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-rw-r--r--gi/posterior-regularisation/prjava/src/hmm/HMMObjective.java351
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diff --git a/gi/posterior-regularisation/prjava/src/hmm/HMMObjective.java b/gi/posterior-regularisation/prjava/src/hmm/HMMObjective.java
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--- a/gi/posterior-regularisation/prjava/src/hmm/HMMObjective.java
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@@ -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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