simplegraphexample.java

常用机器学习算法,java编写源代码,内含常用分类算法,包括说明文档

/* Copyright (C) 2003 Univ. of Massachusetts Amherst, Computer Science Dept.
   This file is part of "MALLET" (MAchine Learning for LanguagE Toolkit).
   http://www.cs.umass.edu/~mccallum/mallet
   This software is provided under the terms of the Common Public License,
   version 1.0, as published by http://www.opensource.org.  For further
   information, see the file `LICENSE' included with this distribution. */
package edu.umass.cs.mallet.grmm.examples;

import edu.umass.cs.mallet.grmm.*;

import java.util.Random;

/**
 * Created: Aug 13, 2004
 *
 * @author <A HREF="mailto:[e-mail]>[e-mail]</A>
 * @version $Id: SimpleGraphExample.java,v 1.1 2004/08/13 20:20:44 casutton Exp $
 */
public class SimpleGraphExample {

  public static void main (String[] args)
  {

    // STEP 1: Create the graph

    Variable[] allVars = {
      new Variable (2),
      new Variable (2),
      new Variable (2),
      new Variable (2)
    };

    UndirectedModel mdl = new UndirectedModel (allVars);

    // Create a diamond graph, with random potentials
    Random r = new Random (42);
    for (int i = 0; i < allVars.length; i++) {
      double[] ptlarr = new double [4];
      for (int j = 0; j < ptlarr.length; j++)
        ptlarr [j] = Math.abs (r.nextDouble ());

      Variable v1 = allVars [i];
      Variable v2 = allVars [(i + 1) % allVars.length];
      mdl.addEdgeWithPotential (v1, v2, ptlarr);
    }

    // STEP 2: Compute marginals

    Inferencer inf = new JunctionTreeInferencer ();
    inf.computeMarginals (mdl);

    // STEP 3: Collect the results
    //   We'll just print them out

    for (int varnum = 0; varnum < allVars.length; varnum++) {
      Variable var = allVars[varnum];
      DiscretePotential ptl = inf.lookupMarginal (var);
      for (int outcome = 0; outcome < 2; outcome++) {
        Assignment assn = new Assignment (var, outcome);
        System.out.println (var+"  "+outcome+"   "+ptl.phi (assn));
      }
      System.out.println ();
    }

  }

}