本文整理汇总了Java中weka.core.pmml.TargetMetaInfo类的典型用法代码示例。如果您正苦于以下问题：Java TargetMetaInfo类的具体用法？Java TargetMetaInfo怎么用？Java TargetMetaInfo使用的例子？那么, 这里精选的类代码示例或许可以为您提供帮助。
TargetMetaInfo类属于weka.core.pmml包，在下文中一共展示了TargetMetaInfo类的1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: distributionForInstance
import weka.core.pmml.TargetMetaInfo; //导入依赖的package包/类
/**
* Classifies the given test instance. The instance has to belong to a
* dataset when it's being classified.
*
* @param inst the instance to be classified
* @return the predicted most likely class for the instance or
* Utils.missingValue() if no prediction is made
* @exception Exception if an error occurred during the prediction
*/
public double[] distributionForInstance(Instance inst) throws Exception {
if (!m_initialized) {
mapToMiningSchema(inst.dataset());
}
double[] preds = null;
if (m_miningSchema.getFieldsAsInstances().classAttribute().isNumeric()) {
preds = new double[1];
} else {
preds = new double[m_miningSchema.getFieldsAsInstances().classAttribute().numValues()];
}
// create an array of doubles that holds values from the incoming
// instance; in order of the fields in the mining schema. We will
// also handle missing values and outliers here.
double[] incoming = m_fieldsMap.instanceToSchema(inst, m_miningSchema);
// In this implementation we will default to information in the Target element (default
// value for numeric prediction and prior probabilities for classification). If there is
// no Target element defined, then an Exception is thrown.
boolean hasMissing = false;
for (int i = 0; i < incoming.length; i++) {
if (i != m_miningSchema.getFieldsAsInstances().classIndex() &&
Double.isNaN(incoming[i])) {
hasMissing = true;
break;
}
}
if (hasMissing) {
if (!m_miningSchema.hasTargetMetaData()) {
String message = "[GeneralRegression] WARNING: Instance to predict has missing value(s) but "
+ "there is no missing value handling meta data and no "
+ "prior probabilities/default value to fall back to. No "
+ "prediction will be made ("
+ ((m_miningSchema.getFieldsAsInstances().classAttribute().isNominal()
|| m_miningSchema.getFieldsAsInstances().classAttribute().isString())
? "zero probabilities output)."
: "NaN output).");
if (m_log == null) {
System.err.println(message);
} else {
m_log.logMessage(message);
}
if (m_miningSchema.getFieldsAsInstances().classAttribute().isNumeric()) {
preds[0] = Utils.missingValue();
}
return preds;
} else {
// use prior probablilities/default value
TargetMetaInfo targetData = m_miningSchema.getTargetMetaData();
if (m_miningSchema.getFieldsAsInstances().classAttribute().isNumeric()) {
preds[0] = targetData.getDefaultValue();
} else {
Instances miningSchemaI = m_miningSchema.getFieldsAsInstances();
for (int i = 0; i < miningSchemaI.classAttribute().numValues(); i++) {
preds[i] = targetData.getPriorProbability(miningSchemaI.classAttribute().value(i));
}
}
return preds;
}
} else {
// construct input parameter vector here
double[] inputParamVector = incomingParamVector(incoming);
computeResponses(incoming, inputParamVector, preds);
}
return preds;
}