xdrm-brackets
/
neuralnet.php
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Management of genome's training with callback + fitness user-defined + generation&genomes management to do in NeuralNetworkCore::learn()

This commit is contained in:
xdrm-brackets 2016-10-27 14:47:08 +02:00
parent db41a36af4
commit e16048b569
5 changed files with 235 additions and 236 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

245
build/neuralnetwork/core/Genome.php
View File

@ -15,9 +15,17 @@
/************************************************ /************************************************
**** LOCAL ATTRIBUTES **** **** LOCAL ATTRIBUTES ****
************************************************/ ************************************************/
public $layers; // Number of layers private $layers; // Number of hidden layers
public $neurons; // Number of neurons per layer
public $synapses; // Synapses between neurons private $inputN; // Number of input neurons
private $neurons; // Number of neurons for each hidden layer
private $outputN; // Number of output neurons
private $synapses; // Synapses between neurons
private $callback; // callback training function
private $fitness; // Genome's fitness
/************************************************ /************************************************
@ -27,8 +35,10 @@
/* CONSTRUCTOR /* CONSTRUCTOR
* *
* -- RANDOM CREATION -- * -- RANDOM CREATION --
* @layers<int> Number of layers to manage * @layers<int> Number of hidden layers to manage
* @neurons<int> Number of neurons per layer * @neurons<int> Number of neurons per hidden layer
* @inN<int> Number of input neurons
* @outN<int> Number of output neurons
* *
* -- CLONING -- * -- CLONING --
* @base<Genom> Genome to clone to * @base<Genom> Genome to clone to
@ -50,8 +60,8 @@
$this->construct_crossover($argv[0], $argv[1]); $this->construct_crossover($argv[0], $argv[1]);
/* (3) If RandomCreation */ /* (3) If RandomCreation */
else if( $argc > 1 && abs(intval($argv[0])) === $argv[0] && abs(intval($argv[1])) === $argv[1] ) else if( $argc > 3 && is_numeric($argv[0]) && is_numeric($argv[1]) && is_numeric($argv[2]) && is_numeric($argv[3]) )
$this->construct_new($argv[0], $argv[1]); $this->construct_new($argv[0], $argv[1], $argv[2], $argv[3]);
/* (4) If InheritanceCreation (clone) */ /* (4) If InheritanceCreation (clone) */
else if( $argc > 0 && $argv[0] instanceof Genome ) else if( $argc > 0 && $argv[0] instanceof Genome )
@ -60,19 +70,23 @@
/* (5) If no match */ /* (5) If no match */
else else
throw new \Error('Invalid Genome constructor\'s arguments.'); throw new \Error('Invalid Genome constructor\'s arguments.');
$this->callback = function(){};
} }
/* BUILDS A Genome RANDOMLY WITH PARAMETERS /* BUILDS A Genome RANDOMLY WITH PARAMETERS
* *
* @layers<int> The number of hidden layers to manage * @layers<int> The number of hidden layers to manage
* @neurons<int> The number neurons per layer * @neurons<int> The number neurons per layer
* @inN<int> Number of input neurons
* @outN<int> Number of output neurons
* *
* @return created<Boolean> If Genome has been successfully created * @return created<Boolean> If Genome has been successfully created
* *
*/ */
private function construct_new($layers=-1, $neurons=-1){ private function construct_new($layers, $neurons, $inN, $outN){
/* (1) Checks parameters */ /* (1) Checks parameters */
if( abs(intval($layers)) !== $layers || abs(intval($neurons)) !== $neurons ) if( abs(intval($layers)) !== $layers || abs(intval($neurons)) !== $neurons || abs(intval($inN)) !== $inN || abs(intval($outN)) !== $outN )
return false; return false;
// set layers // set layers
@ -80,10 +94,12 @@
/* (2) Store number of neurons */ /* (2) Store number of neurons */
$this->neurons = $neurons; $this->neurons = $neurons;
$this->inputN = $inN;
$this->outputN = $outN;
/* (3) Creating random synapses */ /* (3) Creating random synapses */
$this->synapses = []; $this->synapses = [];
for( $i = 0, $l = $layers*pow($neurons,2) ; $i < $l ; $i++ ) for( $i = 0, $l = $neurons*($inN+$outN+$neurons*$layers) ; $i < $l ; $i++ )
$this->synapses[$i] = rand(self::MIN, self::MAX) / self::MAX; $this->synapses[$i] = rand(self::MIN, self::MAX) / self::MAX;
// Success status // Success status
@ -124,19 +140,26 @@
if( !($father instanceof Genome) || !($mother instanceof Genome) ) if( !($father instanceof Genome) || !($mother instanceof Genome) )
return false; return false;
/* (2) Checks number of layers+neurons (same species) */ /* (2) Checks the attributes (same species) */
if( $father->layers !== $mother->layers || $father->neurons !== $mother->neurons ) $diffAttr = $father->layers !== $mother->layers;
$diffAttr = $diffAttr || $father->inputN !== $mother->inputN;
$diffAttr = $diffAttr || $father->neurons !== $mother->neurons;
$diffAttr = $diffAttr || $father->outputN !== $mother->outputN;
if( $diffAttr )
return false; return false;
/* (3) Set layer count */ /* (3) Set layer count */
$this->layers = $father->layers; $this->layers = $father->layers;
/* (4) Set neurons number */ /* (4) Set neurons number */
$this->inputN = $father->inputN;
$this->neurons = $father->neurons; $this->neurons = $father->neurons;
$this->outputN = $father->inputN;
/* (5) Do random crossover for synapses */ /* (5) Do random crossover for synapses */
$this->synapses = []; $this->synapses = [];
for( $i = 0, $l = $this->layers*pow($this->neurons, 2) ; $i < $l ; $i++ ) for( $i = 0, $l = $this->neurons*($this->inputN+$this->outputN+$this->neurons*$this->layers) ; $i < $l ; $i++ )
if( !!rand(0,1) ) $this->synapses[$i] = $father->synapses[$i]; if( !!rand(0,1) ) $this->synapses[$i] = $father->synapses[$i];
else $this->synapses[$i] = $mother->synapses[$i]; else $this->synapses[$i] = $mother->synapses[$i];
@ -145,6 +168,39 @@
} }
/************************************************
**** Setters ****
************************************************/
/* SETS THE CALLBACK FUNCTION
*
* @callback<Function> Callback function to train with
*
*/
public function setCallback($callback=null){
/* (1) Checks @callback argument */
if( !is_callable($callback) )
throw new \Error('Wrong argument for Genome\'s callback function.');
/* (2) Set callback function */
$this->callback = $callback;
}
/* SETS THE FITNESS OF THE GENOME
*
* @fitness<double> Calculated fitness of the genome
*
*/
public function setFitness($fitness=null){
/* (1) Checks @fitness argument */
if( !is_numeric($fitness) )
throw new \Error('Wrong argument for specifying Genome\'s fitness.');
/* (2) Set fitness */
$this->fitness = floatval($fitness);
}
/************************************************ /************************************************
**** Genome Actions **** **** Genome Actions ****
************************************************/ ************************************************/
@ -160,19 +216,9 @@
throw new \Error('Invalid threshold for Genome mutation.'); throw new \Error('Invalid threshold for Genome mutation.');
/* (2) Calculates how many neurons/synapses to mutate */ /* (2) Calculates how many neurons/synapses to mutate */
$neuronsMutations = round( (count($this->neurons) - 1) * $threshold );
$synapsesMutations = round( (count($this->synapses) - 1) * $threshold ); $synapsesMutations = round( (count($this->synapses) - 1) * $threshold );
/* (3) Choose random neurons' indexes */ /* (3) Choose random synapses' indexes */
$iNeurons = [];
while( count($iNeurons) < $neuronsMutations ){
$r = rand(0, count($this->neurons)-1);
if( !in_array($r, $iNeurons) )
$iNeurons[] = $r;
}
/* (4) Choose random synapses' indexes */
$iSynapses = []; $iSynapses = [];
while( count($iSynapses) < $synapsesMutations ){ while( count($iSynapses) < $synapsesMutations ){
$r = rand(0, count($this->synapses)-1); $r = rand(0, count($this->synapses)-1);
@ -181,11 +227,7 @@
$iSynapses[] = $r; $iSynapses[] = $r;
} }
/* (5) Update chosen neurons */ /* (4) Update chosen synapses */
for( $i = 0, $l = count($iNeurons) ; $i < $l ; $i++ )
$this->neurons[$iNeurons[$i]] = rand(self::MIN, self::MAX) / self::MAX;
/* (6) Update chosen synapses */
for( $i = 0, $l = count($iSynapses) ; $i < $l ; $i++ ) for( $i = 0, $l = count($iSynapses) ; $i < $l ; $i++ )
$this->synapses[$iSynapses[$i]] = rand(self::MIN, self::MAX) / self::MAX; $this->synapses[$iSynapses[$i]] = rand(self::MIN, self::MAX) / self::MAX;
} }
@ -193,20 +235,25 @@
/* CALCULATES THE OUTPUT OF THE GENOME /* CALCULATES THE OUTPUT OF THE GENOME
* *
* @input<Array> Input for which we want fitness * @input<Array> Input for which we want fitness
* @callback<Function> [OPTIONAL] Callback function
* *
* @return output<double> Output calculated with this input * @note: will call @callback function with INPUTS as 1st argument and OUTPUTS as 2nd arguments
* *
*/ */
public function process($input=null){ public function train($input=null, $callback=null){
/* (1) Checks @input argument */ /* (1) Checks @input argument */
if( !is_array($input) || count($input) !== $this->neurons ) if( !is_array($input) || count($input) !== $this->inputN )
throw new \Error('Invalid @input for Genome\'s output calculation.'); throw new \Error('Invalid @input for Genome\'s training.');
/* (2) Checks optional @callback argument */
if( is_callable($callback) )
$this->setCallback($callback);
/* [1] Set temporary calculation data /* [1] Set temporary calculation data
=========================================================*/ =========================================================*/
/* (1) Set temporary neurons data */ /* (1) Set temporary neurons data */
$neurons = array_merge( $input, array_fill(0, $this->neurons*$this->layers, 0) ); $neurons = array_merge( $input, array_fill(0, $this->neurons*$this->layers, 0), array_fill(0, $this->outputN, 0) );
/* (2) Set temporary synapses data */ /* (2) Set temporary synapses data */
$synapses = $this->synapses; $synapses = $this->synapses;
@ -216,39 +263,122 @@
=========================================================*/ =========================================================*/
/* (1) For each hidden layer /* (1) For each hidden layer
---------------------------------------------------------*/ ---------------------------------------------------------*/
for( $l = 1 ; $l < $this->layers+1 ; $l++ ){ foreach($this->layersIterator() as $lr=>$l){
/* (2) For each neuron of this layer /* (2) For each neuron of this layer
---------------------------------------------------------*/ ---------------------------------------------------------*/
for( $n = $l*$this->neurons, $nl = ($l+1)*$this->neurons ; $n < $nl ; $n++ ){ foreach($this->neuronsIterator($l) as $nr=>$n){
$neurons[$n] = 0; $neurons[$n] = 0;
/* (3) For each synapse between current neuron and last layer /* (3) For each synapse between current neuron and last layer
---------------------------------------------------------*/ ---------------------------------------------------------*/
for( $s = ($l-1)*pow($this->neurons,2), $sl = $l*pow($this->neurons,2) ; $s < $sl ; $s++ ) foreach($this->synapsesIterator($l, $nr) as $sr=>$s){
$neurons[$n] += $synapses[$s] * $neurons[ floor($s/$this->neurons) ]; // echo "current: n#$nr/l#$l = s#$s * n#$sr\n";
// newNeuron += synapse*lastLayerNeuron // echo "calc: ${neurons[$n]} += ${synapses[$s]} * ${neurons[$sr]}\n";
$neurons[$n] += $synapses[$s] * $neurons[$sr];
/* Divide the sum to make a mean */ // echo "result: ${neurons[$n]}\n\n";
$neurons[$n] /= $this->neurons;
} }
} }
/* (4) Calculates single output
---------------------------------------------------------*/
$output = 0;
for( $n = ($this->layers-1)*$this->neurons, $nl = $this->layers*$this->neurons ; $n < $nl ; $n++ )
$output += $neurons[$n];
/* [3] Returns output }
/* [3] Callback the output layer's values
=========================================================*/ =========================================================*/
return $output / $this->neurons; call_user_func($this->callback, $input, array_slice($neurons, -$this->outputN) );
} }
/************************************************
**** Utilities ****
************************************************/
/* RETURNS ITERATOR THROUGH EACH LAYER
*
* @return indexes<yield> Iterator through each layer (relative->absolute)
*
*/
private function layersIterator(){
for( $l = 1 ; $l < $this->layers+2 ; $l++ )
yield $l => $l;
}
/* RETURNS AN ITERATOR ON THE NEURONS OF A LAYER
*
* @layer<int> Layer to browse
*
* @return indexes<yield> Iterator through the neurons indexes of the layer (relative->absolute)
*
*/
private function neuronsIterator($layer){
/* (1) Formats @layer argument */
if( $layer < 0 ) $layer = 0;
if( $layer > $this->layers+1 ) $layer = $this->layers+1;
$layer = intval($layer);
/* (2) If between input/hidden */
if( $layer === 0 ){
$offset = 0;
for( $n = $offset, $nl = $this->inputN ; $n < $nl ; $n++ )
yield $n-$offset => $n;
/* (3) If between hidden/output */
}else if( $layer == $this->layers+1 ){
$offset = $this->inputN + $this->neurons * $this->layers;
for( $n = $offset, $nl = $this->inputN + $this->neurons * $this->layers + $this->outputN ; $n < $nl ; $n++ )
yield $n-$offset => $n;
/* (2) If in between hidden layer */
}else{
$offset = $this->inputN + $this->neurons * ($layer-1);
for( $n = $offset, $nl = $this->inputN+$this->neurons*$layer ; $n < $nl ; $n++ )
yield $n-$offset => $n;
}
}
/* RETURNS AN ITERATOR ON THE SYNAPSES BETWEEN A NEURON AND ITS PREVIOUS LAYER'S NEURONS
*
* @layer<int> Destination layer
* @neuron<int> Destination neuron
*
* @return indexes<yield> Iterator through the synapses indexes between the destination neuron and the source layer's neurons (neuronIndex->SynapseIndex)
*
*/
private function synapsesIterator($layer, $neuron){
/* (1) Formats @layer argument */
if( $layer < 1 ) $layer = 1;
if( $layer > $this->layers+1 ) $layer = $this->layers+1;
$layer = intval($layer);
$prev = $layer-1;
/* (2) If between input/hidden */
if( $layer === 1 ){
$offset = 0;
for( $s = $offset+$neuron, $sl = $this->inputN*$this->neurons ; $s < $sl ; $s += $this->neurons )
yield ($s-$offset-$neuron)/$this->neurons => $s;
/* (3) If between hidden/output */
}else if( $layer == $this->layers+1 ){
$offset = $this->neurons * ($this->inputN+$this->neurons*$this->layers);
for( $s = $offset+$neuron, $sl = $offset+$this->neurons*$this->outputN ; $s < $sl ; $s += $this->outputN )
yield $this->inputN+$this->neurons*($this->layers-1) + ($s-$offset-$neuron)/$this->outputN => $s;
/* (2) If in between hidden layer */
}else{
$offset = $this->neurons * ($this->inputN+$this->neurons*($prev-1));
for( $s = $offset+$neuron, $sl = $offset+$this->neurons*$this->neurons ; $s < $sl ; $s += $this->neurons )
yield $this->inputN+$this->neurons*($prev-1) + ($s-$offset-$neuron)/$this->neurons => $s;
}
}
/************************************************ /************************************************
**** Serialization **** **** Serialization ****
************************************************/ ************************************************/
@ -263,7 +393,7 @@
$csv = ''; $csv = '';
/* (2) Adds global attributes */ /* (2) Adds global attributes */
$csv .= $this->layers .','. $this->neurons .';'; $csv .= $this->layers .','. $this->inputN .','. $this->neurons .','. $this->outputN .';';
/* (3) Adds synapses data */ /* (3) Adds synapses data */
$csv .= implode(',', $this->synapses); $csv .= implode(',', $this->synapses);
@ -271,7 +401,6 @@
return $csv; return $csv;
} }
/* BUILDS A Genome BASED ON HIS SERIALIZED REPRESENTATION /* BUILDS A Genome BASED ON HIS SERIALIZED REPRESENTATION
* *
* @serialized<String> Serialized representation of a Genome * @serialized<String> Serialized representation of a Genome
@ -286,13 +415,15 @@
throw new \Error('Format error during Genome unserialization.'); throw new \Error('Format error during Genome unserialization.');
/* (2) Get global attributes */ /* (2) Get global attributes */
$global = explode(',', $segments[0]); $globals = explode(',', $segments[0]);
if( count($global) < 2 ) if( count($globals) < 4 )
throw new \Error('Format error during Genome unserialization.'); throw new \Error('Format error during Genome unserialization.');
$this->layers = intval($global[0]); $this->layers = intval($globals[0]);
$this->layers = intval($global[1]); $this->inputN = intval($globals[1]);
$this->layers = intval($globals[2]);
$this->outputN = intval($globals[3]);
/* (3) Get synapses values */ /* (3) Get synapses values */
$this->synapses = explode(',', $segments[1]); $this->synapses = explode(',', $segments[1]);
@ -310,7 +441,7 @@
if( $use_case ){ if( $use_case ){
/* (1) Basic Creation */ /* (1) Basic Creation */
$a = new Genome(2, 3); // 2 layers of 3 neurons each -> randomly filled $a = new Genome(2, 3, 3, 2); // 2 layers of 3 neurons each -> randomly filled + 3 input neurons + 2 output neurons
/* (2) Inheritance */ /* (2) Inheritance */
$b = new Genome($a); // Clone of @a $b = new Genome($a); // Clone of @a

59
build/neuralnetwork/core/NeuralNetworkCore.php
View File

@ -17,7 +17,6 @@
private $numHid; // Number of hidden layer(s) private $numHid; // Number of hidden layer(s)
private $numNeu; // Number of neurons for each hidden layer private $numNeu; // Number of neurons for each hidden layer
private $max; // max values for input and output neurons
private $storage; // path to storage private $storage; // path to storage
/************************************************ /************************************************
@ -221,63 +220,9 @@
**** Sample Setters **** **** Sample Setters ****
************************************************/ ************************************************/
/* SET SAMPLE INPUT/OUTPUT MAX VALUES
*
* @input<Array> Sample input-ordered-like max values
* @output<Array> Sample output-ordered-like max values
*
*/
public function setMaxValues($input, $output){
if( !is_array($input) || !is_array($output) ) return;
$this->max = [
'input' => $input,
'output' => $output,
];
}
/* ADD SAMPLE TO THE NEURAL NETWORK /* ADD SAMPLE TO THE NEURAL NETWORK
*
* @input<Array> Sample's input values
* @output<Array> Sample's output values
*
* @return added<Boolean> If sample has been added
*
*/
public function addSample($input, $output){ public function addSample($input, $output){
/* [1] Check min/max values
=========================================================*/
/* (1) If not set yet */
if( is_null($this->max) )
throw new Error('You must use setMaxValues() before adding samples.');
/* (2) Checking each input */
foreach($input as $i=>$value){
// {2.1} If no max for this value //
if( !isset($this->max['input'][$i]) )
return false;
// {2.2} Checks value //
if( $value > $this->max['input'][$i] )
return false;
}
/* (3) Checking each output */
foreach($output as $i=>$value){
// {2.1} If no max for this value //
if( !isset($this->max['output'][$i]) )
return false;
// {2.2} Checks value //
if( $value > $this->max['output'][$i] )
return false;
}
/* [2] Adds values
=========================================================*/
FileManager::append( $this->storage[1]['filename'], implode(',',$input) .';'. implode(',',$output) ); FileManager::append( $this->storage[1]['filename'], implode(',',$input) .';'. implode(',',$output) );
return true;
} }
@ -344,7 +289,7 @@
/* [2] Creates the neural network /* [2] Creates the neural network
=========================================================*/ =========================================================*/
// best of last generation // best of last generation
$best = new Genome($this->numHid, $this->numNeu); $best = null;
/* (1) For each generation /* (1) For each generation
@ -401,7 +346,6 @@
$json['numHid'] = $this->numHid; $json['numHid'] = $this->numHid;
$json['numNeu'] = $this->numNeu; $json['numNeu'] = $this->numNeu;
$json['max'] = $this->max;
$json['storage'] = $this->storage; $json['storage'] = $this->storage;
/* (3) Returns serialized string */ /* (3) Returns serialized string */
@ -430,7 +374,6 @@
$this->numHid = $json['numHid']; $this->numHid = $json['numHid'];
$this->numNeu = $json['numNeu']; $this->numNeu = $json['numNeu'];
$this->max = $json['max'];
$this->storage = $json['storage']; $this->storage = $json['storage'];
} }

114
build/neuralnetwork/core/NeuralNetworkManager.php
View File

@ -1,114 +0,0 @@
<?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
}
?>

1
config/test_long_file.txt
View File

@ -1 +0,0 @@
aa

52
public/main.php
View File

@ -6,19 +6,18 @@
use \neuralnetwork\core\NeuralNetwork; use \neuralnetwork\core\NeuralNetwork;
use \filemanager\core\FileManager; use \filemanager\core\FileManager;
if( false && 'test_creating_dataset' ){
function behaviour($abc){ function behaviour($abc){
return [($abc[0] & $abc[1]), $abc[1] | $abc[2]]; return [($abc[0] & $abc[1]), $abc[1] | $abc[2]];
} }
if( false && 'test_creating_dataset' ){
echo "Welcome to neural-network.php\n"; echo "Welcome to neural-network.php\n";
echo "-----------------------------\n"; echo "-----------------------------\n";
$nn = NeuralNetwork::create(50, 100); $nn = NeuralNetwork::create(50, 100);
$nn->setMaxValues([1, 1, 1], [1, 1]);
$nn->setHiddenLayersCount(2); $nn->setHiddenLayersCount(2);
$nn->setHiddenLayerNeuronsCount(3); $nn->setHiddenLayerNeuronsCount(3);
@ -30,6 +29,14 @@
$d = [1, 0, 1]; $nn->addSample($d, behaviour($d)); $d = [1, 0, 1]; $nn->addSample($d, behaviour($d));
$d = [1, 1, 0]; $nn->addSample($d, behaviour($d)); $d = [1, 1, 0]; $nn->addSample($d, behaviour($d));
$d = [1, 1, 1]; $nn->addSample($d, behaviour($d)); $d = [1, 1, 1]; $nn->addSample($d, behaviour($d));
$d = [0, 0, 0]; $nn->addSample($d, behaviour($d));
$d = [0, 0, 2]; $nn->addSample($d, behaviour($d));
$d = [0, 2, 0]; $nn->addSample($d, behaviour($d));
$d = [0, 2, 2]; $nn->addSample($d, behaviour($d));
$d = [2, 0, 0]; $nn->addSample($d, behaviour($d));
$d = [2, 0, 2]; $nn->addSample($d, behaviour($d));
$d = [2, 2, 0]; $nn->addSample($d, behaviour($d));
$d = [2, 2, 2]; $nn->addSample($d, behaviour($d));
$nn->store('test/test1', true); $nn->store('test/test1', true);
@ -67,9 +74,42 @@
if( true ){ if( true ){
$g = new Genome(2, 3); $g = new Genome(2, 3, 3, 2);
$fitness = 0;
$g->setCallback(function($input, $output){
global $fitness;
echo "callback output: ".round($output[0]).", ".round($output[1])."\n";
$result = behaviour($input);
if( $output[0] == $result[0] )
$fitness++;
echo $g->process([1, 1, 0]); if( $output[1] == $result[1] )
$fitness++;
});
echo $g->train([0, 0, 0]);
echo $g->train([0, 0, 1]);
echo $g->train([0, 1, 0]);
echo $g->train([0, 1, 1]);
echo $g->train([1, 0, 0]);
echo $g->train([1, 0, 1]);
echo $g->train([1, 1, 0]);
echo $g->train([1, 1, 1]);
echo $g->train([0, 0, 0]);
echo $g->train([0, 0, 2]);
echo $g->train([0, 2, 0]);
echo $g->train([0, 2, 2]);
echo $g->train([2, 0, 0]);
echo $g->train([2, 0, 2]);
echo $g->train([2, 2, 0]);
echo $g->train([2, 2, 2]);
echo "fitness: $fitness\n";
$g->setFitness($fitness);
echo $g->serialize();
} }
// REWRITE TEST // REWRITE TEST