som.m

竞争学习的matlab工具箱

% Basic implementation of the Kohonen Map
%
% Source: 
%    T. Kohonen (1997). Self-Organizing Maps, 2nd. Edition,
%    Springer-Verlag.
%
% NOTE: This implementation aims to be simple and direct. More powerful
% implementations of the SOM can be found in the SOMtoolbox demos.
% 
% Authors: Guilherme A. Barreto
% Date: November 17th 2005

clear; clc; close all;

% Load data
load dataset1.dat;
Dw=dataset1; clear dataset1

% Get size of data matrix (1 input vector per row)
[LEN_DATA DIM_INPUT]=size(Dw);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Define size of the network %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Mx = 4;   % Number of neurons in the X-dimension
My = 4;   % Number of neurons in the Y-dimension
MAP_SIZE = [Mx My];        % Size of SOM map

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Create a CL network structure  %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
sMap = som_map_struct(DIM_INPUT,'msize',MAP_SIZE,'rect','sheet');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Different weights initialization methods %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% sMap  = som_randinit(Dw, sMap);   % Random weight initialization
% sMap  = som_lininit(Dw, sMap);    % Linear weight initialization
I=randperm(LEN_DATA); sMap.codebook=Dw(I(1:Mx*My),:);  % Select Mx*My data vectors at random

Co=som_unit_coords(sMap); % Coordinates of neurons in the map

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Specification of some training parameters %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
si=round(max(Mx,My)/2);  % Initial neighborhood
sf=0.001;                % Final neighborhood
ei=0.8;                  % Initial learning rate
ef=0.001;              % Final learning rate
Nep=100;                % Number of epochs
Tmax=LEN_DATA*Nep;     % Maximum number of iterations
T=0:Tmax;              % Time index for training iteration
eta=ei*power(ef/ei,T/Tmax);  % Learning rate vector
sig=si*power(sf/si,T/Tmax);  % Neighborhood width vector

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Train Kohonen Map (TKM)  %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
counter=zeros(1,Mx*My);  % Counter for the number of victories
for t=1:Nep,  % loop for the epochs
    
    epoch=t, % Show current epoch
    for tt=1:LEN_DATA,
         % Compute distances of all prototype vectors to current input
         Di=sqrt(som_eucdist2(sMap,Dw(tt,:)));

         % Find the BMU (i.e. the one with minimum value for Di)
         [Di_min win] = min(Di);
         counter(win)=counter(win)+1;   % Increment the number of victories of the winner
     
         % Update the weights of the winner and its neighbors
         T=(t-1)*LEN_DATA+tt;    % iteration throughout the epochs
         for i=1:Mx*My,
             % Squared distance (in map coordinates) between winner and neuron i
             D2=power(norm(Co(win,:)-Co(i,:)),2);
             
             % Compute corresponding value of the neighborhood function
             H=exp(-0.5*D2/(sig(T)*sig(T)));
             
             % Update the weights of neuron i
             sMap.codebook(i,:)=sMap.codebook(i,:) + eta(T)*H*(Dw(tt,:)-sMap.codebook(i,:));
         end
    end
    % Quantization error per training epoch
    Qerr(t) = som_quality(sMap, Dw);
end


% Plot prototypes and data altogether
figure, plot(Dw(:,1),Dw(:,2),'+r'), hold on
som_grid(sMap,'Coord',sMap.codebook)
%plot(sMap.codebook(:,1),sMap.codebook(:,2),'b*')
title('Prototype vectors in input space'), hold off

% Plot quantization error evolution per training epoch
figure, plot(Qerr) 
title('Quantization Error per Training Epoch')

% A bar plot of the number of victories per neuron throughout epochs
figure, bar(1:Mx*My,counter)
title('Victories per neuron')