ELM.zip_ELM_elm code_极限学习机_黄广斌_黄广斌elm代码

function [TrainingTime, TestingTime, label,TrainingAccuracy, TestingAccuracy] = ELM(train_label,exper_train,test_label,exper_test, Elm_Type, NumberofHiddenNeurons, ActivationFunction) % Usage: elm(TrainingData_File, TestingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction) % OR: [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy] = elm(TrainingData_File, TestingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction) % % Input: % TrainingData_File - Filename of training data set % TestingData_File - Filename of testing data set % Elm_Type - 0 for regression; 1 for (both binary and multi-classes) classification % NumberofHiddenNeurons - Number of hidden neurons assigned to the ELM % ActivationFunction - Type of activation function: % 'sig' for Sigmoidal function % 'sin' for Sine function % 'hardlim' for Hardlim function % 'tribas' for Triangular basis function % 'radbas' for Radial basis function (for additive type of SLFNs instead of RBF type of SLFNs) % % Output: % TrainingTime - Time (seconds) spent on training ELM % TestingTime - Time (seconds) spent on predicting ALL testing data % TrainingAccuracy - Training accuracy: % RMSE for regression or correct classification rate for classification % TestingAccuracy - Testing accuracy: % RMSE for regression or correct classification rate for classification % % MULTI-CLASSE CLASSIFICATION: NUMBER OF OUTPUT NEURONS WILL BE AUTOMATICALLY SET EQUAL TO NUMBER OF CLASSES % FOR EXAMPLE, if there are 7 classes in all, there will have 7 output % neurons; neuron 5 has the highest output means input belongs to 5-th class % % Sample1 regression: [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy] = elm('sinc_train', 'sinc_test', 0, 20, 'sig') % Sample2 classification: elm('diabetes_train', 'diabetes_test', 1, 20, 'sig') % %%%% Authors: MR QIN-YU ZHU AND DR GUANG-BIN HUANG %%%% NANYANG TECHNOLOGICAL UNIVERSITY, SINGAPORE %%%% EMAIL: EGBHUANG@NTU.EDU.SG; GBHUANG@IEEE.ORG %%%% WEBSITE: http://www.ntu.edu.sg/eee/icis/cv/egbhuang.htm %%%% DATE: APRIL 2004 %%%%%%%%%%% Macro definition REGRESSION=0; CLASSIFIER=1; %%%%%%%%%%% Load training dataset % train_data=load(TrainingData_File); % T=train_data(:,1)'; T = train_label'; % P=train_data(:,2:size(train_data,2))'; P = exper_train'; % clear train_data; % Release raw training data array %%%%%%%%%%% Load testing dataset % test_data=load(TestingData_File); % TV.T=test_data(:,1)'; % TV.P=test_data(:,2:size(test_data,2))'; TV.T = test_label'; TV.P = exper_test'; % clear test_data; % Release raw testing data array NumberofTrainingData=size(P,2); NumberofTestingData=size(TV.P,2); NumberofInputNeurons=size(P,1); if Elm_Type~=REGRESSION %%%%%%%%%%%% Preprocessing the data of classification sorted_target=sort(cat(2,T,TV.T),2); label=zeros(1,1); % Find and save in 'label' class label from training and testing data sets label(1,1)=sorted_target(1,1); j=1; for i = 2:(NumberofTrainingData+NumberofTestingData) if sorted_target(1,i) ~= label(1,j) j=j+1; label(1,j) = sorted_target(1,i); end end number_class=j; NumberofOutputNeurons=number_class; %%%%%%%%%% Processing the targets of training temp_T=zeros(NumberofOutputNeurons, NumberofTrainingData); for i = 1:NumberofTrainingData for j = 1:number_class if label(1,j) == T(1,i) break; end end temp_T(j,i)=1; end T=temp_T*2-1; %%%%%%%%%% Processing the targets of testing temp_TV_T=zeros(NumberofOutputNeurons, NumberofTestingData); for i = 1:NumberofTestingData for j = 1:number_class if label(1,j) == TV.T(1,i) break; end end temp_TV_T(j,i)=1; end TV.T=temp_TV_T*2-1; end % end if of Elm_Type %%%%%%%%%%% Calculate weights & biases start_time_train=cputime; %%%%%%%%%%% Random generate input weights InputWeight (w_i) and biases BiasofHiddenNeurons (b_i) of hidden neurons InputWeight=rand(NumberofHiddenNeurons,NumberofInputNeurons)*2-1; BiasofHiddenNeurons=rand(NumberofHiddenNeurons,1); tempH=InputWeight*P; clear P; % Release input of training data ind=ones(1,NumberofTrainingData); BiasMatrix=BiasofHiddenNeurons(:,ind); % Extend the bias matrix BiasofHiddenNeurons to match the demention of H tempH=tempH+BiasMatrix; %%%%%%%%%%% Calculate hidden neuron output matrix H switch lower(ActivationFunction) case { 'sig','sigmoid' } %%%%%%%% Sigmoid H = 1 ./ (1 + exp(-tempH)); case { 'sin','sine' } %%%%%%%% Sine H = sin(tempH); case { 'hardlim' } %%%%%%%% Hard Limit H = double(hardlim(tempH)); case { 'tribas' } %%%%%%%% Triangular basis function H = tribas(tempH); case { 'radbas' } %%%%%%%% Radial basis function H = radbas(tempH); %%%%%%%% More activation functions can be added here end clear tempH; % Release the temparary array for calculation of hidden neuron output matrix H %%%%%%%%%%% Calculate output weights OutputWeight (beta_i) OutputWeight=pinv(H') * T'; % slower implementation % OutputWeight=inv(H * H') * H * T'; % faster implementation end_time_train=cputime; TrainingTime=end_time_train-start_time_train; % Calculate CPU time (seconds) spent for training ELM %%%%%%%%%%% Calculate the training accuracy Y=(H' * OutputWeight)'; % Y: the actual output of the training data if Elm_Type == REGRESSION TrainingAccuracy=sqrt(mse(T - Y)) ; % Calculate training accuracy (RMSE) for regression case end clear H; %%%%%%%%%%% Calculate the output of testing input start_time_test=cputime; tempH_test=InputWeight*TV.P; clear TV.P; % Release input of testing data ind=ones(1,NumberofTestingData); BiasMatrix=BiasofHiddenNeurons(:,ind); % Extend the bias matrix BiasofHiddenNeurons to match the demention of H tempH_test=tempH_test + BiasMatrix; switch lower(ActivationFunction) case { 'sig','sigmoid' } %%%%%%%% Sigmoid H_test = 1 ./ (1 + exp(-tempH_test)); case { 'sin','sine' } %%%%%%%% Sine H_test = sin(tempH_test); case { 'hardlim' } %%%%%%%% Hard Limit H_test = hardlim(tempH_test); case { 'tribas' } %%%%%%%% Triangular basis function H_test = tribas(tempH_test); case { 'radbas' } %%%%%%%% Radial basis function H_test = radbas(tempH_test); %%%%%%%% More activation functions can be added here end TY=(H_test' * OutputWeight)'; % TY: the actual output of the testing data end_time_test=cputime; TestingTime=end_time_test-start_time_test; % Calculate CPU time (seconds) spent by ELM predicting the whole testing data for jj = 1:size(TY,2) [x,label] = max(TY); end label = label'; if Elm_Type == REGRESSION TestingAccuracy=sqrt(mse(TV.T - TY)) ; % Calculate testing accuracy (RMSE) for regression case end if Elm_Type == CLASSIFIER %%%%%%%%%% Calculate t