Do you need a non-negative square matrix converted to a doubly stochastic matrix? That is, you want all rows and columns to sum to one? Well here’s a package just for that! I had the function lying around for a deep learning project I was working on at Columbia, and I thought it’d be a great opportunity to learn how to publish to PyPi!

It uses the Sinkhorn-Knopp algorithm, which iteratively normalizes the rows and columns of a matrix to 1, until the matrix is doubly stochastic (Sinkhorn, Knopp 1967). It is guaranteed to converge for a non-negative square matrix $A$ if $A \neq 0$, and if $A$ has total support. Matrix $A$ has total support if every positive element of $A$ lies on a positive diagonal. And a positive diagonal of a matrix is defined as, for any permutation of $\sigma = \{1,\dots,N\}$, the sequence where all elements are positive (non-zero in this case).

Just `pip install sinkhorn_knopp`

and you’re good to go. Here’s a quick demo:

Check it out on github, and feel free to add on!