/ani/mrses

function res=mrses_hw_distance(A,B,k,Niter,Ncycle,distmod,block)

  if (nargin<7)

    block=256;

  end

  if (nargin<6)

    distmod=1;

  end

  if (nargin<5)

    Ncycle=1000;

  end

  if (nargin<4)

    Niter=500;

  end

  if (nargin<3)

    k=5;

  end

  if (nargin<2)

    error('As minimum two matrixes needed for MRSES');

  end

  if (nargin>6)

    error('Too much parameters');

  end

  sa=size(A);sb=size(B);

  if (sa(2)==sb(2))

    genes=sa(2);

  else

    error('Features dimension mismatch');

  end

  nA=sa(1); nB=sb(1);

  %optki=zeros(Ncycle,k);

  ctx = mrses_hw();

  mrses_hw(ctx, 1, k, block, single(A), single(B), distmod);

  for icycle=0:block:(Ncycle-1)

    block_size = min(block, Ncycle - icycle);

    %   SELECT k GENES {ki} FOR TEST AND EXCLUDE THEM FROM ALL GENES {ke}

    ki=int16([]); ke=int16([]); 

    for i=1:block_size

	tt=randperm(genes);% randomizing genes

	ki(:,i)=tt(1:k);	% selecting first k

	ke(:,i)=tt(k+1:end);	% the rest unuzed

    end

    cur_dist = mrses_hw(ctx, 10, block_size, ki);

%{

    check_dist = [];

    for i=1:block_size

	check_dist(i) = bmc(A(:,ki(:,i)),B(:,ki(:,i)), distmod); 

    end

    dist_diff = abs(cur_dist - check_dist);

    allowed = abs(check_dist)/10000;

    find ( dist_diff > allowed)

%}

    for iter=1:Niter

	xki=ceil(rand(1,block_size)*k);			% selecting random gen from selected

	xke=ceil(rand(1,block_size)*(genes-k));		% selected random gen from non-selected

	idx_i = sub2ind(size(ki), xki, 1:block_size);

	idx_e = sub2ind(size(ke), xke, 1:block_size);

	t=ki(idx_i);

	ki(idx_i)=ke(idx_e);

	ke(idx_e)=t;

	dist = mrses_hw(ctx, 10, block_size, ki);

%{

	check_dist = [];

        for i=1:block_size

	    check_dist(i)=bmc(A(:,ki(:,i)),B(:,ki(:,i)),distmod); % compute distance between A and B with currently selected genes

	end

	find(dist ~= check_dist);

	dist_diff = abs(dist - check_dist);

	allowed = abs(dist)/10000;

	find ( dist_diff > allowed)

%}

	bad = find(dist < cur_dist);

	idx_i = idx_i(bad);

	idx_e = idx_e(bad);

	t=ki(idx_i);

	ki(idx_i)=ke(idx_e);

	ke(idx_e)=t;

	cur_dist = max(dist, cur_dist);

    end

    optki(:,(icycle+1):(icycle+block_size))=ki;	% save finally selected genes

  end

  mrses_hw(ctx);

  optki=reshape(optki,1,[]);

  [n,g]=hist(optki,1:genes);

  H=[n./Ncycle;g];

  res=flipud(sortrows(H'));

function dist=bmc(x1,x2,distmod)

    c1=cov1(x1);

    c2=cov1(x2);

    c=(c1+c2)./2;

    if (distmod~=2)

	rcorr=2.*log(det(c)./sqrt(det(c1).*det(c2)));

    end

    if (distmod~=3)

	m1=mean(x1);

	m2=mean(x2);

	rmahal=((m2-m1)/c)*(m2-m1)';

    end

    if (distmod==1) 

	dist = rmahal./8+rcorr./4;

    elseif (distmode==2) 

	dist=rmahal;

    else 

	dist=rcorr;

    end

function c = cov1(x, m)

    [rows, cols] = size(x);

    %rows = size(x(:,1))

    if (nargin<2) 

	nX = x - ones([rows,1]) * mean(x);

    else

	nX = x - ones([rows,1]) * m';

    end

    c = nX' * nX / rows;