Bayesian Network Classifiers using libtorch from scratch
Ricardo Montañana Gómez
64970cf7f7
Reviewed-on: #32 Added - Add a new hyperparameter to the BoostAODE class, alphablock, to control the way α is computed, with the last model or with the ensmble built so far. Default value is false. - Add a new hyperparameter to the SPODE class, parent, to set the root node of the model. If no value is set the root parameter of the constructor is used. - Add a new hyperparameter to the TAN class, parent, to set the root node of the model. If not set the first feature is used as root. |
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| bayesnet | ||
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| config | ||
| diagrams | ||
| docs | ||
| lib | ||
| sample | ||
| tests | ||
| .clang-tidy | ||
| .clang-uml | ||
| .gitignore | ||
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| CHANGELOG.md | ||
| CMakeGraphVizOptions.cmake | ||
| CMakeLists.txt | ||
| LICENSE | ||
| logo.png | ||
| Makefile | ||
| README.md | ||
| update_coverage.py | ||
BayesNet
Bayesian Network Classifiers library
Dependencies
The only external dependency is libtorch which can be installed with the following commands:
wget https://download.pytorch.org/libtorch/nightly/cpu/libtorch-shared-with-deps-latest.zip
unzip libtorch-shared-with-deps-latest.zip
Setup
Getting the code
git clone --recurse-submodules https://github.com/doctorado-ml/bayesnet
Release
make release
make buildr
sudo make install
Debug & Tests
make debug
make test
Coverage
make coverage
make viewcoverage
Sample app
After building and installing the release version, you can run the sample app with the following commands:
make sample
make sample fname=tests/data/glass.arff
Models
- TAN
- KDB
- SPODE
- SPnDE
- AODE
- A2DE
- BoostAODE
- BoostA2DE
With Local Discretization
- TANLd
- KDBLd
- SPODELd
- AODELd
Documentation
Manual
Coverage report
Diagrams
UML Class Diagram
Dependency Diagram
