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