本文整理汇总了Java中org.dmg.pmml.regression.RegressionTable类的典型用法代码示例。如果您正苦于以下问题：Java RegressionTable类的具体用法？Java RegressionTable怎么用？Java RegressionTable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。
RegressionTable类属于org.dmg.pmml.regression包，在下文中一共展示了RegressionTable类的11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，您的评价将有助于我们的系统推荐出更棒的Java代码示例。
示例1: marshal
import org.dmg.pmml.regression.RegressionTable; //导入依赖的package包/类
@Test
public void marshal() throws Exception {
RegressionModel regressionModel = new RegressionModel()
.addRegressionTables(new RegressionTable());
PMML pmml = new PMML(Version.PMML_4_3.getVersion(), new Header(), new DataDictionary())
.addModels(regressionModel);
JAXBContext context = JAXBContextFactory.createContext(new Class[]{org.dmg.pmml.ObjectFactory.class, org.dmg.pmml.regression.ObjectFactory.class}, null);
Marshaller marshaller = context.createMarshaller();
ByteArrayOutputStream os = new ByteArrayOutputStream();
marshaller.marshal(pmml, os);
String string = os.toString("UTF-8");
assertTrue(string.contains("<PMML xmlns=\"http://www.dmg.org/PMML-4_3\" version=\"4.3\">"));
assertTrue(string.contains("<RegressionModel>"));
assertTrue(string.contains("</RegressionModel>"));
assertTrue(string.contains("</PMML>"));
}
开发者ID:jpmml，项目名称:jpmml-model，代码行数:24，代码来源:MarshallerTest.java
示例2: createBinaryLogisticClassification
import org.dmg.pmml.regression.RegressionTable; //导入依赖的package包/类
static
public RegressionModel createBinaryLogisticClassification(MathContext mathContext, List<? extends Feature> features, List<Double> coefficients, Double intercept, RegressionModel.NormalizationMethod normalizationMethod, boolean hasProbabilityDistribution, Schema schema){
CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();
if(categoricalLabel.size() != 2){
throw new IllegalArgumentException();
} // End if
if(normalizationMethod != null){
switch(normalizationMethod){
case NONE:
case LOGIT:
case PROBIT:
case CLOGLOG:
case LOGLOG:
case CAUCHIT:
break;
default:
throw new IllegalArgumentException();
}
}
RegressionTable activeRegressionTable = RegressionModelUtil.createRegressionTable(features, coefficients, intercept)
.setTargetCategory(categoricalLabel.getValue(1));
RegressionTable passiveRegressionTable = RegressionModelUtil.createRegressionTable(Collections.<Feature>emptyList(), Collections.<Double>emptyList(), null)
.setTargetCategory(categoricalLabel.getValue(0));
RegressionModel regressionModel = new RegressionModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), null)
.setNormalizationMethod(normalizationMethod)
.setMathContext(ModelUtil.simplifyMathContext(mathContext))
.addRegressionTables(activeRegressionTable, passiveRegressionTable)
.setOutput(hasProbabilityDistribution ? ModelUtil.createProbabilityOutput(mathContext, categoricalLabel) : null);
return regressionModel;
}
开发者ID:jpmml，项目名称:jpmml-converter，代码行数:38，代码来源:RegressionModelUtil.java
示例3: assertState
import org.dmg.pmml.regression.RegressionTable; //导入依赖的package包/类
static
private void assertState(RegressionTable regressionTable, double intercept, boolean hasNumericTerms, boolean hasCategoricalTerms, boolean hasInteractionTerms){
assertEquals((Double)intercept, (Double)regressionTable.getIntercept());
assertEquals(hasNumericTerms, regressionTable.hasNumericPredictors());
assertEquals(hasCategoricalTerms, regressionTable.hasCategoricalPredictors());
assertEquals(hasInteractionTerms, regressionTable.hasPredictorTerms());
}
开发者ID:jpmml，项目名称:jpmml-converter，代码行数:9，代码来源:RegressionModelUtilTest.java
示例4: isDefault
import org.dmg.pmml.regression.RegressionTable; //导入依赖的package包/类
static
private boolean isDefault(RegressionTable regressionTable){
if(regressionTable.hasExtensions()){
return false;
} // End if
if(regressionTable.hasNumericPredictors() || regressionTable.hasCategoricalPredictors() || regressionTable.hasPredictorTerms()){
return false;
}
return (regressionTable.getIntercept() == 0d);
}
开发者ID:jpmml，项目名称:jpmml-evaluator，代码行数:14，代码来源:RegressionModelEvaluator.java
示例5: visit
import org.dmg.pmml.regression.RegressionTable; //导入依赖的package包/类
@Override
public VisitorAction visit(RegressionTable regressionTable){
if(regressionTable.hasCategoricalPredictors()){
List<CategoricalPredictor> categoricalPredictors = regressionTable.getCategoricalPredictors();
for(ListIterator<CategoricalPredictor> it = categoricalPredictors.listIterator(); it.hasNext(); ){
it.set(new RichCategoricalPredictor(it.next()));
}
}
return super.visit(regressionTable);
}
开发者ID:jpmml，项目名称:jpmml-evaluator，代码行数:14，代码来源:RegressionModelOptimizer.java
示例6: encodeModel
import org.dmg.pmml.regression.RegressionTable; //导入依赖的package包/类
@Override
public RegressionModel encodeModel(TensorFlowEncoder encoder){
DataField dataField = encoder.createDataField(FieldName.create("_target"), OpType.CATEGORICAL, DataType.INTEGER);
RegressionModel regressionModel = encodeRegressionModel(encoder);
List<RegressionTable> regressionTables = regressionModel.getRegressionTables();
List<String> categories;
if(regressionTables.size() == 1){
categories = Arrays.asList("0", "1");
RegressionTable activeRegressionTable = regressionTables.get(0)
.setTargetCategory(categories.get(1));
RegressionTable passiveRegressionTable = new RegressionTable(0)
.setTargetCategory(categories.get(0));
regressionModel.addRegressionTables(passiveRegressionTable);
} else
if(regressionTables.size() > 2){
categories = new ArrayList<>();
for(int i = 0; i < regressionTables.size(); i++){
RegressionTable regressionTable = regressionTables.get(i);
String category = String.valueOf(i);
regressionTable.setTargetCategory(category);
categories.add(category);
}
} else
{
throw new IllegalArgumentException();
}
dataField = encoder.toCategorical(dataField.getName(), categories);
CategoricalLabel categoricalLabel = new CategoricalLabel(dataField);
regressionModel
.setMiningFunction(MiningFunction.CLASSIFICATION)
.setNormalizationMethod(RegressionModel.NormalizationMethod.SOFTMAX)
.setMiningSchema(ModelUtil.createMiningSchema(categoricalLabel))
.setOutput(ModelUtil.createProbabilityOutput(DataType.FLOAT, categoricalLabel));
return regressionModel;
}
开发者ID:jpmml，项目名称:jpmml-tensorflow，代码行数:52，代码来源:LinearClassifier.java
示例7: createClassification
import org.dmg.pmml.regression.RegressionTable; //导入依赖的package包/类
static
public MiningModel createClassification(List<? extends Model> models, RegressionModel.NormalizationMethod normalizationMethod, boolean hasProbabilityDistribution, Schema schema){
CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();
if(categoricalLabel.size() < 3 || categoricalLabel.size() != models.size()){
throw new IllegalArgumentException();
} // End if
if(normalizationMethod != null){
switch(normalizationMethod){
case NONE:
case SIMPLEMAX:
case SOFTMAX:
break;
default:
throw new IllegalArgumentException();
}
}
MathContext mathContext = null;
List<RegressionTable> regressionTables = new ArrayList<>();
for(int i = 0; i < categoricalLabel.size(); i++){
Model model = models.get(i);
MathContext modelMathContext = model.getMathContext();
if(modelMathContext == null){
modelMathContext = MathContext.DOUBLE;
} // End if
if(mathContext == null){
mathContext = modelMathContext;
} else
{
if(!Objects.equals(mathContext, modelMathContext)){
throw new IllegalArgumentException();
}
}
Feature feature = MiningModelUtil.MODEL_PREDICTION.apply(model);
RegressionTable regressionTable = RegressionModelUtil.createRegressionTable(Collections.singletonList(feature), Collections.singletonList(1d), null)
.setTargetCategory(categoricalLabel.getValue(i));
regressionTables.add(regressionTable);
}
RegressionModel regressionModel = new RegressionModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), regressionTables)
.setNormalizationMethod(normalizationMethod)
.setMathContext(ModelUtil.simplifyMathContext(mathContext))
.setOutput(hasProbabilityDistribution ? ModelUtil.createProbabilityOutput(mathContext, categoricalLabel) : null);
List<Model> segmentationModels = new ArrayList<>(models);
segmentationModels.add(regressionModel);
return createModelChain(segmentationModels, schema);
}
开发者ID:jpmml，项目名称:jpmml-converter，代码行数:61，代码来源:MiningModelUtil.java
示例8: encodeModel
import org.dmg.pmml.regression.RegressionTable; //导入依赖的package包/类
@Override
public RegressionModel encodeModel(Schema schema){
int[] shape = getCoefShape();
int numberOfClasses = shape[0];
int numberOfFeatures = shape[1];
boolean hasProbabilityDistribution = hasProbabilityDistribution();
List<? extends Number> coef = getCoef();
List<? extends Number> intercepts = getIntercept();
CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();
List<Feature> features = schema.getFeatures();
if(numberOfClasses == 1){
ClassifierUtil.checkSize(2, categoricalLabel);
return RegressionModelUtil.createBinaryLogisticClassification(features, ValueUtil.asDoubles(CMatrixUtil.getRow(coef, numberOfClasses, numberOfFeatures, 0)), ValueUtil.asDouble(intercepts.get(0)), RegressionModel.NormalizationMethod.LOGIT, hasProbabilityDistribution, schema);
} else
if(numberOfClasses >= 3){
ClassifierUtil.checkSize(numberOfClasses, categoricalLabel);
List<RegressionTable> regressionTables = new ArrayList<>();
for(int i = 0, rows = categoricalLabel.size(); i < rows; i++){
RegressionTable regressionTable = RegressionModelUtil.createRegressionTable(features, ValueUtil.asDoubles(CMatrixUtil.getRow(coef, numberOfClasses, numberOfFeatures, i)), ValueUtil.asDouble(intercepts.get(i)))
.setTargetCategory(categoricalLabel.getValue(i));
regressionTables.add(regressionTable);
}
RegressionModel regressionModel = new RegressionModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), regressionTables)
.setNormalizationMethod(RegressionModel.NormalizationMethod.LOGIT)
.setOutput(hasProbabilityDistribution ? ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel) : null);
return regressionModel;
} else
{
throw new IllegalArgumentException();
}
}
开发者ID:jpmml，项目名称:jpmml-sklearn，代码行数:46，代码来源:BaseLinearClassifier.java
示例9: createClassification
import org.dmg.pmml.regression.RegressionTable; //导入依赖的package包/类
static
public MiningModel createClassification(List<? extends Model> models, RegressionModel.NormalizationMethod normalizationMethod, boolean hasProbabilityDistribution, Schema schema){
CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();
// modified here
if(categoricalLabel.size() != models.size()){
throw new IllegalArgumentException();
} // End if
if(normalizationMethod != null){
switch(normalizationMethod){
case NONE:
case SIMPLEMAX:
case SOFTMAX:
break;
default:
throw new IllegalArgumentException();
}
}
MathContext mathContext = null;
List<RegressionTable> regressionTables = new ArrayList<>();
for(int i = 0; i < categoricalLabel.size(); i++){
Model model = models.get(i);
MathContext modelMathContext = model.getMathContext();
if(modelMathContext == null){
modelMathContext = MathContext.DOUBLE;
} // End if
if(mathContext == null){
mathContext = modelMathContext;
} else
{
if(!Objects.equals(mathContext, modelMathContext)){
throw new IllegalArgumentException();
}
}
Feature feature = MODEL_PREDICTION.apply(model);
RegressionTable regressionTable = RegressionModelUtil.createRegressionTable(Collections.singletonList(feature), Collections.singletonList(1d), null)
.setTargetCategory(categoricalLabel.getValue(i));
regressionTables.add(regressionTable);
}
RegressionModel regressionModel = new RegressionModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), regressionTables)
.setNormalizationMethod(normalizationMethod)
.setMathContext(ModelUtil.simplifyMathContext(mathContext))
.setOutput(hasProbabilityDistribution ? ModelUtil.createProbabilityOutput(mathContext, categoricalLabel) : null);
List<Model> segmentationModels = new ArrayList<>(models);
segmentationModels.add(regressionModel);
return createModelChain(segmentationModels, schema);
}
开发者ID:cheng-li，项目名称:pyramid，代码行数:62，代码来源:MiningModelUtil.java
示例10: evaluateRegression
import org.dmg.pmml.regression.RegressionTable; //导入依赖的package包/类
private <V extends Number> Map<FieldName, ?> evaluateRegression(ValueFactory<V> valueFactory, EvaluationContext context){
RegressionModel regressionModel = getModel();
TargetField targetField = getTargetField();
FieldName targetFieldName = regressionModel.getTargetFieldName();
if(targetFieldName != null && !Objects.equals(targetField.getName(), targetFieldName)){
throw new InvalidAttributeException(regressionModel, PMMLAttributes.REGRESSIONMODEL_TARGETFIELDNAME, targetFieldName);
}
List<RegressionTable> regressionTables = regressionModel.getRegressionTables();
if(regressionTables.size() != 1){
throw new InvalidElementListException(regressionTables);
}
RegressionTable regressionTable = regressionTables.get(0);
Value<V> result = evaluateRegressionTable(valueFactory, regressionTable, context);
if(result == null){
return TargetUtil.evaluateRegressionDefault(valueFactory, targetField);
}
RegressionModel.NormalizationMethod normalizationMethod = regressionModel.getNormalizationMethod();
switch(normalizationMethod){
case NONE:
case SOFTMAX:
case LOGIT:
case EXP:
case PROBIT:
case CLOGLOG:
case LOGLOG:
case CAUCHIT:
RegressionModelUtil.normalizeRegressionResult(result, normalizationMethod);
break;
case SIMPLEMAX:
throw new InvalidAttributeException(regressionModel, normalizationMethod);
default:
throw new UnsupportedAttributeException(regressionModel, normalizationMethod);
}
return TargetUtil.evaluateRegression(targetField, result);
}
开发者ID:jpmml，项目名称:jpmml-evaluator，代码行数:43，代码来源:RegressionModelEvaluator.java
示例11: createRegressionTable
import org.dmg.pmml.regression.RegressionTable; //导入依赖的package包/类
@Test
public void createRegressionTable(){
ModelEncoder encoder = new ModelEncoder();
RegressionTable regressionTable = RegressionModelUtil.createRegressionTable(Collections.<Feature>emptyList(), Collections.<Double>emptyList(), null);
assertState(regressionTable, 0d, false, false, false);
Feature feature = SchemaUtil.createConstantFeature(encoder, 3d);
regressionTable = RegressionModelUtil.createRegressionTable(Collections.singletonList(feature), Collections.singletonList(2d), 1d);
assertState(regressionTable, 1d + (2d * 3d), false, false, false);
feature = SchemaUtil.createInteractionFeature(encoder, 3d, FieldName.create("x"), 7d);
regressionTable = RegressionModelUtil.createRegressionTable(Collections.singletonList(feature), Collections.singletonList(2d), 1d);
assertState(regressionTable, 1d, true, false, false);
NumericPredictor numericPredictor = Iterables.getOnlyElement(regressionTable.getNumericPredictors());
assertEquals(FieldName.create("x"), numericPredictor.getName());
assertEquals((Double)(2d * 3d * 7d), (Double)numericPredictor.getCoefficient());
feature = SchemaUtil.createInteractionFeature(encoder, FieldName.create("x1"), 5d, FieldName.create("x2"));
regressionTable = RegressionModelUtil.createRegressionTable(Collections.singletonList(feature), Collections.singletonList(2d), 1d);
assertState(regressionTable, 1d, false, false, true);
PredictorTerm predictorTerm = Iterables.getOnlyElement(regressionTable.getPredictorTerms());
assertEquals((Double)(2d * 5d), (Double)predictorTerm.getCoefficient());
List<FieldRef> fieldRefs = predictorTerm.getFieldRefs();
assertEquals(FieldName.create("x1"), (fieldRefs.get(0)).getField());
assertEquals(FieldName.create("x2"), (fieldRefs.get(1)).getField());
}
开发者ID:jpmml，项目名称:jpmml-converter，代码行数:41，代码来源:RegressionModelUtilTest.java
注：本文中的org.dmg.pmml.regression.RegressionTable类示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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