VPAC

Requirements

• python3 (is preferable)
• numpy
• scipy
• sklearn
• tqdm (only when the VERBOSE is True)

Usage instructions

Download VPAC.

git clone https://github.com/ShengquanChen/VPAC

Load in the data which should be arranged as n_features by n_samples. Fit the model with parameter latent_dim specifying the number of latent dimensions, and n_components the number of mixture components.

from vpac import VPAC
vpac = VPAC(y = data, latent_dim = 5, n_components = 3)
vpac.fit()

Predict posterior probability of each component given the data.

prob = vpac.predict_proba()

License

This project is licensed under the MIT License - see the LICENSE file for details.