使用Java中的WEKA打印预测

Question

我正在尝试用Java中的Weka进行预测，使用朴素贝叶斯分类器，使用以下代码：

JAVA

public class Run {
    public static void main(String[] args) throws Exception {

        ConverterUtils.DataSource source1 = new ConverterUtils.DataSource("./data/train.arff");
        Instances train = source1.getDataSet();
        // setting class attribute if the data format does not provide this information
        // For example, the XRFF format saves the class attribute information as well
        if (train.classIndex() == -1)
            train.setClassIndex(train.numAttributes() - 1);

        ConverterUtils.DataSource source2 = new ConverterUtils.DataSource("./data/test.arff");
        Instances test = source2.getDataSet();
        // setting class attribute if the data format does not provide this information
        // For example, the XRFF format saves the class attribute information as well
        if (test.classIndex() == -1)
            test.setClassIndex(train.numAttributes() - 1);

        // model

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

        Evaluation evaluation = new Evaluation(train);
        evaluation.evaluateModel(naiveBayes, test);
    }
}

TRAIN

@relation weather

@attribute outlook {sunny, overcast, rainy}
@attribute temperature real
@attribute humidity real
@attribute windy {TRUE, FALSE}
@attribute play {yes, no}

@data
sunny,85,85,FALSE,no
sunny,80,90,TRUE,no
...

PREDICT

@relation weather

@attribute outlook {sunny, overcast, rainy}
@attribute temperature real
@attribute humidity real
@attribute windy {TRUE, FALSE}
@attribute play {yes, no}

@data
sunny,85,85,FALSE,?

在预测输出的GUI中

=== Predictions on test split ===

inst#,    actual, predicted, error, probability distribution
     1          ?       2:no      +   0.145 *0.855

如何使用Java获得此输出？我需要使用哪种方法才能获得此功能？

Answer 1

public class Run {
    public static void main(String[] args) throws Exception {

        ConverterUtils.DataSource source1 = new ConverterUtils.DataSource("./data/train.arff");
        Instances train = source1.getDataSet();
        // setting class attribute if the data format does not provide this information
        // For example, the XRFF format saves the class attribute information as well
        if (train.classIndex() == -1)
            train.setClassIndex(train.numAttributes() - 1);

        ConverterUtils.DataSource source2 = new ConverterUtils.DataSource("./data/test.arff");
        Instances test = source2.getDataSet();
        // setting class attribute if the data format does not provide this information
        // For example, the XRFF format saves the class attribute information as well
        if (test.classIndex() == -1)
            test.setClassIndex(train.numAttributes() - 1);

        // model

        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));
    }
}