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Added models for testing kernel gradients in unit tests.

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Neil Lawrence 2013-08-20 19:38:12 +02:00
parent c140b2118a
commit 65702c1448
3 changed files with 102 additions and 34 deletions
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32
GPy/kern/kern.py
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@ -536,35 +536,3 @@ class kern(Parameterized):
else:
raise NotImplementedError, "Cannot plot a kernel with more than two input dimensions"
def objective_and_gradients_dK_dtheta(self, param, X, X2=None):
self._set_param(param)
K = self.K(X, X2)
f = K.sum()
dL_dK = np.ones_like(K)
g = self.dK_dtheta(param, dL_dK, X, X2)
return f, g
def objective_and_gradients_dK_dX(self, param, X, X2=None):
self._set_param(param)
K = self.K(X, X2)
f = K.sum()
dL_dK = np.ones_like(K)
g = self.dK_dX(param, dL_dK, X, X2)
return f, g
def objective_and_gradients_dKdiag_dtheta(self, param, X, X2=None):
self._set_param(param)
Kdiag = self.Kdiag(X)
f = Kdiag.sum()
dL_dK = np.ones_like(Kdiag)
g = self.dKdiag_dtheta(param, dL_dK, X)
return f, g
def objective_and_gradients_dKdiag_dX(self, param, X, X2=None):
self._set_param(param)
Kdiag = self.Kdiag(X)
f = Kdiag.sum()
dL_dK = np.ones_like(Kdiag)
g = self.dK_dX(param, dL_dK, X)
return f, g