AP(affinity propagation）研究

%APCLUSTER Affinity Propagation Clustering (Frey/Dueck, Science 2007)
% [idx,netsim,dpsim,expref]=APCLUSTER(s,p) clusters data, using a set 
% of real-valued pairwise data point similarities as input. Clusters 
% are each represented by a cluster center data point (the "exemplar"). 
% The method is iterative and searches for clusters so as to maximize 
% an objective function, called net similarity.
% 
% For N data points, there are potentially N^2-N pairwise similarities; 
% this can be input as an N-by-N matrix ‘s‘, where s(i,k) is the 
% similarity of point i to point k (s(i,k) needn抰 equal s(k,i)).  In 
% fact, only a smaller number of relevant similarities are needed; if 
% only M similarity values are known (M < N^2-N) they can be input as 
% an M-by-3 matrix with each row being an (i,j,s(i,j)) triple.
% 
% APCLUSTER automatically determines the number of clusters based on 
% the input preference ‘p‘, a real-valued N-vector. p(i) indicates the 
% preference that data point i be chosen as an exemplar. Often a good 
% choice is to set all preferences to median(s); the number of clusters 
% identified can be adjusted by changing this value accordingly. If ‘p‘ 
% is a scalar, APCLUSTER assumes all preferences are that shared value.
% 
% The clustering solution is returned in idx. idx(j) is the index of 
% the exemplar for data point j; idx(j)==j indicates data point j 
% is itself an exemplar. The sum of the similarities of the data points to 
% their exemplars is returned as dpsim, the sum of the preferences of 
% the identified exemplars is returned in expref and the net similarity 
% objective function returned is their sum, i.e. netsim=dpsim+expref.
% 
%     [ ... ]=apcluster(s,p,‘NAME‘,VALUE,...) allows you to specify 
%       optional parameter name/value pairs as follows:
% 
%   ‘maxits‘     maximum number of iterations (default: 1000)
%   ‘convits‘    if the estimated exemplars stay fixed for convits 
%          iterations, APCLUSTER terminates early (default: 100)
%   ‘dampfact‘   update equation damping level in [0.5, 1).  Higher 
%        values correspond to heavy damping, which may be needed 
%        if oscillations occur. (default: 0.9)
%   ‘plot‘       (no value needed) Plots netsim after each iteration
%   ‘details‘    (no value needed) Outputs iteration-by-iteration 
%      details (greater memory requirements)
%   ‘nonoise‘    (no value needed) APCLUSTER adds a small amount of 
%      noise to ‘s‘ to prevent degenerate cases; this disables that.
% 
% Copyright (c) B.J. Frey & D. Dueck (2006). This software may be 
% freely used and distributed for non-commercial purposes.
%          (RUN APCLUSTER WITHOUT ARGUMENTS FOR DEMO CODE)
function [idx,netsim,dpsim,expref]=apcluster(s,p,varargin);
if nargin==0, % display demo
    fprintf(‘Affinity Propagation (APCLUSTER) sample/demo code\n\n‘);
    fprintf(‘N=100; x=rand(N,2); % Create N, 2-D data points\n‘);
    fprintf(‘M=N*N-N; s=zeros(M,3); % Make ALL N^2-N similarities\n‘);
    fprintf(‘j=1;\n‘);
    fprintf(‘for i=1:N\n‘);
    fprintf(‘  for k=[1:i-1,i+1:N]\n‘);
    fprintf(‘    s(j,1)=i; s(j,2)=k; s(j,3)=-sum((x(i,:)-x(k,:)).^2);\n‘);
    fprintf(‘    j=j+1;\n‘);
    fprintf(‘  end;\n‘);
    fprintf(‘end;\n‘);
    fprintf(‘p=median(s(:,3)); % Set preference to median similarity\n‘);
    fprintf(‘[idx,netsim,dpsim,expref]=apcluster(s,p,‘‘plot‘‘);\n‘);
    fprintf(‘fprintf(‘‘Number of clusters: %%d\\n‘‘,length(unique(idx)));\n‘);
    fprintf(‘fprintf(‘‘Fitness (net similarity): %%g\\n‘‘,netsim);\n‘);
    fprintf(‘figure; % Make a figures showing the data and the clusters\n‘);
    fprintf(‘for i=unique(idx)‘‘\n‘);
    fprintf(‘  ii=find(idx==i); h=plot(x(ii,1),x(ii,2),‘‘o‘‘); hold on;\n‘);
    fprintf(‘  col=rand(1,3); set(h,‘‘Color‘‘,col,‘‘MarkerFaceColor‘‘,col);\n‘);
    fprintf(‘  xi1=x(i,1)*ones(size(ii)); xi2=x(i,2)*ones(size(ii)); \n‘);
    fprintf(‘  line([x(ii,1),xi1]‘‘,[x(ii,2),xi2]‘‘,‘‘Color‘‘,col);\n‘);
    fprintf(‘end;\n‘);
    fprintf(‘axis equal tight;\n\n‘);
    return;
end;
start = clock;
% Handle arguments to function
if nargin<2 error(‘Too few input arguments‘);
else
    maxits=1000; convits=100; lam=0.9; plt=0; details=0; nonoise=0;
    i=1;
    while i<=length(varargin)
        if strcmp(varargin{i},‘plot‘)
            plt=1; i=i+1;
        elseif strcmp(varargin{i},‘details‘)
            details=1; i=i+1;
        elseif strcmp(varargin{i},‘sparse‘)
%             [idx,netsim,dpsim,expref]=apcluster_sparse(s,p,varargin{:});
            fprintf(‘‘‘sparse‘‘ argument no longer supported; see website for additional software\n\n‘);
            return;
        elseif strcmp(varargin{i},‘nonoise‘)
            nonoise=1; i=i+1;
        elseif strcmp(varargin{i},‘maxits‘)
            maxits=varargin{i+1};
            i=i+2;
            if maxits<=0 error(‘maxits must be a positive integer‘); end;
        elseif strcmp(varargin{i},‘convits‘)
            convits=varargin{i+1};
            i=i+2;
            if convits<=0 error(‘convits must be a positive integer‘); end;
        elseif strcmp(varargin{i},‘dampfact‘)
            lam=varargin{i+1};
            i=i+2;
            if (lam<0.5)||(lam>=1)
                error(‘dampfact must be >= 0.5 and < 1‘);
            end;
        else i=i+1;
        end;
    end;
end;
if lam>0.9
    fprintf(‘\n*** Warning: Large damping factor in use. Turn on plotting\n‘);
    fprintf(‘    to monitor the net similarity. The algorithm will\n‘);
    fprintf(‘    change decisions slowly, so consider using a larger value\n‘);
    fprintf(‘    of convits.\n\n‘);
end;

% Check that standard arguments are consistent in size
if length(size(s))~=2 error(‘s should be a 2D matrix‘);
elseif length(size(p))>2 error(‘p should be a vector or a scalar‘);
elseif size(s,2)==3
    tmp=max(max(s(:,1)),max(s(:,2)));
    if length(p)==1 N=tmp; else N=length(p); end;
    if tmp>N
        error(‘data point index exceeds number of data points‘);
    elseif min(min(s(:,1)),min(s(:,2)))<=0
        error(‘data point indices must be >= 1‘);
    end;
elseif size(s,1)==size(s,2)
    N=size(s,1);
    if (length(p)~=N)&&(length(p)~=1)
        error(‘p should be scalar or a vector of size N‘);
    end;
else error(‘s must have 3 columns or be square‘); end;

% Construct similarity matrix
if N>3000
    fprintf(‘\n*** Warning: Large memory request. Consider activating\n‘);
    fprintf(‘    the sparse version of APCLUSTER.\n\n‘);
end;
if size(s,2)==3 && size(s,1)~=3,
    S=-Inf*ones(N,N,class(s)); 
    for j=1:size(s,1), S(s(j,1),s(j,2))=s(j,3); end;
else S=s;
end;

if S==S‘, symmetric=true; else symmetric=false; end;
realmin_=realmin(class(s)); realmax_=realmax(class(s));

% In case user did not remove degeneracies from the input similarities,
% avoid degenerate solutions by adding a small amount of noise to the
% input similarities
if ~nonoise
    rns=randn(‘state‘); randn(‘state‘,0);
    S=S+(eps*S+realmin_*100).*rand(N,N);
    randn(‘state‘,rns);
end;

% Place preferences on the diagonal of S
if length(p)==1 for i=1:N S(i,i)=p; end;
else for i=1:N S(i,i)=p(i); end;
end;

% Numerical stability -- replace -INF with -realmax
n=find(S<-realmax_); if ~isempty(n), warning(‘-INF similarities detected; changing to -REALMAX to ensure numerical stability‘); S(n)=-realmax_; end; clear(‘n‘);
if ~isempty(find(S>realmax_,1)), error(‘+INF similarities detected; change to a large positive value (but smaller than +REALMAX)‘); end;


% Allocate space for messages, etc
dS=diag(S); A=zeros(N,N,class(s)); R=zeros(N,N,class(s)); t=1;
if plt, netsim=zeros(1,maxits+1); end;
if details
    idx=zeros(N,maxits+1);
    netsim=zeros(1,maxits+1); 
    dpsim=zeros(1,maxits+1); 
    expref=zeros(1,maxits+1); 
end;

% Execute parallel affinity propagation updates
e=zeros(N,convits); dn=0; i=0;
if symmetric, ST=S; else ST=S‘; end; % saves memory if it‘s symmetric
while ~dn
    i=i+1; 

    % Compute responsibilities
    A=A‘; R=R‘;
    for ii=1:N,
        old = R(:,ii);
        AS = A(:,ii) + ST(:,ii); [Y,I]=max(AS); AS(I)=-Inf;
        [Y2,I2]=max(AS);
        R(:,ii)=ST(:,ii)-Y;
        R(I,ii)=ST(I,ii)-Y2;
        R(:,ii)=(1-lam)*R(:,ii)+lam*old; % Damping
        R(R(:,ii)>realmax_,ii)=realmax_;
    end;
    A=A‘; R=R‘;

    % Compute availabilities
    for jj=1:N,
        old = A(:,jj);
        Rp = max(R(:,jj),0); Rp(jj)=R(jj,jj);
        A(:,jj) = sum(Rp)-Rp;
        dA = A(jj,jj); A(:,jj) = min(A(:,jj),0); A(jj,jj) = dA;
        A(:,jj) = (1-lam)*A(:,jj) + lam*old; % Damping
    end;
    
    % Check for convergence
    E=((diag(A)+diag(R))>0); e(:,mod(i-1,convits)+1)=E; K=sum(E);
    if i>=convits || i>=maxits,
        se=sum(e,2);
        unconverged=(sum((se==convits)+(se==0))~=N);
        if (~unconverged&&(K>0))||(i==maxits) dn=1; end;
    end;

    % Handle plotting and storage of details, if requested
    if plt||details
        if K==0
            tmpnetsim=nan; tmpdpsim=nan; tmpexpref=nan; tmpidx=nan;
        else
            I=find(E); notI=find(~E); [tmp c]=max(S(:,I),[],2); c(I)=1:K; tmpidx=I(c);
            tmpdpsim=sum(S(sub2ind([N N],notI,tmpidx(notI))));
            tmpexpref=sum(dS(I));
            tmpnetsim=tmpdpsim+tmpexpref;
        end;
    end;
    if details
        netsim(i)=tmpnetsim; dpsim(i)=tmpdpsim; expref(i)=tmpexpref;
        idx(:,i)=tmpidx;
    end;
    if plt,
        netsim(i)=tmpnetsim;
        figure(234);
        plot(((netsim(1:i)/10)*100)/10,‘r-‘); xlim([0 i]); % plot barely-finite stuff as infinite
        xlabel(‘# Iterations‘);
        ylabel(‘Fitness (net similarity) of quantized intermediate solution‘);
%         drawnow; 
    end;
end; % iterations
I=find((diag(A)+diag(R))>0); K=length(I); % Identify exemplars
if K>0
    [tmp c]=max(S(:,I),[],2); c(I)=1:K; % Identify clusters
    % Refine the final set of exemplars and clusters and return results
    for k=1:K ii=find(c==k); [y j]=max(sum(S(ii,ii),1)); I(k)=ii(j(1)); end; notI=reshape(setdiff(1:N,I),[],1);
    [tmp c]=max(S(:,I),[],2); c(I)=1:K; tmpidx=I(c);
    tmpdpsim=sum(S(sub2ind([N N],notI,tmpidx(notI))));
    tmpexpref=sum(dS(I));
    tmpnetsim=tmpdpsim+tmpexpref;
else
    tmpidx=nan*ones(N,1); tmpnetsim=nan; tmpexpref=nan;
end;
if details
    netsim(i+1)=tmpnetsim; netsim=netsim(1:i+1);
    dpsim(i+1)=tmpdpsim; dpsim=dpsim(1:i+1);
    expref(i+1)=tmpexpref; expref=expref(1:i+1);
    idx(:,i+1)=tmpidx; idx=idx(:,1:i+1);
else
    netsim=tmpnetsim; dpsim=tmpdpsim; expref=tmpexpref; idx=tmpidx;
end;
if plt||details
    fprintf(‘\nNumber of exemplars identified: %d  (for %d data points)\n‘,K,N);
    fprintf(‘Net similarity: %g\n‘,tmpnetsim);
    fprintf(‘  Similarities of data points to exemplars: %g\n‘,dpsim(end));
    fprintf(‘  Preferences of selected exemplars: %g\n‘,tmpexpref);
    fprintf(‘Number of iterations: %d\n\n‘,i);
    fprintf(‘Elapsed time: %g sec\n‘,etime(clock,start));
end;
if unconverged
    fprintf(‘\n*** Warning: Algorithm did not converge. Activate plotting\n‘);
    fprintf(‘    so that you can monitor the net similarity. Consider\n‘);
    fprintf(‘    increasing maxits and convits, and, if oscillations occur\n‘);
    fprintf(‘    also increasing dampfact.\n\n‘);
end;

s=[1 0.85 0.9 0.5 0.45 0.5 0.4 0.4 0.5 0.45;
   0.85 1 0.85 0.6 0.65 0.7 0.6 0.55 0.8 0.7;
   0.9 0.85 1 0.75 0.7 0.65 0.55 0.5 0.6 0.5;
   0.5 0.6 0.75 1 0.9 0.7 0.7 0.85 0.5 0.45;
   0.45 0.65 0.7 0.9 1 0.9 0.9 0.85 0.6 0.65;
   0.5 0.7 0.65 0.7 0.9 1 0.85 0.75 0.75 0.75;
   0.4 0.6 0.55 0.7 0.9 0.85 1 0.85 0.5 0.55;
   0.4 0.55 0.5 0.85 0.85 0.75 0.85 1 0.3 0.25;
   0.5 0.8 0.6 0.5 0.6 0.75 0.5 0.3 1 0.9;
   0.45 0.7 0.5 0.45 0.65 0.75 0.55 0.25 0.9 1;
    ];
p=median(median(s));

idx =

     1
     1
     1
     5
     5
     5
     5
     5
     9
     9


netsim =

    8.1875


dpsim =

    6.2000


expref =

    1.9875