ELM.rar_MATLAB中elm_elm函数_elm函数调用_matlab elm_matlab elm函数

function [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy] = ELM(TrainingData_File, TestingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction) % TrainingData_File = 'diabetes_train'; % TestingData_File = 'diabetes_test'; % Elm_Type = 1; % NumberofHiddenNeurons = 100; % ActivationFunction = 'sig'; % 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)'; 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 %%===================================================================== %% Gain the number of the classification label = unique(cat(2,T,TV.T)); %The code in below can be replaced by: label = unique(cat(2,T,TV.T));(on Matlab 2012b by Xiao Liu) % 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; %%Gain the number of the classification %%===================================================================== number_class = length(label); NumberofOutputNeurons=number_class; %%%%%%%%%% Processing the targets of training temp_T=zeros(NumberofOutputNeurons, NumberofTrainingData); %The code in below can be replaced by: for i = 1:number_class % temp_T(i,find(T == label(1,i))) = 1; % end; % (on Matlab 2012b by Xiao Liu) for i = 1:number_class temp_T(i,find(T == label(1,i))) = 1; end % 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); %The code in below can be replaced by: for i = 1:number_class % temp_TV_T(i,find(TV.T == label(1,i))) = 1; % end; % (on Matlab 2012b by Xiao Liu) for i = 1:number_class temp_TV_T(i,find(TV.T == label(1,i))) = 1; end % 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'; % implementation without regularization factor //refer to 2006 Neurocomputing paper %OutputWeight=inv(eye(size(H,1))/C+H * H') * H * T'; % faster method 1 //refer to 2012 IEEE TSMC-B paper %implementation; one can set regularizaiton factor C properly in classification applications %OutputWeight=(eye(size(H,1))/C+H * H') \ H * T'; % faster method 2 //refer to 2012 IEEE TSMC-B paper %implementation; one can set regularizaiton factor C properly in classification applications %If you use faster methods or kernel method, PLEASE CITE in your paper properly: %Guang-Bin Huang, Hongming Zhou, Xiaojian Ding, and Rui Zhang, "Extreme Learning Machine for Regression and Multi-Class Classification," submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence, October 2010. end_time_train=cputime; Traini