Topsis_UditGupta_102203835is a Python package for implementing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It is a popular multi-criteria decision-making method used to rank alternatives based on their relative closeness to the ideal solution.
You can install the package using pip:
pip install Topsis_UditGupta_102203835The input CSV file must follow this structure:
- The first column should contain the names of the alternatives (e.g., products, models, or options).
- Subsequent columns should contain the numerical values of the criteria for each alternative.
- The first row should provide headers for all columns.
Suppose you have an input CSV file named data.csv with the following content:
| Model | Price | Battery Life | Performance | Portability |
|---|---|---|---|---|
| m1 | 300 | 10 | 8 | 6 |
| m2 | 250 | 8 | 7 | 9 |
| m3 | 400 | 9 | 9 | 5 |
You want to apply the TOPSIS method with the following parameters:
- Weights for the criteria:
Price(0.5),Battery Life(0.3),Performance(0.2) - Impacts for the criteria:
Price(-),Battery Life(+),Performance(+)
The command would be:
topsis data.csv "0.5,0.3,0.2" "-,+,+" results.csv
### Command-line Usage
After installation, you can use the `topsis` command in the terminal:
```bash
topsis <input_file> <weights> <impacts> <output_file><input_file>: Path to the input CSV file.<weights>: Comma-separated string of weights for the criteria.<impacts>: Comma-separated string of '+' or '-' indicating the desirability of the criteria.<output_file>: Path to the output CSV file to save the results.
topsis data.csv "0.5,0.3,0.2" "+,-,+" results.csvThis command will process the data.csv file using the specified weights and impacts and output the results to results.csv.
- Ensure the input file contains at least three columns: one for alternatives and at least two for criteria.
- All criteria values should be numerical.
- The number of weights and impacts should match the number of criteria columns.
- Impacts must only include + (beneficial) or - (non-beneficial).
This project is licensed under the MIT License. See the LICENSE file for details.