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Editing: KMC2.php

<?php

namespace Rubix\ML\Clusterers\Seeders;

use Rubix\ML\Datasets\Dataset;
use Rubix\ML\Kernels\Distance\Distance;
use Rubix\ML\Kernels\Distance\Euclidean;
use Rubix\ML\Specifications\DatasetIsNotEmpty;
use Rubix\ML\Exceptions\InvalidArgumentException;

use function count;

use const Rubix\ML\EPSILON;

/**
 * K-MC2
 *
 * A fast Plus Plus approximator that replaces the brute force method with a substantially
 * faster Markov Chain Monte Carlo (MCMC) sampling procedure with comparable results.
 *
 * References:
 * [1] O. Bachem et al. (2016). Approximate K-Means++ in Sublinear Time.
 *
 * @category    Machine Learning
 * @package     Rubix/ML
 * @author      Andrew DalPino
 */
class KMC2 implements Seeder
{
    /**
     * The number of candidate nodes in the Markov Chain.
     *
     * @var int
     */
    protected int $m;

/**
     * The distance kernel used to compute the distance between samples.
     *
     * @var Distance
     */
    protected Distance $kernel;

/**
     * @param int $m
     * @param Distance|null $kernel
     * @throws InvalidArgumentException
     */
    public function __construct(int $m = 50, ?Distance $kernel = null)
    {
        if ($m < 1) {
            throw new InvalidArgumentException('M must be greater'
                . " than 0, $m given.");
        }

$this->m = $m;
        $this->kernel = $kernel ?? new Euclidean();
    }

/**
     * Seed k cluster centroids from a dataset.
     *
     * @internal
     *
     * @param Dataset $dataset
     * @param int $k
     * @return list<list<string|int|float>>
     */
    public function seed(Dataset $dataset, int $k) : array
    {
        DatasetIsNotEmpty::with($dataset)->check();

$centroids = $dataset->randomSubset(1)->samples();

$max = getrandmax();

while (count($centroids) < $k) {
            $candidates = $dataset->randomSubsetWithReplacement($this->m)->samples();

$x = array_pop($candidates) ?? [];

$target = end($centroids) ?: [];

$xDistance = $this->kernel->compute($x, $target) ?: EPSILON;

foreach ($candidates as $candidate) {
                $yDistance = $this->kernel->compute($candidate, $target);

$density = min(1.0, $yDistance / $xDistance);

$threshold = rand() / $max;

if ($density > $threshold) {
                    $xDistance = $yDistance;

$x = $candidate;
                }
            }

$centroids[] = $x;
        }

return $centroids;
    }

/**
     * Return the string representation of the object.
     *
     * @internal
     *
     * @return string
     */
    public function __toString() : string
    {
        return "KMC2 (m: {$this->m}, kernel: {$this->kernel})";
    }
}

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