semvec/pypi-packages huggingface dataset:
Topsis-Cherish-102003647 0.4
It is a topsis package.
License: MIT
Keywords: MCDM,102003647,TOPSIS

_for: **Topsis_Cherish_102003647**_
_submitted by: **Cherish Mahajan**_
_Roll no: **102003647**_
_Group: **3COE25**_


Topsis_Cherish_102003647 is a Python library for solving Multiple Criteria Decision Making(MCDM) problems by  Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Topsis-Cherish-102003647.

```bash
pip install Topsis_Cherish_102003647
```

## Usage

Enter csv filename followed by _.csv_ extension, then enter the _weights_ vector with vector values separated by commas, followed by the _impacts_ vector with comma separated signs _(+,-)_

### Input
And using this package in Python as :
```bash
import topsispy as tp
a =[
     [250, 16, 12, 5],
     [200, 16, 8, 3],
     [300, 32, 16, 4],
     [275, 32, 8, 4],
     [225, 16, 16, 2]
   ]
w = [0.25, 0.25, 0.25, 0.25]
sign = [-1, 1, 1, 1]
tp.topsis(a, w, sign)
```

The package can be used via commandline as :
``` bash
$ python [package name] [path of csv as string] [list of weights as string] [list of sign as string]
```
## Example

#### sample.csv

A csv file showing data for different mobile handsets having varying features.

| Model  | Storage space(in gb) | Camera(in MP)| Price(in $)  | Looks(out of 5) |
| :----: |:--------------------:|:------------:|:------------:|:---------------:|
| M1 | 16 | 12 | 250 | 5 |
| M2 | 16 | 8  | 200 | 3 |
| M3 | 32 | 16 | 300 | 4 |
| M4 | 32 | 8  | 275 | 4 |
| M5 | 16 | 16 | 225 | 2 |

weights vector = [ 0.25 , 0.25 , 0.25 , 0.25 ]

impacts vector = [ + , + , - , + ]



### Output:

      TOPSIS RESULTS
      As per the given sample, the output file looks as given below :


| Model  | Storage space(in gb) | Camera(in MP)| Price(in $)  | Looks(out of 5) |Topsis Score  |  Rank  |
| :----: |:--------------------:|:------------:|:------------:|:---------------:|:------------:|:------:|
| M1 | 16 | 12 | 250 | 5 | 0.534277 | 3 |
| M2 | 16 | 8  | 200 | 3 | 0.308368 | 5 |
| M3 | 32 | 16 | 300 | 4 | 0.691632 | 1 |
| M4 | 32 | 8  | 275 | 4 | 0.534737 | 2 |
| M5 | 16 | 16 | 225 | 2 | 0.401046 | 4 |
   


## Other notes

* The first column and first row have only been omitted by the library Topsis-Cherish-102003647 before processing, in attempt to remove indices and headers. So make sure the csv follows the format as shown in sample.csv otherwise the code will have issues in working properly.
* Make sure the csv does not contain categorical values since the code takes into account all the values which are encoded as numerical values.
* The Topsis-Cherish-102003647 works on the sample data and calculates the topsis score according to which it provides the rank to each available option.
ml2ipa 1.0.2
A linguistic tool that generates IPA pronunciation for Malayalam words
License: MIT

## Description
A Python package for generating IPA pronounciation for a Malayalam word or list of Malayalam words.

## Installation
```python
!pip install ml2ipa
```

## Usage
```python
from ml2ipa import get_ipa
get_ipa("മഴ")
```
Output: {'മാമ്പഴം': 'm aː m p a ɻ a m'}

## License
This package is license under MIT license.
Topsis-Nipun-102003674 1.0.0
A Python package for handling problems of Multiple Criteria Decision Making(MCDM) for a given dataset.
License: MIT

# Topsis-Nipun-102003674
Topsis-Nipun-102003674 is a Python package for dealing with Multiple Criteria Decision Making(MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).
Topsis is a method of compensatory aggregation that compares a set of alternatives, 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.

#### Installation
```
Use the package manager pip to install Topsis-Nipun-102003674
```
#### Syntax
```
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
Example:
topsis inputfile.csv 1,2,1,2,1 +,+,-,+,- result.csv
```
#### Example
Sample Input Data
| Name | P1 | P2 | P3 | P4 | P5 |
| --- | --- | --- | --- | --- | --- |
| M1 | 0.71 |0.5|3.8|40.8|11.5|
| M2 | 0.94 | 0.88| 5.3|56.2 |15.83 |
| M3 |0.85 |0.72|4 |30.5 |9.02 |
| M4 |0.61  |0.37|5.4 |56.9 |15.82 |
| M5 |0.91 |0.83|3.4 |53.4 |14.64 |

Weights: 1,1,1,1,1
Impacts: +,+,+,+,+

Sample Output Data
| Name | P1 | P2 | P3 | P4 | P5 |Score|Rank|
| --- | --- | --- | --- | --- | --- |---|---|
| M1 | 0.71 |0.5|3.8|40.8|11.5|0.3015751942839768|5|
| M2 | 0.94 | 0.88| 5.3|56.2 |15.83 |0.97815026808521971|1
| M3 |0.85 |0.72|4 |30.5 |9.02 |0.4172925776259159|4
| M4 |0.61  |0.37|5.4 |56.9 |15.82 |0.5053936295885693|3
| M5 |0.91 |0.83|3.4 |53.4 |14.64 |0.6774035368116197|2

#### Note
1. Enter the path of your input csv file.
2. Enter the weights and impacts vector with each entry separated by commas.
3. Enter the name of output file in .csv format.
4. The Output file will be created in the current working directory

### License
© 2023 Nipun Garg
This repository is licensed under the MIT license.
See LICENCE for details.
Topsis-Vikas-102067010 1.0.8
Calculates Topsis Score and Rank them accordingly
License: MIT
Keywords: TOPSIS

## Topsis_Vikas

# TOPSIS

Submitted By: **Vikas Singh - 102067010**.

Type: **Package**.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Author: **Vikas Singh**.

Maintainer: **Vikas Singh <vs8422135@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-Vikas-102067010
```

### In Command Prompt

```
>> topsis 102067010-data.csv "1,1,1,1" "+,+,-,+" 102067010-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.

| Mobile Name  | Price($) | Storage(GB) | Camera(MP) | Looks |
| Moto g82     | 250      | 16          | 12         | 5     | 
| Samsung S32  | 200      | 16          | 8          | 3     |
| Oppo F17     | 300      | 32          | 16         | 4     | 
| Iphone 14    | 275      | 32          | 8          | 4     | 
| Redmi Note12 | 225      | 16          | 16         | 2     |
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)
| Mobile Name  | Price($) | Storage(GB) | Camera(MP) | Looks | Topsis Score | Rank |
| Moto g82     | 250      | 16          | 12         | 5     | 0.5682       | 3    |
| Samsung S32  | 200      | 16          | 8          | 3     | 0.1658       | 5    |
| Oppo F17     | 300      | 32          | 16         | 4     | 0.8342       | 1    |
| Iphone 14    | 275      | 32          | 8          | 4     | 0.5691       | 2    |
| Redmi Note12 | 225      | 16          | 16         | 2     | 0.3096       | 4    |

<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**