mirror of
https://github.com/Llewellynvdm/php-ml.git
synced 2024-09-22 04:09:01 +00:00
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
63 lines
2.1 KiB
PHP
63 lines
2.1 KiB
PHP
<?php
|
|
|
|
declare(strict_types=1);
|
|
|
|
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);
|
|
}
|
|
}
|