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[spgp minibatch] linear calls the right psicomps and add kernel

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Max Zwiessele 2015-09-04 13:30:15 +01:00
parent bf3c579cc6
commit 8fa5b67089
5 changed files with 16 additions and 13 deletions
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8
GPy/core/sparse_gp.py
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@ -119,9 +119,10 @@ class SparseGP(GP):
if there is missing data, each dimension has its own full_cov of shape NxNxD, and if full_cov is of,
we take only the diagonal elements across N.
For uncertain inputs, the SparseGP bound produces a full covariance structure across D, so for full_cov we
return a NxDxD matrix and in the not full_cov case, we return the diagonal elements across D (NxD).
This is for both with and without missing data. See for missing data SparseGP implementation py:class:'~GPy.models.sparse_gp_minibatch.SparseGPMiniBatch'.
For uncertain inputs, the SparseGP bound produces cannot predict the full covariance matrix full_cov for now.
The implementation of that will follow. However, for each dimension the
covariance changes, so if full_cov is False (standard), we return the variance
for each dimension [NxD].
"""
if kern is None: kern = self.kern
@ -158,6 +159,7 @@ class SparseGP(GP):
mu = np.dot(psi1_star, la) # TODO: dimensions?
if full_cov:
raise NotImplementedError, "Full covariance for Sparse GP predicted with uncertain inputs not implemented yet."
var = np.empty((Xnew.shape[0], la.shape[1], la.shape[1]))
di = np.diag_indices(la.shape[1])
else: