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

%%  som mapping and distinct the classs of significant and not 
%------------------------  Mapping the positve training samples-------------------------------------- 
 function [pos_train_sample,pos_test_sample,neg_train_sample,neg_test_sample,net]  = SOM_choose(pos_sample,neg_sample,sizeout,SOM_epochs,net) 
   P = [pos_sample, neg_sample]; 
   [feature_num sample_num] = size(P);  
   pos_num = size(pos_sample,2); 
   neg_num = size(neg_sample,2); 
   SOM_out_num = [sizeout  sizeout];                  % 输出节点数目    
   SOM_input_num = feature_num;           % 输入节点数目 
    
    MinMaxValue = [zeros(feature_num ,1),ones(feature_num,1)];  
%     net  = newsom( MinMaxValue,SOM_out_num); 
%     net.trainParam.epochs = SOM_epochs;    
%     net.trainParam.show = 1000; 
    
%     -------- som training  
    begin_time = clock; 
%     [net,tr,Y,E,Pf,Af] = train(net,P); 
    Y = sim(net,P); 
    cost_time = etime(clock,begin_time) 
 
    %  map distribution   
    [Maxvalue max_num] = max(Y);     
    distr = zeros(pos_num,2); 
    for(i=1:sample_num) 
        distr(i,1) = floor((max_num(i)-1)/sizeout)+1; 
        distr(i,2) = max_num(i)-( distr(i,1) - 1 )*sizeout+1; 
        distr(i,2) = rem(max_num(i)-1,sizeout)+1; 
    end 
 
     
    distr_pos = distr(1:pos_num,:); 
    distr_neg = distr(pos_num+1:sample_num,:); 
    % plot the distribution of the map result 
    figure 
    for(i=1:pos_num) 
        hold on 
        plot(distr_pos(i,1),distr_pos(i,2),'r*'); 
    end 
    for(i=1:neg_num) 
        hold on 
        plot(distr_neg(i,1),distr_neg(i,2),'bsquare'); 
    end 
    title('som result distribution ') 
     
    % cal the center of the positive class 
    center = floor(mean(distr_pos,1)); 
    plot(center(1),center(2),'rO','LineWidth',2,... 
                'MarkerEdgeColor','k',... 
                'MarkerFaceColor','g',... 
                'MarkerSize',10); 
    % cal the distance of the positive class, the mean distance, the max_num distance 
    dislength = zeros(pos_num,1); 
    dislength = dist(distr_pos,center'); 
    mean_dist = mean( dislength,1); 
    max_dist = max( dislength); 
    thresh_dist = floor((mean_dist+max_dist)/2); 
    train_num = 0; 
    test_num = 0; 
    train_som = zeros(feature_num,pos_num); 
    test_som = zeros(feature_num,pos_num); 
    for(i=1:pos_num) 
        if( dislength(i)<=thresh_dist) 
            train_num = train_num + 1; 
            train_som(:,train_num) = pos_sample(:,i); 
            % choose the train sample 
            hold on  
            plot(distr_pos(i,1),distr_pos(i,2),'c+'); 
        else 
            test_num = test_num + 1; 
            test_som(:,test_num) = pos_sample(:,i); 
        end 
    end 
    pos_train_sample = train_som(:,1:train_num); 
    pos_test_sample = test_som(:,1:test_num); 
     
 
     
   % cal the center of the positive class 
    center = floor(mean(distr_neg,1)); 
   hold on  
    plot(center(1),center(2),'rO','LineWidth',2,... 
                'MarkerEdgeColor','k',... 
                'MarkerFaceColor','g',... 
                'MarkerSize',10); 
 
    % cal the distance of the positive class, the mean distance, the max_num distance 
    dislength = zeros(neg_num,1); 
    dislength = dist(distr_neg,center'); 
    mean_dist = mean( dislength,1); 
    max_dist = max( dislength); 
    thresh_dist = floor((mean_dist+max_dist)/2); 
    train_num = 0; 
    test_num = 0; 
    train_som = zeros(feature_num,neg_num); 
    test_som = zeros(feature_num,neg_num); 
    for(i=1:neg_num) 
        if( dislength(i)<=thresh_dist) 
            train_num = train_num + 1; 
            train_som(:,train_num) = neg_sample(:,i); 
            % choose the train sample 
            hold on  
            plot(distr_neg(i,1),distr_neg(i,2),'Mx'); 
        else 
            test_num = test_num + 1; 
            test_som(:,test_num) = neg_sample(:,i); 
        end 
    end 
    neg_train_sample = train_som(:,1:train_num); 
    neg_test_sample = test_som(:,1:test_num);