cdec cleanup, remove bayesian stuff, parsing stuff

11 files changed, 0 insertions, 3008 deletions

diff --git a/gi/posterior-regularisation/prjava/src/phrase/Agree.java b/gi/posterior-regularisation/prjava/src/phrase/Agree.java
deleted file mode 100644
index 8f7b499e..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/Agree.java
+++ /dev/null
@@ -1,204 +0,0 @@
-package phrase;

-

-import gnu.trove.TIntArrayList;

-

-import io.FileUtil;

-

-import java.io.File;

-import java.io.IOException;

-import java.io.PrintStream;

-import java.util.List;

-

-import phrase.Corpus.Edge;

-

-public class Agree {

-	PhraseCluster model1;

-	C2F model2;

-	Corpus c;

-	private int K,n_phrases, n_words, n_contexts, n_positions1,n_positions2;

-	

-	/**@brief sum of loglikelihood of two

-	 * individual models

-	 */

-	public double llh;

-	/**@brief Bhattacharyya distance

-	 * 

-	 */

-	public double bdist; 

-	/**

-	 * 

-	 * @param numCluster

-	 * @param corpus

-	 */

-	public Agree(int numCluster, Corpus corpus){

-		

-		model1=new PhraseCluster(numCluster, corpus);

-		model2=new C2F(numCluster,corpus);

-		c=corpus;

-		n_words=c.getNumWords();

-		n_phrases=c.getNumPhrases();

-		n_contexts=c.getNumContexts();

-		n_positions1=c.getNumContextPositions();

-		n_positions2=2;

-		K=numCluster;

-		

-	}

-	

-	/**@brief test

-	 * 

-	 */

-	public static void main(String args[]){

-		//String in="../pdata/canned.con";

-		String in="../pdata/btec.con";

-		String out="../pdata/posterior.out";

-		int numCluster=25;

-		Corpus corpus = null;

-		File infile = new File(in);

-		try {

-			System.out.println("Reading concordance from " + infile);

-			corpus = Corpus.readFromFile(FileUtil.reader(infile));

-			corpus.printStats(System.out);

-		} catch (IOException e) {

-			System.err.println("Failed to open input file: " + infile);

-			e.printStackTrace();

-			System.exit(1);

-		}

-		

-		Agree agree=new Agree(numCluster, corpus);

-		int iter=20;

-		for(int i=0;i<iter;i++){

-			agree.EM();

-			System.out.println("Iter"+i+", llh: "+agree.llh+

-					", divergence:"+agree.bdist+

-							" sum: "+(agree.llh+agree.bdist));

-		}

-		

-		File outfile = new File (out);

-		try {

-			PrintStream ps = FileUtil.printstream(outfile);

-			agree.displayPosterior(ps);

-		//	ps.println();

-		//	c2f.displayModelParam(ps);

-			ps.close();

-		} catch (IOException e) {

-			System.err.println("Failed to open output file: " + outfile);

-			e.printStackTrace();

-			System.exit(1);

-		}

-		

-	}

-	

-	public double EM(){

-		

-		double [][][]exp_emit1=new double [K][n_positions1][n_words];

-		double [][]exp_pi1=new double[n_phrases][K];

-		

-		double [][][]exp_emit2=new double [K][n_positions2][n_words];

-		double [][]exp_pi2=new double[n_contexts][K];

-		

-		llh=0;

-		bdist=0;

-		//E

-		for(int context=0; context< n_contexts; context++){

-			

-			List<Edge> contexts = c.getEdgesForContext(context);

-

-			for (int ctx=0; ctx<contexts.size(); ctx++){

-				Edge edge = contexts.get(ctx);

-				int phrase=edge.getPhraseId();

-				double p[]=posterior(edge);

-				double z = arr.F.l1norm(p);

-				assert z > 0;

-				bdist += edge.getCount() * Math.log(z);

-				arr.F.l1normalize(p);

-				

-				double count = edge.getCount();

-				//increment expected count

-				TIntArrayList phraseToks = edge.getPhrase();

-				TIntArrayList contextToks = edge.getContext();

-				for(int tag=0;tag<K;tag++){

-

-					for(int position=0;position<n_positions1;position++){

-						exp_emit1[tag][position][contextToks.get(position)]+=p[tag]*count;

-					}

-					

-					exp_emit2[tag][0][phraseToks.get(0)]+=p[tag]*count;

-					exp_emit2[tag][1][phraseToks.get(phraseToks.size()-1)]+=p[tag]*count;

-					

-					exp_pi1[phrase][tag]+=p[tag]*count;

-					exp_pi2[context][tag]+=p[tag]*count;

-				}

-			}

-		}

-		

-		//System.out.println("Log likelihood: "+loglikelihood);

-		

-		//M

-		for(double [][]i:exp_emit1){

-			for(double []j:i){

-				arr.F.l1normalize(j);

-			}

-		}

-		

-		for(double []j:exp_pi1){

-			arr.F.l1normalize(j);

-		}

-		

-		for(double [][]i:exp_emit2){

-			for(double []j:i){

-				arr.F.l1normalize(j);

-			}

-		}

-		

-		for(double []j:exp_pi2){

-			arr.F.l1normalize(j);

-		}

-		

-		model1.emit=exp_emit1;

-		model1.pi=exp_pi1;

-		model2.emit=exp_emit2;

-		model2.pi=exp_pi2;

-		

-		return llh;

-	}

-

-	public double[] posterior(Corpus.Edge edge) 

-	{

-		double[] prob1=model1.posterior(edge);

-		double[] prob2=model2.posterior(edge);

-		

-		llh+=edge.getCount()*Math.log(arr.F.l1norm(prob1));

-		llh+=edge.getCount()*Math.log(arr.F.l1norm(prob2));

-		arr.F.l1normalize(prob1);

-		arr.F.l1normalize(prob2);

-		

-		for(int i=0;i<prob1.length;i++){

-			prob1[i]*=prob2[i];

-			prob1[i]=Math.sqrt(prob1[i]);

-		}

-		

-		return prob1;

-	}

-	

-	public void displayPosterior(PrintStream ps)

-	{	

-		displayPosterior(ps, c.getEdges());

-	}

-	

-	public void displayPosterior(PrintStream ps, List<Edge> test)

-	{	

-		for (Edge edge : test)

-		{

-			double probs[] = posterior(edge);

-			arr.F.l1normalize(probs);

-

-			// emit phrase

-			ps.print(edge.getPhraseString());

-			ps.print("\t");

-			ps.print(edge.getContextString(true));

-			int t=arr.F.argmax(probs);

-			ps.println(" ||| C=" + t);

-		}

-	}

-	

-}

diff --git a/gi/posterior-regularisation/prjava/src/phrase/Agree2Sides.java b/gi/posterior-regularisation/prjava/src/phrase/Agree2Sides.java
deleted file mode 100644
index 031f887f..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/Agree2Sides.java
+++ /dev/null
@@ -1,197 +0,0 @@
-package phrase;

-

-import gnu.trove.TIntArrayList;

-

-import io.FileUtil;

-

-import java.io.File;

-import java.io.IOException;

-import java.io.PrintStream;

-import java.util.List;

-

-import phrase.Corpus.Edge;

-

-public class Agree2Sides {

-	PhraseCluster model1,model2;

-	Corpus c1,c2;

-	private int K;

-	

-	/**@brief sum of loglikelihood of two

-	 * individual models

-	 */

-	public double llh;

-	/**@brief Bhattacharyya distance

-	 * 

-	 */

-	public double bdist; 

-	/**

-	 * 

-	 * @param numCluster

-	 * @param corpus

-	 */

-	public Agree2Sides(int numCluster, Corpus corpus1 , Corpus corpus2 ){

-		

-		model1=new PhraseCluster(numCluster, corpus1);

-		model2=new PhraseCluster(numCluster,corpus2);

-		c1=corpus1;

-		c2=corpus2;

-		K=numCluster;

-		

-	}

-	

-	/**@brief test

-	 * 

-	 */

-	public static void main(String args[]){

-		//String in="../pdata/canned.con";

-	//	String in="../pdata/btec.con";

-		String in1="../pdata/source.txt";

-		String in2="../pdata/target.txt";

-		String out="../pdata/posterior.out";

-		int numCluster=25;

-		Corpus corpus1 = null,corpus2=null;

-		File infile1 = new File(in1),infile2=new File(in2);

-		try {

-			System.out.println("Reading concordance from " + infile1);

-			corpus1 = Corpus.readFromFile(FileUtil.reader(infile1));

-			System.out.println("Reading concordance from " + infile2);

-			corpus2 = Corpus.readFromFile(FileUtil.reader(infile2));

-			corpus1.printStats(System.out);

-		} catch (IOException e) {

-			System.err.println("Failed to open input file: " + infile1);

-			e.printStackTrace();

-			System.exit(1);

-		}

-		

-		Agree2Sides agree=new Agree2Sides(numCluster, corpus1,corpus2);

-		int iter=20;

-		for(int i=0;i<iter;i++){

-			agree.EM();

-			System.out.println("Iter"+i+", llh: "+agree.llh+

-					", divergence:"+agree.bdist+

-							" sum: "+(agree.llh+agree.bdist));

-		}

-		

-		File outfile = new File (out);

-		try {

-			PrintStream ps = FileUtil.printstream(outfile);

-			agree.displayPosterior(ps);

-		//	ps.println();

-		//	c2f.displayModelParam(ps);

-			ps.close();

-		} catch (IOException e) {

-			System.err.println("Failed to open output file: " + outfile);

-			e.printStackTrace();

-			System.exit(1);

-		}

-		

-	}

-	

-	public double EM(){

-		

-		double [][][]exp_emit1=new double [K][c1.getNumContextPositions()][c1.getNumWords()];

-		double [][]exp_pi1=new double[c1.getNumPhrases()][K];

-		

-		double [][][]exp_emit2=new double [K][c2.getNumContextPositions()][c2.getNumWords()];

-		double [][]exp_pi2=new double[c2.getNumPhrases()][K];

-		

-		llh=0;

-		bdist=0;

-		//E

-		for(int i=0;i<c1.getEdges().size();i++){

-			Edge edge1=c1.getEdges().get(i);

-			Edge edge2=c2.getEdges().get(i);

-			double p[]=posterior(i);

-			double z = arr.F.l1norm(p);

-			assert z > 0;

-			bdist += edge1.getCount() * Math.log(z);

-			arr.F.l1normalize(p);

-			double count = edge1.getCount();

-				//increment expected count

-			TIntArrayList contextToks1 = edge1.getContext();

-			TIntArrayList contextToks2 = edge2.getContext();

-			int phrase1=edge1.getPhraseId();

-			int phrase2=edge2.getPhraseId();

-			for(int tag=0;tag<K;tag++){

-				for(int position=0;position<c1.getNumContextPositions();position++){

-					exp_emit1[tag][position][contextToks1.get(position)]+=p[tag]*count;

-				}

-				for(int position=0;position<c2.getNumContextPositions();position++){

-					exp_emit2[tag][position][contextToks2.get(position)]+=p[tag]*count;

-				}

-				exp_pi1[phrase1][tag]+=p[tag]*count;

-				exp_pi2[phrase2][tag]+=p[tag]*count;

-			}

-		}

-		

-		//System.out.println("Log likelihood: "+loglikelihood);

-		

-		//M

-		for(double [][]i:exp_emit1){

-			for(double []j:i){

-				arr.F.l1normalize(j);

-			}

-		}

-		

-		for(double []j:exp_pi1){

-			arr.F.l1normalize(j);

-		}

-		

-		for(double [][]i:exp_emit2){

-			for(double []j:i){

-				arr.F.l1normalize(j);

-			}

-		}

-		

-		for(double []j:exp_pi2){

-			arr.F.l1normalize(j);

-		}

-		

-		model1.emit=exp_emit1;

-		model1.pi=exp_pi1;

-		model2.emit=exp_emit2;

-		model2.pi=exp_pi2;

-		

-		return llh;

-	}

-

-	public double[] posterior(int edgeIdx) 

-	{

-		return posterior(c1.getEdges().get(edgeIdx), c2.getEdges().get(edgeIdx));

-	}

-	

-	public double[] posterior(Edge e1, Edge e2) 

-	{

-		double[] prob1=model1.posterior(e1);

-		double[] prob2=model2.posterior(e2);

-		

-		llh+=e1.getCount()*Math.log(arr.F.l1norm(prob1));

-		llh+=e2.getCount()*Math.log(arr.F.l1norm(prob2));

-		arr.F.l1normalize(prob1);

-		arr.F.l1normalize(prob2);

-		

-		for(int i=0;i<prob1.length;i++){

-			prob1[i]*=prob2[i];

-			prob1[i]=Math.sqrt(prob1[i]);

-		}

-		

-		return prob1;

-	}

-	

-	public void displayPosterior(PrintStream ps)

-	{	

-		for (int i=0;i<c1.getEdges().size();i++)

-		{

-			Edge edge=c1.getEdges().get(i);

-			double probs[] = posterior(i);

-			arr.F.l1normalize(probs);

-

-			// emit phrase

-			ps.print(edge.getPhraseString());

-			ps.print("\t");

-			ps.print(edge.getContextString(true));

-			int t=arr.F.argmax(probs);

-			ps.println(" ||| C=" + t);

-		}

-	}

-}

diff --git a/gi/posterior-regularisation/prjava/src/phrase/C2F.java b/gi/posterior-regularisation/prjava/src/phrase/C2F.java
deleted file mode 100644
index e8783950..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/C2F.java
+++ /dev/null
@@ -1,216 +0,0 @@
-package phrase;

-

-import gnu.trove.TIntArrayList;

-

-import io.FileUtil;

-

-import java.io.File;

-import java.io.IOException;

-import java.io.PrintStream;

-import java.util.Arrays;

-import java.util.List;

-

-import phrase.Corpus.Edge;

-

-/**

- * @brief context generates phrase

- * @author desaic

- *

- */

-public class C2F {

-	public int K;

-	private int n_words, n_contexts, n_positions;

-	public Corpus c;

-	

-	/**@brief

-	 *  emit[tag][position][word] = p(word | tag, position in phrase)

-	 */

-	public double emit[][][];

-	/**@brief

-	 *  pi[context][tag] = p(tag | context)

-	 */

-	public double pi[][];

-	

-	public C2F(int numCluster, Corpus corpus){

-		K=numCluster;

-		c=corpus;

-		n_words=c.getNumWords();

-		n_contexts=c.getNumContexts();

-		

-		//number of words in a phrase to be considered

-		//currently the first and last word in source and target

-		//if the phrase has length 1 in either dimension then

-		//we use the same word for two positions

-		n_positions=c.phraseEdges(c.getEdges().get(0).getPhrase()).size();

-		

-		emit=new double [K][n_positions][n_words];

-		pi=new double[n_contexts][K];

-		

-		for(double [][]i:emit){

-			for(double []j:i){

-				arr.F.randomise(j);

-			}

-		}

-		

-		for(double []j:pi){

-			arr.F.randomise(j);

-		}

-	}

-	

-	/**@brief test

-	 * 

-	 */

-	public static void main(String args[]){

-		String in="../pdata/canned.con";

-		String out="../pdata/posterior.out";

-		int numCluster=25;

-		Corpus corpus = null;

-		File infile = new File(in);

-		try {

-			System.out.println("Reading concordance from " + infile);

-			corpus = Corpus.readFromFile(FileUtil.reader(infile));

-			corpus.printStats(System.out);

-		} catch (IOException e) {

-			System.err.println("Failed to open input file: " + infile);

-			e.printStackTrace();

-			System.exit(1);

-		}

-		

-		C2F c2f=new C2F(numCluster,corpus);

-		int iter=20;

-		double llh=0;

-		for(int i=0;i<iter;i++){

-			llh=c2f.EM();

-			System.out.println("Iter"+i+", llh: "+llh);

-		}

-		

-		File outfile = new File (out);

-		try {

-			PrintStream ps = FileUtil.printstream(outfile);

-			c2f.displayPosterior(ps);

-		//	ps.println();

-		//	c2f.displayModelParam(ps);

-			ps.close();

-		} catch (IOException e) {

-			System.err.println("Failed to open output file: " + outfile);

-			e.printStackTrace();

-			System.exit(1);

-		}

-		

-	}

-	

-	public double EM(){

-		double [][][]exp_emit=new double [K][n_positions][n_words];

-		double [][]exp_pi=new double[n_contexts][K];

-		

-		double loglikelihood=0;

-		

-		//E

-		for(int context=0; context< n_contexts; context++){

-			

-			List<Edge> contexts = c.getEdgesForContext(context);

-

-			for (int ctx=0; ctx<contexts.size(); ctx++){

-				Edge edge = contexts.get(ctx);

-				double p[]=posterior(edge);

-				double z = arr.F.l1norm(p);

-				assert z > 0;

-				loglikelihood += edge.getCount() * Math.log(z);

-				arr.F.l1normalize(p);

-				

-				double count = edge.getCount();

-				//increment expected count

-				TIntArrayList phrase= edge.getPhrase();

-				for(int tag=0;tag<K;tag++){

-

-					exp_emit[tag][0][phrase.get(0)]+=p[tag]*count;

-					exp_emit[tag][1][phrase.get(phrase.size()-1)]+=p[tag]*count;

-					

-					exp_pi[context][tag]+=p[tag]*count;

-				}

-			}

-		}

-		

-		//System.out.println("Log likelihood: "+loglikelihood);

-		

-		//M

-		for(double [][]i:exp_emit){

-			for(double []j:i){

-				arr.F.l1normalize(j);

-			}

-		}

-		

-		emit=exp_emit;

-		

-		for(double []j:exp_pi){

-			arr.F.l1normalize(j);

-		}

-		

-		pi=exp_pi;

-		

-		return loglikelihood;

-	}

-

-	public double[] posterior(Corpus.Edge edge) 

-	{

-		double[] prob=Arrays.copyOf(pi[edge.getContextId()], K);

-		

-		TIntArrayList phrase = edge.getPhrase();

-		TIntArrayList offsets = c.phraseEdges(phrase);

-		for(int tag=0;tag<K;tag++)

-		{

-			for (int i=0; i < offsets.size(); ++i)

-				prob[tag]*=emit[tag][i][phrase.get(offsets.get(i))];

-		}

-			

-		return prob;

-	}

-

-	public void displayPosterior(PrintStream ps)

-	{	

-		for (Edge edge : c.getEdges())

-		{

-			double probs[] = posterior(edge);

-			arr.F.l1normalize(probs);

-

-			// emit phrase

-			ps.print(edge.getPhraseString());

-			ps.print("\t");

-			ps.print(edge.getContextString(true));

-			int t=arr.F.argmax(probs);

-			ps.println(" ||| C=" + t);

-		}

-	}

-	

-	public void displayModelParam(PrintStream ps)

-	{

-		final double EPS = 1e-6;

-		

-		ps.println("P(tag|context)");

-		for (int i = 0; i < n_contexts; ++i)

-		{

-			ps.print(c.getContext(i));

-			for(int j=0;j<pi[i].length;j++){

-				if (pi[i][j] > EPS)

-					ps.print("\t" + j + ": " + pi[i][j]);

-			}

-			ps.println();

-		}

-		

-		ps.println("P(word|tag,position)");

-		for (int i = 0; i < K; ++i)

-		{

-			for(int position=0;position<n_positions;position++){

-				ps.println("tag " + i + " position " + position);

-				for(int word=0;word<emit[i][position].length;word++){

-					if (emit[i][position][word] > EPS)

-						ps.print(c.getWord(word)+"="+emit[i][position][word]+"\t");

-				}

-				ps.println();

-			}

-			ps.println();

-		}

-		

-	}

-	

-}

diff --git a/gi/posterior-regularisation/prjava/src/phrase/Corpus.java b/gi/posterior-regularisation/prjava/src/phrase/Corpus.java
deleted file mode 100644
index 4b1939cd..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/Corpus.java
+++ /dev/null
@@ -1,288 +0,0 @@
-package phrase;
-
-import gnu.trove.TIntArrayList;
-
-import java.io.*;
-import java.util.*;
-import java.util.regex.Pattern;
-
-
-public class Corpus
-{
-	private Lexicon<String> wordLexicon = new Lexicon<String>();
-	private Lexicon<TIntArrayList> phraseLexicon = new Lexicon<TIntArrayList>();
-	private Lexicon<TIntArrayList> contextLexicon = new Lexicon<TIntArrayList>();
-	private List<Edge> edges = new ArrayList<Edge>();
-	private List<List<Edge>> phraseToContext = new ArrayList<List<Edge>>();
-	private List<List<Edge>> contextToPhrase = new ArrayList<List<Edge>>();
-	public int splitSentinel;
-	public int phraseSentinel;
-	public int rareSentinel;
-
-	public Corpus()
-	{
-		splitSentinel = wordLexicon.insert("<SPLIT>");
-		phraseSentinel = wordLexicon.insert("<PHRASE>");		
-		rareSentinel = wordLexicon.insert("<RARE>");
-	}
-	
-	public class Edge
-	{
-		
-		Edge(int phraseId, int contextId, double count,int tag)
-		{
-			this.phraseId = phraseId;
-			this.contextId = contextId;
-			this.count = count;
-			fixTag=tag;
-		}
-		
-		Edge(int phraseId, int contextId, double count)
-		{
-			this.phraseId = phraseId;
-			this.contextId = contextId;
-			this.count = count;
-			fixTag=-1;
-		}
-		public int getTag(){
-			return fixTag;
-		}
-		
-		public int getPhraseId()
-		{
-			return phraseId;
-		}
-		public TIntArrayList getPhrase()
-		{
-			return Corpus.this.getPhrase(phraseId);
-		}
-		public String getPhraseString()
-		{
-			return Corpus.this.getPhraseString(phraseId);
-		}		
-		public int getContextId()
-		{
-			return contextId;
-		}
-		public TIntArrayList getContext()
-		{
-			return Corpus.this.getContext(contextId);
-		}
-		public String getContextString(boolean insertPhraseSentinel)
-		{
-			return Corpus.this.getContextString(contextId, insertPhraseSentinel);
-		}
-		public double getCount()
-		{
-			return count;
-		}
-		public boolean equals(Object other)
-		{
-			if (other instanceof Edge) 
-			{
-				Edge oe = (Edge) other;
-				return oe.phraseId == phraseId && oe.contextId == contextId; 
-			}
-			else return false;
-		}
-		public int hashCode()
-		{   // this is how boost's hash_combine does it
-			int seed = phraseId;
-			seed ^= contextId + 0x9e3779b9 + (seed << 6) + (seed >> 2);
-			return seed;
-		}
-		public String toString()
-		{
-			return getPhraseString() + "\t" + getContextString(true);
-		}
-		
-		private int phraseId;
-		private int contextId;
-		private double count;
-		private int fixTag;
-	}
-
-	List<Edge> getEdges()
-	{
-		return edges;
-	}
-	
-	int getNumEdges()
-	{
-		return edges.size();
-	}
-
-	int getNumPhrases()
-	{
-		return phraseLexicon.size();
-	}
-	
-	int getNumContextPositions()
-	{
-		return contextLexicon.lookup(0).size();
-	}
-	
-	List<Edge> getEdgesForPhrase(int phraseId)
-	{
-		return phraseToContext.get(phraseId);
-	}
-	
-	int getNumContexts()
-	{
-		return contextLexicon.size();
-	}
-	
-	List<Edge> getEdgesForContext(int contextId)
-	{
-		return contextToPhrase.get(contextId);
-	}
-	
-	int getNumWords()
-	{
-		return wordLexicon.size();
-	}
-	
-	String getWord(int wordId)
-	{
-		return wordLexicon.lookup(wordId);
-	}
-	
-	public TIntArrayList getPhrase(int phraseId)
-	{
-		return phraseLexicon.lookup(phraseId);
-	}
-	
-	public String getPhraseString(int phraseId)
-	{
-		StringBuffer b = new StringBuffer();
-		for (int tid: getPhrase(phraseId).toNativeArray())
-		{
-			if (b.length() > 0)
-				b.append(" ");
-			b.append(wordLexicon.lookup(tid));
-		}
-		return b.toString();
-	}		
-	
-	public TIntArrayList getContext(int contextId)
-	{
-		return contextLexicon.lookup(contextId);
-	}
-	
-	public String getContextString(int contextId, boolean insertPhraseSentinel)
-	{
-		StringBuffer b = new StringBuffer();
-		TIntArrayList c = getContext(contextId);
-		for (int i = 0; i < c.size(); ++i)
-		{
-			if (i > 0) b.append(" ");
-			//if (i == c.size() / 2) b.append("<PHRASE> ");
-			b.append(wordLexicon.lookup(c.get(i)));
-		}
-		return b.toString();
-	}
-	
-	public boolean isSentinel(int wordId)
-	{
-		return wordId == splitSentinel || wordId == phraseSentinel;
-	}
-	
-	List<Edge> readEdges(Reader in) throws IOException
-	{	
-		// read in line-by-line
-		BufferedReader bin = new BufferedReader(in);
-		String line;
-		Pattern separator = Pattern.compile(" \\|\\|\\| ");
-		
-		List<Edge> edges = new ArrayList<Edge>();
-		while ((line = bin.readLine()) != null)
-		{
-			// split into phrase and contexts
-			StringTokenizer st = new StringTokenizer(line, "\t");
-			assert (st.hasMoreTokens());
-			String phraseToks = st.nextToken();
-			assert (st.hasMoreTokens());
-			String rest = st.nextToken();
-			assert (!st.hasMoreTokens());
-
-			// process phrase	
-			st = new StringTokenizer(phraseToks, " ");
-			TIntArrayList ptoks = new TIntArrayList();
-			while (st.hasMoreTokens())
-				ptoks.add(wordLexicon.insert(st.nextToken()));
-			int phraseId = phraseLexicon.insert(ptoks);
-			
-			// process contexts
-			String[] parts = separator.split(rest);
-			assert (parts.length % 2 == 0);
-			for (int i = 0; i < parts.length; i += 2)
-			{
-				// process pairs of strings - context and count
-				String ctxString = parts[i];
-				String countString = parts[i + 1];
-
-				assert (countString.startsWith("C="));
-
-				String []countToks=countString.split(" ");
-				
-				double count = Double.parseDouble(countToks[0].substring(2).trim());
-				
-				TIntArrayList ctx = new TIntArrayList();
-				StringTokenizer ctxStrtok = new StringTokenizer(ctxString, " ");
-				while (ctxStrtok.hasMoreTokens())
-				{
-					String token = ctxStrtok.nextToken();
-					ctx.add(wordLexicon.insert(token));
-				}
-				int contextId = contextLexicon.insert(ctx);
-
-
-				if(countToks.length<2){
-					edges.add(new Edge(phraseId, contextId, count));
-				}
-				else{
-					int tag=Integer.parseInt(countToks[1].substring(2));
-					edges.add(new Edge(phraseId, contextId, count,tag));
-				}
-			}
-		}
-		return edges;
-	}
-	
-	static Corpus readFromFile(Reader in) throws IOException
-	{	
-		Corpus c = new Corpus();
-		c.edges = c.readEdges(in);
-		for (Edge edge: c.edges)
-		{
-			while (edge.getPhraseId() >= c.phraseToContext.size())
-				c.phraseToContext.add(new ArrayList<Edge>());
-			while (edge.getContextId() >= c.contextToPhrase.size())
-				c.contextToPhrase.add(new ArrayList<Edge>());
-			
-			// index the edge for fast phrase, context lookup
-			c.phraseToContext.get(edge.getPhraseId()).add(edge);
-			c.contextToPhrase.get(edge.getContextId()).add(edge);
-		}
-		return c;
-	}
-		
-	TIntArrayList phraseEdges(TIntArrayList phrase)
-	{
-		TIntArrayList r = new TIntArrayList(4);
-		for (int p = 0; p < phrase.size(); ++p)
-		{
-			if (p == 0 || phrase.get(p-1) == splitSentinel) 				
-				r.add(p);
-			if (p == phrase.size() - 1 || phrase.get(p+1) == splitSentinel) 
-				r.add(p);
-		}
-		return r;
-	}
-
-	public void printStats(PrintStream out) 
-	{
-		out.println("Corpus has " + edges.size() + " edges " + phraseLexicon.size() + " phrases " 
-				+ contextLexicon.size() + " contexts and " + wordLexicon.size() + " word types");
-	}
-}
\ No newline at end of file
diff --git a/gi/posterior-regularisation/prjava/src/phrase/Lexicon.java b/gi/posterior-regularisation/prjava/src/phrase/Lexicon.java
deleted file mode 100644
index a386e4a3..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/Lexicon.java
+++ /dev/null
@@ -1,34 +0,0 @@
-package phrase;
-
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-
-public class Lexicon<T>
-{
-	public int insert(T word)
-	{
-		Integer i = wordToIndex.get(word);
-		if (i == null)
-		{
-			i = indexToWord.size();
-			wordToIndex.put(word, i);
-			indexToWord.add(word);
-		}
-		return i;
-	}
-
-	public T lookup(int index)
-	{
-		return indexToWord.get(index);
-	}
-
-	public int size()
-	{
-		return indexToWord.size();
-	}
-
-	private Map<T, Integer> wordToIndex = new HashMap<T, Integer>();
-	private List<T> indexToWord = new ArrayList<T>();
-}
\ No newline at end of file
diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
deleted file mode 100644
index c032bb2b..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
+++ /dev/null
@@ -1,540 +0,0 @@
-package phrase;

-

-import gnu.trove.TIntArrayList;

-import org.apache.commons.math.special.Gamma;

-

-import java.io.BufferedReader;

-import java.io.IOException;

-import java.io.PrintStream;

-import java.util.ArrayList;

-import java.util.Arrays;

-import java.util.List;

-import java.util.concurrent.Callable;

-import java.util.concurrent.ExecutionException;

-import java.util.concurrent.ExecutorService;

-import java.util.concurrent.Executors;

-import java.util.concurrent.Future;

-import java.util.concurrent.LinkedBlockingQueue;

-import java.util.concurrent.atomic.AtomicInteger;

-import java.util.concurrent.atomic.AtomicLong;

-import java.util.regex.Pattern;

-

-import phrase.Corpus.Edge;

-

-

-public class PhraseCluster {

-	

-	public int K;

-	private int n_phrases, n_words, n_contexts, n_positions;

-	public Corpus c;

-	public ExecutorService pool; 

-

-	double[] lambdaPTCT;

-	double[][] lambdaPT;

-	boolean cacheLambda = true;

-

-	// emit[tag][position][word] = p(word | tag, position in context)

-	double emit[][][];

-	// pi[phrase][tag] = p(tag | phrase)

-	double pi[][];

-	

-	public PhraseCluster(int numCluster, Corpus corpus)

-	{

-		K=numCluster;

-		c=corpus;

-		n_words=c.getNumWords();

-		n_phrases=c.getNumPhrases();

-		n_contexts=c.getNumContexts();

-		n_positions=c.getNumContextPositions();

-

-		emit=new double [K][n_positions][n_words];

-		pi=new double[n_phrases][K];

-		

-		for(double [][]i:emit)

-			for(double []j:i)

-				arr.F.randomise(j, true);

-

-		for(double []j:pi)

-			arr.F.randomise(j, true);

-	}

-	

-	void useThreadPool(ExecutorService pool)

-	{

-		this.pool = pool;

-	}

-

-	public double EM(int phraseSizeLimit)

-	{

-		double [][][]exp_emit=new double [K][n_positions][n_words];

-		double []exp_pi=new double[K];

-		

-		for(double [][]i:exp_emit)

-			for(double []j:i)

-				Arrays.fill(j, 1e-10);

-		

-		double loglikelihood=0;

-		

-		//E

-		for(int phrase=0; phrase < n_phrases; phrase++)

-		{

-			if (phraseSizeLimit >= 1 && c.getPhrase(phrase).size() > phraseSizeLimit)

-				continue;

-

-			Arrays.fill(exp_pi, 1e-10);

-			

-			List<Edge> contexts = c.getEdgesForPhrase(phrase);

-

-			for (int ctx=0; ctx<contexts.size(); ctx++)

-			{

-				Edge edge = contexts.get(ctx);

-				

-				double p[]=posterior(edge);

-				double z = arr.F.l1norm(p);

-				assert z > 0;

-				loglikelihood += edge.getCount() * Math.log(z);

-				arr.F.l1normalize(p);

-				

-				double count = edge.getCount();

-				//increment expected count

-				TIntArrayList context = edge.getContext();

-				for(int tag=0;tag<K;tag++)

-				{

-					for(int pos=0;pos<n_positions;pos++){

-						exp_emit[tag][pos][context.get(pos)]+=p[tag]*count;

-					}

-					exp_pi[tag]+=p[tag]*count;

-				}

-			}

-			arr.F.l1normalize(exp_pi);

-			System.arraycopy(exp_pi, 0, pi[phrase], 0, K);

-		}

-

-		//M

-		for(double [][]i:exp_emit)

-			for(double []j:i)

-				arr.F.l1normalize(j);

-			

-		emit=exp_emit;

-

-		return loglikelihood;

-	}

-	

-	public double PREM(double scalePT, double scaleCT, int phraseSizeLimit)

-	{

-		if (scaleCT == 0)

-		{

-			if (pool != null)

-				return PREM_phrase_constraints_parallel(scalePT, phraseSizeLimit);

-			else

-				return PREM_phrase_constraints(scalePT, phraseSizeLimit);

-		}

-		else // FIXME: ignores phraseSizeLimit

-			return this.PREM_phrase_context_constraints(scalePT, scaleCT);

-	}

-

-	

-	public double PREM_phrase_constraints(double scalePT, int phraseSizeLimit)

-	{

-		double [][][]exp_emit=new double[K][n_positions][n_words];

-		double []exp_pi=new double[K];

-		

-		for(double [][]i:exp_emit)

-			for(double []j:i)

-				Arrays.fill(j, 1e-10);

-		

-		if (lambdaPT == null && cacheLambda)

-			lambdaPT = new double[n_phrases][];

-		

-		double loglikelihood=0, kl=0, l1lmax=0, primal=0;

-		int failures=0, iterations=0;

-		long start = System.currentTimeMillis();

-		//E

-		for(int phrase=0; phrase<n_phrases; phrase++)

-		{

-			if (phraseSizeLimit >= 1 && c.getPhrase(phrase).size() > phraseSizeLimit)

-			{

-				//System.arraycopy(pi[phrase], 0, exp_pi[phrase], 0, K);

-				continue;

-			}

-			

-			Arrays.fill(exp_pi, 1e-10);

-			

-			// FIXME: add rare edge check to phrase objective & posterior processing

-			PhraseObjective po = new PhraseObjective(this, phrase, scalePT, (cacheLambda) ? lambdaPT[phrase] : null);

-			boolean ok = po.optimizeWithProjectedGradientDescent();

-			if (!ok) ++failures;

-			if (cacheLambda) lambdaPT[phrase] = po.getParameters();

-			iterations += po.getNumberUpdateCalls();

-			double [][] q=po.posterior();

-			loglikelihood += po.loglikelihood();

-			kl += po.KL_divergence();

-			l1lmax += po.l1lmax();

-			primal += po.primal(scalePT);

-			List<Edge> edges = c.getEdgesForPhrase(phrase);

-

-			for(int edge=0;edge<q.length;edge++){

-				Edge e = edges.get(edge);

-				TIntArrayList context = e.getContext();

-				double contextCnt = e.getCount();

-				//increment expected count

-				for(int tag=0;tag<K;tag++){

-					for(int pos=0;pos<n_positions;pos++){

-						exp_emit[tag][pos][context.get(pos)]+=q[edge][tag]*contextCnt;

-					}

-					

-					exp_pi[tag]+=q[edge][tag]*contextCnt;

-					

-				}

-			}

-			arr.F.l1normalize(exp_pi);

-			System.arraycopy(exp_pi, 0, pi[phrase], 0, K);

-		}

-		

-		long end = System.currentTimeMillis();

-		if (failures > 0)

-			System.out.println("WARNING: failed to converge in " + failures + "/" + n_phrases + " cases");

-		System.out.println("\tmean iters:     " + iterations/(double)n_phrases + " elapsed time " + (end - start) / 1000.0);

-		System.out.println("\tllh:            " + loglikelihood);

-		System.out.println("\tKL:             " + kl);

-		System.out.println("\tphrase l1lmax:  " + l1lmax);

-		

-		//M

-		for(double [][]i:exp_emit)

-			for(double []j:i)

-				arr.F.l1normalize(j);

-		emit=exp_emit;

-		

-		return primal;

-	}

-

-	public double PREM_phrase_constraints_parallel(final double scalePT, int phraseSizeLimit)

-	{

-		assert(pool != null);

-		

-		final LinkedBlockingQueue<PhraseObjective> expectations 

-			= new LinkedBlockingQueue<PhraseObjective>();

-		

-		double [][][]exp_emit=new double [K][n_positions][n_words];

-		double [][]exp_pi=new double[n_phrases][K];

-		

-		for(double [][]i:exp_emit)

-			for(double []j:i)

-				Arrays.fill(j, 1e-10);

-		for(double []j:exp_pi)

-			Arrays.fill(j, 1e-10);

-		

-		double loglikelihood=0, kl=0, l1lmax=0, primal=0;

-		final AtomicInteger failures = new AtomicInteger(0);

-		final AtomicLong elapsed = new AtomicLong(0l);

-		int iterations=0;

-		long start = System.currentTimeMillis();

-		List<Future<PhraseObjective>> results = new ArrayList<Future<PhraseObjective>>();

-		

-		if (lambdaPT == null && cacheLambda)

-			lambdaPT = new double[n_phrases][];

-

-		//E

-		for(int phrase=0;phrase<n_phrases;phrase++) {

-			if (phraseSizeLimit >= 1 && c.getPhrase(phrase).size() > phraseSizeLimit) {

-				System.arraycopy(pi[phrase], 0, exp_pi[phrase], 0, K);

-				continue;

-			}

-

-			final int p=phrase;

-			results.add(pool.submit(new Callable<PhraseObjective>() {

-				public PhraseObjective call() {

-					//System.out.println("" + Thread.currentThread().getId() + " optimising lambda for " + p);

-					long start = System.currentTimeMillis();

-					PhraseObjective po = new PhraseObjective(PhraseCluster.this, p, scalePT, (cacheLambda) ? lambdaPT[p] : null);

-					boolean ok = po.optimizeWithProjectedGradientDescent();

-					if (!ok) failures.incrementAndGet();

-					long end = System.currentTimeMillis();

-					elapsed.addAndGet(end - start);

-					//System.out.println("" + Thread.currentThread().getId() + " done optimising lambda for " + p);

-					return po;

-				}

-			}));

-		}

-		

-		// aggregate the expectations as they become available

-		for (Future<PhraseObjective> fpo : results)

-		{

-			try {

-				//System.out.println("" + Thread.currentThread().getId() + " reading queue #" + count);

-

-				// wait (blocking) until something is ready

-				PhraseObjective po = fpo.get();

-				// process

-				int phrase = po.phrase;

-				if (cacheLambda) lambdaPT[phrase] = po.getParameters();

-				//System.out.println("" + Thread.currentThread().getId() + " taken phrase " + phrase);

-				double [][] q=po.posterior();

-				loglikelihood += po.loglikelihood();

-				kl += po.KL_divergence();

-				l1lmax += po.l1lmax();

-				primal += po.primal(scalePT);

-				iterations += po.getNumberUpdateCalls();

-

-				List<Edge> edges = c.getEdgesForPhrase(phrase);

-				for(int edge=0;edge<q.length;edge++){

-					Edge e = edges.get(edge);

-					TIntArrayList context = e.getContext();

-					double contextCnt = e.getCount();

-					//increment expected count

-					for(int tag=0;tag<K;tag++){

-						for(int pos=0;pos<n_positions;pos++){

-							exp_emit[tag][pos][context.get(pos)]+=q[edge][tag]*contextCnt;

-						}

-						exp_pi[phrase][tag]+=q[edge][tag]*contextCnt;

-					}

-				}

-			} catch (InterruptedException e) {

-				System.err.println("M-step thread interrupted. Probably fatal!");

-				throw new RuntimeException(e);

-			} catch (ExecutionException e) {

-				System.err.println("M-step thread execution died. Probably fatal!");

-				throw new RuntimeException(e);

-			}

-		}

-		

-		long end = System.currentTimeMillis();

-		

-		if (failures.get() > 0)

-			System.out.println("WARNING: failed to converge in " + failures.get() + "/" + n_phrases + " cases");

-		System.out.println("\tmean iters:     " + iterations/(double)n_phrases + " walltime " + (end-start)/1000.0 + " threads " + elapsed.get() / 1000.0);

-		System.out.println("\tllh:            " + loglikelihood);

-		System.out.println("\tKL:             " + kl);

-		System.out.println("\tphrase l1lmax:  " + l1lmax);

-		

-		//M

-		for(double [][]i:exp_emit)

-			for(double []j:i)

-				arr.F.l1normalize(j);

-		emit=exp_emit;

-		

-		for(double []j:exp_pi)

-			arr.F.l1normalize(j);

-		pi=exp_pi;

-		

-		return primal;

-	}

-	

-	public double PREM_phrase_context_constraints(double scalePT, double scaleCT)

-	{	

-		double[][][] exp_emit = new double [K][n_positions][n_words];

-		double[][] exp_pi = new double[n_phrases][K];

-

-		//E step

-		PhraseContextObjective pco = new PhraseContextObjective(this, lambdaPTCT, pool, scalePT, scaleCT);

-		boolean ok = pco.optimizeWithProjectedGradientDescent();

-		if (cacheLambda) lambdaPTCT = pco.getParameters();

-

-		//now extract expectations

-		List<Corpus.Edge> edges = c.getEdges();

-		for(int e = 0; e < edges.size(); ++e)

-		{

-			double [] q = pco.posterior(e);

-			Corpus.Edge edge = edges.get(e);

-

-			TIntArrayList context = edge.getContext();

-			double contextCnt = edge.getCount();

-			//increment expected count

-			for(int tag=0;tag<K;tag++)

-			{

-				for(int pos=0;pos<n_positions;pos++)

-					exp_emit[tag][pos][context.get(pos)]+=q[tag]*contextCnt;

-				exp_pi[edge.getPhraseId()][tag]+=q[tag]*contextCnt;

-			}

-		}

-		

-		System.out.println("\tllh:            " + pco.loglikelihood());

-		System.out.println("\tKL:             " + pco.KL_divergence());

-		System.out.println("\tphrase l1lmax:  " + pco.phrase_l1lmax());

-		System.out.println("\tcontext l1lmax: " + pco.context_l1lmax());

-		

-		//M step

-		for(double [][]i:exp_emit)

-			for(double []j:i)

-				arr.F.l1normalize(j);

-		emit=exp_emit;

-		

-		for(double []j:exp_pi)

-			arr.F.l1normalize(j);

-		pi=exp_pi;

-		

-		return pco.primal();

-	}	

-		

-	/**

-	 * @param phrase index of phrase

-	 * @param ctx array of context

-	 * @return unnormalized posterior

-	 */

-	public double[] posterior(Corpus.Edge edge) 

-	{

-		double[] prob;

-		

-		if(edge.getTag()>=0){

-			prob=new double[K];

-			prob[edge.getTag()]=1;

-			return prob;

-		}

-		

-		if (edge.getPhraseId() < n_phrases)

-			prob = Arrays.copyOf(pi[edge.getPhraseId()], K);

-		else

-		{

-			prob = new double[K];

-			Arrays.fill(prob, 1.0);

-		}

-		

-		TIntArrayList ctx = edge.getContext();

-		for(int tag=0;tag<K;tag++)

-		{

-			for(int c=0;c<n_positions;c++)

-			{

-				int word = ctx.get(c);

-				if (!this.c.isSentinel(word) && word < n_words)

-					prob[tag]*=emit[tag][c][word];

-			}

-		}

-		

-		return prob;

-	}

-	

-	public void displayPosterior(PrintStream ps, List<Edge> testing)

-	{	

-		for (Edge edge : testing)

-		{

-			double probs[] = posterior(edge);

-			arr.F.l1normalize(probs);

-

-			// emit phrase

-			ps.print(edge.getPhraseString());

-			ps.print("\t");

-			ps.print(edge.getContextString(true));

-			int t=arr.F.argmax(probs);

-			ps.println(" ||| C=" + t + " T=" + edge.getCount() + " P=" + probs[t]);

-			//ps.println("# probs " + Arrays.toString(probs));

-		}

-	}

-	

-	public void displayModelParam(PrintStream ps)

-	{

-		final double EPS = 1e-6;

-		ps.println("phrases " + n_phrases + " tags " + K + " positions " + n_positions);

-		

-		for (int i = 0; i < n_phrases; ++i)

-			for(int j=0;j<pi[i].length;j++)

-				if (pi[i][j] > EPS)

-					ps.println(i + " " + j + " " + pi[i][j]);

-

-		ps.println();

-		for (int i = 0; i < K; ++i)

-		{

-			for(int position=0;position<n_positions;position++)

-			{

-				for(int word=0;word<emit[i][position].length;word++)

-				{

-					if (emit[i][position][word] > EPS)

-						ps.println(i + " " + position + " " + word + " " + emit[i][position][word]);

-				}

-			}

-		}

-	}

-	

-	double phrase_l1lmax()

-	{

-		double sum=0;

-		for(int phrase=0; phrase<n_phrases; phrase++)

-		{

-			double [] maxes = new double[K];

-			for (Edge edge : c.getEdgesForPhrase(phrase))

-			{

-				double p[] = posterior(edge);

-				arr.F.l1normalize(p);

-				for(int tag=0;tag<K;tag++)

-					maxes[tag] = Math.max(maxes[tag], p[tag]);

-			}

-			for(int tag=0;tag<K;tag++)

-				sum += maxes[tag];

-		}

-		return sum;

-	}

-

-	double context_l1lmax()

-	{

-		double sum=0;

-		for(int context=0; context<n_contexts; context++)

-		{

-			double [] maxes = new double[K];

-			for (Edge edge : c.getEdgesForContext(context))

-			{

-				double p[] = posterior(edge);

-				arr.F.l1normalize(p);

-				for(int tag=0;tag<K;tag++)

-					maxes[tag] = Math.max(maxes[tag], p[tag]);

-			}

-			for(int tag=0;tag<K;tag++)

-				sum += maxes[tag];

-		}

-		return sum;

-	}

-

-	public void loadParameters(BufferedReader input) throws IOException

-	{	

-		final double EPS = 1e-50;

-		

-		// overwrite pi, emit with ~zeros

-		for(double [][]i:emit)

-			for(double []j:i)

-				Arrays.fill(j, EPS);

-

-		for(double []j:pi)

-			Arrays.fill(j, EPS);

-

-		String line = input.readLine();

-		assert line != null;

-

-		Pattern space = Pattern.compile(" +");

-		String[] parts = space.split(line);

-		assert parts.length == 6;

-

-		assert parts[0].equals("phrases");

-		int phrases = Integer.parseInt(parts[1]);

-		int tags = Integer.parseInt(parts[3]);

-		int positions = Integer.parseInt(parts[5]);

-		

-		assert phrases == n_phrases;

-		assert tags == K;

-		assert positions == n_positions;

-

-		// read in pi

-		while ((line = input.readLine()) != null)

-		{

-			line = line.trim();

-			if (line.isEmpty()) break;

-			

-			String[] tokens = space.split(line);

-			assert tokens.length == 3;

-			int p = Integer.parseInt(tokens[0]);

-			int t = Integer.parseInt(tokens[1]);

-			double v = Double.parseDouble(tokens[2]);

-

-			pi[p][t] = v;

-		}

-		

-		// read in emissions

-		while ((line = input.readLine()) != null)

-		{

-			String[] tokens = space.split(line);

-			assert tokens.length == 4;

-			int t = Integer.parseInt(tokens[0]);

-			int p = Integer.parseInt(tokens[1]);

-			int w = Integer.parseInt(tokens[2]);

-			double v = Double.parseDouble(tokens[3]);

-

-			emit[t][p][w] = v;

-		}

-	}

-}

diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java
deleted file mode 100644
index 646ff392..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java
+++ /dev/null
@@ -1,436 +0,0 @@
-package phrase;

-

-import java.util.ArrayList;

-import java.util.Arrays;

-import java.util.HashMap;

-import java.util.List;

-import java.util.Map;

-import java.util.concurrent.ExecutionException;

-import java.util.concurrent.ExecutorService;

-import java.util.concurrent.Future;

-

-import optimization.gradientBasedMethods.ProjectedGradientDescent;

-import optimization.gradientBasedMethods.ProjectedObjective;

-import optimization.gradientBasedMethods.stats.OptimizerStats;

-import optimization.linesearch.ArmijoLineSearchMinimizationAlongProjectionArc;

-import optimization.linesearch.InterpolationPickFirstStep;

-import optimization.linesearch.LineSearchMethod;

-import optimization.projections.SimplexProjection;

-import optimization.stopCriteria.CompositeStopingCriteria;

-import optimization.stopCriteria.ProjectedGradientL2Norm;

-import optimization.stopCriteria.StopingCriteria;

-import optimization.stopCriteria.ValueDifference;

-import optimization.util.MathUtils;

-import phrase.Corpus.Edge;

-

-public class PhraseContextObjective extends ProjectedObjective

-{

-	private static final double GRAD_DIFF = 0.00002;

-	private static double INIT_STEP_SIZE = 300;

-	private static double VAL_DIFF = 1e-8;

-	private static int ITERATIONS = 20;

-	boolean debug = false;

-	

-	private PhraseCluster c;

-	

-	// un-regularized unnormalized posterior, p[edge][tag]

-	// P(tag|edge) \propto P(tag|phrase)P(context|tag)

-	private double p[][];

-

-	// regularized unnormalized posterior 

-	// q[edge][tag] propto p[edge][tag]*exp(-lambda)

-	private double q[][];

-	private List<Corpus.Edge> data;

-	

-	// log likelihood under q

-	private double loglikelihood;

-	private SimplexProjection projectionPhrase;

-	private SimplexProjection projectionContext;

-	

-	double[] newPoint;

-	private int n_param;

-	

-	// likelihood under p

-	public double llh;

-	

-	private static Map<Corpus.Edge, Integer> edgeIndex;

-	

-	private long projectionTime;

-	private long objectiveTime;

-	private long actualProjectionTime;

-	private ExecutorService pool;

-	

-	double scalePT;

-	double scaleCT;

-	

-	public PhraseContextObjective(PhraseCluster cluster, double[] startingParameters, ExecutorService pool,

-			double scalePT, double scaleCT)

-	{

-		c=cluster;

-		data=c.c.getEdges();

-		n_param=data.size()*c.K*2;

-		this.pool=pool;

-		this.scalePT = scalePT;

-		this.scaleCT = scaleCT;

-		

-		parameters = startingParameters;

-		if (parameters == null)

-			parameters = new double[n_param];

-		

-		System.out.println("Num parameters " + n_param);

-		newPoint = new double[n_param];

-		gradient = new double[n_param];

-		initP();

-		projectionPhrase = new SimplexProjection(scalePT);

-		projectionContext = new SimplexProjection(scaleCT);

-		q=new double [data.size()][c.K];

-		

-		if (edgeIndex == null) {

-			edgeIndex = new HashMap<Edge, Integer>();

-			for (int e=0; e<data.size(); e++)

-			{

-				edgeIndex.put(data.get(e), e);

-				//if (debug) System.out.println("Edge " + data.get(e) + " index " + e);

-			}

-		}

-		

-		setParameters(parameters);

-	}

-

-	private void initP(){

-		p=new double[data.size()][];

-		for(int edge=0;edge<data.size();edge++)

-		{

-			p[edge]=c.posterior(data.get(edge));

-			llh += data.get(edge).getCount() * Math.log(arr.F.l1norm(p[edge]));

-			arr.F.l1normalize(p[edge]);

-		}

-	}

-	

-	@Override

-	public void setParameters(double[] params) {

-		//System.out.println("setParameters " + Arrays.toString(parameters));

-		// TODO: test if params have changed and skip update otherwise

-		super.setParameters(params);

-		updateFunction();

-	}

-	

-	private void updateFunction()

-	{

-		updateCalls++;

-		loglikelihood=0;

-

-		System.out.print(".");

-		System.out.flush();

-

-		long begin = System.currentTimeMillis();

-		for (int e=0; e<data.size(); e++) 

-		{

-			Edge edge = data.get(e);

-			for(int tag=0; tag<c.K; tag++)

-			{

-				int ip = index(e, tag, true);

-				int ic = index(e, tag, false);

-				q[e][tag] = p[e][tag]*

-					Math.exp((-parameters[ip]-parameters[ic]) / edge.getCount());

-				//if (debug)

-					//System.out.println("\tposterior " + edge + " with tag " + tag + " p " + p[e][tag] + " params " + parameters[ip] + " and " + parameters[ic] + " q " + q[e][tag]);

-			}

-		}

-	

-		for(int edge=0;edge<data.size();edge++) {

-			loglikelihood+=data.get(edge).getCount() * Math.log(arr.F.l1norm(q[edge]));

-			arr.F.l1normalize(q[edge]);

-		}

-		

-		for (int e=0; e<data.size(); e++) 

-		{

-			for(int tag=0; tag<c.K; tag++)

-			{

-				int ip = index(e, tag, true);

-				int ic = index(e, tag, false);

-				gradient[ip]=-q[e][tag];

-				gradient[ic]=-q[e][tag];

-			}

-		}

-		//if (debug) {

-			//System.out.println("objective " + loglikelihood + " ||gradient||_2: " + arr.F.l2norm(gradient));		

-			//System.out.println("gradient " + Arrays.toString(gradient));

-		//}

-		objectiveTime += System.currentTimeMillis() - begin;

-	}

-	

-	@Override

-	public double[] projectPoint(double[] point) 

-	{

-		long begin = System.currentTimeMillis();

-		List<Future<?>> tasks = new ArrayList<Future<?>>();

-		

-		System.out.print(",");

-		System.out.flush();

-

-		Arrays.fill(newPoint, 0, newPoint.length, 0);

-		

-		// first project using the phrase-tag constraints,

-		// for all p,t: sum_c lambda_ptc < scaleP 

-		if (pool == null)

-		{

-			for (int p = 0; p < c.c.getNumPhrases(); ++p)

-			{

-				List<Edge> edges = c.c.getEdgesForPhrase(p);

-				double[] toProject = new double[edges.size()];

-				for(int tag=0;tag<c.K;tag++)

-				{

-					// FIXME: slow hash lookup for e (twice)

-					for(int e=0; e<edges.size(); e++) 						

-						toProject[e] = point[index(edges.get(e), tag, true)];

-					long lbegin = System.currentTimeMillis();

-					projectionPhrase.project(toProject);

-					actualProjectionTime += System.currentTimeMillis() - lbegin;

-					for(int e=0; e<edges.size(); e++)

-						newPoint[index(edges.get(e), tag, true)] = toProject[e];

-				}

-			}

-		}

-		else // do above in parallel using thread pool

-		{	

-			for (int p = 0; p < c.c.getNumPhrases(); ++p)

-			{

-				final int phrase = p;

-				final double[] inPoint = point;

-				Runnable task = new Runnable()

-				{

-					public void run()

-					{

-						List<Edge> edges = c.c.getEdgesForPhrase(phrase);

-						double toProject[] = new double[edges.size()];

-						for(int tag=0;tag<c.K;tag++)

-						{

-							// FIXME: slow hash lookup for e

-							for(int e=0; e<edges.size(); e++)

-								toProject[e] = inPoint[index(edges.get(e), tag, true)];

-							projectionPhrase.project(toProject);

-							for(int e=0; e<edges.size(); e++)

-								newPoint[index(edges.get(e), tag, true)] = toProject[e];

-						}

-					}		

-				};

-				tasks.add(pool.submit(task));

-			}

-		}

-		//System.out.println("after PT " + Arrays.toString(newPoint));

-	

-		// now project using the context-tag constraints,

-		// for all c,t: sum_p omega_pct < scaleC

-		if (pool == null)

-		{

-			for (int ctx = 0; ctx < c.c.getNumContexts(); ++ctx)

-			{

-				List<Edge> edges = c.c.getEdgesForContext(ctx);

-				double toProject[] = new double[edges.size()];

-				for(int tag=0;tag<c.K;tag++)

-				{

-					// FIXME: slow hash lookup for e

-					for(int e=0; e<edges.size(); e++)

-						toProject[e] = point[index(edges.get(e), tag, false)];

-					long lbegin = System.currentTimeMillis();

-					projectionContext.project(toProject);

-					actualProjectionTime += System.currentTimeMillis() - lbegin;

-					for(int e=0; e<edges.size(); e++)

-						newPoint[index(edges.get(e), tag, false)] = toProject[e];

-				}

-			}

-		}

-		else

-		{

-			// do above in parallel using thread pool

-			for (int ctx = 0; ctx < c.c.getNumContexts(); ++ctx)

-			{

-				final int context = ctx;

-				final double[] inPoint = point;

-				Runnable task = new Runnable()

-				{

-					public void run()

-					{

-						List<Edge> edges = c.c.getEdgesForContext(context);

-						double toProject[] = new double[edges.size()];

-						for(int tag=0;tag<c.K;tag++)

-						{

-							// FIXME: slow hash lookup for e

-							for(int e=0; e<edges.size(); e++)

-								toProject[e] = inPoint[index(edges.get(e), tag, false)];

-							projectionContext.project(toProject);

-							for(int e=0; e<edges.size(); e++)

-								newPoint[index(edges.get(e), tag, false)] = toProject[e];

-						}

-					}

-				};

-				tasks.add(pool.submit(task));

-			}

-		}

-		

-		if (pool != null)

-		{

-			// wait for all the jobs to complete

-			Exception failure = null;

-			for (Future<?> task: tasks)

-			{

-				try {

-					task.get();

-				} catch (InterruptedException e) {

-					System.err.println("ERROR: Projection thread interrupted");

-					e.printStackTrace();

-					failure = e;

-				} catch (ExecutionException e) {

-					System.err.println("ERROR: Projection thread died");

-					e.printStackTrace();

-					failure = e;

-				}

-			}

-			// rethrow the exception

-			if (failure != null)

-			{

-				pool.shutdownNow();

-				throw new RuntimeException(failure);

-			}

-		}

-		

-		double[] tmp = newPoint;

-		newPoint = point;

-		projectionTime += System.currentTimeMillis() - begin;

-		

-		//if (debug)

-			//System.out.println("\t\treturning " + Arrays.toString(tmp));

-		return tmp;

-	}

-	

-	private int index(Edge edge, int tag, boolean phrase)

-	{

-		// NB if indexing changes must also change code in updateFunction and constructor

-		if (phrase)

-			return tag * edgeIndex.size() + edgeIndex.get(edge);

-		else

-			return (c.K + tag) * edgeIndex.size() + edgeIndex.get(edge);

-	}

-

-	private int index(int e, int tag, boolean phrase)

-	{

-		// NB if indexing changes must also change code in updateFunction and constructor

-		if (phrase)

-			return tag * edgeIndex.size() + e;

-		else

-			return (c.K + tag) * edgeIndex.size() + e;

-	}

-	

-	@Override

-	public double[] getGradient() {

-		gradientCalls++;

-		return gradient;

-	}

-

-	@Override

-	public double getValue() {

-		functionCalls++;

-		return loglikelihood;

-	}

-

-	@Override

-	public String toString() {

-		return "No need for pointless toString";

-	}

-

-	public double []posterior(int edgeIndex){

-		return q[edgeIndex];

-	}

-	

-	public boolean optimizeWithProjectedGradientDescent()

-	{

-		projectionTime = 0;

-		actualProjectionTime = 0;

-		objectiveTime = 0;

-		long start = System.currentTimeMillis();

-

-		LineSearchMethod ls =

-			new ArmijoLineSearchMinimizationAlongProjectionArc

-				(new InterpolationPickFirstStep(INIT_STEP_SIZE));

-		//LineSearchMethod  ls = new WolfRuleLineSearch(

-		//		(new InterpolationPickFirstStep(INIT_STEP_SIZE)), c1, c2);

-		OptimizerStats stats = new OptimizerStats();

-		

-		

-		ProjectedGradientDescent optimizer = new ProjectedGradientDescent(ls);

-		StopingCriteria stopGrad = new ProjectedGradientL2Norm(GRAD_DIFF);

-		StopingCriteria stopValue = new ValueDifference(VAL_DIFF*(-llh));

-		CompositeStopingCriteria compositeStop = new CompositeStopingCriteria();

-		compositeStop.add(stopGrad);

-		compositeStop.add(stopValue);

-		optimizer.setMaxIterations(ITERATIONS);

-		updateFunction();

-		boolean success = optimizer.optimize(this,stats,compositeStop);

-

-		System.out.println();

-		System.out.println(stats.prettyPrint(1));

-		

-		if (success)

-			System.out.print("\toptimization took " + optimizer.getCurrentIteration() + " iterations");

-	 	else

-			System.out.print("\toptimization failed to converge");

-		long total = System.currentTimeMillis() - start;

-		System.out.println(" and " + total + " ms: projection " + projectionTime + 

-				" actual " + actualProjectionTime + " objective " + objectiveTime);

-

-		return success;

-	}

-	

-	double loglikelihood()

-	{

-		return llh;

-	}

-	

-	double KL_divergence()

-	{

-		return -loglikelihood + MathUtils.dotProduct(parameters, gradient);

-	}

-	

-	double phrase_l1lmax()

-	{

-		// \sum_{tag,phrase} max_{context} P(tag|context,phrase)

-		double sum=0;

-		for (int p = 0; p < c.c.getNumPhrases(); ++p)

-		{

-			List<Edge> edges = c.c.getEdgesForPhrase(p);

-			for(int tag=0;tag<c.K;tag++)

-			{

-				double max=0;

-				for (Edge edge: edges)

-					max = Math.max(max, q[edgeIndex.get(edge)][tag]);

-				sum+=max;

-			}	

-		}

-		return sum;

-	}

-	

-	double context_l1lmax()

-	{

-		// \sum_{tag,context} max_{phrase} P(tag|context,phrase)

-		double sum=0;

-		for (int ctx = 0; ctx < c.c.getNumContexts(); ++ctx)

-		{

-			List<Edge> edges = c.c.getEdgesForContext(ctx);

-			for(int tag=0; tag<c.K; tag++)

-			{

-				double max=0;

-				for (Edge edge: edges)

-					max = Math.max(max, q[edgeIndex.get(edge)][tag]);

-				sum+=max;

-			}	

-		}

-		return sum;

-	}

-	

-	// L - KL(q||p) - scalePT * l1lmax_phrase - scaleCT * l1lmax_context

-	public double primal()

-	{

-		return loglikelihood() - KL_divergence() - scalePT * phrase_l1lmax() - scaleCT * context_l1lmax();

-	}

-}
\ No newline at end of file
diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseCorpus.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseCorpus.java
deleted file mode 100644
index 0cf31c1c..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseCorpus.java
+++ /dev/null
@@ -1,193 +0,0 @@
-package phrase;

-

-import io.FileUtil;

-

-import java.io.BufferedInputStream;

-import java.io.BufferedReader;

-import java.io.File;

-import java.io.FileNotFoundException;

-import java.io.IOException;

-import java.io.PrintStream;

-import java.util.ArrayList;

-import java.util.HashMap;

-import java.util.Scanner;

-

-public class PhraseCorpus 

-{

-	public HashMap<String,Integer>wordLex;

-	public HashMap<String,Integer>phraseLex;

-	

-	public String wordList[];

-	public String phraseList[];

-	

-	//data[phrase][num context][position]

-	public int data[][][];

-	public int numContexts;	

-

-	public PhraseCorpus(String filename) throws FileNotFoundException, IOException

-	{

-		BufferedReader r = FileUtil.reader(new File(filename));

-		

-		phraseLex=new HashMap<String,Integer>();

-		wordLex=new HashMap<String,Integer>();

-		

-		ArrayList<int[][]>dataList=new ArrayList<int[][]>();

-		String line=null;

-		numContexts = 0;

-		

-		while((line=readLine(r))!=null){

-			

-			String toks[]=line.split("\t");

-			String phrase=toks[0];

-			addLex(phrase,phraseLex);

-			

-			toks=toks[1].split(" \\|\\|\\| ");

-			

-			ArrayList <int[]>ctxList=new ArrayList<int[]>();

-			

-			for(int i=0;i<toks.length;i+=2){

-				String ctx=toks[i];

-				String words[]=ctx.split(" ");

-				if (numContexts == 0)

-					numContexts = words.length - 1;

-				else

-					assert numContexts == words.length - 1;

-				

-				int []context=new int [numContexts+1];

-				int idx=0;

-				for(String word:words){

-					if(word.equals("<PHRASE>")){

-						continue;

-					}

-					addLex(word,wordLex);

-					context[idx]=wordLex.get(word);

-					idx++;

-				}

-				

-				String count=toks[i+1];

-				context[idx]=Integer.parseInt(count.trim().substring(2));

-				

-				ctxList.add(context);

-			}

-			

-			dataList.add(ctxList.toArray(new int [0][]));

-			

-		}

-		try{

-			r.close();

-		}catch(IOException ioe){

-			ioe.printStackTrace();

-		}

-		data=dataList.toArray(new int[0][][]);

-	}

-

-	private void addLex(String key, HashMap<String,Integer>lex){

-		Integer i=lex.get(key);

-		if(i==null){

-			lex.put(key, lex.size());

-		}

-	}

-	

-	//for debugging

-	public void saveLex(String lexFilename) throws FileNotFoundException, IOException

-	{

-		PrintStream ps = FileUtil.printstream(new File(lexFilename));

-		ps.println("Phrase Lexicon");

-		ps.println(phraseLex.size());

-		printDict(phraseLex,ps);

-		

-		ps.println("Word Lexicon");

-		ps.println(wordLex.size());

-		printDict(wordLex,ps);

-		ps.close();

-	}

-	

-	private static void printDict(HashMap<String,Integer>lex,PrintStream ps){

-		String []dict=buildList(lex);

-		for(int i=0;i<dict.length;i++){

-			ps.println(dict[i]);

-		}

-	}

-	

-	public void loadLex(String lexFilename){

-		Scanner sc=io.FileUtil.openInFile(lexFilename);

-		

-		sc.nextLine();

-		int size=sc.nextInt();

-		sc.nextLine();

-		String[]dict=new String[size];

-		for(int i=0;i<size;i++){

-			dict[i]=sc.nextLine();

-		}

-		phraseLex=buildMap(dict);

-

-		sc.nextLine();

-		size=sc.nextInt();

-		sc.nextLine();

-		dict=new String[size];

-		for(int i=0;i<size;i++){

-			dict[i]=sc.nextLine();

-		}

-		wordLex=buildMap(dict);

-		sc.close();

-	}

-	

-	private HashMap<String, Integer> buildMap(String[]dict){

-		HashMap<String,Integer> map=new HashMap<String,Integer>();

-		for(int i=0;i<dict.length;i++){

-			map.put(dict[i], i);

-		}

-		return map;

-	}

-	

-	public void buildList(){

-		if(wordList==null){

-			wordList=buildList(wordLex);

-			phraseList=buildList(phraseLex);

-		}

-	}

-	

-	private static String[]buildList(HashMap<String,Integer>lex){

-		String dict[]=new String [lex.size()];

-		for(String key:lex.keySet()){

-			dict[lex.get(key)]=key;

-		}

-		return dict;

-	}

-	

-	public String getContextString(int context[], boolean addPhraseMarker)

-	{

-		StringBuffer b = new StringBuffer();

-		for (int i=0;i<context.length-1;i++)

-		{

-			if (b.length() > 0)

-				b.append(" ");

-

-			if (i == context.length/2)

-				b.append("<PHRASE> ");

-			

-			b.append(wordList[context[i]]);

-		}

-		return b.toString();

-	}

-	

-	public static String readLine(BufferedReader r){

-		try{

-			return r.readLine();

-		}

-		catch(IOException ioe){

-			ioe.printStackTrace();

-		}

-		return null;

-	}

-

-	public static void main(String[] args) throws Exception 

-	{

-		String LEX_FILENAME="../pdata/lex.out";

-		String DATA_FILENAME="../pdata/btec.con";

-		PhraseCorpus c=new PhraseCorpus(DATA_FILENAME);

-		c.saveLex(LEX_FILENAME);

-		c.loadLex(LEX_FILENAME);

-		c.saveLex(LEX_FILENAME);

-	}

-}

diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
deleted file mode 100644
index ac73a075..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
+++ /dev/null
@@ -1,224 +0,0 @@
-package phrase;

-

-import java.util.Arrays;

-import java.util.List;

-

-import optimization.gradientBasedMethods.ProjectedGradientDescent;

-import optimization.gradientBasedMethods.ProjectedObjective;

-import optimization.gradientBasedMethods.stats.OptimizerStats;

-import optimization.linesearch.ArmijoLineSearchMinimizationAlongProjectionArc;

-import optimization.linesearch.InterpolationPickFirstStep;

-import optimization.linesearch.LineSearchMethod;

-import optimization.linesearch.WolfRuleLineSearch;

-import optimization.projections.SimplexProjection;

-import optimization.stopCriteria.CompositeStopingCriteria;

-import optimization.stopCriteria.ProjectedGradientL2Norm;

-import optimization.stopCriteria.StopingCriteria;

-import optimization.stopCriteria.ValueDifference;

-import optimization.util.MathUtils;

-

-public class PhraseObjective extends ProjectedObjective

-{

-	static final double GRAD_DIFF = 0.00002;

-	static double INIT_STEP_SIZE = 300;

-	static double VAL_DIFF = 1e-8; // tuned to BTEC subsample

-	static int ITERATIONS = 100;

-	private PhraseCluster c;

-	

-	/**@brief

-	 *  for debugging purposes

-	 */

-	//public static PrintStream ps;

-	

-	/**@brief current phrase being optimzed*/

-	public int phrase;

-

-	/**@brief un-regularized posterior

-	 * unnormalized

-	 * p[edge][tag]

-	*  P(tag|edge) \propto P(tag|phrase)P(context|tag)

-	 */

-	private double[][]p;

-

-	/**@brief regularized posterior

-	 * q[edge][tag] propto p[edge][tag]*exp(-lambda)

-	 */

-	private double q[][];

-	private List<Corpus.Edge> data;

-	

-	/**@brief log likelihood of the associated phrase

-	 * 

-	 */

-	private double loglikelihood;

-	private SimplexProjection projection;

-	

-	double[] newPoint  ;

-	

-	private int n_param;

-	

-	/**@brief likelihood under p

-	 * 

-	 */

-	public double llh;

-	

-	public PhraseObjective(PhraseCluster cluster, int phraseIdx, double scale, double[] lambda){

-		phrase=phraseIdx;

-		c=cluster;

-		data=c.c.getEdgesForPhrase(phrase);

-		n_param=data.size()*c.K;

-		//System.out.println("Num parameters " + n_param + " for phrase #" + phraseIdx);

-		

-		if (lambda==null) 

-			lambda=new double[n_param];

-		

-		parameters = lambda;

-		newPoint = new double[n_param];

-		gradient = new double[n_param];

-		initP();

-		projection=new SimplexProjection(scale);

-		q=new double [data.size()][c.K];

-

-		setParameters(parameters);

-	}

-

-	private void initP(){

-		p=new double[data.size()][];

-		for(int edge=0;edge<data.size();edge++){

-			p[edge]=c.posterior(data.get(edge));

-			llh += data.get(edge).getCount() * Math.log(arr.F.l1norm(p[edge])); // Was bug here - count inside log!

-			arr.F.l1normalize(p[edge]);

-		}

-	}

-	

-	@Override

-	public void setParameters(double[] params) {

-		super.setParameters(params);

-		updateFunction();

-	}

-	

-	private void updateFunction(){

-		updateCalls++;

-		loglikelihood=0;

-

-		for(int tag=0;tag<c.K;tag++){

-			for(int edge=0;edge<data.size();edge++){

-				q[edge][tag]=p[edge][tag]*

-					Math.exp(-parameters[tag*data.size()+edge]/data.get(edge).getCount());

-			}

-		}

-	

-		for(int edge=0;edge<data.size();edge++){

-			loglikelihood+=data.get(edge).getCount() * Math.log(arr.F.l1norm(q[edge]));

-			arr.F.l1normalize(q[edge]);

-		}

-		

-		for(int tag=0;tag<c.K;tag++){

-			for(int edge=0;edge<data.size();edge++){

-				gradient[tag*data.size()+edge]=-q[edge][tag];

-			}

-		}

-	}

-	

-	@Override

-	public double[] projectPoint(double[] point) 

-	{

-		double toProject[]=new double[data.size()];

-		for(int tag=0;tag<c.K;tag++){

-			for(int edge=0;edge<data.size();edge++){

-				toProject[edge]=point[tag*data.size()+edge];

-			}

-			projection.project(toProject);

-			for(int edge=0;edge<data.size();edge++){

-				newPoint[tag*data.size()+edge]=toProject[edge];

-			}

-		}

-		return newPoint;

-	}

-

-	@Override

-	public double[] getGradient() {

-		gradientCalls++;

-		return gradient;

-	}

-

-	@Override

-	public double getValue() {

-		functionCalls++;

-		return loglikelihood;

-	}

-

-	@Override

-	public String toString() {

-		return Arrays.toString(parameters);

-	}

-

-	public double [][]posterior(){

-		return q;

-	}

-	

-	long optimizationTime;

-	

-	public boolean optimizeWithProjectedGradientDescent(){

-		long start = System.currentTimeMillis();

-		

-		LineSearchMethod ls =

-			new ArmijoLineSearchMinimizationAlongProjectionArc

-				(new InterpolationPickFirstStep(INIT_STEP_SIZE));

-		//LineSearchMethod  ls = new WolfRuleLineSearch(

-		//		(new InterpolationPickFirstStep(INIT_STEP_SIZE)), c1, c2);

-		OptimizerStats stats = new OptimizerStats();

-		

-		

-		ProjectedGradientDescent optimizer = new ProjectedGradientDescent(ls);

-		StopingCriteria stopGrad = new ProjectedGradientL2Norm(GRAD_DIFF);

-		StopingCriteria stopValue = new ValueDifference(VAL_DIFF*(-llh));

-		CompositeStopingCriteria compositeStop = new CompositeStopingCriteria();

-		compositeStop.add(stopGrad);

-		compositeStop.add(stopValue);

-		optimizer.setMaxIterations(ITERATIONS);

-		updateFunction();

-		boolean success = optimizer.optimize(this,stats,compositeStop);

-		//System.out.println("Ended optimzation Projected Gradient Descent\n" + stats.prettyPrint(1));

-		//if(succed){

-			//System.out.println("Ended optimization in " + optimizer.getCurrentIteration());

-		//}else{

-//			System.out.println("Failed to optimize");

-		//}

-		//System.out.println(Arrays.toString(parameters));

-		

-		//	for(int edge=0;edge<data.getSize();edge++){

-		//	ps.println(Arrays.toString(q[edge]));

-		//	}

-

-		return success;

-	}

-	

-	public double KL_divergence()

-	{

-		return -loglikelihood + MathUtils.dotProduct(parameters, gradient);

-	}

-	

-	public double loglikelihood()

-	{

-		return llh;

-	}

-	

-	public double l1lmax()

-	{

-		double sum=0;

-		for(int tag=0;tag<c.K;tag++){

-			double max=0;

-			for(int edge=0;edge<data.size();edge++){

-				if(q[edge][tag]>max)

-					max=q[edge][tag];

-			}

-			sum+=max;

-		}

-		return sum;

-	}

-

-	public double primal(double scale)

-	{

-		return loglikelihood() - KL_divergence() - scale * l1lmax();	

-	}

-}

diff --git a/gi/posterior-regularisation/prjava/src/phrase/Trainer.java b/gi/posterior-regularisation/prjava/src/phrase/Trainer.java
deleted file mode 100644
index 6f302b20..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/Trainer.java
+++ /dev/null
@@ -1,257 +0,0 @@
-package phrase;
-
-import io.FileUtil;
-import joptsimple.OptionParser;
-import joptsimple.OptionSet;
-import java.io.File;
-import java.io.IOException;
-import java.io.PrintStream;
-import java.util.List;
-import java.util.Random;
-import java.util.concurrent.ExecutorService;
-import java.util.concurrent.Executors;
-
-import phrase.Corpus.Edge;
-
-import arr.F;
-
-public class Trainer 
-{
-	public static void main(String[] args) 
-	{
-        OptionParser parser = new OptionParser();
-        parser.accepts("help");
-        parser.accepts("in").withRequiredArg().ofType(File.class);
-        parser.accepts("in1").withRequiredArg().ofType(File.class);
-        parser.accepts("test").withRequiredArg().ofType(File.class);
-        parser.accepts("out").withRequiredArg().ofType(File.class);
-        parser.accepts("start").withRequiredArg().ofType(File.class);
-        parser.accepts("parameters").withRequiredArg().ofType(File.class);
-        parser.accepts("topics").withRequiredArg().ofType(Integer.class).defaultsTo(5);
-        parser.accepts("iterations").withRequiredArg().ofType(Integer.class).defaultsTo(10);
-        parser.accepts("threads").withRequiredArg().ofType(Integer.class).defaultsTo(0);
-        parser.accepts("scale-phrase").withRequiredArg().ofType(Double.class).defaultsTo(0.0);
-        parser.accepts("scale-context").withRequiredArg().ofType(Double.class).defaultsTo(0.0);
-        parser.accepts("seed").withRequiredArg().ofType(Long.class).defaultsTo(0l);
-        parser.accepts("convergence-threshold").withRequiredArg().ofType(Double.class).defaultsTo(1e-6);
-        parser.accepts("variational-bayes");
-        parser.accepts("alpha-emit").withRequiredArg().ofType(Double.class).defaultsTo(0.1);
-        parser.accepts("alpha-pi").withRequiredArg().ofType(Double.class).defaultsTo(0.0001);
-        parser.accepts("agree-direction");
-        parser.accepts("agree-language");
-        parser.accepts("no-parameter-cache");
-        parser.accepts("skip-large-phrases").withRequiredArg().ofType(Integer.class).defaultsTo(5);
-        OptionSet options = parser.parse(args);
-
-        if (options.has("help") || !options.has("in"))
-        {
-        	try {
-				parser.printHelpOn(System.err);
-			} catch (IOException e) {
-				System.err.println("This should never happen.");
-				e.printStackTrace();
-			}
-        	System.exit(1);     
-        }
-		
-		int tags = (Integer) options.valueOf("topics");
-		int iterations = (Integer) options.valueOf("iterations");
-		double scale_phrase = (Double) options.valueOf("scale-phrase");
-		double scale_context = (Double) options.valueOf("scale-context");
-		int threads = (Integer) options.valueOf("threads");
-		double threshold = (Double) options.valueOf("convergence-threshold");
-		boolean vb = options.has("variational-bayes");
-		double alphaEmit = (vb) ? (Double) options.valueOf("alpha-emit") : 0;
-		double alphaPi = (vb) ? (Double) options.valueOf("alpha-pi") : 0;
-		int skip = (Integer) options.valueOf("skip-large-phrases");
-		
-		if (options.has("seed"))
-			F.rng = new Random((Long) options.valueOf("seed"));
-		
-		ExecutorService threadPool = null;
-		if (threads > 0)
-			threadPool = Executors.newFixedThreadPool(threads);			
-		
-		if (tags <= 1 || scale_phrase < 0 || scale_context < 0 || threshold < 0)
-		{
-			System.err.println("Invalid arguments. Try again!");
-			System.exit(1);
-		}
-		
-		Corpus corpus = null;
-		File infile = (File) options.valueOf("in");
-		Corpus corpus1 = null;
-		File infile1 = (File) options.valueOf("in1");
-		try {
-			System.out.println("Reading concordance from " + infile);
-			corpus = Corpus.readFromFile(FileUtil.reader(infile));
-			corpus.printStats(System.out);
-			if(options.has("in1")){
-				corpus1 = Corpus.readFromFile(FileUtil.reader(infile1));
-				corpus1.printStats(System.out);
-			}
-		} catch (IOException e) {
-			System.err.println("Failed to open input file: " + infile);
-			e.printStackTrace();
-			System.exit(1);
-		}
-				
-		if (!(options.has("agree-direction")||options.has("agree-language")))
-			System.out.println("Running with " + tags + " tags " +
-					"for " + iterations + " iterations " +
-					((skip > 0) ? "skipping large phrases for first " + skip + " iterations " : "") +
-					"with scale " + scale_phrase + " phrase and " + scale_context + " context " +
-					"and " + threads + " threads");
-		else
-			System.out.println("Running agreement model with " + tags + " tags " +
-	 				"for " + iterations);
-
-	 	System.out.println();
-		
- 		PhraseCluster cluster = null;
- 		Agree2Sides agree2sides = null;
- 		Agree agree= null;
- 		VB vbModel=null;
- 		if (options.has("agree-language"))
- 			agree2sides = new Agree2Sides(tags, corpus,corpus1);
- 		else if (options.has("agree-direction"))
- 			agree = new Agree(tags, corpus);
- 		else
- 		{
- 			if (vb)	
- 			{
- 				vbModel=new VB(tags,corpus);
- 				vbModel.alpha=alphaPi;
- 				vbModel.lambda=alphaEmit;
- 	 			if (threadPool != null) vbModel.useThreadPool(threadPool);
- 			} 
- 			else 
- 			{
- 				cluster = new PhraseCluster(tags, corpus);
- 	 			if (threadPool != null) cluster.useThreadPool(threadPool);
- 				
-	 			if (options.has("no-parameter-cache")) 
-	 				cluster.cacheLambda = false;
-	 			if (options.has("start"))
-	 			{
-	 				try {
-						System.err.println("Reading starting parameters from " + options.valueOf("start"));
-						cluster.loadParameters(FileUtil.reader((File)options.valueOf("start")));
-					} catch (IOException e) {
-						System.err.println("Failed to open input file: " + options.valueOf("start"));
-						e.printStackTrace();
-					}
-	 			}
- 			}
- 		}
-				
-		double last = 0;
-		for (int i=0; i < iterations; i++)
-		{
-			double o;
-			if (agree != null)
-				o = agree.EM();
-			else if(agree2sides!=null)
-				o = agree2sides.EM();
-			else
-			{
-				if (i < skip)
-					System.out.println("Skipping phrases of length > " + (i+1));
-				
-				if (scale_phrase <= 0 && scale_context <= 0)
-				{
-					if (!vb)
-						o = cluster.EM((i < skip) ? i+1 : 0);
-					else
-						o = vbModel.EM();	
-				}
-				else
-					o = cluster.PREM(scale_phrase, scale_context, (i < skip) ? i+1 : 0);
-			}
-			
-			System.out.println("ITER: "+i+" objective: " + o);
-			
-			// sometimes takes a few iterations to break the ties
-			if (i > 5 && Math.abs((o - last) / o) < threshold)
-			{
-				last = o;
-				break;
-			}
-			last = o;
-		}
-		
-		double pl1lmax = 0, cl1lmax = 0;
-		if (cluster != null)
-		{
-			pl1lmax = cluster.phrase_l1lmax();
-			cl1lmax = cluster.context_l1lmax();
-		}
-		else if (agree != null)
-		{
-			// fairly arbitrary choice of model1 cf model2
-			pl1lmax = agree.model1.phrase_l1lmax();
-			cl1lmax = agree.model1.context_l1lmax();
-		}
-		else if (agree2sides != null)
-		{
-			// fairly arbitrary choice of model1 cf model2
-			pl1lmax = agree2sides.model1.phrase_l1lmax();
-			cl1lmax = agree2sides.model1.context_l1lmax();
-		}
-
-		System.out.println("\nFinal posterior phrase l1lmax " + pl1lmax + " context l1lmax " + cl1lmax);
-		
-		if (options.has("out"))
-		{
-			File outfile = (File) options.valueOf("out");
-			try {
-				PrintStream ps = FileUtil.printstream(outfile);
-				List<Edge> test;
-				if (!options.has("test")) // just use the training
-					test = corpus.getEdges();
-				else
-				{	// if --test supplied, load up the file
-					infile = (File) options.valueOf("test");
-					System.out.println("Reading testing concordance from " + infile);
-					test = corpus.readEdges(FileUtil.reader(infile));
-				}
-				if(vb) {
-					assert !options.has("test");
-					vbModel.displayPosterior(ps);
-				} else if (cluster != null) 
-					cluster.displayPosterior(ps, test);
-				else if (agree != null) 
-					agree.displayPosterior(ps, test);
-				else if (agree2sides != null) {
-					assert !options.has("test");
-					agree2sides.displayPosterior(ps);
-				}
-				
-				ps.close();
-			} catch (IOException e) {
-				System.err.println("Failed to open either testing file or output file");
-				e.printStackTrace();
-				System.exit(1);
-			}
-		}
-
-		if (options.has("parameters"))
-		{
-			assert !vb;
-			File outfile = (File) options.valueOf("parameters");
-			PrintStream ps;
-			try {
-				ps = FileUtil.printstream(outfile);
-				cluster.displayModelParam(ps);
-				ps.close();
-			} catch (IOException e) {
-				System.err.println("Failed to open output parameters file: " + outfile);
-				e.printStackTrace();
-				System.exit(1);
-			}
-		}
-		
-		if (cluster != null && cluster.pool != null)
-			cluster.pool.shutdown();
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/phrase/VB.java b/gi/posterior-regularisation/prjava/src/phrase/VB.java
deleted file mode 100644
index cd3f4966..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/VB.java
+++ /dev/null
@@ -1,419 +0,0 @@
-package phrase;

-

-import gnu.trove.TIntArrayList;

-

-import io.FileUtil;

-

-import java.io.File;

-import java.io.IOException;

-import java.io.PrintStream;

-import java.util.ArrayList;

-import java.util.Arrays;

-import java.util.List;

-import java.util.concurrent.Callable;

-import java.util.concurrent.ExecutionException;

-import java.util.concurrent.ExecutorService;

-import java.util.concurrent.Future;

-

-import org.apache.commons.math.special.Gamma;

-

-import phrase.Corpus.Edge;

-

-public class VB {

-

-	public static int MAX_ITER=400;

-	

-	/**@brief

-	 * hyper param for beta

-	 * where beta is multinomial

-	 * for generating words from a topic

-	 */

-	public double lambda=0.1;

-	/**@brief

-	 * hyper param for theta

-	 * where theta is dirichlet for z

-	 */

-	public double alpha=0.0001;

-	/**@brief

-	 * variational param for beta

-	 */

-	private double rho[][][];

-	private double digamma_rho[][][];

-	private double rho_sum[][];

-	/**@brief

-	 * variational param for z

-	 */

-	//private double phi[][];

-	/**@brief

-	 * variational param for theta

-	 */

-	private double gamma[];

-	private static double VAL_DIFF_RATIO=0.005;

-	

-	private int n_positions;

-	private int n_words;

-	private int K;

-	private ExecutorService pool;

-	

-	private Corpus c;

-	public static void main(String[] args) {

-	//	String in="../pdata/canned.con";

-		String in="../pdata/btec.con";

-		String out="../pdata/vb.out";

-		int numCluster=25;

-		Corpus corpus = null;

-		File infile = new File(in);

-		try {

-			System.out.println("Reading concordance from " + infile);

-			corpus = Corpus.readFromFile(FileUtil.reader(infile));

-			corpus.printStats(System.out);

-		} catch (IOException e) {

-			System.err.println("Failed to open input file: " + infile);

-			e.printStackTrace();

-			System.exit(1);

-		}

-		

-		VB vb=new VB(numCluster, corpus);

-		int iter=20;

-		for(int i=0;i<iter;i++){

-			double obj=vb.EM();

-			System.out.println("Iter "+i+": "+obj);

-		}

-		

-		File outfile = new File (out);

-		try {

-			PrintStream ps = FileUtil.printstream(outfile);

-			vb.displayPosterior(ps);

-		//	ps.println();

-		//	c2f.displayModelParam(ps);

-			ps.close();

-		} catch (IOException e) {

-			System.err.println("Failed to open output file: " + outfile);

-			e.printStackTrace();

-			System.exit(1);

-		}

-	}

-

-	public VB(int numCluster, Corpus corpus){

-		c=corpus;

-		K=numCluster;

-		n_positions=c.getNumContextPositions();

-		n_words=c.getNumWords();

-		rho=new double[K][n_positions][n_words];

-		//to init rho

-		//loop through data and count up words

-		double[] phi_tmp=new double[K];

-		for(int i=0;i<K;i++){

-			for(int pos=0;pos<n_positions;pos++){

-				Arrays.fill(rho[i][pos], lambda);

-			}

-		}

-		for(int d=0;d<c.getNumPhrases();d++){

-			List<Edge>doc=c.getEdgesForPhrase(d);

-			for(int n=0;n<doc.size();n++){

-				TIntArrayList context=doc.get(n).getContext();

-				arr.F.randomise(phi_tmp);

-				for(int i=0;i<K;i++){

-					for(int pos=0;pos<n_positions;pos++){

-						rho[i][pos][context.get(pos)]+=phi_tmp[i];

-					}

-				}

-			}

-		}

-		

-	}

-	

-	private double inference(int phraseID, double[][] phi, double[] gamma)

-	{

-		List<Edge > doc=c.getEdgesForPhrase(phraseID);

-		for(int i=0;i<phi.length;i++){

-			for(int j=0;j<phi[i].length;j++){

-				phi[i][j]=1.0/K;

-			}

-		}

-		Arrays.fill(gamma,alpha+1.0/K);

-		

-		double digamma_gamma[]=new double[K];

-		

-		double gamma_sum=digamma(arr.F.l1norm(gamma));

-		for(int i=0;i<K;i++){

-			digamma_gamma[i]=digamma(gamma[i]);

-		}

-		double gammaSum[]=new double [K];

-		double prev_val=0;

-		double obj=0;

-		

-		for(int iter=0;iter<MAX_ITER;iter++){

-			prev_val=obj;

-			obj=0;

-			Arrays.fill(gammaSum,0.0);

-			for(int n=0;n<doc.size();n++){

-				TIntArrayList context=doc.get(n).getContext();

-				double phisum=0;

-				for(int i=0;i<K;i++){

-					double sum=0;

-					for(int pos=0;pos<n_positions;pos++){

-						int word=context.get(pos);

-						sum+=digamma_rho[i][pos][word]-rho_sum[i][pos];

-					}

-					sum+= digamma_gamma[i]-gamma_sum;

-					phi[n][i]=sum;

-					

-					if (i > 0){

-	                    phisum = log_sum(phisum, phi[n][i]);

-					}

-	                else{

-	                    phisum = phi[n][i];

-	                }

-					

-				}//end of  a word

-				

-				for(int i=0;i<K;i++){

-					phi[n][i]=Math.exp(phi[n][i]-phisum);

-					gammaSum[i]+=phi[n][i];

-				}

-				

-			}//end of doc

-			

-			for(int i=0;i<K;i++){

-				gamma[i]=alpha+gammaSum[i];

-			}

-			gamma_sum=digamma(arr.F.l1norm(gamma));

-			for(int i=0;i<K;i++){

-				digamma_gamma[i]=digamma(gamma[i]);

-			}

-			//compute objective for reporting

-

-			obj=0;

-			

-			for(int i=0;i<K;i++){

-				obj+=(alpha-1)*(digamma_gamma[i]-gamma_sum);

-			}

-			

-			

-			for(int n=0;n<doc.size();n++){

-				TIntArrayList context=doc.get(n).getContext();

-				

-				for(int i=0;i<K;i++){

-					//entropy of phi + expected log likelihood of z

-					obj+=phi[n][i]*(digamma_gamma[i]-gamma_sum);

-					

-					if(phi[n][i]>1e-10){

-						obj+=phi[n][i]*Math.log(phi[n][i]);

-					}

-					

-					double beta_sum=0;

-					for(int pos=0;pos<n_positions;pos++){

-						int word=context.get(pos);

-						beta_sum+=(digamma(rho[i][pos][word])-rho_sum[i][pos]);

-					}

-					obj+=phi[n][i]*beta_sum;

-				}

-			}

-			

-			obj-=log_gamma(arr.F.l1norm(gamma));

-			for(int i=0;i<K;i++){

-				obj+=Gamma.logGamma(gamma[i]);

-				obj-=(gamma[i]-1)*(digamma_gamma[i]-gamma_sum);

-			}

-			

-//			System.out.println(phraseID+": "+obj);

-			if(iter>0 && (obj-prev_val)/Math.abs(obj)<VAL_DIFF_RATIO){

-				break;

-			}

-		}//end of inference loop

-		

-		return obj;

-	}//end of inference

-	

-	/**

-	 * @return objective of this iteration

-	 */

-	public double EM(){

-		double emObj=0;

-		if(digamma_rho==null){

-			digamma_rho=new double[K][n_positions][n_words];

-		}

-		for(int i=0;i<K;i++){

-			for (int pos=0;pos<n_positions;pos++){

-				for(int j=0;j<n_words;j++){

-					digamma_rho[i][pos][j]= digamma(rho[i][pos][j]);

-				}

-			}

-		}

-		

-		if(rho_sum==null){

-			rho_sum=new double [K][n_positions];

-		}

-		for(int i=0;i<K;i++){

-			for(int pos=0;pos<n_positions;pos++){

-				rho_sum[i][pos]=digamma(arr.F.l1norm(rho[i][pos]));

-			}

-		}

-

-		//E

-		double exp_rho[][][]=new double[K][n_positions][n_words];

-		if (pool == null)

-		{

-			for (int d=0;d<c.getNumPhrases();d++)

-			{		

-				List<Edge > doc=c.getEdgesForPhrase(d);

-				double[][] phi = new double[doc.size()][K];

-				double[] gamma = new double[K];

-				

-				emObj += inference(d, phi, gamma);

-				

-				for(int n=0;n<doc.size();n++){

-					TIntArrayList context=doc.get(n).getContext();

-					for(int pos=0;pos<n_positions;pos++){

-						int word=context.get(pos);

-						for(int i=0;i<K;i++){	

-							exp_rho[i][pos][word]+=phi[n][i];

-						}

-					}

-				}

-				//if(d!=0 && d%100==0)  System.out.print(".");

-				//if(d!=0 && d%1000==0) System.out.println(d);

-			}

-		}

-		else // multi-threaded version of above loop

-		{

-			class PartialEStep implements Callable<PartialEStep>

-			{

-				double[][] phi;

-				double[] gamma;

-				double obj;

-				int d;

-				PartialEStep(int d) { this.d = d; }

-

-				public PartialEStep call()

-				{

-					phi = new double[c.getEdgesForPhrase(d).size()][K];

-					gamma = new double[K];

-					obj = inference(d, phi, gamma);

-					return this;

-				}			

-			}

-

-			List<Future<PartialEStep>> jobs = new ArrayList<Future<PartialEStep>>();

-			for (int d=0;d<c.getNumPhrases();d++)

-				jobs.add(pool.submit(new PartialEStep(d)));

-		

-			for (Future<PartialEStep> job: jobs)

-			{

-				try {

-					PartialEStep e = job.get();

-					

-					emObj += e.obj;				

-					List<Edge> doc = c.getEdgesForPhrase(e.d);

-					for(int n=0;n<doc.size();n++){

-						TIntArrayList context=doc.get(n).getContext();

-						for(int pos=0;pos<n_positions;pos++){

-							int word=context.get(pos);

-							for(int i=0;i<K;i++){	

-								exp_rho[i][pos][word]+=e.phi[n][i];

-							}

-						}

-					}

-				} catch (ExecutionException e) {

-					System.err.println("ERROR: E-step thread execution failed.");

-					throw new RuntimeException(e);

-				} catch (InterruptedException e) {

-					System.err.println("ERROR: Failed to join E-step thread.");

-					throw new RuntimeException(e);

-				}

-			}

-		}	

-	//	System.out.println("EM Objective:"+emObj);

-		

-		//M

-		for(int i=0;i<K;i++){

-			for(int pos=0;pos<n_positions;pos++){

-				for(int j=0;j<n_words;j++){

-					rho[i][pos][j]=lambda+exp_rho[i][pos][j];

-				}

-			}

-		}

-		

-		//E[\log p(\beta|\lambda)] - E[\log q(\beta)]

-		for(int i=0;i<K;i++){

-			double rhoSum=0;

-			for(int pos=0;pos<n_positions;pos++){

-				for(int j=0;j<n_words;j++){

-					rhoSum+=rho[i][pos][j];

-				}

-				double digamma_rhoSum=Gamma.digamma(rhoSum);

-				emObj-=Gamma.logGamma(rhoSum);

-				for(int j=0;j<n_words;j++){

-					emObj+=(lambda-rho[i][pos][j])*(Gamma.digamma(rho[i][pos][j])-digamma_rhoSum);

-					emObj+=Gamma.logGamma(rho[i][pos][j]);

-				}

-			}

-		}

-		

-		return emObj;

-	}//end of EM

-	

-	public void displayPosterior(PrintStream ps)

-	{	

-		for(int d=0;d<c.getNumPhrases();d++){

-			List<Edge > doc=c.getEdgesForPhrase(d);

-			double[][] phi = new double[doc.size()][K];

-			for(int i=0;i<phi.length;i++)

-				for(int j=0;j<phi[i].length;j++)

-					phi[i][j]=1.0/K;

-			double[] gamma = new double[K];

-

-			inference(d, phi, gamma);

-

-			for(int n=0;n<doc.size();n++){

-				Edge edge=doc.get(n);

-				int tag=arr.F.argmax(phi[n]);

-				ps.print(edge.getPhraseString());

-				ps.print("\t");

-				ps.print(edge.getContextString(true));

-

-				ps.println(" ||| C=" + tag);

-			}

-		}

-	}

-

-	double log_sum(double log_a, double log_b)

-	{

-	  double v;

-

-	  if (log_a < log_b)

-	      v = log_b+Math.log(1 + Math.exp(log_a-log_b));

-	  else

-	      v = log_a+Math.log(1 + Math.exp(log_b-log_a));

-	  return(v);

-	}

-		

-	double digamma(double x)

-	{

-	    double p;

-	    x=x+6;

-	    p=1/(x*x);

-	    p=(((0.004166666666667*p-0.003968253986254)*p+

-		0.008333333333333)*p-0.083333333333333)*p;

-	    p=p+Math.log(x)-0.5/x-1/(x-1)-1/(x-2)-1/(x-3)-1/(x-4)-1/(x-5)-1/(x-6);

-	    return p;

-	}

-	

-	double log_gamma(double x)

-	{

-	     double z=1/(x*x);

-

-	    x=x+6;

-	    z=(((-0.000595238095238*z+0.000793650793651)

-		*z-0.002777777777778)*z+0.083333333333333)/x;

-	    z=(x-0.5)*Math.log(x)-x+0.918938533204673+z-Math.log(x-1)-

-	    Math.log(x-2)-Math.log(x-3)-Math.log(x-4)-Math.log(x-5)-Math.log(x-6);

-	    return z;

-	}

-

-	public void useThreadPool(ExecutorService threadPool) 

-	{

-		pool = threadPool;

-	}

-}//End of  class