apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-10 20:42:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

posterior with one covariance per dimension and param gradient fix

Browse source
This commit is contained in:
Max Zwiessele 2014-02-20 14:09:20 +00:00
parent 46f59f9f64
commit 52ab456bfe
3 changed files with 9 additions and 4 deletions
Download patch file Download diff file Expand all files Collapse all files

3
GPy/core/parameterization/param.py
View file

@ -86,6 +86,9 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parentable
if self._gradient_ is None: if self._gradient_ is None:
self._gradient_ = numpy.zeros(self._realshape_) self._gradient_ = numpy.zeros(self._realshape_)
return self._gradient_ return self._gradient_
@gradient.setter
def gradient(self, val):
self.gradient[:] = val
#=========================================================================== #===========================================================================
# Pickling operations # Pickling operations

7
GPy/inference/latent_function_inference/posterior.py
View file

@ -81,13 +81,16 @@ class Posterior(object):
def covariance(self): def covariance(self):
if self._covariance is None: if self._covariance is None:
#LiK, _ = dtrtrs(self.woodbury_chol, self._K, lower=1) #LiK, _ = dtrtrs(self.woodbury_chol, self._K, lower=1)
self._covariance = self._K - self._K.dot(self.woodbury_inv).dot(self._K) self._covariance = np.tensordot(np.dot(np.atleast_3d(self.woodbury_inv).T, self._K), self._K, [1,0]).T
#self._covariance = self._K - self._K.dot(self.woodbury_inv).dot(self._K)
return self._covariance return self._covariance
@property @property
def precision(self): def precision(self):
if self._precision is None: if self._precision is None:
self._precision, _, _, _ = pdinv(self.covariance) self._precision = np.zeros(np.atleast_3d(self.covariance).shape) # if one covariance per dimension
for p in xrange(self.covariance.shape[-1]):
self._precision[:,:,p] = pdinv(self.covariance[:,:,p])[0]
return self._precision return self._precision
@property @property

3
GPy/util/warping_functions.py
View file

@ -3,8 +3,6 @@
import numpy as np import numpy as np
import scipy as sp
import pylab as plt
class WarpingFunction(object): class WarpingFunction(object):
""" """
@ -39,6 +37,7 @@ class WarpingFunction(object):
def plot(self, psi, xmin, xmax): def plot(self, psi, xmin, xmax):
y = np.arange(xmin, xmax, 0.01) y = np.arange(xmin, xmax, 0.01)
f_y = self.f(y, psi) f_y = self.f(y, psi)
from matplotlib import pyplot as plt
plt.figure() plt.figure()
plt.plot(y, f_y) plt.plot(y, f_y)
plt.xlabel('y') plt.xlabel('y')