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Merge branch 'params' of github.com:SheffieldML/GPy into params

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This commit is contained in:
Alan Saul 2014-03-21 14:22:46 +00:00
commit 1c7ed4ab66
3 changed files with 32 additions and 20 deletions
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36
GPy/testing/kernel_tests.py
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@ -152,7 +152,12 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
if verbose:
print("Checking gradients of Kdiag(X) wrt theta.")
result = Kern_check_dKdiag_dtheta(kern, X=X).checkgrad(verbose=verbose)
try:
result = Kern_check_dKdiag_dtheta(kern, X=X).checkgrad(verbose=verbose)
except NotImplementedError:
result=True
if verbose:
print("update_gradients_diag not implemented for " + kern.name)
if result and verbose:
print("Check passed.")
if not result:
@ -240,9 +245,22 @@ class KernelGradientTestsContinuous(unittest.TestCase):
def test_Add(self):
k = GPy.kern.Matern32(2, active_dims=[2,3]) + GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D)
k += GPy.kern.Matern32(2, active_dims=[2,3]) + GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D)
k.randomize()
self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
def test_Add_dims(self):
k = GPy.kern.Matern32(2, active_dims=[2,self.D]) + GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D)
k.randomize()
self.assertRaises(AssertionError, k.K, self.X)
k = GPy.kern.Matern32(2, active_dims=[2,self.D-1]) + GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D)
k.randomize()
# assert it runs:
try:
k.K(self.X)
except AssertionError:
raise AssertionError, "k.K(X) should run on self.D-1 dimension"
def test_Matern52(self):
k = GPy.kern.Matern52(self.D)
k.randomize()
@ -329,17 +347,11 @@ class KernelTestsNonContinuous(unittest.TestCase):
kern = GPy.kern.IndependentOutputs(k, -1, name='ind_split')
self.assertTrue(check_kernel_gradient_functions(kern, X=self.X, X2=self.X2, verbose=verbose, fixed_X_dims=-1))
class test_ODE_UY(unittest.TestCase):
def setUp(self):
self.k = GPy.kern.ODE_UY(2)
self.X = np.random.randn(50,2)
self.X[:,1] = np.random.randint(0,2,50)
i = np.argsort(X[:,1])
self.X = self.X[i]
self.Y = np.random.randn(50, 1)
def checkgrad(self):
m = GPy.models.GPRegression(X,Y,kernel=k)
self.assertTrue(m.checkgrad())
def test_ODE_UY(self):
kern = GPy.kern.ODE_UY(2, active_dims=[0, self.D])
X = self.X[self.X[:,-1]!=2]
X2 = self.X2[self.X2[:,-1]!=2]
self.assertTrue(check_kernel_gradient_functions(kern, X=X, X2=X2, verbose=verbose, fixed_X_dims=-1))
if __name__ == "__main__":