LOpenGM: Lua bindings for OpenGM

OpenGM is a C++ library for graphical modeling, and inference. The Lua bindings provide a simple way of describing graphs, from Lua, and then optimizing them with OpenGM.

Note: this package is superseded by 'gm', a more general and simple (pure Lua) package for graphical models.

License

LOpenGM Copyright (c) 2011 Clement Farabet (Lua Bindings)

OpenGM Copyright (c) 2010 by Bjoern Andres and Joerg Hendrik Kappes.

This software was developed by Bjoern Andres and Joerg Hendrik Kappes. Enquiries shall be directed to:

[email protected], [email protected]

All advertising materials mentioning features or use of this software must display the following acknowledgement: ``This product includes the OpenGM library developed by Bjoern Andres and Joerg Hendrik Kappes. Please direct enquiries concerning OpenGM to [email protected], [email protected]''.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

• Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
• Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
• All advertising materials mentioning features or use of this software must display the following acknowledgement: ``This product includes the OpenGM library developed by Bjoern Andres and Joerg Hendrik Kappes. Please direct enquiries concerning OpenGM to [email protected], [email protected]''.
• The names of the authors must not be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Install

1/ Torch7 is required:

Dependencies, on Linux (Ubuntu > 9.04):

$ apt-get install gcc g++ git libreadline5-dev cmake wget libqt4-core libqt4-gui libqt4-dev

Dependencies, on Mac OS (Leopard, or more), using Homebrew:

$ brew install git readline cmake wget qt

Then on both platforms:

$ git clone https://github.com/andresy/torch
$ cd torch
$ mkdir build; cd build
$ cmake ..
$ make
$ [sudo] make install

2/ Once Torch7 is available, install this package:

$ [sudo] torch-pkg install opengm

Use the library

API, in very short:

Load/start up package:

require 'opengm'

Construct a graph:

g = opengm.Graph(...)

Optimize a graph:

g:optimize{}

Display a graph, using Graphviz:

g:show{}

A simple complete example:

-- load opengm
require 'opengm'

-- standard factors
f = opengm.factors

-- define variables
variables = {'car', 'person', 'building', 'street', 'vehicle'}

-- define factors
factors = {-- unary factors (prior probabilities of each class):
           {f.prior(0.9),  {'car'}},
           {f.prior(0.01), {'person'}},
           {f.prior(0.7),  {'building'}},
           {f.prior(0.8),  {'street'}},
           {f.prior(0.4),  {'vehicle'}},
           -- Potts factors (joint probabilities):
           {f.band(0),     {'car',      'person'}},
           {f.band(0),     {'person',   'building'}},
           {f.band(0),     {'building', 'street'}},
           {f.band(0),     {'car',      'building'}},
           {f.band(0),     {'building', 'vehicle'}},
           {f.band(0),     {'street',   'vehicle'}},
           {f.band(0),     {'person',   'vehicle'}},
           {f.bimplies(1), {'car',      'vehicle'}}}

-- create graph
g = opengm.Graph(variables, factors)

-- optimize graph
g:optimize{method='a*', verbose=true}

-- print graph
print(g)

Running the script above outputs:

<opengm> optimizing... 
step 1: E=3.99758, c=0
step 2: E=3.63212, c=2.19722
step 3: E=3.63212, c=2.19722
<opengm.Graph>
  + nb of variables: 4
  + nb of factors: 6
  + graph is acyclic
  + current (optimized) variable states: 
    - car [1]
    - person [0]
    - building [0]
    - street [0]
    - vehicle [1]