cdec cleanup, remove bayesian stuff, parsing stuff

3 files changed, 0 insertions, 303 deletions

diff --git a/gi/posterior-regularisation/prjava/src/optimization/projections/BoundsProjection.java b/gi/posterior-regularisation/prjava/src/optimization/projections/BoundsProjection.java
deleted file mode 100644
index 0429d531..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/projections/BoundsProjection.java
+++ /dev/null
@@ -1,104 +0,0 @@
-package optimization.projections;
-
-
-import java.util.Random;
-
-import optimization.util.MathUtils;
-import optimization.util.MatrixOutput;
-
-/**
- * Implements a projection into a box set defined by a and b.
- * If either a or b are infinity then that bound is ignored.
- * @author javg
- *
- */
-public class BoundsProjection extends Projection{
-
-	double a,b;
-	boolean ignoreA = false;
-	boolean ignoreB = false;
-	public BoundsProjection(double lowerBound, double upperBound) {
-		if(Double.isInfinite(lowerBound)){
-			this.ignoreA = true;
-		}else{
-			this.a =lowerBound;
-		}
-		if(Double.isInfinite(upperBound)){
-			this.ignoreB = true;
-		}else{
-			this.b =upperBound;
-		}
-	}
-	
-	
-	
-	/**
-	* Projects into the bounds
-	* a <= x_i <=b
-	 */
-	public void project(double[] original){
-		for (int i = 0; i < original.length; i++) {
-			if(!ignoreA && original[i] < a){
-				original[i] = a;
-			}else if(!ignoreB && original[i]>b){
-				original[i]=b;
-			}
-		}
-	}
-	
-	/**
-	 * Generates a random number between a and b.
-	 */
-
-	Random r = new Random();
-	
-	public double[] samplePoint(int numParams) {
-		double[] point = new double[numParams];
-		for (int i = 0; i < point.length; i++) {
-			double rand = r.nextDouble();
-			if(ignoreA && ignoreB){
-				//Use const to avoid number near overflow
-				point[i] = rand*(1.E100+1.E100)-1.E100;
-			}else if(ignoreA){
-				point[i] = rand*(b-1.E100)-1.E100;
-			}else if(ignoreB){
-				point[i] = rand*(1.E100-a)-a;
-			}else{
-				point[i] = rand*(b-a)-a;
-			}
-		}
-		return point;
-	}
-	
-	public static void main(String[] args) {
-		BoundsProjection sp = new BoundsProjection(0,Double.POSITIVE_INFINITY);
-		
-		
-		MatrixOutput.printDoubleArray(sp.samplePoint(3), "random 1");
-		MatrixOutput.printDoubleArray(sp.samplePoint(3), "random 2");
-		MatrixOutput.printDoubleArray(sp.samplePoint(3), "random 3");
-		
-		double[] d = {-1.1,1.2,1.4};
-		double[] original = d.clone();
-		MatrixOutput.printDoubleArray(d, "before");
-		
-		sp.project(d);
-		MatrixOutput.printDoubleArray(d, "after");
-		System.out.println("Test projection: " + sp.testProjection(original, d));
-	}
-	
-	double epsilon = 1.E-10;
-	public double[] perturbePoint(double[] point, int parameter){
-		double[] newPoint = point.clone();
-		if(!ignoreA && MathUtils.almost(point[parameter], a)){
-			newPoint[parameter]+=epsilon;
-		}else if(!ignoreB && MathUtils.almost(point[parameter], b)){
-			newPoint[parameter]-=epsilon;
-		}else{
-			newPoint[parameter]-=epsilon;
-		}
-		return newPoint;
-	}
-
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/projections/Projection.java b/gi/posterior-regularisation/prjava/src/optimization/projections/Projection.java
deleted file mode 100644
index b5a9f92f..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/projections/Projection.java
+++ /dev/null
@@ -1,72 +0,0 @@
-package optimization.projections;
-
-import optimization.util.MathUtils;
-import optimization.util.MatrixOutput;
-import util.ArrayMath;
-import util.Printing;
-
-
-
-public abstract class Projection {
-
-	
-	public abstract void project(double[] original);
-	
-	
-	/**
-	 *  From the projection theorem "Non-Linear Programming" page
-	 *  201 fact 2.
-	 *  
-	 *  Given some z in R, and a vector x* in X;
-	 *  x* = z+ iif for all x in X 
-	 *  (z-x*)'(x-x*) <= 0 where 0 is when x*=x
-	 *  See figure 2.16 in book
-	 *  
-	 * @param original
-	 * @param projected
-	 * @return
-	 */
-	public boolean testProjection(double[] original, double[] projected){
-		double[] original1 = original.clone();
-		//System.out.println(Printing.doubleArrayToString(original1, null, "original"));
-		//System.out.println(Printing.doubleArrayToString(projected, null, "projected"));
-		MathUtils.minusEquals(original1, projected, 1);
-		//System.out.println(Printing.doubleArrayToString(original1, null, "minus1"));
-		for(int i = 0; i < 10; i++){
-			double[] x = samplePoint(original.length);
-		//	System.out.println(Printing.doubleArrayToString(x, null, "sample"));
-			//If the same this returns zero so we are there.	
-			MathUtils.minusEquals(x, projected, 1);
-		//	System.out.println(Printing.doubleArrayToString(x, null, "minus2"));
-			double dotProd = MathUtils.dotProduct(original1, x);
-			
-		//	System.out.println("dot " + dotProd);
-			if(dotProd > 0) return false;
-		}
-		
-		//Perturbs the point a bit in all possible directions
-		for(int i = 0; i < original.length; i++){
-			double[] x = perturbePoint(projected,i);
-		//	System.out.println(Printing.doubleArrayToString(x, null, "perturbed"));
-			//If the same this returns zero so we are there.	
-			MathUtils.minusEquals(x, projected, 1);
-		//	System.out.println(Printing.doubleArrayToString(x, null, "minus2"));
-			double dotProd = MathUtils.dotProduct(original1, x);
-			
-		//	System.out.println("dot " + dotProd);
-			if(dotProd > 0) return false;
-		}
-		
-		
-		
-		return true;
-	}
-
-	//Samples a point from the constrained set
-	public abstract double[] samplePoint(int dimensions);
-	
-	//Perturbs a point a bit still leaving it at the constraints set
-	public abstract double[] perturbePoint(double[] point, int parameter);
-	
-	
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/projections/SimplexProjection.java b/gi/posterior-regularisation/prjava/src/optimization/projections/SimplexProjection.java
deleted file mode 100644
index f22afcaf..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/projections/SimplexProjection.java
+++ /dev/null
@@ -1,127 +0,0 @@
-package optimization.projections;
-
-
-
-import java.util.Random;
-
-import optimization.util.MathUtils;
-import optimization.util.MatrixOutput;
-
-public class SimplexProjection extends Projection{
-
-	double scale;
-	public SimplexProjection(double scale) {
-		this.scale = scale;
-	}
-	
-	/**
-	 * projects the numbers of the array 
-	 * into a simplex of size. 
-	 * We follow the description of the paper
-	 * "Efficient Projetions onto the l1-Ball
-	 * for learning in high dimensions"
-	 */
-	public void project(double[] original){
-		double[] ds = new double[original.length];
-		System.arraycopy(original, 0, ds, 0, ds.length);
-		//If sum is smaller then zero then its ok
-		for (int i = 0; i < ds.length; i++) ds[i] = ds[i]>0? ds[i]:0;
-		double sum = MathUtils.sum(ds);
-		if (scale - sum >=  -1.E-10 ){
-			System.arraycopy(ds, 0, original, 0, ds.length);
-			//System.out.println("Not projecting");
-			return;
-		}
-		//System.out.println("projecting " + sum + " scontraints " + scale);	
-		util.Array.sortDescending(ds);
-		double currentSum = 0;
-		double previousTheta = 0;
-		double theta = 0;
-		for (int i = 0; i < ds.length; i++) {
-			currentSum+=ds[i];
-			theta = (currentSum-scale)/(i+1);
-			if(ds[i]-theta < -1e-10){
-				break;
-			}
-			previousTheta = theta;
-		}
-		//DEBUG
-		if(previousTheta < 0){
-			System.out.println("Simple Projection: Theta is smaller than zero: " + previousTheta);
-			System.exit(-1);
-		}
-		for (int i = 0; i < original.length; i++) {
-			original[i] = Math.max(original[i]-previousTheta, 0);
-		}
-	}
-	
-	
-	
-	
-	
-
-	/**
-	 * Samples a point from the simplex of scale. Just sample
-	 * random number from 0-scale and then if
-	 * their sum is bigger then sum make them normalize.
-	 * This is probably not sampling uniformly from the simplex but it is
-	 * enough for our goals in here.
-	 */
-	Random r = new Random();
-	public double[] samplePoint(int dimensions) {
-		double[] newPoint = new double[dimensions];
-		double sum =0;
-		for (int i = 0; i < newPoint.length; i++) {
-			double rand = r.nextDouble()*scale;
-			sum+=rand;
-			newPoint[i]=rand;
-		}
-		//Normalize
-		if(sum > scale){
-			for (int i = 0; i < newPoint.length; i++) {
-				newPoint[i]=scale*newPoint[i]/sum;
-			}
-		}
-		return newPoint;
-	}
-	
-	public static void main(String[] args) {
-		SimplexProjection sp = new SimplexProjection(1);
-		
-		
-		double[] point = sp.samplePoint(3);
-		MatrixOutput.printDoubleArray(point , "random 1 sum:" + MathUtils.sum(point));
-		point = sp.samplePoint(3);
-		MatrixOutput.printDoubleArray(point , "random 2 sum:" + MathUtils.sum(point));
-		point = sp.samplePoint(3);
-		MatrixOutput.printDoubleArray(point , "random 3 sum:" + MathUtils.sum(point));
-		
-		double[] d = {0,1.1,-10};
-		double[] original = d.clone();
-		MatrixOutput.printDoubleArray(d, "before");
-		
-		sp.project(d);
-		MatrixOutput.printDoubleArray(d, "after");
-		System.out.println("Test projection: " + sp.testProjection(original, d));
-		
-	}
-	
-	
-	double epsilon = 1.E-10;
-	public double[] perturbePoint(double[] point, int parameter){
-		double[] newPoint = point.clone();
-		if(MathUtils.almost(MathUtils.sum(point), scale)){
-			newPoint[parameter]-=epsilon;
-		}
-		else if(point[parameter]==0){
-			newPoint[parameter]+=epsilon;
-		}else if(MathUtils.almost(point[parameter], scale)){
-			newPoint[parameter]-=epsilon;
-		}
-		else{
-			newPoint[parameter]-=epsilon;
-		}
-		return newPoint;
-	}
-	
-}