前言：注意首先要将weka.jar包加载到相应的路径中去。程序中的数据也是用的weka自带的数据。
扩展：eclipse添加jar包的操作方法：
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
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 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 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(); } } }转载于:https://www.cnblogs.com/wenbaoli/p/5655748.html
