# Topsis_Aniket_102003643
_for: **Topsis Project**_
_submitted by: **Aniket Sharma**_
_Roll no: **102003643**_
_Group: **3COE25**_
Topsis_Aniket_102003643 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_Aniket_102003643.
```bash
pip install Topsis_Aniket_102003643
```
## 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
```
## Other notes
* The first column and first row are removed by the library before processing, in attempt to remove indices and headers. So make sure the csv follows the format as shown in sample.csv.
* Make sure the csv does not contain categorical values
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"description": "# Topsis_Aniket_102003643\n\n_for: **Topsis Project**_\n_submitted by: **Aniket Sharma**_\n_Roll no: **102003643**_\n_Group: **3COE25**_\n\n\nTopsis_Aniket_102003643 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).\n\n## Installation\n\nUse the package manager [pip](https://pip.pypa.io/en/stable/) to install Topsis_Aniket_102003643.\n\n```bash\npip install Topsis_Aniket_102003643\n```\n\n## Usage\n\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 _(+,-)_\n```bash\ntopsis sample.csv \"1,1,1,1\" \"+,-,+,+\"\n```\nor vectors can be entered without \" \"\n```bash\ntopsis sample.csv 1,1,1,1 +,-,+,+\n```\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 _(\" \")_.\n\nTo view usage __help__, use\n```\ntopsis /h\n```\n## Example\n\n#### sample.csv\n\nA csv file showing data for different mobile handsets having varying features.\n\n| Model | Storage space(in gb) | Camera(in MP)| Price(in $) | Looks(out of 5) |\n| :----: |:--------------------:|:------------:|:------------:|:---------------:|\n| M1 | 16 | 12 | 250 | 5 |\n| M2 | 16 | 8 | 200 | 3 |\n| M3 | 32 | 16 | 300 | 4 |\n| M4 | 32 | 8 | 275 | 4 |\n| M5 | 16 | 16 | 225 | 2 |\n\nweights vector = [ 0.25 , 0.25 , 0.25 , 0.25 ]\n\nimpacts vector = [ + , + , - , + ]\n\n### input:\n\n```python\ntopsis sample.csv \"0.25,0.25,0.25,0.25\" \"+,+,-,+\"\n```\n\n### output:\n```\n TOPSIS RESULTS\n-----------------------------\n\n P-Score Rank\n1 0.534277 3\n2 0.308368 5\n3 0.691632 1\n4 0.534737 2\n5 0.401046 4\n\n``` \n\n## Other notes\n\n* The first column and first row are removed by the library before processing, in attempt to remove indices and headers. So make sure the csv follows the format as shown in sample.csv.\n* Make sure the csv does not contain categorical values",
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