diff options
Diffstat (limited to 'gi/posterior-regularisation/prjava/src/optimization/stopCriteria')
8 files changed, 325 insertions, 0 deletions
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; + } + + } +} |