# topsis_nitanshjain_102017025
## What is TOPSIS
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method.<br> TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.
## Installation
```pip install topsis-nitanshjain-102017025```
## Input csv format
Input file contain three or more columns<br>
First column is the object/variable name <br>
From 2nd to last columns contain numeric values only
## How to use it
Python File<br>
```
from topsis.topsis_nitanshjain_102017025 import solve_topsis
solve_topsis()
```
Command Prompt<br>
```
topsis <python_file> <Input Data File> <Weights> <Impacts> <Result File Name>
```
<br>
Example:<br>
```
topsis topsis.py inputfile.csv “1,1,1,1,2” “+,+,+,+,-” result.csv
```
<br><br>
<i>Note: The weights and impacts should be ',' seperated, input file should be in pwd.</i>
## Functions, Parameters and Return Values
```
function = solve_topsis()
parameters = No input parameters
return values = Creates a csv file with the topsis rank and performance score
```
## Sample input data
| Model | P1 | P2 | P3 | P4 | P5 |
| ------------- |:-------------:| -----:|-----:|-----:|-----:|
| M1 | 0.62 | 0.38 | 3.8 | 33.8 | 9.65 |
| M2 | 0.75 | 0.56 | 5.7 | 50.3 | 14.33 |
| M3 | 0.95 | 0.90 | 6.5 | 65.6 | 18.49 |
| M4 | 0.61 | 0.37 | 6.2 | 43.6 | 12.70 |
| M5 | 0.60 | 0.36 | 6.4 | 61.2 | 17.14 |
| M6 | 0.76 | 0.58 | 5.3 | 68.0 | 18.66 |
| M7 | 0.66 | 0.44 | 6.2 | 47.2 | 13.63 |
| M8 | 0.80 | 0.64 | 5.7 | 37.1 | 11.06 |
## Sample output data
| Model | P1 | P2 | P3 | P4 | P5 | Performance Score | Topsis Rank |
| ------------- |:-------------:| -----:|-----:|-----:|-----:| ---: | ---: |
| M1 | 0.62 | 0.38 | 3.8 | 33.8 | 9.65 | 0.317272185 | 8 |
| M2 | 0.75 | 0.56 | 5.7 | 50.3 | 14.33 | 0.452068871 | 4 |
| M3 | 0.95 | 0.90 | 6.5 | 65.6 | 18.49 | 0.689037307 | 1 |
| M4 | 0.61 | 0.37 | 6.2 | 43.6 | 12.70 | 0.340383903 | 7 |
| M5 | 0.60 | 0.36 | 6.4 | 61.2 | 17.14 | 0.367206376 | 6 |
| M6 | 0.76 | 0.58 | 5.3 | 68.0 | 18.66 | 0.481350901 | 3 |
| M7 | 0.66 | 0.44 | 6.2 | 47.2 | 13.63 | 0.372999972 | 5 |
| M8 | 0.80 | 0.64 | 5.7 | 37.1 | 11.06 | 0.51226635 | 2 |
Raw data
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"description": "# topsis_nitanshjain_102017025\n\n## What is TOPSIS\nTechnique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method.<br> TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.\n\n## Installation\n```pip install topsis-nitanshjain-102017025```\n\n## Input csv format\nInput file contain three or more columns<br>\nFirst column is the object/variable name <br>\nFrom 2nd to last columns contain numeric values only\n\n## How to use it\nPython File<br>\n```\nfrom topsis.topsis_nitanshjain_102017025 import solve_topsis\nsolve_topsis()\n```\nCommand Prompt<br>\n```\ntopsis <python_file> <Input Data File> <Weights> <Impacts> <Result File Name>\n```\n<br>\n\nExample:<br>\n```\ntopsis topsis.py inputfile.csv \u201c1,1,1,1,2\u201d \u201c+,+,+,+,-\u201d result.csv\n```\n<br><br>\n<i>Note: The weights and impacts should be ',' seperated, input file should be in pwd.</i> \n\n## Functions, Parameters and Return Values\n\n```\nfunction = solve_topsis()\nparameters = No input parameters\nreturn values = Creates a csv file with the topsis rank and performance score\n```\n\n## Sample input data\n| Model | P1 | P2 | P3 | P4 | P5 |\n| ------------- |:-------------:| -----:|-----:|-----:|-----:|\n| M1 | 0.62 | 0.38 | 3.8 | 33.8 | 9.65 | \n | M2 | 0.75 | 0.56 | 5.7 | 50.3 | 14.33 | \n | M3 | 0.95 | 0.90 | 6.5 | 65.6 | 18.49 | \n | M4 | 0.61 | 0.37 | 6.2 | 43.6 | 12.70 | \n | M5 | 0.60 | 0.36 | 6.4 | 61.2 | 17.14 | \n | M6 | 0.76 | 0.58 | 5.3 | 68.0 | 18.66 | \n | M7 | 0.66 | 0.44 | 6.2 | 47.2 | 13.63 | \n | M8 | 0.80 | 0.64 | 5.7 | 37.1 | 11.06 | \n\n\n## Sample output data\n| Model | P1 | P2 | P3 | P4 | P5 | Performance Score | Topsis Rank |\n| ------------- |:-------------:| -----:|-----:|-----:|-----:| ---: | ---: |\n| M1 | 0.62 | 0.38 | 3.8 | 33.8 | 9.65 | 0.317272185 | 8 | \n| M2 | 0.75 | 0.56 | 5.7 | 50.3 | 14.33 | 0.452068871 | 4 | \n| M3 | 0.95 | 0.90 | 6.5 | 65.6 | 18.49 | 0.689037307 | 1 | \n| M4 | 0.61 | 0.37 | 6.2 | 43.6 | 12.70 | 0.340383903 | 7 | \n| M5 | 0.60 | 0.36 | 6.4 | 61.2 | 17.14 | 0.367206376 | 6 |\n| M6 | 0.76 | 0.58 | 5.3 | 68.0 | 18.66 | 0.481350901 | 3 | \n| M7 | 0.66 | 0.44 | 6.2 | 47.2 | 13.63 | 0.372999972 | 5 | \n| M8 | 0.80 | 0.64 | 5.7 | 37.1 | 11.06 | 0.51226635 | 2 | \n\n \n \n\n\n\n\n \n\n\n\n\n",
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