php-ml/tests/Phpml/Math/Statistic/CovarianceTest.php

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<?php
declare(strict_types=1);
2017-09-02 19:39:59 +00:00
namespace tests\Phpml\Math\Statistic;
use Phpml\Math\Statistic\Covariance;
use Phpml\Math\Statistic\Mean;
use PHPUnit\Framework\TestCase;
class CovarianceTest extends TestCase
{
public function testSimpleCovariance(): void
{
// Acceptable error
$epsilon = 0.001;
// First a simple example whose result is known and given in
// http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf
$matrix = [
[0.69, 0.49],
[-1.31, -1.21],
[0.39, 0.99],
[0.09, 0.29],
[1.29, 1.09],
[0.49, 0.79],
[0.19, -0.31],
[-0.81, -0.81],
[-0.31, -0.31],
[-0.71, -1.01],
];
$knownCovariance = [
[0.616555556, 0.615444444],
[0.615444444, 0.716555556], ];
$x = array_column($matrix, 0);
$y = array_column($matrix, 1);
// Calculate only one covariance value: Cov(x, y)
$cov1 = Covariance::fromDataset($matrix, 0, 0);
$this->assertEquals($cov1, $knownCovariance[0][0], '', $epsilon);
$cov1 = Covariance::fromXYArrays($x, $x);
$this->assertEquals($cov1, $knownCovariance[0][0], '', $epsilon);
$cov2 = Covariance::fromDataset($matrix, 0, 1);
$this->assertEquals($cov2, $knownCovariance[0][1], '', $epsilon);
$cov2 = Covariance::fromXYArrays($x, $y);
$this->assertEquals($cov2, $knownCovariance[0][1], '', $epsilon);
// Second: calculation cov matrix with automatic means for each column
$covariance = Covariance::covarianceMatrix($matrix);
$this->assertEquals($knownCovariance, $covariance, '', $epsilon);
// Thirdly, CovMatrix: Means are precalculated and given to the method
$x = array_column($matrix, 0);
$y = array_column($matrix, 1);
$meanX = Mean::arithmetic($x);
$meanY = Mean::arithmetic($y);
$covariance = Covariance::covarianceMatrix($matrix, [$meanX, $meanY]);
$this->assertEquals($knownCovariance, $covariance, '', $epsilon);
}
}