<?php

	namespace neuralnetwork\core;

	use filemanager\core\FileManager;

	class NeuralNetworkCore implements \Serializable{

		/************************************************
		****            GLOBAL ATTRIBUTES            ****
		************************************************/
		private $maxGnr; // Maximum generation iterations
		private $maxGnm; // Maximum genomes per generation
		private $kptGnm; // Number of genomes kept for each generation
		private $mutThr; // Mutation threshold
		private $fitEnd; // Fitness range to end process
		private $numHid; // Number of hidden layer(s)
		private $numNeu; // Number of neurons for each hidden layer

		private $max;     // max values for input and output neurons
		private $storage; // path to storage

		/************************************************
		****             LOCAL ATTRIBUTES            ****
		************************************************/
		private $gnr;  // Current generation index
		private $gmns; // Current generation's genomes
		private $gnm;  // Current genome index
		private $fit;  // Current fitness


	}


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

		/* (1) Instanciate and configure neural network
		---------------------------------------------------------*/
		/* (1) Initialize neural network to try 100 genomes over 50 generations (max) */
		$neunet = NeuralNetwork::create(100, 50);

		/* (2) Specifies that a fitness greater than 0.89 will stop the neural network */
		$neunet->setFitnessEnd(0.89);

		/* (3) Specifies that it will keep the 2 best genomes of each generation */
		$neunet->setKeptGenomes(2);

		/* (4) Specifies that for each mutation, it must be at maximum 30% different */
		$neunet->setMutationThreshold(0.4);

		/* (5) Specifies the number of hidden layers */
		$neunet->setHiddenLayersCount(2);

		/* (6) Specifies the number of neurons per hidden layer */
		$neunet->setHiddenLayerNeuronsCount(4);


		/* (2) Let's learn
		---------------------------------------------------------*/
		/* (1) Set dataset sample maximum values */
		$neunet->setMaxValues([2,2,7], [10, 10]);
		//                    input    output

		/* (2) Setting our example dataset */
		$neunet->addSample([1.9, 2,   5.3], [6, 0]);
		$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/storage');

		/* (2) [HIDDEN] will create those 2 files */
		'/some/path/to/storage.nn'; // containing neural network configuration
		'/somt/path/to/storage.ln'; // containing neural network learnt values & weights
		'/some/path/to/storage.ex'; // containing neural network samples


		/* (4) And next ?
		---------------------------------------------------------*/
		/* (1) Load your stored neural network */
		$trainednn = NeuralNetwork::load('/some/path/to/storage');

		/* (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

	}

?>