apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-08 19:42:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

added periodic kernels

Browse source
This commit is contained in:
Nicolas 2013-01-21 13:29:25 +00:00
parent eedf1f99d1
commit 20cc336a7a
2 changed files with 58 additions and 1 deletions
Download patch file Download diff file Expand all files Collapse all files

2
GPy/kern/__init__.py
View file

@ -2,5 +2,5 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from constructors import rbf, Matern32, Matern52, exponential, linear, white, bias, finite_dimensional, spline, Brownian, linear_ARD, rbf_sympy, sympykern
from constructors import rbf, Matern32, Matern52, exponential, linear, white, bias, finite_dimensional, spline, Brownian, linear_ARD, rbf_sympy, sympykern, periodic_exponential, periodic_Matern32, periodic_Matern52
from kern import kern

57
GPy/kern/constructors.py
View file

@ -16,6 +16,9 @@ from bias import bias as biaspart
from finite_dimensional import finite_dimensional as finite_dimensionalpart
from spline import spline as splinepart
from Brownian import Brownian as Brownianpart
from periodic_exponential import periodic_exponential as periodic_exponentialpart
from periodic_Matern32 import periodic_Matern32 as periodic_Matern32part
from periodic_Matern52 import periodic_Matern52 as periodic_Matern52part
#TODO these s=constructors are not as clean as we'd like. Tidy the code up
#using meta-classes to make the objects construct properly wthout them.
@ -196,3 +199,57 @@ def sympykern(D,k):
A kernel from a symbolic sympy representation
"""
return kern(D,[spkern(D,k)])
def periodic_exponential(D=1,variance=1., lengthscale=None, period=2*np.pi,n_freq=10,lower=0.,upper=4*np.pi):
"""
Construct an periodic exponential kernel
:param D: dimensionality, only defined for D=1
:type D: int
:param variance: the variance of the kernel
:type variance: float
:param lengthscale: the lengthscale of the kernel
:type lengthscale: float
:param period: the period
:type period: float
:param n_freq: the number of frequencies considered for the periodic subspace
:type n_freq: int
"""
part = periodic_exponentialpart(D,variance, lengthscale, ARD)
return kern(D, [part])
def periodic_Matern32(D,variance=1., lengthscale=None, period=2*np.pi,n_freq=10,lower=0.,upper=4*np.pi):
"""
Construct a periodic Matern 3/2 kernel.
:param D: dimensionality, only defined for D=1
:type D: int
:param variance: the variance of the kernel
:type variance: float
:param lengthscale: the lengthscale of the kernel
:type lengthscale: float
:param period: the period
:type period: float
:param n_freq: the number of frequencies considered for the periodic subspace
:type n_freq: int
"""
part = periodic_Matern32part(D,variance, lengthscale, ARD)
return kern(D, [part])
def periodic_Matern52(D,variance=1., lengthscale=None, period=2*np.pi,n_freq=10,lower=0.,upper=4*np.pi):
"""
Construct a periodic Matern 5/2 kernel.
:param D: dimensionality, only defined for D=1
:type D: int
:param variance: the variance of the kernel
:type variance: float
:param lengthscale: the lengthscale of the kernel
:type lengthscale: float
:param period: the period
:type period: float
:param n_freq: the number of frequencies considered for the periodic subspace
:type n_freq: int
"""
part = periodic_Matern52part(D,variance, lengthscale, ARD)
return kern(D, [part])