leaspy.models.utils.attributes.linear_attributes module
- class LinearAttributes(name, dimension, source_dimension)
Bases:
AbstractManifoldModelAttributes
Attributes of leaspy linear models.
Contains the common attributes & methods to update the linear model’s attributes.
- Parameters:
- namestr
- dimensionint
- source_dimensionint
See also
UnivariateModel
MultivariateModel
- Attributes:
- namestr (default ‘linear’)
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.
- positions
torch.Tensor
[dimension] (default None) positions = realizations[‘g’] such that “p0” = positions
- velocities
torch.Tensor
[dimension] (default None) Always positive: exp(realizations[‘v0’])
- orthonormal_basis
torch.Tensor
[dimension, dimension - 1] (default None) - betas
torch.Tensor
[dimension - 1, source_dimension] (default None) - mixing_matrix
torch.Tensor
[dimension, source_dimension] (default None) Matrix A such that w_i = A * s_i.
Methods
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:
- 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_valuesset[str]
- Elements of set must be either:
all
(update everything)g
correspond to the attributepositions
.v0
(only for multivariate models) correspond to the attributevelocities
. 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 usingv0_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 themixing_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.