最新matlab30个案例分析案例14-SVM神经网络的回归预测分析.docx
Four short words sum up what has lifted most successful individuals above the crowd: a little bit more.-author-datematlab30个案例分析案例14-SVM神经网络的回归预测分析matlab30个案例分析案例14-SVM神经网络的回归预测分析% SVM神经网络的回归预测分析-上证指数开盘指数预测 % 清空环境变量function chapter14tic;close all;clear;clc;format compact;% 数据的提取和预处理% 载入测试数据上证指数(1990.12.19-2009.08.19)% 数据是一个4579*6的double型的矩阵,每一行表示每一天的上证指数% 6列分别表示当天上证指数的开盘指数,指数最高值,指数最低值,收盘指数,当日交易量,当日交易额.load chapter14_sh.mat;% 提取数据m,n = size(sh);ts = sh(2:m,1);tsx = sh(1:m-1,:);% 画出原始上证指数的每日开盘数figure;plot(ts,'LineWidth',2);title('上证指数的每日开盘数(1990.12.20-2009.08.19)','FontSize',12);xlabel('交易日天数(1990.12.19-2009.08.19)','FontSize',12);ylabel('开盘数','FontSize',12);grid on;% 数据预处理,将原始数据进行归一化ts = ts'tsx = tsx'% mapminmax为matlab自带的映射函数% 对ts进行归一化TS,TSps = mapminmax(ts,1,2);% 画出原始上证指数的每日开盘数归一化后的图像figure;plot(TS,'LineWidth',2);title('原始上证指数的每日开盘数归一化后的图像','FontSize',12);xlabel('交易日天数(1990.12.19-2009.08.19)','FontSize',12);ylabel('归一化后的开盘数','FontSize',12);grid on;% 对TS进行转置,以符合libsvm工具箱的数据格式要求TS = TS'% mapminmax为matlab自带的映射函数% 对tsx进行归一化TSX,TSXps = mapminmax(tsx,1,2);% 对TSX进行转置,以符合libsvm工具箱的数据格式要求TSX = TSX'% 选择回归预测分析最佳的SVM参数c&g% 首先进行粗略选择: bestmse,bestc,bestg = SVMcgForRegress(TS,TSX,-8,8,-8,8);% 打印粗略选择结果disp('打印粗略选择结果');str = sprintf( 'Best Cross Validation MSE = %g Best c = %g Best g = %g',bestmse,bestc,bestg);disp(str);% 根据粗略选择的结果图再进行精细选择: bestmse,bestc,bestg = SVMcgForRegress(TS,TSX,-4,4,-4,4,3,0.5,0.5,0.05);% 打印精细选择结果disp('打印精细选择结果');str = sprintf( 'Best Cross Validation MSE = %g Best c = %g Best g = %g',bestmse,bestc,bestg);disp(str);% 利用回归预测分析最佳的参数进行SVM网络训练cmd = '-c ', num2str(bestc), ' -g ', num2str(bestg) , ' -s 3 -p 0.01'model = svmtrain(TS,TSX,cmd);% SVM网络回归预测predict,mse = svmpredict(TS,TSX,model);predict = mapminmax('reverse',predict',TSps);predict = predict'% 打印回归结果str = sprintf( '均方误差 MSE = %g 相关系数 R = %g%',mse(2),mse(3)*100);disp(str);% 结果分析figure;hold on;plot(ts,'-o');plot(predict,'r-');legend('原始数据','回归预测数据');hold off;title('原始数据和回归预测数据对比','FontSize',12);xlabel('交易日天数(1990.12.19-2009.08.19)','FontSize',12);ylabel('开盘数','FontSize',12);grid on;figure;error = predict - ts'plot(error,'rd');title('误差图(predicted data - original data)','FontSize',12);xlabel('交易日天数(1990.12.19-2009.08.19)','FontSize',12);ylabel('误差量','FontSize',12);grid on;figure;error = (predict - ts')./ts'plot(error,'rd');title('相对误差图(predicted data - original data)/original data','FontSize',12);xlabel('交易日天数(1990.12.19-2009.08.19)','FontSize',12);ylabel('相对误差量','FontSize',12);grid on;snapnow;toc;% 子函数 SVMcgForRegress.mfunction mse,bestc,bestg = SVMcgForRegress(train_label,train,cmin,cmax,gmin,gmax,v,cstep,gstep,msestep)%SVMcg cross validation by faruto% by faruto%Email:patrick.lee QQ:516667408 BNU%last modified 2010.01.17%Super Moderator % 若转载请注明：% faruto and liyang , LIBSVM-farutoUltimateVersion % a toolbox with implements for support vector machines based on libsvm, 2009. % Software available at % % Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for% support vector machines, 2001. Software available at% http:/www.csie.ntu.edu.tw/cjlin/libsvm% about the parameters of SVMcg if nargin < 10 msestep = 0.06;endif nargin < 8 cstep = 0.8; gstep = 0.8;endif nargin < 7 v = 5;endif nargin < 5 gmax = 8; gmin = -8;endif nargin < 3 cmax = 8; cmin = -8;end% X:c Y:g cg:accX,Y = meshgrid(cmin:cstep:cmax,gmin:gstep:gmax);m,n = size(X);cg = zeros(m,n);eps = 10(-4);bestc = 0;bestg = 0;mse = Inf;basenum = 2;for i = 1:m for j = 1:n cmd = '-v ',num2str(v),' -c ',num2str( basenumX(i,j) ),' -g ',num2str( basenumY(i,j) ),' -s 3 -p 0.1' cg(i,j) = svmtrain(train_label, train, cmd); if cg(i,j) < mse mse = cg(i,j); bestc = basenumX(i,j); bestg = basenumY(i,j); end if abs( cg(i,j)-mse )<=eps && bestc > basenumX(i,j) mse = cg(i,j); bestc = basenumX(i,j); bestg = basenumY(i,j); end endend% to draw the acc with different c & gcg,ps = mapminmax(cg,0,1);figure;C,h = contour(X,Y,cg,0:msestep:0.5);clabel(C,h,'FontSize',10,'Color','r');xlabel('log2c','FontSize',12);ylabel('log2g','FontSize',12);firstline = 'SVR参数选择结果图(等高线图)GridSearchMethod' secondline = 'Best c=',num2str(bestc),' g=',num2str(bestg), . ' CVmse=',num2str(mse);title(firstline;secondline,'Fontsize',12);grid on;figure;meshc(X,Y,cg);% mesh(X,Y,cg);% surf(X,Y,cg);axis(cmin,cmax,gmin,gmax,0,1);xlabel('log2c','FontSize',12);ylabel('log2g','FontSize',12);zlabel('MSE','FontSize',12);firstline = 'SVR参数选择结果图(3D视图)GridSearchMethod' secondline = 'Best c=',num2str(bestc),' g=',num2str(bestg), . ' CVmse=',num2str(mse);title(firstline;secondline,'Fontsize',12);-
