import GPy
import numpy as np
import matplotlib.pyplot as plt
import GPy.models.state_space_model as SS_model
[docs]def state_space_example():
X = np.linspace(0, 10, 2000)[:, None]
Y = np.sin(X) + np.random.randn(*X.shape) * 0.1
kernel1 = GPy.kern.Matern32(X.shape[1])
m1 = GPy.models.GPRegression(X, Y, kernel1)
print(m1)
m1.optimize(optimizer="bfgs", messages=True)
print(m1)
kernel2 = GPy.kern.sde_Matern32(X.shape[1])
# m2 = SS_model.StateSpace(X,Y, kernel2)
m2 = GPy.models.StateSpace(X, Y, kernel2)
print(m2)
m2.optimize(optimizer="bfgs", messages=True)
print(m2)
return m1, m2