2.7.4.1. Noisy optimization problemΒΆ

Draws a figure explaining noisy vs non-noisy optimization

../../../_images/sphx_glr_plot_noisy_001.png
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(0)
x = np.linspace(-5, 5, 101)
x_ = np.linspace(-5, 5, 31)
def f(x):
return -np.exp(-x**2)
# A smooth function
plt.figure(1, figsize=(3, 2.5))
plt.clf()
plt.plot(x_, f(x_) + .2*np.random.normal(size=31), linewidth=2)
plt.plot(x, f(x), linewidth=2)
plt.ylim(ymin=-1.3)
plt.axis('off')
plt.tight_layout()
plt.show()

Total running time of the script: ( 0 minutes 0.047 seconds)

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