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726cf4cddf
* travis: move coveralls here, decouple from package * composer: use PSR4 * phpunit: simpler config * travis: add ecs run * composer: add ecs dev * use standard vendor/bin directory for dependency bins, confuses with local bins and require gitignore handling * ecs: add PSR2 * [cs] PSR2 spacing fixes * [cs] PSR2 class name fix * [cs] PHP7 fixes - return semicolon spaces, old rand functions, typehints * [cs] fix less strict typehints * fix typehints to make tests pass * ecs: ignore typehint-less elements * [cs] standardize arrays * [cs] standardize docblock, remove unused comments * [cs] use self where possible * [cs] sort class elements, from public to private * [cs] do not use yoda (found less yoda-cases, than non-yoda) * space * [cs] do not assign in condition * [cs] use namespace imports if possible * [cs] use ::class over strings * [cs] fix defaults for arrays properties, properties and constants single spacing * cleanup ecs comments * [cs] use item per line in multi-items array * missing line * misc * rebase
65 lines
2.4 KiB
PHP
65 lines
2.4 KiB
PHP
<?php
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declare(strict_types=1);
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namespace tests\Phpml\Classification\Ensemble;
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use Phpml\Classification\Ensemble\AdaBoost;
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use Phpml\ModelManager;
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use PHPUnit\Framework\TestCase;
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class AdaBoostTest extends TestCase
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{
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public function testPredictSingleSample()
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{
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// AND problem
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$samples = [[0.1, 0.3], [1, 0], [0, 1], [1, 1], [0.9, 0.8], [1.1, 1.1]];
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$targets = [0, 0, 0, 1, 1, 1];
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$classifier = new AdaBoost();
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$classifier->train($samples, $targets);
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$this->assertEquals(0, $classifier->predict([0.1, 0.2]));
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$this->assertEquals(0, $classifier->predict([0.1, 0.99]));
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$this->assertEquals(1, $classifier->predict([1.1, 0.8]));
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// OR problem
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$samples = [[0, 0], [0.1, 0.2], [0.2, 0.1], [1, 0], [0, 1], [1, 1]];
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$targets = [0, 0, 0, 1, 1, 1];
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$classifier = new AdaBoost();
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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(1, $classifier->predict([0.1, 0.99]));
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$this->assertEquals(1, $classifier->predict([1.1, 0.8]));
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// XOR problem
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$samples = [[0.1, 0.2], [1., 1.], [0.9, 0.8], [0., 1.], [1., 0.], [0.2, 0.8]];
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$targets = [0, 0, 0, 1, 1, 1];
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$classifier = new AdaBoost(5);
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$classifier->train($samples, $targets);
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$this->assertEquals(0, $classifier->predict([0.1, 0.1]));
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$this->assertEquals(1, $classifier->predict([0, 0.999]));
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$this->assertEquals(0, $classifier->predict([1.1, 0.8]));
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return $classifier;
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}
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public function testSaveAndRestore(): void
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{
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// Instantinate new Percetron trained for OR problem
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$samples = [[0, 0], [1, 0], [0, 1], [1, 1]];
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$targets = [0, 1, 1, 1];
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$classifier = new AdaBoost();
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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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$filename = 'adaboost-test-'.random_int(100, 999).'-'.uniqid();
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$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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