mirror of
https://github.com/Llewellynvdm/php-ml.git
synced 2024-12-02 01:48:22 +00:00
e83f7b95d5
- Backpropagation using the neuron activation functions derivative - instead of hardcoded sigmoid derivative - Added missing activation functions derivatives - Sigmoid forced for the output layer - Updated ThresholdedReLU default threshold to 0 (acts as a ReLU) - Unit tests for derivatives - Unit tests for classifiers using different activation functions - Added missing docs
50 lines
1.1 KiB
PHP
50 lines
1.1 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Tests\NeuralNetwork\ActivationFunction;
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use Phpml\NeuralNetwork\ActivationFunction\BinaryStep;
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use PHPUnit\Framework\TestCase;
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class BinaryStepTest extends TestCase
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{
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/**
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* @dataProvider binaryStepProvider
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*/
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public function testBinaryStepActivationFunction($expected, $value): void
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{
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$binaryStep = new BinaryStep();
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$this->assertEquals($expected, $binaryStep->compute($value));
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}
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public function binaryStepProvider(): array
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{
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return [
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[1, 1],
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[1, 0],
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[0, -0.1],
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];
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}
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/**
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* @dataProvider binaryStepDerivativeProvider
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*/
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public function testBinaryStepDerivative($expected, $value): void
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{
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$binaryStep = new BinaryStep();
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$activatedValue = $binaryStep->compute($value);
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$this->assertEquals($expected, $binaryStep->differentiate($value, $activatedValue));
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}
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public function binaryStepDerivativeProvider(): array
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{
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return [
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[0, -1],
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[1, 0],
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[0, 1],
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];
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}
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}
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