import numpy as np
import scipy as sp
import GPy
[docs]class SVGP_nonconvex(np.testing.TestCase):
"""
Inference in the SVGP with a student-T likelihood
"""
[docs] def setUp(self):
X = np.linspace(0,10,100).reshape(-1,1)
Z = np.linspace(0,10,10).reshape(-1,1)
Y = np.sin(X) + np.random.randn(*X.shape)*0.1
Y[50] += 3
lik = GPy.likelihoods.StudentT(deg_free=2)
k = GPy.kern.RBF(1, lengthscale=5.) + GPy.kern.White(1, 1e-6)
self.m = GPy.core.SVGP(X, Y, Z=Z, likelihood=lik, kernel=k)
[docs] def test_grad(self):
assert self.m.checkgrad(step=1e-4)
[docs]class SVGP_classification(np.testing.TestCase):
"""
Inference in the SVGP with a Bernoulli likelihood
"""
[docs] def setUp(self):
X = np.linspace(0,10,100).reshape(-1,1)
Z = np.linspace(0,10,10).reshape(-1,1)
Y = np.where((np.sin(X) + np.random.randn(*X.shape)*0.1)>0, 1,0)
lik = GPy.likelihoods.Bernoulli()
k = GPy.kern.RBF(1, lengthscale=5.) + GPy.kern.White(1, 1e-6)
self.m = GPy.core.SVGP(X, Y, Z=Z, likelihood=lik, kernel=k)
[docs] def test_grad(self):
assert self.m.checkgrad(step=1e-4)
[docs]class SVGP_Poisson_with_meanfunction(np.testing.TestCase):
"""
Inference in the SVGP with a Bernoulli likelihood
"""
[docs] def setUp(self):
X = np.linspace(0,10,100).reshape(-1,1)
Z = np.linspace(0,10,10).reshape(-1,1)
latent_f = np.exp(0.1*X * 0.05*X**2)
Y = np.array([np.random.poisson(f) for f in latent_f.flatten()]).reshape(-1,1)
mf = GPy.mappings.Linear(1,1)
lik = GPy.likelihoods.Poisson()
k = GPy.kern.RBF(1, lengthscale=5.) + GPy.kern.White(1, 1e-6)
self.m = GPy.core.SVGP(X, Y, Z=Z, likelihood=lik, kernel=k, mean_function=mf)
[docs] def test_grad(self):
assert self.m.checkgrad(step=1e-4)