本文整理汇总了Java中mulan.classifier.MultiLabelOutput类的典型用法代码示例。如果您正苦于以下问题：Java MultiLabelOutput类的具体用法？Java MultiLabelOutput怎么用？Java MultiLabelOutput使用的例子？那么, 这里精选的类代码示例或许可以为您提供帮助。
MultiLabelOutput类属于mulan.classifier包，在下文中一共展示了MultiLabelOutput类的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: makePredictionInternal
import mulan.classifier.MultiLabelOutput; //导入依赖的package包/类
/**
* {@inheritDoc}
*/
protected MultiLabelOutput makePredictionInternal(Instance instance) {
boolean[] bipartition = new boolean[numLabels];
double[] confidences = new double[numLabels];
marginDifference = new double[numLabels];
leastConfidences = new double[numLabels];
for (int counter = 0; counter < numLabels; counter++) {
Instance transformedInstance = brt.transformInstance(instance, counter);
double distribution[];
try {
distribution = ensemble[counter].distributionForInstance(transformedInstance);
marginDifference[counter] = Math.abs(distribution[0] - distribution[1]);
leastConfidences[counter] = Math.abs(1 - Math.max(distribution[0], distribution[1]));
} catch (Exception e) {
System.out.println(e);
return null;
}
int maxIndex = (distribution[0] > distribution[1]) ? 0 : 1;
// Ensure correct predictions both for class values {0,1} and {1,0}
bipartition[counter] = (maxIndex == 1) ? true : false;
// The confidence of the label being equal to 1
confidences[counter] = distribution[1];
}
MultiLabelOutput mlo = new MultiLabelOutput(bipartition, confidences);
return mlo;
}
示例2: makePrediction
import mulan.classifier.MultiLabelOutput; //导入依赖的package包/类
/**
* Return a MultiLabelOutput object
*
* @param instance
* The instance to test
* @return a MultiLabelOutput object
*/
public MultiLabelOutput makePrediction(Instance instance) {
try {
return classifier.makePrediction(instance);
} catch (Exception e) {
Logger.getLogger(MulanClassifier.class.getName()).log(Level.SEVERE, null, e);
}
return null;
}
示例3: makePredictionInternal
import mulan.classifier.MultiLabelOutput; //导入依赖的package包/类
@Override
protected MultiLabelOutput makePredictionInternal( Instance instance)
throws Exception {
MultiLabelOutput baseout = basebr.makePrediction(instance);
boolean[] bipartition = new boolean[uppermatrix.numAttributes()];
double[] confidences = new double[uppermatrix.numAttributes()];
for (int i = 0; i < bipartition.length; i++) {
int index1 = uppermatrix.attribute(i).value(0).equals("0") ? 1 : 0;
for (int j = 0; j < baseout.getBipartition().length; j++) {
double matval = uppermatrix.instance(j).value(i);
bipartition[i] = bipartition[i]
|| (baseout.getBipartition()[j] && (matval == index1));
if (matval == index1) {
confidences[i] = Math.max(confidences[i], baseout.getConfidences()[j]);
}
}
}
MultiLabelOutput mlo = new MultiLabelOutput(bipartition, confidences);
return mlo;
}
示例4: computeMeanAveragePrecision
import mulan.classifier.MultiLabelOutput; //导入依赖的package包/类
public void computeMeanAveragePrecision() {
MeanAveragePrecision measure = new MeanAveragePrecision(numLabels);
for (int dd = 0; dd < trueLabels.length; dd++) {
if (this.docNumTrueLabels[dd] == 0) {
continue;
}
measure.update(new MultiLabelOutput(predictedScores[dd]), trueLabels[dd]);
}
this.measurements.add(new Measurement("MAP", measure.getValue()));
}
示例5: computeOneError
import mulan.classifier.MultiLabelOutput; //导入依赖的package包/类
public void computeOneError() {
OneError oneError = new OneError();
for (int dd = 0; dd < trueLabels.length; dd++) {
if (this.docNumTrueLabels[dd] == 0) {
continue;
}
oneError.update(new MultiLabelOutput(predictedScores[dd]), trueLabels[dd]);
}
this.measurements.add(new Measurement("One-error", oneError.getValue()));
}
示例6: computeIsError
import mulan.classifier.MultiLabelOutput; //导入依赖的package包/类
public void computeIsError() {
IsError isError = new IsError();
for (int dd = 0; dd < trueLabels.length; dd++) {
if (this.docNumTrueLabels[dd] == 0) {
continue;
}
isError.update(new MultiLabelOutput(predictedScores[dd]), trueLabels[dd]);
}
this.measurements.add(new Measurement("Is-error", isError.getValue()));
}