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beginning of work to make sparse GP ork with RA's EP methods

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James Hensman 2013-02-01 13:55:19 +00:00
parent 346f9dd8bd
commit c025e8b68b
2 changed files with 12 additions and 5 deletions
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2
GPy/likelihoods/EP.py
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@ -31,7 +31,7 @@ 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.precsion = self.tau_tilde
self.precision = self.tau_tilde[:,None]
self.covariance_matrix = np.diag(1./self.precision)
def fit_full(self,K):

15
GPy/models/sparse_GP.py
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@ -70,16 +70,23 @@ class sparse_GP(GP):
self.psi0 = self.kern.psi0(self.Z,self.X, self.X_uncertainty).sum()
self.psi1 = self.kern.psi1(self.Z,self.X, self.X_uncertainty).T
self.psi2 = self.kern.psi2(self.Z,self.X, self.X_uncertainty)
self.psi2_beta_scaled = (self.psi2*(self.beta/sf2)).sum(0)
if self.likelihood.is_heteroscedastic:
self.psi2_beta_scaled = (self.psi2*(self.likelihood.precision.reshape(self.N,1,1)/sf2)).sum(0)
#TODO: what is the likelihood is heterscedatic and there are multiple independent outputs?
else:
self.psi2_beta_scaled = (self.psi2*(self.likelihood.precision/sf2)).sum(0)
else:
self.psi0 = self.kern.Kdiag(self.X,slices=self.Xslices).sum()
self.psi1 = self.kern.K(self.Z,self.X)
tmp = self.psi1*(np.sqrt(self.likelihood.beta)/sf)
if self.likelihood.is_heteroscedastic:
tmp = self.psi1*(np.sqrt(self.likelihood.precision.reshape(self.N,1))/sf)
else:
tmp = self.psi1*(np.sqrt(self.likelihood.precision)/sf)
self.psi2_beta_scaled = np.dot(tmp,tmp.T)
self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm)#+np.eye(self.M)*1e-3)
self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm)
self.V = (self.likelihood.beta/self.scale_factor)*self.Y
self.V = (self.likelihood.precision/self.scale_factor)*self.Y
self.A = mdot(self.Lmi, self.psi2_beta_scaled, self.Lmi.T)
self.B = np.eye(self.M)/sf2 + self.A