本文整理汇总了Java中weka.classifiers.bayes.BayesNet.updateClassifier方法的典型用法代码示例。如果您正苦于以下问题：Java BayesNet.updateClassifier方法的具体用法？Java BayesNet.updateClassifier怎么用？Java BayesNet.updateClassifier使用的例子？那么恭喜您, 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weka.classifiers.bayes.BayesNet的用法示例。
在下文中一共展示了BayesNet.updateClassifier方法的4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: leaveOneOutCV
import weka.classifiers.bayes.BayesNet; //导入方法依赖的package包/类
/**
* LeaveOneOutCV returns the accuracy calculated using Leave One Out cross
* validation. The dataset used is m_Instances associated with the Bayes
* Network.
*
* @param bayesNet : Bayes Network containing structure to evaluate
* @return accuracy (in interval 0..1) measured using leave one out cv.
* @throws Exception passed on by updateClassifier
*/
public double leaveOneOutCV(BayesNet bayesNet) throws Exception {
m_BayesNet = bayesNet;
double fAccuracy = 0.0;
double fWeight = 0.0;
Instances instances = bayesNet.m_Instances;
bayesNet.estimateCPTs();
for (int iInstance = 0; iInstance < instances.numInstances(); iInstance++) {
Instance instance = instances.instance(iInstance);
instance.setWeight(-instance.weight());
bayesNet.updateClassifier(instance);
fAccuracy += accuracyIncrease(instance);
fWeight += instance.weight();
instance.setWeight(-instance.weight());
bayesNet.updateClassifier(instance);
}
return fAccuracy / fWeight;
}
示例2: leaveOneOutCV
import weka.classifiers.bayes.BayesNet; //导入方法依赖的package包/类
/**
* LeaveOneOutCV returns the accuracy calculated using Leave One Out
* cross validation. The dataset used is m_Instances associated with
* the Bayes Network.
* @param bayesNet : Bayes Network containing structure to evaluate
* @return accuracy (in interval 0..1) measured using leave one out cv.
* @throws Exception passed on by updateClassifier
*/
public double leaveOneOutCV(BayesNet bayesNet) throws Exception {
m_BayesNet = bayesNet;
double fAccuracy = 0.0;
double fWeight = 0.0;
Instances instances = bayesNet.m_Instances;
bayesNet.estimateCPTs();
for (int iInstance = 0; iInstance < instances.numInstances(); iInstance++) {
Instance instance = instances.instance(iInstance);
instance.setWeight(-instance.weight());
bayesNet.updateClassifier(instance);
fAccuracy += accuracyIncrease(instance);
fWeight += instance.weight();
instance.setWeight(-instance.weight());
bayesNet.updateClassifier(instance);
}
return fAccuracy / fWeight;
}
示例3: cumulativeCV
import weka.classifiers.bayes.BayesNet; //导入方法依赖的package包/类
/**
* CumulativeCV returns the accuracy calculated using cumulative cross
* validation. The idea is to run through the data set and try to classify
* each of the instances based on the previously seen data. The data set used
* is m_Instances associated with the Bayes Network.
*
* @param bayesNet : Bayes Network containing structure to evaluate
* @return accuracy (in interval 0..1) measured using leave one out cv.
* @throws Exception passed on by updateClassifier
*/
public double cumulativeCV(BayesNet bayesNet) throws Exception {
m_BayesNet = bayesNet;
double fAccuracy = 0.0;
double fWeight = 0.0;
Instances instances = bayesNet.m_Instances;
bayesNet.initCPTs();
for (int iInstance = 0; iInstance < instances.numInstances(); iInstance++) {
Instance instance = instances.instance(iInstance);
fAccuracy += accuracyIncrease(instance);
bayesNet.updateClassifier(instance);
fWeight += instance.weight();
}
return fAccuracy / fWeight;
}
示例4: cumulativeCV
import weka.classifiers.bayes.BayesNet; //导入方法依赖的package包/类
/**
* CumulativeCV returns the accuracy calculated using cumulative
* cross validation. The idea is to run through the data set and
* try to classify each of the instances based on the previously
* seen data.
* The data set used is m_Instances associated with the Bayes Network.
* @param bayesNet : Bayes Network containing structure to evaluate
* @return accuracy (in interval 0..1) measured using leave one out cv.
* @throws Exception passed on by updateClassifier
*/
public double cumulativeCV(BayesNet bayesNet) throws Exception {
m_BayesNet = bayesNet;
double fAccuracy = 0.0;
double fWeight = 0.0;
Instances instances = bayesNet.m_Instances;
bayesNet.initCPTs();
for (int iInstance = 0; iInstance < instances.numInstances(); iInstance++) {
Instance instance = instances.instance(iInstance);
fAccuracy += accuracyIncrease(instance);
bayesNet.updateClassifier(instance);
fWeight += instance.weight();
}
return fAccuracy / fWeight;
}