61 lines
2.0 KiB
PHP
61 lines
2.0 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Tests\Regression;
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use Phpml\ModelManager;
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use Phpml\Regression\SVR;
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use Phpml\SupportVectorMachine\Kernel;
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use PHPUnit\Framework\TestCase;
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class SVRTest extends TestCase
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{
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public function testPredictSingleFeatureSamples(): void
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{
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$delta = 0.01;
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$samples = [[60], [61], [62], [63], [65]];
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$targets = [3.1, 3.6, 3.8, 4, 4.1];
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$regression = new SVR(Kernel::LINEAR);
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$regression->train($samples, $targets);
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self::assertEqualsWithDelta(4.03, $regression->predict([64]), $delta);
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}
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public function testPredictMultiFeaturesSamples(): void
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{
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$delta = 0.01;
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$samples = [[73676, 1996], [77006, 1998], [10565, 2000], [146088, 1995], [15000, 2001], [65940, 2000], [9300, 2000], [93739, 1996], [153260, 1994], [17764, 2002], [57000, 1998], [15000, 2000]];
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$targets = [2000, 2750, 15500, 960, 4400, 8800, 7100, 2550, 1025, 5900, 4600, 4400];
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$regression = new SVR(Kernel::LINEAR);
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$regression->train($samples, $targets);
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self::assertEqualsWithDelta([4109.82, 4112.28], $regression->predict([[60000, 1996], [60000, 2000]]), $delta);
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}
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public function testSaveAndRestore(): void
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{
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$samples = [[60], [61], [62], [63], [65]];
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$targets = [3.1, 3.6, 3.8, 4, 4.1];
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$regression = new SVR(Kernel::LINEAR);
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$regression->train($samples, $targets);
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$testSamples = [64];
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$predicted = $regression->predict($testSamples);
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$filename = 'svr-test'.random_int(100, 999).'-'.uniqid('', false);
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$filepath = (string) tempnam(sys_get_temp_dir(), $filename);
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$modelManager = new ModelManager();
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$modelManager->saveToFile($regression, $filepath);
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$restoredRegression = $modelManager->restoreFromFile($filepath);
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self::assertEquals($regression, $restoredRegression);
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self::assertEquals($predicted, $restoredRegression->predict($testSamples));
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}
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}
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