View file

@ -65,7 +65,7 @@ class GP(model):
self.likelihood._set_params(p[self.kern.Nparam:])
self.K = self.kern.K(self.X,slices1=self.Xslices)
self.K += self.likelihood.variance
self.K += self.likelihood.covariance_matrix
self.Ki, self.L, self.Li, self.K_logdet = pdinv(self.K)
@ -90,7 +90,7 @@ class GP(model):
For a Gaussian (or direct: TODO) likelihood, no iteration is required:
this function does nothing
"""
self.likelihood.fit_full(self.kern.compute(self.X))
self.likelihood.fit_full(self.kern.K(self.X))
self._set_params(self._get_params()) # update the GP
def _model_fit_term(self):