Fast, flexible and easy to use probabilistic modelling in Python.
This release marks a major milestone in the pomegranate saga.
Here is an incomplete summary of the changes. Please see the CHANGELOG for a complete description.
General
Features
torch.masked.MaskedTensor
objectsModels
DenseHMM
and SparseHMM
models which differ in how the transition matrix is encoded, with DenseHMM
objects being significantly fasterHope this is useful to y'all! I know that it is a big change but I think that these changes will pay off. Please reach out with feedback or questions.
This is the last release of the Cython version of pomegranate.
This release contains speed improvements, bug fixes, and many model additions, as well as extensive documentation.
I am releasing the code for HMMs, FSMs, and discrete Bayesian networks for public debugging. The development for HMMs and FSMs is probably finished, but any bugs or feedback are still welcome! Predominately looking for feedback on the Bayesian network implementation and usage.