neuralnet.php/public/main.php

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

	require_once __ROOT__.'/autoloader.php';

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


	if( false && 'test_creating_dataset' ){

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

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

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

		$nn->setMaxValues([1, 1, 1], [1]);
		$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));

		$nn->store('A/B/C/test', true);

	}

	if( false && 'load_neural_network' ){

		$nn = NeuralNetwork::load('A/B/C/test');

	}

	if( true && '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";
	}


	// 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);
	// 	}

?>