public EntityConfidence(double conf, boolean corr, Sequence input, int start, int end) {
this.confidence = conf;
this.correct = corr;
StringBuffer buff = new StringBuffer();
if (input != null) {
for (int j = start; j <= end; j++) {
FeatureVector fv = (FeatureVector) input.get(j);
for (int k = 0; k < fv.numLocations(); k++) {
String featureName = fv.getAlphabet().lookupObject(fv.indexAtLocation(k)).toString();
if (featureName.startsWith("W=") && featureName.indexOf("@") == -1) {
buff.append(featureName.substring(featureName.indexOf('=') + 1) + " ");
}
}
}
}
this.entity = buff.toString();
}
/**
* Classifies an instance using Winnow's weights
*
* @param instance an instance to be classified
* @return an object containing the classifier's guess
*/
public Classification classify(Instance instance) {
int numClasses = getLabelAlphabet().size();
double[] scores = new double[numClasses];
FeatureVector fv = (FeatureVector) instance.getData(this.instancePipe);
// Make sure the feature vector's feature dictionary matches
// what we are expecting from our data pipe (and thus our notion
// of feature probabilities.
assert (instancePipe == null || fv.getAlphabet() == this.instancePipe.getDataAlphabet());
int fvisize = fv.numLocations();
// Set the scores by summing wi*xi
for (int fvi = 0; fvi < fvisize; fvi++) {
int fi = fv.indexAtLocation(fvi);
for (int ci = 0; ci < numClasses; ci++) scores[ci] += this.weights[ci][fi];
}
// Create and return a Classification object
return new Classification(instance, this, new LabelVector(getLabelAlphabet(), scores));
}
