www.pudn.com > SOM_Bp_HybridNetwork_matlab_emulator.rar > SOM_03.m

%------  SOM test-02 -------- 
clear 
clc 
echo off 
% --------------- initial the parameter 
out_num = 3;                  % 输出节点数目 
input_num = 5;                % 输入节点数目 
Epochs = 1;                  % 训练周期 
positive_num = 55;       %  正常图像数目 
negative_num = 75;       %  异常图像数目 
sum_num = positive_num + negative_num; % 总的图像数目 
 
% --------------- read data from the file 
base_path = 'E:\SOMBP\data_source\feature02\';                      % minmax([I1';I2';I3';I4';I5';I6';I7';I8']) 
I1 = load([base_path,'apen.txt']);           % 1 >> 2        1.2563    1.8150         
I2 = load([base_path,'kc.txt']);             % 0 >> 1        0.6299    0.9611 
I3 = load([base_path,'mir.txt']);            % 0 >> 5        0.4802    4.0544 
I4 = load([base_path,'asm.txt']);            % 0 >> 0.01    0.0004    0.0037 
I5 = load([base_path,'idm.txt']);            % 0 >> 0.5       0.0663    0.2906 
I6 = load([base_path,'cont.txt']);           % 0 >> 500     12.2600  337.5440 
I7 = load([base_path,'ent.txt']);            % 5 >> 9        5.8675    7.9858 
I8 = load([base_path,'gln.txt']);            % 10 >> 60      13.4204   53.2048 
% normalize  
P = [(I1'-ones(1,sum_num));I2';(I3'./5);(I4'./0.01);I5'./0.5];%(I6'./500);(I7'-ones(1,sum_num).*5)./4;(I8'-ones(1,sum_num).*10)./50]; 
MinMaxValue = [zeros(input_num ,1),ones(input_num ,1)];  
 
% NEWSOM ---- create the som net  
net = newsom( MinMaxValue,[out_num]); 
net.trainParam.show = 1000; 
% net.parameter 
 
% TRAIN ----- train the net 
Epochs = 0; 
savePath1 = 'E:\SOMBP\save_result\8feature03\'; 
file = '.mat'; 
 
M = 10;%16; 
train_num = [1,10,100,500,1000,2000,3000,5000,10000,20000,30000,50000,100000,200000,500000,1000000]; 
 
time_record = zeros(1,M); 
mix_record = zeros(M,out_num,2); 
% FN_record = zeros(1,M); 
% FP_record = zeros(1,M); 
 
for(i=1:M) 
% train  
    if (i==1) 
        Epochs = 1; 
    else 
        Epochs = train_num(i) - train_num(i-1); 
    end 
    net.trainParam.epochs = Epochs; 
    %  train 
    begin_time = clock; 
    [net,tr,Y,E,Pf,Af] = train(net,P); 
    cost_time = etime(clock,begin_time) 
    time_record(i) = cost_time; 
    %  my sim    
    SOMresult = dist(net.IW{1},P); 
    [minValue minNum] = min(SOMresult); 
    % give some test (stastical test) 
    mixture = zeros(out_num,2);                         %  the positve and negative in classes 
    for(k=1:out_num) 
        temp = 0; 
        for(j=1:positive_num) 
            if( minNum(j)==k) 
                temp = temp + 1; 
            end 
        end 
        mixture(k,1) = temp; 
        temp = 0; 
        for(j=(positive_num+1):sum_num) 
            if( minNum(j)==k) 
                temp = temp + 1; 
            end 
        end 
        mixture(k,2) = temp; 
    end 
    mix_record(i,:,:) = mixture(:,:);  
        
% OUTPUT ----- out put the som data which made as the bp input data 
    savePath2 = [savePath1 'SOMresult' num2str(train_num(i)) file]; 
    save(savePath2, 'SOMresult','mixture', 'net', 'tr', 'Y' ,'E', 'Pf','Af','P');  
end 
    savePath3 = [savePath1 'statis_som'  file]; 
    save(savePath3,'time_record','mix_record'); 
     
     
%     save(savePath3,'time_record','FN_record','FP_record','FN' ,'FP','Sens', 'Spec');   
%     FN = 0;   %  False-negative rate 
%     FP = 0;   %  False-positive rate 
%     Sens = 0; %  1-FN 
%     Spec = 0; %  1-FP 
%     for(j=1:positive_num) 
%         if(minNum(1,j)~=1) 
%             FP = FP+1; 
%         end 
%     end 
%     for(j= (positive_num+1):sum_num) 
%         if(minNum(1,j)==1) 
%             FN = FN+1; 
%         end 
%     end 
%     FN = FN/negative_num; 
%     FP = FP/positive_num; 
%     Sens = 1-FN; 
%     Spec = 1-FP; 
%     FN_record(i) = FN; 
%     FP_record(i) = FP;