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-rw-r--r--gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java234
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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
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@@ -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;
-// }
-
-
-
-
-
-
-
-
-
-
-}