83 lines
2.0 KiB
PHP
83 lines
2.0 KiB
PHP
<?php define('__ROOT__', dirname(dirname(__FILE__)) );
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require_once __ROOT__.'/autoloader.php';
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use \neuralnetwork\core\Genome;
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use \neuralnetwork\core\NeuralNetwork;
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use \filemanager\core\FileManager;
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if( false && 'test_creating_dataset' ){
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function behaviour($abc){
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return [($abc[0] & $abc[1]), $abc[1] | $abc[2]];
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}
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echo "Welcome to neural-network.php\n";
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echo "-----------------------------\n";
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$nn = NeuralNetwork::create(50, 100);
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$nn->setMaxValues([1, 1, 1], [1, 1]);
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$nn->setHiddenLayersCount(2);
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$nn->setHiddenLayerNeuronsCount(3);
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$d = [0, 0, 0]; $nn->addSample($d, behaviour($d));
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$d = [0, 0, 1]; $nn->addSample($d, behaviour($d));
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$d = [0, 1, 0]; $nn->addSample($d, behaviour($d));
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$d = [0, 1, 1]; $nn->addSample($d, behaviour($d));
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$d = [1, 0, 0]; $nn->addSample($d, behaviour($d));
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$d = [1, 0, 1]; $nn->addSample($d, behaviour($d));
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$d = [1, 1, 0]; $nn->addSample($d, behaviour($d));
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$d = [1, 1, 1]; $nn->addSample($d, behaviour($d));
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$nn->store('test/test1', true);
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}
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if( false && 'load_neural_network' ){
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$nn = NeuralNetwork::load('test/test1');
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}
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if( false && 'test_genomes' ){
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/* (1) Basic Creation */
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$a = new Genome(2, 3); // 2 layers of 3 neurons each -> randomly filled
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echo "A : ".$a->serialize()."\n";
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/* (2) Inheritance */
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$b = new Genome($a); // Clone of @aecho "A neurons\n";
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echo "cloning A to B\n";
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echo "B : ".$b->serialize()."\n";
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/* (3) Section Title */
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$b->mutation(0.3); // @b has now mutated with a threshold of 30%
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echo "mutate B\n";
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echo "B : ".$b->serialize()."\n";
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/* (4) Cross-over (father+mother) */
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$c = new Genome($a, $b); // @c is a randomly-done mix of @a and @b
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echo "crossover : A+B -> C\n";
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echo "C : ".$c->serialize()."\n";
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}
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if( true ){
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$g = new Genome(2, 3);
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echo $g->process([1, 1, 0]);
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}
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// REWRITE TEST
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// for( $a = 0, $al = 50 ; $a < $al ; $a++ )
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// for( $b = 0, $bl = 20 ; $b < $bl ; $b++ ){
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// print "genome $b/$bl on generation $a/$al \r";
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// usleep(1000*10);
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// }
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?>
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