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[plotting] gradient plot added

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mzwiessele 2015-10-05 18:47:54 +01:00
parent 5869ece323
commit 2b02082015
12 changed files with 193 additions and 125 deletions
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17
GPy/core/gp.py
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@ -294,7 +294,7 @@ class GP(Model):
likelihood = self.likelihood
return likelihood.predictive_quantiles(m, v, quantiles, Y_metadata=Y_metadata)
def predictive_gradients(self, Xnew):
def predictive_gradients(self, Xnew, kern=None):
"""
Compute the derivatives of the predicted latent function with respect to X*
@ -311,16 +311,19 @@ class GP(Model):
:rtype: [np.ndarray (N*, Q ,D), np.ndarray (N*,Q) ]
"""
dmu_dX = np.empty((Xnew.shape[0],Xnew.shape[1],self.output_dim))
if kern is None:
kern = self.kern
mean_jac = np.empty((Xnew.shape[0],Xnew.shape[1],self.output_dim))
for i in range(self.output_dim):
dmu_dX[:,:,i] = self.kern.gradients_X(self.posterior.woodbury_vector[:,i:i+1].T, Xnew, self.X)
mean_jac[:,:,i] = kern.gradients_X(self.posterior.woodbury_vector[:,i:i+1].T, Xnew, self._predictive_variable)
# gradients wrt the diagonal part k_{xx}
dv_dX = self.kern.gradients_X(np.eye(Xnew.shape[0]), Xnew)
dv_dX = kern.gradients_X(np.eye(Xnew.shape[0]), Xnew)
#grads wrt 'Schur' part K_{xf}K_{ff}^{-1}K_{fx}
alpha = -2.*np.dot(self.kern.K(Xnew, self.X),self.posterior.woodbury_inv)
dv_dX += self.kern.gradients_X(alpha, Xnew, self.X)
return dmu_dX, dv_dX
alpha = -2.*np.dot(kern.K(Xnew, self._predictive_variable), self.posterior.woodbury_inv)
dv_dX += kern.gradients_X(alpha, Xnew, self._predictive_variable)
return mean_jac, dv_dX
def predict_jacobian(self, Xnew, kern=None, full_cov=True):