model module¶
Scalable model¶
This module implements the abstract concept of scalable model which is used by scalable disciplines. A scalable model is built from a input-output learning dataset associated with a function and generalizing its behavior to a new user-defined problem dimension, that is to say new used-defined input and output dimensions.
The concept of scalable model is implemented
through ScalableModel
, an abstract class which is instantiated from:
data provided as an
AbstractFullCache
variables sizes provided as a dictionary whose keys are the names of inputs and outputs and values are their new sizes. If a variable is missing, its original size is considered.
Scalable model parameters can also be filled in. Otherwise the model uses default values.
See also
The ScalableDiagonalModel
class overloads ScalableModel
.
-
class
gemseo.problems.scalable.model.
ScalableModel
(data, sizes=None, **parameters)[source]¶ Bases:
object
Scalable model.
Constructor.
- Parameters
data (AbstractFullCache) – learning dataset.
sizes (dict) – sizes of input and output variables. If None, use the original sizes. Default: None.
parameters – model parameters
-
ABBR
= 'sm'¶
-
compute_bounds
()[source]¶ Compute lower and upper bounds of both input and output variables.
- Returns
lower bounds, upper bounds.
- Return type
dict, dict
-
property
inputs_names
¶ Inputs names.
- Returns
names of the inputs.
- Return type
list(str)
-
property
original_sizes
¶ Original sizes of variables.
- Returns
original sizes of variables.
- Return type
dict
-
property
outputs_names
¶ Outputs names.
- Returns
names of the outputs.
- Return type
list(str)