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aherbert pushed a commit to branch master
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commit 962ddfc467c0488948439528a21445422e6a4dc5
Author: Alex Herbert <aherb...@apache.org>
AuthorDate: Thu Nov 17 15:58:45 2022 +0000
Use parameterized tests
---
.../statistics/distribution/ArgumentUtilsTest.java | 50 ++++++++---------
.../distribution/BetaDistributionTest.java | 48 ++++++++---------
.../distribution/ChiSquaredDistributionTest.java | 63 ++++++++++++----------
3 files changed, 84 insertions(+), 77 deletions(-)
diff --git
a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ArgumentUtilsTest.java
b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ArgumentUtilsTest.java
index 82c09f9..59a33f1 100644
---
a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ArgumentUtilsTest.java
+++
b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ArgumentUtilsTest.java
@@ -17,37 +17,37 @@
package org.apache.commons.statistics.distribution;
import org.junit.jupiter.api.Assertions;
-import org.junit.jupiter.api.Test;
+import org.junit.jupiter.params.ParameterizedTest;
+import org.junit.jupiter.params.provider.ValueSource;
/**
* Test for {@link ArgumentUtils}.
*/
class ArgumentUtilsTest {
- @Test
- void testIsFiniteStrictlyPositive() {
- final double[] good = {Double.MIN_VALUE, 1, 2, 3, Double.MAX_VALUE};
- final double[] bad = {0, -0.0, -Double.MIN_VALUE, -Double.MAX_VALUE,
- Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, Double.NaN};
- for (final double x : good) {
- Assertions.assertTrue(ArgumentUtils.isFiniteStrictlyPositive(x),
() -> Double.toString(x));
- }
- for (final double x : bad) {
- Assertions.assertFalse(ArgumentUtils.isFiniteStrictlyPositive(x),
() -> Double.toString(x));
- }
+ @ParameterizedTest
+ @ValueSource(doubles = {Double.MIN_VALUE, 1, 2, 3, Double.MAX_VALUE})
+ void testIsFiniteStrictlyPositiveTrue(double x) {
+ Assertions.assertTrue(ArgumentUtils.isFiniteStrictlyPositive(x), () ->
Double.toString(x));
}
- @Test
- void testCheckProbability() {
- // Currently this allows -0.0
- final double[] good = {-0.0, 0.0, Double.MIN_VALUE, 1 - 0x1.0p-53, 1};
- final double[] bad = {-Double.MIN_VALUE, -0.1, -1, -2,
Math.nextUp(1.0), 1.1, -Double.MAX_VALUE,
- Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, Double.NaN};
- for (final double p : good) {
- Assertions.assertDoesNotThrow(() ->
ArgumentUtils.checkProbability(p), () -> Double.toString(p));
- }
- for (final double p : bad) {
- Assertions.assertThrows(IllegalArgumentException.class,
- () -> ArgumentUtils.checkProbability(p), () ->
Double.toString(p));
- }
+ @ParameterizedTest
+ @ValueSource(doubles = {0, -0.0, -Double.MIN_VALUE, -Double.MAX_VALUE,
+ Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, Double.NaN})
+ void testIsFiniteStrictlyPositiveFalse(double x) {
+ Assertions.assertFalse(ArgumentUtils.isFiniteStrictlyPositive(x), ()
-> Double.toString(x));
+ }
+
+ @ParameterizedTest
+ @ValueSource(doubles = {-0.0, 0.0, Double.MIN_VALUE, 1 - 0x1.0p-53, 1})
+ void testCheckProbability(double p) {
+ Assertions.assertDoesNotThrow(() -> ArgumentUtils.checkProbability(p),
() -> Double.toString(p));
+ }
+
+ @ParameterizedTest
+ @ValueSource(doubles = {-Double.MIN_VALUE, -0.1, -1, -2, 1.0 + 0x1.0p-52,
1.1, -Double.MAX_VALUE,
+ Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, Double.NaN})
+ void testCheckProbabilityThrows(double p) {
+ Assertions.assertThrows(IllegalArgumentException.class,
+ () -> ArgumentUtils.checkProbability(p), () -> Double.toString(p));
}
}
diff --git
a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BetaDistributionTest.java
b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BetaDistributionTest.java
index 86984ac..30ec9fd 100644
---
a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BetaDistributionTest.java
+++
b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/BetaDistributionTest.java
@@ -73,31 +73,16 @@ class BetaDistributionTest extends
BaseContinuousDistributionTest {
* Precision tests for verifying that CDF calculates accurately in cases
* where 1-cdf(x) is inaccurately 1.
*/
- @Test
- void testCumulativePrecision() {
- // Calculated using WolframAlpha
- checkCumulativePrecision(5.0, 5.0, 0.0001, 1.2595800539968654e-18);
- checkCumulativePrecision(4.0, 5.0, 0.00001, 6.999776002800025e-19);
- checkCumulativePrecision(5.0, 4.0, 0.0001, 5.598600119996539e-19);
- checkCumulativePrecision(6.0, 2.0, 0.001, 6.994000000000028e-18);
- checkCumulativePrecision(2.0, 6.0, 1e-9, 2.0999999930000014e-17);
- }
-
- /**
- * Precision tests for verifying that survival function calculates
accurately in cases
- * where 1-sf(x) is inaccurately 1.
- */
- @Test
- void testSurvivalPrecision() {
+ @ParameterizedTest
+ @CsvSource({
// Calculated using WolframAlpha
- checkSurvivalPrecision(5.0, 5.0, 0.9999, 1.2595800539961496e-18);
- checkSurvivalPrecision(4.0, 5.0, 0.9999, 5.598600119993397e-19);
- checkSurvivalPrecision(5.0, 4.0, 0.99998, 1.1199283217964632e-17);
- checkSurvivalPrecision(6.0, 2.0, 0.999999999, 2.0999998742158932e-17);
- checkSurvivalPrecision(2.0, 6.0, 0.999, 6.994000000000077e-18);
- }
-
- private static void checkCumulativePrecision(double alpha, double beta,
double value, double expected) {
+ "5.0, 5.0, 0.0001, 1.2595800539968654e-18",
+ "4.0, 5.0, 0.00001, 6.999776002800025e-19",
+ "5.0, 4.0, 0.0001, 5.598600119996539e-19",
+ "6.0, 2.0, 0.001, 6.994000000000028e-18",
+ "2.0, 6.0, 1e-9, 2.0999999930000014e-17",
+ })
+ void testCumulativePrecision(double alpha, double beta, double value,
double expected) {
final double tolerance = 1e-22;
final BetaDistribution dist = BetaDistribution.of(alpha, beta);
Assertions.assertEquals(
@@ -107,7 +92,20 @@ class BetaDistributionTest extends
BaseContinuousDistributionTest {
() -> "cumulative probability not precise at " + value + " for a="
+ alpha + " & b=" + beta);
}
- private static void checkSurvivalPrecision(double alpha, double beta,
double value, double expected) {
+ /**
+ * Precision tests for verifying that survival function calculates
accurately in cases
+ * where 1-sf(x) is inaccurately 1.
+ */
+ @ParameterizedTest
+ @CsvSource({
+ // Calculated using WolframAlpha
+ "5.0, 5.0, 0.9999, 1.2595800539961496e-18",
+ "4.0, 5.0, 0.9999, 5.598600119993397e-19",
+ "5.0, 4.0, 0.99998, 1.1199283217964632e-17",
+ "6.0, 2.0, 0.999999999, 2.0999998742158932e-17",
+ "2.0, 6.0, 0.999, 6.994000000000077e-18",
+ })
+ void testSurvivalPrecision(double alpha, double beta, double value, double
expected) {
final double tolerance = 1e-22;
final BetaDistribution dist = BetaDistribution.of(alpha, beta);
Assertions.assertEquals(
diff --git
a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ChiSquaredDistributionTest.java
b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ChiSquaredDistributionTest.java
index 57daa18..6917a97 100644
---
a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ChiSquaredDistributionTest.java
+++
b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ChiSquaredDistributionTest.java
@@ -17,7 +17,10 @@
package org.apache.commons.statistics.distribution;
-import org.junit.jupiter.api.Test;
+import java.util.stream.Stream;
+import org.junit.jupiter.params.ParameterizedTest;
+import org.junit.jupiter.params.provider.Arguments;
+import org.junit.jupiter.params.provider.MethodSource;
/**
* Test cases for {@link ChiSquaredDistribution}.
@@ -50,37 +53,43 @@ class ChiSquaredDistributionTest extends
BaseContinuousDistributionTest {
//-------------------- Additional test cases
-------------------------------
- @Test
- void testAdditionalDensity() {
+ @ParameterizedTest
+ @MethodSource
+ void testAdditionalDensity(double df, double[] points, double[] values) {
// Values have many digits above the decimal point so use relative
tolerance
final DoubleTolerance tol = createRelTolerance(5e-14);
+ testDensity(ChiSquaredDistribution.of(df), points, values, tol);
+ }
- final double[] x = new double[]{-0.1, 1e-6, 0.5, 1, 2, 5};
+ static Stream<Arguments> testAdditionalDensity() {
// R 2.5:
// x <- c(-0.1, 1e-6, 0.5, 1, 2, 5)
- // print(dchisq(x, df=1), digits=17)
- testDensity(ChiSquaredDistribution.of(1), x, new double[] {
- 0.0, 398.942080930342626743, 0.439391289467722435,
0.241970724519143365,
- 0.103776874355148693, 0.014644982561926489}, tol);
- // print(dchisq(x, df=0.1), digits=17)
- testDensity(ChiSquaredDistribution.of(0.1), x, new double[]{
- 0, 2.4864539972849805e+04, 7.4642387316120481e-02,
- 3.0090777182393683e-02, 9.4472991589506262e-03,
8.8271993957607896e-04}, tol);
- // print(dchisq(x, df=2), digits=17)
- testDensity(ChiSquaredDistribution.of(2), x, new double[]{0,
- 0.49999975000006253, 0.38940039153570244,
- 0.30326532985631671, 0.18393972058572117, 0.04104249931194940},
tol);
- // print(dchisq(x, df=10), digits=17)
- testDensity(ChiSquaredDistribution.of(10), x, new double[]{0,
- 1.3020826822918329e-27, 6.3378969976514082e-05,
- 7.8975346316749191e-04, 7.6641550244050524e-03,
6.6800942890542614e-02}, tol);
- // print(dchisq(x, df=100), digits=17)
- testDensity(ChiSquaredDistribution.of(100), x, new double[]{0,
- 0.0000000000000000e+00, 2.0200026568141969e-93,
- 8.8562141121618944e-79, 3.0239224849774644e-64,
2.1290671364111626e-45}, tol);
+ final double[] x = new double[]{-0.1, 1e-6, 0.5, 1, 2, 5};
+ return Stream.of(
+ // print(dchisq(x, df=1), digits=17)
+ Arguments.of(1, x, new double[] {
+ 0, 398.942080930342626743, 0.439391289467722435,
0.241970724519143365,
+ 0.103776874355148693, 0.014644982561926489}),
+ // print(dchisq(x, df=0.1), digits=17)
+ Arguments.of(0.1, x, new double[] {
+ 0, 2.4864539972849805e+04, 7.4642387316120481e-02,
+ 3.0090777182393683e-02, 9.4472991589506262e-03,
8.8271993957607896e-04}),
+ // print(dchisq(x, df=2), digits=17)
+ Arguments.of(2, x, new double[] {
+ 0, 0.49999975000006253, 0.38940039153570244,
+ 0.30326532985631671, 0.18393972058572117,
0.04104249931194940}),
+ // print(dchisq(x, df=10), digits=17)
+ Arguments.of(10, x, new double[] {
+ 0, 1.3020826822918329e-27, 6.3378969976514082e-05,
+ 7.8975346316749191e-04, 7.6641550244050524e-03,
6.6800942890542614e-02}),
+ // print(dchisq(x, df=100), digits=17)
+ Arguments.of(100, x, new double[] {
+ 0, 0.0000000000000000e+00, 2.0200026568141969e-93,
+ 8.8562141121618944e-79, 3.0239224849774644e-64,
2.1290671364111626e-45})
- // TODO:
- // Add more density checks with large DF and x points around the mean
- // and into overflow for the underlying Gamma distribution.
+ // TODO:
+ // Add more density checks with large DF and x points around the
mean
+ // and into overflow for the underlying Gamma distribution.
+ );
}
}
