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[classification] sparse gp classification and dtc update

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This commit is contained in:
Max Zwiessele 2015-09-11 15:08:30 +01:00
parent 4ea5ebaa68
commit 1d354f5cce
14 changed files with 208 additions and 369 deletions
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21
GPy/testing/model_tests.py
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@ -447,16 +447,14 @@ class GradientTests(np.testing.TestCase):
rbflin = GPy.kern.RBF(2) + GPy.kern.Linear(2)
self.check_model(rbflin, model_type='SparseGPRegression', dimension=2)
def test_SparseGPRegression_rbf_linear_white_kern_2D_uncertain_inputs(self):
def test_SparseGPRegression_rbf_white_kern_2D_uncertain_inputs(self):
''' Testing the sparse GP regression with rbf, linear kernel on 2d data with uncertain inputs'''
rbflin = GPy.kern.RBF(2) + GPy.kern.Linear(2)
raise unittest.SkipTest("This is not implemented yet!")
rbflin = GPy.kern.RBF(2) + GPy.kern.White(2)
self.check_model(rbflin, model_type='SparseGPRegression', dimension=2, uncertain_inputs=1)
def test_SparseGPRegression_rbf_linear_white_kern_1D_uncertain_inputs(self):
def test_SparseGPRegression_rbf_white_kern_1D_uncertain_inputs(self):
''' Testing the sparse GP regression with rbf, linear kernel on 1d data with uncertain inputs'''
rbflin = GPy.kern.RBF(1) + GPy.kern.Linear(1)
raise unittest.SkipTest("This is not implemented yet!")
rbflin = GPy.kern.RBF(1) + GPy.kern.White(1)
self.check_model(rbflin, model_type='SparseGPRegression', dimension=1, uncertain_inputs=1)
def test_GPLVM_rbf_bias_white_kern_2D(self):
@ -506,6 +504,17 @@ class GradientTests(np.testing.TestCase):
m = GPy.models.SparseGPClassification(X, Y, kernel=kernel, Z=Z)
self.assertTrue(m.checkgrad())
def test_sparse_EP_DTC_probit_uncertain_inputs(self):
N = 20
X = np.hstack([np.random.normal(5, 2, N / 2), np.random.normal(10, 2, N / 2)])[:, None]
Y = np.hstack([np.ones(N / 2), np.zeros(N / 2)])[:, None]
Z = np.linspace(0, 15, 4)[:, None]
X_var = np.random.uniform(0.1, 0.2, X.shape)
kernel = GPy.kern.RBF(1)
m = GPy.models.SparseGPClassificationUncertainInput(X, X_var, Y, kernel=kernel, Z=Z)
self.assertTrue(m.checkgrad())
def test_multioutput_regression_1D(self):
X1 = np.random.rand(50, 1) * 8
X2 = np.random.rand(30, 1) * 5