This release is a pre-release and may not be stable for production use.
Scikit-ELM
scikit-elm is a scikit-learn compatible Extreme Learning Machine (ELM) regressor/classifier.
It features very high degree of model flexibility: dynamically added classes, partial_fit without performance penalties, wide data format compatibility, optimization and parameter selection without full re-training.
Big Data and out-of-core learning support through dask-powered backend. GPU acceleration support with NVidia hardware, and on macOS through plaidml.
Toolbox is in active development, initial release soon.
1 maintainer
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1 maintainer
Release files for scikit-elm 0.21a0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| scikit-elm-0.21a0.tar.gz | 21.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| scikit_elm-0.21a0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 51.7 kB
Release files / scikit-elm-0.21a0.tar.gz
| Download URL | scikit-elm-0.21a0.tar.gz |
|---|---|
| Size | 21.6 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/3.2.0 pkginfo/1.6.1 requests/2.25.0 setuptools/50.3.2 requests-toolbelt/0.9.1 tqdm/4.53.0 CPython/3.8.5
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Release files / scikit_elm-0.21a0-py3-none-any.whl
| Download URL | scikit_elm-0.21a0-py3-none-any.whl |
|---|---|
| Size | 30.1 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
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twine/3.2.0 pkginfo/1.6.1 requests/2.25.0 setuptools/50.3.2 requests-toolbelt/0.9.1 tqdm/4.53.0 CPython/3.8.5
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