cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 579 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