MultilayerPerceptron::reset() - Code Metrics - Inspection of "Neural networks partial training and persistency (..." - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Push — master ( 3dff40...de5049 )

by Arkadiusz

created 2017-05-23 07:03 UTC

MultilayerPerceptron::reset() A

↳ Parent: MultilayerPerceptron

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\Network;

use Phpml\Estimator;
use Phpml\IncrementalEstimator;
use Phpml\Exception\InvalidArgumentException;
use Phpml\NeuralNetwork\Training\Backpropagation;
use Phpml\NeuralNetwork\ActivationFunction;
use Phpml\NeuralNetwork\Layer;
use Phpml\NeuralNetwork\Node\Bias;
use Phpml\NeuralNetwork\Node\Input;
use Phpml\NeuralNetwork\Node\Neuron;
use Phpml\NeuralNetwork\Node\Neuron\Synapse;
use Phpml\Helper\Predictable;

abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator, IncrementalEstimator
{
    use Predictable;

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

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

    /**
     * @var array
     */
    protected $classes = [];

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

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

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

    /**
     * @var Backpropagation
     */
    protected $backpropagation = null;

    /**
     * @param int                     $inputLayerFeatures
     * @param array                   $hiddenLayers
     * @param array                   $classes
     * @param int                     $iterations
     * @param ActivationFunction|null $activationFunction
     * @param int                     $theta
     *
     * @throws InvalidArgumentException
     */
    public function __construct(int $inputLayerFeatures, array $hiddenLayers, array $classes, int $iterations = 10000, ActivationFunction $activationFunction = null, int $theta = 1)
    {
        if (empty($hiddenLayers)) {
            throw InvalidArgumentException::invalidLayersNumber();
        }

        if (count($classes) < 2) {
            throw InvalidArgumentException::invalidClassesNumber();
        }

        $this->classes = array_values($classes);
        $this->iterations = $iterations;
        $this->inputLayerFeatures = $inputLayerFeatures;
        $this->hiddenLayers = $hiddenLayers;
        $this->activationFunction = $activationFunction;
        $this->theta = $theta;

        $this->initNetwork();
    }

    /**
     * @return void
     */
    private function initNetwork()
    {
        $this->addInputLayer($this->inputLayerFeatures);
        $this->addNeuronLayers($this->hiddenLayers, $this->activationFunction);
        $this->addNeuronLayers([count($this->classes)], $this->activationFunction);

        $this->addBiasNodes();
        $this->generateSynapses();

        $this->backpropagation = new Backpropagation($this->theta);
    }

    /**
     * @param array $samples
     * @param array $targets
     */
    public function train(array $samples, array $targets)
    {
        $this->reset();
        $this->initNetwork();
        $this->partialTrain($samples, $targets, $this->classes);
    }

    /**
     * @param array $samples
     * @param array $targets
     */
    public function partialTrain(array $samples, array $targets, array $classes = [])
    {
        if (!empty($classes) && array_values($classes) !== $this->classes) {
            // We require the list of classes in the constructor.
            throw InvalidArgumentException::inconsistentClasses();
        }

        for ($i = 0; $i < $this->iterations; ++$i) {
            $this->trainSamples($samples, $targets);
        }
    }

    /**
     * @param array $sample
     * @param mixed $target
     */
    abstract protected function trainSample(array $sample, $target);

    /**
     * @param array $sample
     * @return mixed
     */
    abstract protected function predictSample(array $sample);

    /**
     * @return void
     */
    protected function reset()
    {
        $this->removeLayers();
    }

    /**
     * @param int $nodes
     */
    private function addInputLayer(int $nodes)
    {
        $this->addLayer(new Layer($nodes, Input::class));
    }

    /**
     * @param array                   $layers
     * @param ActivationFunction|null $activationFunction
     */
    private function addNeuronLayers(array $layers, ActivationFunction $activationFunction = null)
    {
        foreach ($layers as $neurons) {
            $this->addLayer(new Layer($neurons, Neuron::class, $activationFunction));
        }
    }

    private function generateSynapses()
    {
        $layersNumber = count($this->layers) - 1;
        for ($i = 0; $i < $layersNumber; ++$i) {
            $currentLayer = $this->layers[$i];
            $nextLayer = $this->layers[$i + 1];
            $this->generateLayerSynapses($nextLayer, $currentLayer);
        }
    }

    private function addBiasNodes()
    {
        $biasLayers = count($this->layers) - 1;
        for ($i = 0; $i < $biasLayers; ++$i) {
            $this->layers[$i]->addNode(new Bias());
        }
    }

    /**
     * @param Layer $nextLayer
     * @param Layer $currentLayer
     */
    private function generateLayerSynapses(Layer $nextLayer, Layer $currentLayer)
    {
        foreach ($nextLayer->getNodes() as $nextNeuron) {
            if ($nextNeuron instanceof Neuron) {
                $this->generateNeuronSynapses($currentLayer, $nextNeuron);
            }
        }
    }

    /**
     * @param Layer  $currentLayer
     * @param Neuron $nextNeuron
     */
    private function generateNeuronSynapses(Layer $currentLayer, Neuron $nextNeuron)
    {
        foreach ($currentLayer->getNodes() as $currentNeuron) {
            $nextNeuron->addSynapse(new Synapse($currentNeuron));
        }
    }

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


1			<?php
2
3			declare(strict_types=1);
4
5			namespace Phpml\NeuralNetwork\Network;
6
7			use Phpml\Estimator;
8			use Phpml\IncrementalEstimator;
9			use Phpml\Exception\InvalidArgumentException;
10			use Phpml\NeuralNetwork\Training\Backpropagation;
11			use Phpml\NeuralNetwork\ActivationFunction;
12			use Phpml\NeuralNetwork\Layer;
13			use Phpml\NeuralNetwork\Node\Bias;
14			use Phpml\NeuralNetwork\Node\Input;
15			use Phpml\NeuralNetwork\Node\Neuron;
16			use Phpml\NeuralNetwork\Node\Neuron\Synapse;
17			use Phpml\Helper\Predictable;
18
19			abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator, IncrementalEstimator
20			{
21			use Predictable;
22
23			/**
24			* @var int
25			*/
26			private $inputLayerFeatures;
27
28			/**
29			* @var array
30			*/
31			private $hiddenLayers;
32
33			/**
34			* @var array
35			*/
36			protected $classes = [];
37
38			/**
39			* @var int
40			*/
41			private $iterations;
42
43			/**
44			* @var ActivationFunction
45			*/
46			protected $activationFunction;
47
48			/**
49			* @var int
50			*/
51			private $theta;
52
53			/**
54			* @var Backpropagation
55			*/
56			protected $backpropagation = null;
57
58			/**
59			* @param int $inputLayerFeatures
60			* @param array $hiddenLayers
61			* @param array $classes
62			* @param int $iterations
63			* @param ActivationFunction\|null $activationFunction
64			* @param int $theta
65			*
66			* @throws InvalidArgumentException
67			*/
68			public function __construct(int $inputLayerFeatures, array $hiddenLayers, array $classes, int $iterations = 10000, ActivationFunction $activationFunction = null, int $theta = 1)
69			{
70			if (empty($hiddenLayers)) {
71			throw InvalidArgumentException::invalidLayersNumber();
72			}
73
74			if (count($classes) < 2) {
75			throw InvalidArgumentException::invalidClassesNumber();
76			}
77
78			$this->classes = array_values($classes);
79			$this->iterations = $iterations;
80			$this->inputLayerFeatures = $inputLayerFeatures;
81			$this->hiddenLayers = $hiddenLayers;
82			$this->activationFunction = $activationFunction;
83			$this->theta = $theta;
84
85			$this->initNetwork();
86			}
87
88			/**
89			* @return void
90			*/
91			private function initNetwork()
92			{
93			$this->addInputLayer($this->inputLayerFeatures);
94			$this->addNeuronLayers($this->hiddenLayers, $this->activationFunction);
95			$this->addNeuronLayers([count($this->classes)], $this->activationFunction);
96
97			$this->addBiasNodes();
98			$this->generateSynapses();
99
100			$this->backpropagation = new Backpropagation($this->theta);
101			}
102
103			/**
104			* @param array $samples
105			* @param array $targets
106			*/
107			public function train(array $samples, array $targets)
108			{
109			$this->reset();
110			$this->initNetwork();
111			$this->partialTrain($samples, $targets, $this->classes);
112			}
113
114			/**
115			* @param array $samples
116			* @param array $targets
117			*/
118			public function partialTrain(array $samples, array $targets, array $classes = [])
119			{
120			if (!empty($classes) && array_values($classes) !== $this->classes) {
121			// We require the list of classes in the constructor.
122			throw InvalidArgumentException::inconsistentClasses();
123			}
124
125			for ($i = 0; $i < $this->iterations; ++$i) {
126			$this->trainSamples($samples, $targets);
127			}
128			}
129
130			/**
131			* @param array $sample
132			* @param mixed $target
133			*/
134			abstract protected function trainSample(array $sample, $target);
135
136			/**
137			* @param array $sample
138			* @return mixed
139			*/
140			abstract protected function predictSample(array $sample);
141
142			/**
143			* @return void
144			*/
145			protected function reset()
146			{
147			$this->removeLayers();
148			}
149
150			/**
151			* @param int $nodes
152			*/
153			private function addInputLayer(int $nodes)
154			{
155			$this->addLayer(new Layer($nodes, Input::class));
156			}
157
158			/**
159			* @param array $layers
160			* @param ActivationFunction\|null $activationFunction
161			*/
162			private function addNeuronLayers(array $layers, ActivationFunction $activationFunction = null)
163			{
164			foreach ($layers as $neurons) {
165			$this->addLayer(new Layer($neurons, Neuron::class, $activationFunction));
166			}
167			}
168
169			private function generateSynapses()
170			{
171			$layersNumber = count($this->layers) - 1;
172			for ($i = 0; $i < $layersNumber; ++$i) {
173			$currentLayer = $this->layers[$i];
174			$nextLayer = $this->layers[$i + 1];
175			$this->generateLayerSynapses($nextLayer, $currentLayer);
176			}
177			}
178
179			private function addBiasNodes()
180			{
181			$biasLayers = count($this->layers) - 1;
182			for ($i = 0; $i < $biasLayers; ++$i) {
183			$this->layers[$i]->addNode(new Bias());
184			}
185			}
186
187			/**
188			* @param Layer $nextLayer
189			* @param Layer $currentLayer
190			*/
191			private function generateLayerSynapses(Layer $nextLayer, Layer $currentLayer)
192			{
193			foreach ($nextLayer->getNodes() as $nextNeuron) {
194			if ($nextNeuron instanceof Neuron) {
195			$this->generateNeuronSynapses($currentLayer, $nextNeuron);
196			}
197			}
198			}
199
200			/**
201			* @param Layer $currentLayer
202			* @param Neuron $nextNeuron
203			*/
204			private function generateNeuronSynapses(Layer $currentLayer, Neuron $nextNeuron)
205			{
206			foreach ($currentLayer->getNodes() as $currentNeuron) {
207			$nextNeuron->addSynapse(new Synapse($currentNeuron));
208			}
209			}
210
211			/**
212			* @param array $samples
213			* @param array $targets
214			*/
215			private function trainSamples(array $samples, array $targets)
216			{
217			foreach ($targets as $key => $target) {
218			$this->trainSample($samples[$key], $target);
219			}
220			}
221			}
222

php-ai / php-ml

Push — master ( 3dff40...de5049 )

MultilayerPerceptron::reset() A

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like