How to apply a function / map values of each element in a 2d numpy array/matrix?

Question

Given the following numpy matrix:

import numpy as np
mymatrix = mymatrix = np.matrix(\'-1 0 1; -2 0 2; -4 0 4\')


matrix([[-1,  0,  1],
        [-2,  0,  2]         


        

Answer 1

Apparently the way to apply a function to elements is to convert your function into a vectorized version that takes arrays as input and return arrays as output.

You can easily convert your function to vectorized form using numpy.vectorize as follows:

myfunc_vec = np.vectorize(myfunc)
result = myfunc_vec(mymatrix)

or for a one shot usage:

np.vectorize(myfunc)(mymatrix)

As pointed out by @Divakar, it's better (performance-wise) if you can write an already vectorized function from scratch (using numpy built ufuncs without using numpy.vectorize) like so:

def my_vectorized_func(m):
    return 1/(1+np.exp(-m))  # np.exp() is a built-in ufunc

myvectorized_func(mymatrix)

Since numpy.exp is already vectorized (and math.exp wasn't) the whole expression 1/(1+np.exp(-m)) will be vectorized (and faster that applying my original function to each element).

The following complete example produced the required output:

import numpy as np
mymatrix = mymatrix = np.matrix('-1 0 1; -2 0 2; -4 0 4')
import math
def myfunc(z):
    return 1/(1+math.exp(-z))

np.vectorize(myfunc)(mymatrix) # ok, but slow

def my_vectorized_func(m):
    return 1/(1+np.exp(-m))

my_vectorized_func(mymatrix) # faster using numpy built-in ufuncs

Answer 2

Just in case this helps, scipy has a sigmoid function you can directly call on a matrix.