Below is the syntax highlighted version of SimpsonsRule.java
from §9.3 Symbolic Methods.
/****************************************************************************** * Compilation: javac SimpsonsRule.java * Execution: java SimpsonsRule a b * * Numerically integrate the function in the interval [a, b]. * * % java SimpsonsRule -3 3 * 0.9972993166805203 // true answer = 0.9973002040... * * Observation: this says that 99.7% of time a standard normal random * variable is within 3 standard deviation of its mean. * * % java SimpsonsRule 0 100000 * 1.3299405953976486 // true answer = 1/2 * * Caveat: this is not the best way to integrate the normal density * function. See what happens if you make b very big. * ******************************************************************************/ public class SimpsonsRule { /********************************************************************** * Standard normal distribution density function. * Replace with any sufficiently smooth function. **********************************************************************/ public static double f(double x) { return Math.exp(- x * x / 2) / Math.sqrt(2 * Math.PI); } /********************************************************************** * Integrate f from a to b using Simpson's rule. * Increase N for more precision. **********************************************************************/ public static double integrate(double a, double b) { int N = 10000; // precision parameter double h = (b - a) / (N - 1); // step size // 1/3 terms double sum = 1.0 / 3.0 * (f(a) + f(b)); // 4/3 terms for (int i = 1; i < N - 1; i += 2) { double x = a + h * i; sum += 4.0 / 3.0 * f(x); } // 2/3 terms for (int i = 2; i < N - 1; i += 2) { double x = a + h * i; sum += 2.0 / 3.0 * f(x); } return sum * h; } // sample client program public static void main(String[] args) { double a = Double.parseDouble(args[0]); double b = Double.parseDouble(args[1]); StdOut.println(integrate(a, b)); } }