Topsis Package
Project description
TOPSIS Package
This package implements the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a multi-criteria decision analysis (MCDA) method used for ranking and selecting a set of alternatives based on their distance to an ideal solution.
Features
- TOPSIS Implementation: Rank a set of alternatives based on multiple criteria.
- Ideal and Negative Ideal Solution Calculation: Automatically calculate the positive and negative ideal solutions.
- Normalization: Normalize the decision matrix to make the comparison meaningful.
- Weighted Sum Model: Support for weighted decision matrix for each criterion.
- Distance Calculation: Use Euclidean distance to calculate closeness to ideal solutions.
Installation
You can install the package via pip or manually by cloning the repository.
pip install topsis
Alternatively, you can clone the repository and install the package manually:
git clone https://github.com/yourusername/topsis.git
cd topsis
python setup.py install
Input Format
- Decision Matrix: A list of lists where each inner list represents an alternative, and each element within that list represents the value for a particular criterion.
- Weights: A list where each value represents the weight for a corresponding criterion. The weights should sum up to 1 or should be proportionally normalized.
- Impacts: A list where each value represents the impact for a corresponding criterion. Use
1for beneficial criteria (higher values are better) and-1for non-beneficial criteria (lower values are better).
Output Format
The output will be a ranking of the alternatives based on their closeness to the ideal solution, starting with the best.
Requirements
- Python 3.x
- Pandas
- NumPy
License
This project is licensed under the MIT License. See the LICENSE file for details.
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