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added some caching

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This commit is contained in:
James Hensman 2014-02-27 09:16:36 +00:00
parent a6d3fda234
commit 283b0745aa
2 changed files with 11 additions and 10 deletions
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20
GPy/kern/_src/linear.py
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@ -49,13 +49,8 @@ class Linear(Kern):
self.variances = Param('variances', variances, Logexp())
self.add_parameter(self.variances)
self.variances.add_observer(self, self._on_changed)
def _on_changed(self, obj):
#TODO: move this to base class? isnt it jst for the caching?
self._notify_observers()
#@cache_this(limit=3, reset_on_self=True)
@Cache_this(limit=2)
def K(self, X, X2=None):
if self.ARD:
if X2 is None:
@ -66,7 +61,7 @@ class Linear(Kern):
else:
return self._dot_product(X, X2) * self.variances
#@cache_this(limit=3, reset_on_self=False)
@Cache_this(limit=1, ignore_args(0,))
def _dot_product(self, X, X2=None):
if X2 is None:
return tdot(X)
@ -113,6 +108,7 @@ class Linear(Kern):
def psi1(self, Z, variational_posterior):
return self.K(variational_posterior.mean, Z) #the variance, it does nothing
@Cache_this(limit=1)
def psi2(self, Z, variational_posterior):
ZA = Z * self.variances
ZAinner = self._ZAinner(variational_posterior, Z)
@ -126,9 +122,11 @@ class Linear(Kern):
if self.ARD: self.variances.gradient += tmp.sum(0)
else: self.variances.gradient += tmp.sum()
#psi2
tmp = dL_dpsi2[:, :, :, None] * (self._ZAinner(variational_posterior, Z)[:, :, None, :] * (2. * Z)[None, None, :, :])
if self.ARD: self.variances.gradient += tmp.sum(0).sum(0).sum(0)
else: self.variances.gradient += tmp.sum()
if self.ARD:
tmp = dL_dpsi2[:, :, :, None] * (self._ZAinner(variational_posterior, Z)[:, :, None, :] * Z[None, None, :, :])
self.variances.gradient += 2.*tmp.sum(0).sum(0).sum(0)
else:
self.variances.gradient += 2.*np.sum(dL_dpsi2 * self.psi2(Z, variational_posterior))/self.variances
def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
#psi1
@ -234,9 +232,11 @@ class Linear(Kern):
type_converters=weave.converters.blitz,**weave_options)
@Cache_this(limit=1, ignore_args=(0,))
def _mu2S(self, vp):
return np.square(vp.mean) + vp.variance
@Cache_this(limit=1)
def _ZAinner(self, vp, Z):
ZA = Z*self.variances
inner = (vp.mean[:, None, :] * vp.mean[:, :, None])

1
GPy/kern/_src/rbf.py
View file

@ -113,6 +113,7 @@ class RBF(Stationary):
# Precomputations #
#---------------------------------------#
#TODO: this function is unused, but it will be useful in the stationary class
def _dL_dlengthscales_via_K(self, dL_dK, X, X2):
"""
A helper function for update_gradients_* methods