leaspy.models.utils.attributes.logistic_attributes.LogisticAttributes

class LogisticAttributes(name, dimension, source_dimension)

Bases: AbstractManifoldModelAttributes

Attributes of leaspy logistic models.

Contains the common attributes & methods to update the logistic model’s attributes.

Parameters
namestr
dimensionint
source_dimensionint

See also

UnivariateModel
MultivariateModel
Attributes
namestr (default ‘logistic’)

Name of the associated leaspy model.

dimensionint
source_dimensionint
univariatebool

Whether model is univariate or not (i.e. dimension == 1)

has_sourcesbool

Whether model has sources or not (not univariate and source_dimension >= 1)

update_possibilitiestuple [str] (default (‘all’, ‘g’, ‘v0’, ‘betas’) )

Contains the available parameters to update. Different models have different parameters.

positionstorch.Tensor [dimension] (default None)

positions = exp(realizations[‘g’]) such that “p0” = 1 / (1 + positions)

velocitiestorch.Tensor [dimension] (default None)

Always positive: exp(realizations[‘v0’])

orthonormal_basistorch.Tensor [dimension, dimension - 1] (default None)
betastorch.Tensor [dimension - 1, source_dimension] (default None)
mixing_matrixtorch.Tensor [dimension, source_dimension] (default None)

Matrix A such that w_i = A * s_i.

Methods

get_attributes()

Returns the attributes of the model.

move_to_device(device)

Move the tensor attributes of this class to the specified device.

update(names_of_changed_values, values)

Update model group average parameter(s).
get_attributes()

Returns the attributes of the model.

It is either a tuple of torch tensors or a single torch tensor if there is only one attribute for the model (e.g.: univariate models). For the precise definitions of those attributes please refer to the exact attributes class associated to your model.

Returns
For univariate models:

positions: torch.Tensor

For multivariate (but not parallel) models:

  • positions: torch.Tensor

  • velocities: torch.Tensor

  • mixing_matrix: torch.Tensor

move_to_device(device: device)

Move the tensor attributes of this class to the specified device.

Parameters
devicetorch.device
update(names_of_changed_values, values)

Update model group average parameter(s).

Parameters
names_of_changed_valueslist [str]
Elements of list must be either:

  • all (update everything)

  • g correspond to the attribute positions.

  • v0 (only for multivariate models) correspond to the attribute velocities. When we are sure that the v0 change is only a scalar multiplication (in particular, when we reparametrize log(v0) <- log(v0) + mean(xi)), we may update velocities using v0_collinear, otherwise we always assume v0 is NOT collinear to previous value (no need to perform the verification it is - would not be really efficient)

  • betas correspond to the linear combination of columns from the orthonormal basis so to derive the mixing_matrix.

valuesdict [str, torch.Tensor]

New values used to update the model’s group average parameters

Raises
LeaspyModelInputError

If names_of_changed_values contains unknown parameters.