/** * accuracy at a given coverage percentage * @param cov coverage percentage * @return accuracy value */ public double accuracyAtCoverage(double cov) { assert(cov <= 1 && cov > 0); int numTrials = (int)(Math.round((double)classifications.size()*cov)); int numCorrect = 0; // System.out.println("NumTrials="+numTrials); for(int i= classifications.size()-1; i >= classifications.size()-numTrials; i--) { Classification temp = (Classification)classifications.get(i); if(temp.bestLabelIsCorrect()) numCorrect++; } // System.out.println("Accuracy at cov "+cov+" is "+ //(double)numCorrect/numTrials); return((double)numCorrect/numTrials); }
/** * accuracy at a given coverage percentage * @param cov coverage percentage * @return accuracy value */ public double accuracyAtCoverage(double cov) { assert(cov <= 1 && cov > 0); int numTrials = (int)(Math.round((double)classifications.size()*cov)); int numCorrect = 0; // System.out.println("NumTrials="+numTrials); for(int i= classifications.size()-1; i >= classifications.size()-numTrials; i--) { Classification temp = (Classification)classifications.get(i); if(temp.bestLabelIsCorrect()) numCorrect++; } // System.out.println("Accuracy at cov "+cov+" is "+ //(double)numCorrect/numTrials); return((double)numCorrect/numTrials); }
/** * accuracy at a given coverage percentage * @param cov coverage percentage * @return accuracy value */ public double accuracyAtCoverage(double cov) { assert(cov <= 1 && cov > 0); int numTrials = (int)(Math.round((double)classifications.size()*cov)); int numCorrect = 0; // System.out.println("NumTrials="+numTrials); for(int i= classifications.size()-1; i >= classifications.size()-numTrials; i--) { Classification temp = (Classification)classifications.get(i); if(temp.bestLabelIsCorrect()) numCorrect++; } // System.out.println("Accuracy at cov "+cov+" is "+ //(double)numCorrect/numTrials); return((double)numCorrect/numTrials); }
/** Return the fraction of instances that have the correct label as their best predicted label. */ public double getAccuracy () { int numCorrect = 0; for (int i = 0; i < this.size(); i++) if (this.get(i).bestLabelIsCorrect()) numCorrect++; return (double)numCorrect/this.size(); }
/** Return the fraction of instances that have the correct label as their best predicted label. */ public double getAccuracy () { int numCorrect = 0; for (int i = 0; i < this.size(); i++) if (this.get(i).bestLabelIsCorrect()) numCorrect++; return (double)numCorrect/this.size(); }
/** Return the fraction of instances that have the correct label as their best predicted label. */ public double getAccuracy () { int numCorrect = 0; for (int i = 0; i < this.size(); i++) if (this.get(i).bestLabelIsCorrect()) numCorrect++; return (double)numCorrect/this.size(); }
if (weakLearners[round].classify(inst).bestLabelIsCorrect()) correct[i] = true; else {
if (weakLearners[round].classify(inst).bestLabelIsCorrect()) correct[i] = true; else {
if (weakLearners[round].classify(inst).bestLabelIsCorrect()) correct[i] = true; else {
if(c.bestLabelIsCorrect()){ numCorrectInstances++; totalCorrect+=cpc.getLabelVector().value("correct");
if(c.bestLabelIsCorrect()){ numCorrectInstances++; totalCorrect+=cpc.getLabelVector().value("correct");
if(c.bestLabelIsCorrect()){ numCorrectInstances++; totalCorrect+=cpc.getLabelVector().value("correct");
carrier.setTarget(((LabelAlphabet)getTargetAlphabet()).lookupLabel(classification.bestLabelIsCorrect() ? "correct" : "incorrect")); carrier.setData(new FeatureVector ((Alphabet) getDataAlphabet(), features, false));
carrier.setTarget(((LabelAlphabet)getTargetAlphabet()).lookupLabel(classification.bestLabelIsCorrect() ? "correct" : "incorrect")); carrier.setData(new FeatureVector ((Alphabet) getDataAlphabet(), features, false));
carrier.setTarget(((LabelAlphabet)getTargetAlphabet()).lookupLabel(classification.bestLabelIsCorrect() ? "correct" : "incorrect")); carrier.setData(new FeatureVector ((Alphabet) getDataAlphabet(), features, false));