MonoLayer Perceptron

app/Http/Controllers/PerceptronController.php

@@ -5,6 +5,7 @@ namespace App\Http\Controllers;
 use App\Events\PerceptronInitialization;
 use App\Models\NetworksTraining\ADALINEPerceptronTraining;
 use App\Models\NetworksTraining\GradientDescentPerceptronTraining;
+use App\Models\NetworksTraining\MonoLayerPerceptronTraining;
 use App\Models\NetworksTraining\SimpleBinaryPerceptronTraining;
 use App\Services\DatasetReader\IDataSetReader;
 use App\Services\DatasetReader\LinearOrderDataSetReader;
@@ -13,7 +14,11 @@ use App\Services\IterationEventBuffer\PerceptronIterationEventBuffer;
 use App\Services\IterationEventBuffer\PerceptronLimitedEpochEventBuffer;
 use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
 use App\Services\SynapticWeightsProvider\ZeroSynapticWeights;
+use Illuminate\Contracts\Queue\Job;
 use Illuminate\Http\Request;
+use Illuminate\Support\Facades\DB;
+use Symfony\Contracts\EventDispatcher\Event;
+use Tests\Services\IterationEventBuffer\DullIterationEventBuffer;
 
 class PerceptronController extends Controller
 {
@@ -126,6 +131,8 @@ class PerceptronController extends Controller
 
     public function run(Request $request, ISynapticWeightsProvider $synapticWeightsProvider)
     {
+        $startTime = microtime(true);
+
         $perceptronType = $request->input('type');
         $minError = $request->input('min_error', 0.01);
         $weightInitMethod = $request->input('weight_init_method', 'random');
@@ -135,6 +142,9 @@ class PerceptronController extends Controller
         $sessionId = $request->input('session_id', session()->getId());
         $trainingId = $request->input('training_id');
 
+        // Remove the jobs for the sessionId
+        DB::table('jobs')->where('payload', 'like', '%s:9:\"sessionId\";s:40:\"'. $sessionId .'\";%')->delete();
+
         if ($weightInitMethod === 'zeros') {
             $synapticWeightsProvider = new ZeroSynapticWeights;
         }
@@ -151,6 +161,7 @@ class PerceptronController extends Controller
             'simple' => new SimpleBinaryPerceptronTraining($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, $minError),
+            'monolayer' => new MonoLayerPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError),
             default => null,
         };
 
@@ -160,6 +171,7 @@ class PerceptronController extends Controller
 
         return response()->json([
             'message' => 'Training completed',
+            'execution_time' => microtime(true) - $startTime,
         ]);
     }
 }

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) {

app/Models/Perceptrons/InputNeuron.php

@@ -0,0 +1,26 @@
+<?php
+
+namespace App\Models\Perceptrons;
+
+class InputNeuron extends Perceptron
+{
+    public function __construct(
+    ) {
+        parent::__construct([]);
+    }
+
+    public function setInput(float $input): void
+    {
+        $this->input = $input;
+    }
+
+    public function test(array $inputs): array
+    {
+        return [$this->input];
+    }
+
+    public function activationFunction(float $input): float
+    {
+        return $input; // Identity function for input neurons
+    }
+}

app/Models/Perceptrons/NetworkPerceptron.php

@@ -0,0 +1,76 @@
+<?php
+
+namespace App\Models\Perceptrons;
+
+class NetworkPerceptron extends Perceptron
+{
+    public array $network = [];
+
+    public function __construct(
+        private array $synaptic_weights,
+        private int $inputLayerNeuronsCount,
+        private string $hiddenLayerNeuronClass,
+        private string $outputLayerNeuronClass,
+    ) {
+        parent::__construct($synaptic_weights);
+        $this->initializeNetwork($synaptic_weights);
+    }
+
+    private function initializeNetwork(array $synaptic_weights): void
+    {
+        // Input Layer
+        $this->network[0] = [];
+        foreach (range(0, $this->inputLayerNeuronsCount - 1) as $i) {
+            $this->network[0][] = new InputNeuron();
+        }
+
+        // Hidden Layer
+        for ($layerIndex = 0; $layerIndex < count($synaptic_weights) - 2; $layerIndex++) {
+            $this->network[$layerIndex + 1] = [];
+
+            foreach ($synaptic_weights[$layerIndex] as $neuronWeights) {
+                $this->network[$layerIndex + 1][] = new $this->hiddenLayerNeuronClass($neuronWeights);
+            }
+        }
+
+        // Output Layer
+        $outputLayer = $synaptic_weights[count($synaptic_weights) - 1];
+        $this->network[count($synaptic_weights)] = [];
+
+        foreach ($outputLayer as $neuronWeights) {
+            $this->network[count($synaptic_weights)][] = new $this->outputLayerNeuronClass($neuronWeights);
+        }
+    }
+
+    public function test(array $inputs): array
+    {
+        // Set the inputs for the input layer
+        foreach ($this->network[0] as $index => $inputNeuron) {
+            $inputNeuron->setInput($inputs[$index]);
+        }
+
+        // Pass through the hidden and output layers
+        $output = [];
+        for ($layerIndex = 0; $layerIndex < count($this->network); $layerIndex++) {
+            $lastLayerOutput = $output;
+            $output = [];
+            foreach ($this->network[$layerIndex] as $neuron) {
+                $output[] = $neuron->test($lastLayerOutput)[0];
+            }
+        }
+
+        return $output;
+    }
+
+    public function activationFunction(float $weighted_sum): float
+    {
+        return $weighted_sum;
+    }
+
+    public function setSynapticWeights(array $synaptic_weights): void
+    {
+        parent::setSynapticWeights($synaptic_weights);
+        $this->network = [];
+        $this->initializeNetwork($synaptic_weights);
+    }
+}

app/Models/Perceptrons/Perceptron.php

@@ -2,9 +2,9 @@
 
 namespace App\Models\Perceptrons;
 
-use Illuminate\Database\Eloquent\Model;
+// use Illuminate\Database\Eloquent\Model;
 
-abstract class Perceptron extends Model
+abstract class Perceptron
 {
     public function __construct(
         private array $synaptic_weights,
@@ -12,7 +12,7 @@ abstract class Perceptron extends Model
         $this->synaptic_weights = $synaptic_weights;
     }
 
-    public function test(array $inputs): float
+    public function test(array $inputs): array
     {
         $inputs = array_merge([1], $inputs); // Add bias input
 
@@ -22,7 +22,7 @@ abstract class Perceptron extends Model
 
         $weighted_sum = array_sum(array_map(fn ($input, $weight) => $input * $weight, $inputs, $this->synaptic_weights));
 
-        return $this->activationFunction($weighted_sum);
+        return [$this->activationFunction($weighted_sum)];
     }
 
     abstract public function activationFunction(float $weighted_sum): float;

app/Models/Perceptrons/SimpleBinaryPerceptron2.php

@@ -0,0 +1,18 @@
+<?php
+
+namespace App\Models\Perceptrons;
+
+class SimpleBinaryPerceptron2 extends Perceptron
+{
+    public function __construct(
+        array $synaptic_weights,
+    ) {
+        parent::__construct($synaptic_weights);
+    }
+
+    public function activationFunction(float $weighted_sum): float
+    {
+        // return $weighted_sum >= 0.0 ? 1.0 : -1.0;
+        return $weighted_sum;
+    }
+}

app/Services/DatasetReader/IDataSetReader.php

@@ -8,6 +8,10 @@ interface IDataSetReader
 
     public function getInputSize(): int;
 
+    public function getOutputSize(): int;
+
+    public function getLabels(): array;
+
     public function reset(): void;
 
     public function getLastReadLineIndex(): int;

app/Services/DatasetReader/LinearOrderDataSetReader.php

@@ -49,6 +49,19 @@ class LinearOrderDataSetReader implements IDataSetReader
         return count($this->lines[0]) - 1; // Don't count the label
     }
 
+    public function getOutputSize(): int
+    {
+        // Count the number of unique labels in the dataset
+        $labels = array_map(fn ($line) => end($line), $this->lines);
+        return count(array_unique($labels));
+    }
+
+    public function getLabels(): array
+    {
+        $labels = array_map(fn ($line) => end($line), $this->lines);
+        return array_values(array_unique($labels));
+    }
+
     public function reset(): void
     {
         $this->currentLines = $this->lines;

app/Services/DatasetReader/RandomOrderDataSetReader.php

@@ -55,6 +55,19 @@ class RandomOrderDataSetReader implements IDataSetReader
         return count($this->lines[0]) - 1; // Don't count the label
     }
 
+    public function getOutputSize(): int
+    {
+        // Count the number of unique labels in the dataset
+        $labels = array_map(fn ($line) => end($line), $this->lines);
+        return count(array_unique($labels));
+    }
+
+    public function getLabels(): array
+    {
+        $labels = array_map(fn ($line) => end($line), $this->lines);
+        return array_values(array_unique($labels));
+    }
+
     public function reset(): void
     {
         $this->currentLines = $this->lines;

app/Services/SynapticWeightsProvider/INetworkSynapticWeightsProvider.php

@@ -0,0 +1,8 @@
+<?php
+
+namespace App\Services\SynapticWeightsProvider;
+
+interface INetworkSynapticWeightsProvider
+{
+    public function generate(int $input_size, int $output_size, int $hidden_layers_count, int $hidden_layers_neurons_count): array;
+}

app/Services/SynapticWeightsProvider/SimpleNetworkWeightsProvider.php

@@ -0,0 +1,35 @@
+<?php
+
+namespace App\Services\SynapticWeightsProvider;
+
+use App\Services\SynapticWeightsProvider\INetworkSynapticWeightsProvider;
+
+class SimpleNetworkWeightsProvider implements INetworkSynapticWeightsProvider
+{
+    public function __construct(
+        private ISynapticWeightsProvider $synapticWeightsProvider,
+    ) {
+    }
+
+    public function generate(int $input_size, int $output_size, int $hidden_layers_count, int $hidden_layers_neurons_count): array
+    {
+        $synaptic_weights = [];
+        $lastLayerSize = $input_size;
+
+        // Generate Hidden Layer weights
+        for ($hiddenLayerNeuronIndex = 0; $hiddenLayerNeuronIndex < $hidden_layers_count; $hiddenLayerNeuronIndex++) {
+            for ($neuronIndex = 0; $neuronIndex < $hidden_layers_neurons_count; $neuronIndex++) {
+                $synaptic_weights[] = $this->synapticWeightsProvider->generate($lastLayerSize);
+            }
+            $lastLayerSize = $hidden_layers_neurons_count;
+        }
+
+        // Generate Output Layer weights
+        $synaptic_weights[] = [];
+        for ($outputNeuronIndex = 0; $outputNeuronIndex < $output_size; $outputNeuronIndex++) {
+            $synaptic_weights[count($synaptic_weights) -1][] = $this->synapticWeightsProvider->generate($lastLayerSize);
+        }
+
+        return $synaptic_weights;
+    }
+}

public/data_sets/table_3_1.csv

@@ -1,150 +1,150 @@
-2.8,1.9,-1,-1,1
+2.8,1.9,1
-2.9,1.8,-1,-1,1
+2.9,1.8,1
-2.2,1.5,-1,-1,1
+2.2,1.5,1
-1.3,5.6,-1,1,-1
+1.3,5.6,0
--1.2,1.6,1,-1,-1
+-1.2,1.6,-1
-2.5,1.7,-1,-1,1
+2.5,1.7,1
-3.1,2.3,-1,-1,1
+3.1,2.3,1
-2.8,2.4,-1,-1,1
+2.8,2.4,1
--1.2,1.5,1,-1,-1
+-1.2,1.5,-1
-1.4,6.1,-1,1,-1
+1.4,6.1,0
--1.2,1.3,1,-1,-1
+-1.2,1.3,-1
-3.1,1.8,-1,-1,1
+3.1,1.8,1
-3.3,2.5,-1,-1,1
+3.3,2.5,1
-3.4,2.3,-1,-1,1
+3.4,2.3,1
-1.5,5.9,-1,1,-1
+1.5,5.9,0
-1.3,5.6,-1,1,-1
+1.3,5.6,0
--1.2,1,1,-1,-1
+-1.2,1,-1
-3,2.1,-1,-1,1
+3,2.1,1
-1.4,5.2,-1,1,-1
+1.4,5.2,0
-1.2,5.5,-1,1,-1
+1.2,5.5,0
-1.4,6.7,-1,1,-1
+1.4,6.7,0
--1.3,1.7,1,-1,-1
+-1.3,1.7,-1
-1,5.7,-1,1,-1
+1,5.7,0
-2.6,1.4,-1,-1,1
+2.6,1.4,1
-1.6,6,-1,1,-1
+1.6,6,0
-1.2,5.8,-1,1,-1
+1.2,5.8,0
-1.4,6.1,-1,1,-1
+1.4,6.1,0
-1.3,5.7,-1,1,-1
+1.3,5.7,0
--1.2,1.4,1,-1,-1
+-1.2,1.4,-1
-1,5.5,-1,1,-1
+1,5.5,0
-3.1,2.1,-1,-1,1
+3.1,2.1,1
--1.4,1.7,1,-1,-1
+-1.4,1.7,-1
-1.6,6.3,-1,1,-1
+1.6,6.3,0
--1.2,1.4,1,-1,-1
+-1.2,1.4,-1
-2.7,1.9,-1,-1,1
+2.7,1.9,1
-1.1,5.5,-1,1,-1
+1.1,5.5,0
-3,1.8,-1,-1,1
+3,1.8,1
-3.8,2,-1,-1,1
+3.8,2,1
-2.8,2.1,-1,-1,1
+2.8,2.1,1
-1,5,-1,1,-1
+1,5,0
-2.8,2,-1,-1,1
+2.8,2,1
--1.2,1.6,1,-1,-1
+-1.2,1.6,-1
-1.3,6.6,-1,1,-1
+1.3,6.6,0
-1.6,6,-1,1,-1
+1.6,6,0
-3,1.6,-1,-1,1
+3,1.6,1
--1.1,1.1,1,-1,-1
+-1.1,1.1,-1
-2.9,1.8,-1,-1,1
+2.9,1.8,1
-3,1.8,-1,-1,1
+3,1.8,1
--1.2,1.4,1,-1,-1
+-1.2,1.4,-1
-1,5.8,-1,1,-1
+1,5.8,0
-1.2,5.7,-1,1,-1
+1.2,5.7,0
-2.8,1.5,-1,-1,1
+2.8,1.5,1
--1.4,1.6,1,-1,-1
+-1.4,1.6,-1
--1.2,1.4,1,-1,-1
+-1.2,1.4,-1
-2.8,2.2,-1,-1,1
+2.8,2.2,1
--1.5,1.7,1,-1,-1
+-1.5,1.7,-1
-1,4.9,-1,1,-1
+1,4.9,0
-1.3,5.7,-1,1,-1
+1.3,5.7,0
-3.2,2.3,-1,-1,1
+3.2,2.3,1
--1.3,1.5,1,-1,-1
+-1.3,1.5,-1
--1.2,1.2,1,-1,-1
+-1.2,1.2,-1
--1.2,1.6,1,-1,-1
+-1.2,1.6,-1
-3,2.1,-1,-1,1
+3,2.1,1
--1.4,1.5,1,-1,-1
+-1.4,1.5,-1
-1.3,5.5,-1,1,-1
+1.3,5.5,0
-3.3,2.5,-1,-1,1
+3.3,2.5,1
-3.1,2.4,-1,-1,1
+3.1,2.4,1
-1.8,5.9,-1,1,-1
+1.8,5.9,0
-2.7,1.9,-1,-1,1
+2.7,1.9,1
-1.5,6.3,-1,1,-1
+1.5,6.3,0
--1.3,1.4,1,-1,-1
+-1.3,1.4,-1
-1,5,-1,1,-1
+1,5,0
-3.2,2.3,-1,-1,1
+3.2,2.3,1
--1.3,1.4,1,-1,-1
+-1.3,1.4,-1
-3,2.1,-1,-1,1
+3,2.1,1
-3.2,2,-1,-1,1
+3.2,2,1
-1.3,6.3,-1,1,-1
+1.3,6.3,0
-1.4,7,-1,1,-1
+1.4,7,0
--1.2,1.7,1,-1,-1
+-1.2,1.7,-1
-1.4,6.6,-1,1,-1
+1.4,6.6,0
-1.2,5.8,-1,1,-1
+1.2,5.8,0
-3,2,-1,-1,1
+3,2,1
--1.2,1.6,1,-1,-1
+-1.2,1.6,-1
-1.5,6.2,-1,1,-1
+1.5,6.2,0
-1.5,6.4,-1,1,-1
+1.5,6.4,0
--1.2,1.9,1,-1,-1
+-1.2,1.9,-1
-3.2,2.3,-1,-1,1
+3.2,2.3,1
--1.2,1.5,1,-1,-1
+-1.2,1.5,-1
-1.5,5.4,-1,1,-1
+1.5,5.4,0
-1.5,6,-1,1,-1
+1.5,6,0
-1.5,6.9,-1,1,-1
+1.5,6.9,0
--1.4,1.5,1,-1,-1
+-1.4,1.5,-1
-1.3,6.4,-1,1,-1
+1.3,6.4,0
-2.8,2,-1,-1,1
+2.8,2,1
--1.2,1.5,1,-1,-1
+-1.2,1.5,-1
--1.1,1.5,1,-1,-1
+-1.1,1.5,-1
-3.6,2.5,-1,-1,1
+3.6,2.5,1
-1.1,5.1,-1,1,-1
+1.1,5.1,0
--1.1,1.5,1,-1,-1
+-1.1,1.5,-1
--1.2,1.5,1,-1,-1
+-1.2,1.5,-1
-3.4,2.4,-1,-1,1
+3.4,2.4,1
--1.3,1.3,1,-1,-1
+-1.3,1.3,-1
-3,2.3,-1,-1,1
+3,2.3,1
--1.1,1.5,1,-1,-1
+-1.1,1.5,-1
-1.1,5.6,-1,1,-1
+1.1,5.6,0
-1.3,5.6,-1,1,-1
+1.3,5.6,0
--1.2,1.5,1,-1,-1
+-1.2,1.5,-1
--1.2,1.3,1,-1,-1
+-1.2,1.3,-1
-1.7,6.7,-1,1,-1
+1.7,6.7,0
--1.2,1.3,1,-1,-1
+-1.2,1.3,-1
--1.4,1.3,1,-1,-1
+-1.4,1.3,-1
-2.5,1.8,-1,-1,1
+2.5,1.8,1
--1.4,1.5,1,-1,-1
+-1.4,1.5,-1
-1.5,6.5,-1,1,-1
+1.5,6.5,0
-2.6,2.3,-1,-1,1
+2.6,2.3,1
--1.2,1.4,1,-1,-1
+-1.2,1.4,-1
-1.5,5.6,-1,1,-1
+1.5,5.6,0
-1.3,5.7,-1,1,-1
+1.3,5.7,0
-1.2,6.1,-1,1,-1
+1.2,6.1,0
-3,2.2,-1,-1,1
+3,2.2,1
-3,1.8,-1,-1,1
+3,1.8,1
--1.1,1.4,1,-1,-1
+-1.1,1.4,-1
-1.4,6.8,-1,1,-1
+1.4,6.8,0
--1.6,1.6,1,-1,-1
+-1.6,1.6,-1
--1.2,1.4,1,-1,-1
+-1.2,1.4,-1
-3.2,1.8,-1,-1,1
+3.2,1.8,1
--1.2,1.5,1,-1,-1
+-1.2,1.5,-1
-2.7,1.8,-1,-1,1
+2.7,1.8,1
--1.3,1.4,1,-1,-1
+-1.3,1.4,-1
-1.3,6.2,-1,1,-1
+1.3,6.2,0
--1.3,1.3,1,-1,-1
+-1.3,1.3,-1
-3,1.8,-1,-1,1
+3,1.8,1
-2.7,1.9,-1,-1,1
+2.7,1.9,1
--1.2,1.4,1,-1,-1
+-1.2,1.4,-1
--1.2,1.2,1,-1,-1
+-1.2,1.2,-1
-1.5,6.7,-1,1,-1
+1.5,6.7,0
-2.5,1.9,-1,-1,1
+2.5,1.9,1
-3.3,2.1,-1,-1,1
+3.3,2.1,1
-2.8,1.8,-1,-1,1
+2.8,1.8,1
--1.2,1.3,1,-1,-1
+-1.2,1.3,-1
-3.8,2.2,-1,-1,1
+3.8,2.2,1
-2.5,2,-1,-1,1
+2.5,2,1
-1.3,6.1,-1,1,-1
+1.3,6.1,0
--1.2,1.4,1,-1,-1
+-1.2,1.4,-1
--1.1,1.5,1,-1,-1
+-1.1,1.5,-1
-3,2.3,-1,-1,1
+3,2.3,1
--1.4,1.9,1,-1,-1
+-1.4,1.9,-1
--1.2,1.6,1,-1,-1
+-1.2,1.6,-1
-1,6,-1,1,-1
+1,6,0
-1.3,5.5,-1,1,-1
+1.3,5.5,0

public/data_sets/table_3_5.csv

@@ -1,4 +1,4 @@
-0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,-1,-1,-1
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-0,0,1,0,0,0,0,1,0,0,1,1,1,1,1,0,0,1,0,0,0,0,1,0,0,-1,1,-1,-1
+0,0,1,0,0,0,0,1,0,0,1,1,1,1,1,0,0,1,0,0,0,0,1,0,0,1
-1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,-1,-1,1,-1
+1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,2
-0,0,0,0,0,0,1,1,1,0,0,1,0,1,0,0,1,1,1,0,0,0,0,0,0,-1,-1,-1,1
+0,0,0,0,0,0,1,1,1,0,0,1,0,1,0,0,1,1,1,0,0,0,0,0,0,3

resources/js/components/LinkHeader.vue

@@ -20,6 +20,16 @@ const links = [
         href: '/perceptron',
         data: { type: 'adaline' },
     },
+    {
+        name: 'Mono-couche',
+        href: '/perceptron',
+        data: { type: 'monolayer' },
+    },
+    {
+        name: 'Multi-couche',
+        href: '/perceptron',
+        data: { type: 'multilayer' },
+    },
 ];
 
 const isActiveLink = (link: any) => {

resources/js/components/PerceptronDecisionGraph.vue

@@ -5,7 +5,7 @@ import type {
     BubbleDataPoint,
     Point,
 } from 'chart.js';
-import { computed } from 'vue';
+import { computed, ref } from 'vue';
 import { Chart } from 'vue-chartjs';
 import { colors, gridColor, gridColorBold } from '@/types/graphs';
 import type { Iteration } from '@/types/perceptron';
@@ -16,6 +16,10 @@ const props = defineProps<{
     activationFunction: (x: number) => number;
 }>();
 
+const examplesNumber = computed(() => {
+    return props.cleanedDataset.reduce((sum, dataset) => sum + dataset.data.length, 0);
+});
+
 const farLeftDataPointX = computed(() => {
     if (props.cleanedDataset.length === 0) {
         return 0;
@@ -25,6 +29,15 @@ const farLeftDataPointX = computed(() => {
     );
     return minX;
 });
+const farBottomDataPointY = computed(() => {
+    if (props.cleanedDataset.length === 0) {
+        return 0;
+    }
+    const minY = Math.min(
+        ...props.cleanedDataset.flatMap((d) => d.data.map((point) => point.y)),
+    );
+    return minY;
+});
 const farRightDataPointX = computed(() => {
     if (props.cleanedDataset.length === 0) {
         return 0;
@@ -34,8 +47,20 @@ const farRightDataPointX = computed(() => {
     );
     return maxX;
 });
+const farTopDataPointY = computed(() => {
+    if (props.cleanedDataset.length === 0) {
+        return 0;
+    }
+    const maxY = Math.max(
+        ...props.cleanedDataset.flatMap((d) => d.data.map((point) => point.y)),
+    );
+    return maxY;
+});
 
-function getPerceptronOutput(weightsNetwork: number[][], inputs: number[]): number[] {
+function getPerceptronOutput(
+    weightsNetwork: number[][][],
+    inputs: number[],
+): number[] {
     for (const layer of weightsNetwork) {
         const nextInputs: number[] = [];
 
@@ -59,13 +84,14 @@ function getPerceptronOutput(weightsNetwork: number[][], inputs: number[]): numb
     return inputs;
 }
 
+const nonLinearGraph = ref<boolean>(false);
 function getPerceptronDecisionBoundaryDataset(
     networkWeights: number[][][],
     activationFunction: (x: number) => number = (x) => x,
 ): ChartDataset<
     keyof ChartTypeRegistry,
     number | Point | [number, number] | BubbleDataPoint | null
-> {
+>[] {
     const label = 'Ligne de décision du Perceptron';
     console.log('Calculating decision boundary with weights:', networkWeights);
 
@@ -74,6 +100,7 @@ function getPerceptronDecisionBoundaryDataset(
         networkWeights[0].length == 1 &&
         networkWeights[0][0].length <= 3
     ) {
+        nonLinearGraph.value = false;
         // Unique, 3 weights perceptron
         const perceptronWeights = [...networkWeights[0][0]]; // Copy of the unique perceptron weights
 
@@ -89,7 +116,8 @@ function getPerceptronDecisionBoundaryDataset(
         }
 
         // Simple line
-        return {
+        return [
+            {
                 type: 'line',
                 label: label,
                 data: [
@@ -105,60 +133,67 @@ function getPerceptronDecisionBoundaryDataset(
                 borderColor: '#FFF',
                 borderWidth: 2,
                 pointRadius: 0,
-        };
+            },
+        ];
     } else {
-        function forward(x1: number, x2: number): number {
+        nonLinearGraph.value = true;
-            let activations: number[] = [x1, x2];
 
-            for (const layer of networkWeights) {
+        const bubbleTransparency = '30';
-                const nextActivations: number[] = [];
+        const isInDataThreshold = 0.0;
 
-                for (const neuron of layer) {
-                    const bias = neuron[0];
-                    const weights = neuron.slice(1);
 
-                    let sum = bias;
+        // -------- 1️⃣ Construction des datasets --------
-
+        const datasets: {
-                    for (let i = 0; i < weights.length; i++) {
+            type: string;
-                        sum += weights[i] * activations[i];
+            label: string;
-                    }
+            data: Point[];
-
+            backgroundColor: string;
-                    const activated = activationFunction(sum);
+            pointRadius: number;
-
+            borderWidth: number;
-                    nextActivations.push(activated);
+            order: number;
-                }
+        }[] = [];
-
+        // For the number of neuron in the last layer
-                activations = nextActivations;
+        const lastLayer = networkWeights[networkWeights.length - 1];
-            }
+        for (let i = 0; i < lastLayer.length; i++) {
-
+            const dataset = {
-            return activations[0]; // on suppose sortie unique
+                type: 'scatter',
+                label: label,
+                data: [], // Will be filled with the decision boundary points
+                backgroundColor: colors[i] + bubbleTransparency || '#AAA',
+                pointRadius: 15,
+                borderWidth: 0,
+                order: -1,
+            };
+            datasets.push(dataset);
         }
 
         // -------- 2️⃣ Échantillonnage grille --------
-        const decisionBoundary: Point[] = [];
+        const step =
-        const min = -2;
+            Math.abs(
-        const max = 2;
+                farRightDataPointX.value + 1 - (farLeftDataPointX.value - 1),
-        const step = 0.03;
+            ) / 50;
-        const epsilon = 0.01;
 
-        for (let x = min; x <= max; x += step) {
+        for (
-            for (let y = min; y <= max; y += step) {
+            let x = farLeftDataPointX.value - 1;
-                const value = forward(x, y);
+            x <= farRightDataPointX.value + 1;
-
+            x += step
-                if (Math.abs(value) < epsilon) {
+        ) {
-                    decisionBoundary.push({ x, y });
+            for (
+                let y = farBottomDataPointY.value - 1;
+                y <= farTopDataPointY.value + 1;
+                y += step
+            ) {
+                const values = getPerceptronOutput(networkWeights, [x, y]);
+                values.forEach((v, i) => {
+                    if (v > isInDataThreshold) {
+                        datasets[i].data.push({ x, y });
                     }
+                });
             }
         }
 
         // -------- 3️⃣ Dataset ChartJS --------
-        return {
+        return datasets;
-            type: 'scatter',
-            label: label,
-            data: decisionBoundary,
-            backgroundColor: '#FFFFFF',
-            pointRadius: 1,
-        };
     }
 }
 </script>
@@ -180,6 +215,9 @@ function getPerceptronDecisionBoundaryDataset(
                     text: 'Ligne de décision du Perceptron',
                 },
             },
+            animation: {
+                duration: nonLinearGraph || examplesNumber > 10 ? 0 : 1000, // Disable animations for instant updates
+            },
             layout: {
                 padding: {
                     left: 10,
@@ -228,7 +266,7 @@ function getPerceptronDecisionBoundaryDataset(
                 })),
 
                 // Perceptron decision boundary
-                getPerceptronDecisionBoundaryDataset(
+                ...getPerceptronDecisionBoundaryDataset(
                     props.iterations.length > 0
                         ? props.iterations[props.iterations.length - 1].weights
                         : [[[0, 0, 0]]],

resources/js/components/PerceptronIterationsErrorsGraph.vue

@@ -94,6 +94,9 @@ const datasets = computed<
                     text: 'Nombre d\'erreurs par epoch',
                 },
             },
+            animation: {
+                duration: iterations.length > 100 ? 0 : 1000, // Disable animations for instant updates
+            },
             scales: {
                 x: {
                     stacked: true,

tests/Unit/Training/TrainingTestCase.php

@@ -14,8 +14,8 @@ class TrainingTestCase extends TestCase
         $training->start();
 
         // Assert that the final synaptic weights are as expected withing the margin of error
-        // $finalWeights = $training->getSynapticWeights();
+        $finalWeights = $training->getSynapticWeights();
-        // $this->assertEqualsWithDelta($expectedWeights, $finalWeights, $marginOfError, "Final synaptic weights do not match expected values.");
+        $this->assertEqualsWithDelta($expectedWeights, $finalWeights, $marginOfError, "Final synaptic weights do not match expected values.");
 
         // Assert that the number of epochs taken is as expected
         $this->assertEquals($expectedEpochs, $training->getEpoch(), "Expected training to take $expectedEpochs epochs, but it took {$training->getEpoch()} epochs.");