semvec/pypi-packages huggingface dataset:
TOPSIS-101866015 1.0.0
This Package is for calculating the TOPSIS score and rank of a dataset and storing final result in a csv file
License: MIT
Keywords: TOPSIS,RANKING,DATAFRAME,MCDM

# TOPSIS_Sharannya
By:**Sharannya_101866015**

### Title:Multiple Criteria Decision Making using TOPSIS

## What is TOPSIS:
TOPSIS is an acronym that stands for 'Technique of Order Preference Similarity to the Ideal Solution' and is a pretty straightforward MCDA method.
It is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 with further developments by Yoon in 1987, and Hwang, Lai and Liu in 1993.
#### How to install the TOPSIS package
```buildoutcfg
pip install TOPSIS_101866105
```
#### For Calculating the TOPSIS Score
```buildoutcfg
Topsis data.csv "0.25,0.25,0.25,0.25" "-,+,+,+" result.csv
```

##### Input File(Example:data.csv):
Argument used to pass the path of the input file which conatins a dataset having different fields and to perform the topsis mathematical operations
##### Weights(Example:"0.25,0.25,0.25,0.25")
The weights to assigned to the different parameters in the dataset should be passed in the argument.**It must be seperated by ','.**
##### Impacts(Example:"-,+,+,+"):
The impacts are passed to consider which parameters have a positive impact on the decision and which one have the negative impact.**Only '+' and '-' values should be passed and should be seperated with ',' only**
##### Output File(Example:result.csv):
This argument is used to pass the path of the result file where we want the rank and score to be stored
topsis-yotest 0.0.8
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS

Submitted By: **Yash Shukla 101803617**

---

## What is TOPSIS?

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal **S**olution
(TOPSIS) originated in the 1980s as a multi-criteria decision making method.
TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution,
and greatest distance from the negative-ideal solution.

<br>

## How to install this package:

```
>> pip install Topsis_Yash_101803617
```

### In Command Prompt

```
>> python

>> import Topsis_Yash_101803617
>> Topsis_Yash_101803617.topsis("data.csv","1,1,1,1","+,+,-,+","result.csv")
```

## Input file (data.csv)

The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

| Model | Correlation | R<sup>2</sup> | RMSE | Accuracy |
| ----- | ----------- | ------------- | ---- | -------- |
| M1    | 0.79        | 0.62          | 1.25 | 60.89    |
| M2    | 0.66        | 0.44          | 2.89 | 63.07    |
| M3    | 0.56        | 0.31          | 1.57 | 62.87    |
| M4    | 0.82        | 0.67          | 2.68 | 70.19    |
| M5    | 0.75        | 0.56          | 1.3  | 80.39    |

Weights (`weights`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.

<br>

## Output file (result.csv)

| Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | Topsis_score | Rank |
| ----- | ----------- | ------------- | ---- | -------- | ------------ | ---- |
| M1    | 0.79        | 0.62          | 1.25 | 60.89    | 0.7722       | 2    |
| M2    | 0.66        | 0.44          | 2.89 | 63.07    | 0.2255       | 5    |
| M3    | 0.56        | 0.31          | 1.57 | 62.87    | 0.4388       | 4    |
| M4    | 0.82        | 0.67          | 2.68 | 70.19    | 0.5238       | 3    |
| M5    | 0.75        | 0.56          | 1.3  | 80.39    | 0.8113       | 1    |

<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
funity 0.0.11
A Unity3d installation finder and a command line helper.
License: MIT

# funity

![PyPI](https://img.shields.io/pypi/v/funity)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/funity)
![PyPI - Status](https://img.shields.io/pypi/status/funity)
![PyPI - License](https://img.shields.io/pypi/l/funity)
![PyPI - Format](https://img.shields.io/pypi/format/funity)
![PyPI - Downloads](https://img.shields.io/pypi/dm/funity)

A Unity3d installation finder and command line helper.

## Installation

```sh
pip install funity
```

## Usage

### In Terminal

```sh
python -m funity

# Outputs a JSON-formatted file containing all Unity3d editors found in the current working directory.

# editor.cache
# [
#   "/Applications/Unity/Hub/Editor/2019.2.6f1"
# ]
```

### In Python

```python
from os import getcwd
from pathlib import Path

from funity import UnityEditor, UnityProject, UnityVersion


cache_dir = Path(getcwd()) / 'editor.cache'

# Find all Unity editor installations and cache the results into 'cache_dir'.
editors = UnityEditor.find_in(cache=str(cache_dir))

version = UnityVersion(2019, 2)

# Filter results to only Unity 2019.2.xfx versions.
editors_2019_2 = [e for e in editors if e.version.is_equal_to(version, fuzzy=True)]

# Throw an exception if no compatible Unity version is found.
if not editors_2019_2:
    raise Exception(f'No Unity {version} found.')

# Get the first Unity 2019.2.xfx editor.
editor = editors_2019_2[0]

# Create a UnityProject instance.
project = UnityProject('/Users/you/Projects/HelloWorld')

# Run 'executeMethod' on the Unity project using the Unity editor CLI.
return_code = editor.run(
    '-projectPath', str(project),
    '-buildTarget', 'Win64',
    '-executeMethod', 'BuildPlayerCommand.Execute',
    cli=True,  # Shorthand for '-batchmode', '-nographics', '-quit', '-silent-crashes'.
    log_func=lambda l: print(l, end='')  # Prints all logs from Unity.
)
```

# Changelog

## v0.0.11

- Fixed Unity version detection on Windows installations.

## v0.0.10

- Minor bug fixes.

## v0.0.9

- Added additional UnityProject helper methods.

## v0.0.8

- Added more UnityProject test cases.

## v0.0.7

- Added additional UnityProject helper methods.

## v0.0.6

- Minor bug fixes.

## v0.0.5

- Added support for getting the Unity version for Windows systems.

## v0.0.4

- Added UnityVersion class.
- Updated the README file.

## v0.0.3

- Some bug fixes when fetching the Unity version for Darwin systems.

## v0.0.2

- Added support for getting the Unity version for Darwin systems.