movMF is a distribution model for D-dimensional spherical data. movMF is analogous to Gaussian distribution but in spherical space. This code is based on the article Clustering on the Unit Hypersphere using von Mises-Fisher Distributions of A. Banerjee.
Get and Compile (you need boost):
git clone https://github.com/mrouvier/movMF
cd movMF
make
Generate randomly data:
python data/generate_data.py > data/vec
or
make generate_date
To train a statistical mixture model :
./bin/movmf_train --nb_mixture 128 --nb_iteration_em 10 --train data/vec --save mixture.txt
where nb_mixture is the number of mixture, nb_iteration_em is the number of iteration of Exepctation Maximization (algorithm used to train the mixture model), train is the training file and save is a file, the program save the model to this file.
To test the mixture model :
./bin/movmf_test data/vec mixture.txt
Given a mixture model (mixture.txt) and a testing file (data/vec), the program caculate the log likelihood for each vector.