leaspy.models.utils.attributes.logistic_attributes module

class LogisticAttributes(name, dimension, source_dimension)

Attributes of leaspy logistic models.

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

Parameters:

See also

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_possibilitiesset[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, which is a tuple of three torch tensors.
`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() → Tuple[FloatTensor, FloatTensor, FloatTensor]

Returns the attributes of the model, which is a tuple of three torch tensors.

For the precise definitions of those attributes please refer to the exact attributes class associated to your model.

Returns:

move_to_device(device: device)

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

Parameters:

update(names_of_changed_values, values)

Update model group average parameter(s).

Parameters:

names_of_changed_valuesset[str]

Elements of set 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.