宽度学习 Broad Learning System MATLAB代码（2）：NORB实践

function [TrainingAccuracy,TestingAccuracy,Training_time,Testing_time] = bls_train(train_x,train_y,test_x,test_y,s,C,N1,N2,N3) % Learning Process of the proposed broad learning system %Input: %---train_x,test_x : the training data and learning data %---train_y,test_y : the label %---We: the randomly generated coefficients of feature nodes %---wh:the randomly generated coefficients of enhancement nodes %----s: the shrinkage parameter for enhancement nodes %----C: the regularization parameter for sparse regualarization %----N11: the number of feature nodes per window %----N2: the number of windows of feature nodes %%%%%%%%%%%%%%feature nodes%%%%%%%%%%%%%% tic train_x = zscore(train_x')'; H1 = [train_x .1 * ones(size(train_x,1),1)];y=zeros(size(train_x,1),N2*N1); for i=1:N2 we=2*rand(size(train_x,2)+1,N1)-1; We{i}=we; A1 = H1 * we;A1 = mapminmax(A1); clear we; beta1 = sparse_bls(A1,H1,1e-3,50)'; beta11{i}=beta1; % clear A1; T1 = H1 * beta1; fprintf(1,'Feature nodes in window %f: Max Val of Output %f Min Val %f\n',i,max(T1(:)),min(T1(:))); [T1,ps1] = mapminmax(T1',0,1);T1 = T1'; ps(i)=ps1; % clear H1; % y=[y T1]; y(:,N1*(i-1)+1:N1*i)=T1; end clear H1; clear T1; %%%%%%%%%%%%%enhancement nodes%%%%%%%%%%%%%%%%%%%%%%%%%%%% H2 = [y .1 * ones(size(y,1),1)]; if N1*N2>=N3 wh=orth(2*rand(N2*N1+1,N3)-1); else wh=orth(2*rand(N2*N1+1,N3)'-1)'; end T2 = H2 *wh; l2 = max(max(T2)); l2 = s/l2; fprintf(1,'Enhancement nodes: Max Val of Output %f Min Val %f\n',l2,min(T2(:))); T2 = tansig(T2 * l2); T3=[y T2]; clear H2;clear T2; beta = (T3' * T3+eye(size(T3',1)) * (C)) \ ( T3' * train_y); Training_time = toc; disp('Training has been finished!'); disp(['The Total Training Time is : ', num2str(Training_time), ' seconds' ]); %%%%%%%%%%%%%%%%%Training Accuracy%%%%%%%%%%%%%%%%%%%%%%%%%% xx = T3 * beta; clear T3; yy = result(xx); train_yy = result(train_y); TrainingAccuracy = length(find(yy == train_yy))/size(train_yy,1); disp(['Training Accuracy is : ', num2str(TrainingAccuracy * 100), ' %' ]); tic; %%%%%%%%%%%%%%%%%%%%%%Testing Process%%%%%%%%%%%%%%%%%%% test_x = zscore(test_x')'; HH1 = [test_x .1 * ones(size(test_x,1),1)]; %clear test_x; yy1=zeros(size(test_x,1),N2*N1); for i=1:N2 beta1=beta11{i};ps1=ps(i); TT1 = HH1 * beta1; TT1 = mapminmax('apply',TT1',ps1)'; clear beta1; clear ps1; %yy1=[yy1 TT1]; yy1(:,N1*(i-1)+1:N1*i)=TT1; end clear TT1;clear HH1; HH2 = [yy1 .1 * ones(size(yy1,1),1)]; TT2 = tansig(HH2 * wh * l2);TT3=[yy1 TT2]; clear HH2;clear wh;clear TT2; %%%%%%%%%%%%%%%%% testing accuracy%%%%%%%%%%%%%%%%%%%%%%%%%%% x = TT3 * beta; y = result(x); test_yy = result(test_y); TestingAccuracy = length(find(y == test_yy))/size(test_yy,1); clear TT3; Testing_time = toc; disp('Testing has been finished!'); disp(['The Total Testing Time is : ', num2str(Testing_time), ' seconds' ]); disp(['Testing Accuracy is : ', num2str(TestingAccuracy * 100), ' %' ]);