MonoLayer Perceptron

app/Models/NetworksTraining/ADALINEPerceptronTraining.php

@@ -66,7 +66,7 @@ class ADALINEPerceptronTraining extends NetworkTraining
             foreach ($inputsForCurrentEpoch as $inputsWithLabel) {
                 $inputs = array_slice($inputsWithLabel, 0, -1);
                 $correctOutput = (float) end($inputsWithLabel);
-                $output = $this->perceptron->test($inputs);
+                $output = $this->perceptron->test($inputs)[0];
                 $iterationError = $correctOutput - $output;
                 $this->epochError += ($iterationError ** 2) / 2; // Squared error for the example
             }
@@ -92,7 +92,7 @@ class ADALINEPerceptronTraining extends NetworkTraining
 
     private function iterationFunction(array $inputs, float $correctOutput): float
     {
-        $output = $this->perceptron->test($inputs);
+        $output = $this->perceptron->test($inputs)[0];
 
         $error = $correctOutput - $output;
 

app/Models/NetworksTraining/GradientDescentPerceptronTraining.php

@@ -88,7 +88,7 @@ class GradientDescentPerceptronTraining extends NetworkTraining
 
     private function iterationFunction(array $inputs, float $correctOutput): float
     {
-        $output = $this->perceptron->test($inputs);
+        $output = $this->perceptron->test($inputs)[0];
 
         $error = $correctOutput - $output;
 

app/Models/NetworksTraining/MonoLayerPerceptronTraining.php

@@ -0,0 +1,157 @@
+<?php
+
+namespace App\Models\NetworksTraining;
+
+use App\Events\PerceptronTrainingEnded;
+use App\Models\ActivationsFunctions;
+use App\Models\Perceptrons\GradientDescentPerceptron;
+use App\Models\Perceptrons\NetworkPerceptron;
+use App\Models\Perceptrons\Perceptron;
+use App\Models\Perceptrons\SimpleBinaryPerceptron2;
+use App\Models\Perceptrons\SimpleBinaryPerceptron;
+use App\Services\DatasetReader\IDataSetReader;
+use App\Services\IterationEventBuffer\IPerceptronIterationEventBuffer;
+use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
+use App\Services\SynapticWeightsProvider\SimpleNetworkWeightsProvider;
+use Illuminate\Support\Arr;
+
+class MonoLayerPerceptronTraining extends NetworkTraining
+{
+    private Perceptron $network;
+
+    private array $labels;
+
+    public ActivationsFunctions $activationFunction = ActivationsFunctions::LINEAR;
+    public ?ActivationsFunctions $presentationLayerActivationFunction = ActivationsFunctions::STEP;
+
+    private float $epochError;
+
+    public function __construct(
+        IDataSetReader $datasetReader,
+        protected float $learningRate,
+        int $maxEpochs,
+        ISynapticWeightsProvider $synapticWeightsProvider,
+        IPerceptronIterationEventBuffer $iterationEventBuffer,
+        string $sessionId,
+        string $trainingId,
+        private float $minError,
+    ) {
+        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId);
+        $networkWeightsProvider = new SimpleNetworkWeightsProvider($synapticWeightsProvider);
+        $this->network = new NetworkPerceptron(
+            $networkWeightsProvider->generate(
+                $datasetReader->getInputSize(),
+                $datasetReader->getOutputSize(),
+                0, // No hidden layer
+                0, // No hidden layer neurons
+            ),
+            $datasetReader->getInputSize(),
+            GradientDescentPerceptron::class, // No hidden layer
+            SimpleBinaryPerceptron2::class,
+        );
+        $this->labels = $datasetReader->getLabels();
+    }
+
+    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);
+                $correctOutput = (int) end($nextRow);
+
+                $iterationError = $this->iterationFunction($inputs, $correctOutput);
+
+                // Synaptic weights correction after each example
+                $synaptic_weights = $this->network->getSynapticWeights();
+                $inputs_with_bias = array_merge([1], $inputs); // Add bias input
+
+                // Updates the weights
+                $this->network->setSynapticWeights(
+                    $this->getUpdatedSynapticWeights($synaptic_weights, $iterationError, $inputs_with_bias)
+                );
+
+                // Broadcast the training iteration event
+                $this->addIterationToBuffer(array_sum($iterationError), $this->network->getSynapticWeights());
+            }
+
+            // Calculte the average error for the epoch with the last synaptic weights
+            foreach ($inputsForCurrentEpoch as $inputsWithLabel) {
+                $inputs = array_slice($inputsWithLabel, 0, -1);
+                $correctOutput = (float) end($inputsWithLabel);
+                $iterationError = $this->iterationFunction($inputs, $correctOutput);
+                foreach ($iterationError as $error) {
+                    $this->epochError += ($error ** 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 <= $this->minError;
+        if ($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): array
+    {
+        $outputs = $this->network->test($inputs);
+        $desiredOutput = $this->getDesiredOutputFromCorrectOutput($correctOutput);
+
+        $errors = [];
+        foreach ($outputs as $index => $output) {
+            $error = $desiredOutput[$index] - $output;
+            $errors[] = $error;
+        }
+
+        return $errors;
+    }
+
+    private function getUpdatedSynapticWeights(array $synaptic_weights, array $iterationError, array $inputs): array
+    {
+        $updatedWeights = [];
+        foreach ($synaptic_weights[0] as $neuronIndex => $neuronWeights) { // There is only one layer of weights
+            $updatedNeuronWeights = [];
+            foreach ($neuronWeights as $weightIndex => $weight) {
+                $updatedWeight = $weight + ($this->learningRate * $iterationError[$neuronIndex] * $inputs[$weightIndex]);
+                $updatedNeuronWeights[] = $updatedWeight;
+            }
+            $updatedWeights[] = $updatedNeuronWeights;
+        }
+
+        return [$updatedWeights];
+    }
+
+    private function getDesiredOutputFromCorrectOutput(int $correctOutput): array
+    {
+        $desiredOutput = array_fill(0, count($this->labels), -1);
+        $labelIndex = Arr::first(array_keys($this->labels), fn($key) => $this->labels[$key] == $correctOutput);
+        if ($labelIndex !== null) {
+            $desiredOutput[$labelIndex] = 1;
+        }
+
+        return $desiredOutput;
+    }
+
+    public function getSynapticWeights(): array
+    {
+        return [[$this->network->getSynapticWeights()]];
+    }
+}

app/Models/NetworksTraining/NetworkTraining.php

@@ -16,6 +16,8 @@ abstract class NetworkTraining
      */
     public ActivationsFunctions $activationFunction;
 
+    public ?ActivationsFunctions $presentationLayerActivationFunction = null;
+
     public function __construct(
         protected IDataSetReader $datasetReader,
         protected int $maxEpochs,

app/Models/NetworksTraining/SimpleBinaryPerceptronTraining.php

@@ -71,7 +71,7 @@ class SimpleBinaryPerceptronTraining extends NetworkTraining
 
     private function iterationFunction(array $inputs, int $correctOutput)
     {
-        $output = $this->perceptron->test($inputs);
+        $output = $this->perceptron->test($inputs)[0];
 
         $error = $correctOutput - $output;
         if (abs($error) > $this::MIN_ERROR) {