mGPLVM
Getting Started
Install
Examples
(Bayesian) GPFA
Supervised learning and decoding with manifold GPLVMs
Applying mGPLVM to synthetic neural data generated from circular latents
API
mgplvm package
Subpackages
mgplvm.crossval package
mgplvm.fast_utils package
mgplvm.kernels package
mgplvm.lpriors package
mgplvm.manifolds package
Submodules
Module contents
mgplvm.models package
mgplvm.optimisers package
mgplvm.rdist package
mgplvm.syndata package
Submodules
Module contents
mGPLVM
»
mgplvm package
»
mgplvm.manifolds package
»
mgplvm.manifolds.base module
View page source
mgplvm.manifolds.base module
class
mgplvm.manifolds.base.
Manifold
(
d
)
[source]
Bases:
object
abstract
static
distance
(
x
,
y
)
[source]
Return type
Tensor
abstract
static
expmap
(
x
)
[source]
Return type
Tensor
abstract
gmul
(
x
,
y
)
[source]
Return type
Tensor
abstract
inducing_points
(
n
,
n_z
,
z
=
typing.Union[torch.Tensor,
NoneType]
)
[source]
abstract
static
initialize
(
initialization
,
n_samples
,
m
,
d
,
Y
)
[source]
abstract
inverse
(
x
)
[source]
Return type
Tensor
abstract
static
log_q
(
p
,
x
,
d
,
kmax
)
[source]
Return type
Tensor
abstract
static
logmap
(
x
)
[source]
Return type
Tensor
abstract
lprior
(
g
)
[source]
Return type
Tensor
abstract
property
name
abstract
static
parameterise
(
x
)
[source]
Return type
Tensor