semvec/pypi-packages huggingface dataset:
TOPSIS-Nipun-101803531 0.01
A package for python implemetation of TOPSIS method for multiple criteria decision making
License: MIT
Keywords: SOME,MEANINGFULL,KEYWORDS

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
topsis-jaskaran-101803660 1.0.0
A topsis package.
License: MIT
Keywords: topsis

This is a topsis program using python and is a cli based program. Use python topsis-jaskaran-101803660 <InputDataFile> <Weights> <Impacts> <ResultFileName>

Change Log
==========


0.0.1(13/11/20)
----------------
- First Release

0.0.2(13/11/20)
----------------
- Bug Fixes

0.0.3(13/11/20)
----------------
- More Bug Fixes

1.0.0(13/11/20)
----------------
- Stable Build
TOPSIS-Anubhav-101803051 1.0.1
Compute Topsis Scores/Ranks of a given csv file
License: MIT

# TOPSIS-Anubhav-101803051

## What is TOPSIS
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method.<br> TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.

## Installation
```pip install TOPSIS-Anubhav-101803051```<br>
OR<br>
```pip3 install TOPSIS-Anubhav-101803051```

## Input csv format
Input file contain three or more columns<br>
First column is the object/variable name <br>
From 2nd to last columns contain numeric values only

## How to use it
Command Prompt<br>
```topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>```<br>
Example:<br>
```topsis inputfile.csv “1,1,1,2” “+,+,-,+” result.csv```<br><br>
<i>Note: The weights and impacts should be ',' seperated, input file should be in pwd.</i> 

## Sample input data
|Model|Corr|Rseq|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   |
## Sample output data
|Model|Corr|Rseq|RMSE |Accuracy|Topsis Score       |Rank|
|-----|----|----|-----|--------|-------------------|----|
|M1   |0.79|0.62|1.25 |60.89   |0.7731301458119156 |2   |
|M2   |0.66|0.44|2.89 |63.07   |0.22667595732024362|5   |
|M3   |0.56|0.31|1.57 |62.87   |0.4389494866695491 |4   |
|M4   |0.82|0.67|2.68 |70.19   |0.5237626971836845 |3   |
|M5   |0.75|0.56|1.3  |80.39   |0.8128626132980138 |1   |

## License
© 2020 Anubhav Sharma

This repository is licensed under the MIT license. See LICENSE for details.
TOPSIS-Sunidhi-101983052 2.2.0
A python package to implement TOPSIS on a given dataset
License: MIT

# topsis_pckg
It is a Python package to find out the best value among the different data using the mathematical calculations.
## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install this package.

```bash
pip install TOPSIS-Sunidhi-101983052
```

## About
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

To get started quickly, just use the following:

```bash
topsis filename weights impacts result
```
make ensure the weights and impacts should be in "" 

eg: "1,1,1,1" and "+,-,+,-"

## Pre-requisite
The data should be enclosed in the csv file. There must be more than 2 columns


## Result
the output(outputfilename)  is saved in the project folder with extra 2 columns with topsis score and rank.