Below is the syntax highlighted version of Markov.java
from §1.6 Case Study: PageRank.
/****************************************************************************** * Compilation: javac Markov.java * Execution: java Markov trials * Data files: https://introcs.cs.princeton.edu/java/16pagerank/tinyG.txt * https://introcs.cs.princeton.edu/java/16pagerank/mediumG.txt * * This program reads a transition matrix from standard input and * computes the probabilities that a random surfer lands on each page * (page ranks) after the specified number of matrix-vector multiplies. * * % java Transition < tinyG.txt | java Markov 40 * 0.27303 0.26573 0.14618 0.24723 0.06783 * ******************************************************************************/ public class Markov { public static void main(String[] args) { int trials = Integer.parseInt(args[0]); // number of iterations int n = StdIn.readInt(); // number of pages StdIn.readInt(); // ignore integer required by input format // Read p[][] from StdIn. double[][] p = new double[n][n]; // p[i][j] = prob. surfer moves from page i to page j for (int i = 0; i < n; i++) for (int j = 0; j < n; j++) p[i][j] = StdIn.readDouble(); // Use the power method to compute page ranks. double[] rank = new double[n]; rank[0] = 1.0; for (int t = 0; t < trials; t++) { // Compute effect of next move on page ranks. double[] newRank = new double[n]; for (int j = 0; j < n; j++) { // New rank of page j is dot product of old ranks and column j of p[][]. for (int k = 0; k < n; k++) newRank[j] += rank[k]*p[k][j]; } // Update page ranks. rank = newRank; } // print page ranks for (int i = 0; i < n; i++) { StdOut.printf("%8.5f", rank[i]); } StdOut.println(); StdOut.println(); // print page ranks for (int i = 0; i < n; i++) { StdOut.printf("%2d %5.3f\n", i, rank[i]); } } }