Test additional distributions using setters from parent class.
Rearranges the data to use the naming convention and order of the
abstract methods:
cumulativeTestPoints
cumulativeTestValues
densityTestValues
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/TruncatedNormalDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/TruncatedNormalDistributionTest.java
index ce49e46..48eed2f 100644
--- a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/TruncatedNormalDistributionTest.java
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/TruncatedNormalDistributionTest.java
@@ -17,6 +17,7 @@
package org.apache.commons.statistics.distribution;
+import java.util.Arrays;
import org.junit.jupiter.api.Assertions;
import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;
@@ -26,86 +27,79 @@
* All test values were computed using Python with SciPy v1.6.0.
*/
class TruncatedNormalDistributionTest extends ContinuousDistributionAbstractTest {
- /** Distribution to test with. */
- private TruncatedNormalDistribution distribution = new TruncatedNormalDistribution(1.9, 1.3, -1.1, 3.4);
- /** Percentiles to test with. */
- private double[] ppfValues = new double[]{0, 0.0001, 0.001, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95,
- 0.975, 0.99, 0.999, 0.9999, 1};
- /** Expected cumulative values for percentiles. */
- private double[] cdfValues = new double[]{-1.1, -1.09597275767544, -1.0609616183922, -0.79283350106842,
- -0.505331829887808, -0.192170173599874, 0.21173317261645,
- 0.925791281910463, 1.71399518338879, 2.43413009451536, 2.94473113856785,
- 3.15310057075828, 3.27036798398733, 3.34641874981679, 3.39452729074341,
- 3.39945153287941, 3.4};
- /** Expected density values for percentiles. */
- private double[] pdfValues = new double[]{0.0247422752302618, 0.0249196707321102, 0.0265057408263321,
- 0.0415071096500185, 0.0640403254340905, 0.0971457789636,
- 0.152622492901864, 0.267853863255995, 0.35107475879338, 0.325977522502844,
- 0.25680502248913, 0.222886115806507, 0.203494915087054, 0.190997946666992,
- 0.183167918885238, 0.182370706542209, 0.182281965373914};
- /** Expected distribution mean. */
- private double mean = 1.63375792365723;
- /** Expected distribution variance. */
- private double variance = 1.03158703914439;
- /** Overrides tolerance and sets up distribution. */
+ //---------------------- Override tolerance --------------------------------
+
@BeforeEach
- void setUp() {
+ void customSetUp() {
setTolerance(1e-7);
- super.setUp();
}
+ //-------------- Implementations for abstract methods ----------------------
+
/** {@inheritDoc} */
@Override
public ContinuousDistribution makeDistribution() {
- return distribution;
+ return new TruncatedNormalDistribution(1.9, 1.3, -1.1, 3.4);
}
/** {@inheritDoc} */
@Override
public double[] makeCumulativeTestPoints() {
- return cdfValues;
+ return new double[]{-1.1, -1.09597275767544, -1.0609616183922, -0.79283350106842,
+ -0.505331829887808, -0.192170173599874, 0.21173317261645,
+ 0.925791281910463, 1.71399518338879, 2.43413009451536, 2.94473113856785,
+ 3.15310057075828, 3.27036798398733, 3.34641874981679, 3.39452729074341,
+ 3.39945153287941, 3.4};
}
/** {@inheritDoc} */
@Override
public double[] makeCumulativeTestValues() {
- return ppfValues;
+ return new double[]{0, 0.0001, 0.001, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95,
+ 0.975, 0.99, 0.999, 0.9999, 1};
}
/** {@inheritDoc} */
@Override
public double[] makeDensityTestValues() {
- return pdfValues;
+ return new double[]{0.0247422752302618, 0.0249196707321102, 0.0265057408263321,
+ 0.0415071096500185, 0.0640403254340905, 0.0971457789636,
+ 0.152622492901864, 0.267853863255995, 0.35107475879338, 0.325977522502844,
+ 0.25680502248913, 0.222886115806507, 0.203494915087054, 0.190997946666992,
+ 0.183167918885238, 0.182370706542209, 0.182281965373914};
}
+ //-------------------- Additional test cases -------------------------------
+
/**
* Configures new test values and runs relevant tests in this class and {@link ContinuousDistributionAbstractTest}.
*
- * @param testDistribution distribution to test with.
- * @param expectedPpfValues expected percentiles to test with.
- * @param expectedCdfValues expected cumulative values for percentiles.
- * @param expectedPdfValues expected density values for percentiles.
- * @param expectedMean expected mean.
- * @param expectedVariance expected variance.
+ * @param distribution distribution to test with.
+ * @param cumulativeTestPoints test points for the cumulative probability and density.
+ * @param cumulativeTestValues expected values for the cumulative probability.
+ * @param densityTestValues expected values for the density.
+ * @param mean expected mean.
+ * @param variance expected variance.
*/
private void testAdditionalDistribution(
- TruncatedNormalDistribution testDistribution,
- double[] expectedPpfValues,
- double[] expectedCdfValues,
- double[] expectedPdfValues,
- double expectedMean,
- double expectedVariance) {
- this.distribution = testDistribution;
- this.ppfValues = expectedPpfValues;
- this.cdfValues = expectedCdfValues;
- this.pdfValues = expectedPdfValues;
- this.mean = expectedMean;
- this.variance = expectedVariance;
+ TruncatedNormalDistribution distribution,
+ double[] cumulativeTestPoints,
+ double[] cumulativeTestValues,
+ double[] densityTestValues,
+ double mean,
+ double variance) {
+ setDistribution(distribution);
+ setCumulativeTestPoints(cumulativeTestPoints);
+ setCumulativeTestValues(cumulativeTestValues);
+ setDensityTestValues(densityTestValues);
+ // Use reverse mapping
+ setInverseCumulativeTestPoints(cumulativeTestValues);
+ setInverseCumulativeTestValues(cumulativeTestPoints);
+ // Use the log(density)
+ setLogDensityTestValues(Arrays.stream(densityTestValues).map(Math::log).toArray());
- setUp();
-
- testMoments();
+ testMoments(distribution, mean, variance);
testConsistency();
testSampler();
@@ -127,10 +121,10 @@
void testOneSidedLowerTail() {
testAdditionalDistribution(
new TruncatedNormalDistribution(12, 2.4, Double.NEGATIVE_INFINITY, 7.1),
- new double[]{0, 0.00108276414971883, 0.00433032247708514, 0.0155754809421998, 0.0504271331622245,
- 0.147106879016387, 0.387159643321778, 0.920668099879139, 1},
new double[]{Double.NEGATIVE_INFINITY, 2.20249292901062, 3.00511196424565, 3.80773099948069,
4.61035003471573, 5.41296906995077, 6.21558810518581, 7.01820714042084, 7.1},
+ new double[]{0, 0.00108276414971883, 0.00433032247708514, 0.0155754809421998, 0.0504271331622245,
+ 0.147106879016387, 0.387159643321778, 0.920668099879139, 1},
new double[]{0, 0.00194181137319567, 0.00719165311538403, 0.0238165586714952, 0.0705273999981105,
0.186752027463317, 0.442182309739316, 0.936194292830215, 1.00423817618302},
6.21558810518581,
@@ -142,10 +136,10 @@
void testOneSidedUpperTail() {
testAdditionalDistribution(
new TruncatedNormalDistribution(-9.6, 17, -15, Double.POSITIVE_INFINITY),
- new double[]{0, 0.164539974698729, 0.564800349576255, 0.836443289017693, 0.957226746540945,
- 0.992394081771774, 0.999093968560336, 0.999928403010774, 1},
new double[]{-15, -10.5314720401464, 0.723583450712814, 11.978638941572, 23.2336944324312,
34.4887499232902, 45.7438054141485, 56.9988609050074, Double.POSITIVE_INFINITY},
+ new double[]{0, 0.164539974698729, 0.564800349576255, 0.836443289017693, 0.957226746540945,
+ 0.992394081771774, 0.999093968560336, 0.999928403010774, 1},
new double[]{0.035721742043989, 0.0375137766818179, 0.0312438063187719, 0.0167870518464031,
0.00581865051705663, 0.00130109036611494, 0.000187685186297558, 1.74658560715427e-05, 0},
0.723583450712812,
@@ -157,11 +151,11 @@
void testNoTruncation() {
testAdditionalDistribution(
new TruncatedNormalDistribution(3, 1.1, Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY),
+ new double[]{Double.NEGATIVE_INFINITY, -2.5, -1.4, -0.300000000000001, 0.799999999999999, 1.9, 3,
+ 4.1, 5.2, 6.3, 7.4, 8.49999999996719, Double.POSITIVE_INFINITY},
new double[]{0, 2.86651571879193e-07, 3.16712418331199e-05, 0.00134989803163009, 0.0227501319481792,
0.158655253931457, 0.5, 0.841344746068543, 0.977249868051821, 0.99865010196837,
0.999968328758167, 0.999999713348428, 1},
- new double[]{Double.NEGATIVE_INFINITY, -2.5, -1.4, -0.300000000000001, 0.799999999999999, 1.9, 3,
- 4.1, 5.2, 6.3, 7.4, 8.49999999996719, Double.POSITIVE_INFINITY},
new double[]{0, 1.35156319521299e-06, 0.000121663841604441, 0.00402895310176182, 0.0490826968301709,
0.219973385926494, 0.362674800364939, 0.219973385926494, 0.0490826968301709,
0.00402895310176184, 0.000121663841604441, 1.35156319541454e-06, 0},
@@ -174,10 +168,10 @@
void testLowerTailOnly() {
testAdditionalDistribution(
new TruncatedNormalDistribution(0, 1, Double.NEGATIVE_INFINITY, -5),
- new double[]{0, 0.00196196451357246, 0.00597491488512203, 0.0176247203066899, 0.0503595643590926,
- 0.139390045971621, 0.373761183487683, 0.970943041215359, 1},
new double[]{Double.NEGATIVE_INFINITY, -6.09061174025149, -5.90979018562636, -5.72896863100123,
-5.54814707637611, -5.36732552175098, -5.18650396712585, -5.00568241250073, -5},
+ new double[]{0, 0.00196196451357246, 0.00597491488512203, 0.0176247203066899, 0.0503595643590926,
+ 0.139390045971621, 0.373761183487683, 0.970943041215359, 1},
new double[]{0, 0.0122562922051934, 0.0362705138555484, 0.103883943928261, 0.287967362544455,
0.772570689127439, 2.00601097433085, 5.04113700754108, 5.18650396712585},
-5.18650396712585,
@@ -189,10 +183,10 @@
void testUpperTailOnly() {
testAdditionalDistribution(
new TruncatedNormalDistribution(0, 1, 5, Double.POSITIVE_INFINITY),
- new double[]{0, 0.0290569590230917, 0.626238816822898, 0.860609954247549, 0.949640435971243,
- 0.982375279755296, 0.994025085266282, 0.998038035551444, 1},
new double[]{5, 5.00568241254803, 5.18650396728068, 5.36732552203467, 5.54814707752324,
5.72896863159791, 5.90979018980065, 6.09061174555624, Double.POSITIVE_INFINITY},
+ new double[]{0, 0.0290569590230917, 0.626238816822898, 0.860609954247549, 0.949640435971243,
+ 0.982375279755296, 0.994025085266282, 0.998038035551444, 1},
new double[]{5.18650396712585, 5.04113700634745, 2.00601097272001, 0.772570687951075,
0.287967360711704, 0.103883943573147, 0.0362705129607846, 0.0122562918092027, 0},
5.18650396712585,
@@ -204,8 +198,8 @@
void testNarrowTruncatedRange() {
testAdditionalDistribution(
new TruncatedNormalDistribution(7.1, 9.9, 7.0999999, 7.1000001),
- new double[]{0, 0.5, 1},
new double[]{7.0999999, 7.1, 7.1000001},
+ new double[]{0, 0.5, 1},
new double[]{5000000.00238838, 5000000.00238838, 5000000.00238838},
7.1,
1.13584123966337e-07);
@@ -214,6 +208,12 @@
/** Test mean and variance moments. */
@Test
void testMoments() {
+ final double mean = 1.63375792365723;
+ final double variance = 1.03158703914439;
+ testMoments(makeDistribution(), mean, variance);
+ }
+
+ private void testMoments(ContinuousDistribution distribution, double mean, double variance) {
Assertions.assertEquals(mean, distribution.getMean(), getTolerance());
Assertions.assertEquals(variance, distribution.getVariance(), getTolerance());
}
