Metadata-Version: 2.2
Name: TOPSIS_Prerit_102217030
Version: 1.0.2
Summary: A Python package for performing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis.
Author: Prerit Bhagat
Author-email: preritbhagat.pb@gmail.com
License: MIT
Project-URL: Documentation, https://github.com/Prerit-Bhagat/PYPI_Package#readme
Project-URL: Source, https://github.com/Prerit-Bhagat/PYPI_Package
Keywords: topsis mcdm multi-criteria decision-making ranking analysis
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: pandas>=1.0.0
Requires-Dist: numpy>=1.19.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: keywords
Dynamic: license
Dynamic: project-url
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Dynamic: requires-python
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# TOPSIS Implementation

This package provides a Python implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for multi-criteria decision analysis.

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## Features
- **Ease of Use**: Simple and clear implementation of the TOPSIS algorithm.
- **Weighted Decision Making**: Allows users to define weights for each criterion.
- **Impact Analysis**: Accounts for both positive and negative impacts of criteria.
- **Command-Line Interface**: Execute TOPSIS directly from the terminal with input and output files.

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## Installation

To install the package, use:

pip install TOPSIS_Prerit_102217030


## Usage
Run the TOPSIS analysis using the command-line interface:

topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>

Example
Suppose you have a CSV file data.csv containing a decision matrix where:

The first column is the identifier for alternatives.
The subsequent columns contain numeric data for each criterion.

If you want to apply TOPSIS with weights [1, 1, 1, 2] and impacts [+, +, -, +], use:
python topsis data.csv "1,1,1,2" "+,+,-,+" result.csv

This will generate a result file result.csv with the calculated TOPSIS scores and rankings.
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