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PAC-BAYESIAN DOMAIN ADAPTATION (aka PBDA)
Version 0.901 (August 9, 2013), Released under the BSD-license
https://github.com/pgermain/pbda 
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Author: 
    Pascal Germain. Groupe de Recherche en Apprentissage Automatique de 
    l'Universite Laval (GRAAL).
Reference: 
    Pascal Germain, Amaury Habrard, Francois Laviolette, and Emilie Morvant. 
    A PAC-Bayesian Approach for Domain Adaptation with Specialization to 
    Linear Classifiers.
    International Conference on Machine Learning (ICML) 2013. 
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Thank you for looking at my code!
This program have been tested using Python 3.6 under Linux.
It requires the NumPy and SciPy libraries.
I prepared three small scripts to use PBDA by the command line:
1) pbda_learn.py: Execute the learning algorithm
2) pbda_classify.py: Execute the classification function
3) pbda_reverse_cv.py: Compute a "reverse cross-validation" score
Further usage instructions can be obtained by the following commands:
python pbda_learn.py --help
python pbda_classify.py --help
python pbda_reverse_cv.py --help
The data used in the paper experiments is available here (in svmlight format):
https://researchers.lille.inria.fr/pgermain/data/amazon_tfidf_svmlight.tgz
Pascal Germain.