`Genome::process` first implementation with a single `output` + must add 2 layers to have the last one as the `output`

build/neuralnetwork/core/Genome.php

@@ -16,7 +16,7 @@
 		****             LOCAL ATTRIBUTES            ****
 		************************************************/
 		public $layers;   // Number of layers
-		public $neurons;  // Neurons of the genome
+		public $neurons;  // Number of neurons per layer
 		public $synapses; // Synapses between neurons
 
 
@@ -78,14 +78,12 @@
 			// set layers
 			$this->layers = $layers;
 
-			/* (2) Creating random neurons */
+			/* (2) Store number of neurons */
-			$this->neurons = [];
+			$this->neurons = $neurons;
-			for( $i = 0, $l = $neurons*$layers ; $i < $l ; $i++ )
-				$this->neurons[$i] = rand(self::MIN, self::MAX) / self::MAX;
 
 			/* (3) Creating random synapses */
 			$this->synapses = [];
-			for( $i = 0, $l = pow($neurons, $layers) ; $i < $l ; $i++ )
+			for( $i = 0, $l = $layers*pow($neurons,2) ; $i < $l ; $i++ )
 				$this->synapses[$i] = rand(self::MIN, self::MAX) / self::MAX;
 
 			// Success status
@@ -106,7 +104,7 @@
 
 			/* (2) Clones into this Genome */
 			$this->layers   = $parent->layers;
-			$this->neurons  = array_slice($parent->neurons, 0);
+			$this->neurons  = $parent->neurons;
 			$this->synapses = array_slice($parent->synapses, 0);
 
 			// Success state
@@ -127,21 +125,18 @@
 				return false;
 
 			/* (2) Checks number of layers+neurons (same species) */
-			if( $father->layers !== $mother->layers || count($father->neurons) !== count($mother->neurons) )
+			if( $father->layers !== $mother->layers || $father->neurons !== $mother->neurons )
 				return false;
 
 			/* (3) Set layer count */
 			$this->layers = $father->layers;
 
-			/* (4) Do random crossover for neurons */
+			/* (4) Set neurons number */
-			$this->neurons = [];
+			$this->neurons = $father->neurons;
-			for( $i = 0, $l = count($father->neurons) ; $i < $l ; $i++ )
-				if( !!rand(0,1) ) $this->neurons[$i] = $father->neurons[$i];
-				else              $this->neurons[$i] = $mother->neurons[$i];
 
 			/* (5) Do random crossover for synapses */
 			$this->synapses = [];
-			for( $i = 0, $l = pow(count($this->neurons)/$this->layers, $this->layers) ; $i < $l ; $i++ )
+			for( $i = 0, $l = $this->layers*pow($this->neurons, 2) ; $i < $l ; $i++ )
 				if( !!rand(0,1) ) $this->synapses[$i] = $father->synapses[$i];
 				else              $this->synapses[$i] = $mother->synapses[$i];
 
@@ -195,6 +190,64 @@
 				$this->synapses[$iSynapses[$i]] = rand(self::MIN, self::MAX) / self::MAX;
 		}
 
+		/* CALCULATES THE OUTPUT OF THE GENOME
+		*
+		* @input<Array>											Input for which we want fitness
+		*
+		* @return output<double> 								Output calculated with this input
+		*
+		*/
+		public function process($input=null){
+			/* (1) Checks @input argument */
+			if( !is_array($input) || count($input) !== $this->neurons )
+				throw new \Error('Invalid @input for Genome\'s output calculation.');
+
+
+			/* [1] Set temporary calculation data
+			=========================================================*/
+			/* (1) Set temporary neurons data */
+			$neurons = array_merge( $input, array_fill(0, $this->neurons*$this->layers, 0) );
+
+			/* (2) Set temporary synapses data */
+			$synapses = $this->synapses;
+
+
+			/* [2] Calculates output
+			=========================================================*/
+				/* (1) For each hidden layer
+				---------------------------------------------------------*/
+				for( $l = 1 ; $l < $this->layers+1 ; $l++ ){
+
+					/* (2) For each neuron of this layer
+					---------------------------------------------------------*/
+					for( $n = $l*$this->neurons, $nl = ($l+1)*$this->neurons ; $n < $nl ; $n++ ){
+
+						$neurons[$n] = 0;
+
+						/* (3) For each synapse between current neuron and last layer
+						---------------------------------------------------------*/
+						for( $s = ($l-1)*pow($this->neurons,2), $sl = $l*pow($this->neurons,2) ; $s < $sl ; $s++ )
+							$neurons[$n] += $synapses[$s] * $neurons[ floor($s/$this->neurons) ];
+							// newNeuron += synapse*lastLayerNeuron
+
+						/* Divide the sum to make a mean */
+						$neurons[$n] /= $this->neurons;
+					}
+
+				}
+
+				/* (4) Calculates single output
+				---------------------------------------------------------*/
+				$output = 0;
+
+				for( $n = ($this->layers-1)*$this->neurons, $nl = $this->layers*$this->neurons ; $n < $nl ; $n++ )
+					$output += $neurons[$n];
+
+			
+			/* [3] Returns output
+			=========================================================*/
+			return $output / $this->neurons;
+		}
 
 		/************************************************
 		****              Serialization              ****
@@ -210,12 +263,9 @@
 			$csv = '';
 
 			/* (2) Adds global attributes */
-			$csv .= $this->layers .';';
+			$csv .= $this->layers .','. $this->neurons .';';
 
-			/* (3) Adds neurons data */
+			/* (3) Adds synapses data */
-			$csv .= implode(',', $this->neurons) .';';
-
-			/* (4) Adds synapses data */
 			$csv .= implode(',', $this->synapses);
 
 			return $csv;
@@ -236,16 +286,16 @@
 				throw new \Error('Format error during Genome unserialization.');
 
 			/* (2) Get global attributes */
-			if( !is_numeric($segments[0]) )
+			$global = explode(',', $segments[0]);
+
+			if( count($global) < 2 )
 				throw new \Error('Format error during Genome unserialization.');
 
-			$this->layers = intval($segments[0]);
+			$this->layers = intval($global[0]);
+			$this->layers = intval($global[1]);
 
-			/* (3) Get neurons values */
+			/* (3) Get synapses values */
-			$this->neurons = explode(',', $segments[1]);
+			$this->synapses = explode(',', $segments[1]);
-
-			/* (4) Get synapses values */
-			$this->synapses = explode(',', $segments[2]);
 		}
 
 	}

build/neuralnetwork/core/NeuralNetworkCore.php

@@ -343,9 +343,42 @@
 
 			/* [2] Creates the neural network
 			=========================================================*/
+				// best of last generation
+				$best = new Genome($this->numHid, $this->numNeu);
+
+
+				/* (1) For each generation
+				---------------------------------------------------------*/
+				for( $this->gnr = 0 ; $this->gnr < $this->maxGnr ; $this->gnr++ ){
+
+					/* (1) Initializes genome list for current generation */
+					$this->gnms = [];
+
+					/* (2) For each genome of the generation */
+					for( $this->gnm = 0 ; $this->gnm < $this->maxGnm ; $this->gnm++ ){
+
+						// {2.1} First Generation -> random genomes //
+						if( $this->gnr === 0 )
+							$this->gnms[$this->gnm] = new Genome($this->numHid, $this->numNeu);
+
+						// {2.2} Others generations -> crossover + mutation //
+						else{
+							$this->gnms[$this->gnm] = new Genome($best['father'], $best['mother']);
+							$this->gnms[$this->gnm]->mutation($this->mutThr);
+						}
+
+						/* (3) Calculate fitness */
+						// ... blablabla
+					}
 
 				}
 
+				// TODO: Select best genomes based on fitness
+				$best = null;
+
+		}
+
+
 		/************************************************
 		****          Serialization Methods          ****
 		************************************************/

build/neuralnetwork/storage/A/B/C/test.ex

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

build/neuralnetwork/storage/A/B/C/test.nn

@@ -1 +0,0 @@
-{"maxGnr":50,"maxGnm":100,"kptGnm":2,"mutThr":0.3,"fitEnd":1,"numHid":2,"numNeu":3,"max":{"input":[1,1,1],"output":[1]},"storage":[{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/A\/B\/C\/test.nn","exists":true},{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/A\/B\/C\/test.ex","exists":true},{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/A\/B\/C\/test.ln","exists":true}]}

build/neuralnetwork/storage/_buffer.ex

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

build/neuralnetwork/storage/test/test1.ex

@@ -0,0 +1,8 @@
+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.nn

@@ -0,0 +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}]}

public/main.php

@@ -10,7 +10,7 @@
 	if( false && 'test_creating_dataset' ){
 
 		function behaviour($abc){
-			return [($abc[0] & $abc[1]) ^ $abc[2]];
+			return [($abc[0] & $abc[1]), $abc[1] | $abc[2]];
 		}
 
 		echo "Welcome to neural-network.php\n";
@@ -18,7 +18,7 @@
 
 		$nn = NeuralNetwork::create(50, 100);
 
-		$nn->setMaxValues([1, 1, 1], [1]);
+		$nn->setMaxValues([1, 1, 1], [1, 1]);
 		$nn->setHiddenLayersCount(2);
 		$nn->setHiddenLayerNeuronsCount(3);
 
@@ -31,17 +31,17 @@
 		$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);
+		$nn->store('test/test1', true);
 
 	}
 
 	if( false && 'load_neural_network' ){
 
-		$nn = NeuralNetwork::load('A/B/C/test');
+		$nn = NeuralNetwork::load('test/test1');
 
 	}
 
-	if( true && 'test_genomes' ){
+	if( false && 'test_genomes' ){
 		/* (1) Basic Creation */
 		$a = new Genome(2, 3); // 2 layers of 3 neurons each -> randomly filled
 
@@ -61,9 +61,17 @@
 		$c = new Genome($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);
+
+		echo $g->process([1, 1, 0]);
+	}
+
 	// REWRITE TEST
 	// for( $a = 0, $al = 50 ; $a < $al ; $a++ )
 	// 	for( $b = 0, $bl = 20 ; $b < $bl ; $b++ ){