def gp_quick_condition(lenscale,
obs=None,
hidfcn=None,
domain=linspace(0, 1, 1028),
sigvar=1.,
noisevar2=1e-7):
obs_huh = check_obs(obs)
mu_prior = zeros_like(domain)
covmat_prior = jbgp.K_se(domain, domain, lenscale, sigvar)
if obs_huh:
if isscalar(obs) and obs>0:
if hidfcn is None:
hidfcn = jbgp.sample(domain, mu_prior, covmat_prior, noisevar2)
iobs = rng.randint(len(domain), size=obs)
xobs = domain[iobs]
yobs = hidfcn[iobs]
elif type(obs) is ndarray:
if hidfcn is None:
hidfcn = jbgp.sample(domain, mu_prior, covmat_prior, noisevar2)
iobs = obs
xobs = domain[iobs]
yobs = hidfcn[iobs]
elif type(obs) is list:
assert len(obs)==2, 'if list, must be list of two ndarrays w x and y vals'
assert not hidfcn, 'cannot provide yvals if passing a hidfcn'
iobs = None
xobs = obs[0]
yobs = obs[1]
else:
raise ValueError('obs must be an integer, a nparray, or a list of two nparrays')
post_mu = jbgp.conditioned_mu(domain, xobs, yobs, lenscale, sigvar, noisevar2)
post_covmat = jbgp.conditioned_covmat(domain, covmat_prior, xobs, lenscale, sigvar, noisevar2)
else:
post_mu = mu_prior
post_covmat = covmat_prior
xobs = None
yobs = None
iobs = None
return {'mu': post_mu,
'covmat': post_covmat,
'xobs': xobs,
'yobs': yobs,
'iobs': iobs}
def gp_quick_sample(lenscale,
obs=None,
nsam=1,
hidfcn=None,
domain=linspace(0, 1, 1028),
sigvar=1.,
noisevar2=1e-7):
mu_prior = zeros_like(domain)
covmat_prior = jbgp.K_se(domain, domain, lenscale, sigvar)
post = gp_quick_condition(lenscale, obs, hidfcn, domain, sigvar, noisevar2)
samples = [jbgp.sample(domain, post['mu'], post['covmat'], noisevar2)
for _ in xrange(nsam)]
return {'samples': samples,
'xobs': post['xobs'],
'yobs': post['yobs'],
'iobs': post['iobs']}