I have the code
import numpy as np
import math
pos = np.array([[ 1.72, 2.56],
[ 0.24, 5.67],
[ -1.24, 5.45],
[ -3.17, -0.23],
[ 1.17, -1.23],
[ 1.12, 1.08]])
ref = np.array([1.22, 1.18])
# Insert your solution below
d1 = math.sqrt((pos[0,0]-ref[0])**2 + (pos[0,1]-ref[1])**2)
d2 = math.sqrt((pos[1,0]-ref[0])**2 + (pos[1,1]-ref[1])**2)
d3 = math.sqrt((pos[2,0]-ref[0])**2 + (pos[2,1]-ref[1])**2)
d4 = math.sqrt((pos[3,0]-ref[0])**2 + (pos[3,1]-ref[1])**2)
d5 = math.sqrt((pos[4,0]-ref[0])**2 + (pos[4,1]-ref[1])**2)
d6 = math.sqrt((pos[5,0]-ref[0])**2 + (pos[5,1]-ref[1])**2)
The expected answer is
# [ 1.468, 4.596, 4.928 , 4.611, 2.410, 0.141 ]
Is it possible to make my solution more efficient and short, preferably without the use of a for-loop. Thank you :D
This does the same as your computations. Python's math module is not needed.
np.sqrt(((pos - ref)**2).sum(1))
Out:
[1.46778745, 4.59570452, 4.9279306 , 4.61087844, 2.41051862, 0.14142136]