Metadata-Version: 2.1
Name: Topsis-Prisha-102116052
Version: 0.0.1
Summary: TOPSIS Application
Home-page: UNKNOWN
Author: Prisha Sawhney
Author-email: psawhney_be21@thapar.edu
License: UNKNOWN
Keywords: python,topsis
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
License-File: LICENSE.txt


# Topsis-Prisha-102116052



_for: **Project-1 (UCS654)**_

_submitted by: **Prisha Sawhney**_

_Roll no: **102116052**_

_Group: **3CS10**_





Topsis-Prisha-102116052 is a Python library for dealing with Multiple Criteria Decision Making(MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).



## Installation



Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Topsis-Prisha-102116052.



```bash

pip install Topsis-Prisha-102116052

```



## Usage



Enter csv filename followed by _.csv_ extentsion, then enter the _weights_ vector with vector values separated by commas, followed by the _impacts_ vector with comma separated signs _(+,-)_

```bash

topsis sample.csv "1,1,1,1" "+,-,+,+"

```

or vectors can be entered without " "

```bash

topsis sample.csv 1,1,1,1 +,-,+,+

```

But the second representation does not provide for inadvertent spaces between vector values. So, if the input string contains spaces, make sure to enclose it between double quotes _(" ")_.



To view usage __help__, use

```

topsis /h

```

## 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 = [ 0.25 , 0.25 , 0.25 , 0.25 ]



impacts vector = [ + , + , - , + ]



### input:



```python

topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+"

```



### output:

```

      TOPSIS RESULTS

-----------------------------



    P-Score  Rank

1  0.534277     3

2  0.308368     5

3  0.691632     1

4  0.534737     2

5  0.401046     4



``` 



