_for: **TOPSIS**_
_submitted by: **Ira Vashisht**_
_Roll no: **102017029**_
_Group: **COPC2**_
TOPSIS_Ira_102017029 is a Python library for (TOPSIS).The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method determines the best solution from a set of alternatives with certain attributes. The best alternative is chosen based on its Euclidean distance from the ideal solution. TOPSIS is widely used in various multi-attribute decision making problems such as supply chain logistics, marketing management, environmental management or chemical engineering. Despite the extensive use of this method, there is no free and open-source software (FOSS) for TOPSIS with comprehensive post-analysis extensions
## Installation
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Topsis-Ira-102017029.
## Usage
Enter csv filename followed by _.csv_ extentsion, then enter the _weights_ vector with vector values separated by commas, followed by the _impacts_ vector with comma separated signs _(+,-)_
### INPUT
And using this package in Python as :
```bash
import topsispy as tp
a =[
[250, 16, 12, 5],
[200, 16, 8, 3],
[300, 32, 16, 4],
[275, 32, 8, 4],
[225, 16, 16, 2]
]
w = [0.25, 0.25, 0.25, 0.25]
sign = [-1, 1, 1, 1]
tp.topsis(a, w, sign)
```
The package can be used via commandline as :
```bash
$ python [package name] [path of csv as string] [list of weights as string] [list of sign as string]
```
## Example
#### sample.csv
A csv file showing data for different mobile handsets having varying features.
| Model | Storage space(in gb) | Camera(in MP)| Price(in $) | Looks(out of 5) |
| :----: |:--------------------:|:------------:|:------------:|:---------------:|
| M1 | 16 | 12 | 250 | 5 |
| M2 | 16 | 8 | 200 | 3 |
| M3 | 32 | 16 | 300 | 4 |
| M4 | 32 | 8 | 275 | 4 |
| M5 | 16 | 16 | 225 | 2 |
weights vector = [ 0.25 , 0.25 , 0.25 , 0.25 ]
impacts vector = [ + , + , - , + ]
### Output:
| Model | Storage space(in gb) | Camera(in MP)| Price(in $) | Looks(out of 5) | Topsis Score | Rank |
| :----: |:--------------------:|:------------:|:------------:|:---------------:|:------------:|:-----:|
| M1 | 16 | 12 | 250 | 5 | 0.534277 | 3 |
| M2 | 16 | 8 | 200 | 3 | 0.308368 | 5 |
| M3 | 32 | 16 | 300 | 4 | 0.691632 | 1 |
| M4 | 32 | 8 | 275 | 4 | 0.534737 | 2 |
| M5 | 16 | 16 | 225 | 2 | 0.401046 | 4 |
Raw data
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"description": "_for: **TOPSIS**_\n_submitted by: **Ira Vashisht**_\n_Roll no: **102017029**_\n_Group: **COPC2**_\n\n\nTOPSIS_Ira_102017029 is a Python library for (TOPSIS).The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method determines the best solution from a set of alternatives with certain attributes. The best alternative is chosen based on its Euclidean distance from the ideal solution. TOPSIS is widely used in various multi-attribute decision making problems such as supply chain logistics, marketing management, environmental management or chemical engineering. Despite the extensive use of this method, there is no free and open-source software (FOSS) for TOPSIS with comprehensive post-analysis extensions\n\n## Installation\n\nUse the package manager [pip](https://pip.pypa.io/en/stable/) to install Topsis-Ira-102017029.\n\n\n\n## Usage\n\nEnter csv filename followed by _.csv_ extentsion, then enter the _weights_ vector with vector values separated by commas, followed by the _impacts_ vector with comma separated signs _(+,-)_\n\n### INPUT\nAnd using this package in Python as :\n\n```bash\nimport topsispy as tp\na =[\n [250, 16, 12, 5],\n [200, 16, 8, 3],\n [300, 32, 16, 4],\n [275, 32, 8, 4],\n [225, 16, 16, 2]\n ]\nw = [0.25, 0.25, 0.25, 0.25]\nsign = [-1, 1, 1, 1]\ntp.topsis(a, w, sign)\n```\n\nThe package can be used via commandline as :\n```bash\n$ python [package name] [path of csv as string] [list of weights as string] [list of sign as string]\n```\n## Example\n\n#### sample.csv\n\nA csv file showing data for different mobile handsets having varying features.\n\n| Model | Storage space(in gb) | Camera(in MP)| Price(in $) | Looks(out of 5) |\n| :----: |:--------------------:|:------------:|:------------:|:---------------:|\n| M1 | 16 | 12 | 250 | 5 |\n| M2 | 16 | 8 | 200 | 3 |\n| M3 | 32 | 16 | 300 | 4 |\n| M4 | 32 | 8 | 275 | 4 |\n| M5 | 16 | 16 | 225 | 2 |\n\nweights vector = [ 0.25 , 0.25 , 0.25 , 0.25 ]\n\nimpacts vector = [ + , + , - , + ]\n\n\n### Output:\n\n\n| Model | Storage space(in gb) | Camera(in MP)| Price(in $) | Looks(out of 5) | Topsis Score | Rank |\n| :----: |:--------------------:|:------------:|:------------:|:---------------:|:------------:|:-----:|\n| M1 | 16 | 12 | 250 | 5 | 0.534277 | 3 |\n| M2 | 16 | 8 | 200 | 3 | 0.308368 | 5 |\n| M3 | 32 | 16 | 300 | 4 | 0.691632 | 1 |\n| M4 | 32 | 8 | 275 | 4 | 0.534737 | 2 |\n| M5 | 16 | 16 | 225 | 2 | 0.401046 | 4 |",
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