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