Index

A

acc_stats() (bob.learn.em.GMMMachine method)

B

bob.learn.em module

C

centroids_ (bob.learn.em.KMeansMachine attribute)

E

e_step() (bob.learn.em.ISVMachine method) e_step_d() (bob.learn.em.JFAMachine method) e_step_u() (bob.learn.em.JFAMachine method)

e_step_v() (bob.learn.em.JFAMachine method) enroll() (bob.learn.em.ISVMachine method) (bob.learn.em.JFAMachine method) enroll_using_array() (bob.learn.em.ISVMachine method)

F

finalize_u() (bob.learn.em.JFAMachine method) finalize_v() (bob.learn.em.JFAMachine method) fit() (bob.learn.em.GMMMachine method) (bob.learn.em.ISVMachine method) (bob.learn.em.IVectorMachine method) (bob.learn.em.JFAMachine method) (bob.learn.em.KMeansMachine method) (bob.learn.em.WCCN method) (bob.learn.em.Whitening method)

from_hdf5() (bob.learn.em.GMMMachine class method) (bob.learn.em.GMMStats class method)

G

g_norms (bob.learn.em.GMMMachine property) get_variances_and_weights_for_each_cluster() (bob.learn.em.KMeansMachine method)

GMMMachine (class in bob.learn.em) GMMStats (class in bob.learn.em)

I

init_fields() (bob.learn.em.GMMStats method) initialize() (bob.learn.em.KMeansMachine method) initialize_gaussians() (bob.learn.em.GMMMachine method) is_similar_to() (bob.learn.em.GMMMachine method) (bob.learn.em.GMMStats method) (bob.learn.em.KMeansMachine method)

ISVMachine (class in bob.learn.em) IVectorMachine (class in bob.learn.em)

J

JFAMachine (class in bob.learn.em)

K

KMeansMachine (class in bob.learn.em)

L

linear_scoring() (in module bob.learn.em) load() (bob.learn.em.GMMMachine method) (bob.learn.em.GMMStats method)

log_likelihood (bob.learn.em.GMMStats attribute) log_likelihood() (bob.learn.em.GMMMachine method) log_weighted_likelihood() (bob.learn.em.GMMMachine method) log_weights (bob.learn.em.GMMMachine property)

M

m_step() (bob.learn.em.ISVMachine method) m_step_d() (bob.learn.em.JFAMachine method) m_step_u() (bob.learn.em.JFAMachine method) m_step_v() (bob.learn.em.JFAMachine method)

means (bob.learn.em.GMMMachine property) (bob.learn.em.KMeansMachine property) module bob.learn.em

N

n (bob.learn.em.GMMStats attribute)

nbytes (bob.learn.em.GMMStats property)

P

predict() (bob.learn.em.KMeansMachine method)

project() (bob.learn.em.IVectorMachine method)

R

reset() (bob.learn.em.GMMStats method)

resize() (bob.learn.em.GMMStats method)

S

save() (bob.learn.em.GMMMachine method) (bob.learn.em.GMMStats method) score() (bob.learn.em.ISVMachine method) (bob.learn.em.JFAMachine method)

shape (bob.learn.em.GMMMachine property) (bob.learn.em.GMMStats property) stats_per_sample() (bob.learn.em.GMMMachine method) sum_px (bob.learn.em.GMMStats attribute) sum_pxx (bob.learn.em.GMMStats attribute)

T

t (bob.learn.em.GMMStats attribute) transform() (bob.learn.em.GMMMachine method) (bob.learn.em.ISVMachine method) (bob.learn.em.IVectorMachine method) (bob.learn.em.KMeansMachine method) (bob.learn.em.WCCN method) (bob.learn.em.Whitening method)

V

variance_thresholds (bob.learn.em.GMMMachine property)

variances (bob.learn.em.GMMMachine property)

W

WCCN (class in bob.learn.em)

weights (bob.learn.em.GMMMachine property) Whitening (class in bob.learn.em)