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‎.gitignore‎

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# typical for all available operation systems
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__MACOSX/
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.DS_Store
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Thumbs.db
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# some files
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*.gz
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*.tar
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*.bat
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*.dat
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*.pdf
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*.psd
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*.log
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*.txt
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*.flv
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*.wmv
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*.xlsx
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*.xls
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*.docx
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*.doc
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*.phar
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*.mmdb
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*.csv
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# ide settings
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.idea/

‎README.md‎

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# php-fpgrowth
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PHP implementation of the Frequent Pattern Growth algorithm
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# FP-Growth
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A PHP implementation of the Frequent Pattern Growth algorithm
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## Getting Started
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You can install the package with composer:
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composer require enzomc/php-fpgrowth
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## Usage
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#### Run algorithm
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use EnzoMC\PhpFPGrowth\FPGrowth;
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$support = 3;
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$confidence = 0.7;
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$fpgrowth = new FPGrowth($support, $confidence);
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$transactions = [
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['M', 'O', 'N', 'K', 'E', 'Y'],
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['D', 'O', 'N', 'K', 'E', 'Y'],
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['M', 'A', 'K', 'E'],
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['M', 'U', 'C', 'K', 'Y'],
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['C', 'O', 'O', 'K', 'I', 'E']
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];
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$fpgrowth->run($transactions);
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$patterns = $fpgrowth->getPatterns();
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$rules = $fpgrowth->getRules();
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#### Returned results
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Patterns returns as array of arrays like:
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[
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['ITEM_1,ITEM_2' => 3],
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['ITEM_3' => 5],
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...
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]
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Where key is itemset, value is support of that itemset
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Rules returns as array of arrays like:
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[
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['ITEM_1,ITEM_2' => ['ITEM_3',0.7]],
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['ITEM_4' => ['ITEM_5',0.7]],
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...
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]
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Where keys is left path of rule, value is array, where first item is right path of the rule and second item is confidence of that rule
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#### Result with example transactions
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`var_dump($patterns);`
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array(10) {
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["M"]=>
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int(3)
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["K,M"]=>
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int(3)
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["Y"]=>
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int(3)
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["K,Y"]=>
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int(3)
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["E,O"]=>
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int(4)
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["E,K"]=>
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int(4)
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["E,K,O"]=>
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int(4)
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["O"]=>
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int(4)
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["K,O"]=>
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int(4)
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["K"]=>
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int(5)
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}
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`var_dump($rules);`
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array(7) {
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["M"]=>
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array(2) {
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[0]=>
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string(1) "K"
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[1]=>
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float(1)
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}
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["Y"]=>
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array(2) {
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[0]=>
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string(1) "K"
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[1]=>
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float(1)
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}
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["O"]=>
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array(2) {
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[0]=>
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string(1) "K"
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[1]=>
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float(1)
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}
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["K"]=>
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array(2) {
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[0]=>
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string(1) "O"
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[1]=>
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float(0.8)
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}
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["E,K"]=>
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array(2) {
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[0]=>
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string(1) "O"
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[1]=>
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float(1)
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}
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["E,O"]=>
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array(2) {
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[0]=>
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string(1) "K"
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[1]=>
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float(1)
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}
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["K,O"]=>
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array(2) {
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[0]=>
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string(1) "E"
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[1]=>
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float(1)
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}
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}

‎composer.json‎

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{
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"name": "enzomc/php-fpgrowth",
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"description": "PHP implementation of the Frequent Pattern Growth algorithm",
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"type": "isc",
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"authors": [
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{
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"name": "Ivan Rebrov",
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"email": "rebrvivan95@gmail.com"
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}
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],
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"require": {
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"php": ">=7.0"
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},
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"autoload": {
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"psr-0": {
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"EnzoMC\\PhpFPGrowth\\": "src/"
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}
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}
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}

‎example/example.php‎

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<?php
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use EnzoMC\PhpFPGrowth\FPGrowth;
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$transactions = [
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['M', 'O', 'N', 'K', 'E', 'Y'],
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['D', 'O', 'N', 'K', 'E', 'Y'],
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['M', 'A', 'K', 'E'],
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['M', 'U', 'C', 'K', 'Y'],
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['C', 'O', 'O', 'K', 'I', 'E'],
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];
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$support = 3;
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$confidence = 0.7;
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$fpgrowth = new FPGrowth($support, $confidence);
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$fpgrowth->run($transactions);
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$patterns = $fpgrowth->getPatterns();
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$rules = $fpgrowth->getRules();

‎src/FPGrowth.php‎

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<?php
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namespace EnzoMC\PhpFPGrowth;
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class FPGrowth
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{
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protected $support = 3;
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protected $confidence = 0.7;
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private $patterns;
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private $rules;
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/**
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* @return mixed
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*/
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public function getSupport()
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{
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return $this->support;
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}
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/**
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* @param mixed $support
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*/
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public function setSupport($support)
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{
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$this->support = $support;
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return $this;
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}
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/**
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* @return mixed
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*/
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public function getConfidence()
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{
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return $this->confidence;
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}
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/**
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* @param mixed $confidence
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*/
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public function setConfidence($confidence)
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{
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$this->confidence = $confidence;
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return $this;
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}
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/**
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* @return mixed
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*/
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public function getPatterns()
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{
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return $this->patterns;
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}
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/**
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* @return mixed
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*/
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public function getRules()
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{
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return $this->rules;
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}
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/**
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* FPGrowth constructor.
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* @param $support 1, 2, 3 ...
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* @param $confidence 0 ... 1
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*/
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public function __construct($support, $confidence)
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{
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$this->support = $support;
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$this->confidence = $confidence;
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}
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/**
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* Do algorithm
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* @param $transactions
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*/
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public function run($transactions)
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{
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$this->patterns = $this->findFrequentPatterns($transactions, $this->support);
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$this->rules = $this->generateAssociationRules($this->patterns, $this->confidence);
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}
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protected function findFrequentPatterns($transactions, $support_threshold)
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{
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$tree = new FPTree($transactions, $support_threshold, null, null);
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return $tree->minePatterns($support_threshold);
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}
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protected function generateAssociationRules($patterns, $confidence_threshold)
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{
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$rules = [];
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foreach (array_keys($patterns) as $itemsetStr) {
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$itemset = explode(',', $itemsetStr);
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$upper_support = $patterns[$itemsetStr];
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for ($i = 1; $i < count($itemset); $i++) {
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foreach (self::combinations($itemset, $i) as $antecedent) {
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sort($antecedent);
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$antecedentStr = implode(',', $antecedent);
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$consequent = array_diff($itemset, $antecedent);
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sort($consequent);
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$consequentStr = implode(',', $consequent);
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if (isset($patterns[$antecedentStr])) {
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$lower_support = $patterns[$antecedentStr];
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$confidence = (floatval($upper_support) / $lower_support);
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if ($confidence >= $confidence_threshold) {
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$rules[$antecedentStr] = [$consequentStr, $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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return $rules;
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}
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public static function iter($var)
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{
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switch (true) {
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case $var instanceof \Iterator:
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return $var;
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case $var instanceof \Traversable:
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return new \IteratorIterator($var);
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case is_string($var):
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$var = str_split($var);
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case is_array($var):
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return new \ArrayIterator($var);
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default:
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$type = gettype($var);
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throw new \InvalidArgumentException("'$type' type is not iterable");
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}
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return;
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}
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public static function combinations($iterable, $r)
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{
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$pool = is_array($iterable) ? $iterable : iterator_to_array(self::iter($iterable));
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$n = sizeof($pool);
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if ($r > $n) {
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return;
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}
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$indices = range(0, $r - 1);
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yield array_slice($pool, 0, $r);
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for (; ;) {
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for (; ;) {
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for ($i = $r - 1; $i >= 0; $i--) {
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if ($indices[$i] != $i + $n - $r) {
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break 2;
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}
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}
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return;
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}
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$indices[$i]++;
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for ($j = $i + 1; $j < $r; $j++) {
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$indices[$j] = $indices[$j - 1] + 1;
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}
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$row = [];
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foreach ($indices as $i) {
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$row[] = $pool[$i];
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}
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yield $row;
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}
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}
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}

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