<?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[2])];
	}


	$train = false;
	$guess = !$train;

	if( $train && 'learning_process' ){

		$part = 1;


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

		/* [1] Trying to load neural network
		=========================================================*/
		try{

			$nn = NeuralNetwork::load('test1/test1');
			echo "$part. NeuralNetwork loaded from 'test1/test1'\n"; $part++;

		/* [2] Else, creates it
		=========================================================*/
		}catch(\Exception $e){

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

			$nn->setHiddenLayersCount(3);       // 3 Hidden layers
			$nn->setHiddenLayerNeuronsCount(3); // Composed with 3 neurons each
			$nn->setInputLayerCount(3);         // 3 inputs
			$nn->setOutputLayerCount(1);        // 1 output
			$nn->setMutationThreshold(0.3);     // mutation 30% each generation
			$nn->setFitnessEnd(0);              // Algorith is done when fitness reaches 0

			echo "$part. NeuralNetwork configured\n"; $part++;

			$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($d));
			echo "$part. Samples added to NeuralNetwork\n"; $part++;

			$nn->store('test1/test1', true);
			echo "$part. NeuralNetwork stored to 'test1/test1'\n"; $part++;

		}


		/* [2] Initializing learning routine
		=========================================================*/
		$fitness = 0;
		$max_fit = 0;
		$nn->loadLearningRoutine(function($input, $output){
			global $fitness;
			$fitness -= abs(round($output[0]) - behaviour($input)[0]);
		});
		echo "$part. Learning routine initialized.\n"; $part++;


		/* [3] Learning through generations and genomes
		=========================================================*/
		/* (1) For each generation */
		$last_gnr = -1;
		while( true ){

			if( $nn->gnr > $last_gnr)
				$start = microtime(true);

			$last_gnr = $nn->gnr;

			$max_fit = -1e9;

			/* (2) For each genome */
			while( true ){
				$fitness = 0;

				/* (2.1) Get current genome */
				$g = $nn->getGenome();
				echo "\r[x] gnm ".($nn->gnm+1)."/100 on gnr ".($nn->gnr+1)."/50 - max_fit: $max_fit                 ";

				/* (2.2) Train genome with random samples */
				for( $r = 0 ; $r < 100 ; $r++ )
					$g->train([rand(0,100), rand(0,100), rand(0,100)]);

				/* (2.3) Set fitness & go to next genome */
				if( $fitness > $max_fit ) $max_fit = $fitness;

				$g->setFitness($fitness);

				if( $nn->gnm >= 100-1 )
					break;

				$nn->nextGenome();
			}

			$nn->nextGenome();

			// If generation evolution, notify
			if( $nn->gnr > $last_gnr)
				echo "\n\t".((microtime(true)-$start))."s\n";

			if( is_null($nn->gnr) || $nn->gnr == 50-1 )
				break;

		}


	}


	if( $guess && 'guessing_process' ){
		$part = 1;


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

		/* [1] Trying to load neural network
		=========================================================*/
		try{

			$nn = NeuralNetwork::load('test1/test1');
			echo "$part. NeuralNetwork loaded from 'test1/test1'\n"; $part++;

		/* [2] Else, creates it
		=========================================================*/
		}catch(\Exception $e){

			echo "You must create/train your neural network before using it.\n";
			exit();
		}


		/* [2] Fetch trained genome
		=========================================================*/
		$genome = $nn->getTrainedGenome();
		$genome->setCallback(function($in, $out){
			echo "callback input:  ".implode(',', $in)."\n";
			echo "callback output: ".round($out[0])."\n";
			echo "callback result: ".implode(',', behaviour($in))."\n";
		});

		$genome->train([rand(0,100), rand(0,100), rand(0,100)]);


	}

	if( false ){

		$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);
	// 	}

?>