import gpflow
import tensorflow as tf
import os
import numpy as np
def getData():
rng = np.random.RandomState(1)
N = 30
X = rng.rand(N,1)
Y = np.sin(12*X) + 0.66*np.cos(25*X) + rng.randn(N,1)*0.1 + 3
return X, Y
def getRegressionModel(X, Y):
#build the GPR object
k = gpflow.kernels.Matern52(1)
meanf = gpflow.mean_functions.Linear(1,0)
m = gpflow.models.GPR(X, Y, k, meanf)
m.likelihood.variance = 0.01
print("Here are the parameters before optimization")
m
return m
def optimizeModel(m):
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m)
print("Here are the parameters after optimization")
m
def setModelPriors(m):
#we'll choose rather arbitrary priors.
m.kern.lengthscales.prior = gpflow.priors.Gamma(1., 1.)
m.kern.variance.prior = gpflow.priors.Gamma(1., 1.)
m.likelihood.variance.prior = gpflow.priors.Gamma(1., 1.)
m.mean_function.A.prior = gpflow.priors.Gaussian(0., 10.)
m.mean_function.b.prior = gpflow.priors.Gaussian(0., 10.)
print("model with priors ", m)
def getSamples(m):
hmc = gpflow.train.HMC()
samples = hmc.sample(m, num_samples=100, epsilon = 0.1)
return samples
def runExperiments(sampling=True,outputGraphs=False):
X,Y = getData()
m = getRegressionModel(X,Y)
optimizeModel(m)
if sampling:
setModelPriors(m)
samples = getSamples(m)
if __name__ == '__main__':
runExperiments()
# cProfile.run('runExperiments(plotting=False)')