(learning probabilistic and possibilistic graphical models)
|lnxines.zip||(673 kb)||GNU/Linux executables|
|winines.zip||(498 kb)||Windows console executables|
|ines.zip||(289 kb)||C sources, package version 4.1 (2014.10.24)|
Note: The table package contains some auxiliary programs for preprocessing the data files.
INeS (Induction of Networks Structures) is a set of programs to learn a graphical model (Bayesian network or possibilistic network) from a dataset of sample cases, to generate a random dataset from a Bayesian network, to evaluate learned networks w.r.t. a test dataset and a reference network, and to measure the strengths of conditional dependences.
A brief description of how to apply these programs can be found
in the file
ines/ex/readme in the source package.
djc_local in the directory
ines/djc may also be
If you have trouble executing the programs on Microsoft Windows, check whether you have the Microsoft Visual C++ Redistributable for Visual Studio 2019 (see under "Other Tools and Frameworks") installed, as the programs were compiled with Microsoft Visual Studio 2019.
The theory underlying this program is described in detail in the books:
Note: The learning results reported in these books were produced
on a GNU/Linux system using the
rand48 random number
generator, which is not available on a Microsoft Windows System
(but should be available on an Apple OSX system). In order to avoid
different results on different systems, the above programs now use
a different random number generator by default (see
util/src/random.[ch]). However, on a GNU/Linux system,
the results reported in the books can still be reproduced exactly by
recompiling the programs with
make all ADDFLAGS="-DRAND_LIB48"
which compiles the programs in such a way that they use the
rand48 random number generator.