| Name | Topsis-Arjun-102017005 JSON |
| Version |
1.0.2
JSON |
| download |
| home_page | |
| Summary | A Python package for Multiple Criteria Decision Analysis (MCDA) using TOPSIS Method made by Arjun Khanchandani. |
| upload_time | 2023-01-23 01:41:54 |
| maintainer | |
| docs_url | None |
| author | Arjun Khanchandani |
| requires_python | |
| license | |
| keywords |
python
topsis
mcda
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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No Travis.
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# topsis_arjun_102017005
## What is TOPSIS
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in 1981 as a multi-criteria decision analysis method.<br> TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS).
## Installation
`pip install Topsis-Arjun-102017005`
## How to use it?
Command Prompt
> Topsis-Arjun-102017005 input.csv "1,1,1,1,1" "+,-,+,+,+" output.csv
Command Prompt<br>
```
Topsis-Arjun-102017005 <python_file> <Input Data File> <Weights> <Impacts> <Result File Name>
```
<br>
## Input csv format
Input file contain three or more columns<br>
First column is the variable/quantity name <br>
From 2nd to last columns contain numeric (int/float) values only
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>
## 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_arjun_102017005\n\n## What is TOPSIS\nTechnique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in 1981 as a multi-criteria decision analysis method.<br> TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS).\n\n## Installation\n\n`pip install Topsis-Arjun-102017005`\n\n## How to use it?\n\nCommand Prompt\n\n> Topsis-Arjun-102017005 input.csv \"1,1,1,1,1\" \"+,-,+,+,+\" output.csv\n\nCommand Prompt<br>\n```\nTopsis-Arjun-102017005 <python_file> <Input Data File> <Weights> <Impacts> <Result File Name>\n```\n<br>\n\n## Input csv format\nInput file contain three or more columns<br>\nFirst column is the variable/quantity name <br>\nFrom 2nd to last columns contain numeric (int/float) values only\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## 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## 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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