本文整理汇总了Java中org.dmg.pmml.DataField.addValues方法的典型用法代码示例。如果您正苦于以下问题：Java DataField.addValues方法的具体用法？Java DataField.addValues怎么用？Java DataField.addValues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org.dmg.pmml.DataField的用法示例。
在下文中一共展示了DataField.addValues方法的2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: testBuildCategoricalEncoding
import org.dmg.pmml.DataField; //导入方法依赖的package包/类
@Test
public void testBuildCategoricalEncoding() {
List<DataField> dataFields = new ArrayList<>();
dataFields.add(new DataField(FieldName.create("foo"), OpType.CONTINUOUS, DataType.DOUBLE));
DataField barField =
new DataField(FieldName.create("bar"), OpType.CATEGORICAL, DataType.STRING);
barField.addValues(new Value("b"), new Value("a"));
dataFields.add(barField);
DataDictionary dictionary = new DataDictionary(dataFields).setNumberOfFields(dataFields.size());
CategoricalValueEncodings encodings = AppPMMLUtils.buildCategoricalValueEncodings(dictionary);
assertEquals(2, encodings.getValueCount(1));
assertEquals(0, encodings.getValueEncodingMap(1).get("b").intValue());
assertEquals(1, encodings.getValueEncodingMap(1).get("a").intValue());
assertEquals("b", encodings.getEncodingValueMap(1).get(0));
assertEquals("a", encodings.getEncodingValueMap(1).get(1));
assertEquals(Collections.singletonMap(1, 2), encodings.getCategoryCounts());
}
示例2: buildDummyClassificationModel
import org.dmg.pmml.DataField; //导入方法依赖的package包/类
private static PMML buildDummyClassificationModel(int numTrees) {
PMML pmml = PMMLUtils.buildSkeletonPMML();
List<DataField> dataFields = new ArrayList<>();
DataField predictor =
new DataField(FieldName.create("color"), OpType.CATEGORICAL, DataType.STRING);
predictor.addValues(new Value("yellow"), new Value("red"));
dataFields.add(predictor);
DataField target =
new DataField(FieldName.create("fruit"), OpType.CATEGORICAL, DataType.STRING);
target.addValues(new Value("banana"), new Value("apple"));
dataFields.add(target);
DataDictionary dataDictionary =
new DataDictionary(dataFields).setNumberOfFields(dataFields.size());
pmml.setDataDictionary(dataDictionary);
List<MiningField> miningFields = new ArrayList<>();
MiningField predictorMF = new MiningField(FieldName.create("color"))
.setOpType(OpType.CATEGORICAL)
.setUsageType(MiningField.UsageType.ACTIVE)
.setImportance(0.5);
miningFields.add(predictorMF);
MiningField targetMF = new MiningField(FieldName.create("fruit"))
.setOpType(OpType.CATEGORICAL)
.setUsageType(MiningField.UsageType.PREDICTED);
miningFields.add(targetMF);
MiningSchema miningSchema = new MiningSchema(miningFields);
double dummyCount = 2.0;
Node rootNode = new Node().setId("r").setRecordCount(dummyCount).setPredicate(new True());
double halfCount = dummyCount / 2;
Node left = new Node().setId("r-").setRecordCount(halfCount).setPredicate(new True());
left.addScoreDistributions(new ScoreDistribution("apple", halfCount));
Node right = new Node().setId("r+").setRecordCount(halfCount)
.setPredicate(new SimpleSetPredicate(FieldName.create("color"),
SimpleSetPredicate.BooleanOperator.IS_NOT_IN,
new Array(Array.Type.STRING, "red")));
right.addScoreDistributions(new ScoreDistribution("banana", halfCount));
rootNode.addNodes(right, left);
TreeModel treeModel = new TreeModel(MiningFunction.CLASSIFICATION, miningSchema, rootNode)
.setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT)
.setMissingValueStrategy(TreeModel.MissingValueStrategy.DEFAULT_CHILD);
if (numTrees > 1) {
MiningModel miningModel = new MiningModel(MiningFunction.CLASSIFICATION, miningSchema);
List<Segment> segments = new ArrayList<>();
for (int i = 0; i < numTrees; i++) {
segments.add(new Segment()
.setId(Integer.toString(i))
.setPredicate(new True())
.setModel(treeModel)
.setWeight(1.0));
}
miningModel.setSegmentation(
new Segmentation(Segmentation.MultipleModelMethod.WEIGHTED_MAJORITY_VOTE, segments));
pmml.addModels(miningModel);
} else {
pmml.addModels(treeModel);
}
return pmml;
}