variational bayes inference

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@372 ec762483-ff6d-05da-a07a-a48fb63a330f

1 files changed, 12 insertions, 12 deletions

diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
index 13ac14ba..ccb6ae9d 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
@@ -111,8 +111,9 @@ public class PhraseCluster {
 				TIntArrayList context = edge.getContext();

 				for(int tag=0;tag<K;tag++)

 				{

-					for(int pos=0;pos<n_positions;pos++)

-						exp_emit[tag][pos][context.get(pos)]+=p[tag]*count;		

+					for(int pos=0;pos<n_positions;pos++){

+						exp_emit[tag][pos][context.get(pos)]+=p[tag]*count;

+					}

 					exp_pi[tag]+=p[tag]*count;

 				}

 			}

@@ -248,14 +249,12 @@ public class PhraseCluster {
 	public double PREM_phrase_constraints(double scalePT, int phraseSizeLimit)

 	{

 		double [][][]exp_emit=new double[K][n_positions][n_words];

-		double [][]exp_pi=new double[n_phrases][K];

+		double []exp_pi=new double[K];

 		

 		for(double [][]i:exp_emit)

 			for(double []j:i)

 				Arrays.fill(j, 1e-10);

-		for(double []j:exp_pi)

-			Arrays.fill(j, 1e-10);

-

+		

 		if (lambdaPT == null && cacheLambda)

 			lambdaPT = new double[n_phrases][];

 		

@@ -267,10 +266,12 @@ public class PhraseCluster {
 		{

 			if (phraseSizeLimit >= 1 && c.getPhrase(phrase).size() > phraseSizeLimit)

 			{

-				System.arraycopy(pi[phrase], 0, exp_pi[phrase], 0, K);

+				//System.arraycopy(pi[phrase], 0, exp_pi[phrase], 0, K);

 				continue;

 			}

 			

+			Arrays.fill(exp_pi, 1e-10);

+			

 			// FIXME: add rare edge check to phrase objective & posterior processing

 			PhraseObjective po = new PhraseObjective(this, phrase, scalePT, (cacheLambda) ? lambdaPT[phrase] : null);

 			boolean ok = po.optimizeWithProjectedGradientDescent();

@@ -294,9 +295,12 @@ public class PhraseCluster {
 						exp_emit[tag][pos][context.get(pos)]+=q[edge][tag]*contextCnt;

 					}

 					

-					exp_pi[phrase][tag]+=q[edge][tag]*contextCnt;

+					exp_pi[tag]+=q[edge][tag]*contextCnt;

+					

 				}

 			}

+			arr.F.l1normalize(exp_pi);

+			System.arraycopy(exp_pi, 0, pi[phrase], 0, K);

 		}

 		

 		long end = System.currentTimeMillis();

@@ -313,10 +317,6 @@ public class PhraseCluster {
 				arr.F.l1normalize(j);

 		emit=exp_emit;

 		

-		for(double []j:exp_pi)

-			arr.F.l1normalize(j);

-		pi=exp_pi;

-		

 		return primal;

 	}