Nick Goble February 2016

I have a gaussian function with two independent discrete variables. How do I create a matrix of all possible values?

Basically I have this:

from scip.stats import norm
import pandas as pd

r = pd.Series([1, 2, 3])
k = pd.Series([0.2, 0.3, 0.4, 0.5])
x = 2

mean = x + k
variance = k

# I'm feeding the gaussian function two vectors. 
# I'd like to get a matrix back of all possible combinations. Quickly.

values = norm.pdf(r, mean, variance)

So I'm giving the function norm.pdf two vectors of data, and I'd like a (3x4) matrix returned to me that looks like:

values(1, 0.2)    values(1, 0.3)    values(1, 0.4)    values(1, 0.5)
values(2, 0.2)                                             ...
values(3, 0.2)                                             ...
values(4, 0.2)     ...........       ...........      values(4, 0.5)

I know I could iterate over all items in all arrays, but that takes a lot of time, and I plan on scaling this up quite a bit. I'd like to take advantage of numpy's speed. I've tried vectorizing, but that fails. Any ideas? Thanks!!!

Answers


Marius February 2016

You can apply the pdf to each element of r and automatically put the results in a matrix using:

r.apply(lambda x: pd.Series(norm.pdf(x, mean, variance), index=k))

If you return a Series from apply then the results are automatically unpacked into columns. Output:

            0.2       0.3       0.4       0.5
0  3.037941e-08  0.000111  0.002182  0.008864
1  1.209854e+00  0.806569  0.604927  0.483941
2  6.691511e-04  0.087406  0.323794  0.483941


ebarr February 2016

Use numpy.meshgrid to get all the combinations of inputs:

all_r,all_means =  np.meshgrid(r,mean)
_,all_variances = np.meshgrid(r,variance)
values = norm.pdf(all_r, all_means, all_variances)

This will return the values in a 2-d grid:

print values
# outputs 
array([[  3.03794142e-08,   1.20985362e+00,   6.69151129e-04],
       [  1.11236208e-04,   8.06569082e-01,   8.74062970e-02],
       [  2.18170674e-03,   6.04926811e-01,   3.23793989e-01],
       [  8.86369682e-03,   4.83941449e-01,   4.83941449e-01]])

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Asked in February 2016
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