<?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;
    }
}