Phpml\Regression\MLPRegressor

Complexity

Total Complexity

3

Size/Duplication

Total Lines	69
Duplicated Lines	0 %

Coupling/Cohesion

Components	1
Dependencies	3

Importance

Changes	1
Bugs	0	Features	0

3 Methods

Rating	Name	Duplication	Size	Complexity
A	__construct()	0	7	1
A	train()	0	9	1
A	predictSample()	0	4	1

<?php

declare (strict_types = 1);

namespace Phpml\Regression;


use Phpml\Helper\Predictable;
use Phpml\NeuralNetwork\ActivationFunction;
use Phpml\NeuralNetwork\Network\MultilayerPerceptron;
use Phpml\NeuralNetwork\Training\Backpropagation;

class MLPRegressor implements Regression
{
    use Predictable;

    /**
     * @var MultilayerPerceptron
     */
    private $perceptron;

    /**
     * @var array
     */
    private $hiddenLayers;

    /**
     * @var float
     */
    private $desiredError;

    /**
     * @var int
     */
    private $maxIterations;

    /**
     * @var ActivationFunction
     */
    private $activationFunction;

    /**
     * @param array $hiddenLayers
     * @param float $desiredError
     * @param int $maxIterations
     * @param ActivationFunction $activationFunction
     */
    public function __construct(array $hiddenLayers = [100], float $desiredError, int $maxIterations, ActivationFunction $activationFunction = null)
    {
        $this->hiddenLayers = $hiddenLayers;
        $this->desiredError = $desiredError;
        $this->maxIterations = $maxIterations;
        $this->activationFunction = $activationFunction;
    }


    /**
     * @param array $samples
     * @param array $targets
     */
    public function train(array $samples, array $targets)
    {
        $layers = [count($samples[0])] + $this->hiddenLayers + [count($targets[0])];

        $this->perceptron = new MultilayerPerceptron($layers, $this->activationFunction);

        $trainer = new Backpropagation($this->perceptron);
        $trainer->train($samples, $targets, $this->desiredError, $this->maxIterations);
    }

    /**
     * @param array $sample
     *
     * @return array
     */
    protected function predictSample(array $sample)
    {
        return $this->perceptron->setInput($sample)->getOutput();
    }

}