105 lines
4.5 KiB
PHP
105 lines
4.5 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Tests\Classification\Linear;
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use Phpml\Classification\Linear\Perceptron;
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use Phpml\Exception\InvalidArgumentException;
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use Phpml\ModelManager;
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use PHPUnit\Framework\TestCase;
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class PerceptronTest extends TestCase
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{
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public function testPerceptronThrowWhenLearningRateOutOfRange(): void
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{
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$this->expectException(InvalidArgumentException::class);
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$this->expectExceptionMessage('Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)');
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new Perceptron(0, 5000);
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}
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public function testPerceptronThrowWhenMaxIterationsOutOfRange(): void
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{
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$this->expectException(InvalidArgumentException::class);
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$this->expectExceptionMessage('Maximum number of iterations must be an integer greater than 0');
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new Perceptron(0.001, 0);
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}
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public function testPredictSingleSample(): void
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{
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// AND problem
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$samples = [[0, 0], [1, 0], [0, 1], [1, 1], [0.6, 0.6]];
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$targets = [0, 0, 0, 1, 1];
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$classifier = new Perceptron(0.001, 5000);
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$classifier->setEarlyStop(false);
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$classifier->train($samples, $targets);
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self::assertEquals(0, $classifier->predict([0.1, 0.2]));
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self::assertEquals(0, $classifier->predict([0, 1]));
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self::assertEquals(1, $classifier->predict([1.1, 0.8]));
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// OR problem
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$samples = [[0.1, 0.1], [0.4, 0.], [0., 0.3], [1, 0], [0, 1], [1, 1]];
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$targets = [0, 0, 0, 1, 1, 1];
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$classifier = new Perceptron(0.001, 5000, false);
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$classifier->setEarlyStop(false);
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$classifier->train($samples, $targets);
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self::assertEquals(0, $classifier->predict([0., 0.]));
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self::assertEquals(1, $classifier->predict([0.1, 0.99]));
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self::assertEquals(1, $classifier->predict([1.1, 0.8]));
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// By use of One-v-Rest, Perceptron can perform multi-class classification
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// The samples should be separable by lines perpendicular to the dimensions
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$samples = [
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[0, 0], [0, 1], [1, 0], [1, 1], // First group : a cluster at bottom-left corner in 2D
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[5, 5], [6, 5], [5, 6], [7, 5], // Second group: another cluster at the middle-right
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[3, 10], [3, 10], [3, 8], [3, 9], // Third group : cluster at the top-middle
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];
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$targets = [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2];
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$classifier = new Perceptron();
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$classifier->setEarlyStop(false);
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$classifier->train($samples, $targets);
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self::assertEquals(0, $classifier->predict([0.5, 0.5]));
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self::assertEquals(1, $classifier->predict([6.0, 5.0]));
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self::assertEquals(2, $classifier->predict([3.0, 9.5]));
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// Extra partial training should lead to the same results.
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$classifier->partialTrain([[0, 1], [1, 0]], [0, 0], [0, 1, 2]);
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self::assertEquals(0, $classifier->predict([0.5, 0.5]));
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self::assertEquals(1, $classifier->predict([6.0, 5.0]));
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self::assertEquals(2, $classifier->predict([3.0, 9.5]));
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// Train should clear previous data.
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$samples = [
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[0, 0], [0, 1], [1, 0], [1, 1], // First group : a cluster at bottom-left corner in 2D
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[5, 5], [6, 5], [5, 6], [7, 5], // Second group: another cluster at the middle-right
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[3, 10], [3, 10], [3, 8], [3, 9], // Third group : cluster at the top-middle
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];
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$targets = [2, 2, 2, 2, 0, 0, 0, 0, 1, 1, 1, 1];
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$classifier->train($samples, $targets);
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self::assertEquals(2, $classifier->predict([0.5, 0.5]));
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self::assertEquals(0, $classifier->predict([6.0, 5.0]));
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self::assertEquals(1, $classifier->predict([3.0, 9.5]));
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}
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public function testSaveAndRestore(): void
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{
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// Instantinate new Percetron trained for OR problem
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$samples = [[0, 0], [1, 0], [0, 1], [1, 1]];
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$targets = [0, 1, 1, 1];
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$classifier = new Perceptron();
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$classifier->train($samples, $targets);
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$testSamples = [[0, 1], [1, 1], [0.2, 0.1]];
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$predicted = $classifier->predict($testSamples);
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$filename = 'perceptron-test-'.random_int(100, 999).'-'.uniqid('', false);
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$filepath = (string) tempnam(sys_get_temp_dir(), $filename);
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$modelManager = new ModelManager();
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$modelManager->saveToFile($classifier, $filepath);
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$restoredClassifier = $modelManager->restoreFromFile($filepath);
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self::assertEquals($classifier, $restoredClassifier);
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self::assertEquals($predicted, $restoredClassifier->predict($testSamples));
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}
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}
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