%% grnn
%% 1.初始化环境
%% 清空环境变量
warning off % 关闭报警信息
close all % 关闭开启的图窗
clear % 清空变量
clc % 清空命令行
%% 导入数据
res = xlsread('数据集.xlsx');
%% 清空环境变量
warning off % 关闭报警信息
close all % 关闭开启的图窗
clear % 清空变量
clc % 清空命令行
%% 导入数据
res = xlsread('数据集.xlsx');
%% 划分训练集和测试集
temp = randperm(357);
P_train = res(temp(1: 240), 1: 12)';
T_train = res(temp(1: 240), 13)';
M = size(P_train, 2);
P_test = res(temp(241: end), 1: 12)';
T_test = res(temp(241: end), 13)';
N = size(P_test, 2);
%% 权重初始化
[mm,nn]=size(P_train);
D(1,:)=ones(1,nn)/nn;
%% 弱分类器分类
K=11;
for i=1:K
error(i)=0;
%% 归一化
[trainn ,trains]=mapminmax(P_train,0,1);
p_train=trainn;
testn=mapminmax('apply',P_test,trains);
p_test=testn;
%% 3.创建grnn
spread=0.1;
net=newgrnn(p_train,T_train,spread);
pred_test=sim(net,p_train);
t_test=sim(net,p_test);
%% 4.结果统计
[T_sim1]=round(pred_test);
[ T_sim2(i,:)]=round(t_test);
%统计输出效果
kk1=find(T_sim1==1);
kk2=find(T_sim1==2);
kk3=find(T_sim1==3);
kk4=find(T_sim1==4);
kk5=find(T_sim1==5);
aa(kk1)=1;
aa(kk2)=2;
aa(kk3)=3;
aa(kk4)=4;
aa(kk5)=5;
%统计错误样本数
for j=1:nn
if T_sim1(j)~=T_train(j)
error(i)=error(i)+D(i,j);
end
end
%弱分类器i权重
at(i)=0.2*log((1-error(i))/(error(i)+1.001));
%更新D值
for j=1:nn
D(i+1,j)=D(i,j)*exp(-at(i)*aa(j)*T_sim1(j));
end
%D值归一化
Dsum=sum(D(i+1,:));
D(i+1,:)=D(i+1,:)/Dsum;
end
at=at/sum(at);
T_sim22=round(at*T_sim2);
figure
stem(T_sim22,'bo');
grid on
hold on
plot(T_test,'r*');
legend('测试输出','真实标签')
title('测试集')
xlabel('样本数')
ylabel('分类标签')
hold off
