mirror of
https://github.com/Llewellynvdm/php-ml.git
synced 2024-11-16 02:07:08 +00:00
726cf4cddf
* travis: move coveralls here, decouple from package * composer: use PSR4 * phpunit: simpler config * travis: add ecs run * composer: add ecs dev * use standard vendor/bin directory for dependency bins, confuses with local bins and require gitignore handling * ecs: add PSR2 * [cs] PSR2 spacing fixes * [cs] PSR2 class name fix * [cs] PHP7 fixes - return semicolon spaces, old rand functions, typehints * [cs] fix less strict typehints * fix typehints to make tests pass * ecs: ignore typehint-less elements * [cs] standardize arrays * [cs] standardize docblock, remove unused comments * [cs] use self where possible * [cs] sort class elements, from public to private * [cs] do not use yoda (found less yoda-cases, than non-yoda) * space * [cs] do not assign in condition * [cs] use namespace imports if possible * [cs] use ::class over strings * [cs] fix defaults for arrays properties, properties and constants single spacing * cleanup ecs comments * [cs] use item per line in multi-items array * missing line * misc * rebase
90 lines
3.9 KiB
PHP
90 lines
3.9 KiB
PHP
<?php
|
|
|
|
declare(strict_types=1);
|
|
|
|
namespace tests\Phpml\Classification\Linear;
|
|
|
|
use Phpml\Classification\Linear\Perceptron;
|
|
use Phpml\ModelManager;
|
|
use PHPUnit\Framework\TestCase;
|
|
|
|
class PerceptronTest extends TestCase
|
|
{
|
|
public function testPredictSingleSample(): void
|
|
{
|
|
// AND problem
|
|
$samples = [[0, 0], [1, 0], [0, 1], [1, 1], [0.6, 0.6]];
|
|
$targets = [0, 0, 0, 1, 1];
|
|
$classifier = new Perceptron(0.001, 5000);
|
|
$classifier->setEarlyStop(false);
|
|
$classifier->train($samples, $targets);
|
|
$this->assertEquals(0, $classifier->predict([0.1, 0.2]));
|
|
$this->assertEquals(0, $classifier->predict([0, 1]));
|
|
$this->assertEquals(1, $classifier->predict([1.1, 0.8]));
|
|
|
|
// OR problem
|
|
$samples = [[0.1, 0.1], [0.4, 0.], [0., 0.3], [1, 0], [0, 1], [1, 1]];
|
|
$targets = [0, 0, 0, 1, 1, 1];
|
|
$classifier = new Perceptron(0.001, 5000, false);
|
|
$classifier->setEarlyStop(false);
|
|
$classifier->train($samples, $targets);
|
|
$this->assertEquals(0, $classifier->predict([0., 0.]));
|
|
$this->assertEquals(1, $classifier->predict([0.1, 0.99]));
|
|
$this->assertEquals(1, $classifier->predict([1.1, 0.8]));
|
|
|
|
// By use of One-v-Rest, Perceptron can perform multi-class classification
|
|
// The samples should be separable by lines perpendicular to the dimensions
|
|
$samples = [
|
|
[0, 0], [0, 1], [1, 0], [1, 1], // First group : a cluster at bottom-left corner in 2D
|
|
[5, 5], [6, 5], [5, 6], [7, 5], // Second group: another cluster at the middle-right
|
|
[3, 10], [3, 10], [3, 8], [3, 9], // Third group : cluster at the top-middle
|
|
];
|
|
$targets = [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2];
|
|
|
|
$classifier = new Perceptron();
|
|
$classifier->setEarlyStop(false);
|
|
$classifier->train($samples, $targets);
|
|
$this->assertEquals(0, $classifier->predict([0.5, 0.5]));
|
|
$this->assertEquals(1, $classifier->predict([6.0, 5.0]));
|
|
$this->assertEquals(2, $classifier->predict([3.0, 9.5]));
|
|
|
|
// Extra partial training should lead to the same results.
|
|
$classifier->partialTrain([[0, 1], [1, 0]], [0, 0], [0, 1, 2]);
|
|
$this->assertEquals(0, $classifier->predict([0.5, 0.5]));
|
|
$this->assertEquals(1, $classifier->predict([6.0, 5.0]));
|
|
$this->assertEquals(2, $classifier->predict([3.0, 9.5]));
|
|
|
|
// Train should clear previous data.
|
|
$samples = [
|
|
[0, 0], [0, 1], [1, 0], [1, 1], // First group : a cluster at bottom-left corner in 2D
|
|
[5, 5], [6, 5], [5, 6], [7, 5], // Second group: another cluster at the middle-right
|
|
[3, 10], [3, 10], [3, 8], [3, 9], // Third group : cluster at the top-middle
|
|
];
|
|
$targets = [2, 2, 2, 2, 0, 0, 0, 0, 1, 1, 1, 1];
|
|
$classifier->train($samples, $targets);
|
|
$this->assertEquals(2, $classifier->predict([0.5, 0.5]));
|
|
$this->assertEquals(0, $classifier->predict([6.0, 5.0]));
|
|
$this->assertEquals(1, $classifier->predict([3.0, 9.5]));
|
|
}
|
|
|
|
public function testSaveAndRestore(): void
|
|
{
|
|
// Instantinate new Percetron trained for OR problem
|
|
$samples = [[0, 0], [1, 0], [0, 1], [1, 1]];
|
|
$targets = [0, 1, 1, 1];
|
|
$classifier = new Perceptron();
|
|
$classifier->train($samples, $targets);
|
|
$testSamples = [[0, 1], [1, 1], [0.2, 0.1]];
|
|
$predicted = $classifier->predict($testSamples);
|
|
|
|
$filename = 'perceptron-test-'.random_int(100, 999).'-'.uniqid();
|
|
$filepath = tempnam(sys_get_temp_dir(), $filename);
|
|
$modelManager = new ModelManager();
|
|
$modelManager->saveToFile($classifier, $filepath);
|
|
|
|
$restoredClassifier = $modelManager->restoreFromFile($filepath);
|
|
$this->assertEquals($classifier, $restoredClassifier);
|
|
$this->assertEquals($predicted, $restoredClassifier->predict($testSamples));
|
|
}
|
|
}
|