/**
* Simpson's integration method.
*
* <p>Note that the Commons implementation fails if the lower bound is larger than the upper - in
* this case, the bounds are reversed and the result negated.
*
* @param f The function to integrate, not null
* @param lower The lower bound, not null
* @param upper The upper bound, not null
* @return The result of the integration
*/
@Override
public Double integrate(Function<Double, Double> f, Double lower, Double upper) {
ArgChecker.notNull(f, "function");
ArgChecker.notNull(lower, "lower bound");
ArgChecker.notNull(upper, "upper bound");
try {
if (lower < upper) {
return integrator.integrate(MAX_EVAL, CommonsMathWrapper.wrapUnivariate(f), lower, upper);
}
log.info("Upper bound was less than lower bound; swapping bounds and negating result");
return -integrator.integrate(MAX_EVAL, CommonsMathWrapper.wrapUnivariate(f), upper, lower);
} catch (NumberIsTooSmallException | NumberIsTooLargeException e) {
throw new MathException(e);
}
}
@Test
/** Test of integrator for the sine function. */
public void testSinFunction() {
UnivariateFunction f = new Gaussian(10, 2);
UnivariateIntegrator integrator = new SimpsonIntegrator();
double a, b, expected, tolerance, result;
a = 8;
b = 12;
expected = 0.68269;
tolerance = 0.00001;
tolerance = Math.abs(expected * integrator.getRelativeAccuracy());
result = integrator.integrate(MAX_EVAL, f, a, b);
assertEquals(expected, result, tolerance);
log.info(
"Result: "
+ result
+ ", tolerance: "
+ tolerance
+ " - Relative accuracy: "
+ integrator.getRelativeAccuracy()
+ " - Absolute accuracy: "
+ integrator.getAbsoluteAccuracy()
+ " - Iterations: "
+ integrator.getIterations());
NormalDistribution distribution = new NormalDistribution(10, 2);
result = distribution.cumulativeProbability(a, b);
log.info("Distribution result: " + result);
}