stheno.model.observations module¶
-
class
stheno.model.observations.
AbstractObservations
[source]¶ Bases:
object
Abstract base class for observations.
Also takes in one or multiple tuples of the described arguments.
- Parameters
fdd (
fdd.FDD
) – FDD of observations.y (column vector) – Values of observations.
-
y
¶ Values of observations.
- Type
column vector
-
posterior_kernel
(measure, p_i, p_j)[source]¶ Get the posterior kernel between two processes.
- Parameters
measure (
measure.Measure
) – Prior.p_i (
gp.GP
) – First process.p_j (
gp.GP
) – Second process.
- Returns
- Posterior kernel between the first and
second process.
- Return type
mlkernels.Kernel
-
posterior_mean
(measure, p)[source]¶ Get the posterior kernel of a process.
- Parameters
measure (
measure.Measure
) – Prior.p (
gp.GP
) – Process.
- Returns
Posterior mean of p.
- Return type
mlkernels.Mean
-
stheno.model.observations.
Obs
¶ Shorthand for Observations.
-
class
stheno.model.observations.
Observations
(*args)[source]¶ Bases:
stheno.model.observations.AbstractObservations
Observations.
Takes arguments according to the constructor of
measure.AbstractObservations
.- Parameters
fdd (
fdd.FDD
) – FDDs to corresponding to the observations.y (tensor) – Values of observations.
-
K_x
(measure)[source]¶ Kernel matrix of the data.
- Parameters
measure (
measure.Measure
) – Measure.- Returns
Kernel matrix.
- Return type
matrix
-
posterior_kernel
(measure, p_i, p_j)[source]¶ Get the posterior kernel between two processes.
- Parameters
measure (
measure.Measure
) – Prior.p_i (
gp.GP
) – First process.p_j (
gp.GP
) – Second process.
- Returns
- Posterior kernel between the first and
second process.
- Return type
mlkernels.Kernel
-
posterior_mean
(measure, p)[source]¶ Get the posterior kernel of a process.
- Parameters
measure (
measure.Measure
) – Prior.p (
gp.GP
) – Process.
- Returns
Posterior mean of p.
- Return type
mlkernels.Mean
-
stheno.model.observations.
PseudoObs
¶ Shorthand for PseudoObservations.
-
class
stheno.model.observations.
PseudoObservations
[source]¶ Bases:
stheno.model.observations.AbstractObservations
Observations through inducing points.
Further takes arguments according to the constructor of
measure.AbstractObservations
. Can also take in tuples of inducing points.- Parameters
u (
fdd.FDD
) – Inducing points
-
A
(measure)[source]¶ Parameter of the corrective variance of the kernel of the optimal approximating distribution.
- Parameters
measure (
measure.Measure
) – Measure.- Returns
Corrective variance.
- Return type
matrix
-
K_z
(measure)[source]¶ Kernel matrix of the data.
- Parameters
measure (
measure.Measure
) – Measure.- Returns
Kernel matrix.
- Return type
matrix
-
elbo
(measure)[source]¶ ELBO.
- Parameters
measure (
measure.Measure
) – Measure.- Returns
ELBO.
- Return type
scalar
-
mu
(measure)[source]¶ Mean of optimal approximating distribution.
- Parameters
measure (
measure.Measure
) – Measure.- Returns
Mean.
- Return type
matrix
-
posterior_kernel
(measure, p_i, p_j)[source]¶ Get the posterior kernel between two processes.
- Parameters
measure (
measure.Measure
) – Prior.p_i (
gp.GP
) – First process.p_j (
gp.GP
) – Second process.
- Returns
- Posterior kernel between the first and
second process.
- Return type
mlkernels.Kernel
-
posterior_mean
(measure, p)[source]¶ Get the posterior kernel of a process.
- Parameters
measure (
measure.Measure
) – Prior.p (
gp.GP
) – Process.
- Returns
Posterior mean of p.
- Return type
mlkernels.Mean
-
stheno.model.observations.
SparseObs
¶
-
stheno.model.observations.
SparseObservations
¶