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Changed inference classes to pass null RandomGenerators to distribution constructors. JIRA: MATH-1154.

This commit is contained in:
Phil Steitz 2014-10-13 06:31:45 -07:00
parent 69273dca61
commit a3fdeb4da9
8 changed files with 41 additions and 24 deletions
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5
src/changes/changes.xml
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@ -73,6 +73,11 @@ Users are encouraged to upgrade to this version as this release not
2. A few methods in the FastMath class are in fact slower that their
counterpart in either Math or StrictMath (cf. MATH-740 and MATH-901).
">
<action dev="psteitz" type="add" issue="MATH-1154" >
Changed classes in the inference package that instantiate distributions to
pass null RandomGenerators to avoid initialization overhead for the default
generator.
</action>
<action dev="luc" type="add" issue="MATH-1156" >
Added all Java 8 StrictMath methods to FastMath, so FastMath remains compatible
with newer Java versions.

3
src/main/java/org/apache/commons/math3/stat/inference/BinomialTest.java
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@ -119,7 +119,8 @@ public class BinomialTest {
throw new NullArgumentException();
}
final BinomialDistribution distribution = new BinomialDistribution(numberOfTrials, probability);
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
final BinomialDistribution distribution = new BinomialDistribution(null, numberOfTrials, probability);
switch (alternativeHypothesis) {
case GREATER_THAN:
return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);

14
src/main/java/org/apache/commons/math3/stat/inference/ChiSquareTest.java
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@ -155,8 +155,9 @@ public class ChiSquareTest {
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException, MaxCountExceededException {
ChiSquaredDistribution distribution =
new ChiSquaredDistribution(expected.length - 1.0);
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
final ChiSquaredDistribution distribution =
new ChiSquaredDistribution(null, expected.length - 1.0);
return 1.0 - distribution.cumulativeProbability(chiSquare(expected, observed));
}
@ -311,8 +312,8 @@ public class ChiSquareTest {
checkArray(counts);
double df = ((double) counts.length -1) * ((double) counts[0].length - 1);
ChiSquaredDistribution distribution;
distribution = new ChiSquaredDistribution(df);
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
final ChiSquaredDistribution distribution = new ChiSquaredDistribution(df);
return 1 - distribution.cumulativeProbability(chiSquare(counts));
}
@ -507,8 +508,9 @@ public class ChiSquareTest {
throws DimensionMismatchException, NotPositiveException, ZeroException,
MaxCountExceededException {
ChiSquaredDistribution distribution;
distribution = new ChiSquaredDistribution((double) observed1.length - 1);
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
final ChiSquaredDistribution distribution =
new ChiSquaredDistribution(null, (double) observed1.length - 1);
return 1 - distribution.cumulativeProbability(
chiSquareDataSetsComparison(observed1, observed2));

18
src/main/java/org/apache/commons/math3/stat/inference/GTest.java
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@ -152,10 +152,10 @@ public class GTest {
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException, MaxCountExceededException {
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
final ChiSquaredDistribution distribution =
new ChiSquaredDistribution(expected.length - 1.0);
return 1.0 - distribution.cumulativeProbability(
g(expected, observed));
new ChiSquaredDistribution(null, expected.length - 1.0);
return 1.0 - distribution.cumulativeProbability(g(expected, observed));
}
/**
@ -183,10 +183,10 @@ public class GTest {
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException, MaxCountExceededException {
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
final ChiSquaredDistribution distribution =
new ChiSquaredDistribution(expected.length - 2.0);
return 1.0 - distribution.cumulativeProbability(
g(expected, observed));
new ChiSquaredDistribution(null, expected.length - 2.0);
return 1.0 - distribution.cumulativeProbability(g(expected, observed));
}
/**
@ -472,8 +472,10 @@ public class GTest {
final long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException,
MaxCountExceededException {
final ChiSquaredDistribution distribution = new ChiSquaredDistribution(
(double) observed1.length - 1);
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
final ChiSquaredDistribution distribution =
new ChiSquaredDistribution(null, (double) observed1.length - 1);
return 1 - distribution.cumulativeProbability(
gDataSetsComparison(observed1, observed2));
}

3
src/main/java/org/apache/commons/math3/stat/inference/MannWhitneyUTest.java
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@ -181,7 +181,8 @@ public class MannWhitneyUTest {
final double z = (Umin - EU) / FastMath.sqrt(VarU);
// No try-catch or advertised exception because args are valid
final NormalDistribution standardNormal = new NormalDistribution(0, 1);
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
final NormalDistribution standardNormal = new NormalDistribution(null, 0, 1);
return 2 * standardNormal.cumulativeProbability(z);
}

8
src/main/java/org/apache/commons/math3/stat/inference/OneWayAnova.java
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@ -124,9 +124,10 @@ public class OneWayAnova {
throws NullArgumentException, DimensionMismatchException,
ConvergenceException, MaxCountExceededException {
AnovaStats a = anovaStats(categoryData);
final AnovaStats a = anovaStats(categoryData);
// No try-catch or advertised exception because args are valid
FDistribution fdist = new FDistribution(a.dfbg, a.dfwg);
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
final FDistribution fdist = new FDistribution(null, a.dfbg, a.dfwg);
return 1.0 - fdist.cumulativeProbability(a.F);
}
@ -167,7 +168,8 @@ public class OneWayAnova {
ConvergenceException, MaxCountExceededException {
final AnovaStats a = anovaStats(categoryData, allowOneElementData);
final FDistribution fdist = new FDistribution(a.dfbg, a.dfwg);
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
final FDistribution fdist = new FDistribution(null, a.dfbg, a.dfwg);
return 1.0 - fdist.cumulativeProbability(a.F);
}

11
src/main/java/org/apache/commons/math3/stat/inference/TTest.java
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@ -1056,8 +1056,9 @@ public class TTest {
final double v, final double n)
throws MaxCountExceededException, MathIllegalArgumentException {
double t = FastMath.abs(t(m, mu, v, n));
TDistribution distribution = new TDistribution(n - 1);
final double t = FastMath.abs(t(m, mu, v, n));
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
final TDistribution distribution = new TDistribution(null, n - 1);
return 2.0 * distribution.cumulativeProbability(-t);
}
@ -1086,7 +1087,8 @@ public class TTest {
final double t = FastMath.abs(t(m1, m2, v1, v2, n1, n2));
final double degreesOfFreedom = df(v1, v2, n1, n2);
TDistribution distribution = new TDistribution(degreesOfFreedom);
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
final TDistribution distribution = new TDistribution(null, degreesOfFreedom);
return 2.0 * distribution.cumulativeProbability(-t);
}
@ -1115,7 +1117,8 @@ public class TTest {
final double t = FastMath.abs(homoscedasticT(m1, m2, v1, v2, n1, n2));
final double degreesOfFreedom = n1 + n2 - 2;
TDistribution distribution = new TDistribution(degreesOfFreedom);
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
final TDistribution distribution = new TDistribution(null, degreesOfFreedom);
return 2.0 * distribution.cumulativeProbability(-t);
}

3
src/main/java/org/apache/commons/math3/stat/inference/WilcoxonSignedRankTest.java
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@ -253,7 +253,8 @@ public class WilcoxonSignedRankTest {
final double z = (Wmin - ES - 0.5) / FastMath.sqrt(VarS);
// No try-catch or advertised exception because args are valid
final NormalDistribution standardNormal = new NormalDistribution(0, 1);
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
final NormalDistribution standardNormal = new NormalDistribution(null, 0, 1);
return 2*standardNormal.cumulativeProbability(z);
}