I am calculating Euclidean distances in python. I want to learn how to calculate it without using a for loop. Here is my code,
import numpy as np
import random
A = np.random.randint(5, size=(10, 5))
B = [1, 3, 5, 2, 4]
for i in range(10):
dist = np.linalg.norm(A[i]-B)
print("Distances: ", dist)
Is there anyway in which I can use advanced indexing or any other techniques to calculate the distances without using a for loop? thanks.
Approach #1 : Most straight-forward one with np.linalg.norm
using its axis
param and also leveraging broadcasting
would be -
np.linalg.norm(A-B,axis=1)
Approach #2 : With einsum
-
subs = A - B
out = np.sqrt(np.einsum('ij,ij->i',subs,subs))
Approach #3 : Using (ab)^2 = a^2 + b^2 - 2ab
formula to leverage matrix-multiplication
with np.dot
and np.inner
-
np.sqrt(np.einsum('ij,ij->i',A, A) + np.inner(B,B) - 2*A.dot(B))
You can calculate the Frobenius Norm explicitly:
res = (np.abs(A - B)**2).sum(1)**0.5
This is the default for np.linalg.norm
. Here's a demo:
np.random.seed(0)
A = np.random.randint(5, size=(10, 5))
B = [1, 3, 5, 2, 4]
res = (np.abs(A - B)**2).sum(1)**0.5
array([4.89897949, 5.38516481, 5.29150262, 5.47722558, 5. ,
5.56776436, 6.244998 , 2.23606798, 5.56776436, 4.47213595])
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