PHP code example of emilklindt / laravel-marker-clusterer

    
        
<?php
require_once('vendor/autoload.php');

/* Start to develop here. Best regards https://php-download.com/ */

    

emilklindt / laravel-marker-clusterer example snippets

return [

    /*
    |--------------------------------------------------------------------------
    | Default Clusterer
    |--------------------------------------------------------------------------
    |
    | The default clustering method used when using the DefaultClusterer class
    | ------------------------------------------------------
    |
    | The default formula for calculating distance from one coordinate to
    | another. Possible values are constants of the DistanceFormula enum.
    |
    */

    'default_distance_formula' => \EmilKlindt\MarkerClusterer\Enums\DistanceFormula::MANHATTAN,

    /*
    |--------------------------------------------------------------------------
    | K-means Clustering
    |--------------------------------------------------------------------------
    |
    | K-means algorithm identifies k number of centroids, and then allocates
    | every data point to the nearest cluster.
    |
    */

    'k_means' => [

        /*
        |--------------------------------------------------------------------------
        | Default Maximum Iterations
        |--------------------------------------------------------------------------
        |
        | The default number of maximum iterations of clustering, for example used
        | in K-means clustering, where clustering is repeated untill either reaching
        | convergence (no further changes) or the maximum number of iterations.
        |
        */

        'default_maximum_iterations' => 10,

        /*
        |--------------------------------------------------------------------------
        | Default Maximum Convergence Distance
        |--------------------------------------------------------------------------
        |
        | The maximum distance between iterations to count a cluster as converged,
        | meaning that no further iteration is necessary. A higher value can provide
        | better performance, due to the need of doing less iterations. A lower value
        | will ensure that a cluster has actually converged.
        |
        */

        'default_convergence_maximum' => 100.0,

        /*
        |--------------------------------------------------------------------------
        | Default Maximum Samples
        |--------------------------------------------------------------------------
        |
        | The default number of maximum samples of clustering, for example used
        | in K-means clustering, where the specified number of samples are run
        | to achieve the lowest variance between the centroids.
        |
        | This differs from maximum iterations in that, iterations are executed
        | on the same set of initially random centroids. Each sample instantiates
        | a new set of centroids to iteratively optimize, untill maximum number
        | of iterations or convergence is reached.
        |
        */

        'default_maximum_samples' => 10,
    ],

    /*
    |--------------------------------------------------------------------------
    | Density Based Spatial Clusterer (DBSCAN)
    |--------------------------------------------------------------------------
    |
    | Finds core samples of high density and expands clusters from them.
    |
    */

    'dbscan' => [

        /*
        |--------------------------------------------------------------------------
        | Default Include Noise
        |--------------------------------------------------------------------------
        |
        | Whether to 

$clusters = DefaultClusterer::cluster($markers, $config);

use League\Geotools\Coordinate\Coordinate;
use EmilKlindt\MarkerClusterer\Interfaces\Clusterable;

class Car extends Model implements Clusterable
{
    public function getClusterableCoordinate(): Coordinate
    {
        return new Coordinate([
            $this->lat,
            $this->lng
        ]);
    }
}

$cars = Car::get();

$config = new Config([
    'epsilon' => 10.5,
    'minSamples' => 2
]);

$clusters = DefaultClusterer::cluster($cars, $config);

$config = new Config([
    'k' => 3
]);

$clusters = KMeansClusterer::cluster($cars, $config);

$config = new Config([
    'epsilon' => 10.5,
    'minSamples' => 5
]);

$clusters = DensityBasedSpatialClusterer::cluster($cars, $config);

bash
php artisan vendor:publish --provider="EmilKlindt\MarkerClusterer\MarkerClustererServiceProvider" --tag="config"