PyCave Documentation

PyCave provides well-known machine learning models for the usage with large-scale datasets. This is achieved by leveraging PyTorch’s capability to easily perform computations on a GPU as well as implementing batch-wise training for all models.

As a result, PyCave’s models are able to work with datasets orders of magnitudes larger than datasets that are commonly used with Sklearn. At the same time, PyCave provides an API that is very familiar both to users of Sklearn and PyTorch.

Internally, PyCave’s capabilities are heavily supported by PyBlaze which enables seamless batch-wise GPU training without additional code.

Models

PyCave currently includes the following models:

• pycave.bayes.GMM: Gaussian Mixture Models

• pycave.bayes.HMM: Hidden Markov Models with discrete and Gaussian emissions

• pycave.bayes.MarkovModel: Markov Models

All of these models can be trained on a (single) GPU and using batches of data.

Guides

• Quickstart

Package Reference

• Bayesian Models
  • Gaussian Mixture Model
  • Markov Model
  • Hidden Markov Model

Indices and Tables

• Index

• Module Index

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