Gradient descent training + Added all dataset + graphs improvements

app/Models/GradientDescentPerceptronTraining.php

@@ -0,0 +1,91 @@
+<?php
+
+namespace App\Models;
+
+use App\Events\PerceptronTrainingEnded;
+use App\Services\DataSetReader;
+use App\Services\ISynapticWeightsProvider;
+use App\Services\PerceptronIterationEventBuffer;
+
+class GradientDescentPerceptronTraining extends NetworkTraining
+{
+    private Perceptron $perceptron;
+
+    public ActivationsFunctions $activationFunction = ActivationsFunctions::LINEAR;
+
+    private float $epochError;
+
+    public function __construct(
+        DataSetReader $datasetReader,
+        protected float $learningRate,
+        int $maxIterations,
+        protected ISynapticWeightsProvider $synapticWeightsProvider,
+        PerceptronIterationEventBuffer $iterationEventBuffer,
+        string $sessionId,
+        string $trainingId,
+        private float $minError,
+    ) {
+        parent::__construct($datasetReader, $maxIterations, $iterationEventBuffer, $sessionId, $trainingId);
+        $this->perceptron = new GradientDescentPerceptron($synapticWeightsProvider->generate($datasetReader->getInputSize()));
+    }
+
+    public function start(): void
+    {
+        $this->iteration = 0;
+        do {
+            $this->epochError = 0;
+            $iterationErrorPerWeight = [];
+            $this->iteration++;
+
+            while ($nextRow = $this->datasetReader->getRandomLine()) {
+                $inputs = array_slice($nextRow, 0, -1);
+                $correctOutput = (float) end($nextRow);
+
+                $iterationError = $this->iterationFunction($inputs, $correctOutput);
+                $this->epochError += (1 / 2) * (abs($iterationError) ** 2); // TDDO REMOVEME abs()
+
+                // Store the iteration error for each weight
+                $inputs_with_bias = array_merge([1], $inputs); // Add bias input
+                foreach ($inputs_with_bias as $index => $input) {
+                    $iterationErrorPerWeight[$index][] = $iterationError * $input;
+                }
+
+                // Broadcast the training iteration event
+                $this->addIterationToBuffer($iterationError, [[$this->perceptron->getSynapticWeights()]]);
+            }
+
+            // Synaptic weights correction after each epoch
+            $synaptic_weights = $this->perceptron->getSynapticWeights();
+            $new_weights = array_map(
+                fn($weight, $weightIndex) => $weight + $this->learningRate * array_sum($iterationErrorPerWeight[$weightIndex]),
+                $synaptic_weights,
+                array_keys($synaptic_weights)
+            );
+            $this->perceptron->setSynapticWeights($new_weights);
+
+            $this->datasetReader->reset(); // Reset the dataset for the next iteration
+        } while ($this->iteration < $this->maxIterations && !$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;
+    }
+}