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Fixed test in kern.py to request correct output dim for multioutput covariances.

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This commit is contained in:
Neil Lawrence 2013-11-27 10:07:08 +00:00
parent 58ffdd813e
commit a81b5cfd50
2 changed files with 5 additions and 5 deletions
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4
GPy/kern/kern.py
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@ -861,13 +861,13 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False, X_positive=
if X_positive:
X = abs(X)
if output_ind is not None:
X[:, output_ind] = np.random.randint(kern.parts[0].output_dim, X.shape[0])
X[:, output_ind] = np.random.randint(low=0,high=kern.parts[0].output_dim, size=X.shape[0])
if X2==None:
X2 = np.random.randn(20, kern.input_dim)
if X_positive:
X2 = abs(X2)
if output_ind is not None:
X2[:, output_ind] = np.random.randint(kern.parts[0].output_dim, X2.shape[0])
X2[:, output_ind] = np.random.randint(low=0, high=kern.parts[0].output_dim, size=X2.shape[0])
if verbose:
print("Checking covariance function is positive definite.")

6
GPy/testing/bcgplvm_tests.py
View file

@ -15,7 +15,7 @@ class BCGPLVMTests(unittest.TestCase):
k = GPy.kern.mlp(input_dim) + GPy.kern.bias(input_dim)
bk = GPy.kern.rbf(output_dim)
mapping = GPy.mappings.Kernel(output_dim=input_dim, X=Y, kernel=bk)
m = GPy.models.BCGPLVM(Y, input_dim, kernel = k, mapping=mapping)
m = GPy._models.BCGPLVM(Y, input_dim, kernel = k, mapping=mapping)
m.randomize()
self.assertTrue(m.checkgrad())
@ -28,7 +28,7 @@ class BCGPLVMTests(unittest.TestCase):
k = GPy.kern.mlp(input_dim) + GPy.kern.bias(input_dim)
bk = GPy.kern.rbf(output_dim)
mapping = GPy.mappings.Linear(output_dim=input_dim, input_dim=output_dim)
m = GPy.models.BCGPLVM(Y, input_dim, kernel = k, mapping=mapping)
m = GPy._models.BCGPLVM(Y, input_dim, kernel = k, mapping=mapping)
m.randomize()
self.assertTrue(m.checkgrad())
@ -41,7 +41,7 @@ class BCGPLVMTests(unittest.TestCase):
k = GPy.kern.mlp(input_dim) + GPy.kern.bias(input_dim)
bk = GPy.kern.rbf(output_dim)
mapping = GPy.mappings.MLP(output_dim=input_dim, input_dim=output_dim, hidden_dim=[5, 4, 7])
m = GPy.models.BCGPLVM(Y, input_dim, kernel = k, mapping=mapping)
m = GPy._models.BCGPLVM(Y, input_dim, kernel = k, mapping=mapping)
m.randomize()
self.assertTrue(m.checkgrad())