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MATH-1058 

Precision improvements by using "expm1" and "log1p". Thanks to Sean Owen.


git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1538998 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Gilles Sadowski 2013-11-05 13:51:24 +00:00
parent 67fc63b994
commit 4ebd967c96
7 changed files with 32 additions and 3 deletions
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4
src/changes/changes.xml
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@ -51,6 +51,10 @@ If the output is not quite correct, check for invisible trailing spaces!
</properties>
<body>
<release version="3.3" date="TBD" description="TBD">
<action dev="erans" type="fix" issue="MATH-1058" due-to="Sean Owen">
Precision improvements (for small values of the argument) in "Beta" function
and in "LogNormalDistribution" and "WeibullDistribution".
</action>
<action dev="tn" type="fix" issue="MATH-1055" due-to="Sean Owen">
Fixed some invalid links inside javadoc and added missing deprecated annotations.
</action>

2
src/main/java/org/apache/commons/math3/distribution/LogNormalDistribution.java
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@ -289,7 +289,7 @@ public class LogNormalDistribution extends AbstractRealDistribution {
public double getNumericalVariance() {
final double s = shape;
final double ss = s * s;
return (FastMath.exp(ss) - 1) * FastMath.exp(2 * scale + ss);
return (FastMath.expm1(ss)) * FastMath.exp(2 * scale + ss);
}
/**

2
src/main/java/org/apache/commons/math3/distribution/WeibullDistribution.java
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@ -221,7 +221,7 @@ public class WeibullDistribution extends AbstractRealDistribution {
} else if (p == 1) {
ret = Double.POSITIVE_INFINITY;
} else {
ret = scale * FastMath.pow(-FastMath.log(1.0 - p), 1.0 / shape);
ret = scale * FastMath.pow(-FastMath.log1p(-p), 1.0 / shape);
}
return ret;
}

2
src/main/java/org/apache/commons/math3/special/Beta.java
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@ -218,7 +218,7 @@ public class Beta {
return 1.0;
}
};
ret = FastMath.exp((a * FastMath.log(x)) + (b * FastMath.log(1.0 - x)) -
ret = FastMath.exp((a * FastMath.log(x)) + (b * FastMath.log1p(-x)) -
FastMath.log(a) - logBeta(a, b)) *
1.0 / fraction.evaluate(x, epsilon, maxIterations);
}

8
src/test/java/org/apache/commons/math3/distribution/LogNormalDistributionTest.java
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@ -243,4 +243,12 @@ public class LogNormalDistributionTest extends RealDistributionAbstractTest {
Assert.assertEquals(dist.getNumericalMean(), 0.0, tol);
Assert.assertEquals(dist.getNumericalVariance(), 0.0, tol);
}
@Test
public void testTinyVariance() {
LogNormalDistribution dist = new LogNormalDistribution(0, 1e-9);
double t = dist.getNumericalVariance();
Assert.assertEquals(1e-18, t, 1e-20);
}
}

10
src/test/java/org/apache/commons/math3/distribution/WeibullDistributionTest.java
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@ -63,6 +63,16 @@ public class WeibullDistributionTest extends RealDistributionAbstractTest {
//---------------------------- Additional test cases -------------------------
@Test
public void testInverseCumulativeProbabilitySmallPAccuracy() {
WeibullDistribution dist = new WeibullDistribution(2, 3);
double t = dist.inverseCumulativeProbability(1e-17);
// Analytically, answer is solution to 1e-17 = 1-exp(-(x/3)^2)
// x = sqrt(-9*log(1-1e-17))
// If we're not careful, answer will be 0. Answer below is computed with care in Octave:
Assert.assertEquals(9.48683298050514e-9, t, 1e-17);
}
@Test
public void testInverseCumulativeProbabilityExtremes() {
setInverseCumulativeTestPoints(new double[] {0.0, 1.0});

7
src/test/java/org/apache/commons/math3/special/BetaTest.java
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@ -134,6 +134,13 @@ public class BetaTest {
testRegularizedBeta(0.75, 0.5, 1.0, 2.0);
}
@Test
public void testRegularizedBetaTinyArgument() {
double actual = Beta.regularizedBeta(1e-17, 1.0, 1e12);
// This value is from R: pbeta(1e-17,1,1e12)
TestUtils.assertEquals(9.999950000166648e-6, actual, 1e-16);
}
@Test
public void testLogBetaNanPositive() {
testLogBeta(Double.NaN, Double.NaN, 2.0);