A machine learning interface for isolated temporal sequence classification algorithms in Python.
Temporal sequences are sequences of observations that occur over time. Changing patterns over time naturally provide many interesting opportunities and challenges for machine learning.
This library specifically aims to tackle classification problems for isolated temporal sequences by creating an interface to a number of classification algorithms.
Despite these types of sequences sounding very specific, you probably observe some of them on a regular basis!
Some examples of classification problems for isolated temporal sequences include classifying:
- isolated word utterances in speech audio signals,
- isolated hand-written characters according to their pen-tip trajectories,
- isolated hand or head gestures in a video or motion-capture recording.
Sequentia offers the use of multivariate observation sequences with differing durations in conjunction with the following algorithms and methods.
- Hidden Markov Models (via Pomegranate )
- Multivariate Gaussian Emissions
- Gaussian Mixture Model Emissions (soon!)
- Left-Right and Ergodic Topologies
- Approximate Dynamic Time Warping k-Nearest Neighbors (implemented with FastDTW )
- Long Short-Term Memory Networks (soon!)
- Centering and standardization
- Downsampling (decimation and averaging)
- Filtering (mean and median)
- Discrete Fourier Transform
- Multi-processing for DTW k-NN predictions
Disclaimer: The package currently remains largely untested and is still in its early pre-alpha stages – use with caution!
pip install sequentia
Documentation for the package is available on Read The Docs.
Tutorials and examples
For tutorials and examples on the usage of Sequentia, look at the notebooks here!
All contributions to this repository are greatly appreciated. Contribution guidelines can be found here.
Sequentia © 2019-2020, Edwin Onuonga - Released under the MIT License.
Authored and maintained by Edwin Onuonga.