cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 234 deletions

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;	
-//	}
-	
-
-
-	
-
-	
-
-	
-	
-
-}