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
