private void tallyTree(
Tree<String> tree,
Counter<String> symbolCounter,
Counter<UnaryRule> unaryRuleCounter,
Counter<BinaryRule> binaryRuleCounter) {
if (tree.isLeaf()) return;
if (tree.isPreTerminal()) return;
if (tree.getChildren().size() == 1) {
UnaryRule unaryRule = makeUnaryRule(tree);
symbolCounter.incrementCount(tree.getLabel(), 1.0);
unaryRuleCounter.incrementCount(unaryRule, 1.0);
}
if (tree.getChildren().size() == 2) {
BinaryRule binaryRule = makeBinaryRule(tree);
symbolCounter.incrementCount(tree.getLabel(), 1.0);
binaryRuleCounter.incrementCount(binaryRule, 1.0);
}
if (tree.getChildren().size() < 1 || tree.getChildren().size() > 2) {
throw new RuntimeException(
"Attempted to construct a Grammar with an illegal tree: " + tree);
}
for (Tree<String> child : tree.getChildren()) {
tallyTree(child, symbolCounter, unaryRuleCounter, binaryRuleCounter);
}
}
private void tallyTagging(String word, String tag) {
if (!isKnown(word)) {
totalWordTypes += 1.0;
typeTagCounter.incrementCount(tag, 1.0);
}
totalTokens += 1.0;
tagCounter.incrementCount(tag, 1.0);
wordCounter.incrementCount(word, 1.0);
wordToTagCounters.incrementCount(word, tag, 1.0);
}
/* Returns a smoothed estimate of P(word|tag) */
public double scoreTagging(String word, String tag) {
double p_tag = tagCounter.getCount(tag) / totalTokens;
double c_word = wordCounter.getCount(word);
double c_tag_and_word = wordToTagCounters.getCount(word, tag);
if (c_word < 10) { // rare or unknown
c_word += 1.0;
c_tag_and_word += typeTagCounter.getCount(tag) / totalWordTypes;
}
double p_word = (1.0 + c_word) / (totalTokens + totalWordTypes);
double p_tag_given_word = c_tag_and_word / c_word;
return p_tag_given_word / p_tag * p_word;
}
/* A builds PCFG using the observed counts of binary and unary
* productions in the training trees to estimate the probabilities
* for those rules. */
public Grammar(List<Tree<String>> trainTrees) {
Counter<UnaryRule> unaryRuleCounter = new Counter<UnaryRule>();
Counter<BinaryRule> binaryRuleCounter = new Counter<BinaryRule>();
Counter<String> symbolCounter = new Counter<String>();
for (Tree<String> trainTree : trainTrees) {
tallyTree(trainTree, symbolCounter, unaryRuleCounter, binaryRuleCounter);
}
for (UnaryRule unaryRule : unaryRuleCounter.keySet()) {
double unaryProbability =
unaryRuleCounter.getCount(unaryRule) / symbolCounter.getCount(unaryRule.getParent());
unaryRule.setScore(unaryProbability);
addUnary(unaryRule);
}
for (BinaryRule binaryRule : binaryRuleCounter.keySet()) {
double binaryProbability =
binaryRuleCounter.getCount(binaryRule) / symbolCounter.getCount(binaryRule.getParent());
binaryRule.setScore(binaryProbability);
addBinary(binaryRule);
}
}
public boolean isKnown(String word) {
return wordCounter.keySet().contains(word);
}
public Set<String> getAllTags() {
return tagCounter.keySet();
}
public Tree<String> getBestParseOld(List<String> sentence) {
// TODO: This implements the CKY algorithm
CounterMap<String, String> parseScores = new CounterMap<String, String>();
System.out.println(sentence.toString());
// First deal with the lexicons
int index = 0;
int span = 1; // All spans are 1 at the lexicon level
for (String word : sentence) {
for (String tag : lexicon.getAllTags()) {
double score = lexicon.scoreTagging(word, tag);
if (score >= 0.0) { // This lexicon may generate this word
// We use a counter map in order to store the scores for this sentence parse.
parseScores.setCount(index + " " + (index + span), tag, score);
}
}
index = index + 1;
}
// handle unary rules now
HashMap<String, Triplet<Integer, String, String>> backHash =
new HashMap<
String, Triplet<Integer, String, String>>(); // hashmap to store back propation
// System.out.println("Lexicons found");
Boolean added = true;
while (added) {
added = false;
for (index = 0; index < sentence.size(); index++) {
// For each index+ span pair, get the counter.
Counter<String> count = parseScores.getCounter(index + " " + (index + span));
PriorityQueue<String> countAsPQ = count.asPriorityQueue();
while (countAsPQ.hasNext()) {
String entry = countAsPQ.next();
// System.out.println("I am fine here!!");
List<UnaryRule> unaryRules = grammar.getUnaryRulesByChild(entry);
for (UnaryRule rule : unaryRules) {
// These are the unary rules which might give rise to the above preterminal
double prob =
rule.getScore() * parseScores.getCount(index + " " + (index + span), entry);
if (prob > parseScores.getCount(index + " " + (index + span), rule.parent)) {
parseScores.setCount(index + " " + (index + span), rule.parent, prob);
backHash.put(
index + " " + (index + span) + " " + rule.parent,
new Triplet<Integer, String, String>(-1, entry, null));
added = true;
}
}
}
}
}
// System.out.println("Lexicon unaries dealt with");
// Now work with the grammar to produce higher level probabilities
for (span = 2; span <= sentence.size(); span++) {
for (int begin = 0; begin <= (sentence.size() - span); begin++) {
int end = begin + span;
for (int split = begin + 1; split <= end - 1; split++) {
Counter<String> countLeft = parseScores.getCounter(begin + " " + split);
Counter<String> countRight = parseScores.getCounter(split + " " + end);
// List<BinaryRule> leftRules= new ArrayList<BinaryRule>();
HashMap<Integer, BinaryRule> leftMap = new HashMap<Integer, BinaryRule>();
// List<BinaryRule> rightRules=new ArrayList<BinaryRule>();
HashMap<Integer, BinaryRule> rightMap = new HashMap<Integer, BinaryRule>();
for (String entry : countLeft.keySet()) {
for (BinaryRule rule : grammar.getBinaryRulesByLeftChild(entry)) {
if (!leftMap.containsKey(rule.hashCode())) {
leftMap.put(rule.hashCode(), rule);
}
}
}
for (String entry : countRight.keySet()) {
for (BinaryRule rule : grammar.getBinaryRulesByRightChild(entry)) {
if (!rightMap.containsKey(rule.hashCode())) {
rightMap.put(rule.hashCode(), rule);
}
}
}
// System.out.println("About to enter the rules loops");
for (Integer ruleHash : leftMap.keySet()) {
if (rightMap.containsKey(ruleHash)) {
BinaryRule ruleRight = rightMap.get(ruleHash);
double prob =
ruleRight.getScore()
* parseScores.getCount(begin + " " + split, ruleRight.leftChild)
* parseScores.getCount(split + " " + end, ruleRight.rightChild);
// System.out.println(begin+" "+ end +" "+ ruleRight.parent+ " "+ prob);
if (prob > parseScores.getCount(begin + " " + end, ruleRight.parent)) {
// System.out.println(begin+" "+ end +" "+ ruleRight.parent+ " "+ prob);
// System.out.println("parentrule :"+ ruleRight.getParent());
parseScores.setCount(begin + " " + end, ruleRight.getParent(), prob);
backHash.put(
begin + " " + end + " " + ruleRight.parent,
new Triplet<Integer, String, String>(
split, ruleRight.leftChild, ruleRight.rightChild));
}
}
}
// System.out.println("Exited rules loop");
}
// System.out.println("Grammar found for " + begin + " "+ end);
// Now handle unary rules
added = true;
while (added) {
added = false;
Counter<String> count = parseScores.getCounter(begin + " " + end);
PriorityQueue<String> countAsPriorityQueue = count.asPriorityQueue();
while (countAsPriorityQueue.hasNext()) {
String entry = countAsPriorityQueue.next();
List<UnaryRule> unaryRules = grammar.getUnaryRulesByChild(entry);
for (UnaryRule rule : unaryRules) {
double prob = rule.getScore() * parseScores.getCount(begin + " " + (end), entry);
if (prob > parseScores.getCount(begin + " " + (end), rule.parent)) {
parseScores.setCount(begin + " " + (end), rule.parent, prob);
backHash.put(
begin + " " + (end) + " " + rule.parent,
new Triplet<Integer, String, String>(-1, entry, null));
added = true;
}
}
}
}
// System.out.println("Unaries dealt for " + begin + " "+ end);
}
}
// Create and return the parse tree
Tree<String> parseTree = new Tree<String>("null");
// System.out.println(parseScores.getCounter(0+" "+sentence.size()).toString());
String parent = parseScores.getCounter(0 + " " + sentence.size()).argMax();
if (parent == null) {
System.out.println(parseScores.getCounter(0 + " " + sentence.size()).toString());
System.out.println("THIS IS WEIRD");
}
parent = "ROOT";
parseTree = getParseTreeOld(sentence, backHash, 0, sentence.size(), parent);
// System.out.println("PARSE SCORES");
// System.out.println(parseScores.toString());
// System.out.println("BACK HASH");
// System.out.println(backHash.toString());
// parseTree = addRoot(parseTree);
// System.out.println(parseTree.toString());
// return parseTree;
return TreeAnnotations.unAnnotateTree(parseTree);
}
public Tree<String> getBestParse(List<String> sentence) {
// This implements the CKY algorithm
int nEntries = sentence.size();
// hashmap to store back rules
HashMap<Triplet<Integer, Integer, String>, Triplet<Integer, String, String>> backHash =
new HashMap<Triplet<Integer, Integer, String>, Triplet<Integer, String, String>>();
// more efficient access with arrays, but must cast each time :(
@SuppressWarnings("unchecked")
Counter<String>[][] parseScores = (Counter<String>[][]) (new Counter[nEntries][nEntries]);
for (int i = 0; i < nEntries; i++) {
for (int j = 0; j < nEntries; j++) {
parseScores[i][j] = new Counter<String>();
}
}
System.out.println(sentence.toString());
// First deal with the lexicons
int index = 0;
int span = 1; // All spans are 1 at the lexicon level
for (String word : sentence) {
for (String tag : lexicon.getAllTags()) {
double score = lexicon.scoreTagging(word, tag);
if (score >= 0.0) { // This lexicon may generate this word
// We use a counter map in order to store the scores for this sentence parse.
parseScores[index][index + span - 1].setCount(tag, score);
}
}
index = index + 1;
}
// handle unary rules now
// System.out.println("Lexicons found");
boolean added = true;
while (added) {
added = false;
for (index = 0; index < sentence.size(); index++) {
// For each index+ span pair, get the counter.
Counter<String> count = parseScores[index][index + span - 1];
PriorityQueue<String> countAsPQ = count.asPriorityQueue();
while (countAsPQ.hasNext()) {
String entry = countAsPQ.next();
// System.out.println("I am fine here!!");
List<UnaryRule> unaryRules = grammar.getUnaryRulesByChild(entry);
for (UnaryRule rule : unaryRules) {
// These are the unary rules which might give rise to the above preterminal
double prob = rule.getScore() * parseScores[index][index + span - 1].getCount(entry);
if (prob > parseScores[index][index + span - 1].getCount(rule.parent)) {
parseScores[index][index + span - 1].setCount(rule.parent, prob);
backHash.put(
new Triplet<Integer, Integer, String>(index, index + span, rule.parent),
new Triplet<Integer, String, String>(-1, entry, null));
added = true;
}
}
}
}
}
// System.out.println("Lexicon unaries dealt with");
// Now work with the grammar to produce higher level probabilities
for (span = 2; span <= sentence.size(); span++) {
for (int begin = 0; begin <= (sentence.size() - span); begin++) {
int end = begin + span;
for (int split = begin + 1; split <= end - 1; split++) {
Counter<String> countLeft = parseScores[begin][split - 1];
Counter<String> countRight = parseScores[split][end - 1];
// List<BinaryRule> leftRules= new ArrayList<BinaryRule>();
HashMap<Integer, BinaryRule> leftMap = new HashMap<Integer, BinaryRule>();
// List<BinaryRule> rightRules=new ArrayList<BinaryRule>();
HashMap<Integer, BinaryRule> rightMap = new HashMap<Integer, BinaryRule>();
for (String entry : countLeft.keySet()) {
for (BinaryRule rule : grammar.getBinaryRulesByLeftChild(entry)) {
if (!leftMap.containsKey(rule.hashCode())) {
leftMap.put(rule.hashCode(), rule);
}
}
}
for (String entry : countRight.keySet()) {
for (BinaryRule rule : grammar.getBinaryRulesByRightChild(entry)) {
if (!rightMap.containsKey(rule.hashCode())) {
rightMap.put(rule.hashCode(), rule);
}
}
}
// System.out.println("About to enter the rules loops");
for (Integer ruleHash : leftMap.keySet()) {
if (rightMap.containsKey(ruleHash)) {
BinaryRule ruleRight = rightMap.get(ruleHash);
double prob =
ruleRight.getScore()
* parseScores[begin][split - 1].getCount(ruleRight.leftChild)
* parseScores[split][end - 1].getCount(ruleRight.rightChild);
// System.out.println(begin+" "+ end +" "+ ruleRight.parent+ " "+ prob);
if (prob > parseScores[begin][end - 1].getCount(ruleRight.parent)) {
// System.out.println(begin+" "+ end +" "+ ruleRight.parent+ " "+ prob);
// System.out.println("parentrule :"+ ruleRight.getParent());
parseScores[begin][end - 1].setCount(ruleRight.getParent(), prob);
backHash.put(
new Triplet<Integer, Integer, String>(begin, end, ruleRight.parent),
new Triplet<Integer, String, String>(
split, ruleRight.leftChild, ruleRight.rightChild));
}
}
}
// System.out.println("Exited rules loop");
}
// System.out.println("Grammar found for " + begin + " "+ end);
// Now handle unary rules
added = true;
while (added) {
added = false;
Counter<String> count = parseScores[begin][end - 1];
PriorityQueue<String> countAsPriorityQueue = count.asPriorityQueue();
while (countAsPriorityQueue.hasNext()) {
String entry = countAsPriorityQueue.next();
List<UnaryRule> unaryRules = grammar.getUnaryRulesByChild(entry);
for (UnaryRule rule : unaryRules) {
double prob = rule.getScore() * parseScores[begin][end - 1].getCount(entry);
if (prob > parseScores[begin][end - 1].getCount(rule.parent)) {
parseScores[begin][end - 1].setCount(rule.parent, prob);
backHash.put(
new Triplet<Integer, Integer, String>(begin, end, rule.parent),
new Triplet<Integer, String, String>(-1, entry, null));
added = true;
}
}
}
}
// System.out.println("Unaries dealt for " + begin + " "+ end);
}
}
// Create and return the parse tree
Tree<String> parseTree = new Tree<String>("null");
// System.out.println(parseScores.getCounter(0+" "+sentence.size()).toString());
// Pick the argmax
String parent = parseScores[0][nEntries - 1].argMax();
// Or pick root. This second one is preferred since sentences are meant to have ROOT as their
// root node.
parent = "ROOT";
parseTree = getParseTree(sentence, backHash, 0, sentence.size(), parent);
// System.out.println("PARSE SCORES");
// System.out.println(parseScores.toString());
// System.out.println("BACK HASH");
// System.out.println(backHash.toString());
// parseTree = addRoot(parseTree);
// System.out.println(parseTree.toString());
// return parseTree;
return TreeAnnotations.unAnnotateTree(parseTree);
}
public HashMap<String, Entity> getAllEntities(String handle) {
HashMap<String, Entity> allEntities = new HashMap<String, Entity>();
try {
BufferedReader br = new BufferedReader(new FileReader("data/" + handle + ".txt"));
BufferedWriter bw = new BufferedWriter(new FileWriter("data/" + handle + "_entities.txt"));
BufferedWriter bw1 = new BufferedWriter(new FileWriter("data/" + handle + "_statistics.txt"));
String line = "";
Counter<String> nPhraseCounter = new Counter<String>();
Counter<String> capitalsCounter = new Counter<String>();
while ((line = br.readLine()) != null) {
line = line.replaceAll("RT", "");
TwitterTokenizer tweetTokenizer = new TwitterTokenizer();
for (String token : tweetTokenizer.tokenize(line)) {
token = token.trim();
token =
token.replaceAll(
"( [^a-zA-Z0-9\\.]) | ( [^a-zA-Z0-9\\.] ) | ([^a-zA-Z0-9\\.] )", " ");
ArrayList<String> nPhrases = new ArrayList<String>();
HashSet<String> capitalWords = new HashSet<String>();
try {
Pattern p = Pattern.compile("^[A-Z]+.*");
String[] split = token.split("\\s+");
for (String s : split) {
if (p.matcher(s).matches() && !stopWords.contains(s.toLowerCase())) {
capitalWords.add(s.toLowerCase());
capitalsCounter.incrementCount(s.toLowerCase(), 1.0);
if (allEntities.containsKey(s.trim())) {
Entity e = allEntities.get(s.trim());
if (!e.tweets.contains(line)) {
e.tweets.add(line);
allEntities.put(s.trim(), e);
}
} else {
Entity e = new Entity(s.trim());
e.tweets.add(line);
allEntities.put(s.trim(), e);
}
}
}
} catch (Exception e) {
e.printStackTrace();
}
bw.write("===============================================\n");
bw.write(token + "\n");
System.out.println("token: " + token);
for (String np : npe.extract(token)) {
if (!stopWords.contains(np.trim().toLowerCase())) {
nPhrases.add(np.trim());
nPhraseCounter.incrementCount(np.trim(), 1.0);
if (allEntities.containsKey(np.trim())) {
Entity e = allEntities.get(np.trim());
if (!e.tweets.contains(line)) {
e.tweets.add(line);
allEntities.put(np.trim(), e);
}
} else {
Entity e = new Entity(np.trim());
e.tweets.add(line);
allEntities.put(np.trim(), e);
}
}
}
bw.write("===============================================\n");
bw.write("Noun-Phrases: " + nPhrases.toString() + "\n");
// HashSet<String> capitalWords =
// getCapitalizedWords(token);
if (capitalWords == null) {
bw.write("No capitals\n\n");
} else {
bw.write("Capitals: " + capitalWords.toString() + "\n\n");
}
}
bw.flush();
if (true) continue;
}
PriorityQueue<String> nPhraseQueue = nPhraseCounter.asPriorityQueue();
PriorityQueue<String> capitalQueue = capitalsCounter.asPriorityQueue();
while (nPhraseQueue.hasNext()) {
String np = nPhraseQueue.next();
bw1.write(np + " " + nPhraseCounter.getCount(np) + "\n");
}
bw1.write("=========================================================\n");
while (capitalQueue.hasNext()) {
String cap = capitalQueue.next();
bw1.write(cap + " " + capitalsCounter.getCount(cap) + "\n");
}
bw1.flush();
} catch (Exception e) {
e.printStackTrace();
}
return allEntities;
}
public void train(List<SentencePair> trainingPairs) {
sourceTargetCounts = new CounterMap<String, String>();
sourceTargetDistortions = new CounterMap<Pair<Integer, Integer>, Pair<Integer, Integer>>();
for (SentencePair pair : trainingPairs) {
List<String> sourceSentence = pair.getSourceWords();
List<String> targetSentence = pair.getTargetWords();
targetSentence.add(WordAligner.NULL_WORD);
int m = sourceSentence.size();
int l = targetSentence.size();
for (int i = 0; i < m; i++) {
String sourceWord = sourceSentence.get(i);
for (int j = 0; j < l; j++) {
String targetWord = targetSentence.get(j);
sourceTargetCounts.setCount(sourceWord, targetWord, 1.0);
Pair<Integer, Integer> lmPair = new Pair<Integer, Integer>(l, m);
Pair<Integer, Integer> jiPair = new Pair<Integer, Integer>(j, i);
sourceTargetDistortions.setCount(jiPair, lmPair, 1.0);
}
}
}
// Use Model 1 to train params
double delta = Double.POSITIVE_INFINITY;
for (int i = 0; i < MAX_ITERS && delta > CONVERGENCE; i++) {
CounterMap<String, String> tempSourceTargetCounts = new CounterMap<String, String>();
Counter<String> targetCounts = new Counter<String>();
delta = 0.0;
for (SentencePair pair : trainingPairs) {
List<String> sourceSentence = pair.getSourceWords();
List<String> targetSentence = pair.getTargetWords();
Counter<String> sourceTotals = new Counter<String>();
for (String sourceWord : sourceSentence) {
for (String targetWord : targetSentence) {
sourceTotals.incrementCount(
sourceWord, sourceTargetCounts.getCount(sourceWord, targetWord));
}
}
for (String sourceWord : sourceSentence) {
for (String targetWord : targetSentence) {
double transProb = sourceTargetCounts.getCount(sourceWord, targetWord);
double sourceTotal = sourceTotals.getCount(sourceWord);
tempSourceTargetCounts.incrementCount(sourceWord, targetWord, transProb / sourceTotal);
targetCounts.incrementCount(targetWord, transProb / sourceTotal);
}
}
}
// update t(s|t) values
for (String sourceWord : tempSourceTargetCounts.keySet()) {
for (String targetWord : tempSourceTargetCounts.getCounter(sourceWord).keySet()) {
double oldProb = sourceTargetCounts.getCount(sourceWord, targetWord);
double newProb =
tempSourceTargetCounts.getCount(sourceWord, targetWord)
/ targetCounts.getCount(targetWord);
sourceTargetCounts.setCount(sourceWord, targetWord, newProb);
delta += Math.pow(oldProb - newProb, 2.0);
}
}
delta /= sourceTargetCounts.totalSize();
}
// Maximizing for ibm model 2
delta = Double.POSITIVE_INFINITY;
for (int iter = 0; iter < MAX_ITERS && delta > CONVERGENCE; iter++) {
CounterMap<String, String> tempSourceTargetCounts = new CounterMap<String, String>();
CounterMap<Pair<Integer, Integer>, Pair<Integer, Integer>> tempSourceTargetDistortions =
new CounterMap<Pair<Integer, Integer>, Pair<Integer, Integer>>();
Counter<String> targetCounts = new Counter<String>();
CounterMap<Pair<Integer, Integer>, Integer> targetDistorts =
new CounterMap<Pair<Integer, Integer>, Integer>();
delta = 0.0;
for (SentencePair pair : trainingPairs) {
List<String> sourceSentence = pair.getSourceWords();
List<String> targetSentence = pair.getTargetWords();
CounterMap<Pair<Integer, Integer>, Integer> distortSourceTotals =
new CounterMap<Pair<Integer, Integer>, Integer>();
Pair<Integer, Integer> lmPair =
new Pair<Integer, Integer>(targetSentence.size(), sourceSentence.size());
for (int i = 0; i < sourceSentence.size(); i++) {
String sourceWord = sourceSentence.get(i);
for (int j = 0; j < targetSentence.size(); j++) {
String targetWord = targetSentence.get(j);
Pair<Integer, Integer> jiPair = new Pair<Integer, Integer>(j, i);
double currTransProb = sourceTargetCounts.getCount(sourceWord, targetWord);
double currAlignProb = sourceTargetDistortions.getCount(jiPair, lmPair);
distortSourceTotals.incrementCount(lmPair, i, currTransProb * currAlignProb);
}
}
for (int i = 0; i < sourceSentence.size(); i++) {
String sourceWord = sourceSentence.get(i);
double distortTransSourceTotal = distortSourceTotals.getCount(lmPair, i);
for (int j = 0; j < targetSentence.size(); j++) {
String targetWord = targetSentence.get(j);
Pair<Integer, Integer> jiPair = new Pair<Integer, Integer>(j, i);
double transProb = sourceTargetCounts.getCount(sourceWord, targetWord);
double distortProb = sourceTargetDistortions.getCount(jiPair, lmPair);
double update =
(transProb * distortProb) / (distortTransSourceTotal); // q(j|ilm)t(f|e)/totals
tempSourceTargetCounts.incrementCount(sourceWord, targetWord, update);
tempSourceTargetDistortions.incrementCount(jiPair, lmPair, update);
targetCounts.incrementCount(targetWord, update);
targetDistorts.incrementCount(lmPair, i, update);
}
}
}
// update t(s|t) values
double delta_trans = 0.0;
for (String sourceWord : tempSourceTargetCounts.keySet()) {
for (String targetWord : tempSourceTargetCounts.getCounter(sourceWord).keySet()) {
double oldProb = sourceTargetCounts.getCount(sourceWord, targetWord);
double newProb =
tempSourceTargetCounts.getCount(sourceWord, targetWord)
/ targetCounts.getCount(targetWord);
sourceTargetCounts.setCount(sourceWord, targetWord, newProb);
delta += Math.pow(oldProb - newProb, 2.0);
}
}
// update q(j|ilm) values
double delta_dist = 0.0;
for (Pair<Integer, Integer> jiPair : tempSourceTargetDistortions.keySet()) {
for (Pair<Integer, Integer> lmPair :
tempSourceTargetDistortions.getCounter(jiPair).keySet()) {
double oldProb = sourceTargetDistortions.getCount(jiPair, lmPair);
double tempAlignProb = tempSourceTargetDistortions.getCount(jiPair, lmPair);
double tempTargetDist = targetDistorts.getCount(lmPair, jiPair.getSecond());
double newProb = tempAlignProb / tempTargetDist;
sourceTargetDistortions.setCount(jiPair, lmPair, newProb);
delta_dist += Math.pow(oldProb - newProb, 2.0);
}
}
delta =
(delta_trans / sourceTargetCounts.totalSize()
+ delta_dist / sourceTargetDistortions.totalSize())
/ 2.0;
}
}