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This article introduces the relevant knowledge of "how to add Gaussian noise to data by python3". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Background

Gaussian noise, as its name implies, refers to a class of noise that obeys Gaussian distribution (normal distribution). Sometimes we need to add appropriate Gaussian noise to the standard data to make the data more realistic.

Gaussian normal distribution is integrated into the random library in python, which can be used directly.

We can obtain the processing data of different effects by adjusting the mean and variance of Gaussian noise.

raw data

Gaussian noise sigma = 0. 05

Gaussian noise sigma = 0.1

Gaussian noise sigma = 0.15

Source code import randomimport numpy as npfrom matplotlib import pyplot as pltdef gauss_noisy (x, y): "" add Gaussian noise to the input data: param x: X axis data: param y: y axis data: return: "mu = 0 sigma = 0.05 for i in range (len (x)): X [I] + = random.gauss (mu, sigma) y [I] + = random.gauss (mu) Sigma) if _ name__ = ='_ main__': # generate 50 points in the range 0-5 as test data xl = np.linspace (0,5,50, endpoint=True) yl = np.sin (xl) # add Gaussian noise gauss_noisy (xl, yl) # draw these points plt.plot (xl, yl, linestyle='', marker='.') Plt.show () "how to add Gaussian noise to data by python3" is introduced here. Thank you for your reading. If you want to know more about the industry, you can follow the website, the editor will output more high-quality practical articles for you!

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