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[vardtc] predict with uncertain inputs, the non principled way

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Max Zwiessele 2014-12-03 08:34:11 +00:00
parent d554b1a442
commit 865d8e3851
1 changed files with 8 additions and 5 deletions
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13
GPy/core/sparse_gp.py
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@ -121,12 +121,15 @@ class SparseGP(GP):
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 = kern.psi1(self.Z, Xnew)
mu = np.dot(Kx, self.posterior.woodbury_vector)
Kx = kern.psi1(self.Z, Xnew).T
mu = np.dot(Kx.T, self.posterior.woodbury_vector)
if full_cov:
raise NotImplementedError, "TODO"
Kxx = kern.K(Xnew.mean)
if self.posterior.woodbury_inv.ndim == 2:
var = Kxx - np.dot(Kx.T, np.dot(self.posterior.woodbury_inv, Kx))
elif self.posterior.woodbury_inv.ndim == 3:
var = Kxx[:,:,None] - np.tensordot(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx).T, Kx, [1,0]).swapaxes(1,2)
else:
Kxx = kern.psi0(self.Z, Xnew)
psi2 = kern.psi2(self.Z, Xnew)
var = Kxx - np.sum(np.sum(psi2 * Kmmi_LmiBLmi[None, :, :], 1), 1)
var = (Kxx - np.sum(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx) * Kx[None,:,:], 1)).T
return mu, var