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%% I. 清空环境变量
clear all
clc
%% II. 训练集/测试集产生
%%
% 1. 导入数据
load iris_data.mat
%%
% 2. 随机产生训练集和测试集
P_train = [];
T_train = [];
P_test = [];
T_test = [];
for i = 1:3
temp_input = features((i-1)*50+1:i*50,:);
temp_output = classes((i-1)*50+1:i*50,:);
n = randperm(50);
% 训练集------120个样本
P_train = [P_train temp_input(n(1:40),:)'];
T_train = [T_train temp_output(n(1:40),:)'];
% 测试集------30个样本
P_test = [P_test temp_input(n(41:50),:)'];
T_test = [T_test temp_output(n(41:50),:)'];
end
%% III. ELM创建/训练
IW,B,LW,TF,TYPE\] = elmtrain(P_train,T_train,20,'sig',1); %注意这里是1 分类问题 隐含层神经元20个 %% IV. ELM仿真测试 T_sim_1 = elmpredict(P_train,IW,B,LW,TF,TYPE); T_sim_2 = elmpredict(P_test,IW,B,LW,TF,TYPE); %% V. 结果对比 result_1 = \[T_train' T_sim_1'\]; result_2 = \[T_test' T_sim_2'\]; %% % 1. 训练集正确率 k1 = length(find(T_train == T_sim_1)); n1 = length(T_train); Accuracy_1 = k1 / n1 \* 100; disp(\['训练集正确率Accuracy = ' num2str(Accuracy_1) '%(' num2str(k1) '/' num2str(n1) ')'\]) %% % 2. 测试集正确率 k2 = length(find(T_test == T_sim_2)); n2 = length(T_test); Accuracy_2 = k2 / n2 \* 100; disp(\['测试集正确率Accuracy = ' num2str(Accuracy_2) '%(' num2str(k2) '/' num2str(n2) ')'\]) %% VI. 绘图 figure(2) plot(1:30,T_test,'bo',1:30,T_sim_2,'r-\*') grid on xlabel('测试集样本编号') ylabel('测试集样本类别') string = {'测试集预测结果对比(ELM)';\['(正确率Accuracy = ' num2str(Accuracy_2) '%)' \]}; title(string) legend('真实值','ELM预测值') 