/** * Computes the dot product of the specified example vector and the weight vector associated * with the supplied class. If no label is specified, it defaults to a label of 0 (that is, a * positive example), but this should only be done in binary classification. * * @param example The example object. * @return The score for the given example vector. **/ public double score(Object example) { assert !solverType.equals("MCSVM_CS") && numClasses == 2 : "Cannot call score(Object) in a multi-class classifier."; return score(example, 0); }
/** * Calls the appropriate <code>Learner.setParameters(Parameters)</code> method for this * <code>Parameters</code> object. * * @param l The learner whose parameters will be set. **/ public void setParameters(Learner l) { ((SupportVectorMachine) l).setParameters(this); }
public edu.illinois.cs.cogcomp.lbjava.classify.Feature valueOf(int[] a0, double[] a1, java.util.Collection a2) { if (isClone) { loadInstance(); return instance.valueOf(a0, a1, a2); } return super.valueOf(a0, a1, a2); }
public int getNumClasses() { if (isClone) { loadInstance(); return instance.getNumClasses(); } return super.getNumClasses(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public java.lang.Object[] getExampleArray(java.lang.Object a0) { if (isClone) { loadInstance(); return instance.getExampleArray(a0); } return super.getExampleArray(a0); }
public edu.illinois.cs.cogcomp.lbjava.classify.Feature featureValue(int[] a0, double[] a1) { if (isClone) { loadInstance(); return instance.featureValue(a0, a1); } return super.featureValue(a0, a1); }
public double[] getWeights() { if (isClone) { loadInstance(); return instance.getWeights(); } return super.getWeights(); }
public void countFeatures(edu.illinois.cs.cogcomp.lbjava.learn.Lexicon.CountPolicy a0) { if (isClone) { loadInstance(); instance.countFeatures(a0); return; } super.countFeatures(a0); }
public edu.illinois.cs.cogcomp.lbjava.classify.FeatureVector[] classify(java.lang.Object[][] a0) { if (isClone) { loadInstance(); return instance.classify(a0); } return super.classify(a0); }
public edu.illinois.cs.cogcomp.lbjava.classify.Feature valueOf(java.lang.Object a0, java.util.Collection a1) { if (isClone) { loadInstance(); return instance.valueOf(a0, a1); } return super.valueOf(a0, a1); }
public int getNumClasses() { if (isClone) { loadInstance(); return instance.getNumClasses(); } return super.getNumClasses(); }
public edu.illinois.cs.cogcomp.lbjava.learn.Lexicon demandLexicon() { if (isClone) { loadInstance(); return instance.demandLexicon(); } return super.demandLexicon(); }
public java.lang.Object[] getExampleArray(java.lang.Object a0, boolean a1) { if (isClone) { loadInstance(); return instance.getExampleArray(a0, a1); } return super.getExampleArray(a0, a1); }
public edu.illinois.cs.cogcomp.lbjava.classify.Feature featureValue(edu.illinois.cs.cogcomp.lbjava.classify.FeatureVector a0) { if (isClone) { loadInstance(); return instance.featureValue(a0); } return super.featureValue(a0); }
public double[] getWeights() { if (isClone) { loadInstance(); return instance.getWeights(); } return super.getWeights(); }
public void countFeatures(edu.illinois.cs.cogcomp.lbjava.learn.Lexicon.CountPolicy a0) { if (isClone) { loadInstance(); instance.countFeatures(a0); return; } super.countFeatures(a0); }
public edu.illinois.cs.cogcomp.lbjava.classify.FeatureVector[] classify(java.lang.Object[][] a0) { if (isClone) { loadInstance(); return instance.classify(a0); } return super.classify(a0); }
public edu.illinois.cs.cogcomp.lbjava.classify.Feature valueOf(java.lang.Object a0, java.util.Collection a1) { if (isClone) { loadInstance(); return instance.valueOf(a0, a1); } return super.valueOf(a0, a1); }
public int getNumClasses() { if (isClone) { loadInstance(); return instance.getNumClasses(); } return super.getNumClasses(); }