80 questions
1
vote
1
answer
59
views
numpy.random - size and shape confusion
I was looking through some codes and saw this line, numpy.random.normal(size=x.shape). Where, x = numpy.linspace(1, 2, 100). I don't understand what this does. I've only come across, np.random.normal(...
- 25
1
vote
3
answers
130
views
How can I quickly generate a (large) list of random numbers given a list of seeds as input?
I need to make a function that takes in an array of integers and returns a list of random numbers with the same length as the array. However, there is the restriction that the output random number ...
- 23
2
votes
1
answer
87
views
Confusion regarding the inner workings of NumPy's SeedSequence
In case it matters at all, I'm using Python 3.11.5 64-bit on a Windows 11 Pro desktop computer with NumPy 1.26.4.
In order to try to better understand what NumPy is doing behind the scenes when I ask ...
- 27
0
votes
1
answer
105
views
How to find if two arrays are from uncorrelated distributions?
I have two arrays - array1 and array2. How confidently can I state that they are generated from uncorrelated distributions? Just checking the correlation values would not suffice; I would require p-...
0
votes
1
answer
60
views
How to randomly map a proportion of data value to a specific category?
I have a dataset below which shows if a customer is a return customer or not. The end goal is for all returned customers, I need to map about 25% of them to 'yes 1 purchase' and 75% of them to 'yes &...
- 460
3
votes
2
answers
674
views
Correctly seeding numpy random generator
For my scientific experiments, I usually seed using:
rng = np.random.Generator(np.random.PCG64(seed))
which for the current numpy version is equivalent to
rng = np.random.Generator(np.random....
- 374
2
votes
1
answer
635
views
Scaling of a Standard Normal Distribution
I have a question that is closely related to this one:
Normed histogram y-axis larger than 1
The solution of the above thread was to scale the dimensions of the axis properly.
However, that solution ...
- 75
0
votes
1
answer
203
views
Why does np.random.choice take so much longer than np.random.randint?
I've been reading that np.random.choice is faster than np.random.randint (here and here), however when comparing the speed difference between
import numpy as np
for i in range(100000):
np.random....
- 19
0
votes
1
answer
88
views
How to restore matlab random generator state into numpy?
I can save the state of matlab random generator with the following code
seed = 10;
rng(seed, 'twister');
%... random functions that don't need to be reproduced%
s = rng;
s.Type
s.Seed
s.State
save('...
- 428
0
votes
1
answer
60
views
Create Numpy array from list of arbitrary sized bites
How can I create a numpy array from a python list of bytes objects of an arbitrary (but known) size?
Example:
size = 10
byte_list = [np.random.default_rng().bytes(size) for i in range(100)]
...
- 95
0
votes
1
answer
67
views
Can't assign the "sum of np.random.normal" in a element of array
I am trying to produce random number by random.normal and take the summary of them. Then, I tried to assgin the value to each element in the array sum.
I made a zero array (float type) by np.zeros and ...
- 11
0
votes
0
answers
135
views
Randomly sampling similar distributions
I want to find a robust way to sample distributions that are similar to an existing distribution. This new distribution should be at least r/2 away from the original distribution, but at most r away. ...
- 23
1
vote
3
answers
923
views
Uniformity test on a large set using scipy.stats.kstest
I'm playing around with some hardware entropy creation. I'm using electronic sensors to sample the environment, using A2D with between 12-16 bits of resolution. I'm hypothesizing that the lower-order ...
- 245
-1
votes
1
answer
746
views
Generate bootstrap sample from ndarray
Is there a way to generate a bootstrap sample on an N-dimensional array? I am limited to using numpy==1.19.4
I have already tried using a for loop on the other dimensions to no avail, but the ...
- 122
3
votes
1
answer
1k
views
`numpy.random.normal` generates different numbers on different systems
I'm comparing the numbers generated with np.random.normal on two different systems (details see below) with the following code (I'm using the legacy np.random.seed because this is used by another ...
- 36.4k