This is an automated email from the ASF dual-hosted git repository.
aherbert pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/commons-rng.git
commit fc04e8bd74af009514a6245230d7db0445e15f59
Author: Alex Herbert <aherb...@apache.org>
AuthorDate: Thu Aug 5 15:14:12 2021 +0100
Ignore sampling output files
Renamed to have the prefix 'pdf.' to allow easier identification.
---
.gitignore | 1 +
commons-rng-examples/examples-sampling/HOWTO.md | 2 +-
.../ProbabilityDensityApproximationCommand.java | 42 +++++++++++-----------
3 files changed, 23 insertions(+), 22 deletions(-)
diff --git a/.gitignore b/.gitignore
index 7b6ac0a..ff21ad1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,3 +19,4 @@ site-content*
/.externalToolBuilders/
/maven-eclipse.xml
stdin2testu01
+pdf.*.txt
diff --git a/commons-rng-examples/examples-sampling/HOWTO.md
b/commons-rng-examples/examples-sampling/HOWTO.md
index dc93690..516f4a0 100644
--- a/commons-rng-examples/examples-sampling/HOWTO.md
+++ b/commons-rng-examples/examples-sampling/HOWTO.md
@@ -36,7 +36,7 @@ the commands:
The `density` command computes a histogram of the distribution and uses this
to create an
approximation of the probability denssity function for the distribution. The
data is recorded to
a file named using the sampler. For example to output a histogram of a
-`ZigguratGaussianSampler` to the file `gauss.ziggurat.txt` use:
+`ZigguratGaussianSampler` to the file `pdf.gauss.ziggurat.txt` use:
java -jar target/examples-sampling.jar density -s
ZigguratGaussianSampler
diff --git
a/commons-rng-examples/examples-sampling/src/main/java/org/apache/commons/rng/examples/sampling/ProbabilityDensityApproximationCommand.java
b/commons-rng-examples/examples-sampling/src/main/java/org/apache/commons/rng/examples/sampling/ProbabilityDensityApproximationCommand.java
index 36a46a8..f698e81 100644
---
a/commons-rng-examples/examples-sampling/src/main/java/org/apache/commons/rng/examples/sampling/ProbabilityDensityApproximationCommand.java
+++
b/commons-rng-examples/examples-sampling/src/main/java/org/apache/commons/rng/examples/sampling/ProbabilityDensityApproximationCommand.java
@@ -125,7 +125,7 @@ class ProbabilityDensityApproximationCommand implements
Callable<Void> {
* @param max abscissa of the last bin: every sample larger than or
* equal to that value will increment an additional bin (of infinite
* width) placed after the last "equal-width" bin.
- * @param outputFile Filename.
+ * @param outputFile Filename (final name is "pdf.[filename].txt").
* @throws IOException Signals that an I/O exception has occurred.
*/
private void createDensity(ContinuousSampler sampler,
@@ -158,7 +158,7 @@ class ProbabilityDensityApproximationCommand implements
Callable<Void> {
final double binHalfSize = 0.5 * binSize;
final double norm = 1 / (binSize * numSamples);
- try (PrintWriter out = new PrintWriter(outputFile, "UTF-8")) {
+ try (PrintWriter out = new PrintWriter("pdf." + outputFile + ".txt",
"UTF-8")) {
// CHECKSTYLE: stop MultipleStringLiteralsCheck
out.println("# Sampler: " + sampler);
out.println("# Number of bins: " + numBins);
@@ -201,22 +201,22 @@ class ProbabilityDensityApproximationCommand implements
Callable<Void> {
if (samplers.contains(Sampler.ZigguratGaussianSampler)) {
createDensity(GaussianSampler.of(ZigguratNormalizedGaussianSampler.of(rng),
gaussMean, gaussSigma),
- gaussMin, gaussMax, "gauss.ziggurat.txt");
+ gaussMin, gaussMax, "gauss.ziggurat");
}
if (samplers.contains(Sampler.MarsagliaGaussianSampler)) {
createDensity(GaussianSampler.of(MarsagliaNormalizedGaussianSampler.of(rng),
gaussMean, gaussSigma),
- gaussMin, gaussMax, "gauss.marsaglia.txt");
+ gaussMin, gaussMax, "gauss.marsaglia");
}
if (samplers.contains(Sampler.BoxMullerGaussianSampler)) {
createDensity(GaussianSampler.of(BoxMullerNormalizedGaussianSampler.of(rng),
gaussMean, gaussSigma),
- gaussMin, gaussMax, "gauss.boxmuller.txt");
+ gaussMin, gaussMax, "gauss.boxmuller");
}
if (samplers.contains(Sampler.ModifiedZigguratGaussianSampler)) {
createDensity(GaussianSampler.of(ZigguratSampler.NormalizedGaussian.of(rng),
gaussMean, gaussSigma),
- gaussMin, gaussMax, "gauss.modified.ziggurat.txt");
+ gaussMin, gaussMax, "gauss.modified.ziggurat");
}
final double betaMin = 0;
@@ -225,13 +225,13 @@ class ProbabilityDensityApproximationCommand implements
Callable<Void> {
final double alphaBeta = 4.3;
final double betaBeta = 2.1;
createDensity(ChengBetaSampler.of(rng, alphaBeta, betaBeta),
- betaMin, betaMax, "beta.case1.txt");
+ betaMin, betaMax, "beta.case1");
}
if (samplers.contains(Sampler.ChengBetaSamplerCase2)) {
final double alphaBetaAlt = 0.5678;
final double betaBetaAlt = 0.1234;
createDensity(ChengBetaSampler.of(rng, alphaBetaAlt, betaBetaAlt),
- betaMin, betaMax, "beta.case2.txt");
+ betaMin, betaMax, "beta.case2");
}
final double meanExp = 3.45;
@@ -239,11 +239,11 @@ class ProbabilityDensityApproximationCommand implements
Callable<Void> {
final double expMax = 60;
if (samplers.contains(Sampler.AhrensDieterExponentialSampler)) {
createDensity(AhrensDieterExponentialSampler.of(rng, meanExp),
- expMin, expMax, "exp.txt");
+ expMin, expMax, "exp");
}
if (samplers.contains(Sampler.ModifiedZigguratExponentialSampler)) {
createDensity(ZigguratSampler.Exponential.of(rng, meanExp),
- expMin, expMax, "exp.modified.ziggurat.txt");
+ expMin, expMax, "exp.modified.ziggurat");
}
final double gammaMin = 0;
@@ -252,13 +252,13 @@ class ProbabilityDensityApproximationCommand implements
Callable<Void> {
if
(samplers.contains(Sampler.AhrensDieterMarsagliaTsangGammaSamplerCase1)) {
final double alphaGammaSmallerThanOne = 0.1234;
createDensity(AhrensDieterMarsagliaTsangGammaSampler.of(rng,
alphaGammaSmallerThanOne, thetaGamma),
- gammaMin, gammaMax1, "gamma.case1.txt");
+ gammaMin, gammaMax1, "gamma.case1");
}
if
(samplers.contains(Sampler.AhrensDieterMarsagliaTsangGammaSamplerCase2)) {
final double alphaGammaLargerThanOne = 2.345;
final double gammaMax2 = 70;
createDensity(AhrensDieterMarsagliaTsangGammaSampler.of(rng,
alphaGammaLargerThanOne, thetaGamma),
- gammaMin, gammaMax2, "gamma.case2.txt");
+ gammaMin, gammaMax2, "gamma.case2");
}
final double scalePareto = 23.45;
@@ -267,14 +267,14 @@ class ProbabilityDensityApproximationCommand implements
Callable<Void> {
final double paretoMax = 400;
if (samplers.contains(Sampler.InverseTransformParetoSampler)) {
createDensity(InverseTransformParetoSampler.of(rng, scalePareto,
shapePareto),
- paretoMin, paretoMax, "pareto.txt");
+ paretoMin, paretoMax, "pareto");
}
final double loUniform = -9.876;
final double hiUniform = 5.432;
if (samplers.contains(Sampler.ContinuousUniformSampler)) {
createDensity(ContinuousUniformSampler.of(rng, loUniform,
hiUniform),
- loUniform, hiUniform, "uniform.txt");
+ loUniform, hiUniform, "uniform");
}
final double scaleLogNormal = 2.345;
@@ -284,22 +284,22 @@ class ProbabilityDensityApproximationCommand implements
Callable<Void> {
if (samplers.contains(Sampler.LogNormalZigguratGaussianSampler)) {
createDensity(LogNormalSampler.of(ZigguratNormalizedGaussianSampler.of(rng),
scaleLogNormal, shapeLogNormal),
- logNormalMin, logNormalMax,
"lognormal.ziggurat.txt");
+ logNormalMin, logNormalMax, "lognormal.ziggurat");
}
if (samplers.contains(Sampler.LogNormalMarsagliaGaussianSampler)) {
createDensity(LogNormalSampler.of(MarsagliaNormalizedGaussianSampler.of(rng),
scaleLogNormal, shapeLogNormal),
- logNormalMin, logNormalMax,
"lognormal.marsaglia.txt");
+ logNormalMin, logNormalMax, "lognormal.marsaglia");
}
if (samplers.contains(Sampler.LogNormalBoxMullerGaussianSampler)) {
createDensity(LogNormalSampler.of(BoxMullerNormalizedGaussianSampler.of(rng),
scaleLogNormal, shapeLogNormal),
- logNormalMin, logNormalMax,
"lognormal.boxmuller.txt");
+ logNormalMin, logNormalMax, "lognormal.boxmuller");
}
if
(samplers.contains(Sampler.LogNormalModifiedZigguratGaussianSampler)) {
createDensity(LogNormalSampler.of(ZigguratSampler.NormalizedGaussian.of(rng),
scaleLogNormal, shapeLogNormal),
- logNormalMin, logNormalMax,
"lognormal.modified.ziggurat.txt");
+ logNormalMin, logNormalMax,
"lognormal.modified.ziggurat");
}
if (samplers.contains(Sampler.LevySampler)) {
@@ -309,17 +309,17 @@ class ProbabilityDensityApproximationCommand implements
Callable<Void> {
// Quantile 0.99
final double levyMax = 4.7756e+03;
createDensity(LevySampler.of(rng, levyLocation, levyscale),
- levyMin, levyMax, "levy.txt");
+ levyMin, levyMax, "levy");
}
if (samplers.contains(Sampler.StableSampler)) {
final double stableAlpha = 1.23;
final double stableBeta = 0.25;
- // Quantiles 0.0005 to 0.9995
+ // Quantiles 0.0005 to 0.9995
final double stableMin = -131.9640;
final double stableMax = 200.9239;
createDensity(StableSampler.of(rng, stableAlpha, stableBeta),
- stableMin, stableMax, "stable.txt");
+ stableMin, stableMax, "stable");
}
return null;
