# TOPSIS Package
This repository contains a Python implementation of the **TOPSIS** (Technique for Order of Preference by Similarity to Ideal Solution) method, a multi-criteria decision-making technique. The package supports command-line execution and enables users to calculate TOPSIS scores and rankings based on input data.
---
## **Features**
- Calculates TOPSIS scores and rankings for multi-criteria decision-making.
- Handles both positive and negative impacts of criteria.
- Provides results in a CSV file with scores and rankings.
- Fully customizable through weights and impacts specified as command-line arguments.
---
## **Installation**
To install the package from PyPI:
```bash
pip install topsis-Prince-3619
```
---
## **Usage**
### **Command-Line Interface (CLI)**
Run the TOPSIS program directly from the command line:
```bash
python3 -m Topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```
### **Arguments**
1. `<InputDataFile>`: Path to the input CSV file.
- The first column must contain object names (e.g., M1, M2, M3).
- The remaining columns must contain numeric values for criteria.
2. `<Weights>`: Comma-separated weights for the criteria (e.g., `"1,1,1,2"`).
3. `<Impacts>`: Comma-separated impacts for the criteria (`+` for positive impact, `-` for negative impact).
4. `<ResultFileName>`: Name of the output CSV file where results will be saved.
---
## **Example**
### **Input Data**
Input file (`102203619-data.csv`):
| Model | Criterion 1 | Criterion 2 | Criterion 3 | Criterion 4 |
|-------|-------------|-------------|-------------|-------------|
| M1 | 250 | 16 | 12 | 5 |
| M2 | 200 | 18 | 8 | 3 |
| M3 | 300 | 14 | 16 | 10 |
| M4 | 275 | 17 | 10 | 8 |
### **Command**
```bash
topsis 102203619-data.csv "1,1,1,2" "+,+,-,+" result.csv
```
### **Output File**
Output file (`result.csv`):
| Model | Criterion 1 | Criterion 2 | Criterion 3 | Criterion 4 | Topsis Score | Rank |
|-------|-------------|-------------|-------------|-------------|--------------|------|
| M1 | 250 | 16 | 12 | 5 | 0.5346 | 2 |
| M2 | 200 | 18 | 8 | 3 | 0.3084 | 4 |
| M3 | 300 | 14 | 16 | 10 | 0.6912 | 1 |
| M4 | 275 | 17 | 10 | 8 | 0.5340 | 3 |
---
## **How It Works**
1. **Normalization**:
The decision matrix is normalized using vector normalization:
\[
R_{ij} = \frac{a_{ij}}{\sqrt{\sum_{k=1}^{m} a_{kj}^2}}
\]
2. **Weighted Normalized Matrix**:
Each criterion is weighted according to user-defined weights:
\[
V_{ij} = R_{ij} \cdot w_j
\]
3. **Identify Ideal Solutions**:
- **Positive Ideal Solution (PIS)**: Maximum values for positive impacts and minimum values for negative impacts.
- **Negative Ideal Solution (NIS)**: Minimum values for positive impacts and maximum values for negative impacts.
4. **Calculate Separation Measures**:
- Separation from PIS (\( S_i^+ \)) and NIS (\( S_i^- \)) are computed using Euclidean distance.
5. **Calculate TOPSIS Scores**:
- The score is calculated as:
\[
C_i = \frac{S_i^-}{S_i^+ + S_i^-}
\]
6. **Rank Alternatives**:
- Alternatives are ranked based on their scores, with higher scores indicating better performance.
---
## **Development**
### **Project Structure**
```
topsis-package/
├── topsis/
│ ├── __init__.py
│ ├── __main__.py
├── README.md
├── LICENSE
├── setup.py
├── pyproject.toml
└── MANIFEST.in
```
### **Dependencies**
- `numpy`
- `pandas`
### **Setup for Development**
1. Clone the repository:
```bash
git clone https://github.com/Prince-05/TOPSIS
```
2. Navigate to the project directory:
```bash
cd Topsis-Prince-3619
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
---
## **Testing**
Run the tests to ensure the package works correctly:
```bash
pytest
```
---
## **License**
This project is licensed under the MIT License. See the `LICENSE` file for details.
---
## **Contributing**
Contributions are welcome! Please follow these steps:
1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Submit a pull request with a detailed explanation of your changes.
---
## **Contact**
For any questions or issues, please contact:
- **Name**: Prince
- **Email**: pprince_be22@thapar.edu
- **GitHub**: [Prince-05](https://github.com/Prince-05)
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"description": "# TOPSIS Package\n\nThis repository contains a Python implementation of the **TOPSIS** (Technique for Order of Preference by Similarity to Ideal Solution) method, a multi-criteria decision-making technique. The package supports command-line execution and enables users to calculate TOPSIS scores and rankings based on input data.\n\n---\n\n## **Features**\n- Calculates TOPSIS scores and rankings for multi-criteria decision-making.\n- Handles both positive and negative impacts of criteria.\n- Provides results in a CSV file with scores and rankings.\n- Fully customizable through weights and impacts specified as command-line arguments.\n\n---\n\n## **Installation**\n\nTo install the package from PyPI:\n\n```bash\npip install topsis-Prince-3619\n```\n\n---\n\n## **Usage**\n\n### **Command-Line Interface (CLI)**\nRun the TOPSIS program directly from the command line:\n\n```bash\npython3 -m Topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>\n```\n\n### **Arguments**\n1. `<InputDataFile>`: Path to the input CSV file.\n - The first column must contain object names (e.g., M1, M2, M3).\n - The remaining columns must contain numeric values for criteria.\n\n2. `<Weights>`: Comma-separated weights for the criteria (e.g., `\"1,1,1,2\"`).\n\n3. `<Impacts>`: Comma-separated impacts for the criteria (`+` for positive impact, `-` for negative impact).\n\n4. `<ResultFileName>`: Name of the output CSV file where results will be saved.\n\n---\n\n## **Example**\n\n### **Input Data**\nInput file (`102203619-data.csv`):\n\n| Model | Criterion 1 | Criterion 2 | Criterion 3 | Criterion 4 |\n|-------|-------------|-------------|-------------|-------------|\n| M1 | 250 | 16 | 12 | 5 |\n| M2 | 200 | 18 | 8 | 3 |\n| M3 | 300 | 14 | 16 | 10 |\n| M4 | 275 | 17 | 10 | 8 |\n\n### **Command**\n```bash\ntopsis 102203619-data.csv \"1,1,1,2\" \"+,+,-,+\" result.csv\n```\n\n### **Output File**\nOutput file (`result.csv`):\n\n| Model | Criterion 1 | Criterion 2 | Criterion 3 | Criterion 4 | Topsis Score | Rank |\n|-------|-------------|-------------|-------------|-------------|--------------|------|\n| M1 | 250 | 16 | 12 | 5 | 0.5346 | 2 |\n| M2 | 200 | 18 | 8 | 3 | 0.3084 | 4 |\n| M3 | 300 | 14 | 16 | 10 | 0.6912 | 1 |\n| M4 | 275 | 17 | 10 | 8 | 0.5340 | 3 |\n\n---\n\n## **How It Works**\n1. **Normalization**:\n The decision matrix is normalized using vector normalization:\n \n \\[\n R_{ij} = \\frac{a_{ij}}{\\sqrt{\\sum_{k=1}^{m} a_{kj}^2}}\n \\]\n\n2. **Weighted Normalized Matrix**:\n Each criterion is weighted according to user-defined weights:\n \n \\[\n V_{ij} = R_{ij} \\cdot w_j\n \\]\n\n3. **Identify Ideal Solutions**:\n - **Positive Ideal Solution (PIS)**: Maximum values for positive impacts and minimum values for negative impacts.\n - **Negative Ideal Solution (NIS)**: Minimum values for positive impacts and maximum values for negative impacts.\n\n4. **Calculate Separation Measures**:\n - Separation from PIS (\\( S_i^+ \\)) and NIS (\\( S_i^- \\)) are computed using Euclidean distance.\n\n5. **Calculate TOPSIS Scores**:\n - The score is calculated as:\n \\[\n C_i = \\frac{S_i^-}{S_i^+ + S_i^-}\n \\]\n\n6. **Rank Alternatives**:\n - Alternatives are ranked based on their scores, with higher scores indicating better performance.\n\n---\n\n## **Development**\n\n### **Project Structure**\n```\ntopsis-package/\n\u251c\u2500\u2500 topsis/\n\u2502 \u251c\u2500\u2500 __init__.py\n\u2502 \u251c\u2500\u2500 __main__.py\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 setup.py\n\u251c\u2500\u2500 pyproject.toml\n\u2514\u2500\u2500 MANIFEST.in\n```\n\n### **Dependencies**\n- `numpy`\n- `pandas`\n\n### **Setup for Development**\n1. Clone the repository:\n ```bash\n git clone https://github.com/Prince-05/TOPSIS\n ```\n2. Navigate to the project directory:\n ```bash\n cd Topsis-Prince-3619\n ```\n3. Install dependencies:\n ```bash\n pip install -r requirements.txt\n ```\n\n---\n\n## **Testing**\nRun the tests to ensure the package works correctly:\n```bash\npytest\n```\n\n---\n\n## **License**\nThis project is licensed under the MIT License. See the `LICENSE` file for details.\n\n---\n\n## **Contributing**\nContributions are welcome! Please follow these steps:\n1. Fork the repository.\n2. Create a new branch for your feature or bug fix.\n3. Submit a pull request with a detailed explanation of your changes.\n\n---\n\n## **Contact**\nFor any questions or issues, please contact:\n- **Name**: Prince\n- **Email**: pprince_be22@thapar.edu\n- **GitHub**: [Prince-05](https://github.com/Prince-05)\n\n",
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