代码 rbf分类_基于机器学习的心律失常分类(七)——支持向量机SVM[MATLAB]

 %% I. 清空环境变量

    
 %clear all
    
 %clc
    
  
    
 %% II. 导入数据
    
 %load BreastTissue_data.mat
    
 matrix=DATA(:,1:6);
    
 label=DATA(:,7);
    
 %%
    
 % 1. 随机产生训练集和测试集
    
 n1 = randperm(1700);
    
 n2 = randperm(1700);
    
 n3 = randperm(1700);
    
 n4 = randperm(1700);
    
 %%
    
 % 2. 训练集——4760（6800）个样本
    
 P_train1 = matrix(n1(1:1190),:);
    
 P_train2 = matrix(1700+n2(1:1190),:);
    
 P_train3 = matrix(1700*2+n3(1:1190),:);
    
 P_train4 = matrix(1700*3+n4(1:1190),:);
    
 train_matrix = [P_train1;P_train2;P_train3;P_train4];
    
 T_train1 = label(n1(1:1190),:);
    
 T_train2 = label(1700+n2(1:1190),:);
    
 T_train3 = label(1700*2+n3(1:1190),:);
    
 T_train4 = label(1700*3+n4(1:1190),:);
    
 train_label = [T_train1;T_train2;T_train3;T_train4];
    
  
    
 %%
    
 % 3. 测试集——2040个样本
    
 P_test1 = matrix(n1(1191:end),:);
    
 P_test2 = matrix(1700+n2(1191:end),:);
    
 P_test3 = matrix(1700*2+n3(1191:end),:);
    
 P_test4 = matrix(1700*3+n4(1191:end),:);
    
 test_matrix = [P_test1;P_test2;P_test3;P_test4];
    
 T_test1 = label(n1(1191:end),:);
    
 T_test2 = label(1700+n2(1191:end),:);
    
 T_test3 = label(1700*2+n3(1191:end),:);
    
 T_test4 = label(1700*3+n4(1191:end),:);
    
 test_label = [T_test1;T_test2;T_test3;T_test4];
    
  
    
 %% III. 数据归一化
    
 [Train_matrix,PS] = mapminmax(train_matrix');
    
 Train_matrix = Train_matrix';
    
 Test_matrix = mapminmax('apply',test_matrix',PS);
    
 Test_matrix = Test_matrix';
    
  
    
 %% IV. SVM创建/训练(RBF核函数)
    
 %%
    
 % 1. 寻找最佳c/g参数——交叉验证方法
    
 [c,g] = meshgrid(-10:0.2:10,-10:0.2:10);
    
 [m,n] = size(c);
    
 cg = zeros(m,n);
    
 eps = 10^(-4);
    
 v = 5;
    
 bestc = 1;
    
 bestg = 0.1;
    
 bestacc = 0;
    
 for i = 1:m
    
     for j = 1:n
    
     cmd = ['-v ',num2str(v),' -h 0',' -c ',num2str(2^c(i,j)),' -g ',num2str(2^g(i,j))];
    
     cg(i,j) = svmtrain(train_label,Train_matrix,cmd);     
    
     if cg(i,j) > bestacc
    
         bestacc = cg(i,j);
    
         bestc = 2^c(i,j);
    
         bestg = 2^g(i,j);
    
     end        
    
     if abs( cg(i,j)-bestacc )<=eps && bestc > 2^c(i,j) 
    
         bestacc = cg(i,j);
    
         bestc = 2^c(i,j);
    
         bestg = 2^g(i,j);
    
     end               
    
     end
    
 end
    
 cmd = [' -h 0',' -c ',num2str(bestc),' -g ',num2str(bestg)];
    
  
    
 %%
    
 % 2. 创建/训练SVM模型
    
 model = svmtrain(train_label,Train_matrix,cmd);
    
  
    
 %% V. SVM仿真测试
    
 [predict_label_1,accuracy_1,m] = svmpredict(train_label,Train_matrix,model);
    
 [predict_label_2,accuracy_2,n] = svmpredict(test_label,Test_matrix,model);
    
 result_1 = [train_label predict_label_1];
    
 result_2 = [test_label predict_label_2];
    
  
    
 %% VI. 绘图
    
 figure
    
 plot(1:length(test_label),test_label,'r-*')
    
 hold on
    
 plot(1:length(test_label),predict_label_2,'b:o')
    
 grid on
    
 legend('真实类别','预测类别')
    
 xlabel('测试集样本编号')
    
 ylabel('测试集样本类别')
    
 string = {'测试集SVM预测结果对比(RBF核函数)';
    
       ['accuracy = ' num2str(accuracy_2(1)) '%']};
    
 title(string)
    
  
    
  
    
 %% 混淆矩阵
    
 T_sim=predict_label_2;
    
 T_test=test_label;
    
 H=[];
    
 number_1_1= length(find(T_sim == 1 & T_test == 1));H(1,1)=number_1_1;
    
 number_1_2= length(find(T_sim == 1 & T_test == 2));H(1,2)=number_1_2;
    
 number_1_3= length(find(T_sim == 1 & T_test == 3));H(1,3)=number_1_3;
    
 number_1_4= length(find(T_sim == 1 & T_test == 4));H(1,4)=number_1_4;
    
  
    
 number_2_1= length(find(T_sim == 2 & T_test == 1));H(2,1)=number_2_1;
    
 number_2_2= length(find(T_sim == 2 & T_test == 2));H(2,2)=number_2_2;
    
 number_2_3= length(find(T_sim == 2 & T_test == 3));H(2,3)=number_2_3;
    
 number_2_4= length(find(T_sim == 2 & T_test == 4));H(2,4)=number_2_4;
    
  
    
 number_3_1= length(find(T_sim == 3 & T_test == 1));H(3,1)=number_3_1;
    
 number_3_2= length(find(T_sim == 3 & T_test == 2));H(3,2)=number_3_2;
    
 number_3_3= length(find(T_sim == 3 & T_test == 3));H(3,3)=number_3_3;
    
 number_3_4= length(find(T_sim == 3 & T_test == 4));H(3,4)=number_3_4;
    
  
    
 number_4_1= length(find(T_sim == 4 & T_test == 1));H(4,1)=number_4_1;
    
 number_4_2= length(find(T_sim == 4 & T_test == 2));H(4,2)=number_4_2;
    
 number_4_3= length(find(T_sim == 4 & T_test == 3));H(4,3)=number_4_3;
    
 number_4_4= length(find(T_sim == 4 & T_test == 4));H(4,4)=number_4_4;
    
 H
    
    
    
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/9AIq6jy08MfStTXNHYKzludDZ2GQ.png)