%% 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_dim = size(res, 2) - 1; % 特征维度
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 = categorical(T_train)';
t_test = categorical(T_test )';
%% 数据平铺
% 将数据平铺成1维数据只是一种处理方式
% 也可以平铺成2维数据，以及3维数据，需要修改对应模型结构
% 但是应该始终和输入层数据结构保持一致
P_train = double(reshape(P_train, 12, 1, 1, M));
P_test = double(reshape(P_test , 12, 1, 1, N));
%% 数据格式转换
for i = 1 : M
p_train{i, 1} = P_train(:, :, 1, i);
end
for i = 1 : N
p_test{i, 1} = P_test( :, :, 1, i);
end
%% 创建模型
numFeatures = 12; % 输入特征个数
numHiddenUnits = 40; % 隐含层神经元个数
layers = layerGraph();
layers = addLayers(layers, [
sequenceInputLayer(numFeatures, "Name", "input")
gruLayer( numHiddenUnits, 'OutputMode', 'sequence',"Name", "bigru1")
concatenationLayer(1, 2, "Name", "cat1")
gruLayer( numHiddenUnits, 'OutputMode', 'last', "Name", "bigru3")
concatenationLayer(1, 2, "Name", "cat2")
fullyConnectedLayer(num_class)
softmaxLayer % 分类层
classificationLayer("Name","classificationoutput")]);
layers = addLayers( layers, [
FlipLayer("flip1")
gruLayer( numHiddenUnits, 'OutputMode', 'sequence',"Name", "bigru2" )
FlipLayer("flip2")] );
layers = addLayers(layers, [
FlipLayer("flip3")
gruLayer( numHiddenUnits, 'OutputMode', 'last',"Name", "bigru4" )] );
layers = connectLayers(layers, "input", "flip1");
layers = connectLayers(layers, "flip2", "cat1/in2");
layers = connectLayers(layers, "cat1", "flip3");
layers = connectLayers(layers, "bigru4", "cat2/in2");
%% 参数设置
options = trainingOptions('adam', ... % Adam 梯度下降算法
'MiniBatchSize', 50, ... % 批大小
'MaxEpochs', 100, ... % 最大迭代次数
'InitialLearnRate', 1e-2, ... % 初始学习率为
'LearnRateSchedule', 'piecewise', ... % 学习率下降
'LearnRateDropFactor', 0.5, ... % 学习率下降因子
'LearnRateDropPeriod', 50, ... % 经过 500 次训练后 学习率为 0.01 * 0.5
'Shuffle', 'every-epoch', ... % 每次训练打乱数据集
'Plots', 'training-progress', ... % 画出曲线
'Verbose', false);
%% 训练模型
net = trainNetwork(p_train, t_train, layers, options);
%% 仿真预测
t_sim1 = predict(net, p_train);
t_sim2 = predict(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 ;
%% 查看网络结构
analyzeNetwork(net)
%% 数据排序
[T_train, index_1] = sort(T_train);
[T_test , index_2] = sort(T_test );
T_sim1 = T_sim1(index_1);
T_sim2 = T_sim2(index_2);
%% 绘图
figure
plot(T_train,'r-+','linewidth',1)
hold on
plot(T_sim1,'bo','linewidth',1)
legend('真实值','预测值')
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_test,'r-+','linewidth',2)
hold on
plot(T_sim2,'bo','linewidth',2)
legend('真实值','预测值')
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
%% 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
