error in 4.5.2 Continuous Errors
Mr-Z-W-J opened this issue · 2 comments
from sklearn.gaussian_process import GaussianProcess
model = lambda x: x * np.sin(x)
xdata = np.array([1, 3, 5, 6, 8])
ydata = model(xdata)
gp = GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4, thetaU=1E-1,
random_start=100)
gp.fit(xdata[:, np.newaxis], ydata)
xfit = np.linspace(0, 10, 1000)
yfit, MSE = gp.predict(xfit[:, np.newaxis], eval_MSE=True)
dyfit = 2 * np.sqrt(MSE) # 2*sigma ~ 95% confidence region
How can I compute the MSE ?
The GaussianProcess has been deprecated. You should try using the GaussianProcessregressor
from sklearn.gaussian_process import GaussianProcessRegressor
define the model and draw some data
model = lambda x: x * np.sin(x)
xdata = np.array([1, 3, 5, 6, 8])
ydata = model(xdata)
Compute the Gaussian process fit
gp = GaussianProcessRegressor()
gp.fit(xdata[:, np.newaxis], ydata)
xfit = np.linspace(0, 10, 1000)
yfit, dyfit_ori = gp.predict(xfit[:, np.newaxis],return_std=True)
dyfit = 2 * dyfit_ori # 2*sigma ~ 95% confidence region