# 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
```
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"description": "\r\n# Topsis-Prisha-102116052\r\n\r\n\r\n\r\n_for: **Project-1 (UCS654)**_\r\n\r\n_submitted by: **Prisha Sawhney**_\r\n\r\n_Roll no: **102116052**_\r\n\r\n_Group: **3CS10**_\r\n\r\n\r\n\r\n\r\n\r\nTopsis-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).\r\n\r\n\r\n\r\n## Installation\r\n\r\n\r\n\r\nUse the package manager [pip](https://pip.pypa.io/en/stable/) to install Topsis-Prisha-102116052.\r\n\r\n\r\n\r\n```bash\r\n\r\npip install Topsis-Prisha-102116052\r\n\r\n```\r\n\r\n\r\n\r\n## Usage\r\n\r\n\r\n\r\nEnter 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 _(+,-)_\r\n\r\n```bash\r\n\r\ntopsis sample.csv \"1,1,1,1\" \"+,-,+,+\"\r\n\r\n```\r\n\r\nor vectors can be entered without \" \"\r\n\r\n```bash\r\n\r\ntopsis sample.csv 1,1,1,1 +,-,+,+\r\n\r\n```\r\n\r\nBut 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 _(\" \")_.\r\n\r\n\r\n\r\nTo view usage __help__, use\r\n\r\n```\r\n\r\ntopsis /h\r\n\r\n```\r\n\r\n## Example\r\n\r\n\r\n\r\n#### sample.csv\r\n\r\n\r\n\r\nA csv file showing data for different mobile handsets having varying features.\r\n\r\n\r\n\r\n| Model | Storage space(in gb) | Camera(in MP)| Price(in $) | Looks(out of 5) |\r\n\r\n| :----: |:--------------------:|:------------:|:------------:|:---------------:|\r\n\r\n| M1 | 16 | 12 | 250 | 5 |\r\n\r\n| M2 | 16 | 8 | 200 | 3 |\r\n\r\n| M3 | 32 | 16 | 300 | 4 |\r\n\r\n| M4 | 32 | 8 | 275 | 4 |\r\n\r\n| M5 | 16 | 16 | 225 | 2 |\r\n\r\n\r\n\r\nweights vector = [ 0.25 , 0.25 , 0.25 , 0.25 ]\r\n\r\n\r\n\r\nimpacts vector = [ + , + , - , + ]\r\n\r\n\r\n\r\n### input:\r\n\r\n\r\n\r\n```python\r\n\r\ntopsis sample.csv \"0.25,0.25,0.25,0.25\" \"+,+,-,+\"\r\n\r\n```\r\n\r\n\r\n\r\n### output:\r\n\r\n```\r\n\r\n TOPSIS RESULTS\r\n\r\n-----------------------------\r\n\r\n\r\n\r\n P-Score Rank\r\n\r\n1 0.534277 3\r\n\r\n2 0.308368 5\r\n\r\n3 0.691632 1\r\n\r\n4 0.534737 2\r\n\r\n5 0.401046 4\r\n\r\n\r\n\r\n``` \r\n\r\n\r\n\r\n",
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