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* 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
63 lines
2.1 KiB
PHP
63 lines
2.1 KiB
PHP
<?php
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declare(strict_types=1);
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namespace tests\Phpml\Math\Statistic;
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use Phpml\Math\Statistic\Covariance;
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use Phpml\Math\Statistic\Mean;
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use PHPUnit\Framework\TestCase;
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class CovarianceTest extends TestCase
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{
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public function testSimpleCovariance(): void
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{
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// Acceptable error
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$epsilon = 0.001;
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// First a simple example whose result is known and given in
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// http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf
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$matrix = [
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[0.69, 0.49],
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[-1.31, -1.21],
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[0.39, 0.99],
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[0.09, 0.29],
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[1.29, 1.09],
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[0.49, 0.79],
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[0.19, -0.31],
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[-0.81, -0.81],
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[-0.31, -0.31],
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[-0.71, -1.01],
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];
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$knownCovariance = [
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[0.616555556, 0.615444444],
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[0.615444444, 0.716555556], ];
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$x = array_column($matrix, 0);
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$y = array_column($matrix, 1);
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// Calculate only one covariance value: Cov(x, y)
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$cov1 = Covariance::fromDataset($matrix, 0, 0);
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$this->assertEquals($cov1, $knownCovariance[0][0], '', $epsilon);
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$cov1 = Covariance::fromXYArrays($x, $x);
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$this->assertEquals($cov1, $knownCovariance[0][0], '', $epsilon);
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$cov2 = Covariance::fromDataset($matrix, 0, 1);
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$this->assertEquals($cov2, $knownCovariance[0][1], '', $epsilon);
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$cov2 = Covariance::fromXYArrays($x, $y);
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$this->assertEquals($cov2, $knownCovariance[0][1], '', $epsilon);
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// Second: calculation cov matrix with automatic means for each column
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$covariance = Covariance::covarianceMatrix($matrix);
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$this->assertEquals($knownCovariance, $covariance, '', $epsilon);
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// Thirdly, CovMatrix: Means are precalculated and given to the method
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$x = array_column($matrix, 0);
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$y = array_column($matrix, 1);
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$meanX = Mean::arithmetic($x);
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$meanY = Mean::arithmetic($y);
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$covariance = Covariance::covarianceMatrix($matrix, [$meanX, $meanY]);
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$this->assertEquals($knownCovariance, $covariance, '', $epsilon);
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}
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}
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