106 lines
4.0 KiB
PHP
106 lines
4.0 KiB
PHP
<?php
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namespace App\Models\NetworksTraining;
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use App\Events\PerceptronTrainingEnded;
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use App\Models\ActivationsFunctions;
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use App\Models\Perceptrons\GradientDescentPerceptron;
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use App\Models\Perceptrons\Perceptron;
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use App\Services\DatasetReader\IDataSetReader;
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use App\Services\IterationEventBuffer\IPerceptronIterationEventBuffer;
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use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
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class ADALINEPerceptronTraining 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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IDataSetReader $datasetReader,
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protected float $learningRate,
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int $maxEpochs,
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protected ISynapticWeightsProvider $synapticWeightsProvider,
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IPerceptronIterationEventBuffer $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, $maxEpochs, $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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$this->epoch = 0;
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do {
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$this->epochError = 0;
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$this->epoch++;
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$inputsForCurrentEpoch = [];
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while ($nextRow = $this->datasetReader->getNextLine()) {
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$inputsForCurrentEpoch[] = $nextRow;
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$inputs = array_slice($nextRow, 0, -1);
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$correctOutput = (float) end($nextRow);
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$iterationError = $this->iterationFunction($inputs, $correctOutput);
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// Synaptic weights correction after each example
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$synaptic_weights = $this->perceptron->getSynapticWeights();
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$inputs_with_bias = array_merge([1], $inputs); // Add bias input
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$new_weights = array_map(
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fn($weight, $weightIndex) => $weight + ($this->learningRate * $iterationError * $inputs_with_bias[$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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// Broadcast the training iteration event
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$this->addIterationToBuffer($iterationError, [[$this->perceptron->getSynapticWeights()]]);
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}
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// Calculte the average error for the epoch with the last synaptic weights
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foreach ($inputsForCurrentEpoch as $inputsWithLabel) {
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$inputs = array_slice($inputsWithLabel, 0, -1);
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$correctOutput = (float) end($inputsWithLabel);
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$output = $this->perceptron->test($inputs);
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$iterationError = $correctOutput - $output;
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$this->epochError += ($iterationError ** 2) / 2; // Squared error for the example
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}
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$this->epochError /= $this->datasetReader->getEpochExamplesCount(); // Average error for the epoch
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$this->datasetReader->reset(); // Reset the dataset for the next iteration
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} while ($this->epoch < $this->maxEpochs && !$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 <= $this->minError;
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if ($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 $error;
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}
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public function getSynapticWeights(): array
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{
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return [[$this->perceptron->getSynapticWeights()]];
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}
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}
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