apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-06-11 15:15:15 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

format on save

Browse source
This commit is contained in:
Martin Bubel 2023-10-10 20:00:34 +02:00
parent 5f08c2c139
commit a9e65c965b
1 changed files with 24 additions and 18 deletions
Download patch file Download diff file Expand all files Collapse all files

42
GPy/testing/svgp_tests.py
View file

@ -1,54 +1,60 @@
import numpy as np
import scipy as sp
import GPy
class SVGP_nonconvex(np.testing.TestCase):
"""
Inference in the SVGP with a student-T likelihood
"""
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
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)
k = GPy.kern.RBF(1, lengthscale=5.0) + GPy.kern.White(1, 1e-6)
self.m = GPy.core.SVGP(X, Y, Z=Z, likelihood=lik, kernel=k)
def test_grad(self):
assert self.m.checkgrad(step=1e-4)
class SVGP_classification(np.testing.TestCase):
"""
Inference in the SVGP with a Bernoulli likelihood
"""
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)
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)
k = GPy.kern.RBF(1, lengthscale=5.0) + GPy.kern.White(1, 1e-6)
self.m = GPy.core.SVGP(X, Y, Z=Z, likelihood=lik, kernel=k)
def test_grad(self):
assert self.m.checkgrad(step=1e-4)
class SVGP_Poisson_with_meanfunction(np.testing.TestCase):
"""
Inference in the SVGP with a Bernoulli likelihood
"""
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)
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)
k = GPy.kern.RBF(1, lengthscale=5.0) + GPy.kern.White(1, 1e-6)
self.m = GPy.core.SVGP(X, Y, Z=Z, likelihood=lik, kernel=k, mean_function=mf)
def test_grad(self):
assert self.m.checkgrad(step=1e-4)