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authorPatrick Simianer <simianer@cl.uni-heidelberg.de>2012-11-05 15:29:46 +0100
committerPatrick Simianer <simianer@cl.uni-heidelberg.de>2012-11-05 15:29:46 +0100
commit6f29f345dc06c1a1033475eac1d1340781d1d603 (patch)
tree6fa4cdd7aefd7d54c9585c2c6274db61bb8b159a /gi/posterior-regularisation/prjava/src/optimization
parentb510da2e562c695c90d565eb295c749569c59be8 (diff)
parentc615c37501fa8576584a510a9d2bfe2fdd5bace7 (diff)
merge upstream/master
Diffstat (limited to 'gi/posterior-regularisation/prjava/src/optimization')
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java110
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java128
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/examples/x2y2WithConstraints.java127
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java120
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ConjugateGradient.java92
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/DebugHelpers.java65
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/GradientDescent.java19
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java234
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Objective.java87
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Optimizer.java19
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedAbstractGradientBaseMethod.java11
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java154
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedObjective.java29
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedOptimizer.java10
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/OptimizerStats.java86
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/ProjectedOptimizerStats.java70
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java102
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimizationAlongProjectionArc.java141
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/linesearch/DifferentiableLineSearchObjective.java185
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/linesearch/GenericPickFirstStep.java20
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/linesearch/InterpolationPickFirstStep.java25
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/linesearch/LineSearchMethod.java14
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/linesearch/NonNewtonInterpolationPickFirstStep.java33
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/linesearch/ProjectedDifferentiableLineSearchObjective.java137
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfRuleLineSearch.java300
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfeConditions.java45
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/projections/BoundsProjection.java104
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/projections/Projection.java72
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/projections/SimplexProjection.java127
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/stopCriteria/CompositeStopingCriteria.java33
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/stopCriteria/GradientL2Norm.java30
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedGradientL2Norm.java48
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedProjectedGradientL2Norm.java60
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedValueDifference.java54
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ProjectedGradientL2Norm.java51
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/stopCriteria/StopingCriteria.java8
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ValueDifference.java41
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/util/Interpolation.java37
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/util/Logger.java7
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/util/MathUtils.java339
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/util/MatrixOutput.java28
-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/util/StaticTools.java180
42 files changed, 0 insertions, 3582 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);
- }
-
-
-
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java
deleted file mode 100644
index 2fcb7990..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java
+++ /dev/null
@@ -1,120 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.DifferentiableLineSearchObjective;
-import optimization.linesearch.LineSearchMethod;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.util.MathUtils;
-
-/**
- *
- * @author javg
- *
- */
-public abstract class AbstractGradientBaseMethod implements Optimizer{
-
- protected int maxNumberOfIterations=10000;
-
-
-
- protected int currentProjectionIteration;
- protected double currValue;
- protected double previousValue = Double.MAX_VALUE;;
- protected double step;
- protected double[] gradient;
- public double[] direction;
-
- //Original values
- protected double originalGradientL2Norm;
-
- protected LineSearchMethod lineSearch;
- DifferentiableLineSearchObjective lso;
-
-
- public void reset(){
- direction = null;
- gradient = null;
- previousValue = Double.MAX_VALUE;
- currentProjectionIteration = 0;
- originalGradientL2Norm = 0;
- step = 0;
- currValue = 0;
- }
-
- public void initializeStructures(Objective o,OptimizerStats stats, StopingCriteria stop){
- lso = new DifferentiableLineSearchObjective(o);
- }
- public void updateStructuresBeforeStep(Objective o,OptimizerStats stats, StopingCriteria stop){
- }
-
- public void updateStructuresAfterStep(Objective o,OptimizerStats stats, StopingCriteria stop){
- }
-
- public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stop){
- //Initialize structures
-
- stats.collectInitStats(this, o);
- direction = new double[o.getNumParameters()];
- initializeStructures(o, stats, stop);
- for (currentProjectionIteration = 1; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){
- //System.out.println("\tgradient descent iteration " + currentProjectionIteration);
- //System.out.print("\tparameters:" );
- //o.printParameters();
- previousValue = currValue;
- currValue = o.getValue();
- gradient = o.getGradient();
- if(stop.stopOptimization(o)){
- stats.collectFinalStats(this, o);
- return true;
- }
-
- getDirection();
- if(MathUtils.dotProduct(gradient, direction) > 0){
- System.out.println("Not a descent direction");
- System.out.println(" current stats " + stats.prettyPrint(1));
- System.exit(-1);
- }
- updateStructuresBeforeStep(o, stats, stop);
- lso.reset(direction);
- step = lineSearch.getStepSize(lso);
- //System.out.println("\t\tLeave with step: " + step);
- if(step==-1){
- System.out.println("Failed to find step");
- stats.collectFinalStats(this, o);
- return false;
- }
- updateStructuresAfterStep( o, stats, stop);
-// previousValue = currValue;
-// currValue = o.getValue();
-// gradient = o.getGradient();
- stats.collectIterationStats(this, o);
- }
- stats.collectFinalStats(this, o);
- return false;
- }
-
-
- public int getCurrentIteration() {
- return currentProjectionIteration;
- }
-
-
- /**
- * Method specific
- */
- public abstract double[] getDirection();
-
- public double getCurrentStep() {
- return step;
- }
-
-
-
- public void setMaxIterations(int max) {
- maxNumberOfIterations = max;
- }
-
- public double getCurrentValue() {
- return currValue;
- }
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ConjugateGradient.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ConjugateGradient.java
deleted file mode 100644
index 28295729..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ConjugateGradient.java
+++ /dev/null
@@ -1,92 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.DifferentiableLineSearchObjective;
-import optimization.linesearch.LineSearchMethod;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.util.MathUtils;
-
-
-
-public class ConjugateGradient extends AbstractGradientBaseMethod{
-
-
- double[] previousGradient;
- double[] previousDirection;
-
- public ConjugateGradient(LineSearchMethod lineSearch) {
- this.lineSearch = lineSearch;
- }
-
- public void reset(){
- super.reset();
- java.util.Arrays.fill(previousDirection, 0);
- java.util.Arrays.fill(previousGradient, 0);
- }
-
- public void initializeStructures(Objective o,OptimizerStats stats, StopingCriteria stop){
- super.initializeStructures(o, stats, stop);
- previousGradient = new double[o.getNumParameters()];
- previousDirection = new double[o.getNumParameters()];
- }
- public void updateStructuresBeforeStep(Objective o,OptimizerStats stats, StopingCriteria stop){
- System.arraycopy(gradient, 0, previousGradient, 0, gradient.length);
- System.arraycopy(direction, 0, previousDirection, 0, direction.length);
- }
-
-// public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stop){
-// DifferentiableLineSearchObjective lso = new DifferentiableLineSearchObjective(o);
-// stats.collectInitStats(this, o);
-// direction = new double[o.getNumParameters()];
-// initializeStructures(o, stats, stop);
-// for (currentProjectionIteration = 0; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){
-// previousValue = currValue;
-// currValue = o.getValue();
-// gradient =o.getGradient();
-// if(stop.stopOptimization(gradient)){
-// stats.collectFinalStats(this, o);
-// return true;
-// }
-// getDirection();
-// updateStructures(o, stats, stop);
-// lso.reset(direction);
-// step = lineSearch.getStepSize(lso);
-// if(step==-1){
-// System.out.println("Failed to find a step size");
-// System.out.println("Failed to find step");
-// stats.collectFinalStats(this, o);
-// return false;
-// }
-//
-// stats.collectIterationStats(this, o);
-// }
-// stats.collectFinalStats(this, o);
-// return false;
-// }
-
- public double[] getDirection(){
- direction = MathUtils.negation(gradient);
- if(currentProjectionIteration != 1){
- //Using Polak-Ribiere method (book equation 5.45)
- double b = MathUtils.dotProduct(gradient, MathUtils.arrayMinus(gradient, previousGradient))
- /MathUtils.dotProduct(previousGradient, previousGradient);
- if(b<0){
- System.out.println("Defaulting to gradient descent");
- b = Math.max(b, 0);
- }
- MathUtils.plusEquals(direction, previousDirection, b);
- //Debug code
- if(MathUtils.dotProduct(direction, gradient) > 0){
- System.out.println("Not an descent direction reseting to gradien");
- direction = MathUtils.negation(gradient);
- }
- }
- return direction;
- }
-
-
-
-
-
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/DebugHelpers.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/DebugHelpers.java
deleted file mode 100644
index 6dc4ef6c..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/DebugHelpers.java
+++ /dev/null
@@ -1,65 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import java.util.ArrayList;
-
-import optimization.util.MathUtils;
-
-
-
-public class DebugHelpers {
- public static void getLineSearchGraph(Objective o, double[] direction,
- double[] parameters, double originalObj,
- double originalDot, double c1, double c2){
- ArrayList<Double> stepS = new ArrayList<Double>();
- ArrayList<Double> obj = new ArrayList<Double>();
- ArrayList<Double> norm = new ArrayList<Double>();
- double[] gradient = new double[o.getNumParameters()];
- double[] newParameters = parameters.clone();
- MathUtils.plusEquals(newParameters,direction,0);
- o.setParameters(newParameters);
- double minValue = o.getValue();
- int valuesBiggerThanMax = 0;
- for(double step = 0; step < 2; step +=0.01 ){
- newParameters = parameters.clone();
- MathUtils.plusEquals(newParameters,direction,step);
- o.setParameters(newParameters);
- double newValue = o.getValue();
- gradient = o.getGradient();
- double newgradDirectionDot = MathUtils.dotProduct(gradient,direction);
- stepS.add(step);
- obj.add(newValue);
- norm.add(newgradDirectionDot);
- if(newValue <= minValue){
- minValue = newValue;
- }else{
- valuesBiggerThanMax++;
- }
-
- if(valuesBiggerThanMax > 10){
- break;
- }
-
- }
- System.out.println("step\torigObj\tobj\tsuffdec\tnorm\tcurvature1");
- for(int i = 0; i < stepS.size(); i++){
- double cnorm= norm.get(i);
- System.out.println(stepS.get(i)+"\t"+originalObj +"\t"+obj.get(i) + "\t" +
- (originalObj + originalDot*((Double)stepS.get(i))*c1) +"\t"+Math.abs(cnorm) +"\t"+c2*Math.abs(originalDot));
- }
- }
-
- public static double[] getNumericalGradient(Objective o, double[] parameters, double epsilon){
- int nrParameters = o.getNumParameters();
- double[] gradient = new double[nrParameters];
- double[] newParameters;
- double originalValue = o.getValue();
- for(int parameter = 0; parameter < nrParameters; parameter++){
- newParameters = parameters.clone();
- newParameters[parameter]+=epsilon;
- o.setParameters(newParameters);
- double newValue = o.getValue();
- gradient[parameter]=(newValue-originalValue)/epsilon;
- }
- return gradient;
- }
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/GradientDescent.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/GradientDescent.java
deleted file mode 100644
index 9a53cef4..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/GradientDescent.java
+++ /dev/null
@@ -1,19 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import optimization.linesearch.LineSearchMethod;
-
-
-
-public class GradientDescent extends AbstractGradientBaseMethod{
-
- public GradientDescent(LineSearchMethod lineSearch) {
- this.lineSearch = lineSearch;
- }
-
- public double[] getDirection(){
- for(int i = 0; i< gradient.length; i++){
- direction[i] = -gradient[i];
- }
- return direction;
- }
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java
deleted file mode 100644
index dedbc942..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java
+++ /dev/null
@@ -1,234 +0,0 @@
-package optimization.gradientBasedMethods;
-
-
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.DifferentiableLineSearchObjective;
-import optimization.linesearch.LineSearchMethod;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.util.MathUtils;
-
-public class LBFGS extends AbstractGradientBaseMethod{
-
- //How many previous values are being saved
- int history;
- double[][] skList;
- double[][] ykList;
- double initialHessianParameters;
- double[] previousGradient;
- double[] previousParameters;
-
- //auxiliar structures
- double q[];
- double[] roi;
- double[] alphai;
-
- public LBFGS(LineSearchMethod ls, int history) {
- lineSearch = ls;
- this.history = history;
- skList = new double[history][];
- ykList = new double[history][];
-
- }
-
- public void reset(){
- super.reset();
- initialHessianParameters = 0;
- previousParameters = null;
- previousGradient = null;
- skList = new double[history][];
- ykList = new double[history][];
- q = null;
- roi = null;
- alphai = null;
- }
-
- public double[] LBFGSTwoLoopRecursion(double hessianConst){
- //Only create array once
- if(q == null){
- q = new double[gradient.length];
- }
- System.arraycopy(gradient, 0, q, 0, gradient.length);
- //Only create array once
- if(roi == null){
- roi = new double[history];
- }
- //Only create array once
- if(alphai == null){
- alphai = new double[history];
- }
-
- for(int i = history-1; i >=0 && skList[i]!= null && ykList[i]!=null; i-- ){
- // System.out.println("New to Old proj " + currentProjectionIteration + " history "+history + " index " + i);
- double[] si = skList[i];
- double[] yi = ykList[i];
- roi[i]= 1.0/MathUtils.dotProduct(yi,si);
- alphai[i] = MathUtils.dotProduct(si, q)*roi[i];
- MathUtils.plusEquals(q, yi, -alphai[i]);
- }
- //Initial Hessian is just a constant
- MathUtils.scalarMultiplication(q, hessianConst);
- for(int i = 0; i <history && skList[i]!= null && ykList[i]!=null; i++ ){
- // System.out.println("Old to New proj " + currentProjectionIteration + " history "+history + " index " + i);
- double beta = MathUtils.dotProduct(ykList[i], q)*roi[i];
- MathUtils.plusEquals(q, skList[i], (alphai[i]-beta));
- }
- return q;
- }
-
-
-
-
- @Override
- public double[] getDirection() {
-
- calculateInitialHessianParameter();
-// System.out.println("Initial hessian " + initialHessianParameters);
- return direction = MathUtils.negation(LBFGSTwoLoopRecursion(initialHessianParameters));
- }
-
- public void calculateInitialHessianParameter(){
- if(currentProjectionIteration == 1){
- //Use gradient
- initialHessianParameters = 1;
- }else if(currentProjectionIteration <= history){
- double[] sk = skList[currentProjectionIteration-2];
- double[] yk = ykList[currentProjectionIteration-2];
- initialHessianParameters = MathUtils.dotProduct(sk, yk)/MathUtils.dotProduct(yk, yk);
- }else{
- //get the last one
- double[] sk = skList[history-1];
- double[] yk = ykList[history-1];
- initialHessianParameters = MathUtils.dotProduct(sk, yk)/MathUtils.dotProduct(yk, yk);
- }
- }
-
- //TODO if structures exit just reset them to zero
- public void initializeStructures(Objective o,OptimizerStats stats, StopingCriteria stop){
- super.initializeStructures(o, stats, stop);
- previousParameters = new double[o.getNumParameters()];
- previousGradient = new double[o.getNumParameters()];
- }
- public void updateStructuresBeforeStep(Objective o,OptimizerStats stats, StopingCriteria stop){
- super.initializeStructures(o, stats, stop);
- System.arraycopy(o.getParameters(), 0, previousParameters, 0, previousParameters.length);
- System.arraycopy(gradient, 0, previousGradient, 0, gradient.length);
- }
-
- public void updateStructuresAfterStep( Objective o,OptimizerStats stats, StopingCriteria stop){
- double[] diffX = MathUtils.arrayMinus(o.getParameters(), previousParameters);
- double[] diffGrad = MathUtils.arrayMinus(gradient, previousGradient);
- //Save new values and discard new ones
- if(currentProjectionIteration > history){
- for(int i = 0; i < history-1;i++){
- skList[i]=skList[i+1];
- ykList[i]=ykList[i+1];
- }
- skList[history-1]=diffX;
- ykList[history-1]=diffGrad;
- }else{
- skList[currentProjectionIteration-1]=diffX;
- ykList[currentProjectionIteration-1]=diffGrad;
- }
- }
-
-// public boolean optimize(Objective o, OptimizerStats stats, StopingCriteria stop) {
-// DifferentiableLineSearchObjective lso = new DifferentiableLineSearchObjective(o);
-// gradient = o.getGradient();
-// direction = new double[o.getNumParameters()];
-// previousGradient = new double[o.getNumParameters()];
-//
-// previousParameters = new double[o.getNumParameters()];
-//
-// stats.collectInitStats(this, o);
-// previousValue = Double.MAX_VALUE;
-// currValue= o.getValue();
-// //Used for stopping criteria
-// double[] originalGradient = o.getGradient();
-//
-// originalGradientL2Norm = MathUtils.L2Norm(originalGradient);
-// if(stop.stopOptimization(originalGradient)){
-// stats.collectFinalStats(this, o);
-// return true;
-// }
-// for (currentProjectionIteration = 1; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){
-//
-//
-// currValue = o.getValue();
-// gradient = o.getGradient();
-// currParameters = o.getParameters();
-//
-//
-// if(currentProjectionIteration == 1){
-// //Use gradient
-// initialHessianParameters = 1;
-// }else if(currentProjectionIteration <= history){
-// double[] sk = skList[currentProjectionIteration-2];
-// double[] yk = ykList[currentProjectionIteration-2];
-// initialHessianParameters = MathUtils.dotProduct(sk, yk)/MathUtils.dotProduct(yk, yk);
-// }else{
-// //get the last one
-// double[] sk = skList[history-1];
-// double[] yk = ykList[history-1];
-// initialHessianParameters = MathUtils.dotProduct(sk, yk)/MathUtils.dotProduct(yk, yk);
-// }
-//
-// getDirection();
-//
-// //MatrixOutput.printDoubleArray(direction, "direction");
-// double dot = MathUtils.dotProduct(direction, gradient);
-// if(dot > 0){
-// throw new RuntimeException("Not a descent direction");
-// } if (Double.isNaN(dot)){
-// throw new RuntimeException("dot is not a number!!");
-// }
-// System.arraycopy(currParameters, 0, previousParameters, 0, currParameters.length);
-// System.arraycopy(gradient, 0, previousGradient, 0, gradient.length);
-// lso.reset(direction);
-// step = lineSearch.getStepSize(lso);
-// if(step==-1){
-// System.out.println("Failed to find a step size");
-//// lso.printLineSearchSteps();
-//// System.out.println(stats.prettyPrint(1));
-// stats.collectFinalStats(this, o);
-// return false;
-// }
-// stats.collectIterationStats(this, o);
-//
-// //We are not updating the alpha since it is done in line search already
-// currParameters = o.getParameters();
-// gradient = o.getGradient();
-//
-// if(stop.stopOptimization(gradient)){
-// stats.collectFinalStats(this, o);
-// return true;
-// }
-// double[] diffX = MathUtils.arrayMinus(currParameters, previousParameters);
-// double[] diffGrad = MathUtils.arrayMinus(gradient, previousGradient);
-// //Save new values and discard new ones
-// if(currentProjectionIteration > history){
-// for(int i = 0; i < history-1;i++){
-// skList[i]=skList[i+1];
-// ykList[i]=ykList[i+1];
-// }
-// skList[history-1]=diffX;
-// ykList[history-1]=diffGrad;
-// }else{
-// skList[currentProjectionIteration-1]=diffX;
-// ykList[currentProjectionIteration-1]=diffGrad;
-// }
-// previousValue = currValue;
-// }
-// stats.collectFinalStats(this, o);
-// return false;
-// }
-
-
-
-
-
-
-
-
-
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Objective.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Objective.java
deleted file mode 100644
index 6be01bf9..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Objective.java
+++ /dev/null
@@ -1,87 +0,0 @@
-package optimization.gradientBasedMethods;
-
-
-/**
- * Defines an optimization objective:
- *
- *
- * @author javg
- *
- */
-public abstract class Objective {
-
- protected int functionCalls = 0;
- protected int gradientCalls = 0;
- protected int updateCalls = 0;
-
- protected double[] parameters;
-
- //Contains a cache with the gradient
- public double[] gradient;
- int debugLevel = 0;
-
- public void setDebugLevel(int level){
- debugLevel = level;
- }
-
- public int getNumParameters() {
- return parameters.length;
- }
-
- public double getParameter(int index) {
- return parameters[index];
- }
-
- public double[] getParameters() {
- return parameters;
- }
-
- public abstract double[] getGradient( );
-
- public void setParameter(int index, double value) {
- parameters[index]=value;
- }
-
- public void setParameters(double[] params) {
- if(parameters == null){
- parameters = new double[params.length];
- }
- updateCalls++;
- System.arraycopy(params, 0, parameters, 0, params.length);
- }
-
-
- public int getNumberFunctionCalls() {
- return functionCalls;
- }
-
- public int getNumberGradientCalls() {
- return gradientCalls;
- }
-
- public int getNumberUpdateCalls() {
- return updateCalls;
- }
-
- public String finalInfoString() {
- return "FE: " + functionCalls + " GE " + gradientCalls + " Params updates" +
- updateCalls;
- }
- public void printParameters() {
- System.out.println(toString());
- }
-
- public abstract String toString();
- public abstract double getValue ();
-
- /**
- * Sets the initial objective parameters
- * For unconstrained models this just sets the objective params = argument no copying
- * For a constrained objective project the parameters and then set
- * @param params
- */
- public void setInitialParameters(double[] params){
- parameters = params;
- }
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Optimizer.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Optimizer.java
deleted file mode 100644
index 96fce5b0..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Optimizer.java
+++ /dev/null
@@ -1,19 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.stopCriteria.StopingCriteria;
-
-public interface Optimizer {
- public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stoping);
-
-
- public double[] getDirection();
- public double getCurrentStep();
- public double getCurrentValue();
- public int getCurrentIteration();
- public void reset();
-
- public void setMaxIterations(int max);
-
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedAbstractGradientBaseMethod.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedAbstractGradientBaseMethod.java
deleted file mode 100644
index afb29d04..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedAbstractGradientBaseMethod.java
+++ /dev/null
@@ -1,11 +0,0 @@
-package optimization.gradientBasedMethods;
-
-
-/**
- *
- * @author javg
- *
- */
-public abstract class ProjectedAbstractGradientBaseMethod extends AbstractGradientBaseMethod implements ProjectedOptimizer{
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java
deleted file mode 100644
index 0186e945..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java
+++ /dev/null
@@ -1,154 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import java.io.IOException;
-
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.DifferentiableLineSearchObjective;
-import optimization.linesearch.LineSearchMethod;
-import optimization.linesearch.ProjectedDifferentiableLineSearchObjective;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.util.MathUtils;
-
-
-/**
- * This class implements the projected gradiend
- * as described in Bertsekas "Non Linear Programming"
- * section 2.3.
- *
- * The update is given by:
- * x_k+1 = x_k + alpha^k(xbar_k-x_k)
- * Where xbar is:
- * xbar = [x_k -s_k grad(f(x_k))]+
- * where []+ is the projection into the feasibility set
- *
- * alpha is the step size
- * s_k - is a positive scalar which can be view as a step size as well, by
- * setting alpha to 1, then x_k+1 = [x_k -s_k grad(f(x_k))]+
- * This is called taking a step size along the projection arc (Bertsekas) which
- * we will use by default.
- *
- * Note that the only place where we actually take a step size is on pick a step size
- * so this is going to be just like a normal gradient descent but use a different
- * armijo line search where we project after taking a step.
- *
- *
- * @author javg
- *
- */
-public class ProjectedGradientDescent extends ProjectedAbstractGradientBaseMethod{
-
-
-
-
- public ProjectedGradientDescent(LineSearchMethod lineSearch) {
- this.lineSearch = lineSearch;
- }
-
- //Use projected differential objective instead
- public void initializeStructures(Objective o, OptimizerStats stats, StopingCriteria stop) {
- lso = new ProjectedDifferentiableLineSearchObjective(o);
- };
-
-
- ProjectedObjective obj;
- public boolean optimize(ProjectedObjective o,OptimizerStats stats, StopingCriteria stop){
- obj = o;
- return super.optimize(o, stats, stop);
- }
-
- public double[] getDirection(){
- for(int i = 0; i< gradient.length; i++){
- direction[i] = -gradient[i];
- }
- return direction;
- }
-
-
-
-
-}
-
-
-
-
-
-
-
-///OLD CODE
-
-//Use projected gradient norm
-//public boolean stopCriteria(double[] gradient){
-// if(originalDirenctionL2Norm == 0){
-// System.out.println("Leaving original direction norm is zero");
-// return true;
-// }
-// if(MathUtils.L2Norm(direction)/originalDirenctionL2Norm < gradientConvergenceValue){
-// System.out.println("Leaving projected gradient Norm smaller than epsilon");
-// return true;
-// }
-// if((previousValue - currValue)/Math.abs(previousValue) < valueConvergenceValue) {
-// System.out.println("Leaving value change below treshold " + previousValue + " - " + currValue);
-// System.out.println(previousValue/currValue + " - " + currValue/currValue
-// + " = " + (previousValue - currValue)/Math.abs(previousValue));
-// return true;
-// }
-// return false;
-//}
-//
-
-//public boolean optimize(ProjectedObjective o,OptimizerStats stats, StopingCriteria stop){
-// stats.collectInitStats(this, o);
-// obj = o;
-// step = 0;
-// currValue = o.getValue();
-// previousValue = Double.MAX_VALUE;
-// gradient = o.getGradient();
-// originalGradientL2Norm = MathUtils.L2Norm(gradient);
-// parameterChange = new double[gradient.length];
-// getDirection();
-// ProjectedDifferentiableLineSearchObjective lso = new ProjectedDifferentiableLineSearchObjective(o,direction);
-//
-// originalDirenctionL2Norm = MathUtils.L2Norm(direction);
-// //MatrixOutput.printDoubleArray(currParameters, "parameters");
-// for (currentProjectionIteration = 0; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){
-// // System.out.println("Iter " + currentProjectionIteration);
-// //o.printParameters();
-//
-//
-//
-// if(stop.stopOptimization(gradient)){
-// stats.collectFinalStats(this, o);
-// lastStepUsed = step;
-// return true;
-// }
-// lso.reset(direction);
-// step = lineSearch.getStepSize(lso);
-// if(step==-1){
-// System.out.println("Failed to find step");
-// stats.collectFinalStats(this, o);
-// return false;
-//
-// }
-//
-// //Update the direction for stopping criteria
-// previousValue = currValue;
-// currValue = o.getValue();
-// gradient = o.getGradient();
-// direction = getDirection();
-// if(MathUtils.dotProduct(gradient, direction) > 0){
-// System.out.println("Not a descent direction");
-// System.out.println(" current stats " + stats.prettyPrint(1));
-// System.exit(-1);
-// }
-// stats.collectIterationStats(this, o);
-// }
-// lastStepUsed = step;
-// stats.collectFinalStats(this, o);
-// return false;
-// }
-
-//public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stop){
-// System.out.println("Objective is not a projected objective");
-// throw new RuntimeException();
-//}
-
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedObjective.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedObjective.java
deleted file mode 100644
index c3d21393..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedObjective.java
+++ /dev/null
@@ -1,29 +0,0 @@
-package optimization.gradientBasedMethods;
-
-import optimization.util.MathUtils;
-
-
-/**
- * Computes a projected objective
- * When we tell it to set some parameters it automatically projects the parameters back into the simplex:
- *
- *
- * When we tell it to get the gradient in automatically returns the projected gradient:
- * @author javg
- *
- */
-public abstract class ProjectedObjective extends Objective{
-
- public abstract double[] projectPoint (double[] point);
-
- public double[] auxParameters;
-
-
- public void setInitialParameters(double[] params){
- setParameters(projectPoint(params));
- }
-
-
-
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedOptimizer.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedOptimizer.java
deleted file mode 100644
index 81d8403e..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedOptimizer.java
+++ /dev/null
@@ -1,10 +0,0 @@
-package optimization.gradientBasedMethods;
-
-
-
-public interface ProjectedOptimizer extends Optimizer{
-
-
-
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/OptimizerStats.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/OptimizerStats.java
deleted file mode 100644
index 6340ef73..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/OptimizerStats.java
+++ /dev/null
@@ -1,86 +0,0 @@
-package optimization.gradientBasedMethods.stats;
-
-import java.util.ArrayList;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.Optimizer;
-import optimization.util.MathUtils;
-import optimization.util.StaticTools;
-
-
-public class OptimizerStats {
-
- double start = 0;
- double totalTime = 0;
-
- String objectiveFinalStats;
-
- ArrayList<Double> gradientNorms = new ArrayList<Double>();
- ArrayList<Double> steps = new ArrayList<Double>();
- ArrayList<Double> value = new ArrayList<Double>();
- ArrayList<Integer> iterations = new ArrayList<Integer>();
- double prevValue =0;
-
- public void reset(){
- start = 0;
- totalTime = 0;
-
- objectiveFinalStats="";
-
- gradientNorms.clear();
- steps.clear();
- value.clear();
- iterations.clear();
- prevValue =0;
- }
-
- public void startTime() {
- start = System.currentTimeMillis();
- }
- public void stopTime() {
- totalTime += System.currentTimeMillis() - start;
- }
-
- public String prettyPrint(int level){
- StringBuffer res = new StringBuffer();
- res.append("Total time " + totalTime/1000 + " seconds \n" + "Iterations " + iterations.size() + "\n");
- res.append(objectiveFinalStats+"\n");
- if(level > 0){
- if(iterations.size() > 0){
- res.append("\tIteration"+iterations.get(0)+"\tstep: "+StaticTools.prettyPrint(steps.get(0), "0.00E00", 6)+ "\tgradientNorm "+
- StaticTools.prettyPrint(gradientNorms.get(0), "0.00000E00", 10)+ "\tvalue "+ StaticTools.prettyPrint(value.get(0), "0.000000E00",11)+"\n");
- }
- for(int i = 1; i < iterations.size(); i++){
- res.append("\tIteration:\t"+iterations.get(i)+"\tstep:"+StaticTools.prettyPrint(steps.get(i), "0.00E00", 6)+ "\tgradientNorm "+
- StaticTools.prettyPrint(gradientNorms.get(i), "0.00000E00", 10)+
- "\tvalue:\t"+ StaticTools.prettyPrint(value.get(i), "0.000000E00",11)+
- "\tvalueDiff:\t"+ StaticTools.prettyPrint((value.get(i-1)-value.get(i)), "0.000000E00",11)+
- "\n");
- }
- }
- return res.toString();
- }
-
-
- public void collectInitStats(Optimizer optimizer, Objective objective){
- startTime();
- iterations.add(-1);
- gradientNorms.add(MathUtils.L2Norm(objective.getGradient()));
- steps.add(0.0);
- value.add(objective.getValue());
- }
-
- public void collectIterationStats(Optimizer optimizer, Objective objective){
- iterations.add(optimizer.getCurrentIteration());
- gradientNorms.add(MathUtils.L2Norm(objective.getGradient()));
- steps.add(optimizer.getCurrentStep());
- value.add(optimizer.getCurrentValue());
- }
-
-
- public void collectFinalStats(Optimizer optimizer, Objective objective){
- stopTime();
- objectiveFinalStats = objective.finalInfoString();
- }
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/ProjectedOptimizerStats.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/ProjectedOptimizerStats.java
deleted file mode 100644
index d65a1267..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/ProjectedOptimizerStats.java
+++ /dev/null
@@ -1,70 +0,0 @@
-package optimization.gradientBasedMethods.stats;
-
-import java.util.ArrayList;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.Optimizer;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.gradientBasedMethods.ProjectedOptimizer;
-import optimization.util.MathUtils;
-import optimization.util.StaticTools;
-
-
-public class ProjectedOptimizerStats extends OptimizerStats{
-
-
-
- public void reset(){
- super.reset();
- projectedGradientNorms.clear();
- }
-
- ArrayList<Double> projectedGradientNorms = new ArrayList<Double>();
-
- public String prettyPrint(int level){
- StringBuffer res = new StringBuffer();
- res.append("Total time " + totalTime/1000 + " seconds \n" + "Iterations " + iterations.size() + "\n");
- res.append(objectiveFinalStats+"\n");
- if(level > 0){
- if(iterations.size() > 0){
- res.append("\tIteration"+iterations.get(0)+"\tstep: "+
- StaticTools.prettyPrint(steps.get(0), "0.00E00", 6)+ "\tgradientNorm "+
- StaticTools.prettyPrint(gradientNorms.get(0), "0.00000E00", 10)
- + "\tdirection"+
- StaticTools.prettyPrint(projectedGradientNorms.get(0), "0.00000E00", 10)+
- "\tvalue "+ StaticTools.prettyPrint(value.get(0), "0.000000E00",11)+"\n");
- }
- for(int i = 1; i < iterations.size(); i++){
- res.append("\tIteration"+iterations.get(i)+"\tstep: "+StaticTools.prettyPrint(steps.get(i), "0.00E00", 6)+ "\tgradientNorm "+
- StaticTools.prettyPrint(gradientNorms.get(i), "0.00000E00", 10)+
- "\t direction "+
- StaticTools.prettyPrint(projectedGradientNorms.get(i), "0.00000E00", 10)+
- "\tvalue "+ StaticTools.prettyPrint(value.get(i), "0.000000E00",11)+
- "\tvalueDiff "+ StaticTools.prettyPrint((value.get(i-1)-value.get(i)), "0.000000E00",11)+
- "\n");
- }
- }
- return res.toString();
- }
-
-
- public void collectInitStats(Optimizer optimizer, Objective objective){
- startTime();
- }
-
- public void collectIterationStats(Optimizer optimizer, Objective objective){
- iterations.add(optimizer.getCurrentIteration());
- gradientNorms.add(MathUtils.L2Norm(objective.getGradient()));
- projectedGradientNorms.add(MathUtils.L2Norm(optimizer.getDirection()));
- steps.add(optimizer.getCurrentStep());
- value.add(optimizer.getCurrentValue());
- }
-
-
-
- public void collectFinalStats(Optimizer optimizer, Objective objective){
- stopTime();
- objectiveFinalStats = objective.finalInfoString();
- }
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java
deleted file mode 100644
index c9f9b8df..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java
+++ /dev/null
@@ -1,102 +0,0 @@
-package optimization.linesearch;
-
-import optimization.util.Interpolation;
-
-
-/**
- * Implements Back Tracking Line Search as described on page 37 of Numerical Optimization.
- * Also known as armijo rule
- * @author javg
- *
- */
-public class ArmijoLineSearchMinimization implements LineSearchMethod{
-
- /**
- * How much should the step size decrease at each iteration.
- */
- double contractionFactor = 0.5;
- double c1 = 0.0001;
-
- double sigma1 = 0.1;
- double sigma2 = 0.9;
-
-
-
- double initialStep;
- int maxIterations = 10;
-
-
- public ArmijoLineSearchMinimization(){
- this.initialStep = 1;
- }
-
- //Experiment
- double previousStepPicked = -1;;
- double previousInitGradientDot = -1;
- double currentInitGradientDot = -1;
-
-
- public void reset(){
- previousStepPicked = -1;;
- previousInitGradientDot = -1;
- currentInitGradientDot = -1;
- }
-
- public void setInitialStep(double initial){
- initialStep = initial;
- }
-
- /**
- *
- */
-
- public double getStepSize(DifferentiableLineSearchObjective o) {
- currentInitGradientDot = o.getInitialGradient();
- //Should update all in the objective
- o.updateAlpha(initialStep);
- int nrIterations = 0;
- //System.out.println("tried alpha" + initialStep + " value " + o.getCurrentValue());
- while(!WolfeConditions.suficientDecrease(o,c1)){
- if(nrIterations >= maxIterations){
- o.printLineSearchSteps();
- return -1;
- }
- double alpha=o.getAlpha();
- double alphaTemp =
- Interpolation.quadraticInterpolation(o.getOriginalValue(), o.getInitialGradient(), alpha, o.getCurrentValue());
- if(alphaTemp >= sigma1 || alphaTemp <= sigma2*o.getAlpha()){
-// System.out.println("using alpha temp " + alphaTemp);
- alpha = alphaTemp;
- }else{
-// System.out.println("Discarding alpha temp " + alphaTemp);
- alpha = alpha*contractionFactor;
- }
-// double alpha =o.getAlpha()*contractionFactor;
-
- o.updateAlpha(alpha);
- //System.out.println("tried alpha" + alpha+ " value " + o.getCurrentValue());
- nrIterations++;
- }
-
- //System.out.println("Leavning line search used:");
- //o.printLineSearchSteps();
-
- previousInitGradientDot = currentInitGradientDot;
- previousStepPicked = o.getAlpha();
- return o.getAlpha();
- }
-
- public double getInitialGradient() {
- return currentInitGradientDot;
-
- }
-
- public double getPreviousInitialGradient() {
- return previousInitGradientDot;
- }
-
- public double getPreviousStepUsed() {
- return previousStepPicked;
- }
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimizationAlongProjectionArc.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimizationAlongProjectionArc.java
deleted file mode 100644
index e153f2da..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimizationAlongProjectionArc.java
+++ /dev/null
@@ -1,141 +0,0 @@
-package optimization.linesearch;
-
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.util.Interpolation;
-import optimization.util.MathUtils;
-
-
-
-
-
-/**
- * Implements Armijo Rule Line search along the projection arc (Non-Linear Programming page 230)
- * To be used with Projected gradient Methods.
- *
- * Recall that armijo tries successive step sizes alpha until the sufficient decrease is satisfied:
- * f(x+alpha*direction) < f(x) + alpha*c1*grad(f)*direction
- *
- * In this case we are optimizing over a convex set X so we must guarantee that the new point stays inside the
- * constraints.
- * First the direction as to be feasible (inside constraints) and will be define as:
- * d = (x_k_f - x_k) where x_k_f is a feasible point.
- * so the armijo condition can be rewritten as:
- * f(x+alpha(x_k_f - x_k)) < f(x) + c1*grad(f)*(x_k_f - x_k)
- * and x_k_f is defined as:
- * [x_k-alpha*grad(f)]+
- * where []+ mean a projection to the feasibility set.
- * So this means that we take a step on the negative gradient (gradient descent) and then obtain then project
- * that point to the feasibility set.
- * Note that if the point is already feasible then we are back to the normal armijo rule.
- *
- * @author javg
- *
- */
-public class ArmijoLineSearchMinimizationAlongProjectionArc implements LineSearchMethod{
-
- /**
- * How much should the step size decrease at each iteration.
- */
- double contractionFactor = 0.5;
- double c1 = 0.0001;
-
-
- double initialStep;
- int maxIterations = 100;
-
-
- double sigma1 = 0.1;
- double sigma2 = 0.9;
-
- //Experiment
- double previousStepPicked = -1;;
- double previousInitGradientDot = -1;
- double currentInitGradientDot = -1;
-
- GenericPickFirstStep strategy;
-
-
- public void reset(){
- previousStepPicked = -1;;
- previousInitGradientDot = -1;
- currentInitGradientDot = -1;
- }
-
-
- public ArmijoLineSearchMinimizationAlongProjectionArc(){
- this.initialStep = 1;
- }
-
- public ArmijoLineSearchMinimizationAlongProjectionArc(GenericPickFirstStep strategy){
- this.strategy = strategy;
- this.initialStep = strategy.getFirstStep(this);
- }
-
-
- public void setInitialStep(double initial){
- this.initialStep = initial;
- }
-
- /**
- *
- */
-
- public double getStepSize(DifferentiableLineSearchObjective o) {
-
-
- //Should update all in the objective
- initialStep = strategy.getFirstStep(this);
- o.updateAlpha(initialStep);
- previousInitGradientDot=currentInitGradientDot;
- currentInitGradientDot=o.getCurrentGradient();
- int nrIterations = 0;
-
- //Armijo rule, the current value has to be smaller than the original value plus a small step of the gradient
- while(o.getCurrentValue() >
- o.getOriginalValue() + c1*(o.getCurrentGradient())){
-// System.out.println("curr value "+o.getCurrentValue());
-// System.out.println("original value "+o.getOriginalValue());
-// System.out.println("GRADIENT decrease" +(MathUtils.dotProduct(o.o.gradient,
-// MathUtils.arrayMinus(o.originalParameters,((ProjectedObjective)o.o).auxParameters))));
-// System.out.println("GRADIENT SAVED" + o.getCurrentGradient());
- if(nrIterations >= maxIterations){
- System.out.println("Could not find a step leaving line search with -1");
- o.printLineSearchSteps();
- return -1;
- }
- double alpha=o.getAlpha();
- double alphaTemp =
- Interpolation.quadraticInterpolation(o.getOriginalValue(), o.getInitialGradient(), alpha, o.getCurrentValue());
- if(alphaTemp >= sigma1 || alphaTemp <= sigma2*o.getAlpha()){
- alpha = alphaTemp;
- }else{
- alpha = alpha*contractionFactor;
- }
-// double alpha =obj.getAlpha()*contractionFactor;
- o.updateAlpha(alpha);
- nrIterations++;
- }
-// System.out.println("curr value "+o.getCurrentValue());
-// System.out.println("original value "+o.getOriginalValue());
-// System.out.println("sufficient decrease" +c1*o.getCurrentGradient());
-// System.out.println("Leavning line search used:");
-// o.printSmallLineSearchSteps();
-
- previousStepPicked = o.getAlpha();
- return o.getAlpha();
- }
-
- public double getInitialGradient() {
- return currentInitGradientDot;
-
- }
-
- public double getPreviousInitialGradient() {
- return previousInitGradientDot;
- }
-
- public double getPreviousStepUsed() {
- return previousStepPicked;
- }
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/DifferentiableLineSearchObjective.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/DifferentiableLineSearchObjective.java
deleted file mode 100644
index a5bc958e..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/DifferentiableLineSearchObjective.java
+++ /dev/null
@@ -1,185 +0,0 @@
-package optimization.linesearch;
-
-import gnu.trove.TDoubleArrayList;
-import gnu.trove.TIntArrayList;
-
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.Collections;
-import java.util.Comparator;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.util.MathUtils;
-import optimization.util.StaticTools;
-
-
-
-import util.MathUtil;
-import util.Printing;
-
-
-/**
- * A wrapper class for the actual objective in order to perform
- * line search. The optimization code assumes that this does a lot
- * of caching in order to simplify legibility. For the applications
- * we use it for, caching the entire history of evaluations should be
- * a win.
- *
- * Note: the lastEvaluatedAt value is very important, since we will use
- * it to avoid doing an evaluation of the gradient after the line search.
- *
- * The differentiable line search objective defines a search along the ray
- * given by a direction of the main objective.
- * It defines the following function,
- * where f is the original objective function:
- * g(alpha) = f(x_0 + alpha*direction)
- * g'(alpha) = f'(x_0 + alpha*direction)*direction
- *
- * @author joao
- *
- */
-public class DifferentiableLineSearchObjective {
-
-
-
- Objective o;
- int nrIterations;
- TDoubleArrayList steps;
- TDoubleArrayList values;
- TDoubleArrayList gradients;
-
- //This variables cannot change
- public double[] originalParameters;
- public double[] searchDirection;
-
-
- /**
- * Defines a line search objective:
- * Receives:
- * Objective to each we are performing the line search, is used to calculate values and gradients
- * Direction where to do the ray search, note that the direction does not depend of the
- * objective but depends from the method.
- * @param o
- * @param direction
- */
- public DifferentiableLineSearchObjective(Objective o) {
- this.o = o;
- originalParameters = new double[o.getNumParameters()];
- searchDirection = new double[o.getNumParameters()];
- steps = new TDoubleArrayList();
- values = new TDoubleArrayList();
- gradients = new TDoubleArrayList();
- }
- /**
- * Called whenever we start a new iteration.
- * Receives the ray where we are searching for and resets all values
- *
- */
- public void reset(double[] direction){
- //Copy initial values
- System.arraycopy(o.getParameters(), 0, originalParameters, 0, o.getNumParameters());
- System.arraycopy(direction, 0, searchDirection, 0, o.getNumParameters());
-
- //Initialize variables
- nrIterations = 0;
- steps.clear();
- values.clear();
- gradients.clear();
-
- values.add(o.getValue());
- gradients.add(MathUtils.dotProduct(o.getGradient(),direction));
- steps.add(0);
- }
-
-
- /**
- * update the current value of alpha.
- * Takes a step with that alpha in direction
- * Get the real objective value and gradient and calculate all required information.
- */
- public void updateAlpha(double alpha){
- if(alpha < 0){
- System.out.println("alpha may not be smaller that zero");
- throw new RuntimeException();
- }
- nrIterations++;
- steps.add(alpha);
- //x_t+1 = x_t + alpha*direction
- System.arraycopy(originalParameters,0, o.getParameters(), 0, originalParameters.length);
- MathUtils.plusEquals(o.getParameters(), searchDirection, alpha);
- o.setParameters(o.getParameters());
-// System.out.println("Took a step of " + alpha + " new value " + o.getValue());
- values.add(o.getValue());
- gradients.add(MathUtils.dotProduct(o.getGradient(),searchDirection));
- }
-
-
-
- public int getNrIterations(){
- return nrIterations;
- }
-
- /**
- * return g(alpha) for the current value of alpha
- * @param iter
- * @return
- */
- public double getValue(int iter){
- return values.get(iter);
- }
-
- public double getCurrentValue(){
- return values.get(nrIterations);
- }
-
- public double getOriginalValue(){
- return values.get(0);
- }
-
- /**
- * return g'(alpha) for the current value of alpha
- * @param iter
- * @return
- */
- public double getGradient(int iter){
- return gradients.get(iter);
- }
-
- public double getCurrentGradient(){
- return gradients.get(nrIterations);
- }
-
- public double getInitialGradient(){
- return gradients.get(0);
- }
-
-
-
-
- public double getAlpha(){
- return steps.get(nrIterations);
- }
-
- public void printLineSearchSteps(){
- System.out.println(
- " Steps size "+steps.size() +
- "Values size "+values.size() +
- "Gradeients size "+gradients.size());
- for(int i =0; i < steps.size();i++){
- System.out.println("Iter " + i + " step " + steps.get(i) +
- " value " + values.get(i) + " grad " + gradients.get(i));
- }
- }
-
- public void printSmallLineSearchSteps(){
- for(int i =0; i < steps.size();i++){
- System.out.print(StaticTools.prettyPrint(steps.get(i), "0.0000E00",8) + " ");
- }
- System.out.println();
- }
-
- public static void main(String[] args) {
-
- }
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/GenericPickFirstStep.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/GenericPickFirstStep.java
deleted file mode 100644
index a33eb311..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/GenericPickFirstStep.java
+++ /dev/null
@@ -1,20 +0,0 @@
-package optimization.linesearch;
-
-
-public class GenericPickFirstStep{
- double _initValue;
- public GenericPickFirstStep(double initValue) {
- _initValue = initValue;
- }
-
- public double getFirstStep(LineSearchMethod ls){
- return _initValue;
- }
- public void collectInitValues(LineSearchMethod ls){
-
- }
-
- public void collectFinalValues(LineSearchMethod ls){
-
- }
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/InterpolationPickFirstStep.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/InterpolationPickFirstStep.java
deleted file mode 100644
index 0deebcdb..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/InterpolationPickFirstStep.java
+++ /dev/null
@@ -1,25 +0,0 @@
-package optimization.linesearch;
-
-
-public class InterpolationPickFirstStep extends GenericPickFirstStep{
- public InterpolationPickFirstStep(double initValue) {
- super(initValue);
- }
-
- public double getFirstStep(LineSearchMethod ls){
- if(ls.getPreviousStepUsed() != -1 && ls.getPreviousInitialGradient()!=0){
- double newStep = Math.min(300, 1.02*ls.getPreviousInitialGradient()*ls.getPreviousStepUsed()/ls.getInitialGradient());
- // System.out.println("proposing " + newStep);
- return newStep;
-
- }
- return _initValue;
- }
- public void collectInitValues(WolfRuleLineSearch ls){
-
- }
-
- public void collectFinalValues(WolfRuleLineSearch ls){
-
- }
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/LineSearchMethod.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/LineSearchMethod.java
deleted file mode 100644
index 80cd7f39..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/LineSearchMethod.java
+++ /dev/null
@@ -1,14 +0,0 @@
-package optimization.linesearch;
-
-
-public interface LineSearchMethod {
-
- double getStepSize(DifferentiableLineSearchObjective o);
-
- public double getInitialGradient();
- public double getPreviousInitialGradient();
- public double getPreviousStepUsed();
-
- public void setInitialStep(double initial);
- public void reset();
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/NonNewtonInterpolationPickFirstStep.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/NonNewtonInterpolationPickFirstStep.java
deleted file mode 100644
index 4b354fd9..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/NonNewtonInterpolationPickFirstStep.java
+++ /dev/null
@@ -1,33 +0,0 @@
-package optimization.linesearch;
-
-/**
- * Non newtwon since we don't always try 1...
- * Not sure if that is even usefull for newton
- * @author javg
- *
- */
-public class NonNewtonInterpolationPickFirstStep extends GenericPickFirstStep{
- public NonNewtonInterpolationPickFirstStep(double initValue) {
- super(initValue);
- }
-
- public double getFirstStep(LineSearchMethod ls){
-// System.out.println("Previous step used " + ls.getPreviousStepUsed());
-// System.out.println("PreviousGradinebt " + ls.getPreviousInitialGradient());
-// System.out.println("CurrentGradinebt " + ls.getInitialGradient());
- if(ls.getPreviousStepUsed() != -1 && ls.getPreviousInitialGradient()!=0){
- double newStep = 1.01*ls.getPreviousInitialGradient()*ls.getPreviousStepUsed()/ls.getInitialGradient();
- //System.out.println("Suggesting " + newStep);
- return newStep;
-
- }
- return _initValue;
- }
- public void collectInitValues(WolfRuleLineSearch ls){
-
- }
-
- public void collectFinalValues(WolfRuleLineSearch ls){
-
- }
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ProjectedDifferentiableLineSearchObjective.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ProjectedDifferentiableLineSearchObjective.java
deleted file mode 100644
index 29ccbc32..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/ProjectedDifferentiableLineSearchObjective.java
+++ /dev/null
@@ -1,137 +0,0 @@
-package optimization.linesearch;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.util.MathUtils;
-import optimization.util.MatrixOutput;
-
-
-/**
- * See ArmijoLineSearchMinimizationAlongProjectionArc for description
- * @author javg
- *
- */
-public class ProjectedDifferentiableLineSearchObjective extends DifferentiableLineSearchObjective{
-
-
-
- ProjectedObjective obj;
- public ProjectedDifferentiableLineSearchObjective(Objective o) {
- super(o);
- if(!(o instanceof ProjectedObjective)){
- System.out.println("Must receive a projected objective");
- throw new RuntimeException();
- }
- obj = (ProjectedObjective) o;
- }
-
-
-
- public double[] projectPoint (double[] point){
- return ((ProjectedObjective)o).projectPoint(point);
- }
- public void updateAlpha(double alpha){
- if(alpha < 0){
- System.out.println("alpha may not be smaller that zero");
- throw new RuntimeException();
- }
-
- if(obj.auxParameters == null){
- obj.auxParameters = new double[obj.getParameters().length];
- }
-
- nrIterations++;
-
- steps.add(alpha);
- System.arraycopy(originalParameters, 0, obj.auxParameters, 0, obj.auxParameters.length);
-
- //Take a step into the search direction
-
-// MatrixOutput.printDoubleArray(obj.getGradient(), "gradient");
-
-// alpha=gradients.get(0)*alpha/(gradients.get(gradients.size()-1));
-
- //x_t+1 = x_t - alpha*gradient = x_t + alpha*direction
- MathUtils.plusEquals(obj.auxParameters, searchDirection, alpha);
-// MatrixOutput.printDoubleArray(obj.auxParameters, "before projection");
- obj.auxParameters = projectPoint(obj.auxParameters);
-// MatrixOutput.printDoubleArray(obj.auxParameters, "after projection");
- o.setParameters(obj.auxParameters);
-// System.out.println("new parameters");
-// o.printParameters();
- values.add(o.getValue());
- //Computes the new gradient x_k-[x_k-alpha*Gradient(x_k)]+
- MathUtils.minusEqualsInverse(originalParameters,obj.auxParameters,1);
-// MatrixOutput.printDoubleArray(obj.auxParameters, "new gradient");
- //Dot product between the new direction and the new gradient
- double gradient = MathUtils.dotProduct(obj.auxParameters,searchDirection);
- gradients.add(gradient);
- if(gradient > 0){
- System.out.println("Gradient on line search has to be smaller than zero");
- System.out.println("Iter: " + nrIterations);
- MatrixOutput.printDoubleArray(obj.auxParameters, "new direction");
- MatrixOutput.printDoubleArray(searchDirection, "search direction");
- throw new RuntimeException();
-
- }
-
- }
-
- /**
- *
- */
-// public void updateAlpha(double alpha){
-//
-// if(alpha < 0){
-// System.out.println("alpha may not be smaller that zero");
-// throw new RuntimeException();
-// }
-//
-// nrIterations++;
-// steps.add(alpha);
-// //x_t+1 = x_t - alpha*direction
-// System.arraycopy(originalParameters, 0, parametersChange, 0, parametersChange.length);
-//// MatrixOutput.printDoubleArray(parametersChange, "parameters before step");
-//// System.out.println("Step" + alpha);
-// MatrixOutput.printDoubleArray(originalGradient, "gradient + " + alpha);
-//
-// MathUtils.minusEquals(parametersChange, originalGradient, alpha);
-//
-// //Project the points into the feasibility set
-//// MatrixOutput.printDoubleArray(parametersChange, "before projection");
-// //x_k(alpha) = [x_k - alpha*grad f(x_k)]+
-// parametersChange = projectPoint(parametersChange);
-//// MatrixOutput.printDoubleArray(parametersChange, "after projection");
-// o.setParameters(parametersChange);
-// values.add(o.getValue());
-// //Computes the new direction x_k-[x_k-alpha*Gradient(x_k)]+
-//
-// direction=MathUtils.arrayMinus(parametersChange,originalParameters);
-//// MatrixOutput.printDoubleArray(direction, "new direction");
-//
-// double gradient = MathUtils.dotProduct(originalGradient,direction);
-// gradients.add(gradient);
-// if(gradient > 1E-10){
-// System.out.println("cosine " + gradient/(MathUtils.L2Norm(originalGradient)*MathUtils.L2Norm(direction)));
-//
-//
-// System.out.println("not a descent direction for alpha " + alpha);
-// System.arraycopy(originalParameters, 0, parametersChange, 0, parametersChange.length);
-// MathUtils.minusEquals(parametersChange, originalGradient, 1E-20);
-//
-// parametersChange = projectPoint(parametersChange);
-// direction=MathUtils.arrayMinus(parametersChange,originalParameters);
-// gradient = MathUtils.dotProduct(originalGradient,direction);
-// if(gradient > 0){
-// System.out.println("Direction is really non-descent evern for small alphas:" + gradient);
-// }
-// System.out.println("ProjecteLineSearchObjective: Should be a descent direction at " + nrIterations + ": "+ gradient);
-//// System.out.println(Printing.doubleArrayToString(originalGradient, null,"Original gradient"));
-//// System.out.println(Printing.doubleArrayToString(originalParameters, null,"Original parameters"));
-//// System.out.println(Printing.doubleArrayToString(parametersChange, null,"Projected parameters"));
-//// System.out.println(Printing.doubleArrayToString(direction, null,"Direction"));
-// throw new RuntimeException();
-// }
-// }
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfRuleLineSearch.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfRuleLineSearch.java
deleted file mode 100644
index 5489f2d0..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfRuleLineSearch.java
+++ /dev/null
@@ -1,300 +0,0 @@
-package optimization.linesearch;
-
-import java.io.PrintStream;
-import java.util.ArrayList;
-
-import optimization.util.Interpolation;
-
-
-
-
-/**
- *
- * @author javg
- *
- */
-public class WolfRuleLineSearch implements LineSearchMethod{
-
- GenericPickFirstStep pickFirstStep;
-
- double c1 = 1.0E-4;
- double c2 = 0.9;
-
- //Application dependent
- double maxStep=100;
-
- int extrapolationIteration;
- int maxExtrapolationIteration = 1000;
-
-
- double minZoomDiffTresh = 10E-10;
-
-
- ArrayList<Double> steps;
- ArrayList<Double> gradientDots;
- ArrayList<Double> functionVals;
-
- int debugLevel = 0;
- boolean foudStep = false;
-
- public WolfRuleLineSearch(GenericPickFirstStep pickFirstStep){
- this.pickFirstStep = pickFirstStep;
-
- }
-
-
-
-
- public WolfRuleLineSearch(GenericPickFirstStep pickFirstStep, double c1, double c2){
- this.pickFirstStep = pickFirstStep;
- initialStep = pickFirstStep.getFirstStep(this);
- this.c1 = c1;
- this.c2 = c2;
- }
-
- public void setDebugLevel(int level){
- debugLevel = level;
- }
-
- //Experiment
- double previousStepPicked = -1;;
- double previousInitGradientDot = -1;
- double currentInitGradientDot = -1;
-
- double initialStep;
-
-
- public void reset(){
- previousStepPicked = -1;;
- previousInitGradientDot = -1;
- currentInitGradientDot = -1;
- if(steps != null)
- steps.clear();
- if(gradientDots != null)
- gradientDots.clear();
- if(functionVals != null)
- functionVals.clear();
- }
-
- public void setInitialStep(double initial){
- initialStep = pickFirstStep.getFirstStep(this);
- }
-
-
-
- /**
- * Implements Wolf Line search as described in nocetal.
- * This process consists in two stages. The first stage we try to satisfy the
- * biggest step size that still satisfies the curvature condition. We keep increasing
- * the initial step size until we find a step satisfying the curvature condition, we return
- * success, we failed the sufficient increase so we cannot increase more and we can call zoom with
- * that maximum step, or we pass the minimum in which case we can call zoom the same way.
- *
- */
- public double getStepSize(DifferentiableLineSearchObjective objective){
- //System.out.println("entering line search");
-
- foudStep = false;
- if(debugLevel >= 1){
- steps = new ArrayList<Double>();
- gradientDots = new ArrayList<Double>();
- functionVals =new ArrayList<Double>();
- }
-
- //test
- currentInitGradientDot = objective.getInitialGradient();
-
-
- double previousValue = objective.getCurrentValue();
- double previousStep = 0;
- double currentStep =pickFirstStep.getFirstStep(this);
- for(extrapolationIteration = 0;
- extrapolationIteration < maxExtrapolationIteration; extrapolationIteration++){
-
- objective.updateAlpha(currentStep);
- double currentValue = objective.getCurrentValue();
- if(debugLevel >= 1){
- steps.add(currentStep);
- functionVals.add(currentValue);
- gradientDots.add(objective.getCurrentGradient());
- }
-
-
- //The current step does not satisfy the sufficient decrease condition anymore
- // so we cannot get bigger than that calling zoom.
- if(!WolfeConditions.suficientDecrease(objective,c1)||
- (extrapolationIteration > 0 && currentValue >= previousValue)){
- currentStep = zoom(objective,previousStep,currentStep,objective.nrIterations-1,objective.nrIterations);
- break;
- }
-
- //Satisfying both conditions ready to leave
- if(WolfeConditions.sufficientCurvature(objective,c1,c2)){
- //Found step
- foudStep = true;
- break;
- }
-
- /**
- * This means that we passed the minimum already since the dot product that should be
- * negative (descent direction) is now positive. So we cannot increase more. On the other hand
- * since we know the direction is a descent direction the value the objective at the current step
- * is for sure smaller than the preivous step so we change the order.
- */
- if(objective.getCurrentGradient() >= 0){
- currentStep = zoom(objective,currentStep,previousStep,objective.nrIterations,objective.nrIterations-1);
- break;
- }
-
-
- //Ok, so we can still get a bigger step,
- double aux = currentStep;
- //currentStep = currentStep*2;
- if(Math.abs(currentStep-maxStep)>1.1e-2){
- currentStep = (currentStep+maxStep)/2;
- }else{
- currentStep = currentStep*2;
- }
- previousStep = aux;
- previousValue = currentValue;
- //Could be done better
- if(currentStep >= maxStep){
- System.out.println("Excedded max step...calling zoom with maxStepSize");
- currentStep = zoom(objective,previousStep,currentStep,objective.nrIterations-1,objective.nrIterations);
- }
- }
- if(!foudStep){
- System.out.println("Wolfe Rule exceed number of iterations");
- if(debugLevel >= 1){
- printSmallWolfeStats(System.out);
-// System.out.println("Line search values");
-// DebugHelpers.getLineSearchGraph(o, direction, originalParameters,origValue, origGradDirectionDot,c1,c2);
- }
- return -1;
- }
- if(debugLevel >= 1){
- printSmallWolfeStats(System.out);
- }
-
- previousStepPicked = currentStep;
- previousInitGradientDot = currentInitGradientDot;
-// objective.printLineSearchSteps();
- return currentStep;
- }
-
-
-
-
-
- public void printWolfeStats(PrintStream out){
- for(int i = 0; i < steps.size(); i++){
- out.println("Step " + steps.get(i) + " value " + functionVals.get(i) + " dot " + gradientDots.get(i));
- }
- }
-
- public void printSmallWolfeStats(PrintStream out){
- for(int i = 0; i < steps.size(); i++){
- out.print(steps.get(i) + ":"+functionVals.get(i)+":"+gradientDots.get(i)+" ");
- }
- System.out.println();
- }
-
-
-
- /**
- * Pick a step satisfying the strong wolfe condition from an given from lowerStep and higherStep
- * picked on the routine above.
- *
- * Both lowerStep and higherStep have been evaluated, so we only need to pass the iteration where they have
- * been evaluated and save extra evaluations.
- *
- * We know that lowerStepValue as to be smaller than higherStepValue, and that a point
- * satisfying both conditions exists in such interval.
- *
- * LowerStep always satisfies at least the sufficient decrease
- * @return
- */
- public double zoom(DifferentiableLineSearchObjective o, double lowerStep, double higherStep,
- int lowerStepIter, int higherStepIter){
-
- if(debugLevel >=2){
- System.out.println("Entering zoom with " + lowerStep+"-"+higherStep);
- }
-
- double currentStep=-1;
-
- int zoomIter = 0;
- while(zoomIter < 1000){
- if(Math.abs(lowerStep-higherStep) < minZoomDiffTresh){
- o.updateAlpha(lowerStep);
- if(debugLevel >= 1){
- steps.add(lowerStep);
- functionVals.add(o.getCurrentValue());
- gradientDots.add(o.getCurrentGradient());
- }
- foudStep = true;
- return lowerStep;
- }
-
- //Cubic interpolation
- currentStep =
- Interpolation.cubicInterpolation(lowerStep, o.getValue(lowerStepIter), o.getGradient(lowerStepIter),
- higherStep, o.getValue(higherStepIter), o.getGradient(higherStepIter));
-
- //Safeguard.... should not be required check in what condtions it is required
- if(currentStep < 0 ){
- currentStep = (lowerStep+higherStep)/2;
- }
- if(Double.isNaN(currentStep) || Double.isInfinite(currentStep)){
- currentStep = (lowerStep+higherStep)/2;
- }
-// currentStep = (lowerStep+higherStep)/2;
-// System.out.println("Trying "+currentStep);
- o.updateAlpha(currentStep);
- if(debugLevel >=1){
- steps.add(currentStep);
- functionVals.add(o.getCurrentValue());
- gradientDots.add(o.getCurrentGradient());
- }
- if(!WolfeConditions.suficientDecrease(o,c1)
- || o.getCurrentValue() >= o.getValue(lowerStepIter)){
- higherStepIter = o.nrIterations;
- higherStep = currentStep;
- }
- //Note when entering here the new step satisfies the sufficent decrease and
- // or as a function value that is better than the previous best (lowerStepFunctionValues)
- // so we either leave or change the value of the alpha low.
- else{
- if(WolfeConditions.sufficientCurvature(o,c1,c2)){
- //Satisfies the both wolf conditions
- foudStep = true;
- break;
- }
- //If does not satisfy curvature
- if(o.getCurrentGradient()*(higherStep-lowerStep) >= 0){
- higherStep = lowerStep;
- higherStepIter = lowerStepIter;
- }
- lowerStep = currentStep;
- lowerStepIter = o.nrIterations;
- }
- zoomIter++;
- }
- return currentStep;
- }
-
- public double getInitialGradient() {
- return currentInitGradientDot;
-
- }
-
- public double getPreviousInitialGradient() {
- return previousInitGradientDot;
- }
-
- public double getPreviousStepUsed() {
- return previousStepPicked;
- }
-
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfeConditions.java b/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfeConditions.java
deleted file mode 100644
index dcc704eb..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfeConditions.java
+++ /dev/null
@@ -1,45 +0,0 @@
-package optimization.linesearch;
-
-
-public class WolfeConditions {
-
- /**
- * Sufficient Increase number. Default constant
- */
-
-
- /**
- * Value for suficient curvature:
- * 0.9 - For newton and quase netwon methods
- * 0.1 - Non linear conhugate gradient
- */
-
- int debugLevel = 0;
- public void setDebugLevel(int level){
- debugLevel = level;
- }
-
- public static boolean suficientDecrease(DifferentiableLineSearchObjective o, double c1){
- double value = o.getOriginalValue()+c1*o.getAlpha()*o.getInitialGradient();
-// System.out.println("Sufficient Decrease original "+value+" new "+ o.getCurrentValue());
- return o.getCurrentValue() <= value;
- }
-
-
-
-
- public static boolean sufficientCurvature(DifferentiableLineSearchObjective o, double c1, double c2){
-// if(debugLevel >= 2){
-// double current = Math.abs(o.getCurrentGradient());
-// double orig = -c2*o.getInitialGradient();
-// if(current <= orig){
-// return true;
-// }else{
-// System.out.println("Not satistfying curvature condition curvature " + current + " wants " + orig);
-// return false;
-// }
-// }
- return Math.abs(o.getCurrentGradient()) <= -c2*o.getInitialGradient();
- }
-
-}
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;
- }
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/CompositeStopingCriteria.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/CompositeStopingCriteria.java
deleted file mode 100644
index 15760f18..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/CompositeStopingCriteria.java
+++ /dev/null
@@ -1,33 +0,0 @@
-package optimization.stopCriteria;
-
-import java.util.ArrayList;
-
-import optimization.gradientBasedMethods.Objective;
-
-public class CompositeStopingCriteria implements StopingCriteria {
-
- ArrayList<StopingCriteria> criterias;
-
- public CompositeStopingCriteria() {
- criterias = new ArrayList<StopingCriteria>();
- }
-
- public void add(StopingCriteria criteria){
- criterias.add(criteria);
- }
-
- public boolean stopOptimization(Objective obj){
- for(StopingCriteria criteria: criterias){
- if(criteria.stopOptimization(obj)){
- return true;
- }
- }
- return false;
- }
-
- public void reset(){
- for(StopingCriteria criteria: criterias){
- criteria.reset();
- }
- }
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/GradientL2Norm.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/GradientL2Norm.java
deleted file mode 100644
index 534ff833..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/GradientL2Norm.java
+++ /dev/null
@@ -1,30 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.util.MathUtils;
-
-public class GradientL2Norm implements StopingCriteria{
-
- /**
- * Stop if gradientNorm/(originalGradientNorm) smaller
- * than gradientConvergenceValue
- */
- protected double gradientConvergenceValue;
-
-
- public GradientL2Norm(double gradientConvergenceValue){
- this.gradientConvergenceValue = gradientConvergenceValue;
- }
-
- public void reset(){}
-
- public boolean stopOptimization(Objective obj){
- double norm = MathUtils.L2Norm(obj.gradient);
- if(norm < gradientConvergenceValue){
- System.out.println("Gradient norm below treshold");
- return true;
- }
- return false;
-
- }
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedGradientL2Norm.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedGradientL2Norm.java
deleted file mode 100644
index 4a489641..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedGradientL2Norm.java
+++ /dev/null
@@ -1,48 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.util.MathUtils;
-
-/**
- * Divides the norm by the norm at the begining of the iteration
- * @author javg
- *
- */
-public class NormalizedGradientL2Norm extends GradientL2Norm{
-
- /**
- * Stop if gradientNorm/(originalGradientNorm) smaller
- * than gradientConvergenceValue
- */
- protected double originalGradientNorm = -1;
-
- public void reset(){
- originalGradientNorm = -1;
- }
- public NormalizedGradientL2Norm(double gradientConvergenceValue){
- super(gradientConvergenceValue);
- }
-
-
-
-
- public boolean stopOptimization(Objective obj){
- double norm = MathUtils.L2Norm(obj.gradient);
- if(originalGradientNorm == -1){
- originalGradientNorm = norm;
- }
- if(originalGradientNorm < 1E-10){
- System.out.println("Gradient norm is zero " + originalGradientNorm);
- return true;
- }
- double normalizedNorm = 1.0*norm/originalGradientNorm;
- if( normalizedNorm < gradientConvergenceValue){
- System.out.println("Gradient norm below normalized normtreshold: " + norm + " original: " + originalGradientNorm + " normalized norm: " + normalizedNorm);
- return true;
- }else{
-// System.out.println("projected gradient norm: " + norm);
- return false;
- }
- }
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedProjectedGradientL2Norm.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedProjectedGradientL2Norm.java
deleted file mode 100644
index 5ae554c2..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedProjectedGradientL2Norm.java
+++ /dev/null
@@ -1,60 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.util.MathUtils;
-
-/**
- * Divides the norm by the norm at the begining of the iteration
- * @author javg
- *
- */
-public class NormalizedProjectedGradientL2Norm extends ProjectedGradientL2Norm{
-
- /**
- * Stop if gradientNorm/(originalGradientNorm) smaller
- * than gradientConvergenceValue
- */
- double originalProjectedNorm = -1;
-
- public NormalizedProjectedGradientL2Norm(double gradientConvergenceValue){
- super(gradientConvergenceValue);
- }
-
- public void reset(){
- originalProjectedNorm = -1;
- }
-
-
- double[] projectGradient(ProjectedObjective obj){
-
- if(obj.auxParameters == null){
- obj.auxParameters = new double[obj.getNumParameters()];
- }
- System.arraycopy(obj.getParameters(), 0, obj.auxParameters, 0, obj.getNumParameters());
- MathUtils.minusEquals(obj.auxParameters, obj.gradient, 1);
- obj.auxParameters = obj.projectPoint(obj.auxParameters);
- MathUtils.minusEquals(obj.auxParameters,obj.getParameters(),1);
- return obj.auxParameters;
- }
-
- public boolean stopOptimization(Objective obj){
- if(obj instanceof ProjectedObjective) {
- ProjectedObjective o = (ProjectedObjective) obj;
- double norm = MathUtils.L2Norm(projectGradient(o));
- if(originalProjectedNorm == -1){
- originalProjectedNorm = norm;
- }
- double normalizedNorm = 1.0*norm/originalProjectedNorm;
- if( normalizedNorm < gradientConvergenceValue){
- System.out.println("Gradient norm below normalized normtreshold: " + norm + " original: " + originalProjectedNorm + " normalized norm: " + normalizedNorm);
- return true;
- }else{
-// System.out.println("projected gradient norm: " + norm);
- return false;
- }
- }
- System.out.println("Not a projected objective");
- throw new RuntimeException();
- }
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedValueDifference.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedValueDifference.java
deleted file mode 100644
index 6dbbc50d..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedValueDifference.java
+++ /dev/null
@@ -1,54 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.util.MathUtils;
-
-public class NormalizedValueDifference implements StopingCriteria{
-
- /**
- * Stop if the different between values is smaller than a treshold
- */
- protected double valueConvergenceValue=0.01;
- protected double previousValue = Double.NaN;
- protected double currentValue = Double.NaN;
-
- public NormalizedValueDifference(double valueConvergenceValue){
- this.valueConvergenceValue = valueConvergenceValue;
- }
-
- public void reset(){
- previousValue = Double.NaN;
- currentValue = Double.NaN;
- }
-
-
- public boolean stopOptimization(Objective obj){
- if(Double.isNaN(currentValue)){
- currentValue = obj.getValue();
- return false;
- }else {
- previousValue = currentValue;
- currentValue = obj.getValue();
- if(previousValue != 0){
- double valueDiff = Math.abs(previousValue - currentValue)/Math.abs(previousValue);
- if( valueDiff < valueConvergenceValue){
- System.out.println("Leaving different in values is to small: Prev "
- + (previousValue/previousValue) + " Curr: " + (currentValue/previousValue)
- + " diff: " + valueDiff);
- return true;
- }
- }else{
- double valueDiff = Math.abs(previousValue - currentValue);
- if( valueDiff < valueConvergenceValue){
- System.out.println("Leaving different in values is to small: Prev "
- + (previousValue) + " Curr: " + (currentValue)
- + " diff: " + valueDiff);
- return true;
- }
- }
-
- return false;
- }
-
- }
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ProjectedGradientL2Norm.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ProjectedGradientL2Norm.java
deleted file mode 100644
index aadf1fd5..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ProjectedGradientL2Norm.java
+++ /dev/null
@@ -1,51 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.util.MathUtils;
-
-public class ProjectedGradientL2Norm implements StopingCriteria{
-
- /**
- * Stop if gradientNorm/(originalGradientNorm) smaller
- * than gradientConvergenceValue
- */
- protected double gradientConvergenceValue;
-
-
- public ProjectedGradientL2Norm(double gradientConvergenceValue){
- this.gradientConvergenceValue = gradientConvergenceValue;
- }
-
- public void reset(){
-
- }
-
- double[] projectGradient(ProjectedObjective obj){
-
- if(obj.auxParameters == null){
- obj.auxParameters = new double[obj.getNumParameters()];
- }
- System.arraycopy(obj.getParameters(), 0, obj.auxParameters, 0, obj.getNumParameters());
- MathUtils.minusEquals(obj.auxParameters, obj.gradient, 1);
- obj.auxParameters = obj.projectPoint(obj.auxParameters);
- MathUtils.minusEquals(obj.auxParameters,obj.getParameters(),1);
- return obj.auxParameters;
- }
-
- public boolean stopOptimization(Objective obj){
- if(obj instanceof ProjectedObjective) {
- ProjectedObjective o = (ProjectedObjective) obj;
- double norm = MathUtils.L2Norm(projectGradient(o));
- if(norm < gradientConvergenceValue){
- // System.out.println("Gradient norm below treshold: " + norm);
- return true;
- }else{
-// System.out.println("projected gradient norm: " + norm);
- return false;
- }
- }
- System.out.println("Not a projected objective");
- throw new RuntimeException();
- }
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/StopingCriteria.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/StopingCriteria.java
deleted file mode 100644
index 10cf0522..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/StopingCriteria.java
+++ /dev/null
@@ -1,8 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-
-public interface StopingCriteria {
- public boolean stopOptimization(Objective obj);
- public void reset();
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ValueDifference.java b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ValueDifference.java
deleted file mode 100644
index e5d07229..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ValueDifference.java
+++ /dev/null
@@ -1,41 +0,0 @@
-package optimization.stopCriteria;
-
-import optimization.gradientBasedMethods.Objective;
-import optimization.util.MathUtils;
-
-public class ValueDifference implements StopingCriteria{
-
- /**
- * Stop if the different between values is smaller than a treshold
- */
- protected double valueConvergenceValue=0.01;
- protected double previousValue = Double.NaN;
- protected double currentValue = Double.NaN;
-
- public ValueDifference(double valueConvergenceValue){
- this.valueConvergenceValue = valueConvergenceValue;
- }
-
- public void reset(){
- previousValue = Double.NaN;
- currentValue = Double.NaN;
- }
-
- public boolean stopOptimization(Objective obj){
- if(Double.isNaN(currentValue)){
- currentValue = obj.getValue();
- return false;
- }else {
- previousValue = currentValue;
- currentValue = obj.getValue();
- if(previousValue - currentValue < valueConvergenceValue){
-// System.out.println("Leaving different in values is to small: Prev "
-// + previousValue + " Curr: " + currentValue
-// + " diff: " + (previousValue - currentValue));
- return true;
- }
- return false;
- }
-
- }
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/util/Interpolation.java b/gi/posterior-regularisation/prjava/src/optimization/util/Interpolation.java
deleted file mode 100644
index cdbdefc6..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/util/Interpolation.java
+++ /dev/null
@@ -1,37 +0,0 @@
-package optimization.util;
-
-public class Interpolation {
-
- /**
- * Fits a cubic polinomyal to a function given two points,
- * such that either gradB is bigger than zero or funcB >= funcA
- *
- * NonLinear Programming appendix C
- * @param funcA
- * @param gradA
- * @param funcB
- * @param gradB
- */
- public final static double cubicInterpolation(double a,
- double funcA, double gradA, double b,double funcB, double gradB ){
- if(gradB < 0 && funcA > funcB){
- System.out.println("Cannot call cubic interpolation");
- return -1;
- }
-
- double z = 3*(funcA-funcB)/(b-a) + gradA + gradB;
- double w = Math.sqrt(z*z - gradA*gradB);
- double min = b -(gradB+w-z)*(b-a)/(gradB-gradA+2*w);
- return min;
- }
-
- public final static double quadraticInterpolation(double initFValue,
- double initGrad, double point,double pointFValue){
- double min = -1*initGrad*point*point/(2*(pointFValue-initGrad*point-initFValue));
- return min;
- }
-
- public static void main(String[] args) {
-
- }
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/util/Logger.java b/gi/posterior-regularisation/prjava/src/optimization/util/Logger.java
deleted file mode 100644
index 5343a39b..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/util/Logger.java
+++ /dev/null
@@ -1,7 +0,0 @@
-package optimization.util;
-
-public class Logger {
-
-
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/util/MathUtils.java b/gi/posterior-regularisation/prjava/src/optimization/util/MathUtils.java
deleted file mode 100644
index af66f82c..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/util/MathUtils.java
+++ /dev/null
@@ -1,339 +0,0 @@
-package optimization.util;
-
-import java.util.Arrays;
-
-
-
-public class MathUtils {
-
- /**
- *
- * @param vector
- * @return
- */
- public static double L2Norm(double[] vector){
- double value = 0;
- for(int i = 0; i < vector.length; i++){
- double v = vector[i];
- value+=v*v;
- }
- return Math.sqrt(value);
- }
-
- public static double sum(double[] v){
- double sum = 0;
- for (int i = 0; i < v.length; i++) {
- sum+=v[i];
- }
- return sum;
- }
-
-
-
-
- /**
- * w = w + v
- * @param w
- * @param v
- */
- public static void plusEquals(double[] w, double[] v) {
- for(int i=0; i<w.length;i++){
- w[i] += w[i] + v[i];
- }
- }
-
- /**
- * w[i] = w[i] + v
- * @param w
- * @param v
- */
- public static void plusEquals(double[] w, double v) {
- for(int i=0; i<w.length;i++){
- w[i] += w[i] + v;
- }
- }
-
- /**
- * w[i] = w[i] - v
- * @param w
- * @param v
- */
- public static void minusEquals(double[] w, double v) {
- for(int i=0; i<w.length;i++){
- w[i] -= w[i] + v;
- }
- }
-
- /**
- * w = w + a*v
- * @param w
- * @param v
- * @param a
- */
- public static void plusEquals(double[] w, double[] v, double a) {
- for(int i=0; i<w.length;i++){
- w[i] += a*v[i];
- }
- }
-
- /**
- * w = w - a*v
- * @param w
- * @param v
- * @param a
- */
- public static void minusEquals(double[] w, double[] v, double a) {
- for(int i=0; i<w.length;i++){
- w[i] -= a*v[i];
- }
- }
- /**
- * v = w - a*v
- * @param w
- * @param v
- * @param a
- */
- public static void minusEqualsInverse(double[] w, double[] v, double a) {
- for(int i=0; i<w.length;i++){
- v[i] = w[i] - a*v[i];
- }
- }
-
- public static double dotProduct(double[] w, double[] v){
- double accum = 0;
- for(int i=0; i<w.length;i++){
- accum += w[i]*v[i];
- }
- return accum;
- }
-
- public static double[] arrayMinus(double[]w, double[]v){
- double result[] = w.clone();
- for(int i=0; i<w.length;i++){
- result[i] -= v[i];
- }
- return result;
- }
-
- public static double[] arrayMinus(double[] result , double[]w, double[]v){
- for(int i=0; i<w.length;i++){
- result[i] = w[i]-v[i];
- }
- return result;
- }
-
- public static double[] negation(double[]w){
- double result[] = new double[w.length];
- for(int i=0; i<w.length;i++){
- result[i] = -w[i];
- }
- return result;
- }
-
- public static double square(double value){
- return value*value;
- }
- public static double[][] outerProduct(double[] w, double[] v){
- double[][] result = new double[w.length][v.length];
- for(int i = 0; i < w.length; i++){
- for(int j = 0; j < v.length; j++){
- result[i][j] = w[i]*v[j];
- }
- }
- return result;
- }
- /**
- * results = a*W*V
- * @param w
- * @param v
- * @param a
- * @return
- */
- public static double[][] weightedouterProduct(double[] w, double[] v, double a){
- double[][] result = new double[w.length][v.length];
- for(int i = 0; i < w.length; i++){
- for(int j = 0; j < v.length; j++){
- result[i][j] = a*w[i]*v[j];
- }
- }
- return result;
- }
-
- public static double[][] identity(int size){
- double[][] result = new double[size][size];
- for(int i = 0; i < size; i++){
- result[i][i] = 1;
- }
- return result;
- }
-
- /**
- * v -= w
- * @param v
- * @param w
- */
- public static void minusEquals(double[][] w, double[][] v){
- for(int i = 0; i < w.length; i++){
- for(int j = 0; j < w[0].length; j++){
- w[i][j] -= v[i][j];
- }
- }
- }
-
- /**
- * v[i][j] -= a*w[i][j]
- * @param v
- * @param w
- */
- public static void minusEquals(double[][] w, double[][] v, double a){
- for(int i = 0; i < w.length; i++){
- for(int j = 0; j < w[0].length; j++){
- w[i][j] -= a*v[i][j];
- }
- }
- }
-
- /**
- * v += w
- * @param v
- * @param w
- */
- public static void plusEquals(double[][] w, double[][] v){
- for(int i = 0; i < w.length; i++){
- for(int j = 0; j < w[0].length; j++){
- w[i][j] += v[i][j];
- }
- }
- }
-
- /**
- * v[i][j] += a*w[i][j]
- * @param v
- * @param w
- */
- public static void plusEquals(double[][] w, double[][] v, double a){
- for(int i = 0; i < w.length; i++){
- for(int j = 0; j < w[0].length; j++){
- w[i][j] += a*v[i][j];
- }
- }
- }
-
-
- /**
- * results = w*v
- * @param w
- * @param v
- * @return
- */
- public static double[][] matrixMultiplication(double[][] w,double[][] v){
- int w1 = w.length;
- int w2 = w[0].length;
- int v1 = v.length;
- int v2 = v[0].length;
-
- if(w2 != v1){
- System.out.println("Matrix dimensions do not agree...");
- System.exit(-1);
- }
-
- double[][] result = new double[w1][v2];
- for(int w_i1 = 0; w_i1 < w1; w_i1++){
- for(int v_i2 = 0; v_i2 < v2; v_i2++){
- double sum = 0;
- for(int w_i2 = 0; w_i2 < w2; w_i2++){
- sum += w[w_i1 ][w_i2]*v[w_i2][v_i2];
- }
- result[w_i1][v_i2] = sum;
- }
- }
- return result;
- }
-
- /**
- * w = w.*v
- * @param w
- * @param v
- */
- public static void matrixScalarMultiplication(double[][] w,double v){
- int w1 = w.length;
- int w2 = w[0].length;
- for(int w_i1 = 0; w_i1 < w1; w_i1++){
- for(int w_i2 = 0; w_i2 < w2; w_i2++){
- w[w_i1 ][w_i2] *= v;
- }
- }
- }
-
- public static void scalarMultiplication(double[] w,double v){
- int w1 = w.length;
- for(int w_i1 = 0; w_i1 < w1; w_i1++){
- w[w_i1 ] *= v;
- }
-
- }
-
- public static double[] matrixVector(double[][] w,double[] v){
- int w1 = w.length;
- int w2 = w[0].length;
- int v1 = v.length;
-
- if(w2 != v1){
- System.out.println("Matrix dimensions do not agree...");
- System.exit(-1);
- }
-
- double[] result = new double[w1];
- for(int w_i1 = 0; w_i1 < w1; w_i1++){
- double sum = 0;
- for(int w_i2 = 0; w_i2 < w2; w_i2++){
- sum += w[w_i1 ][w_i2]*v[w_i2];
- }
- result[w_i1] = sum;
- }
- return result;
- }
-
- public static boolean allPositive(double[] array){
- for (int i = 0; i < array.length; i++) {
- if(array[i] < 0) return false;
- }
- return true;
- }
-
-
-
-
-
- public static void main(String[] args) {
- double[][] m1 = new double[2][2];
- m1[0][0]=2;
- m1[1][0]=2;
- m1[0][1]=2;
- m1[1][1]=2;
- MatrixOutput.printDoubleArray(m1, "m1");
- double[][] m2 = new double[2][2];
- m2[0][0]=3;
- m2[1][0]=3;
- m2[0][1]=3;
- m2[1][1]=3;
- MatrixOutput.printDoubleArray(m2, "m2");
- double[][] result = matrixMultiplication(m1, m2);
- MatrixOutput.printDoubleArray(result, "result");
- matrixScalarMultiplication(result, 3);
- MatrixOutput.printDoubleArray(result, "result after multiply by 3");
- }
-
- public static boolean almost(double a, double b, double prec){
- return Math.abs(a-b)/Math.abs(a+b) <= prec || (almostZero(a) && almostZero(b));
- }
-
- public static boolean almost(double a, double b){
- return Math.abs(a-b)/Math.abs(a+b) <= 1e-10 || (almostZero(a) && almostZero(b));
- }
-
- public static boolean almostZero(double a) {
- return Math.abs(a) <= 1e-30;
- }
-
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/util/MatrixOutput.java b/gi/posterior-regularisation/prjava/src/optimization/util/MatrixOutput.java
deleted file mode 100644
index 9fbdf955..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/util/MatrixOutput.java
+++ /dev/null
@@ -1,28 +0,0 @@
-package optimization.util;
-
-
-public class MatrixOutput {
- public static void printDoubleArray(double[][] array, String arrayName) {
- int size1 = array.length;
- int size2 = array[0].length;
- System.out.println(arrayName);
- for (int i = 0; i < size1; i++) {
- for (int j = 0; j < size2; j++) {
- System.out.print(" " + StaticTools.prettyPrint(array[i][j],
- "00.00E00", 4) + " ");
-
- }
- System.out.println();
- }
- System.out.println();
- }
-
- public static void printDoubleArray(double[] array, String arrayName) {
- System.out.println(arrayName);
- for (int i = 0; i < array.length; i++) {
- System.out.print(" " + StaticTools.prettyPrint(array[i],
- "00.00E00", 4) + " ");
- }
- System.out.println();
- }
-}
diff --git a/gi/posterior-regularisation/prjava/src/optimization/util/StaticTools.java b/gi/posterior-regularisation/prjava/src/optimization/util/StaticTools.java
deleted file mode 100644
index bcabee06..00000000
--- a/gi/posterior-regularisation/prjava/src/optimization/util/StaticTools.java
+++ /dev/null
@@ -1,180 +0,0 @@
-package optimization.util;
-
-
-import java.io.File;
-import java.io.PrintStream;
-
-public class StaticTools {
-
- static java.text.DecimalFormat fmt = new java.text.DecimalFormat();
-
- public static void createDir(String directory) {
-
- File dir = new File(directory);
- if (!dir.isDirectory()) {
- boolean success = dir.mkdirs();
- if (!success) {
- System.out.println("Unable to create directory " + directory);
- System.exit(0);
- }
- System.out.println("Created directory " + directory);
- } else {
- System.out.println("Reusing directory " + directory);
- }
- }
-
- /*
- * q and p are indexed by source/foreign Sum_S(q) = 1 the same for p KL(q,p) =
- * Eq*q/p
- */
- public static double KLDistance(double[][] p, double[][] q, int sourceSize,
- int foreignSize) {
- double totalKL = 0;
- // common.StaticTools.printMatrix(q, sourceSize, foreignSize, "q",
- // System.out);
- // common.StaticTools.printMatrix(p, sourceSize, foreignSize, "p",
- // System.out);
- for (int i = 0; i < sourceSize; i++) {
- double kl = 0;
- for (int j = 0; j < foreignSize; j++) {
- assert !Double.isNaN(q[i][j]) : "KLDistance q: prob is NaN";
- assert !Double.isNaN(p[i][j]) : "KLDistance p: prob is NaN";
- if (p[i][j] == 0 || q[i][j] == 0) {
- continue;
- } else {
- kl += q[i][j] * Math.log(q[i][j] / p[i][j]);
- }
-
- }
- totalKL += kl;
- }
- assert !Double.isNaN(totalKL) : "KLDistance: prob is NaN";
- if (totalKL < -1.0E-10) {
- System.out.println("KL Smaller than zero " + totalKL);
- System.out.println("Source Size" + sourceSize);
- System.out.println("Foreign Size" + foreignSize);
- StaticTools.printMatrix(q, sourceSize, foreignSize, "q",
- System.out);
- StaticTools.printMatrix(p, sourceSize, foreignSize, "p",
- System.out);
- System.exit(-1);
- }
- return totalKL / sourceSize;
- }
-
- /*
- * indexed the by [fi][si]
- */
- public static double KLDistancePrime(double[][] p, double[][] q,
- int sourceSize, int foreignSize) {
- double totalKL = 0;
- for (int i = 0; i < sourceSize; i++) {
- double kl = 0;
- for (int j = 0; j < foreignSize; j++) {
- assert !Double.isNaN(q[j][i]) : "KLDistance q: prob is NaN";
- assert !Double.isNaN(p[j][i]) : "KLDistance p: prob is NaN";
- if (p[j][i] == 0 || q[j][i] == 0) {
- continue;
- } else {
- kl += q[j][i] * Math.log(q[j][i] / p[j][i]);
- }
-
- }
- totalKL += kl;
- }
- assert !Double.isNaN(totalKL) : "KLDistance: prob is NaN";
- return totalKL / sourceSize;
- }
-
- public static double Entropy(double[][] p, int sourceSize, int foreignSize) {
- double totalE = 0;
- for (int i = 0; i < foreignSize; i++) {
- double e = 0;
- for (int j = 0; j < sourceSize; j++) {
- e += p[i][j] * Math.log(p[i][j]);
- }
- totalE += e;
- }
- return totalE / sourceSize;
- }
-
- public static double[][] copyMatrix(double[][] original, int sourceSize,
- int foreignSize) {
- double[][] result = new double[sourceSize][foreignSize];
- for (int i = 0; i < sourceSize; i++) {
- for (int j = 0; j < foreignSize; j++) {
- result[i][j] = original[i][j];
- }
- }
- return result;
- }
-
- public static void printMatrix(double[][] matrix, int sourceSize,
- int foreignSize, String info, PrintStream out) {
-
- java.text.DecimalFormat fmt = new java.text.DecimalFormat();
- fmt.setMaximumFractionDigits(3);
- fmt.setMaximumIntegerDigits(3);
- fmt.setMinimumFractionDigits(3);
- fmt.setMinimumIntegerDigits(3);
-
- out.println(info);
-
- for (int i = 0; i < foreignSize; i++) {
- for (int j = 0; j < sourceSize; j++) {
- out.print(prettyPrint(matrix[j][i], ".00E00", 6) + " ");
- }
- out.println();
- }
- out.println();
- out.println();
- }
-
- public static void printMatrix(int[][] matrix, int sourceSize,
- int foreignSize, String info, PrintStream out) {
-
- out.println(info);
- for (int i = 0; i < foreignSize; i++) {
- for (int j = 0; j < sourceSize; j++) {
- out.print(matrix[j][i] + " ");
- }
- out.println();
- }
- out.println();
- out.println();
- }
-
- public static String formatTime(long duration) {
- StringBuilder sb = new StringBuilder();
- double d = duration / 1000;
- fmt.applyPattern("00");
- sb.append(fmt.format((int) (d / (60 * 60))) + ":");
- d -= ((int) d / (60 * 60)) * 60 * 60;
- sb.append(fmt.format((int) (d / 60)) + ":");
- d -= ((int) d / 60) * 60;
- fmt.applyPattern("00.0");
- sb.append(fmt.format(d));
- return sb.toString();
- }
-
- public static String prettyPrint(double d, String patt, int len) {
- fmt.applyPattern(patt);
- String s = fmt.format(d);
- while (s.length() < len) {
- s = " " + s;
- }
- return s;
- }
-
-
- public static long getUsedMemory(){
- System.gc();
- return (Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory())/ (1024 * 1024);
- }
-
- public final static boolean compareDoubles(double d1, double d2){
- return Math.abs(d1-d2) <= 1.E-10;
- }
-
-
-}