merge upstream/master

1 files changed, 0 insertions, 137 deletions

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();
-//		}
-//	}
-	
-}