www.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/FisherLDA.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 for r = 1:5 %进行五次实验 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %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:end);%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:end);%训练样本里属于第一类的样本,不带标签 exper_train_class2=experiment_train(index2,2:end);%训练样本里属于第二类的样本,不带标签 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %计算各类的均值,散布矩阵,求投影方向W %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% mean1=mean(exper_train_class1); %计算第一类训练样本的均值 mean2=mean(exper_train_class2); %计算第二类训练样本的均值 %计算各类训练样本的散布矩阵,它是协方差矩阵的(m-1)倍,m是各类的训练样本数 [m1 n1]=size(exper_train_class1); [m2 n2]=size(exper_train_class2); S1=(m1-1).*cov(exper_train_class1); S2=(m2-1).*cov(exper_train_class2); SW=S1+S2; %也可用下面的方法直接算散布矩阵 % S1=zeros(n1,n1); % S2=zeros(n2,n2); % for i=1:m1 % Rtemp=exper_train_class1(i,:); % S1=S1+(Rtemp-mean1)'*(Rtemp-mean1);%计算一类的散布矩阵 % end % for i=1:m2 % Rtemp=exper_train_class2(i,:); % S2=S2+(Rtemp-mean2)'*(Rtemp-mean2);%计算二类的散布矩阵 % end SW=S1+S2;%计算总类内散布矩阵 W=inv(SW)*((mean1-mean2)');%计算投影方向W %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %对训练样本与测试样本进行降维 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% traing_example_lda_1=W'*exper_train_class1'; traing_example_lda_1=traing_example_lda_1'; traing_example_lda_2=W'*exper_train_class2'; traing_example_lda_2=traing_example_lda_2'; test_example_lda=W'*exper_test'; test_example_lda=test_example_lda'; mean_1=mean(traing_example_lda_1); mean_2=mean(traing_example_lda_2); point=(mean_1+mean_2)/2; %找到分类的阈值 %%%%%%%%%%%%%%%%%%%%% %分类 %%%%%%%%%%%%%%%%%%%%%% for j=1:size(exper_test,1); if test_example_lda(j,1)>point result_class(j,1)=1; else result_class(j,1)=2; end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %分析结果 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% [correct(r,1),error(r,1),ROC(r,:)] = analyse_result(experiment_test,result_class); end %将结果放到一个数组中,便于观察数据 correct = correct'; error = error'; ROC = ROC'; result = [correct;error;ROC]; result = result';