R-LAIR: Riverside Lab for Artificial Intelligence Research
Continuous Time Bayesian Network Reasoning and Learning Engine (CTBN-RLE)
v1.0.2: Install Instructions
How to cite:
============
If you wish to cite this code, please cite the JMLR paper
Christian R. Shelton, Yu Fan, William Lam, Joon Lee, and Jing Xu.
Continuous Time Bayesian Network Reasoning and Learning Engine.
Journal of Machine Learning Research, 2010.
to appear.
a suitable bibtex entry would be
@article{CTBNRLE,
author = "Christian R. Shelton and Yu Fan and William Lam and Joon Lee
and Jing Xu",
title = "Continuous Time {B}ayesian Network Reasoning and Learning
Engine",
journal = "Journal of Machine Learning Research",
note = "to appear",
year = 2010,
}
How to compile:
===============
This code has been tested under Unix with the Gnu tools and Windows
with Visual C++. You may want to read the "Installing Graphviz" section
first if you plan to use the visualization portion of the libraries.
UNIX:
The Makefile in src/ will make ctbn.a (a library of all of the object files).
"make" in demo/ will make the demos (including making the objects in src/
if necessary), and "make" tst/ will make all of the unit/regression tests.
Running the python script tst/unit_test.py will run each of the test
scripts to confirm that the calculations are being performed correctly.
Windows:
Under Visual C++, you must create your own project. Add all of the source
files in src/ and all of the header files in hdr/. Adding one "main" file
(from tst/ or demo/) should allow the entire project to compile. You may
have a number of compile warnings about conversions from "size_t" to "int."
These may be safely ignored.
Installing Graphviz:
====================
graphiviz is a graph layout program available from www.graphviz.org.
The CTBNRLE uses the "dot" executable from it. We have successfully tested
CTBNRLE with graphviz version 2.14.1 (Oct 20, 2007) and version 2.26.3 (Jan
26, 2010).
Install graphviz as per the instructions on their website. If the
executable is not in your path, or you would like to explicitly state
where to find it, you should edit src/graphic.cc and change the definition of
DOTCOMMAND to the string containing the executable (with path) to be executed
to run the "dot" program of graphviz.
Structure:
==========
The base directory is split into multiple directorys:
demo/ This contains example programs on how to use the libraries. They
are often useful on their own (e.g. sample from a CTBN, learn a CTBN
from samples, etc). The README.txt file also has information on
the file extensions used for data files.
doc/ This directory contains information on how to read the code and
use it, literature to read, and funding sources that made this code
base possible.
src/ This directory contains all of the non-header files.
hdr/ This directory contains all of the header files. This, plus the ctbn.a
created in /src are all that is necessary to link a new program to
the libraries here
tst/ This directory contains regression tests that are used to verify that
the code is working
Continuous Time Bayesian Network Reasoning and Learning Engine (CTBN-RLE)
v1.0.2: Install Instructions
How to cite:
============
If you wish to cite this code, please cite the JMLR paper
Christian R. Shelton, Yu Fan, William Lam, Joon Lee, and Jing Xu.
Continuous Time Bayesian Network Reasoning and Learning Engine.
Journal of Machine Learning Research, 2010.
to appear.
a suitable bibtex entry would be
@article{CTBNRLE,
author = "Christian R. Shelton and Yu Fan and William Lam and Joon Lee
and Jing Xu",
title = "Continuous Time {B}ayesian Network Reasoning and Learning
Engine",
journal = "Journal of Machine Learning Research",
note = "to appear",
year = 2010,
}
How to compile:
===============
This code has been tested under Unix with the Gnu tools and Windows
with Visual C++. You may want to read the "Installing Graphviz" section
first if you plan to use the visualization portion of the libraries.
UNIX:
The Makefile in src/ will make ctbn.a (a library of all of the object files).
"make" in demo/ will make the demos (including making the objects in src/
if necessary), and "make" tst/ will make all of the unit/regression tests.
Running the python script tst/unit_test.py will run each of the test
scripts to confirm that the calculations are being performed correctly.
Windows:
Under Visual C++, you must create your own project. Add all of the source
files in src/ and all of the header files in hdr/. Adding one "main" file
(from tst/ or demo/) should allow the entire project to compile. You may
have a number of compile warnings about conversions from "size_t" to "int."
These may be safely ignored.
Installing Graphviz:
====================
graphiviz is a graph layout program available from www.graphviz.org.
The CTBNRLE uses the "dot" executable from it. We have successfully tested
CTBNRLE with graphviz version 2.14.1 (Oct 20, 2007) and version 2.26.3 (Jan
26, 2010).
Install graphviz as per the instructions on their website. If the
executable is not in your path, or you would like to explicitly state
where to find it, you should edit src/graphic.cc and change the definition of
DOTCOMMAND to the string containing the executable (with path) to be executed
to run the "dot" program of graphviz.
Structure:
==========
The base directory is split into multiple directorys:
demo/ This contains example programs on how to use the libraries. They
are often useful on their own (e.g. sample from a CTBN, learn a CTBN
from samples, etc). The README.txt file also has information on
the file extensions used for data files.
doc/ This directory contains information on how to read the code and
use it, literature to read, and funding sources that made this code
base possible.
src/ This directory contains all of the non-header files.
hdr/ This directory contains all of the header files. This, plus the ctbn.a
created in /src are all that is necessary to link a new program to
the libraries here
tst/ This directory contains regression tests that are used to verify that
the code is working
