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[sparse gp] doc changes for missing data

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mzwiessele 2015-03-23 08:48:06 +00:00
parent b47e5ab4bb
commit 7381531d23
1 changed files with 5 additions and 7 deletions
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12
GPy/core/sparse_gp.py
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@ -10,11 +10,6 @@ from parameterization.variational import VariationalPosterior, NormalPosterior
from ..util.linalg import mdot
import logging
from GPy.inference.latent_function_inference.posterior import Posterior
from GPy.inference.optimization.stochastics import SparseGPStochastics,\
SparseGPMissing
#no stochastics.py file added! from GPy.inference.optimization.stochastics import SparseGPStochastics,\
#SparseGPMissing
logger = logging.getLogger("sparse gp")
class SparseGP(GP):
@ -25,6 +20,10 @@ class SparseGP(GP):
(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'.
:param X: inputs
:type X: np.ndarray (num_data x input_dim)
:param likelihood: a likelihood instance, containing the observed data
@ -66,7 +65,6 @@ class SparseGP(GP):
def set_Z(self, Z, trigger_update=True):
if trigger_update: self.update_model(False)
self.unlink_parameter(self.Z)
from ..core import Param
self.Z = Param('inducing inputs',Z)
self.link_parameter(self.Z, index=0)
if trigger_update: self.update_model(True)
@ -120,7 +118,7 @@ class SparseGP(GP):
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.
This is for both with and without missing data. See for missing data SparseGP implementation py:class:'~GPy.models.sparse_gp_minibatch.SparseGPMiniBatch'.
"""
if kern is None: kern = self.kern