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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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5
GPy/kern/_src/stationary.py
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@ -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.