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<?php |
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declare (strict_types = 1); |
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namespace Phpml\Classifier; |
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use Phpml\Classifier\Traits\Predictable; |
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use Phpml\Classifier\Traits\Trainable; |
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use Phpml\Metric\Distance; |
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use Phpml\Metric\Distance\Euclidean; |
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class KNearestNeighbors implements Classifier |
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{ |
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use Trainable, Predictable; |
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/** |
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* @var int |
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*/ |
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private $k; |
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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 int $k |
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* @param Distance|null $distanceMetric (if null then Euclidean distance as default) |
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*/ |
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public function __construct(int $k = 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->k = $k; |
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$this->samples = []; |
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$this->labels = []; |
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$this->distanceMetric = $distanceMetric; |
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} |
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/** |
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* @param array $sample |
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* |
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* @return mixed |
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*/ |
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private function predictSample(array $sample) |
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{ |
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$distances = $this->kNeighborsDistances($sample); |
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$predictions = array_combine(array_values($this->labels), array_fill(0, count($this->labels), 0)); |
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foreach ($distances as $index => $distance) { |
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++$predictions[$this->labels[$index]]; |
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} |
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arsort($predictions); |
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reset($predictions); |
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return key($predictions); |
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} |
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/** |
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* @param array $sample |
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* |
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* @return array |
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* |
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* @throws \Phpml\Exception\InvalidArgumentException |
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*/ |
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private function kNeighborsDistances(array $sample): array |
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{ |
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$distances = []; |
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foreach ($this->samples as $index => $neighbor) { |
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$distances[$index] = $this->distanceMetric->distance($sample, $neighbor); |
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} |
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asort($distances); |
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return array_slice($distances, 0, $this->k, true); |
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} |
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} |
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php-ai /
php-ml
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