1 files changed, 0 insertions, 127 deletions
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;
-	}
-	
-}