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Christian Borgelt's Web Pages

INeS - Induction of Network Structures

(learning probabilistic and possibilistic graphical models)

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32 bit 64 bit (32/64 bit only for executable)
lnxines.zip (560 kb) lnxines.zip (649 kb) GNU/Linux executables
winines.zip (438 kb) winines.zip (486 kb) Windows console executables
ines.zip (285 kb) ines.tar.gz (238 kb) C sources, package version 4.1 (2014.10.24)

Note: The table package contains some auxiliary programs for preprocessing the data files.

Description

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. The scripts djc_prob, djc_poss, and djc_local in the directory ines/djc may also be helpful.

If you have trouble executing the programs on Microsoft Windows, check whether you have the Microsoft Visual C++ Redistributable Packages for Visual Studio 2015 installed, as the library was compiled with Microsoft Visual Studio 2015.

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.