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[dxx] faster numpy version of the gradients_XX

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mzwiessele 2016-05-05 10:13:46 +01:00
parent e0c7118459
commit b16d57f560
2 changed files with 20 additions and 13 deletions
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2
GPy/kern/src/kernel_slice_operations.py
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@ -13,7 +13,7 @@ from paramz.parameterized import ParametersChangedMeta
def put_clean(dct, name, func):
if name in dct:
#dct['_clean_{}'.format(name)] = dct[name]
dct['_clean_{}'.format(name)] = dct[name]
dct[name] = func(dct[name])
class KernCallsViaSlicerMeta(ParametersChangedMeta):

31
GPy/kern/src/stationary.py
View file

@ -237,9 +237,9 @@ class Stationary(Kern):
# d2K_dXdX2 = dK_dr*d2r_dXdX2 + d2K_drdr * dr_dX * dr_dX2:
invdist = self._inv_dist(X, X2)
invdist2 = invdist**2
dL_dr = self.dK_dr_via_X(X, X2) # * dL_dK we perofrm this product later
dL_dr = self.dK_dr_via_X(X, X2) * dL_dK # we perofrm this product later
tmp1 = dL_dr * invdist
dL_drdr = self.dK2_drdr_via_X(X, X2) # * dL_dK we perofrm this product later
dL_drdr = self.dK2_drdr_via_X(X, X2) * dL_dK # we perofrm this product later
tmp2 = dL_drdr * invdist2
l2 = np.ones(X.shape[1])*self.lengthscale**2 #np.multiply(np.ones(X.shape[1]) ,self.lengthscale**2)
@ -250,17 +250,24 @@ class Stationary(Kern):
#tmp1[X==X2.T] -= self.variance # Old version, to be removed
# (seems to have a bug: it is subtracted to the first X1 anyway)
tmp1[invdist2==0.] -= self.variance
tmp3 = (tmp1*invdist2 - tmp2)
if cov: # full covariance
grad = np.empty((X.shape[0], X2.shape[0], X2.shape[1], X.shape[1]), dtype=np.float64)
for q in range(self.input_dim):
tmpdist = (X[:,[q]]-X2[:,[q]].T)
for r in range(self.input_dim):
tmpdist2 = tmpdist*(X[:,[r]]-X2[:,[r]].T) # Introduce temporary distance
if r==q:
grad[:, :, q, r] = np.multiply(dL_dK,(np.multiply((tmp1*invdist2 - tmp2),tmpdist2)/l2[r] - tmp1)/l2[q])
else:
grad[:, :, q, r] = np.multiply(dL_dK,(np.multiply((tmp1*invdist2 - tmp2),tmpdist2)/l2[r])/l2[q])
dist = X[:,None,:] - X2[None,:,:]
t2 = (tmp3[:,:,None,None]*(dist[:,:,:,None]*dist[:,:,None,:]))/l2[None,None,:,None]
t2.T[np.diag_indices(self.input_dim)] -= tmp1.T[None,:,:]
grad = t2/l2[None,None,None,:]
#grad_old = np.empty((X.shape[0], X2.shape[0], X2.shape[1], X.shape[1]), dtype=np.float64)
#
#for q in range(self.input_dim):
# tmpdist = (X[:,[q]]-X2[:,[q]].T)
# for r in range(self.input_dim):
# tmpdist2 = tmpdist*(X[:,[r]]-X2[:,[r]].T) # Introduce temporary distance
# if r==q:
# grad_old[:, :, q, r] = ((tmp3 * tmpdist2)/l2[r] - tmp1)/l2[q]
# else:
# grad_old[:, :, q, r] = (((tmp3 * tmpdist2)/l2[r])/l2[q])
else:
# Diagonal covariance, old code
grad = np.empty((X.shape[0], X2.shape[0], X.shape[1]), dtype=np.float64)
@ -274,7 +281,7 @@ class Stationary(Kern):
#np.sum( - (tmp2*(tmpdist**2)), axis=1, out=grad[:,q])
return grad
def gradients_XX_diag(self, d2L_dK, X, cov=False):
def gradients_XX_diag(self, d2L_dK, X, cov=True):
"""
Given the derivative of the objective d2L_dK, compute the second derivative of K wrt X: