# Mesh the input space for evaluations of the real function, the prediction and
# its MSE
x = np.atleast_2d(np.linspace(-2, 2, 1000)).T
# Instanciate one Gaussian Process model for the stationary Matern kernel and
# one for the non-stationary one
gp_stationary = \
GaussianProcess(corr='matern_1.5', theta0=1e0, thetaL=1e-2, thetaU=1e+2,
random_start=100)
gp_non_stationary = \
GaussianProcess(corr=NonStationaryCorrelation(),
theta0=1e0, thetaL=1e-2, thetaU=1e+2,
random_start=100)
# Fit to data using Maximum Likelihood Estimation of the parameters
gp_stationary.fit(X, y)
gp_non_stationary.fit(X, y)
print("Theta:\n\tStationary: {:.3f} \t Non-stationary: {:.3f}"
.format(gp_stationary.theta_[0], gp_non_stationary.theta_[0]))
print("Posterior probability (negative, average, log):\n\t"
"Stationary: {:.5f} \t Non-stationary: {:.5f}"
.format(gp_stationary.posterior_function_value_,
gp_non_stationary.posterior_function_value_))
# Plot predictions
for title, gp in [("stationary", gp_stationary),
("non-stationary", gp_non_stationary)]:
# Make the prediction on the meshed x-axis (ask for MSE as well)
y_pred, MSE = gp.predict(x, eval_MSE=True)
sigma = np.sqrt(MSE)
# Mesh the input space for evaluations of the real function, the prediction and
# its MSE
x = np.atleast_2d(np.linspace(-2, 2, 1000)).T
# Instanciate one Gaussian Process model for the stationary Matern kernel and
# one for the non-stationary one
gp_stationary = \
GaussianProcess(corr='matern_1.5', theta0=1e0, thetaL=1e-2, thetaU=1e+2,
random_start=100)
gp_non_stationary = \
GaussianProcess(corr=NonStationaryCorrelation(),
theta0=1e0, thetaL=1e-2, thetaU=1e+2,
random_start=100)
# Fit to data using Maximum Likelihood Estimation of the parameters
gp_stationary.fit(X, y)
gp_non_stationary.fit(X, y)
print("Theta:\n\tStationary: {:.3f} \t Non-stationary: {:.3f}".format(
gp_stationary.theta_[0], gp_non_stationary.theta_[0]))
print("Posterior probability (negative, average, log):\n\t"
"Stationary: {:.5f} \t Non-stationary: {:.5f}".format(
gp_stationary.posterior_function_value_,
gp_non_stationary.posterior_function_value_))
# Plot predictions
for title, gp in [("stationary", gp_stationary),
("non-stationary", gp_non_stationary)]:
# Make the prediction on the meshed x-axis (ask for MSE as well)
y_pred, MSE = gp.predict(x, eval_MSE=True)
sigma = np.sqrt(MSE)
