semvec/pypi-packages huggingface dataset:
gismo 0.5.4
GISMO is a NLP tool to rank and organize a corpus of documents according to a query.
Requires Python: <3.15,>=3.10
Keywords: NLP, Random Walk, D-Iteration, TF-I[D]TF

[![Gismo logo](https://github.com/balouf/gismo/raw/master/docs/logo-line.png)](https://balouf.github.io/gismo/)

# A Generic Information Search... With a Mind of its Own!


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GISMO is a NLP tool to rank and organize a corpus of documents according to a query.

Gismo stands for Generic Information Search... with a Mind of its Own.

- Free software: MIT License
- Github: <https://github.com/balouf/gismo/>
- Documentation: <https://balouf.github.io/gismo/>

## Features

Gismo combines three main ideas:

- **TF-IDTF**: a symmetric version of the TF-IDF embedding.
- [**DIteration**](https://arxiv.org/abs/1202.1163): a fast, push-based, variant of the PageRank algorithm.
- **Fuzzy dendrogram**: a variant of the Louvain clustering algorithm.

## Quickstart

Install gismo:

```console
$ pip install gismo
```

Use gismo in a Python project:

```pycon
>>> from gismo.common import toy_source_dict
>>> from gismo import Corpus, Embedding, CountVectorizer, Gismo
>>> corpus = Corpus(toy_source_dict, to_text=lambda x: x['content'])
>>> embedding = Embedding(vectorizer=CountVectorizer(dtype=float))
>>> embedding.fit_transform(corpus)
>>> gismo = Gismo(corpus, embedding)
>>> gismo.rank("Mogwaï")
>>> gismo.get_features_by_rank()
['mogwaï', 'gizmo', 'chinese', 'in', 'demon', 'folklore', 'is']
```

To get the hang of a typical Gismo workflow, you can check the [Toy Example] notebook. For more advanced uses,
look at the other [tutorials] or directly the [reference] section.

## Credits

Thomas Bonald, Anne Bouillard, Marc-Olivier Buob, Dohy Hong for their helpful contribution.

This package was created with [Cookiecutter] and the [francois-durand/package_helper] project template.

# Coverage

[![codecov](https://codecov.io/gh/balouf/gismo/graphs/tree.svg?token=TTMRW7XYS5)](https://codecov.io/gh/balouf/gismo)

[cookiecutter]: https://github.com/audreyr/cookiecutter
[francois-durand/package_helper]: https://github.com/francois-durand/package_helper
[reference]: https://balouf.github.io/gismo/reference.html
[toy example]: https://balouf.github.io/gismo/tutorials/tutorial_toy_example.html
[tutorials]: https://balouf.github.io/gismo/tutorials/index.html
topsis-shivam-102303881 0.0.1
TOPSIS implementation using Python

## TOPSIS-Shivam-102303881

TOPSIS-Shivam-102303881 is a Python package that implements the **TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)** method for multi-criteria decision making (MCDM).

TOPSIS is a widely used decision-making technique that ranks alternatives based on their relative distance from an ideal best solution and an ideal worst solution. This package allows users to easily apply the TOPSIS method on tabular data stored in CSV files using a simple command-line interface.

The package performs proper input validation, handles common user errors, and generates a ranked output with TOPSIS scores, making it suitable for academic assignments as well as practical decision-analysis tasks.

---

## Features
- Command-line implementation of TOPSIS
- Supports multiple decision criteria
- Handles both beneficial (+) and non-beneficial (-) attributes
- Input validation for files, weights, and impacts
- Outputs TOPSIS score and rank for each alternative

---

## Installation

Install the package directly from PyPI:

pip install TOPSIS-Shivam-102303881

---

## Usage

Run the TOPSIS analysis from the command line:

topsis input.csv "1,1,1,2,1" "+,+,+,-,+" output.csv

---

## Input File Format
- The input file must be a CSV file
- The first column should contain the names of alternatives
- All remaining columns must contain numeric values only
- The number of weights and impacts must match the number of criteria columns

---

## Output File
The output CSV file contains:
- All original input columns
- A new column named **Topsis Score**
- A new column named **Rank**, where rank 1 indicates the best alternative

---

## Author
Shivam Kumar 
Roll No: 102303881  

---

## License
This project is developed for academic and educational purposes.