apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-04-30 23:36:23 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Caching functions now take two arguments: self and which, which is the argument which started the update

Browse source
This commit is contained in:
Max Zwiessele 2014-03-24 08:52:23 +00:00
parent 0b5f6ea7c6
commit a493dd085e
7 changed files with 114 additions and 44 deletions
Download patch file Download diff file Expand all files Collapse all files

70
GPy/testing/model_tests.py
View file

@ -58,7 +58,77 @@ class MiscTests(unittest.TestCase):
np.testing.assert_almost_equal(np.diag(K_hat)[:, None], var)
#np.testing.assert_almost_equal(mu_hat, mu)
def test_likelihood_replicate(self):
m = GPy.models.GPRegression(self.X, self.Y)
m2 = GPy.models.GPRegression(self.X, self.Y)
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m.randomize()
m2[:] = m[''].values()
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m.randomize()
m2[''] = m[:]
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m.randomize()
m2[:] = m[:]
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m.randomize()
m2[''] = m['']
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m.kern.lengthscale.randomize()
m2[:] = m[:]
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m.Gaussian_noise.randomize()
m2[:] = m[:]
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m['.*var'] = 2
m2['.*var'] = m['.*var']
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
def test_likelihood_set(self):
m = GPy.models.GPRegression(self.X, self.Y)
m2 = GPy.models.GPRegression(self.X, self.Y)
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m.kern.lengthscale.randomize()
m._trigger_params_changed()
m2.kern.lengthscale = m.kern.lengthscale
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m.kern.lengthscale.randomize()
m._trigger_params_changed()
m2['.*lengthscale'] = m.kern.lengthscale
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m.kern.lengthscale.randomize()
m._trigger_params_changed()
m2['.*lengthscale'] = m.kern['.*lengthscale']
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m.kern.lengthscale.randomize()
m._trigger_params_changed()
m2.kern.lengthscale = m.kern['.*lengthscale']
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
def test_likelihood_replicate_kern(self):
m = GPy.models.GPRegression(self.X, self.Y)
m2 = GPy.models.GPRegression(self.X, self.Y)
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m.kern.randomize()
m2.kern[''] = m.kern[:]
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m.kern.randomize()
m2.kern[:] = m.kern[:]
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m.kern.randomize()
m2.kern[''] = m.kern['']
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m.kern.randomize()
m2.kern[:] = m.kern[''].values()
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
class GradientTests(unittest.TestCase):
def setUp(self):