本文整理汇总了Java中com.rapidminer.operator.ports.metadata.GeneratePredictionModelTransformationRule类的典型用法代码示例。如果您正苦于以下问题：Java GeneratePredictionModelTransformationRule类的具体用法？Java GeneratePredictionModelTransformationRule怎么用？Java GeneratePredictionModelTransformationRule使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
GeneratePredictionModelTransformationRule类属于com.rapidminer.operator.ports.metadata包，在下文中一共展示了GeneratePredictionModelTransformationRule类的5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: AbstractStacking
import com.rapidminer.operator.ports.metadata.GeneratePredictionModelTransformationRule; //导入依赖的package包/类
public AbstractStacking(OperatorDescription description, String... subprocessNames) {
super(description, subprocessNames);
baseInputExtender.start();
baseModelExtender.start();
getTransformer().addRule(baseInputExtender.makePassThroughRule(exampleSetInput));
getTransformer().addRule(new SubprocessTransformRule(getSubprocess(0)));
getTransformer().addRule(
new GeneratePredictionModelTransformationRule(exampleSetInput, modelOutput, PredictionModel.class));
}
示例2: SDRulesetInduction
import com.rapidminer.operator.ports.metadata.GeneratePredictionModelTransformationRule; //导入依赖的package包/类
/** Constructor. */
public SDRulesetInduction(OperatorDescription description) {
super(description, "Training");
getTransformer().addRule(new ExampleSetPassThroughRule(exampleSetInput, trainingInnerSource, SetRelation.EQUAL) {
@Override
public ExampleSetMetaData modifyExampleSet(ExampleSetMetaData metaData) {
AttributeMetaData weightAttribute = new AttributeMetaData("weight", Ontology.REAL, Attributes.WEIGHT_NAME);
weightAttribute.setValueSetRelation(SetRelation.UNKNOWN);
metaData.addAttribute(weightAttribute);
AttributeMetaData specialAttribute = new AttributeMetaData(TIMES_COVERED, Ontology.REAL, TIMES_COVERED);
specialAttribute.setValueSetRelation(SetRelation.UNKNOWN);
metaData.addAttribute(specialAttribute);
return metaData;
}
});
getTransformer().addRule(new SubprocessTransformRule(getSubprocess(0)));
getTransformer().addRule(
new GeneratePredictionModelTransformationRule(exampleSetInput, modelOutput, PredictionModel.class));
addValue(new ValueDouble("performance", "The performance.") {
@Override
public double getDoubleValue() {
return performance;
}
});
addValue(new ValueDouble("iteration", "The current iteration.") {
@Override
public double getDoubleValue() {
return currentIteration;
}
});
}
示例3: SDRulesetInduction
import com.rapidminer.operator.ports.metadata.GeneratePredictionModelTransformationRule; //导入依赖的package包/类
/** Constructor. */
public SDRulesetInduction(OperatorDescription description) {
super(description, "Training");
getTransformer().addRule(new ExampleSetPassThroughRule(exampleSetInput, trainingInnerSource, SetRelation.EQUAL) {
@Override
public ExampleSetMetaData modifyExampleSet(ExampleSetMetaData metaData) {
AttributeMetaData weightAttribute = new AttributeMetaData("weight", Ontology.REAL, Attributes.WEIGHT_NAME);
weightAttribute.setValueSetRelation(SetRelation.UNKNOWN);
metaData.addAttribute(weightAttribute);
AttributeMetaData specialAttribute = new AttributeMetaData(TIMES_COVERED, Ontology.REAL, TIMES_COVERED);
specialAttribute.setValueSetRelation(SetRelation.UNKNOWN);
metaData.addAttribute(specialAttribute);
return metaData;
}
});
getTransformer().addRule(new SubprocessTransformRule(getSubprocess(0)));
getTransformer()
.addRule(new GeneratePredictionModelTransformationRule(exampleSetInput, modelOutput, PredictionModel.class));
addValue(new ValueDouble("performance", "The performance.") {
@Override
public double getDoubleValue() {
return performance;
}
});
addValue(new ValueDouble("iteration", "The current iteration.") {
@Override
public double getDoubleValue() {
return currentIteration;
}
});
}
示例4: AbstractStacking
import com.rapidminer.operator.ports.metadata.GeneratePredictionModelTransformationRule; //导入依赖的package包/类
public AbstractStacking(OperatorDescription description, String ... subprocessNames) {
super(description, subprocessNames);
baseInputExtender.start();
baseModelExtender.start();
getTransformer().addRule(baseInputExtender.makePassThroughRule(exampleSetInput));
getTransformer().addRule(new SubprocessTransformRule(getSubprocess(0)));
getTransformer().addRule(new GeneratePredictionModelTransformationRule(exampleSetInput, modelOutput, PredictionModel.class));
}
示例5: SDRulesetInduction
import com.rapidminer.operator.ports.metadata.GeneratePredictionModelTransformationRule; //导入依赖的package包/类
/** Constructor. */
public SDRulesetInduction(OperatorDescription description) {
super(description, "Training");
getTransformer().addRule(new ExampleSetPassThroughRule(exampleSetInput, trainingInnerSource, SetRelation.EQUAL) {
@Override
public ExampleSetMetaData modifyExampleSet(ExampleSetMetaData metaData) {
AttributeMetaData weightAttribute = new AttributeMetaData("weight", Ontology.REAL, Attributes.WEIGHT_NAME);
weightAttribute.setValueSetRelation(SetRelation.UNKNOWN);
metaData.addAttribute(weightAttribute);
AttributeMetaData specialAttribute = new AttributeMetaData(TIMES_COVERED, Ontology.REAL, TIMES_COVERED);
specialAttribute.setValueSetRelation(SetRelation.UNKNOWN);
metaData.addAttribute(specialAttribute);
return metaData;
}
});
getTransformer().addRule(new SubprocessTransformRule(getSubprocess(0)));
getTransformer().addRule(new GeneratePredictionModelTransformationRule(exampleSetInput, modelOutput, PredictionModel.class));
addValue(new ValueDouble("performance", "The performance.") {
@Override
public double getDoubleValue() {
return performance;
}
});
addValue(new ValueDouble("iteration", "The current iteration.") {
@Override
public double getDoubleValue() {
return currentIteration;
}
});
}