php-ml/tests/Preprocessing/NormalizerTest.php
Marcin Michalski db82afa263 Update to phpunit 8 and bump min php to 7.2 (#367)
* Update to phpunit 8

* Require at least PHP 7.2
2019-04-10 20:42:59 +02:00

138 lines
3.4 KiB
PHP

<?php
declare(strict_types=1);
namespace Phpml\Tests\Preprocessing;
use Phpml\Exception\NormalizerException;
use Phpml\Preprocessing\Normalizer;
use PHPUnit\Framework\TestCase;
class NormalizerTest extends TestCase
{
public function testThrowExceptionOnInvalidNorm(): void
{
$this->expectException(NormalizerException::class);
new Normalizer(99);
}
public function testNormalizeSamplesWithL2Norm(): void
{
$samples = [
[1, -1, 2],
[2, 0, 0],
[0, 1, -1],
];
$normalized = [
[0.4, -0.4, 0.81],
[1.0, 0.0, 0.0],
[0.0, 0.7, -0.7],
];
$normalizer = new Normalizer();
$normalizer->transform($samples);
self::assertEqualsWithDelta($normalized, $samples, $delta = 0.01);
}
public function testNormalizeSamplesWithL1Norm(): void
{
$samples = [
[1, -1, 2],
[2, 0, 0],
[0, 1, -1],
];
$normalized = [
[0.25, -0.25, 0.5],
[1.0, 0.0, 0.0],
[0.0, 0.5, -0.5],
];
$normalizer = new Normalizer(Normalizer::NORM_L1);
$normalizer->transform($samples);
self::assertEqualsWithDelta($normalized, $samples, $delta = 0.01);
}
public function testFitNotChangeNormalizerBehavior(): void
{
$samples = [
[1, -1, 2],
[2, 0, 0],
[0, 1, -1],
];
$normalized = [
[0.4, -0.4, 0.81],
[1.0, 0.0, 0.0],
[0.0, 0.7, -0.7],
];
$normalizer = new Normalizer();
$normalizer->transform($samples);
self::assertEqualsWithDelta($normalized, $samples, $delta = 0.01);
$normalizer->fit($samples);
self::assertEqualsWithDelta($normalized, $samples, $delta = 0.01);
}
public function testL1NormWithZeroSumCondition(): void
{
$samples = [
[0, 0, 0],
[2, 0, 0],
[0, 1, -1],
];
$normalized = [
[0.33, 0.33, 0.33],
[1.0, 0.0, 0.0],
[0.0, 0.5, -0.5],
];
$normalizer = new Normalizer(Normalizer::NORM_L1);
$normalizer->transform($samples);
self::assertEqualsWithDelta($normalized, $samples, $delta = 0.01);
}
public function testStandardNorm(): void
{
// Generate 10 random vectors of length 3
$samples = [];
srand(time());
for ($i = 0; $i < 10; ++$i) {
$sample = array_fill(0, 3, 0);
for ($k = 0; $k < 3; ++$k) {
$sample[$k] = random_int(1, 100);
}
// Last feature's value shared across samples.
$sample[] = 1;
$samples[] = $sample;
}
// Use standard normalization
$normalizer = new Normalizer(Normalizer::NORM_STD);
$normalizer->transform($samples);
// Values in the vector should be some value between -3 and +3
self::assertCount(10, $samples);
foreach ($samples as $sample) {
$errors = array_filter(
$sample,
function ($element) {
return $element < -3 || $element > 3;
}
);
self::assertCount(0, $errors);
self::assertEquals(0, $sample[3]);
}
}
}