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Is my modification on Jeffrey's divergence code correct?

Asked by Mohammad Al Nagdawi on 3 Aug 2017
I use Matlab code available on file exchange to compare the degree of similarity between 2 images I got an unexpected result which has too many NaN.
Therefore, I debug the code to find the issue and try to solve it.
original code
function d=jeffrey_divergence(XI,XJ)
m=size(XJ,1); % number of samples of p
p=size(XI,2); % dimension of samples
assert(p == size(XJ,2)); % equal dimensions
assert(size(XI,1) == 1); % pdist requires XI to be a single sample
d=zeros(m,1); % initialize output array
for i=1:m
for j=1:p
m=(XJ(i,j) + XI(1,j)) / 2;
if m ~= 0 % if m == 0, then xi == xj == 0
d(i,1) = d(i,1) + (XI(1,j) * log(XI(1,j) / m)) + (XJ(i,j) * log(XJ(i,j) / m));
end
end
end
Modified code
function d=jeffrey_divergence(XI,XJ)
m=size(XJ,1); % number of samples of p
p=size(XI,2); % dimension of samples
assert(p == size(XJ,2)); % equal dimensions
assert(size(XI,1) == 1); % pdist requires XI to be a single sample
d=zeros(m,1); % initialize output array
for i=1:m
for j=1:p
m=(XJ(i,j) + XI(1,j)) / 2;
if m ~= 0 % if m == 0, then xi == xj == 0
if XJ(i,j)~=0 & XI(1,j)~=0 % I was added this line
d(i,1) = d(i,1) + (XI(1,j) * log(XI(1,j) / m)) + (XJ(i,j) * log(XJ(i,j) / m));
end
end
end
end
After I add if XJ(i,j)~=0 & XI(1,j)~=0 I get result without any NaN. But I still worry about the result accuracy.
Now I have two questions:
1. Is Jeffrey's divergence code correct? I don't know how to evaluate it!
2. This line of code 'if XJ(i,j)~=0 & XI(1,j)~=0', I added could affect the result accuracy?

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