Gestion de quasi-tout -> FONCTIONNELLLLLLL

build/filemanager/core/FileManager.php

@@ -23,7 +23,7 @@
 			try{
 				fclose( fopen($file, 'w') );
 				return true;
-			}catch(Exception $e){ return false; }
+			}catch(\Exception $e){ return false; }
 		}
 
 
@@ -35,7 +35,7 @@
 		public static function read($file){
 			/* (0) Checks arguments */
 			if( !is_string($file) )
-				throw new \Error('Wrong argument for read(<String>).');
+				throw new \Exception('Wrong argument for read(<String>).');
 
 			/* (1) Initializing driver on file (read-flag) */
 			$driver = new \SplFileObject($file, 'r');
@@ -53,6 +53,54 @@
 			return $read;
 		}
 
+		/* READS A FILE'S SPECIFIC LINE
+		*
+		* @file<String> 										File to read
+		* @line<int>											Line to read
+		*
+		*/
+		public static function readline($file, $line){
+			/* (0) Checks arguments */
+			if( !is_string($file) || intval($line) !== $line )
+				throw new \Exception('Wrong argument for readline(<String>, <int>).');
+
+			/* (1) Initializing driver on file (read-flag) */
+			$driver = new \SplFileObject($file, 'r');
+
+			/* (2) Goto specific line */
+			$driver->seek($line);
+
+			/* (3) Return line's content */
+			if( $driver->key() == $line )
+				return $driver->current();
+			else
+				return null;
+		}
+
+		/* WRITES A FILE'S SPECIFIC LINE
+		*
+		* @file<String> 										File to read
+		* @line<int>											Line to read
+		*
+		*/
+		public static function writeline($file, $line){
+			/* (0) Checks arguments */
+			if( !is_string($file) || intval($line) !== $line )
+				throw new \Exception('Wrong argument for writeline(<String>, <int>).');
+
+			/* (1) Initializing driver on file (read-flag) */
+			$driver = new \SplFileObject($file, 'r');
+
+			/* (2) Goto specific line */
+			$driver->seek($line);
+
+			/* (3) Return line's content */
+			if( $driver->key() == $line )
+				return $driver->current();
+			else
+				return null;
+		}
+
 
 		/* WRITES CONTENT TO A FILE
 		*
@@ -72,7 +120,7 @@
 			/* (1) Erase file */
 			try{
 				fclose( fopen($file, 'w') );
-			}catch(Exception $e){ return false; }
+			}catch(\Exception $e){ return false; }
 
 			/* (2) Get driver (write-flag) */
 			$driver = new \SplFileObject($file, 'r+');

build/neuralnetwork/core/Genome.php

@@ -54,23 +54,24 @@
 			$argv = func_get_args();
 			$argc = count($argv);
 
-
+			/* (2) If RandomCreation */
-			/* (2) If CrossoverCreation */
+			if( $argc > 3 && is_numeric($argv[0]) && is_numeric($argv[1]) && is_numeric($argv[2]) && is_numeric($argv[3]) )
-			if( $argc > 1 && $argv[0] instanceof Genome && $argv[1] instanceof Genome )
-				$this->construct_crossover($argv[0], $argv[1]);
-
-			/* (3) If RandomCreation */
-			else if( $argc > 3 && is_numeric($argv[0]) && is_numeric($argv[1]) && is_numeric($argv[2]) && is_numeric($argv[3]) )
 				$this->construct_new($argv[0], $argv[1], $argv[2], $argv[3]);
 
+			/* (3) If CrossoverCreation */
+			else if( $argc > 1 && $argv[0] instanceof Genome && $argv[1] instanceof Genome )
+				$this->construct_crossover($argv[0], $argv[1]);
+
 			/* (4) If InheritanceCreation (clone) */
 			else if( $argc > 0 && $argv[0] instanceof Genome )
 				$this->construct_inheritance($argv[0]);
 
 			/* (5) If no match */
 			else
-				throw new \Error('Invalid Genome constructor\'s arguments.');
+				throw new \Exception('Invalid Genome constructor\'s arguments.');
 
+			/* (6) Default values */
+			$this->fitness  = null;
 			$this->callback = function(){};
 		}
 
@@ -159,7 +160,7 @@
 
 			/* (5) Do random crossover for synapses */
 			$this->synapses = [];
-			for( $i = 0, $l = $this->neurons*($this->inputN+$this->outputN+$this->neurons*$this->layers) ; $i < $l ; $i++ )
+			for( $i = 0, $l = count($mother->synapses) ; $i < $l ; $i++ )
 				if( !!rand(0,1) ) $this->synapses[$i] = $father->synapses[$i];
 				else              $this->synapses[$i] = $mother->synapses[$i];
 
@@ -180,7 +181,7 @@
 		public function setCallback($callback=null){
 			/* (1) Checks @callback argument */
 			if( !is_callable($callback) )
-				throw new \Error('Wrong argument for Genome\'s callback function.');
+				throw new \Exception('Wrong argument for Genome\'s callback function.');
 
 			/* (2) Set callback function */
 			$this->callback = $callback;
@@ -194,13 +195,27 @@
 		public function setFitness($fitness=null){
 			/* (1) Checks @fitness argument */
 			if( !is_numeric($fitness) )
-				throw new \Error('Wrong argument for specifying Genome\'s fitness.');
+				throw new \Exception('Wrong argument for specifying Genome\'s fitness.');
 
 			/* (2) Set fitness */
 			$this->fitness = floatval($fitness);
 		}
 
 
+		/************************************************
+		****                  Getter                 ****
+		************************************************/
+
+		/* RETURNS THE FITNESS OF THE GENOME
+		*
+		* @return fitness<double> 								Current fitness (or NULL if not defined)
+		*
+		*/
+		public function getFitness(){
+			return $this->fitness;
+		}
+
+
 		/************************************************
 		****              Genome Actions             ****
 		************************************************/
@@ -213,10 +228,10 @@
 		public function mutation($threshold=0.5){
 			/* (1) Checks @threshold argument */
 			if( floatval($threshold) !== $threshold || $threshold < 0 || $threshold > 1 )
-				throw new \Error('Invalid threshold for Genome mutation.');
+				throw new \Exception('Invalid threshold for Genome mutation.');
 
 			/* (2) Calculates how many neurons/synapses to mutate */
-			$synapsesMutations = round( (count($this->synapses) - 1) * $threshold );
+			$synapsesMutations = round( (count($this->synapses) - 1) * rand(0, $threshold*self::MAX)/self::MAX );
 
 			/* (3) Choose random synapses' indexes */
 			$iSynapses = [];
@@ -243,7 +258,7 @@
 		public function train($input=null, $callback=null){
 			/* (1) Checks @input argument */
 			if( !is_array($input) || count($input) !== $this->inputN )
-				throw new \Error('Invalid @input for Genome\'s training.');
+				throw new \Exception('Invalid @input for Genome\'s training.');
 
 			/* (2) Checks optional @callback argument */
 			if( is_callable($callback) )
@@ -366,7 +381,7 @@
 
 			/* (3) If between hidden/output */
 			}else if( $layer == $this->layers+1 ){
-				$offset = $this->neurons * ($this->inputN+$this->neurons*$this->layers);
+				$offset = $this->neurons * ($this->inputN+$this->neurons*($this->layers-1));
 				for( $s = $offset+$neuron, $sl = $offset+$this->neurons*$this->outputN ; $s < $sl ; $s += $this->outputN )
 					yield $this->inputN+$this->neurons*($this->layers-1) + ($s-$offset-$neuron)/$this->outputN => $s;
 
@@ -408,21 +423,21 @@
 		*/
 		public function unserialize($serialized){
 			/* (1) Segmenting data */
-			$segments = explode(';', $serialized);
+			$segments = explode(';', trim($serialized) );
 
 			// Manage segmentation error
-			if( count($segments) < 3 )
+			if( count($segments) < 2 )
-				throw new \Error('Format error during Genome unserialization.');
+				throw new \Exception('Format error during Genome unserialization.');
 
 			/* (2) Get global attributes */
 			$globals = explode(',', $segments[0]);
 
 			if( count($globals) < 4 )
-				throw new \Error('Format error during Genome unserialization.');
+				throw new \Exception('Format error during Genome unserialization.');
 
 			$this->layers  = intval($globals[0]);
 			$this->inputN  = intval($globals[1]);
-			$this->layers  = intval($globals[2]);
+			$this->neurons = intval($globals[2]);
 			$this->outputN = intval($globals[3]);
 
 			/* (3) Get synapses values */

build/neuralnetwork/core/NeuralNetwork.php

@@ -34,14 +34,14 @@
 
 			/* (1) Checks argument */
 			if( is_null($storage=NeuralNetworkCore::getStorage($storage)) )
-				throw new Error('Wrong NeuralNetwork loader\'s argument.');
+				throw new \Exception('Wrong NeuralNetwork loader\'s argument.');
 
 			/* (2) If files doesn't exist, raise error */
-			if( !$storage[0]['exists'] || !$storage[1]['exists'] || !$storage[2]['exists'] )
+			if( !$storage['nn']['exists'] )
-				throw new Error('Loaded storage have file(s) missing.');
+				throw new \Exception('Loaded storage have file(s) missing.');
 
 			/* (3) Unserialize last NeuralNetwork */
-			$last_network = FileManager::read($storage[0]['filename']);
+			$last_network = FileManager::read($storage['nn']['filename']);
 
 			/* (4) Creates and fill instance */
 			$instance = new NeuralNetworkCore(0, 0);

build/neuralnetwork/core/NeuralNetworkCore.php

@@ -11,21 +11,22 @@
 		************************************************/
 		private $maxGnr; // Maximum generation iterations
 		private $maxGnm; // Maximum genomes per generation
-		private $kptGnm; // Number of genomes kept for each generation
 		private $mutThr; // Mutation threshold
 		private $fitEnd; // Fitness range to end process
 		private $numHid; // Number of hidden layer(s)
 		private $numNeu; // Number of neurons for each hidden layer
+		private $inpNeu; // Number of input neurons
+		private $outNeu; // Number of output neurons
 
-		private $storage; // path to storage
+		private $storage;  // path to storage
+		private $callback; // callback training function
 
 		/************************************************
 		****             LOCAL ATTRIBUTES            ****
 		************************************************/
-		private $gnr;  // Current generation index
+		public $gnr;    // Current generation index
-		private $gmns; // Current generation's genomes
+		public $gnm;    // Current genome index
-		private $gnm;  // Current genome index
+		private $genome; // Current genome instance
-		private $fit;  // Current fitness
 
 
 
@@ -70,15 +71,18 @@
 
 			/* (2) Checks file's existence and returns it */
 			return [
-				[
+				'nn' => [
 					'filename' => $absolute_path.'.nn', // will contain neural network data
 					'exists'   => is_file($absolute_path.'.nn')
-				],[
+				], 'ex' => [
 					'filename' => $absolute_path.'.ex', // will contain samples
 					'exists'   => is_file($absolute_path.'.ex')
-				],[
+				], 'gn' => [
-					'filename' => $absolute_path.'.ln', // will contain values & weights
+					'filename' => $absolute_path.'.gn', // will contain genomes of the generation
-					'exists'   => is_file($absolute_path.'.ln')
+					'exists'   => is_file($absolute_path.'.gn')
+				], 'ft' => [
+					'filename' => $absolute_path.'.ft', // will contain genomes' fitness
+					'exists'   => is_file($absolute_path.'.ft')
 				]
 			];
 
@@ -107,17 +111,16 @@
 			/* (2) Default attributes */
 			$default = self::conf()['default'];
 
-			$this->setKeptGenomes($default['genomes_kept']);              // default value
 			$this->setMutationThreshold($default['mutation_threshold']);  // default value
 			$this->setFitnessEnd($default['fitness_end']);                // default value
 			$this->setHiddenLayersCount($default['hidden_layers']);       // default value
 			$this->setHiddenLayerNeuronsCount($default['layer_neurons']); // default value
 			$this->max = null;                                            // default value
 			$this->setStorage($default['storage']);                       // default value
+			$this->callback = function(){};                               // default value
 		}
 
 
-
 		/************************************************
 		****           Attributes Setters            ****
 		************************************************/
@@ -146,12 +149,12 @@
 			/* [2] Initializes files
 			=========================================================*/
 			/* (1) Creates directory/ies */
-			if( !is_dir(dirname(($this->storage[0]['filename']))) )
+			if( !is_dir(dirname(($this->storage['nn']['filename']))) )
-				mkdir( dirname($this->storage[0]['filename']), 0775, true );
+				mkdir( dirname($this->storage['nn']['filename']), 0775, true );
 
 			// Checks
-			if( !is_dir(dirname(($this->storage[0]['filename']))) )
+			if( !is_dir(dirname(($this->storage['nn']['filename']))) )
-				throw new \Error('Error creating directory: '.dirname(($this->storage[0]['filename'])));
+				throw new \Exception('Error creating directory: '.dirname(($this->storage['nn']['filename'])));
 
 			/* (2) Creates files */
 			foreach($this->storage as $file)
@@ -215,14 +218,49 @@
 			$this->numHid = $numHid;
 		}
 
+		/* SET NUMBER OF NEURONS OF THE INPUT LAYER
+		*
+		* @inpNeu<int> 											Number of neurons for the input layer
+		*
+		*/
+		public function setInputLayerCount($inpNeu){
+			if( abs(intval($inpNeu)) !== $inpNeu ) return;
+
+			$this->inpNeu = $inpNeu;
+		}
+
+		/* SET NUMBER OF NEURONS OF THE OUTPUT LAYER
+		*
+		* @outNeu<int> 											Number of neurons for the output layer
+		*
+		*/
+		public function setOutputLayerCount($outNeu){
+			if( abs(intval($outNeu)) !== $outNeu ) return;
+
+			$this->outNeu = $outNeu;
+		}
+
 
 		/************************************************
 		****             Sample Setters              ****
 		************************************************/
 
 		/* ADD SAMPLE TO THE NEURAL NETWORK
+		*
+		* @input<Array>							Set of input of the sample
+		* @output<Array>							Set of output of the sample
+		*
+		*/
 		public function addSample($input, $output){
-			FileManager::append( $this->storage[1]['filename'], implode(',',$input) .';'. implode(',',$output) );
+			/* (1) Checks number of input neurons */
+			if( !is_array($input) || is_null($this->inpNeu) || count($input) != $this->inpNeu )
+				throw new \Exception('Wrong @input argument for addSample() method.');
+
+			/* (2) Checks number of output neurons */
+			if( !is_array($output) || is_null($this->outNeu) || count($output) != $this->outNeu )
+				throw new \Exception('Wrong @output argument for addSample() method.');
+
+			FileManager::append( $this->storage['ex']['filename'], implode(',',$input) .';'. implode(',',$output) );
 		}
 
 
@@ -245,9 +283,9 @@
 				$storage = self::getStorage($path);
 
 				/* (2) Checks if files doesn't exist */
-				if( $storage[0]['exists'] || $storage[1]['exists'] || $storage[2]['exists'] )
+				if( $storage['nn']['exists'] )
 					if( !$override )
-						throw new \Error('This storage already exists, you can only load() it.');
+						throw new \Exception('This storage already exists, you can only load() it.');
 
 
 				/* [2] Creates storage & its files
@@ -264,65 +302,185 @@
 			/* [3] Stores data
 			=========================================================*/
 			/* (1) Stores NeuralNetwork state (attributes) */
-			FileManager::write($storage[0]['filename'], $this->serialize());
+			FileManager::write($storage['nn']['filename'], $this->serialize());
 
 			/* (2) Stores samples */
-			FileManager::write($storage[1]['filename'], FileManager::read($last_storage[1]['filename']));
+			FileManager::write($storage['ex']['filename'], FileManager::read($last_storage['ex']['filename']));
 
 			/* (3) Stores NeuralNetwork values & weights */
-			FileManager::write($storage[2]['filename'], FileManager::read($last_storage[2]['filename']));
+			FileManager::write($storage['gn']['filename'], FileManager::read($last_storage['gn']['filename']));
 
 		}
 
-
+		/* INITIALIZES THE LEARNING ROUTINE
-		/* STARTS THE LEARNING ROUTINE
 		*
 		* @callback<Function> 									Callback function to display current state
 		*
 		*/
-		public function learn($callback=null){
+		public function initLearningRoutine($callback=null){
 			/* [1] Manages @callback argument
 			=========================================================*/
 			if( !is_callable($callback) )
-				$callback = function(){};
+				$this->callback = function(){};
+			else
+				$this->callback = $callback;
 
-			/* [2] Creates the neural network
+			/* [2] Creates the First generation and serialize it
 			=========================================================*/
-				// best of last generation
+			/* (1) Initializes data & storage */
-				$best = null;
+			$this->gnr = 0;
+			$this->gnm = 0;
+			FileManager::write($this->storage['gn']['filename'], '');
+			FileManager::write($this->storage['ft']['filename'], '');
+
+			/* (2) Stores random genomes to storage */
+			for( $g = 0 ; $g < $this->maxGnm ; $g++ ){
+				$gnm = new Genome($this->numHid, $this->numNeu, $this->inpNeu, $this->outNeu);
+				FileManager::append($this->storage['gn']['filename'], $gnm->serialize());
+			}
+
+		}
+
+		/* RETURNS THE CURRENT GENOME
+		*
+		* @return genome<Genome>		 						Returns the current genome
+		*
+		*/
+		public function getGenome(){
+			/* (1) Checks if learning routine has beem started */
+			if( !is_numeric($this->gnm) )
+				throw new \Exception('Learning routine closed.');
+
+			/* (2) Reads the current genome from storage */
+			$serialized = FileManager::readline($this->storage['gn']['filename'], $this->gnm);
+
+			/* (3) Unserializes genome */
+			$this->genome = new Genome(2, 2, 2, 2);
+
+			try{
+
+				$this->genome->unserialize($serialized);
+				$this->genome->setCallback($this->callback);
+
+				return $this->genome;
+
+			/* (4) If error */
+			}catch(Exception $e){ return null; }
+
+		}
+
+		/* ITERATES TO THE NEXT GENOME (IF SAME GENERATION)
+		*
+		*/
+		public function nextGenome(){
+			/* (0) Checks if Genome's fitness has been set
+			---------------------------------------------------------*/
+			if( !($this->genome instanceof Genome) || is_null($this->genome->getFitness()) )
+				throw new \Exception('The learning routine is closed.');
+
+			/* (1) Stores fitness */
+			FileManager::append($this->storage['ft']['filename'], strval($this->genome->getFitness()) );
 
 
-				/* (1) For each generation
+			/* (1) Iterates if possible
-				---------------------------------------------------------*/
+			---------------------------------------------------------*/
-				for( $this->gnr = 0 ; $this->gnr < $this->maxGnr ; $this->gnr++ ){
+			if( $this->gnm < $this->maxGnm-1 ){
 
-					/* (1) Initializes genome list for current generation */
+				$this->gnm++;
-					$this->gnms = [];
 
-					/* (2) For each genome of the generation */
+			/* (2) If must change generation
-					for( $this->gnm = 0 ; $this->gnm < $this->maxGnm ; $this->gnm++ ){
+			---------------------------------------------------------*/
+			}else if( $this->gnr < $this->maxGnr-1 ){
 
-						// {2.1} First Generation -> random genomes //
+				/* (1) Update generation & genome indexes */
-						if( $this->gnr === 0 )
+				$this->gnr++;
-							$this->gnms[$this->gnm] = new Genome($this->numHid, $this->numNeu);
+				$this->gnm = 0;
 
-						// {2.2} Others generations -> crossover + mutation //
+				/* (2) Fetch the whole generation fitness values */
-						else{
+				$ftRead = FileManager::read($this->storage['ft']['filename']);
-							$this->gnms[$this->gnm] = new Genome($best['father'], $best['mother']);
+				$fitnesses = explode("\n", trim($ftRead) );
-							$this->gnms[$this->gnm]->mutation($this->mutThr);
-						}
 
-						/* (3) Calculate fitness */
+				/* (3) Extract @mother & @father indexes */
-						// ... blablabla
+				$iBest = $this->bestFitnesses($fitnesses);
+
+				/* (4) Extract best 2 genomes */
+				$sFather = FileManager::readline($this->storage['gn']['filename'], $iBest[0]);
+				$sMother = FileManager::readline($this->storage['gn']['filename'], $iBest[1]);
+
+				/* (5) Unserializes them */
+				$father = new Genome(2, 2, 2, 2);
+					$father->unserialize($sFather);
+
+				$mother = new Genome(2, 2, 2, 2);
+					$mother->unserialize($sMother);
+
+				/* (6) Create new generation */
+				FileManager::write($this->storage['gn']['filename'], '');
+				FileManager::write($this->storage['ft']['filename'], '');
+
+				for( $g = 0 ; $g < $this->maxGnm ; $g++ ){
+
+					// {6.1} Re-use father //
+					if( $g == 0 )
+						FileManager::append($this->storage['gn']['filename'], $father->serialize());
+
+					// {6.2} Re-use mother //
+					else if( $g == 1 )
+						FileManager::append($this->storage['gn']['filename'], $mother->serialize());
+
+					// {6.3} Do cross-over + mutation for the rest //
+					else{
+						$gnm = new Genome($father, $mother);
+						$gnm->mutation($this->mutThr);
+						FileManager::append($this->storage['gn']['filename'], $gnm->serialize());
 					}
 
 				}
 
-				// TODO: Select best genomes based on fitness
-				$best = null;
 
+			/* (3) If end of process
+			---------------------------------------------------------*/
+			}else{
+				$this->gnr = null;
+				$this->gnm = null;
+			}
 		}
 
+		/************************************************
+		****                 Utility                 ****
+		************************************************/
+
+		/* RETURNS THE 2 BEST FITNESSES FROM A LIST
+		*
+		* @fitnesses<Array> 									List of indexes fitnesses
+		*
+		* @return best<Array> 									List of the 2 indexes of the best fitnesses
+		*
+		*/
+		private static function bestFitnesses($fitnesses=null){
+			/* (1) Checks @fitnesses argument */
+			if( !is_array($fitnesses) )
+				throw new \Exception('Fitness list format error.');
+
+			/* (1) Find father (best)
+			---------------------------------------------------------*/
+			/* (1) Select greatest fitness value */
+			arsort($fitnesses);
+
+			/* (2) Attributes @father & @mother indexes */
+			$iFather = null;
+			$iMother = null;
+			$c = 0;
+			foreach($fitnesses as $k=>$v){
+				if( $c == 0 )      $iFather = $k;
+				else if( $c == 1 ) $iMother = $k;
+				else               break;
+
+				$c++;
+			}
+
+			return [ $iFather, $iMother ];
+		}
 
 		/************************************************
 		****          Serialization Methods          ****
@@ -340,11 +498,12 @@
 			/* (2) Adding attributes */
 			$json['maxGnr']  = $this->maxGnr;
 			$json['maxGnm']  = $this->maxGnm;
-			$json['kptGnm']  = $this->kptGnm;
 			$json['mutThr']  = $this->mutThr;
 			$json['fitEnd']  = $this->fitEnd;
 			$json['numHid']  = $this->numHid;
 			$json['numNeu']  = $this->numNeu;
+			$json['inpNeu']  = $this->inpNeu;
+			$json['outNeu']  = $this->outNeu;
 
 			$json['storage'] = $this->storage;
 
@@ -368,11 +527,12 @@
 			/* (3) Attributing json attributes */
 			$this->maxGnr = $json['maxGnr'];
 			$this->maxGnm = $json['maxGnm'];
-			$this->kptGnm = $json['kptGnm'];
 			$this->mutThr = $json['mutThr'];
 			$this->fitEnd = $json['fitEnd'];
 			$this->numHid = $json['numHid'];
 			$this->numNeu = $json['numNeu'];
+			$this->inpNeu = $json['inpNeu'];
+			$this->outNeu = $json['outNeu'];
 
 			$this->storage = $json['storage'];
 		}

build/neuralnetwork/storage/_buffer.ex

@@ -1,8 +0,0 @@
-0,0,0;0,0
-0,0,1;0,1
-0,1,0;0,1
-0,1,1;0,1
-1,0,0;0,0
-1,0,1;0,1
-1,1,0;1,1
-1,1,1;1,1

build/neuralnetwork/storage/test/test1.ex

@@ -6,3 +6,11 @@
 1,0,1;0,1
 1,1,0;1,1
 1,1,1;1,1
+0,0,0;0,0
+0,0,2;0,2
+0,2,0;0,2
+0,2,2;0,2
+2,0,0;0,0
+2,0,2;0,2
+2,2,0;2,2
+2,2,2;2,2

build/neuralnetwork/storage/test/test1.ft

@@ -0,0 +1,520 @@
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build/neuralnetwork/storage/test/test1.nn

@@ -1 +1 @@
-{"maxGnr":50,"maxGnm":100,"kptGnm":2,"mutThr":0.3,"fitEnd":1,"numHid":2,"numNeu":3,"max":{"input":[1,1,1],"output":[1,1]},"storage":[{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.nn","exists":false},{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.ex","exists":false},{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.ln","exists":false}]}
+{"maxGnr":50,"maxGnm":1000,"kptGnm":2,"mutThr":0.3,"fitEnd":1,"numHid":2,"numNeu":3,"inpNeu":3,"outNeu":2,"storage":{"nn":{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.nn","exists":false},"ex":{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.ex","exists":false},"gn":{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.gn","exists":false},"ft":{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.ft","exists":false}}}

config/neural-network.json

@@ -2,7 +2,6 @@
 	"storage_parent": "/build/neuralnetwork/storage",
 
 	"default": {
-		"genomes_kept":       2,
 		"mutation_threshold": 0.3,
 		"fitness_end":        1,
 		"storage":            "_buffer",

public/main.php

@@ -10,38 +10,103 @@
 		return [($abc[0] & $abc[1]), $abc[1] | $abc[2]];
 	}
 
-	if( false && 'test_creating_dataset' ){
+	if( true && 'test_creating_dataset' ){
+
+		$part = 1;
 
 
 		echo "Welcome to neural-network.php\n";
-		echo "-----------------------------\n";
+		echo "-----------------------------\n\n";
 
-		$nn = NeuralNetwork::create(50, 100);
+		/* [1] Trying to load neural network
+		=========================================================*/
+		try{
 
-		$nn->setHiddenLayersCount(2);
+			$nn = NeuralNetwork::load('test/test1');
-		$nn->setHiddenLayerNeuronsCount(3);
+			echo "$part. NeuralNetwork loaded from 'test/test1'\n"; $part++;
 
-		$d = [0, 0, 0]; $nn->addSample($d, behaviour($d));
+		/* [2] Else, creates it
-		$d = [0, 0, 1]; $nn->addSample($d, behaviour($d));
+		=========================================================*/
-		$d = [0, 1, 0]; $nn->addSample($d, behaviour($d));
+		}catch(\Exception $e){
-		$d = [0, 1, 1]; $nn->addSample($d, behaviour($d));
+
-		$d = [1, 0, 0]; $nn->addSample($d, behaviour($d));
+			$nn = NeuralNetwork::create(50, 1000);
-		$d = [1, 0, 1]; $nn->addSample($d, behaviour($d));
+
-		$d = [1, 1, 0]; $nn->addSample($d, behaviour($d));
+			$nn->setHiddenLayersCount(4);
-		$d = [1, 1, 1]; $nn->addSample($d, behaviour($d));
+			$nn->setHiddenLayerNeuronsCount(3);
-		$d = [0, 0, 0]; $nn->addSample($d, behaviour($d));
+			$nn->setInputLayerCount(3);
-		$d = [0, 0, 2]; $nn->addSample($d, behaviour($d));
+			$nn->setOutputLayerCount(2);
-		$d = [0, 2, 0]; $nn->addSample($d, behaviour($d));
+
-		$d = [0, 2, 2]; $nn->addSample($d, behaviour($d));
+			echo "$part. NeuralNetwork configured\n"; $part++;
-		$d = [2, 0, 0]; $nn->addSample($d, behaviour($d));
+
-		$d = [2, 0, 2]; $nn->addSample($d, behaviour($d));
+			$d = [0, 0, 0]; $nn->addSample($d, behaviour($d));
-		$d = [2, 2, 0]; $nn->addSample($d, behaviour($d));
+			$d = [0, 0, 1]; $nn->addSample($d, behaviour($d));
-		$d = [2, 2, 2]; $nn->addSample($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++;
+
+			$nn->store('test/test1', true);
+			echo "$part. NeuralNetwork stored to 'test/test1'\n"; $part++;
+
+		}
+
+
+		/* [2] Initializing learning routine
+		=========================================================*/
+		$fitness = 0;
+		$max_fit = 0;
+		$nn->initLearningRoutine(function($input, $output){
+			global $fitness;
+			if( $output[0] == behaviour($input)[0] ) $fitness++;
+			if( $output[1] == behaviour($input)[1] ) $fitness++;
+		});
+		echo "$part. Learning routine initialized.\n"; $part++;
+
+
+		/* [3] Learning through generations and genomes
+		=========================================================*/
+		/* (1) For each generation */
+		for( $gnr = 0 ; $gnr < 50 ; $gnr++ ){
+
+			/* (2) For each genome */
+			for( $gnm = 0 ; $gnm < 1000 ; $gnm++ ){
+				$fitness = 0;
+
+				/* (2.1) Get current genome */
+				$g = $nn->getGenome();
+				echo "\r[x] genome ".($nn->gnm+1)."/1000 on generation ".($nn->gnr+1)."/50 - max fitness: $max_fit    ";
+
+				/* (2.2) Train genome with random samples */
+				for( $r = 0 ; $r < 100 ; $r++ )
+					$g->train([rand(0,10), rand(0,10), rand(0,10)]);
+
+				/* (2.3) Set fitness & go to next genome */
+				if( $fitness > $max_fit ) $max_fit = $fitness;
+
+				$g->setFitness($fitness);
+				$nn->nextGenome();
+			}
+
+		}
 
-		$nn->store('test/test1', true);
 
 	}
 
+
+
+
+
 	if( false && 'load_neural_network' ){
 
 		$nn = NeuralNetwork::load('test/test1');
@@ -50,7 +115,7 @@
 
 	if( false && 'test_genomes' ){
 		/* (1) Basic Creation */
-		$a = new Genome(2, 3); // 2 layers of 3 neurons each -> randomly filled
+		$a = new Genome(2, 3, 3, 2); // 2 layers of 3 neurons each -> randomly filled
 
 		echo "A : ".$a->serialize()."\n";
 
@@ -72,7 +137,7 @@
 	}
 
 
-	if( true ){
+	if( false ){
 
 		$g = new Genome(2, 3, 3, 2);
 		$fitness = 0;