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mzwiessele 2018-02-22 23:46:48 +01:00
parent c1b70fd2d1
commit 13179b9275

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@ -4,23 +4,26 @@ import matplotlib.pyplot as plt
import GPy.models.state_space_model as SS_model import GPy.models.state_space_model as SS_model
X = np.linspace(0, 10, 2000)[:, None] def state_space_example():
Y = np.sin(X) + np.random.randn(*X.shape)*0.1 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]) kernel1 = GPy.kern.Matern32(X.shape[1])
m1 = GPy.models.GPRegression(X,Y, kernel1) m1 = GPy.models.GPRegression(X,Y, kernel1)
print(m1) print(m1)
m1.optimize(optimizer='bfgs',messages=True) m1.optimize(optimizer='bfgs',messages=True)
print(m1) print(m1)
kernel2 = GPy.kern.sde_Matern32(X.shape[1]) kernel2 = GPy.kern.sde_Matern32(X.shape[1])
#m2 = SS_model.StateSpace(X,Y, kernel2) #m2 = SS_model.StateSpace(X,Y, kernel2)
m2 = GPy.models.StateSpace(X,Y, kernel2) m2 = GPy.models.StateSpace(X,Y, kernel2)
print(m2) print(m2)
m2.optimize(optimizer='bfgs',messages=True) m2.optimize(optimizer='bfgs',messages=True)
print(m2) print(m2)
return m1, m2