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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;
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