Matlab神经网络的ELM算法-神经网络的ELM算法.rar

function [TrainingTime, TrainingAccuracy, TestingAccuracy] = elm(TrainingData_File, TestingData_File, NumberofHiddenNeurons, ActivationFunction, Elm_Type) % 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 % % 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') % %%%%%%%%%%% Macro definition REGRESSION=0; CLASSIFIER=1; %%%%%%%%%%% Load training dataset train_data=load(TrainingData_File); T=train_data(:,1)'; P=train_data(:,2:size(train_data,2))'; 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))'; 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; switch lower(ActivationFunction) case {'rbf'} BiasofHiddenNeurons=rand(NumberofHiddenNeurons,1); ind=ones(1,NumberofTrainingData); for i=1:NumberofHiddenNeurons this_weight=InputWeight(i,:); extend_weight=this_weight(ind,:)'; if NumberofInputNeurons==1 tempH(i,:)=-((P-extend_weight).^2); else tempH(i,:)=-sum((P-extend_weight).^2); end end BiasMatrix=BiasofHiddenNeurons(:,ind); tempH=tempH./BiasMatrix; clear extend_weight; case {'rbf_gamma'} BiasofHiddenNeurons=rand(NumberofHiddenNeurons,1)*0.5; ind=ones(1,NumberofTrainingData); for i=1:NumberofHiddenNeurons this_weight=InputWeight(i,:); extend_weight=this_weight(ind,:)'; if NumberofInputNeurons==1 tempH(i,:)=-((P-extend_weight).^2); else tempH(i,:)=-sum((P-extend_weight).^2); end end BiasMatrix=BiasofHiddenNeurons(:,ind); tempH=tempH.*BiasMatrix; clear extend_weight; otherwise BiasofHiddenNeurons=rand(NumberofHiddenNeurons,1); %%%%%%%% feedforward NN tempH=InputWeight*P; ind=ones(1,NumberofTrainingData); BiasMatrix=BiasofHiddenNeurons(:,ind); % Extend the bias matrix BiasofHiddenNeurons to match the demention of H tempH=tempH+BiasMatrix; end clear P; % Release input of training data clear BiasMatrix % Release bias matrix by Qinyu 19/04/2004 %%%%%%%%%%% 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 = hardlim(tempH); case {'rbf','rbf_gamma'} %%%%%%%% RBF H = exp(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'; 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; switch lower(ActivationFunction) case {'rbf'} ind=ones(1,NumberofTestingData); for i=1:NumberofHiddenNeurons this_weight=InputWeight(i,:); extend_weight=this_weight(ind,:)'; if NumberofInputNeurons==1 tempH_test(i,:)=-((TV.P-extend_weight).^2); else tempH_test(i,:)=-sum((TV.P-extend_weight).^2); end end BiasMatrix=BiasofHiddenNeurons(:,ind); tempH_test=tempH_test./BiasMatrix; clear extend_weight; case {'rbf_gamma'} ind=ones(1,NumberofTestingData); for i=1:NumberofHiddenNeurons this_weight=InputWeight(i,:);