diff options
Diffstat (limited to 'gi/posterior-regularisation/prjava/src/optimization/projections')
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; - } - -} |