CODEWITHSUNDEEP
pythonarraysnumpyrandom
Jane Sully
Jane Sully

Reputation: 3337

Creating a numpy array with dimension, mean, and variance as parameters?

How do I create a numpy array with the dimensions, mean, and variance as parameters? I see there is a numpy.random.randn function which allows the user to specify dimensions, but that function assumes a mean of 0 and variance of 1.

I am okay with the mean 0 part, but I want to be able to specify a variance each time I am creating a new numpy array.

Thanks for your help!

Upvotes: 2

Views: 3865

Answers (2)

yakobyd
yakobyd

Reputation: 582

From the numpy documentation (numpy.random.randn):

For random samples from N(\mu, \sigma^2), when mu is the mean and sigma is the variance (see Normal Distribution), use:

sigma * np.random.randn(d0, d1, ..., dn) + mu

When (d0, d1, ..., dn) is the shape of the generated array.

Upvotes: 3

jpp
jpp

Reputation: 164653

You may be looking for numpy.random.normal. For example:

import numpy as np

arr = np.random.normal(loc=1, scale=0.50, size=(500, 500))

print(arr.mean())  # 0.9995707343806642
print(arr.std())   # 0.5010967322495354

Here loc represents the mean and scale the standard deviation, i.e. the square root of variance.

Of course, you are drawing samples from a distribution, so you will not have a mean of 1.0 or a standard deviation of 0.50, unless you have a very large array.

Upvotes: 4

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