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caching in place again and working : )

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Max Zwiessele 2014-02-28 11:46:09 +00:00
parent 1d1123fcae
commit 83f5b9377a
4 changed files with 10 additions and 10 deletions
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4
GPy/kern/_src/rbf.py
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@ -159,7 +159,7 @@ class RBF(Stationary):
grad_mu = np.sum(dL_dpsi1[:, :, None] * tmp * dist, 1)
grad_S = np.sum(dL_dpsi1[:, :, None] * 0.5 * tmp * (dist_sq - 1), 1)
#psi2
denom, Zdist, Zdist_sq, mudist, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior)
denom, _, _, mudist, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior)
tmp = psi2[:, :, :, None] / l2 / denom
grad_mu += -2.*(dL_dpsi2[:, :, :, None] * tmp * mudist).sum(1).sum(1)
grad_S += (dL_dpsi2[:, :, :, None] * tmp * (2.*mudist_sq - 1)).sum(1).sum(1)
@ -237,7 +237,7 @@ class RBF(Stationary):
return denom, dist, dist_sq, psi1
#@cache_this(ignore_args=(1,))
@Cache_this(limit=1, ignore_args=(0,))
def _Z_distances(self, Z):
Zhat = 0.5 * (Z[:, None, :] + Z[None, :, :]) # M,M,Q
Zdist = 0.5 * (Z[:, None, :] - Z[None, :, :]) # M,M,Q

10
GPy/kern/_src/stationary.py
View file

@ -69,18 +69,18 @@ class Stationary(Kern):
def dK_dr(self, r):
raise NotImplementedError, "implement derivative of the covariance function wrt r to use this class"
#@Cache_this(limit=5, ignore_args=())
@Cache_this(limit=5, ignore_args=())
def K(self, X, X2=None):
r = self._scaled_dist(X, X2)
return self.K_of_r(r)
#@Cache_this(limit=5, ignore_args=(0,))
@Cache_this(limit=5, ignore_args=(0,))
def _dist(self, X, X2):
if X2 is None:
X2 = X
return X[:, None, :] - X2[None, :, :]
#@Cache_this(limit=5, ignore_args=(0,))
@Cache_this(limit=5, ignore_args=(0,))
def _unscaled_dist(self, X, X2=None):
"""
Compute the square distance between each row of X and X2, or between
@ -94,7 +94,7 @@ class Stationary(Kern):
X2sq = np.sum(np.square(X2),1)
return np.sqrt(-2.*np.dot(X, X2.T) + (X1sq[:,None] + X2sq[None,:]))
#@Cache_this(limit=5, ignore_args=())
@Cache_this(limit=5, ignore_args=())
def _scaled_dist(self, X, X2=None):
"""
Efficiently compute the scaled distance, r.
@ -147,7 +147,7 @@ class Stationary(Kern):
diagonal, where we return zero (the distance on the diagonal is zero).
This term appears in derviatives.
"""
dist = self._scaled_dist(X, X2)
dist = self._scaled_dist(X, X2).copy()
if X2 is None:
nondiag = util.diag.offdiag_view(dist)
nondiag[:] = 1./nondiag