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‎dbfun.m

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function DB=dbfun(data,net)
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% Calculate Davies–Bouldin index
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% Directed Batch Growing Self Organizing Map (DBGSOM)
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% version 1.0 - July 2017
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% Mahdi Vasighi
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% Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
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% Department of Computer Science and Information Technology
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% www.iasbs.ac.ir/~vasighi/
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W=net.W;
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hitcount=net.hitcount;
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winlist=net.winlist;
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[data,~]=prefun(data,'rs');
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for i=1:size(W,2)
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for j=i+1:size(W,2)
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if hitcount(j)~=0 && hitcount(i)~=0;
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di=mean(dist(data(winlist==i,:),W(:,i)));
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dj=mean(dist(data(winlist==j,:),W(:,j)));
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dij=dist(W(:,i)',W(:,j));
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D2(i,j)=(di+dj)/dij;
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D2(j,i)=(di+dj)/dij;
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end
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end
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end
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DB=sum(max(D2))/sum(hitcount~=0);

‎dbgrowfun.m

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function net=dbgrowfun(net,netset,ds,bo_id)
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% Growing phase function in batch mode learning
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% Directed Batch Growing Self Organizing Map (DBGSOM)
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% version 1.0 - July 2017
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% Mahdi Vasighi
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% Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
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% Department of Computer Science and Information Technology
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% www.iasbs.ac.ir/~vasighi/
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W=net.W;
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Err=net.Err;
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grd=net.grd;
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[sorted_Err,x1id]=sort(Err(bo_id),'descend');
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bo_id=bo_id(x1id);
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bo_id=bo_id(Err(bo_id)>=netset.GT); % Run growing phase only for b-node (Err>=GT)
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%Growing phase
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for i=1:length(bo_id)
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new_grd=[];
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W_new_grd=[];
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new_Err=[];
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notallowed_pos=[ismember([grd(:,bo_id(i))+[0;1]]',grd','rows') ismember([grd(:,bo_id(i))+[0;-1]]',grd','rows'),...
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[ismember([grd(:,bo_id(i))+[1;0]]',grd','rows') ismember([grd(:,bo_id(i))+[-1;0]]',grd','rows')]];
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direct_opr=[0 0 1 -1;1 -1 0 0];%[N S E W] directions
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direct_opr(:,notallowed_pos)=[];
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allowed_grd=repmat(grd(:,bo_id(i)),1,size(direct_opr,2))+direct_opr;
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if ~isempty(allowed_grd)
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nbr1_id=find(ds(bo_id(i),:)==1);
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nbr2_id=find(ds(bo_id(i),:)==2);
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%sorting neighbirs of boundary nodes
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[sorted_Err1,x1id]=sort(Err(nbr1_id),'descend');%
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nbr1_id=nbr1_id(x1id);
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[sorted_Err2,x2id]=sort(Err(nbr2_id),'descend');%
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nbr2_id=nbr2_id(x2id);
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if size(allowed_grd,2)==3 && length(nbr1_id)==1 %three possible postion to grow
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sel_nbr2_id = nbr2_id(min(dist(allowed_grd',grd(:,nbr2_id)))==1);
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nbr1_dist = dist(grd(:,nbr1_id)',allowed_grd);
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new_Err=0;
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if isempty(sel_nbr2_id) % if there is no allowed nbr2
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temp_id=find(nbr1_dist~=2);
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new_grd = allowed_grd(:,temp_id(1));
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W_new_grd = 2.*W(:,bo_id(i))-W(:,nbr1_id);
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else % there is nbr2
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[mval,max_id]=max(Err(sel_nbr2_id));
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nbr2_dist = dist(allowed_grd',grd(:,sel_nbr2_id(max_id)));
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new_grd = allowed_grd(:,nbr2_dist==1);
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W_new_grd = (W(:,bo_id(i))+W(:,sel_nbr2_id(max_id)))/2;
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end
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elseif size(allowed_grd,2)==2 && length(nbr1_id)==2%two possible positions to grow
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[~,max_id]=max(Err(nbr1_id));
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[~,min_id]=min(Err(nbr1_id));
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[~,ind]=min(dist(grd(:,nbr1_id(max_id))',allowed_grd));
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sel_nbr2_id = nbr2_id(min(dist(allowed_grd',grd(:,nbr2_id)))==1);
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[~,max2_id]=max(Err(sel_nbr2_id));
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new_grd=allowed_grd(:,ind(1));
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new_Err=0;
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if isempty(sel_nbr2_id) % if there is no adj nbr2
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W_new_grd = 2.*W(:,bo_id(i))-W(:,nbr1_id(min_id)); % can be change (mean with bo)
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else
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if dist(grd(nbr1_id(max_id))',grd(:,sel_nbr2_id(max2_id)))==1 % nbr2 is adj to selected allowed grd
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W_new_grd = (2.*W(:,bo_id(i))-W(:,nbr1_id(min_id))+W(:,sel_nbr2_id(max2_id)))/2;
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else
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W_new_grd = 2.*W(:,bo_id(i))-W(:,nbr1_id(min_id));
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end
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end
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else
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if length(nbr1_id)==3 %one possible position to grow
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new_grd=allowed_grd;
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new_Err=0;
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nbr1_dist = dist(allowed_grd',grd(:,nbr1_id));
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sel_nbr2_id = nbr2_id(min(dist(allowed_grd',grd(:,nbr2_id)))==1);
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if isempty(sel_nbr2_id) % if there is no adj nbr2
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W_new_grd = 2.*W(:,bo_id(i))-W(:,nbr1_id(nbr1_dist==2));
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else
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[~,max2_id]=max(Err(sel_nbr2_id));
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W_new_grd = (2.*W(:,bo_id(i))-W(:,nbr1_id(nbr1_dist==2))+W(:,sel_nbr2_id(max2_id)))/2;
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end
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end
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end
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grd=[grd,new_grd];
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W=[W,W_new_grd];
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Err=[Err,new_Err];
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end
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end
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net.grd=grd;
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net.W=W;

‎dbgsom.m

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function net=dbgsom(data,netset)
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% Training function for DBGSOM
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% Directed Batch Growing Self Organizing Map (DBGSOM)
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% version 1.0 - July 2017
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% Mahdi Vasighi
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% Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
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% Department of Computer Science and Information Technology
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% www.iasbs.ac.ir/~vasighi/
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[n1,m1]=size(data);
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[data,pre_param]=prefun(data,'rs'); %data pre-processing
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data_tmp=data;
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net=dbgsom_init(data); %gsom initialization
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ds=linkdist(net.grd);
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epvec=1:netset.epch;
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netset.pvec=(netset.pmax-netset.pmin)*((netset.epch-epvec)/(netset.epch-1))+netset.pmin;
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netset.lrvec=(netset.amax-netset.amin)*((netset.epch-epvec)/(netset.epch-1))+netset.amin; % Learning rate
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netset.GT=-m1*log(netset.sf);
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fprintf('Data matrix (Samples=%i Variables=%i)\n',n1,m1)
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fprintf('DBGSOM (SF=%3.2f)\n',netset.sf)
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for ep=1:netset.epch
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if ~rem(ep,10)
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fprintf('Training epoch %i\n',ep)
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end
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h=nbrfun(netset,ds,ep);
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[~,net.win_list]=winfun(data,net.W);
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hh=h(:,net.win_list);
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h1=data'*hh';
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h2= true(size(data'))*hh';
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pos = find(h2 > 0);
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net.W(pos)=h1(pos)./h2(pos);
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if ep<netset.epch && ep>1
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%calulcating error of each neuron
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[win_err,win_id]=winfun(data,net.W);
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net.Err=errcalc(win_err,win_id,net.grd);
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% check to find boundary and non-boundary nodes.
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[nb_id,bo_id]=findbound(ds);
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% distibute the error of non-boundary nodes which has a higher error
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% than GT (batch mode)
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for jj=1:length(nb_id)
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if net.Err(nb_id(jj))>=netset.GT
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intersect_id=intersect(find(ds(nb_id(jj),:)==1),bo_id);
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if ~isempty(intersect_id)
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net.Err(intersect_id)=net.Err(intersect_id)+net.Err(nb_id(jj))/8; % error of a nb node is distributed equally between neighbors and itself
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net.Err(nb_id(jj))=net.Err(nb_id(jj))/2;
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end
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end
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end
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net=dbgrowfun(net,netset,ds,bo_id);
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ds=linkdist(net.grd);
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end
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% ploting neurons in data sapce
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if strcmp(netset.vis,'y')
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vistrn(net,data)
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end
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end
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fprintf('[Done]\n')
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[~,winlist]=winfun(data_tmp,net.W);
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[hitcount,~]=hist(winlist,size(net.W,2));
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net.winlist=winlist;
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net.hitcount=hitcount;
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net.pre_param=pre_param;
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net=net_eval(data,net);
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‎dbgsom_3rd.wmap_iris.tif

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‎dbgsom_demo.m

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% Directed Batch Growing Self Organizing Map (DBGSOM)
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% version 1.0 - July 2017
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% Mahdi Vasighi
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% Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
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% Department of Computer Science and Information Technology
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% www.iasbs.ac.ir/~vasighi/
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% Demo: Iris data analysis
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% Directed Batch Growing Self Organizing Map (DBGSOM), version 1.0
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% Copyright (C) 2017 Mahdi Vasighi
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%
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% This program is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% any later version.
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%
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% This program is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with this program. If not, see <http://www.gnu.org/licenses/>.
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close all
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clear
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clc
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load iris % load data matrix (samples x variables)
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netset=setting('dbgsom'); % initialize setting for dbgsom
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netset.epch = 100; % number of epochs
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netset.sf = 0.7; % Spread Factor setting
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netset.vis ='n';
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net=dbgsom(X,netset);
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figure(1);vismap(net,class) %Top-map visulaization
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figure(2);vishit(net) %Hit-map visualization

‎dbgsom_hitmap_iris.tif

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‎dbgsom_init.m

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function net=dbgsom_init(data)
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% DBGSOM initializatio
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% Directed Batch Growing Self Organizing Map (DBGSOM)
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% version 1.0 - July 2017
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% Mahdi Vasighi
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% Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
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% Department of Computer Science and Information Technology
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% www.iasbs.ac.ir/~vasighi/
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% Directed Batch Growing Self Organizing Map (DBGSOM), version 1.0
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% Copyright (C) 2017 Mahdi Vasighi
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%
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% This program is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% any later version.
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%
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% This program is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with this program. If not, see <http://www.gnu.org/licenses/>.
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grd=[0 0;1 0;0 1;1 1]'; %grid positions
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W=mean(data)'*ones(1,4)+0.1*randn(size(data,2),size(grd,2));
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% W=rand(size(X,2),size(grd,2));
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net.W=W;
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net.grd=grd;

‎dbgsom_topmap_iris.tif

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‎dmfun.m

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function DM=dmfun(data,net)
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% Calculate distorsion measure
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% Directed Batch Growing Self Organizing Map (DBGSOM)
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% version 1.0 - July 2017
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% Mahdi Vasighi
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% Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
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% Department of Computer Science and Information Technology
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% www.iasbs.ac.ir/~vasighi/
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% Directed Batch Growing Self Organizing Map (DBGSOM), version 1.0
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% Copyright (C) 2017 Mahdi Vasighi
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%
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% This program is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% any later version.
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%
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% This program is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with this program. If not, see <http://www.gnu.org/licenses/>.
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W=net.W;
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winlist=net.winlist;
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hitcount=net.hitcount;
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[data,~]=prefun(data,'rs');
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ntds=linkdist(net.grd);
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h = exp(-(ntds.^2)/(2*((1)^2)));
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hh=h(:,winlist);
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dst=dist(data,W);
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hitind=hitcount>0;
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dm=hh.*(dst.^2)';
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dm=dm(hitind,:);
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DM=mean(sum(dm./repmat(hitcount(hitind)',1,size(data,1))));

‎errcalc.m

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function Err=errcalc(win_err,win_id,grd)
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% Accumulative error calculation
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% Directed Batch Growing Self Organizing Map (DBGSOM)
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% version 1.0 - July 2017
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% Mahdi Vasighi
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% Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
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% Department of Computer Science and Information Technology
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% www.iasbs.ac.ir/~vasighi/
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% Directed Batch Growing Self Organizing Map (DBGSOM), version 1.0
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% Copyright (C) 2017 Mahdi Vasighi
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%
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% This program is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% any later version.
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%
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% This program is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with this program. If not, see <http://www.gnu.org/licenses/>.
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Err=zeros(1,size(grd,2));
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for i=1:size(grd,2)
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Err(i)=sum(win_err(find(win_id==i)));
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end

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