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improve the documentation for LVMOGP

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Zhenwen Dai 2017-11-23 09:20:05 +00:00
parent 754c67f71d
commit f11090ad8a
6 changed files with 78 additions and 44 deletions
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12
GPy/inference/latent_function_inference/vardtc_md.py
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@ -1,5 +1,5 @@
# Copyright (c) 2017, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from GPy.util.linalg import jitchol, backsub_both_sides, tdot, dtrtrs, dtrtri,pdinv, dpotri
from GPy.util import diag
@ -12,13 +12,7 @@ log_2_pi = np.log(2*np.pi)
class VarDTC_MD(LatentFunctionInference):
"""
An object for inference when the likelihood is Gaussian, but we want to do sparse inference.
The function self.inference returns a Posterior object, which summarizes
the posterior.
For efficiency, we sometimes work with the cholesky of Y*Y.T. To save repeatedly recomputing this, we cache it.
The VarDTC inference method for sparse GP with missing data (GPy.models.SparseGPRegressionMD)
"""
const_jitter = 1e-6

14
GPy/inference/latent_function_inference/vardtc_svi_multiout.py
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@ -1,4 +1,6 @@
#from .posterior import Posterior
# Copyright (c) 2017, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from GPy.util.linalg import jitchol, backsub_both_sides, tdot, dtrtrs, dtrtri,pdinv, dpotri
from GPy.util import diag
from GPy.core.parameterization.variational import VariationalPosterior
@ -10,13 +12,7 @@ log_2_pi = np.log(2*np.pi)
class VarDTC_SVI_Multiout(LatentFunctionInference):
"""
An object for inference when the likelihood is Gaussian, but we want to do sparse inference.
The function self.inference returns a Posterior object, which summarizes
the posterior.
For efficiency, we sometimes work with the cholesky of Y*Y.T. To save repeatedly recomputing this, we cache it.
The VarDTC inference method for Multi-output GP regression (GPy.models.GPMultioutRegression)
"""
const_jitter = 1e-6
@ -100,7 +96,7 @@ class VarDTC_SVI_Multiout(LatentFunctionInference):
- (LcInvMLrInvT.T.dot(LcInvPsi2_cLcInvT).dot(LcInvMLrInvT)*LrInvPsi2_rLrInvT).sum() \
- tr_LrInvPsi2_rLrInvT_LrInvSrLrInvT* tr_LcInvPsi2_cLcInvT_LcInvScLcInvT \
+ 2 * (Y * LcInvPsi1_cT.T.dot(LcInvMLrInvT).dot(LrInvPsi1_rT)).sum() - psi0_c * psi0_r \
+ tr_LrInvPsi2_rLrInvT * tr_LcInvPsi2_cLcInvT
+ tr_LrInvPsi2_rLrInvT * tr_LcInvPsi2_cLcInvT
logL = -N*D/2.*(np.log(2.*np.pi)-np.log(beta)) + beta/2.* logL_A \
-Mc * (np.log(np.diag(Lr)).sum()-np.log(np.diag(LSr)).sum()) -Mr * (np.log(np.diag(Lc)).sum()-np.log(np.diag(LSc)).sum()) \

5
GPy/inference/latent_function_inference/vardtc_svi_multiout_miss.py
View file

@ -1,4 +1,6 @@
#from .posterior import Posterior
# Copyright (c) 2017, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from GPy.util.linalg import jitchol, backsub_both_sides, tdot, dtrtrs, dtrtri,pdinv, dpotri
from GPy.util import diag
from GPy.core.parameterization.variational import VariationalPosterior
@ -11,6 +13,7 @@ log_2_pi = np.log(2*np.pi)
class VarDTC_SVI_Multiout_Miss(LatentFunctionInference):
"""
The VarDTC inference method for Multi-output GP regression with missing data (GPy.models.GPMultioutRegressionMD)
"""
const_jitter = 1e-6