2016-08-05 14:12:39 +00:00
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<?php
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2016-11-20 21:53:17 +00:00
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declare(strict_types=1);
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2016-08-05 14:12:39 +00:00
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namespace Phpml\NeuralNetwork\Network;
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2017-05-17 22:07:14 +00:00
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use Phpml\Estimator;
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2016-08-09 11:27:43 +00:00
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use Phpml\Exception\InvalidArgumentException;
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2017-11-06 07:56:37 +00:00
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use Phpml\Helper\Predictable;
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use Phpml\IncrementalEstimator;
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2016-08-11 11:21:22 +00:00
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use Phpml\NeuralNetwork\ActivationFunction;
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2016-08-09 11:27:43 +00:00
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use Phpml\NeuralNetwork\Layer;
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use Phpml\NeuralNetwork\Node\Bias;
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use Phpml\NeuralNetwork\Node\Input;
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use Phpml\NeuralNetwork\Node\Neuron;
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use Phpml\NeuralNetwork\Node\Neuron\Synapse;
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2017-11-06 07:56:37 +00:00
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use Phpml\NeuralNetwork\Training\Backpropagation;
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2016-08-09 11:27:43 +00:00
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2017-05-23 07:03:05 +00:00
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abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator, IncrementalEstimator
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2016-08-05 14:12:39 +00:00
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{
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2017-05-17 22:07:14 +00:00
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use Predictable;
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2017-05-23 07:03:05 +00:00
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/**
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* @var int
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*/
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private $inputLayerFeatures;
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/**
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* @var array
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*/
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private $hiddenLayers;
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2016-08-09 11:27:43 +00:00
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/**
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2017-05-17 22:07:14 +00:00
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* @var array
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*/
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protected $classes = [];
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/**
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* @var int
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*/
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private $iterations;
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2017-05-23 07:03:05 +00:00
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/**
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* @var ActivationFunction
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*/
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protected $activationFunction;
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/**
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* @var int
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*/
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private $theta;
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2017-05-17 22:07:14 +00:00
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/**
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* @var Backpropagation
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*/
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protected $backpropagation = null;
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/**
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2016-08-09 11:27:43 +00:00
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* @throws InvalidArgumentException
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*/
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2017-11-14 20:21:23 +00:00
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public function __construct(int $inputLayerFeatures, array $hiddenLayers, array $classes, int $iterations = 10000, ?ActivationFunction $activationFunction = null, int $theta = 1)
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2016-08-09 11:27:43 +00:00
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{
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2017-05-17 22:07:14 +00:00
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if (empty($hiddenLayers)) {
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2016-08-09 11:27:43 +00:00
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throw InvalidArgumentException::invalidLayersNumber();
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}
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2017-05-23 07:03:05 +00:00
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if (count($classes) < 2) {
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2017-05-17 22:07:14 +00:00
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throw InvalidArgumentException::invalidClassesNumber();
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}
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2017-05-23 07:03:05 +00:00
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$this->classes = array_values($classes);
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2017-05-17 22:07:14 +00:00
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$this->iterations = $iterations;
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2017-05-23 07:03:05 +00:00
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$this->inputLayerFeatures = $inputLayerFeatures;
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$this->hiddenLayers = $hiddenLayers;
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$this->activationFunction = $activationFunction;
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$this->theta = $theta;
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$this->initNetwork();
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}
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2017-05-17 22:07:14 +00:00
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2017-11-14 20:21:23 +00:00
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private function initNetwork(): void
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2017-05-23 07:03:05 +00:00
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{
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$this->addInputLayer($this->inputLayerFeatures);
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$this->addNeuronLayers($this->hiddenLayers, $this->activationFunction);
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$this->addNeuronLayers([count($this->classes)], $this->activationFunction);
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2017-05-17 22:07:14 +00:00
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2016-08-09 11:27:43 +00:00
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$this->addBiasNodes();
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$this->generateSynapses();
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2017-05-17 22:07:14 +00:00
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2017-05-23 07:03:05 +00:00
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$this->backpropagation = new Backpropagation($this->theta);
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2017-05-17 22:07:14 +00:00
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}
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2017-11-14 20:21:23 +00:00
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public function train(array $samples, array $targets): void
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2017-05-17 22:07:14 +00:00
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{
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2017-05-23 07:03:05 +00:00
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$this->reset();
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$this->initNetwork();
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$this->partialTrain($samples, $targets, $this->classes);
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}
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/**
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2017-07-26 06:22:12 +00:00
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* @throws InvalidArgumentException
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2017-05-23 07:03:05 +00:00
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*/
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2017-11-14 20:21:23 +00:00
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public function partialTrain(array $samples, array $targets, array $classes = []): void
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2017-05-23 07:03:05 +00:00
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{
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if (!empty($classes) && array_values($classes) !== $this->classes) {
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// We require the list of classes in the constructor.
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throw InvalidArgumentException::inconsistentClasses();
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}
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2017-05-17 22:07:14 +00:00
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for ($i = 0; $i < $this->iterations; ++$i) {
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$this->trainSamples($samples, $targets);
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}
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2016-08-09 11:27:43 +00:00
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}
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2017-05-17 22:07:14 +00:00
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/**
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* @param mixed $target
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*/
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2017-05-23 07:03:05 +00:00
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abstract protected function trainSample(array $sample, $target);
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2017-05-17 22:07:14 +00:00
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/**
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* @return mixed
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*/
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2017-05-23 07:03:05 +00:00
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abstract protected function predictSample(array $sample);
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2017-11-14 20:21:23 +00:00
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protected function reset(): void
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2017-05-23 07:03:05 +00:00
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{
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$this->removeLayers();
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}
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2017-05-17 22:07:14 +00:00
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2017-11-14 20:21:23 +00:00
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private function addInputLayer(int $nodes): void
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2016-08-09 11:27:43 +00:00
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{
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$this->addLayer(new Layer($nodes, Input::class));
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}
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2017-11-14 20:21:23 +00:00
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private function addNeuronLayers(array $layers, ?ActivationFunction $activationFunction = null): void
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2016-08-09 11:27:43 +00:00
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{
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foreach ($layers as $neurons) {
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2016-08-11 11:21:22 +00:00
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$this->addLayer(new Layer($neurons, Neuron::class, $activationFunction));
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2016-08-09 11:27:43 +00:00
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}
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}
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2017-11-14 20:21:23 +00:00
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private function generateSynapses(): void
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2016-08-09 11:27:43 +00:00
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{
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$layersNumber = count($this->layers) - 1;
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for ($i = 0; $i < $layersNumber; ++$i) {
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$currentLayer = $this->layers[$i];
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$nextLayer = $this->layers[$i + 1];
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$this->generateLayerSynapses($nextLayer, $currentLayer);
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}
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}
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2017-11-14 20:21:23 +00:00
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private function addBiasNodes(): void
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2016-08-09 11:27:43 +00:00
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{
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$biasLayers = count($this->layers) - 1;
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2016-11-20 21:53:17 +00:00
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for ($i = 0; $i < $biasLayers; ++$i) {
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2016-08-09 11:27:43 +00:00
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$this->layers[$i]->addNode(new Bias());
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}
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}
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2017-11-14 20:21:23 +00:00
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private function generateLayerSynapses(Layer $nextLayer, Layer $currentLayer): void
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2016-08-09 11:27:43 +00:00
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{
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foreach ($nextLayer->getNodes() as $nextNeuron) {
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if ($nextNeuron instanceof Neuron) {
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$this->generateNeuronSynapses($currentLayer, $nextNeuron);
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}
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}
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}
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2017-11-14 20:21:23 +00:00
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private function generateNeuronSynapses(Layer $currentLayer, Neuron $nextNeuron): void
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2016-08-09 11:27:43 +00:00
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{
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foreach ($currentLayer->getNodes() as $currentNeuron) {
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$nextNeuron->addSynapse(new Synapse($currentNeuron));
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}
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}
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2017-05-17 22:07:14 +00:00
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2017-11-14 20:21:23 +00:00
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private function trainSamples(array $samples, array $targets): void
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2017-05-17 22:07:14 +00:00
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{
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foreach ($targets as $key => $target) {
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$this->trainSample($samples[$key], $target);
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}
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}
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2016-08-05 14:12:39 +00:00
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}
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