php-ml/tests/Phpml/Classifier/KNearestNeighborsTest.php
2016-04-08 22:49:17 +02:00

59 lines
2.0 KiB
PHP

<?php
declare (strict_types = 1);
namespace tests\Classifier;
use Phpml\Classifier\KNearestNeighbors;
use Phpml\CrossValidation\RandomSplit;
use Phpml\Dataset\Demo\Iris;
use Phpml\Metric\Accuracy;
class KNearestNeighborsTest extends \PHPUnit_Framework_TestCase
{
public function testPredictSingleSampleWithDefaultK()
{
$samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
$labels = ['a', 'a', 'a', 'b', 'b', 'b'];
$classifier = new KNearestNeighbors();
$classifier->train($samples, $labels);
$this->assertEquals('b', $classifier->predict([3, 2]));
$this->assertEquals('b', $classifier->predict([5, 1]));
$this->assertEquals('b', $classifier->predict([4, 3]));
$this->assertEquals('b', $classifier->predict([4, -5]));
$this->assertEquals('a', $classifier->predict([2, 3]));
$this->assertEquals('a', $classifier->predict([1, 2]));
$this->assertEquals('a', $classifier->predict([1, 5]));
$this->assertEquals('a', $classifier->predict([3, 10]));
}
public function testPredictArrayOfSamples()
{
$trainSamples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
$trainLabels = ['a', 'a', 'a', 'b', 'b', 'b'];
$testSamples = [[3, 2], [5, 1], [4, 3], [4, -5], [2, 3], [1, 2], [1, 5], [3, 10]];
$testLabels = ['b', 'b', 'b', 'b', 'a', 'a', 'a', 'a'];
$classifier = new KNearestNeighbors();
$classifier->train($trainSamples, $trainLabels);
$predicted = $classifier->predict($testSamples);
$this->assertEquals($testLabels, $predicted);
}
public function testAccuracyOnIrisDataset()
{
$dataset = new RandomSplit(new Iris(), $testSize = 0.5, $seed = 123);
$classifier = new KNearestNeighbors($k = 4);
$classifier->train($dataset->getTrainSamples(), $dataset->getTrainLabels());
$predicted = $classifier->predict($dataset->getTestSamples());
$score = Accuracy::score($dataset->getTestLabels(), $predicted);
$this->assertEquals(0.96, $score);
}
}