Matlab基于逻辑回归(Logistic Regression)的数据回归预测，多输入单输出模型

%% 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('data.xlsx'); %% 数据分析 num_size = 0.8; % 训练集占数据集比例 outdim = 1; % 最后一列为输出 num_samples = size(res, 1); % 样本个数 res = res(randperm(num_samples), :); % 打乱数据集（不希望打乱时，注释该行） num_train_s = round(num_size * num_samples); % 训练集样本个数 f_ = size(res, 2) - outdim; % 输入特征维度 %% 划分训练集和测试集 P_train = res(1: num_train_s, 1: f_)'; T_train = res(1: num_train_s, f_ + 1: end)'; M = size(P_train, 2); P_test = res(num_train_s + 1: end, 1: f_)'; T_test = res(num_train_s + 1: end, f_ + 1: end)'; 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, ps_output] = mapminmax(T_train, 0, 1); t_test = mapminmax('apply', T_test, ps_output); % %% 转置以适应模型 % p_train = p_train'; p_test = p_test'; % t_train = t_train'; t_test = t_test'; %% 设置参数 MaxTrainNumber = 5000; % 最大训练次数 Eta = 0.01; % 学习率 %% 训练模型 [t_sim1, t_sim2, loss] = LPtrain(p_train, t_train, p_test, Eta, MaxTrainNumber); %% 数据反归一化 T_sim1 = mapminmax('reverse', t_sim1, ps_output); T_sim2 = mapminmax('reverse', t_sim2, ps_output); % T_sim1 = T_sim1'; % T_sim2 = T_sim2'; %% 均方根误差 error1 = sqrt(sum((T_sim1 - T_train).^2) ./ M); error2 = sqrt(sum((T_sim2 - T_test ).^2) ./ N); %% 相关指标计算 % MAE mae1 = sum(abs(T_sim1 - T_train)) ./ M ; mae2 = sum(abs(T_sim2 - T_test )) ./ N ; disp(['训练集数据的MAE为：', num2str(mae1)]) disp(['测试集数据的MAE为：', num2str(mae2)]) % MAPE maep1 = sum(abs(T_sim1 - T_train)./T_train) ./ M ; maep2 = sum(abs(T_sim2 - T_test )./T_test) ./ N ; disp(['训练集数据的MAPE为：', num2str(maep1)]) disp(['测试集数据的MAPE为：', num2str(maep2)]) % RMSE RMSE1 = sqrt(sumsqr(T_sim1 - T_train)/M); RMSE2 = sqrt(sumsqr(T_sim2 - T_test)/N); disp(['训练集数据的RMSE为：', num2str(RMSE1)]) disp(['测试集数据的RMSE为：', num2str(RMSE2)]) %% 绘图 figure plot(1: M, T_train, 'r-*', 1: M, T_sim1, 'b-o', 'LineWidth', 1) legend('真实值', '逻辑回归(logistic)预测值') xlabel('预测样本') ylabel('预测结果') string = {'训练集预测结果对比'; ['RMSE=' num2str(error1) '%']}; title(string) grid figure plot(1: N, T_test, 'r-*', 1: N, T_sim2, 'b-o', 'LineWidth', 1) legend('真实值', '逻辑回归(logistic)预测值') xlabel('预测样本') ylabel('预测结果') string = {'测试集预测结果对比'; ['RMSE=' num2str(error2) '%']}; title(string) grid %% 损失函数 figure plot(1: MaxTrainNumber, loss, 'r-', 'LineWidth', 1.5) legend('损失函数') xlabel('迭代次数') ylabel('损失函数曲线') string = {'损失函数曲线'}; title(string) grid