cdec cleanup, remove bayesian stuff, parsing stuff

3 files changed, 0 insertions, 1050 deletions

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();

-	}

-}