php-ml/tests/Phpml/Preprocessing/NormalizerTest.php
Mustafa Karabulut cf222bcce4 Linear classifiers: Perceptron, Adaline, DecisionStump (#50)
* Linear classifiers

* Code formatting to PSR-2

* Added basic test cases for linear classifiers
2017-02-16 23:23:55 +01:00

132 lines
3.1 KiB
PHP

<?php
declare(strict_types=1);
namespace tests\Preprocessing;
use Phpml\Preprocessing\Normalizer;
use PHPUnit\Framework\TestCase;
class NormalizerTest extends TestCase
{
/**
* @expectedException \Phpml\Exception\NormalizerException
*/
public function testThrowExceptionOnInvalidNorm()
{
new Normalizer(99);
}
public function testNormalizeSamplesWithL2Norm()
{
$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);
$this->assertEquals($normalized, $samples, '', $delta = 0.01);
}
public function testNormalizeSamplesWithL1Norm()
{
$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);
$this->assertEquals($normalized, $samples, '', $delta = 0.01);
}
public function testFitNotChangeNormalizerBehavior()
{
$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);
$this->assertEquals($normalized, $samples, '', $delta = 0.01);
$normalizer->fit($samples);
$this->assertEquals($normalized, $samples, '', $delta = 0.01);
}
public function testL1NormWithZeroSumCondition()
{
$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);
$this->assertEquals($normalized, $samples, '', $delta = 0.01);
}
public function testStandardNorm()
{
// 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] = rand(1, 100);
}
$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
$this->assertCount(10, $samples);
foreach ($samples as $sample) {
$errors = array_filter($sample,
function ($element) {
return $element < -3 || $element > 3;
});
$this->assertCount(0, $errors);
}
}
}