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* tests: update to PHPUnit 6.0 with rector * fix namespaces on tests * composer + tests: use standard test namespace naming * update travis * resolve conflict * phpstan lvl 2 * phpstan lvl 3 * phpstan lvl 4 * phpstan lvl 5 * phpstan lvl 6 * phpstan lvl 7 * level max * resolve conflict * [cs] clean empty docs * composer: bump to PHPUnit 6.4 * cleanup * composer + travis: add phpstan * phpstan lvl 1 * composer: update dev deps * phpstan fixes * update Contributing with new tools * docs: link fixes, PHP version update * composer: drop php-cs-fixer, cs already handled by ecs * ecs: add old set rules * [cs] apply rest of rules
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 Phpml\Tests\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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