PLEASE USE PYTHON

Now we'll create some artificial data to practice on. We're generating the data by adding noise to the usual square root function. The noise is sampled from a normal distribution. import numpy as np import matplotlib.pyplot as plt x = np.linspace( 0,50,50) ## The domain N 30 ## 30 sample points np. random . seed(22) # so we get the same random stuff shuff np.random.permutation(len(x)) x-pts-sorted (x(shuff][:N]) ## Pick N polnts at random from the domain f-np.sqrt #The target function (no noise yet) sigma 1/2 noise-np. randon . randn (N) *sigma ## Noise sampled from the normal distribution with sd-sigma f-noisy f(x-pts) noise Noisy data - + plt.plot(x,f(x),label-"target",c-'blue' plt.plot (x-pts,f-noisy, 'o', label-"noisy target ",alpha#0.8,c-'orange') plt.legend) plt.show) Now we'll create some artificial data to practice on. We're generating the data by adding noise to the usual square root function. The noise is sampled from a normal distribution. import numpy as np import matplotlib.pyplot as plt x = np.linspace( 0,50,50) ## The domain N 30 ## 30 sample points np. random . seed(22) # so we get the same random stuff shuff np.random.permutation(len(x)) x-pts-sorted (x(shuff][:N]) ## Pick N polnts at random from the domain f-np.sqrt #The target function (no noise yet) sigma 1/2 noise-np. randon . randn (N) *sigma ## Noise sampled from the normal distribution with sd-sigma f-noisy f(x-pts) noise Noisy data - + plt.plot(x,f(x),label-"target",c-'blue' plt.plot (x-pts,f-noisy, 'o', label-"noisy target ",alpha#0.8,c-'orange') plt.legend) plt.show)