semvec/pypi-packages huggingface dataset:
robustocs 0.2.1
Tools for solving robust optimal contribution selection problems
Requires Python: >=3.10

# RobustOCS

[![PyPi](https://img.shields.io/pypi/v/robustocs.svg)](https://pypi.python.org/pypi/robustocs) [![Check Build](https://github.com/Foggalong/RobustOCS/actions/workflows/check-build.yml/badge.svg?branch=main)](https://github.com/Foggalong/RobustOCS/actions/workflows/check-build.yml)

Tools for solving robust optimal contribution selection problems in Python. All code and examples in RobustOCS are fully and freely available for all use under the MIT License.

## Installation

The latest release can be installed from the [PyPI repository](https://pypi.org/project/robustocs/) using

```bash
pip install robustocs
```

Alternatively, it use the latest (potentially unstable) version from GitHub, use

```bash
git clone https://github.com/Foggalong/RobustOCS.git
pip install RobustOCS/
```

Either way, the package depends on Python 3.10+, using [NumPy](https://pypi.org/project/numpy) for linear algebra and [SciPy](https://scipy.org) for sparse matrix objects. As a solver it can either use [Gurobi](https://www.gurobi.com) (commercial) via [gurobipy](https://pypi.org/project/gurobipy) or [HiGHS](https://highs.dev) (free software) via [highspy](https://pypi.org/project/highspy).

## Quick-start

Suppose we have a breeding cohort whose relationships are modelled some matrix $\Sigma$, and whose expected breeding values have mean $\bar{\boldsymbol\mu}$ and variance $\Omega$. If these are [saved in files](https://github.com/Foggalong/RobustOCS/wiki/File-Formats), finding the robust optimal contribution selection is as simple as opening Python and running

```python
import robustocs as rocs

selection, objective, merit, coancestry = rocs.solveROCS(
    sigma_filename="cohort-relationships.txt",
    mu_filename="breeding-means.txt",
    omega_filename="breeding-variances.txt",
    sex_filename="cohort-sexes.txt",
    method='robust', lam=0.5, kappa=1
)
```

where `selection` will be an array of optimal selections, `objective` is a score of the selection, `merit` the expected genetic merit, and `coancestry` the group co-ancestry.

## Documentation

The [GitHub wiki] includes documentation which explains the usage and parameters in more detail, full rundowns of the key functions (including the quick-start's [`solveROCS`]), all alongside some worked examples using the more granular solver functions (the data for which is in [`examples/`](examples/)). This includes a realistic simulated example from [Gregor Gorjanc] and [Ivan Pocrnić].

[`solveROCS`]: https://github.com/Foggalong/RobustOCS/wiki/solveROCS
[GitHub wiki]: https://github.com/Foggalong/RobustOCS/wiki
[Gregor Gorjanc]: https://www.ed.ac.uk/profile/gregor-gorjanc
[Ivan Pocrnić]: https://www.ed.ac.uk/profile/ivan-pocrnic
AVR 0.3.5
AVR is a voice anti-spoofing system that uses deep learning models to detect spoofed audio files.
Requires Python: >=3.6

# AI Voice Recognize (AVR) 🎙️
Usage Instructions

    download the .tar.gz from latest release release OR use pip install AVR
    in your project use import AVR with your imports
    in your code you can now call AVR.query_function(file_path). a function which returns a boolean value
    make sure to use flush() after you finish your use of the AI models for the runtime. 


For inquiries or collaboration:

    Guy Ben Ari: gbenari2@gmail.com
    Ynon Friedman: ynonfridman@gmai.com
poseidon-py 0.1.5
Python implementation of Poseidon hash
Requires Python: >=3.8

poseidon-py
===========

Poseidon hash Python library with Hades permutation `implemented in C <https://github.com/CryptoExperts/poseidon>`_.

Currently, the parameters for poseidon hash are set to define the permutation used in Starknet.

They can be found in *poseidon_params.txt*.

|

Changelog
=========

Version 0.1.5 (2024-08-08)
--------------------------

* Update dependencies

Version 0.1.4 (2024-02-12)
--------------------------

* Add support for Windows (Python 3.12)

Version 0.1.3 (2023-04-27)
--------------------------

* Add support for Windows
* Add support for Python >=3.8

Version 0.1.2 (2023-04-13)
--------------------------

* Remove make dependency

Version 0.1.1 (2023-04-07)
--------------------------

* Initial release
pyhula 1.1.4
Hula package
Requires Python: >=3.6

# pyhula
Hula python编程依赖包。  
A Python pack used by Hula.

## 安装 / Installing
在终端执行如下指令以安装pyhula。   
Input the following code in powershell(cmd.exe) to install pyhula. 
```
pip install pyhula
```

## 激活 / Activating
pyhula使用前需要使用激活工具进行激活。请联系作者，获得对应的激活工具与相关文档。  
pyhula needs to be activated before it can be used. Contact with the author to get the activator and documents.

## 查看版本 / Checking version
* 在控制终端执行pip list 进行查看  
Input "pip list" in powershell(cmd.exe) to get pyhula's verison

* 在程序中执行  
Using the following code.
```
import pyhula
ver = pyhula.get_version()
print(ver)
```

## 使用 / Usage
使用以下代码获取一个UserApi实例后，可以通过UserApi所提供的接口对hula无人机进行控制。接口说明请查看doc/html/中文/index.html文件。  
Use the following codes to create a userApi instance. Its interfaces can be used to control hula plane. Go to doc/html/English/index.html to see the interface specification.
```
import pyhula
api = pyhula.UserApi()


## v 1.0.1

1. 初始版本  
Initial version
face-similarity 0.0.2
A package to compare face similarity from documents and original image.
Requires Python: >=3.6

# Person Image Verification Package

## Overview
The Person Image Verification Package allows users to upload a document containing an image of a person and an original image of a person. It then determines whether the person in both images is the same or different. This package leverages advanced image processing and machine learning techniques to provide accurate results.
A package to compare face similarity using HOG features and cosine similarity.

## Features
- Image Upload: Upload any document containing an image of a person.
- Original Image Comparison: Upload an original image of the person for comparison.
- Verification: Determine whether the person in both images is the same or different.
- Easy Integration: Simple and straightforward integration into any project.

## Installation
`pip install Face_Similarity`

## Example Usage
```python
from face_similarity.similarity import compare_faces

image_path_1 = 'path/to/Document_image.jpg'
image_path_2 = 'path/to/original_image.jpg'

result = compare_faces(image_path_1, image_path_2)
print(result)