I'm creating a numpy array of random values and adding them to an existing array containing 32-bit floats. I'd like to generate the random values using the same dtype as the target array, so that I don't have to convert the dtypes manually. Currently I do this:
import numpy as np
x = np.zeros((10, 10), dtype='f')
x += np.random.randn(*x.shape).astype('f')
What I'd like to do instead of the last line is something like:
x += np.random.randn(*x.shape, dtype=x.dtype)
but randn (and actually none of the numpy.random methods) does not accept a dtype argument.
My specific question is, is it possible to specify a dtype for random numbers when I create them, without having to call astype? (My guess is that the random number generator is 64 bits long, so it doesn't really make sense to do this, but I thought I'd ask if it's possible.)
Q: is it possible to specify a dtype for random numbers when I create them.
A: No it isn't. randn accepts the shape only as randn(d0, d1, ..., dn)
Simply try this:
x = np.random.randn(10, 10).astype('f')
Or define a new function like
np.random.randn2 = lambda *args, dtype=np.float64: np.random.randn(*args).astype(dtype)
x = np.random.randn2(10, 10, dtype='f')
If you have to use your code on the post, try this code instead
x = np.zeros((10, 10), dtype='f')
x[:] = np.random.randn(*x.shape)
This assigns the results of randn to the memory allocated by np.zeros