semvec/pypi-packages huggingface dataset:
LolzteamApi 1.1.28
A library that contains all the methods of the Lolzteam API (Market/Forum/Antipublic)
Requires Python: >=3.9

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## Installation

*You can install the library using pip:*

```commandline
pip install LolzteamApi
```

## Usage

*Import the `LolzteamApi` module and create an instance of the `LolzteamApi` class to start using the API:*

```python
from LolzteamApi import LolzteamApi, Types
api = LolzteamApi(token='your_token')
```

## Documentation

### [Forum](https://github.com/AS7RIDENIED/Lolzteam_Python_Api/blob/main/Documentation/Forum.md) - [Market](https://github.com/AS7RIDENIED/Lolzteam_Python_Api/blob/main/Documentation/Market.md) - [Antipublic](https://github.com/AS7RIDENIED/Lolzteam_Python_Api/blob/main/Documentation/Antipublic.md) - [DelaySynchronizer](https://github.com/AS7RIDENIED/Lolzteam_Python_Api/blob/main/Documentation/DelaySynchronizer.md) - [BBCODE](https://github.com/AS7RIDENIED/Lolzteam_Python_Api/blob/main/Documentation/BBCODE.md)

## Official Lolzteam documentation

### [Forum](https://docs.api.zelenka.guru/?forum) [Market](https://docs.api.zelenka.guru/?market) [Antipublic](https://antipublic.one/docs/?antipublic)
Topsis-Joyy-102117024 0.0.8
TOPSIS Implementation
Keywords: topsis,decision-analysis,similarity,decision-making,multi-criteria-decision

# TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)


## Description

### Topsis-Joyy-102117024

For: Assignment-1(UCS654) Submitted-by: **Joyy Goswami* Roll no: *102117024* Group: *3CS-1**

Topsis-Joyy-102117024 is a Python package for dealing with Multiple Criteria Decision Making(MCDM) problems by using TOPSIS

## Installation

Use the package manager *pip* to install Topsis-Joyy-102117024

pip install Topsis-Joyy-102117024

## 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 (+,-)

bash
python sample.py sample.csv "1,1,1,1,2" "+,+,-,+,+" sample-result.csv


## 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 = [1, 1, 1, 1]
impacts vector = [+,+,-,+]

### Input:

python sample.csv "1,1,1,1" "+,+,-,+" sample-result.csv

### Output:

|   Topsis-score   | Rank |
|-------------|------|
| 0.534277    | 3    |
| 0.308368    | 5    |
| 0.691632    | 1    |
| 0.534737    | 2    |
| 0.401046    | 4    |