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import GPy | |
import GPflow | |
import numpy as np | |
rng = np.random.RandomState(0) | |
X = rng.randn(1000,2) | |
Y = rng.randn(1000,1) | |
Xtest = rng.randn(10,2) | |
# GPy | |
m_gpy = GPy.models.GPRegression(X, Y) | |
m_gpy.optimize() | |
# GPflow | |
m_gpflow = GPflow.gpr.GPR(X, Y, GPflow.kernels.RBF(2)) | |
m_gpflow.optimize() | |
print m_gpy | |
print m_gpflow | |
######################## | |
# Expected Output | |
""" | |
(optimization...) | |
optimization terminated, setting model state | |
-1372.99859855 | |
Name : GP regression | |
Objective : 1372.99859855 | |
Number of Parameters : 3 | |
Number of Optimization Parameters : 3 | |
Updates : True | |
Parameters: | |
GP_regression. | value | constraints | priors | |
rbf.variance | 0.00171168915388 | +ve | | |
rbf.lengthscale | 109.006106489 | +ve | | |
Gaussian_noise.variance | 0.911268070608 | +ve | | |
model.likelihood.variance transform:+ve prior:None | |
[ 0.91126679] | |
model.kern.variance transform:+ve prior:None | |
[ 0.00171146] | |
model.kern.lengthscales transform:+ve prior:None | |
[ 108.981745] | |
""" |
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