66 lines
2.1 KiB
PHP
66 lines
2.1 KiB
PHP
<?php
|
|
|
|
declare(strict_types=1);
|
|
|
|
namespace tests\DimensionReduction;
|
|
|
|
use Phpml\DimensionReduction\LDA;
|
|
use Phpml\Dataset\Demo\IrisDataset;
|
|
use PHPUnit\Framework\TestCase;
|
|
|
|
class LDATest extends TestCase
|
|
{
|
|
public function testLDA()
|
|
{
|
|
// Acceptable error
|
|
$epsilon = 0.001;
|
|
|
|
// IRIS dataset will be used to train LDA
|
|
$dataset = new IrisDataset();
|
|
$lda = new LDA(null, 2);
|
|
$transformed = $lda->fit($dataset->getSamples(), $dataset->getTargets());
|
|
|
|
// Some samples of the Iris data will be checked manually
|
|
// First 3 and last 3 rows from the original dataset
|
|
$data = [
|
|
[5.1, 3.5, 1.4, 0.2],
|
|
[4.9, 3.0, 1.4, 0.2],
|
|
[4.7, 3.2, 1.3, 0.2],
|
|
[6.5, 3.0, 5.2, 2.0],
|
|
[6.2, 3.4, 5.4, 2.3],
|
|
[5.9, 3.0, 5.1, 1.8]
|
|
];
|
|
$transformed2 = [
|
|
[-1.4922092756753, 1.9047102045574],
|
|
[-1.2576556684358, 1.608414450935],
|
|
[-1.3487505965419, 1.749846351699],
|
|
[1.7759343101456, 2.0371552314006],
|
|
[2.0059819019159, 2.4493123003226],
|
|
[1.701474913008, 1.9037880473772]
|
|
];
|
|
|
|
$control = [];
|
|
$control = array_merge($control, array_slice($transformed, 0, 3));
|
|
$control = array_merge($control, array_slice($transformed, -3));
|
|
|
|
$check = function ($row1, $row2) use ($epsilon) {
|
|
// 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
|
|
$row1 = array_map('abs', $row1);
|
|
$row2 = array_map('abs', $row2);
|
|
$this->assertEquals($row1, $row2, '', $epsilon);
|
|
};
|
|
array_map($check, $control, $transformed2);
|
|
|
|
// Fitted LDA object should be able to return same values again
|
|
// for each projected row
|
|
foreach ($data as $i => $row) {
|
|
$newRow = [$transformed2[$i]];
|
|
$newRow2 = $lda->transform($row);
|
|
|
|
array_map($check, $newRow, $newRow2);
|
|
}
|
|
}
|
|
}
|