apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-05 01:32:40 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

The product by default is now on the same space

Browse source
This commit is contained in:
Nicolas 2013-02-20 17:50:33 +00:00
parent a0eca5ec9a
commit 37e7e11b5b
1 changed files with 26 additions and 3 deletions
Download patch file Download diff file Expand all files Collapse all files

29
GPy/kern/kern.py
View file

@ -8,6 +8,7 @@ from ..core.parameterised import parameterised
from kernpart import kernpart
import itertools
from product_orthogonal import product_orthogonal
from product import product
class kern(parameterised):
def __init__(self,D,parts=[], input_slices=None):
@ -137,11 +138,33 @@ class kern(parameterised):
"""
Shortcut for `prod_orthogonal`. Note that `+` assumes that we sum 2 kernels defines on the same space whereas `*` assumes that the kernels are defined on different subspaces.
"""
return self.prod_orthogonal(other)
return self.prod(other)
def prod(self,other):
"""
multiply two kernels defined on the same spaces.
:param other: the other kernel to be added
:type other: GPy.kern
"""
K1 = self.copy()
K2 = other.copy()
newkernparts = [product(k1,k2) for k1, k2 in itertools.product(K1.parts,K2.parts)]
slices = []
for sl1, sl2 in itertools.product(K1.input_slices,K2.input_slices):
s1, s2 = [False]*K1.D, [False]*K2.D
s1[sl1], s2[sl2] = [True], [True]
slices += [s1+s2]
newkern = kern(K1.D, newkernparts, slices)
newkern._follow_constrains(K1,K2)
return newkern
def prod_orthogonal(self,other):
"""
multiply two kernels. Both kernels are defined on separate spaces. Note that the constrains on the parameters of the kernels to multiply will be lost.
multiply two kernels. Both kernels are defined on separate spaces.
:param other: the other kernel to be added
:type other: GPy.kern
"""
@ -158,7 +181,7 @@ class kern(parameterised):
newkern = kern(K1.D + K2.D, newkernparts, slices)
newkern._follow_constrains(K1,K2)
return newkern
def _follow_constrains(self,K1,K2):