function [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy] = ELM(sinc_train.txt,sinc_test.txt,0,1,radbas);% Usage: elm(TrainingData_File, TestingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction)
%function [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
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
endclear 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;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
endclear 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 HtempH_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
endTY=(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
if Elm_Type == REGRESSION TestingAccuracy=sqrt(mse(TV.T - TY)); % Calculate testing accuracy (RMSE) for regression case
endif Elm_Type == CLASSIFIER%%%%%%%%%%Calculate training & testing classification accuracy
MissClassificationRate_Training=0; MissClassificationRate_Testing=0; for i = 1 : size(T, 2) [~, label_index_expected]=max(T(:,i)); [~, 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) [~, label_index_expected]=max(TV.T(:,i)); [~, 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
When i run there is error , i can't understand. plz tell me
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