Added max-product functionality to JTree
[libdai.git] / README
1 libDAI - A free/open source C++ library for Discrete Approximate Inference methods
2 ==================================================================================
3
4 v 0.2.2 - September 30, 2008
5
6
7 Copyright (C) 2006-2008 Joris Mooij [joris dot mooij at tuebingen dot mpg dot de]
8 Radboud University Nijmegen, The Netherlands /
9 Max Planck Institute for Biological Cybernetics, Germany
10
11 with contributions from:
12
13 Martijn Leisink
14 Giuseppe Passino
15 Frederik Eaton
16 Charlie Vaske
17 Bastian Wemmenhove
18 Christian Wojek
19 Claudio Lima
20 Jiuxiang Hu
21 Peter Gober
22 Patrick Pletscher
23 Sebastian Nowozin
24
25
26 ----------------------------------------------------------------------------------
27 This file is part of libDAI.
28
29 libDAI is free software; you can redistribute it and/or modify
30 it under the terms of the GNU General Public License as published by
31 the Free Software Foundation; either version 2 of the License, or
32 (at your option) any later version.
33
34 libDAI is distributed in the hope that it will be useful,
35 but WITHOUT ANY WARRANTY; without even the implied warranty of
36 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
37 GNU General Public License for more details.
38
39 You should have received a copy of the GNU General Public License
40 along with libDAI; if not, write to the Free Software
41 Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
42 ----------------------------------------------------------------------------------
43
44
45 SCIENTISTS: If you write a scientific paper describing research that made
46 substantive use of this program, please (a) mention the fashion in which
47 this software was used, including the version number, with a citation
48 to the literature, to allow replication; (b) mention this software in the
49 Acknowledgements section. The appropriate citation is:
50
51 J. M. Mooij (2008) "libDAI 0.2.2: A free/open source C++ library for Discrete
52 Approximate Inference methods", http://www.libdai.org
53
54 Moreover, as a personal note, I would appreciate it if you would email me with
55 citations of papers referencing this work so I can mention them to my funding
56 agent and tenure committee.
57
58
59 About libDAI
60 ------------
61 libDAI is a free/open source C++ library (licensed under GPL) that provides
62 implementations of various (approximate) inference methods for discrete
63 graphical models. libDAI supports arbitrary factor graphs with discrete
64 variables; this includes discrete Markov Random Fields and Bayesian Networks.
65
66 The library is targeted at researchers; to be able to use the library, a good
67 understanding of graphical models is needed.
68
69
70 Limitations
71 -----------
72 libDAI is not intended to be a complete package for approximate inference.
73 Instead, it should be considered as an "inference engine", providing various
74 inference methods. In particular, it contains no GUI, currently only supports
75 its own file format for input and output (although support for standard file
76 formats may be added later), and provides very limited visualization
77 functionalities.
78
79
80 Features
81 --------
82 Currently, libDAI supports the following (approximate) inference methods:
83
84 * Exact inference by brute force enumeration;
85 * Exact inference by junction-tree methods;
86 * Mean Field;
87 * Loopy Belief Propagation [KFL01];
88 * Tree Expectation Propagation [MiQ04];
89 * Generalized Belief Propagation [YFW05];
90 * Double-loop GBP [HAK03];
91 * Various variants of Loop Corrected Belief Propagation [MoK07, MoR05];
92 * Gibbs sampler;
93 * Conditioned BP [EaG09].
94
95 These inference methods can be used to calculate partition sums, marginals
96 over subsets of variables, and MAP states (the joint state of variables that
97 has maximum probability).
98
99 In addition, libDAI supports parameter learning of conditional probability
100 tables by Expectation Maximization.
101
102
103 Why C++?
104 --------
105 Because libDAI is implemented in C++, it is very fast compared with
106 implementations in MatLab (a factor 1000 faster is not uncommon). libDAI does
107 provide a (limited) MatLab interface for easy integration with MatLab.
108
109
110 Releases
111 --------
112 Releases can be obtained from www.libdai.org
113 License: GNU Public License v2 (or higher).
114
115 libDAI-0.2 December 1, 2006
116 libDAI-0.2.1 May 26, 2008
117 libDAI-0.2.2 September 30, 2008
118
119
120 Acknowledgments
121 ---------------
122 This work is part of the Interactive Collaborative Information Systems (ICIS)
123 project, supported by the Dutch Ministry of Economic Affairs, grant BSIK03024.
124 I would like to thank Martijn Leisink for providing the basis on which libDAI has been built.
125
126
127 Documentation
128 -------------
129 Some doxygen documentation is available. Install doxygen and use "make doc" to build the
130 documentation. If the documentation is not clear enough, feel free to send me an email
131 (or even better, to improve the documentation!).
132
133 A description of the factor graph (.fg) file format can be found in the file FILEFORMAT.
134
135
136 Compatibility
137 -------------
138 The code has been developed under Debian GNU/Linux with the GCC compiler suite.
139 libDAI compiles successfully with g++ versions 3.4, 4.1, 4.2 and 4.3.
140
141 libDAI has also been successfully compiled with MS Visual Studio 2008 under Windows
142 (but not all build targets are supported yet) and with Cygwin under Windows.
143
144 Finally, libDAI has been compiled successfully on MacOS X.
145
146
147 Quick start (linux/cygwin/Mac OS X)
148 -----------------------------------
149 You need:
150 - a recent version of gcc (at least version 3.4)
151 - GNU make
152 - doxygen
153 - graphviz
154 - recent boost C++ libraries (at least version 1.34, or 1.37 for cygwin;
155 version 1.37 shipped with Ubuntu 9.04 is known not to work)
156
157 On Debian/Ubuntu, you can easily install all these packages with a single command:
158 "apt-get install g++ make doxygen graphviz libboost-dev libboost-graph-dev libboost-program-options-dev"
159 (root permissions needed).
160
161 On Mac OS X (10.4 is known to work), these packages can be installed easily via MacPorts.
162 First, install MacPorts according to the instructions at http://www.macports.org/
163 Then, a simple "sudo port install gmake boost doxygen graphviz"
164 should be enough to install everything that is needed.
165
166 On Cygwin, the prebuilt Cygwin package boost-1.33.1-x is known not to work.
167 You can however obtain the latest boost version (you need at least 1.37.0)
168 from http://www.boost.org/ and compile/install it with:
169
170 ./configure
171 make
172 make install
173
174
175 To build the libDAI source, first copy a template Makefile.* to Makefile.conf
176 (for example, copy Makefile.LINUX to Makefile.conf if you use GNU/Linux).
177 Then, edit the Makefile.conf template to adapt it to your local setup.
178 Especially directories may differ from system to system. Finally, run
179
180 make
181
182 If the build was successful, you can test the example program:
183
184 ./example tests/alarm.fg
185
186 or the more elaborate test program:
187
188 tests/testdai --aliases tests/aliases.conf --filename tests/alarm.fg --methods JTREE_HUGIN BP_SEQMAX
189
190
191 Quick start (Windows)
192 ---------------------
193 You need:
194 - A recent version of MicroSoft Visual Studio (2008 works)
195 - recent boost C++ libraries (version 1.34 or higher)
196 - GNU make (can be obtained from http://gnuwin32.sourceforge.net)
197 For the regression test, you need:
198 - GNU diff, GNU sed (can be obtained from http://gnuwin32.sourceforge.net)
199
200 To build the source, copy Makefile.WINDOWS to Makefile.conf. Then, edit
201 Makefile.conf to adapt it to your local setup. Finally, run (from the command line)
202
203 make
204
205 If the build was successful, you can test the example program:
206
207 example tests\alarm.fg
208
209 or the more elaborate test program:
210
211 tests\testdai --aliases tests\aliases.conf --filename tests\alarm.fg --methods JTREE_HUGIN BP_SEQMAX
212
213
214 Quick start (MatLab)
215 --------------------
216 You need:
217 - MatLab
218 - The platform-dependent requirements described above
219
220 First, you need to build the libDAI source as described above for your
221 platform. By default, the MatLab interface is disabled, so before compiling the
222 source, you have to enable it in the Makefile.conf by setting
223 "WITH_MATLAB=true". Also, you have to configure the MatLab-specific parts of
224 Makefile.conf to match your system (in particular, the Makefile variables ME,
225 MATLABDIR and MEX). The MEX file extension depends on your platform; for a
226 64-bit linux x86_64 system this would be "ME=.mexa64", for a 32-bit linux x86
227 system this would be "ME=.mexglx". If you are unsure about your MEX file
228 extension: it needs to be the same as what the MatLab command "mexext" returns.
229 The required MEX files are built by issuing
230
231 make
232
233 from the command line. The MatLab interface is much less powerful than using
234 libDAI from C++. There are two reasons for this: (i) it is boring to write MEX
235 files; (ii) the large performance penalty paid when large data structures (like
236 factor graphs) have to be converted between their native C++ data structure to
237 something that MatLab understands.
238
239 A simple example of how to use the MatLab interface is the following (entered
240 at the MatLab prompt), which performs exact inference by the junction tree
241 algorithm and approximate inference by belief propagation on the ALARM network:
242
243 cd path_to_libdai/matlab
244 [psi] = dai_readfg ('../examples/alarm.fg');
245 [logZ,q,md,qv,qf] = dai (psi, 'JTREE', '[updates=HUGIN,verbose=0]')
246 [logZ,q,md,qv,qf] = dai (psi, 'BP', '[updates=SEQMAX,tol=1e-9,maxiter=10000,logdomain=0]')
247
248 where "path_to_libdai" has to be replaced with the directory in which libDAI
249 was installed. For other algorithms and their parameters, see
250 tests/aliases.conf.