87 lines
3.4 KiB
PHP
87 lines
3.4 KiB
PHP
<?php
|
|
|
|
declare(strict_types=1);
|
|
|
|
namespace Phpml\Tests\DimensionReduction;
|
|
|
|
use Phpml\DimensionReduction\KernelPCA;
|
|
use Phpml\Exception\InvalidArgumentException;
|
|
use Phpml\Exception\InvalidOperationException;
|
|
use PHPUnit\Framework\TestCase;
|
|
|
|
class KernelPCATest extends TestCase
|
|
{
|
|
public function testKernelPCA(): void
|
|
{
|
|
// Acceptable error
|
|
$epsilon = 0.001;
|
|
|
|
// A simple example whose result is known beforehand
|
|
$data = [
|
|
[2, 2], [1.5, 1], [1., 1.5], [1., 1.],
|
|
[2., 1.], [2, 2.5], [2., 3.], [1.5, 3],
|
|
[1., 2.5], [1., 2.7], [1., 3.], [1, 3],
|
|
[1, 2], [1.5, 2], [1.5, 2.2], [1.3, 1.7],
|
|
[1.7, 1.3], [1.5, 1.5], [1.5, 1.6], [1.6, 2],
|
|
[1.7, 2.1], [1.3, 1.3], [1.3, 2.2], [1.4, 2.4],
|
|
];
|
|
$transformed = [
|
|
[0.016485613899708], [-0.089805657741674], [-0.088695974245924], [-0.069761503810802],
|
|
[-0.068049558133392], [-0.054702087779187], [-0.063229228729333], [-0.06852813588679],
|
|
[-0.10098315410297], [-0.15617881000654], [-0.21266832077299], [-0.21266832077299],
|
|
[-0.039234518840831], [0.40858295942991], [0.40110375047242], [-0.10555116296691],
|
|
[-0.13128352866095], [-0.20865959471756], [-0.17531601535848], [0.4240660966961],
|
|
[0.36351946685163], [-0.14334173054136], [0.22454914091011], [0.15035027480881], ];
|
|
|
|
$kpca = new KernelPCA(KernelPCA::KERNEL_RBF, null, 1, 15.);
|
|
$reducedData = $kpca->fit($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): void {
|
|
self::assertEqualsWithDelta(abs($val1), abs($val2), $epsilon);
|
|
}, $transformed, $reducedData);
|
|
|
|
// Fitted KernelPCA object can also transform an arbitrary sample of the
|
|
// same dimensionality with the original dataset
|
|
$newData = [1.25, 2.25];
|
|
$newTransformed = [0.18956227539216];
|
|
$newTransformed2 = $kpca->transform($newData);
|
|
self::assertEqualsWithDelta(abs($newTransformed[0]), abs($newTransformed2[0]), $epsilon);
|
|
}
|
|
|
|
public function testKernelPCAThrowWhenKernelInvalid(): void
|
|
{
|
|
$this->expectException(InvalidArgumentException::class);
|
|
$this->expectExceptionMessage('KernelPCA can be initialized with the following kernels only: Linear, RBF, Sigmoid and Laplacian');
|
|
new KernelPCA(0, null, 1, 15.);
|
|
}
|
|
|
|
public function testTransformThrowWhenNotFitted(): void
|
|
{
|
|
$samples = [1, 0];
|
|
|
|
$kpca = new KernelPCA(KernelPCA::KERNEL_RBF, null, 1, 15.);
|
|
|
|
$this->expectException(InvalidOperationException::class);
|
|
$this->expectExceptionMessage('KernelPCA has not been fitted with respect to original dataset, please run KernelPCA::fit() first');
|
|
$kpca->transform($samples);
|
|
}
|
|
|
|
public function testTransformThrowWhenMultiDimensionalArrayGiven(): void
|
|
{
|
|
$samples = [
|
|
[1, 0],
|
|
[1, 1],
|
|
];
|
|
|
|
$kpca = new KernelPCA(KernelPCA::KERNEL_RBF, null, 1, 15.);
|
|
$kpca->fit($samples);
|
|
|
|
$this->expectException(InvalidArgumentException::class);
|
|
$this->expectExceptionMessage('KernelPCA::transform() accepts only one-dimensional arrays');
|
|
$kpca->transform($samples);
|
|
}
|
|
}
|