How to normalize a 2-dimensional numpy array in python less verbose?
Broadcasting is really good for this:
row_sums = a.sum(axis=1)new_matrix = a / row_sums[:, numpy.newaxis]row_sums[:, numpy.newaxis] reshapes row_sums from being (3,) to being (3, 1). When you do a / b, a and b are broadcast against each other.
You can learn more about broadcasting here or even better here.
Scikit-learn offers a function normalize() that lets you apply various normalizations. The "make it sum to 1" is called L1-norm. Therefore:
from sklearn.preprocessing import normalizematrix = numpy.arange(0,27,3).reshape(3,3).astype(numpy.float64)# array([[ 0., 3., 6.],# [ 9., 12., 15.],# [ 18., 21., 24.]])normed_matrix = normalize(matrix, axis=1, norm='l1')# [[ 0. 0.33333333 0.66666667]# [ 0.25 0.33333333 0.41666667]# [ 0.28571429 0.33333333 0.38095238]]Now your rows will sum to 1.
I think this should work,
a = numpy.arange(0,27.,3).reshape(3,3)a /= a.sum(axis=1)[:,numpy.newaxis]