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52 lines
2.1 KiB
PHP
52 lines
2.1 KiB
PHP
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<?php
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declare(strict_types=1);
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namespace tests\DimensionReduction;
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use Phpml\DimensionReduction\KernelPCA;
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use PHPUnit\Framework\TestCase;
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class KernelPCATest extends TestCase
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{
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public function testKernelPCA()
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{
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// Acceptable error
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$epsilon = 0.001;
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// A simple example whose result is known beforehand
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$data = [
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[2,2], [1.5,1], [1.,1.5], [1.,1.],
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[2.,1.],[2,2.5], [2.,3.], [1.5,3],
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[1.,2.5], [1.,2.7], [1.,3.], [1,3],
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[1,2], [1.5,2], [1.5,2.2], [1.3,1.7],
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[1.7,1.3], [1.5,1.5], [1.5,1.6], [1.6,2],
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[1.7,2.1], [1.3,1.3], [1.3,2.2], [1.4,2.4]
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];
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$transformed = [
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[0.016485613899708], [-0.089805657741674], [-0.088695974245924], [-0.069761503810802],
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[-0.068049558133392], [-0.054702087779187], [-0.063229228729333], [-0.06852813588679],
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[-0.10098315410297], [-0.15617881000654], [-0.21266832077299], [-0.21266832077299],
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[-0.039234518840831], [0.40858295942991], [0.40110375047242], [-0.10555116296691],
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[-0.13128352866095], [-0.20865959471756], [-0.17531601535848], [0.4240660966961],
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[0.36351946685163], [-0.14334173054136], [0.22454914091011], [0.15035027480881]];
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$kpca = new KernelPCA(KernelPCA::KERNEL_RBF, null, 1, 15);
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$reducedData = $kpca->fit($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, $reducedData);
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// Fitted KernelPCA object can also transform an arbitrary sample of the
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// same dimensionality with the original dataset
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$newData = [1.25, 2.25];
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$newTransformed = [0.18956227539216];
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$newTransformed2 = $kpca->transform($newData);
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$this->assertEquals(abs($newTransformed[0]), abs($newTransformed2[0]), '', $epsilon);
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}
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}
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