SVM——更改数据.zip_SVM_similarjry

%% Matlab神经网络43个案例分析 % 基于SVM的回归预测分析——上证指数开盘指数预测 % by 李洋(faruto) % http://www.matlabsky.com % Email:[email protected] % http://weibo.com/faruto % http://blog.sina.com.cn/faruto % 2013.01.01 %% 清空环境变量 function chapter_sh tic; 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 chapter_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.m function [mse,bestc,bestg] = SVMcgForRegress(train_label,train,cmin,cmax,gmin,gmax,v,cstep,gstep,msestep) %SVMcg cross validation by faruto % % by faruto %Email:[email protected] QQ:516667408 http://blog.sina.com.cn/faruto BNU %last modified 2010.01.17 %Super Moderator @ www.ilovematlab.cn % 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 http://www.ilovematlab.cn % % 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; end if nargin < 8 cstep = 0.8; gstep = 0.8; end if nargin < 7 v = 5; end if nargin < 5 gmax = 8; gmin = -8; end if nargin < 3 cmax = 8; cmin = -8; end % X:c Y:g cg:acc [X,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( basenum^X(i,j) ),' -g ',num2str( basenum^Y(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 = basenum^X(i,j); bestg = basenum^Y(i,j); end if abs( cg(i,j)-mse )<=eps && bestc > basenum^X(i,j) mse = cg(i,j); bestc = basenum^X(i,j); bestg = basenum^Y(i,j); end end end % to draw the acc with different c & g [cg,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);