php-ml/tests/Phpml/DimensionReduction/PCATest.php
Mustafa Karabulut a87859dd97 Linear algebra operations, Dimensionality reduction and some other minor changes (#81)
* Lineer Algebra operations

* Covariance

* PCA and KernelPCA

* Tests for PCA, Eigenvalues and Covariance

* KernelPCA update

* KernelPCA and its test

* KernelPCA and its test

* MatrixTest, KernelPCA and PCA tests

* Readme update

* Readme update
2017-04-23 09:03:30 +02:00

58 lines
1.7 KiB
PHP

<?php
declare(strict_types=1);
namespace tests\DimensionReduction;
use Phpml\DimensionReduction\PCA;
use PHPUnit\Framework\TestCase;
class PCATest extends TestCase
{
public function testPCA()
{
// 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
$data = [
[2.5, 2.4],
[0.5, 0.7],
[2.2, 2.9],
[1.9, 2.2],
[3.1, 3.0],
[2.3, 2.7],
[2.0, 1.6],
[1.0, 1.1],
[1.5, 1.6],
[1.1, 0.9]
];
$transformed = [
[-0.827970186], [1.77758033], [-0.992197494],
[-0.274210416], [-1.67580142], [-0.912949103], [0.0991094375],
[1.14457216], [0.438046137], [1.22382056]];
$pca = new PCA(0.90);
$reducedData = $pca->fit($data);
// Due to the fact that the sign of values can be flipped
// during the calculation of eigenValues, we have to compare
// absolute value of the values
array_map(function ($val1, $val2) use ($epsilon) {
$this->assertEquals(abs($val1), abs($val2), '', $epsilon);
}, $transformed, $reducedData);
// Test fitted PCA object to transform an arbitrary sample of the
// same dimensionality with the original dataset
foreach ($data as $i => $row) {
$newRow = [[$transformed[$i]]];
$newRow2= $pca->transform($row);
array_map(function ($val1, $val2) use ($epsilon) {
$this->assertEquals(abs($val1), abs($val2), '', $epsilon);
}, $newRow, $newRow2);
}
}
}