README

A PHP library that Implements the Fuzzy Comprehensive Evaluation method to assist you in the conclusion of qualitative assessment.

Installation

Install using composer:

composer require sensasi-delight/fuzzy-ce-php

Usage

The usage examples of this library are also available on examples folder with detailed description.

Define the evaluation index with their sub-factor of evaluation.

$evaluation_index = [
    'u1' => ['u11', 'u12'],
    'u2' => ['u21', 'u22', 'u23'],
    'u3' => ['u31', 'u32'],
    'u4' => ['u41', 'u42'],
    'u5' => ['u51', 'u52']
];

Define the evaluation weight for each factor.

$weights = [
    'u1' => 0.133,
    'u2' => 0.310,
    'u3' => 0.330,
    'u4' => 0.118,
    'u5' => 0.109,
    'u11' => 0.667,
    'u12' => 0.333,
    'u21' => 0.200,
    'u22' => 0.400,
    'u23' => 0.400,
    'u31' => 0.333,
    'u32' => 0.667,
    'u41' => 0.667,
    'u42' => 0.333,
    'u51' => 0.750,
    'u52' => 0.250
];

Define the scale of assesment

The scale of assesment can be ascending or descending with their grade name depends on your assesment design.

$assesment_scale = [
    'Excellent' => 5,
    'Good' => 4,
    'Medium' => 3,
    'Poor' => 2,
    'Worst' => 1
];

Define assesment data for each evaluation index with their respondent answer.

$assesment_data = [
    "u11" => [
        "expert1" => 5,
        "expert2" => 4,
        "expert3" => 4,
        "expert4" => 4,
        "expert5" => 3,
    ], "u12" => [
        "expert1" => 5,
        "expert2" => 5,
        "expert3" => 4,
        "expert4" => 3,
        "expert5" => 3,
    ], 
    
    ...
    
    "u52" => [
        "expert1" => 4,
        "expert2" => 3,
        "expert3" => 3,
        "expert4" => 3,
        "expert5" => 3,
    ], ...

];

Create the FuzzyCE object and set the required property that you have defined before.

$fuzzyCE = new FuzzyCE(
    $evaluation_index,
    $weights,
    $assesment_scale,
    $assesment_data
);

$fuzzyCE = new FuzzyCE();

$fuzzyCE->set_evaluation_index($evaluation_index);
$fuzzyCE->set_weights($weights);
$fuzzyCE->set_assesment_scale($assesment_scale);
$fuzzyCE->set_assesment_data($assesment_data);

Get the evaluation.

for the evaluation vector:

print_r($fuzzyCE->get_evaluation());

it's should be returning an output:

Array
(
    [Excellent] => 0.2708902
    [Good] => 0.4051536
    [Medium] => 0.3239562
    [Poor] => 0
    [Worst] => 0
)

for the overall evaluation grade:
```
echo $fuzzyCE->get_grade();
```
It's should be returning an output:
```
> Good
```
for the grade score:
```
echo $fuzzyCE->get_grade_score();
```
It's should be returning an output:
```
> 0.4051536
```

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

Fork the Project.
Create your Feature Branch (git checkout -b feature/AmazingFeature).
Commit your Changes (git commit -m 'Add some AmazingFeature').
Push to the Branch (git push origin feature/AmazingFeature).
Open a Pull Request.

License

The code is released under the MIT license.

Contact

Email - zainadam.id@gmail.com

Twitter - @sensasi_DELIGHT

sensasi-delight / fuzzy-ce-php

Maintainers

Details