apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-07 11:02:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Update rv_transformation_tests.py

Browse source
This commit is contained in:
Max Zwiessele 2015-09-14 17:07:54 +01:00
parent 9a6ae576e9
commit 6f1024ce5b
1 changed files with 10 additions and 14 deletions
Download patch file Download diff file Expand all files Collapse all files

20
GPy/testing/rv_transformation_tests.py
View file

@ -14,9 +14,9 @@ class TestModel(GPy.core.Model):
""" """
A simple GPy model with one parameter. A simple GPy model with one parameter.
""" """
def __init__(self): def __init__(self, theta=1.):
GPy.core.Model.__init__(self, 'test_model') GPy.core.Model.__init__(self, 'test_model')
theta = GPy.core.Param('theta', 1.) theta = GPy.core.Param('theta', theta)
self.link_parameter(theta) self.link_parameter(theta)
def log_likelihood(self): def log_likelihood(self):
@ -55,17 +55,13 @@ class RVTransformationTestCase(unittest.TestCase):
# END OF PLOT # END OF PLOT
# The following test cannot be very accurate # The following test cannot be very accurate
self.assertTrue(np.linalg.norm(pdf_phi - kde(phi)) / np.linalg.norm(kde(phi)) <= 1e-1) self.assertTrue(np.linalg.norm(pdf_phi - kde(phi)) / np.linalg.norm(kde(phi)) <= 1e-1)
# Check the gradients at a few random points
checks_failed = 0.
checks = 5
for i in range(checks):
m.theta = theta_s[i]
print(m.theta, m.optimizer_array, m.param_array)
if not m.checkgrad():
checks_failed += 1.
m.checkgrad(1)
self.assertTrue(checks_failed/float(checks) <= .3)
def _test_grad(self):
m = TestModel(np.random.uniform(.5, 1.5, 20))
prior = GPy.priors.LogGaussian(.5, 0.1)
m.theta.set_prior(prior)
m.theta.constrain(trans)
self.assertTrue(m.checkgrad())
def test_Logexp(self): def test_Logexp(self):
self._test_trans(GPy.constraints.Logexp()) self._test_trans(GPy.constraints.Logexp())