Is my modification on Jeffrey's divergence code correct?

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?