I'm trying to vectorize a function that outputs a list. I wish to feed it all values from a numpy list and have it return a matrix, such that each row is an output for an element in the input vector.
import numpy as np
def func(x, n):
o = []
for i in range(n):
o.append(x+i)
return o
vec_func = np.vectorize(func)
matrix = vec_func(np.asarray([0, 1, 2]), 10)
however i get the error
ValueError: setting an array element with a sequence.
how can i fix this?
frompyfunc might be better:
In [525]: def fun(x):
...: return x+.1, x+.2, x+.3
...:
I specify 1 input, 3 output values. It returns dtype object:
In [526]: np.frompyfunc(fun,1,3)(np.arange(5))
Out[526]:
(array([0.1, 1.1, 2.1, 3.1, 4.1], dtype=object),
array([0.2, 1.2, 2.2, 3.2, 4.2], dtype=object),
array([0.3, 1.3, 2.3, 3.3, 4.3], dtype=object))
That's a tuple of 3 arrays. They can be turned into one 2d array with stack:
In [527]: np.stack(_, 1)
Out[527]:
array([[0.1, 0.2, 0.3],
[1.1, 1.2, 1.3],
[2.1, 2.2, 2.3],
[3.1, 3.2, 3.3],
[4.1, 4.2, 4.3]], dtype=object)
I could take it a further step with a astype(float).
I assume, of course, that this is a toy func. For something this simple there's no need to use vectorize.
In [528]: fun(np.arange(5))
Out[528]:
(array([ 0.1, 1.1, 2.1, 3.1, 4.1]),
array([ 0.2, 1.2, 2.2, 3.2, 4.2]),
array([ 0.3, 1.3, 2.3, 3.3, 4.3]))
All that vectorize needs is the otypes parameter:
In [536]: np.vectorize(fun, otypes='ddd')(np.arange(5))
Out[536]:
(array([ 0.1, 1.1, 2.1, 3.1, 4.1]),
array([ 0.2, 1.2, 2.2, 3.2, 4.2]),
array([ 0.3, 1.3, 2.3, 3.3, 4.3]))
If the function returns an array instead of a tuple or list, we could use signature:
In [546]: def fun(x):
...: return np.array([x+.1, x+.2, x+.3])
In [547]: np.vectorize(fun, signature='()->(n)')(np.arange(5))
Out[547]:
array([[ 0.1, 0.2, 0.3],
[ 1.1, 1.2, 1.3],
[ 2.1, 2.2, 2.3],
[ 3.1, 3.2, 3.3],
[ 4.1, 4.2, 4.3]])
Or with the original tuple/list case, wrap it in a lambda, np.vectorize(lambda x:np.array(fun(x)), signature='()->(n)')
Experience suggests that the frompyfunc approach is fastest. vectorize with otypes is a bit slower (but it uses frompyfunc). signature is newer method, using different code, and somewhat slower.
With your new func, the signature approach still works. I added excluded so it doesn't try to broadcast the n argument:
In [553]: np.vectorize(lambda x,n:np.array(func(x,n)), signature='()->(n)',excluded=[1])(np.arange(5),3)
Out[553]:
array([[0, 1, 2],
[1, 2, 3],
[2, 3, 4],
[3, 4, 5],
[4, 5, 6]])
In [554]: np.vectorize(lambda x,n:np.array(func(x,n)), signature='()->(n)',excluded=[1])(np.arange(5),7)
Out[554]:
array([[ 0, 1, 2, 3, 4, 5, 6],
[ 1, 2, 3, 4, 5, 6, 7],
[ 2, 3, 4, 5, 6, 7, 8],
[ 3, 4, 5, 6, 7, 8, 9],
[ 4, 5, 6, 7, 8, 9, 10]])
[[1, 2, 3], [2, 3, 4], [3, 4, 5]]?