def __init__(self,inf=exact,cov=1.0*se(ell=1.0),
mean=zero(),lik=gauss(sn=1.0),X=None,y=None):
""" Set up a gp, perform inference if X and y are provided.
"""
self.inf = inf # inference method
self.cov,self.mean,self.lik = cov,mean,lik # covariance,mean,likelihood
self.post = None # init posterior approximation
if X!=None and y!=None: self.post = self.inference(X,y) # do inference
def test_zero(self,thr=1e-6):
""" Test zero mean.
"""
n,D,X = self.n,self.D,self.X
m = mean.zero()
self.run_verifications(m,thr)
d = np.linalg.norm( m(X) )
self.assertLessEqual(d,thr)
def __init__(self,
inf=exact,
cov=1.0 * se(ell=1.0),
mean=zero(),
lik=gauss(sn=1.0),
X=None,
y=None):
""" Set up a gp, perform inference if X and y are provided.
"""
self.inf = inf # inference method
self.cov, self.mean, self.lik = cov, mean, lik # covariance,mean,likelihood
self.post = None # init posterior approximation
if X != None and y != None:
self.post = self.inference(X, y) # do inference
def test_ep(self,thr=1e-4):
""" Test expectation propagation approximate inference.
"""
im = inf.ep
cf,mf,lf = 1.1*cov.se(ell=0.8),mean.zero(),lik.erf()
self.run_verifications(im,cf,mf,lf,thr)
lf = lik.gauss(sn=0.2)
self.run_verifications(im,cf,mf,lf,thr)
mf = 2.3*mean.one()
self.run_verifications(im,cf,mf,lf,thr)
cf = 1.3*cov.se(ell=[0.8,0.7,0.3])
self.run_verifications(im,cf,mf,lf,thr)
mf = 1.3*mean.linear(a=[0.8,0.7,0.3])
self.run_verifications(im,cf,mf,lf,thr)
def test_exact(self,thr=1e-4):
""" Test exact inference.
"""
im = inf.exact
cf,mf,lf = 1.1*cov.se(ell=0.8),mean.zero(),lik.gauss(sn=1e-5)
self.run_verifications(im,cf,mf,lf,thr)
lf = lik.gauss(sn=0.2)
self.run_verifications(im,cf,mf,lf,thr)
mf = 2.3*mean.one()
self.run_verifications(im,cf,mf,lf,thr)
cf = 1.3*cov.se(ell=[0.8,0.7,0.3])
self.run_verifications(im,cf,mf,lf,thr)
mf = 1.3*mean.linear(a=[0.8,0.7,0.3])
self.run_verifications(im,cf,mf,lf,thr)
