本文整理汇总了Java中com.github.javacliparser.ClassOption.createObject方法的典型用法代码示例。如果您正苦于以下问题：Java ClassOption.createObject方法的具体用法？Java ClassOption.createObject怎么用？Java ClassOption.createObject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.github.javacliparser.ClassOption的用法示例。
在下文中一共展示了ClassOption.createObject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: setLayout
import com.github.javacliparser.ClassOption; //导入方法依赖的package包/类
/**
* Sets the layout.
*/
protected void setLayout() {
int ensembleSize = this.ensembleSizeOption.getValue();
distributorP = new BaggingDistributorProcessor();
distributorP.setEnsembleSize(ensembleSize);
builder.addProcessor(distributorP, 1);
// instantiate classifier
ensemble = new AdaptiveLearner[ensembleSize];
for (int i = 0; i < ensembleSize; i++) {
try {
ensemble[i] = (AdaptiveLearner) ClassOption.createObject(baseLearnerOption.getValueAsCLIString(),
baseLearnerOption.getRequiredType());
} catch (Exception e) {
logger.error("Unable to create members of the ensemble. Please check your CLI parameters");
e.printStackTrace();
throw new IllegalArgumentException(e);
}
ensemble[i].setChangeDetector((ChangeDetector) this.driftDetectionMethodOption.getValue());
ensemble[i].init(builder, this.dataset, 1); // sequential
}
PredictionCombinerProcessor predictionCombinerP = new PredictionCombinerProcessor();
predictionCombinerP.setEnsembleSize(ensembleSize);
this.builder.addProcessor(predictionCombinerP, 1);
// Streams
resultStream = builder.createStream(predictionCombinerP);
predictionCombinerP.setOutputStream(resultStream);
for (AdaptiveLearner member : ensemble) {
for (Stream subResultStream : member.getResultStreams()) { // a learner can have multiple output streams
this.builder.connectInputKeyStream(subResultStream, predictionCombinerP); // the key is the instance id to combine predictions
}
}
ensembleStreams = new Stream[ensembleSize];
for (int i = 0; i < ensembleSize; i++) {
ensembleStreams[i] = builder.createStream(distributorP);
builder.connectInputShuffleStream(ensembleStreams[i], ensemble[i].getInputProcessor()); // connect streams one-to-one with ensemble members (the type of connection does not matter)
}
distributorP.setOutputStreams(ensembleStreams);
}
示例2: setLayout
import com.github.javacliparser.ClassOption; //导入方法依赖的package包/类
/**
* Sets the layout.
*/
protected void setLayout() {
int ensembleSize = this.ensembleSizeOption.getValue();
distributor = new ShardingDistributorProcessor();
distributor.setEnsembleSize(ensembleSize);
this.builder.addProcessor(distributor, 1);
// instantiate classifier
ensemble = new Learner[ensembleSize];
for (int i = 0; i < ensembleSize; i++) {
try {
ensemble[i] = (Learner) ClassOption.createObject(baseLearnerOption.getValueAsCLIString(),
baseLearnerOption.getRequiredType());
} catch (Exception e) {
logger.error("Unable to create members of the ensemble. Please check your CLI parameters");
e.printStackTrace();
throw new IllegalArgumentException(e);
}
ensemble[i].init(builder, this.dataset, 1); // sequential
}
PredictionCombinerProcessor predictionCombiner = new PredictionCombinerProcessor();
predictionCombiner.setEnsembleSize(ensembleSize);
this.builder.addProcessor(predictionCombiner, 1);
// Streams
resultStream = this.builder.createStream(predictionCombiner);
predictionCombiner.setOutputStream(resultStream);
for (Learner member : ensemble) {
for (Stream subResultStream : member.getResultStreams()) { // a learner can have multiple output streams
this.builder.connectInputKeyStream(subResultStream, predictionCombiner); // the key is the instance id to combine predictions
}
}
ensembleStreams = new Stream[ensembleSize];
for (int i = 0; i < ensembleSize; i++) {
ensembleStreams[i] = builder.createStream(distributor);
builder.connectInputShuffleStream(ensembleStreams[i], ensemble[i].getInputProcessor()); // connect streams one-to-one with ensemble members (the type of connection does not matter)
}
distributor.setOutputStreams(ensembleStreams);
}
示例3: setLayout
import com.github.javacliparser.ClassOption; //导入方法依赖的package包/类
/**
* Sets the layout.
*
* @throws Exception
*/
protected void setLayout() {
int ensembleSize = this.ensembleSizeOption.getValue();
distributorP = new BaggingDistributorProcessor();
distributorP.setEnsembleSize(ensembleSize);
builder.addProcessor(distributorP, 1);
// instantiate classifier
ensemble = new Learner[ensembleSize];
for (int i = 0; i < ensembleSize; i++) {
try {
ensemble[i] = (Learner) ClassOption.createObject(baseLearnerOption.getValueAsCLIString(),
baseLearnerOption.getRequiredType());
} catch (Exception e) {
logger.error("Unable to create members of the ensemble. Please check your CLI parameters");
e.printStackTrace();
throw new IllegalArgumentException(e);
}
ensemble[i].init(builder, this.dataset, 1); // sequential
}
PredictionCombinerProcessor predictionCombinerP = new PredictionCombinerProcessor();
predictionCombinerP.setEnsembleSize(ensembleSize);
this.builder.addProcessor(predictionCombinerP, 1);
// Streams
resultStream = builder.createStream(predictionCombinerP);
predictionCombinerP.setOutputStream(resultStream);
for (Learner member : ensemble) {
for (Stream subResultStream : member.getResultStreams()) { // a learner can have multiple output streams
this.builder.connectInputKeyStream(subResultStream, predictionCombinerP); // the key is the instance id to combine predictions
}
}
ensembleStreams = new Stream[ensembleSize];
for (int i = 0; i < ensembleSize; i++) {
ensembleStreams[i] = builder.createStream(distributorP);
builder.connectInputShuffleStream(ensembleStreams[i], ensemble[i].getInputProcessor()); // connect streams one-to-one with ensemble members (the type of connection does not matter)
}
distributorP.setOutputStreams(ensembleStreams);
}