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Merge branch 'gradientsxx' of github.com:SheffieldML/GPy into gradientsxx

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Max Zwiessele 2016-05-04 12:26:19 +01:00
commit da30663228
1 changed files with 17 additions and 20 deletions
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37
GPy/core/gp.py
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@ -335,8 +335,7 @@ class GP(Model):
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):
def predict_jacobian(self, Xnew, kern=None, full_cov=False):
"""
Compute the derivatives of the posterior of the GP.
@ -354,15 +353,11 @@ class GP(Model):
:param X: The points at which to get the predictive gradients.
:type X: np.ndarray (Xnew x self.input_dim)
:param kern: The kernel to compute the jacobian for.
:param boolean full_cov: whether to return the full covariance of the jacobian.
:param boolean full_cov: whether to return the cross-covariance terms between
the N* Jacobian vectors
:returns: dmu_dX, dv_dX
:rtype: [np.ndarray (N*, Q ,D), np.ndarray (N*,Q,(D)) ]
Note: We always return sum in input_dim gradients, as the off-diagonals
in the input_dim are not needed for further calculations.
This is a compromise for increase in speed. Mathematically the jacobian would
have another dimension in Q.
"""
if kern is None:
kern = self.kern
@ -378,27 +373,28 @@ class GP(Model):
dK_dXnew_full[i] = kern.gradients_X(one, Xnew, self._predictive_variable[[i]])
if full_cov:
dK2_dXdX = kern.gradients_XX(one, Xnew, cov=False)
dK2_dXdX = kern.gradients_XX(one, Xnew)
else:
dK2_dXdX = kern.gradients_XX_diag(one, Xnew, cov=False)
dK2_dXdX = np.zeros((Xnew.shape[0], Xnew.shape[1], Xnew.shape[1]))
for i in range(Xnew.shape[0]):
dK2_dXdX[i:i+1,:,:] = kern.gradients_XX(one, Xnew[i:i+1,:])
def compute_cov_inner(wi):
if full_cov:
# full covariance gradients:
var_jac = dK2_dXdX - np.einsum('qnm,miq->niq', dK_dXnew_full.T.dot(wi), dK_dXnew_full)
var_jac = dK2_dXdX - np.einsum('qnm,msr->nsqr', dK_dXnew_full.T.dot(wi), dK_dXnew_full) # n,s = Xnew.shape[0], m = pred_var.shape[0]
else:
var_jac = dK2_dXdX - np.einsum('qim,miq->iq', dK_dXnew_full.T.dot(wi), dK_dXnew_full)
var_jac = dK2_dXdX - np.einsum('qnm,mnr->nqr', dK_dXnew_full.T.dot(wi), dK_dXnew_full)
return var_jac
if self.posterior.woodbury_inv.ndim == 3: # Missing data:
if full_cov:
var_jac = np.empty((Xnew.shape[0],Xnew.shape[0],Xnew.shape[1],self.output_dim))
var_jac = np.empty((Xnew.shape[0],Xnew.shape[0],Xnew.shape[1],Xnew.shape[1],self.output_dim))
for d in range(self.posterior.woodbury_inv.shape[2]):
var_jac[:, :, :, :, d] = compute_cov_inner(self.posterior.woodbury_inv[:, :, d])
else:
var_jac = np.empty((Xnew.shape[0],Xnew.shape[1],Xnew.shape[1],self.output_dim))
for d in range(self.posterior.woodbury_inv.shape[2]):
var_jac[:, :, :, d] = compute_cov_inner(self.posterior.woodbury_inv[:, :, d])
else:
var_jac = np.empty((Xnew.shape[0],Xnew.shape[1],self.output_dim))
for d in range(self.posterior.woodbury_inv.shape[2]):
var_jac[:, :, d] = compute_cov_inner(self.posterior.woodbury_inv[:, :, d])
else:
var_jac = compute_cov_inner(self.posterior.woodbury_inv)
return mean_jac, var_jac
@ -422,10 +418,11 @@ class GP(Model):
mu_jac, var_jac = self.predict_jacobian(Xnew, kern, full_cov=False)
mumuT = np.einsum('iqd,ipd->iqp', mu_jac, mu_jac)
Sigma = np.zeros(mumuT.shape)
if var_jac.ndim == 3:
if var_jac.ndim == 4: # Missing data
Sigma[(slice(None), )+np.diag_indices(Xnew.shape[1], 2)] = var_jac.sum(-1)
else:
Sigma[(slice(None), )+np.diag_indices(Xnew.shape[1], 2)] = self.output_dim*var_jac
Sigma = self.output_dim*var_jac
G = 0.
if mean:
G += mumuT