Below is the syntax highlighted version of TwoPass.java
from §3.3 Designing Data Types.
/****************************************************************************** * Compilation: javac TwoPass.java * Execution: java TwoPass < data.txt * Dependencies: StdIn.java * * Reads in a sequence of real numbers, computes the mean, standard * deviation and 95% approximate confidence interval. * * Note: the two-pass formula is preferred for stability. * * Limitations * ----------- * - accurate subject to overflow of double * * * % java TwoPass * 10.0 5.0 6.0 * 3.0 7.0 32.0 * average = 10.5 * sample variance = 116.3 * 95% approximate confidence interval = [ -10.637125632403283, 31.637125632403283 ] * * % java Average * 0.5000000000000002 0.5000000000000001 * average = 0.5000000000000002 * sample stddev = 1.1102230246251565E-16 * 95% approximate confidence interval = [ 0.5, 0.5000000000000004 ] * ******************************************************************************/ public class TwoPass { private int capacity = 10; private int n = 0; private double[] x = new double[capacity]; private double sumx = 0.0; // double the capacity of the array storing the values private void increaseCapacity() { capacity = capacity * 2; double[] temp = new double[capacity]; for (int i = 0; i < n; i++) temp[i] = x[i]; x = temp; } // add a new value to the dataset public void add(double value) { if (n == capacity) increaseCapacity(); x[n++] = value; sumx = sumx + value; } // return the mean of the n values public double mean() { return sumx / n; } // return the sample variance of the n values public double variance() { double xbar = mean(); double xxbar = 0.0; for (int i = 0; i < n; i++) xxbar += (x[i] - xbar) * (x[i] - xbar); double variance = xxbar / (n - 1); return variance; } // return the sample standard deviation of the n values public double stddev() { return Math.sqrt(variance()); } // test client public static void main(String[] args) { TwoPass dataset = new TwoPass(); while (!StdIn.isEmpty()) { double x = StdIn.readDouble(); dataset.add(x); } double mean = dataset.mean(); double stddev = dataset.stddev(); double lo = mean - 1.96 * stddev; double hi = mean + 1.96 * stddev; // print results StdOut.println("mean = " + mean); StdOut.println("sample stddev = " + stddev); StdOut.print("95% approximate confidence interval = "); StdOut.println("[ " + lo + ", " + hi + " ]"); } }