Say I have the following simple function that I use to generate random numbers:
def my_func():
rvs = np.random.random(size=3)
return rvs[2] - rvs[1]
I want to call this function a number of times, lets say 1000 and I want to store the results in an array, for example:
result = []
for _ in range(1000):
result += [my_func()]
Is there a way to use numpy to vectorize this operation and make everything faster? I don't mind if the workflow changes.
If I understand your question correctly, you just need to use the np.random.rand function:
np.random.rand(1000)
This function create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1).
You can vectorize as follows:
rvs_vect = np.random.rand(10000, 3)
result = rvs_vect[:,2] - rvs_vect[:,1]
rvs_vect[:,1] selects all rows in column 1.
rvs_vect[:,2] selects all rows in column 2.
Execution times for instances of 10000 elements on my machine are about 100 times faster than your solution and the other proposed ones (np.vectorize and list comprehension).
Extras
I have prepared an example for you with Numba. Numba is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code. Although you will not gain substantial advantages over numpy on this type of operation.
import numba as nb
nb.njit
def my_rand(n):
rvs_vect = np.random.rand(n, 3)
return rvs_vect[:,2] - rvs_vect[:,1]
Try this:
import numpy as np
def my_func(arr):
rvs = np.random.random(size=3)
return rvs[2] - rvs[1]
vfunc = np.vectorize(my_func)
result = []
result.append(vfunc([1]*1000))
print(result)
Hope it hepls
Explanation:
np.vectorize is vectorizing the function. In normal cases you will pass a numpy array to a function that performs some task on its elements, but here I just passed an anonymous list for the function to be executed 1000 times rest is as you were doing
my_func in a for loop. No strict-sense vectorization is happening here. See the notes in numpy.org/doc/stable/reference/generated/numpy.vectorize.html