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skipping crossterm tests instead of expected failure

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Max Zwiessele 2013-11-20 14:37:14 +00:00
parent 591c28d494
commit 3a08c0d9ab
1 changed files with 4 additions and 2 deletions
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6
GPy/testing/unit_tests.py
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@ -163,16 +163,18 @@ class GradientTests(unittest.TestCase):
rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2) rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2)
self.check_model(rbflin, model_type='SparseGPRegression', dimension=2) self.check_model(rbflin, model_type='SparseGPRegression', dimension=2)
@unittest.expectedFailure #@unittest.expectedFailure
def test_SparseGPRegression_rbf_linear_white_kern_2D_uncertain_inputs(self): def test_SparseGPRegression_rbf_linear_white_kern_2D_uncertain_inputs(self):
''' Testing the sparse GP regression with rbf, linear kernel on 2d data with uncertain inputs''' ''' Testing the sparse GP regression with rbf, linear kernel on 2d data with uncertain inputs'''
rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2) rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2)
raise unittest.SkipTest("This is not implemented yet!")
self.check_model(rbflin, model_type='SparseGPRegression', dimension=2, uncertain_inputs=1) self.check_model(rbflin, model_type='SparseGPRegression', dimension=2, uncertain_inputs=1)
@unittest.expectedFailure #@unittest.expectedFailure
def test_SparseGPRegression_rbf_linear_white_kern_1D_uncertain_inputs(self): def test_SparseGPRegression_rbf_linear_white_kern_1D_uncertain_inputs(self):
''' Testing the sparse GP regression with rbf, linear kernel on 1d data with uncertain inputs''' ''' Testing the sparse GP regression with rbf, linear kernel on 1d data with uncertain inputs'''
rbflin = GPy.kern.rbf(1) + GPy.kern.linear(1) rbflin = GPy.kern.rbf(1) + GPy.kern.linear(1)
raise unittest.SkipTest("This is not implemented yet!")
self.check_model(rbflin, model_type='SparseGPRegression', dimension=1, uncertain_inputs=1) self.check_model(rbflin, model_type='SparseGPRegression', dimension=1, uncertain_inputs=1)
def test_GPLVM_rbf_bias_white_kern_2D(self): def test_GPLVM_rbf_bias_white_kern_2D(self):