Question

3 Compute Euclidean distance using Numpy Arrays • The Euclidean distance d is given by the following equation: N d(a,b) = (a - b)2 Complete the following Euclidean distance function with two parameters of Numpy arrays • Hint: you may use np.sqrt and np.sum to compute the two Numpy arrays [73]: def euclidean_distance(a,b): return 0 Test your Euclidean distance function using two Numpy arrays [74]: A = np.array(range(100)) B - np array(range(1, 101)) print (euclidean_distance (A,B)) 0 [ ]:

Answer 1

Solution :

You can use the following definition of euclidean_distance(a,b)

def euclidean_distance(a,b):
    return np.sqrt(sum((e1-e2)**2 for e1, e2 in zip(a,b)))

Here sum will sum up the square values (e1-e2)² and zip will return an iterator of tuples where the first item in each passed iterator is paired together.

Code :

import numpy as np
def euclidean_distance(a,b):
    return np.sqrt(sum((e1-e2)**2 for e1, e2 in zip(a,b)))
A=np.array(range(100))
B=np.array(range(1,101))
print(euclidean_distance(A,B))

Code demo for reference :

import numpy as np def euclidean_distance(a,b): return np.sqrt(sum((e1-e2)**2 for ei, e2 in zip(a,b))) A=np.array(range (100)) B=np.array(range (1,101)) print(euclidean_distance (A,B)) 10.0