semvec/pypi-packages huggingface dataset:
apist 2.10
apist, The best framework for api automation testing

### Apist框架介绍

apist致力于接口自动化，可以让测试员在不懂代码的情况下做接口自动化测试，只需要会excel以及遵循相应的规则即可。

### 使用规范
 
 - 第一个sheet是你的所测系统的接口表格，里面放的是接口的各种信息。

 - 第二个sheet名为header，里面放cookie以及user-agent信息，用于请求的授权操作。

 - 第三个sheet名为email，里面放的是个人邮箱的用户名密码等信息，用于接收测试报告。

 - 详见excel模板中的示例。

#### 启动

 - exe文件启动：api -f D://api.xls。

 - 项目中启动： 
   
    from apist.apist import Api  
    Api('api.xls', is_email=False)
ICPOptimize 2.4
Python 3 Implementation of ICP and ICPRE
Keywords: optimization,optimizer,linear,ICP,ICPRE

# ICPOptimize
The Iterative Constrained Pathways Optimizer

ICP is a constrained linear model optimizer built with a focus on memory efficiency, flexibility, and solution interpretability.

## Description

This repository contains implementations of the Iterative Constrained Pathways (ICP) optimization method, the ICP Rule Ensemble (ICPRE), linear classifier, regressor, and other methods. Currently, hinge, least-squares, and absolute-value loss modes are supported, with support for other loss functions planned. Coefficients can be constrained by sign or by arbitrary intervals. L1 & L2 norm constraints are also supported.

Further discussion about and motivation for the methods can be found on my blog:

[nicholastsmith.wordpress.com/2021/05/18/the-iterative-constrained-pathways-optimizer/](https://nicholastsmith.wordpress.com/2021/05/18/the-iterative-constrained-pathways-optimizer/)

## Features

- Linear Classification using Hinge Loss
- Regression Support using L1 and L2 Penalties
- Arbitrary Interval Constraints
- L1 and L2 Coefficient Norm Constraints
- Useful Default Settings
- Support for DataFrames and Sparse Matrices

## Installation

Install via PyPi:

```pip install ICPOptimize```

PyPi Project:

[https://pypi.org/project/ICPOptimize/](https://pypi.org/project/ICPOptimize/)

## Examples

### Rule Ensemble Classifier
```python
from ICP.Models import ICPRuleEnsemble

...

IRE = ICPRuleEnsemble().fit(A[trn], Y[trn])
YP  = IRE.predict_proba(A)
```

### Linear Model
```python
from ICP.Models import ICPLinearRegressor

...
# Fit linear regressor with absolute loss and L_1 norm <= 10
ILR = ICPLinearRegressor(p='l1', L1=10.0).fit(A[trn], Y[trn])
YP  = ILR.predict(A)
```

Further examples are available on the ICPExamples GitHub page:

[https://github.com/nicholastoddsmith/ICPExamples](https://github.com/nicholastoddsmith/ICPExamples)
file-encrypter-decrypter 0.0.3
A package for Encrypting & Decrypting Files
Requires Python: >=3.6
Keywords: encryption decryption crypto Encryption secret cryptography password

[![Downloads](https://static.pepy.tech/personalized-badge/file-encrypter-decrypter?period=total&units=abbreviation&left_color=grey&right_color=brightgreen&left_text=Downloads)](https://pepy.tech/project/file-encrypter-decrypter) ![PyPI](https://img.shields.io/pypi/v/file-encrypter-decrypter?style=flat-square)

# file_encrypter_decrypter

##### A simple package to Encrypt & Decrypt files or Entire Folder contents

## Installation

```bash
pip install file-encrypter-decrypter
```

## Usage/Examples

##### This package can be used to encrypt/decrypt text,images,pdf,word,csv etc
<br/>

```
from file_encrypter_decrypter import File_Encrypter,File_Decrypter,Folder_Encrypter,Folder_Decrypter

File_Encrypter("./dummy.text")
File_Decrypter("./dummy.text")
Folder_Encrypter("/folderpath")
Folder_Decrypter("/folderpath")


The file has been Encrypted Successfully

Loading Encrypted file data
Decrypting the file
File data has been successfully decrypted
All data related to encryption  purged
........................
```

## Authors

[@LpCodes](https://github.com/LpCodes)

## License

[MIT](https://choosealicense.com/licenses/mit/)

## Contributing

Contributions are always welcome!