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

Browse source 
Conflicts:
	GPy/likelihoods/EP.py
This commit is contained in:
James Hensman 2013-02-01 13:56:28 +00:00
commit 64280d7eb6
7 changed files with 136 additions and 136 deletions
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33
GPy/likelihoods/EP.py
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@ -16,6 +16,8 @@ class EP(likelihood):
self.likelihood_function = likelihood_function
self.epsilon = epsilon
self.eta, self.delta = power_ep
self.data = data
self.N = self.data.size
self.is_heteroscedastic = True
#Initial values - Likelihood approximation parameters:
@ -23,6 +25,24 @@ class EP(likelihood):
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.covariance_matrix = np.eye(self.N)
self.Z = 0
self.YYT = None
def predictive_values(self,mu,var):
return self.likelihood_function.predictive_values(mu,var)
def _get_params(self):
return np.zeros(0)
def _get_param_names(self):
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
mu_tilde = self.v_tilde/self.tau_tilde #When calling EP, this variable is used instead of Y in the GP model
@ -31,7 +51,8 @@ class EP(likelihood):
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.precision = self.tau_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):
@ -41,6 +62,8 @@ 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()
@ -73,8 +96,9 @@ class EP(likelihood):
#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])
@ -85,6 +109,7 @@ class EP(likelihood):
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
@ -105,7 +130,7 @@ class EP(likelihood):
For nomenclature see ... 2013.
"""
#TODO: this doesn;t work with uncertain inputs!
#TODO: this doesn;t work with uncertain inputs!
"""
Prior approximation parameters:
@ -246,7 +271,7 @@ class EP(likelihood):
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])