Adrian P. - 4 months ago 13
Python Question

# How to perform an operation on every element in a numpy matrix?

Say I have a function foo() that takes in a single float and returns a single float. What's the fastest/most pythonic way to apply this function to every element in a numpy matrix or array?

What I essentially need is a version of this code that doesn't use a loop:

``````import numpy as np

big_matrix = np.matrix(np.ones((1000, 1000)))

for i in xrange(np.shape(big_matrix)[0]):

for j in xrange(np.shape(big_matrix)[1]):

big_matrix[i, j] = foo(big_matrix[i, j])
``````

I was trying to find something in the numpy documentation that will allow me to do this but I haven't found anything.

Edit: As I mentioned in the comments, specifically the function I need to work with is the sigmoid function,
`f(z) = 1 / (1 + exp(-z))`
.

If `foo` is really a black box that takes a scalar, and returns a scalar, then you must use some sort of iteration. People often try `np.vectorize` and realize that, as documented, it does not speed things up much. It is most valuable as a way of broadcasting several inputs. It uses `np.frompyfunc`, which is slightly faster, but with a less convenient interface.
``````f(z) = 1 / (1 + exp(-z))
There's a `np.exp` function. The rest is simple math.