Moved platform independent build options into Makefile.ALL and documented tests/testdai
[libdai.git] / README
1 libDAI - A free/open source C++ library for Discrete Approximate Inference
2
3 -------------------------------------------------------------------------------
4
5 Version: git HEAD
6 Date: February 4, 2010 - or later
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 J. M. Mooij (2009) "libDAI 0.2.3: A free/open source C++ library for Discrete
38 Approximate Inference", http://www.libdai.org
39
40 Moreover, as a personal note, I would appreciate it if you would email
41 (citations of) papers referencing this work to joris dot mooij at libdai dot
42 org.
43
44 -------------------------------------------------------------------------------
45
46 About libDAI
47
48 libDAI is a free/open source C++ library (licensed under GPL 2+) that provides
49 implementations of various (approximate) inference methods for discrete
50 graphical models. libDAI supports arbitrary factor graphs with discrete
51 variables; this includes discrete Markov Random Fields and Bayesian Networks.
52
53 The library is targeted at researchers. To be able to use the library, a good
54 understanding of graphical models is needed.
55
56 The best way to use libDAI is by writing C++ code that invokes the library; in
57 addition, part of the functionality is accessibly by using the
58
59 * command line interface
60 * (limited) MatLab interface
61 * (experimental) python interface
62 * (experimental) octave interface.
63
64 libDAI can be used to implement novel (approximate) inference algorithms and to
65 easily compare the accuracy and performance with existing algorithms that have
66 been implemented already.
67
68 Features
69
70 Currently, libDAI supports the following (approximate) inference methods:
71
72 * Exact inference by brute force enumeration;
73 * Exact inference by junction-tree methods;
74 * Mean Field;
75 * Loopy Belief Propagation [KFL01];
76 * Fractional Belief Propagation [WiH03];
77 * Tree-Reweighted Belief Propagation [WJW03];
78 * Tree Expectation Propagation [MiQ04];
79 * Generalized Belief Propagation [YFW05];
80 * Double-loop GBP [HAK03];
81 * Various variants of Loop Corrected Belief Propagation [MoK07, MoR05];
82 * Gibbs sampler;
83 * Clamped Belief Propagation [EaG09].
84
85 These inference methods can be used to calculate partition sums, marginals over
86 subsets of variables, and MAP states (the joint state of variables that has
87 maximum probability).
88
89 In addition, libDAI supports parameter learning of conditional probability
90 tables by Expectation Maximization.
91
92 Limitations
93
94 libDAI is not intended to be a complete package for approximate inference.
95 Instead, it should be considered as an "inference engine", providing various
96 inference methods. In particular, it contains no GUI, currently only supports
97 its own file format for input and output (although support for standard file
98 formats may be added later), and provides very limited visualization
99 functionalities. The only learning method supported currently is Expectation
100 Maximization (or Maximum Likelihood if no data is missing) for learning factor
101 parameters.
102
103 Rationale
104
105 In my opinion, the lack of open source "reference" implementations hampers
106 progress in research on approximate inference. Methods differ widely in terms
107 of quality and performance characteristics, which also depend in different ways
108 on various properties of the graphical models. Finding the best approximate
109 inference method for a particular application therefore often requires
110 empirical comparisons. However, implementing and debugging these methods takes
111 a lot of time which could otherwise be spent on research. I hope that this code
112 will aid researchers to be able to easily compare various (existing as well as
113 new) approximate inference methods, in this way accelerating research and
114 stimulating real-world applications of approximate inference.
115
116 Language
117
118 Because libDAI is implemented in C++, it is very fast compared with
119 implementations in MatLab (a factor 1000 faster is not uncommon). libDAI does
120 provide a (limited) MatLab interface for easy integration with MatLab. It also
121 provides a command line interface and experimental python and octave interfaces
122 (thanks to Patrick Pletscher).
123
124 Compatibility
125
126 The code has been developed under Debian GNU/Linux with the GCC compiler suite.
127 libDAI compiles successfully with g++ versions 3.4, 4.1, 4.2 and 4.3.
128
129 libDAI has also been successfully compiled with MS Visual Studio 2008 under
130 Windows (but not all build targets are supported yet) and with Cygwin under
131 Windows.
132
133 Finally, libDAI has been compiled successfully on MacOS X.
134
135 Downloading libDAI
136
137 The libDAI sources and documentation can be downloaded from the libDAI website:
138 http://www.libdai.org.
139
140 Mailing list
141
142 The Google group "libDAI" (http://groups.google.com/group/libdai) can be used
143 for getting support and discussing development issues.
144
145 -------------------------------------------------------------------------------
146
147 Building libDAI under UNIX variants (Linux / Cygwin / Mac OS X)
148
149 You need:
150
151 * a recent version of gcc (at least version 3.4)
152 * GNU make
153 * doxygen
154 * graphviz
155 * recent boost C++ libraries (at least version 1.34 if you have a recent
156 version of GCC, otherwise at least version 1.37; however, version 1.37
157 shipped with Ubuntu 9.04 is known not to work)
158
159 On Debian/Ubuntu, you can easily install all these packages with a single
160 command:
161
162 apt-get install g++ make doxygen graphviz libboost-dev libboost-graph-dev libboost-program-options-dev
163
164 (root permissions needed).
165
166 On Mac OS X (10.4 is known to work), these packages can be installed easily via
167 MacPorts. If MacPorts is not already installed, install it according to the
168 instructions at http://www.macports.org/. Then, a simple
169
170 sudo port install gmake boost doxygen graphviz
171
172 should be enough to install everything that is needed.
173
174 On Cygwin, the prebuilt Cygwin package boost-1.33.1-x is known not to work. You
175 can however obtain the latest boost version (you need at least 1.37.0) from
176 http://www.boost.org/ and compile/install it with:
177
178 ./configure
179 make
180 make install
181
182
183 To build the libDAI source, first copy a template Makefile.* to Makefile.conf
184 (for example, copy Makefile.LINUX to Makefile.conf if you use GNU/Linux). Then,
185 edit the Makefile.conf template to adapt it to your local setup. Especially
186 directories may differ from system to system. Finally, run
187
188 make
189
190 The build includes a regression test, which may take a while to complete.
191
192 If the build was successful, you can test the example program:
193
194 examples/example tests/alarm.fg
195
196 or the more elaborate test program:
197
198 tests/testdai --aliases tests/aliases.conf --filename tests/alarm.fg --methods JTREE_HUGIN BP_SEQMAX
199
200 -------------------------------------------------------------------------------
201
202 Building libDAI under Windows
203
204 You need:
205
206 * A recent version of MicroSoft Visual Studio (2008 is known to work)
207 * recent boost C++ libraries (version 1.37 or higher)
208 * GNU make (can be obtained from http://gnuwin32.sourceforge.net)
209
210 For the regression test, you need:
211
212 * GNU diff, GNU sed (can be obtained from http://gnuwin32.sourceforge.net)
213
214 To build the source, copy Makefile.WINDOWS to Makefile.conf. Then, edit
215 Makefile.conf to adapt it to your local setup. Finally, run (from the command
216 line)
217
218 make
219
220 The build includes a regression test, which may take a while to complete.
221
222 If the build was successful, you can test the example program:
223
224 examples\example tests\alarm.fg
225
226 or the more elaborate test program:
227
228 tests\testdai --aliases tests\aliases.conf --filename tests\alarm.fg --methods JTREE_HUGIN BP_SEQMAX
229
230 -------------------------------------------------------------------------------
231
232 Building the libDAI MatLab interface
233
234 You need:
235
236 * MatLab
237 * The platform-dependent requirements described above
238
239 First, you need to build the libDAI source as described above for your
240 platform. By default, the MatLab interface is disabled, so before compiling the
241 source, you have to enable it in the Makefile.conf by setting
242
243 WITH_MATLAB=true
244
245 Also, you have to configure the MatLab-specific parts of Makefile.conf to match
246 your system (in particular, the Makefile variables ME, MATLABDIR and MEX). The
247 MEX file extension depends on your platform; for a 64-bit linux x86_64 system
248 this would be "ME=.mexa64", for a 32-bit linux x86 system "ME=.mexglx". If you
249 are unsure about your MEX file extension: it needs to be the same as what the
250 MatLab command "mexext" returns. The required MEX files are built by issuing
251
252 make
253
254 from the command line. The MatLab interface is much less powerful than using
255 libDAI from C++. There are two reasons for this: (i) it is boring to write MEX
256 files; (ii) the large performance penalty paid when large data structures (like
257 factor graphs) have to be converted between their native C++ data structure to
258 something that MatLab understands.
259
260 A simple example of how to use the MatLab interface is the following (entered
261 at the MatLab prompt), which performs exact inference by the junction tree
262 algorithm and approximate inference by belief propagation on the ALARM network:
263
264 cd path_to_libdai/matlab
265 [psi] = dai_readfg ('../examples/alarm.fg');
266 [logZ,q,md,qv,qf] = dai (psi, 'JTREE', '[updates=HUGIN,verbose=0]')
267 [logZ,q,md,qv,qf] = dai (psi, 'BP', '[updates=SEQMAX,tol=1e-9,maxiter=10000,logdomain=0]')
268
269 where "path_to_libdai" has to be replaced with the directory in which libDAI
270 was installed. For other algorithms and some default parameters, see the file
271 tests/aliases.conf.
272
273 -------------------------------------------------------------------------------
274
275 Building the documentation
276
277 Install doxygen, graphviz and a TeX distribution and use
278
279 make doc
280
281 to build the documentation. If the documentation is not clear enough, feel free
282 to send me an email (or even better, to improve the documentation and send a
283 patch!). The documentation can also be browsed online at http://www.libdai.org.
284