Optimization with @fitness, only keep fitnesses greater than @this->maxFit (if anti-regression-option enabled) + no_file anymore
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xdrm-brackets
parent
6826434e07
commit
dee73f521a
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@ -227,7 +227,7 @@
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*/
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public function mutation($threshold=1){
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/* (1) Checks @threshold argument */
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if( floatval($threshold) !== $threshold || $threshold < 0 || $threshold > 1 )
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if( !is_numeric($threshold) || $threshold < 0 || $threshold > 1 )
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throw new \Exception('Invalid threshold for Genome mutation.');
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/* (2) Calculates how many neurons/synapses to mutate */
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@ -25,11 +25,11 @@
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/************************************************
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**** LOCAL ATTRIBUTES ****
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************************************************/
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public $maxFit; // Maximum fitness of the previous generation
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public $minFit; // Minimum fitness of the current generation
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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 $maxFit; // Maximum fitness of the previous generation
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private $fitnesses; // The fitnesses of the current generation's genomes
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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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@ -83,9 +83,6 @@
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], 'gn' => [
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'filename' => $absolute_path.'.gn', // will contain genomes of the generation
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'exists' => is_file($absolute_path.'.gn')
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], 'ft' => [
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'filename' => $absolute_path.'.ft', // will contain genomes' fitness
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'exists' => is_file($absolute_path.'.ft')
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], 'ln' => [
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'filename' => $absolute_path.'.ln', // will contain learnt best genomes
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'exists' => is_file($absolute_path.'.ln')
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@ -370,10 +367,10 @@
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/* (1) Initializes data & storage */
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$this->gnr = 0;
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$this->gnm = 0;
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$this->minFit = null;
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$this->maxFit = null;
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FileManager::write($this->storage['gn']['filename'], '');
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FileManager::write($this->storage['ft']['filename'], '');
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// FileManager::write($this->storage['ft']['filename'], '');
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$this->fitnesses = [];
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/* (2) Stores random genomes to storage */
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for( $g = 0 ; $g < $this->maxGnm ; $g++ ){
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@ -399,10 +396,10 @@
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/* (1) Initializes data & storage */
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$this->gnr = 0;
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$this->gnm = 0;
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$this->minFit = null;
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$this->maxFit = null;
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FileManager::write($this->storage['gn']['filename'], '');
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FileManager::write($this->storage['ft']['filename'], '');
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// FileManager::write($this->storage['ft']['filename'], '');
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$this->fitnesses = [];
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/* (2.1) Fetch learnt best genomes */
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$loadedGenomes = [
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@ -481,7 +478,11 @@
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throw new \Exception('The learning routine is closed.');
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/* (1) Stores fitness */
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FileManager::append($this->storage['ft']['filename'], strval($this->genome->getFitness()) );
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$fit = $this->genome->getFitness();
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if( !$this->antReg || is_null($this->maxFit) || $fit > $this->maxFit )
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$this->fitnesses[$this->gnm] = $fit;
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// FileManager::append($this->storage['ft']['filename'], strval($this->genome->getFitness()) );
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/* (1) Iterates if possible
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@ -499,12 +500,12 @@
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$this->gnm = 0;
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/* (2) Fetch the whole generation fitness values */
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$ftRead = FileManager::read($this->storage['ft']['filename']);
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$fitnesses = explode("\n", trim($ftRead) );
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// $ftRead = FileManager::read($this->storage['ft']['filename']);
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// $this->fitnesses = explode("\n", trim($ftRead) );
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/* (3) Checks if fitnessEnd is reached */
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if( min($fitnesses) == $this->fitEnd ){
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if( count($this->fitnesses) > 0 && max($this->fitnesses) == $this->fitEnd ){
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/* (1) Get the 2 best genomes */
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$best = FileManager::readline($this->storage['gn']['filename'], 0);
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@ -522,13 +523,11 @@
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/* (4) Checks if theres a fitness maximum evolution */
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$fitnessEvolution = !$this->antReg || is_null($this->maxFit) && is_null($this->minFit) || max($fitnesses) > $this->maxFit;
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// Extract @mother & @father indexes //
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$iBest = $this->bestFitnesses($this->fitnesses);
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/* (4.1) If evolution -> choose best + cross-over ... */
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if( $fitnessEvolution ){
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// {1} Extract @mother & @father indexes //
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$iBest = $this->bestFitnesses($fitnesses);
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if( !is_null($iBest[0]) ){
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// {2} Extract best 2 genomes //
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$sFather = FileManager::readline($this->storage['gn']['filename'], $iBest[0]);
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@ -541,8 +540,7 @@
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$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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$this->minFit = min($fitnesses);
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$this->maxFit = max($this->fitnesses);
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$this->storeLearntBest();
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@ -566,7 +564,7 @@
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/* (7) Create new generation */
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FileManager::write($this->storage['gn']['filename'], '');
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FileManager::write($this->storage['ft']['filename'], '');
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// FileManager::write($this->storage['ft']['filename'], '');
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for( $g = 0 ; $g < $this->maxGnm ; $g++ ){
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@ -587,6 +585,8 @@
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}
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$this->fitnesses = [];
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/* (3) If end of process
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---------------------------------------------------------*/
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}else{
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@ -611,7 +611,6 @@
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/* (2) Stores data to learnt data */
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FileManager::write($this->storage['ln']['filename'], $best);
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}
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// TODO: Manage @mutThr decreasing to be more precise
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/************************************************
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**** Utility ****
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@ -646,8 +645,8 @@
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$c++;
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}
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/* (3) Anti-regression, if @mother < @father only use @father*/
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if( $fitnesses[$iMother] < $fitnesses[$iFather] )
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/* (3) If mother not found copy father */
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if( is_null($iMother) )
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$iMother = $iFather;
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return [ $iFather, $iMother ];
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@ -2,7 +2,7 @@
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"storage_parent": "/build/neuralnetwork/storage",
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"default": {
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"mutation_threshold": 0.5,
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"mutation_threshold": 1,
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"fitness_end": 1,
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"storage": "_buffer",
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"hidden_layers": 2,
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@ -12,7 +12,7 @@
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$train = true;
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$train = $argc > 1 && $argv[1] == 'train';
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$guess = !$train;
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if( $train && 'learning_process' ){
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@ -34,13 +34,13 @@
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=========================================================*/
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}catch(\Exception $e){
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$nn = NeuralNetwork::create(50, 500);
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$nn = NeuralNetwork::create(50, 100);
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$nn->setHiddenLayersCount(3); // 3 Hidden layers
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$nn->setHiddenLayerNeuronsCount(4); // Composed with 3 neurons each
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$nn->setHiddenLayersCount(5); // 3 Hidden layers
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$nn->setHiddenLayerNeuronsCount(3); // Composed with 3 neurons each
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$nn->setInputLayerCount(3); // 3 inputs
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$nn->setOutputLayerCount(1); // 1 output
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$nn->setMutationThreshold(0.3); // mutation 30% each generation
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$nn->setMutationThreshold(0.5); // mutation 30% each generation
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$nn->setFitnessEnd(-1.5); // Algorithm is done when fitness reaches 0
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$nn->setAntiRegression(true); // That repeats a generation while its fitness is lower than the previous one
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@ -71,12 +71,11 @@
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/* [2] Initializing learning routine
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=========================================================*/
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$defaultMT = 0.3;
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$fitness = 0;
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$max_fit = 0;
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$nn->loadLearningRoutine(function($input, $output){
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global $fitness;
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$fitness -= abs($output[0] - behaviourtest2($input)[0]);
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$fitness -= abs(round($output[0]) - behaviourtest2($input)[0]);
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});
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echo "$part. Learning routine initialized.\n"; $part++;
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@ -94,7 +93,6 @@
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$last_gnr = $nn->gnr;
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$max_fit = -1e9;
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$min_fit = 100;
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/* (2) For each genome */
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while( true ){
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@ -102,19 +100,18 @@
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/* (2.1) Get current genome */
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$g = $nn->getGenome();
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echo "\r[x] gnm ".($nn->gnm+1)."/500 on gnr ".($nn->gnr+1)."/50 - x".($gen_repeat+1)." - fit[$min_fit;$max_fit] ";
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echo "\r[x] gnm ".($nn->gnm+1)."/100 on gnr ".($nn->gnr+1)."/50 - x".($gen_repeat+1)." - fit[$max_fit] ";
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/* (2.2) Train genome with random samples */
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for( $r = 0 ; $r < 100 ; $r++ )
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$g->train([rand(0,10), rand(0,10), rand(0,10)]);
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for( $r = 0 ; $r < 500 ; $r++ )
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$g->train([rand(0,100), rand(0,100), rand(0,100)]);
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/* (2.3) Set fitness & go to next genome */
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if( $fitness > $max_fit ) $max_fit = $fitness;
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if( $fitness < $min_fit ) $min_fit = $fitness;
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$g->setFitness($fitness);
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if( $nn->gnm >= 500-1 )
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if( $nn->gnm >= 100-1 )
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break;
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$nn->nextGenome();
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