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

42 files changed, 0 insertions, 3582 deletions

diff --git a/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java b/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java
deleted file mode 100644
index 25fa7f09..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java
+++ /dev/null
@@ -1,110 +0,0 @@
-package optimization.examples;
-
-
-import optimization.gradientBasedMethods.ConjugateGradient;
-import optimization.gradientBasedMethods.GradientDescent;
-import optimization.gradientBasedMethods.LBFGS;
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.Optimizer;
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.ArmijoLineSearchMinimization;
-import optimization.linesearch.LineSearchMethod;
-import optimization.stopCriteria.GradientL2Norm;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.util.MathUtils;
-
-/**
- * 
- * @author javg
- * f(x) = \sum_{i=1}^{N-1} \left[ (1-x_i)^2+ 100 (x_{i+1} - x_i^2 )^2 \right] \quad \forall x\in\mathbb{R}^N.
- */
-public class GeneralizedRosenbrock extends Objective{
-
-	
-	
-	public GeneralizedRosenbrock(int dimensions){
-		parameters = new double[dimensions];		
-		java.util.Arrays.fill(parameters, 0);
-		gradient = new double[dimensions];
-		
-	}
-	
-	public GeneralizedRosenbrock(int dimensions, double[] params){
-		parameters = params;	
-		gradient = new double[dimensions];
-	}
-	
-	
-	public double getValue() {
-		functionCalls++;
-		double value = 0;
-		for(int i = 0; i < parameters.length-1; i++){
-			value += MathUtils.square(1-parameters[i]) + 100*MathUtils.square(parameters[i+1] - MathUtils.square(parameters[i]));
-		}
-		
-		return value;
-	}
-
-	/**
-	 * gx = -2(1-x) -2x200(y-x^2)
-	 * gy = 200(y-x^2)
-	 */
-	public double[] getGradient() {
-		gradientCalls++;
-		java.util.Arrays.fill(gradient,0);
-		for(int i = 0; i < parameters.length-1; i++){
-			gradient[i]+=-2*(1-parameters[i]) - 400*parameters[i]*(parameters[i+1] - MathUtils.square(parameters[i]));
-			gradient[i+1]+=200*(parameters[i+1] - MathUtils.square(parameters[i]));
-		}	
-		return gradient;
-	}
-
-	
-
-	
-
-	
-	
-	public String toString(){
-		String  res ="";
-		for(int i = 0; i < parameters.length; i++){
-			res += "P" + i+ " " + parameters[i];
-		}
-		res += " Value " + getValue();
-		return res;
-	}
-	
-	public static void main(String[] args) {
-		
-		GeneralizedRosenbrock o = new GeneralizedRosenbrock(2);
-		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		;
-
-		System.out.println("Doing Gradient descent");
-		//LineSearchMethod wolfe = new WolfRuleLineSearch(new InterpolationPickFirstStep(1),100,0.001,0.1);
-		StopingCriteria stop = new GradientL2Norm(0.001);		
-		LineSearchMethod ls = new ArmijoLineSearchMinimization();
-		Optimizer optimizer = new GradientDescent(ls);		
-		OptimizerStats stats = new OptimizerStats();
-		optimizer.setMaxIterations(1000);
-		boolean succed = optimizer.optimize(o,stats, stop);
-		System.out.println("Suceess " + succed + "/n"+stats.prettyPrint(1));
-		System.out.println("Doing Conjugate Gradient descent");
-		o = new GeneralizedRosenbrock(2);
-	//	wolfe = new WolfRuleLineSearch(new InterpolationPickFirstStep(1),100,0.001,0.1);
-		optimizer = new ConjugateGradient(ls);
-		stats = new OptimizerStats();
-		optimizer.setMaxIterations(1000);
-		succed = optimizer.optimize(o,stats,stop);
-		System.out.println("Suceess " + succed + "/n"+stats.prettyPrint(1));
-		System.out.println("Doing Quasi newton descent");
-		o = new GeneralizedRosenbrock(2);
-		optimizer = new LBFGS(ls,10);
-		stats = new OptimizerStats();
-		optimizer.setMaxIterations(1000);
-		succed = optimizer.optimize(o,stats,stop);
-		System.out.println("Suceess " + succed + "/n"+stats.prettyPrint(1));
-
-	}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java b/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java
deleted file mode 100644
index f087681e..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java
+++ /dev/null
@@ -1,128 +0,0 @@
-package optimization.examples;
-
-
-import optimization.gradientBasedMethods.ConjugateGradient;
-
-import optimization.gradientBasedMethods.GradientDescent;
-import optimization.gradientBasedMethods.LBFGS;
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.GenericPickFirstStep;
-import optimization.linesearch.LineSearchMethod;
-import optimization.linesearch.WolfRuleLineSearch;
-import optimization.stopCriteria.GradientL2Norm;
-import optimization.stopCriteria.StopingCriteria;
-
-
-/**
- * @author javg
- *
- */
-public class x2y2 extends Objective{
-
-	
-	//Implements function ax2+ by2 
-	double a, b;
-	public x2y2(double a, double b){
-		this.a = a;
-		this.b = b;
-		parameters = new double[2];
-		parameters[0] = 4;
-		parameters[1] = 4;
-		gradient = new double[2];
-	}
-	
-	public double getValue() {
-		functionCalls++;
-		return a*parameters[0]*parameters[0]+b*parameters[1]*parameters[1];
-	}
-
-	public double[] getGradient() {
-		gradientCalls++;
-		gradient[0]=2*a*parameters[0];
-		gradient[1]=2*b*parameters[1];
-		return gradient;
-//		if(debugLevel >=2){
-//			double[] numericalGradient = DebugHelpers.getNumericalGradient(this, parameters, 0.000001);
-//			for(int i = 0; i < parameters.length; i++){
-//				double diff = Math.abs(gradient[i]-numericalGradient[i]);
-//				if(diff > 0.00001){
-//					System.out.println("Numerical Gradient does not match");
-//					System.exit(1);
-//				}
-//			}
-//		}
-	}
-
-	
-	
-	public void optimizeWithGradientDescent(LineSearchMethod ls, OptimizerStats stats, x2y2 o){
-		GradientDescent optimizer = new GradientDescent(ls);
-		StopingCriteria stop = new GradientL2Norm(0.001);
-//		optimizer.setGradientConvergenceValue(0.001);
-		optimizer.setMaxIterations(100);
-		boolean succed = optimizer.optimize(o,stats,stop);
-		System.out.println("Ended optimzation Gradient Descent\n" + stats.prettyPrint(1));
-		System.out.println("Solution: " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		if(succed){
-			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
-		}else{
-			System.out.println("Failed to optimize");
-		}
-	}
-	
-	public void optimizeWithConjugateGradient(LineSearchMethod ls, OptimizerStats stats, x2y2 o){
-		ConjugateGradient optimizer = new ConjugateGradient(ls);
-		StopingCriteria stop = new GradientL2Norm(0.001);
-
-		optimizer.setMaxIterations(10);
-		boolean succed = optimizer.optimize(o,stats,stop);
-		System.out.println("Ended optimzation Conjugate Gradient\n" + stats.prettyPrint(1));
-		System.out.println("Solution: " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		if(succed){
-			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
-		}else{
-			System.out.println("Failed to optimize");
-		}
-	}
-	
-	public void optimizeWithLBFGS(LineSearchMethod ls, OptimizerStats stats, x2y2 o){
-		LBFGS optimizer = new LBFGS(ls,10);
-		StopingCriteria stop = new GradientL2Norm(0.001);
-		optimizer.setMaxIterations(10);
-		boolean succed = optimizer.optimize(o,stats,stop);
-		System.out.println("Ended optimzation LBFGS\n" + stats.prettyPrint(1));
-		System.out.println("Solution: " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		if(succed){
-			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
-		}else{
-			System.out.println("Failed to optimize");
-		}
-	}
-	
-	public static void main(String[] args) {
-		x2y2 o = new x2y2(1,10);
-		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		o.setDebugLevel(4);
-		LineSearchMethod wolfe = new WolfRuleLineSearch(new GenericPickFirstStep(1),0.001,0.9);;
-//		LineSearchMethod ls = new ArmijoLineSearchMinimization();
-		OptimizerStats stats = new OptimizerStats();
-		o.optimizeWithGradientDescent(wolfe, stats, o);
-		o = new x2y2(1,10);
-		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-//		ls = new ArmijoLineSearchMinimization();
-		stats = new OptimizerStats();
-		o.optimizeWithConjugateGradient(wolfe, stats, o);
-		o = new x2y2(1,10);
-		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-//		ls = new ArmijoLineSearchMinimization();
-		stats = new OptimizerStats();
-		o.optimizeWithLBFGS(wolfe, stats, o);	
-	}
-	
-	public String toString(){
-		return "P1: " + parameters[0] + " P2: " + parameters[1] + " value " + getValue();
-	}
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2WithConstraints.java b/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2WithConstraints.java
deleted file mode 100644
index 391775b7..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2WithConstraints.java
+++ /dev/null
@@ -1,127 +0,0 @@
-package optimization.examples;
-
-
-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.projections.BoundsProjection;
-import optimization.projections.Projection;
-import optimization.projections.SimplexProjection;
-import optimization.stopCriteria.CompositeStopingCriteria;
-import optimization.stopCriteria.GradientL2Norm;
-import optimization.stopCriteria.ProjectedGradientL2Norm;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.stopCriteria.ValueDifference;
-
-
-/**
- * @author javg
- * 
- * 
- *ax2+ b(y2 -displacement)
- */
-public class x2y2WithConstraints extends ProjectedObjective{
-
-
-	double a, b;
-	double dx;
-	double dy;
-	Projection projection;
-	
-	
-	public x2y2WithConstraints(double a, double b, double[] params, double dx, double dy, Projection proj){
-		//projection = new BoundsProjection(0.2,Double.MAX_VALUE);
-		super();
-		projection = proj;	
-		this.a = a;
-		this.b = b;
-		this.dx = dx;
-		this.dy = dy;
-		setInitialParameters(params);
-		System.out.println("Function " +a+"(x-"+dx+")^2 + "+b+"(y-"+dy+")^2");
-		System.out.println("Gradient " +(2*a)+"(x-"+dx+") ; "+(b*2)+"(y-"+dy+")");
-		printParameters();
-		projection.project(parameters);
-		printParameters();
-		gradient = new double[2];
-	}
-	
-	public double getValue() {
-		functionCalls++;
-		return a*(parameters[0]-dx)*(parameters[0]-dx)+b*((parameters[1]-dy)*(parameters[1]-dy));
-	}
-
-	public double[] getGradient() {
-		if(gradient == null){
-			gradient = new double[2];
-		}
-		gradientCalls++;
-		gradient[0]=2*a*(parameters[0]-dx);
-		gradient[1]=2*b*(parameters[1]-dy);
-		return gradient;
-	}
-	
-	
-	public double[] projectPoint(double[] point) {
-		double[] newPoint = point.clone();
-		projection.project(newPoint);
-		return newPoint;
-	}	
-	
-	public void optimizeWithProjectedGradientDescent(LineSearchMethod ls, OptimizerStats stats, x2y2WithConstraints o){
-		ProjectedGradientDescent optimizer = new ProjectedGradientDescent(ls);
-		StopingCriteria stopGrad = new ProjectedGradientL2Norm(0.001);
-		StopingCriteria stopValue = new ValueDifference(0.001);
-		CompositeStopingCriteria compositeStop = new CompositeStopingCriteria();
-		compositeStop.add(stopGrad);
-		compositeStop.add(stopValue);
-		
-		optimizer.setMaxIterations(5);
-		boolean succed = optimizer.optimize(o,stats,compositeStop);
-		System.out.println("Ended optimzation Projected Gradient Descent\n" + stats.prettyPrint(1));
-		System.out.println("Solution: " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		if(succed){
-			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
-		}else{
-			System.out.println("Failed to optimize");
-		}
-	}
-	
-	
-	
-	public String toString(){
-		
-		return "P1: " + parameters[0] + " P2: " + parameters[1] + " value " + getValue() + " grad (" + getGradient()[0] + ":" + getGradient()[1]+")";
-	}
-	
-	public static void main(String[] args) {
-		double a = 1;
-		double b=1;
-		double x0 = 0;
-		double y0  =1;
-		double dx = 0.5;
-		double dy = 0.5	;
-		double [] parameters = new double[2];
-		parameters[0] = x0;
-		parameters[1] = y0;
-		x2y2WithConstraints o = new x2y2WithConstraints(a,b,parameters,dx,dy, new SimplexProjection(0.5));
-		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1] + " a " + a + " b "+b );
-		o.setDebugLevel(4);
-		
-		LineSearchMethod ls = new ArmijoLineSearchMinimizationAlongProjectionArc(new InterpolationPickFirstStep(1));
-		
-		OptimizerStats stats = new OptimizerStats();
-		o.optimizeWithProjectedGradientDescent(ls, stats, o);
-		
-//		o = new x2y2WithConstraints(a,b,x0,y0,dx,dy);
-//		stats = new OptimizerStats();
-//		o.optimizeWithSpectralProjectedGradientDescent(stats, o);
-	}
-	
-	
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java
deleted file mode 100644
index 2fcb7990..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java
+++ /dev/null
@@ -1,120 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.DifferentiableLineSearchObjective;
-import optimization.linesearch.LineSearchMethod;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.util.MathUtils;
-
-/**
- * 
- * @author javg
- *
- */
-public abstract class AbstractGradientBaseMethod implements Optimizer{
-	
-	protected int maxNumberOfIterations=10000;
-	
-	
-	
-	protected int currentProjectionIteration;
-	protected double currValue;	
-	protected double previousValue = Double.MAX_VALUE;;
-	protected double step;
-	protected double[] gradient;
-	public double[] direction;
-	
-	//Original values
-	protected double originalGradientL2Norm;
-	
-	protected LineSearchMethod lineSearch;
-	DifferentiableLineSearchObjective lso;
-	
-	
-	public void reset(){
-		direction = null;
-		gradient = null;
-		previousValue = Double.MAX_VALUE;
-		currentProjectionIteration = 0;
-		originalGradientL2Norm = 0;
-		step = 0;
-		currValue = 0;
-	}
-	
-	public void initializeStructures(Objective o,OptimizerStats stats, StopingCriteria stop){
-		lso =   new DifferentiableLineSearchObjective(o);
-	}
-	public void updateStructuresBeforeStep(Objective o,OptimizerStats stats, StopingCriteria stop){
-	}
-	
-	public void updateStructuresAfterStep(Objective o,OptimizerStats stats, StopingCriteria stop){
-	}
-	
-	public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stop){
-		//Initialize structures
-			
-		stats.collectInitStats(this, o);
-		direction = new double[o.getNumParameters()];
-		initializeStructures(o, stats, stop);
-		for (currentProjectionIteration = 1; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){
-			//System.out.println("\tgradient descent iteration " + currentProjectionIteration);
-			//System.out.print("\tparameters:" );
-			//o.printParameters();
-			previousValue = currValue;
-			currValue = o.getValue();
-			gradient = o.getGradient();
-			if(stop.stopOptimization(o)){
-				stats.collectFinalStats(this, o);
-				return true;
-			}	
-			
-			getDirection();
-			if(MathUtils.dotProduct(gradient, direction) > 0){
-				System.out.println("Not a descent direction");
-				System.out.println(" current stats " + stats.prettyPrint(1));
-				System.exit(-1);
-			}
-			updateStructuresBeforeStep(o, stats, stop);
-			lso.reset(direction);
-			step = lineSearch.getStepSize(lso);
-			//System.out.println("\t\tLeave with step: " + step);
-			if(step==-1){
-				System.out.println("Failed to find step");
-				stats.collectFinalStats(this, o);
-				return false;		
-			}
-			updateStructuresAfterStep( o, stats,  stop);
-//			previousValue = currValue;
-//			currValue = o.getValue();
-//			gradient = o.getGradient();
-			stats.collectIterationStats(this, o);
-		}
-		stats.collectFinalStats(this, o);
-		return false;
-	}
-	
-	
-	public int getCurrentIteration() {
-		return currentProjectionIteration;
-	}
-
-	
-	/**
-	 * Method specific
-	 */
-	public abstract double[] getDirection();
-
-	public double getCurrentStep() {
-		return step;
-	}
-
-
-
-	public void setMaxIterations(int max) {
-		maxNumberOfIterations = max;
-	}
-
-	public double getCurrentValue() {
-		return currValue;
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ConjugateGradient.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ConjugateGradient.java
deleted file mode 100644
index 28295729..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ConjugateGradient.java
+++ /dev/null
@@ -1,92 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.DifferentiableLineSearchObjective;
-import optimization.linesearch.LineSearchMethod;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.util.MathUtils;
-
-
-
-public class ConjugateGradient extends AbstractGradientBaseMethod{
-	
-	
-	double[] previousGradient;
-	double[] previousDirection;
-
-	public ConjugateGradient(LineSearchMethod lineSearch) {
-		this.lineSearch = lineSearch;
-	}
-	
-	public void reset(){
-		super.reset();
-		java.util.Arrays.fill(previousDirection, 0);
-		java.util.Arrays.fill(previousGradient, 0);
-	}
-	
-	public void initializeStructures(Objective o,OptimizerStats stats, StopingCriteria stop){
-		super.initializeStructures(o, stats, stop);
-		previousGradient = new double[o.getNumParameters()];
-		previousDirection = new double[o.getNumParameters()];
-	}
-	public void updateStructuresBeforeStep(Objective o,OptimizerStats stats, StopingCriteria stop){
-		System.arraycopy(gradient, 0, previousGradient, 0, gradient.length);
-		System.arraycopy(direction, 0, previousDirection, 0, direction.length);	
-	}
-	
-//	public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stop){
-//		DifferentiableLineSearchObjective lso = new DifferentiableLineSearchObjective(o);
-//		stats.collectInitStats(this, o);
-//		direction = new double[o.getNumParameters()];
-//		initializeStructures(o, stats, stop);
-//		for (currentProjectionIteration = 0; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){
-//			previousValue = currValue;
-//			currValue = o.getValue();
-//			gradient =o.getGradient();
-//			if(stop.stopOptimization(gradient)){
-//				stats.collectFinalStats(this, o);
-//				return true;
-//			}
-//			getDirection();
-//			updateStructures(o, stats, stop);
-//			lso.reset(direction);
-//			step = lineSearch.getStepSize(lso);	
-//			if(step==-1){
-//				System.out.println("Failed to find a step size");
-//				System.out.println("Failed to find step");
-//				stats.collectFinalStats(this, o);
-//				return false;	
-//			}
-//			
-//			stats.collectIterationStats(this, o);
-//		}
-//		stats.collectFinalStats(this, o);
-//		return false;
-//	}
-	
-	public double[] getDirection(){
-		direction = MathUtils.negation(gradient);
-		if(currentProjectionIteration != 1){
-			//Using Polak-Ribiere method (book equation 5.45)
-			double b = MathUtils.dotProduct(gradient, MathUtils.arrayMinus(gradient, previousGradient))
-			/MathUtils.dotProduct(previousGradient, previousGradient);
-			if(b<0){
-				System.out.println("Defaulting to gradient descent");
-				b = Math.max(b, 0);
-			}
-			MathUtils.plusEquals(direction, previousDirection, b);
-			//Debug code
-			if(MathUtils.dotProduct(direction, gradient) > 0){
-				System.out.println("Not an descent direction reseting to gradien");
-				direction = MathUtils.negation(gradient);
-			}
-		}
-		return direction;
-	}
-	
-	
-	
-
-
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/DebugHelpers.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/DebugHelpers.java
deleted file mode 100644
index 6dc4ef6c..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/DebugHelpers.java
+++ /dev/null
@@ -1,65 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import java.util.ArrayList;
-
-import optimization.util.MathUtils;
-
-
-
-public class DebugHelpers {
-	public static void getLineSearchGraph(Objective o, double[] direction, 
-			double[] parameters, double originalObj,
-			double originalDot, double c1, double c2){
-		ArrayList<Double> stepS = new ArrayList<Double>();
-		ArrayList<Double> obj = new ArrayList<Double>();
-		ArrayList<Double> norm = new ArrayList<Double>();
-		double[] gradient = new double[o.getNumParameters()];
-		double[] newParameters = parameters.clone();
-		MathUtils.plusEquals(newParameters,direction,0);
-		o.setParameters(newParameters);
-		double minValue = o.getValue();
-		int valuesBiggerThanMax = 0;
-		for(double step = 0; step < 2; step +=0.01 ){
-			newParameters = parameters.clone();
-			MathUtils.plusEquals(newParameters,direction,step);
-			o.setParameters(newParameters);
-			double newValue = o.getValue();
-			gradient = o.getGradient();
-			double newgradDirectionDot = MathUtils.dotProduct(gradient,direction);
-			stepS.add(step);
-			obj.add(newValue);
-			norm.add(newgradDirectionDot);
-			if(newValue <= minValue){
-				minValue = newValue;
-			}else{
-				valuesBiggerThanMax++;
-			}
-			
-			if(valuesBiggerThanMax > 10){
-				break;
-			}
-			
-		}
-		System.out.println("step\torigObj\tobj\tsuffdec\tnorm\tcurvature1");
-		for(int i = 0; i < stepS.size(); i++){
-			double cnorm= norm.get(i); 
-			System.out.println(stepS.get(i)+"\t"+originalObj +"\t"+obj.get(i) + "\t" + 
-					(originalObj + originalDot*((Double)stepS.get(i))*c1) +"\t"+Math.abs(cnorm) +"\t"+c2*Math.abs(originalDot));
-		}
-	}
-	
-	public static double[] getNumericalGradient(Objective o, double[] parameters, double epsilon){
-		int nrParameters = o.getNumParameters();
-		double[] gradient = new double[nrParameters];
-		double[] newParameters;
-		double originalValue = o.getValue();
-		for(int parameter = 0; parameter < nrParameters; parameter++){
-			newParameters = parameters.clone();
-			newParameters[parameter]+=epsilon;
-			o.setParameters(newParameters);
-			double newValue = o.getValue();
-			gradient[parameter]=(newValue-originalValue)/epsilon;
-		}	
-		return gradient;
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/GradientDescent.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/GradientDescent.java
deleted file mode 100644
index 9a53cef4..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/GradientDescent.java
+++ /dev/null
@@ -1,19 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import optimization.linesearch.LineSearchMethod;
-
-
-
-public class GradientDescent extends AbstractGradientBaseMethod{
-	
-	public GradientDescent(LineSearchMethod lineSearch) {
-		this.lineSearch = lineSearch;
-	}
-		
-	public double[] getDirection(){
-		for(int i = 0; i< gradient.length; i++){
-			direction[i] = -gradient[i];
-		}
-		return direction;
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java
deleted file mode 100644
index dedbc942..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java
+++ /dev/null
@@ -1,234 +0,0 @@
-package optimization.gradientBasedMethods;
-
-
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.DifferentiableLineSearchObjective;
-import optimization.linesearch.LineSearchMethod;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.util.MathUtils;
-
-public class LBFGS extends AbstractGradientBaseMethod{
-
-	//How many previous values are being saved
-	int history;
-	double[][] skList;
-	double[][] ykList;
-	double initialHessianParameters;
-	double[] previousGradient;
-	double[] previousParameters;
-	
-	//auxiliar structures
-	double q[];
-	double[] roi;
-	double[] alphai;
-	
-	public LBFGS(LineSearchMethod ls, int history) {
-		lineSearch = ls;
-		this.history = history;
-		skList = new double[history][];
-		ykList = new double[history][];
-
-	}
-	
-	public void reset(){
-		super.reset();
-		initialHessianParameters = 0;
-		previousParameters = null;
-		previousGradient = null;
-		skList = new double[history][];
-		ykList = new double[history][];
-		q = null;
-		roi = null;
-		alphai = null;
-	}
-	
-	public double[] LBFGSTwoLoopRecursion(double hessianConst){
-		//Only create array once
-		if(q == null){
-			 q = new double[gradient.length];
-		}
-		System.arraycopy(gradient, 0, q, 0, gradient.length);
-		//Only create array once
-		if(roi == null){
-			roi = new double[history]; 
-		}
-		//Only create array once
-		if(alphai == null){
-			alphai = new double[history];
-		}
-		
-		for(int i = history-1; i >=0 && skList[i]!= null && ykList[i]!=null; i-- ){			
-		//	System.out.println("New to Old proj " + currentProjectionIteration + " history "+history + " index " + i);
-			double[] si =  skList[i];
-			double[] yi = ykList[i];
-			roi[i]= 1.0/MathUtils.dotProduct(yi,si);
-			alphai[i] = MathUtils.dotProduct(si, q)*roi[i];
-			MathUtils.plusEquals(q, yi, -alphai[i]);
-		}
-		//Initial Hessian is just a constant
-		MathUtils.scalarMultiplication(q, hessianConst);
-		for(int i = 0; i <history && skList[i]!= null && ykList[i]!=null; i++ ){
-		//	System.out.println("Old to New proj " + currentProjectionIteration + " history "+history + " index " + i);
-			double beta = MathUtils.dotProduct(ykList[i], q)*roi[i];
-			MathUtils.plusEquals(q, skList[i], (alphai[i]-beta));
-		}
-		return q;
-	}
-	
-	
-	
-	
-	@Override
-	public double[] getDirection() {
-		
-		calculateInitialHessianParameter();
-//		System.out.println("Initial hessian " + initialHessianParameters);
-		return direction = MathUtils.negation(LBFGSTwoLoopRecursion(initialHessianParameters));		
-	}
-	
-	public void calculateInitialHessianParameter(){
-		if(currentProjectionIteration == 1){
-			//Use gradient
-			initialHessianParameters = 1;
-		}else if(currentProjectionIteration <= history){
-			double[] sk = skList[currentProjectionIteration-2];
-			double[] yk = ykList[currentProjectionIteration-2];
-			initialHessianParameters = MathUtils.dotProduct(sk, yk)/MathUtils.dotProduct(yk, yk);
-		}else{
-			//get the last one
-			double[] sk = skList[history-1];
-			double[] yk = ykList[history-1];
-			initialHessianParameters = MathUtils.dotProduct(sk, yk)/MathUtils.dotProduct(yk, yk);
-		}
-	}
-	
-	//TODO if structures exit just reset them to zero
-	public void initializeStructures(Objective o,OptimizerStats stats, StopingCriteria stop){
-		super.initializeStructures(o, stats, stop);
-		previousParameters = new double[o.getNumParameters()];
-		previousGradient = new double[o.getNumParameters()];
-	}
-	public void updateStructuresBeforeStep(Objective o,OptimizerStats stats, StopingCriteria stop){	
-		super.initializeStructures(o, stats, stop);
-		System.arraycopy(o.getParameters(), 0, previousParameters, 0, previousParameters.length);
-		System.arraycopy(gradient, 0, previousGradient, 0, gradient.length);
-	}
-
-	public void 	updateStructuresAfterStep( Objective o,OptimizerStats stats, StopingCriteria stop){
-		double[] diffX = MathUtils.arrayMinus(o.getParameters(), previousParameters);
-		double[] diffGrad = MathUtils.arrayMinus(gradient, previousGradient);
-		//Save new values and discard new ones
-		if(currentProjectionIteration > history){
-			for(int i = 0; i < history-1;i++){
-				skList[i]=skList[i+1];
-				ykList[i]=ykList[i+1];
-			}
-			skList[history-1]=diffX;
-			ykList[history-1]=diffGrad;
-		}else{
-			skList[currentProjectionIteration-1]=diffX;
-			ykList[currentProjectionIteration-1]=diffGrad;
-		}	
-	}
-	
-//	public boolean optimize(Objective o, OptimizerStats stats, StopingCriteria stop) {		
-//		DifferentiableLineSearchObjective lso = new DifferentiableLineSearchObjective(o);		
-//		gradient = o.getGradient();
-//		direction = new double[o.getNumParameters()];
-//		previousGradient = new double[o.getNumParameters()];
-//		
-//		previousParameters = new double[o.getNumParameters()];
-//	
-//		stats.collectInitStats(this, o);
-//		previousValue = Double.MAX_VALUE;
-//		currValue= o.getValue();
-//		//Used for stopping criteria
-//		double[] originalGradient = o.getGradient();
-//		
-//		originalGradientL2Norm = MathUtils.L2Norm(originalGradient);
-//		if(stop.stopOptimization(originalGradient)){
-//			stats.collectFinalStats(this, o);
-//			return true;
-//		}
-//		for (currentProjectionIteration = 1; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){
-//			
-//			
-//			currValue = o.getValue();
-//			gradient  = o.getGradient();
-//			currParameters = o.getParameters();
-//			
-//			
-//			if(currentProjectionIteration == 1){
-//				//Use gradient
-//				initialHessianParameters = 1;
-//			}else if(currentProjectionIteration <= history){
-//				double[] sk = skList[currentProjectionIteration-2];
-//				double[] yk = ykList[currentProjectionIteration-2];
-//				initialHessianParameters = MathUtils.dotProduct(sk, yk)/MathUtils.dotProduct(yk, yk);
-//			}else{
-//				//get the last one
-//				double[] sk = skList[history-1];
-//				double[] yk = ykList[history-1];
-//				initialHessianParameters = MathUtils.dotProduct(sk, yk)/MathUtils.dotProduct(yk, yk);
-//			}
-//			
-//			getDirection();
-//			
-//			//MatrixOutput.printDoubleArray(direction, "direction");
-//			double dot = MathUtils.dotProduct(direction, gradient);
-//			if(dot > 0){				
-//				throw new RuntimeException("Not a descent direction");
-//			} if (Double.isNaN(dot)){
-//				throw new RuntimeException("dot is not a number!!");
-//			}
-//			System.arraycopy(currParameters, 0, previousParameters, 0, currParameters.length);
-//			System.arraycopy(gradient, 0, previousGradient, 0, gradient.length);
-//			lso.reset(direction);
-//			step = lineSearch.getStepSize(lso);
-//			if(step==-1){
-//				System.out.println("Failed to find a step size");
-////				lso.printLineSearchSteps();
-////				System.out.println(stats.prettyPrint(1));
-//				stats.collectFinalStats(this, o);
-//				return false;	
-//			}
-//			stats.collectIterationStats(this, o);
-//			
-//			//We are not updating the alpha since it is done in line search already
-//			currParameters = o.getParameters();
-//			gradient = o.getGradient();
-//			
-//			if(stop.stopOptimization(gradient)){
-//				stats.collectFinalStats(this, o);
-//				return true;
-//			}
-//			double[] diffX = MathUtils.arrayMinus(currParameters, previousParameters);
-//			double[] diffGrad = MathUtils.arrayMinus(gradient, previousGradient);
-//			//Save new values and discard new ones
-//			if(currentProjectionIteration > history){
-//				for(int i = 0; i < history-1;i++){
-//					skList[i]=skList[i+1];
-//					ykList[i]=ykList[i+1];
-//				}
-//				skList[history-1]=diffX;
-//				ykList[history-1]=diffGrad;
-//			}else{
-//				skList[currentProjectionIteration-1]=diffX;
-//				ykList[currentProjectionIteration-1]=diffGrad;
-//			}		
-//			previousValue = currValue;
-//		}
-//		stats.collectFinalStats(this, o);
-//		return false;	
-//	}
-	
-
-
-	
-
-	
-
-	
-	
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Objective.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Objective.java
deleted file mode 100644
index 6be01bf9..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Objective.java
+++ /dev/null
@@ -1,87 +0,0 @@
-package optimization.gradientBasedMethods;
-
-
-/**
- * Defines an optimization objective:
- * 
- * 
- * @author javg
- *
- */
-public abstract  class Objective {
-
-	protected int functionCalls = 0;
-	protected int gradientCalls = 0;
-	protected int updateCalls = 0;
-	
-	protected double[] parameters;
-	
-	//Contains a cache with the gradient
-	public double[] gradient;
-	int debugLevel = 0;
-	
-	public void setDebugLevel(int level){
-		debugLevel = level;
-	}
-	
-	public int getNumParameters() {
-		return parameters.length;
-	}
-
-	public double getParameter(int index) {
-		return parameters[index];
-	}
-
-	public double[] getParameters() {
-		return parameters;
-	}
-
-	public abstract double[] getGradient( );
-	
-	public void setParameter(int index, double value) {
-		parameters[index]=value;
-	}
-
-	public void setParameters(double[] params) {
-		if(parameters == null){
-			parameters = new double[params.length];
-		}
-		updateCalls++;
-		System.arraycopy(params, 0, parameters, 0, params.length);
-	}
-
-	
-	public int getNumberFunctionCalls() {
-		return functionCalls;
-	}
-
-	public int getNumberGradientCalls() {
-		return gradientCalls;
-	}
-	
-	public int getNumberUpdateCalls() {
-		return updateCalls;
-	}
-	
-	public String finalInfoString() {
-		return "FE: " + functionCalls + " GE " + gradientCalls + " Params updates" +
-		updateCalls;
-	}
-	public void printParameters() {
-		System.out.println(toString());
-	}	
-	
-	public abstract String toString();	
-	public abstract double getValue ();
-	
-	/**
-	 * Sets the initial objective parameters
-	 * For unconstrained models this just sets the objective params = argument no copying
-	 * For a constrained objective project the parameters and then set
-	 * @param params
-	 */
-	public  void setInitialParameters(double[] params){
-		parameters = params;
-	}
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Optimizer.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Optimizer.java
deleted file mode 100644
index 96fce5b0..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Optimizer.java
+++ /dev/null
@@ -1,19 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.stopCriteria.StopingCriteria;
-
-public interface Optimizer {
-	public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stoping);
-	
-	
-	public double[] getDirection();
-	public double getCurrentStep();
-	public double getCurrentValue();
-	public int getCurrentIteration();
-	public void reset();
-	
-	public void setMaxIterations(int max);
-	
-		
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedAbstractGradientBaseMethod.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedAbstractGradientBaseMethod.java
deleted file mode 100644
index afb29d04..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedAbstractGradientBaseMethod.java
+++ /dev/null
@@ -1,11 +0,0 @@
-package optimization.gradientBasedMethods;
-
-
-/**
- * 
- * @author javg
- *
- */
-public abstract class ProjectedAbstractGradientBaseMethod extends AbstractGradientBaseMethod implements ProjectedOptimizer{
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java
deleted file mode 100644
index 0186e945..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java
+++ /dev/null
@@ -1,154 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import java.io.IOException;
-
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.DifferentiableLineSearchObjective;
-import optimization.linesearch.LineSearchMethod;
-import optimization.linesearch.ProjectedDifferentiableLineSearchObjective;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.util.MathUtils;
-
-
-/**
- * This class implements the projected gradiend
- * as described in Bertsekas "Non Linear Programming"
- * section 2.3.
- * 
- * The update is given by:
- * x_k+1 = x_k + alpha^k(xbar_k-x_k)
- * Where xbar is:
- * xbar = [x_k -s_k grad(f(x_k))]+
- * where []+ is the projection into the feasibility set
- * 
- * alpha is the step size 
- * s_k - is a positive scalar which can be view as a step size as well, by 
- * setting alpha to 1, then x_k+1 = [x_k -s_k grad(f(x_k))]+
- * This is called taking a step size along the projection arc (Bertsekas) which
- * we will use by default.
- * 
- * Note that the only place where we actually take a step size is on pick a step size
- * so this is going to be just like a normal gradient descent but use a different 
- * armijo line search where we project after taking a step.
- * 
- * 
- * @author javg
- *
- */
-public class ProjectedGradientDescent extends ProjectedAbstractGradientBaseMethod{
-	
-
-	
-	
-	public ProjectedGradientDescent(LineSearchMethod lineSearch) {
-		this.lineSearch = lineSearch;
-	}
-	
-	//Use projected differential objective instead
-	public void initializeStructures(Objective o, OptimizerStats stats, StopingCriteria stop) {
-		lso = new ProjectedDifferentiableLineSearchObjective(o);
-	};
-	
-	
-	ProjectedObjective obj;
-	public boolean optimize(ProjectedObjective o,OptimizerStats stats, StopingCriteria stop){
-		obj = o;
-		return super.optimize(o, stats, stop);
-	}
-	
-	public double[] getDirection(){
-		for(int i = 0; i< gradient.length; i++){
-			direction[i] = -gradient[i];
-		}
-		return direction;
-	}
-	
-	
-
-		
-}
-
-
-
-
-
-
-
-///OLD CODE
-
-//Use projected gradient norm
-//public boolean stopCriteria(double[] gradient){
-//	if(originalDirenctionL2Norm == 0){
-//		System.out.println("Leaving original direction norm is zero");
-//		return true;	
-//	}
-//	if(MathUtils.L2Norm(direction)/originalDirenctionL2Norm < gradientConvergenceValue){
-//		System.out.println("Leaving projected gradient Norm smaller than epsilon");
-//		return true;	
-//	}
-//	if((previousValue - currValue)/Math.abs(previousValue) < valueConvergenceValue) {
-//		System.out.println("Leaving value change below treshold " + previousValue + " - " + currValue);
-//		System.out.println(previousValue/currValue + " - " + currValue/currValue 
-//				+ " = " + (previousValue - currValue)/Math.abs(previousValue));
-//		return true;
-//	}
-//	return false;
-//}
-//
-
-//public boolean optimize(ProjectedObjective o,OptimizerStats stats, StopingCriteria stop){
-//		stats.collectInitStats(this, o);
-//		obj = o;
-//		step = 0;
-//		currValue = o.getValue();
-//		previousValue = Double.MAX_VALUE;
-//		gradient = o.getGradient();
-//		originalGradientL2Norm = MathUtils.L2Norm(gradient);
-//		parameterChange = new double[gradient.length];
-//		getDirection();
-//		ProjectedDifferentiableLineSearchObjective lso = new ProjectedDifferentiableLineSearchObjective(o,direction);
-//		
-//		originalDirenctionL2Norm = MathUtils.L2Norm(direction);
-//		//MatrixOutput.printDoubleArray(currParameters, "parameters");
-//		for (currentProjectionIteration = 0; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){		
-//		//	System.out.println("Iter " + currentProjectionIteration);
-//			//o.printParameters();
-//			
-//			
-//			
-//			if(stop.stopOptimization(gradient)){
-//				stats.collectFinalStats(this, o);
-//				lastStepUsed = step;
-//				return true;
-//			}			
-//			lso.reset(direction);
-//			step = lineSearch.getStepSize(lso);
-//			if(step==-1){
-//				System.out.println("Failed to find step");
-//				stats.collectFinalStats(this, o);
-//				return false;	
-//				
-//			}
-//			
-//			//Update the direction for stopping criteria
-//			previousValue = currValue;
-//			currValue = o.getValue();
-//			gradient = o.getGradient();
-//			direction = getDirection();		
-//			if(MathUtils.dotProduct(gradient, direction) > 0){
-//				System.out.println("Not a descent direction");
-//				System.out.println(" current stats " + stats.prettyPrint(1));
-//				System.exit(-1);
-//			}
-//			stats.collectIterationStats(this, o);
-//		}
-//		lastStepUsed = step;
-//		stats.collectFinalStats(this, o);
-//		return false;
-//	}
-
-//public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stop){
-//	System.out.println("Objective is not a projected objective");
-//	throw new RuntimeException();
-//}
-
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedObjective.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedObjective.java
deleted file mode 100644
index c3d21393..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedObjective.java
+++ /dev/null
@@ -1,29 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import optimization.util.MathUtils;
-
-
-/**
- * Computes a projected objective
- * When we tell it to set some parameters it automatically projects the parameters back into the simplex:
- * 
- * 
- * When we tell it to get the gradient in automatically returns the projected gradient:
- * @author javg
- *
- */
-public abstract class ProjectedObjective extends Objective{
-	
-	public abstract double[] projectPoint (double[] point);
-	
-	public double[] auxParameters;
-	
-	
-	public  void setInitialParameters(double[] params){
-		setParameters(projectPoint(params));
-	}
-	
-	
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedOptimizer.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedOptimizer.java
deleted file mode 100644
index 81d8403e..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedOptimizer.java
+++ /dev/null
@@ -1,10 +0,0 @@
-package optimization.gradientBasedMethods;
-
-
-
-public interface ProjectedOptimizer extends Optimizer{
-	
-	
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/OptimizerStats.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/OptimizerStats.java
deleted file mode 100644
index 6340ef73..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/OptimizerStats.java
+++ /dev/null
@@ -1,86 +0,0 @@
-package optimization.gradientBasedMethods.stats;
-
-import java.util.ArrayList;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.Optimizer;
-import optimization.util.MathUtils;
-import optimization.util.StaticTools;
-
-
-public class OptimizerStats {
-	
-	double start = 0;
-	double totalTime = 0;
-	
-	String objectiveFinalStats;
-	
-	ArrayList<Double> gradientNorms = new ArrayList<Double>();
-	ArrayList<Double> steps = new ArrayList<Double>();
-	ArrayList<Double> value = new ArrayList<Double>();
-	ArrayList<Integer> iterations = new ArrayList<Integer>();
-	double prevValue =0;
-	
-	public void reset(){
-		start = 0;
-		totalTime = 0;
-		
-		objectiveFinalStats="";
-		
-		gradientNorms.clear();
-		steps.clear();
-		value.clear();
-		iterations.clear();
-		prevValue =0;
-	}
-	
-	public void startTime() {
-		start = System.currentTimeMillis();
-	}
-	public void stopTime() {
-		totalTime += System.currentTimeMillis() - start;
-	}
-	
-	public String prettyPrint(int level){
-		StringBuffer res = new StringBuffer();
-		res.append("Total time " + totalTime/1000 + " seconds \n" + "Iterations " + iterations.size() + "\n");
-		res.append(objectiveFinalStats+"\n");
-		if(level > 0){
-			if(iterations.size() > 0){
-			res.append("\tIteration"+iterations.get(0)+"\tstep: "+StaticTools.prettyPrint(steps.get(0), "0.00E00", 6)+ "\tgradientNorm "+ 
-					StaticTools.prettyPrint(gradientNorms.get(0), "0.00000E00", 10)+ "\tvalue "+ StaticTools.prettyPrint(value.get(0), "0.000000E00",11)+"\n");
-			}
-			for(int i = 1; i < iterations.size(); i++){
-			res.append("\tIteration:\t"+iterations.get(i)+"\tstep:"+StaticTools.prettyPrint(steps.get(i), "0.00E00", 6)+ "\tgradientNorm "+ 
-					StaticTools.prettyPrint(gradientNorms.get(i), "0.00000E00", 10)+ 
-					"\tvalue:\t"+ StaticTools.prettyPrint(value.get(i), "0.000000E00",11)+
-					"\tvalueDiff:\t"+ StaticTools.prettyPrint((value.get(i-1)-value.get(i)), "0.000000E00",11)+
-					"\n");
-			}
-		}
-		return res.toString();
-	}
-	
-	
-	public void collectInitStats(Optimizer optimizer, Objective objective){
-		startTime();
-		iterations.add(-1);
-		gradientNorms.add(MathUtils.L2Norm(objective.getGradient()));
-		steps.add(0.0);
-		value.add(objective.getValue());
-	}
-	
-	public void collectIterationStats(Optimizer optimizer, Objective objective){
-		iterations.add(optimizer.getCurrentIteration());
-		gradientNorms.add(MathUtils.L2Norm(objective.getGradient()));
-		steps.add(optimizer.getCurrentStep());
-		value.add(optimizer.getCurrentValue());
-	}
-	
-	
-	public void collectFinalStats(Optimizer optimizer, Objective objective){
-		stopTime();
-		objectiveFinalStats = objective.finalInfoString();
-	}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/ProjectedOptimizerStats.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/ProjectedOptimizerStats.java
deleted file mode 100644
index d65a1267..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/ProjectedOptimizerStats.java
+++ /dev/null
@@ -1,70 +0,0 @@
-package optimization.gradientBasedMethods.stats;
-
-import java.util.ArrayList;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.Optimizer;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.gradientBasedMethods.ProjectedOptimizer;
-import optimization.util.MathUtils;
-import optimization.util.StaticTools;
-
-
-public class ProjectedOptimizerStats extends OptimizerStats{
-	
-	
-	
-	public void reset(){
-		super.reset();
-		projectedGradientNorms.clear();
-	}
-	
-	ArrayList<Double> projectedGradientNorms = new ArrayList<Double>();
-
-	public String prettyPrint(int level){
-		StringBuffer res = new StringBuffer();
-		res.append("Total time " + totalTime/1000 + " seconds \n" + "Iterations " + iterations.size() + "\n");
-		res.append(objectiveFinalStats+"\n");
-		if(level > 0){
-			if(iterations.size() > 0){
-			res.append("\tIteration"+iterations.get(0)+"\tstep: "+
-					StaticTools.prettyPrint(steps.get(0), "0.00E00", 6)+ "\tgradientNorm "+ 
-					StaticTools.prettyPrint(gradientNorms.get(0), "0.00000E00", 10)
-					+ "\tdirection"+
-					StaticTools.prettyPrint(projectedGradientNorms.get(0), "0.00000E00", 10)+
-					"\tvalue "+ StaticTools.prettyPrint(value.get(0), "0.000000E00",11)+"\n");
-			}
-			for(int i = 1; i < iterations.size(); i++){
-			res.append("\tIteration"+iterations.get(i)+"\tstep: "+StaticTools.prettyPrint(steps.get(i), "0.00E00", 6)+ "\tgradientNorm "+ 
-					StaticTools.prettyPrint(gradientNorms.get(i), "0.00000E00", 10)+ 
-					"\t direction "+
-					StaticTools.prettyPrint(projectedGradientNorms.get(i), "0.00000E00", 10)+
-					"\tvalue "+ StaticTools.prettyPrint(value.get(i), "0.000000E00",11)+
-					"\tvalueDiff "+ StaticTools.prettyPrint((value.get(i-1)-value.get(i)), "0.000000E00",11)+
-					"\n");
-			}
-		}
-		return res.toString();
-	}
-	
-	
-	public void collectInitStats(Optimizer optimizer, Objective objective){
-		startTime();
-	}
-	
-	public void collectIterationStats(Optimizer optimizer, Objective objective){		
-		iterations.add(optimizer.getCurrentIteration());
-		gradientNorms.add(MathUtils.L2Norm(objective.getGradient()));
-		projectedGradientNorms.add(MathUtils.L2Norm(optimizer.getDirection()));
-		steps.add(optimizer.getCurrentStep());
-		value.add(optimizer.getCurrentValue());
-	}
-	
-	
-	
-	public void collectFinalStats(Optimizer optimizer, Objective objective){
-		stopTime();
-		objectiveFinalStats = objective.finalInfoString();
-	}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java
deleted file mode 100644
index c9f9b8df..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java
+++ /dev/null
@@ -1,102 +0,0 @@
-package optimization.linesearch;
-
-import optimization.util.Interpolation;
-
-
-/**
- * Implements Back Tracking Line Search as described on page 37 of Numerical Optimization.
- * Also known as armijo rule
- * @author javg
- *
- */
-public class ArmijoLineSearchMinimization implements LineSearchMethod{
-
-	/**
-	 * How much should the step size decrease at each iteration.
-	 */
-	double contractionFactor = 0.5;
-	double c1 = 0.0001;
-	
-	double sigma1 = 0.1;
-	double sigma2 = 0.9;
-
-
-	
-	double initialStep;
-	int maxIterations = 10;
-	
-			
-	public ArmijoLineSearchMinimization(){
-		this.initialStep = 1;
-	}
-	
-	//Experiment
-	double previousStepPicked = -1;;
-	double previousInitGradientDot = -1;
-	double currentInitGradientDot = -1;
-	
-	
-	public void reset(){
-		previousStepPicked = -1;;
-		previousInitGradientDot = -1;
-		currentInitGradientDot = -1;
-	}
-	
-	public void setInitialStep(double initial){
-		initialStep = initial;
-	}
-	
-	/**
-	 * 
-	 */
-	
-	public double getStepSize(DifferentiableLineSearchObjective o) {	
-		currentInitGradientDot = o.getInitialGradient();
-		//Should update all in the objective
-		o.updateAlpha(initialStep);	
-		int nrIterations = 0;
-		//System.out.println("tried alpha" + initialStep + " value " + o.getCurrentValue());
-		while(!WolfeConditions.suficientDecrease(o,c1)){			
-			if(nrIterations >= maxIterations){
-				o.printLineSearchSteps();	
-				return -1;
-			}
-			double alpha=o.getAlpha();
-			double alphaTemp = 
-				Interpolation.quadraticInterpolation(o.getOriginalValue(), o.getInitialGradient(), alpha, o.getCurrentValue());
-			if(alphaTemp >= sigma1 || alphaTemp <= sigma2*o.getAlpha()){
-//				System.out.println("using alpha temp " + alphaTemp);
-				alpha = alphaTemp;
-			}else{
-//				System.out.println("Discarding alpha temp " + alphaTemp);
-				alpha = alpha*contractionFactor;
-			}
-//			double alpha =o.getAlpha()*contractionFactor;
-
-			o.updateAlpha(alpha);
-			//System.out.println("tried alpha" + alpha+ " value " + o.getCurrentValue());
-			nrIterations++;			
-		}
-		
-		//System.out.println("Leavning line search used:");
-		//o.printLineSearchSteps();	
-		
-		previousInitGradientDot = currentInitGradientDot;
-		previousStepPicked = o.getAlpha();
-		return o.getAlpha();
-	}
-
-	public double getInitialGradient() {
-		return currentInitGradientDot;
-		
-	}
-
-	public double getPreviousInitialGradient() {
-		return previousInitGradientDot;
-	}
-
-	public double getPreviousStepUsed() {
-		return previousStepPicked;
-	}
-		
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimizationAlongProjectionArc.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimizationAlongProjectionArc.java
deleted file mode 100644
index e153f2da..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimizationAlongProjectionArc.java
+++ /dev/null
@@ -1,141 +0,0 @@
-package optimization.linesearch;
-
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.util.Interpolation;
-import optimization.util.MathUtils;
-
-
-
-
-
-/**
- * Implements Armijo Rule Line search along the projection arc (Non-Linear Programming page 230)
- * To be used with Projected gradient Methods.
- * 
- * Recall that armijo tries successive step sizes alpha until the sufficient decrease is satisfied:
- * f(x+alpha*direction) < f(x) + alpha*c1*grad(f)*direction
- * 
- * In this case we are optimizing over a convex set X so we must guarantee that the new point stays inside the 
- * constraints.
- * First the direction as to be feasible (inside constraints) and will be define as:
- * d = (x_k_f - x_k) where x_k_f is a feasible point.
- * so the armijo condition can be rewritten as:
- * f(x+alpha(x_k_f - x_k)) < f(x) + c1*grad(f)*(x_k_f - x_k)
- * and x_k_f is defined as:
- * [x_k-alpha*grad(f)]+
- * where []+ mean a projection to the feasibility set.
- * So this means that we take a step on the negative gradient (gradient descent) and then obtain then project
- * that point to the feasibility set. 
- * Note that if the point is already feasible then we are back to the normal armijo rule.
- * 
- * @author javg
- *
- */
-public class ArmijoLineSearchMinimizationAlongProjectionArc implements LineSearchMethod{
-
-	/**
-	 * How much should the step size decrease at each iteration.
-	 */
-	double contractionFactor = 0.5;
-	double c1 = 0.0001;
-	
-	
-	double initialStep;
-	int maxIterations = 100;
-			
-	
-	double sigma1 = 0.1;
-	double sigma2 = 0.9;
-	
-	//Experiment
-	double previousStepPicked = -1;;
-	double previousInitGradientDot = -1;
-	double currentInitGradientDot = -1;
-	
-	GenericPickFirstStep strategy;
-	
-	
-	public void reset(){
-		previousStepPicked = -1;;
-		previousInitGradientDot = -1;
-		currentInitGradientDot = -1;
-	}
-
-	
-	public ArmijoLineSearchMinimizationAlongProjectionArc(){
-		this.initialStep = 1;
-	}
-	
-	public ArmijoLineSearchMinimizationAlongProjectionArc(GenericPickFirstStep strategy){
-		this.strategy = strategy;
-		this.initialStep = strategy.getFirstStep(this);
-	}
-	
-	
-	public void setInitialStep(double initial){
-		this.initialStep = initial;
-	}
-	
-	/**
-	 * 
-	 */
-	
-	public double getStepSize(DifferentiableLineSearchObjective o) {	
-
-		
-		//Should update all in the objective
-		initialStep = strategy.getFirstStep(this);
-		o.updateAlpha(initialStep);	
-		previousInitGradientDot=currentInitGradientDot;
-		currentInitGradientDot=o.getCurrentGradient();
-		int nrIterations = 0;
-	
-		//Armijo rule, the current value has to be smaller than the original value plus a small step of the gradient
-		while(o.getCurrentValue()  >
-			o.getOriginalValue() + c1*(o.getCurrentGradient())){			
-//			System.out.println("curr value "+o.getCurrentValue());
-//			System.out.println("original value "+o.getOriginalValue());
-//			System.out.println("GRADIENT decrease" +(MathUtils.dotProduct(o.o.gradient,
-//					MathUtils.arrayMinus(o.originalParameters,((ProjectedObjective)o.o).auxParameters))));
-//			System.out.println("GRADIENT SAVED" + o.getCurrentGradient());
-			if(nrIterations >= maxIterations){
-				System.out.println("Could not find a step leaving line search with -1");
-				o.printLineSearchSteps();
-				return -1;
-			}
-			double alpha=o.getAlpha();
-			double alphaTemp = 
-				Interpolation.quadraticInterpolation(o.getOriginalValue(), o.getInitialGradient(), alpha, o.getCurrentValue());
-			if(alphaTemp >= sigma1 || alphaTemp <= sigma2*o.getAlpha()){
-				alpha = alphaTemp;
-			}else{
-				alpha = alpha*contractionFactor;
-			}
-//			double alpha =obj.getAlpha()*contractionFactor;
-			o.updateAlpha(alpha);
-			nrIterations++;			
-		}
-//		System.out.println("curr value "+o.getCurrentValue());
-//		System.out.println("original value "+o.getOriginalValue());
-//		System.out.println("sufficient decrease" +c1*o.getCurrentGradient());
-//		System.out.println("Leavning line search used:");
-//		o.printSmallLineSearchSteps();	
-		
-		previousStepPicked = o.getAlpha();
-		return o.getAlpha();
-	}
-	
-	public double getInitialGradient() {
-		return currentInitGradientDot;
-		
-	}
-
-	public double getPreviousInitialGradient() {
-		return previousInitGradientDot;
-	}
-
-	public double getPreviousStepUsed() {
-		return previousStepPicked;
-	}
-		
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/DifferentiableLineSearchObjective.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/DifferentiableLineSearchObjective.java
deleted file mode 100644
index a5bc958e..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/DifferentiableLineSearchObjective.java
+++ /dev/null
@@ -1,185 +0,0 @@
-package optimization.linesearch;
-
-import gnu.trove.TDoubleArrayList;
-import gnu.trove.TIntArrayList;
-
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.Collections;
-import java.util.Comparator;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.util.MathUtils;
-import optimization.util.StaticTools;
-
-
-
-import util.MathUtil;
-import util.Printing;
-
-
-/**
- * A wrapper class for the actual objective in order to perform 
- * line search.  The optimization code assumes that this does a lot 
- * of caching in order to simplify legibility.  For the applications 
- * we use it for, caching the entire history of evaluations should be 
- * a win. 
- * 
- * Note: the lastEvaluatedAt value is very important, since we will use
- * it to avoid doing an evaluation of the gradient after the line search.  
- * 
- * The differentiable line search objective defines a search along the ray
- * given by a direction of the main objective.
- * It defines the following function, 
- * where f is the original objective function:
- * g(alpha) = f(x_0 + alpha*direction)
- * g'(alpha) = f'(x_0 + alpha*direction)*direction
- * 
- * @author joao
- *
- */
-public class DifferentiableLineSearchObjective {
-
-	
-	
-	Objective o;
-	int nrIterations;
-	TDoubleArrayList steps;
-	TDoubleArrayList values;
-	TDoubleArrayList gradients;
-	
-	//This variables cannot change
-	public double[] originalParameters;
-	public double[] searchDirection;
-
-	
-	/**
-	 * Defines a line search objective:
-	 * Receives:
-	 * Objective to each we are performing the line search, is used to calculate values and gradients
-	 * Direction where to do the ray search, note that the direction does not depend of the 
-	 * objective but depends from the method.
-	 * @param o
-	 * @param direction
-	 */
-	public DifferentiableLineSearchObjective(Objective o) {
-		this.o = o;
-		originalParameters = new double[o.getNumParameters()];
-		searchDirection = new double[o.getNumParameters()];
-		steps = new TDoubleArrayList();
-		values = new TDoubleArrayList();
-		gradients = new TDoubleArrayList();
-	}
-	/**
-	 * Called whenever we start a new iteration. 
-	 * Receives the ray where we are searching for and resets all values
-	 * 
-	 */
-	public void reset(double[] direction){
-		//Copy initial values
-		System.arraycopy(o.getParameters(), 0, originalParameters, 0, o.getNumParameters());
-		System.arraycopy(direction, 0, searchDirection, 0, o.getNumParameters());
-		
-		//Initialize variables
-		nrIterations = 0;
-		steps.clear();
-		values.clear();
-		gradients.clear();
-	
-		values.add(o.getValue());
-		gradients.add(MathUtils.dotProduct(o.getGradient(),direction));	
-		steps.add(0);
-	}
-	
-	
-	/**
-	 * update the current value of alpha.
-	 * Takes a step with that alpha in direction
-	 * Get the real objective value and gradient and calculate all required information.
-	 */
-	public void updateAlpha(double alpha){
-		if(alpha < 0){
-			System.out.println("alpha may not be smaller that zero");
-			throw new RuntimeException();
-		}
-		nrIterations++;
-		steps.add(alpha);
-		//x_t+1 = x_t + alpha*direction
-		System.arraycopy(originalParameters,0, o.getParameters(), 0, originalParameters.length);
-		MathUtils.plusEquals(o.getParameters(), searchDirection, alpha);
-		o.setParameters(o.getParameters());
-//		System.out.println("Took a step of " + alpha + " new value " + o.getValue());
-		values.add(o.getValue());
-		gradients.add(MathUtils.dotProduct(o.getGradient(),searchDirection));		
-	}
-
-	
-	
-	public int getNrIterations(){
-		return nrIterations;
-	}
-	
-	/**
-	 * return g(alpha) for the current value of alpha
-	 * @param iter
-	 * @return
-	 */
-	public double getValue(int iter){
-		return values.get(iter);
-	}
-	
-	public double getCurrentValue(){
-		return values.get(nrIterations);
-	}
-	
-	public double getOriginalValue(){
-		return values.get(0);
-	}
-
-	/**
-	 * return g'(alpha) for the current value of alpha
-	 * @param iter
-	 * @return
-	 */
-	public double getGradient(int iter){
-		return gradients.get(iter);
-	}
-	
-	public double getCurrentGradient(){
-		return gradients.get(nrIterations);
-	}
-	
-	public double getInitialGradient(){
-		return gradients.get(0);
-	}
-	
-	
-	
-	
-	public double getAlpha(){
-		return steps.get(nrIterations);
-	}
-	
-	public void printLineSearchSteps(){
-		System.out.println(
-				" Steps size "+steps.size() + 
-				"Values size "+values.size() +
-				"Gradeients size "+gradients.size());
-		for(int i =0; i < steps.size();i++){
-			System.out.println("Iter " + i + " step " + steps.get(i) +
-					" value " + values.get(i) + " grad "  + gradients.get(i));
-		}
-	}
-	
-	public void printSmallLineSearchSteps(){
-		for(int i =0; i < steps.size();i++){
-			System.out.print(StaticTools.prettyPrint(steps.get(i), "0.0000E00",8) + " ");
-		}
-		System.out.println();
-	}
-	
-	public static void main(String[] args) {
-		
-	}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/GenericPickFirstStep.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/GenericPickFirstStep.java
deleted file mode 100644
index a33eb311..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/GenericPickFirstStep.java
+++ /dev/null
@@ -1,20 +0,0 @@
-package optimization.linesearch;
-
-
-public class GenericPickFirstStep{
-	double _initValue;
-	public GenericPickFirstStep(double initValue) {
-		_initValue = initValue;
-	}
-	
-	public double getFirstStep(LineSearchMethod ls){
-		return _initValue;
-	}
-	public void collectInitValues(LineSearchMethod ls){
-		
-	}
-	
-	public void collectFinalValues(LineSearchMethod ls){
-		
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/InterpolationPickFirstStep.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/InterpolationPickFirstStep.java
deleted file mode 100644
index 0deebcdb..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/InterpolationPickFirstStep.java
+++ /dev/null
@@ -1,25 +0,0 @@
-package optimization.linesearch;
-
-
-public class InterpolationPickFirstStep extends GenericPickFirstStep{
-	public InterpolationPickFirstStep(double initValue) {
-		super(initValue);
-	}
-	
-	public double getFirstStep(LineSearchMethod ls){
-		if(ls.getPreviousStepUsed() != -1 && ls.getPreviousInitialGradient()!=0){
-			double newStep = Math.min(300, 1.02*ls.getPreviousInitialGradient()*ls.getPreviousStepUsed()/ls.getInitialGradient());
-		//	System.out.println("proposing " + newStep);
-			return newStep;
-			
-		}
-		return _initValue;
-	}
-	public void collectInitValues(WolfRuleLineSearch ls){
-		
-	}
-	
-	public void collectFinalValues(WolfRuleLineSearch ls){
-		
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/LineSearchMethod.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/LineSearchMethod.java
deleted file mode 100644
index 80cd7f39..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/LineSearchMethod.java
+++ /dev/null
@@ -1,14 +0,0 @@
-package optimization.linesearch;
-
-
-public interface LineSearchMethod {
-	
-	double getStepSize(DifferentiableLineSearchObjective o);
-	
-	public double getInitialGradient();
-	public double getPreviousInitialGradient();
-	public double getPreviousStepUsed();
-	
-	public void setInitialStep(double initial);
-	public void reset();
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/NonNewtonInterpolationPickFirstStep.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/NonNewtonInterpolationPickFirstStep.java
deleted file mode 100644
index 4b354fd9..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/NonNewtonInterpolationPickFirstStep.java
+++ /dev/null
@@ -1,33 +0,0 @@
-package optimization.linesearch;
-
-/**
- * Non newtwon since we don't always try 1...
- * Not sure if that is even usefull for newton
- * @author javg
- *
- */
-public class NonNewtonInterpolationPickFirstStep extends GenericPickFirstStep{
-	public NonNewtonInterpolationPickFirstStep(double initValue) {
-		super(initValue);
-	}
-	
-	public double getFirstStep(LineSearchMethod ls){
-//		System.out.println("Previous step used " + ls.getPreviousStepUsed());
-//		System.out.println("PreviousGradinebt " + ls.getPreviousInitialGradient());
-//		System.out.println("CurrentGradinebt " + ls.getInitialGradient());
-		if(ls.getPreviousStepUsed() != -1 && ls.getPreviousInitialGradient()!=0){
-			double newStep = 1.01*ls.getPreviousInitialGradient()*ls.getPreviousStepUsed()/ls.getInitialGradient();
-			//System.out.println("Suggesting " + newStep);
-			return newStep;
-			
-		}
-		return _initValue;
-	}
-	public void collectInitValues(WolfRuleLineSearch ls){
-		
-	}
-	
-	public void collectFinalValues(WolfRuleLineSearch ls){
-		
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ProjectedDifferentiableLineSearchObjective.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ProjectedDifferentiableLineSearchObjective.java
deleted file mode 100644
index 29ccbc32..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ProjectedDifferentiableLineSearchObjective.java
+++ /dev/null
@@ -1,137 +0,0 @@
-package optimization.linesearch;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.util.MathUtils;
-import optimization.util.MatrixOutput;
-
-
-/**
- * See ArmijoLineSearchMinimizationAlongProjectionArc for description
- * @author javg
- *
- */
-public class ProjectedDifferentiableLineSearchObjective extends DifferentiableLineSearchObjective{
-
-	
-	
-	ProjectedObjective obj;
-	public ProjectedDifferentiableLineSearchObjective(Objective o) {
-		super(o);
-		if(!(o instanceof ProjectedObjective)){
-			System.out.println("Must receive a projected objective");
-			throw new RuntimeException();
-		}
-		obj = (ProjectedObjective) o;
-	}
-
-	
-	
-	public double[] projectPoint (double[] point){
-		return ((ProjectedObjective)o).projectPoint(point);
-	}
-	public void updateAlpha(double alpha){
-		if(alpha < 0){
-			System.out.println("alpha may not be smaller that zero");
-			throw new RuntimeException();
-		}
-		
-		if(obj.auxParameters == null){
-			obj.auxParameters = new double[obj.getParameters().length];
-		}
-		
-		nrIterations++;
-		
-		steps.add(alpha);		
-		System.arraycopy(originalParameters, 0, obj.auxParameters, 0, obj.auxParameters.length);
-		
-		//Take a step into the search direction
-		
-//		MatrixOutput.printDoubleArray(obj.getGradient(), "gradient");
-		
-//		alpha=gradients.get(0)*alpha/(gradients.get(gradients.size()-1));
-	
-		//x_t+1 = x_t - alpha*gradient = x_t + alpha*direction
-		MathUtils.plusEquals(obj.auxParameters, searchDirection, alpha);
-//		MatrixOutput.printDoubleArray(obj.auxParameters, "before projection");
-		obj.auxParameters = projectPoint(obj.auxParameters);
-//		MatrixOutput.printDoubleArray(obj.auxParameters, "after projection");
-		o.setParameters(obj.auxParameters);
-//		System.out.println("new parameters");
-//		o.printParameters();
-		values.add(o.getValue());
-		//Computes the new gradient x_k-[x_k-alpha*Gradient(x_k)]+ 
-		MathUtils.minusEqualsInverse(originalParameters,obj.auxParameters,1);
-//		MatrixOutput.printDoubleArray(obj.auxParameters, "new gradient");
-		//Dot product between the new direction and the new gradient
-		double gradient = MathUtils.dotProduct(obj.auxParameters,searchDirection);
-		gradients.add(gradient);	
-		if(gradient > 0){
-			System.out.println("Gradient on line search has to be smaller than zero");
-			System.out.println("Iter: " + nrIterations);
-			MatrixOutput.printDoubleArray(obj.auxParameters, "new direction");
-			MatrixOutput.printDoubleArray(searchDirection, "search direction");
-			throw new RuntimeException();
-			
-		}
-		
-	}
-	
-	/**
-	 * 
-	 */
-//	public void updateAlpha(double alpha){
-//		
-//		if(alpha < 0){
-//			System.out.println("alpha may not be smaller that zero");
-//			throw new RuntimeException();
-//		}
-//		
-//		nrIterations++;
-//		steps.add(alpha);
-//		//x_t+1 = x_t - alpha*direction
-//		System.arraycopy(originalParameters, 0, parametersChange, 0, parametersChange.length);
-////		MatrixOutput.printDoubleArray(parametersChange, "parameters before step");
-////		System.out.println("Step" + alpha);
-//		MatrixOutput.printDoubleArray(originalGradient, "gradient + " + alpha);
-//
-//		MathUtils.minusEquals(parametersChange, originalGradient, alpha);
-//		
-//		//Project the points into the feasibility set
-////		MatrixOutput.printDoubleArray(parametersChange, "before projection");
-//		//x_k(alpha) = [x_k - alpha*grad f(x_k)]+
-//		parametersChange = projectPoint(parametersChange);
-////		MatrixOutput.printDoubleArray(parametersChange, "after projection");
-//		o.setParameters(parametersChange);
-//		values.add(o.getValue());
-//		//Computes the new direction x_k-[x_k-alpha*Gradient(x_k)]+
-//		
-//		direction=MathUtils.arrayMinus(parametersChange,originalParameters);
-////		MatrixOutput.printDoubleArray(direction, "new direction");
-//		
-//		double gradient = MathUtils.dotProduct(originalGradient,direction);
-//		gradients.add(gradient);		
-//		if(gradient > 1E-10){
-//			System.out.println("cosine " + gradient/(MathUtils.L2Norm(originalGradient)*MathUtils.L2Norm(direction)));
-//			
-//			
-//			System.out.println("not a descent direction for alpha " + alpha);
-//			System.arraycopy(originalParameters, 0, parametersChange, 0, parametersChange.length);
-//			MathUtils.minusEquals(parametersChange, originalGradient, 1E-20);
-//			
-//			parametersChange = projectPoint(parametersChange);
-//			direction=MathUtils.arrayMinus(parametersChange,originalParameters);
-//			gradient = MathUtils.dotProduct(originalGradient,direction);
-//			if(gradient > 0){
-//				System.out.println("Direction is really non-descent evern for small alphas:" + gradient);
-//			}
-//			System.out.println("ProjecteLineSearchObjective: Should be a descent direction at " + nrIterations + ": "+ gradient);
-////			System.out.println(Printing.doubleArrayToString(originalGradient, null,"Original gradient"));
-////			System.out.println(Printing.doubleArrayToString(originalParameters, null,"Original parameters"));
-////			System.out.println(Printing.doubleArrayToString(parametersChange, null,"Projected parameters"));
-////			System.out.println(Printing.doubleArrayToString(direction, null,"Direction"));
-//			throw new RuntimeException();
-//		}
-//	}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfRuleLineSearch.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfRuleLineSearch.java
deleted file mode 100644
index 5489f2d0..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfRuleLineSearch.java
+++ /dev/null
@@ -1,300 +0,0 @@
-package optimization.linesearch;
-
-import java.io.PrintStream;
-import java.util.ArrayList;
-
-import optimization.util.Interpolation;
-
-
-
-
-/**
- * 
- * @author javg
- *
- */
-public class WolfRuleLineSearch implements LineSearchMethod{
-
-	GenericPickFirstStep pickFirstStep;
-	
-	double c1 = 1.0E-4;
-	double c2 = 0.9;
-	
-	//Application dependent
-	double maxStep=100;
-	
-	int extrapolationIteration;
-	int maxExtrapolationIteration = 1000;
-	
-	
-	double minZoomDiffTresh = 10E-10;
-
-	
-	ArrayList<Double> steps;
-	ArrayList<Double> gradientDots;
-	ArrayList<Double> functionVals;
-	
-	int debugLevel = 0;
-	boolean foudStep = false;
-	
-	public WolfRuleLineSearch(GenericPickFirstStep pickFirstStep){
-		this.pickFirstStep = pickFirstStep;
-		
-	}
-	
-	
-
-	
-	public WolfRuleLineSearch(GenericPickFirstStep pickFirstStep,  double c1, double c2){
-		this.pickFirstStep = pickFirstStep;
-		initialStep = pickFirstStep.getFirstStep(this);
-		this.c1 = c1;
-		this.c2 = c2;
-	}
-	
-	public void setDebugLevel(int level){
-		debugLevel = level;
-	}
-	
-	//Experiment
-	double previousStepPicked = -1;;
-	double previousInitGradientDot = -1;
-	double currentInitGradientDot = -1;
-	
-	double initialStep;
-
-	
-	public void reset(){
-		previousStepPicked = -1;;
-		previousInitGradientDot = -1;
-		currentInitGradientDot = -1;
-		if(steps != null)
-			steps.clear();
-		if(gradientDots != null)
-			gradientDots.clear();
-		if(functionVals != null)
-			functionVals.clear();
-	}
-	
-	public void setInitialStep(double initial){
-		initialStep = pickFirstStep.getFirstStep(this);
-	}
-	
-	
-	
-	/**
-	 * Implements Wolf Line search as described in nocetal.
-	 * This process consists in two stages. The first stage we try to satisfy the
-	 * biggest step size that still satisfies the curvature condition. We keep increasing
-	 * the initial step size until we find a step satisfying the curvature condition, we return 
-	 * success, we failed the sufficient increase so we cannot increase more and we can call zoom with 
-	 * that maximum step, or we pass the minimum in which case we can call zoom the same way. 
-	 * 
-	 */
-	public double getStepSize(DifferentiableLineSearchObjective objective){
-		//System.out.println("entering line search");
-		
-		foudStep = false;
-		if(debugLevel >= 1){
-			steps = new ArrayList<Double>();
-			gradientDots = new ArrayList<Double>();
-			functionVals  =new ArrayList<Double>();
-		}
-		
-		//test
-		currentInitGradientDot = objective.getInitialGradient();
-		
-		
-		double previousValue = objective.getCurrentValue();
-		double previousStep = 0;
-		double currentStep =pickFirstStep.getFirstStep(this);
-		for(extrapolationIteration = 0; 
-		extrapolationIteration < maxExtrapolationIteration; extrapolationIteration++){	
-			
-			objective.updateAlpha(currentStep);
-			double currentValue = objective.getCurrentValue();
-			if(debugLevel >= 1){
-				steps.add(currentStep);
-				functionVals.add(currentValue);
-				gradientDots.add(objective.getCurrentGradient());
-			}
-			
-			
-			//The current step does not satisfy the sufficient decrease condition anymore
-			// so we cannot get bigger than that calling zoom.
-			if(!WolfeConditions.suficientDecrease(objective,c1)||					
-					(extrapolationIteration > 0 && currentValue >= previousValue)){
-				currentStep = zoom(objective,previousStep,currentStep,objective.nrIterations-1,objective.nrIterations);
-				break;
-			}
-			
-			//Satisfying both conditions ready to leave
-			if(WolfeConditions.sufficientCurvature(objective,c1,c2)){
-				//Found step
-				foudStep = true;
-				break;
-			}
-			
-			/**
-			 * This means that we passed the minimum already since the dot product that should be 
-			 * negative (descent direction) is now positive. So we cannot increase more. On the other hand
-			 * since we know the direction is a descent direction the value the objective at the current step
-			 * is for sure smaller than the preivous step so we change the order.
-			 */
-			if(objective.getCurrentGradient() >= 0){
-				currentStep =  zoom(objective,currentStep,previousStep,objective.nrIterations,objective.nrIterations-1);
-				break;
-			}
-			
-			
-			//Ok, so we can still get a bigger step, 
-			double aux = currentStep;
-			//currentStep = currentStep*2;
-			if(Math.abs(currentStep-maxStep)>1.1e-2){
-				currentStep = (currentStep+maxStep)/2;
-			}else{
-				currentStep = currentStep*2;
-			}
-			previousStep = aux;
-			previousValue = currentValue;
-			//Could be done better
-			if(currentStep >= maxStep){
-				System.out.println("Excedded max step...calling zoom with maxStepSize");
-				currentStep = zoom(objective,previousStep,currentStep,objective.nrIterations-1,objective.nrIterations);
-			}
-		}
-		if(!foudStep){
-			System.out.println("Wolfe Rule exceed number of iterations");
-			if(debugLevel >= 1){
-				printSmallWolfeStats(System.out);
-//				System.out.println("Line search values");
-//				DebugHelpers.getLineSearchGraph(o,  direction, originalParameters,origValue, origGradDirectionDot,c1,c2);			
-			}
-			return -1;
-		}
-		if(debugLevel >= 1){
-			printSmallWolfeStats(System.out);
-		}
-
-		previousStepPicked = currentStep;
-		previousInitGradientDot = currentInitGradientDot;
-//		objective.printLineSearchSteps();
-		return currentStep;
-	}
-	
-	
-	
-	
-	
-	public void printWolfeStats(PrintStream out){
-		for(int i = 0; i < steps.size(); i++){		
-			out.println("Step " + steps.get(i) + " value " + functionVals.get(i) + " dot " + gradientDots.get(i));
-		}
-	}
-	
-	public void printSmallWolfeStats(PrintStream out){
-		for(int i = 0; i < steps.size(); i++){		
-			out.print(steps.get(i) + ":"+functionVals.get(i)+":"+gradientDots.get(i)+" ");
-		}
-		System.out.println();
-	}
-	
-	
-	
-	/**
-	 * Pick a step satisfying the strong wolfe condition from an given from lowerStep and higherStep
-	 * picked on the routine above.
-	 * 
-	 * Both lowerStep and higherStep have been evaluated, so we only need to pass the iteration where they have
-	 * been evaluated and save extra evaluations.
-	 * 
-	 * We know that lowerStepValue as to be smaller than higherStepValue, and that a point 
-	 * satisfying both conditions exists in such interval.
-	 * 
-	 * LowerStep always satisfies at least the sufficient decrease
-	 * @return
-	 */
-	public double zoom(DifferentiableLineSearchObjective o, double lowerStep, double higherStep,
-			int lowerStepIter, int higherStepIter){
-		
-		if(debugLevel >=2){
-			System.out.println("Entering zoom with " + lowerStep+"-"+higherStep);
-		}
-		
-		double currentStep=-1;
-		
-		int zoomIter = 0;
-		while(zoomIter < 1000){		
-			if(Math.abs(lowerStep-higherStep) < minZoomDiffTresh){
-				o.updateAlpha(lowerStep);
-				if(debugLevel >= 1){
-					steps.add(lowerStep);
-					functionVals.add(o.getCurrentValue());
-					gradientDots.add(o.getCurrentGradient());
-				}
-				foudStep = true;
-				return lowerStep;
-			}	
-	
-			//Cubic interpolation
-			currentStep = 
-				Interpolation.cubicInterpolation(lowerStep, o.getValue(lowerStepIter), o.getGradient(lowerStepIter), 
-						higherStep, o.getValue(higherStepIter), o.getGradient(higherStepIter));
-			
-			//Safeguard.... should not be required check in what condtions it is required
-			if(currentStep < 0 ){
-				currentStep = (lowerStep+higherStep)/2;
-			}
-			if(Double.isNaN(currentStep) || Double.isInfinite(currentStep)){
-				currentStep = (lowerStep+higherStep)/2;
-			}
-//			currentStep = (lowerStep+higherStep)/2;
-//			System.out.println("Trying "+currentStep);
-			o.updateAlpha(currentStep);
-			if(debugLevel >=1){
-				steps.add(currentStep);
-				functionVals.add(o.getCurrentValue());
-				gradientDots.add(o.getCurrentGradient());
-			}
-			if(!WolfeConditions.suficientDecrease(o,c1)
-					|| o.getCurrentValue() >= o.getValue(lowerStepIter)){
-				higherStepIter = o.nrIterations;
-				higherStep = currentStep;
-			}
-			//Note when entering here the new step satisfies the sufficent decrease and
-			// or as a function value that is better than the previous best (lowerStepFunctionValues)
-			// so we either leave or change the value of the alpha low.
-			else{
-				if(WolfeConditions.sufficientCurvature(o,c1,c2)){
-					//Satisfies the both wolf conditions
-					foudStep = true;
-					break;
-				}
-				//If does not satisfy curvature 
-				if(o.getCurrentGradient()*(higherStep-lowerStep) >= 0){
-					higherStep = lowerStep;
-					higherStepIter = lowerStepIter;
-				}
-				lowerStep = currentStep;
-				lowerStepIter = o.nrIterations;
-			}
-			zoomIter++;
-		}
-		return currentStep;
-	}
-
-	public double getInitialGradient() {
-		return currentInitGradientDot;
-		
-	}
-
-	public double getPreviousInitialGradient() {
-		return previousInitGradientDot;
-	}
-
-	public double getPreviousStepUsed() {
-		return previousStepPicked;
-	}
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfeConditions.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfeConditions.java
deleted file mode 100644
index dcc704eb..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfeConditions.java
+++ /dev/null
@@ -1,45 +0,0 @@
-package optimization.linesearch;
-
-
-public class WolfeConditions {
-	
-	/**
-	 * Sufficient Increase number. Default constant
-	 */
-	
-	
-	/**
-	 * Value for suficient curvature:
-	 * 0.9 - For newton and quase netwon methods
-	 * 0.1 - Non linear conhugate gradient
-	 */
-	
-	int debugLevel = 0;
-	public void setDebugLevel(int level){
-		debugLevel = level;
-	}
-	
-	public  static boolean suficientDecrease(DifferentiableLineSearchObjective o, double c1){	
-		double value = o.getOriginalValue()+c1*o.getAlpha()*o.getInitialGradient();
-//		System.out.println("Sufficient Decrease original "+value+" new "+  o.getCurrentValue());
-		return o.getCurrentValue() <= value;
-	}
-	
-	
-
-
-	public static boolean sufficientCurvature(DifferentiableLineSearchObjective o, double c1, double c2){
-//		if(debugLevel >= 2){
-//			double current = Math.abs(o.getCurrentGradient());
-//			double orig = -c2*o.getInitialGradient();
-//			if(current <= orig){
-//				return true;
-//			}else{
-//				System.out.println("Not satistfying curvature condition curvature " + current + " wants " + orig);
-//				return false;
-//			}
-//		}
-		return Math.abs(o.getCurrentGradient()) <= -c2*o.getInitialGradient();
-	}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/projections/BoundsProjection.java b/gi/posterior-regularisation/prjava/src/optimization/projections/BoundsProjection.java
deleted file mode 100644
index 0429d531..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/projections/BoundsProjection.java
+++ /dev/null
@@ -1,104 +0,0 @@
-package optimization.projections;
-
-
-import java.util.Random;
-
-import optimization.util.MathUtils;
-import optimization.util.MatrixOutput;
-
-/**
- * Implements a projection into a box set defined by a and b.
- * If either a or b are infinity then that bound is ignored.
- * @author javg
- *
- */
-public class BoundsProjection extends Projection{
-
-	double a,b;
-	boolean ignoreA = false;
-	boolean ignoreB = false;
-	public BoundsProjection(double lowerBound, double upperBound) {
-		if(Double.isInfinite(lowerBound)){
-			this.ignoreA = true;
-		}else{
-			this.a =lowerBound;
-		}
-		if(Double.isInfinite(upperBound)){
-			this.ignoreB = true;
-		}else{
-			this.b =upperBound;
-		}
-	}
-	
-	
-	
-	/**
-	* Projects into the bounds
-	* a <= x_i <=b
-	 */
-	public void project(double[] original){
-		for (int i = 0; i < original.length; i++) {
-			if(!ignoreA && original[i] < a){
-				original[i] = a;
-			}else if(!ignoreB && original[i]>b){
-				original[i]=b;
-			}
-		}
-	}
-	
-	/**
-	 * Generates a random number between a and b.
-	 */
-
-	Random r = new Random();
-	
-	public double[] samplePoint(int numParams) {
-		double[] point = new double[numParams];
-		for (int i = 0; i < point.length; i++) {
-			double rand = r.nextDouble();
-			if(ignoreA && ignoreB){
-				//Use const to avoid number near overflow
-				point[i] = rand*(1.E100+1.E100)-1.E100;
-			}else if(ignoreA){
-				point[i] = rand*(b-1.E100)-1.E100;
-			}else if(ignoreB){
-				point[i] = rand*(1.E100-a)-a;
-			}else{
-				point[i] = rand*(b-a)-a;
-			}
-		}
-		return point;
-	}
-	
-	public static void main(String[] args) {
-		BoundsProjection sp = new BoundsProjection(0,Double.POSITIVE_INFINITY);
-		
-		
-		MatrixOutput.printDoubleArray(sp.samplePoint(3), "random 1");
-		MatrixOutput.printDoubleArray(sp.samplePoint(3), "random 2");
-		MatrixOutput.printDoubleArray(sp.samplePoint(3), "random 3");
-		
-		double[] d = {-1.1,1.2,1.4};
-		double[] original = d.clone();
-		MatrixOutput.printDoubleArray(d, "before");
-		
-		sp.project(d);
-		MatrixOutput.printDoubleArray(d, "after");
-		System.out.println("Test projection: " + sp.testProjection(original, d));
-	}
-	
-	double epsilon = 1.E-10;
-	public double[] perturbePoint(double[] point, int parameter){
-		double[] newPoint = point.clone();
-		if(!ignoreA && MathUtils.almost(point[parameter], a)){
-			newPoint[parameter]+=epsilon;
-		}else if(!ignoreB && MathUtils.almost(point[parameter], b)){
-			newPoint[parameter]-=epsilon;
-		}else{
-			newPoint[parameter]-=epsilon;
-		}
-		return newPoint;
-	}
-
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/projections/Projection.java b/gi/posterior-regularisation/prjava/src/optimization/projections/Projection.java
deleted file mode 100644
index b5a9f92f..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/projections/Projection.java
+++ /dev/null
@@ -1,72 +0,0 @@
-package optimization.projections;
-
-import optimization.util.MathUtils;
-import optimization.util.MatrixOutput;
-import util.ArrayMath;
-import util.Printing;
-
-
-
-public abstract class Projection {
-
-	
-	public abstract void project(double[] original);
-	
-	
-	/**
-	 *  From the projection theorem "Non-Linear Programming" page
-	 *  201 fact 2.
-	 *  
-	 *  Given some z in R, and a vector x* in X;
-	 *  x* = z+ iif for all x in X 
-	 *  (z-x*)'(x-x*) <= 0 where 0 is when x*=x
-	 *  See figure 2.16 in book
-	 *  
-	 * @param original
-	 * @param projected
-	 * @return
-	 */
-	public boolean testProjection(double[] original, double[] projected){
-		double[] original1 = original.clone();
-		//System.out.println(Printing.doubleArrayToString(original1, null, "original"));
-		//System.out.println(Printing.doubleArrayToString(projected, null, "projected"));
-		MathUtils.minusEquals(original1, projected, 1);
-		//System.out.println(Printing.doubleArrayToString(original1, null, "minus1"));
-		for(int i = 0; i < 10; i++){
-			double[] x = samplePoint(original.length);
-		//	System.out.println(Printing.doubleArrayToString(x, null, "sample"));
-			//If the same this returns zero so we are there.	
-			MathUtils.minusEquals(x, projected, 1);
-		//	System.out.println(Printing.doubleArrayToString(x, null, "minus2"));
-			double dotProd = MathUtils.dotProduct(original1, x);
-			
-		//	System.out.println("dot " + dotProd);
-			if(dotProd > 0) return false;
-		}
-		
-		//Perturbs the point a bit in all possible directions
-		for(int i = 0; i < original.length; i++){
-			double[] x = perturbePoint(projected,i);
-		//	System.out.println(Printing.doubleArrayToString(x, null, "perturbed"));
-			//If the same this returns zero so we are there.	
-			MathUtils.minusEquals(x, projected, 1);
-		//	System.out.println(Printing.doubleArrayToString(x, null, "minus2"));
-			double dotProd = MathUtils.dotProduct(original1, x);
-			
-		//	System.out.println("dot " + dotProd);
-			if(dotProd > 0) return false;
-		}
-		
-		
-		
-		return true;
-	}
-
-	//Samples a point from the constrained set
-	public abstract double[] samplePoint(int dimensions);
-	
-	//Perturbs a point a bit still leaving it at the constraints set
-	public abstract double[] perturbePoint(double[] point, int parameter);
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/projections/SimplexProjection.java b/gi/posterior-regularisation/prjava/src/optimization/projections/SimplexProjection.java
deleted file mode 100644
index f22afcaf..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/projections/SimplexProjection.java
+++ /dev/null
@@ -1,127 +0,0 @@
-package optimization.projections;
-
-
-
-import java.util.Random;
-
-import optimization.util.MathUtils;
-import optimization.util.MatrixOutput;
-
-public class SimplexProjection extends Projection{
-
-	double scale;
-	public SimplexProjection(double scale) {
-		this.scale = scale;
-	}
-	
-	/**
-	 * projects the numbers of the array 
-	 * into a simplex of size. 
-	 * We follow the description of the paper
-	 * "Efficient Projetions onto the l1-Ball
-	 * for learning in high dimensions"
-	 */
-	public void project(double[] original){
-		double[] ds = new double[original.length];
-		System.arraycopy(original, 0, ds, 0, ds.length);
-		//If sum is smaller then zero then its ok
-		for (int i = 0; i < ds.length; i++) ds[i] = ds[i]>0? ds[i]:0;
-		double sum = MathUtils.sum(ds);
-		if (scale - sum >=  -1.E-10 ){
-			System.arraycopy(ds, 0, original, 0, ds.length);
-			//System.out.println("Not projecting");
-			return;
-		}
-		//System.out.println("projecting " + sum + " scontraints " + scale);	
-		util.Array.sortDescending(ds);
-		double currentSum = 0;
-		double previousTheta = 0;
-		double theta = 0;
-		for (int i = 0; i < ds.length; i++) {
-			currentSum+=ds[i];
-			theta = (currentSum-scale)/(i+1);
-			if(ds[i]-theta < -1e-10){
-				break;
-			}
-			previousTheta = theta;
-		}
-		//DEBUG
-		if(previousTheta < 0){
-			System.out.println("Simple Projection: Theta is smaller than zero: " + previousTheta);
-			System.exit(-1);
-		}
-		for (int i = 0; i < original.length; i++) {
-			original[i] = Math.max(original[i]-previousTheta, 0);
-		}
-	}
-	
-	
-	
-	
-	
-
-	/**
-	 * Samples a point from the simplex of scale. Just sample
-	 * random number from 0-scale and then if
-	 * their sum is bigger then sum make them normalize.
-	 * This is probably not sampling uniformly from the simplex but it is
-	 * enough for our goals in here.
-	 */
-	Random r = new Random();
-	public double[] samplePoint(int dimensions) {
-		double[] newPoint = new double[dimensions];
-		double sum =0;
-		for (int i = 0; i < newPoint.length; i++) {
-			double rand = r.nextDouble()*scale;
-			sum+=rand;
-			newPoint[i]=rand;
-		}
-		//Normalize
-		if(sum > scale){
-			for (int i = 0; i < newPoint.length; i++) {
-				newPoint[i]=scale*newPoint[i]/sum;
-			}
-		}
-		return newPoint;
-	}
-	
-	public static void main(String[] args) {
-		SimplexProjection sp = new SimplexProjection(1);
-		
-		
-		double[] point = sp.samplePoint(3);
-		MatrixOutput.printDoubleArray(point , "random 1 sum:" + MathUtils.sum(point));
-		point = sp.samplePoint(3);
-		MatrixOutput.printDoubleArray(point , "random 2 sum:" + MathUtils.sum(point));
-		point = sp.samplePoint(3);
-		MatrixOutput.printDoubleArray(point , "random 3 sum:" + MathUtils.sum(point));
-		
-		double[] d = {0,1.1,-10};
-		double[] original = d.clone();
-		MatrixOutput.printDoubleArray(d, "before");
-		
-		sp.project(d);
-		MatrixOutput.printDoubleArray(d, "after");
-		System.out.println("Test projection: " + sp.testProjection(original, d));
-		
-	}
-	
-	
-	double epsilon = 1.E-10;
-	public double[] perturbePoint(double[] point, int parameter){
-		double[] newPoint = point.clone();
-		if(MathUtils.almost(MathUtils.sum(point), scale)){
-			newPoint[parameter]-=epsilon;
-		}
-		else if(point[parameter]==0){
-			newPoint[parameter]+=epsilon;
-		}else if(MathUtils.almost(point[parameter], scale)){
-			newPoint[parameter]-=epsilon;
-		}
-		else{
-			newPoint[parameter]-=epsilon;
-		}
-		return newPoint;
-	}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/CompositeStopingCriteria.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/CompositeStopingCriteria.java
deleted file mode 100644
index 15760f18..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/CompositeStopingCriteria.java
+++ /dev/null
@@ -1,33 +0,0 @@
-package optimization.stopCriteria;
-
-import java.util.ArrayList;
-
-import optimization.gradientBasedMethods.Objective;
-
-public class CompositeStopingCriteria implements StopingCriteria {
-	
-	ArrayList<StopingCriteria> criterias;
-	
-	public CompositeStopingCriteria() {
-		criterias = new ArrayList<StopingCriteria>();
-	}
-	
-	public void add(StopingCriteria criteria){
-		criterias.add(criteria);
-	}
-	
-	public boolean stopOptimization(Objective obj){
-		for(StopingCriteria criteria: criterias){
-			if(criteria.stopOptimization(obj)){
-				return true;
-			}
-		}
-		return false;
-	}
-	
-	public void reset(){
-		for(StopingCriteria criteria: criterias){
-			criteria.reset();
-		}
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/GradientL2Norm.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/GradientL2Norm.java
deleted file mode 100644
index 534ff833..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/GradientL2Norm.java
+++ /dev/null
@@ -1,30 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.util.MathUtils;
-
-public class GradientL2Norm implements StopingCriteria{
-	
-	/**
-	 * Stop if gradientNorm/(originalGradientNorm) smaller
-	 * than gradientConvergenceValue
-	 */
-	protected double gradientConvergenceValue;
-	
-	
-	public GradientL2Norm(double gradientConvergenceValue){
-		this.gradientConvergenceValue = gradientConvergenceValue;
-	}
-	
-	public void reset(){}
-	
-	public boolean stopOptimization(Objective obj){
-		double norm = MathUtils.L2Norm(obj.gradient);
-		if(norm < gradientConvergenceValue){
-			System.out.println("Gradient norm below treshold");
-			return true;
-		}
-		return false;
-		
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedGradientL2Norm.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedGradientL2Norm.java
deleted file mode 100644
index 4a489641..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedGradientL2Norm.java
+++ /dev/null
@@ -1,48 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.util.MathUtils;
-
-/**
- * Divides the norm by the norm at the begining of the iteration
- * @author javg
- *
- */
-public class NormalizedGradientL2Norm extends GradientL2Norm{
-	
-	/**
-	 * Stop if gradientNorm/(originalGradientNorm) smaller
-	 * than gradientConvergenceValue
-	 */
-	protected double originalGradientNorm = -1;
-	
-	public void reset(){
-		originalGradientNorm = -1;
-	}
-	public NormalizedGradientL2Norm(double gradientConvergenceValue){
-		super(gradientConvergenceValue);
-	}
-	
-	
-	 
-	
-	public boolean stopOptimization(Objective obj){
-			double norm = MathUtils.L2Norm(obj.gradient);
-			if(originalGradientNorm == -1){
-				originalGradientNorm = norm;
-			}
-			if(originalGradientNorm < 1E-10){
-				System.out.println("Gradient norm is zero " +  originalGradientNorm);
-				return true;
-			}
-			double normalizedNorm = 1.0*norm/originalGradientNorm;
-			if( normalizedNorm < gradientConvergenceValue){
-				System.out.println("Gradient norm below normalized normtreshold: " + norm + " original: " + originalGradientNorm + " normalized norm: " + normalizedNorm);
-				return true;
-			}else{
-//				System.out.println("projected gradient norm: " + norm);
-				return false;
-			}
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedProjectedGradientL2Norm.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedProjectedGradientL2Norm.java
deleted file mode 100644
index 5ae554c2..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedProjectedGradientL2Norm.java
+++ /dev/null
@@ -1,60 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.util.MathUtils;
-
-/**
- * Divides the norm by the norm at the begining of the iteration
- * @author javg
- *
- */
-public class NormalizedProjectedGradientL2Norm extends ProjectedGradientL2Norm{
-	
-	/**
-	 * Stop if gradientNorm/(originalGradientNorm) smaller
-	 * than gradientConvergenceValue
-	 */
-	double originalProjectedNorm = -1;
-	
-	public NormalizedProjectedGradientL2Norm(double gradientConvergenceValue){
-		super(gradientConvergenceValue);
-	}
-	
-	public void reset(){
-		originalProjectedNorm = -1;
-	}
-	
-	
-	 double[] projectGradient(ProjectedObjective obj){
-		
-		if(obj.auxParameters == null){
-			obj.auxParameters = new double[obj.getNumParameters()];
-		}
-		System.arraycopy(obj.getParameters(), 0, obj.auxParameters, 0, obj.getNumParameters());
-		MathUtils.minusEquals(obj.auxParameters, obj.gradient, 1);
-		obj.auxParameters = obj.projectPoint(obj.auxParameters);
-		MathUtils.minusEquals(obj.auxParameters,obj.getParameters(),1);
-		return obj.auxParameters;
-	}
-	
-	public boolean stopOptimization(Objective obj){
-		if(obj instanceof ProjectedObjective) {
-			ProjectedObjective o = (ProjectedObjective) obj;
-			double norm = MathUtils.L2Norm(projectGradient(o));
-			if(originalProjectedNorm == -1){
-				originalProjectedNorm = norm;
-			}
-			double normalizedNorm = 1.0*norm/originalProjectedNorm;
-			if( normalizedNorm < gradientConvergenceValue){
-				System.out.println("Gradient norm below normalized normtreshold: " + norm + " original: " + originalProjectedNorm + " normalized norm: " + normalizedNorm);
-				return true;
-			}else{
-//				System.out.println("projected gradient norm: " + norm);
-				return false;
-			}
-		}
-		System.out.println("Not a projected objective");
-		throw new RuntimeException();
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedValueDifference.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedValueDifference.java
deleted file mode 100644
index 6dbbc50d..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedValueDifference.java
+++ /dev/null
@@ -1,54 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.util.MathUtils;
-
-public class NormalizedValueDifference implements StopingCriteria{
-	
-	/**
-	 * Stop if the different between values is smaller than a treshold
-	 */
-	protected double valueConvergenceValue=0.01;
-	protected double previousValue = Double.NaN;
-	protected double currentValue = Double.NaN;
-	
-	public NormalizedValueDifference(double valueConvergenceValue){
-		this.valueConvergenceValue = valueConvergenceValue;
-	}
-	
-	public void reset(){
-		previousValue = Double.NaN;
-		currentValue = Double.NaN;
-	}
-
-	
-	public boolean stopOptimization(Objective obj){
-		if(Double.isNaN(currentValue)){
-			currentValue = obj.getValue();
-			return false;
-		}else {
-			previousValue = currentValue;
-			currentValue = obj.getValue();
-			if(previousValue != 0){
-				double valueDiff = Math.abs(previousValue - currentValue)/Math.abs(previousValue);
-				if( valueDiff  < valueConvergenceValue){
-					System.out.println("Leaving different in values is to small: Prev " 
-							+ (previousValue/previousValue) + " Curr: " + (currentValue/previousValue) 
-							+ " diff: " + valueDiff);
-					return true;
-				}
-			}else{
-				double valueDiff = Math.abs(previousValue - currentValue);
-				if( valueDiff  < valueConvergenceValue){
-					System.out.println("Leaving different in values is to small: Prev " 
-							+ (previousValue) + " Curr: " + (currentValue) 
-							+ " diff: " + valueDiff);
-					return true;
-				}
-			}
-
-			return false;
-		}
-		
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ProjectedGradientL2Norm.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ProjectedGradientL2Norm.java
deleted file mode 100644
index aadf1fd5..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ProjectedGradientL2Norm.java
+++ /dev/null
@@ -1,51 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.util.MathUtils;
-
-public class ProjectedGradientL2Norm implements StopingCriteria{
-	
-	/**
-	 * Stop if gradientNorm/(originalGradientNorm) smaller
-	 * than gradientConvergenceValue
-	 */
-	protected double gradientConvergenceValue;
-	
-	
-	public ProjectedGradientL2Norm(double gradientConvergenceValue){
-		this.gradientConvergenceValue = gradientConvergenceValue;
-	}
-	
-	public void reset(){
-		
-	}
-	
-	 double[] projectGradient(ProjectedObjective obj){
-		
-		if(obj.auxParameters == null){
-			obj.auxParameters = new double[obj.getNumParameters()];
-		}
-		System.arraycopy(obj.getParameters(), 0, obj.auxParameters, 0, obj.getNumParameters());
-		MathUtils.minusEquals(obj.auxParameters, obj.gradient, 1);
-		obj.auxParameters = obj.projectPoint(obj.auxParameters);
-		MathUtils.minusEquals(obj.auxParameters,obj.getParameters(),1);
-		return obj.auxParameters;
-	}
-	
-	public boolean stopOptimization(Objective obj){
-		if(obj instanceof ProjectedObjective) {
-			ProjectedObjective o = (ProjectedObjective) obj;
-			double norm = MathUtils.L2Norm(projectGradient(o));
-			if(norm < gradientConvergenceValue){
-	//			System.out.println("Gradient norm below treshold: " + norm);
-				return true;
-			}else{
-//				System.out.println("projected gradient norm: " + norm);
-				return false;
-			}
-		}
-		System.out.println("Not a projected objective");
-		throw new RuntimeException();
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/StopingCriteria.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/StopingCriteria.java
deleted file mode 100644
index 10cf0522..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/StopingCriteria.java
+++ /dev/null
@@ -1,8 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-
-public interface StopingCriteria {
-	public boolean stopOptimization(Objective obj);
-	public void reset();
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ValueDifference.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ValueDifference.java
deleted file mode 100644
index e5d07229..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ValueDifference.java
+++ /dev/null
@@ -1,41 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.util.MathUtils;
-
-public class ValueDifference implements StopingCriteria{
-	
-	/**
-	 * Stop if the different between values is smaller than a treshold
-	 */
-	protected double valueConvergenceValue=0.01;
-	protected double previousValue = Double.NaN;
-	protected double currentValue = Double.NaN;
-	
-	public ValueDifference(double valueConvergenceValue){
-		this.valueConvergenceValue = valueConvergenceValue;
-	}
-	
-	public void reset(){
-		previousValue = Double.NaN;
-		currentValue = Double.NaN;
-	}
-	
-	public boolean stopOptimization(Objective obj){
-		if(Double.isNaN(currentValue)){
-			currentValue = obj.getValue();
-			return false;
-		}else {
-			previousValue = currentValue;
-			currentValue = obj.getValue();
-			if(previousValue - currentValue   < valueConvergenceValue){
-//				System.out.println("Leaving different in values is to small: Prev " 
-//						+ previousValue + " Curr: " + currentValue 
-//						+ " diff: " + (previousValue - currentValue));
-				return true;
-			}
-			return false;
-		}
-		
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/util/Interpolation.java b/gi/posterior-regularisation/prjava/src/optimization/util/Interpolation.java
deleted file mode 100644
index cdbdefc6..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/util/Interpolation.java
+++ /dev/null
@@ -1,37 +0,0 @@
-package optimization.util;
-
-public class Interpolation {
-
-	/**
-	 * Fits a cubic polinomyal to a function given two points,
-	 * such that either gradB is bigger than zero or funcB >= funcA
-	 * 
-	 * NonLinear Programming appendix C
-	 * @param funcA
-	 * @param gradA
-	 * @param funcB
-	 * @param gradB
-	 */
-	public final static double cubicInterpolation(double a, 
-			double funcA, double gradA, double b,double funcB, double gradB ){
-		if(gradB < 0 && funcA > funcB){
-			System.out.println("Cannot call cubic interpolation");
-			return -1;
-		}
-		
-		double z = 3*(funcA-funcB)/(b-a) + gradA + gradB;
-		double w = Math.sqrt(z*z - gradA*gradB);
-		double min = b -(gradB+w-z)*(b-a)/(gradB-gradA+2*w);
-		return min;
-	}
-	
-	public final static double quadraticInterpolation(double initFValue, 
-			double initGrad, double point,double pointFValue){
-				double min = -1*initGrad*point*point/(2*(pointFValue-initGrad*point-initFValue));
-		return min;
-	}
-	
-	public static void main(String[] args) {
-		
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/util/Logger.java b/gi/posterior-regularisation/prjava/src/optimization/util/Logger.java
deleted file mode 100644
index 5343a39b..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/util/Logger.java
+++ /dev/null
@@ -1,7 +0,0 @@
-package optimization.util;
-
-public class Logger {
-	
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/util/MathUtils.java b/gi/posterior-regularisation/prjava/src/optimization/util/MathUtils.java
deleted file mode 100644
index af66f82c..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/util/MathUtils.java
+++ /dev/null
@@ -1,339 +0,0 @@
-package optimization.util;
-
-import java.util.Arrays;
-
-
-
-public class MathUtils {
-	
-	/**
-	 * 
-	 * @param vector
-	 * @return
-	 */
-	public static double L2Norm(double[] vector){
-		double value = 0;
-		for(int i = 0; i < vector.length; i++){
-			double v = vector[i];
-			value+=v*v;
-		}
-		return Math.sqrt(value);
-	}
-	
-	public static double sum(double[] v){
-		double sum = 0;
-		for (int i = 0; i < v.length; i++) {
-			sum+=v[i];
-		}
-		return sum;
-	}
-	
-	
-	
-	
-	/**
-	 * w = w + v
-	 * @param w
-	 * @param v
-	 */
-	public static void plusEquals(double[] w, double[] v) {
-		for(int i=0; i<w.length;i++){
-			w[i] += w[i] + v[i];
-		}		
-	}
-	
-	/**
-	 * w[i] = w[i] + v
-	 * @param w
-	 * @param v
-	 */
-	public static void plusEquals(double[] w, double v) {
-		for(int i=0; i<w.length;i++){
-			w[i] += w[i] + v;
-		}		
-	}
-	
-	/**
-	 * w[i] = w[i] - v
-	 * @param w
-	 * @param v
-	 */
-	public static void minusEquals(double[] w, double v) {
-		for(int i=0; i<w.length;i++){
-			w[i] -= w[i] + v;
-		}		
-	}
-	
-	/**
-	 * w = w + a*v
-	 * @param w
-	 * @param v
-	 * @param a
-	 */
-	public static void plusEquals(double[] w, double[] v, double a) {
-		for(int i=0; i<w.length;i++){
-			w[i] += a*v[i];
-		}		
-	}
-	
-	/**
-	 * w = w - a*v
-	 * @param w
-	 * @param v
-	 * @param a
-	 */
-	public static void minusEquals(double[] w, double[] v, double a) {
-		for(int i=0; i<w.length;i++){
-			w[i] -= a*v[i];
-		}		
-	}
-	/**
-	 * v = w - a*v
-	 * @param w
-	 * @param v
-	 * @param a
-	 */
-	public static void minusEqualsInverse(double[] w, double[] v, double a) {
-		for(int i=0; i<w.length;i++){
-			v[i] = w[i] - a*v[i];
-		}		
-	}
-	
-	public static double dotProduct(double[] w, double[] v){
-		double accum = 0;
-		for(int i=0; i<w.length;i++){
-			accum += w[i]*v[i];
-		}
-		return accum;
-	}
-	
-	public static double[] arrayMinus(double[]w, double[]v){
-		double result[] = w.clone();
-		for(int i=0; i<w.length;i++){
-			result[i] -= v[i];
-		}
-		return result;
-	}
-	
-	public static double[] arrayMinus(double[] result , double[]w, double[]v){
-		for(int i=0; i<w.length;i++){
-			result[i] = w[i]-v[i];
-		}
-		return result;
-	}
-	
-	public static double[] negation(double[]w){
-		double result[]  = new double[w.length];
-		for(int i=0; i<w.length;i++){
-			result[i] = -w[i];
-		}
-		return result;
-	}
-	
-	public static double square(double value){
-		return value*value;
-	}
-	public static double[][] outerProduct(double[] w, double[] v){
-		double[][] result = new double[w.length][v.length];
-		for(int i = 0; i < w.length; i++){
-			for(int j = 0; j < v.length; j++){
-				result[i][j] = w[i]*v[j];
-			}
-		}
-		return result;
-	}
-	/**
-	 * results = a*W*V
-	 * @param w
-	 * @param v
-	 * @param a
-	 * @return
-	 */
-	public static double[][] weightedouterProduct(double[] w, double[] v, double a){
-		double[][] result = new double[w.length][v.length];
-		for(int i = 0; i < w.length; i++){
-			for(int j = 0; j < v.length; j++){
-				result[i][j] = a*w[i]*v[j];
-			}
-		}
-		return result;
-	}
-	
-	public static double[][] identity(int size){
-		double[][] result = new double[size][size];
-		for(int i = 0; i < size; i++){
-			result[i][i] = 1;
-		}
-		return result;
-	}
-	
-	/**
-	 * v -= w
-	 * @param v
-	 * @param w
-	 */
-	public static void minusEquals(double[][] w, double[][] v){
-		for(int i = 0; i < w.length; i++){
-			for(int j = 0; j < w[0].length; j++){
-				w[i][j] -= v[i][j];
-			}
-		}
-	}
-	
-	/**
-	 * v[i][j] -= a*w[i][j]
-	 * @param v
-	 * @param w
-	 */
-	public static void minusEquals(double[][] w, double[][] v, double a){
-		for(int i = 0; i < w.length; i++){
-			for(int j = 0; j < w[0].length; j++){
-				w[i][j] -= a*v[i][j];
-			}
-		}
-	}
-	
-	/**
-	 * v += w
-	 * @param v
-	 * @param w
-	 */
-	public static void plusEquals(double[][] w, double[][] v){
-		for(int i = 0; i < w.length; i++){
-			for(int j = 0; j < w[0].length; j++){
-				w[i][j] += v[i][j];
-			}
-		}
-	}
-	
-	/**
-	 * v[i][j] += a*w[i][j]
-	 * @param v
-	 * @param w
-	 */
-	public static void plusEquals(double[][] w, double[][] v, double a){
-		for(int i = 0; i < w.length; i++){
-			for(int j = 0; j < w[0].length; j++){
-				w[i][j] += a*v[i][j];
-			}
-		}
-	}
-	
-	
-	/**
-	 * results = w*v
-	 * @param w
-	 * @param v
-	 * @return
-	 */
-	public static  double[][] matrixMultiplication(double[][] w,double[][] v){
-		int w1 = w.length;
-		int w2 = w[0].length;
-		int v1 = v.length;
-		int v2 = v[0].length;
-		
-		if(w2 != v1){
-			System.out.println("Matrix dimensions do not agree...");
-			System.exit(-1);
-		}
-		
-		double[][] result = new double[w1][v2];
-		for(int w_i1 = 0; w_i1 < w1; w_i1++){
-			for(int v_i2 = 0; v_i2 < v2; v_i2++){
-				double sum = 0;
-				for(int w_i2 = 0; w_i2 < w2; w_i2++){
-						sum += w[w_i1 ][w_i2]*v[w_i2][v_i2];	
-				}
-				result[w_i1][v_i2] = sum;
-			}
-		}
-		return result;
-	}
-	
-	/**
-	 * w = w.*v
-	 * @param w
-	 * @param v
-	 */
-	public static  void matrixScalarMultiplication(double[][] w,double v){
-		int w1 = w.length;
-		int w2 = w[0].length;	
-		for(int w_i1 = 0; w_i1 < w1; w_i1++){
-				for(int w_i2 = 0; w_i2 < w2; w_i2++){
-						w[w_i1 ][w_i2] *= v;	
-				}
-		}
-	}
-	
-	public static  void scalarMultiplication(double[] w,double v){
-		int w1 = w.length;
-		for(int w_i1 = 0; w_i1 < w1; w_i1++){
-			w[w_i1 ] *= v;	
-		}
-		
-	}
-	
-	public static  double[] matrixVector(double[][] w,double[] v){
-		int w1 = w.length;
-		int w2 = w[0].length;
-		int v1 = v.length;
-		
-		if(w2 != v1){
-			System.out.println("Matrix dimensions do not agree...");
-			System.exit(-1);
-		}
-		
-		double[] result = new double[w1];
-		for(int w_i1 = 0; w_i1 < w1; w_i1++){
-				double sum = 0;
-				for(int w_i2 = 0; w_i2 < w2; w_i2++){
-						sum += w[w_i1 ][w_i2]*v[w_i2];	
-				}
-				result[w_i1] = sum;
-		}
-		return result;
-	}
-	
-	public static boolean allPositive(double[] array){
-		for (int i = 0; i < array.length; i++) {
-			if(array[i] < 0) return false;
-		}
-		return true;
-	}
-	
-	
-	
-	
-	
-		public static void main(String[] args) {
-			double[][] m1 = new double[2][2];
-			m1[0][0]=2;
-			m1[1][0]=2;
-			m1[0][1]=2;
-			m1[1][1]=2;
-			MatrixOutput.printDoubleArray(m1, "m1");
-			double[][] m2 = new double[2][2];
-			m2[0][0]=3;
-			m2[1][0]=3;
-			m2[0][1]=3;
-			m2[1][1]=3;
-			MatrixOutput.printDoubleArray(m2, "m2");
-			double[][] result = matrixMultiplication(m1, m2);
-			MatrixOutput.printDoubleArray(result, "result");
-			matrixScalarMultiplication(result, 3);
-			MatrixOutput.printDoubleArray(result, "result after multiply by 3");
-		}
-	
-		public static boolean almost(double a, double b, double prec){
-			return Math.abs(a-b)/Math.abs(a+b) <= prec || (almostZero(a) && almostZero(b));
-		}
-
-		public static boolean almost(double a, double b){
-			return Math.abs(a-b)/Math.abs(a+b) <= 1e-10 || (almostZero(a) && almostZero(b));
-		}
-
-		public static boolean almostZero(double a) {
-			return Math.abs(a) <= 1e-30;
-		}
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/util/MatrixOutput.java b/gi/posterior-regularisation/prjava/src/optimization/util/MatrixOutput.java
deleted file mode 100644
index 9fbdf955..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/util/MatrixOutput.java
+++ /dev/null
@@ -1,28 +0,0 @@
-package optimization.util;
-
-
-public class MatrixOutput {
-	public static void printDoubleArray(double[][] array, String arrayName) {
-		int size1 = array.length;
-		int size2 = array[0].length;
-		System.out.println(arrayName);
-		for (int i = 0; i < size1; i++) {
-			for (int j = 0; j < size2; j++) {
-				System.out.print(" " + StaticTools.prettyPrint(array[i][j],
-						"00.00E00", 4) + " ");
-
-			}
-			System.out.println();
-		}
-		System.out.println();
-	}
-	
-	public static void printDoubleArray(double[] array, String arrayName) {
-		System.out.println(arrayName);
-		for (int i = 0; i < array.length; i++) {
-				System.out.print(" " + StaticTools.prettyPrint(array[i],
-						"00.00E00", 4) + " ");
-		}
-		System.out.println();
-	}
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/util/StaticTools.java b/gi/posterior-regularisation/prjava/src/optimization/util/StaticTools.java
deleted file mode 100644
index bcabee06..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/util/StaticTools.java
+++ /dev/null
@@ -1,180 +0,0 @@
-package optimization.util;
-
-
-import java.io.File;
-import java.io.PrintStream;
-
-public class StaticTools {
-
-	static java.text.DecimalFormat fmt = new java.text.DecimalFormat();
-
-	public static void createDir(String directory) {
-
-		File dir = new File(directory);
-		if (!dir.isDirectory()) {
-			boolean success = dir.mkdirs();
-			if (!success) {
-				System.out.println("Unable to create directory " + directory);
-				System.exit(0);
-			}
-			System.out.println("Created directory " + directory);
-		} else {
-			System.out.println("Reusing directory " + directory);
-		}
-	}
-
-	/*
-	 * q and p are indexed by source/foreign Sum_S(q) = 1 the same for p KL(q,p) =
-	 * Eq*q/p
-	 */
-	public static double KLDistance(double[][] p, double[][] q, int sourceSize,
-			int foreignSize) {
-		double totalKL = 0;
-		// common.StaticTools.printMatrix(q, sourceSize, foreignSize, "q",
-		// System.out);
-		// common.StaticTools.printMatrix(p, sourceSize, foreignSize, "p",
-		// System.out);
-		for (int i = 0; i < sourceSize; i++) {
-			double kl = 0;
-			for (int j = 0; j < foreignSize; j++) {
-				assert !Double.isNaN(q[i][j]) : "KLDistance q:  prob is NaN";
-				assert !Double.isNaN(p[i][j]) : "KLDistance p:  prob is NaN";
-				if (p[i][j] == 0 || q[i][j] == 0) {
-					continue;
-				} else {
-					kl += q[i][j] * Math.log(q[i][j] / p[i][j]);
-				}
-
-			}
-			totalKL += kl;
-		}
-		assert !Double.isNaN(totalKL) : "KLDistance: prob is NaN";
-		if (totalKL < -1.0E-10) {
-			System.out.println("KL Smaller than zero " + totalKL);
-			System.out.println("Source Size" + sourceSize);
-			System.out.println("Foreign Size" + foreignSize);
-			StaticTools.printMatrix(q, sourceSize, foreignSize, "q",
-					System.out);
-			StaticTools.printMatrix(p, sourceSize, foreignSize, "p",
-					System.out);
-			System.exit(-1);
-		}
-		return totalKL / sourceSize;
-	}
-
-	/*
-	 * indexed the by [fi][si]
-	 */
-	public static double KLDistancePrime(double[][] p, double[][] q,
-			int sourceSize, int foreignSize) {
-		double totalKL = 0;
-		for (int i = 0; i < sourceSize; i++) {
-			double kl = 0;
-			for (int j = 0; j < foreignSize; j++) {
-				assert !Double.isNaN(q[j][i]) : "KLDistance q:  prob is NaN";
-				assert !Double.isNaN(p[j][i]) : "KLDistance p:  prob is NaN";
-				if (p[j][i] == 0 || q[j][i] == 0) {
-					continue;
-				} else {
-					kl += q[j][i] * Math.log(q[j][i] / p[j][i]);
-				}
-
-			}
-			totalKL += kl;
-		}
-		assert !Double.isNaN(totalKL) : "KLDistance: prob is NaN";
-		return totalKL / sourceSize;
-	}
-
-	public static double Entropy(double[][] p, int sourceSize, int foreignSize) {
-		double totalE = 0;
-		for (int i = 0; i < foreignSize; i++) {
-			double e = 0;
-			for (int j = 0; j < sourceSize; j++) {
-				e += p[i][j] * Math.log(p[i][j]);
-			}
-			totalE += e;
-		}
-		return totalE / sourceSize;
-	}
-
-	public static double[][] copyMatrix(double[][] original, int sourceSize,
-			int foreignSize) {
-		double[][] result = new double[sourceSize][foreignSize];
-		for (int i = 0; i < sourceSize; i++) {
-			for (int j = 0; j < foreignSize; j++) {
-				result[i][j] = original[i][j];
-			}
-		}
-		return result;
-	}
-
-	public static void printMatrix(double[][] matrix, int sourceSize,
-			int foreignSize, String info, PrintStream out) {
-
-		java.text.DecimalFormat fmt = new java.text.DecimalFormat();
-		fmt.setMaximumFractionDigits(3);
-		fmt.setMaximumIntegerDigits(3);
-		fmt.setMinimumFractionDigits(3);
-		fmt.setMinimumIntegerDigits(3);
-
-		out.println(info);
-
-		for (int i = 0; i < foreignSize; i++) {
-			for (int j = 0; j < sourceSize; j++) {
-				out.print(prettyPrint(matrix[j][i], ".00E00", 6) + " ");
-			}
-			out.println();
-		}
-		out.println();
-		out.println();
-	}
-
-	public static void printMatrix(int[][] matrix, int sourceSize,
-			int foreignSize, String info, PrintStream out) {
-
-		out.println(info);
-		for (int i = 0; i < foreignSize; i++) {
-			for (int j = 0; j < sourceSize; j++) {
-				out.print(matrix[j][i] + " ");
-			}
-			out.println();
-		}
-		out.println();
-		out.println();
-	}
-
-	public static String formatTime(long duration) {
-		StringBuilder sb = new StringBuilder();
-		double d = duration / 1000;
-		fmt.applyPattern("00");
-		sb.append(fmt.format((int) (d / (60 * 60))) + ":");
-		d -= ((int) d / (60 * 60)) * 60 * 60;
-		sb.append(fmt.format((int) (d / 60)) + ":");
-		d -= ((int) d / 60) * 60;
-		fmt.applyPattern("00.0");
-		sb.append(fmt.format(d));
-		return sb.toString();
-	}
-
-	public static String prettyPrint(double d, String patt, int len) {
-		fmt.applyPattern(patt);
-		String s = fmt.format(d);
-		while (s.length() < len) {
-			s = " " + s;
-		}
-		return s;
-	}
-	
-	
-	public static long getUsedMemory(){
-		System.gc();
-		return (Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory())/ (1024 * 1024);
-	}
-	
-	public final static boolean compareDoubles(double d1, double d2){
-		return Math.abs(d1-d2) <= 1.E-10;
-	}
-	
-	
-}