www.pudn.com > GramSchmidt.zip > Orth_GramSchmidt.m

function [TrainingTime, TrainingAccuracy, TestingAccuracy]=Orth_GramSchmidt(TrainingData_File, TestingData_File, NumberofHiddenNeurons, ActivationFunction, Problem_Type) 
 
 
% How to run like this : Orth_GramSchmidt( 'abalone_train', 'abalone_test', 10, 'sig', 0); 
 
REGRESSION = 0; 
CLASSIFIER = 1; 
 
%%%%%%%%%%% Load training dataset 
train_data=load(TrainingData_File); 
T=train_data(:,1)'; 
I=train_data(:,2:size(train_data,2))'; 
clear train_data;                                   %   Release raw training data array 
 
%%%%%%%%%%% Load testing dataset 
test_data=load(TestingData_File); 
t_testing=test_data(:,1)'; 
x_testing=test_data(:,2:size(test_data,2))'; 
clear test_data;    
 
NumberofTrainingData=size(I,2); 
NumberofTestingData=size(x_testing,2); 
NumberofInputNeurons=size(I,1); 
NumberofOutputNeurons=size(T,1); 
 
if Problem_Type~=REGRESSION 
    %%%%%%%%%%%% Preprocessing the data of classification 
    sorted_target=sort(cat(2,T,t_testing),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) == t_testing(1,i) 
                break;  
            end 
        end 
        temp_TV_T(j,i)=1; 
    end 
    t_testing = temp_TV_T*2-1; 
end   
clear temp_T; 
 
% Q=zeros(NumberofTrainingData,NumberofTrainingData); 
Htrainout=zeros(NumberofTrainingData,NumberofHiddenNeurons); 
InputWeight=zeros(NumberofInputNeurons,NumberofHiddenNeurons); 
HiddenBias=zeros(1,NumberofHiddenNeurons); 
L=0; 
 
starting_cpu=cputime; 
%%%%%%%%%%%%%%%     Train matrix    %%%%%%%% 
while L1 
            y1=0; 
        elseif x1>0 
            y1=1-x1; 
        else 
            y1=x1+1; 
        end 
    case {'hardlim'} 
        %%%%%%%% Hardlimit 
        x1=w'*x+b; 
        y1=sign(x1); 
    case {'gau'} 
        %%%%%%%% Gaussian 
        x1=w'*x+b; 
        y1=exp(-x1.^2); 
    case {'sig','sigmoid'} 
        %%%%%%%% Sigmoid  
        bias_vector = b*ones(1,size(x,2)); 
        y1=1./(1+exp(-(w'*x+bias_vector))); 
    case {'windows'} 
        %%%%%%%% windows 
        x1=w'*x+b; 
        traina = x1<=1; 
        trainb = x1>=-1;     
        y1 = traina.*trainb+0.0001; 
        %%%%%%%% More activation functions can be added here 
         
end