semvec/pypi-packages huggingface dataset:
pylinlin-owenl131 0.0.1
A nice linear algebra library
Requires Python: >=3.8

# pylinlin

Nice linear algebra library in python

## Goals

- Test-driven development
- Profiling of performance
- Profiling of numerical stability

## TODOs

- LU factorization
- LU factorization with partial pivoting
- QR factorization with householder matrices
- Spectral decomposition
- SVD
mkdocs-helper 1.0.1
A simplified way of creating static markdown sites
Requires Python: >=3.6

# MkDocs Helper

> Make Websites The Easy Way

This package is a bridge between [MkDocs](https://mkdocs.org) and static site hosting, giving capabilities to host the project directly from the command line.

Go to our website, [mkdocs-helper.bd103.repl.co](https://mkdocs-helper.bd103.repl.co/).
Regx-Dev 1.0.2
This package is made especially for developers or web scrapers for saving time while scraping the information.
Requires Python: >=3.6
Keywords: re,phone-scrape,finding

# Regx-Dev #

This package is made to developers for saving them time while playing in scraping the information.

## Feature's of this project

* Find's any format of phone number irrespective of the country.
* Can find links from the string.
* Can extract the mail id from string.
* Removes any links from the string.
* Return's the index of the matching word present in the string as list.
* Can replace any word provided with new word.

# How to install and using it.

`pip install Regx-Dev`

` from Regx-Dev import Regxdev as rd `

* For finding phone number from the string you passed.

  ` result = rd.phone_finder("PASS THE STRNG CONTAINING NUMBEERS") ` returns list of phone numbers 

    Note: This method works only if numbers starts with pre-fix '+' and not containing parentheses.

* For finding links.

  ` result = rd.link_finder("PASS THE STRING") ` returns list of links in the string.

* For extracting mail id's.
  
  ` result = rd.mail_extractor("PASS THE STRING") `

* For removing links.

  ` result = rd.link_remover("PASS THE STRING") `

* This returns a list of tuple's that is the index of matched word you were searching for in the list.

  ` result = rd.word_finder("WORD TO BE FOUND", "PASS THE STRING") `
  
  Example: when you run the above line you get output similar to ` result = [(2, 5), (11, 14)] ` each item are tuples, which contains start index and end index of the word. you can use this tuple and by slicing the string you will have the word.

* To replace any word from the string irrespective of length.
  
  ` result = rd.word_replacer("WORD TO REPLACE", "WORD TO BE REPLACED FOR", "PASS THE STRING") `

  This returns new string with replaced charecter.
TOPSIS-shivansh-101803103 0.0.1
Python Package for TOPSIS

# TOPSIS Package in Python

Submitted by : Shivansh Kumar
Roll no : 101803103

UCS538

* * *
## What Is TOPSIS

TOPSIS is an acronym that stands for 'Technique of Order Preference Similarity to the Ideal Solution' and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those

* * *

## How To Use

The package TOPSIS-shivansh-101803103 can be run though the command line as follows:
```
>> pip install TOPSIS-shivansh-101803103
```
```
>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```
> **Note:**
> 
> *   Usages: 
>     **topsis data.csv "1,1,1,2" "+,+,-,+" result.csv**
> *   **Input File**:
>     
>     > *   Input file contain three or more columns.
>     > *   First column is the object/variable name (e.g. M1, M2, M3, M4...).
>     > *   From 2nd to last columns contain numeric values only.
> *   **Output File**:
>     
>     > *   Result file contains all the columns of input file and two additional columns having ***Topsis Score*** and ***Rank***
> *   The output is created in the form of csv file and stored and also it is displayed.
> *  The impacts given in the command line should be either â€˜+' or â€˜â€“â€™ depending if you want to maximise the column parameter or minimise it.

## Sample Input

Here is a sample set of data which can be used for the following package:

<table><thead><tr><th>Model</th><th>Correlation</th><th>R2</th><th>RMSE</th><th>Accuracy</th></tr></thead><tbody><tr><td>M1</td><td>0.79</td><td>0.62</td><td>1.25</td><td>60.89</td></tr><tr><td>M2</td><td>0.66</td><td>0.44</td><td>2.89</td><td>63.07</td></tr><tr><td>M3</td><td>0.56</td><td>0.31</td><td>1.57</td><td>62.87</td></tr><tr><td>M4</td><td>0.82</td><td>0.67</td><td>2.68</td><td>70.19</td></tr><tr><td>M5</td><td>0.75</td><td>0.56</td><td>1.3</td><td>80.39</td></tr></tbody></table>

## Output of this sample input

The output that will be generated from the following input data will be:

<table><thead><tr><th>Model</th><th align="right">Correlation</th><th align="center">R2</th><th>RMSE</th><th>Accuracy</th><th>Topsis Score</th><th>Rank</th></tr></thead><tbody><tr><td>M1</td><td align="right">0.79</td><td align="center">0.62</td><td>1.25</td><td>60.89</td><td>0.639133014134258</td><td>2.0</td></tr><tr><td>M2</td><td align="right">0.66</td><td align="center">0.44</td><td>2.89</td><td>63.07</td><td>0.212591829692779</td><td>5.0</td></tr><tr><td>M3</td><td align="right">0.56</td><td align="center">0.31</td><td>1.57</td><td>62.87</td><td>0.407845677613051</td><td>4.0</td></tr><tr><td>M4</td><td align="right">0.82</td><td align="center">0.67</td><td>2.68</td><td>70.19</td><td>0.519153239500747</td><td>3.0</td></tr><tr><td>M5</td><td align="right">0.75</td><td align="center">0.56</td><td>1.3</td><td>80.39</td><td>0.828266585193581</td><td>1.0</td></tr></tbody></table>

Here the ranks are given as rank 1 is the best solution according to the weights and impacts given and rank 5 is the worst solution.

* * *
kgpr-core 0.8.3
KubraGen Providers: Core
Requires Python: >=3.6

# KubraGen Providers: Core

[![PyPI version](https://img.shields.io/pypi/v/kgpr_core.svg)](https://pypi.python.org/pypi/kgpr_core/)
[![Supported Python versions](https://img.shields.io/pypi/pyversions/kgpr_core.svg)](https://pypi.python.org/pypi/kgpr_core/)

kgpr_core is a collection of core providers for [KubraGen](https://github.com/RangelReale/kubragen). 

[KubraGen](https://github.com/RangelReale/kubragen) is a Kubernetes YAML generator library that makes it possible to generate
configurations using the full power of the Python programming language.

* Website: https://github.com/RangelReale/kgpr_core
* Repository: https://github.com/RangelReale/kgpr_core.git
* Documentation: https://kgpr_core.readthedocs.org/
* PyPI: https://pypi.python.org/pypi/kgpr_core

### Provider

* Google GKE: ```kgpr_core.google.gke.provider.ProviderGoogleGKE```
* Amazon EKS: ```kgpr_core.amazon.eks.provider.ProviderAmazonEKS```
* DigitalOcean Kubernetes: ```kgpr_core.digitalocean.kubernetes.provider.ProviderDigitalOceanKubernetes```
* KIND: ```kgpr_core.kind.generic.provider.ProviderKINDGeneric```
* K3D: ```kgpr_core.k3d.generic.provider.ProviderK3DGeneric```

## Author

Rangel Reale (rangelreale@gmail.com)