Fixed wrong version number in documentation and README
[libdai.git] / README
1 libDAI - A free/open source C++ library for Discrete Approximate Inference
2
3 -------------------------------------------------------------------------------
4
5 Version: 0.2.5
6 Date: May 9, 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 cite the software appropriately, by mentioning the
31 fashion in which this software was used, including the version number.
32
33 An appropriate citation would be:
34
35 Joris M. Mooij et al. (2010) "libDAI 0.2.5: A free/open source C++ library for
36 Discrete Approximate Inference", http://www.libdai.org
37
38 or in BiBTeX format:
39
40 @misc{mooij2010libdai,
41 author = "Joris M. Mooij et al.",
42 title = "lib{DAI} 0.2.5: A free/open source {C}++ library for {D}iscrete {A}pproximate {I}nference",
43 howpublished = "http://www.libdai.org/",
44 year = 2010
45 }
46
47
48 Moreover, as a personal note, I would appreciate it to be informed about any
49 publications using libDAI at joris dot mooij at libdai dot org.
50
51 -------------------------------------------------------------------------------
52
53 About libDAI
54
55 libDAI is a free/open source C++ library (licensed under GPL 2+) that provides
56 implementations of various (approximate) inference methods for discrete
57 graphical models. libDAI supports arbitrary factor graphs with discrete
58 variables; this includes discrete Markov Random Fields and Bayesian Networks.
59
60 The library is targeted at researchers. To be able to use the library, a good
61 understanding of graphical models is needed.
62
63 The best way to use libDAI is by writing C++ code that invokes the library; in
64 addition, part of the functionality is accessibly by using the
65
66 * command line interface
67 * (limited) MatLab interface
68 * (experimental) python interface
69 * (experimental) octave interface.
70
71 libDAI can be used to implement novel (approximate) inference algorithms and to
72 easily compare the accuracy and performance with existing algorithms that have
73 been implemented already.
74
75 Features
76
77 Currently, libDAI supports the following (approximate) inference methods:
78
79 * Exact inference by brute force enumeration;
80 * Exact inference by junction-tree methods;
81 * Mean Field;
82 * Loopy Belief Propagation [KFL01];
83 * Fractional Belief Propagation [WiH03];
84 * Tree-Reweighted Belief Propagation [WJW03];
85 * Tree Expectation Propagation [MiQ04];
86 * Generalized Belief Propagation [YFW05];
87 * Double-loop GBP [HAK03];
88 * Various variants of Loop Corrected Belief Propagation [MoK07, MoR05];
89 * Gibbs sampler;
90 * Conditioned Belief Propagation [EaG09].
91
92 These inference methods can be used to calculate partition sums, marginals over
93 subsets of variables, and MAP states (the joint state of variables that has
94 maximum probability).
95
96 In addition, libDAI supports parameter learning of conditional probability
97 tables by Expectation Maximization.
98
99 Limitations
100
101 libDAI is not intended to be a complete package for approximate inference.
102 Instead, it should be considered as an "inference engine", providing various
103 inference methods. In particular, it contains no GUI, currently only supports
104 its own file format for input and output (although support for standard file
105 formats may be added later), and provides very limited visualization
106 functionalities. The only learning method supported currently is Expectation
107 Maximization (or Maximum Likelihood if no data is missing) for learning factor
108 parameters.
109
110 Rationale
111
112 In my opinion, the lack of open source "reference" implementations hampers
113 progress in research on approximate inference. Methods differ widely in terms
114 of quality and performance characteristics, which also depend in different ways
115 on various properties of the graphical models. Finding the best approximate
116 inference method for a particular application therefore often requires
117 empirical comparisons. However, implementing and debugging these methods takes
118 a lot of time which could otherwise be spent on research. I hope that this code
119 will aid researchers to be able to easily compare various (existing as well as
120 new) approximate inference methods, in this way accelerating research and
121 stimulating real-world applications of approximate inference.
122
123 Language
124
125 Because libDAI is implemented in C++, it is very fast compared with
126 implementations in MatLab (a factor 1000 faster is not uncommon). libDAI does
127 provide a (limited) MatLab interface for easy integration with MatLab. It also
128 provides a command line interface and experimental python and octave interfaces
129 (thanks to Patrick Pletscher).
130
131 Compatibility
132
133 The code has been developed under Debian GNU/Linux with the GCC compiler suite.
134 libDAI compiles successfully with g++ versions 3.4 up to 4.4.
135
136 libDAI has also been successfully compiled with MS Visual Studio 2008 under
137 Windows (but not all build targets are supported yet) and with Cygwin under
138 Windows.
139
140 Finally, libDAI has been compiled successfully on MacOS X.
141
142 Downloading libDAI
143
144 The libDAI sources and documentation can be downloaded from the libDAI website:
145 http://www.libdai.org.
146
147 Mailing list
148
149 The Google group "libDAI" (http://groups.google.com/group/libdai) can be used
150 for getting support and discussing development issues.
151
152 -------------------------------------------------------------------------------
153
154 Building libDAI under UNIX variants (Linux / Cygwin / Mac OS X)
155
156 Preparations
157
158 You need:
159
160 * a recent version of gcc (at least version 3.4)
161 * GNU make
162 * recent boost C++ libraries (at least version 1.37; however, version 1.37
163 shipped with Ubuntu 9.04 is known not to work)
164 * doxygen (only for building the documentation)
165 * graphviz (only for using some of the libDAI command line utilities)
166
167 On Debian/Ubuntu, you can easily install the required packages with a single
168 command:
169
170 apt-get install g++ make doxygen graphviz libboost-dev libboost-graph-dev libboost-program-options-dev libboost-test-dev
171
172 (root permissions needed).
173
174 On Mac OS X (10.4 is known to work), these packages can be installed easily via
175 MacPorts. If MacPorts is not already installed, install it according to the
176 instructions at http://www.macports.org/. Then, a simple
177
178 sudo port install gmake boost doxygen graphviz
179
180 should be enough to install everything that is needed.
181
182 On Cygwin, the prebuilt Cygwin package boost-1.33.1-x is known not to work. You
183 can however obtain the latest boost version (you need at least 1.37.0) from
184 http://www.boost.org/ and build it as described in the next subsection.
185
186 Building boost under Cygwin
187
188 * Download the latest boost libraries from http://www.boost.org
189 * Build the required boost libraries using:
190
191 ./bootstrap.sh --with-libraries=program_options,math,graph,test --prefix=/boost_root/
192 ./bjam
193
194 * In order to use dynamic linking, the boost .dll's should be somewhere in
195 the path. This can be achieved by a command like:
196
197 export PATH=$PATH:/boost_root/stage/lib
198
199 Building libDAI
200
201 To build the libDAI source, first copy a template Makefile.* to Makefile.conf
202 (for example, copy Makefile.LINUX to Makefile.conf if you use GNU/Linux). Then,
203 edit the Makefile.conf template to adapt it to your local setup. Especially
204 directories may differ from system to system. Platform independent build
205 options can be set in Makefile.ALL. Finally, run
206
207 make
208
209 The build includes a regression test, which may take a while to complete.
210
211 If the build is successful, you can test the example program:
212
213 examples/example tests/alarm.fg
214
215 or the more extensive test program:
216
217 tests/testdai --aliases tests/aliases.conf --filename tests/alarm.fg --methods JTREE_HUGIN BP_SEQMAX
218
219 -------------------------------------------------------------------------------
220
221 Building libDAI under Windows
222
223 Preparations
224
225 You need:
226
227 * A recent version of MicroSoft Visual Studio (2008 is known to work)
228 * recent boost C++ libraries (version 1.37 or higher)
229 * GNU make (can be obtained from http://gnuwin32.sourceforge.net)
230
231 For the regression test, you need:
232
233 * GNU diff, GNU sed (can be obtained from http://gnuwin32.sourceforge.net)
234
235 Building boost under Windows
236
237 Because building boost under Windows is tricky, I provide some guidance here.
238
239 * Download the boost zip file from http://www.boost.org/users/download and
240 unpack it somewhere.
241 * Download the bjam executable from http://www.boost.org/users/download and
242 unpack it somewhere else.
243 * Download Boost.Build (v2) from http://www.boost.org/docs/tools/build/
244 index.html and unpack it yet somewhere else.
245 * Edit the file boost-build.jam in the main boost directory to change the
246 BOOST_BUILD directory to the place where you put Boost.Build (use UNIX /
247 instead of Windows \ in pathnames).
248 * Copy the bjam.exe executable into the main boost directory. Now if you
249 issue "bjam --version" you should get a version and no errors. Issueing
250 "bjam --show-libraries" will show the libraries that will be built.
251 * The following command builds the boost libraries that are relevant for
252 libDAI:
253
254 bjam --with-graph --with-math --with-program_options --with-test link=static runtime-link=shared
255
256 Building libDAI
257
258 To build the source, copy Makefile.WINDOWS to Makefile.conf. Then, edit
259 Makefile.conf to adapt it to your local setup. Platform independent build
260 options can be set in Makefile.ALL. Finally, run (from the command line)
261
262 make
263
264 The build includes a regression test, which may take a while to complete.
265
266 If the build is successful, you can test the example program:
267
268 examples\example tests\alarm.fg
269
270 or the more extensive test program:
271
272 tests\testdai --aliases tests\aliases.conf --filename tests\alarm.fg --methods JTREE_HUGIN BP_SEQMAX
273
274 -------------------------------------------------------------------------------
275
276 Building the libDAI MatLab interface
277
278 You need:
279
280 * MatLab
281 * The platform-dependent requirements described above
282
283 First, you need to build the libDAI source as described above for your
284 platform. By default, the MatLab interface is disabled, so before compiling the
285 source, you have to enable it in Makefile.ALL by setting
286
287 WITH_MATLAB=true
288
289 Also, you have to configure the MatLab-specific parts of Makefile.conf to match
290 your system (in particular, the Makefile variables ME, MATLABDIR and MEX). The
291 MEX file extension depends on your platform; for a 64-bit linux x86_64 system
292 this would be "ME=.mexa64", for a 32-bit linux x86 system "ME=.mexglx". If you
293 are unsure about your MEX file extension: it needs to be the same as what the
294 MatLab command "mexext" returns. The required MEX files are built by issuing
295
296 make
297
298 from the command line. The MatLab interface is much less powerful than using
299 libDAI from C++. There are two reasons for this: (i) it is boring to write MEX
300 files; (ii) the large performance penalty paid when large data structures (like
301 factor graphs) have to be converted between their native C++ data structure to
302 something that MatLab understands.
303
304 A simple example of how to use the MatLab interface is the following (entered
305 at the MatLab prompt), which performs exact inference by the junction tree
306 algorithm and approximate inference by belief propagation on the ALARM network:
307
308 cd path_to_libdai/matlab
309 [psi] = dai_readfg ('../tests/alarm.fg');
310 [logZ,q,md,qv,qf] = dai (psi, 'JTREE', '[updates=HUGIN,verbose=0]')
311 [logZ,q,md,qv,qf] = dai (psi, 'BP', '[updates=SEQMAX,tol=1e-9,maxiter=10000,logdomain=0]')
312
313 where "path_to_libdai" has to be replaced with the directory in which libDAI
314 was installed. For other algorithms and some default parameters, see the file
315 tests/aliases.conf.
316
317 -------------------------------------------------------------------------------
318
319 Building the documentation
320
321 Install doxygen, graphviz and a TeX distribution and use
322
323 make doc
324
325 to build the documentation. If the documentation is not clear enough, feel free
326 to send me an email (or even better, to improve the documentation and send a
327 patch!). The documentation can also be browsed online at http://www.libdai.org.
328