php-ml/tests/Phpml/Classification/Linear/PerceptronTest.php

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<?php
declare(strict_types=1);
namespace tests\Classification\Linear;
use Phpml\Classification\Linear\Perceptron;
use Phpml\ModelManager;
use PHPUnit\Framework\TestCase;
class PerceptronTest extends TestCase
{
public function testPredictSingleSample()
{
// AND problem
$samples = [[0, 0], [1, 0], [0, 1], [1, 1], [0.9, 0.8]];
$targets = [0, 0, 0, 1, 1];
$classifier = new Perceptron(0.001, 5000);
$classifier->train($samples, $targets);
$this->assertEquals(0, $classifier->predict([0.1, 0.2]));
$this->assertEquals(0, $classifier->predict([0.1, 0.99]));
$this->assertEquals(1, $classifier->predict([1.1, 0.8]));
// OR problem
$samples = [[0, 0], [0.1, 0.2], [1, 0], [0, 1], [1, 1]];
$targets = [0, 0, 1, 1, 1];
$classifier = new Perceptron(0.001, 5000);
$classifier->train($samples, $targets);
$this->assertEquals(0, $classifier->predict([0, 0]));
$this->assertEquals(1, $classifier->predict([0.1, 0.99]));
$this->assertEquals(1, $classifier->predict([1.1, 0.8]));
return $classifier;
}
public function testSaveAndRestore()
{
// Instantinate new Percetron trained for OR problem
$samples = [[0, 0], [1, 0], [0, 1], [1, 1]];
$targets = [0, 1, 1, 1];
$classifier = new Perceptron();
$classifier->train($samples, $targets);
$testSamples = [[0, 1], [1, 1], [0.2, 0.1]];
$predicted = $classifier->predict($testSamples);
$filename = 'perceptron-test-'.rand(100, 999).'-'.uniqid();
$filepath = tempnam(sys_get_temp_dir(), $filename);
$modelManager = new ModelManager();
$modelManager->saveToFile($classifier, $filepath);
$restoredClassifier = $modelManager->restoreFromFile($filepath);
$this->assertEquals($classifier, $restoredClassifier);
$this->assertEquals($predicted, $restoredClassifier->predict($testSamples));
}
}