BP、RBF、SVM三种机器学习算法（Matlab完整源码)

% 基于SVM的回归预测分析�?—铜矿品位预�? %% 清空环境变量 function chapter_sh tic; close all; clear; clc; format compact; %% 数据的提取和预处�? num1 = xlsread('train420.csv'); num2 = xlsread('test21.csv'); input_train=num1(:,1:end-1);%ѵ�����ݵ��������� output_train=num1(:,end);%ѵ�����ݵ�������� input_test=num2(:,1:end-1);%�������ݵ��������� output_test=num2(:,end); %�������ݵ��������? % ���ݵ���ȡ��Ԥ���� [inputn,inputps]=mapminmax(input_train',-1,1);%ѵ�����ݵ��������ݵĹ�һ�� inputn_test=mapminmax('apply',input_test',inputps); [outputn,outputps]=mapminmax(output_train',-1,1);%ѵ�����ݵ�������ݵĹ�һ�� putputn_test=mapminmax('apply',output_test',outputps); %% ѡ��ع�Ԥ�������ѵ�SVM����c&g inputn=inputn'; inputn_test=inputn_test'; outputn=outputn'; putputn_test=putputn_test'; % ���Ƚ��д���ѡ��: [bestmse,bestc,bestg] = SVMcgForRegress(outputn,inputn,-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); % ���ݴ���ѡ��Ľ��ͼ�ٽ��о�ϸѡ��: [bestmse,bestc,bestg] = SVMcgForRegress(outputn,inputn,-4,4,-4,4,3,0.5,0.5,0.05); % ��ӡ��ϸѡ���� disp('��ӡ��ϸѡ����'); str = sprintf( 'Best Cross Validation MSE = %g Best c = %g Best g = %g',bestmse,bestc,bestg); disp(str); %% ���ûع�Ԥ�������ѵIJ�������SVM����ѵ�� cmd = ['-c ', num2str(bestc), ' -g ', num2str(bestg) , ' -s 3 -p 0.01']; %cmd = ['-c ', '0.707107', ' -g ', '2' , ' -s 3 -p 0.01']; model = svmtrain(outputn,inputn,cmd); %% SVM����ع�Ԥ�� [predict,mse,sssy] = svmpredict(putputn_test,inputn_test,model); predict = mapminmax('reverse',predict',outputps); predict = predict'; % ��ӡ�ع��� str = sprintf( '������� MSE = %g ���ϵ�� R = %g%%',mse(2,1),mse(3,1)*100); disp(str); %% ������� figure; hold on; plot(output_test,'r-o');%实际�? plot(predict,'k-*');%预测�? legend('Ԥ��ֵ','ʵ��ֵ'); hold off; title('ԭʼ���ݺͻع�Ԥ�����ݶԱ�','FontSize',12); grid on; figure; svmerror = predict - output_test; plot(svmerror,'k-*'); %title('误差�?,12); ylabel('mse','FontSize',12); snapnow; toc; function [mse,bestc,bestg] = SVMcgForRegress(train_label,train,cmin,cmax,gmin,gmax,v,cstep,gstep,msestep) %SVMcg cross validation by faruto 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);