From e26434979adc33bd949566ba7bf02dff64e80a3e Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@cab.ark.cs.cmu.edu>
Date: Tue, 2 Oct 2012 00:19:43 -0400
Subject: cdec cleanup, remove bayesian stuff, parsing stuff

---
 .../prjava/src/hmm/HMM.java                        | 579 ---------------------
 .../prjava/src/hmm/HMMObjective.java               | 351 -------------
 .../prjava/src/hmm/POS.java                        | 120 -----
 3 files changed, 1050 deletions(-)
 delete mode 100644 gi/posterior-regularisation/prjava/src/hmm/HMM.java
 delete mode 100644 gi/posterior-regularisation/prjava/src/hmm/HMMObjective.java
 delete mode 100644 gi/posterior-regularisation/prjava/src/hmm/POS.java

(limited to 'gi/posterior-regularisation/prjava/src/hmm')

diff --git a/gi/posterior-regularisation/prjava/src/hmm/HMM.java b/gi/posterior-regularisation/prjava/src/hmm/HMM.java
deleted file mode 100644
index 17a4679f..00000000
--- a/gi/posterior-regularisation/prjava/src/hmm/HMM.java
+++ /dev/null
@@ -1,579 +0,0 @@
-package hmm;
-
-import java.io.File;
-import java.io.FileNotFoundException;
-import java.io.IOException;
-import java.io.PrintStream;
-import java.util.ArrayList;
-import java.util.Scanner;
-
-public class HMM {
-
-	
-	//trans[i][j]=prob of going FROM i to j
-	double [][]trans;
-	double [][]emit;
-	double []pi;
-	int  [][]data;
-	int [][]tagdata;
-	
-	double logtrans[][];
-	
-	public HMMObjective o;
-	
-	public static void main(String[] args) {
-	
-	}
-	
-	public HMM(int n_state,int n_emit,int [][]data){
-		trans=new double [n_state][n_state];
-		emit=new double[n_state][n_emit];
-		pi=new double [n_state];
-		System.out.println(" random initial parameters");
-		fillRand(trans);
-		fillRand(emit);
-		fillRand(pi);
-
-		this.data=data;
-		
-	}
-	
-	private void fillRand(double [][] a){
-		for(int i=0;i<a.length;i++){
-			for(int j=0;j<a[i].length;j++){
-				a[i][j]=Math.random();
-			}
-			l1normalize(a[i]);
-		}
-	}
-	private void fillRand(double []a){
-		for(int i=0;i<a.length;i++){
-				a[i]=Math.random();
-		}
-		l1normalize(a);
-	}
-	
-	private double loglikely=0;
-	
-	public void EM(){
-		double trans_exp_cnt[][]=new double [trans.length][trans.length];
-		double emit_exp_cnt[][]=new double[trans.length][emit[0].length];
-		double start_exp_cnt[]=new double[trans.length];
-		loglikely=0;
-		
-		//E
-		for(int i=0;i<data.length;i++){
-			
-			double [][][] post=forwardBackward(data[i]);
-			incrementExpCnt(post, data[i], 
-					trans_exp_cnt,
-					emit_exp_cnt,
-					start_exp_cnt);
-			
-			
-			if(i%100==0){
-				System.out.print(".");
-			}
-			if(i%1000==0){
-				System.out.println(i);
-			}
-			
-		}
-		System.out.println("Log likelihood: "+loglikely);
-		
-		//M
-		addOneSmooth(emit_exp_cnt);
-		for(int i=0;i<trans.length;i++){
-		
-			//transition probs
-			double sum=0;
-			for(int j=0;j<trans.length;j++){
-				sum+=trans_exp_cnt[i][j];
-			}
-			//avoid NAN
-			if(sum==0){
-				sum=1;
-			}
-			for(int j=0;j<trans[i].length;j++){
-				trans[i][j]=trans_exp_cnt[i][j]/sum;
-			}
-			
-			//emission probs
-			
-			sum=0;
-			for(int j=0;j<emit[i].length;j++){
-				sum+=emit_exp_cnt[i][j];
-			}
-			//avoid NAN
-			if(sum==0){
-				sum=1;
-			}
-			for(int j=0;j<emit[i].length;j++){
-				emit[i][j]=emit_exp_cnt[i][j]/sum;
-			}
-			
-			
-			//initial probs
-			for(int j=0;j<pi.length;j++){
-				pi[j]=start_exp_cnt[j];
-			}
-			l1normalize(pi);
-		}
-	}
-	
-	private double [][][]forwardBackward(int [] seq){
-		double a[][]=new double [seq.length][trans.length];
-		double b[][]=new double [seq.length][trans.length];
-		
-		int len=seq.length;
-		//initialize the first step
-		for(int i=0;i<trans.length;i++){
-			a[0][i]=emit[i][seq[0]]*pi[i];
-			b[len-1][i]=1;
-		}
-		
-		//log of denominator for likelyhood
-		double c=Math.log(l1norm(a[0]));
-		
-		l1normalize(a[0]);
-		l1normalize(b[len-1]);
-		
-		
-		
-		//forward
-		for(int n=1;n<len;n++){
-			for(int i=0;i<trans.length;i++){
-				for(int j=0;j<trans.length;j++){
-					a[n][i]+=trans[j][i]*a[n-1][j];
-				}
-				a[n][i]*=emit[i][seq[n]];
-			}
-			c+=Math.log(l1norm(a[n]));
-			l1normalize(a[n]);
-		}
-		
-		loglikely+=c;
-		
-		//backward
-		for(int n=len-2;n>=0;n--){
-			for(int i=0;i<trans.length;i++){
-				for(int j=0;j<trans.length;j++){
-					b[n][i]+=trans[i][j]*b[n+1][j]*emit[j][seq[n+1]];
-				}
-			}
-			l1normalize(b[n]);
-		}
-		
-		
-		//expected transition 
-		double p[][][]=new double [seq.length][trans.length][trans.length];
-		for(int n=0;n<len-1;n++){
-			for(int i=0;i<trans.length;i++){
-				for(int j=0;j<trans.length;j++){
-					p[n][i][j]=a[n][i]*trans[i][j]*emit[j][seq[n+1]]*b[n+1][j];
-					
-				}
-			}
-
-			l1normalize(p[n]);
-		}
-		return p;
-	}
-	
-	private void incrementExpCnt(
-			double post[][][],int [] seq, 
-			double trans_exp_cnt[][],
-			double emit_exp_cnt[][],
-			double start_exp_cnt[])
-	{
-		
-		for(int n=0;n<post.length;n++){
-			for(int i=0;i<trans.length;i++){
-				double py=0;
-				for(int j=0;j<trans.length;j++){
-					py+=post[n][i][j];
-					trans_exp_cnt[i][j]+=post[n][i][j];
-				}
-
-				emit_exp_cnt[i][seq[n]]+=py;				
-				
-			}
-		}
-		
-		//the first state
-		for(int i=0;i<trans.length;i++){
-			double py=0;
-			for(int j=0;j<trans.length;j++){
-				py+=post[0][i][j];
-			}
-			start_exp_cnt[i]+=py;	
-		}
-		
-		
-		//the last state
-		int len=post.length;
-		for(int i=0;i<trans.length;i++){
-			double py=0;
-			for(int j=0;j<trans.length;j++){
-				py+=post[len-2][j][i];
-			}
-			emit_exp_cnt[i][seq[len-1]]+=py;	
-		}
-	}
-	
-	public void l1normalize(double [] a){
-		double sum=0;
-		for(int i=0;i<a.length;i++){
-			sum+=a[i];
-		}
-		if(sum==0){
-			return ;
-		}
-		for(int i=0;i<a.length;i++){
-			a[i]/=sum;
-		}
-	}
-	
-	public  void l1normalize(double [][] a){
-		double sum=0;
-		for(int i=0;i<a.length;i++){
-			for(int j=0;j<a[i].length;j++){
-				sum+=a[i][j];
-			}
-		}
-		if(sum==0){
-			return;
-		}
-		for(int i=0;i<a.length;i++){
-			for(int j=0;j<a[i].length;j++){
-				a[i][j]/=sum;
-			}
-		}
-	}
-	
-	public void writeModel(String modelFilename) throws FileNotFoundException, IOException{
-		PrintStream ps=io.FileUtil.printstream(new File(modelFilename));
-		ps.println(trans.length);
-		ps.println("Initial Probabilities:");
-		for(int i=0;i<pi.length;i++){
-			ps.print(pi[i]+"\t");
-		}
-		ps.println();
-		ps.println("Transition Probabilities:");
-		for(int i=0;i<trans.length;i++){
-			for(int j=0;j<trans[i].length;j++){
-				ps.print(trans[i][j]+"\t");
-			}
-			ps.println();
-		}
-		ps.println("Emission Probabilities:");
-		ps.println(emit[0].length);
-		for(int i=0;i<trans.length;i++){
-			for(int j=0;j<emit[i].length;j++){
-				ps.println(emit[i][j]);
-			}
-			ps.println();
-		}
-		ps.close();
-	}
-	
-	public HMM(){
-	
-	}
-	
-	public void readModel(String modelFilename){
-		Scanner sc=io.FileUtil.openInFile(modelFilename);
-	
-		int n_state=sc.nextInt();
-		sc.nextLine();
-		sc.nextLine();
-		pi=new double [n_state];
-		for(int i=0;i<n_state;i++){
-			pi[i]=sc.nextDouble();
-		}
-		sc.nextLine();
-		sc.nextLine();
-		trans=new double[n_state][n_state];
-		for(int i=0;i<trans.length;i++){
-			for(int j=0;j<trans[i].length;j++){
-				trans[i][j]=sc.nextDouble();
-			}
-		}
-		sc.nextLine();
-		sc.nextLine();
-		
-		int n_obs=sc.nextInt();
-		emit=new double[n_state][n_obs];
-		for(int i=0;i<trans.length;i++){
-			for(int j=0;j<emit[i].length;j++){
-				emit[i][j]=sc.nextDouble();
-			}
-		}
-		sc.close();
-	}
-	
-	public int []viterbi(int [] seq){
-		double [][]p=new double [seq.length][trans.length];
-		int backp[][]=new int [seq.length][trans.length];
-		
-		for(int i=0;i<trans.length;i++){
-			p[0][i]=Math.log(emit[i][seq[0]]*pi[i]);
-		}
-		
-		double a[][]=logtrans;
-		if(logtrans==null){
-			a=new double [trans.length][trans.length];
-			for(int i=0;i<trans.length;i++){
-				for(int j=0;j<trans.length;j++){
-					a[i][j]=Math.log(trans[i][j]);
-				}
-			}
-			logtrans=a;
-		}
-		
-		double maxprob=0;
-		for(int n=1;n<seq.length;n++){
-			for(int i=0;i<trans.length;i++){
-				maxprob=p[n-1][0]+a[0][i];
-				backp[n][i]=0;
-				for(int j=1;j<trans.length;j++){
-					double prob=p[n-1][j]+a[j][i];
-					if(maxprob<prob){
-						backp[n][i]=j;
-						maxprob=prob;
-					}
-				}
-				p[n][i]=maxprob+Math.log(emit[i][seq[n]]);
-			}
-		}
-		
-		maxprob=p[seq.length-1][0];
-		int maxIdx=0;
-		for(int i=1;i<trans.length;i++){
-			if(p[seq.length-1][i]>maxprob){
-				maxprob=p[seq.length-1][i];
-				maxIdx=i;
-			}
-		}
-		int ans[]=new int [seq.length];
-		ans[seq.length-1]=maxIdx;
-		for(int i=seq.length-2;i>=0;i--){
-			ans[i]=backp[i+1][ans[i+1]];
-		}
-		return ans;
-	}
-	
-	public double l1norm(double a[]){
-		double norm=0;
-		for(int i=0;i<a.length;i++){
-			norm += a[i];
-		}
-		return norm;
-	}
-	
-	public double [][]getEmitProb(){
-		return emit;
-	}
-	
-	public int [] sample(int terminalSym){
-		ArrayList<Integer > s=new ArrayList<Integer>();
-		int state=sample(pi);
-		int sym=sample(emit[state]);
-		while(sym!=terminalSym){
-			s.add(sym);
-			state=sample(trans[state]);
-			sym=sample(emit[state]);
-		}
-		
-		int ans[]=new int [s.size()];
-		for(int i=0;i<ans.length;i++){
-			ans[i]=s.get(i);
-		}
-		return ans;
-	}
-	
-	public int sample(double p[]){
-		double r=Math.random();
-		double sum=0;
-		for(int i=0;i<p.length;i++){
-			sum+=p[i];
-			if(sum>=r){
-				return i;
-			}
-		}
-		return p.length-1;
-	}
-	
-	public void train(int tagdata[][]){
-		double trans_exp_cnt[][]=new double [trans.length][trans.length];
-		double emit_exp_cnt[][]=new double[trans.length][emit[0].length];
-		double start_exp_cnt[]=new double[trans.length];
-		
-		for(int i=0;i<tagdata.length;i++){
-			start_exp_cnt[tagdata[i][0]]++;
-			
-			for(int j=0;j<tagdata[i].length;j++){
-				if(j+1<tagdata[i].length){
-					trans_exp_cnt[ tagdata[i][j] ] [ tagdata[i][j+1] ]++;
-				}
-				emit_exp_cnt[tagdata[i][j]][data[i][j]]++;
-			}
-			
-		}
-		
-		//M
-		addOneSmooth(emit_exp_cnt);
-		for(int i=0;i<trans.length;i++){
-		
-			//transition probs
-			double sum=0;
-			for(int j=0;j<trans.length;j++){
-				sum+=trans_exp_cnt[i][j];
-			}
-			if(sum==0){
-				sum=1;
-			}
-			for(int j=0;j<trans[i].length;j++){
-				trans[i][j]=trans_exp_cnt[i][j]/sum;
-			}
-			
-			//emission probs
-
-			sum=0;
-			for(int j=0;j<emit[i].length;j++){
-				sum+=emit_exp_cnt[i][j];
-			}
-			if(sum==0){
-				sum=1;
-			}
-			for(int j=0;j<emit[i].length;j++){
-				emit[i][j]=emit_exp_cnt[i][j]/sum;
-			}
-
-			
-			//initial probs
-			for(int j=0;j<pi.length;j++){
-				pi[j]=start_exp_cnt[j];
-			}
-			l1normalize(pi);
-		}
-	}
-	
-	private void addOneSmooth(double a[][]){
-		for(int i=0;i<a.length;i++){
-			for(int j=0;j<a[i].length;j++){
-				a[i][j]+=0.01;
-			}
-			//l1normalize(a[i]);
-		}
-	}
-	
-	public void PREM(){
-		
-		o.optimizeWithProjectedGradientDescent();
-		
-		double trans_exp_cnt[][]=new double [trans.length][trans.length];
-		double emit_exp_cnt[][]=new double[trans.length][emit[0].length];
-		double start_exp_cnt[]=new double[trans.length];
-		
-		o.loglikelihood=0;
-		//E
-		for(int sentNum=0;sentNum<data.length;sentNum++){
-			
-			double [][][] post=o.forwardBackward(sentNum);
-			incrementExpCnt(post, data[sentNum], 
-					trans_exp_cnt,
-					emit_exp_cnt,
-					start_exp_cnt);
-			
-			
-			if(sentNum%100==0){
-				System.out.print(".");
-			}
-			if(sentNum%1000==0){
-				System.out.println(sentNum);
-			}
-			
-		}
-		
-		System.out.println("Log likelihood: "+o.getValue());
-		
-		//M
-		addOneSmooth(emit_exp_cnt);
-		for(int i=0;i<trans.length;i++){
-		
-			//transition probs
-			double sum=0;
-			for(int j=0;j<trans.length;j++){
-				sum+=trans_exp_cnt[i][j];
-			}
-			//avoid NAN
-			if(sum==0){
-				sum=1;
-			}
-			for(int j=0;j<trans[i].length;j++){
-				trans[i][j]=trans_exp_cnt[i][j]/sum;
-			}
-			
-			//emission probs
-			
-			sum=0;
-			for(int j=0;j<emit[i].length;j++){
-				sum+=emit_exp_cnt[i][j];
-			}
-			//avoid NAN
-			if(sum==0){
-				sum=1;
-			}
-			for(int j=0;j<emit[i].length;j++){
-				emit[i][j]=emit_exp_cnt[i][j]/sum;
-			}
-			
-			
-			//initial probs
-			for(int j=0;j<pi.length;j++){
-				pi[j]=start_exp_cnt[j];
-			}
-			l1normalize(pi);
-		}
-		
-	}
-	
-	public void computeMaxwt(double[][]maxwt, int[][] d){
-
-		for(int sentNum=0;sentNum<d.length;sentNum++){
-			double post[][][]=forwardBackward(d[sentNum]);
-			
-			for(int n=0;n<post.length;n++){
-				for(int i=0;i<trans.length;i++){
-					double py=0;
-					for(int j=0;j<trans.length;j++){
-						py+=post[n][i][j];
-					}
-
-					if(py>maxwt[i][d[sentNum][n]]){
-						maxwt[i][d[sentNum][n]]=py;
-					}
-					
-				}
-			}
-			
-			//the last state
-			int len=post.length;
-			for(int i=0;i<trans.length;i++){
-				double py=0;
-				for(int j=0;j<trans.length;j++){
-					py+=post[len-2][j][i];
-				}
-				
-				if(py>maxwt[i][d[sentNum][len-1]]){
-					maxwt[i][d[sentNum][len-1]]=py;
-				}
-				
-			}
-			
-		}
-	
-	}
-	
-}//end of class
diff --git a/gi/posterior-regularisation/prjava/src/hmm/HMMObjective.java b/gi/posterior-regularisation/prjava/src/hmm/HMMObjective.java
deleted file mode 100644
index 70b6c966..00000000
--- a/gi/posterior-regularisation/prjava/src/hmm/HMMObjective.java
+++ /dev/null
@@ -1,351 +0,0 @@
-package hmm;
-
-import gnu.trove.TIntArrayList;
-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;
-
-public class HMMObjective extends ProjectedObjective{
-
-	
-	private static final double GRAD_DIFF = 3;
-	public static double INIT_STEP_SIZE=10;
-	public static double VAL_DIFF=1000;
-	
-	private HMM hmm;
-	double[] newPoint  ;
-	
-	//posterior[sent num][tok num][tag]=index into lambda
-	private int posteriorMap[][][];
-	//projection[word][tag].get(occurence)=index into lambda
-	private TIntArrayList projectionMap[][];
-
-	//Size of the simplex
-	public double scale=10;
-	private SimplexProjection projection;
-	
-	private int wordFreq[];
-	private static int MIN_FREQ=10;
-	private int numWordsToProject=0;
-	
-	private int n_param;
-	
-	public  double loglikelihood;
-	
-	public HMMObjective(HMM h){
-		hmm=h;
-		
-		countWords();
-		buildMap();
-
-		gradient=new double [n_param];
-		projection = new SimplexProjection(scale);
-		newPoint  = new double[n_param];
-		setInitialParameters(new double[n_param]);
-		
-	}
-	
-	/**@brief counts word frequency in the corpus
-	 * 
-	 */
-	private void countWords(){
-		wordFreq=new int [hmm.emit[0].length];
-		for(int i=0;i<hmm.data.length;i++){
-			for(int j=0;j<hmm.data[i].length;j++){
-				wordFreq[hmm.data[i][j]]++;
-			}
-		}
-	}
-	
-	/**@brief build posterior and projection indices
-	 * 
-	 */
-	private void buildMap(){
-		//number of sentences hidden states and words
-		int n_states=hmm.trans.length;
-		int n_words=hmm.emit[0].length;
-		int n_sents=hmm.data.length;
-		
-		n_param=0;
-		posteriorMap=new int[n_sents][][];
-		projectionMap=new TIntArrayList[n_words][];
-		for(int sentNum=0;sentNum<n_sents;sentNum++){
-			int [] data=hmm.data[sentNum];
-			posteriorMap[sentNum]=new int[data.length][n_states];
-			numWordsToProject=0;
-			for(int i=0;i<data.length;i++){
-				int word=data[i];
-				for(int state=0;state<n_states;state++){
-					if(wordFreq[word]>MIN_FREQ){
-						if(projectionMap[word]==null){
-							projectionMap[word]=new TIntArrayList[n_states];
-						}
-			//			if(posteriorMap[sentNum][i]==null){
-			//				posteriorMap[sentNum][i]=new int[n_states];
-			//			}
-						
-						posteriorMap[sentNum][i][state]=n_param;
-						if(projectionMap[word][state]==null){
-							projectionMap[word][state]=new TIntArrayList();
-							numWordsToProject++;
-						}
-						projectionMap[word][state].add(n_param);
-						n_param++;
-					}
-					else{
-						posteriorMap[sentNum][i][state]=-1;
-					}
-				}
-			}
-		}
-	}
-	
-	@Override
-	public double[] projectPoint(double[] point) {
-		// TODO Auto-generated method stub
-		for(int i=0;i<projectionMap.length;i++){
-			
-			if(projectionMap[i]==null){
-				//this word is not constrained
-				continue;
-			}
-			
-			for(int j=0;j<projectionMap[i].length;j++){
-				TIntArrayList instances=projectionMap[i][j];
-				double[] toProject = new double[instances.size()];
-				
-				for (int k = 0; k < toProject.length; k++) {
-					//	System.out.print(instances.get(k) + " ");
-						toProject[k] = point[instances.get(k)];
-				}
-				
-				projection.project(toProject);
-				for (int k = 0; k < toProject.length; k++) {
-					newPoint[instances.get(k)]=toProject[k];
-				}
-			}
-		}
-		return newPoint;
-	}
-
-	@Override
-	public double[] getGradient() {
-		// TODO Auto-generated method stub
-		gradientCalls++;
-		return gradient;
-	}
-
-	@Override
-	public double getValue() {
-		// TODO Auto-generated method stub
-		functionCalls++;
-		return loglikelihood;
-	}
-	
-
-	@Override
-	public String toString() {
-		// TODO Auto-generated method stub
-		StringBuffer sb = new StringBuffer();
-		for (int i = 0; i < parameters.length; i++) {
-			sb.append(parameters[i]+" ");
-			if(i%100==0){
-				sb.append("\n");
-			}
-		}
-		sb.append("\n");
-		/*
-		for (int i = 0; i < gradient.length; i++) {
-			sb.append(gradient[i]+" ");
-			if(i%100==0){
-				sb.append("\n");
-			}
-		}
-		sb.append("\n");
-		*/
-		return sb.toString();
-	}
-
-	
-	/**
-	 * @param seq
-	 * @return posterior probability of each transition
-	 */
-	public double [][][]forwardBackward(int sentNum){
-		int [] seq=hmm.data[sentNum];
-		int n_states=hmm.trans.length;
-		double a[][]=new double [seq.length][n_states];
-		double b[][]=new double [seq.length][n_states];
-		
-		int len=seq.length;
-		
-		boolean  constrained=
-			(projectionMap[seq[0]]!=null);
-
-		//initialize the first step
-		for(int i=0;i<n_states;i++){
-			a[0][i]=hmm.emit[i][seq[0]]*hmm.pi[i];
-			if(constrained){
-				a[0][i]*=
-					Math.exp(- parameters[ posteriorMap[sentNum][0][i] ] );
-			}
-			b[len-1][i]=1;
-		}
-		
-		loglikelihood+=Math.log(hmm.l1norm(a[0]));		
-		hmm.l1normalize(a[0]);
-		hmm.l1normalize(b[len-1]);
-		
-		//forward
-		for(int n=1;n<len;n++){
-			
-			constrained=
-				(projectionMap[seq[n]]!=null);
-			
-			for(int i=0;i<n_states;i++){
-				for(int j=0;j<n_states;j++){
-					a[n][i]+=hmm.trans[j][i]*a[n-1][j];
-				}
-				a[n][i]*=hmm.emit[i][seq[n]];
-				
-				if(constrained){
-					a[n][i]*=
-						Math.exp(- parameters[ posteriorMap[sentNum][n][i] ] );
-				}
-				
-			}
-			loglikelihood+=Math.log(hmm.l1norm(a[n]));
-			hmm.l1normalize(a[n]);
-		}
-		
-		//temp variable for e^{-\lambda}
-		double factor=1;
-		//backward
-		for(int n=len-2;n>=0;n--){
-			
-			constrained=
-				(projectionMap[seq[n+1]]!=null);
-			
-			for(int i=0;i<n_states;i++){
-				for(int j=0;j<n_states;j++){
-					
-					if(constrained){
-						factor=
-							Math.exp(- parameters[ posteriorMap[sentNum][n+1][j] ] );
-					}else{
-						factor=1;
-					}
-					
-					b[n][i]+=hmm.trans[i][j]*b[n+1][j]*hmm.emit[j][seq[n+1]]*factor;
-					
-				}
-			}
-			hmm.l1normalize(b[n]);
-		}
-		
-		//expected transition 
-		double p[][][]=new double [seq.length][n_states][n_states];
-		for(int n=0;n<len-1;n++){
-			
-			constrained=
-				(projectionMap[seq[n+1]]!=null);
-			
-			for(int i=0;i<n_states;i++){
-				for(int j=0;j<n_states;j++){
-					
-					if(constrained){
-						factor=
-							Math.exp(- parameters[ posteriorMap[sentNum][n+1][j] ] );
-					}else{
-						factor=1;
-					}
-					
-					p[n][i][j]=a[n][i]*hmm.trans[i][j]*
-						hmm.emit[j][seq[n+1]]*b[n+1][j]*factor;
-					
-				}
-			}
-
-			hmm.l1normalize(p[n]);
-		}
-		return p;
-	}
-
-	public void optimizeWithProjectedGradientDescent(){
-		LineSearchMethod ls =
-			new ArmijoLineSearchMinimizationAlongProjectionArc
-				(new InterpolationPickFirstStep(INIT_STEP_SIZE));
-		
-		OptimizerStats stats = new OptimizerStats();
-		
-		
-		ProjectedGradientDescent optimizer = new ProjectedGradientDescent(ls);
-		StopingCriteria stopGrad = new ProjectedGradientL2Norm(GRAD_DIFF);
-		StopingCriteria stopValue = new ValueDifference(VAL_DIFF);
-		CompositeStopingCriteria compositeStop = new CompositeStopingCriteria();
-		compositeStop.add(stopGrad);
-		compositeStop.add(stopValue);
-		
-		optimizer.setMaxIterations(10);
-		updateFunction();
-		boolean succed = 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");
-		}
-	}
-	
-	@Override
-	public void setParameters(double[] params) {
-		super.setParameters(params);
-		updateFunction();
-	}
-	
-	private void updateFunction(){
-		
-		updateCalls++;
-		loglikelihood=0;
-	
-		for(int sentNum=0;sentNum<hmm.data.length;sentNum++){
-			double [][][]p=forwardBackward(sentNum);
-			
-			for(int n=0;n<p.length-1;n++){
-				for(int i=0;i<p[n].length;i++){
-					if(projectionMap[hmm.data[sentNum][n]]!=null){
-						double posterior=0;
-						for(int j=0;j<p[n][i].length;j++){
-							posterior+=p[n][i][j];
-						}
-						gradient[posteriorMap[sentNum][n][i]]=-posterior;
-					}
-				}
-			}
-			
-			//the last state
-			int n=p.length-2;
-			for(int i=0;i<p[n].length;i++){
-				if(projectionMap[hmm.data[sentNum][n+1]]!=null){
-					
-					double posterior=0;
-					for(int j=0;j<p[n].length;j++){
-						posterior+=p[n][j][i];
-					}
-					gradient[posteriorMap[sentNum][n+1][i]]=-posterior;
-				
-				}
-			}	
-		}
-		
-	}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/hmm/POS.java b/gi/posterior-regularisation/prjava/src/hmm/POS.java
deleted file mode 100644
index bdcbc683..00000000
--- a/gi/posterior-regularisation/prjava/src/hmm/POS.java
+++ /dev/null
@@ -1,120 +0,0 @@
-package hmm;
-
-import java.io.File;
-import java.io.FileNotFoundException;
-import java.io.IOException;
-import java.io.PrintStream;
-import java.util.HashMap;
-
-import data.Corpus;
-
-public class POS {
-
-	//public String trainFilename="../posdata/en_train.conll";
-	public static String trainFilename="../posdata/small_train.txt";
-//	public static String trainFilename="../posdata/en_test.conll";
-//	public static String trainFilename="../posdata/trial1.txt";
-	
-	public static String testFilename="../posdata/en_test.conll";
-	//public static String testFilename="../posdata/trial1.txt";
-	
-	public static String predFilename="../posdata/en_test.predict.conll";
-	public static String modelFilename="../posdata/posModel.out";
-	public static final int ITER=20;
-	public static final int N_STATE=30;
-	
-	public static void main(String[] args) {
-		//POS p=new POS();
-		//POS p=new POS(true);
-		try {
-			PRPOS();
-		} catch (FileNotFoundException e) {
-			e.printStackTrace();
-		} catch (IOException e) {
-			e.printStackTrace();
-		}
-	}
-
-	
-	public POS() throws FileNotFoundException, IOException{
-		Corpus c= new Corpus(trainFilename);
-		//size of vocabulary +1 for unknown tokens
-		HMM hmm =new HMM(N_STATE, c.getVocabSize()+1,c.getAllData());
-		for(int i=0;i<ITER;i++){
-			System.out.println("Iter"+i);
-			hmm.EM();
-			if((i+1)%10==0){
-				hmm.writeModel(modelFilename+i);
-			}
-		}
-
-		hmm.writeModel(modelFilename);
-		
-		Corpus test=new Corpus(testFilename,c.vocab);
-		
-		PrintStream ps= io.FileUtil.printstream(new File(predFilename));
-		
-		int [][]data=test.getAllData();
-		for(int i=0;i<data.length;i++){
-			int []tag=hmm.viterbi(data[i]);
-			String sent[]=test.get(i);
-			for(int j=0;j<data[i].length;j++){
-				ps.println(sent[j]+"\t"+tag[j]);
-			}
-			ps.println();
-		}
-		ps.close();
-	}
-	
-	//POS induction with L1/Linf constraints
-	public static void PRPOS() throws FileNotFoundException, IOException{
-		Corpus c= new Corpus(trainFilename);
-		//size of vocabulary +1 for unknown tokens
-		HMM hmm =new HMM(N_STATE, c.getVocabSize()+1,c.getAllData());
-		hmm.o=new HMMObjective(hmm);
-		for(int i=0;i<ITER;i++){
-			System.out.println("Iter: "+i);
-			hmm.PREM();
-			if((i+1)%10==0){
-				hmm.writeModel(modelFilename+i);
-			}
-		}
-
-		hmm.writeModel(modelFilename);
-	}
-	
-	
-	public POS(boolean supervised) throws FileNotFoundException, IOException{
-		Corpus c= new Corpus(trainFilename);
-		//size of vocabulary +1 for unknown tokens
-		HMM hmm =new HMM(c.tagVocab.size() , c.getVocabSize()+1,c.getAllData());
-		hmm.train(c.getTagData());
-
-		hmm.writeModel(modelFilename);
-		
-		Corpus test=new Corpus(testFilename,c.vocab);
-		
-		HashMap<String, Integer>tagVocab=
-			(HashMap<String, Integer>) io.SerializedObjects.readSerializedObject(Corpus.tagalphaFilename);
-		String [] tagdict=new String [tagVocab.size()+1];
-		for(String key:tagVocab.keySet()){
-			tagdict[tagVocab.get(key)]=key;
-		}
-		tagdict[tagdict.length-1]=Corpus.UNK_TOK;
-		
-		System.out.println(c.vocab.get("<e>"));
-		
-		PrintStream ps= io.FileUtil.printstream(new File(predFilename));
-		
-		int [][]data=test.getAllData();
-		for(int i=0;i<data.length;i++){
-			int []tag=hmm.viterbi(data[i]);
-			String sent[]=test.get(i);
-			for(int j=0;j<data[i].length;j++){
-				ps.println(sent[j]+"\t"+tagdict[tag[j]]);
-			}
-			ps.println();
-		}
-		ps.close();
-	}
-}
-- 
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