diff options
Diffstat (limited to 'gi/posterior-regularisation/prjava/src/hmm')
| -rw-r--r-- | gi/posterior-regularisation/prjava/src/hmm/HMM.java | 579 | ||||
| -rw-r--r-- | gi/posterior-regularisation/prjava/src/hmm/HMMObjective.java | 351 | ||||
| -rw-r--r-- | gi/posterior-regularisation/prjava/src/hmm/POS.java | 120 |
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();
- }
-}
|