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Merge branch 'newGP' of github.com:SheffieldML/GPy into newGP

Browse source 
Conflicts:
	GPy/likelihoods/EP.py
	GPy/likelihoods/likelihood_functions.py
This commit is contained in:
Ricardo Andrade 2013-02-01 13:19:59 +00:00
commit 879fa138e1
7 changed files with 464 additions and 369 deletions
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21
GPy/likelihoods/EP.py
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@ -1,11 +1,9 @@
import numpy as np
import random
from scipy import stats, linalg
#from ..core import model
from ..util.linalg import pdinv,mdot,jitchol
from ..util.plot import gpplot
from likelihood import likelihood
class EP:
class EP(likelihood):
def __init__(self,data,likelihood_function,epsilon=1e-3,power_ep=[1.,1.]):
"""
Expectation Propagation
@ -20,11 +18,10 @@ class EP:
self.eta, self.delta = power_ep
self.data = data
self.N = self.data.size
self.is_heteroscedastic = True
"""
Initial values - Likelihood approximation parameters:
p(y|f) = t(f|tau_tilde,v_tilde)
"""
#Initial values - Likelihood approximation parameters:
#p(y|f) = t(f|tau_tilde,v_tilde)
self.tau_tilde = np.zeros(self.N)
self.v_tilde = np.zeros(self.N)
@ -51,9 +48,11 @@ class EP:
mu_tilde = self.v_tilde/self.tau_tilde #When calling EP, this variable is used instead of Y in the GP model
sigma_sum = 1./self.tau_ + 1./self.tau_tilde
mu_diff_2 = (self.v_/self.tau_ - mu_tilde)**2
Z_ep = 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
self.Y, self.beta, self.Z = mu_tilde[:,None],self.tau_tilde[:,None], Z_ep
self.variance = np.diag(1./self.beta.flatten())
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.covariance_matrix = np.diag(1./self.precision)
def fit_full(self,K):
"""