apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-21 14:05:14 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

merged

Browse source
This commit is contained in:
James Hensman 2014-02-28 12:07:19 +00:00
commit ba1d6697e5
14 changed files with 356 additions and 122 deletions
Download patch file Download diff file Expand all files Collapse all files

7
GPy/kern/_src/rbf.py
View file

@ -20,7 +20,7 @@ class RBF(Stationary):
"""
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, name='RBF'):
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, name='rbf'):
super(RBF, self).__init__(input_dim, variance, lengthscale, ARD, name)
self.weave_options = {}
@ -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
@ -257,7 +257,6 @@ class RBF(Stationary):
#allocate memory for the things we want to compute
mudist = np.empty((N, M, M, Q))
mudist_sq = np.empty((N, M, M, Q))
exponent = np.zeros((N,M,M))
psi2 = np.empty((N, M, M))
l2 = self.lengthscale **2

8
GPy/kern/_src/stationary.py
View file

@ -69,12 +69,12 @@ 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 _unscaled_dist(self, X, X2=None):
"""
Compute the Euclidean distance between each row of X and X2, or between
@ -88,7 +88,7 @@ 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.
@ -141,7 +141,7 @@ 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