setMaxValues([1, 1, 1], [1, 1]); $nn->setHiddenLayersCount(2); $nn->setHiddenLayerNeuronsCount(3); $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)); $nn->store('test/test1', true); } if( false && 'load_neural_network' ){ $nn = NeuralNetwork::load('test/test1'); } if( false && 'test_genomes' ){ /* (1) Basic Creation */ $a = new Genome(2, 3); // 2 layers of 3 neurons each -> randomly filled echo "A : ".$a->serialize()."\n"; /* (2) Inheritance */ $b = new Genome($a); // Clone of @aecho "A neurons\n"; echo "cloning A to B\n"; echo "B : ".$b->serialize()."\n"; /* (3) Section Title */ $b->mutation(0.3); // @b has now mutated with a threshold of 30% echo "mutate B\n"; echo "B : ".$b->serialize()."\n"; /* (4) Cross-over (father+mother) */ $c = new Genome($a, $b); // @c is a randomly-done mix of @a and @b echo "crossover : A+B -> C\n"; echo "C : ".$c->serialize()."\n"; } if( true ){ $g = new Genome(2, 3); echo $g->process([1, 1, 0]); } // REWRITE TEST // for( $a = 0, $al = 50 ; $a < $al ; $a++ ) // for( $b = 0, $bl = 20 ; $b < $bl ; $b++ ){ // print "genome $b/$bl on generation $a/$al \r"; // usleep(1000*10); // } ?>