mgplvm.lpriors.euclidean module

class mgplvm.lpriors.euclidean.DS(manif, fio=<function fio_id>)[source]

Bases: mgplvm.lpriors.euclidean.LpriorEuclid

forward(x, batch_idxs=None)[source]: x: (n_mc, n_samples, m, d)

property msg

name = 'DS'

property prms

training: bool

class mgplvm.lpriors.euclidean.GP(n, m, n_samples, manif, kernel, ts, n_z=20, d=1, learn_sigma=False)[source]

Bases: mgplvm.lpriors.euclidean.LpriorEuclid

forward(x, batch_idxs=None)[source]: x is a latent of shape (n_mc x n_samples x mx x d) ts is the corresponding timepoints of shape (n_samples x mx)

property msg

name = 'GP'

property prms

training: bool

class mgplvm.lpriors.euclidean.LpriorEuclid(manif)[source]

Bases: mgplvm.lpriors.common.Lprior

training: bool

mgplvm.lpriors.euclidean.fio_ReLU(x)[source]

mgplvm.lpriors.euclidean.fio_id(x)[source]

mgplvm.lpriors.euclidean.fio_tanh(x)[source]