1 files changed, 137 insertions, 0 deletions
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ProjectedDifferentiableLineSearchObjective.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ProjectedDifferentiableLineSearchObjective.java
new file mode 100644
index 00000000..29ccbc32
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ProjectedDifferentiableLineSearchObjective.java
@@ -0,0 +1,137 @@
+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();
+//		}
+//	}
+	
+}