Question

上学期我有一个项目，当给定一组关于汽车的数据时，我必须构建一个模型并使用该模型根据用户输入的数据进行预测（它涉及GUI等）。教授介绍了Weka，但仅以GUI形式进行。我正在重新创建项目，但是这次是使用Weka库。这是有问题的课程：

public class TreeModel {
private J48 model = new J48();
private String[] options = new String[1];
private DataSource source;
private Instances data;
private Evaluation eval;

// Constructor
public TreeModel(String file) throws Exception {
     source = new DataSource(file);
     // By default, the options are set to produce unpruned tree '-U'
     options[0] = "-U";
     data = source.getDataSet();         
     model.setOptions(options);
}

// Overloaded constructor allowing you to choose options for the model
public TreeModel(String file, String[] options) throws Exception {
     DataSource source = new DataSource(file);
     data = source.getDataSet();
     model.setOptions(options);
}

// Builds the decision tree
public void buildDecisionTree() throws Exception {
     data.setClassIndex(data.numAttributes() - 1);
     model.buildClassifier(data);
}

/*
 * Uses cross validation technique to calculate the accuracy.
 * Gives a more respected accuracy that is more likely to hold 
 * with instances not in the dataset.
 */
public void crossValidatedEvaluation(int folds) throws Exception {
    eval = new Evaluation(data);
    eval.crossValidateModel(model, data, folds, new Random());
    System.out.println("The model predicted "+eval.pctCorrect()+" percent of the data correctly.");
}

/*
 * Evaluates the accuracy of a decision tree when using all available data
 * This should be looked at with skepticism (less interpretable)
 */
public void evaluateModel() throws Exception {
     eval = new Evaluation(data);
     eval.evaluateModel(model, data);
     System.out.println("The model predicted "+eval.pctCorrect()+" percent of the data correctly.");
}


/*
 *  Returns a prediction for a particular instance. Will take in an instance 
 *  as a parameter.
 */
public String getPrediction() throws Exception {
    DataSource predFile = new DataSource("./predict.arff");
    Instances pred = predFile.getDataSet();

    Instance predic = pred.get(0);
    pred.setClassIndex(pred.numAttributes() - 1);

    double classify = model.classifyInstance(predic);

    pred.instance(0).setClassValue(classify);
    return pred.instance(0).stringValue(6);
}

// Returns source code version of the model (warning: messy code)
public String getModelSourceCode() throws Exception {
     return model.toSource("DecisionTree");
}   
}

在我的getPrediction（）方法中，我有一个简单的示例，用于获取ARFF文件中实例的预测。问题是我无法弄清楚如何初始化单个Instance对象，然后将要进行预测的数据放在该实例中。我浏览了Instance类的文档，但乍看之下什么都没有。是否可以手动将数据放入实例中，还是需要将预测数据转换为ARFF文件？

Answer 1

此代码段应帮助您构建自己的实例集而无需ARFF文件。下面显示了从具有两个属性的数组创建一组新的实例；纬度和经度。

import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.FastVector;
import weka.core.Instances;

public class AttTest {

    public static void main(String[] args) throws Exception
    {
        double[] one={0,1,2,3};
        double[] two={3,2,1,0};
        double[][] both=new double[2][4];
        both[0]=one;
        both[1]=two;

        Instances to_use=AttTest.buildArff(both);
        System.out.println(to_use.toString());
    }

  public static Instances buildArff(double[][] array) throws Exception
  {
         FastVector      atts = new FastVector();
         atts.addElement(new Attribute("lat")); //latitude
         atts.addElement(new Attribute("lon")); //longitude

         // 2. create Instances object
         Instances test = new Instances("location", atts, 0);

         // 3. fill with data
         for(int s1=0; s1 < array[0].length; s1=s1+1)
         {
             double vals[] = new double[test.numAttributes()];
             vals[0] = array[0][s1];
             vals[1] = array[1][s1];
             test.add(new DenseInstance(1.0, vals));
         }

         return(test);
  }
}

如何在WEKA中为实例创建预测而不用为该实例创建ARFF文件？

1 个答案: