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So many changes

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This commit is contained in:
Ricardo Andrade 2013-02-01 13:17:17 +00:00
parent de53917039
commit 182c4c7d64
7 changed files with 118 additions and 139 deletions
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31
GPy/likelihoods/EP.py
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@ -1,7 +1,7 @@
import numpy as np
import random
from scipy import stats, linalg
from ..core import model
#from ..core import model
from ..util.linalg import pdinv,mdot,jitchol
from ..util.plot import gpplot
@ -18,6 +18,8 @@ class EP:
self.likelihood_function = likelihood_function
self.epsilon = epsilon
self.eta, self.delta = power_ep
self.data = data
self.N = self.data.size
"""
Initial values - Likelihood approximation parameters:
@ -26,6 +28,12 @@ class EP:
self.tau_tilde = np.zeros(self.N)
self.v_tilde = np.zeros(self.N)
#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.Z = 0
self.YYT = None
def predictive_values(self,mu,var):
return self.likelihood_function.predictive_values(mu,var)
@ -35,6 +43,8 @@ class EP:
return []
def _set_params(self,p):
pass # TODO: the EP likelihood might want to take some parameters...
def _gradients(self,partial):
return np.zeros(0) # TODO: the EP likelihood might want to take some parameters...
def _compute_GP_variables(self):
#Variables to be called from GP
@ -42,7 +52,8 @@ class EP:
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 = self.tau_tilde[:,None], mu_tilde[:,None], Z_ep
self.Y, self.beta, self.Z = mu_tilde[:,None],self.tau_tilde[:,None], Z_ep
self.variance = np.diag(1./self.beta.flatten())
def fit_full(self,K):
"""
@ -53,7 +64,7 @@ class EP:
#Initial values - Posterior distribution parameters: q(f|X,Y) = N(f|mu,Sigma)
self.mu = np.zeros(self.N)
self.Sigma = K.copy()
self.Sigma = K.copy() - self.variance.copy()
"""
Initial values - Cavity distribution parameters:
@ -78,14 +89,14 @@ class EP:
self.np1 = [self.tau_tilde.copy()]
self.np2 = [self.v_tilde.copy()]
while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:
update_order = np.arange(self.N)
random.shuffle(update_order)
update_order = np.random.permutation(self.N)
for i in update_order:
#Cavity distribution parameters
self.tau_[i] = 1./self.Sigma[i,i] - self.eta*self.tau_tilde[i]
self.v_[i] = self.mu[i]/self.Sigma[i,i] - self.eta*self.v_tilde[i]
print 1./self.Sigma[i,i],self.tau_tilde[i]
#Marginal moments
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood.moments_match(i,self.tau_[i],self.v_[i])
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood_function.moments_match(self.data[i],self.tau_[i],self.v_[i])
#Site parameters update
Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./self.Sigma[i,i])
Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - self.mu[i]/self.Sigma[i,i])
@ -96,6 +107,7 @@ class EP:
self.Sigma = self.Sigma - Delta_tau/(1.+ Delta_tau*self.Sigma[i,i])*np.dot(si,si.T)
self.mu = np.dot(self.Sigma,self.v_tilde)
self.iterations += 1
print self.tau_tilde[i]
#Sigma recomptutation with Cholesky decompositon
Sroot_tilde_K = np.sqrt(self.tau_tilde)[:,None]*K
B = np.eye(self.N) + np.sqrt(self.tau_tilde)[None,:]*Sroot_tilde_K
@ -116,7 +128,7 @@ class EP:
For nomenclature see ... 2013.
"""
#TODO: this doesn;t work with uncertain inputs!
#TODO: this doesn;t work with uncertain inputs!
"""
Prior approximation parameters:
@ -251,14 +263,13 @@ class EP:
self.np1 = [self.tau_tilde.copy()]
self.np2 = [self.v_tilde.copy()]
while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:
update_order = np.arange(self.N)
random.shuffle(update_order)
update_order = np.random.permutation(self.N)
for i in update_order:
#Cavity distribution parameters
self.tau_[i] = 1./self.Sigma_diag[i] - self.eta*self.tau_tilde[i]
self.v_[i] = self.mu[i]/self.Sigma_diag[i] - self.eta*self.v_tilde[i]
#Marginal moments
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood.moments_match(i,self.tau_[i],self.v_[i])
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood_function.moments_match(data[i],self.tau_[i],self.v_[i])
#Site parameters update
Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./self.Sigma_diag[i])
Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - self.mu[i]/self.Sigma_diag[i])