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Fixing failing tests and misc cleanup

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Alexander Liljengård
2016-05-24 15:12:29 +02:00
parent 5694a2fb30
commit 25b64d53f0
20 changed files with 132 additions and 125 deletions
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43
tests/Numerics/GaussianDistributionTest.php
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@@ -1,33 +1,34 @@
<?php namespace Moserware\Skills\Tests\Numerics;
use Moserware\Numerics\GaussianDistribution;
use Moserware\Skills\Numerics\BasicMath;
use Moserware\Skills\Numerics\GaussianDistribution;
use Moserware\Skills\Tests\TestCase;
class GaussianDistributionTest extends 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);
$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);
$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);
@@ -35,14 +36,14 @@ class GaussianDistributionTest extends TestCase
$m6s7 = new GaussianDistribution(6, 7);
$product2 = GaussianDistribution::multiply($m4s5, $m6s7);
$expectedMean = (4 * BasicMatch::square(7) + 6 * BasicMatch::square(5)) / (BasicMatch::square(5) + BasicMatch::square(7));
$expectedMean = (4 * BasicMath::square(7) + 6 * BasicMath::square(5)) / (BasicMath::square(5) + BasicMath::square(7));
$this->assertEquals($expectedMean, $product2->getMean(), '', GaussianDistributionTest::ERROR_TOLERANCE);
$expectedSigma = sqrt(((BasicMatch::square(5) * BasicMatch::square(7)) / (BasicMatch::square(5) + BasicMatch::square(7))));
$expectedSigma = sqrt(((BasicMath::square(5) * BasicMath::square(7)) / (BasicMath::square(5) + BasicMath::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
@@ -51,15 +52,15 @@ class GaussianDistributionTest extends TestCase
$productDividedByStandardNormal = GaussianDistribution::divide($product, $standardNormal);
$this->assertEquals(2.0, $productDividedByStandardNormal->getMean(), '', GaussianDistributionTest::ERROR_TOLERANCE);
$this->assertEquals(3.0, $productDividedByStandardNormal->getStandardDeviation(),'', GaussianDistributionTest::ERROR_TOLERANCE);
$product2 = new GaussianDistribution((4 * BasicMatch::square(7) + 6 * BasicMatch::square(5)) / (BasicMatch::square(5) + BasicMatch::square(7)), sqrt(((BasicMatch::square(5) * BasicMatch::square(7)) / (BasicMatch::square(5) + BasicMatch::square(7)))));
$m4s5 = new GaussianDistribution(4,5);
$this->assertEquals(3.0, $productDividedByStandardNormal->getStandardDeviation(), '', GaussianDistributionTest::ERROR_TOLERANCE);
$product2 = new GaussianDistribution((4 * BasicMath::square(7) + 6 * BasicMath::square(5)) / (BasicMath::square(5) + BasicMath::square(7)), sqrt(((BasicMath::square(5) * BasicMath::square(7)) / (BasicMath::square(5) + BasicMath::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
@@ -72,16 +73,16 @@ class GaussianDistributionTest extends TestCase
$lpn2 = GaussianDistribution::logProductNormalization($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);
$this->assertEquals(2.6157405972171204, $lrn, '', GaussianDistributionTest::ERROR_TOLERANCE);
}
public function testAbsoluteDifference()
{
// Verified with Ralf Herbrich's F# implementation