clear all
delta= 0.4;

%  iris data set: Input dimmension M=4;  Output dimmension: N=3
%  Wisconsine Brests Cencer data set: Input dimmension M=9;  Output dimmension: N=1
%  taxdata data set: Input dimmension M=71;  Output dimmension: N=1
%  biotrain data set: Input dimmension M=74;  Output dimmension: N=1
M=74;
N=1;
alfa = 0;
%********************************************
%load iris_tr
%Y= IRIS_training_data;
%clear IRIS_training_data

%load taxdata
%[nr,nc]= size(tax);
%wis= tax;

%load wine_a
%Y=wine_a;
%clear wine_a

% load wisconsine
% [nr,nc]=size(wisconsin);
% for i=1:1:nc-1   wis(:,i)= wisconsin(:,i);  end
load biotrain
yy = biotrain(:,2:75);
yy(:,75) = biotrain(:,1);
[nr,nc] = size(yy);
for i=1:1:nc   bio1(:,i) = yy(:,i);  end

% ii=floor(nr/2);
% for i=1:1:ii                    Y(i,:)=wis(i,:);    end
% for i=ii+1:1:nr                 S(i-ii,:)= wis(i,:);   end
% clear wisconsin  wis

ii=15000;
for i=1:1:ii                      Y(i,:) = bio1(i,:);   end
for i=ii+1:1:20000            S(i-ii,:)= bio1(i,:);   end
clear yy  bio1

[nr,nc]=size(Y);

%********************************************************
% training process
%***********************************************************
tic;CPUTIME=cputime;
%************************************************
nodeY = sgnt_generating_batch(Y, M+N, alfa);
%*******************************************************
TOC=toc;
CPUTIME=cputime-CPUTIME 

%*********************************************************
% test process
%*********************************************************
%load iris_te 
%S = IRIS_testing_data;
%clear IRIS_testing_data

%load wine_b
%S=wine_b;
%clear wine_b
[sr,sc]=size(S);

tic;CPUTIME=cputime;

[nr,nc]= size(nodeY);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Err=0;
i5=1;
for j=1:1:sr
   dmin= inf;
   for i=1:1:nr
      if nodeY(i,M+N+2) > 0
         for k=1:1:M  
             w(k)= nodeY(i,k);
             ss(k)= S(j,k);
         end
         d= (ss-w)*(ss-w)';
         if dmin > d   
            dmin= d;
            min= i;
         end
      end
   end
   
   for k=1:1:N
      x1(k)= S(j,M+k);
      x2(k)= nodeY(min,M+k);
   end
   ds= sqrt((x1-x2)*(x1-x2)');
   if ds > delta  
       Err= Err+1; 
       c(i5)=j;  
       i5=i5+1; 
   end
end

accuracy= (1-(Err/sr))*100   
Err=Err
TOC=toc;
CPUTIME2= cputime-CPUTIME