README

   1 libDAI - A free/open source C++ library for Discrete Approximate Inference methods
   2 ==================================================================================
   3
   4 v 0.2.2 - May 26, 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
  12 ----------------------------------------------------------------------------------
  13 This file is part of libDAI.
  14
  15 libDAI is free software; you can redistribute it and/or modify
  16 it under the terms of the GNU General Public License as published by
  17 the Free Software Foundation; either version 2 of the License, or
  18 (at your option) any later version.
  19
  20 libDAI is distributed in the hope that it will be useful,
  21 but WITHOUT ANY WARRANTY; without even the implied warranty of
  22 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  23 GNU General Public License for more details.
  24
  25 You should have received a copy of the GNU General Public License
  26 along with libDAI; if not, write to the Free Software
  27 Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
  28 ----------------------------------------------------------------------------------
  29
  30
  31 SCIENTISTS: please be aware that the fact that this program is released as Free
  32 Software does not excuse you from scientific propriety, which obligates you to
  33 give appropriate credit! If you write a scientific paper describing research
  34 that made substantive use of this program, it is your moral obligation as a
  35 scientist to (a) mention the fashion in which this software was used, including
  36 the version number, with a citation to the literature, to allow replication;
  37 (b) mention this software in the Acknowledgements section.  The appropriate
  38 citation is: J. M. Mooij (2008) libDAI: A free/open source C++ library for
  39 Discrete Approximate Inference methods, http://mloss.org/software/view/77/.
  40 Moreover, as a personal note, I would appreciate it if you would email me with
  41 citations of papers referencing this work so I can mention them to my funding
  42 agent and tenure committee.
  43
  44 What is libDAI?
  45 ---------------
  46 libDAI is a free/open source C++ library (licensed under GPL, see the file
  47 COPYING for more details) that provides implementations of various
  48 (deterministic) approximate inference methods for discrete graphical models.
  49 libDAI supports arbitrary factor graphs with discrete variables (this includes
  50 discrete Markov Random Fields and Bayesian Networks).
  51
  52 libDAI is not intended to be a complete package for approximate inference.
  53 Instead, it should be considered as an "inference engine", providing various
  54 inference methods. In particular, it contains no GUI, currently only supports
  55 its own file format for input and output (although support for standard file
  56 formats may be added), and provides no visualization.
  57
  58 Because libDAI is implemented in C++, it is very fast compared with e.g. MatLab
  59 implementations. libDAI does provide a MatLab interface for easy integration
  60 with MatLab. Currently, libDAI supports the following deterministic approximate
  61 inference methods:
  62
  63     * Mean Field
  64     * (Loopy) Belief Propagation
  65     * Tree Expectation Propagation
  66     * Generalized Belief Propagation
  67     * Double-loop GBP
  68     * Loop Corrected Approximate Inference
  69
  70 Exact inference by JunctionTree is also provided.
  71
  72 Many of these algorithms are not yet available in similar open source software,
  73 to the best of the author's knowledge (open source packages supporting both
  74 directed and undirected graphical models are Murphy's BNT, Intel's PNL and gR).
  75
  76 The library is targeted at researchers; to be able to use the library, a good
  77 understanding of graphical models is needed. However, the code will hopefully
  78 find its way into real-world applications as well.
  79
  80
  81 Rationale
  82 ---------
  83 In my opinion, the lack of open source reference implementations hampers
  84 progress in research on approximate inference. Methods differ widely in terms
  85 of quality and performance characteristics, which also depend in different ways
  86 on various properties of the graphical models. Finding the best approximate
  87 inference method for a particular application therefore often requires
  88 empirical comparisons. However, implementing and debugging these methods takes
  89 a lot of time which could otherwise be spent on research. I hope that this code
  90 will aid researchers to be able to easily compare various (existing as well as
  91 new) approximate inference methods, in this way accelerating research and
  92 stimulating real-world applications of approximate inference.
  93
  94
  95 Releases
  96 --------
  97 Releases can be obtained from http://www.mbfys.ru.nl/~jorism/libDAI.
  98 License: GNU Public License v2 (or higher).
  99
 100 libDAI-0.2      December 1, 2006
 101 libDAI-0.2.1    May 26, 2008
 102
 103
 104 Acknowledgments
 105 ---------------
 106 The development reported here is part of the Interactive Collaborative
 107 Information Systems (ICIS) project, supported by the Dutch Ministry of Economic
 108 Affairs, grant BSIK03024. I would like to thank Martijn Leisink for providing
 109 the basis on which libDAI has been built.
 110
 111
 112 Known issues
 113 ------------
 114 Due to a bug in GCC 3.3.x and earlier (http://gcc.gnu.org/bugzilla/show_bug.cgi?id=20358)
 115 it doesn't compile with these versions (it does compile with GCC version 3.4 and higher).
 116 Workaround: replace the two NAN's in factor.h causing the error messages by -1.
 117
 118
 119 Documentation
 120 -------------
 121 Almost nonexistant. But I'm working on it. In the meantime, I'll provide limited support
 122 by email. The following gives an overview of different methods and their properties
 123 (can be slightly obsolete):
 124
 125 BP
 126     updates     UpdateType SEQFIX,SEQRND,SEQMAX,PARALL
 127     tol         double
 128     maxiter     size_t
 129     verbose     size_t
 130 MF
 131     tol         double
 132     maxiter     size_t
 133     verbose     size_t
 134 HAK
 135     clusters    MIN,DELTA,LOOP
 136     loopdepth
 137     doubleloop  bool
 138     tol         double
 139     maxiter     size_t
 140     verbose     size_t
 141 JTREE
 142     updates     UpdateType  HUGIN,SHSH
 143     verbose     size_t
 144 MR
 145     updates     UpdateType  FULL,LINEAR
 146     inits       InitType    RESPPROP,CLAMPING,EXACT
 147     verbose     size_t
 148 TREEEP
 149     type        TypeType    ORG,ALT
 150     tol         double
 151     maxiter     size_t
 152     verbose     size_t
 153 LC
 154     cavity      CavityType  FULL,PAIR,PAIR2,UNIFORM
 155     updates     UpdateType  SEQFIX,SEQRND(,NONE)
 156     reinit      bool
 157     cavainame   string
 158     cavaiopts   Properties
 159     tol         double
 160     maxiter     size_t
 161     verbose     size_t
 162
 163
 164
 165 Quick start
 166 -----------
 167 You need:
 168 - a recent version of gcc (version 3.4 at least)
 169 - the Boost C++ libraries (under Debian/Ubuntu you can install them using
 170   "apt-get install libboost-dev libboost-program-options-dev")
 171 - GNU make
 172
 173 To build the source, edit the Makefile and then 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/test --aliases tests/aliases.conf --filename tests/alarm.fg --methods JTREE_HUGIN BP_SEQMAX
 184
 185 A description of the factor graph (.fg) file format can be found in the file FILEFORMAT.