For the usual *args technique in python, but the function is np.vectorized. Normally, *args would work, but when I've vectorized the function and the args are a vector, it thinks the all the rows combined are the arguments rather than each row being the argument. I can obviously have my_function(a,b,c,d,e), but my actual function has many many inputs. How do I do this with the *args technique?
import numpy as np
a = np.random.rand(50,1)
b = np.random.rand(50,1)
args = np.random.rand(50,3)
def my_function(a,b,c,d,e):
result = a * b * c * d * e
return result
my_func_vec = np.vectorize(my_function)
res = my_func_vec(a,b,*args)
# TypeError: my_function() takes 5 positional arguments but 52 were given
Using *args will unpack args along the rows. You want to do it along the columns, so you can transpose first.
my_func_vec(a, b, *args.T)
But this example doesn't require np.vectorize, and you should reexamine your actual case to see if it needs it. The code is much faster without it.
%timeit my_function(a, b, *args.T)
14.8 µs ± 490 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
%timeit my_func_vec(a, b, *args.T)
468 µs ± 13.1 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)