semvec/pypi-packages huggingface dataset:
Topsis-101903239-Pushpak 1.0
Topsis Calculation
License: MIT
Keywords: Topsis,TopsisPackage

# Topsis

## A Multi-Criteria Decision Analysis Method

**Submitted By** - Pushpak Kamboj
**Roll No.** - 101903239
**Group** - 3COE9

It is a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) came in the 1980s as a multi-criteria-based decision-making method. TOPSIS chooses the alternative of shortest the Euclidean distance from the ideal solution and greatest distance from the negative ideal solution.

## Installation

This package requires pandas to be pre-installed and python to run.
Install the dependencies and devDependencies and start the server.
**To Install and run in cmd line:**

```sh
pip install Topsis_101903239_Pushpak
import Topsis_101903239_Pushpak
Topsis_Pushpak.Topsis(Input_File_Name.csv,"1,1,1,1 ...","+,-,+,- ...","resultfile.csv")
```
Topsis-Anvi-101903658 0.0.1
A Python package to find TOPSIS for multi-criteria decision analysis method
License: MIT
Keywords: topsis,UCS654,TIET

Topsis Value Calculator

CalcTopsis 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-101903658==0.0.1

In the commandline, you can write as -
    $ python <package_name> <path to 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
    $ python topsis.py data.csv "1,1,1,1" "+,+,-,+" output.csv

This will print all the output attribute values along with the Rank column, in a tabular format

License -> MIT

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

0.0.1 (27/02/2022)
------------------
- First Release
TOPSIS-Riya-102083026 1.0.4
A Python package to get best alternative available using TOPSIS method.
License: MIT

# TOPSIS-Riya-102083026

A python package which implements TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) which is a multi-criteria decision analysis method.

Steps involved in TOPSIS Algorithm:
    
    1. Creating an evaluation matrix (m x n).
    2. Normalization of the matrix so formed such that each metric for a given model lies between 0 and 1.
    3. Creating a weighted normalized matrix i.e. multiplying the metrics with the weights of respective model.
    4. Determining the ideal best and ideal worst alternatives for each criterion.
    5. Calculating the euclidean distance between the target alternative and ideal best and idealworst alternatives.
    6. Calculate the TOPSIS score i.e. similarity to the ideal worst alternative.
    7. Rank the models on the basis of the TOPSIS score thus obtained.s

# Usage

Running the following query on the command-line interface will help you to rank the models i.e. choose the best alternative available from a given set of models based on multiple, usually conflicting criteria.
  
    TOPSIS-Riya-102083026 <data_file_name> <weights> <impacts> <result_file_name>
    Example - TOPSIS-Riya-102083026 data.csv "1,1,1,2" "+,+,-,+" result.csv

Note - Make sure that you run the command in the folder you have stored your data files , else provide the complete path of the file.
Topsis-Tushar-402083001 1.0.1
A Python package for implementing TOPSIS
License: MIT

# TOPSIS-Python

Submitted By: **Tushar Verma**

***

## 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. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

The Topsis-Tushar-402083001 has a function topsis() that takes 4 parameters, i.e., inputFile, weights, impacts, and outputFile, and returns the resulting dataframe having **Topsis Score** and **Rank** as additional columns. 

Where,

* **inputFile:** Can be either csv file or pandas dataframe. Input file must contain three or more columns, where, First column is the object/variable name like M1, M2, M3, M4, etc. Also, from 2nd to last columns must contain numeric values only.
* **weights:** In the form of string having numerical values separated by commas.
* **impacts:** In the form of string having + or - values separated by commas. Here, + refers to positive impact, whereas, - refers to negative impact.
* **outputFile** (optional): csv file in which output of the function will be stored.