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

GPy/testing/likelihood_tests.py

@@ -538,11 +538,9 @@ class TestNoiseModels(object):
         m.checkgrad(verbose=1, step=step)
         #if not m.checkgrad(step=step):
             #m.checkgrad(verbose=1, step=step)
-            #import ipdb; ipdb.set_trace()
             #NOTE this test appears to be stochastic for some likelihoods (student t?)
             # appears to all be working in test mode right now...
         #if isinstance(model, GPy.likelihoods.StudentT):
-        #    import ipdb;ipdb.set_trace()
         assert m.checkgrad(step=step)
 
     ###########

GPy/testing/model_tests.py

@@ -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):

GPy/testing/observable_tests.py

@@ -45,7 +45,7 @@ class Test(unittest.TestCase):
         self._first = None
         self._second = None
 
-    def _trigger(self, which):
+    def _trigger(self, me, which):
         self._observer_triggered = which
         self._trigger_count += 1
         if self._first is not None:
@@ -53,7 +53,7 @@ class Test(unittest.TestCase):
         else:
             self._first = self._trigger
 
-    def _trigger_priority(self, which):
+    def _trigger_priority(self, me, which):
         if self._first is not None:
             self._second = self._trigger_priority
         else: