cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 128 deletions

diff --git a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java b/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java
deleted file mode 100644
index f087681e..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java
+++ /dev/null
@@ -1,128 +0,0 @@
-package optimization.examples;
-
-
-import optimization.gradientBasedMethods.ConjugateGradient;
-
-import optimization.gradientBasedMethods.GradientDescent;
-import optimization.gradientBasedMethods.LBFGS;
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.GenericPickFirstStep;
-import optimization.linesearch.LineSearchMethod;
-import optimization.linesearch.WolfRuleLineSearch;
-import optimization.stopCriteria.GradientL2Norm;
-import optimization.stopCriteria.StopingCriteria;
-
-
-/**
- * @author javg
- *
- */
-public class x2y2 extends Objective{
-
-	
-	//Implements function ax2+ by2 
-	double a, b;
-	public x2y2(double a, double b){
-		this.a = a;
-		this.b = b;
-		parameters = new double[2];
-		parameters[0] = 4;
-		parameters[1] = 4;
-		gradient = new double[2];
-	}
-	
-	public double getValue() {
-		functionCalls++;
-		return a*parameters[0]*parameters[0]+b*parameters[1]*parameters[1];
-	}
-
-	public double[] getGradient() {
-		gradientCalls++;
-		gradient[0]=2*a*parameters[0];
-		gradient[1]=2*b*parameters[1];
-		return gradient;
-//		if(debugLevel >=2){
-//			double[] numericalGradient = DebugHelpers.getNumericalGradient(this, parameters, 0.000001);
-//			for(int i = 0; i < parameters.length; i++){
-//				double diff = Math.abs(gradient[i]-numericalGradient[i]);
-//				if(diff > 0.00001){
-//					System.out.println("Numerical Gradient does not match");
-//					System.exit(1);
-//				}
-//			}
-//		}
-	}
-
-	
-	
-	public void optimizeWithGradientDescent(LineSearchMethod ls, OptimizerStats stats, x2y2 o){
-		GradientDescent optimizer = new GradientDescent(ls);
-		StopingCriteria stop = new GradientL2Norm(0.001);
-//		optimizer.setGradientConvergenceValue(0.001);
-		optimizer.setMaxIterations(100);
-		boolean succed = optimizer.optimize(o,stats,stop);
-		System.out.println("Ended optimzation Gradient Descent\n" + stats.prettyPrint(1));
-		System.out.println("Solution: " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		if(succed){
-			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
-		}else{
-			System.out.println("Failed to optimize");
-		}
-	}
-	
-	public void optimizeWithConjugateGradient(LineSearchMethod ls, OptimizerStats stats, x2y2 o){
-		ConjugateGradient optimizer = new ConjugateGradient(ls);
-		StopingCriteria stop = new GradientL2Norm(0.001);
-
-		optimizer.setMaxIterations(10);
-		boolean succed = optimizer.optimize(o,stats,stop);
-		System.out.println("Ended optimzation Conjugate Gradient\n" + stats.prettyPrint(1));
-		System.out.println("Solution: " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		if(succed){
-			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
-		}else{
-			System.out.println("Failed to optimize");
-		}
-	}
-	
-	public void optimizeWithLBFGS(LineSearchMethod ls, OptimizerStats stats, x2y2 o){
-		LBFGS optimizer = new LBFGS(ls,10);
-		StopingCriteria stop = new GradientL2Norm(0.001);
-		optimizer.setMaxIterations(10);
-		boolean succed = optimizer.optimize(o,stats,stop);
-		System.out.println("Ended optimzation LBFGS\n" + stats.prettyPrint(1));
-		System.out.println("Solution: " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		if(succed){
-			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
-		}else{
-			System.out.println("Failed to optimize");
-		}
-	}
-	
-	public static void main(String[] args) {
-		x2y2 o = new x2y2(1,10);
-		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		o.setDebugLevel(4);
-		LineSearchMethod wolfe = new WolfRuleLineSearch(new GenericPickFirstStep(1),0.001,0.9);;
-//		LineSearchMethod ls = new ArmijoLineSearchMinimization();
-		OptimizerStats stats = new OptimizerStats();
-		o.optimizeWithGradientDescent(wolfe, stats, o);
-		o = new x2y2(1,10);
-		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-//		ls = new ArmijoLineSearchMinimization();
-		stats = new OptimizerStats();
-		o.optimizeWithConjugateGradient(wolfe, stats, o);
-		o = new x2y2(1,10);
-		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-//		ls = new ArmijoLineSearchMinimization();
-		stats = new OptimizerStats();
-		o.optimizeWithLBFGS(wolfe, stats, o);	
-	}
-	
-	public String toString(){
-		return "P1: " + parameters[0] + " P2: " + parameters[1] + " value " + getValue();
-	}
-	
-	
-}