DevianceDetectionHumanABR/Deviance Detection_Humans/findSigClu.m

function [vecSigCl,vecSigClI,nSigCl,sSigCl] = findSigClu(vecCl,hCl)
% IDENTIFY CLUSTERS IN OBSERVED DATA
% This function identifies clusters in the observed data, based on the
% given minimum length of consecutive points. It also counts the number
% of clusters and returns the size of each identified cluster

% convert cluster vector into cluster index vextor
vecClI = vecCl;
vecClI(vecCl~=0) = 1;

% count number of all clusters in vector
vecClIs = [0,vecClI]; % add 0 at start to make sure possible cluster starting at point 1 is considered
vecClIe = [vecClI,0]; % add 0 at the end to make sure possible cluster lasting until the end is considered
startCl = find(diff(vecClIs)==1); % find all points where difference vector is 1 (starting points of clusters in original vector)
endCl = find(diff(vecClIe)==-1); % find all points where difference vector is -1 (ending points of clusters in original vector)
nCl = numel(startCl); % return number of cluster starting points = number of clusters

% replace non-significant clusters by 0s
vecSigClI = vecClI;
for cl = 1:nCl
    if hCl(cl)==0
        vecSigClI(startCl(cl):endCl(cl)) = 0;
    end
end

% create vector that contains only significant clusters
vecSigCl = vecCl.*vecSigClI;

% count number of significant clusters in vector
vecSigClIs = [0,vecSigClI]; % add 0 at start to make sure possible cluster starting at point 1 is considered
vecSigClIe = [vecSigClI,0]; % add 0 at the end to make sure possible cluster lasting until the end is considered
startSigCl = find(diff(vecSigClIs)==1); % find all points where difference vector is 1 (starting points of clusters in original vector)
endSigCl = find(diff(vecSigClIe)==-1); % find all points where difference vector is -1 (ending points of clusters in original vector)
nSigCl = numel(startSigCl); % return number of cluster starting points = number of clusters

% calculate size of each cluster
sSigCl = zeros(nSigCl,1);
for c = 1:nSigCl
    sSigCl(c) = sum(vecSigCl(startSigCl(c):endSigCl(c))); % calulate size of each identified cluster
end
end