semvec/pypi-packages huggingface dataset:
barani 1.0.1
Rubika Library Bot
License: MIT
Keywords: rubika,bot,robot,library,rubikalib,rubikalib.ml,rubikalib.ir,barani,Barani,libraryrobiran,Rubika,Python

How to import hamid's library is as follows:

<< from barani import Robot,Socket >>

An example:

from barani import Robot,Socket

bot = Robot("Your Auth Account")
topsis-prabhnoor-102003560 1.0.1
A Python package to find TOPSIS for multi-criteria decision analysis method
License: MIT
Keywords: UCS654,TIET

Project description
TOPSIS-ANALYSIS
By: Prabhnoor Singh Ghotra

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
```bash
pip install topsis-prabhnoor-102003560==1.0.0
```


### Usage

Arguments Required:
(Assumne we have 3 attributes in dataset.)

You have to required one .csv file. (102003560-data.csv)
Pass weights to each attribute. (e.g.: [1,1,1,1,1])
Pass impacts to each attribute. (e.g.: [+,-,+,-,+])
Pass the name of the file with you want to put on .csv file. (102003560-result-1.csv)


Enter csv filename followed by .csv extension, then enter the weights string with values separated by commas, followed by the impacts string with comma separated signs (+,-) and name of file followed by -.csv- extension in which the user wants the output file

## Example
#### sample.csv
```bash
Fund Name	P1	    P2  	P3	    P4	    P5
M1	        0.84	0.71	6.7	    42.1	12.59
M2	        0.91	0.83	7	    31.7	10.11
M3	        0.79	0.62	4.8	    46.7	13.23
M4	        0.78	0.61	6.4	    42.4	12.55
M5	        0.94	0.88	3.6	    62.2	16.91
M6	        0.88	0.77	6.5	    51.5	14.91
M7	        0.66	0.44	5.3	    48.9	13.83
M8	        0.93	0.86	3.4	    37	    10.55

```

### INPUT
```python
topsis 102003560-data.csv 1,1,1,1,1 +,-,+,-,+ 102003560-result-1.csv
```

### OUTPUT

```bash
Fund Name	P1	        P2	        P3	        P4	        P5	    Topsis Score	Rank
M1	    0.351077437	0.344400588	0.421433661	0.322539084	0.335992288	0.594551725	    2
M2	    0.380333891	0.402609138	0.440303825	0.24286197	0.269807945	0.566246179	    3
M3	    0.330179971	0.300744175	0.301922623	0.357780884	0.353072118	0.485394123	    6
M4	    0.326000478	0.295893463	0.402563497	0.324837462	0.334924798	0.612775882	    1
M5	    0.39287237	0.4268627	0.226441967	0.476530428	0.451281142	0.361550918	    8
M6	    0.367795411	0.373504863	0.408853551	0.394554936	0.397906673	0.538764066	    5
M7	    0.275846558	0.21343135	0.333372896	0.374635658	0.369084459	0.560458621	    4
M8	    0.388692877	0.417161275	0.213861858	0.283466653	0.281550328	0.38966293	    7

```
Topsis-Vanshaj-102003346 0.1.2
Calculates Topsis Score and Rank them accordingly
License: MIT
Keywords: TOPSIS

TOPSIS


How to use
The package Topsis-Vanshaj-102003346 can be run though the command line as follows:

>> pip install Topsis-Vanshaj-102003346
>>python topsis data.csv "1,1,1,1,1" "+,-,+,-,+" result.csv
Sample Input

Fund Name	P1	P2	P3	P4	P5
M1	0.85	0.72	7	37.3	11.47
M2	0.67	0.45	5.5	58.2	16.21
M3	0.83	0.69	3.9	68.3	18.43
M4	0.83	0.69	3.9	42.7	12.03
M5	0.71	0.5	    3.2	57.9	15.58
M6	0.94	0.88	4.7	46.6	13.28
M7	0.68	0.46	7	41.4	12.39
M8	0.95	0.9	    6.5	33.8	10.54


Output of this sample input
The output that will be generated from the following input data will be:

Fund Name	P1	P2	P3	P4	P5	Topsis Score	Rank
M1	0.85	0.72	7	37.3	11.47	0.606515477	2
M2	0.67	0.45	5.5	58.2	16.21	0.571404528	3
M3	0.83	0.69	3.9	68.3	18.43	0.411429976	8
M4	0.83	0.69	3.9	42.7	12.03	0.435972941	6
M5	0.71	0.5	    3.2	57.9	15.58	0.436800052	5
M6	0.94	0.88	4.7	46.6	13.28	0.422424787	7
M7	0.68	0.46	7	41.4	12.39	0.66149433	1
M8	0.95	0.9	    6.5	33.8	10.54	0.530809186	4

The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank** .
Here the ranks are given as rank 1 is the best solution according to the weights and impacts given and rank 8 is the worst solution.