createnetrsom.m

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

function Net = CreateNetRSOM(INNum , RSOMSize , varargin)
%   CreateNetRSOM   Create recurrent self-organizing map (RSOM) network
%                   structure
%
%   Inputs:     INNum:      Number of inputs
%               RSOMSize:   Number of neurons
%               PROPERTIES: See bellow.
%
%   Output:     Net:        RSOM network structure
%
%   Example:
%   1) Create a default network with two inputs and 4x6 map:
%   Net = CreateNetRSOM(1 , [4 , 6]);
%   2) Create a network with one input, 8x8 map, where the
%   neighborhood variance is 2 and learning function is 'RSOMHebbV01':
%   Net = CreateNetRSOM(1 , [8 , 8] , 'RSOMNeiFunP' , 2 , ...
%   'RSOMUnSLFun' , 'RSOMHebbV01');
%
%   This function creates a RSOM network structure that can be passed to
%   'SimNetRSOM' for simulation and learning and it has all necessary
%   information about the network.
%   There are several properties with default values that one can define
%   for the network, as listed bellow. These properties can be assigned in
%   a pairwised manner. Please check RSOMSimV01 and RSOMSimV02 for complete
%   examples on how to override default values.
%
%   Properties:
%   RSOMType:   Can be 'RecSOM' for Recursive Self-Organizing Maps [1] or
%               'SOMSD' for Self-Organizing Maps for Structured Data [2].
%               Default is 'RecSOM'.
%   RSOMTopo:   Topology of the network, can be 'Hex' forhexagonal or
%               'Grid' for reqular square lattice connections. Default is
%               'Hex'.
%   RSOMNeiFunP: Variance parameter for neighborhood function, default = 5.
%   RSOMUnSLFun: Learning function, should be a file name or 'None' if no
%               training is required. Defaults is 'None'.
%   RSOMUnSLStepIN: Learning rate for input weights, default = 0.1.
%   RSOMUnSLStepC: Learning rate for recurrent weights, default = 0.1.
%
%   [1]. T. Voegtlin. "Recursive self-organizing maps". Neur.Netw.,
%   15(8-9):979-991, 2002.
%   [2]. M. Hagenbuchner, A. Sperduti, and A.C. Tsoi. "A Self-Organizing Map
%   for Adaptive Processing of Structured Data". IEEE Transactions on Neural
%   Networks, 14(3):491-505, 2003.
%
%   ---------------------------------------------------------
%   Amir Reza Saffari Azar Alamdari
%   http://www.ymer.org/main.htm , [e-mail]
%   ---------------------------------------------------------

%-----Initialization

%% RSOM
RSOMType    = 'RecSOM';

INRSOMWType = 'Random';
RSOMWType   = 'Random';

RSOMDist    = 'Link';
RSOMTopo    = 'Hex';
RSOMNeiFun  = 'Guass';
RSOMNeiFunP = 5;

RSOMDFun    = 'Euclid';
RSOMCFun    = 'Exp';
RSOMDINP    = 3;
RSOMDCP     = 1;

RSOMUnSLMode    = 'Online';
RSOMUnSLFun     = 'None';
RSOMUnSLStepIN  = 0.1;
RSOMUnSLStepC   = 0.1;

%% Misc.
ShowMessages    = 1;

%% Checking inputs
if length(varargin) > 0
    
    VarCounter  = 1;
    
    while VarCounter < length(varargin)
        CurArg  = varargin{VarCounter};
        
        switch  CurArg
            
            case 'RSOMType'
                RSOMType    = varargin{VarCounter + 1};
                VarCounter  = VarCounter + 2;
            
            case 'INRSOMWType'
                INRSOMWType = varargin{VarCounter + 1};
                VarCounter  = VarCounter + 2;
                
            case 'RSOMWType'
                RSOMWType   = varargin{VarCounter + 1};
                VarCounter  = VarCounter + 2;
                
            case 'RSOMDist'
                RSOMDist    = varargin{VarCounter + 1};
                VarCounter  = VarCounter + 2;
                
            case 'RSOMTopo'
                RSOMTopo    = varargin{VarCounter + 1};
                VarCounter  = VarCounter + 2;
                
            case 'RSOMNeiFun'
                RSOMNeiFun  = varargin{VarCounter + 1};
                VarCounter  = VarCounter + 2;
                
            case 'RSOMNeiFunP'
                RSOMNeiFunP = varargin{VarCounter + 1};
                VarCounter  = VarCounter + 2;
                
            case 'RSOMDFun'
                RSOMDFun    = varargin{VarCounter + 1};
                VarCounter  = VarCounter + 2;
                
            case 'RSOMCFun'
                RSOMCFun    = varargin{VarCounter + 1};
                VarCounter  = VarCounter + 2;
                
            case 'RSOMDINP'
                RSOMDINP = varargin{VarCounter + 1};
                VarCounter  = VarCounter + 2;
                
            case 'RSOMDCP'
                RSOMDCP = varargin{VarCounter + 1};
                VarCounter  = VarCounter + 2;
                
            case 'RSOMUnSLMode'
                RSOMUnSLMode    = varargin{VarCounter + 1};
                VarCounter      = VarCounter + 2;
                
            case 'RSOMUnSLFun'
                RSOMUnSLFun     = varargin{VarCounter + 1};
                VarCounter      = VarCounter + 2;
                
            case 'RSOMUnSLStepIN'
                RSOMUnSLStepIN  = varargin{VarCounter + 1};
                VarCounter      = VarCounter + 2;
                
            case 'RSOMUnSLStepC'
                RSOMUnSLStepC   = varargin{VarCounter + 1};
                VarCounter      = VarCounter + 2;
                
            case 'ShowMessages'
                ShowMessages    = varargin{VarCounter + 1};
                VarCounter      = VarCounter + 2;
                
            otherwise
                error('Unknown input !!!')
                
        end
    end
end

%-----Creating weights
%% RSOM
RSOMNum = prod(RSOMSize);

switch INRSOMWType
    
    case 'Random'
        
        WINRSOM = randn(INNum , RSOMNum);
        
    otherwise
        
        error('Unknown INRSOMWType !!!')
        
end

switch RSOMType
    
    case 'RecSOM'
        
        switch RSOMWType

            case 'Random'

                WRSOM   = randn(RSOMNum , RSOMNum);

            otherwise

                error('Unknown RSOMWType !!!')

        end
        
        TempState   = zeros(RSOMNum , 1);
        
    case 'SOMSD'
        
        switch RSOMWType

            case 'Random'

                WRSOM   = max(RSOMSize)*rand(size(RSOMSize , 2) , RSOMNum);

            otherwise

                error('Unknown RSOMWType !!!')

        end
        
        TempState   = zeros(size(RSOMSize , 2) , 1);
        
    otherwise
        
        error('Unknown RSOMType !!!')
        
end

switch RSOMTopo
    
    case 'Hex'
        
        RSOMPos = hextop(RSOMSize);
        
    case 'Grid'
        
        RSOMPos = gridtop(RSOMSize);
        
    otherwise
        
        error('Unknown RSOMTopo !!!')
        
end

switch RSOMDist
    
    case 'Link'
        
        RSOMPosD    = linkdist(RSOMPos);
        
    otherwise
        
        error('Unknown RSOMDist !!!')
        
end

%-----Creating network object

%% RSOM
DimStr      = struct('INNum' , INNum , 'RSOMNum' , RSOMNum , 'RSOMSize' , RSOMSize);

RSOMStr     = struct('RSOMType' , RSOMType , 'INRSOMWType' , INRSOMWType , 'RSOMWType' , RSOMWType , 'RSOMDist' , RSOMDist , 'RSOMTopo' , RSOMTopo , ...
    'RSOMNeiFun' , RSOMNeiFun , 'RSOMNeiFunP' , RSOMNeiFunP , 'RSOMDFun' , RSOMDFun , 'RSOMCFun' , RSOMCFun , 'RSOMDINP' , RSOMDINP , ...
    'RSOMDCP' , RSOMDCP , 'RSOMPos' , RSOMPos , 'RSOMPosD' , RSOMPosD);

RSOMLStr    = struct('RSOMUnSLMode' , RSOMUnSLMode , 'RSOMUnSLFun' , RSOMUnSLFun , 'RSOMUnSLStepIN' , RSOMUnSLStepIN , ...
    'RSOMUnSLStepC' , RSOMUnSLStepC);

RSOMSStr    = struct('RSOMState' , TempState , 'Epoch' , 1 , 'MaxEpoch' , 100);

RSOMWStr    = struct('WINRSOM' , WINRSOM , 'WRSOM' , WRSOM);

MiscStr     = struct('ShowMessages' , ShowMessages);

%% Net
Net = struct('Dim' , DimStr , 'RSOM' , RSOMStr , 'W' , RSOMWStr , 'Learn' , RSOMLStr , 'State' , RSOMSStr , 'Misc' , MiscStr);

if ShowMessages
    disp('Creating network: Done.')
end

return