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.
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.
Installation
To install the package, use:
pip install TOPSIS_Prerit_102217030
Usage
Run the TOPSIS analysis using the command-line interface:
topsis
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.
Metadata
Release files for Topsis-Prerit-102217030 1.0.8
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| topsis_prerit_102217030-1.0.8.tar.gz | 5.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| Topsis_Prerit_102217030-1.0.8-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 11.5 kB
Release files / topsis_prerit_102217030-1.0.8.tar.gz
| Download URL | topsis_prerit_102217030-1.0.8.tar.gz |
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| Size | 5.5 kB |
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Release files / Topsis_Prerit_102217030-1.0.8-py3-none-any.whl
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| Size | 6.0 kB |
| Tags | Python 3 |
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