semvec/pypi-packages huggingface dataset:
topsis-anubhav-102003551 1.0.0
A Python package to find TOPSIS for multi-criteria decision analysis method
License: MIT
Keywords: UCS654,TIET

Project description
CALCO-O-TOPSIS
By: Anubhav Gupta

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-anubhav-102003551==1.0.0
```


### Usage

Arguments Required:
(Assume we have 5  attributes in dataset.)

You have to required one .csv file. (102003551-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. (102003551-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 102003551-data.csv 1,1,1,1,1 +,-,+,-,+ 102003551-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-rohitThapar-102003482 1.0.0
This is a Python library for handling problems related to Multiple Criteria Decision Making(MCDM)
License: MIT

# TOPSIS

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

Version: **1.0.0**.

Author: **Rohit Thapar**.

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

---

## How to install this package:

```
>> pip install topsis-rohitThapar-102003482
```

### In Command Prompt

```
>> topsis data.csv "1,1,1,1,1" "+,+,-,+,-" output.csv
```

## Input file (data.csv)

    Fund Name          P1        P2        P3        P4        P5    
--  -----------  --------  --------  --------  --------  --------  
 0  M1           0.395818  0.419886  0.221712  0.485574  0.467084      
 1  M2           0.271291  0.196656  0.221712  0.485574  0.4624        
 2  M3           0.413607  0.457091  0.286079  0.45586   0.446787       
 3  M4           0.302422  0.244491  0.479183  0.28917   0.310695       
 4  M5           0.271291  0.196656  0.479183  0.226118  0.252928      
 5  M6           0.413607  0.457091  0.221712  0.226118  0.234713      
 6  M7           0.413607  0.457091  0.286079  0.28917   0.297164      
 7  M8           0.302422  0.244491  0.479183  0.226118  0.253969      

<br>

## Output file (result.csv)
    Fund Name          P1        P2        P3        P4        P5    Topsis Score    Rank
--  -----------  --------  --------  --------  --------  --------  --------------  ------
 0  M1           0.395818  0.419886  0.221712  0.485574  0.467084       0.924217        1
 1  M2           0.271291  0.196656  0.221712  0.485574  0.4624         0.591977        3
 2  M3           0.413607  0.457091  0.286079  0.45586   0.446787       0.864965        2
 3  M4           0.302422  0.244491  0.479183  0.28917   0.310695       0.208741        6
 4  M5           0.271291  0.196656  0.479183  0.226118  0.252928       0.0340168       8
 5  M6           0.413607  0.457091  0.221712  0.226118  0.234713       0.530087        5
 6  M7           0.413607  0.457091  0.286079  0.28917   0.297164       0.577036        4
 7  M8           0.302422  0.244491  0.479183  0.226118  0.253969       0.110191        7

<br>

The output file contains columns of input file along with two additional columns having Topsis_score and Rank