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sparse GPs can now accept kerns for predicting

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James Hensman 2014-05-06 13:05:26 +01:00
parent 7d41001ae1
commit 5fac35b0fd
1 changed files with 10 additions and 7 deletions
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17
GPy/core/sparse_gp.py
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@ -79,29 +79,32 @@ 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, full_cov=False):
def _raw_predict(self, Xnew, full_cov=False, kern=None):
"""
Make a prediction for the latent function values
"""
if kern is None: kern = self.kern
if not isinstance(Xnew, VariationalPosterior):
Kx = self.kern.K(self.Z, Xnew)
Kx = kern.K(self.Z, Xnew)
mu = np.dot(Kx.T, self.posterior.woodbury_vector)
if full_cov:
Kxx = self.kern.K(Xnew)
Kxx = kern.K(Xnew)
var = Kxx - np.dot(Kx.T, np.dot(self.posterior.woodbury_inv, Kx))
#var = Kxx[:,:,None] - np.tensordot(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx).T, Kx, [1,0]).swapaxes(1,2)
var = var.squeeze()
else:
Kxx = self.kern.Kdiag(Xnew)
Kxx = 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)
Kx = 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)
psi2 = self.kern.psi2(self.Z, Xnew)
Kxx = kern.psi0(self.Z, Xnew)
psi2 = kern.psi2(self.Z, Xnew)
var = Kxx - np.sum(np.sum(psi2 * Kmmi_LmiBLmi[None, :, :], 1), 1)
return mu, var