Metadata-Version: 2.1
Name: topsis_debolinaghosh_102103365
Version: 0.2.0
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: numpy

# TOPSIS Package

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a Python package that provides an implementation of the TOPSIS method for multi-criteria decision analysis. This package allows users to evaluate and rank alternative solutions based on a set of criteria.

## Table of Contents
- [Introduction](#introduction)
- [Installation](#installation)
- [Usage](#usage)
- [Command Line Interface](#command-line-interface)
- [Examples](#examples)
- [Contributing](#contributing)
- [License](#license)

## Introduction

Multi-criteria decision analysis (MCDA) is a method for making decisions when there are multiple criteria to consider. TOPSIS is one of the widely used MCDA techniques that helps in ranking alternatives based on their proximity to the ideal solution and their distance to the anti-ideal solution.

This package provides a Python implementation of the TOPSIS method, both as a Python module and a command line tool.

## Installation

You can install TOPSIS using pip:

```bash
pip install topsis-package


To use TOPSIS in your Python code, import the topsis module:

python
Copy code
from topsis_package import topsis

# Your decision matrix
data = {...}

# Your weights and impacts
weights = [...]
impacts = [...]

# Run TOPSIS
result_df = topsis(data, weights, impacts)

# Display the results
print(result_df)

```

## Command Line

TOPSIS also provides a command line interface for convenient usage:

```bash
topsis input.csv <weights> <impacts> result.csv

```

- input.csv: Path to the input CSV file containing the decision matrix.
- weights: Weights for each criterion separated by commas.
- impacts: Impacts for each criterion (+ or -) separated by commas.
- result.csv: Path to the output CSV file where the results will be saved.


## License

This project is licensed under the MIT License - see the LICENSE file for details.


