semvec/pypi-packages huggingface dataset:
coqui 0.0.10
A Python API and CLI to use Coqui services programmatically 
Requires Python: >=3.7,<3.12

# Coqui CLI

🐸CLI lets you use our services programmatically.

## Documentation

Usage documentation available at [coqui-py.readthedocs.io](https://coqui-py.readthedocs.io/).

## Development

```bash
$ python -m pip install flit
# Below, -s means editable mode, symlinked. Using it lets you edit the code and
# test changes without having to reinstall the package.
$ python -m flit install -s
$ coqui --help
$ coqui login --help
$ # etc
```
Topsis-Ananya-102016083 1.1.0
A python package to carry out MCDM using TOPSIS on a dataset with given weights, impacts
Requires Python: >=3.6

# TOPSIS-Python

Submitted By: **Ananya Singh** <br>
Roll Number: **102016083** <br>
Batch: **3CS8**

***
pypi: <https://pypi.org/project/Topsis-Ananya-102016083>
***

## Installation
```pip install Topsis-Ananya-102016083```

## What is TOPSIS

TOPSIS or Technique for Order Preference by Similarity to Ideal Solution 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

<br>

## How to use this package:

Topsis-Ananya-102016083  can be run as in the following example:

### In Command Prompt
```
>> python 102016083.py inputfile.csv "1,1,1,1,1" "-,+,+,+,+" outputfile.csv
```
where 102016083.py contains code for topsis.
<br>

## Working and functions

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria-based decision-making method. TOPSIS chooses the alternative of shortest the Euclidean distance from the ideal solution and greatest distance from the negative ideal solution. 

This following package uses a function topsis_score with 4 parameters: input,weights,impact,output.
It generates an output.csv file using the Input.csv and the parameters weights and impact.
Function normalised normalises the input dataframe.
Function weighted multiplies the weights.
Function checkparameters checks for all conditions to be checked.
Function best returns best ideal value for each column depending on impact sign.
Function worst returns worst ideal value for each column depending on impact sign.
Function euclidean_dist returns euclidean distance value.

<br>

## Dataset

Consider an input.csv file  
First column of file is removed since it is the object/variable name.
File has 3 or more columns.
2nd to last column contains numeric values only.

### input:

```python
topsis inputfile.csv "1,1,1,1,1" "-,+,+,+,+" outputfile.csv
```

### output:

output.csv file will contain the input data along with columns of topsis score and rank.

Weights `w` will be multiplied and are normalised using normalized function in the code.

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

<br>

The rankings are stored in a csv file, with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.

## Debugging and Exception Handling
> The program checks for the following cases:
1. Correct number of parameters.
2. Appropriate message for wrong input.
3. Handling filenotfound error
4. Number of weights, impacts and columns are same
5. Impacts are '-' or '+', separated by ','

## License
Copyright 2023 Ananya Singh
<br>
This repository is licensed under the MIT license.
<br>
See LICENSE for details.
[MIT](https://choosealicense.com/licenses/mit/)
Topsis-Pranav-102003432 0.13
This is a topsis package of version 0.13

## Topsis_Pranav_102003432

# TOPSIS

Submitted By: **Pranav Singh - 102003432**.

Type: **Package**.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **1.0.0**.

Date: **2022-01-22**.

Author: **Pranav Singh**.

Maintainer: **Pranav Singh <psingh2_be20@thapar.edu>**.

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-Pranav-102003432
```

### In Command Prompt

```
>> topsis data.csv "1,1,1,1,1" "+,+,-,+,-" 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 |     P1      | P2 | P3 | P4 | P5 |
| ----- | ----------- | ------------- | ---- | -------- | ---- |
| M1    | 0.7       | 0.5        | 7 | 37    | 11.3 |
| M2    | 0.8        | 0.6          | 7 | 46    | 13.4 |
| M3    | 0.7       | 0.5          | 7 | 48    | 14 |
| M4    | 0.9        | 0.8          | 7 | 44    | 13.2 |
| M5    | 0.9        | 0.9          | 5  | 37    | 11.1 |
| M6    | 0.9        | 0.6          | 3  | 67    | 18 |
| M7    | 0.9        | 0.5          | 7  | 39    | 11.8 |
| M8    | 0.9        | 0.9          | 5  | 46    | 13.2 |

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)

| Model |     P1      | P2 | P3 | P4 | P5 | Topsis Score | Rank |
| ----- | ----------- | ------------- | ---- | -------- | ---- |-----| ----|
| M1    | 0.7       | 0.5        | 7 | 37    | 11.3 | 0.28016 | 5 |
| M2    | 0.8        | 0.6          | 7 | 46    | 13.4 | 0.8292 | 1 |
| M3    | 0.7       | 0.5          | 7 | 48    | 14 | 0.17536 | 8 |
| M4    | 0.9        | 0.8          | 7 | 44    | 13.2 | 0.25 | 7 |
| M5    | 0.9        | 0.9          | 5  | 37    | 11.1 | 0.56483 | 3 |
| M6    | 0.9        | 0.6          | 3  | 67    | 18 | 0.27313 | 6 |
| M7    | 0.9        | 0.5          | 7  | 39    | 11.8 | 0.55075 | 4 |
| M8    | 0.9        | 0.9          | 5  | 46    | 13.2 | 0.65029 | 2 |

<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
Topsis-Sarisha-102003445 0.7
This is a topsis package of version 0.7

## Topsis_Sarisha_102003445

# TOPSIS

Submitted By: *Sarisha Aggarwal - 102003445*.

Type: *Package*.

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

Version: *1.0.0*.

Date: *2023-01-23*.

Author: *Sarisha Aggarwal*.

Maintainer: *Sarisha Aggarwal <saggarwal4_be20@thapar.edu>*.

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

---

## What is TOPSIS?

*Technique for **Order **Preference by **Similarity to **Ideal **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-Sarisha-102003445


### In Command Prompt


>> topsis data.csv "1,1,1,1,1" "+,+,+,+,+" 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 |     P1      | P2 | P3 | P4 | P5 |
| ----- | ----------- | ------------- | ---- | -------- | ---- |
| M1    | 0.7       | 0.5        | 7 | 37    | 11.3 |
| M2    | 0.8        | 0.6          | 7 | 46    | 13.4 |
| M3    | 0.7       | 0.5          | 7 | 48    | 14 |
| M4    | 0.9        | 0.8          | 7 | 44    | 13.2 |
| M5    | 0.9        | 0.9          | 5  | 37    | 11.1 |
| M6    | 0.9        | 0.6          | 3  | 67    | 18 |
| M7    | 0.9        | 0.5          | 7  | 39    | 11.8 |
| M8    | 0.9        | 0.9          | 5  | 46    | 13.2 |

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)

| Model |     P1      | P2 | P3 | P4 | P5 | Topsis Score | Rank |
| ----- | ----------- | ------------- | ---- | -------- | ---- |-----| ----|
| M1    | 0.7       | 0.5        | 7 | 37    | 11.3 | 0.28016 | 5 |
| M2    | 0.8        | 0.6          | 7 | 46    | 13.4 | 0.8292 | 1 |
| M3    | 0.7       | 0.5          | 7 | 48    | 14 | 0.17536 | 8 |
| M4    | 0.9        | 0.8          | 7 | 44    | 13.2 | 0.25 | 7 |
| M5    | 0.9        | 0.9          | 5  | 37    | 11.1 | 0.56483 | 3 |
| M6    | 0.9        | 0.6          | 3  | 67    | 18 | 0.27313 | 6 |
| M7    | 0.9        | 0.5          | 7  | 39    | 11.8 | 0.55075 | 4 |
| M8    | 0.9        | 0.9          | 5  | 46    | 13.2 | 0.65029 | 2 |

<br>
The output file contains columns of input file along with two additional columns having *Topsis_score* and *Rank*