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 In addition, libDAI supports parameter learning of conditional probability
  96 tables by Expectation Maximization.
  97
  98
  99 Why C++?
 100 --------
 101 Because libDAI is implemented in C++, it is very fast compared with
 102 implementations in MatLab (a factor 1000 faster is not uncommon). libDAI does
 103 provide a (limited) MatLab interface for easy integration with MatLab.
 104
 105
 106 Releases
 107 --------
 108 Releases can be obtained from www.libdai.org
 109 License: GNU Public License v2 (or higher).
 110
 111 libDAI-0.2      December 1, 2006
 112 libDAI-0.2.1    May 26, 2008
 113 libDAI-0.2.2    September 30, 2008
 114
 115
 116 Acknowledgments
 117 ---------------
 118 This work is part of the Interactive Collaborative Information Systems (ICIS)
 119 project, supported by the Dutch Ministry of Economic Affairs, grant BSIK03024.
 120 I would like to thank Martijn Leisink for providing the basis on which libDAI has been built.
 121
 122
 123 Documentation
 124 -------------
 125 Some doxygen documentation is available. Install doxygen and use "make doc" to build the
 126 documentation. If the documentation is not clear enough, feel free to send me an email
 127 (or even better, to improve the documentation!).
 128
 129 A description of the factor graph (.fg) file format can be found in the file FILEFORMAT.
 130
 131
 132 Compatibility
 133 -------------
 134 The code has been developed under Debian GNU/Linux with the GCC compiler suite.
 135 libDAI compiles successfully with g++ versions 3.4, 4.1, 4.2 and 4.3.
 136
 137 libDAI has also been successfully compiled with MS Visual Studio 2008 under Windows
 138 (but not all build targets are supported yet) and with Cygwin under Windows.
 139
 140 Finally, libDAI has been compiled successfully on MacOS X.
 141
 142
 143 Quick start (linux/cygwin/Mac OS X)
 144 -----------------------------------
 145 You need:
 146 - a recent version of gcc (at least version 3.4)
 147 - GNU make
 148 - doxygen
 149 - graphviz
 150 - recent boost C++ libraries (at least version 1.34, or 1.37 for cygwin)
 151
 152 On Debian/Ubuntu, you can easily install all these packages with a single command:
 153 "apt-get install g++ make doxygen graphviz libboost-dev libboost-graph-dev libboost-program-options-dev"
 154 (root permissions needed).
 155
 156 On Mac OS X (10.4 is known to work), these packages can be installed easily via MacPorts.
 157 First, install MacPorts according to the instructions at http://www.macports.org/
 158 Then, a simple "sudo port install gmake boost doxygen graphviz"
 159 should be enough to install everything that is needed.
 160
 161 On Cygwin, the prebuilt Cygwin package boost-1.33.1-x is known not to work.
 162 You can however obtain the latest boost version (you need at least 1.37.0)
 163 from http://www.boost.org/ and compile/install it with:
 164
 165   ./configure
 166   make
 167   make install
 168
 169
 170 To build the libDAI source, first copy a template Makefile.* to Makefile.conf
 171 (for example, copy Makefile.LINUX to Makefile.conf if you use GNU/Linux).
 172 Then, edit the Makefile.conf template to adapt it to your local setup.
 173 Especially directories may differ from system to system. Finally, run
 174
 175     make
 176
 177 If the build was successful, you can test the example program:
 178
 179     ./example tests/alarm.fg
 180
 181 or the more elaborate test program:
 182
 183     tests/testdai --aliases tests/aliases.conf --filename tests/alarm.fg --methods JTREE_HUGIN BP_SEQMAX
 184
 185
 186 Quick start (Windows)
 187 ---------------------
 188 You need:
 189 - A recent version of MicroSoft Visual Studio (2008 works)
 190 - recent boost C++ libraries (version 1.34 or higher)
 191 - GNU make (can be obtained from http://gnuwin32.sourceforge.net)
 192 For the regression test, you need:
 193 - GNU diff, GNU sed (can be obtained from http://gnuwin32.sourceforge.net)
 194
 195 To build the source, copy Makefile.WINDOWS to Makefile.conf. Then, edit
 196 Makefile.conf to adapt it to your local setup. Finally, run (from the command line)
 197
 198     make
 199
 200 If the build was successful, you can test the example program:
 201
 202     example tests\alarm.fg
 203
 204 or the more elaborate test program:
 205
 206     tests\testdai --aliases tests\aliases.conf --filename tests\alarm.fg --methods JTREE_HUGIN BP_SEQMAX
 207
 208
 209 Quick start (MatLab)
 210 --------------------
 211 You need:
 212 - MatLab
 213 - The platform-dependent requirements described above
 214
 215 First, you need to build the libDAI source as described above for your
 216 platform. By default, the MatLab interface is disabled, so before compiling the
 217 source, you have to enable it in the Makefile.conf by setting
 218 "WITH_MATLAB=true". Also, you have to configure the MatLab-specific parts of
 219 Makefile.conf to match your system (in particular, the Makefile variables ME,
 220 MATLABDIR and MEX). The MEX file extension depends on your platform; for a
 221 64-bit linux x86_64 system this would be "ME=.mexa64", for a 32-bit linux x86
 222 system this would be "ME=.mexglx". If you are unsure about your MEX file
 223 extension: it needs to be the same as what the MatLab command "mexext" returns.
 224 The required MEX files are built by issuing
 225
 226     make
 227
 228 from the command line. The MatLab interface is much less powerful than using
 229 libDAI from C++. There are two reasons for this: (i) it is boring to write MEX
 230 files; (ii) the large performance penalty paid when large data structures (like
 231 factor graphs) have to be converted between their native C++ data structure to
 232 something that MatLab understands.
 233
 234 A simple example of how to use the MatLab interface is the following (entered
 235 at the MatLab prompt), which performs exact inference by the junction tree
 236 algorithm and approximate inference by belief propagation on the ALARM network:
 237
 238     cd path_to_libdai/matlab
 239     [psi] = dai_readfg ('../examples/alarm.fg');
 240     [logZ,q,md,qv,qf] = dai (psi, 'JTREE', '[updates=HUGIN,verbose=0]')
 241     [logZ,q,md,qv,qf] = dai (psi, 'BP', '[updates=SEQMAX,tol=1e-9,maxiter=10000,logdomain=0]')
 242
 243 where "path_to_libdai" has to be replaced with the directory in which libDAI
 244 was installed. For other algorithms and their parameters, see
 245 tests/aliases.conf.