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 e92c4cebf7a79854e79d91c73d801d4d54be1110
Author: aherbert <aherb...@apache.org>
AuthorDate: Thu Feb 13 11:41:55 2020 +0000
Fix spotbugs for the JMH module.
---
.../EnumeratedDistributionSamplersPerformance.java | 36 +++++++++++++---------
.../resources/spotbugs/spotbugs-exclude-filter.xml | 10 ++++++
2 files changed, 31 insertions(+), 15 deletions(-)
diff --git
a/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/sampling/distribution/EnumeratedDistributionSamplersPerformance.java
b/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/sampling/distribution/EnumeratedDistributionSamplersPerformance.java
index 41bce62..4e74c11 100644
---
a/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/sampling/distribution/EnumeratedDistributionSamplersPerformance.java
+++
b/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/sampling/distribution/EnumeratedDistributionSamplersPerformance.java
@@ -268,6 +268,18 @@ public class EnumeratedDistributionSamplersPerformance {
public static class KnownDistributionSources extends SamplerSources {
/** The cumulative probability limit for unbounded distributions. */
private static final double CUMULATIVE_PROBABILITY_LIMIT = 1 - 1e-9;
+ /** Binomial distribution number of trials. */
+ private static final int BINOM_N = 67;
+ /** Binomial distribution probability of success. */
+ private static final double BINOM_P = 0.7;
+ /** Geometric distribution probability of success. */
+ private static final double GEO_P = 0.2;
+ /** Poisson distribution mean. */
+ private static final double POISS_MEAN = 3.22;
+ /** Bimodal distribution mean 1. */
+ private static final double BIMOD_MEAN1 = 10;
+ /** Bimodal distribution mean 1. */
+ private static final double BIMOD_MEAN2 = 20;
/**
* The distribution.
@@ -275,7 +287,7 @@ public class EnumeratedDistributionSamplersPerformance {
@Param({"Binomial_N67_P0.7",
"Geometric_P0.2",
"4SidedLoadedDie",
- "Poisson_Mean3.14",
+ "Poisson_Mean3.22",
"Poisson_Mean10_Mean20",
})
private String distribution;
@@ -284,13 +296,10 @@ public class EnumeratedDistributionSamplersPerformance {
@Override
protected double[] createProbabilities() {
if ("Binomial_N67_P0.7".equals(distribution)) {
- final int trials = 67;
- final double probabilityOfSuccess = 0.7;
- final BinomialDistribution dist = new
BinomialDistribution(null, trials, probabilityOfSuccess);
- return createProbabilities(dist, 0, trials);
+ final BinomialDistribution dist = new
BinomialDistribution(null, BINOM_N, BINOM_P);
+ return createProbabilities(dist, 0, BINOM_N);
} else if ("Geometric_P0.2".equals(distribution)) {
- final double probabilityOfSuccess = 0.2;
- final double probabilityOfFailure = 1 - probabilityOfSuccess;
+ final double probabilityOfFailure = 1 - GEO_P;
// https://en.wikipedia.org/wiki/Geometric_distribution
// PMF = (1-p)^k * p
// k is number of failures before a success
@@ -300,7 +309,7 @@ public class EnumeratedDistributionSamplersPerformance {
double sum = 0;
int k = 0;
while (k < probabilities.length) {
- probabilities[k] = p * probabilityOfSuccess;
+ probabilities[k] = p * GEO_P;
sum += probabilities[k++];
if (sum > CUMULATIVE_PROBABILITY_LIMIT) {
break;
@@ -311,19 +320,16 @@ public class EnumeratedDistributionSamplersPerformance {
return Arrays.copyOf(probabilities, k);
} else if ("4SidedLoadedDie".equals(distribution)) {
return new double[] {1.0 / 2, 1.0 / 3, 1.0 / 12, 1.0 / 12};
- } else if ("Poisson_Mean3.14".equals(distribution)) {
- final double mean = 3.14;
- final IntegerDistribution dist =
createPoissonDistribution(mean);
+ } else if ("Poisson_Mean3.22".equals(distribution)) {
+ final IntegerDistribution dist =
createPoissonDistribution(POISS_MEAN);
final int max =
dist.inverseCumulativeProbability(CUMULATIVE_PROBABILITY_LIMIT);
return createProbabilities(dist, 0, max);
} else if ("Poisson_Mean10_Mean20".equals(distribution)) {
// Create a Bimodel using two Poisson distributions
- final double mean1 = 10;
- final double mean2 = 20;
- final IntegerDistribution dist1 =
createPoissonDistribution(mean2);
+ final IntegerDistribution dist1 =
createPoissonDistribution(BIMOD_MEAN2);
final int max =
dist1.inverseCumulativeProbability(CUMULATIVE_PROBABILITY_LIMIT);
final double[] p1 = createProbabilities(dist1, 0, max);
- final double[] p2 =
createProbabilities(createPoissonDistribution(mean1), 0, max);
+ final double[] p2 =
createProbabilities(createPoissonDistribution(BIMOD_MEAN1), 0, max);
for (int i = 0; i < p1.length; i++) {
p1[i] += p2[i];
}
diff --git a/src/main/resources/spotbugs/spotbugs-exclude-filter.xml
b/src/main/resources/spotbugs/spotbugs-exclude-filter.xml
index 7507b3e..3448498 100644
--- a/src/main/resources/spotbugs/spotbugs-exclude-filter.xml
+++ b/src/main/resources/spotbugs/spotbugs-exclude-filter.xml
@@ -43,4 +43,14 @@
<BugPattern name="RCN_REDUNDANT_NULLCHECK_WOULD_HAVE_BEEN_A_NPE"/>
</Match>
+ <Match>
+ <!-- Benchmark state classes can expose internal representations.
+ Unwritten fields can be used for baseline values. -->
+ <Class
name="~org.apache.commons.rng.examples.jmh..*(Benchmark|Performance).*"/>
+ <Or>
+ <BugPattern name="EI_EXPOSE_REP"/>
+ <BugPattern name="UWF_UNWRITTEN_FIELD"/>
+ </Or>
+ </Match>
+
</FindBugsFilter>
