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.