Project structure + class bases and use cases for the Neural Network + FileManager to manage large files
This commit is contained in:
xdrm-brackets
commit
228d1d7913
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<?php
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/* [1] On definit les chemins absolus si c'est pas deja fait
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=========================================================*/
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if( !defined('__ROOT__') ) define('__ROOT__', dirname(__FILE__) );
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if( !defined('__CONFIG__') ) define('__CONFIG__', __ROOT__.'/config' );
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if( !defined('__BUILD__') ) define('__BUILD__', __ROOT__.'/build' );
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if( !defined('__PUBLIC__') ) define('__PUBLIC__', __ROOT__.'/public_html' );
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/* ACTIVE LE DEBUGGAGE (WARNING + EXCEPTION)
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*
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*/
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function debug(){
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ini_set('display_errors',1);
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ini_set('display_startup_errors',1);
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error_reporting(-1);
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}
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/* AUTOLOADER
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*
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* @className<String> Nom de la classe appelee
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*
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*/
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function autoloader($className){
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$path = '';
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/* [1] On utilise le namespace pour localiser
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===============================================*/
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// On remplace les '\' par des '/'
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$path = str_replace('\\', '/', $className) . '.php';
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$path = __BUILD__.'/'.$path;
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// Si le fichier existe
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if( file_exists($path) )
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require_once $path; // on inclue le fichier
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}
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// On definit l'autoloader comme autoloader (obvious)
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spl_autoload_register('autoloader', false, true);
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?>
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@ -0,0 +1,83 @@
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<?php
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namespace filemanager\core;
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class FileManager{
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/* READS FILE'S CONTENT
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*
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* @file<String> File to read
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*
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*/
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public static function read($file){
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/* [1] Init driver
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=========================================================*/
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$driver = new \SplFileObject($file, 'r');
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/* [2] Read lines
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=========================================================*/
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$read = '';
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/* (1) Reads lines */
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$line = 0;
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while( $driver->current() ){
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$read .= "\n".$driver->current();
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$driver->next();
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}
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/* [3] Returns read content
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=========================================================*/
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/* (2) Destroy driver */
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$driver = null;
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/* (3) Returns result */
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return $read;
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}
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/* WRITES CONTENT TO A FILE
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*
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* @file<String> File to write to
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* @content<String> Content to write
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*
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*/
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public static function write($file, $content){
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/* (1) Erase file */
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file_put_contents($file, '');
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/* (2) Get driver (write-flag) */
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$driver = new \SplFileObject($file, 'r+');
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/* (3) Writes content */
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$driver->fwrite($content);
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/* (4) Free driver */
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$driver = null;
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}
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/* APPENDS CONTENT TO A FILE
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*
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* @file<String> File to append content to
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* @content<String> Content to append
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*
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*/
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public static function append($file, $content){
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/* (1) Get driver (append-flag) */
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$driver = new \SplFileObject($file, 'a');
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/* (2) append content */
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$driver->fwrite($content.PHP_EOL);
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/* (3) Free driver */
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$driver = null;
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}
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}
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?>
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<?php
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namespace neuralnetwork\core;
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use neuralnetwork\core\NeuralNetworkCore;
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/************************************************
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**** Neural Network's Facade ****
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************************************************/
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class NeuralNetwork{
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/* CHECKS AND STORES PATHS TO STORAGE FILES
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*
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* @path<String> Path to storage
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*
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* @return files<Array> Array containing storage files' information
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* @return error<NULL> Returns NULL if error
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*
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*/
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public static function setStorage($path){
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/* (1) Checks the path */
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if( !preg_match("/^(?P<path>\/(?:[a-z0-9_-]+\/)*)(?P<filename>[a-z0-9_-]+)$/i", $path, $matches) )
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return null;
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/* (2) Checks file's existence and returns it */
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return [
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[
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'filename' => $matches['path'].$matches['filename'] .'.nn', // will contain neural network data
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'exists' => is_file($matches['path'].$matches['filename'] .'.nn')
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],
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[
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'filename' => $matches['path'].$matches['filename'] .'.ex', // will contain samples
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'exists' => is_file($matches['path'].$matches['filename'] .'.ex')
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]
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];
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}
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/* CREATES A NEW NEURAL NETWORK
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*
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* @generations<int> Maximum number of generations to process
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* @genomes<int> Number of genomes per generation
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*
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* @return nn<NeuralNetwork> New NeuralNetwork
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*
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*/
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public static function create($generations=null, $genomes=null){
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/* Propagate instance creation */
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return new NeuralNetworkCore($generations, $genomes);
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}
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/* LOADS A NEURAL NETWORK FROM STORAGE
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*
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* @storage<String> Path to storage
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*
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* @return nn<NeuralNetwork> Loaded NeuralNetwork
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*
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*/
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public static function load($storage=null){
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/* (1) Checks argument */
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if( is_null($storage=self::setStorage($gnr_or_storage)) )
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throw new Error('Wrong NeuralNetwork loader\'s argument.');
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/* (2) If files doesn't exist, raise error */
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if( !$storage[0]['exists'] || !$storage[1]['exists'] )
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throw new Error('Loaded storage have file(s) missing.');
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/* (3) Parses files */
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$last_network = json_decode( FileManager::read($storage[0]['filename']), true);
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/* (4) Creates and fill instance */
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$instance = new NeuralNetworkCore($last_network['generations'], $last_network['genomes']);
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$instance->setStorage($gnr_or_storage);
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$instance->setFitnessEnd($last_network['fitness_end']);
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$instance->setKeptGenomes($last_network['kept_genomes']);
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$instance->setMutationThreshold($last_network['mutation_threshold']);
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$instance->setMaxValues($last_network['max_values']);
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/* (5) Returns instance */
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return $instance;
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}
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}
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?>
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@ -0,0 +1,291 @@
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<?php
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namespace neuralnetwork\core;
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use filemanager\core\FileManager;
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class NeuralNetworkCore{
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/************************************************
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**** GLOBAL ATTRIBUTES ****
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************************************************/
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private $maxGnr; // Maximum generation iterations
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private $maxGnm; // Maximum genomes per generation
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private $kptGnm; // Number of genomes kept for each generation
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private $mutThr; // Mutation threshold
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private $fitEnd; // Fitness range to end process
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private $max; // max values for input and output neurons
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private $storage; // path to storage
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/************************************************
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**** LOCAL ATTRIBUTES ****
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************************************************/
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private $gnr; // Current generation index
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private $gmns; // Current generation's genomes
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private $gnm; // Current genome index
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private $fit; // Current fitness
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/* RETURNS CONFIGURATION CONTENT
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*
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* @return config<Array> Associative array containing config content
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*
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*/
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public static function conf(){
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/* (1) Extract config file's content */
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$fcontent = FileManager::read(__CONFIG__.'/neural-network.json');
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/* (2) Parse json */
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$parsed = json_decode($fcontent, true);
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// If json error -> return empty configuration
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if( !is_array($parsed) )
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return [];
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/* (3) Return parsed data */
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return $parsed;
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}
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/* CONSTRUCTOR
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*
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* @generation<int> Maximum number of generations
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* @genomes<int> Maximum number of genomes per generation
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* OR
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* @storage<String> Storage location
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*
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*/
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public function __construct($generations=null, $genomes=null){
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/* [1] If argument error
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=========================================================*/
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if( intval($generations)!==$generations || intval($genomes) !== $genomes )
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throw new Error('Wrong constructor\'s arguments.');
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/* [2] Initializing attributes
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=========================================================*/
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/* (1) Set attributes */
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$this->maxGnr = intval($generations);
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$this->maxGnm = intval($genomes);
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/* (2) Default attributes */
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$default = self::conf()['default'];
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$this->setKeptGenomes($default['genomes_kept']); // default value
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$this->setMutationThreshold($default['mutation_threshold']); // default value
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$this->setFitnessEnd($default['fitness_end']); // default value
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$this->max = null; // default value
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$this->setStorage($default['storage']); // default value
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}
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/************************************************
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**** Attributes Setters ****
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************************************************/
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/* CHECKS AND STORES PATHS TO STORAGE FILES
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*
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* @path<String> Path to storage
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*
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* @return error<Boolean> Returns if paths are correct
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*
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*/
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public function setStorage($path){
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/* (1) Checks the path */
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if( !preg_match("/^(?P<path>(?:[a-z0-9_-]+\/)*)(?P<filename>[a-z0-9_-]+)$/i", $path, $matches) )
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return false;
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$absolute_path = __ROOT__.self::conf()['storage_parent'].'/'.$matches['path'].$matches['filename'];
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/* (2) Checks file's existence and returns TRUE */
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$this->storage = [
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[
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'filename' => $absolute_path.'.nn', // will contain neural network data
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'exists' => is_file($absolute_path.'.nn')
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],
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[
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'filename' => $absolute_path.'.ex', // will contain samples
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'exists' => is_file($absolute_path.'.ex')
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]
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];
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return true;
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}
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/* SET FITNESS END
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*
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* @fitEnd<Double> Fitness value to stop algorithm if reached
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*
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*/
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public function setFitnessEnd($fitEnd){
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if( !is_numeric($fitEnd) ) return;
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$this->fitEnd = $fitEnd;
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}
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/* SET THE NUMBER OF KEPT GENOMES FOR EACH GENERATION
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*
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* @kptGnm<int> Number of kept genomes for each generation
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*
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*/
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public function setKeptGenomes($kptGnm){
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if( intval($kptGnm) !== $kptGnm ) return;
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$this->kptGnm = $kptGnm;
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}
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/* SET MUTATION THRESHOLD
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*
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* @mutThr<Double> Mutation threshold
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*
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*/
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public function setMutationThreshold($mutThr){
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if( abs(floatval($mutThr)) !== $mutThr ) return;
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$this->mutThr = $mutThr;
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}
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/************************************************
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**** Sample Setters ****
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************************************************/
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/* SET SAMPLE INPUT/OUTPUT MAX VALUES
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*
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* @input<Array> Sample input-ordered-like max values
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* @output<Array> Sample output-ordered-like max values
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*
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*/
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public function setMaxValues($input, $output){
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if( !is_array($input) || !is_array($output) ) return;
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$this->max = [
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'input' => $input,
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'output' => $output,
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];
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}
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/* ADD SAMPLE TO THE NEURAL NETWORK
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*
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* @input<Array> Sample's input values
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* @output<Array> Sample's output values
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*
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* @return added<Boolean> If sample has been added
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*
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*/
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public function addSample($input, $output){
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/* [1] Check min/max values
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=========================================================*/
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/* (1) If not set yet */
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if( is_null($this->max) )
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throw new Error('You must use setMaxValues() before adding samples.');
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/* (2) Checking each input */
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foreach($input as $i=>$value){
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// {2.1} If no max for this value //
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if( !isset($this->max['input'][$i]) )
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return false;
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// {2.2} Checks value //
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if( $value > $this->max['input'][$i] )
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return false;
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}
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/* (3) Checking each output */
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foreach($output as $i=>$value){
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// {2.1} If no max for this value //
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if( !isset($this->max['output'][$i]) )
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return false;
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// {2.2} Checks value //
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if( $value > $this->max['output'][$i] )
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return false;
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}
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/* [2] Adds values
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=========================================================*/
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FileManager::append( $this->storage[1]['filename'], implode(',',$input) .';'. implode(',',$output) );
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return true;
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}
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}
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|
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|
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|
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/************************************************
|
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**** USE CASE ****
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************************************************/
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$use_case = false;
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if( $use_case ){
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/* (1) Instanciate and configure neural network
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---------------------------------------------------------*/
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/* (1) Initialize neural network to try 100 genomes over 50 generations (max) */
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$neunet = NeuralNetwork::create(100, 50);
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/* (2) Specifies that a fitness greater than 0.89 will stop the neural network */
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$neunet->setFitnessEnd(0.89);
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/* (3) Specifies that it will keep the 2 best genomes of each generation */
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$neunet->setKeptGenomes(2);
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/* (4) Specifies that for each mutation, it must be at maximum 30% different */
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$neunet->setMutationThreshold(0.4);
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||||
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||||
|
||||
/* (2) Let's learn
|
||||
---------------------------------------------------------*/
|
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/* (1) Set dataset sample maximum values */
|
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$neunet->setMaxValues([2,2,7], [10, 10]);
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// input output
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||||
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||||
/* (2) Setting our example dataset */
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||||
$neunet->addSample([1.9, 2, 5.3], [6, 0]);
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||||
$neunet->addSample([1.2, 1.2, 0.1], [0, 2]);
|
||||
$neunet->addSample([0, 0, 0], [5, 5]);
|
||||
|
||||
/* (3) Launch learning routine with callback function */
|
||||
$neunet->learn(function($ctx){ // callback with @ctx the current context
|
||||
echo 'generation '. $ctx['generation'] .'/'. $ctx['generations'] ."\n";
|
||||
echo 'genome '. $ctx['genome'] .'/'. $ctx['genomes'] ."\n";
|
||||
echo 'fitness: '. $ctx['fitness'] .'/'. $ctx['fitness_end']. "\n";
|
||||
});
|
||||
|
||||
|
||||
/* (3) What to do next ?
|
||||
---------------------------------------------------------*/
|
||||
/* (1) Store your data */
|
||||
$neunet->store('/some/path/to/filename');
|
||||
|
||||
/* (2) [HIDDEN] will create those 2 files */
|
||||
'/some/path/to/filename.nn'; // containing neural network weights
|
||||
'/some/path/to/filename.ex'; // containing neural network samples
|
||||
|
||||
|
||||
/* (4) And next ?
|
||||
---------------------------------------------------------*/
|
||||
/* (1) Load your stored neural network */
|
||||
$trainednn = NeuralNetwork::load('/some/path/to/filename');
|
||||
|
||||
/* (2) Use it to guess output, with well-formed input */
|
||||
$guessed = $trainednn->guess([1.2, 0.9, 6.1]);
|
||||
|
||||
/* (3) And correct it if needed*/
|
||||
echo "1: ". $guessed[0] ."\n";
|
||||
echo "2: ". $guessed[2] ."\n";
|
||||
echo "is it correct ?\n";
|
||||
// .. some code to manage and read correct output if it was wrong
|
||||
$trainednn->addSample([1.2, 0.9, 6.1], [9, 0]);
|
||||
|
||||
// You can now relaunch a new neural network's learning
|
||||
// or You can just add a few generations to evolve your neural network
|
||||
|
||||
}
|
||||
|
||||
?>
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
0,0,0;0
|
||||
0,0,1;1
|
||||
0,1,0;0
|
||||
0,1,1;1
|
||||
1,0,0;0
|
||||
1,0,1;1
|
||||
1,1,0;1
|
||||
1,1,1;0
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
{
|
||||
"storage_parent": "/build/neuralnetwork/storage",
|
||||
|
||||
"default": {
|
||||
"genomes_kept": 2,
|
||||
"mutation_threshold": 0.3,
|
||||
"fitness_end": 1,
|
||||
"storage": "_buffer"
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1 @@
|
|||
aa
|
||||
|
|
@ -0,0 +1,29 @@
|
|||
<?php define('__ROOT__', dirname(dirname(__FILE__)) );
|
||||
|
||||
require_once __ROOT__.'/autoloader.php';
|
||||
|
||||
use \neuralnetwork\core\NeuralNetwork;
|
||||
use \filemanager\core\FileManager;
|
||||
|
||||
function behaviour($abc){
|
||||
return [($abc[0] & $abc[1]) ^ $abc[2]];
|
||||
}
|
||||
|
||||
echo "Welcome to neural-network.php\n";
|
||||
echo "-----------------------------\n";
|
||||
|
||||
$nn = NeuralNetwork::create(50, 100);
|
||||
|
||||
$nn->setMaxValues([1, 1, 1], [1]);
|
||||
|
||||
$d = [0, 0, 0]; $nn->addSample($d, behaviour($d));
|
||||
$d = [0, 0, 1]; $nn->addSample($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));
|
||||
|
||||
|
||||
?>
|
||||
Loading…
Reference in New Issue