Phpml\NeuralNetwork\Network\LayeredNetwork

Complexity

Total Complexity

17

Size/Duplication

Total Lines	82
Duplicated Lines	0 %

Importance

Changes

0

8 Methods

Rating	Name	Duplication	Size	Complexity
A	addLayer()	0	3	1
A	setTrainedCharacteristics()	0	8	3
A	getLayers()	0	3	1
A	removeLayers()	0	3	1
A	getTrainedCharacteristics()	0	8	2
A	getOutput()	0	8	2
A	setInput()	0	19	6
A	getOutputLayer()	0	3	1

<?php

declare(strict_types=1);

namespace Phpml\NeuralNetwork\Network;

use Phpml\Exception\InvalidArgumentException;
use Phpml\NeuralNetwork\Layer;
use Phpml\NeuralNetwork\Network;
use Phpml\NeuralNetwork\Node\Input;
use Phpml\NeuralNetwork\Node\Neuron;

abstract class LayeredNetwork implements Network
{
    /**
     * @var Layer[]
     */
    protected $layers = [];

    public function addLayer(Layer $layer): void
    {
        $this->layers[] = $layer;
    }

    /**
     * @return Layer[]
     */
    public function getLayers(): array
    {
        return $this->layers;
    }

    public function removeLayers(): void
    {
        unset($this->layers);
    }

    public function getOutputLayer(): Layer
    {
        return $this->layers[count($this->layers) - 1];
    }

    public function getOutput(): array
    {
        $result = [];
        foreach ($this->getOutputLayer()->getNodes() as $neuron) {
            $result[] = $neuron->getOutput();
        }

        return $result;
    }

    /**
     * @param mixed $input
     */
    public function setInput($input): Network
    {
        $firstLayer = $this->layers[0];

        foreach ($firstLayer->getNodes() as $key => $neuron) {
            if ($neuron instanceof Input) {
                $neuron->setInput($input[$key]);
            }
        }

        foreach ($this->getLayers() as $layer) {
            foreach ($layer->getNodes() as $node) {
                if ($node instanceof Neuron) {
                    $node->reset();
                }
            }
        }

        return $this;
    }

    public function getTrainedCharacteristics(): array
    {
        $result = [];
        foreach ($this->layers as $layer) {
            $result[] = $layer->getTrainedCharacteristics();
        }

        return $result;
    }

    public function setTrainedCharacteristics(array $characteristics): void
    {
        if (count($characteristics) != count($this->layers)) {
            throw new InvalidArgumentException('Loaded data does not match the network structure');
        }

        for ($i = 0; $i < count($this->layers); $i++) {

It seems like you are calling the size function count() as part of the test condition. You might want to compute the size beforehand, and not on each iteration.

            $this->layers[$i]->setTrainedCharacteristics($characteristics[$i]);
        }
    }
}