I would like to get my script total execution time down from 4 minutes to less than 30 secs. I have a large 1d array (3000000+) of distances with many duplicate distances. I am trying to write the swiftest function that returns all distances that appear n times in the array. I have written a function in numpy but there is a bottleneck at one line in the code. Swift performance is an issue because the calculations are done in a for loop for 2400 different large distance arrays.
import numpy as np for t in range(0, 2400): a=np.random.randint(1000000000, 5000000000, 3000000) b=np.bincount(a,minlength=np.size(a)) c=np.where(b == 3) #SLOW STATEMENT/BOTTLENECK return c
Given a 1d array of distances [2000000000,3005670000,2000000000,12345667,4000789000,12345687,12345667,2000000000,12345667]
I would expect back an array of [2000000000,12345667] when queried to return an array of all distances that appear 3 times in the main array.
What should I do?