机器学习：eclipse中调用weka的Classifier分类器代码Demo

　　weka中实现了很多机器学习算法，不管实验室研究或者公司研发，都会或多或少的要使用weka，我的理解是weka是在本地的SparkML，SparkML是分布式的大数据处理机器学习算法，数据量不是很大的时候，使用weka可以模拟出很好的效果，决定使用哪个模型，然后再继续后续的数据挖掘工作。

　　下面总结一个eclipse中调用weka的Classifier分类器代码的Demo，通过这个实例，可以进一步跟踪分类算法的原理，查看weka源码，下一节中，介绍最简单的IB1(1NN)算法源码的具体分析。

　　以下是一个调用各种IB1分类器的过程，下一节介绍下IB1算法的源码分析。

package mytest;

import java.io.File;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.lazy.IB1;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.converters.ArffLoader;
//import wlsvm.WLSVM;

public class SimpleClassification {//分类器

    public static void main(String[] args) {
        Instances ins = null;
        Classifier cfs = null;
        try {
            File file = new File("E:\\Develop/Weka-3-6/data/contact-lenses.arff");
//            File file = new File("E:\\yuce/data.csv");
            ArffLoader loader = new ArffLoader();
            loader.setFile(file);
            ins = loader.getDataSet();

            // 在使用样本之前一定要首先设置instances的classIndex，否则在使用instances对象是会抛出异常
            ins.setClassIndex(ins.numAttributes() - 1);
            

            cfs = new IB1();

//            参数设置
//            String[] options=weka.core.Utils.splitOptions("-S 0 -K 2 -D 3 -G 0.0 -R 0.0 -N 0.5 -M 40.0 -C 1.0 -E 0.0010 -P 0.1 -B 0");
//            cfs.setOptions(options);
            
            
            Instance testInst;
            Evaluation testingEvaluation = new Evaluation(ins);
            int length = ins.numInstances();
            for (int i = 0; i < length; i++) {
                testInst = ins.instance(i);
                // 通过这个方法来用每个测试样本测试分类器的效果
                double predictValue = testingEvaluation.evaluateModelOnceAndRecordPrediction(cfs,
                        testInst);
                
                System.out.println(testInst.classValue()+"--"+predictValue);
            }

            System.out.println("分类器的正确率：" + (1 - testingEvaluation.errorRate()));

        } catch (Exception e) {
            e.printStackTrace();
        }

    }

}

步骤的详细解释：

　　1）arff文件中读取数据集，并解析到数据结构Instances 里。

　　2） 创建一个分类器 new IB1();

　　3）设置参数等操作  splitOptions  并且 设置决策属性，一般是最后一个属性： ins.setClassIndex(ins.numAttributes() - 1);

　　4）创建一个评估器new Evaluation(ins)

　　5）交叉验证，并输出测试样本的分类结果及评价参数。testingEvaluation.evaluateModelOnceAndRecordPrediction(cfs, testInst);

data数据集：

@relation contact-lenses

@attribute age             {young, pre-presbyopic, presbyopic}
@attribute spectacle-prescrip    {myope, hypermetrope}
@attribute astigmatism        {no, yes}
@attribute tear-prod-rate    {reduced, normal}
@attribute contact-lenses    {soft, hard, none}

@data
%
%  instances
%
young,myope,no,reduced,none
young,myope,no,normal,soft
young,myope,yes,reduced,none
young,myope,yes,normal,hard
young,hypermetrope,no,reduced,none
young,hypermetrope,no,normal,soft
young,hypermetrope,yes,reduced,none
young,hypermetrope,yes,normal,hard
pre-presbyopic,myope,no,reduced,none
pre-presbyopic,myope,no,normal,soft
pre-presbyopic,myope,yes,reduced,none
pre-presbyopic,myope,yes,normal,hard
pre-presbyopic,hypermetrope,no,reduced,none
pre-presbyopic,hypermetrope,no,normal,soft
pre-presbyopic,hypermetrope,yes,reduced,none
pre-presbyopic,hypermetrope,yes,normal,none
presbyopic,myope,no,reduced,none
presbyopic,myope,no,normal,none
presbyopic,myope,yes,reduced,none
presbyopic,myope,yes,normal,hard
presbyopic,hypermetrope,no,reduced,none
presbyopic,hypermetrope,no,normal,soft
presbyopic,hypermetrope,yes,reduced,none
presbyopic,hypermetrope,yes,normal,none

data详细分析：

　　1）@relation contact-lenses  是表名

　　2）@attribute age {young, pre-presbyopic, presbyopic} 是属性名和属性类型

　　3）@data   是数据集，一个数组的形式。

若data是cvs的格式，weka也支持，最好使用weka的tools工具转化为arff格式的数据集。

输出结果为：

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" 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