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added the raw_predict function in the uncollapsed sparse GP

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James Hensman 2012-12-07 15:51:27 -08:00
parent 4fb28dcbd4
commit 1c0a223329
1 changed files with 9 additions and 8 deletions
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17
GPy/models/uncollapsed_sparse_GP.py
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@ -47,9 +47,7 @@ class uncollapsed_sparse_GP(sparse_GP_regression):
self.V = self.beta*self.Y self.V = self.beta*self.Y
self.psi1V = np.dot(self.psi1, self.V) self.psi1V = np.dot(self.psi1, self.V)
self.psi1VVpsi1 = np.dot(self.psi1V, self.psi1V.T) self.psi1VVpsi1 = np.dot(self.psi1V, self.psi1V.T)
self.Lm = jitchol(self.Kmm) self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm)
self.Lmi = chol_inv(self.Lm)
self.Kmmi = np.dot(self.Lmi.T, self.Lmi)
self.A = mdot(self.Lmi, self.psi2, self.Lmi.T) self.A = mdot(self.Lmi, self.psi2, self.Lmi.T)
self.B = np.eye(self.M) + self.beta * self.A self.B = np.eye(self.M) + self.beta * self.A
self.Lambda = mdot(self.Lmi.T,self.B,sel.Lmi) self.Lambda = mdot(self.Lmi.T,self.B,sel.Lmi)
@ -89,16 +87,19 @@ class uncollapsed_sparse_GP(sparse_GP_regression):
def _raw_predict(self, Xnew, slices): def _raw_predict(self, Xnew, slices):
"""Internal helper function for making predictions, does not account for normalisation""" """Internal helper function for making predictions, does not account for normalisation"""
Kx = self.kern.cross_compute(Xnew)
#TODO Kxx = self.kern.compute_new(Xnew)
mu = mdot(Kx.T,self.Kmmi,self.mu)
tmp = self.Kmmi- mdot(self.Kmmi,self.q_u_cov,self.Kmmi)
var = Kxx - mdot(Kx.T,tmp,Kx) + np.eye(Xnew.shape[0])/self.beta
return mu,var return mu,var
def set_vb_param(self,vb_param): def set_vb_param(self,vb_param):
"""set the distribution q(u) from the canonical parameters""" """set the distribution q(u) from the canonical parameters"""
self.q_u_prec = -2.*vb_param[self.M*self.D:].reshape(self.M,self.M) self.q_u_prec = -2.*vb_param[self.M*self.D:].reshape(self.M,self.M)
self.q_u_prec_L = jitchol(self.q_u_prec) self.q_u_cov, q_u_Li, q_u_L, tmp = pdinv(self.q_u_prec)
self.q_u_cov_L = chol_inv(self.q_u_prec_L) self.q_u_logdet = -tmp
self.q_u_cov = np.dot(self.q_u_cov_L,self.q_u_cov_L.T)
self.q_u_mean = -2.*np.dot(self.q_u_cov,vb_param[:self.M*self.D].reshape(self.M,self.D)) self.q_u_mean = -2.*np.dot(self.q_u_cov,vb_param[:self.M*self.D].reshape(self.M,self.D))
self.q_u_expectation = (self.q_u_mean, np.dot(self.q_u_mean,self.q_u_mean.T)+self.q_u_cov) self.q_u_expectation = (self.q_u_mean, np.dot(self.q_u_mean,self.q_u_mean.T)+self.q_u_cov)