For installing through pip:
pip install verokuTo clone this git repo:
git clone https://github.com/ejlouw/veroku.git
cd veroku/
pip install -r requirements.txt
It is recommended to use a separate conda virtual environment when installing the dependencies, to avoid interfering with existing packages. To get started with conda environments, see the installation guide. For more information on using conda environments see managing environments guide
Veroku currently supports the following probability distributions:
- Categorical (sparse and dense implementations)
- Gaussian
- Gaussian mixture1
- Linear Gaussian2
- Non-linear Gaussian3
1 This class still has some experimental functionality (specifically the Gaussian mixture division methods)
and is, therefore, still in the factors.experimental sub-package.
2 Using the Gaussian class - see the Kalman filter example notebook.
3This implementation is still experimental (see the factors.experimental sub-package).
cluster_graph module to automatically create valid cluster graphs. Inference can be performed in these graphs
using message passing algorithms. Currently only the LBU (Loopy Belief Update) message-passing algorithm is supported.
Example notebooks:
The following distributions, models and features are on the roadmap to be added to veroku:
- Conditional Gaussian
- Dirichlet distribution
- Wishart distribution
- Normal-Wishart distribution
- Plate models
- Structure Learning
- Causal Inference
For the python dependencies see the requirements file.
The following additional dependencies are also required for some functionality (these are not installed automatically
with the pip install):
See https://graphviz.org/download/ for installation instructions.
If you would like to contribute to veroku, please see the contributing guide.
Veroku is released under a 3-Clause BSD license. You can view the license here.