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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
87 lines
3.7 KiB
PHP
87 lines
3.7 KiB
PHP
<?php
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declare(strict_types=1);
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namespace tests\Phpml\Classification\Linear;
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use Phpml\Classification\Linear\Adaline;
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use Phpml\ModelManager;
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use PHPUnit\Framework\TestCase;
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class AdalineTest extends TestCase
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{
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public function testPredictSingleSample(): void
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{
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// AND problem
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$samples = [[0, 0], [1, 0], [0, 1], [1, 1]];
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$targets = [0, 0, 0, 1];
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$classifier = new Adaline();
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$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], [1, 0], [0, 1], [1, 1]];
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$targets = [0, 1, 1, 1];
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$classifier = new Adaline();
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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, $classifier->predict([1.1, 0.8]));
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// By use of One-v-Rest, Adaline can perform multi-class classification
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// The samples should be separable by lines perpendicular to the dimensions
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$samples = [
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[0, 0], [0, 1], [1, 0], [1, 1], // First group : a cluster at bottom-left corner in 2D
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[5, 5], [6, 5], [5, 6], [7, 5], // Second group: another cluster at the middle-right
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[3, 10], [3, 10], [3, 8], [3, 9], // Third group : cluster at the top-middle
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];
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$targets = [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2];
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$classifier = new Adaline();
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$classifier->train($samples, $targets);
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$this->assertEquals(0, $classifier->predict([0.5, 0.5]));
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$this->assertEquals(1, $classifier->predict([6.0, 5.0]));
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$this->assertEquals(2, $classifier->predict([3.0, 9.5]));
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// Extra partial training should lead to the same results.
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$classifier->partialTrain([[0, 1], [1, 0]], [0, 0], [0, 1, 2]);
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$this->assertEquals(0, $classifier->predict([0.5, 0.5]));
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$this->assertEquals(1, $classifier->predict([6.0, 5.0]));
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$this->assertEquals(2, $classifier->predict([3.0, 9.5]));
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// Train should clear previous data.
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$samples = [
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[0, 0], [0, 1], [1, 0], [1, 1], // First group : a cluster at bottom-left corner in 2D
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[5, 5], [6, 5], [5, 6], [7, 5], // Second group: another cluster at the middle-right
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[3, 10], [3, 10], [3, 8], [3, 9], // Third group : cluster at the top-middle
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];
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$targets = [2, 2, 2, 2, 0, 0, 0, 0, 1, 1, 1, 1];
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$classifier->train($samples, $targets);
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$this->assertEquals(2, $classifier->predict([0.5, 0.5]));
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$this->assertEquals(0, $classifier->predict([6.0, 5.0]));
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$this->assertEquals(1, $classifier->predict([3.0, 9.5]));
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}
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public function testSaveAndRestore(): void
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{
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// Instantinate new Percetron trained for OR problem
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$samples = [[0, 0], [1, 0], [0, 1], [1, 1]];
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$targets = [0, 1, 1, 1];
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$classifier = new Adaline();
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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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$filename = 'adaline-test-'.random_int(100, 999).'-'.uniqid();
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$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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