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-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.java119
-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.java83
-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, 3577 insertions, 0 deletions
diff --git a/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java b/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java
new file mode 100644
index 00000000..25fa7f09
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/examples/GeneralizedRosenbrock.java
@@ -0,0 +1,110 @@
+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
new file mode 100644
index 00000000..f087681e
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2.java
@@ -0,0 +1,128 @@
+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
new file mode 100644
index 00000000..391775b7
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/examples/x2y2WithConstraints.java
@@ -0,0 +1,127 @@
+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
new file mode 100644
index 00000000..0a4a5445
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java
@@ -0,0 +1,119 @@
+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("starting iterations: parameters:" );
+// 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("Leave 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
new file mode 100644
index 00000000..28295729
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ConjugateGradient.java
@@ -0,0 +1,92 @@
+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
new file mode 100644
index 00000000..6dc4ef6c
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/DebugHelpers.java
@@ -0,0 +1,65 @@
+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
new file mode 100644
index 00000000..9a53cef4
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/GradientDescent.java
@@ -0,0 +1,19 @@
+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
new file mode 100644
index 00000000..dedbc942
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java
@@ -0,0 +1,234 @@
+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
new file mode 100644
index 00000000..0e2e27ac
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Objective.java
@@ -0,0 +1,83 @@
+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 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
new file mode 100644
index 00000000..96fce5b0
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Optimizer.java
@@ -0,0 +1,19 @@
+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
new file mode 100644
index 00000000..afb29d04
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedAbstractGradientBaseMethod.java
@@ -0,0 +1,11 @@
+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
new file mode 100644
index 00000000..0186e945
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java
@@ -0,0 +1,154 @@
+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
new file mode 100644
index 00000000..c3d21393
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedObjective.java
@@ -0,0 +1,29 @@
+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
new file mode 100644
index 00000000..81d8403e
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedOptimizer.java
@@ -0,0 +1,10 @@
+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
new file mode 100644
index 00000000..6340ef73
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/OptimizerStats.java
@@ -0,0 +1,86 @@
+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
new file mode 100644
index 00000000..d65a1267
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/ProjectedOptimizerStats.java
@@ -0,0 +1,70 @@
+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
new file mode 100644
index 00000000..c9f9b8df
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimization.java
@@ -0,0 +1,102 @@
+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
new file mode 100644
index 00000000..e153f2da
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ArmijoLineSearchMinimizationAlongProjectionArc.java
@@ -0,0 +1,141 @@
+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
new file mode 100644
index 00000000..a5bc958e
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/linesearch/DifferentiableLineSearchObjective.java
@@ -0,0 +1,185 @@
+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
new file mode 100644
index 00000000..a33eb311
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/linesearch/GenericPickFirstStep.java
@@ -0,0 +1,20 @@
+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
new file mode 100644
index 00000000..0deebcdb
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/linesearch/InterpolationPickFirstStep.java
@@ -0,0 +1,25 @@
+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
new file mode 100644
index 00000000..80cd7f39
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/linesearch/LineSearchMethod.java
@@ -0,0 +1,14 @@
+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
new file mode 100644
index 00000000..4b354fd9
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/linesearch/NonNewtonInterpolationPickFirstStep.java
@@ -0,0 +1,33 @@
+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
new file mode 100644
index 00000000..29ccbc32
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/linesearch/ProjectedDifferentiableLineSearchObjective.java
@@ -0,0 +1,137 @@
+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
new file mode 100644
index 00000000..5489f2d0
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfRuleLineSearch.java
@@ -0,0 +1,300 @@
+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
new file mode 100644
index 00000000..dcc704eb
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/linesearch/WolfeConditions.java
@@ -0,0 +1,45 @@
+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
new file mode 100644
index 00000000..0429d531
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/projections/BoundsProjection.java
@@ -0,0 +1,104 @@
+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
new file mode 100644
index 00000000..b5a9f92f
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/projections/Projection.java
@@ -0,0 +1,72 @@
+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
new file mode 100644
index 00000000..eec11bcf
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/projections/SimplexProjection.java
@@ -0,0 +1,127 @@
+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 <= 0){
+ 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
new file mode 100644
index 00000000..15760f18
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/CompositeStopingCriteria.java
@@ -0,0 +1,33 @@
+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
new file mode 100644
index 00000000..534ff833
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/GradientL2Norm.java
@@ -0,0 +1,30 @@
+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
new file mode 100644
index 00000000..4a489641
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedGradientL2Norm.java
@@ -0,0 +1,48 @@
+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
new file mode 100644
index 00000000..5ae554c2
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedProjectedGradientL2Norm.java
@@ -0,0 +1,60 @@
+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
new file mode 100644
index 00000000..6dbbc50d
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/NormalizedValueDifference.java
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 00000000..aadf1fd5
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ProjectedGradientL2Norm.java
@@ -0,0 +1,51 @@
+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
new file mode 100644
index 00000000..10cf0522
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/StopingCriteria.java
@@ -0,0 +1,8 @@
+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
new file mode 100644
index 00000000..e5d07229
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/stopCriteria/ValueDifference.java
@@ -0,0 +1,41 @@
+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
new file mode 100644
index 00000000..cdbdefc6
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/util/Interpolation.java
@@ -0,0 +1,37 @@
+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
new file mode 100644
index 00000000..5343a39b
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/util/Logger.java
@@ -0,0 +1,7 @@
+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
new file mode 100644
index 00000000..af66f82c
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/util/MathUtils.java
@@ -0,0 +1,339 @@
+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
new file mode 100644
index 00000000..9fbdf955
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/util/MatrixOutput.java
@@ -0,0 +1,28 @@
+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
new file mode 100644
index 00000000..bcabee06
--- /dev/null
+++ b/gi/posterior-regularisation/prjava/src/optimization/util/StaticTools.java
@@ -0,0 +1,180 @@
+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;
+ }
+
+
+}