1 代码
%% LVQ神经网络的分类——乳腺肿瘤诊断 %% 清空环境变量 clear all clc warning off %% 导入数据 load data.mat a=randperm(569); Train=data(a(1:500),:); Test=data(a(501:end),:); % 训练数据 P_train=Train(:,3:end)'; Tc_train=Train(:,2)'; T_train=ind2vec(Tc_train); % 测试数据 P_test=Test(:,3:end)'; Tc_test=Test(:,2)'; %% 创建网络 count_B=length(find(Tc_train==1)); count_M=length(find(Tc_train==2)); rate_B=count_B/500; rate_M=count_M/500; net=newlvq(minmax(P_train),20,[rate_B rate_M],0.01,'learnlv1'); % 设置网络参数 net.trainParam.epochs=1000; net.trainParam.show=10; net.trainParam.lr=0.1; net.trainParam.goal=0.1; %% 训练网络 net=train(net,P_train,T_train); %% 仿真测试 T_sim=sim(net,P_test); Tc_sim=vec2ind(T_sim); result=[Tc_sim;Tc_test] %% 结果显示 total_B=length(find(data(:,2)==1)); total_M=length(find(data(:,2)==2)); number_B=length(find(Tc_test==1)); number_M=length(find(Tc_test==2)); number_B_sim=length(find(Tc_sim==1 & Tc_test==1)); number_M_sim=length(find(Tc_sim==2 &Tc_test==2)); disp(['病例总数：' num2str(569)... ' 良性：' num2str(total_B)... ' 恶性：' num2str(total_M)]); disp(['训练集病例总数：' num2str(500)... ' 良性：' num2str(count_B)... ' 恶性：' num2str(count_M)]); disp(['测试集病例总数：' num2str(69)... ' 良性：' num2str(number_B)... ' 恶性：' num2str(number_M)]); disp(['良性乳腺肿瘤确诊：' num2str(number_B_sim)... ' 误诊：' num2str(number_B-number_B_sim)... ' 确诊率p1=' num2str(number_B_sim/number_B*100) '%']); disp(['恶性乳腺肿瘤确诊：' num2str(number_M_sim)... ' 误诊：' num2str(number_M-number_M_sim)... ' 确诊率p2=' num2str(number_M_sim/number_M*100) '%']);
2 结果
result = 1 至 18 列 1 2 1 2 2 1 1 1 1 1 1 1 2 2 2 2 1 1 1 2 1 2 2 1 1 1 1 2 1 1 2 2 2 2 1 1 19 至 36 列 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 2 1 1 2 1 2 1 1 2 2 1 1 1 1 1 1 2 1 2 1 37 至 54 列 1 1 1 2 2 2 1 1 1 2 1 1 1 2 2 1 1 2 1 1 1 2 2 2 1 2 1 2 1 1 2 2 2 1 1 2 55 至 69 列 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 2 1 1 1 1 1 1 1 1 1 2 2 1 2 病例总数：569 良性：357 恶性：212 训练集病例总数：500 良性：315 恶性：185 测试集病例总数：69 良性：42 恶性：27 良性乳腺肿瘤确诊：42 误诊：0 确诊率p1=100% 恶性乳腺肿瘤确诊：19 误诊：8 确诊率p2=70.3704% >>
%% LVQ神经网络的分类——乳腺肿瘤诊断 %% 清空环境变量 clear all clc warning off %% 导入数据 load data.mat a=randperm(569); Train=data(a(1:500),:); Test=data(a(501:end),:); % 训练数据 P_train=Train(:,3:end)'; Tc_train=Train(:,2)'; T_train=ind2vec(Tc_train); % 测试数据 P_test=Test(:,3:end)'; Tc_test=Test(:,2)'; %% 创建网络 count_B=length(find(Tc_train==1)); count_M=length(find(Tc_train==2)); rate_B=count_B/500; rate_M=count_M/500; net=newlvq(minmax(P_train),20,[rate_B rate_M],0.01,'learnlv1'); % 设置网络参数 net.trainParam.epochs=1000; net.trainParam.show=10; net.trainParam.lr=0.1; net.trainParam.goal=0.1; %% 训练网络 net=train(net,P_train,T_train); %% 仿真测试 T_sim=sim(net,P_test); Tc_sim=vec2ind(T_sim); result=[Tc_sim;Tc_test] %% 结果显示 total_B=length(find(data(:,2)==1)); total_M=length(find(data(:,2)==2)); number_B=length(find(Tc_test==1)); number_M=length(find(Tc_test==2)); number_B_sim=length(find(Tc_sim==1 & Tc_test==1)); number_M_sim=length(find(Tc_sim==2 &Tc_test==2)); disp(['病例总数：' num2str(569)... ' 良性：' num2str(total_B)... ' 恶性：' num2str(total_M)]); disp(['训练集病例总数：' num2str(500)... ' 良性：' num2str(count_B)... ' 恶性：' num2str(count_M)]); disp(['测试集病例总数：' num2str(69)... ' 良性：' num2str(number_B)... ' 恶性：' num2str(number_M)]); disp(['良性乳腺肿瘤确诊：' num2str(number_B_sim)... ' 误诊：' num2str(number_B-number_B_sim)... ' 确诊率p1=' num2str(number_B_sim/number_B*100) '%']); disp(['恶性乳腺肿瘤确诊：' num2str(number_M_sim)... ' 误诊：' num2str(number_M-number_M_sim)... ' 确诊率p2=' num2str(number_M_sim/number_M*100) '%']);
