}
public MaxEntOptimizableByLabelDistribution (InstanceList trainingSet, MaxEnt initialClassifier)
{
this.trainingList = trainingSet;
Alphabet fd = trainingSet.getDataAlphabet();
LabelAlphabet ld = (LabelAlphabet) trainingSet.getTargetAlphabet();
// Don't fd.stopGrowth, because someone might want to do feature induction
ld.stopGrowth();
// Add one feature for the "default feature".
this.numLabels = ld.size();
this.numFeatures = fd.size() + 1;
this.defaultFeatureIndex = numFeatures-1;
this.parameters = new double [numLabels * numFeatures];
this.constraints = new double [numLabels * numFeatures];
this.cachedGradient = new double [numLabels * numFeatures];
Arrays.fill (parameters, 0.0);
Arrays.fill (constraints, 0.0);
Arrays.fill (cachedGradient, 0.0);
this.featureSelection = trainingSet.getFeatureSelection();
this.perLabelFeatureSelection = trainingSet.getPerLabelFeatureSelection();
// Add the default feature index to the selection
if (featureSelection != null)
featureSelection.add (defaultFeatureIndex);
if (perLabelFeatureSelection != null)
for (int i = 0; i < perLabelFeatureSelection.length; i++)
perLabelFeatureSelection[i].add (defaultFeatureIndex);
// xxx Later change this to allow both to be set, but select which one to use by a boolean flag?
assert (featureSelection == null || perLabelFeatureSelection == null);
if (initialClassifier != null) {
this.theClassifier = initialClassifier;
this.parameters = theClassifier.parameters;
this.featureSelection = theClassifier.featureSelection;
this.perLabelFeatureSelection = theClassifier.perClassFeatureSelection;
this.defaultFeatureIndex = theClassifier.defaultFeatureIndex;
assert (initialClassifier.getInstancePipe() == trainingSet.getPipe());
}
else if (this.theClassifier == null) {
this.theClassifier = new MaxEnt (trainingSet.getPipe(), parameters, featureSelection, perLabelFeatureSelection);
}
cachedValueStale = true;
cachedGradientStale = true;
// Initialize the constraints
logger.fine("Number of instances in training list = " + trainingList.size());
for (Instance inst : trainingList) {
double instanceWeight = trainingList.getInstanceWeight(inst);
Labeling labeling = inst.getLabeling ();
if (labeling == null)
continue;
//logger.fine ("Instance "+ii+" labeling="+labeling);
FeatureVector fv = (FeatureVector) inst.getData ();
Alphabet fdict = fv.getAlphabet();
assert (fv.getAlphabet() == fd);
// Here is the difference between this code and the single label
// version: rather than only picking out the "best" index,
// loop over all label indices.
for (int pos = 0; pos < labeling.numLocations(); pos++){
MatrixOps.rowPlusEquals (constraints, numFeatures,
labeling.indexAtLocation(pos),
fv,
instanceWeight*labeling.valueAtLocation(pos));
}
assert(!Double.isNaN(instanceWeight)) : "instanceWeight is NaN";
boolean hasNaN = false;
for (int i = 0; i < fv.numLocations(); i++) {
if (Double.isNaN(fv.valueAtLocation(i))) {
logger.info("NaN for feature " + fdict.lookupObject(fv.indexAtLocation(i)).toString());
hasNaN = true;
}
}
if (hasNaN)
logger.info("NaN in instance: " + inst.getName());