A python package that implements dynamic bayesian networks models on time series data
This package is intended to be used for Network Reconstruction of Dynamic Bayesian Networks.
To test the algorithm on the Yeast data set run the bash script. Example to run a Non-Homogeneous Dynamic Bayesian Network
sh yeast_pipeline.sh -m nh-dbn
Where -m denotes the method to use
- 'h-dbn' -> Homogeneous Dynamic Bayesian Network
- 'nh-dbn' -> Non-Homonegeneous Dynamic Bayesian Network
- 'seq-dbn' -> Sequentially Coupled Dynamic Bayesian Network
- 'glob-dbn' -> Globally Coupled Dynamic Bayesian Network
This will be the readme of the package. Github-flavored Markdown for reference.
- Run this command to install the package locally
pip install .
pip install -e .
To be able to edit the source code and (hot-reload) updates?
To run the python profiler use the bash script:
In order to be able to see the profiler results you need to have 'kcachegrind'
sudo apt-get install -y kcachegrind
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
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