crfparams.java

这是一个CRF(条件随机域)算法的实现,希望能对从事算法的有些帮助.

package iitb.CRF;

import java.io.Serializable;
import java.util.*;

/**
 * This class holds all parameters to control various aspects of the CRF model
 *
 * @author Sunita Sarawagi
 *
 */ 


public class CrfParams implements Serializable {
    /** initial value for all the lambda arrays */
    public double initValue = 0;
    /** penalty term for likelihood function is ||lambda||^2*invSigmaSquare/2
	set this to zero, if no penalty needed
    */
    public double invSigmaSquare = 0.01;
    /** Maximum number of iterations over the training data during training */
    public int maxIters = 100;
    /** Convergence criteria for finding optimum lambda using BFGS */
    public double epsForConvergence = 0.001;
    /** The number of corrections used in the BFGS update. */
    public int mForHessian = 7;   

    public String trainerType = "";

    public int debugLvl = 1; // controls amount of status information output

    public boolean doScaling = true;

    public boolean doRobustScale = false;
    
    public boolean reuseM = false;

    public java.util.Properties miscOptions;
    /**
     * constructor with default values as follows.
     * initValue = 0;
     * invSigmaSquare = 1.0;
     * maxIters = 50;
     * epsForConvergence = 0.0001;
     * mForHessian = 7;   
     * trainerType = "";
     * debugLvl = 1;
     */
    public CrfParams() {}

    /** Initialize any parameter using space separated list of name value pairs
     *  Example: "initValue 0.1 maxIters 20" 
     *  will set the initValue param to 0.1 and maxIters param to 20.
     */
    public CrfParams(String args) {
	this(stringToOptions(args));
    }
    static java.util.Properties stringToOptions(String args) {
	java.util.Properties opts = new java.util.Properties();
	StringTokenizer tok = new StringTokenizer(args, " ");
	while (tok.hasMoreTokens()) {
	    String name = tok.nextToken();
	    String value = tok.nextToken();
	    opts.put(name,value);
	}
	return opts;
    }
    
    public CrfParams(java.util.Properties opts) {
	parseParameters(opts);
    }
    public void parseParameters(java.util.Properties opts) {
	miscOptions = opts;
	if (opts.getProperty("initValue") != null) {
	    initValue = Double.parseDouble(opts.getProperty("initValue"));
	} 
	if (opts.getProperty("maxIters") != null) {
	    maxIters = Integer.parseInt(opts.getProperty("maxIters"));
	} 
	if (opts.getProperty("invSigmaSquare") != null) {
	    invSigmaSquare = Double.parseDouble(opts.getProperty("invSigmaSquare"));
	} 
	if (opts.getProperty("debugLvl") != null) {
	    debugLvl = Integer.parseInt(opts.getProperty("debugLvl"));
	} 
	if (opts.getProperty("scale") != null) {
	    doScaling = opts.getProperty("scale").equalsIgnoreCase("true");
	}
	if (opts.getProperty("robustScale") != null) {
	    doRobustScale = opts.getProperty("robustScale").equalsIgnoreCase("true");
	}
	if (opts.getProperty("epsForConvergence") != null) {
	    epsForConvergence = Double.parseDouble(opts.getProperty("epsForConvergence"));
	}
	if (opts.getProperty("mForHessian") != null) {
	    mForHessian = Integer.parseInt(opts.getProperty("mForHessian"));
	}
	if (opts.getProperty("trainer") != null) {
	    trainerType = opts.getProperty("trainer");
	}
	reuseM = Boolean.valueOf(opts.getProperty("reuseM","false")).booleanValue();
    }
};