Affinity Propagation results do not match - r

Question

I am trying to implement the Affinity Propagation clustering algorithm in C++. As part of testing I want to compare my results with well established implementations of the algorithm in Matlab (Link) and in R (package apcluster). Unfortunately, the clusterings do not agree.
To be more precise, the (test) data set is:
0.9411760 0.9702140
0.9607826 0.9744693
0.9754896 0.9574479
0.9852929 0.9489372
0.9950962 0.9234050
1.0000000 0.8936175
1.0000000 0.8723408
0.9852929 0.8595747
1.0000000 0.8893622
1.0000000 0.9191497
In R I typed:
S<-negDistMat(data)
A<-apcluster(S,maxits=1000,convits=100, lam=0.9,q=0.5)
and got:
> A#idx
2 2 2 5 5 9 9 9 9 5
2 2 2 5 5 9 9 9 9 5
In Matlab I just typed:
[idx,netsim,dpsim,expref]=apcluster(S,diag(S));
From the apcluster.m file implementing apcluster (line 77):
maxits=1000; convits=100; lam=0.9; plt=0; details=0; nonoise=0;
This explains the parameters for R, in Matlab their are the default values. Since I'm more comfortable with R concerning Affinity Propagation, for comparison reasons I stuck with Matlab's defaults, just to avoid messing something up unintentionally.
..but got:
>> idx'
ans =
3 3 3 3 5 9 9 9 9 5
In both cases the similarity matrices matched. What could I've missed?
Update:
I've also implemented the Matlab code proposed by Frey & Dueck in their original publication. (You may notice that I omitted noise) and although I can replicate the indexes provided by the former Matlab implementation, Availability and Responsibility matrices differ on some values. The error is less than 0.01 but this is significant.
Their code is:
function [idx,A,R]=frey(S);
N=size(S,1);
A=zeros(N,N);
R=zeros(N,N);
lam=0.9; % Set damping factor
for iter=1:122
% Compute responsibilities
Rold=R;
AS=A+S;
[Y,I]=max(AS,[],2);
for i=1:N
AS(i,I(i))=-realmax;
end;
[Y2,I2]=max(AS,[],2);
R=S-repmat(Y,[1,N]);
for i=1:N
R(i,I(i))=S(i,I(i))-Y2(i);
end;
R=(1-lam)*R+lam*Rold; % Dampen responsibilities
% Compute availabilities
Aold=A;
Rp=max(R,0);
for k=1:N
Rp(k,k)=R(k,k);
end;
A=repmat(sum(Rp,1),[N,1])-Rp;
dA=diag(A);
A=min(A,0);
for k=1:N
A(k,k)=dA(k);
end;
A=(1-lam)*A+lam*Aold; % Dampen availabilities
end;
E=R+A; % Pseudomarginals
I=find(diag(E)>0); K=length(I); % Indices of exemplars
[tmp c]=max(S(:,I),[],2); c(I)=1:K; idx=I(c); % Assignments

Answer 1

I have tried all your code and the problem is caused by the way you supply the input preference. In the first case (R), you specify q=0.5. This means that the input preference p is set to the median of off-diagonal similarities (in your example, this is -0.05129912). If I run the Matlab code as follows (I used Octave, but Matlab should give the same result), I get:
octave:7> [idx,netsim,dpsim,expref]=apcluster(S,-0.05129912);
octave:8> idx'
ans =
2 2 2 5 5 9 9 9 9 5
This is exactly the same as the R result. If I run your Matlab code (with diag(S) being the second argument) and if I run
apcluster(S, p=diag(S))
in R (which sets the input preference to 0 for all samples in both cases), I get 10 one-sample clusters in both cases. So the two results match again, though I could not recover your Matlab result
3 3 3 3 5 9 9 9 9 5
I hope that makes the difference clear.
Cheers, UBod