package edu.stanford.nlp.classify;import java.util.*;import edu.stanford.nlp.util.*;import edu.stanford.nlp.optimization.*;/** @author Dan Klein */public class MaxentClassifierFactory extends AbstractLinearClassifierFactory {  double TOL = 1e-4;  double sigma = 1.0;  boolean verbose = false;  public double[][] trainWeights(int[][] data, int[] labels) {    Minimizer minimizer = new CGMinimizer(!verbose);    DiffFunction objective = new LogConditionalObjectiveFunction(numFeatures(), numClasses(), data, labels, sigma);    double[] weights = minimizer.minimize(objective, TOL, ((HasInitial)objective).initial());    //System.err.println("Final derivative norm2: "+((AbstractCachingDiffFunction)objective).norm2(((AbstractCachingDiffFunction)objective).derivative));    return ((LogConditionalObjectiveFunction)objective).to2D(weights);  }  /** Set the smoothing amount: 1.0 is the default. */  public void setSigma(double sigma) {    this.sigma = sigma;  }  public void setTol(double tol) {    TOL = tol;  }  public void setVerbose(boolean verbose) {    this.verbose = verbose;  }}