Euclidean distance with weights
The suggestion of writing your own weighted L2 norm is a good one, but the calculation provided in this answer is incorrect. If the intention is to calculate
then this should do the job:
def weightedL2(a,b,w): q = a-b return np.sqrt((w*q*q).sum())
Simply define it yourself. Something like this should do the trick:
def mynorm(A, B, w): import numpy as np q = np.matrix(w * (A - B)) return np.sqrt((q * q.T).sum())
If you want to keep using scipy function you could pre-process the vector like this.
def weighted_euclidean(a, b, w): A = a*np.sqrt(w) B = b*np.sqrt(w) return scipy.spatial.distance.euclidean(A, B)However it's look slower than
def weightedL2(a, b, w): q = a-b return np.sqrt((w*q*q).sum())