GPstuff-4.7
所属分类:matlab编程
开发工具:matlab
文件大小:28287KB
下载次数:3
上传日期:2018-11-24 02:07:18
上 传 者:
养乐多
说明: 高斯过程算法工具包GPstuff-4.7
(matlab package for gaussian process GPstuff-4.7)
文件列表:
GPstuff-4.7\ChangeLog.txt (14573, 2016-06-09)
GPstuff-4.7\Contents.m (22839, 2016-06-09)
GPstuff-4.7\diag\acorr.m (773, 2016-06-09)
GPstuff-4.7\diag\acorr2.m (701, 2016-06-09)
GPstuff-4.7\diag\acorrtime.m (1675, 2016-06-09)
GPstuff-4.7\diag\aucs.m (1427, 2016-06-09)
GPstuff-4.7\diag\auct.m (1393, 2016-06-09)
GPstuff-4.7\diag\cipsrf.m (1154, 2016-06-09)
GPstuff-4.7\diag\cmpsrf.m (1289, 2016-06-09)
GPstuff-4.7\diag\Contents.m (2504, 2016-06-09)
GPstuff-4.7\diag\cpsrf.m (1249, 2016-06-09)
GPstuff-4.7\diag\custats.m (670, 2016-06-09)
GPstuff-4.7\diag\cusum.m (773, 2016-06-09)
GPstuff-4.7\diag\derivativecheck.m (2076, 2016-06-09)
GPstuff-4.7\diag\diag_install.m (197, 2016-06-09)
GPstuff-4.7\diag\ext_auc.m (1700, 2016-06-09)
GPstuff-4.7\diag\gbinit.m (912, 2016-06-09)
GPstuff-4.7\diag\gbiter.m (4039, 2016-06-09)
GPstuff-4.7\diag\geyer_icse.m (1895, 2016-06-09)
GPstuff-4.7\diag\geyer_imse.m (1644, 2016-06-09)
GPstuff-4.7\diag\gradcheck.m (2489, 2016-06-09)
GPstuff-4.7\diag\hair.m (1087, 2016-06-09)
GPstuff-4.7\diag\hcs.m (3215, 2016-06-09)
GPstuff-4.7\diag\hct.m (1351, 2016-06-09)
GPstuff-4.7\diag\hpdi.m (923, 2016-06-09)
GPstuff-4.7\diag\idis.m (3881, 2016-06-09)
GPstuff-4.7\diag\ipsrf.m (2114, 2016-06-09)
GPstuff-4.7\diag\kernel1.m (2750, 2016-06-09)
GPstuff-4.7\diag\kernelp.m (1371, 2016-06-09)
GPstuff-4.7\diag\kernels.m (1618, 2016-06-09)
GPstuff-4.7\diag\ksstat.m (3201, 2016-06-09)
GPstuff-4.7\diag\License.txt (35147, 2016-06-09)
GPstuff-4.7\diag\linuxCsource\bbprctile.c (3477, 2016-06-09)
GPstuff-4.7\diag\linuxCsource\convert_for_win (192, 2016-06-09)
GPstuff-4.7\diag\mpsrf.m (2645, 2016-06-09)
GPstuff-4.7\diag\ndhist.m (3948, 2016-06-09)
GPstuff-4.7\diag\psrf.m (3350, 2016-06-09)
GPstuff-4.7\diag\rsqr.m (4939, 2016-06-09)
GPstuff-4.7\diag\score.m (1004, 2016-06-09)
... ...
# GPstuff: Gaussian process models for Bayesian analysis
Maintainer: Aki Vehtari
## REFERENCE
**If you use GPstuff (or otherwise refer to it), use the following reference:**
Jarno Vanhatalo, Jaakko Riihimaki, Jouni Hartikainen, Pasi Jylanki,
Ville Tolvanen, and Aki Vehtari (2013). GPstuff: Bayesian Modeling
with Gaussian Processes. Journal of Machine Learning Research,
14(Apr):1175-1179.
(Available at )
## HOME PAGE
Additional information and illustrations of the features can be found at
and
## INTRODUCTION
The GPstuff toolbox is a versatile collection of Gaussian process
models and computational tools required for inference. The tools
include, among others, various inference methods, sparse
approximations and model assessment methods.
The GPstuff toolbox works (at least) with Matlab versions r2009b
(7.9) or newer (older versions down to 7.7 should work also, but the
code is not tested with them). Most of the functionality works also
with Octave (3.*** or newer, see release notes for details). GPstuff
can also be called from R with RcppOctave package. Most of the code
is written in m-files but some of the most computationally critical
parts have been coded in C.
The code for GPstuff can be found in subfolders. The SuiteSparse
folder contains an exact copy of the SuiteSparse v3.4 toolbox by Tim
Davis:
The SuiteSparse is needed when using compactly supported covariance
functions.
## INSTALLING THE TOOLBOX
If Matlab or Octave is started in the directory of GPstuff,
`startup.m` script will add GPstuff subdirectories to the
path. Alternatively, see `startup.m` for paths to add.
Some of the functions in GPstuff are implemented using C in order to
make the computations faster. In order to use these functions you
need to compile them first. There are two ways to do that:
1) Basic installation without compactly supported covariance functions
* Install the GPstuff package by running `matlab_install` in thisfolder
* With this option you are able to use all the other functions
except for gpcf_ppcs*
2) Installation with compactly supported covariance functions
* Compactly supported (CS) covariance functions are functions that
produce sparse covariance matrices (matrices with zero elements). To
use these functions (gpcf_ppcs*) you need the sparse GP
functionalities in GPstuff which are build over SuiteSparse
toolbox. To take full advantage of the CS covariance functions
install GPstuff by running `matlab_install('SuiteSparseOn')` in the
present directory.
* The function `matlab_install` compiles the mex-files and prints on
the screen, which directories should be added to Matlab paths.
## CONTENTS
The GPstuff packge contains the following subdirectories:
diag dist gp mc misc optim tests* SuiteSparse*
(* not in Octave)
Each folder contains Contents.m, which summarizes the functions
in the folder.
The 'gp' folder contains the main functionalities and demonstration
programs. Other folders contain additional help functions.
## TESTING THE INSTALLATION
Installation can be tested by running command `runtestset('fast')`, which
runs a collection of demos and compares the computed results to pre-saved
results. Running this takes about one hour and it requires Matlab version
2013b or greater for the unit test framework. Alternatively, the
xunit' package can be used instead. The xunit package can be downloaded
from
http://www.mathworks.com/matlabcentral/fileexchange/22846-matlab-xunit-test-framework
## USER GUIDE (VERY SHORT)
It easiest to learn to use the package by running the demos. It is
advisable to open the demo files in text editor and run them line
by line. The demos are documented so that user can follow what
happens on each line.
The basic structure of the program is as follows. The program
consist of separate blocks, which are:
- Gaussian process model structure (GP):
This is a structure that contains all the model information (see
GP_SET) and information structures (GPCF_\*) and likelihood
structures (LIK_\*).
- Covariance function structure (GPCF):
This is a structure that contains all of the covariance function
information (see e.g. GPCF_SEXP). The structure contains the
hyperparameter values, pointers to nested functions that are
related to the covariance function (e.g. function to evaluate
covariance matrix) and hyperprior structure.
- Likelihood structure:
This is a structure that contains all of the likelihood function
information (see e.g. lik_probit). The structure contains the
likelihood parameter values and pointers to nested functions that
are related to the likelihood function (e.g. log likelihood and its
derivatives).
- Inference utilities:
Inference utilities consist of functions that are needed to make
the posterior inference and predictions. These include, among
others,
- GP_OPTIM - Find MAP estimate for hyperparameters
- GP_MC - Markov chain Monte Carlo sampling
- GP_IA - Hyperparameter integration approximations
- GP_PRED - Predictions with Gaussian Process
See more in [User guide](http://arxiv.org/abs/1206.5754)
## LICENSE
This software is distributed under the GNU General Public Licence
(version 3 or later); please refer to the file Licence.txt,
included with the software, for details.
## JMLR DISCLAIMER
THIS SOURCE CODE IS SUPPLIED \AS IS" WITHOUT WARRANTY OF ANY KIND, AND
ITS AUTHOR AND THE JOURNAL OF MACHINE LEARNING RESEARCH (JMLR) AND
JMLR'S PUBLISHERS AND DISTRIBUTORS, DISCLAIM ANY AND ALL WARRANTIES,
INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY
AND FITNESS FOR A PARTICULAR PURPOSE, AND ANYWARRANTIES OR NON
INFRINGEMENT. THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR
USE OF THIS SOURCE CODE, AND NEITHER THE AUTHOR NOR JMLR, NOR JMLR'S
PUBLISHERS AND DISTRIBUTORS, WILL BE LIABLE FOR DAMAGES OF ANY KIND
RESULTING FROM ITS USE. Without limiting the generality of the
foregoing, neither the author, nor JMLR, nor JMLR's publishers and
distributors, warrant that the Source Code will be error-free, will
operate without interruption, or will meet the needs of the user.
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