95 lines
2.3 KiB
PHP
95 lines
2.3 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\DimensionReduction;
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use Phpml\Math\LinearAlgebra\EigenvalueDecomposition;
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use Phpml\Math\Matrix;
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/**
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* Class to compute eigen pairs (values & vectors) of a given matrix
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* with the consideration of numFeatures or totalVariance to be preserved
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*
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* @author hp
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*/
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abstract class EigenTransformerBase
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{
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/**
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* Total variance to be conserved after the reduction
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*
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* @var float
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*/
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public $totalVariance = 0.9;
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/**
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* Number of features to be preserved after the reduction
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*
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* @var int
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*/
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public $numFeatures = null;
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/**
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* Top eigenvectors of the matrix
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*
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* @var array
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*/
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protected $eigVectors = [];
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/**
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* Top eigenValues of the matrix
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*
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* @var array
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*/
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protected $eigValues = [];
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/**
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* Calculates eigenValues and eigenVectors of the given matrix. Returns
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* top eigenVectors along with the largest eigenValues. The total explained variance
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* of these eigenVectors will be no less than desired $totalVariance value
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*/
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protected function eigenDecomposition(array $matrix): void
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{
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$eig = new EigenvalueDecomposition($matrix);
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$eigVals = $eig->getRealEigenvalues();
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$eigVects = $eig->getEigenvectors();
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$totalEigVal = array_sum($eigVals);
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// Sort eigenvalues in descending order
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arsort($eigVals);
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$explainedVar = 0.0;
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$vectors = [];
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$values = [];
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foreach ($eigVals as $i => $eigVal) {
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$explainedVar += $eigVal / $totalEigVal;
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$vectors[] = $eigVects[$i];
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$values[] = $eigVal;
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if ($this->numFeatures !== null) {
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if (count($vectors) == $this->numFeatures) {
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break;
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}
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} else {
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if ($explainedVar >= $this->totalVariance) {
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break;
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}
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}
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}
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$this->eigValues = $values;
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$this->eigVectors = $vectors;
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}
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/**
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* Returns the reduced data
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*/
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protected function reduce(array $data): array
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{
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$m1 = new Matrix($data);
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$m2 = new Matrix($this->eigVectors);
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return $m1->multiply($m2->transpose())->toArray();
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}
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}
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