72 lines
2.3 KiB
PHP
72 lines
2.3 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\NaiveBayes;
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use Phpml\ModelManager;
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use PHPUnit\Framework\TestCase;
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class NaiveBayesTest extends TestCase
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{
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public function testPredictSingleSample(): void
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{
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$samples = [[5, 1, 1], [1, 5, 1], [1, 1, 5]];
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$labels = ['a', 'b', 'c'];
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$classifier = new NaiveBayes();
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$classifier->train($samples, $labels);
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$this->assertEquals('a', $classifier->predict([3, 1, 1]));
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$this->assertEquals('b', $classifier->predict([1, 4, 1]));
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$this->assertEquals('c', $classifier->predict([1, 1, 6]));
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}
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public function testPredictArrayOfSamples(): void
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{
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$trainSamples = [[5, 1, 1], [1, 5, 1], [1, 1, 5]];
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$trainLabels = ['a', 'b', 'c'];
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$testSamples = [[3, 1, 1], [5, 1, 1], [4, 3, 8], [1, 1, 2], [2, 3, 2], [1, 2, 1], [9, 5, 1], [3, 1, 2]];
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$testLabels = ['a', 'a', 'c', 'c', 'b', 'b', 'a', 'a'];
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$classifier = new NaiveBayes();
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$classifier->train($trainSamples, $trainLabels);
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$predicted = $classifier->predict($testSamples);
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$this->assertEquals($testLabels, $predicted);
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// Feed an extra set of training data.
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$samples = [[1, 1, 6]];
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$labels = ['d'];
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$classifier->train($samples, $labels);
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$testSamples = [[1, 1, 6], [5, 1, 1]];
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$testLabels = ['d', 'a'];
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$this->assertEquals($testLabels, $classifier->predict($testSamples));
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}
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public function testSaveAndRestore(): void
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{
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$trainSamples = [[5, 1, 1], [1, 5, 1], [1, 1, 5]];
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$trainLabels = ['a', 'b', 'c'];
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$testSamples = [[3, 1, 1], [5, 1, 1], [4, 3, 8]];
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$testLabels = ['a', 'a', 'c'];
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$classifier = new NaiveBayes();
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$classifier->train($trainSamples, $trainLabels);
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$predicted = $classifier->predict($testSamples);
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$filename = 'naive-bayes-test-'.random_int(100, 999).'-'.uniqid();
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$filepath = 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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$this->assertEquals($classifier, $restoredClassifier);
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$this->assertEquals($predicted, $restoredClassifier->predict($testSamples));
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}
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}
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