simnetrsom.m

a neural network,Recursive SOM and Marge SOM ,can be use for time series and data fit.

function Net = SimNetRSOM(Net , IN)
%   SimNetRSOM  Simulate a RSOM network
%   
%   Inputs:     Net:    Network structure created by 'CreateNetRSOM'
%                       function.
%               IN:     Input time series.
%
%   Outputs:    Net:    Simulated network with results for Winners, State
%                       Variables, and Weights. These values can be
%                       accessed from 'State' field of network structure:
%                       Net.State.Winners , Net.State.RSOMState.
%                       The results of trained weights also can be
%                       addressed from 'W' field: Net.W.WINRSOM ,
%                       Net.W.WRSOM.
%
%   ---------------------------------------------------------
%   Amir Reza Saffari Azar Alamdari
%   http://www.ymer.org/main.htm , [e-mail]
%   ---------------------------------------------------------

%% Initializing
INNum       = Net.Dim.INNum;
RSOMNum     = Net.Dim.RSOMNum;
RSOMSize    = Net.Dim.RSOMSize;

WINRSOM     = Net.W.WINRSOM;
WRSOM       = Net.W.WRSOM;

RSOMType    = Net.RSOM.RSOMType;
RSOMDFun    = Net.RSOM.RSOMDFun;
RSOMCFun    = Net.RSOM.RSOMCFun;
RSOMDINP    = Net.RSOM.RSOMDINP;
RSOMDCP     = Net.RSOM.RSOMDCP;

RSOMUnSLMode    = Net.Learn.RSOMUnSLMode;
RSOMUnSLFun     = Net.Learn.RSOMUnSLFun;

%% Simulating
StopTime    = size(IN , 2);
WinnerInd   = zeros(1 , StopTime);

switch RSOMType
    
    case 'RecSOM'
        
        RSOMState           = zeros(RSOMNum , StopTime + 1);
        RSOMState(: , 1)    = Net.State.RSOMState(: , end);
        
    case 'SOMSD'
        
        RSOMState           = zeros(size(RSOMSize , 2) , StopTime + 1);
        RSOMState(: , 1)    = Net.State.RSOMState(: , end);
        
    otherwise
        
        error('Unknown RSOMType !!!')
        
end

for n = 2:StopTime + 1
    
    I   = IN(: , n - 1);
    
    switch RSOMDFun
        
        case 'Euclid'
            
            DINTemp = sum((WINRSOM - repmat(I , 1 , RSOMNum)).^2 , 1);
            DCTemp  = sum((WRSOM - repmat(RSOMState(: , n - 1) , 1 , RSOMNum)).^2 , 1);
            DTemp   = RSOMDINP*DINTemp + RSOMDCP*DCTemp;
            
            TempWinner          = find(DTemp == min(DTemp));
            WinnerInd(n - 1)    = TempWinner(1);
            
            switch RSOMType
                
                case 'RecSOM'
                    
                    switch RSOMCFun

                        case 'Exp'

                            RSOMState(: , n)    = exp(-DTemp)';

                        otherwise

                            error('Unknown RSOMCFun !!!')

                    end
                    
                case 'SOMSD'
                    
                    [Temp(1) , Temp(2)] = ind2sub(RSOMSize , WinnerInd(n - 1));
                    
                    RSOMState(: , n)    = Temp';

            end
            
            
        otherwise
            
            error('Unknown RSOMDFun !!!')
            
    end
    
    %% Learning
    
    switch RSOMUnSLMode
        
        case 'Online'
            
            switch RSOMUnSLFun
                
                case 'None'
                    
                    
                otherwise
                    
                    Net = feval(RSOMUnSLFun , Net , I , RSOMState(: , n - 1) , WinnerInd(n - 1));
                    
                    WINRSOM     = Net.W.WINRSOM;
                    WRSOM       = Net.W.WRSOM;
                    
            end
            
        otherwise
            
            error('Unknown RSOMUnSLMode !!!')
    end
    
end


%% Storing
Net.State.Winners   = WinnerInd;
Net.State.RSOMState = RSOMState;
Net.State.StopTime  = StopTime;

if Net.Misc.ShowMessages
    disp('Simulating network: Done.')
end

return