mirror of
https://github.com/Llewellynvdm/php-ml.git
synced 2024-12-03 02:18:21 +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
56 lines
1.2 KiB
PHP
56 lines
1.2 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\Gaussian;
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use PHPUnit\Framework\TestCase;
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class GaussianTest extends TestCase
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{
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/**
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* @dataProvider gaussianProvider
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*/
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public function testGaussianActivationFunction($expected, $value): void
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{
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$gaussian = new Gaussian();
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$this->assertEquals($expected, $gaussian->compute($value), '', 0.001);
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}
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public function gaussianProvider(): array
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{
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return [
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[0.367, 1],
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[1, 0],
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[0.367, -1],
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[0, 3],
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[0, -3],
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];
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}
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/**
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* @dataProvider gaussianDerivativeProvider
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*/
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public function testGaussianDerivative($expected, $value): void
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{
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$gaussian = new Gaussian();
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$activatedValue = $gaussian->compute($value);
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$this->assertEquals($expected, $gaussian->differentiate($value, $activatedValue), '', 0.001);
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}
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public function gaussianDerivativeProvider(): array
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{
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return [
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[0, -5],
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[0.735, -1],
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[0.779, -0.5],
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[0, 0],
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[-0.779, 0.5],
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[-0.735, 1],
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[0, 5],
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];
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}
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}
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