function testLayers()
{
$newLayer = new Layer(1);
$this->network->addLayer($newLayer);
$layersCount = count($this->network->getLayers());
$this->assertEquals($layersCount, 3);
$this->assertEquals($this->network->getLayers()[$layersCount - 1], $this->network->getOutputLayer());
$this->assertEquals($this->network->getOutputLayer(), $newLayer);
}
<?php
use Neural\Layer;
use Neural\MultilayerPerceptron;
use Neural\Node\Bias;
use Neural\Node\Input;
use Neural\Node\Neuron;
use Neural\Synapse;
require_once '../vendor/autoload.php';
$p = new MultilayerPerceptron([2, 2, 1]);
//Equivalent to:
$p = new MultilayerPerceptron();
$p->addLayer(new Layer())->toLastLayer()->addNode(new Input())->addNode(new Input())->addNode(new Bias());
$p->addLayer(new Layer())->toLastLayer()->addNode(new Neuron())->addNode(new Neuron())->addNode(new Bias());
$p->addLayer(new Layer())->toLastLayer()->addNode(new Neuron());
//Do not forget to add synapses:
$p->generateSynapses();
//Or you may direct the process:
$neuronFilter = function ($node) {
return $node instanceof Neuron;
};
/** @var Neuron $secondLayerNeuron */
$secondLayerNeuron = iterator_to_array($p->getLayers()[1]->getNodes($neuronFilter))[0];
$input = iterator_to_array($p->getLayers()[0]->getNodes())[0];
$secondLayerNeuron->addSynapse(new Synapse($input));
//and so on...
