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MOT_ANA/Matlab_Scripts/Functions/MOT_ANA_CompareClusters.m
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MOT_ANA_CompareClusters.m
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% C1 and C2 are K x M dimensional, with K the number of clusters and M the
% number of spatio-temporal features
function [RealCorrs,NullCors] = MOT_ANA_CompareClusters(C1,C2,dist)
if size(C2,1) >= size(C1,1)
switch dist
case 'SpatialCorr'
Dis = 1-corr(C1',C2');
ExtraDis1 = 1-corr(C1',C1');
ExtraDis2 = 1-corr(C2',C2');
% First, we use Hungarian algorithm to match
[assignment] = munkres(Dis);
case 'MSE'
for i = 1:size(C1,1)
for j = 1:size(C2,1)
Dis(i,j) = sqrt(sum((C1(i,:)-C2(j,:)).^2));
end
end
for i = 1:size(C1,1)
for j = 1:size(C1,1)
ExtraDis1(i,j) = sqrt(sum((C1(i,:)-C1(j,:)).^2));
end
end
for i = 1:size(C2,1)
for j = 1:size(C2,1)
ExtraDis2(i,j) = sqrt(sum((C2(i,:)-C2(j,:)).^2));
end
end
[assignment] = munkres(Dis);
otherwise
end
% We reorder
C2 = C2(assignment,:);
else
switch dist
case 'SpatialCorr'
Dis = 1-corr(C2',C1');
ExtraDis1 = 1-corr(C1',C1');
ExtraDis2 = 1-corr(C2',C2');
% First, we use Hungarian algorithm to match
[assignment] = munkres(Dis);
case 'MSE'
for i = 1:size(C2,1)
for j = 1:size(C1,1)
Dis(i,j) = sqrt(sum((C2(i,:)-C1(j,:)).^2));
end
end
for i = 1:size(C1,1)
for j = 1:size(C1,1)
ExtraDis1(i,j) = sqrt(sum((C1(i,:)-C1(j,:)).^2));
end
end
for i = 1:size(C2,1)
for j = 1:size(C2,1)
ExtraDis2(i,j) = sqrt(sum((C2(i,:)-C2(j,:)).^2));
end
end
[assignment] = munkres(Dis);
otherwise
end
% We reorder
C1 = C1(assignment,:);
end
switch dist
case 'SpatialCorr'
% We compute the diagonal and off diagonal elements
NewDisMat = 1-corr(C1',C2');
EXTRA1 = jUpperTriMatToVec(ExtraDis1);
EXTRA2 = jUpperTriMatToVec(ExtraDis2);
case 'MSE'
for i = 1:size(C1,1)
for j = 1:size(C2,1)
NewDisMat(i,j) = sqrt(sum((C1(i,:)-C2(j,:)).^2));
end
end
EXTRA1 = jUpperTriMatToVec(ExtraDis1);
EXTRA2 = jUpperTriMatToVec(ExtraDis2);
end
RealCorrs = diag(NewDisMat);
NullCors = jUpperTriMatToVec(NewDisMat);
NullCors = [NullCors;jUpperTriMatToVec(NewDisMat')];
NullCors = [NullCors;EXTRA1;EXTRA2];
figure;
set(gcf,'color','w');
COL{1} = [0.8,0.8,0.8];
COL{2} = [0 0 0];
MakeViolin(NullCors',gca,{''},'',COL,1,1);
hold on;
plot(ones(1,size(C1,1)),RealCorrs,'rd');
MAXVAL = max([NullCors;RealCorrs])+0.05*max([NullCors;RealCorrs]);
MINVAL = min([NullCors;RealCorrs])-0.05*min([NullCors;RealCorrs]);
ylim([MINVAL,MAXVAL]);
end
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