classifierexample.java

Standord Classifier实现了一个基于Java的最大熵分类器。用于模式识别

package edu.stanford.nlp.classify;

import edu.stanford.nlp.dbm.*;

import java.util.*;

public class ClassifierExample {

  protected static String GREEN = "green";
  protected static String RED = "red";
  protected static String WORKING = "working";
  protected static String BROKEN = "broken";

  protected static Datum makeStopLights(String ns, String ew) {
    List features = new ArrayList();
    // Create the north-south light feature
    features.add("NS="+ns);
    // Create the east-west light feature
    features.add("EW="+ew);
    // Create the label
    String label = (ns.equals(ew) ? BROKEN : WORKING);
    return new BasicDatum(features, label);
  }

  public static void main(String[] args) {
    // Create a training set
    List trainingData = new ArrayList();
    trainingData.add(makeStopLights(GREEN, RED));
    trainingData.add(makeStopLights(GREEN, RED));
    trainingData.add(makeStopLights(GREEN, RED));
    trainingData.add(makeStopLights(RED, GREEN));
    trainingData.add(makeStopLights(RED, GREEN));
    trainingData.add(makeStopLights(RED, GREEN));
    trainingData.add(makeStopLights(RED, RED));
    // Create a test set
    Datum workingLights = makeStopLights(GREEN, RED);
    Datum brokenLights = makeStopLights(RED, RED);
    // Build a classifier factory
    MaxentClassifierFactory factory = new MaxentClassifierFactory();
    // Turn on per-iteration convergence updates
    factory.setVerbose(true);
    // Small amount of smoothing
    factory.setSigma(10.0);
    // Build a classifier
    LinearClassifier classifier = (LinearClassifier)factory.trainClassifier(trainingData);
    // Check out the learned weights
    classifier.dump();
    // Test the classifier
    System.out.println("Working instance got: "+classifier.classOf(workingLights));
    classifier.justificationOf(workingLights);
    System.out.println("Broken instance got: "+classifier.classOf(brokenLights));
    classifier.justificationOf(brokenLights);
  }
}