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<?php 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 type |
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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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* @param array $matrix |
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*/ |
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protected function eigenDecomposition(array $matrix) |
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{ |
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$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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* @param array $data |
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* |
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* @return array |
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*/ |
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protected function reduce(array $data) |
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{ |
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$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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php-ai /
php-ml
Our type inference engine has found an assignment to a property that is incompatible with the declared type of that property.
Either this assignment is in error or the assigned type should be added to the documentation/type hint for that property..