Bayesian Network Classifiers using libtorch from scratch
Ricardo Montañana Gómez
f72aa5b9a6
Add hyper parameter to BoostAODE. This hyper parameter decides if we select features with cfs/fcbf/iwss before start building models and build a Spode with the selected features. The hyperparameter is select_features |
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| .vscode | ||
| cmake/modules | ||
| config | ||
| data | ||
| diagrams | ||
| lib | ||
| sample | ||
| src | ||
| tests | ||
| .clang-tidy | ||
| .clang-uml | ||
| .gitignore | ||
| .gitmodules | ||
| CMakeLists.txt | ||
| gcovr.cfg | ||
| LICENSE | ||
| mac_mst.txt | ||
| Makefile | ||
| README.md | ||
BayesNet
Bayesian Network Classifier with libtorch from scratch
0. Setup
Before compiling BayesNet.
boost library
The best option is install the packages that the Linux distribution have in its repository. If this is the case:
sudo dnf install boost-devel
If this is not possible and the compressed packaged is installed, the following environment variable has to be set:
export BOOST_ROOT=/path/to/library/
libxlswriter
cd lib/libxlsxwriter
make
make install DESTDIR=/home/rmontanana/Code PREFIX=
Environment variable has to be set:
export LD_LIBRARY_PATH=/usr/local/lib
Release
make release
Debug & Tests
make debug