Merge branch 'master' of https://github.com/redpony/cdec

3 files changed, 0 insertions, 365 deletions

diff --git a/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java b/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java
deleted file mode 100644
index 25fa7f09..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java
+++ /dev/null
@@ -1,110 +0,0 @@
-package optimization.examples;
-
-
-import optimization.gradientBasedMethods.ConjugateGradient;
-import optimization.gradientBasedMethods.GradientDescent;
-import optimization.gradientBasedMethods.LBFGS;
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.Optimizer;
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.ArmijoLineSearchMinimization;
-import optimization.linesearch.LineSearchMethod;
-import optimization.stopCriteria.GradientL2Norm;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.util.MathUtils;
-
-/**
- * 
- * @author javg
- * f(x) = \sum_{i=1}^{N-1} \left[ (1-x_i)^2+ 100 (x_{i+1} - x_i^2 )^2 \right] \quad \forall x\in\mathbb{R}^N.
- */
-public class GeneralizedRosenbrock extends Objective{
-
-	
-	
-	public GeneralizedRosenbrock(int dimensions){
-		parameters = new double[dimensions];		
-		java.util.Arrays.fill(parameters, 0);
-		gradient = new double[dimensions];
-		
-	}
-	
-	public GeneralizedRosenbrock(int dimensions, double[] params){
-		parameters = params;	
-		gradient = new double[dimensions];
-	}
-	
-	
-	public double getValue() {
-		functionCalls++;
-		double value = 0;
-		for(int i = 0; i < parameters.length-1; i++){
-			value += MathUtils.square(1-parameters[i]) + 100*MathUtils.square(parameters[i+1] - MathUtils.square(parameters[i]));
-		}
-		
-		return value;
-	}
-
-	/**
-	 * gx = -2(1-x) -2x200(y-x^2)
-	 * gy = 200(y-x^2)
-	 */
-	public double[] getGradient() {
-		gradientCalls++;
-		java.util.Arrays.fill(gradient,0);
-		for(int i = 0; i < parameters.length-1; i++){
-			gradient[i]+=-2*(1-parameters[i]) - 400*parameters[i]*(parameters[i+1] - MathUtils.square(parameters[i]));
-			gradient[i+1]+=200*(parameters[i+1] - MathUtils.square(parameters[i]));
-		}	
-		return gradient;
-	}
-
-	
-
-	
-
-	
-	
-	public String toString(){
-		String  res ="";
-		for(int i = 0; i < parameters.length; i++){
-			res += "P" + i+ " " + parameters[i];
-		}
-		res += " Value " + getValue();
-		return res;
-	}
-	
-	public static void main(String[] args) {
-		
-		GeneralizedRosenbrock o = new GeneralizedRosenbrock(2);
-		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1]);
-		;
-
-		System.out.println("Doing Gradient descent");
-		//LineSearchMethod wolfe = new WolfRuleLineSearch(new InterpolationPickFirstStep(1),100,0.001,0.1);
-		StopingCriteria stop = new GradientL2Norm(0.001);		
-		LineSearchMethod ls = new ArmijoLineSearchMinimization();
-		Optimizer optimizer = new GradientDescent(ls);		
-		OptimizerStats stats = new OptimizerStats();
-		optimizer.setMaxIterations(1000);
-		boolean succed = optimizer.optimize(o,stats, stop);
-		System.out.println("Suceess " + succed + "/n"+stats.prettyPrint(1));
-		System.out.println("Doing Conjugate Gradient descent");
-		o = new GeneralizedRosenbrock(2);
-	//	wolfe = new WolfRuleLineSearch(new InterpolationPickFirstStep(1),100,0.001,0.1);
-		optimizer = new ConjugateGradient(ls);
-		stats = new OptimizerStats();
-		optimizer.setMaxIterations(1000);
-		succed = optimizer.optimize(o,stats,stop);
-		System.out.println("Suceess " + succed + "/n"+stats.prettyPrint(1));
-		System.out.println("Doing Quasi newton descent");
-		o = new GeneralizedRosenbrock(2);
-		optimizer = new LBFGS(ls,10);
-		stats = new OptimizerStats();
-		optimizer.setMaxIterations(1000);
-		succed = optimizer.optimize(o,stats,stop);
-		System.out.println("Suceess " + succed + "/n"+stats.prettyPrint(1));
-
-	}
-	
-}
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();
-	}
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2WithConstraints.java b/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2WithConstraints.java
deleted file mode 100644
index 391775b7..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2WithConstraints.java
+++ /dev/null
@@ -1,127 +0,0 @@
-package optimization.examples;
-
-
-import optimization.gradientBasedMethods.ProjectedGradientDescent;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.ArmijoLineSearchMinimizationAlongProjectionArc;
-import optimization.linesearch.InterpolationPickFirstStep;
-import optimization.linesearch.LineSearchMethod;
-import optimization.projections.BoundsProjection;
-import optimization.projections.Projection;
-import optimization.projections.SimplexProjection;
-import optimization.stopCriteria.CompositeStopingCriteria;
-import optimization.stopCriteria.GradientL2Norm;
-import optimization.stopCriteria.ProjectedGradientL2Norm;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.stopCriteria.ValueDifference;
-
-
-/**
- * @author javg
- * 
- * 
- *ax2+ b(y2 -displacement)
- */
-public class x2y2WithConstraints extends ProjectedObjective{
-
-
-	double a, b;
-	double dx;
-	double dy;
-	Projection projection;
-	
-	
-	public x2y2WithConstraints(double a, double b, double[] params, double dx, double dy, Projection proj){
-		//projection = new BoundsProjection(0.2,Double.MAX_VALUE);
-		super();
-		projection = proj;	
-		this.a = a;
-		this.b = b;
-		this.dx = dx;
-		this.dy = dy;
-		setInitialParameters(params);
-		System.out.println("Function " +a+"(x-"+dx+")^2 + "+b+"(y-"+dy+")^2");
-		System.out.println("Gradient " +(2*a)+"(x-"+dx+") ; "+(b*2)+"(y-"+dy+")");
-		printParameters();
-		projection.project(parameters);
-		printParameters();
-		gradient = new double[2];
-	}
-	
-	public double getValue() {
-		functionCalls++;
-		return a*(parameters[0]-dx)*(parameters[0]-dx)+b*((parameters[1]-dy)*(parameters[1]-dy));
-	}
-
-	public double[] getGradient() {
-		if(gradient == null){
-			gradient = new double[2];
-		}
-		gradientCalls++;
-		gradient[0]=2*a*(parameters[0]-dx);
-		gradient[1]=2*b*(parameters[1]-dy);
-		return gradient;
-	}
-	
-	
-	public double[] projectPoint(double[] point) {
-		double[] newPoint = point.clone();
-		projection.project(newPoint);
-		return newPoint;
-	}	
-	
-	public void optimizeWithProjectedGradientDescent(LineSearchMethod ls, OptimizerStats stats, x2y2WithConstraints o){
-		ProjectedGradientDescent optimizer = new ProjectedGradientDescent(ls);
-		StopingCriteria stopGrad = new ProjectedGradientL2Norm(0.001);
-		StopingCriteria stopValue = new ValueDifference(0.001);
-		CompositeStopingCriteria compositeStop = new CompositeStopingCriteria();
-		compositeStop.add(stopGrad);
-		compositeStop.add(stopValue);
-		
-		optimizer.setMaxIterations(5);
-		boolean succed = optimizer.optimize(o,stats,compositeStop);
-		System.out.println("Ended optimzation Projected 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 String toString(){
-		
-		return "P1: " + parameters[0] + " P2: " + parameters[1] + " value " + getValue() + " grad (" + getGradient()[0] + ":" + getGradient()[1]+")";
-	}
-	
-	public static void main(String[] args) {
-		double a = 1;
-		double b=1;
-		double x0 = 0;
-		double y0  =1;
-		double dx = 0.5;
-		double dy = 0.5	;
-		double [] parameters = new double[2];
-		parameters[0] = x0;
-		parameters[1] = y0;
-		x2y2WithConstraints o = new x2y2WithConstraints(a,b,parameters,dx,dy, new SimplexProjection(0.5));
-		System.out.println("Starting optimization " + " x0 " + o.parameters[0]+ " x1 " + o.parameters[1] + " a " + a + " b "+b );
-		o.setDebugLevel(4);
-		
-		LineSearchMethod ls = new ArmijoLineSearchMinimizationAlongProjectionArc(new InterpolationPickFirstStep(1));
-		
-		OptimizerStats stats = new OptimizerStats();
-		o.optimizeWithProjectedGradientDescent(ls, stats, o);
-		
-//		o = new x2y2WithConstraints(a,b,x0,y0,dx,dy);
-//		stats = new OptimizerStats();
-//		o.optimizeWithSpectralProjectedGradientDescent(stats, o);
-	}
-	
-	
-	
-	
-}