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gradients of predictions for Trevor

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This commit is contained in:
James Hensman 2014-08-13 10:36:54 +01:00
parent 3fc603f778
commit fe405271e5
3 changed files with 34 additions and 12 deletions
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18
GPy/kern/_src/stationary.py
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@ -160,20 +160,20 @@ class Stationary(Kern):
"""
invdist = self._inv_dist(X, X2)
dL_dr = self.dK_dr_via_X(X, X2) * dL_dK
#The high-memory numpy way:
#d = X[:, None, :] - X2[None, :, :]
#ret = np.sum((invdist*dL_dr)[:,:,None]*d,1)/self.lengthscale**2
#if X2 is None:
#ret *= 2.
#the lower memory way with a loop
tmp = invdist*dL_dr
ret = np.empty(X.shape, dtype=np.float64)
if X2 is None:
tmp = tmp + tmp.T
X2 = X
[np.einsum('ij,ij->i', tmp, X[:,q][:,None]-X2[:,q][None,:], out=ret[:,q]) for q in xrange(self.input_dim)]
#The high-memory numpy way:
#d = X[:, None, :] - X2[None, :, :]
#ret = np.sum(tmp[:,:,None]*d,1)/self.lengthscale**2
#the lower memory way with a loop
ret = np.empty(X.shape, dtype=np.float64)
[np.sum(tmp*(X[:,q][:,None]-X2[:,q][None,:]), axis=1, out=ret[:,q]) for q in xrange(self.input_dim)]
ret /= self.lengthscale**2
return ret
def gradients_X_diag(self, dL_dKdiag, X):