Merge branch 'master' of https://github.com/redpony/cdec

1 files changed, 0 insertions, 224 deletions

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();	

-	}

-}