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<?php |
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declare (strict_types = 1); |
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namespace Phpml\Regression; |
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use Phpml\Helper\Predictable; |
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use Phpml\NeuralNetwork\ActivationFunction; |
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use Phpml\NeuralNetwork\Network\MultilayerPerceptron; |
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use Phpml\NeuralNetwork\Training\Backpropagation; |
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class MLPRegressor implements Regression |
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{ |
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use Predictable; |
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/** |
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* @var MultilayerPerceptron |
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*/ |
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private $perceptron; |
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/** |
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* @var array |
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*/ |
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private $hiddenLayers; |
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/** |
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* @var float |
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*/ |
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private $desiredError; |
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/** |
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* @var int |
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*/ |
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private $maxIterations; |
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/** |
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* @var ActivationFunction |
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*/ |
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private $activationFunction; |
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/** |
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* @param array $hiddenLayers |
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* @param float $desiredError |
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* @param int $maxIterations |
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* @param ActivationFunction $activationFunction |
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*/ |
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public function __construct(array $hiddenLayers = [100], float $desiredError, int $maxIterations, ActivationFunction $activationFunction = null) |
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{ |
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$this->hiddenLayers = $hiddenLayers; |
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$this->desiredError = $desiredError; |
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$this->maxIterations = $maxIterations; |
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$this->activationFunction = $activationFunction; |
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} |
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/** |
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* @param array $samples |
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* @param array $targets |
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*/ |
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public function train(array $samples, array $targets) |
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{ |
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$layers = [count($samples[0])] + $this->hiddenLayers + [count($targets[0])]; |
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$this->perceptron = new MultilayerPerceptron($layers, $this->activationFunction); |
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$trainer = new Backpropagation($this->perceptron); |
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$trainer->train($samples, $targets, $this->desiredError, $this->maxIterations); |
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} |
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/** |
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* @param array $sample |
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* |
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* @return array |
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*/ |
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protected function predictSample(array $sample) |
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{ |
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return $this->perceptron->setInput($sample)->getOutput(); |
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} |
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} |
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php-ai /
php-ml
