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/HMMObjective.java               | 351 ---------------------
 1 file changed, 351 deletions(-)
 delete mode 100644 gi/posterior-regularisation/prjava/src/hmm/HMMObjective.java

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

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;
-				
-				}
-			}	
-		}
-		
-	}
-	
-}
-- 
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