我尝试通过执行序列化和反序列化来构建weka模型，如weka wikii中的说明所述。使用培训中的bayesnet构建并希望加载该模型进行测试。培训和测试具有相同的属性 过滤器的设置是这样的：weka java加载模型和使用测试数据集

Remove rm = generateFilter(filterOption);

FilteredClassifier fc = new FilteredClassifier();

fc.setFilter(rm);

filterClassifier.setClassifier(randomTree);

filterClassifier.buildClassifier(data);

exportClassifier("randomTree", file, filterClassifier);

导出的代码是这个样子：

private void exportClassifier(String method, String file,

FilteredClassifier filterClassifier) throws IOException,

FileNotFoundException {

System.out.println(file + "." + method + ".model");

ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream(

file + "." + method + ".model"));

oos.writeObject(filterClassifier);

oos.flush();

oos.close();

}

但是当我尝试另一个测试加载它们设置这样的：

public String EvaluateModel(String file, File modelFile) throws Exception {

Instances data = populateInstance(file);

if (data.classIndex() == -1) {

System.out.println("reset index...");

data.setClassIndex(data.numAttributes() - 1);

}

FilteredClassifier classifier = (FilteredClassifier) weka.core.SerializationHelper

.read(new FileInputStream(modelFile));

//classifier.buildClassifier(data);

Evaluation eval = new Evaluation(data);

//eval.crossValidateModel(classifier, data, 10, new Random(1));

eval.evaluateModel(classifier, data);

String summaryString = eval

.toSummaryString("\nResults\n======\n", false);

System.out.println(summaryString);

System.out.println(eval.fMeasure(1) + " " + eval.precision(1) + " "

+ eval.recall(1));

return formatOutput(eval);

}

我喜欢例外：

Exception in thread "main" java.lang.ArrayIndexOutOfBoundsException: 1200

at weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes.getProbability(DiscreteEstimatorBayes.java:106)

at weka.classifiers.bayes.net.estimate.SimpleEstimator.distributionForInstance(SimpleEstimator.java:183)

at weka.classifiers.bayes.BayesNet.distributionForInstance(BayesNet.java:386)

at weka.classifiers.meta.FilteredClassifier.distributionForInstance(FilteredClassifier.java:437)

at weka.classifiers.Evaluation.evaluateModelOnceAndRecordPrediction(Evaluation.java:1439)

at weka.classifiers.Evaluation.evaluateModel(Evaluation.java:1407)

at com.besmart.raynor.dataprocessing.dataprocessor.weka.WekaRunner.EvaluateModel(WekaRunner.java:138)

at com.besmart.raynor.dataprocessing.dataprocessor.weka.WekaBatchRunner.batchReEvaluation(WekaBatchRunner.java:80)

at com.besmart.raynor.dataprocessing.dataprocessor.weka.WekaBatchRunner.main(WekaBatchRunner.java:103)