public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
demandLexicon(); out.println(name + ": " + C + ", " + epsilon + ", " + bias + ", " + solverType);
/** * Take lex and model, and optimize the model by pruning the weights. Any zero weights get pruned. * @param lexicon the lexicon with the feature map. * @param s the support vector machine. */ public SupportVectorMachineOptimizer(SupportVectorMachine s) { super(s.demandLexicon(), s.featurePruningThreshold); this.svm = s; // the numClasses field gets change in the write method to allow for the binary case // which is actually two classes to behave as one class (binary). if (!s.getSolverType().equals("MCSVM_CS") && s.getNumClasses() <= 2) numberclasses = 1; else numberclasses = s.getNumClasses(); // we need to figure out if we have a bias feature introduced this.biasfeatures = svm.getBiasFeatures(); }