在Java代码中使用WEKA中的神经网络类

Question

嗨,我想在WEKA库中使用神经网络进行简单的培训和测试.

但是,我发现它并不是微不足道的,它与库中的NaiveBayes类不同.

任何人都有例子如何在java代码中使用这个类？

Answer 1

以下步骤可能会帮助您:

1. 添加Weka库

从http://www.cs.waikato.ac.nz/ml/weka/downloading.html下载Weka .

从包中找到'Weka.jar'并添加到项目中.

Java Code Snippet

2. 构建神经分类器

   public void simpleWekaTrain(String filepath)
   {
   try{
   //Reading training arff or csv file
   FileReader trainreader = new FileReader(filepath);
   Instances train = new Instances(trainreader);
   train.setClassIndex(train.numAttributes() – 1);
   //Instance of NN
   MultilayerPerceptron mlp = new MultilayerPerceptron();
   //Setting Parameters
   mlp.setLearningRate(0.1);
   mlp.setMomentum(0.2);
   mlp.setTrainingTime(2000);
   mlp.setHiddenLayers(“3?);
   mlp.buildClassifier(train);
   }
   catch(Exception ex){
   ex.printStackTrace();
   }
   }
   

另一种设置参数的方法,

    mlp.setOptions(Utils.splitOptions(“-L 0.1 -M 0.2 -N 2000 -V 0 -S 0 -E 20 -H 3?));

哪里,

L = Learning Rate
M = Momentum
N = Training Time or Epochs
H = Hidden Layers
etc.

3. 神经分类器训练验证

为了评估培训数据,

    Evaluation eval = new Evaluation(train);
    eval.evaluateModel(mlp, train);
    System.out.println(eval.errorRate()); //Printing Training Mean root squared Error
    System.out.println(eval.toSummaryString()); //Summary of Training

要应用K-Fold验证

    eval.crossValidateModel(mlp, train, kfolds, new Random(1));

4. 评估/预测未标记的数据

   Instances datapredict = new Instances(
   new BufferedReader(
   new FileReader(<Predictdatapath>)));
   datapredict.setClassIndex(datapredict.numAttributes() – 1);
   Instances predicteddata = new Instances(datapredict);
   //Predict Part
   for (int i = 0; i < datapredict.numInstances(); i++) {
   double clsLabel = mlp.classifyInstance(datapredict.instance(i));
   predicteddata.instance(i).setClassValue(clsLabel);
   }
   //Storing again in arff
   BufferedWriter writer = new BufferedWriter(
   new FileWriter(<Output File Path>));
   writer.write(predicteddata.toString());
   writer.newLine();
   writer.flush();
   writer.close();