From 9ac87abac855aaaa6c1dcf686b38443092a10ce6 Mon Sep 17 00:00:00 2001
From: desaicwtf <desaicwtf@ec762483-ff6d-05da-a07a-a48fb63a330f>
Date: Thu, 22 Jul 2010 23:54:25 +0000
Subject: variational bayes inference

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@372 ec762483-ff6d-05da-a07a-a48fb63a330f
---
 .../prjava/src/phrase/VB.java                      | 317 +++++++++++++++++++++
 1 file changed, 317 insertions(+)
 create mode 100644 gi/posterior-regularisation/prjava/src/phrase/VB.java

(limited to 'gi/posterior-regularisation/prjava/src/phrase/VB.java')

diff --git a/gi/posterior-regularisation/prjava/src/phrase/VB.java b/gi/posterior-regularisation/prjava/src/phrase/VB.java
new file mode 100644
index 00000000..cc1c1c96
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/phrase/VB.java
@@ -0,0 +1,317 @@
+package phrase;
+
+import gnu.trove.TIntArrayList;
+
+import io.FileUtil;
+
+import java.io.File;
+import java.io.IOException;
+import java.io.PrintStream;
+import java.util.Arrays;
+import java.util.List;
+
+import org.apache.commons.math.special.Gamma;
+
+import phrase.Corpus.Edge;
+
+public class VB {
+
+	public static int MAX_ITER=40;
+	
+	/**@brief
+	 * hyper param for beta
+	 * where beta is multinomial
+	 * for generating words from a topic
+	 */
+	public double lambda=0.1;
+	/**@brief
+	 * hyper param for theta
+	 * where theta is dirichlet for z
+	 */
+	public double alpha=0.000001;
+	/**@brief
+	 * variational param for beta
+	 */
+	private double rho[][][];
+	/**@brief
+	 * variational param for z
+	 */
+	private double phi[][];
+	/**@brief
+	 * variational param for theta
+	 */
+	private double gamma[];
+	
+	private static double VAL_DIFF_RATIO=0.001;
+	
+	/**@brief
+	 * objective for a single document
+	 */
+	private double obj;
+	
+	private int n_positions;
+	private int n_words;
+	private int K;
+	
+	private Corpus c;
+	public static void main(String[] args) {
+		String in="../pdata/canned.con";
+		//String in="../pdata/btec.con";
+		String out="../pdata/vb.out";
+		int numCluster=25;
+		Corpus corpus = null;
+		File infile = new File(in);
+		try {
+			System.out.println("Reading concordance from " + infile);
+			corpus = Corpus.readFromFile(FileUtil.reader(infile));
+			corpus.printStats(System.out);
+		} catch (IOException e) {
+			System.err.println("Failed to open input file: " + infile);
+			e.printStackTrace();
+			System.exit(1);
+		}
+		
+		VB vb=new VB(numCluster, corpus);
+		int iter=20;
+		for(int i=0;i<iter;i++){
+			double obj=vb.EM();
+			System.out.println("Iter "+i+": "+obj);
+		}
+		
+		File outfile = new File (out);
+		try {
+			PrintStream ps = FileUtil.printstream(outfile);
+			vb.displayPosterior(ps);
+		//	ps.println();
+		//	c2f.displayModelParam(ps);
+			ps.close();
+		} catch (IOException e) {
+			System.err.println("Failed to open output file: " + outfile);
+			e.printStackTrace();
+			System.exit(1);
+		}
+	}
+
+	public VB(int numCluster, Corpus corpus){
+		c=corpus;
+		K=numCluster;
+		n_positions=c.getNumContextPositions();
+		n_words=c.getNumWords();
+		rho=new double[K][n_positions][n_words];
+		//to init rho
+		//loop through data and count up words
+		double[] phi_tmp=new double[K];
+		for(int i=0;i<K;i++){
+			for(int pos=0;pos<n_positions;pos++){
+				Arrays.fill(rho[i][pos], lambda);
+			}
+		}
+		for(int d=0;d<c.getNumPhrases();d++){
+			List<Edge>doc=c.getEdgesForPhrase(d);
+			for(int n=0;n<doc.size();n++){
+				TIntArrayList context=doc.get(n).getContext();
+				arr.F.randomise(phi_tmp);
+				for(int i=0;i<K;i++){
+					for(int pos=0;pos<n_positions;pos++){
+						rho[i][pos][context.get(pos)]+=phi_tmp[i];
+					}
+				}
+			}
+		}
+	}
+	
+	private void inference(int phraseID){
+		List<Edge > doc=c.getEdgesForPhrase(phraseID);
+		phi=new double[doc.size()][K];
+		for(int i=0;i<phi.length;i++){
+			for(int j=0;j<phi[i].length;j++){
+				phi[i][j]=1.0/K;
+			}
+		}
+		gamma = new double[K];
+		double digamma_gamma[]=new double[K];
+		for(int i=0;i<gamma.length;i++){
+			gamma[i] = alpha + 1.0/K;
+		}
+		
+		double rho_sum[][]=new double [K][n_positions];
+		for(int i=0;i<K;i++){
+			for(int pos=0;pos<n_positions;pos++){
+				rho_sum[i][pos]=Gamma.digamma(arr.F.l1norm(rho[i][pos]));
+			}
+		}
+		double gamma_sum=Gamma.digamma(arr.F.l1norm(gamma));
+		for(int i=0;i<K;i++){
+			digamma_gamma[i]=Gamma.digamma(gamma[i]);
+		}
+		double gammaSum[]=new double [K];
+		
+		double prev_val=0;
+		obj=0;
+		for(int iter=0;iter<MAX_ITER;iter++){
+			prev_val=obj;
+			obj=0;
+			Arrays.fill(gammaSum,0.0);
+			for(int n=0;n<doc.size();n++){
+				TIntArrayList context=doc.get(n).getContext();
+				double phisum=0;
+				for(int i=0;i<K;i++){
+					double sum=0;
+					for(int pos=0;pos<n_positions;pos++){
+						int word=context.get(pos);
+						sum+=Gamma.digamma(rho[i][pos][word])-rho_sum[i][pos];
+					}
+					sum+= digamma_gamma[i]-gamma_sum;
+					phi[n][i]=sum;
+					
+					if (i > 0){
+	                    phisum = log_sum(phisum, phi[n][i]);
+					}
+	                else{
+	                    phisum = phi[n][i];
+	                }
+					
+				}//end of  a word
+				
+				for(int i=0;i<K;i++){
+					phi[n][i]=Math.exp(phi[n][i]-phisum);
+					gammaSum[i]+=phi[n][i];
+				}
+				
+			}//end of doc
+			
+			for(int i=0;i<K;i++){
+				gamma[i]=alpha+gammaSum[i];
+			}
+			gamma_sum=Gamma.digamma(arr.F.l1norm(gamma));
+			for(int i=0;i<K;i++){
+				digamma_gamma[i]=Gamma.digamma(gamma[i]);
+			}
+			//compute objective for reporting
+			obj=0;
+			
+			for(int i=0;i<K;i++){
+				obj+=(alpha-1)*(digamma_gamma[i]-gamma_sum);
+			}
+			
+			
+			for(int n=0;n<doc.size();n++){
+				TIntArrayList context=doc.get(n).getContext();
+				
+				for(int i=0;i<K;i++){
+					//entropy of phi + expected log likelihood of z
+					obj+=phi[n][i]*(digamma_gamma[i]-gamma_sum);
+					
+					if(phi[n][i]>1e-10){
+						obj+=phi[n][i]*Math.log(phi[n][i]);
+					}
+					
+					double beta_sum=0;
+					for(int pos=0;pos<n_positions;pos++){
+						int word=context.get(pos);
+						beta_sum+=(Gamma.digamma(rho[i][pos][word])-rho_sum[i][pos]);
+					}
+					obj+=phi[n][i]*beta_sum;
+				}
+			}
+			
+			obj-=Gamma.logGamma(arr.F.l1norm(gamma));
+			for(int i=0;i<K;i++){
+				obj+=Gamma.logGamma(gamma[i]);
+				obj-=(gamma[i]-1)*(digamma_gamma[i]-gamma_sum);
+			}
+			
+//			System.out.println(phraseID+": "+obj);
+			if(iter>0 && (obj-prev_val)/Math.abs(obj)<VAL_DIFF_RATIO){
+				break;
+			}
+		}//end of inference loop
+	}//end of inference
+	
+	/**
+	 * @return objective of this iteration
+	 */
+	public double EM(){
+		double emObj=0;
+		
+		//E
+		double exp_rho[][][]=new double[K][n_positions][n_words];
+		for (int d=0;d<c.getNumPhrases();d++){
+			inference(d);
+			List<Edge>doc=c.getEdgesForPhrase(d);
+			for(int n=0;n<doc.size();n++){
+				TIntArrayList context=doc.get(n).getContext();
+				for(int pos=0;pos<n_positions;pos++){
+					int word=context.get(pos);
+					for(int i=0;i<K;i++){	
+						exp_rho[i][pos][word]+=phi[n][i];
+					}
+				}
+			}
+			
+			emObj+=obj;
+		}
+		
+	//	System.out.println("EM Objective:"+emObj);
+		
+		//M
+		for(int i=0;i<K;i++){
+			for(int pos=0;pos<n_positions;pos++){
+				for(int j=0;j<n_words;j++){
+					rho[i][pos][j]=lambda+exp_rho[i][pos][j];
+				}
+			}
+		}
+		
+		//E[\log p(\beta|\lambda)] - E[\log q(\beta)]
+		for(int i=0;i<K;i++){
+			double rhoSum=0;
+			for(int pos=0;pos<n_positions;pos++){
+				for(int j=0;j<n_words;j++){
+					rhoSum+=rho[i][pos][j];
+				}
+				double digamma_rhoSum=Gamma.digamma(rhoSum);
+				emObj-=Gamma.logGamma(rhoSum);
+				for(int j=0;j<n_words;j++){
+					emObj+=(lambda-rho[i][pos][j])*(Gamma.digamma(rho[i][pos][j])-digamma_rhoSum);
+					emObj+=Gamma.logGamma(rho[i][pos][j]);
+				}
+			}
+		}
+		
+		return emObj;
+	}//end of EM
+	
+	public void displayPosterior(PrintStream ps)
+	{	
+		for(int d=0;d<c.getNumPhrases();d++){
+			inference(d);
+			List<Edge> doc=c.getEdgesForPhrase(d);
+			for(int n=0;n<doc.size();n++){
+				Edge edge=doc.get(n);
+				int tag=arr.F.argmax(phi[n]);
+				ps.print(edge.getPhraseString());
+				ps.print("\t");
+				ps.print(edge.getContextString(true));
+
+				ps.println(" ||| C=" + tag);
+			}
+		}
+	}
+
+	double log_sum(double log_a, double log_b)
+	{
+	  double v;
+
+	  if (log_a < log_b)
+	  {
+	      v = log_b+Math.log(1 + Math.exp(log_a-log_b));
+	  }
+	  else
+	  {
+	      v = log_a+Math.log(1 + Math.exp(log_b-log_a));
+	  }
+	  return(v);
+	}
+	
+}//End of  class
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