Machine Learning On Time-Series (MLOTS)
mlots provides Machine Learning tools for Time-Series Classification.
This package builds on (and hence depends on) scikit-learn, numpy, tslearn, annoy, and hnswlib libraries.
It can be installed as a python package from the PyPI repository.
Installation
Install mlots by running:
pip install mlots
After installation, it can be imported to a python environment to be employed.
import mlots
Documentation
The documentation is hosted at readthedocs. Examples of using mlots models are present in the Getting Started section of the documentation.
Contribute
- Issue Tracker: https://github.com/vivekmahato/mlots/issues
- Source Code: https://github.com/vivekmahato/mlots
Support
If you are having issues, please let us know.
License
The project is licensed under the BSD 3-Clause license.
Acknowledgements
We thank Angus Dempster et al. for sharing (open-sourcing) the code for ROCKET and MINIROCKET.
Metadata
Release files for mlots 0.0.7.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| mlots-0.0.7.4-py3-none-any.whl | Python 3 | none | any | Details for mlots-0.0.7.4-py3-none-any.whl |
Release files / mlots-0.0.7.4-py3-none-any.whl
| Download URL | mlots-0.0.7.4-py3-none-any.whl |
|---|---|
| Size | 23.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
ef4512351ab3ca0acbb5684a484356a7aa79391453ccbb376fbd1b4fe428a834
|
|
BLAKE2b-256 checksum How to use checksums |
06fd4e65f01af850700b9e1cc37a4b787200dca698d733c97983acbdf37eb224
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/52.0.0.post20210125 requests-toolbelt/0.9.1 tqdm/4.56.0 CPython/3.8.5
|
Security for mlots 0.0.7.4
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.