cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 102 deletions

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;
-	}
-		
-}