%% 清空环境变量
clc;
clear all
close all
nntwarn off;%关闭警告
%% 载入数据
load data;
% 载入数据并将数据分成训练和预测两类
p_train=p(1:12,:);%p 影响因素，t 实际值
t_train=t(1:12,:);
p_test=p(13,:);
t_test=t(13,:);
%% 交叉验证
desired_spread=[];
mse_max=10e20;
desired_input=[];%期望
desired_output=[];
result_perfp=[];
indices = crossvalind('Kfold',length(p_train),4);%K折交叉验证，随机选择
h=waitbar(0,'正在寻找最优化参数....')%进度条
k=1;
for i = 1:4
perfp=[];
disp(['以下为第',num2str(i),'次交叉验证结果'])%输出
test = (indices == i); train = ~test;%==是逻辑判断，是则等于1，否则等于0
p_cv_train=p_train(train,:);
t_cv_train=t_train(train,:);
p_cv_test=p_train(test,:);
t_cv_test=t_train(test,:);
p_cv_train=p_cv_train';
t_cv_train=t_cv_train';
p_cv_test= p_cv_test';
t_cv_test= t_cv_test';
[p_cv_train,minp,maxp,t_cv_train,mint,maxt]=premnmx(p_cv_train,t_cv_train);%归一化
p_cv_test=tramnmx(p_cv_test,minp,maxp);
for spread=0.1:0.1:2
net=newgrnn(p_cv_train,t_cv_train,spread);
waitbar(k/80,h);
disp(['当前spread值为', num2str(spread)]);
test_Out=sim(net,p_cv_test);%书签
test_Out=postmnmx(test_Out,mint,maxt);
error=t_cv_test-test_Out;
disp(['当前网络的mse为',num2str(mse(error))])
perfp=[perfp mse(error)];
if mse(error)<mse_max
mse_max=mse(error);
desired_spread=spread;
desired_input=p_cv_train;
desired_output=t_cv_train;
end
k=k+1;
end
result_perfp(i,:)=perfp;
end;
close(h)
disp(['最佳spread值为',num2str(desired_spread)])
disp(['此时最佳输入值为'])
desired_input
disp(['此时最佳输出值为'])
desired_output
%% 采用最佳方法建立GRNN网络
net=newgrnn(desired_input,desired_output,desired_spread);
p_test=p_test';
p_test=tramnmx(p_test,minp,maxp);
grnn_prediction_result=sim(net,p_test);
grnn_prediction_result=postmnmx(grnn_prediction_result,mint,maxt);
grnn_error=t_test-grnn_prediction_result';
disp(['GRNN神经网络三项流量预测的误差为',num2str(abs(grnn_error))])
save best desired_input desired_output p_test t_test grnn_error mint maxt
