function [x,g,j,gg] = v_kmeans(d,k,x0,l)
%V_KMEANS Vector quantisation using K-means algorithm [X,ESQ,J]=(D,K,X0,L)
%
%  Inputs:
%
%    D(N,P)  contains N data vectors of dimension P
%    K       is number of centres required
%    X0(K,P) are the initial centres (optional)
%     
%      or alternatively
%
%    X0      gives the initialization method
%            'f'   pick K random elements of D as the initial centres [default]
%            'p'   randomly divide D into K sets and choose the centroids
%    L       gives max number of iterations (use 0 if you just want to calculate G and J)
%
%  Outputs:
%
%    X(K,P)  is output row vectors (omitted if L=0)
%    G       is mean square error
%    J(N)    indicates which centre each data vector belongs to
%    GG(L)   gives the mean square error at the start of each iteration (omitted if L=0)
%
% It is often a good idea to scale the input data so that it has equal variance in each
% dimension before calling V_KMEANS.

%  Originally based on a routine by Chuck Anderson, anderson@cs.colostate.edu, 1996


%      Copyright (C) Mike Brookes 1998
%      Version: $Id: v_kmeans.m 4497 2014-04-23 10:28:55Z dmb $
%
%   VOICEBOX is a MATLAB toolbox for speech processing.
%   Home page: http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%   This program is free software; you can redistribute it and/or modify
%   it under the terms of the GNU General Public License as published by
%   the Free Software Foundation; either version 2 of the License, or
%   (at your option) any later version.
%
%   This program is distributed in the hope that it will be useful,
%   but WITHOUT ANY WARRANTY; without even the implied warranty of
%   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
%   GNU General Public License for more details.
%
%   You can obtain a copy of the GNU General Public License from
%   http://www.gnu.org/copyleft/gpl.html or by writing to
%   Free Software Foundation, Inc.,675 Mass Ave, Cambridge, MA 02139, USA.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

memsize=voicebox('memsize'); 
[n,p] = size(d);
nb=min(n,max(1,floor(memsize/(8*p*k))));    % block size for testing data points
nl=ceil(n/nb);                  % number of blocks
if nargin<4
    l=300;                  % very large max iteration count
    if nargin<3
        x0='f';             % use 'f' initialization mode
    end
end
if ischar(x0)
    if k<n
        if any(x0)=='p'                  % Initialize using a random partition
            ix=ceil(rand(1,n)*k);       % allocate to random clusters
            ix(rnsubset(k,n))=1:k;      % but force at least one point per cluster
            x=zeros(k,p);
            for i=1:k
                x(i,:)=mean(d(ix==i,:),1);
            end
        else                                % Forgy initialization: choose k random points [default] 
            x=d(rnsubset(k,n),:);         % sample k centres without replacement
        end
    else
        x=d(mod((1:k)-1,n)+1,:);    % just include all points several times
    end
else
    x=x0;
end
m=zeros(n,1);           % minimum distance to a centre
j=zeros(n,1);           % index of closest centre
gg=zeros(l,1);
wp=ones(1,p);
kk=1:p;
kk=kk(ones(n,1),:);
kk=kk(:);

if l>0
    for ll=1:l                 % loop until x==y causes a break
        
        % find closest centre to each data point [m(:),j(:)] = distance, index
        
        ix=1;
        jx=n-nl*nb;
        for il=1:nl
            jx=jx+nb;        % increment upper limit
            ii=ix:jx;
            z = disteusq(d(ii,:),x,'x');
            [m(ii),j(ii)] = min(z,[],2);
            ix=jx+1;
        end
        y = x;              % save old centre list
        
        % calculate new centres as the mean of their assigned data values (or zero for unused centres)
        
        nd=full(sparse(j,1,1,k,1));         % number of points allocated to each centre
        md=max(nd,1);                       % remove zeros
        jj=j(:,wp);
        x=full(sparse(jj(:),kk,d(:),k,p))./md(:,wp);    % calculate the new means 
        fx=find(nd==0);
        
        % if any centres are unused, assign them to data values that are not exactly on centres
        % choose randomly if there are more such points than needed
        
        if ~isempty(fx)
            q=find(m~=0);
            if length(q)<=length(fx)
                x(fx(1:length(q)),:)=d(q,:);
            else
                if length(fx)>1
                    [rr,ri]=sort(rand(length(q),1));
                    x(fx,:)=d(q(ri(1:length(fx))),:);
                else
                    x(fx,:) = d(q(ceil(rand(1)*length(q))),:);
                end
            end
        end
        
        % quit if the centres are unchanged
        
        gg(ll)=sum(m,1);
        if x==y
            break
        end
    end
    gg=gg(1:ll)/n;
%     ll % *** DEBUG ***
%     gg' % *** DEBUG ***
    g=gg(end);
else            % if l==0 then just calculate G and J (but rename as X and G)
    ix=1;
    jx=n-nl*nb;
    for il=1:nl
        jx=jx+nb;        % increment upper limit
        ii=ix:jx;
        z = disteusq(d(ii,:),x,'x');
        [m(ii),j(ii)] = min(z,[],2);
        ix=jx+1;
    end
    x=sum(m,1)/n;
    g=j;
end
