Martijn Leisink
Giuseppe Passino
Frederik Eaton
+Charlie Vaske
Bastian Wemmenhove
Christian Wojek
Claudio Lima
Jiuxiang Hu
Peter Gober
Patrick Pletscher
+Sebastian Nowozin
----------------------------------------------------------------------------------
Acknowledgements section. The appropriate citation is:
J. M. Mooij (2008) "libDAI 0.2.2: A free/open source C++ library for Discrete
-Approximate Inference methods", http://mloss.org/software/view/77/.
+Approximate Inference methods", http://www.libdai.org
Moreover, as a personal note, I would appreciate it if you would email me with
citations of papers referencing this work so I can mention them to my funding
* Generalized Belief Propagation [YFW05];
* Double-loop GBP [HAK03];
* Various variants of Loop Corrected Belief Propagation [MoK07, MoR05];
- * Gibbs sampler.
+ * Gibbs sampler;
+ * Conditioned BP [EaG09].
+
+In addition, libDAI supports parameter learning of conditional probability
+tables by Expectation Maximization.
Why C++?
--------
Because libDAI is implemented in C++, it is very fast compared with
implementations in MatLab (a factor 1000 faster is not uncommon). libDAI does
-provide a MatLab interface for easy integration with MatLab.
+provide a (limited) MatLab interface for easy integration with MatLab.
Releases
--------
-Releases can be obtained from http://mloss.org/software/view/77/
+Releases can be obtained from www.libdai.org
License: GNU Public License v2 (or higher).
libDAI-0.2 December 1, 2006
libDAI-0.2.1 May 26, 2008
-libDAI-0.2.2 September 30, 2008
+libDAI-0.2.2 September 30, 2008
Acknowledgments
To build the libDAI source, first copy a template Makefile.* to Makefile.conf
(for example, copy Makefile.LINUX to Makefile.conf if you use GNU/Linux).
Then, edit the Makefile.conf template to adapt it to your local setup.
-Especially directories may change from system to system. Finally, run
+Especially directories may differ from system to system. Finally, run
make
or the more elaborate test program:
tests\testdai --aliases tests\aliases.conf --filename tests\alarm.fg --methods JTREE_HUGIN BP_SEQMAX
+
+
+Quick start (MatLab)
+--------------------
+You need:
+- MatLab
+- The platform-dependent requirements described above
+
+First, you need to build the libDAI source as described above for your
+platform. By default, the MatLab interface is disabled, so before compiling the
+source, you have to enable it in the Makefile.conf by setting
+"WITH_MATLAB=true". Also, you have to configure the MatLab-specific parts of
+Makefile.conf to match your system (in particular, the Makefile variables ME,
+MATLABDIR and MEX). The MEX file extension depends on your platform; for a
+64-bit linux x86_64 system this would be "ME=.mexa64", for a 32-bit linux x86
+system this would be "ME=.mexglx". If you are unsure about your MEX file
+extension: it needs to be the same as what the MatLab command "mexext" returns.
+The required MEX files are built by issuing
+
+ make
+
+from the command line. The MatLab interface is much less powerful than using
+libDAI from C++. There are two reasons for this: (i) it is boring to write MEX
+files; (ii) the large performance penalty paid when large data structures (like
+factor graphs) have to be converted between their native C++ data structure to
+something that MatLab understands.
+
+A simple example of how to use the MatLab interface is the following (entered
+at the MatLab prompt), which performs exact inference by the junction tree
+algorithm and approximate inference by belief propagation on the ALARM network:
+
+ cd path_to_libdai/matlab
+ [psi] = dai_readfg ('../examples/alarm.fg');
+ [logZ,q,md,qv,qf] = dai (psi, 'JTREE', '[updates=HUGIN,verbose=0]')
+ [logZ,q,md,qv,qf] = dai (psi, 'BP', '[updates=SEQMAX,tol=1e-9,maxiter=10000,logdomain=0]')
+
+where "path_to_libdai" has to be replaced with the directory in which libDAI
+was installed. For other algorithms and their parameters, see
+tests/aliases.conf.
projects / libdai.git / blobdiff