def _bfit_get_prior(distribution, em=0):
from csb.statistics import scalemixture as sm
if distribution == 'student':
prior = sm.GammaPrior()
if em: prior.estimator = sm.GammaPosteriorMAP()
elif distribution == 'k':
prior = sm.InvGammaPrior()
if em: prior.estimator = sm.InvGammaPosteriorMAP()
else:
raise AttributeError('distribution')
return prior
def bfit(X, Y, n_iter=10, distribution='student', em=False, full_output=False):
"""
Robust superposition of two coordinate arrays. Models non-rigid
displacements with outlier-tolerant probability distributions.
@param X: (n, 3) input vector
@type X: numpy.array
@param Y: (n, 3) input vector
@type Y: numpy.array
@param n_iter: number of iterations
@type n_iter: int
@param distribution: student or k
@type distribution: str
@param em: use maximum a posteriori probability (MAP) estimator
@type em: bool
@param full_output: if true, return ((R, t), scales)
@type full_output: bool
@rtype: tuple
"""
from csb.statistics import scalemixture as sm
if distribution == 'student':
prior = sm.GammaPrior()
if em:
prior.estimator = sm.GammaPosteriorMAP()
elif distribution == 'k':
prior = sm.InvGammaPrior()
if em:
prior.estimator = sm.InvGammaPosteriorMAP()
else:
raise AttributeError('distribution')
mixture = sm.ScaleMixture(scales=X.shape[0], prior=prior, d=3)
R, t = fit(X, Y)
for _ in range(n_iter):
data = distance(X, transform(Y, R, t))
mixture.estimate(data)
R, t = probabilistic_fit(X, Y, mixture.scales)
if full_output:
return (R, t), mixture.scales
else:
return (R, t)