OS-ELM.zip_OS-ELM_极限学习机_神经网络_神经网络分类_预测

function [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy] = OSELM_VARY(TrainingData_File, TestingData_File, Elm_Type, nHiddenNeurons, ActivationFunction, N0, Block_Range) % Usage: OSELM_VARY(TrainingData_File, TestingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction, N0, Block_Range) % OR: [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy] = OSELM_VARY(TrainingData_File, TestingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction, N0, Block_Range) % % 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 % nHiddenNeurons - Number of hidden neurons assigned to the OSELM % ActivationFunction - Type of activation function: % 'rbf' for radial basis function, G(a,b,x) = exp(-b||x-a||^2) % 'sig' for sigmoidal function, G(a,b,x) = 1/(1+exp(-(ax+b))) % 'sin' for sine function, G(a,b,x) = sin(ax+b) % 'hardlim' for hardlim function, G(a,b,x) = hardlim(ax+b) % N0 - Number of initial training data used in the initial phase of OSLEM, which is not less than the number of hidden neurons % Block_Range - Range from wich the size of data block randomly generated in each iteration of sequential learning phase % % Output: % TrainingTime - Time (seconds) spent on training OSELM % TestingTime - Time (seconds) spent on predicting all testing data % TrainingAccuracy - Training accuracy: % RMSE for regression or correct classification rate for classifcation % TestingAccuracy - Testing accuracy: % RMSE for regression or correct classification rate for classifcation % % 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: OSELM_VARY('mpg_train', 'mpg_test', 0, 25, 'rbf', 75, [10,30]); % Sample2 classification: OSELM_VARY('segment_train', 'segment_test', 1, 180, 'sig', 280, [10,30]); %%%% Authors: %%%% NANYANG TECHNOLOGICAL UNIVERSITY, SINGAPORE %%%% EMAIL: %%%% WEBSITE: %%%% DATE: %%%%%%%%%%% Macro definition REGRESSION=0; CLASSIFICATION=1; %%%%%%%%%%% Load dataset train_data=load(TrainingData_File); test_data=load(TestingData_File); T=train_data(:,1); P=train_data(:,2:size(train_data,2)); TV.T=test_data(:,1); TV.P=test_data(:,2:size(test_data,2)); clear train_data test_data; nTrainingData=size(P,1); nTestingData=size(TV.P,1); nInputNeurons=size(P,2); %%%%%%%%%%%% Preprocessing T in the case of CLASSIFICATION if Elm_Type==CLASSIFICATION sorted_target=sort(cat(1,T,TV.T),1); 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:(nTrainingData+nTestingData) if sorted_target(i,1) ~= label(j,1) j=j+1; label(j,1) = sorted_target(i,1); end end nClass=j; nOutputNeurons=nClass; %%%%%%%%%% Processing the targets of training temp_T=zeros(nTrainingData,nClass); for i = 1:nTrainingData for j = 1:nClass if label(j,1) == T(i,1) break; end end temp_T(i,j)=1; end T=temp_T*2-1; %%%%%%%%%% Processing the targets of testing temp_TV_T=zeros(nTestingData,nClass); for i = 1:nTestingData for j = 1:nClass if label(j,1) == TV.T(i,1) break; end end temp_TV_T(i,j)=1; end TV.T=temp_TV_T*2-1; end clear temp_T temp_TV_T sorted_target start_time_train=cputime; %%%%%%%%%%% step 1 Initialization Phase P0=P(1:N0,:); T0=T(1:N0,:); IW = rand(nHiddenNeurons,nInputNeurons)*2-1; switch lower(ActivationFunction) case{'rbf'} Bias = rand(1,nHiddenNeurons); % Bias = rand(1,nHiddenNeurons)*1/3+1/11; %%%%%%%%%%%%% for the cases of Image Segment and Satellite Image % Bias = rand(1,nHiddenNeurons)*1/20+1/60; %%%%%%%%%%%%% for the case of DNA H0 = RBFun(P0,IW,Bias); case{'sig'} Bias = rand(1,nHiddenNeurons)*2-1; H0 = SigActFun(P0,IW,Bias); case{'sin'} Bias = rand(1,nHiddenNeurons)*2-1; H0 = SinActFun(P0,IW,Bias); case{'hardlim'} Bias = rand(1,nHiddenNeurons)*2-1; H0 = HardlimActFun(P0,IW,Bias); H0 = double(H0); end M = pinv(H0' * H0); beta = pinv(H0) * T0; clear P0 T0 H0; %%%%%%%%%%%%% step 2 Sequential Learning Phase n = N0 + 1; while n <= nTrainingData Block = randint(1,1,Block_Range); if (n+Block-1) > nTrainingData Pn = P(n:nTrainingData,:); Tn = T(n:nTrainingData,:); Block = size(Pn,1); %%%% correct the block size else Pn = P(n:(n+Block-1),:); Tn = T(n:(n+Block-1),:); end switch lower(ActivationFunction) case{'rbf'} H = RBFun(Pn,IW,Bias); case{'sig'} H = SigActFun(Pn,IW,Bias); case{'sin'} H = SinActFun(Pn,IW,Bias); case{'hardlim'} H = HardlimActFun(Pn,IW,Bias); end M = M - M * H' * (eye(Block) + H * M * H')^(-1) * H * M; beta = beta + M * H' * (Tn - H * beta); n = n + Block; end end_time_train=cputime; TrainingTime=end_time_train-start_time_train clear Pn Tn H M; switch lower(ActivationFunction) case{'rbf'} HTrain = RBFun(P, IW, Bias); case{'sig'} HTrain = SigActFun(P, IW, Bias); case{'sin'} HTrain = SinActFun(P, IW, Bias); case{'hardlim'} HTrain = HardlimActFun(P, IW, Bias); end Y=HTrain * beta; clear HTrain; %%%%%%%%%%% Performance Evaluation start_time_test=cputime; switch lower(ActivationFunction) case{'rbf'} HTest = RBFun(TV.P, IW, Bias); case{'sig'} HTest = SigActFun(TV.P, IW, Bias); case{'sin'} HTest = SinActFun(TV.P, IW, Bias); case{'hardlim'} HTest = HardlimActFun(TV.P, IW, Bias); end TY=HTest * beta; clear HTest; end_time_test=cputime; TestingTime=end_time_test-start_time_test if Elm_Type == REGRESSION %%%%%%%%%%%%%% Calculate RMSE in the case of REGRESSION TrainingAccuracy=sqrt(mse(T - Y)) TestingAccuracy=sqrt(mse(TV.T - TY)) elseif Elm_Type == CLASSIFICATION %%%%%%%%%% Calculate correct classification rate in the case of CLASSIFICATION MissClassificationRate_Training=0; MissClassificationRate_Testing=0; for i = 1 : nTrainingData [x, label_index_expected]=max(T(i,:)); [x, label_index_actual]=max(Y(i,:)); if label_index_actual~=label_index_expected MissClassificationRate_Training=MissClassificationRate_Training+1; end end TrainingAccuracy=1-MissClassificationRate_Training/nTrainingData for i = 1 : nTestingData [x, label_index_expected]=max(TV.T(i,:)); [x, label_index_actual]=max(TY(i,:)); if label_index_actual~=label_index_expected MissClassificationRate_Testing=MissClassificationRate_Testing+1; end end TestingAccuracy=1-MissClassificationRate_Testing/nTestingData end