mirror of
https://github.com/Llewellynvdm/php-ml.git
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333 lines
8.2 KiB
PHP
333 lines
8.2 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Association;
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use Phpml\Helper\Predictable;
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use Phpml\Helper\Trainable;
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class Apriori implements Associator
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{
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use Trainable;
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use Predictable;
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public const ARRAY_KEY_ANTECEDENT = 'antecedent';
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public const ARRAY_KEY_CONFIDENCE = 'confidence';
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public const ARRAY_KEY_CONSEQUENT = 'consequent';
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public const ARRAY_KEY_SUPPORT = 'support';
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/**
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* Minimum relative probability of frequent transactions.
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*
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* @var float
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*/
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private $confidence;
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/**
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* The large set contains frequent k-length item sets.
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*
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* @var mixed[][][]
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*/
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private $large = [];
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/**
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* Minimum relative frequency of transactions.
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*
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* @var float
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*/
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private $support;
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/**
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* The generated Apriori association rules.
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*
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* @var mixed[][]
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*/
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private $rules = [];
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/**
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* Apriori constructor.
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*/
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public function __construct(float $support = 0.0, float $confidence = 0.0)
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{
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$this->support = $support;
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$this->confidence = $confidence;
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}
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/**
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* Get all association rules which are generated for every k-length frequent item set.
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*
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* @return mixed[][]
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*/
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public function getRules(): array
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{
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if (count($this->large) === 0) {
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$this->large = $this->apriori();
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}
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if (count($this->rules) > 0) {
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return $this->rules;
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}
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$this->rules = [];
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$this->generateAllRules();
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return $this->rules;
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}
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/**
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* Generates frequent item sets.
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*
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* @return mixed[][][]
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*/
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public function apriori(): array
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{
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$L = [];
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$items = $this->frequent($this->items());
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for ($k = 1; isset($items[0]); ++$k) {
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$L[$k] = $items;
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$items = $this->frequent($this->candidates($items));
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}
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return $L;
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}
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/**
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* @param mixed[] $sample
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*
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* @return mixed[][]
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*/
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protected function predictSample(array $sample): array
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{
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$predicts = array_values(array_filter($this->getRules(), function ($rule) use ($sample): bool {
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return $this->equals($rule[self::ARRAY_KEY_ANTECEDENT], $sample);
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}));
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return array_map(static function ($rule) {
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return $rule[self::ARRAY_KEY_CONSEQUENT];
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}, $predicts);
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}
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/**
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* Generate rules for each k-length frequent item set.
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*/
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private function generateAllRules(): void
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{
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for ($k = 2; isset($this->large[$k]); ++$k) {
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foreach ($this->large[$k] as $frequent) {
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$this->generateRules($frequent);
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}
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}
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}
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/**
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* Generate confident rules for frequent item set.
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*
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* @param mixed[] $frequent
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*/
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private function generateRules(array $frequent): void
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{
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foreach ($this->antecedents($frequent) as $antecedent) {
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$confidence = $this->confidence($frequent, $antecedent);
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if ($this->confidence <= $confidence) {
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$consequent = array_values(array_diff($frequent, $antecedent));
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$this->rules[] = [
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self::ARRAY_KEY_ANTECEDENT => $antecedent,
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self::ARRAY_KEY_CONSEQUENT => $consequent,
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self::ARRAY_KEY_SUPPORT => $this->support($frequent),
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self::ARRAY_KEY_CONFIDENCE => $confidence,
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];
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}
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}
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}
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/**
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* Generates the power set for given item set $sample.
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*
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* @param mixed[] $sample
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*
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* @return mixed[][]
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*/
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private function powerSet(array $sample): array
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{
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$results = [[]];
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foreach ($sample as $item) {
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foreach ($results as $combination) {
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$results[] = array_merge([$item], $combination);
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}
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}
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return $results;
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}
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/**
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* Generates all proper subsets for given set $sample without the empty set.
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*
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* @param mixed[] $sample
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*
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* @return mixed[][]
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*/
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private function antecedents(array $sample): array
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{
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$cardinality = count($sample);
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$antecedents = $this->powerSet($sample);
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return array_filter($antecedents, static function ($antecedent) use ($cardinality): bool {
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return (count($antecedent) != $cardinality) && ($antecedent != []);
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});
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}
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/**
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* Calculates frequent k = 1 item sets.
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*
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* @return mixed[][]
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*/
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private function items(): array
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{
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$items = [];
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foreach ($this->samples as $sample) {
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foreach ($sample as $item) {
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if (!in_array($item, $items, true)) {
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$items[] = $item;
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}
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}
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}
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return array_map(static function ($entry): array {
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return [$entry];
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}, $items);
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}
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/**
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* Returns frequent item sets only.
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*
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* @param mixed[][] $samples
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*
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* @return mixed[][]
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*/
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private function frequent(array $samples): array
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{
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return array_values(array_filter($samples, function ($entry): bool {
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return $this->support($entry) >= $this->support;
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}));
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}
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/**
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* Calculates frequent k item sets, where count($samples) == $k - 1.
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*
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* @param mixed[][] $samples
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*
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* @return mixed[][]
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*/
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private function candidates(array $samples): array
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{
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$candidates = [];
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foreach ($samples as $p) {
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foreach ($samples as $q) {
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if (count(array_merge(array_diff($p, $q), array_diff($q, $p))) != 2) {
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continue;
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}
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$candidate = array_values(array_unique(array_merge($p, $q)));
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if ($this->contains($candidates, $candidate)) {
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continue;
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}
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foreach ($this->samples as $sample) {
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if ($this->subset($sample, $candidate)) {
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$candidates[] = $candidate;
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continue 2;
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}
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}
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}
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}
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return $candidates;
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}
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/**
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* Calculates confidence for $set. Confidence is the relative amount of sets containing $subset which also contain
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* $set.
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*
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* @param mixed[] $set
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* @param mixed[] $subset
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*/
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private function confidence(array $set, array $subset): float
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{
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return $this->support($set) / $this->support($subset);
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}
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/**
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* Calculates support for item set $sample. Support is the relative amount of sets containing $sample in the data
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* pool.
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*
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* @see \Phpml\Association\Apriori::samples
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*
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* @param mixed[] $sample
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*/
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private function support(array $sample): float
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{
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return $this->frequency($sample) / count($this->samples);
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}
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/**
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* Counts occurrences of $sample as subset in data pool.
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*
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* @see \Phpml\Association\Apriori::samples
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*
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* @param mixed[] $sample
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*/
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private function frequency(array $sample): int
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{
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return count(array_filter($this->samples, function ($entry) use ($sample): bool {
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return $this->subset($entry, $sample);
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}));
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}
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/**
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* Returns true if set is an element of system.
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*
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* @see \Phpml\Association\Apriori::equals()
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*
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* @param mixed[][] $system
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* @param mixed[] $set
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*/
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private function contains(array $system, array $set): bool
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{
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return (bool) array_filter($system, function ($entry) use ($set): bool {
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return $this->equals($entry, $set);
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});
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}
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/**
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* Returns true if subset is a (proper) subset of set by its items string representation.
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*
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* @param mixed[] $set
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* @param mixed[] $subset
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*/
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private function subset(array $set, array $subset): bool
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{
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return count(array_diff($subset, array_intersect($subset, $set))) === 0;
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}
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/**
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* Returns true if string representation of items does not differ.
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*
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* @param mixed[] $set1
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* @param mixed[] $set2
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*/
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private function equals(array $set1, array $set2): bool
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{
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return array_diff($set1, $set2) == array_diff($set2, $set1);
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}
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}
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