topsis-Sarvagy-102003553


Nametopsis-Sarvagy-102003553 JSON
Version 1.0.0 PyPI version JSON
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home_pagehttps://github.com/Sarvagy-Jain/topsis_python_package
SummaryA Python package to find TOPSIS for multi-criteria decision analysis method
upload_time2023-01-22 12:52:19
maintainer
docs_urlNone
authorSarvagy Jain
requires_python
licenseMIT
keywords ucs538 tiet
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            Project description
TOPSIS-ANALYSIS
By: Sarvagy Jain

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-SARVAGY-102003553    
```


### Usage

Arguments Required:
(Assumne we have 3 attributes in dataset.)

You have to required one .csv file. (102003553-data.csv)
Pass weights to each attribute. (e.g.: [1,1,1])
Pass impacts to each attribute. (e.g.: [+,-,+])
Pass the name of the file with you want to put on .csv file. (102003553-result.csv)


Enter csv filename followed by .csv extentsion, 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	        0.72	0.52	4.4	    66.6	18.06
M2	        0.71	0.5	    4.9	    48.4	13.63
M3      	0.82	0.67	6.1	    58.2	16.45
M4	        0.67	0.45	4.3	    48.9	13.58
M5	        0.75	0.56	3.3	    60.2	16.2
M6	        0.76	0.58	6.4	    33.3	10.26
M7	        0.85	0.72	3.2	    61.9	16.67
M8	        0.73	0.53	5.8	    36.5	10.89

```

### INPUT
```python
topsis 102003553-data.csv 1,1,1,1,1 +,+,-,+,+ 102003553-result.csv
```

### OUTPUT

```bash
Fund Name	P1	    P2	    P3	    P4	    P5	    Topsis Score	Rank
M1	        0.72	0.52	4.4	    66.6	18.06	0.607089574	    2
M2	        0.71	0.5	    4.9	    48.4	13.63	0.424434575	    6
M3	        0.82	0.67	6.1	    58.2	16.45	0.811786381	    1
M4	        0.67	0.45	4.3	    48.9	13.58	0.346716421	    8
M5	        0.75	0.56	3.3	    60.2	16.2	0.486990207	    4
M6	        0.76	0.58	6.4	    33.3	10.26	0.446021381	    5
M7	        0.85	0.72	3.2	    61.9	16.67	0.568789224	    3
M8	        0.73	0.53	5.8	    36.5	10.89	0.397353478	    7

```









            

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