Question

您好我正在尝试在java中实现我自己的分类器。这是我到目前为止所获得的：

 import weka.core.*;


public class RandomProbability extends Classifier {
    Instances data;

    public RandomProbability ()
    {
        /*DataSource d = new DataSource("C:\\Program Files\\Weka-3-6\\data\\labor.arff");
        data=((Object) d).getSourceData();*/
        DataSource source = null;
        try {
            source = new DataSource("C:\\Program Files\\Weka-3-6\\data\\labor.arff");
        } catch (Exception e1) {
            // TODO Auto-generated catch block
            e1.printStackTrace();
        }

         try {
            Instances instances = source.getDataSet();

             //instances.setClassIndex(instances.numAttributes() - 1);

             // Print header and instances.
             System.out.println("\nDataset:\n");
             System.out.println(instances);

现在问题是我无法对数据集中的数据进行分类（好的或坏的）。我在尝试访问此代码中的单个实例时需要帮助。

Answer 1

您可以使用instanceOf运算符，如下所示：

int countA = 0, countB=0; 
double pred; 
for (int i=0;i<57;i++) { 
    pred = classifyInstance(instances.instance(i)); 
    System.out.println("===== Classified instance ====="); 
    System.out.println("Class predicted:" + instances.classAttribute().value((int) pred)); 
    if (instances.classAttribute().value((int) pred).toString().equals("bad")) { 
         countB++; 
    } else { 
         countA++; 
    } 
}

Answer 2

package dm;
//import javax.activation.DataSource;
import weka.core.converters.ConverterUtils.DataSource;
import weka.classifiers.*;
import weka.core.*;
import org.jfree.data.*;

import weka.core.*;
public class RandomProbability extends Classifier {
    Instances data;

    public RandomProbability ()
    {
        /*DataSource d = new DataSource("C:\\Program Files\\Weka-3-6\\data\\labor.arff");
        data=((Object) d).getSourceData();*/
        DataSource source = null;
        try {
            source = new DataSource("C:\\Program Files\\Weka-3-6\\data\\labor.arff");
        } catch (Exception e1) {
            // TODO Auto-generated catch block
            e1.printStackTrace();
        }
         try {
            Instances instances = source.getDataSet();

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

             // Print header and instances.
             System.out.println("\nDataset:\n");
             System.out.println(instances);

             int total=instances.numInstances();
             System.out.println("total"+total);

             Attribute attr= instances.attribute(16);
             System.out.println("attr"+attr);

             // checking class
             int countA = 0, countB=0;
             double pred;
             for (int i=0;i<57;i++)
             {
                    pred=0;
                    pred = classifyInstance(instances.instance(i));
                    System.out.println("===== Classified instance =====");
                    System.out.println("Class predicted:" + instances.classAttribute().value((int) pred));
                    if (instances.classAttribute().value((int) pred).toString().equals("bad"))
                    {
                        countB++;
                    }
                    else {
                        countA++;
                    } 

             }
             System.out.println("good instances"+countA);
             System.out.println("bad instances"+ countB);

        } catch (Exception e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
    }

    public void buildClassifier (Instances data)
    {

    }

    public double classifyInstance (Instance inst)
    {
        return 0;

    }

}

在java中对数据集进行分类

2 个答案: