semvec/pypi-packages huggingface dataset:
Topsis-Ishtdeep-102003315 0.1.1
This package can be used to calculate the topsis score of multiple component data and rank them accordingly
License: MIT
Keywords: TOPSIS

# TOPSIS Package in Python

Submitted by: Ishtdeep Singh Sodhi

Roll no: 102003315

---

## TOPSIS

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981,with further developments by Yoon in 1987,and Hwang, Lai and Liu in 1993.TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS).

---

## How to use

The package Topsis-Ishtdeep-102003315 can be run though the command line as follows:

```
>> pip install Topsis-Ishtdeep-102003315
```

```
>>topsis data1.csv "1,1,1,1,1" "+,+,+,+,+" result.csv
```

<br>
The output file contains columns of input file along with two additional columns having Topsis_score and Rank.

---
clean-git-branches 1.0.1
Clean_Git_Branches is a command line utility for cleaning stale branches from your local git repository. The program will check for branches that have been deleted on the remote and prompt the user to confirm before deleting them locally.
License: MIT
Requires Python: >=3.10,<4.0

# Clean_Git_Branches

Clean_Git_Branches is a command line utility for cleaning stale branches from your local git repository. The program will check for branches that have been deleted on the remote and prompt the user to confirm before deleting them locally.

## Installation

To install Clean_Git_Branches, simply run the following command:

```pip install Clean_Git_Branches```


## Usage

To use Clean_Git_Branches, navigate to the root of your local git repository in the command line and run the following command:

```clean_git_branches```


The program will then check for stale branches and prompt the user to confirm before deleting them.

By default, the program will also protect certain branches from being deleted (master, main, dev) in case they exist on the remote repository. To change the list of protected branches, you can modify the "protected_branches" variable in the Clean_Git_Branches.py file.

```Please note that this program is only compatible with Git.```


## Contributing

If you would like to contribute to the development of Clean_Git_Branches, please feel free to fork the repository and submit pull requests with your changes.

## License

Clean_Git_Branches is released under the [MIT License](https://opensource.org/licenses/MIT).
sumtex 1.0.0
My package description
License: MIT
Requires Python: >=3.7
Keywords: GPT,latex,summarize

# sumtex

Summarizes a LaTeX file using the openai API and helps you with writing more concise texts.

## Usage
Install `sumtex` via pip
```
pip install sumtex
```

Add you openai API key as environment variable (you can generate an API key [here](https://beta.openai.com/account/api-keys))
```
export OPENAI_API_KEY=sk-er34*****
```

Run `sumtex` on a LaTeX file
```
sumtex my_file.tex
```
TOPSIS-Manjot 1.0.0
Calculates Topsis Score and Rank them accordingly
License: MIT

TOPSIS
Submitted By: Manjot Singh Kandhari - 102017129.

Type: Package.

Title: TOPSIS method for multiple-criteria decision making (MCDM).

Version: 1.0.0.

Date: 22/01/23.

Author: Manjot Singh Kandhari.

Maintainer: Manjot Singh Kandhari smanjot444@gmail.com.

Description: Evaluation of alternatives based on multiple criteria using TOPSIS method.
Topsis-Ekam-102003322 1.2.1
This package is implimentation of multi-criteria decision analysis using topsis
License: MIT
Requires Python: >=3.6
Keywords: topsis

## TOPSIS Package discription

TOPSIS stands for Technique for Oder Preference by Similarity to Ideal Solution. It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. In this Python package Vector Normalization has been implemented.

This package has been created based on class assignment of course UCS654. Ekam COE-7 102003322

## In Command Prompt

> 102003322.py 102003322-data.csv "1,1,1,2" "+,+,-,+" 102003322-result.csv

## Installation

You can install this package using following command
pip install Topsis-Ekam-102003333

## Example

#### sample.csv

A csv file showing data for different mobile handsets having varying features.

| Model | Storage space(in gb) | Camera(in MP) | Price(in $) | Looks(out of 5) |
| :---: | :------------------: | :-----------: | :---------: | :-------------: |
| Model |     Correlation      |      R2       |    RMSE     |    Accuracy     |
|  ---  |         ---          |      ---      |     ---     |       ---       |
|  M1   |         0.79         |     0.62      |    1.25     |      60.89      |
|  M2   |         0.66         |     0.44      |    2.89     |      63.07      |
|  M3   |         0.56         |     0.31      |    1.57     |      62.87      |
|  M4   |         0.82         |     0.67      |    2.68     |      70.19      |
|  M5   |         0.75         |     0.56      |     1.3     |      80.39      |

weights vector = [ 1,1,1,1 ]

impacts vector = [ + , + , - , + ]

### input:

```python
topsis sample.csv "1,1,1,1" "+,+,-,+"
```

### output:

```
      TOPSIS RESULTS
-----------------------------

|Model|Correlation|R2|RMSE|Accuracy|TOPSIS Score|Rank|
|---|---|---|---|---|---|---|
|M1|0.79|0.62|1.25|60.89|0.7722097345612788|2.0|
|M2|0.66|0.44|2.89|63.07|0.22559875426413367|5.0|
|M3|0.56|0.31|1.57|62.87|0.43889731728018605|4.0|
|M4|0.82|0.67|2.68|70.19|0.5238778712729114|3.0|
|M5|0.75|0.56|1.3|80.39|0.8113887082429979|1.0|

```

## License

https://github.com/Ekam3000/Python-Package-TOPSIS/blob/main/LICENSE
Topsis-Meghna-102003663 0.0.0
Topsis Implementation
License: MIT

# Topsis-meghna-102003663
This package is implementation of multi-criteria decision analysis using topsis.
This package will accept three arguments during file execution:
1. dataset.csv //file which contains the models and parameters
2. string of weights separated by commas(,)
3. string of requirements (+/-) separated by commas(,)
// important
install pandas,sys,operator and math libraries before installing this
//
You can install this package using following command
pip install Topsis_Meghna_102003663
topsispragati102017122 0.0.1
This is a Python Package implementing TOPSIS
License: MIT
Keywords: topsis,mcda,TIET

## Topsis Python Package

TopsisPy is a Python Package implementing Topsis method used for multi-criteria decision analysis. Topsis stands for 'Technique for Order of Preference by Similarity to Ideal Solution'.

TopsisPy intends to make the process of TOPSIS simple in python. Key features of the package are -

- Easy to use 
- Numpy based 
- Ideal for Students

## Installation

Install the Package using the command -

bash
   $ pip install topsispragati102017122

    

You may use the Package via commandline as
```bash
   $ topsis [path of csv] [weights as string] [impacts as string] [path of result csv as string]
Topsis-102017152-Shabroz 1.0.4
A package -> Calculates Topsis Score and Rank them accordingly
License: MIT
Keywords: python package ShabrozKaur

#TOPSIS

Submitted By: Shabroz Kaur - 102017152.

Type: Package.

Title: TOPSIS method for multiple-criteria decision making (MCDM).

Version: 1.0.4.

Date: 2023-01-22.

Author: Shabroz Kaur.

Maintainer: Shabroz Kaur skaur2_be20@thapar.edu.

Description: Evaluation of alternatives based on multiple criteria using TOPSIS method..
Topsis-Sailish-102003768 1.0.0
A TOPSIS calculator
License: MIT
Keywords: TOPSIS

## Project description

This is a Python package implementing Topsis method sed for multi-criteria decision analysis. Topsis stands for Technique for Order of Preference by Similarity to Ideal Solution

Just provide your input attributes and it will give you the results

## Installation
$ pip install Topsis-Sailish-102003768

In the commandline, you can write as - $ topsis <input_data_file_name> <weights as strings> <impacts as strings> <result_file_name>

E.g for input data file as data.csv, command will be like $ topsis data.csv "1,1,1,1,1" "+,+,+,+,+" output.csv

This will print all the output attribute values along with the Rank column, in a tabular format in a csv file with given name

License -> MIT
Topsis-Kushagar-102003002 0.0.2
It is a package for performing TOPSIS analysis
License: MIT

## Topsis-Kushagar-102003002

# TOPSIS

Submitted By: **Kushagar Bansal - 102003002**.

Type: **Package**.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **0.0.1**.

Date: **2023-01-20**.

Author: **Kushagar Bansal**.

Maintainer: **Kushagar Bansal <kbansal_be20@thapar.edu>**.

Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**.

---

## What is TOPSIS?

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal **S**olution
(TOPSIS) originated in the 1980s as a multi-criteria decision making method.
TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution,
and greatest distance from the negative-ideal solution.

<br>

## How to install this package:

```
>> pip install Topsis-Kushagar-102003002
```

### In Command Prompt

```
>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```

## Input file (data.csv)

The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

| Model | Correlation | R<sup>2</sup> | RMSE | Accuracy |
| ----- | ----------- | ------------- | ---- | -------- |
| M1    | 0.79        | 0.62          | 1.25 | 60.89    |
| M2    | 0.66        | 0.44          | 2.89 | 63.07    |
| M3    | 0.56        | 0.31          | 1.57 | 62.87    |
| M4    | 0.82        | 0.67          | 2.68 | 70.19    |
| M5    | 0.75        | 0.56          | 1.3  | 80.39    |

Weights (`weights`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.

<br>

## Output file (result.csv)

| Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | Topsis_score | Rank |
| ----- | ----------- | ------------- | ---- | -------- | ------------ | ---- |
| M1    | 0.79        | 0.62          | 1.25 | 60.89    | 0.7722       | 2    |
| M2    | 0.66        | 0.44          | 2.89 | 63.07    | 0.2255       | 5    |
| M3    | 0.56        | 0.31          | 1.57 | 62.87    | 0.4388       | 4    |
| M4    | 0.82        | 0.67          | 2.68 | 70.19    | 0.5238       | 3    |
| M5    | 0.75        | 0.56          | 1.3  | 80.39    | 0.8113       | 1    |

<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
Topsis-Reena-102017169 1.0.4
A package -> Calculates Topsis Score and Rank them accordingly
License: MIT
Keywords: python package ReenaArora

#TOPSIS

Submitted By: Reena Arora - 102017169.

Type: Package.

Title: TOPSIS method for multiple-criteria decision making (MCDM).

Version: 1.0.4.

Date: 2023-01-22.

Author: Reena Arora.

Maintainer: Reena Arora rarora_be20@thapar.edu.

Description: Evaluation of alternatives based on multiple criteria using TOPSIS method..
Topsis-Abhishek-102003364 0.2
This package can be used for implementation of Multiple criteria decision making using Topsis Algorithm. This is a python library for dealing with Multi-Criteria Decision Making (MCDM) problems by using techniques for order of preference by similarity to ideal solution (TOPSIS).
License: MIT
Keywords: topsis,multiple criteria decision

# TOPSIS


Submitted By: **Abhishek Gandhi 102003364**

***

## What is TOPSIS?

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal **S**olution 
(TOPSIS) originated in the 1980s as a multi-criteria decision making method.
TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, 
and greatest distance from the negative-ideal solution. 

<br>

## How to install this package:
```
>> pip install topsis-abhishek-102003364
```


### In Command Prompt
```
>> python <program.py> <InputDataFile> <Weights> <Impacts> <ResultFileName>
>> python 102003364.py data.csv "1,1,1,1" "+,+,-,+" result.csv
```

## Input file (data.csv)

The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

Model | Correlation | R<sup>2</sup> | RMSE | Accuracy
------|------------ | ------------  | ---- | ------------
M1 |	0.79        | 0.62	        | 1.25 | 60.89
M2 |    0.66        | 0.44	        | 2.89 | 63.07
M3 |	0.56        | 0.31	        | 1.57 | 62.87
M4 |	0.82        | 0.67	        | 2.68 | 70.19
M5 |	0.75        | 0.56	        | 1.3  | 80.39

Weights (`weights`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.

<br>

## Output file (result.csv)


Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | Topsis_score | Rank
------|------------ | --------------| ---- | -------- | ------------ | ------ 
M1 |	0.79        | 0.62	        | 1.25 | 60.89    | 0.7722       | 2
M2 |    0.66        | 0.44	        | 2.89 | 63.07    | 0.2255       | 5
M3 |	0.56        | 0.31	        | 1.57 | 62.87    | 0.4388       | 4
M4 |	0.82        | 0.67	        | 2.68 | 70.19    | 0.5238       | 3
M5 |	0.75        | 0.56	        | 1.3  | 80.39    | 0.8113       | 1


<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**