Project description
CALCO-O-TOPSIS
By: Anubhav Gupta
What is TOPSIS?
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.
### Installation
```bash
pip install topsis-anubhav-102003551==1.0.0
```
### Usage
Arguments Required:
(Assume we have 5 attributes in dataset.)
You have to required one .csv file. (102003551-data.csv)
Pass weights to each attribute. (e.g.: [1,1,1,1,1])
Pass impacts to each attribute. (e.g.: [+,-,+,-,+])
Pass the name of the file with you want to put on .csv file. (102003551-result-1.csv)
Enter csv filename followed by .csv extension, then enter the weights string with values separated by commas, followed by the impacts string with comma separated signs (+,-) and name of file followed by -.csv- extension in which the user wants the output file
## Example
#### sample.csv
```bash
Fund Name P1 P2 P3 P4 P5
M1 0.84 0.71 6.7 42.1 12.59
M2 0.91 0.83 7 31.7 10.11
M3 0.79 0.62 4.8 46.7 13.23
M4 0.78 0.61 6.4 42.4 12.55
M5 0.94 0.88 3.6 62.2 16.91
M6 0.88 0.77 6.5 51.5 14.91
M7 0.66 0.44 5.3 48.9 13.83
M8 0.93 0.86 3.4 37 10.55
```
### INPUT
```python
topsis 102003551-data.csv 1,1,1,1,1 +,-,+,-,+ 102003551-result-1.csv
```
### OUTPUT
```bash
Fund Name P1 P2 P3 P4 P5 Topsis Score Rank
M1 0.351077437 0.344400588 0.421433661 0.322539084 0.335992288 0.594551725 2
M2 0.380333891 0.402609138 0.440303825 0.24286197 0.269807945 0.566246179 3
M3 0.330179971 0.300744175 0.301922623 0.357780884 0.353072118 0.485394123 6
M4 0.326000478 0.295893463 0.402563497 0.324837462 0.334924798 0.612775882 1
M5 0.39287237 0.4268627 0.226441967 0.476530428 0.451281142 0.361550918 8
M6 0.367795411 0.373504863 0.408853551 0.394554936 0.397906673 0.538764066 5
M7 0.275846558 0.21343135 0.333372896 0.374635658 0.369084459 0.560458621 4
M8 0.388692877 0.417161275 0.213861858 0.283466653 0.281550328 0.38966293 7
```
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"description": "Project description\r\nCALCO-O-TOPSIS\r\nBy: Anubhav Gupta\r\n\r\nWhat is TOPSIS?\r\nTechnique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS 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```bash\r\npip install topsis-anubhav-102003551==1.0.0\r\n```\r\n\r\n\r\n### Usage\r\n\r\nArguments Required:\r\n(Assume we have 5 attributes in dataset.)\r\n\r\nYou have to required one .csv file. (102003551-data.csv)\r\nPass weights to each attribute. (e.g.: [1,1,1,1,1])\r\nPass impacts to each attribute. (e.g.: [+,-,+,-,+])\r\nPass the name of the file with you want to put on .csv file. (102003551-result-1.csv)\r\n\r\n\r\nEnter csv filename followed by .csv extension, then enter the weights string with values separated by commas, followed by the impacts string with comma separated signs (+,-) and name of file followed by -.csv- extension in which the user wants the output file\r\n\r\n## Example\r\n#### sample.csv\r\n```bash\r\nFund Name\tP1\t P2 P3\t P4\t P5\r\nM1\t 0.84\t0.71\t6.7\t 42.1\t12.59\r\nM2\t 0.91\t0.83\t7\t 31.7\t10.11\r\nM3\t 0.79\t0.62\t4.8\t 46.7\t13.23\r\nM4\t 0.78\t0.61\t6.4\t 42.4\t12.55\r\nM5\t 0.94\t0.88\t3.6\t 62.2\t16.91\r\nM6\t 0.88\t0.77\t6.5\t 51.5\t14.91\r\nM7\t 0.66\t0.44\t5.3\t 48.9\t13.83\r\nM8\t 0.93\t0.86\t3.4\t 37\t 10.55\r\n\r\n```\r\n\r\n### INPUT\r\n```python\r\ntopsis 102003551-data.csv 1,1,1,1,1 +,-,+,-,+ 102003551-result-1.csv\r\n```\r\n\r\n### OUTPUT\r\n\r\n```bash\r\nFund Name\tP1\t P2\t P3\t P4\t P5\t Topsis Score\tRank\r\nM1\t 0.351077437\t0.344400588\t0.421433661\t0.322539084\t0.335992288\t0.594551725\t 2\r\nM2\t 0.380333891\t0.402609138\t0.440303825\t0.24286197\t0.269807945\t0.566246179\t 3\r\nM3\t 0.330179971\t0.300744175\t0.301922623\t0.357780884\t0.353072118\t0.485394123\t 6\r\nM4\t 0.326000478\t0.295893463\t0.402563497\t0.324837462\t0.334924798\t0.612775882\t 1\r\nM5\t 0.39287237\t0.4268627\t0.226441967\t0.476530428\t0.451281142\t0.361550918\t 8\r\nM6\t 0.367795411\t0.373504863\t0.408853551\t0.394554936\t0.397906673\t0.538764066\t 5\r\nM7\t 0.275846558\t0.21343135\t0.333372896\t0.374635658\t0.369084459\t0.560458621\t 4\r\nM8\t 0.388692877\t0.417161275\t0.213861858\t0.283466653\t0.281550328\t0.38966293\t 7\r\n```\r\n\r\n",
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