<?php

namespace App\Console\Commands;

use Illuminate\Console\Command;
use App\Services\Scheduling\DatasetLoader;
use App\Services\Scheduling\GeneticAlgorithmEngine;

class TestGaEngineCommand extends Command
{
    protected $signature = 'ga:test-parameters 
                            {--semester= : Semester dataset} 
                            {--tahun= : Tahun ajaran dataset}';

    protected $description = 'Jalankan pengujian otomatis hyperparameter GA (Populasi, Crossover, Mutasi, Iterasi, & Pengujian Akhir) dan tampilkan tabel Bab 4';

    public function handle()
    {
        $this->info("================================──────────────────────────");
        $this->info("🚀 MEMULAI OTOMASI PENGUJIAN ALGORITMA GENETIKA (BAB 4)");
        $this->info("================================──────────────────────────");

        $semester = $this->option('semester');
        $tahun = $this->option('tahun');

        $datasetLoader = new DatasetLoader();
        $dataset = $datasetLoader->load($semester, $tahun);

        $this->comment("Dataset berhasil dimuat: " . count($dataset->kelasList) . " Kelas, " . count($dataset->guruList) . " Guru, " . count($dataset->ruanganRobotkuIds) . " Ruangan.");

        // 1. TAHAP 1: UJI POPULASI
        $this->newLine();
        $this->info("📌 1. MENJALANKAN TABEL 4.1: PENGUJIAN JUMLAH POPULASI");
        $popVariations = [10, 20, 30, 40, 50];
        $tabel1 = [];
        $bestPop = 20;
        $bestPopFitness = -1;

        foreach ($popVariations as $idx => $pop) {
            $engine = new GeneticAlgorithmEngine(
                populationSize: $pop,
                crossoverRate: 0.5,
                mutationRate: 0.5,
                maxGenerations: 500,
                tournamentSize: 3,
                elitismCount: 2
            );
            $res = $engine->run($dataset);
            $fitness = $res['bestChromosome']->fitness;

            if ($fitness > $bestPopFitness) {
                $bestPopFitness = $fitness;
                $bestPop = $pop;
            }

            $tabel1[] = [
                'Uji Ke-' => $idx + 1,
                'Populasi' => $pop,
                'Pc' => 0.5,
                'Pm' => 0.5,
                'Iterasi' => 500,
                'Fitness Max' => round($fitness, 6),
                'Total Penalti' => $res['bestChromosome']->totalPenalty,
                'Waktu (s)' => round($res['executionTime'], 4),
            ];
        }
        $this->table(['Uji Ke-', 'Populasi', 'Pc', 'Pm', 'Iterasi', 'Fitness Max', 'Penalti', 'Waktu (s)'], $tabel1);
        $this->info("✅ Populasi Terbaik Terpilih: {$bestPop} (Fitness: {$bestPopFitness})");

        // 2. TAHAP 2: UJI CROSSOVER RATE
        $this->newLine();
        $this->info("📌 2. MENJALANKAN TABEL 4.2: PENGUJIAN CROSSOVER RATE (Pc)");
        $pcVariations = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9];
        $tabel2 = [];
        $bestPc = 0.6;
        $bestPcFitness = -1;

        foreach ($pcVariations as $idx => $pc) {
            $engine = new GeneticAlgorithmEngine(
                populationSize: $bestPop,
                crossoverRate: $pc,
                mutationRate: 0.5,
                maxGenerations: 500,
                tournamentSize: 3,
                elitismCount: 2
            );
            $res = $engine->run($dataset);
            $fitness = $res['bestChromosome']->fitness;

            if ($fitness > $bestPcFitness) {
                $bestPcFitness = $fitness;
                $bestPc = $pc;
            }

            $tabel2[] = [
                'Uji Ke-' => $idx + 1,
                'Populasi' => $bestPop,
                'Pc' => $pc,
                'Pm' => 0.5,
                'Iterasi' => 500,
                'Fitness Max' => round($fitness, 6),
                'Total Penalti' => $res['bestChromosome']->totalPenalty,
                'Waktu (s)' => round($res['executionTime'], 4),
            ];
        }
        $this->table(['Uji Ke-', 'Populasi', 'Pc', 'Pm', 'Iterasi', 'Fitness Max', 'Penalti', 'Waktu (s)'], $tabel2);
        $this->info("✅ Crossover Rate Terbaik Terpilih: {$bestPc} (Fitness: {$bestPcFitness})");

        // 3. TAHAP 3: UJI MUTATION RATE
        $this->newLine();
        $this->info("📌 3. MENJALANKAN TABEL 4.3: PENGUJIAN MUTATION RATE (Pm)");
        $pmVariations = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9];
        $tabel3 = [];
        $bestPm = 0.3;
        $bestPmFitness = -1;

        foreach ($pmVariations as $idx => $pm) {
            $engine = new GeneticAlgorithmEngine(
                populationSize: $bestPop,
                crossoverRate: $bestPc,
                mutationRate: $pm,
                maxGenerations: 500,
                tournamentSize: 3,
                elitismCount: 2
            );
            $res = $engine->run($dataset);
            $fitness = $res['bestChromosome']->fitness;

            if ($fitness > $bestPmFitness) {
                $bestPmFitness = $fitness;
                $bestPm = $pm;
            }

            $tabel3[] = [
                'Uji Ke-' => $idx + 1,
                'Populasi' => $bestPop,
                'Pc' => $bestPc,
                'Pm' => $pm,
                'Iterasi' => 500,
                'Fitness Max' => round($fitness, 6),
                'Total Penalti' => $res['bestChromosome']->totalPenalty,
                'Waktu (s)' => round($res['executionTime'], 4),
            ];
        }
        $this->table(['Uji Ke-', 'Populasi', 'Pc', 'Pm', 'Iterasi', 'Fitness Max', 'Penalti', 'Waktu (s)'], $tabel3);
        $this->info("✅ Mutation Rate Terbaik Terpilih: {$bestPm} (Fitness: {$bestPmFitness})");

        // 4. TAHAP 4: UJI ITERASI / GENERASI
        $this->newLine();
        $this->info("📌 4. MENJALANKAN TABEL 4.4: PENGUJIAN JUMLAH ITERASI");
        $iterVariations = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000];
        $tabel4 = [];
        $bestIter = 500;
        $bestIterFitness = -1;

        foreach ($iterVariations as $idx => $iter) {
            $engine = new GeneticAlgorithmEngine(
                populationSize: $bestPop,
                crossoverRate: $bestPc,
                mutationRate: $bestPm,
                maxGenerations: $iter,
                tournamentSize: 3,
                elitismCount: 2
            );
            $res = $engine->run($dataset);
            $fitness = $res['bestChromosome']->fitness;

            if ($fitness > $bestIterFitness && $bestIterFitness < 1.0) {
                $bestIterFitness = $fitness;
                $bestIter = $iter;
            }

            $tabel4[] = [
                'Uji Ke-' => $idx + 1,
                'Populasi' => $bestPop,
                'Pc' => $bestPc,
                'Pm' => $bestPm,
                'Iterasi' => $iter,
                'Fitness Max' => round($fitness, 6),
                'Total Penalti' => $res['bestChromosome']->totalPenalty,
                'Waktu (s)' => round($res['executionTime'], 4),
            ];
        }
        $this->table(['Uji Ke-', 'Populasi', 'Pc', 'Pm', 'Iterasi', 'Fitness Max', 'Penalti', 'Waktu (s)'], $tabel4);
        $this->info("✅ Iterasi Terbaik Terpilih: {$bestIter} (Fitness: {$bestIterFitness})");

        // 5. TAHAP 5: PENGUJIAN AKHIR (RUN 10 KALI)
        $this->newLine();
        $this->info("📌 5. MENJALANKAN TABEL 4.5: PENGUJIAN AKHIR (10 KALI EKSEKUSI)");
        $tabel5 = [];
        $totalFitness = 0;
        $totalTime = 0;

        for ($run = 1; $run <= 10; $run++) {
            $engine = new GeneticAlgorithmEngine(
                populationSize: $bestPop,
                crossoverRate: $bestPc,
                mutationRate: $bestPm,
                maxGenerations: $bestIter,
                tournamentSize: 3,
                elitismCount: 2
            );
            $res = $engine->run($dataset);
            $fitness = $res['bestChromosome']->fitness;
            $time = $res['executionTime'];

            $totalFitness += $fitness;
            $totalTime += $time;

            $tabel5[] = [
                'Uji Ke-' => $run,
                'Populasi' => $bestPop,
                'Pc' => $bestPc,
                'Pm' => $bestPm,
                'Iterasi' => $bestIter,
                'Fitness Terbaik' => round($fitness, 6),
                'Total Penalti' => $res['bestChromosome']->totalPenalty,
                'Run Time (s)' => round($time, 4),
            ];
        }

        $avgFitness = round($totalFitness / 10, 6);
        $avgTime = round($totalTime / 10, 4);

        $tabel5[] = [
            'Uji Ke-' => 'Rata-rata',
            'Populasi' => $bestPop,
            'Pc' => $bestPc,
            'Pm' => $bestPm,
            'Iterasi' => $bestIter,
            'Fitness Terbaik' => $avgFitness,
            'Total Penalti' => 0,
            'Run Time (s)' => $avgTime,
        ];

        $this->table(['Uji Ke-', 'Populasi', 'Pc', 'Pm', 'Iterasi', 'Fitness Terbaik', 'Penalti', 'Run Time (s)'], $tabel5);

        $this->newLine();
        $this->info("================================──────────────────────────");
        $this->info("🎉 PENGUJIAN GA SELESAI DENGAN SUKSES!");
        $this->info("⭐ KOMBINASI TERBAIK: Populasi = {$bestPop}, Pc = {$bestPc}, Pm = {$bestPm}, Iterasi = {$bestIter}");
        $this->info("⭐ RATA-RATA FITNESS  : {$avgFitness}");
        $this->info("⭐ RATA-RATA WAKTU    : {$avgTime} detik");
        $this->info("================================──────────────────────────");
    }
}