Java中的Weka-如何获得IBk、KStar或LWL或

我在stackoverflow和google上搜索了所有这些条件，但没有找到IBk、KStar或LWL。我需要这三个分类工具中的任何一个的一个实例预测。我正在Android studio中进行这项工作

我找到了从其他分类器获得预测的方法，如：

对于J48:来自

对于朴素贝叶斯：来自

但是我不能使用它们，因为我的分类器没有相同的方法……或者我找不到方法

我的代码：

//I skipped code till here because its too much,
//but Data is definetly inside *instances* (I checked with debuger) 

instances.setClassIndex(instances.numAttributes()-1);
//was trying the sam with KStar, LWL, AdditiveRegression, RandomCommittee)
IBk ibk = new IBk();

//I want predicitons for this instance. For the third attribute3
Instance testInst  = new DenseInstance(3);
testInst.setValue(attribute1, 3);
testInst.setValue(attribute2, 16);
testInst.setValue(attribute3, 0);

//I was hopping for some simple way like this: (but this returns nothing)
double rez =0;
String var="";
 try{
        ibk.buildClassifier(instances);
        rez = ibk.classifyInstance(testInst);
    }
        catch(Exception ex)
    {
        Log.e("Error","ex.getMessage()");
    }
 }

Log.w("GIMME RESULTS:",rez);

---

即使是其他分类器也可以，如AdditiveRegression、Bagging、RandomCommittee和DecisionTable——它们在Weka GUI中可以做出很好的预测，但我需要Java中的预测……）

通过测试所有方法找到了它

ibk.buildClassifier(dataSet);
rez2 = ibk.distributionForInstance(i2); //distrib

int result = (int)rez3[0];
//it goes tha same with Kstar

认识到weka中的分类器通常使用离散数据运行（从最小到最大的步骤相等）。我的数据并非都是离散的。Ibk和Kstar能够使用分布式数据，这就是为什么我只能在数据中使用这两个

NaiveBayes naiveBayes = new NaiveBayes();
naiveBayes.buildClassifier(train);

// this does the trick  
double label = naiveBayes.classifyInstance(test.instance(0));
test.instance(0).setClassValue(label);

System.out.println(test.instance(0).stringValue(4));

//I skipped code till here because its too much,
//but Data is definetly inside *instances* (I checked with debuger) 

instances.setClassIndex(instances.numAttributes()-1);
//was trying the sam with KStar, LWL, AdditiveRegression, RandomCommittee)
IBk ibk = new IBk();

//I want predicitons for this instance. For the third attribute3
Instance testInst  = new DenseInstance(3);
testInst.setValue(attribute1, 3);
testInst.setValue(attribute2, 16);
testInst.setValue(attribute3, 0);

//I was hopping for some simple way like this: (but this returns nothing)
double rez =0;
String var="";
 try{
        ibk.buildClassifier(instances);
        rez = ibk.classifyInstance(testInst);
    }
        catch(Exception ex)
    {
        Log.e("Error","ex.getMessage()");
    }
 }

Log.w("GIMME RESULTS:",rez);

ibk.buildClassifier(dataSet);
rez2 = ibk.distributionForInstance(i2); //distrib

int result = (int)rez3[0];
//it goes tha same with Kstar