semvec/pypi-packages huggingface dataset:
decathlonian 1.1.1
A package to easily train powerful image classification models using colab's free TPUs.
Requires Python: >=3.6

The AI team at Décathlon Canada developed a library to help with the training of image 
                        classification models. It is specially made to exploit the free TPUs that are offered
                        in Google colab notebooks. You can find the full documentation 
                        [here](https://decathlonian-doc.herokuapp.com/)
                        
                        ## Version 1.1.1
                        Fix typo in split_train
                        
                        ## Version 1.1.0
                        Remove google scraping
                        
                        ## Version 1.0.0
                        Original release
TOPSIS-101983054-Ritik 1.1
Technique for Oder Preference by Similarity to Ideal Solution.
Requires Python: >=3.6

# Technique for Oder Preference by Similarity to Ideal Solution(TOPSIS) for multiple-criteria decision making (MCDM)

# TOPSIS
It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. Normalisation is usually required as the parameters or criteria are often of incongruous dimensions in multi-criteria problems. Compensatory methods such as TOPSIS allow trade-offs between criteria, where a poor result in one criterion can be negated by a good result in another criterion. This provides a more realistic form of modelling than non-compensatory methods, which include or exclude alternative solutions based on hard cut-offs.
[![License](https://img.shields.io/badge/license-MIT-brightgreen)]&nbsp;![Made with Love in India](https://madewithlove.org.in/badge.svg)
---
## INSTALLATION & USAGE
 **1. Now install the required package by running the following commands :-**
 ```bash
  pip install TOPSIS-RitikKumar-101983054
 ```
 **3.Use the package by running the following commands :-**
 ```bash
  import TOPSIS_RitikKumar_101983054 as ritik_TOPSIS
  ritik_TOPSIS.TOPSIS(inputFilePath,weightList,impactList,OutputFileName)
 ```
 - inputFilePath : is the location of input file
- weightList : this takes the input value of every parameter weights "0.1,0.2,0.3,0.4"
- impactList : this takes the +ve and -ve as input to take whether to maximize or minimize a parameter
- outputFileName : is the location of the csv file where u want to save it
 ---
 # EXAMPLE USAGE
  ```bash
  import TOPSIS_RitikKumar_101983054 as ritik_TOPSIS
  ritik_TOPSIS.TOPSIS('./Input.csv',[0.25,0.25,0.25,0.25],['-', '+', '+', '+'],'Output.csv')
 ```
 ####  INPUT
 | Model 	| Corr 	| Rseq 	| RMSE 	| Accuracy 	|
|:-:	|:-:	|:-:	|:-:	|:-:	|
| M1 	| 250 	| 16 	| 12 	| 5 	|
| M2 	| 200 	| 16 	| 8 	| 3 	|
| M3 	| 300 	| 32 	| 16 	| 4 	|
| M4 	| 275 	| 32 	| 8 	| 4 	|
| M5 	| 225 	| 16 	| 16 	| 2 	|

#### OUTPUT
| Model 	| Corr 	| Rseq 	| RMSE 	| Accuracy 	| Topsis Score 	| Rank 	|
|:-:	|:-:	|:-:	|:-:	|:-:	|-	|-	|
| M1 	| 250 	| 16 	| 12 	| 5 	| 0.5254 	| 2 	|
| M2 	| 200 	| 16 	| 8 	| 3 	| 0.2641 	| 5 	|
| M3 	| 300 	| 32 	| 16 	| 4 	| 0.7358 	| 1 	|
| M4 	| 275 	| 32 	| 8 	| 4 	| 0.52082 	| 3 	|
| M5 	| 225 	| 16 	| 16 	| 2 	| 0.37274 	| 4 	|

 ---
## BUILT BY 🤝
 - [**Ritik Kumar**](https://github.com/ritik-sys)  
[![forthebadge](https://forthebadge.com/images/badges/built-with-love.svg)](https://forthebadge.com)
[![forthebadge](https://forthebadge.com/images/badges/built-by-developers.svg)](https://forthebadge.com)
TOPSIS-Prajjwal-101803111 1.0.1
Topsis Calculator

# TOPSIS-Python


Submitted By: **PRAJJWAL UPADHYAY**


<br>

***

## 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-prajjwal-101803111  can be run as in the following example:



### In Command Prompt
```
>> pip install TOPSIS-Prajjwal--101803111==1.0.1
```
>> python
>>>from topsis_gen.topsis_cal import topsis
>>>topsis("data.csv","1,1,1,2","+,+,-,+")

<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
------------ | ------------- | ------------ | ------------- | ------------
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 (`w`) is not already normalised will be normalised later in the code.

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

<br>

## Output

```
Model   Score    Rank
-----  --------  ----
  1    0.639133    2
  2    0.212592    5
  3    0.407846    4
  4    0.519153    3
  5    0.828267    1
```
<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-Apurv-101803594 1.0.0
TOPSIS Implementation

# TOPSIS - Apurv Madaan - 101803594
Submitted By: Apurv Madaan(101803594)

## What is TOPSIS?
Technique for Order Preference by Similarity to Ideal Solution (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.
## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install **TOPSIS**.
Dependencies and devDependencies will be installed automatically.

```bash
pip install TOPSIS-Apurv-101803594
```

## Usage
##### 1) As a Library:
Import in your python File:
```python
from TOPSIS import topsis
topsis()
```
Run the python file by typing in terminal/cmd:
```sh
python nameOfFile.py nameOfDataFile.csv "weights" "impacts" nameOfOutputFile.csv
```
##### 2) Using Command Promt:

Command line args:
- name of input File(csv format)
- weights(as a string)
- impacts(as a string)
- name of output file(csv format)
Eg. 
```bash
topsis data.csv "1,1,1,1" "+,+,-,+" output.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, R2, Root Mean Squared Error, Correlation, and many more.
|Model|Corr|Rseq|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.
# Output file (output.csv)
|Model|Corr|Rseq|RMSE|Accuracy|Topsis_score       |Rank|
|-----|----|----|----|--------|-------------------|----|
|M1   |0.79|0.62|1.25|60.89   |0.7722097345612788 |2   |
|M2   |0.66|0.44|2.89|63.07   |0.22559875426413367|5   |
|M3   |0.56|0.31|1.57|62.87   |0.43889731728018605|4   |
|M4   |0.82|0.67|2.68|70.19   |0.5238778712729114 |3   |
|M5   |0.75|0.56|1.3 |80.39   |0.8113887082429979 |1   |
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
## License
[MIT](https://choosealicense.com/licenses/mit/)