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app/Models/NetworksTraining/MonoLayerPerceptronTraining.php

@@ -2,7 +2,6 @@
 
 namespace App\Models\NetworksTraining;
 
-use App\Events\PerceptronTrainingEnded;
 use App\Models\ActivationsFunctions;
 use App\Models\Perceptrons\GradientDescentPerceptron;
 use App\Models\Perceptrons\NetworkPerceptron;
@@ -19,6 +18,8 @@ class MonoLayerPerceptronTraining extends NetworkTraining
 
     private array $labels;
 
+    private bool $isRegression;
+
     public ActivationsFunctions $activationFunction = ActivationsFunctions::LINEAR;
 
     public ?ActivationsFunctions $presentationLayerActivationFunction = ActivationsFunctions::STEP;
@@ -36,11 +37,12 @@ class MonoLayerPerceptronTraining extends NetworkTraining
         private float $minError,
     ) {
         parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId);
+        $this->isRegression = $datasetReader->getInputSize() === 1;
         $networkWeightsProvider = new SimpleNetworkWeightsProvider($synapticWeightsProvider);
         $this->network = new NetworkPerceptron(
             $networkWeightsProvider->generate(
                 $datasetReader->getInputSize(),
-                $datasetReader->getOutputSize(),
+                $this->isRegression ? 1 : $datasetReader->getOutputSize(),
                 0, // No hidden layer
                 0, // No hidden layer neurons
             ),
@@ -103,7 +105,7 @@ class MonoLayerPerceptronTraining extends NetworkTraining
     {
         $condition = $this->epochError <= $this->minError;
         if ($condition === true) {
-            event(new PerceptronTrainingEnded('Le perceptron à atteint l\'erreur minimale', $this->sessionId, $this->trainingId));
+            $this->broadcastTrainingEnded('Le perceptron à atteint l\'erreur minimale');
         }
 
         return $condition;
@@ -140,6 +142,10 @@ class MonoLayerPerceptronTraining extends NetworkTraining
 
     private function getDesiredOutputFromCorrectOutput(float $correctOutput): array
     {
+        if ($this->isRegression) {
+            return [$correctOutput];
+        }
+
         $desiredOutput = array_fill(0, count($this->labels), -1);
         $labelIndex = Arr::first(array_keys($this->labels), fn ($key) => $this->labels[$key] == $correctOutput);
         if ($labelIndex !== null) {