elm_train.m

新神经网络 Extreme Learning Machine 比SVM快

function [TrainingTime,TrainingAccuracy] = elm_train(TrainingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction)

% Usage: elm_train(TrainingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction)
% OR:    [TrainingTime, TrainingAccuracy] = elm_train(TrainingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction)
%
% Input:
% TrainingData_File     - Filename of training 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
%
% Output: 
% TrainingTime          - Time (seconds) spent on training ELM
% TrainingAccuracy      - Training 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, TrainingAccuracy, TestingAccuracy] = elm_train('sinc_train', 0, 20, 'sig')
% Sample2 classification: elm_train('diabetes_train', 1, 20, 'sig')
%
    %%%%    Authors:    MR QIN-YU ZHU AND DR GUANG-BIN HUANG
    %%%%    NANYANG TECHNOLOGICAL UNIVERSITY, SINGAPORE
    %%%%    EMAIL:      [e-mail]; [e-mail]
    %%%%    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

NumberofTrainingData=size(P,2);
NumberofInputNeurons=size(P,1);

if Elm_Type~=REGRESSION
    %%%%%%%%%%%% Preprocessing the data of classification
    sorted_target=sort(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
        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;
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 = hardlim(tempH);            
        %%%%%%%% More activation functions can be added here                
end
clear tempH;                                        %   Release the temparary array for calculation of hidden neuron output matrix H

%%%%%%%%%%% Calculate output weights OutputWeight (beta_i)
OutputWeight=pinv(H') * T';
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
    output=Y;    
end
clear H;

if Elm_Type == CLASSIFIER
%%%%%%%%%% Calculate training & testing classification accuracy
    MissClassificationRate_Training=0;

    for i = 1 : size(T, 2)
        [x, label_index_expected]=max(T(:,i));
        [x, label_index_actual]=max(Y(:,i));
        output(i)=label(label_index_actual);
        if label_index_actual~=label_index_expected
            MissClassificationRate_Training=MissClassificationRate_Training+1;
        end
    end
    TrainingAccuracy=1-MissClassificationRate_Training/NumberofTrainingData
end

if Elm_Type~=REGRESSION
    save('elm_model', 'NumberofInputNeurons', 'NumberofOutputNeurons', 'InputWeight', 'BiasofHiddenNeurons', 'OutputWeight', 'ActivationFunction', 'label', 'Elm_Type');
else
    save('elm_model', 'InputWeight', 'BiasofHiddenNeurons', 'OutputWeight', 'ActivationFunction', 'Elm_Type');    
end