semvec/pypi-packages huggingface dataset:
valis-hest 0.0.2
Modified version of Valis used for HEST. Please visit the original Valis repo for more information https://github.com/MathOnco/valis
Requires Python: >=3.9

# valis-hest

Modified version of Valis used in HEST. Please visit the original Valis repo for more information https://github.com/MathOnco/valis


`valis-hest` is a lightweight version of Valis and might not include all valis features.
`valid-hest` also fixes some alignment issues encountered with the official Valis release when aligning the HEST-1k datasets.
Topsis-Prigya-102313061 1.0.3
Command line implementation of TOPSIS method
Requires Python: >=3.6

# TOPSIS Python Package

This project implements the **TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)** method as a command line tool.

TOPSIS is a multi-criteria decision making technique used to rank alternatives based on their distance from an ideal best and ideal worst solution.

---

## Installation

```bash
pip install Topsis-Prigya-102313061
```

---

## Input File Format

The input must be a CSV file with:

- First column: Names of alternatives
- Remaining columns: Numeric criteria values

### Example Input CSV

| Laptop Model | Performance | Battery Life | Weight (kg) | Price ($) |
| ------------ | ----------- | ------------ | ----------- | --------- |
| Model A      | 90          | 8            | 1.8         | 1200      |
| Model B      | 85          | 10           | 1.5         | 1500      |
| Model C      | 88          | 7            | 2.0         | 1000      |
| Model D      | 92          | 9            | 1.6         | 1800      |


### Meaning of Criteria

- Performance : Higher is better (+)
- Battery Life : Higher is better (+)
- Weight : Lower is better (-)
- Price : Lower is better (-)

---

### Parameters Used

- Weights vector = `[1, 1, 1, 1]`
- Impacts vector = `[+, +, -, -]`

---

## Command Line Usage

```bash
topsis data.csv "1,1,1,1" "+,+,-,-" output.csv
```

---

## Output

The output CSV file will contain two additional columns:

- **Topsis Score**
- **Rank**


| Laptop Model | Topsis Score | Rank |
| ------------ | ------------ | ---- |
| Model A      | 0.534277     | 3    |
| Model B      | 0.308368     | 4    |
| Model C      | 0.691632     | 1    |
| Model D      | 0.534737     | 2    |


---

### Notes

- The first column must contain the names of alternatives.
- All remaining columns must contain numeric values only.
- Number of weights and impacts must match the number of criteria columns.

---

## Requirements

- Python 3.6+
- pandas
- numpy

---

## Author

Prigya Goyal

---

## License

This project is released under the **MIT License** for educational purposes.
See the LICENSE file for details.