1 files changed, 49 insertions, 42 deletions

diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
index 7bc63c33..abd868c4 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
@@ -18,21 +18,16 @@ import util.MathUtil;
 public class PhraseCluster {

 	

 	public int K;

-	public double scalePT, scaleCT;

 	private int n_phrases, n_words, n_contexts, n_positions;

 	public Corpus c;

 	public ExecutorService pool; 

 	

 	// emit[tag][position][word] = p(word | tag, position in context)

-	private double emit[][][];

+	double emit[][][];

 	// pi[phrase][tag] = p(tag | phrase)

-	private double pi[][];

+	double pi[][];

 	

-	double alphaEmit;

-	double alphaPi;

-	

-	public PhraseCluster(int numCluster, Corpus corpus, double scalep, double scalec, int threads,

-						 double alphaEmit, double alphaPi)

+	public PhraseCluster(int numCluster, Corpus corpus)

 	{

 		K=numCluster;

 		c=corpus;

@@ -40,33 +35,34 @@ public class PhraseCluster {
 		n_phrases=c.getNumPhrases();

 		n_contexts=c.getNumContexts();

 		n_positions=c.getNumContextPositions();

-		this.scalePT = scalep;

-		this.scaleCT = scalec;

-		if (threads > 0)

-			pool = Executors.newFixedThreadPool(threads);

-		

+

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

 		pi=new double[n_phrases][K];

 		

 		for(double [][]i:emit)

-		{

 			for(double []j:i)

-			{

-				arr.F.randomise(j, alphaEmit <= 0);

-				if (alphaEmit > 0) 

-					digammaNormalize(j, alphaEmit);

-			}

-		}

-		

+				arr.F.randomise(j, true);

 		for(double []j:pi)

-		{

-			arr.F.randomise(j, alphaPi <= 0);

-			if (alphaPi > 0) 

-				digammaNormalize(j, alphaPi);

-		}

+			arr.F.randomise(j, true);

+	}

+	

+	public void initialiseVB(double alphaEmit, double alphaPi)

+	{

+		assert alphaEmit > 0;

+		assert alphaPi > 0;

+		

+		for(double [][]i:emit)

+			for(double []j:i)

+				digammaNormalize(j, alphaEmit);

 		

-		this.alphaEmit = alphaEmit;

-		this.alphaPi = alphaPi;

+		for(double []j:pi)

+			digammaNormalize(j, alphaPi);

+	}

+	

+	void useThreadPool(int threads)

+	{

+		assert threads > 0;

+		pool = Executors.newFixedThreadPool(threads);

 	}

 

 	public double EM()

@@ -116,7 +112,7 @@ public class PhraseCluster {
 		return loglikelihood;

 	}

 	

-	public double VBEM()

+	public double VBEM(double alphaEmit, double alphaPi)

 	{

 		// FIXME: broken - needs to be done entirely in log-space

 		

@@ -216,9 +212,22 @@ public class PhraseCluster {
 		return kl;

 	}

 	

-	public double PREM_phrase_constraints(){

-		assert (scaleCT <= 0);

-		

+	public double PREM(double scalePT, double scaleCT)

+	{

+		if (scaleCT == 0)

+		{

+			if (pool != null)

+				return PREM_phrase_constraints_parallel(scalePT);

+			else

+				return PREM_phrase_constraints(scalePT);

+		}

+		else

+			return this.PREM_phrase_context_constraints(scalePT, scaleCT);

+	}

+

+	

+	public double PREM_phrase_constraints(double scalePT)

+	{

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

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

 		

@@ -226,7 +235,7 @@ public class PhraseCluster {
 		int failures=0, iterations=0;

 		//E

 		for(int phrase=0; phrase<n_phrases; phrase++){

-			PhraseObjective po=new PhraseObjective(this,phrase);

+			PhraseObjective po=new PhraseObjective(this, phrase, scalePT);

 			boolean ok = po.optimizeWithProjectedGradientDescent();

 			if (!ok) ++failures;

 			iterations += po.getNumberUpdateCalls();

@@ -234,7 +243,7 @@ public class PhraseCluster {
 			loglikelihood += po.loglikelihood();

 			kl += po.KL_divergence();

 			l1lmax += po.l1lmax();

-			primal += po.primal();

+			primal += po.primal(scalePT);

 			List<Edge> edges = c.getEdgesForPhrase(phrase);

 

 			for(int edge=0;edge<q.length;edge++){

@@ -272,10 +281,9 @@ public class PhraseCluster {
 		return primal;

 	}

 

-	public double PREM_phrase_constraints_parallel()

+	public double PREM_phrase_constraints_parallel(final double scalePT)

 	{

 		assert(pool != null);

-		assert(scaleCT <= 0);

 		

 		final LinkedBlockingQueue<PhraseObjective> expectations 

 			= new LinkedBlockingQueue<PhraseObjective>();

@@ -294,7 +302,7 @@ public class PhraseCluster {
 				public void run() {

 					try {

 						//System.out.println("" + Thread.currentThread().getId() + " optimising lambda for " + p);

-						PhraseObjective po = new PhraseObjective(PhraseCluster.this, p);

+						PhraseObjective po = new PhraseObjective(PhraseCluster.this, p, scalePT);

 						boolean ok = po.optimizeWithProjectedGradientDescent();

 						if (!ok) failures.incrementAndGet();

 						//System.out.println("" + Thread.currentThread().getId() + " done optimising lambda for " + p);

@@ -322,7 +330,7 @@ public class PhraseCluster {
 				loglikelihood += po.loglikelihood();

 				kl += po.KL_divergence();

 				l1lmax += po.l1lmax();

-				primal += po.primal();

+				primal += po.primal(scalePT);

 				iterations += po.getNumberUpdateCalls();

 

 				

@@ -366,15 +374,14 @@ public class PhraseCluster {
 		return primal;

 	}

 

-	public double PREM_phrase_context_constraints(){

-		assert (scaleCT > 0);

-		

+	public double PREM_phrase_context_constraints(double scalePT, double scaleCT)

+	{	

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

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

 		double[] lambda = null;

 

 		//E step

-		PhraseContextObjective pco = new PhraseContextObjective(this, lambda, pool);

+		PhraseContextObjective pco = new PhraseContextObjective(this, lambda, pool, scalePT, scaleCT);

 		lambda = pco.optimizeWithProjectedGradientDescent();

 

 		//now extract expectations