58 lines
1.7 KiB
PHP
58 lines
1.7 KiB
PHP
<?php
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declare(strict_types=1);
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namespace tests\DimensionReduction;
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use Phpml\DimensionReduction\PCA;
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use PHPUnit\Framework\TestCase;
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class PCATest extends TestCase
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{
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public function testPCA()
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{
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// Acceptable error
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$epsilon = 0.001;
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// First a simple example whose result is known and given in
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// http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf
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$data = [
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[2.5, 2.4],
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[0.5, 0.7],
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[2.2, 2.9],
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[1.9, 2.2],
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[3.1, 3.0],
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[2.3, 2.7],
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[2.0, 1.6],
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[1.0, 1.1],
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[1.5, 1.6],
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[1.1, 0.9]
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];
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$transformed = [
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[-0.827970186], [1.77758033], [-0.992197494],
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[-0.274210416], [-1.67580142], [-0.912949103], [0.0991094375],
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[1.14457216], [0.438046137], [1.22382056]];
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$pca = new PCA(0.90);
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$reducedData = $pca->fit($data);
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// Due to the fact that the sign of values can be flipped
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// during the calculation of eigenValues, we have to compare
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// absolute value of the values
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array_map(function ($val1, $val2) use ($epsilon) {
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$this->assertEquals(abs($val1), abs($val2), '', $epsilon);
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}, $transformed, $reducedData);
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// Test fitted PCA object to transform an arbitrary sample of the
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// same dimensionality with the original dataset
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foreach ($data as $i => $row) {
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$newRow = [[$transformed[$i]]];
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$newRow2= $pca->transform($row);
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array_map(function ($val1, $val2) use ($epsilon) {
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$this->assertEquals(abs($val1), abs($val2), '', $epsilon);
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}, $newRow, $newRow2);
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}
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}
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}
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