Method to get trained genome + anti-regression management + some stuff to do (output neurons issue) + manage `mutThr` decreasing + ...
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xdrm-brackets
parent
ece00a7688
commit
7aefda0bf0
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@ -4,12 +4,14 @@
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use filemanager\core\FileManager;
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use filemanager\core\FileManager;
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// FIXME: Propagation of outputNeurons from NeuralNetwork to Genome
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class Genome implements \Serializable{
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class Genome implements \Serializable{
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/************************************************
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/************************************************
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**** Constants ****
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**** Constants ****
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************************************************/
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************************************************/
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const MIN = 0;
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const MIN = -1e9;
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const MAX = 1e9;
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const MAX = 1e9;
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/************************************************
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/************************************************
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@ -276,39 +278,38 @@
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/* [2] Calculates output
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/* [2] Calculates output
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=========================================================*/
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=========================================================*/
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/* (1) For each hidden layer
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/* (1) For each hidden layer
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---------------------------------------------------------*/
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foreach($this->layersIterator() as $lr=>$l){
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/* (2) For each neuron of this layer
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---------------------------------------------------------*/
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---------------------------------------------------------*/
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foreach($this->layersIterator() as $lr=>$l){
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foreach($this->neuronsIterator($l) as $nr=>$n){
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/* (2) For each neuron of this layer
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$neurons[$n] = 0;
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/* (3) For each synapse between current neuron and last layer
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---------------------------------------------------------*/
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---------------------------------------------------------*/
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foreach($this->neuronsIterator($l) as $nr=>$n){
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foreach($this->synapsesIterator($l, $nr) as $sr=>$s){
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// echo "current: n#$nr/l#$l = s#$s * n#$sr\n";
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$neurons[$n] = 0;
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// echo "calc: ${neurons[$n]} += ${synapses[$s]} * ${neurons[$sr]}\n";
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$neurons[$n] += $synapses[$s] * $neurons[$sr];
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/* (3) For each synapse between current neuron and last layer
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// echo "result: ${neurons[$n]}\n\n";
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---------------------------------------------------------*/
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foreach($this->synapsesIterator($l, $nr) as $sr=>$s){
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// echo "current: n#$nr/l#$l = s#$s * n#$sr\n";
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// echo "calc: ${neurons[$n]} += ${synapses[$s]} * ${neurons[$sr]}\n";
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$neurons[$n] += $synapses[$s] * $neurons[$sr];
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// echo "result: ${neurons[$n]}\n\n";
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}
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if( $l == 1 )
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$neurons[$n] /= $this->inputN;
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else
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$neurons[$n] /= $this->neurons;
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}
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}
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// if( $l == 1 ) $neurons[$n] /= $this->inputN;
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// else $neurons[$n] /= $this->neurons;
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}
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}
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}
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/* [3] Callback the output layer's values
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/* [3] Callback the output layer's values
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=========================================================*/
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=========================================================*/
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echo $this->outputN;
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call_user_func($this->callback, $input, array_slice($neurons, -$this->outputN) );
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call_user_func($this->callback, $input, array_slice($neurons, -$this->outputN) );
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}
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}
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@ -24,8 +24,9 @@
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/************************************************
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/************************************************
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**** LOCAL ATTRIBUTES ****
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**** LOCAL ATTRIBUTES ****
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************************************************/
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************************************************/
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public $gnr; // Current generation index
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private $maxFit; // Maximum fitness of the previous generation
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public $gnm; // Current genome index
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public $gnr; // Current generation index
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public $gnm; // Current genome index
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private $genome; // Current genome instance
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private $genome; // Current genome instance
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@ -123,7 +124,6 @@
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$this->callback = function(){}; // default value
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$this->callback = function(){}; // default value
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}
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}
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/************************************************
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/************************************************
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**** Attributes Setters ****
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**** Attributes Setters ****
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************************************************/
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************************************************/
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@ -315,6 +315,27 @@
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}
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}
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/* RETURNS THE TRAINED GENOME IF IT EXISTS
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*
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* @return trained<Genome> The trained genome
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*
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*/
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public function getTrainedGenome(){
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/* (1) Read file */
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$serialized = FileManager::readline($this->storage['ln']['filename'], 0);
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/* (2) If no data */
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if( is_null($serialized) )
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throw new \Exception('No trained genome found.');
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/* (3) Unserializes it */
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$genome = new Genome(2, 2, 2, 2);
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$genome->unserialize($serialized);
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/* (4) Return genome */
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return $genome;
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}
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/* INITIALIZES THE LEARNING ROUTINE
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/* INITIALIZES THE LEARNING ROUTINE
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*
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*
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* @callback<Function> Callback function to display current state
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* @callback<Function> Callback function to display current state
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/* [2] Creates the First generation and serialize it
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/* [2] Creates the First generation and serialize it
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=========================================================*/
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=========================================================*/
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/* (1) Initializes data & storage */
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/* (1) Initializes data & storage */
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$this->gnr = 0;
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$this->gnr = 0;
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$this->gnm = 0;
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$this->gnm = 0;
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$this->maxFit = null;
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FileManager::write($this->storage['gn']['filename'], '');
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FileManager::write($this->storage['gn']['filename'], '');
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FileManager::write($this->storage['ft']['filename'], '');
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FileManager::write($this->storage['ft']['filename'], '');
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/* (2) Fetch learnt best genomes */
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/* (2.1) Fetch learnt best genomes */
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$loadedGenomes = [
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$loadedGenomes = [
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FileManager::readline($this->storage['ln']['filename'], 0),
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FileManager::readline($this->storage['ln']['filename'], 0),
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FileManager::readline($this->storage['ln']['filename'], 1)
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FileManager::readline($this->storage['ln']['filename'], 1)
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];
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];
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/* (2.2) Checks content */
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if( is_null($loadedGenomes[0]) || is_null($loadedGenomes[1]) )
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return $this->initLearningRoutine($callback);
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/* (3) Unserializes them */
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/* (3) Unserializes them */
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$father = new Genome(2, 2, 2, 2);
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$father = new Genome(2, 2, 2, 2);
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$father->unserialize($loadedGenomes[0]);
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$father->unserialize($loadedGenomes[0]);
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}
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}
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/* RETURNS THE CURRENT GENOME
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/* RETURNS THE CURRENT GENOME
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*
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*
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* @return genome<Genome> Returns the current genome
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* @return genome<Genome> Returns the current genome
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$ftRead = FileManager::read($this->storage['ft']['filename']);
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$ftRead = FileManager::read($this->storage['ft']['filename']);
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$fitnesses = explode("\n", trim($ftRead) );
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$fitnesses = explode("\n", trim($ftRead) );
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/* (3) Extract @mother & @father indexes */
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/* (3) Checks if theres a fitness evolution */
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$iBest = $this->bestFitnesses($fitnesses);
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$fitnessEvolution = is_null($this->maxFit) || max($fitnesses) > $this->maxFit;
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/* (4) Extract best 2 genomes */
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/* (3.1) If evolution -> choose best + cross-over ... */
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$sFather = FileManager::readline($this->storage['gn']['filename'], $iBest[0]);
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if( $fitnessEvolution ){
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$sMother = FileManager::readline($this->storage['gn']['filename'], $iBest[1]);
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/* (5) Unserializes them */
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// {1} Extract @mother & @father indexes //
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$father = new Genome(2, 2, 2, 2);
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$iBest = $this->bestFitnesses($fitnesses);
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$father->unserialize($sFather);
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$mother = new Genome(2, 2, 2, 2);
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// {2} Extract best 2 genomes //
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$mother->unserialize($sMother);
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$sFather = FileManager::readline($this->storage['gn']['filename'], $iBest[0]);
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$sMother = FileManager::readline($this->storage['gn']['filename'], $iBest[1]);
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/* (6) Create new generation */
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// {3} Unserializes them //
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$father = new Genome(2, 2, 2, 2);
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$father->unserialize($sFather);
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$mother = new Genome(2, 2, 2, 2);
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$mother->unserialize($sMother);
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$this->maxFit = max($fitnesses);
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/* (3.2) If regression -> renew generation */
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}else{
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// {1} Extract 2 parents of the current generation //
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$sFather = FileManager::readline($this->storage['gn']['filename'], 0);
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$sMother = FileManager::readline($this->storage['gn']['filename'], 1);
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// {3} Unserializes them //
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$father = new Genome(2, 2, 2, 2);
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$father->unserialize($sFather);
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$mother = new Genome(2, 2, 2, 2);
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$mother->unserialize($sMother);
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/* (4) Stay on the current genertion */
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$this->gnr--;
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}
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/* (7) Create new generation */
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FileManager::write($this->storage['gn']['filename'], '');
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FileManager::write($this->storage['gn']['filename'], '');
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FileManager::write($this->storage['ft']['filename'], '');
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FileManager::write($this->storage['ft']['filename'], '');
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}
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}
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/* (3) If end of process
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/* (3) If end of process
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---------------------------------------------------------*/
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---------------------------------------------------------*/
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}else{
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}else{
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/* (1) Get the 2 best genomes */
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/* (1) Get the 2 best genomes */
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$best = FileManager::readline($this->storage['ln']['filename'], 0);
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$best = FileManager::readline($this->storage['gn']['filename'], 0);
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$best.= FileManager::readline($this->storage['ln']['filename'], 1);
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$best.= FileManager::readline($this->storage['gn']['filename'], 1);
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/* (2) Stores data to learnt data */
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/* (2) Stores data to learnt data */
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FileManager::write($this->storage['ln']['filename'], $best);
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FileManager::write($this->storage['ln']['filename'], $best);
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}
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}
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}
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}
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// TODO: Manage @mutThr decreasing to be more precise
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/************************************************
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/************************************************
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**** Utility ****
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**** Utility ****
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************************************************/
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************************************************/
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0,0,0;0,0
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0,0,0;0
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0,0,1;0,1
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0,0,1;-1
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0,1,0;0,1
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0,1,0;1
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0,1,1;0,1
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0,1,1;0
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1,0,0;0,0
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1,0,0;1
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1,0,1;0,1
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1,0,1;0
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1,1,0;1,1
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1,1,0;2
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1,1,1;1,1
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1,1,1;1
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0,0,0;0,0
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0,0,0;0
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0,0,2;0,2
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0,0,2;-2
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0,2,0;0,2
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0,2,0;2
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0,2,2;0,2
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0,2,2;0
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2,0,0;0,0
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2,0,0;2
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2,0,2;0,2
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2,0,2;0
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2,2,0;2,2
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2,2,0;4
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2,2,2;2,2
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2,2,2;2
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@ -1,776 +1,67 @@
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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1012084.0082879
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|
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|
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34308.736260989
|
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|
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3193.9662763306
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30623.933618266
|
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4168.1352509163
|
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3428.7506150937
|
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3651.1541263728
|
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|
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|
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32740.833633038
|
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|
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2764.0266015432
|
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3520.4908417154
|
|
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626596.20396703
|
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2983.812238522
|
|
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1041.8619238928
|
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6727.0693381596
|
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17122.628720702
|
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3566.2647754844
|
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401686.03286279
|
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3491.0875278019
|
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|
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194144.58176966
|
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714593.49882494
|
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29972.404507814
|
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18416.785776748
|
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589921.1763952
|
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509243.83194714
|
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5188.3167507381
|
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|
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3460.6412591116
|
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|
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91792.305602593
|
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353894.62182512
|
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7518.5994297769
|
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1649687.5393043
|
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61806.161271895
|
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1796.5368954566
|
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3729.1619837528
|
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7706.4471218507
|
|
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237859.66546805
|
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8157.909553844
|
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4244.5019744555
|
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|
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|
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753311.45631428
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12568.571178854
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1657.9964486281
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273437.44828237
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|
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3778.9007197032
|
|
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37022.412768297
|
|
||||||
301256.26634481
|
|
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3919.5331619325
|
|
||||||
12441.214993626
|
|
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4620.4917934806
|
|
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140042.26424163
|
|
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19885.808110774
|
|
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901668.08698742
|
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721005.18631527
|
|
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405399.6975612
|
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501192.46166846
|
|
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170585.72247322
|
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1032998.3769422
|
|
||||||
500565.57176182
|
|
||||||
1091077.0952211
|
|
||||||
1046675.3262274
|
|
||||||
188697.07585277
|
|
||||||
1476.5779017466
|
|
||||||
513639.73069778
|
|
||||||
476846.33350997
|
|
||||||
650119.52917025
|
|
||||||
3634853.7054124
|
|
||||||
880932.34514119
|
|
||||||
118649.93809137
|
|
||||||
369305.54992343
|
|
||||||
607069.83847438
|
|
||||||
87309.298120936
|
|
||||||
41990.403555945
|
|
||||||
44380.833932577
|
|
||||||
505734.44176155
|
|
||||||
2188.1088980416
|
|
||||||
7942.9407678976
|
|
||||||
753929.06981816
|
|
||||||
4693.4459964267
|
|
||||||
25611.124714649
|
|
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3003.9600783587
|
|
||||||
34149.912741398
|
|
||||||
133116.94641351
|
|
||||||
7051.402042614
|
|
||||||
1414.7803108893
|
|
||||||
12171.691564839
|
|
||||||
1429.3815816289
|
|
||||||
166988.31818112
|
|
||||||
837148.44890922
|
|
||||||
2891.7608151147
|
|
||||||
403512.21750897
|
|
||||||
961843.32633155
|
|
||||||
7132.1522687055
|
|
||||||
751875.3768063
|
|
||||||
13125.024323867
|
|
||||||
614777.64236488
|
|
||||||
2910.2742456494
|
|
||||||
508798.92404313
|
|
||||||
227311.21217478
|
|
||||||
|
|
|
||||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,2 @@
|
||||||
|
3,3,3,3;-0.673048732,-0.412463881,-0.736532526,0.829005319,-0.813510365,-0.502031047,0.950266852,0.818343199,0.03824395,0.219597033,-0.339935295,-0.181847521,-0.820436519,-0.762547916,0.165912707,0.799397635,-0.883427944,0.304251916,-0.414343129,0.25450569,-0.472308773,-0.804309556,0.137957493,0.576775783,-0.004366962,-0.494472245,-0.518802319,-0.819242309,0.447490839,0.506983895,0.452629618,-0.248047378,0.468150892,0.291926346,0.947744258,0.536874463,-0.73260191,0.834541621,0.177465943
|
||||||
|
3,3,3,3;-0.673048732,-0.412463881,-0.736532526,0.829005319,-0.813510365,-0.502031047,0.950266852,0.818343199,0.03824395,0.219597033,-0.339935295,-0.181847521,-0.820436519,-0.762547916,0.165912707,0.799397635,-0.883427944,0.304251916,-0.414343129,0.25450569,-0.472308773,-0.804309556,0.137957493,0.576775783,-0.004366962,-0.494472245,-0.518802319,-0.819242309,0.447490839,0.506983895,0.452629618,-0.248047378,0.468150892,0.291926346,0.947744258,0.536874463,-0.73260191,0.834541621,0.177465943
|
||||||
|
|
@ -0,0 +1,2 @@
|
||||||
|
3,3,3,3;-0.673048732,-0.412463881,-0.736532526,0.829005319,-0.813510365,-0.502031047,0.950266852,0.818343199,0.03824395,0.219597033,-0.339935295,-0.181847521,-0.820436519,-0.762547916,0.165912707,0.799397635,-0.883427944,0.304251916,-0.414343129,0.25450569,-0.472308773,-0.804309556,0.137957493,0.576775783,-0.004366962,-0.494472245,-0.518802319,-0.819242309,0.447490839,0.506983895,0.452629618,-0.248047378,0.468150892,0.291926346,0.947744258,0.536874463,-0.73260191,0.834541621,0.177465943
|
||||||
|
3,3,3,3;-0.673048732,-0.412463881,-0.736532526,0.829005319,-0.813510365,-0.502031047,0.950266852,0.818343199,0.03824395,0.219597033,-0.339935295,-0.181847521,-0.820436519,-0.762547916,0.165912707,0.799397635,-0.883427944,0.304251916,-0.414343129,0.25450569,-0.472308773,-0.804309556,0.137957493,0.576775783,-0.004366962,-0.494472245,-0.518802319,-0.819242309,0.447490839,0.506983895,0.452629618,-0.248047378,0.468150892,0.291926346,0.947744258,0.536874463,-0.73260191,0.834541621,0.177465943
|
||||||
|
|
@ -1 +1 @@
|
||||||
{"maxGnr":50,"maxGnm":1000,"mutThr":0.5,"fitEnd":1,"numHid":4,"numNeu":4,"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},"ln":{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.ln","exists":false}}}
|
{"maxGnr":50,"maxGnm":100,"mutThr":0.005,"fitEnd":1,"numHid":3,"numNeu":3,"inpNeu":3,"outNeu":1,"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},"ln":{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.ln","exists":false}}}
|
||||||
|
|
|
||||||
104
public/main.php
104
public/main.php
|
|
@ -7,10 +7,10 @@
|
||||||
use \filemanager\core\FileManager;
|
use \filemanager\core\FileManager;
|
||||||
|
|
||||||
function behaviour($abc){
|
function behaviour($abc){
|
||||||
return [($abc[0] & $abc[1]), $abc[1] | $abc[2]];
|
return [($abc[0] + $abc[1] - $abc[2])];
|
||||||
}
|
}
|
||||||
|
|
||||||
if( true && 'test_creating_dataset' ){
|
if( false && 'learning_process' ){
|
||||||
|
|
||||||
$part = 1;
|
$part = 1;
|
||||||
|
|
||||||
|
|
@ -29,13 +29,13 @@
|
||||||
=========================================================*/
|
=========================================================*/
|
||||||
}catch(\Exception $e){
|
}catch(\Exception $e){
|
||||||
|
|
||||||
$nn = NeuralNetwork::create(50, 1000);
|
$nn = NeuralNetwork::create(50, 100);
|
||||||
|
|
||||||
$nn->setHiddenLayersCount(4);
|
$nn->setHiddenLayersCount(3);
|
||||||
$nn->setHiddenLayerNeuronsCount(4);
|
$nn->setHiddenLayerNeuronsCount(3);
|
||||||
$nn->setInputLayerCount(3);
|
$nn->setInputLayerCount(3);
|
||||||
$nn->setOutputLayerCount(2);
|
$nn->setOutputLayerCount(1);
|
||||||
$nn->setMutationThreshold(0.5);
|
$nn->setMutationThreshold(0.3);
|
||||||
|
|
||||||
echo "$part. NeuralNetwork configured\n"; $part++;
|
echo "$part. NeuralNetwork configured\n"; $part++;
|
||||||
|
|
||||||
|
|
@ -69,10 +69,7 @@
|
||||||
$max_fit = 0;
|
$max_fit = 0;
|
||||||
$nn->loadLearningRoutine(function($input, $output){
|
$nn->loadLearningRoutine(function($input, $output){
|
||||||
global $fitness;
|
global $fitness;
|
||||||
$diff = [ abs($output[0] - behaviour($input)[0]), abs($output[1] - behaviour($input)[1]) ];
|
$fitness -= abs($output[0] - behaviour($input)[0]);
|
||||||
|
|
||||||
if( $diff[0] > 0 ) $fitness += 1 / $diff[0];
|
|
||||||
if( $diff[1] > 0 ) $fitness += 1 / $diff[1];
|
|
||||||
});
|
});
|
||||||
echo "$part. Learning routine initialized.\n"; $part++;
|
echo "$part. Learning routine initialized.\n"; $part++;
|
||||||
|
|
||||||
|
|
@ -80,18 +77,23 @@
|
||||||
/* [3] Learning through generations and genomes
|
/* [3] Learning through generations and genomes
|
||||||
=========================================================*/
|
=========================================================*/
|
||||||
/* (1) For each generation */
|
/* (1) For each generation */
|
||||||
for( $gnr = 0 ; $gnr < 50 ; $gnr++ ){
|
$last_gnr = -1;
|
||||||
|
while( true ){
|
||||||
|
|
||||||
$max_fit = 0;
|
if( $nn->gnr > $last_gnr)
|
||||||
$start = microtime(true);
|
$start = microtime(true);
|
||||||
|
|
||||||
|
$last_gnr = $nn->gnr;
|
||||||
|
|
||||||
|
$max_fit = -1e9;
|
||||||
|
|
||||||
/* (2) For each genome */
|
/* (2) For each genome */
|
||||||
for( $gnm = 0 ; $gnm < 1000 ; $gnm++ ){
|
while( true ){
|
||||||
$fitness = 0;
|
$fitness = 0;
|
||||||
|
|
||||||
/* (2.1) Get current genome */
|
/* (2.1) Get current genome */
|
||||||
$g = $nn->getGenome();
|
$g = $nn->getGenome();
|
||||||
echo "\r[x] genome ".($nn->gnm+1)."/1000 on generation ".($nn->gnr+1)."/50 - max fitness: $max_fit ";
|
echo "\r[x] gnm ".($nn->gnm+1)."/100 on gnr ".($nn->gnr+1)."/50 - max_fit: $max_fit ";
|
||||||
|
|
||||||
/* (2.2) Train genome with random samples */
|
/* (2.2) Train genome with random samples */
|
||||||
for( $r = 0 ; $r < 100 ; $r++ )
|
for( $r = 0 ; $r < 100 ; $r++ )
|
||||||
|
|
@ -101,10 +103,21 @@
|
||||||
if( $fitness > $max_fit ) $max_fit = $fitness;
|
if( $fitness > $max_fit ) $max_fit = $fitness;
|
||||||
|
|
||||||
$g->setFitness($fitness);
|
$g->setFitness($fitness);
|
||||||
|
|
||||||
|
if( $nn->gnm >= 100-1 )
|
||||||
|
break;
|
||||||
|
|
||||||
$nn->nextGenome();
|
$nn->nextGenome();
|
||||||
}
|
}
|
||||||
|
|
||||||
echo "\n\t".((microtime(true)-$start))."s\n";
|
$nn->nextGenome();
|
||||||
|
|
||||||
|
// If generation evolution, notify
|
||||||
|
if( $nn->gnr > $last_gnr)
|
||||||
|
echo "\n\t".((microtime(true)-$start))."s\n";
|
||||||
|
|
||||||
|
if( $nn->gnr == 50-1 )
|
||||||
|
break;
|
||||||
|
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
@ -112,39 +125,44 @@
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
|
if( true && 'guessing_process' ){
|
||||||
|
$part = 1;
|
||||||
|
|
||||||
|
|
||||||
|
echo "Welcome to neural-network.php\n";
|
||||||
|
echo "-----------------------------\n\n";
|
||||||
|
|
||||||
|
/* [1] Trying to load neural network
|
||||||
|
=========================================================*/
|
||||||
|
try{
|
||||||
|
|
||||||
|
$nn = NeuralNetwork::load('test/test1');
|
||||||
|
echo "$part. NeuralNetwork loaded from 'test/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(0,10), rand(0,10), rand(0,10)]);
|
||||||
|
|
||||||
if( false && 'load_neural_network' ){
|
|
||||||
|
|
||||||
$nn = NeuralNetwork::load('test/test1');
|
|
||||||
|
|
||||||
}
|
}
|
||||||
|
|
||||||
if( false && 'test_genomes' ){
|
|
||||||
/* (1) Basic Creation */
|
|
||||||
$a = new Genome(2, 3, 3, 2); // 2 layers of 3 neurons each -> randomly filled
|
|
||||||
|
|
||||||
echo "A : ".$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";
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
if( false ){
|
if( false ){
|
||||||
|
|
||||||
$g = new Genome(2, 3, 3, 2);
|
$g = new Genome(2, 3, 3, 2);
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue