92 lines
3.4 KiB
PHP
92 lines
3.4 KiB
PHP
<?php
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namespace App\Models;
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use App\Events\PerceptronTrainingEnded;
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use App\Services\DataSetReader;
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use App\Services\ISynapticWeightsProvider;
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use App\Services\PerceptronIterationEventBuffer;
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class GradientDescentPerceptronTraining extends NetworkTraining
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{
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private Perceptron $perceptron;
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public ActivationsFunctions $activationFunction = ActivationsFunctions::LINEAR;
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private float $epochError;
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public function __construct(
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DataSetReader $datasetReader,
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protected float $learningRate,
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int $maxIterations,
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protected ISynapticWeightsProvider $synapticWeightsProvider,
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PerceptronIterationEventBuffer $iterationEventBuffer,
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string $sessionId,
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string $trainingId,
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private float $minError,
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) {
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parent::__construct($datasetReader, $maxIterations, $iterationEventBuffer, $sessionId, $trainingId);
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$this->perceptron = new GradientDescentPerceptron($synapticWeightsProvider->generate($datasetReader->getInputSize()));
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}
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public function start(): void
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{
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$this->iteration = 0;
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do {
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$this->epochError = 0;
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$iterationErrorPerWeight = [];
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$this->iteration++;
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while ($nextRow = $this->datasetReader->getRandomLine()) {
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$inputs = array_slice($nextRow, 0, -1);
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$correctOutput = (float) end($nextRow);
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$iterationError = $this->iterationFunction($inputs, $correctOutput);
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$this->epochError += (1 / 2) * (abs($iterationError) ** 2); // TDDO REMOVEME abs()
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// Store the iteration error for each weight
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$inputs_with_bias = array_merge([1], $inputs); // Add bias input
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foreach ($inputs_with_bias as $index => $input) {
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$iterationErrorPerWeight[$index][] = $iterationError * $input;
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}
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// Broadcast the training iteration event
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$this->addIterationToBuffer($iterationError, [[$this->perceptron->getSynapticWeights()]]);
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}
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// Synaptic weights correction after each epoch
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$synaptic_weights = $this->perceptron->getSynapticWeights();
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$new_weights = array_map(
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fn($weight, $weightIndex) => $weight + $this->learningRate * array_sum($iterationErrorPerWeight[$weightIndex]),
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$synaptic_weights,
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array_keys($synaptic_weights)
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);
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$this->perceptron->setSynapticWeights($new_weights);
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$this->datasetReader->reset(); // Reset the dataset for the next iteration
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} while ($this->iteration < $this->maxIterations && !$this->stopCondition());
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$this->iterationEventBuffer->flush(); // Ensure all iterations are sent to the frontend
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$this->checkPassedMaxIterations($this->epochError);
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}
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protected function stopCondition(): bool
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{
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$condition = $this->epochError <= $this->minError && $this->perceptron->getSynapticWeights() !== [[0.0, 0.0, 0.0]];
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if ($condition === true) {
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event(new PerceptronTrainingEnded('Le perceptron à atteint l\'erreur minimale', $this->sessionId, $this->trainingId));
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}
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return $condition;
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}
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private function iterationFunction(array $inputs, int $correctOutput)
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{
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$output = $this->perceptron->test($inputs);
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$error = $correctOutput - $output;
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return $error;
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}
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}
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