/**
* Test of getVariance method, of class
* gov.sandia.cognition.learning.util.statistics.UniformDistribution.
*/
public void testGetVariance() {
System.out.println("PDF.getVariance");
UniformDistribution.PDF instance = this.createInstance().getProbabilityFunction();
double d = instance.getMaxSupport() - instance.getMinSupport();
assertEquals(d * d / 12.0, instance.getVariance(), TOLERANCE);
}
@Override
public void testPDFKnownValues() {
System.out.println("PDF.knownValues");
for (int i = 0; i < 100; i++) {
UniformDistribution.PDF instance = this.createInstance().getProbabilityFunction();
double a = instance.getMinSupport();
double b = instance.getMaxSupport();
double x = (RANDOM.nextDouble() * (b - a + 2)) + a - 1;
double h = 1 / (b - a);
double y;
if (x < a) {
y = 0;
} else if (x > b) {
y = 0;
} else {
y = h;
}
double yhat = instance.evaluate(x);
assertEquals(y, yhat);
assertEquals(h, instance.evaluate(a));
assertEquals(h, instance.evaluate(b));
}
}
/**
* Test of getMean method, of class
* gov.sandia.cognition.learning.util.statistics.UniformDistribution.
*/
public void testGetMean() {
System.out.println("PDF.getMean");
UniformDistribution.PDF instance = this.createInstance().getProbabilityFunction();
assertEquals(
0.5 * (instance.getMinSupport() + instance.getMaxSupport()), instance.getMean(), TOLERANCE);
}
@Override
public void testPDFConstructors() {
System.out.println("PDF Constructor");
UniformDistribution.PDF u = new UniformDistribution.PDF();
assertEquals(UniformDistribution.DEFAULT_MIN, u.getMinSupport());
assertEquals(UniformDistribution.DEFAULT_MAX, u.getMaxSupport());
double a = RANDOM.nextGaussian();
double b = RANDOM.nextDouble() + a;
u = new UniformDistribution.PDF(a, b);
assertEquals(a, u.getMinSupport());
assertEquals(b, u.getMaxSupport());
UniformDistribution.PDF u2 = new UniformDistribution.PDF(u);
assertEquals(u.getMinSupport(), u2.getMinSupport());
assertEquals(u.getMaxSupport(), u2.getMaxSupport());
}
