elm_kernel_train.m

function model = elm_kernel_train(TrainingData,C,Kernel_type, Kernel_para)
%%核ELM只需要调惩罚系数C，核类型，和Kernel_para就是公式里w和b，因为相比于OS-ELM，elm采用的是随机生成；
train_data=TrainingData;
dataLabel=train_data(:,1);
data=train_data(:,2:end);
clear train_data;                                   %   Release raw training data array

NumberofTrainingData=size(data,1);
    %%%%%%%%%%%% Preprocessing the data of classification
    label=unique(dataLabel);
    number_class=numel(label);
    NumberofOutputNeurons=number_class;
    model.label=label;
    model.X=data;
    %%%%%%%%%% Processing the targets of training
    temp=zeros(NumberofTrainingData,NumberofOutputNeurons );
    for i = 1:NumberofTrainingData
        for j = 1:number_class
            if label(j) == dataLabel(i)
                break; 
            end
        end
        temp(i,j)=1;
    end
    dataLabelMP=temp*2-1;%转化成多节点形式

    %%%%%%%%%% Processing the targets of testing
 


%%%%%%%%%%% Training Phase %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
tic;
n = size(dataLabel,1);
Omega_train = kernel_matrix(data,Kernel_type, Kernel_para);
model.beta=((Omega_train+speye(n)/C)\(dataLabelMP)); 
model.TrainingTime=toc;
model.Kernel_type=Kernel_type;
model.Kernel_para=Kernel_para;
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
end