I would like to populate a NumPy array with "accumulated" results from a custom function.
Currently, my code is:
import numpy as np
def f(x, mu):
return mu * x * (1 - x)
def populate(x0, mu, n):
s = np.zeros(n)
x = x0
for i in range(n):
s[i], x = x, f(x, mu)
return s
It does not take advantage of the vectorization performance of NumPy.
Is there any way to improve the speed of creating arrays like this?
s[0] == x0
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