semvec/pypi-packages huggingface dataset:
Topsis-Ikshul-101903458 2.0.0
A package -> Calculates Topsis Score and Rank them accordingly
License: MIT

##TOPSIS-IKSHUL-101903458

# TOPSIS

Submitted By: **Ikshul Dureja 101903458**.

Type: **Package**.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **1.0.0**.

Date: **2022-27-2**.

Author: **Ikshul Dureja**.

Maintainer: **Ikshul Dureja <ikshuldureja130@gmail.com>**.

Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**.

---

## 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-IKSHUL-101903458
```

### In Command Prompt

```
>> 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**
topsis-10191117pip1-yash 1.0.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python



Submitted By: **YASHWARDHAN RATHORE 101917117**
*ASSIGNMENT 4*

***

## 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. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

TOPSIS-YASHWARDHAN_RATHORE_101917117  can be run as in the following example:



### In Command Prompt to run the code:
```
>> topsis data.csv "1,1,1,1" "+,+,-,+"
```
<br>

## Sample dataset

The decision matrix (`a`) should be constructed with each row representing a Model alternative, and each column representing a criterion like Fund Name , P1 ,P2 , P3 , P4 , P5.

Fund Name | P1 | P2 | P3 | P4 | P5
------------ | ------------- | ------------ | ------------- | ------------ | ------------
M1|	0.8	|0.64	|3.5	|37.5	|10.61
M2|	0.86	|0.74	|3.4	|42.2	|11.8
M3|	0.69	|0.48	|5.7	|70	|19.22
M4|	0.65	|0.42	|5.7	|65.5	|18.07
M5|	0.9	|0.81	|6.6	|39.1	|11.85
M6|	0.76	|0.58	|4	|53.5	|14.71
M7|	0.69	|0.48	|6.2	|51.3	|14.67
M8|	0.65	|0.42	|6	|50.2	|14.32



<br>

## Output

```
Row_NO	Performance_score	Rank
1	0.436737	7
2	0.389937	8
3	0.56565	4
4	0.590487	3
5	0.522924	5
6	0.4513	6
7	0.637889	1
8	0.635536	2

```
<br>
The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.
topsis-10191117pip-yash 1.0.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python



Submitted By: **YASHWARDHAN RATHORE 101917117**
*ASSIGNMENT 4*

***

## 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. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

TOPSIS-YASHWARDHAN_RATHORE_101917117  can be run as in the following example:



### In Command Prompt to run the code:
```
>> topsis data.csv "1,1,1,1" "+,+,-,+"
```
<br>

## Sample dataset

The decision matrix (`a`) should be constructed with each row representing a Model alternative, and each column representing a criterion like Fund Name , P1 ,P2 , P3 , P4 , P5.

Model | Correlation | R<sup>2</sup> | RMSE | Accuracy
------------ | ------------- | ------------ | ------------- | ------------
M1|	0.8	|0.64	|3.5	|37.5	|10.61
M2|	0.86	|0.74	|3.4	|42.2	|11.8
M3|	0.69	|0.48	|5.7	|70	|19.22
M4|	0.65	|0.42	|5.7	|65.5	|18.07
M5|	0.9	|0.81	|6.6	|39.1	|11.85
M6|	0.76	|0.58	|4	|53.5	|14.71
M7|	0.69	|0.48	|6.2	|51.3	|14.67
M8|	0.65	|0.42	|6	|50.2	|14.32



<br>

## Output

```
Row_NO	Performance_score	Rank
1	0.436737	7
2	0.389937	8
3	0.56565	4
4	0.590487	3
5	0.522924	5
6	0.4513	6
7	0.637889	1
8	0.635536	2

```
<br>
The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.
Topsis-Archit-101903054 1.1.0
Calculates TOPSIS on the given dataset!
License: MIT

# Topsis-Archit-101903054
It takes the dataset and calculates the TOPSIS and assign the topsis score and rank!

## Installation
```pip install Topsis-Archit-101903054```

## How to use it?
Open terminal Write:
```Topsis <inputFilename.csv> <"Weights"> <"Impacts"> <outputFilename.csv>```

## License

Â© 2022 Archit Kapoor

This repository is licensed under the MIT license. See LICENSE for details.
topsis-10191117-1yash 1.0.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python



Submitted By: **YASHWARDHAN RATHORE 101917117**
*ASSIGNMENT 4*

***

## 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. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

TOPSIS-YASHWARDHAN_RATHORE_101917117  can be run as in the following example:



### In Command Prompt to run the code:
```
>> topsis data.csv "1,1,1,1" "+,+,-,+"
```
<br>

## Sample dataset

The decision matrix (`a`) should be constructed with each row representing a Model alternative, and each column representing a criterion like Fund Name , P1 ,P2 , P3 , P4 , P5.

M1|	0.8	|0.64	|3.5	|37.5	|10.61
M2|	0.86	|0.74	|3.4	|42.2	|11.8
M3|	0.69	|0.48	|5.7	|70	|19.22
M4|	0.65	|0.42	|5.7	|65.5	|18.07
M5|	0.9	|0.81	|6.6	|39.1	|11.85
M6|	0.76	|0.58	|4	|53.5	|14.71
M7|	0.69	|0.48	|6.2	|51.3	|14.67
M8|	0.65	|0.42	|6	|50.2	|14.32




<br>

## Output

```
Row_NO	Performance_score	Rank
1	0.436737	7
2	0.389937	8
3	0.56565	4
4	0.590487	3
5	0.522924	5
6	0.4513	6
7	0.637889	1
8	0.635536	2

```
<br>
The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.
topsis-10191117-yash 1.0.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python



Submitted By: **YASHWARDHAN RATHORE 101917117**
*ASSIGNMENT 4*

***

## 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. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

TOPSIS-YASHWARDHAN_RATHORE_101917117  can be run as in the following example:



### In Command Prompt to run the code:
```
>> topsis data.csv "1,1,1,1" "+,+,-,+"
```
<br>

## Sample dataset

The decision matrix (`a`) should be constructed with each row representing a Model alternative, and each column representing a criterion like Fund Name , P1 ,P2 , P3 , P4 , P5.

Fund Name | P1 | P2 | P3 | P4 | P5
------------ | ------------- | ------------ | ------------- | ------------ | ------------
M1	0.8	0.64	3.5	37.5	10.61
M2	0.86	0.74	3.4	42.2	11.8
M3	0.69	0.48	5.7	70	19.22
M4	0.65	0.42	5.7	65.5	18.07
M5	0.9	0.81	6.6	39.1	11.85
M6	0.76	0.58	4	53.5	14.71
M7	0.69	0.48	6.2	51.3	14.67
M8	0.65	0.42	6	50.2	14.32




<br>

## Output

```
Row_NO	Performance_score	Rank
1	0.436737	7
2	0.389937	8
3	0.56565	4
4	0.590487	3
5	0.522924	5
6	0.4513	6
7	0.637889	1
8	0.635536	2

```
<br>
The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.
topsis-10191117 1.0.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python



Submitted By: **YASHWARDHAN RATHORE 101917117**
*ASSIGNMENT 4*

***

## 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. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

TOPSIS-YASHWARDHAN_RATHORE_101917117  can be run as in the following example:



### In Command Prompt to run the code:
```
>> topsis data.csv "1,1,1,1" "+,+,-,+"
```
<br>

## Sample dataset

The decision matrix (`a`) 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
------------ | ------------- | ------------ | ------------- | ------------
Fund Name	P1	P2	P3	P4	P5
M1	0.8	0.64	3.5	37.5	10.61
M2	0.86	0.74	3.4	42.2	11.8
M3	0.69	0.48	5.7	70	19.22
M4	0.65	0.42	5.7	65.5	18.07
M5	0.9	0.81	6.6	39.1	11.85
M6	0.76	0.58	4	53.5	14.71
M7	0.69	0.48	6.2	51.3	14.67
M8	0.65	0.42	6	50.2	14.32




<br>

## Output

```
Row_NO	Performance_score	Rank
1	0.436737	7
2	0.389937	8
3	0.56565	4
4	0.590487	3
5	0.522924	5
6	0.4513	6
7	0.637889	1
8	0.635536	2

```
<br>
The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.
Topsis-Yash-101903780 0.0.1
A Python package to find TOPSIS for multi-criteria decision analysis method
License: MIT
Keywords: topsis,UCS654,101903780

Topsis Value Calculator

CalcTopsis is a Python package implementing Topsis method sed for multi-criteria decision analysis.
Topsis stands for Technique for Order of Preference by Similarity to Ideal Solution

Just provide your input attributes and it will give you the results


## Installation

$ pip install TOPSIS-101903780==0.0.1

In the commandline, you can write as -
    $ python <package_name> <path to input_data_file_name> <weights as strings> <impacts as strings> <result_file_name>

E.g for input data file as data.csv, command will be like
    $ python topsis.py data.csv "1,1,1,1" "+,+,-,+" output.csv

This will print all the output attribute values along with the Rank column, in a tabular format

License -> MIT

Change Log
==========

0.0.1 (27/02/2022)
------------------
- First Release
dkopt 0.0.4
deck-optimizer is a Python module to support TCG deck building.
License: MIT

# deck-optimizer

[![PyPI version](https://badge.fury.io/py/dkopt.svg)](https://badge.fury.io/py/dkopt)
[![Python Versions](https://img.shields.io/pypi/pyversions/dkopt.svg)](https://pypi.org/project/dkopt/)
[![Docker Publish](https://github.com/hasoya/deck-optimizer/actions/workflows/docker-publish.yml/badge.svg)](https://github.com/hasoya/deck-optimizer/actions/workflows/docker-publish.yml)
[![pytest](https://github.com/hasoya/deck-optimizer/actions/workflows/python-app.yml/badge.svg)](https://github.com/hasoya/deck-optimizer/actions/workflows/python-app.yml)
[![Upload Python Package](https://github.com/hasoya/deck-optimizer/actions/workflows/pypi-publish.yml/badge.svg)](https://github.com/hasoya/deck-optimizer/actions/workflows/pypi-publish.yml)
[![Code Quality](https://app.codacy.com/project/badge/Grade/9cd9ee6c310947029719e7c22af67cb2)](https://www.codacy.com/gh/hasoya/deck-optimizer/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=hasoya/deck-optimizer&amp;utm_campaign=Badge_Grade)
[![Coverage](https://app.codacy.com/project/badge/Coverage/9cd9ee6c310947029719e7c22af67cb2)](https://www.codacy.com/gh/hasoya/deck-optimizer/dashboard?utm_source=github.com&utm_medium=referral&utm_content=hasoya/deck-optimizer&utm_campaign=Badge_Coverage)

**deck-optimizer** is a Python module to support TCG deck building especially Yu-Gi-Oh!. This module suggest the best combination of cards under given conditions.

## Dependency

Use [Docker](https://www.docker.com/) or see [requirements.txt](https://github.com/hasoya/deck-optimizer/blob/main/requirements.txt).

## Usage

Make config yaml file to describe the ideal hand. For example, if you want to draw at least one PSY-Framegear Gamma and keep PSY-Frame Driver in deck, your config file will be written as below.

```yaml:config.yaml
num_hand: 5
num_deck:
  min: 40
  max: 60
condition:
  - name: Gamma
    min: 2
    max: 3
    require: 1
    exact: False
    in_hand: True
  - name: Driver
    min: 1
    max: 1
    require: 1
    exact: True
    in_hand: False
```

Then, run the app by following command.

```bash
docker pull ghcr.io/hasoya/deck-optimizer:v0.0.1
docker run -v /absolute/path/to/config/file:/config.yaml ghcr.io/hasoya/deck-optimizer:v0.0.1 -f /config.yaml
```

You will get the suggestion of deck composition and the probability of the ideal hand.

```bash
The best deck
  Total Num Deck: 40 Num Gamma in deck: 3 Num Driver in deck: 1
The probability of the ideal hand
  30.21%
```