本文整理汇总了Java中com.rapidminer.operator.learner.PredictionModel.apply方法的典型用法代码示例。如果您正苦于以下问题：Java PredictionModel.apply方法的具体用法？Java PredictionModel.apply怎么用？Java PredictionModel.apply使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类com.rapidminer.operator.learner.PredictionModel的用法示例。
在下文中一共展示了PredictionModel.apply方法的3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: apply
import com.rapidminer.operator.learner.PredictionModel; //导入方法依赖的package包/类
@Override
public ExampleSet apply(ExampleSet exampleSet) throws OperatorException {
// retrieving and applying model
PredictionModel model = modelInput.getData(PredictionModel.class);
exampleSet = model.apply(exampleSet);
Attribute weightAttr = exampleSet.getAttributes().getWeight();
if (weightAttr == null) {
weightAttr = Tools.createWeightAttribute(exampleSet);
}
WeightedPerformanceMeasures wp = new WeightedPerformanceMeasures(exampleSet);
WeightedPerformanceMeasures.reweightExamples(exampleSet, wp.getContingencyMatrix(), true);
// recalculate weight attribute statistics
exampleSet.recalculateAttributeStatistics(exampleSet.getAttributes().getWeight());
double maxWeight = exampleSet.getStatistics(exampleSet.getAttributes().getWeight(), Statistics.MAXIMUM);
// fill new table
RandomGenerator randomGenerator = RandomGenerator.getRandomGenerator(this);
int[] remappingIndices = new int[exampleSet.size()];
int i = 0;
for (Example example : exampleSet) {
if (randomGenerator.nextDouble() > example.getValue(weightAttr) / maxWeight) {
example.setValue(weightAttr, 1.0d);
remappingIndices[i] = 1;
}
i++;
}
checkForStop();
SplittedExampleSet splittedExampleSet = new SplittedExampleSet(exampleSet, new Partition(remappingIndices, 2));
splittedExampleSet.selectSingleSubset(1);
return splittedExampleSet;
}
示例2: apply
import com.rapidminer.operator.learner.PredictionModel; //导入方法依赖的package包/类
@Override
public ExampleSet apply(ExampleSet exampleSet) throws OperatorException {
// retrieving and applying model
PredictionModel model = modelInput.getData(PredictionModel.class);
exampleSet = model.apply(exampleSet);
Attribute weightAttr = Tools.createWeightAttribute(exampleSet);
WeightedPerformanceMeasures wp = new WeightedPerformanceMeasures(exampleSet);
WeightedPerformanceMeasures.reweightExamples(exampleSet, wp.getContingencyMatrix(), true);
// recalculate weight attribute statistics
exampleSet.recalculateAttributeStatistics(exampleSet.getAttributes().getWeight());
double maxWeight = exampleSet.getStatistics(exampleSet.getAttributes().getWeight(), Statistics.MAXIMUM);
// fill new table
RandomGenerator randomGenerator = RandomGenerator.getRandomGenerator(this);
int[] remappingIndices = new int[exampleSet.size()];
int i = 0;
for (Example example : exampleSet) {
if (randomGenerator.nextDouble() > example.getValue(weightAttr) / maxWeight) {
example.setValue(weightAttr, 1.0d);
remappingIndices[i] = 1;
}
i++;
}
checkForStop();
SplittedExampleSet splittedExampleSet = new SplittedExampleSet(exampleSet, new Partition(remappingIndices, 2));
splittedExampleSet.selectSingleSubset(1);
return splittedExampleSet;
}
示例3: apply
import com.rapidminer.operator.learner.PredictionModel; //导入方法依赖的package包/类
@Override
public ExampleSet apply(ExampleSet exampleSet) throws OperatorException {
// retrieving and applying model
PredictionModel model = modelInput.getData(PredictionModel.class);
exampleSet = model.apply(exampleSet);
Attribute weightAttr = exampleSet.getAttributes().getWeight();
if (weightAttr == null) {
weightAttr = Tools.createWeightAttribute(exampleSet);
}
WeightedPerformanceMeasures wp = new WeightedPerformanceMeasures(exampleSet);
WeightedPerformanceMeasures.reweightExamples(exampleSet, wp.getContingencyMatrix(), true);
// recalculate weight attribute statistics
exampleSet.recalculateAttributeStatistics(exampleSet.getAttributes().getWeight());
double maxWeight = exampleSet.getStatistics(exampleSet.getAttributes().getWeight(), Statistics.MAXIMUM);
// fill new table
RandomGenerator randomGenerator = RandomGenerator.getRandomGenerator(this);
int[] remappingIndices = new int[exampleSet.size()];
int i = 0;
for (Example example: exampleSet) {
if (randomGenerator.nextDouble() > example.getValue(weightAttr) / maxWeight) {
example.setValue(weightAttr, 1.0d);
remappingIndices[i] = 1;
}
i++;
}
checkForStop();
SplittedExampleSet splittedExampleSet = new SplittedExampleSet(exampleSet, new Partition(remappingIndices, 2));
splittedExampleSet.selectSingleSubset(1);
return splittedExampleSet;
}