diff options
Diffstat (limited to 'gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods')
13 files changed, 0 insertions, 996 deletions
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java deleted file mode 100644 index 2fcb7990..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java +++ /dev/null @@ -1,120 +0,0 @@ -package optimization.gradientBasedMethods; - -import optimization.gradientBasedMethods.stats.OptimizerStats; -import optimization.linesearch.DifferentiableLineSearchObjective; -import optimization.linesearch.LineSearchMethod; -import optimization.stopCriteria.StopingCriteria; -import optimization.util.MathUtils; - -/** - * - * @author javg - * - */ -public abstract class AbstractGradientBaseMethod implements Optimizer{ - - protected int maxNumberOfIterations=10000; - - - - protected int currentProjectionIteration; - protected double currValue; - protected double previousValue = Double.MAX_VALUE;; - protected double step; - protected double[] gradient; - public double[] direction; - - //Original values - protected double originalGradientL2Norm; - - protected LineSearchMethod lineSearch; - DifferentiableLineSearchObjective lso; - - - public void reset(){ - direction = null; - gradient = null; - previousValue = Double.MAX_VALUE; - currentProjectionIteration = 0; - originalGradientL2Norm = 0; - step = 0; - currValue = 0; - } - - public void initializeStructures(Objective o,OptimizerStats stats, StopingCriteria stop){ - lso = new DifferentiableLineSearchObjective(o); - } - public void updateStructuresBeforeStep(Objective o,OptimizerStats stats, StopingCriteria stop){ - } - - public void updateStructuresAfterStep(Objective o,OptimizerStats stats, StopingCriteria stop){ - } - - public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stop){ - //Initialize structures - - stats.collectInitStats(this, o); - direction = new double[o.getNumParameters()]; - initializeStructures(o, stats, stop); - for (currentProjectionIteration = 1; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){ - //System.out.println("\tgradient descent iteration " + currentProjectionIteration); - //System.out.print("\tparameters:" ); - //o.printParameters(); - previousValue = currValue; - currValue = o.getValue(); - gradient = o.getGradient(); - if(stop.stopOptimization(o)){ - stats.collectFinalStats(this, o); - return true; - } - - getDirection(); - if(MathUtils.dotProduct(gradient, direction) > 0){ - System.out.println("Not a descent direction"); - System.out.println(" current stats " + stats.prettyPrint(1)); - System.exit(-1); - } - updateStructuresBeforeStep(o, stats, stop); - lso.reset(direction); - step = lineSearch.getStepSize(lso); - //System.out.println("\t\tLeave with step: " + step); - if(step==-1){ - System.out.println("Failed to find step"); - stats.collectFinalStats(this, o); - return false; - } - updateStructuresAfterStep( o, stats, stop); -// previousValue = currValue; -// currValue = o.getValue(); -// gradient = o.getGradient(); - stats.collectIterationStats(this, o); - } - stats.collectFinalStats(this, o); - return false; - } - - - public int getCurrentIteration() { - return currentProjectionIteration; - } - - - /** - * Method specific - */ - public abstract double[] getDirection(); - - public double getCurrentStep() { - return step; - } - - - - public void setMaxIterations(int max) { - maxNumberOfIterations = max; - } - - public double getCurrentValue() { - return currValue; - } -} diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ConjugateGradient.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ConjugateGradient.java deleted file mode 100644 index 28295729..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ConjugateGradient.java +++ /dev/null @@ -1,92 +0,0 @@ -package optimization.gradientBasedMethods; - -import optimization.gradientBasedMethods.stats.OptimizerStats; -import optimization.linesearch.DifferentiableLineSearchObjective; -import optimization.linesearch.LineSearchMethod; -import optimization.stopCriteria.StopingCriteria; -import optimization.util.MathUtils; - - - -public class ConjugateGradient extends AbstractGradientBaseMethod{ - - - double[] previousGradient; - double[] previousDirection; - - public ConjugateGradient(LineSearchMethod lineSearch) { - this.lineSearch = lineSearch; - } - - public void reset(){ - super.reset(); - java.util.Arrays.fill(previousDirection, 0); - java.util.Arrays.fill(previousGradient, 0); - } - - public void initializeStructures(Objective o,OptimizerStats stats, StopingCriteria stop){ - super.initializeStructures(o, stats, stop); - previousGradient = new double[o.getNumParameters()]; - previousDirection = new double[o.getNumParameters()]; - } - public void updateStructuresBeforeStep(Objective o,OptimizerStats stats, StopingCriteria stop){ - System.arraycopy(gradient, 0, previousGradient, 0, gradient.length); - System.arraycopy(direction, 0, previousDirection, 0, direction.length); - } - -// public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stop){ -// DifferentiableLineSearchObjective lso = new DifferentiableLineSearchObjective(o); -// stats.collectInitStats(this, o); -// direction = new double[o.getNumParameters()]; -// initializeStructures(o, stats, stop); -// for (currentProjectionIteration = 0; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){ -// previousValue = currValue; -// currValue = o.getValue(); -// gradient =o.getGradient(); -// if(stop.stopOptimization(gradient)){ -// stats.collectFinalStats(this, o); -// return true; -// } -// getDirection(); -// updateStructures(o, stats, stop); -// lso.reset(direction); -// step = lineSearch.getStepSize(lso); -// if(step==-1){ -// System.out.println("Failed to find a step size"); -// System.out.println("Failed to find step"); -// stats.collectFinalStats(this, o); -// return false; -// } -// -// stats.collectIterationStats(this, o); -// } -// stats.collectFinalStats(this, o); -// return false; -// } - - public double[] getDirection(){ - direction = MathUtils.negation(gradient); - if(currentProjectionIteration != 1){ - //Using Polak-Ribiere method (book equation 5.45) - double b = MathUtils.dotProduct(gradient, MathUtils.arrayMinus(gradient, previousGradient)) - /MathUtils.dotProduct(previousGradient, previousGradient); - if(b<0){ - System.out.println("Defaulting to gradient descent"); - b = Math.max(b, 0); - } - MathUtils.plusEquals(direction, previousDirection, b); - //Debug code - if(MathUtils.dotProduct(direction, gradient) > 0){ - System.out.println("Not an descent direction reseting to gradien"); - direction = MathUtils.negation(gradient); - } - } - return direction; - } - - - - - - -} diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/DebugHelpers.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/DebugHelpers.java deleted file mode 100644 index 6dc4ef6c..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/DebugHelpers.java +++ /dev/null @@ -1,65 +0,0 @@ -package optimization.gradientBasedMethods; - -import java.util.ArrayList; - -import optimization.util.MathUtils; - - - -public class DebugHelpers { - public static void getLineSearchGraph(Objective o, double[] direction, - double[] parameters, double originalObj, - double originalDot, double c1, double c2){ - ArrayList<Double> stepS = new ArrayList<Double>(); - ArrayList<Double> obj = new ArrayList<Double>(); - ArrayList<Double> norm = new ArrayList<Double>(); - double[] gradient = new double[o.getNumParameters()]; - double[] newParameters = parameters.clone(); - MathUtils.plusEquals(newParameters,direction,0); - o.setParameters(newParameters); - double minValue = o.getValue(); - int valuesBiggerThanMax = 0; - for(double step = 0; step < 2; step +=0.01 ){ - newParameters = parameters.clone(); - MathUtils.plusEquals(newParameters,direction,step); - o.setParameters(newParameters); - double newValue = o.getValue(); - gradient = o.getGradient(); - double newgradDirectionDot = MathUtils.dotProduct(gradient,direction); - stepS.add(step); - obj.add(newValue); - norm.add(newgradDirectionDot); - if(newValue <= minValue){ - minValue = newValue; - }else{ - valuesBiggerThanMax++; - } - - if(valuesBiggerThanMax > 10){ - break; - } - - } - System.out.println("step\torigObj\tobj\tsuffdec\tnorm\tcurvature1"); - for(int i = 0; i < stepS.size(); i++){ - double cnorm= norm.get(i); - System.out.println(stepS.get(i)+"\t"+originalObj +"\t"+obj.get(i) + "\t" + - (originalObj + originalDot*((Double)stepS.get(i))*c1) +"\t"+Math.abs(cnorm) +"\t"+c2*Math.abs(originalDot)); - } - } - - public static double[] getNumericalGradient(Objective o, double[] parameters, double epsilon){ - int nrParameters = o.getNumParameters(); - double[] gradient = new double[nrParameters]; - double[] newParameters; - double originalValue = o.getValue(); - for(int parameter = 0; parameter < nrParameters; parameter++){ - newParameters = parameters.clone(); - newParameters[parameter]+=epsilon; - o.setParameters(newParameters); - double newValue = o.getValue(); - gradient[parameter]=(newValue-originalValue)/epsilon; - } - return gradient; - } -} diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/GradientDescent.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/GradientDescent.java deleted file mode 100644 index 9a53cef4..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/GradientDescent.java +++ /dev/null @@ -1,19 +0,0 @@ -package optimization.gradientBasedMethods; - -import optimization.linesearch.LineSearchMethod; - - - -public class GradientDescent extends AbstractGradientBaseMethod{ - - public GradientDescent(LineSearchMethod lineSearch) { - this.lineSearch = lineSearch; - } - - public double[] getDirection(){ - for(int i = 0; i< gradient.length; i++){ - direction[i] = -gradient[i]; - } - return direction; - } -} diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java deleted file mode 100644 index dedbc942..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java +++ /dev/null @@ -1,234 +0,0 @@ -package optimization.gradientBasedMethods; - - -import optimization.gradientBasedMethods.stats.OptimizerStats; -import optimization.linesearch.DifferentiableLineSearchObjective; -import optimization.linesearch.LineSearchMethod; -import optimization.stopCriteria.StopingCriteria; -import optimization.util.MathUtils; - -public class LBFGS extends AbstractGradientBaseMethod{ - - //How many previous values are being saved - int history; - double[][] skList; - double[][] ykList; - double initialHessianParameters; - double[] previousGradient; - double[] previousParameters; - - //auxiliar structures - double q[]; - double[] roi; - double[] alphai; - - public LBFGS(LineSearchMethod ls, int history) { - lineSearch = ls; - this.history = history; - skList = new double[history][]; - ykList = new double[history][]; - - } - - public void reset(){ - super.reset(); - initialHessianParameters = 0; - previousParameters = null; - previousGradient = null; - skList = new double[history][]; - ykList = new double[history][]; - q = null; - roi = null; - alphai = null; - } - - public double[] LBFGSTwoLoopRecursion(double hessianConst){ - //Only create array once - if(q == null){ - q = new double[gradient.length]; - } - System.arraycopy(gradient, 0, q, 0, gradient.length); - //Only create array once - if(roi == null){ - roi = new double[history]; - } - //Only create array once - if(alphai == null){ - alphai = new double[history]; - } - - for(int i = history-1; i >=0 && skList[i]!= null && ykList[i]!=null; i-- ){ - // System.out.println("New to Old proj " + currentProjectionIteration + " history "+history + " index " + i); - double[] si = skList[i]; - double[] yi = ykList[i]; - roi[i]= 1.0/MathUtils.dotProduct(yi,si); - alphai[i] = MathUtils.dotProduct(si, q)*roi[i]; - MathUtils.plusEquals(q, yi, -alphai[i]); - } - //Initial Hessian is just a constant - MathUtils.scalarMultiplication(q, hessianConst); - for(int i = 0; i <history && skList[i]!= null && ykList[i]!=null; i++ ){ - // System.out.println("Old to New proj " + currentProjectionIteration + " history "+history + " index " + i); - double beta = MathUtils.dotProduct(ykList[i], q)*roi[i]; - MathUtils.plusEquals(q, skList[i], (alphai[i]-beta)); - } - return q; - } - - - - - @Override - public double[] getDirection() { - - calculateInitialHessianParameter(); -// System.out.println("Initial hessian " + initialHessianParameters); - return direction = MathUtils.negation(LBFGSTwoLoopRecursion(initialHessianParameters)); - } - - public void calculateInitialHessianParameter(){ - if(currentProjectionIteration == 1){ - //Use gradient - initialHessianParameters = 1; - }else if(currentProjectionIteration <= history){ - double[] sk = skList[currentProjectionIteration-2]; - double[] yk = ykList[currentProjectionIteration-2]; - initialHessianParameters = MathUtils.dotProduct(sk, yk)/MathUtils.dotProduct(yk, yk); - }else{ - //get the last one - double[] sk = skList[history-1]; - double[] yk = ykList[history-1]; - initialHessianParameters = MathUtils.dotProduct(sk, yk)/MathUtils.dotProduct(yk, yk); - } - } - - //TODO if structures exit just reset them to zero - public void initializeStructures(Objective o,OptimizerStats stats, StopingCriteria stop){ - super.initializeStructures(o, stats, stop); - previousParameters = new double[o.getNumParameters()]; - previousGradient = new double[o.getNumParameters()]; - } - public void updateStructuresBeforeStep(Objective o,OptimizerStats stats, StopingCriteria stop){ - super.initializeStructures(o, stats, stop); - System.arraycopy(o.getParameters(), 0, previousParameters, 0, previousParameters.length); - System.arraycopy(gradient, 0, previousGradient, 0, gradient.length); - } - - public void updateStructuresAfterStep( Objective o,OptimizerStats stats, StopingCriteria stop){ - double[] diffX = MathUtils.arrayMinus(o.getParameters(), previousParameters); - double[] diffGrad = MathUtils.arrayMinus(gradient, previousGradient); - //Save new values and discard new ones - if(currentProjectionIteration > history){ - for(int i = 0; i < history-1;i++){ - skList[i]=skList[i+1]; - ykList[i]=ykList[i+1]; - } - skList[history-1]=diffX; - ykList[history-1]=diffGrad; - }else{ - skList[currentProjectionIteration-1]=diffX; - ykList[currentProjectionIteration-1]=diffGrad; - } - } - -// public boolean optimize(Objective o, OptimizerStats stats, StopingCriteria stop) { -// DifferentiableLineSearchObjective lso = new DifferentiableLineSearchObjective(o); -// gradient = o.getGradient(); -// direction = new double[o.getNumParameters()]; -// previousGradient = new double[o.getNumParameters()]; -// -// previousParameters = new double[o.getNumParameters()]; -// -// stats.collectInitStats(this, o); -// previousValue = Double.MAX_VALUE; -// currValue= o.getValue(); -// //Used for stopping criteria -// double[] originalGradient = o.getGradient(); -// -// originalGradientL2Norm = MathUtils.L2Norm(originalGradient); -// if(stop.stopOptimization(originalGradient)){ -// stats.collectFinalStats(this, o); -// return true; -// } -// for (currentProjectionIteration = 1; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){ -// -// -// currValue = o.getValue(); -// gradient = o.getGradient(); -// currParameters = o.getParameters(); -// -// -// if(currentProjectionIteration == 1){ -// //Use gradient -// initialHessianParameters = 1; -// }else if(currentProjectionIteration <= history){ -// double[] sk = skList[currentProjectionIteration-2]; -// double[] yk = ykList[currentProjectionIteration-2]; -// initialHessianParameters = MathUtils.dotProduct(sk, yk)/MathUtils.dotProduct(yk, yk); -// }else{ -// //get the last one -// double[] sk = skList[history-1]; -// double[] yk = ykList[history-1]; -// initialHessianParameters = MathUtils.dotProduct(sk, yk)/MathUtils.dotProduct(yk, yk); -// } -// -// getDirection(); -// -// //MatrixOutput.printDoubleArray(direction, "direction"); -// double dot = MathUtils.dotProduct(direction, gradient); -// if(dot > 0){ -// throw new RuntimeException("Not a descent direction"); -// } if (Double.isNaN(dot)){ -// throw new RuntimeException("dot is not a number!!"); -// } -// System.arraycopy(currParameters, 0, previousParameters, 0, currParameters.length); -// System.arraycopy(gradient, 0, previousGradient, 0, gradient.length); -// lso.reset(direction); -// step = lineSearch.getStepSize(lso); -// if(step==-1){ -// System.out.println("Failed to find a step size"); -//// lso.printLineSearchSteps(); -//// System.out.println(stats.prettyPrint(1)); -// stats.collectFinalStats(this, o); -// return false; -// } -// stats.collectIterationStats(this, o); -// -// //We are not updating the alpha since it is done in line search already -// currParameters = o.getParameters(); -// gradient = o.getGradient(); -// -// if(stop.stopOptimization(gradient)){ -// stats.collectFinalStats(this, o); -// return true; -// } -// double[] diffX = MathUtils.arrayMinus(currParameters, previousParameters); -// double[] diffGrad = MathUtils.arrayMinus(gradient, previousGradient); -// //Save new values and discard new ones -// if(currentProjectionIteration > history){ -// for(int i = 0; i < history-1;i++){ -// skList[i]=skList[i+1]; -// ykList[i]=ykList[i+1]; -// } -// skList[history-1]=diffX; -// ykList[history-1]=diffGrad; -// }else{ -// skList[currentProjectionIteration-1]=diffX; -// ykList[currentProjectionIteration-1]=diffGrad; -// } -// previousValue = currValue; -// } -// stats.collectFinalStats(this, o); -// return false; -// } - - - - - - - - - - -} diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Objective.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Objective.java deleted file mode 100644 index 6be01bf9..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Objective.java +++ /dev/null @@ -1,87 +0,0 @@ -package optimization.gradientBasedMethods; - - -/** - * Defines an optimization objective: - * - * - * @author javg - * - */ -public abstract class Objective { - - protected int functionCalls = 0; - protected int gradientCalls = 0; - protected int updateCalls = 0; - - protected double[] parameters; - - //Contains a cache with the gradient - public double[] gradient; - int debugLevel = 0; - - public void setDebugLevel(int level){ - debugLevel = level; - } - - public int getNumParameters() { - return parameters.length; - } - - public double getParameter(int index) { - return parameters[index]; - } - - public double[] getParameters() { - return parameters; - } - - public abstract double[] getGradient( ); - - public void setParameter(int index, double value) { - parameters[index]=value; - } - - public void setParameters(double[] params) { - if(parameters == null){ - parameters = new double[params.length]; - } - updateCalls++; - System.arraycopy(params, 0, parameters, 0, params.length); - } - - - public int getNumberFunctionCalls() { - return functionCalls; - } - - public int getNumberGradientCalls() { - return gradientCalls; - } - - public int getNumberUpdateCalls() { - return updateCalls; - } - - public String finalInfoString() { - return "FE: " + functionCalls + " GE " + gradientCalls + " Params updates" + - updateCalls; - } - public void printParameters() { - System.out.println(toString()); - } - - public abstract String toString(); - public abstract double getValue (); - - /** - * Sets the initial objective parameters - * For unconstrained models this just sets the objective params = argument no copying - * For a constrained objective project the parameters and then set - * @param params - */ - public void setInitialParameters(double[] params){ - parameters = params; - } - -} diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Optimizer.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Optimizer.java deleted file mode 100644 index 96fce5b0..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/Optimizer.java +++ /dev/null @@ -1,19 +0,0 @@ -package optimization.gradientBasedMethods; - -import optimization.gradientBasedMethods.stats.OptimizerStats; -import optimization.stopCriteria.StopingCriteria; - -public interface Optimizer { - public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stoping); - - - public double[] getDirection(); - public double getCurrentStep(); - public double getCurrentValue(); - public int getCurrentIteration(); - public void reset(); - - public void setMaxIterations(int max); - - -} diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedAbstractGradientBaseMethod.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedAbstractGradientBaseMethod.java deleted file mode 100644 index afb29d04..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedAbstractGradientBaseMethod.java +++ /dev/null @@ -1,11 +0,0 @@ -package optimization.gradientBasedMethods; - - -/** - * - * @author javg - * - */ -public abstract class ProjectedAbstractGradientBaseMethod extends AbstractGradientBaseMethod implements ProjectedOptimizer{ - -} diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java deleted file mode 100644 index 0186e945..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedGradientDescent.java +++ /dev/null @@ -1,154 +0,0 @@ -package optimization.gradientBasedMethods; - -import java.io.IOException; - -import optimization.gradientBasedMethods.stats.OptimizerStats; -import optimization.linesearch.DifferentiableLineSearchObjective; -import optimization.linesearch.LineSearchMethod; -import optimization.linesearch.ProjectedDifferentiableLineSearchObjective; -import optimization.stopCriteria.StopingCriteria; -import optimization.util.MathUtils; - - -/** - * This class implements the projected gradiend - * as described in Bertsekas "Non Linear Programming" - * section 2.3. - * - * The update is given by: - * x_k+1 = x_k + alpha^k(xbar_k-x_k) - * Where xbar is: - * xbar = [x_k -s_k grad(f(x_k))]+ - * where []+ is the projection into the feasibility set - * - * alpha is the step size - * s_k - is a positive scalar which can be view as a step size as well, by - * setting alpha to 1, then x_k+1 = [x_k -s_k grad(f(x_k))]+ - * This is called taking a step size along the projection arc (Bertsekas) which - * we will use by default. - * - * Note that the only place where we actually take a step size is on pick a step size - * so this is going to be just like a normal gradient descent but use a different - * armijo line search where we project after taking a step. - * - * - * @author javg - * - */ -public class ProjectedGradientDescent extends ProjectedAbstractGradientBaseMethod{ - - - - - public ProjectedGradientDescent(LineSearchMethod lineSearch) { - this.lineSearch = lineSearch; - } - - //Use projected differential objective instead - public void initializeStructures(Objective o, OptimizerStats stats, StopingCriteria stop) { - lso = new ProjectedDifferentiableLineSearchObjective(o); - }; - - - ProjectedObjective obj; - public boolean optimize(ProjectedObjective o,OptimizerStats stats, StopingCriteria stop){ - obj = o; - return super.optimize(o, stats, stop); - } - - public double[] getDirection(){ - for(int i = 0; i< gradient.length; i++){ - direction[i] = -gradient[i]; - } - return direction; - } - - - - -} - - - - - - - -///OLD CODE - -//Use projected gradient norm -//public boolean stopCriteria(double[] gradient){ -// if(originalDirenctionL2Norm == 0){ -// System.out.println("Leaving original direction norm is zero"); -// return true; -// } -// if(MathUtils.L2Norm(direction)/originalDirenctionL2Norm < gradientConvergenceValue){ -// System.out.println("Leaving projected gradient Norm smaller than epsilon"); -// return true; -// } -// if((previousValue - currValue)/Math.abs(previousValue) < valueConvergenceValue) { -// System.out.println("Leaving value change below treshold " + previousValue + " - " + currValue); -// System.out.println(previousValue/currValue + " - " + currValue/currValue -// + " = " + (previousValue - currValue)/Math.abs(previousValue)); -// return true; -// } -// return false; -//} -// - -//public boolean optimize(ProjectedObjective o,OptimizerStats stats, StopingCriteria stop){ -// stats.collectInitStats(this, o); -// obj = o; -// step = 0; -// currValue = o.getValue(); -// previousValue = Double.MAX_VALUE; -// gradient = o.getGradient(); -// originalGradientL2Norm = MathUtils.L2Norm(gradient); -// parameterChange = new double[gradient.length]; -// getDirection(); -// ProjectedDifferentiableLineSearchObjective lso = new ProjectedDifferentiableLineSearchObjective(o,direction); -// -// originalDirenctionL2Norm = MathUtils.L2Norm(direction); -// //MatrixOutput.printDoubleArray(currParameters, "parameters"); -// for (currentProjectionIteration = 0; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){ -// // System.out.println("Iter " + currentProjectionIteration); -// //o.printParameters(); -// -// -// -// if(stop.stopOptimization(gradient)){ -// stats.collectFinalStats(this, o); -// lastStepUsed = step; -// return true; -// } -// lso.reset(direction); -// step = lineSearch.getStepSize(lso); -// if(step==-1){ -// System.out.println("Failed to find step"); -// stats.collectFinalStats(this, o); -// return false; -// -// } -// -// //Update the direction for stopping criteria -// previousValue = currValue; -// currValue = o.getValue(); -// gradient = o.getGradient(); -// direction = getDirection(); -// if(MathUtils.dotProduct(gradient, direction) > 0){ -// System.out.println("Not a descent direction"); -// System.out.println(" current stats " + stats.prettyPrint(1)); -// System.exit(-1); -// } -// stats.collectIterationStats(this, o); -// } -// lastStepUsed = step; -// stats.collectFinalStats(this, o); -// return false; -// } - -//public boolean optimize(Objective o,OptimizerStats stats, StopingCriteria stop){ -// System.out.println("Objective is not a projected objective"); -// throw new RuntimeException(); -//} - diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedObjective.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedObjective.java deleted file mode 100644 index c3d21393..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedObjective.java +++ /dev/null @@ -1,29 +0,0 @@ -package optimization.gradientBasedMethods; - -import optimization.util.MathUtils; - - -/** - * Computes a projected objective - * When we tell it to set some parameters it automatically projects the parameters back into the simplex: - * - * - * When we tell it to get the gradient in automatically returns the projected gradient: - * @author javg - * - */ -public abstract class ProjectedObjective extends Objective{ - - public abstract double[] projectPoint (double[] point); - - public double[] auxParameters; - - - public void setInitialParameters(double[] params){ - setParameters(projectPoint(params)); - } - - - - -} diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedOptimizer.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedOptimizer.java deleted file mode 100644 index 81d8403e..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/ProjectedOptimizer.java +++ /dev/null @@ -1,10 +0,0 @@ -package optimization.gradientBasedMethods; - - - -public interface ProjectedOptimizer extends Optimizer{ - - - - -} diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/OptimizerStats.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/OptimizerStats.java deleted file mode 100644 index 6340ef73..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/OptimizerStats.java +++ /dev/null @@ -1,86 +0,0 @@ -package optimization.gradientBasedMethods.stats; - -import java.util.ArrayList; - -import optimization.gradientBasedMethods.Objective; -import optimization.gradientBasedMethods.Optimizer; -import optimization.util.MathUtils; -import optimization.util.StaticTools; - - -public class OptimizerStats { - - double start = 0; - double totalTime = 0; - - String objectiveFinalStats; - - ArrayList<Double> gradientNorms = new ArrayList<Double>(); - ArrayList<Double> steps = new ArrayList<Double>(); - ArrayList<Double> value = new ArrayList<Double>(); - ArrayList<Integer> iterations = new ArrayList<Integer>(); - double prevValue =0; - - public void reset(){ - start = 0; - totalTime = 0; - - objectiveFinalStats=""; - - gradientNorms.clear(); - steps.clear(); - value.clear(); - iterations.clear(); - prevValue =0; - } - - public void startTime() { - start = System.currentTimeMillis(); - } - public void stopTime() { - totalTime += System.currentTimeMillis() - start; - } - - public String prettyPrint(int level){ - StringBuffer res = new StringBuffer(); - res.append("Total time " + totalTime/1000 + " seconds \n" + "Iterations " + iterations.size() + "\n"); - res.append(objectiveFinalStats+"\n"); - if(level > 0){ - if(iterations.size() > 0){ - res.append("\tIteration"+iterations.get(0)+"\tstep: "+StaticTools.prettyPrint(steps.get(0), "0.00E00", 6)+ "\tgradientNorm "+ - StaticTools.prettyPrint(gradientNorms.get(0), "0.00000E00", 10)+ "\tvalue "+ StaticTools.prettyPrint(value.get(0), "0.000000E00",11)+"\n"); - } - for(int i = 1; i < iterations.size(); i++){ - res.append("\tIteration:\t"+iterations.get(i)+"\tstep:"+StaticTools.prettyPrint(steps.get(i), "0.00E00", 6)+ "\tgradientNorm "+ - StaticTools.prettyPrint(gradientNorms.get(i), "0.00000E00", 10)+ - "\tvalue:\t"+ StaticTools.prettyPrint(value.get(i), "0.000000E00",11)+ - "\tvalueDiff:\t"+ StaticTools.prettyPrint((value.get(i-1)-value.get(i)), "0.000000E00",11)+ - "\n"); - } - } - return res.toString(); - } - - - public void collectInitStats(Optimizer optimizer, Objective objective){ - startTime(); - iterations.add(-1); - gradientNorms.add(MathUtils.L2Norm(objective.getGradient())); - steps.add(0.0); - value.add(objective.getValue()); - } - - public void collectIterationStats(Optimizer optimizer, Objective objective){ - iterations.add(optimizer.getCurrentIteration()); - gradientNorms.add(MathUtils.L2Norm(objective.getGradient())); - steps.add(optimizer.getCurrentStep()); - value.add(optimizer.getCurrentValue()); - } - - - public void collectFinalStats(Optimizer optimizer, Objective objective){ - stopTime(); - objectiveFinalStats = objective.finalInfoString(); - } - -} diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/ProjectedOptimizerStats.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/ProjectedOptimizerStats.java deleted file mode 100644 index d65a1267..00000000 --- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/stats/ProjectedOptimizerStats.java +++ /dev/null @@ -1,70 +0,0 @@ -package optimization.gradientBasedMethods.stats; - -import java.util.ArrayList; - -import optimization.gradientBasedMethods.Objective; -import optimization.gradientBasedMethods.Optimizer; -import optimization.gradientBasedMethods.ProjectedObjective; -import optimization.gradientBasedMethods.ProjectedOptimizer; -import optimization.util.MathUtils; -import optimization.util.StaticTools; - - -public class ProjectedOptimizerStats extends OptimizerStats{ - - - - public void reset(){ - super.reset(); - projectedGradientNorms.clear(); - } - - ArrayList<Double> projectedGradientNorms = new ArrayList<Double>(); - - public String prettyPrint(int level){ - StringBuffer res = new StringBuffer(); - res.append("Total time " + totalTime/1000 + " seconds \n" + "Iterations " + iterations.size() + "\n"); - res.append(objectiveFinalStats+"\n"); - if(level > 0){ - if(iterations.size() > 0){ - res.append("\tIteration"+iterations.get(0)+"\tstep: "+ - StaticTools.prettyPrint(steps.get(0), "0.00E00", 6)+ "\tgradientNorm "+ - StaticTools.prettyPrint(gradientNorms.get(0), "0.00000E00", 10) - + "\tdirection"+ - StaticTools.prettyPrint(projectedGradientNorms.get(0), "0.00000E00", 10)+ - "\tvalue "+ StaticTools.prettyPrint(value.get(0), "0.000000E00",11)+"\n"); - } - for(int i = 1; i < iterations.size(); i++){ - res.append("\tIteration"+iterations.get(i)+"\tstep: "+StaticTools.prettyPrint(steps.get(i), "0.00E00", 6)+ "\tgradientNorm "+ - StaticTools.prettyPrint(gradientNorms.get(i), "0.00000E00", 10)+ - "\t direction "+ - StaticTools.prettyPrint(projectedGradientNorms.get(i), "0.00000E00", 10)+ - "\tvalue "+ StaticTools.prettyPrint(value.get(i), "0.000000E00",11)+ - "\tvalueDiff "+ StaticTools.prettyPrint((value.get(i-1)-value.get(i)), "0.000000E00",11)+ - "\n"); - } - } - return res.toString(); - } - - - public void collectInitStats(Optimizer optimizer, Objective objective){ - startTime(); - } - - public void collectIterationStats(Optimizer optimizer, Objective objective){ - iterations.add(optimizer.getCurrentIteration()); - gradientNorms.add(MathUtils.L2Norm(objective.getGradient())); - projectedGradientNorms.add(MathUtils.L2Norm(optimizer.getDirection())); - steps.add(optimizer.getCurrentStep()); - value.add(optimizer.getCurrentValue()); - } - - - - public void collectFinalStats(Optimizer optimizer, Objective objective){ - stopTime(); - objectiveFinalStats = objective.finalInfoString(); - } - -} |