semvec/pypi-packages huggingface dataset:
102217106-topsis 1.0.0
A simple TOPSIS implementation package.
Requires Python: >=3.6


# TOPSIS Package



## Overview

The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is a widely used multi-criteria decision analysis (MCDA) technique designed to evaluate and rank alternatives based on multiple criteria. This Python package offers a straightforward implementation of the TOPSIS method.

## Features

- User-friendly functionality to perform the TOPSIS method on decision matrices.
- Allows customization of weights for each criterion.
- Supports both beneficial and non-beneficial criteria (impacts).
- Provides preference scores and rankings for alternatives.

    Parameters:
        data (list of lists): The decision matrix (alternatives x criteria).
        weights (list): Weights assigned to each criterion.
        impacts (list): Use '+' for beneficial criteria and '-' for non-beneficial criteria.

    Returns:
        list: Preference scores for each alternative.
        list: Ranks of each alternative.

## Installation

You can install the *Topsis Package* using pip from PyPI.

### From PyPI:

Install the package using pip:
```bash
pip install 102217106_topsis
```
package-topsis 0.3
A Python package for performing TOPSIS analysis
Requires Python: >=3.6

# TOPSIS Package

A Python package for performing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis.

## Installation
You can install this package using pip:

```bash
pip install package-topsis

from package_topsis import topsis

data = [
    [250, 16, 12, 5],
    [200, 16, 8, 3],
    [300, 32, 16, 4],
    [275, 32, 8, 4],
    [225, 16, 16, 2],
]
weights = [0.25, 0.25, 0.25, 0.25]
impacts = ['+', '+', '-', '+']

rankings = topsis(data, weights, impacts)
print(rankings)  # Output: [3, 1, 2, 5, 4]
package-102217249 1.0.0
A Python package for performing TOPSIS analysis
Requires Python: >=3.6

# TOPSIS Package

A Python package for performing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis.

## Installation
You can install this package using pip:

```bash
pip install package-topsis

from package_102217249 import topsis

data = [
    [250, 16, 12, 5],
    [200, 16, 8, 3],
    [300, 32, 16, 4],
    [275, 32, 8, 4],
    [225, 16, 16, 2],
]
weights = [0.25, 0.25, 0.25, 0.25]
impacts = ['+', '+', '-', '+']

rankings = topsis(data, weights, impacts)
print(rankings)  # Output: [3, 1, 2, 5, 4]
ncsu-courses 0.4.0
A Python library for retrieving data about NCSU's course offerings and sections
Requires Python: >=3.13

# NCSU Courses

A Python library for getting data about NCSU's course offerings. Contains functionality for retrieving historical and current courses and their sections, including instructor, meeting information, seats, and more.

This library uses the same API that [this](https://webappprd.acs.ncsu.edu/php/coursecat/) website does.

## Courses

Use `ncsu_courses.courses.get_courses(subject, term)` to get all courses for a given subject during the given term. A course has data such as a curriculum and code, a description, title, etc.

## Sections

A section of a course is a specific instance of the course being offered during a certain term. A section contains information such as instructor, meeting days and times, meeting location, seats, etc. Use `ncsu_courses.courses.get_sections(subject, term)` to get all sections for all classes of the given subject during the given term.

## Subjects

Subjects represent broad areas of study. Each course's name begins with the letters of the subject that it belongs to (ex. CSC 111 is a Computer Science course because CSC represents the Computer Science subject). The `ncsu_courses.subjects.get_all_subjects(term)` function returns a list of all subjects that exist during the current term.

## Terms

A term is identified by a year and a session (Fall, Spring, Summer 1, or Summer 2). Use `ncsu_courses.term.Term` to represent a specific term. Terms are passed to the API as integers representing a combination of year and session, and the `Term` object handles this internally.

## Catalog Courses

A catalog course is a course listed in the catalog, not associated with any specific term. Use `ncsu_courses.catalog.get_catalog_courses(subject)` to get a generator over all catalog courses.