Fix linting

app/Models/NetworksTraining/ADALINEPerceptronTraining.php

@@ -52,7 +52,7 @@ class ADALINEPerceptronTraining extends NetworkTraining
                 $synaptic_weights = $this->perceptron->getSynapticWeights();
                 $inputs_with_bias = array_merge([1], $inputs); // Add bias input
                 $new_weights = array_map(
-                    fn($weight, $weightIndex) => $weight + ($this->learningRate * $iterationError * $inputs_with_bias[$weightIndex]),
+                    fn ($weight, $weightIndex) => $weight + ($this->learningRate * $iterationError * $inputs_with_bias[$weightIndex]),
                     $synaptic_weights,
                     array_keys($synaptic_weights)
                 );
@@ -73,7 +73,7 @@ class ADALINEPerceptronTraining extends NetworkTraining
             $this->epochError /= $this->datasetReader->getEpochExamplesCount(); // Average error for the epoch
 
             $this->datasetReader->reset(); // Reset the dataset for the next iteration
-        } while ($this->epoch < $this->maxEpochs && !$this->stopCondition());
+        } while ($this->epoch < $this->maxEpochs && ! $this->stopCondition());
 
         $this->iterationEventBuffer->flush(); // Ensure all iterations are sent to the frontend
 
@@ -86,6 +86,7 @@ class ADALINEPerceptronTraining extends NetworkTraining
         if ($condition === true) {
             event(new PerceptronTrainingEnded('Le perceptron à atteint l\'erreur minimale', $this->sessionId, $this->trainingId));
         }
+
         return $condition;
     }
 

app/Models/NetworksTraining/GradientDescentPerceptronTraining.php

@@ -62,14 +62,14 @@ class GradientDescentPerceptronTraining extends NetworkTraining
             // Synaptic weights correction after each epoch
             $synaptic_weights = $this->perceptron->getSynapticWeights();
             $new_weights = array_map(
-                fn($weight, $weightIndex) => $weight + $this->learningRate * array_sum($epochCorrectorPerWeight[$weightIndex]),
+                fn ($weight, $weightIndex) => $weight + $this->learningRate * array_sum($epochCorrectorPerWeight[$weightIndex]),
                 $synaptic_weights,
                 array_keys($synaptic_weights)
             );
             $this->perceptron->setSynapticWeights($new_weights);
 
             $this->datasetReader->reset(); // Reset the dataset for the next iteration
-        } while ($this->epoch < $this->maxEpochs && !$this->stopCondition());
+        } while ($this->epoch < $this->maxEpochs && ! $this->stopCondition());
 
         $this->iterationEventBuffer->flush(); // Ensure all iterations are sent to the frontend
 
@@ -82,6 +82,7 @@ class GradientDescentPerceptronTraining extends NetworkTraining
         if ($condition === true) {
             event(new PerceptronTrainingEnded('Le perceptron à atteint l\'erreur minimale', $this->sessionId, $this->trainingId));
         }
+
         return $condition;
     }
 

app/Models/NetworksTraining/NetworkTraining.php

@@ -3,9 +3,9 @@
 namespace App\Models\NetworksTraining;
 
 use App\Events\PerceptronTrainingEnded;
+use App\Models\ActivationsFunctions;
 use App\Services\DatasetReader\IDataSetReader;
 use App\Services\IterationEventBuffer\IPerceptronIterationEventBuffer;
-use App\Models\ActivationsFunctions;
 
 abstract class NetworkTraining
 {
@@ -13,7 +13,6 @@ abstract class NetworkTraining
 
     /**
      * @abstract
-     * @var ActivationsFunctions
      */
     public ActivationsFunctions $activationFunction;
 
@@ -23,13 +22,14 @@ abstract class NetworkTraining
         protected IPerceptronIterationEventBuffer $iterationEventBuffer,
         protected string $sessionId,
         protected string $trainingId,
-    ) {
-    }
+    ) {}
+
+    abstract public function start(): void;
 
-    abstract public function start() : void;
     abstract protected function stopCondition(): bool;
 
-    protected function checkPassedMaxIterations(?float $finalError) {
+    protected function checkPassedMaxIterations(?float $finalError)
+    {
         if ($this->epoch >= $this->maxEpochs) {
             $message = 'Le nombre maximal d\'epoch a été atteint';
             if ($finalError) {
@@ -40,7 +40,8 @@ abstract class NetworkTraining
         }
     }
 
-    protected function addIterationToBuffer(float $error, array $synapticWeights) {
+    protected function addIterationToBuffer(float $error, array $synapticWeights)
+    {
         $this->iterationEventBuffer->addIteration($this->epoch, $this->datasetReader->getLastReadLineIndex(), $error, $synapticWeights);
     }
 

app/Models/NetworksTraining/SimpleBinaryPerceptronTraining.php

@@ -13,6 +13,7 @@ use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
 class SimpleBinaryPerceptronTraining extends NetworkTraining
 {
     private Perceptron $perceptron;
+
     private int $iterationErrorCounter = 0;
 
     public ActivationsFunctions $activationFunction = ActivationsFunctions::STEP;
@@ -51,7 +52,7 @@ class SimpleBinaryPerceptronTraining extends NetworkTraining
                 $this->addIterationToBuffer($error, [[$this->perceptron->getSynapticWeights()]]);
             }
             $this->datasetReader->reset(); // Reset the dataset for the next iteration
-        } while ($this->epoch < $this->maxEpochs && !$this->stopCondition());
+        } while ($this->epoch < $this->maxEpochs && ! $this->stopCondition());
 
         $this->iterationEventBuffer->flush(); // Ensure all iterations are sent to the frontend
 
@@ -64,6 +65,7 @@ class SimpleBinaryPerceptronTraining extends NetworkTraining
         if ($condition === true) {
             event(new PerceptronTrainingEnded('Le perceptron ne commet plus d\'erreurs sur aucune des données', $this->sessionId, $this->trainingId));
         }
+
         return $this->iterationErrorCounter == 0;
     }
 
@@ -79,9 +81,10 @@ class SimpleBinaryPerceptronTraining extends NetworkTraining
         if ($error !== 0) { // Update synaptic weights if needed
             $synaptic_weights = $this->perceptron->getSynapticWeights();
             $inputs_with_bias = array_merge([1], $inputs); // Add bias input
-            $new_weights = array_map(fn($weight, $input) => $weight + $this->learningRate * $error * $input, $synaptic_weights, $inputs_with_bias);
+            $new_weights = array_map(fn ($weight, $input) => $weight + $this->learningRate * $error * $input, $synaptic_weights, $inputs_with_bias);
             $this->perceptron->setSynapticWeights($new_weights);
         }
+
         return $error;
     }