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Fix FITC for multiple output dimensions #821

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Fix FITC for multiple output dimensions #821

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4 changes: 2 additions & 2 deletions GPy/inference/latent_function_inference/fitc.py
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Expand Up @@ -68,13 +68,13 @@ def inference(self, kern, X, Z, likelihood, Y, mean_function=None, Y_metadata=No


# Compute dL_dKmm
vvT_P = tdot(v.reshape(-1,1)) + P
vvT_P = tdot(v) + P
dL_dK = 0.5*(Kmmi - vvT_P)
KiU = np.dot(Kmmi, U.T)
dL_dK += np.dot(KiU*dL_dR, KiU.T)

# Compute dL_dU
vY = np.dot(v.reshape(-1,1),Y.T)
vY = np.dot(v, Y.T)
dL_dU = vY - np.dot(vvT_P, U.T)
dL_dU *= beta_star
dL_dU -= 2.*KiU*dL_dR
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13 changes: 12 additions & 1 deletion GPy/testing/fitc.py
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Expand Up @@ -16,12 +16,18 @@ def setUp(self):
self.Y1D = np.sin(self.X1D) + np.random.randn(N, 1) * 0.05

######################################
# # 2 dimensional example
# # 2 dimensional example with 1 dimensional output

# sample inputs and outputs
self.X2D = np.random.uniform(-3., 3., (N, 2))
self.Y2D = np.sin(self.X2D[:, 0:1]) * np.sin(self.X2D[:, 1:2]) + np.random.randn(N, 1) * 0.05

######################################
# # 2 dimensional example with 2 dimensional output

# sample inputs and outputs
self.Y2D2D = np.sin(self.X2D) + np.random.randn(N, 2) * 0.05

def test_fitc_1d(self):
m = GPy.models.SparseGPRegression(self.X1D, self.Y1D)
m.inference_method=GPy.inference.latent_function_inference.FITC()
Expand All @@ -32,3 +38,8 @@ def test_fitc_2d(self):
m.inference_method=GPy.inference.latent_function_inference.FITC()
self.assertTrue(m.checkgrad())

def test_fitc_2d2d(self):
m = GPy.models.SparseGPRegression(self.X2D, self.Y2D2D)
m.inference_method=GPy.inference.latent_function_inference.FITC()
self.assertTrue(m.checkgrad())