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added unittests for sparse gp with uncertain inputs

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Max Zwiessele 2013-07-17 14:50:02 +01:00
parent 0870301144
commit c280cdac90
1 changed files with 25 additions and 2 deletions
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27
GPy/testing/unit_tests.py
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@ -23,7 +23,7 @@ class GradientTests(unittest.TestCase):
self.X2D = np.random.uniform(-3., 3., (40, 2)) self.X2D = np.random.uniform(-3., 3., (40, 2))
self.Y2D = np.sin(self.X2D[:, 0:1]) * np.sin(self.X2D[:, 1:2]) + np.random.randn(40, 1) * 0.05 self.Y2D = np.sin(self.X2D[:, 0:1]) * np.sin(self.X2D[:, 1:2]) + np.random.randn(40, 1) * 0.05
def check_model_with_white(self, kern, model_type='GPRegression', dimension=1): def check_model_with_white(self, kern, model_type='GPRegression', dimension=1, uncertain_inputs=False):
# Get the correct gradients # Get the correct gradients
if dimension == 1: if dimension == 1:
X = self.X1D X = self.X1D
@ -36,7 +36,10 @@ class GradientTests(unittest.TestCase):
noise = GPy.kern.white(dimension) noise = GPy.kern.white(dimension)
kern = kern + noise kern = kern + noise
m = model_fit(X, Y, kernel=kern) if uncertain_inputs:
m = model_fit(X, Y, kernel=kern, X_variance=np.random.rand(X.shape[0], X.shape[1]))
else:
m = model_fit(X, Y, kernel=kern)
m.randomize() m.randomize()
# contrain all parameters to be positive # contrain all parameters to be positive
self.assertTrue(m.checkgrad()) self.assertTrue(m.checkgrad())
@ -141,6 +144,26 @@ class GradientTests(unittest.TestCase):
rbf = GPy.kern.rbf(2) rbf = GPy.kern.rbf(2)
self.check_model_with_white(rbf, model_type='SparseGPRegression', dimension=2) self.check_model_with_white(rbf, model_type='SparseGPRegression', dimension=2)
def test_SparseGPRegression_rbf_linear_white_kern_1D(self):
''' Testing the sparse GP regression with rbf and white kernel on 2d data '''
rbflin = GPy.kern.rbf(1) + GPy.kern.linear(1)
self.check_model_with_white(rbflin, model_type='SparseGPRegression', dimension=1)
def test_SparseGPRegression_rbf_linear_white_kern_2D(self):
''' Testing the sparse GP regression with rbf and white kernel on 2d data '''
rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2)
self.check_model_with_white(rbflin, model_type='SparseGPRegression', dimension=2)
def test_SparseGPRegression_rbf_linear_white_kern_2D_uncertain_inputs(self):
''' Testing the sparse GP regression with rbf, linear and white kernel on 2d data with uncertain inputs'''
rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2)
self.check_model_with_white(rbflin, model_type='SparseGPRegression', dimension=2, uncertain_inputs=1)
def test_SparseGPRegression_rbf_linear_white_kern_1D_uncertain_inputs(self):
''' Testing the sparse GP regression with rbf, linear and white kernel on 1d data with uncertain inputs'''
rbflin = GPy.kern.rbf(1) + GPy.kern.linear(1)
self.check_model_with_white(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):
""" Testing GPLVM with rbf + bias and white kernel """ """ Testing GPLVM with rbf + bias and white kernel """
N, input_dim, D = 50, 1, 2 N, input_dim, D = 50, 1, 2