Below is the syntax highlighted version of Transition.java
from §1.6 Case Study: PageRank.
/****************************************************************************** * Compilation: javac Transition.java * Execution: java Transition < input.txt * Data files: https://introcs.cs.princeton.edu/java/16pagerank/tinyG.txt * https://introcs.cs.princeton.edu/java/16pagerank/mediumG.txt * * This program is a filter that reads links from standard input and * produces the corresponding transition matrix on standard output. * First, it processes the input to count the outlinks from each page. * Then it applies the 90-10 rule to compute the transition matrix. * It assumes that there are no pages that have no outlinks in the * input (see Exercise 1.6.3). * * % more tinyG.txt * 5 * 0 1 * 1 2 1 2 * 1 3 1 3 1 4 * 2 3 * 3 0 * 4 0 4 2 * * % java Transition < tinyG.txt * 5 5 * 0.02 0.92 0.02 0.02 0.02 * 0.02 0.02 0.38 0.38 0.20 * 0.02 0.02 0.02 0.92 0.02 * 0.92 0.02 0.02 0.02 0.02 * 0.47 0.02 0.47 0.02 0.02 * ******************************************************************************/ public class Transition { public static void main(String[] args) { int n = StdIn.readInt(); // number of pages int[][] counts = new int[n][n]; // counts[i][j] = # links from page i to page j int[] outDegree = new int[n]; // outDegree[i] = # links from page i to anywhere // Accumulate link counts. while (!StdIn.isEmpty()) { int i = StdIn.readInt(); int j = StdIn.readInt(); outDegree[i]++; counts[i][j]++; } StdOut.println(n + " " + n); // Print probability distribution for row i. for (int i = 0; i < n; i++) { // Print probability for column j. for (int j = 0; j < n; j++) { double p = 0.90*counts[i][j]/outDegree[i] + 0.10/n; StdOut.printf("%7.5f ", p); } StdOut.println(); } } }