rpcl.m

竞争学习的matlab工具箱

% MATLAB implementation of Rival-Penalized Competitive Learning (RPCL)
% Source: 
%    L. Xu, A. Krzyzak and E. Oja 1993, "Rival penalized competitive
%    learning for clustering analysis, RBF net, and curve detection," 
%    IEEE Trans. Neural Networks, vol. 4, no. 4, p. 636--648. 
%
%    Note: According to the RPCL algorithm, once training is completed,
%          prototypes located far away from the data clusters should 
%          be eliminated.
% 
% Code authors: Guilherme A. Barreto
% Date: October 18th 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);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Create a SOM structure  %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Mx = 16;   % Number of neurons
MAP_SIZE = [Mx 1];        % Size of SOM map (always use 1-D 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),:);  % Select Mx*My data vectors at random

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Train the RPCL algorithm   %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Ep=200;      % number of training epochs
alpha=0.05;  % Learning rate of the 1st winner
eta=0.001;   % Learning rate of the 2nd winner (rival)

counter=zeros(Mx,1);   % Counter for the number of victories
freq=zeros(Mx,1);      % Relative frequency of victories
for t=1:Ep,  % loop for training epochs
    Epoca=t,
    
    % Shuffle input data vectors at each training epoch
    I=randperm(LEN_DATA);  % shuffle the row indices
    Dw=Dw(I,:);
    
    for tt=1:LEN_DATA, % loop for iteration within an epoch
        
        % Plain Euclidean distances from current input to all neurons
        Di=sqrt(som_eucdist2(sMap,Dw(tt,:))); % Compute Euclidean distances for all neurons
        
        % Weighted Euclidean distances 
        WDi=freq.*Di;
        
        % Find the 1st winner using Wdi
        [WDi1 win1]=min(WDi);
        
        % Find the 2nd winner using Wdi
        WDi(win1)=10^4;  % Insert a large value to avoid the 1st winner to be chosen again
        [WDi2 win2]=min(WDi);
        
        % Reward the 1st winner
        sMap.codebook(win1,:)=sMap.codebook(win1,:) + alpha*(Dw(tt,:)-sMap.codebook(win1,:));
        
        % Penalize the 2nd winner (rival)
        sMap.codebook(win2,:)=sMap.codebook(win2,:) - eta*(Dw(tt,:)-sMap.codebook(win2,:));        
        
        % Update counter of victories of the 1st winner
        counter(win1)=counter(win1)+1;
        
        % Update the relative frequency of victories of the winner
        freq(win1)= counter(win1)/sum(counter);
    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
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,counter)
title('Victories per neuron')