2023年全国大学生数学建模竞赛-针对蔬菜类商品定价与补货决策的研究-支撑材料

clc,clear data = xlsread('问题2数据表.xlsx'); %% % 花菜类 y1 = data(:, 1); x1 = data(:, 2); %% % 花叶类 y2 = data(:, 3); x2 = data(:, 4); %% % 辣椒类 y3 = data(:, 5); x3 = data(:, 6); %% % 茄类 y4 = data(:, 7); x4 = data(:, 8); %% % 食用菌 y5 = data(:, 9); x5 = data(:, 10); %% % 水生根茎类 y6 = data(:, 11); x6 = data(:, 12); %% Fit: '花菜类销量与平均定价关系函数'. function [fitresult, gof] = createFit(x, y) [xData, yData] = prepareCurveData( x, y ); % Set up fittype and options. ft = fittype( 'poly2' ); opts = fitoptions( 'Method', 'LinearLeastSquares' ); opts.Robust = 'LAR'; % Fit model to data. [fitresult, gof] = fit( xData, yData, ft, opts ); % Plot fit with data. figure( 'Name', '花菜类销量与平均定价关系函数' ); h = plot( fitresult, xData, yData ); legend( h, 'y vs. x', '花菜类销量与平均定价关系函数', 'Location', 'NorthEast', 'Interpreter', 'none' ); % Label axes xlabel( 'x', 'Interpreter', 'none' ); ylabel( 'y', 'Interpreter', 'none' ); grid on %% Fit: '花叶类销量与平均定价关系函数'. function [fitresult, gof] = createFit(x2, y2) [xData, yData] = prepareCurveData( x2, y2 ); % Set up fittype and options. ft = fittype( 'poly2' ); opts = fitoptions( 'Method', 'LinearLeastSquares' ); opts.Robust = 'LAR'; % Fit model to data. [fitresult, gof] = fit( xData, yData, ft, opts ); % Plot fit with data. figure( 'Name', '花叶类销量与平均定价关系函数' ); h = plot( fitresult, xData, yData ); legend( h, 'y2 vs. x2', '花叶类销量与平均定价关系函数', 'Location', 'NorthEast', 'Interpreter', 'none' ); % Label axes xlabel( 'x2', 'Interpreter', 'none' ); ylabel( 'y2', 'Interpreter', 'none' ); grid on %% Fit: '辣椒类销量与平均定价关系函数'. function [fitresult, gof] = createFit(x3, y3) [xData, yData] = prepareCurveData( x3, y3 ); % Set up fittype and options. ft = fittype( 'poly2' ); opts = fitoptions( 'Method', 'LinearLeastSquares' ); opts.Robust = 'LAR'; % Fit model to data. [fitresult, gof] = fit( xData, yData, ft, opts ); % Plot fit with data. figure( 'Name', '辣椒类销量与平均定价关系函数' ); h = plot( fitresult, xData, yData ); legend( h, 'y3 vs. x3', '辣椒类销量与平均定价关系函数', 'Location', 'NorthEast', 'Interpreter', 'none' ); % Label axes xlabel( 'x3', 'Interpreter', 'none' ); ylabel( 'y3', 'Interpreter', 'none' ); grid on %% Fit: '茄类销量与平均定价关系函数'. function [fitresult, gof] = createFit(x4, y4) [xData, yData] = prepareCurveData( x4, y4 ); % Set up fittype and options. ft = fittype( 'poly2' ); opts = fitoptions( 'Method', 'LinearLeastSquares' ); opts.Robust = 'LAR'; % Fit model to data. [fitresult, gof] = fit( xData, yData, ft, opts ); % Plot fit with data. figure( 'Name', '茄类销量与平均定价关系函数' ); h = plot( fitresult, xData, yData ); legend( h, 'y4 vs. x4', '茄类销量与平均定价关系函数', 'Location', 'NorthEast', 'Interpreter', 'none' ); % Label axes xlabel( 'x4', 'Interpreter', 'none' ); ylabel( 'y4', 'Interpreter', 'none' ); grid on %% Fit: '食用菌销量与平均定价关系函数'. function [fitresult, gof] = createFit(x5, y5) [xData, yData] = prepareCurveData( x5, y5 ); % Set up fittype and options. ft = fittype( 'poly2' ); opts = fitoptions( 'Method', 'LinearLeastSquares' ); opts.Robust = 'LAR'; % Fit model to data. [fitresult, gof] = fit( xData, yData, ft, opts ); % Plot fit with data. figure( 'Name', '食用菌销量与平均定价关系函数' ); h = plot( fitresult, xData, yData ); legend( h, 'y5 vs. x5', '食用菌销量与平均定价关系函数', 'Location', 'NorthEast', 'Interpreter', 'none' ); % Label axes xlabel( 'x5', 'Interpreter', 'none' ); ylabel( 'y5', 'Interpreter', 'none' ); grid on %% Fit: '水生根茎类销量与平均定价关系函数'. function [fitresult, gof] = createFit(x6, y6) [xData, yData] = prepareCurveData( x6, y6 ); % Set up fittype and options. ft = fittype( 'poly2' ); opts = fitoptions( 'Method', 'LinearLeastSquares' ); opts.Robust = 'LAR'; % Fit model to data. [fitresult, gof] = fit( xData, yData, ft, opts ); % Plot fit with data. figure( 'Name', '水生根茎类销量与平均定价关系函数' ); h = plot( fitresult, xData, yData ); legend( h, 'y6 vs. x6', '水生根茎类销量与平均定价关系函数', 'Location', 'NorthEast', 'Interpreter', 'none' ); % Label axes xlabel( 'x6', 'Interpreter', 'none' ); ylabel( 'y6', 'Interpreter', 'none' ); grid on