neuralnet.php/build/neuralnetwork/core/Genome.php

<?php

	namespace neuralnetwork\core;

	use filemanager\core\FileManager;

	class Genome implements \Serializable{

		/************************************************
		****                Constants                ****
		************************************************/
		const MIN = 0;
		const MAX = 1e9;

		/************************************************
		****             LOCAL ATTRIBUTES            ****
		************************************************/
		private $layers; // Number of hidden layers

		private $inputN;  // Number of input neurons
		private $neurons; // Number of neurons for each hidden layer
		private $outputN; // Number of output neurons

		private $synapses; // Synapses between neurons

		private $callback; // callback training function

		private $fitness;  // Genome's fitness


		/************************************************
		****           Genome Construction           ****
		************************************************/

		/* CONSTRUCTOR
		*
		* -- RANDOM CREATION --
		* @layers<int> 											Number of hidden layers to manage
		* @neurons<int> 										Number of neurons per hidden layer
		* @inN<int>												Number of input neurons
		* @outN<int>											Number of output neurons
		*
		* -- CLONING --
		* @base<Genom>											Genome to clone to
		*
		* -- CROSS-OVER CREATION --
		* @father<Genome>										First parent
		* @mother<Genome>										Second parent
		*
		*
		*/
		public function __construct(){
			/* (1) Get arguments */
			$argv = func_get_args();
			$argc = count($argv);

			/* (2) If RandomCreation */
			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 \Exception('Invalid Genome constructor\'s arguments.');

			/* (6) Default values */
			$this->fitness  = null;
			$this->callback = function(){};
		}

		/* BUILDS A Genome RANDOMLY WITH PARAMETERS
		*
		* @layers<int> 											The number of hidden layers to manage
		* @neurons<int> 										The number neurons per layer
		* @inN<int>												Number of input neurons
		* @outN<int>											Number of output neurons
		*
		* @return created<Boolean>								If Genome has been successfully created
		*
		*/
		private function construct_new($layers, $neurons, $inN, $outN){
			/* (1) Checks parameters */
			if( abs(intval($layers)) !== $layers || abs(intval($neurons)) !== $neurons || abs(intval($inN)) !== $inN || abs(intval($outN)) !== $outN )
				return false;

			// set layers
			$this->layers = $layers;

			/* (2) Store number of neurons */
			$this->neurons = $neurons;
			$this->inputN  = $inN;
			$this->outputN = $outN;

			/* (3) Creating random synapses */
			$this->synapses = [];
			for( $i = 0, $l = $neurons*($inN+$outN+$neurons*$layers) ; $i < $l ; $i++ )
				$this->synapses[$i] = rand(self::MIN, self::MAX) / self::MAX;

			// Success status
			return true;
		}

		/* BUILDS A Genome BASED ON A PARENT
		*
		* @parent<Genome> 										Parent genome to clone into children
		*
		* @return created<Boolean> 								If cloned Genome has been created successfully
		*
		*/
		private function construct_inheritance($parent=null){
			/* (1) Checks parent type */
			if( !($parent instanceof Genome) )
				return false;

			/* (2) Clones into this Genome */
			$this->layers   = $parent->layers;
			$this->neurons  = $parent->neurons;
			$this->synapses = array_slice($parent->synapses, 0);

			// Success state
			return true;
		}

		/* BUILDS A Genome BASED ON TWO PARENTS
		*
		* @father<Genome> 										First parent ($father)
		* @mother<Genome> 										Second parent ($mother)
		*
		* @return created<Boolean> 								If crossed-over Genome has been created successfully
		*
		*/
		private function construct_crossover($father=null, $mother=null){
			/* (1) Checks parent type */
			if( !($father instanceof Genome) || !($mother instanceof Genome) )
				return false;

			/* (2) Checks the attributes (same species) */
			$diffAttr = $father->layers !== $mother->layers;
			$diffAttr = $diffAttr || $father->inputN !== $mother->inputN;
			$diffAttr = $diffAttr || $father->neurons !== $mother->neurons;
			$diffAttr = $diffAttr || $father->outputN !== $mother->outputN;

			if( $diffAttr )
				return false;

			/* (3) Set layer count */
			$this->layers = $father->layers;

			/* (4) Set neurons number */
			$this->inputN  = $father->inputN;
			$this->neurons = $father->neurons;
			$this->outputN = $father->inputN;

			/* (5) Do random crossover for synapses */
			$this->synapses = [];
			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];

			// Success state
			return true;
		}


		/************************************************
		****                 Setters                 ****
		************************************************/

		/* SETS THE CALLBACK FUNCTION
		*
		* @callback<Function> 									Callback function to train with
		*
		*/
		public function setCallback($callback=null){
			/* (1) Checks @callback argument */
			if( !is_callable($callback) )
				throw new \Exception('Wrong argument for Genome\'s callback function.');

			/* (2) Set callback function */
			$this->callback = $callback;
		}

		/* SETS THE FITNESS OF THE GENOME
		*
		* @fitness<double> 										Calculated fitness of the genome
		*
		*/
		public function setFitness($fitness=null){
			/* (1) Checks @fitness argument */
			if( !is_numeric($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             ****
		************************************************/

		/* APPLIES A MUTATION ON THE Genome WITH A SPECIFIC @threshold
		*
		* @threshold<double> 									Mutation threshold
		*
		*/
		public function mutation($threshold=1){
			/* (1) Checks @threshold argument */
			if( floatval($threshold) !== $threshold || $threshold < 0 || $threshold > 1 )
				throw new \Exception('Invalid threshold for Genome mutation.');

			/* (2) Calculates how many neurons/synapses to mutate */
			$synapsesMutations = round( (count($this->synapses) - 1) * rand(0, $threshold*self::MAX)/self::MAX );

			/* (3) Choose random synapses' indexes */
			$iSynapses = [];
			while( count($iSynapses) < $synapsesMutations ){
				$r = rand(0, count($this->synapses)-1);

				if( !in_array($r, $iSynapses) )
					$iSynapses[] = $r;
			}

			/* (4) Update chosen synapses */
			for( $i = 0, $l = count($iSynapses) ; $i < $l ; $i++ )
				$this->synapses[$iSynapses[$i]] = rand(self::MIN, self::MAX) / self::MAX;
		}

		/* CALCULATES THE OUTPUT OF THE GENOME
		*
		* @input<Array>											Input for which we want fitness
		* @callback<Function>								[OPTIONAL] Callback function
		*
		* @note: will call @callback function with INPUTS as 1st argument and OUTPUTS as 2nd arguments
		*
		*/
		public function train($input=null, $callback=null){
			/* (1) Checks @input argument */
			if( !is_array($input) || count($input) !== $this->inputN )
				throw new \Exception('Invalid @input for Genome\'s training.');

			/* (2) Checks optional @callback argument */
			if( is_callable($callback) )
				$this->setCallback($callback);


			/* [1] Set temporary calculation data
			=========================================================*/
			/* (1) Set temporary neurons data */
			$neurons = array_merge( $input, array_fill(0, $this->neurons*$this->layers, 0), array_fill(0, $this->outputN, 0) );

			/* (2) Set temporary synapses data */
			$synapses = $this->synapses;


			/* [2] Calculates output
			=========================================================*/
				/* (1) For each hidden layer
				---------------------------------------------------------*/
				foreach($this->layersIterator() as $lr=>$l){

					/* (2) For each neuron of this layer
					---------------------------------------------------------*/
					foreach($this->neuronsIterator($l) as $nr=>$n){

						$neurons[$n] = 0;

						/* (3) For each synapse between current neuron and last layer
						---------------------------------------------------------*/
						foreach($this->synapsesIterator($l, $nr) as $sr=>$s){
							// echo "current: n#$nr/l#$l = s#$s * n#$sr\n";
							// echo "calc:    ${neurons[$n]} += ${synapses[$s]} * ${neurons[$sr]}\n";
							$neurons[$n] += $synapses[$s] * $neurons[$sr];
							// echo "result:  ${neurons[$n]}\n\n";
						}

						if( $l == 1 )
							$neurons[$n] /= $this->inputN;
						else
							$neurons[$n] /= $this->neurons;
					}




				}


			/* [3] Callback the output layer's values
			=========================================================*/
			call_user_func($this->callback, $input, array_slice($neurons, -$this->outputN) );
		}


		/************************************************
		****                Utilities                ****
		************************************************/

		/* RETURNS ITERATOR THROUGH EACH LAYER
		*
		* @return indexes<yield> 								Iterator through each layer (relative->absolute)
		*
		*/
		private function layersIterator(){
			for( $l = 1 ; $l < $this->layers+2 ; $l++ )
				yield $l => $l;
		}

		/* RETURNS AN ITERATOR ON THE NEURONS OF A LAYER
		*
		* @layer<int> 											Layer to browse
		*
		* @return indexes<yield> 								Iterator through the neurons indexes of the layer (relative->absolute)
		*
		*/
		private function neuronsIterator($layer){
			/* (1) Formats @layer argument */
			if( $layer < 0               ) $layer = 0;
			if( $layer > $this->layers+1 ) $layer = $this->layers+1;

			$layer = intval($layer);

			/* (2) If between input/hidden */
			if( $layer === 0 ){
				$offset = 0;
				for( $n = $offset, $nl = $this->inputN ; $n < $nl ; $n++ )
					yield $n-$offset => $n;

			/* (3) If between hidden/output */
			}else if( $layer == $this->layers+1 ){
				$offset = $this->inputN + $this->neurons * $this->layers;
				for( $n = $offset, $nl = $this->inputN + $this->neurons * $this->layers + $this->outputN ; $n < $nl ; $n++ )
					yield $n-$offset => $n;

			/* (2) If in between hidden layer */
			}else{
				$offset = $this->inputN + $this->neurons * ($layer-1);
				for( $n = $offset, $nl = $this->inputN+$this->neurons*$layer ; $n < $nl ; $n++ )
					yield $n-$offset => $n;
			}
		}

		/* RETURNS AN ITERATOR ON THE SYNAPSES BETWEEN A NEURON AND ITS PREVIOUS LAYER'S NEURONS
		*
		* @layer<int> 											Destination layer
		* @neuron<int> 											Destination neuron
		*
		* @return indexes<yield> 								Iterator through the synapses indexes between the destination neuron and the source layer's neurons (neuronIndex->SynapseIndex)
		*
		*/
		private function synapsesIterator($layer, $neuron){
			/* (1) Formats @layer argument */
			if( $layer < 1               ) $layer = 1;
			if( $layer > $this->layers+1 ) $layer = $this->layers+1;

			$layer = intval($layer);
			$prev  = $layer-1;

			/* (2) If between input/hidden */
			if( $layer === 1 ){
				$offset = 0;
				for( $s = $offset+$neuron, $sl = $this->inputN*$this->neurons ; $s < $sl ; $s += $this->neurons )
					yield ($s-$offset-$neuron)/$this->neurons => $s;


			/* (3) If between hidden/output */
			}else if( $layer == $this->layers+1 ){
				$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;

			/* (2) If in between hidden layer */
			}else{
				$offset = $this->neurons * ($this->inputN+$this->neurons*($prev-1));
				for( $s = $offset+$neuron, $sl = $offset+$this->neurons*$this->neurons ; $s < $sl ; $s += $this->neurons )
					yield $this->inputN+$this->neurons*($prev-1) + ($s-$offset-$neuron)/$this->neurons => $s;
			}
		}


		/************************************************
		****              Serialization              ****
		************************************************/

		/* SERIALIZES A Genome
		*
		* @return serialized<String> 							Serialized representation of the Genome
		*
		*/
		public function serialize(){
			/* (1) Initialize result */
			$csv = '';

			/* (2) Adds global attributes */
			$csv .= $this->layers .','. $this->inputN .','. $this->neurons .','. $this->outputN .';';

			/* (3) Adds synapses data */
			$csv .= implode(',', $this->synapses);

			return $csv;
		}

		/* BUILDS A Genome BASED ON HIS SERIALIZED REPRESENTATION
		*
		* @serialized<String> 									Serialized representation of a Genome
		*
		*/
		public function unserialize($serialized){
			/* (1) Segmenting data */
			$segments = explode(';', trim($serialized) );

			// Manage segmentation error
			if( count($segments) < 2 )
				throw new \Exception('Format error during Genome unserialization.');

			/* (2) Get global attributes */
			$globals = explode(',', $segments[0]);

			if( count($globals) < 4 )
				throw new \Exception('Format error during Genome unserialization.');

			$this->layers  = intval($globals[0]);
			$this->inputN  = intval($globals[1]);
			$this->neurons = intval($globals[2]);
			$this->outputN = intval($globals[3]);

			/* (3) Get synapses values */
			$this->synapses = explode(',', $segments[1]);
		}

	}




	/************************************************
	****                 USE CASE                ****
	************************************************/
	$use_case = false;
	if( $use_case ){

		/* (1) Basic Creation */
		$a = new Genome(2, 3, 3, 2); // 2 layers of 3 neurons each -> randomly filled + 3 input neurons + 2 output neurons

		/* (2) Inheritance */
		$b = new Genome($a);   // Clone of @a

		/* (3) Section Title */
		$b->mutation(0.3); // @b has now mutated with a threshold of 30%

		/* (4) Cross-over (father+mother) */
		$c = new Genome($a, $b); // @c is a randomly-done mix of @a and @b

	}

?>