<?php define('__ROOT__', dirname(dirname(__FILE__)) );

	require_once __ROOT__.'/autoloader.php';

	use \neuralnetwork\core\Genome;
	use \neuralnetwork\core\NeuralNetwork;
	use \filemanager\core\FileManager;

	function behaviour($abc){
		return [($abc[0] & $abc[1]), $abc[1] | $abc[2]];
	}

	if( false && 'test_creating_dataset' ){


		echo "Welcome to neural-network.php\n";
		echo "-----------------------------\n";

		$nn = NeuralNetwork::create(50, 100);

		$nn->setHiddenLayersCount(2);
		$nn->setHiddenLayerNeuronsCount(3);

		$d = [0, 0, 0]; $nn->addSample($d, behaviour($d));
		$d = [0, 0, 1]; $nn->addSample($d, behaviour($d));
		$d = [0, 1, 0]; $nn->addSample($d, behaviour($d));
		$d = [0, 1, 1]; $nn->addSample($d, behaviour($d));
		$d = [1, 0, 0]; $nn->addSample($d, behaviour($d));
		$d = [1, 0, 1]; $nn->addSample($d, behaviour($d));
		$d = [1, 1, 0]; $nn->addSample($d, behaviour($d));
		$d = [1, 1, 1]; $nn->addSample($d, behaviour($d));
		$d = [0, 0, 0]; $nn->addSample($d, behaviour($d));
		$d = [0, 0, 2]; $nn->addSample($d, behaviour($d));
		$d = [0, 2, 0]; $nn->addSample($d, behaviour($d));
		$d = [0, 2, 2]; $nn->addSample($d, behaviour($d));
		$d = [2, 0, 0]; $nn->addSample($d, behaviour($d));
		$d = [2, 0, 2]; $nn->addSample($d, behaviour($d));
		$d = [2, 2, 0]; $nn->addSample($d, behaviour($d));
		$d = [2, 2, 2]; $nn->addSample($d, behaviour($d));

		$nn->store('test/test1', true);

	}

	if( false && 'load_neural_network' ){

		$nn = NeuralNetwork::load('test/test1');

	}

	if( false && 'test_genomes' ){
		/* (1) Basic Creation */
		$a = new Genome(2, 3); // 2 layers of 3 neurons each -> randomly filled

		echo "A : ".$a->serialize()."\n";

		/* (2) Inheritance */
		$b = new Genome($a);   // Clone of @aecho "A neurons\n";
		echo "cloning A to B\n";
		echo "B : ".$b->serialize()."\n";

		/* (3) Section Title */
		$b->mutation(0.3); // @b has now mutated with a threshold of 30%
		echo "mutate B\n";
		echo "B : ".$b->serialize()."\n";

		/* (4) Cross-over (father+mother) */
		$c = new Genome($a, $b); // @c is a randomly-done mix of @a and @b
		echo "crossover : A+B -> C\n";
		echo "C : ".$c->serialize()."\n";

	}


	if( true ){

		$g = new Genome(2, 3, 3, 2);
		$fitness = 0;
		$g->setCallback(function($input, $output){
			global $fitness;
			echo "callback output: ".round($output[0]).", ".round($output[1])."\n";
			$result = behaviour($input);
			if( $output[0] == $result[0] )
				$fitness++;

			if( $output[1] == $result[1] )
				$fitness++;
		});

		echo $g->train([0, 0, 0]);
		echo $g->train([0, 0, 1]);
		echo $g->train([0, 1, 0]);
		echo $g->train([0, 1, 1]);
		echo $g->train([1, 0, 0]);
		echo $g->train([1, 0, 1]);
		echo $g->train([1, 1, 0]);
		echo $g->train([1, 1, 1]);


		echo $g->train([0, 0, 0]);
		echo $g->train([0, 0, 2]);
		echo $g->train([0, 2, 0]);
		echo $g->train([0, 2, 2]);
		echo $g->train([2, 0, 0]);
		echo $g->train([2, 0, 2]);
		echo $g->train([2, 2, 0]);
		echo $g->train([2, 2, 2]);

		echo "fitness: $fitness\n";
		$g->setFitness($fitness);
		echo $g->serialize();
	}

	// REWRITE TEST
	// for( $a = 0, $al = 50 ; $a < $al ; $a++ )
	// 	for( $b = 0, $bl = 20 ; $b < $bl ; $b++ ){
	// 		print "genome $b/$bl on generation $a/$al     \r";
	// 		usleep(1000*10);
	// 	}

?>