/**
* Convert a single Tree[String] to Tree[StateSet]
*
* @param tree
* @param numStates
* @param tagNumberer
* @return
*/
public static short[] initializeSubStateArray(
List<Tree<String>> trainTrees,
List<Tree<String>> validationTrees,
Numberer tagNumberer,
short nSubStates) {
// boolean dontSplitTags) {
// first generate unsplit grammar and lexicon
short[] nSub = new short[2];
nSub[0] = 1;
nSub[1] = nSubStates;
// do the validation set so that the numberer sees all tags and we can
// allocate big enough arrays
// note: although this variable is never read, this constructor adds the
// validation trees into the tagNumberer as a side effect, which is
// important
StateSetTreeList trainStateSetTrees = new StateSetTreeList(trainTrees, nSub, true, tagNumberer);
@SuppressWarnings("unused")
StateSetTreeList validationStateSetTrees =
new StateSetTreeList(validationTrees, nSub, true, tagNumberer);
StateSetTreeList.initializeTagNumberer(trainTrees, tagNumberer);
StateSetTreeList.initializeTagNumberer(validationTrees, tagNumberer);
short numStates = (short) tagNumberer.total();
short[] nSubStateArray = new short[numStates];
short two = nSubStates;
Arrays.fill(nSubStateArray, two);
// System.out.println("Everything is split in two except for the root.");
nSubStateArray[0] = 1; // that's the ROOT
return nSubStateArray;
}
/**
* @param previousGrammar
* @param previousLexicon
* @param grammar
* @param lexicon
* @param trainStateSetTrees
* @return
*/
public static double doOneEStep(
Grammar previousGrammar,
Lexicon previousLexicon,
Grammar grammar,
Lexicon lexicon,
StateSetTreeList trainStateSetTrees,
boolean updateOnlyLexicon,
int unkThreshold) {
boolean secondHalf = false;
ArrayParser parser = new ArrayParser(previousGrammar, previousLexicon);
double trainingLikelihood = 0;
int n = 0;
int nTrees = trainStateSetTrees.size();
for (Tree<StateSet> stateSetTree : trainStateSetTrees) {
secondHalf = (n++ > nTrees / 2.0);
boolean noSmoothing = true, debugOutput = false;
parser.doInsideOutsideScores(stateSetTree, noSmoothing, debugOutput); // E Step
double ll = stateSetTree.getLabel().getIScore(0);
ll =
Math.log(ll)
+ (100 * stateSetTree.getLabel().getIScale()); // System.out.println(stateSetTree);
if ((Double.isInfinite(ll) || Double.isNaN(ll))) {
if (VERBOSE) {
System.out.println("Training sentence " + n + " is given " + ll + " log likelihood!");
System.out.println(
"Root iScore "
+ stateSetTree.getLabel().getIScore(0)
+ " scale "
+ stateSetTree.getLabel().getIScale());
}
} else {
lexicon.trainTree(stateSetTree, -1, previousLexicon, secondHalf, noSmoothing, unkThreshold);
if (!updateOnlyLexicon) grammar.tallyStateSetTree(stateSetTree, previousGrammar); // E Step
trainingLikelihood += ll; // there are for some reason some sentences that are unparsable
}
}
lexicon.tieRareWordStats(unkThreshold);
// SSIE
((SophisticatedLexicon) lexicon).overwriteWithMaxent();
return trainingLikelihood;
}
