85 lines
3.2 KiB
PHP
85 lines
3.2 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Tests\Classification;
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use Phpml\Classification\KNearestNeighbors;
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use Phpml\Math\Distance\Chebyshev;
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use Phpml\ModelManager;
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use PHPUnit\Framework\TestCase;
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class KNearestNeighborsTest extends TestCase
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{
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public function testPredictSingleSampleWithDefaultK(): void
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{
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$samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
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$labels = ['a', 'a', 'a', 'b', 'b', 'b'];
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$classifier = new KNearestNeighbors();
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$classifier->train($samples, $labels);
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self::assertEquals('b', $classifier->predict([3, 2]));
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self::assertEquals('b', $classifier->predict([5, 1]));
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self::assertEquals('b', $classifier->predict([4, 3]));
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self::assertEquals('b', $classifier->predict([4, -5]));
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self::assertEquals('a', $classifier->predict([2, 3]));
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self::assertEquals('a', $classifier->predict([1, 2]));
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self::assertEquals('a', $classifier->predict([1, 5]));
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self::assertEquals('a', $classifier->predict([3, 10]));
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}
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public function testPredictArrayOfSamples(): void
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{
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$trainSamples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
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$trainLabels = ['a', 'a', 'a', 'b', 'b', 'b'];
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$testSamples = [[3, 2], [5, 1], [4, 3], [4, -5], [2, 3], [1, 2], [1, 5], [3, 10]];
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$testLabels = ['b', 'b', 'b', 'b', 'a', 'a', 'a', 'a'];
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$classifier = new KNearestNeighbors();
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$classifier->train($trainSamples, $trainLabels);
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$predicted = $classifier->predict($testSamples);
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self::assertEquals($testLabels, $predicted);
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}
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public function testPredictArrayOfSamplesUsingChebyshevDistanceMetric(): void
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{
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$trainSamples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
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$trainLabels = ['a', 'a', 'a', 'b', 'b', 'b'];
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$testSamples = [[3, 2], [5, 1], [4, 3], [4, -5], [2, 3], [1, 2], [1, 5], [3, 10]];
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$testLabels = ['b', 'b', 'b', 'b', 'a', 'a', 'a', 'a'];
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$classifier = new KNearestNeighbors(3, new Chebyshev());
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$classifier->train($trainSamples, $trainLabels);
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$predicted = $classifier->predict($testSamples);
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self::assertEquals($testLabels, $predicted);
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}
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public function testSaveAndRestore(): void
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{
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$trainSamples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
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$trainLabels = ['a', 'a', 'a', 'b', 'b', 'b'];
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$testSamples = [[3, 2], [5, 1], [4, 3], [4, -5], [2, 3], [1, 2], [1, 5], [3, 10]];
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// Using non-default constructor parameters to check that their values are restored.
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$classifier = new KNearestNeighbors(3, new Chebyshev());
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$classifier->train($trainSamples, $trainLabels);
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$predicted = $classifier->predict($testSamples);
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$filename = 'knearest-neighbors-test-'.random_int(100, 999).'-'.uniqid('', false);
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$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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