    001    package calhoun.analysis.crf.features.generic;
    002    import java.util.List;
    003
    004    import org.apache.commons.logging.Log;
    005    import org.apache.commons.logging.LogFactory;
    006
    007    import calhoun.analysis.crf.AbstractFeatureManager;
    008    import calhoun.analysis.crf.CacheStrategySpec;
    009    import calhoun.analysis.crf.FeatureList;
    010    import calhoun.analysis.crf.FeatureManagerEdge;
    011    import calhoun.analysis.crf.ModelManager;
    012    import calhoun.analysis.crf.CacheStrategySpec.CacheStrategy;
    013    import calhoun.analysis.crf.io.InputSequence;
    014    import calhoun.analysis.crf.io.TrainingSequence;
    015    import calhoun.util.Assert;
    016
    017    /** learns transition probabilities from the data and then creates a single feature for all edge transitions.
    018     * <p>
    019     * <b>Notes:</b>
    020     * <ul>
    021     * <li> Returns the log probability of the transitions.
    022     * <li> Uses a CONSTANT cache strategy since the values returns are independent of position.
    023     * <li> When learning, initializes each edge count with a pseudocount of 1.
    024     * </ul>
    025     */
    026    public class WeightedEdges extends AbstractFeatureManager<Object> implements FeatureManagerEdge<Object> {
    027            private static final long serialVersionUID = 8477631359065280630L;
    028            private static final Log log = LogFactory.getLog(WeightedEdges.class);
    029
    030            int startIx;
    031            ModelManager manager;
    032            float[][] transitions;
    033
    034            @Override
    035            public CacheStrategySpec getCacheStrategy() {
    036                    return new CacheStrategySpec(CacheStrategy.CONSTANT);
    037            }
    038
    039            public String getFeatureName(int featureIndex) {
    040                    Assert.a(featureIndex == startIx, "Invalid feature index: ", featureIndex, ". Must be ", startIx);
    041                    return "WeightedEdges";
    042            }
    043
    044            public int getNumFeatures() {
    045                    // The is a single feature that is the log transition probabilities from the data.
    046                    return 1;
    047            }
    048
    049            public void evaluateEdge(InputSequence<?> seq, int pos, int prevState, int state, FeatureList result) {
    050                    result.addFeature(startIx, transitions[prevState][state]);
    051            }
    052
    053            public void train(int startingIndex, ModelManager modelInfo, List<? extends TrainingSequence<?>> data) {
    054                    log.debug("Training edges");
    055                    startIx = startingIndex;
    056                    manager = modelInfo;
    057                    int nStates = manager.getNumStates();
    058
    059                    // Count transitions from the training data
    060                    transitions = new float[nStates][nStates];
    061                    for (int j=0; j<nStates; j++) {
    062                            for (int k=0; k<nStates; k++) {
    063                                    transitions[j][k] = (float) 1.0; // pseudocounts
    064                            }
    065                    }
    066
    067                    //DoubleMatrix2D transitions  = new DenseDoubleMatrix2D(nStates, nStates);
    068                    for(TrainingSequence<?> seq : data) {
    069                            // Start at 1 because there is no transition for the first element of the sequence.
    070                            for(int pos = 1; pos < seq.length(); ++pos) {
    071                                    int start = seq.getY(pos-1);
    072                                    int end = seq.getY(pos);
    073                                    transitions[start][end] += (float) 1.0;
    074                            }
    075                    }
    076
    077                    log.debug("The transition logprobabilities are as follows (row is the FROM state and column is the TO state");
    078                    for (int j=0; j<nStates; j++) {
    079                            String s = modelInfo.getStateName(j);
    080                            float rowtotal = (float) 0.0;
    081                            for (int k=0; k<nStates; k++) {
    082                                    rowtotal += transitions[j][k];
    083                            }
    084                            for (int k=0; k<nStates; k++) {
    085                                    transitions[j][k] = (float) Math.log(transitions[j][k] / rowtotal);
    086                                    s = s + "\t" + transitions[j][k];
    087                            }
    088                            log.debug(s);
    089                    }
    090            }
    091    }