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2016-INS-RVFL-Comp-Eval-Classification.m
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function [train_accuracy,test_accuracy]=RVFL_train_val(trainX,trainY,testX,testY,option)
% This is the function to train and evaluate RVFL for classification
% problem.
% Option.N : number of hidden neurons
% Option.bias: whether to have bias in the output neurons
% option.link: whether to have the direct link.
% option.ActivationFunction:Activation Functions used.
% option.seed: Random Seeds
% option,mode 1: regularized least square, 2: Moore-Penrose pseudoinverse
% option.RandomType: different randomnization methods. Currently only support Gaussian and uniform.
% option.Scale Linearly scale the random features before feedinto the nonlinear activation function.
% In this implementation, we consider the threshold which lead to 0.99 of the maximum/minimum value of the activation function as the saturating threshold.
% Option.Scale=0.9 means all the random features will be linearly scaled
% into 0.9* [lower_saturating_threshold,upper_saturating_threshold].
%option.Scalemode Scalemode=1 will scale the features for all neurons.
% Scalemode=2 will scale the features for each hidden
% neuron separately.
% Scalemode=3 will scale the range of the randomization for
% uniform diatribution.
% This software package has been developed by Le Zhang(c) 2015
% based on this paper: A Comprehensive Evaluation of Random Vector Functional Link Neural Network Variants
% For technical support and/or help, please contact [email protected]
% This package has been downloaed from https://sites.google.com/site/zhangleuestc/
if ~isfield(option,'N')|| isempty(option.N)
option.N=100;
end
if ~isfield(option,'bias')|| isempty(option.bias)
option.bias=false;
end
if ~isfield(option,'link')|| isempty(option.link)
option.link=true;
end
if ~isfield(option,'ActivationFunction')|| isempty(option.ActivationFunction)
option.ActivationFunction='radbas';
end
if ~isfield(option,'seed')|| isempty(option.seed)
option.seed=0;
end
if ~isfield(option,'RandomType')|| isempty(option.RandomType)
option.RandomType='Uniform';
end
if ~isfield(option,'mode')|| isempty(option.mode)
option.mode=1;
end
if ~isfield(option,'Scale')|| isempty(option.Scale)
option.Scale=1;
end
if ~isfield(option,'Scalemode')|| isempty(option.Scalemode)
option.Scalemode=1;
end
rand('state',option.seed);
randn('state',option.seed);
U_trainY=unique(trainY);
nclass=numel(U_trainY);
trainY_temp=zeros(numel(trainY),nclass);
% 0-1 coding for the target
for i=1:nclass
idx= trainY==U_trainY(i);
trainY_temp(idx,i)=1;
end
[Nsample,Nfea]=size(trainX);
N=option.N;
if strcmp(option.RandomType,'Uniform')
if option.Scalemode==3
Weight= option.Scale*(rand(Nfea,N)*2-1);
Bias= option.Scale*rand(1,N);
% fprintf('linearly scale the range of uniform distribution to %d\n', option.Scale);
else
Weight=rand(Nfea,N)*2-1;
Bias=rand(1,N);
end
else if strcmp(option.RandomType,'Gaussian')
Weight=randn(Nfea,N);
Bias=randn(1,N);
else
error('only Gaussian and Uniform are supported')
end
end
Bias_train=repmat(Bias,Nsample,1);
H=trainX*Weight+Bias_train;
switch lower(option.ActivationFunction)
case {'sig','sigmoid'}
if option.Scale
Saturating_threshold=[-4.6,4.6];
Saturating_threshold_activate=[0,1];
if option.Scalemode==1;
[H,k,b]=Scale_feature(H,Saturating_threshold,option.Scale);
elseif option.Scalemode==2
[H,k,b]=Scale_feature_separately(H,Saturating_threshold,option.Scale);
end
end
H = 1 ./ (1 + exp(-H));
case {'sin','sine'}
if option.Scale
Saturating_threshold=[-pi/2,pi/2];
Saturating_threshold_activate=[-1,1];
if option.Scalemode==1
[H,k,b]=Scale_feature(H,Saturating_threshold,option.Scale);
elseif option.Scalemode==2
[H,k,b]=Scale_feature_separately(H,Saturating_threshold,option.Scale);
end
end
H = sin(H);
case {'hardlim'}
H = double(hardlim(H));
case {'tribas'}
if option.Scale
Saturating_threshold=[-1,1];
Saturating_threshold_activate=[0,1];
if option.Scalemode==1
[H,k,b]=Scale_feature(H,Saturating_threshold,option.Scale);
elseif option.Scalemode==2
[H,k,b]=Scale_feature_separately(H,Saturating_threshold,option.Scale);
end
end
H = tribas(H);
case {'radbas'}
if option.Scale
Saturating_threshold=[-2.1,2.1];
Saturating_threshold_activate=[0,1];
if option.Scalemode==1
[H,k,b]=Scale_feature(H,Saturating_threshold,option.Scale);
elseif option.Scalemode==2
[H,k,b]=Scale_feature_separately(H,Saturating_threshold,option.Scale);
end
end
H = radbas(H);
case {'sign'}
H = sign(H);
end
if option.bias
H=[H,ones(Nsample,1)];
end
if option.link
switch option.Scalemode
case 1
trainX_temp=trainX.*k+b;
H=[H,trainX_temp];
case 2
[trainX_temp,ktr,btr]=Scale_feature_separately(trainX,Saturating_threshold_activate,option.Scale);
H=[H,trainX_temp];
otherwise
H=[H,trainX];
end
end
H(isnan(H))=0;
if option.mode==2
beta=pinv(H)*trainY_temp;
else if option.mode==1
if ~isfield(option,'C')||isempty(option.C)
option.C=0.1;
end
C=option.C;
if N<Nsample
beta=(eye(size(H,2))/C+H' * H) \ H'*trainY_temp;
else
beta=H'*((eye(size(H,1))/C+H* H') \ trainY_temp);
end
else
error('Unsupport mode, only Regularized least square and Moore-Penrose pseudoinverse are allowed. ')
end
end
trainY_temp=H*beta;
Y_temp=zeros(Nsample,1);
% decode the target
for i=1:Nsample
[maxvalue,idx]=max(trainY_temp(i,:));
Y_temp(i)=U_trainY(idx);
end
Bias_test=repmat(Bias,numel(testY),1);
H_test=testX*Weight+Bias_test;
switch lower(option.ActivationFunction)
case {'sig','sigmoid'}
%%%%%%%% Sigmoid
if option.Scale
if option.Scalemode==1
H_test=H_test.*k+b;
elseif option.Scalemode==2
nSamtest=size(H_test,1);
kt=repmat(k,nSamtest,1);
bt=repmat(b,nSamtest,1);
H_test=H_test.*kt+bt;
end
end
H_test = 1 ./ (1 + exp(-H_test));
case {'sin','sine'}
if option.Scale
if option.Scalemode==1
H_test=H_test.*k+b;
elseif option.Scalemode==2
nSamtest=size(H_test,1);
kt=repmat(k,nSamtest,1);
bt=repmat(b,nSamtest,1);
H_test=H_test.*kt+bt;
end
end
H_test = sin(H_test);
case {'hardlim'}
H_test = double(hardlim(H_test));
case {'tribas'}
if option.Scale
if option.Scalemode==1
H_test=H_test.*k+b;
elseif option.Scalemode==2
nSamtest=size(H_test,1);
kt=repmat(k,nSamtest,1);
bt=repmat(b,nSamtest,1);
H_test=H_test.*kt+bt;
end
end
H_test = tribas(H_test);
case {'radbas'}
if option.Scale
if option.Scalemode==1
H_test=H_test.*k+b;
elseif option.Scalemode==2
nSamtest=size(H_test,1);
kt=repmat(k,nSamtest,1);
bt=repmat(b,nSamtest,1);
H_test=H_test.*kt+bt;
end
end
H_test = radbas(H_test);
case {'sign'}
H_test = sign(H_test);
end
if option.bias
H_test=[H_test,ones(numel(testY),1)];
end
if option.link
switch option.Scalemode
case 1
testX_temp=testX.*k+b;
H_test=[H_test,testX_temp];
case 2
nSamtest=size(H_test,1);
kt=repmat(ktr,nSamtest,1);
bt=repmat(btr,nSamtest,1);
testX_temp=testX.*kt+bt;
H_test=[H_test,testX_temp];
otherwise
H_test=[H_test,testX];
end
end
H_test(isnan(H_test))=0;
testY_temp=H_test*beta;
Yt_temp=zeros(numel(testY),1);
for i=1:numel(testY)
[maxvalue,idx]=max(testY_temp(i,:));
Yt_temp(i)=U_trainY(idx);
end
train_accuracy=length(find(Y_temp==trainY))/Nsample;
test_accuracy=length(find(Yt_temp==testY))/numel(testY);
end
function [Output,k,b]=Scale_feature(Input,Saturating_threshold,ratio)
Min_value=min(min(Input));
Max_value=max(max(Input));
min_value=Saturating_threshold(1)*ratio;
max_value=Saturating_threshold(2)*ratio;
k=(max_value-min_value)/(Max_value-Min_value);
b=(min_value*Max_value-Min_value*max_value)/(Max_value-Min_value);
Output=Input.*k+b;
end
function [Output,k,b]=Scale_feature_separately(Input,Saturating_threshold,ratio)
nNeurons=size(Input,2);
k=zeros(1,nNeurons);
b=zeros(1,nNeurons);
Output=zeros(size(Input));
min_value=Saturating_threshold(1)*ratio;
max_value=Saturating_threshold(2)*ratio;
for i=1:nNeurons
Min_value=min(Input(:,i));
Max_value=max(Input(:,i));
k(i)=(max_value-min_value)/(Max_value-Min_value);
b(i)=(min_value*Max_value-Min_value*max_value)/(Max_value-Min_value);
Output(:,i)=Input(:,i).*k(i)+b(i);
end
end