基于MATLAB实现的ELM 极限学习机的核函数 MATLAB程序，用于深入研究和改进极限学习机+使用说明文档.rar

function [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy,TY] = elm_kernel(TrainingData_File, TestingData_File, Elm_Type, Regularization_coefficient, Kernel_type, Kernel_para) % 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 % Regularization_coefficient - Regularization coefficient C % Kernel_type - Type of Kernels: % 'RBF_kernel' for RBF Kernel % 'lin_kernel' for Linear Kernel % 'poly_kernel' for Polynomial Kernel % 'wav_kernel' for Wavelet Kernel %Kernel_para - A number or vector of Kernel Parameters. eg. 1, [0.1,10]... % 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_kernel('sinc_train', 'sinc_test', 0, 1, ''RBF_kernel',100) % Sample2 classification: elm_kernel('diabetes_train', 'diabetes_test', 1, 1, 'RBF_kernel',100) % %%%% Authors: MR HONG-MING ZHOU 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: MARCH 2012 %%%%%%%%%%% 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 C = Regularization_coefficient; NumberofTrainingData=size(P,2); NumberofTestingData=size(TV.P,2); 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 if of Elm_Type end %%%%%%%%%%% Training Phase %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% tic; n = size(T,2); Omega_train = kernel_matrix(P',Kernel_type, Kernel_para); OutputWeight=((Omega_train+speye(n)/C)\(T')); TrainingTime=toc %%%%%%%%%%% Calculate the training output Y=(Omega_train * OutputWeight)'; % Y: the actual output of the training data %%%%%%%%%%% Calculate the output of testing input tic; Omega_test = kernel_matrix(P',Kernel_type, Kernel_para,TV.P'); TY=(Omega_test' * OutputWeight)'; % TY: the actual output of the testing data TestingTime=toc %%%%%%%%%% Calculate training & testing classification accuracy if Elm_Type == REGRESSION %%%%%%%%%% Calculate training & testing accuracy (RMSE) for regression case TrainingAccuracy=sqrt(mse(T - Y)) TestingAccuracy=sqrt(mse(TV.T - TY)) end if Elm_Type == CLASSIFIER %%%%%%%%%% Calculate training & testing classification accuracy MissClassificationRate_Training=0; MissClassificationRate_Testing=0; for i = 1 : size(T, 2) [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/size(T,2) for i = 1 : size(TV.T, 2) [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/size(TV.T,2) end %%%%%%%%%%%%%%%%%% Kernel Matrix %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function omega = kernel_matrix(Xtrain,kernel_type, kernel_pars,Xt) nb_data = size(Xtrain,1); if strcmp(kernel_type,'RBF_kernel'), if nargin<4, XXh = sum(Xtrain.^2,2)*ones(1,nb_data); omega = XXh+XXh'-2*(Xtrain*Xtrain'); omega = exp(-omega./kernel_pars(1)); else XXh1 = sum(Xtrain.^2,2)*ones(1,size(Xt,1)); XXh2 = sum(Xt.^2,2)*ones(1,nb_data); omega = XXh1+XXh2' - 2*Xtrain*Xt'; omega = exp(-omega./kernel_pars(1)); end elseif strcmp(kernel_type,'lin_kernel') if nargin<4, omega = Xtrain*Xtrain'; else omega = Xtrain*Xt'; end elseif strcmp(kernel_type,'poly_kernel') if nargin<4, omega = (Xtrain*Xtrain'+kernel_pars(1)).^kernel_pars(2); else omega = (Xtrain*Xt'+kernel_pars(1)).^kernel_pars(2); end elseif strcmp(kernel_type,'wav_kernel') if nargin<4, XXh = sum(Xtrain.^2,2)*ones(1,nb_data); omega = XXh+XXh'-2*(Xtrain*Xtrain'); XXh1 = sum(Xtrain,2)*ones(1,nb_data); omega1 = XXh1-XXh1'; omega = cos(kernel_pars(3)*omega1./kernel_pars(2)).*exp(-omega./kernel_pars(1)); else XXh1 = sum(Xtrain.^2,2)*ones(1,size(Xt,1)); XXh2 = sum(Xt.^2,2)*ones(1,nb_data); omega = XXh1+XXh2' - 2*(Xtrain*Xt'); XXh11 = sum(Xtrain,2)*ones(1,size(Xt,1)); XXh22 = sum(Xt,2)*ones(1,nb_data); omega1 = XXh11-XXh22'; omega = cos(kernel_pars(3)*omega1./kernel_pars(2)).*exp(-omega./kernel_pars(1)); end end