Wiki

app/Models/NetworksTraining/ADALINEPerceptronTraining.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\Perceptron;
@@ -84,7 +83,7 @@ class ADALINEPerceptronTraining 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;

app/Models/NetworksTraining/GradientDescentPerceptronTraining.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\Perceptron;
@@ -80,7 +79,7 @@ class GradientDescentPerceptronTraining 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;

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) {

app/Models/NetworksTraining/MultiLayerPerceptronTraining.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;
@@ -116,7 +115,7 @@ class MultiLayerPerceptronTraining 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;

app/Models/NetworksTraining/NetworkTraining.php

@@ -33,7 +33,7 @@ abstract class NetworkTraining
     protected function checkPassedMaxIterations(?float $finalError)
     {
         if ($this->epoch >= $this->maxEpochs) {
-            $message = 'Le nombre maximal d\'epoch a été atteint';
+            $message = 'Le nombre maximal d\'époques a été atteint';
             if ($finalError) {
                 $message .= " avec une erreur finale de $finalError";
             }
@@ -42,6 +42,12 @@ abstract class NetworkTraining
         }
     }
 
+    protected function broadcastTrainingEnded(string $reason): void
+    {
+        $this->iterationEventBuffer->flush();
+        event(new PerceptronTrainingEnded($reason, $this->sessionId, $this->trainingId));
+    }
+
     protected function addIterationToBuffer(float $error, array $synapticWeights)
     {
         $this->iterationEventBuffer->addIteration($this->epoch, $this->datasetReader->getLastReadLineIndex(), $error, $synapticWeights);