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/Parzen_smooth.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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %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(1:100),:);%测试样本 exper_test=experiment_test(:,2:ns);%测试样本,不带标签 experiment_train=samples(p(101:300),:);%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); [m1 n1]=size(exper_train_class1); [m2 n2]=size(exper_train_class2); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %计算类条件概率密度 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %构造一个正态函数,以每个训练样本为中心(均值) SIGMA=eye(16); R1=zeros(m,1);%记录待测样本在第一类的测试结果 R2=zeros(m,1);%记录待测样本在第二类的测试结果 %对第一类 Result=zeros(m,1);%记录最后分类结果 for i=1:m for j=1:m1 R1(i)=R1(i)+mvnpdf(exper_test(i,:),exper_train_class1(j,:),SIGMA); end end R1=R1./m1; %对第二类 for i=1:m for j=1:m2 R2(i)=R2(i)+mvnpdf(exper_test(i,:),exper_train_class2(j,:),SIGMA); end end R2=R2./m2; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %画类条件概率密度图,并分类 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %绘图 plot(R1,'-r','LineWidth',2); hold on; plot(R2,'-b','LineWidth',2); xlabel ('待测样本'); ylabel ('类条件概率密度 P(x|wi)'); title ('类条件概率密度图'); %分类 for i=1:m if R1(i,1)>R2(i,1) Result(i,1)=1; else Result(i,1)=2; end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %分析结果 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% [correct,error,ROC] = analyse_result(experiment_test,Result);