I have an array a with size n*2, I want to add a guassian noise to each value a[i,j] with mean= a[i,j] and standard deviation = 0.2* a[i,j] to each number in the array. Could you please help me with that?
Here is a sample of array:
import numpy as np
a = np.random.rand(10,2)
>Solution :
If you look at the documentation, the np.random.normal function takes an array-like for both the mean and stdev arguments, so
x = np.random.rand(10, 2)
noise = np.random.normal(x, 0.2*x)
x + noise
However, I don’t quite follow your reasoning behind using each value in the array as the mean… if you want to add noise to data, why not use loc=0? That would then add +/- a tiny bit of Gaussian distributed noise to each of the values without heavily skewing each value.
For instance, if x[i,j] == 6, and you added noise centered on ~G(6, 1.2), then x[i, j] would be as large as 12 on average, which isn’t so much adding noise as it is fundamentally changing the data.
Here, taking 6 to be the value at some given coordinate x[i, j],
In [12]: 6 + np.random.normal(6, 0.2*6)
Out[12]: 13.812965429428406
Your 6 turned into 13.81 — is this your intention?
Depending on each of the distributions, you’d could be consistently shifting all your data in the positive direction, which is almost certainly not what you intend. If you’re simply trying to add jitter to your data, you should use loc=0.