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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')
