Added ADALINE training

app/Http/Controllers/PerceptronController.php

@@ -3,6 +3,7 @@
 namespace App\Http\Controllers;
 
 use App\Events\PerceptronInitialization;
+use App\Models\NetworksTraining\ADALINEPerceptronTraining;
 use App\Models\NetworksTraining\GradientDescentPerceptronTraining;
 use App\Models\NetworksTraining\SimpleBinaryPerceptronTraining;
 use App\Services\DatasetReader\IDataSetReader;
@@ -32,8 +33,9 @@ class PerceptronController extends Controller
                 $learningRate = 0.015;
                 $maxIterations = 150;
                 break;
-            case 'gradientdescent':
+            case 'gradientdescent' || 'adaline':
                 $learningRate = 0.00003;
+                break;
         }
 
         return inertia('PerceptronViewer', [
@@ -88,6 +90,10 @@ class PerceptronController extends Controller
                                 $dataset['defaultLearningRate'] = 0.3;
                                 $dataset['defaultMinError'] = 0.125;
                                 break;
+                            case 'adaline':
+                                $dataset['defaultLearningRate'] = 0.05;
+                                $dataset['defaultMinError'] = 0.125;
+                                break;
                         }
                         break;
                     case 'table_2_9':
@@ -95,7 +101,7 @@ class PerceptronController extends Controller
                             case 'simple':
                                 $dataset['defaultLearningRate'] = 0.015;
                                 break;
-                            case 'gradientdescent':
+                            case 'gradientdescent' || 'adaline':
                                 $dataset['defaultLearningRate'] = 0.001;
                                 break;
                         }
@@ -123,7 +129,7 @@ class PerceptronController extends Controller
         $weightInitMethod = $request->input('weight_init_method', 'random');
         $dataSet = $request->input('dataset');
         $learningRate = $request->input('learning_rate', 0.015);
-        $maxIterations = $request->input('max_iterations', 100);
+        $maxEpochs = $request->input('max_iterations', 100);
         $sessionId = $request->input('session_id', session()->getId());
         $trainingId = $request->input('training_id');
 
@@ -132,21 +138,22 @@ class PerceptronController extends Controller
         }
 
         $iterationEventBuffer = new PerceptronIterationEventBuffer($sessionId, $trainingId);
-        if ($maxIterations > config('perceptron.limited_broadcast_iterations')) {
-            $iterationsInterval = (int)($maxIterations / config('perceptron.limited_broadcast_iterations'));
+        if ($maxEpochs > config('perceptron.limited_broadcast_iterations')) {
+            $iterationsInterval = (int)($maxEpochs / config('perceptron.limited_broadcast_iterations'));
             $iterationEventBuffer = new PerceptronLimitedEpochEventBuffer($sessionId, $trainingId, $iterationsInterval);
         }
 
-        $dataSetReader = $this->getDataSetReader($dataSet);
+        $datasetReader = $this->getDataSetReader($dataSet);
 
 
         $networkTraining = match ($perceptronType) {
-            'simple' => new SimpleBinaryPerceptronTraining($dataSetReader, $learningRate, $maxIterations, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId),
-            'gradientdescent' => new GradientDescentPerceptronTraining($dataSetReader, $learningRate, $maxIterations, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError),
+            'simple' => new SimpleBinaryPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId),
+            'gradientdescent' => new GradientDescentPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError),
+            'adaline' => new ADALINEPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError),
             default => null,
         };
 
-        event(new PerceptronInitialization($dataSetReader->lines, $networkTraining->activationFunction, $sessionId, $trainingId));
+        event(new PerceptronInitialization($datasetReader->lines, $networkTraining->activationFunction, $sessionId, $trainingId));
 
         $networkTraining->start();
 

app/Models/NetworksTraining/ADALINEPerceptronTraining.php

@@ -0,0 +1,105 @@
+<?php
+
+namespace App\Models\NetworksTraining;
+
+use App\Events\PerceptronTrainingEnded;
+use App\Models\ActivationsFunctions;
+use App\Models\Perceptrons\GradientDescentPerceptron;
+use App\Models\Perceptrons\Perceptron;
+use App\Services\DatasetReader\IDataSetReader;
+use App\Services\IterationEventBuffer\IPerceptronIterationEventBuffer;
+use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
+
+class ADALINEPerceptronTraining extends NetworkTraining
+{
+    private Perceptron $perceptron;
+
+    public ActivationsFunctions $activationFunction = ActivationsFunctions::LINEAR;
+
+    private float $epochError;
+
+    public function __construct(
+        IDataSetReader $datasetReader,
+        protected float $learningRate,
+        int $maxEpochs,
+        protected ISynapticWeightsProvider $synapticWeightsProvider,
+        IPerceptronIterationEventBuffer $iterationEventBuffer,
+        string $sessionId,
+        string $trainingId,
+        private float $minError,
+    ) {
+        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId);
+        $this->perceptron = new GradientDescentPerceptron($synapticWeightsProvider->generate($datasetReader->getInputSize()));
+    }
+
+    public function start(): void
+    {
+        $this->epoch = 0;
+        do {
+            $this->epochError = 0;
+            $this->epoch++;
+
+            $inputsForCurrentEpoch = [];
+
+            while ($nextRow = $this->datasetReader->getNextLine()) {
+                $inputsForCurrentEpoch[] = $nextRow;
+                $inputs = array_slice($nextRow, 0, -1);
+                $correctOutput = (float) end($nextRow);
+
+                $iterationError = $this->iterationFunction($inputs, $correctOutput);
+
+                // Synaptic weights correction after each example
+                $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]),
+                    $synaptic_weights,
+                    array_keys($synaptic_weights)
+                );
+                $this->perceptron->setSynapticWeights($new_weights);
+
+                // Broadcast the training iteration event
+                $this->addIterationToBuffer($iterationError, [[$this->perceptron->getSynapticWeights()]]);
+            }
+
+            // Calculte the average error for the epoch with the last synaptic weights
+            foreach ($inputsForCurrentEpoch as $inputsWithLabel) {
+                $inputs = array_slice($inputsWithLabel, 0, -1);
+                $correctOutput = (float) end($inputsWithLabel);
+                $output = $this->perceptron->test($inputs);
+                $iterationError = $correctOutput - $output;
+                $this->epochError += ($iterationError ** 2) / 2; // Squared error for the example
+            }
+            $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());
+
+        $this->iterationEventBuffer->flush(); // Ensure all iterations are sent to the frontend
+
+        $this->checkPassedMaxIterations($this->epochError);
+    }
+
+    protected function stopCondition(): bool
+    {
+        $condition = $this->epochError <= $this->minError && $this->perceptron->getSynapticWeights() !== [[0.0, 0.0, 0.0]];
+        if ($condition === true) {
+            event(new PerceptronTrainingEnded('Le perceptron à atteint l\'erreur minimale', $this->sessionId, $this->trainingId));
+        }
+        return $condition;
+    }
+
+    private function iterationFunction(array $inputs, int $correctOutput)
+    {
+        $output = $this->perceptron->test($inputs);
+
+        $error = $correctOutput - $output;
+
+        return $error;
+    }
+
+    public function getSynapticWeights(): array
+    {
+        return [[$this->perceptron->getSynapticWeights()]];
+    }
+}