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<?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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}