60 lines
2.2 KiB
PHP
60 lines
2.2 KiB
PHP
<?php
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declare(strict_types=1);
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namespace tests\Classification\Linear;
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use Phpml\Classification\Linear\DecisionStump;
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use Phpml\ModelManager;
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use PHPUnit\Framework\TestCase;
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class DecisionStumpTest extends TestCase
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{
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public function testPredictSingleSample()
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{
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// Samples should be separable with a line perpendicular to any dimension
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// given in the dataset
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// First: horizontal test
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$samples = [[0, 0], [1, 0], [0, 1], [1, 1]];
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$targets = [0, 0, 1, 1];
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$classifier = new DecisionStump();
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$classifier->train($samples, $targets);
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$this->assertEquals(0, $classifier->predict([0.1, 0.2]));
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$this->assertEquals(0, $classifier->predict([1.1, 0.2]));
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$this->assertEquals(1, $classifier->predict([0.1, 0.99]));
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$this->assertEquals(1, $classifier->predict([1.1, 0.8]));
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// Then: vertical test
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$samples = [[0, 0], [1, 0], [0, 1], [1, 1]];
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$targets = [0, 1, 0, 1];
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$classifier = new DecisionStump();
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$classifier->train($samples, $targets);
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$this->assertEquals(0, $classifier->predict([0.1, 0.2]));
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$this->assertEquals(0, $classifier->predict([0.1, 1.1]));
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$this->assertEquals(1, $classifier->predict([1.0, 0.99]));
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$this->assertEquals(1, $classifier->predict([1.1, 0.1]));
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return $classifier;
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}
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public function testSaveAndRestore()
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{
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// Instantinate new Percetron trained for OR problem
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$samples = [[0, 0], [1, 0], [0, 1], [1, 1]];
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$targets = [0, 1, 1, 1];
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$classifier = new DecisionStump();
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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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$filename = 'dstump-test-'.rand(100, 999).'-'.uniqid();
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$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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