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Yes, there are two ways the MI() function could have been implemented.
Version one, MI() takes the the joint probability P(X,Y) as 2D matrix and then internally converts it to a joint probability vector xy[i,j] through indexing.
Version two (which is what I implemented), MI() takes the the joint probability P(X,Y) directly as probability vector AFTER the user converts their 2D matrix to a joint probability vector { xy[i,j] }.
I chose version two to give users maximum flexibility on how to define their joint probabilities.
I hope this clarifies the usability?
Any concrete suggestions for improvement are always welcome.
Hello,
I have problems understanding the way the use of
MI(x, y, xy, unit = "log2")
is intended.The documentation states:
How can
xy
be a vector?If I understand mutual information correctly it should be a 2D matrix, so that
xy[i,j]
gives the joint probability, right?https://en.wikipedia.org/wiki/Mutual_information#In_terms_of_PMFs_for_discrete_distributions
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