通过weka.jar包来进行数据预处理

本文主要是介绍通过weka.jar包来进行数据预处理，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

打开eclipse ,在对应的工程下右击，选择Build Path ->选择Configure Build Path ->选择Libraries ->点击Add External JARs ->然后到你的jar包所在路径选择它。即可。

一、特征选择

[java]  view plain copy  
 print ? 
 package learning;  
   
 import weka.attributeSelection.ASEvaluation;  
 import weka.attributeSelection.InfoGainAttributeEval;  
 import weka.attributeSelection.Ranker;  
 import weka.core.Instances;  
 import weka.core.converters.ConverterUtils.DataSink;  
 import weka.core.converters.ConverterUtils.DataSource;  
 import weka.filters.Filter;  
 import weka.filters.supervised.attribute.AttributeSelection;  
   
   
 /**feature selection via weka 
  *  
  * @author wenbaoli 
  * 
  */  
 public class featureSelect {  
   
     /** 
      *  
      * @param arg 
      */  
     public static void main(String[] arg){  
           
         try {  
   
             System.out.println("++++++++++++Example3:Feature Selection Via Weka.+++++++++");  
               
             System.out.println("Step1:load data...");  
             String fn = "E:/weka/data/iris.arff";  
             DataSource source = new DataSource(fn);  
             Instances instances = source.getDataSet();  
               
             System.out.println("Step2:feature selction...");  
             featureSelect fs = new featureSelect();  
               
             int k = 2;  
             AttributeSelection as = new AttributeSelection();  
               
             Ranker rank = new Ranker();  
             rank.setThreshold(0.0);  
             rank.setNumToSelect(k);  
               
             ASEvaluation ae = new InfoGainAttributeEval();  
           
             as.setEvaluator(ae);  
             as.setSearch(rank);  
             as.setInputFormat(instances);  
             Instances reductData = Filter.useFilter(instances, as);  
               
             System.out.println("Step3:保存规约后的数据到新文件...");  
             DataSink.write("E:/weka/data/iris_reducted.arff", reductData);  
             System.out.println("Finished...");  
               
               
         } catch (Exception e) {  
             e.printStackTrace();  
         }     
     }  
       
 }  

二、缺失值处理

[java]  view plain copy  
 print ? 
 package learning;  
   
 import weka.core.Instances;  
 import weka.core.converters.ConverterUtils.DataSink;  
 import weka.core.converters.ConverterUtils.DataSource;  
   
   
 /**Missing value Handling via weka 
  *  
  * @author wenbaoli 
  * 
  */  
 public class missingHandle {  
   
     /** 
      *  
      * @param arg 
      */  
     public static void main(String[] arg) {  
           
         try {  
             System.out.println("+++++++++++++Example 2 :Missing Value Handling.++++++++++++++");  
               
             System.out.println("Step1:load data...");  
               
             String fn = "E:weka/data/labor.arff";  
               
             DataSource source = new DataSource(fn);  
               
             Instances instances = source.getDataSet();  
             int dim = instances.numAttributes();  
             int num = instances.numInstances();  
               
             System.out.println("Step2:缺失值处理...");  
             double[] meanV = new double[dim];  
             for (int i = 0; i < meanV.length; i++) {  
                 meanV[i] = 0;  
                 int count = 0;  
                 for (int j = 0; j < num; j++) {  
                     if(!instances.instance(j).isMissing(i)){  
                         meanV[i] += instances.instance(j).value(i);  
                         count++;  
                     }  
                 }  
                 meanV[i] = meanV[i]/count;  
                 System.out.println(meanV[i]);  
             }  
               
               
             for (int i = 0; i < meanV.length; i++) {  
                 meanV[i] = 0;  
                 int count = 0;  
                 for (int j = 0; j < num; j++) {  
                     if(instances.instance(j).isMissing(i)){  
                         instances.instance(j).setValue(i, meanV[i]);  
                     }  
                 }  
                   
                   
             }  
               
             System.out.println("Step3:保存数据到新文件...");  
               
             DataSink.write("E:weka/data/labor_missingValueHandled.arff", instances);  
             System.out.println("Finished.");  
         } catch (Exception e) {  
             e.printStackTrace();  
         }  
           
           
     }  
 }  

三、归一化处理

[java]  view plain copy  
 print ? 
 package learning;  
   
   
   
 import weka.core.Attribute;  
 import weka.core.Instance;  
 import weka.core.Instances;  
 import weka.core.converters.ConverterUtils.DataSink;  
 import weka.core.converters.ConverterUtils.DataSource;  
 import weka.filters.Filter;  
 import weka.filters.unsupervised.attribute.Normalize;  
   
   
 /**normalize data via weka 
  *  
  * @author wenbaoli 
  * 
  */  
 public class normalizeTest {  
   
     /** 
      *  
      * @param arg 
      */  
     public static void main(String[] arg) {  
           
           
         String file = "cpu.arff";  
         String file_norm = "norm_" + file;  
         //对数据进行归一化  
         try {  
         System.out.println("+++++++++++++Example 1 : Normalize Data via weka.+++++++++");  
           
         System.out.println("Step1:读取数据...");  
         DataSource source = new DataSource("E:/Weka/data/" + file);  
         Instances instances = source.getDataSet();  
           
         System.out.println("Step2:原数据打印...");  
         System.out.println("---------------------------------");  
         int attributeNo = instances.numAttributes();  
         for (int i = 0; i < attributeNo; i++) {  
             Attribute attr = instances.attribute(i);  
             System.out.print(attr.name() + "\t");  
               
         }  
         System.out.println();  
           
         int instanceNo = instances.numInstances();  
         for (int i = 0; i < instanceNo; i++) {  
             Instance ins = instances.instance(i);  
             System.out.print(ins.toString() + "\t");  
             System.out.println();  
         }  
           
         System.out.println("Step3:归一化...");  
         Normalize norm = new Normalize();  
         norm.setInputFormat(instances);  
           
         //归一化关键步骤：  
         Instances newInstances = Filter.useFilter(instances, norm);  
           
         System.out.println("Step4:归一化之后的数据(打印)...");  
         System.out.println("---------------------------------");  
           
         //打印属性名  
         int numOfAttributes = newInstances.numAttributes();  
         for (int i = 0; i < numOfAttributes; i++) {  
             Attribute attribute = newInstances.attribute(i);  
             System.out.print(attribute.name() + "\t");  
               
         }  
         System.out.println();  
           
         //打印实例  
         int numOfInstance = newInstances.numInstances();  
         for (int i = 0; i < numOfInstance ; i++) {  
             Instance instance = newInstances.instance(i);  
             System.out.print(instance.toString() + "\t");  
             System.out.println();  
         }  
         //发现一个问题：这把标签label也给归一化了。。。。。。。。。。这样可以吗？？？？？？？  
           
         System.out.println("Step5:保存归一化的新数据到新文件...");  
         System.out.println("-----------------------");  
         DataSink.write("E:/Weka/data/" +file_norm, newInstances);  
         System.out.println("Congratulations.");  
         } catch (Exception e) {  
             e.printStackTrace();  
         }  
           
           
     }  
 }  