%% 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
%% 读取数据
res = xlsread('data.xlsx');
%% 分析数据
num_class = length(unique(res(:, end))); % 类别数（Excel最后一列放类别）
num_res = size(res, 1); % 样本数（每一行，是一个样本）
num_size = 0.7; % 训练集占数据集的比例
res = res(randperm(num_res), :); % 打乱数据集（不打乱数据时，注释该行）
flag_conusion = 1; % 标志位为1，打开混淆矩阵（要求2018版本及以上）
%% 设置变量存储数据
P_train = []; P_test = [];
T_train = []; T_test = [];
%% 划分数据集
for i = 1 : num_class
mid_res = res((res(:, end) == i), :); % 循环取出不同类别的样本
mid_size = size(mid_res, 1); % 得到不同类别样本个数
mid_tiran = round(num_size * mid_size); % 得到该类别的训练样本个数
P_train = [P_train; mid_res(1: mid_tiran, 1: end - 1)]; % 训练集输入
T_train = [T_train; mid_res(1: mid_tiran, end)]; % 训练集输出
P_test = [P_test; mid_res(mid_tiran + 1: end, 1: end - 1)]; % 测试集输入
T_test = [T_test; mid_res(mid_tiran + 1: end, end)]; % 测试集输出
end
%% 数据转置
P_train = P_train'; P_test = P_test';
T_train = T_train'; T_test = T_test';
%% 得到训练集和测试样本个数
M = size(P_train, 2);
N = size(P_test , 2);
%% 数据归一化
[p_train, ps_input] = mapminmax(P_train, 0, 1);
p_test = mapminmax('apply', P_test, ps_input);
t_train = ind2vec(T_train);
t_test = ind2vec(T_test );
%% 节点个数
inputnum = size(p_train, 1); % 输入层节点数
hiddennum = 15; % 隐藏层节点数
outputnum = size(t_train, 1); % 输出层节点数
%% 构建网络
net = newff(p_train, t_train, hiddennum);
%% 设置训练参数
net.trainParam.epochs = 50; % 训练次数
net.trainParam.goal = 1e-3; % 目标误差
net.trainParam.lr = 0.01; % 学习率
net.trainParam.showWindow = 0; % 关闭窗口
%% 参数设置
fun = @getObjValue; % 目标函数
dim = inputnum * hiddennum + hiddennum * outputnum + ...
hiddennum + outputnum; % 优化参数个数
lb = -1 * ones(1, dim); % 优化参数目标下限
ub = 1 * ones(1, dim); % 优化参数目标上限
pop = 20; % 数量
Max_iteration = 20; % 最大迭代次数
%% 优化算法
[Best_score,Best_pos,curve] = POA(pop, Max_iteration, lb, ub, dim, fun);
%% 把最优初始阀值权值赋予网络预测
w1 = Best_pos(1 : inputnum * hiddennum);
B1 = Best_pos(inputnum * hiddennum + 1 : inputnum * hiddennum + hiddennum);
w2 = Best_pos(inputnum * hiddennum + hiddennum + 1 : inputnum * hiddennum + hiddennum + hiddennum*outputnum);
B2 = Best_pos(inputnum * hiddennum + hiddennum + hiddennum * outputnum + 1 : ...
inputnum * hiddennum + hiddennum + hiddennum * outputnum + outputnum);
net.Iw{1, 1} = reshape(w1, hiddennum, inputnum);
net.Lw{2, 1} = reshape(w2, outputnum, hiddennum);
net.b{1} = reshape(B1, hiddennum, 1);
net.b{2} = B2';
%% 网络训练
net.trainParam.showWindow = 1; % 打开窗口
net = train(net, p_train, t_train);
%% BP网络预测
t_sim1 = sim(net, p_train);
t_sim2 = sim(net, p_test);
%% 数据反归一化
T_sim1 = vec2ind(t_sim1);
T_sim2 = vec2ind(t_sim2);
%% 性能评价
error1 = sum((T_sim1 == T_train)) / M * 100 ;
error2 = sum((T_sim2 == T_test )) / N * 100 ;
%% 绘图
figure()
plot(T_sim1,'o','Color',[255 0 255]./255,'linewidth',1)
hold on
plot(T_train,'-+','Color',[0 0 0]./255,'linewidth',1)
legend('预测值','真实值')
xlabel('预测样本')
ylabel('预测结果')
string = {'训练集预测结果对比'; ['准确率=' num2str(error1) '%']};
title(string)
xlim([1, M])
grid
figure
plot(T_sim2,'o','Color',[250 180 60]./255,'linewidth',2)
hold on
plot(T_test,'-+','Color',[0 0 0]./255,'linewidth',2)
legend('预测值','真实值')
xlabel('预测样本')
ylabel('预测结果')
string = {'测试集预测结果对比'; ['准确率=' num2str(error2) '%']};
title(string)
xlim([1, N])
grid
%% 优化曲线
figure
plot(curve, 'linewidth',1.5);
title('POA')
xlabel('The number of iterations')
ylabel('Fitness')
grid on;
%% 混淆矩阵
if flag_conusion == 1
figure
cm = confusionchart(T_train, T_sim1);
cm.Title = 'Confusion Matrix for Train Data';
cm.ColumnSummary = 'column-normalized';
cm.RowSummary = 'row-normalized';
figure
cm = confusionchart(T_test, T_sim2);
cm.Title = 'Confusion Matrix for Test Data';
cm.ColumnSummary = 'column-normalized';
cm.RowSummary = 'row-normalized';
end
