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observable pattern through and thorugh

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Max Zwiessele 2014-02-26 15:46:14 +00:00
parent 26aeb5e1db
commit 65fd6dd24e
11 changed files with 64 additions and 80 deletions
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1
GPy/kern/_src/linear.py
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@ -9,7 +9,6 @@ from ...util.linalg import tdot
from ...util.misc import fast_array_equal, param_to_array
from ...core.parameterization import Param
from ...core.parameterization.transformations import Logexp
from ...util.caching import cache_this
class Linear(Kern):
"""

5
GPy/kern/_src/rbf.py
View file

@ -6,6 +6,7 @@ import numpy as np
from scipy import weave
from ...util.misc import param_to_array
from stationary import Stationary
from GPy.util.caching import Cache_this
class RBF(Stationary):
"""
@ -166,7 +167,7 @@ class RBF(Stationary):
return target
#@cache_this TODO
@Cache_this(limit=1)
def _psi1computations(self, Z, vp):
mu, S = vp.mean, vp.variance
l2 = self.lengthscale **2
@ -179,7 +180,7 @@ class RBF(Stationary):
#@cache_this TODO
@Cache_this(limit=1)
def _psi2computations(self, Z, vp):
mu, S = vp.mean, vp.variance

5
GPy/kern/_src/ssrbf.py
View file

@ -6,7 +6,6 @@ from kern import Kern
import numpy as np
from ...util.linalg import tdot
from ...util.config import *
from ...util.caching import cache_this
from stationary import Stationary
class SSRBF(Stationary):
@ -155,7 +154,7 @@ class SSRBF(Stationary):
# Precomputations #
#---------------------------------------#
@cache_this(1)
#@cache_this(1)
def _K_computations(self, X, X2):
"""
K(X,X2) - X is NxQ
@ -175,7 +174,7 @@ class SSRBF(Stationary):
self._K_dist2 = -2.*np.dot(X, X2.T) + (np.sum(np.square(X), axis=1)[:, None] + np.sum(np.square(X2), axis=1)[None, :])
self._K_dvar = np.exp(-0.5 * self._K_dist2)
@cache_this(1)
#@cache_this(1)
def _psi_computations(self, Z, mu, S, gamma):
"""
Z - MxQ

5
GPy/kern/_src/stationary.py
View file

@ -9,6 +9,7 @@ from ...util.linalg import tdot
from ... import util
import numpy as np
from scipy import integrate
from ...util.caching import Cache_this
class Stationary(Kern):
def __init__(self, input_dim, variance, lengthscale, ARD, name):
@ -39,15 +40,18 @@ class Stationary(Kern):
def dK_dr(self, r):
raise NotImplementedError, "implement the covaraiance function as a fn of r to use this class"
@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,))
def _dist(self, X, X2):
if X2 is None:
X2 = X
return X[:, None, :] - X2[None, :, :]
@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
@ -61,6 +65,7 @@ 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=())
def _scaled_dist(self, X, X2=None):
"""
Efficiently compute the scaled distance, r.