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einsumming in stationary

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James Hensman 2014-02-28 13:37:26 +00:00
parent 659afaf241
commit d5658660a6
1 changed files with 15 additions and 11 deletions
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26
GPy/kern/_src/stationary.py
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@ -13,13 +13,13 @@ from ...util.caching import Cache_this
class Stationary(Kern):
"""
Stationary kernels (covaraince functions).
Stationary kernels (covariance functions).
Stationary covariance fucntion depend only on r, where r is defined as
r = \sqrt{ \sum_{q=1}^Q (x_q - x'_q)^2 }
The covaraince function k(x, x' can then be written k(r).
The covariance function k(x, x' can then be written k(r).
In this implementation, r is scaled by the lengthscales parameter(s):
@ -28,7 +28,7 @@ class Stationary(Kern):
By default, there's only one lengthscale: seaprate lengthscales for each
dimension can be enables by setting ARD=True.
To implement a stationary covaraince function using this class, one need
To implement a stationary covariance function using this class, one need
only define the covariance function k(r), and it derivative.
...
@ -74,6 +74,11 @@ class Stationary(Kern):
r = self._scaled_dist(X, X2)
return self.K_of_r(r)
@Cache_this(limit=3, ignore_args=())
def dK_dr_via_X(self, X, X2):
#a convenience function, so we can cache dK_dr
return self.dK_dr(self._scaled_dist(X, X2))
@Cache_this(limit=5, ignore_args=(0,))
def _unscaled_dist(self, X, X2=None):
"""
@ -117,11 +122,11 @@ class Stationary(Kern):
self.lengthscale.gradient = 0.
def update_gradients_full(self, dL_dK, X, X2=None):
r = self._scaled_dist(X, X2)
K = self.K_of_r(r)
dL_dr = self.dK_dr(r) * dL_dK
self.variance.gradient = np.einsum('ij,ij,i', self.K(X, X2), dL_dK, 1./self.variance)
#now the lengthscale gradient(s)
dL_dr = self.dK_dr_via_X(X, X2) * dL_dK
if self.ARD:
#rinv = self._inv_dis# this is rather high memory? Should we loop instead?t(X, X2)
#d = X[:, None, :] - X2[None, :, :]
@ -129,11 +134,11 @@ class Stationary(Kern):
#self.lengthscale.gradient = -((dL_dr*rinv)[:,:,None]*x_xl3).sum(0).sum(0)/self.lengthscale**3
tmp = dL_dr*self._inv_dist(X, X2)
if X2 is None: X2 = X
[np.copyto(self.lengthscale.gradient[q:q+1], -np.sum(tmp * np.square(X[:,q:q+1] - X2[:,q:q+1].T))/self.lengthscale[q]**3) for q in xrange(self.input_dim)]
self.lengthscale.gradient = np.array([np.einsum('ij,ij,...', tmp, np.square(X[:,q:q+1] - X2[:,q:q+1].T), -1./self.lengthscale[q]**3) for q in xrange(self.input_dim)])
else:
r = self._scaled_dist(X, X2)
self.lengthscale.gradient = -np.sum(dL_dr*r)/self.lengthscale
self.variance.gradient = np.sum(K * dL_dK)/self.variance
def _inv_dist(self, X, X2=None):
"""
@ -153,9 +158,8 @@ class Stationary(Kern):
"""
Given the derivative of the objective wrt K (dL_dK), compute the derivative wrt X
"""
r = self._scaled_dist(X, X2)
invdist = self._inv_dist(X, X2)
dL_dr = self.dK_dr(r) * dL_dK
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
@ -168,7 +172,7 @@ class Stationary(Kern):
tmp *= 2.
X2 = X
ret = np.empty(X.shape, dtype=np.float64)
[np.copyto(ret[:,q], np.sum(tmp*(X[:,q][:,None]-X2[:,q][None,:]), 1)) for q in xrange(self.input_dim)]
[np.einsum('ij,ij->i', tmp, X[:,q][:,None]-X2[:,q][None,:], out=ret[:,q]) for q in xrange(self.input_dim)]
ret /= self.lengthscale**2
return ret