gradientascent.java

来自「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」· Java 代码 · 共 66 行

/* Copyright (C) 2002 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. */




/** 
   @author Andrew McCallum <a href="mailto:[e-mail]">[e-mail]</a>
 */

package edu.umass.cs.mallet.base.maximize;

import edu.umass.cs.mallet.base.util.MalletLogger;
import java.util.logging.*;

// Gradient Ascent

public class GradientAscent implements Maximizer.ByGradient
{
	private static Logger logger = MalletLogger.getLogger(GradientAscent.class.getName());

	double initialStepSize = 0.2;
	double tolerance = 0.001;
	int maxIterations = 200;
	LineMaximizer.ByGradient lineMaximizer = new BackTrackLineSearch();

	// "eps" is a small number to recitify the special case of converging
	// to exactly zero function value
	final double eps = 1.0e-10;
	
	public GradientAscent ()
	{
	}

	public boolean maximize (Maximizable.ByGradient maxable)
	{
		return maximize (maxable, maxIterations);
	}
	
	public boolean maximize (Maximizable.ByGradient maxable, int numIterations)
	{
		int iterations;
		double step = initialStepSize;
		double fret;
		double fp = maxable.getValue ();
    double[] xi = new double [maxable.getNumParameters()];
		maxable.getValueGradient(xi);

		for (iterations = 0; iterations < numIterations; iterations++) {
			logger.info ("At iteration "+iterations+", cost = "+fp);
			step = lineMaximizer.maximize (maxable, xi, step);
			fret = maxable.getValue ();
			if (2.0*Math.abs(fret-fp) <= tolerance*(Math.abs(fret)+Math.abs(fp)+eps))
				return true;
			fp = fret;
			maxable.getValueGradient(xi);
		}
		return false;
	}
	
}