Penalty Model - Linear Programming
Generates penalty models using scipy.optimize’s Linear Programming capability. Serves as a factory and cache for penaltymodel.
On install, penaltymodel-lp registers an entry point that can be read by
penaltymodel. It will be used automatically by any project that uses penaltymodel’s
get_penalty_model function.
Installation
To install:
pip install penaltymodel-lpTo build from souce:
cd penaltymodel_lp
pip install -r requirements.txt
python setup.py installLicense
Released under the Apache License 2.0
1 maintainer
Release files for penaltymodel-lp 0.1.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| penaltymodel-lp-0.1.6.tar.gz | 5.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| penaltymodel_lp-0.1.6-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 12.9 kB
Release files / penaltymodel-lp-0.1.6.tar.gz
| Download URL | penaltymodel-lp-0.1.6.tar.gz |
|---|---|
| Size | 5.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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twine/3.7.1 importlib_metadata/4.8.3 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.6.8
|
Release files / penaltymodel_lp-0.1.6-py3-none-any.whl
| Download URL | penaltymodel_lp-0.1.6-py3-none-any.whl |
|---|---|
| Size | 7.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
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| Uploaded via |
twine/3.7.1 importlib_metadata/4.8.3 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.6.8
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