Finished release 0.2.4
[libdai.git] / README
1 libDAI - A free/open source C++ library for Discrete Approximate Inference
2
3 -------------------------------------------------------------------------------
4
5 Version: 0.2.4
6 Date: February 11, 2010
7 See also: http://www.libdai.org
8
9 -------------------------------------------------------------------------------
10
11 License
12
13 libDAI is free software; you can redistribute it and/or modify it under the
14 terms of the GNU General Public License as published by the Free Software
15 Foundation; either version 2 of the License, or (at your option) any later
16 version.
17
18 libDAI is distributed in the hope that it will be useful, but WITHOUT ANY
19 WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
20 PARTICULAR PURPOSE. See the GNU General Public License for more details.
21
22 You should have received a copy of the GNU General Public License
23 along with libDAI in the file COPYING. If not, see http://www.gnu.org/licenses/
24
25 -------------------------------------------------------------------------------
26
27 Citing libDAI
28
29 If you write a scientific paper describing research that made substantive use
30 of this program, please:
31
32 * mention the fashion in which this software was used, including the version
33 number, with a citation to the literature, to allow replication;
34 * mention this software in the Acknowledgements section.
35
36 An appropriate citation would be:
37
38 Joris M. Mooij et al. (2010) "libDAI 0.2.4: A free/open source C++ library for
39 Discrete Approximate Inference", http://www.libdai.org
40
41 or in BiBTeX format:
42
43 @misc{mooij2010libdai,
44 author = "Joris M. Mooij et al.",
45 title = "lib{DAI} 0.2.4: A free/open source {C}++ library for {D}iscrete {A}pproximate {I}nference",
46 howpublished = "http://www.libdai.org/",
47 year = 2010
48 }
49
50
51 Moreover, as a personal note, I would appreciate it if you would email
52 (citations of) papers referencing this work to joris dot mooij at libdai dot org.
53
54 -------------------------------------------------------------------------------
55
56 About libDAI
57
58 libDAI is a free/open source C++ library (licensed under GPL 2+) that provides
59 implementations of various (approximate) inference methods for discrete
60 graphical models. libDAI supports arbitrary factor graphs with discrete
61 variables; this includes discrete Markov Random Fields and Bayesian Networks.
62
63 The library is targeted at researchers. To be able to use the library, a good
64 understanding of graphical models is needed.
65
66 The best way to use libDAI is by writing C++ code that invokes the library; in
67 addition, part of the functionality is accessibly by using the
68
69 * command line interface
70 * (limited) MatLab interface
71 * (experimental) python interface
72 * (experimental) octave interface.
73
74 libDAI can be used to implement novel (approximate) inference algorithms and to
75 easily compare the accuracy and performance with existing algorithms that have
76 been implemented already.
77
78 Features
79
80 Currently, libDAI supports the following (approximate) inference methods:
81
82 * Exact inference by brute force enumeration;
83 * Exact inference by junction-tree methods;
84 * Mean Field;
85 * Loopy Belief Propagation [KFL01];
86 * Fractional Belief Propagation [WiH03];
87 * Tree-Reweighted Belief Propagation [WJW03];
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 Belief Propagation [EaG09].
94
95 These inference methods can be used to calculate partition sums, marginals over
96 subsets of variables, and MAP states (the joint state of variables that has
97 maximum probability).
98
99 In addition, libDAI supports parameter learning of conditional probability
100 tables by Expectation Maximization.
101
102 Limitations
103
104 libDAI is not intended to be a complete package for approximate inference.
105 Instead, it should be considered as an "inference engine", providing various
106 inference methods. In particular, it contains no GUI, currently only supports
107 its own file format for input and output (although support for standard file
108 formats may be added later), and provides very limited visualization
109 functionalities. The only learning method supported currently is Expectation
110 Maximization (or Maximum Likelihood if no data is missing) for learning factor
111 parameters.
112
113 Rationale
114
115 In my opinion, the lack of open source "reference" implementations hampers
116 progress in research on approximate inference. Methods differ widely in terms
117 of quality and performance characteristics, which also depend in different ways
118 on various properties of the graphical models. Finding the best approximate
119 inference method for a particular application therefore often requires
120 empirical comparisons. However, implementing and debugging these methods takes
121 a lot of time which could otherwise be spent on research. I hope that this code
122 will aid researchers to be able to easily compare various (existing as well as
123 new) approximate inference methods, in this way accelerating research and
124 stimulating real-world applications of approximate inference.
125
126 Language
127
128 Because libDAI is implemented in C++, it is very fast compared with
129 implementations in MatLab (a factor 1000 faster is not uncommon). libDAI does
130 provide a (limited) MatLab interface for easy integration with MatLab. It also
131 provides a command line interface and experimental python and octave interfaces
132 (thanks to Patrick Pletscher).
133
134 Compatibility
135
136 The code has been developed under Debian GNU/Linux with the GCC compiler suite.
137 libDAI compiles successfully with g++ versions 3.4, 4.1, 4.2 and 4.3.
138
139 libDAI has also been successfully compiled with MS Visual Studio 2008 under
140 Windows (but not all build targets are supported yet) and with Cygwin under
141 Windows.
142
143 Finally, libDAI has been compiled successfully on MacOS X.
144
145 Downloading libDAI
146
147 The libDAI sources and documentation can be downloaded from the libDAI website:
148 http://www.libdai.org.
149
150 Mailing list
151
152 The Google group "libDAI" (http://groups.google.com/group/libdai) can be used
153 for getting support and discussing development issues.
154 -------------------------------------------------------------------------------
155
156 Building libDAI under UNIX variants (Linux / Cygwin / Mac OS X)
157
158 Preparations
159
160 You need:
161
162 * a recent version of gcc (at least version 3.4)
163 * GNU make
164 * doxygen
165 * graphviz
166 * recent boost C++ libraries (at least version 1.34 if you have a recent
167 version of GCC, otherwise at least version 1.37; however, version 1.37
168 shipped with Ubuntu 9.04 is known not to work)
169
170 On Debian/Ubuntu, you can easily install all these packages with a single
171 command:
172
173 apt-get install g++ make doxygen graphviz libboost-dev libboost-graph-dev libboost-program-options-dev
174
175 (root permissions needed).
176
177 On Mac OS X (10.4 is known to work), these packages can be installed easily via
178 MacPorts. If MacPorts is not already installed, install it according to the
179 instructions at http://www.macports.org/. Then, a simple
180
181 sudo port install gmake boost doxygen graphviz
182
183 should be enough to install everything that is needed.
184
185 On Cygwin, the prebuilt Cygwin package boost-1.33.1-x is known not to work. You
186 can however obtain the latest boost version (you need at least 1.37.0) from
187 http://www.boost.org/ and compile/install it with:
188
189 ./configure
190 make
191 make install
192
193
194 Building libDAI
195
196 To build the libDAI source, first copy a template Makefile.* to Makefile.conf
197 (for example, copy Makefile.LINUX to Makefile.conf if you use GNU/Linux). Then,
198 edit the Makefile.conf template to adapt it to your local setup. Especially
199 directories may differ from system to system. Platform independent build
200 options can be set in Makefile.ALL. Finally, run
201
202 make
203
204 The build includes a regression test, which may take a while to complete.
205
206 If the build is successful, you can test the example program:
207
208 examples/example tests/alarm.fg
209
210 or the more extensive test program:
211
212 tests/testdai --aliases tests/aliases.conf --filename tests/alarm.fg --methods JTREE_HUGIN BP_SEQMAX
213
214 -------------------------------------------------------------------------------
215
216 Building libDAI under Windows
217
218 Preparations
219
220 You need:
221
222 * A recent version of MicroSoft Visual Studio (2008 is known to work)
223 * recent boost C++ libraries (version 1.37 or higher)
224 * GNU make (can be obtained from http://gnuwin32.sourceforge.net)
225
226 For the regression test, you need:
227
228 * GNU diff, GNU sed (can be obtained from http://gnuwin32.sourceforge.net)
229
230 Building libDAI
231
232 To build the source, copy Makefile.WINDOWS to Makefile.conf. Then, edit
233 Makefile.conf to adapt it to your local setup. Platform independent build
234 options can be set in Makefile.ALL. Finally, run (from the command line)
235
236 make
237
238 The build includes a regression test, which may take a while to complete.
239
240 If the build is successful, you can test the example program:
241
242 examples\example tests\alarm.fg
243
244 or the more extensive test program:
245
246 tests\testdai --aliases tests\aliases.conf --filename tests\alarm.fg --methods JTREE_HUGIN BP_SEQMAX
247
248 -------------------------------------------------------------------------------
249
250 Building the libDAI MatLab interface
251
252 You need:
253
254 * MatLab
255 * The platform-dependent requirements described above
256
257 First, you need to build the libDAI source as described above for your
258 platform. By default, the MatLab interface is disabled, so before compiling the
259 source, you have to enable it in Makefile.ALL by setting
260
261 WITH_MATLAB=true
262
263 Also, you have to configure the MatLab-specific parts of Makefile.conf to match
264 your system (in particular, the Makefile variables ME, MATLABDIR and MEX). The
265 MEX file extension depends on your platform; for a 64-bit linux x86_64 system
266 this would be "ME=.mexa64", for a 32-bit linux x86 system "ME=.mexglx". If you
267 are unsure about your MEX file extension: it needs to be the same as what the
268 MatLab command "mexext" returns. The required MEX files are built by issuing
269
270 make
271
272 from the command line. The MatLab interface is much less powerful than using
273 libDAI from C++. There are two reasons for this: (i) it is boring to write MEX
274 files; (ii) the large performance penalty paid when large data structures (like
275 factor graphs) have to be converted between their native C++ data structure to
276 something that MatLab understands.
277
278 A simple example of how to use the MatLab interface is the following (entered
279 at the MatLab prompt), which performs exact inference by the junction tree
280 algorithm and approximate inference by belief propagation on the ALARM network:
281
282 cd path_to_libdai/matlab
283 [psi] = dai_readfg ('../examples/alarm.fg');
284 [logZ,q,md,qv,qf] = dai (psi, 'JTREE', '[updates=HUGIN,verbose=0]')
285 [logZ,q,md,qv,qf] = dai (psi, 'BP', '[updates=SEQMAX,tol=1e-9,maxiter=10000,logdomain=0]')
286
287 where "path_to_libdai" has to be replaced with the directory in which libDAI
288 was installed. For other algorithms and some default parameters, see the file
289 tests/aliases.conf.
290
291 -------------------------------------------------------------------------------
292
293 Building the documentation
294
295 Install doxygen, graphviz and a TeX distribution and use
296
297 make doc
298
299 to build the documentation. If the documentation is not clear enough, feel free
300 to send me an email (or even better, to improve the documentation and send a
301 patch!). The documentation can also be browsed online at http://www.libdai.org.