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prediction code need updating, started with woodbury vector, but how to predict variance in sparse gp with uncertain inputs?

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Max Zwiessele 2014-02-28 12:09:03 +00:00
parent a67bf9d04d
commit af50fa3e57
1 changed files with 6 additions and 6 deletions
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12
GPy/core/sparse_gp.py
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@ -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