semvec/pypi-packages huggingface dataset:
debandshit 0.4.1
VapourSynth Debanding Functions Collection
Requires Python: >=3.9

# debandshit, a collection of wrappers and functions for debanding

This repository is intended to be a maintained version of `debandshit` until [Z4STIN](https://github.com/kgrabs) continues maintaining it himself.

The original v0.3.0 of `debandshit.py` that this is built on from can be found [here](https://pastebin.com/NGFBhCCn).

## How to install

If you have the old `debandshit.py` module,
remove that from your system first.

Install `debandshit` with the following command:

```sh
$ pip install vs-debandshit
```

## Dependencies

debandshit depends on the following third-party scripts:

- [mvsfunc](https://github.com/HomeOfVapourSynthEvolution/mvsfunc)
- [vardefunc](https://pypi.org/project/vardefunc/)
- [vsutil](https://pypi.org/project/vsutil/)

And the following VapourSynth libraries:

- [fmtconv](https://github.com/EleonoreMizo/fmtconv)
basicMLpy 1.0.9.1
A collection of simple machine learning algorithms
Requires Python: >=3.8

# basicMLpy <br />
basicMLpy is a package that implements simple machine learning algorithms. It currently contains eight modules that implement multiple machine learning techniques for supervised learning.<br />
### The basicMLpy.regression module contains the following functionalities:
* Linear Regression 
* Ridge Regression 
### The basicMLpy.classification module contains the following functionalities:
* Multiclass classification through the IRLS(Iteratively Reweighted Least Squares) algorithm
### The basicMLpy.nearest_neighbors module contains the following functionalities:
* An implementation of the K-Nearest Neighbors algorithm, that can fit both classification and regression problems
### The basicMLpy.model_selection module contains the following functionalities:
* A Cross-Validation algorithm for the functions presented by the basicMLpy package
### The basicMLpy.ensemble module contains the following functionalities:
* An implementation of the Random Forests algorithm for regression and classification
* An implementation of the AdaBoost algorithm for classification
* An implementation of the Gradient Boosting algorithm for regression
### The basicMLpy.decomposition module contains the following functionalities:
* An implementation of the SVD decomposition algorithm
* An implementation of the PCA algorithm
### The basicMLpy.loss_functions module contains the following functionalities:
* Multiple functions for error evaluation, e.g. MSE, MAE, exponential loss, etc.
### The basicMLpy.utils module contains the following functionalities:
* Useful functions utilized all throughout the other models.
## Installation <br />
To install basicMLpy run the following command: <br />
`pip install basicMLpy` <br />
## Package's site and documentation <br />
https://henrysilvacs.github.io/basicMLpy/
## Dependencies <br />
basicMLpy requires Python >= 3.8, Numpy >= 1.19, Scipy >= 1.5.2, scikit-learn >= 0.23. <br />
## On Github <br />
https://github.com/HenrySilvaCS/basicMLpy
## On Pypi <br />
https://pypi.org/project/basicMLpy/
## Some thoughts <br />
This is a work in progress project, so more functionalities will be added with time.
## Author <br />
Henrique Soares AssumpÃ§Ã£o e Silva
BL2-skingen 1.4.0
Utility to generate png files from Borderlands 2 in-game skin files.

# Borderlands 2 Skin generator
Utility to generate png files from Borderlands 2 in-game skin files.

# How to use:
## Installation via PyPi
This project is available on [PyPi](https://pypi.org/project/BL2_skingen/), however pre-compiled binaries only come for Python 3.7, Python 3.8 64bit.
If this meets your requirements, you can install it using `pip install BL2_skingen`, which will create an entry script called `skingen.exe`

## Manual installation
 * Download and extract the repo somewhere.
 * Make sure you have the following python packages installed: [Cython](https://pypi.org/project/Cython/), [Pillow](https://pypi.org/project/Pillow/) and [numpy](https://pypi.org/project/numpy/).
   * You can do so with: `pip install -r requirements.txt`
 * Navigate a terminal to the project's root folder.
 * Compile the .pyx files to binaries by running `py setup.py build_ext --inplace`.
## Usage
 * To extract the packets from Borderlands 2 use [UE Viewer/umodel](https://www.gildor.org/en/projects/umodel). The filepaths are hardcoded to locate the files the way UE Viewer extracts them.
  * If you installed the PyPi package, the entry script will be placed in `%PYTHONPATH%/Scripts` and should be accessible with `skingen` anywhere if the location is in `PATH`.
  * If you installed the script manually, navigate a terminal to the directory, the script can be run there with `py <Installationdir>/skingen.py`
 * For help on options, run the script without any arguments.
 Example : `skingen -in C:\Skinfiles\CD_Assasin_OrangeD_SF -out C:\Skinfiles\GEN -exc-head`

If a result did not conform to your expectations (and it's damn likely it won't), feel free to open up an issue.