114 lines
2.5 KiB
PHP
114 lines
2.5 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Clustering;
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use Phpml\Math\Distance;
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use Phpml\Math\Distance\Euclidean;
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class DBSCAN implements Clusterer
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{
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/**
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* @var float
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*/
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private $epsilon;
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/**
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* @var int
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*/
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private $minSamples;
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/**
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* @var Distance
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*/
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private $distanceMetric;
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/**
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* @param float $epsilon
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* @param int $minSamples
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* @param Distance $distanceMetric
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*/
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public function __construct($epsilon = 0.5, $minSamples = 3, Distance $distanceMetric = null)
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{
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if (null === $distanceMetric) {
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$distanceMetric = new Euclidean();
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}
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$this->epsilon = $epsilon;
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$this->minSamples = $minSamples;
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$this->distanceMetric = $distanceMetric;
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}
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/**
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* @param array $samples
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*
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* @return array
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*/
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public function cluster(array $samples)
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{
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$clusters = [];
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$visited = [];
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foreach ($samples as $index => $sample) {
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if (isset($visited[$index])) {
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continue;
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}
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$visited[$index] = true;
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$regionSamples = $this->getSamplesInRegion($sample, $samples);
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if (count($regionSamples) >= $this->minSamples) {
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$clusters[] = $this->expandCluster($regionSamples, $visited);
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}
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}
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return $clusters;
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}
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/**
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* @param array $localSample
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* @param array $samples
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*
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* @return array
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*/
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private function getSamplesInRegion($localSample, $samples)
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{
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$region = [];
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foreach ($samples as $index => $sample) {
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if ($this->distanceMetric->distance($localSample, $sample) < $this->epsilon) {
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$region[$index] = $sample;
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}
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}
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return $region;
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}
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/**
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* @param array $samples
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* @param array $visited
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*
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* @return array
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*/
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private function expandCluster($samples, &$visited)
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{
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$cluster = [];
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$clusterMerge = [[]];
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foreach ($samples as $index => $sample) {
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if (!isset($visited[$index])) {
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$visited[$index] = true;
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$regionSamples = $this->getSamplesInRegion($sample, $samples);
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if (count($regionSamples) > $this->minSamples) {
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$clusterMerge[] = $regionSamples;
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}
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}
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$cluster[$index] = $sample;
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}
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$cluster = \array_merge($cluster, ...$clusterMerge);
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return $cluster;
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}
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}
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