Backpropagation::isResultWithinError() - Code Metrics - Inspection of "Neural networks improvements" - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — master (#89)

unknown

created 2017-05-16 07:48 UTC

Backpropagation::isResultWithinError() A

↳ Parent: Project

Complexity

Conditions	4
Paths	3

Size

Total Lines	10
Code Lines	5

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
dl	0
loc	10
c	0
b	0
f	0
rs	9.2
cc	4
eloc	5
nc	3
nop	3

<?php

declare(strict_types=1);

namespace Phpml\NeuralNetwork\Training;

use Phpml\NeuralNetwork\Network;
use Phpml\NeuralNetwork\Node\Neuron;
use Phpml\NeuralNetwork\Training;
use Phpml\NeuralNetwork\Training\Backpropagation\Sigma;

class Backpropagation implements Training
{
    /**
     * @var Network
     */
    private $network;

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

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

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

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

    /**
     * @param Network $network
     * @param int     $theta
     */
    public function __construct(Network $network, int $theta = 1, int $maxIterations = 10000)
    {
        $this->network = $network;
        $this->theta = $theta;
        $this->maxIterations = $maxIterations;
    }

    /**
     * @param array $samples
     * @param array $targets
     */
    public function train(array $samples, array $targets)
    {
        for ($i = 0; $i < $this->maxIterations; ++$i) {
            $this->trainSamples($samples, $targets);
        }
    }

    /**
     * @param array $samples
     * @param array $targets
     */
    private function trainSamples(array $samples, array $targets)
    {
        foreach ($targets as $key => $target) {
            $this->trainSample($samples[$key], $target);
        }
    }

    /**
     * @param array $sample
     * @param mixed $target
     */
    private function trainSample(array $sample, $target)
    {

        // Feed-forward.
        $this->network->setInput($sample)->getOutput();

        $layers = $this->network->getLayers();
        $layersNumber = count($layers);

        $targetClass = $this->network->getTargetClass($target);
interface User
{
    /** @return string */
    public function getPassword();
}

class MyUser implements User
{
    public function getPassword()
    {
        // return something
    }

    public function getDisplayName()
    {
        // return some name.
    }
}

class AuthSystem
{
    public function authenticate(User $user)
    {
        $this->logger->info(sprintf('Authenticating %s.', $user->getDisplayName()));
        // do something.
    }
}

        // Backpropagation.
        for ($i = $layersNumber; $i > 1; --$i) {
            $this->sigmas = [];
            foreach ($layers[$i - 1]->getNodes() as $key => $neuron) {

                if ($neuron instanceof Neuron) {
                    $sigma = $this->getSigma($neuron, $targetClass, $key, $i == $layersNumber);
                    foreach ($neuron->getSynapses() as $synapse) {
                        $synapse->changeWeight($this->theta * $sigma * $synapse->getNode()->getOutput());
                    }
                }
            }
            $this->prevSigmas = $this->sigmas;
        }
    }

    /**
     * @param Neuron $neuron
     * @param int    $targetClass
     * @param int    $key
     * @param bool   $lastLayer
     *
     * @return float
     */
    private function getSigma(Neuron $neuron, int $targetClass, int $key, bool $lastLayer): float
    {
        $neuronOutput = $neuron->getOutput();
        $sigma = $neuronOutput * (1 - $neuronOutput);

        if ($lastLayer) {
            if ($targetClass == $key) {
                $value = 1;
            } else {
                $value = 0;
            }
            $sigma *= ($value - $neuronOutput);
        } else {
            $sigma *= $this->getPrevSigma($neuron);
        }

        $this->sigmas[] = new Sigma($neuron, $sigma);

        return $sigma;
    }

    /**
     * @param Neuron $neuron
     *
     * @return float
     */
    private function getPrevSigma(Neuron $neuron): float
    {
        $sigma = 0.0;

        foreach ($this->prevSigmas as $neuronSigma) {
            $sigma += $neuronSigma->getSigmaForNeuron($neuron);
        }

        return $sigma;
    }
}


1			<?php
2
3			declare(strict_types=1);
4
5			namespace Phpml\NeuralNetwork\Training;
6
7			use Phpml\NeuralNetwork\Network;
8			use Phpml\NeuralNetwork\Node\Neuron;
9			use Phpml\NeuralNetwork\Training;
10			use Phpml\NeuralNetwork\Training\Backpropagation\Sigma;
11
12			class Backpropagation implements Training
13			{
14			/**
15			* @var Network
16			*/
17			private $network;
18
19			/**
20			* @var int
21			*/
22			private $theta;
23
24			/**
25			* @var int
26			*/
27			private $maxIterations;
28
29			/**
30			* @var array
31			*/
32			private $sigmas;
33
34			/**
35			* @var array
36			*/
37			private $prevSigmas;
38
39			/**
40			* @param Network $network
41			* @param int $theta
42			*/
43			public function __construct(Network $network, int $theta = 1, int $maxIterations = 10000)
44			{
45			$this->network = $network;
46			$this->theta = $theta;
47			$this->maxIterations = $maxIterations;
48			}
49
50			/**
51			* @param array $samples
52			* @param array $targets
53			*/
54			public function train(array $samples, array $targets)
55			{
56			for ($i = 0; $i < $this->maxIterations; ++$i) {
57			$this->trainSamples($samples, $targets);
58			}
59			}
60
61			/**
62			* @param array $samples
63			* @param array $targets
64			*/
65			private function trainSamples(array $samples, array $targets)
66			{
67			foreach ($targets as $key => $target) {
68			$this->trainSample($samples[$key], $target);
69			}
70			}
71
72			/**
73			* @param array $sample
74			* @param mixed $target
75			*/
76			private function trainSample(array $sample, $target)
77			{
78
79			// Feed-forward.
80			$this->network->setInput($sample)->getOutput();
81
82			$layers = $this->network->getLayers();
83			$layersNumber = count($layers);
84
85			$targetClass = $this->network->getTargetClass($target);
			0 ignored issues – show Bug introduced 2017-05-16 07:33 UTC by Report Bug Copy Issue Report It seems like you code against a concrete implementation and not the interface `Phpml\NeuralNetwork\Network` as the method `getTargetClass()` does only exist in the following implementations of said interface: `Phpml\NeuralNetwork\Network\MultilayerPerceptron`. Let’s take a look at an example: interface User { /** @return string / public function getPassword(); } class MyUser implements User { public function getPassword() { // return something } public function getDisplayName() { // return some name. } } class AuthSystem { public function authenticate(User $user) { $this->logger->info(sprintf('Authenticating %s.', $user->getDisplayName())); // do something. } } In the above example, the authenticate() method works fine as long as you just pass instances of MyUser. However, if you now also want to pass a different implementation of User which does not have a getDisplayName() method, the code will break. Available Fixes Change the type-hint for the parameter: class AuthSystem { public function authenticate(MyUser $user) { / ... / } } Add an additional type-check: class AuthSystem { public function authenticate(User $user) { if ($user instanceof MyUser) { $this->logger->info(/* ... /); } // or alternatively if ( ! $user instanceof MyUser) { throw new \LogicException( '$user must be an instance of MyUser, ' .'other instances are not supported.' ); } } } Note:* PHP Analyzer uses reverse abstract interpretation to narrow down the types inside the if block in such a case. Add the method to the interface: interface User { /** @return string / public function getPassword(); /* @return string */ public function getDisplayName(); } Loading history...
86
87			// Backpropagation.
88			for ($i = $layersNumber; $i > 1; --$i) {
89			$this->sigmas = [];
90			foreach ($layers[$i - 1]->getNodes() as $key => $neuron) {
91
92			if ($neuron instanceof Neuron) {
93			$sigma = $this->getSigma($neuron, $targetClass, $key, $i == $layersNumber);
94			foreach ($neuron->getSynapses() as $synapse) {
95			$synapse->changeWeight($this->theta * $sigma * $synapse->getNode()->getOutput());
96			}
97			}
98			}
99			$this->prevSigmas = $this->sigmas;
100			}
101			}
102
103			/**
104			* @param Neuron $neuron
105			* @param int $targetClass
106			* @param int $key
107			* @param bool $lastLayer
108			*
109			* @return float
110			*/
111			private function getSigma(Neuron $neuron, int $targetClass, int $key, bool $lastLayer): float
112			{
113			$neuronOutput = $neuron->getOutput();
114			$sigma = $neuronOutput * (1 - $neuronOutput);
115
116			if ($lastLayer) {
117			if ($targetClass == $key) {
118			$value = 1;
119			} else {
120			$value = 0;
121			}
122			$sigma *= ($value - $neuronOutput);
123			} else {
124			$sigma *= $this->getPrevSigma($neuron);
125			}
126
127			$this->sigmas[] = new Sigma($neuron, $sigma);
128
129			return $sigma;
130			}
131
132			/**
133			* @param Neuron $neuron
134			*
135			* @return float
136			*/
137			private function getPrevSigma(Neuron $neuron): float
138			{
139			$sigma = 0.0;
140
141			foreach ($this->prevSigmas as $neuronSigma) {
142			$sigma += $neuronSigma->getSigmaForNeuron($neuron);
143			}
144
145			return $sigma;
146			}
147			}
148

php-ai / php-ml

Pull Request — master (#89)

Backpropagation::isResultWithinError() A

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