CODEWITHSUNDEEP
pythonarraysnumpy
mik
mik

Reputation: 110

vectorize numpy operations

I have a 1 dimensional numpy ndarray V. The operation I have is H = (V/a)**b, where a and b are just scalars. This is simple, but I need to run multiple experiments (thousands) where I try different variations of a's and b's. Its easy to do this with for loops, but I want this to run as fast as possible so I want to vectorize. So, lets say I have generated 1D ndarrays of a and b as well. given the 3 1D arrays V, a, and b, how do I go about doing this efficiently? Thanks!

Upvotes: 1

Views: 70

Answers (2)

MSeifert
MSeifert

Reputation: 152587

That's where broadcasting comes in, you can add dimensions so the arrays broadcast along each axis:

(V/a[:, None])**b[:, None, None]

This adds one trailing dimension to a and two trailing dimensions to b and broadcasting will make sure you'll get a 3D array. That works because broadcasting will always "broadcast" along the last axis.

For example:

>>> import numpy as np
>>> V = np.array([1,2,3])
>>> a = np.array([0.1, 0.2, 0.3])
>>> b = np.array([1, 0.5, 0.34])

>>> a[:, None]
array([[ 0.1],
       [ 0.2],
       [ 0.3]])

>>> b[:, None, None]
array([[[ 1.  ]],

       [[ 0.5 ]],

       [[ 0.34]]])

>>> (V/a[:, None])**b[:, None, None]
array([[[ 10.        ,  20.        ,  30.        ],
        [  5.        ,  10.        ,  15.        ],
        [  3.33333333,   6.66666667,  10.        ]],

       [[  3.16227766,   4.47213595,   5.47722558],
        [  2.23606798,   3.16227766,   3.87298335],
        [  1.82574186,   2.5819889 ,   3.16227766]],

       [[  2.18776162,   2.7691737 ,   3.17849276],
        [  1.72842206,   2.18776162,   2.51114059],
        [  1.50583981,   1.90602666,   2.18776162]]])

Note that this may take a lot of memory. So it may not be possible to use this if all your arrays are very long.

For example if all arrays contain 1000 values the result will contain 1 billion elements which takes a lot of RAM (~7.5GB with float64s).

Upvotes: 0

javidcf
javidcf

Reputation: 59681

You can do something like this:

import numpy as np

V = np.arange(100.0)
a = np.random.rand(50)
b = np.random.rand(50)

result = (V[np.newaxis, :] / a[:, np.newaxis]) ** b[:, np.newaxis]

result will have size 50x100, so result[i] will be the result of using the parameters a[i] and b[i].

Upvotes: 1

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