semvec/pypi-packages huggingface dataset:
Topsis-Udit-102203558 1.0.0
A Python package for implementing the TOPSIS method.
Requires Python: >=3.6

# Topsis-Udit-102203558
A Python package for implementing the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method.

## Installation
```bash
pip install Topsis-Udit-102203558
```

## Usage
```python
from topsis import topsis

# Example usage
topsis('input_file.csv', 'weights', 'impacts', 'output_file.csv')
```

## Author
Udit Arora
102217092-Aditya 0.1.1
Topsis Package by Aditya Pandey
Requires Python: >=3.6
Keywords: python, topsis, decision-making

# Topsis Python Package

**Developed by:** Aditya Pandey - 102217092

## Overview

The Topsis (Technique for Order of Preference by Similarity to Ideal Solution) method is a popular multi-criteria decision-making technique. It helps to evaluate and select the best alternative from a set of options based on their proximity to the ideal solution. This Python package implements the Topsis method, making it easy to apply the technique on datasets with multiple criteria.

## Features
- Computes Topsis scores based on input data, weights, and impacts.
- Ranks alternatives according to the Topsis methodology.
- Supports data input through Excel files.
- Provides an easy-to-use command-line interface for seamless integration.

## Installation

To install the Topsis Python package, use `pip` with the following command:

```bash
pip install 102217092-Aditya-Pandey
 ```

## Usage

### Command-Line Input

To use the Topsis package from the command line, run the following command:

```sh
python <program.py> <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

- **Input File Type**: The input file must be an Excel file.
- **Data Format**: The second to last columns of the data file MUST contain numeric values.
- **Impacts**: Impacts should be either '+' (positive) or '-' (negative).
- **Weights and Impacts**: Weights and impacts should be enclosed in double quotes and separated by commas.
- **Output**: The output will include a 'Topsis Score' column and a 'Rank' column added to the data. The results will be saved to a CSV file specified in the command-line arguments.

## Example

### Command-Line Input Example

```sh
python 102217092.py 102217092-data.csv "1,1,1,1,1" "+,+,-,+,-" 102217092-result.csv
```
mau 4.0.2
A lightweight template-driven markup language
Requires Python: >=3.7

# Mau v4

Mau is a lightweight markup language heavily inspired by [AsciiDoc](https://asciidoctor.org/docs/what-is-asciidoc), [Asciidoctor](https://asciidoctor.org/) and [Markdown](https://daringfireball.net/projects/markdown/).

As I wasn't satisfied by the results I got with those tools I decided to try to write my own language and the tool to render it.

I am currently using Mau to render posts on my blog [The Digital Cat](https://www.thedigitalcatonline.com) and the online version of my book ["Clean Architectures in Python"](https://www.thedigitalcatbooks.com). I also used it to transpile the code of the book to Markua, to be able to publish the book on Leanpub using their toolchain.

## Quick start

To install Mau use `pip`

``` sh
pip install mau
```

Then install at least one visitor plugin. You probably want to start with `mau-html-visitor`

``` sh
pip install mau-html-visitor
```

To convert Mau sources into HTML just run

``` sh
mau -i source.mau -o destination.html -f html
```

Check out Mau [documentation](https://project-mau.github.io/) for further information.

## Pelican plugin

There is a Pelican plugin that enables you to use Mau in your blog. Check it at https://github.com/pelican-plugins/mau-reader.

## Support

You may report bugs or missing features use the [issues page](https://github.com/Project-Mau/mau/issues).
If you want to ask for help or discuss ideas use the [discussions page](https://github.com/Project-Mau/mau/discussions)
topsis-Aabharan-Gupta-102203075 1.0.0
This Python package implements the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method for rank calculation. It helps in ranking alternatives based on multiple criteria by determining their proximity to the ideal and negative-ideal solutions. The package is efficient and easy to use for decision-making tasks.
Requires Python: >=3.6

TOPSIS Package
The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) package is a Python-based tool for multi-criteria decision-making. It evaluates and ranks alternatives based on their relative closeness to the ideal and anti-ideal solutions.
Installation
pip install -r requirements.txt
Usage
python topsis.py <input_file> <weights> <impacts> <output_file>
Topsis-SanatKGupta-102203079 0.1.0
A Python library for TOPSIS method for MCDM problems
Requires Python: >=3.6

# Topsis-SanatKGupta-102203079

## Project Description

**Topsis-SanatKGupta-102203079** is a Python library that implements the **Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)** for solving **Multiple Criteria Decision Making (MCDM)** problems. It ranks alternatives based on their proximity to the ideal solution, taking into account various criteria with associated weights and impact directions.

This library can be used for decision-making problems where you have multiple alternatives and criteria, such as selecting the best product based on multiple features or evaluating alternatives in project management.

**Submitted by**: Sanat Kumar Gupta  
**Roll No**: 102203079  
**Group**: 3C52  
## Installation

To install **Topsis-SanatKGupta-102203079**, use the package manager `pip`:

```bash
pip install Topsis-SanatKGupta-102203079
```
## Usage
```bash
topsis <csv_filename> <weights_vector> <impacts_vector>
```
* csv_filename: Path to your .csv file.
* weights_vector: A comma-separated list of weights representing the importance of each criterion.
* impacts_vector: A comma-separated list of signs (+ or -), specifying whether each criterion should be maximized (+) or minimized (-).
```bash
topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+"
```
## Output
```bash
TOPSIS RESULTS
-----------------------------

    P-Score  Rank
1  0.534277     3
2  0.308368     5
3  0.691632     1
4  0.534737     2
5  0.401046     4
```
## Code Explanation
### Input Validation:

The validate_inputs function ensures that the data contains at least three columns, that all numeric values are valid, and that the weights and impacts match the number of criteria.
### TOPSIS Algorithm:

- Normalization: The criteria values are normalized so that they are comparable by dividing each value by the square root of the sum of squares for that column.
- Weighting: After normalization, the values are multiplied by the weights to give more importance to certain criteria.
- Ideal and Negative Ideal Solution: The ideal best and worst values are calculated for each criterion based on whether the criterion is maximized or minimized.
- Distance Calculation: The Euclidean distance is computed for each alternative's distance from the ideal and negative ideal solutions.
- Ranking: The Topsis score is calculated as the ratio of the distance from the worst solution to the sum of distances from both the best and worst solutions, and alternatives are ranked accordingly.
### Important Notes
- The first row and column of the CSV file are considered headers and indices, respectively, and are removed before processing.
- Ensure that your CSV file contains only numeric values for the criteria. Categorical data will cause errors during processing.
- If the input vectors (weights and impacts) contain spaces, enclose them in double quotes (").

## License
This project is licensed under the MIT License.
Topsis-Nitin-Goyal-102203614 0.1.0
A Python package for implementing the TOPSIS method
Requires Python: >=3.6

# Topsis-Nitin_Goyal-102203614

This Python library delivers a robust implementation of the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) algorithm, designed for solving multi-criteria decision-making problems.

Key Features
Standardizes decision matrices for consistency.
Incorporates weights to reflect the relative importance of criteria.
Computes TOPSIS scores and ranks alternatives effectively.
Installation
You can install this package using pip:

bash
Copy
Edit
pip install Topsis-YourName-102203614
How to Use
Execute the TOPSIS method by providing:

A CSV file containing the decision matrix.
A set of weights for the criteria.
Positive (+) or negative (-) impacts for each criterion.
Example Command:
bash
Copy
Edit
python script.py input_data.csv "1,2,3" "+,-,+,-" output_result.csv
License
This library is distributed under the MIT License. For more details, refer to the license.txt file included in the repository.
Topsis-Dushar-102217166 0.1
A Python package to implement the Topsis method

# Topsis-Dushar-102217166

This is a Python package to implement the Topsis (Technique for Order Preference by Similarity to Ideal Solution) method.

## Installation

You can install the package via pip:

pip install Topsis-Dushar-102217166

## Usage

After installation, you can use the `topsis` command line tool like this:

python -m topsis 102217166-data.csv "1,1,1,3,2" "+,+,-,-,+" 102217166-result.csv