Neuron::reset() - Code Metrics - Inspection of "Add load/save feature for MultilayerPerceptron tra..." - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — master (#399)

unknown

created 2019-07-24 12:49 UTC

Neuron::reset() A

↳ Parent: Neuron

Complexity

Conditions	1
Paths	1

Size

Total Lines	4
Code Lines	2

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
dl	0
loc	4
rs	10
c	0
b	0
f	0
cc	1
eloc	2
nc	1
nop	0

<?php

declare(strict_types=1);

namespace Phpml\NeuralNetwork\Node;

use Phpml\Exception\InvalidArgumentException;
use Phpml\NeuralNetwork\ActivationFunction;
use Phpml\NeuralNetwork\ActivationFunction\Sigmoid;
use Phpml\NeuralNetwork\Node;
use Phpml\NeuralNetwork\Node\Neuron\Synapse;

class Neuron implements Node
{
    /**
     * @var Synapse[]
     */
    protected $synapses = [];

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

    /**
     * @var float
     */
    protected $output = 0.0;

    /**
     * @var float
     */
    protected $z = 0.0;

    public function __construct(?ActivationFunction $activationFunction = null)
    {
        $this->activationFunction = $activationFunction ?: new Sigmoid();
    }

    public function addSynapse(Synapse $synapse): void
    {
        $this->synapses[] = $synapse;
    }

    /**
     * @return Synapse[]
     */
    public function getSynapses(): array
    {
        return $this->synapses;
    }

    public function getOutput(): float
    {
        if ($this->output === 0.0) {

            $this->z = 0;
            foreach ($this->synapses as $synapse) {
                $this->z += $synapse->getOutput();
            }

            $this->output = $this->activationFunction->compute($this->z);
        }

        return $this->output;
    }

    public function getDerivative(): float
    {
        return $this->activationFunction->differentiate($this->z, $this->output);
    }

    public function reset(): void
    {
        $this->output = 0.0;
        $this->z = 0.0;
    }

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

        return $result;
    }

    public function setTrainedCharacteristics(array $characteristics): void
    {
        // length of weights should equals number of synapses
        if (count($characteristics) != count($this->synapses)) {
            throw new InvalidArgumentException('Loaded weights should match the neuron structure');
        }

        for ($i = 0; $i < count($characteristics); $i++) {
for ($i=0; $i<count($array); $i++) { // calls count() on each iteration
}

// Better
for ($i=0, $c=count($array); $i<$c; $i++) { // calls count() just once
}
            $this->synapses[$i]->setWeight($characteristics[$i]);
        }
    }
}


1			<?php
2
3			declare(strict_types=1);
4
5			namespace Phpml\NeuralNetwork\Node;
6
7			use Phpml\Exception\InvalidArgumentException;
8			use Phpml\NeuralNetwork\ActivationFunction;
9			use Phpml\NeuralNetwork\ActivationFunction\Sigmoid;
10			use Phpml\NeuralNetwork\Node;
11			use Phpml\NeuralNetwork\Node\Neuron\Synapse;
12
13			class Neuron implements Node
14			{
15			/**
16			* @var Synapse[]
17			*/
18			protected $synapses = [];
19
20			/**
21			* @var ActivationFunction
22			*/
23			protected $activationFunction;
24
25			/**
26			* @var float
27			*/
28			protected $output = 0.0;
29
30			/**
31			* @var float
32			*/
33			protected $z = 0.0;
34
35			public function __construct(?ActivationFunction $activationFunction = null)
36			{
37			$this->activationFunction = $activationFunction ?: new Sigmoid();
38			}
39
40			public function addSynapse(Synapse $synapse): void
41			{
42			$this->synapses[] = $synapse;
43			}
44
45			/**
46			* @return Synapse[]
47			*/
48			public function getSynapses(): array
49			{
50			return $this->synapses;
51			}
52
53			public function getOutput(): float
54			{
55			if ($this->output === 0.0) {
			0 ignored issues – show introduced 2019-05-15 06:10 UTC by Report Bug Copy Issue Report The condition `$this->output === 0.0` is always `false`. Loading history...
56			$this->z = 0;
57			foreach ($this->synapses as $synapse) {
58			$this->z += $synapse->getOutput();
59			}
60
61			$this->output = $this->activationFunction->compute($this->z);
62			}
63
64			return $this->output;
65			}
66
67			public function getDerivative(): float
68			{
69			return $this->activationFunction->differentiate($this->z, $this->output);
70			}
71
72			public function reset(): void
73			{
74			$this->output = 0.0;
75			$this->z = 0.0;
76			}
77
78			public function getTrainedCharacteristics(): array
79			{
80			$result = [];
81			foreach ($this->synapses as $synapse) {
82			$result[] = $synapse->getWeight();
83			}
84
85			return $result;
86			}
87
88			public function setTrainedCharacteristics(array $characteristics): void
89			{
90			// length of weights should equals number of synapses
91			if (count($characteristics) != count($this->synapses)) {
92			throw new InvalidArgumentException('Loaded weights should match the neuron structure');
93			}
94
95			for ($i = 0; $i < count($characteristics); $i++) {
			0 ignored issues – show Performance Best Practice introduced 2019-07-24 12:54 UTC by Report Bug Copy Issue Report It seems like you are calling the size function `count()` as part of the test condition. You might want to compute the size beforehand, and not on each iteration. If the size of the collection does not change during the iteration, it is generally a good practice to compute it beforehand, and not on each iteration: for ($i=0; $i<count($array); $i++) { // calls count() on each iteration } // Better for ($i=0, $c=count($array); $i<$c; $i++) { // calls count() just once } Loading history...
96			$this->synapses[$i]->setWeight($characteristics[$i]);
97			}
98			}
99			}
100

php-ai / php-ml

Pull Request — master (#399)

Neuron::reset() A

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like