ELM：ELM实现鸢尾花种类测试集预测识别正确率(better)结果对比—Jason niu

load iris_data.mat 

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);

    P_train = [P_train temp_input(n(1:40),:)'];
    T_train = [T_train temp_output(n(1:40),:)'];

    P_test = [P_test temp_input(n(41:50),:)'];
    T_test = [T_test temp_output(n(41:50),:)'];
end

[IW,B,LW,TF,TYPE] = elmtrain(P_train,T_train,20,'sig',1);

T_sim_1 = elmpredict(P_train,IW,B,LW,TF,TYPE);
T_sim_2 = elmpredict(P_test,IW,B,LW,TF,TYPE);

result_1 = [T_train' T_sim_1'];
result_2 = [T_test' T_sim_2'];

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) ')'])

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) ')'])

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 = {'ELM：ELM实现鸢尾花种类测试集预测识别正确率(better)结果对比—Jason niu';['(正确率Accuracy = ' num2str(Accuracy_2) '%)' ]};
title(string)
legend('真实值','ELM预测值')