485 lines
15 KiB
PHP
485 lines
15 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Classification;
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use Phpml\Classification\DecisionTree\DecisionTreeLeaf;
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use Phpml\Exception\InvalidArgumentException;
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use Phpml\Helper\Predictable;
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use Phpml\Helper\Trainable;
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use Phpml\Math\Statistic\Mean;
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class DecisionTree implements Classifier
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{
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use Trainable;
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use Predictable;
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public const CONTINUOUS = 1;
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public const NOMINAL = 2;
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/**
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* @var int
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*/
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public $actualDepth = 0;
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/**
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* @var array
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*/
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protected $columnTypes = [];
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/**
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* @var DecisionTreeLeaf
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*/
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protected $tree;
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/**
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* @var int
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*/
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protected $maxDepth;
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/**
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* @var array
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*/
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private $labels = [];
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/**
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* @var int
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*/
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private $featureCount = 0;
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/**
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* @var int
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*/
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private $numUsableFeatures = 0;
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/**
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* @var array
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*/
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private $selectedFeatures = [];
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/**
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* @var array|null
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*/
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private $featureImportances;
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/**
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* @var array
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*/
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private $columnNames = [];
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public function __construct(int $maxDepth = 10)
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{
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$this->maxDepth = $maxDepth;
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}
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public function train(array $samples, array $targets): void
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{
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$this->samples = array_merge($this->samples, $samples);
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$this->targets = array_merge($this->targets, $targets);
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$this->featureCount = count($this->samples[0]);
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$this->columnTypes = self::getColumnTypes($this->samples);
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$this->labels = array_keys(array_count_values($this->targets));
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$this->tree = $this->getSplitLeaf(range(0, count($this->samples) - 1));
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// Each time the tree is trained, feature importances are reset so that
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// we will have to compute it again depending on the new data
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$this->featureImportances = null;
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// If column names are given or computed before, then there is no
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// need to init it and accidentally remove the previous given names
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if ($this->columnNames === []) {
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$this->columnNames = range(0, $this->featureCount - 1);
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} elseif (count($this->columnNames) > $this->featureCount) {
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$this->columnNames = array_slice($this->columnNames, 0, $this->featureCount);
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} elseif (count($this->columnNames) < $this->featureCount) {
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$this->columnNames = array_merge(
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$this->columnNames,
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range(count($this->columnNames), $this->featureCount - 1)
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);
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}
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}
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public static function getColumnTypes(array $samples): array
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{
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$types = [];
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$featureCount = count($samples[0]);
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for ($i = 0; $i < $featureCount; ++$i) {
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$values = array_column($samples, $i);
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$isCategorical = self::isCategoricalColumn($values);
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$types[] = $isCategorical ? self::NOMINAL : self::CONTINUOUS;
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}
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return $types;
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}
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/**
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* @param mixed $baseValue
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*/
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public function getGiniIndex($baseValue, array $colValues, array $targets): float
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{
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$countMatrix = [];
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foreach ($this->labels as $label) {
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$countMatrix[$label] = [0, 0];
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}
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foreach ($colValues as $index => $value) {
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$label = $targets[$index];
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$rowIndex = $value === $baseValue ? 0 : 1;
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++$countMatrix[$label][$rowIndex];
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}
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$giniParts = [0, 0];
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for ($i = 0; $i <= 1; ++$i) {
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$part = 0;
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$sum = array_sum(array_column($countMatrix, $i));
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if ($sum > 0) {
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foreach ($this->labels as $label) {
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$part += ($countMatrix[$label][$i] / (float) $sum) ** 2;
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}
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}
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$giniParts[$i] = (1 - $part) * $sum;
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}
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return array_sum($giniParts) / count($colValues);
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}
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/**
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* This method is used to set number of columns to be used
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* when deciding a split at an internal node of the tree. <br>
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* If the value is given 0, then all features are used (default behaviour),
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* otherwise the given value will be used as a maximum for number of columns
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* randomly selected for each split operation.
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*
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* @return $this
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*
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* @throws InvalidArgumentException
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*/
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public function setNumFeatures(int $numFeatures)
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{
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if ($numFeatures < 0) {
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throw new InvalidArgumentException('Selected column count should be greater or equal to zero');
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}
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$this->numUsableFeatures = $numFeatures;
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return $this;
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}
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/**
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* A string array to represent columns. Useful when HTML output or
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* column importances are desired to be inspected.
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*
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* @return $this
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*
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* @throws InvalidArgumentException
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*/
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public function setColumnNames(array $names)
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{
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if ($this->featureCount !== 0 && count($names) !== $this->featureCount) {
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throw new InvalidArgumentException(sprintf('Length of the given array should be equal to feature count %s', $this->featureCount));
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}
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$this->columnNames = $names;
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return $this;
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}
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public function getHtml(): string
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{
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return $this->tree->getHTML($this->columnNames);
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}
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/**
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* This will return an array including an importance value for
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* each column in the given dataset. The importance values are
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* normalized and their total makes 1.<br/>
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*/
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public function getFeatureImportances(): array
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{
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if ($this->featureImportances !== null) {
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return $this->featureImportances;
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}
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$sampleCount = count($this->samples);
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$this->featureImportances = [];
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foreach ($this->columnNames as $column => $columnName) {
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$nodes = $this->getSplitNodesByColumn($column, $this->tree);
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$importance = 0;
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foreach ($nodes as $node) {
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$importance += $node->getNodeImpurityDecrease($sampleCount);
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}
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$this->featureImportances[$columnName] = $importance;
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}
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// Normalize & sort the importances
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$total = array_sum($this->featureImportances);
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if ($total > 0) {
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array_walk($this->featureImportances, function (&$importance) use ($total): void {
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$importance /= $total;
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});
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arsort($this->featureImportances);
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}
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return $this->featureImportances;
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}
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protected function getSplitLeaf(array $records, int $depth = 0): DecisionTreeLeaf
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{
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$split = $this->getBestSplit($records);
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$split->level = $depth;
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if ($this->actualDepth < $depth) {
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$this->actualDepth = $depth;
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}
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// Traverse all records to see if all records belong to the same class,
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// otherwise group the records so that we can classify the leaf
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// in case maximum depth is reached
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$leftRecords = [];
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$rightRecords = [];
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$remainingTargets = [];
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$prevRecord = null;
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$allSame = true;
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foreach ($records as $recordNo) {
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// Check if the previous record is the same with the current one
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$record = $this->samples[$recordNo];
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if ($prevRecord !== null && $prevRecord != $record) {
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$allSame = false;
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}
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$prevRecord = $record;
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// According to the split criteron, this record will
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// belong to either left or the right side in the next split
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if ($split->evaluate($record)) {
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$leftRecords[] = $recordNo;
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} else {
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$rightRecords[] = $recordNo;
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}
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// Group remaining targets
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$target = $this->targets[$recordNo];
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if (!array_key_exists($target, $remainingTargets)) {
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$remainingTargets[$target] = 1;
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} else {
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++$remainingTargets[$target];
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}
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}
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if ($allSame || $depth >= $this->maxDepth || count($remainingTargets) === 1) {
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$split->isTerminal = true;
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arsort($remainingTargets);
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$split->classValue = (string) key($remainingTargets);
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} else {
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if (isset($leftRecords[0])) {
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$split->leftLeaf = $this->getSplitLeaf($leftRecords, $depth + 1);
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}
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if (isset($rightRecords[0])) {
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$split->rightLeaf = $this->getSplitLeaf($rightRecords, $depth + 1);
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}
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}
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return $split;
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}
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protected function getBestSplit(array $records): DecisionTreeLeaf
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{
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$targets = array_intersect_key($this->targets, array_flip($records));
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$samples = (array) array_combine(
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$records,
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$this->preprocess(array_intersect_key($this->samples, array_flip($records)))
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);
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$bestGiniVal = 1;
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$bestSplit = null;
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$features = $this->getSelectedFeatures();
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foreach ($features as $i) {
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$colValues = [];
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foreach ($samples as $index => $row) {
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$colValues[$index] = $row[$i];
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}
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$counts = array_count_values($colValues);
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arsort($counts);
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$baseValue = key($counts);
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if ($baseValue === null) {
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continue;
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}
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$gini = $this->getGiniIndex($baseValue, $colValues, $targets);
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if ($bestSplit === null || $bestGiniVal > $gini) {
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$split = new DecisionTreeLeaf();
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$split->value = $baseValue;
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$split->giniIndex = $gini;
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$split->columnIndex = $i;
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$split->isContinuous = $this->columnTypes[$i] === self::CONTINUOUS;
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$split->records = $records;
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// If a numeric column is to be selected, then
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// the original numeric value and the selected operator
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// will also be saved into the leaf for future access
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if ($this->columnTypes[$i] === self::CONTINUOUS) {
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$matches = [];
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preg_match("/^([<>=]{1,2})\s*(.*)/", (string) $split->value, $matches);
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$split->operator = $matches[1];
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$split->numericValue = (float) $matches[2];
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}
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$bestSplit = $split;
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$bestGiniVal = $gini;
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}
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}
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return $bestSplit;
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}
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/**
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* Returns available features/columns to the tree for the decision making
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* process. <br>
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*
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* If a number is given with setNumFeatures() method, then a random selection
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* of features up to this number is returned. <br>
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*
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* If some features are manually selected by use of setSelectedFeatures(),
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* then only these features are returned <br>
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*
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* If any of above methods were not called beforehand, then all features
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* are returned by default.
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*/
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protected function getSelectedFeatures(): array
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{
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$allFeatures = range(0, $this->featureCount - 1);
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if ($this->numUsableFeatures === 0 && count($this->selectedFeatures) === 0) {
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return $allFeatures;
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}
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if (count($this->selectedFeatures) > 0) {
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return $this->selectedFeatures;
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}
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$numFeatures = $this->numUsableFeatures;
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if ($numFeatures > $this->featureCount) {
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$numFeatures = $this->featureCount;
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}
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shuffle($allFeatures);
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$selectedFeatures = array_slice($allFeatures, 0, $numFeatures);
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sort($selectedFeatures);
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return $selectedFeatures;
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}
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protected function preprocess(array $samples): array
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{
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// Detect and convert continuous data column values into
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// discrete values by using the median as a threshold value
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$columns = [];
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for ($i = 0; $i < $this->featureCount; ++$i) {
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$values = array_column($samples, $i);
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if ($this->columnTypes[$i] == self::CONTINUOUS) {
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$median = Mean::median($values);
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foreach ($values as &$value) {
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if ($value <= $median) {
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$value = "<= {$median}";
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} else {
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$value = "> {$median}";
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}
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}
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}
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$columns[] = $values;
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}
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// Below method is a strange yet very simple & efficient method
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// to get the transpose of a 2D array
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return array_map(null, ...$columns);
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}
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protected static function isCategoricalColumn(array $columnValues): bool
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{
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$count = count($columnValues);
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// There are two main indicators that *may* show whether a
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// column is composed of discrete set of values:
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// 1- Column may contain string values and non-float values
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// 2- Number of unique values in the column is only a small fraction of
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// all values in that column (Lower than or equal to %20 of all values)
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$numericValues = array_filter($columnValues, 'is_numeric');
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$floatValues = array_filter($columnValues, 'is_float');
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if (count($floatValues) > 0) {
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return false;
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}
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if (count($numericValues) !== $count) {
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return true;
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}
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$distinctValues = array_count_values($columnValues);
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return count($distinctValues) <= $count / 5;
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}
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/**
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* Used to set predefined features to consider while deciding which column to use for a split
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*/
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protected function setSelectedFeatures(array $selectedFeatures): void
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{
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$this->selectedFeatures = $selectedFeatures;
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}
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/**
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* Collects and returns an array of internal nodes that use the given
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* column as a split criterion
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*/
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protected function getSplitNodesByColumn(int $column, DecisionTreeLeaf $node): array
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{
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if ($node->isTerminal) {
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return [];
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}
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$nodes = [];
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if ($node->columnIndex === $column) {
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$nodes[] = $node;
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}
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$lNodes = [];
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$rNodes = [];
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if ($node->leftLeaf !== null) {
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$lNodes = $this->getSplitNodesByColumn($column, $node->leftLeaf);
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}
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if ($node->rightLeaf !== null) {
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$rNodes = $this->getSplitNodesByColumn($column, $node->rightLeaf);
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}
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return array_merge($nodes, $lNodes, $rNodes);
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}
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/**
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* @return mixed
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*/
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protected function predictSample(array $sample)
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{
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$node = $this->tree;
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do {
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if ($node->isTerminal) {
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return $node->classValue;
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}
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if ($node->evaluate($sample)) {
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$node = $node->leftLeaf;
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} else {
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$node = $node->rightLeaf;
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}
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} while ($node);
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return $this->labels[0];
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}
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}
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