apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-09 03:52:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

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

Browse source
This commit is contained in:
Max Zwiessele 2015-09-04 13:30:15 +01:00
parent bf3c579cc6
commit 8fa5b67089
5 changed files with 16 additions and 13 deletions
Download patch file Download diff file Expand all files Collapse all files

8
GPy/core/sparse_gp.py
View file

@ -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:

8
GPy/kern/_src/add.py
View file

@ -85,15 +85,15 @@ class Add(CombinationKernel):
[target.__iadd__(p.gradients_XX_diag(dL_dKdiag, X)) for p in self.parts]
return target
@Cache_this(limit=2, force_kwargs=['which_parts'])
@Cache_this(limit=1, force_kwargs=['which_parts'])
def psi0(self, Z, variational_posterior):
return reduce(np.add, (p.psi0(Z, variational_posterior) for p in self.parts))
@Cache_this(limit=2, force_kwargs=['which_parts'])
@Cache_this(limit=1, force_kwargs=['which_parts'])
def psi1(self, Z, variational_posterior):
return reduce(np.add, (p.psi1(Z, variational_posterior) for p in self.parts))
@Cache_this(limit=2, force_kwargs=['which_parts'])
@Cache_this(limit=1, force_kwargs=['which_parts'])
def psi2(self, Z, variational_posterior):
psi2 = reduce(np.add, (p.psi2(Z, variational_posterior) for p in self.parts))
#return psi2
@ -128,7 +128,7 @@ class Add(CombinationKernel):
raise NotImplementedError("psi2 cannot be computed for this kernel")
return psi2
@Cache_this(limit=2, force_kwargs=['which_parts'])
@Cache_this(limit=1, force_kwargs=['which_parts'])
def psi2n(self, Z, variational_posterior):
psi2 = reduce(np.add, (p.psi2n(Z, variational_posterior) for p in self.parts))
#return psi2

3
GPy/kern/_src/linear.py
View file

@ -125,6 +125,9 @@ class Linear(Kern):
def psi2(self, Z, variational_posterior):
return self.psicomp.psicomputations(self.variances, Z, variational_posterior)[2]
def psi2n(self, Z, variational_posterior):
return self.psicomp.psicomputations(self.variances, Z, variational_posterior, return_psi2_n=True)[2]
def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
dL_dvar = self.psicomp.psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, self.variances, Z, variational_posterior)[0]
if self.ARD:

4
GPy/kern/_src/psi_comp/__init__.py
View file

@ -35,9 +35,9 @@ class PSICOMP_RBF(Pickleable):
class PSICOMP_Linear(Pickleable):
@Cache_this(limit=2, ignore_args=(0,))
def psicomputations(self, variance, Z, variational_posterior):
def psicomputations(self, variance, Z, variational_posterior, return_psi2_n=False):
if isinstance(variational_posterior, variational.NormalPosterior):
return linear_psi_comp.psicomputations(variance, Z, variational_posterior)
return linear_psi_comp.psicomputations(variance, Z, variational_posterior, return_psi2_n=return_psi2_n)
elif isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
return sslinear_psi_comp.psicomputations(variance, Z, variational_posterior)
else:

6
GPy/models/sparse_gp_minibatch.py
View file

@ -171,8 +171,7 @@ class SparseGPMiniBatch(SparseGP):
variational_posterior=self.X,
Z=self.Z, dL_dpsi0=full_values['dL_dpsi0'],
dL_dpsi1=full_values['dL_dpsi1'],
dL_dpsi2=full_values['dL_dpsi2'],
psi0=self.psi0, psi1=self.psi1, psi2=self.psi2)
dL_dpsi2=full_values['dL_dpsi2'])
self.kern.gradient += kgrad
@ -182,8 +181,7 @@ class SparseGPMiniBatch(SparseGP):
variational_posterior=self.X,
Z=self.Z, dL_dpsi0=full_values['dL_dpsi0'],
dL_dpsi1=full_values['dL_dpsi1'],
dL_dpsi2=full_values['dL_dpsi2'],
psi0=self.psi0, psi1=self.psi1, psi2=self.psi2)
dL_dpsi2=full_values['dL_dpsi2'])
else:
#gradients wrt kernel
self.kern.update_gradients_diag(full_values['dL_dKdiag'], self.X)