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mean functions now working for svgp. with tests

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This commit is contained in:
James Hensman 2015-03-26 16:20:17 +00:00
parent 02f2bb5c76
commit 72de607199
5 changed files with 83 additions and 18 deletions
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16
GPy/core/sparse_gp.py
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@ -19,7 +19,7 @@ class SparseGP(GP):
This model allows (approximate) inference using variational DTC or FITC
(Gaussian likelihoods) as well as non-conjugate sparse methods based on
these.
This is not for missing data, as the implementation for missing data involves
some inefficient optimization routine decisions.
See missing data SparseGP implementation in py:class:'~GPy.models.sparse_gp_minibatch.SparseGPMiniBatch'.
@ -39,7 +39,7 @@ class SparseGP(GP):
"""
def __init__(self, X, Y, Z, kernel, likelihood, inference_method=None,
def __init__(self, X, Y, Z, kernel, likelihood, mean_function=None, inference_method=None,
name='sparse gp', Y_metadata=None, normalizer=False):
#pick a sensible inference method
if inference_method is None:
@ -53,7 +53,7 @@ class SparseGP(GP):
self.Z = Param('inducing inputs', Z)
self.num_inducing = Z.shape[0]
GP.__init__(self, X, Y, kernel, likelihood, inference_method=inference_method, name=name, Y_metadata=Y_metadata, normalizer=normalizer)
GP.__init__(self, X, Y, kernel, likelihood, mean_function, inference_method=inference_method, name=name, Y_metadata=Y_metadata, normalizer=normalizer)
logger.info("Adding Z as parameter")
self.link_parameter(self.Z, index=0)
@ -61,7 +61,7 @@ class SparseGP(GP):
def has_uncertain_inputs(self):
return isinstance(self.X, VariationalPosterior)
def set_Z(self, Z, trigger_update=True):
if trigger_update: self.update_model(False)
self.unlink_parameter(self.Z)
@ -110,8 +110,8 @@ class SparseGP(GP):
def _raw_predict(self, Xnew, full_cov=False, kern=None):
"""
Make a prediction for the latent function values.
Make a prediction for the latent function values.
For certain inputs we give back a full_cov of shape NxN,
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.
@ -136,6 +136,9 @@ class SparseGP(GP):
else:
Kxx = kern.Kdiag(Xnew)
var = (Kxx - np.sum(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx) * Kx[None,:,:], 1)).T
#add in the mean function
if self.mean_function is not None:
mu += self.mean_function.f(Xnew)
else:
psi0_star = self.kern.psi0(self.Z, Xnew)
psi1_star = self.kern.psi1(self.Z, Xnew)
@ -165,4 +168,5 @@ class SparseGP(GP):
var[i] = var_
else:
var[i] = np.diag(var_)+p0-t2
return mu, var