# GCPROCPY
This is a Python implementation based on https://github.com/AskExplain/gcproc/blob/1_prepare
To be done: * Using a zero-shot NN to provide good intialization. * Extend the technique to multi-linear agebra. This is of interest in biological structures data where there is metadata that can be used. * generalized assigment problem. * Impuation learning * Kernelized version.
Metadata
Release files for GCProc 0.1.0
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Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| GCProc-0.1.0.tar.gz | 7.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| GCProc-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 16.5 kB
Release files / GCProc-0.1.0.tar.gz
| Download URL | GCProc-0.1.0.tar.gz |
|---|---|
| Size | 7.1 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.9.7
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Release files / GCProc-0.1.0-py3-none-any.whl
| Download URL | GCProc-0.1.0-py3-none-any.whl |
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| Size | 9.4 kB |
| Tags | Python 3 |
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twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.9.7
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