From af50fa3e57e969e6d2571e78aca8743a631aefb3 Mon Sep 17 00:00:00 2001 From: Max Zwiessele Date: Fri, 28 Feb 2014 12:09:03 +0000 Subject: [PATCH] prediction code need updating, started with woodbury vector, but how to predict variance in sparse gp with uncertain inputs? --- GPy/core/sparse_gp.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/GPy/core/sparse_gp.py b/GPy/core/sparse_gp.py index 751a295d..b06ffbc7 100644 --- a/GPy/core/sparse_gp.py +++ b/GPy/core/sparse_gp.py @@ -85,11 +85,11 @@ class SparseGP(GP): self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z) self.Z.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'].T, self.Z, self.X) - def _raw_predict(self, Xnew, X_variance_new=None, full_cov=False): + def _raw_predict(self, Xnew, full_cov=False): """ Make a prediction for the latent function values """ - if X_variance_new is None: + if not isinstance(Xnew, VariationalPosterior): Kx = self.kern.K(self.Z, Xnew) mu = np.dot(Kx.T, self.posterior.woodbury_vector) if full_cov: @@ -100,13 +100,13 @@ class SparseGP(GP): Kxx = self.kern.Kdiag(Xnew) var = (Kxx - np.sum(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx) * Kx[None,:,:], 1)).T else: - Kx = self.kern.psi1(self.Z, Xnew, X_variance_new) - mu = np.dot(Kx, self.Cpsi1V) + Kx = self.kern.psi1(self.Z, Xnew) + mu = np.dot(Kx, self.posterior.woodbury_vector) if full_cov: raise NotImplementedError, "TODO" else: - Kxx = self.kern.psi0(self.Z, Xnew, X_variance_new) - psi2 = self.kern.psi2(self.Z, Xnew, X_variance_new) + Kxx = self.kern.psi0(self.Z, Xnew) + psi2 = self.kern.psi2(self.Z, Xnew) var = Kxx - np.sum(np.sum(psi2 * Kmmi_LmiBLmi[None, :, :], 1), 1) return mu, var