001 package calhoun.analysis.crf.features.generic;
002
003 import java.util.Arrays;
004 import java.util.List;
005
006 import calhoun.analysis.crf.AbstractFeatureManager;
007 import calhoun.analysis.crf.FeatureList;
008 import calhoun.analysis.crf.FeatureManagerNode;
009 import calhoun.analysis.crf.ModelManager;
010 import calhoun.analysis.crf.io.InputSequence;
011 import calhoun.util.Assert;
012
013 /** indicator functions that evaluate to true for a selected set of start states at the first position in the sequence. */
014 public class EndFeatures extends AbstractFeatureManager<Object> implements FeatureManagerNode<Object> {
015 private static final long serialVersionUID = 5206582215565363768L;
016 int startIx;
017 int[] endStates;
018 String[] names;
019
020 int[] configStates = new int[] {0, 1};
021
022 public void setStates(int[] config) {
023 configStates = config;
024 }
025
026 public String getFeatureName(int featureIndex) {
027 Assert.a(featureIndex - startIx < names.length, "Invalid feature index");
028 return names[featureIndex - startIx];
029 }
030
031 public int getNumFeatures() {
032 return names.length;
033 }
034
035 public void evaluateNode(InputSequence<?> seq, int pos, int state, FeatureList result) {
036 if(pos == seq.length()-1) {
037 int index = endStates[state];
038 if(index != -1) {
039 result.addFeature(index, 1);
040 }
041 }
042 }
043
044 /** Edge features don't train based on the data. Just set up based on the model. */
045 public void train(int startingIndex, ModelManager modelInfo, List data) {
046 startIx = startingIndex;
047 endStates = new int[modelInfo.getNumStates()];
048 Arrays.fill(endStates, -1);
049 names = new String[configStates.length];
050 for(int i=0; i<configStates.length; ++i) {
051 endStates[configStates[i]] = startIx+i;
052 names[i] = "End."+modelInfo.getStateName(configStates[i]);
053 }
054 }
055 }