| Name | Deepak-102003483 JSON |
| Version |
1.1.10
JSON |
| download |
| home_page | |
| Summary | Topsis Package |
| upload_time | 2023-01-22 19:11:02 |
| maintainer | |
| docs_url | None |
| author | Deepak Aggarwal |
| requires_python | |
| license | MIT License 102003483 Copyright (c) 2023 Deepak Aggarwal 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
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Deepak-102003483
## What is TOPSIS
Topsis stands for <b>T</b>echnique for <b>O</b>rder <b>P</b>reference by <b>S</b>imilarity to <b>I</b>deal <b>S</b>olution (TOPSIS). Topsis was originated back in the 1980s and was used for making decisions which are subjected to multiple-criteria.
<br>
TOPSIS takes in the use of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.
## Package Installation
```pip install Deepak-102003483==1.1.10```
## Input File in CSV Format
Input file must contain <b>Three or more columns</b>
<br>
First column contains the <b>Object Name / Variable Name </b>
<br>
Rest of the other columns contains only numeric values
## Usage Method
Command Prompt<br>
```
python <python_file> <Input_Data_File> <Weights> <Impacts> <Result_File_Name>
```
<br>
<br>
python_file -> Python Code file for Topsis Calculation <br>
Input_Data_File -> CSV file name <br>
Weights -> Weights for each Column <br>
Impacts -> Maximaization('+'), Minimization('-') <br>
Result_File_Name -> CSV file name to store result <br>
Example:<br>
```
python 102003483.py 102003483-data.csv “1,1,1,1,1” “+,-,+,-,+” 102003483-result-1.csv
python 102003483.py 102003483-data.csv “2,2,3,3,4” “-,+,-,+,-” 102003483-result-2.csv
```
<br><br>
<i>Note: The Weights and Impacts should be comma (',') seperated and Input CSV file should be in pwd(Present Working Directory).</i>
## Functions and Return Values
```
function = topsis_102003483()
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": "# Deepak-102003483\n\n## What is TOPSIS\nTopsis stands for <b>T</b>echnique for <b>O</b>rder <b>P</b>reference by <b>S</b>imilarity to <b>I</b>deal <b>S</b>olution (TOPSIS). Topsis was originated back in the 1980s and was used for making decisions which are subjected to multiple-criteria.\n<br> \nTOPSIS takes in the use of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.\n\n## Package Installation \n```pip install Deepak-102003483==1.1.10```\n\n## Input File in CSV Format\nInput file must contain <b>Three or more columns</b>\n<br>\nFirst column contains the <b>Object Name / Variable Name </b>\n<br>\nRest of the other columns contains only numeric values\n\n## Usage Method\n\nCommand Prompt<br>\n```\npython <python_file> <Input_Data_File> <Weights> <Impacts> <Result_File_Name>\n```\n<br>\n\n<br>\npython_file -> Python Code file for Topsis Calculation <br>\nInput_Data_File -> CSV file name <br>\nWeights -> Weights for each Column <br>\nImpacts -> Maximaization('+'), Minimization('-') <br>\nResult_File_Name -> CSV file name to store result <br>\n\n\nExample:<br>\n```\npython 102003483.py 102003483-data.csv \u201c1,1,1,1,1\u201d \u201c+,-,+,-,+\u201d 102003483-result-1.csv\npython 102003483.py 102003483-data.csv \u201c2,2,3,3,4\u201d \u201c-,+,-,+,-\u201d 102003483-result-2.csv\n```\n<br><br>\n<i>Note: The Weights and Impacts should be comma (',') seperated and Input CSV file should be in pwd(Present Working Directory).</i> \n\n## Functions and Return Values\n\n```\nfunction = topsis_102003483()\nreturn values = Creates a CSV file with the Topsis Rank and Performance Score\n```\n\n## Sample input data\n| Fund Name | 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| Fund Name | P1 | P2 | P3 | P4 | P5 | Topsis Score | 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",
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