gpflow_vs_gpy.py

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]

"""