CODEWITHSUNDEEP
pythonnumpy
user3371637
user3371637

Reputation:

Nested numpy operations

I have a function like this:

def foo(v, w):
    return sum(np.exp(v/w))

Where v in the beginning is a numpy array and w a number. Now I want to plot the value of this function for more values of w, so I need a function that works for different sizes of vectors. My solution for now is the obvious one

r = []
for e in w:
    r.append(foo(v, e))

but I wonder if there is a better way to do it. Also, I want to stay low on memory, so I need to avoid create a big matrix, then applying the function to every value and sum over the columns (the length of v is more than 5e+4 and the length of w is 1e+3).

Thanks

Upvotes: 1

Views: 413

Answers (1)

wwii
wwii

Reputation: 23753

If you cannot determine an upper bound for the length of v and ensure that you don't exceed the memory requirements, I think you will have to stay with your solution.

If you can determine an upper bound for length of v and meet your memory requirements using a Mx1000 array, you can do this.

import numpy as np
v = np.array([1,2,3,4,5])
w = np.array([10.,5.])
c = v / w[:, np.newaxis]
d = np.exp(c)
e = d.sum(axis = 1)

>>> 
>>> v
array([1, 2, 3, 4, 5])
>>> w
array([ 10.,   5.])
>>> c
array([[ 0.1,  0.2,  0.3,  0.4,  0.5],
       [ 0.2,  0.4,  0.6,  0.8,  1. ]])
>>> d
array([[ 1.10517092,  1.22140276,  1.34985881,  1.4918247 ,  1.64872127],
       [ 1.22140276,  1.4918247 ,  1.8221188 ,  2.22554093,  2.71828183]])
>>> e
array([ 6.81697845,  9.47916901])
>>>

Upvotes: 1

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