Topsis-Prabhjot-102003106


NameTopsis-Prabhjot-102003106 JSON
Version 1.1.6 PyPI version JSON
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SummaryTopsis Calculation Package
upload_time2023-01-22 18:21:02
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authorPrabhjot Singh Bhatia
requires_python
licenseMIT License 102003106 Copyright (c) 2023 Prabhjot Singh Bhatia Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
keywords python topsis mcdm
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            # Topsis-Prabhjot-102003106

## 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-Prabhjot-102003106```

## Input csv format
Input file must contain three or more columns
<br>
First column is the object/variable name 
<br>
2nd to last columns contain numeric values only

## How to use it
Python File<br>
```
which includes complete code for topsis calculation
```
Command Prompt<br>
```
python <python_file> <Input Data File> <Weights> <Impacts> <Result File Name>
```
<br>

Example:<br>
```
python codefile.py inputfile.csv “1,1,1,1,2” “+,+,+,+,-” result.csv
python 102003106.py 102003106-data.csv “1,1,1,1,2” “+,+,+,+,-” 102003106-result.csv
```
<br><br>
<i>Note: The weights and impacts should be ',' seperated, input file should be in pwd.</i> 

## Functions and Return Values

```
function = topsis_calc()
return values = Creates a csv file with the topsis rank and performance score
```

## Sample input data
| Fund Name       | 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
| Fund Name       | P1 | P2 | P3 | P4 | P5 | Topsis Score | 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           |

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    "description": "# Topsis-Prabhjot-102003106\r\n\r\n## What is TOPSIS\r\nTechnique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method.\r\n<br> \r\nTOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.\r\n\r\n## Installation\r\n```pip install Topsis-Prabhjot-102003106```\r\n\r\n## Input csv format\r\nInput file must contain three or more columns\r\n<br>\r\nFirst column is the object/variable name \r\n<br>\r\n2nd to last columns contain numeric values only\r\n\r\n## How to use it\r\nPython File<br>\r\n```\r\nwhich includes complete code for topsis calculation\r\n```\r\nCommand Prompt<br>\r\n```\r\npython <python_file> <Input Data File> <Weights> <Impacts> <Result File Name>\r\n```\r\n<br>\r\n\r\nExample:<br>\r\n```\r\npython codefile.py inputfile.csv \u201c1,1,1,1,2\u201d \u201c+,+,+,+,-\u201d result.csv\r\npython 102003106.py 102003106-data.csv \u201c1,1,1,1,2\u201d \u201c+,+,+,+,-\u201d 102003106-result.csv\r\n```\r\n<br><br>\r\n<i>Note: The weights and impacts should be ',' seperated, input file should be in pwd.</i> \r\n\r\n## Functions and Return Values\r\n\r\n```\r\nfunction = topsis_calc()\r\nreturn values = Creates a csv file with the topsis rank and performance score\r\n```\r\n\r\n## Sample input data\r\n| Fund Name       | P1 | P2 | P3 | P4 | P5 |\r\n| ------------- |:-------------:| -----:|-----:|-----:|-----:|\r\n| M1    | 0.62 | 0.38 | 3.8 | 33.8 | 9.65  | \r\n | M2    | 0.75 | 0.56 | 5.7 | 50.3 | 14.33 | \r\n | M3    | 0.95 | 0.90 | 6.5 | 65.6 | 18.49 | \r\n | M4    | 0.61 | 0.37 | 6.2 | 43.6 | 12.70 | \r\n | M5    | 0.60 | 0.36 | 6.4 | 61.2 | 17.14 | \r\n | M6    | 0.76 | 0.58 | 5.3 | 68.0 | 18.66 | \r\n | M7    | 0.66 | 0.44 | 6.2 | 47.2 | 13.63 | \r\n | M8    | 0.80 | 0.64 | 5.7 | 37.1 | 11.06 | \r\n\r\n\r\n## Sample output data\r\n| Fund Name       | P1 | P2 | P3 | P4 | P5 | Topsis Score | Rank |\r\n| ------------- |:-------------:| -----:|-----:|-----:|-----:| ---: | ---: |\r\n| M1    | 0.62 | 0.38 | 3.8 | 33.8 | 9.65  |  0.317272185       | 8           | \r\n| M2    | 0.75 | 0.56 | 5.7 | 50.3 | 14.33 |  0.452068871       | 4           | \r\n| M3    | 0.95 | 0.90 | 6.5 | 65.6 | 18.49 |  0.689037307       | 1           | \r\n| M4    | 0.61 | 0.37 | 6.2 | 43.6 | 12.70 |  0.340383903       | 7           | \r\n| M5    | 0.60 | 0.36 | 6.4 | 61.2 | 17.14 |  0.367206376       | 6           |\r\n| M6    | 0.76 | 0.58 | 5.3 | 68.0 | 18.66 |  0.481350901       | 3           | \r\n| M7    | 0.66 | 0.44 | 6.2 | 47.2 | 13.63 |  0.372999972       | 5           | \r\n| M8    | 0.80 | 0.64 | 5.7 | 37.1 | 11.06 |  0.51226635        | 2           | \r\n",
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Prabhjot Singh Bhatia
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