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/home/infinio/public_html/moodle/lib/mlbackend/php/phpml/src/Phpml/NeuralNetwork/Training

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✏️ Editing: Backpropagation.php

<?php

declare(strict_types=1);

namespace Phpml\NeuralNetwork\Training;

use Phpml\NeuralNetwork\Node\Neuron;
use Phpml\NeuralNetwork\Training\Backpropagation\Sigma;

class Backpropagation
{
    /**
     * @var float
     */
    private $learningRate;

/**
     * @var array
     */
    private $sigmas = [];

/**
     * @var array
     */
    private $prevSigmas = [];

public function __construct(float $learningRate)
    {
        $this->setLearningRate($learningRate);
    }

public function setLearningRate(float $learningRate): void
    {
        $this->learningRate = $learningRate;
    }

public function getLearningRate(): float
    {
        return $this->learningRate;
    }

/**
     * @param mixed $targetClass
     */
    public function backpropagate(array $layers, $targetClass): void
    {
        $layersNumber = count($layers);

// Backpropagation.
        for ($i = $layersNumber; $i > 1; --$i) {
            $this->sigmas = [];
            foreach ($layers[$i - 1]->getNodes() as $key => $neuron) {
                if ($neuron instanceof Neuron) {
                    $sigma = $this->getSigma($neuron, $targetClass, $key, $i == $layersNumber);
                    foreach ($neuron->getSynapses() as $synapse) {
                        $synapse->changeWeight($this->learningRate * $sigma * $synapse->getNode()->getOutput());
                    }
                }
            }

$this->prevSigmas = $this->sigmas;
        }

// Clean some memory (also it helps make MLP persistency & children more maintainable).
        $this->sigmas = [];
        $this->prevSigmas = [];
    }

private function getSigma(Neuron $neuron, int $targetClass, int $key, bool $lastLayer): float
    {
        $neuronOutput = $neuron->getOutput();
        $sigma = $neuron->getDerivative();

if ($lastLayer) {
            $value = 0;
            if ($targetClass === $key) {
                $value = 1;
            }

$sigma *= ($value - $neuronOutput);
        } else {
            $sigma *= $this->getPrevSigma($neuron);
        }

$this->sigmas[] = new Sigma($neuron, $sigma);

return $sigma;
    }

private function getPrevSigma(Neuron $neuron): float
    {
        $sigma = 0.0;

foreach ($this->prevSigmas as $neuronSigma) {
            $sigma += $neuronSigma->getSigmaForNeuron($neuron);
        }

return $sigma;
    }
}

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