Reseaux-de-neurones-artificiels-Perceptrons/app/Http/Controllers/PerceptronController.php

<?php

namespace App\Http\Controllers;

use App\Events\PerceptronInitialization;
use App\Models\GradientDescentPerceptronTraining;
use App\Models\SimpleBinaryPerceptronTraining;
use App\Services\DataSetReader;
use App\Services\ISynapticWeightsProvider;
use App\Services\PerceptronIterationEventBuffer;
use App\Services\PerceptronLimitedEpochEventBuffer;
use App\Services\ZeroSynapticWeights;
use Illuminate\Http\Request;

class PerceptronController extends Controller
{
    /**
     * Display the specified resource.
     */
    public function index(Request $request)
    {
        $perceptronType = $request->query('type');

        $learningRate = 0.01;
        $maxIterations = 200;
        $minError = 0.6;

        switch ($perceptronType) {
            case 'simple':
                $learningRate = 0.015;
                $maxIterations = 150;
                break;
            case 'gradientdescent':
                $learningRate = 0.00003;
        }

        return inertia('PerceptronViewer', [
            'type' => $perceptronType,
            'sessionId' => session()->getId(),
            'datasets' => $this->getDatasets($perceptronType),
            'minError' => $minError,
            'learningRate' => $learningRate,
            'maxIterations' => $maxIterations,
        ]);
    }

    private function getDatasets(string $perceptronType): array
    {
        $dataSetsDirectory = public_path('data_sets');
        $files = scandir($dataSetsDirectory);
        $datasets = [];
        foreach ($files as $file) {
            if (pathinfo($file, PATHINFO_EXTENSION) === 'csv') {
                $dataset = [];
                $dataset['label'] = str_replace('.csv', '', $file);
                $dataSetReader = new DataSetReader($dataSetsDirectory . '/' . $file);
                $dataset['data'] = [];
                switch (count($dataSetReader->lines[0])) {
                    case 3:
                        foreach ($dataSetReader->lines as $line) {
                            $dataset['data'][] = [
                                'x' => $line[0],
                                'y' => $line[1],
                                'label' => $line[2],
                            ];
                        }
                        break;
                    case 2:
                        foreach ($dataSetReader->lines as $line) {
                            $dataset['data'][] = [
                                'x' => $line[0],
                                'y' => $line[1],
                                'label' => 1,
                            ];
                        }
                        break;
                    default:
                        $dataset['data'] = null; // Not supported for viewing
                        break;
                }

                switch ($dataset['label']) {
                    case 'logic_and_gradient':
                        switch ($perceptronType) {
                            case 'gradientdescent':
                                $dataset['defaultLearningRate'] = 0.3;
                                break;
                        }
                        break;
                    case 'table_2_9':
                        switch ($perceptronType) {
                            case 'simple':
                                $dataset['defaultLearningRate'] = 0.015;
                                break;
                            case 'gradientdescent':
                                $dataset['defaultLearningRate'] = 0.001;
                                $dataset['defaultMinError'] = 2.0;
                                break;
                        }
                        break;
                    case 'table_2_11':
                        $dataset['defaultMinError'] = 1.0;
                        break;
                }
                $datasets[] = $dataset;
            }
        }
        return $datasets;
    }

    private function getDataSetReader(string $dataSet): DataSetReader
    {
        $dataSetFileName = "data_sets/{$dataSet}.csv";
        return new DataSetReader($dataSetFileName);
    }

    public function run(Request $request, ISynapticWeightsProvider $synapticWeightsProvider)
    {
        $perceptronType = $request->input('type');
        $minError = $request->input('min_error', 0.01);
        $weightInitMethod = $request->input('weight_init_method', 'random');
        $dataSet = $request->input('dataset');
        $learningRate = $request->input('learning_rate', 0.015);
        $maxIterations = $request->input('max_iterations', 100);
        $sessionId = $request->input('session_id', session()->getId());
        $trainingId = $request->input('training_id');

        if ($weightInitMethod === 'zeros') {
            $synapticWeightsProvider = new ZeroSynapticWeights();
        }

        $iterationEventBuffer = new PerceptronIterationEventBuffer($sessionId, $trainingId);
        if ($maxIterations > config('perceptron.limited_broadcast_iterations')) {
            $iterationsInterval = (int)($maxIterations / config('perceptron.limited_broadcast_iterations'));
            $iterationEventBuffer = new PerceptronLimitedEpochEventBuffer($sessionId, $trainingId, $iterationsInterval);
        }

        $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),
            default => null,
        };

        event(new PerceptronInitialization($dataSetReader->lines, $networkTraining->activationFunction, $sessionId, $trainingId));

        $networkTraining->start();

        return response()->json([
            'message' => 'Training completed',
        ]);
    }
}