semvec/pypi-packages huggingface dataset:
harchetan 1.0.0
TOPSIS
License: MIT

# TOPSIS-Harchetan-101803246
TOPSIS is an acronym that stands for "Technique of Order Preference Similarity to the Ideal Solution" and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those. 

## Installation 
'''pip install TOPSIS-Harchetan-101803246==1.0.0'''

## License
Â©  2020 Harchetan
This repository is licensed under the MIT license. 
See LICENSE for details
TOPSIS-Shivansh-101803103 1.0.0
Python Package for TOPSIS
License: MIT

# TOPSIS Package in Python

Submitted by : Shivansh Kumar
Roll no : 101803103

UCS538

* * *
## What Is TOPSIS

TOPSIS is an acronym that stands for 'Technique of Order Preference Similarity to the Ideal Solution' and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those

* * *

## How To Use

The package TOPSIS-Shivansh-101803103 can be run though the command line as follows:
```
>> pip install TOPSIS-Shivansh-101803103
```
```
>> topsis data.csv "1,1,1,2" "+,+,-,+" result.csv
```
> **Note:**
> 
> *   Usages: 
>     **topsis data.csv "1,1,1,2" "+,+,-,+" result.csv**
> *   **Input File**:
>     
>     > *   Input file contain three or more columns.
>     > *   First column is the object/variable name (e.g. M1, M2, M3, M4...).
>     > *   From 2nd to last columns contain numeric values only.
> *   **Output File**:
>     
>     > *   Result file contains all the columns of input file and two additional columns having ***Topsis Score*** and ***Rank***
> *   The output is created in the form of csv file and stored and also it is displayed.
> *  The impacts given in the command line should be either + or - depending if you want to maximise the column parameter or minimise it.

## Sample Input

Here is a sample set of data which can be used for the following package:

<table><thead><tr><th>Model</th><th>Correlation</th><th>R2</th><th>RMSE</th><th>Accuracy</th></tr></thead><tbody><tr><td>M1</td><td>0.79</td><td>0.62</td><td>1.25</td><td>60.89</td></tr><tr><td>M2</td><td>0.66</td><td>0.44</td><td>2.89</td><td>63.07</td></tr><tr><td>M3</td><td>0.56</td><td>0.31</td><td>1.57</td><td>62.87</td></tr><tr><td>M4</td><td>0.82</td><td>0.67</td><td>2.68</td><td>70.19</td></tr><tr><td>M5</td><td>0.75</td><td>0.56</td><td>1.3</td><td>80.39</td></tr></tbody></table>

## Output of this sample input

The output that will be generated from the following input data will be:

<table><thead><tr><th>Model</th><th align="right">Correlation</th><th align="center">R2</th><th>RMSE</th><th>Accuracy</th><th>Topsis Score</th><th>Rank</th></tr></thead><tbody><tr><td>M1</td><td align="right">0.79</td><td align="center">0.62</td><td>1.25</td><td>60.89</td><td>0.639133014134258</td><td>2.0</td></tr><tr><td>M2</td><td align="right">0.66</td><td align="center">0.44</td><td>2.89</td><td>63.07</td><td>0.212591829692779</td><td>5.0</td></tr><tr><td>M3</td><td align="right">0.56</td><td align="center">0.31</td><td>1.57</td><td>62.87</td><td>0.407845677613051</td><td>4.0</td></tr><tr><td>M4</td><td align="right">0.82</td><td align="center">0.67</td><td>2.68</td><td>70.19</td><td>0.519153239500747</td><td>3.0</td></tr><tr><td>M5</td><td align="right">0.75</td><td align="center">0.56</td><td>1.3</td><td>80.39</td><td>0.828266585193581</td><td>1.0</td></tr></tbody></table>

Here the ranks are given as rank 1 is the best solution according to the weights and impacts given and rank 5 is the worst solution.

* * *
capablerobot-usbregister 1.3.1.2
Windows command line tool to assist in registering USB drivers
License: MIT
Requires Python: >=3.6,<4.0
Keywords: usb,hardware

# Capable Robot : USB Register

Windows command line tool & python library to assist in registering USB drivers.

This repository includes binaries built from the [libwdi](https://github.com/pbatard/libwdi) project.  Specifically:

- `wdi-64bit.exe` : 64-bit binary of [wdi-simple](https://github.com/pbatard/libwdi/blob/master/examples/wdi-simple.c), version v1.3.1.
- `wdi-32bit.exe` : 32-bit binary of [wdi-simple](https://github.com/pbatard/libwdi/blob/master/examples/wdi-simple.c), version v1.3.1.

The python wrapper automatically runs the appropiate binary based on your computers's architecture.

The wrapper also includes registration settings for the following products, enabling error-free device registration.

- [Capable Robot Programmable USB Hub](https://capablerobot.com/products/programmable-usb-hub/)


Pull requests to add additional devices will be gladly accepted.

## Installation

```
    pip install capablerobot_usbregister
```

## Usage

```

    Usage: usbregister [OPTIONS] COMMAND [ARGS]...

    Options:
      --verbose  Increase logging level.
      --help     Show this message and exit.

    Commands:
      list      Print the known devices which can be registered to drivers.
      device    Register the specified device.
      register  Registers driver based on Vendor ID, Product ID, and interface ID.

```