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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 from ..util.linalg import mdot
import logging 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") logger = logging.getLogger("sparse gp")
class SparseGP(GP): class SparseGP(GP):
@ -24,6 +19,10 @@ class SparseGP(GP):
This model allows (approximate) inference using variational DTC or FITC This model allows (approximate) inference using variational DTC or FITC
(Gaussian likelihoods) as well as non-conjugate sparse methods based on (Gaussian likelihoods) as well as non-conjugate sparse methods based on
these. 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 :param X: inputs
:type X: np.ndarray (num_data x input_dim) :type X: np.ndarray (num_data x input_dim)
@ -66,7 +65,6 @@ class SparseGP(GP):
def set_Z(self, Z, trigger_update=True): def set_Z(self, Z, trigger_update=True):
if trigger_update: self.update_model(False) if trigger_update: self.update_model(False)
self.unlink_parameter(self.Z) self.unlink_parameter(self.Z)
from ..core import Param
self.Z = Param('inducing inputs',Z) self.Z = Param('inducing inputs',Z)
self.link_parameter(self.Z, index=0) self.link_parameter(self.Z, index=0)
if trigger_update: self.update_model(True) 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 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). 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 if kern is None: kern = self.kern