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Diffstat (limited to 'gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java')
| -rw-r--r-- | gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/LBFGS.java | 234 |
1 files changed, 0 insertions, 234 deletions
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; -// } - - - - - - - - - - -} |