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Moved UnitTests to tests/ and Skills to src/

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Alexander Liljengård
2016-05-24 13:53:56 +02:00
parent 11b5033c8a
commit 4ab0c5d719
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106
tests/Numerics/GaussianDistributionTest.php Normal file
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<?php
namespace Moserware\Numerics;
require_once 'PHPUnit/Framework.php';
require_once 'PHPUnit/TextUI/TestRunner.php';
require_once(dirname(__FILE__) . '/../../Skills/Numerics/GaussianDistribution.php');
use \PHPUnit_Framework_TestCase;
class GaussianDistributionTest extends PHPUnit_Framework_TestCase
{
const ERROR_TOLERANCE = 0.000001;
public function testCumulativeTo()
{
// Verified with WolframAlpha
// (e.g. http://www.wolframalpha.com/input/?i=CDF%5BNormalDistribution%5B0%2C1%5D%2C+0.5%5D )
$this->assertEquals( 0.691462, GaussianDistribution::cumulativeTo(0.5),'', GaussianDistributionTest::ERROR_TOLERANCE);
}
public function testAt()
{
// Verified with WolframAlpha
// (e.g. http://www.wolframalpha.com/input/?i=PDF%5BNormalDistribution%5B0%2C1%5D%2C+0.5%5D )
$this->assertEquals(0.352065, GaussianDistribution::at(0.5), '', GaussianDistributionTest::ERROR_TOLERANCE);
}
public function testMultiplication()
{
// I verified this against the formula at http://www.tina-vision.net/tina-knoppix/tina-memo/2003-003.pdf
$standardNormal = new GaussianDistribution(0, 1);
$shiftedGaussian = new GaussianDistribution(2, 3);
$product = GaussianDistribution::multiply($standardNormal, $shiftedGaussian);
$this->assertEquals(0.2, $product->getMean(), '', GaussianDistributionTest::ERROR_TOLERANCE);
$this->assertEquals(3.0 / sqrt(10), $product->getStandardDeviation(), '', GaussianDistributionTest::ERROR_TOLERANCE);
$m4s5 = new GaussianDistribution(4, 5);
$m6s7 = new GaussianDistribution(6, 7);
$product2 = GaussianDistribution::multiply($m4s5, $m6s7);
$expectedMean = (4 * square(7) + 6 * square(5)) / (square(5) + square(7));
$this->assertEquals($expectedMean, $product2->getMean(), '', GaussianDistributionTest::ERROR_TOLERANCE);
$expectedSigma = sqrt(((square(5) * square(7)) / (square(5) + square(7))));
$this->assertEquals($expectedSigma, $product2->getStandardDeviation(), '', GaussianDistributionTest::ERROR_TOLERANCE);
}
public function testDivision()
{
// Since the multiplication was worked out by hand, we use the same numbers but work backwards
$product = new GaussianDistribution(0.2, 3.0 / sqrt(10));
$standardNormal = new GaussianDistribution(0, 1);
$productDividedByStandardNormal = GaussianDistribution::divide($product, $standardNormal);
$this->assertEquals(2.0, $productDividedByStandardNormal->getMean(), '', GaussianDistributionTest::ERROR_TOLERANCE);
$this->assertEquals(3.0, $productDividedByStandardNormal->getStandardDeviation(),'', GaussianDistributionTest::ERROR_TOLERANCE);
$product2 = new GaussianDistribution((4 * square(7) + 6 * square(5)) / (square(5) + square(7)), sqrt(((square(5) * square(7)) / (square(5) + square(7)))));
$m4s5 = new GaussianDistribution(4,5);
$product2DividedByM4S5 = GaussianDistribution::divide($product2, $m4s5);
$this->assertEquals(6.0, $product2DividedByM4S5->getMean(), '', GaussianDistributionTest::ERROR_TOLERANCE);
$this->assertEquals(7.0, $product2DividedByM4S5->getStandardDeviation(), '', GaussianDistributionTest::ERROR_TOLERANCE);
}
public function testLogProductNormalization()
{
// Verified with Ralf Herbrich's F# implementation
$standardNormal = new GaussianDistribution(0, 1);
$lpn = GaussianDistribution::logProductNormalization($standardNormal, $standardNormal);
$this->assertEquals(-1.2655121234846454, $lpn, '', GaussianDistributionTest::ERROR_TOLERANCE);
$m1s2 = new GaussianDistribution(1, 2);
$m3s4 = new GaussianDistribution(3, 4);
$lpn2 = GaussianDistribution::logProductNormalization($m1s2, $m3s4);
$this->assertEquals(-2.5168046699816684, $lpn2, '', GaussianDistributionTest::ERROR_TOLERANCE);
}
public function testLogRatioNormalization()
{
// Verified with Ralf Herbrich's F# implementation
$m1s2 = new GaussianDistribution(1, 2);
$m3s4 = new GaussianDistribution(3, 4);
$lrn = GaussianDistribution::logRatioNormalization($m1s2, $m3s4);
$this->assertEquals(2.6157405972171204, $lrn, '', GaussianDistributionTest::ERROR_TOLERANCE);
}
public function testAbsoluteDifference()
{
// Verified with Ralf Herbrich's F# implementation
$standardNormal = new GaussianDistribution(0, 1);
$absDiff = GaussianDistribution::absoluteDifference($standardNormal, $standardNormal);
$this->assertEquals(0.0, $absDiff, '', GaussianDistributionTest::ERROR_TOLERANCE);
$m1s2 = new GaussianDistribution(1, 2);
$m3s4 = new GaussianDistribution(3, 4);
$absDiff2 = GaussianDistribution::absoluteDifference($m1s2, $m3s4);
$this->assertEquals(0.4330127018922193, $absDiff2, '', GaussianDistributionTest::ERROR_TOLERANCE);
}
}
?>