From e26434979adc33bd949566ba7bf02dff64e80a3e Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@cab.ark.cs.cmu.edu>
Date: Tue, 2 Oct 2012 00:19:43 -0400
Subject: cdec cleanup, remove bayesian stuff, parsing stuff

---
 .../prjava/src/phrase/PhraseObjective.java         | 224 ---------------------
 1 file changed, 224 deletions(-)
 delete mode 100644 gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java

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

diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
deleted file mode 100644
index ac73a075..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
+++ /dev/null
@@ -1,224 +0,0 @@
-package phrase;
-
-import java.util.Arrays;
-import java.util.List;
-
-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.linesearch.WolfRuleLineSearch;
-import optimization.projections.SimplexProjection;
-import optimization.stopCriteria.CompositeStopingCriteria;
-import optimization.stopCriteria.ProjectedGradientL2Norm;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.stopCriteria.ValueDifference;
-import optimization.util.MathUtils;
-
-public class PhraseObjective extends ProjectedObjective
-{
-	static final double GRAD_DIFF = 0.00002;
-	static double INIT_STEP_SIZE = 300;
-	static double VAL_DIFF = 1e-8; // tuned to BTEC subsample
-	static int ITERATIONS = 100;
-	private PhraseCluster c;
-	
-	/**@brief
-	 *  for debugging purposes
-	 */
-	//public static PrintStream ps;
-	
-	/**@brief current phrase being optimzed*/
-	public int phrase;
-
-	/**@brief un-regularized posterior
-	 * unnormalized
-	 * p[edge][tag]
-	*  P(tag|edge) \propto P(tag|phrase)P(context|tag)
-	 */
-	private double[][]p;
-
-	/**@brief regularized posterior
-	 * q[edge][tag] propto p[edge][tag]*exp(-lambda)
-	 */
-	private double q[][];
-	private List<Corpus.Edge> data;
-	
-	/**@brief log likelihood of the associated phrase
-	 * 
-	 */
-	private double loglikelihood;
-	private SimplexProjection projection;
-	
-	double[] newPoint  ;
-	
-	private int n_param;
-	
-	/**@brief likelihood under p
-	 * 
-	 */
-	public double llh;
-	
-	public PhraseObjective(PhraseCluster cluster, int phraseIdx, double scale, double[] lambda){
-		phrase=phraseIdx;
-		c=cluster;
-		data=c.c.getEdgesForPhrase(phrase);
-		n_param=data.size()*c.K;
-		//System.out.println("Num parameters " + n_param + " for phrase #" + phraseIdx);
-		
-		if (lambda==null) 
-			lambda=new double[n_param];
-		
-		parameters = lambda;
-		newPoint = new double[n_param];
-		gradient = new double[n_param];
-		initP();
-		projection=new SimplexProjection(scale);
-		q=new double [data.size()][c.K];
-
-		setParameters(parameters);
-	}
-
-	private void initP(){
-		p=new double[data.size()][];
-		for(int edge=0;edge<data.size();edge++){
-			p[edge]=c.posterior(data.get(edge));
-			llh += data.get(edge).getCount() * Math.log(arr.F.l1norm(p[edge])); // Was bug here - count inside log!
-			arr.F.l1normalize(p[edge]);
-		}
-	}
-	
-	@Override
-	public void setParameters(double[] params) {
-		super.setParameters(params);
-		updateFunction();
-	}
-	
-	private void updateFunction(){
-		updateCalls++;
-		loglikelihood=0;
-
-		for(int tag=0;tag<c.K;tag++){
-			for(int edge=0;edge<data.size();edge++){
-				q[edge][tag]=p[edge][tag]*
-					Math.exp(-parameters[tag*data.size()+edge]/data.get(edge).getCount());
-			}
-		}
-	
-		for(int edge=0;edge<data.size();edge++){
-			loglikelihood+=data.get(edge).getCount() * Math.log(arr.F.l1norm(q[edge]));
-			arr.F.l1normalize(q[edge]);
-		}
-		
-		for(int tag=0;tag<c.K;tag++){
-			for(int edge=0;edge<data.size();edge++){
-				gradient[tag*data.size()+edge]=-q[edge][tag];
-			}
-		}
-	}
-	
-	@Override
-	public double[] projectPoint(double[] point) 
-	{
-		double toProject[]=new double[data.size()];
-		for(int tag=0;tag<c.K;tag++){
-			for(int edge=0;edge<data.size();edge++){
-				toProject[edge]=point[tag*data.size()+edge];
-			}
-			projection.project(toProject);
-			for(int edge=0;edge<data.size();edge++){
-				newPoint[tag*data.size()+edge]=toProject[edge];
-			}
-		}
-		return newPoint;
-	}
-
-	@Override
-	public double[] getGradient() {
-		gradientCalls++;
-		return gradient;
-	}
-
-	@Override
-	public double getValue() {
-		functionCalls++;
-		return loglikelihood;
-	}
-
-	@Override
-	public String toString() {
-		return Arrays.toString(parameters);
-	}
-
-	public double [][]posterior(){
-		return q;
-	}
-	
-	long optimizationTime;
-	
-	public boolean optimizeWithProjectedGradientDescent(){
-		long start = System.currentTimeMillis();
-		
-		LineSearchMethod ls =
-			new ArmijoLineSearchMinimizationAlongProjectionArc
-				(new InterpolationPickFirstStep(INIT_STEP_SIZE));
-		//LineSearchMethod  ls = new WolfRuleLineSearch(
-		//		(new InterpolationPickFirstStep(INIT_STEP_SIZE)), c1, c2);
-		OptimizerStats stats = new OptimizerStats();
-		
-		
-		ProjectedGradientDescent optimizer = new ProjectedGradientDescent(ls);
-		StopingCriteria stopGrad = new ProjectedGradientL2Norm(GRAD_DIFF);
-		StopingCriteria stopValue = new ValueDifference(VAL_DIFF*(-llh));
-		CompositeStopingCriteria compositeStop = new CompositeStopingCriteria();
-		compositeStop.add(stopGrad);
-		compositeStop.add(stopValue);
-		optimizer.setMaxIterations(ITERATIONS);
-		updateFunction();
-		boolean success = 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");
-		//}
-		//System.out.println(Arrays.toString(parameters));
-		
-		//	for(int edge=0;edge<data.getSize();edge++){
-		//	ps.println(Arrays.toString(q[edge]));
-		//	}
-
-		return success;
-	}
-	
-	public double KL_divergence()
-	{
-		return -loglikelihood + MathUtils.dotProduct(parameters, gradient);
-	}
-	
-	public double loglikelihood()
-	{
-		return llh;
-	}
-	
-	public double l1lmax()
-	{
-		double sum=0;
-		for(int tag=0;tag<c.K;tag++){
-			double max=0;
-			for(int edge=0;edge<data.size();edge++){
-				if(q[edge][tag]>max)
-					max=q[edge][tag];
-			}
-			sum+=max;
-		}
-		return sum;
-	}
-
-	public double primal(double scale)
-	{
-		return loglikelihood() - KL_divergence() - scale * l1lmax();	
-	}
-}
-- 
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