cdec cleanup, remove bayesian stuff, parsing stuff

13 files changed, 0 insertions, 996 deletions

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();
-	}
-	
-}