semvec/pypi-packages huggingface dataset:
Topsis-Khushnoor-102303219 1.1.1
A Python package for TOPSIS MCDM problems

# TOPSIS-Khushnoor-102303219

**Project:** UCS654 Project 1  
**Submitted by:** Khushnoor Kaur  
**Roll No:** 102303219  

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a
multi-criteria decision-making (MCDM) method used to rank alternatives based on
their distance from the ideal best and ideal worst solutions.

---

## Installation

```bash
pip install topsis-khushnoor-102303219
```

## Usage

This package provides a command-line tool called topsis.
```bash
topsis <InputDataFile> <Weights> <Impacts> <OutputResultFileName>
```

Example
```bash
topsis sample.csv "1,1,1,2" "+,+,-,+" result.csv
```

## Input Format

The input file must be a CSV file with:
* First column as alternative names
* Remaining columns as numerical criteria

Sample Input (sample.csv)

| Model | Storage space | Camera | Price | Looks |
|-------|---------------|--------|-------|-------|
| M1    | 16            | 12     | 250   | 5     |
| M2    | 16            | 8      | 200   | 3     |
| M3    | 32            | 16     | 300   | 4     |

## Output

The output file will contain the original columns plus the Topsis Score and Rank.

| P-Score  | Rank |
|----------|------|
| 0.534277 | 2    |
| 0.308368 | 3    |
| 0.691632 | 1    |

## Other notes:

* The first column (alternatives) is ignored during TOPSIS computation.
* All criteria values must be numerical.
* Weights and impacts must match the number of criteria.
* Impacts must be either + (benefit) or - (cost).

## License:

[MIT](https://choosealicense.com/licenses/mit/)
datastudio-cli 0.2.5
A command line client for running DataStudio Datakits
Requires Python: >=3.11

# DataStudio datakit CLI

A command line client for running DataStudio datakits.

Usage documentation can be found [here](https://docs.datastudioapp.com).


## Development

To install and test locally, navigate to the datakit directory you want to
test.
```
cd /path/to/datakit
```

Create a virtualenv and install the CLI via pip in local mode:
```
python -m venv .venv
source .venv/bin/activate
pip install -e [/path/to/cli]
```

You can now run the CLI script with:
```
ds
```