gpr.Gaussian_noise.variance = noise**2
gpr.Gaussian_noise.variance.fix()
# Run optimization
gpr.optimize()
# Obtain optimized kernel parameters
l = gpr.rbf.lengthscale.values[0]
sigma_f = np.sqrt(gpr.rbf.variance.values[0])
# Compare with previous results
assert (np.isclose(l_opt, l))
assert (np.isclose(sigma_f_opt, sigma_f))
# Plot the results with the built-in plot function
gpr.plot()
import numpy as np
from sklearn.gaussian_process import GaussianProcess
from matplotlib import pyplot as pl
np.random.seed(1)
def f(x):
"""The function to predict."""
return x * np.sin(x)
#----------------------------------------------------------------------
# First the noiseless case
# https://sheffieldml.github.io/GPy/
# See also https://gpytorch.ai/
#import sys
#sys.path.append("/home/kpmurphy/github/GPy")
import GPy
rbf = GPy.kern.RBF(input_dim=1, variance=1.0, lengthscale=1.0)
gpr = GPy.models.GPRegression(X_train, Y_train, rbf)
# Fix the noise variance to known value
gpr.Gaussian_noise.variance = noise**2
gpr.Gaussian_noise.variance.fix()
# Run optimization
gpr.optimize();
# Display optimized parameter values
#display(gpr)
# Obtain optimized kernel parameters
l = gpr.rbf.lengthscale.values[0]
sigma_f = np.sqrt(gpr.rbf.variance.values[0])
# Compare with previous results
assert(np.isclose(l_opt, l))
assert(np.isclose(sigma_f_opt, sigma_f))
# Plot the results with the built-in plot function
gpr.plot();
