public boolean optimize () { return optimize (Integer.MAX_VALUE); }
public boolean optimize () { return optimize (Integer.MAX_VALUE); }
public boolean optimize () { return optimize (Integer.MAX_VALUE); }
for (int i = 0; i < numIterations; i++) { try { converged = bfgs.optimize(1); iteration++; logger.info ("CRF finished one iteration of maximizer, i="+i); for (int i = 0; i < numIterations; i++) { try { converged = bfgs.optimize (1); iteration++; logger.info ("CRF finished one iteration of maximizer, i="+i);
for (int i = 0; i < numIterations; i++) { try { converged = bfgs.optimize(1); iteration++; logger.info ("CRF finished one iteration of maximizer, i="+i); for (int i = 0; i < numIterations; i++) { try { converged = bfgs.optimize (1); iteration++; logger.info ("CRF finished one iteration of maximizer, i="+i);
private double[][] optimizeQ(InstanceList data, Classifier p, boolean firstIter) { int numLabels = data.getTargetAlphabet().size(); double[][] base; if (firstIter) { base = null; } else { base = new double[data.size()][numLabels]; for (int ii = 0; ii < data.size(); ii++) { p.classify(data.get(ii)).getLabelVector().addTo(base[ii]); } } PRAuxClassifierOptimizable optimizable = new PRAuxClassifierOptimizable(data,base,q); LimitedMemoryBFGS bfgs = new LimitedMemoryBFGS(optimizable); try { bfgs.optimize(); } catch (Exception e) { e.printStackTrace(); } bfgs.reset(); try { bfgs.optimize(); } catch (Exception e) { e.printStackTrace(); } return base; } }
private double[][] optimizeQ(InstanceList data, Classifier p, boolean firstIter) { int numLabels = data.getTargetAlphabet().size(); double[][] base; if (firstIter) { base = null; } else { base = new double[data.size()][numLabels]; for (int ii = 0; ii < data.size(); ii++) { p.classify(data.get(ii)).getLabelVector().addTo(base[ii]); } } PRAuxClassifierOptimizable optimizable = new PRAuxClassifierOptimizable(data,base,q); LimitedMemoryBFGS bfgs = new LimitedMemoryBFGS(optimizable); try { bfgs.optimize(); } catch (Exception e) { e.printStackTrace(); } bfgs.reset(); try { bfgs.optimize(); } catch (Exception e) { e.printStackTrace(); } return base; } }
private double[][] optimizeQ(InstanceList data, Classifier p, boolean firstIter) { int numLabels = data.getTargetAlphabet().size(); double[][] base; if (firstIter) { base = null; } else { base = new double[data.size()][numLabels]; for (int ii = 0; ii < data.size(); ii++) { p.classify(data.get(ii)).getLabelVector().addTo(base[ii]); } } PRAuxClassifierOptimizable optimizable = new PRAuxClassifierOptimizable(data,base,q); LimitedMemoryBFGS bfgs = new LimitedMemoryBFGS(optimizable); try { bfgs.optimize(); } catch (Exception e) { e.printStackTrace(); } bfgs.reset(); try { bfgs.optimize(); } catch (Exception e) { e.printStackTrace(); } return base; } }