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 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 \(\{ a_{1,\sigma(1)},\dots, a_{N,\sigma(N)} \}\) 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:
import numpy as np
from sinkhorn_knopp import sinkhorn_knopp as skp
sk = skp.SinkhornKnopp()
P = [[.011, .15], [1.71, .1]]
P_ds = sk.fit(P)
print P_ds
[[ 0.06102561 0.93897439]
[ 0.93809928 0.06190072]]
print np.sum(P_ds, axis=0)
[ 0.99912489 1.00087511]
print np.sum(P_ds, axis=1)
[ 1., 1.]Check it out on github, and feel free to add on!