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some tests for the svgp, and some changes to the likelihoods

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This commit is contained in:
James Hensman 2015-03-24 14:11:50 +00:00
parent 53081c704d
commit 534e0df606
3 changed files with 37 additions and 3 deletions
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2
GPy/likelihoods/bernoulli.py
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@ -77,7 +77,7 @@ class Bernoulli(Likelihood):
return Z_hat, mu_hat, sigma2_hat return Z_hat, mu_hat, sigma2_hat
def variational_expectations(self, Y, m, v, gh_points=None): def variational_expectations(self, Y, m, v, gh_points=None, Y_metadata=None):
if isinstance(self.gp_link, link_functions.Probit): if isinstance(self.gp_link, link_functions.Probit):
if gh_points is None: if gh_points is None:

4
GPy/likelihoods/student_t.py
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@ -35,8 +35,8 @@ class StudentT(Likelihood):
self.log_concave = False self.log_concave = False
def parameters_changed(self): #def parameters_changed(self):
self.variance = (self.v / float(self.v - 2)) * self.sigma2 #self.variance = (self.v / float(self.v - 2)) * self.sigma2
def update_gradients(self, grads): def update_gradients(self, grads):
""" """

34
GPy/testing/svgp_tests.py Normal file
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@ -0,0 +1,34 @@
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
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)
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)
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)
def test_grad(self):
assert self.m.checkgrad(step=1e-4)