apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-09 12:02:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Revert "merge devel mrd"

Browse source 
This reverts commit 3f625a9347, reversing
changes made to dc6faeb303.
This commit is contained in:
Max Zwiessele 2013-04-23 14:02:15 +01:00
parent 3f625a9347
commit 0c8b83454f
8 changed files with 61 additions and 202 deletions
Download patch file Download diff file Expand all files Collapse all files

70
GPy/kern/constructors.py
View file

@ -25,7 +25,6 @@ from symmetric import symmetric as symmetric_part
from coregionalise import coregionalise as coregionalise_part
from rational_quadratic import rational_quadratic as rational_quadraticpart
from rbfcos import rbfcos as rbfcospart
from independent_outputs import independent_outputs as independent_output_part
#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.
@ -166,40 +165,34 @@ def Brownian(D,variance=1.):
part = Brownianpart(D,variance)
return kern(D, [part])
try:
import sympy as sp
from sympykern import spkern
from sympy.parsing.sympy_parser import parse_expr
sympy_available = True
except ImportError:
sympy_available = False
import sympy as sp
from sympykern import spkern
from sympy.parsing.sympy_parser import parse_expr
if sympy_available:
def rbf_sympy(D,ARD=False,variance=1., lengthscale=1.):
"""
Radial Basis Function covariance.
"""
X = [sp.var('x%i'%i) for i in range(D)]
Z = [sp.var('z%i'%i) for i in range(D)]
rbf_variance = sp.var('rbf_variance',positive=True)
if ARD:
rbf_lengthscales = [sp.var('rbf_lengthscale_%i'%i,positive=True) for i in range(D)]
dist_string = ' + '.join(['(x%i-z%i)**2/rbf_lengthscale_%i**2'%(i,i,i) for i in range(D)])
dist = parse_expr(dist_string)
f = rbf_variance*sp.exp(-dist/2.)
else:
rbf_lengthscale = sp.var('rbf_lengthscale',positive=True)
dist_string = ' + '.join(['(x%i-z%i)**2'%(i,i) for i in range(D)])
dist = parse_expr(dist_string)
f = rbf_variance*sp.exp(-dist/(2*rbf_lengthscale**2))
return kern(D,[spkern(D,f)])
def rbf_sympy(D,ARD=False,variance=1., lengthscale=1.):
"""
Radial Basis Function covariance.
"""
X = [sp.var('x%i'%i) for i in range(D)]
Z = [sp.var('z%i'%i) for i in range(D)]
rbf_variance = sp.var('rbf_variance',positive=True)
if ARD:
rbf_lengthscales = [sp.var('rbf_lengthscale_%i'%i,positive=True) for i in range(D)]
dist_string = ' + '.join(['(x%i-z%i)**2/rbf_lengthscale_%i**2'%(i,i,i) for i in range(D)])
dist = parse_expr(dist_string)
f = rbf_variance*sp.exp(-dist/2.)
else:
rbf_lengthscale = sp.var('rbf_lengthscale',positive=True)
dist_string = ' + '.join(['(x%i-z%i)**2'%(i,i) for i in range(D)])
dist = parse_expr(dist_string)
f = rbf_variance*sp.exp(-dist/(2*rbf_lengthscale**2))
return kern(D,[spkern(D,f)])
def sympykern(D,k):
"""
A kernel from a symbolic sympy representation
"""
return kern(D,[spkern(D,k)])
del sympy_available
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):
"""
@ -325,14 +318,3 @@ def rbfcos(D,variance=1.,frequencies=None,bandwidths=None,ARD=False):
"""
part = rbfcospart(D,variance,frequencies,bandwidths,ARD)
return kern(D,[part])
def independent_outputs(k):
"""
Construct a kernel with independent outputs from an existing kernel
"""
for sl in k.input_slices:
assert (sl.start is None) and (sl.stop is None), "cannot adjust input slices! (TODO)"
parts = [independent_output_part(p) for p in k.parts]
return kern(k.D+1,parts)