gusucode.com > 用mushrooms数据对模式识别课程讲述的各种模式分类方法matlab源码程序 > pattern-recognition-simulation/liner.m
clc; clear; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %读取数据,取16个特征 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% samples = textread('data2000.txt'); samples = samples(:,[1:6,9:15,19:22]); %17列 第1列标号,16列特征 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %对样本进行归一化处理 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% [ms ns]=size(samples); TMax=max(samples); TMin=min(samples); % 第一列是样本标签,从第二列开始归一化 for i=2:ns samples(:,i)=(samples(:,i)-TMin(i))/(TMax(i)-TMin(i)); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %使用随机下采样(random subsampling)的方法对,将样本分为测试样本,第一类训练样本,第二类训练样本 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% p = randperm(2000);%对1:2000的整数随机排序 experiment_test=samples(p(1:500),:);%测试样本 exper_test=experiment_test(:,2:ns);%测试样本,不带标签 experiment_train=samples(p(501:2000),:);%训练样本 index1=find(experiment_train(:,1)==1);%找到训练样本中第一类的行号 index2=find(experiment_train(:,1)==2);%找到训练样本中第二类的行号 exper_train_class1=experiment_train(index1,2:ns);%训练样本里属于第一类的样本,不带标签 exper_train_class2=experiment_train(index2,2:ns);%训练样本里属于第二类的样本,不带标签 [m n]=size(exper_test); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %计算每个待测样本到每个训练样本类的Mahalanobis 距离 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% R1=mahal(exper_test,exper_train_class1); R2=mahal(exper_test,exper_train_class2); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %分类 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %分类, for j=1:m if R1(j,1)<=R2(j,1) Result(j,1)=1; else Result(j,1)=2; end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %分析结果 ,计算准确率, 错误率, 敏感性, 特异性,误判率,漏判率 % 准确率:(有毒的(1)判断为有毒(1)+可食的(2)判断为可食(2))/测试样本总数(500) % 错误率:1-准确率 % 敏感性:蘑菇有毒(1),且判为有毒(1) % 特异性:蘑菇可食(2),且判为可食(2) % 误判率:实际可食(2),但判为有毒(1) % 漏判率:实际有毒(1),但判为可食(2) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% [correct,error,ROC] = analyse_result(experiment_test,Result);