gpml-matlab-v1.3-2006-09-08
所属分类：matlab编程
开发工具：matlab
文件大小：813KB
下载次数：4
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被猫嫌弃的鱼
说明： 高斯过程(GP)模型中推理和预测的实现。它实现了在《Rasmussen & Williams:机器学习的高斯过程》(麻省理工学院出版社，2006)和《Nickisch & Rasmussen:二进制高斯过程分类的近似》(JMLR, 2008)中讨论的算法。该函数的优点在于灵活性、简单性和可扩展性。该函数具有一定的灵活性，首先通过定义均值函数和协方差函数来确定遗传算法的性质。其次，它允许指定不同的推理过程，如精确推理和期望传播(EP)。第三，它允许指定似然函数，如高斯函数或拉普拉斯函数(用于回归)和累积逻辑函数(用于分类)。简单性是通过一个简单的函数和紧凑的代码实现的。可扩展性是通过模块化设计来保证的，允许为已经相当广泛的推理方法、均值函数、协方差函数和似然函数库轻松添加扩展。
(Gaussian Processes for Machine Learning , the MIT press, 2006 and Nickisch & Rasmussen: Approximations for Binary Gaussian Process Classification , JMLR, 2008. The strength of the function lies in its flexibility, simplicity and extensibility. The function is flexible as firstly it allows specification of the properties of the GP through definition of mean function and covariance functions. Secondly, it allows specification of different inference procedures, such as e.g. exact inference and Expectation Propagation (EP). Thirdly it allows specification of likelihood functions e.g. Gaussian or Laplace (for regression) and e.g. cumulative Logistic (for classification). Simplicity is achieved through a single function and compact code.)
文件列表:
gpml-matlab (0, 2006-09-08)
gpml-matlab\doc (0, 2006-03-29)
gpml-matlab\doc\classification.html (34439, 2006-03-27)
gpml-matlab\doc\alg31.gif (43172, 2005-11-07)
gpml-matlab\doc\style.css (77, 2006-01-30)
gpml-matlab\doc\alg32.gif (34064, 2005-11-07)
gpml-matlab\doc\index.html (3349, 2006-03-29)
gpml-matlab\doc\alg35.gif (66673, 2006-02-10)
gpml-matlab\doc\alg36.gif (32226, 2006-02-10)
gpml-matlab\doc\alg51.gif (60084, 2006-01-31)
gpml-matlab\doc\alg52.gif (41085, 2006-02-10)
gpml-matlab\doc\figepp.gif (17875, 2006-03-10)
gpml-matlab\doc\figepp2.gif (23299, 2006-03-10)
gpml-matlab\doc\figlapp.gif (17854, 2006-03-09)
gpml-matlab\doc\figlapp2.gif (25557, 2006-03-09)
gpml-matlab\doc\regression.html (16598, 2006-03-29)
gpml-matlab\doc\figl1.gif (21386, 2005-10-26)
gpml-matlab\doc\alg21.gif (31996, 2005-10-26)
gpml-matlab\doc\sparse-approx.html (7738, 2006-03-29)
gpml-matlab\doc\fig2d.gif (36772, 2006-03-14)
gpml-matlab\doc\fig2de1.gif (35364, 2006-03-14)
gpml-matlab\doc\figlf.gif (16078, 2006-03-27)
gpml-matlab\doc\fig2de2.gif (42683, 2006-03-14)
gpml-matlab\doc\fig2de3.gif (38483, 2006-03-14)
gpml-matlab\doc\fig2dl1.gif (34059, 2006-03-14)
gpml-matlab\doc\fig2dl2.gif (40431, 2006-03-14)
gpml-matlab\doc\fig2dl3.gif (38375, 2006-03-14)
gpml-matlab\doc\figl.gif (6109, 2006-03-27)
gpml-matlab\doc\figlm.gif (16712, 2006-03-27)
gpml-matlab\gpml (0, 2006-09-08)
gpml-matlab\gpml\binaryEPGP.m (7565, 2006-09-08)
gpml-matlab\gpml\binaryLaplaceGP.m (7940, 2006-05-10)
gpml-matlab\gpml\Contents.m (2206, 2006-04-07)
gpml-matlab\gpml\Copyright (775, 2006-02-09)
gpml-matlab\gpml\gprSRPP.m (2963, 2006-03-30)
gpml-matlab\gpml\minimize.m (8995, 2006-09-08)
gpml-matlab\gpml\solve_chol.c (1236, 2006-02-09)
gpml-matlab\gpml\solve_chol.m (991, 2006-02-08)
gpml-matlab\gpml\sq_dist.c (1931, 2006-02-08)
... ...
ABOUT THESE PROGRAMS
====================
This collection of matlab programs implement and demonstrates some fo the
algorithms described in the book Rasmussen and Williams: "Gaussian Processes
for Machine Learning", the MIT Press 2006.
There are 3 subdirectories: gpml, gpml-demo and doc.
gpml: contains code which implements the algorithms. Please see the Copyright
notice contained in the file named "Copyright".
gpml-demo: contains matlab scripts with names "demo_*.m". These provide small
demonstrations of the various programs provided.
doc: contains four html files providing documentation. The best place to start
is index.html, the other pages are linked from there. This information
is also available from http://www.GaussianProcess.org/gpml/code
When running the demos, it is assumed that your current directory is the
gpml-demo directory. Otherwise, you should manually add both the gpml-demo and
gpml directories to the matab path.
VERSION
=======
The current version of the programs is dated: 2006-09-08.
CHANGES FROM PREVIOUS VERSIONS
==============================
Changes from the 2006-05-10 version:
------------------------------------
covRQard.m: bugfix: replaced x with x' and z with z' in line 36
covRQiso.m: bugfix: replaced x with x' and z with z' in line 28
minimize.m: correction: replaced "error()" with "error('')", and
made a few cosmetic changes
binaryEPGP.m: added the line "lml = -n*log(2);" in line 77. This change
should be largely inconsequential, but occationally may save things
when the covariance matrix is exceptionally badly conditioned.
Changes from the 2006-04-12 version:
------------------------------------
added the "erfint" function to "binaryLaplaceGP.m". The erfint function
was missing by mistake, preventing the use of the "logistic" likelihood.
Changes from the 2006-03-29 version:
------------------------------------
added files: "covProd.m" and "covPeriodic.m"
changes: "covSEiso.m" was changed slightly to avoid the use of persistent
variables
DATASETS
========
The datasets needed for some of the demos can be downloaded from
http://www.GaussianProcess.org/gpml/data
ABOUT MEX FILES
===============
Some of the programs make use of the mex facility in matlab for more efficient
implementation. However, if you don't know about how to compile mex files, you
do not need to worry about this - the code should run anyway. If you do
compile the mex files, this should be automatically detected, and the program
will run more efficiently. Particularly the demonstrations of classification
on the usps digits require a lot of computation.
COMPILING MEX FILES
===================
As mentioned above, it is not necessary to compile the mex files, but it can
speed up execution considerably. We cannot give a detailed account, but here
are some hints:
Generally, you just type "mex file.c" at the matlab prompt or in your shell to
compile, where "file.c" is the program you want to compile. There is a Makefile
which works for unix/linux on x86 machines. Just type "make".
In some cases (solve_chol.c), routines from the lapack numerical library are
used. This should pose no problem on linux. On windows, you have to 1) remove
the trailing underscore from the name of the lapack function ("dpotrs", two
occurences) and 2) pass the location of the lapack library to mex, ie
something like
mex file.c /extern/lib/win32/lcc/libmwlapack.lib
where is the root of your matlab installation. If your installation
doesn't include the libmwlapack.lib you may be able to get it from
http://www.cise.ufl.edu/research/sparse/umfpack/v4.4/UMFPACKv4.4/UMFPACK/
MATLAB/lcc_lib/
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