This project consists of a gpflow 2 implementation of the variational Generalized Wishart Process , based on the Generalized Wishart Process. The implementation consists of the exact Wishart Process model and likelihood, as well as the factorized approximation. In addition, a multi output kernel is added which allows several input channels to share the same kernel (and thus learn the same lengthscale).
| package | version |
|---|---|
| gpflow | 2.1.4 |
| tensorflow | 2.5.0 |
| tensorflow_probability | 0.12.1 |
| tensorboard | 2.5.0 |
| matplotlib | 3.3.2 |
| numpy | 1.19.2 |
| h5py | 3.1.0 |
| scikit-learn | 0.24.2 |
| pandas | 1.3.2 |
| tqdm | 4.62.3 |
├── data # Folder for offline data
├── logs # Saving trained models and training logs
├── analyses # Training scripts and jupyter notebook examples
├── src # Source files
│ ├── models
| ├── kernels
| ├── likelihood
└── README.md
Run "tensorboard --logdir logs/" in command line